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--- title: The Extraction and High Antiproliferative Effect of Anthocyanin from Gardenblue Blueberry authors: - Fengyi Zhao - Jialuan Wang - Weifan Wang - Lianfei Lyu - Wenlong Wu - Weilin Li journal: Molecules year: 2023 pmcid: PMC10054926 doi: 10.3390/molecules28062850 license: CC BY 4.0 --- # The Extraction and High Antiproliferative Effect of Anthocyanin from Gardenblue Blueberry ## Abstract Blueberries are rich in flavonoids, anthocyanins, phenolic acids, and other bioactive substances. Anthocyanins are important functional components in blueberries. We collected 65 varieties of blueberries to investigate their nutritional and functional values. Among them, Gardenblue had the highest anthocyanin content, with 2.59 mg/g in fresh fruit. After ultrasound-assisted solvent extraction and macroporous resin absorption, the content was increased to 459.81 mg/g in the dried powder. Biological experiments showed that Gardenblue anthocyanins (L1) had antiproliferative effect on cervical cancer cells (Hela, 51.98 μg/mL), liver cancer cells (HepG2, 23.57 μg/mL), breast cancer cells (MCF-7, 113.39 μg/mL), and lung cancer cells (A549, 76.10 μg/mL), and no apparent toxic effects were indicated by methyl thiazolyl tetrazolium (MTT) assay, especially against HepG2 cells both in vitro and in vivo. After combining it with DDP (cisplatin) and DOX (doxorubicin), the antiproliferative effects were enhanced, especially when combined with DOX against HepG2 cells; the IC50 value was 0.02 μg/mL. This was further evidence that L1 could inhibit cell proliferation by inducing apoptosis. The detailed mechanism might be L1 interacting with DNA in an intercalation mode that changes or destroys DNA, causing apoptosis and inhibiting cell proliferation. The findings of this study suggest that L1 extract can be used as a functional agent against hepatoma carcinoma cells. ## 1. Introduction Blueberry (Vaccinium L.) is a new type of berry fruit tree that has been cultivated on a large scale in China. It is one of the five healthy fruits recommended by the World Food and Agriculture Organization because of its pleasant color, sweet and sour taste, and excellent nutrition including organic acids, phenolic acids, and flavonoids [1,2,3]. Blueberries are rich in bioactive compounds, mainly flavones and other polyphenolic compounds [4]. They have a wide range of pharmacological effects, including anticancer effects [5], antioxidant effects [6,7], antiviral effects [8], anti-inflammatory effects [9,10], hypoglycemic effects [11,12], improvement of dyslipidemia [13], anti-obesity effects [14,15] and the prevention and treatment of age-related degenerative diseases [16] and cardiovascular diseases [17]. Anthocyanins account for 50~$80\%$ of the total polyphenol content in blueberry fruits. Our group analyzed the anthocyanins in the Gardenblue blueberry type. We found that the main anthocyanin components were cyanidin and pelargonidin, which were glycosylated or acylated forms of glucose, galactose, and arabinose. Most studies have focused on the crude extract of blueberries, and it is still necessary to explore the exact effective substances with significant biological activity. The mechanism of their curative effect is also worth further study. Anthocyanins are secondary metabolites generated via the synthesis pathways of phenylpropionic acid and flavonoids in plants [18]. Their basic structure consists of two benzene rings (C6-C3-C6) connected by a central three-carbon chain. The major common anthocyanins in plants include pelargonidin, cyanidin, delphinidin, peonidin, petunidin, and malvidin [19]. Usually, anthocyanins are formed through glycosidic bonds with one or more molecules of glucose, rhamnose, galactose, xylose, or arabinose, etc. The glycosides and hydroxyl groups can also form acylated anthocyanins through ester bonds with one or more molecules of coumaric acid, ferulic acid, caffeic acid, p-hydroxybenzoic acid, or other aromatic acids and fatty acids in anthocyanins. More than 500 anthocyanins are known in nature. Pharmacological studies suggest that anthocyanins possess physiological activities, such as promoting retinoid resynthesis, improving microcirculation, antiproliferative and anti-inflammation effects, and reducing blood glucose and blood lipid levels [20]. Cancer is a serious threat to human life and health. At present, the main tumor treatments are surgery, radiotherapy, and chemotherapy, but these are traumatic to the human body and have serious side effects. As a natural plant pigment, anthocyanins have biological activities, and the study of their antiproliferative effects has attracted increasing attention from researchers [21,22,23,24,25]. These effects are related to their excellent antioxidant activity, anti-DNA breakage effect, inhibition of cell proliferation, induction of apoptosis, anti-angiogenesis effect, and other activities. In many reports, anthocyanins have been considered to have effects against colon cancer, skin cancer, prostate cancer, leukemia, and breast cancer, among others. In recent years, many research results have shown that blueberry anthocyanin can inhibit the proliferation of cancer cells. Wang and co-workers found it had a significant inhibitory effect on mice melanoma cells (B16F10) in a dose-dependent manner in vitro [26]. Zhou et al. reported blueberry anthocyanin had a certain inhibitory effect on HepG2 cells and induced HepG2 cells apoptosis in a dose-dependent manner in vitro [27]. The mechanism of apoptosis is related to the mitochondrial apoptosis pathway, mitogen-activated protein kinase (MAPK) signaling pathway, p53 signaling pathway, apoptosis signaling pathway in the endoplasmic reticulum (ER) stress response, the TGF-β signaling pathway, and so on. Due to the damage drugs cause cells, there are also studies on the inhibition effect of blueberry anthocyanin combined with current drugs on cancer cells and the protective effect on normal cells. One study showed that acylated blueberry anthocyanin combined with cyclophosphamide could increase the enzyme activities of total superoxide dismutase (T-SOD), cat, and glutathione peroxidase (GSH PX) in mouse cancer liver tissue [28]. Another study showed for the first time that blueberry anthocyanin has an inhibitory effect on human colon cancer cells (HCT-116), which is mainly related to the induction of apoptosis, cell cycle arrest of G0/G1, reactive oxygen species regulation, and reduction of matrix metalloproteinases [29]. Among these combined conventional drugs, doxorubicin (DOX, also termed Adriamycin) and cisplatin (DDP) have attracted more attention. DOX is widely used as a first-choice anticancer drug for many cancers and is one of the most effective anticancer drugs developed because of its apoptosis-inducing activity [30]. Similar to DOX, DDP exerts anticancer activity via multiple mechanisms, but its most acceptable mechanism involves generation of DNA lesions by interacting with purine bases on DNA followed by activation of several signal transduction pathways, which finally lead to apoptosis [31]. We chose drugs that combine DOX and DDP to enhance the antiproliferative effect of blueberry anthocyanin extract and to explore its possible mechanism related to apoptosis and DNA. In this work, 65 varieties of blueberries were collected and their main phytochemicals and polyphenol qualities were investigated. We found that the content of Gardenblue anthocyanins was the highest. After further purification, we explored their inhibition of cancer cell proliferation both in vitro and in vivo, as well as the effect of combined drugs and the possible mechanism (Scheme 1). We hoped to establish a theoretical basis for developing blueberries’ value and contribute to exploring future antiproliferative drugs from natural foods or plants through an easier extraction method. ## 2.1. Main Phytochemicals Analysis A total of 65 varieties (lines) of blueberries were investigated for their phytochemical profiles, including soluble solids, organic acids, solidity–acid ratio, and total anthocyanin content (Table 1). A high content of soluble solids indicated that sugar content was high, which generally means the fruit is sweet. From Table 1, the average soluble solid content was $11.33\%$, and the content of Vernon was the highest with $15.63\%$, followed by Homebell ($14.77\%$), Tifblue ($13.93\%$), Beckyblue ($13.73\%$), Powderblue ($13.67\%$), Brightwell ($13.6\%$), Pink Lemonade ($13.57\%$), Anna ($13.57\%$), Onslow ($13.17\%$), Chandler ($13.03\%$), and others. The high total acid content suggested the fruit is generally acidic. Caroline blue had the highest total acid content with $1.44\%$ among the investigated blueberry varieties, followed by Sweetheart ($1.0\%$), Ozarkblue ($0.94\%$), Summit ($0.87\%$), Biloxi ($0.86\%$), Berkeley ($0.80\%$), Sharpblue ($0.79\%$), Baldwin ($0.78\%$), Zhongzhi ‘2’ ($0.77\%$), Springhigh ($0.77\%$), and others. The solidity–acid ratio is the ratio of soluble solid to total acid content, higher content indicating sweeter fruit. The results showed Anna was the sweetest, with the highest solidity–acid ratio of 32.38, followed by Jewel (27.76), Beckyblue (27.74), Zhaixuan ‘4’ (27.11), Ruble (26.21), and others. Blueberries have abundant anthocyanins, which are the main component relevant to health. Anthocyanin content is an important index of blueberry fruit quality. According to the investigation of total anthocyanins, Gardenblue had the highest anthocyanin content with 259.46 mg/100 g. The other varieties of blueberries in the top five were Zhaixuan ‘4’ (229.89 mg/100 g), Vernon (228.93 mg/100 g), Centurion (220.85 mg/100 g), and Ruble (192.70 mg/100 g). We further investigated the main polyphenols of the Gardenblue blueberries, including phenols, anthocyanins, ellagic acid, and flavonoids (Table 2). The contents of Gardenblue’s main nutrients were all enhanced by nearly $60\%$ by converting the fresh fruit into dried powder (extract, 459.81 mg/g). Anthocyanins represented the highest content of the main nutrients and are an important functional component in Gardenblue blueberries, belonging to the group of phenols. Lanmei 1 blueberries have the highest anthocyanin content on the domestic market, at 443.08 mg/g [32]. Gardenblue has a higher anthocyanin content (459.81 mg/g, dried powder) than Lanmei 1 and has more potential to be developed as a functional food product with added value. ## 2.2. Component Analysis The analysis via UPLC-QTOF-MS2 showed that there were five types of anthocyanidin: delphintin (m/z 303), cyanidin (m/z 287.1), petunidin (m/z 317), malvidin (m/z 331.1), and peonidin (m/z 301.1), and the sugar residues associated with them were glucoside, galactoside, and arabinoside in Figure 1. Peak 5 and peak 7 had the same molecular ion peak (m/z 449.1) and molecular fragment (m/z 287.1). According to the order of peak production, it was inferred from the literature [33] that peak 5 was cyanidin-3-galactoside and peak 7 was cyanidin-3-glucoside. Peak 8 and peak 9 had the same molecular ion peak (m/z 479.1) and molecular fragment (m/z 317.1). According to the peak sequence and the literature [18], it was speculated that peak 8 was petunidin-3-galactoside, peak 9 was petunidin-3-glucoside, and peak 6 was delphintin-arabinoside or xyloside. Peak 14 and 16 had the same molecular ion peak (m/z 493.1) and molecular fragment (m/z 331), which were speculated to be malvidin-3-galactoside, malvidin-3-glucoside, and malvidin-3-arabinoside or xyloside, respectively. Peak 17 and peak 19 had the same molecular ion peak (m/z 433.1) and molecular fragment (m/z 303.1), which were formed by m/z 433.1 losing a neutral fragment with a mass number of 132. The latter has the same mass number as five-carbon sugars (arabinoside or xyloside) and peonidin after losing a water molecule. Other results are displayed in Table 3. In summary, the highest content of anthocyanins in blueberry fruit was malvidin-3-glucoside. ## 2.3.1. Antiproliferative Activities In order to compare the antiproliferative activity of purified Gardenblue anthocyanin (L1) and well-known antiproliferative drugs, the effects of L1, DDP, DOX, L1-DDP (L1 combined with cisplatin), and L1-DOX (L1 combined with doxorubicin) on the growth of human cancer cell lines Hela, HepG2, MCF-7, and A549, and the normal cell HUVEC, were evaluated in vitro according to IC50 values. DDP and DOX were used as the positive control. The results are listed in Table 4. According to the results, the toxicity of L1 was lower than DDP, DOX, L1-DDP, and L1-DOX for HUVEC cells. For Hela cells, the combined administration of L1 and DDP or DOX led to more effective anti-Hela activity than individual incubation. For the HepG2 cells, the IC50 value was smaller in total than the other four cells, especially L1 combined with DOX, which had the highest anti-HepG2 activity (0.02 μg/mL). L1 combined with DDP had higher activity against HepG2 cells than L1. For MCF-7 cells, the IC50 values of L1 combined with DDP (30.45 μg/mL) and DOX (6.97 μg/mL) were smaller than L1 (113.39 μg/mL) and DDP (39.88 μg/mL), which suggested L1 combined with both DOX and DDP had higher activity against MCF-7 cells than L1. For A549 cells, the IC50 values of L1 combined with DDP (47.07 μg/mL) and DOX (0.55 μg/mL) were smaller than L1 (76.10 μg/mL), which indicated that the combination of DDP or DOX had additive and synergistic effects on A549 cells. According to Wang’s report, blueberry malvidin-3-galactoside suppressed the development of hepatocellular carcinoma cells (HepG2) [34]. The cytotoxicity for the nonmalignant cell line (HUVEC cells) was also investigated to characterize the selectivity expressed according to selectivity index (SI) (SI = (IC50 for nonmalignant cell line HUVEC)/(IC50 for human tumor cell line)) [35], illustrated in Figure 2. SI was an important aspect for prospective pharmacological applications. DDP and DOX displayed good cytotoxicity to human tumor lines but relatively low selectivity (SI < 1). It was one of the important aims of our work to reduce cytotoxicity to nonmalignant cells. After being combined with L1, it was found that L1-DDP and L1-DOX demonstrated a moderate-to-good cytotoxic activity against human cancer cells and obviously enhanced selectivity towards HepG2, MCF-7, and A549 compared to DDP and DOX. In general, whether L1 was combined with DOX or DDP, the antiproliferative activity was significantly enhanced compared to L1, which revealed that a combination of drugs can improve the treatment effect. Among all tested materials, L1 combined with DOX had the smallest IC50 value of 0.02 μg/mL against HepG2 cells, which suggested that L1 combined with DOX was more effective at inhibiting cultured HepG2 cell survival, and it may be a potential antiproliferative drug. ## 2.3.2. Induction of Apoptosis Blueberry anthocyanins have been found to play a role in mitochondrial-mediated apoptosis [34]. Apoptosis plays a core role in cancer because its induction in cancer cells is the key to successful treatment [35]. Apoptosis is controlled by genes of orderly cell death. It is not a passive process, but an active process involving a series of activation, expression, and regulation of genes. It is not a pathological condition or a phenomenon of self-injury, but is required to adapt better to the environment and is an essential death process [36]. The apoptosis assay can provide important information for studying the action mode of cells. In our previous work, L1 was found to have higher anti-HepG2 activity; therefore, we further studied whether L1 induced apoptosis in HepG2 cells. The flow cytometric analysis is shown in Figure 3. After L1 was incubated with HepG2 cells with the concentration range from 12.5 to 50 μg/mL, the number of live cells reduced and that of apoptotic cells increased, exhibiting a dosage-dependent effect, as in Figure 3. When the concentrations of L1 were 12.5, 25, and 50 μg/mL, the early apoptotic rates were $21.1\%$, $40.8\%$, and $42.5\%$, and the late apoptotic rates were $13.7\%$, $20.9\%$, and $38.2\%$, respectively. During the induction of the apoptosis process, live cells tended to develop toward apoptotic cells with increased concentration. The reported results distinctly illustrated that L1 can inhibit cell proliferation by inducing apoptosis. ## 2.3.3. Detection of Intracellular Reactive Oxygen Species (ROS) As previously reported, excess cellular levels of ROS cause damage to proteins, nucleic acids, lipids, membranes, and organelles, which can lead to activation of cell death processes such as apoptosis [37]. There is powerful evidence that ROS are the underlying cause of many chronic diseases that involve enhanced intracellular oxidative stress [38]. The effect of representative L1 on cellular ROS levels in HepG2 cancer cells was detected using flow cytometry. HepG2 cells were incubated with L1 (25 μg/mL) for 48 h, and the results are shown in Figure 4. The percentage of ROS was $21.2\%$, while the control was $4.1\%$. After adding NAC (N-acetylcysteine), a known ROS inhibitor, the percentage fell back to $9.9\%$. Generally, these data indicated that L1 could induce the enhancement of ROS production, which might cause apoptosis. ## 2.3.4. Confocal Fluorescence Images The induced apoptosis process was made visible via confocal fluorescence images (Figure 5), since L1 was able to inhibit cell proliferation by inducing apoptosis. The nucleus stained with PI (propidium iodide) presented red fluorescence while the cytoplasm stained with Annexin V-FITC presented green fluorescence. Normal cells were not stained by fluorescence, while apoptotic cells were stained only by green fluorescence, and necrotic cells were stained by both green and red fluorescence. In the negative control, there was scarcely any green and red fluorescence signal, which suggested the cells were in a normal state. After incubation with L1 (25 μg/mL) at 37 °C for 48 h, the green fluorescence signal was strong inside the cytoplasm and the red fluorescence signal was very weak, demonstrating the apoptotic ability of L1. ## 2.3.5. In Vivo Experiment L1 was administered to mice in in vivo experiments for further investigation. In this experiment, tumor model mice with HepG2 cells injected intravenously with L1 (10 mg/kg) from day 1 to day 25, every 3 days, were used. As shown in Figure 6a–c, the volume and weight of tumor model mice were reduced clearly after injection with L1 (10 mg/kg) compared to PBS control. For L1, the average volume of the HepG2 tumor was 0.495 cm3, while the PBS control was 1.478 cm3. The weight of the tumor was 0.60 g when the PBS control was 1.69 g. The relative tumor proliferation rate (T/C) was $35.56\%$ and the tumor inhibition rate was up to $64.86\%$. In Figure 6d, no significant toxicity was observed in the heart, liver, spleen, lung, kidney, or brain tissues of the mice injected with L1, which displayed no obvious changes in the morphology of these organs. In Figure 6e, compared to PBS control, CD31 immunohistochemical staining with tumor model mice was conducted on the 25th day after injecting L1 (10 mg/kg), and they exhibited an obviously decreased tumor angiogenesis rate, which showed L1 could inhibit tumor growth. As Wang reported, higher doses of blueberry anthocyanin had significant inhibitory effects on the migration and invasion activity of HepG2 cells at 80 mg/kg in vivo, accompanied by a decrease in cytoplasm and a sparse cell distribution and suppressed tumor growth [34]. Overall, these results clearly suggested that L1 had high anti-HepG2 activity both in vitro and in vivo experiments and demonstrated that L1 possesses great promise as a nontoxic antiproliferative drug and future clinical treatment option. ## 2.3.6. DNA Binding Modes Rowe et al. reported that when DNA damage is introduced into cells from exogenous or endogenous sources, there is an increase in the amount of intracellular ROS that is not directly related to cell death but related to apoptosis [39]. Intercalation is well known to strongly influence the properties of DNA and has been reported as a preliminary step in mutagenesis. Many drugs can induce apoptosis through intercalating or binding with DNA, such as DOX [40]. We further investigated whether L1 had similar activity to DOX in terms of DNA-binding properties. The Salmon Sperm DNA (fDNA) binding modes were evaluated with ethidium bromide (EB) fluorescence displacement experiments. EB had no perceptible emissions in buffer solution. After adding DNA, the fluorescence intensity improved, which was considered to be due to its strong intercalation with DNA base pairs. The intercalation of the compound with the base pairs of DNA was confirmed when the fDNA-EB emissions were decreased or quenched upon adding a compound [41,42,43]. As expected, the emission intensity reduced (shown in Figure 7) by adding L1 to fDNA-EB, which showed that L1 can bind to DNA at the sites occupied by EB and that it can interact with DNA via intercalation. The interaction between L1 and DNA was studied using fluorescence spectroscopy. The results of the fluorescence spectrum showed that L1 could induce fluorescence quenching in the fDNA-EB system, and the degree of fluorescence quenching increased with the increase in concentration. The above tests indicated that L1 interacted with DNA in an intercalation mode. Compounds can interact with DNA in an intercalation mode, which can change or destroy DNA, thus causing apoptosis, which is also one of the reasons for the inhibition of cell proliferation activity of L1. ## 3. Discussion The solidity–acid ratio is one of the important indexes of fruit quality, which largely depends on the kinds of sugar contained in the fruit class, quantity, and organic acid content. High-acid and low-sugar fruit is experienced as sour and is poorly accepted by people, and low-acid and high-sugar fruit tastes weak and does not meet the requirements for fresh food. In addition, fruit sweetness is related to the type of sugar. Consumers may choose fresh blueberries based on their appearance, sweetness and acidity, taste, flavor, and overall feeling. The Gardenblue blueberry has become a popular choice among consumers and has several advantages, including a higher yield, medium fruit size, longer maturation period, and better taste, smell, and storage qualities. The most important thing to consider is that the Gardenblue blueberry has the highest anthocyanin content with 2.59 mg/g of fresh fruit and 459.81 mg/g of dried powder compared to other blueberries collected in the same period and place. Anthocyanin can inhibit the proliferation of cancer cells, which suggests Gardenblue anthocyanin has higher antiproliferative effects. The antiproliferative effect of Gardenblue anthocyanin (L1) on cancer cells has been evaluated. Compared to three other cancer cells, L1 has the highest antiproliferative effect on HepG2 cells. We further proved that a combination of drugs can improve the treatment effect by using L1 combined with DOX and obtaining the smallest IC50 value of 0.02 μg/mL against HepG2 cells. According to the analysis of the selectivity index, it was found that L1-DDP and L1-DOX demonstrated a moderate-to-good cytotoxic activity against human cancer cells and obviously enhanced selectivity towards HepG2, MCF-7, and A549 cells compared to DDP and DOX, especially towards HepG2 cells. This result suggested that L1 combined with DOX was more effective at inhibiting cultured HepG2 cell survival, and it may be a potential antiproliferative drug. In vivo experiments indicated that anthocyanins can prevent and treat cancer, with a relative tumor proliferation rate (T/C) of $35.56\%$ and a tumor inhibition rate of up to $64.86\%$, which is higher than the result in our previous paper covering the anticancer activity of natural products, suggesting that it has excellent antiproliferative effects [30,36]. L1 could induce the enhancement of ROS production, which might cause apoptosis. The further mechanism might be that L1 interacted with DNA in an intercalation mode, which can change or destroy DNA, thus causing apoptosis and inhibiting cancer cell proliferation. As Wang reports, blueberry anthocyanin suppressed the proliferation, polarization, migration, and invasion activities of HepG2 cells in vitro by regulating the protein expression of cyclin D1/B/E, caspase-3, cleaved caspase-3, Bax, c-Jun N-terminal kinase (JNK), and p-p38, activating phosphatase and tensin homologue deleted on chromosome 10 (PTEN), accompanied by a decrease in the phosphorylation-AKT (p-AKT) level, and lowering the protein expression levels of matrix metalloproteinase 2 (MMP-2) and matrix metalloproteinase 9 (MMP-9). In vivo, blueberry anthocyanin promoted the apoptosis of liver tumor cells, as determined by immunohistochemistry [34]. Similarly, Yang et al. found that blueberries had a very significant inhibitory effect on breast, non-small-cell lung, and colon cancer cells, and explored the possible mechanisms for this, such as apoptosis induction and inhibiting cell proliferation. A combination of suboptimal concentrations of equimolar anthocyanidins of berries suppressed the growth of two aggressive non-small-cell lung cancer cell lines, and a variety of berry mixtures with diverse anthocyanins had a therapeutic effect in non-small-cell lung cancer and prevented its future recurrence and metastasis. For cancer prevention, blueberries’ ability to decrease DNA damage (the first step of cancer) is seen as promising. Bioactive compounds of blueberries can regulate the expression of genes following DNA damage [5]. Blueberry anthocyanins may be recommended for the treatment of HepG2 cells. Besides anthocyanins, ellagitannins, and their gut microbiota-derived metabolites, other important bioactive molecules found in blueberries were shown to trigger autophagy in human colorectal cancer cells and to induce apoptosis by increasing the expression of proapoptotic proteins p21 and p53 and decreasing the anti-apoptotic protein expression of B-cell lymphoma-2 (Bcl-2). Moreover, the proapoptotic effect was achieved through downregulation of the phosphatidylinositol 3-kinase/amino threonine protein kinase (PI3K/AKT) signaling pathway [44]. With the rise in consumer awareness regarding the need for healthy eating habits, researchers have been striving to find alternative natural sources of additives that, while being completely safe, may also have health benefits. Gardenblue contains five anthocyanins (delphinidin, peonidin, petunidin, cyanidin, and malvidin) and can inhibit the growth of cancer cells and induced apoptosis, which suggests that it may be a potential healthy food or drug. ## 4.1. General Materials A total of 65 varieties of blueberries were collected during the ripening period (from May to July) in 2022 from the Institute of Botany, Jiangsu Province, and the Chinese Academy of Sciences, Baima field. Analytical reagents (AR) and solvents were used in the general experimental procedure unless otherwise stated. Purifications were performed using flash chromatography on microporous resin (HPD-100B). UV–vis adsorption spectra were recorded by a TU-1810 spectrophotometer. Annexin V-FITC/PI was purchased from Shanghai Beyotime Biotechnology Co. LTD, Shanghai, China. Reagents and compounds, including MTT, DMEM, FBS, and penicillin/streptomycin, were all commercially purchased from Nanjing Keybionet Biotechnology Co. LTD. Hela, HepG2, MCF-7, A549, and HUVEC cells were purchased from the National Collection of Authenticated Cell Cultures. Other equipment included an EX-200A Electronic Balance, ZD-2 Automatic Potential Titrator, PL-5-B Low-speed Centrifuge, Philips Blender HR2838, Agilent 1260UHPLC-6530 Q-TOF MS, NIB610 microscope, KQ-300DE ultrasonic cleaner, BD Accuri C6 flow cytometry, and Zeiss LSM 900 Laser Scanning Confocal Microscope. ## 4.2. Soluble Solids Content Pal-1 was used to measure the soluble solid content of 65 varieties of fresh blueberry fruit (unit: °Brix). ## 4.3. Titratable Acid Content Determination For the Determination of Total Acid in Food method from GB/T12456-2008, the determination result was calculated using citric acid. A total of 50 g of fresh blueberries was broken up and 3 g was taken out and placed into a 50 mL centrifuge tube whereupon 20 mL of deionized water was added. The mixture underwent ultrasonic treatment at 35 °C and 60 Hz for 20 min and was centrifuged at 5000 rpm for 5 min. The supernatant was transferred to a 100 mL beaker and the initial pH value of the solution was measured using a pH meter. The NaOH standard solution (0.1 mol/L) was titrated until the pH value of the solution was 8.0, which was the end point. The amount of NaOH was recorded and the titratable acid content was calculated [36]. ## 4.4. Extraction and Purification Improving upon previous work, 2 kg of frozen Gardenblue blueberry fruits were hydrolyzed with $0.02\%$ pectinase at 40–50 °C for 2 h and then centrifuged at 4000 rpm for 5 min. We extracted the precipitates with $50\%$ ethanol (containing $0.1\%$ formic acid by volume) three times. After ultrasound-assisted extraction at 40 °C and 60 Hz for 30 min, the extract was centrifuged at 5000 rpm for 5 min [20,36]. We filtrated the above extract, retaining the supernatant; evaporated the ethanol, retaining the aqueous solution; and adsorbed with a macroporous resin column (HPD-100B). The solvent was eluted with $50\%$ ethanol. We repeated the adsorption, collected the eluent, removed the solvent, and freeze dried the resulting substance. The final product obtained was Gardenblue anthocyanins (L1). ## 4.5. Component Analysis An Agilent 1260 Infinity ultra-high-performance liquid chromatography system was used for separation according to Agilent Poroshell 120 SB-AQ (3.0 × 100 mm, 2.7 μm) columns. The mobile phase consisted of $0.1\%$ formic acid water (A) and acetonitrile (B), using gradient elution: 0 min, $10\%$B; 15 min, $15\%$B; 20 min, $15\%$B; 30 min, $30\%$B; 35 min, $40\%$B; 36 min, $90\%$B; and 41 min, $90\%$B. The flow rate was 0.5 mL/min and the column temperature was 30 °C. The detector was an Agilent 6530 Accurate-Mass Tandem Quadrupole-Time of Flight Mass Spectrometer (Q-TOFMS) with an electrospray ion source (ESI). The positive ion mode was used to detect the mass spectrometry. The detection parameters were DAD wavelength: 280 nm, capillary voltage: +4.0 kV, sprayer pressure: 50 psi, dry temperature: 350 °C, Vcap: 3500 V, breakage voltage: 150 V, and scanning range: m/z 100–1000, and Nitrogen was used as the atomizing and desolvating gas with a flow rate of 1 L/min. The reference standards used to identify the compounds were cyanidin-3-O-glucoside, delphintin-3-O-glucoside, malvidin-3-glucoside, peonidin-3-glucoside, and petunidin-3-glucoside. ## 4.6. Cell Culture, Antitumor Activities, and Cytotoxicity Assay Hela, HepG2, MCF-7, A549, and HUVEC cells were seeded in 96-well plates with a density of 1 × 104 cells per well. After 12 h of incubation at $5\%$ CO2 and 37 °C, the culture media were removed and the cells were incubated with DDP, DOX, L1-DDP (L1 combined with cisplatin), and L1-DOX (L1 combined with doxorubicin) dissolved in DMEM at different concentrations (each concentration was repeated three times) for 48 h at $5\%$ CO2 and 37 °C. Subsequently, we removed the culture media, and the new culture medium containing MTT (1 mg/mL) was added, followed by incubation for 4 h to allow the formation of formazan dye. After removing the medium, 200 μL DMSO was added to each well to dissolve the formazan crystals. Absorbance was measured at 595 nm in a microplate photometer. Cell viability values were determined (at least three times) according to the following formula: cell viability (%) = the absorbance of experimental group/the absorbance of blank control group × $100\%$ [30,35,36]. ## 4.7. Induction of Apoptosis Assay We further investigated whether L1 could induce apoptosis. DMSO was used as a negative control. HepG2 cells (1 × 106) were cultured in 35 mm dishes and incubated at 37 °C for 24 h. After incubation with DMSO at 5 μg/mL, L1 at 12.5, 25, and 50 μg/mL was added for 48 h (each concentration was repeated three times and the incubation time was the optimum), and then the treated cells were washed, trypsinized (non-EDTA, ethylene diamine tetraacetic acid), and centrifuged (2000 rpm/min). Next, the cells were collected and resuspended in 500 μL buffer solution loaded with Annexin V-FITC apoptosis detection reagent (with 5 μL Annexin V-FITC and 5 μL PI). The Annexin V-FITC stained cells were incubated for 5–15 min in the dark, and approximately 1 × 104 cells were collected and 80,000 events for flow cytometry analysis with a single 488 nm argon laser were conducted with BD Accuri C6 flow software and cytometry [36]. ## 4.8. Detection of Intracellular Reactive Oxygen Species (ROS) HepG2 cells were cultured in a 6-well plate overnight and then incubated with L1 (0, 25 μg/mL) for 48 h. The cells were then incubated with dihydrodichlorofluorescein diacetate (DCFH-DA) (10 μM) for 30 min at 37 °C in the dark. Finally, the cells were collected and washed in PBS and the samples were analyzed via flow cytometry. Excitation wavelengths were 488 nm and emission wavelengths were 535 nm [36]. ## 4.9. Confocal Fluorescence Images HepG2 cells were seeded on 35 mm glass dishes for 24 h and then incubated with L1 (25 μg/mL) for 48 h at $5\%$ CO2 and 37 °C before undergoing fluorescence imaging. After incubation, the culture medium was removed and cells were washed with PBS (pH = 7.4) and then incubated with fresh medium. Confocal fluorescence images were taken with the excitation of Annexin V and PI channels [36]. ## 4.10. In Vivo Experiment The in vivo experiment was undertaken using Nanjing Keygen Biotech. Co. Ltd., Nanjing, China. To develop the tumor model, 1 × 106 HepG2 cells were subcutaneously injected into the right armpit of Balb/C nude mice. Two groups of HepG2-tumor-bearing mice with five mice per group were randomly chosen in our experiment: [1] PBS (as a control), [2] L1. After the size of the tumors reached 80 mm3, all agents, including PBS and L1 solutions, were administrated via an intravenous injection (dose = 10 mg/kg). During the next 25 days, the tumor size of each mouse in our experiments was measured with a vernier caliper every 3 days. To accurately evaluate the growth inhibition of tumors, the mice were sacrificed after 25 days, and then their tumors were collected, photographed, and weighed. Sections of tumor, heart, kidney, liver, lung, and spleen tissues of different groups harvested on the 25th day were observed using H&E staining and then examined by a pathologist. The tumor size was calculated as the volume = 0.5 × (tumor length) × (tumor width)2. The inhibition efficiency of tumor growth was calculated according to the equation: inhibition efficiency (%) = (1 − the weight of experimental group/the weight of control group) × $100\%$. CD31 immunohistochemical staining with mice was conducted on the 25th day after an intravenous injection of L1 (10 mg/kg) and PBS control [36]. ## 4.11. DNA Binding Modes A volume of 3 mL of DNA-EB mixture solution (CDNA/CEB = 10) was added to the sample pool, and an equal volume of compound sample was added to the sample pool each time, which caused an increase in the concentration ratio between L1 and DNA. Fluorescence spectra were measured at an excitation wavelength of 520 nm [36]. ## 4.12. Data Analysis Statistical software SPSS 16.0 (IBM, Chicago, IL, USA) was used to analyze the data, including the correlation analysis. One-way ANOVA was used to compare mean values, and Duncan post hoc multiple comparisons were used ($p \leq 0.05$). Origin 2019b (OriginLab Crop., Northampton, MA, USA) was used to analyze the IC50 values with linear fitting. All experiments were repeated three times, and the results represent the mean ± standard deviation. ## 5. Conclusions In this study, Gardenblue blueberries were found to have rich phytochemicals, such as polyphenols, anthocyanin, ellagic acid, and flavonoid. The highest content of anthocyanins in blueberries was malvidin-3-glucoside. Gardenblue anthocyanin extract had high antiproliferative effects both in vitro and in vivo, especially on HepG2 cells. A combination of drugs with additive and synergistic effects demonstrated a moderate-to-good cytotoxic activity against human cancer cells and obviously enhanced selectivity towards HepG2 compared to DDP and DOX, which suggested that Gardenblue anthocyanin extract may be developed as a potential antiproliferative agent by combining it with drugs in the future. The mechanism for this may be that Gardenblue anthocyanins interact with DNA in an intercalation mode, which could change or destroy DNA, cause apoptosis and inhibiting cancer cell proliferation. These findings require further research of more possible mechanisms. However, the antiproliferative effect decreases according to when the bioavailability of anthocyanins is low, thus further work on bioavailability will be needed in future. This research may contribute to the future development of antiproliferative drugs from natural foods or plants. ## Figures, Scheme and Tables Scheme 1: The creative idea of the article. Figure 1: UPLC-QTOF-MS/MS total ion flow diagram of anthocyanin components in blueberries. Figure 2: The selectivity index (SI) of L1, DDP, DOX, L1-DDP, and L1-DOX. (SI = (IC50 for normal cell line HUVEC)/(IC50 for human tumor cell line)). Figure 3: Apoptosis assay of HepG2 cells treated with L1. (a) The control was HepG2 cells treated with DMSO (negative control), followed by L1 at 12.5, 25, and 50 μg/mL. (b) Apoptotic rate of control and L1 at 12.5, 25, and 50 μg/mL, including early apoptosis and late apoptosis. Figure 4: ROS generation assay of L1 in HepG2 cells. Cells not treated with L1 were used as the control for 48 h, while other cells were treated with L1 (25 μg/mL) and added NAC after treatment with L1 (25 μg/mL) for 48 h, respectively. Figure 5: Confocal fluorescence images of HepG2 cells incubated with L1 (25 μg/mL) for 48 h at 37 °C. Scale bars are 20 μm. Figure 6: (a) Change in tumor volume of mice injected with L1 (10 mg/kg) compared with PBS control; (b) change in body weight of mice injected with L1 (10 mg/kg) compared with PBS control; (c) the tumor weight of mice injected with L1 (10 mg/kg) compared with PBS control after 25 days; (d) haematoxylin and eosin staining of the brain, heart, liver, spleen, lung, and kidney tissues collected from mice on the 25th day after intravenous injection of L1 (10 mg/kg), compared to that from mice on the 25th day after intravenous injection of PBS as a control; (e) CD31 immunohistochemical staining of tumor model mice on the 25th day after an intravenous injection of L1 (10 mg/kg) and PBS control. Scale bar = 20 μm. Error bars are based on standard errors of the mean ($$n = 3$$). 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--- title: The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy authors: - Beatrice Campanella - Stefano Legnaioli - Massimo Onor - Edoardo Benedetti - Emilia Bramanti journal: Metabolites year: 2023 pmcid: PMC10055013 doi: 10.3390/metabo13030393 license: CC BY 4.0 --- # The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy ## Abstract Saliva is an easily sampled matrix containing a variety of biochemical information, which can be correlated with the individual health status. The fast, straightforward analysis of saliva by vibrational (ATR-FTIR and Raman) spectroscopy is a good premise for large-scale preclinical studies to aid translation into clinics. In this work, the effects of saliva collection (spitting/swab) and processing (two different deproteinization procedures) were explored by principal component analysis (PCA) of ATR-FTIR and Raman data and by investigating the effects on the main saliva metabolites by reversed-phase chromatography (RPC-HPLC-DAD). Our results show that, depending on the bioanalytical information needed, special care must be taken when saliva is collected with swabs because the polymeric material significantly interacts with some saliva components. Moreover, the analysis of saliva before and after deproteinization by FTIR and Raman spectroscopy allows to obtain complementary biological information. ## 1. Introduction Saliva is a matrix rich of biochemical information. The term “salivaomics” was introduced in 2008 to indicate the complexity and the importance of knowing the various “omic” constituents of saliva (https://iadr.abstractarchives.com/abstract/2008Dallas-100600/salivaomics-knowledge-base-skb, accessed on 27 February 2023). It is quite clear that whole-mouth saliva contains a variety of high- (proteins and nucleic acids) or low-molecular-weight compounds (salts, organic and inorganic acids, sugars, and nitrogenous bases.) and that its analysis might disclose clinically relevant information regarding the oral and systemic health status [1,2] (and references therein). Saliva collection is noninvasive and straightforward; it has high patient compliance, and it can be easily repeated [3,4]. For this reason, many biological and bioanalytical techniques (chromatographic and spectroscopic) have been developed in the last 15 years to investigate salivaomics through targeted and untargeted methods [5,6]. Attenuated total reflectance-Fourier transformed infrared spectroscopy (ATR-FTIR) is a nondestructive/microdestructive, fast, and cost-effective spectroscopic approach that requires in principle minimal sample handling to collect information from biological samples, tissues, cells, or biofluids Several reviews report on the application of mid-infrared (IR) as a promising tool in human saliva [2,7,8,9,10,11,12,13]. The analysis of saliva as a diagnostic specimen by ATR-FTIR in tandem with chemometric analysis has experienced a rapid growth over the last decade, and even more in the last 2–3 years. In 1996, a new quantitative method based on transmittance FTIR was developed to evaluate thiocyanate concentrations in 5 µL of dried human saliva [14] using the band at 2058 cm−1. More than 10 years later, Khaustova et al. developed an ATR-FTIR method to rapidly assess the biochemical properties of the saliva (total protein concentration, glucose, secretory immunoglobulin A, urea, amylase, cortisol, and inorganic phosphate) [15]. Recently, FTIR has been applied to study saliva from diabetic patients [16,17,18,19,20,21] and patients with oral pathologies [22,23] and to identify cancer biomarkers [4,24,25] and COVID-19-related biomarkers [26,27,28,29]. Recently, ATR-FTIR spectra in tandem with chemometric have been employed to analyze the spectral changes in semen, saliva, and urine in violent crimes during dry out, allowing to estimate their time since deposition [30]. Raman spectroscopy can yield complementary information to IR spectroscopy as the two techniques rely on different processes and selection rules. The inherently weak Raman signals of biological molecules, often overwhelmed by sample fluorescence, are counterbalanced by the fact that Raman spectra are mostly unaffected by water bands and exhibit sharper signals compared to IR. The application of Raman to saliva analysis was recently reviewed by Hardy et al. [ 31]. Although sample preparation in vibrational spectroscopy is minimal, several methodological features are critical to obtain reproducible, comparable spectra of saliva [8,13,32]. Thus, it is crucial to standardize the preanalytical step, including both saliva sampling [2,33] and sample preparation, to obtain time- and cost-effective procedures and to minimize sample handling and possible contaminations. Saliva composition depends on the collection method, as well as on the nature and duration of salivation stimulation, subject hydration status, collection timing, etc. [ 2,33]. In many studies, vibrational spectra are acquired on dried samples, adopting a drying time variable between 3 min (directly drying the saliva sample onto the plate of the ATR device) and 24 h (after drying onto various supports for ATR and scattering analysis). Table 1 summarizes the main works published in which FTIR spectroscopy was employed for the analysis of saliva, focusing on the brief descriptions of the preanalytical steps. Basically, in all the works, the spectra were recorded on dried samples, i.e., after the removal of water. Water bands may indeed affect the sensitivity and reproducibility in the detection of several sample components, especially for IR. In the last few years, our research group has extensively studied the salivary metabolites by liquid and gas chromatography approaches [45,46,47,48,49,50]. The analysis of saliva by ATR-FTIR and Raman provides complementary, fast, and holistic information on the sample, which includes low-molecular-weight (MW) metabolites and (macromolecules proteins, carbohydrates, and lipids), both having a high diagnostic value for local and systemic disorders. The aim of this work is to investigate the effect of saliva sampling (spitting method or sampling with commercial polymeric swab) on the vibrational spectra (ATR-FTIR and Raman) acquired before and after deproteinization with two methods (protein precipitation with ethanol or using 3 kDa cut-off centrifugation units). The spitting method may indeed simplify the sampling, meeting patient compliance (especially for children) and reducing costs and risks. Saliva contains about 0.1–1.5 mg/mL protein [51], and the saliva deproteinization may simplify the spectral information, allowing the analyst to focus on the analytical window of interest. In all cases, information remains complex, and the coupling with chemometrics is crucial to extract information from the vibrational spectra. An easy “printing” of sample dried spots (SDSs) prepared on polypropylene (PP) sheets onto ATR crystal is described for the fast, interference-free acquisition of FTIR spectra. Our work implements the information recently reported by Paschotto et al. [ 4], who investigated ATR-FTIR absorption of saliva sampled with different collection methods (spitting method vs. soaking) and processing protocols (dried unprocessed, dried supernatant after centrifugation, and dried concentrate), confirming the need of standardized collection–processing protocols based on the biochemical component analysis. Paschotto et al. investigated the effects of sampling using cotton swabs, and they applied centrifugation conditions at low g values, probably removing cells and bacteria. They did not investigate the deproteinization effect, nor were both FTIR and Raman spectroscopy used. In our work, the concentrations of the main metabolites in saliva after the various sample handling procedures were also determined by RP-HPLC-DAD [49] to focus on the possible artefacts of saliva sampling and sample handling. ## 2.1. Chemicals Sulfuric acid for HPLC analysis was employed (V800287 VETEC ≥ $85\%$ Sigma-Aldrich, Milan, Italy). Methanol for RP-HPLC was purchased from Carlo Erba (Rodano, Italy). Preparation and dilution of samples and solutions were performed gravimetrically using ultrapure MilliQ water (18.2 MΩ cm−1 at 25 °C, Millipore, Bedford, MA, USA). Standard solutions for HPLC (TraceCERT®, 1000 mg/L in water) were purchased from Sigma-Aldrich, Milan, Italy. Analyte stock and working solutions were prepared as previously reported [49]. ## 2.2. Experimental Design: Saliva Sample Collection and Processing Whole, nonstimulated saliva samples were collected from 10 nominally healthy volunteers. The study was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all volunteers who agreed to provide saliva samples. A fasting period of at least 8 h was required, and volunteers did not brush or rinse the oral cavity with mouthwash before sampling. Exclusion criteria included the existence of any oral disease or a systemic pathology, alcohol consumption, smokers, or systemic medication usage. The pattern of samples analyzed was the following: The volunteers were asked to spit into sterile polypropylene tubes (about 2 mL for each subject). Saliva samples were pooled, homogenized in vortex, and stored in a freezer at −20 °C. For the analysis, pooled saliva was thawed at room temperature and subdivided into two processing groups: one half (“salivette” in this work) was loaded onto Salivette® swabs (2 mL/swab) for 5 min as physiological time for the adsorption of the whole saliva, centrifuged at 4500× g for 10 min at 4 °C (Eppendorf™ 5804R Centrifuge), and pooled again. Second half was used as is (unprocessed saliva, “saliva” in this work). This procedure was chosen to perform the methodological comparison exactly on the same sample, avoiding changes in saliva composition due to presence of the swab. Both saliva and salivette samples were fractionated in three parts: (i) a part was analyzed as is (named saliva and salivette); (ii) a part was deproteinized by ultracentrifugation (30 min) using Microcon® Centrifugal Filters with cut-off 3 kDa (Merk, Milan, Italy) (named saliva_CO and salivette_CO); (iii) a total of 100 μL of saliva or salivette was mixed with 900 μL ethanol (EtOH) (10-fold dilution), cooled at −20 °C for 2 h, and centrifuged at 14,000 rpm (10,000× g) for 30 min in a refrigerated centrifuge (named saliva_EtOH and salivette_EtOH). The solution remaining in the upper part of 3 kDa cut-off filtering units was also analyzed by ATR-FTIR to characterize the HMW compounds (“HMWsaliva_CO” and “HMWsalivette_CO”). ## 2.3. ATR-FTIR Analysis Five drops (50 μL each) of sample were deposited onto a polypropylene (PP) sheet by a micropipette (Eppendorf Research Plus pipette, Eppendorf AG) and air-dried at room temperature overnight. Spectra were recorded in ATR mode on sample dried spots (SDSs) using a Frontiers FTIR spectrometer (Perkin Elmer, Milan, Italy), equipped with a diamond-attenuated total reflectance (ATR) sampling accessory. The flat sample press tip (2 mm diameter) was employed to “stamp” the sample from the SDSs (Figure 1). After this, the PP sheet was removed. The microamount “printed” on the ATR diamond window was enough to obtain reliable and reproducible spectra. Using this method, at least 3 spectra can be recorded from 3 different areas of one single SDS. Spectra were recorded in 4000–600 cm−1 spectral range with a 4 cm−1 resolution, with 32 scans for the background and the sample. For each analysis, the diamond sampling window and the sample press tip were cleaned with $70\%$ ethanol v/v. Mid-infrared (MIR) spectra were acquired on 3–5 different SDSs. Saliva_EtOH and salivette_EtOH sample spectra were acquired after the deposition of 3 μL of the samples directly onto the ATR crystal as ethanol evaporates in less than 15 s. HMWsaliva_CO and HMWsalivette_CO samples were analyzed by wiping (w) the tip wetted with the sample onto ATR crystal (samples dried in less than 15 s) or by “printing” (p) from SDSs. ## 2.4. Raman Analysis Five drops (10 μL each) of sample were deposited onto a glass slide covered with an aluminum foil and air-dried at room temperature overnight. Spectra were recorded with a Renishaw inVia confocal micro-Raman system, coupled with an optical Leica DLML microscope equipped with an NPLAN objective 50×. The laser sources were a diode laser with a wavelength of 785 nm and an He–Ne laser with a wavelength of 633 nm. The spectrometer consisted of a single-grating monochromator (1200 or 1800 lines mm−1 according to the selected laser wavelength), coupled with a CCD detector, a RenCam 578 × 400 pixels (22 µm × 22 µm) cooled by a Peltier element. The spectral calibration of the instrument was performed on the 520.5 cm−1 band of a pure silicon crystal. Spectra were acquired with 633 nm laser source at 5.5 mW and with 785 nm laser source at 40 mW, 5 accumulations of 10 s each. ## 2.5. RP-HPLC-DAD Analysis Saliva, salivette, saliva_CO, and salivette_CO samples were 5-fold diluted in 5 mM sulfuric acid, filtered using a 0.20 μm RC Mini-Uniprep (Agilent Technologies, Milan, Italy) filter, injected in the HPLC system (Vinj = 5 μL), and analyzed as previously reported [49]. Saliva_EtOH and salivette_EtOH were directly injected in the HPLC system (Vinj = 5 μL). ## 2.6. Data Processing Principal component analysis (PCA) was carried out on the mean-centered column-wise spectra to investigate possible clustering of samples. ATR spectra were standardized by using standard normal variate (SNV) to minimize unwanted contributions (e.g., global intensity effects or baseline shifts). Raman spectra were treated to remove cosmic rays, and then Savitzky–Golay (zero-order derivative, third-degree polynomial order, and a window size equal to 9 data points) and Asymmetric Least Squares algorithms were applied for smoothing and baseline correction, respectively. The analysis was performed with the open-source Chemometric Agile Tool (CAT) program (http://www.gruppochemiometria.it/index.php/software/19-download-the-r-based-chemometric-software, accessed on 27 February 2023) and by a tailored in-house R-script (R version 3.6.3 (R Development Core Team 2012) and R-Studio, Version 1.1.463) using the R-package mdatool. ## 3.1. ATR-FTR Analysis of Saliva/Salivette Dried Spots: Effect of Deproteinization Method ATR-FTIR spectra were recorded on microspots “printed” from the dried spots on the ATR diamond window. The flat sample press tip (2 mm diameter) was employed to “stamp” the sample from the dried spots. After this, the PP sheet was removed. This procedure, not previously reported, allows in principle to prepare samples quickly onto a low-cost support and to obtain reliable and reproducible spectra using a microamount of sample. Using this method, at least three spectra can be recorded from three different areas of one single dried spot obtained from 50 μL. Figure 2 shows a representative ATR-FTIR spectrum of a saliva dried spot. Figure 3 shows the spectra of all the analyzed samples before and after SNV normalization. The absorption bands of lipids, proteins, carbohydrates, and nucleic acids are evidenced. The IR spectrum of saliva is in fact a superposition of the absorption spectra of all these components in proportion to their concentration, following the Lambert–Beer law. The sampling and the deproteinization method employed evidenced major changes in the FTIR spectra of dried spots in the 1750–600 cm−1 fingerprint region and in the N–H and OH stretching regions (3800–1600 cm−1) and overlaid the latter in the region of C–H stretching in CH2 and CH3 (3000–2850 cm−1). The FTIR spectrum of almost all samples examined showed the characteristic FTIR features of biological samples: the peaks of proteins at 1656–1642 cm−1 (Amide I, C=O stretching), 1542 cm−1 (Amide II, N–H bending), and 1237 cm−1 (Amide III); nucleic acids (1100–850 cm−1); P=O asymmetrical and symmetrical stretching vibrations of PO2 phosphodiester groups from phosphorylated molecules (1125 cm−1 and 1076 cm−1); and C–O stretching vibration coupled with C–O bending of the C–OH groups of carbohydrates (including glucose, fructose, and glycogen) at 1035 cm−1. The absorptions typical of proteins (Amide I, II, and III) were not observed in the saliva_CO and salivette_CO samples, i.e., after deproteinization by 3 kDa cut-off filtering. The spectral region 1080–950 cm−1 also includes the sugar moieties of glycosylated proteins, (e.g., salivary amylase and mucins). Several authors report the assignment of specific bands in the fingerprint region to immunoglobins (1560–1464 cm−1 associated to IgG, 1420–1289 cm−1 and 1160–1028 cm−1 related to IgM, and 1285–1237 cm−1 designed to IgA) [28]. However, the salivary proteome is a complex protein mixture resulting from the activity of salivary glands and serum, from mucosal and/or immune cells, or from micro-organisms containing amylase (representing about $20\%$ of total proteins), mucins (about $20\%$), $6\%$ human serum albumin, $10\%$ lysozyme, $10\%$ IgA and IgG, lactoferrin, proline-rich proteins, histatins, cathelicidins, defensins, glycoproteins, lipoproteins, statherin, and matrix metalloproteases [2,52,53]. Human saliva contains also inorganic compounds (sodium, potassium, calcium, magnesium, chloride, and phosphate) and organic nonprotein components, such as bilirubin, creatinine, glucose, lactic and uric acids [2], and references therein. The differences among the various sample groups, corresponding to different saliva preparation modes, were better evidenced, and the information from the spectra were extracted using principal component analysis (PCA). The results derived from the PCA on the FTIR spectra are shown in the PC1–PC2 score plots (Figure 4a), explaining $87.8\%$ of the total variance. PC1 is responsible for the separation of samples deproteinized using 3 kDa cut-off, which show positive values of PC1 (Figure 4b, blue line) with respect to the other samples on the left side of the plot. Interestingly, the HMWsaliva_CO and HMWsalivette_CO samples (MW > 3 kDa) cluster between unprocessed samples and saliva_CO/salivette_CO samples, without significant differences if analyzed by wiping the tip onto ATR crystal (w) or by “printing” from dried spots (p). PC2 (Figure 4b, red line) separates all samples treated with EtOH that show positive values of PC2 with respect to all the others. Figures S1 and S2 show the PC1–PC3 and PC2–PC3 scores (a) and loading plots (b), explaining $67.2\%$ and $30.9\%$ of the total variance, respectively. The PC1 loading plot (Figure 4b, blue line) clearly shows positive values of 4000–3100 cm−1 absorptions related to OH and NH stretching vibrations, negative values of Amide I and Amide II bands typical of proteins due to C=O and C–N stretching vibrations, respectively, of the bands assigned to unsaturated C=CH stretching of lipids (at 3000 cm−1), symmetric -CH3 stretching at 2922 cm−1 due primarily to proteins, and symmetric -CH2 stretching at 2854 cm−1 due to lipids and proteins, and bending (at 1450 and 1378 cm−1) of the CH2 and CH3 groups. In the region of 3600–2900 cm−1, the absorption bands of the primary and secondary amines (-NH2 and -NHR) are observed; the peaks at 3300–3200 cm−1 are assigned to O–H vibrations; N–H stretching is typically around 3364–3517 cm−1 and usually show a medium, somewhat broad signal (usually considerably less broad than a typical OH stretching). The positive values of PC1 at 3200–3300 cm−1 reflect the higher contents of water in all saliva and salivette samples after deproteinization with 3 kDa units. Another important region of the FTIR spectrum is the spectral range 1180–800 cm−1 that originates from various C–C/C–O stretching vibrations in sugar moieties, P–O stretching of phosphate groups in phosphorylated proteins, and nucleic acids and low-MW compounds. The 1032 cm−1 band is usually attributed to the C–O stretching vibration in glycogen, while lactic acid has peaks at 1032 and 916 cm−1. Thus, the absorptions of low-MW metabolites in saliva/salivette spectra after 3 kDa cut-off ultrafiltration characterize PC1 components. The negative value in PC1, for these samples, of Amide I (1666–1622 cm−1) and Amide II bands (1556 cm−1), typical of proteins, also indicates that ultracentrifugation using 3 kDa cut-off is the only effective method for saliva deproteinization. The negative bands at 1137, 1078, 950, and 830 cm−1 of PC1 could be due to the removal of high-MW carbohydrates and nucleic acids from the saliva and salivette samples after cut off or the removal of phosphorylated molecules. The typical absorptions of high-MW compounds that characterize saliva and salivette samples are better evidenced in the negative components of PC3 (Figures S1 and S2, green line). The PC2 loading plot shows remarkable positive values peaking at 3736, 3461, 3397 cm−1, 3022sh, 2962, 2926, 2878sh, and 2857 cm−1, characteristic of lipids. Positive values are also observed at 1750, 1719, and 1687 cm−1 and assigned to the C=O ester groups of lipids and cortisols and C=C stretching of cholesterol. These components are responsible for the clustering of the saliva_EtOH and salivette_EtOH samples. Among low-MW saliva components detected by FTIR, cortisol, phosphates, lactic acid, and urea are of interest from a medical point of view because their concentrations vary during physiological stress [44]. Our results suggest that the deproteinization in ethanol is not effective, in agreement with Araki, who reported that ethanol mostly precipitates non-protein nitrogen [54]. Table 2 shows with more detail the principal assignment of saliva MIR absorptions [7,10]. Negative values of the PC2 loading plot are observed at 1553, 1450, 1403, and 1321 cm−1. The differences between the saliva and salivette samples mainly rely on marked negative peaks of PC2 (Figure 4b), i.e., the absorptions at 1553 cm−1 (amide II), 1042 with shoulders at 1137 and 1018 cm−1, and 849 cm−1. These absorptions, typical of C–O–C symmetric and asymmetric vibrations of sugar moieties of heavily glycosylated proteins (e.g., mucins [31]) (Table 2), let us hypothesize that the polymeric swab (Salivette®) may adsorb proteins characterized by HMW and/or high degrees of glycosylation. ## 3.2. Choice of PP Support and Effect of Dried Spot Volume Fifty μL was the optimized volume for the analysis of dried spots by FTIR that allowed to obtain “printed mini-spots” of suitable thickness to record high-quality FTIR spectra. If a smaller amount of sample is available for the analysis, e.g., 10 μL, the sample can be dried on PP and eventually gently scratched and microamounts analyzed by ATR-FTIR without significant changes in the spectra. The same experimental design performed on dried spots drop-casted onto aluminum foil did not gave satisfying, reproducible results likely because of the irregular thickness of the saliva dried spots or the rigidity of the aluminum foil. The good reproducibility of the saliva dried spots obtained on PP support may be also due to the hydrophobicity of the PP sheet itself. The ATR-FTIR measurements directly performed on the dried spots onto PP or aluminum foil have interference bands (data not shown for brevity) of the support employed unless higher volumes (≥50 µL) were used to obtain films of suitable thickness. ## 3.3. HPLC Analysis of Main Metabolites in Saliva/Salivette Samples The concentrations of the main metabolites in saliva after the various sample handling procedures were determined by RP-HPLC-DAD [49]. Figure 5 shows the comparison of the concentration (mean and SD) of seven main metabolites determined in the saliva/salivette samples before and after deproteinization with 3 kDa cut-off filtration. The injection of the saliva_EtOH and salivette_EtOH samples did not give meaningful results likely because the precipitation in ethanol favors reaction/degradation of LMW metabolites (e.g., the decrease in the peak of uric acid and the increase in an unassigned peak at tR = 4.348 min) and the disappearance of the peaks of pyruvic acid, valine (VAL), lactic acid, and propionic acid (Figure S3a,b). Figure S3c shows, as an example, UV/visible spectra of the peak at tR = 5269 min (orange line) of the saliva_CO sample, which is due to uric acid, and UV/visible spectra of the peaks at tR = 5.2599 (purple line) and 4.35 min (blue line) of the saliva_EtOH sample. Both these peaks have the absorption characteristics of uric acid, but only the peak at 5.2599 has the same retention time of uric acid standard solution. The results show that for most of the metabolites the sampling by spitting or by swab does not affect their quantitation (lactic, propionic, uric acids, and valine). For other metabolites (creatinine and pyruvic acid), the salivette swab seems to partially adsorb the analyte. The filtering with cut-off filtration units instead does not affect their quantitation. ## 3.4. Raman Analysis on Saliva Dried Spots Raman spectra were acquired from saliva dried spots on PP, glass, and aluminum foil-covered glass. The signals of PP strongly interfere with the analysis, while the spectra collected from samples onto glass were characterized by a poor S/N ratio. The deposition onto aluminum, as verified also by Bedoni and coworkers [55], is rather correlated with well-defined Raman bands, which are easily associable to the vibrational signatures of several biomolecules. Figure 6 shows the comparison of Raman spectra acquired at 785 nm of saliva before (Figure 6a) and after (Figure 6b) filtering with 3 kDa filters. The characteristic features of proteins are clearly recognizable in the spectra of both saliva and salivette, dominating the investigated spectral region. In the spectra obtained after the cut-off at 3 kDa, the only signals related to proteins are the out-of-ring breathing of tyrosine (824 cm−1), the C–C stretching of the proline ring (926 cm−1), the C–C stretching of the protein β-sheet (978 cm−1), and the band of Amide III (centered at 1255 cm−1). Saliva treatment with filters to remove large biomolecules is thus necessary in Raman spectroscopy to obtain information from smaller metabolites. Protein precipitation with EtOH, instead, gives Raman spectra with high noise and low-intensity signals, and no reliable information could be deduced from them. The PCA was applied to the preprocessed dataset acquired at 785 nm, obtaining a $95.6\%$ of variance explained by the first two PCs (Figures S4 and S5). Saliva and salivette spectra cluster together and are clearly separated from the other samples along PC2. It appears, thus, that the Salivette® swab does not retain/release any compound at a significant concentration for Raman. The spectra of saliva_CO and salivette_CO are separated along PC1, while they appear indistinguishable along PC2, and a detailed analysis of the spectra revealed that salivette_CO samples show Raman signals at a lower intensity with respect to those of saliva_CO. As would be expected, the samples treated with EtOH form a close-packed cluster separated from the other groups. Spectra acquisition with a laser in the visible range is further complicated by molecular fluorescence. Specifically, we could not register any Raman working at 532 nm regardless of the processing protocol, while at 633 nm, protein removal with 3 kDa filters was necessary. In this case, the spectra of saliva_CO and salivette_CO mostly resemble those acquired at 785 nm, though the spectral bands are broader and less defined. ## 4. Conclusions Vibrational spectroscopy (ATR-FTIR and Raman) of saliva in tandem with chemometrics is potentially a straightforward technique for pathology biomarker research and for personalized medicine screening to facilitate the diagnosis and follow up of patients during pharmacological therapies once biomarkers have been identified. Multivariate analysis suggests that both Raman and FTIR spectral patterns are not affected by the saliva collection method (spitting or swab). The deproteinization method, instead, may affect the results of saliva-based vibrational spectroscopy, most of all because saliva contains nonprotein nitrogen that precipitates in ethanol [54]. Thus, the collection–processing protocol should be based on the biochemical component suitable to obtain differential diagnoses or to extract information on specific biomarkers [4]. As for the other spectrochemical approaches, FTIR is in fact advantageous for providing holistic information, but the extraction of information from the spectra is a key point to make this information useful for clinical purposes. Although saliva collection by cotton swabs is not invasive, the spitting/drooling method is even easier and minimizes patient hassle, and it is cost-effective in repeated “personal monitoring” when the dynamics of salivary metabolites would be required. Raman analysis before and after protein removal with cut-off filters allows to obtain complementary information. It is not trivial or negligible to highlight that the development of methods based on vibrational spectroscopies, coupled with easy preanalytical steps (sampling/processing) and portable infrared and Raman spectrophotometers would in principle favor bedside applications. Lastly, the saliva deposition of multiple spots onto low-cost PP sheets and the acquisition of spectra on “printed” microamounts of SDSs transferred onto ATR diamond window is fast and novel, and the samples dry simultaneously, and it allows to obtain reproducible conditions and spectra, even when small amounts of sample are available. ## References 1. Sreebny L.M.. Saliva in health and disease: An appraisal and update. Int. Dent. J. (2000) 50 140-161. DOI: 10.1111/j.1875-595X.2000.tb00554.x 2. Khurshid Z., Zohaib S., Najeeb S., Zafar M.S., Slowey P.D., Almas K.. Human Saliva Collection Devices for Proteomics: An Update. Int. J. Mol. Sci. (2016) 17. DOI: 10.3390/ijms17060846 3. Slowey P.D., Streckfus C.F.. 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--- title: The Predictive Value of Monocyte/High-Density Lipoprotein Ratio (MHR) and Positive Symptom Scores for Aggression in Patients with Schizophrenia authors: - Ning Cheng - Huan Ma - Ke Zhang - Caiyi Zhang - Deqin Geng journal: Medicina year: 2023 pmcid: PMC10055014 doi: 10.3390/medicina59030503 license: CC BY 4.0 --- # The Predictive Value of Monocyte/High-Density Lipoprotein Ratio (MHR) and Positive Symptom Scores for Aggression in Patients with Schizophrenia ## Abstract Background and Objectives: Schizophrenia with aggression often has an inflammatory abnormality. The monocyte/high-density lipoprotein ratio (MHR), neutrophil/high-density lipoprotein ratio (NHR), platelet/high-density lipoprotein ratio (PHR) and lymphocyte/high-density lipoprotein ratio (LHR) have lately been examined as novel markers for the inflammatory response. The objective of this study was to assess the relationship between these new inflammatory biomarkers and aggression in schizophrenia patients. Materials and Methods: We enrolled 214 schizophrenia inpatients in our cross-sectional analysis. They were divided into the aggressive group ($$n = 94$$) and the non-aggressive group ($$n = 120$$) according to the Modified Overt Aggression Scale (MOAS). The severity of schizophrenia was assessed using the Positive and Negative Syndrome Scale (PANSS). The numbers of platelets (PLT), neutrophils (NEU), lymphocytes (LYM), monocytes (MON) and the high-density lipoprotein (HDL) content from subjects were recorded. The NHR, PHR, MHR and LHR were calculated. We analyzed the differences between those indexes in these two groups, and further searched for the correlation between inflammatory markers and aggression. Results: Patients with aggression had higher positive symptom scores ($$p \leq 0.002$$). The values of PLT, MON, MHR and PHR in the aggressive group were considerably higher ($p \leq 0.05$). The NHR ($r = 0.289$, $p \leq 0.01$), LHR ($r = 0.213$, $p \leq 0.05$) and MHR ($r = 0.238$, $p \leq 0.05$) values of aggressive schizophrenia patients were positively correlated with the total weighted scores of the MOAS. A higher MHR (β = 1.529, OR = 4.616, $$p \leq 0.026$$) and positive symptom scores (β = 0.071, OR = 1.047, $$p \leq 0.007$$) were significant predictors of aggression in schizophrenia patients. Conclusions: The MHR and the positive symptom scores may be predictors of aggressive behavior in schizophrenia patients. The MHR, a cheap and simple test, may be useful as a clinical tool for risk stratification, and it may direct doctors’ prevention and treatment plans in the course of ordinary clinical care. ## 1. Introduction Schizophrenia, with a global prevalence of $1\%$, is a multi-system disease with no recognized cause, and is one of the fifteen leading causes of disability [1,2]. It accounts for about $12.3\%$ of the global disease burden [3]. Schizophrenia is often demonstrated as a disorder of thought and behavior, including hallucinations, delusions, and disrupted speech and thought patterns, as well as cognitive symptoms such as deficits in working memory and cognitive flexibility [4]. It can lead to a deterioration of the patient’s social functioning, and places great stress on their families and on society as a whole [5]. In a nationwide study of violent behavior in schizophrenia patients conducted in the United States, 1410 patients underwent clinical and violent behavior evaluation. It was discovered that up to $19.1\%$ of participants had engaged in violent behavior within the previous six months, with $3.6\%$ of the participants exhibiting severe violent behavior [6]. The prevalence of aggressive behavior in hospitalized patients with schizophrenia in China ranged between $15.3\%$ and $53.2\%$ [7]. A study found that agitation, including aggression, is frequently the main or initial symptom of a patient receiving medical care or being admitted to the hospital [8]. According to research, about $\frac{1}{5}$ patients hospitalized to acute psychiatric facilities may engage in violent behavior. Similar factors that contribute to violence in individual patients also contribute to levels of violence in psychiatric units (male gender, diagnosis of schizophrenia, substance use and lifetime history of violence) [9]. During acute episodes of schizophrenia, aggressive behavior is a common feature. As a result of their aggression, they may cause emergency events and tricky problems, including issues of legal liability and public security [10]. Hunter claimed that there were 973 beds in a forensic mental institution, and a total of 134 major injuries per year. The cautious estimate for the average cost per injury was $5719, for a total annual loss of $766,290 [11]. The health, safety and general wellbeing of patients, staff and others are endangered due to violent and aggressive behaviors, which cause substantial financial and medical costs. Therefore, more clinical indicators are needed to assess the risk of aggression in schizophrenia. However, the etiology and mechanism of aggressive conduct are not completely understood at present. Studies from cat models found that inflammatory mediators are one of the reasons for aggressive behavior [12,13,14,15]. Furthermore, the application of proinflammatory proteins can boost defensive anger in cats [13]. In other animal models, aggressive behavior was induced by the injection of proinflammatory cytokines in the key brain areas [15]. Similarly, a growing body of studies have found that abnormal levels of inflammatory markers are associated with aggressive behavior, emotional management and cognitive difficulties in patients with mental disorders [16,17,18]. Moreover, Fanning et al. found that childhood trauma has a significant effect on violent and aggressive behavior in adult schizophrenia. There is long-lasting and low-grade inflammation following exposure to adversities in childhood. Immune system disruptions affect brain processes related to controlling aggressive behavior [19,20]. Immunologic dysfunction is also implicated as a primary factor in the schizophrenia pathomechanism, according to a growing body of solid evidence [21,22,23]. One of the hypotheses is that immune system abnormalities may be one of the etiologies for schizophrenia. Lymphocytes (LYM), neutrophils (NEU) and monocytes (MON) play significant roles in the inflammatory response [21]. Interestingly, there is growing evidence that suggests platelets (PLT) play a role in schizophrenia pathophysiology via the serotonin route and inflammation theory [24,25]. Increased levels of blood lymphocytes, monocytes, neutrophils and platelets, along with an increase in severity, were associated with the onset of aggressive behavior in patients with schizophrenia [23]. Aggressive conduct in schizophrenia has been shown to positively correlate with the neutrophil–lymphocyte ratio [26]. High-density lipoprotein (HDL) is one type of cholesterol. Due to HDL’s antioxidant properties, low-density lipoprotein (LDL), a risk factor for coronary heart disease, is protected from oxidation, decreasing or eliminating LDL’s ability to harm endothelial cells [27]. HDL has anti-inflammatory properties as well. Modified HDL inhibits the expression of adhesion molecules that are activated by cytokines, mediates cholesterol outflow from peripheral tissues, and eventually slows the progression of inflammation. The level of HDL fluctuates when inflammation occurs in the body, and it can prevent the activation of monocytes [28]. Thromboxane 2 secretion, fibrin linkage and platelet aggregation are all inhibited by HDL. HDL is a crucial part of lipid rafts. The lipid rafts are involved in cell signaling and cytoskeletal communication [29]. It has been shown that lipid rafts play an important role in neurodegenerative diseases [30]. The lipid rafts affected by cholesterol can change synaptic transmission and nerve plasticity, and can also affect the levels of serotonin and dopamine [29]. 5-hydroxyindoleacetic acid (5-HIAA), a central nervous system metabolite of serotonin, has been linked in studies to impulsive and violent behavior. Considering the roles that NEU, MON, LYM, PLT and HDL play in inflammation, PHR, MHR, LHR and NHR have recently been considered as newer inflammatory indicators. In various inflammatory illnesses, those indicators also were proposed as prospective markers of inflammatory responses and oxidative stress. Numerous studies found that the NHR and MHR were associated with the occurrence, development and prognosis of cardiovascular disease, ischemic stroke, cancer, erectile dysfunction, chronic kidney disease, Parkinson’s disease and so on [31,32,33,34]. The MHR, PHR, NHR and LHR are new types of inflammatory response markers that combine inflammation and anti-inflammation. Some scholars found the relevance of this class of inflammatory markers to psychiatric disorders. Such inflammatory markers have also been correlated with depressive disorders [35,36]. In 2021, Sahpolat et al. first found significant differences in the MHR between patients with schizophrenia and healthy populations in a small sample study [37]. In 2022, Yanyan Wei et al. recruited 13,329 patients with schizophrenia, 6005 patients with bipolar disorder and 5810 healthy people for a study, and subsequently found that the MHR could be used as a distinguishing factor between healthy people and SCZ patients [38]. Although it is true that there are relatively few inflammatory markers of this class in research on psychiatric disorders—which is in the exploratory phase—there are studies demonstrating the relationship of this class of indicators to psychiatric diseases. Currently, numerous studies have evaluated the levels of the NHR, MHR, LHR and PHR in patients with mental disorders. However, the relationship between these ratios and aggression has been investigated less. In this study, we will investigate the relationship between the PHR, MHR, NHR and LHR and aggression in schizophrenia patients, similar to investigating the relationship between CRP, IL-6 and IL-10 and aggression. We predicted that these inflammatory indicators would be different in schizophrenia patients, with and without aggressive behavior. Therefore, the purpose of our study was to identify the indicators listed above that were significantly different between schizophrenia patients who displayed violence and those who did not. We also hoped to find potential biomarkers which could assess aggression in schizophrenia. Based on the results of previous studies, the following were our hypotheses: [1] Indicators of immunoinflammatory activity differ between the aggressive and non-aggressive groups; [2] the MHR, PHR, NHR or LHR may be predictive indicators of aggression in schizophrenia patients. ## 2.1. Participants All of the schizophrenia inpatients were enrolled in The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University from January 2021 to June 2022 ($$n = 214$$). The following were the inclusion criteria: [1] inpatients between the ages of 18 to 65; [2] meeting the diagnostic criteria for schizophrenia based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5); [3] no psychiatric-related medications, immunosuppressive drugs or lipid-lowering drugs for at least three months. The exclusion criteria were as follows: [1] severe endocrine disorders, immunological disorders or physical illnesses (such as heart disease, diabetes, thyroid disease, inflammation and others); [2] pregnancy and the use of prescription weight-loss drugs or glucocorticoids, anti-arrhythmics or insulin; [3] people undergoing hypolipidemic treatment, or those who previously had hormonal problems (statins). Within 24 h of being admitted to the hospital, each patient consented to a mental evaluation using psychiatric scales, and an interview. For the patient group, the PANSS was employed to assess the severity of the schizophrenia [39]. The scale consists of the positive symptom, the negative symptom and the general psychopathology. The MOAS, including verbal aggression, aggression against property, auto-aggression and physical aggression, was used to assess the aggression [40]. The PANSS and MOAS were assessed in the time frame of all information, mainly about one week before admission. The aggressive behaviors were evaluated using the MOAS. According to previous studies [40], subjects with total weighted scores greater than five were assigned to the aggressive group. We also defined this as the occurrence of aggression. Subjects were split into two groups based on the MOAS’s evaluation: the aggressive group ($$n = 94$$) and the nonaggressive group ($$n = 120$$). Each participant’s medical history was examined to learn about their length of sickness, as well as prior and present attempts at suicide and aggressiveness. Additionally, the study controlled and monitored the impact of confounders such as gender, age, marriage and family history of psychosis. All participants gave informed consent after being told of the study’s aim and purpose. Demographic data, including gender, age, education, length of illness, family history of psychosis, marriage and body mass index (BMI) were collected. Blood samples from post-admission patients are taken the next morning. Blood sample processing was carried out with the Sysmex XN-1000 fully automated blood cell analyzer manufactured by Sysmex Japan. The test results included the number of platelets, neutrophils, lymphocytes and monocytes, and the level of HDL. A homogeneous enzymatic technique using polyethylene glycol and dextran sulfate was used to measure HDL cholesterol. The MHR was determined using the formula MHR = MON/HDL. The same method was used to calculate the LHR, NHR and PHR. ## 2.2. Statistical Analysis Statistical data processing was performed in the SPSS26.0 (SPSS Inc., Chicago, IL, USA) software package. For each of the investigated variables, the Shapiro–Wilk test was used to assess whether the distribution was normal. Continuous variables were provided as the median and interquartile range (IQR), and the Mann–Whitney U test was utilized for analysis because the majority of the data were non-normally distributed. The chi-square test was employed to examine classification comparisons in this study instead of Fisher’s exact test, because no cells had an anticipated number of values ≤ 5. The Spearman correlation in the aggressive group was used to analyze the blood indexes and aggression. A binary logistic regression model only contained variables with $p \leq 0.05.$ The likelihood ratio was analyzed to choose the predictors of aggression. For all analyses, a two-sided $p \leq 0.05$ was regarded as statistically significant. ## 2.3. Ethics Statement Our investigation was carried out after the Helsinki Declaration. The Ethics Committee of the Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University reviewed and approved this study (approval number: 2022011807). We appreciate everyone who committed to taking part in this study. ## 3.1. Comparison of Demographical and Clinical Characteristics between Groups This study included a total of 214 schizophrenia inpatients. Demographic data and clinical characteristics are shown in Table 1. The aggressive group consisted of 94 patients, including 55 males and 39 females. There was a statistically significant gender difference between the two groups ($$p \leq 0.028$$). The aggressive group of patients was disproportionately male. In comparison to the non-aggressive group, the aggressive group had a much higher percentage of males. The positive symptom scores ($$p \leq 0.002$$) significantly varied between the two groups about the clinical characteristics. Marriage ($$p \leq 0.433$$), age ($$p \leq 0.32$$), length of illness ($$p \leq 0.149$$), the total scores of the PANSS ($$p \leq 0.116$$), the negative symptom scores ($$p \leq 0.878$$), the general psychopathology scores ($$p \leq 0.350$$), the years of education ($$p \leq 0.593$$) and the family history of mental illness ($$p \leq 0.584$$) did not differ statistically significantly between the two groups. ## 3.2. Comparison of Hematological Parameters in the Two Groups The values of hematological parameters were from schizophrenia, with and without aggression, including HDL, NEU, PLT, LYM and MON, PHR, LHR, NHR as well as the MHR in Table 2. All of the aforementioned indices—aside from HDL, NEU, LYM, NHR, and LHR—revealed differences between the two groups. ## 3.3. Serum Concentration, (a) PLT, (b) MON, (c) MHR and (d) PHR in Schizophrenia Patients by Sex, with and without Aggression We also compared the concentrations of PLT, MON, and the PHR and MHR in men and women between the two studied groups (Figure 1). In male patients, but not in females, the value of PLT was significant in the aggressive group and non-aggressive group ($$p \leq 0.015$$, Figure 1a). However, the significant difference in MON was only observed in female patients with schizophrenia. There was a higher level of MON in female schizophrenia patients with aggression, compared with the non-aggressive female group ($$p \leq 0.019$$, Figure 1b). Moreover, the mean value of the MHR was significantly higher in female patients with aggression in comparison to females with non-aggressive schizophrenia ($$p \leq 0.050$$, Figure 1c). In female patients with schizophrenia, it was found that aggressive patients had significantly higher values of the PHR ($$p \leq 0.041$$, Figure 1d). ## 3.4. Correlations among the NHR, LHR, MHR, PHR and the MOAS in the Aggressive Group As shown in Table 3, in the aggressive group, we performed a Spearman correlation analysis of the values of the NHR, LHR, PHR and MHR and aggression. It showed the correlation between inflammation and aggression of different dimensions, including verbal aggression, aggression against property, auto-aggression and physical aggression. The value of the NHR was significantly positively correlated with total weighted scores ($r = 0.289$, $p \leq 0.01$) and auto-aggression ($r = 0.319$, $p \leq 0.01$). The LHR was positively correlated with total weighted scores ($r = 0.213$, $p \leq 0.05$) and physical aggression ($r = 0.215$, $p \leq 0.05$). The parameters of the PHR only positively correlated with auto-aggression ($r = 0.227$, $p \leq 0.05$). The MHR displayed a positive correlation with total weighted scores ($r = 0.238$, $p \leq 0.01$) and physical aggression ($r = 0.230$, $p \leq 0.01$). ## 3.5. The Related Factors for Aggression in Schizophrenia In Table 4, we performed a logistic multivariate regression analysis. In this model, the MHR remained a significant predictor of aggression in patients with schizophrenia. The occurrence of aggressive behavior was used as the dependent variable. In binary logistic regression analysis, the covariates NHR, PHR, MHR, LHR, the positive symptom scores, and gender were all taken into consideration. The result which operated by likelihood ratio revealed that a higher MHR (β = 1.529, OR = 4.616, $p \leq 0.05$) and positive symptom scores (β = 0.071, OR = 1.047, $p \leq 0.05$) were significant predictors of aggression, whereas gender ($$p \leq 0.055$$), NHR ($$p \leq 0.692$$), LHR ($$p \leq 0.264$$) and PHR ($$p \leq 0.321$$) were not. ## 4. Discussion This study suggested that abnormal inflammation occurs in schizophrenia patients with aggression. All of the hypotheses were verified. Our analysis detected a significant increase in the values of MON, PLT, PHR and MHR in schizophrenia patients with aggression. This result is consistent with our hypothesis. The present study accesses the predictive values of the MHR for aggression in schizophrenia patients. This is consistent with our earlier theory. These results indicated that an elevated MHR and higher positive symptom scores were remarkably associated with aggression in schizophrenia patients. Neutrophils are the most abundant granulocyte type, accounting for $50\%$ to $70\%$ of all human leukocytes [41]. They serve as the body’s first line of defense against invasive diseases [42]. Additionally, neutrophils contribute to cell signaling and recruitment [43]. One study found that patients with higher neutrophil counts also had higher PLT and MON counts [44]. We found no distinctions between NEU, LYM, LHR and NHR in the two groups in the current investigation. We hypothesize that this is not only related to the traits associated with NEU and LYM, but also to the inflammatory hypothesis pathophysiology of schizophrenia. Although NEU are the body’s first line of defense, a number of cytokines control them. The majority of NEU are already produced, and have a short lifespan during the acute phase of inflammation [45]. Lymphocytes are the smallest type of white blood cells. They are mainly involved in the immune response process, including antibody production and cell-mediated immunity [46]. According to an earlier study, both psychiatric and neurodegenerative diseases have been linked to chronic inflammation of specific brain areas, which is defined by an infiltration of peripheral immune cells that may aggravate brain damage and lead to symptomatic clinical presentation [46]. Therefore, it seems reasonable that the results for NEU and LYM, which are often observed in the early phases of inflammation, are not significant in our study. As the largest white blood cells in the body, monocytes are crucial for the release of cytokines that are pro-inflammatory and pro-oxidant [47]. One of the key cells involved in chronic inflammatory conditions is the monocyte. A study found that anxiety, depression and hostile emotions promote monocyte accumulation [48]. In many physiological and pathological circumstances, activated platelets exhibit inflammatory properties, and can also control endothelial cell permeability [49]. Secondly, a major source of peripheral 5-hydroxytryptamine (5-HT) linked to aggressive behavior is platelets [50]. One study found that first-episode schizophrenia patients had considerably greater mid-term platelet levels than did healthy controls [51]. This is consistent with the results we observed. Our research adds support to the inflammatory theory of schizophrenia, and offers new perspectives on the relationship between inflammation and aggression, mood, and other behaviors. A few studies revealed a link between aggression and inflammation, but no particular biological mechanism was shown [26,52,53,54]. A study reported that the attack score and concentration of 5-hydroxyindoleacetic acid in cerebrospinal fluid showed a strong negative connection, indicating that the severity of such violent behavior was inversely connected to 5-HT [55]. Inflammatory factors or inflammatory cells not only participate in the autoimmune reaction leading to brain dysfunction, but also decrease the activity of dopamine and 5-HT in the frontal lobe and hippocampus [56]. According to one study, oxidative stress may play a role in neuro-modulatory activity; it indirectly affects aggressiveness [57]. HDL-C has anti-inflammatory, anti-thrombotic, and antioxidative stress properties, in addition to improving lipid profiles [58]. Thus, the antioxidant function of HDL should not be ignored. Additionally, HDL can reduce inflammatory reactions and prevent native low-density lipoprotein (LDL) from oxidizing [59]. Inhibiting the cycle of inflammatory response processes and preventing monocytes from differentiating into macrophages are two effects of HDL-C. By limiting the growth of the progenitor cells that produce monocytes, HDL-C also works to counteract the pro-inflammatory and pro-oxidant actions of monocytes [58]. As a result, HDL has an indirect impact on many inflammatory cell types, in addition to its direct anti-inflammatory and antioxidant effects on the CNS. Low cholesterol levels have been linked to an increased risk of hostile, aggressive or suicidal conduct, according to numerous research studies [60,61]. Our findings contradict prior research, since we found no link between aggressiveness or impulsivity and HDL cholesterol levels. This could be as a result of the fact that we did not examine the connection between other cholesterol types, such as LDL and triglycerides, and aggression. Another reason for the above results may be that the influences of diet, habits and sports were not considered. As for their complex relationship, composite indicators that include the NHR, LHR, MHR and PHR may be more trustworthy than a single measure in representing inflammatory levels. The MHR, PHR, NHR and LHR are a new group of inflammation markers that combine inflammation and anti-inflammation. They are regarded as basic, low-cost laboratory measures that can detect systemic inflammation in a variety of disorders. These indicators are inflammatory markers that are quick, simple, inexpensive and reproducible. They can highlight symptoms of systemic inflammation in a variety of illnesses, such as cardiovascular disease, ischemic stroke, cancer, erectile dysfunction, chronic kidney disease and Parkinson’s disease [31,32,33,34]. The NHR not only has strong predictive value for Parkinson’s disease, but is also closely related to disease duration [32]. The MHR, a novel inflammatory-oxidative stress biomarker, has been found by Ylmaz. et to be helpful in predicting the outcome of patients with primary nephrotic syndrome [62]. Significantly positive correlations between the MHR and LHR and cardiometabolic risk variables were observed [63]. Researchers also discovered that the PHR is a valid biomarker of nascent metabolic syndrome [64]. Abundant research indicates that those blood indexes can be regarded as independent predictors. Similar to earlier research projects, our findings demonstrated that aggressive behavior in schizophrenia is associated with significantly higher PLT and MON counts [26,51]. Significant differences in the MHR and PHR were observed between the two groups. The NHR, MHR, PHR and LHR have been linked to schizophrenia severity in earlier research [37], but there was no evidence linking them to the likelihood of schizophrenic violence. According to the results of our study, patients who exhibited aggressive behavior had higher MHR and PHR values than the non-aggressive group. The MHR may be able to forecast when hostility will appear in schizophrenic individuals. This is the study’s most significant finding, since it suggests potential biomarkers for predicting aggression. It is important to note that the MHR correlation OR’s confidence interval in this study ranged from nearly 1.2 to 17.750, which suggests that the correlation may be anything from negligible to extremely significant. We speculate that the cause for this occurrence may be related to the modest variance in the MHR or the small sample size. To overcome this issue, we thought that future studies could increase the sample size, or that the MHR values could be examined after a logarithmic transformation, which would then enhance the model’s ability to forecast the future. In conclusion, the MHR may be a potential predictor of violence in schizophrenia patients, but many more research studies are needed to fully assess its predictive significance. Prior studies reported a lot of different results about the association of inflammation and severity of psychiatric symptomatology in patients with mental illness. The study of injecting lipopolysaccharide into healthy volunteers showed that serum IL-6 increased in the peripheral immune system, and symptoms such as depression, anxiety and cognitive decline were caused [65]. Ranjit et al. found a positive association between hostility and the circulating levels of CRP and IL-6 [17]. IL-6 levels were found to be significantly higher in intermittent explosive disorder (IED) patients compared to controls, according to Emil et al. [ 54]. A different study on athletes found that when a game was coming up, football players’ rage triggered their immune system, leading to elevated IL-1 levels [66]. In 2017, Zhang et found that the hsCRP/IL-10 ratio was positively correlated with aggression [67]. There is a lack of consensus in public research on the molecular biomarkers of violent aggressiveness in schizophrenia patients. Even while some progress has been achieved, it is still only the beginning stages of exploration. At present, the mechanism of violent aggressive behavior in schizophrenia is still unclear. For the purposes of the research on inflammatory factors and their relationship, we presumptively believe that inflammatory factors not only have the ability to directly affect particular brain regions, but also to activate the distal neural circuit by triggering the primary triggers at a distance, which in turn trigger the chain reaction [68,69]. Since we cannot fully explain all violent behaviors in schizophrenic patients by high inflammatory activity in the body, we need to evaluate aggressive behavior comprehensively in the context of more situations. Our findings demonstrated that gender disparities were significantly different, with more men among the aggressive subjects. According to many previous studies, men are more likely than women to engage in violent behavior [60,70,71]. It suggested that gender may play an important role in aggression. Aggression was observed more frequently in male patients with acute psychotic episodes, which is consistent with earlier findings [70,71]. First of all, we guess that those female patients tend to exhibit more frequent indirect aggression. They perceive the hostility and aggressiveness brought on by their insanity as a failure of self-control and self-preservation. Male patients, in contrast, may exhibit direct-physical aggression and verbal violence. Therefore, they are more likely to require physical restraints. Yet a study about borderline personality disorder showed that gender difference was not significant in aggression [72]. Some researchers found that girls engaged in more indirect aggression, such as cyber-aggression, when compared with boys [73]. This may imply that different genders have different forms of aggression. When preventing attacks, we should design unique strategies for each gender. Secondly, men and women have distinct hormone levels and thought processes, which may potentially have an effect on the occurrence of violent conduct. According to certain research, testosterone plays a complex role in social interaction in humans, and also promotes violence [74]. By changing the link between brain activity and the “threshold” for aggression, sex hormones promote persistent aggressive behavior [75]. However, in this study, the levels of hormones were not measured. Nevertheless, studies in Mongolian gerbils have shown that androgens can alter pro-social responses over both short- and long-term periods, promoting or inhibiting pro-social behavior depending on the social situation, but not affecting aggression [76]. Aggressive patients’ positive symptom scores were greater than those of non-aggressive patients in this study. This is consistent with the findings of previous studies [77,78,79]. More severe psychotic symptoms could be a mediator of greater violence. Based on these results, we can speculate that aggressive patients have more abundant and severe clinical symptoms. Aggression risk was higher in psychotic patients, particularly those with schizophrenia. The positive symptom scores were significant predictors of aggression, according to logistic regression. Therefore, we should be more cautious in evaluating the mood and aggressiveness of schizophrenia patients who present to the hospital with abundant clinical symptoms and more severe conditions. It can help us to better protect the personal safety of patients and healthcare workers. Medications such as clozapine and valproate have an impact on patients’ levels of aggression as well as on their blood lipids and blood cells [80,81,82]. Atypical antipsychotics, including clozapine, risperidone, quetiapine and ziprasidone, were widely believed to be the most effective medications for treating individuals with aggressive and violent behavior in the past [83,84]. At the moment, clozapine may be the only antipsychotic drug that effectively curbs violent behavior [80]. An increasing number of studies indicated that antipsychotic medicines’ multi-receptor mechanism of action frequently leads to metabolic problems in patients, including weight gain, hyperglycemia and hyperlipidemia [81]. In order to reduce the impact of confounding factors on this study, we made it a requirement for those enrolled to be off their medication for 3 months or more. The participants also had no previous history of dyslipidemia, and no history of statin use. ## Study Limitations and Future Prospects This study has several limitations. Firstly, this was a cross-sectional design study. The causation could not be demonstrated, and a longitudinal study should be performed. A large number of studies are still needed to further validate our findings. Secondly, our subjects were not first-episode schizophrenia patients, and an analysis of the different immune inflammatory states still needs to be added. Additionally, we did not use a healthy control group of participants. Thirdly, other influencing factors were controlled, such as smoking, alcoholism, the levels of hormones, job, diet and exercise. Complex factors may contribute to aggressive behavior in schizophrenia, including substance abuse, gender, age, hallucination/delusional beliefs, poor impulse control, emotion, neurogenic trophic factor, hormone levels and so on [85]. In this study, we were not concerned with these affecting factors. Our study group was reduced because we excluded a subset of individuals with somatic disease, and concentrated on aggressiveness and immunological inflammation. One of the limitations of this study was the effects from prior medications (≥3 months) or other drugs. However, there are other forms of cholesterol which may also influence the inflammatory marker to HDL ratio, which were not included in our study. As a result, we view the findings of this study as a “potential prediction”, and numerous other future investigations are still required to assess their predictive validity. We want to underline that neither a correlation nor a straightforward comparison indicated a causal connection between the values that were investigated. In conclusion, this study aimed to assess the relationship between inflammation and schizophrenia with aggression. Our research found that the level of inflammatory cells was different in schizophrenia with aggression or without. Further investigation revealed that the MHR and positive symptom scores were significant predictors of aggression. These results may help researchers identify variables that predict aggression in schizophrenia patients. The MHR can be quickly and affordably acquired. In the course of general clinical care, it can be utilized as a clinical tool for risk assessment, and can direct doctors in preventative and treatment planning. Our research adds support to the inflammatory theory of schizophrenia, and offers new perspectives on the relationship between inflammation and aggression, mood, and other behaviors. ## 5. Conclusions In this study, the value of the MHR and the positive symptom scores may be predictors of aggressive behavior in schizophrenia patients. These preliminary results, however, require validation in sizable prospective investigations. ## References 1. Pillinger T., D’Ambrosio E., McCutcheon R., Howes O.D.. Correction to: Is psychosis a multisystem disorder? A meta-review of central nervous system, immune, cardiometabolic, and endocrine alterations in first-episode psychosis and perspective on potential models. Mol. Psychiatry (2019) 24 928. DOI: 10.1038/s41380-018-0275-2 2. 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--- title: A Qualitative Study to Compare Barriers to Improving Food Security among Households with Young Children in the U.S. as Perceived by Different Types of Stakeholders before and during COVID-19 authors: - Elder Garcia Varela - Jamie Zeldman - Isabella Bolivar - Amy R. Mobley journal: Nutrients year: 2023 pmcid: PMC10055020 doi: 10.3390/nu15061438 license: CC BY 4.0 --- # A Qualitative Study to Compare Barriers to Improving Food Security among Households with Young Children in the U.S. as Perceived by Different Types of Stakeholders before and during COVID-19 ## Abstract This qualitative study aimed to determine the perceived barriers of different community stakeholders’ to providing resources for improving food security in households with young children in the U.S. Community stakeholders working with low-income families with children 0–3 years of age in Florida were recruited to represent healthcare ($$n = 7$$), community/policy development ($$n = 6$$), emergency food assistance ($$n = 6$$), early childhood education ($$n = 7$$), and nutrition education ($$n = 6$$) sectors. In 2020, one-on-one interviews were conducted with each stakeholder in via Zoom, using an interview script based on the PRECEDE–PROCEED model and questions to capture the impacts of COVID-19. The interviews were audio-recorded, transcribed verbatim, and analyzed using a deductive thematic approach. A cross-tab qualitative analysis was used to compare data across categories of stakeholders. Healthcare professionals and nutrition educators indicated stigma, community/policy development stakeholders indicated a lack of time, emergency food assistance personnel indicated a limited access to food, and early childhood professionals indicated a lack of transportation as the main barriers to food security prior to COVID-19. COVID-19 impacts included the fear of virus exposure, new restrictions, lack of volunteers, and a lack of interest in virtual programming as barriers to food security. As perceived barriers may vary with respect to providing resources to improve food security in families with young children and the COVID-19 impacts persist, coordinated policy, systems, and environmental changes are needed. ## 1. Introduction Food insecurity is a public health problem affecting millions of households with young children in the U.S [1]. In 2020, 15.3 percent (2.5 million) of U.S. households with children under the age of six reported being food insecure [1]. Food insecurity has been associated with poor diet quality [2,3,4,5,6], impacting the physical, cognitive, developmental, and social growth of young children [7,8,9,10,11]. Specifically, children under three who live in food-insecure households are more likely to be iron deficient, be at higher risk of cognitive–developmental problems, and experience more hospitalizations [12,13,14]. The World Health Organization declared COVID-19 a global pandemic in March 2020 [15], impacting many households, specifically those living in low-income communities [16,17,18]. Unemployment rates related to COVID-19 resulted in individuals reporting difficulty in obtaining healthy and affordable foods for themselves and their children [18,19,20,21], exacerbating existing racial/ethnic and socioeconomic inequities [22,23]. While the existing literature provides evidence of an association between food insecurity and adverse health outcomes in young children [14,24,25,26,27,28], minimal research has been conducted to address the barriers to food security in households with children under three [18]. The research suggests community stakeholders offer a unique perspective regarding community needs, barriers, and opportunities [29]. For this study, community stakeholders included key individuals, groups, and/or organizations who share a vested interest in a specific topic or subpopulation, such as food security in households with young children. Thus, engaging community stakeholders in the research process is beneficial to [1] obtain a better understanding of the community’s needs and priorities; [2] increase community buy-in in the proposed program; and [3] develop a sense of shared responsibility for community health [29,30]. Research also shows that the meaningful and equitable engagement of multiple stakeholder groups can contribute to developing and implementing better quality, more acceptable, and relevant health programs, policies, and services [31,32]. By bringing different perspectives, these stakeholders provide valuable input on the processes, outcomes, and lessons learned from their niche that can contribute to reducing existing health disparities [31,32]. Nonetheless, little is known about how perceived barriers may vary among different types of stakeholders with respect to improving food security in households with young children. The objective of this study was to explore the perceptions of different types of community stakeholders (i.e., healthcare providers, early childhood education specialists, community health planning and policy development professionals, emergency food assistance providers, and nutrition education professionals) regarding the barriers to providing and delivering services and resources to individuals with children under three years of age who experienced food insecurity before and during COVID-19. Identifying the factors that impact access to services and resources for improving food security and how these perceived barriers may vary by the type of stakeholder is a critical step in adapting existing programs and policies to improve the health and quality of life of food-insecure households with young children. ## 2. Materials and Methods Qualitative, in-depth interviews were conducted with community stakeholders in Florida. The Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (Supplementary File S1), a 32-item checklist for interviews and focus groups, guided the reporting of study findings. ## 2.1. Study Participants and Sampling Purposive sampling was supplemented with convenience sampling to recruit participants for this study [33]. Potential participants included community stakeholders with a vested interest in supporting families with young children experiencing food insecurity. We defined community stakeholders as individuals working in local groups, organizations, and businesses who directly provide services and/or resources to improve the food security status of families with children under three years of age. Multiple stakeholders throughout the state of Florida were identified from publicly available information online (i.e., websites, reports, etc.) in various sectors, including healthcare, community health planning and policy development, emergency food assistance, early childhood education, and nutrition education. Participants were recruited via email and phone by a member of the research team (E.G.V.) to seek their participation with the goal of including somewhat equal representation from various categories or types of stakeholders (i.e., healthcare providers, early childhood education specialists, community health planning and policy development professionals, emergency food assistance providers, and nutrition education professionals). Flyers were included in electronic communication to be shared with individuals within the targeted organizations. This paper does not report the stakeholders’ employers and job titles to retain the anonymity of the participants. After completing the in-depth interview (response rate of $85\%$), stakeholders who participated in this study received monetary compensation through a $30 electronic gift card. Table 1 illustrates participants’ characteristics by stakeholder type. A total of 32 community stakeholders participated in semi-structured interviews, including healthcare providers ($$n = 8$$), early childhood education specialists ($$n = 7$$), community health planning and policy development professionals ($$n = 6$$), emergency food assistance providers ($$n = 6$$), and nutrition education professionals ($$n = 5$$). Overall, the majority of stakeholders interviewed were non-Hispanic ($91\%$), white ($64\%$) females with at least a Bachelor’s degree education level ($84\%$) and an average of 8 years of experience providing services and resources to individuals with children under the age of three years. ## 2.2. Data Collection Data were collected through in-depth, semi-structured interviews via Zoom. The interviews lasted approximately 90 min and were conducted between November 2019 and August 2021 by one of the study members (E.G.V.). The semi-structured approach included follow-up questions for further exploration of the topic. The interview guide was developed based on the Predisposing, Reinforcing, and Enabling Constructs in the Educational Diagnosis and Evaluation (PRECEDE) component of the PRECEDE–PROCEED model (Appendix A). The PRECEDE–PROCEED model provides a comprehensive approach to addressing individuals’ health and quality of life by assessing the needs for designing, implementing, and evaluating health-promotion programs [34]. Thus, this study utilized this model to inform the development of a community-based intervention that addresses the needs of individuals with young children who are experiencing food and nutrition insecurity. The interviews were audio recorded. Permission to record was obtained from the participants via a waiver of documentation of informed consent and verbally at the beginning of the interview. The interviews were transcribed verbatim via Zoom and cross-checked by a study member for accuracy. The interviews were conducted until data saturation was reached, which was guided by the seven parameters identified by Hennink et al. 2017 [35]. A brief demographic survey was also administered via Qualtrics (Appendix B) to summarize the participant characteristics. ## 2.3. Data Analysis The data analysis was conducted by three female researchers, E.G.V., J.Z., and I.B., with $80\%$ of the transcripts being independently double-coded and compared for consistency [36]. For the double-coded interviews, disagreements between researchers were resolved through discussion [37]. For the single-coded interviews, any uncertainty in coding by one researcher was discussed with the other two researchers before categorization. Specifically, data were analyzed via traditional text analysis using a deductive thematic approach [37]. As such, the PRECEDE component of the PRECEDE–PROCEED model was conceptually used to identify the themes and subthemes. A qualitative cross-tabulation analysis compared pre- and existing COVID-19 data among the different types of stakeholders, organized into five categories (i.e., healthcare, community health planning and policy development, emergency food assistance, early childhood development, and nutrition education). For this paper, the following questions were selected for qualitative analysis: [1] What were the main barriers to providing or delivering services or resources to these families prior to COVID-19? [ 2] What were the main barriers in providing or delivering services or resources services or resources to these families during the COVID-19 response? [ 3] What resources are lacking/not available or are needed, especially for families with infants and toddlers ages 0–3 years old? Findings from the other questions were analyzed and presented in a previously published manuscript [38]. The demographic questionnaire was analyzed using IBM SPSS Statistics 29. ## 3.1. Thematic Analysis Results The results were organized according to Phase 4: Educational and Ecological Assessment of the PRECEDE–PROCEED model and compared among the different types of stakeholders. Table 2 illustrates the predisposing, reinforcing, and enabling factors that impact stakeholders across different sectors to provide and/or deliver services and/or resources to this population. ## 3.1.1. Barriers to Providing and/or Delivering Services and/or Resources to Improve Food Security among Households with Children under Three before COVID-19 Lack of or limited access to transportation (enabling factor). Early childhood education specialists suggested that a lack of or limited access to transportation was their biggest challenge for program participation. One early childhood education specialist stated, “I think one of the big [issues], I would say, is transportation for the parent and child to get to the activity or to where we’re having the program” (P4). Although a lack of or limited access to transportation was not the most salient barrier for other stakeholders (i.e., healthcare providers, emergency food assistance providers, and nutrition education professionals), they also mentioned that transportation was a challenge for their clients in receiving services and/or resources. In contrast, community health planning and policy development professionals did not mention a lack of or limited access to transportation as a barrier. Logistical issues with recruiting and retaining participants (reinforcing factor). Stakeholders also mentioned logistical issues (i.e., scheduling, length of visit, cost, etc.) to recruiting and retaining participants as some of the most frequent challenges across sectors (i.e., healthcare, early childhood education, community health planning and policy development, and nutrition education). One participant mentioned, “the time that it takes to go into the office, the number of office visits, you know the frequencies up to four times a year and more depending on if you have multiple children and if their appointments are not aligned… it is a challenge for them to receive assistance” (P17). While this was the second-highest-mentioned barrier across stakeholder types, it was not the top mention for any of the identified groups. Further, emergency food assistance providers did not mention logistical issues with recruiting and retaining participants as barriers to providing and/or delivering services to their clients. Pride and stigma (predisposing factor). Stakeholders suggested pride and the stigma associated with accessing resources and/or services were a challenge to supporting individuals with young children. Community health planning and policy development, healthcare, and nutrition education stakeholders suggested pride and stigma were issues in providing and delivering services to the target population. Specifically, healthcare providers indicated pride and stigma were two of the most challenging barriers to providing services and resources to households with young children. One participant mentioned, “There’s a lot of pride. They really don’t want to rely on services. They don’t want to be looking for a handout. It’s really trying to break down that barrier and let them know that we’re not judging them” (P2). While a lack of or limited access to transportation, logistical issues with recruiting and retaining participants, and pride and stigma were the most frequently mentioned barriers across the different types of stakeholders, each group also expressed its unique challenges. For instance, community health planning and policy development stakeholders noted that individuals’ lack of time for seeking out or receiving services and resources was their biggest challenge to providing service and resources before COVID-19. Further, nutrition education professionals mentioned an inequality of resources across neighborhoods as the most salient barrier when recommending services and/or resources to their program participants. ## 3.1.2. Barriers to Providing and/or Delivering Services and/or Resources to Improve Food Security among Households with Children under Three during COVID-19 COVID-19 restrictions (enabling factor). Overall, stakeholders across all sectors (i.e., healthcare, community health planning and policy development, early childhood education, emergency food assistance, and nutrition education) suggested that COVID-19 restrictions (i.e., social distancing, self-isolation, shutdowns, curfews, etc.) were some of the main barriers to providing and/or delivering services and/or resources during COVID-19. Specifically, community health planning and policy development stakeholders mentioned that COVID-19 restrictions prevented them from providing their services during regular hours. One participant stated, “We’re only open from nine to one… and then sometimes, you know, we can only take appointments every 10 to every 15 min. So, we usually take between 18 to 20 appointments per day. That is a real challenge” (P9). Virtual programming limitations (enabling and predisposing factor). Stakeholders mentioned that because of COVID-19 restrictions, they had to adjust their programming to reach participants in different forms, including through online platforms. However, virtual programming brought challenges to providing or delivering services and resources. While virtual programming challenges (i.e., consent, face-to-face preference, etc.) did not affect all stakeholders, early childhood education professionals, healthcare providers, and nutrition education professionals stated that not having the flexibility to meet in person with their clients restricted their ability to access the population they served. Additionally, limited access to technology and internet connectivity made it particularly challenging for individuals to stay connected and consistently engage in programming. One stakeholder mentioned, “Everybody doesn’t necessarily have a computer in their home or really reliable Internet service. So, while I want to believe, we were still reaching a good number of people, I mean, we do have to face the reality that we were not probably reaching nearly as many people as we were before simply because, you know, just lack of technology, not comfortable with it, not savvy with it. Also, I would imagine, in the grand scheme of things, when people were trying to pay bills and, at the height of this, you know, keeping the Internet on might not have been the top priority” (P29). Fear of COVID-19 exposure (predisposing factor). Stakeholders also mentioned that the fear of COVID-19 exposure was one of the most salient challenges when providing or delivering services and/or resources to households with young children during COVID-19. Healthcare providers mentioned that families were not making their scheduled appointments primarily due to the fear of being exposed to the virus. One stakeholder stated, “A lot of families didn’t feel comfortable coming to the doctor. You know, understanding that a doctor’s office might be a higher chance of getting COVID from other patients. So, we had a lot of concerns, and we’re correcting for it now, but there’s still a backlog of a lot of families that did not seek routine care, so their basic needs were not identified. And now, I’m worried that we will end up missing a lot of families that we sort of would have seen more routinely and have screened for before the pandemic through basic primary care” (P23). Nevertheless, not all types of stakeholders (i.e., emergency food assistance and early childhood education) identified a fear of COVID-19 exposure as a barrier to providing or delivering services. Lack or limited number of volunteers (reinforcing factor). Stakeholders also mentioned the lack of or limited number of volunteers was a barrier to providing or delivering resources and services to individuals with young children. Specifically, emergency food assistance and healthcare providers mentioned this barrier as their most salient challenge in providing services and/or resources. One stakeholder mentioned, “Our volunteers are aging out because we’re a 34-year-old pantry, and so they’re aging out. And a lot of them are afraid to come around, you know, other people, so we’ve been really struggling providing volunteers, younger volunteers, healthy volunteers than we had before” (P15). Similar to the fear of COVID-19 exposure, not all types of stakeholders identified the lack or limited number of volunteers as their number one barrier to providing or delivering services in the community. While COVID-19 restrictions, virtual programming limitations, fear of COVID-19 exposure, and lack of or a limited number of volunteers were the most salient barriers across all types of stakeholders during the COVID-19 pandemic, stakeholders across sectors also recognized that the barriers identified before COVID-19 were exacerbated during the pandemic. For instance, one participant mentioned, “At the height of the pandemic, accessing food was difficult because of transportation. You know, limited availability of public transport” (P25). Additionally, stakeholders mentioned that COVID-19 restrictions disrupted their employment status due to the lack of or limited accessibility to childcare options during the pandemic. ## 3.1.3. Services and Resources Needed among Households with Children under Three during the COVID-19 Pandemic Access to affordable and high-quality childcare services. Overall, most stakeholders across the different sectors indicated that families with children under the age of three needed access to affordable and quality childcare services. Healthcare providers specifically stated that affordable childcare services are inadequate and prevent mothers from continuing to work and support their families. For example, one participant said, “Quality daycare and preschool is probably number one for that age group because if parents have that, then, the kids, you know, the kids would be well cared for, and the parents could be able to then go to school, find jobs, be able to do what they need to do to support their family” (P27). Marketing strategies to increase awareness about available services and resources. Stakeholders suggested that better communication and marketing strategies are needed to raise individuals’ awareness about the available services and resources in their communities. Specifically, nutrition education and community health planning and policy development professionals mentioned marketing strategies as the most-needed strategy. One participant stated, “There’s a lack of overall knowledge. So even if sometimes the resource is available, they may not know about it because you know no one’s ever told them, or they just don’t know where to find it. Better communication or marketing strategies are one of the main things a lot of the health services are lacking out there” (P32). Centralized referral system. Stakeholders across the different sectors (i.e., healthcare, early childhood education, emergency food assistance, and nutrition education) also suggested the need for a centralized referral system so that individuals are evaluated, efficiently matched, and directed to the programs and/or services that address their needs. One emergency food assistance provider mentioned, “I think continuity of service is extremely important, and that technology piece that we share in terms of a direct flow referral process is [important]. For example, if a client goes to seek assistance at a baby-friendly pantry and they’re able to offer access to a diaper pantry, it would be great to have a direct referral system where they’re able to then refer the client to us. We’re able to close that loop for food assistance because we know folks are seeking assistance…one economic trade-off, then they are likely in need of another.” ## 4. Discussion Understanding the specific needs and barriers faced by different types of community stakeholders can inform the development and adaptation of programs and/or strategies that better fit the needs of those implementing the program and those receiving the services and resources [18,39,40]. As the previous research suggested, a multi-sector response is essential to coordinating community support and increasing the access to unmet social needs, specifically with respect to addressing food security [16,41,42,43]. Moreover, exploring the barriers and needs of different types of stakeholders during times of economic, political, or social crises (i.e., the COVID-19 pandemic) is critical for identifying the short- and long-term implications of the infrastructure that impacts the development and implementation of policies and programs intended to improve the health and quality of life of individuals in these communities [16,18,44]. Specifically, the COVID-19 pandemic exacerbated existing barriers (i.e., a lack of or limited transportation, pride, stigma, etc.) to providing services and resources to individuals with young children. It also generated a new set of challenges with additional implications. For instance, COVID-19-related restrictions, such as social distancing, limited hours of operation, and shutdowns, impacted all community sectors (i.e., healthcare, early childhood education, community health planning and policy development, emergency food assistance, and nutrition education). However, healthcare providers, stated that individuals were particularly reluctant to bring their children in for routine check-ups due to the potential risk of exposure to COVID-19. Recent studies have identified similar findings, suggesting that caregivers missed routine pediatric care due to the fear of contracting COVID-19, increasing the risk of morbidity and mortality associated with treatable and preventable health conditions [45,46]. Despite the efforts to transition to virtual programming amidst the COVID-19 outbreak, stakeholders faced many challenges in reaching participants and providing high-quality services to their clients. Early childhood education professionals, healthcare providers, and nutrition education professionals experienced many challenges in staying connected and engaging with individuals with young children. Recent studies have identified similar findings. suggesting that programs that adapted their delivery of services to a virtual platform during the COVID-19 pandemic were able to support individuals who did not feel comfortable receiving in-person services and resources [47,48]. However, similar studies also suggested that these innovative strategies further exposed existing inequalities in low-income communities, including a lack of technology and internet access [47,49,50]. While stakeholders across groups shared similar perspectives regarding the barriers to providing and/or delivering services and/or resources, each type of stakeholder group experienced different barriers before and during COVID-19. For instance, none of the stakeholder groups suggested the same barrier as their main (i.e., most frequently mentioned) barrier when discussing challenges to providing services and resources before COVID-19. Similarly, none of the stakeholder groups reported the same barrier as their top barrier before and during COVID-19. These findings provide additional evidence of the importance of involving key stakeholders to inform the development and implementation of health promotion programs and policies [31,51]. Community program implementers play an essential role, especially in identifying the assets and resources available to meet the needs of the populations they serve, in helping to identify the needs, in informing best practices, and in adapting health promotion programming [31,51,52]. Overall, stakeholders (i.e., healthcare providers, early childhood education professionals, community health planning and policy development specialists, and nutrition education professionals) expressed the need to improve the knowledge and awareness of individuals about the programs and resources available to them to reduce food insecurity. The research has identified similar findings, suggesting a lack of community outreach opportunities and marketing strategies to improve individuals’ awareness about community resources and assistance programs [53,54,55,56]. Furthermore, stakeholders (i.e., healthcare providers, early childhood education professionals, emergency food assistance providers, and nutrition education professionals) also addressed the need for a centralized referral system to better address the needs of community members. A recent study found that linking clinical services to community-based resources is a promising strategy for assisting individuals with chronic disease prevention and management [57]. Finally, stakeholders (i.e., healthcare providers, early childhood education professionals, community health planning and policy development specialists, and nutrition education professionals) also expressed the need for affordable, high-quality childcare services for their clients. When compared with all the other types of stakeholder groups, community health planning and policy development specialists did not share many suggestions for improving the food security of the target population. Nevertheless, they highlighted the importance of affordable healthcare services and resources to improve the well-being of these individuals and their children. ## Limitations While this study offers an overview of the unique barriers faced by various types of community stakeholders when providing services to individuals with young children experiencing food insecurity, these findings may not be transferable to other settings and populations. Additionally, given that a purposive sampling method was supplemented by convenience sampling to recruit participants for this study and that most participants were female, the participants’ responses may not represent the perspectives of all stakeholders working or providing services within the selected sectors. Likewise, the views of the community stakeholders may reflect the needs of the communities in Florida in which they provide services. Moreover, although the overall number of participants ($$n = 32$$) was adequate for a qualitative study, the number of recruited participants per type of community stakeholder group was modest and may not be reflective of the perceptions of all stakeholders that might identify themselves as part of these groups. Lastly, because interviews were conducted during or a few months after the onset of the COVID-19 pandemic, participants’ responses may represent their present-day experiences. While rigorous data collection techniques were employed to minimize potential bias, future research should explore the perceptions of caregivers of young children as they may indicate different barriers or rank the identified barriers in a different order. ## 5. Conclusions Despite the current availability and implementation of existing programs and policies to improve food security in the U.S., various barriers continue to prevent the provision of services or resources by community stakeholders to households with young children. Additionally, these barriers were further exacerbated or new barriers emerged as a result of COVID-19. Interestingly, these barriers often varied across stakeholders, indicating a potential need to centralize the delivery or availability of food security and related services and resources for families with young children. Understanding the existing gaps and potential opportunities within each sector is important for supporting adapting programs and policies, especially after experiencing the impact of COVID-19. 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--- title: 'Infrared Thermography in Symptomatic Knee Osteoarthritis: Joint Temperature Differs Based on Patient and Pain Characteristics' authors: - Luca De Marziani - Angelo Boffa - Lucia Angelelli - Luca Andriolo - Alessandro Di Martino - Stefano Zaffagnini - Giuseppe Filardo journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10055129 doi: 10.3390/jcm12062319 license: CC BY 4.0 --- # Infrared Thermography in Symptomatic Knee Osteoarthritis: Joint Temperature Differs Based on Patient and Pain Characteristics ## Abstract The aim of this study was to evaluate osteoarthritis (OA) patients with infrared thermography to investigate imaging patterns as well as demographic and clinical characteristics that influence knee inflammation. Forty patients with one-sided symptomatic knee OA were included and evaluated through knee-specific PROMs and the PainDETECT Questionnaire for neuropathic pain evaluation. Thermograms were captured using a thermographic camera FLIR-T1020 and temperatures were extracted using the software ResearchIR for the overall knee and the five ROIs: medial, lateral, medial patella, lateral patella, and suprapatellar. The mean temperature of the total knee was 31.9 ± 1.6 °C. It negatively correlated with age (rho = −0.380, $$p \leq 0.016$$) and positively correlated with BMI (rho = 0.421, $$p \leq 0.007$$) and the IKDC objective score (tau = 0.294, $$p \leq 0.016$$). Men had higher temperatures in the knee medial, lateral, and suprapatellar areas ($$p \leq 0.017$$, $$p \leq 0.019$$, $$p \leq 0.025$$, respectively). Patients with neuropathic pain had a lower temperature of the medial knee area (31.5 ± 1.0 vs. 32.3 ± 1.1, $$p \leq 0.042$$), with the total knee negatively correlating with PainDETECT ($$p \leq 0.045$$). This study demonstrated that the skin temperature of OA symptomatic knees is influenced by demographic and clinical characteristics of patients, with higher joint temperatures in younger male patients with higher BMI and worst objective knee scores and lower temperatures in patients affected by neuropathic pain. ## 1. Introduction Osteoarthritis (OA) is a common form of degenerative joint disease affecting the adult population, characterized by articular cartilage loss within the synovial joints and associated with hypertrophy of the bone (osteophytes and subchondral bone sclerosis), thickening of the capsule, and synovial inflammation [1]. An important aspect of OA is the interpatient variability in clinical and structural manifestations [2,3]. This heterogeneity may be one of the major factors associated with the complexity of OA management and the difficulties in developing one-fits-all therapeutic strategies. In fact, nowadays, no conservative therapies have been proven to arrest or modify the disease progression, nor to be highly effective for symptomatic relief [4,5]. For this reason, the identification of specific OA features could help to better identify different diseases patterns and target treatments and manage each patient according to the specific disease phase and manifestation [6]. Among the key aspects investigated, lot of attention has been placed on the inflammatory process involving OA joints. Inflammation plays a central role in the pathophysiology of OA, with the involvement of the synovial membrane and the release of pro-inflammatory cytokines. These factors induce chondrocytes to produce degradative enzymes of the extracellular matrix and inhibit both tissue repair and regeneration [7]. During the inflammatory process, there is an increase of blood flow that can manifest clinically with redness and heat, as well as with joint swelling and pain [8]. As many available treatments target the inflammatory process, the quantification of the inflammatory component in the OA process could help to better characterize patients with OA, favoring a more targeted treatment [9,10]. In this light, the temperature is a key physical property, as its values detected by infrared thermography could reflect the articular inflammatory process [11]. Therefore, infrared thermography has been proposed as a tool for OA diagnosis and monitoring of disease state, progression, and response to medical treatment, in particular in relation to the inflammatory components [12]. However, although the use of infrared thermography for the evaluation of patients with OA is growing, data are still sparse and evidence on thermographic findings in OA patients is lacking. The aim of this study was to evaluate OA patients with infrared thermography, to investigate imaging patterns as well as demographic and clinical characteristics that could influence the skin temperature of the knee of patients affected by symptomatic OA. ## 2. Materials and Methods This study was approved by the hospital ethics committee of the IRCCS Istituto Ortopedico Rizzoli, Italy (n. 0017413). Patients were enrolled by orthopedic physicians between December 2021 and April 2022 in a research outpatient clinic focused on patients with knee OA. Informed consent was obtained from each patient for study participation. Male or female patients with one-sided symptomatic knee OA (Kellgren–Lawrence grade ≥ 2) with a history of chronic pain or swelling (at least 6 months) were included in the study. The following exclusion criteria were used for selection: history of trauma or intra-articular injection therapy within 6 months before treatment or knee surgery within 12 months; presence of any concomitant knee lesion causing pain or swelling, neoplasms, dermatological and vascular conditions, systemic disorders (e.g., uncontrolled diabetes), metabolic disorders of the thyroid, severe cardiovascular diseases, rheumatoid arthritis, inflammatory arthropathy, hematological diseases, infections, immunodepression, antidepressant, anticoagulants, or antiaggregant therapy; and use of nonsteroidal anti-inflammatory drugs in the 5 days before the investigation. Forty consecutive patients were enrolled according to the inclusion/exclusion criteria. Among them, 26 patients were men and 14 women, with a mean age of 61.3 ± 9.3 years and a mean body mass index (BMI) of 25.2 ± 3.0. All demographic and clinical patients’ characteristics are reported in Table 1. After enrollment in the study and just before the infrared thermography evaluation, patients were clinically assessed thorough knee-specific patient reported outcome measurements (PROMs) including the International Knee Documentation Committee (IKDC) subjective and objective scores, the Knee injury and Osteoarthritis Outcome Score (KOOS) sub-scales, the EuroQol Visual Analog Scale (EQ-VAS), the Tegner score, the Visual Analogue Scale (VAS) for the symptomatic knee pain, and the PainDETECT Questionnaire for the neuropathic pain evaluation. For the last score, patients with values lower than 13 were considered negative for neuropathic pain, while patients with values higher or equal to 13 were considered positive for neuropathic pain. Subjective clinical questionnaires were compiled by patients with the support of the clinician, while the IKDC objective score was evaluated by the clinician. Moreover, all participants underwent weight-bearing antero-posterior to assess the baseline OA severity according to the Kellgren–Lawrence classification. Finally, the skin temperature of the knee affected by symptomatic OA was evaluated with thermography imaging as reported below. After data collection, further analyses were performed to determine the demographic and clinical parameters that influenced the skin temperature of the OA knee. ## 2.1. Infrared Imaging Procedure and Analysis The infrared imaging evaluation was performed in a dedicated outpatient clinic shielded from direct sunlight and with a temperature controlled and set at 23.0 °C and a mean humidity of 45 ± $3\%$. The image acquisition was always performed in the same time slot between 14:00 pm and 17:00 pm in order to minimize the circadian variations of the temperature. According to Marins et al. [ 13], participants were asked to sit for ten minutes without touching their knee before the thermal image acquisition without pants, socks, shoes, and with light clothing such as a t-shirt on the top. Participants were asked to stand on a designated floor map. The thermograms of the symptomatic knee were captured using a thermographic camera FLIR T1020 (FLIR® Systems, Täby, Sweden), which has 1024 × 768 pixels of resolution and a thermal sensitivity of 0.02 °C. The camera was positioned at 1 m of distance from the subject, adjusted to their patellar height and positioned perpendicular to the knee. An anterior view image was obtained for each patient using the autofocus modality. Then, maintaining the same knee position, an anatomical marker (a 2 cm diameter circular sticker) was placed on the center of the patella and a second anterior view image was obtained to facilitate the precise subsequent localization of the patella in infrared images. The two anterior images (one with and one without the patellar marker) were aligned side by side on the computer screen, and a template indicating the region of interests (ROIs) was centered over the patella of the unmarked image, using the marked image as a guide (Figure 1) [14]. The ROIs were defined as follows: the patellar area was a square 6 cm in width, divided in “medial patella” and “lateral patella” (6 cm high and 3 cm wide each); the “suprapatellar” area was the area 3 cm over the patella; and the “medial” and “lateral” areas were the regions 3 cm under the patella and on its medial and lateral sides, respectively. The mean temperatures, as well as maximum and minimum temperatures, were extracted using the software ResearchIR (FLIR® Systems, Sweden) for the overall knee area and the 5 ROIs: medial, lateral, medial patella, lateral patella, and suprapatellar. ## 2.2. Statistical Analysis All continuous data were expressed in terms of the mean and the standard deviation of the mean and range; the categorical data were expressed as frequency and percentages. The Shapiro–Wilk test was performed to test normality of continuous variables. The Levene test was used to assess the homoscedasticity of the data. The repeated measures general linear model (GLM) with Sidak test for multiple comparisons was performed to assess the differences in different areas. The ANOVA test was performed to assess the between groups differences of continuous, normally distributed, and homoscedastic data; the Mann–Whitney non-parametric test was used otherwise. The ANOVA test, followed by the post hoc Sidak test for pairwise comparisons, was performed to assess the among groups differences of continuous, normally distributed, and homoscedastic data; the Kruskal–Wallis non-parametric test, followed by the post hoc Mann–Whitney test with Bonferroni correction for multiple comparisons, was used otherwise. The Spearman rank correlation was used to assess correlations between temperature and continuous data; the Kendall tau rank correlation was used for ordinal data. With 40 patients, a post hoc power equal to 0.9 was obtained with the Kendall’s ordinal correlation between the IKDC objective score and the total mean knee temperature. For all tests, $p \leq 0.05$ was considered significant. All statistical analyses were performed using SPSS v.19.0 (IBM Corp., Armonk, NY, USA). ## 3. Results The mean temperatures of the evaluated OA knees are shown in Table 2. The mean temperature of the total knee was 31.9 ± 1.6 °C. Analyzing the mean temperature of the different areas, the patella (both medial and lateral areas) was found to be colder than all other areas of the knee. In particular, the mean temperature of the medial area was higher than medial patella and lateral patella areas (both $p \leq 0.0005$). The mean temperature of the lateral area was higher than medial patella ($$p \leq 0.019$$) and lateral patella areas ($$p \leq 0.048$$). The mean temperature of the suprapatellar area was higher than medial patella and lateral patella areas (both $p \leq 0.0005$). No significant differences were found among the medial, lateral, and suprapatellar areas (p = n.s.). The mean temperature of the total knee negatively correlated with age (rho = −0.380, $$p \leq 0.016$$) and positively with BMI (rho = 0.421, $$p \leq 0.007$$), with higher temperatures in patients younger and with higher BMI. This correlation was also confirmed for all sub-areas, except for the medial patellar area in relation to age, as shown in Table 3. Males tended to be warmer than females, with higher mean temperatures of the total knee (32.2 ± 1.2 vs. 31.4 ± 0.8, $$p \leq 0.051$$), medial area (32.4 ± 1.1 vs. 31.5 ± 0.7 $$p \leq 0.017$$), lateral area (32.2 ± 1.1 vs. 31.3 ± 0.9, $$p \leq 0.019$$), and suprapatellar area (32.4 ± 1.2 vs. 31.5 ± 0.8 $$p \leq 0.025$$), while no significant differences between sexes were found for the patella areas. Regarding clinical scores, the mean temperature of the total knee correlated with the IKDC objective score (tau = 0.294, $$p \leq 0.016$$), and this correlation was confirmed for all sub-areas (Figure 2). Patients with neuropathic pain had a lower mean temperature of the medial knee area than patients without neuropathic pain (31.5 ± 1.0 vs. 32.3 ± 1.1, $$p \leq 0.042$$), although only a tendency was found for the total knee and the remaining sub-areas. Moreover, the mean temperature of the total knee and the medial area negatively correlated with the PainDETECT Questionnaire (rho = −0.319, $$p \leq 0.045$$, rho = −0.366, $$p \leq 0.020$$, respectively), while a tendency was found for the other four sub-areas (Figure 3). The mean temperature of the medial knee area correlated with the VAS pain scale (rho = −0.361, $$p \leq 0.022$$), while a tendency was found for the mean temperature of the total knee (rho = −0.298, $$p \leq 0.062$$), the lateral area (rho = −0.291, $$p \leq 0.068$$), and the lateral area (rho = −0.301, $$p \leq 0.060$$). Finally, the mean temperatures of the total knee and other sub-areas were not influenced by other factors such as side, symptom duration, Kellgren–Lawrence grade, previous surgery, and smoke. ## 4. Discussion The main finding of this study is that the skin temperature of knees affected by OA is influenced by demographic and clinical characteristics of patients, including age, sex, BMI, and objective and subjective scores. In particular, higher joint temperatures were found in younger male patients with higher BMI and worst objective knee scores, while lower temperatures were found in patients affected by neuropathic pain. The detection of knee temperature through infrared thermography can be useful for better profiling patients with different knee OA patterns. Over the years, the use of infrared thermography has gained a growing interest in the clinical research to evaluate musculoskeletal disorders, quantifying the skin temperature in order to better characterize the properties and course of a specific disease [15,16,17]. The study of joint temperature could potentially improve the diagnosis and therapy of orthopedic pathologies, including knee diseases such as OA [18,19]. A recent systematic review of the literature underlined the correlation between surface skin temperature and joints’ inflammatory and degenerative diseases, including rheumatic pathologies and OA [20]. In particular, a correlation was shown between thermal findings and diseases’ presence and stage, as well as the clinical assessment of disease activity and response to treatment, supporting infrared thermography’s role in the study and management of rheumatic diseases and OA. Nevertheless, evidence on infrared thermography application for knee OA is still limited, especially on elucidating demographic and clinical characteristics that could influence the temperature of the knee in patients affected by symptomatic OA. The current study demonstrated that different factors could influence the skin temperature of symptomatic OA knees. Age showed a negative correlation with the skin temperature of the knee, with younger patients having higher temperatures compared to older patients. The lower temperature of the knees of elderly patients is ascribable to the decrease in body temperature with aging, probably due to a reduction in basal metabolic rate and a lower muscle component compared to the younger population [21,22,23]. Moreover, a reduction in the overall skin temperature with aging has been justified by the reduction in core temperature in older adults due to a reduction in metabolic processes and to an alteration in the heat dissipation through the skin [22,24,25]. This was reported in healthy subjects by Ferreira et al., who found that young subjects’ limbs’ temperature was higher compared to the elderly subjects’ limbs [26]. The current study confirmed the difference of temperature based on age also for knees affected by symptomatic OA, with lower skin temperatures in older patients considering both the overall knee temperature and most sub-areas. In fact, a further sub-analysis confirmed this correlation for all sub-regions but the region above the patellar bone. The analysis of knee sub-regions is important when investigating knee OA thermographic patterns. Different temperatures have been detected among the different knee areas, with the area corresponding to the patella reporting the coldest temperature while the medial and suprapatellar areas reporting the highest temperatures. The patella area of the investigated knee OA patients showed a distinct thermal pattern compared to the other areas. In fact, its skin temperature was colder than all other areas of the knee, as previously reported in the literature also in healthy subjects. A description of the thermal image of the normal knee was previously given by several authors who described the thermographic image of a normal knee to be characterized by symmetry in the image of the two knees with an isothermal oval area corresponding to the patella [27,28]. The lower skin temperature of the patellar area could be explained by the fact that the skin tends to be colder above tendons and bones than above muscles [29,30]. Accordingly, the patella represents the coldest area for knee OA patients, and this could be probably linked to the fact that this area of the knee is the furthest from the intra-articular synovium due to the interposition of the patellar bone. In fact, the skin temperature of the knee could reflect the joint inflammatory process, characterized by an increased vascularization of the synovial membrane, which could be easily detected in areas without underlying bone [31]. Other aspects that influenced the skin temperature of the knee OA patients studied were sex and BMI. Neves et al. evaluated the influence of gender and body fat on temperature. They discovered that women exhibit lower values of surface temperature than man on the trunk and upper and lower limbs [32]. The literature data showed that women differ from men in thermal responses to exogenous heat load and heat loss, as well as to endogenous heat load during exercise, because they usually have a larger ratio of body surface to body mass, a greater subcutaneous fat content, and lower exercise capacity. Perhaps also a lower blood volume in women than in men may limit their heat exchange [33]. Moreover, men have a significantly higher muscle mass and lower body fat percentage than women [34]. Finally, this can be also explained by the fact that women have a lower metabolic rate than men [35]. Accordingly, the data emerged from the current study showed that men have higher knee skin temperature than women. More controversial findings have been found regarding BMI. Higher temperatures were detected for all areas of the knee in patients with higher BMI. This finding is not in line compared to what has been reported in the literature for the overall temperature in obese subjects. In fact, subjects with increased body fat percentage showed lower temperatures of the lower limbs compared to normal-weight individuals [32]. This discrepancy could be explained by the increased inflammatory component that characterized knees of overweight or obese patients who are also affected by OA, probably resulting in an increase of the skin temperature at the OA knee level [36]. In fact, it has been demonstrated that obesity is associated with a chronic inflammatory environment at joint level, which can increase biomarkers of synovial inflammation [37,38,39,40,41]. The obesity-related dyslipidemia can also contribute to OA pathogenesis and increase of inflammation by an increasing matrix metalloproteinases production in joint tissues [42]. Moreover, obese patients present a higher mechanical loading on their joints, resulting in an altered activation of multiple inflammatory pathways, such as interleukin 1-beta (IL-1β) and tumor necrosis factor-alpha (TNF-α) release, chondrocyte apoptosis induction, synovial inflammation, and subchondral bone dysfunction, all contributing to OA [43]. Future studies should investigate the correlation between the skin temperature of the knee and the inflammatory biomarker profile of the affected joint and analyze these findings in relation to the clinical status of the patients. The clinical evaluation of the knee OA joints in this series showed a significant positive correlation between the skin temperature and the IKDC objective score. This score evaluates objective features of the knee including joint effusion [44]. The current study demonstrated that OA knees with a worse objective clinical status are characterized by a higher temperature, probably related to a high inflammatory component of the joint, with consequent high functional limitations [45]. This finding was similar to a previous study which showed a correlation between higher skin temperature of the knee and worst Western Ontario and McMaster Universities Osteoarthritis (WOMAC) stiffness and function scores [46]. Conversely, the subjective scores analyzed in this case series did not correlate with the skin temperature of the evaluated knee, and the VAS pain score actually showed a negative correlation. The controversial findings with respect to the pain perception and temperature can be explained by another result of this study, where the pain experience was investigated also in terms of another aspect, the neuropathic pain component. This was investigated with the painDETECT questionnaire, a score evaluating the contribution of neuropathic pain in the pain perceived by the patient [47,48]. A significant contribution of neuropathic pain is present in $23\%$ of patients with knee or hip OA, with a typical symptomatologic pattern with burning pain, shooting pain or lancinating pain, tactile allodynia, and pain patterns [47,49]. Patients with a neuropathic pain showed a lower skin temperature of the knee at medial area compared with patients without neuropathic pain. A possible explanation for this result is that the pain in patients with neuropathic pain is not related to a significant inflammatory component, while it could be due to a central sensitization and an impaired pain modulation [50]. This hypothesis is confirmed by a study conducted by Ohtori S. et al., who found a tendency for negative correlation between the painDETECT score and the amount of joint fluid, with less joint fluid in patients with neuropathic pain [51]. Therefore, the evaluation of patients with symptomatic knee OA but with a “low” temperature should always be investigated for the presence of neuropathic pain, although future targeted studies are needed to better elucidate this aspect. This study has some limitations. First of all, the sample size could limit the statistical power to better investigate correlations among sub-groups. Therefore, while this is one of the largest studies on symptomatic knee OA patients, future studies with larger populations should confirm the correlation found in the current study. Second, knees were evaluated with an anterior view alone, while further information could be obtained through lateral and posterior thermal acquisitions. Third, a control group of non-symptomatic knee OA patients or non-OA knee patients could have helped understanding the role of thermography in detecting changes of the temperature related to the severity or the presence of OA disease. Another possible weakness is that the evaluation of the neuropathic pain was performed based only on the PainDETECT questionnaire rather than on specific clinical and instrumental exams. Future studies should better investigate the influence of the neuropathic pain component on skin temperature of OA knees. Moreover, the thermographic evaluation of the knee skin temperature could be influenced by the time of the day chosen, and further studies should investigate the behavior of the knee skin temperature during the daytime. Additionally, future studies will have to better characterize the temperature differences of the knee affected by symptomatic knee OA compared to the healthy contralateral one. Finally, the method of thermographic image acquisition and analysis was based on the previous literature, but no method has been described as the gold standard in this field. It is possible that different settings, different lenses, and different devices could be more suitable for such evaluations in the clinical practice. Therefore, future studies should help standardize more the use of thermography for the evaluation of patients with knee OA in order to confirm its potential in identifying different disease patterns both in the research setting and in the clinical practice. This could have the potential to better address patients with knee OA, improving diagnosis, management, and treatment, with a possible socioeconomic and healthcare impact in the future. ## 5. 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--- title: 'Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease' authors: - Andrea I. Luppi - S. Parker Singleton - Justine Y. Hansen - Danilo Bzdok - Amy Kuceyeski - Richard F. Betzel - Bratislav Misic journal: bioRxiv year: 2023 pmcid: PMC10055141 doi: 10.1101/2023.03.16.532981 license: CC BY 4.0 --- # Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease ## Abstract Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain’s network architecture. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; $$n = 17$$ 000 patients, $$n = 22$$ 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies. ## INTRODUCTION The brain is a complex system of interconnected units that dynamically transitions through diverse activation states supporting cognitive function [18, 19, 24, 25, 129, 131]. Large-scale, noninvasive techniques like functional magnetic resonance imaging (fMRI) provides a way to map activation patterns to cognitive functions [7, 32, 76, 93, 115, 159]. Healthy brain function requires the ability to flexibly transition between different patterns of brain activation, to engage the corresponding cognitive functions in response to environmental and task demands. In turn, the neurophysiological dynamics of the human brain are both constrained and supported by the network organisation of the structural connectome: the white matter fibers that physically connect brain regions [3, 61, 62, 96, 138]. However, the exact mechanisms by which the brain’s network architecture shapes its capacity to transition between cognitively relevant activation patterns remain largely unknown, and an intense focus of inquiry in neuroscience [6, 17, 73, 130]. Network control theory is a computational paradigm that explicitly operationalises how the architecture and dynamics of a network supports transitions between activation states [52, 73, 80]. Originally developed in the physics and engineering literature [80, 81, 126], network control theory conceptualises the state of a dynamical system at a given time as a linear function of three elements: (i) the previous state, (ii) the structural network linking system units, and (iii) input injected into the system to control it. In the context of the brain, such input can intuitively take the form of task modulation [14, 16, 51] or other perturbations from the environment, but potentially also pharmacological or direct electromagnetic stimulation [72, 91, 124, 134], or endogenous signals from elsewhere in the brain [92]. This approach is widely applicable across the breadth of neuroscience, from C. elegans and drosophila [73, 158] to rodents and primates [52, 73], and across human development [78, 106, 140], health, and disease [11, 14, 16, 58, 107, 134, 165, 166]. In humans, network control can be used to study the transition between brain states. Such state-to-state transitions can be formalised as a dynamical process that unfolds over the connectome’s network architecture, reconstructed from diffusion-weighted imaging (DWI). Of particular relevance is the quantification of control energy. Control energy refers to the magnitude of input that needs to be provided to the system in order to drive its trajectory from an initial state to a desired target state [52, 80]. In the context of transitions between brain states, the cost of transitions may correspond to the magnitude of exogenous stimulation (e.g. transcranial magnetic stimulation, deep brain stimulation, intracranial stimulation [36, 91, 97, 134] or the dose of a pharmacological intervention [16, 49, 124]), but also to endogenous effort, as reflected by cognitive demand [90, 92]. When seeking to operationalise this promising framework, conventional studies on control energy in the human brain have consistently adopted one of two strategies for defining brain states. One strategy is to define brain states as co-activations of cognitively relevant brain circuits, operationalised as the canonical intrinsic connectivity networks of the brain [14, 22, 51, 58, 70]. The downside of this approach is that intrinsic networks identified from functional MRI are limited in number (usually only 7–8 [20, 23, 110, 129, 163]), providing a correspondingly limited repertoire compared with the space of possible functional activation patterns. The second strategy typically involves defining brain states as random activation patterns [58, 73], whose number is then virtually limitless, but at the expense of being cognitively ambiguous. Here we overcome these challenges by investigating how network architecture supports transitions between cognitive topographies. We define cognitive topographies (i.e., cognitively relevant brain states) as meta-analytic patterns of cortical activation pertaining to over 100 cognitive terms, obtained by aggregating over 15 000 functional MRI studies from the NeuroSynth atlas [159]. This approach represents a large-scale generalisation of recent work that defined cognitively relevant brain states in terms of task-based fMRI contrast maps [16, 68]. In addition to generalising the set of possible start and target states under consideration, we also provide two key extensions to the scope of the control inputs under investigation. First, we consider the potential role of deficiencies in the capacity of brain regions to act as sources of endogenous control signals, due to pathology. We operationalise this using cortical thickness abnormalities for 11 neurological, psychiatric, and neurodevelopmental disorders from the ENIGMA consortium, summarising contrasts between 17 000 patients and 22 000 controls [56, 77, 141, 142]. Second, we investigate the potential role of pharmacological control on cognitive transitions by defining inputs as the regional expression of 18 neurotransmitter receptors and transporters, quantified from in vivo positron emission tomography (PET) scans in > 1 200 subjects [55]. Overall, we combine multiple databases from different neuroimaging modalities (functional MRI, diffusion MRI tractography, cortical morphometry, PET) to investigate how the brain’s network architecture shapes its capacity to transition between a large number of experimentally defined cognitive topographies, and how this capacity can be reshaped by perturbing control inputs due to disease or pharmacology. ## RESULTS Network control allows us to ask how brain network structure supports transitions between cognitively-relevant brain states [52, 92] (Fig. 1a). We define the network whose activity is to be controlled as the human structural connectome (obtained as a consensus of $$n = 100$$ Human Connectome Project [148] subjects’ connectomes reconstructed from diffusion MRI tractography; see Methods). We define the cognitive topographies as meta-analytic brain activation patters from the NeuroSynth atlas (Methods). With definitions of the network and its state in hand, we consider the problem of network controllability: how can the system be driven to specific target states by internal or external control inputs (Fig. 1b)? Beginning with uniform inputs applied to all regions, we are interested in the relative energetic cost of transitions between different cognitive topographies (Fig. 1c). ## Transitions between cognitive topographies We first evaluate the control energy required to transition between each pair of cognitive topographies (“brain states”) from NeuroSynth [159]. The NeuroSynth meta-analytic engine provides meta-analytic functional activation maps associated with 123 cognitive and behavioural terms from the Cognitive Atlas [109], ranging from general terms (“attention”, “emotion”) to specific cognitive processes (“motor control”, “autobiographical memory”), behavioural states (“eating”, “sleep”), and emotional states (“fear”, “anxiety”). Each map is a vector encoding the statistical strength of differential activation for task-based neuroimaging studies that include a cognitive term versus studies that do not include that term, at each spatial location, based on the published literature. Although NeuroSynth uses activation maps as the inputs for its term-based meta-analysis, its outputs are not activation maps per se, but rather they reflect statistical association tests. Applying control inputs uniformly to all brain regions, we compute the optimal energy cost for each of the 15 129 possible transitions between cognitive topographies. We find that optimal control energy can vary by nearly 10-fold across different transitions (Fig. 2a–c). As a result of these differences, for several combinations of source and target cognitive topographies ($36\%$) a direct transition is not the most energy-efficient. Rather, the control energy required to transition between them can be reduced if an intermediate transition is made to some other state (Fig. S1). The transition energy between each pair of states correlates with the Euclidean distance between their NeuroSynth vector representations (Spearman’s $r = 0.99$, $p \leq 0.001$): the more distant two patterns are, the more energy will be required, and consequently the average energy to reach each target state correlates with the mean (Spearman’s $r = 0.49$, $p \leq 0.001$) and standard deviation (Spearman’s $r = 0.96$, $p \leq 0.001$) of the corresponding NeuroSynth map (Fig. S2a–d). However, mean and variance of the NeuroSynth activation patterns are coarse descriptions of each pattern. This is because they disregard all information about the neuroanatomical distribution of activations. Additionally, Euclidean distance alone cannot fully account for the observed results: Euclidean distance is symmetric, whereas we observe that transition energy is asymmetric. Specifically, we observe that target states (columns of the matrix) exhibit greater variability than start states (rows), suggesting that the destination of a transition—the desired target topography—may play a more prominent role than the current state in determining the ease or difficulty of the transition (Fig. 2a–c). After partialling out the effect of each NeuroSynth map’s mean and standard deviation, we find that its transition cost is related to both topological features of the structural connectome (especially whether high-valued nodes are easy or difficult to reach using a diffusion process), and the map’s spatial alignment with the unimodal-transmodal cortical hierarchy (Fig. S2e–g). We confirm the observation of transition asymmetry by showing that the variability (standard deviation) of the transition energy matrix is higher across target states (mean = 1.17 × 106, SD = 8.74 × 104) than across start states (mean = 4.35 × 105, SD = 1.35 × 105; t[244] = 50.52, $p \leq 0.001$, Cohen’s $d = 6.42$) (Fig. 2d). To investigate this asymmetry further, we also compute a measure of transition asymmetry between each pair of brain states i and j, as the difference in control energy required to move from i to j, versus moving from j to i. Averaging across start states provides, for each target state, a measure of whether that brain state is overall easier to reach than leave (negative values) or harder to reach than leave (positive values) from other states. As expected, this measure is positively correlated with the overall transition cost to reach a given state (Spearman’s $r = 0.73$, $p \leq 0.001$). We find that the majority of cognitive topographies are slightly easier to reach than to leave, but this is counterbalanced by a small number of cognitive topographies that are substantially harder to reach than to leave. In particular, hard-to-reach cognitive topographies include those pertaining to language-related cognitive operations (e.g., “language”, “reading”, “speech production”) and those pertaining to memory (e.g., “memory”, “autobiographical memory”, “semantic memory”)(Fig. 2e,f). We confirm this observation quantitatively via empirical permutation tests (1, 000 permutations): we consider the six data-driven cognitive domains identified by Beam et al. [ 7]: memory, cognition, inference, emotion, vision, and language. Among the terms in our list of 123 that have been assigned to one of these six domains, we find that terms pertaining to “memory” ($$p \leq 0.007$$) and “language” ($$p \leq 0.045$$) exhibit higher median value of asymmetry than would be expected by chance (“emotion”: $$p \leq 0.640$$; “inference”: $$p \leq 0.696$$; “cognition”: $$p \leq 0.542$$; “vision”: $$p \leq 0.730$$). Altogether, we find that the energetic ease or difficulty of transitioning between two given cognitive topographies appears to be primarily driven by the identity of the target state. ## Connectome wiring supports efficient cognitive transitions Having considered how transition energy varies as a function of different origin and target cognitive topographies, we now turn our attention to the network itself. We assess how much the observed effects are due to topology and geometric embedding. To address this question, we implement two classes of null models [150]. We first consider a null model that preserves weight distribution and degree sequence [88]; and a “geometry-preserving” rewired null, preserving degree sequence and weight distribution, but also the approximate wiring cost (length of connections) [12]. For each specific null model, we re-estimate the control energy 500 times, and compare the resulting distribution of all-to-all mean transition energy against the distribution obtained using the empirical structural connectome of the human brain. We find that the human brain outperforms both null models: transitions are significantly less energy-expensive on the human connectome than on either null (all $p \leq 0.001$; Fig. 3), suggesting that the unique wiring architecture of the human connectome supports efficient transitions between cognitively relevant topographies. We also find a significant difference between the nulls: rewired networks are more energy-efficient when they preserve both the degree sequence and the geometric properties of the human connectome, than when only the degree sequence is preserved. In other words, the geometric embedding of the human connectome accounts for a substantial portion of its energy-efficiency—but not all of it. We also show that these observations, obtained from a consensus connectome, remain true at the single-subject level: subject-level matrices of transition energy correlate with the group-level matrix, and individual connectomes outperform corresponding degree-preserving and degree- and cost-preserving nulls (Fig. S3 and Table S1). ## Effect of disease on transitions Up to this point, we investigated both the role of the transition sources and targets, and the role of the underlying network. We now turn to the remaining element of the network control theory as a framework for dynamics on neuronal networks: the control inputs. To this end, we consider how pathology-associated variations in cortical thickness may impact the connectome’s ability to facilitate transitions between cognitive topographies. We focus on cortical thickness by making the simplifying assumption that the amount of input energy that a region is capable of injecting into the network is a function of its total number of excitatory neurons (since most inter-regional projections are known to originate from excitatory neurons)—and that this, in turn, is related to cortical thickness (since most neurons are excitatory). This approach is conceptually related to the approach recently employed by Singleton and colleagues [124], who applied non-uniform control inputs according to the normalised density of the serotonin 2A receptor expressed in each region (as quantified by PET). We consider different patterns of abnormal cortical thickness associated with 11 neurological diseases and neuropsychiatric disorders (collectively referred to as “disorders” here, for brevity), as quantified by the ENIGMA consortium and recent related publications [56, 77, 141, 142]. For each of 11 ENIGMA disorders, we modulate the regional control input according to the regional pattern of increases or decreases in cortical thickness associated with that disorder (Fig. 4). In particular, we seek to disentangle the role of overall changes in cortical thickness, versus their specific neuroanatomical distribution. To this end, and to remove the potential confound of the mean and variance of each distribution, we compare the transition energy associated with each pattern of cortical thickness, with randomly rotated versions of the same pattern, preserving the original brain map’s spatial autocorrelation and the overall distribution of values, but randomising the neuroanatomical locations [86]. For each disorder, we z-score the empirical control energies against the resulting null distributions of transition energies, separately for each target cognitive topography. For a given combination of disorder and target state, a negative z-score indicates that the real disorder-associated pattern requires overall less energy than the null patterns, to induce a transition to the target state in question. A positive z-score indicates the opposite. Overall difference in the cost of all pairwise transitions, compared with baseline, is shown in (Fig. S4): as expected, since most alterations in cortical thickness are reductions, the overall control input is diminished, and thus the transition cost is higher. Our results show that for some disorders, such as schizophrenia, bipolar disorder, or the temporal lobe epilepsies, the neuroanatomical distribution of cortical abnormalities is such that transition costs are on average lower than would be expected by only considering equivalent but randomly distributed changes in cortical thickness. In contrast, other conditions such as autism, ADHD, depression and 22.q syndrome incur transition costs that exceed what would be expected based on randomly distributed cortical abnormalities (Fig. 4). ## Effect of pharmacological manipulations on transitions Finally, mimicking the effects of endogenous or pharmacological modulation, we modulate control inputs in proportion to the regional expression of different neurotransmitter receptors and transporters, quantified from in vivo PET. We consider a total of 18 PET maps [55]. This allows us to evaluate how each receptor’s distribution favours transitions towards different cognitive topographies. As above, we account for the overall distribution of values in each receptor map by comparing it with randomly rotated null maps having the same distribution of values and spatial autocorrelation, but different association with neuroanatomy [86]. Our results show that some cognitive topographies are more susceptible than others to facilitation via receptor-informed stimulation, whereas others exhibit little benefit (Fig. 5). Additionally, we find differences between receptors in terms of their propensity to facilitate transitions, over and above the mere effect of increased input (i.e., performing better than randomly rotated counterparts). Specifically, the dopamine transporter and D1 receptor, the mu-opioid receptor, and the histamine H3 receptor maps performed best (Fig. 5). Our computational framework identifies these receptors and transporters as those whose neuroanatomical distribution is mostly suited to facilitate transitions towards a variety of cognitive topographies. ## Replication and validation We repeated our analyses using different parameter settings for network control theory; different reconstructions of the human connectome (with a different parcellation, and also in a separate DWI dataset); and a different way of defining cognitive topographies (based on the expert-curated BrainMap database [39, 76]). The Supplementary Information shows results with different implementations of the controllability framework [70]: we consider the time horizon T for control, adjacency matrix normalisation factor c, and the effect of normalising each map to unit Euclidean norm (Fig. S5, S6, S7, S8 and Tables S4, S5). We also show that our results can be replicated using a different reconstruction of the empirical human connectome, obtained from Diffusion Spectrum Imaging (Lausanne consensus dataset; [151]); and using a functional rather than anatomical parcellation of the human cerebral cortex to define network nodes (Schaefer-100 [120]) (Fig. S9). Using the Lausanne consensus connectome, we see that the group-matrices of transition energy obtained using the HCP and Lausanne consensus connectomes are positively correlated (Spearman’s $r = .99$, $p \leq 0.001$). We also confirm that transitions between cognitive topographies are more energy-efficient on the human connectome than on degree-preserving nulls ($$p \leq 0.003$$). Although we do not find a significant difference between the empirical human connectome, and the distribution of null networks preserving both degree and wiring cost ($$p \leq 0.266$$), the superior energy-efficiency of the human connectome is confirmed at the single-subject level (Fig. S10a,b and Table S2). Both group-level and subject-level results are also replicated with network nodes defined by the Schaefer-100 atlas (Fig. S10c,d and Table S3). The Lausanne dataset recapitulates the results about simulating pharmacological intervention (Fig. S12a). For Schaefer-parcellated data, we also observe a prominent role of D1 receptor and dopamine transporters, as before, but also acetylcholine and noradrenaline transporters (Fig. S12b). Lausanne dataset results are also consistent with HCP results in terms of the impact of cortical abnormality patterns (note that this analysis could not be repeated with the Schaefer-100 atlas, since ENIGMA data are only available in a single parcellation) (Fig. S11). Finally, we show that analogous results can be obtained if instead of the automated NeuroSynth meta-analytic engine, we derive 66 cognitive topographies from BrainMap, an expert-curated database of published voxel coordinates from neuroimaging studies that are significantly activated or deactivated during tasks [39, 76]. As with NeuroSynth, we observe asymmetry of transitions, such that targets exhibit significantly greater variability in transition energy (mean = 8.54 × 104, SD = 6.46 × 103) than sources (mean = 4.27 × 104, SD = 9.07 × 103; t[130] = 31.21, $p \leq 0.001$, Cohen’s $d = 5.40$) (Fig. S13). Likewise we find that, among the BrainMap terms that have been assigned to one of the six cognitive ontology domains from [7], memory-related terms exhibit higher median value of asymmetry than would be expected by chance ($$p \leq 0.043$$). We also replicate the result that transitions between cognitive topographies require significantly less energy on the human connectome than on degree-preserving and degree-and cost-preserving null networks, with the geometry-preserving nulls being significantly closer to the human connectome than degree-preserving ones (all $p \leq 0.001$), Fig. S13). ## DISCUSSION In the present report, we investigated how the brain’s network architecture shapes its capacity to transition between behaviourally defined cognitive topographies. We also systematically examined how transition between cognitive topographies could be reshaped by disease or pharmacological intervention. By taking into account network structure, functional activation and chemoarchitecture, our results provide a first step towards designing interventions that selectively manipulate cognitively relevant activation. Up to this point, a comprehensive “look-up table” charting the transitions between cognitively relevant brain states has remained elusive. The present approach permits exploration of the full range of possible transitions between experimentally defined brain states with a cognitive interpretation. In this sense, our work provides a large-scale generalisation of recent advances [16, 68] that defined brain states in terms of β maps from a task-based fMRI contrast, or electrocorticography signal power associated with memory task performance [134]. Importantly, we have also expanded the network control framework to include naturalistic, empirically defined forms of control input, such as of receptor density (as could be exogenously engaged by pharmacological agents) and cortical abnormalities (as could arise endogenously from pathology). We find that transitions between different cognitive topographies are not symmetric, but rather directional. Namely, some cognitively relevant brain states are substantially harder to reach than others, regardless of start state. These observations are consistent with the notion that state-to-state transition cost can perhaps be framed as cognitive demand [92]: in particular, transitioning to a more cognitively demanding 2-back task requires more control energy than transition to an easier task [16]. However, “cognitive demand” is a multifaceted construct with a variety of distinct possible operationalisations [47]: it remains to be determined which of these different interpretations is best aligned with network control energy. This endeavour will be facilitated by the approach introduced here, which enables the computational assessment of transition costs between any number of experimentally defined tasks from the literature. Consistent with our work, a recent report identified a transition asymmetry between artificial “bottom-up” and “top-down” states, defined as recruiting different portions of the cytoarchitectonic sensory-fugal axis [106], finding that top-down states (i.e., involving a greater proportion of higher-order cortices) are more demanding. Indeed, we find that association of the target state with the putative unimodal-transmodal functional hierarchy is a predictor of transition cost. Moreover, the variability in ease-of-transition that we observe highlights cognitive topographies related to language and especially memory among those with the greatest asymmetry in transition cost. Both of these domains emerge gradually over human development [30, 100], and both language and autobiographical memory have long been argued (though not without controversy) to be “uniquely human” [84, 123, 136, 144]. Although there is variability among cognitive topographies in terms of transition cost, the wiring of the human connectome generally facilitates more efficient transitions than alternative topologies [73, 132, 139, 150]. Specifically, the human connectome enables transitions at lower cost compared to randomly rewired nulls that preserve degree sequence, suggesting that this efficiency is imparted by network topology, rather than low-level features such as the density and degree. Importantly, this efficiency can be partly attributed to the geometry of wiring lengths: when this was accounted for in our geometry-preserving null models, the connectome’s advantage was substantially diminished. Our work contributes to a growing appreciation for how network topology and geometry shape efficient communication [12, 19, 66, 133] and brain function [2, 96, 114, 138]. We also identified several factors that contribute to transition costs. Our results pertaining to Euclidean distance predicting transition costs are in line with those of Karrer and colleagues [70] and Stiso and colleagues [134], who found a monotonic increase of both minimum and optimal control energy with increasing distance between initial and target states. In terms of the states themselves, the best architectural predictor of transition cost to a given cognitive topography is its network-based variance (Fig. S2). A distribution of values over a network’s nodes has high network-based variance if nodes with high values are relatively difficult to reach using a diffusion process [29]. Network control theory predicts that control energy will diffuse along the network’s paths [132]. Therefore, we can interpret our results as showing that if a state requires great activation at nodes that are difficult to reach via diffusion, the corresponding state will be harder to reach. Indeed, network-based variance is related to bi-directional communicability between nodes—a known predictor of transition energy between states [14, 51, 106]. In other words, if a desired pattern of activations has low divergence from the pattern of diffusion-based proximities between nodes (low network-based variance), then that pattern will be easier to reach through network control. Finally, we systematically quantified alternative naturalistic forms of control, via neurotransmitter receptor engagement and disorder-related aberrations in cortical morphology. Disorder-associated patterns of cortical abnormalities reshape the brain’s capacity to support transitions in ways that are not uniform, but rather both disorder-specific and brain state-specific. Of note, we find that depression and especially ADHD, both of which involve widespread attentional deficits [71, 145], are characterised by overall transition costs that exceed what would be expected if the corresponding cortical abnormalities were spatially distributed in a random fashion. Other disorders (schizophrenia, epilepsy) exhibited the opposite pattern. This suggests that localized perturbations in the connectome can attenuate some types of state transitions and promote others, potentially providing a mechanistic link between regional anatomical changes and changes in cognitive capacity across disease categories. This observation is consistent with results in the healthy population, where increased controllability is advantageous for cognitive performance in some regions, but disadvantageous in others [91]. Our results pertaining to pathology are complementary to applications of network control theory that evaluated transitions between random states or intrinsic connectivity networks, based on patients’ reorganised connectomes [11, 14, 58]—including a recent report that temporal lobe epilepsy induces deficits in control energy that are predictive of metabolic deficits quantified by FDG-PET [58]. In contrast, here we used the healthy connectome and simulated the effect of altered control input using abnormal cortical thickness (a change in control inputs rather than controlled network), and assessed the cost of transitioning between cognitive topographies that were experimentally defined. Since disorders typically involve both regional and connectomic alterations, in the future we expect that combining the two approaches will provide an even more fine-grained characterisation of how disease reshapes the brain’s capacity to transition between brain states. We observed a similar principle when control inputs were guided by empirically-derived receptor and transporter maps. We find that the more difficult cognitive topographies to reach also appear to be those that can most benefit from pharmacological intervention. Here dopamine transporters and D1 receptors, histamine H3 receptors, and mu-opioid receptors appear well-positioned to facilitate transitions. These results are consistent with the use of modafinil and methylphenidate as cognitive enhancers and to treat symptoms of ADHD: both drugs engage the dopaminergic system by blocking dopamine transporter as one of their main mechanisms of action [38, 46, 64, 65, 74, 83, 87, 95, 135, 146, 152–154, 157]. Likewise, H3-receptor antagonist drugs such as pitolisant are being evaluated for potential treatment of ADHD symptoms [37, 57, 105, 155]. Collectively, the predictions generated by our report highlight numerous potential clinical and non-clinical applications. Although preliminary, these results provide a first step towards designing protocols that selectively promote transitions to desired cognitive topographies in specific diseases. In addition, outcomes of this computational screening could be further tested in vivo by engaging different neurotransmitter systems through targeted pharmacological manipulations [16, 124], and evaluating the degree to which they facilitate switching between specific experimentally-defined cognitive topographies. The present work should be interpreted with respect to several important methodological considerations. Network control theory models neural dynamics as noise-free, and under assumptions of linearity and time invariance [52, 70, 73]. Recent work has begun to introduce stochasticity in the network control framework for the brain, with promising results—though still within the context of linear systems [68]. Although the brain is a nonlinear system, it has been shown that non-linear dynamics can be locally approximated by linear dynamics [26, 62], including through the application of dynamic causal models [40, 41]. In fact, evidence suggests that linear models may even outperform nonlinear ones at the macroscopic scale of BOLD signals [103, 122]. Finally, the predictions of linear network control theory have found successful translation to nonlinear systems [97, 143], and even at predicting the effects of direct intracranial electrical stimulation in humans [134]. Additionally, we made several simplifying assumptions about the control input provided by each region based on its cortical thickness or receptor/transporter expression. We have treated summary statistics from NeuroSynth’s term-based meta-analysis as representing relative activation; we also acknowledge that the mapping of functional activation to psychological terms in NeuroSynth does not distinguish activations from deactivations [159]. However, we believe that our replication with cognitive topographies defined using BrainMap [39] provides reassurance about the validity of our approach. Although the ENIGMA consortium provides datasets from large cohorts with standardised pipelines, ensuring robust results, the patient populations may exhibit co-morbidities and/or be undergoing treatment. In addition, and of particular relevance for the present modelling approach, the available maps do not directly reflect changes in tissue volume, but rather the effect size (magnitude of between-group difference) of patient-control statistical comparisons (though note that our use of spatial autocorrelation-preserving null models accounts for the mean and variance of each map; see Methods). Moreover, many more disorders, diseases, and conditions exist than the ones considered here. The same limitation applies to the PET data: the atlas of neurotransmitter receptors, though extensive, does not include all receptors. However, our computational workflow can readily be extended to accommodate new cognitive topographies, receptors, or disease maps of interest. Finally, here we did not consider the role of the subcortex, which is not present in ENIGMA disorder maps, and needs different treatment both in terms of spatial null models, and in terms of PET imaging. This approach has also been adopted in recent applications of network control theory to the human connectome [106, 107]. Overall, our approach based on network control theory provides a computational framework to evaluate the propensity of the connectome to support transitions between cognitively-relevant brain patterns. This framework lends itself to interrogating transitions between specific states of interest, and modelling the impact of global perturbations of the connectome, or regional cortical heterogeneities, or specific neurotransmitter systems. We anticipate that future work may combine different facets of this approach to evaluate in silico which potential pharmacological treatments may best address the specific cognitive difficulties associated with a given disorder or brain tissue lesion. ## Human structural connectome from Human Connectome Project We used diffusion MRI (dMRI) data from the 100 unrelated subjects of the HCP 900 subjects data release [148]. All HCP scanning protocols were approved by the local Institutional Review Board at Washington University in St. Louis. The diffusion weighted imaging (DWI) acquisition protocol is covered in detail elsewhere [48]. The diffusion MRI scan was conducted on a Siemens 3T Skyra scanner using a 2D spin-echo single-shot multiband EPI sequence with a multi-band factor of 3 and monopolar gradient pulse. The spatial resolution was 1.25 mm isotropic. TR=5500 ms, TE=89.50ms. The b-values were 1000, 2000, and 3000 s/mm2. The total number of diffusion sampling directions was 90, 90, and 90 for each of the shells in addition to 6 b0 images. We used the version of the data made available in DSI Studio-compatible format at http://brain.labsolver.org/diffusion-mri-templates/hcp-842-hcp-1021 [160]. We adopted previously reported procedures to reconstruct the human connectome from DWI data. The minimally-preprocessed DWI HCP data [48] were corrected for eddy current and susceptibility artifact. DWI data were then reconstructed using q-space diffeomorphic reconstruction (QSDR [162]), as implemented in DSI Studio (www.dsi-studio.labsolver.org). QSDR is a model-free method that calculates the orientational distribution of the density of diffusing water in a standard space, to conserve the diffusible spins and preserve the continuity of fiber geometry for fiber tracking. QSDR first reconstructs diffusion-weighted images in native space and computes the quantitative anisotropy (QA) in each voxel. These QA values are used to warp the brain to a template QA volume in Montreal Neurological Institute (MNI) space using a nonlinear registration algorithm implemented in the statistical parametric mapping (SPM) software. A diffusion sampling length ratio of 2.5 was used, and the output resolution was 1 mm. A modified FACT algorithm [161] was then used to perform deterministic fiber tracking on the reconstructed data, with the following parameters [82]: angular cutoff of 55°, step size of 1.0 mm, minimum length of 10 mm, maximum length of 400 mm, spin density function smoothing of 0.0, and a QA threshold determined by DWI signal in the cerebrospinal fluid. Each of the streamlines generated was automatically screened for its termination location. A white matter mask was created by applying DSI Studio’s default anisotropy threshold (0.6 Otsu’s threshold) to the spin distribution function’s anisotropy values. The mask was used to eliminate streamlines with premature termination in the white matter region. Deterministic fiber tracking was performed until 1, 000, 000 streamlines were reconstructed for each individual. For each individual, their structural connectome was reconstructed by drawing an edge between each pair of regions i and j from the Desikan-Killiany cortical atlas [27] if there were white matter tracts connecting the corresponding brain regions end-to-end; edge weights were quantified as the number of streamlines connecting each pair of regions, normalised by ROI distance and size. A group-consensus matrix A across subjects was then obtained using the distance-dependent procedure of Betzel and colleagues, to mitigate concerns about inconsistencies in reconstruction of individual participants’ structural connectomes [13]. This approach seeks to preserve both the edge density and the prevalence and length distribution of inter- and intra-hemispheric edge length distribution of individual participants’ connectomes, and it is designed to produce a representative connectome [13, 96]. The final edge density was $27\%$, and the weight associated with each edge was computed as the mean non-zero weight across participants. ## Alternative structural connectome from Lausanne dataset A total of $$n = 70$$ healthy participants (25 females, age 28.8 ± 8.9 years old) were scanned at the Lausanne University Hospital in a 3-Tesla MRI Scanner (Trio, Siemens Medical, Germany) using a 32-channel head coil [50]. Informed written consent was obtained for all participants in accordance with institutional guidelines and the protocol was approved by the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland. The protocol included [1] a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence sensitive to white/gray matter contrast (1 mm in-plane resolution, 1.2 mm slice thickness), and [2] a diffusion spectrum imaging (DSI) sequence (128 diffusion-weighted volumes and a single b0 volume, maximum b-value 8 000 s/mm2, 2.2 × 2.2 × 3.0 mm voxel size). Structural connectomes were reconstructed for individual participants using deterministic streamline tractography and divided according to the Desikan-Killiany grey matter parcellation. White matter and grey matter were segmented from the MPRAGE volumes using the FreeSurfer version 5.0.0 open-source package, whereas DSI data preprocessing was implemented with tools from the Connectome Mapper open-source software, initiating 32 streamline propagations per diffusion direction for each white matter voxel. Structural connectivity was defined as streamline density between node pairs, i.e., the number of streamlines between two regions normalized by the mean length of the streamlines and the mean surface area of the regions, following previous work with these data [53, 151]. ## Network control energy Network control theory models the brain as a linear, time-invariant control system [52]. In the general context of linear control theory, the evolutionary dynamics of the state x(t) is formulated as an equation relating the first-order derivative of the state x(t), x˙, to the state variable x itself, and the control input. For this system, given the initial and target states, the control trajectory moving from the initial to target states is determined by the interaction matrix A, the input matrix B, and the control input u(t), in the form of [1] x˙=Ax(t)+Bu(t) The state interaction matrix A characterizes the relationships between system elements, determining how the control system moves from the current state to the future state. The structural connectivity matrix A serves as a linear operator that maps each state, x, to the rate of change of that state. This linear transformation can be described in terms of the evolutionary modes of the system consisting of the N eigenvectors of A and their associated eigenvalues. The matrix A is then normalised to avoid infinite growth of the system over time: [2] Anorm=Aλ(A)max+c-I Here, I denotes the identity matrix of size N × N, and λ(A)max denotes the largest eigenvalue of the system. To normalize the system, we must specify the parameter c, which determines the rate of stabilization of the system. Here we use $c = 0$ for our main analyses, such that the system approaches its largest mode over time. We also report results for $c = 0.01$×λ(A)max, whereby all modes decay, and the system goes to zero over time. The control input matrix B denotes the location of control nodes on which we place the input energy. If we control all brain regions, B corresponds to the N × N identity matrix with ones on the diagonal and zeros elsewhere. If we control only a single brain region i, B reduces to a single N × N diagonal matrix with a one in the ith element of the diagonal, and zeros elsewhere. The control input u(t) denotes the amount of energy injected into each control node at each time point t. Intuitively, u(t) can be summarized over time to represent the total energy consumption during transition from an initial state to a final state. In the brain control analysis framework, a state refers to a vector x(t) of N elements, which encodes the neurophysiological activity map across the whole brain. In the current work, x(t) is the meta-analytic activation of each region associated with each cognitively relevant term, aggregated over studies in the NeuroSynth database. This computational approach allows us to compute the transition energy as the optimal energy required to transition between each pair of cognitive topographies in finite time. To explore the energetic efficiency of the structural brain network in facilitating the transition between cognitive topographies, we adopted the optimal control framework to estimate the control energy required to optimally steer the brain through these state transitions [14, 51, 58]. Optimality is defined in terms of jointly minimising the combination of both the length of the transition trajectory from an initial source state (x[0]=x0 to the final target state x(T)=xT over the time horizon T (to avoid spurious, unrealistically long trajectories), and the required unique control input u(t) summarised over the length of this trajectory: [3] utκ=argminuκJuκ=argminuκ∫0T(xT−xt⊤xT−xt)+ρukt⊤ukt)dt where xT-x(t)⊤xT-x(t) is the distance between the state at time t and the final state xT,T is the finite amount of time given to reach the final state, and ρ is the relative weighting between the cost associated with the length of the transition trajectory and the input control energy. The equation is solved using forward integration. J(u(t)κ) is the cost function defined to find the unique optimal control input u(t)κ. Here, following common practice, we set ρ equal to 1, corresponding to equal weighting [14, 70]. We set $T = 1$ [14], but we also report results with $T = 3$ [70] (Fig. S5, S6, S7, S8). ## Cognitive topographies from NeuroSynth Continuous measures of the association between voxels and cognitive categories were obtained from NeuroSynth, an automated term-based meta-analytic tool that synthesizes results from more than 15 000 published fMRI studies by searching for high-frequency key words (such as “pain” and “attention” terms) that are systematically mentioned in the papers alongside fMRI voxel coordinates (https://github.com/neurosynth/neurosynth, using the volumetric association test maps [159]). This measure of association strength is the tendency that a given term is reported in the functional task-based neuroimaging study if there is activation observed at a given voxel. Note that the tool does not distinguish between areas that are activated or deactivated in relation to the term of interest, nor the degree of activation, only that certain brain areas are frequently reported in conjunction with certain words. Although more than a thousand terms are catalogued in the NeuroSynth engine, we refine our analysis by focusing on cognitive function and therefore we limit the terms of interest to cognitive and behavioural terms. These terms were selected from the Cognitive Atlas, a public ontology of cognitive science [109], which includes a comprehensive list of neurocognitive terms and has been previously used in conjunction with NeuroSynth [54]. This approach totaled to $t = 123$ terms, ranging from umbrella terms (“attention”, “emotion”) to specific cognitive processes (“visual attention”, “episodic memory”), behaviours (“eating”, “sleep”), and emotional states (“fear”, “anxiety”). The probabilistic measure reported by Neurosynth can be interpreted as a quantitative representation of how regional fluctuations in activity are related to psychological processes. ## Alternative cognitive topographies from BrainMap Whereas NeuroSynth is an automated tool, BrainMap is an expert-curated repository: it includes the brain coordinates that are significantly activated during thousands of different experiments from published neuroimaging studies [39, 76]. As a result, NeuroSynth terms and BrainMap behavioural domains differ considerably. Here, we used maps pertaining to 66 unique behavioural domains (the same as in [54]), obtained from 8,703 experiments. Experiments conducted on unhealthy participants were excluded, as well as experiments without a defined behavioural domain. ## Network null models We used two different network null models to disambiguate the role of connectome topology and geometric embedding in shaping control energy [150]. The first null model is the well-known Maslov-Sneppen degree-preserving rewired network, whereby edges are swapped so as to randomise the topology while preserving the exact binary degree of each node (degree sequence), and the overall distribution of edge weights [88]. As a second, more stringent null model, we adopted a null model that in addition to preserving exactly the same degree sequence and exactly the same edge weight distribution as the original network, also approximately preserves the original network’s edge length distribution (based on Euclidean distance between regions), and the weight-length relationship [12]. For each null model, we generated a population of 500 null networks starting from the empirical connectome, and computed the control energy between each pair of cognitive brain states from NeuroSynth, as done for the empirical connectome. We compared the overall control energy between all possible states obtained from the empirical connectome and from the distribution of null instances, by obtaining a z-score. ## Spatial null models To evaluate the role of regional neuroanatomical features, we implemented a permutation-based null model, termed spin test [86, 150]. For each map, parcel coordinates were projected onto the spherical surface and then randomly rotated and original parcels were reassigned the value of the closest rotated parcel (10 000 repetitions) [151]. In addition to preserving the distribution of cortical values, this null model also preserves the spatial autocorrelation present in the data. ## Predictors of transition energy We characterised each cognitive brain state from NeuroSynth in terms of its relationship with several well-known graph-theoretic properties of the structural connectome. From the consensus connectome we computed the binary and weighted degree (also known as node strength) of each region. We also computed the participation coefficient of each region, based on the modular assignment of each region to the well-known intrinsic connectivity networks [163]. We computed the Spearman correlation between each of these vectors, and each of the 123 brain maps from NeuroSynth. Additionally, we also computed the correlation between each NeuroSynth map, and the principal gradient of variation in functional connectivity [85], believed to reflect the hierarchical organisation of cortical information-processing. ## Network-based variance For each NeuroSynth map, we also computed as an additional predictor a recently developed measure termed “network variance” [29]. The traditional notion of variance of a distribution (sum of squared differences from the mean) assumes that observations are independent. However, this assumption is almost invariably violated in the case of distributions on a graph, where the graph’s nodes (whose values correspond to the distribution’s observations) are connected to each other, genereating dependencies. The notion of spatial autocorrelation [86] can be cast as a special case of this situation, whereby the graph connecting nodes is the graph of spatial distances between them (e.g., Euclidean distance). Devriendt and colleagues provided the network variance as a generalization of variance to distributions on a graph: [4] var(p)=12∑Ni,jp(i)p(j)d2 In other words, a distribution on a graph has high network variance if most of the mass (here: activity) is concentrated at nodes that are poorly connected with the rest of the network. This relies on defining a suitable measure of distance on a graph. Devriendt and colleagues noted that the geodesic distance (length of the shortest path between two nodes) may be a suitable candidate, but recommended using the effective resistance instead [28, 29]. Like geodesic distance, the effective resistance is predicated on the length of the paths between a pair of nodes. However, unlike the geodesic distance, effective resistance does not only consider the shortest path between two nodes, but rather it takes into account paths of all length along the graph, such that two nodes are less distant the more paths exist between them, thereby reflecting the full topology of the network. The resistance distance ωij between nodes i and j is large when nodes i and j are not well connected in the network, such that only few, long paths connect them, resulting in a long time for a random walker to reach one node from another, whereas a small ωij means that they are well connected through many, predominantly short paths i and j [28]. Up to a constant, the effective resistance can be computed as the “commute time”: the mean time it takes a random walker to go from node i to node j and back, for all pairs of nodes i and j [21]. Concretely, effective resistance on a graph is computed as [5] ωij=ei-ej⊤Qei-ej where the unit vectors have entries (ei)$k = 1$ if k = i and zero otherwise, and where Q is the Moore-Penrose pseudoinverse of the graph’s Laplacian matrix. For each NeuroSynth map, we computed its network variance using as distance measure the effective resistance on the consensus connectome. Since the network variance requires the distribution on the graph’s nodes to be positive and sum to 1, each map’s values were rescaled so that the minimum was 0, and then divided by their sum. We then used these characterisations of the NeuroSynth maps (correlation with connectome graph-theoretic properties; correlation with the cortical hierarchy; and network variance) as predictors against the average energy required to transition to each cognitive brain state. We performed multiple partial correlations, using each characterisation in turn as predictor (after partialling out the effects of mean and traditional variance of each NeuroSynth map). ## Dominance analysis As an alternative approach, to consider all predictors together and evaluate their respective contributions, we performed a dominance analysis with all five predictors. Dominance analysis seeks to determine the relative contribution (“dominance” of each independent variable to the overall fit (adjusted R2)) of the multiple linear regression model (https://github.com/dominance-analysis/dominance-analysis) [4]. This is done by fitting the same regression model on every combination of predictors (2p − 1 submodels for a model with p predictors). Total dominance is defined as the average of the relative increase in R2 when adding a single predictor of interest to a submodel, across all 2p − 1 submodels. The sum of the dominance of all input variables is equal to the total adjusted R2 of the complete model, making the percentage of relative importance an intuitive method that partitions the total effect size across predictors. Therefore, unlike other methods of assessing predictor importance, such as methods based on regression coefficients or univariate correlations, dominance analysis accounts for predictor–predictor interactions and is interpretable. ## Disease-associated patterns of cortical thickness abnormality from the ENIGMA database Spatial maps of cortical thickness were collected for the available 11 neurological, neurodevelopmental, and psychiatric disorders from the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) consortium [56, 141, 142] and the Enigma toolbox and recent related publications (https://github.com/MICA-MNI/ENIGMA) [77]: 22q11.2 deletion syndrome [137], attention-deficit/hyperactivity disorder [63], autism spectrum disorder [149], idiopathic generalized epilepsy [156], right temporal lobe epilepsy [156], left temporal lobe epilepsy [156], depression [121], obsessive-compulsive disorder [15], schizophrenia [147], bipolar disorder [59], and Parkinson’s disease [75]. The ENIGMA consortium is a data-sharing initiative that relies on standardized image acquisition and processing pipelines, such that disorder maps are comparable [141]. Altogether, over 17 000 patients were scanned across the eleven disorders, against almost 22 000 controls. The values for each map are z-scored effect sizes (Cohen’s d) of cortical thickness in patient populations versus healthy controls. Imaging and processing protocols can be found at http://enigma.ini.usc.edu/protocols/. For every brain region, we constructed an 11-element vector of disorder abnormality, where each element represents a disorder’s cortical abnormality at the region. These values were then added to the B matrix of uniform control inputs, to provide regional heterogeneity. This approach is motivated by the expectation that regions with decreased thickness should have lower capacity to exert control inputs, and vice-versa. Recent work adopted a similar approach to model the regional control input provided at each region, in terms of the regional density of receptor expression, such that regions expressing the receptor to a greater extent are understood to exert greater control input [124]. The largest increase in cortical thickness across all disorders is 0.87 (expressed in terms of Cohen’s d), whereas the largest decrease is 0.59. Thus, across all diseases, the entries in the input matrix B were always positive, bound between 0.41 and 1.87. Since the distributions of cortical abnormality associated with the various ENIGMA diseases and disorders are different, changing the distribution of control inputs also changes the overall amount of control energy that is being injected into the system (in some cases leading to an overall increase, or an overall decrease), and consequently the control energy that is required to transition between brain states. Therefore, an appropriate null model to evaluate the effects of disease-associated patterns of grey matter abnormality on brain state transitions should preserve the overall spatial distribution of control inputs associated with each map, while changing the spatial location. Rather than simply randomising the distribution of cortical thickness abnormalities, we opted to adopt the spin-based null model, which also preserves the spatial autocorrelation present in the data [86, 150]. We then obtained a z-score of the control energy required to transition between each pair of states (here considering only the reduced set of 25 states, to reduce computational burden), for the empirical ENIGMA map against the distribution of spin-null maps. Thus, a positive z-score indicates that the empirical pattern of cortical thickness abnormality associated with a disease is more energetically demanding than would be expected if the abnormalities were occurring at random on the cortex; whereas a negative z-score indicates that the pattern requires less energy than if the abnormalities were occurring at random (but with equivalent spatial autocorrelation). ## Receptor maps from Positron Emission Tomography Receptor densities were estimated using PET tracer studies for a total of 18 receptors and transporters, across 9 neurotransmitter systems, recently made available by Hansen and colleagues at https://github.com/netneurolab/hansen_receptors [55]. These include dopamine (D1 [67], D2 [117, 125, 128, 164], DAT [35], norepinephrine (NET [9, 31, 79, 116], serotonin (5-HT1A [119], 5-HT1B [42, 89, 98, 108, 118, 119], 5-HT2A [10], 5-HT4 [10], 5-HT6 [111, 112] 5-HTT [10]), acetylcholine (α4β2 [5, 60], M1 [99], VAChT [1, 8], glutamate (mGluR5 [34, 127] NMDA [44, 45], GABA (GABAA [104])), histamine (H3 [43]), cannabinoid (CB1 [33, 101, 102, 113]) and opioid (MOR [69]). Volumetric PET images were registered to the MNI-ICBM 152 non-linear 2009 (version c, asymmetric) template, averaged across participants within each study, then parcellated and receptors/transporters with more than one mean image of the same tracer (5-HT1B, D2, VAChT) were combined using a weighted average [55]. For the control energy analysis, each PET map was scaled between 0 and 1, and its regional values were added to the B matrix, following recent work [124]. Since the PET distributions are different, changing the distribution of control inputs also changes the overall amount of control energy that is being injected into the system, and consequently the control energy that is required to transition between brain states. Therefore, an appropriate null model to evaluate which receptors are especially well-poised to facilitate brain state transitions, in terms of their spatial location, should preserve the overall distribution of control inputs associated with each receptor, while changing the spatial location. Rather than simply randomising the distribution of receptor densities, we opted to adopt the more stringent spin test null [86, 150]. In addition to preserving the distribution of regional receptor densities, this null model also preserves the spatial autocorrelation present in the data. We then evaluated how often (as a percentage) a transition between two given brain states was found to require less control energy when using an empirical receptor map to determine the control inputs, than when using a spin-randomised version of the same map. Due to the computationally intensive nature of this procedure (10 000 repetitions for each of 18 PET maps), we only considered transitions between the reduced set of 25 brain states, instead of the whole 123. ## References 1. Aghourian M., Legault-Denis C., Soucy J.-P., Rosa-Neto P., Gauthier S., Kostikov A., Gravel P., Bédard M.-A.. Quantification of brain cholinergic denervation in alzheimer’s disease using pet imaging with [18f]-feobv. Molecular Psychiatry (2017) 22 1531-1538. PMID: 28894304 2. Akarca D., Vértes P. E., Bullmore E. T., Baker K., Gathercole S. E., Holmes J., Kievit R. A., Manly T., Bathelt J., Bennett M., Bignardi G., Bishop S., Bottacin E., Bridge L., Brkic D., Bryant A., Butterfield S., Byrne E. 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--- title: Aging impairs cold-induced beige adipogenesis and adipocyte metabolic reprogramming authors: - Corey D. Holman - Alexander P. Sakers - Ryan P. Calhoun - Lan Cheng - Ethan C. Fein - Christopher Jacobs - Linus Tsai - Evan D. Rosen - Patrick Seale journal: bioRxiv year: 2024 pmcid: PMC10055201 doi: 10.1101/2023.03.20.533514 license: CC BY 4.0 --- # Aging impairs cold-induced beige adipogenesis and adipocyte metabolic reprogramming ## Abstract The energy-burning capability of beige adipose tissue is a potential therapeutic tool for reducing obesity and metabolic disease, but this capacity is decreased by aging. Here, we evaluate the impact of aging on the profile and activity of adipocyte stem and progenitor cells (ASPCs) and adipocytes during the beiging process. We found that aging increases the expression of Cd9 and other fibro-inflammatory genes in fibroblastic ASPCs and blocks their differentiation into beige adipocytes. Fibroblastic ASPC populations from young and aged mice were equally competent for beige differentiation in vitro, suggesting that environmental factors suppress adipogenesis in vivo. Examination of adipocytes by single nucleus RNA-sequencing identified compositional and transcriptional differences in adipocyte populations with age and cold exposure. Notably, cold exposure induced an adipocyte population expressing high levels of de novo lipogenesis (DNL) genes, and this response was severely blunted in aged animals. We further identified natriuretic peptide clearance receptor Npr3, a beige fat repressor, as a marker gene for a subset of white adipocytes and an aging-upregulated gene in adipocytes. In summary, this study indicates that aging blocks beige adipogenesis and dysregulates adipocyte responses to cold exposure and provides a unique resource for identifying cold and aging-regulated pathways in adipose tissue. ## Introduction Brown and beige fat cells are specialized to burn calories for heat production and have the capacity to reduce obesity and metabolic disease. Brown adipocytes are localized in dedicated brown adipose tissue (BAT) depots, whereas beige adipocytes develop in white adipose tissue (WAT) in response to cold exposure, and other stimuli (Wang and Seale, 2016). Adult humans possess thermogenic adipose depots that appear to resemble rodent beige adipose tissue (Jespersen et al., 2013; Wu et al., 2012). Brown and beige adipocytes share similar cellular features such as abundant mitochondria, multilocular lipid droplets, and expression of thermogenic genes like Uncoupling Protein-1 (UCP1). UCP1, when activated, dissipates the mitochondrial proton gradient, leading to high levels of substrate oxidation and heat production (Cannon and Nedergaard, 2004). Brown and beige adipocytes can also produce heat via other UCP1-independent futile cycles (Chouchani et al., 2019). Increasing beige fat development in mice reduces obesity and improves insulin sensitivity, whereas ablation of beige fat in mice causes metabolic dysfunction (Cederberg et al., 2001; Cohen et al., 2014; Seale et al., 2011; Shao et al., 2016; Stine et al., 2016). Furthermore, transplantation of human beige adipocytes into obese mice reduces liver steatosis and improves metabolic health (Min et al 2016). Beige adipocytes develop via the de novo differentiation of adipocyte stem and progenitor cells (ASPCs) or through induction of the thermogenic program in adipocytes (Ferrero et al., 2020; Sakers et al., 2022; Shao et al., 2019). Human and mouse thermogenic adipose tissue activity declines with aging, predisposing to cardiometabolic disease and limiting the potential of brown/beige fat-targeted therapies (Becher et al., 2021; Berry et al., 2017; Cypess et al., 2012; Rogers et al., 2012; Wang et al., 2019; Yoneshiro et al., 2011). In mice, beige adipose tissue is reduced by ‘middle-age’ (i.e., 1-year-old), preceding many of the damaging effects of old age on organ function (Berry et al., 2017; Goncalves et al., 2017; Rogers et al., 2012). The aging-associated decline in beige fat activity can occur independently of increases in body weight (Rogers et al., 2012; St-Onge, 2005). A variety of processes and pathways have been linked to the aging-induced deficit in beige fat formation, including diminished proliferation and cellular senescence of ASPCs (Berry et al., 2017), increased fibrosis (Wang et al., 2019), increased inflammation (Amiya Kumar Ghosh, 2019), accumulation of anti-adipogenic regulatory cells (Nguyen et al., 2021), and reduced adrenergic tone (Rogers et al., 2012). However, a comprehensive understanding of how cold exposure and aging affect ASPC identity, adipogenesis, and adipocyte phenotypic switching remains elusive. We applied ASPC lineage tracing, along with unbiased single-cell and single-nucleus RNA sequencing (scRNA-seq; snRNA-seq) to profile the beiging process and evaluate the impact of aging on this process. We found that aging modulates the gene program of multiple fibroblastic ASPC populations and blocks the differentiation of these cells into beige adipocytes in vivo. snRNA-seq analysis revealed four types of adipocytes defined by different responses to cold exposure and aging: beige, Npr3-high, de novo lipogenesis (DNL)-low, and DNL-high. Notably, DNL-high adipocytes were defined by a marked induction of DNL genes during cold exposure in young compared to aged animals. A white adipocyte subpopulation in young mice was marked by expression of Natriuretic peptide receptor-3 (Npr3), which was also increased in adipocyte populations from aged mice. Altogether, this study shows that aging blocks cold-stimulated adipocyte reprogramming and ASPC adipogenesis, while implicating suppression of natriuretic peptide signaling and DNL as contributing to the aging-mediated decline in beige fat formation. ## Aging impairs iWAT beiging To study the impact of aging on beige adipose tissue development, we exposed young (9-week-old) and middle aged (57-week-old) C57BL/6 mice to 6°C for either 3 or 14 days. All mouse groups were first acclimated to 30°C (thermoneutrality [TN]) for 3 weeks to reduce beige adipose tissue to baseline (low) levels. Following acclimation, TN-housed mice remained at 30°C; acute cold mice (3D) were transitioned to 6°C after 11 days for the final 3 days; and chronic cold mice (14D) were moved to 6°C for two weeks (Figure 1A). As expected, the aged mice weighed more and had larger iWAT depots than the young mice (Figure S1A,B). Cold exposure greatly and progressively increased the expression levels of thermogenic genes Ucp1, Cidea, Dio2 and Ppargc1a in young iWAT, and the activation of these genes was significantly blunted in aged mice, especially at the 3D time point (Figure 1B). Immunofluorescence (IF) staining showed a robust induction of UCP1 protein in multilocular adipocytes of young iWAT at 3D of cold exposure, which was further increased at 14D. The induction of UCP1+ beige adipocytes was severely reduced in aged animals, with strikingly few UCP1+ adipocytes detected. At 14D, the beige adipocytes looked morphologically similar in young and aged mice, though there were fewer in aged animals (Figure 1C). At both ages, beige adipocytes were more prominent in the inguinal versus dorsolumbar region of iWAT, consistent with other reports (Barreau et al., 2016; Chi et al., 2018; Dichamp et al., 2019), and beiging was largely absent in the dorsolumbar region of aged mice (Figure S1C–D, F). To determine if the beiging response was delayed in aged mice, we exposed young and aged mice at 6°C for 6 weeks. At this time point, the iWAT of aged mice exhibited a larger deficit in thermogenic gene expression compared to young animals (Figure 1D). Thermogenic gene levels in interscapular BAT were similar between young and aged mice, at TN and after cold exposure, indicating that the inhibitory effects of aging were selective to WAT (Figure S1E). Next, we examined beige fat formation in young and aged animals upon treatment with the β3-selective adrenergic agonist CL-316,243 (CL). CL acts in an adipose tissue autonomous manner to stimulate beige fat biogenesis, bypassing the central nervous system pathways that mediate the cold response. Acute CL treatment for only 1-hour increased Ucp1 expression in in iWAT of young mice to a much greater extent than in aged mice (Figure 1E). Chronic CL exposure for 5 days also induced much higher expression levels of Ucp1 and Cidea in iWAT of young compared to aged mice (Figure 1F). Taken together, these results demonstrate that beige adipose tissue induction is severely impaired in middle aged mice. ## Aging blocks beige adipogenesis from Pdgfra+ ASPCs To determine the contribution of fibroblastic ASPCs to beige adipocytes during cold exposure, we performed lineage tracing using Pdgfra-CreERT2;R26RtdTomato reporter mice. Pdgfra expression marks multiple ASPC populations, including preadipocytes (Merrick et al., 2019; Sakers et al., 2022). Young and aged reporter mice were treated with tamoxifen for 5 days at TN (30°C; “pulse”) to activate Cre and induce tdTomato expression in Pdgfra+ cells. Following a 9 day washout period, mice were transferred to 6°C (cold) for two weeks (“chase”) (Figure 2A). We observed near complete and specific labeling of ASPCs during the pulse period, with ~$95\%$ of PDGFRα+ cells in iWAT from young and aged mice displaying tdTomato expression (Figures 2B, S2A). The proportion of PDGFRa+ cells in iWAT was similar between young and aged mice (Figure 2B). No tdTomato-expressing adipocytes were observed after the pulse (Figure S2B). After 14 days of cold exposure, we detected many newly developed beige adipocytes from ASPCs in young mice (visible as tdTomato+/UCP1+ multilocular adipocytes). By contrast, very few ASPC-derived (tdTomato+) adipocytes were detected in the beige fat areas of aged iWAT at day 14 (Figures 2C). Quantifying across the entire length of iWAT pads revealed that most beige adipogenesis occurred in the inguinal region and was ~12-fold lower in aged compared to young mice (Figure 2D,E). However, the overall contribution of Pdgfra+ ASPCs to beige adipocytes was relatively low, even in young animals, with <$20\%$ of beige adipocytes expressing tdTomato. ## Single cell expression profiling of ASPCs We previously identified three main fibroblastic ASPC populations in iWAT: DPP4+ cells, ICAM1+ preadipocytes, and CD142+ cells. All these cell types express Pdgfra and have the capacity to undergo adipogenic differentiation (Merrick et al., 2019). To test whether aging dysregulates one or more of these ASPC types, we performed scRNA-seq on stromal vascular cells from iWAT of young and aged animals, maintained at TN, or following transition to cold for 3 or 14 days (Figure 1A). ASPCs were enriched by removing immune (CD45+) cells using fluorescence activated cell sorting (FACS). We integrated the datasets from all conditions together and performed clustering analysis. The following cell populations were annotated based on their expression of cell type-specific marker genes: four fibroblast populations (Dpp4+; Icam1+ preadipocytes; Cd142+, Spp1+), two populations of endothelial cells (Pecam1+); smooth muscle cells/pericytes (Myh11+, Pdgfrb+); Schwann cells (Mpz+); and residual immune cells (Ptprc+) (Figures 3A–C). We did not identify any cell population specific to either aging or cold exposure. In this regard, we did not identify ‘aging-dependent regulatory cells (ARCs)’, which were previously defined as ASPCs expressing Lgals3 and other inflammatory genes (Figure S3A) (Nguyen et al., 2021). The expression levels of identity markers of the ASPC populations were not modulated during cold exposure or aging (Figure S3B). Differential gene expression analyses identified aging-modulated genes in ASPCs (Figure 3D). Notably, expression of Cd9, previously identified as a fibrogenic marker, was upregulated with age in Dpp4+ cells and preadipocytes (Marcelin et al., 2017). Pltp and Gpnmb were also elevated by aging across all ASPC populations and temperature conditions. Genes downregulated by aging in all ASPC populations included Meg3, Itm2a and Gpc3 and Postn. Of note, Postn encodes an extracellular matrix protein that was previously reported to regulate adipose tissue expansion and decrease in expression during aging (Graja et al., 2018). ## ASPCs from aged mice are competent for beige adipogenesis ex vivo We next evaluated if ASPCs from young and aged animals exhibit cell-autonomous differences in adipogenic differentiation capacity. We FACS-purified DPP4+, ICAM1+ and CD142+ cells from the iWAT of young and aged mice, plated them in culture and induced adipocyte differentiation. Using a minimal differentiation stimulus consisting of insulin only (Min), ICAM1+ and CD142+ cells underwent more efficient differentiation into lipid droplet-containing adipocytes, and expressed higher levels of adipocyte genes (Adipoq and Fabp4) than DPP4+ cells, consistent with prior work (Figures 4A,B) (Merrick et al., 2019). DPP4+ and CD142+ cells from young and aged mice underwent adipocyte differentiation and induced adipocyte genes with equivalent efficiency. Unexpectedly, ICAM1+ cells from aged mice exhibited greater differentiation capacity than those from young mice, as evidenced by higher expression levels of Adipoq and Fabp4 (Figures 4A,B). Maximal stimulation with a full cocktail of adipogenic inducers (Max) produced similar and robust differentiation in all ASPC populations from young or aged mice (Figures 4C,D). To assess whether young and aged ASPCs behave differently when cultured as a mixed heterogeneous population, we isolated the stromal vascular fraction (SVF) for adipogenesis assays. Again, SVF cell cultures from young and aged mice displayed similar adipogenic differentiation efficiency following either Min or Max stimulation (Figures 4E,F). Finally, we treated differentiated adipocyte cultures with the pan-adrenergic agonist isoproterenol for 4 hours to evaluate thermogenic gene activation (i.e., beiging). Basal levels of Ucp1 expression were lower in DPP4+ cells compared to other ASPC types, but all ASPC populations activated Ucp1 expression to high and similar levels in response to isoproterenol treatment, and did not differ by age (Figure 4G). We also did not observe an aging-related difference in the levels of Ucp1 induction in SVF-derived adipocyte cultures (Figure 4H). Together, these data suggest that the beige adipogenic capacity of ASPCs is not intrinsically compromised in aged mice, and therefore the in vivo deficit in beige adipogenesis could be due to non-ASPC-autonomous effects. ## Single nucleus RNA sequencing uncovers adipocyte heterogeneity To determine the effects of aging and cold exposure on adipocyte gene profiles, we performed snRNA-seq analyses of iWAT samples using the same experimental paradigm described above (Figure 1A). We integrated all the conditions together for analyses from two separate runs. Similar cell types were captured as with scRNA-seq (Figure 3A), but with the addition of mature adipocyte populations (Figure 5A). This dataset also has increased representation from immune cells since there was no negative selection against CD45+ cells. As with the single-cell data set, we did not identify any aging-specific cell populations (Figure S4A). However, we observed striking gene expression differences in the adipocyte cluster across age and temperature. Most obvious, and expectedly, was the emergence and expansion of a distinct beige adipocyte population, marked by expression of Ucp1 and other thermogenic genes, during cold exposure (Figure 5B). To focus on adipocyte responses, we reintegrated the snRNA-seq data using only the adipocytes, which revealed four main clusters (Figures 5D–F). All adipocyte clusters displayed similarly high mRNA levels of canonical adipocyte markers Fabp4 and Plin1. Beige adipocytes, marked by high expression of many thermogenic genes (i.e., Ppargc1a, Esrrg, Cidea, Gk, Prdm16 and Ucp1), were the most distinctive cluster and were largely absent at TN in young and aged mice. These cells began to appear in young mice after 3 days of cold exposure, and were further increased at 14 days. By contrast, in aged mice, beige cells were barely detectable at 3 days of cold exposure and were present at greatly reduced numbers than in young mice at 14 days (Figure 5E). This analysis also revealed three sub-populations of ‘white’ adipocytes. ‘ Npr3-high’ adipocytes were enriched for expression of Npr3, Synpo2, Prr16, and Tshr, expressed higher levels of white fat marker genes Leptin (Lep) and Nnat, and exhibited the lowest expression levels of thermogenic (beige) genes (Gesta et al., 2007; Rosell et al., 2014). Two additional white adipocyte clusters were designated as ‘de novo lipogenesis (DNL)-low’ and ‘DNL-high’ cells, both of which expressed lower levels of Npr3 and shared selective expression of Fgf14. DNL-high cells uniquely expressed Ces1f and Gsta3 and activated high levels of DNL pathway genes (i.e., Fasn, Acss2 and Acly) upon cold exposure (Figure 5F). Interestingly, Adiponectin (Adipoq) was differentially expressed across adipocyte clusters, with higher levels in Npr3-high and DNL-high cells. Quantification of adipocyte nuclei from this data set suggested that the proportions of Npr3-high and DNL-high adipocytes remain stable across temperature, with aged mice having more Npr3-high adipocytes. The proportion of beige adipocytes increased during cold exposure, while DNL-low adipocytes decreased with cold exposure in both young and aged mice (Figure 5G). ## Aging dysregulates gene programming in adipocyte populations To evaluate the global effects of cold exposure and aging on adipocytes, we performed differential gene expression analysis between young and aged adipocytes within each cluster. DNL-high and beige adipocytes exhibited the most dramatic expression changes between young and aged animals (Figures 6A–B, S4B–C). At TN, DNL-high cells from aged animals expressed lower levels of several genes, including Fkbp5, Spon1 and Adam12. Interestingly, Npr3, in addition to marking Npr3-high cells, was increased by aging in DNL-high adipocytes and to a lesser extent in other adipocyte populations (Figure 6C,D). In young animals, Npr3 expression was downregulated by cold exposure in the three white adipocyte populations, and this downregulation was blunted in aged animals (Figure 6D). Gene expression analysis of whole iWAT pads confirmed that Npr3 mRNA levels were progressively decreased by cold exposure and elevated in aged versus young mice under all temperature conditions (Figure 6E). Npr3 expression levels were also increased in isolated primary adipocytes from aged relative to young mice (Figure 6F). Expression levels of the G-protein coupled NP receptors Npr1 or Npr2 were not modulated by cold or aging in iWAT or iWAT adipocytes (Figure S4D,E). We also observed a striking activation of the DNL gene program (Acly, Fasn, Acaca, Scd1, etc.) in DNL-high and beige adipocytes during cold exposure (Figures 6G,H). The induction of these genes during cold exposure, exemplified by Acly expression, was a cluster-defining attribute of DNL-high cells, which did not express beige markers like Ucp1 even after 14 days of cold exposure. Of note, we found two types of beige (Ucp1+) adipocytes, distinguished by the presence vs. absence of high DNL gene levels (i.e., Ucp1+; DNL+ and Ucp1+;DNL(−)), with the latter arising first during cold exposure (3D vs. 14D) (Figures 6G, S4F,G). Importantly, the induction of DNL genes was nearly completely blocked in DNL-high cells and reduced in beige cells of aged animals (Figure 6G). Indeed, the top aging downregulated genes in adipocytes from cold exposed mice correspond to DNL and related pathways, especially in DNL-high cells (Figure S4I). Lastly, at the whole tissue level, we observed robust induction of Acly in iWAT of young relative to aged mice with increasing duration of cold exposure (Figure S4H). Taken together, these results implicate the suppression of natriuretic peptide signaling and DNL in the aging-related impairment of beige fat formation. ## Discussion Thermogenic adipose tissue activity declines during aging of mice and humans, correlating with increases in fat mass and susceptibility to cardiometabolic diseases (Berry et al., 2017; Cypess et al., 2009; Pfannenberg et al., 2010; Rogers et al., 2012; Saito et al., 2009; Wang et al., 2019; Yoneshiro et al., 2011). Our study provides a comprehensive unbiased profile of the adipose tissue beiging process and reveals pathways dysregulated by aging in ASPCs and adipocytes. Beige adipocytes develop via the de novo differentiation of ASPCs or through activation of the thermogenic gene program in mature adipocytes. Previous studies defined three populations of fibroblastic ASPCs in iWAT, namely Dpp4+ cells, Icam1+ preadipocytes, and Cd142+ cells. Aging or cold exposure did not induce dramatic shifts in either the proportions, or gene expression signatures of any of these ASPC types, suggesting that these cell populations are stably maintained across a range of conditions. In support of this, aging did not diminish the cell-intrinsic adipogenic capacities of these ASPC populations, when subjected to adipogenesis assays ex vivo. Notably, we did not observe the emergence of aging-dependent regulatory cells (ARCs), previously described as modulated ASPCs co-expressing ASPC and immune marker genes, which have the capacity to suppress adipocyte differentiation (Nguyen et al., 2021). However, we did observe the induction of ARC-selective gene markers (i.e., Lgals3, Cd36) specifically in immune cells (Ptprc+, Adgre1+) from aged mice in both our scRNA-seq and snRNA-seq datasets. This Lgals3/Cd36 gene signature has also been described in Lin+ macrophages and CD45+ lipid-associated (LAM) macrophages (Burl et al., 2018; Jaitin et al., 2019). Overall, our results suggest that aging-induced alterations to the systemic milieu or adipose tissue environment are responsible for the block in beige adipogenesis. Gene expression analyses identified several genes that were altered by aging across multiple ASPC types and temperature conditions. The top aging-upregulated gene was Cd9, which was previously identified as a marker of fibrogenic (fibrosis-generating) progenitor cells (Marcelin et al., 2017). Cd9 encodes for a tetraspanin protein implicated in various processes that could affect adipogenesis, including extracellular vesicle production, cell adhesion, inflammation, and platelet activation (Brosseau et al., 2018). Aging also upregulated the expression of Pltp and Gpnmb, which are both linked to the regulation of inflammation and fibrosis (Prabata et al., 2021; Saade et al., 2021). Conversely, Meg3, Itm2a and Postn were consistently downregulated across all ASPC populations from aged versus young mice. Of note, Periostin (Postn) is an extracellular matrix protein that regulates adipose tissue lipid storage, and its levels were previously shown to decrease in several adipose tissue depots during aging (Graja et al., 2018). We were surprised by the limited (<$20\%$) contribution of fibroblastic (Pdgfra+) ASPCs, (which includes Pparg-expressing preadipocytes), to beige adipocytes during cold exposure. Of note, we also observed tdTomato+, unilocular white adipocytes upon cold exposure, suggesting the bi-potential fate of Pdgfra+ cells. Previous studies in mice using an adipocyte fate tracking system show that a high proportion of beige adipocytes arise via the de novo differentiation of ASPCs as early as 3 days of cold (Wang et al., 2013). However, the relative contribution from ASPC differentiation and direct adipocyte conversion to the formation of beige adipocytes depends highly on the experimental conditions, especially cold exposure history (Shao et al., 2019). Mice housed at TN from birth undergo high rates of de novo beige adipogenesis upon first cold exposure, whereas mice reared at room temperature acquire many ‘dormant’ beige adipocytes that can be re-activated by cold exposure (Rosenwald et al., 2013; Shao et al., 2019). Based on these findings, we presume that mature (dormant beige) adipocytes serve as the major source of beige adipocytes in our cold-exposure paradigm. However, long-term cold exposure also recruits smooth muscle cells to differentiate into beige adipocytes; a process that we did not investigate here (Berry et al., 2016; Long et al., 2014; McDonald et al., 2015; Shamsi et al., 2021). The beiging process is associated with a dramatic remodeling of adipose tissue structure and metabolic function. We applied snRNA-seq analysis to investigate the cold response of iWAT adipocytes in young and aged animals, leading us to identify four adipocyte clusters: beige adipocytes and three “white” subsets: Npr3-high, DNL-low and DNL-high adipocytes. Npr3-high adipocytes were enriched for expression of white fat-selective genes and exhibit the lowest levels of thermogenic genes (Rosell et al., 2014; Ussar et al., 2014). Interestingly, Npr3 also upregulated by aging in all white adipocytes. Previous studies show that obesity also increases Npr3 levels in adipose tissue of mice and humans (Gentili et al., 2017; Kovacova et al., 2016). NPR3 represses beige fat development and adipocyte thermogenesis by functioning as a clearance receptor for natriuretic peptides (NPs), thereby reducing their lipolytic and thermogenic effects (Bordicchia et al., 2012; Coue et al., 2018; Moro et al., 2004; Sengenès et al., 2000; Sengenes et al., 2003). Together, these results suggest that Npr3-high adipocytes may impede beige fat development in a cell non-autonomous manner by reducing NP signaling. Moreover, high NPR3 levels in aged animals could contribute to the block in beige fat development, and targeting this pathway may be a promising avenue to elevate beige fat activity. We were also intrigued by the dramatic induction of DNL genes in beige adipocytes and DNL-high cells during cold exposure. Previous work established that cold stimulates opposing pathways of lipid oxidation and lipogenesis in thermogenic fat tissue (Mottillo et al., 2014; Sanchez-Gurmaches et al., 2018; Yu et al., 2002). The co-occurrence of these two processes is unusual and may provide a mechanism to ensure the continued availability of fatty acids to fuel thermogenesis and/or provide critical metabolic intermediates, such as acetyl-CoA. The Granneman lab demonstrated that high expression of the lipid catabolic enzyme MCAD and lipogenic enzyme FAS occurred in separate populations of iWAT adipocytes upon stimulation with a β3-adrenergic agonist for 3–7 days (Lee et al., 2017). We identified two subsets of UCP1+ beige adipocytes, distinguished by the presence vs. absence of high levels of DNL genes (i.e., Ucp1+; DNL-high and Ucp1+; DNL-low). Interestingly, the Ucp1+; DNL-high cells accumulated later during cold exposure (14D), suggesting that fully cold-adapted beige adipocytes express both pathways simultaneously. Of note, the induction of Acly and other lipogenic genes was very severely impaired in aged animals. Related to this point, Martinez Calejman and colleagues showed that Acly deficiency in brown adipocytes caused a whitened phenotype, coupled with an unexpected and unexplained reduction in Ucp1 expression (Martinez Calejman et al., 2020). We speculate that high levels of ACLY may be required to support thermogenic gene transcription by supplying and efficiently shuttling acetyl-CoA for acetylation of histones or other proteins. Aging is a complex process, and unsurprisingly, many pathways have been linked to the aging-related decline in beiging capacity. For example, increased adipose cell senescence, impaired mitochondrial function, elevated PDGF signaling and dysregulated immune cell activity during aging diminish beige fat formation (Benvie et al., 2023; Berry et al., 2017; Goldberg et al., 2021; Nguyen et al., 2021). Of note, older mice exhibit higher body and fat mass, which is associated with metabolic dysfunction and reduced beige fat development. While the effects of aging and altered body composition are difficult to separate, previous studies suggest that the beiging deficit in aged mice is not solely attributable to changes in body weight (Rogers et al., 2012). Further studies, including additional time points across the aging continuum may help clarify the role of aging and ascertain when beiging capacity decreases. In summary, this work shows that aging impairs beige adipogenesis through non-cell-autonomous effects on adipose tissue precursors and by disrupting adipocyte responses to environmental cold exposure. Expression profiling at the single-cell level reveals adipocyte heterogeneity, including two different types of UCP1+ beige adipocytes. Finally, aging-dysregulated pathways, including natriuretic peptide signaling and lipogenesis, may provide promising targets for unlocking beige adipocyte development. ## Mice All animal procedures were approved and performed under the guidance of the University of Pennsylvania Institutional Animal Care and Use Committee. Young (4 weeks) and aged (52 weeks) C57BL/6 male mice were obtained from the National Institute of Aging (C57BL/6JN) or Jackson Laboratories (C57BL/6J, stock number 000664). Mice were housed at 30°C for 3 weeks, then were either: maintained at 30°C for 2 weeks (TN); kept at 30°C for 11 more days before moving to 6°C for 3 days (3D cold) or moved to 6°C for 14 days (14D cold). Mice were single housed during the final two week temperature treatment and provided with a nestlet and shepherd shack. For experiments with CL316,243 (CL, Sigma-C5976), mice were housed at 30°C for 5 weeks, followed by intraperitoneal (IP) injection of 1 mg/kg/d CL either 1 hour prior to tissue harvest or for 5 days. PdgfraCreERT2 mice were obtained from Dr. Brigid Hogan (Duke University) (Chung et al., 2018) and crossed with Rosa26tdTomato (strain: B6.Cg-Gt(ROSA)26Sortm14(CAG-tdTomato)Hze/J, stock no. 007914). To induce Cre activity, tamoxifen (Sigma, T5648) dissolved in corn oil (Sigma, C8267) was injected intraperitonially (IP) into mice at a dose of 100 mg/kg/d for 5 days. For all iWAT processing other than histology, the inguinal lymph node was removed. ## Histology and Immunofluorescence Tissues were fixed overnight in $4\%$ paraformaldehyde, washed with PBS, dehydrated in ethanol, paraffin-embedded and sectioned. Following deparaffinization, slides were subjected to heat antigen retrieval in a pressure cooker with Bulls Eye Decloaking buffer (Biocare), unless otherwise noted. Slides were incubated in primary antibody overnight and secondary antibody conjugated to peroxidase and then developed using Tyramide Signal Amplification (TSA, Akoya Biosciences). Samples were stained with either hematoxylin and eosin or the following antibodies: anti-red fluorescent protein (RFP) (rabbit; 1:500; Rockland #600–401-379), anti-UCP1 (rabbit, 1:2000, AstraZeneca), and anti-PLIN1 (rabbit, 1:200 Cell Signaling #3470). Slides were imaged on an inverted fluorescence microscope (Keyence BZ-X710). For quantification of tdTomato-expressing adipocytes, full-length iWAT slices were tile imaged, stitched, exported as a BigTiff, and quantified using the Count Tool in Photoshop (Adobe). ## SVCs. As previously described (Merrick et al 2019, Wang et al 2019), iWAT tissue was dissected, minced gently and digested with Collagenase Type I (1.5 units/ml; Worthington) and Dispase II (2.4 units/ml; Roche) in DMEM/F12 containing $1\%$ fatty acid-free bovine serum albumin (Gold Biotechnology) in a gentleMACS dissociator (Miltenyi Biotec) on program “37 MR ATDK-1.” The digestion was quenched with DMEM/F12 containing $10\%$ FBS, and the dissociated cells were passed through a 100 μm filter and spun at 400 × g for 4 mins. The pellet was resuspended in red blood cell lysis buffer (BioLegend), incubated for 4 mins at RT, then quenched with DMEM/F12 containing $10\%$ serum. Cells were passed through a 70 μm filter, spun, resuspended, then passed through a final 40 μm filter, spun at 400 × g for 4 minutes and plated or underwent further processing for FACS. Mice were not pooled unless indicated. ## Adipocytes. Tissue went through the same process as above, except after digestion and quenching, adipocyte/SVF slurry was filtered through a 200 μm filter and centrifuged at 50 × g for 3 mins at RT. Using a 20 mL syringe and 1.5-inch, 25G needle, media containing the SVCs was removed from below the adipocytes (and saved if concurrently isolating SVCs), leaving only the adipocytes in the tube. Adipocytes were washed twice with the same media as quenching, transferred to 2 mL tubes, spun a final time, media was removed from below the adipocytes again, and TRIzol was added for RNA extraction. Mice were not pooled. ## FACS DPP4+, ICAM1+, and CD142+ cells were isolated as previously described (Merrick et al 2019). Briefly, SVCs from the subcutaneous adipose of mice ($$n = 2$$–5) were pooled and resuspended in FACS buffer (HBSS containing $3\%$ FBS; Fisher), then incubated for 1 hr at 4°C with the following antibodies: CD26 (DPP4)-fluorescein isothiocyanate (FITC) (Biolegend, 137806; 1:200), anti-mouse ICAM1-phycoerythrin (PE)/Cy7 (Biolegend, 116122; 1:100), anti-mouse CD45-allophycocyanin (APC)/Cy7 (Biolegend, 103116; 1:1000), anti-mouse CD31-APC-Fire (Biolegend, 102528; 1:1000), and anti-mouse CD142 (Sino Biological, 50413-R001, 1:100; or R&D Systems, AF3178, 1:50). Anti-mouse CD142 antibodies were conjugated with Biotium Mix-n-Stain CF647 (Sigma, MX647S100). For lineage tracing pulse analysis, SVCs were isolated from individual mice without pooling. SVCs were stained with anti-mouse CD31, anti-mouse CD45, and anti-mouse CD140a (PDGFRΑ) (PE/Cy7) (Biolegend, 135912; 1:100). In all FACS experiments, cells were stained with 4′,6-diamidino-2-phenylindole (DAPI) (Roche, 10236276001; 1:10,000) for 5 minutes, then washed three times with FACS buffer to remove unbound antibodies. Cells were sorted with a BD FACS Aria cell sorter (BD Biosciences) equipped with a 100 μm nozzle and the following lasers and filters: DAPI, 405 and $\frac{450}{50}$ nm; FITC, 488 and $\frac{515}{20}$ nm; mTomato, 532 and $\frac{610}{20}$ nm; PE/Cy7, 532 and $\frac{780}{60}$ nm; CF647, 640 and $\frac{660}{20}$ nm; and APC/Cy7 and APC-Fire, 640 and $\frac{780}{60}$ nm. All compensation was performed at the time of acquisition in Diva software by using compensation beads (BioLegend, A10497) for single-color staining and SVCs for negative staining and fluorescence (DAPI and tdTomato). ## Adipocyte precursor cells. All cells were cultured in DMEM/F12 containing $10\%$ FBS and Primocin (50 ng/ml) (InvivoGen, ant-pm-1). DPP4+, ICAM1+, and CD142+ populations were FACS purified, plated on CellBind 384-well plates (Corning) at 15–25K cells/well, and incubated for 48 (25K cells) to 72 hours (15K cells) to facilitate attachment before the induction of adipogenic differentiation. For whole SVF, SVCs were isolated and plated in a 48 well CellBind plate (Corning) at a high confluency of one mouse per 18 wells. No cells were passaged after plating to maintain adipogenic competency. Differentiation was carried out with either maximum adipogenic cocktail, max: 500 μM isobutylmethylxanthine (Sigma, I7018), 10 μM dexamethasone (Sigma, D4902), 125 μM indomethacin (Sigma, I8280), 1 μM rosiglitazone (Cayman Chemical, 11884), 1 nM T3 (Sigma, T6397), and 20 nM insulin (Novolin) or a minimal adipogenic cocktail, min: 20 nM insulin. For the max adipogenic cocktail induction, cells were incubated with cocktail for 2 days and then transferred to adipogenic maintenance medium for the remaining 6 days (1 μM rosiglitazone, 1 nM T3, and 20 nM insulin). For all conditions, medium was changed every 2 days, and cells were harvested on day 8 of differentiation. For drug treatments, cells were treated for 4 hrs on day 8 with 1 μM isoproterenol (Sigma, I6504). Adipogenesis was assessed by staining with Biodipy $\frac{493}{503}$ (Invitrogen, D3922) for lipid droplet accumulation and Hoechst 33342 (Thermo Fisher, 62249) for nuclei number. The cells were imaged on a Keyence inverted fluorescence microscope (BZ-X710) by using DAPI (excitation, $\frac{360}{40}$ nm; emission, $\frac{460}{50}$ nm) and green fluorescent protein (excitation, $\frac{470}{40}$ nm; emission, $\frac{525}{50}$ nm) filters. Individual wells were imaged in their entirety at 4x magnification, and at 20x to see morphology. 384-well plates were not stained and imaged in brightfield due to low cell number recovery from FACS prior to RNA extraction. ## RNA Extraction. Total RNA was extracted using TRIzol (Invitrogen) combined with PureLink RNA Mini columns (Thermo Fisher, 12183025) for tissue and SVC cells or by PicoPure RNA Isolation Kit (Applied Biosystems, KIT0204) for 384-well plate populations and adipocytes. Prior to the addition of chloroform, all tissue and primary adipocytes in TRIzol included an extra spin at max speed for 10 minutes at RT, then TRIzol was removed from below the lipid layer to avoid lipid contamination disrupting the subsequent phase separation with chloroform. Chloroform was added to the lipid-free TRIzol, spun for 15 mins at 12,000 × g and the aqueous layer was removed and added to columns. mRNA was quantified using a Nanodrop and reverse transcribed to cDNA using the ABI High-Capacity cDNA Synthesis kit (ABI, 4368813). Real-time PCR was performed on a QuantStudio5 qPCR machine using SYBR green fluorescent dye (Applied Biosystems). Fold changes were calculated using the ddCT method, with TATA binding Protein (Tbp) mRNA serving as a normalization control. ## Single Cell RNA-seq Samples. Cells were flow sorted to isolate live (DAPI−) cells and remove debris. We enriched non-immune cells by sorting out CD45+ cells. Next-generation sequencing libraries were prepared using the Chromium Next GEM Single Cell 3’ Reagent kit v3.1 (10x Genomics, 1000121) per manufacturer’s instructions. Libraries were uniquely indexed using the Chromium Single Index Kit T Set A, pooled, and sequenced on an Illumina NovaSeq 6000 sequencer in a paired-end, dual indexing run by the CHOP Center for Applied Genomics at the University of Pennsylvania. Sequencing for each library targeted 20,000 mean reads per cell. ## Single Nucleus RNA-seq Samples. Nuclei were isolated from frozen mouse iWAT samples as previously described, with the following modifications to integrate hash multiplexing and FANS-assisted nuclear quality thresholding and sample pooling (Drokhlyansky et al., 2020; Slyper et al., 2020). Briefly, 300 mg of flash-frozen adipose samples were held on dry ice until immediately before nuclei isolation, and all sample handling steps were performed on ice. Each sample was placed into a gentleMACS C tube (Miltenyi Biotec, 130–093-237) with 2 mL freshly prepared TST buffer ($0.03\%$ Tween 20 (Bio-Rad), $0.01\%$ Molecular Grade BSA (New England Biolabs), 146 mM NaCl (ThermoFisher Scientific), 1 mM CaCl2 (VWR International), 21 mM MgCl2 (Sigma Aldrich), and 10 mM Tris-HCl pH 7.5 (ThermoFisher Scientific) in ultrapure water (ThermoFisher Scientific)) with 0.2 U/μL of Protector RNase Inhibitor (Sigma Aldrich, RNAINH-RO). gentleMACS C tubes were then placed on the gentleMACS Dissociator (Miltenyi Biotec) and tissue was dissociated by running the program “mr_adipose_01” three times, and then incubated on ice for 10 minutes. Lysate was passed through a 40 μm nylon filter (CellTreat) and collected into a 50 mL conical tube (Corning). Filter was rinsed with 3 mL of freshly prepared ST buffer (146 mM NaCl, 1 mM CaCl2, 21 mM MgCl2; 10 mM Tris-HCl pH 7.5) with 0.2 U/μL RNase Inhibitor, and collected into the same tube. Flow-through was passed through a 20 μm pre-separation filter (Miltenyi Biotec) set on top of a 5 mL FACS tube (Corning) and collected into the same tube. Suspension was centrifuged in a swinging-bucket centrifuge (Eppendorf) at 500 × g for 5 minutes at 4°C with brake set to low. Following centrifugation, supernatant was removed and 5 mL of PBS pH 7.4 (ThermoFisher Scientific) with $0.02\%$ BSA and 0.2 U/μL RNase Inhibitor was added without resuspending the nuclear pellet. Sample was centrifuged again at 500 × g for 5 minutes at 4°C with brake set to low. Following centrifugation, supernatant was removed, and the nuclear pellet was resuspended in 1 mL PBS-$0.02\%$ BSA with 0.2 U/μL RNase Inhibitor. Each sample was split into two 500 μL aliquots and transferred to new 5 mL FACS tubes for subsequent hashing. Each aliquot of resuspended nuclei was stained with NucBlue (ThermoFisher, R37605), labeled with 1 μg of a unique TotalSeq anti-Nuclear Pore Complex Proteins Hashtag Antibody (Biolegend), and then incubated on ice for 30 minutes. Suspension was centrifuged at 500 × g for 5 minutes at 4°C with brake set to low. Following centrifugation, 450 μL of supernatant was removed and the nuclear pellet was resuspended in 450 μL PBS-$0.02\%$ BSA with 0.2 U/μL RNase Inhibitor. For nuclear quality thresholding, fluorescence-activated nuclear sorting (FANS) was implemented to collect 4,000–4,300 nuclei from hashtagged aliquots directly into a shared well of a 96-well PCR plate (Thermo Scientific) containing 24.6 μL of 10X RT Reagent B with 1U/uL RNase Inhibitor on a Beckman Coulter MoFlo AstriosEQ fitted with a 70 μm nozzle. High-quality nuclei were selected by initial gating at 360 nm with laser filter 405–$\frac{448}{59}$ followed by SSC-H and FSC-H to remove doublets and unlysed cells. Once all sample aliquots were FANS-sorted, the pool of 43,000 nuclei was loaded on the 10x Chromium controller (10x Genomics) according to the manufacturer’s protocol. cDNA and gene expression libraries were generated according to the manufacturer’s instructions (10x Genomics). Libraries of hashtag oligo fractions were generated according to the manufacturer’s instructions (Biolegend). cDNA and gene expression library fragment sizes were assessed with a DNA High Sensitivity Bioanalyzer Chip (Agilent). cDNA and gene expression libraries were quantified using the Qubit dsDNA High Sensitivity assay kit (ThermoFisher, Q32854). Gene expression libraries were multiplexed and sequenced on the Nextseq 500 (Illumina) using a 75-cycle kit and the following read structure: Read 1: 28 cycles, Read 2: 55 cycles, Index Read 1: 8 cycles. ## Single Cell RNA Sequencing Data was processed using the Cell Ranger pipeline (10x Genomics, v.3.1.0) for demultiplexing and alignment of sequencing reads to the mm10 transcriptome and creation of feature-barcode matrices. The cell ranger output files were read into R (version 4.1.1) and processed utilizing the standard Seurat CCA integrated workflow (version 4.3.0). Each of the six samples went through a first phase of filtering, where only cells that recorded more than 200 features and only features present in a minimum of 3 cells were kept. Each sample was filtered prior to downstream analysis on nCount_RNA, nFeature_RNA, and mitochondrial percentages. Samples were then normalized using a LogNormalization method with a scaling factor of 10000 followed by FindVariableFeatures using Variance Stabilization Transformation with the top 6000 features to be returned. The samples were scored on their cell cycle phases which would be used in the regression later. The FindIntegrationAnchors function using the CCA reduction method and IntegrateData was utilized to integrate the data together. The integrated data-set was then scaled in which mitochondrial percentage and cell cycle state was regressed out. A principal component analysis was performed and the top 15 dimensions were kept. Uniform Manifold and Projection (UMAP) was run on the dataset, in addition to FindNeighbors and FindClusters. Differential gene expression between clusters was performed using the FindMarkers function with the Wilocox test in Seurat. Violin plots and individual UMAP plots were all generated using the Seurat toolkit VlnPlot and FeaturePlot functions, respectively. Heatmaps were generated utilizing the pheatmap package (version 1.0.12). ## Single Nucleus RNA Sequencing Raw sequencing reads were demultiplexed to FASTQ format files using bcl2fastq (Illumina; version 2.20.0). Digital expression matrices were generated from the FASTQ files using Cell Ranger (Zheng et al., 2017)(version 6.1.2) with the option to include intronic reads (--include-introns). Reads were aligned against the GRCm38 mouse genome assembly and gene counts were obtained, per-droplet, by summarizing exonic and intronic UMIs that overlapped with the GENCODE mouse annotation (release 24) for each gene symbol. In order to adjust for downstream effects of ambient RNA expression within mouse nuclei, we used the “remove-background” module from CellBender (Fleming et al., 2022) (version 0.2.0) to remove counts due to ambient RNA molecules from the count matrices and to estimate the true cells. Genes were subsequently filtered such that only genes detected in two or more cells and with at least 6 total counts (across all cells) were retained. Sample demultiplexing via hashtag oligonucleotide sequences (HTOs) was performed with the Cumulus sc/snRNA-Seq processing pipeline (Li et al., 2020). Specifically, HTO quantification was performed with the Cumulus Tool on Feature Barcoding, which provided a cell-by-HTO count matrix. This HTO count matrix, along with the gene count matrices generated via Cell Ranger (above) were used to assign each cell to their respective sample(s) with the demuxEM program. Only cells that were identified as singlets were retained (i.e. no cells identified as a multiplet or unassignable) in the per-sample CellBender-ed gene count matrices. Cellbender output files were read into R (version 4.1.1) and processed utilizing the standard Seurat CCA and later RPCA integration workflows (version 4.3.0). Each of the hashed samples (24 in total) were merged with their respective pair to have a total of twelve samples consisting of six different groups. Each sample was filtered prior to downstream analysis based on their nCount_RNA, nFeature_RNA, and mitochondrial percentages. Samples were then normalized using a LogNormalization method with a scaling factor of 10000 followed by FindVariableFeatures using a Variance-Stabilizing Transformation as the method with the top 2000 features to be returned. The FindIntegrationAnchors function using the CCA reduction method and IntegrateData was utilized to integrate the data together. The integrated data-set was then scaled on which mitochondrial percentage was regressed. A principal component analysis was performed in which only the top 18 dimensions were retained. Uniform Manifold and Projection (UMAP), FindNeighbors, and FindClusters with a resolution of 0.4 was performed on the dataset. To remove doublets in the dataset, we used the package scDblFinder (1.8.0) and their function scDblFinder with the parameters of samples set to our twelve samples, dbr set to NULL, dbr.sd set to 1, clusters set to FALSE, and multiSampleMode set to split. The object was then subsetted to only contain expected singlets. Differential gene expression between clusters was performed using the FindMarkers function with the Wilocox test in Seurat. Violin plots and individual UMAP plots were all generated using the Seurat toolkit VlnPlot and FeaturePlot functions, respectively. Heatmaps were generated utilizing the dittoSeq package (1.9.1) and pheatmap package (version 1.0.12). After identifying the adipocyte population, we subsetted our object on that population, extracting the raw RNA counts on the cells for each of the six samples (YTN, OTN, Y3D, O3D, Y14D, O14D) (Y is young, O is “Old” or as referred to in this paper, Aged). These samples were then integrated together using the standard RPCA integration workflow. There was no further filtering done on the reintegrated adipocyte population. Samples were normalized using a LogNormalization method with a scaling factor of 10000 followed by FindvariableFeatures using a Variance-Stabilizing Transformation as the method with the top 2000 features to be returned. The function SelectIntegrationFeatures was performed on the dataset where it was then scaled on which mitochondrial percentage was regressed, and principal components were found using the ScaleData and RunPCA functions. The FindIntegrationAnchors function using the ROCA reduction method and a k.anchors of 20 and IntegrateData was utilized to integrate the data together. After integration, the dataset was then scaled in which mitochondrial percentage was regressed on again. A principal component analysis was performed in which only the top 18 dimensions were retained. Uniform Manifold and Projection (UMAP), FindNeighbors, and FindClusters with a resolution of 0.2 was performed on the dataset. Differential gene expression between clusters was performed using the FindMarkers function with a Wilcoxon signed-rank test as the method in Seurat. Violin plots and individual UMAP plots were all generated using the Seurat toolkit VlnPlot and FeaturePlot functions, respectively. Heatmaps were generated utilizing the dittoSeq package (1.9.1) and pheatmap package (version 1.0.12). Enrichment analysis was performed on the positively expressed genes with a log2 fold change (LFC) > 0.25 and a P adjusted value < 0.01 on comparison of the young 14 days cold and old 14 days cold groups in the DNL high cluster. The generated gene list, which was in order of significance, was fed into g:Profiler (version 0.2.1) using default parameters except with modifications to query as an ordered query against the ‘mmusculus’ database, a gSCS correction method for multiple testing, with domain scope set to annotated, and sources set to the Reactome database. The top six enriched pathways yielded from the database were taken and displayed in order of P adjusted value. ## Statistical methods All bar graphs represent the mean ± SEM. A Student’s t-test was used when 2 groups were compared. Where multiple conditions were compared, we applied two-way ANOVA with a Tukey correction for multiple comparisons. Only the Young vs. Aged comparisons were depicted on graphs for clarity, with additional multiple comparisons provided below. P values are indicated by asterisks and defined as $p \leq 0.05$, $p \leq 0.01$ and $p \leq 0.001.$ All statistics were calculated with GraphPad Prism Version 10.0.3. ## Funding: NIH grants DK120982 and DK121801 to P.S.; T32 HD083185 to C.D.H; T32 DK007314 to E.F.; RC2 DK116691 to E.D.R. ## Data and materials availability: scRNA-seq and snRNA-seq datasets are deposited in the Gene Expression Omnibus (GEO) under the superseries accession number GSE227441. Data analysis pipelines used for processing of raw sequencing data, integration and clustering can be obtained from: https://github.com/calhounr/Aging-impairs-cold-induced-beige-adipogenesis-and-adipocyte-metabolic-reprogramming ## References 1. Amiya Kumar Ghosh M.O.B., Mau Theresa, Qi Nathan, Yung Raymond. Adipose Tissue Senescence and Inflammation in Aging is Reversed by the Young Milieu*. J Gerontol A Biol Sci Med Sci* (2019) 74 1709-1715. PMID: 30590424 2. 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--- title: 'A Consequence of Immature Breathing induces Persistent Changes in Hippocampal Synaptic Plasticity and Behavior: A Role of Pro-Oxidant State and NMDA Receptor Imbalance' authors: - Alejandra Arias-Cavieres - Alfredo J. Garcia journal: bioRxiv year: 2023 pmcid: PMC10055328 doi: 10.1101/2023.03.21.533692 license: CC BY 4.0 --- # A Consequence of Immature Breathing induces Persistent Changes in Hippocampal Synaptic Plasticity and Behavior: A Role of Pro-Oxidant State and NMDA Receptor Imbalance ## Abstract Underdeveloped breathing results from premature birth and causes intermittent hypoxia during the early neonatal period. Neonatal intermittent hypoxia (nIH) is a condition linked to the increased risk of neurocognitive deficit later in life. However, the underlying mechanistic consequences nIH-induced neurophysiological changes remains poorly resolved. Here, we investigated the impact of nIH on hippocampal synaptic plasticity and NMDA receptor (NMDAr) expression in neonatal mice. Our findings indicate that nIH induces a pro-oxidant state, leading to an imbalance in NMDAr subunit composition that favors GluN2A over GluN2B expression, and subsequently impairs synaptic plasticity. These consequences persist in adulthood and coincide with deficits in spatial memory. Treatment with the antioxidant, manganese(III) tetrakis(1-methyl-4-pyridyl)porphyrin (MnTMPyP), during nIH effectively mitigated both immediate and long-term effects of nIH. However, MnTMPyP treatment post-nIH did not prevent the long-lasting changes in either synaptic plasticity or behavior. Our results underscore the central role of the pro-oxidant state in nIH-mediated neurophysiological and behavioral deficits and importance of stable oxygen homeostasis during early life. These findings suggest that targeting the pro-oxidant state during a discrete window may provide a potential avenue for mitigating long-term neurophysiological and behavioral outcomes when breathing is unstable during early postnatal life. ## Introduction Human neonates are born prematurely (less than 37 weeks of gestation) are at an increased risk of Apneas of Prematurity, a condition that results in breathing instabilities causing intermittent hypoxemia and subsequently, oxidative stress (Di Fiore & Raffay, 2021; Di Fiore & Vento, 2019). While instabilities due to immature breathing resolve with continued postnatal development, the occurrence of oxidative stress during early life is hypothesized to be a principal contributor to causing disturbances in the neonatal brain (Panfoli et al., 2018) and the emergence of neurobehavioral deficits associated with intellectual disability and autism spectrum disorder in humans (Poets, 2020). Indeed, oxidative stress has been documented in animal models exposed to perinatal IH (Garcia et al., 2016; Souvannakitti, Kumar, Fox, & Prabhakar, 2009; Souvannakitti et al., 2010). Additionally, perinatal IH also causes anatomical and neurophysiological changes that are associated with deficit in affective and cognitive behaviors later in life (Cai, Tuong, & Gozal, 2011; Goussakov, Synowiec, Yarnykh, & Drobyshevsky, 2019; Vanderplow et al., 2022). However, extent to which the pro-oxidant state contributes to neurophysiological and behavioral changes caused by nIH remains poorly resolved. During normal postnatal development, NMDAr subunit composition changes where GluN2A subunit expression progressively increases and surpasses GluN2B to become the predominant GluN2 isoform in adulthood (Paoletti, Bellone, & Zhou, 2013). As GluN2 subunit composition is an important determinant to both biophysical and downstream signaling properties of the NMDAr (Hansen et al., 2018; Paoletti et al., 2013; Paoletti & Neyton, 2007), disruption to the normal transition of GluN2 subunit composition may impact neurophysiological properties and neurocognition later in life. Here, we characterize the immediate and long-term consequences of nIH on the hippocampus and behavioral performance related to spatial learning and memory. Here we show that the IH-dependent pro-oxidant state is linked to remodeling of GluN2 subunit composition and is associated with impaired NMDAr-dependent synaptic plasticity in the neonatal hippocampus. These nIH-dependent phenomena persist later in life and coincide with deficits related to spatial memory. While MnTMPyP treatment during IH prevents the emergence of these phenomena, antioxidant treatment after IH is ineffective. In addition to demonstrating a previously undescribed role for nIH-dependent oxidative to stress perturb the normal development trajectory of NMDAr-dependent physiology and behavior later in life, this study identifies an interventional window effective for mitigating the impact of nIH-dependent oxidative stress in promoting long-lasting consequences on neurophysiology and behavior. ## Study Approval. All animal protocols were approved by the Institutional of Animal Care and Use Committee at the University of Chicago, in accordance with National Institute of Health guidelines. ## Animals. Mice were housed in AAALAC-approved facilities with a 12 hour/12-hour light-dark cycle and ad libitum to food and water. Mice used in this study were from a C57BL/6 background. To allow ad libitum access for nutrition, pups of both sexes and their dam were exposed together to either room air (control), nIH, nIH with daily saline administration (nIHsaline) or nIH with daily MnTMPyP administration (nIHMn). We examined age-matched control adult mice (P55 to P60; Adultcontrol), nIH mice after approximately six weeks (42 ± 4 days) of recovery in room air recovery (AdultnIH), nIHMn mice after six weeks recovery in room air (AdultnIH-Mn), and nIH mice that received approximately six weeks (42 ± 4 days) of MnTMPyP after IH exposure (Adult Rec-Mn). Body mass immediately following IH and six weeks following IH exposure were similar to age-matched controls (Supplement 1). Mice treated with MnTMPyP (Enzo Life Sciences, Cat #ALX-430-070) received a single daily dose (5mg/kg i.p.) at the beginning of each day for the aforementioned treatment regimens. ## Intermittent hypoxia exposure. As previously described (Garcia et al., 2016), the nIH paradigm was executed during the light cycle and lasted for 8±1 hours per day for ten consecutive days. Exposure to nIH began on P4 to P5. A single hypoxic cycle was achieved by flowing $100\%$ N2 into the chamber for approximately 60 sec. This created a hypoxic environment where the nadir O2 chamber reached 4.5 ± $1.5\%$ for approximately 10 seconds immediately followed by an air break (19 ± $2.5\%$ O2; 300 sec). ## Slice Preparation for Electrophysiology. Coronal hippocampal slices were prepared from mice either 24 hours following the end of nIH (P14 to P15) or approximately six weeks nIH after (P55 to P60). Mice were anesthetized with isoflurane and euthanized by rapid decapitation. The brain was rapidly harvested and blocked, rinsed with cold artificial cerebrospinal fluid (aCSF) and mounted for vibratome sectioning. The mounted brain tissue was submerged in aCSF (4°C; equilibrated with $95\%$ O2, $5\%$ CO2) and coronal cortico-hippocampal brain slices (350 µm thick) were prepared. Slices were immediately transferred into a holding chamber containing aCSF equilibrated with $95\%$ O2, $5\%$ CO2 (at 20.5±1°C). Slices were allowed to recover a minimum of one hour prior to the transfer into recording chamber and were used up to eight hours following tissue harvest. The composition of aCSF (in mM):118 NaCl, 10 Glucose, 20 sucrose, 25 NaHCO3, 3.0 KCl, 1.5 CaCl2, 1.0 NaH2PO4 and 1.0 MgCl2. The osmolarity of aCSF was 305–315 mOsm/L and equilibrated with $95\%$ O2/$5\%$ CO2, the pH was 7.42 ± 0.2. Recordings were made at 30 ± 1 C in $95\%$ O2 $5\%$ CO2. ## Extracellular recording of the field excitatory postsynaptic potential (fEPSP). The extracellular recording of the fEPSP was established in aCSF (31.0 ± 2oC, equilibrated with $95\%$ O2 $5\%$ CO2) superfused and recirculated over the preparation. The stimulation electrode, a custom constructed bipolar electrode composed of twisted Teflon coated platinum wires (wire diameter:127 µm, catalog number 778000, AM Systems.), was positioned in the Schaffer Collateral and recording electrode (<2 MΩ) was placed into the stratum radiatum of the CA1. The intensity of the electrical current (100–400 µA; 0.1–0.2 ms duration) was set to the minimum intensity required to generate the $50\%$ maximal fEPSP. The fEPSP was evoked every 20 s. After 10 minutes of recording the baseline fEPSP, LTP was induced using Theta Burst Stimulation (TBS: four trains of 10 bursts at 5 Hz, each burst was comprised four pulses at 100 Hz; LTPTBS). Following stimulation, recordings continued for up to one hour. The fEPSP slope was normalized to baseline values. D-L AP5 (50 µM, Sigma-Aldrich, Cat#A5282) was used to block NMDAr; TCN-213 (5 µM, Tocris, Cat#4163) was used to block GluN2A and ifenprodil (5 µM, Tocris, Cat#0545) was used to block GluN2B. The current stimulus was set at the minimum current value (150 to 250 µA) required to evoke the initial fEPSP at $50\%$ the maximal value. Recordings were made using either a Multiclamp 700B (Molecular Devices, San Jose, CA, USA) or using a differential amplifier (AM system, Washington, DC, USA). ## Nuclear Western Immunoblot Assay. Entire hippocampus mouse was rapidly dissected, and samples were by homogenizing using N-PER (Thermo Fisher Scientific, Cat#87792) in cold ice by following manufacturer instructions. Briefly, cytoplasmic fragment was obtained by homogenizing tissue using a tissue grinder and then by pipetting in cytoplasmic extraction buffers. After isolation of cytoplasmic fragment, the insoluble pellet that contains nuclear proteins was suspended in nuclear extraction buffer and separated by centrifugation. Halt Protease Inhibitor (Thermo Fisher Scientific, Cat#78429) was added into cytoplasmic and nuclear extraction buffers to prevent protein degradation. Samples were boiled for 15 min in loading buffer (Bio-Rad, Hercules, CA, USA) and denaturated by β-mercaptoethanol at 75°C before loading 50–60 µg protein onto 4–$20\%$ Mini-PROTEAN TGX Stain-FreeTM Protein Gels (Bio-Rad, Hercules, CA, USA) and electrophoresed at 120 V for 120 min, then gels were transferred to PVDF membrane (Bio-Rad) using Transfer-Blot Turbo System (BIO-RAD, Hercules, CA, USA). Later, membranes were subsequently blocked for 2 h at room temperature with $5\%$ bovine serum albumin (BSA) (Sigma-Aldrich, MN, USA) in Tris buffered saline (TBS) (Bio-Rad, Hercules, CA, USA). Membranes were incubated under constant shaking with primary antibodies: monoclonal mouse anti-HIF1a (1:500; Abcam Cat# ab1, RRID:AB_296474) and monoclonal rabbit anti-TBP (1:2000; Cell Signaling Technology Cat# 44059, RRID:AB_2799258). After washing three times with TBS-Tween $0.3\%$ for 20 min, the membranes were incubated for 1.5 hr at room temperature with appropriate secondary antibodies. Finally, the membranes were washed three times with TBS-Tween $0.3\%$ for 20 min and immunoreactive proteins were detected with Super Signal™ West Femto reagents according to the manufacturer instructions (Thermo Fisher Scientific, Cat#34095). Signals were captured with the ChemiDoc system (Bio-Rad, Hercules, CA, USA). The IMAGE J image program (National Institutes of Health, USA) was used to quantify optical band intensity. ## Whole cell Western Immunoblot Assay. Entire hippocampus mouse was rapidly dissected, and samples were by homogenizing using M-PER™ (Thermo Fisher Scientific, Cat#78501) Halt Protease Inhibitor (Thermo Fisher Scientific, Cat#78429) in cold ice. Samples were centrifuged at 12 rpm for 15 min at 4°C and the pellet was discarded. Samples were boiled for 15 min in loading buffer (Bio-Rad, Hercules, CA, USA) and denaturated by β-mercaptoethanol at 75°C before loading 20–30 µg protein onto 4–$20\%$ Mini-PROTEAN TGX Stain-FreeTM Protein Gels (Bio-Rad, Hercules, CA, USA) and electrophoresed at 120 V for 120 min, then gels were transferred to PVDF membrane (Bio-Rad) using Transfer-Blot Turbo System (BIO-RAD, Hercules, CA, USA). Later, membranes were subsequently blocked for 2 h at room temperature with $5\%$ non-fat milk (Bio-Rad, Hercules, CA, USA) or $5\%$ bovine serum albumin (BSA) (Sigma-Aldrich, MN, USA) in Tris buffered saline (TBS) (Bio-Rad, Hercules, CA, USA). Membranes were incubated under constant shaking with primary antibodies: anti rabbit GluN1 (1:2000; Abcam Cat# ab109182, RRID:AB_10862307) anti-rabbit GluN2A (1:2000; Cell Signaling Technology Cat# 4205, RRID:AB_2112295), anti-rabbit GluN2B (1:2000; Cell Signaling Technology Cat# 14544, RRID:AB_2798506), anti-rabbit NOX2 (1:2000; Abcam Cat# ab129068, RRID:AB_11144496) anti-rabbit NOX4 (1:500; Novus Cat# NB110–58851B, RRID:AB_1217375 and anti-mouse GAPDH (1: 10.000; Abcam Cat# ab8245, RRID:AB_2107448). Incubations were performed at 4°C overnight in $5\%$ non-fat milk or BSA. After washing three times with TBS-Tween $0.2\%$ for 15min, the membranes were incubated for 1.5 h at room temperature with appropriate secondary antibodies. Finally, the membranes were washed three times with TBS-Tween 0.2 % for 15 min and immunoreactive proteins were detected with enhanced chemiluminescence reagents according to the manufacturer instructions (Bio-Rad, Hercules, CA USA). Signals were captured with the ChemiDoc system (Bio-Rad, Hercules, CA, USA). The IMAGE J image program (National Institutes of Health, USA) was used to quantify optical band intensity. ## TBARS assay. Whole cell protein lysates were isolated from entire hippocampal tissues using RIPA buffer (Thermo Fisher Scientific, Catalogue No. R0278) in the presence of protease and phosphatase inhibitors (Thermo Fisher Scientific, Catalogue No. 78429) in cold ice. Protein lysates were immediately processed and stored at −80oC until used. The amount of lipid-peroxidation was determined using a TBARS Assay Kit (Cayman Chemical, Cat#10009055), per manufacturer instructions. Absorbance was measured between 530–540 nm using a plate reader. The analysis for determine MDA values was made according with per manufacturer instructions. ## Barnes maze. The Barnes maze performed in using a custom made opaque white circular acrylic platform (92.4 cm in diameter) with 20 equidistant holes (5.08 cm in diameter and 2.54 cm from the edge). The platform was elevated (30 cm from the floor) ground and surrounded by four identical walls (27.94 cm high). By default, each hole was closed with a fixed piece of opaque acrylic that could be removed to lead to a dark exit box. Lighting was achieved through diffuse overhead fluorescent lighting such that all holes were equally lit. An overhead camera was suspended above the maze. Data collection and posthoc analysis was performed using CinePlex Video Tracking System (Plexon, Dallas, TX). As previously described (Arias-Cavieres et al., 2020), the task was performed using a four-day protocol consisting of one training trial per day for three consecutive days and a probe trial on the fourth day. Barnes Maze began after 6 weeks exposure to IH with respective controls run at the same time. For the training trials, all, but one of the holes (exit hole), were closed. An exit box with a small ramp was placed directly underneath the exit hole. Animals were given a maximum of six minutes to locate the exit and if unable to locate the exit, they were gently guided to the exit. If the mouse found and entered the exit before the six minutes were over, the trial ended at the time that the mouse entered the exit, and the mouse was promptly returned to its home cage. During the probe trial, all holes were closed, and the animal was given six minutes to explore the maze. The entire arena was sanitized in-between trials. Entry probability for each hole during the probe trail was calculated by the following: EPn= $100\%$ × XnXtotal where EPn is the entry probability for hole n; Xn = number of entries into hole n; and Xtotal is sum of entries across all holes during the probe trial. ## Object Location Task. The Object Location task was performed using a modified protocol described by (Wimmer, Hernandez, Blackwell, & Abel, 2012). The protocol was modified for mice. The procedure included three phases: open field; familiarization, and object location. Each phase was performed in acrylic open arena (W: 33 cm, L:36 cm, H: 33 cm). During the open field phase, the mouse was habituated and allowed to freely explore the arena for 10 min. The familiarization consisted of three consecutives (session duration: 5 min; intersession duration: 5 min), where the mouse was placed in the arena and allowed to explore two different objects (i.e., object A and object B). The time exploring both objects were recorded. The object location phase occurred 24 hr following familiarization where one object presented in the familiarization was repositioned (B). The times exploring the relocated object (object B) was used to compared with the other group. To eliminate odor cues between trials, the experimental apparatus and all objects were cleaned with $75\%$ ethanol after each trial. Mice behavior was recorded with a video camera positioned over the behavioral apparatus and the collected videos were analyzed with the ANY-MAZE software (Stoelting Co., Wood Dale, IL, USA). ## Statistical Analysis. Statistics were performed using Prism 6 (GraphPad Software, Inc.; RRID: SCR_015807) and results were plotted in either Prism 6 or Origin (Origin Labs;2018b). Comparisons between two groups were conducted using unpaired two-tailed t-test with Welch’s correction or paired comparisons, where appropriate. The equality of variances between two groups was determined with an F test. Analyses involved comparisons beyond more than two groups were assessed by a one-way ANOVA of means followed by a posthoc Bonferroni’s multi-comparison test. Unless otherwise stated, data are presented as mean ± S.E.M, and where appropriate, individual responses overlaid over box plots. The upper and lower limit of each box represented 25th and 75th percentile of the cohort, respectively, while the inter-limit line within the box represented median. The Box plot error bars represented maximum and minimum values in the data set. Significance was defined as $P \leq 0.05.$ ## nIH promotes a pro-oxidant state, increases nuclear HIF1a upregulates NADPH Oxidase and promotes a pro-oxidant state in the neonatal hippocampus. Measurements of malondialdehyde (MDA) content in neonatal hippocampal homogenates revealed that MDA levels were increased with nIH (Figure 1A, control: 7.2 ± 0.82 nmol • mg−1 protein and nIH:18.59 ± 1.65 nmol • mg−1 protein, $$P \leq 0.004$$; $$n = 6$$ preparations per condition). This was accompanied by greater nuclear content of the pro-oxidant transcription factor, HIF1a (Figure 1B, control= 1.06 ± 0.04 and nIH=1.59±0.12, $$P \leq 0.010$$; $$n = 5$$ per condition) and increased expression of two isoforms of the pro-oxidant enzyme NADPH oxidase (NOX), NOX-2 (Figure 1C control= 1.02±0.02 and nIH=1.31±0.12; $$P \leq 0.045$$; $$n = 6$$ per condition) and NOX-4 (Figure 1D, NOX-4: control= 0.96±0.03 and nIH=1.30±0.07; $$P \leq 0.003$$; $$n = 5$$ per condition). ## Attenuation of synaptic plasticity by nIH is associated with changed NMDAr subunit composition. In the adult rodent IH-dependent increased nuclear HIF1a and the shift toward a pro-oxidant state is associated with impaired NMDAr-dependent synaptic plasticity (Arias-Cavieres, Fonteh, Castro-Rivera, & Garcia, 2021; Arias-Cavieres et al., 2020). Therefore, we next characterized how nIH impacted LTP evoked by theta-burst stimulation (LTPTBS) in neonatal hippocampal slices from control and nIH mice. LTPTBS was evoked in area CA1 in both control (Figure 2A, black, $$n = 7$$ slices; $$n = 6$$ mice) and nIH (Figure 2A, red, $$n = 6$$; $$n = 5$$) and blockade of NMDAr, using AP5 [50 µM], prevented LTPTBS in both groups (Figure 2A, control: green, $$n = 4$$, $$n = 4$$; nIH: blue, $$n = 4$$, $$n = 4$$). However, the magnitude of LTPTBS was smaller in nIH slices as compared to control (Figure 2A, control: 77.02±$5.9\%$ versus nIH: 35.10±$6.8\%$ over baseline, $$P \leq 0.009$$). Such a difference may have resulted from an overall downregulation in NMDAr expression or a change in receptor subunit composition. While expression of GluN1, the obligatory subunit of the NMDAr, was similar between control and nIH (Figure 2B, control: 1.06±0.01, $$n = 5$$; nIH: 1.11±0.09, $$n = 5$$; $$P \leq 0.55$$), GluN2 subunit composition appeared to be different between the groups. GluN2A expression was reduced following nIH (Figure 2C, control: 1.07 ± 0.04, $$n = 5$$; nIH: 0.88 ± 0.02, $$n = 5$$; $$P \leq 0.036$$); whereas, GluN2B expression following nIH was greater that control (Figure 2D, control: 1.06±0.03; $$n = 5$$; nIH: 1.38±0.09; $$n = 5$$, $$P \leq 0.014$$). These differences coincided with a larger GluN2B:GluN2A ratio following IH (Figure 2E, $$P \leq 0.0019$$). As GluN2 subunit composition is a significant factor dictating NMDAr-dependent physiology (A. Kumar, Thinschmidt, & Foster, 2019; S. S. Kumar & Huguenard, 2003; Paoletti et al., 2013), we sought determine whether changed LTPTBS following nIH was related to the changes in GluN2 subunit composition. In control slices, the magnitude of LTPTBS was reduced when blocking either the GluN2A containing NMDAr with TCN-213 (Figure 2F, orange, 29.87 ± $3.07\%$ over baseline; $$n = 7$$ slices, $$n = 6$$) or the GluN2B containing NMDAr with ifenprodil (Figure 2F, cyan, 46.67 ± $3.87\%$ over baseline; $$n = 6$$ slices, $$n = 6$$). When both agents were applied LTPTBS was not effectively evoked (Figure 2F, olive, 6.37 ± $2.03\%$ over baseline; $$n = 4$$ slices, $$n = 4$$). Following nIH, LTPTBS appeared to be unaffected by TCN-213 (Figure 2G; light yellow, 37.58 ± $2.24\%$ over baseline; $$n = 4$$ slices, $$n = 4$$ mice) when compared to treated nIH slices (from Figure 2A), yet ifenprodil appeared to prevent LTPTBS following nIH (Figure 2G; light blue, 6.13 ± $1.16\%$ over baseline, $$n = 5$$ slices, $$n = 5$$). Collectively, these results suggest that NMDAr-dependent plasticity appears to be almost exclusively driven by GluN2B containing receptors and is associated with the nIH-mediated remodeling of GluN2 subunit composition to favor GluN2B expression over GluN2A. ## MnTMPyP administration during IH mitigates IH-dependent molecular, biochemical and neurophysiological changes in the neonatal hippocampus. To determine the contribution of the nIH-dependent pro-oxidant state to the observed changes in the neonatal hippocampus, we administered, the superoxide anion scavenger, MnTMPyP to a cohort of mice during nIH. While MDA content in neonatal hippocampi from the control and the cohort receiving MnTMPyP during nIH (nIHMn) were similar, MDA content in nIH cohort treated with saline vehicle (nIHsaline) was greater than control (Figure 3A; control: 9.31 ± 0.98 nmol per mg protein; nIHsaline: 16.75 ± 1.78 nmol per mg protein; nIHMn: 11.33 ± 0.93 nmol per mg protein; $$P \leq 0.0011$$; $$n = 5$$ per group). Nuclear HIF1a content (Figure 3B, control: 0.99 ± 0.07; nIHsaline: 1.32±0.10 and nIHMn: 1.03 ± 0.05, $$P \leq 0.017$$; $$n = 5$$ per group), NOX2 (Figure 3C, control: 1.03±0.03, nIHsaline: 1.32 ± 0.051 and nIHMn: 0.99 ± 0.046, $$P \leq 0.024$$; $$n = 6$$ per group) and NOX4 were increased in nIHsaline (Figure 3D, control: 1.02± 0.03; nIHsaline: 1.35±0.08 and nIHMn: 1.10 ± 0.01, $$P \leq 0.0034$$; $$n = 4$$ per group) and appeared to be unchanged in nIHMn. GluN2A expression was also reduced in nIHsaline while expression appeared to be unchanged in nIHMn (Figure 3E, control: 1,08 ± 0.07; nIHSaline: 0.68 ± 0.06; nIHMn: 0.92 ± 0.06; $$P \leq 0.0086$$; $$n = 6$$ per group). Furthermore, while GluN2B expression was increased in nIHsaline, GluN2B expression appeared to be unchanged in nIHMn (Figure 3F, control: 1.03 ± 0.030; nIHsaline: 1.38± 0.12; nIHMn: 1.02 ± 0.059, $$P \leq 0.010$$; $$n = 5$$ per group). Similarly, the magnitude of NMDAr-dependent in nIHMn was greater than nIH (Figure 3G, nIHMn: 66.65±6.20 % over the baseline; $$n = 7$$, $$n = 5$$) and the sensitivity to of LTP to ifenprodil was evident when compared to nIH (Figure 3H, 33.26 ± 5.03 % over the baseline; $$n = 7$$, $$n = 5$$). ## IH causes persistent deficits in NMDAr-dependent synaptic plasticity and NR2 subunit remodeling that can be mitigated by MnTMyPyP administration. To assess the long-term consequences of nIH, we characterized LTP in adult mice allowed to recover from nIH for six weeks in room air (AdultnIH). In hippocampal slices from AdultnIH, the magnitude of LTP was smaller compared to that in Adultcontrol preparations (Figure 4A; Adultcontrol (black): 67.58 ± $4.08\%$ over baseline; $$n = 6$$ slices, $$n = 6$$ mice; AdultnIH (red): 39.47±$2.76\%$ over baseline; $$n = 7$$ slices, $$n = 7$$ mice; $$P \leq 0.003$$). To determine the physiological consequences of GluN2 levels, we explore GluN2A and GluN2B subunit contribution on LTP. To determine whether the difference in LTP magnitude observed in AdultnIH hippocampal slices was related to GluN2 subunit composition, the sensitivity of LTP to TCN-213 and ifenprodil was assessed. In Adultcontrol slices, NMDAr-dependent LTP was appeared largely dependent on the GluN2A containing NMDAr and independent of GluN2B containing NMDAr as TCN-213 suppressed LTP (Figure 4B, orange, 19.53 ± $1.97\%$ over baseline; $$n = 4$$ slices, $$n = 4$$ mice) while ifenprodil minimally affected LTP (Figure 4B, blue, 50.04 ± $3.59\%$ over baseline; $$n = 4$$ slices, $$n = 4$$ mice). In contrast LTP from AdultnIH slices appeared dependent on GluN2B containing NMDAr and independent of GluN2A containing NMDAr as ifenprodil blocked LTP (Figure 4C, blue: 6.94±1.04 over baseline; $$n = 6$$, $$n = 5$$) yet was insensitive to TCN-213 (Figure 4C, orange, 32.08±5.1 over baseline; $$n = 5$$, $$n = 3$$). As MnTMPyP prevented the immediate effects of nIH on LTP, we assessed how nIHMn and MnTMPyP administration following nIH (AdultREC-Mn) impacted LTP in adult hippocampal slices. The magnitude of LTP from adult mice from the AdultREC-Mn was smaller in magnitude of LTP from AdultnIH-Mn (Figure 4D, AdultnIH-Mn: green, 61.35 ±$8.39\%$ over baseline; $$n = 6$$ slices, $$n = 6$$ mice; AdultREC-Mn: blue, 24.50 ± $4.87\%$ over baseline; $$n = 7$$ slices, $$n = 7$$ mice; $$P \leq 0.0030$$). We next examined biochemical and protein expression in the adult hippocampus. Neither HIF1a nor NOX isoforms were different from Adultcontrol in AdultnIH, AdultnIH-Mn, or AdultREC-Mn (Supplement 2). Similarly, no differences in GluN1 subunit expression was found across experimental groups (Figure 4E, control=1.00±0.07; AdultnIH=0.99±0.07; AdultnIH-Mn=1±0.05 and AdultREC-Mn=0.95±0.03, $$n = 5$$; $$P \leq 0.93$$). However, differences in both GluN2A and GluN2B subunit expression were evident. In AdultnIH and AdultREC-Mn, GluN2A expression was suppressed when compared to Adultcontrol, yet no difference was evident in AdultnIH-Mn (Figure 4F, Adultcontrol: 1±0.05; AdultnIH: 0.51±0.72; AdultnIH-Mn: 0.92 ±0.07 and AdultREC-Mn: 0.80 ±0.07, $$n = 5$$; $$P \leq 0.0014$$). While GluN2B expression was increased in AdultnIH, no differences were observed in AdultnIH-Mn and AdultREC-Mn (Figure 4E, Adultcontrol: 1.02 ±0.03; AdultnIH: 1.47±0.11, AdultnIH-Mn: 1.14 ±0.08, and AdultREC-Mn: 0.98 ±0.13, $$n = 4$$; $$P \leq 0.018$$). ## Spatial memory deficits occurring in adult mice previously exposed to nIH can be mitigated by MnTMPyP. To determine whether the persistent changes in synaptic plasticity and NMDAr subunit expression due to neonatal IH corresponded with behavioral deficits related to spatial learning and memory, we next examined the performance of control adult mice (Adultcontrol) and AdultnIH and in the Barnes maze and Object Location task. In the Barnes maze, locomotor activity between control ($$n = 14$$) and AdultnIH ($$n = 20$$) did not appear to be different as velocity and distance travelled were similar (Supplement 3). Moreover, training control and AdultnIH, exhibited progressive decreases in total latency to exit across of three training session (Supplement 4). During the probe trial, the initial distance (Figure 5A (middle); control: 0.13 ± 0.017 m versus AdultnIH: 0.33± 0.05 m, $$P \leq 0.0019$$) and latency to initial entry (Figure 5A (right); control: 43.04± 10.44 s versus AdultnIH: 83.13±15.99 s, $$P \leq 0.04$$) into the exit zone were greater in AdultnIH. These differences were accompanied by a smaller entry probability into the exit zone in AdultnIH (Figure 5B (right); control: 12.63±$1.42\%$ versus AdultnIH: 7.14 ± $1.21\%$, $$P \leq 0.0067$$). During the open field session in the object location task, Adultcontrol ($$n = 14$$) and AdultnIH ($$n = 20$$) exhibited similar velocities, distance travelled, time in the periphery and time in the center (Supplement 5), suggesting no locomotor differences between groups. During familiarization session, control and AdultnIH both exhibited a progressive decrease in exploration times (Supplement 6) and similar exploration times with both objects (Supplement 6). These data suggested that both groups became learned the location of both objects and did not have preference for either of the objects tested. During the probe trial of the Object location task, control mice exhibit greater exploration time compared to nIH (Figure 5C: control =28.81±3.38s and nIH=19.42±2.63s; $$P \leq 0.028$$) We sought to determine whether adult behavioral performance was impacted by MnTMPyP administration during nIH (i.e., AdultnIH-Mn) or by MnTMPyP administration following nIH (i.e., AdultREC-Mn). Both AdultnIH-Mn ($$n = 16$$) and AdultREC-Mn ($$n = 13$$) exhibited progressive reductions in distance traveled across training session without changed velocities (Supplement 7). This corresponded with progressive improvement exiting the Barnes maze during the three training sessions (Supplement 4). During the probe trial, the initial distance (Figure 5D (left); AdultnIH-Mn: 0.13 ± 0.016 m versus AdultREC-Mn: 0.27± 0.05 m, $$P \leq 0.039$$) and latency to initial entry (Figure 5D (right); AdultnIH-Mn: 43.83± 9.03 s versus AdultREC-Mn: 84.56±15.79s, $$P \leq 0.037$$) into the exit zone were less in AdultnIH-Mn when compared to AdultREC-Mn. AdultnIH-Mn also showed a greater entry probability into the exit zone when compared to AdultREC-Mn (Figure 5E; AdultnIH-Mn: 11.45±$1.52\%$ versus AdultREC-Mn: 7.43 ± $5.18\%$, $$P \leq 0.040$$). During the open field session of the object location task, AdultnIH-Mn ($$n = 16$$) and AdultREC-Mn ($$n = 13$$) exhibited similar velocities, distance travelled, time in the periphery and time in the center (Supplement 8), suggesting no locomotor differences between groups. During familiarization, AdultnIH-Mn and AdultREC-Mn both also exhibited a progressive decrease in exploration times (Supplement 5) and similar times exploring both objects (Supplemental 5). However, during the probe trial of the Object location task, exploration time for the moved object (i.e., object B) was greater in the AdultnIH-Mn when compared to AdultREC-Mn (Figure 5F; AdultnIH-Mn: 27.21 ± 2.51s vs AdultREC-Mn: 20.44±1.31, $$P \leq 0.024$$) ## Discussion Intermittent hypoxia can be experienced across a lifetime. While most-prominently associated with untreated sleep apnea in adolescents (Narang & Mathew, 2012) and adults (Ramirez et al., 2013), intermittent hypoxia may also be experienced by children suffering from autonomic dysautonomias, such as that observed in Rett Syndrome and other conditions (Carroll et al., 2015; Glaze, Frost, Zoghbi, & Percy, 1987; Ramirez et al., 2020). In perinatal life, intermittent hypoxia may be experienced embryonically when sleep apnea is left untreated during pregnancy (Dominguez, Street, & Louis, 2018)); whereas, in premature infants, it occurs with apneas of prematurity (Martin, Wang, Koroglu, Di Fiore, & Kc, 2011). Each of these conditions have associations with neurocognitive impairment (Poets, 2020; Ramirez et al., 2013; Slattery et al., 2023), leading to the hypothesis that IH exposure, independent of life stage, impacts neurophysiology and behavior. Over the past three decades, the majority of research examining this hypothesis has been almost exclusively focused on examining how IH impacts neurophysiology in the adult brain. Here, we demonstrate a role for the nIH-dependent pro-oxidant state to remodel NMDAr subunit composition and to cause deficits in synaptic plasticity in the hippocampus. These phenomena are evident immediately follow nIH and persist later in life where they coincide with neurobehavioral deficit. Importantly, our results also show that antioxidant administration has a discrete window to be effective in preventing the nIH-dependent phenomena observed. As our study used subjects with no known genetic predisposition for neurophysiological or behavioral deficits, our findings underscore the critical importance for stable oxygen homeostasis and suggest that neonatal intermittent hypoxia is an independent risk factor for negatively impacting neurophysiological development and behavior later in life. Recent advancements in understanding how IH impacts synaptic plasticity and neurogenesis in the adult hippocampus has established a mechanistic framework by which IH-dependent HIF1a signaling promotes a pro-oxidant condition (Arias-Cavieres et al., 2020; Chou et al., 2013; Khuu et al., 2021), to suppress neurogenesis (Khuu et al., 2021; Khuu et al., 2019) and impair synaptic plasticity (Arias-Cavieres et al., 2021; Arias-Cavieres et al., 2020; Khuu et al., 2019) as these phenomena are mitigated with antioxidant treatment (Arias-Cavieres et al., 2021; Arias-Cavieres et al., 2020; Khuu et al., 2019). nIH also promotes a pro-oxidant state that coincides with increased nuclear HIF1a signaling and upregulation in NOX isoforms known to be increased by IH-dependent HIF1a activity in the adult (Arias-Cavieres et al., 2020; Chou et al., 2013; Peng et al., 2014; Peng et al., 2006). However, rather than downregulating the obligatory subunit of the NMDAr, as observed in adult hippocampus (Arias-Cavieres et al., 2021; Arias-Cavieres et al., 2020), it appears that the IH-dependent pro-oxidant state perturbs the normal transition of predominant expression of GluN2B subunit to GluN2A in the in the neonatal hippocampus. The conversion of GluN2 subunit predominance from GluN2B to GluN2A in the brain normally occurs during early postnatal development. In the primary visual cortex of dark-reared animals, GluN2 subunit remodeling is rapidly precipitated by a single one-hour exposure to a visual stimulus (Philpot, Sekhar, Shouval, & Bear, 2001; Quinlan, Philpot, Huganir, & Bear, 1999) demonstrating that GluN2 subunit dominance can be shaped by early life experience. Consistent with this view, experiencing short repeated oscillations in oxygenation in early postnatal effectively influences the transition in NR2 subunit identity within hippocampus. Additionally, our findings indicate that this experience-dependent phenomenon occurring in early life persists to affect hippocampal synaptic properties and associated behavioral outcomes later in life. As GluN2 subunit composition dictates the biophysical, pharmacological and signaling properties of the NMDAr (Cull-Candy & Leszkiewicz, 2004; Traynelis et al., 2010; Vyklicky et al., 2014), which in turn, influences synaptic timing and summation properties (S. S. Kumar & Huguenard, 2003; Lei & McBain, 2002; Paoletti et al., 2013), the differences in subunit composition would be predicted to impact NMDAr dependent physiology. Indeed, the nIH-dependent deficits to LTP appeared to result from changed GluN2 subunit composition as LTP following nIH was prevented by blocking the GluN2B containing NMDAr and yet, was insensitive to blockade of the GluN2A containing NMDAr. As CAMKII activity is associated with GluN2B containing NMDAr (Hosokawa et al., 2021; Paoletti et al., 2013; Tang et al., 2020), the continued predominance of GluN2B activity may increase CAMKII activity and subsequently affect hippocampal neurophysiology and associated behaviors. However, only MnTMPyP treatment during nIH effectively mitigated the downregulation of GluN2A receptors while also preventing the nIH-dependent effects on LTP and behavioral performance. Thus, while further resolution is required to determine how GluN2B gain of function and GluN2A loss of function each contribute to the emergence and persistence of neurophysiological deficits observed following nIH, our findings with MnTMPyP suggest that GluN2A subunit downregulation is a significant factor contributing to deficits in response to nIH. Our experiments using MnTMPyP demonstrated that the ability to mitigate the pro-oxidant state during nIH effectively prevents the immediate and long-term consequences of nIH on the hippocampus and behavioral performance. However, MnTMPyP administration following nIH neither mitigated the downregulation of GluN2A nor the impaired synaptic plasticity. When compared to adult mice that received MnTMPyP during nIH, adult mice treated with MnTMPyP following nIH also appeared to have memory deficits as indicated by performance in both the Barnes maze and object location task. Additionally, protein analysis in adult hippocampal tissue following nIH indicated that neither nuclear HIF1a nor NOX isoforms are elevated relative to that in adult mice left in room air. Together these findings strongly suggest that data suggest that while the pro-oxidant state that correlates with increased HIF1a signaling and elevated NOX enzymes may initiate changes in the neonatal hippocampus, the persistent effects of nIH on the hippocampus and behavior later in life are not driven by a persistent elevation in signaling or activity related to these proteins and the pro-oxidant state. Rather, it appears that nIH-mediated pro-oxidant activity initiates a lasting program that remodels hippocampal NMDAr identity and synaptic plasticity to cause behavioral impairments later in life. This lasting program activated by nIH has yet to be determined. Despite the well-recognized clinical importance of stable oxygenation in early perinatal life, an understanding of the mechanistic role that intermittent hypoxia has on neurodevelopmental outcomes is very limited when compared to the depth of knowledge into the genetic and molecular determinants of intellectual disability. Advancements toward understanding and addressing the consequences of mutations such as SYNGAP1 (Clement et al., 2012; Hamdan et al., 2009; Yang et al., 2023; Zoghbi & Bear, 2012), Fragile X Messenger Ribonucleoprotein 1 (D’Incal, Broos, Torfs, Kooy, & Vanden Berghe, 2022; Leal, Comprido, & Duarte, 2014; Monday, Kharod, Yoon, Singer, & Castillo, 2022), and MECP2 (Banerjee, Miller, Li, Sur, & Kaufmann, 2019) continue to grow. However, as the severity of such neurocognitive deficits often varies among individuals with such genetic predispositions, the extent to which environment interacts with genetic predisposition contribute to augment the severity of neurocognitive deficits is an important issue to be resolved by future work. ## Funding Sources: This work was supported by NIH PO 1 HL 144454 (AJG), NIH R01 NS10742101 (AJG) ## References 1. Arias-Cavieres A., Fonteh A., Castro-Rivera C. I., Garcia A. J.. 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--- title: High Prevalence of Clostridioides difficile Ribotype 176 in the University Hospital in Kosice authors: - Katarina Curova - Martin Novotny - Lubos Ambro - Anna Kamlarova - Viera Lovayova - Vladimir Hrabovsky - Leonard Siegfried - Pavol Jarcuska - Peter Jarcuska - Annamaria Toporova journal: Pathogens year: 2023 pmcid: PMC10055383 doi: 10.3390/pathogens12030430 license: CC BY 4.0 --- # High Prevalence of Clostridioides difficile Ribotype 176 in the University Hospital in Kosice ## Abstract Dysbiosis of the gut microbiota, caused by antibiotics, plays a key role in the establishment of Clostridioides difficile CD). Toxin-producing strains are involved in the pathogenesis of Clostridioides difficile infection (CDI), one of the most common hospital-acquired infections. We cultured a total of 84 C. difficile isolates from stool samples of patients hospitalized at Louis Pasteur University Hospital in Kosice, Slovakia, that were suspected of CDI and further characterized by molecular methods. The presence of genes encoding toxin A, toxin B, and binary toxin was assessed by toxin-specific PCR. CD ribotypes were detected using capillary-based electrophoresis ribotyping. A total of $96.4\%$ of CD isolates carried genes encoding toxins A and B, and $54.8\%$ of them were positive for the binary toxin. PCR ribotyping showed the presence of three major ribotypes: RT 176 ($$n = 40$$, $47.6\%$); RT 001 ($$n = 23$$, $27.4\%$); and RT 014 ($$n = 7$$, $8.3\%$). Ribotype 176 predominated among clinical CD isolates in our hospital. The proportion of RT 176 and RT 001 in four hospital departments with the highest incidence of CDI cases was very specific, pointing to local CDI outbreaks. Based on our data, previous use of antibiotics represents a significant risk factor for the development of CDI in patients over 65 years of age. ## 1. Introduction The human gut microbiota is a complex, dynamic, and heterogenous population of microorganisms, including bacteria, fungi, archae, and viruses. They interact with each other, as well as with the host, and significantly affect the host during homeostasis and disease. The number of microorganisms inhabiting the gastrointestinal tract has been estimated to exceed 1014, with the predominance of bacteria types belonging to the Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria phyla [1,2,3]. The importance of microbiota for human health is enormous. In particular, the role of gut microbiota in shaping the host’s intestinal epithelium, production of beneficial compounds for the host, protection against pathogens, and regulation of host immunity has been extensively studied and proved [4,5,6,7]. External factors (antibiotics, dietary components, stress) and host factors can induce dysbiosis of the gut microbiota. Dysbiosis affects the structure and function of the gut microbiota, which leads to the selective enumeration of certain microbiota members, including pathobionts, and to dysregulated production of microbial products or metabolites potentially harmful to the host. As a result of the aforementioned pathological processes, a wide spectrum of diseases is being developed, in particular, irritable bowel syndrome, inflammatory bowel disease, colon cancer, or Clostridioides difficile infection, metabolic diseases (metabolic syndrome, obesity, and diabetes), autoimmune diseases (celiac disease, systemic lupus erythematosus, and others), allergies, and neural disorders (depression and anxiety) [3,8,9]. Clostridioides difficile is a spore-forming anaerobic Gram-positive bacillus, taxonomically belonging to the Firmicutes phylum. The CD is proved to be a part of normal gut microbiota; however, in very low amounts. Some strains of C. difficile can produce toxins that are involved in the pathogenesis of CDI [8,10]. Generally, toxigenic strains of C. difficile produce toxin A (enterotoxin, TcdA) and toxin B (cytotoxin, TcdB). In addition, 6–$30\%$ of C. difficile strains produce another toxin called binary toxin (C. difficile transferase, CDT). It is often found in so-called hypervirulent strains responsible for outbreaks of CDI in hospital settings [11,12]. Dysbiosis of the gut microbiota, predominantly by antibiotics (ATBs), plays a key role in the establishment of CD and also in the increased toxins secretion by a greater number of vegetative cells. Subsequent damage to the intestinal barrier stimulates a severe inflammatory response that leads to diarrhea. Symptoms of CDI range from mild to severe diarrhea, pseudomembranous colitis, toxic megacolon, bowel perforation, and sepsis [13,14,15]. Since the emergence of hypervirulent CD ribotype BI/NAP$\frac{1}{027}$ (RT 027) in 2000, the epidemiological situation of CDI has been changing to become one of the most common hospital-acquired infections [10]. The spread of hypervirulent strains in hospitals and healthcare settings has led to increased rates of morbidity, mortality, and medical costs worldwide. In addition to RT 027, other ribotypes also appear and circulate in Europe [16,17]. Therefore, a deeper molecular characterization of causative CD strains is required to identify a common RT cluster in a suspected CD outbreak or to monitor the emergence of new RTs. The aim of our study was to investigate gene-encoding toxins and ribotypes of CD isolates among patients hospitalized at Louis Pasteur University Hospital (LP UH) in Kosice. We also summarize the key risk factors of CDI, the distribution of CD ribotypes in hospital departments, and treatment strategies of CDI in our set of patients. ## 2.1. Study Design and Patients Patients admitted to LP UH in Kosice between 1 January 2020 and 31 December 2020, who were at least 18 years of age at the time of admission, with suspicion of CDI and positive glutamate dehydrogenase (GDH) rapid test result, were included in this study. All stool samples of patients with suspicion of CDI were analyzed for the detection of GDH enzyme and toxins A/B by an immunochromatographic assay (Intermedical CLOSTRIDIUM TRIO TOXIN A/B/GDH) at the Department of Clinical Microbiology of LP UH in Kosice. Whereas the risk of acquiring infection with C. difficile is disproportionately higher in people aged over 65 compared to younger people, the group of patients older than 65 years was separated for the clinical characteristics and antibiotics used for treatment before onset of CDI. ## 2.2. Cultivation and Identification Stool samples with positive GDH tests were submitted for C. difficile isolation via conventional culture methods. Each sample was pretreated using the alcohol-shock method. A total of $70\%$ methyl alcohol (1 mL) was added to the stool sample (~1 mL, used directly), and the mixture was vortexed for 10 s every 15 min and incubated at room temperature for 45 min. Deposit (50–75 µL) was inoculated onto Brazier´s Clostridium difficile Selective agar (Oxoid). The inoculated plates were immediately transferred to the anaerobic workstation (Whitley A35, Don Whitley Scientific, Bingley, UK) and incubated at 36 °C for 48 h. The species identification of putative colonies was performed by MALDI TOF mass spectrometry (Bruker Daltonics, Billerica, MA, USA). ## 2.3. Isolation of Genomic DNA C. difficile genomic DNA was extracted from the grown colonies on Brazier´s agar using the GRISP isolation kit (GRS Genomic DNA kit) in accordance with the manufacturer´s instructions. Extracted DNA samples were stored at −20 °C until used for PCR analysis. ## 2.4. Detection of Toxin Genes All DNA samples were amplified for the 16S rDNA, tcdA, tcdB, cdtA, and cdtB genes of C. difficile in a single multiplex PCR, as described by Persson et al. [ 18]. The total PCR mixture final volume was 25 µL with 1.5 µL of genomic DNA, 12.5 µL of One Taq Master Mix (BioLabs), 4.75 µL of nuclease-free water, and 12 primers with the corresponding volume of each primer depending on its concentration. The amplification was carried out in Biometra thermal cycler (Biometra TOne 96 G, 230 V, Analytik Jena, Jena, Germany) according to the following protocol: one cycle of 30 s at 94 °C; 30 cycles of 30 s at 94 °C; 40 s at 53 °C; then 70 s at 68 °C; and the final extension of 5 min at 68 °C. The sizes of the amplification products were as follows: 1062 bp for the 16S-rRNA; 629 bp for the tcdA; 410 bp for the tcdB; 262 bp for the cdtB; and 221 bp for the cdtA gene. Amplification products were separated in $1.5\%$ agarose gel in Tris-acetate-EDTA buffer, stained by ECO Safe (Uniscience), and visualized by UV light. The image was captured by Gel Doc TMEZ System (BIO-RAD) and analyzed using Image Lab Software (BIO-RAD). ## 2.5. PCR Ribotyping PCR ribotyping was performed according to the standardized protocol for capillary-based electrophoresis of PCR ribotyping [19] with primers described by Bidet et al. [ 20]. These primers are complementary to the 3′ end of the 16S rRNA gene and the 5′ end of the 23S rRNA gene and allow amplification of the variable intergenic spacer region. The total PCR mixture consisted of 1 µL of genomic DNA, 12.5 µL of Hotstar Taq Master Mix (BioLabs), 10 µL of nuclease-free water, and 0.3 µL of each primer. The PCR protocol included one cycle of 30 s at 94 °C, 30 cycles of 20 s at 94 °C, 45 s at 52 °C, then 50 s at 68 °C, and the final cycle of 5 min at 68 °C. PCR fragments were analyzed in an ABI3130 automatic genetic analyzer (Thermo Fisher Scientific, St. Louis, MO, USA) in a 50 cm capillary loaded with Pop 7 polymer (Thermo Fisher Scientific). LIZ 1200 was used as the size standard (Thermo Fisher Scientific). Each sample length of fragments was determined using the GeneMapper v4.1 software (Thermo Fisher Scientific). The ribotyping profiles were compared with the Webribo database [21]. ## 2.6. Antimicrobial Susceptibility Testing The susceptibility of CD isolates to antimicrobials metronidazole (MTZ), vancomycin (VA), tigecycline (TGC), doxycycline (DO), tetracycline (TTC), and rifampicin (RIF) was tested by disc diffusion test on Wilkins–Chalgren agar (Oxoid). After the application of bacterial suspension (density 0.5 McFarland) and six antibiotic discs, the plates were incubated in the anaerobic workstation at 36 °C for 48 h. Inhibition zone diameters were evaluated using the BACMED 6iG2 automated AST reader and analyzer. Subsequently, BACMED 6iG2 automatically calculated the minimum inhibitory concentration (MIC) using an expert system. CD isolates were classified as susceptible or resistant to ATBs based on the breakpoints for interpretation of MIC issued by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) or the Clinical and Laboratory Standards Institute (CLSI). ## 2.7. Statistical Analysis For statistical comparison of the results, statistical methods of data processing and results evaluation were used. Processed research data were entered into the data tables and visualized in a graphical form (SPSS 21, GraphPad Prism 9). Statistical analysis by Wilcoxon test was used to determine whether there was a significant difference between the characteristics of patients (clinical characteristics, comorbidities, antibiotics, and the number of ATB classes) for the group of patients over 65 years of age. All p-values of less than 0.05 were considered statistically significant. ## 3.1. Characteristics of Patients and C.difficile Isolates From January 2020 to December 2020, GDH positivity was confirmed in 108 stool samples from 102 patients. After discarding 24 samples due to duplicity, lack of material, and negative culture, a total of 84 C. difficile isolates taken from hospitalized patients suspected of CDI were recovered and further characterized. From the total number of 84 CD isolates, 40 ($47.6\%$) were obtained from females and 44 ($52.4\%$) from males. The age of patients ranged from 31 to 98, and 70 ($83.3\%$) patients were older than 65. As it comes to the origin of CDI, most of the cases—75 ($89.3\%$) were hospital-acquired CDI (HA CDI), 4 ($4.8\%$) cases were community-acquired CDI (CA CDI), 3 ($3.6\%$) cases were recurrent CDI, and in 2 ($2.4\%$) cases the origin of CDI was unknown. A total of 17 ($20.2\%$) patients died. In patients with HA CDI, hospitalization in the same department was recorded in 42 cases, previous hospitalization in the same hospital during the last 4 weeks in 32 cases, and hospitalization in another hospital during the last 4 weeks in 2 cases as the origin of CDI. The clinical characteristics of patients included in this study are presented in Table 1. Statistical analysis did not confirm the significance between the clinical characteristics of patients for the group of patients over 65 years of age and also between comorbidities for the group over 65 years of age. In the same way, no statistical differences were confirmed for the group of patients under 65 years of age. A total of 68 CDI patients ($80.9\%$) were treated with antibiotics (ATBs) within three months before the episode of CDI, while 49 ($58.3\%$) patients used two and more ATBs belonging to different classes within the same period. The most commonly used ATBs were cephalosporins ($$n = 41$$, $8.8\%$), fluoroquinolones ($$n = 23$$, $27.4\%$), clindamycin ($$n = 18$$, $21.4\%$), trimetoprim/sulphametoxazole ($$n = 11$$, $13.1\%$), and macrolides ($$n = 10$$, $11.9\%$). Details about the types and number of ATB classes used by patients are summarized in Table 2. Statistical analysis confirmed a statistically significant difference between the group of antibiotics used most frequently before the onset of CDI (cephalosporins, fluoroquinolones, clindamycin, trimetoprim/sulphametoxazole, macrolides, carbapenems) for the group of patients over 65 years of age. Significance was demonstrated at the $95\%$ level of significance ($p \leq 0.0029$). On the other hand, the significance between the group of antibiotics less frequently used before the onset of CDI (aminoglycosides, metronidazole, amoxicillin/clavulanic acid) for the group over 65 years of age and also between the number of ATB classes for the group over 65 years of age was not confirmed by statistical analysis. No statistical differences were confirmed for the group of patients under 65 years of age. Distribution of CD isolates per hospital department was as follows: 20 cases from the 4th Department of Internal Medicine (providing comprehensive curative-preventive care within internal medicine); 18 cases from Infectology; 17 cases from Traumatology; 12 cases from the 1st Department of Internal Medicine (profiling in gastroenterology); 3 cases from Neurosurgery; 3 cases from the 1st Department of Surgery; 3 cases from Pneumology; 2 cases from the 2nd Department of Surgery; 2 cases from Hematology; 1 case from Urology; 1 case from Psychiatry; 1 case from Orthopedics; and 1 case from Department of Anesthesiology and Intensive Medicine. For the ATB treatment of CDI, vancomycin was used in 49 cases, both vancomycin and metronidazole—in 22 cases, metronidazole in 7 cases, and fidaxomicin in one case. Fecal microbiota transplantation (FMT) was used in addition to ATBs in two cases. To clarify, fidaxomicin and FMT were applied to treat recurrent CDI. In three cases, there was no antibiotic treatment due to an asymptomatic course, and in two cases, treatment was unknown. ## 3.2. Toxins of C. difficile Isolates Among the 84 stool samples selected for the study, the analysis of toxins A/B by rapid test revealed 69 ($82.1\%$) positive stool samples. The presence or absence of toxin genes was determined by multiplex PCR. In total, 81 ($96.4\%$) CD isolates carried genes tcdA and tcdB encoding toxins A and B. A total of 46 of them ($54.8\%$) also carried genes cdtA and cdtB encoding binary toxin. Three different toxin gene profiles were identified among the C. difficile isolates: 46 ($54.8\%$) had tcdA+ tcdB+ cdtA+ cdtB+ genotype; 35 ($41.6\%$) had tcdA+ tcdB+ cdtA− cdtB− genotype; and three ($3.6\%$) isolates were non-toxigenic (tcdA− tcdB− cdtA− cdtB− genotype). In this study, 12 ($14.3\%$) toxigenic C. difficile isolates were identified from stool samples previously evaluated by the rapid test as toxin A/B negative; 7 had tcdA+ tcdB+ cdtA+ cdtB+ genotype, and 5 had tcdA+ tcdB+ cdtA− cdtB− genotype. ## 3.3. Ribotypes of C. difficile Isolates By PCR ribotyping, three ribotypes were detected with the highest frequency: RT 176 ($$n = 40$$, $47.6\%$); RT 001 ($$n = 23$$, $27.4\%$); and RT 014 ($$n = 7$$, $8.3\%$). The remaining ribotypes ($$n = 12$$) were RT 020, RT 010, RT 027, RT 078, RT 003, and new RTs. They did not exceed three isolates per RT profile. In two cases, ribotyping failed (Figure 1). Table 3 indicates the association of CD ribotypes with three toxin gene profiles identified among the C. difficile isolates. The highest incidence of CDI and frequently isolated RTs was observed in four hospital departments (Figure 2). The isolates of RT 176 were most common in the fourth Department of Internal Medicine ($$n = 15$$), Infectology ($$n = 10$$), and the first Department of Internal Medicine ($$n = 6$$), and the isolates of RT 001 were most common in Traumatology ($$n = 15$$). ## 3.4. Antimicrobial Resistance of C. difficile Isolates Antimicrobial susceptibility testing of CD isolates to six antibiotics and evaluation using the BACMED 6iG2 revealed 31 ($36.9\%$) isolates susceptible to all tested ATBs. The resistance to RIF was observed most frequently in 47 out of 84 CD isolates ($56\%$). Among these isolates, the majority ($$n = 40$$) belonged to RT 176. Five isolates ($6\%$) were resistant to VA, and four isolates ($4.8\%$) to TTC and DO. Three ($3.6\%$) isolates were resistant to TGC, and one isolate ($1.2\%$) to MTZ. ## 4. Discussion C. difficile is one of the most frequently reported nosocomial microorganisms. Several risk factors contribute to the development of CDI, including the use of antibiotics, proton pump inhibitors, different comorbidities (arterial hypertension, diabetes mellitus, malignancies, chronic kidney diseases, COVID-19), older age, and long-term hospitalization [22,23]. In our cohort, we recorded $80.3\%$ of patients aged over 65. The use of antibiotics within 3 months before the onset of CDI was confirmed among $81.4\%$ of patients, and taking proton pump inhibitors during the mentioned time period among $50\%$ of patients. The most common comorbidities were arterial hypertension ($66.6\%$), diabetes mellitus ($22.6\%$), oncological diseases ($15.4\%$), and COVID-19 ($10.7\%$). Similar findings have been reported by other studies [23,24,25,26]. The most significant influence on the incidence of CDI in hospital settings is the use of two and more classes of antibiotics, which alter the gut microbiota and are responsible for the physiological protection of the gastrointestinal tract against colonization by pathogens, including CD [27]. In our cohort, the use of two and more classes of ATB was detected in $58.3\%$ of patients. Some ATBs, such as cephalosporins, clindamycin, and, more recently, fluoroquinolones, are known to carry a higher risk of CDI than others. In particular, cephalosporins have emerged as the ATBs with the highest relative and attributable risk of CDI [27]. During the COVID-19 pandemic, the number of CDI patients receiving high-dose antibiotic therapy with predispositions to antibiotic-associated diarrhea and the development of CDI has increased rapidly in some countries [25]. In our study, the most frequently used ATBs were cephalosporins ($48.8\%$), fluoroquinolones ($27.3\%$), clindamycin ($21.4\%$), trimetrophim/sulphamethoxazole ($13.1\%$), and macrolides ($11.9\%$). Amoxicillin/clavulanic acid ($7.1\%$) was a less frequent ATB. In addition, statistical analysis confirmed the existence of a statistically significant risk of ATB use in the group of patients aged over 65. Significance was demonstrated at the $95\%$ level of significance ($p \leq 0.0029$). Antibiotic policy within individual countries and regions is very specific and different, which can be subsequently observed in relation to CDI. According to a study from Israel [28], $48.8\%$ of patients were treated with cephalosporins and $6\%$ with macrolides. In the study summarizing the ATB use in 9 hospitals from 7 European countries [29], fluoroquinolones were used by $24.4\%$ of patients. In the study from Korea [30], clindamycin was used by $13.61\%$ and trimetrophim/sulphamethoxazole by $19.78\%$ of patients. The usage frequency of amoxicillin/clavulanic acid ranges from $0.19\%$ to $46.9\%$, as demonstrated by several studies [25,29,31,32,33]. Currently, the antibiotics vancomycin and fidaxomicin are the first-line antibiotics for the treatment of CDI. Metronidazole should only be used in mild to moderate disease courses among younger patients who have no or few risk factors for recurrence. The treatment success rate among these patients is up to $89\%$ [22,34,35]. Fidaxomicin has a high efficacy against CD without a significant negative effect on the gut microbiota. Czepiel et al. [ 10] indicate that the efficacy of fidaxomicin is comparable to vancomycin, and in some groups, it is even more effective in reducing CDI recurrence. In the case of recurrent CDI, fecal microbiota transplantation is the second line of treatment [36], with a success rate of 80–$92\%$ among patients suffering from recurrent and primary CDI [36,37]. In our study, $49\%$ of patients were treated with vancomycin, $22\%$ with vancomycin/metronidazole, and $7\%$ with metronidazole. Fidaxomicin was used in one and FMT in two of the three cases of recurrent infections during the year 2020. In these three patients, the treatment was successful, and no further recurrences occurred. Antibiotic misuse and the emergence of new hypervirulent C. difficile strains, mainly in nosocomial environments, led to a global increase in CDI incidence rate [15,16]. In Europe, the hypervirulent epidemic RT 027 strain was first reported in England in 2005 and has rapidly spread to other European countries [38]. Other ribotypes, such as RT 176, are genetically and phenotypically similar to RT 027 [39]. CDI caused by these RTs is associated with local outbreaks and a higher rate of complications and recurrences [40]. A number of other RTs are also emerging and circulating in Europe, namely, RT $\frac{001}{072}$, RT $\frac{014}{020}$, RT 140, RT 002, RT 010, and RT 078 [16,17]. In our study, RT 176 was confirmed in 40 CD ($47.6\%$) isolates, representing the most frequently occurring ribotype in the total set of 84 CD isolates. This RT was also dominant among 46 toxigenic CD isolates carrying gene-encoding toxin A, toxin B, and binary toxin. On the other hand, RT 027 was detected in only one isolate ($1.2\%$). RT 176 is common in Eastern European countries (Czech Republic, Poland, Hungary, Slovakia) [39] with the varying incidence among hospitals, regions, and countries. Several studies have already investigated the prevalence of hypervirulent CD strains in Slovakia. A 5-month study carried out by Krehelova et al. [ 41] in three eastern Slovakian hospitals in 2017 found RT 176 in 3 out of 66 CD isolates ($4.5\%$) recovered from GDH-positive stool samples. The LP UH is in the same region, but the incidence of RT 176 is higher. Novakova et al. [ 42] provided a 6-month study in a tertiary-care center in the northern part of Slovakia between January and July 2016. During this period, she examined 114 toxin-A/B-positive stool samples. RT 176 was detected in 75 ($65.8\%$) CD isolates and was found to be the cause of CDI outbreaks among patients. In a national CDI surveillance in Slovakia (October 2016–December 2016), a total of 78 CD isolates from 12 hospitals were analyzed. The incidence of RT 176 was $29.5\%$ ($$n = 23$$) [43]. In the Czech Republic, Krutova et al. reported a 26.7 % incidence of RT 176 for the period of 2013–2015 [39] and $11.6\%$ for October–December 2017 [44]. Beran et al. [ 45] reported an incidence of $60.9\%$ in 2011–2012, and Kracik et al. [ 46] $31.3\%$ in 2017–2018. The incidence of RT 176 at the level of $6.7\%$ was confirmed in Hungary [47], while in Poland, it was $2.8\%$ [48]. RT 027 probably occurs only rarely in Slovakia. This is indicated by the results of our study, as well as the study of Novakova [43], reporting $1.3\%$, and Krehelova [41], reporting $0\%$ incidence. RT 027 is also rare in the neighboring Czech Republic [39,44,45,46]. The predominance of RT 027 in recent years has been noted in Poland and Hungary, with the incidence of $82.4\%$ and $45.8\%$, respectively [47,48]. The second and third most prevalent ribotypes in this study, RT 001 ($27.3\%$) and RT 014 ($8.3\%$), respectively, are characterized by the production of toxins A and B. Both RTs were already known to be circulating in European countries, including Slovakia, reporting $56.1\%$ of RT 001 [41], $59\%$ of RT 001 [43], the Czech Republic with $24\%$ and $33.5\%$ of RT 001 [39,44], $9.4\%$ and $13\%$ of RT 014 [45,49], Hungary with $11.4\%$ of RT 014 [47], and Germany with $22.2\%$ of RT 014 [50]. The proportion of individual ribotypes in the four departments of LP UH with the highest incidence of CDI cases was very specific and pointed to local outbreaks of CDI. RT 176 was the main ribotype that caused four local outbreaks in the fourth Department of Internal Medicine, three outbreaks in the Infectology, and two in the first Department of Internal Medicine. RT 001 was responsible for three local outbreaks in Traumatology and was found to be predominant in this department. Finally, ribotype 014 was also associated with local outbreaks in two departments (Infectology and Hematology). Transfers of patients among all these departments were observed and contributed to the emergence of new infections. Our results indicate that the proportions of CD ribotypes are probably highly connected to the specific hospital departments, and the nosocomial transmission of CD strains from one department to another is common. Similar findings are stated by several studies [46,51,52]. Most of these outbreaks were recorded between August and December, during the second wave of the COVID-19 pandemic. The situation in Slovak healthcare during this period was very problematic. In hospitals, including LP UH, there were not enough beds for patients with COVID-19 and other diseases, and the medical staff was exhausted and undersized. Strict antibiotic stewardship programs aimed to reduce the use of broad-spectrum antibiotics (third-generation cephalosporines, fluoroquinolones, macrolides, and others) were not followed. In contrast, ATB prescription and use were disproportionately high. The health condition of many patients, especially those over the age of 65, requires prolonged hospitalization. In the field of prevention and control of the spread of CDI, a team of experts was established in LP UH even before the outbreak of the COVID-19 pandemic. This team was very active in CDI surveillance and efforts to implement and manage preventive measures, including personal protective equipment, patient isolation, visit restrictions, and continuous education of the medical and cleaning staff. From the summer of 2020, it was not possible to continue the mentioned activities due to the worsening situation in the hospital. Not only the SARS CoV-2 virus but also C. difficile spread rapidly in hospital departments. The situation finally stabilized, and team activities resumed in 2022. In recent years, an increasing number of CD isolates resistant to antibiotics has been observed [27]. In our cohort, results of antimicrobial susceptibility testing point to $36.9\%$ of CD isolates susceptible to all tested ATBs. A total of $56\%$ of CD isolates were resistant to rifampicin. Between July 2011 and July 2014, CD’s resistance to rifampicin was notable in Hungary, Italy, and the Czech Republic, with percentage values of 38.7–$56.6\%$, 36.6–$47.0\%$, and 40.0–$64\%$, respectively [53]. Sholeh et al. [ 54], in a systematic review, described $42.3\%$ resistant isolates. Aptekorz et al. [ 48] found $40.5\%$ resistant isolates in 13 hospitals in Poland, with over $90\%$ of them belonging to RT 027 and $33.3\%$ to RT 176. Among our isolates, all isolates belonging to RT 176 ($$n = 40$$) exhibited rifampicin resistance. Vancomycin resistance in $6\%$ and metronidazole resistance in $1.2\%$ of CD isolates was found in this study. Vancomycin resistance is higher compared to the CDI surveillance of 2016 in Slovakia [43], where no reduced susceptibility to vancomycin was observed. While Freeman et al. [ 53] reported rare resistance to vancomycin ($0.1\%$) and metronidazole ($0.2\%$) in their study, Sholeh et al. [ 54] reported higher vancomycin and metronidazole resistance ($3.7\%$ and $3.2\%$). Finally, $4.8\%$ of CD isolates were resistant to tetracycline and doxycycline, and $3.6\%$ of isolates to tigecycline. Our results are comparable to the results of Sholeh et al. [ 54] for tigecycline ($1.6\%$) but not for tetracycline ($18.2\%$). The resistance of CD isolates to ATBs may create a survival advantage for resistant strains causing therapeutic failure and increasing chances of recurrence. This effect we observed in three of five CD isolates exhibiting vancomycin resistance (death in one case and recurrence in two cases). It is considered that CD’s resistance to ATBs can be multifactorial, and the need for thorough monitoring of clinical CD isolates is very high as it could help to identify new genotypic and phenotypic characteristics. ## 5. Conclusions In conclusion, this is the first study of C. difficile isolates in the Louis Pasteur University Hospital in Kosice, Slovakia, which assessed complete cultivation and molecular biology-based characterization of C. difficile isolated from hospitalized patients over a period of one year. It is clear that previous consumption of antibiotics, especially cephalosporins, fluoroquinolones, and clindamycin, represents a significant risk factor for the development of CDI among patients aged over 65 years. This finding indicates a need for better management of antibiotic treatment, focused on careful selection and/or reduction of ATB prescriptions. CD genotype tcdA+ tcdB+ cdtA+ cdtB+ and the ribotype 176 characterized by rifampicin resistance predominated among clinical C. difficile isolates. The occurrence of RT 176 and RT 001 in selected departments and transmission among departments points to the epidemic situation. 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Infect. Control. (2020.0) 9 158. DOI: 10.1186/s13756-020-00815-5
--- title: Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 (Abhd2) as an enzyme that metabolizes phosphatidylcholine and cardiolipin authors: - Tara R. Price - Donnie S. Stapleton - Kathryn L. Schueler - Marie K. Norris - Brian W. Parks - Brian S. Yandell - Gary A. Churchill - William L. Holland - Mark P. Keller - Alan D. Attie journal: bioRxiv year: 2023 pmcid: PMC10055419 doi: 10.1101/2023.03.23.533902 license: CC BY 4.0 --- # Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 (Abhd2) as an enzyme that metabolizes phosphatidylcholine and cardiolipin ## Abstract We and others have previously shown that genetic association can be used to make causal connections between gene loci and small molecules measured by mass spectrometry in the bloodstream and in tissues. We identified a locus on mouse chromosome 7 where several phospholipids in liver showed strong genetic association to distinct gene loci. In this study, we integrated gene expression data with genetic association data to identify a single gene at the chromosome 7 locus as the driver of the phospholipid phenotypes. The gene encodes α/β-hydrolase domain 2 (Abhd2), one of 23 members of the ABHD gene family. We validated this observation by measuring lipids in a mouse with a whole-body deletion of Abhd2. The Abhd2KO mice had a significant increase in liver levels of phosphatidylcholine and phosphatidylethanolamine. Unexpectedly, we also found a decrease in two key mitochondrial lipids, cardiolipin and phosphatidylglycerol, in male Abhd2KO mice. These data suggest that Abhd2 plays a role in the synthesis, turnover, or remodeling of liver phospholipids. ## Introduction Lipids play a variety of roles in physiology, including providing structure, signaling and as fuel sources. Disruptions to lipid metabolism can lead to disease states, such as obesity[1, 2], insulin resistance[3, 4], cardiovascular disease[5, 6], and hepatic steatosis[7, 8]. Manipulations to lipid composition in plasma, tissues, and organelles can have a profound impact on disease susceptibility. For example, alterations in the fatty acid compositions of lipids in the endoplasmic reticulum (ER) have been shown to affect obesity-associated ER stress and to improve glucose metabolism in a leptin-deficient mouse model of obesity[9]. Improvements in detection methods and their sensitivity, such as untargeted lipidomics, have allowed for discovery of previously undefined roles of lipids in physiology. Within the past decade, a new class of lipids (fatty acid esters of hydroxy fatty acids, FAHFAs) have been discovered[10]. For example, the identification of FAHFAs as a novel bioactive lipid class has opened a new field of study into their roles in normal physiology and metabolic disease[11–13]. Commensurate with the diversity of lipids is the diversity of enzymes that metabolize lipids. One substantial challenge is discovering the in vivo substrates of lipid metabolizing enzymes and the enzymes responsible for synthesis and turnover of newly discovered lipids. We have used genetics to assist us in establishing a causal link between enzymes and their substrates. When we perform lipidomic surveys in the context of a segregating population, we can identify loci where specific lipid species are genetically associated with loci harboring genes that encode highly plausible candidate enzymes responsible for the metabolism of the lipids. In a prior study, we showed that the substrate and product of an enzyme in glycosphingolipid metabolism mapped to a locus containing that enzyme [14]. This was proof of principle that genetics could be used to de-orphanize lipid metabolism enzymes. The same study identified several ABHD members as modulators of lipid classes [14]. In validation experiments, ABHD1 and ABHD3 overexpression revealed distinct specificity for lipid classes and acyl chain lengths. The ABHD family of such enzymes (α/β -hydrolase domain) has 23 known members, which are characterized by a α/β -hydrolase fold and a catalytic serine hydrolase domain [15, 16]. ABHD6 is the most characterized lipase in this family, with a wide variety of physiological roles, including adipose biology, islet insulin secretion, and in cold tolerance [17–20]. ABHD3, another lipase, was shown to selectively modulate phospholipids with C14 acyl chain lengths[21]. The biological roles of many ABHD family members are still being discovered. Here, we incorporate murine liver untargeted, mass spectrometry-based lipidomics and quantitative trait loci (QTL) genetics to identify α/β-hydrolase domain 2 (Abhd2) as a novel driver of hepatic phospholipids. ## Identification of ABHD2 as novel driver of liver phosphatidylcholine In a recent genetic screen of circulating and hepatic lipids in Diversity Outbred (DO) mice, we identified a quantitative trait locus (QTL) for multiple phospholipids (PC and PE) on chromosome 7 at ~79 Mbp[22]. In parallel, we performed RNA-sequencing to survey the liver transcriptome in the same DO mice that were used for the lipidomic survey, enabling us to identify expression QTL (eQTL) for all genes. We found a strong association of the abundance of the Abhd2 mRNA with SNPs located near the Abhd2 gene (a cis-eQTL) with a LOD of 65. This QTL co-mapped with the phospholipid QTL on Chr 7 (Figure 1A). The DO mice segregate alleles from eight founder strains. We can identify the contribution of each allele to a given phenotype and display the allele effect patterns. The allele effect patterns for the phospholipids and the Abhd2 eQTL were similar, partitioning the founder haplotypes into two subgroups: CAST and WSB versus B6, A/J, NOD and 129 (Figure 1B). However, the directionality of the haplotype separation was different for the phospholipids and Abhd2 expression. Whereas alleles derived from CAST and WSB were associated with high expression of Abhd2, the same alleles were associated with lower abundance of phospholipids (Figure 1B). Thus, the phospholipids and Abhd2 expression show shared, but inverted genetic architecture. Next, we identified the SNPs most strongly associated with the phospholipids and the expression of Abhd2. The QTL for PC-20:4 peaks at ~79.2 Mbp and includes a block of SNPs with strongest association, which span from ~79.2 to ~79.4 Mbp (Figure 1C). The gene for Abhd2 is located ~79.3 Mbp, right under the SNPs with strongest association to PC-20:4. The SNP association profile for the Abhd2 cis-eQTL was the same as that for PC-20:4, suggesting a common genetic architecture for the lipids and Abhd2 expression. There are 67 protein-coding and non-coding genes that are located between 78.2 and 80.2 Mbp on Chr 7 (Supplemental Table S1). We used mediation analysis to identify a causal gene driver from among the genes present at the phospholipid QTL. In mediation analysis, QTL for a lipid is conditioned on the expression of all other genes, including those at the locus to which the lipid maps. If the genetic signal of the lipid QTL decreases upon conditioning of the expression level of a specific gene, that gene becomes a strong candidate as a driver for the lipid. We focused on the QTL for PC-20:4, as this demonstrated the strongest genetic signal (Figure 1A). Mediation of the PC-20:4 QTL against the expression of Abhd2 in liver resulted in a large drop in the LOD score for the PC 20:4 QTL (Figure 1D). To extend these observations, we asked if Abhd2 is a strong driver for all phospholipids mapping to the chromosome 7 QTL. For the seven phospholipids with a QTL to the Abhd2 gene locus, mediation against Abhd2 expression resulted in the largest drop in the LOD scores (Figure 2). In summary, the inverse allele effects for the phospholipid versus Abhd2 expression profiles strongly suggest that Abhd2 functions as a negative driver of the hepatic phospholipid QTL on chromosome 7. ## Experimental validation of Abhd2 as a driver of liver phospholipids To determine if Abhd2 is a key driver of liver phospholipids, we obtained a whole-body knockout of Abhd2 from Dr. Polina Lishko at UC Berkeley[23, 24]. Wildtype (WT) and Abhd2 knockout (Abhd2KO) mice were maintained on the same Western diet (WD), high in fat and sucrose, that was provided to DO mice used for the lipidomic genetic screen[14]. To experimentally validate the genetic prediction that Abhd2 is a driver of liver phospholipids (PC and PE), we performed MS-based lipidomics on liver tissue from male and female WT and Abhd2KO mice. A total of 583 unique lipid species were quantified (Table S2–S3), including 67 and 50 PC and PE lipids, respectively. Figure 3 highlights the liver lipids that were the most differentially abundant between WT and Abhd2KO mice. Female Abhd2KO mice had 21 liver lipids decreased and 9 lipids increased (Figure 3A), whereas male Abhd2KO mice showed 44 and 16 liver lipids decrease and increased, respectively (Figure 3B). Consistent with the prediction from the genetic screen, the PC and PE species that mapped to the Chr 7 QTL were significantly increased in liver from both male and female Abhd2KO mice (Figure 3C). In addition to PC and PE, other lipids were significantly altered in the livers of Abhd2KO mice. For example, several species of cardiolipin (CL) (Figure 3D) and phosphatidylglycerol (PG) (Figure S1A) were significantly reduced in livers from male, but not female, Abhd2KO mice. CL and PG are synthesized in mitochondria[25] and have play important roles in mitochondrial function[26]. To determine if the decrease in CL and PG levels in Abhd2KO males reflect a change in mitochondrial number, we performed quantitative PCR for several mitochondrial-encoded genes. In both male and female Abhd2KO mice, the expression of eight mitochondrial-encoded genes was not significantly different in WT vs. Abhd2KO mice (Figure S1B). These results suggest that lower levels of hepatic CL and PG in male Abhd2KO mice are not the consequence of reduced mitochondrial number. It is therefore more likely that Abhd2 plays a key role in the metabolism of these two mitochondrial lipids. To provide additional support for Abhd2 in regulating hepatic PG and CL levels, we asked if there was genetic association for CL and PG lipid species in liver among DO mice. We identified several QTL for both lipids, including a hotspot on chromosome 3 at ~46 Mbp where several CL species co-mapped (Table S4). CL-$\frac{16.0}{18.1}$/$\frac{16.0}{18.1}$ yielded the strongest genetic signal on chromosome 3, with a LOD of ~12, along with possible secondary QTL on chromosomes 7 and 13 (Figure 4A). Interestingly, the gene Abhd18, which is relatively uncharacterized but has been localized to mitochondria[27], is physically located at the CL QTL on chromosome 3, raising the possibility that Abhd18 and Abhd2 work in concert to regulate CL levels. While no CL species mapped to the Abhd2 gene locus on chromosome 7, conditioning CL on PC-$\frac{20.4}{22.6}$ as an additive covariate resulted in CL acquiring a QTL to the Abhd2 locus (Figure 4B). This QTL on chromosome 7 of CL adjusted by PC demonstrates an allele pattern that is similar to the cis-eQTL for Abhd2, and the inverse of the PC QTL (Figure 4B), consistent with CL being a downstream product of Abhd2-dependent metabolism of PC. Similar results were observed for two PG lipids; when conditioned on PC-$\frac{20.4}{22.6}$, QTL were acquired to the Abhd2 gene locus (Table S5). Changes in fatty acyl composition (number of carbons and degree of saturation) have been associated with differential response to metabolic stressors[28, 29]. Therefore, we evaluated the composition of the acyl chains in PC, PG, and CL lipids in WT and Abhd2KO mice (Figure S1C–E). Both PC and PG lipid classes were equally represented by acyl chain lengths of C16 and C18; in CLs, however, C18 comprised more than $95\%$ of the acyl chains (Figure S1C–E). PCs were primarily composed of saturated fatty acids, PGs had similar monounsaturated and saturated fatty acyl chains (~$43\%$ and $50\%$, respectively), while $80\%$ of CL fatty acyl chains contained two double bonds (Figure S1C–E). Acyl chain length and degree of saturation for PC, PG, and CL species were not different in Abhd2KO mice. Taken together, these results suggest that ABHD2 is not involved in specific alteration of the acyl chain composition of phospholipids. ## Physiological characterization of Abhd2KO mice While the increase in hepatic phospholipids we observed in the Abhd2KO mouse confirms the predictions from the genetic screen that Abhd2 is a negative driver of these lipids, it does not inform us about the physiological role of Abhd2. To gain a better understanding of this, we performed a series of physiological measurements in WT and Abhd2KO mice. WT and Abhd2KO mice demonstrated similar growth curves (Figure S2A, S2E), and comparable fasting glucose, insulin, and triglyceride profiles (Figure S2B–D, S2F–H). At ~24 weeks of age, body weight did not differ among female mice (WT 32.2 ± 1.4g vs. Abhd2KO 36.0 ± 11.9g, Figure S3A); however, Abhd2KO females showed greater fat mass (Figure S3B), fat mass percentage (Figure S3C) and decreased lean mass percentage (Figure S3D). Male mice did not differ in body weights or body composition (Figure S3F–H). To evaluate a role for Abhd2 deletion on broad metabolic pathways, we performed an oral glucose tolerance test (oGTT) to assess whole-body insulin signaling and glucose homeostasis, a β3-adrenergic receptor agonist tolerance tests (β3TT) to examine differences in adipose lipolysis and glucose metabolism, and a fast/re-feed (FRF) paradigm to probe liver lipolysis/lipogenesis pathways. During the oGTT, no differences in plasma glucose, insulin, or c-peptide levels were observed for male or female WT vs. Abhd2KO mice (Figure S4). Administration of CL-316,243 (a β3-adrenergic receptor agonist) resulted in a marginal increase in plasma glucose in male Abhd2KO mice during the β3TT (Figure S5). However, area under the curve (AUC) for glucose, insulin, free fatty acids, and glycerol were all unchanged in Abhd2KO mice (Figure S5). Similarly, circulating fatty acids were not different for WT vs. Abhd2KO mice during the fast/re-feed paradigm (Figure S6). Another member of the ABHD family of enzymes, ABHD6, has been shown to have a direct effect on islet insulin secretion by hydrolyzing monacylglycerols, inhibiting MUNC13-1 action and thereby regulating insulin granule release[20]. To directly evaluate the effect of ABHD2 on pancreatic β-cell function, we determined insulin secretion from cultured islets isolated from WT and Abhd2KO mice. Insulin secretion in response to varying glucose concentrations or monoacylglycerol (2-arachidonoylglycerol or 1-palmitoylglycerol) was the same for WT and Abhd2KO mice (Figure S7). Given that hepatic phospholipids have been shown to play a major role in lipoprotein metabolism and cholesterol homeostasis[30–33], we measured circulating total cholesterol and triglycerides (TG) in WT and Abhd2KO mice. Total cholesterol and TG were not different in Abhd2KO mice (Figure S8A–B). To assess whole-body cholesterol metabolism, we measured biliary and hepatic cholesterol content. These remained unchanged in Abhd2KO mice (Figure S8C). Hepatic cholesterol levels showed a marginal increase in male, but not female Abhd2KO mice (Figure S8D). To assess lipoprotein classes (e.g., LDL, HDL), we performed fast protein liquid chromatography (FPLC) on plasma from WT and Abhd2KO mice. Cholesterol in the individual FPLC fractions did not differ between genotypes of females (Figure S8E) or males (Figure S8F). No differences were detected for total cholesterol across the lipoprotein fractions for WT vs. Abhd2KO mice (Figure S8G). Given the marginal increase in hepatic cholesterol levels in male Abhd2KO mice (Figure S8D), we evaluated hepatic LDL receptor (LDLR) protein levels by western blot analysis. LDLR protein was not different between female (Figure S8H) or male (Figure S8I) WT and Abhd2KO mice (Figure S8J). Taken together, while our data supports Abhd2 as a driver of several hepatic phospholipid, and cardiolipin (in male) species, we were unable to link these changes to differences in serum lipoproteins, suggesting that the role of Abhd2 in phospholipid metabolism is confined to intracellular lipids. ## Discussion Genetic diversity plays a pivotal role in lipid metabolism and homeostasis. By leveraging genetic diversity of murine populations, it is possible to define novel drivers of physiological traits, including lipid classes. Through untargeted mass spectrometry-based lipidomics in the context of a genetic screen, our study is the first to nominate and validate Abhd2 as a genetic driver of hepatic phosphatidylcholine and phosphatidylethanolamine. Phospholipid species (PC and PE) which mapped to chromosome 7 were increased in livers of knockout mice (both sexes), following the substrate signature prediction of our genetic screen. By integrating lipidomics and transcriptomics, we show how a mouse genetic screen can be used to identify novel drivers of hepatic lipids. Abhd2 has been previously characterized as a monoacylglycerol lipase with potent effects on male fertility[24] and ovulation in female mice[23]. In sperm, Abhd2 is activated by progesterone, cleaves monoacylglycerols (1-arachadonoylglycerol and 2-arachadonoylglycerol) to remove the inhibition of the CatSper calcium channel thereby allowing for sperm activation. In a gene-trap mouse model of age-related emphysema, loss of Abhd2 resulted in decreased PC levels in bronchoalveolar lavage[34]. These Abhd2-deficient mice had increased lung macrophage infiltration and inflammatory markers and spontaneously developed emphysema with aging. It is interesting that their study showed a decrease in PC lipids with loss of Abhd2, whereas PCs increased in livers of our whole-body Abhd2KO mice, perhaps highlighting tissue-specific roles of Abhd2. Nevertheless, Abhd2 appears to have a causative role in PC species homeostasis. Our study is the first do demonstrate an in vivo role for Abhd2 in phospholipid regulation in non-reproductive tissues. An unexpected finding was a decrease in cardiolipins and phosphatidylglycerols in male Abhd2KO mice. Cardiolipins comprise ~$20\%$ of the inner mitochondrial membrane, whereas phosphatidylglycerols reside in the outer mitochondrial membrane[26]. To explore a genetic association between PC and CL or PG, we performed QTL analyses in which the PC lipid showing strongest association to the Abhd2 gene locus (PC-$\frac{20.4}{22.6}$) was used as an additive covariate when mapping CL or PG. This QTL analysis yielded an intriguing result: CL and PG acquired QTL at the Abhd2 locus with an inverted allele signature to that for the PC. This inverted allele signature is also indicative of a substrate signature, where an increase in PC is associated with a decrease in PG and CL. Thus, Abhd2, through its effect on PC, may indirectly play a role in the synthesis of CL species. One hypothesis for Abhd2’s effect on CL biosynthesis is through the role of an acyltransferase. Abhd2 contains two enzymatic motifs: the canonical serine hydrolase motif and the highly conserved HxxxxD acyltransferase motif between H120 and D125. Synthesis of CL involves a transfer of a fatty acyl chain from PC or PE phospholipids to monolysocardiolipin (MLCL) to form mature CL species. Four MLCL species were detected in our liver samples (Table S3). In males, there was a 2.5-fold reduction in one MLCL species (MLCL-56:6) in Abhd2KO mice. If Abhd2 affected mature CL synthesis through a direct fatty acyl chain transfer to MLCL, an increase in MLCL species would be expected. Therefore, the reduction in MLCL indicates Abhd2’s role is likely upstream of mature CL synthesis. Since PG is also required for CL synthesis, it’s also possible that the reduction in CL concentrations is secondary to alterations in PG concentrations[25]. In our initial QTL analyses of all liver lipids, we identified a CL hotspot on chromosome 3 at ~46Mbp, which includes the ABHD enzyme, Abhd18. Recently, Abhd18 was shown to reside in the mitochondria[27]; however, its mechanism has not been well characterized. In the STRING protein-protein association network database (string-db.org), Abhd2 and Abhd18 are predicted to have an interaction, although this has not been experimentally validated[35]. It is possible that Abhd2 mediates utilization of PC or its acyl chains in the synthesis of CL and PG, or that it interacts with another mitochondrial enzyme, such as Abhd18, to affect these changes. It is important to note that changes to mitochondrial lipids were only observed in male Abhd2KO mice, whereas the increase in PC and PE phospholipids occurred in both sexes. Progesterone-induced activation of Abhd2 is required for its lipid cleavage function and regulating ovulation in females [23, 24]; however, the effect of male sex hormones on Abhd2 has not been demonstrated. In a study of cerebral cortex development, a perinatal testosterone spike in male mice drove mitochondrial lipid composition and maturation[36]. It is possible that Abhd2 is required for testosterone-dependent regulation of mitochondrial lipid synthesis or maturation. Reduced abundance of PG and CL lipids may indicate a reduction in total mitochondrial number or a defect in the IMM leading to altered metabolic function. As a surrogate for mitochondrial number, we measured expression of key mitochondrial genes by qPCR but did not see a sex-specific or genotype effect. Thus, the decrease in CL and PG does not appear to be due to a reduction in mitochondrial number but does not rule out altered mitochondrial function in liver from Abhd2KO mice. The monoacylglycerol lipase, Abhd6, has also been shown to modulate mitochondrial lipid metabolism[37, 38]. However, the changes in lipid class concentrations were in the opposite direction of the Abhd2 lipids. Loss of Abhd6 results in an increase in liver PG, which was attributed to defective degradation of lysophosphatidylglycerol (LPG)[37]. Another group later showed increased plasma concentrations of bis(monoacylglycerol)phosphate (BMP) in mice lacking Abhd6 and in humans with a loss-of-function mutation in ABHD6[38]. Both BMP and CL synthesis require PG as a precursor[39]; therefore, it is possible that the reduction of PG and CL content in the Abhd2KO livers may reflect alterations in one or both of these pathways. Loss of Abhd2 has been previously shown to regulate vascular smooth muscle migration and induce blood vessel intima hyperplasia after a cuff experiment in a mouse model[40]. The same group showed an increase in macrophage ABHD2 expression abundance in vulnerable plaques in humans[41], but no mechanism of action was determined. In human genome-wide association studies[42], there is a significant region on chromosome 15 associated with coronary artery disease. This locus sits between two genes: ABHD2 and MFGE8. Soubeyrand et al. showed that deletion of the intergenic locus results in a marked increase in MFGE8 expression but did not affect the expression of ABHD2[43]. Knockdown of MFGE8 in coronary smooth muscle cell and monocytes inhibited proliferation, indicating MFGE8 as the causal gene for CAD-associated at this locus[43]. Splice variants of MFGE8 have been associated with reduced risk of atherosclerosis in FinnGen, a large Finnish biobank study[44]. However, an in vivo role for MFGE8 has not been established. In our genetic screen, hepatic expression of Mfge8 did not significantly correlate with hepatic lipids or plasma lipoproteins. We did not observe a difference in plasma lipoproteins with Abhd2 deletion. We did not assess any indicators of vascular smooth muscle physiology or blood pressure. Thus, ABHD2 is likely not the causative gene for with the CAD-associated region on chromosome 15 in human GWAS. With biochemical approaches alone it is challenging to discover novel candidate substrates for known enzymes. Through integration of gene expression data with untargeted, mass-spectrometry lipidomics, we identified a hepatic phospholipid hotspot on chromosome 7 and nominated Abhd2 as a novel driver of PC, PE, and cardiolipin. Using a whole-body knockout mouse model, we validated Abhd2 as the causative gene for several PC and PE lipids, and cardiolipin, precisely as predicted by the QTL analysis. Our study demonstrates the power of metabolite QTL analysis to discover novel candidate substrates for enzymes. ## Mouse genetic screen to nominate novel drivers of hepatic lipid metabolism Details of the mouse genetic screen has been previously described[22]. Briefly, 500 Diversity Outbred (DO) mice were obtained from Jackson Laboratories (Bar Harbor, ME) and maintained on a high-fat, high-sucrose diet (TD.08811, Envigo, Madison, WI) for 16 weeks. Livers were collected for transcriptomics and untargeted mass spectrometry-based lipidomics. Mapping of gene expression and phenotypes were performed to identify quantitative trait loci (QTL) and nominate candidate drivers for individual lipid species. Genome scans were completed with R/qtl2 software[45], using sex and wave as additive covariates. To investigate genetic associations between mitochondrial lipid classes and phosphatidylcholines mapping to chromosome 7, genome QTL scans were performed with sex, wave, and PC-20:$\frac{4}{22}$:6 as additive covariates. A logarithm of odds (LOD) greater than 6.0 was used as the threshold for identifying a QTL. Mediation analysis to establish causality was performed using conditional regression of the target phenotype on gene expression of candidate gene and the locus genotype[46]. ## Abhd2 mouse housing and maintenance Whole-body Abhd2 heterozygous mice were a kind gift of Dr. Polina Lishko at University of California – Berkley. All animal work was approved by the Institutional Animal Care and Use Committee at University of Wisconsin-Madison under protocol #A005821. Heterozygous mice were bred to produce knockout mice and wild-type littermate controls. All mice were housed at the University of Wisconsin – Madison animal facilities with standard 12-hour light/dark cycles. Animals were weaned and provided a high-fat, high-sucrose diet (TD.08811, Envigo, Madison, WI) and water ad libitum. At 23–25 weeks of age, mice were euthanized by carbon dioxide asphyxiation and exsanguinated by cardiac puncture. Whole blood was collected with EDTA, centrifuged at 10,000xg for 10 minutes at 4°C and plasma separated. Tissues were collected, snap frozen in liquid nitrogen, and stored at −80°C until assay. ## in vivo Physiologic Measurements At 6, 10 and 14 weeks of age, mice were fasted four hours and blood collected by retroorbital bleed for measurement of plasma glucose (#23-666-286, FisherScientific), insulin (#SRI-13K, MilliporeSigma) and triglycerides (#TR22421, ThermoFisher). At age 16 weeks, mice were subjected to a 24-hour fast and 6-hour refeed to assess hepatic lipid storage during energy deficits. Body weights and whole blood were collected at 0, 24, and 30 hours and plasma measured for non-esterified fatty acids (NEFA) using the Wako Linearity Set (#999-34691, #995-34791, # 991-34891, # 993-35191, FisherScientific). in vivo insulin action was assessed at 18 weeks of age by an oral glucose tolerance test at as previously described[22]. Mice were fasted for four hours and given a 2 g/kg BW glucose dose by oral gavage. Blood was collected by retroorbital eye bleed and assayed for glucose, insulin, and c-peptide concentrations. β3-adrenergic receptor agonist tolerance tests (β3TT) were performed at 20 weeks of age on four-hour fasted mice. Mice were dosed with 1 mg/kg BW of CL-316,243 by i.p. injection. Blood, collected by retroorbital eye bleed, was assayed for glucose, non-esterified fatty acids, glycerol, and insulin content. ## Liver Lipidomics Frozen tissues were sectioned to 10mg on dry ice and added to phosphate buffered saline (PBS) and methanol containing internal stable isotope metabolomics standards (Table S4). Tissues were mechanically homogenized (Qiagen TissueLyser) for 5 minutes at maximum frequency (30.0 Hz/s). 20µL of homogenate was removed for protein quantification (Pierce BCA Protein Assay Kit). Samples were mixed with methyl tertiary-butyl ether (MTBE), vortexed, centrifuged, and supernatant was transferred into new tube. Original samples were re-extracted with MTBE: Methanol: dd-H2O (10:3:2.5), vortexed, centrifuged, and supernatant was transferred into tubes with first extraction’s supernatant. Samples were evaporated in a speed-vac and then resuspended with isopropyl alcohol: acetonitrile: dd-H2o (8:2:2). Samples were then vortexed and centrifuged before transferring supernatant to glass vials (Agilent Technologies). Samples were analyzed by liquid chromatography- tandem mass spectrometry (LC-MS) with a 6545 UPLC-QToF mass spectrometer for non-targeted lipidomics. Results from LC-MS experiments were collected using Agilent Mass Hunter Workstation and analyzed using the software package Agilent Mass Hunter Quant B.07.00. Lipid species were quantified based on exact mass and fragmentation patterns and verified by lipid standards. Mass spectrometry was performed at the Metabolomics Core Facility at the University of Utah. Mass spectrometry equipment was obtained through NCRR Shared Instrumentation Grant 1S10OD016232-01, 1S10OD018210-01A1 and 1S10OD021505-01. ## Liver, Bile, and Plasma Cholesterol Total cholesterol in undiluted plasma and bile was assessed with Infinity Cholesterol reagent (TR13421, Thermo Scientific, Waltham, MA) and concentrations determined by a standard curve. Liver cholesterol was extracted by homogenizing 50 mg of tissue in a TissueLyser with 1 mL chloroform:isopropanol:IGEPAL CA-630 (7:11:0.1). The organic phase was collected and dried at 50°C. Dried lipids were resuspended in 200μL cholesterol assay buffer (MAK043, Millipore Sigma, St. Louis, MO) and total cholesterol determined following manufacturer’s protocol. To analyze lipoprotein size distributions, plasma was analyzed using a Superose 6 10-300GL column and size-exclusion fast protein liquid chromatography (FPLC). Fractions were assayed for total cholesterol and triglycerides as previously described[47]. ## RT-PCR for mitochondrial genes For mitochondrial gene analyses, DNA was isolated from liver samples ($$n = 5$$/sex/genotype) with an overnight incubation in proteinase K. Isolated DNA was dried and resuspended in ultrapure water for qPCR analysis. Mitochondrial gene expression (primers in Table S5) were normalized to the nuclear cystic fibrosis transmembrane conductance receptor (Cftr) and fold-change calculated using the 2−∆∆Ct method. ## Western blot analysis Tissues were lysed in RIPA buffer and total protein determined by Pierce BCA assay (#23225, ThermoFisher Scientific) to ensure equal loading. Samples (15–30 ug) were heat inactivated with 4X Laemmli dye containing $4\%$ 2-mercaptoethanol at 70°C for 10 minutes and run on $7.5\%$ tris-glycine gels following standard protocols. PVDF membranes were stained for total protein with $0.1\%$ ponceau S in $5\%$ acetic acid, and then probed for the protein of interest. For blotting of FPLC-separated plasma lipoprotein fractions, 25 uL of each fraction was incubated with 4X Laemmli dye containing $4\%$ 2-mercaptoethanol at 70°C for 10 minutes and probed for protein as described above. Primary and secondary antibodies are listed in Table S6. ## Statistical Analyses Statistical analysis of in vivo mouse data and tissue assays were performed by ANOVA followed by Tukey’s post-hoc analysis. Lipidomics data were analyzed using MetaboAnalystR[48]: liver lipid concentrations (pmol lipid/mg liver) were log-transformed, normalized by Pareto scaling, and then fold change calculated. Unless noted, data are presented as mean ± standard error. Differences were considered significant at $p \leq 0.05.$ ## References 1. Bays HE, Toth PP, Kris-Etherton PM, Abate N, Aronne LJ, Brown WV. Obesity, adiposity, and dyslipidemia: A consensus statement from the National Lipid Association.. Journal of Clinical Lipidology. (2013.0) 7 304-83. DOI: 10.1016/j.jacl.2013.04.001 2. Klop B, Elte JW, Cabezas MC. Dyslipidemia in obesity: mechanisms and potential targets.. Nutrients. (2013.0) 5 1218-40. DOI: 10.3390/nu5041218 3. Lee S, Norheim F, Gulseth HL, Langleite TM, Aker A, Gundersen TE. 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--- title: Formulation and Evaluation of Xanthan Gum Microspheres for the Sustained Release of Metformin Hydrochloride authors: - Madiha Melha Yahoum - Selma Toumi - Hichem Tahraoui - Sonia Lefnaoui - Mohammed Kebir - Abdeltif Amrane - Aymen Amin Assadi - Jie Zhang - Lotfi Mouni journal: Micromachines year: 2023 pmcid: PMC10055444 doi: 10.3390/mi14030609 license: CC BY 4.0 --- # Formulation and Evaluation of Xanthan Gum Microspheres for the Sustained Release of Metformin Hydrochloride ## Abstract This work aimed to formulate xanthan gum microspheres for the encapsulation of metformin hydrochloride, according to the process of ionotropic gelation. The obtained microparticles, based on various fractions of xanthan gum (0.5–1.25), were subjected to different physico-chemical tests and a drug release study. Microspheres with an average size varying between 110.96 μm and 208.27 μm were obtained. Encapsulation efficiency reached $93.11\%$ at a $1.25\%$ biopolymer concentration. The swelling study showed a swelling rate reaching $29.8\%$ in the gastric medium (pH 1.2) and $360\%$ in the intestinal medium (pH 6.8). The drug release studies showed complete metformin hydrochloride release from the beads, especially those prepared from xanthan gum at the concentration of $1.25\%$, in intestinal medium at $90.00\%$ after 6 h. However, limited and insignificant drug release was observed within the gastric medium ($32.50\%$). The dissolution profiles showed sustained release kinetics. ## 1. Introduction Improving the quality of drug treatments has always been one of the major concerns of humans. It is within this framework that technical progress is generally dependent on the development of much more efficient systems based on new materials with improved properties and better adapted to current health requirements. Modified-release forms such as hydrophilic matrices [1,2] or microparticles such as microspheres and microcapsules fit into this perspective [3]. Microencapsulation is a process by which it is possible to produce individualized microparticles using a polymeric material intended to contain an active substance in an amount of $5\%$ to $90\%$. This technique is applied to several fields, such as food, pharmacy, cosmetics, or other fields including catalysis. In the pharmaceutical field, encapsulation is used for different purposes, namely the immobilization and the protection of the active ingredient, masking some of its undesirable organoleptic proprieties, such as odor, or even making it possible to control and trigger the release of drugs [4,5]. Microparticles are divided into two distinct types: microcapsules and microparospheres. Microcapsules are reservoir particles made up of a solid or (a more or less) viscous liquid core of active substance, surrounded by a continuous solid shell of coating material. The microspheres are particles made up of a continuous macromolecular or lipid network forming a matrix in which the active ingredient is finely dispersed, in the form of molecules of fine solid particles or even droplets of solutions [3]. Polysaccharides represent the largest category of materials used as encapsulating agents, due to their multiple advantages, particularly their biocompatibility and biodegradability, as well as their low toxicity and gelling capacity. Among these biopolymers, alginate, pectin or chitosan, and xanthan gum, are largely used for the encapsulation of active substances such as drugs and essential oils, as well as viable cells [6,7,8]. In the present work, the choice was directed towards xanthan gum due to its valuable proprieties and biosafety, and also for its rheological characteristics and high viscosity, which make it stand out from the rest of the biopolymers. Furthermore, xanthan gum is non-toxic to health and to the environment, having a production route considered to be sustainable [9]. Xanthan gum, due to its specific proprieties, is currently used in drug delivery systems for sustained-release activity. Formulations based on xanthan gum offer an effective sustained release due to the more branched polysaccharide structure than other biopolymers, such as alginate, pectin or chitosan, due to which the drug is entrapped, hence decreasing its diffusion [9]. Xanthan gum (XG) is a branched anionic heteropolysaccharide produced by a Gram [-] bacterium, Xanthomonas compestris. The primary structure of xanthan gum consists of a chain of pentasaccharide units. Each unit contains D-glucose, D-mannose and D-glucuronate in a 2:2:1 molar ratio, respectively. In an aqueous solution, xanthan gum exhibits an ordered helical conformation as a quintuple helix. This anionic polysaccharide is completely soluble in hot or cold water and hydrates rapidly once dispersed, facilitating water retention and producing highly viscous solutions even at low concentrations. Moreover, xanthan is characterized by its high thermal stability and exceptional rheological properties. Indeed, this biopolymer exhibits excellent thermal stability, where its solutions retain uniform viscosities over a wide range of temperatures [9]. Furthermore, it was recently reported that xanthan gum has a potential blood-sugar-lowering and -stabilizing effect. This last particular property makes xanthan gum an excellent candidate for the administration of active antidiabetic substances [10,11]. Metformin hydrochloride (MTH) is an antidiabetic active ingredient belonging to the biguanide family [12], administered for the treatment of type II diabetes as a regulator of blood sugar levels through an increase in insulin action and the inhibition of gluconeogenesis [13]. Metformin improves hepatic insulin sensitivity and is therefore recommended in the treatment of polycystic ovary syndrome in obese patients with insulin resistance [14]. Some researchers reported the potential anticancer activity of metformin against breast cancer [15]. Metformin is characterized by its low oral bioavailability and short half-life of around 1.5 to 3 h [16], requiring several administrations per day (500 mg or 850 mg once to twice daily), hence negatively influencing the good compliance with the treatment and therefore its effectiveness, and it also causes gastrointestinal discomfort such as diarrhea. Additionally, when metformin hydrochloride is administered orally, it undergoes alterations as it passes through the digestive tract, where the pH is very acidic, in addition to the presence of enzymes. These major drawbacks make metformin a good candidate for sustained release by using a suitable dosage form such as polymeric microparticles or microcapsules. In addition to the control of the drug release, these systems offer the more effective protection of active ingredients against degradation (pH, enzymes), thus increasing the bioavailability, in addition to better patient compliance and an increase in comfort and safety via a greater reduction in side effects [17]. Several studies have reported the use of polymeric microparticle encapsulation systems for the sustained release of MTH using diverse types of polymers and preparation techniques. MTH mucoadhesive microcapsules based on sodium alginate and gum karaya were developed by Kumar et al. [ 18], using emulsification ionotropic gelation. Another study was conducted by Nath et al. [ 19] on the development of floating microcapsules for metformin hydrochloride based on Eudragit RL 100 and cellulose acetate butyrate by the nonaqueous emulsion–solvent evaporation method. Silva et al. [ 20] used poly(L-lactic acid) and carboxymethyl cellulose microparticles produced by the double emulsion–solvent evaporation technique for MTH sustained release. Furthermore, Choudhury et al. [ 21] prepared cellulose acetate microspheres by emulsion–solvent evaporation for metformin hydrochloride’s gastric retention. Pectin microspheres were developed by Banerjee et al. [ 22], using different ratios of the drug pectin, and also with different polymers, namely ethyl cellulose, hydroxypropylcellulose (HPMC) and Acrycoat S100. Bioadhesive chitosan microparticles for the oromucosal drug administration of MTH were prepared by Madsen et al. [ 23], using the spray-drying method. Hariyadi et al. [ 24] resorted to an ionotropic gelation technique for the preparation of MTH-loaded alginate microspheres. Cydonia oblonga mucilage/alginate mucoadhesive microspheres were assessed by Noreen et al. [ 25], as a potential sustained-release and mucoadhesive system to improve MTH bioavailability. However, very little work has been done on the microencapsulation of MTH using xanthan gum. A single study was conducted in 2016 by Ramalingam Nethaji et al. [ 26], where sodium alginate and different concentrations of natural mucoadhesive polymers such as xanthan gum and guar gum were used to develop gastroretentive formulations via the ionic gelation technique. In this study, the effect of the xanthan gum/guar gum mixture ratio on the properties of the microparticles obtained and on the release of MTH in the gastric medium (1.2) was evaluated. Optimal results were obtained with mixtures containing a higher fraction of xanthan. This contributed to the choice of polymer in this present study, alongside the other advantages offered by this polysaccharide. In the literature, no study has been reported on the encapsulation of active substances into microparticles based exclusively on xanthan gum. In all the existing research, XG is often used either in the form of a mixture with other natural or synthetic polymers or else in the form of carboxymethyl xanthan [27,28,29,30,31]. Our present study is therefore a first. The main objective of this work was to formulate an encapsulation system for metformin hydrochloride, based on xanthan gum, to control and modulate its release. The method used to develop this system was ionotropic gelation in the presence of aluminum chloride as a crosslinking agent. The microparticles obtained were then subjected to several physicochemical characterizations, such as particle size measurement, the swelling rate and the release kinetics in gastric and intestinal simulated media. ## 2.1. Materials Metformin hydrochloride (MTH) was generously provided as a gift by Saidal Medea (Medea, Algeria); xanthan gum (XG), aluminium trichloride (AlCl3), sodium chloride (NaCl) and hydrochloric acid (HCl) were purchased from Sigma Aldrich (Hamburg, Germany). All other chemicals used were of analytical grade and were also purchased from Sigma Aldrich (Hamburg, Germany). ## 2.2. Fourier Transform Infrared (FTIR) Analysis The compatibility study assessed by FTIR spectroscopy was conducted on the raw materials, namely MTH, XG and their mixture, and also on the formulated microspheres. The spectra with a resolution of 4 cm−1, using 10 scans, were then recorded in the range of 4000–500 cm−1 using a Fourier transform infrared spectrophotometer (Shimadzu, Tokyo, Japan). ## 2.3. Viscosity Measurement of Xanthan Gum Hydrogels This test consisted of studying the variation in viscosity as a function of xanthan concentration. The rotational viscometer HAAKE Visco-Tester (VT5) was used for the viscosity measurements of xanthan gum solutions at the different concentrations of $0.5\%$, $0.75\%$, $1\%$ and $1.25\%$. The tests were carried out at ambient temperature using an SP3 spindle at a shear rate of 60 rpm. ## 2.4. Formulation and Characterization of MTH Microparticles Different solutions of xanthan gum (XG) were first prepared by the dispersion of a defined mass ($0.5\%$, $0.75\%$, $1\%$ and $1.25\%$) of this polymer in an aqueous solution of NaCl (0.1 M) under vigorous magnetic stirring (Table 1). The obtained solutions were left to rest overnight. Subsequently, the MTH was introduced into each xanthan gum solution and the mixture thus obtained was extruded dropwise using a 10 mL syringe into an AlCl3 solution at the concentration of $5\%$ (w/v) with a rate of 0.5 mL/min. The capsules formed were left in the AlCl3 solution with magnetic stirring at 100 rpm for 1 h. ## 2.5. Particle Size Measurement The particle size of the obtained microspheres was measured by a digital caliper having an accuracy of 0.001 mm. A random choice of microparticles for each analysis was made. A statistical analysis of the mean and the variation coefficient was carried out. ## 2.6. Swelling Study of MTH Microparticles The swelling study was carried out in two dissolution media at pH 1.2 and pH 6.8 as a simulation of the gastric and intestinal medium, respectively. Approximately 50 mg of dry microparticles was carefully introduced into 25 mL of the dissolution medium. At regular time intervals, the particles were removed from the dissolution medium and stripped of excess liquid, before being weighed using a precision analytical balance. The swelling rates (SR) were calculated by the following equation [32]:[1]SR (%)=Wg−WiWi where Wi and Wg are the mass of the microspheres in the initial state (at time t0) and in the swollen state (at time t), respectively. ## 2.7. Encapsulation Efficiency The encapsulation efficiency (EE) values were calculated by an indirect assay of the amount of unencapsulated metformin hydrochloride (MTH) present in the filtrate recovered after filtration of the formulation of the capsules, at a wavelength of 234 nm. They were calculated by the following formula [9]:[2] EE (%)=Cn−C0C0 where C0 is the concentration of initial MTH and Cn is the concentration of unencapsulated MTH. ## 2.8. Calibration Curve Before establishing the calibration curves, a scan in the UV–Vis domain (Perkin Elmer, Villebon-sur-yvette, France) was first carried out, in order to determine the optimum wavelength, λmax, corresponding to the absorption of the active principle metformin hydrochloride (MTH) in different media of dissolution. For the determination of λmax, 0.1 mg/mL solutions of metformin were prepared in buffers at pH 1.2 and pH 6.8. The results showed maximum absorption peaks defined at a wavelength λmax = 234 nm. The calibration curves represent the variation in the absorbance (A) as a function of the solution’s concentration (A = f(c)). For metformin, the optical density measurements for solutions whose concentration varied between 0.006 mg/mL and 0.03 mg/mL were carried out in the two different buffer media, which were gastric buffer pH 1.2 and intestinal buffer pH 6.8 at 234 nm. ## 2.9. In Vitro Release Study The in vitro dissolution study was carried out at two different pH values corresponding to simulated gastric media at pH 1.2 and intestinal media at pH 6.8 using a USP dissolution apparatus II, at the temperature of 37 ± 0.5 °C and the stirring speed of 100 rpm. An accurately weighted sample of microcapsules (100 mg) was introduced into the basket and placed in the dissolution medium. At defined time intervals [33], a 10 mL sample was withdrawn and filtered with a syringe filter (0.45 μm), and then replaced by the same volume with the dissolution medium. The samples obtained were then analyzed by UV–Vis (PerkinElmer Lambda 25) at the wavelength of 234 nm, after suitable dilution. ## 2.10. Statistical Analysis The statistical analysis of all results was achieved via ANOVA using Tukey’s multiple comparison test. A p-value <0.05 was considered statistically significant. All experiments were carried out in triplicate. ## 3.1. Fourier Transform Infrared (FTIR) Analysis FTIR spectroscopy gives information on chemical bonds and molecular structures but also allows the detection of the presence of intermolecular interactions in a mixture of compounds. The FTIR spectrum of xanthan gum (XG) shows characteristic absorption bands at 1015.55 cm−1, 1407.66 cm−1, 1602.34 cm−1 and 1732.13 cm−1, corresponding, respectively, to the elongation of the ether function C–O–C, the C–H bonds of methyl groups and the asymmetric vibrations of COO–, as well as the elongation of CO esters (acetyls). The characteristic peak at 3317.00 cm−1 is attributed to the elongation of the OH hydroxyl groups [34,35,36,37]. The FT-IR spectrum of pure metformin hydrochloride shows two bands of valence vibration of the N–H bond of primary C–N–H lying in the region of 3400 cm−1 and 3100 cm−1. The absorbances at 3367 cm−1, 3290 cm−1 and 3149 cm−1 correspond to the symmetric and asymmetric elongation of primary N–H. An intense band positioned at 1621 cm−1 is assigned to the in-plane deformation of the NH2 bond. The bands at 735 cm−1 and 936 cm−1 of medium to low intensity are assigned to C–H and CN–C bond swings, respectively. Three deformation bands of the CH3 cluster of medium intensity are observed at 1413 cm−1, 1448 cm−1 and 1472 cm−1. Two characteristic bands located at 1038 cm−1 and 1166 cm−1 are attributed to the C–N valence vibration of a secondary amine. Two elongation vibrations of the C–Cl group are noted at 578 and 632 cm−1 [12,16]. The FTIR analysis of xanthan gum, metformin hydrochloride and their mixture confirmed the compatibility of MTH with XG. The appearance of the main characteristic peaks of MTH and XG on the spectrum of the mixture (MTH/XG) was noticed (Figure 1), with the observation of the N–H stretching of the primary amine group of metformin in the range of 3400 to 3100 cm−1, along with the presence of two bands at 1038 cm−1 and 1166 cm−1 [38] ascribed to C–N stretching, as well as the N–H deformation at 1602.34 cm−1 [12,16]. Characteristic absorption bands of XG were also observed at 1015.55 cm−1, 1407.66 cm−1 and 1732.13 cm−1, corresponding, respectively, to the elongation of the ether COC function, the CH bonds of the methyl groups [39] and the asymmetric vibrations of COO–, as well as the elongation of the acetyl group [34,35,36,37]. The FTIR spectra of the formulated microspheres, depicted in Figure 2, also demonstrate the absence of interactions between XG and MTH. ## 3.2. Viscosity Measurement of Xanthan Gum Hydrogels The viscosity of a polymer solution defines its ability to resist flow, which essentially depends on the hydrodynamic volume of the polymer chains. Thus, it varies greatly depending on the average length of the chains and the polymer concentration, but also on the operating conditions (solvent, pH, temperature, ionic strength), which influence both the conformation and the flexibility of the chains [40]. Figure 3 represents the results of viscosity measurement as a function of the concentration of xanthan gum solution. The results obtained show that the apparent viscosity increases as a function of the concentration. Indeed, the more the polymer concentration increases, the more the polysaccharide chains intertwine, thus causing the greater entanglement of the network and consequently causing the rigidity of the hydrogel and its resistance to flow to become more pronounced. These results led us to limit the use of xanthan gum to concentrations ranging from $0.5\%$ to $1.25\%$ (w/v). Indeed, at concentrations below $0.5\%$, the viscosity of the hydrogels obtained is very low, and, beyond $1.25\%$, the xanthan solution becomes highly viscous, and therefore obtaining capsules would be almost impossible. The extrusion of the xanthan gum solution through a syringe at concentrations greater than $1.25\%$ is difficult to achieve and leads to the formation of misshapen microparticles. ## 3.3. Particle Size Measurement The microspheres obtained (Figure 4) all had a homogeneous spherical shape, both in the wet and dry states. Figure 5 illustrates the results of the particle size measurements. These results show that the size of the capsules varies from one formula to another depending on the concentration of xanthan gum. Indeed, the average diameter of the capsules for formula F1 is 110.96 ± 0.032 µm, and it increases with the increasing concentration of xanthan gum to finally reach a value of 208.27 ± 0.02 µm for formula F4, composed of $1.25\%$ xanthan gum. This can be explained by the fact that the viscosity of the initial solution increases with the increase in the concentration of biopolymer; this leads to the formation of much larger capsules during extrusion through the syringe. Similar results were reported by Ramalingam Nethaji et al. [ 26] for metformin microspheres based on different ratios of sodium alginate, guar gum and xanthan gum. Kalpana et al. [ 41] also reported that the higher the concentration of chitosan, the higher the particle size of MTH microparticles. The results obtained by Hariyadi et al. [ 24] demonstrated the same tendency. All of these studies attributed the larger particle size with increasing concentrations of polymer to the greater viscosity of the media, which results in larger dimensions of the droplets formed. ## 3.4. Swelling Study The swelling study based on gravimetry allows one to establish the kinetics of the penetration of the dissolution medium in the capsules. This study establishes its absorption rate and its increase in volume over time. This method consists of measuring the amount of liquid absorbed by the capsules as a function of time until equilibrium. The results of the swelling study are illustrated by Figure 6, where it is clearly shown that the swelling of the polymeric micro-sized spheres increases with time. Additionally, the swelling rate at pH 1.2 (Figure 6a) is much lower than at pH 6.8 (Figure 6b). At an acidic pH (pH slightly below 3.5), the neutralization of the charges leads to the bringing together of the chains, which insolubilize and precipitate. Below pH 3, the risk of chain hydrolysis is very high, which explains the low swelling rates in the pH 1.2 medium [42]. Xanthan gum is an anionic polysaccharide due to the presence of carboxylic functions –COOH in its structure. The slowing down of the swelling kinetics in an acid medium can be explained by the fact that the carboxylic groups are in the undissociated form (–COOH) at pH 1.2, therefore clearly reducing the electrostatic repulsion due to the neutralization of the charges and leading to the rapprochement of the chains. For pH values between 3.5 and 10, strong electrostatic repulsions between carboxylate groups tend to separate the chains from each other, producing viscous and stable solutions [42]. These data are in agreement with the high swelling rates obtained in pH 6.8 media and distilled water. The results of the swelling study are depicted in Figure 6. It is observed that the swelling rate of the particles increases as the concentration of polymer concentration increases at pH 1.2 (Figure 6a) as well as at pH 6.8 (Figure 6b). The formula F1, based on $0.5\%$ of XG, presented the lowest swelling rate against the formula F4, which showed a greater percentage and good swelling rate. These results are in total agreement with those found by Ramalingam Nethaji et al. [ 26], who attributed the increase in swelling index to the greater relative density in higher xanthan and guar gum concentrations and linked this to the presence of pores and cavities on the microspheres. Khonsari et al. [ 43] reported similar results for MTH microparticles based on Carbomer 934p and ethylcellulose and attributed the higher swelling to the fact that the liquid enters the particles through pores and binds to large particles, breaking the hydrogen bond. ## 3.5. Entrapment Efficiency Figure 7 illustrates the results of metformin entrapment efficiency (EE) as a function of XG concentration, where it is clearly shown that the rates of encapsulation of MTH vary between $76.75\%$ and $93.11\%$, depending on the different formulas studied. It is also noted that the rate of encapsulation increases with the increase in the concentration of XG. Indeed, when the xanthan concentration is $0.5\%$ (F1), the encapsulation rate is $76.75\%$; this rate reaches $82.27\%$ when the xanthan gum concentration is $0.75\%$ (F2) and passes to $86.94\%$ in the formula F3 ($1.0\%$ XG), and finally reaches a maximum of $93.11\%$ at XG concentrations of $1.25\%$ (F4). These findings are in accordance with numerous literature references, including Ramalingam Nethaji et al. [ 26], Khonsari et al. [ 43] and Kalpana et al. [ 41]. This may be explained by the fact that the increase in the concentration of the biopolymer leads to the greater crosslinking of the latter by the Al3+ ions contained in the crosslinking solution. This causes the capsules to harden more quickly, preventing the active ingredient from emerging and being expelled to the outside environment. The low concentrations of xanthan gum lead to the formation of capsules whose membrane is much thinner, and therefore the presence of pores on the surface allows the early release of MTH and thus a lower rate of encapsulation. ## 3.6. Calibration Curve The calibration curves were obtained by measuring the absorbance (A) at λ = 234 nm. The results obtained are shown in Figure 8. The calibration curves (A = f(c)) of metformin hydrochloride in simulated gastric and intestinal media were found to be linear in the concentration range of 0.006 mg/mL to 0.03 mg/mL, having a coefficient of regression value R2 = 0.997 at pH 1.2 (Figure 8a) and R2 = 0.996 at pH6.8 (Figure 8b). ## 3.7. In Vitro Release Study The dissolution profiles show (Figure 9) an increase in the levels of MTH released as a function of time according to a non-linear relationship. In the simulated gastric media pH 1.2 (Figure 9a), it is observed that the maximum release rates of MTH are greater in the F1 formulas (0.5 of XG %), with a value of $32.5\%$ in 2.0 h, while this rate decreases when increasing the concentration of xanthan gum. Indeed, the release rates of metformin hydrochloride are, respectively, $20.8\%$, $15.3\%$ and $10.5\%$, for F2 ($0.75\%$ of XG), F3 ($1.0\%$ of XG) and F4 ($1.25\%$ XG). These results indicate that with the increase in the biopolymer concentration, the hydrophilic matrix forming the capsules is more resistant to the release of metformin hydrochloride because of the entanglement of the polysaccharide chains, which becomes more and more important, hence reducing the porosity of the XG core. The results of the in vitro release study of metformin hydrochloride as a function of time from the microcapsules of xanthan gum in the intestinal medium at pH 6.8 are shown in Figure 9b. These dissolution profiles show the gradual and continuous release of metformin over time. Formula F1, containing $0.5\%$ XG, releases $90.2\%$ of MTH in 3.0 h, while this rate decreases when increasing the concentration of xanthan gum, where the release rates of MTH are $90.4\%$ after 4.0 h in formula F2 (0.75 XG%), $99.65\%$ after 5.0 h in formula F3 ($1.0\%$ XG), $95.9\%$ and finally a rate of $90.6\%$ after 6.0 h in formula F4 ($1.25\%$ XG). At pH 6.8, the gel of the membrane is in a more hydrated state, increasing the permeability and the rate of MTH released. The xanthan gum is very hydrophilic; its network will swell and the aluminum ions will gradually diffuse in the external medium, which has the consequence of increasing the dimensions of the meshes of the network, and consequently the diffusion of MTH, until it degrades completely, because the polymer chains become too far from each other and the hydrogel layer disintegrates [44,45]. It is observed that the release rate of MTH is more prolonged in the acidic gastric simulated media compared to the intestinal simulated media. This may be due to the ionization of the xanthan gum, which is lower at an acidic pH due to the presence of –COOH groups onto the backbone of XG, thereby reducing the swelling of the polymeric network. The diffusion of the drug through the less swollen gelatinous polymer mass becomes more difficult, leading to more prolonged release. This result constitutes a promising practical approach for the development of gastroretentive delivery systems for the improvement of MTH bioavailability [29]. ## 4. Conclusions The encapsulation of metformin hydrochloride (MTH) in xanthan gum (XG) micrometric particles has been successfully carried out using the ionotropic gelation method. Initially, the interaction study between XG and MTH was conducted by Fourier transform infrared (FTIR) spectroscopy and, then, the xanthan gum hydrogels at different concentrations were submitted to viscosity testing. The results of the FTIR analysis demonstrated the absence of interactions between MTH and XG, and the viscosity measurements led us to limit the use of xanthan gum to concentrations ranging from $05\%$ to $1.25\%$ (w/v). Afterward, the obtained particles were also characterized for their macroscopic appearance, particle size and encapsulation efficiency, in addition to the swelling and drug release studies. Particle size measurements confirmed the micrometric size of the xanthan particles, which varied between 110.96 ± 0.032 µm and 110.96 ± 0.032 µm. Furthermore, the encapsulation efficiency assessment demonstrated that the encapsulation rates of MTH varied between $76.75\%$ and $93.0\%$, depending on the XG concentration, reaching a maximum in formula F4, which contained $1.25\%$ of XG (F4). Low concentrations of xanthan gum led to the formation of microparticles with a much thinner membrane and therefore allowed the early release of MTH and a lower encapsulation rate. Additionally, this study showed that the swelling rate at pH 1.2 reached a maximum of $29.8\%$, while it was $360\%$ at pH 6.8, at xanthan gum concentrations of $1.25\%$ (F4). The release kinetics are also influenced upon increasing the biopolymer concentration. The kinetics of release of MTH were very low in the pH 1.2 medium, with higher rates for formula F1 ($0.5\%$ of XG), while this rate decreased when increasing the concentration of xanthan gum. At pH 6.8, the kinetics of release were more prolonged in time, with maximum release rates of $90.0\%$ after 6 h found in formula F4 ($1.25\%$ GX). These results indicate that with an increasing biopolymer concentration, the hydrophilic matrix forming the xanthan gum capsules is more resistant to the release of metformin hydrochloride. The promising findings of this study demonstrate that it is worth exploring different operational conditions in future studies to optimize even further the amount of metformin hydrochloride encapsulated in the xanthan gum microparticles. 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--- title: 'Cardiovascular disease and type 2 diabetes in older adults: a combined protocol for an individual participant data analysis for risk prediction and a network meta-analysis of novel anti-diabetic drugs' authors: - Valerie Aponte Ribero - Heba Alwan - Orestis Efthimiou - Nazanin Abolhassani - Douglas C Bauer - Séverine Henrard - Antoine Christiaens - Gérard Waeber - Nicolas Rodondi - Baris Gencer - Cinzia Del Giovane journal: medRxiv year: 2023 pmcid: PMC10055459 doi: 10.1101/2023.03.13.23287105 license: CC BY 4.0 --- # Cardiovascular disease and type 2 diabetes in older adults: a combined protocol for an individual participant data analysis for risk prediction and a network meta-analysis of novel anti-diabetic drugs ## Abstract ### Introduction Older and multimorbid adults with type 2 diabetes (T2D) are at high risk of cardiovascular disease (CVD) and chronic kidney disease (CKD). Estimating risk and preventing CVD is a challenge in this population notably because it is underrepresented in clinical trials. Our study aims to [1] assess if T2D and haemoglobin A1c (HbA1c) are associated with the risk of CVD events and mortality in older adults, [2] develop a risk score for CVD events and mortality for older adults with T2D, [3] evaluate the comparative efficacy and safety of novel antidiabetics. ### Methods and analysis For Aim 1, we will analyse individual participant data on individuals aged ≥65 years from five cohort studies: the Optimising Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older People study; the Cohorte Lausannoise study; the Health, Aging and Body Composition study; the Health and Retirement Study; and the Survey of Health, Ageing and Retirement in Europe. We will fit flexible parametric survival models (FPSM) to assess the association of T2D and HbA1c with CVD events and mortality. For Aim 2, we will use data on individuals aged ≥65 years with T2D from the same cohorts to develop risk prediction models for CVD events and mortality using FPSM. We will assess model performance, perform internal-external cross validation, and derive a point-based risk score. For Aim 3, we will systematically search randomized controlled trials of novel antidiabetics. Network meta-analysis will be used to determine comparative efficacy in terms of CVD, CKD, and retinopathy outcomes, and safety of these drugs. Confidence in results will be judged using the CINeMA tool. ### Ethics and dissemination Aims 1 and 2 were approved by the local ethics committee (Kantonale Ethikkommission Bern); no approval is required for Aim 3. Results will be published in peer-reviewed journals and presented in scientific conferences. ## INTRODUCTION Type 2 diabetes (T2D) is highly prevalent, affecting one in ten adults aged 20-79 years worldwide, and the prevalence rises to almost $25\%$ in individuals aged 75-79 years [1, 2]. Cardiovascular (CVD) and chronic kidney diseases (CKD) are life-threatening complications of T2D. A large proportion of older individuals aged ≥65 years with T2D are multimorbid, where multimorbidity is defined as the presence of two or more chronic medical conditions [3]. Prevention of CVD and CKD is therefore critical in this population [4, 5]. In the following, we address three major open issues in the preventive care of adults with T2D: [1] the association of T2D and hemoglobin A1c (HbA1c) with the risk of CVD events and mortality in older adults; [2] the accurate prediction of CVD events and mortality risk in older adults with T2D; [3] the comparative efficacy and safety of novel antidiabetics to prevent CVD and CKD complications. ## Are T2D and HbA1c associated with the incidence of CVD events and mortality in older adults, and is T2D a coronary risk equivalent in this population? It is uncertain whether T2D is independently predictive of incidence of CVD events and mortality in older and multimorbid adults, as studies have observed a decreasing association with older age [6-9]. A recent systematic review of studies evaluating CVD risk factors in people aged ≥60 years found that in two thirds of the studies, T2D was identified as a predictor of incident CVD [6]. Among studies with a mean age ≥75 years, however, only around one third retained T2D as a predictor for CVD in the final models. It is also disputed whether T2D is a coronary heart disease (CHD) risk equivalent among older adults. Two large contemporary studies found a lower risk of developing CHD in diabetics without prior CHD compared to non-diabetics with previous CHD [10, 11]. Yet, two studies that were conducted exclusively in older adults reported a similar risk of CVD across these two groups, supporting the status of diabetes as a coronary risk equivalent in this population [12, 13]. Intensive glycemic control and diabetes overtreatment can result in harms such as increased risks of severe hypoglycemia and mortality, which may outweigh clinical benefits in older populations [14]. Therefore, recent guidelines recommend less stringent targets of HbA1c, and different targets according to individual’s health status, for older or multimorbid adults [15-18]. However, recommendations on optimal HbA1c targets are based on low-level evidence. Prospective observational studies that assessed the association of Hb1Ac with the risk of CVD and/or mortality among older adults with and without T2D reported conflicting results and were mostly limited to mortality outcomes [19-24]. Further assessment of these associations in a large sample of older and multimorbid adults is needed. A single cohort might have limited power to detect such an association. Analysing multiple cohorts increases power, precision, and might give insight into the heterogeneity of the association across different populations and settings. Analysis of individual participant data (IPD) allows for harmonization of analyses across studies and use of additional information that would not be possible with aggregate data, and is therefore the most powerful method for summarizing evidence from multiple cohorts. ## Can CVD events and mortality be accurately predicted in older adults with T2D? Guidelines for the management of T2D recommend using risk scores to identify adults who are at high risk of CVD events [25]. However, most CVD risk-estimation tools developed for adults with T2D have not focused on older people [26], and recent external validation studies found that existing scores had poor predictive performance in older age groups [27, 28]. A risk score that can accurately predict the risk of CVD events and mortality in older and multimorbid adults is therefore needed. It is particularly relevant to identify, among older adults with T2D, those with a higher risk of CVD events, as they might benefit from medical treatment. ## Which novel anti-diabetic drug has the best benefit-risk profile for prevention of CVD and CKD, and should the medical management differ by age or glycemic control? Novel anti-diabetic drugs have emerged, including sodium-glucose co-transporter-2 (SGLT-2) inhibitors, glucagon-like peptide 1 (GLP-1) receptor agonists (RA), and dipeptidyl peptidase-4 (DPP-4) inhibitors [29]. These drugs have been shown to have cardiovascular and renal benefits and have low risk of hypoglycemia [29]. However, recommendations differ according to the type of novel anti-diabetic drug. For example, the European Society of Cardiology (ESC) and the European Association for the Study of Diabetes (EASD) recommend treating patients with prevalent CVD or at high/very high CVD risk with either empagliflozin or liraglutide to reduce CVD events, whereas it is recommended to treat patients with SGLT2 inhibitors rather than GLP-1 RA to reduce the risk of hospitalization due to heart failure [30, 31]. Moreover, the therapeutic effect of novel antidiabetic drugs may vary between drugs, between younger and older adults [32], and according to baseline HbA1c levels [33]. A network meta-analysis (NMA) can be used to make such comparisons, utilizing all available data [34]. Only a few NMAs have compared the classes of novel anti-diabetic drugs with each other in terms of preventing CVD, CKD or mortality [35-40]. None of the published NMAs have provided results according to baseline levels of HbA1c and only one reported analysis stratified in younger and older adults separately [39]. It is therefore timely to conduct an up-to-date, comprehensive, and high-quality NMA to assess, overall and according to age and baseline HbA1c levels, the benefit and safety profile of novel anti-diabetic drugs in adults with T2D. Our overarching goal is to improve CVD risk prediction among multimorbid older adults with T2D and to compare the benefits and harms of novel anti-diabetic drugs. We endeavour to accomplish this goal with three specific aims: Methods are described separately for each aim in the following section. ## Patient and public involvement Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research. ## Aim 1: Assessing the association of T2D and HbA1c with the risk of CVD events and mortality, and evaluating if T2D is a coronary risk equivalent, in older adults All results will be reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline for cohort studies [41]. ## Study design and participants For Aim 1 of this study, we will use IPD on participants aged ≥65 years from five prospective cohort studies (Table 1): the Optimising Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older People (OPERAM) study [42, 43]; the Cohorte Lausannoise (CoLaus) study [44]; the Health, Aging, and Body Composition (Health ABC) study [45]; the Health and Retirement Study (HRS) [46]; and the Survey of Health, Ageing and Retirement in Europe (SHARE) [47, 48]. Study descriptions are available in the Supplementary Appendix S1. Data from all participants with and without T2D aged ≥65 years at baseline will be included for this analysis. We expect to include data from at least 20’861 participants (Table 1). Final data on the number of patients included in the analyses will be determined once data collection has been completed. For the development of the CVD events and mortality risk prediction model, we will use IPD on the subgroup of participants aged ≥65 years with T2D at baseline from the same sources as in Aim 1 (Table 1). The estimated sample size available for this analysis is 4’202 individuals with T2D. ## Exposures The exposures of interest are (i) presence of T2D at baseline, (ii) HbA1c levels at baseline, and (iii) no T2D and prior CHD versus T2D and no prior CHD at baseline. Definitions are available in the Supplementary Appendix S2. ## Outcomes The primary outcome is a composite of incidence of CVD event or all-cause death. We decided to include all-cause death in the primary outcome rather than CVD-related death only, as mortality is high in older individuals, with various causes leading to death, and focusing on CVD-related death alone might exaggerate a potentially unimportant safety signal. Secondary outcomes will be the individual components of the composite: incidence of a fatal or non-fatal CVD event, and all-cause death. The outcome of CVD event will be defined according to the cohort study included. We will use data from the entire available follow-up of participants. Outcomes will be censored at the time of any CVD event, death, or the last follow-up assessment (whichever comes first). If multiple CVD events have been observed, only the first will be considered. Adjudicated outcomes will be used in the analyses whenever possible. Unadjudicated outcomes will be included when no adjudication was performed in the study. A summary of the adjudication procedures across the studies is provided in the Supplementary Appendix S3. The primary and secondary outcomes will be the same as for Aim 1. We will estimate the 5- and 10-year risks of these outcomes. ## Statistical analysis We will perform an IPD analysis on the five cohort studies using a two-step approach, in which models will first be fit to each study separately and results subsequently meta-analysed [52, 53]. We will use a flexible survival parametric model to analyse the association between each outcome and i) T2D (as a dichotomous variable), and ii) HbA1c levels (as a continuous variable), and estimate the respective hazard ratios and $95\%$ confidence intervals (CIs) [54]. For the secondary outcome on fatal and non-fatal CVD event, we will use a competing-risk model with non-CVD-related deaths as a competing event and estimate sub-hazard ratios [55]. A potential non-linear relationship for continuous variables such as HbA1c will be accounted for by including splines in the model. In order to determine if T2D is a coronary risk equivalent among multimorbid older adults, we will compare the risk of CVD events and death of non-diabetic adults who had a previous CHD event to that of diabetic adults with no prior CHD [12]. We will perform an analysis adjusted for baseline age and sex. We will further adjust our models for the following covariates: body mass index, prior CVD, smoking status, alcohol consumption, systolic blood pressure, hypertension treatment, total cholesterol, high-density lipoprotein cholesterol, and treatment for high cholesterol. Subgroup analyses will be conducted to detect effect modification or significant interaction terms that need to be included in the model. Subgroups will be age (≥ 75 years vs < 75 years), sex (women vs men) and prior CVD (yes vs no). For the assessment of the predictive value of HbA1c, we will also perform stratified analyses by presence of T2D at baseline. In individuals with baseline T2D, we will conduct a subgroup analysis by treatment with hypoglycaemic medications (including insulins, glinides and sulfonylureas; yes vs no) if sufficient data is available, and a sensitivity analysis in which HbA1c will be categorized as <$7.5\%$, ≥$7.5\%$ to <$8.4\%$ and ≥ $8.5\%$ [15]. We will use multiple imputation methods to impute missing data for the analyses [56, 57], For the development of the risk-prediction models, we will fit flexible survival parametric models to the IPD from the cohort studies using a one-step approach [54, 60]. We will assess the heterogeneity of baseline risk and predictor effects as recommended by Debray et al. [ 60]. If baseline risks are not considered to be very different across the cohorts, we will derive a single model with a random intercept using IPD from all cohorts [61]. Otherwise, we will estimate study-specific intercepts and give guidance on choosing the most appropriate intercept for a population [62]. We will use splines to model potential non-linear relationships between continuous variables and the outcome. For the secondary outcome of fatal and non-fatal CVD event, we will use a competing-risk model [55, 63]. We will use multiple imputation to impute missing data for the analyses [56, 57]. ## Power estimation To assess if our sample size is sufficient, we calculated the power to detect an increased risk of the primary outcome (CVD event and overall mortality) in older people with T2D. We varied the risks at 5-year follow-up in older people without T2D between 12 to $25\%$ with a minimum relative risk of 1.2 for individuals with versus without T2D, based on 1) the mortality risk at 1-year follow-up in OPERAM that was ($18\%$) [43], and 2) the mortality risk at 5-years follow-up from the Cardiovascular Health Study ($12\%$) [58], respectively. Given an expected sample size of 20’861 and 4’202 adults without and with T2D, respectively, power is at least $99\%$ (alpha = 0.05; two-sided test). ## Aim 2: Development of risk-prediction models for CVD events and mortality in older adults Results from all analyses will be reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement (TRIPOD) guidelines [59]. ## Predictor selection Using predictors that have a causal relationship with the outcome may improve transportability of clinical prediction models [64]. Therefore, we will map the causal relationship between potential candidate predictors and CVD events and mortality using directed acyclic graphs (DAGs). Potential candidate predictors were identified from previously reported CVD risk scores for individuals with T2D [28], listed in Table 2, and will be collected from all five data cohorts if available. Final predictors to be included in the model will be based on the DAGs, clinical guidance regarding practical usability of the model, and availability across the cohorts. ## Assessing model performance and model validation The model’s performance will be assessed using measures for discrimination, calibration, and overall performance. For discrimination, we will calculate Harrell’s concordance (c) statistic for time-to-event data [65]. Calibration will be assessed using calibration plots, the Greenwood-D’Agostino-Nam test of calibration and expected-to-observed ratio [66, 67]. Overall performance of the model will be evaluated using Nagelkerke’s R2 [66]. We will validate the model internally using bootstrapping to calculate optimism-corrected c-statistics [68]. We will also follow an internal-external cross-validation method [60]. This method allows us to examine differences in the performance of the model across studies and assess the generalizability and applicability of the developed model. Based on these validation exercises, we may make adjustments to the risk-prediction model (e.g, by excluding strongly heterogeneous predictors, or by including interaction effects) [60]. ## Calculation of a novel risk score and implementation of an online risk prediction tool We will develop an online risk-prediction tool of the final model using the Shiny R package [69]. If a model including only linear predictors provides good predictive performance, we will also create a point-based risk scoring system to facilitate clinical use of the risk-prediction model. We will assign integer points to each predictor and predictor level of the final model, according to the system of the Framingham risk score [70]. ## Sample size estimation We calculated the required sample size for the risk-prediction model [71]. Assuming an outcome prevalence of $15\%$, an R2 of $15\%$ in the primary outcome, and 19 predictors (assuming no splines), the minimum sample size required for a new model development is 1’043 participants (corresponding to 8 events per predictor parameters [EPP]). For an R2 of $10\%$, a sample size of 1’614 would be required. For an outcome prevalence of $25\%$ (with an R2 of $15\%$), the sample size is the same with a higher EPP of 14. Therefore, our sample size of 4’202 is deemed adequate. ## Aim 3: Systematic review and NMA to evaluate the comparative efficacy and safety of novel antidiabetic drugs in individuals with T2D This systematic review and NMA is registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD 42022310243). We will adhere to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) extension statement for reporting of systematic reviews incorporating network meta-analyses of healthcare interventions [72]. ## Eligibility criteria Studies that meet the following criteria will be included: [1] randomized controlled trials that included adults 18 years or older with T2D; [2] studies that included at least one of the following novel anti-diabetic drugs: SGLT-2 inhibitors, GLP1-RA, and DPP-4 inhibitors; [3] studies that included a control group of either placebo, no drug, another novel anti-diabetic drug, or older anti-diabetic drugs (metformin, insulin secretagogues, alpha-glucosidase inhibitors, and thiazolidinediones). Studies that only included older anti-diabetic drugs and compared them with each other or placebo will be excluded. ## Primary outcomes The primary outcomes will be [1] incidence of major adverse cardiovascular events (MACE), defined as the composite of cardiovascular mortality, nonfatal myocardial infarction (MI), and nonfatal stroke; [2] renal composite outcome as defined in each trial, such as a composite of adjudication-confirmed end-stage renal disease (ESRD), death due to renal failure, new onset macro-albuminuria, or a sustained decrease of at least $40\%$ in estimated glomerular filtration rate from baseline to less than 60 ml per minute per 1.73 m2 of body-surface area [31, 73]; and [3] diabetic retinopathy as defined by each trial, including vitreous haemorrhage, onset of diabetes-related blindness, or the need for retinal photocoagulation [74]. ## Secondary outcomes Secondary outcomes will include CV mortality; heart failure; myocardial infarction (fatal and non-fatal); coronary and/or peripheral revascularization; all strokes (fatal and non-fatal); all-cause mortality; and HbA1c level measured at follow-up. The following safety outcomes will be assessed: proportion of participants with at least one serious adverse event (e.g., severe hypoglycemia, lower limb amputation, bone fracture, and diabetic ketoacidosis); proportion of participants with a specific serious adverse event; and proportion of participants who withdrew due to adverse events, such as hypoglycemia. ## Information sources The following databases will be searched for eligible studies: MEDLINE, Embase, Cochrane Library, and clinical trial registries (http://clinicaltrials.gov/ and the World Health Organization). We will hand-search the reference lists of all articles, texts, and other reviews on the topic we retrieved, and contact authors and researchers active in the field for more data. We will not apply language and time restrictions to our search. ## Identification and selection of studies Two researchers will independently select studies, extract and collect data in a two-step process. First, we will screen the titles and abstracts. Second, we will read the full texts of all potentially relevant studies and determine the final list of studies to include. When discrepancies arise that cannot be resolved by consensus between the two researchers, a third senior author will be consulted. ## Data extraction Two reviewers will extract data into pre-specified data extraction forms [75]. For each study, we will extract information on study characteristics (e.g., setting, study design, sample size, follow-up), participant characteristics (e.g., age, sex, duration of diabetes, BMI, presence of comorbidities, previous CVDs, baseline HbA1c levels), interventions and controls (e.g., dose, frequency of intervention) and outcomes. For binary outcomes, we will extract the number of patients with the event, the relative risk, odds ratio and hazard ratio and their CIs. We may consider combining hazard ratio and relative risk. For continuous outcomes, when follow-up data are not reported and only change from baseline is available, we will use the latter [76]. We will use published standard deviation (SD), where available. If SD are not available from the publication, SD will be calculated from p values, t-values, CIs or standard errors [77]. ## Risk of bias assessment We will use the Cochrane Collaboration ‘risk of bias’ tool to assess risk of bias (RoB) for each included study [78, 79]. Bias will be evaluated in the following five domains: [1] sequence generation [2] allocation concealment, [3] blinding of participants, personnel, and outcome assessors, [4] incomplete outcome data, and [5] selective outcome reporting. Studies will be classified as having a high, low, or unclear risk of bias overall and for each of the five domains. Two reviewers will independently assess the risk of bias in selected studies. Disagreements will be resolved by discussion and, if needed, by consulting a third senior author. ## Assessing clinical and methodological heterogeneity within and across comparisons of drugs In each pairwise comparison, patient characteristics, drugs and outcome definitions of included studies should be similar [78]. We will produce descriptive statistics for studies and assess their similarity in each comparison. If the assumption of transitivity can be defended, [34] we will compare the distribution of the potential effect modifiers across the different pairwise comparisons [80, 81]. We will assess transitivity for the following possible effect modifiers: dose, frequency or duration of drug, diabetes duration at baseline, sex, high vs low cardiovascular risk trials, baseline levels of HbA1c, and risk of bias. If we find evidence of important differences across comparisons, we will explore the effects of potential effect modifiers with network meta-regression or subgroup analysis. We assume that all treatments are jointly randomizable. ## Data analysis For each outcome, we will conduct pairwise random-effects meta-analyses. Pooled relative effects will be shown along with their $95\%$ CIs. If transitivity is deemed plausible, we will perform a random effects NMA including all studies. For drug ranking, we will use the P-score to provide a hierarchy for each outcome separately and a revised version of the P-score that accounts for multiple outcomes and of the clinical importance value. This will allow us to assess how much harm can be tolerated for a certain benefit [82]. We will estimate the variance of random effects for each pairwise comparison in standard pairwise meta-analyses and assess the magnitude of heterogeneity by visually inspecting the forest plots, by calculating prediction intervals and calculating the I2 statistic [83]. We will assess the magnitude of heterogeneity by comparing the estimated value with empirical distributions, and by examining prediction intervals [84, 85]. Statistical disagreement between direct and indirect effect sizes will be evaluated both using local (node-splitting) and global approaches (design-by-treatment test) [83]. We will assess the existence of small study effects and publication bias with a contour-enhanced funnel plot for each pairwise comparison with more than 10 studies, and by running Egger’s test. For rare outcomes, we will use a NMA model for rare events [86]. We will use STATA version 16 software and R for our analysis (http://methods.cochrane.org/cmi/network-meta-analysis-toolkit) [87, 88]. NMA results will be presented in league tables and forest plots [87]. We will present trade-offs between benefits and harms for each treatment in a two-dimensional plot. We will judge the confidence in the evidence derived from NMA with the CINeMA (http://cinema.ispm.ch/) tool [89, 90]. We will stratify analysis according to low versus high HbA1c levels, by age (more or less than 65 years), and cardiovascular risk trials (high vs low). We will perform sensitivity analysis by excluding a) trials with less than 12 months of follow-up and b) studies with risk of bias. ## ETHICS AND DISSEMINATION Aims 1 and 2 of this study were approved by the local ethics committee (Kantonale Ethikkommission Bern). No ethics approval was required for Aim 3. The results of this study will be published within multiple articles in peer-reviewed journals and presented in meetings. ## Funding This work is supported by the Swiss National Science Foundation grant number (325130_204361 / 1 to Dr. Baris Gencer and Prof. Gérard Waeber). This work is part of the project “OPERAM: Optimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly” supported by the European Union's Horizon 2020 research and innovation programme under the grant agreement No 6342388, and by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0137. The opinions expressed and arguments employed herein are those of the authors and do not necessarily reflect the official views of the EC and the Swiss government. This work uses data from the Health, Aging and Body Composition Study supported by National Institute on Aging (NIA) Contracts N01-AG-6-2101; N01-AG-6-2103; N01-AG-6-2106; NIA grant R01-AG028050, and NINR grant R01-NR012459. This study was funded in part by the Intramural Research Program of the NIH, National Institute on Aging. This work uses data from the CoLaus∣PsyCoLaus Study. The CoLaus study was supported by research grants from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, Switzerland and the Swiss National Science Foundation (grants no: 3200B0–105993, 3200B0-118308, 33CSCO-122661, 33CS30-139468, 33CS30-148401 and 33CS30_177535). This work uses data from SHARE Waves 1, 2, 3, 4, 5, 6, 7, and 8 (DOIs: 10.6103/SHARE.w1.800, 10.6103/SHARE.W2.800, 10.6103/SHARE.w3.800, 10.6103/SHARE.w4.800, 10.6103/SHARE.w5.800, 10.6103/SHARE.w6.800, 10.6103/SHARE.w7.800, 10.6103/SHARE.w8.800, 10.6103/SHARE.w8ca.800), see Börsch-Supan et al. [ 2013] for methodological details. The SHARE data collection has been funded by the European Commission, DG RTD through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N°211909, SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982, DASISH: GA N°283646) and Horizon 2020 (SHARE-DEV3: GA N°676536, SHARE-COHESION: GA N°870628, SERISS: GA N°654221, SSHOC: GA N°823782, SHARE-COVID19: GA N°101015924) and by DG Employment, Social Affairs & Inclusion through VS $\frac{2015}{0195}$, VS $\frac{2016}{0135}$, VS $\frac{2018}{0285}$, VS $\frac{2019}{0332}$, and VS $\frac{2020}{0313.}$ Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C, RAG052527A) and from various national funding sources is gratefully acknowledged (see www.share-project.org). 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--- title: Dietary Intake and Circulating Amino Acid Concentrations in Relation with Bone Metabolism Markers in Children Following Vegetarian and Omnivorous Diets authors: - Jadwiga Ambroszkiewicz - Joanna Gajewska - Joanna Mazur - Katarzyna Kuśmierska - Witold Klemarczyk - Grażyna Rowicka - Małgorzata Strucińska - Magdalena Chełchowska journal: Nutrients year: 2023 pmcid: PMC10055473 doi: 10.3390/nu15061376 license: CC BY 4.0 --- # Dietary Intake and Circulating Amino Acid Concentrations in Relation with Bone Metabolism Markers in Children Following Vegetarian and Omnivorous Diets ## Abstract Scientific studies reported that most vegetarians meet the total protein requirements; however, little is known about their amino acid intakes. We aimed to assess dietary intake and serum amino acid levels in relation to bone metabolism markers in prepubertal children on vegetarian and traditional diets. Data from 51 vegetarian and 25 omnivorous children aged 4–9 years were analyzed. Dietary intake of macro- and micronutrients were assessed using the nutritional program Dieta 5®. Serum amino acid analysis was performed using high-pressure liquid chromatography technique, 25-hydroxyvitamin D and parathormone–electrochemiluminescent immunoassay, and bone metabolism markers, albumin, and prealbumin levels using enzyme-linked immunosorbent assay. Vegetarian children had a significantly lower intake of protein and amino acids with median differences of about 30–$50\%$ compared to omnivores. Concentrations of four amino acids (valine, lysine, leucine, isoleucine) in serum varied significantly by diet groups and were lower by 10–$15\%$ in vegetarians than meat-eaters. Vegetarian children also had lower ($p \leq 0.001$) serum albumin levels compared to omnivores. Among bone markers, they had higher ($p \leq 0.05$) levels of C-terminal telopeptide of collagen type I (CTX-I) than omnivores. Correlation patterns between amino acids and bone metabolism markers differed in the vegetarian and omnivore groups. Out of bone markers, especially osteoprotegerin was positively correlated with several amino acids, such as tryptophan, alanine, aspartate, glutamine, and serine, and ornithine in vegetarians. Vegetarian children consumed apparently sufficient but lower protein and amino acids compared to omnivores. In circulation these differences were less marked than in the diet. Significantly lower amino acid intake and serum levels of valine, lysine, leucine, and isoleucine as well as the observed correlations between serum amino acids and biochemical bone marker levels indicated the relations between diet, protein quality, and bone metabolism. ## 1. Introduction Nutritional habits have been considered an important modifiable factor affecting bone health. The organic matrix of bone consists of collagen and a variety of non-collagenous proteins, so adequate dietary intake of protein, which plays structural, kinetic, catalytic, and signaling roles, seems to be essential for optimal acquisition and maintenance of bone mass [1,2]. Special attention has been focused on a balanced diet and adequate protein intake in childhood and adolescence periods of intensive growth and development. Children require more energy and nutrients per body weight unit compared to adults to obtain normal development of the endocrine, neural, and immunological systems [3,4,5]. They need not only adequate dietary protein intake but also adequate amounts of amino acids (AA), including essential amino acids (EAA), which cannot be synthesized endogenously. Amino acids may be essential precursors for the synthesis of many important molecules and regulate key metabolic pathways and processes that are significant to the health, growth, and homeostasis of organisms [6]. Thus, an optimal balance among amino acids in the diet and in the circulation deserves special attention. Recent data demonstrated that AAs should also be viewed as specific and selective signaling molecules in bone cells [7,8]. Bone mass can be elevated by amino acid-induced increases in calcium absorption efficiency, osteoblast proliferation and bone mineralization, synthesis of type I collagen, circulating levels of insulin-like growth factor-I (IGF-I) and alkaline phosphatase, suppressed osteoclast differentiation and reduced bone resorption [9,10]. Certain AA types, particularly amino acids from the aromatic group (phenylalanine, tryptophan, tyrosine) can stimulate an increase in intracellular calcium and extracellular signal-regulated kinase (ERK) phosphorylation/activation [8,11]. The branched-chain amino acids (BCAA) such as valine, leucine, and isoleucine are the most potent stimulators of muscle protein synthesis, which is critical also for maintaining adequate bone strength and density [12,13]. Over the last years, vegetarian diets which exclude the consumption of meat, have been gaining popularity in industrialized countries, including families with young children [14]. Additionally, a growing number of children have been born to vegetarian mothers [15]. Although it is accepted that appropriately planned vegetarian diets are nutritionally adequate for individuals during all stages of the life cycle [16,17], concerns exist about their potential insufficiency in regard to some nutrients, especially in vegans. Several observational studies provided evidence for health benefits from a plant-based diet, including reduced risk of chronic diseases; however, the majority of research has been conducted on adult’ populations and little is known about the possible health consequences of vegetarianism in infants and children [18,19,20,21,22,23,24,25]. Vegetarian diets may be connected with lesser bioavailability and insufficiency of nutrients such as protein (amino acid composition), minerals (calcium, iron, zinc), and vitamins (vitamin B12, vitamin D), which play a critical role in maintaining muscle mass and bone health [1,26]. Inadequate nutrient composition, especially at a younger age, could contribute to bone remodeling imbalance, failure to achieve optimal bone mass, and higher risk of osteoporosis in later life. There are reports suggesting that long-term vegetarian diets, especially a restricted vegan diet, are associated with lower bone mineral density (BMD) and higher risk of bone fractures compared to those on an omnivorous diet [27,28,29]. Apart from measuring BMD, biochemical bone metabolism markers such as osteocalcin (OC), cross-linking telopeptide of collagen type I (CTX-I), osteoprotegerin (OPG), IGF-I, parathormone (PTH), and 25-hydroxyvitamin D are clinically useful in the assessment of bone turnover [30,31]. To assess the balance between the processes of bone formation and resorption, the OC/CTX-I ratio can be use. Subjects following vegetarian diets seem to have increased parathormone levels and bone resorption markers [32]. Whether potential differences in amino acid profile caused by consuming vegetarian diets can affect bone status in children and adolescents has not yet been investigated. Therefore, the aims of the present study are to (i) compare dietary intake and serum concentrations of amino acids in prepubertal children following vegetarian and omnivorous diets, (ii) investigate serum levels of bone metabolism markers in both studied groups, and (iii) assess relationships within serum amino acids and bone marker concentrations in the examined groups of children. ## 2.1. Subjects In this cross-sectional study, we recruited 76 prepubertal children (age range 4–9 years) between June 2020 and December 2021 from a group of consecutive patients attending the Department of Nutrition at the Institute of Mother and Child in Warsaw (Poland). All studied children were Caucasians. Among them, 51 children were following vegetarian diets, free of meat products, with $82.7\%$ of them described as lacto-ovo-vegetarians (include dairy and eggs); $7.7\%$ of them lacto-vegetarians (consume dairy but not eggs) and $9.6\%$ with more restrictive dietary pattern such as vegans (exclude both dairy and eggs). The examined children remained under regular medical and nutritionist care. We recruited the maximum possible number of prepubertal children consuming a vegetarian diet since birth. The vegan children were breastfed by vegan mothers. The inclusion criteria regarding vegetarians were: being on a diet excluding meat from birth, having no signs of puberty, generally healthy (without development and nutrition disorders), normal-weight with BMI z-score between −1 and +1 [33]. The exclusion criteria were: not in the prepubertal period, history of low birth weight, gastrointestinal diseases accompanied by malabsorption, history of chronic infection, and drug consumption, except for standard vitamin D supplementation. The control group included 25 healthy children following a traditional omnivorous diet (including meat, poultry, fish, dairy, and eggs). Each participant underwent a basic medical check-up and anthropometric examination using calibrated instruments. Height was measured with a stadiometer and recorded with a precision of 0.5 cm. Weight was assessed unclothed with a calibrated scale to the nearest 0.1 kg. Body mass index (BMI) was calculated as body weight (kg) divided by height squared (m2). Based on the data from the questionnaire completed by the parents, the studied children (vegetarians and omnivores) had similar physical activity (PA) and accumulated about 60−90 min/day of moderate-to-vigorous physical activity (MVPA) and approximately 30 min per day of vigorous physical activity (VPA). The protocol of this study was in accordance with the Helsinki Declaration of Principles and approved by the Ethics Committee of the Institute of Mother and Child (decision number $\frac{6}{2020}$, date of approval 6 April 2020). The parents of the participants were comprehensively informed about the study details and signed the informed consent form. ## 2.2. Dietary Assessment Assessment of dietary intakes in the studied children was done using diet record methodology and had been described in more detail previously [34]. After being advised by a nutritionist, the parents of the studied children prepared a food diary for their children. During the visit, the nutritionist asked for detailed information about the recorded foods and drinks, such as portion sizes and preparation methods using a photo album of products and dishes [35]. When necessary, the food diary was corrected in the presence of the child and parent. The data of the three-day dietary records (two weekdays and one weekend day) were selected and entered into the nutritional software program Dieta 5® (National Food and Nutrition Institute, Warsaw, Poland) [36]. The obtained data were compared with the current age- and sex-specific dietary recommendations for Polish children [37]. The average daily dietary energy, protein, fiber, calcium, phosphorus, magnesium, vitamin D, and amino acid intakes were assessed in 61 ($80\%$) of the studied children: 41 ($80.4\%$) of vegetarians and 20 ($80\%$) of omnivores. Due to the assay method used, data of the intake of 18 amino acids (expressed in mg per day) were estimated. ## 2.3. Biochemical Analyses For biochemical measurements, venous blood samples (3.5 mL) were collected in the morning hours after an overnight fast to avoid short-term dietary influence and diurnal variations. The samples were centrifuged at 2500× g for 10 min at 4 °C and serum was obtained. Serum samples were aliquoted at 100 or 200 μL portions into Eppendorf tubes and stored at −80 °C no longer than two months until assay. Biochemical parameters were assessed in all the studied children, except for PTH concentration, which was determined in 61 ($80.3\%$) subjects. Serum concentrations of 25-hydroxyvitamin D and PTH were determined by electrochemiluminescent immunoassay (ECLIA) using kits from DiaSorin Inc. (Stillwater, OK, USA) on a Liaison analyzer with a precision of the coefficient of variation (CV) of 6.0–$9.8\%$. Serum amino acid analysis was performed by high-pressure liquid chromatography reversed-phase separation and fluorescence detection (Ex-340 nm and Em-460 nm) using the HPLC-RF-10AXL system (Shimadzu, Kyoto, Japan). Primary amino acids were derivatized with o-phthalaldehyde 3-mercaptopropionic acid (OPA) and then with 9-fluorenylmethyl chloroformate (FMOC). Chromatographic separation was achieved by gradient elution (mobile phase 1 with 40 mmol/L phosphate buffer, pH 7.8) and the organic mobile phase 2 with $45\%$ acetonitrile + $45\%$ methanol + $10\%$ water) on column C18 (Gemini 5 µm C18/ODS, 11OA; Phenomenex 4.6 × 150 mm, Torrance, CA, USA). Serum levels of albumin and prealbumin were assayed using ELISA kits from Bioassay Technology Laboratory (Jiaxing, China), in which intra-assay CVs were below $8\%$ and inter-assay CVs were below $10\%$. The limit of detection was 0.52 mg/mL for albumin and 2.51 µg/mL for prealbumin. Concentrations of bone metabolism markers (OC, CTX-I, OPG, IGF-I) were assessed using enzyme-linked immunosorbent assay (ELISA), according to the manufacturer’s instructions. Serum levels of OC and CTX-I were detected using N-MID Osteocalcin and Serum CrossLaps (CTX-I) kits from Immunodiagnostic Systems (Boldon, UK). The limit of detection was 0.5 ng/mL for OC, and 0.020 ng/mL for CTX-I. The intra- and interassay CVs were: 1.3–$2.2\%$ and 2.7–$5.1\%$ for OC and 1.7–$3.0\%$ and 2.6–$10.9\%$ for CTX-I, respectively. OPG concentrations were assessed using a kit from DRG Instruments GmbH (Marburg, Germany) with a limit of detection of 0.03 pmol/L, intra-assay CV between 2.5 and $4.9\%$, and inter-assay CV between 1.7 and $9.0\%$. Serum IGF-I was determined using a kit from Mediagnost (Reutlingen, Germany), where the analytical sensitivity was 0.091 ng/mL, intra-assay CV was between 5.08 and $6.65\%$, and inter-assay CV was between 5.53 and $6.56\%$. ## 2.4. Statistical Analyses The obtained data were statistically analyzed using IBM Statistics for Windows version 27.0 (Amonk, NY, USA, IBM Corp.). All variables were tested for normality using the Kolmogorov–Smirnov test. Data are presented as frequency (percentage), means ± standard deviation (SD) for normally distributed data or medians and interquartile ranges (IQR) for skewed distribution. The ratio of OC to CTX concentrations was calculated. Groups differences were assessed using Student’s t-test or the Mann–Whitney U test, as appropriate. Univariate correlation analyses were performed separately in the vegetarian and omnivore groups using the Spearman rank correlation (rho) coefficient. For bone markers showing a significant correlation with more amino acids in the vegetarian group, a multiple linear regression model was estimated using a stepwise method of variable selection. Regression parameter values, R-sq coefficient, and selected covariance statistics for multicollinearity assessment are shown. A tolerance greater than 0.1 or a variance inflation factor (VIF) less than 5 is considered to indicate no covariance. A p-value of less than 0.05 was considered to be statistically significant. ## 3. Results All participants were healthy, normal weight, prepubertal children. Vegetarians and omnivores were comparable in terms of age, sex, and body mass index (Table 1). Analyzing the children’s diets, we observed that both studied groups had similar total energy intake. Vegetarians had a significantly lower percentage of energy from protein ($p \leq 0.001$), a higher percentage of energy from carbohydrates ($p \leq 0.05$), and a similar percentage of energy from fat compared with omnivores. Moreover, dietary intake of protein (in grams per day) was significantly lower ($p \leq 0.001$) in children on a vegetarian diet compared to meat-eaters. As expected, in the vegetarian diet about $63\%$ of the protein comes from plant origin and $37\%$ from animal sources, while in omnivores it was $34\%$ and $66\%$, respectively. In addition, vegetarians had significantly higher ($p \leq 0.01$) intake of fiber and lower ($p \leq 0.05$) intake of calcium and vitamin D. Dietary intakes of phosphorus and magnesium were comparable between the groups. As shown in Table 2, dietary intake of all amino acids, including essential AAs was significantly lower ($p \leq 0.001$) in vegetarian children than in omnivores, except for cysteine intake. The largest percentage differences between the diet groups were for intakes of lysine and methionine, which were about $48\%$ and $43\%$ less in vegetarians than in omnivores. The amino acid concentrations in the serum of children on vegetarian and omnivorous diets are presented in Table 3. Serum levels of four of the 24 amino acids varied between the diet groups, being significantly lower ($p \leq 0.05$) in vegetarians than in omnivores. The largest median differences were for essential AAs: isoleucine (about −$18\%$), lysine (about −$16\%$), leucine (about −$14\%$), and valine (about −$10\%$), yet still within the normal ranges according to Blau et al. [ 38]. Moreover, serum aspartate, glutamate, ornithine, and taurine tended to be lower (by about 8–$12\%$) in children on a vegetarian diet than in omnivores; however, these differences were not significant. Vegetarian children had lower ($p \leq 0.001$) serum albumin levels compared to omnivores and comparable prealbumin concentrations (Table 4). Among the bone metabolism markers, serum concentration of bone resorption marker (CTX-I) was significantly higher ($p \leq 0.05$) in vegetarians than omnivores. Moreover, serum levels of IGF-I and PTH tended to be higher in these children. Other bone marker concentrations, such as osteocalcin, osteoprotegerin, and 25-hydroxyvitamin D were comparable between the diet groups. The similar serum vitamin D levels in our studied groups of children can be explained by the fact that the majority of participants (vegetarians and omnivores) took vitamin D supplements (an average dose of 500 ± 200 IU/day). Assessing correlations between albumin/prealbumin and bone markers levels, we found that serum albumin concentration negatively correlated with OPG (r = −0.308, $$p \leq 0.028$$) and prealbumin with PTH levels (r = −0.533, $$p \leq 0.006$$) in vegetarian children. In the omnivorous group, serum prealbumin was correlated with PTH concentration (r = −0.886, $$p \leq 0.019$$). Additionally, albumin/prealbumin levels were associated with serum amino acids (data not shown). In vegetarians, albumin negatively correlated with methionine (r = −0.331, $$p \leq 0.017$$), tryptophan (r = −0.371, $$p \leq 0.007$$), valine (r = −0.330, $$p \leq 0.018$$), tyrosine (r = −0.438, $$p \leq 0.001$$), alanine (r = −0.348, $$p \leq 0.012$$), and proline (r = −0.310, $$p \leq 0.027$$). In omnivores, albumin level positively correlated with phenylalanine ($r = 0.457$, $$p \leq 0.033$$) and lysine ($r = 0.463$, $$p \leq 0.030$$). Prealbumin levels did not correlate with amino acids in omnivores, while in vegetarians they inversely correlated with methionine (r = −0.291, $$p \leq 0.038$$), leucine (r = −0.297, $$p \leq 0.036$$), and glutamine (r = −0.289, $$p \leq 0.040$$). The pattern of correlations between serum amino acids and bone metabolism marker concentrations was different in vegetarians and omnivores (Figure 1). In vegetarians, we observed that osteocalcin concentrations were significantly negatively correlated with lysine (r = −0,311, $$p \leq 0.026$$) and glutamate (r = −0.325, $$p \leq 0.020$$), however, positively with glutamine ($r = 0.327$, $$p \leq 0.019$$) levels. CTX-I levels were inversely associated with lysine (r = −0.364, $$p \leq 0.009$$) and ornithine (r = −0.369, $$p \leq 0.004$$) while directly with tryptophan ($r = 0.307$, $$p \leq 0.029$$) concentrations. We observed positive correlations between osteoproegerin and tryptophan ($r = 0.373$, $$p \leq 0.007$$), alanine ($r = 0.294$, $$p \leq 0.042$$), aspartate ($r = 0.314$, $$p \leq 0.025$$), glutamine ($r = 0.287$, $$p \leq 0.041$$), serine ($r = 0.278$, $$p \leq 0.048$$), and negative with ornithine (r = −0.280, $$p \leq 0.048$$) concentrations. Among the other bone markers, only 25-hydroxyvitamin D concentrations were positively correlated with phenylalanine ($r = 0.299$, $$p \leq 0.014$$) levels in this group of children. In omnivores, serum osteocalcin was directly correlated with tryptophan ($r = 0.398$, $$p \leq 0.048$$) and indirectly with glycine (r = −0.425, $$p \leq 0.034$$) levels, while CTX-I was negatively correlated with isoleucine (r = −0.656, $$p \leq 0.001$$), leucine (r = −0.543, $$p \leq 0.006$$), asparagine (r = −0.418, $$p \leq 0.038$$), and proline (r = −0.412, $$p \leq 0.041$$) concentrations. OPG was negatively correlated with ornithine (r = −0.424, $$p \leq 0.034$$) and taurine (r = −0.405, $$p \leq 0.045$$), while IGF-I with cysteine (r = −0.393, $$p \leq 0.050$$). The levels of 25-hydroxyvitamin D were directly correlated with several amino acids such as phenylalanine ($r = 0.450$, $$p \leq 0.024$$), threonine ($r = 0.579$, $$p \leq 0.002$$), leucine ($r = 0.374$, $$p \leq 0.050$$), lysine ($r = 0.473$, $$p \leq 0.017$$), arginine ($r = 0.452$, $$p \leq 0.023$$), asparagine ($r = 0.476$, $$p \leq 0.016$$), glutamate ($r = 0.439$, $$p \leq 0.028$$), serine ($r = 0.664$, $$p \leq 0.001$$), and tyrosine ($r = 0.542$, $$p \leq 0.005$$). However, PTH concentrations were negatively related with methionine (r = −0.891, $$p \leq 0.001$$), phenylalanine (r = −0.901, $$p \leq 0.001$$), isoleucine (r = −0.821, $$p \leq 0.023$$), leucine (r = −0.964, $$p \leq 0.001$$), lysine (r = −0.786, $$p \leq 0.036$$), asparagine (r = −0.919, $$p \leq 0.003$$), serine (r = −0.786, $$p \leq 0.036$$), tyrosine (r = −0.857, $$p \leq 0.014$$), and taurine (r = −0.750, $$p \leq 0.043$$) levels. Table 5 shows the results of multiple linear regression estimation with osteoprotegerin as the dependent variable. Six significantly correlating amino acids (tryptophan, alanine, aspartate, glutamine, serine, ornithine) and albumin were included as independent variables. Three factors entered the final model, in order: alanine, ornithine and aspartate. The model explains $35.1\%$ of the variation in osteoprotegerin in the vegetarian group, and the tolerance and VIF indices are highly favorable. ## 4. Discussion The adequacy of dietary protein intake from vegetarian diets has been discussed in the literature. Because the overall protein quality of a vegetarian diet is estimated to be about 80–$90\%$ compared with the meat-eater diet (mainly due to the lower digestibility of plant protein), it has been suggested that the dietary requirements of vegetarians should be increased by about $20\%$ [26,39]. Most studies were conducted on adult vegetarians [40,41,42,43]. Despite lower average protein intake in the plant-based dietary patterns, researchers reported that vegetarians had protein intakes still within the recommended range [44,45]. The present study showed that children following a vegetarian diet had lower but sufficient dietary intake of protein. They also had lower dietary intake of amino acids with the highest median differences in essential AAs. Despite this, the serum levels of amino acids were within the reference values, but significantly lower in the case of lysine, leucine, isoleucine, and valine than in omnivores. Adequate protein intake during development is critical to ensure optimal peak bone mass in later life. Insufficient protein intake leads to growth retardation during early life stages and poor bone quality in adults. The effect of protein on bone metabolism is related to the capacity to provide essential AAs for the synthesis of the bone collagen matrix. Protein restriction is also associated with a decrease in the synthesis of IGF-I, an anabolic factor for bone and muscle. In an animal model, Rouy et al. [ 46] described lower bone mineral density and reduced bone turnover markers in animals fed a soy-based protein-restricted diet compared to a diet containing normal amount of protein as well as with a casein-based protein-restricted diet. This can be partly related to a difference in AA profile, as casein is richer than soy in methionine, proline, serine, threonine, glutamine, valine, tyrosine, isoleucine, and leucine with a possible effect on bone protein turnover. Thus, protein quality appears critical to ensure adequate growth and optimal peak bone mass. Moreover, animals subjected to a methionine restricted diet had significantly lower BMD and bone mineral content (BMC), possibly due to decreased IGF-I levels leading to a disrupted IGF-I/IGFBP axis [47]. Only a few studies analyzed the amino acid profile in vegetarians, mostly in adults [48,49,50]. The authors found that dietary intakes of AAs were the lowest in vegans followed by vegetarians, then fish-eaters, and the highest in meat-eaters. The largest differences were found for lysine and methionine intakes and plasma levels of essential amino acids between diet groups [48,49]. In vegan children, Hovinen et al. [ 51] reported lower protein intake and plasma concentrations of leucine, isoleucine, lysine, phenylalanine, and valine. Generally, the dietary profile of AA was less optimal in plant foods than in animal foods. Plant-based proteins contain lower amounts of certain essential amino acids (e.g., leucine, lysine, methionine) and higher amounts of arginine, glutamine, and glycine compared with animal-based proteins [1,52]. Existing data demonstrated a direct effect of AAs on bone metabolism through complex cellular pathways. Fini et al. [ 53] conducted a study on primary osteoblast cultures from normal and osteopenic rats. They reported the effects of lysine and arginine on the stimulation of intestinal calcium absorption and the cross-linking process of bone collagen, which is essential for bone matrix formation. Arginine is also involved in the synthesis of IGF-I and proline, which acts as substrate for collagen production. Treatment with lysine and arginine resulted in an increased level of bone formation markers such as procollagen I C-terminal propeptide (PICP) and alkaline phosphatase. In turn, the combination of phenylalanine, tyrosine, and tryptophan treatment may lead to reduced osteoclast differentiation, and result in a lower rate of resorption [54]. Serum albumin and prealbumin (also called transthyretin) are widely used biomarkers in assessing nutritional status [55]. Caso et al. [ 56] observed a reduced rate of albumin synthesis in adult vegetarians compared with subjects consuming a traditional diet, suggesting that different food sources might have a different effect on albumin synthesis. Albumin synthesis might be responsive to a reduction in amino acid availability, a consequence of the lower digestibility, and AA score as well as higher fiber content of vegetarian diets. In our study, vegetarian children compared with omnivores, had significantly lower but still in the normal range serum albumin levels. Additionally, its level was significantly associated with several serum amino acids, such as methionine, tryptophan, valine, tyrosine, alanine, and proline. Lower levels of serum albumin in our examined vegetarian children compared to omnivores might be related to the different proportions of animal/plant protein in the diets [57]. Individuals following a vegetarian diet usually had lower dietary intake with poorer bioavailability not only of protein and essential amino acids but also minerals (e.g., calcium) and vitamins (vitamin D, vitamin B12), which are particularly important for optimal bone health [58]. Research on bone status in subjects on vegetarian diets remains limited. Some reports suggested that vegetarians, especially vegans have increased parathormone levels, higher bone turnover rate, resulting in decreased bone mineral density compared with omnivores [27,28,32,59,60]. Moreover, the results from studies on the association between hypoalbuminemia with osteoporosis are conflicting [61]. Albumin and prealbumin are not only markers of nutrition but are also associated with inflammation. Numerous proinflammatory cytokines have been implicated in the regulation of osteoblasts and osteoclasts playing a potential role in bone remodeling [55,62]. The interpretation of amino acid intakes and serum concentrations in regard to their impact on bone metabolism is difficult because of the limited research in this field and a relative lack of reference values of these parameters for children. In the present study, vegetarian children had higher serum levels of CTX-I and comparable concentrations of osteocalcin and the OC/CTX-I ratio, suggesting no significant difference in bone turnover rate. Analyzing the correlations, we observed that several amino acids were associated with bone metabolism markers, but their patterns were different in vegetarian and omnivorous children. An interesting finding is that in vegetarian children we noted positive correlations between OPG with several amino acids. A multiple linear regression analysis confirmed that osteoprotegerin is significantly associated with alanine, ornithine, and aspartate. OPG plays a role in the regulation of bone formation/resorption processes through the RANKL/RANK/OPG pathway. It acts as a decoy receptor, which after binding to the receptor activator of nuclear factor kappa B (RANK), blocks the possibility of binding its ligand (RANKL) and consequently decreases bone resorption [63]. An elevated ratio of RANKL/OPG is associated with an imbalance in osteoclastic/osteoblastic activity, leading to increased bone resorption and decreased bone mass. Recent data demonstrated that amino acids should be viewed as specific and selective signaling molecules in bone cells [59]. Specific AAs may preferentially bind to a calcium-sensing receptor, modulate its activation by calcium ions, and thus potentially impact bone turnover. Amino acids are also required for bone marrow stromal cells to promote differentiation into osteoblasts, synthesis of type I collagen, circulating levels of IGF-I and alkaline phosphatase [9,10]. Certain AA types, particularly amino acids from the aromatic group, can stimulate increases in intracellular calcium and ERK phosphorylation/activation and may impact an early stage of osteoclast differentiation by suppressing the attachment of osteoclasts and the resorption process. The branched-chain AAs, are the most potent stimulators of muscle protein synthesis, which is critical for the maintenance of adequate muscle mass, bone strength, and bone density. The dietary intakes as well as circulating levels of these amino acids were indeed lower in the vegetarian children we examined compared with omnivores. Leucine has a direct effect on the regulation of protein turnover through the mTOR (mammalian target of rapamycin) kinase signaling pathway, valine and isoleucine maintain a balance among BCAAs, while lysine acts among others via the regulation of nitric oxide synthesis [6,64]. It seems that different subclasses of amino acids have varying effects in the body, thus, paying attention to amino acid composition may be relevant to the prevention of bone abnormalities. The skeleton is a dynamic and metabolically active tissue, and is exquisitely sensitive to its microenvironment [28,57,60]. For example, glutamate and aspartate might be higher (20–70-fold) in the bone microenvironment than in the circulation. Long-term follow-up studies are needed to clarify the consequences of lower protein, essential amino acids, calcium, vitamin D, and albumin in children on a vegetarian diet on bone health. We realize that our study has several limitations that may impact our interpretation. First, the cross-sectional design does not allow to establish causal relationships. However, this is the first observational study performing the evaluation of dietary and serum amino acid levels in relation with bone metabolism markers in prepubertal children consuming vegetarian and omnivorous diets. Second, our study was limited to prepubertal children who had their nutritional and biochemical data measured at the same time. Thus, our sample size was relatively small, especially for the group of control children. The paper only gives an example of multivariate analysis, as the small sample size is a limitation here. However, the sample was homogenous and unique. All participants were of Polish origin comparable in terms of age, sex, and development stage. Large groups of healthy children are rarely studied because of ethical considerations regarding blood sampling in healthy children. Third, we used three-day (two weekdays and one weekend day) food records to assess dietary intake of macro- and micronutrients. Although not ideal, this method is the most practical and commonly used. In our study, about $20\%$ of the studied children had missing data in the dietary assessment. Fourth, we did not measure bone mineral density and our results regarding bone markers were based on single measurements. However, we assessed bone formation as well as bone resorption markers and could calculate the OC/CTX-I ratio, assessing the rate of bone turnover. In a future study, we are planning to assess the novel bone markers such as RANKL, sclerostin, Dickkopf-related protein-1 (Dkk-1). There are several advantages to using a wide panel of bone markers involved in the regulation of bone turnover through signaling pathways. Since significant correlations between AAs and OPG concentrations were observed in the examined vegetarians, it is worth evaluating the regulatory RANK/RANKL/OPG system. Moreover, the Wnt-beta-catenin signaling pathway, with sclerostin and Dickkopf-related protein-1 (Dkk-1), is crucial in bone metabolism. To summarize, we observed that children on vegetarian diets consumed apparently sufficient but lower protein and amino acids than omnivores. However, in circulation, the amino acid concentrations were less marked than in the diet. Despite vegetarians having higher levels of bone resorption marker CTX-I, they had a similar concentration of osteocalcin and the OC/CTX-I ratio compared to omnivores. The observed significant associations between serum levels of bone metabolism markers and amino acid concentrations confirm an existing link between protein quality and bone turnover. Particularly, significant relationships of osteoprotegerin with alanine, ornithine, and aspartate might suggest an impact of diet on the bone regulatory pathway. 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--- title: 'Long-Term Adverse Effects of Mild COVID-19 Disease on Arterial Stiffness, and Systemic and Central Hemodynamics: A Pre-Post Study' authors: - Mario Podrug - Pjero Koren - Edita Dražić Maras - Josip Podrug - Viktor Čulić - Maria Perissiou - Rosa Maria Bruno - Ivana Mudnić - Mladen Boban - Ana Jerončić journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10055477 doi: 10.3390/jcm12062123 license: CC BY 4.0 --- # Long-Term Adverse Effects of Mild COVID-19 Disease on Arterial Stiffness, and Systemic and Central Hemodynamics: A Pre-Post Study ## Abstract COVID-19-associated vascular disease complications are primarily associated with endothelial dysfunction; however, the consequences of disease on vascular structure and function, particularly in the long term (>7 weeks post-infection), remain unexplored. Individual pre- and post-infection changes in arterial stiffness as well as central and systemic hemodynamic parameters were measured in patients diagnosed with mild COVID-19. As part of in-laboratory observational studies, baseline measurements were taken up to two years before, whereas the post-infection measurements were made 2–3 months after the onset of COVID-19. We used the same measurement protocol throughout the study as well as linear and mixed-effects regression models to analyze the data. Patients ($$n = 32$$) were predominantly healthy and young (mean age ± SD: 36.6 ± 12.6). We found that various parameters of arterial stiffness and central hemodynamics—cfPWV, AIx@HR75, and cDBP as well as DBP and MAP—responded to a mild COVID-19 disease. The magnitude of these responses was dependent on the time since the onset of COVID-19 as well as age (pregression_models ≤ 0.013). In fact, mixed-effects models predicted a clinically significant progression of vascular impairment within the period of 2–3 months following infection (change in cfPWV by +1.4 m/s, +$15\%$ in AIx@HR75, approximately +8 mmHg in DBP, cDBP, and MAP). The results point toward the existence of a widespread and long-lasting pathological process in the vasculature following mild COVID-19 disease, with heterogeneous individual responses, some of which may be triggered by an autoimmune response to COVID-19. ## 1. Introduction In December 2019, the first official case of coronavirus disease (COVID-19) was detected in the Chinese city of Wuhan. Since its first breakout, the virus has swiftly spread over the world, and the World Health Organization (WHO) declared a pandemic in March 2020. At the time of writing, there had been 660,378,145 confirmed cases of COVID-19, with 6,691,495 fatalities globally [1]. While COVID-19 was initially thought of as an acute respiratory illness, it is now recognized as a complex multisystemic disease with extensive and deleterious cardiovascular involvement [1,2]. In addition to direct consequences and complications due to acute COVID-19 infection, a recent study showed that 12 months after the onset of COVID-19 infection, up to $25\%$ of patients who were otherwise healthy and free of underlying diseases exhibited the long COVID-19 syndrome [3]. Subclinical myocardial and vascular dysfunction have been linked to worse outcomes and an increased risk of death in patients with COVID-19 disease [4]. Even in patients with mild COVID-19 disease severity, the infection has been linked to impaired subclinical markers of cardiovascular and endothelial function [5]. It is presumed that COVID-19-associated vascular disease complications may be precipitated by direct endothelium damage [6] or immune-mediated vascular damage [7,8]. However, it is unknown to what extent structural alterations of the vascular wall occur in addition to endothelial damage. Even fewer data exist regarding the long-term effects of COVID-19 infection on vascular structure and function. Current fragmented evidence suggests that COVID-19 disease reduces systemic vascular function and increases arterial stiffness [9,10]. Arterial stiffness is a vascular aging phenomenon that refers to a loss of arterial compliance or changes in artery wall characteristics [11]. Arterial stiffness worsens with age and exposure to risk factors that hasten the stiffening process [12,13]. Various measures of arterial stiffness and central hemodynamics can reveal a decline in arterial elasticity brought on by structural wall changes in the arterial system. The most validated and direct measure of arterial stiffness is the carotid–femoral pulse wave velocity (cfPWV) (Townsend, Wilkinson et al., 2015). In addition, augmentation indices are indirect measures of arterial stiffness which are believed to capture the negative impact of systolic wave reflection on cardiac workload [14]. Finally, the central blood pressures refer to the pressure in the ascending aorta. These are the pressures that the target organs are subjected to, and they are lower than brachial cuff pressures due to arterial pressure amplification [15]. We hypothesized that even mild cases of COVID-19 disease could have long-term detrimental effects on arterial structure and function. To investigate this, we examined individual pre- and post-infection changes in arterial stiffness as well as systemic and central hemodynamic parameters in patients diagnosed with mild COVID-19. Baseline measurements were taken up to two years before a participant became infected, and post-infection measurements were taken two to three months after the onset of the disease. ## 2.1. Study Design This is a pre–post study design in which measures of arterial stiffness and central hemodynamic were recorded in a group of participants before and after the COVID-19 infection. All the recordings were made between October 2019 and April 2022 in the Laboratory for Vascular Aging at the University of Split School of Medicine. To assess arterial stiffness and central hemodynamic parameters prior to COVID-19 infection, we utilized the stored recordings of enrolled participants from in-laboratory observational studies that applied the same measurement protocol as was used for the post-COVID-19 measurements. The post-COVID-19 measurement was performed between 8 and 12 weeks after the COVID-19 infection had ended, as evidenced by the absence of symptoms. This timeframe corresponded to 50 ± 2 to 90 ± 2 days after the onset of the first symptoms. The maximum amount of time between pre- and post-COVID measurements was set at 24 months. ## 2.2. Participants The participants who had their arterial stiffness and central hemodynamic outcomes measured in our laboratory prior to infection with COVID-19 and who were afterwards infected with the virus were considered eligible for inclusion in the study. For all of those invited to the study, COVID-19 diagnosis was made by real-time Polymerase Chain Reaction test. During their first visit to the laboratory (pre-COVID measurements), all the participants underwent a medical history, and those with arrhythmias, cerebrovascular sickness, pregnancy, surgery amputation, oncology disease, psychiatric disease, infections throughout the trial duration, medical nonadherence, those that were unable to provide fully informed written consent or had any other serious medical condition that may affect data interpretation were excluded from the study. While this was not originally our inclusion criteria, all of the participants reported mild severity of COVID-19. In total, we invited 36 adults to participate in our study, with 32 ($89\%$) agreeing to take part. All participants provided written informed consent to participate in the study, which conformed to the Declaration of Helsinki. ## 2.3. Study Procedures Before undergoing testing, participants filled out a health history questionnaire, which inquired about personal and family medical history as well as medication use. They arrived for testing in a fasted state having abstained from food, caffeine, or smoking for at least 3 h and from exercise, alcohol and smoking for 24 h before testing. Those taking vasoactive medications (3 or $9\%$) maintained the same dosage throughout the duration of the study. All study procedures were carried out in a quiet, temperature-neutral environment with the temperature range of 21–23 °C after participants had lain supine for 10 min. To avoid possible confounding, each participant was recorded at the same time of day and with the same device during both visits. ## 2.4. Study Measurements The arterial stiffness and central hemodynamics measurements were taken in accordance with the American Heart Association’s recommendations for improving and standardizing vascular research on arterial stiffness (Townsend, Wilkinson et al., 2015). Office blood pressure (BP) was measured during each visit using the validated oscillometric sphygmomanometer (Welch Allyn Connex ProBP 3400 digital blood pressure monitor with SureBP technology). The BP measurements were taken in a supine position after 5 min of resting and prior to PWV measurements. The participants did not change body posture between the two measurements. Carotid–femoral pulse wave velocity (cfPWV); central blood pressures including: central systolic (cSBP) and diastolic (cDBP) blood pressures and pulse pressure (cPP); pulse pressure amplification; augmentation pressure (AP); augmentation indices: AIx calculated as AP/PP, AIx©75—AIx calculated as AP/PP and normalized to the heart rate of 75 beats per minute (bpm), and AIx index calculated as the ratio of late to early systolic pressure P2/P1; and heart rate (HR) were measured by either applanation tonometry using the Sphygmocor CvMS device (Atcor Medical, Sydney, Australia) or by the hybrid applanation tonometer—oscillometric device SphygmoCor Xcel (Atcor Medical, Sydney, Australia), as described previously [16,17]. While the validation studies comparing two devices indicated that they were comparable in terms of assessment of carotid–femoral pulse wave velocity (cfPWV) and augmentation index (AIx) [18,19], each participant was recorded using only one device to ensure that intra-individual changes are not affected by the type of device used. A single operator (M. P.) carried out all of the measurements. For cfPWV measurements, recordings were performed on the right carotid and the right femoral artery. Central BPs and other parameters derived from the pulse wave analysis (PWA) were estimated after calibration of the pulse waveform recorded at the radial artery to mean and diastolic brachial pressures. We used the subtracted distance method to calculate the wave travel distance. The method was chosen over the direct method as per recommendation by the latest guideline [20]. ## 2.5. Sample Size Considerations Thirty-two participants are sufficient to detect moderate to strong effects on parameter changes using a simple linear regression model and strong effects when two-predictor linear regression model is used. Namely, using a two-predictor multiple linear regression model with α = 0.05, f2 = 0.35, and power of $80\%$, a sample size of 31 is required to detect a strong association between pre–post changes in vascular function/structure and potential predictors. For estimations based on a simple linear regression model, the sample size of 32 was sufficient to detect moderate to strong associations under assumptions of α = 0.05, f2 = 0.27, and power of $80\%$. ## 2.6. Data Analysis To describe the distribution of a quantitative variable, we used mean and standard deviation (SD) or median and interquartile range (IQR), depending on the shape of the distribution. To decide if a distribution is asymmetrical, we used skewness and kurtosis tests for normality. The distribution of a qualitative variable was described with absolute and relative frequencies. We utilized one-sample tests to determine whether a pre–post change in a parameter was statistically significant: either the parametric one-sample t-test or its non-parametric counterpart, the sign rank test, depending on the symmetry of the variable’s distribution. There were two sets of the regression models developed. We employed simple or multiple linear regression (MLR) models to identify predictors of the change from baseline (pre-COVID) for different arterial stiffness and hemodynamic parameters. These models were preferred as they use individual pre–post changes as the dependent variable. In addition, we built mixed-effects regression models to identify predictors affecting the values of a modeled parameter. Due to the fact that mixed-effects regression models employ repeated measurements of a parameter, these models had greater analytical power than simple or MLR models. The model building was performed in two steps. In the first step, potential predictors—including age, sex, the amount of time that passed since the start of COVID infection, the amount of time that passed between the pre- and post-COVID-19 measurements, pre-COVID baseline values of a modeled parameter, and the type of device used to estimate its values—were used as single predictors in a simple linear regression or a mixed-effects model. For the final model, only those predictors that were significant at the 0.1 level or higher were considered (p ≤ 0.01). Requirements for inclusion in the final model were significance at the 0.05 level or an increase in adjusted R2 of at least $2\%$ and a p-value of less than 0.2. The above-mentioned potential predictors that were initially evaluated were selected to estimate the dependence of parameter values on the time that passed since the COVID infection and account for potential confounding variables. As an example, even though we did not anticipate any significant changes in vascular function over a 24-month period in the predominantly young participants (Table 1), we included the time between the first and second measurements as a potential predictor to control for its confounding effect. We interpreted the strength of association between a predictor and a modeled parameter by applying the Cohen’s effect size magnitudes for R2 (small from 0.02 to <0.13, medium from 0.13 to <0.26, large from ≥0.26) to the adjusted R2 of a single predictor model. [ 21] As this is an exploratory study, no control for multiple testing was performed. The analysis was performed in STATA (version 17.0, Stata Corp. LP, College Station, TX, USA). We applied the significance level of $$p \leq 0.05.$$ ## 3. Results In this study, 32 participants were recruited; each participant visited the laboratory twice; and all of their data were collected. Table 1 shows their demographic and clinical characteristics at baseline prior to the COVID-19 infection. The participants were predominantly young (≤40 years) and healthy with only $9\%$ ($$n = 3$$) of the cohort being hypertensive. None of the participants had dyslipidemia, and only two had diabetes ($6\%$, one person was also hypertensive). The majority of the cohort was overweight or obese ($69\%$) and did not smoke ($78\%$). The average time since the onset of COVID-19 infection in our sample was mean ± SD: 73 ± 10 days, with this time ranging from 51 to 92 days. The median time that elapsed between two measurements was 327.5 days (IQR, 129 to 458), with the range between 74 and 730 days. The majority of participants, 23 or $72\%$, were recorded with the SphygmoCor XCEL device. In terms of the severity of COVID-19 infections, none of our participants have developed any of the cardiovascular, pulmonary, thromboembolic, or other COVID-19-associated complications, and there were no hospitalizations. Participants were evenly distributed according to the year they became infected (chi-square test, $$p \leq 0.084$$). There was no significant pre–post change in weight: median change 0 kg, $95\%$ CI from −0.3 to 0.5. When we looked to see if the mean individual pre–post changes were significantly different from 0, we found no significant pre–post change in any of the arterial stiffness or hemodynamic parameters tested (p ≥ 0.122). We did, however, see an average increase of 0.19 m/s in carotid–femoral pulse wave velocity (cfPWV) from pre-infection values but at the significance level of 0.1 ($$p \leq 0.052$$). Table 2 shows the distribution of vascular parameters at baseline and after the infection. Further analysis, however, revealed a widespread and complex pattern of confounders that affect the pre–post infection changes, and parameter values, in the majority of the assessed arterial stiffness and hemodynamic parameters (Table 3 and Table 4). The size of these changes, as well as the direction in which they went, were dependent not only on the length of recovery time that had passed since the onset of the COVID-19 infection, confirming the existence of a response to the COVID-19 infection, but also, and more commonly, on cardiovascular health status at baseline (ascertained by age of an individual at baseline or the value of a parameter at baseline), as well as the amount of time that had passed between the two measurements. In accordance with the Cohen’s interpretation of R2, the majority of identified associations, regardless or the type of model, were moderate to strong [21]. ## 3.1. Arterial Stiffness—cfPWV Regarding the cfPWV response to COVID-19 infection, defined as the pre–post change in this parameter, we found that post-infection values increased by 0.19 m/s ($95\%$ CI −0.04 to 0.41) but only at the significance level of 0.1 ($$p \leq 0.052$$). We also found no evidence that age, time since the onset of COVID 19, time between measurements, or cfPWV baseline values influence individual cfPWV responses. Individual pre–post changes were also significant at the 0.1 level according to the mixed-effects model (Table 4). However, age and time were moderately and positively associated with the cfPWV change since the onset of COVID infection at a group level. This model, which explains $32\%$ of the intra-individual variability and $28\%$ of variation at the group level, predicts an increase of 1.14 m/s in the average cfPWV value as a result of variation in the time since the onset of COVID-19 infection (51–92 days). The relationship between cfPWV and two predictors is shown in Figure 1. Although the age dependence is to be expected, we included it for comparison purposes. The change in cfPWV was not determined by the pre–post change in HR or the baseline HR value, nor was it affected by the change in BMI or the baseline BMI value (p ≥ 0.308). ## 3.2. Arterial Stiffness—Augmentation Indices As previously stated, no significant pre–post changes in augmentation indices were observed following the COVID-19 infection (p ≥ 0.244). However, we discovered that the pre–post changes increased with age in AP and all of the AIx indices: Aix AP/PP, Aix P2/P1, and AIx@HR75 (Table 3). Except for the pre–post change in Aix P2/P1, which was moderately associated with age, this dependency was generally weak (Table 3). Aside from age, which was found to be a common predictor of AIx pre–post changes, we discovered additional time-related predictors of these changes in Aix P2/P1 and AIx@HR75 indices. Time since the onset of COVID-19 infection was a moderate and positive predictor of pre–post changes in the AIx@HR75, accounting for $20\%$ of their variance (Table 3, Figure 2). Within a range of 51 to 92 days after the onset of COVID infection, the AIx@HR75 pre–post change was predicted to move from −$5\%$ to +$10\%$. ## 3.3. Peripheral and Central Hemodynamics We found no significant changes from baseline in any peripheral or central hemodynamics parameters (p ≥ 0.122). Pre–post changes from baseline in SBP, cSBP, PP, and cPP were negatively dependent on their baseline values (weak—cSBP and cPP, moderate—SBP, strong—PP; Table 3). For example, given the range of baseline values for the PP parameter, the predicted post-COVID increase in PP ranges from +4 to -11 mmHg. It should also be noted that we also found a significant association of pre–post changes in PP with the time from onset of COVID-19, but at a 0.1 level of significance ($$p \leq 0.098$$). The variable was not included in the final model of pre–post PP changes because it did not meet our protocol’s inclusion requirements. Using the mixed-effects models, we were able to identify that age, and to a lesser extent the time since acquiring COVID-19, were positively associated with parameter values for DBP, cDBP, MAP, and cfPWV (Table 4). According to these models, the estimated change in average pressure values caused by variations in the amount of time that has passed since the beginning of the COVID-19 infection is as follows: DBP is envisaged to increase by 8.1 mmHg, cDBP by 7.6 mmHg, and MAP by 7.6 mmHg. As for individual pre–post changes, we were unable to identify significant mean changes nor the associations of these changes with any of the predictors listed above, nor were we able to find significant individual pre–post changes within mixed-effects models. Such a finding suggests that individual changes are likely heterogeneous and that that the effect that we identified at the group level is probably an average effect. ## 3.4. The Age Dependence of Pre-Post Changes in Investigated Parameters We examined the pattern of pre–post changes in age dependence to see if there is a possibility that age modifies responses to COVID-19 infection. The scatter plots in Figure 3a–c depict a distinct pattern for those aged under and over 40. We demonstrated that the change in AIx P2/P1 from baseline for those over 40 years old is significantly greater than 0 (median $6\%$, $95\%$ CI 0.7–$24\%$, $$p \leq 0.005$$), whereas no significant change was observed for those under 40 years old ($$p \leq 0.976$$) (Figure 3a). ## 4. Discussion This is the first study to compare pre- and post-COVID-19 infection levels across a wide range of arterial stiffness and hemodynamics parameters in the same group of participants. We discovered that the responses of the vascular system to a mild COVID-19 disease, defined here as systematic, individual pre–post differences in investigated parameters, are not simple in the sense that COVID-19 on average either increases or decreases a parameter in infected patients by a comparable amount of measurement units. In fact, except for a non-significant trend for cfPWV, we were unable to detect any parameter with a mean pre–post COVID-19 change that differed from 0. Instead, responses to COVID-19 infection are dynamic and depend on the time since the onset of COVID-19 infection. We identified such time-dependent responses in the arterial stiffness parameters—cfPWV and AIx@HR75, the central hemodynamic parameter—cDBP, and the systemic hemodynamics parameters—DBP and MAP; and we showed that their values increased with the length of time that passed from the onset of COVID-19 infection, independent of age or other confounders. In addition, the vascular impairment predicted by our models for observation period of two to three months post-infection is clinically significant as shown by an increase in cfPWV of +1.4 m/s, +$15\%$ in AIx@HR75, +8 mmHg in DBP, and +7.6 mmHg in cDBP and MAP. The finding that the longer the period from COVID-19 infection the worse the vascular impairment was surprising, as we expected inflammation burden associated with COVID-19 to decrease with time. While we can only speculate on what causes this phenomenon, emerging evidence suggests that it stems from a failure to resolve autoantibodies observed during the acute phase of disease [22,23,24], or alternatively, that generating de novo pathogenic autoimmune responses post-recovery contributes to long COVID with evidence of residual inflammatory cytokines [25,26,27]. Hence, what we observed at the group level, 2–3 months after infection, may be related to inflammation-induced arterial stiffening in some individuals [28], which is caused by inflammation from an autoimmune response or chronic inflammation that precedes one [29]. Furthermore, the heterogeneous responses observed in our study could be explained by the fact that inflammation in post-recovery was not triggered in all patients. Indeed, a recent study found that the circulating levels of anti-/extractable nuclear autoantibodies (ANA/ENAs) were higher at 3 months post-recovery in patients who had COVID-19 and were free from autoimmune diseases at the time of infection compared to healthy and non-COVID infection groups. High circulating ANA/ENA titers, which correlated with long COVID symptoms, were maintained up to 6 months after recovery but significantly reduced by 12 months. Even after 12 months, several pathogenic ANA/ENAs were still detectable in up to $30\%$ of COVID survivors. Furthermore, a retrospective study of 4 million participants found an increased risk of autoimmune diseases in patients with COVID-19 with an adjusted hazard ratio for different autoimmune diseases ranging from 1.78 to 3.21 [30]. All the time-dependent responses to COVID-19 disease were also affected by age in a way that each additional year at baseline added to vascular impairment post-infection. The effect of age was not the result of the time gap between pre- and post-COVID-19 measurements, as this confounding variable was controlled for in our analyses. In addition, we could not assign the effect of age only to the increased variability of investigated parameter with age [16], because in that case, pre–post differences would go in both directions—positive and negative, and we would not be able to find an increasingly positive relationship with age. Age, however, may modulate the response to a mild COVID-19 disease in arterial stiffness and central hemodynamics parameters in different age groups. Previous studies have suggested an age modulation of vascular responses to various triggers, including an infection [31,32], and the association of age with autoimmune inflammation [33]. While our results suggest the role of age as a modifiable factor in the response to mild COVID-19 disease, such a role should be further examined in studies with a larger sample size. We detected the responses to COVID-19 disease in a variety of arterial stiffness measures and measures of its hemodynamic consequences, including: the direct (cfPWV) and indirect (augmentation index) measures of arterial stiffness as well as central (cDBP) hemodynamic parameter. Each of these three parameters represents a distinct aspect of the atherosclerotic process, which involves morphological and/or functional alterations to the vessel wall [34]. Therefore, the simultaneous detection of responses to COVID-19 disease in various vascular structure and function parameters supports the existence of a widespread and long-term pathological process in the vasculature following infection [35]. So far, only a handful of studies investigated the effect of COVID-19 infection on arterial stiffness and central hemodynamics. Most of them were case control studies with small sample sizes (10–22 per arm) comparing patients recovering from COVID-19 with controls [4,9,10]. Despite the possibly limited power of these studies, their results support our conclusions regarding the existence of vascular impairment after COVID-19. The fact that cfPWV is increased in participants after the COVID-19 infection when compared to controls was found in several studies performed on: young healthy patients and their controls 3–4 weeks after the onset of COVID-19 (increase of 0.7 m/s) [9], acutely ill elderly patients (increase of 3.3 m/s) [4], as well as middle-aged patients that were compared to controls at 4 months (increase of 2.05 m/s) [36] and 12 months (increase of 1.15 m/s) after the COVID-19 onset [37]. Aix, like cfPWV, has been found to be higher in COVID-19-infected participants compared to controls. A $10\%$ increase in the augmentation indices AIX AP/PP and AIx@HR75 has been reported in those infected with COVID19 when comparing 15 young adults 3–4 weeks after a positive COVID-19 test to healthy young controls. [ 10]. Finally, in terms of cSBP, at the 4- and 12-month follow-ups, COVID-19 patients have had a persistent increase of 10 mmHg in cSBP compared to controls [36,37]. In addition, Akpek et al. [ 38] reported an increase in systemic hemodynamics parameters during short-term follow-up in patients diagnosed with COVID-19. Only one case control study did not find significant differences in arterial stiffness parameters—PWV and AIx75—at 4 weeks post-infection when young adults who were infected with COVID-19 were compared with their controls [39]. In addition, two small longitudinal studies reported results contradicting our findings. In the first study that followed 14 young participants from the first to sixth month post-infection, the authors reported a decrease in cfPWV (decrease by 0.82 m/s), SBP (by 11 mmHg), MAP (by 11 mmHg) with time; and no change in time was found for AIx@HR75 [40]. The second study followed 10 young adults for 6 months after the COVID-19 infection and found that SBP and DBP decreased throughout the study: with SBP decreasing by 15 mmHg and DBP decreasing by 10 mmHg [41]. Given that both of these longitudinal studies reported participant attrition on very small sample sizes, used inappropriate statistics (mean and standard deviation) to describe the distribution of limited data, and removed outliers from a small sample size [40], the reported results could be the result of methodological issues. On the other side, the lack of a uniform individual response to COVID-19 in the investigated parameters of vascular structure and function, which may be the consequence of age modulation (and possibly modulation by other factors), may have caused such results. Our study had some limitations, the most significant of which was that the sample size only allowed us to detect moderate to strong associations. This means that even though we may have confidence in the significant associations observed in our study, we may have overlooked a relationship between the time since the onset of COVID-19 and several other parameters. For example, pre–post changes in PP were significantly associated with the time from COVID at the lower significance level of 0.1. As our sample size was limited by the number of recent pre-COVID recordings in our laboratory, we were not able to expand the sample size further. Another potential drawback is the possibility that age and perhaps other factors relate to moderate responses to COVID-19 and that certain patient subgroups respond differently to COVID-19 than it was predicted in the overall model for that parameter. The fact that our models did not detect that individual pre–post changes in the tested parameters are significantly different from 0 but were able to detect in the mixed-effects models that parameter values depend on time from COVID-19, at the level of the entire sample, suggests that heterogeneous responses are possible. If this was the case, given that our results suggested an average response to COVID-19 disease, further analyses should be performed in studies with larger sample sizes. Finally, because MAP in our study was estimated rather than directly measured, its estimation may be unreliable for some individuals [42], which could affect the accuracy of parameters derived from pulse wave analysis. However, since we were able to identify general patterns of change and interdependence, we do not expect this had a significant impact on the results. The findings of this study demonstrated that there is a widespread and long-lasting pathological process in the vasculature following the mild COVID-19 infection which keeps deteriorating during 2–3 months post-infection. In light of the recent finding that up to $25\%$ of otherwise healthy and disease-free patients exhibited the long COVID-19 syndrome 12 months after the onset of COVID-19 infection [3], and in light of the fact that vascular impairment increases the risk of future cardiovascular events, it is crucial that future studies explore these changes with larger sample sizes and with more synchronous population regarding the onset of COVID-19. ## 5. Conclusions We found that various parameters of arterial stiffness and central hemodynamics respond simultaneously to the mild COVID-19 disease in predominantly healthy individuals. While we were unable to demonstrate this effect on all of the parameters tested, the worsening of values of those found to be responsive (cfPWV, AIx@HR75, cDBP, DBP, and MAP) points toward the existence of a widespread and long-lasting pathological process in the vasculature following the infection. 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--- title: Associating Frailty and Dynamic Dysregulation between Motor and Cardiac Autonomic Systems authors: - Patricio Arrué - Kaveh Laksari - Nima Toosizadeh journal: ArXiv year: 2023 pmcid: PMC10055481 license: CC BY 4.0 --- # Associating Frailty and Dynamic Dysregulation between Motor and Cardiac Autonomic Systems ## Abstract Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20-seconds with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. Using convergent cross-mapping (CCM) the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed. A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals ($p \leq 0.01$, effect size=0.81±0.08). Using logistic models pre-frailty and frailty were identified with sensitivity and specificity of $82\%$ to $89\%$, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty. ## Introduction Frailty is an aging syndrome related to low physiological reserves in organs and systems and is associated with increased risk of hospitalization, adverse therapy outcomes, disability, and mortality [1]. Muscle loss (sarcopenia), and weakness (dynapenia) are the main symptoms of frailty [2], which are triggered by metabolic and hormonal derangements (3–6), and the so called “heightened inflammatory state” [7], caused by excessive levels of C-reactive protein (CRP), proinflammatory cytokines interleukin 6 (IL-6), and white blood cells and tumor necrosis factor-alpha (TNFalpha) (7–9). Consequently, frailty is highly associated with a decrease in motor function performance [10]. Furthermore, frailty is associated with an impaired cardiac autonomic nervous system (ANS) because of alterations in the action potential on the sinoatrial node myocytes, which impacts the cardiac function and the heart rate variability (HRV) [11]. While research showed alterations in several physiological systems, the association between frailty and dynamic interconnection between cardiac and motor systems is still unclear. Indeed, the human body is a complex network of several physiological systems, where intricate dynamics exist between these systems to maintain homeostasis [12,13]. Accurate identification of the level of physiological reserve requires a collection of information across multiple physiological systems (12–15), rather than only system-specific evaluations. To explore the extent of dynamic behaviors within and across physiological systems, principles of network physiology has been introduced. The concept of network physiology claims that dysregulation of interactions between physiological systems leads to loss of resilience and the ability to recover from stressors [14], which is inherent to the concept of frailty. We have previously developed a methodology for assessing frailty that incorporates an upper-extremity function (UEF) and corresponding heart rate (HR) response to physical activity. The UEF test consists of repetitive and rapid elbow flexion and extension [16], during which several kinematics features representing dynapenia are measured using motion sensors [1]. Since UEF involves upper-extremity motion, it is feasible to perform for bedbound patients and where walking tests are difficult for frail older adults. In our recent research we showed that HR dynamics, measured by changes in HR due to the UEF physical function (i.e., HR dynamics), were significantly associated with frailty [17]. Combining UEF motor and cardiac functions, we were able to identify frailty with higher accuracy compared to models including each of the motor or HR dynamics parameters separately [18]. Nevertheless, it is unclear whether frailty can influence the interconnection between motor and HR dynamics, and whether applying interconnection measures improve frailty identification. The goal of the current study was to determine the effect of frailty on the interconnection between motor and cardiac systems. Build upon our previous research, the main hypothesis was that due to frailty, a weaker interconnection would exist between motor and HR performance. Recently, the concept of interconnection assessment within different physiological systems has gained attention (19–24). Granger causality is a classical approach that identifies causality between variables based on the removal of one to determine the predictability of the other variable [25], but its usage is limited to linear systems that have stationary behaviors, or for those in which variables are strongly coupled [26]. In contrast, convergent cross-mapping (CCM) assesses the non-linear directional interactions of variables in a complex dynamic system, based on state-space reconstruction of time series collected from each system [27]. The secondary hypothesis was that the accuracy of frailty identification would be improved using additional CCM interconnection parameters compared to models incorporating each of motor and HR parameters individually. ## Participants Older adult participants (≥65 years) were recruited between October 2016 and March 2018. Participants were recruited from primary, secondary, and tertiary health care settings such as primary and community care providers, assisted living facilities, retirement homes, and aging service organizations. The inclusion criteria were 1) being 65 years or older; 2) the ability to walk a minimum distance of 4.57 m (15 ft) for frailty assessment; and 3) the ability to read and sign an informed consent. The exclusion criteria were: 1) severe motor disorders (Parkinson’s disease, multiple sclerosis, or recent stroke); 2) severe upper-extremity disorders (e.g., elbow bilateral fractures or rheumatoid arthritis); 3) cognitive impairment identified by a Mini Mental State Examination (MMSE) score ≤ 23 [28]; 4) terminal illness; 5) diseases/treatments that can bias the HR measurements (including arrhythmia and use of pacemaker); and 6) usage of β-blockers or similar medications that can influence HR. Written informed consent was obtained from all participants. The study was approved by the University of Arizona Institutional Review Board. All research was performed in accordance with the relevant guidelines and regulations, according to the principles expressed in the Declaration of Helsinki [29]. ## Frailty assessment and clinical measures The frailty assessment was executed using the five-component Fried phenotype as the gold standard [1]. The Fried phenotype considers five criteria: 1) unintentional weight loss of 4.54 kg (10 pounds) or more in the previous year; 2) grip strength weakness (adjusted with body mass index (BMI) and sex); 3) slowness based on the required time to walk 4.57 m (15 ft) (adjusted with height and sex); 4) self-reported exhaustion based on a short two-question version of Center for Epidemiological Studies Depression (CESD); and 5) self-reported low energy expenditure based on a short version of Minnesota Leisure Time Activity Questionnaire [30]. Participants were categorized into three frailty groups, which were non-frail if they met none of the criteria, pre-frail if they met one or two criteria, and frail if they met three or more criteria. Clinical measures collected included: 1) MMSE and Montreal Cognitive Assessment (MoCA) for cognition [28,31]; 2) comorbidity based on Charlson Comorbidity Score (CCI) [32]; and 3) depression using Patient Health Questionnaire (PHQ-9) [33]. These measures were considered as adjusting variables in the statistical analysis because they could potentially influence physical activity and the cardiovascular system performance. ## UEF test After frailty assessment and clinical measures, participants were asked to sit on a chair and rest for two minutes to regain normal resting status. Participants then performed the UEF task of elbow flexion-extension as quickly as possible for 20 seconds with the right arm. After the UEF task, participants rested on the chair for another two minutes. We have shown that UEF results are similar on both left and right hands [34]. Before the test, participants practiced the UEF test with their non-dominant arm to become familiar with the protocol. The protocol was explained to participants and using exact same verbal instruction they were encouraged only once, before elbow flexion, to do the task as fast as possible. Wearable motion sensors (triaxial gyroscope sensors, BioSensics LLC, Cambridge, MA, sampling frequency=100 Hz; Figure 1A) were used to measure forearm and upper arm motion, and ultimately the elbow angular velocity. Angular velocity data from gyroscopes were filtered using first-order high pass butter-worth filter with a cutoff of 2.5 Hz. Maximums and minimums of the angular velocity signal were detected, and subsequently, elbow flexion cycles were identified. Motor performance was assessed to represent: 1) slowness based on speed of elbow flexion; 2) flexibility based on range of motion, 3) weakness based on strength of upper-extremity muscles; 4) speed variability and motor accuracy; 5) fatigue based on reduction in speed during the 20-second task, and 6) number of flexion cycles. A sub-score was assigned for each of those features, determined previously based on multivariable ordinal logistic models, with the Fried frailty categories as the dependent variable and UEF parameters plus demographic information as independent variables [17]. The normalized UEF motor score from zero (resiliency) to one (extreme frailty) was computed as the sum of sub-scores corresponding to performance results and demographic information (i.e., BMI) [16]. More details about UEF validation, repeatability, and the normalized score are explained in previous research [16,34,35]. HR was recorded using a wearable ECG device with two electrodes and one built-in accelerometer (360° eMotion Faros, Mega Electronics, Kuopio, Finland; ECG sampling frequency=1000 Hz and accelerometer sampling frequency=100 Hz; Figure 1A). One ECG electrode was placed on the upper mid-thorax and the other one inferior to the left rib cage. The placement of the electrodes on the left chest would minimize the movement artifacts due to UEF test with the right arm. ECG data was analyzed for 20 seconds of baseline, 20-second UEF, and 30 seconds of recovery. RR intervals (successive R peaks of the QRS signal) were computed using the Pan-Tompkins algorithm [36]. The automated peak detection process was manually inspected by two researchers (PA and NT). Previously two types of HR parameters were extracted, one representing baseline HR and HR variability (HRV), and one representing HR dynamics (changes in HR during UEF and HR recovery after the task) [37]. Briefly, HR dynamics included time to reach maximum and minimum HR, as well as percent increase and decrease in HR during activity and recovery periods, respectively. In addition to previously developed parameters, in the current study, the interconnection between motor and HR data were assessed using CCM. ## CCM analysis We quantitatively assessed the directional nonlinear interactions between HR and motor data using CCM. An overview of the method is summarized in Figure 1. CCM tests whether a historical trace of HR can predict motor performance (or inversely, whether a historical trace motor performance can predict HR). To calculate the CCM, we first created evenly sampled data of synchronized HR and motor function with a sampling frequency of 10Hz, using spline interpolation (Figure 1B). Each HR data point represents average HR values over 0.1 seconds. Corresponding motor data represent the angular displacement travelled during each 0.1 second of UEF. For calculating motor performance, motor function Mf was defined by: [1] Mfi=∫titi+0.1ωedt, where ωe represents the angular velocity of the elbow. Taken’s embedding theorem generally guarantees that the space state of a dynamic system could be represented from a single-observed time series X as an E-dimensional manifold [38]. The shadow or reconstructed manifold, denoted by MX, consists of an E -dimensional data with lagged coordinates (τ) of the variable: [2] MX=〈X(t),X(t−τ),X(t−2τ) …X(t−(E−1)τ)〉. Subsequently, we reconstructed E-dimensional manifolds from each of these two time series [38] (Figure 1C). Dimension (E) of four was selected based on the average false nearest neighbor approach [39]. A time lag (τ) of 1 second was used for analysis based on the delayed mutual information method [40]. We predicted one time series (e.g., motor function) by historical records of the other signal (e.g., shadow manifold of HR) using a k-nearest neighbor technique. For a dimension E, we determined E + 1 nearest neighbors and identified indices of each data points in manifolds (MX). Using these indices for one manifold (e.g., motor data X(t)), we found corresponding neighbors in the second manifold (e.g., HR data Y(t)) (Figure 1D), and then predicted X(t) to Y^(t) as the weighted mean of E + 1 points in the second manifold [41]: [3] Y^(t)=∑$i = 1$E+1wiY(ti), where wi weights are calculated based on the Euclidean distances between MY and its ith nearest neighbor on X(t). The Pearson correlation coefficient and the normalized root-mean-square-error (NRMSE) between the predicted and original time series were calculated to assess the strength of interconnections (Figure 1E). NRMSE was calculated by normalizing the RMSE between the predicted and the ground truth with respect to the standard deviation of observations. As documented in previous studies, the correlation coefficient is expected to increase with increasing the time-series length (i.e., library length, Figure 1F). For the current study, the correlation and NRMSE values were calculated at the maximum library length (Figure 1F). Significant effects of frailty on CCM correlation values were observed for interconnections in both directions, i.e., predicting HR time series based on motor function (motor to HR) and predicting motor function based on HR (HR to motor) as reported in Table 2, Figure 2 and 3. Pre-frail/frail older adults showed smaller correlations in CCM for both directions, compared to non-frail older adults. There was also a significant effect of frailty on NRMSE values ($p \leq 0.05$); for both motor and HR CCM predictions, NRMSE values were significantly smaller among non-frail compared to pre-frail/frail ($p \leq 0.05$). Within the stepwise regression analysis, UEF score, HR percent increase, and CCM Motor-to-HR parameters were selected as independent predictors of frailty categories (non-frail vs. pre-frail/frail). Using these three parameters, pre-frailty/frailty was predicted with an AUC, sensitivity, and specificity of 0.91, 0.89, and 0.83 (Table 3, Figure 4), which had a $7\%$ higher AUC than models that included only individual motor or HR parameters as predictors. ## Statistical analysis Analysis of variance (ANOVA) models were used to evaluate the differences in demographic information between the frailty groups, except for sex. Instead, the chi-square (χ2) test was used to assess the difference in sex categories among frailty groups. CCM parameters were compared between frailty groups using multivariable ANOVA models; age, sex, and BMI were considered as adjusting variables since they have been previously associated with motor and cardiac performance and frailty (16,42–44). Cohen’s effect size (d) was estimated. ANOVA analyses for comparing CCM parameters across frailty groups were repeated with clinical measures with significant association with frailty as covariates. To assess the additional value of interconnection measures compared to previous models with individual motor and HR parameters, logistic regression models were implemented with frailty as the dependent variable and HR, motor, and CCM parameters as independent parameters. A stepwise parameter selection based on Akaike information criterion (AIC) values was implemented to identify independent predictive variables. The area under the curve (AUC) with $95\%$ confidence interval (CI) was calculated using receiver operator characteristics (ROC) curves for each predicting model. Statistical analyses were done using JMP (Version 16, SAS Institute Inc., Cary, NC, USA), and statistical significance was concluded when $p \leq 0.05.$ ## Participants and clinical measures Fifty-six participants were recruited for the study, including 12 non-frail (age=76.92±7.32 years), 40 pre-frail (age=80.53±8.12 years), and four frail individuals (age=88.25±4.43 years). Of note, due to the small number of frail participants ($$n = 4$$), frail and pre-frail groups were merged for the statistical analysis. A summary of demographics is presented in Table 1. There was no significant difference in demographic parameter between the frailty groups ($p \leq 0.10$). Among clinical measures, CCI comorbidity and PHQ-9 depression scores were significantly different between frailty groups ($p \leq 0.03$, Table 1). ## Effect of frailty on system interconnections As hypothesized, a significantly weaker interconnection between motor and cardiac systems was observed among pre-frail and frail older adults compared to non-frail individuals (Table 2 and Figure 2). Indeed, these results are consistent with expected changes due to aging-related physiological dysregulation. Autonomic nervous system (ANS) regulates heart activity during exercise by signals from the central nervous system [45] and feedback mechanisms from the exercise pressor reflex (group III and IV muscle afferents) [46] and the arterial baroreflex, which controls blood pressure and consequently cardiac output [47]. Previous studies have shown that exercise pressor reflex is impacted by aging (48–51), which would potentially alter the interconnection between the motor and cardiac systems. Nevertheless, the effect is still controversial and further research is needed to fully understand this interconnection pathway. One potential explanation is that frailty leads to an altered control of motor to cardiac system by affecting exercise pressor reflex. Nevertheless, this hypothesis should be investigated in future research. In addition to exercise pressor reflex, the observed weaker CCM values among pre-frail and frail older adults may be explained by the general concepts of homeostatic physiological dysregulation and heightened inflammatory state [7,52]. In this regard, aging and more specifically frailty can be caused by breakdowns of key regulatory processes and excessive increase of immune factors, leading to the loss of homeostasis and functional impairment (3,5–7,9). Different methods have been used previously to identify physiological dysregulation, such as Mahalanobis multivariate statistical distance and principal component analysis. Mahalanobis multivariate statistical distance is a multivariate model built to assess dysregulation within relevant blood-based biomarkers for frailty, such as red blood cell count, IL-6, CRP, calcium, and hemoglobin [53]. This method showed that the increase in the multivariate distance is accelerated with age, which represents the loss of integration of the system physiology. Similarly, the principal component analysis approach considered the variability of blood-based biomarkers, and consequently was showed to be an independent frailty predictor [54]. Both methods included information from multiple systems to assess frailty, analogously to how CCM parameters were computed from HR and motor time-series, and how they were associated with physiological dysregulation and frailty status. ## Frailty identification using multimodal models Current findings confirmed that assessing two physiological systems of motor and cardiac autonomic control, and especially the dynamic interaction between them, can improve frailty identification compared to models that focus on individual physiological systems in isolation. These two physiological systems were selected in this study as they are strongly associated with frailty. Muscle loss and weakness (sarcopenia and dynapenia) are the main symptoms of frailty, caused by inflammatory, metabolic, and hormonal derangements (2–10). Motor deficits and muscle weakness are commonly assessed using walking speed or grip strength tests (Fried phenotype) or counting deficits/disorders (Rockwood deficit index) [55,56]. Nevertheless, performing walking tests in the clinical setting is cumbersome, and many frail older adults have walking disabilities. Grip strength, on the other hand, only measures muscle strength and cannot reveal other aspects of motor deficits. We have previously validated the sensor based UEF motor task to accurately detect systematic decrements in motor function associated with frailty, including slowness, weakness, inflexibility, fatigue, and motor variability [35,57]. In addition to the motor system, the implemented method included cardiac autonomic control. Previous research showed an association between frailty and an impaired ANS because of alterations in electrical conduction and action potential morphology [58,59]. The presence of a compromised neurohormonal homeostasis associated with ANS dysfunction is, in turn, associated with health complications [60,61]. HRV (i.e., variability in RR intervals within QRS-waves) during resting have been used for assessing ANS dysfunction, and has been proposed as a “vital sign” (62–64). However, between-subject and diurnal variability exists in resting HRV (e.g., due to breathing regulation and environmental factors (65–67)). Here, a novel measure of HR dynamics (HR response to physical activity) was introduced as a direct measure of sympathetic (during activity) and parasympathetic (during recovery) performance. The advantages of HR dynamics over HRV are twofold: 1) by normalizing the HR response to the resting condition, we will reduce between-subject and diurnal variability [17]; and 2) we directly assess ANS performance and cardiac physiological reserve in response to a controlled stressor (physical task), to establish a stress-response model that can be further used for assessing interconnection measures. As the last component, within the current study, we investigated the interconnection between physiological systems in response to stress caused by the physical task. The concept of stress-response testing for quantifying frailty has become the subject of recent research. Evidence suggests that differences in physiological reserve between non-frails and frails are subtle under the basal condition [68]. Implementing provocative testing accentuates frailty-related alterations in measurable dynamics of physiological systems in response to stimuli. The provocative UEF test is designed to be hard enough to stress motor and cardiac systems, and not too demanding, so they can be incorporated in a routine clinical setting for frail older adults, especially those with walking disabilities. Simultaneous assessment of motor and heart function in this manner allows us to accurately quantify the dysregulation of interconnection between these systems. Further, the motion artifacts are minimum with the proposed testing, with HR measurement acquiring from the left side while the participant perform the physical task on the right side. ## Limitations and Further work Despite the promising findings of the current study, there are some limitations related to the recruited sample. First, the sample of community dwelling older adult selected for this study was small. Second, there was a limited number of frail participants, and therefore, pre-frail and frail groups were merged. Third, participants with arrhythmia and those who require β-blockers and pacemakers were excluded from the study. Also, test-retest reliability of CCM parameters were not investigated here. Therefore, the interconnection analysis should be confirmed in larger studies incorporating test-retest reliability measures. Additionally, we used time-series library lengths that may not provide accurate results for some participants, since some HR data may have a higher level of short-term complexity, leading to less dense attractor shadow manifolds and consequently a non-completely developed convergence of CCM parameters. Possible solutions would be to perform longer arm tests; however, this will lead to more physical demand on frail older adults. ## Conclusions and clinical implications In the present work a novel quantification of interconnection between motor and cardiac autonomic systems was implemented for frailty assessment. We demonstrated that CCM parameters showed weaker interconnection between motor and cardiac systems among pre-frail/frail older adults compared to non-frails. The new CCM parameters also showed promising results in improving frailty prediction within logistic models. The simplicity of the investigated UEF test permits performing it even for hospitalized bed-bound patients, for predicting therapy complications, in-hospital outcomes, and rehabilitation strategies. We expect to present this multimodal test as an alternative to accurate but impractical frailty assessment tools such the Fried phenotype, when patients are not able to walk. Further, commercialized wearable devices are now allow accurate assessment of HR and motion. Showing the proof of concept in the current study, in our future investigation, we will develop an easy-to-use app for Smart Watch for identifying frailty using simultaneous measures of motor and cardiac functions. ## Data availability The datasets used for the current study are available from the corresponding author on reasonable request. ## References 1. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J. Frailty in older adults: Evidence for a phenotype. Journals Gerontol - Ser A Biol Sci Med Sci (2001.0) 56 146-57 2. Clark BC, Manini TM. What is dynapenia?. Tissue Eng (2012.0) 28 495-503 3. Serviddio G, Romano AD, Greco A, Rollo T, Bellanti F, Altomare E. Frailty syndrome is associated with altered circulating redox balance and increased markers of oxidative stress. Int J Immunopathol Pharmacol (2009.0) 22 819-27. PMID: 19822098 4. Johar H, Emeny RT, Bidlingmaier M, Reincke M, Thorand B, Peters A. 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--- title: 'Proportion and factors influencing client satisfaction with delivery services in health facilities in the Sissala East Municipality, Ghana: A cross‐sectional study' authors: - Alijata Braimah - Gifty A. Aninanya - Ebenezer Senu journal: Health Science Reports year: 2023 pmcid: PMC10055499 doi: 10.1002/hsr2.1166 license: CC BY 4.0 --- # Proportion and factors influencing client satisfaction with delivery services in health facilities in the Sissala East Municipality, Ghana: A cross‐sectional study ## Abstract ### Background and Aims Client satisfaction is the difference between the healthcare services delivered and the needs of the client. Anecdotal evidence suggests the quality of maternal health and delivery services in Ghana especially in the Upper West Region is appalling. Moreover, there is a paucity of data on clients' satisfaction with maternal and delivery services rendered by healthcare. This study, therefore, assessed clients' satisfaction with delivery services and their associated factors. ### Methods This analytical cross‐sectional study included 431 women who had delivered in the last 7 days from four health facilities within Sissala East Municipality using a multistage and simple random sampling technique. A well‐structured questionnaire was used to collect sociodemographic and client satisfaction data. All statistical analyses were done using Statistical Package for Social Sciences Version 26.0 and GraphPad Prism Version 8.0. A $p \leq 0.05$ was considered statistically significant. ### Results Clients’ satisfaction with general delivery services was rated as $80.3\%$ and was significantly associated with process‐related factors ($p \leq 0.0001$) and structural‐related factors ($p \leq 0.0001$) of the health facilities. This study found that health facilities' delivery services differed significantly and were associated with clients’ satisfaction ($p \leq 0.0001$). Moreover, age group ($$p \leq 0.0200$$), occupation ($$p \leq 0.0090$$), kind of delivery ($$p \leq 0.0050$$), and delivery outcome ($p \leq 0.0001$) were significantly associated with client satisfaction with delivery services. ### Conclusion More than two‐thirds of women are satisfied with delivery services within selected health facilities in the Sissala East municipality, although satisfaction within health facilities differs. Furthermore, age group, occupation, kind of delivery, delivery outcome, process, and structural‐related factors significantly contribute to client satisfaction with delivery services. To provide more comprehensive coverage of customers' satisfaction with delivery services in the municipality, strategies such as free maternal health initiatives and health education on the significance of facility delivery should be reinforced. ## INTRODUCTION Globally, maternal deaths have witnessed a significant reduction compared to maternal deaths in the year 2000. An estimated number of 495,000 maternal deaths were recorded globally, and the 2017 global maternal deaths were estimated to be 279,000 ($35\%$). 1 Moreover, in 2017, an estimated 515,000 pregnancy‐related deaths were recorded globally, and 30 million women suffered from maternal‐related complications in developing countries yearly. 2 Over $98\%$ of these maternal deaths were recorded in resource‐poor countries. 3 Currently, the African Region accounts for $65\%$ (two‐thirds) of maternal deaths in low‐ and middle‐income countries. 1 In Ghana, maternal mortality is reported to have decreased sharply from $\frac{760}{100}$,000 live births in 1990 to 319 per every 100,000 live births in 2015. 4 Previous studies showed that preterm birth and stillbirths are associated with maternal problems, such as hypertensive disorders of pregnancy, nonobstetric complications, obstetric hemorrhage, and pregnancy‐related infections. 5, 6 Reduction in maternal mortality can be achieved by making health services available, accessible, and of quality to women in cases of complications related to pregnancy and childbirth. 7, 8, 9 The most common evidence‐based practice for improving quality care includes active referral and actionable information systems, adequate flow of communication, respect, dignity preservation, supporting women emotionally, engagement of talented and motivated human resources, and making available essential physical infrastructure or resources. 10 Client satisfaction could be viewed as the difference between the healthcare services delivered and the needs of the client. This measurement gives an opportunity to reduce maternal mortality due to the fact that the mothers will obey the instructions of the healthcare provider once they are satisfied with the delivery of care which meets their needs, and health authorities use the feedback from clients to improve upon service delivery which will ultimately help in reducing mortalities. 11 Furthermore, client satisfaction is the evaluation of healthcare received. It is usually based on their perception of the health service rendered to them and the physical structures and relationships which exist between health providers, the performance of their roles, while others see clients' satisfaction as a comparison between their expectations and what they experienced. 12, 13 Ghana has made some gains toward addressing maternal and under‐five [5] mortality during the Millenium Development Goals (MDGs) era. Currently, maternal mortality stands at 310 in every 100,000 live births and $\frac{52}{1000}$ live births for children under‐five [5] years. 5 Antenatal coverage nationally in 2018 was $89\%$, while pregnant women who received skilled birth delivery accounted for $79\%$. 5 Attitudes portrayed by healthcare providers, facility characteristics, and sociodemographic characteristics of pregnant women seeking care have been linked to facility delivery services. 14, 15, 16 However, evidence suggests that the quality of maternal health services and delivery services especially in the Upper West Region of Ghana is appalling although the region recorded $87\%$ in antenatal care (ANC) and $68.7\%$ in supervised deliveries in 2018. 17 Recent unpublished data from the Municipal Health Directorate of Sissala East shows that ANC attendance for 2019 and 2018 was 2439 and 2805, respectively, whereas deliveries for the same respective years were also recounted to be 2272 and 2456, respectively. 18 Stillbirths for 2019 and 2018 were also recorded as 20 and 22, respectively. 18 In addition, there is a paucity of data on clients’ satisfaction with maternal and child health services rendered by healthcare workers in health facilities in the Upper West Region, including the Sissala East Municipality. Anecdotal evidence suggests that some pregnant women are not happy with some of the maternal and child health services received. The consequences of pregnant women not being attended to or served by skilled healthcare providers could result in complications for the mother and babies. In the unfortunate event, maternal and neonatal deaths may occur and hence the need to explore the factors accounting for clients' satisfaction with delivery services in the municipality. ## Study design In achieving the main objective of the study, an analytical cross‐sectional design was adopted using the quantitative approach. The design was used to determine the factors that influence client satisfaction with care in a specific population at a single moment in time. 19 ## Study site The study was conducted in four health facilities such as Tumu Municipal Hospital, Wellembelle Health Center, Sakai Health Center, and Kulfuo Health Center within Sissala East Municipality. The Sissala municipality is located in Ghana's Upper West Region's northeastern enclave. It is located between 1.300 W longitude and 10.000 N and 11.000 N latitude and shares a border with Burkina Faso on the north, Kassena‐Nankana West, and Builsa District on the east, West Mamprusi District on the south, Wa East, and Daffiama‐Bussie‐Issah districts on the south, and Sissala West District on the west. About 4 out of 10 people aged 12 and above are married ($52.7\%$), $1\%$ are divorced, and $0.8\%$ are separated. By the age of 25–29, moreover, half of the females ($79.8\%$) are married, compared to just under half of males ($44.4\%$). Previous statistics showed $78.5\%$ of married adults have never attended school, compared to $30.8\%$ of unmarried people. 20 Tumu which is the capital and has a municipal hospital which is the only referral facility for the municipality and receives referrals from the Sissala West district. ## Study population This study included all women who had delivered in the last 7 days and between the age brackets of 18–49 years at the time of conducting the study in the four selected health facilities. Exclusion criteria include pregnant women in labor. Postnatal mothers who had exceeded the seventh day following delivery and had reported to the facility were also excluded from the study. ## Sample size calculation The women of fertility age population in the district is estimated as 16,087. The sample size was then obtained by Yamane's formula for a random sample size of a known population. 21 n=N1+N(e)2, where n is the minimum sample size required, N is the estimated population [16,087], e is the margin of error ($5\%$) at a $95\%$ confidence interval $$n = 160871$$+16087(0.05)2=390.3[390]. Hence, a minimum of 390 participants were required for the study. Accounting for $10\%$ nonresponse of the respondents and to increase statistical power, 431 women were recruited for the study. ## Sampling technique Multistage sampling and simple random sampling were used to employ in selecting the submunicipals and health centers within the submunicipals, respectively. Thus, at the municipal level, the Tumu Municipal Hospital, which is the main referral facility, was purposively selected for the study. The municipal has seven submunicipals through which it provides healthcare services to individuals within its catchment area and to ensure that some important characteristics of the population are fairly represented, a simple random sampling through balloting without replacement was used to select three submunicipals from the seven submunicipal. This was done through balloting where the names of all submunicipals were written on paper, folded, and placed in one bowl for the rural submunicipal and the other bowl for the urban submunicipality. With one's eyes closed, two submunicipals from the rural bowl and one from the urban submunicipals were picked. Next was picking two health facilities under the already chosen two rural submunicipal and picking one health facility in the urban submunicipal category from the one that was selected. The facilities for the study include the Tumu Municipal Hospital and other selected health centers. Respondents in these facilities were selected consecutively provided they accepted to participate in the study and sign the consent form. ## Instruments and procedures for data collection and measurement of client satisfaction Instruments for data collection comprised a structured questionnaire developed based on studies done earlier among delivery clients. 14, 22 The primary data included raw data collected using questionnaires which were done by the researcher and four research assistants. The questionnaire consists of questions related to the rating of hygiene of the facility, the attitude of healthcare providers toward clients, the competence of care providers, general health information on labor and 7 days postnatal, and client–healthcare provider interaction on the sociodemographic characteristics information of respondents was also obtained. All clients who came to the selected facilities were questioned once they accepted to partake in the study and after signing the agreement form. The questionnaires were developed in English before translating into Sissali language and back to English by Mr. Batong Khalid of SILDEP, nongovernmental organization. Data collection was carried out at the health facility. Four hundred and thirty‐one questionnaires were administered to the respondents in four health facilities where labor and delivery services are conducted. Data collection started on February 24, 2021 after obtaining ethical approval on February 11, 2021 and ended in April 2021. An average of 30 min was spent on each questionnaire. The questionnaires were administered in English, Sissali, or the Dagaare language. The researcher entered selected health facilities to identify postnatal mothers who delivered day 1 up to the 7th day by questioning them on the date of delivery and postnatal day. Eligible participants were selected from each facility. The researcher explained the purpose of the study in the preferred language of the respondent to gain their consent and cooperation to participate in the study. ## Measures for quality control A pretest was conducted among 20 respondents in the Nabulo subdistrict who were not part of the study. This facility that was chosen shared similar characteristics with the respondents who were used in the study. This was done to confirm the appropriateness of the data collection tools. It was tested for clarity, and construction of the questions, and questions that are not clear will be revised. The principal researcher monitored the research team to ensure that the interviews were well conducted in the selected study areas. Four research assistants were recruited and trained to assist the researcher in the administration of the questionnaire. Due diligence was done to ensure that the assistants collected complete data on each respondent. All returned questionnaires on each day were checked for completeness and data were entered into Microsoft Excel 2019 for analysis. ## Data management and statistical analysis Collected data were entered, cleaned, and coded using Microsoft Excel 2019. All statistical analyses were done using the Statistical Package for Social Sciences Version 26.0, and GraphPad Prism Version 8.0 (GraphPad Software, www.graphpad.com). All categorical variables were represented as frequency and percentages. Bar charts were used to represent the proportion of the general client satisfaction process and structural‐related client satisfaction at delivery. A χ 2 test statistic was used to determine the factors associated with general process and structural‐related client satisfaction at delivery among study participants. A p‐value of <0.05 was considered statistically significant. ## Sociodemographic characteristics of study participants Of the 431 women recruited using multistage and simple random sampling techniques and included in the statistical analyses, more than half were from Tumu Municipal Hospital ($51.0\%$), $25.5\%$ were from Kulfuo Health Center, $14.4\%$ were from Wellembelle Health Center, and $9.0\%$ were from Sakai Health Center. Most were within 18–22 years ($33.6\%$), followed by 28–32 years ($27.8\%$), 23–27 years ($26.2\%$), and a few were 38–42 years ($2.6\%$) and 43–47 years ($0.3\%$). Over $90\%$ ($90.3\%$) were married and most had given birth multiple times ($60.3\%$) with $39.7\%$ who had given birth for the first time. Moreover, about one‐quarter had never attended school ($26.2\%$), while more than half had primary or secondary education ($52.2\%$), with some who had tertiary education ($15.8\%$), vocational, or technical skills ($5.8\%$). Most participants were farmers ($32.9\%$) and housewives ($28.8\%$) with an average monthly income of Gh₵100.00 to Gh₵300.00 ($83.3\%$). The majority of women had a normal vaginal delivery ($82.4\%$), with some who had planned cesarean delivery ($8.4\%$), an emergency cesarean delivery ($6.5\%$), and vaginal delivery assisted by equipment ($2.8\%$) (Table 1). Table 1 \| Variable \| Frequency (n = 431) \| Percentage (%) \| \| --- \| --- \| --- \| \| Number of deliveries \| Number of deliveries \| Number of deliveries \| \| Tumu Municipal Hospital \| 220 \| 51.0 \| \| Wellembelle Health Center \| 62 \| 14.4 \| \| Sakai Health Center \| 39 \| 9.0 \| \| Kulfuo Health Center \| 110 \| 25.5 \| \| Age groups (years) \| Age groups (years) \| Age groups (years) \| \| 18–22 \| 145 \| 33.6 \| \| 23–27 \| 113 \| 26.2 \| \| 28–32 \| 120 \| 27.8 \| \| 33–37 \| 41 \| 9.5 \| \| 38–42 \| 11 \| 2.6 \| \| 43–47 \| 1 \| 0.3 \| \| Marital status \| Marital status \| Marital status \| \| Single \| 42 \| 9.7 \| \| Married \| 389 \| 90.3 \| \| Parity \| Parity \| Parity \| \| Primipara \| 171 \| 39.7 \| \| Multipara \| 260 \| 60.3 \| \| Educational level \| Educational level \| Educational level \| \| Never attended school \| 113 \| 26.2 \| \| Primary/secondary \| 225 \| 52.2 \| \| Vocational/technical \| 25 \| 5.8 \| \| Tertiary \| 68 \| 15.8 \| \| Occupation \| Occupation \| Occupation \| \| Trader \| 47 \| 10.9 \| \| Farmer \| 142 \| 32.9 \| \| Housewife \| 124 \| 28.8 \| \| Student \| 51 \| 11.8 \| \| Self‐employed \| 67 \| 15.5 \| \| Average monthly income \| Average monthly income \| Average monthly income \| \| 100gh–300Gh₵ \| 359 \| 83.3 \| \| 400gh–600Gh₵ \| 49 \| 11.4 \| \| 700gh–900Gh₵ \| 12 \| 2.8 \| \| 1000gh and above \| 11 \| 2.6 \| \| Kind of delivery \| Kind of delivery \| Kind of delivery \| \| Normal vaginal delivery \| 355 \| 82.4 \| \| Vaginal delivery assisted by equipment \| 12 \| 2.8 \| \| Planned cesarean delivery \| 36 \| 8.4 \| \| Emergency cesarean delivery \| 28 \| 6.5 \| \| Outcome of delivery \| Outcome of delivery \| Outcome of delivery \| \| Normal \| 397 \| 92.1 \| \| With complications \| 34 \| 7.9 \| ## Proportion of general client satisfaction with delivery services Respondents were asked to rate their general satisfaction with the delivery services in the various health facilities, the majority of them rated it as satisfactory ($80.3\%$) while $19.7\%$ rated as unsatisfactory (Figure 1). Figure 1: Proportion of general client satisfaction with delivery services in health facilities in the Sissala East Municipality, Ghana. ## Proportion client satisfaction stratified by process‐ and structural‐related factors during delivery services Of 346 women who were satisfied with general delivery services, over $90\%$ were satisfied with process‐related factors ($95.1\%$). Of 85 women unsatisfied with general delivery services, majority ($80.0\%$) were unsatisfied with process‐related factors. A significant association was, therefore, observed between satisfaction with general delivery services and that of process‐related factors ($p \leq 0.0001$) (Figure 2A). Figure 2: Proportion client satisfaction stratified by process (A) and structural (B) related factors during delivery services among women in health facilities in the Sissala East Municipality, Ghana. Moreover, this study found a significant association between women satisfaction with general delivery services and structural‐related factors of health facilities ($p \leq 0.0001$). This is due to over three‐quarter women ($77.6\%$) among those unsatisfied with general delivery services that were equally unsatisfied with structural‐related factors ($p \leq 0.0001$) (Figure 2B). ## Factors associated with client satisfaction during delivery services This study found that the health facilities within the Sissala East Municipality delivery services differ and were significantly associated with participant satisfaction ($p \leq 0.0001$). Moreover, the age group of study participants ($$p \leq 0.0200$$) and occupation ($$p \leq 0.0090$$) were significantly associated with satisfaction with delivery services. Similarly, the kind of delivery ($$p \leq 0.0050$$) and delivery outcome ($p \leq 0.0001$) were significantly associated with general satisfaction with delivery services. However, no significant association was found between the marital status of women ($$p \leq 0.1800$$), parity ($$p \leq 0.0960$$), educational level ($$p \leq 0.1350$$), women's average monthly income ($$p \leq 0.9160$$), and satisfaction with delivery services (Table 2). Table 2 \| Satisfaction with delivery services \| Satisfaction with delivery services.1 \| Satisfaction with delivery services.2 \| Unnamed: 3 \| \| --- \| --- \| --- \| --- \| \| Variable \| Satisfied (n = 346) \| Unsatisfied (85) \| p Value \| \| Facility name \| \| \| <0.0001 \| \| Tumu Municipal Hospital \| 154 (44.5) \| 66 (77.6) \| \| \| Wellembelle Health Center \| 59 (17.1) \| 3 (3.5) \| \| \| Sakai Health Center \| 36 (10.4) \| 3 (3.5) \| \| \| Kulfuo Health Center \| 97 (28.03) \| 13 (15.4) \| \| \| Marital status \| \| \| 0.1800 \| \| Single \| 37 (10.7) \| 5 (5.9) \| \| \| Married \| 309 (89.3) \| 80 (94.1) \| \| \| Age group (years) \| \| \| 0.0200 \| \| Less than 20 \| 48 (13.8) \| 6 (7.1) \| \| \| 20–29 \| 211 (61.0) \| 44 (51.8) \| \| \| 30–39 \| 83 (24.0) \| 33 (38.8) \| \| \| 40–49 \| 4 (1.2) \| 2 (2.4) \| \| \| Parity \| \| \| 0.0960 \| \| Primipara \| 144 (41.6) \| 27 (31.8) \| \| \| Multipara \| 202 (58.4) \| 58 (68.2) \| \| \| Educational level \| \| \| 0.1350 \| \| Never attended school \| 98 (28.3) \| 18 (21.2) \| \| \| Primary/secondary \| 177 (51.2) \| 48 (56.5) \| \| \| Vocational/technical \| 18 (5.2) \| 4 (4.7) \| \| \| Tertiary \| 53 (15.3) \| 15 (17.6) \| \| \| Occupation \| \| \| 0.0090 \| \| Trader \| 29 (8.4) \| 18 (21.2) \| \| \| Farmer \| 122 (35.3) \| 20 (23.5) \| \| \| Housewife \| 101 (29.2) \| 23 (27.1) \| \| \| Student \| 42 (12.1) \| 9 (10.6) \| \| \| Self‐employed \| 52 (15.0) \| 15 (17.6) \| \| \| Average monthly income \| \| \| 0.9160 \| \| 100gh–300gh \| 290 (83.8) \| 69 (81.2) \| \| \| 400gh–600gh \| 38 (11.0) \| 11 (12.9) \| \| \| 700gh–900gh \| 9 (2.6) \| 3 (3.5) \| \| \| 1000gh and above \| 9 (2.6) \| 2 (2.4) \| \| \| Kind of delivery \| \| \| 0.0050 \| \| Normal vaginal delivery \| 295 (85.3) \| 60 (70.6) \| \| \| Vaginal delivery assisted by equipment \| 6 (1.7) \| 6 (7.1) \| \| \| Planned cesarean delivery \| 26 (7.5) \| 10 (11.8) \| \| \| Emergency cesarean delivery \| 19 (5.5) \| 9 (10.6) \| \| \| Delivery outcome \| Delivery outcome \| Delivery outcome \| Delivery outcome \| \| Normal \| 331 (95.7) \| 66 (77.6) \| <0.0001 \| \| With complications \| 15 (4.3) \| 19 (22.4) \| \| ## Association between processes‐related factors and level of satisfaction with delivery services Of the process‐related factors, the reception given at the health facility ($p \leq 0.0001$), the privacy provided during stay in the health facility and delivery ($p \leq 0.0001$), the level of respect received from the health workers ($p \leq 0.0001$), and the support given by the health workers ($p \leq 0.0001$) were significantly associated with the level of general client satisfaction with delivery services at the health facilities. Moreover, the information given by the health workers during labor delivery and after delivery ($p \leq 0.0001$), the waiting time between arriving at the health facility and seeing a health worker ($$p \leq 0.0004$$), and the satisfaction with clinical examination performed by health workers during labor and delivery ($p \leq 0.0001$) were significantly associated with the level of general client satisfaction with delivery services at the health facilities. Similarly, this study observed the level of assistance, attention and care given during and after delivery ($p \leq 0.0001$) significantly influenced satisfaction with delivery services at health centers. Thus, each process‐related factor contributes to a level of client satisfaction and must be monitored together for total client satisfaction with delivery services at various health facilities (Table 3). Table 3 \| Level of satisfaction with delivery satisfaction \| Level of satisfaction with delivery satisfaction.1 \| Level of satisfaction with delivery satisfaction.2 \| Level of satisfaction with delivery satisfaction.3 \| Level of satisfaction with delivery satisfaction.4 \| Unnamed: 5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variable \| Very satisfied (n = 186) \| Satisfied (n = 231) \| Neutral (n = 13) \| Unsatisfied (n = 1) \| p Value \| \| Are you satisfied with the reception given to you at the health facility \| \| \| \| \| <0.0001 \| \| Very satisfied \| 90 (48.4) \| 46 (19.9) \| 2 (15.4) \| 0 (0.0) \| \| \| Satisfied \| 90 (48.4) \| 173 (74.9) \| 9 (69.2) \| 0 (0.0) \| \| \| Neutral \| 5 (2.7) \| 5 (2.2) \| 2 (15.4) \| 0 (0.0) \| \| \| Unsatisfied \| 1 (0.5) \| 5 (2.2) \| 0 (0.0) \| 1 (100.0) \| \| \| Very unsatisfied \| 0 (0.0) \| 2 (0.9) \| 0 (0.0) \| 0 (0.0) \| \| \| Is your satisfaction ok with the privacy provided during your stay in the health facility and delivery? \| \| \| \| \| <0.0001 \| \| Very satisfied \| 89 (47.8) \| 41 (17.7) \| 2 (15.4) \| 0 (0.0) \| \| \| Satisfied \| 89 (47.8) \| 173 (74.9) \| 9 (69.2) \| 1 (100.0) \| \| \| Neutral/somewhat satisfied \| 5 (2.7) \| 14 (6.1) \| 2 (15.4) \| 0 (0.0) \| \| \| Unsatisfied \| 3 (1.7) \| 3 (1.3) \| 0 (0.0) \| 0 (0.0) \| \| \| Very unsatisfied \| 0 (0.0) \| 0 (0.0) \| \| \| \| \| Are you satisfied with the level of respect you received from the health workers? \| \| \| \| \| <0.0001 \| \| Very satisfied \| 75 (40.3) \| 42 (18.2) \| 4 (30.8) \| 0 (0.0) \| \| \| Satisfied \| 107 (57.5) \| 169 (73.2) \| 3 (23.1) \| 0 (0.0) \| \| \| Neutral \| 4 (4.3) \| 18 (7.8) \| 6 (46.2) \| 0 (0.0) \| \| \| Unsatisfied \| 0 (0.0) \| 1 (0.4) \| 0 (0.0) \| 1 (100.0) \| \| \| Very unsatisfied \| 0 (0.0) \| 1 (0.4) \| 0 (0.0) \| 0 (0.0) \| \| \| Are you satisfied with the support given by the health workers? \| Are you satisfied with the support given by the health workers? \| \| \| \| <0.0001 \| \| Very satisfied \| 82 (44.1) \| 41 (17.7) \| 1 (7.7) \| 0 (0.0) \| \| \| Satisfied \| 99 (53.2) \| 176 (76.2) \| 7 (53.8) \| 0 (0.0) \| \| \| Neutral \| 5 (2.7) \| 13 (5.6) \| 3 (23.1) \| 0 (0.0) \| \| \| Unsatisfied \| 0 (0.0) \| 1 (0.4) \| 1 (7.7) \| 1 (100.0) \| \| \| Very unsatisfied \| 0 (0.0) \| 0 (0.0) \| 1 (7.7) \| 0 (0.0) \| \| \| Did not receive any drugs \| 0 (0.0) \| 0 (0.0) \| 0 (0.0) \| 0 (0.0) \| \| \| With the information given you by the health workers during labor, delivery, and after delivery \| \| \| \| \| <0.0001 \| \| Very satisfied \| 65 (34.9) \| 51 (22.1) \| 0 (0.0) \| 0 (0.0) \| \| \| Satisfied \| 109 (58.6) \| 169 (73.2) \| 10 (76.9) \| 0 (0.0) \| \| \| Neutral \| 8 (4.3) \| 8 (3.5) \| 1 (7.7) \| 0 (0.0) \| \| \| Unsatisfied \| 3 (1.6) \| 2 (0.9) \| 1 (7.7) \| 1 (100.0) \| \| \| Very unsatisfied \| 1 (0.5) \| 1 (0.4) \| 1 (7.7) \| 0 (0.0) \| \| \| What was the waiting time between arriving at the health facility and seeing a health worker? \| \| \| \| \| 0.0004 \| \| Far too long \| 11 (5.9) \| 22 (9.5) \| 1 (7.7) \| 0 (0.0) \| \| \| Long \| 28 (15.1) \| 40 (17.3) \| 3 (23.1) \| 1 (100.0) \| \| \| Neutral \| 41 (22.0) \| 89 (38.5) \| 6 (46.2) \| 0 (0.0) \| \| \| I was helped immediately; I did not have to wait \| 106 (57.0) \| 80 (34.6) \| 3 (23.1) \| 0 (0.0) \| \| \| Related to the clinical examination performed by health workers during labor and delivery, were you satisfied with it? \| Related to the clinical examination performed by health workers during labor and delivery, were you satisfied with it? \| Related to the clinical examination performed by health workers during labor and delivery, were you satisfied with it? \| Related to the clinical examination performed by health workers during labor and delivery, were you satisfied with it? \| Related to the clinical examination performed by health workers during labor and delivery, were you satisfied with it? \| Related to the clinical examination performed by health workers during labor and delivery, were you satisfied with it? \| \| Very satisfied \| 77 (41.4) \| 42 (18.2) \| 3 (23.1) \| 1 (100.0) \| <0.0001 \| \| Satisfied \| 99 (53.2) \| 174 (75.3) \| 2 (15.4) \| 0 (0.0) \| \| \| Neutral \| 6 (3.2) \| 11 (4.8) \| 8 (61.5) \| 0 (0.0) \| \| \| Unsatisfied \| 0 (0.0) \| 3 (1.3) \| 0 (0.0) \| 0 (0.0) \| \| \| Very unsatisfied \| 4 (2.2) \| 1 (0.4) \| 0 (0.0) \| 0 (0.0) \| \| \| Satisfaction with the level of assistance by health workers during delivery \| \| \| \| \| <0.0001 \| \| Very satisfied \| 73 (39.2) \| 29 (12.6) \| 2 (15.4) \| 0 (0.0) \| \| \| Satisfied \| 107 (57.5) \| 181 (78.4) \| 6 (46.2) \| 0 (0.0) \| \| \| Neutral \| 5 (2.7) \| 18 (7.8) \| 5 (38.5) \| 0 (0.0) \| \| \| Unsatisfied \| 0 (0.0) \| 1 (0.4) \| 0 (0.0) \| 1 (100.0) \| \| \| Very unsatisfied \| 1 (0.5) \| 2 (0.9) \| 0 (0.0) \| 0 (0.0) \| \| \| With the attention and care given to your newborn baby after delivery, were you satisfied? \| \| \| \| \| <0.0001 \| \| Very satisfied \| 89 (47.8) \| 33 (14.3) \| 2 (15.4) \| 1 (100.0) \| \| \| Satisfied \| 89 (47.8) \| 177 (76.6) \| 9 (69.2) \| 0 (0.0) \| \| \| Neutral \| 6 (3.2) \| 16 (6.9) \| 2 (15.4) \| 0 (0.0) \| \| \| Unsatisfied \| 0 (0.0) \| 2 (0.9) \| 0 (0.0) \| 0 (0.0) \| \| \| Very unsatisfied \| 2 (1.1) \| 3 (1.3) \| 0 (0.0) \| 0 (0.0) \| \| \| With the level of attention and care given to you after delivery, are you satisfied with it? \| \| \| \| \| <0.0001 \| \| Very satisfied \| 78 (41.9) \| 38 (16.5) \| 2 (15.4) \| 0 (0.0) \| \| \| Satisfied \| 100 (53.8) \| 170 (73.6) \| 6 (46.2) \| 1 (100.0) \| \| \| Neutral \| 7 (3.8) \| 19 (8.2) \| 4 (30.8) \| 0 (0.0) \| \| \| Unsatisfied \| 1 (0.5) \| 2 (0.9) \| 0 (0.0) \| 0 (0.0) \| \| \| Very unsatisfied \| 0 (0.0) \| 2 (0.9) \| 1 (7.7) \| 0 (0.0) \| \| ## Association between structural‐related factors and level of satisfaction with delivery services Of the structural‐related factors, there was a significant association observed between the medical examination satisfaction by a health worker ($p \leq 0.0001$), level of assistance given by the caregiver(s) during the delivery ($p \leq 0.0001$), satisfaction with drugs prescribed by the health worker(s) ($p \leq 0.0001$), and level of general client satisfaction with delivery services at the health facilities. Furthermore, drugs provided at the health facility ($p \leq 0.0001$), satisfaction with medical equipment available at the facility ($p \leq 0.0001$), and hygiene at the facility ($p \leq 0.0001$) were significantly associated with the level of general client satisfaction with delivery services. This study's findings show that each structural‐related factor singly affects the level of client satisfaction with delivery services and must be wholesomely enhanced for adequate client satisfaction at various health facilities (Table 4). Table 4 \| Level of satisfaction with delivery satisfaction \| Level of satisfaction with delivery satisfaction.1 \| Level of satisfaction with delivery satisfaction.2 \| Level of satisfaction with delivery satisfaction.3 \| Level of satisfaction with delivery satisfaction.4 \| Unnamed: 5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variable \| Very satisfied (n = 186) \| Satisfied (n = 231) \| Neutral (n = 13) \| Unsatisfied (n = 1) \| p Value \| \| Medical examination satisfaction by a health worker \| \| \| \| \| <0.0001 \| \| Very satisfied \| 77 (41.4) \| 42 (18.2) \| 3 (23.1) \| 1 (100.0) \| \| \| Satisfied \| 99 (53.2) \| 174 (75.3) \| 2 (15.4) \| 0 (0.0) \| \| \| Neutral \| 6 (3.2) \| 11 (4.8) \| 8 (61.5) \| 0 (0.0) \| \| \| Unsatisfied \| 0 (0.0) \| 3 (1.3) \| 0 (0.0) \| 0 (0.0) \| \| \| Very unsatisfied \| 4 (2.2) \| 1 (0.4) \| 0 (0.0) \| 0 (0.0) \| \| \| Level of assistance given by caregiver (s) during delivery \| \| \| \| \| <0.0001 \| \| Very satisfied \| 73 (39.2) \| 29 (12.6) \| 2 (15.4) \| 0 (0.0) \| \| \| Satisfied \| 107 (57.5) \| 181 (78.4) \| 6 (46.2) \| 0 (0.0) \| \| \| Neutral \| 5 (2.7) \| 18 (7.8) \| 15 (115.4) \| 0 (0.0) \| \| \| Unsatisfied \| 0 (0.0) \| 1 (0.4) \| 0 (0.0) \| 1 (100.0) \| \| \| Very unsatisfied \| 1 (0.5) \| 2 (0.9) \| 0 (0.0) \| 0 (0.0) \| \| \| Drugs prescribed by the health worker(s) \| \| \| \| \| <0.0001 \| \| Very satisfied \| 49 (26.3) \| 34 (14.7) \| 2 (15.4) \| 1 (100.0) \| \| \| Satisfied \| 100 (53.8) \| 142 (61.5) \| 5 (38.5) \| 0 (0.0) \| \| \| Neutral \| 14 (7.5) \| 26 (11.3) \| 2 (15.4) \| 0 (0.0) \| \| \| Unsatisfied \| 3 (1.6) \| 5 (2.2) \| 3 (23.1) \| 0 (0.0) \| \| \| Very unsatisfied \| 1 (0.5) \| 3 (1.3) \| 0 (0.0) \| 0 (0.0) \| \| \| Drugs provided at the health facility? \| \| \| \| \| <0.0001 \| \| Very satisfied \| 60 (32.3) \| 25 (10.8) \| 2 (15.4) \| 0 (0.0) \| \| \| Satisfied \| 91 (48.9) \| 152 (65.8) \| 5 (38.5) \| 0 (0.0) \| \| \| Neutral \| 12 (6.5) \| 21 (9.1) \| 4 (30.8) \| 0 (0.0) \| \| \| Unsatisfied \| 2 (1.1) \| 7 (3.0) \| 2 (15.4) \| 1 (100.0) \| \| \| Very unsatisfied \| 0 (0.0) \| 1 (0.4) \| 0 (0.0) \| 0 (0.0) \| \| \| Did not receive any drugs \| 24 (12.9) \| 25 (10.8) \| 0 (0.0) \| 0 (0.0) \| \| \| Satisfaction with medical equipment available at the facility \| \| \| \| \| <0.0001 \| \| Very satisfied \| 64 (34.4) \| 32 (13.9) \| 2 (15.4) \| 0 (0.0) \| \| \| Satisfied \| 107 (57.5) \| 164 (71.0) \| 4 (30.8) \| 0 (0.0) \| \| \| Neutral \| 11 (5.9) \| 25 (10.8) \| 2 (15.4) \| 0 (0.0) \| \| \| Unsatisfied \| 2 (1.1) \| 8 (3.5) \| 4 (30.8) \| 1 (100.0) \| \| \| Very unsatisfied \| 2 (1.1) \| 2 (0.9) \| 1 (7.7) \| 0 (0.0) \| \| \| Hygiene at the facility \| \| \| \| \| <0.0001 \| \| Very satisfied \| 82 (44.1) \| 34 (14.7) \| 5 (38.5) \| 0 (0.0) \| \| \| Satisfied \| 90 (48.4) \| 164 (71.0) \| 2 (15.4) \| 0 (0.0) \| \| \| Neutral \| 8 (4.3) \| 17 (7.4) \| 4 (30.8) \| 0 (0.0) \| \| \| Unsatisfied \| 2 (1.1) \| 11 (4.8) \| 1 (7.7) \| 1 (100.0) \| \| \| Very unsatisfied \| 4 (2.2) \| 5 (2.2) \| 1 (7.7) \| 0 (0.0) \| \| ## DISCUSSION This study assessed clients' satisfaction with delivery services and their associated factors. Clients' satisfaction with general delivery services was rated as $80.3\%$ and was significantly associated with process‐related factors and structural‐related factors of the health facilities. This study found that health facility delivery services differ and were significantly associated with participant satisfaction. Moreover, age group, occupation, kind of delivery, and delivery outcome were significantly associated with client satisfaction with delivery services. Of the process‐related factors, the reception given at the health facility, the privacy provided during stay in the health facility and delivery, the level of respect received, the support given, the information given by the health workers during labor, delivery, and after delivery, and waiting time were significantly associated with the level of general client satisfaction with delivery services. Of the structural‐related factors, a significant association was observed between medical examination, level of assistance, satisfaction with drugs prescribed, medical equipment available at the facility, hygiene at the facility, and level of general client satisfaction with delivery services. Clients' satisfaction with general delivery services rated as $80.3\%$ is in line with what was observed in a comprehensive evaluation of women's satisfaction with maternal services in underdeveloped countries. 23 The present study could have positive health outcomes for clients as satisfied clients are likely to patronize delivery and related services in the future. The satisfaction with general delivery services was significantly associated with process‐related factors and structural‐related factors of the health facilities. The age group of study participants and occupation being significantly associated with client satisfaction with delivery services are consistent with the Hoseini et al. study which found age, and occupation as variables that are significant to the maternal level of satisfaction with delivery services. 24 Moreover, the findings of health facilities within the Sissala East Municipality, the kind of delivery, and the delivery outcome being significantly associated with client satisfaction with delivery services are consistent with the studies of Melese et al. and Karoni et al. who found the type of health facility, the mode of delivery being normal vaginal delivery, vaginal delivery assisted, planned cesarean delivery, or emergency cesarean delivery, and delivery outcomes to be factors significantly influencing client satisfaction with delivery services. 25, 26 On the mode of delivery, Karoni et al. reported that mothers who had normal vaginal delivery had a satisfaction level of $65.6\%$ while mothers with cesarean section delivery were $57.2\%$ satisfied in Bahir Dar city in North West Ethiopia. However, an earlier study by Tesfaye et al. in South West Ethiopia reported that women who had undergone a cesarean section were four times more likely to be satisfied than mothers with normal vaginal delivery. 11 It could, therefore, be argued that other factors besides the mode of delivery could account for client satisfaction with delivery services. Furthermore, the study suggests that the satisfaction of clients with delivery services at health facilities is linked to the nature of reception and treatment pregnant women receive in these facilities. 27 This finding is consistent with the present study in that the reception and support given at the health facility influence clients' satisfaction. Privacy and confidentiality of clients in a health facility are deemed a relevant indicator of good service delivery and a key determinant of client satisfaction with the service provider as indicated by some studies. 23, 26, 28 The current study showed that respondents were satisfied with service delivery due to the privacy and confidentiality clients received in the study area. Gitobu et al. in a study conducted in Kenya, however, found more than half of the respondents felt dissatisfied due to lack of privacy. 29 This finding confirms the privacy provided during stay in the health facility and delivery are important factors of client satisfaction. Studies by Srivastava et al. and Odonkor et al. found respect, as well as courtesy, according to clients at health facilities during delivery services by health workers as a hallmark of client satisfaction. 23, 30 This was further strengthened in the current study as over $90\%$ of participants pointed out the respect received from health workers as a major contributor to satisfaction. The findings of information given by the health workers during labor, delivery, and after delivery, satisfaction with drugs prescribed by the health workers, were significantly associated with the level of general client satisfaction with delivery and are in agreement with the studies of Ampofo et al., Peprah et al., and Panth et al. which showed similar findings contributing to client satisfaction. 31, 32, 33 In addition, our study also demonstrated waiting time, medical examination, medical equipment available at the facility, and hygiene at the facility are associated with the level of general client satisfaction with delivery services. Nonetheless, this quantitative study is limited only to selected health facilities in the Tumu municipality and for that matter, future researchers should include all health facilities (institutions) in the Upper West Region through a mixed‐methods approach. Moreover, delivery clients were the only participants that took part in the study and so future studies should interview both health staff and clients so that the responses provided could be validated. The study was also conducted among delivery clients in the wards of the health facilities selected and so it is possible that the majority of the clients reported clients' satisfaction with the services rendered because they wanted to please the health staff who were around the ward during the survey. ## CONCLUSION More than two‐thirds of women were satisfied with delivery services within selected health facilities in the Tumu municipality in the Upper West Region, although client satisfaction within health facilities differs. Moreover, age group, occupation, kind of delivery, delivery outcome, process, and structural‐related factors significantly contributed to client satisfaction with delivery services. For more thorough coverage of clients' satisfaction with delivery services in the municipality, interventions such as free maternal health initiatives and health education on the importance of facility delivery should be enhanced. ## AUTHOR CONTRIBUTIONS Alijata Braimah: Conceptualization; data curation; investigation; methodology; resources; writing—original draft; writing—review and editing. Gifty A. Aninanya: Conceptualization; resources; supervision; writing—original draft; writing—review and editing. Ebenezer Senu: Conceptualization; data curation; formal analysis; methodology; software; writing—original draft; writing—review and editing. ## CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. ## ETHICS STATEMENT Ethics for the study was approved by the Kwame Nkrumah University of Science and Technology ethics board. An introductory letter was also obtained from the University for Development Studies, which was used in obtaining permission from the Health Directorate of the Upper West Region. Lastly, a component of an informed consent form was explained to the respondents and they agreed to participate in the study, and the form was signed by both the researcher and respondents to ensure confidentiality. Informed consent was obtained from the respondent after the consent form had been explained to the respondents on their rights in taking part in the study. Other aspects such as the privacy and confidentiality of respondents will also be highlighted to them. Respondents were also informed that participation in the study was voluntary and they agreed to participate. Data was stored in the cupboard/computer under keys and lock and only opened when necessary. ## TRANSPARENCY STATEMENT The lead author Ebenezer Senu affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. ## DATA AVAILABILITY STATEMENT All data generated or analyzed during this study are included in this article and can be requested from the corresponding author. ## References 1. 1 World Health Organization . Maternal Mortality: Evidence Brief. World Health Organization; 2019.. Maternal Mortality: Evidence Brief (2019) 2. Alemu Y, Aragaw A. Early initiations of first antenatal care visit and associated factor among mothers who gave birth in the last six months preceding birth in Bahir Dar Zuria Woreda North West Ethiopia. Reprod Health (2018) 15 203. PMID: 30541562 3. 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--- title: Common Variable Immunodeficiency Patient Fecal Microbiota Transplant Recapitulates Gut Dysbiosis authors: - Joud Hajjar - Anita Voigt - Margaret Conner - Alton Swennes - Stephanie Fowler - Chadi Calarge - Danielle Mendonca - Dominique Armstrong - Cheng-Yen Chang - Jolan Walter - Manish Butte - Tor Savidge - Julia Oh - Farrah Kheradmand - Joseph Petrosino journal: Research Square year: 2023 pmcid: PMC10055500 doi: 10.21203/rs.3.rs-2640584/v1 license: CC BY 4.0 --- # Common Variable Immunodeficiency Patient Fecal Microbiota Transplant Recapitulates Gut Dysbiosis ## Abstract ### Purpose Patients with non-infectious complications have worse clinical outcomes in common variable immunodeficiency (CVID) than those with infections-only. Non-infectious complications are associated with gut microbiome aberrations, but there are no reductionist animal models that emulate CVID. Our aim in this study was to uncover potential microbiome roles in the development of non-infectious complications in CVID. ### Methods We examined fecal whole genome shotgun sequencing from patients CVID, and non-infectious complications, infections-only, and their household controls. We also performed Fecal Microbiota transplant from CVID patients to Germ-Free Mice. ### Results We found potentially pathogenic microbes Streptococcus parasanguinis and Erysipelatoclostridium ramosum were enriched in gut microbiomes of CVID patients with non-infectious complications. In contrast, Fusicatenibacter saccharivorans and Anaerostipes hadrus, known to suppress inflammation and promote healthy metabolism, were enriched in gut microbiomes of infections-only CVID patients. Fecal microbiota transplant from non-infectious complications, infections-only, and their household controls into germ-free mice revealed gut dysbiosis patterns in recipients from CVID patients with non-infectious complications, but not infections-only CVID, or household controls recipients. ### Conclusion Our findings provide a proof of concept that fecal microbiota transplant from CVID patients with non-infectious complications to Germ-Free mice recapitulates microbiome alterations observed in the donors. ## Introduction Common variable immunodeficiency (CVID) is the most common treatable inborn error of immunity in adults [1, 2]. It is characterized by low immunoglobulin (Ig) levels (IgG, IgA, and/or IgM) and recurrent infections due to B-cell defects [3]. Clinically, CVID patients present with two broad phenotypes: those with infections-only (INF) and those with additional autoimmune and autoinflammatory complications known as non-infectious complications (NIC) [1, 4, 5]. Nearly $60\%$ of CVID patients develop NIC, which manifests as cytopenia, inflammatory bowel disease (IBD)-like disease, chronic lung disease, and lymphoproliferation [1, 6-9]. In addition, NIC-CVID patients have a significant increase in morbidity and mortality compared to INF-CVID patients [10-12]. Thus, there is a pressing need to improve our understanding of NIC-CVID. Several recent studies have suggested involvement of the gut microbiome in CVID-associated immune dysregulation. Specifically, bacteria and their associated products translocate across “leaky” gut epithelium into systemic circulation, as evidenced by the detection of circulating lipopolysaccharide (LPS) or bacterial DNA[13-15]. Furthermore, LPS activates an immune response through the recognition of microbe-associated molecular patterns [16, 17], releasing pro-inflammatory cytokines [18]. Other clinical studies have also shown that the gut microbial composition is altered in CVID patients (i.e., dysbiosis), particularly in NIC-CVID [14, 19, 20]. Furthermore, 16S ribosomal RNA (rRNA) gene sequencing data from CVID patient stool samples showed lower within-sample taxonomic diversity (i.e., alpha diversity) compared to controls [19, 21]. Reduced alpha diversity and increased circulating LPS concentration are also more common in NIC-CVID compared to INF-CVID patients, suggesting that the translocation of certain bacteria may be implicated in immune dysregulation observed in NIC-CVID [22]. In fact, IgG replacement reduces circulating LPS concentrations, suggesting it may reduce gut bacterial translocation [13], or that polyclonal IgG blocks LPS activity in other ways. Additionally, when mucosal integrity is disrupted, some pathobionts, such as Acinetobacter baumanni, induce inflammation by triggering mucosal intestinal macrophages to produce inflammatory cytokines [23]. However, it remains unclear if the gut microbiome in NIC-CVID patients is distinct from that in INF-CVID patients and whether NIC-CVID gut dysbiosis can be recapitulated in animal models. Additionally, microbiome diversity, enrichment, and the specific taxa linked to CVID phenotypes remain unclear. To address these questions, we examined the gut microbiome composition in NIC-CVID and IFN-CVID patients, as well as their household controls. First, we established the baseline composition of the INF-CVID and NIC-CVID microbiomes at species-level resolution. Then, we comprehensively assessed the gut microbiome using metagenomic whole genome shotgun sequencing (mWGS) from 11 CVID patients (6 NIC-CVID and 5 INF-CVID) and their household controls. Finally, because CVID is a rare disease with no widely accepted animal models, we performed fecal microbiota transplant (FMT) from these CVID patients and household controls into germ-free C57Bl/6J GF mice to assess the feasibility of modeling CVID gut dysbiosis in mice. ## Recruitment Of Cvid Patients Patients were diagnosed with CVID by their treating clinical immunologists. Table S1 summarizes patients' characteristics. We excluded patients on immune suppressive medications, with an acute infection/illness, and those who received antibiotics 30 days before enrollment. We defined NIC-CVID patients as having severe forms of autoimmunity/immune dysregulation associated with CVID [1] (i.e., Granulomatous interstitial lung disease, colitis, nodular regenerative hyperplasia, lymphoproliferation, and severe cytopenia). Common autoimmunity, such as hypothyroidism alone, was not considered NIC. ## Fecal Microbiome Transplant FMT experiments were performed as described previously [24]. Fecal matter was thawed, diluted (100 mg/1 ml sterile PBS), passed through a 40μm strainer thrice, and then frozen at − 80°C. GF(C57BL/6J) mice (males and females, age 8–12 weeks) were orally gavaged (2–3 times over 1 week at 200μl/dose) with fecal matter from either NIC-CVID or INF-CVID patients or a healthy donor. Mice were allowed 30 days for the microbiome to stabilize. Blood and feces were collected at baseline and 30 days later. ## Whole Genome Shotgun Sequencing Libraries were prepared using the Nextera XT DNA Library Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions, except for using one-quarter of the recommended reaction volume. WGS was predominantly carried out using a 2× 150bp (paired-end) sequencing protocol on the NovaSeq 6000 Sequencing System (Illumina), according to the manufacturer's manual. Sequencing was conducted at the Genome Technologies core facility at the Jackson Laboratory for Genomic Medicine, Farmington, USA. ## Data Processing Samples with fewer than 1.4 million reads were excluded, leaving 22 samples from CVID patients, 15 household controls, and 112 mouse fecal samples for analysis. Relative proportions were used for all analyses. All taxonomic features at the species level with a mean relative abundance of $0.01\%$ (denoise function, [25] across the dataset were removed from the dataset to reduce potential false positives and allow for multiple hypothesis correction. ## Biomarker Discovery With Lefse LEfSe pipeline [26] was used with default parameters (LDA score log[10] > 2.0) to identify discriminant taxa between sample groups. We opted to categorize our metagenomic profiles based on MetaPhlAn 4. MetaPhlAn 4 is a tool for profiling microbiome communities and uses a database of unique, clade-specific gene markers. It assigns fragments by mapping them against the gene markers database [27]. MetaPhlAn is associated with higher accuracy and lower rates of false positivity [27, 28]. ## Statistical analysis We used the R-based software Agile Toolkit for Incisive Microbial Analyses (ATIMA) [29] to generate plots visualizing alpha diversity (richness and evenness), beta diversity (in-between sample differences), and taxa abundances (phylum-genus) through box plots, Principal Coordinate Analysis (PCoA) ordinations, and heatmaps. ATIMA enables rarefied and non-rarefied relative abundance analysis. We analyzed categorical variables using the non-parametric Mann-Whitney and Kruskal-Wallis tests for variables with 2 groups or ≥ 3 groups, respectively. P-values were adjusted for multiple comparisons using the FDR (false discovery rate) algorithm. The inter-group dissimilarities (beta diversity) in gut microbiota composition were measured using the Bray–Curtis distance metrics. Bray Curtis dissimilarity quantifies the differences in species populations between two different sites. The resulting number is between 0 and 1, with 0 denoting the highest similarity (two samples share the same species) and 1 denoting the highest dissimilarity. ## Gut microbiome alpha diversity is comparable between NIC-CVID and INF-CVID patients, as well as their household controls Greater diversity within each sample, known as alpha diversity, is often associated with a stable microbiome and healthy metabolism [30, 31]. To determine the effect of CVID on gut microbiome richness and diversity, we performed mWGS on the gut microbiome of NIC-CVID and INF-CVID patients, as well as their household controls (Fig. 1a). We found microbial alpha diversity was not statistically significantly different between NIC-CVID and INF-CVID patients or their household controls. Still, notably, Alpha diversity in the NIC-CVID participants was qualitatively lower compared to INF-CVID, and household controls (Fig. 1b). Nor did we detect any significant differences in alpha diversity between NIC-CVID and their matched household control or between INF-CVID and their matched household control were observed (Figs. 1c and d). ## Nic-cvid And Inf-cvid Patients Exhibit Dissimilar Gut Microbiome Composition Beta diversity captures differences in microbiota composition between two groups [32]. To identify potential associations between gut microbial composition and CVID phenotype, we used the Bray–Curtis dissimilarity matrix to cluster the metagenome using ATIMA (Agile Toolkit for Incisive Microbial Analysis), developed by the Center for Metagenomics and Microbiome Research at Baylor College of Medicine [29, 33]. CVID patients' bacterial microbiomes clustered separately from household controls and INF-CVID patients (Figs. 2a and 2b). Next, we compared each CVID phenotype with their household controls. The microbial composition of NIC-CVID patients was distinct from that of their household controls (Fig. 2c), whereas the microbiota composition of INF-CVID patients did not significantly differ from that of their household controls (Fig. 2d). We next compared inter-group dissimilarities in gut microbiota composition. We found the NIC-CVID group had greater microbiota variation from their household controls compared to the other groups (Fig. 2e). These findings indicate that NIC-CVID is associated with a significant shift in gut microbiome composition that overcomes the similarities that can be shared due to kinship and diet [34-36]. ## Distinct Microbial Species Are Associated With Nic-cvid And Inf-cvid Patients We used linear discriminant analysis (LDA) and LDA effect size (LEfSe) to identify microbes differentially associated with NIC-CVID or INF-CVID [26]. LEfSe couples standard tests for statistical significance with additional tests encoding biological consistency and effect relevance to determine the features, such as organisms, clades, operational taxonomic units, genes, or functions, most likely to explain differences between classes [26]. We found significant differences in the gut microbiome composition of NIC-CVID and INF-CVID patients at the species level (Fig. 3). The discriminant species for the NIC-CVID group were Streptococcus parasanguinis and Erysipelatoclostridium ramosum. Both are pathobionts reported to cause severe infections in immunocompromised hosts [37, 38]. In contrast, the microbiome of INF-CVID patients showed a preponderance of several microbes associated with anti-inflammatory effects, including Fusicatenibacter saccharivorans, Dorea longicatena, and Blautia faecie [39-41]. Additionally, we identified in the gut microbiome of INF-CVID patients an enrichment of microbes that are associated with healthy metabolism, including Anaerostipes hadrus [42], Coprococcus catus [43], and Roseburia hominis [44]. ## A New Cvid-fmt Gut Dysbiosis Model In Gf Mice Although CVID is considered the most common treatable inborn error of immunity in adults, it is still a rare and heterogeneous disease. A broader understanding of the role of the gut microbiome and its impact on immune regulation, in CVID patients, remains unclear. To determine the degree to which FMT would recapitulate differences in microbial composition observed in our human participants, we compared microbial communities between fecal matter from CVID patients, household controls, and FMT-recipient mice (Fig. 4a). GF mice have low serum and fecal IgA and underdeveloped Peyer patches, as well as small and underdeveloped mesenteric lymph nodes [45]. In addition, introducing normal flora into GF mice restores their capacity to produce mucosal and systemic immune responses [46]. Consistent with these findings, our pilot studies showed that GF(C57Bl/6J) mice had undetectable serum IgA, variable serum IgG, and low fecal IgA/IgG levels (0–10 μg/ml and 0–3 ng/ml, respectively) at baseline (Figure S1a, b). Four weeks following FMT, serum IgA levels increased in all FMT recipients (Figure S1c). In addition, serum IgG increased, (Figure S1d), whereas fecal IgG levels remained low (0–6 ng/ml, similar to fecal IgG levels in WT C57Bl/6J mice) housed in a specific pathogen-free facility. We noted interesting differences when we compared the immunoglobulin levels between FMT groups. First, there was no significant difference in serum IgA among FMT recipients following FMT (Figure S1e). In contrast, total serum IgG was higher in both NIC-FMT and INF-FMT recipients, compared to CTL-FMT recipients. ( Figure S1f). Notably, the increase in IgG subclasses differed per FMT group. IgG2b was significantly higher in both NIC-FMT and INF-FMT recipients compared to CTL-FMT (Figure S1g), while IgG2c was higher in INF-CTL compared to all other groups (Figure S1h). We measured IgG2c instead of IgG2a because C57BL/6 mice produce this isotype in place of IgG2a [47]. IgG2c in mice is produced as a result of Th1 response and INFγ production [48, 49], while IgG2b binds to FcγRIII and IV, activating FcγRs, which has been shown to induce autoimmunity, such as arthritis [50] and thrombocytopenia [51]. Although, taken together, the antibody responses in CVID-FMT recipients may indicate an inflammatory response to FMT compared to CTL-FMT recipients, the findings should be interpreted with caution and require replication. To prevent the development of anti-commensal antibody responses in FMT recipients, [52], we pretreated GF mice with 100 μg anti-mouse CD20 mAb intraperitoneally every two weeks, to prevent the development of anti-commensal antibody responses in FMT recipients, [52]. Figure S2a and b show our flow cytometry gating strategy to assess mouse blood for B-cells before and after anti-CD20 depletion. Figure S2c shows successful B-cell depletion following anti-CD20 treatment. With this approach, we induced relative hypogammaglobulinemia (Figure S3a-d). The rationale for B-cell depletion is to prevent the production of specific antibodies to new antigens [53], generating a humoral immune defect that resembles CVID. No significant differences in FMT engraftment or mouse health were noted in mice treated with anti-CD20 mAb. ## Fmt From Cvid Patients To Gf Mice Recapitulates Cvid Patients' Gut Dysbiosis We examined broad community metrics, including alpha and beta diversity, to characterize the overall similarity between donor and recipient communities. Four weeks following FMT, there was a significant difference in microbial richness and alpha diversity between NIC-FMT, INF-FMT, and CTL-FMT recipients (Fig. 4b). We also found a significant difference in gut microbial richness and alpha diversity between NIC-FMT and INF-FMT recipients (Fig. 4c). In addition, beta diversity measurements using unweighted and weighted UniFrac distances revealed that the gut microbiome composition was significantly different between the three FMT groups (Fig. 4d). Notably, the microbiota composition of NIC-FMT recipients was distinct from INF-FMT recipients (Fig. 4e). In addition, inter-group analysis in gut microbiota composition identified dissimilarities between FMT recipients, most notably between CVID-FMT and control-FMT recipients (Fig. 4f). Taken together, these results demonstrate that GF-FMT mouse recipients predominantly exhibited gut microbiome compositional aberrations resembling what was seen in CVID donors. We compared the relative abundance of the top 25 most abundant taxa between human fecal donors and FMT recipients (Fig. 5a). There was no statistically significant difference in the relative abundance between human donors and their respective FMT recipient mice in any of the FMT experiments. An exception was Klebsiella sp., which was present in low abundance in one NIC-CVID patient but was not detected in the mice. Bacteroides sp, Clostridium sp., and Akkermansia muciniphila had the highest relative abundance in both humans and mice. Finally, we examined species-level differences between NIC-FMT and INF-FMT recipients. A representation of the mice's fecal microbiome that compares the relative abundance of the top 25 most abundant taxa between NIC-FMT and INF-FMT recipients is shown in (Fig. 5b). Similar to what we observed in CVID patients, NIC-FMT recipients had a higher relative abundance of microbes that can potentially cause opportunistic infections in immunocompromised individuals, including Dysgonomonas mossii and Negativebacillus massiliensis. D. mossii is a Gram-negative, anaerobic, coccobacillus-shaped bacteria within the phylum Bacteroidetes that has been reported to cause opportunistic infections in patients with type 1 diabetes and cancer [54-56]. Similarly, N. massiliensis is a rare microbe that caused meningitis in a patient with Whipple Syndrome [57]. On the other hand, INF-FMT recipients had a higher relative abundance of potentially beneficial microbes, including Clostridium symbiosum and Parabacteroides distasonis. C. symbiosum is a short-chain fatty acid producer associated with immunomodulatory and anti-inflammatory effects [58]. Adding C. Symbiosum to the microbiota of a malnutrition mouse model ameliorated growth and metabolic abnormalities in the recipient mice [59]. P. distasonis is one of 18 core members in the human gut microbiota [60] and thought to have critical physiological functions in its hosts. P. distasonis produces succinate (which activates gut glucogenesis) and transforms primary bile acids into secondary bile acids [61]. Both succinate and secondary bile acids can promote gut barrier integrity and reduce inflammation in the gut of obese mice [62]. Taken together, our mWGS analysis of fecal matter from CVID patients and FMT-recipient GF mice revealed a high level of similarity between humans and mice, both in diversity metrics and in potential function. Both NIC-CVID patients and NIC-FMT recipients harbored potential pathogenic microbes associated with opportunistic infections in immunocompromised hosts, whereas INF-CVID patients and INF-FMT recipients harbored microbes with beneficial metabolic functions and potential anti-inflammatory capacity. ## Discussion In the present study, we performed mWGS on the gut microbiomes from NIC-CVID and INF-CVID patients, as well as their healthy household controls. To overcome intra-individual microbial variations that can be missed when only a single sample collection is used, we collected two samples from each patient and household control for a more accurate assessment of the microbiome composition [63]. Additionally, we included healthy household members as a control for diet and environmental factors [64]. Flousehold members share more of their gut microbes compared to unrelated individuals, and intimate partners share even more gut microbiota than other household members [35, 65]. Using these robust methods, we were able to further characterize gut microbiome composition in CVID patients. We identified specific microbes that were more abundant in NIC-CVID patients, including S. parasanguinis and E. ramosum. S. parasanguinis is predominantly an oral cavity microbe that belongs to the viridans group streptococci (VGS). Although VGS are generally considered to be of low pathogenic potential in immunocompetent individuals, they can cause invasive diseases such as endocarditis, intra-abdominal infection, and shock [66]. S. parasanguinis is known to produce hydrogen peroxide [67] and has been reported to cause invasive infections, such as infective endocarditis and pneumonia, in immunocompromised hosts [37, 68]. Additionally, the presence of S. parasanguinis in the gut is associated with dysbiosis in inflammatory bowel disease patients, owing to oxidative stress resistance in such bacteria [69]. Hence, it is plausible that S. parasanguinis contributes to gut dysbiosis and immune dysregulation in NIC-CVID. We also found that E. ramosum is more abundant in the gut microbiome of NIC-CVID patients. E. ramosum belongs to the Clostridia group and has been shown to cause severe infections, particularly in immunocompromised patients [38]. Interestingly, E. ramosum produces an IgA protease that is capable of cleaving human IgA [70]. E. ramosum has been shown recently to be over 80-fold enriched in individuals with selective IgA deficiency, especially in those with autoreactive anti-IgA antibodies, suggesting a potential role for this pathobiont as an autoimmune trigger [71]. In INF-CVID patients, we noted an increased abundance of several microbes associated with potential anti-inflammatory effects, including F. saccharivorans, D. longicatena, and B. faecie. We also identified microbes associated with healthy metabolism, including A. hadrus, C. catus, R. hominis, Blautia massiliensis, and Firmicutes bacterium. The most abundant bacteria in INF-CVID patients was F. saccharivorans, a species of the Clostridia class. Its abundance is associated with ulcerative colitis remission [39]. In contrast, its decrease is associated with increased ulcerative colitis disease activity, which has been attributed to its immunomodulatory effects and its ability to induce IL-10 production in humans and mice [39, 72]. Similarly, the presence of D. longicatena in the gut microbiome is associated with Crohn's disease remission [40]. The second most abundant bacteria in the gut of INF-CVID patients was A. hadrus, a human-derived butyrate-producing strain. In contrast, A. hardus was shown in mice to be beneficial by increasing butyrate levels in the gut and harmful by potentially causing worse chemically-induced colitis [42]. Butyrate is produced when gut microbes ferment dietary fiber and is considered a health-promoting molecule due to its anti-inflammatory [73] and anti-neoplastic potential [74]. We also revealed that two of the Blautia species were enriched in the INF-CVID gut microbiome. Blautia sp can metabolize polymethoxyflavones, which are major bioactive flavonoids with various biological activities, including anti-inflammatory and anti-cancer effects [41, 75]. Finally, we observed Firmicutes was enriched in INF-CVID patients. Two studies that used 16S rRNA gene sequencing for CVID gut microbiomes identified an increase in some Firmicutes in CVID [14, 76] metabolize polymethoxyflavones, which are major bioactive flavonoids with various biological activities, including anti-inflammatory and anti-cancer effects produces butyrate and supports healthy metabolism [77]. Notably, Firmicutes harbors H2-oxidizing properties that promote more efficient energy extraction from food [78]. Although an abundance of Firmicutes in the gut microbiome is associated with obesity [77, 79], this property of Firmicutes might be beneficial in CVID patients, as many with enteropathy develop malnutrition [80]. Overall, the gut microbiome in INF-CVID patients was enriched with microbes that have been associated with a healthy metabolism and anti-inflammatory effects. In contrast, the NIC-CVID microbiome was enriched with inflammation-associated microbes, especially in the immunocompromised host. In addition to our comprehensive characterization of the CVID gut microbiome, we provided a proof of concept that FMT from CVID patients to GF mice recapitulates the microbiome alterations seen in CVID patients. As far as we are aware, our model is the first to use B-cell depletion, to induce hypogammaglobulinemia, and prevent the generation of specific antibody response against transplanted human microbiota, creating an antibody defect that resembles CVID immunophenotype. Even though the highest abundance of microbes in mice was not the same as in CVID patients, we noted the same potential pathogenicity and function in the gut microbiome of both mice and humans. The relative abundance of microbes associated with opportunistic infections and potential pro-inflammatory capacities were enriched in the NIC-CVID patients. On the other hand, microbes associated with a healthy metabolism and potential anti-inflammatory capacities were enriched in INF-CVID patients and INF-FMT recipients. In future studies, we believe this model may allow us to assess the impact of microbiome manipulation on immune responses and test therapeutics to ameliorate microbiome-associated immune dysregulation in CVID patients. Although we did not detect a significant difference in alpha diversity between CVID patients and household controls, or between NIC-CVID and INF-CVID, we noted that Alpha diversity in the NIC-CVID participants was qualitatively lower compared to INF-CVID, and household controls. Previous studies showed that alpha diversity was lower in CVID patients compared to a general population healthy control and household controls using 16S rRNA gene sequencing [14, 20]. However, smaller studies using mWGS showed that CVID patients (with no significant complications) had increased bacterial diversity compared to their household controls [81]. Unlike 16S rRNA sequencing, mWGS reads all genomic DNA in a sample, rather than just one specific region of DNA, which allows the identification and profiling of all microbial genes present in the sample (the metagenome). Thus, metagenomic profiling often provides species-level assignment [82]. Our study has some limitations. Owing to the rare nature of inborn errors of immunity, this study comprised a small sample size. Also, our strict exclusion criteria eliminated patients with acute illness, infection, or recent use of antimicrobial agents. However, our longitudinal design mitigated these limitations to a degree. Additionally, household controls allowed us to control for shared diets and environments [31, 35]. Finally, assessing two samples from each subject helps overcome some intra-individual microbial variations and provides a more accurate assessment of the microbiome composition [63]. Our goal for this study was not to generate a CVID mouse model but rather to create a gut dysbiosis model that could potentially be used to further model mucosal immune dysregulation in CVID. In addition, the model developed in this study may allow us to assess the impact of microbiome manipulation on immune responses and test therapeutics to ameliorate immune dysregulation in an immunocompromised host. 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--- title: Automated evaluation of cardiac contractile dynamics and aging prediction using machine learning in a Drosophila model authors: - Aniket Pant - Yash Melkani - Girish C. Melkani journal: Research Square year: 2023 pmcid: PMC10055502 doi: 10.21203/rs.3.rs-2635745/v1 license: CC BY 4.0 --- # Automated evaluation of cardiac contractile dynamics and aging prediction using machine learning in a Drosophila model ## Abstract The Drosophila model has proven tremendously powerful for understanding pathophysiological bases of several human disorders including aging and cardiovascular disease. Relevant high-speed imaging and high-throughput lab assays generate large volumes of high-resolution videos, necessitating next-generation methods for rapid analysis. We present a platform for deep learning-assisted segmentation applied to optical microscopy of Drosophila hearts and the first to quantify cardiac physiological parameters during aging. An experimental test dataset is used to validate a Drosophila aging model. We then use two novel methods to predict fly aging: deep-learning video classification and machine-learning classification via cardiac parameters. Both models suggest excellent performance, with an accuracy of $83.3\%$ (AUC 0.90) and $77.1\%$ (AUC 0.85), respectively. Furthermore, we report beat-level dynamics for predicting the prevalence of cardiac arrhythmia. The presented approaches can expedite future cardiac assays for modeling human diseases in Drosophila and can be extended to numerous animal/human cardiac assays under multiple conditions. ## Introduction Cardiovascular disease (CVD) continues to be among the leading causes of death and disability in the United States and a major public health burden. Despite other risk factors, aging is one of the major risk factors for advancing CVD. As the modern human population is increasingly adapting to lifestyles that mimic shift work further exacerbates CVD risks1. The Drosophila melanogaster (fruit flies) model system will allow us to comprehensively examine the potential cardiac benefit of a simple lifestyle modification and identify the relevant molecular and physiological changes. Gaining pathophysiological insights is essential for designing therapies and in vivo models including Drosophila models and modern technologies including machine learning are powerful tools for understanding several aspects of human pathophysiology, including aging and CVD. Despite some morphological and functional differences, Drosophila and human hearts conserved pathways appear to govern form and function2–9. Several genetic and non-genetic risks for heart diseases in humans also increase disease risks in Drosophila during aging. Moreover, mutant flies carrying genetic variants that are associated with a higher risk for CVD result in a similar outcome in the fly3,4,6. Furthermore, flies like humans living in industrial societies, consume some of their daily caloric intake at night and nutritional challenges that compromise cardiac function in humans have similar effects in flies10–12. Progress in the high-speed recording of optical cardiac imagery has led to remarkable development in the application of Drosophila, zebrafish, and embryonic mouse heart for cardiac modeling13. For example, current canonical methods using Semi-Automatic Optical Heart Analysis (SOHA) software offers methods for measuring relevant cardiac parameters during aging and under multiple disease conditions13. However, the SOHA software requires manual selection of points of interest in diastole and systole phases, allowing the software to track changes in heart morphology. Moreover, Dong et. al demonstrated the use of 3D convolutional architectures for segmentation of Drosophila heart images studied via optical coherence microscopy (OCM) setups14. The authors reported robust segmentation ($92\%$ IoU) of OCM videos, with capabilities for calculating EDD, ESD, heart area and heart rate. Similarly, Lee et. al demonstrate Drosophila heart-beat counting using segmentation of optical coherence tomography (OCT) recordings15. They demonstrate similar segmentation of OCM videos and also apply PCA procedure for heart morphology reconstruction. Work by Klassen et al. enabled in vivo imaging of Drosophila aging models with a fluorescence imaging and conventional image-processing techniques for high-resolution, unanesthetized beating patterns16. Using a conventional computer vision approach, the authors provide methods for automatic segmentation and parameter (chamber diameter, fractional shortening, systolic interval, cardiac output, and heart wall velocity) calculations. We find much discussion of successful automatic analysis techniques for use with optical coherence microscopy but find a lack of literature surrounding similar techniques in standard high-resolution optical microscopy setups, likely due to added complexity in visualized heart morphology. Through literature review, we find that a limited amount of cardiac physiological data has been analyzed using machine learning from morphological data collected with OCT or OCM techniques, with quantification of all relevant cardiac parameters. Moreover, heart analyses using machine learning techniques in the literature have not tested rigorous Drosophila aging or disease models. Furthermore, there is a lack of automated methods for analysis of high-speed Drosophila cardiac optical recordings in general optical microscopy setups. Modern video volumes outgrow the use of manual analysis techniques, necessitating automatic analysis methods. Our method differs from others in that it allows for automatic analysis of optical cardiac recordings at high spatial and temporal resolutions. Furthermore, we are the first to provide all cardiac statistics via a deep learning assisted pipeline. We attempt a solution with a well-known deep learning-assisted medical image segmentation architecture, the UNet. Recent developments in machine learning have enabled state-of-the-art dense image segmentation platforms, such as the development of full convolutional networks (FCNs)17, SegNets18, and UNets19–22. Historically, UNet architectures have been applied for segmentation cell nuclei, spleen, and liver segmentation23,24, COVID-19 prognosis25, and more. Human heart analysis has been a central focus in medical segmentation efforts 26–28. In 2020, Ouyang et. al26 demonstrated the use of convolutional architectures for segmentation and beat-level analysis of human echocardiograms. They demonstrate real-time prediction of ejection fraction with comparable or less variance than that of human predictions. In this work, we draw inspiration from similar applications of medical image segmentation and apply UNet architectures to segmentation and beat-level analysis of Drosophila cardiac recordings. To augment human analysis of Drosophila cardiac recordings, we apply techniques in medical segmentation for analysis of high-speed optical recordings. We calculate multiple diagnostic cardiac parameters for assessing cardiac function from videos alone. A 2D attention-based UNet architecture is applied for frame-level dense segmentation of cardiac recordings. Using only the generated annotations, we calculate beat-level cardiac parameters and beating patterns. Cardiac parameters are calculated on a per-beat resolution. We used $$n = 54$$ hearts for model training and validated an experimental aging model with $$n = 177$$ hearts. This deep-learning approach to Drosophila cardiac analysis facilitates robust analysis of Drosophila cardiac physiology and enables access to future downstream analysis techniques. We investigate the use of both Drosophila cardiac statistics and cardiac videos for fly age prediction as an example downstream task. We determine that conventional machine learning and deep learning methods can be applied for predicting fly age, suggesting that morphological features evident in video recording can be an effective predictor of an aging phenotype. ## Overview of presented machine learning pipeline Analysis of Drosophila high-speed cardiac recordings requires high-throughput and accurate measurement techniques. Current methods involve tedious analyses with large amounts of personnel hours, necessitating automatic next-generation methods. In this work, we propose three machine learning enabled pipelines that is well-suited for rapid analysis of Drosophila cardiac recordings. Depicted in Fig. 1, we establish two unique tasks: [1] heart wall detection via semantic segmentation and [2] age prediction. In the presented segmentation pipeline (Fig. 1), a user begins by uploading high-speed cardiac recordings to a central workstation via a file-transfer protocol. After this, the user selects videos for segmentation and analysis and provides them to our segmentation model, which is made accessible via a PyTorch interface29. Then, using our segmentation model, heart wall regions are tagged on a frame-by-frame basis, allowing us to easily calculate average heart diameter and area per-frame. After results have been generated per frame, users may vary network parameters such as selecting a region-of-interest in diameter calculations and a model confidence threshold. Supplementary Fig. 1 (.mp4) demonstrates a video-format output of our network results for a given heart, for five seconds. Once a satisfactory segmentation is acquired, users may export time-resolved morphological data. Finally, using the exported morphological data, we make available relevant cardiac statistics for assessing function and rhythmicity. Example Python notebooks are provided for segmenting and analyzing heart videos with easy to use, interactive user-interfaces. Two separate pipelines are presented for age prediction. One such pipeline involves using segmentation-calculated cardiac statistics for age-prediction. Age-prediction via cardiac statistics is powered via a logistic classification model. Such a logistic classification approach enables transparent reporting of model interpretation via black-box methods such as calculation of SHAPly scores. Furthermore, we present a fully deep learning-based pipeline for age classification. Through use of raw video frames, we determine that convolutional neural networks can predict fly age with high accuracy and specificity. Additional detail for both methods is provided in the Method section. ## Representative output from our deep learning pipeline Fig. 2 demonstrates a representative output from our deep learning pipeline. This includes frame-by-frame heart wall output, visualized beating patterns, time-resolved morphological data, and calculated cardiac statistics on a representative level. Heart morphology is captured on a per-frame basis, denoted by overlaid red layers (Fig. 2a), as indicated in the methods section. Qualitative inspection suggests excellent agreement between detected heart regions and ground truth through all stages of the cardiac cycle. Fig. 2b, d, f present representative model outputs for a 1-week male heart. Alternatively, Fig. 2c, e, g present representative model outputs for a 5-week male heart. We trace the annotation over time at a single vertical slice, creating a mechanical mode (M-Mode) image, demonstrated in Fig. 2b, c. Again, one may note excellent agreement between annotated and ground truth MMode, indicating excellent capture of the heart wall dynamics. Furthermore, periods of diastolic and systolic intervals are automatically labeled with red and green vertical lines. Fig. 2d, e depicts time-resolved beating patterns constructed via measurement of heart diameter through segmented heart walls. Average diameter is calculated by measuring each vertical pixel-slice distance in our frame-by-frame heart walls. This is further discussed in the Methods section. The time-resolved beating patterns clearly demonstrate differences in contractility and arrhythmia, making neural-network captured aging phenotypes clear on a representative basis. These are further quantified in tabulated cardiac statistics for a single heart ($$n = 1$$), made available in Fig. 2f, g. These aging phenotypes are presented for a larger-scale study in Figs. 3, 4. ## Experimental validation of Drosophila aging model A study investigating cardiac aging phenotypes was performed on a cohort population. We demonstrate that our model can capture aging phenotypes presented in existing literature with a fraction of the effort required from canonical analysis techniques (SOHA). We calculate a full suite of cardiac statistics including diastolic diameter (Fig. 3a), systolic diameter (Fig. 3b), fractional shortening (Fig. 3c), heart rate (Fig. 3d), and arrythmia index (Fig. 3e). Furthermore, we provide select temporal statistics including heart period, diastolic intervals, and systolic intervals in the SI (Supplementary Fig. 2). Fig. 4 presents model-detected contractile dynamics, including aging phenotypes in spatial and temporal modes. Supplementary Fig. 3 depicts heart fractional shortening and heart period, as a function of gender and age at the beat-level. ## Deep learning recovers aging phenotypes including contractile dynamics in a Drosophila cardiac model We find that expected directional trends in cardiac function and arrythmia are captured successfully across aging groups and genders (Fig. 3). Spatial (cardiac morphology) associated statistics are first discussed. We demonstrated a statistically significant decrease in diastolic diameter in male aging groups and no change in diastolic diameter across female aging groups, with agreement between experimental machine-learning (ML) and SOHA groups. Systolic diameter demonstrates little variance across aging groups with agreement between ML SOHA groups, except for a weak statistically significant difference in the ML female aging group ($$p \leq 0.049$$). Furthermore, we observe a strong statistically significant decrease of cardiac function (fractional shortening) across both male and female aging ($p \leq 0.001$). R2 measurements between ML and SOHA data are high for diastolic and systolic diameter measurements, at R2 = 0.76 and R2 = 0.69, respectively. However, R2 is low for fractional shortening measurements at R2 = 0.32. We demonstrate a strong reduction in cardiac function with age across both male and female groups in ML and SOHA studies, as expected. This agrees with morphological parameters quantified in Fig. 4, with a statistically significant reduction in stroke volume (Fig. 4a) and cardiac output (Fig. 4b). Methods for calculating all discussed parameters are made available in the Methods section. Next, we discuss temporal associated cardiac statistics. We find that our model can accurately capture changes in heart rate, with expected significant differences across aging groups in both ML and SOHA datapoints. Similar agreement is presented in studied heart period, diastolic and systolic interval data across ML and SOHA groups. The model captures an accurate evolution of cardiac arrythmia in female aging data in both ML and SOHA groups but exhibits an unexpected significant difference in male aging groups. R2 measurements between ML and SOHA data are high for temporal statistics including heart rate, heart period, diastolic interval, and systolic intervals, at R2 = 0.88, R2 = 0.91, R2 = 0.89, and R2 = 0.63, respectively. However, R2 is low for arrythmia index measurements at R2 = 0.46. We observe a small increase in cardiac arrythmia with aging in both ML and SOHA groups. While heart period and derivative temporal parameters demonstrate statistically significant shifts with aging, changes in rhythmicity and contractile latency are not exhibited with aging (Figs. 3, 4). Latency is quantified in Fig. 4c, d, concluding that no significant difference in latency to peak contraction and latency between peak contraction to peak relaxation is expressed in our aging model across aging in gender-separated data. This signals agreement with previously presented results in measured arrythmia shifts with aging. We also demonstrate our ability to capture beat-level contractile and temporal dynamics in beating patterns. In both male and female groups, we see a large flattening across 1-week and 5-week fractional shortening distributions, indicating a large increase in contractility variance (Supplementary Fig. 3 a, b). Furthermore, a general decrease in contractility with aging is depicted on a beat-level, as expected from cohort-level data. The reverse trend is true in heart period distribution, with flattening across both male and female aging groups but an increase, on average, in beat length (Supplementary Fig. 3c, d). Furthermore, we employ beat-level dynamics to detect brachy- and tachycardia arrythmias. Supplementary Fig. 4 quantifies prevalence and significant shifts in exhibited tachy- and brachy- cardiac arrythmia across our aging model. We find a significant decrease in average SI length during tachycardiac events for female specimen with aging (Supplementary Fig. 4). Additionally, we observe a significant increase in average DI length during brachycardiac events for both male and female specimen through aging. We note an exceptionally small n-value at $$n = 3$$ for brachycardiac events in young female hearts. We visualize cohort-level and beat-level data for SI and DI distributions (Supplementary Fig. 4c). Beat-level data suggests a widening distribution in both SI and DI for aging male and female specimen. ## Prediction of aging in Drosophila data We investigate the use of machine learning approaches for age classification in Drosophila datasets (Figs. 5, 6). We apply logistic regression for age classification (Fig. 5). We note high accuracy in experimental classification, with an accuracy of $77.1\%$ and AUROC of 0.85 (Fig. 5a, b). We observe the highest inaccuracies to be found in old hearts falsely classified as young. The presented results suggest that cardiac statistics are accurate predictors of aging phenotypes. We demonstrate the use of SHAP values for assessing drivers of aging function, enabling interpretable machine learning (Fig. 5c). Descriptors are sorted in predictive importance, from top to bottom. Thus, we note that SHAP-methods determine fractional shortening (FS) as a main predictor of fly age, followed by diastolic diameter (DD). Using a deep convolutional neural network, we train a video classifier for assessing fly age from cardiac video without knowledge of cardiac parameters. Results of this technique are presented in Fig. 6. The constructed model architecture along with data preparation are discussed in the Methods section. Using a k-fold cross-validation procedure, we perform experimental validation on $$n = 497$$ cardiac samples, with group breakdowns of $$n = 118$$ for 1wm, $$n = 156$$ for 1wf, $$n = 121$$ for 5wm, and $$n = 98$$ for 5wf. We observe an average AUROC score of 0.90 (Fig. 6b) and an average accuracy of $83.3\%$, demonstrating excellent performance, further depicted in the confusion matrix (Fig. 6a). Qualitatively, we note excellent separation of age likelihood as determined by our model, with a high separation between young and old likelihoods (Fig. 6c). The presented results suggest a neural architecture can recover fly age from cardiac video based on spatial data from select frames. We note that the highest number of inaccuracies found in young hearts falsely predicted as old ($22\%$ of samples). Overall, we determine that deep learning classification models can model aging directly on raw cardiac video with a high accuracy, suggesting such models may possess capabilities for capturing morphological or rhythmic features in image pairs. Furthermore, we determine that both cardiac parameters and videos can be applied for highly accurate aging prediction in Drosophila models. ## Discussion We present methods for the machine and deep-learning analysis of Drosophila optical cardiac videos. We find that deep segmentation models can accurately recover important contractile and temporal dynamics in Drosophila heart beating patterns. Our approach allows us to bypass the time-consuming human involvement required in existing canonical software such as SOHA13. Moreover, our automated machine-learning analysis will be helpful to erase human errors in marking heart edges under contraction and relaxation conditions. This is one of the vital steps for automated analysis as three of the main cardiac parameters, including diastolic and systolic diameters as well as fractional shortening (cardiac performance) depends on accurately marking cardiac edges. Such cardiac edge marking is automated in Dong et al.14 for OCM and Klassen et al.16 for fluorescent microscopy, but yet to be automated in an optical microscopy approach. Dong et al.14 provides utilities for heart beat calculation. We provide a full suite of cardiac statistics that may be automatically calculated using our deep model. The presented code can readily provide calculated cardiac statistics including diastolic and systolic diameters/intervals, fractional shortening, ejection fraction, heart period/rate as well as quantify heartbeat arrhythmicity. Our model may be applied on readily available consumer hardware. In our study, we employ the Tesla P100, but suggest the model can be used with lower-end, consumer graphics models. Our method may potentially aid researchers with a higher fidelity, reproducible, and more automatic approach to Drosophila cardiac modeling beyond the capabilities of human technicians. We use a 2D deep segmentation model to generate heart-wall segmentations on a frame-by-frame basis. We find that the employed segmentation model allows us to recover accurate heart-wall segmentations, thus, recover accurate beating patterns and cardiac statistics on a frame-by-frame resolution. Using our deep learning method, we note fly aging expresses significant reduction in cardiac function (contractility) and an increase in cardiac dysrhythmia. Similarly, our model detects significant changes in aged heart rate and heart period, as well as underlying parameters including diastolic and systolic intervals. Furthermore, such annotations open opportunities in precise time-resolved study of Drosophila cardiac morphologies in optical micrography assays (Fig. 2). Such analysis is not possible using canonical analysis software. For example, deep learning assisted modeling of congenital muscular dystrophies in Drosophila cardiac morphologies may reveal unique physiological information30,31. To our knowledge, this is the first platform for deep learning assisted segmentation applied to standard high-resolution, high-speed optical microscopy of Drosophila hearts and is the first to quantify all relevant parameters, including directly quantifying ejection fraction. Cited works such as Ouyang et. al26 report a limited amount of cardiac parameters and provide ejection fraction as a model-derived value on human echocardiography data. Ouyang et. al studied failing hearts as a contrasting model to demonstrate differences in beat-level ejection fraction but did not include age dependence. Similarly, Lee et. al15 report segmentation on Drosophila OCM video, with fewer provided cardiac parameters including heart-rate, ESD, EDD, and FS, with some discussion on aging phenotypes. We further enable understanding of contractile dynamics via our beat-level capabilities (Figs. 3, 4, Supplementary Fig. 3). Through per-frame analysis, we quantify contractility through measurement of morphological parameters quantifying stroke performance (Figs. 3, 4) and latency (Fig. 4). We note significant reduction in spatial beating modes (FS, SV, CO) with aging, but little to no dependence on aging for temporal beating character (AI, latency) (Fig. 4). However, we do note significant shifts with aging in beat lengths, indicated in modeling of HP and derivative parameters. Beat-level investigation elucidates per-beat information regarding cardiac arrythmia’s including tachycardia and brachycardiac arrythmias (Supplementary Fig. 3). We observe a significant, large increase in DI length during brachycardiac events in 5wk male flies. We demonstrate capabilities of predicting fly age using experimentally calculated cardiac statistics with excellent agreement (Fig. 5). We also use a 2D video classification model to predict fly ages between 1-week and 5-week groups (Fig. 6). The ability to classify age via both raw video frames (Fig. 6) and cardiac statistics suggests that experimentally calculated cardiac statistics are physiologically salient and model age dependence. Additionally, the ability to predict age with only video frames suggests that deep learning models can capture morphological and rhythmic patterns in Drosophila cardiac video data. This has important implications for detecting phenotypes mimicking or delaying aging of Drosophila hearts. To our knowledge, we are the first work to determine that deep neural networks can capture such cardiac physiological features of the heart that induce aging in cardiac videos. In the future, such classification could be extended to classify healthy and compromised hearts in mutant studies and may include further quantification through methods such as GradCAM32. Current limitations include validation of parameters including calculation region of interest and suitable confidence thresholds. In the future, we hope to overcome this limitation. We report preliminary results in the Supplementary Fig. 3 and Supplementary Fig. 4 of using our trained segmentation approach without specifying a region of interest for morphological calculation (referred to as “No ROI”). Furthermore, we homogenously employ a pre-selected threshold, enabling a user to analyze samples with no human input or supervision. Presented results demonstrate excellent agreement in temporal statistics, including high accuracy in heart rate/period, diastolic and systolic intervals (with comparison to data collected via SOHA software), but strong disagreement in the arrythmia index. Spatial statistics, however, demonstrate strong disagreement in systolic diameter, driving further disagreement in fractional shortening. We propose further developments in dataset size, domain adaptability, and self-supervision may lead to stronger spatial performance in the case of completely hands-free usage. Future applications of the discussed techniques include enabling deep learning assisted studies of cardiac mutation models, other small animal models (e.g., commonly studied zebrafish and mice models), and parameter calculation in human heart models. Furthermore, quantification of measured uncertainty techniques may be applied to qualify certainty of heart analyses. Massive volumes of Drosophila cardiac data collected in the lab necessitate advanced methods for automated analysis of cardiac physiologies and morphologies on a beat-by-beat basis. In summary, we evaluate the use of deep segmentation models for high-fidelity analysis of cardiac physiologies in high-speed Drosophila cardiac optical recordings. We demonstrate that the presented deep segmentation models can be applied for accurately expressing known Drosophila phenotypes in aging across male and female 1-week and 5-week groups. Furthermore, we demonstrate that developed deep video classification methods can be successfully applied to Drosophila fly age-classification using only video clips with exceptional accuracy. We hope these models can be applied in the future to expedite laboratory analysis and power next-generation Drosophila cardiological model analysis. ## High-speed cardiac recording Physiological cardiac parameters such as heart rate, heart period, diastolic and systolic diameters, diastolic and systolic intervals, cardiac rhythmicity, and cardiac performance (% fractional shortening) will be determined for each fly group to detect cardiac defects using established protocols4–7. To avoid any circadian variability in cardiac function, all assays will be performed between ZT4 and ZT8. Briefly, in these semi-intact heart preparations, nerve input is eliminated so that endogenous pacing can be observed. Direct immersion optics is used in conjunction with a digital high-speed camera (up to 150 frame/s, Hamamatsu EM-CCD) to record contraction movements using the image capture software HC Image (Hamamatsu Corp.). ## Dataset curation A standard library of high-speed cardiac optical recordings is procured for training. We employ a total of 54 training videos, with an $85\%$/$15\%$ train/validation split. Training videos are captured in grayscale format with a total of 500 frames per video from a complete 6000 frames, indicating 2.5 seconds of video. Training videos include 40 videos with wildtype and 14 videos with genetically engineered flies. Researchers used the Computer Vision Annotation Tool (CVAT) software to produce high-quality masks on a per-frame basis. Annotations spanned heart walls that were clearly identifiable – annotations were clipped in the presence of pericardial occlusion, change of heart regions, and unclear tissue. Annotation masks were reviewed for accuracy before usage in deep-learning experiments. Testing videos are captured in identical formats. We procure 177 videos for experimental validation, with $$n = 46$$ for 1-week males, $$n = 43$$ for 1-week females, $$n = 44$$ for 5-week males, and $$n = 44$$ for 5-week females. ## Deep-learning development and training Model design was performed in Python using the PyTorch deep learning framework29. Our study employs a modified Attention-UNet architecture for semantic segmentation22. The model contains a symmetric encoder/decoder architecture containing 8, 16, 32, 64, and 128 filters, employing a rectangular convolutional kernel. Validation loss was optimized via Dice Loss33. Our model employs a random weight initialization and the Adam34 optimizer and a 16-image batch size. For experimental testing, the model epoch with the lowest validation loss was employed. During the training process, the neural network samples data via a data point’s corresponding diameter, as measured via its human-annotated mask. 75 frames per measured diameter are randomly sampled to be included in the training dataset – this is done to minimize class imbalance in our segmentation task. After frame sampling, 3D samples are constructed by appending frames at (t − 4, t, t + 4) indices, allowing us to encode temporal information in 3D convolutions. After sampling and augmentation, the model used a total of 114750 images for training with 19350 images used for validation. The model was trained for a total of 30 epochs, yielding a total of approximately 30 compute hours. Models were trained on a Tesla P100 GPU on the UAB Cheaha supercomputer. ## Calculation of cardiac parameters Once a video has been selected for analysis, each frame t is converted into a three-channel image with the t − 4 frame and the t + 4 frame. Images are then processed via neural network. On a Tesla P100, we estimate this process to take approximately 103 seconds for a video with 5990 frames (58.16 FPS). From sigmoidal activations, the user determines an acceptable threshold and region-of-interest (ROI) through visual confirmation. Using provided values, average heart diameter for each frame is measured. Processing codes for the identification of diastolic intervals (DI) and systolic intervals (SI) is provided in the paper repository (GitHub). The diastolic diameter (DD) for each DI is taken to be the largest diameter attained during the DI. Similarly, the systolic diameter (SD) for each SI is taken to be the smallest diameter attained over its duration. We derive additional cardiac parameters from the collected DD, SD, DI, and SI statistics. Equations for fractional shortening (FS), ejection fraction (EF), heart period (HP), heart rate (HR), and arrhythmia index (AI) can be found in literature6. Stroke volume and cardiac output calculations follow those in Klassen et al.16, along with peak contraction velocity and peak contraction to relaxation latencies16. Velocity information is procured via numeric differentiation of frame-level time-resolved diameter data. Extraction of beat-level data enables capturing of SI and DI arrythmias, referred to as tachycardiac and brachycardiac arrythmias. After identifying all SI and DI at the beat-level, tachycardiac data is selectively filtered from all SI events with a length over 0.5 sec. Brachycardiac events are extracted from DI data with a length over 1.0 sec. These filtering parameters are extracted analysis performed by Occur et al.4. ## Experimental validation We procure high-speed videos of $$n = 46$$ for 1-week males, $$n = 43$$ for 1-week females, $$n = 44$$ for 5-week males, and $$n = 44$$ for 5-week females to experimentally validate our model. For each heart, we calculate time-resolved beating patterns and cardiac statistics using our deep-learning approach. For each heart, an end-to-end analysis takes approximately 2 minutes. Next, each heart was identically examined in the canonical software for small-animal cardiac analysis, titled SOHA13. We employ the SOHA software for experimental validation of our model using blind data. Statistics comparing age-dependent phenotypes are calculated using generally available Python packages. For comparison of aging groups, we calculate t-Test significance values. Furthermore, for a quantitative view of our model performance, we calculate pairwise coefficient of determination information (R2) score via the Scikit-Learn Python library35. A close agreement between deep-learning calculated data and SOHA data indicates high model performance. ## Classification of Drosophila age Calculated cardiac statistics are exported from experimental studies. A dataset is labelled with cardiac statistics and corresponding fly age (1-week, 5-week). We produce a predictive model combining calculated cardiac statistics and fly age via logistic classification. To fit this logistic model, we employed the Scikit-Learn Python library. Our logistic model was fit on experimentally predicted DD, SD, FS, DI, SI, HP, and AI parameters. A k-fold cross-validation with $k = 5$ folds was used to evaluate model performance. Using such a configuration achieved a high testing accuracy with our model, as quantified in the Results section. To elucidate the connection between cardiac physiological parameters and age-dependence, we employ the SHapley Additive explanations (SHAP)36 technique. Doing so enables model transparency and insights into main determinants of aging phenotypes. We also investigate the use of deep learning models for the classification of Drosophila age from cardiac recordings. For this task, the sum of the squared difference of each pixel between a frame t0 and frame t is saved as timeseries data and are normalized between 0 and 1. This data is binarized with a threshold of 0.5. The resulting timeseries is composed of alternating regions of consecutive ones and consecutive zeros. The frame corresponding to the center of each region was saved until 96 frames had been collected. This along with the duration between each frame was taken as the input for the model. The model passes the 96-frame clip through 3 convolution blocks each consisting of two 2D convolutional layers followed by a max pooling layer. The result is then flattened and combined with the duration data before being passed into three dense layers and a final sigmoid output layer. Our dataset consisted of $$n = 118$$ for 1-week males, $$n = 156$$ for 1-week females, $$n = 121$$ for 5-week males, and $$n = 98$$ for 5-week females. 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--- title: Sodium-Glucose Cotransporter-2 Inhibitors for Hyperglycemia in Phosphoinositide 3-kinase Pathway Inhibition authors: - Michael A Weintraub - Dazhi Liu - Raymond DeMatteo - Marcus DaSilva Goncalves - James Flory journal: Research Square year: 2023 pmcid: PMC10055504 doi: 10.21203/rs.3.rs-2655905/v1 license: CC BY 4.0 --- # Sodium-Glucose Cotransporter-2 Inhibitors for Hyperglycemia in Phosphoinositide 3-kinase Pathway Inhibition ## Abstract ### Purpose Phosphoinositide 3-kinase (PI3K) inhibition is used for the treatment of certain cancers, but can cause profound hyperglycemia and insulin resistance, for which sodium-glucose cotransporter-2 (SGLT2) inhibitors have been proposed as a preferred therapy. The objective of this research is to assess the effectiveness and safety of SGLT2 inhibitors for hyperglycemia in PI3K inhibition. ### Methods We conducted a single-center retrospective review of adults initiating the PI3k inhibitor alpelisib. Exposure to different antidiabetic drugs and adverse events including diabetic ketoacidosis (DKA) were assessed through chart review. Plasma and point-of-care blood glucoses were extracted from the electronic medical record. Change in serum glucose and the rate of DKA on SGLT2 inhibitor versus other antidiabetic drugs were examined as co-primary outcomes. ### Results We identified 103 patients meeting eligibility criteria with median follow-up of 85 days after starting alpelisib. When SGLT2 inhibitors were used to treat hyperglycemia, they were associated with a decrease in mean random glucose by −54 mg/dL ($95\%$ CI −99 to −8) in adjusted linear modeling. Five cases of DKA were identified, two occurring in patients on alpelisib plus SGLT2 inhibitor. Estimated incidence of DKA was: alpelisib plus SGLT2 inhibitor, 24 DKA cases per 100 patient-years ($95\%$ CI 6, 80); alpelisib with non-SGLT2 inhibitor antidiabetic drugs, 7 ($95\%$ CI 0.1, 34); alpelisib only, 4 ($95\%$ CI 0.1, 21). ### Conclusions SGLT2 inhibitors are effective treatments for hyperglycemia in the setting of PI3K inhibition, but given possible adverse events, SGLT2 inhibitors should be used with caution. ## Introduction The phosphoinositide 3-kinase (PI3K) pathway is critically important for tumor progression–promoting cell growth, motility, survival, and metabolism. In breast cancer, the PI3K pathway can be hyperactivated by somatic mutations in PIK3CA, which encodes the catalytic subunit of PI3K (P110α). These mutations are found in $40\%$ of breast tumors, and associate with resistance to conventional anticancer agents [1]. Tumor PI3K signaling can be therapeutically targeted by small molecules that specifically bind and inhibit P110α-like taselisib, serabelisib, inavolisib, and alpelisib. Alpelisib was shown to improve clinical outcomes in postmenopausal women and men with hormone receptor-positive, HER2-negative, PIK3CA-mutant advanced breast cancer in the SOLAR-1 trial (Study Assessing the Efficacy and Safety of Alpelisib Plus Fulvestrant in Men and Postmenopausal Women With Advanced Breast Cancer Which Progressed on or After Aromatase Inhibitor Treatment [NCT02437318])[2]. In addition to blocking the action of mutant PI3K in the tumor, PI3K inhibitors systemically inhibit PI3K in host tissues, which impairs the intracellular action of insulin and induces insulin resistance. PI3K inhibitor driven insulin resistance causes hyperglycemia, which can be severe [3, 4]. Of 284 patients who received alpelisib in SOLAR-1, hyperglycemia occurred in $63.7\%$ of patients, with fasting plasma glucose levels exceeding 250 mg/dL in $36.6\%$[2]. Severe hyperglycemia caused by alpelisib has also led to diabetic ketoacidosis (DKA) in some case reports [5–7]. These on-target adverse hyperglycemic events were reported in other trials of P110α inhibitors, as well as inhibitors of AKT, another targetable kinase in the PI3K signaling cascade [8]. Hyperglycemia related to systemic inhibition of PI3K and AKT can result in emergency room visits, inpatient admissions, treatment interruptions, dose reductions, and treatment discontinuation. In a retrospective study of 251 cancer patients new to PI3Kα inhibitor use, we found hyperglycemia caused dose interruption in $13\%$, dose reduction in $11\%$, and hospitalization in $3\%$ of patients [9]. Of note, this population largely consisted of clinical trial participants for whom baseline type 1 or 2 diabetes was an exclusion criterion. In ‘real world’ settings, where patients with diabetes are not excluded, hyperglycemia may be a major challenge to using PI3K or AKT inhibitors. Approaches to hyperglycemia management in patients who receive alpelisib and other PI3K/AKT inhibitors are not clearly established. Blocking the PI3K/AKT pathway can induce a highly insulin resistant phenotype in which some antidiabetic drug classes, particularly insulin and insulin secretagogues, might be ineffective. In SOLAR-1, the study protocol was amended about half-way through enrollment to encourage the use of metformin as a first-line anti-hyperglycemic agent which resulted in its use in $87.1\%$ of patients with hyperglycemia [10]. However, the effectiveness of metformin in the setting of PI3K inhibitor use has still not been systematically studied and the effects of other antidiabetic drug classes in this setting are even less well-understood. Choice of an antidiabetic agent in the setting of PI3K/AKT inhibitor use may have implications beyond the quality of glycemic control. For example, high levels of endogenous or exogenous insulin might overcome the therapeutic blockade of PI3K and limit the anti-cancer efficacy, as has been shown in animal models [3]. Metformin is recommended as a first-line agent for the treatment of alpelisib-induced hyperglycemia, even though its effectiveness in the setting of PI3K blockade is not established [10]. Of alternative antidiabetic drug classes, sodium-glucose cotransporter-2 (SGLT2) inhibitors have received special attention. In animal models with cancer exposed to PI3K inhibitors, SGLT2 inhibitors reduced glucose and insulin levels more than metformin and intriguingly were also associated with reduced cancer progression [11–13]. In humans, several clinical case reports describe impressive glycemic responses to SGLT2 inhibitors in patients on alpelisib [3, 14, 15]. In our prior exploratory analysis we found SGLT2 inhibitor use was associated with larger glucose reductions than other classes of antidiabetic drugs (e.g., metformin) [9]. However, SGLT2 inhibitors are not without risk. We and others have identified cases of DKA in patients receiving alpelisib and SGLT2 inhibitors [5, 9, 16]. SGLT2 inhibitor therapy increases the risk for euglycemic DKA, which could be exacerbated by alpelisib-induced insulin resistance. Therefore, we conducted this follow-up retrospective cohort study with two goals: to test the hypothesis that SGLT2 inhibitors reduce glucose more effectively than other classes of antidiabetic drugs in patients on alpelisib, and to describe the rate of DKA with and without SGLT2 inhibitor use in alpelisib-treated patients. ## Research Design And Methods This is a single-center retrospective review of patients aged > 18 years initiating alpelisib treatment at a tertiary cancer center, Memorial Sloan Kettering (MSK). The research protocol was approved by MSK’s Institutional Review Board. Patients were potentially eligible if they initiated alpelisib on any date prior to May 26, 2022. To ensure that findings of the current work were not duplicative of a previous publication, patients were excluded if any of their data were included in that publication [9]. No exclusion criteria by cancer type, or stage were imposed, although it was expected that nearly all patients received alpelisib according to its clinical indication for metastatic breast cancer in patients with activating PI3K mutations. Alpelisib users were identified by a database query for electronic prescriptions for alpelisib. Alpelisib use was confirmed by manual chart review by two reviewers (any of MAW, DL, RD, JF). Baseline comorbidities, height, weight, body mass index (BMI, in kg/m2), random serum and point-of-care glucoses, serum creatinine, serum albumin, serum bicarbonate, calculated anion gap, and HbA1c levels were extracted from the electronic medical record (EMR). Estimated glomerular filtration rate (eGFR) was calculated from serum creatinine using the CKD-EPI creatinine-based 2021 equation [17]. Start and stop dates and indications for alpelisib, all antidiabetic therapies (metformin, SGLT2 inhibitors, dipeptidyl-peptidase 4 [DPP4] inhibitors, glucagon-like peptide receptor agonists [GLP1-RA], sulfonylurea, thiazolidinedione, meglitinide, and insulin), and corticosteroids (prednisone, hydrocortisone, methylprednisolone, and dexamethasone) were captured by manual chart review. Alpelisib interruptions, dose reductions, and discontinuations, along with the reason for these events, were also captured by manual chart review, as were dates and reasons for hospitalizations. All chart reviews were conducted by two reviewers and discrepancies in medication exposure dates were resolved by consensus among three members of the study team. Days spent inpatient (excluding the day on which hospitalization occurred) were excluded from the analysis because outpatient antidiabetic drugs are typically held and replaced with insulin during hospital admission. Follow-up ended with permanent alpelisib discontinuation, death, loss to follow-up (defined as three or more months with no encounters in the MSK system), or on $\frac{6}{30}$/2022. The co-primary outcomes were change in random glucose levels (mg/dL) as measured from serum or point-of-care testing at MSK facilities, and incidence of DKA. Home glucose monitoring, including continuous glucose monitoring, was not captured. Rates of hospitalization due to hyperglycemia as well as alpelisib interruptions, dose reductions, and discontinuation due at least in part to hyperglycemia were reported along with a composite of all these events as ‘hyperglycemia-related treatment disruption.’ Rates of death and progression of disease were also reported. DKA was defined as events satisfying all of the following diagnostic criteria upon presentation: [1] serum bicarbonate ≤ 18 mmol/L and/or blood pH ≤ 7.3, [2] anion gap > 10 mEq/L, and [3] presence of serum and/or urine ketones.[18] These criteria were applied by two chart reviewers. Rates of DKA were calculated, both overall and stratified into three exposure categories for patients on alpelisib: [1] no antidiabetic drugs, [2] on antidiabetic drugs excluding SGLT2 inhibitors, and [3] on SGLT2 inhibitors (with or without other antidiabetic drugs). Each case was also described with respect to initial symptoms, vital signs, laboratory data (eg, serum and point-of-care glucose levels), medications, clinical management, and outcome. Change in blood glucose levels associated with each time varying exposure were described and analyzed using a mixed linear model, hierarchical at the patient level. The analysis of change in blood glucose level was restricted to time periods when patients were taking alpelisib. Patients on antidiabetic drugs at baseline were excluded from this specific analysis, as were patients who did not have random glucoses measured both at baseline and during follow-up. Antidiabetic drugs and corticosteroid exposure were included as time-varying variables (e.g., a patient who took metformin for only a portion of their follow-up time would be classified as metformin-exposed during that period, but not before or afterwards). Unadjusted changes in glucose level from baseline associated with exposure to each antidiabetic drug class and to corticosteroids are reported. In the primary adjusted analysis, antidiabetic drugs and steroid exposure were all included together in the mixed linear model as time-varying covariates. Results were further adjusted for baseline age, sex, date of alpelisib initiation, eGFR, glucose levels, and BMI. In sensitivity analysis, to address concerns about adjustment for mediators, the mixed linear model was repeated as a marginal structural model (MSM), with stabilized inverse probability weights (IPW) applied to each period of follow-up. For example, if patients with no reduction of serum or point-of-care glucose levels after starting an SGLT2 inhibitor were likely to then stop SGLT2 inhibitor, this could create a bias in favor of SGLT2 inhibitor because non-responders to SGLT2 inhibitors would contribute less follow-up time. Including the initial change in glucose levels as a covariate would potentially adjust for this bias, but such adjustment could itself produce bias by ‘adjusting away’ the initial effects of SGLT2 inhibitor use on glucose levels, even though such an effect would be a mediator of SGLT2 inhibitor benefit, not a confounder. By using weights instead of covariates, an MSM is able to adjust for such potential mediators without producing bias.[19] MSM was conducted separately for metformin, SGLT2 inhibitors, and for corticosteroids. For each MSM, the IPW was calculated using exposure history to metformin, SGLT2 inhibitor, and corticosteroids as well as prior random glucose readings. Other antidiabetic drug classes were not included in the MSM because there were insufficient observations for the models to calculate IPW to converge. Models were also adjusted for non-time varying covariates measured at baseline: age, sex, date of alpelisib initiation, eGFR, glucose levels, and BMI. Work in preclinical models has indicated that choice of antidiabetic drug may have implications not only for glucose levels on alpelisib, but on levels of insulin (or C-peptide). Differences in insulin level could be clinically significant in this context because high insulin levels could in theory override PI3K inhibition and undermine the anti-cancer effect of alpelisib and similar drugs. In an exploratory analysis, random C-peptide levels were also extracted from the electronic medical record and levels of C-peptide summarized in four exposure categories for patients: [1] not on alpelisib (ie, before or after exposure, since all study patients were on alpelisib at some point), [2] on alpelisib but no antidiabetic drugs, [3] on alpelisib with antidiabetic drugs excluding SGLT2 inhibitors, and [4] on alpelisib and SGLT2 inhibitor (with or without other antidiabetic drugs). ## Results We identified 103 eligible patients who initiated treatment with alpelisib between October 23, 2019 and May 26, 2022 (Table 1). The median age of the cohort was 61 years (interquartile range [IQR] 55, 68). Of the patients, 102 ($99\%$) were female, and 101 ($99\%$) had metastatic breast cancer; two patients with ovarian cancer received alpelisib on a clinical trial protocol. In terms of race, 83 patients ($81\%$) self-identified as White, eight ($8\%$) as Asian, eight ($8\%$) as Black, and four ($4\%$) as other or were unknown; in terms of ethnicity, seven ($7\%$) identified as Hispanic. Median baseline HbA1c was $5.5\%$ (IQR 5.2, 5.8 [37 mmol/mol; IQR 33, 40]), in the 47 patients ($46\%$) with available data. Eight patients ($8\%$) had type 2 diabetes diagnosed at baseline. The median BMI was 25.6 (IQR 22.5, 28.9) and eGFR was 85 (IQR 76, 98). At the time of alpelisib initiation, six patients ($6\%$) were already taking antidiabetic drugs (Table 1). Of those taking antidiabetic drugs, one was taking a meglitinide, four were taking metformin alone, and one was taking metformin and a DPP4 inhibitor. After starting alpelisib, patients had median follow-up of 85 days (IQR 40, 212) (Table S1). Of the 103 patients, 63 ($61\%$) permanently discontinued alpelisib during the follow-up period. Hyperglycemia was the primary or contributing cause in 11 of the 63 cases ($17\%$). The median random glucose level prior to alpelisib initiation was 100 mg/dL (IQR 92,114). The mean follow-up glucose level after alpelisib treatment was 146 mg/dL. Metformin was prescribed to 34 patients ($33\%$), SGLT2 inhibitor to 11 ($11\%$), insulin to seven ($7\%$), DPP4 inhibitor to three ($3\%$), sulfonylurea to two ($2\%$), and thiazolidinedione to two ($2\%$) during alpelisib treatment. In the cohort, 21 patients ($20\%$) experienced hyperglycemia-related treatment disruption, five ($5\%$) presented with DKA, 36 ($35\%$) experienced progression of disease and discontinued alpelisib, and 21 ($20\%$) died. In the analysis of the effect of antihyperglycemics on mean random glucose levels, 77 patients ($75\%$) were included (after exclusion of six patients ($6\%$) on antidiabetic drugs at baseline and 20 ($20\%$) without glucose measurements during the follow-up period). An average of seven glucose readings over mean follow-up of 166 days were analyzed. In unadjusted mixed linear modeling, eight of 77 patients ($10\%$) had time with SGLT2 inhibitor exposure, which was associated with lower mean glucose levels compared to SGLT2 inhibitor unexposed time in adjusted linear analysis (−54 mg/dL [$95\%$ CI −99 to −8]). Time with metformin exposure, as contributed by 31 of 77 patients ($40\%$) who received metformin, was also associated with lower mean glucose levels in adjusted linear analysis (−34 mg/dL [$95\%$ CI −60 to −7]) (Fig. 1). Confidence intervals (CIs) for other antidiabetic drug classes were wide and crossed null in the setting of limited sample size: four patients ($5\%$) received insulin, which had adjusted glucose reduction of −43 mg/dL ($95\%$ CI −111 to 28); two patients received a sulfonylurea (−4 mg/dL; $95\%$ CI −94 to 92); two patients received a thiazolidinedione (−45 mg/dL; $95\%$ CI −144 to 55), and two patients received a DPP4 inhibitor (−73 mg/dL; $95\%$ CI −157 to 10). Corticosteroids were used in the treatment of 11 of 77 patients ($14\%$) and were associated with significantly increased mean glucose values (+71 mg/dL; $95\%$ CI 42 to 98). MSM showed an association between SGLT2 inhibitor exposure and glucose reduction but not for metformin (Fig. 1). MSM could not be carried out on other antidiabetic drugs because insufficient data were available to run the statistical models needed. We identified five cases of DKA (Table 2). Of these, two occurred in patients on concomitant SGLT2 inhibitor and alpelisib (with one patient also on metformin and one patient also on a sulfonylurea), two occurred in patients receiving concomitant metformin and alpelisib, and one occurred in a patient on no antidiabetic drugs. Patients receiving concomitant alpelisib and SGLT2 inhibitor had an incidence of 24 DKA cases per 100 patient-years ($95\%$ CI 6–80). In comparison, patients receiving alpelisib concomitant with non-SGLT2 inhibitor antidiabetic drugs (e.g., metformin) experienced seven DKA cases per 100 patient-years ($95\%$ CI 0.1, 34), and patients on alpelisib only experienced four DKA cases per 100 patient-years ($95\%$ CI 0.1, 21) (Table 3). In an exploratory analysis, a total of 49 random (i.e., non-fasting) C-peptide levels were measured in 23 patients ($22\%$). Serum glucose levels were measured simultaneously for 48 of the 49 C-peptides (Supplementary Fig. 1A). Twelve samples were collected when patients were not receiving alpelisib (median serum C-peptide level = 4.0 ng/mL; IQR 2.3,.1), 13 were measured during periods when patients received alpelisib without any antidiabetic drugs (median 9.9 ng/mL; IQR 4.1, 13.8), 14 were measured during periods when patients received concomitant alpelisib and antidiabetic drugs other than an SGLT2 inhibitor (median 10.6 ng/mL; IQR 7.7, 14.5), and 10 were measured during periods when patients received concomitant alpelisib and an SGLT2 inhibitor (4.6 ng/mL; IQR 4.0, 8.1). Unadjusted mixed linear modeling showed increased C-peptide levels in patients on alpelisib ($P \leq 0.01$) and alpelisib plus other antidiabetic drugs ($P \leq 0.01$), but not on alpelisib plus SGLT2 inhibitor (Supplementary Fig. 1B). ## Conclusions Findings from this retrospective review show that SGLT2 inhibitor use is consistently associated with substantial reductions in glucose levels in patients on alpelisib. However, the occurrence of two cases of DKA in 11 patients ($18\%$) on an SGLT2 inhibitor is concerning. SGLT2 inhibitors may be a uniquely effective antidiabetic drug class in patients on alpelisib, but the risk of DKA must be considered and mitigated. These findings may also be relevant to managing hyperglycemia in patients taking other drugs that inhibit the PI3K/AKT pathway. The glucose-lowering effectiveness for SGLT2 inhibition in this study is consistent with previous animal data, clinical case reports, and exploratory observational analyses [9, 12, 14, 15]. This study’s strengths relative to previous work are that it is hypothesis testing rather than exploratory, controlled for important confounders (particularly steroid use), and includes sensitivity analysis using an MSM. This study was too small to establish whether SGLT2 inhibitors are more effective than other agents, such as metformin, in the setting of alpelisib use. However, SGLT2 inhibitors are now the only drug class with consistent evidence of glucose-lowering effectiveness in the context of patients receiving alpelisib. Alpelisib increases C-peptide levels in patients, a marker of insulin production [20, 21]. Our data suggest that SGLT2 inhibitors may abrogate this effect. This finding is consistent with preclinical data showing that SGLT2 inhibitors, but not metformin, could prevent high endogenous insulin levels in animal models on PI3K inhibitors [3]. Researchers have speculated that SGLT2 inhibitors might improve clinical response to PI3K inhibition through this mechanism, since high insulin levels might be reactive to PI3K inhibition and allow tumors to escape the anticancer effect. The exploratory findings here support further investigation of that hypothesis. These potential benefits from SGLT2 inhibitor use with alpelisib need to be weighed against the potential for harm. The two cases of DKA observed in patients treated with a combination of alpelisib and an SGLT2 inhibitor imply a rate of approximately one case per four person-years of exposure, far higher than the DKA rates of approximately one case per 1,000 patient-years seen in patients with type 2 diabetes who take an SGLT2 inhibitor [22]. While two cases comprise a small sample, it is supported further by our previous study, which identified one unambiguous case of DKA in 15 patients treated with an SGLT2 inhibitor while using PI3K or AKT inhibitors [9]. Prescribers who choose to use an SGLT2 inhibitor with alpelisib should be aware of this potential risk. Limitations of this analysis include that the results regarding effectiveness are limited by small sample size and potential for time varying confounding. For example, the decision to start any antidiabetic drug might be accompanied by lifestyle modifications that are not captured in EMR data. Given uncertainty about the relative effectiveness of different antidiabetic drug classes in the unusual clinical setting of PI3K inhibition, continuous glucose monitoring may help optimize care and collect more robust data on the effectiveness of different drug classes. Finally, the hypothesis that SGLT2 inhibitors might have anticancer effects requires prospective study. Several relevant clinical trials are currently recruiting, including in “Alpelisib, Fulvestrant and Dapagliflozin for the Treatment of HR+, HER2−, PIK3CA Mutant Metastatic Breast Cancer” (NCT05025735) and TIFA (Targeting Insulin Feedback to Enhance Alpelisib: A Phase 2 Randomized Control Trial in Metastatic PIK3CA-mutant Hormone-Receptor Positive Breast Cancer [NCT05090358]). Balancing the risks and benefits of SGLT2 inhibitors in patients on alpelisib is challenging. Due to the risk of DKA, clinical practice at our institution is not to use SGLT2 inhibitors as first-line agents, as many patients can achieve adequate glycemic control with metformin and lifestyle modification. However, SGLT2 inhibitors are our preferred second-line choice either in addition to or in place of metformin. We advise patients of the risk of DKA and that poor dietary intake is likely to increase the risk, so that they should hold the SGLT2 inhibitor on any day they are fasting or consuming less than $50\%$ of their usual caloric intake. The value of home urine ketone monitoring is unclear because asymptomatic ketosis may be common on SGLT2 inhibitors and may not warrant intervention [23]. In summary, these study findings can aid in treatment decisions when managing PI3K inhibitor-induced hyperglycemia. While these findings apply to alpelisib specifically, they likely generalize to other PI3Kα inhibitors, pan-PI3K inhibitors, and potentially to other inhibitors on the PI3K/AKT pathway. We recommend that metformin and lifestyle modification should be the first-line treatment for alpelisib-induced hyperglycemia, given possible effectiveness for glucose lowering and a lack of serious adverse events. SGLT2 inhibitors are a reasonable second-line strategy with the caveat that providers should monitor for the development of DKA. Further research on the comparative effectiveness of other antidiabetic drug classes is warranted. ## Funding: This work has been supported in part by the Memorial Sloan Kettering Cancer Center Support Grant/Core Grant (P30 CA008748) from the National Cancer Institute of the National Institutes of Health. ## Conflict of interest statement: M.A.W.: None to report D.L., R.D.: None to report. M.D.G.: Received consulting fees from Novartis, Pfizer, and Scorpion Therapeutics; he is an inventor on a patent (pending) for Combination Therapy for PI3K-associated Disease or Disorder; and he is a co-founder, shareholder, and consultant of Faeth Therapeutics. J.F.: Hagens Berman Sobol Shapiro LLP (provision of services). ## Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. ## References 1. Anderson EJ, Mollon LE, Dean JL, Warholak TL, Aizer A, Platt EA. A Systematic Review of the Prevalence and Diagnostic Workup of PIK3CA Mutations in HR+/HER2-Metastatic Breast Cancer. Int J Breast Cancer (2020) 2020 3759179. DOI: 10.1155/2020/3759179 2. André F, Ciruelos E, Rubovszky G, Campone M, Loibl S, Rugo HS. Alpelisib for PIK3CA-Mutated, Hormone Receptor-Positive Advanced Breast Cancer. N Engl J Med (2019) 380 1929-40. DOI: 10.1056/NEJMoa1813904 3. Hopkins BD, Pauli C, Du X, Wang DG, Li X, Wu D. Suppression of insulin feedback enhances the efficacy of PI3K inhibitors. Nature (2018) 560 499-503. DOI: 10.1038/s41586-018-0343-4 4. Goncalves MD, Hopkins BD, Cantley LC. Phosphatidylinositol 3-Kinase, Growth Disorders, and Cancer. N Engl J Med (2018) 379 2052-62. DOI: 10.1056/NEJMra1704560 5. Carrillo M, Rodriguez RM, Walsh CL, McGarvey M. Alpelisib-Induced Diabetic Ketoacidosis: A Case Report and Review of Literature. AACE Clin Case Rep (2021) 7 127-31. DOI: 10.1016/j.aace.2020.11.028 6. Farah SJ, Masri N, Ghanem H, Azar M. Diabetic ketoacidosis associated with alpelisib treatment of metastatic breast cancer. AACE Clin Case Rep (2020) 6 e349-e51. DOI: 10.4158/ACCR-2020-0452 7. Nguyen R, Musa A, Samantray J. Alpelisib-Induced Diabetic Ketoacidosis. Cureus (2021) 13 e14796. DOI: 10.7759/cureus.14796 8. Nitulescu GM, Van De Venter M, Nitulescu G, Ungurianu A, Juzenas R, Peng Q. The Akt pathway in oncology therapy and beyond (Review). 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Phosphatidylinositol 3-Kinase α-Selective Inhibition With Alpelisib (BYL719) in PIK3CA-Altered Solid Tumors: Results From the First-in-Human Study. J Clin Oncol (2018) 36 1291-9. DOI: 10.1200/JCO.2017.72.7107 22. Dawwas GK, Flory JH, Hennessy S, Leonard CE, Lewis JD. Comparative Safety of Sodium-Glucose Cotransporter 2 Inhibitors Versus Dipeptidyl Peptidase 4 Inhibitors and Sulfonylureas on the Risk of Diabetic Ketoacidosis. Diabetes Care (2022) 45 919-27. DOI: 10.2337/dc21-2177 23. Min SH, Oh TJ, Baek SI, Lee DH, Kim KM, Moon JH. Degree of ketonaemia and its association with insulin resistance after dapagliflozin treatment in type 2 diabetes. Diabetes Metab (2018) 44 73-6. DOI: 10.1016/j.diabet.2017.09.006
--- title: Risk factors for eight common cancers revealed from a phenome-wide Mendelian randomisation analysis of 378,142 cases and 485,715 controls authors: - Molly Went - Amit Sud - Charlie Mills - Abi Hyde - Richard Culliford - Philip Law - Jayaram Vijayakrishnan - Ines Gockel - Carlo Maj - Johannes Schumacher - Claire Palles - Martin Kaiser - Richard Houlston journal: Research Square year: 2023 pmcid: PMC10055507 doi: 10.21203/rs.3.rs-2587058/v1 license: CC BY 4.0 --- # Risk factors for eight common cancers revealed from a phenome-wide Mendelian randomisation analysis of 378,142 cases and 485,715 controls ## Abstract For many cancers there are few well-established risk factors. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify causal relationships. We performed a MR-PheWAS of breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, comprising 378,142 cases and 485,715 controls. To derive a more comprehensive insight into disease aetiology we systematically mined the literature space for supporting evidence. We evaluated causal relationships for over 3,000 potential risk factors. In addition to identifying well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we provide evidence for specific factors, including dietary intake, sex steroid hormones, plasma lipids and telomere length as determinants of cancer risk. We also implicate molecular factors including plasma levels of IL-18, LAG-3, IGF-1, CT-1, and PRDX1 as risk factors. Our analyses highlight the importance of risk factors that are common to many cancer types but also reveal aetiological differences. A number of the molecular factors we identify have the potential to be biomarkers. Our findings should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app (https://mrcancer.shinyapps.io/mrcan/) to visualise findings. ## INTRODUCTION Cancer is currently the third major cause of death with an estimated 18.1 million new cases and nearly 10 million cancer deaths in 20201. By 2030 it is predicted there are likely to be 26 million new cancer cases and 17 million cancer-related deaths annually2. Such projections have renewed efforts to identify risk factors to inform cancer prevention programmes. For many cancers, despite significant epidemiological research, there are few established risk factors. Although randomised-controlled trials (RCTs) are the gold standard for establishing causal relationships, they are often impractical or unfeasible because of cost, time, and ethical issues. Conversely, case-control studies can be complicated by biases such as reverse causation and confounding. Mendelian randomisation (MR) is an analytical strategy that uses germline genetic variants as instrumental variables (IVs) to infer causal relationships (Fig. 1A)3. The random assortment of these genetic variants at conception mitigates against reverse causation bias. Moreover, in the absence of pleiotropy (i.e. the presence of an association between variants and disease through additional pathways), MR can provide unconfounded disease risk estimates. Elucidating disease causality using MR is gaining popularity especially given the availability of data from large genome-wide association studies (GWAS) and well-developed analytical frameworks3. Most MR studies of cancer have been predicated on assumptions about disease aetiology or have sought to evaluate purported associations from conventional observational epidemiology3,4. A recently proposed agnostic strategy, termed MR-PheWAS, integrates the phenome-wide association study (PheWAS) with MR methodology to identify causal relationships using hitherto unconsidered traits5. To identify causal relationships for eight common cancers (breast, prostate, colorectal, lung, endometrial, oesophageal, renal and ovarian), and reveal intermediates of risk, we conducted a MR-PheWAS study. We integrated findings with a systematic mining of the literature space to provide supporting evidence and derive a more comprehensive description of disease aetiology (Fig. 1B)6. ## Ethics approval The analysis was undertaken using published GWAS data, hence ethical approval was not required. ## Study design Our study had four elements. Firstly, the identification of genetic variants serving as instruments for exposure traits under investigation; secondly, the acquisition of GWAS data for the eight cancers; thirdly, MR analysis; fourthly, triangulation through literature mining to provide supporting evidence for causal relationships (Fig. 1B). ## Genetic variants serving as instruments Single nucleotide polymorphisms (SNPs), considered genetic instruments, were identified from published studies or MR-Base (Supplementary Table 1). For each SNP, the corresponding effect estimate on a trait expressed in per standard deviation (SD) units (assuming a per allele effect) and standard error (SE) was obtained. Only SNPs with a minor allele frequency <0.01 and a trait association of P-values <5 × 10−8 in a European population GWAS were considered as instruments. We excluded correlated SNPs at a linkage disequilibrium threshold of r2 >0.01, retaining SNPs with the strongest effect. For binary traits we restricted our analyses to traits with a medical diagnosis, excluding cancer. We removed duplicate exposure traits based on manual curation. ## Cancer GWAS summary statistics To examine the association of each genetic instrument with cancer risk, we used summary GWAS effect estimates from: [1] Online consortia resources, for breast (BCAC; https://bcac.ccge.medschl.cam.ac.uk/. accessed July 2022) and prostate cancer (PRACTICAL; http://practical.icr.ac.uk/; accessed July 2022)7,8; [2] GWAS catalogue (https://www.ebi.ac.uk/gwas/), for ovarian, endometrial and lung cancers (accessed September 2022)9-11; [3] Investigators of published work, for colorectal cancer (CRC), renal cell carcinoma (RCC) and oesophageal cancer12-14. Cancer subtype summary statistics were available for lung, breast and ovarian cancers. As the UK Biobank was used to obtain genetic instruments for many traits investigated, the CRC and oesophageal GWAS association statistics were recalculated from primary data excluding UK Biobank samples to avoid sample overlap bias (Table 1). Single nucleotide polymorphisms were harmonised to ensure that the effect estimates of SNPs on exposure traits and cancer risk referenced the same allele (Supplementary Table 2)15. ## Statistical analysis For each SNP, causal effects were estimated for cancer as an odds ratio (OR) per SD unit increase in the putative risk factor (ORSD), with $95\%$ confidence intervals (CIs), using the Wald ratio16. For traits with multiple SNPs as IVs, causal effects were estimated under an inverse variance weighted random-effects (IVW-RE) model as the primary measurement as it is robust in the presence of pleiotropic effects, provided any heterogeneity is balanced at mean zero (Supplementary Table 3-6)17. Weighted median estimate (WME) and mode-based estimates (MBE) were obtained to assess the robustness of findings (Supplementary Table 7)18,19. Directional pleiotropy was assessed using MR-Egger regression (Supplementary Table 8)20. The MR Steiger test was used to infer the direction of causal effect for continuous exposure traits (Supplementary Table 9)21. For this we estimated the proportion of variance explained (PVE) using Cancer Research UK lifetime risk estimates for each tumour type (Supplementary Table 10)22. A leave-one-out strategy under the IVW-RE model was employed to assess the potential impact of outlying and pleiotropic SNPs (Supplementary Table 11)23. Because two-sample MR of a binary risk factor and a binary outcome can be biased, we primarily considered whether there exists a significant non-zero effect, and only report ORs for consistency24. Statistical analyses were performed using the TwoSampleMR package v0.5.6 (https://github.com/MRCIEU/TwoSampleMR) in R (v3.4.0)15. ## Estimation of study power The power of MR to demonstrate a causal relationship depends on the PVE by the instrument25. We excluded instruments with a F-statistic <10 since these are considered indicative of evidence for weak instrument bias26. We estimated study power, stipulating a P-value of 0.05 for each target a priori across a range of effect sizes as per Brion et al. ( Supplementary Table 1)27. Since power estimates for binary exposure traits and binary outcomes in a two-sample setting are unreliable, we did not estimate study power for binary traits24. ## Assignment of statistical significance The support for a causal relationship with non-binary traits was categorised into four hierarchical levels of statistical significance a priori: robust (PIVW-RE <1.4×10−5; corresponding to a P-value of 0.05 after Bonferroni correction for multiple testing ($\frac{0.05}{3}$,500), PWME or PMBE <0.05, true causal direction and >1 IVs), probable (PIVW-RE <0.05, PWME or PMBE <0.05, true causal direction and >1 IVs), suggestive (PIVW-RE <0.05 or PWALD <0.05), and non-significant (PIVW-RE ≥0.05 or PWALD ≥0.05) (Supplementary Table 12). While non-significant associations can be due to low statistical power, they also indicate that a moderate causal effect is unlikely. For binary traits we classified associations as being supported ($P \leq 0.05$) or not supported ($P \leq 0.05$; Supplementary Tables 13-16). ## Support for causality To strengthen evidence for causal relationships identified from the MR analysis we exploited the semantic predications in Semantic MEDLINE Database (SemMedDB), which is based on all PubMed citations28. Within SemMedDB pairs of terms connected by a predicate which are collectively known as ‘literature triples’ (i.e. ‘subject term 1’ – predicates – ‘object term 2’). These literature triples represent semantic relationships between biological entities derived from published literature. To interrogate SemMedDB we queried MELODI Presto and EpiGraphDB to facilitate data mining of epidemiological relationships for molecular and lifestyle traits29,30. For each putative risk factor-cancer pair the set of triples were overlapped and common terms identified to reveal causal pathways and inform aetiology. Based on the information profile of all literature mined triples, we considered literature spaces with >50 literature triples as being viable, corresponding to $90\%$ of the information content31. We complemented this systematic text mining by referencing reports from the World Cancer Research Fund/American Institute for Cancer Research, and the International Agency for Cancer Research Global Cancer Observatory, as well as querying specific putative relationships in PubMed32,33. ## Phenotypes and genetic instruments After filtering we analysed 3,661 traits, proxied by 336,191 genetic variants in conjunction with summary genetic data from published GWAS of breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers (Table 1; Supplementary Table 17). The number of SNPs used as genetic instruments for each trait ranged from one to 1,335. Figure 2 shows the power of our MR study to identify causal relationships between each of the genetically defined traits and each cancer type. The median PVE by SNPs used as IVs for each of the 3,661 traits evaluated as risk factors was $3.4\%$ (0.01–$84\%$). Our power to demonstrate causal relationships a priori for each cancer type reflects in part inevitably the size of respective GWAS datasets (Supplementary Table 1). ## Causal associations identified by MR To aid interpretation we grouped traits related to established cancer risk factors (i.e. smoking, obesity and alcohol) and those for which current evidence is inconclusive into the following categories: cardiometabolic; dietary intake; anthropometrics; immune and inflammatory factors; fatty acid (FA) and lipoprotein metabolism; lifestyle, reproduction, education and behaviour; metabolomics and proteomics; miscellaneous. Out of the 27,066 graded associations, MR analyses provided robust evidence for a causal relationship with 123 phenotypes ($0.5\%$ of total MR analyses), 174 with probable evidence ($0.6\%$ of total), 1,652 with suggestive evidence ($6\%$ of total). Across the eight cancer types, the largest number of robust associations were observed for endometrial cancer with 37 robust associations, followed by renal cancer ($$n = 32$$), CRC ($$n = 21$$), lung ($$n = 20$$), breast ($$n = 10$$), oesophageal ($$n = 3$$) and prostate cancer ($$n = 1$$). No robust MR associations were observed for ovarian cancer (Supplementary Table 3). Across all of the cancer types anthropometric traits showed the highest number of robust MR defined causal relationships ($$n = 32$$; $0.1\%$), followed by lifestyle, reproduction, education and behaviour ($$n = 17$$; $0.06\%$). No robust associations were observed for dietary intake or cardiometabolic categories (Supplementary Table 3). To visualise the strength and direction of effect of the causal relationship between each of the traits examined and risk of each cancer type and, where appropriate, their respective subtypes we provide a R/Shiny app (https://mrcancer.shinyapps.io/mrcan/). Fig. 3 shows a screenshot of the app for selected traits across the eight different types of cancer. Many of the identified causal relationships, especially those that were statistically robust or probable, have been reported in previous MR studies and are related to established risk factor categories4,32,33. Notably: (i) the relationship between metrics of increased body mass index (BMI) with an increased risk of colorectal, lung, renal, endometrial and ovarian cancers34; (ii) cigarette smoking with an increased risk of lung cancer35; (iii) higher alcohol consumption and increased risk of oesophageal, colorectal, lung, renal, endometrial and ovarian cancers36; (iv) reduced physical activity and sedentary behaviour with an increased risk of multiple cancers, including breast, lung, colorectal and endometrial37. As anticipated, exposure traits pertaining to cigarette smoking were not causally related to lung cancer in never smokers. Paradoxically, but as reported in previous MR analyses, increased BMI was associated with reduced risk of prostate and breast cancer, and an inverse relationship between smoking and prostate cancer risk was observed34,38. Our analysis also supports the reported relationship between higher levels of sex hormone-binding globulin with reduced endometrial cancer risk and a relationship between testosterone with risk of endometrial cancer and breast cancers39,40. Notably, exposure traits related to testosterone levels were only causally associated with luminal-A and luminal-B breast cancer subtypes. With respect to dietary intake our analysis demonstrated probable associations between genetically predicted higher levels of coffee, oily fish, and cheese intake with reduced CRC risk and suggestive associations between genetically predicted beef and poultry intake and elevated CRC risk. We found suggestive associations between genetically predicted high serum vitamin B12 with colorectal and prostate cancer, serum calcium and 25-hydroxyvitamin-D with RCC, low blood selenium with colorectal and oesophageal cancers and methionine and zinc with reduced CRC risk. We observed no association between genetically predicted blood levels of circulating carotenoids or vitamins B6 and E for any of the cancers. In terms of glucose homeostasis, no relationship between genetically predicted blood glucose or glycated haemoglobin was shown for any of the eight cancers. However, higher levels of genetically predicted levels of fasting insulin and insulin growth factor 1 (IGF-1) and lower proinsulin showed associations with CRC. Additionally, a suggestive association between proinsulin and renal cancer, fasting insulin and lung and endometrial cancers, and IGF-1 levels and breast cancer was observed. Amongst genetically predicted higher levels of lipoproteins, the only associations were a probable association between high density lipoprotein cholesterol (HDL-C) and breast cancer and suggestive associations between low density lipoprotein cholesterol (LDL-C) with CRC, and total cholesterol and ovarian cancer. Genetically predicted levels of plasma FAs showed an association with reduced cancer risk. Specifically, for the omega-6 polyunsaturated FAs, lower levels of arachidonic acid (20:4n6) and gamma-linoleic acid (18:3n6) and higher levels of linoleic acid (18:2n6) and adrenic acid (22:4n6)) with reduced risk of CRC; for the omega-3 polyunsaturated FAs (alpha-linoleic acid, eicosapentaenoic acid, docosahexaenoic acid) and breast cancer risk, and arachidonic acid and endometrial cancer. A relationship between longer lymphocyte telomere length (LTL) and an increased risk of six of the eight cancer types was identified - robust with respect to renal and lung cancers, probable for breast and prostate cancers and suggestive for colorectal and ovarian cancers. In addition to a robust association between higher HLA-DR dendritic plasmacytoid levels and risk of prostate cancer, 26 probable associations between genetically predicted levels of other circulating immune and inflammatory factors were shown across the cancers studied. These included higher levels of IL-18 with reduced risk of lung cancer, with specificity for lung cancer in never smokers. Our MR analysis provides support for the known relationship between colonic polyps and CRC41, benign breast disease and breast cancer42, oesophageal reflux with risk of oesophageal cancer (Supplementary Table 13)43. Other associations included possible relationships between pulmonary fibrosis and lung cancer44, as well as the relationship between a diagnosis of schizophrenia and lung cancer, which has been observed in conventional epidemiological studies45. It was noteworthy, however, that we did not find evidence to support the purported relationship between hypertension and risk of developing RCC. Similarly, our analysis did not provide evidence to support a causal relationship between either type 1 or type 2 diabetes and an increased cancer risk. ## Literature-mined support for MR causal relationships To provide support for the associations and to gain molecular insights into the underlying biological basis of relationships we performed triangulation through systematic literature mining. We identified 55,105 literature triples across the eight different cancer types and 680,375 literature triples across the MR defined putative risk factors (Supplementary Table 18). Overlapping risk factor-cancer pairings from our MR analysis yielded on average 49 potential causal relationships. Supplementary Table 19 stratifies the literature space size by trait category while recognising that causal relationships with a small literature space could be reflective of deficiencies in semantic mapping relationships with large literature spaces support triangulation. Supplementary Table 20 provides the complete list of potential mediators for each trait. Illustrating the use of triangulation using a large literature space (defined herein as >50 triples) to support causal relationships, Fig. 4 highlights four notable examples (IGF-1, LAG-3, IL-18, and PRDX1). IGF-1, which is reported to play a role in multiple cancers, appears to mediate its effect in part through beta-catenin and BRAF signalling, modulating CRC and breast cancer risk46,47. Whilst LAG-3 inhibition is an attractive therapeutic target in restoring T-cell function, we demonstrate genetically elevated LAG-3 levels as being associated with reduced CRC, endometrial and lung cancer. In all three of these cancers, the association appears to be at least partly mediated through IL-10 and the seemingly paradoxical relationship between LAG-3 levels and tumourgenesis suggests potentiation of T-cell function by serum LAG-3 rather than cell membrane expressed LAG-348. We identify genetically predicted IL-18 levels as being associated with an increased risk of lung cancer. Our literature mining also supports a role for the decoy inhibitory protein, IL-18BP as being a mediator of lung cancer risk as well as IL-10, IL-12, IL-4 and TNF49. Finally, PRDX1, a member of the peroxiredoxin family of antioxidant enzymes, interacts with the androgen receptor to enhance its transactivation resulting in increased EGFR-mediated signalling and an increased prostate cancer risk46. ## DISCUSSION By performing a MR-PheWAS we have been able to agnostically examine the relationship between multiple traits and the risk of eight different cancer types, restricted only by the availability of suitable genetic instruments. Importantly, many of the traits we examined have not previously been the subject of conventional epidemiological studies or been assessed by MR. Even for risk factors that were examined in many previous analyses, the number of cases and controls in our study has afforded greater power to identify potential causal associations. This has allowed us to exclude large causal effects on cancer risk for the majority of exposure traits examined in our study. In addition to identifying causal relationships for the well-established lifestyle traits, which validates our approach, we implicate other lifestyle factors that have been putatively associated by observational epidemiology contributing to cancer risk. For example, the protective effects of physical activity, coffee, oily fish, fresh/dried fruit intake for CRC risk. A number of the causal relationships we identify have been the subject of studies of individual traits and include the association between longer LTL with increased risk of RCC and lung cancers; sex steroid hormones and risk of breast and endometrial cancer; and circulating lipids with CRC and breast cancer. Using genetic instruments for plasma proteome constituents has allowed us to identify hitherto unexplored potential risk factors for a number of the cancers, including: the cytokine like molecule, FAM3D, which plays a role in host defence against inflammation associated carcinogenesis with lung cancer50; the autophagy associated cytokine cardiotrophin-1 with lung, endometrial, prostate and breast cancer and the tumour progression associated antigen CD63 with endometrial cancer51,52. Levels of these and other plasma proteins potentially represent biomarkers worthy of future prospective studies. Clustering of MR causal effect sizes for each trait cancer relationship highlights the importance of risk factors common to many cancers but also reveal differences in their impact in part likely to be reflective of underlying biology (Fig. 5). A principal assumption in MR is that variants used as IVs are associated with the exposure trait under investigation. We therefore used SNPs associated with exposure traits at genome-wide significance. Furthermore, only IVs from European populations were used to limit bias from population stratification. Our MR analysis does, however, have limitations. Firstly, we were limited to studying phenotypes with genetic instruments available, moreover traits such as food intake or television watching can be highly correlated with other exposures making deconvolution of the causal risk factor problematic53,54. Secondly, correcting for multiple testing guards against false positives especially when based on a single exposure outcome. However, the potential for false negatives is not unsubstantial. Since we have not adjusted for between trait correlations, our associations are inevitably conservative. Thirdly, for a number of traits, we had limited power to demonstrate causal associations of small effect. Fourthly, not unique to our MR analysis, is the inability of our study to deconvolute time-varying effects of genetic variants as evidenced by the relationship between obesity and breast cancer risk55. Finally, as with all MR studies, excluding pleiotropic IVs is challenging. To address this, we incorporated information from weighted median and mode-based estimate methods, to classify the strength of causal associations. However, there are inevitably limitations to such modelling as exemplified by the strong relationship between plasma FA and risk of CRC which has been shown to be driven by the pleiotropic FADS locus which has a profound effect on the metabolism of multiple FA through its gene expression56. A major concern articulated regarding any MR-PheWAS is the need to provide supporting evidence from alternative sources. Herein we have sought to address this by conducting a systematic interrogation of the literature space and potentially identify intermediates to explain relationships. Although literature mined data is inevitably noisy and driven by publication bias, we have been able to provide a narrative of the causal relationships for a number of risk factors, which are attractive candidates for molecular validation. Complementary studies are required to delineate the exact biological mechanisms underpinning associations. Our analysis does however highlight important targets for primary prevention of cancer in the population. The limited power to robustly characterise relationships between exposure traits and cancer in this study, provides an impetus for larger MR studies. Finally, we recognise that MR is not infallible and replication and triangulation of findings using different data sources, and if possible, benchmarking against RCTs is highly desirable. 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--- title: 'Risk factors for preterm labor: An Umbrella Review of meta-analyses of observational studies' authors: - Ioannis Mitrogiannis - Evangelos Evangelou - Athina Efthymiou - Theofilos Kanavos - Effrosyni Birbas - George Makrydimas - Stefania Papatheodorou journal: Research Square year: 2023 pmcid: PMC10055511 doi: 10.21203/rs.3.rs-2639005/v1 license: http://www.nature.com/reprints --- # Risk factors for preterm labor: An Umbrella Review of meta-analyses of observational studies ## Abstract Preterm birth defined as delivery before 37 gestational weeks, is a leading cause of neonatal and infant morbidity and mortality. Understanding its multifactorial nature may improve prediction, prevention and the clinical management. We performed an umbrella review to summarize the evidence from meta-analyses of observational studies on risks factors associated with PTB, evaluate whether there are indications of biases in this literature and identify which of the previously reported associations are supported by robust evidence. We included 1511 primary studies providing data on 170 associations, covering a wide range of comorbid diseases, obstetric and medical history, drugs, exposure to environmental agents, infections and vaccines. Only seven risk factors provided robust evidence. The results from synthesis of observational studies suggests that sleep quality and mental health, risk factors with robust evidence should be routinely screened in clinical practice, should be tested in large randomized trial. Identification of risk factors with robust evidence will promote the development and training of prediction models that could improve public health, in a way that offers new perspectives in health professionals. ## Introduction Preterm Birth (PTB) is defined as delivery before 37 gestational weeks and is a leading cause of infant morbidity and mortality [1-4]. 15 million babies are estimated to be born preterm every year and the PTB rate ranges between 5–$18\%$ worldwide [3] (PTB rates in USA:12–$13\%$ [1, 2]; in Europe is 5–$9\%$ [2]). Advances in neonatology and the administration of corticosteroids before birth have improved significantly the prognosis of babies born preterm. In contrast, although vigorous research, costing millions of dollars, was carried out during the last 40 years, focusing in the prediction and prevention of preterm birth its incidence remains relatively unchanged. The most probable explanation is that preterm birth is a syndrome, rather than a single disease and many different causes may be responsible [161]. Numerous systematic reviews and meta-analyses have assessed various, non-genetic risk factors of preterm labor. Several environmental and clinical parameters such as present pregnancy characteristics, previous pregnancy history [4], infections [7, 8], environmental exposures, pharmaceutical factors [9, 10] and surgical interventions have been proposed as plausible factors related to PTB. Identifying robust risk factors for PTB should either help us define a study population for specific interventions, allocate available resources effectively and allow risk-specific treatment and understanding the mechanism leading to PTB [1]. However, the exact causes of this syndrome are still mostly unknown and the contribution of every risk factor in terms of prevention is still questionable. To our knowledge there is no previous effort to summarize existing evidence of meta-analyses of non-genetic risk factors for PTB. We conducted an umbrella review across published meta-analyses of observational studies with the goal to map the existing evidence and critically evaluate the reported associations applying stringent criteria that assess potential systematic biases and we highlight previously studied associations that provide robust evidence of association. ## Description of Eligible meta-analyses The search identified 2769 items, of which 2239 were excluded after review of the title and abstract (Fig. 1, PRISMA Flowchart). Of the remaining 530 articles that were reviewed in full text, eight articles did not report the appropriate information for the calculation of excess of statistical significance (either because the total sample size was missing or the study-specific relative risk estimates were missing), and 98 articles were excluded because a larger systematic review or meta-analysis investigating the same risk factor was available. From the 223 comparisons, we further excluded the ones that included one or two studies (53 comparisons). Therefore, 219 articles were analyzed, of which 133 were systematic reviews without any quantitative component and 86 were meta-analyses. The 86 eligible meta-analyses [26-109, 116-118] included data on 170 comparisons and 1511 primary studies. ## Summary Effect-sizes And Significant Findings Three to 152 studies, with a median of nine studies, were included per meta-analysis. The median number of case and control subjects in each study was 88 and 529, respectively. The median number of case and control subjects in each meta-analysis was 98 and 807, respectively. The number of cases was greater than 1000 in 98 comparisons. Overall, 578 ($45\%$) individual studies observed nominally statistically significant results. 40 meta-analyses used the Newcastle–Ottawa Scale to assess qualitatively the included primary studies. One meta-analysis used assessment criteria for non-randomized observational studies adapted from Duckitt and Harrington, 3 meta-analyses used the Methodological Index for Non-Randomized Studies (MINORS) and 38 meta-analyses used other assessment tools. Four meta-analyses did not perform any quality assessment. Details of the 170 comparisons that included 1511 individual study estimates are summarized in Supplemental Table 1. Of the 170 comparisons, 100(58,$8\%$) had nominally statistically significant findings at $P \leq 0.05$ using the random-effects model, of which 94 reported an increased risk and six a decreased risk for preterm birth (preconception care vs no care, magnesium supplementation vs placebo,single vs double embryo transfer, high gestational weight gain vs normal gestational weight gain, IPI following miscarriage< 6m vs > 6m, greenery including only a 100-m NDVI buffer). Of these, a total of 62(36,$5\%$) associations presented statistically significant effect at $P \leq 0.001$, while only 41 (24,$1\%$) remained significant after the application of a more stringent P-value threshold of $P \leq 10$−6 (Supplemental Table 2) ## Between-study Heterogeneity And Prediction Intervals Forty-five (26,$5\%$) comparisons had large (I2 ≥ $50\%$ and ≤ $75\%$) and forty-nine comparisons (28,$8\%$) had very large (I2 > $75\%$) heterogeneity estimates (Supplemental Table 1). When calculating the $95\%$ Pis, the null value was excluded in only thirty two (18,$8\%$) comparisons. ## Small-study Effects Evidence for statistically significant small-study effects (Egger test $P \leq 0.10$ and random-effects summary estimate larger compared with the point estimate of the largest study in the meta-analysis) was identified in 41 (24,$1\%$) comparisons (Supplemental Table 1). ## Test Of Excess Statistical Significance Evidence of excess-statistical-significance bias were observed in 12($7\%$) associations, with statistically significant ($P \leq 0.05$) excess of positive studies under any of the three assumptions for the plausible effect size, i.e. the fixed-effects summary, random-effects summary or results of the largest study (Supplemental Table 1). In addition, the observed and expected number of positive studies showed that, overall, the excess of positive results was driven by meta-analyses with large estimates of heterogeneity (I2 > $50\%$). ## Grading Of Evidence The summary of the epidemiological credibility for 170 associations of risk factors for PTB is shown in Supplemental Table 1. Seven of the 170 associations ($4.1\%$) were supported by robust evidence (fetus with isolated single umbilical artery, maternal personality disorder, sleep-breathing disorder, prior induced termination of pregnancy with vacuum aspiration, low gestational gain weight and interpregnancy interval following miscarriage less than 6 months) (Supplemental Table 4). 26 associations ($15.3\%$) were supported by highly suggestive evidence (Intimate partner violence, cancer survivors, placenta previa, velamentous cord insertion, African/Black ethnicity, Aboriginal ethnicity, first trimester bleeding, unmarried women, obstetric cholestasis, severe maternal morbidity (hemorrhagic and hepatic disorders), Body Mass Index (BMI) > 40 kg/m2, cocaine exposure, endometriosis, prior surgical termination of pregnancy, maternal age > = 45 years, pregnancy with chronic kidney disease, underweight women, LEEP, LLETZ for CIN, any type of treatment for CIN with a cone depth of ≥ 10-12mm compared to untreated CIN, any type of treatment for CIN with a cone depth of ≥ 15-17mm compared to untreated CIN and PCOS). 16 associations (9,$4\%$) were supported by suggestive evidence. Regarding the environmental risk factors, higher residential greenness did not technically qualify to be categorized as robust evidence because the random effects p-value was 3.25 x 10−6 but fulfilled all other criteria.. The rest of the associations regarding different levels of exposure to air pollutants (PM2,5, NO2) in all windows of exposure were classified as weak. We followed a 4-level grading (robust, highly suggestive, suggestive and weak) to evaluate the strength of the evidence based on the following criteria: number of cases, summary random-effects P-value, between-studies heterogeneity, $95\%$ PI, small study effects bias and excess statistical significance[110]. This grading approach based on these parameters was used because it allows for an objective, standardized classification of the level of evidence and has been previously shown that provides consistent results with other more subjective grading schemes [111, 112]. As most of the environmental risk factors included meta-analyses did not report the number of cases or the sample size of the studies included, we were unable to estimate the power of each meta-analysis and the excess significance test for these factors so we did not include excess statistical significance in the grading of these evidence. Briefly, meta-analyses were considered to be supported by robust evidence if: the association was supported by more than 1000 cases, a highly significant association (the random effects model had a P-value ≤ 10−6, a threshold that is considered to substantially reduce false positive findings) [113, 114, 115], there was absence of high heterogeneity based on I2 < $50\%$, the $95\%$ PI excluded the null value, and there was no evidence of small study effects or excess statistical significance. Highly suggestive evidence required more than 1000 cases, a highly significant association (a random-effects P-value ≤ 10−6), and the largest study in the meta-analysis was nominally significant. Associations based on metaanalyses a random-effects P-value ≤ 10−3 and included more than 1000 cases [113, 114, 115] were graded as suggestive evidence. The remaining nominally significant associations were graded as weak evidence ($P \leq 0.05$). We need to highlight that this specific grading scheme focuses on the reduction of false positive findings and the evaluation of potential biases in the studied associations. Therefore, the set of criteria used here is not ideal for a detailed evaluation of non-significant associations and to distinguish insufficient evidence from robust evidence of no association. That would require a different approach and another set of criteria altogether that would focus on the power of the meta-analyses to observe a significant effect, which was beyond the scope of our review. Statistical analyses were performed using STATA version 14 (StataCorp, Texas, USA) ## Discussion In this umbrella review we evaluated the current evidence, derived from meta-analyses of observational studies on the association between various risk factors and PTB. Overall, from the 170 associations that have been examined, only a minority had strongly significant results with no suggestion of bias, as can be inferred by substantial heterogeneity between studies, small study effects, and excess significance bias. Seven risk factors were supported by robust evidence, including amphetamine exposure, isolated single umbilical artery, maternal personality disorder, sleep disordered breathing measured with objective assessment, prior induced termination of pregnancy with vacuum aspiration compared to no termination, low gestational weight gain compared to normal weight gain, and interpregnancy interval following miscarriage less than 6 months. Several others had highly suggestive evidence including intimate partner violence and unmarried women, cancer survivors, Black race, placental complications, hemorrhagic and hepatic disorders, endometriosis, chronic kidney disease and treatments for CIN. ## Interpretation In The Light Of Evidence Apart from risk factors that have been well incorporated in the clinical screening system, we identified a few that are not receiving the attention they should during pregnancy follow up despite the fact that they demonstrate robust evidence. The World Health Organization (WHO) encourages women who experienced a previous miscarriage to wait for a minimum of 6 months before the next conception to achieve optimal outcome and reduce obstetric complications such as preterm birth [119]. Contrary to the findings of the research on which WHO based its recommendations, some studies reported that the risk of adverse obstetric outcomes including preterm birth is lower in women who conceived less than 6 months after a pregnancy loss [120, 125, 126], while synthesizing all available data provided the same conclusion [105]. This meta-analysis included eight studies, performed two analyses: one including the study of Conde Agudelo 2004 [121] and one excluding it, and robust results were obtained after excluding the study. While this was a large retrospective study on which the WHO guidelines for delaying pregnancy for at least 6 months [119] are based, it did not differentiate between induced and spontaneous abortions and used data from many countries where induced abortion is illegal[121], therefore should be interpreted with caution. After a miscarriage, there is a very small burden on the folate reserve and thus miscarriage is not very likely to lead to folate deficiency in the postpartum period, so miscarriage and delivery later in pregnancy can have differential effects on subsequent pregnancy. This could explain the reduced risk of adverse outcomes in a short IPI after a miscarriage [122] but not after delivery. In support of this hypothesis, there is evidence to suggest that late miscarriages (after 12 weeks of gestation) are associated with worse outcomes in the subsequent pregnancy [123]. In addition, most women who attempt another pregnancy soon after a miscarriage are likely to be motivated to take better care of their health and consequently result in better pregnancy outcomes [124]. Another plausible reason may be that those who conceive soon after a miscarriage are naturally more fertile and younger and consequently have better pregnancy outcomes. Another association with robust evidence was pregnant women with sleep breathing disorders. This meta-analysis clearly demonstrated the increased risk profile of women who experience SBD not only for preterm birth but for other pregnancy outcomes. Regarding plausible mechanisms, the association between SDB and intermittent maternal hypoxia as well as the link with conditions synonymous with impaired placental function such as pre-eclampsia suggest a multifactorial cause, with both physiologic changes associated with pregnancy and placental dysfunction involved. This robust association has clear implications for obstetric practice. First, given the rapidly increasing worldwide obesity rates, SDB is likely to become more prevalent in the pregnant population and is worthy of being screened for. Second, the increased risk for both adverse intrapartum and perinatal outcomes demonstrated in this review strongly support the need for increased surveillance of this cohort. Third, public health education programs must take into account the specific maternal and perinatal risks and promote education about the significance of obstructive sleep apnea symptoms and the need for women to discuss this with their obstetric caregivers. In alignment with this suggestion, women with personality disorders could be identified early through mental health screening, where targeted health interventions and multidisciplinary management can be implemented in order to reduce poor outcomes for the baby/child and woman. This early identification and support also have the potential to enable the prevention of maladaptive development trajectories within the mother infant relationship [128, 129]. Regarding induced termination of pregnancy with vacuum aspiration, our results should be interpreted with caution because it is unclear whether two of the five included studies come from the same population, therefore the variance of the pooled estimate may be artificially narrower. Furthermore, it is important that clinical examination and medical history includes risk factors which are not well known, identified in meta-analysis with highly suggestive evidence. To be more specific regarding highly suggestive evidence, there were a few that are well known and used to classify pregnancies as high risk for PTB such as therapies for cervical intraepithelial neoplasia, advanced maternal age, placental pathology, race, first trimester bleeding and maternal comorbidities. There were also included factors that are not routinely screened in the obstetric population such as intimate partner violence, cancer survivors and being unmarried. When it comes to intimate partner violence exposure during pregnancy, this meta-analysis included 30 studies examining the risk of PTB [28]. Two possible pathways have been described which could lead to adverse perinatal outcomes [140, 141]. One is the direct exposure to violence consisted of either physical assault directly to the abdomen or sexual abuse. Direct exposure has been associated with pregnancy complications such as premature rupture of membranes, uterine contractions and placental damage, too[140, 142]. On the other hand, indirect exposure to violence trigger biological mechanisms, such as smoking, alcohol or drug use, inadequate prenatal care and weight gain that contribute to adverse birth outcomes [140, 145-156]. Women with history of abuse by their partner is believed to have less support, lower levels of self-esteem and higher levels of stress, too [140, 142, 145, 153, 160]. All these factors contribute to the indirect mechanism “theory” associated to preterm birth. As a result, healthcare professionals/institutes follow screening protocols in some nations or clinical guidelines, in order to detect and take care of these cases [157-159]. Another association that demonstrates highly suggestive evidence is pregnant women, whom survived cancer. This meta-analysis included fourteen studies which described the incidence of PTB [30]. Regarding plausible mechanisms, it is believed that radiotherapy treatment protocols for cancer, especially irradiation of the abdomen is harmful both for the uterine vasculature and the uterus muscular development. This leads to a reduction in uterine elasticity and uterine volume [143, 144]. Uterine volume can also be smaller due to hormonal deficiency, caused by ovarian failure [144]. This could lead to preterm delivery. However, there is a possible association between the dosage of radiotherapy and risk of PTB, something which still has not been examined due to the obscuring of pooling dosages in previous studies. Higher radiations doses may reflect to higher risk of PTB. In addition, we should highlight the fact that the population of cancer survivors following advancing treatment grows and the prevalence of PTB cases in these groups is going to rise, in regards. Maternal marital status plays also a role in PTB, but healthcare professionals rarely consider it as a risk factor. This meta-analysis consists of 21 studies comparing unmarried women to married ones, identifying an increased risk of PTB [43]. Regarding the ways in which unmarried women are associated to PTB, it is suggested that the quality of relationship between biological maternal and paternal figures is more important than their legal status [136, 137]. Moreover, a biological father might be more caring or supportive of the birth compared to another family member or partner. Mother psychosocial stress level depends on the support that she receives from her familiar environment [138, 139], but a variety of other factors should be taken into consideration before interpreting these results. With regards to health practitioner’s point of view, the importance of obtaining social history information during clinical exam, lies in identifying pregnancies at risk for PTB and offering new perspectives. This information should be focused on rarely screened factors in every-day routine, which support highly suggestive evidence. Regarding environmental risk factors, increased residential greenness was associated with a protective effect on the risk of PTB. Although this finding was categorized as having suggestive evidence, the p-value of the random effect estimate was very close to the stringent threshold of < 10 – 6. Acknowledging the detrimental projected effect of climate change in greenness and given that it is one of the few protective risk factors for PTB, serious efforts should be made to maintain and grow residential greenness. Possible mechanisms include among others amelioration of the effects of air pollutants, reduction of stress and increase in physical activity [116]. There were also suggestive evidence for early pregnancy exposure to PM2.5 and the risk of PTB. This association has been debated in the literature with conflicting results about the timing and magnitude of effect and is less robust than other associations that have been shown to have strong evidence for associations [130] such as birthweight. In the current umbrella review, we applied a transparent and replicable set of criteria and statistical tests to evaluate and categorize the level of existing observational evidence. Although, 58,$8\%$ of associations in the included meta-analyses report a nominally ($P \leq 0.05$) statistically significant random-effects summary estimate, when stringent P value was considered ($P \leq 10$−6), the proportion of significant associations decreased to 24,$1\%$. 94 (55,$3\%$) associations had large or very large heterogeneity, while when we calculated the $95\%$ prediction intervals, which further account for heterogeneity, we found that the null value was excluded in less than half of the associations. Only seven ($4.1\%$) of the assessed risk factors found to provide robust evidence, indicating that several published meta-analyses of observational studies in the field could be susceptible to biases and the reported associations in the existing studies are often exaggerated. The ability to modify those factors, mainly those related to mental health and sleep quality screening, through screening and clinical interventions or public health policy measures remains to be established. Furthermore, there is no guarantee that even a convincing observational association for a modifiable risk factor would necessarily translate into large preventive benefits for preterm birth if these risk factors were to be modified [93]. With obesity becoming a global epidemic, the assessment of the strength of the evidence supporting the impact of overweight and obesity in sleep breathing disorders could allow the identification of women at high risk for adverse outcomes and allow better prevention. Obesity is generating an unfavorable metabolic environment from early gestation; therefore, initiation of interventions for weight loss during pregnancy might be belated to prevent or reverse adverse effects, which highlights the need of weight management strategies before conception [68, 103, 104, 131]. PTB does not only increase the risk for maternal and infant complications, but also significantly increases a woman’s risk of cardiovascular disease (CVD) after pregnancy, therefore primary prevention [12, 132-134] is extremely important. Our assessment has certain limitations. Umbrella reviews focus on existing systematic reviews and meta-analyses and therefore some studies may have not been included either because the original systematic reviews did not identify them, or they were too recent to be included. In the current assessment we used all available data from observational studies, therefore the meta-analysis estimates may partly reflect the biases from which the original studies suffer from. Statistical tests of bias in the body of evidence (small study effect and excess significance tests) offer hints of bias, not definitive proof thereof, while the Egger test is difficult to interpret when the between-study heterogeneity is large. These tests have low power if the meta-analyses include less than 10 studies and they may not identify the exact source of bias [23, 25, 135]. More specifically, in our study, all robust evidence applied to meta-analyses with less than 10 studies, therefore the results of publication bias should be interpreted with caution. Furthermore, we did not appraise the quality of the individual studies on our own, since this should be included in the original meta-analysis and it was beyond the scope of the current umbrella review. However, we recorded whether and how they performed a quality assessment of the synthesized studies. Lastly, we cannot exclude the possibility of selective reporting for some associations in several studies. For example, perhaps some risk factors were more likely to be reported, if they had statistically significant results. ## Conclusion The present umbrella review of meta-analyses identified 170 unique risk factors for preterm birth. Our analysis identified seven risk factors with robust evidence and strong epidemiological credibility pertaining to isolated single umbilical artery, amphetamine exposure, maternal personality disorder, sleep breathing disorders, induced termination of pregnancy with vacuum aspiration, low gestational weight gain and interpregnancy interval following miscarriage of less than 6 months. As previously suggested, the use of standardized definitions and protocols for exposures, outcomes, and statistical analyses may diminish the threat of biases, allow for the computation of more precise estimates and will promote the development and training of prediction models that could promote public health. ## Methods We conducted an umbrella review which is a comprehensive and systematic approach that collects and critically evaluates all systematic reviews and meta-analyses performed on a specific research topic [11]. We used previously described, standardized methods that have been already used in previously published umbrella reviews referring to risk factors related to various outcomes [13-16] and have been elaborated below. A protocol for this umbrella review was registered in the International prospective register of systematic review (PROSPERO 2021 CRD42021227296) ## Search Strategy Two researchers (A.E., I.M.) independently searched PubMed database from inception to December 2020, in order to identify systematic reviews and meta-analyses of studies that examine the association between risk factors and preterm birth. The search strategy included combinations of the Medical Subject Headings (MESH) terms, key words and word variants for terms “preterm birth” AND (“systematic review” OR “meta-analysis”). Titles and abstracts were screened and potentially eligible articles were retrieved for full text evaluation. A detailed description of our search strategy is provided in the supplement (Supplemental Table 3). ## Eligibility Criteria And Data Extraction We included systematic reviews with meta-analyses investigating the association between various types of exposures and PTB. Specifically, we included studies with singleton pregnancies and studies where PTB was evaluated as primary outcome. Case report or series and individual participant data meta-analyses were excluded. We also excluded studies that set time limits on time span or were performed on a restricted setting (i.e. conducted for one specific country). Furthermore, we excluded studies that assessed PTB as a secondary outcome, studies including multiple pregnancies, and studies that assessed genetic or over -omics features as risk factor for PTB. All studies were compared to avoid the possibility of duplicate or overlapping samples. If more than one meta-analysis referring to the same research question were eligible, the one with the largest amount of component studies with data on individual studies’ effect sizes retained for the main analysis Publications whom the estimates of the studied associations, such as relative risks (RR) and $95\%$ confidence intervals (CIs), were not reported or could not be retrieved/calculated were excluded from the analysis. For the non-environmental risk factors, we also excluded meta-analyses that did not provide the number of cases in the exposed and non-exposed groups, which is used for the calculation of the excess significance tests. For the environmental risk factors, since most commonly they report the results as per unit(s) increase in exposure and everyone is exposed, we included them even if they did not report the number of cases and total sample size. Eligible articles were screened by four independent reviewers (AE/IM and EB/TK). Any disagreement between reviewers was resolved by consensus or after evaluation of a third author (SP or EE). The data of eligible studies were extracted in a predefined data extraction form recording for each study the first author, journal, year of publication, the examined risk factors and the number of reviewed studies. Either the study specific relative risk estimates (risk ratio, odds ratio, hazard ratio, incidence rate ratio) and the confidence intervals were extracted or the mean and the standard deviation for continuous outcomes were also noted in this form. We also extracted exposed and control group used; outcome assessed; study population; exposure characteristics; number of studies in the meta-analysis; meta-analysis metric and method; effect estimate with the corresponding $95\%$ confidence interval; number of cases and total sample size; I2 metric and the corresponding χ2 p-value for the Q test; and Egger’s regression P-value. ## Assessment Of Summary Effect And Heterogeneity We re-calculated summary effects and $95\%$ Confidence Intervals (CIs) for each meta-analysis via fixed and random effects model [17, 18]. $95\%$ prediction intervals (PI) were also computed for the summary random-effects estimates, which further account for between-study heterogeneity indicating the uncertainty for the effect that would be expected in a new study examining the same correlation [19, 20]. A PI describes the variability of the individual study estimates around the summary effect size and represents the range in which the effect estimate of a new study is expected to lie. The largest study considered as the most precise with a difference between the point estimate and the upper or lower $95\%$ confidence interval less than 0.20. If the largest study presented a statistically significant effect, then we recorded this as a part of the grading criteria. Between study heterogeneity was assessed and P-value of the χ2-based Cochran Q test and the I2 metric for inconsistency (reflecting either diversity or bias) was reported, too. I2 metric were used to indicate the ratio of between study-variance over the sum of within and between-study variances, ranging from 0–$100\%$ [21]. Values exceeding $50\%$ or $75\%$ are usually considered to represent large or very large heterogeneity, respectively. $95\%$ Confidence intervals were calculated as per Ioannidis et al. [ 22]. ## Assessment Of Small-study Effect Small studies tend to give substantially larger estimates of effect size when compared to larger studies. We evaluated the evidence of the presence of the small study effect, in order to identify publication and other selective reporting biases. They can also reflect genuine heterogeneity, chance, or other reasons for differences between small and large studies [23]. We evaluated whether smaller (less precise) studies lead to inflated effect estimates comparted to than larger studies. We used the regression asymmetry test proposed by Egger, that examines the potential existence of small study effects via funnel plot asymmetry [24]. Egger’s test fits a linear regression of the study estimates on their standard errors weighted by their inverse variance. Indication of small study effects based on the Egger’s asymmetry test was claimed when P-value ≤ 0.10. This is considered as an indication of publication bias, Indication of small study effects based on the Egger’s asymmetry test was claimed when P-value ≤ 0.10 and the random effects summary estimate was larger compared to the point estimate of the largest (most precise) study in the meta-analysis. ## Excess Statistical Significance Evaluation The excess significant test was applied to evaluate the existence of relative excess of significant findings in the published literature for any reason (e.g. publication bias, selective reporting of outcomes or analyses). The number of expected positive studies is estimated by a chi-squared-based test and being compared to the observed number of studies with statistically significant results ($P \leq 0.05$) [25]. A binomial test evaluated whether the number of positive studies in a meta-analysis was too large according to the power that these studies have to detect plausible effects at α = 0.05. In brief, observed versus expected studies for each meta-analysis were compared separately and this comparison also extended to groups of many meta-analysis after summing the observed and expected studies from each meta-analysis. The power of each component study was calculated using the fixed-effects summary, the random effects summary, or the effect size of the largest study (smallest SE) as the plausible effect size [15]. An algorithm using non-central t distribution was used to calculate the power of each study [26]. Excess statistical significance for single meta-analyses was claimed at $P \leq 0.10$ (one-sided $P \leq 0.05$, with observed > expected as previously proposed), given the power to detect a specific excess will be low, especially with few positive studies.[25] ## Data availability: Relevant data to our study are mainly included in the article, tables and supplemental material. 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--- title: Both Saccharomyces boulardii and Its Postbiotics Alleviate Dextran Sulfate Sodium-Induced Colitis in Mice, Association with Modulating Inflammation and Intestinal Microbiota authors: - Xinge Xu - Jingwei Wu - Yuxin Jin - Kunlun Huang - Yuanyuan Zhang - Zhihong Liang journal: Nutrients year: 2023 pmcid: PMC10055518 doi: 10.3390/nu15061484 license: CC BY 4.0 --- # Both Saccharomyces boulardii and Its Postbiotics Alleviate Dextran Sulfate Sodium-Induced Colitis in Mice, Association with Modulating Inflammation and Intestinal Microbiota ## Abstract Objective: To investigate the effect of Saccharomyces boulardii and its freeze-dried and spray-dried postbiotics on the intervention and potential mechanism of dextran sulfate sodium (DSS)-induced ulcerative colitis in mice. [ Methods] After the acclimation period of C67BL/6J mice, a colitis model was constructed by applying $2\%$ DSS for 7 d, followed by 7 d of intervention. Subsequently, the disease activity index (DAI), organ index, colon length, colon HE staining of pathological sections, ELISA for blood inflammatory factors (Interleukin (IL)-1β, IL-6, IL-10, Tumor necrosis factor (TNF)-α), Real time quantitative polymerase chain reaction (RT-qPCR) to determine the levels of colonic inflammatory factors (IL-1β, IL-6, IL-10, TNF-α), Occludin gene expression, and intestinal flora were assessed to evaluate the protective effects of S. boulardii and its postbiotics on colitis in mice. Results: Compared with the DSS group, S. boulardii and the postbiotics interventions effectively improved colonic shortening and tissue damage, increased the expression of intestinal tight junction protein, reduced the secretion of pro-inflammatory factors, increased the secretion of anti-inflammatory factors, and maintained the homeostasis of intestinal microorganisms. Postbiotics intervention is better than probiotics. Conclusions: S. boulardii and its postbiotics can effectively alleviate DSS-induced colitis in mice through modulating host immunity and maintaining intestinal homeostasis. Postbiotics are promising next-generation biotherapeutics for ulcerative colitis treatment. ## 1. Introduction Inflammatory bowel diseases (IBD) are a group of chronic intestinal inflammatory diseases that mainly include ulcerative colitis (UC) and Crohn’s disease (CD) [1]. UC is a chronic inflammatory disease affecting the colon and rectum that mostly manifests as abdominal pain, diarrhea, and rectal bleeding [2]. UC has become a global disease in recent years, and its prevalence and incidence are still on the rise, especially in the rapidly developing newly industrialized countries, posing a serious threat to people’s quality of life and health [3]. A combination of environmental factors, genetic susceptibility, epithelial barrier defects, and dysregulated immune response contribute to the development and progression of UC [4]. The gut microbiota, as a major environmental driver, profoundly influence the immune composition of the host under physiological and pathological conditions, and disturbances in the gut microbiota lead to immunological dysregulation, which may underlie diseases such as inflammatory bowel disease [5]. It has been noted that although some medical treatments such as steroids, immunomodulators, and antibodies are available for UC patients, there are problems of low treatment response and high incidence of recurrences, thus UC treatment imposes a considerable financial burden on the healthcare system, highlighting the need for novel therapeutics to improve disease management [6,7]. Studies have shown that the use of probiotics can reduce the symptoms associated with UC and that their effects may be related to the regulation of the gut microbiota, thus attracting much attention [8]. Saccharomyces boulardii is a type of S. cerevisiae, a nonpathogenic and probiotic yeast, which belongs to facultative anaerobic fungi and exerts anti-cancer, immune modulation, antibacterial, antiviral, and antioxidant functions in the body [9]. It was found to be the most abundant fungal genus in healthy individuals [10]. S. boulardii has been used as a probiotic since the 1950s for prevention and treatment of antibiotic-associated diarrhea and has demonstrated efficacy in pilot studies in patients with inflammatory bowel disease (IBD) [11]. However, it should not be overlooked that the exact mechanism by which S. boulardii relieves the symptoms of colitis is not fully understood. Furthermore, some safety concerns have been raised about the use of this probiotic, with a small number of studies finding fungemia [12]. It has also been shown that non-curative Escherichia coli will cause damage to the wall of S. boulardii, affecting its ability to exert a direct probiotic effect on pathogenic E. coli. As a result of that, the intestinal microbiota contains high levels of non-curative E. coli, which will affect the efficiency of S. boulardii in the intestine [13]. All this evidence questions the safety of this preventative biotherapy in clinical use, so it would be of interest to develop products that not only resemble the functional repertoire of probiotics but also pose no or the near absence of risk to users. Postbiotics are a new mean of intervening in the gut ecosystem that have emerged. In May 2021, the International Scientific Association of Probiotics and Prebiotics (ISAPP) published a consensus statement on postbiotics, stating that postbiotics refer to “preparations of inanimate microorganisms and/or their components that confers a health benefit on the host” [14]. The efficacy of postbiotics is based on microbial metabolites, proteins, lipids, carbohydrates, vitamins, organic acids, cell wall components, or other complex molecules produced in the fermentation matrix. At this point the viability of the microorganism is no longer an important criterion. Therefore, postbiotics have advantages that are unmatched by probiotics, including a well-defined chemical structure, safe dosing parameters, longer shelf life, and better absorption, metabolism, and organismal distribution. The use of postbiotics can obtain probiotic-like efficacy while avoiding the problems of low bioavailability of bacteria, unstable effects, easy transmission of drug-resistant genes, easily cause microecological imbalance, increased microbial translocation, opportunistic infection or enhanced inflammatory response, etc. It is considered a better and safer strategy and will be a new direction for future research in the field of probiotics [15,16]. At present, research on postbiotic function is still in its infancy, and further studies would be needed to unveil other beneficial effects and clarify the probiotic mechanisms of postbiotics. Furthermore, there is a lack of relevant studies directly comparing the clinical benefits of postbiotics and probiotics to support their application. The aim of this work was to characterize the therapeutic effects of S. boulardii and its postbiotics on a DSS-induced colitis model in mice, focusing on the alteration of the intestinal microbiota and its contribution to colitis-related parameters. It also explores the role of S. boulardii and its postbiotics in the intestinal microbial ecosystem to reveal the potential mechanisms of its intestinal anti-inflammatory activity, and to initially compare the effectiveness between postbiotics and probiotics. The highlight is that this study visually compares the differences in the therapeutic effects of the S. boulardii and its postbiotics to provide a theoretical basis and practical guidance for the development of effective modalities to alleviate colitis and related inflammatory bowel diseases. ## 2.1. Animals Male specific pathogen-free (SPF) C57BL/6 J mice (age 6–8 weeks, weight 18–22 g) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (SCXK (Beijing, China) 2021-0006). All mice were housed in a standard SPF environment in the animal center (SYXK (Jing) 2020-0052). Five mice were maintained in each individually ventilated cage (temperature, 22 ± 2 °C; relatively humidity, 40–$70\%$; standard 12 h/12 h light/dark cycle). ## 2.2. Chemicals and Reagents Yeast Extract Peptone Dextrose Medium was purchased from Qingdao Hi-Tech Park Haibo Biotechnology Dextran sulfate sodium (DSS, w/v. molecular mass 36–50 kDa) was purchased from Yisheng Biotechnology Co., Ltd. (Shanghai, China). RNA Extraction Kit (TransZol™ Up Plus RNA Kit) and cDNA Synthesis Kit (TransScript® One-Step gDNA Removal and cDNA Synthesis SuperMix) were purchased from Beijing TransGen Biotechnology Co., Ltd. (Beijing, China). SuperReal Color Fluorescence Quantitative Premix Reagent was purchased from Tiangen Biochemical Technology Co., Ltd. (Beijing, China). ## 2.3. Probiotics and Postbiotics Saccharomyces boulardii was isolated from Angel Fubon Colibri Ken feed additive, strain conservation number CCTCC NO.M 2012116. Freeze-dried S. boulardii postbiotic: YPD liquid culture was sterilized at 121 °C for 15 min, and after cooling to 55–60 °C, the S. boulardii strain was inoculated and incubated at 30 °C with shaking at 200 rpm until the stable stage. The fermentation broth was divided into portions, and after the pre-freezing was completed, the powder was freeze-dried by a freeze-dryer, and the lyophilized postbiotic sample was obtained. Spray-dried S. boulardii postbiotic: YPD liquid culture was sterilized at 121 °C for 15 min, and after cooling to 55–60 °C, the S. boulardii strain was inoculated and incubated at 30 °C with shaking at 200 rpm until the stable stage. The spray-drying conditions were set as follows: the inlet temperature was set at 135–140 °C, the outlet air temperature was between 48 °C and 50 °C, the inlet flow rate and the drying air flow rate were 7.5 mL/min and 32.5 m3/h, respectively. The powder obtained was the spray-dried postbiotic sample. ## 2.4. Animal Experiment After fifty C57BL/6J male mice were acclimatized and fed in the experimental environment for 1 week, the mice were divided into five groups ($$n = 10$$): Control, DSS, BLD (S. boulardii), D-BLD (Freeze-dried S. boulardii postbiotic), and P-BLD (Spray-dried S. boulardii postbiotic) groups. Mice in the DSS, BLD, D-BLD, and P-BLD groups were administered with DSS ($2\%$) in drinking water daily for 7 days. The $2\%$ DSS solution was changed once a day for 7 days. The mice were weighed daily and their disease activity index (DAI) was assessed according to the scoring criteria as shown in Supplementary Table S1. Then, probiotics and postbiotics were administered to mice in BLD, D-BLD, and P-BLD groups by oral gavage from day 7 for 7 consecutive days. While the mice in Control and DSS groups were gavaged with 200 μL of sterile phosphate buffered saline (PBS) solution. Both freeze-dried and spray-dried S. boulardii postbiotics were dissolved in PBS solution (Figure 1). From day 7, all animals had unrestricted access to food and water. After the last feeding, mice were fasted without water for 12 h and were sacrificed on day 15. Before the mice were sacrificed by cervical dislocation, the blood samples were taken from the posterior orbital venous plexus. The serum was obtained and then stored at −80 °C. The organs were dissected immediately. The liver, kidney, and spleen were weighed to calculate organ indices. Organ index (organ index of spleen, liver, and kidney) = organ weight/body weight × $100\%$. The length of colon was measured. A segment of the colon was flushed with sterile water and then fixed in $4\%$ paraformaldehyde for subsequent histopathological analysis. The colon and cecal contents were snap-frozen in liquid nitrogen instantly, and then kept at −80 °C for further investigation. ## 2.5. Histopathological Analysis The distal colon (2 cm from the anus) was taken and fixed in $4\%$ paraformaldehyde for histopathological assessment. Paraformaldehyde-fixed colon tissues embedded in paraffin and sectioned into 4 µm thick sections. Paraffin sections were dewaxed and stained with hematoxylin and eosin. A SUNNY EX20 biological microscope (Ningbo Sunny Instruments Co., Ltd., Ningbo, China) was used to examine the sections. ## 2.6. Measurement of Serum Inflammatory Indicators The blood samples were placed at rest for 2 h and then centrifuged once at 4 °C (3500 r/min, 15 min) to collect the serum. The colonic concentration of inflammatory factors including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and IL-10 were measured by corresponding ELISA kits according to the instructions of manufacturer. ## 2.7. Quantitative Real-Time Polymerase Chain Reaction Analysis for mRNA Expression Total RNA was extracted from the colon using TransZolTM Up Plus RNA Kit according to the manufacturer’s protocols. The extracted RNA was reverse transcribed to complementary DNA (cDNA) using a reverse transcriptase kit. The cDNA was analyzed by quantitative real-time PCR (qRT-PCR) using SuperReal PreMix Plus (SYBR Green) (Tiangen Biochemical Technology Co., Ltd., Beijing, China). Each reaction was subjected to the following cycling conditions: a pre-cycling stage at 95 °C for 15 min, followed by 40 cycles at 95 °C for 15 s, 60 °C for 20 s, and 72 °C for 25 s. The 2−ΔΔCt method was used to determine the messenger RNA (mRNA) expression levels of cytokines and tight junction protein in colon tissue relative to the expression of GAPDH. The expression levels of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), anti-inflammatory cytokines (IL-10), and tight junction protein (occludin) were evaluated. The sequences of the primers used in this study are provided in Table 1. ## 2.8. Gut Microbiota Analysis Microbial genomic DNA was extracted and the bacterial V3–V4 regions of the 16 S rRNA were amplified and sequenced on a Nova-Seq platform (Illumina, San Diego, CA, USA). The raw data were filtered and analyzed by QIIME (v1.9.1). UPARSE v7.0.1001 was used to cluster the OTUs at an identity threshold of $97\%$. NMDS plots were analyzed using PAST v2.17 based on Bray–Curtis distance. Heat maps were drawn using HemI software (v1.0.3.7). Determinations of alpha and beta diversities were also conducted in QIIME (v1.9.1). The Majorbio Cloud Platform was used to create principal coordinates analysis (PCoA) visualizations (www.majorbio.com, accessed on 1 December 2022). In addition, this study used the Majorbio cloud platform (www.majorbio.com, accessed on 1 December 2022) to undertake a Bray–Curtis distance-based analysis of similarity (ANOSIM). Following the linear discriminant analysis (LDA), the LDA effect size (LEfSe) analysis was conducted to identify the differential bacterial taxa from the level of phylum to genus with two filters ($p \leq 0.05$ and LDA score > 2). PICRUSt was used to predict the functional profiles of microbial communities, and STAMP (version 2.1.3) was used to evaluate statistically significant differences (http://kiwi.cs.dal.ca/Software/STAMP, accessed on 1 February 2023). ## 2.9. Statistical Analysis All data were expressed as means ± SEM and analyzed using GraphPad Prism 9.0 program (GraphPad Software, San Diego, CA, USA). All data differences were analyzed by one-way analysis of variance (ANOVA), followed by Tukey’s test, and $p \leq 0.05$ was considered as statistically significant. ## 3.1. Both S. boulardii and Its Postbiotics Can Alleviate the Symptoms of DSS-Induced Colitis in Mice By constructing a model of DSS-induced colitis, it was possible to detect and compare the beneficial effects of S. boulardii and its postbiotic application. The survival curves showed that S. boulardii and its postbiotics treatment temporarily delayed the death of the mice, which showed higher survival rates (Figure 2A). Weight loss, a typical feature of colitis, was significantly lower in mice in the DSS group compared with the Control group (Figure 2B, $p \leq 0.01$), the intervention reversed the weight loss, with the BLD and P-BLD groups ($p \leq 0.01$) showing better weight recovery than the D-BLD group ($p \leq 0.05$). The liver, kidney, and spleen indices of the mice in each group were examined, which can reflect the biological function of the organ to some extent. Among them, the spleen, as an important peripheral immune response organ, is capable of producing antibodies and other active substances, and is stimulated by immune activation in DSS-induced colitis thus causing enlargement. The results showed that the spleen index increased in the DSS group compared with the Control group, with a significant difference ($p \leq 0.05$). There were no significant changes for liver and kidney indices among all the groups (Figure 2C,D). ## 3.2. Both S. boulardii and Its Postbiotics Alleviated DSS-Induced Colonic Shortening and Pathological Changes in Mice Shortening of colon length is one of the typical features of DSS-induced colitis. The colon length of mice in the DSS group was significantly shorter than that in the Control group ($p \leq 0.01$), and the colon length of mice in the three groups with S. boulardii and postbiotics intervention basically returned to normal, with no significant difference from that in the Control group ($p \leq 0.05$) as shown in Figure 3A,B. The results of Hematoxylin-eosin staining of colon sections are shown in Figure 3C. Under the light microscope, the Control group had normal colonic tissue structure, while the DSS group had disordered colonic tissue structure, incomplete colonic mucosa, irregular arrangement, damage or disappearance of glands and glandular lumen, and structural abnormalities of the crypt. Compared with the DSS group, S. boulardii and its postbiotics intervention could alleviate the damage of intestinal mucosa and crypt, inflammatory cell infiltration caused by DSS, and improve the histopathological changes in the colon caused by DSS. Among them, the degree of lesion reduction was more obvious in the postbiotic intervention group, which showed that the mucosa was basically intact, inflammation was less severe, and the crypt was basically intact and close to normal colonic tissue. Based on the above results, it can be concluded that both S. boulardii and its postbiotics can alleviate DSS-induced shortening of colonic length and lesions of colonic structural damage. The postbiotics’ effect may be superior to that of S. boulardii, but more evidence is needed for support. ## 3.3. Both S. boulardii and Its Postbiotics Regulated Serum Cytokine Levels in DSS-Induced Colitis Mice The increase of pro-inflammatory factors and decrease of anti-inflammatory factors are typical features of colitis, which can reflect the development process and severity of colitis. The effect of S. boulardii and its postbiotics on serum inflammatory factors in mice with DSS-induced colitis was detected using an ELISA kit, and the results are shown in Figure 4. The levels of IL-6 and TNF-α in serum were significantly increased in the DSS group compared with the Control group ($p \leq 0.001$). Compared with DSS, the levels of TNF-α ($p \leq 0.01$) and IL-6 were significantly lower ($p \leq 0.001$) after the intervention of S. boulardii (i.e., BLD group). The intervention effect was more obvious in D-BLD and P-BLD groups, which showed a significant decrease in IL-6 and TNF-α levels ($p \leq 0.001$) and IL-1β levels ($p \leq 0.05$). There was no significant difference in IL-10 levels between the groups. The results showed that both S. boulardii and its postbiotics were able to alleviate the symptoms of colitis caused by DSS by reducing the expression of pro-inflammatory factors, and the intervention effects of postbiotics were better than those of S. boulardii. In summary, this shows that postbiotics may have stronger immunomodulatory effects. ## 3.4. Both S. boulardii and Its Postbiotics Modulated the Expression Levels of Inflammatory Factors and Tight Junction Proteins in the Colons of Mice with DSS-Induced Colitis To further test the immunomodulatory effects of S. boulardii and its postbiotics, the expression of mRNA levels of the colonic inflammatory factors IL-1β, IL-6, IL-10, and TNF-α in each group of mice was examined using RT-qPCR in this study. As shown in Figure 5A, the gene expression levels of IL-1β and TNF-α were significantly higher ($p \leq 0.001$) and the level of IL-10 was significantly lower ($p \leq 0.01$) in the colon of the DSS group compared with the Control group. Compared with DSS, the colonic IL-1β levels were significantly lower and IL-10 levels were significantly higher after intervention with Saccharomyces boulardii and spray-dried postbiotics (BLD and P-BLD groups) ($p \leq 0.001$). The most significant intervention effect was observed in the D-BLD group, which showed a significant decrease in IL-1β and TNF-α levels and a significant increase in IL-10 levels ($p \leq 0.001$). There was no significant difference in IL-6 levels between the groups. Remarkably, it was observed that the intervention with S. boulardii and P-BLD did not decrease the expression of TNF-α and its level was even equal to that of the DSS group. In summary, both S. boulardii and its postbiotics can alleviate the inflammatory response of colon caused by DSS, among which the effect of freeze-dried postbiotics is the most obvious. Disruption of the intestinal barrier is one of the characteristics of the pathogenesis of patients with ulcerative colitis. To investigate the effect of S. boulardii and its postbiotic elements on the intestinal barrier in mice, the mRNA expression level of the tight junction protein occludin in the colon of mice was analyzed, as shown in Figure 5B. Compared with the Control group, the expression level of occludin in the DSS group was reduced significantly ($p \leq 0.05$); it was slightly increased after the intervention of S. boulardii and postbiotics, but with no significant difference between the groups ($p \leq 0.05$). ## 3.5. Both S. boulardii and Its Postbiotics Modified Gut Microbiota in DSS-Induced Colitis Mice Intestinal microorganisms are crucial for the development of inflammatory bowel disease, and improving colitis by regulating intestinal flora is a current research hotspot. The sequences of the genes from the V3-V4 regions of the intestinal contents of mice in each experimental group were sequenced with high throughput to detect changes in the intestinal flora. ## 3.5.1. Gut Microbiota Overall Structure Was Altered by Both S. boulardii and Its Postbiotics The overall structure of the intestinal flora was evaluated by diversity analysis of single samples (α diversity) and diversity analysis between samples (β diversity). The Chao index as well as the Simpson index reflected the α diversity of the intestinal microbial populations, and Figure 6A,B shows that based on the analysis of OTU level, the Chao index of the intestinal flora of DSS-induced colitis mice and the Simpson index were not statistically different, indicating that DSS treatment, S. boulardii, and its postbiotic intervention did not significantly alter the intestinal microbial community richness and homogeneity in colitis mice. The Venn diagram provides a visualization of the number of OTUs common and specific to multiple sample groups. The results are shown in Figure 6C. The Control, DSS, BLD, D-BLD, and P-BLD groups each had 503, 362, 292, 311, and 351 OTUs, and the number of endemic OTUs were 24, 33, 4, 11, and 15, respectively. Based on OTU abundance, principal component analysis (PCA) was used to determine overall differences in microbial communities among groups. The PLS-DA scale directly reflected differences between and within groups. PCoA and NMDS analyses were used to assess differences in microbiota structure. The results showed that the samples showed significant clustering, with significant differences ($p \leq 0.01$) in the structure of the flora between groups. The samples of the DSS group were far from the Control group, and the intervention of S. boulardii and its postbiotic elements changed the intestinal flora structure of mice back toward the Control group. All these results showed that the intervention of S. boulardii and its postbiotics would influence the structure of gut microorganisms. ## 3.5.2. Gut Microbiota Composition Was Regulated by Both S. boulardii and Its Postbiotics The composition of the intestinal microbial community and its structure play a crucial role in maintaining intestinal microbial homeostasis. Figure 7A–C analyzes the relative abundance of intestinal microorganisms in each experimental group at both the phylum and genus levels, with the Control group flora reflecting the composition of the normal mouse intestinal flora, to determine the differences in community composition between groups. As shown in Figure 7A, at the gate level, Bacteroidota ($70.81\%$), Firmicutes ($17.87\%$), Actinobacteriota ($9.22\%$), and Patescibacteria ($0.95\%$) were the four major groups of bacteria in the intestine of normal mice. Compared with the Control group, the relative abundance of Bacteroidota ($64.84\%$) and Actinobacteriota ($2.07\%$) decreased and the relative abundance of Firmicutes ($27.46\%$) and Patescibacteria ($3.72\%$) increased in the mice of the DSS group. Both S. boulardii and postbiotics interventions reversed these changes and showed a microbial community structure more similar to that of the Control group. As shown in Figure 7B, at the genus level, norank_f__Muribaculaceae ($64.70\%$), Ileibacterium ($9.25\%$), Bifidobacterium ($6.06\%$), Alloprevotella ($4.41\%$), and Dubosiella ($3.85\%$) were the main components of the intestinal flora genus level in Control group mice, which are key genera for maintaining intestinal microecological stability. Community structure changed after DSS induction, when norank_f__Muribaculaceae ($37.96\%$), Prevotellaceae_UCG-001 ($8.15\%$), Alistipes ($7.78\%$), and Lactobacillus ($7.40\%$) became the major components of their flora, and the relative abundance of all four major groups of the Control group was significantly reduced. S. boulardii and postbiotics could improve the DSS-induced intestinal flora dysbiosis and make the community composition closer to the normal level. The intervention effect was obvious, as follows: the relative abundance of norank_f__Muribaculaceae was adjusted upward from $37.96\%$ to $69.10\%$, $62.68\%$, and $63.15\%$, respectively, for the BLD, D-BLD, and P-BLD groups, which were close to the normal level ($64.70\%$). However, there were differences in community composition among the groups, with Dubosiella ($10.27\%$), Dubosiella ($4.83\%$), and Alistipes ($5.79\%$) being the second most abundant groups in relative abundance in the BLD, D-BLD, and P-BLD groups, respectively. The D-BLD group had better community regulation than the other groups and showed a community composition closer to that of the Control group. In order to clarify the differences among groups, we used linear discriminant analysis (LDA) of the effect amount (LEfSe) to determine the biomarkers with rich differences between groups. As shown in Figure 7C,D, each group is rich in different microbial communities, and species with significant differences are expressed by an LDA score greater than 2, which reflects the impact of species with significant differences in components. The results showed that gut microbiota in the mice of the DSS group was increased dramatically in Clostridia class, RF39, and Mycoplasma order. In addition, the DSS group had an increase in the relative abundance of Erysipelatoclostridiaceae, UCG-010, and Mycoplasmataceae at family level, and Lachnonospiraceae_NK4A136_group and Erysipelatoclostridium at genus level. Compared to the Control group, there were more diverse changes in the structure of the DSS colonic microbiota. The BLD group significantly increased the abundance of Turcibacter at the genus level. While the P-BLD group significantly increased the abundance of Oscillospirales and Christensenellales at the order level, Ruminococcaceae, Christensenellaceae, and Defluviitaleaceae at the family level, and Turicibacter and Defluviitaleaceae_UCG-011 at the genus level. The results suggested that S. boulardii and postbiotics may regulate the gut microbiota to achieve a new balance by restoring the abundance of dominant bacteria. ## 3.5.3. Functional Profile of the Gut Microbiome Was Changed by Both S. boulardii and Its Postbiotics In order to understand the potential function of colonic microbiota, we used the PICRUSt2 method to predict the KEGG function spectrum of intestinal microbiota based on 16S rRNA gene amplification sequence. Figure 8 shows the first 20 KEGG pathways between each group. After DSS induction, probiotics and postbiotics interventions, many pathways have changed significantly. The intestinal microbiota was mainly involved in multiple pathways such as global and overview maps, carbohydrate metabolism, amino acid metabolism, energy metabolism, and metabolism of cofactors and vitamins. The cardiovascular disease pathway decreased in the DSS group, the D-BLD group, and the P-BLD group compared to the Control group. With regard to drug resistance, the antimicrobial pathway increased in the DSS group compared with the BLD group and the P-BLD group. The administration of S. boulardii intervention reduced the decline in development and regeneration, immune disease, and substance dependence. Furthermore, D-BLD caused an increase in the development and regeneration pathway. ## 4. Discussion Ulcerative colitis is a recurrent and persistent chronic inflammatory bowel disease with increasing incidence. It is a complex disease involving host, microorganisms, and other environmental factors, and its pathogenesis is still unclear, with strong drug dependence and more side effects. Thus, exploring effective nutritional intervention therapies with low toxic side effects has become a hot concern. Probiotics have been shown to improve the pathological signs of UC. The new concept of postbiotics has been proposed as a safe and effective intervention with great promise. However, very few studies have compared the actual effects of postbiotics and probiotics. The DSS-induced experimental colitis model is a well-established model that can be used to understand the pathogenesis of UC [17]. With the DSS model, we investigated for the first time the alleviative effect and mechanism of action of the probiotic S. boulardii and its postbiotics, with a view to its future application in UC treatment. Our results showed that both S. boulardii and its postbiotic elements could effectively alleviate weight loss, reduce colonic tissue damage, regulate the balance between pro/anti-inflammatory cytokines in serum and colon, promote the expression of colonic tight junction protein, and regulate the stability of intestinal microecology in mice, and these factors together alleviated the disease of DSS-induced colitis. Additionally, the combined therapeutic effects of the postbiotics were better than those of S. boulardii. Firstly, we measured some macroscopic indicators, which were similar to the symptoms usually observed in some previous animal studies (Table S2) [18,19,20,21,22,23,24,25] and human UC [26]. DSS-induced colitis mice showed weight loss, shortened colon length, and the presence of severe histological lesions, indicating that a DSS-induced colitis mouse model was successfully established in this study. In contrast, both the administration of S. boulardii and the postbiotics intervention were able to affect the survival rate, weight loss, colonic lesions, and length shortening in mice with colitis, indicating that it showed a healing effect on colitis by potentially reversing the above parameters. However, the results showed that there was no significant difference between the intervention group and the Control and DSS groups, except for the body weight, which was significantly different from the DSS group, which was somewhat different from the results of previous studies (Table S2). It may be due to the different substances used, referring to different probiotic strains and postbiotic preparations, and different experimental designs such as experimental period and DSS dosage, which caused some of the results to be different. The cytokine responses characterizing the inflammatory bowel diseases (IBDs) are the key pathophysiological factors controlling the initiation, evolution, and eventual resolution of these forms of inflammation [27]. Clinical studies have found that pro-inflammatory cytokines are highly correlated with the severity of colitis in patients with UC [14]. Studies have shown that fresh colonic mucus biopsies from UC patients show elevated mRNA expression levels of IL-1β, TNF-α, and IL-6 [28]. This is similar to the results we obtained, where serum TNF-α and IL-6 levels and mRNA expression levels of colonic TNF-α and IL-1β were significantly higher in the DSS group of mice than in the Control group. Studies have shown that probiotics have therapeutic effects on controlling inflammation and maintaining UC carcinogenesis in remission [29]. For example, S. boulardii has been shown to inhibit TNF-α and IL-6 levels as well as other pro-inflammatory cytokines such as IL-1β and IL-8 R, demonstrating that S. boulardii can reduce colonic inflammation and regulate inflammatory gene expression [30]. This was also confirmed by our study, in which S. boulardii significantly reduced DSS-induced serum TNF-α, IL-6, and colonic IL-1β mRNA expression levels in mice. In contrast, the two postbiotics showed a superior ability to intervene, as evidenced by their ability to significantly reduce DSS-induced serum TNF-α, IL-6, and IL-1β levels, as well as colonic IL-1β mRNA expression. D-BLD also significantly reduced colonic TNF-α mRNA expression. These are our new findings after comparison, suggesting that postbiotics may have a stronger immunomodulatory ability than probiotics, and that there are differences between postbiotics, which deserve more in-depth study, such as their specific mechanisms. IL10 is an immunosuppressive cytokine produced by B cells, T cells, macrophages, and some non-hematopoietic cells upon stimulation [31]. IL-10 has a broad effect in immunoregulation and host defense, as it affects both the innate and adaptive immune systems [32]. We also examined the levels of this conventional anti-inflammatory factor, and although there was no significant difference in serum levels between the groups, IL-10 mRNA expression levels in the colon were inhibited by DSS ($p \leq 0.01$). This was improved by the intervention of S. boulardii and postbiotics, and the postbiotics were superior to the probiotics. The above results suggested that both S. boulardii and its postbiotics have immunomodulatory abilities and can improve the balance of pro/anti-inflammatory factors in the serum and colon of DSS-induced mice, while the postbiotics have a stronger immunomodulatory ability compared with probiotics. It is well documented that pro-inflammatory cytokines are involved in the pathogenesis of colitis, and in addition to their contribution to intestinal mucosal inflammation, they are also associated with the intestinal epithelial barrier. For example, cytokines of the IL-10 family are essential for maintaining the integrity and homeostasis of the tissue epithelial barrier, suggesting that they can promote the innate immune response of the tissue epithelium, limit the damage caused by viral and bacterial infections, and promote the healing process of damaged tissues caused by inflammation [33,34,35]. Intestinal epithelial cells (IECs) maintain a fundamental immunoregulatory function that influences the development and homeostasis of mucosal immune cells. The intestinal epithelial barrier is extremely important to protect the host from exogenous pathogens [36]. The association between increased bacterial translocation and risk of developing inflammatory bowel disease (IBD) suggests a central role for dysregulated epithelial barrier function in either the etiology or the pathology of intestinal inflammation and IBD [37]. Damage in the intestinal epithelium, as observed during UC, seriously affects barrier function and results in malabsorption, chronic inflammation, and diarrhea [38]. DSS-induced colitis is manifested by significant damage to intestinal epithelial cells and loss of intestinal barrier function. Since the expression of tight junction protein is a major determinant of intestinal barrier function, we investigated the mRNA expression levels of colonic occludin in various groups of mice. The upregulation of tight junction protein expression in the intervention group provides an alternative explanation for the anti-colitis effect of S. boulardii and postbiotics. In conclusion, in the present study, we found that S. boulardii and postbiotics may have positive effects on tight junctions. Therefore, more future investigations are warranted to determine the effects of S. boulardii and postbiotics on tight junctions, in order to provide viable basis for clinical treatments for acute colitis. In addition to intestinal permeability, the homeostasis of the gut microbiota is another important factor of concern. The symbiotic gastrointestinal microbiota provides a variety of beneficial services to the healthy host, including maintenance of immune homeostasis, regulation of gastrointestinal development, and enhanced metabolic capacity [39]. To better understand the possible role of gut flora in UC, we examined the changes in gut flora in various groups of mice and compared them. Some previous studies have shown that inflammatory bowel disease can reduce the diversity of gut microbiota, change the composition of gut microbiota and lead to the destruction of the intestinal microecology [40,41,42,43]. Our results showed that, although there was no significant difference in alpha diversity between the DSS group compared to the postbiotic or probiotic groups, the intervention modulated the beta diversity of the fecal microbiota in mice with colitis. Both S. boulardii and postbiotics interventions significantly altered the structure and composition of the intestinal microflora and reshaped the intestinal microbial changes caused by DSS. To understand the difference of microbial composition among different groups, we analyzed microbial composition at the phylum level and the genus level. The DSS-induced colitis model involves the actions of various microbes, such as Firmicutes, Bacteroidetes, and Patescibacteria. Bacteroides and Firmicutes are the main dominant phyla in gut microbiota, which are involved in the host’s energy homeostasis regulation [44]. Accumulating evidence demonstrated that Firmicutes and Bacteroidota played a critical role in UC development [45]. For example, one study showed that Bacteroidota displayed negative correlations with the metrics of UC activity, so the loss of these species is suggested to result from UC exacerbation [46]. This could be because Bacteroidetes species adhering to the mucosal surface may be unable to inhabit the niche of the extensively damaged mucosa without sufficient mucin production in highly severe UC [47]. Firmicutes play a significant role in gut homeostasis through the production of metabolites, which enhances the gastrointestinal barrier and mucosal immune functions [48]. Herein, based on metagenomics sequencing, the abundance of Bacteroidota decreased in the DSS group, while that of Firmicutes increased, but both phyla showed opposite trends after S. boulardii and postbiotics treatments. This result is similar to previous reports [49], some studies have also reached the opposite results [50], but we confirmed that S. boulardii and postbiotics interventions can remodel the DSS-induced intestinal microbiota on the phylum level to more closely resemble normal mice. At the genus level, Muribaculaceae which belong to Bacteroidota, was the major colonic microbiota in each treatment group. Muribaculaceae is a major microbiota occurring in the intestinal tract of various animals and has been identified as a fermenter capable of producing succinate, acetate, and propionate [51]. Our results showed that Muribaculaceae was significantly reduced in the DSS group which was reversed by S. boulardii and postbiotics interventions. In contrast to Muribaculaceae, our results showed that abundance of the Lachnospiraceae_NK4A136_group in the DSS group was relatively higher than that of the Control group. Previously, Lachnospiraceae exhibited higher abundance in both DSS-treated mice and IBD patients [52]. The Lachnospiraceae_NK4A136_group positively correlated with the pathological characteristics of chemically induced mouse models of colitis, including DSS [53]. In the model of chronic colitis, we show that imbalance of Muribaculaceae and Lachnospiraceae caused by DSS were ameliorated in the mice orally gavaged with S. boulardii and postbiotics interventions. Similar to our findings, the probiotic yeast BR14 [54] and L. plantarum JS19 [55] rebalanced the composition of gut microbiota in the mice with DSS-induced colitis by increasing the abundance of Muribaculaceae and decreasing the abundance of Lachnospiraceae. We also used imputed relative abundances of KEGG pathways in each sample to predict changes in metabolic function in microbiomes. For the imputed relative abundances of KEGG pathways in different groups, the greatest statistical differences were observed for the development and regeneration, cardiovascular disease, and immune disease. Although the analysis shows fewer KEGG difference paths between groups, these results indicated that S. boulardii and postbiotics treatments significantly affected these pathways in this study. It is important to note that, while PICRUSt2 is a relatively robust metagenomic prediction tool, it is entirely dependent on the quality of the reference database used and is therefore incapable of accounting for strain variations present in a given community [56,57]. At the very least, these results warrant further investigation into the role of the gut microbiota in DSS-induced colitis. A recent study showed that UC is indeed characterized by abnormal mucosal immune response, but microbial factors (changes in the composition of intestinal microbial flora) and epithelial cell abnormalities (abnormalities in the function of epithelial barrier) can promote this response [58]. Therefore, in general, our direct comparison of probiotic S. boulardii and its postbiotics showed that they have a certain degree of UC-related symptoms relief, but postbiotics have a stronger ability to alleviate UC inflammatory symptoms and regulate intestinal microbiota. However, we acknowledge that this result has certain limitations and still needs to be supported by more evidence, for the following reasons: 1. This study uses the model of acute colitis, and the treatment intervention time is short. In the future, it may be possible to obtain more obvious or accurate experimental results through a longer modeling and treatment cycle, such as using the model of chronic colitis, and extending the intervention cycle. 2. There is a lack of general criteria for the evaluation of results. For example, it is not yet possible to judge the difference between the therapeutic effects of epigenetic agents and conventional therapeutic drugs, such as mesalazine, to judge its therapeutic efficiency more accurately. 3. Although epigenetic agents are generally considered to be of low risk, existing studies cannot provide evidence to show the safety of S. boulardii postbiotics, and a large number of animal models and human clinical studies are still needed to verify this hypothesis. In conclusion, postbiotics as an extension direction of probiotics has great potential, such as how Lactobacillus plantarum-derived postbiotics ameliorate acute alcohol-induced liver injury [59]. Therefore, the multiple applications of postbiotics will be an effective supplement to probiotics and a driving force for the development of the total health industry [60]. ## 5. Conclusions In summary, the present study demonstrates that S. boulardii and its postbiotics can effectively alleviate DSS-induced colitis in mice by reducing the inflammatory response and maintaining intestinal homeostasis. Our findings highlight the differential function of probiotics and even different postbiotics in colitis remission, it would be of interest to further explore the exact mechanisms of action of probiotics and postbiotics. In the future, more precision research should be carried out on specific postbiotics prepared by different strains. To the best of our knowledge, this is the first study to systematically compare the performance of probiotic S. boulardii and its postbiotics derived from the same strain in an experimental colitis model. 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--- title: Association of a Healthy Lifestyle with Mortality in Older People authors: - Catherine Robb - Prudence Carr - Jocasta Ball - Alice Owen - Lawrence J. Beilin - Anne B. Newman - Mark R. Nelson - Christopher M Reid - Suzanne G. Orchard - Johannes T Neumann - Andrew M. Tonkin - Rory Wolfe - John J. McNeil journal: Research Square year: 2023 pmcid: PMC10055537 doi: 10.21203/rs.3.rs-2541145/v1 license: CC BY 4.0 --- # Association of a Healthy Lifestyle with Mortality in Older People ## Abstract ### Background Unhealthy lifestyle behaviours such as smoking, high alcohol consumption, poor diet or low physical activity are associated with morbidity and premature mortality. Public health guidelines provide recommendations for adherence to these four factors, however, their impact on the health of older people is less certain. ### Methods The study involved 11,340 Australian participants (median age 7.39 [Interquartile Range (IQR) 71.7, 77.3]) from the ASPirin in Reducing Events in the Elderly study, followed for a median of 6.8 years (IQR: 5.7, 7.9). We investigated whether a point-based lifestyle score based on adherence to guidelines for a healthy diet, physical activity, non-smoking and moderate alcohol consumption was associated with all-cause and cause-specific mortality. ### Results In multivariable adjusted models, compared to those in the unfavourable lifestyle group, individuals in the moderate lifestyle group (Hazard Ratio (HR) 0.73 [$95\%$ CI 0.61, 0.88]) and favourable lifestyle group (HR 0.68 [$95\%$ CI 0.56, 0.83]) had lower risk of all-cause mortality. A similar pattern was observed for cardiovascular related mortality and non-cancer/non-cardiovascular related mortality. There was no association of lifestyle with cancer-related mortality. Stratified analysis indicated larger effect sizes among males, those ≤ 73 years old and among those in the aspirin treatment group. ### Conclusions In a large cohort of initially healthy older people, reported adherence to a healthy lifestyle is associated with reduced risk of all-cause and cause-specific mortality. ## Introduction The incidence of chronic disease increases with age and are major contributors to mortality amongst older individuals [1]. Cardiovascular disease (CVD), cancer and dementia are the most common and costly of these diseases leading to premature mortality, but are partially preventable [2]. Common unhealthy lifestyle behaviours, such as smoking [3], high alcohol consumption [4], adverse dietary patterns [5] or low physical activity [6], have each been associated with morbidity and premature mortality in middle to older-age [7]. As a result, public health authorities in various countries have provided recommendations related to these behaviours in order to preserve good health. However, it is less certain whether reported adherence to these recommendations is associated with appreciable benefit among older people [8]. Previous studies have suggested that a combination of healthy lifestyle behaviours incurs the strongest benefit in promoting healthy longevity [9] and reduced risk of premature mortality [10]. A meta-analysis of 15 studies including 531,804 participants from the United States, Europe, China and Japan (mean follow-up 13.24 years) reported that up to $66\%$ of premature deaths could be attributable to a combination of unhealthy lifestyle characteristics including smoking, high alcohol consumption, physical inactivity and a poor diet [10],[2,11,12]. Currently, the studies underpinning health guidelines and lifestyle recommendations for the wider population are mainly derived from middle-aged cohorts. The few studies focussing exclusively on older people have limited sample-sizes and/or focussed exclusively on all-cause mortality while neglecting to investigate the impacts on cause specific mortality [11, 13, 14]. Clarifying the relationship between adherence and advice concerning lifestyle behaviours and mortality may help prioritise preventive advice and policy recommendations among progressively aging populations. The Aspirin in Reducing Events in the Elderly (ASPREE) study, a cohort of initially healthy older people, is uniquely suited to investigate whether a combination of healthy lifestyle behaviours in community-dwelling older people is associated with a prolonged life span and reduced cause specific mortality [15]. ## Study population and trial design This current analysis is based on data from the Australian participants in the ASPREE ($$n = 16$$,703), and ASPREE trial sub-sets: the ASPREE-eXTension (ASPREE-XT) study [15-17] and the ASPREE Longitudinal Study of Older Persons (ALSOP) sub-study ($$n = 14$$,892) [18]. ASPREE was a large, randomised, double-blind, placebo-controlled trial investigating the efficacy of 100mg of aspirin on disability free survival in healthy men and women who were 70 years of age or older. Details of ASPREE and the primary results of the study have been published previously [15-17]. Briefly, all participants were required to be in good health, with no prior cardiovascular disease events, dementia or major physical disability and expected to survive for at least five years at the time of enrolment. All participants provided written informed consent. Following completion of the ASPREE clinical trial (2010 – June 2017), the ASPREE-XT observation follow-up period commenced (ongoing). Data in this analysis includes those collected up to the participants’ second annual ASPREE-XT visit [2020]. ASPREE and ASPREE-XT were approved by the local ethics committees and is registered on clinicaltrials.gov on $\frac{24}{12}$/2009 (NCT01038583). The ALSOP sub-study is a longitudinal cohort study involving approximately $90\%$ of the Australian participants in ASPREE and ASPREE-XT. Details of the ALSOP study methodology and baseline characteristics have been published elsewhere [18]. Participation involved voluntary completion of a set of medical and social questionnaires mostly administered during the first year following ASPREE study enrolment, and again after three years of ASPREE participation. ## Lifestyle score The lifestyle score was constructed based on four modifiable lifestyle factors (alcohol consumption, smoking status, physical activity and diet) known to be associated with chronic disease onset [11, 19-23]. The score was created by allocating one-point for adherence to each of the 4 lifestyle behaviours defined on the basis of national and international recommendations (Table S1). The lifestyle score ranged from 0 to 4, with higher scores indicating higher adherence to healthy lifestyle recommendations. The lifestyle score was also subsequently categorised in 3 groups, as unfavourable (lifestyle score ≤ 1), moderate (lifestyle score = 2), and favourable (lifestyle score ≥ 3). ## Assessment of lifestyle factors Full details of the assessment of the lifestyle factors and classification is described in detail in Table S1. Briefly, baseline smoking status was categorised as either current or no current smoking (including former smokers). A previous study using ASPREE data found moderate alcohol consumption to be associated with a reduced risk of CVD and all-cause mortality, as reported previously [4, 24]. In the current analyses, participants were therefore categorised as either having moderate alcohol consumption or not, defined as those reporting between 51–100 grams of alcohol per week (approximating to an average of 0.7 to 1.4 standard Australian alcoholic beverages a day) at baseline, synonymous to previous cut-offs [24] and informed by current NHMRC guidelines [25]. The World Health Organization (WHO) and Australian government guidelines for adults aged ≥ 65 years recommend at least 30 minutes of moderate activity at least five days per week [26, 27]. Therefore, participants were categorised into either engaging in low weekly activity: no or light activity; or high: moderate or vigorous activity based on their responses at baseline. Dietary data was not available at baseline; therefore, year-three diet was utilised instead. Diet was assessed by a 49-item simple food frequency questionnaire, covering major food groups. Consumption was assessed over five predefined categories of responses ranging from “never/rarely” to “every day or several times a day”. A healthy diet was based on the consumption of at least four of seven commonly eaten food groups, following recommendations on dietary priorities for cardiometabolic health [28], and previously used elsewhere [22]. ## Outcomes Methodological details on the ascertainment of all-cause and categorisation of cause-specific mortality have been published [16, 17] and further detail is provided in Table S2. Briefly, all deaths and underlying cause were adjudicated by clinicians masked to treatment allocation and confirmed by review of at least two independent sources such as family report, clinical record or public death notice, and via a final cross check through linkage with the National Death Indices. The primary outcome was all-cause mortality. Secondary outcomes included cancer-related mortality, CVD-related mortality (including stroke, coronary and cardiovascular related death) and non-CVD/non-cancer mortality (the latter referred to as ‘other’ mortality). ## Statistical analyses Participants were included in the current analysis if they had completed both baseline and year-three ALSOP questionnaires (Fig. 1). Baseline characteristics were reported using descriptive statistics and stratified by the lifestyle score categories (unfavourable, moderate, favourable). Comparison of baseline characteristics across categories was made using the χ2 test, analysis of variance (ANOVA) or Kruskal- Wallis test, as appropriate. Cox proportional hazards models were used to estimate hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for the association between the lifestyle score and all-cause and cause-specific HRs for each of cancer, CVD and ‘Other’ mortality. Competing risk Kaplan–Meier curves were plotted to illustrate the cumulative effect of lifestyle categories on all-cause and cause-specific mortality. Analyses were adjusted for age (continuous), sex (male/female), aspirin treatment allocation (100mg enteric coated aspirin/placebo) (model 1), education (≥ 12-years/< 12-years), living status (alone/with others), and socioeconomic status (IRSAD deciles, continuous) (model 2). Full details on the ascertainment of these study measurements have been described in detail previously [15, 18]. To investigate whether a priori selected factors modified the association between the lifestyle score and all-cause mortality, we performed analyses stratified by median age (≥ 74/<74 years), sex (male/female), education (≥ 12-years/< 12-years), Body Mass Index (BMI) (≥ 25 vs < 25 kg/m2), baseline diagnosis of type-2 diabetes (yes/no) and baseline diagnosis of hypertension (yes/no) and aspirin treatment allocation. Interaction was tested by including a cross-product term along with the main effect terms in the models. In exploratory analyses, we investigated the association between the individual lifestyle factors with all-cause and cause-specific mortality and combinations of individual lifestyle factors with all-cause mortality. In sensitivity analyses we explored alternative definitions of adherence to the lifestyle factors to assess whether significant differences in associations with all-cause mortality would result. For example, there is debate over whether the ‘protective’ effect of moderate alcohol consumption is real or spurious due to residual confounding [29]. Therefore, we created a second variable allocating participants to either moderate/low/never vs high. This variable was associated with all-cause mortality alone and as part of the lifestyle score. Second, we excluded former alcohol consumers and former smokers (who quit <15-years ago), who may have stopped due to various health reasons, possibly introducing bias from reverse causality. Third, owing to the utility of year-three diet, in sensitivity analyses, we tested the association between the same lifestyle score, but now comprised of year-three smoking, alcohol, physical activity and diet, with all-cause mortality. Assuming lifestyle does not change significantly, and owing to the fact that participants must have survived the first three years to be included in the analysis, we hypothesise that the HRs will remain equivocal to those utilising the baseline lifestyle score. Statistical analyses were conducted using Stata (version 17; College Station, TX: StataCorp LLC). A two-sided p-value of ≤ 0.05 was considered statistically significant. The proportional hazards assumption was assessed and met using log-log Kaplan-Meier survival plots. ## Results A total of 11,340 Australian participants were included in the current analysis, and followed for a median of 6.8 years (IQR: 5.7, 7.9) years. At study entry, the median age was 73.9 (IQR 71.7–77.3) years and $54.2\%$ were female. A total of 702 ($6.2\%$) participants died during the follow-up period. ## Baseline characteristics Baseline characteristics of included participants according to the lifestyle score categories as well as by all-cause mortality are shown in Table 1 and Table S3, respectively. Overall, $20.4\%$ of participants adhered to one or no lifestyle factors (unfavourable lifestyle), $44.2\%$ of participants adhered to two lifestyle factors (moderate lifestyle) and $35.3\%$ adhered to three or four lifestyle factors (favourable lifestyle). There was a significantly higher proportion of younger participants, females, those living with others and those with a higher education in the favourable lifestyle group. A higher proportion of individuals with diagnosed vascular risk factors (i.e. hypertension, diabetes, dyslipidaemia), who were on antihypertensives and statins, who were pre-frail/frail, with depressive symptoms and/or higher BMI, waist circumference and systolic blood pressure were in the unfavourable lifestyle group. There were no differences across lifestyle groups based on aspirin treatment allocation. Description and prevalence of the lifestyle factors in the population are shown in Table S1. With regard to adherence to healthy lifestyle factors, $97.5\%$ reported no current smoking, $22.3\%$ reported moderate alcohol consumption, $33.3\%$ met the ‘healthy diet’ criteria and $66.9\%$ of participants reported engagement in weekly moderate/vigorous physical activity. ## Association between lifestyle and mortality Rates of all-cause mortality and cause-specific mortality are shown in Table 2, Table 3 and Figure. For all-cause mortality, compared to participants with no or one healthy lifestyle factors, the multivariable adjusted HRs were 0.73 ($95\%$ CI 0.61, 0.88) for two factors, 0.70 ($95\%$ CI 0.57, 0.86) for three factors, and 0.56 ($95\%$ CI 0.37, 0.86) for four factors, p-trend < 0.0001 (Table 2; Fig. 2A). When evaluated as a continuous variable, each additional lifestyle factor was associated with a $16\%$ lower risk of all-cause mortality (HR for a one-point increase: 0.78 [$95\%$ CI 0.71, 0.87]). A similar association was observed for CVD-related (Table 3, Fig. 2B) and ‘Other’ mortality (Table 3, Fig. 2C). The same dose-response association was observed when the lifestyle scores were divided into three categories as shown in Table 2, Fig. 2A (all-cause mortality) and Table 3, Fig. 2B-C (cause-specific mortality). In multivariable adjusted models, compared with those in the unfavourable lifestyle group, individuals in the moderate lifestyle group had a $27\%$ lower risk of all-cause mortality [HR 0.73 ($95\%$ CI 0.61, 0.88]), and individuals in the favourable lifestyle group had a $32\%$ lower risk of all-cause mortality (HR 0.59 [$95\%$ CI 0.47, 0.73]), p-trend < 0.001 (Table 2; Fig. 2A). In absolute terms, among 1000 individuals in the unfavourable lifestyle group, a crude total of 39 deaths could be averted during the median follow-up period of 6.8 years if they were in the favourable lifestyle group. With regard to risk of CVD and ‘Other’ mortality, the same dose-response association was observed, with larger effect size for CVD mortality (Table 3; Fig. 2B-C). There was no association between lifestyle groups and cancer-related mortality (Table 3; Fig. 2D). ## Additional analyses Additional analyses of specific lifestyle characteristics are presented in Tables S4-S6. Each individual lifestyle factor was associated with a lower risk of all-cause mortality, CVD mortality and ‘Other’ mortality. Specifically, effect sizes of each lifestyle factor on risk of all-cause mortality were highest and statistically significant for smoking status (HR 0.42 [$95\%$ CI 0.30, 0.59]) and physical activity (HR 0.84 [$95\%$ CI 0.72, 0.98]), with non-significant trends for alcohol consumption (HR 0.92 [$95\%$ CI 0.62, 1.37]) and diet (HR 0.90 [$95\%$ CI 0.76, 1.06]). Similar trends were observed for CVD mortality and ‘Other’ mortality. Further analyses according to different combinations of two, three and four healthy lifestyle factors prevalent in at least $2\%$ of the population compared to no or one healthy lifestyle factors are shown in Table S7. None of the observed associations were as protective of all-cause mortality as the combination of 4 factors (HR 0.57 [$95\%$ CI 0.37, 0.86]). Table S8 shows results of the stratified analyses by selected health, demographic and anthropometric factors on the association between the lifestyle score categories and all-cause mortality. No statistically significant differences were observed in the associations between the healthy lifestyle score categories and all-cause mortality by age, sex, education, BMI, diabetes, hypertension and aspirin treatment. Nonetheless, trends indicate larger effect sizes among males, those at or below the median age of 73-years-old and among those in the aspirin treatment group. ## Sensitivity analyses Results from the various sensitivity analyses are presented in the Tables S9-S12, and page 14 of the supplement. Briefly, excluding former smokers/drinkers (Table S9), the alternative categorisation of alcohol consumption (supplement p 14) and the alternative lifestyle score (Table S10 and S11) did not alter the results. Multivariable adjusted HRs on the association between the year-three lifestyle score categories and risk of all-cause mortality remained equivocal (Table S12). ## Discussion In this cohort of 11,340 community dwelling healthy older Australians, we examined the association between a healthy lifestyle score and all-cause mortality, cancer-related mortality, CVD-related mortality and ‘other’ causes over a median follow-up time of 6.8 years (IQR: 5.7, 7.9). We found that a healthy lifestyle score at baseline, comprising of four common and potentially modifiable lifestyle factors (non-smoking, moderate alcohol consumption, a healthy dietary pattern and physical activity) was associated with prolonged lifespan in a dose-response relationship, such that each additional lifestyle factor was associated with a $16\%$ lower risk of all-cause mortality, $25\%$ lower risk of CVD mortality and $22\%$ lower risk of ‘Other’ mortality. The current data found no association between lifestyle groups and cancer-related mortality. The benefits of a healthy lifestyle on all-cause mortality had larger effect sizes among males, those ≤ 73 years old and among those in the aspirin treatment group, although interactions were not statistically significant. This is one of the largest and most comprehensive studies, conducted exclusively within community dwelling older people, reporting the association between a lifestyle composite score based on adherence to international health behaviour recommendations and all-cause plus cause-specific mortality. The results are largely in agreement with previous reports investigating different combinations of healthy lifestyle characteristics. A similar study of older Chinese people ($$n = 11$$,224, aged 65–90 years) reported that, compared to those without any unhealthy factors, those who had a high BMI, poor sleep, unhealthy diet, no physical activity, consumed alcohol and currently smoked were 1.34 ($95\%$ CI 1.02, 1.76) times more likely to die from any cause over a ten-year follow-up period [14]. Another 18-year follow-up study of Swedish older adults (75 +years of age; $$n = 1$$,810) reported a median survival of 5.4 years longer among those who had a healthy BMI, never smoked or drank alcohol, engaged in leisure activities and moderate levels of physical activity versus those who did not [13]. “ The healthy aging: a longitudinal study in Europe” (HALE) study ($$n = 1$$,507) reported that 70 to 90-year-old community-dwelling people who did not smoke, consumed a Mediterranean diet, reported moderate alcohol consumption and was physically active had a $50\%$ lower rate of all-cause and cause-specific mortality over 10-years, including CVD and cancer-related mortality [11]. Similar protective effects of a composite lifestyle score, typically including at least diet, physical activity, smoking and alcohol, have been reported among middle-aged cohorts from different counties including Japan [30], China [12], United States of America (USA) [2], Australia [31] and the United Kingdom (UK) [32]. Results reported here and previously, provide compelling evidence to suggest that individuals reporting a healthy lifestyle in older age have a significantly reduced risk of earlier mortality. The results also demonstrate that current international recommendations for moderate physical activity, no smoking, a healthy dietary pattern and moderate alcohol consumption may still provide a useful predictor of longevity among this older aged cohort. We found no relationship between a healthy lifestyle on risk of overall cancer-related death, which is contrary to findings previously reported in the HALE study as well as among studies of younger cohorts [11]. Although, it is possible that the lack of a broader link with cancer reflects the very small percentage of current smokers in ASPREE. The HALE study was conducted via survey only and among a demographic born up to 40-years earlier than ASPREE participants. Some methodological points may impact the conclusions of this study. In order to construct the healthy lifestyle score, we dichotomised each lifestyle factor according to pre-defined cut-off points. Different threshold values may have resulted in different risk estimates. However, the choice of cut-off was largely based on national and international public health recommendations [25-28]. In sensitivity analyses we trialled different cut-offs and multi-levels but the results remained largely unchanged (Tables S10, S11 and supplement p 14). The approach of designating compliance versus non-compliance allows a simple objective classification to assess the health impact of lifestyle and can inform a clear public health message. Future modifications of this approach may involve differential weighting of the health impact of each lifestyle measure. Owing to the absence of baseline dietary data, we utilised year-three dietary data as an alternative replacement within the baseline lifestyle score. It is not certain whether dietary behavior had significantly changed over this three-year period. Dietary changes can occur in older people due to factors such as oral health, income, marital status, medication or change of residence [33]. Nonetheless, as healthy lifestyle habits are characteristic of a person’s way of living, and given ASPREE is an especially healthy cohort, majority of participants were unlikely to show a substantial change in general dietary habits over a three-year period [34]. Furthermore, the year-three lifestyle score was associated with all-cause and cause-specific mortality with similar effect size to associations between baseline lifestyle and mortality, sanctioning this assumption. Finally, given we do not have detailed information about mid-life lifestyle behaviour in the ASPREE cohort, we cannot confirm whether observed associations are not driven by behaviour earlier in life. Healthy lifestyle behaviours in older age may reflect a long-standing approach to healthy living which, in turn, may be driving these observations. Our results still, however, highlight the benefits of identifying healthy lifestyle factors as predictors of likely future mortality, even among already healthy older people. ## Strengths and limitations There are several strengths of our study. ASPREE is a well characterised, large and contemporary cohort of older people who had reached age 70-years or more in relatively good health [18]. Furthermore, rigorous methods for the ascertainment of cause-specific mortality ensured highly accurate endpoints. The investigation of not only all-cause but cause-specific mortality is a further strength. There are also several potential limitations. First, the ASPREE cohort is comprised of initially healthy volunteers for a clinical trial who are more likely to be attentive to maintaining a healthy lifestyle, hence, may represent a healthier sample of older people compared with the general population. Second, the cohort is largely Caucasian, educated and drawn from communities with access to universal healthcare as reflected by the extensive use of preventive medications including statins (in $30\%$) and antihypertensive agents (in $51\%$). Therefore, our results may not be applicable among other socioeconomic and ethnic groups as well as among those residing in lower-to middle-income countries. Third, due to the progressive nature of noncommunicable disease leading to death, with declining function often preceding and possibly influencing lifestyle behaviour, we cannot rule out reverse causality as a partial explanation for these observations. Nonetheless, although survivor bias is a common limitation in healthy cohort studies, our censoring of death events at three-years may also help to mitigate reverse causality. Finally, although potential confounders were considered in multivariable analyses, residual confounding cannot be ruled out. Furthermore, other unmeasured lifestyle and environmental factors may also play a role in determining risk of death. However, demonstrating that these four common lifestyle behaviors are associated with prolongation of an individual’s lifespan remains an important public health message. ## Conclusion In a well-characterised population of healthy older people, moderate exercise, a healthy dietary pattern, moderate alcohol consumption and non-smoking was associated with $44\%$ reduced risk of all-cause mortality, when compared to those complying to ≤ 1 healthy lifestyle factor. Previous multi-lifestyle interventions among healthy older people has proven beneficial in reducing risk for CVD and cognitive decline [35-37], whereas evidence from single domain lifestyle interventions are less convincing [38]. Furthermore, these studies, plus others [39], indicate that simple and effective methods for lifestyle modification is possible among older people. Findings here further suggest the importance of engaging or continuing to engage in multiple healthy lifestyle behaviours in older age and may encourage further multi-lifestyle interventions at both the population level as well as on an individual level. ## Funding: This work was supported by the National Institute on Aging and the National Cancer Institute at the National Institutes of Health [grant numbers U01AG029824, U19AG062682]; the National Health and Medical Research Council of Australia [grant numbers 334047, 1127060]; Monash University; and the Victorian Cancer Agency. J.McN. is supported through an NHMRC Leadership Fellowship [IG 1173690]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. ## Data sharing Upon official request and submission of an expression of interest to the ASPREE data management team, with support from the PI (Professor John McNeil), data may be made available. Deidentified participant data would be accessed via a secure remote server with an accompanying data dictionary and manual including all source data worksheets. Upon submission of the expression of interest, data can be made available within a timely manner. ## References 1. 1.Mortality and global health estimates, https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates (30 November 2022, date last accessed).. Mortality and global health estimates 2. Li Y, Pan A, Wang DD, Liu X, Dhana K, Franco OH. 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--- title: Mediating role of obesity on the association between disadvantaged neighborhoods and intracortical myelination authors: - Lisa Kilpatrick - Keying Zhang - Tien Dong - Gilbert Gee - Hiram Beltran-Sanchez - May Wang - Jennifer Labus - Bruce Naliboff - Emeran Mayer - Arpana Gupta journal: Research Square year: 2023 pmcid: PMC10055549 doi: 10.21203/rs.3.rs-2592087/v1 license: CC BY 4.0 --- # Mediating role of obesity on the association between disadvantaged neighborhoods and intracortical myelination ## Abstract We investigated the relationship between neighborhood disadvantage (area deprivation index [ADI]) and intracortical myelination (T1-weighted/T2-weighted ratio at deep to superficial cortical levels), and the potential mediating role of the body mass index (BMI) and perceived stress in 92 adults. Worse ADI was correlated with increased BMI and perceived stress (p's<.05). Non-rotated partial least squares analysis revealed associations between worse ADI and decreased myelination in middle/deep cortex in supramarginal, temporal, and primary motor regions and increased myelination in superficial cortex in medial prefrontal and cingulate regions ($p \leq .001$); thus, neighborhood disadvantage may influence the flexibility of information processing involved in reward, emotion regulation, and cognition. Structural equation modelling revealed increased BMI as partially mediating the relationship between worse ADI and observed myelination increases ($$p \leq .02$$). Further, trans-fatty acid intake was correlated with observed myelination increases ($$p \leq .03$$), suggesting the importance of dietary quality. These data further suggest ramifications of neighborhood disadvantage on brain health. ## Introduction Living in a disadvantaged neighborhood (area deprivation) is linked to worse health outcomes, including poor brain health1. Disadvantaged neighborhoods can be stressful, and which can then alter brain structure and function, including decreased brain volume2. The key mechanisms that underlying the link between neighborhood conditions and brain health remain unclear, but one possible pathway might be through obesity. People who live in disadvantaged neighborhoods are at higher risk of obesity due to the poor quality of available foods and environments that hamper physical activity3, 4, 5. In particular, neighborhood disadvantage is associated with an increased intake of calories from trans-fatty acids (TFAs) and sodium6. TFAs (high in fried fast food) are known to contribute to obesity, especially abdominal obesity7, 8. Additionally, chronic neighborhood stressors (allostatic load) can impact eating behaviors9, 10, increasing desire for highly palatable, but unhealthy foods used as a coping response11, 12. Numerous neuroimaging studies have demonstrated that stress can alter brain structure and function, leading to food cravings, contributing to an increased risk for obesity13, 14, 15. Further, high body mass index (BMI) has been shown to mediate the impact of living in a disadvantaged neighborhood on reduced brain volume, suggesting its importance in the negative impact of neighborhood disadvantage on brain health16. Neighborhood disadvantage, high BMI, and chronic stress have also been shown to impact intracortical myelination17, 18, 19. Intracortical myelination, which refers to the myelination of axons in the cortical gray matter, affects the timing and integration of signals from multiple axons, and is critical for neural synchrony and fine-tuning of cortical circuits, affecting cognitive functioning20, 21, 22. Additionally, intracortical myelination is actively involved in brain remodeling and plasticity throughout the lifespan23, 24. Myelinated components vary by cortical layer, with a large fraction of intracortical myelin ensheathing the axons of inhibitory internerons in upper cortical layers (layers 1-3); specifically, parvalbumin-positive basket cells, which play an important role in gamma network oscillations that support a variety of cognitive processes25. In deeper cortical layers, myelin predominantly ensheaths non-gamma-aminobutyric acid (non-GABA) axons, presumably from long-distance excitatory pyramidal neurons, which integrate thousands of synaptic inputs and act on targets in other cortical regions and subcerebral structures26, 27, 28. Further, cortical layers vary in terms of specific inputs and outputs and information processing functions (e.g., subcortical vs. intercortical input, feedback vs. forward processes)29. Accordingly, examining intracortical myelination at different cortical layers can inform how alterations in cell populations, processes, and communication routes may be affected by adverse or stressful environments, such as living in a disadvantaged neighborhood. We, therefore, investigated the relationship between the area deprivation index (ADI) and intracortical myelination at multiple cortical levels, as well as the role of potential mediators, including BMI and stress (Graphical Abstract). In addition, we investigated the relationship between TFA intake and intracortical myelination in a subset of participants with diet data. We hypothesized that worse ADI would be associated with higher BMI and stress levels, with negative effects on intracortical myelination in reward-related, emotion regulation, and cognitive regions, related to poor dietary quality characterized by high TFA intake. ## Sample characteristics Participant characteristics, including TFA intake, are summarized in Table 1. In this sample, ADI was positively correlated with BMI ($r = 0.27$, $$p \leq 0.01$$) and the Perceived Stress Scale (PSS) score ($r = 0.22$, $$p \leq 0.04$$), but BMI and PSS were not correlated ($r = 0.04$, $$p \leq 0.73$$). ## Regions showing a relationship between ADI and intracortical myelination Non-rotated partial least squares correlational (PLSC) analysis revealed worse ADI as associated with increased myelination in medial prefrontal and cingulate regions (hereupon referred to as ADI-positive regions), involved in reward-related processing, emotional regulation, and higher cognition30, 31, 32, and decreased myelination in supramarginal, middle temporal, and primary motor regions (hereupon referred to as ADI-negative regions), components of the extended mirror system involved in social interaction ($p \leq 0.001$) (Figure 1)33, 34, 35. Regions with a positive association were more extensive at middle/superficial cortical levels, while those with a negative association were more extensive at middle/deep cortical levels. Intracortical myelination was averaged across all ADI-positive regions, and across all ADI-positive regions, for further analysis. ## Mediation of the relationship between ADI and intracortical myelination The final model in the structural equation modelling (SEM) analysis is shown in Figure 2; for simplicity, sex and age are not shown in the model, but were used as control variables. The model had a high chi-squared p-value (χ2[2]=0.025, $$p \leq 0.988$$), comparative fit index of 1.0, and standardized root mean square residual of 0.002, indicating good model fit. BMI had significant positive direct effects on both brain variables (ADI-positive regions: $p \leq 0.001$; ADI-positive regions: $$p \leq 0.03$$). Additionally, BMI partially mediated the relationship between ADI and the average intracortical myelination in ADI-positive regions, but not ADI-negative regions (indirect effect of ADI on ADI-positive regions via BMI: $$p \leq 0.02$$; on ADI-negative regions: $$p \leq 0.09$$). Although stress (PSS score) had a negative direct effect on both brain variables, statistical significance was not reached (ADI-positive regions: $$p \leq 0.33$$; ADI-positive regions: $$p \leq 0.31$$). Stress also failed to reach significance as a mediator of the relationship between ADI and the average intracortical myelination in these regions (indirect effect of ADI on ADI-positive regions via stress: $$p \leq 0.36$$; on ADI-negative regions: $$p \leq 0.32$$). ## Relationship between TFA intake and intracortical myelination Correlations between TFA intake and intracortical myelination are shown in Table 2. Significant positive correlations were observed between the average intracortical myelination in ADI-positive regions and trans-octadecenoic (elaidic) acid and total TFA intake (p’s<0.05). No significant correlations were observed for the average intracortical myelination in ADI-positive regions. ## Discussion We examined the relationship between ADI and myelin content, as assessed by the T1-weighted/T2-weighted (T1w/T2w) ratio, at different levels of the cortical ribbon, as well as potential mediation by factors associated with ADI, namely, BMI and stress. We found that ADI was associated with increased myelination in medial prefrontal and cingulate cortices at more superficial levels (we refer to these regions as ADI-positive regions); these regions are involved in reward-related, emotion regulation, and higher cognitive processes30, 31, 32. This association was partially mediated by increased BMI. We also found ADI associated with decreased myelination in supramarginal, middle temporal, and primary motor cortices at deeper levels (we refer to these regions as ADI-negative regions); these regions are components of the mirror neuron system, involved in social interaction33, 34, 35. BMI did not appear to mediate this relationship. Further, perceived stress was associated with ADI but not myelination. Cortical layers differ in the type of axons myelinated and information processing functions, with superficial cortex receiving top-down information that can modulate feed-forward and feed-back processes in deeper layers26, 27, 28, 29. Thus, our findings provide new insights as to the nature of affected information processing pathways under worse ADI, as discussed below. ## ADI and increased superficial intracortical myelination In the present study, worse ADI was associated with increased myelination signal in more superficial cortex in medial prefrontal and cingulate regions. These results are largely similar to a previous developmental study on the relationship between disadvantage due to low socioeconomic status (SES) and intracortical myelination as assessed by the T1w/T2w ratio. This previous study found that lower parental SES was associated with increased intracortical myelination in frontal, temporal, medial parietal, and occipital regions in children and adolescents36. Thus, disadvantage due to ADI or individual SES may be associated with increased intracortical myelination in overlapping regions. The maturation of intracortical myelination is protracted in humans, especially in prefrontal regions, peaking at 30-45 years of age depending on the brain region37. Accordingly, intracortical myelination within a normative range may be needed for optimal function, with both reduced and excessive myelination being problematic38. Consistent with the latter, animal studies have found that under conditions of excessive myelination (i.e., beyond axonal demand) mistargeting to cell bodies occurs readily39. Thus, our finding that worse ADI and increased BMI are associated with increased myelination in more superficial cortex in medial prefrontal and cingulate regions may imply excessive and disorganized myelination in the upper layers of the cortex. Superficial cortex receives top-down information from subcortical and cortical regions and is thought to enable flexible and state-dependent processing of feedforward sensory input arriving in deeper layers29, 40. One may speculate that myelination mistargeting in superficial cortex could negatively influence the context and flexibility of information processing in affected regions, which in the present study, comprised the prefrontal and cingulate cortices. Given the involvement of these regions in reward-related processing, emotional regulation, and higher cognition, this interpretation is similar to previous behavioral studies showing an impact of neighborhood disadvantage on these functions throughout the lifespan41, 42, 43, 44. However, in contrast with the current results, studies using markers of intracortical myelination other than the T1w/T2w ratio have mainly found reductions associated with neighborhood or socioeconomic disadvantage. For example, a study using magnetization transfer as a myelin-sensitive marker, found that living in a disadvantaged neighborhood before the age of 12 years was associated with slower myelin growth in adolescents and young adults in sensorimotor, cingulate, and prefrontal cortices18. Another study using magnetization transfer found that SES in adulthood was associated with decreased entorhinal cortical myelination45. Although the T1w/T2w ratio is sensitive to intracortical myelin content, accumulating evidence suggests that it is not a straightforward proxy for intracortical myelin46, 47. Given our finding that increased intracortical myelination in more superficial, but not deeper, cortical levels of prefrontal and cingulate regions was mediated by BMI, we entertained an alternative explanation of our results. We considered it possible that the T1w/T2w signal at the upper BMI range was affected by a fatty acid-rich cortical environment, with lipid droplet accumulation and lipid-laden astrocytes, due to blood-brain barrier disruption and increased transport of fatty acids under obese conditions48, 49, 50. In support of this, we found that total TFA intake, largely driven by trans-octadecenoic (elaidic) acid intake, was correlated with increased superficial cortical myelination in ADI-positive regions. Although industrial TFAs such as partially hydrogenated oil have been banned in the United States because of health concerns (effective 2020), the process of repeatedly cooking oil at high temperatures can cause high levels of TFAs in fried fast foods51. Additionally, some meat and dairy products naturally contain small amounts of TFA52. Higher intake of TFA, including elaidic acid, is associated with an increased risk of dementia53, 54. TFAs can be incorporated into cell membranes, including myelin55. Poor-quality diet, such as a diet high in fried fast foods, is thought to be one of the factors of worse ADI that contributes to obesity and worse health outcomes3, 4, 5, 56. Thus, our results could suggest that a diet high in TFA under worse ADI may create a fatty acid-rich environment in superficial cortical layers, become incorporated into cell membranes and disrupt information processing in affected regions. ## ADi and decreased intracortical myelination We also found that worse ADI was associated with decreased myelination in middle/deep cortex in supramarginal, middle temporal, and primary motor regions. These regions are components of the mirror neuron system, involved in understanding the actions of others and in interpersonal coordination in activities (e.g. imitation, cooperation)33, 34, 35. As middle/deep levels were involved, feed-forward and feed-back processes and intercortical and subcortical-cortical communication in these regions may be affected with worse ADI29. Animal studies suggest that myelin plasticity is a major component in the response to stress57. In addition, a previous study using the T1w/T2w ratio as a measure of intracortical myelination found that higher perceived stress was associated with lower intracortical myelination in the right supramarginal gyrus19. However, in the present study, perceived stress was not significantly associated with decreased myelination in ADI-negative regions, which included the left supramarginal gyrus. Thus, mediation of the association between worse ADI and decreased intracortical myelination in these regions remains unclear. ## Limitations The present study has several limitations. Although the T1w/T2w ratio is sensitive to myelin, it may not be a straightforward proxy, as it does not correlate well with myelin-related gene expression and other measures58. Confirmation studies using other acquisition protocols sensitive to intracortical myelin are needed. Additionally, we assessed current ADI at one point in time; we did not have information regarding the length of residence, nor did we have historical data on ADI in younger ages. A previous study found that, although both child and adult SES showed correlations with intracortical myelination, childhood SES showed robust associations even after controlling for adult SES, suggesting a lasting imprint, which may also hold for neighborhood-level factors such as ADI42, 45. ## Conclusions We found that worse ADI was associated with decreased myelination in middle/deep cortex in supramarginal, middle temporal, and primary motor regions, potentially impacting intercortical and subcortical-cortical communication of the mirror neuron system, important for understanding the actions of others and cooperative behavior. ADI was also associated with increased myelination in the superficial cortex in medial prefrontal and cingulate regions, which was partially mediated by increased BMI. Further, this increased myelination was positively correlated with TFA intake. Thus, obesogenic features of neighborhood disadvantage may disrupt the flexibility of information processing involved in reward, emotion regulation, and cognition. These results provide new information regarding the ramifications of living in a disadvantaged neighborhood on brain health. Further research on the mediating factors involved in the impact of ADI on the brain during development and adulthood is needed. ## Participants Participants comprised 92 adults (27 men; 65 women) recruited from the Los Angeles area who completed a neuroimaging session including both T1-weighted (T1w) and T2-weighted (T2w) scans (enabling the calculation of intracortical myelination) and provided residential address information. Exclusion criteria were as follows: major neurological condition, current or past psychiatric illness, vascular disease, weight loss/abdominal surgery, substance use disorder, use of medications that interfere with the central nervous system, pregnant or breastfeeding, strenuous exercise regimen (> 8 h/week of continuous exercise), weight > 400 pounds, or metal implants. In addition, individuals with poor quality images were excluded. Image quality was evaluated using Qi1, which reflects the proportion of voxels with intensity corrupted by artifacts normalized by the number of voxels in the background, from the MRI Quality Control tool (MRIQC)30. Qi1 > 0.00506 for T1w images and > 0.0030 for T2w images was considered to indicate poor image quality31. All procedures were approved by the Institutional Review Board at the University of California, Los Angeles’s Office of Protection for Research Subjects (Nos. 16–000187, 15-001591). All participants provided written informed consent. ## Assessments Basic demographic data, as well as weight and height, were collected. BMI was calculated as weight divided by the square of the height (kg/m2). ADI was originally developed by the Health Resources and Services Administration several decades ago and is updated periodically. We used the 2020 ADI in the Neighborhood Atlas®32, based on the residential address provided by the participant. This atlas ranks census block groups, which are considered as similar to neighborhoods, on 17 neighborhood-level measures reflecting income, education, employment, and housing quality, within state and nationally. We employed the California State ADI, which is provided in deciles (scores range 1–10), with higher values indicating greater deprivation/disadvantage. Participants also completed the Perceived Stress Scale (PSS), which is a 10-item questionnaire that assesses feelings of stress during the prior month33. Scores range from 0 to 40, with higher scores indicating greater stress. Diet information was collected using the VioScreen Graphical Food Frequency System (Viocare Technologies, Inc., Princeton, NJ), and was available in a subset of participants ($$n = 81$$) as 11 participants did not provide this information. The VioScreen System provides information on nutrient intake, including fatty acid intake. We focused on the intake of individual TFAs (trans-hexadecenoic acid, trans-octadecenoic acid [elaidic acid], and trans-octadecadienoic acid [linolelaidic acid]), as well as the total TFA intake. TFA intake was measured as a component of a poor-quality diet known to contribute to obesity, especially abdominal obesity, and have harmful effects on brain cell membranes, including myelin7, 8, 34. ## Imaging acquisition and preprocessing T1w and T2w structural images were obtained for the non-invasive assessment of intracortical myelination using a 3.0T Siemens Prisma MRI scanner (Siemens, Erlangen, Germany). Spin echo fieldmaps were also acquired in anterior-posterior and posterior-anterior directions for distortion correction. The acquisition parameters for high-resolution T1w images were as follows: echo time, 1.81 ms; repetition time, 2500 ms; slice thickness, 0.8 mm; number of slices, 208; voxel matrix, 320×300; and voxel size, 1.0×1.0×0.8 mm. The parameters for the T2w images were as follows: echo time, 564 ms; repetition time, 3200 ms; slice thickness,.8 mm; number of slices, 208; voxel matrix, 320×300; and voxel size, 1.0×1.0×0.8 mm. Imaging data were preprocessed using Human Connectome Project (HCP) pipelines, including volume segmentation and cortical surface reconstruction with FreeSurfer 6.035, 36. Intracortical myelination was estimated by the T1w/T2w ratio at multiple levels within the cortical ribbon37, 38. Specifically, the T1w/T2w ratio was calculated at $5\%$ increments in cortical thickness from the gray-white boundary to the gray-cerebrospinal fluid boundary and averaged within 4 cortical ribbon levels as follows: $5\%$-$25\%$ (deep cortex), $30\%$-$50\%$ (lower-middle cortex), $55\%$-$75\%$ (upper-middle cortex), and $80\%$-$100\%$ (superficial cortex). The 4 resulting myelin maps were parcellated using the HCP multi-modal parcellation 1.0 atlas, resulting in 360 cortical regions with 4 intracortical myelination estimates each39. ## Statistical analysis Partial correlation coefficients, controlling for sex and age, were calculated to determine individual factors associated with worse ADI using SPSS version 28 (IBM Crop., Albany, NY, USA), with bootstrapping (5000 samples). P-values <.05 were considered statistically significant. Non-rotated partial least squares correlational (PLSC) analysis was applied to identify regions correlated with ADI according to cortical level, using freely available code (http://www.rotman-baycrest.on.ca/pls)40. PLSC is a multivariate analytical technique that identifies weighted patterns of variables in two blocks of variables that maximally covary with each other40, 41. In this study, one block comprised demographic variables (ADI, age, sex) and one block comprised parcellated intracortical myelin values (at a specific cortical level). Weights were preset to identify brain regions sensitive to ADI but not sex or age. Reliability of identified regions was assessed using bootstrap estimation (5000 samples); regions with a bootstrap ratio > 3.1 ($p \leq .001$) were considered significant. Myelin data was extracted from significant brain clusters to calculate an average of all regions/levels with a positive relationship with ADI and an average of all regions/levels with a negative relationship with ADI, for further analysis. Structural equation modeling (SEM) was applied to investigate the mediation of relationships between ADI and significant findings from the PLSC analysis. SEM was performed in R Studio using the lavaan package42. Input variables comprised ADI, BMI, PSS score, average myelination in ADI-positive regions, and average myelination in ADI-negative regions, as well as sex and age as control variables. Data were standardized prior to fitting the model. Missing values were estimated using the maximum likelihood (4 participants had missing PSS scores). Model fit was assessed using the chi-squared p-value, comparative fit index, and standardized root mean square residual. Model paths with a p-value <.05 were considered significant. ## Funding: This research was supported by grants from the National Institutes of Health including R01 MD015904 (AG), K23 DK106528 (AG), R03 DK121025(AG), T32 DK07180 (TD), ULTR001881/DK041301 (UCLA CURE/CTSI Pilot and Feasibility Study (AG), R01 DK048351 (EAM), P30 DK041301; and pilot funds provided for brain scanning by the Ahmanson-Lovelace Brain Mapping Center. 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--- title: 'Barriers and facilitators to implementing Advanced HIV Disease screening at secondary referral hospital -Malawi: Asequential exploratory mixed method-study' authors: - Brany Mithi - Agatha Bula - Lester Kapanda - Fatsani Ngwalangwa - Evanson Z Sambala journal: Research Square year: 2023 pmcid: PMC10055552 doi: 10.21203/rs.3.rs-2602019/v1 license: CC BY 4.0 --- # Barriers and facilitators to implementing Advanced HIV Disease screening at secondary referral hospital -Malawi: Asequential exploratory mixed method-study ## Abstract ### Background Malawi continues to register increased HIV/AIDs mortality despite increased expansion of ART services. One of the strategies for reducing AIDS related deaths outlined in the Malawi National HIV Strategic Plan (NSP) is scaling up screening for AHD in all antiretroviral therapy (ART) screening sites. This study investigated factors influencing the implementation of the advanced HIV disease (AHD) screening package at Rumphi District Hospital, Malawi. ### Methods We conducted a mixed method, sequential exploratory study from March, 2022 to July, 2022. The study was guided by a consolidated framework of implementation research (CFIR). Interviews were administered to key healthcare providers, purposively selected from various hospital departments. Transcripts were organized and coded using NVivo 12 software with thematically predefined CFIR constructs. Newly HIV-positive client records extracted from ART cards (July –Dec, 2021) were analyzed using STATA 14 which generated table of proportions, means and standard deviations. ### Results Out of 101 data records of the new ART clients reviewed, $60\%$ {($$n = 61$$) had no documented results for CD4 Cell count as a baseline screening test for AHD. Four major themes emerged as barriers: complexity of the intervention, poor work coordination, limited resources to support the expansion of point of care services for AHD, knowledge and information gap among providers. Technical support from MoH implementing partners and the availability of committed focal leaders coordinating HIV programs emerged as major facilitators of AHD screening package. ### Conclusion The study has identified major contextual barriers to AHD screening affecting work coordination and client linkage to care. Improving coverage of AHD screening services would therefore require overcoming the existing barriers such communication and information gaps. ## Background Despite the significant progress in expanding access to antiretroviral therapy (ART) in Malawi, AIDS-related deaths plateaued over the recent years [1, 2]. HIV Epidemiological Estimates for Malawi indicate that 10 800 deaths were registered among 987 000 adults (15 + years) living with HIV in 2020 [3]. The prevailing HIV mortality rates have been attributed to opportunistic infections (OIs) such as Tuberculosis (TB), and Cryptococcol meningitis often associate with Advanced HIV Disease (AHD) [4]. The impact of these opportunistic infectious diseases has been immense on the performance of healthcare systems in poorly resourced settings particularly in sub-Saharan Africa [5, 6]. While Malawi has made gains in the UNAIDS 95-95-95 treatment and viral load suppression targets ($88.3\%$, $97.9\%$, and $96.9\%$), it is yet to reach the $95\%$ target for HIV positive individuals who know their status [1]. The majority of HIV positive individuals unaware of their status often present with AHD due to late HIV diagnosis which increases the risk of morbidity, mortality as well as HIV transmission [7, 8]. Therefore, identifying newly diagnosed HIV clients in advanced HIV stage through CD4 Cell count testing and linking them to care, provide a means for preventing morbidity and mortality among people living with HIV (PLWH) initiating ART [9]. One of the strategies for reducing AIDS mortality outlined in the Malawi National HIV Strategic Plan (NSP) is scaling up screening for AHD in all antiretroviral therapy (ART) screening sites [10]. This strategy compliments the 2017 WHO-recommended AHD package of care integrated in the Malawi HIV Clinical Management guideline [11]. WHO defines advanced HIV disease as CD4 Cell count of less than 200 cells/μL or HIV clinical stages 3 and 4 [9]. Since the roll- out of the AHD screening package in 2020 in Malawi, several people living with HIV have been diagnosed with opportunistic infections and linked to care, owing to the use of point of care rapid devices such as Urine LAM, Cryptococcol antigen (CrAg) and PIMA machine for CD4 cell count. Subsequently, AIDS mortality rate due to OIs de-masked by immune-reconstitution inflammatory syndrome (IRIS) often common in the first month of ART initiation has gone down [8, 9]. Considering the high prevalence of TB and Cryptococcal meningitis among clients with AHD, there is need to scale up AHD screening as an entry point to care for newly diagnosed HIV positive clients of which majority are asymptomatic[4]. However, the implementation of AHD screening in various healthcare facilities has not been smooth. The HIV coverage quality and impact network (CQUIN) project in Malawi singled out COVID-19 as the major obstacle to delivery of AHD package of care as it led to decreased in the number of PLHIV attendance and delays in AHD trainings for healthcare providers [2]. Studies conducted in tertiary referral hospitals have also established barriers at the system and healthcare providers’ level such as limited trained personnel, and inconsistencies in testing as key challenges in implementation of AHD diagnostic tests that often leads to non-compliance to AHD guidelines and poor identification of AHD cases [8, 10, 11]. Although Ministry of Health (MoH)’s response has focused on conducting mentorship and onsite supervisions as well as expanding screening sites, without understanding the nature and root cause of the existing contextual barriers, interventions are likely to be ineffective. Currently, local data about contextual barriers from secondary referral facilities is missing. Most previous AHD studies focused on hospitalized patients in tertiary referral hospitals. Our study, therefore, sought to investigate factors influencing implementation of AHD screening package among newly HIV-diagnosed clients in one of the secondary referral facilities in Malawi. The findings will provide a framework for designing strategies for overcoming existing implementation barriers. In our study, we applied the Consolidated Framework for Implementation Research (CFIR) as an analytical framework to explore contextual barriers and facilitators from multiple levels within the healthcare system [16]. The CFIR was selected because it provides a comprehensive understanding of the contextual determinants of health service innovations of proven effectiveness [11, 12]. This conceptual framework comprises of five major domains; intervention characteristic, outer setting, inner setting, characteristic of individuals, and the process. These domains have a total of 39 constructs that ensure maximum exploration of contextual factors influencing the implementation of an intervention. CFIR has been used in a number of interventional studies and programmes ranging from communicable and non-communicable diseases, to nutrition and others[14, 15, 16, 17]. The Fig. 1 shows a Consolidated Framework for Implementation Research, adapted from Yuan Lu, 2018 [23]. ## Study design and site We conducted an exploratory mixed methods study to explore the contextual factors and gain a more comprehensive understanding of the impact of the barriers to delivery of AHD screening services. Data was collected sequentially in two phases: the first phase involved collection and analysis of qualitative data. This was followed by the building approach where themes and participant quotes of preliminary qualitative findings informed data collection procedure for secondary quantitative variables. Figure 2 describe the process of mixed method data merging and reporting. The study was conducted at Rumphi District Hospital, Northern Malawi. The district covers an area of 4,769 km.2, with a population of 128 360 people. At the time of data collection, the hospital ART Clinic supported by Lighthouse organization, had 8 659 clients on ART The facility was chosen because it implements the AHD management package of care and faces various implementation barriers including shortage of AHD trained providers and the impact of COVID 19 pandemic according to the MoH department of HIV/AIDS CQUIN report [2]. AHD screening was collaboratively done by laboratory staff, clinicians and nurses with all the three AHD point of care tests (CD4 Cell count, serum CrAg and Urine LAM) conducted in the main laboratory. ## Study population and sample size We purposively selected a homogenous sample of 10 health care workers including laboratory staff, nurses and clinicians who were actively involved in the implementation of the AHD management package of care. In-charges were tasked to identify key informants through snowballing. The addition of quantitative data with 101 ART reviewed records of newly diagnosed HIV positive clients covering two quarterly months (July to Dec, 2021) helped to further validate themes from qualitative data analyses. ## Qualitative data collection Using the CFIR selected constructs, we designed an interview guide which was pre-tested during demonstration interviews between research assistants and four healthcare workers at the hospital to determine the flow and appropriateness of the questions. All interviews were conducted in English but participants were allowed to express themselves in Chichewa, the local language. Each IDI lasted for about 30–40 minutes. During the interview process, field notes were written, highlighting important points and any new information emerging. All IDIs in local language were audio recorded, transcribed and translated into English. Data for newly HIV-diagnosed client was collected with details such as patient name, age, date of HIV diagnosis and whether the client was checked for CD4 Cell count or not. The final list was de-identified and assigned numbers before transferring it into an excel sheet, ready for analysis. ## Qualitative data analysis We first analyzed qualitative data soon after the IDI using deductive method where data was coded according to the CFIR framework of analysis, with themes categorized based on defined barriers or facilitators. A thematic analysis was employed in order to find patterns across the interview data in relation to our research questions. During the initial phase of analysis, BM and AG reviewed the interview transcripts for accuracy. Following familiarization with data, a codebook was developed based CFIR domains and most applicable constructs to guide coding and categorization of data using NVivo software version 12 (QSR International). The codebook which had clearly defined parent and child nodes was continuously reviewed and edited by authors. During the analysis, transcripts were entered into NVivo where codes were indexed to sections of the transcripts. Coded text categories were further examined for patterns. Finally, data was abstracted and interpreted, with direct quotations of the some respondents’ opinions included in the final report under various themes. ## Quantitative data analysis ART data records in the excel sheet was imported into STATA 14.0 software programme and run to generate descriptive statistical analyses in form of proportions, mean, range and standard deviation. Findings were presented in form of tables and figures. ## Ethical Consideration The study was approved by the College of Medicine Research and Ethics Committee (COMREC), certificate number P$\frac{.01}{22}$/3542. Consent process was followed with each of the interviewees and all interviews were conducted at a place convenient to the participants. Both qualitative and quantitative data with personal information was de-identified. ## Demographic characteristic of participants Table 1 summarizes the total number and background characteristics of participants involved in the study. Out of the 10 IDIs, four ($40\%$) were clinicians, three ($30\%$) laboratory scientists, and the other three ($30\%$) were nurses. Most of the participants were males ($$n = 8$$, $80\%$). Participants age range was 27 to 59 years old with a mean age of 43.3 (± 8.2). These participants have worked in hospital between 10 to 25 years (Mean = 15.5, ± 5.0). $50\%$ ($$n = 5$$) were degree holders of various health related qualifications, while about $40\%$ ($$n = 4$$) had diplomas. The majority of the participants attended either a formal AHD training or an on-job training and were working in departments which are actively involved in AHD screening. Table 2 displays the background characteristics of the newly HIV diagnosed clients from ART data records of July to December, 2021. There were 101 study participants with a balance distribution of sex (Male = $49.5\%$, Female = $50.5\%$) and mean age of 35years. All major qualitative themes indicated that healthcare level barriers significantly contributed to poor linkage to care among eligible new ART clients. Respondents from ART clinic highlighted poor communication structures, distance and congestion at the laboratory as factors that led to clients long waiting time and loss to follow ups. Thus, the subsequent analysis of secondary data of ART client records provided evidence of the impact of the existing barriers on client access to AHD screening. For instance, out of the 101 ART records reviewed, $60\%$ {($$n = 61$$) of the newly HIV-diagnosed clients had no documented results for AHD with CD4 cell count as a baseline test. Overall, both the third and fourth quarterly months had fewer clients screened for AHD {3rd Q, $38\%$ [$\frac{19}{50}$]; 4th Q, $41\%$ [$\frac{21}{51}$]}. As shown in Fig. 3, low AHD screening rate was registered for four consecutive months of August $33\%$ ($\frac{4}{12}$), September $25\%$ ($\frac{5}{20}$), October $35\%$ ($\frac{6}{17}$) and November $30\%$ ($\frac{4}{14}$). ## Contextual barriers and facilitators of AHD screening implementation Due to overlapping of CFIR domains and constructs, our findings on facilitators have been presented in four major themes as informed by CFIR framework; 1). Complexity of the intervention, 2). Limited supporting resources, 3). Poor work coordination, 4). Knowledge gap. Enablers of implementation have been presented thematically as external and internal facilitators. More barriers and facilitators has been summarized in the Table 3 and Table 4 respectively. ## Complexity of the intervention The implementation process was found to be affected by factors relating to the complexity of AHD screening. Providers reported the implementation process is quite involving and cumbersome with the inclusion of various departments and multiple lab tests which also increases patients waiting time. Any new HIV positive client has to undergo rapid screening tests for TB-LAM and CrAg if CD4 count was less than 200 cells/μL, before finally linked to treatment. Moreover, results recording was reported to be a challenge owing to a substandard register for the three AHD rapid tests. One of the respondents from the laboratory commented: “We have been given a CD4 register from the MoH, but it is missing these other tests. So if you are documenting these tests results, you will be using a separate improvised register which is cumbersome.” [ Lab tech]. This result in missing data as other technicians are reportedly not documenting results. ## Limited supporting resources Respondents acknowledged the absence of supporting resources such as posters and testing algorithm which contributed to poor execution of AHD screening by the majority providers who never attended any formal training. This was classified as a potential barrier from the inner setting. One respondent from ART clinic said: “The government has delayed to provide the posters because we should have pasted some in the wards for our friends who have less knowledge to understand what’s going on” [ART Clinician]. Most respondents argued that it was difficult for other departments to start conducting the AHD diagnostic tests outside laboratory without necessary laboratory equipment such as centrifuge, pipettes, and waste management facilities. Responding to the question as what could be the way forward, a DHMT member said: “the laboratory should continue providing the POCT services up until the facility has enough resources to extend it to ART clinic” [DHMT rep]. As a way of preparing the ART clinic for POCT testing and to ensure services are not interrupted due to staff engagements, a representative from Lighthouse suggested that ‘lay cadres be trained in conducting rapid tests to prevent service interruption due to staff engagements. “ In other sites they trained the HDAs and ART Clerks so it becomes easy when all nurses and clinicians are not available” [LH Clinician] ## Poor work coordination Physical and technological barriers in the inner setting affected work coordination among teams involved in the execution of the intervention. Respondents raised concerns about the distance between ART clinic and the laboratory which contributed to poor tracking of new ART clients. They proposed relocation of POCT services to ART clinic as a solution to client loss to follow up due to distance and congestion at the laboratory. One provider made reference to the policy that require AHD testing services to be conducted at the ART Clinic, arguing that the current arrangement defeated the intended purpose of providing quick services to the eligible clients. “ If the patient has to go to the lab to access testing services rather than within the ART clinic then it’s no longer point of care.” [ LH Nurse]. Absence of such communication systems compromised formal and informal information sharing practices which had a negative impact on work coordination among different working teams. Without ground telephone system in place, implementers used personal gadgets for communication and often walked on foot to and from laboratory whenever they run short of airtime to inquire about testing services. “ Sometimes there are stockouts of some reagents in the laboratory. It’s very hard to know until you send a client and comes back without a test” [ART Nurse]. ## Knowledge gap Respondents emphasized that knowledge gap among healthcare providers significantly contributed to poor adherence to guidelines as well as under-screening of AHD among newly HIV-diagnosed clients. Untrained providers were said to have decreased self-efficacy to conduct AHD screening. One of the participants argued that trained AHD providers were not adequate such that some departments lacked trained healthcare personnel: “In the male ward department, I think am the only one trained which means if am not available then we have problems[Clinician Mw]. ## External facilitators Besides the availability of MoH policy documents such as guidelines and SOPs supporting implementation of AHD screening, the facility was found to be well supported by MoH and PEPFAR implementing partners. For instance, Lighthouse organization provided technical support in form of personnel and data collecting tools, in addition to facilitation of integrated HIV programme review meetings at district level. On the other hand, MoH facilitated integrated HIV mentorships and supervision programmes as well as bi-annual AHD review meetings where teams from different district hospitals come together and learn from each other. “ This week a team from Department of HIV/AIDs has been going around in different health facilities carrying out mentorship programmes”[MA] ## Internal facilitators Additionally, respondents mentioned that the availability of two AHD TOT trained implementation leads who were also coordinators for HIV Testing Services and ART helped to facilitate the implementation of the AHD screening package. One of the coordinators further explained that their primary responsibility was to ensure that AHD providers comply with the available guidelines: “So as a trainer and Coordinator I look to it that the samples are collected adequately and run according to the specified AHD guidelines…The ART coordinator makes sure that that all who have tested HIV positive for the first time undergo CD4 test as the baseline test”[focal]. The implementation leads demonstrated in-depth knowledge of AHD management package and the zeal to support the implementation efforts. ## Discussion In this mixed methods study, most of the contextual barriers were interlinked and cross-cutting among CFIR domains, with the majority emerging from the inner setting. Major implementation barriers in the inner setting include unavailable communication systems and information gap as well as inadequate resources to support expansion of POCT to other screening sites. Merging and integration of the qualitative and quantitative findings, showed evidence of impact of barriers as expressed in qualitative themes and the coverage expressed as proportions in the surveyed ART clients. Most respondents reported poor AHD screening coordination among implementers due to communication challenges owing to the absence of ground telephone system and other social platforms for communicating and sharing relevant AHD updates. Communication system barriers existing between healthcare providers and their clients have also been documented elsewhere [24]. Thus, the use of social mobile platforms (SMPs) with instant messaging applications such as WhatsApp is highly recommended. In Ghana, WhatsApp platforms successfully facilitated networking and communication among healthcare workers which offered collaborative opportunities for TB screening and case detection [15]. SMPs provide a fast and affordable means of information sharing between healthcare providers in the management of clients [20, 21, 22]. Some respondents also suggested quarterly cross departmental meetings to facilitate information sharing of AHD updates among implementers. AHD services could also be integrated into ART and TB programmes which are well established and sufficiently supported by MoH PEPFAR implementing partners. Integration of related health programs reduces duplication of services, is cost-effective, and as well as efficient [23, 24]. Facility work flow and distance were also identified as potential barriers as they led to long AHD screening pathways for clients. Similar findings were also reported in a study conducted in the Eastern Africa where overcrowding, long waiting times and lack of resources emerged as prominent barriers [30]. In our study, we noted long client waiting time primarily due to the fact that testing was conducted only in a medical laboratory which also performed other testing services for in-patients and out-patients. Most respondents wished the Point of Care Testing (POCT) for AHD diagnostic services were stationed at ART clinic where the majority of new ART clients report for care. This was underlined as a solution to the problem of long waiting time and loss to follow-up due to congestion at the main laboratory. However, financial and diagnostic resources are required for the establishment of a functional compartment for AHD services at ART clinic. A recent study at a tertiary healthcare facility in Malawi estimated a capital investment cost of establishing and equipping an advanced HIV disease room for diagnostic tests to be U$10,708 [12]. This amount is affordable for MoH with the support from the U.S. President’s Emergency Plan for AIDs Relief (PEPFAR) which has recently focused on decreasing mortality among PLWH by addressing advanced HIV Disease and its associated opportunistic infections [31]. As a means to increase access to knowledge and information about AHD screening, formal AHD trainings are highly recommended. In our study, stakeholders alluded that healthcare providers with less knowledge of the intervention lacked confidence and self-efficacy when conducting AHD screening in various screening points within the hospital. This led to low screening coverage as many eligible clients were missed out. Such findings corresponds with programme evaluation reports which highlights inadequately trained providers as a stumbling block to a successful implementation of the HIV programmes [27, 28, 29, 30]. Limited trained personnel is a common challenge in the delivery of AHD screening and diagnostic services [14]. Therefore, improving knowledge and information through both formal and informal training is therefore paramount [36]. There is also urgent need for hospital implementation leads to make use of learning platforms such as morning handover reporting and weekly ground ward rounds which we found not to be adequately utilized by nurses and clinicians. Our investigation also revealed that posters for AHD screening were not available in all screening points. Nursing and clinical staff with less knowledge of the intervention had no access to such reference material. This widened the knowledge and information gap and partly contributed to non-compliance of AHD screening guidelines. Posters with information about eligibility criteria and testing algorithm could be a significant step towards improving adherence to guidelines by health care providers. One advantage of using posters is that they are readily available, hence provides instant knowledge and awareness of the intervention [37]. Most trained healthcare workers perceive the AHD screening package of care to have more clinical benefits to their clients. This is a facilitator of the AHD screening as health providers are motivated by positive clinical outcomes of the intervention. Many scientific papers have reported various benefits of the WHO AHD management package of care which includes improvement in diagnosis and management of opportunistic conditions, leading to improvement of quality of life for PLWH who would otherwise die to HIV related complications [2, 9, 30, 34, 35]. Supervision also tops as strong facilitators of implementation of interventional programmes in HIV/TB [40]. Similar findings emerged in our study in which external networks and partnerships with other health organizations significantly contributed to efficient delivery of the AHD screening services through capacity building. With MoH led mentorship and supervision programmes, major implementation gaps were identified and quickly addressed. Besides, Lighthouse organization technical team was very key in supporting AHD package through facilitation of AHD trainings, review meetings and involvement in reporting monthly data for evaluation. A successful implementation of an intervention also require presence of experienced focal leaders to oversee the implementation process [41]. Our study site had two focal persons: one with a laboratory background coordinating HIV Testing Services (HTS), while the other one with a clinical background, coordinated ART services. Their extensive knowledge of the AHD management package of care, and high level of commitment, facilitated the implementation of this complex intervention. Globally, there is a tremendous need for well-trained leaders in both healthcare and advocacy, especially in countries with high HIV prevalence and limited human resource capacity [38, 39]. This enables smooth implementation of interventional programmes aiming at reducing the burden of HIV infections and mortality. ## Study limitations We acknowledge that the study wasn’t done on large scale, involving multiple health facilities implementing the WHO recommended AHD package of care due to time and financial constraints. We also wished we could have interviewed clients who underwent AHD screening services to better understand their experiences. One of the strength of this study is that we employed mixed methods, of which qualitative and quantitative data triangulation improved validity of the findings. ## Conclusion The study has identified major contextual barriers to AHD screening, affecting work coordination and client linkage to care. The study findings provide an insight of the existing contextual barriers as well as comprehensive approaches for HIV programme implementers to design barrier-tailored strategies for optimizing AHD screening services in secondary healthcare facilities. Improving access and coverage of AHD screening services would therefore require overcoming the existing barriers in the facility set-up. There is also need to maintain good institutional partnerships with MoH and other external organizations to ensure continued technical support towards AHD screening. Use of CFIR framework of analysis and mixed methods study designs in evaluation of ## Funding The research was supported by the Fogarty International Center of the National Institutes of Health (NIH) under the Award Number D43TW010060. The content does not represent the official views of the NIH. The funders had no role in the study design, data collection and analysis. 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--- title: Antidiabetic and Anticancer Potentials of Mangifera indica L. from Different Geographical Origins authors: - Rizwan Ahmad - Aljawharah Alqathama - Mohammed Aldholmi - Muhammad Riaz - Ashraf N. Abdalla - Fatema Aljishi - Ebtihal Althomali - Mohd Amir - Omeima Abdullah - Muntathir Ali Alamer - Deema Alaswad - Wala Alsulais - Ahad Alsulays journal: Pharmaceuticals year: 2023 pmcid: PMC10055559 doi: 10.3390/ph16030350 license: CC BY 4.0 --- # Antidiabetic and Anticancer Potentials of Mangifera indica L. from Different Geographical Origins ## Abstract Mango fruit is well known for its nutritional and health benefits due to the presence of a plethora of phytochemical classes. The quality of mango fruit and its biological activities may change depending upon the variation in geographical factors. For the first time, this study comprehensively screened the biological activities of all four parts of the mango fruit from twelve different origins. Various cell lines (MCF7, HCT116, HepG2, MRC5) were used to screen the extracts for their cytotoxicity, glucose uptake, glutathione peroxidase activity, and α-amylase inhibition. MTT assays were carried out to calculate the IC50 values for the most effective extracts. The seed part from Kenya and Sri Lanka origins exhibited an IC50 value of 14.44 ± 3.61 (HCT116) and 17.19 ± 1.60 (MCF7). The seed part for Yemen Badami (119 ± 0.08) and epicarp part of Thailand (119 ± 0.11) mango fruit showed a significant increase in glucose utilization (50 μg/mL) as compared to the standard drug metformin (123 ± 0.07). The seed extracts of Yemen Taimoor seed (0.46 ± 0.05) and Yemen Badami (0.62 ± 0.13) produced a significant reduction in GPx activity (50 μg/mL) compared to the control cells (100 μg/mL). For α-amylase inhibition, the lowest IC50 value was observed for the endocarp part of Yemen Kalabathoor (108.8 ± 0.70 μg/mL). PCA, ANOVA, and Pearson’s statistical models revealed a significant correlation for the fruit part vs. biological activities, and seed part vs. cytotoxicity and α-amylase activity ($$p \leq 0.05$$). The seed of mango fruit exhibited significant biological activities; hence, further in-depth metabolomic and in vivo studies are essential to effectively utilize the seed part for the treatment of various diseases. ## 1. Introduction Mangifera indica (mango) is one of the most prevalent tropical fruits belonging to the genus Mangifera, which involves about 30 species of fruit trees in the Anacardiaceae family [1]. Although mango is known to be native to India, it is now widely cultivated in several countries, Mainly China, Thailand, Indonesia, Mexico, and Pakistan. Several parts of mango, including fruits, leaves, flowers, bark, and roots, have been traditionally used to treat numerous diseases. For instance, an aqueous extract of mango stem bark obtained by decoction has been used for the treatment of diarrhea, menorrhagia, anemia, cutaneous infection, and diabetes [2]. Additionally, it is commonly used in Cuba to enhance the quality of life of cancer patients [3]. Numerous pharmacological studies on mango fruits have been conducted to validate the traditional uses of Mangifera indica in the management of diverse diseases. These studies demonstrated that mango fruits have antioxidant, anticancer, and antidiabetic activities [4]. For example, an in vitro study of five Indian mango cultivars showed that different solvent extracts of mango peel have antioxidant, antimicrobial, and anti-inflammatory activities [5]. Regarding the anticancer effect, a recent study revealed that the ethanolic extract of mango peel has antioxidant and cytotoxic effects on lung cancer cell lines [6]. Another study illustrated the antiproliferation effect of the acetone extracts of peel and pulp of different mango cultivars in hepatocellular carcinoma (HepG2) [7]. Furthermore, a group of researchers observed oxidative stress mediated apoptosis induced by ethanolic extract of mango kernels in cultured estrogen receptor-positive breast cancer (MCF-7) [8]. For the antidiabetic potential, mango peel extract exhibited antidiabetic properties via improving antioxidant enzymes in diabetic rats [9]. Additionally, another study found that the methanolic extracts of mango fruits (pulp) offered antidiabetic effects by inhibiting α-amylase and α-glucosidase activity [10]. Moreover, an in vivo study found that long-term administration (21 days) of aqueous and methanolic extract of mango seed effectively reduces blood glucose levels in diabetic rats [11]. Collectively, a considerable amount of the literature proposed the potential application of mango fruit extract in the management of diabetes and cancer. The medicinal benefits of mango are attributed to many bioactive compounds, including polyphenols, terpenoids, carotenoids, and phytosterols [12]. Mangiferin is the prominent potent active constituent of mango with multiple pharmacological properties involving anti-inflammatory, antioxidant, antidiabetic, and anticancer [13]. Mangiferin (1,3,6,7-tetrahydroxyxanthone-C2-β-D- glucoside) is a polyphenol and is mainly isolated from all parts of the Mangifera indica tree (fruits, leaves, stem bark, and roots). Different mango cultivars exhibit a wide diversity in mango fruit characteristics (shape, size, color, texture, taste, and aroma) and their phytochemical profiles. Consequently, the bioactivity of each cultivar might be varied. Similarly, various parts of an individual mango fruit (peel, flesh, seed) possess many biological effects due to the variation in the type and quantity of chemical compounds [14]. Indeed, only Nam Doc Mai peel extract showed anti-proliferative effects when a study compared the methanolic extract of peel and pulp from three mango varieties (Irwin, Nam Doc Mai, and Kensington Pride) for their growth-inhibitory potentials on MCF-7 human breast cancer cells. Overall, fruit parts and habitat differences are essential factors to consider when evaluating medicinal plants’ bioactivity. To the best of our knowledge, this is a first-time study to evaluate the anticancer and antidiabetic activities of an aqueous extract of four separate parts (epi-, meso-, endocarp, and seed) of eight mango fruits from different geographical origins (Indian, Egyptian, Pakistani, Yemen, Thailand, Sri Lanka, Kenya, and Vietnam). In addition, it assesses the correlation between the Mangiferin amount and the observed biological activities. To illustrate, the cytotoxicity potential of the mango fruit extract was tested in selected cancer cell lines (Human breast adenocarcinoma, Human colorectal carcinoma, and Hepatocellular carcinoma) and normal human fetal lung fibroblast. Also, the effect of mango extracts on the glutathione peroxidase enzyme was assessed. Lastly, the antidiabetic activity was investigated by the inhibition of α-amylase, as well as the glucose uptake capacity in hepatocellular carcinoma. ## 2.1. Determination of the Cytotoxic Activities The cytotoxicity was observed in the range of (%) 34–91 for HCT116 and 34–98 for MCF7. The extracts with cytotoxic effect >$50\%$ were selected for further MTT assay in order to find their IC50 values (Table 1): India Badami (large green) seed (HCT116 (37 ± 0.11), MCF7 (37 ± 0.11)), India Totapuri (long green) seed (HCT116 (45 ± 0.16), MCF7 (34 ± 0.01)), Egypt (reddish green) epicarp (HCT116 (41 ± 0.12), MCF7 (38 ± 0.08)), Egypt (reddish green) seed (HCT116 (50 ± 0.11), MCF7 (47 ± 0.07)), Kenya (large round red) seed (HCT116 (34 ± 0.08), MCF7 (38 ± 0.11)), Sri Lanka (large yellowish green) seed (HCT116 (39 ± 0.12), MCF7 (34 ± 0.05)), Thailand (large green) seed (HCT116 (35 ± 0.15), MCF7 (36 ± 0.01)), Vietnam (large yellowish green) seed (HCT116 (42 ± 0.17), MCF7 (39 ± 0.05)), Yemen Badami (large yellow) seed (HCT116 (42 ± 0.17), MCF7 (39 ± 0.03)), Yemen Kalabathoor (large round red) endocarp (HCT116 (50 ± 0.11), MCF7 (43 ± 0.01)), Yemen Kalabathoor (large round red) seed (HCT116 (48 ± 0.13), MCF7 (40 ± 0.02)), Yemen Taimoor (large yellowish green) seed (HCT116 (42 ± 0.21), MCF7 (38 ± 0.05)), and Yemen Taimoor (small reddish green) seed (HCT116 (36 ± 0.12), MCF7 (39 ± 0.07)). Six different concentrations with the addition of an MRC5 cell line were utilized for selectivity determination. The mango fruit extract from Kenya (large round red) with seed part was the most effective against HCT116 (14.44 ± 3.61), whereas the extract from Sri Lanka (large yellowish green) seed revealed a significant effect against MCF7 cell line (17.19 ± 1.60). Likewise, the extracts from Kenya (large round red) seed (14.44 ± 3.61 and 25.96 ± 1.08), Vietnam (large yellowish green) seed (26.54 ± 1.10 and 20.17 ± 1.24), and Thailand (large green) seed (20.01 ± 0.88 and 24.63 ± 2.53) showed more selective cytotoxicity towards MCF7 and HCT116 cell lines respectively, as compared to MRC5 cell line. The Sri Lankan (large yellowish green) seed extract exhibited a comparatively more significant effect against HCT116 (20.53 ± 1.56), MCF7 (17.19 ± 1.60), and MRC5 cell line (20.79 ± 1.59). The data are shown in Table 2. ## 2.2. The effect of Extracts on Glucose Uptake Using HepG2 The cell viability was initially evaluated using MTT assay to check for the cytotoxic effect of extracts (100 μg/mL) on HepG2 followed by confirmation of the actual dose for glucose utilization assay. The MTT assay confirmed a lack of cytotoxicity (100 μg/mL) for all the extracts except the following: Yemen Taimoor (small reddish green) seed, Thailand (large green) seed, Kenya (large round red) seed, Vietnam (large yellowish green) seed, Yemen Kalabathoor (large round red) endocarp, Yemen Badami (large yellow) seed, Yemen Taimoor (large yellowish green) seed, and Yemen Kalabathoor (large round red) seed, which showed no cytotoxicity at 50 μg/mL (data not shown). The cells treated with the extracts of Thailand (large green) epicarp (119 ± 0.11), Kenya (large round red) mesocarp (118 ± 0.07), Yemen Taimoor (large yellowish green) epicarp (115 ± 0.05), Yemen Badami (large yellow) endocarp (114 ± 0.06), Indian Alphonso (small round green) mesocarp (114 ± 0.05), Kenya (large round red) endocarp (112 ± 0.06), Thailand (large green) mesocarp (110 ± 0.05), Thailand (large green) endocarp (109 ± 0.05), Yemen Badami (large yellow) epicarp (108 ± 0.04), and Kenya (large round red) epicarp (105 ± 0.02) exhibited significant ($p \leq 0.05$) increase in glucose uptake and utilization in HepG2 cells (100 μg/mL). The extract for Yemen Badami (large yellow) seed (119 ± 0.08) showed a significant increase in glucose utilization at 50 μg/mL, whereas the results for both extracts of Yemen Badami (large yellow) seed (119 ± 0.08) and Thailand (large green) epicarp (119 ± 0.11) were comparable with the result of control (metformin = 123 ± 0.07) used in this study (Table 1). ## 2.3. The Effect of Extracts on Glutathione Peroxidase Activity (GPx) Using HepG2 For relative GPx activity, MTT-assisted cell viability was evaluated as described earlier. The tested concentration (100 μg/mL) showed a lack of cytotoxic effect for all the extracts except the following: Yemen Taimoor (small reddish green) seed, Thailand (large green) seed, Kenya (large round red) seed, Vietnam (large yellowish green) seed, Yemen Kalabathoor (large round red) endocarp, Yemen Badami (large yellow) seed, Yemen Taimoor (large yellowish green) seed, and Yemen Kalabathoor (large round red) seed where no sign of cytotoxicity was observed at 50 μg/mL (data not shown). A significant ($p \leq 0.05$) decrease in GPx activity was observed for cells treated with the extracts (100 μg/mL) of Egypt (reddish green) endocarp (0.25 ± 0.13), Thailand (large green) mesocarp (0.36 ± 0.12), Thailand (large green) endocarp (0.42 ± 0.11), Yemen Taimoor (large yellowish green) epicarp (0.53 ± 0.04), India Totapuri (long green) seed (0.53 ± 0.14), Sri Lanka (large yellowish green) seed (0.54 ± 0.23), Yemen Taimoor (large yellowish green) endocarp (0.57 ± 0.32), Yemen Kalabathoor (large round red) mesocarp (0.60 ± 0.17), and Sri Lanka (large yellowish green) epicarp (0.61 ± 0.11), as compared to the control cells. Extracts of Yemen Taimoor (small reddish-green) seed (0.46 ± 0.05) and Yemen Badami (large yellow) seed (0.62 ± 0.13) showed a significant reduction for GPx activity at a dose of 50 μg/mL. The data for GPx activity are shown in Table 1. ## 2.4. The Effect of Extracts on the α-Amylase Activity The initial screening (500 μg/mL) for α-amylase activity revealed inhibition of >$50\%$ for Yemen Kalabathoor (large round red) endocarp (60 ± 0.05), Vietnam (large yellowish green) seed (58 ± 0.07), India Badami (large green) seed (55 ± 0.13) extracts, and Yemen Taimoor (small reddish-green) seed (54 ± 0.06), as shown in Table 1. These extract samples were further investigated at six different concentrations for the determination of the IC50 values. The extract for Yemen Kalabathoor (large round red) endocarp showed a comparatively low IC50 value (μg/mL) of 108.8 ± 0.70, as compared to the standard drug acarbose (78.41 ± 0.67 μg/mL). The IC50 values for the tested extracts are shown in Table 3. ## 2.5. Statistical Analysis The results are expressed with a mean (±standard deviation (SD)) from at least three independent experiments. The data were analyzed for statistical significance between the treated and control group with the help of GraphPad Prism V-9.2.0 (GraphPad, San Diego, CA, USA) at $p \leq 0.05$, whereas for correlations and component analysis, SPSS (statistical package for the social sciences) V 22.0 was used. ## 2.5.1. Descriptive Analysis The descriptive analysis showed a range with a mean (±SD) of 34–91 and 68.6 (±17.84) for HCT116, 34–98, and 64.81 (±19.28) for MCF7, 95–119, and 102.02 (±6.67) for glucose uptake, 0.25–1.30 and 0.722 (±0.21) for GPx, and 1–60 and 18.70 (±15.16) for α-amylase (Table 1). ## Pearson’s Correlation The statistical analysis for Pearson’s analysis showed a significant correlation between geographical origin and MG amount (−0.603, $$p \leq 0.000$$). With regard to biological activities, a significant correlation was observed for the fruit part vs. HT116 (−0.550, $$p \leq 0.000$$), MCF7 (−0.442, $$p \leq 0.002$$), and α-amylase activity (0.315, $$p \leq 0.029$$). Glucose uptake and GPx activities do not correlate with the geographical origin, fruit part, MG amount, or biological activities (Table 4). ## Component Analysis for Variance The principal component analysis (PCA), an eigenvalue-based statistical tool, is employed to reduce the dimensions of a large dataset into principal components based on %variability. The PCA maximizes the variance for uncorrelated variables based on an individual and cumulative variance, with an impactful display of the relevant correlated variables in the same component. Herein, three components, PC1 ($33.09\%$), PC2 ($20.48\%$), and PC3 ($14.65\%$), with a cumulative % variability of 68.23, were observed. For PC1 with major % variability, the variables presented were fruit part (−0.70), HCT116 (0.92), MCF7 (0.87), and α-amylase activity (−0.60), geographical origin (−0.91) and MG amount (0.83) were observed in PC2, whereas glucose uptake (0.62) and GPx activity (0.79) were observed in PC3, i.e., with the least % variability. The KMO-Bartlett’s test of sphericity showed a high X2-value of 129.78 with a significance value of 0.00 ($$p \leq 0.05$$) the data for PCA are shown in Table 5. A graphical presentation of the components observed with their corresponding eigenvalues is shown in Figure 1. ## Variables Correlation with ANOVA ANOVA with inter- and intra-correlation for the data variables also confirmed the outcomes from Pearson’s correlation and PCA analysis. The correlation of geographical origin with MG amount (−0.603), as well as fruit part with HCT116 (−0.550), MCF7 (−0.442), and α-amylase activity (0.315), was observed to be significant (0.00) with a high F-value of 627.81. The correlation for within and between groups, along with the sum of squares and mean square values, is shown in Table 6 ($$p \leq 0.05$$). ## 3. Discussion It is a well-known fact that variations in the geographical origin of a plant or fruit significantly affect its quality in terms of phytochemical profile and biological activities. The main aim of this study was to compare the quality variation among mango fruits obtained from different geographical origins. Mango fruits from eight different cultivars were collected and processed for green extraction and characterization of phytochemical profile based on MG amount in four parts of each mango fruit (epi-, endo-, mesocarp, and seed). The details regarding the novel green extraction and LCMSMS characterization for MG amount in these four parts of eight cultivars are described in our previous study [15]. The extracts from these mango fruit samples were screened for biological activities consisting of cytotoxicity (HCT116, MCF7, MRC5 cell lines), glucose uptake assay and GPx activity in HepG2 cells, and α-amylase activity. Statistical models were created to establish a correlation between the phytochemicals present in the mango fruit with the biological activities tested. For cytotoxicity (HCT116, MCF7, MRC5), a general followed by an in-depth screening was performed to determine the IC50 values. The extract samples with <$50\%$ of cell viability were studied further at six different concentrations using an additional cell line of MRC5. In general, the seed parts of these mango cultivars were observed with considerable cytotoxic potential. The seed extract from Kenya mango (large and round) was seen to be more cytotoxic towards the tested cell lines (HCT116, MCF7) in general screening, whereas the MTT assay for selectivity against the three cell lines (HCT116, MCF7, and MRC5) suggested Sri Lanka (large yellowish green) seed extract with the lowest IC50 value followed by Thailand (large green) seed extract. Though various cytotoxic studies have been reported for mango fruit extract including the following: aqueous flesh extract [16], peel and flesh for Australian cultivars [14], peel extract from six Brazilian cultivars [17,18], seed extract [8,19,20] as well as skin and flesh extract [21], this is a first-time study to report a comprehensive characterization for the whole mango fruit parts from eight different cultivars. With regard to phytochemicals in mango fruit, a good amount of MG was observed in all the fruit parts of these mango cultivars. Though seeds were reported with an average amount of MG, the epi- and mesocarp parts of the mango fruit presented the highest MG amount. MG and MG gallate [22], along with phenols and oleanolic acid, have been reported to possess significant cytotoxic potential [6]. The 48 extracted samples from the eight cultivars exhibited a significant ($p \leq 0.05$) increase in glucose uptake and utilization in HepG2 cell lines. The epicarps (Thailand (large green), Yemen Taimoor (large yellowish green), Yemen Badami (large yellow), Kenya (large round red)), endocarps (Yemen Badami (large yellow), Kenya (large round red), Thailand (large green)), and mesocarps (Kenya (large round red), Indian Alphonso (small round green), Thailand (large green)) parts were more effective as compared to the seeds of the mango fruits (100 μg/mL). In addition, the extracts of Yemen Badami (large yellow) seed and Thailand (large green) epicarp revealed a significant increase in glucose uptake at 50 μg/mL. As mentioned earlier, the MG amount was found more in the mentioned parts of these different cultivars fruit samples, epicarp, and mesocarp in particular. Mangiferin has been reported to promote glucose utilization and metabolism in a dose-dependent manner [23], promote PPARα-induced free fatty acids metabolism in HepG2 cells [24], enhance the utilization of peripheral glucose [23], and decrease β-cells apoptosis [25]. The GPx (glutathione peroxidase) enzyme prevents cell damage via the reduction of free radicals [26,27]; however, with an increase in free radicals in various disease conditions, an increase in GPx level is usually witnessed, which returns to its normal level with the use of specific therapeutic agents [28,29]. An elevated level of GPx prevents oxidative damage and inflammation, but this may block apoptotic cell death, resulting in a higher survival rate for the altered cells. This shows a complex role for GPx in the development and progression of cancer due, in part, to its role in the modulation of intracellular ROS [29]. The extracts were tested for a potential role in GPx enzyme inhibition. Herein again, the epicarps (Yemen Taimoor (large yellowish green), Sri Lanka (large yellowish green)), mesocarps (Thailand (large green), Yemen Kalabathoor (large round red)), and endocarps (Egypt (reddish green), Thailand (large green), Yemen Taimoor (large yellowish green)) parts of these mango fruits samples showed considerable reduction of GPx activity in HepG2 cells. A significant activity reduction (as compared to the positive control, i.e., tannic acid) was recorded for the extracts of Egypt (reddish green) endocarp and Thailand (large green) mesocarp; however, in vivo assays are required to further investigate and confirm the effect of these extracts on GPx level. The more MG amount in the mango fruits epicarp and mesocarp may be suggested to play a role in GPx restoration and its normalization to the normal level, as reported previously [30,31]. One of the investigative targets for diabetes management is the control of postprandial glucose levels via inhibition of the carbohydrate-digesting enzyme, i.e., α-amylase in the intestine [32,33]. In order to find the antidiabetic potential for the mango fruit parts, α-amylase inhibition activity was investigated. The general screening results suggested considerable activity for the extracts of Yemen Kalabathoor (large round red) endocarp, Vietnam (large yellowish green) seed, India Badami (large green) seed, and Yemen Taimoor (small reddish-green) seed. The extracts from these cultivars were studied further for selectivity (i.e., being selectively cytotoxic to cancer cells and less cytotoxic to normal cells. Extracts with lower cytotoxicity to normal cells are considered a selective extract against cancer cell lines) and IC50 value determination, where the endocarp from Yemen Kalabathoor (large round red) exhibited considerably low IC50 values when compared to the standard drug acarbose. The amount of MG in the endocarp is reported to be higher compared to seeds; hence, MG, along with some other constituents, may be responsible for the α-amylase activity. Previous studies have confirmed the α-amylase inhibitory role of mango peel-extracted MG [34,35,36]. Furthermore, in vivo studies on mango peel extract also supported the antidiabetic potential for MG [9,34,37]. However, phenolic compounds may also play a vital role as suppressors of postprandial hyperglycemia at different concentrations [38]. The outcomes of the biological activities suggest the presence of more than one bioactive phytochemical in mango fruit and its different parts. Albeit MG was observed with an average amount in all the 48 samples extracted for mango fruit, higher in epicarp and mesocarp parts, a systemic in-depth metabolomic analysis may be more useful to obtain a broader profile of the phytochemicals present in different varieties of the mango fruit (prone to variation based on the change in geography and other factors) vs. the effectiveness in biological activities of these samples. Moreover, the active phytochemicals (based on biological assays) need to be isolated and tested further via in vivo studies in different concentrations. The isolated compounds might show lower activity compared to the mixture of compounds in the extract. The statistical models were applied to show the correlation among the biological activities of the different parts of the mango fruit from different cultivars. Pearson’s test revealed a significant correlation between the geographical origin vs. MG amount and fruit part vs. cytotoxicity (HCT116, MCF7) and α-amylase activity. Glucose uptake assay and GPx inhibition showed no correlation with the mentioned biological activities. For the fruit part, it was the seed part of the fruit that appeared as the most effective in cytotoxicity and α-amylase inhibitory activity. Likewise, the PCA analysis showed more %variability (intra-correlation) for PC1 with components of fruit part, HCT116, MCF7, and α-amylase. Geographical origin and MG amount were placed in PC2, whereas glucose uptake and GPx activities were grouped in PC3 with the least % variability, suggesting a more significant correlation for the seed fruit part with cytotoxicity and α-amylase activity. The ANOVA with inter- and intra-correlation for the dataset further confirmed an alike significant correlation with a high F-value as reported for Pearson’s and PCA analysis. The outcomes of the statistical model suggest a prominent role for the seed part of the mango fruit in various biological activities. It is noteworthy to mention that it is the first time to report the mangiferin-based characterization of the different mango fruit parts (epi-, endo-, mesocarp, and seed) from eight different cultivars (48 samples) with an extensive biological screening of cytotoxicity (HCT116, MCF7, MRC5), glucose utilization (HepG2), GPx inhibition (HepG2), and α-amylase inhibition. None of the literature is available to be compared with the outcomes herein. The authors suggest a detailed metabolomic or phytochemical analysis to establish a prominent role in a series of biological activities for the seed part of the mango fruit. These in vitro studies may be followed by appropriate in vivo pharmacological experiments in order to confirm the role of mango seed, which may become a good source of phytochemicals, food products, and nutraceuticals to be utilized for the management and cure of various ailments. ## 4.1. Collection and Preparation of Samples Fresh mango fruit varieties from different geographical origins (Indian, Egyptian, Pakistani, Yemen, Thailand, Sri Lanka, Kenya, and Vietnam) were collected from local markets at Khobar, Eastern Province, Kingdom of Saudi Arabia. A green ultrasonic-assisted extraction (using water as a solvent at a temperature of 40 °C) was performed for four different parts (epi-, meso-, endocarp, and seed) of every individual mango fruit, as reported in our previous study [15]. ## 4.2. Cells and Microorganisms Human breast adenocarcinoma (MCF7: ATCC-HTB22); Human colorectal carcinoma (HCT116: ATCC-CCL247); Hepatocellular carcinoma (HepG2: ATCC-HB8065); normal human fetal lung fibroblast (MRC5: ATCC-CCL171). ## 4.3. Chemicals and Reagents Dimethyl sulfoxide (DMSO from Sigma Aldrich, St. Louis, MO, USA); RPMI-1640 (Roswell Park Memorial Institute Medium), DMEM (Dulbecco’s Modified Eagle Media), FBS (fetal bovine serum), penicillin (10,000 units/mL), and streptomycin (10,000 µg per mL) from Gibco, Life Technologies, Carlsbad, CA, USA; MTT assay reagent and α-amylase reagent Sigma Aldrich); Glucose uptake assay Kit (GAGO20) from Sigma Aldrich, St Louis, MO, USA; glutathione peroxidase Kit (ab102530) from Abcam, Cambridge, UK; doxorubicin (98.0–$102.0\%$ (HPLC)) from Sigma Aldrich and metformin ($97\%$) from Merck, whereas Microplate reader used was from BIORAD, PR 4100, Hercules, CA, USA. ## 4.4. Characterization and Standardization of the Extracts The extracts were quantified for Mangiferin amount (MG amount) in all the mango fruit samples. UPLCMS/MS was applied to develop and validate a green, efficient, and fast analytical method for MG quantification [15]. Though reported in our previous study, the MG amount found in these samples is presented in the table below for ease of understanding of the correlation. ## 4.5. Cell Culture All cell lines were cultured and maintained according to the procedure reported by Ahmad et al. [ 39]. ## 4.6. Determination of Cytotoxicity and Selectivity The cytotoxicity of the extracts was evaluated by MTT assay, as previously reported [40] and the already reported method of Ahmad et al. [ 39]. ## 4.7. Glucose Uptake Assay Odeyemi et al. modified method was used for glucose uptake assay [39,41]. ## 4.8. Determination of Glutathione Peroxidase Activity The assay was performed according to the manufacturer’s protocol, and our group reported procedures [26,28,39]. ## 4.9. α-Amylase Inhibition Activity The inhibitory activity of α-amylase was ascertained as described by Quan et al. [ 42]. The extracts were initially tested for 500 μg/mL, extracts that inhibited the enzyme were further evaluated for 1000, 500, 100, 50, 25, and 10 μg/mL, with acarbose as a positive control [39]. Absorbance was measured using a multi-plate reader at 550 nm for each well, calculating the percentage of inhibition utilizing the following equation:% inhibition = (A − C/B − C) × 100, where A = the absorbance of the reaction mixture in the presence of the extract, B = the absorbance of the mixture without the enzyme, and C = the absorbance of the reaction mixture in the absence of any extract. ## 5. Conclusions This study investigated the quality of eight different cultivars of mango fruit in terms of the biological activities of the fruit parts. All parts of the mango fruit showed cytotoxicity, glucose uptake, GPx, and α-amylase inhibition. The statistical model suggested a significant correlation with most activities attributable to the seed part of the mango fruit. MG was correlated to the screened biological activities; however, a comprehensive phytochemical characterization with advanced in vivo studies and clinical trials may further confirm the role of mango fruit for the cure of diseases in the form of food products, nutraceuticals, or isolated medicinal compounds. The study assessed quality variation among different mango cultivars and established a correlation between phytochemistry and the biological activity of mango fruit and its parts from different geographical origins. ## References 1. Shah K., Patel M., Patel R., Parmar P.. Pharmacogn. Rev. (2010) 4 42-48. 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--- title: Quantifying met and unmet health needs for HIV, hypertension and diabetes in rural KwaZulu-Natal, South Africa authors: - Urisha Singh - Stephen Olivier - Diego Cuadros - Alison Castle - Yumna Moosa - Jonathan Alex Edwards - Hae-Young Kim - Mark J. Siedner - Frank Tanser - Emily B. Wong journal: Research Square year: 2023 pmcid: PMC10055615 doi: 10.21203/rs.3.rs-2702048/v1 license: CC BY 4.0 --- # Quantifying met and unmet health needs for HIV, hypertension and diabetes in rural KwaZulu-Natal, South Africa ## Abstract ### Background The convergence of infectious and non-communicable diseases (NCDs) in South Africa poses a challenge to health systems. Here we establish a framework to quantify met and unmet health needs for individuals living with infectious and NCDs. ### Methods We screened adult residents >15 years of age within the uMkhanyakude district in KwaZulu-Natal, South Africa for HIV, hypertension (HPTN) and diabetes mellitus (DM). For each condition, individuals were defined as having no unmet health needs (absence of condition), met health need (condition that is well controlled), or one or more unmet health needs (including diagnosis, engagement in care, or treatment optimization). We analyzed met and unmet health needs for individual and combined conditions and investigated their geospatial distribution. ### Findings Of 18,041 participants, 9,898 ($55\%$) had at least one chronic condition. 4,942 ($50\%$) of these individuals had at least one unmet health need ($18\%$ needed treatment optimization, $13\%$ needed engagement in care, and $19\%$ needed diagnosis). Unmet health needs varied by disease: $93\%$ of people with DM, $58\%$ of people with HPTN and $21\%$ of people with HIV had unmet health needs. Geospatially, met health needs for HIV were widely distributed, unmet health needs had specific sites of concentration whilst the need for diagnosis for all three conditions was co-located. ### Interpretation Whilst people living with HIV are predominantly well-controlled, there is a high burden of unmet health needs for people living with HPTN and DM. Adaptation of HIV models of care to integrate HIV and NCD services is of high priority. ### Funding Fogarty International Center / NIH (R21TW01167, D43TW010543 K24HL166024), Bill and Melinda Gates Foundation, the South African Department of Science and Innovation, South African Medical Research Council and South African Population Research Infrastructure Network (SAPRIN). This research was funded in part, by the Wellcome Trust [Grant number 201433/Z/16/A]. For open access, the author has applied a CC by public copyright license to any Author Accepted Manuscript version arising from this submission. ## Background Infectious diseases, including HIV and tuberculosis (TB), have dominated the burden of disease in sub-Saharan Africa (SSA) for decades [1]. However, like other low- and middle-income countries, regions within SSA are experiencing an epidemiological transition in which prevalence of chronic non-communicable diseases (NCDs) is increasing [2]. These NCDs include diabetes mellitus (DM) [3, 4], hypertension (HPTN) and cardiovascular diseases [5, 6], chronic respiratory diseases [7], chronic renal diseases [8], mental and substance use disorders [9, 10] and cancers [11]. Whilst the transition of disease burden has predominantly included shifts from infectious diseases to NCDs globally, numerous studies in South Africa (SA) and some from SSA have reported a convergence of infectious diseases and NCDs (12–17). Managing the convergence of illness is of even greater concern since the advent of COVID-19 because poorly controlled multimorbidity has been associated with a higher risk of severe outcome from COVID-19 [18, 19]. Additionally, an increase in ageing among people living with HIV, as a result of the success of antiretroviral therapy (ART), has seen a subsequent increase in NCDs amongst this group resulting in recognition for the need of integrated infectious disease and NCD care and prevention programs in order to avoid a reversal in health gains made through ART [13, 17, 20, 21]. The United Nations sustainable development goal number 3, which aims to ensure healthy lives and promote well-being for all at all ages, advocates for the integration of infectious disease and NCD prevention and treatment [22]. However, the extent to which health needs of individuals with each of these conditions overlap within individuals and communities, and thus the most efficient and effective approach of designing a health systems response, is not well established. Here, we used results from a multimorbidity survey conducted in rural SA [12], to assess the multi-disease health needs for individuals and communities in rural KwaZulu-Natal and describe a needs scale which assesses health needs for infectious and NCDs. ## Study setting and population We analysed data from the Vukuzazi study, a cross-sectional health screening survey of individuals aged 15 or older in the uMkhanyakude district of KwaZulu-Natal, South Africa between 2018–2020, which collected information on HIV, DM and HPTN, described previously [12, 23]. The Vukuzazi study was embedded within an ongoing Africa Health Research Institute (AHRI) surveillance, the Population intervention Platform (PIP), which conducts multiple household, demographic, and health surveys annually on the surveillance population, and since 2017 has incorporated a clinic surveillance system (ClinicLink) that captures clinic attendance in the 11 primary health facilities in the PIP surveillance area [24]. Thus, enabling us to leverage pre-existing demographic, clinic and health data to enrich the Vukuzazi study. ## Data collected at Vukuzazi Detailed questionnaires were used to assess individual’s diagnosis and treatment history for each disease. Anthropometric measures and blood pressure were collected according to the WHO STEPS protocol. Blood samples were collected for assessment of glycosylated hemoglobin (HbA1C) and HIV immunoassay testing. Positive HIV immunoassay tests were followed by a reflex HIV-1 RNA viral load assessment [12]. ## Data from PIP and ClinicLink Data from PIP was taken from the most recent PIP surveillance before Vukuzazi. Socioeconomic status was captured with SES index using principal components analysis. Self-reported measures of perceived overall health, residence status and geolocation of residence was routinely collected as part of the PIP general health and socio-demographic questionnaire. The number of clinic visits individuals made in the year prior to Vukuzazi was obtained through linkage with the ClinicLink system [24]. Participants included in the study were geo-located to their respective homesteads using the comprehensive geographic information system [25]. ## Definition of the needs score and health states For this analysis, we defined five health states, based on parallel diagnostic criteria, for each of the three chronic diseases included in this analysis: (i) free of the condition, (ii) diagnosed and optimally treated, (iii) diagnosed and sub-optimally treated, (iv) diagnosed but not engaged in care and (v) undiagnosed but with a positive screening test in Vukuzazi (Table 1). The health system needs of each state were captured by a needs score in which the lowest score (zero) represented absence of disease and thus no immediate needs from the health system and the highest score (four) represented individuals who had the highest health needs and required diagnosis, engagement in care, treatment optimization and provision of chronic medication (Figure 1). Individuals with a needs score of 1, who were diagnosed with the health condition, engaged in care and had their condition optimally controlled on chronic medications were defined as having met health needs. Individuals who had needs score of 2–4, represented those individuals who required one or more health need not currently in place (Figure 1) and were defined as having unmet health needs. Need scores were calculated for individual diseases and for all three diseases combined. In the combined analysis, individuals with more than one disease were assigned the highest needs score for each of their diseases. ## Geospatial analysis Data visualization analysis of the distribution of health needs for each condition and for all three diseases combined were generated using continuous surface maps of the prevalence distribution of each need. Spatial interpolations were generated with the use of a standard Gaussian kernel interpolation method (with a search radius of 3 km), which has been used and validated in this population for mapping multiple HIV outcomes in the area of study [25]. Maps were created using the ArcGIS Pro software (http://www.esri.com). ## Statistical analysis We described the prevalence of each need score by disease and for all diseases combined. We then compared the descriptive features of individuals falling within each combined need score. Need scores were compared using Pearson’s Chi-squared test and non-parametric data were analysed using the Kruskal-Wallis rank sum test. Statistical analyses were done in the R statistical software (version 4.2.1). ## Ethical considerations The Vukuzazi study was approved by the University of KwaZulu-Natal Biomedical Research Ethics Committee and the institutional review board of Mass General Brigham. Written consent was obtained from all participants ## Results A total of 18,041 individuals were enrolled in the study, representing $50\%$ of the 36,097 eligible residents of the Demographic surveillance area (DSA) who were >15 years of age. Over half of participants ($54.9\%$, 9,$\frac{898}{18}$,041) had at least one of the three health conditions measured (Figure 2a). Of those individuals with health conditions, $61.7\%$ had HIV, $46.6\%$ had HPTN and $17.6\%$ had DM (Figure 2b). Of the participants found to have a chronic health condition, the patterns of met and unmet health needs differed by individual illness (Figure 2c). While $78.3\%$ of HIV-positive participants had their health needs met (diagnosed and utilising chronic medication for optimal disease control), only $6.9\%$ of participants with DM and $41.8\%$ of participants with HPTN had their health needs fully met (Figure 2c). Unmet health needs for individuals with HIV was predominantly driven by the need for treatment optimisation and diagnosis (need score 4), with few participants ($2.5\%$) requiring engagement in care (need score 3). By contrast, for HPTN and DM, all three unmet needs, including engagement in care, contributed significantly to the high levels of unmet health needs. Although $66\%$ and $40\%$ of participants with DM and HPTN were aware of their diagnosis, respectively, they either received suboptimal treatment (need score 2) or were not engaged in care (need score 3, Figure 2c). When we assessed the health needs of the population for all three disease conditions combined, we found that of the $55\%$ of participants who had at least one of the three health conditions, half had their health needs met and half had at least one unmet health need (Figure 2d). Among those participants with unmet health needs, $18.2\%$ were diagnosed and on treatment that required optimization (need score 2), $12.9\%$ were diagnosed but not engaged in care (need score 3) and $18.8\%$ were undiagnosed and were therefore in need of further diagnostic testing, engagement in care, optimisation of treatment, provision of chronic medication and routine monitoring (need score 4) (Figure 2d). Distribution of sex, age, BMI, perceived general health state, number of clinic visits in the last year, distance to nearest clinic, residence location and socio-economic status differed between people with no health needs and people with different health needs scores (Table 2). Two thirds of participants were female ($68\%$, 12,$\frac{229}{18}$,041). With regards to age, results showed a distribution of health needs that varied between age categories. For example, 25–44-year-olds represented the plurality of the people with well-controlled chronic disease (needs score 1, $47\%$) and undiagnosed chronic disease (needs score 4, $37\%$) whereas 45–64-year-olds represented the plurality of people with suboptimally controlled chronic disease (needs score 2, $42\%$) and chronic disease that was diagnosed but not treated (needs score 3, $43\%$). Nearly $56\%$ of the cohort were overweight or obese (i.e., BMI > 25kg/m2). These individuals were underrepresented among those without health needs ($44\%$) and over-represented among those with each of the health needs: needs score 1 ($61\%$), needs score 2 ($72\%$), needs score 3 ($77\%$) and needs score 4 ($68\%$) (Table 2). Despite having unmet health needs, participants with undiagnosed and uncontrolled illnesses had an overall perception of good or very good health. A majority of participants ($87\%$) who were undiagnosed and had uncontrolled illness (need score 4) reported during the PIP survey that they perceived their health as good ($55\%$) or very good ($32\%$) (Table 2). Similarly, $72\%$ of participants who required optimization of treatment (needs score 2) and $75\%$ of participants who required engagement in care (needs score 3), and thus were collectively deemed to be poorly controlled, reported their perceived health status as good or very good. Many individuals with unmet health needs had visited a clinic in the year prior to engaging in the Vukuzazi study (Table 2). Overall, $42\%$ of participants who were undiagnosed and uncontrolled (need score 4), $57\%$ of participants who required engagement in care (need score 3) and $72\%$ of participants who required optimisation of treatment (need score 2) visited a clinic in the past year, with most participants with unmet health needs having ≥2 visits (Table 2). Individuals who resided in rural areas were over-represented among people who had diagnosed chronic illness but were not engaged in care (needs score 3); they comprised $86\%$ of people in this needs group compared to $60\%$, $58\%$ and $49\%$ of needs score groups 1, 2 and 4 respectively (Table 2, $p \leq 0.001$). People with the furthest distance to the nearest clinic were similarly over-represented among people who were diagnosed but not engaged in care (needs score 3): the median distance to the nearest clinic for these individuals was 3.29km compared to 2.46km, 2.53km and 2.27km for needs score 1, 2 and 4 respectively. Having previously observed the lack of geospatial overlap for prevalence of HIV, DM and HPTN within this DSA [12], we sought to assess the geospatial distribution of health needs for these conditions in this area (Figure 3). Needs score 1 was widely distributed throughout the DSA indicating that the need for chronic medication is present across the entire DSA for all three conditions. In contrast needs score 2 and 3 (the need for treatment optimisation and engagement in care respectively), were specifically concentrated in the more rural areas of the demographic surveillance area for all three conditions. Specifically, the need for optimisation of treatment for HPTN and the need for engagement in care for HPTN and DM was concentrated in the northern portion of the surveillance area and the need for optimisation of treatment for DM was higher in the south-eastern portion of the surveillance area. Needs scores 2 and 3 had low density in the Southern-eastern corner of the DSA, the most densely populated region in the surveillance area, while needs score 4 showed overlap for all three conditions within this region indicating a possible target area for diagnostic interventions (Figure 3). ## Discussion Leveraging data from a large community-based multi-disease survey in rural KwaZulu-Natal, we introduce and implement a health needs framework to conceptualize met and unmet health needs of communities impacted by the overlapping infectious and NCD epidemics in SA. The framework allows determination of comparable needs across chronic disease and promotes comparison by sociodemographic and other health determinants. In our cohort in rural SA, we found that approximately half of people living with chronic illness in this community have unmet health needs. Use of this health needs framework also allows geographic visualizations that illustrate co-localization of individuals with undiagnosed infectious and NCDs. Geospatial data visualization by health needs also illustrates that analyzing populations by their health needs provides useful disaggregation that is obscured when people with a given condition are analyzed in a group without regard to their health needs. Importantly, our framework demonstrates that analysing chronic illness separately and implementing public health approaches in silos misses the opportunity for integration of communicable and NCD chronic care. Consideration should be given to health systems designed to be agnostic to conditions and to people suffering from multiple chronic diseases. We found that over half of the individuals that engaged in community-based health screening had at least one health need for the diagnosis or management of HIV, DM or HPTN, but that the met or unmet status of these needs differed markedly between HIV and NCDs. Over $70\%$ of the participants with HIV, who were widely distributed throughout the geospatial area, were well controlled on ART. This demonstrates the successful public health response to HIV in its ability to diagnose, optimally treat and monitor patients with a chronic infection across a large rural area. However, it also highlights the stark contrast between HIV and NCD responses: $93\%$ of people who screened positive for diabetes and $58\%$ of people who screened positive for hypertension have unmet health needs in this same community. The lack of NCD disease control is in line with those reported in other studies in the region (26–28). For example, the South African National Health and Nutrition Examination Survey (SANHANES), which considered prevalence of unmet health needs in SA, estimated that $91.5\%$ of hypertensive patients and $80.6\%$ of diabetic patients had an unmet health [26, 27]. Smaller studies in the province of Mpumalanga also reported high prevalence of uncontrolled HPTN (between 54.2–$56.8\%$) [28]. While these studies assessed health needs of people with HPTN and DM, our study provided a framework for the assessment of these health needs simultaneously with HIV. This revealed a notable discrepancy between the health system’s ability to meet the health needs of people with communicable and NCDs. Our results highlight the massive need for improved NCD care in rural SA, with health systems currently geared to reach a wide target population for HIV, creative adaptation of existing health programs and frameworks could be successful in treating multiple chronic illnesses concurrently. Unmet health needs also varied by disease and geospatial location in this community. For HIV, most participants with unmet health needs required diagnosis ($10.2\%$) or optimisation of treatment ($8.9\%$). Very few participants ($2.5\%$) required engagement in care, despite a known diagnosis. These data indicate that individuals who have been diagnosed with HIV have mostly been engaged in care and are receiving optimal ART. Conversely, for the NCDs, $65.9\%$ of people who knew they had DM and $39.7\%$ of people who knew they had HPTN required either engagement in care ($33.2\%$ and $15.4\%$ for DM and HPTN respectively) or optimisation of treatment ($33.7\%$ and $24.3\%$ for DM and HPTN respectively). These differences could partially reflect difficulties in accessing care, since individuals requiring engagement in care tended to live furthest from clinic (3.29km) and were more likely to live in in a rural setting ($86\%$) compared to those with other need scores. In contrast the need for treatment optimisation (need score 2) was strongly associated with increasing age and BMI. Individuals with this health need were predominantly >45 years of age ($75\%$) and were typically overweight ($25\%$) or obese ($47\%$). The association between increased BMI and suboptimal treatment of HPTN, DM or other chronic illnesses has been reported in other studies in which links between obesity, treatment resistant hypertension and altered pharmacological activity of drugs have been reported with use of multiple agents suggested [29]. Collectively these data support institution of decentralised patient-centred treatment programs which consider patient parameters like barriers to healthcare access, BMI and age when providing treatment for NCDs. We found that the need for diagnosis (need score 4) was greater for individuals with DM (27,$2\%$) and HPTN ($18.5\%$) than HIV ($10.2\%$). Individuals with this health need for all three conditions were concentrated in the southern portion of the surveillance area, the most densely populated region in this study. Collectively, this data shows a need to improve access to testing for NCDs. It also shows an opportunity for targeted integrated interventions for NCDs and HIV in this community. There is a possibility that healthcare facilities may have missed opportunities to address the health needs of patients with a diagnosis requiring treatment optimisation (needs score 2) or engagement in care (need score 3) or even undiagnosed patients requiring a diagnosis (need score 4) because the majority of these participants had visited a clinic in the area two or more times in the year before engaging in the Vukuzazi study but still had unmet health needs at the time of the survey also showing the need for improved integrated healthcare. Our study has several limitations. Firstly, only three chronic disease conditions were considered in this study. Nonetheless, the proposed framework offers flexibility and can be extended to other conditions. Secondly, Vukuzazi only enrolled half of the eligible population which may have biased description of health needs and their associations in directions hard to anticipate based on known differences between the sampled and unsampled population [12]. We acknowledge that people who screened positive for DM and HPTN required confirmatory testing prior to confirmation of diagnosis, and that this testing could rule out disease requiring immediate treatment; thus, we may have overestimated the burden of undiagnosed disease [30]. Lastly, we acknowledge that it is an oversimplification to ascribe “no health needs” to people who screen negative for disease because it neglects the need for interventions targeting disease prevention, which may be critical for optimal community health. In summary, we have introduced a needs framework that allows for interrogation of health needs for multiple diseases concurrently despite their individualised prevention, treatment and diagnostic parameters. This novel framework provides a way to conceptualize and measure individual and community health needs for people living in communities with high rates of infectious and NCDs. Applying this framework shows that half of the people living with HIV, DM or HPTN in a South African community have unmet health needs, that the unmet needs are particularly high in people living with NCDs. Furthermore, the granularity of this framework identifies unanticipated geospatial patterns of health need distribution that may inform strategies for improving rural health. 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--- title: 'A Novel Drug Delivery System: Hyodeoxycholic Acid-Modified Metformin Liposomes for Type 2 Diabetes Treatment' authors: - Minghao Hu - Tingting Gou - Yuchen Chen - Min Xu - Rong Chen - Tao Zhou - Junjing Liu - Cheng Peng - Qiang Ye journal: Molecules year: 2023 pmcid: PMC10055618 doi: 10.3390/molecules28062471 license: CC BY 4.0 --- # A Novel Drug Delivery System: Hyodeoxycholic Acid-Modified Metformin Liposomes for Type 2 Diabetes Treatment ## Abstract Metformin is a first-line drug for the clinical treatment of type 2 diabetes; however, it always leads to gastrointestinal tolerance, low bioavailability, short half-life, etc. Liposome acts as an excellent delivery system that could reduce drug side effects and promote bioavailability. Hyodeoxycholic acid, a cholesterol-like structure, can regulate glucose homeostasis and reduce the blood glucose levels. As an anti-diabetic active ingredient, hyodeoxycholic acid modifies liposomes to make it overcome the disadvantages of metformin as well as enhance the hypoglycemic effect. By adapting the thin-film dispersion method, three types of liposomes with different proportions of hyodeoxycholic acid and metformin were prepared (HDCA:ME-(0.5:1)-Lips, HDCA:ME-(1:1)-Lips, and HDCA:ME-(2:1)-Lips). Further, the liposomes were characterized, and the anti-type 2 diabetes activity of liposomes was evaluated. The results from this study indicated that three types of liposomes exhibited different characteristics—Excessive hyodeoxycholic acid decreased encapsulation efficiency and drug loading. In the in vivo experiments, liposomes could reduce the fasting blood glucose levels, improve glucose tolerance, regulate oxidative stress markers and protect liver tissue in type 2 diabetic mice. These results indicated that HDCA:ME-(1:1)-Lips was the most effective among the three types of liposomes prepared and showed better effects than metformin. Hyodeoxycholic acid can enhance the hypoglycemic effect of metformin and play a suitable role as an excipient in the liposome. ## 1. Introduction Diabetes is a common chronic disease. According to the International Diabetes Federation, the number of people with diabetes has reached 463 million globally in 2019. It is estimated that the number of patients with diabetes will increase to 578 million by 2030 [1]. Diabetes is a kind of disease that occurs when blood glucose levels are high and is caused by insufficient insulin secretion or impaired insulin action. Diabetes is classified as type 1 diabetes and type 2 diabetes [2,3]. Type 2 diabetes accounts for more than $90\%$ of diabetic cases [4]. Sustained high blood glucose levels can damage the eye, kidney, cardiovascular and nervous systems, etc. Diabetes usually results in many complications, such as diabetic nephropathy, diabetic neuropathy, and diabetic foot [5]. These complications often lead to blindness and disability, even death, thereby seriously endangering human health. Metformin has remarkable hypoglycemic effects and is used as a first-line drug for the treatment of type 2 diabetes. Although metformin could lead to side effects in the gastrointestinal system (nausea, diarrhea, etc.), clinical trials have proved few safety concerns for metformin. Moreover, long-term oral administration increases the risk of lactic acidosis [6]. Due to the transmembrane difficulty, low bioavailability and short half-life in vivo, metformin is difficult to be fully utilized clinically [7,8]. Developing sustained-release preparations is an effective way for improving bioavailability and reducing side effects. Liposome is composed of a lipid bilayer and can encapsulate hydrophilic and lipophilic drugs. Liposomes are similar in nature and function to the biological membrane and can deliver drugs to the cell interior by fusing with cell membranes [9]. Compared with traditional drug delivery systems, liposomes exhibit outstanding characteristics of high drug loading, sustained-release, low toxicity, biocompatibility and biodegradability [10]. Appropriate active ingredients added to liposomes not only play the role of excipients but also enhance the efficacy of the main drug [11]. In Peony and Licorice stomach floating tablets, chitosan as excipients could assist the formation of drug tablets and promote the healing of gastric ulcer wounds [12,13]. Thus, it is feasible to design a new drug delivery system based on the above strategy [14]. A previous study indicated that the nanoparticles modified by ginsenoside could enhance the anti-tumor effect of drugs, reduce the drug side effects and improve bioavailability and targeting [15]. Moreover, a nanostructured lipid carrier with naringin-containing coix seed oil demonstrated synergistic anti-tumor activities against hepatocellular carcinoma [16]. In several new drug delivery systems, liposomes have received special attention. In ancient China, pig bile was used for diabetes treatment [17]. A modern study also suggested that hyocholic acid and its derivatives were closely related to diabetes and could be considered new biomarkers for diabetes [18]. Further studies had found that hyodeoxycholic acid (HDCA) up-regulated the production and secretion of glucagon-like peptide-1 (GLP-1) in intestinal endocrine cells by simultaneously activating the G-protein-coupled BA receptor TGR5 and inhibiting the Fani-like X receptor (FXR), thereby inducing the secretion of insulin and reducing blood glucose levels [19]. Thus, it was revealed that HDCA could regulate glucose homeostasis and reduce blood glucose levels. To achieve excellent hypoglycemic effects and cholesterol-like structure, cholic acid and its derivatives can also be used as excipients for liposome modification, which would enhance the efficacy, bioavailability, and targeted delivery of the main drug. Chen et al. prepared cholic acid-modified biphenyl diester liposomes and observed that liposomes modified by cholic acid could enhance drug accumulation in the liver parenchymal cells [20]. Tan demonstrated that the anti-cerebral ischemia activity of baicalin liposomes modified with cholic acid was significantly higher than that of ordinary baicalin liposomes, indicating the enhancement of the therapeutic of baicalin by cholic acid [21]. Therefore, based on the findings from the above studies, HDCA-modified liposomes containing metformin are used to treat type 2 diabetes and are found to be better than using the metformin alone. In this study, HDCA was used as an excipient for the modification of liposomes. Three different liposomes (HDCA:ME-(0.5:1)-Lips, HDCA:ME-(1:1)-Lips, and HDCA:ME-(2:1)-Lips) were prepared by the thin-film dispersion method. Further, the particle size and polydispersion index (PDI), encapsulation rate (EE), and drug loading (DL) of liposomes were determined. The activities of the three different proportions of liposomes against type 2 diabetes in vivo were sequentially evaluated by measuring fasting blood glucose, glucose tolerance, biochemical markers, and pathological tissue. ## 2.1. Preparation and Characterization of Liposomes Three types of HDCA-modified metformin liposomes (HDCA:ME-(0.5:1)-Lips, HDCA:ME-(1:1)-Lips, and HDCA:ME-(2:1)-Lips) were prepared by the thin-film dispersion method [22]. To evaluate the stability of liposomes, the particle size and PDI were determined. The particle sizes of the three liposomes were about 196.36 nm, 232.30 nm, and 332.40 nm, respectively, and the PDI of all three liposomes was smaller than 0.30 (Table 1). The EE of the liposomes was $67.15\%$, $61.28\%$ and $59.53\%$, and the DL was $5.52\%$, $5.35\%$, and $5.16\%$, respectively (Table 1). The particle size of HDCA:ME-(0.5:1)-Lips was less than 200 nm, while those of HDCA:ME-(1:1)-Lips and HDCA:ME-(2:1)-Lips were greater than 200 nm, indicating that HDCA:ME-(0.5:1)-Lips could easily pass through the membrane and escape physical removal [23]. Moreover, EE and DL gradually decreased with the increase in HDCA dosage, indicating the importance of HDCA dosage for EE and DL of liposomes. ## 2.2.1. Reduction in the Fasting Blood Glucose Level The effects of different liposomes on the fasting blood glucose levels in 12 h fasting mice are shown in Table 2. As shown in Table 2, the fasting blood glucose levels of the model group mice ranged from 18.26 to 20.48 mmol/L. The blood glucose levels of the model group were significantly higher ($p \leq 0.05$) than those of the normal group, indicating that the models of type 2 diabetic mice were successfully established. The fasting blood glucose levels of the liposome groups and the metformin group significantly decreased from day 7 to day 21 ($p \leq 0.05$, $p \leq 0.01$). Considering oral administration of metformin for 21 days, the blood glucose levels of the HDCA:ME-(0.5:1)-Lips group were close to that of the metformin group; the levels of the HDCA:ME-(1:1)-Lips group were slightly lower than those of the metformin group; and the hypoglycemic effect of HDCA:ME-(2:1)-Lips group was higher than that of the metformin group. Thus, the hypoglycemic effect of the HDCA:ME-(1:1)-Lips group was slightly better than the metformin group. The body weight of mice from six groups showed no significant difference after 21 days of administration (Figure 1a). The metformin group significantly reduced the food intake of type 2 diabetic mice after 2 weeks of treatment ($p \leq 0.01$) (Figure 1b). After 3 weeks of administration, the food intake of the three liposome groups decreased significantly when compared with the model group. It was observed that a slight decrease in food intake in the HDCA:ME-(0.5:1)-Lips group after the treatment of 3 weeks. However, there was no significant difference between the HDCA:ME-(1:1)-Lips and HDCA:ME-(2:1)-Lips groups. ## 2.2.2. Improvement in the Oral Glucose Tolerance In this study, an oral glucose tolerance test was performed. After the administration of glucose, the blood glucose levels of the mice were measured at different time intervals (Figure 2). The results demonstrated that the blood glucose levels of all the mice increased rapidly within 30 min, and then gradually decreased from 30 min to 120 min. The blood glucose levels of the model group were significantly higher than those of normal group ($p \leq 0.05$). The blood glucose levels of the liposome groups and the metformin group significantly decreased ($p \leq 0.05$) when compared with the model group. Between 30 and 120 min, the blood glucose levels of the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips group were higher than those of the metformin group, while the blood glucose levels of the HDCA:ME-(1:1)-Lips group were lower than those of the metformin group. The efficacy of the HDCA:ME-(1:1)-Lips group was slightly better than the metformin group. In three liposome groups, the time of effect in the HDCA:ME-(1:1)-Lips group was significantly faster than those in HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips group. The analysis of blood glucose at different time intervals, indicated that the improvement effect of HDCA:ME-(1:1)-Lips on glucose tolerance was significantly higher than that of metformin in type 2 diabetic mice. Furthermore, a flattening hypoglycemic trend of the HDCA:ME-(1:1)-Lips group was observed between 90 and 120 min, revealing that HDCA:ME-(1:1)-Lips group demonstrated a certain sustained-release effect. ## 2.2.3. Regulation of the Biochemical Indices Total cholesterol (TC) and triglyceride (TG) are important clinical indicators of blood lipid [24]. The pathological elevation of TC increases the risk of hyperlipoproteinemia, atherosclerosis, and diabetes [25]. High levels of TG can lead to atherosclerosis, diabetes, and pancreatitis [26]. As shown in Figure 3a,b, the TC and TG of the model group were higher than those of the normal group ($p \leq 0.05$). The high levels of TC and TG were reduced in the metformin group and the liposome groups ($p \leq 0.05$). The TC levels in the HDCA:ME-(1:1)-Lips group were higher than those in the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups. The TG levels in the three HDCA-ME-Lips groups were higher than those in the metformin group. Thus, HDCA:ME-(1:1)-Lips group performed the best. As type 2 diabetes is characterized by insulin resistance, insulin (INS) levels can reflect the effectiveness of drugs [27]. As shown in Figure 3c, there were a significant increase in the serum INS levels of the model group ($p \leq 0.$ 01), indicating that type 2 diabetic mice were successfully modeled. There was a reduction in the INS levels in the metformin group and the three liposome groups reduced the levels of INS ($p \leq 0.05$) when compared with the model group. The INS levels in the HDCA:ME-(1:1)-Lips group were significantly higher than those in the metformin group. However, the INS levels in the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups were lower than those in the metformin group. These results demonstrated that the HDCA:ME-(1:1)-Lips group could better increase INS sensitivity and improve the symptoms of INS resistance in type 2 diabetic mice. Glucagon-like peptide 1 (GLP-1) is considered to be a key factor in postprandial glucose homeostasis regulation [28]. It was reported that metformin and bile acid can significantly increase GLP-1 secretion [29]. As shown in Figure 3d, significant increases in the GLP-1 levels in the metformin group and the three liposome groups were observed. The GLP-1 levels in three liposome groups were obviously higher than those in the metformin group. Superoxide dismutase (SOD), catalase (CAT), and malonaldehyde (MDA) are important indicators of oxidative stress and play a significant role in glucose metabolism [30,31,32,33]. The activity of SOD and CAT was significantly reduced in the model group when compared with that in the normal group ($p \leq 0.01$) (Figure 3d–f). When diabetic mice were treated with metformin and three types of liposomes, the activity of SOD and CAT increased significantly ($p \leq 0.05$). When compared with the metformin group, the activity of SOD and CAT was enhanced in the HDCA:ME-(1:1)-Lips group. However, the activity of SOD and CAT in the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups were lower than that in the metformin group. The MDA content of the model group was much higher than that of the normal group ($p \leq 0.01$). The MDA content of the metformin group and the liposome groups were effectively reduced when compared with that of the model group. Moreover, the reduction in MDA content of the HDCA:ME-(1:1)-Lips group was significantly better than that of the metformin group. These results indicated that HDCA-ME-Lips may relieve oxidative stress and abnormal glucose metabolism caused by oxidative stress, thereby regulating the oxidative decomposition of glucose to decrease blood glucose. The effects of three types of liposomes on different biochemical indices were compared in Table 3. The TC and TG levels of the HDCA:ME-(1:1)-Lips group were lower than those of the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups. The down-regulation in INS levels and up-regulation in GLP-1 levels of the HDCA:ME-(1:1)-Lips group was significantly better than those of the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups. Furthermore, we also observed that the SOD, CAT, and MDA levels in the three liposome groups there existed a significant difference. The activity of SOD and CAT in the HDCA:ME-(1:1)-Lips group was higher than that in the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups. However, the MDA content of the HDCA:ME-(1:1)-Lips group was lower than that of the HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups. HDCA:ME-(1:1)-Lips showed stronger than HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips. ## 2.2.4. Protection of the Liver Tissue The results of the histopathological examination revealed significant differences in the morphology and the number of mice hepatocytes in the different experimental groups (Figure 4). The hepatocytes of the normal group had clear boundaries and round nuclei, which were surrounded by rich cytoplasm (Figure 4a). The model group exhibited infiltration of inflammatory cells, cell swelling, focal necrosis, and cytoplasm osteoporosis. The cells appear translucent and polygonal-shaped (Yellow arrow) (Figure 4b). The local necrosis and lymphocyte infiltration in type 2 diabetic mice hepatocytes were significantly relieved in the metformin group and the liposomes groups (Figure 4c–f). In HDCA:ME-(0.5:1)-Lips and HDCA:ME-(2:1)-Lips groups, the intercellular lacuna was significantly enlarged, the cytoplasm was clear, and the nucleus was atrophic. In the HDCA:ME-(1:1)-Lips group, there were fewer swollen and necrotic cells and stickier cytoplasm. Moreover, the cell shape is more regular, and the nuclei are arranged more neatly. There were fewer symptoms of lymphocyte infiltration and focal necrosis in the HDCA:ME-(1:1)-Lips group than those in the metformin group, indicating that HDCA:ME-(1:1)-Lips effectively alleviated inflammation and protected the tissue structure of the liver in type 2 diabetic mice. ## 3. Discussion In this study, three different proportions HDCA-modified ME liposomes were prepared. The study evaluating the effects of liposomes on mice with type 2 diabetes came up with the following conclusions. ## 3.1. Effect of HDCA Dosage on Liposomes As a hydrophilic small molecular drug, metformin can be encapsulated by liposomes [34,35]. Three types of HDCA-modified metformin liposomes were prepared by the thin-film dispersion method, which was simple and reproducible. The characterization of the three types of liposomes indicated that the dosage of HDCA is an important factor governing the morphology of liposomes. The particle size of a liposome determines its ability to penetrate biofilms. The smaller particle sizes of the liposomes indicate stronger penetration ability, which is favorable for the drugs to reach target organs or target cells [36]. Moreover, the effect of HDCA dosage on EE and DL should not be ignored. As shown in Table 1, EE and DL of the liposomes gradually decreased with the increase in the HDCA dosage. It is worth noting that the more HDCA dosage in the preparation of liposomes indicated thicker film formation, which makes the preparation of liposomes more difficult. These results indicated that excessive excipients would reduce EE and DL, which in turn, would affect the efficacy of the liposomes to a certain extent. Therefore, choosing appropriate excipients and drugs is the key to the successful preparation of liposomes. ## 3.2. Anti-Type 2 Diabetes Activity of Liposomes Further, the effect of HDCA-modified metformin liposomes on type 2 diabetes mice was investigated. Liposomes significantly decreased the fasting blood glucose levels in type 2 diabetic mice ($p \leq 0.05$) and improved oral glucose tolerance ($p \leq 0.05$). TC and TG of the diabetic patients were maintained at high levels, which may lead to cardiovascular complications [37,38]. Liposomes significantly reduced TC and TG levels in type 2 diabetic mice ($p \leq 0.05$). INS resistance is a characteristic of type 2 diabetes [39]. The INS levels were significantly decreased by the liposomes ($p \leq 0.05$), indicating that liposomes could better increase insulin sensitivity and improve the symptoms of INS resistance in type 2 diabetic mice. The liposomes also can increase the secretion of GLP-1, which is a crucial factor for postprandial glucose homeostasis. SOD, CAT, and MDA are oxidative stress indicators, which can reflect glucose metabolism in several ways [40,41]. Liposomes significantly increased SOD and CAT activity and decreased MDA content in the liver, demonstrating that liposomes could regulate glycolipid metabolism and reduce blood sugar by relieving oxidative stress to a certain extent. The histopathological examination demonstrated that lymphocyte infiltration and local necrosis were reduced by liposomes, indicating an effective reduction in inflammation and protection of liver tissue structure in type 2 diabetic mice. This helped control the progression of diabetes and related complications. Among three types of liposomes, HDCA:ME-(1:1)-Lips significantly reduced the fasting blood glucose levels, improved glucose tolerance, and regulated biochemical indices (TC, TG, INS, GLP-1, SOD, CAT, and MDA) in type 2 diabetic mice. The anti-diabetic effect of HDCA:ME-(1:1)-Lips was also significantly better than that of metformin, HDCA:ME-(1:1)-Lips and HDCA:ME-(1:1)-Lips. In addition, HDCA:ME-(1:1)-Lips was found to protect liver tissue better than metformin (Figure 4f). Therefore, we speculated that HDCA achieved synergistic hypoglycemic effect in the liposomes. ## 3.3. Potential of HDCA as an Excipient for Liposomes HDCA, a steroid derivative, is similar in structure to cholesterol [42]. HDCA as an excipient can replace cholesterol for the stabilization of the phospholipid bilayer. The biparental nature of HDCA makes it possible to modify liposomes [43]. It is noteworthy that HDCA could regulate glucose homeostasis and reduce blood glucose, which was considered a potential natural ingredient in the treatment of type 2 diabetes [44]. In this study, as an excipient for liposome modification, HDCA has strong hypoglycemic activity and can be used for the treatment of type 2 diabetic mice synergistically with metformin. On the other hand, HDCA can reduce the destruction of metformin liposomes in the gastrointestinal tract and improve the stability of liposomes in vivo. In addition, HDCA-modified metformin liposomes may reduce the side effects of metformin as well as improve its bioavailability and half-life. However, this still needs to be verified in future experiments. In conclusion, HDCA in liposomes plays a very crucial role as excipients and auxiliary anti-type 2 diabetes ingredients and has a significant potential for the treatment of type 2 diabetes. This strategy of liposomes modified with natural anti-diabetic active ingredients is expected to be applied to other type 2 diabetes drugs for improving their hypoglycemic efficacy and reducing their side effects. ## 4.1. Materials Metformin hydrochloride and hyodeoxycholic acid were purchased from Shanghai Yi En Chemical Technology Co., Ltd. (Shanghai, China); Lecithin was bought from Chengdu Kelon Chemical Co., Ltd. (Chengdu, China). The assay kits for total cholesterol (TC), total triglycerides (TG), superoxide dismutase (SOD), catalase (CAT), and malonaldehyde (MDA) were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). The insulin (INS) and Glucagon-like peptide 1 (GLP-1) assay kit was purchased from Wuhan ABclonal Biotechnology Co., Ltd. (Wuhan, China). Acetonitrile was HPLC grade, and all other reagents were analytical grade. Purified deionized water was used throughout the experiment. ## 4.2. Preparation of Liposomes Liposomes were prepared by the thin-film dispersion method. Certain proportions of lecithin and HDCA were precisely weighed in a pear-shaped bottle and dissolved in 10 mL of trichloromethane-methanol (3:1) solution using ultrasonication. Subsequently, the mixed solution was placed on a rotary evaporator (RE-52AA, ShangHai Yarong Biochemistry Instrument Factory, Shanghai, China), and the organic solvent was removed under a vacuum at 40 °C. When a uniform film was formed on the bottle, a 10 mL PBS solution of metformin (10 mg/mL) was added. Then the film was hydrated at 40 °C for 1 h. The liposome suspension was ultrasonicated for 10 min for uniform dispersion. Then, the suspension was filtered through 0.45 and 0.22 μm microporous membranes and stored at 4 °C. 100 mg of metformin was added to 50 mg, 100 mg, and 200 mg HDCA to obtain three different proportions of liposomes, namely, HDCA:ME-(0.5:1)-Lips, HDCA:ME-(1:1)-Lips, and HDCA:ME-(2:1)-Lips. ## 4.3.1. Particle Size and Polydispersity Index (PDI) The particle sizes and polydispersity index (PDI) of liposomes were determined using a nanoparticle size analyzer (Litesizer 500, Anton Paar, Graz, Austria). The liposomes were diluted 20-fold with purified water before measurement, and each measurement was repeated three times. ## 4.3.2. Encapsulation Efficiency (EE) and Drug Loading (DL) One mL of liposome was centrifuged for 30 min at 12,000 r/min. The supernatant was collected and was placed in a 10 mL bottle. The anhydrous ethanol was used for constant volume and the solution was mixed by vortex for 5 min. The solution was further diluted with anhydrous ethanol to obtain the desired concentration. The absorbance of metformin was determined by the ultraviolet/visible spectrophotometer (UV-2550, Shimadzu, Kyoto, Japan) at 232 nm. The concentration of free metformin (Cfree) was calculated using the following standard curve: $y = 0.0758$x + 0.1413 (R2 = 0.9998; $$n = 6$$). The standard curve was constructed using a series of standard metformin solutions with concentrations ranging from 4.65 µg/mL to 74.4 µg/mL. The total metformin concentration (Ctotal) was determined by adding another 1 mL liposome in a 10 mL volumetric bottle. Further, 4 mL of DMSO was added to the bottle and the volume was made up by anhydrous ethanol. The solution was then mixed by vortex for 5 min. Then solution was further diluted with anhydrous ethanol to obtain the desired concentration. Equations [1] and [2] were used to calculate the EE and DL of the liposomes, respectively. [ 1] EE%= Ctotal−Cfree Ctotal×$100\%$ [2]DL%=encapsulated drug contentweight of carrier×$100\%$ ## 4.4. Animal Experiments KM mice (18–22 g, male) were purchased from Spearfish (Beijing, China) Biotechnology Co. All mice were acclimatized to the new environment for one week, during which they were kept in light for 12 h and dark for 12 h per day, with appropriate diet control and free access to water. Mice were prohibited from ingesting food for 12 h before the experiment, but drinking water was not restricted. All the animal experimental protocols were approved by the Animal Research Ethics Committee of Chengdu University of Traditional Chinese Medicine (NO. 2022-55). ## 4.4.1. Establishment of the Type 2 Diabetic Mice Model One hundred mice were reared for 7 days before the experiment. The blood glucose levels of mice were measured with a glucose meter (yuyue glucometer, Shenzhen, China) before preparation for modeling. No abnormalities in mice were observed. Mice were randomly divided into the model group and the normal group, then were given a normal diet and distilled water. There were ninety mice in the model group and ten mice in the normal group. Mice of the model group were fasted for 12 h and injected intraperitoneally with $0.4\%$ streptozotocin (STZ) solution (0.2 mL per mouse with a corresponding dose of 50 mg/kg) for 5 days. On the fifth day after injection, food and water were normally provided. The fasting blood glucose levels were weekly measured. Since this method requires stable blood glucose levels for 1–2 weeks, fasting blood glucose levels were measured during the second week. The mice having blood glucose levels between 11.1 and 20 mmol/L were selected as the successful type 2 diabetes mice. ## 4.4.2. Grouping and Administration According to the fasting blood glucose levels, thirty STZ-treated type 2 diabetes mice were randomly divided into five groups with 6 mice in each group. Another six untreated mice were randomly selected as the normal group. Each group was administered the following compounds orally for 21 days: [1] Normal group mice were administered $0.9\%$NaCl; [2] Model group mice were administered $0.9\%$NaCl; [3] Metformin group mice were administered metformin hydrochloride at a dose of 100 mg/kg/d; [4] HDCA:ME-(0.5:1)-Lips group mice were administered HDCA:ME-(0.5:1)-Lips at a dose of 100 mg/kg/d; [5] HDCA:ME-(1:1)-Lips group mice were administered HDCA:ME-(1:1)-Lips at a dose of 100 mg/kg/d; and [6] HDCA:ME-(2:1)-Lips group mice were administered HDCA:ME-(2:1)-Lips at a dose of 100 mg/kg/d. ## 4.4.3. Measurement of Fasting Blood Glucose Levels Before measurement, the mice were prohibited from ingesting food for 12 h. The blood glucose levels of caudal vein blood were measured by a glucose meter at 0, 7, 14, and 21 days after administration. ## 4.4.4. Oral Glucose Tolerance Test After mice were administered with the aforementioned compounds for 21 days, the oral glucose tolerance test was performed. Mice were not allowed to ingest food for 12 h and then orally administered 2.0 g/kg glucose. The blood glucose levels were measured at 0, 30, 60, and 120 min after the glucose administration. ## 4.4.5. Assessment of Biochemistry Indices Serum was collected by centrifugation for 10 min at 3000 r/min using a refrigerated centrifuge at 4 °C. Biochemical indices including TG, TC, INS and GLP-1 were measured using the assay kit following the manufacturer’s instructions. According to the ratio of liver tissue weight (g): normal saline (mL) = 1:9, homogenization of liver tissues was performed. The supernatants were collected by centrifugation for 10 min at 8000 r/min at 4 °C. The hepatic SOD, CAT, and MDA were determined normatively using assay kits. ## 4.4.6. Histopathological Examination Liver tissues were embedded in paraffin and the paraffin sections were sliced and stained with hematoxylin–eosin (HE). The tissues were visualized by a CX41 microscope (Olympus, Tokyo, Japan) equipped with MDX4 (Lilai, Chengdu, China) digital camera system under 400× magnification. ## 4.5. Statistical Analysis All the experiments were repeated at least 3 times. All the experimental data were presented as mean ± standard deviation (SD). One-way analysis of variance was performed on the data using SPSS 26.0 software (IBM, Chicago, IL, USA). $p \leq 0.05$ was considered statistically significant. ## 5. Conclusions In this study, three HDCA-modified metformin liposomes (HDCA:ME-(0.5:1)-Lips, HDCA:ME-(1:1)-Lips, and HDCA:ME-(2:1)-Lips) were prepared by the thin-film dispersion method based on the different proportions of HDCA and metformin. The three types of liposomes exhibited different characteristics (particle size, EE, and DL), which was related to the dosage of HDCA. When the dosage of HDCA increased, the particle size, EE and DL of liposomes also decreased obviously. In addition, liposomes showed significant anti-diabetic effects in type 2 diabetic mice. Liposomes could reduce the fasting blood glucose levels, improve glucose tolerance and regulate biochemical indexes related to glucose metabolism. Histopathological examination manifested that liposomes alleviated liver inflammation in mice and showed a protective effect on liver. Furthermore, the anti-diabetic effect of HDCA:ME-(1:1)-Lips group was significantly better than that of the metformin group, which confirmed that HDCA played a synergistic role in the treatment of type 2 diabetic mice. HDCA:ME-(1:1)-Lips was the most effective among the three types of liposomes. 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--- title: Nicotinamide Adenine Dinucleotide Precursor Suppresses Hepatocellular Cancer Progression in Mice authors: - Nengzhi Pang - Qianrong Hu - Yujia Zhou - Ying Xiao - Wenli Li - Yijie Ding - Yunan Chen - Mingtong Ye - Lei Pei - Qiuyan Li - Yingying Gu - Yan Sun - Evandro Fei Fang - Mianrong Chen - Zhenfeng Zhang - Lili Yang journal: Nutrients year: 2023 pmcid: PMC10055624 doi: 10.3390/nu15061447 license: CC BY 4.0 --- # Nicotinamide Adenine Dinucleotide Precursor Suppresses Hepatocellular Cancer Progression in Mice ## Abstract Targeting Nicotinamide adenine dinucleotide (NAD) metabolism has emerged as a promising anti-cancer strategy; we aimed to explore the health benefits of boosting NAD levels with nicotinamide riboside (NR) on hepatocellular carcinoma (HCC). We established three in vivo tumor models, including subcutaneous transplantation tumor model in both Balb/c nude mice (xenograft), C57BL/6J mice (allograft), and hematogenous metastatic neoplasm in nude mice. NR (400 mg/kg bw) was supplied daily in gavage. In-situ tumor growth or noninvasive bioluminescence were measured to evaluate the effect of NR on the HCC process. HepG2 cells were treated with transforming growth factor-β (TGF-β) in the absence/presence of NR in vitro. We found that NR supplementation alleviated malignancy-induced weight loss and metastasis to lung in nude mice in both subcutaneous xenograft and hematogenous metastasis models. NR supplementation decreased metastasis to the bone and liver in the hematogenous metastasis model. NR supplementation also significantly decreased the size of allografted tumors and extended the survival time in C57BL/6J mice. In vitro experiments showed that NR intervention inhibited the migration and invasion of HepG2 cells triggered by TGF-β. In summary, our results supply evidence that boosting NAD levels by supplementing NR alleviates HCC progression and metastasis, which may serve as an effective treatment for the suppression of HCC progression. ## 1. Introduction Liver cancer is the sixth most common cancer worldwide and was the third leading cause of cancer-related death globally in 2020 [1]. The most common type of liver cancer is hepatocellular carcinoma (HCC), accounting for ~$90\%$ of the cases [2]. Despite the availability of various treatment strategies for managing HCC, poor overall prognosis is often encountered because of the development of metastasis and recurrence [3]. Early-stage HCC tumors can be treated with curative options such as resection and local ablation, resulting in a 5-year survival rate of over $70\%$. However, symptomatic advanced-stage cases with portal invasion, distant metastasis, or lymph node metastasis have a median survival period of only 1–1.5 years [4,5]. Although hepatic resection can effectively treat early-stage HCC tumors, the recurrence of HCC remains a major concern due to the development of micro-metastases following resection and the emergence of de novo tumors in a highly carcinogenic microenvironment [6]. Post hepatic resection, a five-year recurrence rate has been reported to be as high as $70\%$ [6]. Therefore, there is an urgent need to explore new strategies to repress the progression of HCC. Nicotinamide adenine dinucleotide (NAD) is a coenzyme mediating redox reaction in various metabolic pathways. It also serves as a vital substrate for NAD-consuming enzymes such as sirtuins, PARPs, and CD38. NAD and its metabolites have been found to affect several biological processes, including those linked to cancer development, such as energy metabolism, DNA repair, epigenetic modifications, inflammation, stress resistance, and circadian rhythms [7]. Thus, NAD metabolism is a promising therapeutic target for cancer treatment [8,9]. Anti-cancer strategies targeting NAD metabolism aim to inhibit cancer cells’ survival and proliferation by reducing NAD levels, mainly through interfering with NAD-biosynthetic machinery [9,10]. Although some studies have reported that reduced NAD levels inhibit the migration and invasion ability of HCC cells in vitro [11,12], other studies have indicated that NAD depletion actually promotes tumorigenesis, tumor progression, and metastasis [13,14,15,16,17,18,19]. Moreover, boosting NAD with NAD precursors has been shown to suppress tumor development and metastasis both in vivo and in vitro [13,15,16,17]. To sum up, the role of NAD in the complex process of tumor development and metastasis is controversial, and it remains unknown whether NAD repletion can suppress or promote the development and metastasis of HCC. Nicotinamide riboside (NR) is a potent NAD precursor that naturally exists in milk and has already been made available as a nutraceutical [20]. Oral NR has been shown to significantly elevate NAD levels in human blood and mouse liver [21]. NR supplementation has shown promising results in the therapy of numerous cardiovascular, neurodegenerative, and metabolic disorders [20]. Furthermore, previous studies have shown that NR supplementation can attenuate alcohol-induced liver injuries, protect against liver fibrosis, and promote liver regeneration [22,23,24]. However, only a few studies have investigated the potential prophylactic or therapeutic role of NR in cancer [13,25], and none have explored its effect on cancer metastasis. In this study, we sought to test the effect of boosting NAD on tumor metastasis prevention. The findings suggest that NR supplementation could serve as a promising therapeutic strategy for preventing HCC progression. ## 2.1. Cell Culture and Treatment Human liver cancer cell line HepG2 and murine hepatoma cell line Hepa1-6 were from American Type Culture Collection (ATCC). Human liver cell line LO2 was from the National Collection of Authenticated Cell Cultures. Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (GibcoTM, Thermo Fisher Scientific Inc., Waltham, MA, USA) supplemented with $10\%$ fetal bovine serum (FBS) (GibcoTM, Thermo Fisher Scientific Inc., Waltham, MA, USA) and $1\%$ penicillin-streptomycin (GibcoTM, Thermo Fisher Scientific Inc., Waltham, MA, USA) at 37 °C in a humidified, $5\%$ CO2 atmosphere. ## 2.2. Animal Experiments All animal experiments in this study were approved by the Animal Care and Protection Committee of Sun Yat-sen University (No. 2018-001 and 2022001319). The mice were purchased from Guangdong Medical Laboratory Animal Center (Guangzhou, Guangdong, China), housed in temperature-controlled animal facilities with 12 h of artificial light/dark cycles, and had ad libitum access to water and food. NR administrated in animal experiments was from ChromaDex (Irvine, CA, USA). The mice were sacrificed as long as the mouse had suffered from rapid weight loss of 15–20 percent within a few days or had shown a state of cachexia. For the subcutaneous xenograft model, a total of 5 × 106 HepG2 cells suspended in a 100 μL mixture of PBS and Matrigel (Corning, NY, USA) (1:1), were injected subcutaneously into the rear flanks of five-week-old male BALB/C nude mice. The tumor volumes were measured twice weekly using a digital vernier caliper and calculated as length × width2 × 0.52. When the tumor volume reached 300 mm3, mice were randomly subjected to the treatment of 400 mg/kg/day NR (NR group, $$n = 7$$) or saline vehicle (Control group, $$n = 6$$) by daily gavage. The body weights and tumor sizes of the mice were measured every two days. For the hematogenous metastatic neoplasm model, GFP-and-luciferase-labeled HepG2 cells (2.5 × 106 cells in 100 μL PBS) were injected into the lateral tail veins of five-week-old male BALB/C nude mice. Two hours after tumor cells injection, mice were injected i.p. with D-luciferin (150 mg/kg), then anesthetized and placed in an imaging chamber. Images (IVIS Spectrum, Perkin Elmer, Waltham, MA, USA) were taken to confirm whether an equal amount of tumor cells was successfully transfused. Mice were randomly subjected to the treatment of 400 mg/kg/day NR (NR group, $$n = 7$$) or saline vehicle (Control group, $$n = 7$$) by daily gavage the next day. To monitor the progression of tumor metastasis, mice were imaged by noninvasive bioluminescence every 16 days and before sacrifice. The lungs of mice were dissected and imaged. Quantification of bioluminescence was performed by Living Image 4.3.1 software (Perkin Elmer, Waltham, MA, USA). For the subcutaneous allograft model, a total of 5 × 106 Hepa1-6 cells were injected subcutaneously into the rear flanks of five-week-old male C57BL/6J mice. Seven days later, mice were randomly subjected to the treatment of 400 mg/kg/day NR (NR group, $$n = 9$$) or saline vehicle (Control group, $$n = 12$$) by daily gavage the next day. The tumor volumes were measured every three days. All organ samples, including tumor samples, were collected and weighed, then immediately frozen and stored at −80 °C until analysis, or fixed in formalin for Hematoxylin-eosin (H&E) staining. ## 2.3. Human Samples Human liver tissues were collected from patients pathologically diagnosed with HCC undergoing hepatectomy at affiliated hospitals of Sun Yat-sen University. The study was approved by the Human Research Ethics Committee of Sun Yat-sen University. Written informed consents were signed by patients before recruitment. Tissue samples were collected as HCC tissues and normal liver tissues (tissues far from the HCC tumor). All tissue samples were quickly frozen on dry ice or in liquid nitrogen and stored at −80 °C until analysis. A total of 38 patients were recruited in this study. The demographic and pathological characteristics of patients are shown in Table S1. ## 2.4. Establishment of GFP- and Luciferase-Labeled HepG2 Cell Line To establish the GFP and luciferase-labeled HepG2 cell line (HepG2-GL), HepG2 cells were transduced by ultra-purified lentivirus particles containing a plasmid encoding luciferase and eGFP (EX-hLUC-Lv201, Genecopoeia, Rockville, MD, USA) following the manufacturer’s protocol. After 72 h of transduction, puromycin was added into cell culture media at a final concentration of 2 μg/mL to select stably transduced cells. Puromycin-resistant cells were plated onto 96-well culture plates at a density of approximately one cell per well. A monoclonal cell line with the highest fluorescence intensity of GFP in fluorescence microscope view was expanded for further experiment. In addition, a dual-luciferase assay was conducted to check the firefly luciferase expression of HepG2-GL cells using Luc-Pair™ Duo-Luciferase Assay Kit 2.0 (Genecopoeia) following the manufacturer’s protocol. ## 2.5. Cell Viability Analysis Cell viability was determined by the CellTiter 96® AQueous Non-Radioactive Cell Proliferation Assay (G1112, Promega, Madison, WI, USA) composed of a novel tetrazolium compound (MTS) (Promega) and an electron coupling reagent (PMS) (P9625, Sigma-Aldrich, St. Louis, MO, USA). Cells were seeded on 96-well plates and NR (ChromaDex) was administrated for 48 h. Cell viability was measured by a microplate reader and quantified at 490 nm absorbance. ## 2.6. Migration and Invasion Assay For wound healing assay, HepG2 cells were seeded in 12-well plates. Cells at 90–$100\%$ confluency were pretreated with 1 µg/mL Mitomycin C (HY-13316, MedChemExpress, Monmouth Junction, NJ, USA) for 1 h to exclude the effect of cell proliferation on the results of migration assays. Then, the cells were gently scratched with a sterile 10 μL pipette tip to create a wound. Cells were cultured in the FBS-free medium containing 4 ng/mL TGF-β1(240-B, R&D systems, Minneapolis, MN, USA) and 1 mM NR. The images of the migrating cells were captured at 0 h and 48 h after scratching by a light microscope (Leica, Wetzlar, Germany). The area of the wound was calculated with ImageJ 1.51j8 software. Values for analysis were expressed as the percentage of wound healing area to the initial wound area. Transwell migration and invasion assay were performed using an 8.0 µm pore polycarbonate membrane insert (3422, Corning Inc., Corning, NY, USA) according to the manufacturer’s protocol. For the invasion assay, the insert was pre-coated with matrigel (356234, Corning Inc., Corning, NY, USA). HepG2 cells (2 × 104 for migration, 3 × 104 for invasion) in 200 µL of FBS-free medium containing 4 ng/mL TGF-β1 and 1 mM NR were cultured in the upper chambers, whereas 700 µL of DMEM ($10\%$ FBS) containing 4 ng/mL TGF-β1 was added to the lower chambers. After being incubated at 37 °C for 24 h, cells were fixed with $4\%$ paraformaldehyde and stained with crystal violet. Images of cells on the bottom side of the inserts were taken for five random views, and the number of migrated or invaded cells was counted by ImageJ software. ## 2.7. Metabolites Quantification For metabolites quantification, quick-frozen human HCC tissues were thoroughly grounded by mortar-grinder and homogenized with $80\%$ methanol/water (v/v) solution by tissue lyser. After centrifugation, the vacuum-dried samples were resuspended in acetonitrile/water (v/v) solution. Metabolites of cells were extracted with $80\%$ methanol/water (v/v) solution and vacuum dried. The metabolites were resuspended in acetonitrile/water (v/v) solution for LC/MS analysis. The LC/MS assay of the NAD metabolome was described previously [26]. Data were collected using UPLC (Agilent 1290, Agilent Technologies, California, USA)-TOF (Agilent 6538) and analyzed using Agilent MassHunter Qualitative B.07.00 software. NAD metabolites were separated and analyzed using a Hypercarb column (100 × 2.1 mm, 3 μm, ThermoFisher Scientific, Waltham, MA, USA) in UPLC-QTOF System (Infinity/6538, Agilent Technologies, California, USA), as previously described [27]. NAD levels of mouse tumor tissues were measured using NAD+/NADH Assay Kit with WST-8 (S0175, Beyotime, Shanghai, China) according to the manufacturer’s instructions. The amounts of total NAD/NADH or NADH were measured at 450 nm absorbance by a microplate reader. The amount of NAD is equal to the total NAD/NADH content minus the amount of NADH. ## 2.8. Statistical Analysis Results are presented as individual data points or mean ± standard error of the mean (SEM). Statistical analysis was performed using Graphpad Prism 8.0 software. Comparisons between groups were analyzed by unpaired t test, Mann–Whitney test, Kruskal–Wallis test, Mixed-effects model, or two-way ANOVA. $p \leq 0.05$ was considered statistically significant. ## 3.1. Reduced Nicotinamide Adenine Dinucleotide (NAD) Level Was Found in Human Hepatocellular Carcinoma (HCC) Tissues Cancer cells have a large demand for nicotinamide adenine dinucleotide (NAD) and its metabolites to support various crucial cellular processes, such as biosynthesis, energy metabolism, and antioxidant defense. Cellular NAD may be phosphorylated into nicotinamide adenine dinucleotide phosphate (NADP), whose reduced form, NADPH, provides the primary reducing power to eliminate ROS [7]. In this study, we examined NAD and NADP levels in human liver samples. NAD levels in HCC tissues were found to be significantly lower than those in normal liver tissues (Figure 1A). Similarly, NADP levels were remarkably decreased in HCC tissues compared to normal liver tissues (Figure 1B). ## 3.2. Nicotinamide Riboside (NR) Supplementation Alleviated Weight Loss Caused by Hepatocellular Carcinoma (HCC) in Immunodeficient Mice Based on the reduction of NAD content in HCC tissues, we applied NR, a potent NAD precursor, to restore NAD levels for exploring the role of NAD in HCC progression. We established a subcutaneous cell-derived xenograft model and administrated NR via gavage (Figure 2A). At the beginning of the intervention, the body weights of mice in different groups were similar (Table 1). During the 40-day NR intervention, we found that NR-treated mice exhibited a significant reduction in weight loss compared to control mice (Figure 2B,C and Figure S1A). At the end of the intervention, the NR-treated mice had an $11.8\%$ decrease in body weight, which was considerably lower than the $18.61\%$ decrease from the control mice (Table 1). In addition, there was no difference in daily food intake among groups of mice during the intervention period, indicating that NR did not affect the appetite of the mice (Figure S1B). These results suggested that NR supplementation is able to alleviate the weight loss caused by HCC in this animal model. ## 3.3. Nicotinamide Riboside (NR) Supplementation Inhibited Spontaneous Lung Metastasis of Hepatocellular Carcinoma (HCC) without Inhibiting In Situ Tumor Growth in Immunodeficient Mice We investigated whether NR is able to relieve the tumor burden of mice by inhibiting in situ tumor growth. However, there were no statistical differences in both the tumor volume and the tumor index between the control and NR groups (Figure 3A and Figure S1C). NR increased the NAD levels of subcutaneous tumor of mice (Figure S1D). Cancer metastasis is an important part of HCC progression leading to the deterioration of physical health, including rapid weight loss. Due to this, we focused on whether NR supplementation suppressed tumor metastasis in the subcutaneous xenograft model. We found that the lung-to-body weight ratio was significantly higher in the control group compared to the NR group (Figure 3B). Considering the possibility of lung metastasis, we performed histological examinations and found that NR treatment significantly decreased the number and area of lung metastasis nodules in mice compared to the control mice (Figure 3C,D). The incidence rate of tumor metastasis was significantly lower in the NR ($28.6\%$, $\frac{2}{7}$) group comparing to the control group ($100\%$, $\frac{6}{6}$) (Figure 3D). These results suggested that NR supplementation is able to inhibit the spontaneous lung metastasis of HCC in the subcutaneous xenograft model. ## 3.4. Nicotinamide Riboside (NR) Supplementation Inhibited Hematogenous Multi-Organ Metastasis of Hepatocellular Carcinoma (HCC) in Immunodeficient Mice To further validate the inhibitory effect of NR supplementation on cancer metastasis, we established a GFP and luciferase-labeled HepG2 cell line (HepG2-GL) and used the HepG2-GL cells to perform a hematogenous metastasis model. Bioluminescence imaging was applied to better monitor the metastasis process of HCC cells (Figure 4A). Firstly, we found that NR supplementation contributed to the maintenance of body weight of tumor-bearing mice (Figure 4B). Overall, $42.9\%$ ($\frac{3}{7}$) of mice in the control group suffered from dramatic weight loss in the late intervention period, whereas all of the NR-supplemented mice retained the ability to gain weight and remained active (Figure 4C). Next, we analyzed the noninvasive bioluminescence images throughout the experiment. At the begin of the experiment, mice in the NR and the control groups were successfully injected with HepG2-GL cells of the same viability and amount, confirmed by equal bioluminescence signals on day 0 (Figure 4D). After 32 days of intervention, the NR-treated mice were found to possess significantly lower overall tumor burden compared to the control mice, as shown by the bioluminescence signals (Figure 4D). It is worth noting that at the end of the intervention, $57\%$ ($\frac{4}{7}$) of the control mice were found to suffer from distinctly more severe tumor burden compared to all of the NR-treated mice (Figure 4E). As for the bioluminescence data of lung, no notable difference could be found in quantified bioluminescence between the two groups (Figure 4F,G). Besides, the lung-to-body weight ratios were significantly higher in the control group compared to the NR group (Figure 4H). We found macroscopic pulmonary tumor nodule in the H&E staining of the control mice (Figure 4I). In addition, we found that the incidences of HCC cells metastasizing to the head, bone, and abdomen were all lower in the NR group compared to the control group (Figure 4J, Table 2), suggesting that NR was able to reduce the incidence of the multi-organ metastasis of HCC cells. Taken together, the results showed that NR is able to relieve tumor burden in mice by inhibiting multi-organ metastasis of HCC. ## 3.5. Nicotinamide Riboside (NR) Supplementation Suppressed the Subcutaneous Tumor Growth in Immunocompetent Mice Considering the potential anti-tumor effect of the immunity system in the tumor process, we also established subcutaneous cell-derived allograft models in immunocompetent mice and daily administrated NR supplementation in gavage (Figure 5A). During the two weeks of NR treatment, we found that NR significantly suppressed subcutaneous tumor growth in the NR-treated mice compared to the control mice (Figure 5B). NR supplementation prolonged the survival time of tumor-bearing mice (Figure 5C). Note that starting day 17, four of the mice in the control group were sacrificed due to rapid weight loss or obvious response lags. After two weeks of treatment, the numbers of smaller tumor were more in NR group than control group, however, the mean tumor volume was not statistically significant in two groups (Figure 5B,D). ## 3.6. Nicotinamide Riboside (NR) Inhibited the Migration and Invasion of HepG2 Cells We treated normal hepatocyte LO2 and HCC cell line HepG2 cells with different concentrations of NR and found that NR treatment at concentrations below 2 mM had no significant effect on the viability of LO2 and HepG2 cells (Figure 6A). These results suggested that short-term treatment of NR below 2 mM was not cytotoxic to either LO2 or HepG2 cells. For this reason, NR concentrations of 1mM were selected for subsequent experiments. Transforming growth factor-β (TGF-β) is identified as one of the most potent inducers of epithelial–mesenchymal transition (EMT), which renders tumor cells more invasive and ultimately leads to tumor metastasis [28,29]. TGF-β decreased the NAD levels of HepG2 cells, however, the addition of NR to TGF-β-treated cells significantly restored the compromised NAD levels (Figure S3). To determine whether NR is able to inhibit the TGF-β-induced migration and invasion of HepG2 cells, we performed wound-healing and transwell assays. Our results revealed that the wound-healing ability of TGF-β-treated HepG2 cells was higher than that of control cells at 48 h, and this effect of TGF-β was reversed by NR supplement (Figure 6B). The transwell assay further confirmed that the migratory and invasion potential of HepG2 cells were notably elevated by TGF-β at 24 h, which were significantly reduced by NR treatment to the level of the control cells (Figure 6C). Taken together, this implies that NR is able to inhibit the migration and invasion of HCC cells triggered by TGF-β in vitro. ## 4. Discussion In this study, we report for the first time that nicotinamide riboside (NR) supplementation could alleviate cancer metastasis in tumor-bearing mice and enhance the maintenance of body weight at the late stage of cancer. Our results reveal that NAD precursor may be a novel treatment for the prevention of HCC progression. Our results showed that decreased levels of nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP) were found in HCC tissues compared to non-carcinoma normal tissues. This suggests that NAD pool’s balance was disturbed during HCC formation. To explore the role of NAD in HCC progression, we established three different mice tumor models (with daily NR supply to replenish NAD pools). Surprisingly, we found that NR supplementation remarkably suppressed weight loss and cancer metastasis in immunodeficient mice. The energy metabolism of cancer cells is characterized by a shift of energy supply from mitochondrial respiration to glycolysis [30]. Note that glycolysis is a process where NAD production is low. During tumorigenesis, hyperproliferative tumor cells have acute demands of nucleic acid synthesis and NADPH [31]. This leads to the regeneration of NADPH, which consumes NADP [31]. NAD and NADP could possibly decrease during tumorigenesis. This corresponds to our finding of the obvious decrease in levels of NAD and NADP between HCC tissues and normal tissues. NAD participates in the regulation of multiple cellular processes including energy metabolism, antioxidation, aging, and cell death [32]. The relationship between NAD and cancer is still elusive. Existing evidence indicates that the depletion of NAD through inhibiting the NAD salvage pathway can sensitize cancer cells to anti-tumor drugs, inhibit DNA repair, and trigger cell death [8]. Several studies reported that anti-tumor drugs in combination with inhibition of NAMPT, a rate-limiting enzyme in the NAD salvage pathway, can synergistically inhibit cancer cell proliferation [33]. NAD boosting as a strategy for treating cancer has caught some research spotlight recently. Prevention of the aging-related decline of NAD can hinder the metabolic reprogramming of tumors, which is the first step of tumorigenesis [34]. Moreover, literature research has demonstrated that inhibition of NAD de novo synthesis restrained PARP-1 activity, which subsequently suppressed DNA repair and led to liver tumorigenesis [13]. As mentioned, NR is a potent NAD precursor. In the mouse model, NR supplementation helped resume NAD pools and prevent DNA damage-induced liver tumorigenesis [13]. In breast cancer objects, supplementation of NAD precursors (niacin or nicotinamide) inhibited lung and multi-organ metastasis of tumors by inducing autophagy [15]. Consistent with these findings, our study observed that NR supplementation restored NAD pools and significantly inhibited cancer metastasis in both subcutaneous xenograft and hematogenous metastasis models. Our study also showed that NR supplementation suppressed in situ tumor growth in a subcutaneous allograft model established in immunocompetent mice. Taken together, our study indicated that NR supplementation can restore NAD pools, which in turn suppresses hepatocellular cancer progression in mice. This provides a new evidence to support the potential anti-tumor prospect via boosting NAD levels. Cancer metastasis is the major cause of cancer-related death [35,36]. Patients with cancer metastasis are often accompanied with poor prognosis. The process of cancer metastasis is complex and dynamic. An increasing number of researchers have started to focus on the pro-metastatic function of transforming growth factor-β (TGF-β) during cancer progression [28,37]. TGF-β family proteins are able to induce epithelial-mesenchymal transition (EMT), a key mechanism of the initial cancer metastasis, via activating canonical SMAD and/or non-SMAD signaling pathway [37,38]. During EMT, tumor cells lose apical-basal polarity and cell–cell junctions and acquire front-back polarity; this causes the tumor cells to become more invasive [39]. Recently, a TGF-β-targeting drug has been identified as a promising therapy for cancer [40]. Previous studies confirmed that a decrease in NAD levels could affect the activities of NAD-dependent enzymes, such as the SIRT family proteins, and then promote the TGF-β signaling pathway [16,41]. Activating NAD-dependent enzymes or replenishing NAD by NAD precursors could attenuate TGF-β-related diseases [41,42,43,44]. In our study, we observed that TGF-β significantly promoted the migration and invasion of HepG2 cells in vitro, whereas NR supplementation was found to inhibit such effects. In future, we aim to further investigate the underlying mechanism of NR in counteracting TGF-β-triggered cancer metastasis. We discovered that NR helped preserve the body weight of tumor-bearing mice. Ongoing body weight loss is a hallmark of cancer cachexia, which is defined as a multiorgan syndrome characterized by substantial weight loss [45,46,47,48]. The weight loss during cancer cachexia is primarily due to skeletal muscle and adipose tissues wasting on account of energy balance disorder and anorexia [45,46,47,48]. Cancer cachexia severely impairs life quality, weakens physical and emotional function, and reduces tolerability to anti-tumor therapy [49]. Patients with cancer cachexia also have poor clinical prognosis [49]. Improving nutrition status can help ameliorate cancer cachexia. For example, applying mirtazapine is a novel cancer adjuvant therapy. Mirtazapine could increase food intake and nutrition status in tumor-bearing mice, even though it had no significant effect on tumor growth [50]. In a model established by mouse colon carcinoma cells, preemptive intake of NR was found to significantly ameliorate cancer cachexia. Dietary intake of NR can prevent cancer-induced muscle atrophy and weight loss but not the growth of transplanted tumor [51]. Consistently, our study found no differences in tumor weight between the NR group and control in the subcutaneous xenograft model. Nevertheless, even though no food intake difference was found between the two groups, the loss of body weight in control was significantly higher than in the NR group. During intervention, we observed that the mice in the control group were depressed and exhibited response lags, whereas the mice in the NR group were more active in contrast. At the end of the experiment, mice in the control group had less subcutaneous fat content compared to the mice in the NR group (Figure S1E). This suggests that NR may be able to improve the nutrition status and life quality of advanced HCC mice. In our xenograft model, no difference in transplanted tumor size was found between control and NR group. This could be due to: NR treatment did not start until the subcutaneous tumor volume exceeded 300 mm3; the high amount of HCC cells inoculated caused the speedy growth of the transplanted tumor; and once tumor has grown large enough (i.e., exceeded 300 mm3), anti-tumor medicine instead of nutrition supplement is required. Although we found significant differences in the number and area of lung metastatic nodules between the two groups via H&E-stained lung sections, this evaluation approach is rather limited. Bioluminescence imaging is more accurate in monitoring cancer metastasis. However, it is solely used in a mouse model established by fluorescent proteins or luciferase-labeled tumor cells. In our future studies, we aim to explore the protective effect of NR on transplanted tumor growth and cancer metastasis. This can be achieved by conducting a slow-growth xenograft model established by luciferase-labeled cells and a prophylactic NR intervention. In our allograft model, transplanted tumor size was significantly lower in the NR group than in the control, which was different from the results in the xenograft model. This indicated that NR can suppress tumor growth in early tumorigenesis. Furthermore, this implied that anti-tumor immunity, especially T cells, might contribute to the inhibitory effect of NR on tumor growth. Taken together, these may explain why the inhibition of subcutaneous tumor growth by NR was limited to immunocompetent mice instead of nude mice lacking mature T cells in our study. However, in the allograft model, neither significant body weight loss nor lung metastasis was found in tumor-bearing mice (Figure S2). This could possibly be due to the fact that the experiment period was too short for advanced cancer to occur. In summary, we found that replenishing NAD pools by NR could inhibit tumor growth and cancer metastasis as well as promote the overall health status of tumor-bearing mice. Our study revealed that boosting NAD by NR supplementation could be a novel strategy for the prevention of HCC progression. Our future studies will focus on the mechanisms of the effects of NR on HCC, and further clinical trials regarding NR are warranted. ## References 1. Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F.. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. (2021) 71 209-249. DOI: 10.3322/caac.21660 2. 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--- title: 'Early Probiotic Supplementation of Healthy Term Infants with Bifidobacterium longum subsp. infantis M-63 Is Safe and Leads to the Development of Bifidobacterium-Predominant Gut Microbiota: A Double-Blind, Placebo-Controlled Trial' authors: - Akari Hiraku - Setsuko Nakata - Mai Murata - Chendong Xu - Natsumi Mutoh - Satoshi Arai - Toshitaka Odamaki - Noriyuki Iwabuchi - Miyuki Tanaka - Takahisa Tsuno - Masahiko Nakamura journal: Nutrients year: 2023 pmcid: PMC10055625 doi: 10.3390/nu15061402 license: CC BY 4.0 --- # Early Probiotic Supplementation of Healthy Term Infants with Bifidobacterium longum subsp. infantis M-63 Is Safe and Leads to the Development of Bifidobacterium-Predominant Gut Microbiota: A Double-Blind, Placebo-Controlled Trial ## Abstract Bifidobacteria are important intestinal bacteria that provide a variety of health benefits in infants. We investigated the efficacy and safety of Bifidobacterium longum subsp. infantis (B. infantis) M-63 in healthy infants in a double-blind, randomized, placebo-controlled trial. Healthy term infants were given B. infantis M-63 ($$n = 56$$; 1 × 109 CFU/day) or placebo ($$n = 54$$) from postnatal age ≤ 7 days to 3 months. Fecal samples were collected, and fecal microbiota, stool pH, short-chain fatty acids, and immune substances were analyzed. Supplementation with B. infantis M-63 significantly increased the relative abundance of Bifidobacterium compared with the placebo group, with a positive correlation with the frequency of breastfeeding. Supplementation with B. infantis M-63 led to decreased stool pH and increased levels of acetic acid and IgA in the stool at 1 month of age compared with the placebo group. There was a decreased frequency of defecation and watery stools in the probiotic group. No adverse events related to test foods were observed. These results indicate that early supplementation with B. infantis M-63 is well tolerated and contributes to the development of Bifidobacterium-predominant gut microbiota during a critical developmental phase in term infants. ## 1. Introduction The establishment of a healthy gut microbiota in the early developmental stages of human life plays an important role in later health [1]. Bifidobacteria are important intestinal bacteria that provide a variety of health benefits for infants, including the production of vitamins and organic acids [2], maintenance of gut homeostasis [3], improved vaccination response [4,5], prevention from infection [6], suppression of gut inflammation [7], and allergy prevention [8]. Reduction of bifidobacteria in infants could cause a variety of disorders and has been associated with an increased prevalence of obesity, diabetes, metabolic disorders, and all-cause mortality in later life [9,10,11]. The formation of Bifidobacterium-dominant microbiota is delayed in infants born by cesarean section [12], in newborns who used antibiotics at birth [13], and in low-birth-weight infants. Low-birth-weight infants with delayed colonization of Bifidobacterium have been reported to be at high risk for sepsis and necrotizing enterocolitis [14]. Human milk oligosaccharides (HMOs), a complex mixture of over 200 molecular species including fucose and sialic acid, are the third most abundant solids in breast milk after lactose and lipids and are the selective growth factors of bifidobacteria. HMOs are not digested in the small intestine because they cannot be broken down by the infant itself; instead, they reach the large intestine, where they are selectively capitalized upon by intestinal bacteria such as bifidobacteria and Bacteroides. The Bifidobacterium longum subsp. infantis (B. infantis) microorganisms have metabolic pathways that allow them to preferentially uptake and utilize HMOs, such as fucosyl-lactose (FL) transporters, fucosidases, and sialidases [15], and they utilize HMOs for their own bifidobacterial growth. B. infantis, which becomes the predominant bacterial species in the gut due to HMOs in breast milk, benefits infant health by promoting the maturation of immune function, inhibiting excessive inflammation, enhancing gut barrier function, and increasing acetate production [16]. Administration of probiotics to infants could maintain mucosal barrier integrity, regulate appropriate bacterial colonization, and modulate immune function by promoting IgA secretion in the mucosa and suppressing intestinal inflammation [17]. Early postnatal use of probiotics such as Bifidobacterium spp. in low-birth-weight infants has been reported to be effective in preventing sepsis and necrotizing enterocolitis and establishing enteral nutrition early by the formation of a Bifidobacterium-dominant microbiota [18,19,20,21]. In clinical studies in which B. infantis was administered to preterm or undernourished infants, anti-inflammatory effects and growth-promoting effects on infant development were reported [10,22]. However, current guidelines from the European Society of Pediatric Gastroenterology, Hepatology and Nutrition and the American Academy of Pediatrics state that there is insufficient evidence to recommend the use of probiotics for all neonates [17]. B. infantis M-63 was isolated from the feces of infants [22] and has been shown to grow well in human breast milk due to its high capability to capitalize on HMOs [23,24] and its tolerance to lysozyme [25]. From clinical studies in which probiotics containing B. infantis M-63 and a mixture of several other species of Bifidobacterium and Lactobacillus were administered to infants and children, the following effects have been reported: early establishment of Bifidobacterium-predominant gut microbiota and suppression of intestinal staphylococcal colonization in low-birth-weight infants [19], suppression of dysbiosis and growth promotion in neonates with congenital gastrointestinal surgical conditions (CGISC) [26], improvement of symptoms of gastrointestinal discomfort such as overflowing milk and bloating in colicky infants [27], reduction in the duration of crying or agitation [28], prevention and alleviation of the onset of allergies such as seasonal rhinitis and atopic dermatitis [29], alleviation of functional constipation [3], and improvement of QOL in children with functional gastrointestinal disorders [30]. However, the effect of administering the M-63 strain alone to full-term infants during the early postnatal period has not been examined to date. In this study, the effects of B. infantis M-63 as a single probiotic on gut microbiota formation, intestinal environment, gastrointestinal function, and immune parameters in healthy full-term infants up to 3 months of age were investigated in a double-blind, randomized, placebo-controlled trial. ## 2.1. Study Population Between October 2019 and August 2021, healthy women who were scheduled to give birth or deliver a healthy full-term baby at Matsumoto City Hospital were recruited and subsequently provided written informed consent for participation in the study. Eligible participants were apparently healthy children born within 7 days whose gestational age at birth was ≥37 weeks and ˂42 weeks. Exclusion criteria for mothers were as follows: mothers who were diagnosed with any severe liver, renal, cardiovascular, respiratory, endocrine, metabolic, or mental disease or planned to administer any other probiotic supplements to their infants during the study period. Mothers with gestational diabetes or gestational hypertension were not excluded. Exclusion criteria for infants were as follows: infants born with multiple births and infants born with medical complications such as small for gestational age (SGA), large for gestational age (LGA), blood, liver, heart, kidneys, digestive disease, or suspected immunodeficiency or who had exposure to any oral or intravenous antibiotics or who were judged to be inappropriate to participate in the trial by the principal investigator. ## 2.2. Study Design This study was a single-center, placebo-controlled, double-blinded randomized trial of probiotics in healthy full-term infants conducted in Matsumoto City Hospital. This study protocol was performed in compliance with the Helsinki Declaration of 1975 as revised in 2013 and the Ethical Guidelines for Medical and Health Research Involving Human Subjects proposed by the Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labor and Welfare; it was reviewed and approved by the Research Ethics Committee of Matsumoto City Hospital (27 September 2019). This study was registered on the UMIN Clinical Trials Registry (UMIN000038351). Prior to the initiation of the study, the investigator recruited participants from mothers giving birth at Matsumoto City Hospital and explained the details of the study according to the informed consent form. After giving birth, mothers completed a written consent form as a surrogate, and the infants who met the eligibility criteria were stratified by randomization schemes based on delivery mode (vaginal delivery/cesarean section) or sex (male/female) and assigned to two groups. Stratified randomization was utilized because delivery mode and gender have been shown to be influencing factors on early intestinal gut microbiota [31]. The infants in each group were fed B. infantis M-63 (1 billion CFU/1.0 g of sachet) or placebo (sterilized dextrin only/1.0 g of sachet) daily from within 7 days after birth to 3 months after birth. The test foods were suspended in a small amount of sterile water in a sterilized feeding bottle at room temperature, and mothers fed the suspension to their infants using a feeding bottle or a sterilized medicine dropper. After giving birth, the mother received support at the hospital to promote breastfeeding as much as possible, and the infants who were judged to require supplementation with infant formula were begun on mixed nutrition. None of the infants used HMOs-fortified infant formula. ## 2.3. Infant Gastrointestinal (GI) Tolerability and Health Examination Infant GI tolerability and health was assessed by mothers on a daily basis from Day 1 until 3 months of age during test food supplementation period. Mothers were instructed to record the following information about their infants in daily logs: consumption of the amount of test foods, intake of any oral antibiotics or medicines, any symptoms including episodes of fever (≥38 °C), and hospital visits. For the 7 days before 1 week, 1 month, and 3 months of age, mothers recorded in daily logs the number of breast milk-fed and infant formula-fed infants, number of stools, stool consistency, duration and episodes of crying, number of regurgitations and vomiting episodes after feeding. Consistency of stool was assayed by mothers using a modified Amsterdam infant scale [32] (4-point scale with 1: watery; 2: soft; 3: formed; 4: hard). Episodes of crying were defined as more than 30 min per day. Episodes of colic were defined as 3 h per day for at least 3 days per week [33]. The investigator examined infant health and physical growth (length, body weight, and head circumference) immediately after birth and during hospitalization after delivery, before starting the study, and at 1 month and 3 months of age and checked the daily logs recorded by mothers. A safety evaluation was conducted on all participants who consumed the study food once or more. Throughout the study period, all adverse events related to subjective and objective symptoms were recorded in the daily logs. The degree of symptoms and the causal relationship were evaluated according to the revised “National Cancer Institute, Common Terminology Standards for Adverse Events Version 4.0, Japanese Translation JCOG Version”. ## 2.4. Fecal Sample Collection Two types of fecal samples were prepared: fresh fecal samples were collected before intake and at 1 month of age, and fecal samples with the preservative solution, guanidine thiocyanate solution, were collected before intake, 1 week after intervention, and at 1 month and 3 months of age. Both fecal samples were collected from infants’ diapers in each stool collection tube (Techno Suruga Laboratory Co., Ltd., Shizuoka, Japan). Fresh fecal samples were collected within 24 h of the hospital visit and were stored in a foamed styrene box with cooling agent at home, whereas fecal samples with the preservative solution were stored at room temperature at home. Both samples taken at home were transferred to the hospital and stored in −20 °C freezers. After being transported to the laboratory with dry ice, they were stored at −80 °C before DNA extraction. Fresh fecal samples were used for the analysis of real-time PCR, pH, SCFAs, and biomarkers, whereas fecal samples with the preservative solution were used for 16S rRNA gene sequencing. All participant samples were blinded to the process and analysis. ## 2.5. Fecal DNA Extraction and 16S rRNA Gene Sequencing Fecal DNA was extracted according to previously described methods [34]. In brief, 200 μL of fecal sample in GuSCN solution was lysed with glass beads (300 mg, 0.1 mm diameter) and 300 μL of lysis buffer (No. 10 buffer, Kurabo Industries Ltd., Osaka, Japan) using a FastPrep-245G homogenizer (MP Biomedicals LLC, Santa Ana, CA, USA) at a 5-power level for 45 s with 5 min cooling intervals on ice. After centrifugation at 12,000 rpm for 5 min, DNA was extracted from 200 μL of the supernatant by utilizing the GENE PREP STAR PI-480 instrument (Kurabo Industries Ltd., Osaka, Japan) according to the manufacturer’s protocol. Amplification of the V3-V4 region of the bacterial 16S rRNA gene through PCR and subsequent DNA sequencing were carried out as previously described [35], using by the Illumina MiSeq instrument (Illumina, San Diego, CA, USA). Following the removal of sequences that aligned with data from the Genome Reference Consortium human build 38 (GRCh38) and phiX reads from the raw Illumina paired-end reads, the remaining sequences were analyzed using the QIIME2 software package version 2017.10 (https://qiime2.org/). DADA2 [36] was employed to remove potential chimeric sequences, followed by trimming 30 and 90 bases of the 3′ region of the forward and reverse reads, respectively. Taxonomical classification was conducted by utilizing the naive Bayes classifier trained on Greengenes13.8 with a $99\%$ threshold of full-length sequence operational taxonomic units. Alpha diversities were calculated using QIIME2 software. We used R software (ver. 3.6.0) for principal coordinate analysis (PCoA) based on Jensen–Shannon distance (JSD) and partitioning around medoid (PAM) clustering [37]. The optimal number of clusters was estimated by utilizing the Calinski–Harabasz (CH) index. ## 2.6. Quantification of Bifidobacterium Species by Real-Time PCR Fecal DNA for real-time PCR was extracted as previously described [38]. In brief, 200 μL of fecal sample in GTC buffer was lysed using a Precellys Evolution (Bertin Instruments, FRA) system, and DNA was extracted using the GENE PREP STAR PI-480 instrument (Kurabo Industries Ltd., Osaka, Japan) in accordance with the manufacturer’s protocol. Real-time PCR was conducted using an ABI PRISM 7500 fast real-time PCR system (Thermo Fisher Scientific, Waltham, MA, USA) with TB Green Premix Ex Taq™ Tli RNaseH Plus (TaKaRa Bio Inc., Shiga, Japan) in accordance with the manufacturer’s protocol. Quantification of each Bifidobacteria species was performed using the following primers (Table S1). Primers and amplification methods used were determined based on prior studies [39,40,41]. Bacterial copy numbers were determined by utilizing bacterial solutions with established counts as a standard. Duplicate assays were performed for all samples. ## 2.7. Fecal pH, Short-Chain Fatty Acids (SCFAs) and Biomarker Analysis Fecal pH was measured with an electrode-fitted pH meter LAQUAtwinB-712 (HORIBA, Kyoto, Japan) after suspending fecal samples in Milli-Q water and sterilizing at 85 °C for 15 min for sterilization of fecal samples. Fecal SCFAs (acetate, propionate, n-butyrate, iso-butyrate, n-valerate, iso-valerate, n-caproic acid) in ethyl acetate extract were determined by gas chromatography system GC-FID (7890B, Agilent Technologies, Santa Clara, CA, USA) and a DB-WAXetr column (30 m, 0.25 mm id, 0.25 μm film thickness, 1.2 mL/min) as described previously [42]. Commercially available ELISA kits were used to determine fecal secretory immunoglobulin A (sIgA: Human IgA ELISA Core Kit, LABISKOMA, Seoul, Republic of Korea) and calprotectin (IDK® Calprotectin MRP $\frac{8}{14}$ ELISA kit, Immundiagnostik AG, Bensheim, Germany). ## 2.8. Sample Size In a previous study on Japanese infants [15], the relative abundance of bifidobacteria in feces at 1 month of age was 55.40 ± $40\%$. Assuming the relative abundance of bifidobacteria would increase to $75\%$ by intake of test food, to detect an intergroup difference with $80\%$ power and α = 0.05 and a $15\%$ attrition rate, 75 infants were needed in each group. ## 2.9. Primary and Secondary Outcomes Evaluation of the primary outcome involved determining whether B. infantis M-63 supplementation could increase the relative abundance of Bifidobacteriaceae, the proportion of infants with Bifidobacteriaceae-predominant microbiota, and the fecal copy number of Bifidobacteriaceae in healthy term infants. Secondary outcomes included the effect of B. infantis M-63 on the abundance of intestinal bacteria and diversity, GI tolerability (stool frequency and consistency, number of regurgitation and vomiting after feeding), duration and episodes of crying, episodes of fever (≥38 °C), hospital visit, fecal pH, SCFAs, sIgA, and calprotectin. ## 2.10. Statistics Intergroup differences in the microbiota at the amplicon sequence variant (ASV) level were analyzed by ALDEx2 [43]. A Q-value < 0.05 was considered significant. To assess the variation in microbiota composition explained by each factor, a permutational multivariate analysis of variance (PERMANOVA) test for JSD was used for multivariate analysis. For the relative abundance of Bifidobacteria, a normal log transformation was performed for each time point and for each test food group, and the mean ± standard error of the mean (SEM) was calculated. When the assumption of a normal distribution was not ruled out, an analysis of covariance (ANCOVA) was performed with the values at each time point as the objective variable, the test food group as the explanatory variable, and the baseline values as covariates. The changes from the baseline value were calculated for each time point, and a paired t test was performed. When the assumption of a normal distribution was not verified, the change from the baseline values was calculated for each time point and compared between the groups using the Wilcoxon rank sum test. To identify changes from baseline values, a Wilcoxon signed ranks test was performed. The same analysis was performed for the absolute number of bifidobacteria per gram of feces (copy number), occupancy of each bacterial species and diversity index in gut microbiota analysis, and physical and chemical analysis of stool (pH, amount of short-chain fatty acids, IgA, and calprotectin, pH). For frequency of defecation and fecal characteristics, the average number of defecations (times/day) and average fecal characteristics (score/times) were calculated for each time point and analyzed in the same way. The percentage of infants whose most prevalent bacterial species was Bifidobacterium was calculated for each time point, and Fisher’s exact test was performed. The breastfeeding rate was defined as the number of times breastfeeding occurred relative to the number of feedings. Correlations between the breastfeeding rate and the relative abundance of Bifidobacterium were analyzed by calculating Spearman’s rank correlation coefficients. For the health condition of the infants, duration and frequency of crying, frequency of regurgitation and vomiting, frequency of fever, and hospital visits, the mean values per week were calculated for each time point, and a Wilcoxon rank sum test was performed. For the primary endpoint, subgroups were analyzed according to the following factors affecting gut microbiota: [1] mode of delivery (vaginal delivery, cesarean section); [2] whether antibiotics were administered to the mother during delivery; and [3] mode of nutrition (exclusively breastfeeding, mixed feeding, exclusively formula feeding). For height, weight, and head circumference of children, the mean ± SE was calculated for each time point, and Student’s t test was performed. Adverse events and side effects were tested for incidence (person/person) using Fisher’s exact test in the ITT population of patients that consumed the study foods. Statistical analysis software was IBM SPSS Statistics (version 28.0), and a p value of < 0.05 was adopted to indicate a significant difference. ## 3.1. Infant and Maternal Characteristics Figure 1 shows the process from subject enrollment to analysis: 111 subjects were enrolled, and after randomization (54 in the placebo group and 57 in the M-63 group), one subject in the M-63 group withdrew consent before consuming the test food, and one subject in the placebo group was excluded from the analysis due to noncompliance involving intake of less than $50\%$ of the test food. The analysis was performed in the per protocol set (PPS) population (53 patients in the placebo group and 56 patients in the M-63 group). No infant received antibiotics or oral probiotics other than the test food, although one infant used antibacterial eye drops due to discharge from the eyes during the study period. The subject background of the newborns and mothers is shown in Table 1. There were no significant differences between groups in the number of weeks of gestation, mode of delivery (vaginal delivery, cesarean section), sex, height at birth, weight, head circumference, APGAR score (5 min after birth), and intake rate of test foods among the newborns. There were also no significant differences between the two groups in maternal age, number of deliveries (first-time mothers and term mothers), prepregnancy body mass index (BMI, kg/m2), weight gain during pregnancy, number of women taking antibiotics at delivery, history of allergies, and smoking habits. ## 3.2. Infant Feeding The intake rate of the test foods was 92.4 ± $1.4\%$ for the placebo group and 94.4 ± $0.8\%$ for the M-63 group with no significant difference between groups ($$p \leq 0.5082$$). Table S2 shows the number of feedings and the percentage of breastfeeding in each group at 1 week, 1 month, and 3 months of age (calculated from the number of breastfeeds/number of feedings per day). The percentage of breastfeeding increased in both groups at 1 month and 3 months of age with no significant differences between the groups. At 1 month and 3 months of age, $27.5\%$ and $52.3\%$ of the infants were exclusively breastfed, $70.6\%$ and $41.3\%$ were mixed fed, and very few were exclusively formula fed: one case at 1 week, two cases at 1 month, and seven cases at 3 months of age. Therefore, when subgroup analysis was performed, depending on the infant’s feeding status, the analysis was performed in the groups of either exclusive breastfeeding or mixed feeding with exclusive formula feeding. There were 14 cases of mothers taking antibiotics during the lactation period (6 in the placebo group and 8 in the M-63 group), and there was no significant difference between the two groups. The reasons for taking the drugs were mastitis (three cases), cold (two cases), treatment of cesarean section wounds (one case), uterine restoration failure (one case), treatment of placental abruption (one case), asthma (one case), sinusitis (one case), cystitis (one case), dental treatment (one case), stye (one case), and unknown (one case). ## 3.3. Microbiota Analysis We then investigated the fecal microbiota of all samples during the intervention. PCoA data clearly showed two enterotypes (Figure 2a,b) enriched in Bifidobacterium (enterotype 1) and several taxa, such as Enterobacteriaceae (enterotype 2, Figure 2c–f). Within a week of birth, 101 of 106 subjects had enterotype 2 microbiota (Figure 3a). However, the enterotype of all subjects in the M-63 group except one transitioned to the Bifidobacterium-dominated enterotype (enterotype 1) after 1 week of administration of B. infantis M-63, while the enterotype of most subjects in the placebo group was stable in enterotype 2 (Figure 3). This polarization was maintained until the end of the intervention. ## 3.4. Bifidobacterial Colonization The relative abundance of Bifidobacterium and the percentage of infants for whom Bifidobacterium spp. was the most dominant bacterial genus are shown in Table 2. There was no significant difference between the two groups in the relative abundance of Bifidobacterium before intake of the test food, and the relative abundance of Bifidobacterium increased with the age of the infants (1 week, 1 month, and 3 months of age), and a significant increase was observed in the M-63 group over the placebo group. The percentage of infants in the M-63 group with Bifidobacterium as the most dominant genus was also significantly higher than that of the placebo group at the first week of intake and at one month of age. This trend was also observed in all subgroups, regardless of the delivery method (vaginal or cesarean section), regardless of the mode of nutrition in the infants or whether antibiotics were used by the mothers at delivery. The relative abundance of bifidobacteria in the placebo group was significantly lower in infants born to mothers who received antibiotics at delivery than in those whose mothers did not receive antibiotics before intake and was also lower in the placebo group at 1 week (Table S3). There was no significant difference in the relative abundance of bifidobacteria between cesarean section and vaginal delivery infants within the population whose mothers received antibiotics in the placebo group, and no association was found between mode of delivery and the relative abundance of bifidobacteria. Even in the case of vaginal delivery, bifidobacteria were found to be significantly lower in the antibiotic-treated group than in the antibiotic-untreated group. ## 3.5. Bacterial Species of Colonized Bifidobacteria The abundance and detection rate of the genus Bifidobacterium and each Bifidobacterium species at 1 month after birth, measured by quantitative PCR, are shown in Table 3. There were no significant differences in the abundance and detection rate of the genus Bifidobacterium and each Bifidobacterium species before the ingestion point. However, the abundance and detection rate of the genera Bifidobacterium and B. infantis in the M-63 group were significantly higher than those in the placebo group. In short, the results of quantitative PCR at 1 month of age showed that most of the Bifidobacterium species in the M-63 group were B. infantis, whereas B. infantis was barely detected in the placebo group and B. breve and B. longum subsp. longum (B. longum) were detected in the placebo group. These results indicate that B. infantis was the main Bifidobacterium species that colonized the intestines of the infants in the M-63 group, while B. breve and B. longum were the main species in the placebo group. ## 3.6. Correlation between Breastfeeding and Bifidobacterial Occupancy We analyzed the correlation at 1 month of age between the breastfeeding rate and the relative abundance of bifidobacteria (Figure 4). A significant positive correlation was seen between the breastfeeding rate and the relative abundance of Bifidobacterium in the M-63 group. However, there was no significant positive correlation between them in the placebo group. Although the breastfeeding rate was more than $80\%$, some infants did not have increased bifidobacteria in the gut in the placebo group. ## 3.7. Gut Fermentation Patterns and Immunologic Parameters in Stools The pH, amount of short-chain fatty acid, IgA, and calprotectin in the stools before intake and one month after birth are shown in Table 4. In the M-63 group, the pH in the stools was significantly lower and the amount of acetic acid in the stools increased at 1 month of age compared to the placebo group. There was no difference between the two groups in terms of calprotectin in the feces, but IgA levels in the feces were significantly higher in the M-63 group than in the placebo group at one month of age. ## 3.8. GI Tolerability and Health Condition of Infants The number of defecations per day and fecal characteristics of the infants are shown in Figure 5. The placebo group showed a decrease in defecation frequency from 1 to 3 months of age, whereas the M-63 group showed a gradual decrease in defecation frequency from 1 week to 3 months of age, with a significant decrease in the M-63 group compared to the placebo group”. “ Watery” stools and “soft” stools were significantly decreased in the M-63 group as compared to the placebo group at 1 month of age (Figure 5a). Stratified analysis by type of nutrition is shown in Figure 5b,c. In the exclusively breastfed infants, defecation frequency was lower in the M-63 group than in the placebo group at 1 and 3 months of age (Figure 5b), and in the mixed-fed infants and exclusively formula-fed infants, defecation frequency was lower in the M-63 group than in the placebo group at 1 week and 1 month of age (Figure 5c). These results indicate that M-63 modulates the defecation function of infants regardless of the type of nutrition. The number of times (times/day) and average duration (minutes/time) that infants cried for more than 30 min are shown in Table S4. Neither group included infants who cried for an average of more than 3 h per day or who were suspected of having so-called “colic”, and there were no significant differences between the two groups. The number of times and number of infants with regurgitation and vomiting of milk are shown in Table S5. There were significantly fewer infants in the M-63 group than in the placebo group who had regurgitation of milk at 1 week after intake. Both regurgitation and vomiting of milk were mild and not pathological. The growth of the infant’s height, weight, and head circumference at 1 and 3 months after birth was favorable and comparable in both groups (Table S6). ## 3.9. Adverse Events Adverse events during the study period are shown in Table S7. No adverse events attributable to bifidobacteria administration were identified, and the incidence of respiratory, gastrointestinal, and skin symptoms did not differ between the two groups. ## 4. Discussion In this study, B. infantis M-63 was administered as a single species probiotic at a dose of 1 billion/day to healthy full-term infants up to 3 months of age to investigate its effects on gut microbiota formation, intestinal environment, gastrointestinal function and fecal immune parameters. Administration of B. infantis M-63 was associated with a decreased frequency of defecation and watery stools (Figure 5), suggesting that B. infantis M-63 may modulate gastrointestinal function in infants. Ingestion of B. infantis M-63 promoted the formation of Bifidobacterium-dominant gut microbiota within one week (Figure 2 and Figure 3), and this effect was confirmed even in infants with low postnatal bifidobacterial occupancy, such as cesarean section infants and newborn infants born to mothers who had taken antibiotics during delivery (Table 2). In the M-63 group, the relative abundance of Bifidobacterium was higher as the frequency of breast milk intake increased (Figure 4), and the main Bifidobacterium species that colonized the infants was B. infantis (Table 3), indicating that B. infantis is compatible with breast milk. Administration of B. infantis M-63 was shown to decrease stool pH by increasing the amount of acetic acid in the stool and to increase IgA in the stool (Table 4), which plays an important role in mucosal immunity. No serious adverse events were observed in neonates that could be attributed to B. infantis M-63 administration (Table S7). These results indicate that administration of B. infantis M-63 at 1 billion/day to healthy full-term neonates has a beneficial effect on the health maintenance of infants by promoting the formation of Bifidobacterium-dominant gut microbiota. The frequency of defecation during the intervention decreased over the first 3 months of life in both groups (Figure 5). This is consistent with reports that defecation frequency decreases physiologically during the neonatal period. This may be due to the maturation of the digestive and absorptive capacity of nutrients and water as the intestinal tract grows [44,45,46,47]. The frequency of defecation was significantly decreased in the M-63 group compared to the placebo group, especially the decrease in watery stools, which was remarkable. A clinical trial using B. infantis EVC001 in neonates showed a similar effect [48]. The authors speculate that B. infantis consumption promoted the maturation of the intestinal mucosa, based on the fact that Bifidobacterium enhances the barrier function of the intestinal tract [6] and that B. infantis increases the expression of mRNA for tight junction proteins in intestinal epithelial cells [49]. On the other hand, in the present study, a modulatory effect of B. infantis M-63 administration on defecation frequency and fecal characteristics was observed from 1 week after ingestion. Since this effect was observed immediately after ingestion, it is possible that administration of M-63 directly regulates intestinal functions via a decrease in intestinal pH, etc., rather than promoting the development of intestinal functions. Considering these results and reports, it is possible that administration of M-63 to neonates regulates intestinal functions through direct modulatory effects via a decrease in intestinal pH, etc., in addition to promoting the development of intestinal functions. The gut microbiota of breastfed infants was reported to be dominated by Bifidobacterium until the cessation of breastfeeding [50,51]. Even though almost all infants were breastfed or mixed-fed at 1 week after intake (Table S2), in the placebo group, the percentage of infants where Bifidobacterium was the most dominant genus was $41.5\%$, and this proportion was still only $51\%$ at 1 month of age (Table 2). Furthermore, B. infantis was not detected in any except four of the placebo group infants by 1 month of age (Table 3). On the other hand, B. infantis was not detected before intake in the M-63 group but was detected in 54 of 56 infants at 1 month of age (Table 3), and the proportion of infants with Bifidobacterium as the most prevalent species increased sharply to $94.6\%$ (Table 2). The majority of bifidobacteria that caused the increased with intake of M-63 were B. infantis, while the amounts of B. longum, B. bifidum, and B. breve, which were present before the intervention, did not change significantly (Table 3). It has been reported that B. infantis is undetectable in many adults and is detected in infants after 2 months of age, indicating the possibility of horizontal transmission from the skin or environment rather than vertical transmission from mother to infant during delivery [52]. In the present study, B. infantis was detected in most of the infants in the M-63 group at 1 week after intake (Table 3), i.e., less than 1 month after birth, and we speculate that the B. infantis M-63 administered soon after birth colonized the intestine and formed a bifidobacteria-dominant gut microbiota earlier than in the nonintervention population. In the M-63 group, there was a significant positive correlation between the breastfeeding rate and the relative abundance of Bifidobacterium (Figure 4), and the main Bifidobacterium species that colonized the infants in the M-63 group was B. infantis (Table 3). These results suggest that breast milk intake increases bifidobacteria in the infant gut, especially B. infantis among the bifidobacteria species. On the other hand, no correlation was found between the proportion of breastfeeding and bifidobacteria occupancy in the placebo group (Figure 4). A certain number of infants in the placebo group showed no bifidobacterial colonization even though they were breastfed a higher proportion of the time. These results suggest that there are cases in which Bifidobacteria do not colonize even with breastfeeding and that supplementation with B. infantis in the early postnatal period and either breastfeeding or supplementation of alternative breast milk components may be necessary to establish a high percentage of Bifidobacteria. Maternal antimicrobial use at delivery has been reported to have a stronger effect than the mode of delivery on the gut microbiota, in particularly colonization of bifidobacteria [13,53]. In the present study, neonates of mothers who used antimicrobials at delivery had lower bifidobacterial occupancy within the first 7 days of life, whereas mode of delivery had no clear effect on neonatal bifidobacterial abundance (Table S3), suggesting that the establishment of bifidobacteria in the infant intestinal tract is more influenced by the mother’s use of antimicrobials during delivery than by the mode of delivery. Administration of B. infantis M-63 was found to be effective in increasing bifidobacterial abundance, which was low due to the use of antimicrobials at delivery, by 1 week after administration (Table 2). Evaluation of intestinal metabolites at 1 month of intervention showed a significant increase in acetic acid in stools and a decrease in n-butanoic acid in the M-63 group compared to the placebo group (Table 4). This is consistent with reports of other bifidobacteria administered neonatally and changes in short-chain fatty acids in the gut [54]. Acetic acid and butyric acid are beneficial short-chain fatty acids and have been reported to change composition gradually in the infant’s intestine [55]. Butyrate is an important short-chain fatty acid that is a major source of energy for intestinal epithelial cells, but it is not a major source during lactation as it has been reported to increase with intestinal Clostridiales occupancy after lactation cessation [55]. Patients in the M-63 group showed a significant decrease in fecal pH after 1 month of intervention compared to the placebo group (Table 4), and there was a significant negative correlation between the number of bifidobacteria in the feces and pH (ρ = −0.716, $p \leq 0.01.$ A review of studies of infant fecal pH and gut microbiota over the past 100 years indicates an increase in infant fecal pH and a concomitant decrease in bifidobacteria, a major component of infant gut commensal bacteria [56]. More interestingly, in relation to B. infantis, infants lacking B. infantis have been reported to have significantly higher fecal pH, higher levels of potential pathogens and mucus-eating bacteria in their intestinal flora, and signs of chronic enteritis, suggesting that acidification in the gut by B. infantis may contribute to the suppression of harmful bacteria associated with the induction of inflammation [56]. The study confirmed that consumption of B. infantis M-63 has the effect of increasing acetic acid concentration and lowering pH in the gut, which may have beneficial effects on the health of newborns. Ingestion of B. infantis M-63 resulted in increased IgA in stool one month after intervention (Table 4). IgA is a mucosal immunoglobulin that predominates in mucosal tissues such as the intestinal tract and plays a crucial role in protection against antigens, toxins, and potential pathogens. It has been reported that IgA is increased in breast-fed infants due to the supply of IgA from breast milk. Although the IgA concentration in breast milk was not measured in this study, the breast milk intake rate was similar to that of the placebo group (Table S2), and no significant correlation was observed between the breast milk intake rate and IgA concentration in placebo group (ρ = −0.005, $$p \leq 0.973$$). A trend toward increased IgA was observed in the M-63 group even in the mixed-fed infants and exclusively formula-fed infants (M-63: 1832 ± 249 µg/g, placebo: 1352 ± 187 µg/g; $$p \leq 0.299$$), suggesting that the increase in B. infantis may have promoted IgA secretion from the intestinal immune tissues. Similar to our findings, bifidobacteria in the intestine, especially B. infantis and B. breve, have been demonstrated to increase fecal sIgA and anti-poliovirus-specific IgA in healthy full-term infants. Acetic acid has been reported to promote IgA secretion in the intestinal tract by regulating the IgA class switch of B cells by intestinal dendritic cells via GPR43, thereby maintaining host-intestinal bacterial symbiosis and exhibiting anti-inflammatory effects [57]. The effect of B. infantis M-63 on the host immune response requires detailed analysis, but ingestion of B. infantis M-63 may promote IgA secretion into the intestinal tract by increasing acetic acid in the intestinal tract. Frese et al. [ 58] reported that administration of B. infantis EVC001 at a dose of 18 billion CFU/day to full-term infants significantly increased bifidobacteria in the intestine. In the present study, administration of a lower dose of B. infantis M-63 at 1 billion CFU/day to full-term infants significantly increased B. infantis in the gut (Table 2), and the relative abundance of bifidobacteria, most of which were presumed to be B. infantis species in the M-63 group, correlated with the proportion of breastfeeding. B. infantis is known to have a generally high capacity to utilize HMOs [59], and B. infantis M-63 has also been reported to have a high capacity to utilize the major HMOs in human breast milk [23,24]. It is likely that B. infantis M-63 preferentially utilizes HMOs in the gut of breast-fed infants, allowing it to grow efficiently and form a Bifidobacterium-dominated microbiota even at low doses. Mothers recorded the number of episodes and duration of crying for more than 30 min and the number of instances of regurgitation and vomiting milk in their diaries for the seven days prior to 1 week, 1 month, and 3 months of age. There was no difference between the groups in the number of episodes and duration of crying for more than 30 min (Table S4). Regarding regurgitation and vomiting milk, the frequency of regurgitation tended to be higher in the M-63 group at the age of 3 months, but they were not pathological or serious events (Table S5). There were no differences between both groups in other adverse events recorded in the logs during the study period (Table S7), and no adverse events were identified that could be attributed to the consumption of the test food. There was also no difference in the growth of the infants up to 3 months of age (Table S6). These results indicate that ingestion of B. infantis M-63 is safe and well tolerated in neonates. Potential limitations of this study are that it was conducted at a single center in Japan and that we were unable to evaluate the effects of the composition of breast milk on the infant’s gut microbiota and immune indices. However, the effects of breast milk should have been limited to some extent, as confirmed by the fact that breast milk frequency was studied and was comparable in both feeding groups. To obtain a comprehensive understanding of early bifidobacterial intervention and its beneficial effects on healthy full-term infants, a detailed analysis of the association between gut microbiota and clinical benefit is needed, and the results of this analysis will be reported as a separate study. Further studies, including long-term follow-ups, are also needed to determine the impact of early bifidobacterial intervention on the health of growing infants. ## 5. Conclusions In conclusion, supplementation with B. infantis M-63 in healthy term infants was well tolerated and beneficially modulated the infant gut microbiota toward higher Bifidobacterium levels, accompanied by softer stool consistency. 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--- title: 'Instant Coffee Is Negatively Associated with Telomere Length: Finding from Observational and Mendelian Randomization Analyses of UK Biobank' authors: - Yudong Wei - Zengbin Li - Hao Lai - Pengyi Lu - Baoming Zhang - Lingqin Song - Lei Zhang - Mingwang Shen journal: Nutrients year: 2023 pmcid: PMC10055626 doi: 10.3390/nu15061354 license: CC BY 4.0 --- # Instant Coffee Is Negatively Associated with Telomere Length: Finding from Observational and Mendelian Randomization Analyses of UK Biobank ## Abstract Telomere length, as a biomarker of accelerated aging, is closely related to many chronic diseases. We aimed to explore the association between coffee consumption and telomere length. Our study included 468,924 participants from the UK Biobank. Multivariate linear models (observational analyses) were conducted to evaluate the associations of coffee intake, instant coffee intake, and filtered coffee intake with telomere length. In addition, we evaluated the causality of these associations in Mendelian randomization (MR) analyses by four methods (inverse-variance weighted (IVW), MR pleiotropy residual sum and outlier (MR-PRESSO), MR-Egger, and weighted median). Observational analyses indicated that coffee intake and instant coffee intake were negatively correlated with telomere length, which was equal to 0.12 year of age-related decrease in telomere length for each additional cup of coffee intake ($p \leq 0.001$), and 0.38 year of age-related decrease in telomere length for each additional cup of instant coffee intake ($p \leq 0.001$), respectively. There was no significant correlation between filtered coffee and telomere length ($$p \leq 0.862$$). Mendelian randomization analyses supported the results of observational analyses. Coffee intake was found to have a causal effect on telomere length through weighted median analysis ($$p \leq 0.022$$), and instant coffee intake had a causal effect on telomere length through IVW analysis ($$p \leq 0.019$$) and MR-PRESSO analysis ($$p \leq 0.028$$). No causal relationship was found between filtered coffee intake and telomere length ($p \leq 0.05$). Coffee intake, particularly instant coffee, was found to have an important role in shortening telomere length. ## 1. Introduction Telomeres, protein-protected short sequences of DNA repeats located at the ends of chromosomes, are shortened with each somatic cell cycle. Telomeres preserve hereditary information by keeping chromosomes stable, and shorten after each cell division [1,2]. Therefore, telomere length, as a biological indicator of aging, dictates the cell’s proliferative history [3]. Telomere length is linked to a variety of aging-related disorders, such as diabetes, cancers, Alzheimer’s disease, and cardiovascular disease [4,5,6,7,8]. In addition, telomere length is heritable and attributed to gender and ethnicity [9,10], and also linked to environmental and lifestyle factors such as exercise, smoking, and dietary habits [11,12,13,14]. There is a growing interest in one’s lifestyle and its potential effect on telomere length [12,15]. Coffee, as one of the most popular beverages, has been studied for its effect on health [16,17,18]. Several studies have investigated the link between coffee consumption and telomere length, but the results were controversial. A cross-sectional study of 5826 adults based on the National Health and Examination Survey (NHANES) found a positive correlation between coffee consumption and telomere length [19]. Another cross-sectional study based on 4780 women in the Nurses’ Health Study also found that coffee consumption was positively associated with telomere length [20]. A random control trial (RCT) of 37 chronic hepatitis C patients still found this positive relationship between them [21]. The other three observational studies, which respectively included 1638, 840, and 28 subjects, found no statistical association between telomere length and coffee consumption [22,23,24]. Coffee contained chlorogenic acid, caffeine, diterpenoids, and other active substances, while commercial instant coffee also contained sugar, creamer, and other flavoring agents which may account for different health effects [25,26]. Previous studies were limited by small sample sizes, and they neither classified the coffee type nor explored the causality between coffee consumption and telomere length. Mendelian randomization (MR) is a method to explore evidence for causality [27]. MR utilizes the random distribution of genetic variants at conception to reduce and limit residual confounding and reverse causality [28]. It makes use of genetic variants as instrumental variables (IV) which are significantly related to risk factors and provides a reliable causal estimate [27,29]. MR has been used to explore the causal estimate between coffee consumption and health outcomes, such as nonalcoholic fatty liver disease [30], osteoarthritis [31], kidney stones [32], and Alzheimer’s disease [33], but the casual association between coffee consumption and telomere length was unknown [19,20,21,22,23,24]. In this paper, we aimed to investigate the relationship and causality between coffee consumption and telomere length based on UK biobank data, by using observational and MR analyses. Understanding coffee’s effect on telomere length might help discover new pathways by which coffee consumption influences health and longevity [15,26]. ## 2.1. Study Population UK biobank collected the health information of more than 500,000 participants aged 37–73 from 2006–2010. At the participants’ baseline visit, trained staff assisted in completing the touch-screen questionnaire on lifestyle factors, health-related information, and food frequency questionnaires. Meanwhile, participants accepted a comprehensive physical exam, and their biological samples were collected. The 24-h dietary recall questionnaires were collected five times online from April 2009 to June 2012. All the procedure was conducted according to the Declaration of Helsinki principles, and its protocol was reviewed and approved by the Northwest Multi-Center Research Ethics Committee (11/NW/0382). Opt-in written informed consent was obtained. The telomere length of 472,525 participants was assessed at the baseline survey. Details of the UK biobank’s explanations, design, and questionnaire are available elsewhere [34]. There were 502,409 participants at baseline, and we included 472,525 participants with telomere length data. In the food frequency questionnaire, 470,754 participants had data on coffee intake data. After adjusting for covariables, 467,329, 347,490, and 3,403,374 participants were in model 1, model 2, and model 3, separately. In the 24-h dietary recall questionnaire, there were 111,192 individuals with instant coffee intake records and 58,771 with filtered coffee intake records. For instant coffee, 110,610, 85,829 and 84,842 participants remained in model 1, model 2, and model 3, respectively. For filtered coffee, there were 58,434, 46,843, and 46,504 participants in model 1, model 2, and model 3, respectively. The flowchart of study participants is demonstrated in Figure 1. ## 2.2. Assessment of Telomere Length DNA was taken from peripheral blood leukocytes by the UK biobank for an array genotyping project. Research staff used multiple quantitative polymerase chain reaction (PCR) methods to measure leukocytes’ telomere length. This was represented by the ratio (T/S) of telomere repeat copy number (T) relative to a single copy gene (S). The details of operation and adjustment for technical factors are described elsewhere [35]. Our study used the Z-standardized log-telomere length measure, following the previous study [15]. In order to suggest the clinical implication of telomere length change, we transformed the 𝛽 coefficient into the equivalent years of age-related change in telomere length for each additional cup of coffee consumption. We divided the 𝛽 coefficient by 0.023, which is the 𝛽 coefficient of telomere length decreasing in age-relation per year computed in UK biobank studies [15,35]. ## 2.3. Assessment of Coffee Consumption We chose three coffee-related exposure variables from the dietary questionnaire: coffee intake, instant coffee intake, and filtered coffee intake. The coffee intake was collected from the food frequency questionnaire at baseline: “How many cups of coffee do you drink each day (including decaffeinated coffee)?”. Participants would input the number of cups, or select an option from “Less than one”, “Do not know”, or “Prefer not to answer”. Information on instant coffee intake and filtered coffee intake was gathered from the five-times questionnaires of 24-h dietary recall after they answered “Yes” in the “Did you drink any coffee yesterday”. The questions were “How many cups/mugs of instant coffee did you drink yesterday?” and “How many cups/mugs of filter/americano/cafetiere coffee did you drink yesterday?”. Participants would make the choice from the number of cups. Instant coffee and filtered coffee were counted as “6+” if the coffee consumption exceeded 6 cups. Participants were included if they completed only one of five recalls. If participants filled out the questionnaire with more than one recall, the mean intake would represent their consumption. ## 2.4. Assessment of Covariables We included socio-demographic, health-related, lifestyle-related, and dietary characteristics as covariates for the multivariate analyses [12,15,19,20]. Socio-demographics included age, sex, ethnicity, Townsend deprivation index, and qualification. The Townsend Deprivation Index was a census-based measure of material deprivation that factored in aspects including lack of a car, overcrowded living conditions, homeownership status, and unemployment. Health-related characteristics included body-mass index (BMI), white blood cell count (WBC), C-reactive protein (CRP), vascular/heart problems diagnosed by the doctor, cancer diagnosed by the doctor, and diabetes diagnosed by the doctor. The BMI was calculated as diving weight (kg) by the square of height (m2), which were both measured at the baseline visit. The lifestyle-related characteristics contained total MET-minutes/week of physical activity, smoking status, and alcohol intake frequency. The metabolic equivalent task minutes per week for walking, including moderate activity and vigorous activity, were designated as a summary of the overall MET-minute/week of physical activity, which was calculated based on the International Physical Activity Questionnaire. Dietary characteristics were represented by the frequency of red-meat intake (beef, mutton, and pork), processed meat intake, oily fish intake, fruit intake (fresh and dried), and vegetable intake (cooked and salad/raw). These were all collected through a standardized and validated food frequency questionnaire (FFQ) to calculate habitual dietary intake. All measurements were gathered from the baseline survey. ## 2.5. Observational Analyses We concluded the data of coffee intake at baseline. Besides, we clarified the coffee type into instant coffee and filtered coffee. Baseline characteristics were described by percentage (N (%)), and telomere length was calculated by the mean and standard deviation (x ± SD). One-way analysis of variance was conducted to compare the significant differences in telomere length between groups. We performed multivariate linear regression as observational analyses, and established three models to explore the relationship between coffee intake and telomere length. Model 1 included age (year; continuous), gender (male and female; categorical), ethnicity (white and others; categorical), and BMI (kg/m2; continuous) as covariates. Model 2 expanded on model 1 by including Townsend deprivation index (score; continuous), qualification (college or university degree, advanced/advanced subsidiary/national vocational qualification/higher national diploma/higher national certificate/other professional qualifications, ordinary levels/general certificate of secondary education/certificate of secondary education, and none of the above; categorical), WBC (10⁹ cells/L; continuous), CRP (mg/L; continuous), vascular/heart problems diagnosed by the doctor (none, heart attack, angina, stroke, and high blood pressure; categorical), cancer diagnosed by the doctor (yes, and no/don’t know; categorical), diabetes diagnosed by the doctor (yes, and no/don’t know; categorical), total MET-minutes/week physical activity (minutes/week; continuous), smoking status (current, previous and never; categorical), alcohol intake frequency (daily or almost daily, there or four times a week, once or twice a week, once to three times a month, special occasions only, and never; categorical). On the basis of model 1 and model 2, model 3 added the frequency (never, less than once a week, once a week, 2–4 times a week, and >4 times a week; categorical) of beef intake, mutton intake, pork intake, processed meat intake, and oily fish intake; and the consumption (tablespoon/day; continuous) of fresh fruit intake, dried fruit intake, cooked vegetable intake, and salad/raw vegetable intake. We separately excluded the participants in these three models if they lacked information on exposure factors and covariables, or answered the questionnaire with “Do not know” or “Prefer not to answer”. The coefficient in linear regression was tested by t-test. ## 2.6. Mendelian Randomization Analyses To investigate the causal association between coffee consumption and telomere length, we conducted MR analyses using four methods: IVW, MR-PRESSO, MR-Egger, and weighted median. The IVW method is based on the assumption that there is no pleiotropy (IVs affect telomere length through alternative pathways) and that all IVs are valid [29]. The MR-Egger approach can show a valid causal effect estimate even though all IVs are invalid [36]. The weighted median approach requires that at least half of IVs are valid [29]. The MR-PRESSO approach identifies potential IV abnormalities by thoroughly testing and automatically removing identified abnormalities to provide an unbiased causal effect result [37]. In order to reduce the influence of confounding factors (including race), we chose both exposure and outcome populations from the UK biobank. Single nucleotide polymorphisms (SNPs) were extracted from the genome-wide association studies (GWAS) of the UK biobank as IVs. We selected independent SNPs (r2 < 0.001, window size = 10,000 kb) of coffee consumption at a level of $p \leq 5$ × 10−6. In addition, we calculated the F-score to assess the instrumental strength of SNPs. SNPs with an F score below 10 were considered weak IVs and would be removed [36]. We conducted the following sensitivity analyses to evaluate the robustness of MR results. [ 1] The fixed-effect model was performed if there was no heterogeneity in the IVW analysis; otherwise, the random-effect model was used. [ 2] We detected the horizontal pleiotropy by MR-Egger intercept [36] and MR-PRESSO global test [37]. [ 3] The leave-one-out analysis was performed to assess whether the casual effect result was driven by any single SNP. All statistical analyses were performed in R v4.1.0 (R Foundation, Vienna, Austria). “ TwoSampleMR” and “MRPRESSO” packages were used for MR analyses [37,38]. $p \leq 0.05$ was considered to be statistically significant. ## 3.1. Population Characteristics In general, the baseline characteristics of participants in the UK Biobank are demonstrated in Table 1, in which $55.6\%$ of the participants were younger than 60 years old, $54.2\%$ were female, and $91.1\%$ were white. The BMI of $67.0\%$ of participants was over 24.9 kg/m2, $30.8\%$ had more than 50 h (3000 min) of total MET physical activity per week, $22.7\%$ of participants were at a high CRP level (>3 mg/L) [39], $29.8\%$ of participants had vascular disease, $7.6\%$ cancer, $5.2\%$ diabetes, $50.5\%$ of the participants drank more than one cup of coffee each day, and $69.1\%$ and $55.3\%$ of individuals who drank instant or filtered coffee yesterday consumed more than one cup. In the univariable analysis of coffee intake, the significant differences in telomere length included age ($p \leq 0.001$), sex ($p \leq 0.001$), ethnicity ($p \leq 0.001$), BMI ($p \leq 0.001$), qualification ($p \leq 0.001$), Townsend deprivation index ($p \leq 0.001$), MET physical activity ($p \leq 0.001$), WBC ($p \leq 0.001$), CRP ($p \leq 0.001$), smoking status ($p \leq 0.001$), alcohol intake ($p \leq 0.001$), vascular/heart problem ($p \leq 0.001$), diabetes ($p \leq 0.001$), oily fish intake ($p \leq 0.001$), processed meat intake ($p \leq 0.001$), beef intake ($p \leq 0.001$), mutton/lamb intake ($p \leq 0.001$), pork intake ($p \leq 0.001$), and salad/raw vegetable intake ($p \leq 0.001$) (Table 1). In addition, we clarified the coffee type into instant coffee and filtered coffee in the univariable analysis. Instant coffee and filtered coffee also showed significant differences in these variables. However, cooked vegetable intake ($p \leq 0.001$, $p \leq 0.001$), fresh fruit intake ($p \leq 0.001$, $$p \leq 0.007$$), and coffee consumption ($p \leq 0.001$, $$p \leq 0.045$$) significantly differed in coffee intake and instant coffee intake. Cancer ($p \leq 0.001$, $$p \leq 0.005$$), and dried fruit intake ($p \leq 0.001$, $p \leq 0.001$) only showed significant differences in coffee intake and filtered coffee intake. ## 3.2. Observational Analyses of Coffee Consumption on Telomere Length The results of the multivariable analyses of coffee consumption on telomere length are shown in Table 2. The p1, p2, and p3 represented the significance of 𝛽 coefficient in model 1, model 2, and model 3, separately. Coffee intake was inversely associated with telomere length in model 1 and was equal to 0.22 year in age-related decrease in telomere length for each additional cup of coffee intake ($95\%$ confidence interval (CI): −0.28, −0.16; p1 < 0.001). The inverse association of coffee intake with telomere length still existed in model 2 (equal to 0.13 year age-related decrease in telomere length, $95\%$ CI: −0.20, −0.06; p2 < 0.001) and model 3 (equal to 0.12 year age-related decrease in telomere length, $95\%$ CI: −0.19, −0.05; p3 < 0.001). Instant coffee intake had a negative correlation with telomere length in all three models, which was equal to 0.58 year age-related decrease in telomere length for each additional cup of instant coffee intake ($95\%$ CI:−0.78, −0.38; p1 < 0.001), 0.39 year age-related decrease in telomere length for each additional cup of instant coffee intake ($95\%$ CI: −0.62, −0.16; p2 < 0.001), and 0.38 year age-related decrease in telomere length for each additional cup of instant coffee intake ($95\%$ CI: −0.61, −0.15; p3 < 0.001), separately. However, the association of filtered coffee intake with telomere length was not statistically significant in the three models (p1 = 0.952, p2 = 997, p3 = 0.862). ## 3.3. MR Analyses of Coffee Consumption on Telomere Length To check whether there was a causal connection between coffee consumption and telomere length, we further conducted two-sample MR analyses (Table 3). We identified 109, 21, and 20 SNPs for coffee intake, filtered coffee intake, and instant coffee intake, respectively. Heterogeneity was observed in the IVW analyses of coffee intake ($p \leq 0.001$) and filtered coffee intake ($$p \leq 0.006$$). Then the random-effect model was performed in the IVW analyses. No horizontal pleiotropy ($p \leq 0.05$) was found in the MR-Egger test. However, significant horizontal pleiotropy was represented in the MR-PRESSO test of coffee intake ($p \leq 0.001$) and filtered coffee intake ($$p \leq 0.010$$). We thus chose the corrected causal effect estimates. The weighted median analysis suggested that coffee intake might be negatively casual associated with telomere length, equal to 3.13 ($95\%$ CI: −5.80, −0.46; $$p \leq 0.022$$) years of age-related decrease in telomere length for each additional cup of coffee intake. IVW analysis (equal to 0.85 year age-related decrease in telomere length for each additional cup of instant coffee intake, $95\%$ CI: −1.56, −0.14; $$p \leq 0.019$$) and MR-PRESSO analysis (equal to 0.85 year age-related decrease in telomere length for each additional cup of instant coffee intake, $95\%$ CI: −1.55, −0.15; $$p \leq 0.028$$) indicated that instant coffee intake was negatively causally associated with telomere length. No significant difference was found in any MR analysis of filtered coffee intake ($p \leq 0.05$). The leave-one-out analysis suggested that the causal effect results of coffee intake and filtered coffee intake in the IVW analyses were not driven by any single SNP (Supplementary Tables S1 and S2). However, rs2472297 and rs2726351 might affect the result of instant coffee intake of IVW analysis (Supplementary Table S3), even MR-PRESSO analysis indicating that there are no outliers. Therefore, the result of instant coffee intake in IVW analysis requires careful consideration. ## 4. Discussion In this study, we investigated the association between coffee consumption and telomere length using the data from UK biobank. We found that coffee intake and instant coffee intake were negatively correlated with telomere length, but there was no significant correlation between filtered coffee and telomere length through the observational analyses. Mendelian randomization analyses supported the results of observational analyses. We found that instant coffee had a causal relationship with telomere length in the IVW and MR-PRESSO approach, and coffee intake had a causal relationship with telomere length in the weighted median analysis. However, filtered coffee did not have a causal relationship with telomere length in the four MR analyses. To the best of our knowledge, this was the first study to evaluate the causal association between coffee consumption and telomere length in the UK biobank with a large population size. Previous research on the association between telomere length and coffee has not yielded a consistent result. Three studies, including a Nurses’ Health Study, an NHANES survey, and a randomized controlled trial of chronic hepatitis C patients, revealed that higher coffee consumption was associated with longer telomere length [19,20,21]. Other epidemiological studies indicated that there was no association between coffee consumption and telomere length [22,23,24]. Our results indicated that coffee intake was negatively correlated with telomere length when the coffee subtype was not considered. After exploring the relationship by subtype, instant coffee intake showed a negative association with telomere length, while filtered coffee intake did not. More than half of the UK biobank participants preferred drinking instant coffee [40]. We inferred that instant coffee played a key role in the association that coffee consumption had a negative effect on telomere length. Previous studies did not take coffee types into account, and the inconsistent results might be attributed to small sample sizes or a lack of a classification of coffee type. Instant coffee might shorten telomere length, and it might lead to the occurrence and development of diseases. The health effects of instant coffee, which varied from other subtypes of coffee, might be caused by their different ingredients. The mineral lead in instant coffee was more abundant than that in other coffee types, and long-term consumption of instant coffee may result in excessive lead [41]. Additional substances added to commercial instant coffee, such as creamer and flavoring agents, might partially explain the negative effect [25,26]. Some studies have investigated the effects of coffee subtypes on health. Ground coffee could reduce the risk of type 2 diabetes, whereas instant coffee might increase the risk [42,43]. Instant coffee consumption has been proven to be associated with obesity [44,45]. Compared to women who did not regularly drink coffee, those who drank instant coffee had a higher risk of developing breast cancer [46]. Instant coffee was regarded as a risk factor for Alzheimer’s disease and frailty in the elderly [47,48]. Instant coffee might have the effect of shortening telomere length, and might lead to the occurrence and development of diseases. Therefore, we emphasized the importance of coffee types and the consumption of instant coffee at an appropriate amount. More research needs to identify whether the ingredients in instant coffee results in shorter telomere length. Our study has several limitations. First, coffee consumption was assessed by frequency questionnaire, while instant coffee intake and filtered coffee intake were based on 24-h recall questionnaires, which might have led to biases in the results. Second, in the 24-h recall questionnaire, the participants who consumed more than six cups were recorded as “6+”, which might weaken the association because of information loss. Third, we didn’t further classify coffee types with milk. Coffee with milk (including expresso, cappuccino, and latte) might have effects on telomere length. Fourth, we relaxed the p-value threshold ($p \leq 5$ × 10−6) of SNPs due to lack of sufficient SNPs (less than three) after linkage disequilibrium, resulting in the reduced robustness of MR results. Fifth, in the observational analyses, although exhaustive adjustment was conducted in the multivariable analyses, residuals or unmeasured confounders could not be excluded [49,50,51]. Finally, our analyses were limited to individuals of European ancestry. Generalization to other ethnic or regional populations requires careful consideration. ## 5. Conclusions In summary, we found that both observational and Mendelian randomization analyses indicated that coffee intake, especially instant coffee, might reduce telomere length, while filtered coffee did not. The type of coffee plays a key role in the effect of coffee consumption on telomere length. 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--- title: Autonomic Neuropathy is Associated with More Densely Interconnected Cytokine Networks in People with HIV authors: - Steven Lawrence - Bridget R. Mueller - Emma K. T. Benn - Seunghee Kim-Schulze - Patrick Kwon - Jessica Robinson-Papp journal: Research Square year: 2023 pmcid: PMC10055631 doi: 10.21203/rs.3.rs-2670770/v1 license: CC BY 4.0 --- # Autonomic Neuropathy is Associated with More Densely Interconnected Cytokine Networks in People with HIV ## Abstract ### Introduction. The autonomic nervous system (ANS) plays a complex role in the regulation of the immune system, with generally inhibitory effects via activation of β-adrenergic receptors on immune cells. We hypothesized that HIV-associated autonomic neuropathy (HIV-AN) would result in immune hyperresponsiveness which could be depicted using network analyses. ### Methods. Forty-two adults with well-controlled HIV underwent autonomic testing to yield the Composite Autonomic Severity Score (CASS). The observed range of CASS was 2–5, consistent with normal to moderate HIV-AN. To construct the networks, participants were divided into 4 groups based on the CASS (i.e., 2, 3, 4 or 5). Forty-four blood-based immune markers were included as nodes in all networks and the connections (i.e., edges) between pairs of nodes were determined by their bivariate Spearman’s Rank Correlation Coefficient. Four centrality measures (strength, closeness, betweenness and expected influence) were calculated for each node in each network. The median value of each centrality measure across all nodes in each network was calculated as a quantitative representation of network complexity. ### Results. Graphical representation of the four networks revealed greater complexity with increasing HIV-AN severity. This was confirmed by significant differences in the median value of all four centrality measures across the networks (p≤0.025 for each). ### Conclusion. Among people with HIV, HIV-AN is associated with stronger and more numerous positive correlations between blood-based immune markers. Findings from this secondary analysis can be used to generate hypotheses for future studies investigating HIV-AN as a mechanism contributing to the chronic immune activation observed in HIV. ## Introduction The autonomic nervous system (ANS) plays a key role in regulating the function of both innate and adaptive immunity.1 The sympathetic, parasympathetic and sensory branches of the ANS are all involved, with local effects mediated by direct sympathetic innervation of primary and secondary lymphoid organs, and modulation of systemic inflammation occurring via: 1) surveillance of blood borne immune mediators by the autonomic sensory system, and 2) the indirect effects of the parasympathetic nervous system on the spleen. Given the important effects of the ANS on the immune system, it is expected that disruption of autonomic function, for example autonomic neuropathy (AN), might cause changes in immune function, however this is currently understudied. AN is a relatively common, but often subclinical, feature of many chronic systemic diseases that strain the metabolic capacity of peripheral axons, with two common examples being diabetes mellitus and HIV.2, 3 Interactions between AN and the immune system may be particularly relevant in the case of people with HIV given pre-existing abnormalities in immune function some of which persist despite optimal antiretroviral treatment.4, 5 HIV-associated AN (HIV-AN) is generally mild to moderate in severity and characterized by partial loss of autonomic nerve fibers of different types,3 thus its effects on immune function might be expected to vary on the basis of which fibers were impaired. For example, given that the sympathetic branch of the ANS generally has an inhibitory effect on immune cells via the action of norepinephrine on β-adrenergic receptors,1 immune cells in dennervated locations might be expected to display exaggerated responsiveness or a lower threshold for response to antigenic stimuli or proinflammatory cytokines. If autonomic sensory fibers are lost, critical information regarding systemic levels of inflammatory mediators may not efficiently reach the central nervous system (CNS),6 blunting its modulatory capacity, which might be compounded by loss of the systemic anti-inflammatory capacity of parasympathetic fibers.7 Taken together these effects might lead to a loss of specificity and restraint in immune responsiveness. Importantly, these changes might not result in the elevation of any one immune marker but rather dysfunction of the system as a whole. Prior studies have used cytokine network analyses to quantify such global immune activation in people with HIV,8, 9 as well as other conditions such as cancer,10, 11 chronic fatigue syndrome,12 and pulmonary arterial hypertension.13 Such analyses generate graphical representations of cytokine networks based on bivariate correlations between individual cytokines, and can be used to quantify and compare network characteristics between groups of patients. In general, these studies have shown denser, more interconnected networks in inflammatory disease states compared to controls, and have suggested such findings may represent a muted form of “cytokine storm. ”9 To our knowledge, such analyses have not previously been used to study the relationship between autonomic and immune function. Herein we describe network analyses examining the relationships between 44 blood-based immune markers in people with HIV with varying degrees of HIV-AN. We hypothesized that more severe HIV-AN would result in denser, more complex networks, reflecting a tendency toward an “all or nothing” immune response due to a loss of autonomic modulation. We also report an exploratory analysis using a machine learning algorithm to back-predict HIV-AN severity from immune marker data. ## Methods Study overview. This is a secondary data analysis of participants recruited to two other studies of HIV-AN, (one cross-sectional observational study and one pilot clinical trial), conducted at a single academic medical center.14, 15 Both studies had the same inclusion/exclusion criteria and recruited from the same patient population. The baseline procedures for the clinical trial were identical to those of the single cross-sectional visit in the observational study. In the case of data collected in the clinical trial, only baseline data (i.e., prior to any intervention) were used herein. Brie y, included participants in both parent studies were adults (≥ 18 years) living with HIV and treated with combined antiretroviral therapy (CART) for at least 3 months, with HIV-1 plasma RNA load of ≤ 100 copies/ml. Confounders for autonomic dysfunction (e.g. diabetes, interfering medications) were exclusionary. All procedures were performed in accordance with a protocol approved by the Institutional Review Board of the Icahn School of Medicine at Mount Sinai (ISMMS). All participants provided written informed consent. The Composite Autonomic Severity Score (CASS) was calculated for each participant based on the results of a standardized battery of autonomic function tests.16 This battery has been described previously and consists of: quantitative sudomotor axon re ex testing (QSART), heart rate response to deep breathing (HRDB), Valsalva maneuver (VM), and tilt table testing.16, 17 In general the CASS is scored on a scale of 0–10, with higher numbers indicating worse autonomic function. The observed range for CASS in the data was 2 to 5, consistent with normal to moderately abnormal autonomic function, thus for the purposes of this analysis participants were divided into four groups based on CASS score (i.e., score of 2, 3, 4 or 5). Blood samples were processed to isolate the plasma within one hour of phlebotomy, stored at −80°C, and used only once (without repeated freeze thaw cycles) according to standard laboratory protocol.14 Plasma samples were then analyzed by our institution’s Human Immune Monitoring Center, using a bead-based ELISA method by Milliplex xMAP technology (Millipore, Billerica, MA) with a Luminex 200 system (Luminex Corporation, Austin, TX). We used a premixed 41 plex human cytokine/chemokine panel which includes: EGF, Eotaxin, G-CSF, GM-CSF, IFNα2, IFNγ, IL-10, IL-12P40, IL-12P70, IL-13, IL-15, IL-17A, IL-1RA, IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IP-10, MCP-1, MIP-1α, MIP-1β, RANTES, TNFα, TNFβ, VEGF,FGF-2, TGF-α, FLT-3L, Fractalkine, GRO, MCP-3, MDC, PDGF-AA, PDGF-AB/BB, sCD40L. Additional analytes were sCD14, sCD163 which are monocyte activation markers commonly used in studies of people with HIV.18, 19 We also measured high mobility group box-1 (HMGB-1), an inflammatory biomarker which has been studied both in the context of HIV and autonomic function.20, 21 Results were expressed as mean fluorescence intensity (MFI); laboratory quality assurance procedures have been described previously.14 Generation of Networks. All analyses and figures were generated using R statistical computing language. The package qgraph (1.9.2) was used for network and graphical analysis, and caret (6.0.93) for model building. Participants were divided into four groups based on CASS scores (i.e. score of 2, 3, 4 or 5) and a network was generated for each group with the 44 blood-based immune markers as nodes and the bivariate Spearman’s rank correlation coefficients as weighted, non-directional edges between nodes. All bivariate correlations regardless of strength or directionality were included in the creation of the network. However, including all edges in the network figures made them illegible and so a cutoff of an absolute value of 0.7 or greater was used as the criterion for inclusion of an edge in the figures. Positive correlations between nodes were depicted as blue and negative correlations as red; with wider darker edges representing stronger correlations. In the main network figures, node color was chosen based on functional groupings of the immune markers (angiogenic growth factors = red; anti-inflammatory = blue; chemokines = green; multifunctional growth factors = purple; hematopoetic growth factors = orange; interferons = yellow; monocyte activation markers = brown; pro-inflammatory = pink; type 2 inflammatory = grey). To further illustrate network structure, we performed exploratory graphical analysis to establish communities of nodes within each network using the Walktrap algorithm which identifies communities in large networks via random walks, assigning each node to a single community. This is based on the idea that during a random walk one is more likely to become “trapped” in a highly interconnected community. The random walks are used to compute distances between nodes based on their partial correlations, and nodes are assigned into communities via bottom-up hierarchical clustering which initially assumes each node is a singleton cluster and then successively merges pairs of clusters according to the variance explained. In contrast to other methods such as eigenvector decomposition, spine glass, or label propagation, the Walktrap algorithm is sensitive to edge weights and is among the most stable for determining communities within networks.22, 23 Descriptions of Network and Node Characteristics. For each node within each network, four centrality measures were calculated using the centrality function in R within the qgraph package which is based on the method of Opsahl et al for weighted networks.24, 25 The four centrality measures were: node strength, expected influence, closeness centrality and betweenness centrality. Strength represents the sum of the absolute value of the weights of all edges connected to the node. Strength is the simplest measure of a node’s overall importance or involvement in a weighted network, reflecting especially local involvement given that it accounts only for direct (i.e., one step) connections. Expected influence is similar to node strength except that the original sign (negative or positive) of the edge weight is retained, which may help identify nodes with prominent negative associations. Closeness and betweenness centrality are both based on the calculation of the shortest (i.e., most efficient) paths through the network to get from a given node to all other nodes. In a weighted network, the shortest path calculation considers both the number of nodes and the absolute value of edge strengths on the path (preferring fewer nodes and higher strength edges). Once all shortest paths are calculated, closeness centrality for a given node represents the average distance from the node to all other nodes traveling along the shortest paths. Betweenness centrality represents the number of shortest paths that pass through the node. A node with high betweenness centrality may be conceptualized as a “gatekeeper” connecting different parts of a network.26 Additional statistical analyses. While the scope of our network analysis was primarily descriptive in nature, statistical analyses were performed to evaluate interplay among the centrality measures and explore the relationship between immune and autonomic function. More Specifically, the Kruskal Wallis test was used to compare the centrality measures between networks. Machine learning-based classification methods were applied to examine whether the immune mediator data could be used to back-predict the CASS score of individual participants. Specifically, we chose an ordinal random forest model given the ordered nature of CASS scores and the robustness of the model to differences in group sizes.27 Given that our overarching hypothesis was that HIV-AN would affect the relationships between immune mediators rather than their individual levels, we transformed the raw immune mediator data into z-scores, as the distance (and direction) from the mean was a stronger classifier than the raw data. We used data from $60\%$ of the participants in a training set and the remaining $40\%$ in the validation set modeling using repeated cross validation. A confusion matrix (i.e., a cross-tabulation of actual versus predicted CASS score) was created and used to estimate performance metrics for the model: accuracy, macro F1 score (given its robustness to imbalanced groups),28 sensitivity, specificity. Finally, we used variable importance to determine which immune markers contributed most to the model’s classification. Variable importance reflects the effect of variable permutation on the model’s accuracy. If the variable is unimportant, permutation of its values has little effect on model accuracy and variable importance tends toward zero; in contrast highly important variables yield a scaled value closer to 100.27 ## Participants. Participant characteristics are summarized in Table 1. Overall, the sample had a mean age of 57 years, was racially and ethnically diverse and about three-quarters male. Many participants had longstanding HIV infection, with a mean self-reported disease duration of 20 years, but all were currently well-controlled (as per study inclusion/exclusion criteria). Women were particularly underrepresented in the CASS = 3 group, in which only one of the 17 participants was female ($$p \leq 0.044$$). Otherwise, there were no significant demographic differences across the groups. ## Characteristics of the Networks. Figure 1 displays the correlation networks between immune markers for participants in each of the four CASS groups, with positive correlations in blue and negative correlations in red (only correlations with an absolute value ≥ 0.7 are shown). Increasing density of positive correlations with higher CASS scores (i.e., worse autonomic function) can be appreciated visually. Regarding negative correlations, there were a total of 22 bivariate negative correlations involving multiple nodes in the CASS = 2 network. In contrast, strong negative correlations were much more circumscribed in the other networks. Specifically, in the CASS = 3 network, a single marker, HMGB-1, was a member of all the strong bivariate negative correlations. In the CASS = 4 network there was only one strong negative bivariate correlation (RANTES to FLT3). In the CASS = 5 network, all the strong negative correlations within the main body of the network involved a single node (GRO). Thus, in contrast to the positive correlations, negative correlations tended to decrease in diversity with increasing CASS. Network metrics are summarized in Table 2. All centrality measures differed significantly between the four networks. Strength, expected influence and closeness centrality tended to increase with CASS. In the case of strength and expected influence, this indicates that in the higher CASS networks nodes had significantly more numerous and stronger correlations with other nodes. For strength, which uses the absolute values of the correlation strength, this was a nearly linear increase, whereas for expected influence, which accounts for the correlation strength and direction, the increase occurred between the CASS = 2 and CASS = 3 networks and was then relatively at. Median closeness centrality increased linearly from CASS = 2 to CASS = 5, indicating a steady decrease in the shortest distance path between nodes overall, another reflection of more highly interconnected networks in the higher CASS networks. Betweenness centrality, which reflects how often a node is on the shortest path between other nodes, displayed a distinct pattern, being on average higher in the CASS = 2 network and lower in the other three. This is likely because the greater number of paths through the higher CASS networks decreases the likelihood that an individual node is on the shortest path between any two other nodes, or stated another way: with so many paths, fewer nodes have key bridging functions. Figure 2 demonstrates that in general there was no clear difference in the centrality measures between different functional classes of immune mediators. Monocyte activation markers and angiogenic growth factors (brown and red lines respectively in Fig. 2) tended to separate somewhat from the other categories, however, these were categories which held only two markers each. ## Communities within the Networks. The Walktrap algorithm identified between five and eight communities within each network as depicted in Fig. 3, in which the node colors have changed to reflect community but otherwise the networks remain the same as Fig. 2. Given the “bottom-up” approach of the algorithm, the first community in each network (community #1, shown in red) is typically the largest and most heterogenous and generally contains the nodes with lower centrality measures, whereas communities #2 through #8 are generally progressively smaller and more insular. The CASS = 2 and CASS = 3 networks had five communities each, whereas the CASS = 4 network had eight communities and the CASS = 5 community had six. Examination of the communities within the CASS = 2 network suggests several elements of structure which were not present in the other networks. For example, community #5 (orange) was comprised of three nodes (TNF-β, GMCSF and IL-1 receptor antagonist (IL-1RA)) with: 1) very strong positive correlations with one another (rho > 0.9 for all); 2) strong positive connections to nodes in community #2 (blue) with high positive expected influence (FGF and IL-1b); and 3) strong negative connections to nodes in community #1 (red) with high negative expected influence (IP10 and PDGFAABB). These connections resulted in a relatively high expected influence for community #5 (median = 6.0, compared with 3.8 for the network overall). Community #4 (purple) was also characterized by strong correlations between its four members (IL-3, IL-9, IL12p40 and IL-15) and high expected influence (median = 9.1) based in part on strong positive correlations with the three highest expected influence nodes in network #1 (TGF-α, IL-4 and IL-7, median expected influence = 8.0). Community #3 (green) has two subgroups. One subgroup is comprised of EGF, VEGF and sCD40L which are all strongly positively correlated with one another and tend to be negatively correlated with the remaining members of community #3. Community #2 (blue) has one negatively correlated couplet (MCP-1 and IL-5) which is relatively separate from the other community members which are connected by multiple positive correlations. In contrast to the complex community relationships in the CASS = 2 network, in the other CASS networks most of the communities are defined by particularly strong positive connections between multiple nodes, although there are a few notable exceptions. In the CASS = 3 network, community #4 (purple) contains HMGB-1 and several nodes with which it has strong negative connections. Similarly, in the CASS = 5 network, community #2 (blue) contains some strong negative correlations between GRO and other community members, and community #5 (orange) is characterized mostly by negative correlations with HMGB-1 and IFN-α at its center ## Predicting CASS group membership from immune mediator data. The ordinal random forest model had $73\%$ accuracy, indicating that it correctly assigned $73\%$ of participants overall (i.e., across all CASS scores) to their correct CASS group. The model’s macro F1 score (an alternative performance metric which accounts for imbalance in the number of participants in each CASS group) was $72\%$, similar to accuracy. However, the model’s predictive value was not significantly different ($p \leq 0.05$) from the no information rate (i.e., the rate of correct classification achievable by chance alone based on the known distribution of CASS scores). Sensitivity and specificity of the model by CASS score is summarized in Table 3. As shown, the model correctly classified all participants with a CASS of 2, 4, or 5, but incorrectly classified $40\%$ of participants with a CASS of 3. Examination of variable importance revealed that the top five immune markers contributing to the predictive value of the model (variable importance factors of > 75 for each) were: TGF-α, MDC, IL-7, IL12p40, and IL-15. ## Discussion In this study, we used network analyses to depict and describe differences in relationships between immune markers in people living with HIV with normal autonomic function and varying degrees of HIV-AN. We found that, overall, positive correlations between immune markers increased with worsening autonomic function, reflected by increases in median centrality measures in the higher CASS networks. Similarly, strong negative correlations between immune markers decreased in their strength and diversity with increasing CASS. Given the cross-sectional, associational nature of these data and the relatively small sample size, this work cannot be used to establish causative mechanisms, but can be used to generate hypotheses regarding the possible effects of HIV-AN on immune networks. We hypothesize that at least some of the observed correlations between immune markers are reflections of regulatory feedback loops. Both positive and negative feedback loops are key features of biologic systems. In general, negative feedback loops are appropriate for providing stability or maintenance of the status quo. In contrast, positive feedback loops are useful for rapidly amplifying a physiologic process in response to a threat, and an external control is typically required to terminate the response. Given that a main function of the immune system is to respond quickly and effectively to perturbations, such as infection or tissue damage, one would expect positive feedback loops to be prominent in its structure, and indeed this is the case. For example, in the innate immune system, activated neutrophils can directly enhance their own activity and movement toward a target via autocrine and paracrine signaling, including the release of certain chemokines.29 The process of T-cell activation provides an example of a positive feedback loop within the adaptive immune system. Dendritic cells present antigen to T-cells and also provide costimulatory signaling to activate the T-cell. The activated T-cell then provides positive feedback to the dendritic cell resulting in increased expression of costimulatory signaling molecules.30 Given that chronic HIV is a proinflammatory state, and inflammation is characterized by positive feedback loops, our networks suggest the hypothesis that the ANS may be an important external interrupter of inflammatory positive feedback loops. Specifically, in the CASS = 2 network, the presence of multiple negative correlations dispersed throughout the network could reflect underlying negative feedback loops mediated by a properly functioning ANS, which are then lost in the higher CASS networks. Pre-clinical models suggest anatomic and physiologic bases for such a phenomenon. Anatomically, under normal circumstances the sympathetic nervous system (SNS) has the potential to regulate the immune system in a nuanced and site specific manner, given that SNS fibers are a pervasive presence in all lymphoid organs.1 SNS fibers also have the ability to respond directly to certain pathogens via activation of toll-like receptors (TLRs).31 Moreover it has been hypothesized that SNS fibers innervating secondary lymphoid organs may display neuroplasticity, for example, retracting from the spleen in order to disinhibit an appropriate inflammatory response, and then later returning to curtail it.32 In addition to these local mechanisms, the SNS can also exert systemic effects via the sympatho-adrenal-medullary (SAM) axis through which the SNS stimulates the adrenal medulla to release catecholamines directly into the systemic circulation.33 In addition to this neuroanatomy, a significant body of research documents the physiologic response of diverse immune cells to norepinephrine (NE), the main neurotransmitter of the SNS. Immune cell response to NE, as recently and comprehensively reviewed,31 is permitted by the presence of β- and to a lesser extent α-adrenergic receptors (β-AR and α-AR respectively) on both innate and adaptive immune cells. In general, β-ARs are lower affinity but more numerous, and so NE at the relatively high concentrations supplied by local SNS terminals results in greater activation of β-AR which has a generally suppressive effect. In contrast, α-ARs which have a generally pro-inflammatory effect, are less numerous but higher affinity and so may be preferentially activated by diffuse low level catecholamines, such as is released via the SAM axis (e.g. in response to chronic stress).34 The sensory and parasympathetic branches of the ANS (both contained predominantly within the vagus nerves) are also important regulators of immune function, despite their apparent lack of significant direct innervation of lymphoid organs.1 Vagal sensory fibers detect the presence of cytokines in the circulation, and convey this information to the CNS with cytokine-specific ring patterns referred to as “neurograms. ”6 Moreover, blocking of vagal sensory fibers lessens CNS-mediated responses to infection including fever and sickness behavior.35 The effect of the parasympathetic on immune function has also been well described and is sometimes referred to as the cholinergic anti-inflammatory pathway, given that acetylcholine (ACh) is its main neurotransmitter.7 Although certain portions of the pathway remain uncertain, it is clear that activation of vagal parasympathetic nerve fibers ultimately inhibits the release of inflammatory mediators (e.g. IL-6 and TNF-α) into the circulation from splenic macrophages in response to a systemic inflammatory stimulus such as lipopolysaccharide (LPS).36 Treated HIV has been conceptualized as a state of chronic low-level exposure to antigenic stimuli such as LPS due to alteration of the gut microbiome and increased translocation across a compromised gut barrier.37 Thus it is possible that partial loss of sympathetic, parasympathetic, and sensory autonomic fibers in HIV-AN leads to a decreased ability to control the intensity and breadth of immune responsiveness in people living with HIV. Indeed, prior work has shown more closely correlated cytokine networks in people living with HIV compared to uninfected controls,8, 9 although not in the context of autonomic dysfunction. This study has important limitations. The sample size is small, and this is a secondary data analysis of data collected from a single academic center and so the findings require replication in a larger cohort in which such a network analysis is a pre-planned primary outcome. Another limitation is that machine learning models are better suited to larger sample sizes, although among such models, ordinal random forests have been proposed as more adaptable for smaller sample sizes. We have previously shown that HIV-AN is associated with increased burden of medical comorbidities,38 and so it is possible that the observed increases in network density at higher CASS scores are not due to the autonomic dysfunction but rather due to other medical issues. Moreover, women and men were not evenly distributed between the four CASS groups and so it is possible that differences in immune function could be related to sex-based differences. It is a limitation of this network analysis approach that such potential confounders cannot be readily controlled for. Despite these limitations this study provides preliminary evidence supporting a potential role of HIV-AN in the state of chronic immune activation observed in people living with HIV. Future work is needed to replicate the findings in larger cohorts, and to employ methodology which will enable more precise mechanistic characterization of autonomic-immune interactions. These may include combinations of serum catecholamine measurements, “whole body” imaging of sympathetic innervation with radiotracers, detailed immune cell phenotyping, and advanced computational methods to identify phenotypes of autonomic and immune function and their intersections. ## References 1. Nance DM, Sanders VM. 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--- title: Development of cardiometabolic risk factors following endocrine therapy in women with breast cancer authors: - Eileen Rillamas-Sun - Marilyn L. Kwan - California Carlos - Richard Cheng - Romain Neugebauer - Jamal S. Rana - Mai Nguyen-Huynh - Zaixing Shi - Cecile A. Laurent - Valerie S. Lee - Janise M. Roh - Yuhan Huang - Hanjie Shen - Dawn L. Hershman - Lawrence H. Kushi - Heather Greenlee journal: Research Square year: 2023 pmcid: PMC10055634 doi: 10.21203/rs.3.rs-2675372/v1 license: CC BY 4.0 --- # Development of cardiometabolic risk factors following endocrine therapy in women with breast cancer ## Abstract ### Purpose: Studies comparing the effect of aromatase inhibitor (AI) and tamoxifen use on cardiovascular disease (CVD) risk factors in hormone-receptor positive breast cancer (BC) survivors report conicting results. We examined associations of endocrine therapy use with incident diabetes, dyslipidemia, and hypertension. ### Methods: The Pathways Heart Study examines cancer treatment exposures with CVD-related outcomes in Kaiser Permanente Northern California members with BC. Electronic health records provided sociodemographic and health characteristics, BC treatment, and CVD risk factor data. Hazard ratios (HR) and $95\%$ con dence intervals (CI) of incident diabetes, dyslipidemia, and hypertension in hormone-receptor positive BC survivors using AIs or tamoxifen compared with survivors not using endocrine therapy were estimated using Cox proportional hazards regression models adjusted for known confounders. ### Results: In 8,985 BC survivors, mean baseline age and follow-up time was 63.3 and 7.8 years, respectively; $83.6\%$ were postmenopausal. By treatment, $77.0\%$ used AIs, $19.6\%$ used tamoxifen, and $16.0\%$ used neither. Postmenopausal women who used tamoxifen had an increased rate (HR: 1.43, $95\%$ CI: 1.06–1.92) of developing hypertension relative to those who did not use endocrine therapy. Tamoxifen use was not associated with incident diabetes, dyslipidemia, or hypertension in premenopausal BC survivors. Postmenopausal AI users had higher hazard rates of developing diabetes (HR: 1.37, $95\%$ CI: 1.05–1.80), dyslipidemia (HR: 1.58, $95\%$ CI: 1.29–1.92) and hypertension (HR: 1.50, $95\%$ CI: 1.24–1.82) compared with non-endocrine therapy users. ### Conclusion: Hormone-receptor positive BC survivors treated with AIs may have higher rates of developing diabetes, dyslipidemia, and hypertension over an average 7.8 years post-diagnosis. ## Introduction An estimated $83\%$ of all breast cancers (BC) are hormone receptor-positive [1], and endocrine therapy is typically the primary adjuvant treatment for these cancers. Endocrine therapies have been effective at treating hormone receptor-positive BC with five-year survival rates ranging from 89–$92\%$ [1]. Studies have shown that the long-term cardiovascular impact of endocrine therapy is an important health concern for BC survivors and an active area of research [2–4]. The most used endocrine therapies are selective estrogen-receptor modulators (i.e., tamoxifen) and aromatase inhibitors (AIs), which have different modes of action in treating hormone receptor-positive BC. Tamoxifen blocks estrogen from binding to receptors on breast cancer cells, but mimics estrogen in other tissue cells [1, 4]. Tamoxifen is a treatment used in both premenopausal and postmenopausal women. Via a different mechanism, AIs reduce endogenous estrogen production by blocking aromatase, which is the enzyme that converts androgens to estrogens in non-ovarian tissues [4]. Since the ovaries produce the majority of endogenous estrogen, AIs are primarily used in postmenopausal women whose ovarian function has ceased. AI use has been associated with lower breast cancer recurrence and mortality compared with tamoxifen [5, 6], but the impact of AIs on cardiovascular health should be considered in the adjuvant setting [7]. Studies have been published on the cardiovascular disease (CVD) risks associated with endocrine therapies [6, 8–15], including several meta-analyses and systematic reviews [3, 16, 17]. Observational studies often directly compare AI and tamoxifen users, however endogenous estrogen, which premenopausal women have in higher concentrations, is generally associated with favorable cardiometabolic factors, such as reduction in cardiomyocyte hypertrophy, inammation, and atherosclerosis [2, 4, 18] and improvements in lipid pro les and insulin resistance [18, 19]. Since AIs work by suppressing endogenous estrogen, AIs may more negatively affect the cardiovascular system than tamoxifen. Thus, opposing effects on the cardiovascular system between the two therapies challenge the capability to distinguish whether associations between endocrine therapy use and CVD are due to a protective effect of tamoxifen or a deleterious effect of AIs. Clearer results might emerge by comparing AI or tamoxifen users to hormone-receptor positive breast cancer survivors who did not use endocrine therapy for treatment. Therefore, we examined the association of AI or tamoxifen use on the risk of developing diabetes, dyslipidemia, and hypertension in a large population of hormone-receptor positive BC survivors. To minimize confounding and improve clarity in potential associations, AI-only and tamoxifen-only users were separately compared with BC survivors who had an indication, but did not use any endocrine therapy for cancer treatment. Finally, since higher body mass index (BMI) is a risk factor for diabetes, dyslipidemia, and hypertension, we explored whether these relationships varied by baseline BMI category. ## Study Population The Pathways Heart Study is a prospective cohort study within Kaiser Permanente Northern California (KPNC) whose aim is improve understanding of CVD risks and outcomes associated with cancer treatments in women with a history of BC. Women were eligible for participation in the Pathways Heart Study if they were diagnosed with American Joint Committee on Cancer (AJCC) Stage I-IV invasive BC between November 2005 and March 2013, at least 21 years of age, and had active KPNC membership for one year or longer at the time of their BC diagnosis. Eligible study participants for the Pathways Heart Study were identified from KPNC electronic health records (EHR). For this analysis, participants who did not have hormone-receptor positive BC, were diagnosed at AJCC Stage IV, and/or were underweight (defined as BMI < 18.5 kg/m2) were excluded. Furthermore, women with a history of using both therapies were excluded, given the exposure of interest was history using AI only or tamoxifen only for cancer treatment. Thus, the comparison group was hormone-receptor positive BC survivors with no history taking AI or tamoxifen for their BC treatment. ## Measures Data were obtained from the KPNC EHR and included KPNC membership enrollment, sociodemographic characteristics, select clinical measures, and incident and prevalent diabetes, dyslipidemia, and hypertension. Menopausal status at baseline (defined as date of BC diagnosis) was not available in the EHR for all women, so a baseline age cutoff of 51 years or older was used to characterize postmenopausal status [20]. BMI was calculated as weight, in kilograms, divided by height, in meters, squared. BMI values were grouped into standardized categories used to characterize people with normal weight, overweight, and obesity [21]. Use of AI or tamoxifen endocrine therapy use was identified KPNC outpatient pharmacy data. Characterization of incident diabetes, dyslipidemia, and hypertension using KPNC EHR data has been described in detail [22]. Incident diabetes was identified the KPNC Diabetes Registry [23] based on one or more ICD-$\frac{9}{10}$-CM principal inpatient diagnosis codes, two or more outpatient diagnoses in the previous five years, two or more fasting blood glucose lab result of ≥ 126 mg/dL on separate days over the last two years, or one or more prescribed diabetes medication. Women with incident dyslipidemia had two separate diagnosis codes of ICD-9-CM 272.0–272.4, ICD-10 E78.00, E78.01, E78.1-R78.5 or a combination of two or more of the following: one diagnosis code as above, a low-density lipoprotein cholesterol result of ≥ 160 mg/dL, or a dispensed lipid-lowering medication such as a statin or other antilipemic agent. Incident hypertension was defined as either: 1) having two or more hypertension diagnoses of ICD-9-CM 401.xx (or equivalent ICD-10-CM codes) at primary care visits in the previous two years, or 2) one or more primary care hypertension diagnoses and either one or more hospitalization with a primary or secondary hypertension diagnosis in the previous two years, or 3) one or more dispensed hypertension medication in the previous 6 months [24]. If two diagnosis codes were required to identify incidence of the cardiometabolic risk factor, the earliest diagnosis date was considered the diagnosis date. ## Statistical Analysis Baseline demographic and health characteristics of the study population by AI or tamoxifen use and menopausal status were described using means and standard deviations (SD) and frequencies. Women with history of AI or tamoxifen use were compared with hormone-receptor positive BC survivors who did not use any endocrine therapy for their BC treatment. Since tamoxifen is indicated for both premenopausal and postmenopausal women, analyses stratified tamoxifen users by menopausal status. Analysis for AI users were in postmenopausal women only. Outcomes of interest were incident diabetes, dyslipidemia, and hypertension; women with a history of these conditions at baseline were excluded from the analysis of that specific risk factor. Cox proportional hazards regression models were used to estimate the hazard rate ratios (HR) and $95\%$ confidence intervals (CI) of newly developed diabetes, dyslipidemia, and hypertension in AI or tamoxifen users compared with hormone-receptor positive non-users of endocrine therapy. Models adjusted for race/ethnicity, baseline age, smoking status, BMI, cancer stage, history of chemotherapy, history of radiation therapy, and prevalent diabetes, dyslipidemia, and hypertension (when not the incident outcome). To evaluate the HR and $95\%$ CI of developing diabetes, dyslipidemia, and hypertension within each of the three BMI groups, Cox proportional hazards regression models were stratified by participants with normal weight, overweight, and obesity. To determine whether the HRs were statistically different between the BMI groups, we evaluated Cox proportional hazards regression models that included two multiplicative interaction terms, one for endocrine therapy use (AI or tamoxifen) by overweight and one for endocrine therapy use (AI or tamoxifen) by obesity. These interaction terms compared whether the HR for AI (or tamoxifen) users with overweight (or obesity) relative to AI (or tamoxifen) users with normal weight were statistically different, which would suggest that the associations of these endocrine therapies on the development of these cardiometabolic factors varied by baseline BMI category. P-values ≤ 0.05 were considered statistically significant and analyses were completed using SAS version 9.4 (SAS Institute Inc., Cary, NC). ## Results The KPNC EHR identified8,985 hormone-receptor positive BC survivors eligible for analysis, of whom $16.4\%$ ($$n = 1$$,472) were premenopausal and $83.6\%$ ($$n = 7$$,513) were postmenopausal. Among premenopausal BC survivors, $82.3\%$ ($$n = 1$$,212) used tamoxifen. Among postmenopausal survivors, $7.3\%$ ($$n = 546$$) used tamoxifen, while $77.0\%$ ($$n = 5$$,788) used AIs. Participants in our sample were followed over mean (SD) 7.8 (3.8) years. Table 1 describes the baseline characteristics of the study population by menopausal status and endocrine therapy use. For premenopausal women with hormone-receptor positive BC, tamoxifen and non-users of endocrine therapy were similar in neighborhood education and income, BMI, smoking status, and prevalence of diabetes, dyslipidemia, and hypertension. However, tamoxifen users were slightly younger (mean age 44.0 vs. 46.2 years, $p \leq 0.001$), more likely to be Asian or Pacific Islander ($24.7\%$ vs. $15.4\%$, $$p \leq 0.001$$), AJCC Stage II or III ($46.9\%$ vs. $29.3\%$, $p \leq 0.001$), and have received chemotherapy ($57.2\%$ vs. $31.5\%$, $p \leq 0.001$). Among postmenopausal hormone-receptor positive BC survivors, tamoxifen, AI, and non-users of endocrine therapy had different baseline characteristics (Table 1). Compared with non-endocrine users, postmenopausal tamoxifen users had similar racial/ethnic distributions, neighborhood incomes, and BMI levels. However, tamoxifen users were younger (mean age 66.6 vs. 71.1 years, $p \leq 0.001$), diagnosed at AJCC Stages II-III ($30.6\%$ vs. $23.1\%$, $$p \leq 0.001$$), never smokers ($57.9\%$ vs. $51.3\%$, $$p \leq 0.04$$), have a history of chemotherapy ($18.5\%$ vs. $9.5\%$, $p \leq 0.001$) and radiation ($61.4\%$ vs. $49.4\%$, $p \leq 0.001$), and were less likely to have diabetes ($13.0\%$ vs. $18.5\%$, $$p \leq 0.005$$), dyslipidemia ($46.2\%$ vs. $52.2\%$, $$p \leq 0.02$$), and hypertension ($47.8\%$ vs. $57.0\%$, $p \leq 0.001$). In contrast, postmenopausal AI users were similar in neighborhood education levels, smoking status, and prevalence of diabetes, dyslipidemia, and hypertension relative to non-endocrine users, but were more likely to be Asian or Pacific Islander ($13.1\%$ vs. $9.8\%$, $p \leq 0.001$), AJCC Stage II ($32.8\%$ vs. $18.3\%$, $p \leq 0.001$), with obesity ($40.1\%$ vs. $30.5\%$, $p \leq 0.001$), and have received chemotherapy ($29.9\%$ vs. $9.5\%$, $p \leq 0.001$) and radiation therapy ($68.9\%$ vs. $49.4\%$, $p \leq 0.001$). The hazard rate ratios of incident diabetes, dyslipidemia, and hypertension by baseline menopausal status for tamoxifen or AI users relative to non-users of endocrine therapies are shown in Table 2. Tamoxifen use was not associated with developing diabetes or dyslipidemia in either premenopausal or postmenopausal BC survivors, even after adjusting for known confounders. However, postmenopausal tamoxifen users had a HR of 1.43 ($95\%$ CI: 1.06, 1.92) of developing hypertension relative to postmenopausal non-endocrine users, after adjustment. AI use was associated with higher HR of incident diabetes, dyslipidemia, and hypertension in unadjusted models, and these hazard rates remained statistically significant after adjustment (Table 2). The HR of diabetes attenuated from 1.74 ($95\%$ CI: 1.34, 2.25) in unadjusted models to 1.37 ($95\%$ CI: 1.05, 1.80) after adjusting for confounders. For dyslipidemia, adjustment also slightly attenuated the unadjusted HR of 1.62 ($95\%$ CI: 1.34, 1.96), but remained high at 1.58 ($95\%$ CI: 1.29, 1.92). However, the 1.50 ($95\%$ CI: 1.24, 1.82) adjusted HR of hypertension was an increase from 1.39 ($95\%$ CI: 1.16, 1.67) in unadjusted models. The association of developing diabetes, dyslipidemia, and hypertension by BMI category in AI or tamoxifen users compared with non-endocrine users is shown in Table 3. The HR of developing diabetes, dyslipidemia, or hypertension in premenopausal tamoxifen users relative to premenopausal non-users of endocrine therapy and in postmenopausal AI users compared with postmenopausal non-users of endocrine therapy was not different between the BMI groups. Similarly, the HR of incident dyslipidemia and hypertension by BMI group were similar for postmenopausal tamoxifen users relative to postmenopausal non-users of endocrine therapy (Table 3). However, postmenopausal tamoxifen users with obesity had a HR of 2.05 ($95\%$ CI 1.13, 3.74) for diabetes relative to postmenopausal non-users of endocrine therapy with obesity. This HR was close to statistically different to the HR of 0.76 ($95\%$ CI: 0.26, 2.20) for incident diabetes found in postmenopausal tamoxifen users with normal weight relative to postmenopausal non-users of endocrine therapy with normal weight (p-value for interaction = 0.08). ## Discussion This analysis of endocrine therapy use in 8,985 hormone-receptor positive BC survivors followed over an average 7.8 years showed that AI use was associated with higher rates of incident diabetes, dyslipidemia, and hypertension in postmenopausal women, and that these rates did not vary by BMI group at baseline. Tamoxifen use was not associated with developing dyslipidemia in either premenopausal or postmenopausal women or by BMI group at baseline. However, postmenopausal tamoxifen users may have higher hypertension rates and postmenopausal tamoxifen users with obesity might have higher rates of incident diabetes than tamoxifen users with normal weight. We reported an increased risk of developing diabetes among postmenopausal AI users, but no association was detected among premenopausal tamoxifen users compared with hormone-receptor positive BC survivors with no history of endocrine therapy use for BC treatment. Further, we found a possible higher diabetes risk in postmenopausal tamoxifen users with obesity when compared with the diabetes risk in postmenopausal tamoxifen users with normal weight. Previously published studies on these relationships are inconsistent. In a study of 133,171 BC survivors identified the National Health Insurance Service database of Korea, incident diabetes was associated with a statistically significant hazard ratio of 1.24 for postmenopausal AI users and of 1.24 and 1.26 for premenopausal and postmenopausal tamoxifen users, respectively, relative to non-users [25]. A similar investigation using 22,257 BC survivors from the Taiwanese National Health Insurance Research Database reported a 1.32 rate of diabetes in tamoxifen survivors compared with non-endocrine therapy users, but a protective association (HR = 0.68, $95\%$ CI: 0.60–0.78) with incident diabetes in AI users [26]. A meta-analysis of seven observational studies described a pooled adjusted diabetes risk of 1.30 ($95\%$ CI: 1.20, 1.40) for tamoxifen users and no association for AI users compared with non-users [27]. Prior publications have also reported no association of diabetes risk in tamoxifen users when compared with non-users [28] or AI users [29], while another study reported no relationship with incident diabetes in either tamoxifen or AI users when compared with healthy non-BC controls [30]. Like many of these previous studies, we also compared endocrine therapy users with BC survivors who had no history of endocrine therapy use for treatment, but we limited our sample to only those with hormone-receptor positive BC and it is unclear whether only hormone-receptor positive cases were examined in these other studies. Prior studies also did not appear to stratify by menopausal status or BMI groups, so impacts of aging ovarian function and overweight or obesity make comparisons with our analyses challenging. AI only, but not tamoxifen only, users in our analysis were at increased rate of developing dyslipidemia. Our nding of no association with dyslipidemia in pre- and postmenopausal tamoxifen users is generally consistent with the existing literature. Studies suggest favorable lipid pro les, including decreased total cholesterol and low-density lipoprotein cholesterol and increased high-density lipoprotein cholesterol, for tamoxifen users in both premenopausal and postmenopausal BC survivors [31–42]. In contrast, prior research frequently reports no significant changes in lipid pro les with AI use [19, 34, 35, 41, 43, 44]. Furthermore, studies investigating dyslipidemia as an outcome endpoint reported no association in BC survivors comparing AI to tamoxifen users [40, 43]. Since tamoxifen use is associated with improvements in lipid biomarkers, its cardioprotective effects may mask any possible deleterious impacts of AI use when compared with each other. It is possible that our use of women with a history of hormone-receptor positive BC who did not use endocrine therapy as a comparison group may provide more clarity about these relationships and contribute to an explanation of our divergent findings. We recommend re-examining this analysis with these same comparators in another large sample of hormone-receptor positive BC women. We also found that postmenopausal tamoxifen and AI users both had higher rates of developing hypertension relative to their postmenopausal non-endocrine therapy user counterparts. Although no variations were observed by BMI group at baseline, postmenopausal AI users with normal weight, overweight, and obesity at baseline all had similar and statistically significant elevated risks of incident hypertension, suggesting robustness in these findings and minimal impact of BMI on these associations. Fewer studies have been published on hypertension risk and endocrine therapy use in BC survivors. A cross-sectional study by Blaes et al. reported higher mean systolic blood pressure in 34 women with hormone-receptor positive BC survivors taking AIs compared with 25 postmenopausal women without BC [45]. However, a meta-analysis using 10 clinical trials by Boszkiewicz et al. found no association of AI use with systemic hypertension when compared with tamoxifen only users or non-users [43]. Similarly, studies have reported no relationship between tamoxifen use and higher systolic or diastolic blood pressures in postmenopausal women [38, 46]. These studies had follow-up times of two years or less and therefore, longer-term associations of AI and tamoxifen use on blood pressure could not be determined. Longer follow-up of endocrine therapy users may result in positive associations with incident hypertension. More studies are needed to con rm our findings. This study has limitations. We did not consider specific details of endocrine therapy, such as dose, duration of use, length of time since treatment concluded during the follow-up period, and specific types of AIs (e.g., steroidal vs. non-steroidal), which might have introduced bias if large variations of these details occurred within our study sample. We excluded women with history of using both tamoxifen and AI, although we acknowledge that sequential use of these endocrine therapies for treatment of hormone-receptor positive BC is common clinical practice. Whether these same results would be observed in BC survivors who took tamoxifen while premenopausal and then switched to AIs after menopause is not known. We also examined cardiometabolic risk factors as binary outcomes and did not include specific, physiological biomarker levels, such as total cholesterol, lipoproteins, blood glucose and insulin, and systolic and diastolic blood pressures. Finally, it is unclear why the hormone-receptor positive BC survivors in our comparison group did not use endocrine therapy for their treatment, despite it being the standard of care for this type of BC. In general, women in the comparison groups were older, diagnosed with lower stages of BC, and less likely to have any history of BC treatment, including radiation, chemotherapy, and surgery. Although we adjusted for these variables in our statistical models, there may still be unmeasured confounding. A future study using the Pathways Heart Study data to improve our understand of the reasons why women do not undergo treatment is in development. Strengths of the study included a large sample size of hormone-receptor positive BC survivors with ample statistical power to examine tamoxifen-only and AI-only associations and relationships by baseline menopausal status and BMI groups. We conducted analyses separately for tamoxifen users and AI users and used a comparison group of non-endocrine therapy hormone-receptor positive BC survivors, resulting in clearer associations on the impacts of these therapies on cardiometabolic risk. Studies comparing tamoxifen and AI users were likely confounded due to the potential cardioprotective bene ts of tamoxifen use, thus challenging the ability to identify potential associations of cardiometabolic risk with AI use [4]. In conclusion, postmenopausal hormone-receptor positive BC survivors treated with AIs may have increased risk of incident diabetes, dyslipidemia, or hypertension over an average 7.8 years of follow-up, while postmenopausal tamoxifen users may have increased risk of developing hypertension. It is unknown whether these associations in cardiometabolic risk factors translates to increased risk of future cardiovascular and cerebrovascular disease or poorer long-term cardiovascular health in these women. 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--- title: Prevalence of depression among elderly women in India-An intersectional analysis of the Longitudinal Ageing Study in India (LASI), 2017-2018 authors: - Paramjot Panda - Prashansa Dash - Manas Behera - Trupti Mishra journal: Research Square year: 2023 pmcid: PMC10055648 doi: 10.21203/rs.3.rs-2664462/v1 license: CC BY 4.0 --- # Prevalence of depression among elderly women in India-An intersectional analysis of the Longitudinal Ageing Study in India (LASI), 2017-2018 ## Abstract Epidemiological transition in India shows a shift in disease burden from youth to the elderly. As Life Expectancy increases, a greater burden is placed on the state, society, and families in India. Mental health disorders are insidious, debilitating Non-Communicable Diseases (NCDs) that afflict people, their families, and generations down the line. Globally, depression is the leading cause of mental health-related disability. It is estimated that mental illness contributes to $4.7\%$ of Disability Adjusted Life Years (DALYs) in India. It is predicted that by 2026, the elderly's sex ratio will increase to 1,060 feminizing ageing. Research has shown that elderly women in developed countries like the United States are more prone to depression. Chronic morbidities are more common in women than in men, and they may suffer from poor vision, depression, impaired physical performance, and elder abuse. Mostly widowed, economically dependent, lacking proper food and clothing, fearing the future, and lacking proper care, they have difficulty coping with these health problems. There are surprisingly few studies on elderly female depression. Therefore, we want to hypothesize the prevalence of depression among women in different regions and demographic groups in India, and what factors may contribute to these differences. Using intersectional analysis with the data from Wave 1 [2017-2018] of the (Longitudinal Ageing Study in India) LASI ($$n = 16$$,737) we were able to explore the intersecting patterns between different variables and how people are positioned simultaneously and position themselves in different multiple categories based on the type of place of residence, age and level of education. Through the study we further aim to determine the prevalence of depression among elderly female in the age group of 60 in different states using the Chloropleth map. The findings of the study highlight the significance of the place of residence in the development of depression among elderly women, with the rural area being associated with a higher prevalence of depression compared to urban area. When compared to people with higher literacy, those with low literacy were significantly associated with depression. State-wise, there is a huge difference between the prevalence of elderly women depression in rural and urban areas. The study highlights the vulnerability of elderly women to depression. It is possible for the government to develop programs that address the needs of elderly women, both in urban and rural areas, to reduce depression. Multi-factor approaches to mental health, which consider age, literacy, and location, are essential. Programs targeting specific populations can be developed to address depression's root causes.. ## Introduction People and their families, and generations down the line, suffer greatly from mental health disorders, an insidious, often debilitating form of Non Communicable Diseases(NCDs)1. The greatest mental health-related burden is attributed to depression, which is a leading cause of disability worldwide2. Mental health impairment worsens a number of NCDs risk factors, such as poor lifestyle choices resulting in obesity, inactivity, and tobacco abuse, poor health literacy, and a lack of access to health promotion activities3-5. As per the WHO Global Burden of Disease Report globally, $4.4\%$ of the population is estimated to be depressed in 2015 6. The report also says that around the world, 322 million people are suffering from depression 7. There is a higher incidence of depression among females ($5.1\%$) than among males ($3.6\%$). It is estimated that this number is growing each year. Even though women live longer than men, research shows they are more prone to certain diseases, which can ultimately shorten their lifespan8. Strokes, depression9, Alzheimer's10, and autoimmune diseases11 such as multiple sclerosis and rheumatoid arthritis12 are among them. Most of these people live in the South-East Asia Region and the Western Pacific Region, due to the fact that these regions have much larger populations than the others (which include India and China, for example)13-15. According to estimates from 2015, the number of Years Lived with Disability (YLD) related to depression amounted to more than 50 million around the world7. In terms of the prevalence of non-fatal health loss, depression disorders are ranked as one of the most significant contributors ($7.5\%$ of all YLD) 6. As per the WHO report for low- and middle-income countries, depression poses a significant public health challenge due to its comorbidity with chronic physical disease 16. It is estimated that depression has a 2-4-fold higher prevalence in patients with chronic diseases such as cancer, diabetes mellitus, stroke, or cardiovascular disease, and the disease may last longer 16. Mental disorders such as depression and anxiety contribute to the escalation of non-communicable diseases through non-adherence to treatment 17. Those with mental disorders may have a harder time accessing healthcare, treatments may require behavioural changes that may be harder for them. Stigma associated with mental disorders is identified as a barrier to access the health facilities 3,18. According to the findings of a population-based study19, it was found that older people with multimorbidity are more likely to experience depressive symptoms in later life 19. Researchers have identified that functional health factors acts as a mediator between multimorbidity and depression, especially when it comes to older women and very few have reported it 20. Also most of the researches have recommended further longitudinal research to understand functional and behavioural health in multimorbidity-depression relationships20-22. The WHO says depression is $50\%$ more prevalent in women than in men, and Indians are among the most depressed worldwide18 As India is the most populous country and the largest democracy, is now emerging as the sixth-largest economy in the world. It is recently seeing a demographic transition with increase in elderly population. In accordance with the United Nations definition of a "Graying Nation", a country is defined as a greying country where the percentage of people who are over 60 years of age is at least $7\%$ of its total population23. There were almost $7.7\%$ of people in India, at the dawn of the millennium, were old, and this figure increased to $8.6\%$ in 2011, and $9.4\%$, in 2017 24. Also most of the researcher have forecasted by seeing the trend that by the year 2050, there will be 20 % of the elderly people (almost 300 million) 25-27. From 2011 through 2041, India is forecast to gain a demographic advantage due to a larger proportion of the population in the working age group 25. And after 2041, when the aging burden shall begin, the older population may contribute to second demographic growth by accumulating capital from their savings accumulated during their working years 28. But this depends on developing financial markets, a healthy older population, and social security, all of which seems to be daunting at the moment. Additionally, due to the epidemiological transition, a large portion of the burden of disease has been shifted from the youth to the elderly29. Non Communicable Diseases (NCDs) exceeded $50\%$ in the 30–34 age group and were highest at $78.8\%$ in the 65–69 age group 29. An increasing Life Expectancy can be attributed to increased longevity and growing society, but it can also be attributed to an increased demand for healthcare facilities, placing an increased burden on the state, society, and families in India19. Out-of-pocket health expenses account for more than $70\%$ of health expenditures in India, leaving the older population vulnerable to health problems 30.The Disability Adjusted Life Year (DALY) rate between 1990 and 2016 was the highest for diseases such as diabetes ($80.0\%$), ischaemic heart disease ($33.9\%$), and sense organ diseases (mainly vision and hearing loss disorders $21.7\%$) 31. In India, mental illness is prevalent and pervasive, especially among older adults living in a distressed socioeconomic situation 29. Researches have reported due to the social stigma of mental illness in older adults and the lack of trained mental health professionals, the prevalence of mental illness among older adults is higher than the reported figures 32,33.There were 197 million people in India who lived with a mental disorder in 2017. Of those people, 45 million suffered from depression and another 44 million from anxiety 34. Mental disorders are a major contributor to the total number of DALYs in India, and their share increased from 2·$5\%$ in 1990 to 4·$7\%$ in 201734. There is a high incidence of depression among the elderly population of India with females being predominant in the group 35. As per the census 201136 the majority of older Indian adults live in rural areas, over $70\%$ of whom are illiterate, while over half of them do not have a source of income 37. Quacks, folk healers, or AYUSH practitioners provide health and mental health care to older adults in rural areas, since allopathic doctors and hospitals are far away in urban areas and elderly people often have difficulty approaching hospitals 38. Though there are scarce research publications available related to depression among elderly population in rural area, but those available shows that as a result of different population characteristics, depression prevalence is slightly but significantly higher in rural areas than in urban areas 39. However, these studies are conducted in a small population. In a study in South India it was revealed that the prevalence of depression varies among rural and urban area35,40. In India National Mental Health Policy,2014 aims to reduce distress, disability, exclusion morbidity and premature mortality associated with mental health problems across the life-span of a person 41. Nevertheless, a larger part of the policy is focused on ensuring the mental health of the population as a whole, with little emphasis on mental health of the elderly 42. The reality is, with the presence of mental health disorders and comorbid conditions, the elderly population is more likely to suffer from mental health problems, contributing to a larger burden of dual disease in the country as a whole 43. Though mental disorders are studied in different parts of India, including in the National Mental Health Survey there has been very limited resource available which highlights the prevalence of state-wise and gender-wise depression its association with disability-adjusted life years (DALYs). Researches have revealed that female elders are more likely to suffer from physical and mental disabilities that greatly reduce their quality of life44. Studies in the United States have shown that elderly women are more susceptible to depression, experience longer and more persistent depression, and have lower mortality rates once depressed44,45. There is no doubt that late-life depression poses a significant public health problem since it is widespread and expensive, associated with disability, re-hospitalization, and even death among those with chronic diseases46,47. According to the literature Compared to elderly men, women are more likely to suffer from chronic morbidity48, poor vision49, cataracts, high blood pressure, back pain/slipped disk, malnutrition50, depression10, impaired physical performance51, and elder abuse52, women have difficulty coping with these health problems because they are widowed, economically dependent, lack proper food and clothing15, fear the future, lack care, and suffer from progressive health decline 53. However, existing policies and programmatic capacities are inadequate and lack gender sensitivity to address the socioeconomic and health needs of women. By utilizing secondary data from Longitudinal Ageing Study of India (LASI), this study fills a gap by identifying the prevalence of common mental disorders like depression in elderly females in India by identifying a number of factors related to it, especially depression, in females in India and its trend with respect to age, economic status, place of residence, marital status, alcohol consumption, tobacco consumption, and physical activity. The study also shows its trend in urban as well as rural areas. It will provide valuable insight to policymakers so that they can develop the necessary policy implications to address the rapidly increasing rate of depression among the female elderly population in India. ## Aim and Objective: What is the prevalence of depression among women in different regions and demographic groups in India, and what factors may contribute to these differences? ## Results Their distribution was calculated using descriptive statistics by gender, age, place of residence, and education status. Binary Logistic Regression was used to estimate the prime factors associated with depression since depression was a binary yes or no variable. Unadjusted odd ratio was obtained through the first regression model to control other variables. Our second regression model takes into account other variables such as age, place of residence, marital status, education, employment, household income, alcohol, tobacco, physical activity, and yoga to determine the risk factors for depression. Using intersectional analysis, we explored how people are positioned simultaneously according to their place of residence, their age, and their level of education, as well as how they position themselves in different multiple categories. To understand the distribution of depression prevalence among Indian states and Union Territories, we have plotted the Choropleth map using the GeoDa software. The study collected data on various variables such as age, place of residence, marital status, education, employment status, household income, alcohol consumption, tobacco consumption, physical activity, and depression. Age was categorized into four groups, and place of residence was categorized into rural and urban areas. Marital status was categorized as a nominal variable, and education status was based on the number of years of schooling. Employment status was reported as a nominal variable with Yes for those who were currently working and No for those who were not. Household income was categorized into five groups. Alcohol and tobacco consumption were captured as nominal variables with Yes for those who consumed them and No for those who did not. Physical activity and yoga were reported as a nominal variable with Yes for those who did them regularly and No for those who did not. The study's outcome variable was depression, which was assessed using the Composite International Diagnostic Interview-Short Form (CIDI-SF) scale, with Yes for those who had depressive symptoms and No for those who did not. The study ensured reliability and validity by training non-clinicians to collect data using the CIDI-SF tool in the local language. Table 1 The study involved 14553 women who were 60 years old or older, and those below 60 were excluded from the analysis. The percentage of participants in the 60-64 age group was $33.6\%$, while the proportion decreased as age increased, with women over 75 accounting for $24.4\%$. This could be due to increased life expectancy and better access to healthcare. The majority of participants ($63.8\%$) were from rural areas, and most ($90.2\%$) were married. Nearly half ($46.2\%$) had some basic education, while $31.6\%$ had completed 10 or more years of education. The majority ($60.7\%$) of the women were employed. Household income was distributed equally across most wealth categories. Only a small percentage of participants consumed alcohol ($3.9\%$), while $19\%$ used tobacco and only $12.2\%$ engaged in regular exercise. ## Geoda Software Fig 1 The data indicates that the highest prevalence of depression was observed in certain states, with Punjab, Uttar Pradesh, Madhya Pradesh, and Gujarat having rates of $25.4\%$, $34.5\%$, $25.1\%$, and $26\%$, respectively. The breakdown by state can provide policymakers with valuable insights into which areas are experiencing a greater burden of depression. This information can be used to identify patterns and develop effective strategies to address the problem. Table 3 regression analysis was conducted to determine the risk factors associated with depression. Initially, an unadjusted odds ratio was calculated to examine the relationship between the independent variables and the outcome variable. Subsequently a second model was developed to identify the risk factors. Certain variables, such as Age, were grouped into categories (60-70, 71-80, and over 81) to ensure consistency, while Place of Residence was classified as either Rural or Urban, with Urban serving as the reference group. Marital Status was recategorized as Married, Widowed, or Separated, with Married being the reference group. Education was divided into two categories, Illiterate and Literate, with Literate as the reference group. Finally, Wealth Quintile was categorized as Poor, Middle, and Rich, with individuals in the Rich category serving as the reference group. The table shows that participants living in rural areas had higher odds of developing depression in the unadjusted odds ratio, but it was not statistically significant. However, after adjusting for other variables, the odds of developing depression among rural residents increased to 1.26 (1.15-1.336), which was statistically significant with $p \leq 0.05.$ Additionally, individuals who were widowed or separated had 1.53 (1.40-1.65) times higher odds of developing depression compared to married participants. Low levels of literacy were also significantly associated with the development of depression, with people having higher levels of literacy being less likely to develop depression due to better cognitive abilities and knowledge about health. Furthermore, individuals belonging to the poor wealth quintile had higher odds of developing depression than those in the rich wealth quintile, with the odds decreasing as wealth increased. Consumption of tobacco was a risk factor for depression, with those who consumed tobacco having 1.20 times higher odds of developing depression compared to non-users. Although alcohol consumption was also a risk factor, adjusting for other variables changed the direction of the relationship, with a significant p-value of less than 0.05. Finally, engaging in physical activity was initially a protective factor against depression, but after adjusting for other variables, it became a risk factor with odds of 1.01 (0.94-1.09) and $p \leq 0.05.$ The study discovered that depression was prevalent in rural areas, particularly in states such as Uttar Pradesh, Bihar, Madhya Pradesh, Maharashtra, Himachal Pradesh, Punjab, Uttarakhand, Rajasthan, Arunachal Pradesh, Nagaland, Tripura, Meghalaya, Assam, Jharkhand, Odisha, and Chhattisgarh, where the prevalence was over $75\%$. In contrast, urban areas in Tamil Nadu, Maharashtra, Madhya Pradesh, Delhi, and Lakshadweep had a depression prevalence of over $75\%$. The data provided shows the prevalence of depression across different age groups, literacy statuses, and locations. The prevalence of depression is higher among illiterate individuals compared to literate individuals across all age groups and locations. Depression rates are also generally higher among rural populations compared to urban populations, especially for illiterate individuals. This information could be useful for healthcare professionals, policymakers, and organizations that aim to address and prevent depression in different populations. For instance, the higher prevalence of depression among illiterate individuals suggests that interventions that focus on improving literacy rates could potentially have a positive impact on reducing depression rates. Additionally, the higher prevalence of depression among rural populations highlights the need for targeted interventions and resources to address mental health in rural areas. However, it’s important to note that the data only provides information on the prevalence of depression and does not provide any information on the causes or risk factors for depression among these populations. Furthermore, the data only includes a limited set of intersectional variables, and other factors such as socioeconomic status, gender, or race could also be important in understanding depression rates in different populations. ## Discussion The study participants consisted of 16,737 women aged 60 and above, with participants under the age of 60 excluded from the analysis. The demographic characteristics of the participants were analysed and several important findings were observed. The age group of 60-64 years was the largest with $33.6\%$ of the participants, while the proportion of participants decreased as age increased, with $24.4\%$ of the participants aged over 75 years. This increase in the proportion of older participants is likely due to improved access to healthcare and increased life expectancy. The majority of the study participants, $63.8\%$, were from rural areas, and $90.2\%$ were married. A significant proportion, $46.2\%$, had received basic schooling, while $31.6\%$ had completed at least 10 years of education. The employment status of the participants showed that $60.7\%$ were working, with household income being relatively equal among the different wealth categories. The study also found that a small proportion of the participants, $3.9\%$, consumed alcohol, while $19\%$ consumed tobacco, and only $12.2\%$ reported regularly exercising. These findings are similar to the results of the 54, which reported that a majority of women in India reside in rural areas, have low levels of education and employment, and engage in unhealthy behaviours such as tobacco and alcohol consumption. It is important to note that these findings provide important insights for policymakers and public health practitioners, highlighting the need for targeted interventions to address the health needs of older women in India, particularly those in rural areas. Further research is needed to understand the impact of demographic and socioeconomic factors on health and well-being in this population, and to develop effective strategies to address the challenges faced by older women in India. The findings of the study on depression levels and aging are similar to the Global Adult Tobacco Survey (GATS) 55, which also found that tobacco use is associated with increased levels of depression. GATS also found that individuals with higher levels of education and those who are employed are less likely to use tobacco and have lower levels of depression. Similarly, the study found that physical activity is associated with lower levels of depression, while GATS found that individuals who are physically active are less likely to use tobacco. However, GATS did not specifically address the relationship between wealth quintiles and depression levels. The study's finding that depression levels were higher among rural participants compared to urban participants is not addressed in GATS. The findings of the study are consistent with other reports regarding depression and risk factors 56,57. The study found that depression levels increased with age from 60 to over 75 years, which is similar to other studies that have shown that older adults are at increased risk for depression 35,58. Additionally, the study found that depression levels were higher among individuals who consumed alcohol or tobacco, which is also in line with other reports that have linked substance use with increased risk for depression 59,60. However, the study also found some unique findings. For example, the study found that a higher proportion of rural participants reported increased levels of depression compared to urban participants, which is not typically reported in other studies. Additionally, the study found that regularly engaging in physical activity was associated with lower levels of depression, but after adjusting for other variables, physical activity became a risk factor, which is also not a commonly reported finding. The study's findings on the associations between depression and education, wealth quintile, and marital status are also consistent with other reports. Education, wealth, and marital status have all been linked with mental health, and the study's findings provide further support for these relationships 27,61. Overall, the study adds to the growing body of literature on depression and its risk factors, particularly in older adults. Further research is needed to better the findings of this study that rural areas have a higher prevalence of depression compared to urban areas aligns with previous studies and reports on the topic. This disparity could be due to factors such as lack of access to healthcare, lower socio-economic status, and cultural stigma surrounding mental health in rural areas 60,62. However, it's worth noting that while the study found that depression levels were higher in rural areas, there were also some urban areas like Chandigarh, Delhi and Lakshadweep where depression levels were higher. This indicates that the relationship between urbanization and depression levels is complex and not straightforward. Further research is needed to fully understand the factors contributing to the higher levels of depression in both rural and urban areas. understand the complex relationships between depression, demographics, and risk factors. The findings of the study highlight the significance of the place of residence in the development of depression among elderly women, with the rural area being associated with a higher prevalence of depression compared to the urban area. The results show that the prevalence of depression among illiterate women in the rural area was $20.5\%$ for women aged 65-69, and $18.9\%$ for women over 75 years of age, compared to a prevalence of $13\%$ for women over 75 years of age in the urban area. This shows a reduction of 6 points in the prevalence of depression in the urban area compared to the rural area which is in line with the other studies 23,35. Additionally, the study found that the trend of depression prevalence increased with age in the rural area and decreased with age in the urban area. Similarly, the prevalence of depression was found to be higher among literate women over 75 years of age in the rural area ($28.6\%$) compared to urban area ($14\%$) which was just half the prevalence in rural area. The intersectional analysis indicates that the type of place of residence is the major factor for the development of depression, although the underlying cause was not established in the study. The strength of this study lies in its use of intersectional analysis, which considers the intersection of multiple factors, such as age, literacy, and place of residence, in the development of depression. This provides a more nuanced understanding of the complex relationships between these factors and the development of depression. However, the weakness of this study is that the underlying cause of the relationship between place of residence and depression was not established. Further research is needed to understand the specific factors that contribute to the higher prevalence of depression in the rural area and the protective effect of urban residency on depression. Additionally, the study is limited by the small sample size and the lack of data on other possible risk factors for depression. Overall, the study provides important insights into the relationship between place of residence and depression among elderly women and highlights the importance of considering multiple factors in the analysis of mental health outcomes. Policy implications and recommendations based on this study would include: Limitations of the study: ## Methodology The data from the Longitudinal ageing study in India (LASI) Wave 1 (2017–2018) was used to understand the burden of depression among older women above 60 Years in India and to explore the geographic distribution of depression in India. A public domain LASI dataset was obtained from the Gateway to Ageing Portal once the abstract submission was approved. In light of the fact that the data is secondary data, both national and international forums have approved the use of the data. A number of filters were applied to the data to obtain 16,637 samples from women aged 60 and older. Their distribution was calculated using descriptive statistics by gender, age, place of residence, and education status. The data from the Longitudinal ageing study in India (LASI) Wave 1 (2017–2018) was used to understand the burden of depression among the older women above 60 Y in India and to explore the geographic distribution of the depression in India. LASI is the first longitudinal dataset in India to provide a reliable basis for designing policies and programmes for the older population's social, health, and economic wellbeing. LASI uses Computer-Assisted Personal Interview (CAPI) technology, internationally Harmonized/ Gold Standard Survey Protocol, Comprehensive Range of Biomarkers. Multistage stratified area probability cluster sampling design is used for selecting the representative sample in each stage. The eligibility criteria was older adults aged 45 Years and above (including spouses irrespective of age).The eventual unit of observation of LASI was LASI-eligible household (LEH) with at least one-member age 45 and above. LASI adopted a multistage stratified area probability cluster sampling design to arrive at the eventual units of observation. All the 30 Indian States and six Union Territories were selected for the survey. The states were further divided in to Districts, Sub districts, Talukas, Tehsils and Blocks. The samples were selected in four stages, where in the first state was for selection of Primary Sampling Unit (PSU) and second and third stage was for selection of Secondary Sampling Unit (SSU), and fourth stage was for selection of households. For assessing depression, the tools used are Centre for Epidemiologic Studies Depression (CES-D) to find the symptoms of depression and Composite International Diagnostic Interview-Short Form (CIDI-SF) scale to diagnose major depression. For more details please refer to LASI India Report 2020 at 29. It is a cross sectional study aimed to explore the factors responsible to determine the prevalence among elderly women of 60 and above in India using the intersectional analysis. We were able to explore the intersecting patterns between different variables and how people are positioned simultaneously and position themselves in different multiple categories based on the type of place of residence, age and level of education. Through the study we further aim to determine the prevalence of depression among elderly female in the age group of 60 in different states using the Chloropleth map. Binary Logistic Regression was used to estimate the prime factors associated with depression since depression was a binary yes or no variable. Odds Ratio was calculated using clinical and demographic variables. Unadjusted odd ratio was obtained through the first regression model to control other variables. Our second regression model takes into account other variables depicted in Fig:4 to determine the risk factors for depression with a confidence interval of.001. Using intersectional analysis, we explored how people are positioned simultaneously according to their place of residence, their age, and their level of education, as well as how they position themselves in different multiple categories and if conditional probability of depression exists in all categories. Our next step was to compute the prevalence of depression at the state level. To understand the distribution of depression prevalence among Indian states and Union Territories, we have plotted the Choropleth map using the GeoDa software. ## Conclusion In conclusion, the study analysed the prevalence of depression among elderly women aged 65 to 69 and more than 75 in rural and urban areas. The results showed that the prevalence of depression among rural elderly women was higher compared to urban elderly women. However a disparity among states was found. The trend of prevalence increased with age among rural women and decreased with age among urban women. Additionally, the intersectional analysis showed that the type of place of residence was the major factor for the development of depression. The study highlights the need for mental health policies and interventions to address the higher prevalence of depression among elderly rural women. This may involve providing access to mental health services, creating community support systems, and raising awareness about mental health in rural areas. Furthermore, there is a need for further research to understand the underlying causes of depression among rural elderly women. While the intersectional analysis provides a useful insight into the complex relationship between demographic factors and mental health, it is important to acknowledge its limitations. The study did not establish the cause of depression, and it was beyond the scope of this analysis. Moreover, the study relied on self-reported data, which may be subject to bias. In summary, the study provides valuable information on the prevalence of depression among elderly women in rural and urban areas and the importance of considering the intersection of demographic factors in understanding mental health. 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--- title: 'A Three-Arm Randomized Controlled Trial Using Ecological Momentary Intervention, Community Health Workers, and Video Feedback at Family Meals to Improve Child Cardiovascular Health: The Family Matters Study Design' authors: - Jerica M. Berge - Amanda C. Trofholz - Marah Aqeel - Kristin Norderud - Allan Tate - Angela R. Fertig - Katie Loth - Tai Mendenhall - Dianne Neumark-Sztainer journal: Research Square year: 2023 pmcid: PMC10055649 doi: 10.21203/rs.3.rs-2662682/v1 license: CC BY 4.0 --- # A Three-Arm Randomized Controlled Trial Using Ecological Momentary Intervention, Community Health Workers, and Video Feedback at Family Meals to Improve Child Cardiovascular Health: The Family Matters Study Design ## Abstract ### Background: Numerous observational studies show associations between family meal frequency and markers of child cardiovascular health including healthful diet quality and lower weight status. Some studies also show the “quality” of family meals, including dietary quality of the food served and the interpersonal atmosphere during meals, is associated with markers of child cardiovascular health. Additionally, prior intervention research indicates that immediate feedback on health behaviors (e.g., ecological momentary intervention (EMI), video feedback) increases the likelihood of behavior change. However, limited studies have tested the combination of these components in a rigorous clinical trial. The main aim of this paper is to describe the Family Matters study design, data collection protocols, measures, intervention components, process evaluation, and analysis plan. ### Methods/design: The Family Matters intervention utilizes state-of-the-art intervention methods including EMI, video feedback, and home visiting by Community Health Workers (CHWs) to examine whether increasing the quantity (i.e., frequency) and quality of family meals (i.e., diet quality, interpersonal atmosphere) improves child cardiovascular health. Family Matters is an individual randomized controlled trial that tests combinations of the above factors across three study Arms: [1] EMI; [2] EMI+Virtual Home Visiting with CHW+Video Feedback; and [3] EMI+Hybrid Home Visiting with CHW+Video Feedback. The intervention will be carried out across 6 months with children ages 5-10 ($$n = 525$$) with increased risk for cardiovascular disease (i.e., BMI ≥$75\%$ile) from low income and racially/ethnically diverse households and their families. Data collection will occur at baseline, post-intervention, and 6 months post-intervention. Primary outcomes include child weight, diet quality, and neck circumference. ### Discussion: This study will be the first to our knowledge to use multiple innovative methods simultaneously including ecological momentary assessment, intervention, video feedback and home visiting with CHWs within the novel intervention context of family meals to evaluate which combination of intervention components are most effective in improving child cardiovascular health. The Family Matters intervention has high potential public health impact as it aims to change clinical practice by creating a new model of care for child cardiovascular health in primary care. ### Trial Registration: This trial is registered in clinicaltrials.gov (Trial ID: NCT02669797). Date recorded $\frac{5}{02}$/22. ## Background Cardiovascular disease (CVD) is a highly prevalent public health problem [1,2]. CVD is the leading cause of death for one in four adults in the US and affects over $30\%$ of minoritized populations [1]. While CVD peaks in middle age, risk factors begin in childhood and may provide a critical window for intervening to mitigate risk [3]. Children ages 7-10 are at a key age when precursors of CVD begin to be observed, but before the manifestation of disease such as high blood pressure, body mass index (BMI), cholesterol [3,4] and less healthful dietary intake, fewer hours of physical activity, and more sedentary behaviors [5]. To date, there has been low to moderate success with lifestyle behavior interventions for children at risk for CVD and the persistent disparities across race/ethnicity calls for a new and innovative way to intervene [6]. Prior research has identified evidence-based intervention targets and strategies that when combined may provide an innovative approach for improving child cardiovascular health (CVH). Prior interventions to increase child CVH have not been anchored around a specific family context/routine such as family meals. Instead, interventions often take a “kitchen sink approach” targeting multiple home environment factors (e.g., eating, physical activity, sedentary behavior, parenting) across multiple contexts (i.e., home, school, daycare). These interventions have had limited success [18]. Family meals are unique in that they create a nexus where multiple parenting and familial behaviors related to childhood obesity occur simultaneously (e.g., parent feeding practices, interpersonal behaviors, availability of healthy foods, portion size, modeling healthy eating) and can be intervened on–which rarely occurs in any other context. Furthermore, intervening on one specific context/routine (i.e., family meals) may also seem more doable to parents [19]. Second, some observational studies have shown the need to examine family meal “quality” (i.e., dietary intake, interpersonal atmosphere), in addition to family meal quantity, to better understand key protective factors of family meals [7,12,20]. Specifically, prior studies have shown associations between interpersonal interactions (e.g., non-controlling food parenting practices, positive communication/connection) during meals and better diet quality of foods served at family meals (e.g., fruits/vegetables, whole grains), lower child weight status, and higher child diet quality [20,21]. The few existing RCTs examining family meal frequency and child CVD risk found that solely increasing the frequency of family meals was not associated with lower weight status in children [17]. Thus, interventions targeting both family meal quality and quantity will have a higher likelihood of improving child CVH. Additionally, studies have identified barriers to carrying out family meal routines such as busy schedules, parental stress, lack of food prep/cooking skills, and child behaviors (e.g., picky eating) [22,23]. Research by our team showed that parents experiencing high stress levels earlier in the day, were less likely to have family meals, served less healthy foods at mealtimes, and were more likely to engage in controlling feeding practices later the same day [24,25]. Interventions including family meal quantity and quality, as well as strategies to reduce barriers (e.g., stress) to carrying out family meals are needed. Third, research shows that providing immediate feedback on behavior (i.e., ecological momentary intervention (EMI), video feedback) within a specific context (e.g., family meals) results in more behavior change over time [26], compared to solely utilizing parent education [18]. These findings suggest that teaching parents what to do is not enough, rather watching one’s own behavior(s) and receiving feedback that reinforces positive behaviors or prompts different behaviors is necessary. Meta-analyses show that video feedback in parenting interventions is feasible, has low participant burden, results in moderate to large effects on parenting behaviors, and results in sustainable behavior change [27]. Ecological momentary intervention (EMI), or mobile health (mHealth), uses smartphones to send text messages to participants to intervene on behaviors in real-time as they unfold, moment-by-moment, over time and across contexts [28]. For example, a participant responds to a text earlier in the day regarding their stress level and source(s) of stress (e.g., too many things to do) then, an EMI message is sent providing suggestions to support them in making a healthful choice for family meals in the face of stress (e.g., tip for making a quick pasta meal more healthful by adding a vegetable stir in) [29,30]. EMI studies from other fields have shown significant improvement in targeted behaviors (e.g., medication compliance, smoking cessation) [31,32], high feasibility [32], validity and reliability [33,34], few logistical problems [26], and low burden [35]. Fourth, interventions utilizing community health workers (CHWs) who can meet participants “where they are at,” both with regard to readiness for change and in their own environment (i.e., home visiting) are associated with better outcomes [36]. CHWs link care across clinic and home contexts and have high success with addressing obesity [36], diabetes [36] and other chronic conditions [36]. In addition, given the rise of virtual technology during the COVID-19 pandemic, the need to test virtual versus in-person measurement and delivery of home visiting interventions in a rigorous RCT is key to confirm the benefits of these approaches [37]. The main aim of the Family Matters Intervention is to target a well-documented family context associated with child CVH (i.e., family meals) using innovative real-time methods (i.e., EMI, video feedback) with CHWs in both virtual and in-person delivery modes to increase child CVH using a three-arm RCT (see Figure 1). The three Arms include: EMI (Arm 1); EMI+Virtual Home Visiting (HV) with CHW+Video Feedback (Arm 2); and EMI+Hybrid HV with CHW+Video Feedback (Arm 3). Our overall hypothesis is that increasing both the quantity and quality of family meals will improve child CVH. Our main study hypotheses include (see Figure 1): ## Theoretical Framework Family Systems Theory (FST) [38] guides the current study. According to FST, the family environment is the most proximal influence on child CVH [39,40]. FST suggests that intervening on individual-level behavior (e.g., dietary intake) has limited success unless the family-level behavior sustaining or overriding the individual-level behavior (e.g., fruits/vegetables served at family meals, food parenting practices) changes too [39,41]. FST also suggests that healthful behaviors learned in one family context (e.g., family dinner meal) will generalize to other family contexts (e.g., breakfast, lunch, snacks) [41,42]. Thus, in the current study it is expected that positive parenting practices learned in the family meal context will generalize to other eating occasions and contribute to child CVH overall. Also, including multiple family members (e.g., parents, grandparents, siblings) in the intervention increases the likelihood of sustainable family-level change [7,20]. ## Methods This protocol has been written following the guidelines of the Standard Protocol Items Recommendations for Interventional Trials (SPIRIT) checklist (Additional file 1). A SPIRIT figure is also provided below to demonstrate the flow of the study (see Figure 2). ## Study Design The Family Matters intervention is a single site RCT with child as the unit of randomization and analysis (see Figure 3). The study is funded by the National Institutes of Health (HL151978) and is registered at clinicaltrial.gov (Trial ID: NCT02669797; May 2, 2022). This RCT lasts 12 months for each family, with a four month active intervention phase, a two month maintenance phase, and data collection at baseline, 6 months (i.e., post-intervention), and 12 months (i.e., 6 months post-intervention). All study materials are created in both English and Spanish. ## Study Recruitment Children ($$n = 525$$) and their families are recruited via family medicine and pediatric primary care clinics in Minneapolis and St. Paul, MN. Recruitment is ongoing for 42 months. Eligible children receive a letter inviting participation. Parents then fill out a REDCap survey assessing eligibility criteria. ## Study Arms and Randomization Families are randomized into one of three intervention Arms: [1] EMI; [2] EMI+Virtual HV with a CHW+Video Feedback; and [3] EMI+Hybrid HV with a CHW+Video Feedback. All Arms receive 16 weeks (4 months) of EMI stress reduction and family meal tip messages via smartphones. Arms 2 and 3 additionally receive eight home visits by CHWs focused on family meal quantity and quality, a meal preparation activity, and video feedback on their family meal behaviors/patterns every-other-week for 16 weeks. Arm 2 receives all of these components virtually and Arm 3 receives these components half in person and half virtual (hybrid). In between weeks, families in Arms 2 and 3 complete a Try-it-Yourself activity to apply the new skills/behaviors they have been taught. All Arms receive an 8-week (2 months) maintenance phase allowing for progressively less support so they can increase self-efficacy and sustainability of behavior change. Once participants complete their baseline data collection visit, they are randomized into one of three study Arms ($$n = 175$$ per Arm). If households have multiple eligible children, one child is randomly selected to minimize bias that could affect generalizability due to parent selection. Randomization is stratified by five racial/ethnic groups (African American, Hispanic, Native American, Asian American, White; n≈105 per race group). Block randomization schedules were produced in PASS 2021 (Kaysville, Utah) to account for the racial/ethnic stratification. Schedules are maintained by the biostatistician to keep team members blinded. ## Virtual Data Collection. Once child eligibility is confirmed, baseline data collection occurs via a virtual zoom visit including: guided anthropometry [43] and neck circumference measurements, a child 24hr. dietary recall, registration for one-week of Ecological Momentary Assessment (EMA) on their phone [44], and training on video recording of family meals. Following the virtual visit, a 14-day observational period ensues including a parent online survey, two additional 24-hr dietary recalls, ten days of EMA measuring parent stress and parenting practices, and a 2-day video-recorded family meal observation period (1 weeknight, 1 weekend night) measuring family meal quality (i.e., dietary, interpersonal) [45]. Virtual data collection occurs at baseline, 6 months (post-intervention), and 12 months (6-month post-intervention). Primary and secondary outcome measures are described in Table 1 and are collected at all three data collection time points in all Arms. Virtual protocols are based on ours [37] and other’s [7,37] prior studies. Data collection tools and databases (i.e., REDCap) include features to support HIPAA compliance and allows for data checks to ensure data quality during data entry. Access to data collection tools and databases including REDCap and Box will be strictly limited and regulated through personal user profiles. Both of these platforms are password protected and all data will be regularly backed up into a password-protected database. ## Measures. This study has three primary child outcomes: BMI%ile [46], neck circumference [47], and diet quality [48,49]. Secondary outcomes include family meal quantity, meal dietary quality [21], meal interpersonal quality [50], parent outcomes: BMI [51], neck circumference [47], food-related parent practices [52], coping skills, sibling BMI%ile, and others [8,13,39,53–60] (see Table 1). ## Blinding and Investigator Allocation Concealment. As with most behavioral interventions, it is not possible to double blind this RCT. However, this study incorporates measurement staff and investigator blinding as much as possible to minimize bias. For example, the intervention is administered by CHWs who are not involved with measurement team responsibilities or meetings and measurement team members are blinded to participant study Arm assignment and are not involved with intervention team responsibilities or meetings. The biostatistician is the only completely unblinded member of the research team and will be overseeing data management and analyses throughout the trial and will have restricted access to the final study dataset. ## Measurement Team Training and Supervision. Measurement team members are trained, engage in role-plays, conduct mock visits, and are closely supervised by the measurement team director according to best practice [7,63]. Table 2 describes these processes in depth. All practice, certification, and data collection visits are video recorded to allow for thorough supervision of visits where both the measurement team member and their supervisor gives feedback. Measurement team members are also trained on the Iowa Family Interaction Rating Scale (IFIRS) for video coding of family meals and the Nutrition Data System for Research (NDS-R) for dietary recalls [61]. Staff only code families in which they did not participate in the measurement visit [7,20,21]. Practice videos are used until coders reach $95\%$ inter-rater reliability and $100\%$ after consensus meetings; $25\%$ of videos are double coded and checked at a 1:5 ratio to ensure high inter-rater reliability and fidelity to protocols. For NDS-R, quality assurance is conducted on $100\%$ of recalls [64]. ## Retention Plan. To minimize attrition in all study Arms, the following retention strategies are used, based on our successful prior studies with >$95\%$ retention rate and best practice [63,65]: [1] gather extensive participant contact information (e.g., phone numbers, email addresses, home/work addresses, emergency contacts); [2] tailor preferred forms of contact to participants (e.g., texts, phone, email); [3] utilize primary care electronic medical record (EMR) databases for updated contact information; [4] send tracking postcards during important cultural celebrations (e.g., Hispanic Heritage Month, Native American Heritage Day); and [5] use ongoing tracking databases (e.g., LexisNexis, White Pages). Additionally, at 9 months families are sent a small gift (e.g., reusable grocery bag with the Family Matters logo) and a short survey asking them to update their contact information. ## Ethical Considerations. The University of Minnesota’s Institutional Review Board (IRB) Human Subjects Committee approved all protocols used in the study. Prior to enrollment into the study, participants are provided with detailed information about the study by our research team via consent and assent forms including study aims and detailed procedures. Participants are informed that their participation is voluntary and that they have the right to withdraw from the study without any consequences at any point. They will be assured of anonymity in participation and confidentially of any data they provide throughout the study, through the use of study IDs and the storage of sensitive information in secure online platforms (i.e., REDCap and Box). Participants can be enrolled into the study only after they have provided written consent and assent forms to our research team. ## Regulatory Oversight/Monitoring. All study modifications will be communicated with and regulated by the IRB. Even though the study is expected to pose minimal risk, the Data Safety Monitoring Board (DSMB), in collaboration with the study investigators will closely monitor recruitment, process evaluation, and retention activities. The DSMB will meet yearly with the study investigators and staff, or more often as needed, for oversight of the study. Any adverse events will be reported to the NHLBI and the IRB at the University of Minnesota. This trial is also registered in the OnCore clinical trial management system and is audited by the Medical School at the University of Minnesota. ## INTERVENTION The Family Matters three-arm intervention, known as the Family Matters Program to our study families, components and dose are described below. ## Study Arm #1: EMI Parents randomized to study Arm 1 receive EMI text messages twice a day for 16 weeks via their smartphone. A study smartphone is provided for use if needed. ## EMI. Our prior research showed parental stress early in the day was associated with more controlling food parenting practices and serving more unhealthful foods (e.g., fast food) at dinner the same night [66]. Therefore, in all Arms, parents receive EMI text messages to their phones that include two parts. First, the parent is sent a text message with a survey link, between 11am-2pm to report their stress level (i.e., scale of 0-10) and sources of stress (e.g., child demands, busy at home/work, social media). Second, a text message is sent back to the participant from a bank of tips (approximately 50 tips per source of stress) for the particular source of stress they reported. This tailored tip is intended to help them cope with the reported stress and increase the likelihood that they will still carry out a family meal the same evening in the face of stress [66]. After the tip is sent, parents are also asked to report whether or not the tip was helpful, which then adjusts their individual EMI algorithm so there is an increased likelihood of them receiving more or less of these types of tips. If parents report no stress, they receive a tip to facilitate having a family meal (e.g., recipe ideas, meal prep tips, mealtime conversation starters). ## Study Arm #2: EMI+Virtual Home Visiting (HV) with CHW+Video Feedback Parents randomized to study Arm 2 receive all elements of study Arm 1, in addition to home visiting by a CHW. Visits by CHWs are virtual via zoom and occur every-other-week (8 total) simultaneously with the 16 weeks of EMI. In between CHW home visits, families complete “Try-it-Yourself” activities (8 total) to reinforce new behaviors and meal preparation skills (e.g., batch cooking recipe, shopping scavenger hunt, stress reduction coping skills). ## Home Visiting. CHWs carry out the 60-90-minute HVs using Motivational Interviewing (MI) [67,68] and psychoeducation [35]. The visits focus on family meal quantity and quality factors [7,20,21] known to be associated with child CVH. Family members are taught specific skills through didactic and interactive session activities (e.g., AHA Slides, Figma games) to improve family meal processes and behaviors. Session content and activities are described in Table 3. A SMART goal (i.e., specific, measurable, achievable, relevant and time-bound) [69] is set at the end of each session related to the content delivered in the home visit and their video feedback. ## Video Feedback. Every other week, starting during home visit three, families video-record and upload one family meal via their smartphone (6 total meals). CHWs watch videos in between home visits to identify specific clips to show family members at the next visit that highlight both strengths and growth areas regarding interpersonal interactions and dietary patterns. During HVs, CHWs engage family members in a conversation–using MI skills [67] where both the CHW and family members identify positive behaviors seen in the videos and areas for growth, based on session content that families have been learning. ## “Try-it-Yourself” Activities. Families are given food-related (e.g., recipes, meal planning strategies) and interpersonal (e.g., food prep with kids, family meal communication game, stress reduction) activities to try out in between visits to increase their self-efficacy in preparing family meals on their own and reinforce messages they are taught during HVs. The study child and all siblings in the home are also given an activity book with games that reinforce session content. ## Study Arm #3: EMI+Hybrid HV with CHW+Video Feedback Parents randomized to study Arm 3 receive all elements of study Arm 2, but they are delivered hybrid. Specifically, CHWs meet in-person with families every other HV and then virtually via zoom on the other weeks. Families also engage in two cooking demonstration activities with the CHW during in-person HVs to reinforce messages taught, share easy recipes (e.g., batch cooking, one ingredient for multiple meals), and teach food prep skills to increase family’s self-efficacy for having family meals. This Arm is important to examine whether relationship building and creating an atmosphere conducive to health behavior change requires an in-person component. This Arm is also critical to examine COVID’s impact on moving research to virtual modes. ## Maintenance Phase After completing four months of the intervention, all study Arms transition to a two-month maintenance phase, based on best practice [70]. For all study Arms, EMI family meal tips are reduced to the three days per week that parents reported their highest stress levels during the 16-week active intervention phase. Stress profiles corresponding to the high risk stress days are created for each participant to maximize intervention uptake and subsequent sustainability [24,25]. ## Community Health Workers (CHWs) Training and Supervision. Interventionists are racially/ethnically diverse CHWs, with half being Spanish speaking. CHWs are trained/certified in MI [67], SMART goals [69], the intervention content for eight HV sessions, video feedback skills [7,27], and HV protocols. The CHW supervision process provides multiple levels of supervision throughout training and intervention delivery (see Table 2). Co-investigators who provide supervision and the intervention director (MA) are trained in MI and are licensed mental health clinicians (JB, TM) or registered dietitians (KL, DNS). All role-plays, certification, and family intervention visits in all study Arms are video-recorded. Both the CHW and supervisor watch and give feedback on the video-recordings, which allows for thorough feedback. Just as families receive video feedback on their recorded family meals from the CHWs as part of the intervention, the CHWs are given feedback as well, thus creating a parallel process that models to the CHW how to give feedback that is collaborative, focusing on both strengths and areas of growth, with their own intervention families. ## Process Evaluation A robust feasibility and process evaluation protocol was designed for this intervention (see Table 4), to ensure feasibility, generalizability, and dissemination into primary care and other health care settings [70,71]. ## Overview. This study is powered for three pairs of tests [72] to evaluate intervention effectiveness: (a) Arm 2 vs. 1, (b) Arm 3 vs. 1, and (c) Arm 2 vs. 3 over three time points. Multi-level, general linear mixed models (MLMM) with a clinic random intercept that nests participants within clinics to address any clinic differences in the recruitment populations, with participant random slopes for time to examine intervention treatment effects, and conditional fixed effects regression models (within-child analytic contrast against baseline), are the primary analytical models for all study hypotheses. Participants’ randomized condition will be examined irrespective of adherence to the study protocol in accordance with an intent-to-treat (ITT) analysis. After the intervention has been fully administered, data will be assessed for balance across arms, outliers, missingness, and other modeling assumptions. Although randomization is expected to produce balance on measured and unmeasured characteristics, variables will be considered for inclusion as controls in adjusted analyses to reduce test statistic variance [73]. We expect little missing data based on our prior work, but if needed, we will employ methods recommended for clinical trials to minimize analytical assumptions required when missing data are present (e.g., follow up all randomized participants prior to unblinding [74], evaluating if results from the primary analysis differ when sensitivity analyses are performed [75]). Reasons for participant withdrawal and non-adherence will be analyzed and reported in the final ITT analysis [76]. ## Sample Size and Power Computations. Study design features were accounted for in powering the study that required increases in sample size to minimize an inflated experiment-wise error rate (EER) due to three pairwise tests between each treatment arm for three primary outcomes (i.e., BMI%ile, neck circumference, and the Healthy Eating Index (HEI)). Accounting for these nine tests, sample size was determined using a conservative two-sided critical value of $z = 2.77$ ($$P \leq 0.006$$) to achieve experiment-wise Type I error of 0.05. Our power calculations were based on prior studies showing that a decline of two BMI%ile points was a clinically meaningful difference in children with overweight/obesity [46]. BMI%ile is a continuous outcome with a variance of 18.8. Eighteen-month follow up data with a comparable cohort provided intraclass correlation coefficient estimates to inform sample size determination (BMI%ile ICC). At $80\%$ power and multiple-outcome and pairwise testing corrected EER of 0.05, with a sample size of 525, we will be able to detect a minimum average difference in BMI%ile as small as 1.67 (or 0.38 SD) with $15\%$ attrition. This magnitude translates to approximately a 2.8lb difference in a six year-old boy who is 3.8 feet tall and 45 lbs, or approximately a 7.5 ounce per month change in weight by the post-intervention endpoint. ## Aim 1 (Primary Outcomes): Examine intervention effects on markers of child CVH including BMI%ile, diet quality, neck circumference. Treatment condition mean differences on the three primary outcomes will be examined at the post-intervention primary endpoint (6 months after baseline). Sample size determination allows for primary outcome standardized effect size assessment of all three outcomes of at least 0.38, which is a small-to-moderate minimum detectable effect. ## Aim 2 (Secondary Outcomes): Examine intervention effects on family, parental, and sibling factors. Family meal quantity and quality, food parenting practices and stress, and sibling BMI %ile outcomes are powered at similar levels as in Aim 1 with the ability to detect standardized effect sizes as small as 0.38. ## Sub-group Exploratory Analyses: Analyses exploring whether interaction effects depend on participant sex, race/ethnicity, and seasonality will also be conducted. These post-hoc analyses will examine whether the intervention has synergistic effects in specific populations or during different seasonal contexts. Post-hoc analyses will be conducted to explore the interaction of child/parent sex and baseline weight status on intervention treatment effects to determine whether the intervention is particularly effective in certain subpopulations. ## Other Exploratory Hypotheses: A model incorporating an interaction effect of treatment arm crossed with the change in family meal quality and quantity between observation periods will be used to evaluate if increases (or decreases) in the quality and quantity of family meals correspond with synergistically favorable (or unfavorable) child outcomes. This analysis will inform whether intervention effects depend on participants’ changes in family meal quality and quantity. This analysis is powered to detect a between-within intervention slope difference over the 6-month intervention period of as little as 9.6 ounces per month, depending on whether participants had high or low change in the moderating variables. Seasonality robustness checks will also be performed to evaluate whether results differ substantively for participants who received the intensive intervention during the summer months. ## Discussion The Family Matters intervention has high potential public health impact as it aims to change clinical practice by creating a new model of care for child CVH in primary care. Research in this field is needed given the low to moderate success of lifestyle behavior interventions for children at risk for CVD and the persistent high prevalence of disparities across race/ethnicity groups. The state-of-the-art measures being used including EMA, EMI, and video feedback combined with the novel intervention context of family meals and CHWs as interventionists will greatly advance the field. In addition, the three-arm study design will allow for testing which combinations of intervention components are most effective in improving child CVH by race/ethnicity as well as whether a virtual or hybrid Arm is more effective. Dependent on study findings, this intervention will be disseminated to other primary care settings. ## Funding Research is supported by grant number R$\frac{61}{33}$ HL151978 from the National Heart, Lung, and Blood Institute (PI: Jerica Berge). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung and Blood Institute or the National Institutes of Health. ## Availability of data and materials The datasets generated from the study will be available from the corresponding author upon reasonable request. ## References 1. Benjamin E, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP. 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The Motivational Interviewing Treatment Integrity Code (MITI 4): Rationale, Preliminary Reliability and Validity. J Subst Abuse Treat (2016.0) 65 36-42. PMID: 26874558 72. Chang YW, Tsong Y, Dong X, Zhao Z. Sample Size Determination for a Three-Arm Equivalence Trial of Normally Distributed Responses. J Biopharm Stat (2014.0) 24 1190-202. DOI: 10.1080/10543406.2014.948552 73. Friedman L, Furberg C, DeMets D, Reboussin D, Granger C. Fundamentals of Clinical Trials (2015.0) 74. Shih WJ. Current Controlled Trials in Cardiovascular Medicine Problems in dealing with missing data and informative censoring in clinical trials. Current Controlled Trials in Cardiovascular Medicine (2002.0) 75. Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ (2011.0) 342 d40. PMID: 21300711 76. Schafer J. Analysis of Incomplete Multivariate Data (1997.0)
--- title: Playgrounds Location and Patterns of Use authors: - Deborah Young - Thomas L McKenzie - Sarah Eng - Meghan Talarowski - Bing Han - Stephanie Williamson - Emily Galfond - Deborah A Cohen journal: Research Square year: 2023 pmcid: PMC10055650 doi: 10.21203/rs.3.rs-2697497/v1 license: CC BY 4.0 --- # Playgrounds Location and Patterns of Use ## Abstract Playgrounds have features that benefit visitors, including opportunities to engage in outdoor physical activity. We surveyed 1350 adults visiting 60 playgrounds across the U.S. in Summer 2021 to determine if distance to the playground from their residence was associated with weekly visit frequency, length of stay, and transportation mode to the site. About $\frac{2}{3}$ of respondents living within ½ mile from the playground reported visiting it at least once per week compared with $14.1\%$ of respondents living more than a mile away. Of respondents living within ¼ mile of playgrounds, $75.6\%$ reported walking or biking there. After controlling for socio-demographics, respondents living within ½ mile of the playground had 5.1 times the odds ($95\%$ CI: 3.68, 7.04) of visiting the playground at least once per week compared with those living further away. Respondents walking or biking to the playground had 6.1 times the odds ($95\%$ CI: 4.23, 8.82) of visiting the playground at least once per week compared with respondents arriving via motorized transport. For public health purposes, city planners and designers should consider locating playgrounds ½ mile from all residences. Distance is likely the most important factor associated with playground use. ## Introduction Playgrounds are built environments that provide features offering multiple health-related benefits to visitors. They provide venues for youth and adult physical activity,[1] opportunities to develop fine and gross motor skills and foster the development of the vestibular system,[2] create new collaborations and friendships,[3, 4] and spark creativity and problem-solving through brain development.[4] Additionally, time spent outdoors calms the stress response,[4, 5] can help with emotional regulation,[4, 6] supports improved immune system health,[7] and improves eye function.[8] The placement of playgrounds is associated with frequency of use. Molina-Garcia et al found that children living in neighborhoods with more playgrounds acquired more physical activity than children in neighborhoods with fewer playgrounds.[9] Others have noted similar results.[10] National guidelines recommend that children obtain 60 minutes of moderate-to-vigorous physical activity per day.[11] Further, the 2016 National Physical Activity Plan states that playgrounds are critical sites to support active recreation.[11] In the National Study of Playgrounds, we assessed how design and specific playground features was associated with the number of users and time spent in the playgrounds. After accounting for factors like neighborhood population density and percent of households in poverty, we found that specific innovative playground features accounted for approximately $43\%$ more use.[12] However, physical activity associated with playgrounds is not strictly dependent on time spent in the playground but is also associated with how people get to the playgrounds. Non-motorized travel is a way for adults and youths to obtain physical activity. When playgrounds are located near residential areas, it is possible to walk or bike to enjoy their facilities. Meanwhile, only $20\%$ of homes in the United States are located within a ½ mile of a park.[4] We examined the association of distance from home and transportation modality with the frequency and duration of playground use across 60 playgrounds in 10 US cities that were a part of the National Study of Playgrounds. We determined how distance from home to playgrounds was associated with use and active transport. ## Materials And Methods The National Study of Playgrounds was designed to assess the impact of non-schoolyard based playground design on physical activity.[12] Playgrounds with innovative designs ($$n = 3$$ per city) were compared to matched traditional post-and-platform playgrounds ($$n = 3$$ per city) in the metropolitan areas of Boston, Chicago, Cincinnati, Denver, Houston, Los Angeles, Memphis, New York, San Francisco, and Seattle (i.e., 60 total playgrounds). The playgrounds were matched by socio-economic aspects of the surrounding census tract, including household poverty level and racial/ethnic demographics. As no personal identifying information was gathered, the study was determined by the RAND Institutional Review Board to be exempt from Human Subjects Review. To understand playground utilization, we created a two-page self-administered questionnaire that was completed by adult playground visitors. The instrument included questions about adult and child(ren) demographic information, transportation to the playground, approximate distance from home, typical frequency of using the playground, reasons for choosing the playground, favorite playground features, and the perceived safety and maintenance of the playground. The questionnaire was administered most frequently in English, but Spanish questionnaires were available. For this report, we focused on the following questions: 1) How did you get to this playground today (response options: on foot, non-motorized vehicle, motor vehicle, public transport, other)? 2) How often do you usually come to this playground (first time, 5–7 times per week, 2–4 times per week, once per week, 2–3 times per month, several times per year, a few times per year)? 3) On a typical day when you come to the playground, how long do you stay (0–30 minutes, 31–60 minutes, more than 1 hour but less than 2 hours, 2–3 hours, more than 3 hours)? Respondents marked how far the playground was from their residence on a local map and responses were coded as 0-.25 mile,.25-.50 mile,.50 – 1 mile, and more than 1 mile. Data collectors were trained in all aspects of the National Study of Playgrounds protocol and they visited playgrounds to administer the questionnaire on Wednesdays, Fridays, Saturdays, and Sundays during a single week in Summer, 2021. They approached people in the playground who appeared to be older than 18 years of age. After introducing themselves and explaining they were part of a national study funded by the National Institutes of Health, they asked potential responders if they were willing to complete a short survey, after which they would receive a $5 gift card. No name, address, or identifying information was obtained. The goal was to collect at least 20 completed questionnaires per playground over the course of the week. ## Analysis Descriptive statistics were used to summarize survey responses by distance from home and mode of transportation to playground (condensed into walk/bike transportation and other transportation modes). Comparisons across groups for categorical and continuous variables were done using Chi-square test and Kruskal-Wallis one-way analysis of variance, respectively. Frequency of visits and duration of stay were modeled using logistic regression, in which visiting the playground once per week or more and staying more than 1 hour were the outcomes of interest. Frequency of visits and duration of stay variables were highly correlated variables, so the models were run separately with the same covariates. Regression models were adjusted for gender, age, marital status, and relationship to child (married parent, non-married parent, other relationship), education status, race/ethnicity, playground type, and city. The significance level for descriptive statistics and logistic regression models was set to 0.05. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC). ## Results Across the 60 playgrounds 1,365 surveys were completed ($76.6\%$ response of invites). Of these, complete data were missing for 15 respondents, leaving an analytic data set of 1,350 persons. Most respondents were women ($69.9\%$) and respondents reported a mean age of 38.5 ± 10.9 (Table 1). Most reported they were White ($50.6\%$), with $16.4\%$ reporting being Hispanic, $10.3\%$ Black, $10.6\%$ Asian/Pacific Islander, $0.3\%$ American Indian/Alaska Native, and $11.9\%$ indicating multiple races/ethnicities. The majority ($59.6\%$) reported being employed full-time and over two-thirds reported having a bachelor’s degree or higher. There were few meaningful demographic differences for reported distance of the playground from one’s residence, although respondents living within ½ mile or ¼ mile from the playgrounds were more likely to be of Hispanic ethnicity or Black race, respectively. There was also a tendency for respondents living closer to the playgrounds to report having a lower educational level, being single, and less likely to be the parent of the child(ren) they brought to the playground. ( Table 1). Most reported bringing an average of 2.0 ± 1.80 children with them on the day they completed the survey. Reported distance from one’s residence was associated with the frequency of playground visits. About $\frac{2}{3}$ of respondents living within ¼ mile or ½ mile from the playground reported visiting the playground at least once per week ($66.1\%$ and $63.2\%$, respectively) compared with $14.1\%$ of respondents living more than a mile away. Living closer was associated with shorter visits than living further away. Respondents living close to the playground most often identified convenience as the reasons for visiting that particular playground (Table 2). Reporting that there were no other playgrounds nearby and that friends and other children used that particular playground were significantly endorsed more often by respondents living near the playground than respondents living further away. Irrespective of distance from home, $95\%$ perceived the playground were either “safe” or “very safe.” ( data not shown). A total of 524 respondents ($39\%$) reported they had walked or biked to the playground. As presented in Table 2, walking or biking to playgrounds was more prevalent for respondents living closer to the playground. For respondents living within ¼ mile of playgrounds, $75.6\%$ reported arriving by non-motorized transportation whereas only $8.6\%$ of those living more than 1 mile away reported that transportation mode. Table 3 displays results indicating that those who walked or biked were likely to visit playgrounds more often, although their visits were shorter than those using a motorized vehicle. Almost $\frac{2}{3}$ of respondents ($64.2\%$) who walked or biked reported visiting the playground at least once per week compared with $21.7\%$ of those who arrived by vehicle. Nearly one-half of respondents ($49.3\%$) who walked or biked reported staying more than one hour, compared to $58\%$ of those arriving by vehicle. Tables 4 and 5 present results of the logistic regressions with distance from one’s residence (Table 4) and travel mode (Table 5) as the main exposures and the outcomes of frequency of playground visits and duration of playground visits. After controlling for socio-demographics and playground type, people who reported living within ½ mile of the playground were 5.06 times ($95\%$ CI: 3.68, 7.04) more likely to visit the playground at least once per week compared with respondents living further away (Table 4). Those living closer were less likely to report staying an hour or longer (OR: 0.64; $95\%$ CI: 0.47, 0.86) compared to people living at least ½ mile from the playground. Respondents reporting walking or biking to the playground were 6.12 times ($95\%$ CI: 4.23, 8.82) more likely to visit the playground at least once per week compared with those arriving via motorized transport. Respondents who walked or bicycled were $37\%$ less likely to stay longer than one hour (OR: 0.63; $95\%$ CI: 0.47, 0.83) compared with those arriving via motorized transport. ## Discussion Results of this national study provide strong evidence that playground location is associated with the frequency of playground use. When playgrounds are convenient, people visit them more frequently and use non-motorized transportation to get there. While there are many considerations when selecting playground sites, proximity to residential areas is of utmost importance. Ensuring that safe sidewalks and bike lanes are conveniently located may enhance non-motorized travel to playgrounds. Residents living within ½ mile of a playground were four times more likely to visit it at least once per week. Veitch and colleagues found that going to parks as a family at least weekly was associated with more frequent play in a park or playground compared with families who visited less regularly.[13] Only $20\%$ of homes in the U.S. are located within ½ mile of a park.[4] In contrast, in a study of over 9,000 individuals surveyed in eight Latin American countries, $49\%$ reported accessibility to parks and $82\%$ reported access to playgrounds (accessibility defined as within a 20-min walking distance).[14] City planners, urban designers, and parks departments should consider the importance of playground locations, as well as active transport networks, when creating new or reconfiguring existing neighborhoods. Siting playgrounds within ¼ to ½ mile of every resident should be a goal for all major cities. Respondents who walked/biked to playgrounds or lived within ½ mile of them reported having shorter visits than those who drove or lived further away. While we did not query why people might stay longer at playgrounds, many of the playgrounds that were closer to residential areas were smaller and had fewer amenities than others and may not have supported as long of a stay time. Additionally, some of the 60 playgrounds chosen for this study were situated within parks, and people may have left the playground to use other park facilities. Nonetheless, the additional frequency of playground visits may compensate for shorter visits and influence the time to engage in play and physical activity. Play is an essential building block for youth development. Outdoor play, which often occurs in playgrounds, stimulates problem-solving and creative thinking, provides opportunities for social interactions, and can improve emotional well-being and mood.[15] We and others[13, 16] found that when playgrounds are available in neighborhoods there is a greater likelihood of children playing outdoors. More children could reap the benefits of outdoor play if more playgrounds were conveniently located. Another strength of playgrounds being located near residential areas are the opportunities for walking and biking to them. Proximity thus provides additional opportunities for children and adults to meet the national guidelines for physical activity.[11] For adults, moderate-intensity physical activity can be quantified as walking 1 mile at a pace of 2.5–4.0 mph,[11] or roughly in 15–25 minutes. Thus, an adult living within ½ mile of a playground could obtain up to 25 minutes of the minimum of 150 minutes/week recommendation for each visit. The study had limitations. Surveys were conducted only during the summer, and respondents may have answered differently during other seasons. Not all adults agreed to complete the survey, so the respondents may not be representative of all adults visiting the playgrounds. How often respondents reported their frequency of visits and typical duration of stay were self-reported and could not be verified. All 60 playgrounds in the study were either built or renovated in the previous 10 years and, therefore, are not representative of all playgrounds. Nonetheless, there were many study strengths. Trained assessors visited 60 playgrounds across 10 cities in the U.S., thus being able to provide national rather than only city or regional information. Additionally, surveys were completed by more than 1,300 adults who were visiting the playgrounds. ## Conclusion The location of playgrounds near residential areas was associated with greater frequency of their use and the use of active (i.e., non-motorized transportation) to get there. City planners and urban designers should consider finding spaces for playgrounds that are ideally within ¼ to ½ mile of residential location. This will result in more opportunities for physical activity for children and adults and likely contribute to public health. ## References 1. Talarowski M, Cohen DA, Williamson S, Han B. Innovative playgrounds: use, physical activity, and implications for health. Public Health (2019) 174 102-109. PMID: 31326759 2. Ayres AJ. Sensory Integration and the Child (2005) 3. Gil-Madrona P, Martinez-Lopez M, Prieto-Ayuso A. Contribution of public playgrounds to motor, social, and creative development and obesity reduction in children. Sustainability (2019) 11 4. Yogman M, Garner A, Hutchinson J. The Power of Play: A Pediatric Role in Enhancing Development in Young Children. Pediatrics (2018) 142 5. Kondo MC, Jacoby SF, South EC. Does spending time outdoors reduce stress? A review of real-time stress response to outdoor environments. Health Place (2018) 51 136-150. PMID: 29604546 6. Taylor AF, Kuo FE. Children with attention deficits concentrate better after walk in the park. J Atten Disord (2009) 12 402-409. PMID: 18725656 7. Garn H, Potaczek DP, Pfefferle PI. The Hygiene Hypothesis and New Perspectives-Current Challenges Meeting an Old Postulate. Front Immunol (2021) 12 637087. PMID: 33815389 8. He X, Sankaridurg P, Wang J. Time Outdoors in Reducing Myopia: A School-Based Cluster Randomized Trial with Objective Monitoring of Outdoor Time and Light Intensity. Ophthalmology (2022) 129 1245-1254. PMID: 35779695 9. Molina-Garcia J, Menescardi C, Estevan I, Queralt A. Associations between Park and Playground Availability and Proximity and Children’s Physical Activity and Body Mass Index: The BEACH Study. Int J Environ Res Public Health (2021) 19 10. Galvez MP, McGovern K, Knuff C. Associations between neighborhood resources and physical activity in inner-city minority children. Acad Pediatr (2013) 13 20-26. PMID: 23312856 11. Piercy KL, Troiano RP, Ballard RM. The Physical Activity Guidelines for Americans. JAMA (2018) 320 2020-2028. PMID: 30418471 12. Cohen DA, Talarowski MR, Han B. Playground Design and Physical Activity. Am J Prev Med (2022) 13. Veitch J, Bagley S, Ball K, Salmon J. Where do children usually play? A qualitative study of parents’ perceptions of influences on children’s active free-play. Health Place (2006) 12 383-393. PMID: 16814197 14. Barreno M, Sisa I, Yepez Garcia MC. Association between built environment and physical activity in Latin American countries: a multicentre cross-sectional study. BMJ Open (2021) 11 e046271 15. Burdette HL, Whitaker RC. Resurrecting free play in young children: looking beyond fitness and fatness to attention, affiliation, and affect. Arch Pediatr Adolesc Med (2005) 159 46-50. PMID: 15630057 16. Xu H, Wen LM, Hardy LL, Rissel C. Mothers’ Perceived Neighbourhood Environment and Outdoor Play of 2- to 3.5-Year-Old Children: Findings from the Healthy Beginnings Trial. Int J Environ Res Public Health (2017) 14
--- title: Integrative Blood-Based Characterization of Oxidative Mitochondrial DNA Damage Variants Implicates Mexican Americans’ Metabolic Risk for Developing Alzheimer’s Disease authors: - Danielle Marie Reid - Robert C. Barber - Harlan P. Jones - Roland J. Thorpe - Jie Sun - Zhengyang Zhou - Nicole R. Phillips journal: Research Square year: 2023 pmcid: PMC10055654 doi: 10.21203/rs.3.rs-2666242/v1 license: CC BY 4.0 --- # Integrative Blood-Based Characterization of Oxidative Mitochondrial DNA Damage Variants Implicates Mexican Americans’ Metabolic Risk for Developing Alzheimer’s Disease ## Abstract Alzheimer’s Disease (AD) continues to be a leading cause of death in the US. As the US aging population (ages 65+) expands, the impact will disproportionately affect vulnerable populations, e.g., Hispanic/Latinx population, due to their AD-related health disparities. Age-related regression in mitochondrial activity and ethnic-specific differences in metabolic burden could potentially explain in part the racial/ethnic distinctions in etiology that exist for AD. Oxidation of guanine (G) to 8-oxo-guanine (8oxoG) is a prevalent lesion and an indicator of oxidative stress and mitochondrial dysfunction. Damaged mtDNA (8oxoG) can serve as an important marker of age-related systemic metabolic dysfunction and upon release into peripheral circulation may exacerbate pathophysiology contributing to AD development and/or progression. Analyzing blood samples from Mexican American (MA) and non-Hispanic White (NHW) participants enrolled in the Texas Alzheimer’s Research & Care Consortium, we used blood-based measurements of 8oxoG from both buffy coat PBMCs and plasma to determine associations with population, sex, type-2 diabetes, and AD risk. Our results show that 8oxoG levels in both buffy coat and plasma were significantly associated with population, sex, years of education, and reveal a potential association with AD. Furthermore, MAs are significantly burdened by mtDNA oxidative damage in both blood fractions, which may contribute to their metabolic vulnerability to developing AD. ## Introduction Alzheimer’s disease (AD) is the most common form of dementia, characterized by symptoms of cognitive decline such as memory deficits, impaired problem-solving, difficulty communicating, and additional cerebral incompetencies1,2. This heterogenous neurodegenerative disease is commonly known for its neurotoxic pathophysiological properties including the accumulation of amyloid beta (Ab) plaques and tangles of hyperphosphorylated tau protein1–3. However, impaired mitochondrial function and chronic inflammation are frequently reported and can be considered as contributors to the observed endophenotypic manifestations of cognitive impairment (CI) likely caused by Alzheimer’s2–8. Particularly, non-Hispanic Whites (NHWs) appear to exhibit an inflammatory endophenotype4–7, while Mexican Americans (MAs) present a metabolic endophenotype5. This evidence may point to population-specific biological and environmental factors influencing CI. Importantly, current established biomarkers are invasive, expensive, and have limited accessibility (i.e., cerebral spinal fluid testing, neuroimaging). Identifying blood-based biomarkers capable of predicting disease onset and assessing disease progression (e.g., preclinical AD, mild cognitive impairment [MCI], and AD) is of great importance to understand the pathophysiological heterogeneity of AD which will inform ultimately more precise therapeutics. Type-2 diabetes (T2D) is a considerable risk factor for AD due to its comorbid association with CI; however, the precise pathophysiological mechanisms connecting these two complex diseases are unclear. As the aging Hispanic/Latinx population is expected to exponentially increase compared to other ethnic/racial groups, the healthcare burden affecting this population is expected to worsen due to age-related diseases (e.g., AD and diabetes)1,9. It has been previously reported that the Ser[326]Cys polymorphism (rs1052133) in 8-Oxoguanine glycosylase (OGG1), an important glycosylase of the base excision repair pathway involved in the recognition and excision of 8oxoG within DNA, delays repair of oxidative DNA damage10 and is associated with T2D risk in MAs13. Recent observations suggest OGG1 regulates cellular energy metabolism and thus shapes metabolic phenotypes during high-fat diet exposure, indicating DNA damage repair may be important to metabolic health11–13. Studies by Komakula et al., and Sampath et al., revealed that functional OGG1 prevented obesity and metabolic dysfunction upon induction in response to a high-fat diet potentially via direct or indirect expression changes of PGC-1a and fatty acid oxidation 11,12. Similarly, reduced expression of PGC-1a is consistently observed in T2D patients14,15 and is linked to elevated ROS levels and decreased levels of b-oxidation enzymes16. AD disproportionately affects Hispanics/Latinos compared to NHWs; this is thought to be due to the increased prevalence of metabolic syndrome, obesity, cardiovascular health risks, and diabetes9,17,18. These observations combined with our previous data indicating MAs have elevated levels of oxidative damage compared to NHWs, leads us to the premise that mitochondrial health may hold greater biological importance in the development of age-related disease for individuals with Hispanic/Latinx ancestry19. Compared to the nuclear genome, mitochondrial DNA is especially susceptible to oxidative damage. Elevated levels of ROS generate oxidative stress (OS) and an oxidative environment capable of oxidatively damaging important biomolecules and may cause detrimental effects in genomic coding regions20–22. Due to the low oxidation potential of guanine, the most common forms of oxidative DNA damage are: 8-oxo-7,8-dihydroguanine (8oxoG) and 8-oxo-7,8-dihydrodeoxyguanine (8oxodG)21–26. Oxidation of guanine possesses unique mutagenic properties that when left unrepaired, can perturb cellular function through several mechanisms and effect protein-DNA binding (e.g., transcription factors)21,22,25,26. Furthermore, oxidized guanine can result in missense mutations and often modify the activity of downstream products, such as RNAs and proteins20–22, 25,27. Due to the nature of the mitochondrial genome within various cell types and tissues, oxidative DNA damage will influence distinctive pathophysiological outcomes in tissue with differing function depending on the location, metabolic activity, and enzymatic processes 26. In response to mitochondrial damage there are QC processes at the cellular, organellar, and molecular level that are employed to preserve mitochondrial integrity28. As mitochondria work in dynamic networks, fusion with nearby mitochondria can recover function; however, excessive damage undermines fusion activity resulting in fragmented mitochondria that are removed through mitophagy and/or apoptosis28. Growing evidence implicates oxidative DNA damage as a primary and secondary contributor to pathology observed in the AD continuum29–32. Recent evidence from our lab analyzing blood-based indices of mtDNA copy number (CN) and cell-free mtDNA (cf-mtDNA) to investigate mitochondrial dysfunction in complex disease (T2D and CI) among MAs showed that mtDNA CN was significantly associated with both T2D and CI18. Also, cf-mtDNA was found to be higher in individuals with either disease, reaching significant levels in individuals with both diseases compared to normal controls18. Cellular mtDNA CN is an indicator of mitochondrial biogenesis and cellular energetics18,33, which can be used as a measurement of mitochondrial health34. Circulating cell-free mitochondrial DNA (ccf-mtDNA) has been increasingly studied as a biomarker for systemic inflammation during cellular stress or apoptosis. In this process, mtDNA fragments are released by cells into the bloodstream, where their bacterial origins cause them to be recognized as a damage associated molecular pattern (DAMP), thereby eliciting an inflammatory response through activation of innate immune cells18, 34–36. There are numerous studies assessing ccf-mtDNA as a clinical diagnostic and predictive biomarker (e.g., in affective disorders, and mitochondrial, autoimmune, neurological, and cardiovascular diseases) 34,35, 37–40, and accumulating evidence indicates that mitochondrial dysfunction and mtDNA damage are correlated with disease severity and levels of ccf-mtDNA35,39. Our mtDNA CN and cf-mtDNA data can be considered as a proxy for our more recent data evaluating mitochondrial 8oxoG variant load since it does not capture if mutation has occurred. More recently in a similar population-based cohort, we discovered that mtDNA variants indicative of 8oxoG were significantly elevated in MAs compared to NHWs and was associated with sex, education, and suggestive for cognitive function19. Correspondingly, Miller, et al., revealed AD neurons compared to age-matched controls had significantly elevated levels of somatic single nucleotide variants (sSNVs) than anticipated when considering sSNVs are known to increase with age32, and the distribution of the variants are presumed to occur secondary to developing disease pathology32. Further analysis established potential mechanisms of oxidative DNA damage developed from 8oxoG (nonsynonymous mutations, e.g., C > A) that might contribute to the significant increase of sSNVs in AD, especially in protein-coding genes32. The increased substitution mutations could be a result of increased ROS and OS, a common feature observed in AD brains, the CNS, and periphery, which can contribute to inflammation and mitochondrial dysfunction32,41. This accumulating evidence may point at a mutational signature important to AD pathophysiology influencing the differing endophenotype reported in AD, particularly of those with different mitochondrial capacity, metabolic health, and comorbidity risk such as those linked to ethnic/racial AD health disparities. Oxidative transversion substitution mutations may be associated with increased mitochondrial dysfunction due to impaired mitochondrial capacity, metabolic health, and/or lifestyle affecting mitochondrial health, and contribute to the continuous progression of AD until a clinical endpoint, death. In this current study, our objective was to use a blood-based measurement of 8oxoG sSNVs as an indicator of impaired mitochondrial function to investigate the role of mitochondria in pathophysiology of complex disease by [1] characterizing associations to population and sex, [2] highlighting burdened genomic regions that influence mitochondrial function, and [3] determining differences in buffy coat or plasma on evaluating AD risk and/or endophenotype. ## Results Descriptive statistics for the cohort analyzed for cellular mitochondrial 8oxoG variants are displayed in Table 1. As anticipated, MMSE, CDR sum, and years of education in both populations had significantly different means based on cognitive status. Age significantly differed in MAs between cognitive groups and years of education was lower compared to NHWs. Directly genotyped and imputed APOE frequencies for each population is shown in Supplementary Table 1. Genotype frequencies for APOE and OGG1 by cognitive status in each population are shown in Supplementary Table 2. Hardy-Weinberg proportions for APOE and OGG1 in both populations separately and together indicates the genotype frequencies are in Hardy-Weinberg equilibrium and both the observed and expected genotype frequencies are not significantly different (Supplementary Tables 3 and 4). ## Evaluation of 8oxoG Variant Count in the Buffy Coat of MA and NHW TARCC Participants In the MA population 8oxoG variant count was significantly reduced for subjects reporting depression compared to those without depression; mean = 6.548 and 7.704, respectively (Supplemental Fig. 1). Tobacco abuse demonstrated an approach for significance in association with 8oxoG demonstrating a higher variant load compared to non-smokers in MAs; mean = 7.935 and 7.048, respectively (Supplemental Fig. 2). These trends were not observed in the NHW cohort. Multiple linear regression model predictions in the whole cohort were performed to assess the associations with 8oxoG and to determine if there are predictive interactions. Sex regarding females ($$p \leq 0.0007$$), years of education ($$p \leq 0.0055$$), BMI ($$p \leq 0.0288$$), and tobacco abuse ($$p \leq 0.0086$$) were significantly associated with 8oxoG variant count, and the population-sex interaction demonstrated a significant interaction effect ($$p \leq 0.0038$$) (Supplementary Table 5). Further analysis of 8oxoG variant count in both population and sex via two-way ANOVA indicates population is significantly associated ($p \leq 0.0001$), while sex was marginally significant ($$p \leq 0.0922$$). In the subsequent multiple linear regression model, a diabetes × cognition interaction was evaluated and showed a significant association with population ($p \leq 0.0001$), sex ($$p \leq 0.0429$$), years of education ($$p \leq 0.0109$$), BMI ($$p \leq 0.0254$$), and tobacco abuse ($$p \leq 0.0155$$); although the interaction effect was not significant (Supplementary Table 6). Cognitive status with respect to AD displayed a suggestive association compared to controls ($$p \leq 0.0556$$). Previously derived 8oxoG variant load for each subject corresponding to 8oxoG “hotspots”19 were further analyzed via multiple linear regression prediction models assessing a population × sex and years of education interaction effect, and a diabetes × cognition interaction effect was less informative than analyzing total 8oxoG variant count (Supplementary Tables 7 and 8). Population stratification lost significant statistical associations (Supplementary Tables 9 and 10) that were observed in Supplementary Tables 7 and 8. Multiple linear regression modelling in MAs indicated that total 8oxoG was significantly associated with cognitive status, sex, years of education, and tobacco abuse (Table 2). BMI did not show significant association in MAs as compared to the regression models investigating interactive effects (Supplementary Tables 5–6). Modelling within NHWs did not demonstrate any associations (Table 3). Additional prediction modelling used cognitive status as a binary variable to combine the effects of AD and MCI compared to NCs and showed similar results to the models with greater resolution on cognitive status (Supplementary Tables 11–618). ## Assessment of ccf-mtDNA 8oxoG Variant Count in MA and NHW TARCC Participants The subset of participants included for the ccf-mtDNA 8oxoG variants were selected from subjects included in the buffy coat analysis to compare the blood fractions collected from the same visit (Table 4). Age, sex, and years of education were considered confounding variables for CI and were utilized with the aim to pairwise match AD with NCs to help reduce the risk of confounders influencing false associations to AD due to the smaller sample size. Genotype frequencies obtained and imputed for APOE in each population and genotype frequencies for APOE and OGG1 distributed by cognitive status in each population are shown (Supplementary Tables 19 and 20). Testing for Hardy-Weinberg Equilibrium for APOE and OGG1 were insignificant for the total cohort and within each population, indicating that the genotype frequencies are in equilibrium (Supplementary Tables 21 and 22). Although our attempt to match samples based on age was unsuccessful; a Pearson’s correlation was performed to exclude age as a potential cofounder and showed age does not need to be considered a covariate in our dataset as it was not correlated with total ccf-mtDNA 8oxoG variant count (Supplementary Fig. 3). ## MAs, especially females, have a greater burden in total ccf-8oxoG variant count In the whole cohort ccf-8oxoG variant count was significantly elevated in the MA population compared to NHWs; mean = 0.8500 and 0.7160, respectively (Fig. 1). Ccf-8oxoG variant count did not significantly differ based on cognitive status or sex (Supplementary Fig. 4 and Supplementary Fig. 5). A significant population · sex interaction was not observed (Fig. 2); however, ccf-8oxoG variant count was significantly elevated in MA females compared to NHW females (mean = 0.8702 and 0.7771, respectively). Despite cognitive phenotype, MAs had an elevated 8oxoG sSNV burden compared to NHWs (Fig. 3), yet there was not a significant difference for 8oxoG variant count in each population when assessing for sex and cognitive phenotype (Fig. 4). Results of multiple linear regression modelling of ccf-mtDNA 8oxoG variant count in the whole cohort with respect to population- sex and education interactive effects while considering APOE status as a dosage effect (i.e., no ε4 allele, one ε4 allele, two ε4 alleles) indicated a significant positive association for APOE and the population · sex interaction (Supplementary Table 23). When assessing for a diabetes × cognition interaction, a significant association between ccf-8oxoG variant count and both population and APOE was determined (Supplementary Table 24). Additional multiple linear regression models were performed to characterize associations with 8oxoG “hotspots” in the whole cohort (Supplementary Fig. 6 and Supplementary Tables 25–26). The regression modelling for both interaction effects, population × sex and education, and diabetes × cognition in the whole cohort did not observe the same associations for 8oxoG “hotspots”, indicating these variants may not be informative in this context. ## Population-specific associations to ccf-8oxoG variant count Population stratification for the multiple linear regression models did not demonstrate any statistical significance for ccf-8oxoG count in MAs (Table 5); however, in the NHW population AD and diabetes was marginally significant (Table 6). Further, the model showed significant statistical association with sex and age, as well. Multiple linear regression models for 8oxoG “hotspots” in each population were performed and no associations were observed in the MA population; however, in the NHW population a significant negative association to age was discerned (Supplementary Tables 27–28). Although the significant association to age in the NHW population was previously observed in the non-“hotspot” stratified regression model, generally the “hotspot” stratified analyses were less informative. ## Discussion Ethnic/racial differences in developing cognitive impairment are known to exist, yet reports investigating biological, behavioral, and lifestyle factors that lead to differential mechanisms of neurodegeneration in populations more heavily burdened by cognitive decline are limited. Here, we investigated the predictability of 8oxoG variant count from two blood fractions in assessing risk for cognitive decline in two populations. Additionally, haplogroup-specific associations to ccf-mtDNA 8oxoG variants were performed. We hypothesized that indirectly evaluating mitochondrial dysfunction through cellular mitochondrial 8oxoG sSNVs may serve as a better biomarker for MAs due to their observed metabolic endophenotype and burden, while ccf-mtDNA 8oxoG variants may provide improved utility as a biomarker for NHWs due to their inflammatory endophenotype. Altogether, our results confirm MAs, especially females, show greater mtDNA oxidative damage compared to NHWs. Tobacco abuse trended for significance with increasing cellular 8oxoG mutational load in MAs; however, interestingly, non-depressed individuals showed elevated cellular 8oxoG variant burden. Stratified regression analysis by population in buffy coat PBMCs demonstrated an association with AD, sex, education, and tobacco abuse, while this was not observed in NHWs. Similarly, ccf-8oxoG variant load was significantly higher in MAs, and MA females had elevated levels compared to males. Ccf-8oxoG stratified regression analysis for NHWs showed a suggestive association with normal cognition in younger aged males without diabetes. This may indicate that oxidative variants from ccf-mtDNA are reduced in older NHW females with AD and diabetes comorbidity; however, the potential reason for this is unclear. There is growing evidence indicating the protective effects of estrogen against OS related damage42,43. Estrogen deficiency post menopause is associated with increased levels of OS, higher blood levels of free fatty acids, and reduced antioxidant defense42,43, so it is expected that aging females would demonstrate elevated levels of oxidative damage. Nonetheless, due to our observation it begs the question if there are sex-related differences for the age-related decline in mitophagy. Depression is a known risk factor for developing MCI and AD, and a study demonstrated a depressive endophenotype of MCI and AD in MAs44. Furthermore, numerous studies report MAs experience more depressive symptoms compared to other Hispanic/Latino subpopulations45,46, as well as NHWs47–52. Accumulating evidence indicates that individuals with depression have higher levels of 8oxoG and oxidative damage, as OS encompasses a critical role in depression pathophysiology through the activity of ROS53,54. Substantial evidence indicates a higher prevalence of depression and depressive symptoms among MAs, yet our data denoted that non-depressed MAs exhibited elevated levels of 8oxoG variants in buffy coat PBMCs. However, depression appeared to be underrepresented because of limited reports of depression in MAs with MCI and AD. Subsequent regression models in the whole cohort and MA population show a negative trend between 8oxoG variant count in buffy PBMCs and non-depressed MAs. Surveying for the presence or absence of depression may have poor resolution when investigating cognitive associations compared to assessing for a collection of depressive symptoms. Previous studies report distinct clustering of depressive symptoms is imperative when studying the connection between cognition and depression55,56. Our results for 8oxoG and depression among MAs warrants further investigation by implementing depressive symptoms and/or other indicators of depression. Our results revealed increased levels of 8oxoG variants in buffy PMBCs of MAs with a history of tobacco abuse. Smoking tobacco and exposure to tobacco smoke has been shown to cause elevated levels of 8oxoG compared to non-smokers57–60 because of the various carcinogens contained within61,62. As previously mentioned, carcinogens readily form DNA adducts and can lead to OS through the production of ROS. Additionally, cigarette smoke has been recognized to cause chronic inflammation leading to increasing OS which further results in accumulating oxidative damage61,63. A recent study reported that smoking tobacco increased risk for cognitive decline in aging MAs64. Following linear regression models including MAs all demonstrated a significant link with tobacco abuse. These results seem to indicate tobacco abuse as a strong modifiable risk factor for increased mitochondrial oxidative damage in MAs and demonstrates the importance of addressing such behaviors to prevent increased risk for CI in this population. Our higher resolution linear regression models in the whole cohort from buffy coat PBMCs established that population (MA), tobacco abuse, BMI, education, and population × sex were statistically associated with 8oxoG somatic variants. AD was suggestively associated with cellular 8oxoG sSNVs when assessing an interaction between diabetes and cognitive status. These results were not observed in the NHW population, and perhaps of interest is the fact that many of the coefficients were in the opposite direction (though not significant). Increasing evidence connects AD and T2D, showing a greater risk for cognitive decline due to T2D, and robust correlations indicate high blood sugar is associated with the presence of Ab plaques65. Brain dysfunction is frequently observed in earlier stages of T2D, and hemoglobin A1C (established biomarker for T2D) has been related to decline in functional memory and hippocampal size65. Links between T2D and AD implicate mitochondrial dysfunction as a participating factor in the development and/or progression of neurodegeneration and may be of exceptional importance for ethnic/racial differences in disease severity and manifestation. This evidence further suggests that mitochondrial health could be a contributor to the unexplained disparities in CI among MAs, especially since MAs are at great risk for metabolic disorders. It is important to note that this study has its limitations including [1] measuring 8oxoG lesions indirectly, [2] small sampling for the plasma dataset, [3] lacking biochemical, metabolic, and inflammatory phenotypes, [4] missing most nDNA variants, [5] solely sampling blood tissue, [6] evaluating in one cohort, and [7] APOE in the NHW model could have reduced power due to a larger number of missingness compared to MAs. To better understand the biological and mechanistic roles of mitochondrial dysfunction and oxidative DNA damage in this context, future studies should incorporate larger sampling, include more biological markers indicative of metabolic health and systemic inflammation, determine if utilizing MMSE, CDR sum, and/or other neuropsychological tests for cognitive function strengthens our power to further support/validate our results, and characterize the nuclear genetic background associated with the mtDNA for each subject. Future studies should also investigate the mechanistic role of mitochondrial processes, such as mitophagy and mitochondrial quality control and sensing, play in contributing to the observed differences in cellular and ccf-mtDNA 8oxoG sSNVs between MAs and NHWs in relation to cognitive decline. Additionally, future studies will aim to validate the applicability of peripheral cellular and cell-free pathophysiological phenotypes as biomarkers for assessing brain pathology, disease risk, and/or disease stage. These studies will also investigate expression of DNA repair machinery, the role of sex hormones, and validate oxidative mtDNA load using alternative methods. Uniquely, our data specifically point to novel, population-based effects in 8oxoG damage in cellular and cell-free mtDNA. Overall, our results indicate both cellular and ccf-8oxoG variants mtDNA are significantly elevated in MAs compared to NHWs and indicate sex-differences (Fig. 5). Notably, our results from evaluating oxidized cellular mtDNA compared to ccf-mtDNA presents the possibility that these biomarkers may have significant predictive capability in MAs, especially females, compared to NHWs due to the observed statistical significance of assessing various independent variables. This evidence implies mitochondrial dysfunction in cellular mtDNA may be distinctly related to disease pathology in MAs with cognitive decline, whereas in ccf-mtDNA displayed poor associations in MAs compared to NHWs. This cumulative evidence supports the notion that [1] blood-based signatures of mitochondrial dysfunction differ between ethnic/racial populations, [2] cellular and ccf-mtDNA possess different functionality in potentially developing pathophysiological conditions, and [3] ethnic/racial differences exist in the manifestation of neurodegeneration through the assessment of mitochondrial oxidative DNA damage from different blood fractions. ## Cohort: The Texas Alzheimer’s Research and Care Consortium (TARCC) is a population-based collaborative longitudinal research initiative that has expanded between several Texas medical research institutions66. TARCC explores factors that may attribute to the development and progression of cognitive impairment due to AD in the MA population compared to their NHW counterparts. ## Participants: The study received institutional review board approval under the University of North Texas Health Science Center IRB #1330309–1 and all experiments were performed in accordance with relevant guidelines and regulations. Informed written consent was obtained from participants and/or their legally authorized proxies to take part in the study and allow publication of findings before data collection. Volunteer aging participants enrolled in TARCC annually complete a medical evaluation, clinical interview, neuropsychological testing, and blood draw. Eligible participants obtained categorical clinical diagnoses of ‘Alzheimer’s disease’, ‘Mild Cognitive Impairment’, and ‘Normal Control’ based on the criteria provided by the National Institute for Neurological Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association67. Additional information regarding the inclusion and exclusionary criteria of TARCC has been discussed elsewhere44. This study included NHW and MA subjects ($$n = 559$$; Table 1) diagnosed with Alzheimer’s Disease ($$n = 104$$), Mild Cognitive Impairment ($$n = 127$$), or normal cognition ($$n = 328$$). Obtained buffy coat samples from NHWs ($$n = 261$$) and MAs ($$n = 299$$) and a subset of plasma samples from 62 NHWs and 57 MAs ($$n = 119$$; Table 4) collected at the same visit were selected to match the distribution of subjects with respect to age, sex, and type-2 diabetes among both populations. The plasma subset did not include individuals diagnosed with MCI. These samples were analyzed to characterize cellular and circulating cell-free mtDNA (ccf-mtDNA) oxidative damage from blood. ## DNA Extraction: DNA from both the buffy coat and plasma was extracted individually from 200 mL of each sample using the Mag-Bind Blood & Tissue DNA HDQ 96 kit (Omega Bio-tek, Norcross, GA). Buffy coat and plasma DNA extractions were conducted using the Hamilton Microlab STARlet automated liquid handler (Hamilton Company, Reno, NV) and manually, respectively. ## Whole mtDNA amplification: The whole mitochondrial genome and large mtDNA fragments for each sample were amplified using the REPLI-g Human Mitochondrial DNA kit (Qiagen, Venlo, Netherlands) following the manufacturer’s protocol. This kit uses the high fidelity proofreading phi29 DNA polymerase capable of both rolling circle and multiple displacement amplification in combination of random hexamers68. Mitochondrial genome amplification was performed in order to increase mtDNA levels relative to nuclear DNA to enhance mtDNA coverage for whole genome sequencing. Amplified product was quantified via Qubit dsDNA BR assay on the Qubit 4 fluorometer (Invitrogen™, Thermo Fisher Scientific, Waltham, MA) for each sample and a small test size of approximately 12 samples were evaluated to determine the distribution of amplicon sizes using the 4200 TapeStation System (Agilent Technologies, Santa Clara, CA) following the manufacturer’s protocol. The Genomic DNA ScreenTape and corresponding reagents were used to determine the presence of mtDNA fragments from 200 bp to the whole genome. ## mtDNA Sequencing: The Nextera XT™ DNA Library Preparation kit (Illumina, San Diego, CA) was used to prepare the sample library for sequencing following the manufacturer’s protocol. All samples were sequenced on the NextSeq 550 Sequencer (Illumina) platform with high output kit v2.5 generating paired-end reads of 150bp for 300 cycles. The buffy coat samples had an average read depth of 1855X, while the plasma samples had an average read depth of 3970X. ## Sequence Mapping/Alignment and Variant Calling: Raw mtDNA gzipped FASTQ pairs generated for each sample were aligned to the reference genome hg38 via BWA-MEM69 (v0.7.17) using the default parameter for mapping to generate SAM files. Post-alignment SAM files were processed with SAMtools (v.1.9) to produce BAM files that were subsequently sorted, indexed, and statistically assessed by coordinate70. Resultant processed aligned sequence reads within the BAM files were assigned to a single new read-group through the Picard tool “AddOrReplaceReadGroups”71. Duplicate reads resulting from sample preparation, or the sequencing instrument were removed from each sample single new read-group BAM file with the GATK4 Spark application of the Picard tool “MarkDuplicates”72. The BAM files were then indexed with SAMtools (v.1.9)70 and used for somatic variant calling including low allelic fractions and excluding read orientation base qualities (Phred score) under 30. High-depth mitochondrial somatic variants were called via the GATK4 variant caller, Mutect2, utilizing the mitochondria mode72,73. ## Oxidation Artifact Assessment: Picard tool, CollectOxoGMetrics, was used to calculate Phred-scaled probability scores for basecalls to differentiate biological alternative basecalls from technical oxidative damage due to 8oxoG (http://broadinstitute.github.io/picard). Readers are encouraged to review the study reported by Costello et al., for a comprehensive analysis of Next Generation Sequencing 8oxoG artifact generation and detection74. A text file was generated for each sample and were subjected to manual review to exclude technical oxidative artifacts with a Phred score below 30. ## Identification of Variants Indicative of Oxidative Damage: The variant call files were manually assessed to identify 8oxoG transversions within the mitochondrial genome. The process of identifying these specific oxidative transversions has been previously described19. Variants indicative of 8oxoG damage for each subject from the buffy coat portion were summed and normalized by accounting for read depth (variant count per 1000 read depth) to evaluate group differences based on the following variables: population, cognition, sex, type-2 diabetes, comorbidity (cognitive impairment and diabetes), both APOE and OGG1 genotype, and lifestyle factors. Variants indicative of 8oxoG damage from plasma was summed and normalized by accounting for read depth and were log10 transformed to evaluate group differences with the same variables described above. 8oxoG variant “hotspots” were identified as variant locations with at least 25 participants in the cohort observed. ## APOE and OGG1 Genotyping Imputation: Genome-wide SNP profiles were generated using the Illumina Infinium Multi-Ethnic Global Array which types 1.7 million SNPs. Standard filtering based on SNP missingness, individual missingness, and minor allele frequency ($5\%$) was conducted according to Anderson et al., 201075. Genetic imputation of APOE (rs7412 and rs429358 for individuals missing APOE genotypes) and OGG1 (rs1052133) was performed using Impute2 based on the 1000 Genomes Project Phase 3 data; probabilistic genotypes for were called at a threshold of 0.8. ## Statistical Analyses: Statistical analyses were performed using Microsoft Excel, IBM SPSS software (v. 27.0), R software (v. 4.2.0), and GraphPad Prism software (v. 9.4.0). Welch’s t-test (two-tailed) and two-way ANOVA were performed on 8oxoG mutational load to compare between both population groups. 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--- title: Central circuit controlling thermoregulatory inversion and torpor-like state authors: - Domenico Tupone - Shelby Hernan - Pierfrancesco Chiavetta - Shaun Morrison - Georgina Cano journal: Research Square year: 2023 pmcid: PMC10055657 doi: 10.21203/rs.3.rs-2698203/v1 license: CC BY 4.0 --- # Central circuit controlling thermoregulatory inversion and torpor-like state ## Abstract To maintain core body temperature in mammals, the CNS thermoregulatory networks respond to cold exposure by increasing brown adipose tissue and shivering thermogenesis. However, in hibernation or torpor, this normal thermoregulatory response is supplanted by “thermoregulatory inversion”, an altered homeostatic state in which cold exposure causes inhibition of thermogenesis and warm exposure stimulates thermogenesis. Here we demonstrate the existence of a novel, dynorphinergic thermoregulatory reflex pathway between the dorsolateral parabrachial nucleus and the dorsomedial hypothalamus that bypasses the normal thermoregulatory integrator in the hypothalamic preoptic area to play a critical role in mediating the inhibition of thermogenesis during thermoregulatory inversion. Our results indicate the existence of a neural circuit mechanism for thermoregulatory inversion within the CNS thermoregulatory pathways and support the potential for inducing a homeostatically-regulated, therapeutic hypothermia in non-hibernating species, including humans. ## Introduction The core body temperature (TCORE) of mammals is normally maintained within a narrow range that is optimal for enzymatic reactions and cellular function. The neural circuitry for normal thermoregulation alters the neural outflows to thermoeffector tissues in response to changes in skin and core temperatures to minimize deviations in TCORE1. In particular, brown adipose tissue (BAT) and skeletal muscle (shivering) thermogenesis are increased during exposure to a low ambient temperature (TAMB) and inhibited in a warm TAMB2–6. However, when certain mammals enter torpor or hibernation7–9, their brain circuits for thermoregulation switch from a normal to an inverted state in which the response to a low TAMB is an inhibition of thermogenesis, which induces hypothermia and a reduction in energy consumption. We have recently discovered that the central activation of A1-adenosine receptors induces a torpor-like state in rats10–12, and that the hypothermia and hypometabolism that characterize this torpor-like state arises from the induction of a novel thermoregulatory state of thermogenesis that we have called thermoregulatory inversion (TI)10, 13. In the TI state, as in natural torpor/hibernation, the CNS control of thermogenesis is inverted, such that thermogenesis is inhibited in a cold TAMB, and stimulated in response to a warm TAMB. In normal thermoregulation1, cold and warm signals from the skin thermoreceptors are transmitted via the dorsal horn to the parabrachial nuclei (PBN). Cold-responsive neurons in the external lateral PBN (elPBN)4, 14 and warm-responsive neurons in the dorsolateral PBN (dlPBN)3, 14 relay thermosensory signals to the preoptic area of the hypothalamus (POA). Responding to these cutaneous thermal signals, POA outputs regulate the balance of inhibitory1, 15 and excitatory1, 16, 17 inputs to thermogenesis-promoting neurons in the dorsomedial hypothalamus (DMH), that in turn excite thermogenesis premotor neurons in the medullary rostral raphe pallidus (rRPa)1. During skin cooling, excitation of thermogenesis-promoting DMH neurons prevails and thermogenesis is augmented. Conversely, in a warm environment, net inhibition of these DMH neurons reduces the excitatory drive to thermogenesis premotor neurons in the rRPa, and thermogenesis is reduced. Clearly, the neural circuitry supporting normal thermoregulation cannot be controlling TCORE in torpor/hibernation, since in these states skin cooling inhibits thermogenesis and TCORE falls7, 18. The central neural circuitry underlying the control of thermogenesis during the TI state has not been delineated. The current study demonstrates the existence of novel thermoregulatory reflex pathways between the PBN and DMH bypassing the POA to control thermogenesis and TCORE in the TI state. This novel circuit could represent the neural basis for the cooling-evoked hypothermia and hypometabolic state observed during natural torpor/hibernation, and could facilitate the induction of the TI state and a sustained hypothermia10, 13, 19 in species that do not have an endogenous ability to enter torpor/hibernation. Implementation of pharmacological and physiological strategies to induce therapeutic hypothermia and the resulting hypometabolism would have extensive clinical applications for increasing survival and recovery of function following ischemic incidents (stroke, cardiac arrest, neonatal encephalopathy), as well as for reducing metabolic demands during extended space flights. ## Inhibition of POA induces the TI state Descending projections from the POA, including those to the DMH and rRPa, mediate the cutaneous and core temperature-evoked changes in BAT thermogenesis during normal thermoregulatory reflex responses3, 4, 13, 15, 16, 20–23. Neurons in the POA play a critical role in the induction of torpor in mice8, 24,25. Transection of pathways between the POA and the DMH (pre-DMH transX) establishes the novel thermoregulatory state of TI, in which the normal BAT thermogenic responses to skin cooling and skin warming are inverted13. To test the hypothesis that POA neuronal activity is required for the induction of the TI state, we determined the BAT sympathetic nerve activity (SNA) and thermogenic responses to skin cooling and to skin warming after injecting muscimol into the POA to inhibit local neurons. In naïve, anesthetized rats, the normal thermoregulatory response to skin cooling is a prompt increase in BAT SNA (ΔTSKIN = −5.6 ± 0.5°C from a baseline of 36.7 ± 0.3°C; ΔBAT SNA: + 983.9 ± $152.1\%$ of precooling control; $$n = 10$$, $$p \leq 0.0001$$; Figs. 1A, 1Ba). Conversely, skin warming produces a strong inhibition of BAT SNA and thermogenesis (Fig. 1A). Bilateral nanoinjections of isotonic saline (vehicle) in the POA did not affect basal levels of BAT SNA (pre-saline in POA: 216.6 ± $74.9\%$BL; post-saline in POA: 238.8 ± $122.6\%$BL; $$n = 3$$, $$p \leq 0.7262$$, Fig. 1Bb), and skin cooling produced the stereotypic increase in BAT SNA (Δ TSKIN = −5.1 ± 2.3°C; Δ BAT SNA: +1305.3 ± $166.6\%$ of pre-saline control; $$n = 3$$, $$p \leq 0.0159$$; Fig. 1Bc) characteristic of a normal cold-defense response. Bilateral nanoinjections of muscimol into the POA (Fig. 1C) did not alter the level of BAT SNA (ΔTSKIN = −0.1 ± 0.2°C; Δ BAT SNA: +0.1 ± $83.7\%$ of pre-muscimol control; $$n = 10$$, $$p \leq 0.4996$$; Figs. 1A, 1Bd). In marked contrast to saline nanoinjections in the POA (Fig. 1Bc), nanoinjections of muscimol into the POA induced the TI state in which skin cooling reduced BAT SNA (ΔTSKIN = −8.9 ± 0.8°C from a baseline of 39.4 ± 0.6°C; Δ BAT SNA: 438.08 ± $103.0\%$ of precooling control; $$n = 10$$, $$p \leq 0.0011$$; Figs. 1A, 1Be). Also characteristic of the TI state13, skin rewarming following muscimol injection in the POA increased BAT SNA and reversed the cooling-induced inhibition of BAT SNA (Figs. 1A, 1Be). ## Pre-dmh Transx Inverts The Normal Thermoregulatory Shivering Response Since skeletal muscle shivering is the most signi cant source of thermoregulatory thermogenesis in humans26, it is important to determine if the thermoregulatory circuitry controlling shivering22 can also be manipulated to transition to the TI state in which cooling would inhibit shivering and allow TCORE to fall as it does in torpor/hibernation. ## Partial pre-DMH transX eliminates cold-evoked shivering In naïve, anesthetized rats, the normal thermoregulatory response to skin and core cooling is a prompt increase in shivering, registered as an increase in nuchal muscle EMG (nEMG) (ΔTSKIN = −8.3 ± 1.1°C from a baseline of 35.56 ± 0.7°C; ΔnEMG: +526.6 ± $195.0\%$ of pre-cooling control; $$n = 9$$, $$p \leq 0.002$$; Figs. 2A, 2Da). Conversely, skin rewarming produces a strong inhibition of nuchal muscle shivering (Fig. 2A), returning the nEMG to the low levels observed in warm rats. A pre-DMH transX to −9 mm from the dorsal surface of the brain did not produce any change in nEMG in warm rats (TSKIN = 36.4 ± 0.2°C; ΔnEMG: −99.9 ± $88.4\%$ of pre-transX control; $$n = 5$$, $$p \leq 0.5$$; Figs. 2A, 2Db). However, as with BAT SNA13, this partial pre-DMH transX completely prevented the increase in shivering nEMG in response to skin cooling (ΔTSKIN = −10.8 ± 1.2°C from a baseline of 36.7 ± 0.5°C; ΔnEMG: +57.1 ± $17.8\%$ of pre-cooling control; $$n = 5$$, $$p \leq 0.125$$; Figs. 2A, 2Dc). ## Complete pre-DMH transX inverts the thermoregulatory shivering response With TCORE and TSKIN in a warm condition and nEMG at a low, non-shivering level, a complete pre-DMH transX to −10 mm from the dorsal brain surface produced an immediate and remarkable increase in nEMG and nuchal muscle shivering (TSKIN = 35.7 ± 0.1°C; ΔnEMG: +347.1 ± $145.7\%$ of pre-transX control; $$n = 6$$, $$p \leq 0.0156$$; Figs. 2A, 2Dd). Paralleling the pre-DMH transX-evoked activation of BAT SNA in warm conditions13, the pre-DMH transX-induced activation of shivering nEMG in rats with a warm TSKIN is indicative of the TI state. The TI state for shivering was con rmed by the demonstration that skin cooling inhibited these warming-evoked shivering nEMG responses. Following a pre-DMH transX, skin cooling consistently decreased nEMG (ΔTSKIN = −10.3 ± 1.4°C from a baseline of 38.6 ± 0.9°C; ΔnEMG: −366.65 ± $212.3\%$ of pre-cooling nEMG; $$n = 5$$, $$p \leq 0.0313$$; Figs. 2A, 2De). Subsequent skin rewarming consistently increased shivering nEMG (Fig. 2A). ## A glutamatergic excitation of DMH neurons is necessary for the skin warming-evoked increases in BAT SNA and BAT thermogenesis and in shivering EMG during TI The CNS circuits for the normal cold-defensive activation of BAT and shivering thermogenesis require a glutamatergic activation of neurons in the DMH that project to thermogenic premotor neurons in the rRPa1, 6, 16, 22, 27. In the TI state, skin warming activates DMH neurons that project to rRPa (Extended Data Fig. 2). Is a glutamatergic excitation of thermogenesis-promoting neurons in the DMH also required for the skin warming-induced activation of BAT and shivering thermogenesis in the TI state (Figs. 1, 2)? Following a pre-DMH transX, the TI state was validated by demonstrating that skin warming (TSKIN = 38.2 ± 0.6°C) resulted in an activation of BAT SNA (BAT SNA: 878.8 ± $184.3\%$BL; Fig. 3A). Subsequent bilateral nanoinjections of AP5/CNQX in the DMH (Fig. 3C) eliminated the warm-evoked increase in BAT SNA (ΔBAT SNA: −836.0 ± $194.4\%$BL; $$n = 6$$, $$p \leq 0.0039$$; Figs. 3A, 3B). The abrupt fall in BAT SNA resulted in a decrease in TBAT (−0.7 ± 0.1°C, $$n = 6$$, $$p \leq 0.0018$$) and in expired CO2 (−0.4 ± $0.1\%$, $$n = 6$$, $$p \leq 0.0044$$; Fig. 3B). In rats in the TI state after a pre-DMH transX and with a warm skin (TSKIN = 38.5 ± 1.1°C) and an actively shivering nEMG (243.7 ± $114.2\%$BL; Fig. 2A), bilateral nanoinjections of AP5/CNQX in the DMH (Figs. 2B, 2C) reversed the warm-evoked activation of shivering nEMG (ΔnEMG: −217.0 ± $104.1\%$BL; $$n = 6$$, $$p \leq 0.0313$$; Figs. 2A, 2Df). In the TI state after pre-DMH transX, bilateral nanoinjection of saline (vehicle) in the DMH (Fig. 3F) had no effect on the skin warming-evoked activation of BAT SNA (pre-saline BAT SNA: 750.2 ± $306.1\%$BL, post-saline BAT SNA: 700.9 ± $230.4\%$BL; $$n = 4$$, $$p \leq 0.3260$$; Figs. 3D, 3E). Additionally, the cold-evoked inhibition of BAT SNA characteristic of the TI state was unaffected by saline nanoinjections in the DMH (ΔTSKIN = −11.7 ± 0.8°C from a baseline of 38.9 ± 0.5°C; ΔBAT SNA: −513.2 ± $108.3\%$ of pre-cooling control; $$n = 4$$, $$p \leq 0.0089$$; Figs. 3D, 3E). These results indicate that in the TI state increases in BAT and shivering thermogenesis are dependent on a glutamatergic excitation of thermogenesis-promoting neurons in the DMH. Since a complete pre-DMH transX (Fig. 2D) or a nanoinjection of muscimol in POA (Fig. 1C) induces a robust TI state, it seems unlikely that the source of the glutamatergic input to the DMH required for the skin warming-evoked activation of BAT and shivering thermogenesis in the TI state is located within the POA, as it is for normal thermoregulation 16, but rather from neurons located caudal to the pre-DMH transX. To provide evidence that in the TI state skin warming activates DMH neurons that project to rRPa, we examined the Fos expression (Fos-ir) in DMH neurons that were retrogradely-labeled with FluoroGold (FG) injected into the rRPa in warm-exposed rats after a pre-DMH transX (Warm-T rats). A significantly higher percentage of rRPa-projecting (FG-ir) neurons in the DMH were double-labeled (FGFos) in Warm-T rats than in Cold-T rats (Warm-T: 22.38 ± $4.01\%$ FGFos/FG vs. Cold-T: 11.34 ± $1.3\%$ FGFos/FG, $$n = 4$$, $$p \leq 0.02319$$, Extended Data Fig. 2). ## A glutamatergic excitation of PBN neurons is necessary for the skin warming-evoked increase in BAT SNA during TI The central afferent pathways from cutaneous thermoreceptors, which drive a glutamatergic excitation of neurons in two subnuclei of the PBN (dlPBN and elPBN3, 4, 14), comprise the sensory components for the normal thermoregulatory reflex control of thermogenesis. Is a glutamatergic excitation of PBN neurons also essential for the inverted skin thermoreceptor regulation of thermogenesis in the TI state? In the TI state following a pre-DMH transX, skin cooling consistently decreased BAT SNA (ΔTSKIN = −7.0 ± 1.1°C from a baseline of 37.4 ± 0.7°C; ΔBAT SNA: −411.4 ± $98.4\%$ of pre-cooling control; $$n = 5$$, $$p \leq 0.0007$$; Figs. 4A, 4C) and skin rewarming consistently increased BAT SNA (ΔTSKIN = + 6.6 ± 1.3°C from a baseline of 30.4 ± 0.8°C; ΔBAT SNA: +359.1 ± $89.3\%$ of pre-cooling control; $$n = 5$$, $$p \leq 0.0012$$; Figs. 4A, 4C). During skin warming (TSKIN = 40.6 ± 1.0°C, $$n = 5$$) and an elevated BAT SNA (515.11 ± $130.7\%$BL; $$n = 5$$), bilateral nanoinjections of AP5/CNQX into the PBN (Fig. 4D) promptly decreased BAT SNA (ΔBAT SNA: −329.6 ± $112.5\%$BL, $$n = 5$$, $$p \leq 0.0313$$; Figs. 4A, 4B). The long-lasting (> 1 hr) inhibition of the skin warming-evoked activation of BAT SNA resulted in a decrease in TBAT (−0.6 ± 0.2°C, $$n = 3$$, $$p \leq 0.0355$$) and in expired CO2 (−0.2 ± $0.1\%$, $$n = 5$$, $$p \leq 0.0387$$; Fig. 4B). Thus, in the TI state, the inverted control of thermogenesis by skin thermoreceptors requires a glutamatergic activation of neurons in the PBN. In the TI state and with the skin kept warm, bilateral nanoinjections of isotonic saline (vehicle) in the PBN had no effect on the warm-evoked activation of BAT SNA (pre-saline in PBN BAT SNA: 333.5 ± $134.4\%$BL, post-saline in PBN BAT SNA: 281.33 ± $80.5\%$BL; $$n = 5$$, $$p \leq 0.5231$$; Figs. 4A, 4C). In the TI state, nanoinjection of saline in the PBN also had no effect on the characteristic cold-evoked inhibition of BAT SNA (ΔTSKIN = −9.9 ± 1.8°C from a baseline of 38.7 ± 0.6°C; ΔBAT SNA: −212.2 ± $52.6\%$ of pre-cooling BAT SNA; $$n = 4$$, $$p \leq 0.0089$$; Figs. 4A, 4C). ## PBN neurons projecting to DMH are activated during the inverted thermoregulatory thermogenic responses in the TI state The skin thermoreceptor-mediated modulation of thermogenesis in the TI state requires both the descending thermogenesis-promoting pathways from DMH to the rRPa (Figs. 2,3; Extended Data Fig. 2), as well as an ionotropic glutamate receptor-mediated excitation of neurons in the specific regions of the PBN (Fig. 4) receiving thermosensory signals from second-order thermosensory neurons in the spinal dorsal horn. Since the inverted regulation of thermogenesis in the TI state occurs in the absence of direct POA inputs to the DMH (Figs. 1, 2, 3), we tested the hypothesis that PBN neurons with direct projections to the DMH are activated during skin thermoreceptor stimulation in the TI state. DMH-projecting neurons in the PBN are activated during skin warming in anesthetized, naïve rats and in anesthetized, pre-DMH transX rats. We compared anatomical assessments of PBN neuronal activation (Fos-ir) in 4 groups of anesthetized rats: naïve rats and pre-DMH transX rats during skin warming and during skin cooling. Our injections of the retrograde tracer, cholera toxin subunit b (CTb), in the DMH overlapped with DMH neurons retrogradely-labeled following injections of another retrograde tracer, Fluorogold (FG), in the rRPa (Extended Data Figs. 1A, 1B), and resulted in CTb retrograde labeling of neurons in the elPBN and dlPBN (Extended Data Fig. 1C). This basic anatomical result is consistent with the potential for PBN neurons to directly influence the activity of thermogenesis-promoting neurons in the DMH. There was no difference between the number of CTb-ir neurons in the elPBN and in the dlPBN in the 4 treatment groups ($p \leq 0.05$, Fig. 5E). To analyze the extent of Fos expression in dlPBN and elPBN neurons (Fig. 5A) that were retrogradely labeled from CTb injections in DMH (Fig. 5D), we calculated the percent of CTb-labeled neurons in elPBN and in dlPBN that were also Fos-ir (% CTbFos/CTb; Fig. 5C) in PBN sections at 4 consecutive rostro-caudal levels separated by approximately 100 μm. During skin warming in anesthetized, naive rats (Warm-N), we observed Fos-ir in both dlPBN (total: 16.60 ± $2.06\%$) and elPBN (total: 17.75 ± $3.58\%$) neurons that projected to DMH (Figs. 5A, 5C). In anesthetized pre-DMH transX rats with a warm skin (Warm-T), we also found Fos-ir DMH-projecting neurons in both dlPBN (total: 16.93 ± $2.19\%$) and elPBN (total: 13.89 ± $2.50\%$) (Figs. 5A, 5C), and at comparable levels to those observed in the Warm-N rats. Pattern of activation of DMH-projecting neurons in the PBN in anesthetized, naive rats and in anesthetized, pre-DMH transX rats after cold exposure Skin cooling in anesthetized, naïve rats (Cold-N), a condition in which thermogenesis is strongly stimulated, activated 27 ± $0.95\%$ of the total elPBN neurons that project to DMH (Figs. 5B, 5C). Skin cooling in anesthetized, pre-DMH transX rats (Cold-T), a condition in which thermogenesis is inhibited, activated significantly fewer DMH-projecting neurons in elPBN (14.73 ± $2.31\%$; $$n = 5$$, $$p \leq 0.003$$; Figs. 5B, 5C) than in the Cold-N group. This reduction was most prominent at the Intermediate-1 level of the elPBN (Cold-N: 33.09 ± $3.65\%$ vs. Cold-T: 14.52 ± $4.27\%$, $$n = 5$$, $$p \leq 0.0151$$; Fig. 5C). Skin cooling in anesthetized, naive rats (Cold-N) also activated DMH-projecting neurons in dlPBN (11.79 ± $0.81\%$). A similar fraction (12.98 ± $0.91\%$) of the DMH-projecting neurons in dlPBN was also activated by skin cooling in anesthetized, pre-DMH transX rats (Cold-T) (Figs. 5B, 5C). These data support the idea that in anesthetized, naive rats, activation of DMH-projecting neurons in elPBN and in dlPBN contributes to the regulation of the discharge of thermogenesis-promoting neurons in DMH during normal thermoregulatory cold defense. In addition, our nding that the activation of DMH-projecting neurons in elPBN was lower in Cold-T than in Cold-N rats would be consistent with a reduced excitation of DMH neurons from their elPBN inputs in the TI state, when skin cooling reduces thermogenesis (Figs. 1, 2, 3). PBN neurons with direct projections to the DMH are activated during warm and cold exposure in naïve, free-behaving rats We sought to establish that PBN neurons with projections to the DMH were also activated during skin thermoreceptor stimulation in free-behaving rats. One week following CTb injections in the DMH, these naïve, free-behaving rats were exposed to either a warm or a cold TAMB, and Fos expression was quanti ed in DMH-projecting PBN neurons. Since there was no difference between the number of elPBN and dlPBN CTb-retrogradely neurons in warm- or cold-exposed free-behaving rats (elPBN: 140.0 ± 12.93 vs. 175.8 ± 7.56, $$n = 5$$, $$p \leq 0.06$$; dlPBN: 114.0 ± 10.63 vs. 146.6 ± 22.06, $$n = 5$$, $$p \leq 0.2275$$), we expressed the double-labeled PBN neuron counts as a percentage of the number of retrogradely-labeled PBN neurons (% CTbFos/CTb) throughout the dlPBN and elPBN subdivisions of the PBN. During warm exposure in naïve, free-behaving rats, a condition in which we expect low levels of thermogenesis, we observed similar percentages of CTbFos/CTb in DMH-projecting neurons in the dlPBN (7.68 ± $1.74\%$) and within elPBN (5.67 ± $0.95\%$) (Figs. 6A, 6C). During cold exposure, when thermogenesis should be activated, Fos-ir was also observed in DMH-projecting neurons within dlPBN (3.78 ± $0.93\%$) and within elPBN (19.41 ± $3.64\%$) (Figs. 6B, 6C). Of the dlPBN neurons that projected to DMH, a significantly greater percentage expressed Fos-ir in warm-exposed rats than in cold-exposed rats ($$n = 5$$ per group; $$p \leq 0.0416$$; Fig. 6C). In contrast, for elPBN neurons that projected to the DMH, a significantly greater percentage expressed Fos-ir in cold-exposed rats than in warm-exposed rats ($$n = 5$$ per group; $$p \leq 0.0032$$; Fig. 6C). In these same rats, we observed FG-ir neurons in the DMH that expressed Fos-ir after cold exposure (Extended Data Fig. 2A). These anatomical data indicate that neurons in both the dlPBN and the elPBN project to the region of the DMH containing thermogenesis-promoting neurons (Fig. S1C). These populations of PBN neurons take part in the control of DMH thermoregulatory neurons during normal thermoregulation, in naïve, free-behaving rats. ## Dynorphinergic Neurons In Pbn Project To Dmh The region of the PBN containing DMH-projecting neurons (Figs. 5A, 6A, 6B) also contains Dynorphin (Dyn) neurons14, 28. The intermediate-1 level subpopulation of DMH-projecting neurons in dlPBN is activated during skin cooling in pre-DMH transX rats (Fig. 5C), a TI state in which skin cooling inhibits thermogenesis by reducing the discharge of DMH neurons. These ndings, coupled with Dyn exerting an inhibitory influence on neuronal activation (through activation of κ-opioid receptors29–31), prompted us to test the hypothesis that Dyn, potentially released from terminals of the PBN neurons projecting to the DMH, plays a role in the cooling-evoked inhibition of thermogenesis characteristic of the TI state (Figs. 1, 2). Initially, we sought to determine if any of the Dyn neurons in the PBN project to the region of the DMH containing thermogenesis-promoting neurons, and if such a neuronal population is activated during normal thermoregulation and/or in the TI state. To identify Dyn neurons in the PBN and whether they express vesicular glutamate transporter 2 (VGluT2) or vesicular GABA transporter (VGAT), we performed in situ hybridization (ISH) with RNAScope on sequential brain sections containing the PBN. Dyn neurons were observed in several PBN subdivisions along its entire rostro-caudal extent (Fig. 7A), but the strongest transcript labeling was in dense clusters located in the dlPBN at the intermediate-1 and − 2 levels (Fig. 7A). All Dyn neurons in these clusters express VGluT2 transcripts (in a sample of 375 Dyn neurons in 2 sections counted bilaterally, all neurons colocalized VGluT2). None of the Dyn neurons in PBN express VGAT transcripts (Fig. 7A). To determine if the Dyn neurons in the PBN project to the DMH, we performed immunohistochemistry (IHC) for Dyn and CTb in brains from rats that had been injected with CTb in DMH and treated with intracerebroventricular (ICV) colchicine. We observed colocalization of CTb and Dyn mainly in the dense clusters of Dyn neurons in the dlPBN (Fig. 7B). Thus, there is a concentration of VGluT2-expressing Dyn neurons in the dlPBN region, and many of these Dyn neurons project to the region of the DMH that contains thermogenesis-promoting neurons. To determine whether the activity of DMH-projecting Dyn neurons in dlPBN could influence the level of thermogenesis in normal thermoregulation or in the TI state, we performed a co-detection procedure to identify DMH-projecting (CTb labeling with IHC) Dyn (pDyn transcripts with ISH) neurons in PBN that were activated (c-fos with ISH) by cutaneous thermal stimuli in naïve and pre-DMH transX rats. We observed DMH-projecting (CTb) Dyn neurons in dlPBN that were activated (c-fos) during skin warming in anesthetized naïve (Warm-N) rats (Fig. 7C), a condition in which we expect thermogenesis to be inhibited. Noticeably, fewer Dyn neurons in the dlPBN were activated in Cold-N rats than in either Warm-N or Cold-T rats (Fig. 7D). Our observation that DMH-projecting Dyn neurons in the dlPBN are activated in Warm-N rats, when inhibitory influences on the discharge of thermogenesis-promoting neurons in DMH predominate, is consistent with a signi cant thermogenesis-inhibiting role for these DMH-projecting Dyn neurons in the dlPBN. Such a role for Dyn neurons in the dlPBN is also supported by our nding that more of them are activated in Warm-N and Cold-T rats when thermogenesis is inhibited than in Cold-N rats, when thermogenesis is active (Fig. 7D). ## Dynorphin In Dmh Inhibits Normal, Cold-evoked Bat Sna And Bat Thermogenesis Having identified a dynorphinergic projection from the PBN to the region of the DMH containing thermogenesis-promoting neurons, we sought to determine if Dyn in the DMH would affect normal, cold-evoked BAT SNA and BAT thermogenesis. Since the degradation products of exogenous Dyn by extracellular peptidases lead to non-specific activation of NMDA receptors32, we pretreated the DMH with the peptidase inhibitor Amastatin. In anesthetized naïve rats with a cool skin (TSKIN = 35.2 ± 0.4°C) and an activated BAT SNA, bilateral nanoinjections of Amastatin into the DMH (Fig. 8C) did not affect BAT SNA (pre-Amastatin: 787.5 ± $33.0\%$ BL, post-Amastatin: 816.7 ± $163.06\%$ BL, $$n = 4$$, $$p \leq 0.8459$$; Figs. 8A, 8B) or TBAT (pre-Amastatin: 36.4 ± 0.7°C, post-Amastatin: 36.6 ± 0.6°C; $$n = 4$$, $$p \leq 0.0546$$; Figs. 8A, 8B). Subsequent nanoinjections of Dyn in the same site promptly inhibited normal, cold-defensive BAT SNA (pre-Dyn: 613.0 ± $116.6\%$BL, post-Dyn: 61.7 ± $39.4\%$BL; $$n = 4$$, $$p \leq 0.0142$$), which caused a signi cant decrease in TBAT (pre-Dyn: 36.2 ± 0.6°C, post-Dyn: 36.8 ± 0.7°C; $$n = 4$$, $$p \leq 0.0469$$; Figs. 8A, 8B). Following Dyn nanoinjection in the DMH, subsequent skin cooling no longer activated BAT SNA (Fig. 8A). Additionally, skin warming had no effect on the post-Dyn completely inhibited level of BAT SNA (Fig. 8A), indicating that Dyn nanoinjection in the DMH does not induce the TI state in which skin warming activates BAT SNA (cf. Figures 1, 3). ## A κ-opioid receptor antagonist in the DMH prevents the cold-evoked inhibition of BAT thermogenesis during TI Since a population of Dyn-expressing neurons in PBN projects to the DMH, and Dyn nanoinjection into the DMH inhibits normal, cold-evoked BAT SNA and reduces BAT thermogenesis, we tested the hypothesis that Dyn, acting via k-opioid33 receptors in the DMH, contributes to the cold-evoked inhibition of BAT SNA in the TI state. In the TI state following pre-DMH transX, skin cooling consistently decreased BAT SNA (ΔTSKIN = −8.0 ± 1.5°C from a baseline of 37.3 ± 0.3°C; ΔBAT SNA: −1139.9 ± $323.9\%$BL; $$n = 6$$, $$p \leq 0.0085$$; Figs. 8D, 8E). With a warm TSKIN (39.0 ± 0.7°C) and an active BAT SNA (1426.9 ± $277.0\%$BL), bilateral nanoinjections of nor-BNI in the DMH (Fig. 8F) prevented any subsequent cold-evoked inhibitions of BAT SNA (ΔTSKIN = −9.3 ± 2.2°C from a baseline of 39.0 ± 0.7°C; ΔBAT SNA: −280.4 ± $172.6\%$BL; $$n = 6$$, $$p \leq 0.0825$$; Fig. 8D, 8E). Thus, in the TI state, nor-BNI administration into the DMH resulted in an 82.1 ± $19.2\%$ reduction in the cold-evoked inhibition of BAT SNA, indicating that Dyn release in the DMH is necessary for the skin cooling-evoked reduction in thermogenesis in the TI state. ## Blockade of central κ-opioid receptors reduces the hypothermic response to ICV administration of an adenosine 1A receptor agonist in free-behaving rats In free-behaving rats exposed to a cool TAMB, central administration of the adenosine 1A receptor (A1A-R) agonist, CHA, produces a progressive hypothermia consistent with an inverted regulation of thermogenesis, characteristic of the TI state13. Since Dyn release in the DMH is required for the skin cooling-evoked inhibition of thermogenesis in the TI state in anesthetized rats (Figs. 8D, 8E), we determined if blockade of central κ-opioid receptors with ICV nor-BNI would affect the cooling-evoked hypothermia during the TI state induced by the central administration of CHA in free-behaving rats. One hour after reducing the TAMB from 25°C to 15°C, free-behaving rats chronically instrumented for TCORE recording, received an ICV pretreatment of either $0.9\%$ saline vehicle (5 μl) or nor-BNI, followed after 10 minutes by an ICV injection of CHA (1 mM, 5μl). During the 1 h exposure to the TAMB of 15°C prior to pretreatment, the rats maintained a normal TCORE of 36.7 ± 0.1°C ($$n = 3$$; Fig. 8G), re ecting a normal thermoregulatory cold-defense response. Following saline pretreatment, administration of CHA elicited a prompt reduction in TCORE (Fig. 8G), which reached a minimum of 22.4 ± 0.2°C (ΔTCORE = −14.2 ± 0.2°C from a baseline of 36.7 ± 0.2°C, $$n = 3$$) at 7 h:36 min ± 7 min following CHA injection (Fig. 8G). Following pretreatment with nor-BNI, injection of CHA also elicited a rapid reduction in TCORE (Fig. 8G). However, the fall in TCORE (ΔTCORE = −7.2 ± 0.8°C from a baseline of 36.7 ± 0.3°C, $$n = 3$$) following CHA administration was significantly less after pretreatment with nor-BNI than after saline pretreatment ($p \leq 0.001$, Bonferroni post-hoc test). Additionally, because the rate of decline in TCORE was the same in both saline and nor-BNI pretreatment conditions, the minimum TCORE of 29.5 ± 0.8°C after CHA administration was reached at a shorter time (4 h:56 min ± 50 min, $$p \leq 0.0448$$) after the nor-BNI pretreatment than after the saline pretreatment. The nding that during the TI state in free-behaving rats10 pretreatment with nor-BNI reduced the maximum hypothermia but not the rate of decline in TCORE suggests that Dyn, acting via central κ-opioid receptors, plays a permissive role in sustaining the skin cooling-induced inhibition of thermogenesis that is a hallmark of the TI state13. ## Discussion Torpor/hibernation, naturally expressed by only a few species, is a complex behavioral phenotype7 with a unique metabolic state in which TCORE and whole-body energy expenditure are markedly reduced through an inhibition of the normal, cold-defensive responses (BAT and shivering thermogenesis) that are essential for maintaining TCORE in a cold environment34. We have discovered that a torpor-like state, featuring a shifted homeostasis closely resembling the physiological alterations observed in natural torpor, can be induced in rats10, 13, a species that does not naturally express torpor/hibernation in a cold environment. Further, the hypothermia and hypometabolism of this torpor-like state are due to a switch (Extended Fig. 3) within the CNS circuitry regulating body temperature, such that in the TI state stimulation of cold skin thermoreceptors produces an inhibition of thermogenesis10, 13. The present study reveals components of the principal afferent and efferent neural mechanisms underlying the TI state. We demonstrate that the well-known central thermoregulatory components, the PBN and the DMH1–4, 6, 22, mediate an inverted skin thermoreceptor regulation of thermogenesis during the TI state, which is independent from the integration of POA neuronal function required during normal thermoregulation. Our study also led to the discovery of a novel thermoregulatory function for a direct dynorphinergic pathway between the dlPBN and the DMH (Figs. 7, 10), playing an essential role in mediating the cold-evoked inhibition of thermogenesis that is a prominent characteristic of both the TI state and the torpor/hibernation state. Neuraxis transection rostral to the DMH is sufficient to eliminate the normal, POA-dependent, skin thermoreceptor-mediated regulation of thermogenesis and to establish the TI state, in which thermogenesis is still controlled by skin thermoreceptors, but now responds to skin warming and cooling in an inverted13 way. This and our current nding that muscimol-induced inhibition of POA neurons also establishes the TI state are consistent with a model in which the switch from the normal to the TI thermoregulatory state requires a change in the activity of a population of POA neurons that significantly alters an input to the DMH. Recent studies to identify ‘torpor’ neurons in mice8, 24, 25 have also concluded that neurons in the POA are essential for establishing the hypothermic torpor state, including those Q neurons that project to the DMH8 and estrogen-sensitive neurons that project to medial hypothalamic areas25. However, none of these studies has proposed a neural circuit through which such ‘torpor neurons’ would regulate thermogenesis to induce hypothermia. Paralleling the thermore ex circuit controlling BAT thermogenesis, cold-induced shivering is dependent on the activation of shivering thermogenesis-promoting neurons in the DMH that project to shivering premotor neurons in the rRPa22. Remarkably, the switch to the TI state of thermoregulation inverts the skin thermoreceptor-mediated regulation of shivering (Fig. 2), just as it does for BAT thermogenesis (Fig. 3). The warming-induced activation of BAT in the TI state requires an ionotropic glutamate receptor-mediated excitation of DMH neurons (Fig. 3), just as the cold-induced activation of BAT does in the normal thermoregulatory state3. Together, these ndings strongly support our conclusions that a modulation of inputs to neurons in the DMH is necessary to induce the TI state, and that the principal thermoregulatory mechanism underlying the cooling-induced hypothermia in the TI state is a widespread inhibition of thermogenesis at the level of the thermogenesis-promoting neurons in the DMH (Extended Data Fig. 2, 3). Our demonstration that blockade of ionotropic glutamate receptors in the PBN prevents the warm-induced activation of BAT thermogenesis characteristic of the TI state indicates that ascending warm and cold thermoregulatory signaling, mediated by glutamatergic inputs to PBN neurons, is essential for the skin thermoreceptor-mediated control of thermogenesis in the TI state, as it is for normal thermoregulation1 in both the rat3, 4 and mouse14. Our discovery that the TI state occurs when the normal thermoregulatory connections between the POA and the DMH are severed (Fig. 3) or inhibited (Fig. 1), indicates the existence of POA-independent thermoregulatory pathways through which thermosensory signaling transmitted via PBN neurons can influence the activity of thermogenesis-promoting neurons in the DMH (Extended Data Fig. 3). Further, the inversion of the effects of cold and warm skin thermoreceptors on BAT and shivering thermogenesis in the TI state must arise from a “neuronal switch” that alters the balance between the skin thermoreceptor signaling that ascends from the PBN to the POA and the thermoreceptor signaling that short-circuits the POA to directly influence thermogenesis-promoting neurons in the DMH (Extended Data Fig. 3). We identified connections between neurons in the dlPBN and elPBN regions of the PBN and neurons in the region of the DMH that contains thermogenesis-promoting neurons that project to the rRPa. Furthermore, we determined, in free-behaving and anesthetized naïve rats, that many of these DMH-projecting PBN neurons are active during normal thermoregulatory responses to skin cooling and skin warming. These ndings support the existence of novel thermoregulatory inputs from the PBN to the thermogenesis-promoting neurons in the DMH that could act in concert with those from the POA to contribute to the normal thermoregulatory control of thermogenesis. More specifically, we identified a population of DMH-projecting Dyn neurons in the dlPBN region that is activated during skin warming in naïve rats, when inhibitory influences on the discharge of thermogenesis-promoting neurons in DMH predominate. Dyn activated k-opioid receptor, has a potent inhibitory influence on thermogenesis-promoting neurons in the DMH, leading to a nearly complete reversal of the normal cold-evoked activation of BAT SNA and BAT thermogenesis (Fig. 8). These observations are consistent with a signi cant, but previously undescribed, thermogenesis-inhibiting role for these DMH-projecting Dyn neurons in the dlPBN during normal thermoregulation. More Dyn neurons in the dlPBN were activated during skin cooling in the TI state, when thermogenesis is inhibited, than during skin cooling in naïve rats, when thermogenesis is active. In addition, blockade of the κ-opioid receptors for Dyn in the DMH markedly reduced the cold-evoked inhibition of BAT SNA in the TI state in anesthetized rats, and significantly reduced the maximum hypothermia induced in our torpor-mimicking CHA model of TI in awake rats13. Thus, Dyn acting via κ-opioid receptors in the DMH plays a necessary, permissive role in sustaining the skin cooling-induced inhibition of thermogenesis that is a hallmark of TI. However, it is important to note that activation of Dyn receptors in the DMH is not sufficient to induce the TI state. If the VGluT2-expressing Dyn neurons in dlPBN are glutamatergic, their role in inhibiting thermogenesis could be explained by their targeting of GABAergic interneurons in the DMH35. However, on the basis of the recent demonstration that the DMH axon terminals of VGluT2-expressing neurons in the POA express VGAT15, a marker for GABAergic terminals, we propose that the VGluT2-expressing Dyn neurons in the dlPBN might release both Dyn and GABA from their terminals in the DMH to provide a direct inhibitory regulation of thermogenesis-promoting neurons in the DMH. Together, these results provide strong support for our discovery of a novel thermoregulatory role for the dynorphinergic pathway from the dlPBN to the DMH in the inhibitory regulation of thermogenesis, both in the normal thermoregulatory state when skin warming inhibits thermogenesis, as well as in the TI state when skin cooling inhibits thermogenesis. The fact that the neural circuitry underlying the TI state is present and functional in the rat, a species that does not express natural torpor, suggests that this neuronal substrate could be accessed for human clinical applications involving the management of hypothermia following ischemic incidents or for the reduction of metabolic demands during extended space ights. Further, the existence of an alternative thermoregulatory circuit mediating the TI state regulation of thermogenesis and energy expenditure could provide insight into the neural mechanisms underlying conditions in which thermoregulation (post-surgical shivering, anaphylactic or septic hypothermia) or energy metabolism (obesity) is regulated in a seemingly unphysiological manner. ## Animals Male Sprague Dawley rats (300–400 g, Charles River Laboratories) were maintained in a standard 12 hr/12 hr, light/dark cycle (lights on at 0900) with ad libitum access to standard chow and water. Experiments were performed in accordance with the Guide for the Care and Use of Laboratory Animals, 8th Edition (National Research Council, National Academies Press, 2010) and protocols were approved by the Institutional Animal Care and Use Committee of Oregon Health and Science University. ## Procedures for recording BAT sympathetic nerve activity (SNA) or muscle shivering EMG Rats were anesthetized initially with $3\%$ isoflurane in $100\%$ O2 and transitioned to urethane (0.8 g/kg) and chloralose (80mg/kg) following cannulation of a femoral artery and vein. Heart rate (HR) was derived from the femoral arterial pressure (AP) signal. Rats were positioned in a stereotaxic frame with the incisor bar at −4 mm below interaural zero and a spinal clamp installed on the T10 vertebra used to maintain the spine in a rigid and elevated position (detailed in 13). Rats were paralyzed with D-tubocurarine (0.3 mg initial dose, 0.1 mg/h supplements) and artificially ventilated with $100\%$ O2 (60–70 cycles/min, tidal volume 3 – 3.5 ml). Thermocouples (Physitemp Instruments with Sable Systems International meter) were placed (a) on the shaved abdominal skin to measure the skin temperature (TSKIN) beneath a waterperfused blanket wrapped around the rat’s trunk, (b) 6 cm into the rectum to measure TCORE, and (c) into the medial aspect of the left interscapular BAT pad to measure BAT temperature (TBAT). TCORE was normally maintained at ~ 37°C by perfusing the water blanket with warm water. As required for specific experimental protocols, TSKIN and TCORE were adjusted by changing the temperature of the water perfusing the thermal blanket. Postganglionic BAT sympathetic nerve activity (SNA) was recorded from the central cut end of a small nerve bundle dissected from the ventral surface of the right interscapular BAT pad after dividing the fat pad along the midline and reflecting it laterally. BAT SNA was recorded with bipolar hook electrodes, ltered (1–300 Hz), and ampli ed (20,000x; Cyberamp 380, Axon Instruments). The viability and correct identification of the isolated BAT nerve were veri ed by signi cant increases in BAT SNA evoked by skin cooling. A similar surgical preparation was used for experiments in which a shivering EMG was recorded with a bipolar electrode inserted into a nuchal (neck) muscle. Nuchal EMG (nEMG) recordings were performed in rats anesthetized initially with $3\%$ isoflurane in $100\%$ O2, and subsequently transitioned to a continuous intravenous infusion of inactin (85 mg/ml at 0.2 ml/h). Rats were arti cially ventilated but were not paralyzed. ## Pre-DMH transection (pre-DMH transX) A cranial window (~ 4×4 mm) was made just behind the bregma and centered on the sagittal suture. The dura mater was carefully dissected from the superior sagittal sinus and removed throughout the cranial window to allow transection of the brain without damage to the sinus or the major vessels converging on it. A transection knife (15 mm long, 2 mm wide, and 0.1 mm thick) was mounted vertically in a stereotaxic manipulator and positioned perpendicular to the sagittal sinus at −1.5 mm caudal to bregma and with the medial edge on the midline. After a slight lateral retraction of the sagittal sinus, the knife was inserted into the brain sequentially on the left and right sides of the superior sagittal sinus to a depth of either − 9 mm (partial transection) or approximately − 10 mm (complete transection). ## Drug nanoinjection procedures Intraparenchymal brain nanoinjections of drugs were performed as previously described10, 16, 35 via glass micropipettes, using a pressure injection system (Toohey model IIe). For repeated nanoinjections at the same site, the micropipette was retracted vertically, emptied, rinsed with saline, re lled, and repositioned at the original dorsoventral coordinate. The injection sites were marked with fluorescent polystyrene microspheres (1:10 dilution of FluoroSpheres F8797, F8801, or F8803, Invitrogen). With the incisor bar positioned at −4 mm, bilateral nanoinjections (120 nl each) in the PBN were performed at −8.9 mm caudal to bregma, 2.2 mm from the midline, and 5.5 mm below the brain surface; and in the DMH at −3.2 mm caudal to bregma, 0.4 mm from the midline, and 7.8 mm below the brain surface. Bilateral nanoinjections (180 nl each) were performed bilaterally in the medial preoptic area (MPA; −0.4 mm caudal to bregma, ± 0.4 mm from the midline, and − 7.5 mm below the brain surface), and in the median preoptic area (MnPO; at bregma on the midline, −6.5 mm below the brain surface). Following experimental procedures, rats were perfused transcardially with isotonic saline, followed by $4\%$ paraformaldehyde (PFA) in 10 mM sodium phosphate buffered saline (PBS; pH 7.4). The brains were removed, post xed in $4\%$ PFA (2 h), equilibrated overnight in $30\%$ sucrose, and sectioned (60 μm coronal sections) to localize the fluorescent spots indicating the centers of the injection sites. The coordinates used for the brain intraparenchymal injections were adapted from a rat brain atlas 36 and from our previous studies involving these brain regions 1, 13, 16. ## Drugs The A1 adenosine receptor (A1AR) agonist n6-cyclohexyladenosine (CHA, 1mM, Sigma Aldrich); the NMDA receptor antagonist (2R)-amino-5-phosphonopentanoate (AP5, 5 mM, Tocris); the AMPA/kainate receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dione disodium salt hydrate (CNQX, 5 mM, Tocris); the peptidase inhibitor (2S,3R)-3-Amino-2-hydroxy-5-methylhexanoyl-Val-Val-Asp hydrochloride hydrate (Amastatin, 1 mM, Sigma Aldrich); Dynorphin A (100 μM, Tocris); and the κ-opioid receptor antagonist nor-Binaltorphimine dihydrochloride (nor-BNI, 27 μM intraparenchymal or 1.6 mM ICV, Tocris) were dissolved in isotonic saline. ## Experiments in free-behaving rats Central administration of the A1AR agonist, CHA, produces the TI state, featuring a dramatic fall in TCORE due to an inhibition of thermogenesis in a cold TAMB10. Rats were anesthetized with $2\%$ isoflurane in $100\%$ O2 and instrumented for chronic recording of physiological variables as previously described 10. Rats were implanted with an intraperitoneal implantable temperature probe (Anipill®) for recording of TCORE. A guide cannula (C315G-26GA, PlasticsOne) was stereotaxically positioned in the lateral ventricle for intracerebroventricular (ICV) injection of drugs. The cannula was secured to the skull with screws and dental acrylic. Following the surgical procedure, rats were treated with buprenorphine (0.1 mg/kg), penicillin G (40,000 units/kg) and hydrated with isotonic saline (5 ml, subcutaneous). Each rat recovered for 7 days in a temperature-controlled recording chamber at an ambient temperature (TAMB) of 25°C and received daily meloxicam (1 mg/kg orally) for the rst 3 days to reduce post-surgical in ammation. For ICV injections of CHA and nor-BNI, 5 μl of drug solution were injected over 2 minutes through an internal cannula connected to a 25 μl Hamilton syringe. Rats were brie y removed from the recording chamber, the ICV injection procedure was performed in less than 5 minutes, and the rats were immediately returned to the recording chamber for continued data acquisition. ## Neuroanatomy For anatomical tracing experiments, adult male Sprague Dawley rats (240–400 g) were anesthetized with 2–$3\%$ isoflurane in $100\%$ O2, and stereotaxically injected with cholera toxin subunit b (CTb) conjugated with Alexa-488 (1 mg/ml, 120 nl) into the right DMH (bregma: 3.2 mm caudal, 0.4 mm lateral, 7.5 mm ventral to the brain surface; incisor bar at −4 mm), and FluoroGold (FG, $2\%$, 30 nl) into the rRPa (relative to lambda: 3.0 mm caudal, 0.0 mm lateral, 9.2 mm ventral to the brain surface; incisor bar at −4 mm). Rats were pretreated with intramuscular injections of an antibiotic (40,000 units/kg penicillin G) and an analgesic (1 mg/kg meloxicam), and subcutaneous injection of isotonic saline (3 ml). One week after tracer injections, free-behaving rats were exposed to a cold ambient (TAMB: 10°C) or to a warm ambient (TAMB: 30°C) for 2 h to elicit Fos expression as an indicator of neuronal activation. To maximize Fos expression, rats were maintained for 24 h at a TAMB of 30°C prior to the 2 h cold exposure or maintained for 24 h at a TAMB of 10°C prior to the 2 h warm exposure. We employed retrograde transport, combined with Fos expression, to identify DMH-projecting neurons in PBN that are activated following induction of TI in anesthetized rats. Seven days prior to the terminal experiment, rats were injected with non-conjugated CTb into the right DMH as detailed above, with some rats also receiving an injection of FG into the rRPa to identify the DMH region containing thermogenesis-promoting neurons 5, 35. Following a 7-day recovery period for retrograde transport of CTb, rats were anesthetized with 2–$3\%$ isoflurane, placed in the stereotaxic frame, and prepared for pre-DMH transX surgery. Four groups were studied: (a) cold naïve (Cold-N) rats were maintained with a cold skin (TSKIN < 35°C) for 1 h of baseline recordings with a stable TSKIN and TCORE, after which they received a sham brain transection surgery (transection knife lowered into the cortex) and were then maintained with a cold skin for 2 h; (b) warm naïve (Warm-N) rats were treated similarly, but with a skin warming (TSKIN > 35°C); (c) Cold-T rats were maintained with a cold skin for 1 h of baseline recording and then received a complete pre-DMH transX, followed by 2 h with a cold skin; (d) Warm-T rats were treated similarly, but with skin warming throughout the experiment. Following the sham or complete transX, rats were anesthetized with pentobarbital (80 mg/kg i.p.) and transcardially perfused with saline followed by $4\%$ PFA. The brains were removed and post- xed in $4\%$ PFA for 12 h and equilibrated overnight in $30\%$ sucrose in PBS. Serial coronal sections (20 μm and 40 μm) were cut RNAse free in a microtome, collected sequentially in 6 sets (4 × 40 um, 2 × 20 um), and stored in cryoprotectant at −20°C. An immuno fluorescence procedure was performed to label DMH-projecting PBN neurons and rRPa-projecting DMH neurons that express Fos in response to cold or warm exposure (from free-behaving and from anesthetized rats). Sections (40 μm) containing the PBN or the DMH were washed in PBS, blocked for 1 h in PBS with $0.3\%$ Triton-X 100 (PBST) containing $3\%$ normal donkey serum, and incubated overnight at room temperature in PBST containing the primary antibodies for Fos (rabbit anti-c-Fos, 1:2K; Encor, cat#: RPCA-c-Fos) and CTb (goat anti-CTb, 1:5K; Calbiochem, cat#: 227040). After several washes in PBS, sections were incubated for 2 h in PBST containing the secondary species-specific antibodies Alexa Fluor-594 donkey anti-rabbit IgG (1:500) and Alexa Fluor-488 donkey anti-goat IgG (1:250), both from Jackson ImmunoResearch. After incubation, sections were washed in PBS, mounted on Superfrost Plus slides, air-dried and coverslipped. DMH-projecting neurons in the PBN activated by cold or warm exposure displayed green cytoplasmic (CTb) and red nuclear (c-Fos) fluorescence. rRPa-projecting neurons in DMH (from rats injected with FG into the rRPa) displayed cytoplasmic FG and red nuclei when activated. To identify the phenotypes of PBN neurons, mRNA transcripts for pro-Dynorphin (pDyn), vesicular glutamate transporter 2 (VGluT2), and vesicular GABA transporter (VGAT) were detected by in situ hybridization (ISH) using the RNAScope Multiplex Fluorescent v2 assay (Advanced Cell Diagnostics) in 20 μm-sections containing the PBN, following manufacturer instructions. mRNA transcripts were labeled with Opal uorophores (Akoya Biosciences): Opal-520 (1:500, green) for VGAT, Opal-570 (1:2K, red) for pDyn, and Opal-690 (1:500, far-red; assigned blue color in captured images) for VGluT2. Slides were coverslipped with anti-fade mounting medium (Pro-Long Gold, Invitrogen). To characterize DMH-projecting (CTb-labeled) PBN neurons that express c-Fos in response to cold or warm exposure in the 4 groups of isoflurane-anesthetized rats, we used the RNA-Protein Co-detection ancillary kit (Advanced Cell Diagnostics). c-fos and pDyn mRNA transcripts were detected by ISH using the RNAScope procedure, whereas CTb was labeled with immuno fluorescence. Sections (20 μm) containing the PBN were rst incubated in the primary goat anti-CTb antibody (1:500) overnight at 4°C, then washed in PBST and subjected to the RNAscope Multiplex Fluorescent v2 assay following manufacturer instructions (with minor modi cations). mRNA transcripts were labeled with Opal uorophores (Opal-570, red) and (Opal-690, far-red assigned blue color). After RNAScope procedure, slides were washed in PBST and incubated for 2 h in the species-specific secondary antibody for CTb (Alexa-488 donkey anti-goat, 1:200). Slides were washed in PBST and coverslipped with anti-fade mounting medium. CTb-labeled neurons displayed cytoplasmic green fluorescence, whereas c-fos and pDyn mRNA transcripts were labeled as red and far-red (blue) punctate, respectively. In some cases, the colors were reversed for c-fos and pDyn transcripts. All slides processed for IHC and ISH were visualized at an Olympus BX-51 fluorescence microscope, and images were captured using Simple PCI software (C-Imaging Systems). Brightness and contrast were adjusted using Adobe Photoshop. The CTb antigenicity is severely impaired by the protease step of the ISH procedure, causing a substantial decrease in CTb labeling. In addition, the pDyn and c-fos transcript signals are very strong; therefore, the number of triple-labeled neurons (CTb-pDyn-c-fos) was significantly underestimated with this co-detection procedure. To further visualize Dyn-containing neurons in the PBN, a group of rats ($$n = 3$$) were injected, after 7 days recovery from previous injection of CTb in DMH, by an ICV injection of colchicine, which disrupts axonal transport and concentrates the neuropeptide in the soma. Rats were injected, under general anesthesia, with 10 μl of colchicine (25 mM in $10\%$ DMSO saline) ICV. Rats were allowed to survive for 24–36 h after colchicine injection and were perfused with PFA. Brains were removed and processed for IHC as described above. ## Data acquisition Physiological variables were digitized (Micro 1401 MKII; Cambridge Electronic Design) at the following rates: BAT SNA (1 kHz), TBAT (5 Hz), TCORE (5 Hz), TSKIN (5 Hz), TPAW (5 Hz), expired CO2 (200 Hz), AP (200 Hz), EKG (10–300 Hz, 1 kHz), EMG (10–300 Hz, 5 kHz) and recorded into a computer hard drive for subsequent analysis (Spike 2, CED). Continuous measures (4 s bins) of BAT SNA and nEMG amplitudes were calculated as the root mean square (rms, square root of the total power in the 0.1 to 20 Hz band for BAT SNA, and in the 0.1–500 Hz band for EMG) value of sequential 4-s segments of the BAT SNA and nEMG signals 13, 22. ## Data and statistical analysis For analysis of the physiological variables, the data were averaged into 30s bins, and group data were reported as mean ± standard error of the mean (SEM). To account for slight differences in BAT SNA and nEMG recording characteristics (e.g., tissue-electrode contact, ampli er noise, etc.) among experiments, values for BAT SNA and nEMG throughout each experiment were expressed as ‘percent baseline’ (%BL), where baseline values for BAT SNA and for nEMG were the low levels recorded under warm TSKIN and warm TCORE conditions when BAT SNA and nEMG are at their minimum levels in naïve rats. Treatment effects on BAT SNA or nEMG are quanti ed as the difference between pre-treatment (control) and post-treatment levels of BAT SNA and in nEMG. For analysis of anatomical data, neurons expressing Fos, CTb, and CTbFos were counted in the PBN in the side ipsilateral to the CTb injection in DMH. Counting was performed in the dlPBN and elPBN in 4 sections (separated 100 μm) that were labeled as “rostral” (Bregma: −8.90 mm), “intermediate-1” (Bregma: −9.00 mm), “intermediate-2” (Bregma: −9.10 mm), and “caudal” (Bregma: −9.20 mm), based on the Paxinos rat brain atlas 36. Counts at each rostro-caudal level or total counts (sum of counts from the 4 levels) were used in different analysis. The counts of double-labeled (CTbFos) neurons were normalized to the number of CTb-labeled neurons (% CTbFos/CTb) to counteract variability among CTb injections. Neurons expressing Fos, FG, and FGFos were counted in one DMH hemi-section, located contralateral to the CTb injection in DMH, at the level where the main FG-ir cluster is located (i.e., neurons projecting to the rRPa). The counts of double-labeled (FGFos) neurons were normalized to the number of FG-labeled neurons (% FGFos/FG) to counteract variability among FG injections. Data are reported as mean ± SEM. All statistics were performed using Prism software (version 6, GraphPad Software Inc.). Paired one- or two-tailed t-tests, one-way ANOVA followed by t-test, and a two-way ANOVA followed by post-hoc Bonferroni correction were used for statistical comparisons, as described in figure legends. Statistical results with $p \leq 0.05$ were considered significant. ## Funding National Institutes of Health grant #NS099234 (DT, GC) National Institutes of Health grant #NS091066 (SFM) National Institutes of Health grant #OD010996 (GC) ## Data and materials availability All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. ## References 1. Morrison S.F., Nakamura K.. Central Mechanisms for Thermoregulation. Annu Rev Physiol (2019) 81 285-308. PMID: 30256726 2. Morrison S.F., Madden C.J., Tupone D.. Central control of brown adipose tissue thermogenesis. Front Endocrinol (Lausanne) (2012) 3 3. Nakamura K., Morrison S.F.. A thermosensory pathway mediating heat-defense responses. Proc Natl Acad Sci U S A (2010) 107 8848-8853. PMID: 20421477 4. Nakamura K., Morrison S.F.. A thermosensory pathway that controls body temperature. Nat Neurosci (2008) 11 62-71. PMID: 18084288 5. Cano G.. 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--- title: 'Typical Diagnostic Reference Levels of Common Indications for Computed Tomography Scans Among Adult Patients in Uganda: a Cross-sectional Study' authors: - Festo Kiragga - Geoffrey Erem - Harriet Kisembo - John Mark Kasumba Mayanja - Aloysius G. Mubuuke - Ethel Nankya - Kevina Nalwoga journal: Research Square year: 2023 pmcid: PMC10055658 doi: 10.21203/rs.3.rs-2683913/v1 license: CC BY 4.0 --- # Typical Diagnostic Reference Levels of Common Indications for Computed Tomography Scans Among Adult Patients in Uganda: a Cross-sectional Study ## Abstract ### Background Medical exposure to ionizing radiation has increased due to an increase in the number of computerized tomography (CT) scan examinations performed. The International Commission on Radiological Protection (ICRP) recommends indication-based diagnostic reference levels (IB-DRLs) as an effective tool that aids in optimizing CT scan radiation doses. In many low-income settings, there is a lack of IB-DRLs to support optimization of radiation doses. ### Objective To establish typical DRLs for common CT scan indications among adult patients in Kampala, Uganda. ### Methodology: A cross sectional study design was employed involving 337 participants enrolled from three hospitals using systematic sampling. The participants were adults who had been referred for a CT scan. The typical DRL of each indication was determined as the median value of the pooled distribution of CTDIvol (mGy) data and the median value of the pooled distribution of total DLP (tDLP)(mGy.cm) data from three hospitals. Comparison was made to anatomical, and indication based DRLs from other studies. ### Results $54.3\%$ of the participants were male. The following were typical DRLs for: acute stroke (30.17mGy and 653mGy.cm); head trauma (32.04mGy and 878mGy.cm); interstitial lung diseases/ high resolution chest CT scan (4.66mGy and 161mGy.cm); pulmonary embolism (5.03mGy and 273mGy.cm); abdominopelvic lesion (6.93mGy and 838mGy.cm) and urinary calculi (7.61mGy and 975mGy.cm). Indication based total Dose Length Product (tDLP) DRLs was lower than tDLP DRLs of a whole anatomical region by $36.4\%$ on average. Most of the developed typical IB-DLP DRLs were lower or comparable to values from studies in Ghana and Egypt in all indications besides urinary calculi while they were higher than values in a French study in all indications besides acute stroke and head trauma. ### Conclusion Typical IB-DRLs is a good clinical practice tool for optimization of CT doses and therefore recommended for use to manage CT radiation dose. The developed IB-DRLs varied from international values due to differences in selection of CT scan parameters and standardization of CT imaging protocols may narrow the variation. This study can serve as baseline for establishment of national indication-based CT DRLs in Uganda. ## Background The technological advancement in Computerized Tomography (CT) has increased its clinical applications with multidetector CT (MDCT) scans worldwide [1, 2]. CT scanners contribute the highest ($43\%$) collective effective dose of radiation to the population among all medical imaging modalities[1]. This increases the long-term risk for some cancers by $2\%$ [3]. The International Commission on Radiological Protection (ICRP) among other international bodies has issued recent calls for development and utilization of CT indication based-DRLs (IB-DRLs) for better optimization of CT doses per examination within or among CT facilities in preference to CT anatomical based DRLs(4–8). This added onto the Bonn Call for action by IAEA and WHO to strengthen radiation dose optimization through DRL establishment and use within a decade from 2017[9]. The IB-DRL is superior to a single anatomical DRL that cannot logically optimize doses for all the different indications with different image quality requirements in a body region as indicated by ICRP, IAEA and European Society of Radiology (4–6, 8). However, IB-DRLs are yet to be considered a priority by the Atomic Energy Councils in low Middle-Income Countries (LMICs) as evidenced by limited published data on IB-DRLs from some studies conducted in Ghana, Egypt and Nigeria (10–12). This current study was conducted in Uganda, a low-resource setting, but with increasing use of CT in patient care due to the availability, relative affordability, high resolution images and fast image acquisition of CT equipment [13]. Currently, in many LMICs, there is limited published data on DRLs [14]. Some of such studies that have been conducted to establish anatomical CT DRLs in adults and pediatrics (15–19). However, CT IB-DRLs are lacking at national level and for smaller groups of hospitals in Uganda. DRLs are recommended as one of the tools that can be employed to minimize ionizing radiation used in medical exposures to as low as reasonably achievable levels (ALARA) [6]. Therefore, this study sought to establish typical IB-DRLs for common CT scan indications among adult patients in Kampala, Uganda to aid CT dose optimization since national indication based-DRLs (IB-DRLs) are lacking. Findings from the study can be used by many other LMIC settings to establish their own IB-DRLs. ## Study design A cross-sectional design was used. ## Selection of the participating CT facilities At the time of the study, the central region of Uganda where Kampala is located, had 13 out of the 25 CT scanners in the country. Since the study’s target was to establish DRLs using less than 10 CT scanner rooms/hospitals, according to the available financial and time resources, the type of DRL it was to develop was a typical DRL as defined in ICRP publication 135[6]. For ethical reasons the hospitals were anonymized and coded with alphabetical letters. Five tertiary hospitals with a high bed capacity, wide range of radiological services and functional CT scanners within Kampala were selected randomly to represent the public sector (Hospital C), private not for profit sector (Hospital A) and private for-profit sector (Hospitals B, D and E). A survey was done at the selected hospitals to ascertain the monthly number of adult head, chest and abdominopelvic CT scan examinations performed and their clinical indications with the results presented in Table 1. Hospitals with the highest number of head CT examinations were A, B and C. Hospitals with the highest number of chest CT examinations were A, D and E. Hospitals that performed the highest number adult CT examinations in most body regions were selected as the study sites and these included Hospitals A, B and C. Permission was sought from the administrations of the three study sites, each of which had one CT scanner. ## Selection of the most common adult CT scan indications The two indications most frequently examined for among head CT scans, chest CT scans and abdominopelvic CT scans at each of the five hospitals were identified and are presented in Table 1. The indications that were most frequently examined for across all the five hospitals or across most of the hospitals were selected as the most common indications for adult CT scans and these included head trauma and acute stroke among head CT scans, pulmonary embolism (PE) and interstitial lung diseases/high resolution chest CT scan (ILD/HRCT) among chest CT scans and abdominopelvic lesion (ABDPL) and urinary calculi (UC) among abdominopelvic CT scans. ## Inclusion and exclusion criteria Patients who had been referred to the hospital CT units for examination were screened to check for eligibility which included: age of 18 years and above (adult); a common CT scan indication, informed consent; weight of 50–90kg for those with the indication of either interstitial lung disease, pulmonary embolism, abdominopelvic lesion or urinary calculi as the size of the chest and abdomen varies with body weight; and any body weight for head trauma and acute stroke as the size of the head does not vary substantially with change in weight [6]. CT examinations with incomplete raw data on the CT console and those with mixed indications (e.g., stroke or brain mass) were excluded. Since data was not collected from large electronic data bases, and was collected for a few participants using paper forms, it was important to restrict the weight range to minimize variation in the CT doses of indications within the chest and abdomen regions whose size varies with body weight. Therefore DRLs of PE, ILD/HRCT, ABDPL and UC were developed for a standardized adult of 50–90 kg as suggested by ICRP [6]. ## Sampling and sample size estimation Twenty participants were recruited for each CT indication per hospital as recommended by ICRP besides for urinary calculi that received few participants during the study duration [6]. A total sample size of 337 participants were recruited in this study. Participants were recruited by systematic sampling for each indication at each hospital according to the number of CT examinations performed at a particular hospital for a specific indication. ## Quality assurance tests performed on the CT scanners Prior to data collection, quality control (QC) tests were performed on all the CT scanners to verify the radiation doses displayed on the consoles. These tests included radiation surveys using a Geiger Muller Counter to ascertain the safety of the CT bunkers; image quality performance tests done using the CIRS Model 610–05 AAPM CT Performance Phantom and they included CT Number uniformity, CT image noise, CT number accuracy and linearity, spatial resolution and scale tests; CT dose delivery accuracy test done using the 16 cm and 32 cm diameter polymethylmethacrylate (PMMA) CTDI phantoms plus the RaySafe CT dosimetry kit to measure the actual radiation dose output from the CT scanner (CTDIvol and DLP). There was a discrepancy between the actual CT dose emitted by the CT scanner and the CT dose displayed on the console as shown below. There was need to correct the CT doses displayed on the consoles into the actual emitted doses. The actual radiation dose output from the CT scanners was recorded as X and the dose displayed on the CT console was recorded as Y for head CT examinations and abdomen CT examinations. A formula for calculating the actual radiation dose emitted by the CT scanner during an examination was then developed as (Y)/conversion factor. The conversion factor being (Y)/(X). The conversion factors for head CT examinations and abdomen CT examinations were interpolated for each CT scanner to get the specific conversion factor for chest CT examinations. These conversion factors were used on the collected displayed CT doses as there were no in-country Siemens biomedical engineers at the time of the study to go on with CT machine calibration using the conversion factors. The IAEA recommends use of conversion factors to verify the displayed CT radiation doses [4]. ## Data collection Among the patients who had been referred for a CT examination at the hospital CT unit, those who met the study’s eligibility criteria by age and CT indication were approached to obtain informed consent. The consented participants with the indications of acute stroke and head trauma were enrolled. The consented participants with the indications of PE, ILD/HRCT, ABDPL and UC were weighed and those within 50–90 kg were enrolled into the study. The age, gender, weight and CT scan indication of the participants were recorded. After the CT examination had been performed by a radiographer, the CT scan parameters (which included mAs, kVp, pitch, rotation time (Trot), slice thickness, scan length, number of scan sequences and contrast use) and the radiation dose output from the CT scanners (CTDIvol and DLP) were recorded from the CT console. The quality of all CT images of the recruited participants was assessed by a radiologist at each hospital, using a scale adopted from the IAEA CT dose data collection tool, as acceptable, higher than needed or unacceptable and was recorded too. All data was recorded onto a paper form using a CT data collection tool adapted from the international atomic energy agency (IAEA). ## Study variables The study variables were kVp, total mAs, effective mAs, slice thickness, scan length, contrast use, number or range of scan sequences, total DLP, CTDIvol. Image quality was assessed using a 3-point scale adapted from the International Atomic Energy Agency (IAEA) CT dose data collection tool. Every image was scored for overall quality by the radiologist on a 3-point scale which included: 1 = Acceptable, 2 = Higher than needed and 3 = Unacceptable. ## Data management and analysis Data was de-identified, checked daily for completeness and kept under lock and key. It was entered into excel for cleaning, error checks, editing and storage. Using excel, the recorded CT dose (Y) was converted into the actual radiation dose emitted by the CT scanner during examination of a participant using the formula; actual radiation dose emitted (A) = (Y)/conversion factor. The actual CT doses and other data were then exported into and analyzed using R and R studio version 4.10. The Shapiro test was used to test for normality of the data and the data was found to be skewed. Descriptive analysis of the data was performed to include frequencies, the median and interquartile range. The typical IB-DRLs were determined following the method recommended in ICRP publication 135 [6]. The typical DRL for each indication was determined as the median value of the average CTDIvol of the whole examination and the median value of the total DLP using dose data of 60 participants combined from all three hospitals, for all indications besides urinary calculi. For urinary calculi, the typical DRL was developed using data from a total of only 30 participants: 20 from Hospital B and 10 from Hospital A. Only 7 participants were recruited at Hospital C during the study period, therefore these were very few to be included in the calculation of the typical DRL for urinary calculi as the minimum number of participants that can be included in the calculation of a DRL for most examinations generally maybe 10 [4, 6]. In addition, an overall IB-DRL for each indication was calculated as the 75th percentile of the median CTDIvol values and the median total DLP values from the three hospitals for comparison to national DRLs from other studies including anatomical based national DRLs in Uganda where the study was conducted from and IB-DRLs from international studies in Ghana, Egypt and Ghana [10, 11, 17, 20]. ## Results The purpose of this study was to establish typical DRLs for common indications of CT scans among adult patients. ## Characteristics of the sample population There were 154 ($45.74\%$) females. The overall median age was 53.5(36.25–67.75) years with the youngest participants being those with head trauma at 35(28–45) years and the eldest being those with stroke at 57.5(44.5–70) years. The overall median weight for all indications in the study excluding head trauma and stroke was 74(63.6–79.7) kg. The distribution of the study sample by age, weight, and gender in each CT indication at all hospitals combined is presented in Table 2. ## CT scanner characteristics All the study hospitals possessed CT scanners manufactured by Siemens with that of Hospital C being a 16-slice scanner and the other characteristics of the CT scanners are presented in Table 3. ## Scanning parameters per CT indication All CT dose data for each indication was collected from images of acceptable quality as subjectively assessed by radiologist. The median x-ray tube kilovoltage of 130 kVp was used to acquire CT images for each indication unlike for pulmonary embolism (PE) in which 110 (110– 114.25) kVp was used. Contrast material was only used in the indications of PE, ABDPL and UC. The rest of the scan parameters that were used to perform the CT examinations for each indication in the study are presented in Table 4. ## Typical IB-DRLs The typical CTDIvol DRLs of indications within the head, chest and abdomen were comparable with a varifying factor (vf) of 1.1. The typical DLP DRL of head trauma was 1.34-fold higher than for acute stroke. The typical DLP DRL for PE was 1.7-fold higher than for ILD/HRCT. The typical DLP DRL for UC was higher to that of ABDPL by a vf of 1.2-fold. The developed typical DRLs at 50th percentile plus the overall DRLs at the 75th percentile are presented in Table 5. ## Comparison of the developed overall DRLs at 75th percentile (P) to published anatomical based national DRLs (AB-NDRLs) in Uganda. The CTDIvol DRLs of all indications were lower than the AB-CTDIvol DRLs of the corresponding anatomical regions. A comparison of the current study’s DRLs for the various indications to the Ugandan AB-NDRLs of the corresponding anatomical regions revealed that the DLP DRLs were only lower than the AB-national DLP values in the indications of head trauma (lower by $27.4\%$), acute stroke (lower by $42.4\%$), and ILD/HRCT (lower by $39.4\%$) [17] as presented in Table 6. ## Comparison of the overall IB- DRLs at 75th percentile in the current study to some of the published national IB-DRLs at 75th percentile. The CTDIvol DRLs were lower than Ghana’s [10] in all indications (acute stroke, head trauma, PE, ABDPL and UC) and lower than Egypt’s [11] in ILD/HRCT as presented in Fig. 1. The CTDIvol DRLs were lower than France’s [20] in acute stroke and head trauma, comparable to France’s [20] in ABDPL and UC and only higher than France’s [20] in ILD/HRCT as presented in Fig. 1. The DLP DRLs were mostly lower than those of Ghana [10] in the indications of acute stroke, head trauma and PE, but higher than Ghana’s [10] only in UC and only comparable to Ghana’s [10] in ABDPL as presented in Fig. 2. The DLP DRL of ILD/HRCT was also lower than Egypt’s [11] as presented in Fig. 2. The current study’s DLP DRLs were mostly higher than those of France [20] in the indications ILD/HRCT, PE, ABDPL and UC and were only lower than France’s [20] values in the indications of acute stroke and head trauma as presented in Fig. 2. ## Discussion The purpose of this study was to establish typical DRLs for common CT indications among adult patients in Uganda. In this study, IB-DRLs for common indications of CT examinations among adults were developed as typical DRLs set at the 50th percentile of the pooled distribution of dose data from $12\%$ ($\frac{3}{25}$) of the CT scanners in Uganda, a low-income country. The common CT scan indications were similar to those in a study within Ghana and in Europe [10, 21]. The overall median weight for all indications excluding head trauma and stroke was 74(63.6–79.7) kg with pulmonary embolism having the most heavy patients with 78(71.5–85) kg and interstitial lung diseases having the least heavy patients with 69.5(60–77) kg. This overall median weight was within that of a standard adult population defined by ICRP with 70+/−20kg. These findings were also similar to those of other studies in which heavier participants suffered more recurrent episodes of venous thromboembolism and pulmonary embolism [22] and lower weight was associated with disease progression in interstitial lung diseases [23]. ## X-ray tube voltage (measured in peak kilovoltage, kVp): The tube voltage of 130kVp that was used for all indications besides pulmonary embolism was higher than values used in other studies for example higher than 118 (±8.3) to 121.8(±7.4) in Ghana [10], 120 kVp in Egypt [11] and (100 to 120) kVp in France [20]. The tube voltage of 110 (110– 114.25) kVp for pulmonary embolism was similar to trends in some studies for example (100–120)kVp in France [20] while this kVp for PE was lower than values in other studies for example 117.8(±4.0) in Ghana. There is potential to reduce CT radiation doses by lowering kVp for patients of smaller body weight, especially for ILD/HRCT by setting a specific kVp for a particular weight category on the CT machines at the hospitals as kVp in the recent CT machine models is fixed for an examination and is not modulated automatically during an examination to suit body size like it happens for x-ray tube current [24]. There is hope though that newer CT scanner models may bring further improvements in the range of available tube voltages and more advanced automatic tube voltage selection tools which can automatically alter kVp according to patient size to achieve ALARA doses [25]. ## X-ray tube current-time product (measured in milliampere per second, mAs): Indications that did not use contrast material like ILD/HRCT generally had a lower effective mAs and a lower total mAs and compared to those that used contrast material like ABDPL, because of the lower attenuation in the absence of iodinated contrast material in the noncontrasted examinations. Indications within the chest generally had a lower total mAs than those in the abdomen and head because of the lower density/attenuation of the chest tissues for which tube current modulation software automatically reduces the mAs during the scan to minimize radiation dose [24]. This trend was similar to the trend in the mAs used in a study within Ghana [10]. ## Scan length: The scan length for head trauma was longer than that for acute stroke due to the inclusion of a longer part of the neck to rule out concomitant cervical spine injury for early management to mitigate complications similar to a studies in Ghana and Uganda [10, 15, 16, 19]. The scan length for PE was shorter than that for ILD/HRCT due to the exclusion of the most peripheral chest parts in which emboli are not generally seen within the terminal pulmonary arterioles. This trend was similar to that in a study within Ghana [10]. This trend of scan length was largely within the recommended limits of the anatomical extent for PE examinations which extends from the aortic arch to the base of the hear [26]. The scan length for UC was shorter than that for ABDPL probably due the focus on calculi within the upper urinary system as per the scan request form with less inclusion of the most distal parts of the pelvis after the urinary bladder This was dissimilar to findings in a study within Ghana [10] in which both ABDPL and kidney stones had similar scan lengths. This was also dissimilar to the recommendation by ESR that advises the scan length for UC to extend longer than that of ABDPL (appendicitis) i.e., from inferior margin of T10 to lower edge urinary bladder (approximately at lower edge of pubis symphysis) for UC and from inferior margin of T10 to superior border of pubis symphysis [26]. Scan length depends on the height of the participant and the extent of the anatomy that has to be demonstrated therefore it is important to keep it within limits that answer the clinical question for the CT scan. ## Number of scan sequences: Only a precontrast scan sequence was used to examine head trauma, acute stroke and ILD/HRCT as the tissues provide adequate natural contrast to allow visualization of the questioned pathology in these indications similar to a study within Ghana [10]. In the chest, the scan sequences for PE were more than for ILD/HRCT partly due to the higher image quality requirements with contrast use and partly due to the inclusion extra sequences at some hospitals for example a precontrast HRCT phase to rule COVID-19 pneumonia during the pandemic and a delayed phase to rule emboli in pulmonary veins. These findings differed from other studies in Ghana and France that used less sequences (1–2) [10, 20]. In the abdomen, a higher-than-expected number of scan sequences were used for UC contrary to a single noncontrast sequence which is usually adequate as calculi provide high contrast to visualize them easily [4, 27]. Many postcontrast scan sequences were used in UC and ABPL mainly due to absence of standardized scan protocols optimized for these indications and these findings differed from other studies that used one noncontrast scan sequence for UC and one postcontrast scan sequence for ABDPL in Ghana, France and Europe [10, 20, 21]. There is potential to reduce CT doses by lowering the number of scan sequences for PE, ABDPL and UC through adherence to the developed indication-based examination protocols where they exist or development of the optimized indication-based scan protocols where they are absent. ## Slice thickness: The acquisition slice thickness was similar for all indications across all body regions even for pulmonary embolism and HRCT/ILD which require thinner slice acquisition due to the need for higher image quality. The slice thickness for indications in the head(head trauma and acute stroke) and for abdominopelvic indications (ABPL and UC) was expectedly comparable as the indications in both regions do not necessarily require very thin slice thickness to have diagnostic images similar to findings in a study within Ghana [10]. The slice thickness finding for PE differed from findings of a study within Ghana in which the slice thickness for PE was lower than that of other indications in the chest region [10]. It is important to note that image quality in the current study was assessed subjectively and was adequate for PE similar to the high image quality in the cited study [10] that assessed the quality objectively. There is need to assess image quality objectively in future studies to better assess if a slice thickness greater than 1mm provides good enough image quality for PE assessment. ## The typical DRLs The CTDIvol DRLs were observed to be comparable for different indications within the same body regions similar to a study in Ghana [10]. The CTDIvol DRLs in the head region (acute stroke and head trauma) and abdomen region (abdominopelvic lesion and urinary calculi) were similar due to use of similar CT scan parameters (effective mAs). The CTDIvol DRLs in the indications within the chest region (ILD and PE) ended up being comparable due to use of a combination of CT scan parameters that raise and reduce CTDIvol i.e., in ILD, the lower pitch raises the CTDIvol while the lower effective mAs reduces the CTDIvol while in PE, the higher effective mAs raises the CTDIvol while the higher pitch reduces the CTDIvol. The DLP DRLs were observed to differ among indications with the same body region due to the differences in image quality needs and use of different scan parameters. The DLP DRL for head trauma being higher than for acute stroke can be explained by a longer scan length due to the need to rule out cervical spine injury similar to a study within Ghana [10]. The DLP DRL for PE being higher than for ILD/HRCT can be explained by the higher image quality need that requires contrast use and therefore a higher total mAs, plus the use of a higher-than-expected number of scan sequences. This trend of CT doses within the chest findings were similar those in a study within France [20]. The DLP DRL for UC being unexpectedly higher to that for ABDPL can be explained by the absence or less frequent utilization of an optimized scan protocol for UC which allowed room for use of postcontrast sequences which were high in number than those for ABDPL. This finding was different from that in other studies in which the DLP DRL for UC was lower than for ABDPL due to use of a single noncontrast scan sequence for UC in an optimized protocol [10, 11, 20]. ## Comparison of the IB-DRLs to anatomical based national DRLs (AB-NDRLs) in Uganda. The CTDIvol DRLs of all indications being lower than corresponding anatomical based values was probably due to use of a lower total mAs. The IB-DLP DRLs for head trauma, acute stroke and ILD/HRCT being lower than AB-DLP DRLs for head CT and chest CT scans respectively can be explained mainly using a lower total mAs. The need for lower image quality requirements without need for contrast media may have further contributed to the DLP DRLs of acute stroke, head trauma and ILD/HRCT being lower. Some of the current study’s indication based DLP DRLs were lower than DRLs of an anatomical region by $36.4\%$ on average similar to findings in a studies within Finland [28] and Switzerland [29] that found IB-DRLs lower by $20\%$ and by 21–$32\%$ respectively. However, the DLP DRL for PE was higher than for chest CT scans due to use of thinner acquisition slice thickness and generally a higher need for higher image quality similar to another study in Switzerland [29]. The DLP DRL for ABDPL in the current study ended up being comparable to the DLP value for abdomen CT scans (Erem et al., 2022) due to a combination of CT scan parameters that raise the DLP including a longer scan length of 49.12 (46.29–53.09)cm and a thinner slice thickness of 3 (1–5) mm in ABDPL examinations compared to a scan length of 41.25 (32.0–63.3) cm and to a slice thickness of 3.75mm slice thickness that were used in abdomen CT scans (Erem et al., 2022), and due to use of a lower total mAs of 4783 (3496–7088) mAs which reduces the DLP compared to 6115.5 (3619–9869) mAs of abdomen CT scans (Erem et al., 2022). The finding of the DLP DRL of an ABDPL being comparable to the DLP of abdomen CT scans [17] differed from findings in a study within Switzerland[29] that found the DLP DRL for appendicitis to be lower than for abdomen CT scans because this cited study in Switzerland used optimized protocols for appendicitis with probably fewer scan sequences. The DLP DRL for UC in the current study ended up being unexpectedly comparable to the high DLP value for abdomen CT scans [17] due to a combination of CT scan parameters that raise the DLP including many scan sequences that included many postcontrast phases (0–6), a longer scan length of 46.88 (42.83–49.13) cm and a thinner slice thickness of 3 (1–5) mm in UC examinations compared to 41.25 (32.0–63.3) cm scan length and to 3.75mm slice thickness that were used in abdomen CT scans [17], and due to use of a lower total mAs of 4888 (3324–6221) mAs which reduces the DLP compared to 6115.5 (3619–9869) mAs of abdomen CT scans [17]. The finding of the DLP DRL of UC being comparable to the DLP of abdomen CT scans [17] differed from findings in a study within Switzerland[29] that found the DLP DRL for kidney stones to be lower than for abdomen CT scans because this cited study in Switzerland used optimized protocols for kidney stones with probably fewer scan sequences. ## Acute Stroke The CTDIvol and DLP DRLs of acute stroke being much lower than values in Ghana [10]was mainly due to use of a lower effective mAs of 169-(160–203) compared to the higher tube loading of 238.0 (± 80) mAs in Ghana[10]. The reasons for the CTDIvol and DLP DRLs for acute stroke being lower than France’s values [20] were not ascertained because very few CT scan parameters were mentioned in the cited study within France which limited more comparative analysis. ## Head Trauma The CTDIvol and DLP DRLs of head trauma being much lower than values in Ghana [10] was mainly due to use of a lower effective mAs of 181(168–206) compared to the mAs used in Ghana (229.4 ± 73.5 mAs) [10]. The reasons for the CTDIvol DRL for head trauma being lower than, and the DLP DRL for head trauma being comparable to, the corresponding values in France[20] were not ascertained as very few scan parameters were mentioned in the cited study in France for more comparative analysis. ## ILD/HRCT The CTDIvol and DLP DRLs of ILD/HRCT being much lower than the values in Egypt [11] was probably due to use of a lower effective mAs of 61.5 compared to the mAs used in Egypt within a range of (100 minimum mAs to 430 maximum mAs)[11]. The CTDIvol and DLP DRLs for ILD/HRCT being higher than values in France [20] was due to use of a higher kilovoltage of 130 compared to 100 kVp used in most examinations (61 %) the cited study within France[20]. ## PE The CTDIvol and DLP DRLs of PE being lower than values in Ghana [10] was probably due to use of a wider slice thickness of 3 (1–5) mm compared to Ghana’s (2.20 ± 1.7)mm, a lower peak tube kilovoltage of 110 (110– 114.25) kVp compared to Ghana’s (117.8 ± 4.0)kVp, and a lower effective mAs of 98 compared to Ghana’s tube loading of (167.5 ± 92.9) mAs [10]. Even though the current study’s DRLs for PE were lower than Ghana’s, the quality of PE examination images was of acceptable quality to make a diagnosis as assessed by radiologists. The reason for the CTDIvol DRL of PE being lower than the value in France [20] was unknown as the cited study did not mention its scan parameters which limited comparative analysis. The corresponding DLP DRL for PE was instead higher than France’s value [20] due to use of a higher tube peak kilovoltage of 110 (110–114.25) compared to 100kVp used in most examinations ($55\%$) in the French study [20], probably due to frequent use of unoptimized examination protocols with a higher number of scan sequences (1–4) and probably due to a longer scan length compared to the usually optimized PE protocols in European countries[26]. ## ABDPL The CTDIvol DRL being lower than Ghana’s value [10] was due to using a lower effective mAs of 98(81.33) compared to Ghana’s tube loading mAs of (137.0 ± 91.4)[10]. In a comparison to the cited study in Ghana[10], it was observed that most of the scan parameters used in the current study to examine an ABDPL were higher than those in Ghana for example, the current study used a higher number of postcontrast scan sequences(1–3), longer scan length 49.12 (46.29–53.09)cm, higher kilovoltage(130kVp), and thinner slice thickness 3 (1–5)mm compared to Ghana’s single postcontrast scan sequence[1], 45.99(±4.3)cm scan length, 118.7(±7.5)kVp and 5.40(±2.6)mm slice thickness respectively [10]. For this reason, the DLP DRL of ABDPL in the current study was expected to be much higher than Ghana’s value but was instead comparable due to use of a lower effective mAs of 98 compared to Ghana’s tube loading mAs of (137.0 ± 91.4)[10]. The reason for the CTDIvol DRL for ABDPL being comparable to France’s value [20] was not ascertained as the cited study did not mention much about its scan parameters which limited comparative analysis. The corresponding DLP DRL for ABDPL being higher than France’s value [20] was due to use of a higher tube peak kilovoltage of 130 compared to $\frac{100}{120}$ kVp used for most examinations ($59\%$) in the French study [20] and probably due to frequent use of unoptimized examination protocols with a higher number of scan sequences (2–4) compared to the usually optimized protocols for ABDPL in European countries[26]. ## UC The CTDIvol DRL of UC being lower than Ghana’s value [10] was due to use of a lower effective mAs of 105 compared to Ghana’s (138.4 ± 98.4) mAs [10]. The DLP DRL for UC being higher than Ghana’s value was probably due to use of a higher kVp [130], thinner slice thickness 3 (1–5) mm and a higher total number of (1–7) scan sequences compared to Ghana’s 118(±8.3) kVp, 5.3(±2.5) mm and only one noncontrast scan sequence in the examination respectively [10]. The reason for the CTDIvol DRL of UC being comparable to France’s value [20] was not ascertained because the French study did not mention much about their CT scan parameters which limited comparative analysis. The DLP DRL for UC being higher than France’s value [20] was due to use of a higher tube peak kilovoltage of 130 compared to 100 kVp used in most examinations ($69\%$) in this cited French study[20] and probably due to frequent use of unoptimized examination protocols with a higher number of scan sequences (1–7) which included (0–6) postcontrast sequences compared to the optimized UC protocol with only one noncontrasted scan sequence in the French study[20]. Like in other published studies, the overall DRLs of some indications at 75th P varied significantly from national IB-DRLs in other countries mainly due to the difference in the scan parameters chosen for use in the examinations including kilovoltage, acquisition slice thickness, effective mAs and number of scan sequences plus the use of protocols that are less optimized for indications [10, 11, 21, 30]. ## Study strengths and limitations The strengths of this study include having performed QC tests on the CT scanners, use of actual CT scanner output radiation doses to develop the IB-DRLs following corrections of the doses and the development of DRLs using the required minimum of 20 participants per CT scanner room for most indications as recommended by ICRP. The main study limitation was having selected three [3] out of 13 ($23\%$) CT facilities in Kampala and central region of Uganda, the developed typical IB-DRLs may be less representative of the doses and practices used during examinations of the selected CT indications country-wide. However, findings from the study still give an indication that locally determining typical DRLs can optimize the use of CT equipment. Further studies should be conducted using more CT facilities to develop IB-DRLs for the common CT indications that are more representative of other settings. ## Conclusion The typical IB-DRLs determined in this study were 30.17mGy and 653mGy.cm for acute stroke, 32.04mGy and 878mGy.cm for head trauma, 4.66mGy and 161mGy.cm for interstitial lung diseases/ high resolution chest CT scan, 5.03mGy and 273mGy.cm for pulmonary embolism, 6.93mGy and 838mGy.cm for abdominopelvic lesion and 7.61mGy and 975mGy.cm for urinary calculi. The developed typical IB-DRLs are recommended for use to optimize CT radiation doses among adults. Most of the developed typical IB-DLP DRLs were lower or comparable to DRLs from studies in Ghana and Egypt while they were higher than DRLs in the French study due to differences in selection of CT scan parameters. Standardized indication-based examination protocols for the common CT indications should be developed for indications where they do not exist, and their use strengthened to minimize variation in the DRLs in comparison to international DRL values. This can also optimize the use of CT equipment especially in low resource settings where it is not yet widely available. ## Funding: Research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health, US Department of State’s Office of the US Global AIDS Coordinator and Health Diplomacy (S/GAC), and President’s Emergency Plan for AIDS Relief (PEPFAR) under Award Number 1 R25TW011213. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ## Availability of data and materials: All the necessary original data and materials used in this study have been included. ## References 1. 1.Radiation UNSCotEoA. UNSCEAR 2008 Report: Sources and effects of ionizing radiation. Report to the General Assembly. 2010;1.. UNSCEAR 2008 Report: Sources and effects of ionizing radiation (2010) 1 2. Brenner DJ, Hall EJ. 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--- title: 'Self-objectification during the perinatal period: The role of body surveillance in maternal and infant wellbeing' authors: - Lauren M. Laifer - Olivia R. Maras - Gemma Sáez - Sarah J. Gervais - Rebecca L. Brock journal: Research Square year: 2023 pmcid: PMC10055659 doi: 10.21203/rs.3.rs-2714781/v1 license: CC BY 4.0 --- # Self-objectification during the perinatal period: The role of body surveillance in maternal and infant wellbeing ## Abstract Pregnancy represents a unique time during which women’s bodies undergo significant physical changes (e.g., expanding belly, larger breasts, weight gain) that can elicit increased objectification. Experiences of objectification set the stage for women to view themselves as sexual objects (i.e., self-objectification) and is associated with adverse mental health outcomes. Although women may experience heightened self-objectification and behavioral consequences (such as body surveillance) due to the objectification of pregnant bodies in Western cultures, there are remarkably few studies examining objectification theory among women during the perinatal period. The present study investigated the impact of body surveillance, a consequence of self-objectification, on maternal mental health, mother-infant bonding, and infant socioemotional outcomes in a sample of 159 women navigating pregnancy and postpartum. Utilizing a serial mediation model, we found that mothers who endorsed higher levels of body surveillance during pregnancy reported more depressive symptoms and body dissatisfaction, which were associated with greater impairments in mother-infant bonding following childbirth and more infant socioemotional dysfunction at 1-year postpartum. Maternal prenatal depressive symptoms emerged as a unique mechanism through which body surveillance predicted bonding impairments and subsequent infant outcomes. Results highlight the critical need for early intervention efforts that not only target general depression, but also promote body functionality and acceptance over the Western “thin ideal” of attractiveness among expecting mothers. ## Introduction Self-objectification—seeing the self as a sexual object—has been recognized as an important contributor to women’s mental health since the phenomenon was formally introduced to the psychological literature in the form of objectification theory two and a half decades ago (Fredrickson & Roberts, 1997; Roberts et al., 2018). According to this framework, by living in a culture in which women are commonly reduced to their bodily appearance, women learn to view their bodies from a third person’s perspective (i.e., self-objectify, Fredrickson & Roberts, 1997) and often engage in persistent body surveillance (McKinley & Hyde, 1996). Further, many women feel pressure to fit cultural ideals of attractiveness and may experience body shame and dissatisfaction if their bodies do not align with these often-unattainable standards (McKinley & Hyde, 1996; Tiggemann & Lynch, 2001). Self-objectification and its behavioral consequences, such as body surveillance, set the stage for adverse mental health outcomes that disproportionately affect women (e.g., anxiety, depression, eating disorders; Fitzsimmons-Craft & Bardone-Cone, 2012; Jones & Griffiths, 2015; Roberts et al., 2018; Rubin & Steinberg, 2011; Sun, 2018) and can interfere with parenting and child wellbeing (Chapman et al., 2021; Deave et al. ,2008; Galbally & Lewis, 2017; Herba et al., 2016). The current study presents a novel conceptual framework in which self-objectification, as manifested by persistent body surveillance, is significantly linked to maternal mental health during pregnancy (i.e., body dissatisfaction, depression) and undermines infant socioemotional functioning through impaired mother-infant bonding following childbirth. ## Objectification Theory And The Consequences Of Self-objectification Objectification theory posits that “women are most targeted for objectification during their years of reproductive potential” (Fredrickson & Roberts, 1997, p. 192). Indeed, objectification (reduction to appearance and sexual body parts; loss of autonomy; denial of subjectivity) is heightened during key stages in which girls and women undergo physical changes (e.g., puberty), and it may also be heightened during pregnancy. Specifically, pregnant bodies become “public property,” with people looking at, commenting on, and even touching the bodies of pregnant women (Kukla, 2005). Further, women may experience increased body surveillance and related body dissatisfaction across pregnancy and postpartum as their bodies become more removed from a potentially internalized “thin ideal” of attractiveness. These bodily changes may also be connected to other facets of self-objectification (Talmon & Ginzburg, 2016), such as feeling like their autonomy and freedoms are restricted (Sutton et al., 2011). Women may feel like their pregnant bodies have become hyper-visible, while other aspects of their personhood have been rendered invisible. Indeed, a systematic review of research on self-objectification and motherhood by Beech and colleagues [2020] revealed that self-objectification among mothers is associated with a range of negative outcomes, such as difficulties breastfeeding, fear of childbirth, depression, and disordered eating. Despite these possibilities, remarkably few studies have examined whether the tenets of objectification theory apply to the perinatal period (Beech et al., 2020; Brock et al., 2021; Rubin & Steinberg, 2011). Because of the significant changes that women’s bodies undergo during pregnancy and postpartum (e.g., expanding belly, larger breasts, weight gain), the present investigation focused on body surveillance (see Talmon & Ginzburg, 2016, for other important facets of self-objectification). We posit that bodily changes during pregnancy and concomitant increases in objectification from others may cause women to engage in more persistent body surveillance and experience associated mental health problems (e.g., body dissatisfaction, depression). These decrements in mental health may, in turn, undermine the quality of mother-infant interactions (see McNamara et al., 2019 for a review). Increasingly, researchers have examined whether markers of self-objectification among mothers, such as body surveillance, and associated mental health consequences spill over into parenting and child development. Although much of this research has focused on women with adolescent children (e.g., Arroyo & Andersen, 2016; Katz-Wise et al., 2013), research also provides evidence for the intergenerational transmission of body dissatisfaction and disordered eating behaviors in younger children (Rodgers et al., 2013; Spiel et al., 2012). For example, maternal body dissatisfaction is prospectively associated with lower child body esteem in middle childhood (Rodgers et al., 2020). Maternal body dissatisfaction has also been linked to the use of more controlling feeding practices (e.g., food restriction, pressure to eat) with preschool-age children (Blissett & Haycraft, 2011; Duke et al., 2004; Rodgers et al., 2013; Webb & Haycraft, 2019), which may interfere with children’s regulatory capacities by teaching them to view eating as a primary strategy for emotion regulation (Farrow et al., 2015). Despite growing evidence that body surveillance in mothers and associated mental health consequences (e.g., depression, body dissatisfaction) may negatively impact children, comparatively less is known about the impact of body surveillance on infant socioemotional functioning. Thus, we extend these considerations to examine whether body surveillance impacts not only maternal mental health, but also infants by undermining mother-infant bonding following childbirth. We posit that increased body surveillance, resulting from a culture that persistently objectifies women’s bodies, is linked to maternal mental health concerns and the likelihood that mothers experience difficulties bonding with their infants. ## Body Surveillance and Body Dissatisfaction During Pregnancy Research on body dissatisfaction during pregnancy has demonstrated mixed findings (Coker & Abraham, 2015; Fuller-Tyszkiewicz et al., 2020; Loth et al., 2011; Skouteris et al., 2005). Presumably, there are important individual differences in how women experience their body transformation across pregnancy. Some women may embrace this transformation and become more appreciative of what their bodies are physically capable of – nurturing and supporting a developing fetus (Rubin & Steinberg, 2011). This appreciation of body functionality, in turn, may buffer against distress related to the rapid physical changes that occur across pregnancy and postpartum (Clark et al., 2009). Alternatively, some pregnant women might be more susceptible to societal pressures around their bodies and continue to hold their bodies to unrealistic beauty standards, focusing more on body image than functionality (Johnson et al., 2004). For instance, some mothers may aspire to gain minimal gestational weight and to return to their pre-pregnancy figure, or “bounce back,” quickly after childbirth (Watson et al., 2015). Perceived sociocultural pressure to remain thin is associated with maternal distress and body dissatisfaction during pregnancy and the postpartum (Dryer et al., 2020; Fuller-Tyszkiewicz et al., 2013; Kamysheva et al., 2008; Lovering et al., 2018). Further, gaining less than the recommended amount of weight during pregnancy is associated with higher risk of preterm birth and low birthweight (Han et al., 2011), which predict self-regulatory difficulties as early as infancy (Arpi & Ferrari, 2013). ## Body Surveillance and Depression During Pregnancy Body surveillance is associated with higher levels of depressive symptoms during the perinatal period (Rodgers et al., 2018; Rubin & Steinberg, 2011). These associations are alarming given that pregnant women are already at increased risk for depression during the perinatal period, with one in five women endorsing depressive symptoms across pregnancy and postpartum (Underwood et al., 2016; Woolhouse et al., 2015). In the United States, over half of women with perinatal depression go undetected, undiagnosed, and untreated for this condition (Cox et al., 2016). This represents a significant public health burden given that antenatal depression contributes to the proliferation of a range of mental health concerns in both parents and children (Hentges et al., 2019; Waters et al., 2014). ## Maternal Mental Health And Infant Development Maternal psychopathology during pregnancy, particularly depression, is a robust predictor of poor child outcomes, including increased risk for child psychopathology (Barker et al., 2011; Goodman et al., 2011; Goodman & Gotlib, 1999; Szekely et al., 2021). Indeed, perinatal depression predicts socioemotional difficulties (e.g., crying for long periods of time) as early as infancy (Field, 2017; Porter et al., 2019). Mother-infant bonding (i.e., the emotional tie between mother and infant; Bicking Kinsey & Hupcey, 2013), is a salient mechanism through which depression can undermine child functioning (Lefkovics et al., 2014; Slomian et al., 2019). In particular, bonding during the first 6 months postpartum is critical to infant socioemotional development, as infants largely depend on their caregivers to regulate their emotions (Rosenblum et al., 2009), and early mother-infant bonding impairments predict infant socioemotional difficulties as early as 6-months postpartum (Ramsdell & Brock, 2021). Women who report higher levels of depression during and after pregnancy tend to demonstrate greater impairments in mother-infant bonding (Moehler et al., 2006; Nonnenmacher et al., 2016; O’Higgins et al., 2013), and research suggests that negative cognitions associated with perinatal depression may undermine maternal motivation to bond with the infant following childbirth (Muzik & Borovska, 2011). Although maternal depression is a robust predictor of bonding impairments and associated infant maladjustment, researchers also posit that body dissatisfaction during pregnancy impacts mothers’ developing bonds with their infants and, subsequently, child socioemotional functioning (Bergmeier et al., 2020). Indeed, the physical changes that occur over the course of pregnancy–and how these changes are perceived and experienced–represent one of the first ways in which mothers interact with their babies. Women who embrace the bodily changes associated with pregnancy may be more likely to engage emotionally with their babies prior to childbirth, whereas women who feel negatively about these changes and experience greater body dissatisfaction may face more bonding difficulties (Kirk & Preston, 2019). Further, some women may experience a loss of agency and control over their own bodies during pregnancy (Kinloch & Jaworska, 2021). This perceived loss of control, which is associated with maternal distress (Hodgkinson et al., 2014), might also interfere with antenatal attachment. ## The Present Study Objectification theory would suggest that women may be at heightened risk for self-objectification during pregnancy, which can compromise their mental health, and past research suggests a robust link between maternal mental health and bonding difficulties. Taken together, this work suggests that elevations in self-objectification and its correlates (e.g., body surveillance, body dissatisfaction, depression) during pregnancy might ultimately undermine healthy infant socioemotional development. Building on recent work applying objectification theory to motherhood (e.g., Beech et al., 2020), we present a novel conceptual framework (see Fig. 1) in which mothers who report greater body surveillance during pregnancy–a marker of self-objectification–experience higher levels of prenatal depressive symptoms and body dissatisfaction that, in turn, uniquely predict greater mother-infant bonding impairments following childbirth, thereby undermining infant socioemotional functioning at age 1. An integration of research and theory in the areas of objectification and maternal-infant health has the potential to impact both maternal and infant wellbeing by identifying largely overlooked intervention targets during pregnancy (i.e., body surveillance and body dissatisfaction) that arise as a consequence of living in a culture of persistent objectification. ## Participants and Procedures The present study is part of a multi-method, longitudinal study examining how couples navigate the transition from pregnancy to postpartum; thus, participants also completed other procedures beyond the scope of the present study. All participants identified as cisgender upon study entry. Most women were in the second ($38.4\%$) or third ($58.5\%$) trimester of pregnancy. On average, there was one child living at home during pregnancy (SD= 1.18); more than half of women ($57.9\%$) had no children and were experiencing the transition into parenthood for the first time. The majority of women were married ($84.9\%$). Annual household income ranged from less than $9,999 to more than $90,000, with a median household income of $60,000 to $69,999. Nearly half ($47.8\%$) reported earning $50,000 to 59,999 or less which converges with federal guidelines for defining low-income status (Roberts et al., 2012). Reflecting the Midwestern region where the study was conducted, women were primarily White ($89.3\%$), and $9.4\%$ identified as Hispanic or Latina. On average, women were 28.67 years of age (SD = 4.27), and most women were employed at least 16 hours per week ($74.2\%$). Modal education was a bachelor’s degree ($46.5\%$). During follow-up assessments, it was determined that one infant was diagnosed with trisomy 21, and one mother experienced a miscarriage. As such, those families were excluded from analyses to focus on women with typically developing infants ($50\%$ male) for a final sample of 157 perinatal women. There were four waves of data collection spanning February 2016 to April 2019. To address the aims of the present study, we assessed body surveillance, body dissatisfaction, and depressive symptoms using self-report questionnaires administered to mothers during the appointment. We assessed mother-infant bonding at 1-month postpartum ($M = 1.12$ months, SD = 0.29) and 6-months postpartum ($M = 6.32$ months, SD = 0.36) using a self-report questionnaire. Additionally, when the infant turned 1year of age ($M = 12.80$ months, SD = 0.76), both parents reported on infant socioemotional dysfunction. All procedures were approved by the University of Nebraska-Lincoln Institutional Review Board. ## Body Surveillance. The Body Surveillance subscale of the Objectified Body Consciousness Scale (OBCS, (McKinley & Hyde, 1996) was used to assess body surveillance, an important manifestation of self-objectification. During pregnancy, mothers rated the degree to which they persistently monitored their bodily appearance on a scale from one (strongly disagree) to six (strongly agree), with a not applicable option (coded as missing) for items that did not apply. The Body Surveillance subscale contains 8 items, including “During the day, I think about how I look many times” and “I rarely worry about how I look to other people” (reverse coded). Items were averaged with higher scores indicating more body surveillance (Cronbach’s α =.85). ## Depression. Maternal depressive symptoms were assessed using the General Depression subscale of the Inventory of Depression and Anxiety Symptoms (IDAS-II; Watson et al., 2012). The IDAS-II is a 99-item self-report questionnaire designed to assess general and specific symptom dimensions of depression and related anxiety disorders. Participants rated their feelings and experiences during the past two weeks on a scale from 1 (not at all) to 5 (extremely). The general depression subscale consists of 20 items (e.g., “I felt inadequate,” “I felt discouraged about things”), with possible scores ranging from 20 to 100 (Cronbach’s α = 0.84). ## Body Dissatisfaction. The Eating Pathology Symptoms Inventory (EPSI; Forbush et al., 2013, 2014) was used to assess body dissatisfaction reported by mothers during pregnancy. The EPSI is a factor analytically derived scale of eating disorder (ED) symptoms. The Body Dissatisfaction subscale consists of 7 items (e.g., “I did not like how clothes fit the shape of my body,” “I wished the shape of my body was different”) and captures the higher-order, shared dimension among ED symptoms. Participants rated how frequently each statement applied to them during the past month on a scale from 0 (never) to 4 (very often). Items responses were summed, with possible scores ranging from 0 to 28 (Cronbach’s α = 0.88). ## Impaired Mother-Infant Bonding. Postpartum mother-infant bonding was assessed using the Postpartum Bonding Questionnaire (PBQ; Brockington et al. 2001). The PBQ is a 25-item, factor-analytically derived, parent-report measure of a parent’s feelings or attitudes toward their baby. The PBQ assesses impaired bonding, rejection and anger, anxiety about care, and risk of abuse, represented as four subscales that can be summed for a total score. Participants rated their agreement with a series of statements on a 6-point Likert scale. Positive responses (e.g., “I feel close to my baby”) were scored from 0 (always) to 5 (never), while negative responses (e.g., “My baby irritates me”) were scored from 0 (never) to 5 (always). Items were summed to generate a total score, with low scores denoting good bonding and high scores indicating impaired bonding. Scores at 1- and 6-months postpartum were internally consistent (Cronbach’s α = 0.88 at 1 month and Cronbach’s α = 0.86 at 6 months). Scores at each time point were highly correlated ($r = .76$, $p \leq .001$) and were thus aggregated to provide a robust measure of mother-infant impaired bonding during the first 6 months postpartum. ## Infant Socioemotional Dysfunction. The Ages and Stages Questionnaire: Social-Emotional, Second Edition (ASQ:SE-2; (Squires et al., 2015) was used to assess socioemotional dysfunction when the infant turned one year of age. Participants reported how frequently their infant had engaged in a series of behaviors (e.g., “Smiles at you and family members?”, “ Cries for long periods of time?”) using the following scale: often or always (score = 1), sometimes (score = 5), and rarely or never (score = 10). They were also asked to indicate if this is a concern (score = 5). Items were aggregated to obtain an overall score ranging from 0 to 345 (reverse coding items that represent competencies), with higher scores indicating greater infant socioemotional dysfunction. The correlation between maternal and paternal reports was significant ($r = .33$, $p \leq .001$). Therefore, scores were aggregated to obtain a score of infant socioemotional dysfunction based on multiple parental reports to produce a less biased and more reliable estimate (Lengua et al., 2008). The ASQ:SE-2 has demonstrated good reliability and validity, and there was adequate internal consistency in the present sample (Cronbach’s α =.71). ## Data Analytic Plan We tested a series of mediation models in Mplus 8.0. ( Muthén & Muthén, 2010). Missing data were addressed with full information maximum likelihood estimation (covariance coverage ranged from.74 to 1.00), which retains all participants and is preferred over more traditional approaches for handling missing data that introduce bias (e.g., pairwise deletion; Enders, 2010). A series of demographic characteristics (e.g., maternal age, relationship duration, first-time parenthood status, minority racial/ethnic identity, and low-income status) were screened for potential inclusion as control variables. First-time parenthood status was associated with mother-infant bonding and was therefore included as a control. We also controlled for week of pregnancy when the initial assessment occurred to account for differing time intervals between the pregnancy and follow-up assessments across participants. Mediation models were just-identified. To test for mediation, a nonparametric resampling method (bias-corrected bootstrap) with 10,000 resamples was performed to derive the $95\%$ confidence intervals for indirect effects (Preacher et al., 2007). Bias-corrected bootstrapped confidence intervals were used to determine significance of effects given they are robust to violations of univariate and multivariate normality. Data management and analysis procedures for this project were registered (https://osf.io/hprk8), and we made no deviations from that plan. Because we had prior knowledge of data from this longitudinal study, we did not preregister study hypotheses. ## Results Descriptive statistics and correlations are reported in Table 1. As expected for a community sample, levels of body surveillance, general depression, and body dissatisfaction in mothers during pregnancy were relatively low, as were impairments in bonding during the first 6 months postpartum and infant socioemotional dysfunction at 1-year postpartum. There was a large correlation between body surveillance and body dissatisfaction during pregnancy ($r = .54$, $p \leq .001$), as well as a moderate correlation between body surveillance and general depression ($r = .30$, $p \leq .001$). Body surveillance in mothers was significantly correlated with impaired bonding during the first 6 months postpartum ($r = .16$, $p \leq .05$). There was a moderate correlation between general depression and body dissatisfaction during pregnancy ($r = .34$, $p \leq .001$). Small but significant correlations between general depression and impaired bonding ($r = .26$, $p \leq .001$) and between body dissatisfaction and impaired bonding ($r = .23$, $p \leq .01$) emerged. Last, impaired bonding was associated with greater infant socioemotional difficulties ($r = .21$, $p \leq .01$). ## Mediation Model With General Depression As Critical Mediator First, we tested a serial mediation model with body surveillance → general depression → impaired mother-infant bonding → infant socioemotional dysfunction. Full model results are reported in Table 2 and Fig. 2a. Greater body surveillance was associated with greater general depression during pregnancy, $95\%$ CI [1.29, 4.12]. Further, greater maternal depression predicted higher levels of impaired mother-infant bonding over the first 6 months postpartum, $95\%$ CI [.05,.32]. In turn, bonding difficulties predicted greater socioemotional dysfunction for infants at 1 year of age, $95\%$ CI [.04,.77]. The overall indirect effect of body surveillance on infant socioemotional dysfunction through maternal general depression and impaired mother-infant bonding was significant, $95\%$ CI [.04,.59]. ## Mediation Model With Body Dissatisfaction As Critical Mediator Next, we tested a serial mediation model with body surveillance → body dissatisfaction → impaired mother-infant bonding → infant socioemotional dysfunction. Full model results are reported in Table 2 and Fig. 2b. Greater body surveillance was associated with greater body dissatisfaction during pregnancy, $95\%$ CI [2.45, 4.15]. Further, greater body dissatisfaction predicted higher levels of impaired mother-infant bonding over the first 6 months postpartum, $95\%$ CI [.01,.48]. In turn, bonding difficulties predicted greater socioemotional dysfunction for infants at 1 year of age, $95\%$ CI [.08,.79]. The overall indirect effect of body surveillance on infant socioemotional dysfunction through maternal body dissatisfaction and impaired mother-infant bonding was significant, $95\%$ CI [.03, 1.00]. ## Integrated Model With Depression And Body Dissatisfaction As Parallel Mediators Finally, we tested an integrated model with general depression and body dissatisfaction as parallel mediators in a larger serial mediation model. We covaried the residuals of general depression and body dissatisfaction as they are both dimensions of maternal mental health. Full model results are reported in Table 2 and Fig. 2c. Greater body surveillance was associated with greater maternal depression, $95\%$ CI [1.26, 4.11], and body dissatisfaction during pregnancy, $95\%$ CI [2.44, 4.15]. Further, greater maternal depression associated with body surveillance predicted higher levels of impaired mother-infant bonding over the first 6 months postpartum, controlling for body dissatisfaction, $95\%$ CI [.02,.30]. In turn, bonding difficulties predicted socioemotional dysfunction for infants at 1 year of age, $95\%$ CI [.04,.76]. The overall indirect effect of body surveillance on infant socioemotional dysfunction at 1-year postpartum through maternal general depression and impaired mother-infant bonding was significant, $95\%$ CI [.03,.55]. Notably, when controlling for depression, body dissatisfaction was no longer a significant mechanism through which body surveillance impacted mother-infant bonding and infant socioemotional dysfunction. ## Discussion Living in a culture of persistent objectification, women may self-objectify and experience societal pressure to modify their bodies to achieve the thin ideal. During pregnancy, a period in which the body undergoes rapid changes to support fetal development, women who have internalized these messages and engage in more body surveillance may be at increased risk for negative mental health consequences, including body dissatisfaction and depression (Beech et al., 2020; Brock et al., 2021; Rubin & Steinberg, 2011). Maternal mental health, in turn, can undermine the mother-infant relationship and infant socioemotional functioning (McNamara et al., 2019; Slomian et al., 2019). By integrating research and theory in the areas of objectification and maternal-infant health, we found support for a novel conceptual framework in which self-objectification during pregnancy, as manifested by body surveillance, contributes to impaired mother-infant bonding and infant socioemotional functioning at 1-year postpartum through maternal mental health difficulties during pregnancy (i.e., body dissatisfaction and depression). Specifically, we found that mothers who endorsed higher levels of body surveillance also reported higher levels of depressive symptoms and body dissatisfaction during pregnancy. In turn, depressive symptoms and body dissatisfaction were associated with greater mother-infant bonding impairments during the 6 months following childbirth, which contributed to subsequent infant socioemotional dysfunction at 1-year postpartum (i.e., difficulties self-soothing, feeding, and sleeping). When examining maternal depressive symptoms and body dissatisfaction during pregnancy as parallel mechanisms, results suggested that maternal depressive symptoms uniquely contribute to bonding impairments and subsequent maladjustment. Thus, maternal prenatal depression, which was moderately correlated with body dissatisfaction, might be a particularly salient pathway through which body surveillance undermines bonding and infant development. A potential explanation for this finding is that body dissatisfaction during pregnancy could be a prodromal symptom of an underlying mood disorder (Chan et al., 2020; Roomruangwong et al., 2017) or a risk factor for elevations in prenatal depression (Riquin et al., 2019). Indeed, a recent study found that risk of perinatal depression was four times higher in women dissatisfied with their body image (Riquin et al., 2019). Ultimately, results from the present study suggest that persistent depressed mood might be more detrimental to mother-infant bonding (e.g., by undermining maternal motivation and leading to disengagement) than unique aspects of body dissatisfaction. Nonetheless, given that it might contribute to risk for depression, body dissatisfaction remains an important target for investigations of prenatal mental health, particularly in perinatal research pursued within an objectification framework. ## Theoretical Implications The present work makes several theoretical contributions to the literature on objectification. First, while pregnancy is a time when women may experience greater objectification and related consequences due to bodily changes, only a handful of studies (e.g., Rubin & Steinberg, 2011; Brock et al., 2021) have examined body surveillance, body dissatisfaction, and depression during this period. Thus, this study adds to limited research demonstrating that objectification theory, as originally posited by Fredrickson and Roberts [1997] and expanded over the years (Roberts et al., 2018) also applies to pregnant women. Second, to our knowledge, the present study is the first research to link body surveillance to the early mother-infant relationship and infant socioemotional functioning via prenatal depression and body dissatisfaction. While extant research has revealed an association between body surveillance and related mental health outcomes among mothers and children, no research to date has linked these variables during infancy. Further, results isolate a key developmental cascade in which maternal mental health during pregnancy, prior to the birth of the child, predicts early parenting behaviors and infant socioemotional functioning. Researchers increasingly recognize the pregnancy-postpartum transition as a critical window for intervention and assert that this “may be the most important way to ensure healthy child development” (Saxbe et al., 2018). Thus, prenatal maternal mental health represents a critical target for reducing risk for infant socioemotional difficulties, and results of the present study identify features of maternal mental health that have received limited attention in past research (i.e., body surveillance and body dissatisfaction) yet appear to have important implications for infant development. ## Limitations And Future Research Directions It is important to acknowledge that the sample was comprised of women in committed relationships with men; participants also primarily identified as White and were from middle-class backgrounds, thereby limiting the generalizability of the results. There is a need for research examining objectification theory among more diverse populations (e.g., among sexual, gender, and racial minorities). For example, people of color, as well as sexual and gender minorities, experience unique forms of objectification, such as racialized sexual objectification and body policing (Flores et al., 2018). These additional forms of objectification may place pregnant people at even greater risk for self-objectification and related adverse mental health outcomes. There is also increasing recognition that researchers and clinicians alike must broaden their conceptualizations of pregnancy to include the experiences of not only cisgender women, but also transgender and nonbinary individuals (Moseson et al., 2020; Roosevelt et al., 2021). There were also limitations to our measurement approach. First, while the present work examined body surveillance as a manifestation of self-objectification and downstream consequences identified by objectification theory (e.g., body dissatisfaction, depression, Roberts et al., 2018) as well as novel consequences (e.g., infant outcomes), some aspects of the objectification model remain untested in pregnant women. We did not measure specific types of objectification that pregnant women may experience, such as objectification directed at their size and shape (e.g., because their bodies no longer conform to feminine ideals of thinness) or involving denial of autonomy and subjectivity (e.g., because their bodies become public property). Relatedly, we only included one indicator of self-objectification. Thus, future research should examine how other indicators of self-objectification, such as internalized objectifying views (Noll & Fredrickson, 1998) and beliefs (Lindner et al., 2017), as well as non-bodily indicators of self-objectification (e.g., feeling invisible or lacking autonomy; Talmon & Ginzburg, 2016), might impact infant socioemotional functioning. To conduct this important work, measures that Specifically assess objectification and self-objectification in pregnant women will need to be developed and validated. Second, our measure of body dissatisfaction was not Specifically designed for pregnancy and therefore may not capture specific appearance-related concerns associated with pregnancy (e.g., stretch marks, having a prototypical “baby bump”). Future research should consider newly developed measures, such as the Body Understanding Measure for Pregnancy Scale (BUMPs; Kirk & Preston, 2019) or the Body Experience during Pregnancy Scale (BEPS; Talmon & Ginzburg, 2018), that measure other facets of the body experience during pregnancy (e.g., body agency, estrangement, and visibility; satisfaction with appearing pregnant; weight gain concerns; physical burdens of pregnancy). Third, all data were collected using self-report questionnaires, raising the possibility of shared method bias. Although most objectification research has relied on self-report measures, there is increasing evidence that innovative approaches, such as eye tracking technology, can be utilized to assess the objectifying gaze, which may contribute to self-objectification (Gervais et al., 2013; Karsay et al., 2018). Fourth, measures of body surveillance, body dissatisfaction, and depression were gathered at the same time point. Although objectification theory posits that body surveillance contributes to subsequent body dissatisfaction and depression (Fredrickson & Roberts, 1997), it is also possible that maternal depression contributes to increased body surveillance. Thus, future studies examining these constructs at different time points across pregnancy are necessary to establish causality. Finally, other factors of potential relevance to the study aims warrant attention in future research. For example, we did not examine the impact of objective measures of weight, such as pre-pregnancy and pregnancy body-mass index (BMI) and gestational weight gain, on body surveillance, general depression, and body dissatisfaction during pregnancy. Research on this topic is particularly important given that pregnant people with higher BMI are more likely to experience weight stigma (Mulherin et al., 2013; Parker & Pausé, 2018), which has the potential to exacerbate maternal prenatal mental health concerns and, in turn, infant outcomes. In addition, given that pregnancy and childbirth experiences may contribute to bonding impairments (e.g., Hanko et al., 2020; Sockol et al., 2014), research examining whether perinatal complications moderate the associations between prenatal body surveillance, body dissatisfaction, depression, and mother-infant bonding during the postpartum is warranted. ## Practice Implications The present study sheds light on the importance of early interventions targeting not only maternal prenatal depression, but also body surveillance and dissatisfaction, to promote healthy infant development. Because body surveillance is a consequence of living in a culture that persistently objectifies women’s bodies, prevention efforts must begin at the societal level, long before people become pregnant. For instance, media campaigns can raise awareness of the insidious nature of valuing the appearance of girls and women over their other attributes and can help change perceptions of beauty by promoting body positivity and acceptance (e.g., #AerieReal and Dove’s Real Beauty campaign). Specific efforts to target objectification during pregnancy and the postpartum period are also warranted, such as campaigns promoting real images of mothers and their infants. For example, Mothercare’s #BodyProudMums is aimed at normalizing and celebrating the diversity and beauty of post-baby bodies. Broader dissemination of campaigns of this nature has the potential to promote maternal well-being. Finally, emerging evidence suggests that social media can be leveraged for the delivery of brief interventions to improve maternal body image and wellbeing (Wallis et al., 2021). Unfortunately, societal change is slow, and objectification continues to manifest in ways that justify the patriarchy despite collective advances (Roberts et al., 2018). Therefore, beyond broad prevention efforts, there is also a need for targeted interventions informed by careful screening. Providers who interact regularly with pregnant women (e.g., obstetricians, nurses, midwives) could screen for elevations in body surveillance and associated body dissatisfaction and, when indicated, deliver brief interventions to disrupt self-objectification by promoting embodiment, which emphasizes positive self-talk, body functionality and agency, and experiencing the body from a subjective position rather than viewing themselves as sexual objects (Piran, 2017). It is critical that providers avoid protective paternalism and benevolent sexism discourses (e.g., restricting women’s behaviors during pregnancy to protect the fetus; Sutton et al., 2011). Instead, providers should counteract societal objectification and related self-objectification in ways that normalize the experience of body surveillance and body dissatisfaction during pregnancy and empower expectant mothers to prioritize their own mental health. Indeed, research suggests that pregnant women may be especially motivated to make behavioral changes that promote maternal and infant health (Ayyala et al., 2020); thus, pregnancy may be a promising developmental window for the delivery of interventions targeting self-objectification, body surveillance, and the cascade of negative mental health outcomes. Additionally, despite the prevalence and underdiagnosis of perinatal depression (Cox et al., 2016; Underwood et al., 2016), there continues to be a critical need for universal screening and multidisciplinary approaches to maternal mental health care from a range of providers (e.g., obstetrics and gynecology, family medicine, and pediatric care providers; Muzik & Borovska, 2011) assessing multiple indicators of risk. For example, results highlight the utility of screening for body surveillance and dissatisfaction as an early manifestation of depressive symptoms, which can be done briefly and as part of routine prenatal care (Riquin et al., 2019; Stunkard et al., 1983). Further, perinatal depression screening can be effectively implemented by health and social service professionals with limited background in mental health (Segre et al., 2011). Professionals who come in regular contact with pregnant women (e.g., physicians, social workers, nurses) but do not have formal training in the assessment of depression could facilitate discussions of how women are relating to their bodies as they change throughout pregnancy and the postpartum. This approach has the potential to identify women who would benefit from intervention but might otherwise be overlooked by current screening practices. More generally, our results suggest that doctors and clinicians might benefit from a broader conceptualization of maternal mental health during pregnancy including other dimensions of the perinatal experience, such as body shame and dissatisfaction. Women are routinely weighed throughout pregnancy for important medical reasons (e.g., to monitor fetal growth); however, routine weight assessments have the potential to increase body surveillance and adversely impact perinatal mental health. Thus, healthcare providers might consider approaching conversations about weight with sensitivity and with the goal of promoting a healthy pregnancy and baby. For instance, the National Institute of Child and Human Development’s Pregnancy for Every Body initiative aims to help people of all sizes achieve a healthy pregnancy (National Institute of Child and Human Development, 2019). In addition, providing psychoeducation on the natural bodily changes that occur across pregnancy may help mothers adjust to changing body ideals (Beech et al., 2020). By emphasizing body functionality, maternal healthcare providers may help women shift their focus away from their appearance-related concerns (Alleva & Tylka, 2021; Beech et al., 2020). Finally, interventions for pregnant couples that seek to increase partner support may be particularly beneficial given evidence that partners may play a unique role in enhancing maternal body satisfaction during pregnancy (Watson et al., 2016). Indeed, given evidence that intimate partner humanization during pregnancy is associated with less body surveillance in mothers (Brock et al., 2021), it is important for interventions targeting self-objectification and its related consequences to include not only pregnant women, but also their partners. ## Conclusion The present study demonstrated that body surveillance during pregnancy impacts infant socioemotional functioning at 1-year postpartum through increased prenatal depressive symptoms and body dissatisfaction and impaired mother-bonding during the 6 months following childbirth. Further, results suggested that maternal depressive symptoms may uniquely contribute to bonding impairments and subsequent child outcomes. This work expands on the limited body of research applying objectification theory to the experience of pregnancy and childbirth and supports a novel conceptual framework within which maternal self-objectification, manifested as body surveillance during pregnancy, impacts infant development as early as 1-year postpartum. Results highlight the potential utility of prenatal interventions guided by objectification theory to reduce the consequences of sexual objectification on mothers and their children. ## Data Availability Statement: This study complied with Transparency and Openness Promotion (TOP) Guidelines. The study PI, Rebecca L. Brock (rebecca.brock@unl.edu), should be contacted to request access to research materials, analysis code, and data. Data management and analysis procedures for this project are registered at https://osf.io/hprk8, and we made no deviations from that plan. ## References 1. Alleva J. M., Tylka T. L.. Body functionality: A review of the literature. 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--- title: 'Acupuncture in Patients with Diabetic Peripheral Neuropathy-Related Complaints: A Randomized Controlled Clinical Trial' authors: - Joanna Dietzel - Isabel V. Habermann - Sebastian Hörder - Katrin Hahn - Gesa Meyer-Hamme - Miriam Ortiz - Kevin Hua - Barbara Stöckigt - Marie Bolster - Weronika Grabowska - Stephanie Roll - Sylvia Binting - Stefan N. Willich - Sven Schröder - Benno Brinkhaus journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10055667 doi: 10.3390/jcm12062103 license: CC BY 4.0 --- # Acupuncture in Patients with Diabetic Peripheral Neuropathy-Related Complaints: A Randomized Controlled Clinical Trial ## Abstract Background: Diabetic polyneuropathy (DPN) is a common complication of diabetes, which presents with a loss of sensorimotor function or pain. This study assessed the effectiveness and safety of acupuncture as a treatment for DPN-related complaints. Methods: In this randomized controlled trial, patients with type II diabetes and symptoms of neuropathy in the lower limbs were included. A total of 12 acupuncture treatments were administered over 8 weeks. The waitlist control group received the same acupuncture treatment starting at week 16 (after baseline). Results: A total of 62 patients were randomized (acupuncture group $$n = 31$$; control group $$n = 31$$). The primary outcome was overall complaints, and it was reduced with a significant difference of 24.7 on a VAS (CI $95\%$ 14.8;34.7, $p \leq 0.001$) between both groups in favor of acupuncture. Reductions were recorded for the secondary outcomes VAS pain, neuropathic pain symptom inventory (NPSI), emotional dimensions of pain, SF-12, and diabetic peripheral neuropathic pain impact (DPNPI) after the intervention and at the follow-ups in the acupuncture group. Adverse reactions were minor and transient. Conclusions: Acupuncture leads to a significant and lasting reduction in DPN-related complaints when compared to routine care and is well tolerated, with minor side effects. ## 1. Introduction According to the International Diabetes Federation, $9.3\%$ (463 million) of the global population aged 20–79 is affected by diabetes. The numbers are increasing, and so are diabetes-related comorbidity and complications with diabetic peripheral neuropathy among them (DPN) [1]. About 13–$46\%$ of diabetic patients suffer from DPN [2]. The clinical presentation of sensorimotor neuropathy is highly variable. Patients may be asymptomatic or suffer from pain and dysesthesias in the feet and lower legs. The damage of sensory nerves may result in tingling, burning, lancinating or shooting pain, hyperalgesia, and numbness, and because of DPN, atrophy of small foot muscles can occur [3]. DPN may have distressing and incapacitating complications such as the development of foot ulceration, Charcot neuroarthropathy, the amputation of lower limbs, and a higher risk of falls and fractures [4,5]. Moreover, DPN has negative effects on mental health and psychosocial functioning, leading to depression, anxiety, and a lower health-related quality of life [5]. Despite the high prevalence of symptomatic DPN, there is still no satisfactory disease-modifying therapy [6]. Furthermore, according to a Cochrane meta-analysis, even enhanced glycemic control does not prevent the development and progression of DPN in type II diabetes [7]. The administration of multiple drugs, such as antidepressants, anticonvulsants, opioids, and topical treatments, is often necessary to reduce pain, albeit increasing the risk of side effects through polypharmacy. Still, adequate symptom relief is difficult to achieve. Both pain and polypharmacy have a negative impact on quality of life. Moreover, it has no effects on sensory deficits, such as numbness. Nonpharmacological approaches, such as transcutaneous electrical nerve stimulation, have been shown to produce improvements in pain outcomes [8]. Thus, further study of other possible treatments, especially regarding quality of life and the long-term effects, remains of high importance. Acupuncture has long-lasting positive effects on chronic pain syndromes [9] and is considered a safe treatment with fewer side effects compared to many pharmaceuticals [10]. There is evidence showing a positive effect of acupuncture on neuropathies of different etiologies, including DPN-related symptoms [11,12,13]. An increase in local microcirculation through acupuncture is postulated to induce a certain degree of improved supply to the neural tissue [13]. The aim of the multicenter ACUpuncture in Diabetic Peripheral Neuropathy (ACUDPN) trial is to confirm the hypothesis that 12 treatments of acupuncture are safe and efficient for the treatment of DPN-related symptoms. ## 2. Materials and Methods The trial was approved by the Ethics Committee in Berlin (EA$\frac{1}{183}$/18) and Hamburg and was executed according to the principles of Good Clinical Practice and the Declaration of Helsinki. Informed written and oral consent was given by all patients prior to the beginning of the study. The trial is registered on ClinicalTrials.gov NCT03755960. A report on the study protocol was already published earlier [14]. The randomized, controlled, two-armed, multicenter, parallel-group ACUDPN trial was conducted between February 2019 and April 2021 at the German Charité Universitätsmedizin Berlin and at an outpatient clinic for TCM at the University Medical Center Hamburg-Eppendorf in Hamburg, Germany. Patients were recruited through poster advertising on public transport and flyers in medical practices, podiatrists’ offices, and in strategic areas on the university campus and university hospitals. Patients received free acupuncture treatment. Patients with type II diabetes of any sex, aged between 18 and 80, with a clinical diagnosis of DPN, were invited to participate. Other important inclusion criteria were overall complaints of at least 40 mm on a visual analog scale (VAS 0 mm = no complaints to 100 mm = worst imaginable complaints), no adjustments in medications related to DPN in the past 4 weeks, and pathological nerve conduction parameters regarding the suralis nerve (SNAP < 6 µV or nerve conduction velocity < 42 m/s); the absence of other causes for peripheral neuropathy. Participants were excluded if they had severe polyneuropathy with paresis of proximal muscles, obesity BMI > 35 kg/m2, ongoing anticoagulation, bleeding tendency due to thrombocytopenia, severe peripheral arterial occlusive disease, gangrene and ulcers in lower limbs, traumatic damage to nerves or vessels in the lower leg, used opioids or suffered from drug, alcohol, or medication abuse, or used ongoing nonpharmacological therapies for DPN such as psychotherapy or physical therapy. Patients were asked not to initiate other treatments for DPN-related symptoms during the study to avoid confounding. They had to be not pregnant or breastfeeding and had to provide written and verbal consent to participate in the trial. During the first month of screening eligibility criteria, age and BMI were adjusted to increase the number of eligible patients from max age 75 to max age 80 and from max BMI 30 kg/m2 to max BMI35 kg/m2. Patients were randomly assigned at a 1:1 ratio in two groups (acupuncture or waiting list) using a computer-generated randomization list (prepared by SAS 9.4, SAS Institute Inc., Cary, NC, USA). The randomization list was kept at the study center in Berlin and was not accessible by the enrolling study physician. It revealed only one result at a time. If the study physician found a subject to be eligible for participation, a study nurse conducted the randomization and notified the study physician of the result by phone. Patients and physicians were not blinded regarding treatment allocation. Statisticians were blinded. Patients were enrolled in the study for a period of 24 weeks. The intervention group received a total of 12 acupuncture sessions in 8 weeks, with subsequent 16 weeks of follow-up. Details of the intervention have been published previously [14]. The control group was put on a waiting list for acupuncture treatment and completed a series of follow-ups for the first 16 weeks. Acupuncture treatment for the control group started at week 16 and ended at week 24 with the final follow-up. The same acupuncture treatment protocol (as in the intervention group) was applied. Routine care was continued in both groups throughout the trial. The study was designed to examine the overall effects of acupuncture on DPN. Therefore, we compared acupuncture in addition to routine care only. The semi-standardized ACUDPN treatment protocol was developed based on Chinese medicine (CM) theory in consensus with German experts of CM. It included mandatory bilateral acupuncture of ST 34, ST 40, SP 6, KI 3, LV 3, and the four EX-LE-10 Bafeng (Figure 1). If necessary, GB 34, GB 39, GB 41, SP 4, SP 9, SP 10, KI 7, ST 36, and ST 41 could be added, as well as a heat source over the toes in case of very cold feet. All acupuncture points were on the lower extremities. A minimum of 18 needles was used per session and a maximum of 24. The acupuncture treatment was carried out with sterile, single-use, stainless-steel 0.25 × 30 mm (manufactured by Dong Bang AcuPrime) and 0.25 × 40 mm needles (manufactured by PHOENIX). The skin was disinfected before needle insertion. Depending on the anatomical site and tissue, needles were inserted 1–2 cm perpendicular to the skin and rotated until the achievement of the needle sensation (De Qi). There was no manual stimulation after that. Needles were removed after 25 min. Acupuncturists were trained and certified with at least more than 120 h of experience. All outcome parameters were assessed at weeks 8, 16, and 24 for both groups. Additionally, during the first 8 weeks of the study, both groups were asked to record their weekly VAS overall DPN-related complaints and VAS pain in their diaries. The primary outcome parameter was the difference in the overall DPN-related complaints, measured with a 0–100 mm VAS (0 = no complaints, 100 = worst imaginable complaints) at 8 weeks between the groups. The secondary outcome parameters during the first 8 weeks were changes in VAS overall DPN-related complaints and VAS pain intensity, measured weekly from baseline to week 8. They were reassessed at weeks 16 and 24. The following parameters were assessed at weeks 8 and 16 between the groups: the Neuropathic Pain Symptom Inventory (NPSI), assessing subdimensions of neuropathic pain on an 11-point scale, changes in the German affective dimension of pain scale “Schmerz Empfindungsskala” (SES), the assessment of the general health-related quality of life using Short Form-12 (SF-12), as well as the disease-specific quality of life with the diabetic peripheral neuropathic pain impact (DPNPI) score, and the 7-point patient global impression of change (PGIC) scale. An intragroup analysis for the acupuncture group assessed the long-term effects until week 24. Patients were asked for adverse events in their diaries and at treatment appointments. Occurring adverse events were graded for severity and classified as treatment or nontreatment-related. Withdrawals and dropouts with reasons were documented. The sample size of 90 patients (45 per group) was calculated on the basis of an MCID of 1.5 points on a VAS and to provide $80\%$ power. A total of $15\%$ was added to account for dropouts, so 110 patients were planned. The changes in the overall number of DPN-related complaints (measured by the VAS) between the baseline and week 8 were used as the primary outcome parameter. The primary analysis of the primary endpoint was conducted with an analysis of the covariance (ANCOVA). The treatment group (acupuncture/control) and study center were included as the fixed-effect factors in the model, and the baseline value of the overall number of VAS-DPN-related complaints was used as a fixed covariate. The adjusted means were derived from this model and presented along with the two-sided $95\%$ confidence intervals in each treatment group and the p-value of the group comparison (significance level of $5\%$, two-sided). The calculation was performed using the full analysis set (FAS) based on the intention-to-treat principle (ITT), evaluating each patient in the treatment group as randomized without the replacement of missing values. All further analyses were considered exploratory, without adjustment for multiple testing. Similarly, the secondary outcomes were analyzed with an ANCOVA or logistic regression (depending on the scale of the outcome), including the treatment group and study center as fixed-effect factors and the respective baseline value (where applicable) as a fixed covariate. A subgroup analysis determined the effects of acupuncture on the subdimensions of neuropathic dysesthesias, such as tingling, numbness, and pain. Data assessment was performed using SAS for Windows, Version 9.4 or higher (SAS Institute, Cary, NC, USA), SPSS version 26 or higher (IBM SPSS Statistics for Windows, Armonk, NY, USA: IBM Corp). We conducted semi-structured qualitative interviews with 10 study participants to learn more about the subjective experience with DPN, medical care, acupuncture treatments, and study participation. The sample included participants from both groups who had completed the acupuncture treatment at least 1 week prior and were selected based on age, study group, and gender. Participants were recruited on a rolling basis as they completed the acupuncture treatment. Thus, the participants of the control group differed from the RCT because they were interviewed after receiving acupuncture and not during their waiting period. All those invited to the interview agreed to participate. All interviews were recorded, transcribed, pseudonymized, and analyzed deductively and inductively using qualitative content analysis. The analysis was carried out with MAXQDA® Standard 2018 (18.2.4) software. The results of the quantitative and qualitative evaluation were triangulated to possibly supplement and deepen the results. Detailed descriptions of the methods and results of the qualitative study and the triangulation will be published separately. Due to strong restrictions on research with direct patient contact caused by the COVID-19 pandemic, the trial was terminated prematurely. Consequently, the previously calculated sample size of 110 patients (90 patients (45 per group) to provide $80\%$ power plus $15\%$ to account for estimated dropouts) was not reached. Originally, we planned to include “study center” as a fixed effect in the statistical models for the primary and secondary endpoints. However, due to the smaller sample size, the study center was not included as a fixed effect in the statistical models for primary and secondary endpoints in the predefined statistical analysis plan. Instead, the study center was included as a random effect in the analyses. The inclusion of a patient with an HbA1c below $6.5\%$ represented a further protocol deviation with no impact on statistical analysis. ## 3. Results In total, 292 patients were screened for eligibility, and 230 did not meet the eligibility criteria. The main reasons for not being eligible for the trial were DPN-related causes other than type II diabetes, anticoagulation, age over 80, the use of opioids, a BMI higher than 35, and nerve conduction measurements that did not meet the inclusion criteria (see Figure 2). The study did not recruit the planned number of patients due to its early discontinuation over the COVID-19 pandemic, in which patient contact was limited and reduced. A total of 62 patients met the inclusion criteria and were randomized into the intervention or control group (31 patients per group), with five participants having dropped out before the end of the study, three patients discontinued the trial in the intervention group, one due to increased tingling during the acupuncture, and two because of the COVID-19 pandemic during the follow-up; in the control group, two patients dropped out during follow-up because of the COVID-19 pandemic. At baseline, there were no relevant differences in the demographic characteristics, comorbidities, or concomitant medications between the two groups (Table 1). The higher number of men in the cohort reflects the epidemiology of the condition, which has a higher prevalence in men. Neither did the clinical inspection of the feet (regarding the condition of the skin: petechiae, ulcers, hypercornification, and palpability of pulses) reveal any differences between the groups (data not shown). The proportion of patients with a longer duration of neuropathy was higher in the waiting group ($61.3\%$) compared with the acupuncture group ($45.2\%$). Three patients in the acupuncture group and no patients in the control group had received acupuncture treatments against neuropathy in the past. The expectation regarding the effectiveness of acupuncture globally was not different between the groups, though more patients in the acupuncture group ($80.6\%$) than in the control group ($51.6\%$) expected a marked improvement in their DPN symptoms through the acupuncture treatment when asked at baseline. In the acupuncture group, 17 patients had DPN-specific medication at baseline vs. 22 in the control group. At the end of the intervention (week 8), the difference between the groups regarding VAS overall complaints was 24.7 mm ($95\%$ CI 14.8; 34.7, $p \leq 0.001$) in favor of the acupuncture group. The change in VAS overall complaints at week 8 compared to baseline was 34.8 ($95\%$ CI 27.8; 41.8) in the acupuncture group vs. 59.5 ($95\%$ CI 52.4; 66.6) in the control group (see Figure 3). Subgroup analysis was conducted for the primary endpoint by the duration of neuropathy (>5 or ≤5) and BMI (>30 or ≤30). We observed no effect of the subgroups on the primary endpoint. The analysis for the remaining previously defined subgroups was not carried out due to an insufficient number of participants per group. Secondary outcomes The effect for VAS overall complaints was still persistent at week 16, with a clinically meaningful difference of 18.9 mm (CI $95\%$ 8.1; 29.8, $p \leq 0.001$) between the groups in favor of acupuncture. At week 8, the VAS pain showed a clinically meaningful difference of 28.7 mm (CI $95\%$ 17.5; 39.9, $p \leq 0.001$) between the groups in favor of acupuncture. The effect observed in week 8 persisted into week 16, with a difference of 25.1 mm (CI $95\%$ 13.8; 36.4, $p \leq 0.001$) between the groups. The NPSI, the SES, and the disease-specific impact on quality of life (DPNPI) scores also showed clinically meaningful differences between the groups in favor of the acupuncture in week 8 ($p \leq 0.05$); the effects persisted into week 16 ($p \leq 0.05$) (Table 2 and Figure 4). The SF-12 score did not reflect the results from the DPNPI and did not reveal clinically meaningful differences in favor of the acupuncture group, neither at week 8 nor at week 16 (Table 2). The supplementary analysis of the weekly VAS data (from the patient diaries) showed that the first differences between the groups were found starting from week 4 onwards, with a difference of 16.7 mm (CI$95\%$ 4.8; 28.6, $p \leq 0.05$) in VAS overall complaints and of 16.9 mm (CI$95\%$ 7.1;26.8, $$p \leq 0.001$$) in VAS pain. From week 16 on, the control group received acupuncture, so an intergroup comparison for the final follow-up for week 24 was not possible. The reduction in VAS overall complaints and VAS pain in the control group (after receiving acupuncture from week 16 on) was 13.5 ± 19.6 and 13.5 ± 21.6, respectively, compared to week 24. On average, the reduction was slightly smaller than that observed in the treatment/acupuncture group from baseline to week 8, but it exceeded the MCID. An explorative pre–post comparison for the acupuncture group showed that the reduction in the VAS overall complaints persisted, with a clinically relevant mean of 19.4 mm (baseline (SD) 58.5 ± 11.9 vs. 39.1 ± 23.8 at week 24. Additional pre–post comparisons of outcomes after the acupuncture treatments in both groups can be found in the Appendix A (Table A1). The PGIC at week 8 showed that in the intervention group, 25 patients reported improvements, five reported no change, and one reported worsening, whereas in the control group, at week 24 (at the end of their acupuncture treatments), 21 participants reported improvements, five reported no change, and three reported the worsening of global symptoms. For further evaluations of the outcome parameters in the control group after acupuncture, see supplement. No meaningful changes were found in the amount of medication taken in either group; in the acupuncture group, $17.6\%$ of patients reduced their medication during the 8 weeks of the study intervention and another $11\%$ until week 16. In the control group, $13.6\%$ of patients had reduced their on-demand medication at week 8 and $4.5\%$ more at week 16. During 660 acupuncture treatments administered over the course of the study, 43 adverse events in total were related to acupuncture; of those, 18 were small hematomas at single needling sites, and seven were transient paraesthesia, with one of these leading to a patient dropping out of the intervention group. Additionally, five patients reported transient pain at needling sites, five patients reported tiredness after single treatments, four had a transitory intensifying of DPN-related symptoms that resolved in the days after a single session, one patient reported cramps at thigh muscle after needling, one felt light-headed, and one reported itching at the needling site. The details of the qualitative substudy and the triangulation will be published separately. Most interviewed patients reported an improvement in DPN-related symptoms, particularly numbness, pain, and increased mobility. This was reflected in our primary and secondary outcomes. Some interviewees reported that the effect was stronger when receiving two treatments per week rather than only one. Most of the participants favored further acupuncture treatments and were satisfied with our medical assistance, which they reported was easily integrated into their everyday lives. In this randomized controlled clinical trial, 12 acupuncture treatments over 8 weeks led to a significant reduction in overall DPN complaints. Furthermore, the patients achieved a clinically meaningful reduction in pain and reported a decrease in the neurological pain symptom inventory (NPSI) and the affective dimension of pain score (SES). Moreover, there was an improvement in the disease-specific impact on the quality of life, primarily due to an improvement in sleep and pain (DPNPI). The clinical effects persisted in the intervention group after the last acupuncture. Two months after the end of the intervention, overall DPN complaints were still reduced in a clinically meaningful manner compared to the control group. They were still detectable for up to four months after the end of the acupuncture in the intervention group. ## 4. Discussion Previous studies have used only pain or neurography as the primary outcome to study the effect of acupuncture on DPN-related symptoms [11,12]. But since DPN produces a variety of neuropathic symptoms (some of them painless), we chose a scale for overall complaints as the primary outcome instead. This allowed for a better estimation of the effect of acupuncture treatment. In addition, our study is characterized by a particularly long follow-up of 16 weeks, which is unmatched by previous trials. The lasting effects in overall complaints, pain, and symptom scores up to week 24 promise persistent symptom relief. In patient care, this effect could possibly be prolonged by repeated acupuncture treatments. However, this common practice has not been investigated yet. This trial added to the evidence that, despite their chronic underlying condition, patients with neuropathy could benefit from acupuncture regarding their neuropathic symptoms. The study also provided a (so far) unique follow-up length and data on the intermediate and long-term effects of this specific acupuncture treatment regimen. Further, through the provision of acupuncture treatment in the waitlist control, the trial provided data on the reproducibility of the results. Finally, despite the study being underpowered due to its early discontinuation over the COVID-19 pandemic, the primary endpoint results still reached significance. However, in the absence of a sham procedure, including the blinding of patients, certain effects should be considered. The placebo effect should be addressed at this point, which could have influenced the outcomes. The fact that patients knew they were getting the treatment may have influenced their evaluation of the VAS and all the other subjective scores in both groups. Further factors could have influenced the results; in the acupuncture group, more patients had experience with previous acupuncture treatments for various reasons ($58.1\%$ vs. $51.6\%$), and $9.1\%$ vs. $0\%$ had previous acupuncture against neuropathy; moreover, the acupuncture group showed higher efficacy expectations due to acupuncture at baseline. However, based on another publication, this high expectation might not have necessarily had a strong influence on the outcomes [16]. The comparable treatment effects of the control group from week 16–24 should be considered as well, which add important aspects of the replicability of effects. Besides, the trial does not provide information on the specificity of the acupuncture points. A placebo-controlled study would be necessary to further evaluate the impact of acupuncture on this condition, and depending on the choice of the placebo (sham needle vs. sham points), the specific effects of the selected points might be called into question, as sham acupuncture with needling in the proximity of the real points might elicit comparable local effects. The patients in the control group suffered longer and from more severe neuropathy at baseline than in the intervention group, which may explain why this group did not show the same extent of benefit after receiving the treatments from weeks 16 to 24. The difference was otherwise somewhat minimized, with a higher baseline complaint score in the intervention group. Further, it is not possible to say whether the reduction in DPN-specific medication in the intervention group persisted between weeks 9–15, as we did not collect such data for this period. The study was conducted exclusively on patients with type 2 diabetes; thus, further studies are required to assess the transferability of the effects to type 1 diabetes-induced polyneuropathy. The results are consistent with previous sham-controlled studies, showing an improvement in neuropathic symptoms and sleep quality [11,13]. The results of our nerve conduction assessments, along with clinical examination scores, will be presented and discussed in another publication. The results of the quantitative and qualitative analysis align, as most interviewees in the qualitative study reported positive effects from acupuncture on their DPN symptoms. The effect on health-related quality of life measured by the SF-12 was not consistent with the otherwise positive results of the DPNPI in favor of the intervention group and with the findings of previous studies, which reported either improvements or no significant changes in the quality of life compared with the control group [11,12,16]. The control group experienced smaller effects right after the acupuncture treatment than the acupuncture group did. This could be related to the fact that the waiting list group had a higher proportion of severe neuropathy and a higher percentage of patients with neuropathic symptoms for more than 5 years. This may indicate that acupuncture should rather be recommended for moderate and mild symptoms and that patients should receive acupuncture as early as possible in the course of the disease. We ran a subgroup analysis to determine the effects, but due to too small sizes of the subgroups, the results of the analysis were not conclusive. This study has shown that acupuncture is a safe and valuable addition to routine care. The study population represented a quite typical sample of patients with DPN. Over $50\%$ of the patients had used acupuncture in the past for various reasons. Acupuncture has been popular in Germany for many years, and in major cities like Berlin and Hamburg, it is broadly available. In addition, statutory health insurance companies cover acupuncture treatment for chronic low back pain and chronic pain due to knee arthrosis, which is frequently used by patients. Our treatment protocol is a practical guideline for everyday clinical practice, as it provides a thought-out treatment concept while leaving room for individual therapy approaches. Our multicenter trial showed that our protocol could be applied in different treatment contexts. As a result of our wide eligibility criteria, our findings may be applicable to a broad population. Future acupuncture trials for DPN should integrate objective parameters, such as vibration fork testing or nerve conduction studies. Further placebo-controlled studies could investigate the specific effect of acupuncture points on this condition or if a repetition of only a few acupuncture treatments could prolong the significant improvements over a longer period and, therefore, represent a useful addition to routine DPN care. ## 5. Conclusions Acupuncture leads to significant and lasting reductions in DPN-related complaints compared to routine care and is well tolerated with minor side effects. More high-quality clinical trials are needed. ## References 1. Saeedi P., Petersohn I., Salpea P., Malanda B., Karuranga S., Unwin N., Colagiuri S., Guariguata L., Motala A.A., Ogurtsova K.. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas. Diabetes Res. Clin. Pract. (2019) 157 107843. DOI: 10.1016/j.diabres.2019.107843 2. Papanas N., Ziegler D.. 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--- title: 2-Deoxyglucose and hydroxychloroquine HPLC-MS-MS analytical methods and pharmacokinetic interactions after oral co-administration in male rats authors: - Dongxiao Sun - Sangyub Kim - Deepkamal Karelia - Yibin Deng - Cheng Jiang - Junxuan Lü journal: Research Square year: 2023 pmcid: PMC10055671 doi: 10.21203/rs.3.rs-2675386/v1 license: CC BY 4.0 --- # 2-Deoxyglucose and hydroxychloroquine HPLC-MS-MS analytical methods and pharmacokinetic interactions after oral co-administration in male rats ## Abstract Our previous work has shown a synergistic tumoricidal action of the hexokinase (HK) inhibitor 2-deoxyglucose (2-DG) and the autophagy inhibitor chloroquine (CQ) on HK2-addicted prostate cancers in animal models through intraperitoneal injections. Here we developed high performance liquid chromatography-tandem mass spectrometry (HPLC-MS-MS) methods for 2-DG and clinically favored drug hydroxychloroquine (HCQ) and explored PK interaction of the orally administered drugs in a jugular vein cannulated male rat model, which allowed serial blood collection before and 0.5, 1, 2, 4 and 8 h after a single gavage dose of each drug alone or simultaneously after appropriate washout periods between the drugs. The results demonstrated a rapid and satisfactory separation of 2-DG standard from common monosaccharides by HPLC-MS-MS multi-reaction monitoring (MRM) and the presence of endogenous “2-DG”. Application of the HPLC-MS-MS 2-DG and HCQ methods to sera samples of 9 evaluable rats showed a peak time (Tmax) of 2-DG of 0.5 h after 2-DG dosing alone or with HCQ and glucose-like PK behavior. With a seemingly bi-modal time course for HCQ, the Tmax for HCQ dosing alone (1.2 h) was faster than that for the combination (2 h; $$p \leq 0.013$$, 2-tailed t-test). After combination dosing, the peak concentration (Cmax) and area under the curve (AUC) of 2-DG were decreased by $54\%$ ($p \leq 0.0001$) and $52\%$, whereas those for HCQ were decreased by $40\%$ ($$p \leq 0.026$$) and $35\%$, respectively, compared to single dosing. The data suggest significant negative PK interactions between the two oral drugs taken simultaneously and warrant optimization efforts for the combination regimen. ## Introduction 2-Deoxyglucose (2-DG) is a synthetic glucose analog in which the 2-hydroxyl group is replaced by a hydrogen atom. 2-DG, like glucose, enters the glucose-loving cancer cells through the glucose transporter and is phosphorylated by a hexokinase (HK), the first and rate-limiting enzyme in glycolysis. Not further metabolizable, 2-DG-6-phosphate accumulates in cells and competitively inhibits HK and glycolysis, leading to cancer cell cycle arrest and autophagy, which promotes cell survival through antagonizing apoptosis [1]. Due to its cytostatic nature, 2-DG monotherapy has little efficacy in early stage human clinical trials for the treatment of cancer including prostate cancer [2]. For decades, chloroquine (CQ) and hydroxychloroquine (HCQ) are orally available drugs for preventing and treating malaria caused by mosquito bites [3]. Known as lysosomotropic autophagy inhibitors, they are also indicated for treating and managing autoimmune diseases such as lupus and rheumatoid arthritis [4]. Based on the rationale that an induction of autophagy in HK2-addicted prostate cancer cells by 2-DG counteracts against their cell death by apoptosis, our previous work has shown a synergistic tumoricidal action of the 2-DG and CQ combination through intraperitoneal injections in several prostate cancer animal models [1]. Because 2-DG exposure of prostate cancer cells took between 4 to 6 h to activate AMP-activated protein kinase (AMPK) that drove the autophagy induction, the animal daily dosing regimen (Monday – Friday) was based on such a signaling consideration and carried out with 2-DG injection in the morning and CQ injection in the afternoon [1]. Pharmacokinetic (PK) interaction is one of the main drug-drug interactions (DDIs) and a major cause of medication error [5]. Given that cancer patients receive multiple concomitant treatments, such as chemotherapy, radiotherapy, hormonal therapy, and supportive care agents, they are at high risk for DDIs. The oral administration of the cancer treatment has become common over decades because of availability of oral anticancer agents and substantial benefits of the use, including convenience, no needle injury, self-medication, low infection risk, and high patient compliance. Since 2-DG and HCQ are taken by human patients orally, knowledge of their DDIs will inform the optimal dosing regimen to achieve therapeutic benefit over harm. Whereas the PK behavior of each drug has been extensively documented in rodents and humans, their PK interactions, if any, after simultaneous oral administration have not been reported. ## Theoretical Methodologically, different instrumental protocols have been reported for 2-DG previously. One of the early methods for determining the purity of 2-DG preparations was gas chromatography. Because 2-DG is a nonvolatile, high melting point solid, it was made more volatile by derivatization using N-trimethylsilylimidazole in pyridine [6]. Another method analyzed the presence of tritiated 3H-2-DG in rat muscle using chromatography-radioisotopic (RI) detection [7]. Yet another non-derivatization measurement of 2-DG in topical formulations used high performance liquid chromatography (HPLC)-ultraviolet detection (UV) at 195 nm [8], due to the lack of a chromophore absorbing above 200 nm in 2-DG. The analytical columns included a μBondapak 10 μm NH2 column and a Varian Micropak 10 μm NH2 column. As 2-DG has a very short retention time on these columns, plus the low sensitivity and selectivity of UV detection, the HPLC-UV method performed poorly at separating 2-DG from the highly abundant glucose and other structurally related monosaccharides in blood samples. The development of fluorescence detection a decade ago enhanced the sensitivity for 2-DG analysis through derivatization with 2-aminobenzoic acid in the presence of sodium cyanoborohydride at 80°C for 45 min [9]. The subsequent separation by HPLC and detection by fluorescence took an additional 1 h for each sample. The serious drawbacks of the fluorescence method included the extra reaction steps, the long HPLC time per sample and not directly measuring the actual analyte [9]. In summary, no direct method with high sensitivity, selectivity and operational efficiency has been reported for 2-DG quantitation in biological fluid samples. In contrast, HPLC with tandem mass spectrometry (HPLC-MS-MS) has been used for detection of CQ-family drugs, including hydroxychloroquine (HCQ) and its major metabolites [10]. HPLC-MS-MS is a powerful analytical technique that combines the separating power of HPLC with the highly sensitive and selective mass analysis capability of triple quadrupole MS. Herein, we developed a new HPLC-MS-MS method for the separation and quantification of 2-DG in rat serum and refined the HPLC-MS-MS detection of HCQ. We applied these analytical protocols for 2-DG and HCQ drug-drug PK interactions in a jugular vein cannulated rat model, which afforded serial PK blood collections following the gavage administration of each drug alone or their combination. ## Materials Hydroxychloroquine sulfate (HCQ) was purchased from TCI Chemicals (Portland, OR, USA) (catalog H1306). Deuterated hydroxychloroquine-d4 (HCQ-d4) were purchased from Toronto Research Chemicals (Toronto, ON, Canada) as the analytical internal standard (IS). 2-Deoxy-D-glucose (2-DG) was purchased from Sigma-Aldrich (St. Louis, MO, USA) (catalog number D6134). 13C6-2-deoxy-D-glucose (13C6-2-DG) and 13C1-2-DG (in initial method testing) were purchased from Toronto Research Chemicals (Toronto, ON, Canada) as IS for 2-DG. Formic acid was purchased from J. T. Baker (Phillipsburg, New Jersey, USA). Optima LC-MS grade water, acetonitrile and methanol and other chemicals were purchased from Fisher Scientific (Fair Lawn, New Jersey, USA). ## HPLC-MS-MS analysis method for 2-DG A new method was developed using Sciex 6500 + QTrap MS coupled with an ExionLC separation system (Waltham, MA, USA) and was able to separate 2-DG from other simple sugars including D-glucose, fructose, mannose, and galactose (Fig. S1). A Luna 3 μm NH2 analytical column (2 × 100 mm, Phenomenex, Torrance, CA, USA) was used. The isocratic elution was carried out using a flow rate of 0.5 mL/min with water as mobile phase A ($17\%$) and acetonitrile as mobile phase B ($83\%$). The column was kept at 30 °C during the separation procedure. The Sciex 6500 + Q Trap mass spectrometer was equipped with an electrospray ionization probe operated in negative mode. The decluster potential (DP) was – 30 V; the entrance potential (EP) was – 10 V, the collision energy (CE) was – 12 V for 2-DG and – 19 V for 13C6-2-DG, and the collision cell exit potential (CXP) was – 11 V for 2-DG and – 10 V for 13C6-2-DG. The curtain gas (CUR) was 35 L/h, the collision gas (CAD) was medium. The ion spray voltage was – 4500 V, the temperature was 300 °C. The flow rate for gas 1 was 25 L/h and for gas 2 was 25 L/h. The multiple reaction monitoring mode (MRM) was used to analyze and quantify 2-DG, with the transitions of m/z 163 > 85 for 2-DG and 169 > 89 for 13C6-2-DG. All peaks were integrated and quantified by Sciex OS 1.5 software. The quantification limit (signal/noise > = 10) for 2-DG was 10 ng/mL, and the detection limit (signal/noise = 3) was 2.5 ng/mL. ## HPLC-MS-MS analysis method for HCQ An EXionLC separation system with a 1.7 μm Acquity UPLC BEH C18 analytical column (2.1 × 50 mm, Waters, Dublin, Ireland) was used to separate HCQ from other serum constituents. Gradient elution was conducted using a flow rate of 0.3 mL/min with the following conditions: initiate at $2\%$ mobile phase B (acetonitrile) and $98\%$ mobile phase A ($0.1\%$ formic acid in water), and linear gradient to $98\%$ mobile phase B in 2 minutes, and keep the mobile phase B at $98\%$ for another 2 minutes to flush the column before back to the initial conditions to equilibrate the column. HCQ was analyzed using a Sciex 6500 + Q Trap mass spectrometry, equipped with an electrospray ionization probe operated in positive mode. The decluster potential (DP) was 70 V for; the entrance potential (EP) was 10 V, the collision energy (CE) was 32 V and the collision cell exit potential (CXP) was 15 V, while the curtain gas (CUR) was 35 L/h, the collision gas (CAD) was set on high. The ion spray voltage was 4000 V, the temperature was 300 °C. The flow rate for gas 1 and gas 2 was 25 L/h, respectively. The MRM was used to analyze and quantify HCQ and its internal standard HCQ-d4, with the transitions of m/z 336 > 247 for HCQ and m/z 340 > 251 for HCQ-d4. All peaks were integrated and quantified by Sciex OS 1.5 software. The quantification limit (signal/noise > = 10) for HCQ was 0.1 ng/mL, and the detection limit (signal/noise = 3) was 0.025 ng/mL. ## PK experiments: serial blood collection after 2-DG, HCQ single oral dose or combo dose The animal work had been conducted with the approval of the Institutional Animal Care and Use Committee of Penn State College of Medicine, Hershey, PA campus. Jugular vein cannulated CD male rats (200–300 g) were purchased from Charles River, Wilmington, MA. They were housed individually to prevent damage to the catheter. They were provided free access to water and rodent chow pellets. After quarantine and acclimation, they were used in the PK dosing sequence as shown in Table 1. The 2-DG was gavage-administered at 372 mg/kg body weight. HCQ was gavaged at 124 mg/kg body weight. These dosages were based on our early mouse efficacy studies [1] with inter-species allometric dose conversion adjustment. The combo dosing was delivered sequentially within 2 minutes of each other. On each day of experiment, dosing started the PK clock as 0 h. Sequential blood collection (approximately 0.3 ml per time point) were performed at the indicated time points ± 5 minutes (Table 1), followed by proper washout periods based on prior knowledge of PK behaviors of each drug. Serum (no heparin or EDTA anticoagulant) samples were stored at −80°C for later analyses by LC-MS-MS-multiple reaction monitoring (MRM) as detailed above. Nine sets of complete data were evaluable for the results ($$n = 9$$ rats). The data were plotted graphically as timepoint mean ± SEM vs. blood collection time post dose. ## Serum sample preparation procedure for 2-DG measurement A stock solution of 2-DG was prepared in water and serially-diluted into working solutions of 25 – 10,000 ng/mL. 13C6-2-DG was dissolved in DMSO at 2.5 mg/mL as stock solution and was diluted to 25,000 ng/mL by methanol as the working solution. All solutions were kept at –20°C before use. Standards were made by spiking 5 μL 2-DG standard working solution and 5 μL 13C6-2-DG internal standard working solution into 40 μL acetonitrile / methanol ($\frac{50}{50}$) with final concentrations of 25 ng/mL to 10,000 ng/mL. Serum samples were processed through organic solvent extraction. After spiking 5 μL 13C6-2-DG working solution into 5 μL serum, followed by adding 40 μL acetonitrile/methanol ($\frac{50}{50}$) to precipitate proteins by votexing and subsequent centrifugation at 8765× g for 10 min at 4°C. The supernatant was transferred to HPLC vials before loading onto HPLC-MS-MS system. ## Serum sample preparation procedure for HCQ measurement A stock solution of HCQ was prepared in water and diluted into working solutions of 10 – 5,000 ng/mL. HCQ-d4 working solution was prepared in water at 100 ng/mL. All solutions were kept at –20°C before use. For standard curve, 5 μL HCQ serially-diluted working solutions and 5 μL HCQ-d4 working solution were spiked into 10 μL blank serum. Then, 30 μL methanol containing0.1 % formic acid was added to precipitate the proteins. After vortexing and subsequent centrifugation at 8,765 g for 10 minutes at 4°C, the supernatant was loaded to HPLC-MS-MS system, with final concentrations of 1 ng/mL to 500 ng/mL for the HCQ linear standard curve. Standard curves were constructed by plotting the ratio of peak area of analyte to peak area of the corresponding IS versus the nominal analyte concentration. Serum samples were processed the same way as that for standard samples: 5 μL HCQ-d4 working solution was spiked into 10 μL serum, and 35 μL methanol containing.1 % formic acid was added to precipitate the proteins before the supernatant was loaded onto the HPLC-MS-MS system. ## Data presentation and Statistical analyses For graphical visualization, timepoint group mean and SEM were plotted against blood collection time. Peak time Tmax and peak drug concentration Cmax were determined based on individual rat data and compared between single dosing vs. combo dosing by 2 tailed t-test and a variance setting that was appropriate to the dataset. Area under the curve AUC0 – xh was estimated based on timepoint mean data plots (by weighing paper cutout to 0.1 mg). Post-peak half-life t$\frac{1}{2}$ was estimated based on semi-log plots of timepoint group mean data. ## Novel HPLC-MS/MS analysis method for 2-DG For the analysis of 2-DG by HPLC-MS/MS, negative mode was applied due to the molecular structure and chemical property of 2-DG and its internal standard (IS) 13C6-2-DG. By infusion under Q1 scan analysis, [M-H]– was found at m/z 163 for 2-DG (Fig. 1A) and m/z 169 for IS 13C6-2-DG. By product ion scan, a major fragment of 2-DG was found at m/z 85 (Fig. 1A) and m/z 163 → 85 transition was used for MRM. For IS 13C6-2-DG fragments at m/z 105 and m/z 89 were found (Fig. 1B). As m/z 169 > 89 showed higher density MRM peak than m/z 169 > 105, m/z 169 → 89 transition was selected for MRM of the IS. Because of structural similarities with many simple sugars such as glucose, fructose, mannose, galactose, etc. the separation of 2-DG from these sugars, especially the highly abundant glucose, in serum is always difficult. A Luna 3 μ NH2 column (2.0 × 100 mm) was chosen for the separation here. The normal phase column was flushed by isopropanol for hours to change it to a reversed phase column for more mobile phase selections. After careful optimization, an isocratic program of water:acetonitrile (17:83) was used to successfully separate 2-DG from other monosaccharides (Fig. S1). MRM profiles of 2-DG standard (Fig. 1C) and 13C6-2-DG IS (Fig. 1F) showed single peak over baseline. However, an endogenous peak was detected to elute at the same retention time as that of 2-DG standard, as well as with identical molecular ion and fragment (m/z $\frac{163}{85}$) with 2-DG in pre-dose (control) serum (serum-115) (Fig. 1D), and it was separated from other sugars (retention time 2–2.4 min). The chromatograph pattern of 1 h post dose serum sample from the same 2-DG-treated rat (serum-117) (Fig. 1E) showed sharply increased peak intensity at the same retention time with 2-DG standard and the endogenous peak only (also see Fig. S2). This pattern was recapitulated by spiking 2-DG into the pre-dose control serum (Fig. S3). Because of presence of the endogenous “2-DG” peak in the pre-dose serum, the 2-DG working standard solutions were therefore made in water instead of in a blank control serum, with linear range of 25 ng/mL to 10,000 ng/mL for the 2-DG standard curve. Since the rats ate laboratory rodent chow pellets made of practical feed ingredients, the endogenous “2-DG” peak in the pre-dosing control serum could be an isomer(s) of 2-DG, possibly a deoxy sugar. Biologically important deoxy sugars include many examples, such as 6-deoxy-L-galactose, a constituent of cell membrane glycoproteins and glycolipids; 6-deoxy-L-mannose, which presents in plant glycosides; 6-deoxy-D-glucose, a natural product found in Pogostemon cablin, Salmonella enterica, and other organisms (https://lotus.nprod.net/). Separation of the exogenously dosed 2-DG from the endogenous deoxy sugar was not the focus of the current work. Future study may identify the component of the endogenous peak. ## HPLC-MS/MS analysis method for HCQ To optimize MS conditions, ESI source in positive mode was applied and [M + H]+ was found at m/z 336 for HCQ (Fig. 2A) and m/z 340 for HCQ-d4 as IS (Fig. 2B) under Q1 scan analysis. MS/MS product ion scan for the fragmentation of the molecular ions detected specific product ion at m/z 247 for HCQ (Fig. 2A) and m/z 251 for HCQ-d4 (Fig. 2B). Thus, MRM transition of m/z 336 → 247 was selected for quantification of HCQ while m/z 340 → 251 was selected for quantification of IS HCQ-d4. To achieve high resolution and efficiency for the analysis, the chromatography conditions were optimized for HCQ analysis with a five-minute gradient HPLC program using a 1.7 μm C18 column. Sharp peaks with clear baseline were achieved for HCQ standards (Fig. 2C) and HCQ-d4 IS(Fig. 2E). When different organic solvents were compared for the extraction of HCQ from serum, methanol plus 0. % formic acid was found to be the most efficient for a high recovery. Similar patterns were observed for HCQ and HCQ-d4 peaks in serum sample after extraction (Fig. 2D and 2E). The analytical method developed was specific for the analysis of HCQ in serum, showing no endogenous interfering components at the retention time of the analyte. The linear range of the method was 0.5–500 ng/mL for HCQ which covered the distribution of the concentrations from all serum samples. ## 2-DG PK metrics The newly developed method for measuring 2-DG in serum was used to analyze 2-DG levels across different time points. The endogenous “2-DG” peak detected in each pre-dose sample for single dose (902 ± 134 ng/ml) was not significantly different from that for combination dose (758 ± 66 ng/ml) (2-tailed t-test, $$p \leq 0.359$$). The net 2-DG concentrations for each rat were therefore obtained by subtracting the corresponding pre-dose “2-DG” value. The population PK curve was plotted as the timepoint mean of the group. When dosed alone, 2-DG (Fig. 3A, triangles, solid line) was taken up rapidly and peaked at 0.5 h and returned to pre-dose level by 2 h, as expected from glucose PK. However, when 2-DG and HCQ were dosed together, the Cmax for 2-DG was decreased by $54\%$ (2-tailed t-test, $$p \leq 0.000028$$) and the AUC0 – 4h was reduced by $52\%$ (Fig. 3, crosses, dashed line) (Table 2). Nevertheless, there was no change of the Tmax (0.5 h) or the apparent post-peak elimination rate or half-life, t$\frac{1}{2}$ (Fig. 3B, see parallel lines with identical slopes in the semi-log plot) (Table 2). ## HCQ PK metrics The serum HCQ concentration vs. time profiles for the HCQ post single dose or combined with 2-DG are shown in Fig. 4A, with a seemingly bi-modal time course under each dosing condition. When HCQ was dosed alone (Fig. 4A, circles), Tmax was on average 1.2 h (5 rats with 0.5 h; 4 rats with 2 h, out of 9 rats); Cmax was on average 626 ng/ml (Table 2). The combo dosing with 2-DG (Fig. 4A, squares) resulted in a slower absorption with Tmax = 2 h (all 9 rats at 2 h; 2-tailed t-test, $$p \leq 0.013$$) and Cmax was lowered by $40\%$ (377 ng/ml; 2-tailed t-test, $$p \leq 0.026$$) (Table 2). The cumulative HCQ exposure based on AUC0 – 8h from combo dosing was decreased by approximately $35\%$ (Table 2). The post-peak elimination rate (Fig. 4B, semi-log plot) was not affected by combo dosing, but did show much shallower slopes (therefore longer half-life t$\frac{1}{2}$) than that for 2-DG (Fig. 2B) (Table 2), as expected from previous HCQ PK information from the literature [3, 10]. ## Methodological advantages The HPLC-MS-MS methods reported above could measure 2-DG and HCQ in serum with high sensitivity, efficiency and resolution. The small volume of bio fluid (5–10 μL serum) needed is an advantage of these sensitive methods. Only 5 minutes running time for each method makes them ideal for higher-throughput bioanalysis as well as routine PK studies of each of these drugs. It is further noteworthy that the ability of the HPLC-MS-MS method for 2-DG to efficiently separate other monosaccharides from 2-DG lends the method for adaptation to studying these sugars with greater specificity in medicine and in many other fields. ## Implications for 2-DG and HCQ combination therapy of HK2-addicated cancers Application of these methods to the rat sera samples indicated a mutual interference of the uptake/absorption between the two drugs if orally taken simultaneously, with a more profound effect of HCQ on 2-DG than vice versa. In retrospection, the daily dosing regimen of 2-DG in the morning and CQ in the afternoon used in our prostate cancer mouse models [1] inadvertently avoided the brunt of a negative DDI with the observed synergistic anti-cancer outcome. The rat PK interaction data warrant additional animal modeling work for optimization of dosing sequence to minimize the negative DDI in future human translation trials for therapy of HK2-addicted CRPC or cancers of same metabolophenotype in other organ sites. ## Implications for other human clinical indications Beside treating malaria and certain autoimmune diseases clinically [4], HCQ is approved as a third line add-on drug for glycemic control in India for type II diabetes patients with consistent efficacy and remarkable safety for long term use [11, 12]. Although improvement of circulating insulin level and tissue insulin sensitivity have been most often cited as its putative anti-hyperglycemia mechanisms, our observed negative PK interactions in the rat model suggest yet another and more direct mode of interaction in that HCQ decreases glucose absorption (by inference from 2-DG PK) in the gastrointestinal tract. Consistent with this speculation, two weeks of HCQ and rapamycin dual autophagy inhibitor treatment decreased 2-FDG PET-uptake from an iv dose (by inference glucose) by the tumor in sarcoma patients in Taiwan [13]. In fact, hypoglycemia is a stated side effect of HCQ use in non-diabetic patients (Hydroxychloroquine Tablets: Package Insert / Prescribing Information - Drugs.com). In contrast to the global failure of HCQ for COVID-19 treatment, 2-DG has been shown in Phase II and Phase III trials in India to improve the outcome of COVID patients as much as a median reduction of 2.5 days to achieve normalization of specific vital signs parameters when compared with standard of care [14]. How 2-DG and HCQ affect the absorption of the other at the gastrointestinal level awaits further investigation. ## Conclusions The cutting-edge analytical methodologies of HPLC-MS-MS MRM enabled the assessment of PK metrics of 2-DG and HCQ in a rat model. The data suggest significant negative PK interactions between the two oral drugs taken simultaneously. The data warrant additional animal modeling work for optimizing their dosing sequence to minimize the negative DDIs in future human translation trials for cancer therapy or other indications. ## Data Availability Statement: The data that support the findings of this study are available in the supplementary material of this article ## References 1. Wang L, Wang J, Xiong H, Wu F, Lan T, Zhang Y. Co-targeting hexokinase 2-mediated Warburg effect and ULK1-dependent autophagy suppresses tumor growth of PTEN- and TP53-deficiency-driven castration-resistant prostate cancer. EBioMedicine (2016) 7 50-61. PMID: 27322458 2. Stein M, Lin H, Jeyamohan C, Dvorzhinski D, Gounder M, Bray K. Targeting tumor metabolism with 2-deoxyglucose in patients with castrate-resistant prostate cancer and advanced malignancies. Prostate (2010) 70 1388-1394. PMID: 20687211 3. Browning DJ. 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Gounder MK, Lin H, Stein M, Goodin S, Bertino JR, Kong AN. A validated bioanalytical HPLC method for pharmacokinetic evaluation of 2-deoxyglucose in human plasma. Biomedical chromatography: BMC (2012) 26 650-654. PMID: 21932382 10. Chhonker YS, Sleightholm RL, Li J, Oupicky D, Murry DJ. Simultaneous quantitation of hydroxychloroquine and its metabolites in mouse blood and tissues using LC-ESI-MS/MS: An application for pharmacokinetic studies. J Chromatogr B Analyt Technol Biomed Life Sci (2018) 1072 320-327 11. Pal R, Banerjee M, Kumar A, Bhadada SK. Glycemic efficacy and safety of hydroxychloroquine in type 2 diabetes mellitus: A systematic review and meta.analysis of relevance amid the COVID-19 pandemic. Int J Non-Commun Dis (2020) 5 184-193 12. Chakravarti HN, Nag A. Efficacy and safety of hydroxychloroquine as add-on therapy in uncontrolled type 2 diabetes patients who were using two oral antidiabetic drugs. J Endocrinol Invest (2021) 44 481-492. PMID: 32594451 13. 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--- title: Lactobacillus salivarius WZ1 Inhibits the Inflammatory Injury of Mouse Jejunum Caused by Enterotoxigenic Escherichia coli K88 by Regulating the TLR4/NF-κB/MyD88 Inflammatory Pathway and Gut Microbiota authors: - Zhen Wei - Ziqi He - Tongyao Wang - Xiaoxuan Wang - Tiancheng Wang - Miao Long journal: Microorganisms year: 2023 pmcid: PMC10055675 doi: 10.3390/microorganisms11030657 license: CC BY 4.0 --- # Lactobacillus salivarius WZ1 Inhibits the Inflammatory Injury of Mouse Jejunum Caused by Enterotoxigenic Escherichia coli K88 by Regulating the TLR4/NF-κB/MyD88 Inflammatory Pathway and Gut Microbiota ## Abstract Replacing antibiotics with probiotics has become an important way to safely and effectively prevent and treat some gastrointestinal diseases. This study was conducted to investigate whether Lactobacillus salivarius WZ1 (L.S) could reduce the inflammatory injury to the mouse jejunum induced by Escherichia coli (ETEC) K88. Forty Kunming mice were randomly divided into four groups with 10 mice in each group. From day 1 to day 14, the control group and the E. coli group were administered with normal saline each day, while the L.S group and the L.S + E. coli group were gavaged with Lactobacillus salivarius WZ1 1 × 108 CFU/mL each day. On the 15th day, the E. coli group and the L.S + E. coli group were intragastrically administered ETEC K88 1 × 109 CFU/mL and sacrificed 24 h later. Our results show that pretreatment with Lactobacillus salivarius WZ1 can dramatically protect the jejunum morphological structure from the changes caused by ETEC K88 and relieve the morphological lesions of the jejunum, inhibiting changes in the mRNA expressions of TNF-α, IL-1β and IL-6 and the protein expressions of TLR4, NF-κB and MyD88 in the intestinal tissue of mice caused by ETEC K88. Moreover, pretreatment with Lactobacillus salivarius WZ1 also increased the relative abundance of beneficial genera such as Lactobacillus and Bifidobacterium and decreased the abundance of harmful genera such as Ralstonia and Helicobacter in the gut. These results demonstrate that Lactobacillus salivarius WZ1 can inhibit the inflammatory damage caused by ETEC K88 in mouse jejunum by regulating the TLR4/NF-κB/MyD88 inflammatory pathway and gut microbiota. ## 1. Introduction Escherichia coli is the most common bacterial infection causing diarrhea in animals [1,2]. It causes diarrhea, hemorrhagic colitis and dysentery in weak, malnourished and immunocompromised calves, especially those that have not acquired maternal antibodies through colostrum feeding [3]. Diarrhea Escherichia coli can be divided into six categories according to virulence: enterotoxigenic Escherichia coli (ETEC), Shiga toxin-producing Escherichia coli (STEC), enteropathogenic Escherichia coli (EPEC), enteroinvasive Escherichia coli (EIEC), enteroaggregative Escherichia coli (EAEC) and enterohemorrhagic Escherichia coli (EHEC) [4]. Among them, ETEC is the most serious, and its pathogenic types have different specific adhesins and enterotoxins. Fimbriae adhesins K88 (F4), K99 (F5), 987P (F6) and F41 (F7) play an important role in ETEC infection in newborn animals. These virulence factors can colonize bacteria in intestinal mucosa and cause intestinal osmotic imbalance, resulting in diarrhea [5,6,7]. Infection is characterized by acute watery diarrhea, rapid dehydration and collapse within hours, vomiting, stomach cramps, headache and, rarely, mild fever [8]. ETEC infection can increase the concentration of inflammatory factors in serum, the jejunum and the colon, causing the body to produce an inflammatory response, reducing the expression of tight junction proteins and mRNA in the intestinal epithelium of the jejunum and colon, and destroying the intestinal barrier [9]. Currently, antibiotics are commonly used in production to treat diarrhea caused by Escherichia coli, but these antibiotics affect not only the target pathogens, but also the beneficial microbes in the gut, leading to the growth of the gut microbiota associated with the disease [10]. Furthermore, in many cases, producers add antibiotics to feed to reduce the chance of infection, which is non-therapeutic, irresponsible and leads to a rapid increase in resistant bacteria [11]. Therefore, determining antibiotic alternatives to prevent or treat diarrhea is a current research hotspot. Probiotics are “live microorganisms that, when ingested in adequate amounts, are beneficial to the health of the host” [12]. Probiotics can compete for receptors and adhere to cells, then colonize and remain viable in the gut, effectively compete with pre-existing pathogenic microorganisms through the acidification and production of antimicrobial compounds, affect the enzymatic activation of bacterial toxin receptor modification, and improve the body’s immune defense against pathogenic microorganisms [13,14]. Therefore, many studies on the use of probiotics to replace antibiotics in the prevention or treatment of infectious diarrhea caused by pathogenic bacteria in livestock are underway. Lactobacillus salivarius belongs to the genus Lactobacillus and the family Lactobacillaceae. It is a Gram-positive bacterium and an important member of the symbiotic flora of humans and animals [15]. Lactobacillus salivarius is frequently isolated from animal gut, human milk and other sources. It has the ability to modulate gut microbiota, produce antimicrobial substances, stimulate protective immune responses, inhibit fecal enzyme activity and produce the short-chain fatty acids that allow the gut to undergo acidification [16]. Experiments have shown that Lactobacillus salivarius UCC118 can secrete bacteriocin Abp118, which exhibits antibacterial activity and can inhibit the growth of various pathogenic bacteria [17]; heat-inactivated Lactobacillus salivarius CECT 5713 can inhibit Streptococcus mutans [18]; and Lactobacillus salivarius LS01 supernatant has inhibitory effects on Escherichia coli and Staphylococcus aureus [19]. Studies have shown that Lactobacillus salivarius has good application in improving animal health, such as in combating intestinal inflammation through the production and activity of regulatory T cells, macrophages, natural killer cells and dendritic cells [20]; reducing pro-inflammatory cytokines in mouse splenocytes [21]; improving muscle strength and endurance after exercise [22]; and significantly reducing fasting blood glucose levels in diabetic mice [23]. Therefore, Lactobacillus salivarius has been incorporated into microecological preparations, and using this bacterium in actual production has good development prospects. The findings from our unpublished research suggest that Lactobacillus salivarius WZ1, isolated and identified by our laboratory, was resistant to acid and bile salts and had a good inhibitory effect on pathogenic bacteria such as Escherichia coli, Salmonella, Staphylococcus aureus and Clostridium perfringens. However, it is not clear whether this strain can prevent the intestinal damage caused by ETEC K88 in vivo and exert its protective mechanisms. The purpose of this study was to investigate the mechanism of Lactobacillus salivarius WZ1 and its effect on ETEC K88–induced jejunum inflammatory injury in mice and to explore whether it could affect intestinal immune function and gut microbiota, thereby inhibiting the intestinal injury caused by ETEC K88. This lays a theoretical foundation for the clinical application of Lactobacillus salivary WZ1 in preventing calf diarrhea. ## 2.1. Strains and Culture Growth Conditions Lactobacillus salivarius WZ1 (NCBI accession number: ON005140) was isolated from healthy calves, and ETEC K88 standard strain (CVCC196) was purchased from the China Culture Collection Center and preserved by our laboratory. Lactobacillus salivarius WZ1 was cultured in MRS broth (Solearbio, Beijing, China), and ETEC K88 was cultured in NB broth (Solearbio, Beijing, China) at 37 °C, 130 rpm/min for 24 h. ## 2.2. Test Grouping Specific-pathogen-free (SPF) grade Kunming mice (6-week-old, $$n = 40$$, body weight 30 ± 2 g) were purchased from Liaoning Changsheng Biotechnology Co., Ltd. (Shenyang, China). Before the experiment, the mice were given a one-week acclimation period where the temperature was kept at 23 ± 2 °C and a 12 h light–dark cycle was used. The experiments were permitted by the Shenyang Agricultural University under the Laboratory Animal Care Ethics Committee (People’s Republic of China Animal Ethics Regulations and Guidelines) for animal experiments (Permit No. 264SYXK2011-0001, September 2018). A total of 40 Kunming mice were randomly divided into 4 groups: the CON group (blank control group, $$n = 10$$); the L.S group (Lactobacillus salivarius WZ1 group, $$n = 10$$); the E. coli group (ETEC K88 group, $$n = 10$$); and the L.S + E. coli group (Lactobacillus salivarius WZ1 + ETEC K88 group, $$n = 10$$). Fourteen days before the start of the experiment, the CON group and the E. coli group were administered normal saline each day, while the L.S group and the L.S + E. coli group were intragastrically administered Lactobacillus salivarius WZ1 1 × 108 CFU/mL each day [24]. On the 15th day, the E. coli group and the L.S + E. coli group were intragastrically administered ETEC K88 1 × 109 CFU/mL [25], while the CON group and the L.S group were gavaged with normal saline. The mice were sacrificed after 24 h [26]. ## 2.3. Histological Evaluation (HE) of Jejunum We took 10 mL centrifuge tubes, added 7 mL of tissue fixative to each tube, and marked them for future use. In the absence of mechanical damage, mouse jejunum tissue samples of approximately 1–2 cm were selected and placed in the prepared tissue fixative for measurement of villus length and crypt depth, followed by HE staining and observation with a microscope (Savier, Wuhan, China). ## 2.4. Detection of Inflammatory Factors by RT-qPCR The tissue samples were placed on ice, and 0.1 g tissue samples were weighed with sterilized scissors and tweezers. The samples were placed into a sterilized mortar with a small amount of liquid nitrogen and quickly ground into powder with a grinding pestle. The powder was removed with a sterilizing spoon and inserted into a 1.5 mL deenzymatic centrifuge tube, and 1 mL RNA lysate was added to make tissue homogenate. A total RNA extraction kit (Novizan, Nanjing, China) was used to extract RNA from the mouse jejunum tissues. An amount of 12,000 g was centrifuged at 4 °C for 5 min, after which the supernatant was discarded and let dry. We dissolved the precipitate with 20 μL of sterile enzyme-free deionized water and used an instrument to detect the concentration of extracted RNA. Residual genomic DNA from RNA templates was removed (the loading volume was 1 ng/extracted RNA concentration), and the RNA was reverse transcribed into cDNA using HiScript® III RT SuperMix for qPCR (+gDNA wiper) (Novizan, Nanjing, China). The cDNA obtained from the reaction was used to detect the mRNA expression of TNF-α, IL-1β and IL-6 in the tissue using ChamQ Universal SYBR qPCR Master Mix (Novizan, Nanjing, China). GAPDH was used as the internal reference primer for mRNA. The experimental results were expressed using the 2−△△Ct method to express the changes in mRNA expression in the treatment group relative to the control group. The primers used in this study are listed in Table 1. RT-qPCR reaction procedure: stage 1: 95 °C, 30 s; stage 2: 95 °C, 10 s; 60 °C, 30 s; 95 °C, 15 s (34 ×); and stage 3: 60 °C, 60 s; 95 °C, 15 s. ## 2.5. Western Blot Mice jejunal tissue proteins were extracted using a tissue whole protein extraction kit (Solearbio, Beijing, China), the concentrations of the extracted total protein were determined using the Omni-EasyTM ready-to-use BCA protein quantification kit (Yase, Shanghai, China), and the formula was obtained: sample protein concentration = (OD 562 nm–0.1371)/0.7618. The proteins were separated and transferred to PVDF membranes using a PAGE gel rapid prep kit (Yase, Shanghai, China), and blocked with $5\%$ nonfat dry milk (Solearbio, Beijing, China) for 2 h at room temperature on a shaker. After the primary antibodies (TLR4, NF-κB, MyD88, Occludin, ZO-1, Claudin-1) were prepared at a dilution ratio of 1:1000, the PVDF membranes were placed in the primary antibody and incubated at 4 °C with a shaker for 2 h. They were then washed with 1 × TBST, put in the secondary antibody (HRP* Goat Anti-Mouse IgG (H + L) and HRP * Goat Anti-Rabbit IgG (H + L)), and incubated at room temperature for 1 h on a shaker. Finally, we used the Omni-EasyTM basic chemiluminescence detection kit (Yase, Shanghai, China) and incubated the samples at room temperature for 3 to 5 min in the dark. We employed the Shanghai Qinxiang CLiNX gel imaging system to observe the results, used Gel Quant software to detect the gray value of the target protein and the internal reference protein, and calculated the relative expression of the protein according to the calculation formula. ## 2.6. Analysis of Gut Microbiota We prepared several sterile cryopreservation tubes, put the mouse cecum contents into cryopreservation tubes under aseptic conditions, immediately placed them into liquid nitrogen, and then set them at −80 °C for later use. After extracting the total DNA from the sample, primers were designed according to the conserved regions and sequencing adapters were added to the ends of the primers, PCR amplification was performed, and the products were purified, quantified and homogenized to form a sequencing library. Qualified libraries were sequenced with an Illumina HiSeq 2500 sequencing system (Beijing, BioMark Biotechnology Co., Ltd., Beijing, China). The sequencing results were analyzed using OTU, a species distribution histogram, alpha diversity, beta diversity (PCA, PCoA), LEfSe and ANOVA. The raw sequencing data generated from this study were deposited in NCBI SRA1 under the BioProject accession number PRJNA835604 (https://www.ncbi.nlm.nih.gov/sra/PRJNA835604 (accessed on 6 May 2022)). ## 2.7. Data Statistics and Analysis All experiments were independently repeated at least three times. All test data were preliminarily calculated and counted using Microsoft Excel, and then one-way variance calculation and analysis were carried out using SPSS data analysis software (Figures 2–5,9 and 13). The test values are expressed in the form of mean ± standard error (mean ± SE). The difference significance judgment took $p \leq 0.05$ as a significant difference and $p \leq 0.01$ as an extremely significant difference. The results of this test were calculated and plotted in Figures 1–5 using Graphpad Prism 8 and Office, and in Figures 6–13 using the microbial diversity analysis platform (BioMark Biotechnology Co., Ltd., Beijing, China). ## 3.1. Pathological Changes in Jejunum The HE results for the mouse jejunum are shown in Figure 1. The intestinal villi in the CON group were arranged in an orderly manner without rupture (Figure 1A). Compared with the CON group, the intestinal villi in the E. coli group were disordered and obviously broken (Figure 1C). Compared with the E. coli group, the intestinal villi in the L.S + E. coli group were arranged relatively neatly, and the fracture phenomenon was significantly reduced (Figure 1D). We measured the villus length and crypt depth of the small intestine and calculated the ratio of the two, with the results shown in Figure 2. The villus length in the E. coli group was shorter than that in the CON group ($p \leq 0.01$). The villus length in the L.S + E. coli group was longer than that in the E. coli group ($p \leq 0.01$). Compared with the CON group, the crypt depth of the E. coli group increased ($p \leq 0.01$), indicating that the stimulation of ETEC K88 led to a great increase in the crypt depth, resulting in a large number of immature cells with secretory functions, which caused diarrhea. The crypt depth of the L.S + E. coli group was significantly smaller than that of the E. coli group and had no significant difference from the CON group. The ratio between villus length and crypt depth (V/C) indicated the functional status of the intestine, and the decreased ratio indicated the decreased absorptive function and the increased secretory function of the intestine, thus causing diarrhea, while the increased ratio indicated the improved absorptive capacity of the intestine. The figure shows that the V/C of the E. coli group decreased significantly ($p \leq 0.01$), while the V/C of the L.S + E. coli group was significantly higher than that of the E. coli group ($p \leq 0.01$). In conclusion, Lactobacillus salivarius WZ1 can effectively inhibit the negative functional changes caused by ETEC K88 in the intestinal tract. ## 3.2. The mRNA Expression of Inflammatory Factors As shown in Figure 3, the test results reveal that, compared with the CON group, the mRNA expressions of TNF-α, IL-1β and IL-6 in the intestinal tissue of the E. coli group were increased ($p \leq 0.01$), and the mRNA expression of IL-4 was decreased ($p \leq 0.01$), indicating that ETEC K88 could increase the secretion of the pro-inflammatory cytokines TNF-α, IL-1β and IL-6 and decrease the secretion of anti-inflammatory factor IL-4 in the intestinal tract of the mice. Compared with the E. coli group, the mRNA expressions of TNF-α, IL-1β and IL-6 in the intestinal tissue of the L.S + E. coli group were decreased ($p \leq 0.01$), and the mRNA expression of IL-4 was increased ($p \leq 0.01$), indicating that feeding with Lactobacillus salivarius WZ1 can effectively prevent the inflammatory response to ETEC K88 in the intestinal tract of mice. The intestinal tract plays a significant protective role. ## 3.3. Inflammatory Pathway Protein Expression As shown in Figure 4, compared with the control group, the protein expressions of TLR4, NF-κB and MyD88 in the jejunum in the E. coli group were significantly increased ($p \leq 0.01$), indicating that ETEC K88 activated the TLR4/NF-κB/MyD88 pathway, causing inflammatory damage to intestinal tissue. Compared with the E. coli group, the protein expressions of TLR4, NF-κB and MyD88 in the jejunum in the L.S + E. coli group were significantly decreased ($p \leq 0.01$). This result indicates that Lactobacillus salivarius WZ1 can effectively prevent the inflammatory damage caused by ETEC K88 to the intestinal tract. ## 3.4. Tight Junction Protein Expression As shown in Figure 5, compared with the control group, ETEC K88 caused a significant decrease in the protein expressions of Occludin, ZO-1 and Claudin-1 in the jejunum ($p \leq 0.01$), and compared with the E. coli group, the expression of related proteins was significantly higher ($p \leq 0.01$). This result demonstrates that Lactobacillus salivarius WZ1 can promote intestinal epithelial tight junction protein production in intestinal cells, strengthen the intestinal mucosal barrier and effectively prevent the invasion of pathogenic bacteria. ## 3.5. Gut Microbiota Analysis The microbial diversity of the mice cecum was based on the Illumina HiSeq sequencing platform, and paired-end cDNA libraries were constructed and sequenced (16 s, v3 + v4 b). First, we used Trimmatic v0.33 software to filter the raw reads obtained via sequencing; then, we used cutadapt 1.9.1 software to identify and remove the primer sequence, and obtain high-quality reads without primer sequences; afterwards, we used FLASH v1.2.7 software to splice the high-quality reads of each sample through overlay, and the resulting splicing sequence was the clean reads; finally, UCHIME v4.2 software was used to identify and remove the chimeric sequence to obtain the final effective data (effective reads). In this study, 28 samples were sequenced and 2,239,382 pairs of reads were obtained. A total of 2,219,181 clean reads were generated after quality control and splicing. At least 79,040 clean reads were generated in each sample, with an average of 79,256 clean reads. ## 3.5.1. OTU Analysis OTUs, or taxonomic operating units, are the same markers artificially assigned to a taxon (strain, species, genus, group, etc.) for convenience of analysis in phylogenetic studies or population genetics studies. All sequences can be divided into OTUs according to different similarity levels, with each OTU corresponding to a representative sequence. We used Usearch software to cluster reads at a $97.0\%$ similarity level to obtain our OTUs. The total number of OTUs obtained in the test results was 475 (Figure 6). The numbers of OTUs in the CON group, L.S group, E. coli group and L.S + E. coli group were 453, 467, 463 and 467, respectively, with no significant differences. The Venn diagram (Figure 7) shows that the numbers of unique OTUs in the CON group compared with the E. coli group were 9 and 19, respectively, while the numbers of unique OTUs in the E. coli group and the L.S + E. coli group were 6 and 10, respectively. ## 3.5.2. Species Distribution Histogram With SILVA as the reference database, the naive Bayesian classifier was used to carry out taxonomic annotation on the feature sequence, and the species classification information corresponding to each feature can be obtained. Then, the community composition of each sample at each level (phylum, class, order, family, genus, species) was counted, and the species abundance table at different taxonomic levels was generated using QIIME software. As shown in Figure 8, the distribution of species at class level (Figure 8A), family level (Figure 8B) and genus level (Figure 8C) was made according to the distribution of the relative content of species in the sample. The figure shows the species with the top 10 abundances. At the class level (Figure 8A), the abundance ratios of Bacteroidia and Gammaproteobacteria in the E. coli group were significantly higher than those in the CON group, while the L.S + E. coli group demonstrated a significant decreasing trend. At the family level (Figure 8B), the abundance ratio of Lachnospiraceae in the E. coli group was significantly lower than that in the CON group, and the abundance ratio of Muribaculaceae and Prevotellaceae compared with the CON group increased significantly, while the L.S + E. coli group demonstrated the opposite trend of the E. coli group. At the genus level (Figure 8C), the abundance ratios of uncultured_bacterium_f_Muribaculaceae and Alloprevotella in the E. coli group were significantly higher than those in the CON group, while the abundance ratios of uncultured bacterium f Lachnospiraceae and Ruminiclostridium exhibited a downward trend, especially in the Lachnospiraceae NK4A136 group. Significantly, the L.S + E. coli group still demonstrated the opposite trend of the E. coli group. ## 3.5.3. Alpha Diversity Analysis The Chao1 and Ace indices measure species abundance and the number of species. The Shannon and Simpson indices are used to measure species diversity and are influenced by species abundance and species evenness in the sample community. As shown in Figure 9A,B, compared with the E. coli group, the Ace index and the Chao1 index were higher in the L.S + E. coli group ($p \leq 0.01$, $p \leq 0.05$). As shown in Figure 9C,D, compared with the L.S group, the Shannon index and Simpson index were lower in the L.S + E. coli group ($p \leq 0.05$). It can be seen that ETEC K88 infection reduced the species abundance and species diversity of gut microbiota, and pretreatment with Lactobacillus salivarius WZ1 revised this situation. ## 3.5.4. Beta Diversity Analysis PCA and PCoA analyses were performed according to beta diversity based on distance. As shown in Figure 10A, the contribution rates of the two principal components to the sample differences were $27.56\%$ and $13.28\%$, and there were differences among the groups, but the differences were not significant. As shown in Figure 10B, the contribution rates of the two principal components to the sample differences were $17.75\%$ and $9.60\%$, respectively, and there were differences among the groups. There were differences between the E. coli group and the CON group, indicating that ETEC K88 changed the composition of the gut microbiota. In addition, there were also differences between the L.S + E. coli group and the E. coli group, indicating that Lactobacillus salivarius WZ1 modified the intestinal microbial population changed by ETEC K88. ## 3.5.5. LEfSe Analysis As shown in Figure 11, Ralstonia was the dominant species (LDA > 3) in the E. coli group, and its abundance was higher than that in the CON group. However, Ralstonia (LDA < 0) was not the dominant species in the L.S + E. coli group. In contrast, beneficial bacteria such as Lactobacillus (LDA > 2) and Bifidobacterium (LDA > 3) were the dominant species in the L.S + E. coli group. Therefore, ETEC K88 caused a negative change in the gut microbiota, while the addition of Lactobacillus salivarius WZ1 caused a positive change in the gut microbiota. As shown in Figure 12A, ETEC K88 significantly decreased the abundance of Lachnospiraceae, and as shown in Figure 12B, ETEC K88 significantly increased the abundance of Ralstonia. These results indicate that ETEC K88 reduced the abundance of beneficial bacteria and increased the abundance of harmful bacteria in the gut. ## 3.5.6. ANOVA Analysis As shown in Figure 13, compared with the E. coli group, the relative abundances of Ralstonia and Helicobacter were lower in the L.S + E. coli group ($p \leq 0.01$), indicating that the diarrhea caused by ETEC K88 may be related to these two species, and Lactobacillus salivarius WZ1 effectively decreased these two harmful bacteria that cause diarrhea. ## 4. Discussion The small intestine is an important part of the digestion and absorption of nutrients. The villi increase the contact area between the intestine and the nutrients, which is conducive to the absorption of more nutrients, and prevent colonization by harmful bacteria through their swaying motion [27,28]. Villus length, crypt depth and villus length to crypt depth (V/C) ratio are all important indicators of gut maturity and functional capacity. Therefore, the ratio of V/C corresponds to a relatively healthy gut system. We included high brush border enzyme activity, shorter villus length and lower intestinal absorptivity [29,30,31]. Compared with this experiment, ETEC K88 infection shortened the intestinal villi ($p \leq 0.01$) and increased the depth of the crypts ($p \leq 0.01$), resulting in a decrease in V/C ($p \leq 0.01$), suggesting that the absorption capacity of the small intestine was reduced, which is the key to causing diarrhea. Experiments have shown that TLR4/NF-κB/MyD88 signaling is involved in the body’s inflammatory response [32,33], the transient overactivation of NF-κB–mediated signaling leads to acute inflammation, and the release of a series of pro-inflammatory cytokines, including TNF-α, IL-1β and IL-6, can lead to tissue damage [34]. This was consistent with the test results in the E. coli group, where the protein expressions of TLR4, NF-κB and MyD88 were increased ($p \leq 0.01$), and the mRNA expressions of TNF-α, IL-1β and IL-6 were increased ($p \leq 0.01$). Lactobacillus salivarius can inhibit the adhesion of ETEC K88 to IPEC-J2 cells and, at the same time, has the ability to reduce the pro-inflammatory cytokines IL-1β, TNF-α, IL-8 and TLR4 and significantly reduce the phosphorylation of p38 MAPK and p65 NF-κB, which indicates that Lactobacillus salivarius may reduce inflammation-related cytokines by inhibiting the phosphorylation of p38 MAPK and blocking the NF-κB signaling pathway [35]. IL-6 has been reported to play a key role in the amplification of inflammatory signals in the gut, and it can inhibit the NF-κB pathway and pro-inflammatory cytokine IL-6 production to reduce intestinal inflammation [36]. This is consistent with the test results showing that the protein expression of TLR4, NF-κB and MyD88 and the mRNA expression of TNF-α, IL-1β and IL-6 in the L.S + E. coli group were lower than those of the E. coli group ($p \leq 0.01$). Studies have shown that TNF-α and IL-1β levels are also associated with the TLR4/NF-κB/MyD88 pathway [37]. This indicates that Lactobacillus salivarius WZ1 can inhibit the mRNA expression of TNF-α, IL-1β and IL-6 in intestinal cells ($p \leq 0.01$) and the protein expression of the TLR4/NF-κB/MyD88 pathway ($p \leq 0.01$). Thus, the release of pro-inflammatory factors TNF-α, IL-1β and IL-6 was reduced, and the intestinal inflammatory damage caused by ETEC K88 was finally inhibited. The intestinal mucosa is composed of epithelial cells, which form an active barrier through the expression of pro-inflammatory genes, the secretion of inflammatory cytokines, and the recruitment of inflammatory cells to protect the subepithelial tissue from invasion by pathogenic bacteria, intestinal barrier dysfunction or intestinal passage. Increased permeability is an important condition in the pathogenesis of different diseases, and the main determinant of permeability is the integrity of intestinal epithelial tight junctions (TJs) [38,39]. An impaired intestinal tight junction (TJ) barrier is considered an important pathogenic factor in intestinal and systemic inflammation, and impairment of the intestinal tight junction (TJ) barrier can lead to paracellular infiltration by harmful pathogenic bacteria, thereby causing inflammatory responses, including Crohn’s disease, necrotizing enterocolitis, ulcerative colitis, alcoholic hepatitis and various infectious diarrhea syndromes [40]. Studies have shown that dietary supplementation of $0.1\%$ and $0.2\%$ Lactobacillus salivarius can significantly increase the levels of anti-inflammatory cytokines in serum and tight junction proteins Occludin, Claudin-1 and ZO-1 in LPS-stressed piglets, while serum pro-inflammatory factors TNF-α, IL-1β and IL-6 were significantly downregulated [41]. Multiple studies have shown that drug- or probiotic-induced enhancement of the intestinal epithelial TJ barrier prevents the development of intestinal inflammation in various mouse models of IBD [42,43,44,45]. This is consistent with the results of this experiment, where the protein expression levels of intestinal epithelial tight junction–related proteins Occludin, Claudin-1 and ZO-1 in the L.S + E. coli group were higher than those in the E. coli group ($p \leq 0.01$), and the mRNA expressions of TNF-α, IL-1β and IL-6 in the tissue were lower than those in the E. coli group ($p \leq 0.01$). Experiments have shown that the immune system plays an important role in regulating the function of the intestinal TJ barrier, and the opening of this barrier induced by pro-inflammatory cytokines is an important mechanism leading to TJ barrier damage under various inflammatory conditions in the intestine [46]. IL-1β, a typical pro-inflammatory cytokine, increases intestinal epithelial TJ permeability in animal and in vitro cell culture model systems [47]. Therefore, IL-1β plays an important role in intestinal inflammation, and its induced increase in intestinal epithelial TJ permeability is mediated by the p38 kinase activation of the ATF-2 and ATF-3 regulation of MLCK gene activity [48]. Furthermore, the IL-1β–induced increase in Caco-2 TJ permeability was mediated by NF-κB activation, and siRNA inhibition of NF-κB activation or NF-κB p65 depletion prevented IL-1β from increased Caco-2 TJ permeability. IL-1β also caused the downregulation of Occludin protein expression, decreased Occludin mRNA expression and interfered with Occludin junction localization, so the regulation of Occludin by IL-1β was also regulated by NF-κB activation [49]. This is consistent with the results of this experiment, where the expression of the NF-κB protein and the mRNA expression of IL-1β in the E. coli group were increased ($p \leq 0.01$), while the protein expression of Occludin was significantly decreased ($p \leq 0.01$). The normal gut microbiota consists of 100 trillion different microbes, most of which are bacteria, including more than 1100 common species, with at least 160 in each individual [50]. Gut microbiota composition balance is important to ensure the stability of gut homeostasis, and microbiota dysbiosis is thought to play an important role in the pathogenesis of inflammatory bowel disease [51,52]. Trials have demonstrated bacterial dysbiosis in fecal samples from calves with diarrhea, with less diversity and fewer observable species in the diseased calves compared with the healthy controls [53]. It has also been reported that Mycoplasma gallisepticum complicated with Escherichia coli infection significantly reduces the Chao1 index of intestinal microbes, while an intake of Lactobacillus salivarius improves the composition of intestinal microbes [54]. Combined with the alpha diversity analysis results of this experiment, the Ace index of the E. coli group was lower than that of the L.S + E. coli group ($p \leq 0.01$), and the Chao1 index was lower in the L.S + E. coli group ($p \leq 0.05$). This indicates that ETEC K88 infection may reduce the population of beneficial bacteria and reduce the species abundance and species diversity of gut microbiota, while the addition of Lactobacillus salivarius WZ1 improves this situation. Experiments have shown that Escherichia coli, Enterobacter and Enterococcus rapidly proliferate in feces after 7 days of Escherichia coli infection in mice [55]. This is consistent with the experimental results showing that the abundance ratios of Bacterodia and Gammaproteobacteria in the E. coli group were significantly higher than those in the CON group. The oral administration of Lactobacillus salivarius to suckling pigs for 10 days can increase the number of fecal lactobacilli [56]. This is consistent with the results showing that the addition of Lactobacillus salivarius WZ1 in the LEfSe analysis can increase the abundance of Lactobacillus in the gut, indicating that Lactobacillus salivarius WZ1 can increase the abundance of beneficial bacteria in the gut, thereby inhibiting the growth of harmful bacteria and protecting gut health. In conclusion, Lactobacillus salivarius WZ1 can inhibit the inflammatory damage caused by ETEC K88 in the mouse jejunum by regulating the TLR4/NF-κB/MyD88 inflammatory pathway and gut microbiota. ## 5. Conclusions Lactobacillus salivarius WZ1 can inhibit the inflammatory damage caused by ETEC K88 in the mouse jejunum by regulating the TLR4/NF-κB/MyD88 inflammatory pathway. It can also promote the secretion of intestinal epithelial tight junction protein, enhance the intestinal epithelial barrier, and increase the abundance of beneficial bacteria in the intestinal tract, including Lactobacillus and bifidobacterium. The bacteriostatic substances produced by Lactobacillus salivarius WZ1 will be further studied and purified. This will lay a foundation for clinical use of Lactobacillus salivary to prevent calf diarrhea in the future. ## References 1. Viidu D.A., Motus K.. Implementation of a pre-calving vaccination programme against rotavirus, coronavirus and enterotoxigenic. BMC Vet. Res. 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--- title: Microbial Populations in Ruminal Liquid Samples from Young Beefmaster Bulls at Both Extremes of RFI Values authors: - Nelson Manzanares-Miranda - Jose F. Garcia-Mazcorro - Eduardo B. Pérez-Medina - Anakaren Vaquera-Vázquez - Alejandro Martínez-Ruiz - Yareellys Ramos-Zayas - Jorge R. Kawas journal: Microorganisms year: 2023 pmcid: PMC10055678 doi: 10.3390/microorganisms11030663 license: CC BY 4.0 --- # Microbial Populations in Ruminal Liquid Samples from Young Beefmaster Bulls at Both Extremes of RFI Values ## Abstract The gut microbiota is involved in the productivity of beef cattle, but the impact of different analysis strategies on microbial composition is unclear. Ruminal samples were obtained from Beefmaster calves ($$n = 10$$) at both extremes of residual feed intake (RFI) values (5 with the lowest and 5 with the highest RFI) from two consecutive days. Samples were processed using two different DNA extraction methods. The V3 and V4 regions of the 16S rRNA gene were amplified using PCR and sequenced with a MiSeq instrument (Illumina). We analyzed 1.6 million 16S sequences from all 40 samples (10 calves, 2 time points, and 2 extraction methods). The abundance of most microbes was significantly different between DNA extraction methods but not between high-efficiency (LRFI) and low-efficiency (HRFI) animals. Exceptions include the genus Succiniclasticum (lower in LRFI, $$p \leq 0.0011$$), and others. Diversity measures and functional predictions were also mostly affected by DNA extraction methods, but some pathways showed significant differences between RFI levels (e.g., methylglyoxal degradation, higher in LRFI, $$p \leq 0.006$$). The results suggest that the abundance of some ruminal microbes is associated with feed efficiency and serves as a cautionary tale for the interpretation of results obtained with a single DNA extraction method. ## 1. Introduction In animal production systems, feed efficiency refers to the ability of an animal to turn feed ingredients into products for human consumption, such as meat and milk. Feed efficiency is a complex multifaceted trait, under the control of several interrelated biological processes and management regimens [1,2]. These biological processes include those associated with the animal (e.g., energy metabolism) and also the composition and function of the digestive microbiota [1]. While the relationship between the gut microbiota and the health and productivity of animals has been well studied, more details are still needed to better understand the nature and characteristics of this relationship. There are key characteristics of the gut microbiota that make this subject difficult to investigate in the context of feed efficiency, such as its relationship with the animal’s genetics. One study of beef cattle ($$n = 48$$) from three breeds showed that the differential microbial features observed between efficient and inefficient steers tended to be specific to breeds, suggesting that interactions between host genotype and the rumen microbiome contribute to the variations in feed efficiency [3]. Another, larger study of 709 beef cattle also showed that breed, sex, and diet were major factors associated with the variation in rumen microbiota [4]. Moreover, it has been shown that high-RFI (residual feed intake) animals spent about 10 min longer eating than their more efficient, low-RFI contemporaries [2], and that growing beef heifers with low RFI had a lower occurrence of non-feeding events (i.e., where cattle are at the feed face but do not consume any feed, [5]), thus suggesting additional factors (e.g., anatomical or physiological) related to feed efficiency that are not directly linked with either genetics or the gut microbes. Finally, the gut microbiota is relatively constant over time, but it may also show patterns of variation that could potentially mask dietary and even host genetics effects [6,7]. The gut microbiota is affected by many factors, but the data we obtain from gut microbial populations in ruminants and other animals is also dependent on methodological factors, such as the source of material for DNA extraction [8], preservation methods of samples [9], DNA extraction methods [10,11], PCR primer choice [12], the data analysis strategies employed [13], and statistical considerations of the resulting data [14]. The variations among methodologies are important because they can provide different views of the structure and composition of microbial populations. The objective of this study was to investigate microbial populations in ruminal contents in beef cattle with LRFI (more efficient) and HRFI (less efficient) using two different DNA extraction methods. ## 2.1. Experimental Animals This study was conducted in compliance with the current Mexican legislation (NOM-062-ZOO-1999) and revised by the Committee of Animal Research and Experimentation (CARE) at MNA de México (Protocol # 04132021). A total of 33 young beefmaster bulls and 18 calves were included in a feed efficiency test at the Centro de Investigación en Producción Agropecuaria (CIPA) of the Universidad Autonoma de Nuevo Leon (UANL). The adaptation period lasted 14 days and the evaluation period was 78 days. Animals were individually fed a diet that as dry matter consisted of $50\%$ ground corn, $16\%$ Klein grass hay, $10\%$ wet distillers’ grains, $8\%$ cane molasses, $2.5\%$ vitamin and mineral mix, and other ingredients (Supplementary Table S1). On day 5 after the end of the evaluation period, we obtained the RFI values from a GrowSafe system for tracking the feed consumption of individual animals [15] and ruminal liquid samples were obtained from 10 calves at both extremes of RFI values (5 with the lowest and 5 with the highest RFI) from two consecutive days (Day 1 and Day 2, to consider inter-day variation) using SELEKT equipment (Nimrod Veterinary Products Ltd., Moreton-in-Marsh, Gloucestershire, UK). The steel collector tip in this equipment does not allow the passage of solids, which can clog the hose, and samples were not filtered because this may discard some microbial populations. Ruminal samples (~30 mL) were placed in a 50 mL tube containing 10 mL of ethanol at $95\%$ for better microbiome stability [9], and frozen at −20 °C until DNA extraction and volatile fatty acid (VFA) analysis. ## 2.2. DNA Extraction DNA extraction was performed at the Laboratorio Central Regional del Norte using bead beading coupled with two available commercial kits: PureLink Genomic DNA kit (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA, method A) and Wizard Genomic DNA Purification kit (PROMEGA, Madison, WI, USA, method B). Briefly, samples were thawed at room temperature and four aliquots of 1.5 mL (2 for each method) were obtained from each sample ($$n = 20$$, 10 calves with two time points). The samples were centrifuged at 13,000 RPM for 5 min and the supernatant discarded. The pellets in each set of 2 tubes were mixed in one tube which was the source material for each DNA extraction. Approximately 100 µL of silica beads were added to the tubes, mixed in a FastPrep®-24 (Santa Ana, CA, USA) equipment, and the subsequent steps were performed using the instructions included in the user’s manuals. Both methods use purification columns and are similar in their procedures with the exception of a longer incubation time with proteinase K in method A. DNA was quantified using a NanoDrop and visualized in an agarose gel for a qualitative assessment of DNA quality. DNA samples were shipped to the National Laboratory of Genomics for Biodiversity (LANGEBIO, CINVESTAV, Irapuato, Mexico) for further PCR and 16S rRNA gene sequencing. ## 2.3. PCR and Sequencing PCR was performed using primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 785R (5′-GGACTACHVGGGTATCTAATCC-3′), covering the semi-conserved regions V3 and V4 of the 16S rRNA gene (approximately 463 nucleotides, from nucleotide 341 to nucleotide 804). The PCR products were sequenced using a MiSeq instrument (Illumina, San Diego, CA, USA) at LANGEBIO. ## 2.4. Bioinformatics The results were analyzed using QIIME2 v.2021.11 [16]. Quality filtering was performed using DADA2 [17] using 120 nucleotides to remove low-quality regions of the sequences. The method to remove chimeras in the DADA2 plugin was consensus (chimeras are detected in samples individually, and sequences found chimeric in a sufficient fraction of samples are removed). The output feature table was filtered to remove features appearing in less than 4 samples and with less than 20 in frequency [18]. The filtered table was used for taxonomic assignments, and we did not remove any taxa (e.g., Cyanobacteria and Chloroflexi, note that the relevance of these and other taxa in gut microbial ecology is debatable) unless there were issues with low prevalence. The filtered table was also used for alpha and beta diversity analyses using several metrics in the diversity plugin of QIIME2. ## 2.5. Microbial Taxa Abundance The relative proportions of 16S reads have historically been the data of choice to perform comparisons of microbial taxa in studies of gut microbiota in ruminants [19,20,21] and other animal species [13]. However, it is well known that relative abundance can lead to spurious correlations, originally pointed out by Pearson more than a century ago [22]. We performed a series of experiments to test the performance of the centered log-ratio (clr) transformation [23] and applied this transformation to the raw number of sequences obtained from the filtered table (see “Centered log-ratio transformation” in Supplementary Information). Transformations were performed at each phylogenetic level separately. ## 2.6. Prediction of Functional Profiles We used PICRUSt2 [24] for the prediction of metagenome functions based on 16S marker gene sequencing profiles. The filtered feature table was used for this analysis. The resulting pathways counts were clr-transformed prior to the statistical comparison between LRFI and HRFI ($$n = 20$$ each). ## 2.7. Analysis of Volatile Fatty Acids Acetic, propionic, and butyric fatty acids were measured using a standard methodology outlined by M.L. Galyean from Texas Tech University in his manual on Laboratory Procedures in Animal Nutrition Research, in a flame ionization detector in a Varian 3400 CX gas chromatograph (Palo Alto, CA, USA). Briefly, ruminal fluid samples were centrifuged, and 5 mL of the supernatant was mixed with 1 mL of meta-phosphoric acid-2EB solution. The mixture was kept in cold, centrifuged, and the supernatant used for GLC injection. ## 2.8. Statistical Analysis Productive parameters were compared using a Mann–Whitney test. Taxa abundance based on clr-transformed data was analyzed with the MIXED procedure (PROC MIXED) in SAS University Edition (release 3.81) using the clr-transformed data from each taxon as the dependent variable, and day of sampling, DNA extraction method, and RFI, as independent variables (i.e., fixed effects), without random effects. In the case of having residuals with non-normal distributions, the non-parametric 1-way procedure (PROC NPAR1WAY) was used with the disadvantage of analyzing independent variables separately. Alpha diversity metrics were analyzed using the Kruskal–Wallis test. Volatile fatty acids between LRFI and HRFI were compared using Student’s t-test for independent samples. ## 3.1. Samples In the calves, RFI values varied from −1.56 (lowest) to 4.76 (highest). One animal in the LRFI group could not be sampled (the steel collector tip did not enter the esophagus after several attempts) and we had to choose the animal with the next closest RFI value. The animal with the highest RFI value (4.76) from the HRFI group was not selected because the value was further than three standard deviations from the mean. Less efficient, HRFI animals, spent >20 min longer eating than LRFI animals, due to more visits per day not to the duration per feeding event (Table 1). ## 3.2. Sequencing Results The sequencing procedure was successfully performed in all 40 DNA samples (10 calves, 2 time points, 2 DNA extraction methods). Method A yielded a lower DNA concentration and higher ratios of absorbance at $\frac{260}{230}$ nanometers (nm), while the absorbances at $\frac{260}{280}$ nm were similar between the two methods. A summary of the sequencing results is shown in Table 2. ## 3.3. Variation Analysis To gain insights into the factors associated with the abundance in microbial groups, we calculated the variation between time points, DNA extraction methods, and RFI groups, using both the relative abundance of taxa and the clr-transformed data at the phylum level (see “Variation analysis” in Supplementary Information). A total of 20 taxa were discovered at the phylum level, but for this and other analyses, we removed two taxa because of low prevalence (phylum OD1 and 1 phylum from an unassigned kingdom). The removal of these two taxa allowed us to conduct the analysis of variance using data from 18 taxa at the phylum level (Supplementary Table S2). Except for Tenericutes, this analysis showed that the variation in microbial abundance was always higher between DNA extraction methods compared to the variation between days of sampling and between high- (LRFI) and low-efficiency (HRFI) animals. ## 3.4. Differences in Microbial Abundances between LRFI and HRFI At the phylum level, there was no effect of the day of collection but there was an effect of the DNA extraction method across most microbial groups ($p \leq 0.05$). Three taxa showed a statistical difference ($p \leq 0.05$) between RFI levels (Chloroflexi, Elusimicrobia, and SR1). It is interesting that the DNA extraction method did not affect the abundance of all taxa (Bacteroidetes, Verrucomicrobia, TM7, and Tenericutes did not show a difference between DNA extraction methods, Table 3). At the class level, there was no difference between days of sampling, but again most taxa were significantly different between DNA extraction methods (Supplementary Table S3). Anaerolineae (phylum Chloroflexi), Bacilli (Firmicutes), and an unassigned class of phylum SR1 were found to be significantly different between RFI levels (Supplementary Table S3). It is interesting to note that similar to the analysis of phyla, the strong effect of the DNA extraction method did not affect the abundance of all taxa at the class level. The abundance of most taxa at the order level also showed significant differences between DNA extraction methods. Anaerolineales, Lactobacillales (class Bacilli), SR1, and RF32 (Proteobacteria) were found to be significantly different between RFI levels (Supplementary Table S4). It was interesting to note that there was no difference in Lactobacillales and other taxa between the two extraction methods. The order CW040 (phylum TM7, previously shown in ruminal contents of dairy heifers, [25]) again showed a trend for significance between RFI groups ($$p \leq 0.07$$) (Supplementary Table S4). At the genus level, we detected 83 taxa (after filtering microbes with low abundance and prevalence). Prevotella (average: $28.7\%$), an unclassified member of the order Bacteroidales ($6.7\%$) and the families Succinivibrionaceae ($5.4\%$), Ruminococcaceae ($5\%$) and Lachnospiraceae ($4.8\%$) were the most abundant taxa accounting for ~$50\%$ of all microbial populations. There was no difference between sampling days and the abundance of most taxa was also different between DNA extraction methods (Supplementary Table S5). Most taxa did not show differences between LRFI and HRFI. Interesting exceptions include the propionate producer Succiniclasticum (family Veillonellaceae within the Firmicutes; note that the NCBI taxonomy database catalogs this taxon within the order Negativicutes) that was detected in all 40 samples with an average of $3.5\%$ of all 16S sequences and lower values in LRFI animals ($$p \leq 0.0011$$) without the effect of extraction method. Other taxa that showed differences between LRFI and HRFI include the hemicellulose fermenter [26] BS11 (Bacteroidetes, detected in 36 of 40 samples with an average of $1\%$, higher in LRFI animals with $$p \leq 0.0021$$, and with a significant effect of extraction method), SHD-231 (family Anaerolinaceae, Chloroflexi, detected in 32 of 40 samples with an average of $0.07\%$, lower in LRFI animals with $$p \leq 0.0059$$, and with a significant effect of extraction method), RF32 (Alphaproteobacteria, detected in 33 of 40 samples with an average of $0.1\%$, higher in LRFI animals with $$p \leq 0.0069$$, and with a significant effect of extraction method), and others (Figure 1, Figure 2, Figure 3 and Figure 4, Supplementary Table S5). It is interesting to note that the differences between RFI groups also applied for relative abundances of Succiniclasticum ($$p \leq 0.0022$$, also without the effect of extraction method) and RF32 ($$p \leq 0.0399$$), but not for SHD-231 ($$p \leq 0.0911$$) and BS11 ($$p \leq 0.1062$$), although the residuals were not normally distributed for the last three, because of the presence of outliers. ## 3.5. Alpha Diversity Alpha diversity refers to within-sample diversity and was calculated using four metrics. Samples from method A showed a higher number of features ($$p \leq 0.0002$$, Figure 5), evenness ($$p \leq 0.0005$$), faith ($$p \leq 0.06$$), and Shannon ($p \leq 0.0001$) compared to the results using method B. There was no significant difference between LRFI and HRFI in any of the metrics (Table 4 and Table 5). There was also no significant difference between days of sampling in any of the metrics. ## 3.6. Beta Diversity Beta diversity refers to between-sample diversity and was calculated using four metrics. Using the filtered data, Bray–Curtis distances showed significant differences between DNA extraction methods ($$p \leq 0.001$$) and RFI ($$p \leq 0.002$$) levels (Figure 6). Additional comparisons showed that these differences between RFI levels applied to both method A ($$p \leq 0.023$$) and method B ($$p \leq 0.044$$). Similarly, Jaccard distances showed significant differences between DNA extraction methods ($$p \leq 0.001$$) and RFI ($$p \leq 0.002$$) levels. Additional comparisons showed that these differences between RFI levels applied to both method A ($$p \leq 0.026$$) and method B ($$p \leq 0.039$$). Unweighted UniFrac distances were significantly different between DNA extraction methods ($$p \leq 0.001$$) and almost reached significance for the comparison of LRFI and HRFI ($$p \leq 0.066$$), but additional comparisons between LRFI and HRFI within each method did not show differences ($p \leq 0.1$). Weighted UniFrac distances were also significantly different between methods ($$p \leq 0.001$$) and were not different between LRFI and HRFI ($$p \leq 0.107$$). Additional comparisons revealed a significant difference between LRFI and HRFI using samples from method A ($$p \leq 0.002$$) but not from method B ($$p \leq 0.312$$). ## 3.7. Functional Predictions Using PICRUSt2 Genes related to the superpathway of methylglyoxal degradation ($$p \leq 0.006$$) and polyamine biosynthesis ($$p \leq 0.006$$), and dTDP-N-acetylthomosamine biosynthesis ($$p \leq 0.009$$) were higher in LRFI. Other significant differences were found for genes related to L-lysine fermentation to acetate and butanoate ($p \leq 0.001$), pyruvate fermentation to acetone ($$p \leq 0.001$$), NAD salvage pathway ($$p \leq 0.002$$), arginine, ornithine, and proline interconversion ($$p \leq 0.002$$), glucose oxidative degradation ($$p \leq 0.004$$), succinate fermentation to butanoate ($$p \leq 0.008$$), 1,4-dihydroxy-2-naphthoate biosynthesis ($$p \leq 0.009$$), and phylloquinol biosynthesis ($$p \leq 0.009$$) (all lower in LRFI). ## 3.8. Volatile Fatty Acids The concentration of VFAs in ruminal liquid samples was expressed as mM per L, converted to percentages, and analyzed using Student’s t-test. There was no significant difference in any fatty acid between LRFI and HRFI ($$n = 10$$ in each group, 5 animals with 2 sampling days, $p \leq 0.18$, Table 6). These results were not unexpected because of the well-known high rates of utilization and absorption of VFAs and the ability of multiple microbes to produce and use these compounds in vivo. ## 4. Discussion In this study, the DNA extraction method proved to be key to delineating differences in ruminal microbes. The effect of DNA extraction is well-known in gut microbial ecology but is an important concern in studies of the rumen microbiome because many methods have been used in the literature depending on costs and availability, and even large studies of hundreds of animals have employed only one method [4]. Considering our results and other results about the effect of DNA extraction [10], we suggest, with reservations, employing more than one DNA extraction method and either combining the resulting DNA material to sequence only one sample (less expensive), or sequencing the DNA material from each method. This approach may provide a more comprehensive view of the actual rumen microbiome. Other authors have used only one DNA extraction method but have suggested even more complicated approaches, such as running small trials of several 16S regions to measure the discriminatory power of each region [27]. These and other suggestions may not only be unfeasible for some laboratories but may also prove to be inaccurate. For instance, we invite our colleagues to think about the criteria for objectively deciding which DNA extraction method (or 16S region) is better than others to truly reflect the microbial populations in their natural environment. The taxa that show significant differences between animals with LRFI and HRFI are important for our understanding of feed efficiency, but care must be taken in the source of material for analysis because the liquid and solid fractions differ greatly in their microbial populations [8,28] and both are different from the bacteria attached to the rumen wall (epimural bacteria). Na and Guan [29] reviewed the role of rumen epithelial host–microbe interactions in cattle feed efficiency. In that study, the authors suggest at least three functions of epimural bacteria: tissue recycling, urea hydrolysis, and oxygen scavenging. Species of Ruminococcus, Streptococcus, Prevotella, and other bacteria in ruminal fluid have shown ureolytic activity in vitro [30] but it is challenging to prove this phenomenon in epimural bacteria. Moreover, even small differences in diets can promote differences or patterns of abundance in microbial taxa that could interfere with our understanding of microbial contributions to feed efficiency. For instance, Streptococcus in rumen fluid was correlated with RFI in 85 Braham steers on a low protein diet ($8.8\%$ crude protein) but not in a high protein diet ($13.5\%$ crude protein) [31], but this has not been investigated in epimural bacteria. This issue is not trivial since the choice of liquid samples responds to the ease and speed of taking samples and avoiding invasive fistulas. Succiniclasticum is an interesting taxon that showed significantly lower abundance in high-efficiency, LRFI animals, without a DNA extraction effect. It was first described in 1995 as a small, rod-shaped ruminal bacterium capable of converting succinate to propionate as the sole energy-yielding mechanism [32]. The isolated microbe did not ferment carbohydrates, produce urease, or reduce nitrate, and depended on rumen fluid and yeast extract for good growth [32]. The same author with other colleagues later showed that Schwartzia succinivorans was another ruminal bacterium utilizing succinate as the sole energy source that also depended on rumen fluid and yeast extract [33] but in this study, we did not detect differences in this taxon (Supplementary Table S5). Interestingly, Myer et al. [ 34] also showed that Succiniclasticum was detected at the greatest abundance in low-efficiency, mixed breeds steers, more specifically in animals in the subgroup ADGLow-ADFIHigh. Succiniclasticum was found in greater proportion in the liquid fraction of beef steers on a low-quality forage diet [35] and was mostly undetected on forage diets but was more abundant in a high-grain diet [36]. Another study showed that Succiniclasticum was the most abundant taxon ($9.4\%$, compared to $3.5\%$ in this study) in rumen liquid from slaughtered finishing bulls [37], and Petri et al. [ 38] showed that this and other taxa were particularly prevalent during ruminal acidosis in the rumen epimural microbiota. One study showed that feed restriction was associated with a large reduction in an uncharacterized Succinivibrionaceae species (OTU-S3004), with important differences between the liquid and solid fraction [39], and Luo et al. [ 40] showed that a high-concentrate diet (forage-to-concentrate ratio = 20:80) plus niacin, increased the abundance of Succiniclasticum and other taxa in ruminal fluid from cannulated Jinjiang cattle. Since different fractions of ruminal contents differ greatly in their microbial populations [8,28], future studies should consider looking at both the solid and the liquid fractions, as well as the rumen wall, to better understand the relationship between feed efficiency and this taxon. The question of why Succiniclasticum is lower in highly efficient animals deserves attention. This taxon was present in all 40 samples and at high abundance ($3.5\%$), which suggests that it is a member of the core rumen microbiome of beef cattle (another study suggested that this also applies to other ruminant animal species, see [41]). To explain the lower abundance of Succiniclasticum in efficient animals, Myer et al. [ 34] suggested a phenomenon of resource competition because several members of another propionate producer (unknown member of the family Veillonellaceae) were decreased in the same ADGLow-ADFIHigh subgroup. While this hypothesis is feasible, the rationale behind the belief in a relationship between the abundance of propionate producers and feed efficiency is debatable. For instance, it is also possible to think of a rumen ecosystem with more efficient microbes that require fewer numbers to produce the same or more propionate (in this regard, interesting new concepts such as modularity are now being applied in ruminal microbial ecology, [42]). It is also possible that the oxaloacetate is used in other, anabolic routes, rather than serving for succinate formation [43]. On the other hand, grass-fed cattle have lower propionate concentrations compared to grain-fed cattle [42], but this phenomenon may relate to the digestibility of the feed, ruminal passage rate, and the sampling time after the last meal. Moreover, it is believed that the solids of rumen contents are the “chief” substrate for succinate production [44] but here we did not investigate the presence of microbes in solid contents. The production of propionate from succinate is also linked to H2 concentrations which regulate the thermodynamics of rumen fermentation [43,45] that in turn serve to regulate microbial abundances. Finally, the original observation of dependence on yeast extract [32] is worth looking at. The hemicellulose fermenter BS11 [26] from Bacteroidetes was detected in most samples with an average of $1\%$ and showed a higher abundance in LRFI animals. It is interesting that this taxon was found in higher abundance in conditions where there was a need for more efficiency (enriched in winter diets in Alaskan moose, [26]). Chloroflexi is another interesting taxon that is commonly found in treatment plants where they feed on lysed bacterial cells and degrade complex organic compounds [46]. Representatives of this taxon have also been found in the human and animal gut microbiota [47,48], including ruminal fluid from dairy heifers [49]. In this study, the genus SHD-231 (family Anaerolinaceae, Chloroflexi) was affected by both the DNA extraction method and RFI level (lower in LRFI animals). Members of the family Anaerolinaceae are common in anaerobic digesters [50] but the relevance of this taxon in ruminal microbial ecology and feed efficiency remains unknown. Without measures of gene expression, the predictions suggested by PICRUSt are usually not very informative, but some features may deserve attention. Genes related to the superpathway of methylglyoxal degradation were higher in LRFI. Methylglyoxal is produced by ruminal bacteria in response to low nitrogen levels in the rumen [51]. Methylglyoxal is a highly toxic, alternative end product of glucose fermentation that is produced by bacteria when there is carbohydrate excess, and nitrogen limitation and can kill cells and inhibit protein synthesis [52,53]. A gene involved in methylglyoxal degradation, lactoylglutathione lyase (glo1), has been suggested as a strong candidate biomarker of rumen microbiome in less efficient animals because of its higher relative abundance in a shotgun metagenomic sequencing study [54]. Interestingly, Petri et al. [ 55] showed that carbohydrate metabolism based on the glyoxylate pathway is increased in correlation with Succiniclasticum, adding another valuable piece of information about this microbe. The limitations of this study include the low number of animals at each extreme of RFI values, as larger studies often result in more precise and useful results for the scientific community and the beef cattle industry. The inclusion of more breeds can also be something to consider in future studies because breed is strongly associated with feed efficiency [3]. Also, the diet used in this study is not the diet used in commercial feedlots, and it remains controversial whether the feed efficiency results can be extrapolated. Moreover, this and other studies about feed efficiency in cattle have analyzed the rumen of the microbiota of males only, and if the microbiota can be inherited [4], then the relevance of microbial inheritance is questionable because the outspring destined for beef cattle fattening operations have no contact with the microbiota from the father, or even with their real mothers in females that have been inseminated. Finally, the collection of samples from other anatomical sites (e.g., the lower gut) can provide a more complete understanding of the complex relationship between the gut microbiota and feed efficiency [56,57], and other molecular techniques such as quantitative real-time PCR could help to confirm the relative abundance and variation in microbial taxa. In conclusion, this paper shows a strong effect of the DNA extraction method in describing ruminal microbial communities and the existence of several microbes that could be related to feed efficiency based on RFI calculations, such as Succiniclasticum, and members of Bacteroidetes and Chloroflexi. It is interesting to note that all these groups were not necessarily the most abundant, which fits with the idea that low-abundance microbes display complex interactions that help maintain community stability [58]. The implications of these findings in beef cattle operations include a more precise understanding of the key microbial players affecting feed efficiency, which may result in more useful strategies to help produce more and better meat with the same amount or less feed. ## References 1. Cantalapiedra-Hijar G., Abo-Ismail M., Carstens G.E., Guan L.L., Hegarty R., Kenny D.A., McGee M., Plastow G., Relling A., Ortigues-Marty I.. 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--- title: 'Serotonin-Derived Fluorophore: A Novel Fluorescent Biomaterial for Copper Detection in Urine' authors: - Mariagrazia Lettieri - Simona Scarano - Laura Caponi - Andrea Bertolini - Alessandro Saba - Pasquale Palladino - Maria Minunni journal: Sensors (Basel, Switzerland) year: 2023 pmcid: PMC10055690 doi: 10.3390/s23063030 license: CC BY 4.0 --- # Serotonin-Derived Fluorophore: A Novel Fluorescent Biomaterial for Copper Detection in Urine ## Abstract We took advantage of the fluorescent features of a serotonin-derived fluorophore to develop a simple and low-cost assay for copper in urine. The quenching-based fluorescence assay linearly responds within the concentration range of clinical interest in buffer and in artificial urine, showing very good reproducibility (CVav% = $4\%$ and $3\%$) and low detection limits (16 ± 1 μg L−1 and 23 ± 1 μg L−1). The Cu2+ content was also estimated in human urine samples, showing excellent analytical performances (CVav% = $1\%$), with a limit of detection of 59 ± 3 μg L−1 and a limit of quantification of 97 ± 11 μg L−1, which are below the reference value for a pathological Cu2+ concentration. The assay was successfully validated through mass spectrometry measurements. To the best of our knowledge, this is the first example of copper ion detection exploiting the fluorescence quenching of a biopolymer, offering a potential diagnostic tool for copper-dependent diseases. ## 1. Introduction Copper ion is essential to living systems, regulating many physiological functions, acting as a cofactor of numerous enzymes (e.g., dopamine β-hydroxylase, tyrosinase, and cytochrome c oxidase [1]), contributing to cellular and tissue growth, and working as an antioxidant [2]. The World Health Organization (WHO) recommends an intake of 30 µg per kilogram of body weight per day [3]. However, an excess of this heavy metal leads to protein structure modification, interfering with the exchange of zinc in metalloproteins and compromising most cellular functions [4]. Recently, it was demonstrated that an accumulation of Cu2+ affects the activity of dopaminergic neurotransmitters, potentially leading to neurodegenerative disorders and mental issues, including anxiety, depression, language, and cognitive impairment [5,6,7]. In this context, a few fluorescent methods were developed for Cu2+ quantitative analysis in urine [8,9,10,11,12,13,14]. In this study, for the first time, the fluorescent properties of a novel fluorophore derived from serotonin (SE) self-oxidation and polymerization were exploited for sensitive Cu2+ detection in human urine without matrix pretreatment. SE is a neurotransmitter involved in multiple important physiological processes, such as memory, learning, anxiety, depression, cognition, vomiting, and vasoconstriction [15]. Very few studies describe the formation of SE oligomers [16,17,18,19], including polyserotonin (PSE) obtained using horseradish peroxidase (HRP) [19], and serotonin-based nanoparticles (PSE-NPs) [16,17] as multifunctional material for free radical scavenging, bioelectrical, and biomedical applications. In this paper, the highly emitting SE-derivative fluorophore (SEDF) was obtained by heating the SE monomer at an alkaline pH (Scheme 1a). SEDF was characterized by means of UV-Vis spectroscopy and mass spectrometry. The fluorescent properties of SEDF were outstanding when compared with the fluorescent properties of polymers obtained using other endogenous neurotransmitters, i.e., polydopamine (PDA) and polynorepinephrine (PNE) [20,21]. The analytical performances of the fluorescence detection strategy were firstly evaluated by determining the Cu2+ in buffer and in artificial urine. Then, based on these results, we proceeded to determine the Cu2+ concentration in human urine samples (Scheme 1b) observing the reference values sufficient to detect the early stage of diseases associated with Cu2+ accumulation, such as chronic liver disease, acute hepatitis, and, primarily, Wilson’s disease (WD), a rare inherited disorder that can lead to excess storage of copper in the liver, brain, and other organs [22,23]. Patients who suffer from WD have high urinary copper values between ca. 200 and 400 μg per day [24,25]. Values of Cu2+ higher than 100 µg per day are strongly indicative of this disease [23]. Common diagnostic assays for WD are based on the ceruloplasmin (Cp) concentration and on hepatic and urinary Cu2+ detection [23,26]. However, the Cp test is inaccurate for the estimation of free Cu2+, whereas the evaluation of hepatic copper requires an invasive procedure and, often, the heterogeneous Cu2+ distribution within the liver leads to false negative results. Differently, the experimental procedure here developed for copper estimation would be noninvasive, fast, very simple, green, and low cost if compared to the reference instrumental approach for copper quantification in urine samples, such as inductively coupled plasma mass spectrometry (ICP-MS) [27], here used to validate the fluorescence method. This work aims to propose a new diagnostic tool for copper-dependent diseases, representing, to the best of our knowledge, the first example of copper ions detection exploiting the fluorescence quenching of a biopolymer. ## 2.1. Chemicals Serotonin (C10H12N2O, $98\%$ pure), tris(hydroxymethyl)aminomethane (C4H11NO3, $99\%$ pure), sodium hydroxide (NaOH, ≥$99\%$ pure), hydrochloric acid (HCl, $37\%$ w/w, ≥$99\%$ pure), sodium chloride (NaCl, ≥$99\%$ pure), potassium chloride (KCl, ≥$99\%$ pure), lithium chloride (LiCl, $99\%$ pure), and magnesium chloride (MgCl2, $98\%$ pure) were obtained from Thermo Fisher Scientific (Parma, Italy). Barium chloride (BaCl2, ≥$99\%$ pure), copper sulfate (CuSO4, ≥$99\%$ pure), bismuth (III) nitrate (Bi(NO3)3·5H2O, $98\%$ pure), aluminum chloride (AlCl3, $98\%$ pure), zinc (II) sulphate (ZnSO4·7H2O, $98\%$ pure), cadmium (II) sulfate (CdSO4·8H2O, ≥$99\%$ pure), mercury (II) chloride (HgCl2, $98\%$ pure), silver chloride (AgCl, $99\%$ pure), gold (III) chloride (HAuCl4·3H2O, $99\%$ pure), nickel (II) chloride (NiCl2·6H2O, $99\%$ pure), chloroplatinic acid solution (H2PtCl6, $99\%$ pure), palladium (II) oxide (PdO, $99\%$ pure), iron (III) chloride (FeCl3 × 6H2O, $97\%$ pure), potassium permanganate (KMnO4, $98\%$ pure), potassium chromate (K2Cr2O7, ≥$99\%$ pure), cobalt (II) chloride (CoCl2·6H2O, $98\%$ pure), ruthenium (IV) oxide (RuO2, ≥$99\%$ pure), acetonitrile (C2H3N, ≥$99\%$ pure), DHB (2,5-dihydroxy benzoic acid, C7H6O4, $98\%$ pure), glutamic acid (1 μM, C5H9NO4, $99\%$ pure), glucose (2 μM, C6H12O6, $99\%$ pure), ascorbic acid (2 μM,C6H8O6, ≥$99\%$ pure), hypoxanthine (9 μM, C5H4N4O, ≥$99\%$ pure), uric acid (4.5 μM, C5H4N4O3, ≥$99\%$ pure), creatinine (4 μM, C4H7N3O, ≥$99\%$ pure), urea (0.5 M, CH4N2O, ≥$99\%$ pure), sodium citrate (2 μM, Na3C6H5O7, ≥$99\%$ pure), trifluoroacetic acid (TFA, C2HF3O2, $98\%$ pure), quinine sulfate (C20H26N2O6S, $99\%$ pure, C40H50N4O8S), sulfuric acid (H2SO4 ≥ $98\%$ pure), 1-butanol (C4H10, HPLC purity reagent grade), water (H2O, HPLC purity reagent grade), Triton™ X100 (C14H22O(C2H4O)n, $98\%$ purity reagent grade BioXtra), and tetramethylammonium hydroxide solution (TMAH, 25 wt% in H2O, $99\%$ pure) were purchased from Merck (Milan, Italy). The artificial urine (AU) was from LCTech GmbH (Obertaufkirchen, Germany). Human urine samples were collected from those destined for destruction at the Clinical Pathology Laboratory of the University Hospital in Pisa. Spiked samples were obtained and tested. Fluorescence measurements and ICP-MS experiments on the urine samples were performed in the Clinical Pathology Laboratory of the University Hospital in Pisa. ## 2.2. Instrumentation The temperature-controlled synthesis reaction of SEDF was performed using a Thermomixer comfort (Eppendorf, VWR International, Milan, Italy). The fluorescence experiments were performed with fluorimeter FP-6500 (Jasco, Easton, Pennsylvania, USA) using an excitation and emission wavelength of λex = 350 nm and λem = 450 nm, respectively (emission bandwidth: 10 nm; excitation bandwidth: 5 nm; data pitch: 1 nm; scanning speed: 100 nm min−1; sensitivity: low) and microplate readers Fluoroskan Ascent (Thermo Fisher Scientific, Milan, Italy) by selecting as filter pairs λex = 390 nm and λem = 460 nm, corresponding to the excitation and emission wavelengths, respectively. The absorbance measurements were obtained using a SPECTROstar Nano UV-Visible Spectrophotometer (Ortenberg, Germany) in 1.0 cm quartz cells at 20 °C. The mass spectrometry measurements, acquired in the positive-ion mode, were performed with a MALDI-TOF/TOF Ultraflex III (Bruker Daltonics, Milan, Italy) (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) by mixing the samples in a 1:1 ratio with 20 ng μL−1 DHB (2,5-dihydroxy benzoic acid) as matrix dissolved in $70\%$ of acetonitrile, $25\%$ trifluoroacetic acid, and $5\%$ ethanol. The mass spectra were acquired in the positive-ion mode. ## 2.3.1. Synthesis of Serotonin-Derived Fluorophore A total of 2 g L−1 of serotonin solution was dissolved in a 10 mM TRIS buffer pH 9.00 and heated at 60 °C for 2 h. Subsequently, the sample was left for 10 min at room temperature, then centrifuged 2 times for 10 min at 10,000 rpm. Finally, the supernatant was collected and stored at 4 °C after the addition of 5 mM HCl. ## 2.3.2. Quantum Yield Calculation The fluorescence quantum yield (Q) of the SEDF was estimated using quinine sulfate dissolved in 0.1 M H2SO4 as the fluorescence reference standard of the known quantum yield (QR), as reported by Lakowicz [28]. The quantum yield of the SEDF was calculated using the following equation: Q = QR × I/IR × ODR/OD × n2/n2R where I is the integrated fluorescence intensity, OD is the optical density at the excitation wavelength, and n is the refractive index of the solvent. The subscript R indicates the same parameters for the reference fluorophore, in this case quinine sulfate. The absorbances at the wavelength of excitation (λex = 350 nm) were kept at $A = 0.05$ to avoid the inner filter effect [28], thus ensuring a linear dependence of the fluorescence signals to the sample concentration. ## 2.3.3. Copper Determination via the Fluorescence Quenching-Based Method Stock Cu2+ solutions were prepared in 10 mM Tris pH 9.00 and in artificial urine. The different volumes of copper solution were mixed with solutions of previously synthesized SEDF (see above), immediately recording the fluorescence signal. The decrease in the fluorescence in dependence on the copper concentration was expressed as F0/F, where F0 is the fluorescence of the SEDF without any metal addition, and F is the fluorescence recorded after the copper addition in buffer, artificial urine, and real urine samples. All measurements were performed at 25 °C in quadruplicates, at least. The limit of detection (LOD) and limit of quantification (LOQ) were calculated by applying the following equations: LOD = 3 × SDblank/slope and LOQ = 10 × SDblank/slope, where the standard deviation of the blanks (SDblank) was divided by the slope of the relative calibration plots. ## 2.3.4. ICP-MS Measurements The method here developed was validated using an Agilent Technologies 7900 ICP-MS (Santa Clara, CA, USA) equipped with an ASX-500 Series autosampler and a peristaltic pump for the sample injection. The analyses were performed with the following acquisitions parameters: carrier gas flow rate of 0.80 L min−1 (Ar), aerosol dilution flow rate of 0.50 L min−1 (Ar), plasma gas flow rate of 15 L min−1 (Ar), collision gas flow rate of 4.3 × 10−3 L min−1 (He), RF power of 1550 W, stabilization time of 20 s, peak pattern of 3 point, 3 replicates, 100 sweeps per replicate, and a peristaltic pump speed of 0.1 rps. The system control and data acquisition and processing were carried out using the ICP-MS MassHunter Workstation® software, version 5.1. For the pre-analytical procedure, the Agilent environmental calibration standard mix, including copper (63Cu) at a concentration of 10,000 µg L−1, was diluted in deionized water to prepare an initial calibration curve ranging from 0 to 2500 µg L−1. The calibration points at 0, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, and 250 µg L−1 were obtained by dilution with water:1-butanol = 98.5:1.5 (v/v) added with Triton X100, 10 µL L−1, and Tetramethylammonium hydroxide solution (TMAH $25\%$ in H2O), 100 µL L−1. Each calibration point was added with the appropriate amount of germanium (72Ge) as the internal standard. Two quality control points were added to attest the accuracy of the analysis. The urine samples, previously centrifuged at 1230 rpm for 15 min to remove the sediment, were analyzed after a dilution of 1:100 with water: 1-butanol solution with the appropriate amount of the internal standard. ## 3.1. Synthesis and Characterization of Serotonin-Derived Fluorophore (SEDF) Recently, Jeon et al. reported the synthesis of insoluble polyserotonin nanoparticles from the self-oxidation of the SE monomer heated for 2 h at 60 °C in TRIS buffer at pH 9.00 [16]. Repeating the same procedure, we collected the supernatant solution instead of the precipitated nanoparticles, thus obtaining the serotonin-derived fluorophore (SEDF). The SEDF solution was brown in color with a λmax = 470 nm, indicative of a larger conjugated system with respect to the serotonin monomer showing the spectrum typical of a colorless indole derivative with maximum absorbance values at 280 nm and 300 nm (Figure 1a). Analogously, the fluorescence spectra of the SEDF showed a large emission band of approximately 450 nm, whereas in the same conditions the SE monomer was not fluorescent (Figure 1b). Previous works refer to an SE dimer [29,30], while others describe an oligomer obtained through the electrochemical oxidation of serotonin [31,32,33]. To better understand the nature of the serotonin derivative obtained here, we performed mass spectrometry experiments by MALDI-TOF/TOF instrumentation. The peaks highlighted in Figure 2 (m/z 176.689, 350.978, 502.098, and 672.180) are compatible with monomeric, dimeric, trimeric, and tetrameric structures, possibly obtained upon the linkage through the benzene ring (Scheme 1) [19]. However, the determination of the SEDF structure is out of the scope of this study and requires a much deeper analysis. The influence of the synthetic parameters, such as pH, reaction time, and starting monomer concentration, on the fluorescent properties of the SEDF were tested (Figure 3) and, notably, the best fluorescence performances of the supernatant coincide with the best conditions for the size-controlled synthesis of polyserotonin nanoparticles [16]. In detail, the influence of the pH on the fluorescence of the SEDF was evaluated between 2.00 and 12.00. The solutions of the SEDF when excited at 350 nm showed emission spectra with a λmax of approximately 400 nm at pH 2.00–4.00, 425 nm at pH 6.00–7.00, and 450–475 nm at pH 8.00–12.00 (Figure 3a,b). The intensity of the fluorescence emission was strongly reduced at a pH below 6.00 or above 11.00. Moreover, highly basic conditions led to heterogeneous mixtures with large scattering phenomena (data not shown). As shown in Figure 3b, the buffer at pH 9.00 resulted in the best fluorescence reproducibility with good fluorescence intensity, and this condition was used for the subsequent studies. The influence of the temperature on the fluorescence of the SEDF at pH 9.00 for 2 h was evaluated between 40 °C and 90 °C (Figure 3c,d). The fluorescence emission of the SEDF increased with the temperature, and 60 °C resulted in the temperature of choice, representing a good compromise in terms of the signal intensity and reproducibility (Figure 3d). The influence of the reaction time on the fluorescence of the SEDF was evaluated between 30 min and 5 h at pH 9.00 and 60 °C. The fluorescent spectrum of the SEDF was already visible after 30 min of reaction, reaching the maximum intensity after 1 h (Figure 3e,f). However, the best reproducibility was achieved after 2 h of reaction (Figure 3f). Finally, the influence of SE monomer concentration on the fluorescence of SEDF after 2 h at pH 9.00 and 60 °C (Figure 4a,b) was investigated. The largest fluorescence increase was obtained for SE up to 0.5 g L−1, reaching a plateau at 2 g L−1 SE, which was fixed as the monomer concentration for the following experiments. The synthetic product was stabilized by adding 5 mM of HCl that stopped the oxidation process. The fluorescence intensity in buffer was stable up to 4 h, decreasing to $75\%$ of the starting value after one month (Figure S1). The fluorescence quantum yield (Q) of the SEDF was estimated in water, as reported in the assay protocol, and the obtained value of 0.025 was higher than the Q reported for the PSE obtained enzymatically (0.017) [19] and here calculated also for the PDA (0.019) and PNE (0.008), indicating the good fluorescent properties of such conjugated serotonin. ## 3.2. SEDF Fluorescence Quenching by Metal Ions The effect of several solutions (0.5 mM) of alkaline, alkaline earth (Figure 5a), and transition metals (Figure 5b,c) on the SEDF fluorescence was evaluated, and it was found that only the latter quenched the fluorescence of the SEDF, in particular, Au3+, Cr2O72−, Fe3+, and Cu2+ species (Figure 5c), which is in agreement with literature that reports on the fluorescence quenching of indole derivatives by copper ions and a few other metals [28,34,35]. The Fe3+ exhibited a prominent fluorescence quenching, as previously observed for the fluorescent derivatives of dopamine used to quantify iron [36,37]. The fluorescence quenching was also large for Au3+ and Cr2O72− ions but associated with low data reproducibility, which is also due to the formation of gold nanoparticles. Based on these results, and considering that, to the best of our knowledge, there are no studies on copper ion detection exploiting the fluorescence quenching of a polymer derived by an endogenous molecule, such as SEDF, we decided to apply this method to real urine samples, where the presence of large amounts of copper ions would be indicative of a pathological condition (see below), whereas the concomitant presence of the other quenching ions, in particular Au3+ and Cr2O72−, is insignificant in urine [38,39], and the urinary level of iron in healthy people (0.2 mg L−1, 0.003 mM) is approximately two orders of magnitude below the concentration of Fe3+ here tested (0.5 mM), and one order of magnitude below the minimum amount of Cu2+ here spiked in urine (0.05 mM, vide infra). However, there is also the possible interference of larger urinary levels, as a consequence of other iron-overload diseases, which could be simply minimized by using the proper sequestering buffer, as already reported [40,41]. Moreover, we also evaluated the interference of the main organic compounds in urine on SEDF fluorescence quenching. As shown in Figure 5d, urea, uric acid, creatinine, and citrate, in the physiological concentration ranges [42], did not lead to a fluorescence quenching. This was also true for glucose, hypoxanthine, glutamic acid, and ascorbic acid at the concentration that determines the pathological state. Accordingly, we proceeded to determine the Cu2+ concentration in buffer solution, artificial urine, and human urine samples. ## 3.3. Copper Quantification via Quenching-Based Bioanalytical Assay The SEDF fluorescence spectra and dose-response plots in the buffer and in artificial urine (AU) samples are reported in Figure 6. The fluorescence signal was reported as F0/F, where F0 is the fluorescence intensity of the SEDF, and F is the emission signal of the SEDF after the copper addition. The fluorescence intensity at 450 nm after the Cu2+ addition was plotted versus the Cu2+ concentration in the range of 0.05–0.5 mM, following a linear trend with remarkable analytical performance (Table S1), both in the buffer and in AU samples, as confirmed by the correlation coefficient R2 = 0.990 for both and by the linear fitting equations F0/$F = 4.68$ × [Cu2+] + 0.87 used for the Cu2+ detection in buffer and F0/$F = 3.38$ × [Cu2+] + 0.93 for AU. The excellent assay reproducibility was highlighted by the low variability (CVav% = $4\%$ for buffer and $3\%$ for AU), with an LOD of 16 ± 1 μg L−1 and an LOQ of 54 ± 2 μg L−1 in buffer solution and an LOD of 22 ± 1 μg L−1 and LOQ of 75 ± 3 μg L−1 in AU (Table S1). Notably, these concentrations are below the values of copper associated to the pathological state of Wilson’s diseases [24,25], stimulating the application of this assay to human urine samples. ## 3.4. Copper Detection in Human Urine Samples With the aim to design a bioassay for the urinary Cu2+ quantification for clinical purposes, we transferred our experiments in ELISA-type 96-well microplates, allowing for the immediate and simultaneous analysis of several human urine samples and reducing the turnaround time for the analysis. Another advantage offered by the method here developed is the direct analysis of the urine samples without any pretreatment. The urine samples were preliminarily assayed by ICP-MS to determine the starting copper physiological concentration, resulting in the nM range (Table S3). Among them, the urine sample n. 3 was selected to be used as a calibrator in the real matrix by Cu2+ standard additions (red line in Figure 7), since its Cu2+ level represents a mean value within the series. The CVav% ($1\%$) and the slope reported in Table S3 highlight the high reproducibility and sensitivity of the method, even in the complex real matrix, with an LOD and LOQ of 59 ± 3 μg L−1 and 97 ± 11 μg L−1, respectively. Subsequently, 100 μL of each urine sample was spiked with different known Cu2+ concentrations (0.05 mM; 0.20 mM, and 0.40 mM, see Table 1) and added to 1800 μL of the SEDF solution. These spiked samples were analyzed in parallel by fluorescence and ICP-MS (Table 1 and Figure 7). Despite the complexity and the variability of the analyzed urine samples, the results clearly highlight the very good accuracy of the assay with respect to the calibrator, even if the urine sample n. 2 showed a slightly higher variability, likely due to the marked turbidity and the darker color of the sample. This allows us to foresee the realistic applicability of the method as a diagnostic tool, covering a broad range of Cu2+ concentrations expected in this type of patient. ## 3.5. Assay Performance Compared to Other Fluorescence-Based Method for Copper Detection The quenching-based assay here designed was finally compared to the recently developed fluorescence-based methods for Cu2+ detection in urine [8,9,10,11] (Table S4), i.e., carbon dots (CDs) combined with covalent organic frameworks (COFs), chitosan/L-histidine-stabilized silicon nanoparticles (CS/L-His–SiNPs), nitrogen-doped quantum carbon dots (ND-CQDs), and blue CDs (bCDs) combined with green quantum dots (gQDs). These methods offer better LODs values [8,9] (except in Li et al. and Zhang et al., in which the LOD values were not calculated for human urine samples [10,11]). However, these nanostructures require long and laborious synthetic procedures. Contrarily, herein, SEDF was rapidly (2 h) synthesized and, for the first time, this novel biomaterial was applied for diagnostic purposes. ## 4. Conclusions In this study, the fluorescent properties of a serotonin-derived fluorophore were successfully exploited for urinary Cu2+ determination via a quenching-based bioanalytical method. To the best of our knowledge, this is the first example of a quenching-based assay that exploits the fluorescent properties of a polymer derived by an endogenous molecule. The SEDF’s spectroscopic features were deeply investigated, and different techniques were applied to characterize this novel material, including mass spectrometry confirming its oligomeric state. The experimental conditions were optimized to obtain the highest emission signal and to improve the assay sensitivity and reproducibility. The whole protocol was performed within 2 h in line with clinical requirements. The Cu2+ ions were firstly quantified in buffer and in artificial urine obtaining very low variability, high sensitivity, and low LOD and LOQ values. Then, the assay was applied to estimate the Cu2+ concentration in human urine samples from volunteers and, finally, validated by ICP-MS obtaining very good recovery values and excellent analytical performances in a real matrix, namely, a CVav% of $1\%$, an LOD of 59 ± 3 μg L−1, and an LOQ of 97 ± 11 μg L−1. These values are below the concentrations of urinary Cu2+ that, for example, determine the pathological state of Wilson’s disease (60–240 μg L−1), enabling the applicability of the method for Cu2+ measurement in routine clinical practice without the need of sample pretreatment. ## Figures, Scheme and Table Scheme 1: (a) Putative SEDF structure obtained by SE monomer polymerization upon linkage through the benzene ring after 2 h at 60 °C in 10 mM TRIS buffer pH 9; (b) Cu2+ detection in a 96−well microplate by reading the SEDF fluorescence quenching upon a [Cu2+] increase in urine. Figure 1: (a) UV-Vis spectra* of the SE monomer (red line, right cuvette) and synthetic SEDF (black line, left cuvette) in 10 mM TRIS pH 9.00; (b) fluorescence excitation (red, λem = 450 nm) and emission (black, λex = 350 nm) spectra of serotonin-derivative fluorophore (SEDF, thick line) and serotonin (SE, dashed line).* Figure 2: MALDI-TOF/TOF mass spectrum of the SE-derivative fluorophore (SEDF) obtained by heating 2 g L−1 serotonin at 60 °C for 2 h in 10 mM TRIS at pH 9.00. The m/z values compatible with SE monomer (a), dimer (b), trimer (c), and tetramer (d) molecules are reported. Figure 3: Fluorescence emission (λex = 350 nm, λem = 450 nm) of serotonin-derivative fluorophore (SEDF) obtained by heating 2.0 g L−1 SE monomer. The influence of pH 2–12 (a,b), temperature 40 °C–90 °C (c,d), and reaction time 0.5–5 h (e,f) on the fluorescence emission spectra (left) and intensity at 450 nm (right). The error bars represent the standard deviation ($$n = 4$$). Figure 4: Influence of the serotonin monomer concentrations (0, 0.001, 0.005, 0.010, 0.050, 0.100, 0.200, 0.300, 0.400, 0.500, 1.000, and 2.000 g L−1) on the emission signal of the serotonin−derivative fluorophore synthesized after 2 h at 60 °C in alkaline conditions (pH 9.00). The fluorescence emission spectra upon excitation at 350 nm (a) and corresponding fluorescence emission intensity at 450 nm (b). The error bars represent the standard deviation ($$n = 4$$). Figure 5: Fluorescence of the SEDF in 10 mM TRIS at pH 9.00 after the addition of 0.5 mM alkaline and alkaline earth metals (a), transition metals (b,c), and organic compounds occurring in urine (d). The error bars represent the standard deviation ($$n = 4$$). Figure 6: *Fluorescence spectra and the relative calibration plots of the SEDF after copper ion addition in buffer (a,c) and in artificial urine samples (b,d). The fluorescence was recorded at 450 nm (λex = 350 nm) and reported as a F0/F versus Cu2+ concentration (0, 0.010, 0.050, 0.100, 0.165, 0.250, 0.350, and 0.450 mM), where F0 and F represent the fluorescence intensity of the SEDF before and after the copper addition, respectively. The error bars represent the standard deviation ($$n = 4$$). The analytical parameters are reported in Table S1.* Figure 7: Calibration of Cu2+ in human urine (sample 3, red dots and fitting) and its quantification in the four urine samples, indicated by the different colors (1—green, 2—violet, 3—black, and 4—blue). The fluorescent signal is reported as F0/F at λem = 450 nm (λex = 350 nm) versus the Cu2+ concentration, where F0 and F represent the fluorescence intensity of the SEDF before and after the copper addition, respectively. The error bars represent the standard deviation ($$n = 4$$). 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--- title: 'Body Composition, Nutritional Intake Assessment, and Perceptions about Diet for Health and Performance: An Exploratory Study for Senior Futsal Players' authors: - Sílvia Zambujo Brum - Bela Franchini - Ana Pinto Moura journal: Nutrients year: 2023 pmcid: PMC10055704 doi: 10.3390/nu15061428 license: CC BY 4.0 --- # Body Composition, Nutritional Intake Assessment, and Perceptions about Diet for Health and Performance: An Exploratory Study for Senior Futsal Players ## Abstract This study aims to assess the body composition and nutritional intake of senior male futsal players from the II Futsal Division—Azores Series and explore their individual viewpoints regarding the benefits and barriers of healthy eating and performance. Two groups were identified: those who only completed the sociodemographic questionnaire and the anthropometric data (Group 1, $$n = 48$$), and those who additionally had their food intake assessed using three 24-h dietary recalls and were interviewed (Group 2, $$n = 20$$). Although most of the players have a healthy body composition, those from Group 2 had a significantly higher Body Mass Index, showing that they are under “pre-obesity”, and have a higher percentage of body fat compared to the players from Group 1. Findings from the nutritional intake assessment revealed that players from Group 2 met dietary recommendations for protein, but not for energy and carbohydrate, and they slightly exceeded recommendations for fat. Findings from the interviews revealed that most of these players reported low levels of satisfaction with their sport performance, explained by their deviation from a healthy eating practice in their daily lives. They recognized the need to alter their diets, identifying food items that should be taken and avoided. ## 1. Introduction Futsal, or indoor soccer, is a team sport characterized by its high intensity, with intermittent and acyclic activity. It demands strength levels for shots, starts, quick changes of direction and the ability to repeatedly sprint during the game, which simultaneously requires the aerobic and anaerobic energy systems [1,2,3,4,5]. These characteristics require a high physical, technical and tactical capability. The body composition [6,7], the power of the lower limbs, the oxygen uptake, the range of motion, the repeated sprint ability, anaerobic fitness and passing skill, agility and coordination can contribute to the optimum performance of the futsal player [1,2]. These performance factors can be affected by the athlete’s body composition, as it is well-accepted that, for example, excessive body fat can potentially impair performance in team sports and, conversely, a greater percentage of muscle skeletal mass tends to increase sport performance as it contributes to energy production during high-intensity activities and enhances players’ force production capabilities [8]. According to the systematic review by Spyrou et al. [ 3], futsal players display a low percentage of fat, between professional and semi-professional players. Therefore, these physiological demands involve specific nutritional requirements of each athlete according to their age, sex, team sport and position. Several organizations have been establishing guidelines for nutritional requirements for team sports to better improve sports performance [9], even though nutritional guidelines for futsal were not identified in the literature. Deficiencies in energy can have implications for players’ performance, including a loss of muscle mass, disturbances to immune function, decreased bone mineral density, increased susceptibility to injury and increased prevalence of symptoms of overtraining [10]. Moreover, players who do not achieve energy recommendations or an appropriate balance of macronutrients may find that this does not allow for training adaptations and recovery. However, according to systematic reviews of energy and macronutrient intakes of team sport athletes, athletes tend not to comply with general dietary recommendations, particularly for fat, carbohydrates, and energy intake [9,11]. Thus, it is important to better understand the main barriers that hamper players’ eating habits for better health and performance [12,13,14]. This research aims to assess the anthropometric data and the nutritional intake of senior male futsal players of the II Futsal Division—Azores Series. These results may be useful to determine what could be changed to improve player performance. It also generates data for comparison in future research in the field of athletes’ body composition, nutritional intake, and performance. Additionally, this research aims to explore the players’ perceptions of benefits and barriers to healthy eating, and their influence on performance. These factors should be considered when developing strategies to support senior male futsal players to eat optimally for health and performance at home, work and in sports environments. ## 2.1. Participants and Study Design Participants were senior male futsal players of the National Championship Futsal Division II—Azores Series. In August 2018 (season $\frac{2018}{2019}$), this consisted of eight teams, of which only five were assessed for this research. The teams included 161 players of the Football Association of Angra do Heroísmo, registered in the Portuguese Football Federation [15]. From 161 players, only 68 agreed to participate in this study. The League runs a season from August until May, with a pre-season tournament that starts in the second week of September until the third or fourth week of October, and the league starts usually in the first week of November, going until mid-May. To achieve the proposed research aims, the research was comprised of two different phases that were carried out between August and November 2018. The first phase consisted of the assessment of the futsal players’ body composition and sociodemographic data. In the second phase, futsal players’ nutritional intake was measured, and they were interviewed face-to-face to better understand their eating habits and their previously obtained anthropometric data. The study began with different sessions, one for each team, in which the research aims were clearly explained to the futsal players. In these open/clarification sessions, participants were also invited to further participate in the anthropometric, 24-h dietary recall and interview sessions. They were instructed on how they should correctly complete the self-administered 24-h dietary recall. At the end of these sessions, the printed 24-h dietary recall questionnaires were distributed to the players, and they also filled out a brief sociodemographic questionnaire. Following the Helsinki statement, the study was approved by the Football Association of Angra do Heroísmo, as well as by each of the five different clubs in which the study was carried out, for ethical clearance. Informed consent was obtained from all the futsal players and all data were processed under rigorous anonymity, guaranteeing adequate Data Protection procedures. ## 2.2.1. Phase 1: Futsal Players’ Body Composition and Sociodemographic Data Anthropometric data were collected by the first author, a qualified nutritionist, and were collected following the protocol of The International Society for the Advancement of Kinanthropometry (ISAK) [16]. Following this, height was measured using a Seca® stadiometer (cm, sensitivity 0.1 cm), weight using a Tanita® BC-606 scale (kg, sensitivity 0.1 kg) and thickness of three skinfolds (tricipital, abdominal and thigh) in mm with a Jamar Medical Sammons Preston® skinfold calliper (sensitivity 1 mm). For each athlete, triple anthropometric measurements were obtained and the mean of the three measures was used to calculate the sum of each of the three skinfolds. The anthropometric measurements were carried out in a reserved room in the training halls for greater privacy of the players. The measurements were taken before their training sessions, at the same time, from 7 p.m. to 9 p.m. The brief sociodemographic questionnaire considered the following variables: age, education, and occupation. ## 2.2.2. Phase 2: Futsal Players’ Nutrition Intake Assessment and Perception of Diet and Its Role on Sport Performance The futsal players’ dietary assessments relied on a 24-h dietary recall, for which players were invited to auto-record all food and drinks that they consumed for three different days, corresponding to “training”, “rest” and “competition” periods. This was undertaken because the dietary needs of players vary from day to day, depending on the activity [17]. The 24-h dietary recall applied was adapted from the National Food and Physical Activity Survey (IAN-AF) and contained guidelines for correct completion, prompting the athlete to mark down everything he had ingested, including sips or tests [18]. The questionnaire contained three tables to be filled in on the different days requested (rest, training, and competition), with the following data: date, time of waking up and going to bed, mealtime, meal, place, who they had the meal with, quantity, unit, food/drink, brand and method of cooking. It also contained images based on homemade measures for food quantification, adapted from the IAN-AF Food Quantification Manual, and a possible intake for one day was provided as an example. The athlete was asked not to change their food consumption because they were recording their intake [18]. Face-to-face semi-structured interviews were the method of choice to obtain in-depth descriptions of the diet that players practice (regarding healthy eating habits) and their opinions about how they relate the impact of eating to their sports performance. A semi-structured interview guide of open-ended questions was developed, considering the following dimensions: (i). perceptions of healthy eating, (ii). benefits and barriers towards eating healthily and (iii). the role of food and diet on health and sports performance. The players were interviewed individually by the first author, at a time and place of their choice, usually in sports halls at the end of the day, before the participants’ training. All interviews were audio-recorded. They were anonymously transcribed verbatim and then handed to the participants to be read and validated. ## 2.3. Data Analysis The percentage of fat mass (FM) was calculated according to the improved equation developed by Giro et al. [ 19] for futsal players and the resulting subject health status was classified according to Gallagher et al. ’s method [20]. The Body Mass Index (BMI) was calculated using the Quetelet index: body mass/height2 (kg/m2), according to the WHO classification [21]. The Mann–Whitney U test was used for comparison between player groups, regarding their participation along the different phases of the research. Energy and macronutrient intakes were estimated using the Nutrium® software v.2019 converting food into nutrients, using homemade measures and, in the case of whole packaged food and beverages, the corresponding amount. Additionally, recipes were also considered. The average intake of energy and macronutrients was calculated by assessing the different 24-h recall data, considering that athletes followed a regular weekly schedule with three days of training, three days of rest and one day of competition [20]. Results regarding anthropometric, body composition and nutritional intake data are expressed as mean ± standard deviation (SD), maximum and minimum. The level of significance was set at $p \leq 0.05.$ The IBM SPSS Statistics v. 25.0 software package was used to analyse data. Interviews were analysed using a thematic analysis procedure [22]. Themes were identified inductively, and the content was analysed both in terms of manifest and latent themes, an analytical process that involves a progression from description to interpretation of data [23]. The transcripts of the interviews were processed through a qualitative data analysis software, QSR NVivo 12Pro®, and a comprehensive process of data coding and identification of themes, consistencies and discrepancies across themes was undertaken and explored to provide an in-depth understanding of the texts. Extracts were not exclusively assigned to separate themes and the overlap between themes in the data was used to inform the broader analysis [22]. Once groups of themes were created, constant comparison was used on the internal homogeneity and external heterogeneity of the categories [24]. To support the analysis, calculation of the number of participants who mentioned a particular theme was performed as the best indicator of a prevalent theme [25]. To illustrate the analysis, direct quotes by the participants were transcribed, serving as a description of the topic explored. The quotes used in this text were translated into English. ## 3.1. Sample Characterization A total of 68 futsal players’ body composition and sociodemographic data were collected. All participants were male, the majority were young adults, aged between 17 and 39 years. The sample is mostly composed of single individuals, with a higher percentage of participants having a low education level education and being professionally occupied (Table 1). Two groups of futsal players were identified, considering their participation along the different phases of the research: (i). those who only completed the sociodemographic and the anthropometric data ($$n = 48$$, Group 1—partial participation) and (ii). those who additionally had their food intake assessed using the three 24-h dietary recalls and were later interviewed ($$n = 20$$, Group 2—full participation). ## 3.2. The Players’ Body Composition Altogether, our players have a “normal weight”, as they presented a mean of 24.7 ± 3.5 kg/m2 of BMI and have their FM in a healthy state (Table 2). However, comparison between the two groups confirmed that players from Group 2 have significantly higher BMI (26.8 ± 4.8 kg/m2), showing that they are under the “pre-obesity” nutrition status. Although both groups have a healthy FM, futsal players belonging to Group 2 also have a higher FM ($15.6\%$) compared to the players from Group 1 (Table 2). ## 3.3. Nutritional Intake Assessment Table 3 considers the average intake of energy and macronutrients from the three days from the 20 futsal players who responded to the three 24-h dietary recall questionnaires: three days of training, three days of rest and one day of competition. Overall, players reported consuming a mean total daily energy intake of 2445 kcal, which was a slight underconsumption regarding the ISSN energy requirements for athletes [26]. The carbohydrate intake (3.4 g/kg of body weight), which contributed $41.7\%$ of total energy (TE), fell below the ISSN recommended intakes of 5 to 8 g/kg/day [26]. The average protein intake was 1.9 g/kg of body weight, within the recommendations for athletes according to Jäger et al. [ 28], which can vary between 1.4 g/kg at 2.0 g/kg of body weight. The average lipids intake was 83.9 g, contributing to $30.1\%$ of total energy (TE), slightly exceeding the recommended fat intake [26]. ## 3.4. Interviews: Futsal Perception of Diet and Its Role in Futsal Practice Two broad levels of analysis were identified, which combined many dimensions, cutting across the different topics of discussion: (i). eating habits and (ii). sport performance. All 20 participants from Group 2 discussed these two broad levels in their interviews. The eating habits topic represented the internal forces that influence the players’ eating behaviours. The sports performance level considered the possible relations that our players perceived between diet and futsal practice. ## 3.4.1. Healthy Eating: Perceived Concept, Benefits and Barriers When asking our players to explain what “healthy eating” meant to them, they listed specific food items that should be eaten (e.g., F and V, fish and meat and to a lesser extent, water and dairy products), and those that should be avoided or taken in lesser quantities, namely high energy density foods such as fried foods, fast food, burgers, sugar and foods with added sugar; and to a lesser extent alcoholic drinks, cereals and derivatives and salt. To achieve healthy eating, some of our players also referred to the idea of a balanced and varied diet, allowing them to obtain different nutrients, such as protein, vitamins or carbohydrates “…that are necessary for an all-day” (P90). To a small extent ($$n = 3$$), healthy eating was discussed in terms of cooking techniques, where it was found that grilled foods were considered healthier than fried foods. For our players, food is a crucial aspect of their lives, and $75\%$ of them believed that compared to those who do not practice sports: “… the players should have attentive care of their diet”, P79, because “practicing a sport incites other necessities” P63. Eating healthy allows them to have better wellbeing (“The greatest benefit is the physical well-being that we feel”, P65), and enables them to have better sport performance (“Because an athlete needs to have strength to run or whatever else the sport demands, the athlete might need more nutrients and more…”, P46) and weight control: “There are two benefits that are essential to me. Firstly, weight control: in my practice I feel that weight control boosts my output not only in my athletic endeavours but in my day-to-day life. The other benefit I see is health”, P01. Despite these perceived, strongly linked benefits, only $35\%$ of the 20 players interviewed believed that they eat healthily, $45\%$ of them mentioned that they deviate from healthy eating practices in their daily lives, and $20\%$ of them reported that their healthy eating practices are not always consistent, as they eat healthily “sometimes”, P52, or as said P63: “more or less, … I’ve may days”. Additionally, most of our interviewed players ($$n = 19$$/20) reported that sometimes they eat too much, particularly on the weekends, summer holidays or other special occasions. In this context, our participants identified different related barriers that hamper healthy eating practices (Table 4). Nevertheless, two main barriers emerged among the opinions of our players: lack of availability of healthy foods and lack of time to prepare healthy meals. In fact, as most of our participants were young adults and actively working (Table 1), some of them experienced difficulties: “… to find healthy food to eat at working place”, P59. Others, especially younger players, recognized that their parents or other family members (e.g., sister or grandmother) are often responsible for the food preparation in their households, limiting their influence by not allowing them to participate much in labour-intensive meals: “…my mother makes the food I eat whatever is made…” P05. Others, on the other hand, perceived time pressure as a main barrier, and may think that they do not have time to prepare healthy meals when eating at home, instead seeking out convenience food, as reported by P65: “…many times I have to make some quick food, or I need something convenient to eat”. Unhealthy habits, difficulties in giving up favourite foods (e.g., chocolates, cakes) and economic constraints were also barriers cited by our players (Table 4). ## 3.4.2. Sport Performance: Perceived Body Shape and Strategies to Improve Performance In general, our players were not used to assessing their own body composition, as only $35\%$ of them ($$n = 7$$/20) reported performing assessments. The main parameters assessed by these players, performed at different time intervals (daily, weekly, or monthly), were weight, lean mass, FM, BMI and how their clothes fit. They explained that these measurements allowed them to control the development of their body parameters to better adapt eating and training: “To see if everything is within what I consider to be a standard for an athlete… to evaluate above all a continuity of what I am accomplishing in the gym or with my diet”, P79. The other players ($$n = 13$$/20) reported that they were not used to self-assessing their own body composition, essentially for lack of interest ($$n = 10$$, “I never gained the habit…”, P46; “Sometimes I just don’t remember…”, P65; “I’m not really comfortable with my body right now…”, P84), and to a lesser extent, for inadequate conditions or instruments to do it ($$n = 2$$, “… I don’t have a place to do it…”, P65, “…I don’t have a scale at home…” P18). Additionally, 15 of them recognized low levels of satisfaction with their sport performance, compared to four players who expressed that they are in good shape. As a result, they essentially expressed worries about food and performance associations, in terms of fatigue, weight gain and general malaise (Table 5). Thus, to cope with this dissonance, our players identified different strategies that may improve their sports performance, namely: improving eating habits, training more, losing weight, motivation, resting more and supplementation (Table 6). Typically, the dominant strategy for improving sports performance focuses on how to improve eating habits. For this, they identified specific foods that influence their performance in a negative way, and thus should be avoided (e.g., red meat protein, sugar and foods with added sugar, foods with high energy density, dairy products, alcoholic beverages, and salt). Food items that influence their performance in a positive way, and thus should be eaten, were also identified: meat and fish, cereals and derivatives, F and V and dairy products. Curiously, supplementation was not a hot topic for our players, as only nine out of the twenty participants reported having used supplementation and only one of them considered supplementation as a possibility for improving sports performance (Table 6). ## 4. Discussion As for basic anthropometric measurements, the senior futsal players showed a mean height of 1.74 ± 0.07 m and a mean weight of 75.2 ± 12 kg. These results were similar to the means found in other studies with Portuguese futsal players: 1.75 ± 0.07 m and 71.5 ± 7.6 kg [29], and 1.76 ± 0.08 m and 72.8 ± 1.0 kg [19]. This study also shows that players are globally eutrophic (BMI 24.7 ± 3.5 kg/m2) and have an average FM of 19.1 ± $4.0\%$, which may be considered slightly higher, but within range, when compared with other professional and semi-professional Portuguese futsal players, who present an average FM of 15.8 ± $3.2\%$ [19]. In fact, in futsal practice, lower FM tends to favour maximum player performance, as this sport demands high-intensity and intermittent actions that require high physical effort from the players [1]. The excess FM causes greater energy expenditure, therefore hindering the post-exertion recovery process [30]. When compared with the literature, the players in this study showed higher triceps skinfold (13.3 ± 5.5) compared to Médici et al. and Stubbs-Gutierrez et al. [ 31,32] The thigh skinfold (15.9 ± 6.0) was similar to Médici et al. and Giro et al. [ 19,31] and different from Stubbs-Gutierrez et al. and Bonatto et al. [ 32,33]. The abdominal skinfold (17.7 ± 7.0 mm) was similar to Médici et al. and Bonatto et al. [ 31,33] and higher compared to Giro et al. and Stubbs-Gutierrez et al. [ 19,32]. After dividing the sample by those who fulfilled all the research phases, differences were observed between Group 1 and Group 2. The players from Group 2 had significantly higher BMIs (26.8 ± 4.8 kg/m2, $$p \leq 0.003$$), compared to the players of Group 1, showing that they are under the “pre-obesity” ponderal status. In the same way, significant differences were observed in the abdominal skinfold ($$p \leq 0.035$$), which was greater in Group 2 (20.7 ± 8.0 mm) than in Group 1 (16.5 ± 6.7 mm). Following the results of the majority of studies on sport nutrition [9], our findings show that the total energy and carbohydrate intakes of Group 1 players did not meet sports nutrition recommendations for energy and carbohydrate (i.e., ISSN) [26]. In contrast, they slightly exceeded or met recommendations for fat and protein, respectively (ISSN, ACSM) [26,27]. Considering alcohol consumption, the present study confirms a great dispersion of alcohol intake (ranging from 0–145 g), reinforcing that this is one of the most common dietary mistakes in a sports environment [33]. As a result, negative consequences on sports performance can be found, since the intake of carbohydrates is essential to maintaining and replenishing glycogen stores in futsal players, with the penalty of not being able to maintain the same high physical intensity. As futsal is a sport characterized by intermittent intensity exercises, both aerobic and anaerobic, it is urgent to maintain a high glycaemic level, with the threat of affected motor performance due to failure of the muscular system [9,11]. This is particularly relevant, as findings from the qualitative phase revealed that most of the interviewed players ($79\%$ of participants of Group 2) recognized low levels of satisfaction with their sport performance, which could be explained by their deviation from healthy eating practices in their daily lives. Two main barriers emerged among opinions of our players that may explain why healthy eating practices may not be followed: (i). lack of availability of healthy foods and (ii). lack of time to prepare healthy meals [12,13,14]. According to these players, they are exposed to an environment that hampers their choice of healthy foods. For those who actively work (most of the players, Table 1), the majority of the food service sector keeps focusing more on the hedonic features of consumer demand and less on a nutritious consumer-oriented menu, hampering them in finding healthy foods to eat in their workplaces. At home, especially for younger players, they are not able to influence the meals served, since they eat meals that have already been decided upon without their opinion by other household members. Younger players should have greater involvement in food shopping and meal preparation because the contribution to food tasks—namely preparing meals—is associated with healthier eating. Additionally, people with perceived time pressure may think that they do not have the time to prepare healthy meals and may seek out convenience food solutions (such as eating in restaurants, frozen main courses or ready-made meals) rather than cooking from basic ingredients [34]. Facing a lack of fitness, players from Group 2 stated in the interview sessions that they were not used to assessing their body composition. This reported behaviour could be explained by the fact that our participants are senior players who probably do not have the support of sports dietitians for high-performance nutrition, or of medical staff for monitoring and optimizing fitness, body composition and performance outcomes, as usually happens with players that participate in professional team sports [11]. Additionally, players from Group 2 expressed worries about food and performance associations, resulting from their own experience, in terms of fatigue, weight gain and general malaise. Recognizing the strong link between “eating habits” and “sport performance”, they rely on improving eating habits to improve sports performance [12,13,35] and recognized the need to alter their diets. For this, these players polarized food items as those that should be taken and those that should be avoided, in line with current sports nutrition recommendations, namely the inclusion of fruits, vegetables and lean protein foods, and the avoidance of energy-dense nutrient-poor foods [14]. Moreover, some players described healthy eating as the ingestion of a variety of foods in moderation, in accordance with the Portuguese New Food Guide [36]. In fact, eating healthily enabled them to achieve better wellbeing, better sport performance and weight control. This reinforces that in the sports context, wellbeing is related to the physical health dimension [37] and the negative influence of excess weight on performance [13,14]. This research has some of the following limitations. The sample size enrolled in the 24-h dietary recall for three days is small, hampering the assessment of the dietary intake of the adult senior players population of the II Futsal Division—Azores Series. This information should be taken into consideration to assess whether all the players are complying with the current recommendations; as discussed above, most players of different sports do not comply with the energy and carbohydrate recommendations, which leads to a dietary pattern that hinders their sports performance [9]. As a result, there is a need for future research assessing the dietary intake of futsal players. Although the 24-h dietary recall is considered a valid method and very used for assessing the nutritional intake of the players [38], there is a bias regarding the fact that the estimated portion sizes and intakes could be underestimated or overrated, as they are self-reported, and that it relies on memory [39]. Moreover, as the interviews with the players had a qualitative focus, the findings are not generalizable to the entirety of the II Futsal Division—Azores Series futsal cohort. Furthermore, the perspectives of the multiple stakeholders involved (e.g., coaches, sport directors, players’ family members) were not compared in the present study. Future research is required to explore the factors that influence athletes’ dietary intakes. ## 5. Conclusions Most of the players have a healthy body composition, as they are globally eutrophic and have an average FM, which is considered normal for futsal players. However, most of those who had their nutritional intake assessed had BMI values outside the normal range, had an inadequate distribution of macronutrients, had low energy and carbohydrate intakes and consumed alcohol. 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--- title: The Hypolipidemic Characteristics of a Methanol Extract of Fermented Green Tea and Spore of Eurotium cristatum SXHBTBU1934 in Golden Hamsters authors: - Fuhang Song - Kai Zhang - Jinpeng Yang - Annette S. Wilson - Caixia Chen - Xiuli Xu journal: Nutrients year: 2023 pmcid: PMC10055714 doi: 10.3390/nu15061329 license: CC BY 4.0 --- # The Hypolipidemic Characteristics of a Methanol Extract of Fermented Green Tea and Spore of Eurotium cristatum SXHBTBU1934 in Golden Hamsters ## Abstract Fuzhuan brick tea (FBT), a distinctive Chinese dark tea with the predominant fungus of Eurotium cristatum, offered significant health benefits to Chinese people. In the current study, the in vivo bioactivities of E. cristatum (SXHBTBU1934) fermented green tea and spores of E. cristatum fermented on wheat were investigated, respectively. The methanol extract of fermented green tea and spore of E. cristatum both showed potent lipid-lowering activity in the blood of a high-fat diet induced hyperlipidemia model in golden hamsters and significantly reduced the accumulation of fat granules in the liver. These results indicated that the key active components were produced by E. cristatum. Chemical investigations suggested similar components in the two extracts and led to the identification of a new alkaloid, namely variecolorin P [1], along with four known structurally related compounds, [-]-neoechinulin A [2], neoechinulin D [3], variecolorin G [4], and echinulin [5]. The structure of the new alkaloid was elucidated by HRESIMS, 1H, 13C, and 2D NMR analysis. The lipid-lowering activity of these compounds was evaluated using an oleic acid-induced HepG2 cell line model. Compound 1 significantly reduced the lipid accumulation in the HepG2 cell line with an IC50 value of 0.127 μM. ## 1. Introduction The continuous ingestion of a high-fat diet and decreased energy expenditure cause the excessive accumulation of fat, which lead to chronic diseases such as hepatic steatosis, cardiovascular diseases, and type 2 diabetes [1,2,3]. Obesity has become a widespread public health issue all over the world, most of which comes from the excessive accumulation of fat [4]. To treat obesity and avoid related diseases, increasing amounts of studies focused on exploring a new dietary effective functional food, to slow down fatty liver disease and regulate or improve intestinal microbiota with limited side effects [5]. Fuzhuan brick tea (FBT) is a popular beverage in China due to its unique flavor and variety of health-promoting functions. FBT or metabolites isolated from FBT exhibited a variety of bioactivities against metabolic diseases, including regulating the expression of multiple genes which mediated regulation of blood lipids [6,7,8,9,10,11], preventing obesity [12,13,14], hyperlipidemia [15] and hyperglycemia [10,16,17], ameliorating colitis [18], anti-bacterial [19], anti-oxidation [20], and modulating gut microbiota [21]. Eurotium cristatum, the predominant microorganism in FBT, was considered a probiotic that can alleviate the obesity of rodents induced by a high-fat diet [12]. The theabrownins from dark tea fermented by E. cristatum PW-1 were proved to show hypolipidemic activity in high-fat zebrafish [22]. The active metabolites secreted by E. cristatum may enhance the human immune system [23]. Further investigation of biomolecules from the spore of E. cristatum will promote the development of new functional foods. Traditionally, FBT is cooked with milk or butter, which suggests that both water-soluble and lipid-soluble constituents could be extracted. As methanol is a good organic solvent to extract most of the polar and nonpolar compounds, in this study, it was used for extraction. The lipid-lowering activities of the methanol extract of E. cristatum fermented green tea and spore from E. cristatum fermented wheat were investigated, respectively in a high-fat-diet-induced hyperlipidemia model in golden hamsters. Furthermore, the chemical composition and bioactivity of the extracts were investigated. ## 2.1. Fungal Material, FBT, Spore of E. cristatum, and Extraction Strain SXHBTBU1934 was isolated from FBT, bought from the market of Xixian New District, Shaanxi Province, China, and grown on a potato dextrose agar plate at 28 °C for 7 days. The genomic DNA of SXHBTBU1934 was extracted using DNA quick Plant System (Tiangen). The Internal Transcribed Spacer (ITS) sequence was amplified by using a conventional primer pair of ITS4 (5′-TCCTCCGCTTATTGATATGC-3′) and ITS5 (5′-GGAAGTAAAAGTCGTAACAAGG-3′). PCR products were sequenced by Beijing Qingke Biotechnology Co., Ltd. (Beijing, China). Strain SXHBTBU1934 was identified as E. cristatum based on gene sequence analysis of ITS. The strain was deposited at the Beijing Technology and Business University, Beijing, China. Strain SXHBTBU1934 was inoculated on ten potato dextrose agar plates and cultured for 5 days at 28 °C. In total, 10 mL of distilled water was added to the plate to wash out the spore (3 times), and the spore suspension was combined as seed. In total, 5 mL of the seed was inoculated into twenty 1000 mL conical flasks, each containing 100 g green tea and 30 mL distilled water which was sterilized for 15 min at 121 °C. The inoculated flasks were incubated stationary at 28 °C for 20 days. The fermented tea by SXHBTBU1934 was dried by air. In total, 2 kg of dried fermented tea was soaked in 5 L methanol for 24 h and extracted three times. The organic solvent was evaporated in vacuo at 45 °C to yield a brown crude extract, which was used for HPLC-MS analysis and in vivo experiments. For comparison, 10 g fermented tea by SXHBTBU1934 was added to 100 mL fresh milk and boiled for 2 min. Then, 1 mL methanol was added to a 2 mL aliquot of the milk extract and centrifuged (10,000 rpm) for 3 min. In total, 1 mL of supernatant was filtered by a 0.45 µm filter for HPLC analysis. The green tea without fermentation was also extracted with methanol. Spores of E. cristatum were collected by Bio-tea Co., Ltd. of Shaanxi Biotech Group (Xianyang, China). E. cristatum was fermented on wheat on a large scale and the spores were collected as mentioned above. Briefly, the fermented wheat was washed three times with distilled water and the water was combined to collect the spore suspension, which was then centrifuged at 4000 rpm for 20 min. The supernatant was discarded and the spore pellet was dried by vacuum freeze-drying. ## 2.2. HPLC-MS Analysis of Extracts The HPLC-MS analysis was performed on an Agilent 1200-MSD HPLC-MS system with reversed-phase Agilent Eclipse XDB-C8 column (4.6 mm i.d. × 150 mm, 5 μm, Agilent, Santa Clara, CA, USA) at 28 °C. Mobile phase A consisted of ultrapure water containing $0.1\%$ methanolic acid, and mobile phase B was $100\%$ acetonitrile. Gradient elution was performed by 10–$100\%$ B with a linear change within 15 min, then $100\%$ acetonitrile for 5 min. Absorbance was monitored at 254 nm. ## 2.3. Compound Isolation The crude extract from the spore of E. cristatum was dissolved in MeOH at a concentration of 100 mg/mL. The sample was subjected to preparative HPLC using a reversed-phase Agilent Eclipse XDB-C8 column (9.4 mm i.d. × 250 mm, 5 μm, Agilent, Santa Clara, CA, USA) at 28 °C, with a gradient elution of MeCN in H2O from 40–$80\%$ within 20 min to yield compounds 1–5. ## 2.4. Structure Determination NMR spectra were obtained on a Bruker Avance 500 spectrometer at 25 °C (operating at 500 MHz for 1H-NMR, 125 MHz for 13C-NMR) with residual solvent peaks as references (CDCl3-d6: δH 7.26, δC 77.16). High-resolution electrospray ionization mass spectrometry (HR-ESIMS) measurements were obtained on an Accurate-Mass-Q-TOF LC/MS 6520 instrument (Santa Clara, CA, USA) in the positive ion mode. Optical rotations were measured on a Perkin-Elmer Model 343 polarimeter. Compound structures were elucidated by analyzing 1H NMR, 13C NMR, Heteronuclear Multiple Bond Correlation (HMBC), Heteronuclear Single Quantum Correlation (HSQC), 1H-1H Correlation Spectroscopy (COSY), and comparison with the previously reported data. The structure formula was finally confirmed by HR-ESIMS. ## 2.5. Animal Experimental Design In total, 50 healthy specific pathogen-free male golden hamsters (6 weeks old with a similar body weight of 100 g) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). Hamsters were maintained in a temperature-controlled room (23 °C) with a 12 h:12 h light–dark cycle (lights on from 8:00 to 20:00) and allowed one week acclimatization period [24]. Deionized water was provided continuously for hamsters to drink freely. All procedures using hamsters in this study were conducted by the recommendations in the Guide for the Care and Use of Laboratory Animals of the People’s Republic of China. Golden hamsters were allowed one week of the adaptation period. Then, the hamsters were divided into two groups, which included 10 and 40 hamsters, respectively. In total, 10 hamsters were fed a normal diet as the control group (ND), and 40 hamsters in the experiment group were fed a high-fat diet (HFD) for rapid body weight growth. After 2 weeks, 30 hamsters from the HFD group with higher body weight (weight gain > 8 g) were selected and divided into 3 groups: high-fat diet control group (HFD), high-fat diet with methanol extract of fermented green tea intervention group (MET), and high-fat diet with the spore intervention group (ST). The dried methanol extract of fermented green tea and the dried spore of E. cristatum were both dissolved or suspended in $0.5\%$ boxymethylcellulose sodium (80 mg/mL) for the in vivo experiment. The hamsters in MET and ST groups were intragastrically administered 400 mg of dissolved fermented green tea extract or spore suspension per kg body weight (5 mL/kg body weight), once daily for 2 weeks. The hamsters in the ND and HFD groups were administered $0.5\%$ boxymethylcellulose sodium in the same way. After the last administration, all of the hamsters fasted for 12 h [25,26]. ## 2.6. Serum and Liver Collection/Biochemical Analyses Blood was collected from the retro-orbital plexus after the hamsters fasted for 12 h. The blood samples were centrifuged at 3000 rpm for 5 min to separate the serum for further biochemical analyses. Sections of fresh liver were put into a glass homogenizer and homogenized with 9 times (w/w) of physiological saline. The homogenized liver was centrifuged at 3000 rpm for 5 min and the supernatant was collected for further analysis. Total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels in serum and liver, glucose, insulin, leptin, and free fatty acids (FFA) levels in serum were analyzed using commercial kits following the manufacturer’s instructions. Free fatty acid (FFA) assay kit, mouse insulin ELISA kit, and mouse leptin ELISA kit were purchased from Beijing Solarbio Science & Technology Co, Ltd., Beijing, China. Mouse glucose assay kit, total cholesterol (TC) content assay kit, triglyceride (TG) content assay kit, HDL-cholesterol assay kit, and LDL-cholesterol assay kit were purchased from BioSino Bio-Technology & Science Inc., Beijing, China. ## 2.7. Cell Culture and Treatment Human hepatoma HepG2 cells were obtained from the Beijing Union Medical Cell Resource Center (Basic Medical Cell Center, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences). The cells were cultured in DMEM containing $10\%$ FBS and $1\%$ penicillin–streptomycin and incubated with $5\%$ CO2 at 37 °C. Cells were allowed to grow to approximately $80\%$ confluency and were sub-cultured at a ratio of 1:3 [27]. In this study, to simulate the process of nonalcoholic fatty liver disease in vitro, 0.2 mM oleic acid (OA) was added to the culture medium for 48 h to induce human HepG2 to establish a nonalcoholic fatty liver cell model [28]. ## 2.8. Cell Viability Assay HepG2 cells were seeded into 96-well plates after achieving logarithmic growth and cultured overnight at 37 °C with $5\%$ CO2. After treatment with different concentrations of compounds (dissolved in DMSO) for 24 h, 10 μL CCK-8 solution (Cell Counting Kit-8, Sigma-Aldrich (St. Louis, MO, USA)) was added to each well and the plate was incubated for 2 h in the incubator. Then, the absorbance at 450 nm was measured using a microplate reader. The normal control group was treated with the same amount of DMSO as the experiment group [29]. Cell viability was calculated by: cell viability (%) = (ODdrug/ODcontrol) × $100\%$. ## 2.9. Bodipy Staining After treatment with compounds for 24 h in a 96-well plate, the supernatant of the HepG2 cells was discarded and cells were washed twice with PBS, then the cells were fixed with $4\%$ paraformaldehyde at room temperature for 20 min. After removing the fixative reagent, the cells were washed twice with PBS. HepG2 cells were stained with 100 μL Bodipy (2 μg/mL, Thermo Fisher Scientific (Waltham, MA, USA)) in the dark at 25 °C for 30 min [30]. The fluorescence was measured using a fluorescence plate reader (Ex = 500 nm, Em = 550 nm, POLARstar, BMG Labtech (Offenburg, Germany)) [29]. The inhibition rate was calculated by comparison with the control group which was just treated with DMSO. ## 2.10. Oil Red O Staining HepG2 cells were treated with different concentrations of compounds for 24 h, then were washed twice with PBS and fixed with $4\%$ paraformaldehyde for 15–20 min. The cells were washed with PBS and stained with Oil Red O solution (50 µL per well, Sigma-Aldrich) at 25 °C for 1 h. After washed with PBS for 3–5 times, the lipid droplets and cell morphology were observed by a light microscope and photographed [29]. The area of lipid droplets was statistically compared for IC50 calculation with the control group which was treated with DMSO using Image J software. ## 2.11. Statistical Analysis Statistical analyses were performed using GraphPad Prism 8.0.2 (GraphPad Software, San Diego, CA, USA). The statistical significance of differences between groups was calculated by ordinary one-way ANOVA. The experimental data are presented as mean ± standard deviations (SD). Statistical significance is denoted by * for $p \leq 0.05$, for $p \leq 0.01$, * for $p \leq 0.001$, and ** for $p \leq 0.0001.$ ## 3.1. Spores of E. cristatum Share Similiar Components as E. cristatum Fermented Green Tea As FBT is traditionally cooked with butter or milk, we investigated the difference between the milk and methanol extracts of E. cristatum-fermented green tea, as well as the methanol extract of the spores. The extracts were analyzed by HPLC with a reversed-phase column. As shown in Figure 1, after fermentation by E. cristatum, milk, and methanol extracts showed several low-polarity HPLC peaks between the retention times of 13–18 min compared with the extract of green tea without fermentation. For the methanol extract of spore, more peaks with retention times between 7 and 13 min were found. As this extract shows an abundance of compounds, it was selected for further investigation for compound isolation. ## 3.2. Methanol Extract of Fermented Green Tea, as Well as Spore Suspension Alleviated HFD-Induced Body Weight and Ratio of Liver Weight to Body Weight The methanol and milk extracts of fermented green tea shared similar HPLC peaks, while the methanol extract of spores of E. cristatum contained more different peaks than the other two. Therefore, we selected the methanol extract of fermented green tea and spore for further investigation. The in vivo effects of the methanol extract of fermented green tea and the spore suspension on body weight and liver weight were studied in a high-fat induced hyperlipidemic model in golden hamsters. After 2 weeks of treatment with a high-fat diet, the body weight of golden hamsters in the HFD group increased to 139.3 ± 6.9 g, which showed a significant difference compared with the NC group (128.8 ± 5.6 g). Interestingly, after following 2 weeks of 400 mg/kg of the methanol extract of fermented green tea or spore suspension intervention, body weight was significantly controlled in groups of MET (155.7 ± 13.8 g, $p \leq 0.01$) and ST (158.5 ± 10.4 g, $p \leq 0.05$) (Figure 2A) while the body weight of the high-fat diet control group increased to 176.9 ± 9.2 g. The ratios of liver weight to body weight of hamsters in the MET and ST groups were significantly decreased to 0.0408 ± 0.0035 and 0.0411 ± 0.0031, respectively, $p \leq 0.001$ compared with the HFD group (0.0524 ± 0.0078). These results showed that the methanol extract of fermented green tea and spore suspension of E. cristatum were both able to inhibit the increase of body weight and the ratio of liver weight to body weight in golden hamsters. ## 3.3. Effect of Methanol Extract of Fermented Green Tea and Spore Suspension of E. cristatum on Lipid Levels in Serum and Liver The TC, TG, HDL-C, and LDL-C levels in the serum of golden hamsters for each group are shown in Figure 3. After four weeks of HFD induction, serum lipid levels, including TC, TG, HDL-C, and LDL-C, significantly increased from 4.30 ± 0.50, 1.94 ± 0.60, 2.68 ± 0.21, and 0.74 ± 0.22 mmol/L to 11.45 ± 2.08, 6.18 ± 1.92, 3.87 ± 0.25 and 2.89 ± 0.74 mmol/L, respectively (all groups with $p \leq 0.0001$). Methanol extract from fermented green tea treatment caused a significant decrease in TC (8.89 ± 2.01 mmol/L, $p \leq 0.01$), TG (3.60 ± 1.53 mmol/L, $p \leq 0.01$), and LDL-C (2.02 ± 0.50 mmol/L, $p \leq 0.01$) levels in comparison with HFD control (11.45 ± 2.08 mmol/L, 6.18 ± 1.92 mmol/L, and 2.89 ± 0.74 mmol/L, respectively). Interestingly, treatment with spores of E. cristatum also reduced the TC, TG, and LDL-C levels to 7.86 ± 1.40 mmol/L ($p \leq 0.0001$), 3.60 ± 1.60 mmol/L ($p \leq 0.01$), and 1.59 ± 0.36 mmol/L ($p \leq 0.0001$), respectively. However, neither of the treatments showed a significant effect on serum HDL-C levels. The TC, TG, HDL-C, and LDL-C levels in the livers of golden hamsters for each group were also investigated. As shown in Figure 4, methanol extract of fermented green tea and spore suspension of E. cristatum decreased TC (from 0.64 ± 0.17 to 0.45 ± 0.14 and 0.46 ± 0.14 mmol/100 g liver, respectively, both $p \leq 0.05$) and LDL-C (from 0.57 ± 0.13 to 0.39 ± 0.14 and 0.42 ± 0.10 mmol/100 g liver, with $p \leq 0.01$ and $p \leq 0.05$, respectively) levels in liver compared with the HFD control. Both interventions did not show significant effects on TG and HDL-C levels in the liver of golden hamsters. To detect the effect of these samples on lipids accumulation in the liver, microscopic observations were performed by the hematoxylin and eosin (H&E) staining method. The liver fat granules in groups treated with methanol extract from fermented green tea and spore suspension of E. cristatum were significantly reduced compared with the HFD group (Figure 5). These results indicated that the methanol extract of fermented green tea and spore suspension can both reduce the accumulation of lipid droplets in the hamster’s liver after 2 weeks intervention. ## 3.4. Methanol Extract of Fermented Green Tea and Spore Suspension Improve Diabetes-Related Biomarkers in Serum The effects of these samples on biomarker levels in serum were evaluated. When hamsters were treated with HFD, the levels of serum glucose, insulin, leptin, and FFA were all significantly increased compared with the ND group, indicating that intake of HFD caused damage to the normal liver function of hamsters (Figure 6). After two weeks of intervention with methanol extract from fermented green tea and spore suspension of E. cristatum, the levels of blood glucose, insulin, and FFA were reduced significantly, while the average levels of leptin did not show a significant decrease. ## 3.5. Isolation and Structure Elucidation of Compounds from Spore Both the methanol extract of fermented green tea and spore suspension from fermented wheat reduced the body weight, and serum lipid levels in HFD induced hyperlipidemia model in hamsters. To identify the key components displaying these bioactivities, the chemical components of spore extract were investigated. The spore was extracted by methanol and then purified by preparative HPLC to yield compounds 1–5 (Figure 7). Compound 1 was isolated as a light yellow amorphous powder. The molecular formula of 1 was determined to be C24H29N3O3 based on its high-resolution electrospray ionization mass spectrum (HR-ESIMS) (m/z [M + H]+ 408.2289, calculated for C24H30N3O3, 408.2289), accounting for 12 degrees of unsaturation (Figure S1). The 1H, 13C and HSQC NMR spectra of 1 (Figures S3–S5, Table 1) showed the presence of one doublet methyl group [δH 1.60/δC 21.0 (20-Me)], four singlet methyl groups [δH 1.55/δC 27.4 (18-Me), δH 1.55/δC 27.5 (19-Me), δH 1.51/δC 19.2 (C-24), δH 25.0/δC 140 (C-25)], one sp3 methylene group [δH 3.24 and 2.92/δC 33.7 (C-21)], one sp3 oxygenated methine [δH 3.01/δC 64.4 (C-22)], one 1,2,3-trisubstituted benzene moiety [δC 126.3 (C-3a), δH 7.19 (d, 8.0)/δC 117.8 (C-4), δH 7.08 (dd, 8.0, 8.0)/δC 121.1 (C-5), δH 6.99 (8.0)/δC 122.7 (C-6), δC 122.2 (C-7), δC 134.2 (C-7a)], one terminal double bond [δH 6.07/δC 144.3 (C-16), δH 5.15 and 5.17/δC 112.9 (C-17)], one olefinic double bond [δH 7.22/δC 112.7 (C-8), δC 112.4 (C-9)], one sp3 oxygenated quaternary carbon [δC 60.4 (C-23)], two sp2 quaternary carbons [δC 144.6 (C-2), δC 103.2 (C-3)], as well as two carbonyl carbons [δC 160.1 (C-10), δC 165.8 (C-13)]. All these data indicated that compound 1 owns a skeleton of indole-containing diketopiperazine alkaloids. 1H-1H COSY spectrum (Figure 8 and Figure S6) indicated the moieties of C-4/C-5/C-6, C-12/C-20, and C-21/C-22. In the HMBC spectra, the correlations from H-4 to C-3, C-6, and C-7a, from H-6 to C-7a, from H-NH-1 to C-2, C-3, and C-3a confirmed the indole moiety. The long range HMBC crossing peaks from H3-18 and H3-19 to C-2, C-15, and C-16 revealed the isopentene moiety (C15/C16/C17/C18/C19) and the connection between C-2 and C-15. The methyl diketopiperazine and the linkage between C-3 and C-8 were suggested by the HMBC correlations from H3-20 to C-12 and C-13, from H-NH-14 to C-8, C-9, C-10, C-12, and C-13, and from H-8 to C-2, C-3a, and C-10. The oxygenated side chain was indicated by the HMBC correlations from H3-24 and H3-25 to C-22 and C-23. C-21 attachment to C-7 was revealed by the HMBC correlations from H-6 to C-21 and from H-21 to C-6, C-7, and C-7a. Combined with molecular formula and chemical shifts for C-22 (δC 64.4) and C-23 (δC 60.4), there should be an epoxy bond between C-2 and C-3. Thus, the structure of 1 was determined as shown in Figure 8 and named variecolorin P. The alanine moiety in 1 was determined as L by comparing its optical rotation ([a]D25 -30) with the reported analog variecolorin M ([a]D25 -25) [31]. Four known indole-containing diketopiperazine analogs, [-]-neoechinulin A [2] [32], neoechinulin D [3] [33], variecolorin G [4] [34] and echinulin [5] [35] were also isolated and characterized by comparing their molecular weight and NMR data with those reported in the literature. The HPLC peaks of these four compounds as well as compound 1 are labeled in Figure 1. ## 3.6. Compound 1 Attenuated OA-Induced Lipid Accumulation in HepG2 Cells All of the compounds were assessed using an in vitro cell-based model in which NAFLD was simulated by inducing excessive oleic acid influx into the HepG2 cell line. In the primary screening, with Bodipy staining, compounds 1, 2, and 5 attenuated the accumulation of lipids in HepG2 cells with high cell viability at 100, 100, and 25 μg/mL (Table 2). As a new compound, compound 1 was further tested in the Oil Red O assay and showed a significant inhibitory effect against the lipid accumulation with IC50 of 0.127 μM. ## 4. Discussion People with metabolic syndrome normally have conditions including increased blood pressure, abnormal serum levels of blood sugar, cholesterol or triglyceride, and excess body fat. These conditions increase the risk of heart disease, stroke, and type 2 diabetes [36,37]. FBT, a kind of Chinese traditional beverage, is considered to originate in the 16th century (the Ming Dynasty of China) [38]. Chemical investigation of FBT revealed that there are many specific compounds in FBT produced by E. cristatum compared with other teas [7,8,9,39,40,41]. The in vivo health benefits of water crude extract of FBT have been investigated [14,15,21,42]. Limited studies focused on the in vivo evaluation of organic solvent extracts of FBT and spore of E. cristatum [10,43]. Therefore, we studied the in vivo health benefits of methanol extracts from fermented green tea and spore of E. cristatum on a high-fat-diet-induced hyperlipidemia model in hamsters. Furthermore, the chemical components of the spores were analyzed, isolated, and their bioactivity against hyperlipidemia was evaluated. Our results showed that both the methanol extract from fermented green tea and the spore suspension of E. cristatum successfully alleviated the HFD-induced body weight in hamsters. In particular, the ratios of liver weight to body weight were also reduced after two weeks of treatment with interventions compared to HFD control. A study of water extract of FBT on a high-fat diet (HFD)-induced obese mouse assay reported that FBT dramatically ameliorated obesity [14]. E. cristatum is non-pathogenic and could survive in the mouse intestine. It can prevent HFD-induced obesity in C57BL/6J mice [12], which is similar to our results. Hyperlipidemia, also known as high cholesterol, means an increase of triacylglycerol (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and a decrease of high-density lipoprotein cholesterol (HDL-C) in blood. HDL absorbs cholesterol from blood and transports it to the liver and improves the metabolism of cholesterol. High levels of HDL lower the risk for cardiovascular diseases [44]. LDL, made up of an outer rim of lipoprotein with a cholesterol center, can accumulate in the walls of blood vessels. High levels of LDL raise the incidence of metabolic diseases [45]. Our study revealed that the methanol extract and spore of E. cristatum both can significantly decrease the levels of TC, TG, and LDL-C in serum, and TC and LDL-C in the liver with no significant effects on HDL-C level in serum and liver. A previous study on a hexane extract of FBT [43] reported consistent results with our study. As for the spore of E. cristatum, our study reported that a methanol extract of spores of E. cristatum showed effects on TG, TC, HDL-C, and LDL-C in serum and liver in hamsters. Non-alcoholic fatty liver disease (NAFLD) is a leading cause of cirrhosis and hepatocellular carcinoma induced by unhealthy diets [46], which is mainly characterized by fat deposition in the liver [47,48]. Liu and co-workers evaluated the hypoglycemic effect of the water extract of spore of E. cristatum using a Hep-G2 cell hypoglycemic model and observed the evident increase in glucose consumption [10]. Our results indicated that both the methanol extracts of fermented green tea and spores of E. cristatum reduced the accumulation of fatty granules in the livers of hamsters. FBT was reported to have no significant effect on blood glucose homeostasis in HFD mice, while high doses of FBT significantly reduced blood glucose levels in fasting animals [14]. The current study showed that the methanol extract of fermented green tea and spores reduced the blood glucose in hamsters. High insulin levels led to decreased lipid utilization, which will increase the lipid accumulation and aggravate obesity and hyperlipidemia [49]. Zhou and co-workers reported the levels of insulin were decreased after the compound Fuzhuan brick intervention. Compared with glucose, free fatty acids (FFA, or non-esterified fatty acids, NEFA) account for a greater energy flux through the circulation [50,51]. However, a high concentration of FFA induces hepatic toxicity and ectopic lipid deposition, which plays a key role in the pathogenesis of NAFLD [52,53,54]. This study revealed that FBT and spore inhibited the increase of FFA concentration induced by a high-fat diet for the first time. Leptin, a hormone secreted by adipose tissue, can regulate food intake, increase energy release, inhibit the synthesis of adipocytes, and then reduce body weight by participating in the regulation of glucose, fat, and energy metabolism [55]. A high-fat diet feeding increased leptin and affected the body’s glucose and lipid metabolism [56]. A previous study reported that a water extract of dark tea significantly reduced leptin mRNA in the liver, but no significant difference in fasting blood leptin was observed. Similarly, our results showed that two weeks of administration of FBT and spore did not reduce serum leptin. These results demonstrated that real health beneficial components in FBT are mostly contributed by the predominant fungus of E. cristatum, while raw tea provided nutrition during the fermentation process. The chemical compositions of FBT were greatly changed during the fermentation process by E. cristatum [39,40]. Different classes of metabolites, including polyphenols, phenolic acids, flavones, and their glycosides, terpenoids, alkaloids, steroids, tannins, and fatty acids, have been characterized in FBT [38]. However, limited studies focused on the identification of small molecules with bioactivities related to metabolic syndrome [10,43]. Our study investigated the methanol extract and compounds isolated from the spore of E. cristatum with hypolipidemic activity, which indicated the potential developmental value of the spore for functional food and pharmaceutical prospects of compound variecolorin P. ## 5. Conclusions Our results indicated that the methanol extract of fermented green tea and spores of E. cristatum share similar secondary metabolites. Furthermore, both of them improved the hypolipidemic characteristics and obesity in a golden hamster model. Chemical investigation of spores of E. cristatum led to the identification of a new indole-containing diketopiperazine alkaloid, variecolorin P [1], together with four known analogs, including, [-]-neoechinulin A [2], neoechinulin D [3], variecolorin G [4], and echinulin [5]. Variecolorin P inhibited the accumulation of lipids in HepG2 cells with IC50 of 0.127 μM. Further studies on variecolorin P will reveal the potential value of FBT as a functional food for metabolic diseases. ## References 1. Makri E., Goulas A., Polyzos S.A.. Epidemiology, pathogenesis, diagnosis and emerging treatment of nonalcoholic fatty liver disease*. Arch. Med. Res.* (2021) 52 25-37. DOI: 10.1016/j.arcmed.2020.11.010 2. 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--- title: Development and Stability of a New Formulation of Pentobarbital Suppositories for Paediatric Procedural Sedation authors: - Matthieu Lebrat - Yassine Bouattour - Coralie Gaudet - Mouloud Yessaad - Mireille Jouannet - Mathieu Wasiak - Imen Dhifallah - Eric Beyssac - Ghislain Garrait - Philip Chennell - Valérie Sautou journal: Pharmaceutics year: 2023 pmcid: PMC10055724 doi: 10.3390/pharmaceutics15030755 license: CC BY 4.0 --- # Development and Stability of a New Formulation of Pentobarbital Suppositories for Paediatric Procedural Sedation ## Abstract Pentobarbital is a drug of choice to limit motion in children during paediatric procedural sedations (PPSs). However, despite the rectal route being preferred for infants and children, no pentobarbital suppositories are marketed, and therefore they must be prepared by compounding pharmacies. In this study, two suppository formulations of 30, 40, 50, and 60 mg of pentobarbital sodium were developed using hard-fat Witepsol® W25 either alone (formulation F1) or with oleic acid (formulation F2). The two formulations were subjected to the following tests described in the European Pharmacopoeia: uniformity of dosage units, softening time, resistance to rupture, and disintegration time. The stability of both formulations was also investigated for 41 weeks of storage at 5 ± 3 °C using a stability-indicating liquid chromatography method to quantify pentobarbital sodium and research breakdown product (BP). Although both formulae were compliant to uniformity of dosage, the results were in favour of a faster disintegration of F2 compared to F1 (−$63\%$). On the other hand, F1 was found to be stable after 41 weeks of storage unlike F2 for which several new peaks were detected during the chromatographic analysis, suggesting a shorter stability of only 28 weeks. Both formulae still need to be clinically investigated to confirm their safety and efficiency for PPS. ## 1. Introduction Paediatric Procedural Sedation (PPS) is often required for children who undergo a radiologic imaging exam. Indeed, the patients need to stay motionless during the image acquisition sequence, which can be difficult for paediatric patients. Two pharmacological options are available to obtain the patient’s stationary state: a general anaesthesia or a sedation. The latter option does not require resources, which can be limited, such as the presence of an anaesthesiologist or an operating theatre [1,2,3,4]. Nonetheless, almost all the nondissociative drugs that can be used as sedatives in PPS (such as opioids, benzodiazepines, barbiturates (including pentobarbital), etomidate, and propofol) can lead to general anaesthesia with the loss of airway reflexes, depending on the dosage [2]. Their judicious use by trained health-care providers must therefore be well monitored. In the last century, there was rapid growth of PPS with practitioners increasing in skills, an intensification of outpatient settings, and the widespread ethical imperative of managing patient anxiety [5]. The core purpose of PPS for image acquisition in an outpatient setting is to limit invasive acts; thus, an orally or rectally administered drug is needed. Oral chloral hydrate has been an option for many years, but it induces effects that are unreliable for children older than 3 years old [2] and is a potentially carcinogenetic drug that justifies restricting its use according, for example, to the French drug regulatory agency (ANSM) [6], even if other agencies, such as the EMA or FDA, have not as yet published any reservations concerning its safety. In addition, according to a study by Mason et al. ( $$n = 1316$$ patients), it has a higher incidence of adverse events than pentobarbital in infants during imaging [7]. The rectal (transmucosal) route is particularly convenient for PPS, especially for infants, because it is not invasive, does not pose any choking risks, and does not need to be taste-masked [8]. It also has the advantage of a rapid and more predictable onset than the oral route [9]. Rectally administered drugs, which are described in the literature as being possible to use for PPS, are benzodiazepines, chloral hydrate, ketamine, and barbiturates; however, according to Lam et al. [ 9] in their 2018 systemic review, rectal midazolam (the main benzodiazepine used for PPS) was the least effective of these drugs, and ketamine induced very common adverse effects (hypersalivations and hallucinations, for example). They reported a sedating efficacy of 60–$75\%$ for midazolam in most trials versus 80–$95\%$ for the other drugs. The only other rectal option appears to be pentobarbital, which was the third most used sedative for head computerized tomography in children in 2017 in the US (cohort of $$n = 24$$,418 patients [10]. Apart from the US, pentobarbital in the rectal form is also used in France, but it has not been used in the UK since its withdrawal in the 1960s due to its potential for abuse [11] and therefore is not mentioned in the NICE Sedation in children and young people guideline, updated in 2019 [12]. However, as part of their systematic review of needle-free pharmacological sedation techniques in paediatric patients, de Rover et al. reaffirmed the interest and need for better alternative sedation techniques to the I.V. or I.M. route in the imaging procedure context [13]. Pentobarbital is a barbiturate acting as a positive allosteric modulator of γ-aminobutyric acid (GABA) receptors when it interacts with its binding site, which is distinct from the GABA and benzodiazepine ones. Pentobarbital potentiates the effect of GABA on its receptor and increases the mean open duration of the GABA-A receptor channel. Unlike benzodiazepines, at high doses, it directly activates the chloride channel. It has also been reported that barbiturates block the α-amino-3-hydroxy-5-methylisoxazol-4-propionate (AMPA) and kainite receptors, which are glutamate receptors [14]. Its paediatric dosing for the oral or rectal route is usually of 3–6 mg/kg, maximum 100 mg per dose (for patients younger than 4 years old) and 1.5–3 mg/kg, maximum 100 mg per dose (if older than 4 years old), but it should be avoided in patients with porphyria and may produce paradoxical excitement, which is a common adverse event (1–$10\%$ frequency) [9,15]. Because pentobarbital is not marketed in any dosage form allowing a rectal administration, formulations of pentobarbital (and more specifically of pentobarbital sodium, as the salt form is readily available in pharmaceutical grade), such as those made by hospital pharmacists, are needed. Belo et al. [ 16] recently developed a hydrogel formulation for rectal administration containing pentobarbital sodium at a concentration of 25 mg/mL. However, this formulation presented some disadvantages, such as containing sodium benzoate, which is an excipient with a known effect, especially on hormone levels and on inflammation, and can also have an irritating action on the gastric mucosa [17]. Furthermore, hydrogels need to be stored as stock solutions and then divided into single doses to be given to patients. In addition, they also need a special device to be administered, which could complexify the administration process. On the other hand, suppositories are a well-known and greatly used dosage form, especially for the paediatric population, notably because of their manufacturability and established quality control procedures as well as their ease of administration and possible systemic effects [8]. The use of pentobarbital sodium suppositories has already been described (for the sedation of children undergoing auditory brainstem response testing) [3], but the details of the formulation and its stability are not publicly available. The objective of this study was therefore to present and compare the pharmaceutical formulation and stability properties of two different formulations of pentobarbital sodium suppositories for paediatric procedural sedation. ## 2.1. Chemicals Pharmaceutical pentobarbital sodium powder (CAS 57-33-0, batch 2019100735, exp. $\frac{30}{12}$/2023) and oleic acid (CAS 112-80-1, batch 20002698002, exp. $\frac{31}{10}$/2022) were supplied by Inresa (Bartenheim, France). Witepsol W25 (batch 19090001/A, exp. $\frac{05}{2023}$) was supplied by Cooper (Melun, France). Deionised water was purchased from Fresenius (Sèvres, France). The High-Performance Liquid Chromatography (HPLC)-grade methanol was obtained from Carlo Erba reagents (CAS 67-56-1, Val de Reuil, France). Hydrogen peroxide $30\%$ was supplied by Cooper (CAS 7722-84-1, Melun, France). Potassium dihydrogen phosphate (CAS 7778-77-0; KH2PO4) was purchased from Sigma-Aldrich (St. Louis, MO, USA). HCl (CAS 7647-01-0), NaOH (CAS 1310-73-2), and 1-Octanol (CAS 111-87-5) were purchased from Honeywell via a local vendor (MC2, Clermont-Ferrand, France). ## 2.2. Formula Determination and Preparation All compounds used were of pharmaceutical grade, and the pentobarbital that was weighed, used, and quantified was in its sodium salt form. Two formulations were developed to prepare suppositories with a total mass of 1.1 g, containing 30, 40, 50, and 60 mg of pentobarbital sodium and Witepsol® W25 as a base either alone (formulation F1) or with oleic acid added to solubilize pentobarbital in Witepsol® W25 (formulation F2). The range of strengths (from 30 mg to 60 mg of active ingredient) was chosen according to pentobarbital paediatric dosing (3–6 mg/kg, if <4 years old, max. 100 mg; 1.5–3 mg/kg, if >4 years old, max. 100 mg) to cover different paediatric weights starting approximately at 1 year old. The suppositories were prepared using 1 g single-use plastic moulds, which could contain 1.1 g of Witepsol® W25 when completely filled. The amounts of oleic acid used in F2 were determined by calculating the displacement factor (f) in the Witepsol® W25 base [18] (see supplementary data file S1). For F1, because the formulation contained pentobarbital sodium at a concentration equal to or lower than $5\%$ of the total weight of the suppository, f was considered negligible [19]. Table 1 gives the details of the quantities used for each formulation (per suppository). To obtain the exact quantity of the pentobarbital base, the mass of pentobarbital sodium must be multiplied by 0.911 (as the molar masses of pentobarbital sodium and pentobarbital are 248.25 g/mol and 226.27 g/mol, respectively). The components were weighed to prepare batches of 70 units. For F1, pentobarbital sodium powder was triturated with about half the needed quantity of Witepsol® W25 in a mortar, then transferred into a glass beaker. This mixture was melted for 10 min in a double boiler at 50 °C. In another double boiler, the rest of the Witepsol® W25 was also melted at 50 °C for 10 min; then, it was added to the beaker containing the pentobarbital sodium. The preparation was mixed for 20 min using an agitation rod, with the temperature maintained at 50 °C. For F2, the suppositories were prepared by melting the Witepsol® W25 in a double boiler at 80 °C; then, the pentobarbital sodium powder was slowly added under gentle agitation until the mixture reached a milky state. The oleic acid was then added drop by drop, whilst still mixing the preparation until obtaining a clear yellow solution. Finally, the suppositories were cast in single-use plastic moulds (LGA S.A.S, La-Seyne-Sur-Mer, France) whilst still being stirred regularly. The suppositories were left 45 min at room temperature before being stored in a refrigerator (5 ± 3 °C) for a minimum of 24 h before the analyses were performed. ## 2.3. Characterisation of the Suppositories After the preparation by experienced operators of three batches (each batch containing 70 units) per dosage and per formulation, the suppositories were subjected to the below-mentioned pharmaceutical technical procedures recommended by monograph 1145 Rectal Preparations of the European Pharmacopoeia (Ph. Eur.), with $$n = 3$$ (one from each batch) unless specified otherwise. ## 2.3.1. Uniformity of Dosage Units The analysis of the uniformity of the units in both formulae was performed according to monograph 2.9.40 “Uniformity of dosage units” of the Ph. Eur. on each prepared batch [20]. After sampling 10 units per dosage form and pentobarbital sodium extraction (see Section 2.4.2.2), the amount of pentobarbital sodium in each suppository was determined by measuring the absorbance at 240 nm by UV-visible spectrophotometry using a Jasco V-670 spectrometer (Jasco Corporation, Lisses, France) with a quartz measuring cell. The calibration was validated following the preparation of 3 calibration curves and 18 control points each day for three days. The linearity was obtained for pentobarbital sodium concentrations of 12 to 28 µg/mL. The matrix effect (investigated by comparing calibration curves obtained with and without excipients) was found to be negligible. The equation of the regression curve that was used was $Y = 0.0350471$X + 0.0443862 with Y being the absorbance measured at 240 nm and X the pentobarbital sodium concentration in the aqueous phase after extraction and dilution in deionized water (µg/mL). The determination coefficient R2 was 0.998. The experimental acceptance values (EAVs) of the 10 quantified units were calculated and should not be higher than $15\%$. Suppositories of compliant batches were selected for further testing. In addition, to compare the ease of preparation between F1 and F2, four operators with no previous experience whatsoever in suppository preparation produced (following only the preparation instructions sheet) one batch of the 30 mg dosage of each formula following this order: two operators prepared F1 then F2 batches, and conversely, the other two operators started with F2 then F1, in order to reduce the learning bias. The EAVs for each batch were calculated, and compliance with 2.9.40 monography was assessed. ## 2.3.2. Resistance to Rupture/Hardness The hardness measurements were performed using an SBT apparatus (Erweka, Germany) at room temperature (22 ± 0.5 °C) to evaluate the mechanical resistance of the suppositories to crushing, i.e., to determine the mass at which the suppository breaks or crushes at room temperature [21]. One suppository of each dosage per formula was placed between two jaws applying a mass of 600 g, and a mass of 200 g was added to the rod attached to the upper jaw every minute until the suppository was crushed. The time taken to crush each tested suppository was recorded. This test was adapted from monograph 2.9.24 of the Ph. Eur. [ 22]. ## 2.3.3. Softening Time Determination of Lipophilic Suppositories The softening time was measured following monograph 2.9.22 of the Ph. Eur. ( apparatus A) [23]. Briefly, each tested suppository was introduced by its tip into a glass tube containing 10 mL of water and placed in a water bath equilibrated at 36.5 ± 0.5 °C. A rod was immediately introduced after the suppository introduction. After the cover was put on the tube (this being the start of the time measurements), the time that elapsed until the rod sank down to the bottom of the glass tube and the mark ring reached the upper level of the plastic cover was noted. ## 2.3.4. Disintegration Test The disintegration tests were performed following recommendations adapted from monograph 2.9.2 of the Ph. Eur., using a reciprocating cylinder dissolution apparatus (BIO-DIS reciprocating cylinder apparatus, USP Apparatus 3, Agilent, USA), at 37 °C using 250 mL phosphate buffer solution at pH 6.8 as dissolution medium, at 15 dips per minute during 15 min. The disintegration time was determined visually and confirmed by measuring the concentration of pentobarbital sodium released in the phosphate buffer solution at that time, using the UV-visible spectrophotometric method described in Section 2.3.1. ## 2.4. Stability Study: Design and Analyses The stability of the pentobarbital sodium suppositories was studied for 41 weeks on one batch of 30 mg and one batch of 60 mg suppositories of each formulation conditioned in unopened, single-use plastic moulds. The pentobarbital suppositories were stored in a refrigerator (Liebherr, Bulle, Switzerland) at 5 ± 3 °C monitored every day until analysis. After preparation (day 1) and after 2, 4, 16, 28, and 41 weeks, five units (ten for day 1) of each formulation and dosage form were submitted to a visual inspection, pentobarbital quantification and breakdown products (BPs) research (i.e., looking specifically for products resulting from the degradation of pentobarbital). ## 2.4.1. Visual Inspection The suppositories were opened from their single-use plastic mould into glass test tubes and were visually inspected under daylight and under polarized white light from an inspection station (LV28, Allen and Co., Liverpool, UK). The appearance and colour of the suppositories were noted, and a screening for visible inhomogeneity or colour change was performed. ## 2.4.2.1. Preparation of Pentobarbital Standard Solutions Pentobarbital stock solutions (150 µg/mL) were prepared by accurately weighing 3 mg of pentobarbital sodium. The volume was made up to the mark with deionized water in 20 mL volumetric flask. The solutions were stored in the dark at 5 ± 3 °C for a maximum of 7 days (stability for 7 days confirmed in aqueous solution by Anderson et al. [ 24]). ## 2.4.2.2. Pentobarbital Extraction Method The data concerning the validation of the extraction method are provided in the supplementary data file S1. Each suppository was put into an Erlenmeyer flask and melted using a water bath at 50 °C. Then, 10 mL of 1-Octanol was added to the Erlenmeyer flask and ultrasonicated for 15 min. A total of 20 mL of 0.1 N NaOH solution was then added, and the mixture was homogenized before the Erlenmeyer flasks were then emptied into a separating funnel, previously rinsed with the 0.1 N NaOH solution. The separating funnels were inverted 20 times and left to decant at room temperature. After 24 h, the lower aqueous phase was withdrawn for UV-visible spectrophotometry analysis, after performing appropriate dilution in deionized water (depending on the suppository dosage to be in the range of linearity (see previous Section 2.3.1)). For HPLC analysis, the lower phase was diluted $\frac{1}{10}$th to obtain the target concentration of 150 µg/mL. ## 2.4.2.3. Stability Indicating UV-DAD Chromatographic Method The chromatographic analyses were performed on a Prominence-I LC-2030C 3D (Shimadzu France SAS, Marne La Vallée, France) using LabSolutions® Lite software (5.82 version, Shimadzu France SAS, Marne La Vallée, France) to collect and process the data (e.g., peak time and peak area). The compact system contained an integrated degasser, an analytical pump, a thermostated autosampler, and a thermostated column compartment. Diode array detection from 190 nm to 800 nm was used to assess peak purity and detect breakdown products. Pentobarbital quantification was performed at 214 nm. The chromatographic separation was performed on an EC $\frac{250}{4.6}$ Nucleodur C18 HTec column (250 × 4.6 mm, 5 µm particle size; Macherey-Nagel, Düren, Germany). The pentobarbital content in the suppositories was determined using a stability-indicating HPLC method slightly adapted from a previously published method by Ajemni et al. [ 25]. In brief, the stationary phase (C18) and the mobile phase (0.01 M phosphate buffer pH3 adjusted with HCl (Phase A) and methanol (Phase B), 40:60, v/v) were used, but we switched the isocratic elution to a gradient one (Table 2), at a flow rate of 1.2 mL/min; injection volume was 25 µL. The column oven and rack temperature were set at 40 °C to liquefy the base of our suppositories in case some particles were still present after the liquid–liquid extraction. The matrix effect was evaluated by comparing three calibration curves (slopes and intercepts) obtained from sodium pentobarbital only (pharmaceutical quality) with those containing sodium pentobarbital in the presence of all the excipients. Linearity was initially confirmed by preparing one calibration curve daily for three days using five concentrations of sodium pentobarbital at 10, 100, 200, 300, and 350 μg/mL, diluted in deionized water. The calibration regression curves were weighted by 1/C (C = concentration of pentobarbital) to minimize the weight of extreme points on the prediction of the mathematical model. Each calibration curve should have a determination coefficient R2 equal to or higher than 0.999. Homogeneity of the curves was verified using a Cochran test. ANOVA tests were applied to determine applicability. Each day for three days, six solutions of pentobarbital sodium 150 µg/mL were prepared, analysed, and quantified using a calibration curve. To verify the method’s precision, repeatability was estimated by calculating the mean relative standard deviation (RSD) of intraday analysis, and intermediate precision was evaluated using an RSD of interday analysis. Both RSDs should be less than $5\%$. Specificity was assessed by comparing the UV spectra DAD detector of each analysis to a pure sodium pentobarbital UV spectrum (raw material for pharmaceutical use). Method accuracy was demonstrated by evaluating the recovery of five theoretical concentrations to experimental values found using the mean curve equation, and results should be found within the range of 95–$105\%$. The overall accuracy profile was constructed according to Hubert et al. [ 26,27,28]. In order to exclude potential interference of degradation products with pentobarbital quantification, sodium pentobarbital 150 µg/mL stock solutions were subjected to the following forced degradation conditions: 10.8 N hydrochloric acid for 2, 19, 24, 72, and 192 h at 25 °C and 50 °C; 10.8 N sodium hydroxide for 2, 24, 48, 72, and 192 h at 25 °C and 50 °C; $30\%$ hydrogen peroxide for 7 days at 50 °C; $3\%$ hydrogen peroxide for 7 days at 50 °C and thermal degradation for 7 days at 80 °C. Susceptibility to light was analysed 3 times after solution preparation for up to 7 days using an UVA light (5.83 Mlux/h) in climatic chamber at 25 °C (Binder GmbH, Tuttlingen, Germany). ## 2.4.3. Data Analysis—Acceptability Criteria The stability of pentobarbital sodium suppositories was assessed using the following parameters: visual aspect of the suppositories, pentobarbital concentration, and presence or absence of BPs. The study was conducted following the methodological guidelines adapted from the International Conference on Harmonisation for stability studies [29], notably topic Q1A (R2). A variation of concentration outside the 90–$110\%$ range of the initial pentobarbital quantity (including the limits of a $95\%$ confidence interval of the measures) could be considered as a sign of instability after verification of the dosage content in all units. The presence of BPs and the variation of the physicochemical parameters were also considered a sign of pentobarbital instability. The observed pentobarbital suppositories must be homogenous and of unchanged colour. ## 3.1. Characterisation of the Suppositories All results of the characterization of both formulations are presented in Table 3. We found that all batches were compliant with monograph 2.9.40 (EAV < $15\%$) when prepared by experienced operators. When oleic acid was used (formulation F2), we noticed a reduction (−$63\%$ on average) in the softening time for all dosages, ranging, for example, from 8 min 15 s for formulation F1 to 3 min 20 s for formulation F2 (for the 30 mg dosage). We found that the resistance to rupture of the suppository and the disintegration time were also reduced in F2 (−$80\%$ and −$42\%$, respectively, on average for all dosages). On the other hand, the preparation of suppositories by operators with no experience demonstrated that the success rate for the production of suppositories with F2 was $100\%$ versus $25\%$ for the F1. The production process of suppositories with oleic acid was also found to be simpler. The uniformity of dosage results are presented in Table 4. ## 3.2. Pentobarbital Quantification and Breakdown Product (BP) Research: Method Validation Results The pentobarbital retention time was 7.2 ± 0.2 min (Figure 1). The chromatographic method used was found to be linear for concentrations ranging from 10 to 350 μg/mL. The average weighted regression equation was $Y = 34$,854X + 22,873, where X is the pentobarbital concentration in the aqueous phase after extraction (in μg/mL), and Y is the surface area of the corresponding chromatogram peak. The average determination coefficient R2 of the three calibration curves was 0.999. No matrix effect was detected, as the sodium pentobarbital retention time, the slope, and the intercept of the calibration curve did not change significantly with the matrix. The relative mean trueness bias coefficients were less than $1.5\%$, except for the 100 μg/mL calibration point, for which it was $1.86\%$. The mean repeatability RSD coefficient and mean intermediate precision RSD coefficient were less than $2.91\%$. The accuracy profile constructed with the data showed that the limits of the $95\%$ confidence interval coefficients were all within $5\%$ of the expected value, except for the 300 and 350 μg/mL calibration points, for which the lower range limits were −$5.23\%$ and −$5.52\%$, respectively. The detection limit was theoretically calculated to be 0.5 μg/mL, yet the experimental signal-to-noise ratio at that concentration was 90, thus indicating that a lower detection limit could be reached. The limit of quantification was theoretically calculated and then fixed at 1.5 μg/mL (experimental signal-to-noise ratio of 218) with a mean relative bias of −$9.47\%$. The reference chromatogram of 150 µg/mL sodium pentobarbital at 214 nm is well defined with no visible impurities as shown in Figure 1. After forced degradation (see the detailed results presented in Table 5) in highly stressful conditions, BPs were detected, particularly in alkaline-forced conditions where after 48 h and 7 days at 10.8 N of NaOH at 50 °C, $9.7\%$ and $30.9\%$ of pentobarbital degradation, respectively, was found. No degradation was detected after 8 days of 10.8 N of HCl at 50 °C. No BPs were detected when pentobarbital solutions were exposed to UV-vis light for 7 days at 25 °C or placed in oxidative conditions with $30\%$ H2O2 at 50 °C for 7 days. After 7 days at 80 °C, a loss of $11.2\%$ of pentobarbital was noticed, with four breakdown products detected at 3.3, 11.0, 11.8, and 12.3 min. All BPs were detected with a resolution higher than 1.5 from the pentobarbital peak. The chromatograms obtained from these forced degradations are shown in Figure 2. ## 3.3. Physical Stability All samples stayed homogeneous and white. Their appearance under daylight and polarized light was unchanged during the study for both tested formulations, and there was no visible change of colour or inhomogeneity. ## 3.4. Chemical Stability The statistical pentobarbital quantity distribution of the 10 initial samples of each batch is illustrated below with their box plot in Figure 3. The increased variability of the formulation F1 (without oleic acid) was again confirmed. Throughout the stability study, for the 30 mg pentobarbital suppositories with and without oleic acid conditions, the mean pentobarbital quantities did not vary by more than $7.01\%$ ($95\%$CI: 105.97–$108.05\%$ of the mean initial quantity, at the 16-week time point), as presented in Figure 4A. For the 60 mg pentobarbital suppositories without oleic acid, the mean pentobarbital quantities did vary by $14.96\%$ from the mean initial quantity ($95\%$ CI: 112.89–$117.03\%$) at the 28-week time point, as presented in Figure 4B. For the 60 mg pentobarbital suppositories with oleic acid, the mean pentobarbital quantities did not vary by more than $6.97\%$ ($95\%$CI: 104.88–109.06 of the mean initial quantity), except after 2 weeks of storage for which an isolated decrease in the amount of pentobarbital in the tested suppositories was detected, without the emergence of any BP. The chromatograms showed no sign of previously identified pentobarbital BPs during the length of study for both types of pentobarbital formulation at 5 ± 3 °C (some representative chromatograms are presented in Figure 5). Nonetheless, we can see that on the chromatograms of suppositories with oleic acid, small peaks were detectable at the 41-week time point (Figure 5A,B with retention times between 2.5 and 5.5 min). The peaks do not have retention times corresponding to the detected BPs produced during forced degradation. Throughout the study, the purity of the pentobarbital peak was confirmed by spectral analysis and was $100\%$ (see supplementary data file S1 for an example after 41 weeks of storage). ## 4. Discussion In this work, we studied two formulations of pentobarbital suppositories for potential use in paediatric patients for PPS. The results show that whilst formulation F2 containing oleic acid possessed characteristics that could favour its use in PPS, such as faster softening and disintegration times compared to formulation F1, as well as being easier to produce, the materialisation of unknown compounds over time, especially at the 41-week time point, does raise questions about its stability past 28 weeks, which was not the case for formulation F1. Because oral pentobarbital could have poor palatability with a high refusal or vomiting risk among children, the rectal route (which has been demonstrated to be a useful route to administer pentobarbital [30]) avoids these disadvantages [31,32]. Witepsol® W25 was chosen as the base for these suppositories because of its melting point (between 32 and 35.5 °C) and its high hydroxyl values (between 20 and 50 mg KOH/g) [33], which theoretically could have helped to solubilize pentobarbital. Unfortunately, this was not the case experimentally. In formulation F2, oleic acid was used to improve the pentobarbital solubility in Witepsol® W25. This amphiphilic chemical agent was chosen because of its physicochemical properties: as a fatty acid (which means it is soluble in a lipophilic base), the presence of an acid function allowed a clear solution to be obtained. This could be explained by the transformation of sodium pentobarbital to the molecular state by protonation. Indeed, because oleic acid has a carboxylic acid function (pKa = 5.02), pentobarbital initially in the anionic form present in Witepsol® W25 captures the proton of the carboxylic acid function and thus changes into its molecular form, thus increasing its lipophilicity [34,35]. Furthermore, its presence as a fatty acid in our diet is in favour of its security for paediatric patients [36]. Its use as an excipient in pharmaceuticals and as an emulsifying or solubilizing agent in aerosol products has also been commonly reported [37]. We demonstrated that the use of oleic acid enabled an improvement in the ease of preparation of pentobarbital suppositories, as we found $100\%$ compliance with the 2.9.40 monography of the Ph. Eur. by four operators with no experience whatsoever in preparing suppositories, versus $25\%$ when F1 was prepared by the same operators. However, the preparation of pentobarbital suppositories without oleic acid remains possible but needs increased technical practice. In this study, we found a difference in the physical properties between the formulations of pentobarbital suppositories. In fact, formulation F2 containing oleic acid possessed a lower softening time (less than 3.5 min for F2 vs. about 10 min for F1), lower resistance to rupture (1 kg for F2 vs. 4.5 kg for F1), and an accelerated disintegration time (2.5 to 3 min for F2 vs. 6 to 7 min for F1). All these values are comparable to those of suppositories containing other drugs, such as acetaminophen [38], sulpiride [33], or indomethacin [39]. They are also in favour of a rapid dissolution and thus a rapid action [40] of F2 compared to F1. Sedatives are used in children whenever an examination requires the patient’s immobility. This is particularly the case for image acquisition during magnetic resonance imaging or auditory evoked potentials, for example. The quality of the examination is related to a rapid onset, a moderate duration of action, and a minimisation of adverse effects [3]. For this reason, rectal solutions and hydrogels were developed to shortcut the lag time due to suppository dissolution [16], but suppositories have the advantage of being easy to handle and do not need a device to be administered. Although other formulations containing pentobarbital have been described in the literature, all of them contain at least one ingredient that is not recommended for paediatric patients. For example, the US2538127A patent formula [41] contains propylene glycol monostearate, for which safety of use with infant and children is still debatable [42]. Despite the need for such a product, rectal dosage forms are currently not marketed, and pharmaceutical compounding remains the only and indispensable way to treat patients with pentobarbital using the rectal route. In this work, we used only safe and easily available components to prepare the pentobarbital suppositories. The development of a new pentobarbital sodium suppository drug dosage form is critical although batch sizes are limited (benchtop batch), and the quality must be well defined and evaluated. In addition, the scale up of this formulation could be performed in the case of shared production between different hospitals. Lastly, we demonstrated that we reached a total dissolution of pentobarbital in its base using oleic acid in the F2 formula, which could improve the manufacturability and could enable industrial production if desired. This work therefore presents an easy and theoretically safe solution for the preparation of pentobarbital suppositories for paediatric use. To conduct the stability study, an existing analytical method for pentobarbital identification and quantification [25] was used to assess the physicochemical stability of our pentobarbital suppositories, after being adapted. The choice of the quantification wavelength at 214 nm assured maximum sensitivity with minor influence of the mobile phase and excipients, and the $\frac{1}{10}$th dilution of the extracted aqueous phase before chromatographic analysis also favoured the detection of minute quantities of breakdown products in the pentobarbital suppositories. Pentobarbital solutions were analysed after forced degradation conditions, and BPs were then monitored. The detected BPs had different retention times compared to the ones found in previous studies, such as Ajemni et al. [ 25], which is normal as the liquid chromatography method was modified. The forced degradation results are consistent with previous studies that demonstrated a strong pentobarbital resistance to multiple stress conditions [24,25,43,44]. Nevertheless, some variations were noted in terms of the results found in forced degradation studies. Indeed, Ajemni et al. found a $49.1\%$ pentobarbital degradation after exposition to oxidative stress ($3\%$ H2O2, 50 °C, 48 h), whereas with the same experimental conditions, we found no signs of pentobarbital degradation, even after 7 days of contact. A stronger stress condition was therefore investigated (with H2O2 $30\%$, 50 °C for 7 days), but it too did not induce any pentobarbital degradation (Table 4). This reproducibility limitation could be attributed to variations in protocol (sample preparation) but could be investigated further. Other stress conditions were consistent with our findings. During the stability study, the quantity of pentobarbital in the 30 mg suppositories stayed within the 90–$110\%$ range of the initial pentobarbital quantity, for both formulations. For the 60 mg formulation, it is noteworthy that an important variation of the pentobarbital concentration (in % of T0 quantity) was observed with formulation F1, as seen in Figure 4. Indeed, at several times during the study, the mean quantity of pentobarbital quantified in the tested suppositories was above the 90–$110\%$ range of the initial pentobarbital quantity and could therefore be interpreted as a sign of instability. However, no breakdown products were detected. This could be explained by an inhomogeneity of the initial sampling of the ten units of 60 mg pentobarbital suppositories without oleic acid, especially as the mean quantity in the units analysed at the start of the study was lower than the theoretical value (58.29 mg versus 60 mg). The EAV calculated at day 1 for our batches produced supports this hypothesis of inhomogeneity because it was the highest for this 60 mg pentobarbital series without oleic acid (0.1052). For both formulations and dosage forms, after 41 weeks of storage, all pentobarbital peaks remained pure, as demonstrated by the peak purity analysis of the HPLC data (see supplementary data file S1). The shelf life was evaluated in refrigerated conditions (5 ± 3 °C) to preserve the suppositories’ hardness as the excipients, especially oleic acid, are sensitive to temperature and tend to soften when the temperature increases. Nonetheless, regarding the chromatogram analysis, we observed new peaks for the F2 suppositories, as seen in Figure 5. This could possibly be due to a degradation of the oleic acid because these new peaks possessed retention times that were different to those of the BPs detected during the forced degradation tests and their UV spectra did not present any similarities to that of pentobarbital. Further studies investigating the stability of oleic acid would be necessary to assess and characterize its degradation over time. Without this confirmation, the shelf life of formulation F2 should be limited to 28 weeks (approximatively 7 months). On the other hand, this study demonstrated that formulation F1 did not present any signs of pentobarbital degradation for 41 weeks (approximatively 10 months). Overall, this stability study presents strong data in favour of the physicochemical stability of both formulations of 30 and 60 mg pentobarbital suppositories when conditioned in plastic moulds and stored at refrigerated temperatures. The stability of pentobarbital in aqueous solutions has been studied, mainly for liquid injectable and rectal usage [24,44], but there were no relevant data regarding the stability of solid, suppository forms. Our study allows us to address this data weakness. The most recent rectal form study published was about a rectal hydrogel. The authors reported less than a $5\%$ loss of pentobarbital after 90 days at 2 to 8 °C and 22 to 25 °C and did not mention the detection of any breakdown product [16]. However, clinical investigations of these developed formulae need to be conducted in order to confirm their adequacy for PPS for paediatric patients. As oleic acid is reported to be a penetration enhancer [45], it could amplify and accelerate the sedation caused by pentobarbital. For this reason, a clinical study should be conducted with a pharmacokinetic characterisation in order to confirm the safety and the efficiency of these suppositories. ## 5. 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--- title: 'Novel Biomarkers for Inflammatory Bowel Disease and Colorectal Cancer: An Interplay between Metabolic Dysregulation and Excessive Inflammation' authors: - Mohamed Salla - Jimmy Guo - Harshad Joshi - Marilyn Gordon - Hitesh Dooky - Justine Lai - Samantha Capicio - Heather Armstrong - Rosica Valcheva - Jason R. B. Dyck - Aducio Thiesen - Eytan Wine - Levinus A. Dieleman - Shairaz Baksh journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10055751 doi: 10.3390/ijms24065967 license: CC BY 4.0 --- # Novel Biomarkers for Inflammatory Bowel Disease and Colorectal Cancer: An Interplay between Metabolic Dysregulation and Excessive Inflammation ## Abstract Persistent inflammation can trigger altered epigenetic, inflammatory, and bioenergetic states. Inflammatory bowel disease (IBD) is an idiopathic disease characterized by chronic inflammation of the gastrointestinal tract, with evidence of subsequent metabolic syndrome disorder. Studies have demonstrated that as many as $42\%$ of patients with ulcerative colitis (UC) who are found to have high-grade dysplasia, either already had colorectal cancer (CRC) or develop it within a short time. The presence of low-grade dysplasia is also predictive of CRC. Many signaling pathways are shared among IBD and CRC, including cell survival, cell proliferation, angiogenesis, and inflammatory signaling pathways. Current IBD therapeutics target a small subset of molecular drivers of IBD, with many focused on the inflammatory aspect of the pathways. Thus, there is a great need to identify biomarkers of both IBD and CRC, that can be predictive of therapeutic efficacy, disease severity, and predisposition to CRC. In this study, we explored the changes in biomarkers specific for inflammatory, metabolic, and proliferative pathways, to help determine the relevance to both IBD and CRC. Our analysis demonstrated, for the first time in IBD, the loss of the tumor suppressor protein Ras associated family protein 1A (RASSF1A), via epigenetic changes, the hyperactivation of the obligate kinase of the NOD2 pathogen recognition receptor (receptor interacting protein kinase 2 [RIPK2]), the loss of activation of the metabolic kinase, AMP activated protein kinase (AMPKα1), and, lastly, the activation of the transcription factor and kinase Yes associated protein (YAP) kinase, that is involved in proliferation of cells. The expression and activation status of these four elements are mirrored in IBD, CRC, and IBD-CRC patients and, importantly, in matched blood and biopsy samples. The latter would suggest that biomarker analysis can be performed non-invasively, to understand IBD and CRC, without the need for invasive and costly endoscopic analysis. This study, for the first time, illustrates the need to understand IBD or CRC beyond an inflammatory perspective and the value of therapeutics directed to reset altered proliferative and metabolic states within the colon. The use of such therapeutics may truly drive patients into remission. ## 1. Introduction Inflammation is a complex defense mechanism against biological and chemical insults. Although beneficial, persistent inflammation can cause cellular damage, resulting in many diseases, including IBD and CRC. Inflammation is an essential immune response that involves controlled activation of NFκB and production of cytokines, promoting healing of damaged epithelial cells and defense against pathogenic agents. However, chronic inflammation of the gastrointestinal (GI) tract occurs with associated symptoms of diarrhea, abdominal pain, and weight loss. As many as 1 in 150 Canadians are diagnosed with IBD, with the prevalence for IBD in the world at 1 case per 250 persons, annually [1,2,3]. Several IBD patients will develop colorectal cancer (CRC) in less than 20 years. IBD includes ulcerative colitis (UC) and Crohn’s disease (CD), but the root cause of IBD is unknown. Current research suggests a combination of genetic predisposition, enhanced autophagic response, epigenetic modulation, and microbiome disruptions may be involved [4]. Current IBD medications include aminosalicylic acids, disease-modifying antirheumatic drugs (DMARDs), steroids, biologic medications, and novel small molecule drugs [5,6]. Drivers of IBD include a complex interplay of environmental, dietary, genetic, and microbial determinants, that will initiate and perpetuate inflammation of the gastrointestinal tract [7]. It can occur at any age, from early childhood to adulthood, and can have life-long complications. It presents with diarrhea (often bloody), abdominal cramping, nausea, and overall discomfort. Severe cases will lead to hemi- or total colectomy of part or all of the colon, due to colonic erosion caused by enhanced inflammation. Reasons for surgical resection are many: some include fistulizing, stricturing disease, perforation, toxic mega-colon, and severe inflammation not responding to medical therapy [8]. Therapeutic intervention in treating IBD has greatly benefited from the understanding of the interplay of the innate and adaptive immune systems, identifying novel biomarkers of disease pathogenesis, the mapping of genetic factors predisposing individuals to IBD, and to the realization that our microbiome is a major determining factor of disease severity. Numerous genes have been identified as initiating or driving the pathogenesis of IBD and thus influencing intestinal homeostasis [9,10]. These genes include, but are not limited to, cytokine factors (TNF-α, IL-10), cytokine receptors (IL-17R and IL-23R), transcription factors (NFκB and JAK/STAT pathway), kinases (PTPN22, Tyk2, RIPK2), apoptotic elements (CARD9 and caspase 11), and elements involved in autophagic signaling (ATG16L, IRGM, NOD2) [11]. Innate immunity provides a first-line of defense against not only invading microbial insults, but also tissue damage, and functions to activate tissue repair, inflammation, and microbial clearance. The surface of the GI tract is covered by a single layer of epithelial cells that functions as a physical barrier to the microflora found in the lumen, and is continually being stimulated by the natural microflora in the gut, and thus can develop tolerance to some insults. It can be disturbed/damaged during the pathogenesis of inflammatory disorders such as IBD [12,13,14]. Active IBD is characterized by pronounced infiltration of the lamina propria with innate immunity cells (macrophages, dendritic, and natural killer cells), as well as a later phase of infiltration of adaptive immune cells (B and T lymphocytes) stimulating the production of T regulatory cells (Treg), Th1, Th2, and Th17 cytokines [14,15,16]. Following inflammatory directed injury, the intestinal epithelial cell layer integrity needs to be re-established in a timely manner by the process of epithelial restitution (or resealing of the epithelial barrier), wound healing, and/or increased epithelial proliferation [17,18,19]. This will avoid a direct exposure of the lamina propria immune cells to the intestinal microflora and activation of an unnecessary inflammatory response [20]. There were more than two million new CRC cases diagnosed, and about one million CRC-related deaths in 2020, worldwide, representing $10\%$ of the global cancer incidence and cancer related deaths. Although total cases of CRC have been declining worldwide, at a rate of $3\%$ per year since 1990, it is surprising that there is an increase in CRC, by more than $2\%$ per year since 1992, in individuals below 50 years of age. Furthermore, it is estimated that about $40\%$ of CRC patients will die from their disease each year [21]. At diagnosis, more than $20\%$ of CRC patients already have established metastases [4,22,23] and it is known that CRC can spread to common metastatic sites such as lymph nodes, liver, lungs, and peritoneum. The majority of patients are asymptomatic during early-stage CRC, when diagnosed as a result of screening. Thus, symptomatic presentation usually reflects relatively advanced CRC. Colonoscopy is the most accurate and versatile diagnostic test for CRC, and treatment options for CRC patients include radiation therapy, surgical removal of the tumor followed by adjuvant chemotherapy, and then targeted therapy using antibody-based therapies or small molecule inhibitors. Most CRC tumors arise within pre-existing adenomas, which harbor some of the genetic fingerprints of malignant lesions. Appearance of malignant lesions can take 10–15 years to arise (a clinical “remission period”), giving clinicians a window of opportunity to screen and subsequently remove these premalignant or early malignant lesions [24]. Thus, there is a need to better understand the inflammatory mediators/molecular drivers involved in promoting the pathogenesis of IBD-related CRC. In this study, we searched for biomarkers of IBD and CRC that also appeared in IBD-CRC case study patients. In addition, we explored the use of several animal models susceptible to inflammation injury, including a knockout of the tumor suppressor protein, Ras association domain family protein 1A (RASSF1A). The RASSF family of proteins contains ten related family members [25,26,27]. 1A is a tumor suppressor gene epigenetically silenced in cancer, without epigenetic loss of the other isoforms of RASSF1. RASSF6 and 8 may be involved in modulating NFκB by unknown mechanisms [28,29]. Direct association with K-Ras has been only observed for RASSF2, 4, 5A, 6, and 9 [30,31,32]. Rassf1a−/− mice are viable, fertile, and retain expression of isoform 1C and other RASSF gene family members. They have increased tumor incidence by 12–16 months of age (especially in the breast, lung, gastrointestinal tract, and immune system, e.g., B-cell related lymphomas) and develop tumors in response to chemical carcinogens [33,34]. Beyond six months, we have observed spontaneous colitis-like phenotype in Rassf1a−/− mice, that was accompanied by increased cytokine production (unpublished observations), indicating a possible role for 1A in regulating inflammation. Several reports indicate a role for 1A in mitosis, linked to its co-localization on microtubules, influencing the anaphase-promoting-complex [35,36,37,38], and links to centrosomes/spindle body during mitosis [38,39]. 1A is epigenetically silenced in IBD patients [40], which undermines its ability to restrict NFκB activation and prevent uncontrolled intestinal inflammation [41]. Our analysis revealed the importance of four biomarkers that were involved in tumor suppression, proliferation, inflammation, and metabolism within the colon, to suggest the need to find therapeutics to IBD that will reset abnormal inflammation, metabolism, proliferation, and epigenetic silencing, in order to drive patients into full remission. ## 2.1. The Tumor Suppressor Gene, RASSF1A, Is Epigenetically Silenced in IBD For this study, we collected over 500 blood samples and >300 biopsy samples from pediatric, adult IBD, and non-IBD/control patients, by attending regular clinic appointments at the University of Alberta hospitals. Patient demographics and disease sub-types are outlined in Table 1 and Table 2 of the Section 4.2. We explored both epigenetic silencing of RASSF1A in patients with IBD, and if the intracellular NOD2 pathogen recognition receptor/RIPK2 pathway may be driving the inflammation in the colon of IBD patients. Previously, we utilized two pyrosequencing assays, covering 32 CpGs in the RASSF1A promoter, to explore the epigenetic silencing of RASSF1A in numerous cancers, including CRC [42]. That analysis revealed hotspots for epigenetic silencing between CpG1 and 8, that were also observed in a CRC patient that had liver metastasis [42], to suggest similar points of origin (the “CRC epigenetic signature”). Interestingly, analysis in peripheral blood of non-IBD, UC, and CD patients revealed a similar CRC epigenetic signature, with an epigenetic hotspot between CpG1 and CpG8 (Figure 1A, circled), to suggest similarity to CRC. In fact, we should rename it, “IBD-CRC epigenetic signature” now that we have isolated a similar hotspot in IBD. These analyses would then suggest a loss of expression of RASSF1A in tissue sections from IBD patients. We evaluated the expression of RASSF1A in intestinal biopsies from non-IBD, UC, and CD patients by immunohistochemical staining. Using an in-house developed monoclonal antibody to RASSF1A, we observed robust staining of descending colon sections from non-IBD patients, but reduced or no staining in UC or CD patients (Figure 1B), whereby the RASSF1A positive staining is reduced by >$50\%$ in most IBD patients (Figure 1B). This supports our epigenetic data to confirm the significant loss of RASSF1A, a tumor suppressor gene, in the descending colon of IBD patients. RASSF1A epigenetic inactivation can thus be observed in both cancers and inflammatory diseases such as IBD, and may be a robust molecular driver of IBD-related CRC. ## 2.2. The NOD2/RIPK2 Intracellular Pathogen Pathway Is a Molecular Driver of Inflammation in IBD Inflammation is characterized by the hyperactivation of transcription factors (such as NFκB) through multiple pathways (both classical and non-classical), that includes TNF-R1 and the pathogen recognition pathway involving Toll like receptors (TLR) [43,44,45,46,47] and NOD2, an intracellular pattern recognition receptor. NOD2 is mainly stimulated by bacterial products containing muramyl dipeptide (MDP), and requires the obligate kinase RIPK2, to promote an autophagic response or a non-classical NFκB activation response [48]. Mice with genetic disruption of the Nod/Ripk2, have a dysbiotic intestinal flora, resulting in altered susceptibility to intestinal inflammation [49], as well as increased joint inflammation [50]. In addition, the loss of Ripk2 has been demonstrated to result in the inability of cells to carry out mitophagy, leading to enhanced mitochondrial production of superoxide/reactive oxygen species, and accumulation of damaged mitochondria, that will trigger a caspase-11-dependent inflammasome activation [51,52]. We have previously published that, similar to most solid cancers, robust methylation of RASSF1A in inflammatory breast cancer (IBC) patients correlates with loss of expression [53]. Furthermore, we can observe a positive correlation between active RIPK2 (as monitored by RIPK2 pY474 antibody) and methylation status of RASSF1A in IBC tumor samples [53], to suggest expression loss of RASSF1A with increased levels of RASSF1A CpG methylation, and increased activation of active RIPK2. RIPK2 is controlled by complex posttranslational modification events, including autophosphorylation at several sites, including phosphorylation on tyrosine (Y) at position 474 and serine (S) 176 [54,55,56]. Phospho(p)-S176 RIPK2 antibodies do not perform well in detecting active RIPK2 on tissue sections, and thus we created our own RIPK2 antibody to detect the tyrosine (Y) phosphorylation at amino acid 474, that promotes an active RIPK2 (our RIPK2 pY474 antibody). This has been proven to work in both human and animal tissues. As obtained for IBC, IBD patients also had robust detection of active RIPK2 (Figure 2A), at >2 fold, in biopsy sections from IBD patients vs. non-IBD patients, suggesting its importance in driving colonic inflammation in IBD patients. Furthermore, elevated inflammation was confirmed in these tissue sections by analysis of myeloperoxidase (MPO) activity in the descending colon tissue samples from IBD patients (Figure 2B), to reveal similar fold changes in MPO activity in IBD when compared to non-IBD. ## 2.3. Both AMPKα1 Activity and Insulin Production Are Altered Metabolic Parameters in IBD Patients Metabolic pathways are influenced by numerous factors, ranging from stress, exercise, diet, genetics, and the gut microbiota [57]. Cancer arises due to unique reprograming of cells, to switch from aerobic respiration to rely more on glycolysis (known as the Warburg Effect) [58,59,60,61]. This is needed due to the hypoxic environment that most cancer cells find themselves in and the need to survive. Metabolic distress syndrome in IBD patients is understudied, but the AMP activated protein kinase (AMPK) [62,63,64,65,66] and mTOR pathway components have been suggested to be important [67]. In addition, the gut microbiota can control fatty-acid oxidation in the host, via suppression of the AMPKs. Interestingly, a common diabetic drug targeting AMPK, metformin, has been documented to reduce the incidence of CRC in diabetics [63,68]. Thus, we are beginning to realize that the link between metabolism, IBD-CRC, and the microbiome, may significantly contribute to disease progression towards malignancy. AMPK is a heterotrimeric fuel-sensing enzyme, that is activated by decreases in a cell’s energy state. When activated, it initiates metabolic and genetic events, that restore ATP levels by stimulating processes that generate ATP (e.g., fatty acid oxidation) and inhibiting others that consume ATP, but are not acutely required for survival (e.g., triglyceride and protein synthesis, cell proliferation [69]). When ATP levels fall, there is a corresponding increase in intracellular AMP levels, and AMPK is activated both allosterically by AMP and by phosphorylation of the catalytic subunit (α) on threonine (T) 172 by an upstream AMPK-kinase, LKB1. We carried out analysis of active AMPK by using the specific phospho-antibody, (AMPKα1) pT172. Surprisingly, we observed a significant > 50–$70\%$ loss of detection in IBD patients of active AMPK in the descending colon sections, shown in Figure 3A, to suggest an importance of AMPK to the metabolic stability of the colon. Equally as surprising, we detected insulin production in non-IBD colonic sections of IBD patients, that is almost completely lost in both CD and UC patients (Figure 3B). Insulin production in the colon has been reviewed in 2001 [70,71], suggesting that gut insulin may be involved in the response of the gastrointestinal tract to food. Furthermore, it was reported in 2019, that insulin may be able to promote cell death and transport in colon cancer [72]. Our analysis clearly revealed altered metabolism in the colon of IBD patients, related to the loss of active AMPK and insulin production, two outcomes that will significantly contribute to the metabolic distress syndrome in IBD patients and affect their ability to recover. We subsequently explored the ability to regain a normal metabolic state, in terms of normalized AMPK levels, with the use of metformin and resveratrol, two activators of AMPK. We have previously detailed some of the molecular changes during inflammation injury following acute dextran sodium sulphate (DSS)-treatment of Rassf1a knockout mice [41]. The use of DSS in the Rassf1a−/− or Rassf1a+/− mice, resulted in severe inflammation injury, with <$20\%$ survival of the mice (Figure 3C, left panel). When metformin is fed to the Rassf1a+/− mice at 2 g/L, we observed a dramatic recovery from inflammation injury (Figure 3C, left panel), with a regain of normal levels of active AMPK in both lysates and IHC sections from the colons of metformin-treated animals (Figure 3C, right panel). This can also be observed for treatment utilizing another AMPK activator, resveratrol, in the food pellet, during DSS induced inflammation injury (Supplementary Figure S1A). Similar to the use of metformin, we can observe a regain of activation status of AMPK in colon lysates of resveratrol-treated animals (Supplementary Figure S1B). Interestingly, in the spontaneous model of IBD, the Il-10−/− mice, we observed a loss of active AMPK upon DSS treatment (Supplementary Figure S1B), suggesting a role for AMPK and proper regulation of metabolic homoeostasis in IBD. The metabolic reset provided by metformin or resveratrol, appears to be sufficient to allow for >$80\%$ survival of these normally DSS-sensitive animals. Although human IBD is far more complex than normally observed in the DSS model, the result with the use of 2 g/L of metformin may provide a framework for a human trial for the use of metformin to treat or manage acute to severe IBD. ## 2.4. The Activation Status of Proliferation Driver and Transcription Factor Yes Associated Protein (YAP) Is Elevated in IBD Patients YAP (known as Yorkie [Yki] in Drosophila) is a key driver of proliferation, linked to the TEAD family of transcription factors, and an end effector of the Hippo pathway [73]. Removal of either Yki from intestinal stem cells in Drosophila or YAP in Yap−/− mice, revealed poor survival and decreased epithelial cell proliferation in response to DSS treatment (characteristics similar to what we have observed in DSS-treated Rassf1a−/−) [74,75]. RASSFA is an upstream modulator of the Hippo pathway [76] and there has been documented observations linking AMPK to YAP biology. We have observed previously that in the DSS treated Rassf1a−/− knockout model we can detect a robust increase in active YAP (YAP pY357 levels), suggesting abnormal YAP transcriptional activity upon inflammation injury [41]. In this study, we explored the proliferative status of YAP in human IBD colon sections and observed substantial detection, in both CD and UC patients, of YAP pY357 and YAP pS94, two activation states of YAP (Figure 4A,B). Both activation states of YAP have been demonstrated to drive proliferation in numerous cells [77]. We suspect that this activity is driven by abnormal inflammation and increased proliferation, that may be an interesting predictor of abnormal growth/malignancy if inflammation is not controlled. ## 2.5. Correlations between the Biomarkers Explored in This Study Disease prediction is extremely difficult unless robust biomarkers are identified, that truly reflect the molecular changes that may occur. For IBD, there are no reliable biomarkers to date, mainly because the focus has been on inflammatory drivers. Thus far, we have evidence that inflammatory drivers, metabolic, and proliferative markers all intersect to drive IBD development. We have identified the tumor suppressor gene RASSF1A; the obligate kinase of the intracellular pattern recognition NOD2 receptor RIPK2; the metabolic regulator AMPK; and the proliferative driver YAP, as possible molecular drivers of IBD. We carried out correlation analyses to better understand the relationships between these markers. Figure 5A reveals significant correlations between active RIPK2 and active AMPK and active YAP; significant correlations between RASSF1A expression and active RIPK2, active YAP, and active AMPK, suggesting an interplay of these four molecular drivers of inflammation, leading to metabolic and proliferative changes in the colon of IBD patients. Furthermore, Figure 5B illustrates that the changes in RIPK2, YAP, AMPK, and RASSF1A appear more pronounced in patients with a long-standing disease, of >10 years. Interestingly, with respect to the activation of RIPK2, standard IBD drug treatment does not appear to resolve the issue of abnormal RIPK2 activation (Figure 5C), nor does clinical remission reflect changes in RIPK2 activity (Figure 5D). In addition, there appears to be weak or no correlation between active RIPK2 and changes in c-reactive protein (CRP) or faecal calprotectin (Supplemental Figure S2B). RIPK2 is a molecular driver of inflammation, and Figure 5C,D suggest that, although some patients may be in clinical remission, their RIPK2 status should be monitored, before levels escalate to those found in patients with long-standing disease. ## 2.6. Correlations between Leukocytes and Matched Biopsies from IBD Patients Detection of biomarkers is limited to accessibility of biological material, in order to analyze the biomarker. We explored the possibility of detecting the activation status of some of the biomarkers identified thus far, in the blood of IBD patients. We looked at several matched blood/biopsy samples, and found identical patterning of changes between non-IBD and IBD patients. We had robust detection of RIPK pY474, loss of both AMPK α1 T172 and RASSF1A (Figure 6), and robust detection of YAP pY357 in leukocytes (Figure 6D). Interestingly, for both RIPK2 the AMPK, the changes appear to be equal or more robust in the leukocyte fraction. These data suggest that a non-invasive option could be developed, to monitor the status of RIPK2, AMPK, and YAP. ## 2.7. RASSF1A, RIPK2, and YAP Have Robust Changes in Patients That Have IBD and Progress to CRC Genetically and epigenetically, we are just beginning to uncover the susceptibility loci between IBD and CRC [78]. In general, abnormal molecular pathways in IBD (both CD and UC) link to regulators of inflammation, autophagy, and cell death pathways, whereas abnormal molecular pathways of CRC link to growth control and proliferative signaling pathways. With access to biopsies and tissue blocks at different time points from individual case studies (patients A–D that progressed from IBD to CRC), we explored the expression status of RASS1A, RIPK2, AMPK, and YAP, and studied whether more profound changes were observed when CRC was compared to UC with no CRC. Figure 7 (left panel) and Supplemental Figure S3 shows the snapshot of biomarker staining during no disease, UC, and CRC stages, as determined by endoscopy and pathological analyses. The right panel is a quantitative summary of numerous sections in each category for five UC-CRC patients, illustrating the changes that occurred in these biomarkers as the patient progressed from no disease, to UC, to CRC. What is clearly evident, is that (a) during clinical remission, these biomarkers are still elevated and may be causing low level inflammation that may trigger relapses an/or CRC progression; and (b) the changes observed for each biomarker appear to be more robust in UC patients that progress to CRC than in UC patients with no CRC. We believe biomarker levels during UC may be utilized to better monitor patients that are predisposed to developing CRC. Further analysis is required in a larger dataset. ## 3. Discussion and Conclusions IBD includes Crohn’s disease and ulcerative colitis, both of which are highly prevalent [79,80]. Current treatment is extensive and often requires lifelong immunotherapy. The majority of adult treatments involve biologics such as anti-TNFα antibodies (infliximab/remicade) and steroidal products. However, $50\%$ of patients lose response to infliximab, and patients cannot be on prolonged use of steroids. Several other therapies include aminosalicylates, corticosteroids, and disease-modifying anti-rheumatic drugs (azathioprine, cyclosporine, methotrexate), to mention a few [81,82,83]. Those with long-standing colitis (UC > 10 years) are at increased risk for CRC requiring invasive colonoscopies every 1–2 years, as screening biomarkers have not been identified. In this study, we have explored not just the inflammatory drivers of IBD but also the epigenetic, metabolic, and proliferative drivers. RASSF1A is one of the most epigenetically silenced tumor suppressors in cancers, that restricts NFκB activity [41]. We have demonstrated the importance of RASSF1A and YAP during acute inflammation [41] and have evidence of a role of both RASSF1A and YAP in driving chronic inflammation and malignant transformation (unpublished observations). We have also demonstrated in inflammatory breast cancer (IBC), that there is a correlation between loss of expression of RASSF1A and activation of RIPK2, as indicated by increased detection with the RIPK2 pY474 phosphospecific antibody [53]. Similar to what we published for IBC and CRC, RASSF1A is epigenetically silenced in IBD (Figure 1), with an identical epigenetic signature to CRC, the “IBD-CRC epigenetic signature”, suggesting that IBD is epigenetically a predisposing factor to CRC. The consequence of the loss of RASSF1A expression, is elevated phosphorylation of RIPK2 on Y474 (and thus activation of RIPK2, Figure 2), loss of AMPK activity as measured by detection levels of (AMPKα1) T172 (Figure 3), and increased YAP activity (as measured by both pY357 and pS94 phospho-levels, Figure 4). We can observe a very good correlation between RASSF1A expression and active RIPK2 and active AMPK (Figure 5). We have evidence that RASSF1A can physically restrict association of RIPK2 with NOD2, to prevent activation of RIPK2 (Said et al., manuscript in preparation), to explain a molecular link between epigenetic levels of RASSF1A and the activation of the NOD2/RIPK2 pathogen pathway. Lastly, in breast cancer and a few other cancers, AMPK is thought to modulate the activation status of YAP, to control it proliferative capacity [84]. Thus, in the absence of RASSFA, RIPK2 activation is uncontrolled and thus contributes significantly to the inflammatory response. The loss of RASSF1A also results in an altered metabolic state, with a loss of AMPK activity, loss of colonic insulin production, and an increased YAP proliferative capacity, as observed in patient sections and in our DSS-induced inflammation model (Figure 3, Supplementary Figures S1 and S2). We speculate that abnormal RIPK2 activity during acute to chronic inflammation episodes, may promote stromal inflammation, proliferation, and altered metabolism, leading to malignant transformation. Furthermore, the finding that the loss of AMPK activity, coupled to the loss of insulin production in the colon, identifies two molecular drivers of metabolic syndrome disorder in IBD patients. Loss of AMPK is robustly lost in CD and UC patients, as is insulin production. In this study, we demonstrated that, when a reset of metabolism occurs with metformin or resveratrol, active RIPK2 is significantly inhibited (Figure 3C and Supplementary Figure S1), AMPK levels are restored, and YAP S94 activity (and to a large extent YAP Y357 activity) are inhibited, suggesting that a metabolic reset with metformin or resveratrol treatment can alleviate RIPK2-driven inflammation (Supplementary Figures S1 and S2). When a RIPK2 inhibitor is administered to our mouse model of inflammation, we can observe a regain of AMPK levels and significant loss of YAP activity (Supplementary Figure S2), suggesting that inhibiting RIPK2 can also lead to a metabolic reset and may promote remission in IBD patients. Thus, one can suggest the use of both metformin and RIPK2 inhibitors as treatment schemes for IBD, depending on the activation status of RIPK2 or AMPK (please see Figure 8). Our RIPK2 inhibitor was extensively characterized for inhibition of RIPK2 [85], and we have evidence that the tumor suppressor RASSF1A, can physically associate with RIPK2, and keeps in check its ability to drive inflammation (Said et al., manuscript in preparation). There are many cellular consequences to the loss of AMPK activation, that will need to be explored further in IBD patients. It is thought that AMPK can inhibit the growth of cancer cells by switching off protein synthesis and cell proliferation [62,86]. Several epidemiological studies have suggested that diabetes is associated with an increased risk of certain cancers [59]. Indeed, hyperglycemia and hyperinsulinemia are thought to promote the growth of cancer cells in diabetic patients [87]. Metformin, the most commonly prescribed oral anti-diabetic medication and an activator of AMPK, has been shown to have strong anti-proliferative and/or pro-apoptotic properties in several cancer cell lines, and may be of benefit to diabetic cancer patients [88]. Although controversial, the anti-cancer effect may be independent of AMPK activation [89]. Interestingly, several studies have shown that the use of metformin is associated with lower risk of colon and pancreatic cancer in type 2 diabetic (T2D) patients [90]. We performed a database search for the prevalence of IBD in insulin and metformin users in Alberta, and preliminary analysis indicates that IBD is prevalent in approximately 1 in 300 insulin mono-users and 1 in 900 metformin mono-users, when compared to 1 in 150 non-users of insulin or metformin (unpublished information). These data are in line with the results of Tseng [2020], who compared the risk of IBD between users and non-users of metformin [91], and others that suggest a role for metformin in managing inflammation [92]. The results of our database analysis are also in line with our animal model results, that clearly show that metformin intake was sufficient to reverse the damaging effects of DSS-induced inflammation injury (a colitis model) in the inflammation sensitive Rassf1a−/− mouse. Thus, insulin, and more importantly metformin, protected against the occurrence of IBD in Alberta. Serum/colon tissue AMPK levels will help in deciding if metformin may be a useful drug as a primary or a co-treatment for IBD. The most interesting aspect of our study was the detection of biomarker changes between biopsy and blood (leukocytes). In a small subset of IBD patients, with disease > 10 years, we can observe matched changes in active RIPK2, active AMPK, RASSF1A, and active YAP (Figure 6). Interestingly, there was a more robust difference in RIPK2 status in the leukocyte fraction, suggesting further exploration of the use of an active RIPK2 biomarker status in determining therapy selection, as modeled in Figure 8. The most important markers may be RIPK2 and YAP, to indicate inflammation and the beginning signs of hyperproliferation, respectively, and possible progression to CRC. The biomarkers identified in this manuscript may be useful in understanding the origins of acute and chronic intestinal inflammation, what sustains it, and how it promotes the malignant state if uncontrolled. There is a need to better understand the inflammatory mediators/molecular drivers involved in promoting the pathogenesis of CRC, and the biomarkers identified in this study may be essential starting points to a better understanding of the origins of malignant transformation. Eliminating inflammation through novel modulation of metabolism, proliferation, and epigenetics, will influence the composition of the microbiome and the metabolic and inflammatory state of the colonic microenvironment, and significantly contribute to rational drug design. Proposing novel combination therapies will not only help IBD patients but may aid in CRC prediction and reduce the incidence of CRC. We propose that controlling intestinal inflammation is an important cancer prevention strategy, that would decrease the incidences of CRC and possible other inflammatory driven cancers. The biomarkers identified in this study will help in identifying at risk populations of long-standing IBD in a non-invasive manner, or at least warrant surveillance colonoscopies when needed. IBD and IBD-CRC require a multidisciplinary approach, to identify novel targets for disease prevention (or screening), to offer novel, precision therapeutics, that will reduce disease symptoms and unnecessary costs and procedures. ## 4.1. Patient Collection We have collaborated with researchers in the Centre for Excellence in Gastrointestinal Inflammation and Immunity Research (CEGIIR), at the University of Alberta, the Alberta IBD Consortium, and the Alberta Health Services pathology bank, to obtain patient biopsy samples as necessary. Samples were collected from patients with active disease, having gross inflammation, and from those in remission from areas showing no gross inflammation, when available. Biomarker analysis was conducted on a subset of these patients, to time constraints. This was carried out under research ethics protocol # Pro00077868. We also received blood and tumor biopsy samples from colorectal cancer patients, through collaborations with the Cross Cancer Institute Tumor Bank and Dr. Oliver Bathe (University of Calgary). All biopsy and tumor samples were flash-frozen in liquid nitrogen and stored at −80 °C. Samples were transferred to Z-Fix fixative for 24–36 h in order to fix the samples, prior to being dehydrated and paraffin-embedded, as described previously [41]. Biopsy samples were also mounted for IHC immunoblotting, to assess the usefulness of novel IBD biomarkers identified in the mouse models. This was carried out under research ethics protocol # Pro00077868. ## 4.2. Patient Chart Review Retrospective chart reviews were completed for all case study patients and the majority of the IBD patients. The inclusion criteria were as follows: newly diagnosed patients (<5 years) and patients with long-standing IBD disease (>10 years). Patients with other co-morbidities such as diabetes, all cancers, celiac disease, or irritable bowel syndrome were excluded. Tissue samples were obtained for immunoblotting and immunohistochemistry, as indicated, in addition to blood samples for leukocyte analysis. This was carried out under research ethics protocol # Pro00001523. Demographics are indicated in Table 1 and Table 2 in results Section 2.1. ## 4.3. Acute Mouse Model of Inflammation Intestinal colitis can be modeled in mice by the addition of $3\%$ dextran sodium sulfate (DSS, #160110, molecular weight of 10,000, MP Biomedicals, Santa Ana, CA, USA) in drinking water for 7 days (acute treatment), to induce injury, followed by regular drinking water for recovery (as described previously) [20]. DSS irritates the colonic mucosa, resulting in epithelial wall breakdown, microflora invasion activating TLR-expressing epithelial cells, and mucosal injury. Mice were monitored over time for weight changes, as well as clinical symptoms of colitis such as rectal bleeding and diarrhea. Animals were euthanized once rectal bleeding became grossly apparent. Animals were sacrificed at various points, and blood, colon, kidney, and liver samples collected for molecular analysis. The animal ethics research of the University of Alberta in Edmonton, AB, Canada approved the study (under protocol numbers #461 and 639). ## 4.4. Tissue Collection, Preparation and Pathology Scoring and Colon Lysates Colon samples were isolated, fixed in Z-Fix (Anatech 170) and paraffin-embedded. All inflammation scores were obtained utilizing blinded scoring by a gastrointestinal pathologist (Dr. Aducio Thiesen), based on infiltration of enterocytes, neutrophils, lamina propria cellularity, crypt structure, and epithelial hyperplasia (scored as 0–2, where 2 = maximal injury) [41]. For colon lysates, samples intended for protein analysis were flushed with 1X PBS, to clear fecal matter, and immediately placed in ice-cold T-PER lysis buffer (Thermo Fisher Scientific, Waltham, MA, USA) with fresh aprotinin ($0.1\%$), phenylmethylsulfonyl fluoride (PMSF) ($0.2\%$), sodium pyrophosphate (NaPP) ($0.1\%$), sodium orthovanadate ($1\%$), and a protease inhibitor cocktail. Samples were then homogenized using a Fisher PowerGen handheld homogenizer, and centrifuged at 4 °C, at max speed, for 10 min. The supernatants, containing proteins, were stored at −80 °C for further use. Protein concentration was determined using the Bradford protein assay. ## 4.5. Immunohistochemistry Colon samples from selected patients were fixed in formaldehyde, paraffin embedded, and mounted. Samples were rehydrated and antigen retrieval performed using boiling sodium citrate. Immunodetection of proteins of interest was carried out using 1:100 dilution of antibodies against the desired proteins, followed by signal amplification, using a biotin-labelled secondary antibody, streptavidin-HRP amplification, and final detection using metal-enhanced 3, 3′-diaminobenzidine (DAB). ## 4.6. Immunoblotting Polyacrylamide mini gels, $5\%$ stacking and $7.5\%$ separating, were prepared as published previously [41]. ## 4.7. Immunoblotting Leukocyte Fractions Patient peripheral blood was drawn into an EDTA-containing tube (about 5–10 mL) and red cell lysis buffer (bicarbonate-buffered ammonium chloride solution: $0.826\%$ NH4Cl, $0.1\%$ KHCO3, $0.0037\%$ Na4EDTA in H2O) was added, at a ratio of 20:1 (lysis buffer/blood), and incubated for 10 min. Once the erythrocyte fraction was lysed, the samples were centrifuged for 10 min at 400× g or 3500 rpm, using a tissue culture centrifuge. The leukocyte pellet was washed twice with 1X PBS before being divided into three fractions: one fraction, containing ½ sample, was used for immunoblotting. Cells were lysed in lysis buffer containing 50 mM HEPES (pH 7.5), 150 mM NaCl, 1 mM MgCl2, 1.5 mM EDTA, $0.5\%$ Triton X-100, 20 mM β-glycerolphosphate, 100 mM NaF, and 0.1 mM PMSF. Total lysates were assayed (Bradford) for protein concentration and loaded, after boiling (7 min), in 4X SDS-PAGE Sample Buffer, such that approximately 40 µg were loaded onto $10\%$ SDS-polyacrylamide gels, run, transferred and immunoblotted as previously published [20]. Blocking was done in $5\%$ BSA (in TBS-T) and primary/secondary antibody diluted in $5\%$ BSA (in TBS-T). ## 4.8. DNA Methylation Analysis by Pyrosequencing Pyrosequencing, to determine methylation status, was carried out using the Qiagen Pyromark Advanced kit, according to the manufacturer’s instructions, as previously carried out [68]. Briefly, genomic DNA isolated from mouse colonic tissue, was bisulfite modified using the Qiagen Epitect Bisulfite Conversion kit, using the instructions for “sodium bisulfite conversion of unmethylated cytosines in DNA from low-concentration solutions”, according to the manufacturer’s instructions. The resulting bisulfite-modified DNA was then used as a template to amplify the region of interest, using a biotinylated primer set for the first exon of Rassf1a, provided by Qiagen (cat # PM00416290). The Assay1 covers 11 CpGs in promoter and 1 CpG in exon1 of the RASSF1A. The Assay2 covers 20 CpG (Cytosine-phosphate-Guanine) located right upstream of the 12 CpGs covered by the Assay1 [41]. The PCR was performed using a PyroMark PCR Kit (Qiagen, Germantown, MD, USA), in a volume of 25 µL, containing 12.5 µL of 2× PyroMark PCR Master Mix, 1.25 µL of each PCR primer (5 µM), 2.5 µL of 10× Coral Load Concentrate, 6.5 µL high purity water, and 1 µL of bisulfite-treated template DNA. The PCR cycling programme for both primer sets was composed of an initial Taq activation/DNA denaturation step at 95 °C for 15 min, followed by 50 cycles of denaturation at 95 °C for 30 s, annealing at 58 °C for 30 s, and elongation at 72 °C for 30 s. The program was finished by a final elongation step at 72 °C for 10 min. The PCR product (7 µL) was visualized by gel electrophoresis, and 10 µL was subjected to the sample preparation process for pyrosequencing. The sequencing results were analyzed using the Advanced PyroMark software (Qiagen). A control PCR reaction, without template DNA (non-template control), was included in the assay. Pyromark assays were carried out two times, for accuracy. ## 4.9. Myloperoxidase Activity Analysis All activity assays were performed in duplicate or triplicate, on 96-well microtiter plates, and analyzed with a microplate reader. Peroxidase activity, with 3,3′,5,5′-tetramethylbenzidine (TMB, Sigma, Oakville, ON, Canada), was measured as described previously [26]. Briefly, 10 μL of sample was combined with 80 μL 0.75 mM H2O2 (Sigma) and 110 μL TMB solution (2.9 mM TMB in $14.5\%$ DMSO (Sigma), and 150 mM sodium phosphate buffer at pH 5.4), and the plate was incubated at 37 °C for 5 min. The reaction was stopped by adding 50 μL of 2 M H2SO4 (Sigma), and absorption was measured at 450 nm, to estimate MPO activity [41,93]. ## 4.10. Antibodies The following antibodies were utilized for this study: rabbit anti-RIPK2 (Santa Cruz sc-22763), rabbit anti-pY 474 RIPK2 (in-house made), rabbit anti-ERK1 (Santa Cruz sc-93) and rabbit anti-ERK2 (Santa Cruz sc-154), rabbit anti-(AMPKα1) pT172 (Cell Signalling #2535S), mouse anti-(AMPKα1) (Cell Signalling #3532), rabbit anti-YAP Y357 (Phospho-YAP (Tyr357), Sigma-Aldrich Y4645), rabbit anti-YAP pS94 (a generous gift of Dr. Marius Sudol), and mouse anti-RASSF1A monoclonal antibody (in-house made). All in-house antibodies were validated using purified proteins or positive and negative cells. Rabbit anti-pY 474 RIPK2 can now be purchased from QED Biosciences (San Diego, CA, USA), that documents the characterization of rabbit anti-pY 474 RIPK2. Mouse anti-RASSF1A was characterized by ELISA analysis using RASSF1A gel slices, and only high-titer clones were selected and purified. ## 4.11. Statistical Analyses Statistical analyses were performed using one-way or two-way ANOVA with Tukey or Bonferroni post hoc tests, respectively, or Students t-test (two-tailed), as indicated using the GraphPad Prism 5 software. All statistics were non-parametric. Results are considered significant if the p-value is <0.05. All experiments were carried out at least three times with biological replicates. Error bars in all graphs represent the standard error. For all data analysis, samples were taken from UC and CD patients at inclusion criteria of being diagnosed as IBD, with no sex bias or age bias, for this study. These inclusion criteria were carried out before statistical analysis was performed. The number of samples was determined using the sample size calculation: n = Z2 [p(1 − p)]/(D2) and within the 90–$95\%$ confidence interval. 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--- title: 'GANs for Medical Image Synthesis: An Empirical Study' authors: - Youssef Skandarani - Pierre-Marc Jodoin - Alain Lalande journal: Journal of Imaging year: 2023 pmcid: PMC10055771 doi: 10.3390/jimaging9030069 license: CC BY 4.0 --- # GANs for Medical Image Synthesis: An Empirical Study ## Abstract Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been trained to replicate. One recurrent theme in medical imaging, is whether GANs can also be as effective at generating workable medical data, as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study, to gauge the benefits of GANs in medical imaging. We tested various GAN architectures, from basic DCGAN to more sophisticated style-based GANs, on three medical imaging modalities and organs, namely: cardiac cine-MRI, liver CT, and RGB retina images. GANs were trained on well-known and widely utilized datasets, from which their FID scores were computed, to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images and the original data. The results reveal that GANs are far from being equal, as some are ill-suited for medical imaging applications, while others performed much better. The top-performing GANs are capable of generating realistic-looking medical images by FID standards, that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggest that no GAN is capable of reproducing the full richness of medical datasets. ## 1. Introduction During the last decade, machine learning has been widely adopted, mainly due to the advent of deep neural networks and their state-of-the-art results on a variety of medical imaging tasks. Meanwhile, the introduction of generative adversarial networks (GANs) by [1], drove generative modeling and data synthesis to levels of quality never before achieved. The research on GANs grew at an ever increasing pace, with each iteration pushing back the limits of image quality. Perhaps, one notable breakthrough in image quality came from [2] and their Big GAN. Not so long after, another drastic jump in the quality and diversity of generated images came with Style GAN [3], which exhibited highly realistic high-resolution human faces. Motivated by the impressive results achieved by GANs on natural images, the goal of this work is to evaluate how well these machines perform on medical data, an area well-known for its smaller datasets and strict anatomical requirements. Recent reviews have been published, analyzing the use of GANs in medical image analysis [4,5,6]. The distinctiveness of our work is the empirical evaluation of the benefits of GAN-generated data in this context, in addition to the large hyperparameters analysis of the different approaches. ## 1.1. Medical Image Analysis Medical image analysis aims to un-invasively extract information about a patient’s medical condition. Medical images are images acquired from one of multiple modalities, be it magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), or ultrasound (US), to name a few. The acquired images are generally processed using image analysis and/or computer vision techniques, to extract certain useful information about the data at hand, for example, to classify whether the case is normal or pathological. One of the most routine tasks in clinical practice is image contouring, or segmentation. Image segmentation is the operation of outlining parts of the images that belong to certain classes of interest. For example, in the case of cardiac MRI, one may delineate the left ventricular cavity and myocardium, with the objective of measuring blood volumes and contraction rates. In recent years, machine learning and deep learning garnered a large interest from the medical imaging community, due to their unprecedented achievements in a large swath of computer vision tasks. However, machine learning software have not yet been widely adopted in clinical practice, largely due to the fact that neural networks are still error prone under certain conditions (domain adaptation, different acquisition protocols, missing data, etc). One reason for this, derives from the fact that fully-annotated medical imaging datasets are much smaller than those in other areas. For example, the gold standard computer vision ImageNet [7] dataset, contains more than 14 million annotated images, while a typical medical image dataset is three to four orders of magnitude smaller. This is because the creation of medical imaging datasets is costly and difficult, due to the sensitive nature of the data and the highly specific domain knowledge required to reliably annotate it. The paucity of training data in medical imaging, has made the search for other means of acquiring training sets an active area of research [8]. ## 1.2. Synthetic Data and Medical Imaging Recently, GANs have received growing attention from the medical research community, with the hope of using them to synthesize realistic-looking medical images. For example, [9] trained a GAN to synthesis new T1-weighted brain MRIs, with comparable quality to real images, and [10] succeeded in generating high resolution skin lesion images which experts could not reliably tell apart from real images. In [11], they took advantage of GANs to generate brain MRIs that achieved high scores both in qualitative and quantitative evaluation. In [12], the authors showed that GAN-generated images of lung cancer nodules are nearly indistinguishable from real images, even by trained radiologists. GANs were also used as a means for generating more training data. In [13], the authors trained a GAN to generate synthetic brain tumor MRIs, and evaluated the performance of subsequent segmentation networks trained with the generated data. Looking at the reported results, the segmentation networks trained solely with synthetic data do not come close to those trained with real data, performance wise. Likewise, Ref. [ 14] proposed a combination of a variational autoencoder and a GAN, as a data augmentation framework for an image segmentation problem. Here again, the use of GANs to train downstream neural networks produced mixed (and yet more or less convincing) results. As reported in the survey paper by [15], the application of GANs in medical imaging extends beyond image synthesis to other tasks, such as domain adaptation, classification, and reconstruction, to name a few. For these applications, the capability of GANs to generate realistic looking images, has led to a partial disregard of the usefulness of the generated medical images, or whether they hold any value compared to real data in routine clinical tasks. In light of these publications, one might wonder how useful GANs truly are in medical imaging. In this paper, we set out to evaluate the richness and the benefit of using GAN-generated data in the context of medical imaging. We assess their performances on three datasets of different organs and different modalities. ## 2. Generative Adversarial Networks Adversarial networks in general, and GANs (Figure 1) more specifically, are trained to play a minimax game between a generator network, which tries to maximize a certain objective function, in tandem with a discriminator network, which tries to minimize that same objective function, hence the adversarial denomination. In their most basic formulation, GANs are trained to optimize the following loss function [1]:[1]minGmaxDV(D,G)=Ex∼pdata(x)[logD(x)]+Ez∼pz(z)[log(1−D(G(z)))]. here, G(z) is the generator network, with parameters θG. It is fed with a random variable z∼pz, sampled from a given prior distribution, that G tries to map to x∼pdata. To achieve this, another network D (aka the discriminator), with parameters θD, is trained to differentiate between real samples x∼pdata from a given dataset and fake samples x^∼pθG(x\|z) produced by the generator. In doing so, the generator is pushed to gradually produce more and more realistic samples, with the goal of making the discriminator misclassify them as real. ## 2.1. GAN Selection The number of papers published on GANs has been growing steadily in recent years. This has been underlined by a recent survey paper [16], which reported no less than 460 references. Given this large palette of models, we based our choice on those that are the most widely adopted and/or ushered an improvement to the quality of generated images. We also selected GANs based on their ability to fit on a single 12 GB GPU, to be able to evaluate the architectures accessible to researchers with constrained computing resources. Training GANs can be tricky. Since learning involves two opposing networks, GANs are known for suffering from several training problems, the following three being among the most widely documented. Convergence. GANs (and adversarial training in general) often suffer from a lack of a defined convergence state. This is because the training process involves two networks pushing in opposite directions, without one out matching the other. This has been frequently proven to be a difficult task. For example, the generator could become too powerful and learn to fool the discriminator with faulty output. It could also happen that the discriminator reaches a $50\%$ accuracy effectively outputting random guesses, which does not help the generator learn any meaningful information about the true data distribution. Vanishing Gradients. As GANs train a generator with the output of a discriminator, whenever the discriminator significantly outperforms the generator, its loss goes to zero, pushing the retropropagated gradient to a smaller and smaller value, hence the vanishing gradient name. Because of that, the generator does not get enough gradient updates and sees its learning stall, to some sub-optimal solutions [17]. Mode Collapse. Of all the challenges that obstruct the training of powerful GANs, mode collapse might be the most difficult one to deal with. Mode collapse occurs when the generator gets stuck outputting only one (or a few) modes of the input data distribution. An example could be a generator producing images of healthy subjects, while ignoring the diseased ones. This pitfall leads to a loss of diversity in the generated datasets, that can greatly hurt the performance of subsequent networks trained with these generated data. In regards of the aforementioned criteria and the different challenges, we selected the following GANs for our study. ## 2.1.1. DCGAN Deep convolutional GANs [18] were the first GANs to use convolutional layers, compared to the inital GAN which used only fully connected layers. With its simplicity, DCGAN is often the de facto baseline GAN one implements. DCGANs showed a considerable jump in image quality and training stability, while providing some useful insights on the network design (use of strided convolutions instead of pooling layers, extensive use of BatchNorm, etc.). To our knowledge, DCGAN is among the most widely implemented GANs, as of today. ## 2.1.2. LSGAN Least Squares GANs [19] use a different loss for the discriminator than the original GANs, which helps to alleviate certain challenges and improves the generated sample quality. LSGANs replace the cross entropy loss of the original GAN, with the mean squared error, which mitigates the vanishing gradient problem, leading to a more stable learning process. ## 2.1.3. WGAN and WGAN-GP Wasserstein GANs [20] were considered to be a major breakthrough, to overcome GAN training challenges. In particular, they are known to reduce the effect of mode collapse and stabilize the learning procedure. The idea is to use a Wasserstein earth-mover distance as the GAN loss function, together with some other optimization tricks, such as weight clipping and gradient penalty (WGAN-GP). ## 2.1.4. HingeGAN (Geometric GAN) Introduced by [21], HingeGANs substitute the original GAN loss for a margin maximization loss, which theoretically converges to a Nash equilibrium between the generator and discriminator. As for WGAN and LSGAN, HingeGAN has the sole benefit of easing the optimization process. ## 2.1.5. SPADE GAN Spatially adaptative denormalization (SPADE) GANs [22], are a member of the so-called image-to-image translation GAN family. SPADE GANs produce state-of-the-art results on a wide range of datasets, producing high quality images, perfectly aligned to a semantic input mask. SPADE GANs come as an improvement of the previously published pix2pix [23] model. SPADE GANs are considered to be the state-of-the-art conditional GANs. ## 2.1.6. Style Based GANs StyleGAN [24], often considered as the state-of-the-art generative neural network, introduces multiple tricks to GANs borrowed from previous works, such as progressive GANs [25], that gradually train the GAN with different resolutions, which leads to better quality and a more stable training process. StyleGAN also comes with a greatly modified generator, which includes adaptive instance normalization blocks (AdaIN), the injection of noise at every level of the network, and use an 8-layer MLP mapping function on the input latent vector z→. ## 2.2. Evaluation Metrics Broadly speaking, the metrics used to quantify the effectiveness of GANs are the same as those used to evaluate traditional image synthesis tasks. This boils down to computing a similarity distance between a set of images. In their early stages, GANs were evaluated using the traditional metrics such as Peak Signal to Noise Ratio (PSNR) [26] or Structural Similarity Index Measure (SSIM) [27]. As the field advanced, more image quality metrics emerged, and became the de facto evaluation criteria, such as Learned Perceptual Image Patch Similarity (LPIPS) [28], Inception Score (IS) [29], and the Frechet Inception Distance (FID) [30]. The Frechet Inception Distance (FID), first introduced by [30], makes use of a pretrained inception network on the ImageNet [7] dataset, to assess the quality of GAN generated images. The FID is a distance between the distribution of the GAN sampled images and the real dataset used to train the GAN. Generated samples and real images are fed to the pretrained inception network and the mean and covariance of the activations in the final block, assumed to be of a Gaussian distribution, are collected for both sets, then the Frechet distance is computed between both. The FID is computed on a learned feature space and was shown to correlate well to human visual perception [28]. However, it still suffers from a number of drawbacks [31], most prominently, it suffers from a high bias [32]. In addition, FID can not detect a GAN that memorizes the training set [33]. The FID is defined as the Frechet distance between two Gaussians, as shown in Equation [2], where N(μ1,σ1) is the Gaussian distribution of the inception features of the real images, and N(μ2,σ2) the Gaussian distribution of the inception features of the generated images. In this work, we use the FID metric, as it evolves in tandem with human perception. In addition, it makes use of the original dataset to compute a distance in a learned feature space. In addition to the FID metric, we also consider the Dice score evaluation metric, obtained on a segmentation task with a U-Net network trained on the generated dataset. [ 2]FID((μ1,σ1),(μ2,σ2))=∥μ1−μ2∥22+Trσ1+σ2−2σ1σ$\frac{21}{2}$ ## 3. Material and Methods To make informed decisions about the usefulness of GANs in medical imaging as a source of synthetic data, we had to take into account different GANs and cover a diverse set of image modalities. In parallel, a wide range of hyperparameters had to be covered, to assess their effect on the GANs at hand. ## 3.1. Hyperparameters Search GANs are known for their sensitivity to tweaking of the hyperparameters [33]. In order to achieve a fair comparison between the selected GANs, we covered a wide spectrum of hyperparameters (some affecting the GAN architecture), through a vast hyperparameter search, totaling roughly 500 GPU-days. We retained the best performing runs with regards to the reference metric FID, for its correlation with subjective evaluation. Moreover, since the number of runs needed to sweep a large hyperparameter space grows exponentially with the number of hyperparameters we set to optimize over, we chose a number of sensible initial configurations for each dataset/GAN pair, mostly based on their default configuration. Table 1 lists the hyperparameters we searched over. Iterating over these hyperparameters enabled us to find the set that worked best for each GAN/dataset pair. In addition, this hyperparameter search also gave us a look at how the training stability was affected by the selected hyperparameters. Note that, some combinations were only tested for specific GANs, such as “weight clipping” for WGAN or “gradient penalty” for WGAN-GP. ## 3.2. GANs Setup The training of the DCGAN, LSGAN, WGAN, and HingeGAN followed the same protocol. A traditional fully convolutional network architecture, with a standard generator and discriminator composed of upconvolutions and strided convolutions, respectively, was implemented, as a basis of our DCGAN. Then the loss function was swapped, to convert it to either an LSGAN, a WGAN, or a hingeGAN. For StyleGAN and SPADE GAN, we relied on the publicly available implementations, without any change to the networks’ architecture. Figure 2 schematically summarizes the architecture of each GAN. ## 3.3. GAN Training Tricks In order to make the GAN training process more stable, we relied on a few tricks, that have been shown to be useful in this regard. Label smoothing. First applied to GANs by [29], label smoothing consists of replacing the true classification labels given to the discriminator, to a smooth value α. Feature matching. Also introduced by [29], feature matching adds another objective to the generator of the GAN, which consists in minimizing a distance between the activations of the discriminator for real and generated data. Differentiable augmentation. Presented by [34], differentiable augmentation imposes various types of augmentation on the fake and real samples fed into the discriminator, yielding a more stable training and better convergence. ## 3.4. GAN Evaluation in Medical Imaging While image fidelity is fundamentally important for practitioners to deliver a good diagnostic, the visual acuity of generated images cannot be the sole marker to assess the true performance of GANs. In this paper, we want to assess how rich and diverse a synthetically generated dataset really is, in the context of medical imaging. Thus, to verify the medical viability of GAN-generated images, we independently trained a second network, as a downstream task, on the GANs-generated datasets, and compared its results to those obtained on the original (real) datasets. In this work, we choose semantic segmentation as a downstream task to evaluate our GAN generated datasets, as it is a common task in a clinical workflow. This assessment sets a common evaluation protocol for every GAN. This evaluation is also insightful, considering that the objective for using GANs is often to artificially increase the size of a dataset and thus provide more training data to a subsequent task [13,14]. This approach has been explored before, with GANs trained on natural images, and evaluated through a classification task [37,38]. ## 3.5. Datasets To cover a good spectrum of image and medical applications, we picked three different datasets based on their imaging modalities, their organ of interest, and their size, namely, cardiac cine-MR images, liver CT, and retina imaging. These datasets offer a varied selection of data. Different dataset sizes are present, from large (SLiver07), to moderate (ACDC), to small (IDRiD). Coupled with that, different image modalities and organ shapes are considered. Figure 3 shows an example of images from each of the datasets. ## 3.5.1. ACDC The Automated Cardiac Diagnosis Challenge (ACDC) dataset [39], consists of 150 exams (100 training and 50 testing) of short-axis cardiac cine-MRI, acquired at the University Hospital of Dijon (all from different patients). The exams are divided into five evenly distributed subgroups (four pathological, plus one healthy subject groups) and further split into 100 exams (1902 2D slices) for training, with 50 exams (1078 2D slices) held out, for testing. The pixel spacing varies from 0.7 mm to 1.9 mm, with a slice spacing between 5 mm to 10 mm. The exams come with multi-structure segmentation masks for the right ventricular cavity, the left ventricular cavity, and the left ventricular myocardium, at end-diastole and end-systole times. ## 3.5.2. SLiver07 The Segmentation of the Liver Competition 2007 (SLIVER07) [40] dataset, contains 40 CT volumes of the liver, enhanced with contrast agent. Most livers are pathological and include at least one tumor. The pixel spacing ranges from 0.55 mm to 0.8 mm and the inter-slice gap between 1 mm to 3 mm. The 40 CT datasets are randomly split in three groups: a group of 20 volumes for training, another group of 10 volumes for validation, and the remaining 10 volumes for testing. For our study, we only use the 20 training volumes provided with manual segmentations for the liver, which totals 4159 2D slices. ## 3.5.3. IDRiD The Indian Diabetic Retinopathy Image Dataset (IDRiD) [41], contains a total of 516 retinal fundus images of normal and pathological cases. Images are provided with disease grading ground truth for the full dataset, and segmentation masks for 81 images. We used part of the 81 images for our study, specifically, the 54 training images with the optical disc segmentation masks. ## 3.6. Dataset Generation For our study, our selected GANs were trained on the aforementioned datasets, with the goal of synthesizing new medical data. The overarching objective of this study was to assess whether or not GANs offer a reliable framework for synthesizing realistic and diverse medical images. To examine how well GANs manage to learn the original data distribution, a large number of images was sampled from each of our trained GANs, which we later used to train a segmentation network. To be able to train a downstream segmentation network, the different GANs were trained on the joint distribution of the image and the mask, by concatenating the channel axis. We did so for every GAN except for SPADE, which is by nature conditioned on a segmentation mask. Once properly trained with the right set of hyperparameters, each GAN was used to generate a dataset of 10,000 images, by randomly sampling the input latent space. No further processing was performed on the generated datasets, as the objective was to gauge the quality of the raw images output by the GANs. Figure 4 shows some examples of images generated by each GAN on the three datasets. ## 4. Experiments and Results This section goes through the experiments and results obtained by each GAN on each dataset. ## 4.1. Hyperparameter Search and Overall Results The hyperparameter search performed on DCGAN, LSGAN, WGAN, and HingeGAN revealed interesting insights. The first one, is that some GANs are very sensitive to their hyperparameters. To underline this, the FID score obtained for every set of hyperparameters, for each GAN and each dataset, are shown in Figure 5. As can be seen, the HingeGAN has the lowest variance and, overall, the best FID score. On the other hand, DCGAN and LSGAN are overall much more sensitive to hyperparameter tweaking. This is inline with our qualitative experience, as the training of DCGAN and LSGAN often ended up producing degenerated images. SPADE and Style GAN were not included in the graph, due to the shear amount of training time they required (it took respectively 10 and 30 days to train them), but also due to their remarkable stability. Empirical evidence obtained with different hyperparameters on a few epochs, suggests that their FID variance is much lower than that of HingeGAN, hence why they ended up with top results with almost no hyperparameter tweaking. Another insight comes from the impact a dataset has on the performances of GANs. As can be seen from Figure 5, the larger the reference dataset is, the better the resulting FID will be. It goes from IDRiD, the smallest datset, with FID values well above 150, to ACDC, with FIDs values roughly between 100 and 150, and finally SLiver07, the largest dataset, with most FID values being below 100. A similar trend can be seen in Figure 6, where the overall FID values for every GAN are shown against the number of convolutional filters in the discriminator network. This shows how volatile GANs can be when trained on smaller datasets, such as IDRiD. Similar plots with other hyperparameters can be found in the Supplementary Materials. The best FID score obtained for each GAN and each dataset is shown in the third column of Table 2. Examples of generated images can also be seen in Figure 4 (and in high resolution in the Supplementary Materials). The two best models, by far, are StyleGAN and SPADE GAN. The most extreme case is for IDRiD, where a SPADE GAN got a surprising FID of 1.09 and remarkably vivid images, in Figure 4. ## 4.2. Segmentation Evaluation The true value of the generated images was validated with a downstream segmentation network, trained on the synthetic data instead of the original (real) data. To do so, 10,000 new images were generated for each dataset and a U-Net [42] was trained to predict the segmentation mask. The architecture of the used U-Net can be found in the Supplementary Materials. Then the U-Net was trained on the generated dataset. We predict the masks with this trained segmentation network on the test set of each of our original datasets (i.e., ACDC, SLiver07, and IDRiD). The Dice score of the prediction with the ground truth masks of the test set was computed, which will constitute our Dice score evaluation. The Dice score evaluation metric can be defined as: [3]Dice(Y,Y^)=2\|Y∩Y^\|\|Y\|+\|Y^\| where Y is the ground truth segmentation mask and Y^ is the predicted segmentation mask. The last column of Table 2 contains the Dice score obtained on the real test set of each dataset. Unsurprisingly, as suggested by the FID scores, StyleGAN and SPADE GAN achieve the highest Dice scores on all the datasets, with StyleGAN reaching $87\%$ Dice on the ACDC dataset, $2\%$ less than when training with the original data. These results reveal three important things about GANs in medical imaging. First, simpler models such as DCGAN, LSGAN, WGAN, and HingeGAN perform systematically poorly on every dataset, despite an intensive hyperparameter search. This suggest that these models might be ill-suited for medical imaging applications. Second, despite their visual similarity, GAN-generated datasets do not have the same richness as real datasets. This is illustrated by the fact that, despite being trained on far more images, none of the GAN Dice scores equal or outperform the ones obtained on the original datasets. Moreover, the generated datasets, when used as augmentation data, achieve similar performance to traditional augmentation techniques (rotations, shifts, flips), illustrated by the Dice score of training with a mix of the original data and generated data, and the augmented original data only. Third, while the FID score is a good proxy to distinguish the best methods from the least effective ones, it does not correlate well with an application score, such as the Dice score. For example, the FID score of 29.06 of StyleGAN on SLiver07 suggests that the produced images are much more accurate than those of SPADE GAN (FID = 47.62). However, the resulting Dice scores show that SPADE GAN is significantly better than any other model. A similar comment can be made for IDRiD and ACDC, as StyleGAN and SPADE GAN got similar Dice scores but very different FIDs. As for the FID score of 1.09 obtained by SPADE GAN, the associated $82\%$ Dice score suggests that the network has most likely memorized the training set. This might be attributed to the small size of the IDRiD dataset, as well as to the simple shape of the input segmentation mask. To further analyze whether the FID score is a reliable medical imaging metric, we plotted the InceptionNet latent space of the generated images obtained with the most and the least effective GANs, i.e., DCGAN and StyleGAN (c.f. top row of Figure 7, plots were obtained with UMap [43]). In parallel, we plotted the U-Net latent space for the same images and the same GANs (cf. bottom row of Figure 7). While the red and the blue InceptionNet scatter plot distributions are very similar for DCGAN and StyleGAN, the U-Net ones reveal much more distinctive patterns. Indeed, the U-Net distributions of StyleGAN follow very similar distributions (hence suggesting that the synthetic images of StyleGAN are visually very close to those of the original dataset), while the ones from DCGAN show a clear case of mode collapse. This underlines a fundamental limit of the FID metric: since the InceptionNet was trained on ImageNet (a non-medical dataset), its use in medical imaging must be made with great care. ## 4.3. Visual Turing Test Considering how realistic looking some of the GAN-generated images are, we asked four medical experts, each with more than 15 years of experience in cardiology, to classify fake and real cine MRI images generated by StyleGAN and from the ACDC dataset. Each expert was shown 100 images, consisting of a $\frac{50}{50}$ mixture of real and synthetic images, and was asked to classify it based only on their visual appreciation. The accuracy of the classification performed by the experts was equal to $60\%$ (+/−$10\%$). This result shows how visually accurate the generated images are. ## 5. Discussion In this section, we go through the aspects that play a major role in the process of training GANs with medical data. ## 5.1. Training Volatility Throughout this work, the training instability of GANs was a recurrent theme, underlying how slight hyperparameter adjustments can considerably affect the training process. In contrast, GANs were not equally sensitive to the selected hyperparameters. While it is true that DCGAN and LSGAN showed the highest variability, it came to be easier to train WGAN and HingeGAN, which were less sensitive to hyperparameter selection. Moreover, even though the state-of-the-art GANs, such as SPADE and StyleGAN, seem to be the only viable pick to produce images of high quality, they still suffer from long training times and can sometimes lead to overfitting and “Memory GAN”, i.e., a GAN that outputs the training set. Likewise, in the case of the smaller GANs, finding the right set of hyperparameters was not always simple. To illustrate this point, we went through a total of 1500 training runs with different hyperparameter combinations. Most of the runs led to models that could not generate meaningful images, while the remaining runs did not always fair well when evaluated with the FID, or through the image segmentation task. Concurrently, although a considerable amount of hyperparameters were explored, we did not have enough GPUs to go through a GAN architecture search, which could have provided better performance. ## 5.2. FID and Image Quality We relied on the FID score to monitor the training of the GANs. We also compared FID to a domain specific evaluation (segmentation Dice score). This process, enabled us to better understand to what extent an FID metric, optimized for natural images, can be used in medical imaging. Our results reveal that the FID score continuously improves as the training of any GAN moves forward. In contrast, the FID score could not be consistently relied on as a measure of the image quality when used as training input for subsequent tasks. Table 2 clearly shows that a lower FID score, does not always yield better performance on a subsequent task of image segmentation. These results make it interesting to ask whether metrics grounded in domain specific knowledge, could help make GANs easier to evaluate and compare. ## 5.3. Data Scale When comparing the results on the three datasets, an important trend related to the performance of the GANs and the data is visible. When the size of the input dataset is exceedingly small, as is the case for the IDRiD dataset in our study, the expected benefit of training a GAN to increase the dataset size, quickly dissipates, as they often overfit, which can have an adverse effect on the subsequent task. In parallel, when the input dataset is highly unbalanced, portrayed by the SLiver07 dataset in our study, with only $5\%$ foreground pixels, the trained GANs can further exacerbate this imbalance, as they will ultimately learn the underlying biases of the training data. The variety in the input dataset matters as much as the number of data points. Depending on the variety of the data, the GAN can overfit to a single mode in the input distribution or truly learn the overall distribution. Moreover, most of the prevalent GAN architectures deal with 2D images, which is not necessarily the best format to train with when dealing with medical imaging data, as it might have been acquired in 3D. This might further explain the poor performance shown on the liver CT dataset. ## 5.4. Compute Scale It should be kept in mind that training a GAN is often computationally intensive (typically because it involves two or more networks), and requires a large amount of memory. In addition, training GANs requires a lot of hyperparameter tuning, which may or may not lead to better results when considering the downstream tasks the generated data is intended for. This also affects more sophisticated GANs which, despite their good performances, which can fool medical experts, require large computing resources to train. For example, the StyleGAN took roughly 30 days to train on the ACDC dataset, with an NVidia Titan V GPU, with 12 GB of memory. Yet, StyleGAN did not always offer a guarantee to the usefulness of the generated samples (Dice score of 0.36 for StyleGAN on the SLiver07 dataset). ## 5.5. Medical Worth As there is no automated objective way to assess whether a medical image conveys the information for the diagnosis it is intended for, we based our analysis on a proxy task, that aims to mimic the process for which a dataset is created, and compared its performance to that of the original data. Here the proxy task is the evaluation of the segmentation performed by a U-Net, and the results are evaluated by the Dice score. The results show that, although most of the images generated by the tested GANs fail in reaching the baseline performance, some of the more advanced ones manage to close the gap. However, when subjectively assessing the images generated by the larger GANs, we can still see that they exhibit a remarkable degree of complexity and quality. This might be related to the smaller scale of the datasets in medical imaging, and the difference in their nature with the original datasets for which most of the GANs were tailored. Likewise, a considerable amount of the medical data is acquired in a 3D fashion and voxel wise, e.g., CT. Typical GANs might not capture the full extent of the medical information, when trained solely on 2D views. Indeed, this makes exploring GANs specially made for medical data an interesting research avenue, and could lead to an improvement in quality and ultimately clinical usability. ## 6. Conclusions Currently the use of deep learning approaches in medical image analysis remains hindered by the limited access to large annotated datasets. To address this limitation, we have probed both the limitations and promising aspects of generative adversarial networks as medical image synthesis tools, through an experimental approach on three different datasets. As a result, GANs’ effectiveness as a source of medical imaging data was found to not always be reliable, even if the produced images are nearly indistinguishable from real data. Tangentially, the results point to the fact that traditional metrics used to evaluate GANs are less robust than task based evaluations. All in all, this study should drive more research on GANs that take into account the different subtleties of medical data and hopefully lead to better generative models. ## References 1. Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y.. 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--- title: The Role of Mu Opioid Receptors in High Fat Diet-Induced Reward and Potentiation of the Rewarding Effect of Oxycodone † authors: - Asif Iqbal - Abdul Hamid - Syed Muzzammil Ahmad - Kabirullah Lutfy journal: Life year: 2023 pmcid: PMC10055773 doi: 10.3390/life13030619 license: CC BY 4.0 --- # The Role of Mu Opioid Receptors in High Fat Diet-Induced Reward and Potentiation of the Rewarding Effect of Oxycodone † ## Abstract Excessive high fat diet (HFD) consumption can induce food addiction, which is believed to involve the communication between the hypothalamus and mesolimbic dopaminergic neurons, originating in the ventral tegmental area (VTA) and projecting to the nucleus accumbens (NAc). These brain areas are densely populated with opioid receptors, raising the possibility that these receptors, and particularly mu opioid receptors (MORs), are involved in rewards elicited by palatable food. This study sought to investigate the involvement of MORs in HFD-induced reward and if there is any difference between male and female subjects in this response. We also assessed if exposure to HFD would alter the rewarding action of oxycodone, a relatively selective MOR agonist. The place conditioning paradigm was used as an animal model of reward to determine if short-time (STC, 2 h) or long-time (LTC, 16 h) conditioning with HFD induces reward or alters the rewarding action of oxycodone. Male and female C57BL/6J mice as well as MOR knockout and their wildtype littermates of both sexes were tested for basal place preference on day 1 and then conditioned with an HFD in one chamber and a regular chow diet (RCD) in another chamber for 2 h on alternate days. Three sets of STC were used, followed by a set of LTC. Each set of conditioning consisted of two conditioning with RCD and two conditioning with HFD. Mice were tested for place preference after each set of STC and again after LTC. Controls were conditioned with RCD in both conditioning chambers. Following the last place preference test, mice were treated with oxycodone and conditioned in the HFD-paired chamber and with saline in the RCD-paired chamber for one hour once a day to explore the possibility if the HFD could alter oxycodone reward. The result showed that HFD induced conditioned place preference (CPP) in male but not female subjects. However, oxycodone conditioning elicited reward in both male and female mice of the HFD group but not the control group, showing that prior conditioning with HFD potentiated the rewarding action of oxycodone. The latter response was mediated via MORs, as it was blunted in MOR knockout mice. Similarly, HFD-induced CPP was blunted in male MOR knockout mice, suggesting sexual dimorphism in this response. ## 1. Introduction An increase or decrease in food consumption is regulated by several orexigenic or anorexigenic neurons located in the central nervous system (CNS). These neurons are activated or deactivated through signals elicited by hormones secreted from the CNS or periphery, such as the stomach, intestine, and pancreas. These neurons also communicate with the ventral tegmental area (VTA) via the paraventricular nucleus of the hypothalamus (PVN). The dopaminergic neurons in the VTA project to the nucleus accumbens (NAc) [1], a brain area often referred to as the hedonic hotspot [2]. The intake of palatable food beyond the energy requirement activates the reward circuit, causing dopamine release in the NAc [3], thereby leading to binge eating and possibly food addiction. There is also evidence showing alterations in mesolimbic dopaminergic neurons in obese individuals, as observed with addictive drugs (for reviews, see [4,5]). There is also evidence showing that animals exposed to HFD express enhanced cocaine reward [6], but blunted ethanol preference [7]. However, it is unclear whether HFD consumption is associated with reward. Thus, one of the goals of this study was to assess if conditioning with HFD would induce reward. Both palatable diet and addictive drugs activate the mesolimbic dopaminergic system (for reviews, see [5,8]). The endogenous opioid system, consists of endogenous opioids and their receptors, has been implicated in feeding and diet-induced obesity [9]. In this regard, palatable food exposure has been shown to increase MOR expression among obese rats [10]. Similarly, excessive HFD consumption has been implicated in dysregulated dopamine and opioid gene expression [11]. In addition, the overeating of palatable food has been shown to involve the endogenous opioid system [12]. Moreover, consumption of either palatable or non-palatable/regular food has been reported to induce the release of endogenous opioid peptides [13]. Additionally, local administration of MOR antagonists, such as naltrexone, in the NAc and amygdala has been reported to reduce palatable diet intake [14,15]. Overall, these observations suggest that endogenous opioid peptides can alter food consumption and may play a functional role in food reward. Thus, in the current research, we have combined the place conditioning paradigm with food intake to assess if conditioning with HFD induces rewards in mice and if the duration of conditioning with HFD would be important in the acquisition of a conditioned place preference (CPP). Considering that MOR is implicated in the palatability of food, we also determined the role of MOR in HFD-induced CPP. To address these issues, we used MOR knockout and their wildtype controls to investigate whether there is any difference in HFD consumption and HFD-induced reward between mice of the two genotypes. Not only endogenous opioids, but also exogenous opioids may be involved in food intake and food reward. For instance, the local administration of a selective MOR agonist in the NAc, amygdala, hypothalamus, or VTA has been shown to increase palatable diet intake [16,17,18]. Conversely, a previous report has shown the lowering of food liking after treatment with a non-selective MOR antagonist [19]. Furthermore, MORs have a strong correlation with the intake of palatable diet intake [20]. Together, these findings suggest that there is a crosstalk between food reward and the rewarding actions of opioids. We hypothesized that prior exposure to HFD induces sensitization via the release of endogenous opioids acting at MORs. Thus, we also assessed if a prior conditioning with HFD would alter the rewarding action of oxycodone, a relatively selective MOR agonist. Evidence exists to suggest a strong relationship between sex hormones and opioids. Interestingly, populations of neurons containing proopiomelanocortin (POMC, the beta-endorphin precursor) have been reported to express estrogen receptors (αERs). In addition, POMC mRNA fluctuates throughout the phases of the estrus cycle, and estrogen depletion via ovariectomy in females has been reported to decrease POMC mRNA [21]. Furthermore, knocking out the αER on POMC neurons leads to an increase in food consumption and body weight in female but not male mice [21]. Moreover, both MOR and αER are present in the arcuate nucleus of the hypothalamus (ARC) and NAc [1,22,23], raising the possibility that an interaction between these two receptor types is likely, and thus male/female differences may be present in food intake and food reward or in the involvement of MORs in food reward. Considering that the rewarding actions of drugs of abuse can be different between males and females [24], we also assessed if sex-related differences exist in HFD-induced reward, crosstalk between food reward and oxycodone reward, or in the involvement of MORs in these responses. ## 2.1. Subjects Age-matched male (25–30 g) and female (20–21 g) C57BL/6J mice as well as male (30–32 g) and female (20–21 g) mice lacking MOR backcrossed for 12 generations on a C57BL/6J mouse strain bred in-house were used throughout. Each group consisted of six male and six female C57BL/6J mice or mice lacking MOR and their wildtype littermates. The original breeders of each line were purchased from Jackson Laboratories (Bar harbor, ME, USA). Subjects were kept one mouse per cage in a temperature-controlled (22 ± 3 °C) under a 12 h/12 h light/dark cycle (6 am light on and 6 pm light off). Animals had access to regular laboratory chow and water ad libitum except during the experiment. All the procedures were in accord with the NIH guideline for the use of animals in research and approved by the Institutional Animal Care and Use Committee (IACUC) at Western University of Health Sciences (Pomona, CA, USA). ## 2.2. Drug Oxycodone hydrochloride, purchased from Sigma Aldrich (St. Louis, MO, USA), was dissolved in normal saline ($0.9\%$ sodium chloride solution) and injected intraperitoneally (i.p.) to each mouse during the conditioning at a dose of (5 mg/kg) per body weight. ## 2.3. Diets High fat diet (HFD) for rodents was purchased from Research Diets (New Brunswick, NJ 08901, USA). The diet contains 60 Kcal % fat (Code name D12492, which contains 245 gm of fat generated from lard, 200 gm lactic protein and 125 gm carbohydrate in each 773.85 gm). The regular chow diet (RCD) for rodents contains $18\%$ protein (protein $18.6\%$, fat $6.2\%$, and carbohydrate $44.2\%$) and was purchased from Teklad Global Diet (Madison, WI, USA). ## 2.4. To Determine If Binge Eating of HFD Induces CPP or Alter Oxycodone Reward and If Sex-Related Differences Exist in These Responses We used an unbiased and counterbalanced place conditioning paradigm, widely used as an animal model of reward [25], to determine if a high fat diet would induce CPP or alter the rewarding effect of oxycodone. We used both male and female mice and assessed if sex-related differences exist in HFD-induced CPP. The place conditioning paradigm is the same as that of our earlier reports [26,27,28]. Briefly, in this paradigm, animals are conditioned with a drug or another agent in one of the conditioning chambers and with an inert substance, usually saline or another vehicle, and animals are tested for place preference toward the conditioning chambers before and after the conditioning. The conditioning chambers are distinguishable from each other by the inclusion of visual, olfactory and tactile cues. If the agent is rewarding, the animal will prefer the drug-paired chamber over the vehicle-paired chamber. In contrast, if the drug is aversive, the animal will avoid the drug-paired chamber. The place conditioning protocol consisted of three phases: [1] preconditioning, [2] conditioning, and [3] postconditioning. The preconditioning test was conducted on day 1 to assess the baseline place preference of each mouse toward the conditioning chambers. On this day, each mouse was placed in the gray central neutral chamber with both guillotine doors opened and allowed to explore the conditioning chambers for 15 min. The conditioning chambers were distinguishable from each other by the presence of visual cues, i.e., one of which had one-inch horizontal and the other one vertical black and white stripes. The amount of time that the mice spent in each chamber was recorded. Subjects who spent more than $67\%$ or less than $33\%$ of the total time (900 s) in any of the chambers were excluded from the remainder of the study, as per an earlier report [29]. Mice were then divided into two groups: [1] the HFD group and [2] the control (RCD) group. The HFD group received conditioning in the next four days, two with the HFD in one of the conditioning chambers or paired chamber (PCh) and two with the RCD in the opposite chamber or non-paired chamber (NPCh). The HFD was paired with the vertically striped chamber for some mice and with the horizontally striped chamber for other mice. We had an equal number of male and female mice assigned to each chamber. The control group received conditioning with RCD in both chambers, but one of the conditioning chambers was considered as the PCh and the other as NPCh. The conditioning was carried out from 3–5 pm (2 h) each day. On day 6, mice were then tested for place preference for 15 min (test 1), as described for day 1. Given that we did not observe any preference toward the PCh, we continued with two additional sets of conditioning and tested mice for placed preference. After the third test for place preference, they received conditioning for 16 h (6 pm–10 am) for the next four days. Mice were then tested for place preference. The rationale for overnight conditioning was that we hypothesized that a longer conditioning with HFD would induce reward. We then tested if HFD conditioning for the rewarding action of oxycodone to assess if prior conditioning with HFD would alter the rewarding action of oxycodone, a relatively selective MOR agonist. To this end, the day after the last conditioning, mice were conditioned with oxycodone (5 mg/kg, i.p.) in the PCh and saline in the NPCh for one hour. The choice of the dose of oxycodone was according to an earlier study [30]. On the following day, mice received the alternate treatment and were conditioned to the opposite chamber. Twenty-four h later, mice were tested for a place preference toward the chambers, as described for day 1. Figure 1 illustrates a schematic presentation of the place conditioning protocol. ## 2.5. To Assess the Role of MOR in HFD-Induced Reward and If Sex-Related Differences Exist in These Responses The experimental procedure was the same as described above, except MOR knockout and wildtype mice were used, and mice of both genotypes were exposed to HFD or RCD on alternative days. Given that we did not observe any place preference in mice of the RCD group, we did not include the control group in this experiment in order to reduce the number of mice used. ## 2.6. Data Analysis The data are presented as means ± standard errors of the mean (±SEM) of the amount of time that mice spent in the paired chamber, and the amount of food (g) and calories (g) consumed in the conditioning chambers. All data were analyzed using a three-way analysis of variance (ANOVA) followed by the Fisher’s LSD post hoc test for multiple comparison. $p \leq 0.05$ was considered statistically significant. Each group/genotype contained six male and six to eight female mice. ## 3.1. Conditioning with a HFD for 16 h but Not 2 h Induced CPP and Enhanced the Rewarding Action of Oxycodone in Male and Female C57BL/6J Mice Figure 2 illustrates the amount of time that male (right panel) and female (left panel) mice spent in the paired chamber (PCh) in RCD and HFD groups. A mixed-effect ANOVA revealed a significant effect of diet (F [1, 204] = 38.93; $p \leq 0.0001$) and session (F [5, 204], =3.98; $p \leq 0.01$) and a trend for the effect of sex (F [1, 204] = 2.69; $$p \leq 0.10$$) but no interaction between the factors ($p \leq 0.05$). Subsequent analysis of data in male mice (Figure 2, left half of the panel) showed that STC with HFD failed to induce preference toward the PCh in these mice, as there was no significant difference between the baseline preference (BL) vs. Test 1, 2 or 3 ($p \leq 0.05$). On the other hand, when mice were exposed to LTC with HFD (labeled in the figure as ON), they spent more time in the PCh on the test day after LTC with HFD than the day of baseline (BL) test for preference toward the PCh ($p \leq 0.05$; Figure 2, ON vs. BL). In addition, there was a significant ($p \leq 0.05$) increase in the amount of time that mice of the HFD group spent in the PCh on this test day than mice of the RCD group (Figure 2). In contrast, mice in the RCD group did not exhibit any preference toward the PCh on any of the test day compared to the initial test for baseline preference toward this chamber ($p \leq 0.5$). Similarly, oxycodone (5 mg/kg) failed to induce preference toward the PCh in these mice (Figure 2, $p \leq 0.05$). In contrast, mice of the HFD group exhibited a significantly greater preference toward the PCh after oxycodone conditioning compared to baseline preference test day (Figure 2; $p \leq 0.01$). In addition, there was a significant increase in the amount of time that mice in the HFD group spent in the PCh on this day compared to mice in the RCD group ($p \leq 0.01$; Figure 2). While there was a significant preference toward the PCh in male mice after LTC conditioning with HFD and oxycodone conditioning, female mice failed to exhibit a preference toward the PCh on any test day vs. baseline preference test day ($p \leq 0.05$; Figure 2, right half of the panel), suggesting that some male/female differences exist in HFD-induced preference toward the PCh. Female mice of the HFD group spent significantly more time in the PCh than mice of the RCD group ($p \leq 0.05$). A trend toward greater preference toward the PCh following the LTC conditioning with HFD was observed in male vs. female mice ($$p \leq 0.05$$). Together, these results suggest that LTC with HFD induced conditioned place preference (CPP) in male but not female mice. We also observed a greater CPP response following oxycodone conditioning in both male and female mice of the HFD group compared to RCD group. ## 3.2. LTC but Not STC with HFD Induced CPP and Enhanced the Rewarding Action of Oxycodone in Wild-Type (MOR+/+) but Not MOR Knockout (MOR−/−) Mice Figure 3 depicts the amount of time that male (left panel) and female (right panel) mice lacking MOR (open squares) and their wildtype controls (closed squares) of the RCD and HFD groups spent in the PCh. A mixed effect ANOVA revealed a significant effect of genotype (F [1, 60] = 41.48, $p \leq 0.0001$) and session and genotype interaction (F [5, 60] = 3.27, $p \leq 0.05$), but no significant effect of other factors or interaction between other factors ($p \leq 0.05$). Fisher’s LSD post hoc test showed that male wildtype mice spent more time in the PCh following the LTC with HFD ($p \leq 0.05$) and after oxycodone conditioning ($p \leq 0.01$) compared to the initial test for basal preference toward this chamber (Figure 3, the left panel, closed squares). These mice also exhibited greater preference compared to male knockout mice after LTC with HFD and oxycodone conditioning ($p \leq 0.05$). Although the baseline appears different between wildtype and knockout mice, it did not reach the level of significance ($$p \leq 0.09$$), showing that the difference in preference following LTC and oxycodone was not due to baseline differences between wildtype and knockout mice. As we observed in C57BL/6J mice, female wildtype mice failed to exhibit any CPP except following oxycodone conditioning, as evidenced by a significant increase in the amount of time that female C57BL/6J mice spent in the PCh on the test day following oxycodone conditioning compared to baseline test (Figure 3, right panel, closed squares). However, the difference between male and female mice was not present in these mice although there was a trend ($$p \leq 0.08$$) toward an increase in the amount of time that male mice spent in the PCh following LTC with HFD compared to female wildtype mice. While male mice lacking MOR did not exhibit place preference or place aversion after conditioning with HFD or oxycodone, female mice expressed aversion following oxycodone conditioning, i.e., there was a significant decrease in the amount of time that these mice spent in the PCh on the test day following oxycodone conditioning compared to the baseline test ($p \leq 0.05$). ## 3.3. Food Intake and Calorie Consumption between Male and Female C57BL/6J Mice of the RCD and HFD Groups Food intake (upper panel) and calorie consumption (lower panel) in male and female mice is shown in Figure 4. Three-way repeated measures ANOVA revealed a significant effect of session (F [7, 192] = 255.1, $p \leq 0.0001$), sex (F [1, 192] = 10.44, $p \leq 0.01$) and diet (F [1, 192] = 274.40, $p \leq 0.0001$). There was also a significant interaction between diet and session (F [7, 192] = 7.36, $p \leq 0.0001$) but no other interactions were observed. The Fisher’s LSD post hoc test showed that the HFD intake was higher in males compared to females in the last session of the LTC with HFD ($p \leq 0.0001$; Figure 4; upper panel). There was also a robust trend toward an increase in HFD intake in male vs. female mice on the first session of the LTC with HFD ($$p \leq 0.06$$). Similarly, calorie intake was higher in the male mice of the HFD group (Figure 4, lower panel). Data analyses revealed a significant effect of session (F [7, 192] = 270.9, $p \leq 0.0001$), sex (F [1, 192] = 12.05, $p \leq 0.001$) and diet (F [1, 192] = 982.6, $p \leq 0.0001$). Fisher’s LSD post hoc test showed that there was an increase in caloric intake in male compared to female mice on the last two sessions of conditioning with HFD (Figure 4, bottom panel). ## 3.4. Comparison of Food Intake between Male and Female MOR Knockout vs. Wildtype Mice Figure 5 depicts food consumption in male and female wildtype (upper panel) and knockout (lower panel) mice. A mixed effect ANOVA of data showed a significant effect of session (F [7, 176] = 201.8; $p \leq 0.0001$) and sex (F [1, 176] = 18.5; $p \leq 0.0001$), but no effect of genotype and the interactions between factors ($p \leq 0.05$). Subsequent analyses revealed a significant ($p \leq 0.001$) increase in HFD intake in male than female wildtype mice on day 17 (D17; Figure 5, top panel). There was also an increase in HFD intake in male wildtype than male knockout mice on this day ($p \leq 0.01$). For the calorie intake, a mixed effect ANOVA revealed a similar result, i.e., a significant ($p \leq 0.001$) increase in calorie intake in male compared to female wildtype mice as well as in male wildtype vs. male knockout mice on day 17 (D17; Figure 5, lower panel). ## 4. Discussion The main findings of the current study are that male but not female mice exhibited a significant place preference only following long access to HFD in the conditioning chamber. The preference becomes more robust following conditioning with oxycodone, which failed to induce any significant preference in control mice, i.e., mice conditioned with a RCD in both conditioning chambers. These findings reveal that LTC with HFD induces CPP in male but potentiates oxycodone-induced reward in both male and female mice, as we only observed CPP following oxycodone in mice of the HFD but not the RCD group. The action of oxycodone was mediated via MOR, as we did not observe any CPP in MOR knockout mice of the HFD group. We also observed that male mice lacking MOR failed to exhibit any CPP following STC or LTC, suggesting that the rewarding action of HFD may involve MOR, but male/female differences may exist in this response. Previous studies have shown that the overconsumption of fat-rich palatable diets induced drug-like reward hyposensitivity [31]. Importantly, exposure to HFD elicits changes in accumbal dopamine [32,33,34,35]. However, to the best of our knowledge, no prior study has evaluated whether HFD would induce reward. Therefore, using the place conditioning paradigm as a model of reward [25], we assessed if conditioning with HFD for 2 h, which may lead to binge eating in both male and female mice (Figure 4), could induce reward in C57BL/6J mice. Our data showed that short-term (2 h) conditioning with HFD failed to induce reward in male or female C57BL/6J mice. When male and female mice received 16-h conditioning with HFD, it elicited a significant CPP response in male but not female mice. We link this CPP response to the increased HFD consumption compared to 2 h conditioning, when animals had access for a short time to the HFD. There was a 2–3-fold increase in food intake during the LTC compared to STC. Although STC led to binge eating, it did not induce CPP in male or female mice. Thus, the duration of conditioning and the amount of HFD consumed may be essential to result in a significant preference toward the chamber paired with HFD. It is tempting to propose that a longer exposure time to the HFD may have increased dopamine in the NAc, leading to a significant CPP response. An earlier study claimed that palatable foods recruit the endogenous opioid system [36]. Another study reported that intermittent sugar intake led to opioid dependence [37]. Considering that exposure to HFD elicits changes in accumbal dopamine [32,33,34,35], that the MOR agonists increase extracellular dopamine [38,39], and that the endogenous opioid system has been implicated in the palatability of food [16,40], we proposed that prior conditioning with HFD would enhance the rewarding action of oxycodone, a mu opioid receptor agonist [41]. To test this possibility, we used a single conditioning paradigm and a low oxycodone dose (5 mg/kg, i.p.) which did not induce CPP in male or female control mice (RCD group) in the current study. In contrast, both male and female mice conditioned with HFD in one conditioning chamber and RCD in the opposite chamber (HFD group) displayed a robust CPP response following the single conditioning with oxycodone when paired with the HFD-paired chamber (PCh). Our results are consistent with previous reports [42,43,44] suggesting that prior exposure to HFD enhances the rewarding action of addictive drugs; in this case, oxycodone. However, given that it is generally thought that palatable food is rewarding (for a review, see [45]) and that mice already showed CPP following LTC with HFD, it is unclear whether conditioning with HFD potentiated the oxycodone reward or the enhanced reward following oxycodone was a result of the CPP induced by HFD. Thus, future studies are needed to delineate between these two possibilities. Chronic HFD exposure has been reported to alter MOR gene expression differentially between male and female mice [29,46]. Previous research has reported that fat-rich palatable foods are rewarding [45] and may cause the release of endogenous opioids known to govern palatable food intake [47]. Thus, we hypothesized that HFD induces reward, and that MOR is involved in this response. We used MOR knockout mice and their wildtype littermates to test our hypothesis. We also determined if any sex-related difference exists in the CPP response induced by HFD or the potentiation of oxycodone reward. We observed a significant CPP response in male but not female wildtype mice after LTC with HFD and following oxycodone conditioning. This response was blunted in male mice lacking MOR, suggesting that MOR may be involved in HFD-induced reward in male mice. Despite this, the food intake was the same between mice of the two genotypes except on the last conditioning day (day 17). Thus, the lack of CPP in knockout mice was not due to a decrease in food intake, as they were consuming an equal amount of HFD compared their wildtype littermates. Together, these findings suggest that MOR is necessary for the expression of CPP induced by LTC with HFD and oxycodone in mice, but sexual dimorphism exists in this response, which requires further investigation. For example, it would be necessary to determine the interaction between sex hormones and MOR, as its expression can be altered during the phases of the estrous cycle [48]. The NAc can be a potential target to assess for the interaction between MOR and sex hormones, as both MOR and αER are present in the NAc, and this brain area is implicated in food reward and drug reward [1,22,23]. The results of the current study should be interpreted with caution, because our study was underpowered, especially in female mice, given that we did not record phases of the estrous cycle in these mice. Given that there was no significant difference between the amount of time that C57BL/6J and MOR wildtype mice spent in the PCh (Supplementary date), we combined the results of these mice with their respective sex group, which led to an unequal sample size between the HFD group and the RCD group as well as between wildtype mice and knockout mice, and may have confounded the interpretation of the data. ## 5. 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--- title: 'Occupational Physical Activity and Cardiometabolic Risk Factors: A Cross-Sectional Study' authors: - Montserrat Gómez-Recasens - Silvana Alfaro-Barrio - Lucia Tarro - Elisabet Llauradó - Rosa Solà journal: Nutrients year: 2023 pmcid: PMC10055795 doi: 10.3390/nu15061421 license: CC BY 4.0 --- # Occupational Physical Activity and Cardiometabolic Risk Factors: A Cross-Sectional Study ## Abstract Contradictory data exist on the impact of occupational physical activity (OPA) on cardiovascular health. We aimed to evaluate the association between OPA and cardiometabolic risk factors. A cross-sectional study was performed in an environmental services company in 2017 (Spain). OPA was classified by work categories as being low (≤3 METs) or moderate−high (>3 METs). Multiple linear and logistic binary regression models were used to assess the associations between OPA and cardiometabolic risk factors related to obesity, blood pressure, blood lipids, and associated medical conditions, adjusted by age, sex, alcohol consumption, and global physical activity. In total, 751 employees were included (547 males and 204 females), and $55.5\%$ ($$n = 417$$) had moderate−high OPA. Significant inverse associations were observed between OPA and weight, body mass index, waist circumference, waist−hip ratio, and total cholesterol both overall and in males. OPA was significantly inversely related to dyslipidemia overall and in both sexes, while the overweight plus obesity rate was inversely related only in the total and male populations. OPA was associated with a better cardiometabolic risk factor profile, particularly in males. The fact that our models were also adjusted by global physical activity highlights the associations obtained as being independent of leisure time physical activity effects. ## 1. Introduction The workplace has been identified as a suitable environment to promote healthy life-styles for chronic disease prevention [1] due to the large population involved and the increasing trend in working hours/day [2,3]. Workplace environment contributes to the vitality of workers in order to prolong their working life. Health promotion programs in the workplace have been shown to have a positive impact on workers’ physical and mental health [4,5]. Currently, there is an increase in sedentary lifestyles caused by a mechanization of work, technology, etc., in the workplace, and by changes in the environment and society outside workplace [6]. Sedentary lifestyles are associated with worse health and an increase in early morbidity and mortality. It is known that leisure-time physical activity (LTPA), the physical activity (PA) performed in free time, decreases the risk of chronic diseases such as cardiovascular disease (CVD), obesity, diabetes mellitus (DM), hypertension, and some types of cancer [7,8]. The role of occupational PA (OPA), performed at work on health, however, still needs to be defined, independent of the LTPA performed. Studies focused on OPA and CVD risk have reported di-verse results. In some studies, a beneficial effect of OPA on cardiovascular and total mortality has been described [9,10]. One study in patients with diabetes concluded that not only LTPA, but also occupational and commuting PAs, are important components of a healthy lifestyle among patients with diabetes [9]. Another study in non-risk cardiovascular individuals concluded that the lack of LTPA and a sedentary occupation are associated with an increased risk of ischemic heart disease death [10]. Data from the Danish Nurse Cohort Study showed that a low influence at work (defined as the level of influence an individual normally has on the organization of their daily work) was the key factor for increased risk of ischemic heart disease in nurses exposed to strenuous OPA [11]. In some studies, low OPA has been shown to be poorly or not associated with health status [12], and meta-analyses data showed no interrelationship between PA at work and CVD [13]. Data from the Copenhagen City Study concluded that a higher LTPA is associated with reduced adverse cardiovascular events and all-cause mortality risk, while a higher OPA is associated with increased risks, independent of each other [14]. Previous data in the frame of this study have shown that high OPA was associated with an increased risk of all-cause mortality and myocardial infarction only when LTPA was low or moderate [14]. Concerning sex differences, data from the meta-analyses of prospective studies showed that high OPA levels were associated with an increased risk of mortality in men, but not in women (with even a tendency for an inverse association) [12]. Recent data from the Norwegian Cause of Death Registry, with 437,378 participants, showed that moderate to high OPA contributed to longevity in men [15]. However, OPA did not seem to affect longevity in women [15]. These differences can suggest a gender response to OPA or the fact that men are more likely to be involved in physically demanding work than women, causing dissimilar stress on the cardiovascular system [12]. OPA has also been associated with cardiometabolic risk factors. A low OPA has been associated with an increase in hypertensive status in Korean women [16]. However, a protective effect of OPA on diabetes and hypertension was reported in a cohort of 5,157 participants [17]. In contrast, in a Taiwanese cohort of 3,296 workers, a high OPA was associated with a lower risk of abdominal adiposity and elevated triglycerides and diastolic blood pressure (DBP), but with a higher risk of elevated systolic blood pressure (SBP) [18]. Metabolic syndrome (MS) incidence and impaired insulin resistance were significantly related to nonmanual labor, a proxy for low OPA activity, in a cohort of 2,348 middle-aged Korean men [19]. Therefore, PA could have differential health effects depending on whether LTPA or OPA was considered. The possible opposite health effects of LTPA and OPA have been typified as the so-called PA health paradox [20], in which, while PA in the leisure time improves health, OPA could be associated with a positive or negative impact on health. The results of recent cross-sectional analyses in the frame of the Copenhagen City Study highlight the importance of considering the PA health paradox, at least for some risk factors for CVD [20]. In this context, the aim of the present study was to evaluate the association between OPA and cardiometabolic risk factors in adults, both men and women, from 18 to 65 years old. Our hypothesis was that OPA would be independently related to cardiometabolic risk in both men and women. ## 2.1. Design of the Study The present cross-sectional study was carried out from 1 January to 31 December 2017 at the company Fomento de Construcciones y Contratas (FCC, www.fcc.es, accessed on 15 January 2023), a Spanish building company, S.A. Delegation of Catalonia. The FCC Group handles environmental services, end-to-end water management, infrastructures, cement, and real estate management. All participants signed a written informed consent form. The study design was approved and agreed upon by the security and health committees of all company worksites and worksite unions. A certificate of ethical approval from the Global Security and Health Committee of the FCC S.A. Delegation, and another from Catalan Public Services, were also obtained. The present cross-sectional study followed the STROBE criteria [21] (Table S1). ## 2.2. Participants and Public Involvement Participants involved in the approval of the ethics statement were employees of FCC S.A. Delegation, aged 18 to 65, who were active workers during the present cross-sectional study. ## 2.3. Inclusion and Exclusion Criteria To be eligible for inclusion, participants had to [1] be an active employee (not on sick leave) of the FCC S.A. Delegation with at least one year of service, [2] be ≥18 to 65 years old, and [3] had to have a medical visit in 2017. Exclusion criteria were those who did not fulfill all of the inclusion criteria. ## 2.4. Data Collection Every year, the company performs an optional medical check-up for all employees and collects data on age, sex, anthropometric measurements, routine laboratory biochemical parameters, lifestyle characteristics, and diagnosed medical conditions. Data from the 2017 visit were used in this study. Measurements and data of employees were collected by the physician and the nurse of the company. They worked together with the whole research team on the data analyses. The medical check-up had a duration of 30–40 min per employee. Data collected were anthropometric and medical conditions, blood sample analyses, and lifestyle characteristics using questionnaires, as specified in the following sections. The objective of check-ups was to contribute to assess the health of the employees and they were not linked to any employment requirement. ## 2.5. Anthropometric Data and Associated Medical Conditions Anthropometric data were weight (kg) measured with a roman scale (calibrated every year); height (m) measured using a wall-mounted stadiometer (Tanita Leicester Portable; Tanita Corp., Barcelona, Spain); waist circumference (WC) (cm) measured above the iliac crest; and hip circumference (cm) using a 150 cm anthropometric steel measuring tape, following the Lohman manual [22]. Body mass index (BMI) (kg/m2) was calculated and categorized using the World Health Organization (WHO) thresholds (BMI ≥25 kg/m2 as overweight (OW) and ≥30 kg/m2 as obesity). Diagnoses of abdominal obesity (WC ≥102 cm in men and ≥88 cm in women) were assessed [23,24]. The waist−hip ratio was calculated as the ratio between WC and hip circumference. A high waist−hip ratio was considered to be unhealthy when >1 for men and >0.9031 for women [23]. SBP and DBP were collected (mmHg) using the automatic sphygmomanometer OMRON HEM-907; Peroxfarma, Barcelona, Spain), which was calibrated every year following the correct standards. Pulse pressure was calculated as SBP minus DBP. Employees were sitting at rest for approximately 10 min with the arterial pressure monitor on the arm, the physician measured the arterial pressure three times, and the mean of the three measurements, with 1 min interval in between them, was used. Hypertension was defined as an SBP ≥140 mmHg and/or DBP ≥90 mmHg [25] or the use of hypotensive drugs. ## 2.6. Laboratory Data and Associated Medical Conditions Fasting blood samples were taken with participants in the fasting state through a catheter in the antecubital vein. Blood was collected in Vacutainer tubes with K2EDTA anticoagulant. Blood samples were centrifuged at 1500× g for 15 min, and 2.8 mL of plasma was finally recovered. Protease Inhibitor Cocktail (PIC; Sigma-Aldrich, Tres Cantos, Spain) was added to the plasma at a $\frac{1}{100}$ (1 μL of PIC for 100 μL of plasma) concentration. All of the samples were stored at −80 °C until processing. Plasma glucose, total cholesterol, and triglyceride values were obtained by standardized routine laboratory methods. Hypercholesterolemia was defined as a total cholesterol ≥200 mg/dL, hypertriglyceridemia as triglycerides ≥ 150 mg/dL, and dyslipidemia was considered when both cholesterol and triglycerides were higher than these thresholds. DM was reported by the employee and assessed by the family physician. ## 2.7. Lifestyle Data Tobacco consumption was registered in the clinical history of each employee fulfilled in the check-up visit. Participants were categorized as smokers or nonsmokers according to a specific question concerning whether they smoked or not, and how many cigarettes/day. In addition, alcohol consumption was registered by a question done in the check-up visit, concerning whether they drank alcohol or not, and the quantification of the alcohol and type of alcohol consumed [26]. Participants were categorized as [1] nondrinkers or with [2] low alcohol consumption (<28 standard drink units (SDUs)/week in males and <17 SDUs/week in females), [3] medium alcohol consumption (>28 SDUs/week in males and >17 SDUs/week in females), and [4] high alcohol consumption (>28 SDUs/week in males and >17 SDUs/week in females when participants were taking any kind of medication or if they had a chronic disease). Global PA (including both OPA and LTPA) was recorded by means of the Catalan Physical Activity Questionnaire [27] based on the International Physical Activity Questionnaire [28], and was classified as high, moderate, or low, according to the GPAQ Analysis Guide [28]. OPA was registered according to the Compendium of Physical Activities [29] and work categories of the International Labour Organization [30]. OPA was classified as low when there were ≤3 metabolic equivalents of task (METs) in work hours/day and moderate−high when there were >3 METs in work hours/day, according to the GPAQ questionnaire [31]. In this questionnaire, the questions, referred to last week, were [1] “How many days did you do vigorous physical activity?”, [ 2] “How many days did you do at least a short period of moderate-intensity physical activity?”, [ 3] “How many days did you walk at least 10 min?”. Possible answers were “From 1 to 7 days”, and minutes of the most representative day were also asked. In addition, a fourth question, “How many hours did you sit on a non-holiday day? ( Choose the most representative day)”, was included. ## 2.8. Employee OPA and Socioeconomic Characteristics Employees were classified in work categories based on the International Labour Organization [30] (Table S2) and linked with socioeconomic level into one of the following: (a) managers: administrative and commercial managers/production and specialized service managers (high socioeconomic levels: directors and managers); (b) drivers and mobile plant operators (medium socioeconomic level: intermediate occupation); (c) supervisor of operators (medium socioeconomic level: intermediate occupation); (d) cleaners and helpers (low and very low socioeconomic level: primary qualified, half-qualified and non-qualified); and (e) plant and machine operators and assemblers (low and very low socio-economic level: primary qualified, half-qualified, and nonqualified). ## 2.9. Sample Size Calculation In 2012, the National Health Survey of Spain (ENSE $\frac{2011}{12}$), carried out by the Ministry of Health, Social Services, and Equality, in collaboration with the National Institute of Statistics, showed that from the 21,007 adults who answered the survey, 8640 employees were active, and from them, $14.8\%$ presented hypercholesterolemia, $12.7\%$ hypertension, and $3.9\%$ DM, which are three cardiometabolic risk factors. On this basis, we selected hypercholesterolemia as the most frequent cardiometabolic risk factor in our population. Accepting an alpha risk of 0.05 and a beta risk of 0.2 in a two-sided test, 253 participants in each group would be necessary to detect a difference greater than or equal to 10 mg/dL in the total cholesterol. The standard deviation for total cholesterol in a southern European population has been estimated to be 37 mg/dL [32]. A drop-out rate of $10\%$ was anticipated. ## 2.10. Statistical Analyses Continuous variables were presented as the mean and standard deviation (SD), and categorical variables were presented as percentages. Shapiro−Wilk test was used for assessing the parametricity of the variables. ANOVA was used to compare continuous variables, and the chi-square test was used to compare the categorical variables. Logistic regression models were used to analyze the associations between OPA and cardiometabolic risk factors regarding categorical (dichotomic) variables, and multivariate linear regression models were used for continuous variables. All of the models were adjusted by age, sex, alcohol consumption, and global PA. All data were analyzed using SPSS V.27.0 for Windows (SPSS Inc., Chicago, IL, USA). The level of statistical significance was set to $p \leq 0.05.$ ## 3. Results Seven hundred fifty-one employees were included. Most of the population were male ($72.7\%$ ($$n = 546$$/751)). The mean (±SD) age of the population was 45.2 (±9.2) years. A total of $51.7\%$ ($$n = 388$$) of employees were nonqualified (categorized as very low socioeconomic status employees). Table 1 shows the characteristics of the population depending on their OPA. Almost half of the employees, $44.5\%$ ($$n = 334$$), were categorized as having low OPA. The percentage of females in the low OPA group ($10.8\%$, $$n = 36$$) was significantly lower than that in the high OPA group ($40.5\%$, $$n = 169$$) ($p \leq 0.001$). In females, the mean age was higher in the high OPA group than in the low OPA group ($$p \leq 0.003$$). Tobacco consumption was higher in males with high OPA than in those with low OPA ($$p \leq 0.029$$). In the total population, BMI, WC, waist−hip ratio, cholesterol, and triglycerides were lower ($p \leq 0.05$) in the high OPA group than in the low OPA group. Cohen’s d and effect size r for differences between low and high OPA groups were: $d = 0.211$, $r = 0.105$; $d = 0.364$, $r = 0.179$; $d = 0.421$, $r = 0.205$; $d = 0.264$, $r = 0.131$; $d = 0.240$, $r = 0.119$ for BMI, WC, waist−hip ratio, cholesterol, and triglycerides, respectively. When the analyses were performed by sex, BMI, WC, waist−hip ratio, and total cholesterol were significantly lower ($p \leq 0.05$) in males with high OPA than in those with low OPA. Concerning females, those in the high OPA group had higher levels of SBP and DBP ($p \leq 0.05$) than those in the low OPA group, reaching a borderline value for pulse pressure ($$p \leq 0.061$$). Concerning medical conditions (Table 2), in the total population, lower rates of overweight plus obesity, DM, hypertriglyceridemia, and dyslipidemia ($p \leq 0.05$) were observed in the high OPA group compare with the low OPA group. When analyses were performed by sex, males presented a similar pattern to that of the global population. Table 3 shows the association between OPA and cardiometabolic risk factors adjusted by age, sex, alcohol consumption, and global PA. In both the total and male populations, significant inverse associations were observed between OPA and weight, BMI, WC, waist−hip ratio, and total cholesterol ($p \leq 0.05$), pointing out an improvement, with lower values, in these parameters at high OPA levels. In contrast, in the female group, only a borderline positive association ($$p \leq 0.063$$) between OPA and SBP was observed, suggesting a possible, but not confirmed, increase in SBP when OPA increased. Table 4 shows the association between OPA and cardiometabolic risk medical conditions. Significant inverse associations, with odds ratios lower than 1, were observed for OPA and dyslipidemia in the total population and in both sexes ($p \leq 0.05$). Thus, an increase in OPA was associated with a better lipid profile both in the total population and by gender. Similarly, inverse significant associations were also observed between OPA and overweight plus obesity in the total population and in males ($p \leq 0.05$), pointing out the beneficial effects of OPA avoiding weight gain. In males, a borderline inverse association was obtained between OPA and DM ($$p \leq 0.051$$), suggesting a possible, but not confirmed, decrease in DM incidence when OPA increased. In agreement with the data obtained in Table 1, a direct significant association of OPA and tobacco consumption was obtained in males ($$p \leq 0.048$$), pointing out higher smoking habits in the high OPA group. No significant associations were observed in females among OPA and cardiometabolic risk medical conditions, other than dyslipidemia. ## 4. Discussion The results of the present study show that OPA is associated with a better profile of some cardiometabolic risk factors, particularly in males. Although differences in the females could point to a bias according to sex, they are probably related to the low prevalence of females in the low OPA group. As other studies with results in the same line that ours have identified, gender differences could also be attributed to a non-objective OPA measurement; also, men are more likely to be involved in physically demanding work than women, or gender differences in the cardiometabolic risk factors’ response to OPA occur [10,33,34]. High OPA was associated with less weight, BMI, WC, waist−hip ratio, and total cholesterol values in the total and male populations. Consequently, the rate of overweight plus obesity was lower at high OPA in these populations. Males with high OPA had less DM incidence, and in both sexes, dyslipidemia was lower at high OPA values. In our study, obesity and overweight and their related parameters were consistently reduced with an increase in OPA, both in univariate and multivariate analyses, in both the total and male populations. Our results differ from those obtained in the Chilean National Health Survey 2009–2010 [17], in the sense that in this study [14], OPA was not associated with obesity outcomes. In contrast, our results agree with those obtained in a Taiwanese survey, in which individuals with high OPA had a lower incidence of abdominal adiposity or hypertriglyceridemia [15]. Our results also agree with those obtained in a nurse’s survey in which work posts with low OPA, such as managers or supervisors, were significantly more likely to be overweight or obese than staff nurses [35]. We observed a beneficial effect of OPA on dyslipidemia in both sexes. Our data agree with those reported by the CESCAS I study in 7,512 adults from South American populations, with a better profile of lipid parameters associated with high OPA [36], in which a full adjustment of the models by potential confounding variables, including age, sex, and PA, as in our study, was performed. Contradictory data have been obtained concerning OPA and blood pressure in females. In a cross-sectional study, female cleaners with high OPA showed increased SBP and pulse pressure [37], but opposite data have also been reported [13]. In our study, when raw data were evaluated, both SBP and DBP were higher in high OPA females than in low OPA females. When data were adjusted by age, alcohol consumption, and global PA, however, only a borderline direct association of SBP with OPA remained. Thus, from our data, an association between high OPA levels and a better profile of several cardiometabolic risk factors exists. However, how do our results fit within the so-called PA health paradox with an increased risk of CVD with high OPA levels? One factor that could explain the discrepancies between the protective effect of OPA on cardiometabolic risk factors, but the contrary when CVD incidence is evaluated, is that high OPA workers have a low socioeconomic status, which is a well-known factor for CVD risk [38]. The fact that in our study we used occupation categories as a proxy for OPA does not allow us to adjust the models for this variable. Low socioeconomic status is linked with high tobacco consumption [39], as is reflected in our study in the relationship between high OPA and tobacco consumption. Anxiety and mood disorders, such as depression, which is also well-known CVD risk factors [40], are more prevalent in lower socioeconomic groups than in higher socioeconomic groups [41]. An increase in inflammatory status, low work control, fatigue, and exhaustion, among others, have been proposed as factors for explaining the CVD risk associated with high OPA [42]. Thus, factors involved in atherogenic risk, other than those examined in this study, could account for explaining the paradox. Another proposed explanation for the PA paradox is the differences in the characteristics of PA. LTPA often includes dynamic movements at conditioning intensity levels sufficient to improve cardiorespiratory fitness over short time periods with enough recovery time. In contrast, work often requires static and other non-conditioning activities over several hours per day without sufficient recovery time [20]. Differences among results in studies assessing the association of OPA and cardiometabolic risk factors could also be attributed to [1] the heterogeneity of the populations involved; [2] differences in the possible confounding variables used in the models; and [3] differences in OPA measurements, from proxies of occupational categories to direct measurement units. The study has several limitations. First, as a cross-sectional study, it cannot provide cause-effect relationships but only associations. Second, the main variable, the OPA measurement, was collected by questionnaire and not directly measured with an accelerometer. As previously mentioned, we used occupation categories as a proxy for OPA activity, which did not allow us to adjust the model by socioeconomic status. Third, we only had data on global PA (OPA + LTPA) and thus were unable to adjust only for LTPA. Fourth, males represented most of the sample. Therefore, the generalizability of our findings was limited. From our results, workers with a low OPA must be aware of the risk of sedentarism and be encouraged to compensate with the practice of LTPA and/or active com-muting PA. Data from the 2014–2016 Survey of Health of Wisconsin show that individuals classified as having low OPA levels were less likely to meet the U.S. Federal reported aerobic PA guidelines than individuals who were classified as having high OPA levels [43]. Similarly, nurses with passive jobs were significantly less likely to perform aerobic PA [35]. Interventions addressing modifiable behavioral risk factors for chronic disease would be advisable in low OPA workers. The limited number of interventions made at present, however, did not permit us to draw any conclusion on the proper interventions to be performed or their cost/effectiveness [44]. Further investigation of the association between OPA and health is needed. This issue has been recently reinforced by the WHO guidelines on PA and sedentary behavior [45]. ## 5. Conclusions In our study, OPA was associated with a better profile of cardiometabolic risk factors, particularly in males. The lack of associations obtained in the female group, except in the case of dyslipidemia, could be related to the low sample size of females in the low OPA group. The fact that our models were also adjusted by global PA highlights the associations obtained as being independent of the effects of LTPA. From our results, workers with low OPA activity must be aware of the risk of sedentarism and be encouraged to compensate with the practice of LTPA and/or active commuting PA. In addition, interventions addressing modifiable behavioral risk factors for chronic disease would be advisable in low OPA activity workers. Further investigation of the association between OPA and health is needed. Future actions for improving cardiometabolic risk factors could consider the results of this cross-sectional study and sex differences. ## References 1. Burton J.. 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--- title: Anti-Obesity and Anti-Inflammatory Effects of Novel Carvacrol Derivatives on 3T3-L1 and WJ-MSCs Cells authors: - Ivana Cacciatore - Sonia Spalletta - Annalisa Di Rienzo - Vincenzo Flati - Erika Fornasari - Laura Pierdomenico - Piero Del Boccio - Silvia Valentinuzzi - Erica Costantini - Elena Toniato - Stefano Martinotti - Carmela Conte - Antonio Di Stefano - Iole Robuffo journal: Pharmaceuticals year: 2023 pmcid: PMC10055808 doi: 10.3390/ph16030340 license: CC BY 4.0 --- # Anti-Obesity and Anti-Inflammatory Effects of Novel Carvacrol Derivatives on 3T3-L1 and WJ-MSCs Cells ## Abstract [1] Background: Obesity, a complex metabolic disease resulting from an imbalance between food consumption and energy expenditure, leads to an increase in adipocytes and chronic inflammatory conditions. The aim of this paper was to synthesize a small series of carvacrol derivatives (CD1-3) that are able to reduce both adipogenesis and the inflammatory status often associated with the progression of the obesity disease. [ 2] Methods: The synthesis of CD1-3 was performed using classical procedures in a solution phase. Biological studies were performed on three cell lines: 3T3-L1, WJ-MSCs, and THP-1. The anti-adipogenic properties of CD1-3 were evaluated using western blotting and densitometric analysis by assessing the expression of obesity-related proteins, such as ChREBP. The anti-inflammatory effect was estimated by measuring the reduction in TNF-α expression in CD1-3-treated THP-1 cells. [ 3] Results: CD1-3—obtained through a direct linkage between the carboxylic moiety of anti-inflammatory drugs (Ibuprofen, Flurbiprofen, and Naproxen) and the hydroxyl group of carvacrol—have an inhibitory effect on the accumulation of lipids in both 3T3-L1 and WJ-MSCs cell cultures and an anti-inflammatory effect by reducing TNF- α levels in THP-1 cells. [ 4] Conclusions: Considering the physicochemical properties, stability, and biological data, the CD3 derivative—obtained by a direct linkage between carvacrol and naproxen—resulted in the best candidate, displaying anti-obesity and anti-inflammatory effects in vitro. ## 1. Introduction Obesity, one of the global issues for public health, plays a central role in the onset of the metabolic syndrome and its implications [1,2,3,4]. It results from an imbalance between energy intake and energy expenditure and leads to hyperplasia and adipocyte hypertrophy [5]. It is associated with chronic inflammatory conditions, an increase in cytokines release, and inflammatory process activation [6,7]. Tumor Necrosis Factor-alpha (TNF-α), produced by both lymphoid cells and non-lymphoid cells, is the key regulator in the inflammatory process initiation and potentiation, and its levels are elevated in adipose tissues [8]. Moreover, TNF-α induces Cyclooxygenase-2 (COX-2) expression [9]. The role of TNF-α in obesity-related insulin resistance has been recently suggested. Furthermore, in the adipose tissue of several obesity models, TNF-α expression levels are high. The neutralization of TNF-α in experimental models improves insulin sensitivity in muscle and adipose tissue via the insulin tyrosine kinase receptor pathway [10]. Non-steroidal anti-inflammatory drugs (NSAIDs) have anti-inflammatory, analgesic, and antipyretic activities. Their mechanism of action is due to both the inhibition of cyclooxygenases (COX-1 and COX-2) [11] and the modulation of peroxisome proliferator-activated receptors (PPAR)—specifically, PPAR-γ, a central adipogenic regulator [12]. High doses of NSAIDs can modulate PPAR activation in vitro [13] and up-regulate the adipogenic differentiation process. Notably, indomethacin and ibuprofen can induce adipocyte differentiation in C3H10T$\frac{1}{2}$ cells, 3T3-L1, and hepatocytes (both in vitro and in vivo) by activating the PPAR-y receptor [14,15]. Ibuprofen and naproxen stimulate adipogenesis by activating PPAR isoforms and inhibiting prostaglandin H[2] synthases [16,17]. Flurbiprofen is another cyclooxygenases inhibitor, but unlike other NSAIDs, it has an anti-obesity effect [18]. It belongs to the class of drugs for the treatment of obesity due to its ability to reduce the accumulation of unfolded proteins, which causes endoplasmic reticulum stress, which is responsible for obesity development. Its anti-obesity properties are due to its capacity to attenuate leptin resistance and the subsequent amelioration of glucose deprivation-induced leptin resistance, which is involved in the pathophysiology of obesity [19]. NSAIDs, however, have a lot of adverse effects: enhanced gut permeability and inflammation, anemia, malabsorption, and mucosal ulceration. Their prolonged use increases the risk of gastrointestinal bleeding, ulcerations, perforations, and cardiovascular and cerebrovascular effects [20,21]. Many natural products contained in foods, such as carvacrol ([2-methyl-5-(1-methyl-ethyl)phenol] or Car), can reduce adipogenesis [22,23,24,25]. It is a mono-terpenoid present in essential oils of aromatic plants, such as oregano and thyme. It inhibits obesity in HFD-fed mice by diminishing body weight and visceral fat-pad weights and reducing plasma lipid levels [26]. Car lowers the levels of pro-inflammatory cytokines in adipose tissues by impeding toll-like receptor 2 (TLR2)- and TLR4-mediated signaling [27,28,29]. It has also been demonstrated that Car possesses a wide range of properties, including antimicrobial and anti-inflammatory activity [30]. In the last decade, medicinal chemistry-based approaches (prodrugs, codrugs, derivatives, and/or hybrids) were also used by us to improve the efficacy and pharmacokinetic properties of Car [31,32,33,34,35]. Using these strategies, Car was linked to amino acids (to improve water solubility) [31], cinnamic acid derivatives (to obtain topic drugs for the treatment of skin infections) [32], antioxidants (to enhance its potency as an antimicrobial and antifungal drug) [33], and benzyl moiety, especially meta- or para-substituted (to obtain dual inhibitors of H. pylori strains and AGS cell proliferation) [34]. In this context, considering the wide potential of this terpenoid as a pharmaceutical compound, the aim of this work was to develop a small series of Car derivatives (CD1-3) with anti-adipogenic and anti-inflammatory properties endowed with reduced side effects typical of NSAIDs. Car was chosen as a moiety to link, by an ester linkage, to ibuprofen (CD1), flurbiprofen (CD2), and naproxen (CD3) for several reasons: (a) to reduce the adipogenic effects that characterize the selected NSAIDs; (b) to keep the anti-inflammatory efficacy of the novel derivatives inalterable; (c) to mask the carboxylic functions of the selected NSAIDs that are correlated with gastrointestinal toxicity; and (d) to obtain no toxic derivatives by the introduction of Car, since it is considered GRAS (Generally Recognized As Safe) and approved for food use. Particularly, in the present study, the physicochemical properties, stability profiles, and biological evaluation of CD1-3 on undifferentiated and differentiated 3T3-L1 and Wj1-MSCs cells were reported. The anti-inflammatory profile was assessed on THP1-cells, evaluating the TNF-α release after treatment with CD1-3. ## 2.1. Synthesis The syntheses of CD1-3 are shown in Scheme 1. As reported by de Oliveira Pedrosa Rolim et al. [ 35], (S)-5-isopropyl-2-methylphenyl 2-(4-isobutilphenyl) propanoate (CD1) was obtained by the esterification of the carboxyl group of (R,S)-2-[4-(2-methylpropyl)phenyl]-propanoic acid with the -OH of Car, in the presence of benzotriazol-1-yloxytri(dimethylamine) hexafluorophosphate (BOP) as a coupling agent. ( R)-5-isopropyl-2-methyl-phenyl-2-(6-methoxynaphthalen-2-yl) propanoate (CD2) and (S)-5-isopropyl-2-methyl-phenyl-2-(6-methoxynaphthalen-2-yl) propanoate (CD3) were prepared through the conjugation of the appropriate carboxyl acid, 2-(2-fluoro-[1,1′-biphenyl]-4-yl) propanoic acid (Flurbiprofen) or 2-(6-methoxynaphthalen-2-yl)propanoic acid (Naproxen), respectively, and Car by using N,N′-Dicyclohexylcarbodiimide (DCC) and 4-dimethylaminopyridine (DMAP). The structures of CD1-3 were verified by 1H- and 13C-NMR and HR-MS spectra (NMR and MS spectra are reported in the Supporting Information). The purity of CD1-3 was >$97\%$, as stated by HPLC chromatograms (Supporting Information). ## 2.2. In Silico Evaluation of Physicochemical Properties The SwissADME software was used to predict pharmacokinetic and physicochemical properties, such as water solubility, Lipinski’s rule parameters, topological polar surface area (TPSA), molar refractivity (MR), and Rotable Bonds (RB) [36]. Lipinski’s rule of five includes some properties, such as the water/octanol partition coefficient (log P), molecular weight (MW), Hydrogen Bond Acceptors (HBA), and Hydrogen Bond Donors (HBD). A compound is considered to possess good absorption and permeability when characterized by an MW < 500, an HBD < 5, a log p value < 5, and an HBA < 10. According to Table 1, CD1-3, even if they have a LogP slightly higher than 5, possess good physicochemical properties that are necessary to develop a drug for oral administration. ## 2.3. Stability Studies Hydrolysis studies of CD1-3 were achieved at 37 °C in simulated gastric fluid (hydrochloric acid buffer, pH 1.2), phosphate buffer (pH 7.4), and human plasma (Table 2) [37]. All derivatives were more stable in an acidic environment (pH 1.2) than in physiological conditions (pH 7.4). As we expected, the presence of the ester linkage resulted in more susceptibility to the basic environments compared to the acidic one. Additionally, in plasma fluid, CD1-3 were very stable, displaying a t$\frac{1}{2}$ greater than 2.5 h, which would ensure the achievement of the target site, following oral administration and avoiding the degradation. CD3 resulted in the derivative endowed with the best physicochemical properties, being more water soluble, less lipophilic, and more stable in human plasma compared to CD1-2 (Table 1 and Table 2). These data confirmed that the presence of 2-methoxynaphthalene moiety as a scaffold in CD3 ensures an elevated stability against the proteolysis due to its steric hindrance compared to the isobutyl benzene or fluoro-biphenyl structures present in CD1 and CD2, respectively. ## 2.4. Anti-Adipogenic Activity 3T3-L1 and WJ-MSCs cells were chosen as experimental models to evaluate the in vitro inhibitory effects of CD1-3 on lipid accumulation and assess the expression of anti-obesity-related proteins following CD1-3 treatment. Following our previous work, the Oil Red O staining was used to assess the cell differentiation into mature adipocytes (Figures S1 and S2), and both cell lines were grown in an adipogenic differentiation medium and treated with 25 μM Car [27]. Full differentiation was completed following 7 days in 3T3-L1 cells (Figure S1) and following 17 days in WJ-MSCs cells (Figure S2). As we previously reported [27], at 5 days of differentiation, Car has no effect on 3T3-L1 differentiation, while it reduced their differentiation by $40\%$ at 7 days, suggesting that it drastically reduces adipogenic differentiation in 3T3-L1 cells. Similarly, Car (25 μM) reduced the differentiation in two subcultures of Wharton’s jelly WJ-MSCs, WJ-1 and WJ-2, albeit to a lesser extent than 3T3-L1 cells ($30\%$ vs. $50\%$), due to the toxicity-induced mortality of Car in WJ-MSCs cells (Figure S2). After the differentiation, both cell lines were treated with the derivatives CD1-3 and a physical mixture of the selected NSAID (Ibuprofen (I), Flurbiprofen (F), and Naproxen (N)) and 25 μM Car (in equimolar ratio) to verify if the combination of the two pharmacophores into a single molecule had a synergic effect compared to the single molecules. As shown in Figure 1, ibuprofen, flurbiprofen, and naproxen showed a strong adipogenic action compared to the control (diff − Car). In fact, the 3T3-L1 cells treated with the selected NSAIDs anticipated the total differentiation at 5 days of differentiation, which is comparable to the 7 days of the control. In the presence of the physical mixture of NSAIDs plus Car (25 μM), the adipogenic activity of the NSAIDs continued, but it was slightly slowed down by Car. On the other hand, CD1-3 demonstrated a drastic reduction in adipogenesis, which was comparable to that of Car alone; notably, CD1 and CD3, containing ibuprofen and naproxen, respectively, showed the highest antiadipogenic activity compared to CD2. At the same time, the subculture WJ-1 was grown in an adipogenic differentiation medium until total differentiation (17 days) and treated with NSAIDs alone, a physical mixture of the selected NSAID and 25 μM Car, and CD1-3. As shown in Figure 2, ibuprofen, flurbiprofen, and naproxen showed an adipogenic action compared with the control (diff − Car). Compared to 3T3-L1 cells, both the physical mixtures (selected NSAID and Car) and CD1-3 reduced adipogenesis. ## 2.4.1. Effect of CD1–3 on ChREBP Activity during Adipogenic Differentiation Adipogenesis is a permanent process in adult adipose tissues during which preadipocytes, if subjected to appropriate stimuli, can proliferate and differentiate [38]. The transcription factor carbohydrate response element binding protein (ChREBP) is the main regulator of the adipogenic differentiation process [39]. Since the transcription factor, ChREBP, is the major factor responsible for de novo lipogenesis, we analyzed its modulation by NSAIDs alone, a physical mixture of the selected NSAID and 25 μM Car, and CD1-3 in 3T3-L1 cells. Western blot and densitometric analysis showed that ChREBP protein levels increased during adipogenic differentiation, whereas they were reduced by both Car and CD1-3 (Figure 3). The maximum expression of ChREBP was at 7 days of differentiation (Figure 3A), which was comparable to NSAIDs (Figure 3B–D). The highest reduction in ChREBP protein levels was obtained after treatment with CD3 (Figure 3D). It has been reported that the reduction in ChREBP expression levels by various anti-adipogenic compounds is a valid therapeutic method for counteracting obesity, so CD3 possessing this property could be proposed for a deepened investigation. ## 2.4.2. Effect of CD1–3 on TNF-α Release in LPS-Treated THP-1 Cells The chronic systemic low-grade inflammation of white adipose tissue is associated with obesity, which can lead to metabolic syndrome and insulin resistance. The inflammatory process is triggered by the synthesis and secretion of pro-inflammatory cytokines in response to an inflammatory insult. The irregular secretion of adipokines stimulates the recruitment and invasion of macrophages to the adipose tissue and increases levels of proinflammatory cytokines. In particular, TNF-α is known as an adipokine overexpressed in the human adipose and muscle tissues of overweight people, with a crucial role in mediating insulin resistance [40]. To evaluate the anti-inflammatory effect of CD1-3, THP-1 cells were used [41]. These cells derive from the peripheral blood of a childhood case of acute monocytic leukemia and are used as a model for human monocytes. Indeed, when stimulated by phorbol myristate acetate (PMA), they can differentiate into macrophage-like cells resembling the properties of mature macrophages (N). The potential anti-inflammatory effect of CD1-3 on THP-1-derived macrophages through the release of TNF-α levels was analyzed by the ELISA assay. To obtain an in vitro inflammatory experimental model, THP-1 cells were stimulated with LPS, a major inducer of the production and release of TNF-α [42]. THP-1 cells were also treated with NSAIDs. We showed that, compared with untreated cells, TNF-α production was remarkably increased after LPS stimulation for 24 h (TNF-α = 87.20 ± 2.605 pg/mL), while the treatment with CD1-3 for 24 h led to a reduction in TNF-α levels with respect to the LPS-treated cells (CD1 = 46.70 ± 2.246 pg/mL; CD2 = 36.14 ± 17.791 pg/mL) (Figure 4). Interestingly, THP-1 cells showed a drastic decrease in TNF-α levels when treated with CD3 (2.0 ± 6.595 pg/mL). Statistical significance was reported in comparison to both LPS-treated cells and untreated cells (Figure 4). In this study, based on the clue that Car, one of the main components of oregano and thyme, displayed an anti-adipogenic activity, we screened a small series of Car derivatives to discover more effective drugs endowed with both anti-inflammatory properties and reduced adipogenic activities. The literature data report a strong correlation between obesity and several pathological complications such as cardiovascular or renal dysfunctions, diabetes, and oxidative stress [43,44,45,46]. Notably, obesity is often also associated with the pro-inflammatory state that characterizes the above-cited pathologies [47]. The enhancement of proinflammatory cytokines (IL-6, TNF-α, and IL-1β) and an imbalance of adipokines are involved in the development and growth of the adipose tissue. Recent findings report that some phytochemicals are able to inhibit biochemical markers related to adipogenesis and inflammation. Extracts of S. coreana Nakai, a bamboo tree of South Corea, are endowed with both anti-adipogenic activities by avoiding macrophage chemotaxis and anti-inflammatory properties by inhibiting the NF-kB pathway [48]. The same properties are possessed by the extract of rhizomes of A. helferiana or of the Ligularia taquetii Nakai [49,50]. In the literature, there are several examples of natural molecules with dual functions, but they are not commercially available since their safety and efficacy have yet to be deeply studied. At the same time, it is well known that the prolonged treatment of inflammatory conditions using classical NSAIDs can favor the development of the adipogenesis process. Despite a considerable number of natural compounds, few examples of synthetic molecules as dual-acting (anti-inflammatory and anti-adipogenic activities) drugs are reported in the literature [51,52]. In fact, it is rare to find synthetic derivatives with both activities. Starting from these considerations, CD1-3 were developed as novel resources in the development of anti-inflammatory and anti-obesity drugs. Gathering together all the obtained data, we can assume that the CD1-3, obtained through a direct linkage between Car and NSAIDs, resulted in molecules endowed with good drug-like properties and quite good stability for an eventual oral administration. An ester linkage was introduced in all derivatives, but the derivative endowed with the best physicochemical properties resulted in CD3 being more water soluble, less lipophilic, and more stable in human plasma compared to CD1-2. These data proved that the presence of the 2-methoxynaphthalene moiety as a scaffold in CD3 could ensure great stability against the proteolysis due to its steric hindrance compared to the isobutyl benzene or fluoro-biphenyl moieties present in CD1 and CD2, respectively. As already reported in the literature, we observed that ibuprofen, naproxen, and flurbiprofen stimulate adipogenesis in both adipogenic cell models, murine 3T3-L1 and human WJ–MSCs. In prolonged therapies with NSAIDs, among their side effects, there is the stimulation of preadipocytes, which leads to the formation of new adipose tissue, which is involved in the development of various pathologies. Our derivative—in particular, CD3—is able to reduce adipogenesis and, at the same time, possesses an anti-inflammatory effect due to the presence of naproxen. This molecule could reduce the formation of novel adipose tissue, so it could be beneficial for long anti-inflammatory therapies. ## 3. Materials and Methods All of the reagents were supplied by Sigma-Aldrich Co. (St. Louis, MO, USA). Chromatographic columns (Merck 60, 230–400 mesh ASTM silica gel) were used for purifications. NMR spectra were recorded with a Varian VXR-300 spectrometer (Varian Medical Systems, Inc., Paolo Alto, CA, USA). The analyses suggested by the symbols of the elements were within ± $0.4\%$ of the theoretical values. The liquid chromatography system was an Agilent 1260 Infinity II HPLC (Agilent, Santa Clara, CA, USA) containing a 1260 Infinity II Quaternary Pump (model G7111A), a 1260 Infinity II auto-sampler (model G7129A), a 1260 Infinity II Multicolumn Thermostat (model G7116A), and a 1260 Infinity II Diode Array Detector (model G7115A). The data were developed by employing the software Agilent OpenLAB CDS LC ChemStation. The separation was achieved using a Poroshell 120 EC-C18 (150 × 4.6 mm i.d., particle size 4 μm; Agilent, Santa Clara, CA, USA), kept at 20 °C. The samples were run using $20\%$ water (A) and $80\%$ acetonitrile (B), enriched with trifluoroacetic acid ($0.1\%$ v/v) in isocratic elution for 30 min. The flow rate was 0.8 mL/min. The UV detector was set at 254 nm. For the high-resolution mass spectrometry analysis, 10 μg/mL of each investigated compound was dissolved in ACN/H2O $\frac{80}{20}$ with $0.1\%$ of formic acid and injected into the MS spectrometer through a syringe pump at a flow rate of 5 μL/min. A Thermo Fischer Orbitrap FusionTM TribridTM, working in MS scan in an m/z range of 80 to 500 m/z by utilizing the Orbitrap as the detector type at 240,000 of mass resolution (FWHM), was used. CD1-3 spectra were recorded in the positive ion mode. ## 3.1.1. Synthesis of (S)-5-isopropyl-2-methyl-phenyl 2-(4-isobutilphenyl) Propanoate (CD1) Ibuprofen (1.44 mmol) was solubilized in 6 mL of N, N-dimethylformamide (DMF), 6 mL of dichloromethane (DCM), and 6 mL of triethylamine (TEA). Then, 1.1 eq mmol of BOP (1.58 mmol) was added to the solution refrigerated in an ice-water bath and kept under stirring for 30′. Similarly, another solution of Car (1 eq, 1.44 mmol), DCM (3 mL), and TEA (1.8 mL) was kept under stirring for 30′. The solution containing Car was added to that containing ibuprofen, and the reaction was followed by 4 h at 40 °C. After the removal of solvents under reduced pressure, 10 mL of H2O was added to the residue, which was washed three times with AcOEt (20 mL). The organic phases were treated sequentially with 1 M HCl, H2O, 1 M NaHCO3, and H2O, and then they were collected and evaporated. The crude product after chromatography (eluent: hexane/ethyl acetate, $\frac{9.5}{0.5}$) was obtained as an oil. (S)-5-isopropyl-2-methylphenyl 2-(4-isobutilphenyl) propanoate (CD1). Yield: $22\%$; Rf = 0.79, hexane/ethyl acetate, $\frac{9.5}{0.5}$; 1H NMR (300 MHz, CDCl3) δ: 0.99 (6H, d, $J = 6.9$ Hz), 1.29 (6H, d, $J = 6.9$ Hz), 1.73 (3H, d, $J = 7.2$ Hz), 1.92 (3H, s), 1.94 (1H, m), 2.54 (2H, d, $J = 6.9$ Hz), 2.91 (1H, m), 4.05 (1H, q), 6.89 (1H, s), 7.05 (1H, d, $J = 7.8$ Hz), 7.12 (1H, d, $J = 8.1$ Hz), 7.23 (2H, d, $J = 7.5$ Hz), 7.40 (2H, d, $J = 7.8$ Hz); 13C NMR (75 MHz, CDCl3) δ: 15.47 (CH3), 18.25 (CH3), 22.39 (CH3), 22.43 (CH3), 23.97 (CH3), 24.01 (CH3), 30.31 (CH), 33.64 (CH), 45.14 (CH2), 45.36 (CH), 119.79 (CH), 123.99 (CH), 127.33 (C), 127.53 (2 × CH), 129.51 (2 x CH), 130.92 (CH), 137.46 (C), 140.88 (C), 147.93 (C), 149.34 (C), 172,89 (CO). Calcd for C23H30O2: C, 81.61; H, 8.93; O, 9.45. Found: C, 81.50; H, 8.89; O, 9.60. HR-MS (ESI) m/z: [M + H]+ = 339.2310. ## 3.1.2. General Procedures for the Synthesis of CD2 and CD3 CD2 and CD3 were synthesized using flurbiprofen or naproxen (1 eq), respectively, and dissolved in tetrahydrofuran (THF) (20 mL) prior to the addition of DCC (1 eq), and the mixture was stirred for 1 h at rt. Then, Car (1 eq) and DMAP (0.033 eq) were added, and the mixture was stirred for 24 h at rt. After the filtration and evaporation of the solvent, DCM was added, and the solution was treated with NaHCO3 and dried over Na2SO4. After the evaporation of the DCM and the purification on the silica gel with DCM as the eluent, the Car derivatives were obtained as oils. (R)-5-isopropyl-2-methyl-phenyl-2-(6-methoxynaphthalen-2-yl) propanoate (CD2). Yield: $78\%$; Rf = 0.78, CH2Cl2; 1H NMR (300 MHz, CDCl3) δ: 1.28 (6H, d, $J = 7.2$ Hz), 1.75 (3H, d, $J = 7.2$ Hz), 2.04 (3H, s), 2.93 (1H, m), 4.09 (1H, q), 6.92 (1H, s), 7.08 (1H, d, $J = 9.3$ Hz), 7.16 (1H, d, $J = 7.8$ Hz), 7.35 (2H, m), 7.46 (1H, m), 7.54 (3H, m), 7.65 (2H, d, $J = 9.9$ Hz); 13C NMR (75 MHz, CDCl3) δ: 15.61 (CH3), 18.36 (CH3), 23.94 (2 × CH3), 33.61 (CH), 45.19 (CH), 115.40 (CH), 115.71 (CH), 119.64 (CH), 123.85 (CH), 124.17 (CH), 127.15 (CH), 127.79 (CH), 128.54 (CH), 128.99 (2 x CH), 130.99 (CH), 135.49 (C), 141.37 (C), 141.47 (C), 148.09 (C), 149.18 (C), 158.18 (C), 161.47 (C), 172.18 (CO). Calcd for C25H25FO2: C, 79.76; H, 6.69; F, 5.05; O, 8.50. Found: C, 79.74; H, 6.65; F, 5.07; O, 8.54. HR-MS (ESI) m/z: [M + H]+ = 377.1900. (S)-5-isopropyl-2-methyl-phenyl-2-(6-methoxynaphthalen-2-yl) propanoate (CD3). Yield: $15\%$; Rf = 0.86, CH2Cl2; 1H NMR (300 MHz, CDCl3) δ: 1.27 (6 H, d, $J = 7.2$ Hz), 1.81 (3H, d, $J = 7.2$ Hz), 1.94 (3H, s), 2.89 (1H, m), 3.95 (3H, s), 4.18 (1H, q), 6.88 (1H, s), 7.04 (1H, d, $J = 9.3$ Hz), 7.11 (1H, d, $J = 7.8$ Hz), 7.25 (2H, m), 7.62 (1H, d, $J = 9.9$ Hz), 7.77 (2H, t), 7.87 (1H, s). 13C NMR (75 MHz, CDCl3) δ: 15.61 (CH3), 18.52 (CH3), 23.93 (CH3), 23.97 (CH3), 33.60 (CH), 45.63 (CH), 55.31 (CH3), 105.70 (CH), 119.15 (CH), 119.73 (CH), 124.02 (CH), 126.37 (CH), 127.29 (C), 127.35 (CH), 129.06 (2 x CH), 129.39 (CH), 130.92 (C), 133.94 (C), 135.28 (C), 147.99 (C), 149.30 (C), 157.81 (C), 172.89 (CO). Calcd for C24H26O3: C, 79.53; H, 7.23; O, 13.24. Found: C, 79.48; H, 7.25; O, 13.27. HR-MS (ESI) m/z: [M + H]+ = 363.1947. ## 3.2. In Silico Evaluation of Physicochemical Properties The prediction of physicochemical properties for CD1-3 was performed through the online SwissADME program (online access: 20 April 2022 at https://swissadme.ch) to obtain relative results of physicochemical parameters (water solubility, logP, MW, polar surface area, number of HBD and HBA, and number of rotary bonds) [37]. ## 3.3. Stability in Gastrointestinal Fluids The SGF and SIF fluids, respectively, were arranged according to USP requirements. The stock solutions containing CD1-3 were added up to preheated SGF and SIF and placed in a 37 °C shaking water bath. At programmed time points—0, 15, 30, and 60′ for SGF and 0, 60, 120, and 180′ for SIF—100 μL was deproteinized with 100 μL of ice-cold ACN containing $0.5\%$ v/v of formic acid and positioned into micro-centrifuge tubes. The samples were centrifuged at 4 °C and 12,000 rpm for 10′. After filtration, the supernatant was evaluated by HPLC. ## 3.4. Human Plasma Stability Human plasma was acquired from 3H Biomedical (Uppsala, Sweden, Europe). Initially, a stock solution containing CD1-3 was treated with 0.02 M phosphate buffer (pH 7.4) to give a final volume of 1 mL ($80\%$ plasma), and then it was added to a pre-heated (37 °C) plasma fraction to stop enzymatic hydrolysis. Samples of 100 μL were taken at various times, and 200 μL of 0.01 M HCl in MeOH was used to stop the enzymatic activity. After centrifugation for 5 min at 5000× g, the supernatant was examined by HPLC. ## 3T3-L1 Mouse embryonic 3T3-L1 cells, purchased from ATCC (Manassas, VA, USA), were cultured in low-glucose (1 g/L) D-MEM medium (Dulbecco’s modified Eagle’s medium) (Lonza) supplemented with $10\%$ FBS, L-glutamine 0.584 g/L, and $1\%$ penicillin/streptomycin at 37 °C in $5\%$ CO2. ## WJ-MSC The mesenchymal stem cells WJ-MSCs were extracted from human umbilical cords of full-term births. After the removal of blood vessels, the Wharton’s jelly was finely cut, washed with serum-free (D-MEM) (Lonza), and centrifuged for 5 min at 250× g at rt. The pellets obtained were first treated with collagenase type IV (2 mg/mL) (Sigma, Merck Millipore, Darmstadt, Germany) for 18 h at 37 °C and then with $2.5\%$ trypsin-EDTA (GIBCO) for 30′ at 37 °C, under stirring. The cells obtained from two subcultures, WJ-1 and WJ-2, were washed in PBS and cultured in hMSC (Human Mesenchymal Stem Cells) growth medium (Lonza) in $5\%$ CO2 at 37 °C for 3–4 days until reaching confluence. Institutional Review Board approval for the cell culture was received for all experiments from the Institutional Committee for Human Experimentation of Chieti Hospital No. $\frac{1879}{09}$COET. This approval concerns the authorization to harvest the umbilical cord to obtain mesenchymal stem cells (WJ-MSCs) and their use only for in vitro research. ## THP-1 The human monocytic cell line THP-1 (American Type Culture Collection, Manassas, Va.) was grown in RPMI 1640 (Sigma–Aldrich; Merck Millipore, Darmstadt, Germany) medium supplemented with 2 mM L-glutamine, $1\%$ penicillin/streptomycin, and $10\%$ FBS at 37 °C in a humidified atmosphere of $5\%$ CO2. ## 3.5.2. Adipogenic Differentiation 3T3-L1 and WJ-MSCs cells were grown to 70–$80\%$ confluence in D-MEM supplemented with $10\%$ FBS, 0.584 g/L glutamine, 5 mM glucose, 5 mM sodium acetate, and $1\%$ penicillin/streptomycin. The medium was changed every 48 h, and the viability was checked with trypan blue. At approximately $80\%$ confluence, differentiation was induced in a selective medium (glucose D-MEM supplemented with $10\%$ FCS, 1.7 mol/L insulin, 0.5 mM 3-isobutyl-1-methylxanthine (IBMX), 1 μM dexamethasone, and $1\%$ penicillin/streptomycin) for 5 and 7 days in 3T3-L1 and for 17 days in WJ-MSCs, until complete differentiation. The differentiation of the different cell cultures in the presence of 25 μM Car, NSAIDs (ibuprofen, flurbiprofen, or naproxen), the physical mixture of selected NSAIDs and 25 μM Car, and CD1–3 was evaluated. The differentiation test, as lipid accumulation in adipocytes, was revealed by oil red-O staining. The expression of the transcription factor ChREBP was evaluated with western blotting. ## 3.5.3. Oil-Red O Staining to Detect Adipogenic Differentiation The intracellular lipid accumulation in mature adipocytes, during adipogenesis, was determined by Oil-Rd O staining (Sigma-Aldrich Co., St. Louis, MO, USA) in 3T3-L1 at 5 and 7 days and in WJ-MSC at 17 days of differentiation. The differentiated cells were fixed in $4\%$ formaldehyde in PBS for 10′, washed with $60\%$ isopropanol (2-PrOH), and stained with $0.2\%$ Oil-Red O in $60\%$ 2-PrOH for 10′. To remove the unincorporated dye, the cells were washed many times with H2O and destained in $60\%$ 2-PrOH for 15′. The red-stained cells were observed with an optical Zeiss microscope and photographed. Lipid quantification was determined by dissolving stained cellular oil droplets in $60\%$ 2-PrOH and quantified spectrophotometrically at 580 nm. ## 3.5.4. Induction of Cell Differentiation (Stimulation of Transformation into Tissue Macrophages) The induction of cell differentiation was performed upon reaching $80\%$ confluence. THP-1 cells (~2 × 105 cells/mL) were plated as a control group w/o treatment, treated overnight with 100 ng/mL Car, NSAIDs (ibuprofen, flurbiprofen, or naproxen), and CD1–3, or supplemented with 100 ng/mL lipopolysaccharide (LPS derived from E. coli purchased from Sigma–Aldrich, Saint Louis, MO, USA). The cells then were collected by centrifuging for 8′ at 1000 rpm, and cell extracts were made according to standard procedures. For macrophage differentiation, the THP-1 cells (~2 × 105/mL) were incubated with 100 ng/mL PMA (Phorbol myristate acetate) (Sigma–Aldrich, Saint Louis, MO, USA) for 3 days at 37 °C. After removing the PMA-containing media, the cells were incubated for a further 4 days in a conventional medium that was treated earlier with 25 μL of each compound alone or supplemented with 100 ng/mL LPS, according to a specific time-course. THP-1-derived macrophages were detached by Trypsin-EDTA 1X (Euro Clone) treatment for 10′ at 37 °C. Differentiated cells were counted using the vital dye Trypan–blue solution ($0.4\%$) (Sigma–Aldrich, Saint Louis, MO, USA) in a Burker chamber and analyzed for viability [42]. The cells were then lysed to obtain a sample containing both intracellular and released cytokines in the cell culture. ## 3.5.5. ELISA TNF-α levels were measured in cell culture supernatants using a Quantikine solid-phase ELISA kit (R&D System, MN, USA), following the manufacturer’s instructions. The plate was read in a microplate reader (GloMax® Multi Detection System, Promega, MI, Italy) at 450 nm. TNF-α levels (pg/mL) were obtained based on the calibration curves prepared with the cytokine standard obtained by the supplier. The intra- and inter-assay reproducibility was >$90\%$. The minimum detectable (MDD) was 4.8 pg/mL. ## 3.5.6. Western Blotting The cells were incubated on ice for 20′ in a lysis buffer containing 50 mM Tris. Cl pH 7.6, $1\%$ Triton X100, $0.1\%$ SDS, 250 mM NaCl, 5 mM EDTA, proteases, and phosphatases inhibitor cocktail (Thermo Fisher Scientific, Waltham, MA, USA). The samples were then centrifuged at full speed in a refrigerated microfuge, and the supernatant was recovered and assayed for protein content using the Bradford assay (Bio-Rad Laboratories, Milan, Italy). Protein extracts were run on precast 4–$12\%$ Bis-Tris protein gels (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) and transferred onto PVDF Stacks (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) through the iBlot 2 semidry apparatus (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). The membranes were then stained with Ponceau Red (Sigma-Aldrich, Milano, Italy) to verify the proper protein transfer and blocked at rt for 1 h with $5\%$ non-fat dry milk in TBST containing $0.1\%$ Tween20. The protein level was analyzed by western blotting by incubating the membranes O/N at 4 °C with an anti-ChREBP (1:100) (Santa Cruz Biotechnology, Heidelberg, Germany) antibody diluted in $5\%$ non-fat milk in TBST $0.1\%$ Tween20. The membranes were then washed three times for 10′ with TBST and incubated for 1 h at rt with anti-rabbit HRP-conjugated secondary antibody (Santa Cruz Biotechnology, Heidelberg, Germany) diluted $\frac{1}{2000}$ in TBST containing $5\%$ non-fat milk. The membranes were washed three times for 10′, incubated in Super Signal West Pico (Thermo Fisher Scientific Inc, Pierce Biotechnology, Rockford, IL, USA) chemiluminescent substrate, and detected using a ChemiDoc XRSplus imaging system (Bio-Rad Laboratories, Milano, Italy). The optical densities of the blot bands were ultimately revealed by employing a computer-assisted densitometer (ImageJ, U.S. National Institutes of Health, Bethesda, MD, USA) and normalized versus the β-actin housekeeping protein internal control. ## 4. Conclusions To date, many scientific efforts have been made to understand the complex mechanism underlying obesity. However, no effective anti-obesity drugs have been discovered. In this study, CD1 and CD3 were found to have an inhibitory effect on lipid accumulation in both 3T3-L1 and WJ-MSCs cells, but CD3—obtained by a direct linkage between carvacrol and naproxen—showed additional pharmacological and drug-like properties. Derivative CD3, endowed with the best physicochemical properties and stability, was able to neutralize the inflammatory effect of LPS through a decrease in TNF-α levels and reduced the expression levels of ChREBP, which is the major factor responsible for de novo lipogenesis. In the future, in vivo studies are required to confirm the anti-obesity effects of Car derivatives—especially CD3—and gain new insight into the mechanism by which CD3 reduces the adipogenesis of 3T2-L1 and WJ-MSCs preadipocytes and TNF-α-induced inflammation in THP-1 cells. ## Figures, Scheme and Tables Scheme 1: Experimental conditions for the synthesis of CD1-3: (a) ibuprofen, BOP, TEA, and dry DCM/DMF (1:1) at 0 °C for 30′ and then at 40 °C for 4 h; (b) flurbiprofen, DCC, DMAP, and dry THF for 24 h at rt; (c) naproxen, DCC, DMAP, and dry THF for 24 h at rt. Figure 1: Effect of CD1-3 on adipocyte differentiation in 3T3-L1 cells. (A) 3T3-L1 preadipocytes were cultivated in an adipogenic differentiation medium and treated with NSAIDs alone, a physical mixture of selected NSAID and 25 μM Car, and CD1-3. (B) The visualization of triglyceride accumulation was conducted by Oil Red O staining. Lipid accumulation was observed and measured at 5 days of differentiation by spectrophotometry. Data are produced as the mean ± SD ($$n = 3$$). $p \leq 0.05$: treated cells vs. control (diff − Car) and Car (diff + Car).* Figure 2: Effect of CD1-3 on adipocyte differentiation in subculture Wj1-MSCs cells. (A) The subculture WJ-1 was grown in an adipogenic differentiation medium until total differentiation (17 days) and treated with NSAIDs alone, a physical mixture of selected NSAID and 25 μM Car, and CD1-3. (B) The visualization of triglyceride accumulation was conducted by Oil Red O staining. Lipid accumulation was measured by spectrophotometry. Data are produced as the mean ± SD ($$n = 3$$). $p \leq 0.05$: treated cells vs. control (diff − Car) and Car (diff + Car).* Figure 3: Effect of CD1-3 on ChREBP activity during the adipogenic process. (A) Western blot and densitometric analysis of ChREBP in 3T3 cells undergoing adipogenic differentiation for, respectively, 5 to 7 days without (diff − Car) or with (diff + Car) 25 μM Car; (B–D) Western blot and densitometric analysis of ChREBP in 3T3-L1 cells undergoing adipogenic differentiation for 7 days without (−Car) or with (+Car) 25 μM Car; NSAID alone (I = Ibuprofen, F = Flurbiprofen, N = Naproxen); a physical mixture of selected NSAID and 25 μM Car; and CD1-3. Anti-β-actin was used as the protein loading control. Bars depict the means ± SE. ($$n = 2$$). Figure 4: Reduction in TNF-α levels by CD1-3 in THP1-cells. TNF-α secretion was determined by ELISA assay in LPS-treated-THP-1 cells (100 ng/mL), NSAID (I = Ibuprofen, F = Flurbiprofen, N = Naproxen), or CD1-3 derivates (25 μM) for 24 h. 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--- title: Patellofemoral Angle, Pelvis Diameter, Foot Posture Index, and Single Leg Hop in Post-Operative ACL Reconstruction authors: - Ahmet Serhat Genç - Nizamettin Güzel journal: Medicina year: 2023 pmcid: PMC10055810 doi: 10.3390/medicina59030426 license: CC BY 4.0 --- # Patellofemoral Angle, Pelvis Diameter, Foot Posture Index, and Single Leg Hop in Post-Operative ACL Reconstruction ## Abstract Background and Objectives: Anterior cruciate ligament (ACL) injuries occur as a result of the deterioration of the static and dynamic stability of the knee. One of the structures involved in providing static stability is the patellofemoral angle (Q angle). The aim of this study was to investigate the relationships between Q angle, pelvis diameter, lower extremity length, and foot posture index (FPI) in patients who had undergone ACL reconstruction (ACLR) with the semitendinosus/gracilis (ST/G) technique on both the operated and non-operated sides. Materials and Methods: Twenty-five male recreational athletic patients between the ages of 18 and 35 who had undergone semitendinosus/gracilis (ST/G) anterior cruciate ligament reconstruction at least 6 months earlier were included in the study. Femur length, lower extremity length, pelvis diameter, and Q angle measurements, total foot posture index (FPI) scores, and single leg hop (SLH) and triple hop distance (THD) test results were determined on the operated and non-operated sides. Results: When the findings of the patients were evaluated statistically between the operated and non-operated sides, no significant differences were found in Q angle, femur length, and lower extremity length ($p \leq 0.05$). In terms of FPI scores, a significant difference was found only in the inversion/eversion of the calcaneus (CALC) parameter ($p \leq 0.05$). When the single hop test (SLHT) results were evaluated statistically on the operated and non-operated sides, the results were in favor of the non-operated side ($p \leq 0.05$). In the correlation analysis conducted for both the operated and non-operated sides, positive and significant correlations were found only between SLH and THD ($p \leq 0.05$). No significant difference was found in the other parameters. Conclusions: The fact that ST/G ACLR 6th month post-operative findings revealed similar results in Q angle, lower extremity length, and total FPI scores between the operated and non-operated sides showed that the 6-month process did not cause a difference in these parameters. However, it was found that the operated sides showed lower findings compared to non-operated sides for SLHTs, although these findings were within normal ranges in terms of the limb symmetry index. ## 1. Introduction The anterior cruciate ligament (ACL) is one of the most important structures providing stabilization of the tibiofemoral joint, since it prevents anterior tibial translation and rotation in the knee [1]. ACL arthroscopic reconstruction is a procedure applied to restore anterior cruciate ligament function in individuals with anterior cruciate ligament deficiency and to reduce the risk of osteoarthritis and degeneration in other soft tissues of the knee joint that may occur in the future [2,3]. ACL injuries occur as a result of deterioration in tibiofemoral joint stability provided by the static and dynamic stability mechanisms of the knee [4]. One of the structures involved in providing static stability is the patellofemoral angle (Q angle) [5]. The Q angle is defined as the angle between a line drawn from the anterior superior iliac spine (ASIS) to the center of the patella and another line drawn from the center of the patella to the center of the tibial tubercle [6]. It is thought that when the Q angle exceeds the limit of 15–20°, it causes deterioration in the knee extensor mechanism and patellofemoral pain, with an increasing tendency of the patella to slide laterally [7]. Abnormally low values have also been associated with various problems [8]. In addition to causing injuries related to the knee, the Q angle can also be affected by many factors, such as femur length, pelvis width, and postural disorders [9]. When the gender factor is considered, it can be seen that pelvis width, which is one of the factors that changes the Q angle, is higher in women than in men [10,11]. Foot posture differences can lead to postural stability and musculoskeletal problems [12,13]. It is thought that the risk of injury is elevated, and sports performance is negatively affected, since structural deviations of the foot (pronation or supination displacement, high or low arch of the foot) may cause biomechanical deviations [14,15,16]. The association between foot morphology and lower extremity injuries is not clear; however, studies in the literature show both weak and strong correlations between arch structure and biomechanical characteristics of the lower extremity [17,18]. Single leg hop tests (SLHT) have become a widely adopted assessment tool in the rehabilitation of patients following anterior cruciate ligament (ACL) reconstruction, as well as a key factor in determining their readiness to resume sports participation. Researchers have stated that SLHTs are very important for measuring leg strength on a single joint, and they are commonly used to evaluate the functional states of individuals, identify asymmetries between the operated and non-operated sides, and follow developments in the limb [19,20,21]. Given all of these considerations, this study aimed to examine and evaluate the relationship between patellofemoral angle and foot posture index in patients who had undergone semitendinosus/gracilis (hamstring autograft) anterior cruciate ligament reconstructions (ACLR) and to compare surgically repaired knees with healthy knees. ## 2.1. Participants The subject group of the study consisted of 25 male recreational athletes between the ages of 18 and 35 who had undergone conventional ACLR with the semitendinosus/gracilis (ST/G) technique (Table 1). The inclusion criteria were as follows: patients who had a diagnosis of ACL rupture on one knee only; did not have comorbid meniscal, chondral, or other ligament injuries; did not have any other neuromuscular or musculoskeletal system injuries or a history of contralateral knee surgery or injury; and had undergone semitendinosus/gracilis (hamstring autograft) ACLR at least six months earlier. The optimal number of subjects to be included in the study was determined using the GPower 3.1.3. program. Before starting the study, the subjects were informed in detail and included in the study after signing an “Informed Consent Form”. ## 2.2. Experimental Design The study was evaluated as a retrospective cohort that included 25 males who had undergone the conventional ACLR (ST/G) technique by the same surgeon. The retrospective cohort part of the study included only post-operative 6th month Q angle, pelvis width, foot posture index (FPI), femur length, lower extremity length, single leg hop (SLH), and triple hop distance (THD) measurements. Additionally, prospective evaluations of patients undergoing ACL reconstruction included the assessment of Lysholm, Tegner, and International Knee Documentation Committee (IKDC) scores both prior to and at least 6 months following the operation. All measurements (Q angle, pelvis width, FPI, femur length, lower extremity length, single leg hop, and triple hop) were taken on the same day at the same hour (12:00–16:00). While taking the measurements, the single hop and triple hop test measurements were made after all the other tests were applied so that their findings would be correct against acute fatigue. ## 2.3.1. Hop Tests SLH: The patients stood on one foot on a line drawn to the floor, with the big toe touching the line. They were asked to jump as far as possible with one leg with their arms on both sides. The distance the patient jumped was determined by measuring the distance from the start line to the heel of the landing leg at the end of a single jump [22]. THD: In this test, the patients stood on one leg again. They jumped three times in a row, going as far as possible. The distance taken at the end of the three jumps was measured. The measurement was made again by measuring the distance from the start line to the heel of the landing leg at the end of three jumps [23]. ## 2.3.2. Anthropometric Measures Pelvis width was determined by measuring the distance between both the anterior and superior iliac spines in centimeters. Femur length was determined by measuring the distance between the trochanter major crest and the medial condyle in centimeters. The lengths of the leg and the distance between the anterior superior iliac spine (ASIS) or umbilical region and the medial malleolus were measured. All lengths were recorded in centimeters. Q angle (quadriceps angle): This angle was measured from the right knee with the subject in the supine position on a horizontal table and the quadriceps muscle relaxed, with both lower extremities in full extension. The measurements were made using an Insize brand digital goniometer. Marks were placed on the anterior superior iliac spine, the center of the patella, and the tibial tubercle, and the midpoint of the goniometer was placed on the center of the patella. One arm of the goniometer was aligned to the ASIS point, while the other arm was aligned to the tibial tubercle point, and the Q angle was recorded in degrees [3]. FPI: It was first defined by Redmond et al. at a national podiatry conference in Australia. The original version of the index consisted of eight criteria, and it was referred to as the “FPI-8”. However, according to later studies, the last two criteria were found to be problematic, so the index was revised by deleting them. As a result, the “FPI-6” was developed. It is an assessment method with proven validity and reliability. It provides information about the general posture of the foot. The “FPI-6” consists of six criteria:[1]Talus head palpation (TH)[2]The curvature below and above the lateral malleolus (LATM)[3]Inversion and eversion of the os calcaneus (CALC)[4]Protrusion in the talonavicular joint area (TNJ)[5]Medial longitudinal arch alignment (MA)[6]Abduction and adduction of the forefoot when compared with the rearfoot (ABD) Based on observation, each criterion is evaluated on five scales between −2 and +2 and scored accordingly. The total score (between −12 and +12) is the result of the entire evaluation. Positive values indicate pronation, and negative values indicate supination. A value between −5 and −12 shows severe supination of the foot, while a value between −1 and −4 shows supination. A value between 0 and +5 shows the normal position of the foot, a value between +6 and +9 shows pronation of the foot, and a value between +10 and +12 shows severe pronation of the foot [24,25]. ## 2.4. Statistical Analysis Statistical analyses were performed using the SPSS 21 software package. Descriptive statistics were used to present the results as mean and standard deviation. The normality assumption was assessed using the Shapiro–Wilk test, while Levene’s test was used to assess homogeneity. Paired sample t-tests were used to compare paired groups (i.e., the operated and non-operated limbs). Pearson’s correlation test was used to detect associations between parameters. Additionally, effect sizes were calculated using Cohen’s d effect size, which is calculated as (M2–M1)/SDpooled. Effect sizes were categorized as weak (d < 0.2), moderate (0.2 ≤ d < 0.5), or strong (d ≥ 0.5). Statistical significance was set at $p \leq 0.05.$ ## 3. Results When the lower extremity lengths and quadriceps angles of the operated and non-operated sides were evaluated, no significant differences were found between the Q angle ($$p \leq 0.668$$, $95\%$ CI = −1.17–1.79), femur length ($$p \leq 0.515$$, $95\%$ CI = −0.64–0.33), and lower extremity length ($$p \leq 0.904$$, $95\%$ CI = −0.72–0.64) values (Table 2, Figure 1). When the TFPI scores of the operated and non-operated sides were compared, no significant differences were found between the TH ($$p \leq 0.574$$, $95\%$ CI = −0.19–0.11), LATM ($$p \leq 0.574$$, $95\%$ CI = −0.11–0.19), TNJ ($$p \leq 0.425$$, $95\%$ CI = −0.12–0.29), MA ($$p \leq 0.327$$, $95\%$ CI = −0.04–0.12), ABD ($$p \leq 0.265$$, $95\%$ CI = −0.34–0.10), and TFPI ($$p \leq 0.574$$, $95\%$ CI = −0.56–0.32) values. On the other hand, statistical significance was found for CALC ($$p \leq 0.043$$, $95\%$ CI = −0.31–−0.01) values (Table 3, Figure 2). When the SLH (single leg hop) and triple hop distance test results of the operated and non-operated sides were evaluated, statistical significance was found for SLH ($$p \leq 0.004$$, $95\%$ CI = −16.64–−3.60) and THD ($$p \leq 0.022$$, $95\%$ CI = −41.99–−3.61) (Table 4, Figure 3). When the correlations between pelvis diameter, Q angle, FPI, lower extremity length, and SLHT on the operated side were examined, positive correlations were found between TFAPI and ABD ($r = 0.554$), TFFPI and TNJ ($r = 0.444$), ABD and CALC ($r = 0.520$), TFAPI and TH ($r = 0.646$), TFAPI and LATM ($r = 0.730$), TFAPI and CALC ($r = 0.713$), and THD and SLH ($r = 0.917$). Negative and significant correlations were found between SLH and CALC (r = −0.475) and THD and CALC (r = −0.441) (Table 5). When the correlations between pelvis diameter, Q angle, foot posture index, lower extremity length, and SLH tests on the non-operated side were examined, positive correlations were found between LATM and TH ($r = 0.492$), ABD and CALC ($r = 0.485$), CALC and TH ($r = 0.517$), CALC and LATM ($r = 0.600$), ABD and LATM ($r = 0.510$), TFAPI and TH ($r = 0.729$), TFAPI and LATM ($r = 0.667$), TFAPI and CALC ($r = 0.794$), TFAPI and ABD ($r = 0.567$), and THD and SLH ($r = 0.867$). Negative and significant correlations were found between TNJ and Pelvis D. (r = −0.403), SLH and CALC (r= −0.413), and THD and CALC (r = −0.427) (Table 6). ## 4. Discussion The results of our study did not show any significant differences between the operated and non-operated sides in the 6th month post-operative Q angle, lower extremity length, or total FPI scores of patients who had undergone ST/G ACLR surgery. These results indicated that the operated side reached the healthy side in Q angle, lower extremity length, and FPI over a period of approximately 6 months. It was found that the operated sides showed lower results than the non-operated sides for SLHTs. However, when these results were evaluated in terms of the limb symmetry index (LSI), they were found to be within the normal range. Although significant differences were not found in the total FPI scores, significance was found only in the inversion/eversion of the calcaneus score between the operated and non-operated sides. The most important results in the correlation analyses evaluated on both the operated and non-operated sides was the negative and significant correlations between the CALC parameter in FPI and SLHTs. Studies have been found in the literature that examined Q angles, SLHTs, and some physical characteristics in subjects after different ACL autograft methods. Dhillon et al. [ 26] did not find any significant differences in patients who had undergone ACLR with the patellar tendon graft method when compared with the control group in terms of pre-operative and post-operative Q angle (preop −13.86°, postop −12°). A Q angle of 8–15° is considered normal in men, while a Q angle of 12–19° is considered normal in women [27,28,29,30,31]. When Hertel et al. [ 32] compared the Q angles of 20 healthy subjects (11.84°) and 20 patients who had undergone ACLR (11.16°), they did not find any significant differences. An increase in this angle leads to uneven distribution of weight on the knee joint, exposing the medial or lateral knee joint compartments to more stress and joint disorders. Moreover, it may cause collapse in the medial arch of the foot because it creates an increase in joint pronation [7,33]. Increasing the subtalar will cause traction in the medial knee joint, lateral compression tension, supination in the transverse tarsal joint, and increased internal rotation-flexion-adduction angles in the hip joint. On the contrary, following an increased subtalar joint supination angle, there will be medial compression in the knee joint, lateral traction tension, pronation in the transverse tarsal joint, and increased external rotation-extension-abduction angles in the hip joint. In addition, an increase in the Q angle may cause a collapse in the medial arch of the foot [34]. In a study conducted in 2009, Barrios et al. showed that the subtalar joint supination angles were directly proportional to the Q angle and that the subtalar joint supination and tibial mechanical axis measurements were important in the estimation of varus stresses on the knee joint [35]. Although researchers have studied the norm values of the Q angle, there are also studies reporting that there may be differences in these angular values depending on certain factors, such as gender, age, and measurement position [29,36,37,38]. While some researchers have reported that the measurements on knee flexion according to knee extension position were not reliable [10,39], others have stated that there were no differences between Q angle measurements in extension and flexion of the knee and that the differences could be due to errors in determining measurement points or because the measurements were taken by different researchers [40]. When evaluated with all this information in the literature, the findings of our study that there were similar results on the operated and non-operated sides in terms of the 6th month Q angle indicate that the subjects had a good rehabilitation process after their ACLR, especially for the operated sides. It should also be considered that the 6-month process may lead to similar results in Q angles, depending on foot domination. Indeed, these results also show the main limitations of our study. The fact that no measurements were taken before ACLR has caused us not to show pre-operative and post-operative differences and to make interpretations based only on the literature. However, in the literature-supported findings, as in our study, the absence of any difference in Q angles after ACLR is thought to be due to the fact that ACLR does not include any procedure that would affect the patellofemoral angle. However, changes in the Q angle may occur when there are additional injuries that accompany the ACL tear. This situation may arise with detailed studies being carried out with a prospective design. Besides knee injuries, the Q angle can be affected by many other factors, such as femur length, pelvis width, and posture disorders [9]. In their study, Murat et al. [ 41] found a negative weak correlation between Q angle and femur length, regardless of gender. In addition, while it is claimed that high Q angle values are directly proportional to the width of the pelvis, some studies have not been able to confirm this result [27,28,42,43,44]. No significant correlation was found between pelvis width and Q angle in our study. This result also supports the literature information above, and it is thought that this may be due to the fact that our subjects were all male. As a matter of fact, it is known that women have higher pelvis widths and diameters than men due to their anatomical structures; therefore, studies on anthropometry have shown different levels of correlation in women compared with men. In a study conducted by Hertel et al. [ 32], the Q angle was found to be 12.7° in women and 10.2° in men, regardless of injury history, which showed that women had significantly higher Q angle results than men. Another factor affecting the Q angle is ankle deformities [45]. Any injury or deformity in the foot or ankle disrupts the body’s biomechanics, starting from the knee, and if no precautions are taken, the problems in the body worsen [46]. Researchers have reported that with an increased Q angle, the foot tends toward pronation, and the amount of load carried on the medial side increases. Conversely, decreased Q angle causes supination of the foot and more load on the lateral side [33]. Considering that foot posture differences between the operated and non-operated sides may cause musculoskeletal problems, such as problems in the Q angle in FPI values after ST/G ACLR, no difference was found between the operated and non-operated sides in terms of total FPI scores. However, although both the operated and non-operated sides showed significant differences in the CALC parameter, both revealed findings close to pronation, but no significant correlation was found between the CALC results and the Q angles of the subjects. It is known that high-level athletes have flexible pronation in the calcaneum, and the deviation of the forefoot varies in almost all normal individuals. In addition, directions for palpation and curvature of the talus head are variable in almost all normal individuals. For this reason, it is important to evaluate kinematic analyses that will provide clearer findings instead of FPI in future studies. The SLHT is commonly used after ACLR to assess functional performance [47,48,49,50,51]. SLHTs are frequently utilized in clinical practice to assess limb asymmetries between the operated and non-operated sides and to monitor the progress of lower extremity development following ACL reconstruction [19,20,21]. Studies on healthy individuals have reported that the difference between the limbs for SLH and THD tests between conventional SLHTs is, at most, 10–$15\%$ [52,53]. While one study found >$90\%$ similarity between the limbs in all of the participants that were tested with conventional SLHTs, another study conducted on healthy and athletic groups with a history of ACLR did not find limb asymmetry in conventional SLHTs [54,55]. In our study, it was found that although the operated sides showed much lower results than the non-operated sides, the results were still within the normal ranges in terms of LSI. Other important results of our study are the negative and significant correlations between the CALC parameter, which is an FPI score, and the SLH and THD tests. This shows that increased pronation in foot posture negatively affects performance in SLHTs. Indeed, since it is known that high Q angles correlate with feet that have high pronation [33], especially in high LSI rates that may occur in SLHTs after ACLR, the Q angle and FPI indices should be evaluated. All our results show that the main limitation of our study is the lack of evaluation of healthy subjects as a control group. The fact that we did not have healthy subjects in our study made us unable to clearly explain the relationship between operated and non-operated sides in terms of all parameters. ## 5. Conclusions The fact that the ST/G ACLR 6th month post-operative results were similar between the operated and non-operated sides in Q angle, lower extremity length, and total FPI scores showed that the 6-month-long process did not cause a difference in these parameters. However, it was found that the operated sides showed lower results than the non-operated sides for SLHTs, although these results were within normal ranges in terms of the limb symmetry index. 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--- title: 'Causes of HIV Treatment Interruption during the Last 20 Years: A Multi-Cohort Real-Life Study' authors: - Andrea De Vito - Elena Ricci - Barbara Menzaghi - Giancarlo Orofino - Canio Vito Martinelli - Nicola Squillace - Lucia Taramasso - Giuseppe Vittorio De Socio - Chiara Molteni - Laura Valsecchi - Cecilia Costa - Benedetto Maurizio Celesia - Giustino Parruti - Giovanni Francesco Pellicanò - Eleonora Sarchi - Antonio Cascio - Giovanni Cenderello - Katia Falasca - Antonio Di Biagio - Paolo Bonfanti - Giordano Madeddu journal: Viruses year: 2023 pmcid: PMC10055812 doi: 10.3390/v15030720 license: CC BY 4.0 --- # Causes of HIV Treatment Interruption during the Last 20 Years: A Multi-Cohort Real-Life Study ## Abstract In the last years, many antiretroviral drugs (ART) have been developed with increased efficacy. Nowadays, the main reasons for treatment switches are adverse events, proactive strategy or simplification. We conducted a retrospective cohort study to investigate the reason for treatment interruption in the last 20 years. We merged data of eight cohorts of the SCOLTA project: lopinavir/r (LPV), atazanavir/r (ATV), darunavir/r or /c (DRV), rilpivirine (RPV), raltegravir (RAL), elvitegravir/c (EVG), dolutegravir (DTG) and bictegravir (BIC). We included 4405 people with HIV (PWH). Overall, 664 ($15.1\%$), 489 ($11.1\%$), and 271 ($6.2\%$) PWH interrupted the treatment in the first, second, and third years after starting a new ART. Looking at the interruption in the first year, the most frequent causes were adverse events ($3.8\%$), loss to follow-up ($3.7\%$), patients’ decisions ($2.6\%$), treatment failure ($1.7\%$), and simplification ($1.3\%$). In the multivariate analysis regarding experienced patients, treatment with LPV, ATV, RPV or EVG/c, having less than 250 CD4 cells/mL, history of intravenous drug use, and HCV positivity were associated with an increased risk of interruption. In naive people, only LPV/r was associated with an increased risk of interruption, while RPV was associated with a lower risk. In conclusion, our data on more than 4400 PWH show that adverse events have represented the most frequent cause of treatment interruptions in the first year of ART ($3.84\%$). Treatment discontinuations were more frequent during the first year of follow-up and decreased thereafter. First-generation PI in both naïve and experienced PWH, and EVG/c, in experienced PWH, were associated with a higher risk of treatment interruptions. ## 1. Introduction Thanks to the introduction of antiretroviral regimens (ART), HIV infection has become a chronic condition in which, at present, therapy should be continued lifelong [1]. Furthermore, effective ART has an important impact on public health by preventing the transmission of HIV [2,3]. Several new treatments and strategies have been proposed in the last twenty years, with the introduction of second generation boosted PI, integrase inhibitors (INSTI), and second generation of NNRTI. The guidelines have undergone multiple changes for the treatment strategy and the first-line regimens. Until 2015, the decision to start ART was based on the CD4 number and viral load, with the suggestion to begin antiretroviral treatment only if the CD4 count was below 350 cells/mm3 and the viral load was higher than 100,000 copies/mL [4]. With the publications of the INSIGHT START Study Group trial [5,6], it was proven that starting antiretroviral therapy in people with HIV (PWH) with CD4+ count >500 cells/mm3 provided significant benefits over waiting after the CD4+ count had declined to 350 cells/mm3. Also, the first-line regimens have been changed in the last 20 years. For example, in 2003 EACS guidelines, treatment with three different Nucleoside Reverse Transcriptase Inhibitors (NRTI) was suggested, while we now know how this approach can lead to the emergence of mutations and viral load rebound [7,8]. Many first-line regimens have been downgraded to alternative regimens since other more effective and better-tolerated treatments have been approved [9,10,11,12,13,14,15]. In particular, all protease inhibitors (PI) (lopinavir/ritonavir [LPV/r], atazanavir/ritonavir [ATV/r], and darunavir ritonavir or cobicistat [DRV/r or /c]), are not recommended for naïve PWH; only DRV/ is suggested as alternative regimens, and in treatment switch [16]. In the last EACS guidelines, INSTIs represent the preferred class for first-line therapy and as an option for optimization in experienced PWH due to their high efficacy, genetic barrier, and tolerability [12,13,17,18,19,20,21,22]. However, in the last few years, the scientific community focused on this class’s possible role in weight gain, particularly dolutegravir (DTG) [11,21,23,24]. Regarding Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTI), only rilpivirine (RPV) and doravirine (DOR) are recommended by EACS guidelines, thanks to their excellent efficacy and safety [14,25,26,27,28]. The SCOLTA project is a multicenter observational study started in 2002 following prospectively HIV-infected people who began to take newly introduced antiretroviral drugs. The project aims to identify toxicities and adverse effects (AEs) in a real-life setting. The SCOLTA project uses an online pharmacovigilance program and involves 29 Italian Infectious Disease Centers [29]. We aimed to investigate the reason for treatment interruption in the last 20 years during the first year and the entire follow-up, collecting data for all interruption causes, including adverse events. ## 2. Materials and Methods We conducted a retrospective cohort study, merging the data of eight different cohorts of the SCOLTA project: LPV/r (2002–2006), ATV (2003–2008), DRV/r or /c (2006–2019), RPV (2013–2017), raltegravir (RAL; 2007–2014), elvitegravir/c (EVG; 2014–2019), dolutegravir (DTG; 2014-ongoing) and bictegravir (BIC; 2019-ongoing) cohorts. The cohorts’ complete data collection and follow-up procedures have been previously described [29]. Briefly, we collected demographical information, risk factors for HIV infection, viral-immunological data, and the causes of treatment interruption. Discontinuation was defined as PLW stopping the use of the cohort drug. Both ART naïve- and experienced patients can be included in SCOLTA if they are >18 years old and sign a written informed consent. Clinical data collected include sex, age, ethnicity, weight, height, CDC stage, and previous ART history. Laboratory data include HIV-RNA, CD4+ cell count, CD4/CD8 ratio, and biochemical data. All information is prospectively collected in a central database every six months in an anonymized form. All AEs causing drug discontinuation are collected when they occur and categorized according to a standardized toxicity grade scale [30]. Virological failure was defined at the time of the first of two consecutive HIV-RNA above the threshold of 50 copies/mL occurring in people who had previous HIV RNA < 50 copies/mL [29]. The original study protocol was approved on 18 September 2002, and new protocol amendments were approved on 13 June 2013, and 3 March 2020, by the coordinating center at Hospital “L. Sacco”-University of Milan and after that by all participating centers. In addition, written consent for study participation was obtained from all participants. The study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments and by Italian national laws. ## Statistical Analysis Data were described using mean and standard deviation (SD) for normally distributed continuous variables, median and interquartile range (IQR) for not normally distributed continuous variables, and frequency (%) for categorical and ordinal variables. Rates were calculated as the number of discontinuations per 100 patient-years (PY). Using unconditional multiple logistic regression, we estimated rate ratios (RR) and corresponding $95\%$ confidence interval (CI) of regimen discontinuation for any causes over the first year of treatment. In the logistic regression equation, we included the variables significantly associated with the outcomes after accounting for age and sex. The Cox proportional-hazards model was used to estimate the hazard ratio (HR) and $95\%$ CI of regimen discontinuation for any cause over the follow-up period. DTG cohort was used as the reference in both analyses. The significance level was set at <0.05. Statistical analysis was performed using the SAS/STAT statistical package (version 9.4; SAS Institute Inc., Cary, NC, USA). ## 3. Results We included 4405 people, 690 treated with LPV/r, 527 with ATV/r, 646 with DRV/r or DRV/c, 344 with RPV, 468 with RAL, 332 with EVG/c, 1126 with DTG, and 272 with BIC. The characteristics of PWH by regimen are reported in Table 1. Overall, 1424 ($32.32\%$) PWH interrupted the treatment; in particular, 664 ($15.1\%$), 489 ($11.1\%$), and 271 ($6.2\%$) were interrupted in the first, second, and third year, respectively. Looking at the interruption in the first year, the most frequent causes were AEs ($3.8\%$), loss to follow-up ($3.7\%$), patients’ decisions ($2.6\%$), treatment failure ($1.7\%$), and simplification ($1.3\%$). In particular, interruption due to AEs was higher in DTG, RPV, and LPV/r groups, while therapeutic failure was experienced more frequently in EVG/c, LPV/r, and ATV/r groups. In addition, there was a substantial loss to follow-up for LPV/r and ATV/r groups. Finally, the patient’s decision to interrupt the treatment proportion was higher for all boosted drugs (LPV, ATV, DRV, and EVG). The causes of interruption during the first year of treatment are summarized in Table 2. Over the whole observation period, 1718 ART-experienced PWH interrupted the cohort regimens (discontinuation rate $\frac{14.8}{100}$ py, $95\%$ CI 14.0–15.6), whereas the corresponding figure was 17.8 ($95\%$ CI 15.7–19.6) for ART-naïve people ($$p \leq 0.007$$). Discontinuations due to adverse events had a rate of $\frac{2.5}{100}$ PY ($95\%$ CI 2.5–2.9) and 4.0 ($95\%$ CI 3.2–5.1), respectively ($$p \leq 0.0001$$). To identify the risk factors associated with all causes of regimen interruption during the first year of observation, we performed a multivariate analysis separately for ART-experienced and ART-naïve people. Available variables were analyzed as associated with interruption in a model adjusted for sex and age. The complete model included the variable significantly associated with regimen interruption at the age and sex-adjusted analysis (Table 3). In ART-experienced PWH, interruptions were considerably higher in association with female sex, age > 50 years, detectable HIV-RNA at study entry, CDC stage C and CD4+ > 500 cells/mm3. Compared to DTG, treatment with LPV, ATV, RPV or EVG/c were associated with an increased risk of interruption. In naïve people, the multivariate model showed that a higher risk of discontinuation was associated with LPV regimens. In contrast, ART-naïve PWH on RPV had a lower risk of interruption, as compared to DTG (Table 4). During the first year, 133 interruptions due to adverse events occurred in ART-experienced and 36 in ART-naïve PWH. In ART-experienced PWH, discontinuations were associated with age >50 years (RR 2.02, $95\%$ CI 1.40–2.92) and being on treatment with DRV (RR 0.52, $95\%$ CI 0.28–0.95) and RAL (RR 0.22, $95\%$ CI 0.08–0.61) as compared to DTG. In ART-naïve people, no variables were significantly associated with discontinuations due to adverse events. Looking at three years of follow-up, the proportion of interruptions due to adverse events decreased from $\frac{84}{489}$ ($17.2\%$) in the second to $\frac{28}{271}$ ($10.3\%$) in the third year (Figure 1). Finally, we analyzed the discontinuations over the entire follow-up. As in the previous model, available variables were analyzed in a model adjusted for sex and age. In addition, the complete model included the variable significantly associated with regimen interruption at the age and sex-adjusted analysis. At the multivariate analysis for all causes of interruption during the entire follow-up (Table 5), the group of ART-experienced PWH women, non-Caucasian people, PWID, and those who entered the cohorts with detectable HIV-RNA showed an increased risk of regimen discontinuation. A higher level of CD4+ cells at study entry was associated with a lower risk of interruption. Compared to DTG, a higher risk of discontinuation was observed in LPV, ATV, RPV, and EVG-treated PWH. In the group of naïve PWH, HCV antibody positivity and treatment with LPV and RAL were associated with an increased risk of treatment interruption. On the contrary, starting therapy with RPV was associated with a lower risk (Table 6). ## 4. Discussion In this study, we evaluated with the same methodology all discontinuations in PWH receiving different new antiretroviral drugs during 20 years of follow-up. We found a high discontinuation rate in the LPV/r cohort and a halving of discontinuation rates for the newer regimens. In naïve patients, the rates were comparable to other studies in the literature. Toxicities have represented the leading cause of treatment interruption since the early 2000s. d’Arminio Monforte et al. in 2000 found that naïve PWH who did not experience toxicities discontinued the treatment in less than $10\%$ of cases during the first year of therapy [31], while the treatment interruption due to AEs was $21.1\%$. In the Swiss cohort, a similar incidence of treatment interruption for toxicity between 2000–2005 was found [32]. Also, Robinson et al., in their cohort, found a percentage of interruption around $55\%$, in particular, $24.8\%$ due to toxicity. In the multivariate analysis, IDU history and PI-based regimens were associated with an increased risk of discontinuation during the first year of treatment [33]. In 2010, Cicconi et al. analyzed the discontinuation between 1997 and 2007 in the ICONA cohort. Overall, they found that people who started treatment between 2003–2007 had a lower risk of discontinuation for toxicities but a higher for simplification [34]. In 2013, Abgrall et al. published a paper on 21,801 patients from 18 cohorts in Europe and North America who started first-line regimens between 2002 and 2009 [35]. The cumulative percentages of modification, interruption, and death during the first three years were 47, 12, and $2\%$, respectively. The main reason for discontinuation was AEs ($40\%$). Patients on lopinavir/r and other protease inhibitors had higher rates of modification and interruption. In 2016 Kanters et al. conducted a network meta-analysis on all clinical trials published before 5 July 2015, finding that treatment with DTG and RAL was associated with lower discontinuation for AEs [15]. In naïve, the higher rates of discontinuations of LPV and RAL and in PWID and HCV antibody-positive PWH could represent an adherence proxy since the two antiretroviral drug regimens were primarily prescribed twice a day. The need to treat hepatitis C could partly explain the LPV discontinuation in the HCV subgroups due to pharmacokinetic interactions. Also, RAL discontinuation may be partly linked to simplification requests by PWH. Another interesting paper by Di Biagio et al. found that between 2008 and 2014, the leading cause of discontinuation was not toxicity but simplification, thus confirming that the new drugs had a better safety profile [36]. If for naïve patients, many studies analyzed the incidence and predictor of discontinuation, for experienced patients, there are fewer studies, and many explored the discontinuation only for the second-line regimens. Moreover, most studies are conducted in resource-limited countries [37,38,39]. If it is true that EACS 11.0 guidelines removed first-generation PIs from the recommended and alternative treatment, these drugs represent the preferred second-line choice in many countries. Ross et al. conducted a multicenter cohort study between 2015 and 2017 in 12 Asia-Pacific sites about second-line treatment; among 1378 PWH, $93\%$ of people received regimens with PIs ($55\%$ LPV, $39\%$ ATV); of these patients, $7\%$ interrupted the treatment due to virologic failure [40]. Onoya et al. conducted in 2016 a retrospective study to analyze predictors of early drug substitutions and treatment interruptions in a multicenter South African cohort between 2004 and 2013. Overall, $11.7\%$ had a drug substitution, and $6.3\%$ interrupted the treatment. All patients in their cohort had a PIs-based treatment containing LPV/r or ATV/r [38]. Also, most of the second-line treatment in Brazil was PI-based ($91\%$) [39]. Regarding INSTI-based regimens, De La Mata et al. analyzed 598 treatment-experienced PWH, of which 199 started second-line treatment containing INSTI, and 399 patients were considered highly experienced [41]. Among the second-line treatment group, $5.4\%$ discontinued the treatment. The only factor associated with an increased risk of discontinuation in their cohort was female gender (HR 2.53 ($95\%$ CI: 1.10–5.82); female gender was associated with an increased risk of discontinuation also in our study, independently from the type of treatment (RR 1.18, $95\%$CI 1.02–1.37). In our cohort, there was an high interruption of DTG in the first year for adverse events, in part due to SNC adverse events, already described in many studies [17,42,43,44]. However, DTG interruptions due to AEs were counterbalanced by lower proportion of discontinuations due to other causes and rapidly declined over the study period, thus resulting in the RRs and HRs showed in Table 3, Table 4, Table 5 and Table 6. The comparatively low number of discontinuations for each cause, the necessity for competing risk analyses, and adjustment for repeated analyses would have resulted in non-statistically significant findings, with risk estimates and low clinical interest confidence interval. Our study has some limitations. Firstly, it is a retrospective study, and the data were from an existing database. Secondly, there is a lack of information such as stratification of the number of MSM and heterosexual transmissions, clinical information about patients’ comorbidities and data about the treatment adherence. Furthermore, not all drug regimens are present in our study; for example, the EFV cohort is not present. However, many drugs no longer recommended in Europe represent the first choice in many resource-limited countries, in particular, efavirenz (EFV) as first-line regimens, and LPV or ATV as savage treatment, in combination with two NRTI. The strengths of this study are the real-life setting, the large sample size, the inclusion of many regimens, and the extensive period considered. ## 5. Conclusions Our data on more than 4400 PWH show that adverse events have represented the most frequent cause of treatment interruptions. Treatment discontinuations were more frequent during the first year of follow-up and decreased thereafter. 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--- title: Enzymatic Glucose Fiber Sensor for Glucose Concentration Measurement with a Heterodyne Interferometry authors: - Cheng-Chih Hsu - Wan-Yu Chung - Chun-Yi Chang - Chyan-Chyi Wu - Cheng-Ling Lee journal: Sensors (Basel, Switzerland) year: 2023 pmcid: PMC10055821 doi: 10.3390/s23062990 license: CC BY 4.0 --- # Enzymatic Glucose Fiber Sensor for Glucose Concentration Measurement with a Heterodyne Interferometry ## Abstract In this study, we developed a glucose fiber sensor incorporating heterodyne interferometry to measure the phase difference produced by the chemical reaction between glucose and glucose oxidase (GOx). Both theoretical and experimental results showed that the amount of phase variation is inversely proportional to glucose concentration. The proposed method provided a linear measurement range of the glucose concentration from 10 mg/dL to 550 mg/dL. The experimental results indicated that the sensitivity is proportional to the length of the enzymatic glucose sensor, and the optimum resolution can be obtained at a sensor length of 3 cm. The optimum resolution of the proposed method is better than 0.6 mg/dL. Moreover, the proposed sensor demonstrates good repeatability and reliability. The average relative standard deviation (RSD) is better than $10\%$ and satisfied the minimum requirement for point-of-care devices. ## 1. Introduction The Centers for Disease Control and Prevention (CDC) released the latest scientific data on diabetes in the United States in 2020, showing that about $10\%$ (29.1 million) of the population of the United States has diabetes. The report also noted that the cost of diabetes-related treatment in the United States in 2012 was estimated at approximately $245 billion [1]. The International Diabetes Federation announced that diabetes continues to be a growing health burden in low- and middle-income countries, with 592 million people living with diabetes in 2035, twice as many as in 2013 [2]. Numerous clinical studies have confirmed that controlling lower blood glucose levels can reduce risk factors for cardiovascular disease, and that self-monitoring of blood glucose levels can be effectively performed using commercial blood glucose meters. Guidelines from the National Institutes of Health (NIH) recommend that patients with type 2 diabetes typically perform self-testing before meals, after meals, and before bedtime [1,3]. Therefore, measuring glucose concentration with a blood glucose meter is essential to reduce the costs associated with diabetes. Blood glucose concentration measurement methods can be divided into non-invasive methods [4,5,6] and invasive methods [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. While invasive methods do not provide painless measurements, these methods can avoid individual patient differences (including race, skin color, skin composition, skin thickness, and complex blood components) and increase the reliability of clinical diagnosis. Therefore, currently available methods for clinical monitoring of blood glucose levels are based on the invasive (in vitro) methods. The invasive method can be categorized into electrochemical methods [7,8] and optical methods [9,10,11,12,13,14,15,16,17,18,19,20,21]. Many review papers [7,8] pointed out the advantages and challenges of the electrochemical method for blood glucose concentration monitoring. The most important issue with electrochemical methods is to reduce or eliminate calibration procedures while taking into account measurement accuracy to improve the convenience of measurement [7]. In contrast to electrochemical methods, optical methods [9,10,11,12,13,14,15,16,17,18,19,20,21] are measured non-contact. There is no contact between the electrode and the test piece as in electrochemical measurement, eliminating the calibration problems caused by contact measurement. In addition, due to the diversity of optical measurement methods, the design of the sensor can measure the concentration of the sample according to the change in the physical characteristics of the sample (such as refractive index (RI), polarization state, optical rotation power, light intensity, wavelength shift, etc.). Lin [9] proposed a heterodyne refractometer to determine the refractive index and chiral parameter of the various concentration of glucose solution. Based on its optical configuration, when it is incident on the sample at critical angle, the phase difference of the interference signal will be discontinuous, and then the RI and the corresponding glucose concentration will be obtained. The results showed that the optimal resolution of RI could achieve 10−5. Bhardwaj et al. [ 10] developed double tapered Mach–Zehnder interferometer for RI measurement of glucose solution at different concentrations. Their results showed that the minimum concentration variation of glucose solution was $2\%$. Chiu et al. [ 11] developed a novel measurement method for small rotation power of glucose solution. To control the azimuth angle of a half-wave plate, the rotation power of glucose solution can be obtained. Their results showed that a wide measurement range of glucose concentration and the resolution of the rotation power can be better than 1.6 × 10−5 °/mm. Upadhyay et al. [ 12] proposed a double D-shaped fiber Bragg grating (FBG) for RI measurement of glucose concentration varied from $0\%$ to $50\%$. Their results indicated that the sensitivity of double D-shaped FBG was better than that of D-shaped FBG, and the optimal sensitivity could reach 47.37 nm/RIU. Zhong et al. [ 13] proposed a glucose sensor based on helical intermediate period fiber grating (HIPFG) structure. The sensitivity of HIPEG glucose sensor was about 0.026 nm/(mg/mL) and the detection limit can achieve 1 mg/mL. Wu [14] fabricated s-shaped long period fiber grating (LPFG) and immobilized glucose oxidase (GOx) on the s-shaped LPFG surface for glucose concentration measurement. The measurement range of glucose concentration was covered 0 wt%–1 wt% with high linearity, and the sensitivity of Wu’s method could reach 6.229 dB/wt%. Azkune et al. [ 15] modified polymer fiber surface with phenylboronic acid (PBA) and Alizarin Red S(ARS) for glucose sensors. Based on the evanescent wave characteristics at the U-shaped fiber/sample interface, the transmitted light will be related to the sample concentration. Their method showed a detection limit of 0.1 M for glucose concentration. Hsu et al. [ 16] developed a circular heterodyne polarimeter and fabricated a reusable enzymatic glucose sensor for measuring glucose concentration. For glucose solutions, the repeatability and resolution of the proposed system were better than $95\%$ and 0.88 mg/dL, respectively. The reusable glucose sensor could be reused consecutively 100 times for application, and it provided a similar response efficiency. Badmos et al. [ 17] developed an enzymatic long-period fiber grating (LPFG) glucose sensor. Based on LPFG’s dual sensing peaks, their method provided two measurement ranges for glucose concentration. The optimal linear measurement range was 0.1 mg/mL to 3.2 mg/mL with a detection wavelength of 1787 nm. Zhou et al. [ 18] modified 4-vinylphenylboronic acid (4-VPBA) on the surface of the helical long-period grating (HLPG) for glucose sensors. The linear range of the glucose concentration covered 0.18–3 mg/mL and preserved similar sensitivity over 3 weeks of the sensor. Lee et al. [ 19] fabricated a glucose sensor by constructing a long-period grating on a panda-type polarization maintaining (PM) fiber and immobilized GOx on the surface. The results showed that the transmitted intensity of linear horizontal polarization (LHP) was inversely proportional to the glucose concentration around the wavelength of 1548 nm. Compared to the results of LHP, the results of the linear vertical polarization (LVP) exhibited redshift as glucose concentration increased around the wavelength of 1606 nm. Zhang et al. [ 20] developed an MSM-SPR sensor with the structure of multimode fiber/single-mode fiber with surface plasmon resonance structure/multimode fiber for glucose concentration measurement. MSM-SPR sensor combined with a glucose emzymatic reaction device consisting of GOx modified polystyrene (GOx-PS) and MnO2 can detect the RI variation of gluconic acid at different glucose concentrations. Results showed that the glucose enzymatic reaction device could be reused 10 times without significant difference. Previous work [21] fabricated an enzymatic fiber sensor by immobilizing GOx on the core surface of a single mode fiber (SMF) to measure the glucose concentration in human serum. Results showed that the sensor could be reused 13 times within 1 week, and the theoretical resolution could be better than 0.15 mg/dL. To increase the number of applications of fiber type glucose sensor, this study optimized the technology of GOx immobilized on the no-core fiber surface. Therefore, the new glucose sensor can be reused nearly three times more than the sensor produced by the previous work [21]. The glucose sensor had a single mode fiber (SMF)/enzymatic no-core fiber/single mode fiber structure, and the results showed that the measurement sensitivity was proportional to the length of the enzymatic no-core fiber. Two ends of the SMF adopted FC type bare fiber adapter to easily replace the glucose sensor. Based on the optical configuration of the proposed system, the phase variation could achieve a phase stability of 0.07° in 30 s. Therefore, theoretical resolutions were about 1.154 and 0.577 mg/dL for sensors with lengths of 1 cm and 3 cm, respectively. These findings suggest that the proposed sensors and measuring devices can serve as alternative systems for in vitro clinical examinations and become green clinical diagnostic systems for long-term care centers in future. ## 2. Principles Figure 1a shows the measurement setup of the proposed method. The heterodyne light source was generated by an electro-optical modulator with 1k Hz modulation frequency. The heterodyne light was guided into the enzymatic glucose sensor constructed with enzymatic no-core fiber with single-mode fiber (SMF) spliced at both ends. The two ends of the SMF are with FC type bare fiber adaptor. The diagram of the sensor structure is shown in Figure 1b. When light is incident into the enzymatic no-core fiber at an angle of θ1 through a single-mode fiber, total internal reflection (TIR) will occur at the boundary between the enzymatic no-core fiber and the test medium. Therefore, the phase shift between p- and s- polarizations can be obtained and written as [22] [1]δ=δp−δs=2tan−1(sin2θ2−n2tanθ2sinθ2) where n=n3n2 indicated the ratio between the refractive indices of no-core fiber (n2) and test medium (n3); θ2=90°−θ1, which is shown in Figure 1c. Depending on the numerical aperture (NA) of objective lens and the refractive index (n1) of SMF, the maximum value of θ1 will be in the range of 4° to 9°. Obviously, the light beam will exhibit multiple TIR in no-core fiber and the number of TIRs can be expressed as [2]m=L2d⋅tanθ2 where L and d are the length and diameter of no-core fiber, respectively. Based on the Jones calculation [23], the interference signal detected by detector (D) can be written as [3]It=12[1+cos(ωt+mδ)]=12[1+cos(ωt+φ)] The total phase shift (ϕ) can be obtained immediately by a lock-in amplifier with phase-lock technology. When a sample is injected into the sensing area, glucose oxidase (GOx) catalyzes the conversion of glucose to gluconic acid and hydrogen peroxide [24]. Thus, the total phase shift (ϕ) of TIR changes as the chemical reaction progresses, and the heterodyne light carries this time-varying signal. Therefore, by measuring ϕ at different glucose concentrations, the calibration curve of phase versus glucose concentration can be obtained. According to Equations [1] and [2], the relationship between the phase shift and refractive index (RI) of test medium can be seen in Figure 2a; the number of TIR and incident angle (θ1) can be seen in Figure 2b. Figure 2a shows that the phase shift is inversely proportional to RI of the test medium, and the optimum variation of phase shift will be approximated of 0.08° as θ1 = 5°. Figure 2b shows that the TIR number is proportional to the length of no-core fiber, and the longer the length of enzymatic no-core fiber (sensing area), the more times of TIR. ## 3.1. Sensor Fabrication The enzymatic glucose sensor was constructed with enzymatic no-core fiber with single-mode fiber (SMF). These fibers were purchased from commercially available manufactures where no-core fibers (Prime Optical Fiber Corporation, Hsinchu, Taiwan, model:NCF125) and SMFs (Thorlabs Inc., Newton, NJ, USA, model: SM600) have a cladding diameter of 125 μm. The numerical aperture of SMF is 0.1–0.14. The no-core fiber sensor was treated with $1\%$ (v/v) 3-(trimethoxysilyl)propyl aldehyde in absolute ethanol for 30 min at room temperature. After washing with ethanol for few times, the fiber sensor was dried by N2 air gun, followed by heating at 100 °C for 45 min. To immobilize GOx on SiO2 with covalent bonding via the amino linkage aldehyde group of fiber sensor surface, the modified surface was then covered with a 175 µg/mL GOx in 10 mM phosphate buffered saline (PBS) solution at pH 7 for 1 h. Unreacted aldehyde groups were quenched by immersion in 15 mM Tris buffer solution (pH 7.5) for 10 min at room temperature. The fabrication procedure is shown in Figure 3. After fabrication of the enzymatic no-core fiber sensor, the effectiveness of GOx on the sensor surface will be verified using a standard validation method provided by the World Health Organization, (WHO) [25]. Glucose present in the test solution will be oxidized by GOx to form gluconic acid and hydrogen peroxide. Hydrogen peroxide will be converted to water and oxygen by peroxidase (POD). The oxygen acceptor 4-4-aminophenazinone absorbs oxygen and forms a pink chromogen together with phenol. Therefore, when the sensor was placed in a solution containing glucose, POD, and 4-4-aminophenazone, the activity of GOx on the sensor surface could be determined by the appearance or absence of pink color. In addition, for the convenience of sensor replacement, the two ends of the SMF are connected to the light source and detector with FC type bare fiber adaptor. As shown in Figure 4, Figure 4a shows the photo of the actual enzymatic no-core fiber sensor, and Figure 4b is the verification result of GOx activity. Figure 4c shows the reusable behavior by consecutive chromogen test and the darker the pink color, the higher the enzyme activity. Thus, the proposed sensor can be reused at least 30 times. ## 3.2. Performance of the Proposed Method Various concentrations of glucose were prepared to demonstrate the performance of the proposed method. In this study, glucose solutions were prepared by dissolving glucose anhydrous in DI (deionized) water, and the glucose concentration was within the range of 10–550 mg/dL. Figure 5 shows the phase–time response curves of the proposed sensor with various lengths of enzymatic no-core fiber. As the sample was injected onto the sensing area, GOx catalyzed glucose and changed the RI of test medium. Therefore, the total phase shift varied as the reaction progressed. The results showed that the phase variation was inversely proportional to the glucose concentration, and the reaction was terminated within 3 s. In general, the refractive index increases with the increase in glucose concentration. The simulation results in Figure 2a show that as the refractive index increases, the phase shift decreases. The experimental results are consistent with the theoretical predictions. Figure 6 shows the results of consecutive tests of sensors with different lengths at various glucose concentrations. The glucose concentration of the test sample is controlled at 150 mg/dL and 450 mg/dL for the sensor with a length of 1 cm, and the glucose concentration of the test sample for the sensor with a length of 3 cm is controlled at 450 mg/dL. A test sample was injected with a volume of 1 cc directly with a micropipette and reacted with the sensor for 1–2 s. Next, the reacted liquid was aspirated out. In the consecutive testing, DI water was not used to clean the sensor. Terminal phase deviations can be caused by interference caused by improper aspiration of reactants, residual reactants on the sensor surface, and short sampling time frame. To prevent interference, the injection system can be replaced with an autosampler, which continuously injects sample and deionized water to clean the sensor surface. In contrast to the consecutive testing, in a reliability test, the test sample reacted with the sensor for 10 s, the reacted liquid was sucked out, and then the sensor was cleaned twice with 10 cc of DI water. After each sensor has been used 10 times, it was replaced with another new sensor for the next set of 10 experiments. The reliability evaluation was performed by calculating the relative standard deviation (RSD) of 50 replicate experiments for each glucose concentration in accordance with Clarke’s method [26]. The evaluation results are shown in Figure 7, and the values in the region between the red and blue lines indicate the measurement result within ±$20\%$ of the reference concentration. The average RSD of the proposed sensors with lengths of 1 cm and 3 cm were $9\%$ and $6\%$, respectively. These results demonstrated that the proposed sensors have good reliability and met the minimum requirement (±$15\%$) for point-of-care devices provided by US Food and Drug Administration (FDA) [27]. Figure 8 indicates that the calibration curve measured of the proposed method with various lengths of sensor. The symbols ◯, ☐, and I represent the average value of 10 measured data sets and the standard deviation of each concentration measured by the proposed sensor with 1 cm and 3 cm long sensors, respectively. The slope of the calibration curve indicates the sensitivity of the proposed method; the phase variation was approximately −0.026° for 1 mg/dL for the measurement by the sensor length of 1 cm and −0.052° for 1 mg/dL for the measurement by the sensor length of 3 cm. The shaper slope indicates a higher sensitivity, and obviously the sensor length of 3 cm provided higher sensitivity for glucose concentration measurement. The results show that the linear measurement range of the proposed method covers glucose concentrations from 10 to 550 mg/dL. ## 4. Discussion Resolution ΔC of the proposed method can be achieved by calculating the ratio of the phase error \|Δϕ\| of the proposed method to the slope S of the calibration curve, which is expressed as [4]\|ΔC\|=\|ΔφS\| According to Equations [1] and [2], the phase error ∆ϕ is a function of length of enzymatic no-core fiber and the ratio between the refractive indices of no-core fiber and test medium, which can be derived and expressed as [5]Δφ=∂φ(L,n)∂L\|ΔL\|+∂φ(L,n)∂n\|Δn\|=δ2dtanθ2\|ΔL\|−nLsinθ2d((tanθ2sinθ2)2+sin2θ2−n2)sin2θ2−n2\|Δn\| where \|ΔL\| and \|Δn\| are the errors of the length of enzymatic no-core fiber and the ratio between the refractive indices of no-core fiber and test medium, respectively. The reason for \|ΔL\| may be that the length fails to be precisely controlled when cutting the no-core fiber and \|ΔL\| can be less than 2 mm. The source of \|Δn\| may be an improper temperature control around the test sample and sensor, as well as laser wavelength stability. Theoretically, within 1 °C of temperature change, \|Δn\| is less than 0.001. The simulation result was shown in Figure 9a. When \|ΔL\| is 1 mm and \|Δn\| is 0.001, \|Δϕ\| is 0.03°. If \|ΔL\| is controlled at 0.5 mm and the temperature is controlled within 0.5 °C, \|Δn\| will be within 5 × 10−4, and therefore \|Δϕ\| will be close to 0.01°. With reference to the analysis of Wu [28], the residual nonlinearity phase error was evaluated, and the results indicate that phase error was less than 0.02°, which is shown in Figure 9b. Based on the error analysis and considering \|ΔL\|, \|Δn\|, and residual nonlinearity phase error, the theoretical phase error \|Δϕ\| can be better than 0.03°. Unfortunately, imperfect temperature control of the solution, unexpected electronic variations, and the residual nonlinearity periodic error of the measurement apparatus also affected the phase error of the proposed method. The practical phase error of the proposed method is indicated by evaluating the phase stability of the measurement system, and the results are shown in Figure 9c. The practical phase error of the proposed system was 0.07° within 30 s. Based on the error analysis, the resolution of the proposed method can be estimated using Equation [4], and it is summarized in Table 1. If only residual phase error is considered as the phase error of the proposed system, the optimum resolution of the glucose solution can reach 0.615 and 0.308 mg/dL for the sensor lengths of 1 cm and 3 cm, respectively. According to the concept of detection limit (DL) proposed by Barrios et al. [ 29,30], \|ΔC\| can be regarded as the DL of the proposed method. Therefore, for glucose solution samples, the DL of the proposed method can be better than 3 mg/dL. Interferences in blood samples (e.g., L-ascorbic acid, methylmalonic acid, glycine, urea, etc.) may affect the accuracy of glucose concentration measurement. Wu et al. [ 31] evaluated the effect of these interferents on an optical glucose sensor with a GOx sensing layer. Their results showed a difference of less than $2.3\%$ between the measurement of glucose concentration in samples with and without interference. Therefore, those potential interferents may not have a significant impact on the accuracy of the proposed sensor. Table 2 summarizes the performance comparison results between the proposed sensor and the related work cited in Section 1. The proposed method yields a reusable glucose sensor with acceptable detection limits, fast response time, and wide measurement range. ## 5. Conclusions This work demonstrates the feasibility of an enzymatic glucose sensor and integrates into a heterodyne interferometry for measuring the glucose concentration. Experimental results indicate that the resolution of the proposed sensor is strongly related to the length of the enzymatic sensor. The resolution increases with increasing sensor length, and the optimum resolution is approximately 0.577 mg/dL. Moreover, the limit of detection of the proposed method is approximately 1.346 mg/dL. This work further demonstrates the repeatability of the proposed sensor, maintaining an acceptable phase–time response under 10 consecutive uses for sensors of different lengths. Importantly, the proposed method is highly promising for its repeatability and reliability required by the FDA. Based on these findings, a reusable and reliable enzymatic sensor was fabricated and integrated into an apparatus with high sensitivity and a high-resolution system for glucose concentration measurements. 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--- title: Berberine Rescues D-Ribose-Induced Alzheimer‘s Pathology via Promoting Mitophagy authors: - Chuanling Wang - Qian Zou - Yinshuang Pu - Zhiyou Cai - Yong Tang journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10055824 doi: 10.3390/ijms24065896 license: CC BY 4.0 --- # Berberine Rescues D-Ribose-Induced Alzheimer‘s Pathology via Promoting Mitophagy ## Abstract Mitochondrial dysfunction is considered an early event of Alzheimer disease (AD). D-ribose is a natural monosaccharide that exists in cells, especially in mitochondria, and can lead to cognitive dysfunction. However, the reason for this is unclear. Berberine (BBR) is an isoquinoline alkaloid that can target mitochondria and has great prospect in the treatment of AD. The methylation of PINK1 reinforces the burden of Alzheimer’s pathology. This study explores the role of BBR and D-ribose in the mitophagy and cognitive function of AD related to DNA methylation. APP/PS1 mice and N2a cells were treated with D-ribose, BBR, and mitophagy inhibitor Mdivi-1 to observe their effects on mitochondrial morphology, mitophagy, neuron histology, AD pathology, animal behavior, and PINK1 methylation. The results showed that D-ribose induced mitochondrial dysfunction, mitophagy damage, and cognitive impairment. However, BBR inhibition of PINK1 promoter methylation can reverse the above effects caused by D-ribose, improve mitochondrial function, and restore mitophagy through the PINK1–Parkin pathway, thus reducing cognitive deficits and the burden of AD pathology. This experiment puts a new light on the mechanism of action of D-ribose in cognitive impairment and reveals new insights in the use of BBR for AD treatment. ## 1. Introduction Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease classically characterized by memory loss, cognition impairment, and progressive β-amyloid (Aβ) and phosphorylated tau accumulation, ultimately causing loss of neurons and synapses, brain atrophy, and even death [1]. Between 2000 and 2019, the number of deaths due to AD increased by more than $145\%$ [1], and AD has become an important global public health problem. Despite extensive basic and clinical research, no effective therapeutic strategies for AD have been found. Therefore, it is an urgent task to explore the pathogenesis and find novel biomarkers and therapies of AD. D-ribose is found in all living cells and obtained from diet and endogenous synthesis. It is a key component of ribonucleic acid (RNA), acetyl coenzyme A, and adenosine triphosphate (ATP) [2]. It has been reported that the level of D-ribose in AD patients was significantly higher than that of cognitively intact individuals of similar age, which might be a new potential diagnostic biomarker for AD [3]. D-ribose is danger to health if consumed in excess [4]. For example, chronic overconsumption of D-ribose resulted in depression/anxiety and spatial memory impairment in mice [2]. High levels of D-ribose in serum and urine are involved in diabetic encephalopathy [5]. The administration of D-ribose produced high levels of advanced glycation end products (AGEs), including the hyperphosphorylation of Tau protein, in the brain of C57BL/6 mice and N2a cells [6], suggesting that high levels of D-ribose intake may have a damaging effect on the nervous system. However, the specific mechanism needs further study. Cells produce D-ribose, which is essential for ATP production in mitochondria [7]. Mitochondria produce $99\%$ of ATP, which is the main energy source of a highly active brain, and so mitochondrial quality control is crucial. The PINK1 (PTEN induced kinase 1)–Parkin (parkin RBR E3 ubiquitin protein ligase) pathway is a classic pathway of mitophagy, which is the key to maintaining mitochondrial homeostasis, and mitochondrial dysfunction is considered to be an early event of AD. Studies have shown that mitophagy in AD patients and AD models is significantly reduced [8,9]; Aβ and P-Tau hinder the PINK1–Parkin pathway, increase the production of ROS, destroy the mitochondrial membrane, and aggravate the mitochondrial structural and functional abnormalities [10,11], suggesting that the neuropathology of AD forms a vicious cycle with mitophagy disorder. Interestingly, D-ribose was considered to inhibit the autophagy of AD and induce the generation of Aβ [12], although whether it affects mitophagy remains to be studied. Berberine (BBR) has a neuroprotective effect that can cross the blood–brain barrier and inhibit the formation of amyloid plaque deposits and neurofibrillary tangles, significantly improving memory and cognitive dysfunction [13]. Studies have shown that BBR activates PINK1–Parkin-dependent mitophagy, inhibits mitochondrial damage, reduces ROS generation, protects cardiac function, and reduces kidney injury [14,15]. Additionally, BBR can alleviate mitochondrial abnormalities in neurons by maintaining mitochondrial membrane potential, increasing mitochondrial density and length, and improving mitochondrial movement and transport [16], indicating that BBR has a special protective effect on mitochondria. Our previous work has preliminarily proved that BBR can enhance autophagy and reduce D-ribose-induced Aβ [12]; however, whether this is achieved by protecting mitochondrial function remains unknown. DNA methylation is the most common epigenetic modification studied in AD, without changing the DNA sequence but regulating gene expression, which is particularly sensitive to environmental stimuli and affects cognitive function [16]. Studies have revealed that PINK1 methylation reinforces the burden of Alzheimer’s pathology [17]. Interestingly, BBR has been shown to participate in the regulation of gene methylation [18]. Therefore, this study explores the effects of D-ribose on the mitophagy and pathological and cognitive changes in AD models, and observes whether BBR resists the effects of D-ribose by affecting PINK1 methylation, providing a new experimental basis for D-ribose-induced cognitive dysfunction and BBR for the treatment of AD. ## 2.1. Berberine Ameliorates D-Ribose-Induced Mitochondrial Dysfunction via Promoting Mitophagy Mitochondrial membrane potential (ΔΨm), ROS production, and Cytc release can reflect the functional status of mitochondria and are closely linked to apoptosis. Flow cytometry analysis of ΔΨm showed that the D group had weakened red fluorescence, enhanced green fluorescence, and decreased the ratio of the JC-1 aggregate/JC-1 monomer fluorescence compared with the control group, showing that D-ribose caused a significant decrease in ΔΨm (Figure 1A–C). However, ΔΨm increased after the treatment with BBR (DB group), but decreased again after the addition of Midivi-1 (DBM group) or siRNA knockdown of PINK1 (DBS group). Similarly, the results of ROS production (Figure 1D–F), Cytc expression (Figure 1G,H) and apoptosis (Figure 1I,J) in each group showed the same trend as the results of ΔΨm, suggesting that D-ribose-induced a decrease in ΔΨm, overload of ROS, Cytc release, and ultimately lead to cell death. Treatment with berberine can reverse D-ribose-induced mitochondrial dysfunction via promoting mitophagy. ## 2.2. Berberine Revives D-Ribose-Induced Mitophagy Dysfunction through PINK1–Parkin Pathway Mitophagy is the key to maintaining mitochondrial quality, and PINK1–Parkin-induced mitophagy can revive dysfunctional or damaged mitochondria. Therefore, mitophagy was investigated to further clarify the protective role of berberine in Alzheimer’s disease. Electron microscopy showed that autophagy vesicles were observed in N2a cells of the Ctrl and DB groups. Moreover, mitochondria of the Ctrl and DB groups were basically normal with uniform matrix in the hippocampus of APP/PS1 mice, while mitochondria of the D and DBM groups were significantly swollen and cristae were ruptured (Figure 2A). These results suggest that berberine can protect mitochondrial structure damaged by D-ribose through mitophagy. Mito Tracker (green) and lyso Tracker (red) were then used to observe the fusion of mitochondria and autolysosomes. The co-localization results showed that berberine increased the co-localization of mitochondria and lysosomes through the mitophagy, which counteracted the effect of D-ribose (Figure 2B,C). In addition, compared with the Ctrl group, the co-localization of LC3A/B and TOMM20 (Figure 2F,G), PINK1 and Parkin (Figure 2D,E) in the D group was significantly reduced, which blocked PINK1–Parkin-induced mitophagy. Berberine reversed the above results caused by D-ribose, suggesting that berberine revives D-ribose-induced mitophagy dysfunction through the PINK1–Parkin pathway. The detection of the PINK1–Parkin-mitophagy pathway showed that D-ribose could inhibit the expression of the key mitophagy-associated proteins PINK1, Parkin, and LC3B, both in vitro (Figure 3A–F) and in vivo (Figure 3G–J). Furthermore, P62 was cleared and the expression of LC3B increased after berberine intervention, suggesting that the PINK1–Parkin-mitophagy pathway was activated by berberine. ## 2.3. Berberine Mitigates Alzheimer’s-Like Pathology Induced by D-Ribose via Promoting Mitophagy Compelling studies have shown that impaired mitochondrial function occurs earlier than pathological changes, accelerating the deposition of Aβ and hyperphosphorylation of Tau protein in AD models and patients [10,19,20]. This study showed that the expression of Aβ, P-TAU (T205), P-TAU (T231) and P-TAU (S396) in the hippocampus and cortex of APP/PS1 mice in the D group was significantly increased compared with the control group (Figure 4A). Compared with the D group, the above indexes in DB group were decreased. After addition of mitophagy inhibitor Midivi-1 in DBM group, the expression of these proteins was significantly increased again. Meanwhile, the results of immunohistochemical staining also showed the same effect (Figure 4B), suggesting that D-ribose can increase the burden of AD pathology, while berberine reduced the pathological changes of AD by promoting mitophagy. Neuron damage is considered to be one of the important characteristics of AD, so we performed H & E staining and Nissl staining to investigate the morphological alteration and neuron loss, respectively. As shown in Figure 5A, the neurons in the control group were arranged neatly, with full and normal morphology, clear membrane boundary, pale red cytoplasm, blue nucleus and clear nucleolus. Compared with the control group, the neurons in DB group were arranged irregularly, with loss of morphology, enlarged gap, nuclear pyknosis, and cellular vacuolization accompanied by necrosis. However, BBR can rescue the effect of D-ribose. Compared with the D group, the morphology of neurons in DB group was greatly improved; However, compared with the DB group, neurons in the DBM group with midivi-1 showed nuclear pyknosis and cellular vacuolization. The same trend as H & E staining was observed in Nissl staining (Figure 5B). In the control group, neurons were complete in structure and rich in blue Nissl bodies, while the D group was incomplete in structure, vague in shape, and uneven in Nissl staining in cytoplasm; Compared with the D group, the morphology of neurons in DB group with BBR was plump and regular, and Nissl bodies were abundant. However, compared with DB group, Nissl staining in DBM group was not uniform. These results suggest that BBR can improve the cerebral cortex and hippocampal pathological injuries cause by D-ribose through mitophagy. ## 2.4. BBR Ameliorates Cognitive Impairment of APP/PS1 Mice Induced by D-Ribose via Mitophagy The literature shows that the level of D-ribose in serum and urine is negatively correlated with cognitive function and induce anxiety-like behavior [2,21,22]. Therefore, the cognition and mood were checked through behavioral assessment in APP/PS1 mice. The Morris water maze test was used to study the spatial learning and memory. Compared with the control group, the mice in the D group had longer escape latency, shorter target quadrant time, and were more likely to deviate from the platform position; After adding BBR, the escape latency in DB group was significantly shorter than that in the D group, meanwhile, the time in the platform and target quadrant was longer; However, after adding the mitophagy inhibitor Midivi-1, the DBM group showed similar results with the D group, and the average speed of each group was not different (Figure 6A–F). The two-way shuttle (TWS) avoidance task can be used to evaluate the memory of mice [23]. The trend was similar to that of MWM. Compared with the control group, the shuttle times in the D group were significantly reduced and the escape latency was increased. The addition of BBR could significantly reverse the effect of D-ribose, but the DBM group showed fewer shuttle times and longer escape latency after the addition of Midivi-1 (Figure 6G,H). The above results suggest that D-ribose destroys the spatial cognitive, learning, and memory abilities of mice, and BBR can antagonize the D-ribose effect through mitophagy. Anxiety was assessed using a field test. Compared with the control group, the mice of the D group spent a longer time in the periphery and a shorter time and distance in center, showing more anxiety-like behavior. The effect of D-ribose was not affected after taking BBR or Midivi-1 (Figure 6I–O), suggesting that BBR could not relieve the anxiety induced by D-ribose in APP/PS1 mice. ## 2.5. BBR Inhibits PINK1 Promoter Methylation The above results suggest that BBR can protect mitochondrial function through the PINK1–Parkin pathway against a D-ribose inducer, but the specific regulation of mitophagy remains unclear. In general, DNA hypermethylation in promoter regions can inhibit gene expression. Literature has shown that the CpGs methylation of PINK1 is closely related to the burden of AD pathology [17], and both glycoylation and BBR are involved in epigenetic regulation [18,24]. Therefore, the level of PINK1 promoter methylation was detected in this study. The results showed that the methylation of PINK1 promoter in the D group was increased compared with the control group after adding D-ribose. However, BBR (DB group) could significantly reduce methylation of PINK1 promoter, which has the same effect as DNA methylation inhibitor 5-Azacytidine (DA group) (Figure 7A,B). Meanwhile, in both cell and animal models, compared with the control group, the D group had increased expression of DNMT1, but decreased expression of PINK1. Compared with the D group, PINK1 was overexpressed, while DNMT1 was reduced in the DB group and DA group (Figure 7C–G). Increased promoter methylation and reduced expression of PINK1 was observed, suggesting that the promotion of PINK1 promoter methylation by D-ribose was one of the reasons for the decreased expression of PINK1, while BBR could reverse the effect of D-ribose and lead to increased PINK1 expression, which promoted mitophagy and maintained mitochondrial homeostasis. ## 3. Discussion Progress in the understanding of the extent and role of berberine in Alzheimer’s disease has increased substantially in the past decade. Numerous variables have been discovered in which berberine is involved in the Alzheimer’s pathophysiological processes, such as senile plaques, neurofibrillary tangles, acetylcholinesterase enzyme, oxidative stress, neuroinflammation, and others [13], leaving us with a very interesting question in its pharmacological mechanism and neuroprotective role. This study showed that berberine can reverse D-ribose-induced mitochondrial dysfunction while berberine revives D-ribose-induced mitophagy dysfunction through the PINK1–Parkin pathway. Berberine mitigates cognitive impairment and Alzheimer’s-like pathology induced by D-ribose via promoting mitophagy, through which berberine inhibits PINK1 promoter methylation and promotes its expression. This study may provide potential interventions centered on the regulation of PINK1–Parkin-dependent mitophagy and offer therapeutic strategies for the treatment of Alzheimer’s disease. Berberine, an isoquinoline alkaloid, has multiple pharmacological effects, including its purported antioxidant and antimicrobial properties for a series of diseases, such as obesity [25], diabetes [26], hyperlipidemia [27], heart failure [14], H. pylori infection [28], and colonic adenoma prevention [29]. Numerous hypotheses about the ways in which berberine may help with Alzheimer’s disease also have been raised, including retarding oxidative stress and neuroinflammation [13,30], limiting the pathogenesis of extracellular amyloid plaques and intracellular neurofibrillary tangles [13,31]. Previously, our group has reported that BBR attenuates cognitive deficits and limits hyperphosphorylation of tau via inhibiting the activation of NF-kappaB signaling and retarding oxidative stress and neuro-inflammation [32]. Berberine alleviates amyloid-beta pathology in the brain of APP/PS1 mice via inhibiting beta/gamma-secretases activity, enhancing alpha-secretases [33], and activating LKB1/AMPK signaling [34]. BBR ameliorates D-ribose-induced amyloid-beta pathology via inhibiting mTOR/p70S6K signaling and improves spatial learning and memory [12]. However, more detailed investigations are warranted to clarify the role of berberine in limiting Alzheimer’s-like pathologies. Advanced glycation end products (AGEs) is involved in the onset and exacerbation of Alzheimer’s disease while numerous studies favored that glycation help extracellular β-amyloid deposition as neuritic plaques and intracellular accumulation of hyperphosphorylated tau as neurofibrillary tangles [35,36]. The interactions of AGEs with the receptors of AGEs (RAGE) result in β-amyloid deposition and accumulation of hyperphosphorylated tau and further downstream inflammatory cascade events in Alzheimer’s pathogenesis [36,37]. A growing body of research shows that D-ribose-induced ribosylation and excessive AGE production play an important role in the formation of amyloid plaques and neurofibrillary tangles [3,12]. Recently, we have found that BBR mitigates D-ribose-induced amyloid pathology while BBR promotes autophagic lysosomal pathway by suppressing mTOR/p70S6K signaling, regulates the activity of autophagy-related proteins Beclin1, Atg3, and LC3B, and promotes Aβ clearance [12]. However, the underlying mechanism by which BBR inhibits D-ribose-induced amyloid pathology involving ribosylation-induced autophagy dysfunction remains unclear. An abundant literature links the modulation of autophagy to altered Alzheimer’s pathogenesis [38,39]. Mitophagy, a specialized form of macroautophagy, selectively degrades damaged and dysfunctional mitochondria which contribute to normal aging and a wide spectrum of age-related diseases [40], including Parkinson’s disease and Alzheimer’s disease [41,42]. Hence, maintaining a healthy mitophagy status in aged individuals might be a beneficial strategy. This study has discovered that berberine ameliorates D-ribose-induced mitochondrial dysfunction through enhancing mitophagy while D-ribose-induced ΔΨm increase, ROS decreases, Cytc release less and ultimately cell death occurs after the treatment with berberine. Mitophagy has been physiologically responsible for mitochondrial quality control and mitochondrial ROS balance by regulating mitochondrial trafficking and mitochondrial quality control and removing damaged mitochondria [43,44]. Growing evidence supports the contribution of mitophagy impairment to Alzheimer’s disease [45]. Defective mitophagy is thought to be responsible for the accumulation of damaged mitochondria, which leads to cellular dysfunction and death in Alzheimer’s disease [46]. PTEN-induced putative kinase 1 (PINK1) and Parkin are involved in a common pathway to regulate mitophagy and mitochondrial dynamics. PINK1–Parkin mainly regulates ubiquitin-dependent mitophagy to ensure the maintenance of mitochondrial health and homeostasis, and to deliver defective mitochondria to the lysosome for degradation [44,47]. The current knowledge has displayed the molecular mechanisms underlying mitophagy dysregulation in Alzheimer’s disease, especially in relation to the PINK1–Parkin-mediated mitophagy [46,48]. We found that berberine alleviates Alzheimer’s-like pathology induced by D-ribose via promoting mitophagy while berberine revives the PINK1–Parkin pathway. However, how berberine influences the PINK1–Parkin pathway is not well understood. There is growing evidence for the prominent role of DNA methylation (DNAm) in Alzheimer’s disease while DNA hypermethylation in promoter regions can inhibit gene expression [49,50]. Studies have demonstrated that the CpGs methylation of PINK1 reinforces the burden of Alzheimer’s pathology [17,51]. This study found that BBR reversed the level of PINK1 promoter methylation by D-ribose and enhanced PINK1 expression. Therefore, BBR reversed the level of PINK1 promoter methylation by D-ribose and enhanced PINK1 expression. BBR ameliorates Alzheimer’s pathology and cognitive impairment of APP/PS1 mice induced by D-ribose through inhibiting PINK1 promoter methylation. This study found that berberine ameliorates D-ribose-induced mitochondrial dysfunction via promoting mitophagy, and second, berberine promotes D-ribose-induced mitophagy dysfunction through the PINK1–Parkin pathway. Third, berberine alleviates the burden of Alzheimer’s pathology induced by D-ribose via promoting mitophagy and improves cognitive impairment. Finally, this study discovered that berberine rescues D-ribose-induced Alzheimer’s pathology via inhibiting PINK1-promoter methylation (Figure 8). As BBR has been used clinically for many years, it might have applicable potential in regulating mitophagy and improving Alzheimer’s pathology and cognitive impairment. ## 4.1. Animals, Cell Lines, and Reagents APP/PS1 mice were purchased from Cavens Biogle (Suzhou, China) Model Animal Research Co., Ltd. and were raised in the Department of Laboratory animal center of Chongqing Medical University. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of Chongqing Medical University (date of approval, 26 February 2022). The murine neuroblastoma cell line Neuro2a (N2a) was purchased from Procell Life Science and Technology Co., Ltd. (CL-0168, Wuhan, China), and was maintained in Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 (DMEM/F12, Gibco, Carlsbad, CA, USA) with $10\%$ fetal bovine serum (FBS, Invigentech, Irvine, CA, USA) in a $5\%$ CO2 incubator at 37 °C. The reagents and antibodies used in this study were as follows: BBR (Chengdu Jinghua Pharmaceutical Co., Ltd., Sichuan, China), D-ribose (V900389, Sigma-Aldrich, St. Louis, MO, USA), Mdivi-1 (HY-15886, MedChemExpress, Monmouth Junction, NJ, USA), 5-Azacytidine (HY-10586, MedChemExpress, Monmouth Junction, NJ, USA). CST and Abcam antibodies come from MA, USA. Proteintech and SAB antibodies come from Chicago and Maryland, respectively. Beyotime and Bioss antibodies came from Shanghai and Beijing, China, respectively. Information about antibodies is shown in Table 1. ## 4.2. Experimental Design and Drug Treatment Information about the experimental design is shown in Table 2: ## 4.3. Behavioral Tests Morris water maze test (MWM). The test was performed, as previously described, in a 120 cm round basin filled with water (24–25 °C; depth, 40 cm) with white paint added [12]. Briefly, there were two phases: In the training period, the mice were tested four times daily for 4 days with a hidden platform (8 cm diameter, 1.5 cm underwater). Probe trials, during which the platform was removed and mice were placed in the opposite quadrant, were assayed for 1 min. The data were obtained by a video tracking software called ANY-Maze (Stoelting Co., Wood Dale, IL, USA). All tests were performed from 8:00 to 12:00. For the two-way shuttle (TWS) avoidance task, a shuttle box (30 cm × 30 cm × 20 cm) was divided into two equal sized compartments by an opaque partition with a channel. Both compartments were electrifiable and equipped with lighting equipment. Each mouse was put into the box with its back to the door and allowed to adapt for 5 min (walk freely, without light and foot shock), and then 30 cycle experiments were conducted. The experiment used light as the stimulation condition for 5 s. If the mice did not pass through the door to the adjacent chamber after an interval of 5 s, they were given a 0.3 MA, 5 s electric shock. The experimental interval was 30 s. The first day was the training period. Each mouse was repeated 30 times, and the TWS avoidance task was performed 72 h later. The test records include: the escape latency, the time when the mouse first moved to the adjacent chamber; shuttle times, including active avoidance times (shuttle to the other side after turning on the light) and passive avoidance times (shuttle to the other side after electric shock); and escape failure (the mice did not shuttle after being given a shock). For the open field test, the mice were placed in a Plexiglas test device with a size of 50 × 50 × 40 cm. The device was divided into 16 equal squares, with the middle 4 squares forming the central area and the remaining 12 squares forming the peripheral areas. Each mouse was placed in the central area and recorded for 5 min. The recording software was ANY-Maze. The surfaces of the equipment were cleaned and dried after each tested mouse. The total distance traveled, the mean speed, and the time and distances in the central and corner were all recorded. ## 4.4. Mitochondrial Membrane Potential Assay Beyotime Biotech provided the mitochondrial membrane potential (ΔΨm) assay kit (C2003S, Shanghai, China). ΔΨm was detected using a fluorescence microscope and flow cytometry. The cells were seeded in a 6-well plate. After the intervention, the collected cells were resuspended in 0.5 mL cell culture medium, added with 0.5 mL JC-1 working solution, gently mixed, and then incubated at 37 °C for 20 min. They were centrifuged 600× g at 4 °C for 3 min to precipitate cells. The cells were washed twice with 1 mL JC-1 buffer and analyzed using flow cytometry (Beckman Coulter, Brea, CA, USA). Or, the cells were washed once with PBS, and then 1 mL of medium and 1 mL of JC-1 working solution were added and incubated at 37 °C for 20 min. After that, the cells were washed twice with JC-1 buffer and observed with fluorescence microscope (RX50, SUNNY, Ningbo, China). ## 4.5. Reactive Oxygen Species (ROS) Detection According to the instructions of the reactive oxygen species analysis kit (s0033s, Beyotime), dichlorofluorescin diacetate (DCFDA) was used as the probe to evaluate the intracellular ROS level. Briefly, incubation took place for 20 min at 37 °C with 10 μM DCFH-DA. Cells were washed three times with serum-free medium and then detected using a fluorescence microscope and flow cytometry (Beckman Coulter, USA). ## 4.6. Apoptosis Assays N2a cells were seeded in a 6-well plate. After the intervention, 5 × 104 cells were suspended in 195 μL binding buffer and incubated with 5 μL of Annexin V-FITC and 10 μL of PI at room temperature for 15 min in the dark. Flow cytometry (CytoFlex, Beckman Coulter, Brea, CA, USA) was immediately performed on stained cells. ## 4.7. Mitochondria (Mito) and Lysosome (Lyso) Tracker Staining On a 24-well plate, N2a cells were seeded on cover slips. After treatment, the cells were incubated with 60 nM Mito-Tracker Green (C1048, Beyotime) and 50 nM Lyso-Tracker Red (C1046, Beyotime) for 30 min at 37 °C with $5\%$ CO2. Lastly, the staining solution was removed; cells were added to fresh medium and observed by means of a fluorescence microscope. ## 4.8. Small Interfering RNA (siRNA) Interference siRNAs were purchased from Ruibo Company (Guangzhou, China) and transfected according to the instructions. The PINK1 siRNA sequence was-CCAAGCGCGTGTCTGACCC-. ## 4.9. Transmission Electron Microscopy (TEM) Hippocampal tissues and N2a cells were prefixed in $2.5\%$ glutaraldehyde, and fixed in $1\%$ osmium tetroxide with 1 mm3 size. Afterwards, the samples were dehydrated in acetone, infiltrated with Epox 812, and embedded. Sections were prepared and stained with citric acid lead and uranylacetate. Lastly, sections were examined under a JEM-1400-FLASH Transmission Electron Microscope (JEOL; Tokyo, Japan). ## 4.10. Mitochondrial Isolation and Western Blotting (WB) Mitochondria were extracted with the mitochondria extraction kit of tissues (C3606) and cells (C3601) provided by Beyotime, and mitochondria and cytoplasmic proteins of Hippocampal tissues and N2a cells were retained for subsequent WB. Briefly, samples were lysed in RIPA buffer with PMSF and phosphatase inhibitor cocktail A (P1081, Beyotime) for 30 min at 4 °C, then centrifuged, and the supernatant was collected for SDS-PAGE. After blocking, the first antibody was incubated with the membranes at 4 °C overnight, followed by secondary antibody incubation for 1 h at 37 °C. Membranes were visualized on an image analysis system (Fusion, Germany). Antibodies are listed in Table 1. ## 4.11. Histological Analysis and Immunohistochemistry The mice were anesthetized with $8\%$ chloral hydrate and then perfused with physiological saline and $4\%$ paraformaldehyde. Brain tissues were fixed in $4\%$ paraformaldehyde for 3 days, embedded in wax and cut into 5 µm and 10 µm slices, which were xylene dewaxed, then ethanol dehydrated. Then, sections were stained for hematoxylin and eosin (H & E) and Nissl staining (C0117, Beyotime) according to the manufacturer’s instructions. Immunohistochemistry was performed as described [17]. The primary antibodies were incubated overnight at 4 °C, and secondary antibodies were incubated for 1 h at room temperature. A Leica DM500 light microscope (Leica Microsystems, Wetzlar Germany) was used to observe the histological sections. ## 4.12. Immunofluorescent Staining Cells were fixed with $4\%$ formaldehyde for 30 min and treated with QuickBlock™ Blocking Buffer (P0260, Beyotime) for 15 min at room temperature. Double-labeling immunofluorescent staining was performed to assess colocalization for PINK1 and Parkin, TOMM20 and LC3A/B. Incubation of primary antibodies at 4 °C overnight and of fluorescent secondary antibodies at 37 °C for one hour was performed. We purchased fluorescent secondary antibodies from SAB: anti-mouse IgG 488 (L3036), anti-rabbit IgG 594(L3017). Then, the fluorescent microscope was used to inspect the cells after the nuclear staining with DAPI (C1006, Beyotime). ImageJ/Fiji (National Institutes of Health, NIH) was used for image analysis in all the above experimental results. ## 4.13. Bisulfite Sequencing PCR (BSP) The methylation of PINK1 promoter in hippocampus was detected by BSP. 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--- title: A Case Study of Dysfunctional Nicotinamide Metabolism in a 20-Year-Old Male authors: - Karen L. DeBalsi - John H. Newman - Laura J. Sommerville - John A. Phillips - Rizwan Hamid - Joy Cogan - Joshua P. Fessel - Anne M. Evans - Undiagnosed Diseases Network - Adam D. Kennedy journal: Metabolites year: 2023 pmcid: PMC10055858 doi: 10.3390/metabo13030399 license: CC BY 4.0 --- # A Case Study of Dysfunctional Nicotinamide Metabolism in a 20-Year-Old Male ## Abstract We present a case study of a 20-year-old male with an unknown neurodegenerative disease who was referred to the Undiagnosed Diseases Network Vanderbilt Medical Center site. A previous metabolic panel showed that the patient had a critical deficiency in nicotinamide intermediates that are generated during the biosynthesis of NAD(H). We followed up on these findings by evaluating the patient’s ability to metabolize nicotinamide. We performed a global metabolic profiling analysis of plasma samples that were collected: [1] under normal fed conditions (baseline), [2] after the patient had fasted, and [3] after he was challenged with a 500 mg nasogastric tube bolus of nicotinamide following the fast. Our findings showed that the patient’s nicotinamide N-methyltransferase (NNMT), a key enzyme in NAD(H) biosynthesis and methionine metabolism, was not functional under normal fed or fasting conditions but was restored in response to the nicotinamide challenge. Altered levels of metabolites situated downstream of NNMT and in neighboring biochemical pathways provided further evidence of a baseline defect in NNMT activity. To date, this is the only report of a critical defect in NNMT activity manifesting in adulthood and leading to neurodegenerative disease. Altogether, this study serves as an important reference in the rare disease literature and also demonstrates the utility of metabolomics as a diagnostic tool for uncharacterized metabolic diseases. ## 1. Introduction Nicotinamide adenine dinucleotide (NAD(H)) is a coenzyme that plays an essential functional role in many biological processes. It serves as a redox carrier and hydride donor in energy metabolism [1], as a co-substrate for several enzymes involved in DNA repair and gene expression [2,3,4,5], and as a nucleotide analog in DNA ligation and RNA capping [6,7]. Through these functions, NAD(H) and its metabolites impact energy metabolism, DNA repair, epigenetics, inflammation, and the stress response. The importance of NAD(H) in maintaining homeostasis is shown by the negative impacts that a decrease in NAD(H) levels has on human health. NAD(H) depletes naturally with age [8,9] and is thought to be an underlying cause of many age-related pathologies, including diabetes [10], non-alcoholic fatty liver disease [11], Alzheimer’s disease [12], and atherosclerosis [13]. Genetic deficiency of NAD(H), such as in the case of glutamine synthetase deficiency or mitochondrial myopathy, is associated with a poor prognosis unless treatment is initiated [14,15]. NAD(H) is synthesized through three different pathways: the de novo, Preiss-Handler, and salvage, which start with tryptophan, nicotinate, and nicotinamide, respectively. Normally, in mammalian cells, NAD(H) synthesis primarily occurs through the salvage pathway [16]. Here, nicotinamide is converted to either 1-methylnicotinamide by the enzyme nicotinamide N-methyltransferase (NNMT) or to nicotinamide mononucleotide (NMN) by nicotinamide phophoribosyltransferase (NAMPT). NMN is then conjugated to ATP and converted to NAD(H) by NMN adenylyltransferase (NMNAT). The salvage pathway is coupled with several NAD+-consuming enzymes, including poly (ADP-ribose) polymerases (PARPs), sirtuins, CD38, CD157, and SARM1 [17]. These enzymes consume NAD+ to catalyze several reactions, including ones that generate nicotinamide. With the generation of nicotinamide, the salvage pathway can start over again. Despite nearly a century’s worth of findings that have characterized the roles of NAD(H) and its metabolites in health and disease, our understanding remains incomplete. Here, we present a case study of a critically ill 20-year-old male with an undiagnosed neurodegenerative disease and abnormalities in nicotinamide metabolism. Having ruled out multiple diagnoses, his care team turned to untargeted metabolomics to obtain a global metabolic profile in hopes of identifying the underlying cause(s) of his illness. The findings of that study identified an error in the patient’s nicotinamide metabolism. While this discovery did not directly impact the patient’s care or lead to a conclusive diagnosis, it demonstrates the value of metabolomics in revealing deep phenotypic insight into the underlying pathology of rare metabolic diseases and justifies its continued use in diagnostic and precision medicine initiatives. ## 2. Case Study The subject was a normal 18-year-old male college student who developed upper respiratory symptoms with nasal congestion, cough, non-specific headache, and mild fever. A nasal swab for influenza A was negative. No other family members were ill prior to or at the onset of the patient’s symptoms. The acute symptoms persisted for over two weeks. A chest radiograph and a CT sinus image were not informative. The acute illness regressed but never completely resolved. Approximately one and a half months after the onset of flu-like symptoms, the patient experienced intermittent loss of balance, which resulted in several falls. Over the next 16 months, he had periodic episodes of confusion and deteriorated to becoming non-verbal and wheelchair-dependent (Figure 1). One and a half years after the onset of the flu symptoms, he required a percutaneous gastric feeding tube and was given a tracheostomy with oxygen supplementation. Approximately 1.5 years after the flu symptoms appeared, he was admitted to the Mayo Clinic and then to Johns Hopkins Medical Center for study. The differential diagnosis was thought to include prion disease, paraneoplastic syndrome, inborn error(s) of metabolism, autoimmune encephalitis, and other genetic neurodegenerative diseases. His family history was negative for similar illnesses, and his personal history was negative for drug use, unusual exposures, psychiatric disorders, and trauma. He had traveled to Central America within the prior two years without illness. A brain MRI showed only bilateral putamen T2 hyperintensities. An EEG showed moderate changes consistent with encephalopathy. His cerebrospinal fluid (CSF) showed elevated total Tau protein (2936 ng/mL) and was positive for 14-3-3, a clinical marker of Creutzfeldt-Jakob disease (CJD) [18,19]. However, an RT-QulC test ruled out CJD and other prion diseases. His CSF was also negative for numerous infections, including herpes simplex virus, Epstein-Barr virus, Varicella zoster virus, John Cunningham virus, enterovirus, West Nile virus, and human herpes virus 6. His CSF IgG and oligoclonal bands were normal, and cytology was negative for tumor cells. Immune studies ruled out autoimmune vasculitis, systemic lupus erythematosus, anti-N-methyl-d-aspartate (NMDA) encephalitis, and paraneoplastic syndrome. His blood levels of ceruloplasmin, copper, and complement C3 and C4 were also normal. His PET scan was normal. His electromyogram was non-specific and showed no evidence of peripheral neuropathy. A therapeutic trial of plasmapheresis conducted over a total of 5 sessions did not improve neurological function. Whole exosome and mitochondrial DNA sequencing were performed. These tests revealed maternally transmitted heterozygous variants of unknown significance (VUS) in the RNASEH1 (RNASEH1 INM 002936.5:c.299G>A:p. Arg100His) and LRSAM1 (NM_138361:c.375C>A:p. Asn125Lys), which encode proteins involved in DNA repair and cell adhesion, respectively. Single gene testing for Machado-Joseph syndrome and hexanucleotide repeats of C9orf72 were negative. No rare genetic variants associated with encephalopathy were found. At this point, 3 years had passed since the onset of his flu-like symptoms. Given the extensive negative workup, he was referred to the Undiagnosed Diseases Network (UDN) program at Vanderbilt University Medical Center (VUMC). Due to the patient’s delicate health he did not travel to VUMC for his UDN visit. A telemedicine appointment was done, and all blood draws for diagnostic workups were performed locally. Whole genome sequencing (WGS) of both the patient and his parents did not reveal any shared or de novo candidate variants. The patient had a maternally inherited heterozygous VUS (NM 018210.3: c.805A>G:pArg269Gly) in NAXD, a gene involved in NAD(H) metabolism. Yet no second variant, either coding or non-coding, was found. A metabolic profile of the patient’s plasma revealed the absence of 1-methylnicotinamide and low N1-methyl-2-pyridone-5-carboxamide, two biochemical intermediates formed during the metabolism of nicotinamide into NAD(H) via the salvage pathway. Related metabolic compounds showed no obvious abnormalities, and his acylcarnitine and amino acid profiles were normal. A previous dose of NADH did not appear to have affected the patient’s condition. In attempts to further characterize the patient’s suspected defect in nicotinamide metabolism, one of the UDN physicians traveled to the patient’s home to perform a nicotinamide challenge. It had now been three and a half years since the patient first fell ill. By this time, he was completely bedridden with spastic reflexes and contractures. He required round-the-clock care, which his family provided to the highest degree of excellence. To perform the nicotinamide challenge, the patient was subjected to a 4-h fast and then immediately challenged with a 500 mg liquid bolus of nicotinamide. Plasma samples were collected: [1] under normal fed conditions, [2] immediately following the fast, and [3] 8 h after the challenge. Global metabolic profiling of those plasma samples revealed an error in metabolism related to nicotinamide N-methyltransferase (NNMT) activity. A reexamination of his WGS data did not show any candidate NNMT variants. Sadly, by the time this error of metabolism was discovered, the patient’s condition had deteriorated considerably. He died in March 2022, approximately four years after the onset of his illness. In the several hours leading up to his death, he experienced tachycardia to the 150–170 level, which gave way to cardiac arrest. There was no apparent sepsis, and the patient did not experience oxygen desaturation. Even though our findings did not lead to a conclusive diagnosis they did reveal metabolic defects that may have contributed, either directly or indirectly, to the patient’s complex disease. Our motivations for presenting the findings of this global profiling study are to ensure that this information will be available in the rare disease literature for future reference and to show the impact that metabolomics can have on characterizing rare metabolic diseases for both diagnostic and research purposes. ## 3.1. Sample Collection As shown in Figure 2, the first whole blood sample (baseline) was collected from the patient under normal fed conditions three months before the metabolic profiling study was conducted. On the day preceding the study, the patient was subjected to a 4-h fast, and two paired whole blood samples were taken immediately following the fast. The next day, after the normal diet had resumed, the subject was given a 500 mg bolus of liquid nicotinamide through his gastric feeding tube to initiate nicotinamide metabolism. The last blood sample was taken 8 h after the challenge. All blood samples were collected into EDTA tubes by a trained phlebotomist. Plasma was isolated from whole blood by centrifugation and stored at −80 °C until analysis according to Metabolon’s sample handling protocol [20]. ## 3.2. Sample Processing Protein was precipitated from plasma by shaking with methanol on a SPEXC 2000 Geno/Grinder and centrifugation. For quality control (QC) purposes, several recovery standards were added to each sample before extraction. The extracted supernatants were divided into 4 aliquots and then placed on a sample evaporator (SPE-Dry 96) to remove organic solvents. Dried extracts were stored overnight under nitrogen. Solvent-only blanks were extracted using an identical method in every set to ensure curated biochemicals met a 3:1 signal-to-noise ratio. A plasma QC sample was extracted with 4 technical replicates in every set to monitor reproducibility. ## 3.3. Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC/MS-MS) The patient’s global metabolic profile was resolved via untargeted ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC/MS-MS) according to validated methods [21,22,23]. Briefly, all samples were subjected to four different chromatography methods. Each of the 4 aliquots of dried extracts were reconstituted in a solvent optimized for each method. Aliquot #1 was analyzed using acidic positive ion conditions optimized for hydrophilic compounds. The extract was gradient-eluted from a C18 column (Waters™ UPLC BEH C18-2.1 × 100 mm, 1.7 µm) using water and methanol containing $0.05\%$ perfluoropentanoic acid (PFPA) and $0.1\%$ formic acid (FA). Aliquot #2 was analyzed using acidic positive ion conditions optimized for hydrophobic compounds. The extract was gradient eluted from the same C18 column using methanol, acetonitrile, water, $0.5\%$ PFPA, and $0.01\%$ FA. Aliquot #3 was analyzed using basic negative ion-optimized conditions on a dedicated C18 column. The extract was eluted from the column with methanol, water, and 6.5 mM ammonium bicarbonate (pH 8.0). Aliquot #4 was analyzed using negative ionization after eluting from an HILIC column (Waters UPLC BEH Amide 2.1 × 150 mm, 1.7 um) using a gradient consisting of water and acetonitrile with 10 mM ammonium formate (pH 10.8). ## 3.4. Compound Identification and Data Analysis Compounds were identified by comparing the mass-to-charge (m/z), retention time, and associated fragmentation spectra in each sample to a library of standard chemical entities as described [21,22,23,24]. Technical replicates of plasma QC samples were extracted in each plate and interspersed throughout the run to monitor the analytical variability of biochemicals. All sample sets met our acceptance criteria of <$10\%$ relative standard deviation (RSD) for recovery standard variability and <$15\%$ RSD for instrument variability. Raw peak values from samples were used to derive the relative quantitation of each compound identified in the patient relative to a healthy reference population [25]. All values were log-transformed, then converted to Z-scores using rankit regression to estimate the mean and standard deviation as described in [26]. This analysis determined how many standard deviations the raw intensity of a given metabolite rose above or fell below the mean intensity of that metabolite in a dataset. Analyses were conducted in R [27] and Omicsoft Array Studio version 7.2. [ 28]. ## 4.1. Confirmation of Analytical Precision To confirm an acceptable precision and accuracy of the metabolomics data, we analyzed 6 technical replicates of plasma. A total of 943 metabolites were detected, with 872 detected in all 6 replicates. Those 872 metabolites had a mean RSD of $9.04\%$ and a median RSD of $3.7\%$. A total of 43 internal and recovery standards were analyzed to assess variability in metabolite recovery across instruments. The metabolites detected in those standards had a mean and median RSD of $5.1\%$ and $4.1\%$, respectively, meeting Metabolon’s acceptance criteria for variability (see Compound Identification and Data Analysis). ## 4.2. Nicotinamide Metabolism Under physiologic conditions, the enzyme N-methyltransferase (NNMT) metabolizes nicotinamide to 1-methylnicotinamide (Figure 3). While the role of 1-methylnicotinamide in physiologic processes remains the subject of study, it is hypothesized to be a signaling molecule that plays a role in energy metabolism [29], coagulation [30], and T-cell activity in cancer [31]. A previous metabolic profile showed the patient had a total absence of 1-methylnicotimide even though he was receiving daily high-dose supplementation of nicotinamide via standard enteric nutrition. Therefore, to evaluate the patient’s ability to absorb and metabolize nicotinamide, we performed global, untargeted metabolic profiling of plasma collected at baseline 4 h after fasting and 8 h after being challenged with 500 mg of liquid nicotinamide (Figure 4). The patient’s nicotinamide levels were in the 100th percentile at all three time points, confirming the effect of high nicotinamide supplementation. The patient’s baseline and fasting levels of 1-methylnicotinamide were below the 2.5th percentile of the reference population, suggesting that his NNMT function was decreased. Surprisingly, the nicotinamide challenge restored his 1-methylnicotinamide levels to normal (Figure 4B), indicating that the patient had some functional NNMT, though its action was only induced by a large dose of nicotinamide following a fast. This finding also suggests that the metabolic defect at play favored the conversion of nicotinamide to nicotinamide riboside under baseline conditions. Interestingly, nicotinamide riboside levels were normal (40th percentile) at baseline and after fasting and rose only slightly (44th percentile) after the nicotinamide challenge (Figure 4C). While nicotinamide mononucleotide (NMN) was not detected at any time point, NAD+ was detected, but only at very low levels near the limit of detection. This is likely due to relatively low levels of both these intermediates in plasma. We next examined metabolite levels in the de novo NAD(H) biosynthesis pathway (Figure 5A). Here, tryptophan is converted to kynurenine, which is then converted to quinolinate. Quinolinate goes through a series of reactions that result in the generation of NAD(H). Tryptophan, kynurenine, and quinolinate were all within the low normal range at all three time points (Figure 5B–F), suggesting that the patient’s metabolic defect was confined to the salvage NAD(H) biosynthesis pathway. ## 4.3. Methionine Metabolism To better characterize the patient’s metabolic profile and further investigate the role of NNMT in his symptoms, we examined biochemicals in the methionine metabolism pathway. Methionine is an essential amino acid found in meat, fish, and dairy products. It is one of several precursors of cysteine, which is converted to glutathione in the tissues. Glutathione plays a vital role in antioxidant defense, nutrient metabolism, regulation of gene expression, DNA and protein synthesis, and cell proliferation. NNMT plays a role in methionine metabolism by converting the homocysteine precursor, S-adenosylmethionine (SAM), to S-adenosylhomocysteine (SAH). SAH is then converted to homocysteine, which is either converted back to methionine or cystathionine, leading to downstream generation of cystine and glutathione (Figure 6A). Methionine was absent in the patient’s plasma at baseline and after fasting (Figure 6B), but the nicotinamide challenge restored methionine levels to the low normal range (7th percentile). This suggests that the reduced function of NNMT under baseline and fasting conditions impaired the conversion of SAM to SAH. This would then impair the conversion of homocysteine back to methionine. Interestingly, cystathionine, the metabolite directly downstream of homocysteine, was within the normal range at all three time points (Figure 6C). Cysteine, which is immediately downstream of cystathionine, was significantly elevated ($p \leq 0.05$) at all time points (Figure 6D). These data further suggest that NNMT function was minimal at baseline and that its function was restored in response to a nicotinamide bolus, resulting in a correction of metabolic pathways affected by low NNMT activity. ## 4.4. NAD(H) Production Since NAD(H) is difficult to profile directly, we investigated the patient’s ability to produce this molecule by examining purine metabolism pathways. Purine metabolism maintains cellular pools of adenylate and guanylate that are derived through the synthesis and degradation of purine nucleotides. In purine catabolism, inosine is converted to hypoxanthine via salvage pathway reactions. Hypoxanthine is converted to xanthine and then to urate through reactions that require the reduction of NAD+ to NADH (Figure 7A). At baseline and after fasting, the patient’s inosine and hypoxanthine levels were above the 97.5th percentile of the normal reference population (Figure 7B,C). The nicotinamide challenge brought hypoxanthine levels down to the normal range. Xanthine levels were within the normal range at all three time points but fell from the 47th percentile to the 19th percentile in response to the nicotinamide challenge (Figure 7D). While the change in xanthine was not significant, it, along with a reduction in hypoxanthine, suggests that this patient could make NAD(H) via nicotinamide metabolism. ## 4.5. Inflammatory Response We observed a significant elevation in several polyunsaturated fatty acids (PUFAs) essential for generating lipid inflammatory mediators. PUFAs can be classified as n-3 or n-6 fatty acids based on their chemical structure. n-3 and n-6 fatty acids are ligands for the nuclear receptors NFκB, PPAR, and SREBP-1c, which regulate the expression of various genes involved in inflammatory signaling and lipid metabolism [32]. n-3 PUFAs tend to down-regulate the expression of inflammatory genes and the synthesis of lipids, while n-6 PUFAs tend to upregulate these functions [33,34]. Thus, the ratio of n-3 to n-6 PUFAs in the cells strongly impacts cellular processes, including cell death and survival. Here, we found that the n-3 PUFAs stearidonate, docosahexaenoate, docosapentaenoate, linolenate, and eicosapentaenoate were above the normal range at baseline and after fasting. Levels of stearidonate, docosapentaenoate, and eicosapentaenoate were above the normal range at baseline and after fasting (Figure 8A–E). We did not observe elevations in any n-6 PUFAs. Stearidonate, docosapentaenoate, and eicosapentaenoate levels remained above normal after the nicotinamide challenge, while docosahexaenoate and linolenate fell into the normal range. These data suggest that the patient’s inflammatory response was dampened under baseline conditions and was not significantly affected by the nicotinamide challenge. We note that the patient demonstrated a significant increase in the anti-inflammatory mediators cortisol and cortisone following the challenge. However, neither of these mediators rose above the normal reference range (Figure 8F–G). ## 5. Discussion Nicotinamide N-methyltransferase (NNMT) is a cytosolic enzyme that catalyzes the conversion of nicotinamide to 1-methylnicotinamide in the salvage pathway of NAD(H) biosynthesis and the conversion of S-adenosylmethionine to S-adenosylhomocysteine in the methionine metabolic pathway. It actively mediates genome-wide epigenetic and transcriptional changes through hypomethylation of repressive chromatin marks. It is also a crucial player in the biosynthesis of NAD(H) molecules, and vital for normal energy metabolism, DNA repair, gene expression, and stress response [35,36]. Similarly to NAD(H), maintaining homeostatic levels of NNMT is considered important for preserving human health. Aberrantly high expression of NNMT has been linked to the development of obesity [37], type 2 diabetes [38], various cancers [39,40,41], neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease [42,43,44], and pulmonary hypertension [45]. On the other hand, abnormally low serum levels of NNMT have been reported in bipolar mania patients [46], and higher incidents of familial schizophrenia have been correlated with lower expression of NNMT in the frontal cortex [47]. While aberrant expression of NNMT is associated with various diseases, the mechanisms through which it drives disease progression is an ongoing focus of investigation. For example, in cancer, NNMT is highly expressed, which is thought to facilitate dysfunctional metabolism leading to tumor growth [48]. Still, at present, there is a paucity of evidence to show that the dysfunction of NNMT activity itself is a key driver of disease. Undoubtedly, the role of NNMT in health and disease is complex, and our understanding of the mechanisms that govern its role remains to be fully understood. To our knowledge, this is the first report of a patient with decreased NNMT function that either manifested in or became apparent in adulthood. The only clue to the underlying cause of the patient’s disease was the absence of 1-methylnicotinamide and low N1-methyl-2-pyridone-5-carboxamide in his plasma, which was revealed by a metabolic profiling test performed prior to the study described herein. To further investigate a probable defect in nicotinamide metabolism, we performed untargeted global profiling of plasma samples collected at baseline, after fasting, and after receiving a 500 mg bolus of nicotinamide through his gastric feeding tube. Our findings revealed that the patient had severely low NNMT function, which was restored to normal in response to the nicotinamide challenge. Two metabolites that rely on NNMT for biosynthesis, 1-methylnicotinamide, and methionine, increased from undetectable levels to the normal range in response to the nicotinamide challenge, thus demonstrating the presence of functional NNMT (Figure 4 and Figure 6). Interestingly, while the nicotinamide challenge increased 1-methylnicotinamide levels from nil to normal, the amount of nicotinamide riboside increased only slightly, suggesting that the patient’s metabolic defect heavily favored the conversion of nicotinamide to nicotinamide riboside at baseline. No other overt defect in nicotinamide biosynthesis was found (Figure 5). Examination of purine metabolism showed a high accumulation of hypoxanthine and its direct downstream intermediate xanthine at baseline and after fasting. Hypoxanthine is converted to xanthine, which is then converted to urate by two reactions that require the oxidation of NADH to NAD+. The nicotinamide challenge increased the conversion of NADH to NAD+, as evidenced by the reduction of hypoxanthine and xanthine, suggesting that the patient could indeed biosynthesize NAD(H) (Figure 7). The patient exhibited higher than normal expression of inflammatory polyunsaturated fatty acids (PUFAs) and lower than normal expression of anti-inflammatory PUFAs. The nicotinamide challenge did not affect this expression profile (Figure 8). Altogether, our data demonstrated functional NAD(H) biosynthesis and a defect in NNMT function of unknown origin that could be rescued with a high dose of nicotinamide after fasting. Interestingly, rescuing NNMT function did not impact the patient’s health status. This suggests that either the patient’s condition had deteriorated to the point where treatment was no longer effective or that there was/were other metabolic defect(s) at play that were not captured in the plasma metabolome. One limitation of this study was the lack of metabolic profiling of muscle tissue where NAD(H) and other intermediates of nicotinamide are easier to detect. Profiling the patient’s CSF could have also provided further insight into his nicotinamide metabolism and potentially revealed other mechanisms to explain his symptoms. Finally, we note that in the absence of a conclusive genetic cause of the disease along with the age of disease onset, we cannot categorically say whether the patient’s error in nicotinamide metabolism was inborn or acquired. 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--- title: An Enhanced Dissolving Cyclosporin-A Inhalable Powder Efficiently Reduces SARS-CoV-2 Infection In Vitro authors: - Davide D’Angelo - Eride Quarta - Stefania Glieca - Giada Varacca - Lisa Flammini - Simona Bertoni - Martina Brandolini - Vittorio Sambri - Laura Grumiro - Giulia Gatti - Giorgio Dirani - Francesca Taddei - Annalisa Bianchera - Fabio Sonvico - Ruggero Bettini - Francesca Buttini journal: Pharmaceutics year: 2023 pmcid: PMC10055879 doi: 10.3390/pharmaceutics15031023 license: CC BY 4.0 --- # An Enhanced Dissolving Cyclosporin-A Inhalable Powder Efficiently Reduces SARS-CoV-2 Infection In Vitro ## Abstract This work illustrates the development of a dry inhalation powder of cyclosporine-A for the prevention of rejection after lung transplantation and for the treatment of COVID-19. The influence of excipients on the spray-dried powder’s critical quality attributes was explored. The best-performing powder in terms of dissolution time and respirability was obtained starting from a concentration of ethanol of $45\%$ (v/v) in the feedstock solution and $20\%$ (w/w) of mannitol. This powder showed a faster dissolution profile (Weibull dissolution time of 59.5 min) than the poorly soluble raw material (169.0 min). The powder exhibited a fine particle fraction of $66.5\%$ and an MMAD of 2.97 µm. The inhalable powder, when tested on A549 and THP-1, did not show cytotoxic effects up to a concentration of 10 µg/mL. Furthermore, the CsA inhalation powder showed efficiency in reducing IL-6 when tested on A549/THP-1 co-culture. A reduction in the replication of SARS-CoV-2 on Vero E6 cells was observed when the CsA powder was tested adopting the post-infection or simultaneous treatment. This formulation could represent a therapeutic strategy for the prevention of lung rejection, but is also a viable approach for the inhibition of SARS-CoV-2 replication and the COVID-19 pulmonary inflammatory process. ## 1. Introduction Cyclosporine-A (CsA) is a cyclic peptide with an immunosuppressive action, administered for the treatment of various pathologies that share uncontrolled activation of the immune system, e.g., atopic dermatitis and psoriasis. Since entering the market in 1983, CsA, a calcineurin inhibitor peptide, has been widely used in the treatment of various autoimmune conditions characterised by the strong activation of the immune system [1]. The success of this molecule is related to its selective and reversible inhibition of the production of pro-inflammatory cytokines by T-lymphocytes [2]. CsA is intravenously and orally (as soft capsules) administered and is currently used for the prevention of allograft rejection in various organ transplantations. Indeed, the continuous activation of T-cells in the transplanted lung is the key factor bringing on bronchiolitis obliterans syndrome (BOS) characterised by extensive fibroproliferation and loss of lung functionality [3]. BOS is considered a marker of chronic rejection and causes $30\%$ of deaths after lung transplantation [4,5]. Despite the efficacy of CsA, severe adverse side effects including nephrotoxicity, hepatotoxicity, hypertension, and neurotoxicity, usually arise during chronic treatment with CsA [6,7]. Moreover, the delivery of a sufficient and reproducible amount of CsA can hardly be achieved by oral administration because of its poor aqueous solubility, its pre-systemic metabolism at the gut level [8,9], and its erratic absorption related to interindividual variability, food intake, and by comorbidities such as diabetes [10,11]. Overall, the oral bioavailability of CsA is around $30\%$, which entails a dosage range between 5 and 15 mg/kg/day, and the need to carefully monitor the patient’s drug plasma concentration over time [12]. For this reason, pulmonary administration would be a promising strategy for the treatment of lung transplant patients, given the possibility of (i) avoiding pre-systemic metabolism and obtaining high drug local concentrations, (ii) having a rapid onset of action, and (iii) administering lower doses than the oral route with limited systemic exposure to the drug. In this regard, the administration of a 100 µg intratracheal dose of CsA to rats, in addition to being effective in reducing lung inflammation, led to a distribution of CsA in the side effect-related organs that were one hundred times lower than that of an oral dose of 10 mg/kg [13]. The pulmonary administration of a dose of just 5 mg of CsA by nebulisation of a propylene glycol solution was able to produce an improvement in lung-transplanted patients’ conditions, expressed as forced expiratory volume in one second (FEV1) [14]. This study demonstrated a strong relationship between the administration of CsA directly to the lungs and an increased anti-rejection effect. Further clinical trials have confirmed the benefits of direct pulmonary administration of CsA by nebulisation in patients who underwent single or double lung transplantation [15] or in BOS patients [16]. Besides the effect on the prevention of allograft rejection, CsA has also been widely studied as a potential anti-viral drug [17,18,19]. In 2011, de Wilde and colleagues first demonstrated the in vitro inhibitory activity of CsA at micromolar concentrations on the replication of different coronavirus genera [20]. The effective inhibition of replication towards SARS-CoV-2 has also recently been demonstrated by Fenizia et al. on the human lung epithelium Calu3 cell line [19]. In addition, CsA anti-inflammatory and immunomodulatory activities would be beneficial in containing the cytokine storm experienced by many COVID-19 patients, leading to airway damage and respiratory loss of function [19,21]. COVID-19 is currently treated using antiviral drugs such as molnupiravir [22], nirmatrelvir [23], ritonavir [24], and remdesivir [25], anti-inflammatory drugs (dexamethasone [26]), immunomodulatory agents such as baricitinib and tocilizumab, an anti-IL-6 antibody [27], and monoclonal antibodies against the receptor binding domain such as sotrovimab [28]. The aim of this work was the development of a highly respirable formulation of CsA obtained by spray drying with excipients already approved for inhalation. A critical parameter for the evaluation of the quality of the produced powders was the Weibull dissolution time obtained from the in vitro release rate profile. The most promising powder was then further analysed in terms of tolerability, reduction of inflammation, and antiviral activity in terms of SARS-CoV-2 reduction of infection in Vero E6 cells. ## 2. Materials and Methods CsA (Metapharmaceutical, Barcelona, Spain) was purchased from ACEF (Fiorenzuola d’Arda, Italy). HPMC extra-dry capsules for use in dry powder inhalers, Quali-V®-I size #3, were provided by Qualicaps (Madrid, Spain), while the high-resistance dry powder inhaler RS01 was gifted by Plastiape (Lecco, Italy). Mannitol was purchased from Roquette (Lestrem, France) and glycine was purchased from Sigma Aldrich (Merck, Milano, Italy). All other chemicals used were obtained from commercial suppliers and were at least of analytical grade. The human lung adenocarcinoma cell line A549 (CRM-CCL-185), monocytic cell line THP-1 (TIB-202), and Vero E6 (CRL-1586) were purchased from American Type Culture Collection (ATCC, Manassas, VA, USA). ## 2.1. Preparation of CsA Spray-Dried Powders The spray-dried (SD) CsA powders were obtained starting from a solution of water and ethanol $96\%$ with a variable ratio according to the design of experiment (DOE) containing $1\%$ (w/v) solids. The effect of different amounts of excipients on the yield of production, respirability, residual solvent, and dissolution rate was assessed. The experiments were designed by means of the Design-Expert 12 software (Stat-Ease, Inc., Minneapolis, MN, USA). A half-fractional factorial design with three factors at two levels and three additional centre points for curvature check was applied, requiring a total of 11 experiments, as detailed in Table 1. The mannitol (10–$20\%$ w/w) and glycine (0–$5\%$ w/w) content in the dry formulation and ethanol (45–$60\%$ v/v) concentration in the feed solution were the three factors investigated, fixed at two levels equally distant from the central point. CsA raw material (CsA_rm) was solubilised in ethanol where the solubility of CsA is more than 100 mg/g [29], while mannitol and glycine were solubilised in water at room temperature. The aqueous solution was added to the CsA solution under magnetic stirring (160 rpm). The CsA remained in solution in all the ranges of water added (from 40 to 55 % v/v) in the hydroalcoholic solution. To produce the powders, 50 mL solution was spray dried (Mini Spray Dryer B-290, Büchi, Flawill, Switzerland) using the following parameters: inlet temperature 140 °C, drying air flow rate 742 L/h, aspiration 35 m3/h, solution feed rate of 3.5 mL/min, and a nozzle diameter 0.7 mm. Under these conditions, an outlet temperature of 80–87 °C was measured. An analysis of variance (ANOVA) was performed to investigate the effect of factors on the critical quality attributes (CQAs). In detail, the CQAs selected were the production yield, the percentage of residual solvent, and the Weibull dissolution time obtained from the dissolution profile. The probability value of the model was considered significant when lower than 0.05. ## 2.2. CsA Quantification by High-Performance Liquid Chromatography (HPLC) The quantification of CsA in the spray-dried powders was achieved by dissolving 20 mg of powder in 25 mL of water:acetonitrile 40:60. Six samples were prepared and analysed by HPLC. The drug content analysis was conducted after the powder preparation and during the stability study. CsA was quantified using an HPLC (LC-10, Shimadzu, Kyoto, Japan) equipped with a UV–Vis detector, set at a wavelength of 230 nm and using the column Nova-Pak C18 (3.9 × 150 mm, 4 µm; Waters, Italy). The mobile phase was constituted by a mixture of $65\%$ acetonitrile and $35\%$ ultrapure water, acidified at $0.1\%$ with trifluoroacetic acid. The column temperature was set at 65 °C and the flow rate was fixed at 1.6 mL/min. The injection volume was 10 µL, the run time of was 10 min and the retention time for the CsA was about 5 min. The method linearity was over the range 0.1–2 mg/mL. ## 2.3. Aerodynamic Performance Characterisation The screening of the aerodynamic performance of all CsA batches produced was achieved using Fast Screening Impactor (FSI; Copley Scientific, Nottingham, UK), with a 65 L/min insert to provide a 5 μm cut-off size. The FSI was connected to an SCP5 vacuum pump (Copley Scientific, Nottingham, UK) through a critical flow controller (TPK Copley Scientific, Nottingham, UK). A flow rate of 65 L/min, measured with a DFM 2000 Flow Meter (Copley Scientific, UK), was required to activate the RS01 (Plastiape, Lecco, Italy) device at a 4 kPa pressure drop. The TPK actuation time was adjusted so that a volume of 4 L of air was drawn through the inhaler. The content of one capsule, filled with 20 mg of powder, was discharged and each experiment was repeated three times. The amount of CsA present in the formulation was in the range between 15 to 18 mg according to the formulation drug content. CsA was quantified by HPLC using to the method reported in Section 2.2. The emitted fraction (EF) was calculated as the percentage ratio between the total CsA mass recovered in FSI and the CsA loaded in the capsule. The respirable fraction (RF) was calculated as the percentage ratio between the mass of particles with an aerodynamic diameter less than 5 µm and the emitted dose. The same analysis setup was maintained to further investigate the aerodynamic performance using the Next Generation Impactor (NGI; Copley Scientific, Nottingham, UK). To obtain a more accurate analysis and avoid the eventual particles bouncing, the cups of the impactor were coated using a solution of $2\%$ (w/v) Tween 20 in ethanol. As above, the content of one capsule of 20 mg was aerosolised and the CsA in the NGI was collected and quantified by HPLC. The metered dose (MD) is the total mass of the drug, quantified by HPLC, recovered in the inhaler and the impactor (induction port, stages 1 to 7, and Micro Orifice Collector (MOC)). The emitted dose (ED) is the amount of drug leaving the device and entering the impactor (induction port, stages 1 to 7, and MOC). The mass median aerodynamic diameter (MMAD) was determined by plotting the cumulative percentage of mass less than the stated aerodynamic diameter for each NGI stage from 1 to 7, on a probability scale versus the aerodynamic diameter of the stage on a logarithmic scale. The fine particle dose (FPD) is defined as the mass of drug with an aerodynamic diameter less than 5 μm (calculated from the log-probability plot equation) and the extra fine particle dose (EFPD) is the mass of the drug with an aerodynamic diameter less than 2 μm. The fine particle fraction (FPF) and the extra fine particle fraction (EFPF) were calculated as the percentage ratio between the FPD or EFPD, respectively, and the ED. ## 2.4. Thermogravimetric Analysis (TGA) The analysis was carried out using the TGA/DSC 1 STARe System (Mettler Toledo, Columbus, OH, USA) to determine the loss on drying (LOD), i.e., the percentage of residual humidity and solvents present in the powder at the end of the manufacturing process. For this purpose, approximately 4 mg of powder was placed in a pan of aluminium oxide, and the analysis was carried out in a nitrogen flow at 80 mL/min. The temperature was increased from 25 °C to 150 °C with a rate of 10 °C/min. The LOD was measured in the range 25–125 °C. ## 2.5. Dissolution Profile of Respirable Particle Fraction In vitro dissolution tests to compare the dissolution performance of CsA powders were conducted using RespiCellTM [30], an innovative vertical diffusion cell apparatus. The apparatus comprises a 170 cm3 receiving cell filled with the dissolution media, and the sampling was performed through the side arm. The apparatus constitutes two portions: the upper part acts as a donor chamber and the lower part is a receptor chamber maintained under magnetic stirring at 180 rpm. The receptor was filled with 170 mL of medium consisting of phosphate-buffered saline (PBS) containing $0.2\%$ of sodium dodecyl sulphate and the cell was connected to a heating thermostat (Lauda eco silver E4, DE) set at 37 ± 0.5 °C. The dissolution was carried out on the RF of the powder, following separation by FSI. In the case of spray-dried CsA, four capsules of 20 mg were aerosolised for each experiment and the analysis was performed in triplicate. In the case of the raw material, the content of ten capsules was aerosolised due to the low respirability of the material. The filter (Type A/E glass filter 7.6 cm diameter, Pall Corp.) containing the mass of powder < 5 µm was then placed on the diffusion area of the RespiCell and 2 mL of PBS containing $0.2\%$ of SDS was added before starting the dissolution to create a thin liquid layer on the powder bed. At fixed intervals, 1 mL of the receiving solution was removed and replaced with 1 mL of fresh buffer to maintain a constant volume inside the receptor chamber. Finally, at the end of the experiment, the residual undissolved powder was recovered by washing out the filter with 10 mL of ethanol:water (50:50 v/v). The samples were quantified by HPLC according to the method described. The drug dissolved was expressed as a percentage of CsA dissolved relative to the total CsA recovered at the end of the test both on the filter and receptor compartment. The dissolution profiles were analysed by means of the Weibull equation [31] in order to determine the time parameter, recognised as the time at which the 63.2 per cent of the drug was dissolved. ## 2.6. Morphological Analysis by SEM Particle morphology was determined by scanning electron microscopy (SEM, Zeiss AURIGA, Zeiss, Oberkochen, Germany) and was operated under high-vacuum conditions with an accelerating 1.0 kV voltage at a magnification of 5k times. The powders were deposited on adhesive black carbon tabs pre-mounted on aluminium stubs and imaged without undergoing any metallisation process. ## 2.7. Viability Study on A549 and THP-1 A549 cells (seeding 104 cells/well), following overnight culture, and THP-1 cells (seeding 5 × 104 cells/well), immediately after seeding in a 96-well plate at 37 °C, were exposed to the following treatments: vehicle (DMSO $0.5\%$ in PBS), CsA_rm (1, 10 µg/mL), spray-dried powder CsA_M20 (containing $20\%$ w/w of mannitol) (1, 10 µg/mL of CsA), and mannitol 2 µg/mL. Cell viability was quantified using the MTS assay. Briefly, 20 μL of 3-(tributylammonium)-propyl methanethiosulfonate bromide solution (MTS, 1 mg/mL) was added to each well and, following 4 h incubation at 37 °C, the supernatants were collected. The absorbance of each well was measured at 490 nm on a microplate reader (Sunrise™ powered by Magellan™ data analysis software, TECAN, Mannedorf, Switzerland). The impact of the various treatments on cell viability was expressed as the percentage of viability with respect to vehicle-treated cells. ## 2.8. Co-Culture Assays and Cytokine Determination For the co-cultures, A549 cells (105 cells/well) were seeded at the bottom and THP-1 cells (105 cells/well) were plated on the insert (0.4 μm pore polyester filter) of Transwell culture plates (#3470, Corning Inc., Corning, NY, USA), with the two cell cultures being physically separated to avoid direct contact, according to the method described by Li et al. [ 32]. After 24 h co-culture, the cells were exposed to the following treatments: vehicle (DMSO $0.5\%$ in PBS), CsA_rm 10 µg/mL, CsA_M20 at 10 µg/mL in respect to CsA, mannitol 2 µg/mL in DMSO $0.5\%$ in PBS. After 1 h, LPS 1 μg/mL (Escherichia coli O55:B5; cat# L6529; Sigma Aldrich, Merck, Milano, Italy) was added to the culture and maintained for 24 h. Cells incubated with the vehicle and not exposed to LPS were used as the control. The concentration of IL-6 in the conditioned media was subsequently determined using an ELISA kit (Boster Biological Technology, Milano, Italy; cat. no. IL-6, EK0410), according to the manufacturer’s protocol and expressed as pg/mL. ## 2.9. Cell Treatment and Viral Replication Inhibition Assay The inhibitory effect of CsA_M20, CsA_rm, and mannitol on viral replication on Vero E6 cell cultures was tested against Omicron subvariant BA.1 (lineage B.1.1.529.BA.1). The viral strain was isolated from a residual clinical specimen conferred to the Unit of Microbiology, Greater Romagna Area Hub Laboratory (Cesena, Italy). The sample underwent an anonymisation procedure in order to adhere to the regulations issued by the local Ethical Board (AVR-PPC P09, rev.2; based on Burnett et al., 2007 [33]). Detailed descriptions of Vero E6 cell culture and propagation, as well as titration and isolation of the virus from biological samples, are reported in the Supplementary Materials. The day before treatment and infection, Vero E6 cells were seeded at a density of 2 × 106 cells/well in 96-well plates and allowed to attach for 16 to 24 h at 37 °C, $5\%$ CO2. On the day of infection, each tested compound stock suspension in PBS was freshly diluted in cell culture medium containing $2\%$ FBS. CsA_rm was tested at concentrations of 8, 16, 32, and 64 µM, corresponding to 9.6, 19.2, 38.4, and 76.9 µg/mL; CsA_M20 was diluted to obtain the same CsA concentrations considering the exact CsA content in the powder (determined by HPLC) of about $80\%$ (w/w). The selected CsA concentrations, in the case of powder CsA_M20, involved the presence of dissolved mannitol at concentrations of 2.4, 4.8, 9.6, and 19.2 µg/mL since mannitol represents $20\%$ (w/w) of the formulation. These values were then adopted when mannitol was applied to the cells and tested as vehicle alone. To better determine at which level the viral replication cycle was inhibited, the cells were subjected to different treatment regimens: treatment 1 h before infection (pre-treatment), treatment 2 h after infection (post-infection), and treatment during infection (simultaneous). Each treatment lasted one hour then was removed. Antiviral efficacy was tested against the viral concentration of 0.0005 moi. Infected cultures were incubated for one hour at 37 °C to allow viral adsorption then the supernatant was removed, and cells were washed with PBS. Treated and infected cultures, were incubated with cell medium at 37 °C, $5\%$ CO2 for 72 h. For each treatment protocol, the cell culture was infected directly with the virus suspension to assess viral replication in the absence of any potential inhibition. ## 2.10. SARS-CoV-2 Nucleic Acid Quantification Viral replication in treated and untreated cell cultures was evaluated by qRT-PCR by comparing the cycle threshold (Ct) values of each treated sample (Ct treated) and its corresponding untreated control (Ct control) obtained after 72 h of incubation. For this purpose, the Allplex SARS-CoV-2 Extraction-Free system (Seegene Inc., Seoul, South Korea) was used. It consists of a real-time qRT-PCR multiplex assay based on the use of TaqMan probes. The sample preparation, reaction setup, and analysis were performed according to the manufacturer’s instructions and the details are described in the Supplementary Materials. Positive and negative controls were included in each run. Fluorescent signals were acquired after every amplification cycle. By comparing the Ct values referring to the N-gene of each treated sample and its corresponding untreated control obtained at the end of the test, the percentage of infectivity reduction was calculated, as follows:% viral infectivity reduction=Ct treated−Ct controlCt0−Ct control∗100 where Ct0 represents the cycle threshold at the time of treatment application. Cells treated with the same treatment protocols, but not infected, were used to assess the effects on cell viability. To quantify cell viability, after the incubation period, the cell monolayers were fixed and stained using $4\%$ formaldehyde solution in crystal violet; absorbance was read at 595 nm. For each tested compound concentration, the percentage of viable cells for each tested concentration was calculated, setting the mean absorbance value of the cell control wells (neither treated nor infected cells) as $100\%$ viability. None of the CsA_M20, CsA, or mannitol concentrations significantly compromised the cell viability. ## 2.11. Stability Studies Stability studies were conducted on CsA_M20 spray-dried powder by storing the capsules containing 20 mg of powder at 25 °C and $60\%$ of relative humidity (RH) and 40 °C and $75\%$ of RH. The CsA content and in vitro aerodynamic performance by NGI were studied after 1 and 3 months of storage. ## 2.12. Statistical Analysis Statistical analysis was conducted using the analysis of variance (ANOVA test) with a post hoc test using Prism 9 (GraphPad Software, v.9.4.0). Data were considered to be statistically significant when the p-value was < 0.05 (* = $p \leq 0.05$; ** = $p \leq 0.01$). ## 3.1. CsA Dry Powder Development by DOE CsA is a lipophilic molecule with a logP of 3 and poor water solubility (3.69 mg/L at 37 °C), falling into class II of the Biopharmaceutical Classification System (BCS) among molecules with low water solubility and high permeability [34]. These physico-chemical properties limit the bioavailability of CsA, and many studies have been performed to improve the dissolution profile of CsA including the use of nanoparticles incorporated into microparticles by spray drying or spray freeze drying [13,35,36]. Moreover, the direct deposition of CsA to the lung could be an effective strategy in preventing lung rejection due to the high local drug availability also enhanced by the avoidance of intestinal pre-systemic metabolism. The low water solubility of CsA represents an issue for the development of an inhalation product both from the point of view of the formulation and the release of the drug on site. In the case of a nebulisation product, a CsA solution using propylene glycol as a solvent [14] or a liposomal formulation has been proposed to increase the pulmonary exposure of the drug. Despite the good performance in clinical trials, the CsA solution for nebulisation did not reach the market, perhaps because of the possible irritant effect of the solvent used [37,38]. Other clinical trials conducted using inhaled liposomal CsA demonstrated the capability of the drug to increase BOS-free survival [39,40]. Compared to a CsA liquid nebulisation, the use of a CsA inhalation powder offers numerous advantages: the powder can be administered by a quick inhalation act and, as a solid-state formulation, the stability of the product is increased. On the other hand, the development of a powder containing CsA requires particular attention to be paid to the choice of excipients and the production technique capable of improving the release of the drug from the solid particles. In this context, some strategies have been proposed to enhance pulmonary release and absorption, such as the construction of CsA particles with pulmonary surfactants or with hydroxypropyl-beta-cyclodextrin and hydrosoluble chitosan [41,42,43]. In this work, the spray-drying process and water-soluble excipients were chosen to develop physically stable CsA respirable particles with improved dissolution. Mannitol was selected as it is currently approved for pulmonary administration [44] and is widely used in particle engineering. The addition of glycine was investigated to promote powder deaggregation and aerosolisation. A preliminary study was carried out to identify the most suitable amount of mannitol to add to the formulation and subsequently to keep it as a starting point for a more in-depth investigation by DOE. Figure 1A illustrates the EF and RF of powders containing CsA and mannitol in the two ratios of 80:20 (CsA_M20) and 50:50 (CsA_M50), spray-dried starting from a solution containing $45\%$ (v/v) ethanol in water. Similar EF and RF values were shown by the two CsA–mannitol powders: the EF was around $85\%$ and RF was about 68–$70\%$. On the contrary, CsA_rm, which had a volume median diameter of 7.67 µm, had a large deposition in the induction port of the impactor, which led to a very low RF of $6\%$. Both of the CsA spray-dried powders exhibited a faster dissolution rate than the CsA_rm: approximately $87\%$ of the spray-dried powder was dissolved after 3 h of the experiment, while only $50\%$ of the raw material was dissolved (Figure 1B). However, the addition of mannitol in different quantities did not lead to a difference in the release profiles of CsA_M20 and CsA_M50. This preliminary test shows that, when mannitol exceeded $20\%$ (w/w) in the powder composition, it no longer had any positive effect on the formulation for either of the qualitative parameters studied. Hence, with the purpose of limiting the amount of powder to inhale, it was decided that the amount of mannitol in the formulation would remain fixed at $20\%$. A screening DoE was set up to investigate the influence of excipients on the quality of the powders. The effect of the ethanol content in the feedstock solution and the addition of glycine along with mannitol on the CQAs of the powders were investigated and are illustrated in Table 2. The yield of the process and the loss on drying (LOD) describe the quality of the spray-drying process, whereas the powder’s aerodynamic behaviour (i.e., RF) and the dissolution time are related to the quality of the formulation. The residual solvent in the dried powder could affect not only its chemical stability, but also its respirability over time, as it could modify the powder’s properties. An ANOVA of the responses for the selected factorial model was performed. The generated model was not significant for the yield of production and for the respirable fraction. In fact, the yield value was similar for all powders, regardless of the composition of the stock solution. In general, the results indicate that the process was efficient in terms of the amount of powder produced and was robust. The yield of the manufacturing process was in the range of 55–$65\%$ for all powders. In all cases, the microparticles did not give rise to visible aggregates and the powders were not electrostatic. Not only the process was considered robust with acceptable values, but also, regarding the respirable fraction, the composition of the feed solution did not have a significant impact within the investigated ranges. Conversely, the ANOVA revealed that the model was significant for the LOD and WDT with probability values of 0.033 and 0.006, respectively (Table 3). Furthermore, the robustness of the relationship between the model and the variables analysed was high, as indicated by the R2 values. Figure 2 illustrates the perturbation graph of WDT versus the three critical factors and contour plot of LOD and WDT as a function of ethanol and glycine proportion. Ethanol is the main factor influencing the different degrees of residual solvents in the particles. As the percentage of ethanol increases, the LOD value approaches zero per cent. On the contrary, glycine had a negative effect on the powder LOD: the presence of this excipient increased the amount of residual solvent in the powder; hence, it was not beneficial for the formulation quality aspects. According to this model, the percentage of mannitol also positively influences the LOD; however, this would seem to be a parameter deriving from the combined effect of ethanol and glycine. A low LOD value is important because it usually correlates with improved peptide stability in a solid-state formulation and decreases the possibility of mannitol recrystallisation. The graph in Figure 2b illustrates the trend of the LOD as the ethanol and glycine vary. The main contribution to the variation in the dissolution time is due to glycine and ethanol, while the effect of mannitol was not significant. Therefore, as the percentage of ethanol and glycine increases, the time to dissolve the $63.2\%$ of the API rises (see Figure 2c). Mannitol did not have a statistically significant effect, although it was indicated as a factor reducing WDT, i.e., leading to a faster dissolution rate (see Figure 2a). In general, the spray-drying process was always able to produce particles with an enhanced dissolution rate compared to the non-formulated CsA (WDT of 169.0 min). Among all of the formulations, the powder CsA_M20, which was prepared starting from a feed solution containing $45\%$ ethanol and without glycine, had the lowest WDT of approximately 59 min. The drug release profile of CsA_M20 was similar to that obtained by Yamasaki et al. ( WDT of approx. 62 min) when CsA was precipitated in nanoparticles and spray-dried into nano-matrix structures with lactose mannitol and lecithin [35]. However, although the dissolution profile of the engineered powders was improved compared to the raw material, it is still a rather slow dissolution rate, which places undissolved particles at risk of removal by mucociliary clearance or phagocytosis. Therefore, in vivo studies will be useful to fully prove the beneficial effect of such formulations. The observed behaviour indicated that when the particle composition consisted only of mannitol and CsA, this was more favourable for dissolution and in terms of residual solvent content. The reason why the composite CsA particles have a higher dissolution rate than the raw material is because during particle formation, the mannitol precipitates together with the CsA, forming a solid structure where the two materials are intimately dispersed. In contact with an aqueous medium, the mannitol dissolves immediately, leaving the CsA, with a high surface area, free for dissolution. Interestingly, the presence of glycine lowered the release of CsA, although it is a hydrophilic excipient, but less hygroscopic than mannitol. Given the significance of the data, it will be worthwhile to further investigate the effect of the interactions between the factors and the CQAs using a full factorial DOE. The ethanol content of the feed solution also influenced the morphology of the microparticles obtained. When the ethanol was $45\%$ (Figure 3A), the particles appeared to be less inflated and more corrugated than the particles produced from a solution containing $60\%$ ethanol (Figure 3B), where a greater number of large, fractured particles were observed. This behaviour is in agreement with what was reported for the production of amikacin spray-drying powders [45]: the particles are much larger or exploded when the evaporation rate is rapid, and therefore the precipitation of the solute occurs early. The evaporation rate increases as the percentage (v/v) of ethanol in the feed solution rise. With regard to the solid state of the produced CsA powders, all were amorphous, as evidenced by the typical halo of the X-ray pattern (see Supplementary Materials). The structure of the CsA raw material was also amorphous before spray drying and no crystallinity peaks were observed in the powders after production. From this first part of the work, CsA_M20 was selected as the best-performing powder and was then further characterised and tested for its tolerability, anti-inflammatory and antiviral activity. ## 3.2. Full Characterisation of the CsA_M20 Spray-Dried Powder The CsA drug loading in the CsA_M20 powder after its production was 76.3 ± $1.4\%$. This value agreed with the theoretical one ($80\%$) considering that the powder had a solvent content, determined by TGA, of about $2\%$. The aerodynamic particle size distribution of the powder CsA_M20, assessed by NGI, showed that the formulation had a very high respirability. The emitted amount of powder from the RS01 device was 16 mg (corresponding to $90\%$ of the metered dose) containing 13.2 mg of CsA. The FPD was 8.8 mg of CsA, which corresponds to an FPF of 66.5 % (Table 4). The favourable aerodynamic behaviour can be attributed both to the poor cohesiveness of the particles and their good flowability and to the efficient deaggregation mechanism of the RS01 device. From Figure 4, illustrating the deposition of the CsA in the NGI, it is possible to observe that most of the particles were collected in stages 2, 3, and 4 and about $4\%$ was collected in the MOC capturing particles with a size lower than 0.5 µm. This led to obtaining an MMAD value of 2.97 µm. A clinical trial evaluating the CsA anti-inflammatory efficacy in BOS by the nebulisation of 300 mg demonstrated that a deposition of CsA greater than 5 mg in the lung correlates with an improvement in lung functionality, and 12 mg was indicated as an anti-rejection protective dose [14]. In light of these results, it can be considered that the FPD of 8.8 mg, generated by the aerosolisation of 20 mg of CsA_M20, is in the correct therapeutic range for the prevention of BOS. Regarding the management of the COVID-19 infection, there are no efficacy or pharmacokinetic data upon the delivery of CsA by inhalation. However, COVID-19 patients who received 300 mg of CsA orally showed positive results on survival [46]. At this dose, the amount of CsA available to the lung will have been very low, but still sufficient to dampen the inflammatory reaction of the respiratory tract. Inhalation administration would make it possible to obtain equal or higher efficacy in the face of a reduction in the administered dosage and reduced systemic exposure. Stability analyses on the CsA_M20 powder stored in HPMC capsules, conducted at 1 and 3 months in standard and accelerated conditions, provided drug content values in a range between 78 and $82\%$ without being significantly different from the time zero ($p \leq 0.05$). In the aerodynamic assessment, the CsA ED was around 13 mg and the FPD was in the range of 8–9 mg, independently of the storage conditions and the check time of the analysis (Table 4). These data, albeit preliminary, show that the use of mannitol as a bulking excipient was able to protect the physicochemical stability of the formulation, preserving its initial characteristics. The use of Quali_V®_I capsules in this work, specifically produced for DPI, with optimised puncturing properties and internal lubricant features, certainly contributed to this positive achievement [47]. Finally, the CsA_M20 showed a differential scanning calorimetry profile at three months equal to that at time zero, evidencing that the powder did not undergo solid-state transformations during the observation time (see Supplementary Materials). ## 3.3. CsA_M20 Cytotoxicity and Anti-Inflammatory Efficiency The viability of human lung adenocarcinoma cell line A549 and monocytic cell line THP-1 was not affected by the various CsA tested treatments, which were well tolerated by cells, as reported in Figure 5. Indeed, under these conditions, neither CsA_rm nor the spray-dried powder of CsA containing mannitol displayed any cytotoxic effect on the two cell cultures compared to the vehicle ($0.5\%$ DMSO in PBS). IL-6 is a pro-inflammatory cytokine involved in numerous cellular processes such as proliferation and survival. Furthermore, the high serum levels of IL-6 in patients who have undergone a lung transplant were a marker for the development of chronic lung allograft dysfunction [48,49]. In parallel, it was observed that COVID-19 infection is accompanied by an aggressive inflammatory response with the release of a large amount of pro-inflammatory serum cytokines in an event known as a “cytokine storm” [50]. In particular, IL-6 was reported to be a potential predictor for the development of severe COVID-19, since elevated levels of this cytokine were associated with critical patient conditions such as acute respiratory distress syndrome and the need for mechanical ventilation [50]. As IL-6 is the most frequently reported cytokine to be increased in COVID-19 patients and as IL-6 elevated levels have been associated with higher mortalities, this cytokine was selected in this work to test the CsA anti-inflammatory effect. The levels of IL-6 were determined by ELISA test 24 h after the treatment of cell co-cultures exposed to LPS. The levels of the cytokine were significantly reduced either by CsA_rm or by formulated CsA compared to the vehicle (Figure 6). Mannitol, used as an excipient in the formulation, also showed a slight anti-inflammatory effect albeit not statistically significant, as already reported in vivo [51]. The results confirm that through the spray-drying process, it was possible to construct highly respirable particles with improved dissolution rates, preserving the CsA anti-inflammatory effect. An inhaled powder of CsA, therefore, represents a favourable therapeutic strategy to avoid the triggering of a vigorous immune reaction in the lungs. Consequently, this action would limit the production of cytokines and their consequent spillover into the circulatory system, preventing the systemic cytokine storm. ## 3.4. In Vitro Anti-Viral Efficacy against SARS-CoV-2 As mentioned before, CsA has been shown to have a direct inhibitory effect on the replication of different types of coronaviruses, including SARS-CoV-2. For this purpose, orally administrated CsA has also been the subject of clinical trials, reporting positive results on the survival of patients affected by COVID-19 [46,52]. Moreover, to date, a further ten clinical trials are ongoing, although the results have not yet become available, indicating the high interest in CsA for the treatment of this disease. In light of these considerations, the last part of the study explored the inhibition activity of CsA_M20 powder on viral replication in Vero E6 cells in comparison to the CsA_rm. Furthermore, different types of treatment (pre-treatment, post-treatment, or simultaneous regimen) were adopted to assess the more effective one to contain the virus. The infected cells were treated with CsA_rm, CsA_M20, or mannitol powders applied according to the different treatments. Figure 7 illustrates the virus infectivity reduction in relation to the CsA concentrations applied. The effect of mannitol alone was as well assessed since it is a component of the engineered CsA powder. The range of CsA concentrations investigated was selected according to the one proposed by de Wilde et al. [ 20]. A $100\%$ viral infectivity reduction corresponds to the maximal reduction in the viral load. During the pre-treatment, only the highest CsA concentration applied (76.9 µg/mL) showed an antiviral effect. The reduction of viral infectivity was $78\%$ when the drug was formulated as a spray-dried powder and was statistically superior to the raw material, which reduced the infection by $58\%$. At lower concentrations, CsA did not have any relevant antiviral effect. Similarly, mannitol did not produce inhibitory effects at any of the tested concentrations. At the lowest concentrations (19.2 and 9.6 µg/mL) of all of the treatments, even greater viral growth was observed in the treated samples compared to the control; this is identified by the negative value of the infectivity percentage. To interpret the data, it should be mentioned that the cell culture medium containing CsA was replaced with fresh medium before applying the virus; therefore, the drug that interacted with the pathogen replication was only the fraction that was internalised by the cell. The fact that CsA_M20 has superior efficacy to CsA_rm could be due to the higher solubility of these composite particles, possibly increasing the host intracellular concentration of the drug where the virus was replicating. These positive inhibition results show that CsA is active not only against SARS-CoV, as shown in 2011 by de Wilde et al. [ 20], but also on the SARS type CoV-2 responsible for the current sanitary emergency. It was demonstrated that CsA treatment rendered the virus RNA and protein synthesis almost undetectable [20]. In parallel, the reduction of cyclophilins did not interfere with the SARS-CoV replication. Finally, a further blocking mechanism has been recently in silico demonstrated: through molecular docking, CsA was able to bind and block two membrane proteins (TMPRSS2 and CTSL) necessary for SARS-CoV-2 to penetrate the host cell [53]. Differently from the pre-treatment condition, in the post-treatment regimen, the viral inhibitory activity was present for all CsA concentrations tested except for the lowest one. Furthermore, the CsA_M20 powder was always more effective than the CsA_rm, although statistically superior only at the concentration of 19.2 and 76.9 μg/mL. Mannitol, as in the previous case, showed a slight activity of reducing infectivity. With regard to the adopted protocol, in this case, the treatment was applied after the virus had been allowed to absorb and then removed from the culture. Therefore, as in the case of the pre-treatment, the block of the virus infection presumably took place within the host cell, where the viruses remained after washing resided. The engineered CsA powder had, in these conditions, superior efficacy likely due to its enhanced dissolution, leading to a higher amount of the drug entering the host cell where the virus was replicating. In the simultaneous treatment, the CsA_M20 and CsA_rm powders performed similarly at the two highest concentrations tested where the inhibition reached 75–$80\%$. This trend changed at 19.2 and 9.6 µg/mL, at which only raw CsA showed an antiviral effect of $30\%$ significantly higher than that of CsA_M20 ($5\%$). This was the only experimental protocol in which the cells were exposed to the virus simultaneously with the treatment, therefore the only situation in which the drug–virus interaction took place both in the extracellular compartment and subsequently intracellularly. The inhibition data of the CsA_rm highlight that an interaction may occur between drug suspension and the virus, which does not happen in the case of the more soluble CsA_M20 powder. In fact, the members of the Coronaviridae family possess a phospholipid envelope, therefore an interaction between the pure CsA_rm and the viral membrane would be possible. It is known that CsA binds lipid membranes following the classic hydrophobic effect and that CsA affects the membranes in a concentration-dependent manner by the perturbation of the organisation of fatty chains [54]. Hence, it can be hypothesised that in the case of the CsA_rm, the solid particles create a concentration at the particle–virus interface close to saturation, higher than that generated by the CsA_M20 solubilised in the medium. This difference could explain the high ability to interact with the cell membrane of the virus. Furthermore, the presence of solid particles could represent a further obstacle to infection as they act as a physical barrier and reduce the surface area available for virus adsorption. In contrast, CsA_M20, which was successfully dissolved in the medium, could little hinder the interaction between the virus and the host cell membrane. In this regard, the creation of a polymeric barrier is exploited as a system to inhibit virus–cell interaction by numerous commercially available nasal sprays to antagonise the infection. In summary, the most effective treatment regimens were post-infection or simultaneous infection treatment. In both cases, the infectivity of SARS-CoV-2 was reduced and in the case of post-treatment, more efficiently by the CsA_M20 powder than the raw material. This post-infection approach is also the most plausible considering that pharmacological treatment commonly follows and does not simultaneously accompany the entry of the virus. Moreover, the raw material, although effective, cannot be administered as such due to its low respirability. At variance, the prophylactic treatment, despite the in vitro data, has not been proven to be effective except at the highest concentration tested, probably because in cases of treatment with a lower dosage, an effective drug concentration is not internalised and retained by the cells. ## 4. Conclusions The work demonstrated that, through the modulation of mannitol and ethanol, it was possible to achieve an inhalation powder with high respirability (FPF of $66.5\%$) and improved CsA release (WDT of 59.5 min). This aspect is of crucial importance considering that CsA has a very low oral bioavailability and therefore a rapid lung release would be extremely advantageous to obtain high pulmonary exposure. Besides the fact that the inhalation powder developed could represent an advantageous strategy in the prevention of lung transplant rejection, the collected findings provide strong in vitro evidence that this therapeutic approach could be efficient in the reduction of SARS-CoV-2 infectivity, especially as a post-infection treatment. CsA_M20 powder, applied to cells one hour after contact with the virus, was able to inhibit its replication by $93\%$. Finally, the CsA-engineered powder showed an anti-inflammatory effect in terms of IL-6 reduction that could also be useful in containing the COVID-19 cytokine storm in the lungs. ## References 1. Fahr A.. Cyclosporin Clinical Pharmacokinetics. Clin. Pharmacokinet. (1993) 24 472-495. DOI: 10.2165/00003088-199324060-00004 2. Forsythe P., Paterson S.. Ciclosporin 10 Years on: Indications and Efficacy. Vet. Rec. (2014) 174 13-21. DOI: 10.1136/vr.102484 3. Tissot A., Danger R., Claustre J., Magnan A., Brouard S.. 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--- title: Role of Nutrition in the Etiopathogenesis and Prevention of Nonalcoholic Fatty Liver Disease (NAFLD) in a Group of Obese Adults authors: - Daniela Metro - Martina Buda - Luigi Manasseri - Francesco Corallo - Davide Cardile - Viviana Lo Buono - Angelo Quartarone - Lilla Bonanno journal: Medicina year: 2023 pmcid: PMC10055888 doi: 10.3390/medicina59030638 license: CC BY 4.0 --- # Role of Nutrition in the Etiopathogenesis and Prevention of Nonalcoholic Fatty Liver Disease (NAFLD) in a Group of Obese Adults ## Abstract Nonalcoholic fatty liver disease (NAFLD) is liver damage characterized by an accumulation of triglycerides in hepatocytes of >$5\%$ (due to an alteration of the balance of the lipid metabolism in favour of lipogenesis compared to lipolysis) that is not induced by the consumption of alcohol. The pathology includes simple steatosis and nonalcoholic steatohepatitis, or NASH (steatosis associated with microinflammatory activities), which can evolve in $15\%$ of subjects with hepatic fibrosis to cirrhosis and the development of hepatocellular carcinoma. The aim of this study is to report the role of macro- and micronutrients in the pathogenesis and prevention of NAFLD in obese subjects. A total of 22 obese or overweight patients with hepatic steatosis were monitored periodically, evaluating their eating habits, fasting glycaemia, lipid picture, liver enzymes, anthropometric parameters, nutrition status, liver ultrasound, oxidative stress, and adherence to the Mediterranean diet. A statistical analysis shows a significant positive relationship between total cholesterol and the Mediterranean adequacy index (MAI) (r = −0.57; $$p \leq 0.005$$) and a significant negative relationship between ALT transaminases and the MAI (r = −0.56; $$p \leq 0.007$$). Nutrition and diet are important factors in the pathogenesis and prevention of NAFLD. The dietary model, based on the canons of the Mediterranean diet, prevents and reduces the accumulation of fat in hepatocytes. Therefore, in agreement with other studies in the literature, we can state that a dietary model characterized by foods rich in fibre, carotenoids, polyphenols, ω3 fatty acids, folic acid, and numerous other molecules is inversely correlated with the serum levels of ALT transaminases, an enzyme whose level increases when the liver is damaged and before the most obvious symptoms of organ damage appear. ## 1. Introduction Nonalcoholic fatty liver disease is liver damage characterized by an accumulation of triglycerides in hepatocytes of >$5\%$ (due to an alteration of the balance of the lipid metabolism in favour of lipogenesis compared to lipolysis) that is not induced by the consumption of alcohol or by other etiological factors that cause liver disease, including drugs, toxins, infectious diseases, etc. [ 1]. NAFLD includes simple steatosis and nonalcoholic steatohepatitis, or NASH (steatosis associated with microinflammatory activities), which can evolve in $15\%$ of subjects with hepatic fibrosis to cirrhosis and the development of hepatocellular carcinoma [2]. In fact, NAFLD progresses in four stages, which includes, in the first stage, the deposition of fat in the liver (NAFL, or nonalcoholic fatty liver). The second stage is characterized by excess fat storage in the liver and NASH (nonalcoholic steatohepatitis) inflammation. Persistent inflammation can cause a scar to form in the liver: this stage is called fibrosis (third stage). The fourth stage is cirrhosis, which is the most severe form of NAFLD, with impaired liver cell structure and function [3]. NAFLD is associated with age, gender, race, and ethnicity. Several genetic and epigenetic factors, a sedentary lifestyle, sleep (sleep apnoea syndrome), and diet composition, may play a role in the pathogenesis of NAFLD and NASH [4,5,6]. NAFLD is frequently associated with obesity, type II diabetes [7,8], dyslipidaemia (hypertriglyceridemia, rather than hypercholesterolemia, and low HDL levels), hyperinsulinemia, and insulin resistance and, more generally, with metabolic syndrome [9]. In most cases, NAFLD is considered to be the hepatic expression of “metabolic syndrome”, a set of clinical conditions that have insulin resistance as a common etiopathogenetic denominator. Metabolic syndrome is characterized by the presence, in the same patient, of abdominal obesity with a circumference of >102 cm in men and of >88 cm in women in addition to two or more of the following parameters: fasting hyperglycaemia, hypertriglyceridemia, reduced HDL cholesterol, and arterial hypertension. The diagnostic process includes the calculation of the body mass index (BMI) and the measurement of the waist. In fact, most patients with NAFLD/NASH have visceral obesity, which favours the establishment of insulin resistance, an important pathogenetic factor of NAFLD and NASH [10]. Its role in the pathogenesis of NAFLD is mainly linked to the ability to promote the accumulation of fat in hepatocytes. Finally, in NAFLD there is an increase in transaminases. ALTs are higher than ASTs, with an ALT/AST ratio > 1, which is contrary to what occurs in alcoholic liver diseases. Since nutrition is an important pathogenetic factor in NAFLD/NASH, the Mediterranean diet seems to be the nutrition model to follow. Several studies indicate that, due to its food’s richness in fibre, carotenoids, omega-3, and folic acid, it is inversely correlated with serum transaminase levels, insulin resistance, and the severity of chronic liver disease and that it would appear to prevent NAFLD [10,11,12,13,14,15,16,17,18]. NAFLD is a multisystem disease, and several clinical features of this disease have been linked to cognitive disorders [19] to the extent that they are considered to be complications of this condition [20,21]. It is not yet clear whether it is NAFLD that causes these cognitive disorders, but we do know that associated conditions, such as vascular dysfunction, systemic inflammation, obstructive sleep apnoea, and atherosclerosis, contribute to cognitive dysfunction [22,23]. Cognitive deficits would appear to affect psychomotor speed, visuospatial functions [24], attention, memory [25], executive functions, and abstract reasoning [26]. It would seem that an influence of the disorder on the cognitive sphere is more likely in women than in men but only if a highly inflammatory state is present [27]. In addition to cognitive effects, NAFLD appears to affect mood by causing depression and anxiety [20,28], but these results as well as those for the cognitive sphere [29] are rather equivocal. Food intake also has an effect on cognitive performance in patients with a high risk of NAFLD: higher nutrient intake and better diet quality correlate with a better immediate and delayed memory [30]. The role of macro- and micronutrients in the pathogenesis and prevention of NAFLD in obese subjects is reported in the present study. Moreover, active meal management activities are proposed, and their effects on cognitive functions have been measured. ## 2. Materials and Methods All patients involved within this study were administered Montreal Cognitive Assessment (MoCA) to examine their cognitive profile. MoCA is a rapid tool for the assessment of cognitive functioning. It assesses several cognitive domains: attention and concentration, executive functions, memory, language, visuoconstructive skills, abstraction, calculation, and orientation. The administration time of this test is 10 min, and scores range from 0 to 30, with a score of 26 and above generally considered normal. Since cognitive impairment could affect adherence to treatment and the treatment itself, patients who scored below 26 were not taken into account in the study. After the test’s administration, 22 obese or overweight patients with a mean age of 43 years (range 27–64 years), who were teetotal, who were nonsmoking, and who had hepatic steatosis evidenced by hepatic ultrasonography, were found to be eligible for recruitment. These patients were monitored periodically for six months. The following were evaluated:Eating habits;Blood chemistry tests (fasting glycaemia, lipid picture (total cholesterol and HDL cholesterol), and liver enzymes (AST and ALT transaminases));Anthropometric parameters (body weight, height, BMI calculation, and waist circumference);Nutrition status (bioimpedance test, FM (fat mass), and FFM (lean mass));Liver ultrasound;Oxidative stress;Adherence to the Mediterranean diet. Patients were also subjected to a low-calorie diet (1300–1400 Kcal), the total caloric intake (TCI) consisting of $20\%$ protein, $25\%$ lipids, and $55\%$ carbohydrates. The dietary recommendations for patients with NAFLD are as follows:[1]Carbohydrates should be 45–$60\%$ of TCI (simple carbohydrates < $10\%$ of TCI);[2]Lipids should be 20–$35\%$ of TCI (2–4 g/day of omega-3);[3]Proteins should be 20–$25\%$ of TCI. The indicators of oxidative stress, analysed in plasma before and after the administration of the diet, were malonylaldehyde (MDA) and reduced glutathione (GSH). In particular, the oxidative damage, caused by radical species, was evaluated by quantifying the lipid peroxidation expression of oxidative damage by determining the plasma levels of MDA. The modulation of antioxidant defences was evaluated by analysing the plasma levels of reduced glutathione. The analytical procedures used for the determination of the above parameters have been described in our previous work [31,32]. Adherence to the Mediterranean diet was calculated using the MAI (Mediterranean adequacy index) according to the method of Fidanza et al. [ 33]. The MAI was calculated from 18 food groups, was expressed as a percentage of the total energy consumed, and was obtained by dividing the sum of the percentages of total energy of food groups typical of the Mediterranean diet (bread, cereals, legumes, potatoes, vegetables, fresh fruits, nuts, fish, wine, and olive oil) by the percentage of total energy of food groups less typical of the Mediterranean diet (milk, dairy products, meat, eggs, animal fats and margarines, sweet drinks, sweets, and sugar). Specifically, the formula used was MAI=% of energy bread+cereals+legumes dry and fresh+potatoes+vegetables+fresh fruit+nuts+fish+wine+vegetables oils% of energy milk+dairy products+meat+eggs+animal fats and margarine+sweet beverages+cakes+pies and cookies+sugar The value of the MAI for the Italian Mediterranean population was between 4.0 and 8.5. Adherence to the Mediterranean diet was ensured through regular monitoring of the food diary filled out by patients. Indeed, the detection of eating habits and individual food consumption was carried out by administering questionnaires and using a reasoned nutrition atlas and by executing the following:(a)Recording reminder of the foods eaten during meals in a day by means of a quantitative assessment;(b)Registering remembrance of foods consumed habitually and recently through quantitative assessment and registering frequency of consumption. The consumption of the various food groups consumed was quantified in terms of grams per day. It was therefore possible to trace the daily calorie intake. We also decided to assess whether the use of this reasoned atlas combined with the person’s active involvement in managing their own meals influenced cognitive performance. Activities such as compilation of diaries, preparing meals or snacks, researching and using recipes, deciding on and organising the daily menu, and preparing shopping lists are often included in ecological rehabilitation programmes. Although these aspects are often taken for granted, these complex skills require the use and coordination of several cognitive abilities [34,35,36]. They enhance personal and instrumental autonomy; may increase flexible thinking; and stimulate memory, attention, and visuospatial abilities [37,38]. Evidence suggests that changing these health behaviours can benefit cognitive function [39,40]. Therefore, at the end of the diet, MoCA was readministered to assess any changes. ## 3. Statistical Analysis The continuous variables were expressed as the mean ± the standard deviation, whereas the categorical variables were expressed as frequencies and percentages. A paired t-test or Wilcoxon signed-rank test was used to determine if there was a difference between T0 (baseline) and T1 (after 6 months). The correlations between the variables were computed using Spearman’s coefficient or Pearson correlation. Analyses were performed using an open-source R3.0 software package. A $95\%$ confidence level was set with a $5\%$ alpha error. Statistical significance was set at $p \leq 0.05.$ ## 4. Results The demographic and clinical characteristics of the sample are reported in Table 1. The comparison between T0 and T1 showed highlighted significant differences in the clinical variables ($p \leq 0.01$) (Table 1; Figure 1). The patients did not show a significant improvement in cognitive performance, measured with the MoCA, between T0 and T1. Spearman correlation showed a significant negative relationship between ALT and MAI (r = −0.48; $$p \leq 0.02$$) (Figure 2). ## 5. Discussion Nutrition and diet are important factors in the pathogenesis and prevention of NAFLD. Numerous studies indicate that the eating habits of NAFLD patients differ significantly from control populations. Among micronutrients, diets rich in fats and carbohydrates play a role in the pathogenesis, prevention, and treatment of NAFLD [41,42,43,44]. Simple sugars, sucrose, and fructose, among carbohydrates [45,46], as well as saturated fatty acids (SFAs) [47,48], trans fatty acids [49], and ω6 fatty acids [50] play a role in the pathogenesis of NAFLD. Dietary models typical of the “Western Diet”, consisting of a combination of highly processed foods, sweets, and drinks enriched with simple sugars as well as red meats and refined cereals, are also considered risk factors for the onset and progression of the disease [51,52]. On the other hand, dietary fibre, foods containing carbohydrates with a low glycaemic index [53,54], as well as monounsaturated fatty acids (MUFAs) and ω3 fatty acids have beneficial effects against NAFLD. Among the micronutrients, vit. C [27], vit. E [55,56,57], vit. D [31], polyphenols [58,59], and antioxidant molecules prevent the pathogenesis of NAFLD. Recent scientific testimonies highlight that the increasing use of fructose has favoured the onset and spread of metabolic syndrome and, in particular, of diabetes and that it plays a particularly important role in the development of NAFLD by favouring fibrosis and the progression of NASH [60]. The fructose contained in fruit and honey is present together with glucose and sucrose. By consuming fruit, you also take in fibre, vitamins, and antioxidants, which attenuate the effects of simple sugars on the liver. Fructose can also be used by the food industry as a sweetener in some products, such as drinks and snacks, especially those intended for children. Scientific data have highlighted the risk, linked to its high lipogenetic power, of promoting the onset of insulin resistance. In fact, the increase in the use of beverages rich in fructose increases fat mass, lipogenesis, and inflammation and induces insulin resistance and hypertriglyceridemia, particularly in overweight people [61,62]. It was also observed that, in rats, a diet rich in sucrose resulted in the stimulation of lipogenesis and related enzymatic activities in the liver [63]. Additionally, ω3 fatty acids are important regulators of the genes responsible for the degenerative metabolic pathway of fatty acids and determine the downregulation of the synthesis and deposit of triglycerides [64]. Some studies have shown that ω3 fatty acids reduce hepatic steatosis, improve insulin resistance, and reduce inflammation markers [65]. The dietary model, based on the canons of the Mediterranean diet, prevents and reduces the accumulation of fat in hepatocytes. Observational and experimental studies have affirmed that this dietary model, characterized by foods rich in fibre, carotenoids, polyphenols, ω3 fatty acids, folic acid, and numerous other molecules, is inversely correlated with the serum levels of transaminases, insulin resistance, and the severity of chronic liver diseases. The Mediterranean diet (MD) is not only associated with a low incidence of some chronic degenerative diseases, cancer mortality, and cardiovascular disease; in fact, observational studies and clinical experiences have also highlighted a preventive role against obesity and type II diabetes. The traditional MD is characterized by plant foods: in fact, it is rich in wheat, cereals (including bread and pasta), legumes, fresh fruits, vegetables, nuts, and olive oil. Red meats (beef and lamb) and meat derivatives must be consumed in low quantities. The consumption of fish and chicken is moderate; alcohol (red wine) is consumed in moderate quantities during meals. There is moderate consumption of milk and dairy products as well as animal fats in the form of butter; lard and cream are not included in the diet. The nutrients in the MD that show potentially protective effects are (a) complex carbohydrates, (b) fibres, (c) monounsaturated fatty acids (ω3), (d) polyunsaturated fatty acids (ω3), and (e) bioactive compounds. Bioactive compounds are substances, which are almost all of vegetable origin, that are capable of modulating biological activities and important physiological functions. In fact, multiple effects are attributed to these compounds: antioxidants, anti-inflammatories, the modulation of detoxification enzymes, and the stimulation of the immune system. The main bioactive compounds present in food are (a) carotenoids, (b) polyphenols, and (c) vitamins (vitamins C and E). Furthermore, several studies have shown that MD foods as a whole are more important for the longevity of individual nutrients. The MD, however, is not characterized by a simple table of foods contained in the classic pyramid developed in the nineties, but it is, above all, a lifestyle that has as its keywords seasonality, conviviality, tradition, frugality, zero waste, and physical activity. This can ensure one’s well-being, longevity, and sustainability. After all, the meaning of the word diet, from the Ancient Greek word dìaita, meaning “lifestyle”, is the search for a balance with oneself, with others, and the environment: a search for well-being that reconciles taste and health. Recent studies have shown that adherence to the MD tends to decrease, especially in overweight and obese subjects [66]. Several factors contribute to the development and progression of NASH and fibrosis: insulin resistance, hyperinsulinemia, mitochondrial dysfunction, oxidative stress, lipid peroxidation, an increased production of proinflammatory cytokines, hepatic stellate cell activation, and apoptosis [67]. Hepatic stellate cells induce fibrosis and collagen deposition [68]. Oxidative stress (alterations in the balance between prooxidant and antioxidant activities) is due to an increase in reactive oxygen species (ROS). The oxidation of fatty acids is an active source of ROS. This increase in ROS produces damage to DNA and proteins, damages the structure and function of membranes through lipid peroxidation, and increases the release of proinflammatory cytokines [69,70,71,72]. Finally, the results of various experimental and clinical studies suggest that oxidative stress is associated with many of the components of metabolic syndrome, diabetes mellitus, hypertension, obesity, dyslipidaemia, and inflammation [73], and an inverse association between the Mediterranean diet and prevalence is also demonstrated for metabolic syndrome [74]. ## 6. 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--- title: Molecular Mechanisms Underlying TNFα-Induced Mitochondrial Biogenesis in Human Airway Smooth Muscle authors: - Debanjali Dasgupta - Sanjana Mahadev Bhat - Alexis L. Price - Philippe Delmotte - Gary C. Sieck journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10055892 doi: 10.3390/ijms24065788 license: CC BY 4.0 --- # Molecular Mechanisms Underlying TNFα-Induced Mitochondrial Biogenesis in Human Airway Smooth Muscle ## Abstract Proinflammatory cytokines such as TNFα mediate airway inflammation. Previously, we showed that TNFα increases mitochondrial biogenesis in human ASM (hASM) cells, which is associated with increased PGC1α expression. We hypothesized that TNFα induces CREB and ATF1 phosphorylation (pCREBS133 and pATF1S63), which transcriptionally co-activate PGC1α expression. Primary hASM cells were dissociated from bronchiolar tissue obtained from patients undergoing lung resection, cultured (one–three passages), and then differentiated by serum deprivation (48 h). hASM cells from the same patient were divided into two groups: TNFα (20 ng/mL) treated for 6 h and untreated controls. Mitochondria were labeled using MitoTracker green and imaged using 3D confocal microscopy to determine mitochondrial volume density. Mitochondrial biogenesis was assessed based on relative mitochondrial DNA (mtDNA) copy number determined by quantitative real-time PCR (qPCR). Gene and/or protein expression of pCREBS133, pATF1S63, PCG1α, and downstream signaling molecules (NRFs, TFAM) that regulate transcription and replication of the mitochondrial genome, were determined by qPCR and/or Western blot. TNFα increased mitochondrial volume density and mitochondrial biogenesis in hASM cells, which was associated with an increase in pCREBS133, pATF1S63 and PCG1α expression, with downstream transcriptional activation of NRF1, NRF2, and TFAM. We conclude that TNFα increases mitochondrial volume density in hASM cells via a pCREBS133/pATF1S63/PCG1α-mediated pathway. ## 1. Introduction Airway inflammation is one of the major contributors to the pathophysiology of airway diseases including asthma [1,2], chronic obstructive pulmonary disease (COPD) [3,4], chronic bronchitis [5], and COVID-19 [6,7,8]. Previous studies, including those from our laboratory, have shown that acute exposure to proinflammatory cytokines such as TNFα results in airway smooth muscle (ASM) hyperreactivity in response to agonist stimulation, leading to energetic stress [9,10]. In addition, TNFα induces the proliferation of ASM cells, which also increases energy demand [11]. In ASM cells, as in other eukaryotic cells, ATP demand is met by mitochondrial respiration and oxidative phosphorylation [11]. Previous studies from our laboratory have reported that in human ASM (hASM) cells, mitochondrial volume density and mitochondrial biogenesis, denoted by relative mitochondrial DNA (mtDNA) copy number are increased in response to TNFα exposure for 24 h, with a corresponding increase in the expression of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) [11]. Other studies have shown that PGC1α acts as a transcriptional coactivator that binds to the regulatory region of nuclear respiratory factors (NRFs) within the cell’s nucleus [12,13,14,15,16,17]. Downstream, NRFs activate transcription of transcription factor A mitochondrial (TFAM) [16,17], which promotes the replication and transcription of the mitochondrial genome [18,19]. The cAMP-response element binding protein (CREB) is a nuclear bZIP (basic leucine zipper) family of proteins that acts as a transcription factor upon phosphorylation at its specific serine residue 133 (Ser133) [20,21,22]. Phosphorylated CREB (pCREBS133) specifically binds to the cAMP response element (CRE) present in the promoter region of its target genes and initiates transcription [20,21,22]. In human hepatocytes [23] and human skeletal muscle [24], it has been shown that pCREBS133 binds to a conserved CREB binding site within the PGC1α promoter region, thereby increasing the transcription of the PGC1α gene. Other studies have shown that TNFα induces a transient increase in pCREBS133 in various tissue types including the optic nerve [25] and endothelial cells [26,27]. To date, no study in hASM cells has explored the role of pCREBS133 in the transcriptional activation of PGC1α. Activating transcription factor 1 (ATF1) has a similar sequence to CREB with a homologous phosphorylation domain [28,29,30]. Phosphorylation of pCREBS133 is often associated with phosphorylation of ATF1 at serine 63 (pATF1S63), and together, pCREBS133 and pATF1S63 have been reported to function as transcriptional co-activators for downstream gene targets [28,29]. However, whether pATF1S63 transcriptionally activates PGC1α has not been explored. As shown in Figure 1, we hypothesize that TNFα induces pCREBS133/pATF1S63 phosphorylation in hASM cells transcriptionally co-activating PGC1α, which in turn transcriptionally activates expression of downstream NRFs and TFAM, ultimately leading to mtDNA replication and mitochondrial biogenesis. ## 2.1. Dissociated Bronchiolar Cells Exhibited hASM Phenotype Before experimentation, dissociated cells were phenotyped based on the expression of α-SMA by immunocytochemical analysis. In accordance with our previously published studies, hASM cells expressing α-SMA uniformly were larger and displayed a fusiform shape. In each of the patient samples used in this study, hASM cells expressing α-SMA were significantly more prevalent among dissociated cells constituting ~$95\%$ of the total dissociated cell population (Figure 2A). This finding was further validated by Western blots for α-SMA in cell homogenates (Figure 2B, Supplementary Figure S1). However, bronchiolar samples from three patients among the nine initial patient samples displayed no detectable α-SMA protein expression relative to total protein and were thus excluded from subsequent analyses. Therefore, confirmed hASM cell samples from six patients were used in all but two experiments. In both mitochondrial imaging and ChIP analyses, samples from only five patients were used due to an inadequate number of hASM cells within a priori criteria of three passages. ## 2.2. TNFα Increased Mitochondrial Volume Density in hASM Cells In the imaging studies, only larger fusiform shaped hASM cells were included in the analysis of mitochondrial volume density. Mitochondrial volume density was measured in hASM cells treated with TNFα for 6 h (Figure 3A,B) and compared to untreated time-matched controls. After 6 h exposure to TNFα, mitochondrial volume density was significantly greater in hASM cells compared to untreated control hASM cells (* $p \leq 0.05$) (Figure 3C–E). There was no effect of TNFα exposure on total hASM cell volume [11]. In Figure 3C, data from individual cells (six cells from five patients, $$n = 30$$ cells) showed that 6 h of TNFα treatment increased mitochondrial volume density compared to untreated control cells. In each patient, mitochondrial volume density in TNFα-treated hASM cells was significantly greater compared to untreated control cells (Figure 3D). Figure 3E summarizes the results of TNFα treatment compared to untreated control hASM cells across all five patients indicating an ~3.4-fold increase in mitochondrial volume density in TNFα-treated cells (* $p \leq 0.05$). ## 2.3. TNFα Increases mtDNA Copy Number and Mitochondrial Biogenesis in hASM Cells In the present study, mitochondrial biogenesis was reflected by an increase in mtDNA copy number relative to nuclear DNA. After 6 h of TNFα exposure, there was a significant increase in mtDNA copy number relative to nuclear DNA in hASM cells, indicating an increase in mitochondrial biogenesis (* $p \leq 0.05$; Figure 4A,B). The expressions of two human mitochondrial genes, ND1/SLCO2B1 and ND5/SERPINA1, were used as a measure for mtDNA copy number. After normalization with nuclear genes SLCO2B1 and SERPINA1, both ND1 and ND5 levels were significantly elevated in TNFα-treated hASM cells by ~1.2-fold (* $p \leq 0.05$ for each gene, $$n = 6$$ patient samples). In Figure 4A, the expression of both mtDNA genes increased in TNFα-treated cells in all six patients, although in one patient, only a minimal increase for each gene was observed. Figure 4B summarizes results for all six patient samples, showing that TNFα exposure induced an overall ~$15\%$ increase in the relative copy number of the two human mtDNA genes ND1 and ND5 compared with untreated hASM cells (* $p \leq 0.05$). ## 2.4. TNFα-Induced pCREBS133 and pATF1S63 Phosphorylation In hASM cells, 6 h exposure to TNFα increased pCREBS133 phosphorylation by ~1.7-fold (* $p \leq 0.05$, $$n = 6$$ patient samples) compared to untreated control hASM cells (Figure 5A–C, Supplementary Figure S2). Figure 5B compares pCREBS133 levels in hASM cells exposed to 6 h TNFα versus untreated control cells from all six patients. Note that TNFα increased pCREBS133 levels in all six patient hASM samples, although the effects varied across patient samples. Figure 5C summarizes the results from all six patients showing an overall ~$69\%$ increase in pCREBS133 phosphorylation in TNFα-treated hASM cells compared to untreated controls (* $p \leq 0.05$). The antibody (Table 1) used for detecting pCREBS133 cross-reacts and detects the phosphorylated form of activating transcription factor 1 (pATF1S63) (Figure 5A). After 6 h TNFα exposure, pATF1S63 levels increased in hASM cells compared to untreated control cells from all six patients (Figure 5D; * $p \leq 0.05$, $$n = 6$$ patient samples). Figure 5E summarizes the results from all six patients, showing an ~1.7-fold increase in pATF1S63 levels in TNFα-treated compared to untreated control hASM cells (* $p \leq 0.05$, $$n = 6$$ patient samples). Total CREB levels were not changed after TNFα treatment (Supplementary Figure S3). ## 2.5. pCREBS133 Transcriptionally Activates PGC1α in TNFα-Treated hASM Cells To investigate the relationship between pCREBS133 and mitochondrial biogenesis, bioinformatics analysis was performed to identify the pCREBS133 binding site in the regulatory region of PGC1α gene, a key regulator of mitochondrial biogenesis. In silico analyses using TF-bind and EPD revealed the presence of a putative pCREBS133 binding site in the promoter region of PGC1α gene (Figure 6A) at −129 bases upstream to the translational start site. The EPD database also detected a binding site for pATF1S63 at the same location. Due to the sequence homology and presence of homologous phosphorylation domain, pATF1S63 acts as co-transcriptional activator with pCREBS133 for several genes and cell signaling pathways [30,31]. To examine the binding of pCREBS133 to this putative binding site, ChIP assay was performed utilizing the hASM cells from five patients treated with TNFα for 6 h compared to untreated time-matched control cells from the same patients. qPCR amplification of the immunoprecipitated chromatin from ChIP revealed that pCREBS133 bound to the putative binding site in the PGC1α promoter in all five samples (Figure 6B). Interestingly, pCREBS133 occupancy at the PGC1α promoter was increased by ~2.3 fold in TNFα-treated hASM cells, which was in proportion to the enhanced phosphorylation of pCREBS133 (Figure 6B-D, * $p \leq 0.05$; $$n = 5$$ patient samples). ## 2.6. TNFα Increases the Expression of PGC1α in hASM Cells As the major regulator of mitochondrial biogenesis, the protein expression of PGC1α was quantified. In 6 h TNFα-treated hASM cells, PGC1α expression was increased by ~2-fold (* $p \leq 0.05$; $$n = 6$$ patient samples) compared with untreated control hASM cells (Figure 7A–C). Figure 7B shows that PGC1α was elevated in hASM cells after TNFα exposure compared to untreated control cells in all six patients. Figure 7C summarizes results from all six patients, showing that 6 h TNFα exposure increased PGC1α expression by ~$98\%$ in hASM cells compared to untreated control cells (* $p \leq 0.05$). ## 2.7. PGC1α Transcriptionally Activates Expression of Downstream Gene Targets In cells, PGC1α mediates the activation of a downstream signaling cascade involving expression of NRF1, NRF2 and TFAM as major regulators of transcription and replication of the mitochondrial genome [18,32,33]. In hASM cells from each patient sample, 6 h TNFα exposure increased mRNA expression of NRF1, NRF2 and TFAM compared to untreated cells from the same patient (Figure 8A,C,E; * $p \leq 0.05$; $$n = 6$$ patient samples). Overall, across all six patient samples, there was an ~$89\%$ increase in NRF1 mRNA expression after 6 h TNFα treatment (Figure 8B; * $p \leq 0.05$), an ~$71\%$ increase in NRF2 mRNA expression (Figure 8D; * $p \leq 0.05$), and an ~$87\%$ increase in TFAM mRNA expression (Figure 8F; * $p \leq 0.05$). NRF1 protein levels were also increased after 6 h exposure to TNFα (Supplementary Figure S5; * $p \leq 0.05$; $$n = 6$$ patient samples). Summarizing the data from six patients, NRF1 protein levels increased by ~1.12 fold in TNFα-treated hASM cells (Supplementary Figure S5; * $p \leq 0.05$; $$n = 6$$ patient samples). ## 3. Discussion The key findings of the present study indicate that TNFα induces an increase in mitochondrial biogenesis (relative mtDNA copy number) and mitochondrial volume density in hASM cells via a pCREBS133/pATF1S63/PGC1α dependent pathway. We found that 6 h exposure to TNFα-induced pCREBS133 and pATF1S63 phosphorylation, transcriptional activation of the PGC1α gene, and an increase in PGC1α protein expression. Downstream, we found that the TNFα-induced elevation of PGC1α transcriptionally activates gene expression of NRF1, NRF2, and TFAM, the genes responsible for mtDNA replication, transcription, and mitochondrial biogenesis. ## 3.1. TNFα Increases Mitochondrial Volume Density in hASM Cells The results of the present study confirmed the results of a previous study from our lab, which reported that 24 h TNFα exposure increases mitochondrial volume density in hASM cells [11]. In the present study, hASM cells were treated with TNFα for only 6 h; however, the increase in mitochondrial volume density was comparable to that found after 24 h exposure. As in our previous study, mitochondria were labeled with MitoTracker Green and imaged in 3D using confocal microscopy. Previously, we compared the use of 3D confocal imaging to determine mitochondrial volume density to the use of electron microscopy (EM) used as a gold standard [34,35]. We found that MitoTracker labeling and 3D confocal imaging yielded mitochondrial volume densities in hASM cells that were comparable to EM. However, the confocal imaging technique has the major advantage of mitochondrial volume density measurements in many more cells [34,35]. The 3D confocal imaging technique has the additional advantage over conventional 2D EM techniques that the orientation of mitochondria within the cell does not affect mitochondrial volume estimates [11,36]. ## 3.2. TNFα Increases Mitochondrial Biogenesis in hASM Cells Mitochondrial biogenesis defines the process by which mitochondrial DNA replicates, and if greater than the rate of mitochondrial degradation (via mitophagy), leads to an increase in mitochondrial volume density. Mitochondrial biogenesis is a multistep process involving the synthesis of new mtDNA that translates into mitochondrial protein. In the present study, we measured the mtDNA copy number of two genes normalized to nuclear DNA as a reflection of mitochondrial biogenesis. The results of the present study are consistent with those of a previous study in which we found that 24 h TNFα exposure increases mtDNA copy number relative to nuclear DNA (mitochondrial biogenesis) in hASM cells [11]. The results of the present study extend these previous findings by showing that an increase in mitochondrial biogenesis occurs in hASM cells after only 6 h TNFα exposure, although the increase in mtDNA copy number after 6 h TNFα exposure was less pronounced than that observed after 24 h TNFα exposure [11]. As we previously found, the TNFα-induced increase in mtDNA copy number was consistent with an increase in PGC1α expression and mitochondrial volume density in hASM cells [11]. ## 3.3. TNFα Increases pCREBS133 and pATF1S63 Phosphorylation in hASM Cells The results of the present study showed that 6 h TNFα exposure increased CREB phosphorylation at Ser133 (pCREBS133). pCREBS133 is a transcription factor localized to the nucleus and activated in response to a variety of metabolic, trophic, and inflammatory inducers [20,22]. After phosphorylation, pCREBS133 binds to the CRE sequence, a highly conserved nucleotide sequence found in the upstream promoter region of its target gene, where it plays a significant role in activating the transcription of target genes. In this transcriptional pathway, pCREBS133 associates with other co-activators and adaptor proteins, such as pATF1S63 and CREB-binding protein (CBP), and thereby induces the transcription of its target genes [30,31,37]. Previously published studies reported that pro-inflammatory cytokines such as TNFα play a significant role in pCREBS133 activation [27,38,39]. Consistent with the results of the present study in hASM cells, it was previously reported that TNFα induced a transient increase in pCREBS133 phosphorylation in the optic nerve [25]. In human umbilical vein endothelial cells (HUVEC), TNFα induces pCREBS133 via mitogen-activated protein kinase (p38-MAPK)-mediated pathway, with downstream binding to CRE and transactivation of target genes [26]. The phosphorylation of pCREBS133 is mediated by several calcium dependent serine––threonine kinases such as cAMP-dependent protein kinase A (PKA), protein kinase C (PKC; including PKCε), and calmodulin kinases (CaMKs; e.g., CaMK-IV) that respond to calcium fluxes from the extra-cellular domain or the intracellular calcium stores [20,27,37,40,41,42]. Previous studies from our lab and other labs demonstrated that TNFα exposure increases cytosolic calcium and maintains agonist-mediated calcium homeostasis followed by activation of calcium-dependent signaling pathways [43,44] in hASM cells. In the present study, the TNFα-induced increase in pCREBS133 and pATF1S63 in hASM cells was observed and its association with a downstream signaling cascade affecting mitochondrial biogenesis was illustrated. There is little information available regarding the upstream regulatory kinases for pCREBS133 phosphorylation in response to TNFα exposure. ## 3.4. pCREBS133 Transcriptionally Activates PGC1α Expression in hASM Cells The results of the present study showed that pCREBS133 binds to the promotor region of the PGC1α gene in hASM cells. These results are consistent with previous studies reporting the presence of a CREB binding site in the upstream promoter region of the PGC1α gene [23,24,45]. The CREB binding site is highly conserved in humans and mice and regulates the pCREBS133-induced transcription of PGC1α in different cell types [23,24,45]. The results of the present study confirmed that pCREBSer133 binds to this specific binding site in hASM cells and that 6 h exposure to TNFα increased the promotor occupancy by pCREBS133 in PGC1α gene promoter in hASM cells. The transcriptional activation by pCREBS133 induced by TNFα was also reflected by an increase in PGC1α protein expression. ## 3.5. PGC1α Activates Transcription of NRFs and TFAM in hASM Cells To further explore the signaling cascade mediated by the TNFα-induced increase in pCREBS133 and PGC1α expression, we focused on the signaling pathway downstream to PGC1α, which acts as a major factor regulating mitochondrial biogenesis [12,16]. Previous studies reported that PGC1α directly binds to the regulatory region of the NRF1 gene and induces mRNA expression [12,16,32,46]. Consistent with this, we found that TNFα induces an increase in NRF1 mRNA and NRF1 protein levels in hASM cells. NRF1 is known to bind to the upstream regulatory region of the TFAM gene and accelerate mtDNA replication [47,48]. We found that TNFα increased TFAM gene expression in hASM cells. We also found that TNFα exposure increased NRF2 mRNA expression. NRF2, like NRF1, binds to the regulatory region of other mitochondrial genes (TOMM20 and several antioxidant genes) and modulates the structure and function of mitochondria [49,50,51,52]. ## 3.6. TNFα Induces an Increase in Metabolic Demand in hASM Cells Previous studies from our lab reported that 24 h TNFα exposure increases force generation and ATP hydrolysis in porcine ASM [10,44]. The increase in force generation in porcine ASM is due to a TNFα-induced increase in contractile protein expression [10,44]. The TNFα-induced increase in ATP consumption also relates to an increase in cross-bridge cycling rate reflecting reduced internal loading [10,44]. Furthermore, TNFα also increases hASM cell proliferation thereby increasing energetic demand [11]. Thus, TNFα increases metabolic demand, which appears to trigger a homeostatic response to increase ATP production by promoting mitochondrial biogenesis and mitochondrial volume density in hASM cells. In addition, by increasing mitochondrial volume density, the demand for ATP production and oxygen consumption per mitochondrion is reduced, thus limiting reactive oxygen species (ROS)-induced oxidative stress [9,11]. ## 3.7. Clinical Significance In the current COVID-19 pandemic, we have become keenly aware of the negative impact of airway inflammation, which is mediated by several pro-inflammatory cytokines including TNFα. The signaling pathways triggered by pro-inflammatory cytokines activate pathophysiological mechanisms underlying respiratory diseases such as asthma, chronic bronchitis, and chronic obstructive pulmonary disease (COPD). Previous studies from our lab demonstrated that TNFα exposure increases metabolic demand in hASM cells consistent with airway hyper-contractility found in many respiratory diseases. We hypothesize that the metabolic/oxidative stress induced by airway inflammation triggers a homeostatic response to increase ATP production via mitochondrial biogenesis and increased mitochondrial volume density. The current study explored a novel mechanism in hASM cells by which TNFα can mitigate the negative impact of inflammation-induced hyper-contractility, ATP consumption, increased ROS production, and oxidative stress-related cell death. ## 3.8. Experimental Limitations and Future Studies In this study, hASM cells were treated with 20 ng/mL TNFα for 6 h. The serum concentration of TNFα is ~50–100 pg/mL but varies across the patients. Upon inflammatory stimulation, the serum concentration of TNFα may increase up to 5 ng/mL. However, tissue concentration of cytokines are almost certainly higher than serum concentration, especially in inflamed tissue. For this reason, most in vitro studies have used concentrations of TNFα between 10–100 ng/mL [53]. Previous studies from our lab reported that 20 ng/mL TNFα induced an ER stress response involving autophosphorylation of IRE1α and downstream splicing of the transcription factor X-box binding protein 1 (XBP1) [11,54]. TNFα-induced activation of the ER stress response in hASM cells is apparent by 3–6 h exposure. Additionally, the 20 ng/mL TNFα concentration induced ROS production in hASM cells. Importantly, the present study explored the signaling pathway underlying TNFα-induced mitochondrial biogenesis and increased mitochondrial volume density. We selected the 6 h time point to include effects of TNFα on gene expression as well as protein levels. We confirmed that 6 h exposure to 20 ng/mL TNFα induced mitochondrial biogenesis and increased mitochondrial volume density comparable to the effects previously observed at 24 h exposure. The study was not designed to examine gain/loss of function. The mechanism underlying TNFα-induced pCREBS133 phosphorylation and involvement of the specific kinases remains unknown and will be the focus of future studies. Future studies may focus on different kinases and their contribution to TNFα-induced pCREBS133 phosphorylation in hASM cells. Using specific activators or inhibitors (for gain and loss of function) for pCREBS133 phosphorylation would be valuable to further confirm the effect of pCREBS133 phosphorylation on this TNFα-induced mitochondrial biogenesis. ## 4.1. Dissociation of Cells from Bronchiolar Tissue During lung resection surgeries, bronchiolar tissue samples were obtained from anonymous female and male patients ranging in age from 49 to 71 years old who were currently non-smokers with no history of respiratory disease (Figure 9). The research protocol was reviewed by the Institutional Review Board at Mayo Clinic and considered to be exempt (IRB #16-009655). Patient consent was obtained during pre-surgical evaluation. After pathological assessment of the lung tissue, normal regions were identified, and third to sixth generation bronchi were dissected (Figure 9). From the bronchiolar tissue, the smooth muscle layer was further dissected for enzymatic digestion using papain and collagenase and an ovomucoid/albumin separation method was employed for the dissociation of cells following the manufacturer’s instructions (Worthington Biochemical, Lakewood, NJ, USA) [11,54]. Cells were cultured in phenol red-free DMEM/F-12 (Invitrogen, Carlsbad, CA, USA) medium with a pH of 7.4, supplemented with $10\%$ fetal bovine serum (Cat. No. A3840002, Gibco, Thermo Fisher Scientific, Rockford, IL, USA) and maintained at 37 °C, $5\%$ CO2, $95\%$ air. ## 4.2. Phenotyping Dissociated hASM Cells The dissociated bronchiolar cells were phenotyped in two ways depending on experimental use. In cell imaging studies, hASM phenotype was determined by the expression of α-smooth muscle actin (α-SMA) based on immunohistochemistry as previously described [11]. Briefly, dissociated cells were plated (~10,000 cells per well) in Nunc™ Lab-Tek™ II 8-well multi-chamber plastic microscope slides (Cat. No. 154534, Nunc™ Lab-Tek™ II Chamber Slide™, Thermo Fisher Scientific, Rockford, IL, USA) in DMEM/F-12 media. The cells were fixed in $4\%$ paraformaldehyde (Cat. No. 28908, Thermo Fisher Scientific, Rockford, IL, USA) diluted in 1X phosphate buffered saline (PBS) for 10 min at room temperature and blocked for 1 h using a blocking buffer containing $10\%$ normal donkey serum (NDS) (Cat. No. D9663, Sigma Aldrich, St. Louis, MO, USA), $0.2\%$ triton X-100 and 1X PBS at room temperature with gentle agitation to prevent non-specific antibody binding. Cells were incubated overnight at 4 °C with target-specific primary antibodies (α-SMA and fibroblast specific protein 1, FSP1/S100A4) at a dilution of 1:500 in antibody diluent solution ($2.5\%$ normal donkey serum, $0.25\%$ sodium azide, $0.2\%$ triton X-100, 1X PBS) (Table 1). The cells were incubated in species-specific fluorophore-conjugated secondary antibodies (Jackson Immunoresearch, West Grove, PA, USA) at a concentration of 1:400 diluted in antibody diluent at room temperature for 1 h to detect the target proteins. Stained cells were mounted using Fluoro-Gel II mounting medium with 4′,6-Diamidino-2-Phenylindole, Dihydrochloride (DAPI) (Cat. No. 17985-50, Electron Microscopy Sciences, Hatfield, PA, USA) (Thermo Fisher Scientific, Rockford, IL, USA). Dissociated cells were imaged to distinguish α-SMA expressing hASM cells from FSP1 expressing fibroblasts (Figure 2A), and the percentage of hASM cells and fibroblasts were determined as a fraction of total dissociated cells (determined from DAPI). Morphologically, hASM cells were distinctly different from fibroblasts, with a long and fusiform shape compared to smaller fibroblasts (Figure 2A). Based on the single cell-based phenotyping, among the dissociated cell population, hASM cells constituted ~$95\%$ of all dissociated cells with the remainder being fibroblasts. For molecular and biochemical measurements, cell homogenates were used. Cell homogenates were phenotyped by the expression of α-SMA (determined by Western blot) relative to total protein (>$2\%$ for inclusion of sample). In brief, protein samples extracted from lung-dissociated cells were denatured and run in SDS-PAGE, followed by transfer to PVDF membrane. The PVDF membrane was probed with α-SMA specific antibody. The α-SMA-specific band intensity was quantified using Image Lab software (version 6.0.1) and normalized to the total protein loaded in the lane (Figure 2B). ## 4.3. Experimental Design Figure 2 depicts the experimental design used in the present study. From the dissected smooth muscle layer of bronchiolar samples, the dissociated cells were cultured for 1–3 passages. Dissociated cells were only used from culture passages 2–3. Only dissociated bronchiolar cells expressing α-SMA were included in the present study. Based on the absence of α-SMA immunoreactivity and/or α-SMA protein expression (Western blot), samples from three patients were excluded from further analysis. After differentiating cells by serum deprivation for 48 h, the phenotype of hASM cells was confirmed by immunocytochemistry (Figure 2) with >$90\%$ displaying the hASM phenotype (i.e., α-SMA expression). In each patient, hASM cells were split into two experimental groups: TNFα-treated and untreated controls. In the TNFα-treated group, hASM cells were exposed to media containing 20 ng/mL TNFα for 6 h. The concentration of TNFα and duration of exposure were selected based on previous studies exploring the dose and time course of TNFα effects on induction of endoplasmic reticulum (ER) stress and mitochondrial remodeling in hASM cells [11,54]. In particular, we found that 24 h TNFα exposure increased mitochondrial biogenesis and mitochondrial volume density in hASM cells. The shorter 6 h exposure period was selected to explore upstream regulatory signaling pathways responsible for the effects on mitochondrial biogenesis and volume density. For example, we previously found that 24 h TNFα (20 ng/mL) exposure increases the cytosolic calcium response to muscarinic stimulation in hASM [10]. We also found that acute (3–6 h) TNFα (20 ng/mL) exposure induced an ER stress response in hASM cells involving phosphorylation of the inositol requiring enzyme 1-alpha (IRE1α) [54]. Based on these previous results, we selected a period of 6 h exposure to 20 ng/mL TNFα. Mitochondrial DNA (mtDNA) and mRNA expression in muscle homogenates was analyzed by quantitative real-time PCR. Bioinformatic analysis and ChIP was done for gene target identification. Statistical analysis on mitochondrial volume density in individual hASM cells was based on morphometry of six hASM cells per sample times two groups per sample (TNFα-treated or untreated) and five patients ($$n = 30$$ hASM cells per group) using a one-way ANOVA for repeated measures. Statistical analyses of protein expression, mtDNA copy number, and mRNA expression were based on $$n = 6$$ hASM samples (patients) using a paired t-test. In all analyses, statistical significance was set at * $p \leq 0.05.$ ## 4.4. Protein Extraction and Western Blot The hASM cells were lysed in 1X Cell Lysis Buffer (Cat. No. 9803, Cell Signaling Technology, Danvers, MA, USA) supplemented with a protease inhibitor cocktail (Cat. No. 11836170001, Roche, Millipore Sigma, Burlington, MA 01803, USA) and phosphatase inhibitors (PhosSTOP, Cat. No. 4906845001, Roche, Millipore Sigma, Burlington, MA 01803, USA). Protein concentrations were quantified using a DC (detergent-compatible) protein assay that utilizes the principle of the well-documented Lowry-based assay (Bio-Rad, Berkeley, CA, USA). An amount of 60–80 mg of total protein from each sample was denatured in 1X Laemmli sample buffer with beta-mercaptoethanol at 100 °C for 3 min. After denaturation, the samples were loaded onto stain-free polyacrylamide gel (Bio-Rad, Berkeley, CA, USA) and run via SDS-PAGE. Total protein content in each lane was visualized, imaged, and analyzed using the ChemiDoc MP Imaging System (Bio-Rad, Berkeley, CA). The proteins from the gel were then transferred to a polyvinylidene difluoride (PVDF) membrane (Bio-Rad, Berkeley, CA, USA) using the Trans-Blot Turbo system (Cat. No. 1704150EDU, Bio-Rad, Berkeley, CA, USA). After transfer, the membranes were blocked using $5\%$ non-fat dry milk to prevent non-specific binding of antibodies followed by overnight incubation with primary antibodies designed to recognize and bind to the protein of interest (Table 1). Horseradish peroxidase conjugated species-specific secondary antibodies were used to detect the primary antibody targets and amplify the signal for easier detection (1:7500 dilution). Bands were developed by incubating the PVDF membrane in chemiluminescent SuperSignal West Dura Extended Duration Substrate (Cat. No. PIA34075, Thermo Fisher Scientific, Rockford, IL) for 3 min and visualized using the ChemiDoc MP Imaging System. Band intensity was quantified using Image Lab software (version 6.0.1) and normalized to the total protein visualized in each lane. For pCREBS133 and total CREB detection, the same blot was used to avoid gel-to-gel variation. The blot was probed with rabbit monoclonal pCREBS133 specific antibody (Table 1) that also cross-reacts and detects the phosphorylated form of pATF1S63. The blots used for pCREBS133 and pATF1S63 detection were stripped with Restore Western Blot Stripping Buffer (Cat. No. 46428, Thermo Fisher Scientific, Rockford, IL, USA) to remove the pCREBS133/pATF1S63 specific antibody, and the stripped blot was reprobed with mouse monoclonal CREB specific antibody for total CREB detection (Supplementary Figure S3). We were unable to validate an antibody for total ATF1. All gel images and full blot images are available in supplementary figures (Supplementary Figures S1–S5). ## 4.5. Labeling and Confocal Imaging of Mitochondria in hASM Cells For confocal imaging, dissociated cells were plated on m-slide 8-well ibiTreat chambers (Cat. No. 80826, ibidi GmbH, Gräfelfing, Germany). Mitochondria were labeled with 200 nM MitoTracker Green FM (Cat. No. M7514, Thermo Fisher Scientific, Rockford, IL; excitation wavelength: 490 nm; emission wavelength: 516 nm) in serum-free DMEM/F-12 media, pre-warmed at 37 °C for 15 min in the incubator followed by extensive washing with Hanks’ Balanced Salt Solution (HBSS) (Cat. No. H6648, Sigma Aldrich, St. Louis, MO 68178, USA). Mitochondria in hASM cells (distinguished by their size and fusiform shape) were imaged using a Nikon Eclipse A1 laser scanning confocal microscope with a ×$\frac{60}{1.4}$ NA oil-immersion objective at 12-bit resolution into a 1024×1024-pixel array. The dynamic range for imaging was set by first scanning a region containing no MitoTracker fluorescence signal and then a second region of interest containing maximum MitoTracker fluorescence. A series of 0.5 mm optical slices were acquired for each image. The images obtained were deconvolved using NIS Elements (Nikon Instruments Inc., Melville, NY, USA) to improve signal-to-noise in the images and thereby improve contrast and edge detection. The voxel dimensions of each deconvolved optical slice were 0.207 × 0.207 × 0.5 µm. Multiple hASM cells were visualized in each microscopic field. Based on an a priori power analysis of variance in mitochondrial volume density measurements in untreated hASM cells, six individual hASM cells per treatment group (TNFα-treated or untreated) from five bronchial samples (patients) were analyzed ($$n = 30$$). ## 4.6. Measurement of Mitochondrial Volume Density After deconvolution of each optical slice, the Z-series of images was reconstructed in 3D and the boundaries of each hASM cell were delineated using ImageJ-Fiji software (https://imagej.nih.gov/ij/) (ImageJ 1.53t) (Figure 3A). After background correction and ridge filter detection, mitochondria within hASM cells were identified by thresholding to create a binary image and then skeletonized using the ImageJ mitochondrial analyzer plugin [11,36]. Mitochondrial volume density was calculated as the ratio of mitochondrial volume (number of voxels containing thresholded MitoTracker fluorescence) within the cell to the total volume of the delineated hASM cell [11,34,35,55,56]. ## 4.7. Genomic DNA Extraction and Quantification of mtDNA Genomic DNA was extracted from TNFα-treated hASM cells and untreated control hASM cells using QIAamp DNA Mini Kit (Cat. No. 51304, Qiagen, Hilden, Germany) as per manufacturer’s protocol and quantified using Nanodrop spectrophotometer (Thermo Fisher Scientific, Rockville, IL, USA). The relative copy number of human mtDNA was quantified using Human Mitochondrial DNA Monitoring Primer Set (Cat. no. 7246, Takara Bio USA, Mountain View, CA, USA). Briefly, genomic DNA from TNFα-treated hASM cells and untreated control hASM cells was subjected to quantitative real-time PCR (qPCR) using SYBR green master mix (Cat. No. 04707516001, LightCycler® 480 SYBR Green I Master, Roche Scientific, Thermo Fisher Scientific, Rockville, IL) as per the manufacturer’s instructions. The primer set contains two nuclear gene-specific primers, solute carrier organic anion transporter family member 2B1 (SLCO2B1) and serpin family A member 1 (SERPINA1)]; and two mtDNA specific primers, NADH dehydrogenase subunit 1 (ND1) and NADH: ubiquinone oxidoreductase core subunit 5 (ND5)] (Cat. no. 7246, Takara Bio USA, Mountain View, CA, USA). The relative quantification of mtDNA copy number was represented as the difference in cycle threshold (Ct) values for mtDNA and nuclear DNA. ## 4.8. Bioinformatic Analysis for Transcription Factor Binding Site Prediction Total promoter sequence of PGC1α was obtained from the University of California Santa Cruz (UCSC) Genome Browser, selecting the most updated genome assembly (Human GRCh38/hg38). Transcription factor binding sites for pCREBS133 and pATF1S63 were predicted using two databases. TF-bind was used as a tool for searching transcription factor binding motifs in target DNA sequence (Figure 7A) that utilizes the weight matrix available in eukaryotic transcription factor database TRANSFAC R.3.4, developed by Dr. Wingender et al. ( tfbind.hgc.jp). The transcription factor binding site was further confirmed using Eukaryotic Promoter Database (EPD), which is an annotated non-redundant collection of eukaryotic POL II promoters with experimentally validated transcriptional start sites [57]. ## 4.9. Chromatin Immunoprecipitation Assay (ChIP) Chromatin Immunoprecipitation Assay (ChIP) assay was performed in hASM cells treated with TNFα for 6 h and compared with the untreated control as per the manufacturer’s instruction (Cat. No. 26157, Thermo Fisher Scientific, Rockville, IL). Paraformaldehyde-fixed hASM cells were treated with micrococcal nuclease (MNase) to remove extranuclear DNA, followed by sonication. Sheared chromatin was immunoprecipitated with CREB specific antibody (Cat. No. 4820, Cell Signaling Technology, Danvers, MA, USA) and the antibody–chromatin–protein complex was purified using Protein-A/G beads. We were unable to validate an antibody for total ATF1. In the assays, positive (RNA Polymerase II that targets GAPDH) and negative controls (IgG) were included as per the manufacturer’s instruction. Immunoprecipitated chromatin was eluted and purified, and qPCR was performed to estimate the abundance of CREB at the promoter region of the target gene PGC1α using specific primers (Table 2). Ct values were normalized to the negative control (IgG). Data were interpreted as the ratio of precipitated DNA to the total input of genomic DNA and compared between control and TNFα-treated groups. ## 4.10. RNA Extraction, cDNA Preparation and qPCR Total RNA was extracted from hASM cells treated with TNFα for 6 h and untreated control using RNeasy extraction kit (Cat. No. 74104, Qiagen, Hilden, Germany) as per the manufacturer’s instruction. In brief, hASM cells were lysed and subjected to ethanol-mediated extraction of RNA. Genomic DNA was removed by an on-column DNase treatment. Extracted RNA samples were quantified using a nanodrop spectrophotometer (Thermo Fisher Scientific, Rockville, IL, USA). A total of 500 ng of RNA was used for complementary DNA (cDNA) synthesis followed by qPCR using specific primer sets (Table 2) to estimate the mRNA expression of target genes (NRF1, NRF2 and TFAM). ## 4.11. Statistical Analysis For each of the experiments, hASM cells were dissociated from bronchial tissue samples obtained from both female and male patients. Sex is an important biological variable, but the study was not powered to detect sex differences. Patient samples were included only if dissociated cells displayed a hASM phenotype as confirmed by the expression of α-SMA by immunocytochemistry and Western blot. hASM cells dissociated from the same patient and same passage (1–3 passages) were split into two groups, TNFα groups and untreated controls. Thus, hASM cells from each patient served as their own controls in assessing the impact of TNFα treatment. The number of samples required to detect a difference of >$20\%$ between TNFα-treated vs. untreated control hASM cells were determined by an a priori power analysis (α = 0.05, β = 0.80). Shapiro–Wilk test was employed to confirm a normal distribution in the data. The effect of TNFα treatment on mitochondrial volume density in individual hASM cells was based on measurements from six hASM cells per treatment group (TNFα-treated or untreated) from five bronchial samples (patients) using a one-way ANOVA for repeated measures ($$n = 30$$). Since hASM cells dissociated from each patient served as their own control, a paired t-test was performed for statistical analyses using GraphPad Prism 9 software. Statistical significance was indicated as * $p \leq 0.05.$ All experimental findings were presented in two ways. For comparisons between TNFα-treated and untreated sets within each patient, “symbol and line” scatter plots were used to show individual data points for each group. Each color represents one bronchial sample (patient), squares represent TNFα-treated samples, and circles represent untreated controls. For comparisons between TNFα-treated and untreated controls across the six patients, data were presented as box-and-whisker plots, showing the median and minimum to maximum distribution of the datasets. ## 5. Summary and Conclusions The results of the present study provide new insight into the mechanisms underlying TNFα-mediated mitochondrial biogenesis in hASM cells. Exposing hASM cells to TNFα for 6 h activates the pCREBS133 signaling pathway (Figure 1), involving transcriptional activation of PGC1α gene expression. Downstream, PGC1α mediates transcriptional activation of NRF1, NRF2, and TFAM expression, which in turn are responsible for mtDNA synthesis, mitochondrial biogenesis, and an increase in mitochondrial volume density in hASM cells. 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--- title: 'Clustering of Activity-Related Behaviors in Relation to Self-Reported Causes of Stress among Pre-Adolescents: Results from a National Epidemiological Study' authors: - Rena I. Kosti - Thomas Tsiampalis - Matina Kouvari - Ioannis Gketsios - Aikaterini Kanellopoulou - Venetia Notara - George Antonogeorgos - Andrea Paola Rojas-Gil - Ekaterina N. Kornilaki - Areti Lagiou - Demosthenes B. Panagiotakos journal: Life year: 2023 pmcid: PMC10055894 doi: 10.3390/life13030622 license: CC BY 4.0 --- # Clustering of Activity-Related Behaviors in Relation to Self-Reported Causes of Stress among Pre-Adolescents: Results from a National Epidemiological Study ## Abstract An epidemiological study was conducted among 1728 10–12-year-old students ($55.1\%$ girls) and their parents during 2014–2016 in Greece. This study aimed to identify the dominant clusters of physical activity/sedentariness among preadolescents and investigate their association with self-reported sources of stress. Children’s physical activity levels and sources of stress were evaluated using validated questionnaires that assessed daily hours of activities, both on weekdays and on weekends, including physical activity, screen-based sedentary time, and non-screen-based sedentary time. The k-means algorithm of cluster analysis was applied. Three clusters of children’s physical activity/sedentariness were revealed. Cluster 1 was characterized as “Inactive-Non sedentary”, cluster 2 as “Active –Non-screen sedentary”, and cluster 3 as “Inactive-Sedentary”. Parental needs/expectations were associated with physical activity patterns ($$p \leq 0.009$$), i.e., children assigned to the third and second clusters had $36\%$ and $51\%$ lower odds to be stressed due to parental requirements [(OR for cluster 3 = 0.64, $95\%$ CI = 0.41–0.99), (OR for cluster 2 = 0.49, $95\%$ CI = 0.32–0.76)], compared with their first-cluster counterparts. Considering the need to promote physical activity in early life stages, the identification of these complex activity-related patterns along with their significant interaction with parental expectations as a cause of stress could enhance the effectiveness of targeted behavior change interventions among those parent–child dyads most in need. ## 1. Introduction Since the beginning of this century, compelling evidence has underscored the beneficial effects of physical activity and the detrimental consequences of sedentariness on various health outcomes, well-being, quality of life, and longevity [1,2]. The pleiotropic benefits of physical activity on human health start from the early stages of life and continue across the lifespan. However, a mounting body of evidence underlines the declining trends in physical activity and increased sedentary behavior across childhood and adolescence [3,4]. The period that a child transitions into adolescence is a determinant of the formulation of various behaviors including habits related to physical activity [5]. Given that the adoption of healthy behaviors tracking into adulthood has a cumulative effect on health, the instilling of healthy habits during this period is of crucial importance [6,7]. In the past few years, findings from a systematic review and meta-analysis, which synthesized evidence from 163 observational studies, challenged the view that “physical activity” and “sedentary habits” are mutually exclusive behaviors, implying that engagement in high levels of sport is not necessarily displaced by excessive amounts of television watching or vice versa by children and adolescents [8]. Towards this concept, clusters of physical activity and sedentary behavior habits have been reported in the literature demonstrating that many different patterns exist [9,10]. The configuration of these patterns is the result of the combination of psychosocial, personal, and environmental factors [11]. In this context, findings from a systematic review suggested a reciprocal and bidirectional association between psychological stress and activity-related behaviors [12]. It has been also remarked that in youth, the higher the exposure to more stressors, the lower the likelihood to be physically active and the higher the likelihood to adopt a sedentary lifestyle [13]. Moreover, the literature suggests that causes of stress are dependent on the adolescents’ stage [14]. However, there is scarce evidence regarding the dominant stressors in preadolescence [15] and their association with activity-related patterns. The hypothesis of the present study is that sources of stress have differential effects on activity-related patterns. Thus, the aim of the present study was to identify the dominant clusters of physical activity/sedentariness among preadolescents and to investigate their association with self-reported sources of stress. ## 2.1. Design and Setting The present study is a school-based cross-sectional study, conducted in the school period of 2014–2016. Forty-seven primary schools were randomly selected among the school of the Peloponnese peninsula (i.e., Sparta, Kalamata, and Pyrgos), the capital city of the island of Crete (i.e., Heraklion), and the greater Athens metropolitan area, using a list provided by the Greek Ministry of Education and Religious Affairs. The sampling was representative of the reference population according to the census in 2011; the area covered has approximately $75\%$ of the total Greek population and represents large urban and rural municipalities. ## 2.2. Sample All children aged 10–12 years old attending the fifth and sixth grades of the selected primary schools, along with their parents, were invited to participate in the study. A total of 1728 children ($54\%$ girls) were finally enrolled; the participation rate within schools ranged from $95\%$ to $100\%$ without any significant differences between the studied areas. ## 2.3.1. Assessment of Other Children’s Characteristics Sociodemographic characteristics, i.e., age, sex, place of birth, ethnicity, number of siblings, and birth order, were recorded during the interview. In addition, weight (in kg) and height (in m) were measured and recorded by a trained investigator using a scale and a measuring-tape, over skin-tight clothes with no shoes. Body mass index (BMI) was calculated (kg/m2), and children were then categorized as underweight, normal weight, overweight, or obese, according to the International Obesity Task Force BMI cut-off points [16]. Sleep duration (in hours) was calculated as the interval between the reported bedtime and the reported hour children usually woke up. Given that sleep duration was based on a self-reported bedtime and wake-up time, it was actually a self-reported sleep duration. To evaluate children’s level of adherence to the Mediterranean diet, the KIDMED questionnaire was used, which was originally developed by Serra-Majem et al. [ 2004] [17] to combine the Mediterranean diet guidelines for adults, as well as the general dietary guidelines for children (i.e., breakfast skipping) in a single index. According to the KIDMED index, a score of 0–3 reflects poor adherence to the Mediterranean diet, a score of 4–7 describes average adherence with improvement needed to adjust intake to Mediterranean patterns, and a score of 8–12 is good adherence. ## 2.3.2. Assessment of Children’s Physical Activity Children’s leisure-time physical activity was estimated through a special, validated questionnaire, i.e., the Physical Activity and Lifestyle Questionnaire (PALQ) [18], in which children reported the mean daily hours, both on weekdays and weekends (0–1 h, 1–2 h, 2–3 h, 3–5 h, and >5 h), they spent (i) studying for school, (ii) computer use, (iii) TV watching, (iv) reading extracurricular books, (v) playing board games, and (vi) playing electronic games. In addition, children were asked if they were involved in a Sport Club, as well as the frequency (times per week (1 time, 2 times, 3 times, 4 times, 5 times, 6 times) and duration of their training each time (~30 min, 30–60 min, 60–90 min, >90 min)). Children were also asked about the time they walk on a daily basis for their various obligations (<15 min, 15–30 min, 31–45 min, 46–60 min, >60 min). Based on the aforementioned questions, three new variables were created, depicting the frequency (minutes/week) of (i) physical activity (e.g., playing sport, walking, etc.), ( ii) screen-based sedentary time (e.g., frequency of TV watching, playing board games, etc.), and (iii) non-screen-based sedentary time (e.g., frequency of studying for school). Finally, children were asked about the method of travelling to school (walking, by car, another way (school bus, bicycle, etc.)), as well as the years of systematic physical exercise. ## 2.3.3. Assessment of Sources of Stress in Children Children’s sources of stress were evaluated using specific questions about the (i) parents’ requirements/expectations from them, (ii) teachers’ requirements/expectations from them (iii) school performance, (iv) pressed schedule of activities, and (v) peer’s pressure (i.e., from classmates or friends), based on the Adolescent Stress Questionnaire (ASQ) [19] translated and evaluated in several countries, including Greece [20]. ## 2.3.4. Assessment of Parents’ Physical Activity Level and Other Characteristics Each parent provided information about their age (in years), ethnicity ((i) Greek, (ii) Other), place of birth, highest educational level ((i) Basic/Secondary level: Primary school/Graduate of high school/Graduate of general or technical lyceum, (ii) Higher: Graduate of Technical University/Graduate of Higher Education Institute/Postgraduate/PhD), occupational status ((i) Employed/Retired: State employee/Private employee/Freelance/Retired, (ii) Unemployed), marital status ((i) Married, (ii) Single: Widow-widower/Divorced/Cohabitation), as well as the annual family income. Parents’ physical activity was also recorded and evaluated according to weekly frequency and categorized as follows: (i) 0 times/week, (ii) 1–2 times/week, and (iii) more than 3 times/week. In addition, self-reported weight and height were recorded, and body mass index (BMI) was calculated (kg/m2); parents were then categorized as underweight, normal weight, overweight or obese, according to the WHO standards for the BMI cut-off points. ## 2.4.1. Cluster Analysis of Children’s Physical Activity Behaviors The k-means algorithm of cluster analysis (CA) with the K-nearest-means classifier was applied to define the clusters of children with common physical activity behaviors. The frequency of physical activity, screen-based sedentary time, and non-screen-based sedentary time were converted into Z-scores (standardized) and entered into the cluster algorithm, which was run 100 times in order to reduce the effect of random splitting. The analyses were performed for two to five clusters, and the best cluster solution was chosen in terms of the amount of explained variation, the size (≥$10\%$ of the sample) and interpretation of each cluster, and its stability. Finally, a four-cluster solution was decided to be optimal. ## 2.4.2. Other Data Analyses Children’s and parents’ categorical characteristics are presented as relative frequencies (%) and continuous characteristics are presented as mean (Standard Deviation (SD)) values. Associations between the categorical characteristics and children’s clusters of physical activity behaviors were evaluated through the Pearson Chi-square test, while the One-way Analysis of Variance (ANOVA) was used for the continuous characteristics. The normality of continuous characteristics’ distribution was tested through the P-P plot and the Kolmogorov–Smirnov test. In addition, the association between the sources of stress and the children’s clusters of physical activity behaviors was evaluated through the Pearson Chi-square test, while logistic regression analysis was also performed in order to compare the different clusters of children regarding their odds to be stressed due to their parents’ requirements. Logistic regression results are presented as Odds Ratio (ORs) and their corresponding $95\%$ Confidence Intervals (CIs). All statistical analyses were conducted using Stata 14.0 (M. Psarros & Assoc., Sparti, Greece). ## 3.1. Children’s Physical Activity Patterns Several clustering schemes (i.e., with two, three, four, and five clusters) were applied, but the most informative was the one with three clusters (as revealed using the dendrogram and the Bayesian Information Criterion—BIC). The highest percentage of children belonged to Cluster 2, i.e., $45\%$, followed by Cluster 1, i.e., $43\%$, and Cluster 2, i.e., $12\%$. Figure 1 illustrates the results from the cluster analysis (presented as Z-scores) concerning children’s physical activity patterns, using the time engaged in physical activities, screen-based sedentary time, and non-screen-based sedentary time. In particular, cluster 1 was characterized by time spent on physical and sedentary activities (both screen-based and non-screen-based) below the average, and thus, named as “Inactive-Non-Sedentary pattern”. Cluster 2 was characterized by physical activity time above the average, but by screen-based sedentary time below the average and characterized as an “Active–Non-screen Sedentary pattern”, and cluster 3 was characterized by sedentary time above the average irrespective of screen-based, or non-screen based, and by physical activity time below the average and was thus characterized as an “Inactive-Sedentary pattern”. ## 3.2. Demographic, Lifestyle, and Activity-Related Characteristics of Children In Table 1, the lifestyle characteristics of children according to the physical activity cluster to which they belonged are presented. Children with a more sedentary pattern reported unhealthier dietary habits, a higher likelihood to consume fast food, and fewer sleeping hours when compared to more physically active children. Significant differences were observed among the three clusters regarding all lifestyle and activity parameters, except for the frequency of breakfast consumption ($$p \leq 0.721$$) and the number of meals including snacks ($$p \leq 0.082$$). Regarding the other activity parameters, as presented, the highest screen-based and non-screen-based time was observed among the children belonging to the third cluster, while the highest physical activity time was observed among the children in the second cluster. In addition, children belonging to the second cluster were more likely to walk for their everyday obligations, while it is also worth noting the fact that children in the third cluster were more likely to consume and order more fast-food. Finally, the highest percentage of children with a high level of adherence to the Mediterranean diet was observed among children in the second cluster, while the lowest level of adherence to the Mediterranean diet was observed among children belonging to the first cluster. In Table 2, the demographic and socioeconomic characteristics of the children and their parents, according to the physical activity cluster to which they belonged, are presented. In general, there were no significant differences among the physical activity clusters. However, it is worth noting the fact that the highest percentage ($85\%$) of single-parent (mother) families was observed among the children belonging to the first cluster, while it is also worth noting the fact that the third cluster (sedentary pattern) was dominated by boys. ## 3.3. Prevalence of Stress and Its Main Sources According to Children’s Activity-Related Patterns Regarding the stress levels of children, it was found that $87.9\%$ of the children reported having stress, yet there was no significant difference among the three different clusters in terms of activity-related behaviors ($$p \leq 0.135$$). However, after investigating the main sources of stress, parental requirements/expectations were found to be significantly associated with children's physical activity patterns ($$p \leq 0.009$$). Specifically, a higher percentage of children was found to be stressed due to their parental requirements in the third cluster ($47.6\%$), followed by the children in the first cluster ($36.7\%$). On the other side, the lowest percentage of children stressed due to their parents’ requirements was observed among the children in the second cluster ($30.5\%$) (Figure 2). Compared to children belonging to the third cluster, those belonging to the first cluster had $36\%$ lower odds to be stressed due to their parental requirements (OR = 0.64, $95\%$ CI 0.41, 0.99), while those belonging to the second cluster had $51\%$ lower odds to be stressed due to their parental requirements (OR 0.49, $95\%$ CI0.32, 0.76). ## 4. Discussion The present study aimed to explore the dominant clusters of physical activity and sedentary patterns in Greek preadolescents and to investigate their association with sources of stress. Multivariate data analysis revealed that parental expectations, as a source of stress, were strongly associated with children’s activity-related behaviors. Despite the limitations posed by the study design, our findings are important from a public health perspective. In particular, based on the knowledge that parental expectations, as a stressor in early adolescence, may shape the attitude of children towards physical activity, tailor-made parent–child interventions should be developed for those groups who exhibit the most unhealthy activity-related behavior. In line with the current literature [9,10], our results revealed that beyond the commonly identified Inactive–Sedentary behavior pattern, different clusters such as Active–Non-screen sedentary and Inactive–Non-Sedentary were also recognized, confirming that multiple clusters of activity-related patterns dominate in early life stages. This finding is justified by the fact that despite the confirmed inverse association between physical activity and sedentary behavior, the effect estimate is small. This has implications that these behaviors do not necessarily replace one another [8] and can even exist simultaneously throughout the day. In addition, children who followed a sedentary lifestyle pattern were also more likely to be engaged in unhealthy dietary habits with a higher likelihood of consuming fast foods and fewer sleeping hours. These findings are consistent with the literature confirming that obesogenic and unhealthy behaviors are “aggregated” as combined risk patterns [21,22,23], with potential synergistic detrimental effects on well-being [24]. Furthermore, children who were inactive, although non-sedentary, followed less healthy dietary patterns with a higher likelihood of belonging to single-parent families. Indeed, research showed that single-parent families had children who exhibited poorer dietary habits [25] and were prone to sedentariness [26], likely because parents’ involvement in physical activities plays a significant role in children’s physical activity [27]. The present study also found that although the vast majority of children reported stress from various sources (school, family, and friends/peers), a non-significant association was found among the different clusters of activity-related patterns and preadolescent stress. This is in line with the study by Gerber and Puhse [2008] [28], which generally supported the role of physical activity as a moderator of the health–illness relationship. Moreover, a recently conducted meta-analysis found a significant association between mental health and physical activity in adolescents, but not in children [29]. With respect to whether the different causes of children’s stress have the same influence on activity-related behaviors, our findings underlined that only parental expectations showed a significant interaction with children’s physical activity status. In particular, the higher the parental expectations, the higher the sedentary behavior of preadolescents and the lower their physical activity. Although school stress has been recognized as a relatively common stressor in pre-adolescence [14,30], conflict with parents may increase when children ask for greater autonomy as they negotiate their boundaries and responsibilities [31]. Moreover, although meta-analytic findings provide evidence for a positive association between parental educational expectations and a child’s academic achievement [32], when parental expectations exceed the child’s achievement, which puts greater pressure on children’s life, this could cause academic stress via parental pressure among school students [33]. Thus, in the present study, one could assume that parental expectations as a stressor reported by children may imply that their parent’s expectations are greater than their internal ones as regards their academic achievement. It is also worth mentioning that children that followed the “Active-Non-screen sedentary pattern” had the lowest odds of being stressed by their parent’s expectations. This is justified by the fact that all types of sedentary activity do not have the same influence on mental well-being [34]. To this issue, relaxing activities such as playing an instrument may have a beneficial effect on mental well-being [29]. In contrast, children who face stressful events have a higher likelihood of being engaged in screen sedentary activities (i.e., Internet) for fun or socializing in their attempt to manage their bad mood [35]. The literature suggests that culture shapes the interpretation of daily stressful experiences [36]. ‘ Albeit parent- and school-related stressors as perceived by adolescents are common among different cultures, differences were observed in coping behaviors” [15]. The significant role of parental expectations as the only stressor on children’s activity-related behavior observed in the sample of Greek pre-adolescents could imply the “grade-hunting” parental attitude, which is common in Greek families Given the bidirectional association between psychological stress and activity-related behaviors [12], the beneficial effects of physical activity on children’s stress levels may be attributed to neurobiological, psychosocial, and behavioral mechanisms [37]. To the best of our knowledge, this is one of the very few studies that have investigated the level of physical activity in pre-adolescence using the concept of clustering various kinds of activities—structured (e.g., sports) or unstructured (e.g., walking)—with sedentary behaviors and sources of stress. However, inherent limitations may exist, principally due to the cross-sectional nature of the study. First, the findings of the present study cannot be generalized to the entire preadolescent population. Additionally, the possible reporting bias of preadolescents cannot be ignored, although the presence of an investigator clarifying any misconceptions about the questionnaire increased the validity of the answers. Moreover, the lack of the use of accelerometers along with the self-reported answers relevant to children’s stress levels could influence the accuracy of the findings. ## 5. Conclusions Considering the need to promote physical activity in early life stages, the identification of these complex activity-related patterns along with their significant interaction with parental expectations could enhance the effectiveness of targeted behavior change interventions among those parent–child dyads most in need. In particular, tailor-made counselling interventions to parents as regards the influence of their expectations in shaping activity-related behaviors of children are warranted. 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--- title: 'Effects of Early Nutrition Factors on Baseline Neurodevelopment during the First 6 Months of Life: An EEG Study' authors: - Dylan Gilbreath - Darcy Hagood - Graciela Catalina Alatorre-Cruz - Aline Andres - Heather Downs - Linda J. Larson-Prior journal: Nutrients year: 2023 pmcid: PMC10055905 doi: 10.3390/nu15061535 license: CC BY 4.0 --- # Effects of Early Nutrition Factors on Baseline Neurodevelopment during the First 6 Months of Life: An EEG Study ## Abstract Throughout infancy, the brain undergoes rapid changes in structure and function that are sensitive to environmental influences, such as diet. Breastfed (BF) infants score higher on cognitive tests throughout infancy and into adolescence than formula fed (FF) infants, and these differences in neurocognitive development are reflected in higher concentrations of white and grey matter as measured by MRI. To further explore the effect diet has on cognitive development, electroencephalography (EEG) is used as a direct measure of neuronal activity and to assess specific frequency bands associated with cognitive processes. Task-free baseline EEGs were collected from infants fed with human milk (BF), dairy-based formula (MF), or soy-based formula (SF) at 2, 3, 4, 5, and 6 months of age to explore differences in frequency bands in both sensor and source space. Significant global differences in sensor space were seen in beta and gamma bands between BF and SF groups at ages 2 and 6 months, and these differences were further observed through volumetric modeling in source space. We conclude that BF infants exhibit earlier brain maturation reflected in greater power spectral density in these frequency bands. ## 1. Introduction Infancy is marked by the rapid emergence of cognitive, behavioral, and social-emotional functions that have been shown to be sensitive to environmental influences during this critical period of development [1]. Infant diet is increasingly recognized as crucial for optimal myelination [2], neurogenesis [3], structural development of early anatomical architecture [4,5], and cognitive development [4,6,7]. As such, the influence of diet on neurodevelopment could have lifelong effects on the structure and function of the brain. While the structural and functional effects of specific nutrient deficiencies, such as iron [4,8,9] and docosahexaenoic acid (DHA) [3,9], on the developing brain have been well-studied in infants, the more subtle differences resulting from infant feeding behaviors are not yet fully elucidated. Exclusive human milk feeding until 6 months of age and continued human milk feeding for the first two years of life is recommended by the American Academy of Pediatrics (AAP) [10,11] and is widely regarded as being the optimal nutrition source in infants for cognitive development. According to a 2018 CDC report, only $25.8\%$ of infants are exclusively breastfed until 6 months, with many mothers either supplementing or exclusively using formula [11]. Infants who are formula fed (FF) are then recommended dairy-based formula (MF), with soy-based formulas (SF) being the last choice, which is often made because of dietary constraints [10]. While infant formulas contain similar micronutrients and macronutrients inherent in human milk, such as short-, medium-, and long-chain polyunsaturated fatty acids (PUFAS), iron, phospholipids, choline, and DHA, the exact composition and concentration of these essential nutrients can vary [4]. Cognitive differences have been documented between breastfed (BF) and FF children, measured by a slight but significantly higher intelligence quotient (IQ) and Bayley Scales of Infant Developmental (BSID) scores in children who were BF [12,13,14], and these effects were shown to persist into adolescence [7]. Higher IQ in BF children may be confounded by the fact that in high-income countries, breastfed children normally come from a higher socioeconomic group than formula-fed children, and these cognitive differences may reflect a higher parental educational level [12]. However, studies controlling for this potential confound have found similar results with the BF infants scoring higher on cognitive exams later in life [15,16,17]. These findings together suggests that BF infants have a neurodevelopmental advantage, yet cognitive tests alone cannot give insight into the neurophysiological underpinnings responsible for such differences in cognitive outcomes. Advancements in imaging modalities have made it possible to evaluate brain development in infants. Numerous studies in MRI have demonstrated greater white [4,14] and grey [18,19] matter volumes in infants who were BF compared to MF or SF infants, and these studies suggest that nutritional differences in the commercial formulas are the primary cause. While MRI provides a good basis for the structural effects of diet on the brain, the temporal dynamics and underlying neuronal activity is understudied. The brain is a complex system of rhythmic activity, and these rhythms or oscillations modulate mental experience and determine how the brain processes environmental input [20]. These rhythms develop in infancy and have been used to better understand the emergence of learning [21,22], language processing [23], and the general development [24] of neural networks in infants. EEG is a direct measure of this neuronal activity—representative of the population firing of neuronal ensembles—and is reflective of underlying cognitive processes. While EEG gives excellent temporal resolution on the order of milliseconds, its coarser spatial resolution is a known limiting factor. However, using source space modeling algorithms allows for a more accurate model to determine where the electrical signals originate from the cortex [25] and gives greater insight into the topological patterns of spectral power. In this way, EEG can measure the spectral dynamics of the developing brain, giving greater insight into the maturation of underlying neuronal processes while also giving an increasingly accurate spatial resolution with the implementation of source space modeling. Because this study is interested in spectral dynamics and its implication for neuronal maturation, EEG is the ideal modality. It is thought that the emergence of oscillatory frequency bands, known as spectral power, mirrors the underlying maturation of cortical networks [26,27]. These frequencies in adults are delta (2–4 Hz), theta (5–7 Hz), alpha (8–12 Hz), beta (15–29 Hz), and gamma (30–45 Hz). In general, spectral power in higher frequencies, such as beta and gamma, increase with age, while spectral power in lower frequencies, such as delta and theta, tend to decrease into early adulthood [28]. Higher frequency band power is associated with cognitive processing [23,27], although the majority of studies examining the power spectrum in infants and children focuses on the lower spectra, with alpha being the most commonly studied frequency as it correlates strongly to known visual cues [29]. The power spectrum is actively developing during infancy, and lower frequencies predominate for the first decade of life; so, some studies shift the canonical adult frequencies to those that have known behavioral and functional correlates in infants [30]. Many studies in infants and early childhood, however, choose to preserve the adult frequencies for two reasons: [1] the literature concerning when and how the power spectrum develops is sparse, and [2] both activation and co-activation of higher frequency bands in the adult range, such as beta and gamma, have been shown in infants [31,32]. This co-activation or cross-frequency-coupling has been demonstrated in infants in the one- to three-month age range, with beta and gamma coupling observed as a mechanism for early speech discrimination [32]. While the power in gamma and beta is low during infancy, it does exist and has been shown to be responsible for a number of developing cognitive processes, including the perceptual binding of objects [31]. For these reasons, this paper will use the adult power spectrum to remain consistent with the literature and to avoid potential changes in these frequencies as we compare them across development. The primary aim of the current study was to determine whether differences arose in the power spectra in both sensor and source space reconstructions between BF, MF, and SF infants at 2, 3, 4, 5, and 6 months of age using a high-density 128 channel EEG. Because previous work in diet and cognition reports a slight cognitive advantage in BF infants [6,13,17,19,23,33], we predict that the BF group will have a greater global concentration of the high frequency bands associated with cognitive processing—beta and gamma—across age ranges, and this concentration will be reflected in the prefrontal cortex in our source reconstructions. ## 2.1. Participants Data were collected from 536 healthy term infants (>37 weeks gestational age, between 2.73–4.09 kg) that were enrolled in the Beginnings study (www.clinicaltrials.gov URL last accessed on 21 March 2023, ID#: NCT00616395), a longitudinal cohort study examining the effect of infant diet on physiological and cognitive development. Infants were recruited between 1 and 2 months of age, and as a result many missed their 2-month-old visit for EEG collection leading to fewer participants in this particular age group. Parents selected to exclusively provide their infants a BF, MF, or SF diet. MF and SF standard formulas were supplemented with DHA and arachidonic acid to better mimic the nutritional composition of breastmilk [34]. To qualify, infants must remain on the same diet from the age of 2 months, and mothers of enrolled infants are reported to have abstained from alcohol, tobacco, and/or medications while both pregnant and during lactation. Each infant stayed on the same diet until 12 months of age, with complementary foods optionally introduced after 4 months of age. Other exclusion criteria include voluntary withdrawal at any point during the study, failure to obtain usable EEG data due to excessive artifact, developmental or neurological disorders, and a change in selected diet after the age of 2 months. The total group composition of infants whose EEG data were analyzed is summarized in Table 1. Informed consent was obtained from parents prior to study participation, and the study’s protocol was approved by the Institutional Review Board of the University of Arkansas for Medical Sciences. ## 2.2. Anthropometrics and Behavioral Assessments Anthropometric measures (height, weight, and head circumference) and infant diet history were obtained during each visit. Gestational age, birth weight, and birth length were also obtained per parental reports. Licensed psychological examiners conducted behavioral assessments on both the infants and their mothers. The infants underwent the second edition of the Bayley Scales of Infant Developmental (BSID) at ages 3 and 6 months to obtain the mental developmental index (MDI) and psychomotor development index (PDI) [35], and the mothers took the second edition of the Wechsler abbreviated scale of intelligence (WASI) to derive their full-scale IQ score during the 3-month visit [36]. ## 2.3. EEG Recordings and Signal Processing Eyes-open high-density (128 channel nets) EEGs were collected in infants at ages 2, 3, 4, 5, and 6 months old. EEGs were collected during a task-free video baseline for each age group over the course of approximately 5 min. Although a silent video played to promote wakefulness, these data are considered resting state EEGs and are in line with the current literature [24]. EEGs were preprocessed in Matlab using the standard Harvard Automated Processing Pipeline for Electroencephalography (HAPPE) [37], in which data were band-pass filtered (0.5–45 Hz), bad channels were rejected, and artifacts were removed via wavelet-enhanced thresholding and ICA with automated component rejection. The HAPPE was designed to clean pediatric EEGs, which are known to be noisier than adult EEGs, and this standardization should improve reproducibility across studies. EEGs were then segmented in 10 s epochs, and segments were rejected if artifacts exceeding ±200 amps remained. Data were then re-referenced to a global mean using the references electrode standardization technique (REST) [38]. EEGs containing >$70\%$ bad channels or segments were rejected as were EEGs with an R Pre/Post wavelet thresholding value below 0.2 for our frequency range of interest as calculated by the HAPPE. A minimum of 10 artifact-free segments per subject was required for subsequent analysis using the Brainstorm software package [39]. A standardized infant brain atlas [40] was used to calculate the boundary element head model for each subject age, and sensor locations were projected along the surface generated in line with standard fiducials. Power spectral density (PSD) was calculated using Welch’s method over 1 s epochs with $50\%$ overlap and averaged across all 128 sensors to provide a global metric for the following frequency bands: delta (2–4 Hz), theta (5–7 Hz), alpha (8–12 Hz), beta (15–29 Hz), and gamma (30–45 Hz). The PSDs were then normalized by their relative power in each frequency band. Noise covariance matrixes were calculated for each subject from an individual epoch, and the diagonal noise covariance matrix was used for source estimates that were calculated per subject using the minimum norm method sLORETA [25]. Normalized PSD values were then calculated in source space using the same methodology and frequency bands as sensor space. ## 2.4. Statistical Analyses The effects of the dietary group on each frequency band, infant BSID scores, and maternal WASI scores were determined by an analysis of variance (ANOVA) for each age group. Post hoc t tests were used for detecting differences between means of the individual dietary groups, and significance was set at $p \leq 0.05.$ To control for potential covariates for the observed effects of the dietary group on PSD, a secondary analysis was preformed using a general linear model to explore the interaction of biological sex with the following between-subjects measures: gestational age, weight at birth, maternal WASI score, and head circumference at the time of the EEG. Multiple comparisons were corrected using Sidak’s method, and significance was set at $p \leq 0.05.$ Statistical testing was accomplished using SPSS Statistics 28. ## 3.1. Anthropometrics and Behavioral Assessments A significant main effect of the dietary group was not observed for the BSID, MDI, and PDI assessments at 3 months of age, which is consistent with a previous study’s findings [13]; however, it was observed at 6 months of age for the MDI ($F = 3.0$, $$p \leq 0.049$$). Post hoc tests observed significant differences in BF vs. MF ($$p \leq 0.027$$) and BF vs. SF ($$p \leq 0.041$$) for the MDI scores and a trend toward significance between BF vs. SF ($$p \leq 0.051$$) for the PDI score for the six-month age group. Height, weight, and head circumference were also assessed at each EEG visit, with height and head circumference being significant solely at 6 months of age. Infant weight was significant at 2, 3, 5, and 6 months of age. In addition, birth length was not significant in any age group while birth weight was significant at 5 months of age, and gestational age was significant at 3, 4, 5, and 6 months of age. Results are summarized in Table 2. Maternal WASI scores were also calculated at the six-month visit and were found to have a significant effect on infant diet choice ($F = 11.5$, p ≤ 0.001) for that age group. ## 3.2. Spectral Power in Sensor Space Significant differences between the dietary group and delta, theta, and alpha were not observed for any age group. Previous studies using a subset of this data observed regional cortical differences inferred from the EEG sensor placements [33,41]; however, our global analysis exploring gross differences in band power did not observe these effects. At 2 months of age, significant differences in the dietary group were seen in gamma ($F = 3.215$, $$p \leq 0.04$$), and further post hoc testing found BF infants had significantly higher gamma than SF ($$p \leq 0.014$$). Post hoc testing in the two-month-olds revealed that BF infants also had significantly higher beta than SF ($$p \leq 0.028$$). These results are mirrored in the six-month-olds, with BF infants having significantly higher beta ($$p \leq 0.029$$) and gamma ($$p \leq 0.048$$) than the SF infants (Figure 1). The MF infants did not differ from the BF or the SF infants at any age range or frequency band. ## 3.3. Spectral Power Covariate Analysis in Sensor Space Adjusting for gestational age, weight at time of birth, head circumference, maternal WASI, and sex revealed significant interactions between sex, dietary group, and PSD. Differences in delta, theta, and alpha were not observed at any ages. Differences in beta were seen in the two-, four-, and six-month-olds, and differences in gamma remain at 2 months of age. In the two-month-olds, female infants had a significant effect of the dietary group on beta ($F = 3.405$, $$p \leq 0.035$$) and gamma ($F = 3.232$, $$p \leq 0.041$$), and pairwise comparisons revealed this was between BF and SF groups in both cases (beta: $$p \leq 0.043$$, gamma: $$p \leq 0.040$$). There were also sex differences within dietary groups, with BF females having significantly higher beta ($F = 5.617$, $$p \leq 0.018$$) and gamma ($F = 5.895$, $$p \leq 0.016$$) than their male counterparts. Analysis of the four-month-olds observed that MF females had significantly higher beta ($F = 7.541$, $$p \leq 0.006$$) than MF males; however, there was not a significant effect of the dietary group on PSD at this age. The univariate analysis of the dietary group was shown to have a significant effect on beta at 6 months of age ($F = 3.288$, $$p \leq 0.038$$); however, pairwise comparisons did not find significant differences between groups (BF vs. MF, $$p \leq 0.987$$; BF vs. SF, $$p \leq 0.138$$; MF vs. SF, $$p \leq 0.051$$). Significant differences between sex, dietary group, and beta were observed ($F = 3.069$, $$p \leq 0.48$$), and these differences were primarily due to MF females having higher beta than SF females ($$p \leq 0.041$$). MF females also had higher beta than MF males ($F = 4.530$, $$p \leq 0.034$$). There were no significant effects of the dietary group or sex on gamma in the six-month-olds. Importantly, adjusting for covariates decreased our study populations as not every subject had every covariate measure. These results are visualized in Figure 2. ## 3.4. Spectral Power in Source Space To explore the regional differences in spectral power by the dietary group, a source space analysis was performed for the frequencies in each age range found to be significant at the sensor level. In the two-month-olds, this analysis revealed the greatest concentration of prefrontal activation of both gamma and beta in the BF infants, with the least prefrontal activity in the SF infants (Figure 3A,B). Higher prefrontal activation in these frequency bands is consistent with our results in sensor space and provides a more accurate spatial reconstruction of the regional activation seen in previous studies [41]. Source space reconstructions for the six-month-olds revealed similar patterns of higher prefrontal activation in higher frequencies for the BF groups compared to the MF and particularly the SF groups (Figure 4A,C). In addition to the higher concentration of power in the prefrontal cortex, higher temporal beta in both hemispheres is seen in the six-month BF infants (only right hemisphere shown) compared to the other groups (Figure 4B). Source reconstructions also reveal a slight hemispheric asymmetry, with more beta/gamma power in the right hemisphere in the six-month-olds across dietary groups (Figure 4). ## 4. Discussion This study is the first to track diet-related changes in the entire resting state global power spectrum over the first six postnatal months of life in both sensor and source space. Although we did find significant changes globally in beta and gamma in the two- and six-month-olds, the lack of significance in other age ranges and frequency bands suggests that nutrition has a specific effect on neurodevelopment in these two critical periods. Furthermore, our covariate analysis observed that these differences may also be largely driven by biological sex. These neurodevelopmental differences seen in the electrophysiology are reflected in both the cognitive and motor assessments as measured by the BSID assessment, with the BF infants exhibiting slightly higher scores on both behavioral assessments. In addition, infants in this study were all healthy, and general developmental results should map onto studies investigating spectral content throughout infancy that did not consider diet as a confound to neural maturation. ## 4.1. Age-Related Development of Higher Frequencies Many studies acknowledge the importance of the first 1000 days after birth for neurodevelopment, with nutritional factors being seen as the primary factor for optimization [1]. From birth until early adulthood, neurodevelopment consists of two driving factors: progressive, including myelination, neuronal and glial proliferation, and synaptogenesis; and regressive, including apoptosis and synaptic pruning. These factors are often concentrated in temporally distinct periods [42]. The period from birth to 3 months of age sees the greatest amount of volumetric growth [43], which is correlated with an increase in synaptogenesis, neurotrophin serum levels [44], γ-aminobutyric acid (GABA)-ergic neurons [45], myelination [2,4,5,14], and the overall proliferation of both glial and neurons in the brain [46]. Throughout these significant and dynamic changes, the brain is particularly sensitive to nutritional deficits [2]. Early nutrition is known to affect neuroanatomy, neurochemistry, and neurophysiology because of the substrates it provides for the synthesis and activation of growth factors [47]. The primary research focus in studies of neuronal maturation has centered on the structural neuronal architecture that emerges as a function of aging and/or diet, with less emphasis on the resultant functional changes. These functional changes can be evaluated using non-invasive methods, such as EEG, with developmental analysis of the power spectrum used to infer development of different populations of neurons. The differences we observed in our unadjusted model at 2 and 6 months of age between BF and SF infants occurred only in beta and gamma frequency bands, which the literature suggests may reflect development of the GABAergic system and the role it plays in functional neural network architecture [48,49]. Coherent high-frequency oscillations in the gamma range are observed as early as the first 3 to 4 weeks of development [27] and are known to increase from infancy to early adulthood [50]. These high-frequency oscillations are known to facilitate the release of neurotrophins, such as a brain-derived neurotrophic factor (BDNF), which is essential for the survival and proliferation of immature neurons [51,52]. BDNF is regarded as the primary driver of GABAergic development [53,54], and higher levels of BDNF are observed in infants who are BF than those who are FF [55,56]. BDNF regulates the maturation of GABAergic networks through its role in synaptic development, which in turn controls BDNF levels through the post-synaptic release in a positive feedback loop [57]. These networks of interneurons producing GABA mediate gamma activity [49] and are known to migrate rapidly until 6 months of age, with progressively slowing migration patterns until 2 years of age [58]. These immature migratory GABAergic neurons act as excitatory neurons until they reach their target site at which point GABA will act as an inhibitory neurotransmitter similar to its action in adults [59,60,61]. The early patterns of excitatory transmission increase synaptic development and are thought to be responsible for gamma band activity [49], which peaks at approximately 2 months of age after which is declines steeply [61]. In addition, animal studies exploring the maturation of the GABAergic system revealed earlier maturation in females than in males, which could explain the sex differences observed in our secondary analysis at 2 months of age [62]. We propose that the higher global gamma power observed at 2 and 6 months of age between BF and SF infants in sensor space is indicative of an earlier maturation of the emerging GABAergic system and subsequent excitatory/inhibitory balance of the central nervous system as a result of subtle early nutritional differences. While gamma is analyzed in infancy—though almost exclusively in terms of its role in perceptual binding [29]—beta has yet to be characterized although it is thought to be generated in a manner similar to that of gamma [49]. Increases in beta power correlate positively with increasing age and are thought to be a marker of neuronal maturation [63]. While gamma is posited to be indicative of cognition and attention, less is known about the role of beta in infancy. One study found that increases in beta are associated with the acquisition of motoric skills during infancy, reflective of one of its roles in the adult motor cortex [64]. Higher beta in infancy is associated with increased attention [65], while lower beta is associated with a decrease in cognitive developmental scores [66]. These findings are consistent with our study in which we found that higher beta in BF vs. SF is associated with higher BSID scores at 2 and 6 months of age. Beta power could therefore be indicative of faster maturing motor and/or attentional network. Our secondary analysis revealed sex related differences between males and females within dietary groups, with BF females having higher beta and gamma at 2 months of age than BF males, and MF females having higher beta than MF males at 4 and 6 months of age. This effect in beta, particularly in the MF groups, has yet to be explored in the literature, and future studies are needed to explore these differences longitudinally. The effects of sex in infancy and early childhood are often mixed [67], and sex is routinely not considered as a biological variable of interest. ## 4.2. Regional Development of Beta/Gamma Defined in Source Space We hypothesized that differences in spectral power between dietary groups would be visually distinct in source space, with the BF infants having a higher beta/gamma power in frontal regions compared to SF infants based on previous findings in the literature [31,41]. Our results support this hypothesis, although the differences at 2 months are more apparent than those in the six-month age group. This difference may be due to the increased neuronal migration to the anterior cortex that occurs during this early postnatal period [58,68]. Further, not only is there higher power in beta/gamma bands in the BF two-month-old infants, but there is also a higher degree of disbursement throughout the frontal cortex. This is seen to a degree in the six-month-olds as well, particularly in frontal gamma. Our analysis also revealed a greater degree of temporal beta in the BF infants at 6 months of age. Beta is associated with visual attention in this area in adults [69] and infants [70]; we suggest that the increased regional beta observed at 6 months of age is related to the use of a video to engage the infant’s attention during rest state EEG acquisition and may be indicative of a greater degree of visual attention. If that is the case, this may be an important consideration in future studies as many researchers use a video/visual baseline in infancy, and this has a potential to be a confound in connectivity or coherency studies examining the temporal lobes. ## 4.3. Dietary Effects on Neurocognitive Testing Our results are consistent with an extensive literature reporting that BF infants score higher than FF on cognitive testing during infancy, and that these effects persist throughout childhood and to adolescence [4,6,12,16,17,18,71]. In the literature, there are two primary theories to explain this observation: [1] The majority of these studies occur in high-income countries, and the decision to breastfeed is heavily associated with a higher socio-economic and educational status such that higher cognitive scores may be more indicative of having access to better prenatal care or being raised in a more enriched environment; or [2] the specific nutritional content of human milk—particularly the lipid fraction—is optimal for neurodevelopment. To circumvent the confound of socio-economic status, researchers have examined the cognitive scores of BF vs. FF infants in lower-income countries in which the decision to breastfeed is independent of income and educational status. A series of these studies took place in Brazil, with studies reporting that BF infants still scored higher on cognitive tests than their FF counterparts [16,17], while a study in the Philippines in which breastfeeding is inversely related to socio-economic status revealed that children at 8.5 years of age who were BF as infants scored several points higher in IQ tests [71]. In addition, a study controlling for maternal educational status and IQ maintains BF infants score higher than FF infants on subsequent cognitive assessments [72]. From these studies, a tentative conclusion that maternal socio-economic and educational status does not play the primary role in infant development can be drawn. However, maternal WASI was still used in our covariate analysis because of its potential to have a confounding effect. Studies focused on the components of human milk that result in its optimization as an early nutritional source have largely focused on its lipid composition, with conflicting results. Many researchers have emphasized the importance of PUFAs, including DHA, because of their known role in promoting synaptogenesis and myelination [12]. Yet, SF infants supplemented with DHA continue to exhibit lower cognitive scores overall [41,73] and reduced language-related neural responses [74], while an extensive review reported no differences in cognitive outcomes in BF infants who were supplemented with additional DHA compared to BF infants who were not supplemented [75]. These results contrast with the finding that infants fed formula containing higher levels of long-chain PUFAs had better neurodevelopmental outcomes [4]. These conflicting results may be due to an inherent variability in the commercial formula used or may be due to other nutritional factors not yet well established. Recent research examining the positive effect of human milk oligosaccharides on the infant gut microbiome and modulation of the immune system has emerged [76] and has shown a positive effect on cognition in animal studies [77]. Because the composition of these oligosaccharides is unique to humans and not found in formula, human milk oligosaccharides may explain cognitive differences in BF vs. FF infants; however, more research in this area is needed [78]. In addition, human milk is known to have a positive effect on the infant microbiome [79], ultimately resulting in fewer allergies [80] and decreasing the risk for certain pathologies, such as necrotizing enterocolitis [81]; however, the effect of this microbiome on cognition and neurodevelopment has yet to be elucidated. ## 4.4. Strengths and Limitations Strengths of this study include a large sample size across multiple early developmental timepoints for each dietary group and the implementation of source space modeling, which is relatively rare in the pediatric EEG literature despite it increasing the spatial accuracy of EEG. Although this study is longitudinal in nature, it is important to highlight that each age group did not consist of the exact same participants due to either a missed visit or unreadable data for that timepoint. Additionally, while our study did control for infant diet until 4 months of age, data were not collected on the effect of complementary foods integrated at 4 months of age if parents chose to do so, and the accompanying changes in breastmilk or formula feeding if complementary foods were introduced. ## 5. Conclusions We observed significantly higher global beta and gamma in BF infants at 2 and 6 months of age at the sensor level, these results were then explored in source space in which regional differences in the frontal cortex were found. Higher beta in the frontal and temporal lobes are new findings for this age group, while the higher gamma observed is largely supported by the literature. Our secondary analysis looking at covariates showed that these findings are largely driven by sex differences. Importantly, our study looked at resting state metrics of neurodevelopment; resting-state EEG analysis is well-suited for developmental longitudinal studies and has the potential to unveil the underlying mechanisms of neurodevelopment [82]. Future directions using these data include increasing time-points until 6 years of age as well as connectivity and coherency studies. ## References 1. Cusick S.E., Georgieff M.K.. 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--- title: Antidiabetic Treatment before Hospitalization and Admission Parameters in Patients with Type 2 Diabetes, Obesity, and SARS-CoV-2 Viral Infection authors: - Patricia-Andrada Reștea - Mariana Mureșan - Adrian Voicu - Tunde Jurca - Annamaria Pallag - Eleonora Marian - Laura Grațiela Vicaș - Ionuț I. Jeican - Carmen-Bianca Crivii journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10055907 doi: 10.3390/jpm13030392 license: CC BY 4.0 --- # Antidiabetic Treatment before Hospitalization and Admission Parameters in Patients with Type 2 Diabetes, Obesity, and SARS-CoV-2 Viral Infection ## Abstract Background: SARS-CoV-2 viral infection is a current and important topic for patients with comorbidities of type 2 diabetes and obesity, associated with increased risk of mortality and morbidity. This study aims to analyze, compare and describe admission parameters in patients with type 2 diabetes, obesity, and SARS-CoV-2 infection based on whether they received insulin therapy before hospital admission. Methods: Our study enrolled patients diagnosed with type 2 diabetes, obesity, and SARS-CoV-2 viral infection, 81 patients without insulin treatment before hospital admission, and 81 patients with insulin at “Gavril Curteanu” Municipal Clinical Hospital of Oradea, Romania, between August 2020 and March 2022. RT-PCR/rapid antigen tests were used for detecting SARS-CoV-2 viral infection. Results: The severe form of COVID-19 was found in $66\%$ of all patients ($65\%$ in the group without insulin and $67\%$ in the group with insulin). Oxygen saturation at the time of hospital admission was greater or equal to $90\%$ in $62\%$ of all patients. The most associated comorbidities we founded in this study were: hypertension in $75\%$ of all patients (grade two hypertension $63\%$ in the group without insulin and $64\%$ in the group with insulin), ischemic heart disease in $35\%$ of patients ($25\%$ in the group without insulin and $44\%$ in the group with insulin, $$n = 0$.008$), heart failure in $9.3\%$ of all patients ($8.6\%$ in the group without insulin and $10\%$ in the group with insulin). CRP and procalcitonin are increased in both groups at hospital admission, with a slightly higher trend in the group with insulin therapy before hospital admission. We found that $56\%$ of patients in the group with insulin treatment were with uncontrolled diabetes on admission. Only $10\%$ of patients required a change in antidiabetic treatment with insulin therapy at discharge. In our study, $89\%$ of all patients did not require short-term home oxygen therapy at discharge. Conclusions: Antidiabetic therapy taken before hospital admission did not protect patients against cytokine storm in COVID-19, but is very important in the pathophysiological stage of comorbidities. Paraclinical parameters at hospitalization showed differences in correlation with oral antidiabetic treatment like metformin or insulin therapy. Changing the antidiabetic treatment for a small percentage of patients in the group who had not been receiving insulin therapy before discharge was necessary. It is necessary for future studies to see all changes involved in antidiabetic treatment in patients with diabetes type 2 and obesity after SARS-CoV2 viral infection and its long-term evolution. ## 1. Introduction A major concern of the last two years, SARS-CoV-2 infections have prompted many studies, but there are still many unanswered questions about the association between insulin resistance, pancreatic beta cell damage, antidiabetic treatment, and infection with the novel coronavirus [1]. In COVID-19, a link was found between the dysfunction of the endothelium caused by associated comorbidities, vascular damage caused by SARS-CoV-2 viral infection, multiorgan failure, and mortality [2]. Infectious disorders are more likely to appear in a patient with diabetes, a substantial risk factor for coronavirus disease [3]. Obesity increases the risk of developing type 2 diabetes and respiratory infection because of its association with altering immune cells, insulin resistance, and low-grade inflammation [4]. Obesity and type 2 diabetes are both risk factors for SARS-CoV-2 viral infection and increase mortality risk [5]. The question is why some infected patients with type 2 diabetes and obesity had higher mortality than others with the same comorbidities [6]. It appears that there is a connection between the viral entry of SARS-COV2 and the use of insulin in the treatment of diabetic patients [7]. Apparently, infected patients who are at risk for diabetes or who have a history of type 2 diabetes and use insulin would require more frequent admission to intensive care [8]. Literature data show that insulin treatment would be linked with adverse outcomes in diabetic patients with COVID-19 [9,10,11]. Patients with COVID-19 have an aggressive inflammatory response, especially in severe forms, with the release of pro-inflammatory cytokines in cytokine storm; cytokine storm plays an important role in severe lung damage, multiple organ dysfunction syndromes, admission to the intensive care unit, and mortality [12]. The chronic inflammatory state in patients with type 2 diabetes and obesity facilitates the cytokine storm [13]. Type 2 diabetes and obesity can be associated with complications in patients with SARS-CoV2 viral infection [14,15,16]. The goal of this study was to assess and compare admission parameters in individuals with type 2 diabetes, obesity, and SARS-CoV-2 virus infection based on the type of antidiabetic medication they were using before hospitalization and how their antidiabetic medication changed after discharge. ## 2.1. Study Design This study enrolled patients diagnosed with type 2 diabetes, obesity, and SARS-CoV-2 viral infection, 81 patients without insulin treatment before hospital admission, and 81 patients with insulin treatment before hospital admission at “Gavril Curteanu” Municipal Clinical Hospital of Oradea, Romania, between August 2020 and March 2022. RT-PCR/rapid antigen tests were used for detecting SARS-CoV-2 viral infection. This study was conducted in accordance with the Declaration of Helsinki. Before taking part in the study, each subject provided their informed, written agreement for inclusion. The protocol of the study was approved by the Ethics Committee of “Gavril Curteanu” Municipal Clinical Hospital of Oradea (No. $\frac{32652}{16.11.2020}$) and by the Ethics Commission of Oradea University (No. 5/A,21.09.2020). Inclusion criteria were: type 2 diabetes, obesity, and COVID-19 confirmed by RT-PCR/rapid antigen test. Exclusion criteria were: patients with type 1 diabetes, patients without type 2 diabetes, patients without obesity, and patients without COVID-19 confirmed by the RT-PCR/rapid antigen test. This study included two groups of patients with type 2 diabetes, obesity, and SARS-CoV-2 confirmed infection, one group with oral antidiabetic treatment like metformin (81 patients) and another with insulin treatment (81 patients). ## 2.2. Data Collection Data were collected from the electronic medical records and included the following parameters: demographic characteristics (age, gender, origin), COVID-19 severity, oxygen saturation on admission, other comorbidities, BMI, diabetic ketoacidosis on admission, uncontrolled diabetes on admission, transfer to intensive care unit, intubation and mechanical ventilation, short-term oxygen therapy for COVID-19 patient at discharge, insulin treatment on discharge and survival rate. The laboratory parameters collected were: inflammatory, hematological, biochemical, coagulation parameters and electrolytes. ## 2.3. Statistical Analysis The statistical analysis was performed using the following software: R version 4.1.2 (R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria), RStudio 2022.07.1 + 554 “Spotted Wakerobin” and JAMOVI version 1.8.3.0. Since the data used are not part of a population with a normal distribution (p-value < 0.05 with the Shapiro–Wilk test), only non-parametric tests were used for the statistical analysis. For Table 1 and Table 2 we used: Median (IQR); n (%), Wilcoxon rank sum test, Fisher’s exact test, Pearson’s Chi-squared test. For Table 3 we used: Median (IQR); n (%), Wilcoxon rank sum test. For survival analysis, we used the Kaplan–Meier curves and the log-rank test included in survival R package. [ 17] ## 3.1. General Characteristics of Patients at Hospital Admission Of the 162 patients, 81 ($50\%$) without insulin therapy and 81 ($50\%$) with insulin therapy were included in our study. The female patients prevailed ($60\%$) compared to men ($40\%$). The patients were aged between 39–91, with an increased percentage between 61–70 years old—$38\%$ of patients, followed by the range 71–80 years old—$29\%$, with an average of 68 years. The patients from urban areas were $52\%$. BMI (median IQR) in the group without insulin was 32.90, characteristic of type 1 obesity, and in the group with insulin, BMI was 32.40 (median IQR of body mass index). The demographic analysis of the sample, according to insulin therapy before hospitalization, is summarized in Table 1. ## 3.2. The Severity of Disease, and Comorbidities at Hospitalization The severe form of COVID-19 was found in $66\%$ of all patients ($65\%$ in the group without insulin and $67\%$ in the group with insulin). The most associated comorbidities were summarized in the Table 2 below. ## 3.3. Laboratory Parameters at Hospital Admission During the laboratory tests performed at admission lymphocyte number was lower than normal laboratory value ($68\%$ of the patients in the group not receiving insulin and $53\%$ of the patients in the group with insulin). The following parameters were found to be with high values: C-reactive protein ($93\%$ of the patients not receiving insulin treatment and $95\%$ of those on insulin therapy, p-value = 0.035), fibrinogen ($86\%$ of the patients in the group without insulin and $74\%$ of those in the group with insulin), Creatinekinase ($28\%$ of the patients in the group without insulin and $41\%$ of those in the group with insulin, $$p \leq 0.020$$). The results are summarized in Table 3 below. ## 4. Discussions The severe form of COVID-19 was found in $66\%$ of all patients ($65\%$ in the group without insulin and $67\%$ in the group with insulin). Oxygen saturation in our study was greater or equal to $90\%$ in $62\%$ of all patients. Ischemic heart disease was founded in $25\%$ of patients in group without insulin and in 44 % of patients in group with insulin therapy, with statistically significant the p-value 0.008. The human pancreas can be a target of SARS-CoV-2 infection, and β-cell infection could contribute to the metabolic dysregulation/diabetes observed in patients with COVID-19 [18,19]. It is known that the novel coronavirus can influence the host cell metabolism and change the glycolysis process of the host [7,8,9,10]. Oral antidiabetic treatment in COVID-19 patients was associated with a decreased mortality rate, but insulin therapy increased the risk of adverse outcomes in type 2 diabetes [20,21]. In a randomized controlled study, metformin as an oral antidiabetic had a possible effect against severe forms of COVID-19, but high doses do not increase anti-inflammatory effects [22]. In a recent article, it was stated that metformin decreased viral entry of novel coronavirus into cells and protect against fibrosis, and had a protective effect in the microcirculation [23]. It seems that oral antidiabetic like metformin interferences with IL-6 cytokines levels involved in cytokines storm [23]. In a randomized study on metformin versus placebo in COVID-19, it was found that oral antidiabetic treatment had no clinical effects on ambulatory patients with diabetes and SARS-CoV-2 infection [24]. SARS-CoV-2 infection also interacts with epithelial cells of multiple organs and with the endothelium [25,26]. Evans et al. [ 27] have shown the crucial role of endotheliitis induced by SARS-CoV-2 and the fact that endothelium is an important source of inflammatory cytokines in the cytokine storm of COVID-19. Endothelial dysfunction is already present in comorbidities including diabetes, obesity, and cardiovascular disease, and the endothelial dysfunction brought on by SARS-CoV-2 infection is added to this [28]. There are theories proposed for the immunomodulatory and anti-inflammatory effects of some oral antidiabetics in diabetic patients with SARS-CoV-2 infection [29]. Clinical studies showed that diabetes in patients with COVID-19 disease is a risk factor for transfer to an intensive care unit and may be an independent factor for survival in SARS-CoV-2 viral infection [30,31,32]. In other studies, obesity in patients with SARS-CoV-2 viral infection was considered an independent risk factor for transfer to the intensive care unit [33,34]. For the prognosis, it is important how patients were treated during hospitalization and how they were treated before, but we analyzed the condition of patients at admission according to antidiabetic treatment before hospital admission because was necessary to identify from the beginning aspects that can show the severity of the disease. The patient’s condition at admission may be severe from home or worsen during admission. There are situations in which patients have been admitted with severe forms of COVID-19 in both groups, and our study showed the connection between the admission parameters and the treatment without insulin or with insulin at home. Most of the patients who were admitted to the hospital did not follow an antidiabetic diet, and during the COVID-19 pandemic, they no longer visited the doctor for blood glucose control and treatment modification due to pandemic restrictions or because of concern about contracting the SARS-CoV-2 virus. Laboratory parameters at hospitalization showed significant statistical differences in correlation with the type of antidiabetic treatment. CRP and procalcitonin are increased in both groups at hospital admission, with a slightly higher trend in the group with insulin therapy before hospital admission. Other studies have shown that a higher level of CRP, D-dimers, and decreased lymphocyte number are items included in cytokine storm in COVID-19 disease [35,36,37]. In other studies, metformin, sulfonylurea, and sodium-glucose Cotransporter-2 Inhibitors decreased mortality risk, but insulin therapy and gliptins are linked with a high risk of severe outcome and mortality in COVID-19 [38]. Literature data showed that in severe forms of COVID-19, oxygen therapy for the short term at home was recommended for hypoxemic patients, according to the protocols [39,40]. We found that the majority of all patients in this study did not require short-term oxygen therapy at home. Some patients in the group without insulin treatment needed a change in antidiabetic treatment after SARS-CoV-2 viral infection, requiring insulin therapy at discharge. The severity of COVID-19 in diabetic patients was primarily influenced by the pathophysiological stage of the disease according to literature data. The limitation of our study is given by the small number of patients, small groups, included in a single center, only admission parameters, case control study with the same number of patients in both groups. Our findings may be useful for the medical community adding information about antidiabetic treatment before hospitalization and admission parameters in patients with type 2 diabetes, obesity, and SARS-CoV-2 Viral Infection. ## 5. Conclusions This research points out that antidiabetic therapy taken before hospital admission did not protect patients against cytokine storm in COVID-19, because the evolution of SARS-CoV-2 viral infection was primarily influenced by the pathophysiological stage of comorbidities. A small percentage of patients in the insulin-free group required a change in antidiabetic treatment at discharge in management with insulin therapy, and a high percentage of patients in both groups did not require short-term home oxygen therapy. 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--- title: TLR4/Inflammasomes Cross-Talk and Pyroptosis Contribute to N-Acetyl Cysteine and Chlorogenic Acid Protection against Cisplatin-Induced Nephrotoxicity authors: - Amira M. Badr - Layla A. Al-Kharashi - Hala Attia - Samiyah Alshehri - Hanaa N. Alajami - Rehab A. Ali - Yasmen F. Mahran journal: Pharmaceuticals year: 2023 pmcid: PMC10055908 doi: 10.3390/ph16030337 license: CC BY 4.0 --- # TLR4/Inflammasomes Cross-Talk and Pyroptosis Contribute to N-Acetyl Cysteine and Chlorogenic Acid Protection against Cisplatin-Induced Nephrotoxicity ## Abstract Background: Cisplatin (Cp) is an antineoplastic agent with a dose-limiting nephrotoxicity. Cp-induced nephrotoxicity is characterized by the interplay of oxidative stress, inflammation, and apoptosis. Toll-4 receptors (TLR4) and NLPR3 inflammasome are pattern-recognition receptors responsible for activating inflammatory responses and are assigned to play a significant role with gasdermin (GSDMD) in acute kidney injuries. N-acetylcysteine (NAC) and chlorogenic acid (CGA) have documented nephroprotective effects by suppressing oxidative and inflammatory pathways. Therefore, the current study aimed to investigate the contribution of the upregulation of TLR4/inflammasomes/gasdermin signaling to Cp-induced nephrotoxicity and their modulation by NAC or CGA. Methods: A single injection of Cp (7 mg/kg, i.p.) was given to Wistar rats. Rats received either NAC (250 mg/kg, p.o.) and/or CGA (20 mg/kg, p.o.) one week before and after the Cp injection. Results: Cp-induced acute nephrotoxicity was evident by the increased blood urea nitrogen and serum creatinine and histopathological insults. Additionally, nephrotoxicity was associated with increased lipid peroxidation, reduced antioxidants, and elevated levels of inflammatory markers (NF-κB and TNF-α) in the kidney tissues. Moreover, Cp upregulated both TLR4/NLPR3/interleukin-1beta (IL-1β) and caspase-1/GSDMD-signaling pathways, accompanied by an increased Bax/BCL-2 ratio, indicating an inflammatory-mediated apoptosis. Both NAC and/or CGA significantly corrected these changes. Conclusions: This study emphasizes that inhibition of TLR4/NLPR3/IL-1β/GSDMD might be a novel mechanism of the nephroprotective effects of NAC or CGA against Cp-induced nephrotoxicity in rats. ## 1. Introduction Cisplatin (cis-diamminedichloroplatinum II, Cp) is one of the most effective antineoplastic agents, used to treat a variety of malignant tumors [1]. It has been known that Cp exerts its cytotoxic effect through alkylating the DNA double helix, resulting in intra-strand and inter-strand adducts, which explains why it inhibits cell division and has the greatest efficacy in rapidly reproducing cells [2]. Despite its potential effectiveness in prolonging the patient survival rate, its dose-dependent nephrotoxicity is still not completely resolved [3,4]. Cp nephrotoxicity is encountered in 30–$45\%$ of patients receiving Cp; accordingly, the discontinuation of therapy and limitation of the overall clinical outcome will be the final result [5,6]. Therefore, there is an urgent need for a comprehensive understanding of the Cp-induced nephrotoxic-signaling pathways and optimal nephroprotective treatment to increase the clinical applicability of Cp. Cp-induced nephrotoxicity is a complex process involving an interplay between oxidative stress, inflammation, and apoptosis [1,6]. In this context, it has been reported that acute kidney injury (AKI) and hyperuricemia are among the common clinical manifestations of Cp nephrotoxicity [7]. Studies have gradually uncovered the complex inflammatory pathways that are involved in Cp-induced AKI [8,9], e.g., Toll-4 receptors (TLR4) and inflammasomes. TLR4 and NLR family pyrin domain-containing 3 (NLRP3) inflammasomes are pattern recognition receptors responsible for activating inflammatory responses. They were recently assigned to play a plausible role in kidney injuries [9,10,11,12,13]. The expression of NLPR3, as well as pro-interleukin (IL)-1beta (pro-IL-1β), is triggered by nuclear factor-kB (NF-kB). NF-kB is activated by various stimuli, such as lipopolysaccharides binding to TLR4 and reactive oxygen species (ROS). NLRP3-mediated caspase-1 activation induces the cleavage of pro-IL-1β and pro-IL-18, which are recently known to be implicated in Cp-induced nephrotoxicity [13,14]. Accordingly, the inhibition of NLRP3 was found to ameliorate Cp-induced tissue injuries not only in the kidney, but also in the liver [13,15]. In addition to activating IL-β, caspase-1 can activate and cleave gasdermin D (GSDMD). GSDMD has the ability to form pores in the cell membranes, with the resultant leakage of cytoplasmic contents and inflammatory mediators, including IL-1β and IL-18, inducing a type of inflammatory cell death known as pyroptosis; the Greek root “pyro” means fever, and it is a sign of inflammation. Pyroptosis is a type of programmed cell death that is activated by inflammatory caspases, mainly caspase-1 [16,17]. Pyroptosis differs from apoptosis in many aspects, including function and cell morphology. However, the main hallmark is that in pyroptosis, DNA fragmentation occurs, while the nucleus remains intact [16,18]. Pyroptosis was found to be implicated in the development of kidney diseases, including ischemia-reperfusion injury [19,20], diabetic nephropathy [21], fibrosis [22], and lupus nephritis [18]. Accordingly, the activation of caspase-1/GSDMD pathway was documented to play a role in pyroptotic inflammatory responses of Cp-induced AKI [17]. Additionally, one study recently reported that Cp might have induced pyroptosis in breast cancer through an NLRP3/caspase-1/GSDMD pathway [23]. In addition to pyroptosis, apoptotic cell death also plays a role in the development of renal diseases [24]. The rate of apoptosis of kidney tubular epithelial cells is the key pathophysiological alteration occurring during ischemia/reperfusion, and it determines the level of kidney damage [25]. Moreover, diabetic kidney disease is also associated with tubular atrophy and epithelial cell apoptosis [24]. Cisplatin nephrotoxicity is also associated with the induction of apoptosis, even at low concentrations. This is mostly associated with the production of ROS, which damages mitochondrial membrane lipids and culminates in increased caspase-3 activity. Additionally, nephroprotection mediated by various pharmacological modalities is associated with a reduction in caspase-3 activity [26,27,28,29,30], further confirming the role of caspase-3 in Cp-induced nephrotoxicity [26,28,29,30]. The use of antioxidant and anti-inflammatory drugs to help minimize Cp-induced nephrotoxicity has gained considerable interest [31]. N-acetylcysteine (NAC), the acetylated variant of the amino acid L-cysteine, has a well-documented antioxidant and anti-inflammatory activity and is widely used as an antidote for acetaminophen toxicity [32,33]. Chlorogenic acid (CGA) is a phenolic compound widely found in fruits, vegetables, coffee, and tea, and it also has well-characterized antioxidant and anti-inflammatory properties [34,35]. Indeed, the antioxidant and anti-inflammatory properties of NAC [36,37] and CGA [38,39] have been demonstrated to promote nephroprotection. Few studies have recently linked the inflammasome pathway to their hepatoprotective effect [40,41]. However, no data is available about the contribution of TLR4/inflammasomes/pyroptosis signaling inhibition to NAC and CGA nephroprotective activity. Therefore, the present study was conducted to answer the upcoming questions: [1] Does TLR4 crosstalk with NLPR3/IL-1β signaling in Cp-induced AKI, and if so; [2] Does the inhibition of TLR4 and NLRP3/IL-1β signaling play a role in NAC- and CGA-mediated nephroprotection against Cp-induced nephrotoxicity, and how is this nephroprotection effect associated with caspase 1/GSDMD-induced pyroptosis? ## 2.1. NAC and/or CGA Ameliorated Cp-Induced Nephrotoxicity To confirm the nephroprotective effect of NAC and/or CGA on the acute nephrotoxicity induced by a single dose of Cp, biochemical renal function indices were measured, and a histopathological examination was conducted. Table 1 shows that Cp increased the kidney index by $42\%$ compared to the control group. In addition, Cp caused a significant rise in both BUN and serum creatinine to about $778\%$, $$p \leq 0.0001$$ and $903\%$, $$p \leq 0.0003$$, respectively, compared to the control values. However, the administration of NAC or CGA significantly decreased BUN (by $74\%$ and $65\%$, respectively) and serum creatinine (by $55\%$ and $47\%$, respectively) compared to the Cp group. In addition, the combined group showed superior correction of both BUN and serum creatinine over NAC or CGA (by $82\%$ and $60\%$, respectively). Nevertheless, no significant changes have been found in NAC, CGA, or the combined groups in values of kidney indexes when compared to the Cp group. Besides renal biochemical functions, histopathological alterations in kidney specimens were assessed using H &E staining, as shown in Figure 1. No histopathological alterations were found in the control group; it shows regular histological features of renal parenchyma with apparent intact renal corpuscles (star) and renal tubular segments with almost intact tubular epithelium (arrow), as well as intact vasculatures (Figure 1(A1,A2)). On the other hand, the injection of Cp shows diffuse records of severe tubular degenerative changes at corticomedullary junctions with abundant figures of cystic dilations and necrotic tubular segments in both proximal and distal tubules (red star), with moderate forms of intraluminal desquamated necrotic epithelial cells (red arrow) with mild interstitial inflammatory cells infiltrates (Figure 1(B1,B2)). Meanwhile, the administration of NAC shows almost intact morphological features of renal parenchyma and tubular epithelium (black arrow) with few focal records of degenerated tubular cells with pyknotic nuclei (red arrow) (Figure 1(C1,C2)). In contrast, the CGA group demonstrates almost intact morphological features of renal parenchyma and tubular epithelium (black arrow) with minimal records of dilated (red star) or degenerated tubular segments (red arrow), as well as minimal inflammatory cells infiltrate (Figure 1(D1,D2)). Moreover, a combined group of both NAC and CGA shows apparent intact renal parenchyma and tubular epithelium (black arrow) with minimal dilatation of a few tubular segments, as well as occasional periglomerular inflammatory cells infiltrates (arrowhead) (Figure 1(E1,E2)). The scoring of renal tissue damage was represented in Table 2. Collectively, these results indicate that NAC and CGA alleviates Cp-induced nephrotoxicity. ## 2.2. NAC and/or CGA Reduced Cp-Induced Oxidative Stress As shown in Table 1, seven days post-Cp injection, renal tissues showed massive oxidative stress confirmed by the upregulation of lipid peroxidation (reaching about $203\%$, $$p \leq 0.0001$$) and GPx activity (approximately $1480\%$, $$p \leq 0.0001$$) as well as the significant reduction of antioxidants and catalase activity (approximately $57\%$, $$p \leq 0.0069$$, and approximately $19\%$, $$p \leq 0.0001$$, respectively) compared with renal tissues of the control group. Nonetheless, both NAC and CGA counteracted the oxidative stress marker changes. NAC and CGA nearly normalized the levels of malondialdehyde and antioxidants and improved the cisplatin-induced changes in GPx levels. In addition, NAC and CGA increased the catalase activity to about $248\%$ and $168\%$ of the Cp group, respectively. The combined group showed approximately the same results as either the NAC- or the CGA-treated group. ## 2.3. Effect of NAC and/or CGA on Cp-Induced Inflammatory Signalling Responses The expression of the common inflammatory markers was assessed in kidney homogenates of different groups, and the results were represented in Figure 2. The renal expressions of NF-κB and TNF-α demonstrated a distinct upregulation in the Cp-treated group compared to the control group ($166\%$, $$p \leq 0.0067$$ & $148\%$, $$p \leq 0.0025$$, respectively). However, the administration of NAC and/or CGA significantly reduced inflammatory responses of Cp in terms of NF-κB ($37\%$ reduction for NAC group, $43\%$ reduction for CGA group, and $42\%$ reduction for the combined group). At the same time, both NAC and/or CGA corrected this increment of TNF-α by approximately $38.7\%$ for the NAC-treated group, $45\%$ for the CGA-treated group, and $32\%$ for the combination group. ## 2.4. NAC and/or CGA Inhibited the TLR4/NLPR3/IL-1β and Caspase-1/GSDMD Signaling in Cp-Induced Nephrotoxicity To further explore the molecular mechanisms underlying Cp-induced acute nephrotoxicity, the fundamental role of the TLR4/NLPR3/IL-1β pathway, along with the GSDMD and caspase-1, was investigated using western blot analysis of the protein expression of TLR4, NF-κB, NLPR3, caspase-1, and IL-1β, as shown in Figure 3 and Figure 4. The resulting immunoblotting analysis in the Cp-treated group showed sharp upregulation in protein expression levels (4.4 folds for TLR4, 3.7 folds for NF-κB, 28.6 folds for NLPR3, seven folds for caspase-1, and 19 folds for IL-1β) when compared with the control group (Figure 3 and Figure 4A–E). On the other hand, the treatment of Cp-injected rats with 250 mg/kg NAC or 20 mg/kg CGA one week before and after Cp injection significantly reduced the upregulation of these protein expressions. However, rats treated with both NAC and CGA showed superior downregulation of those proteins (about $80\%$-reduction for TLR4, $80\%$fold reduction for NF-κB, $80\%$ reduction for NLPR3, $68\%$ reduction in caspase-1, and $55\%$ reduction for IL-1β) and protein expression levels compared to the Cp-group (Figure 3 and Figure 4A–E). Concomitantly, Figure 3 and Figure 4F show western blot analysis of GSDMD. The intense induction of both proteins’ expressions occurred following Cp injection (3-fold increment for GSDMD) compared to the control values. However, NAC and/or CGA administration corrected this increment and nearly normalized the proteins’ expression levels, as shown in Figure 3 and Figure 4F. ## 2.5. Inhibition of Apoptotic Markers by NAC’s and/or CGA’s The involvement of apoptotic pathways in Cp-mediated nephrotoxicity has been documented. Therefore, the Bax/BCL2 ratio was assessed to explore the renoprotective mechanisms of NAC and/or CGA. At the end of the experiment, the Cp-injected rats demonstrated intense upregulation of the Bax/BCL2 ratio (to about 1.57-fold, $$p \leq 0.0024$$) compared to the control rats (Figure 5A). On the other hand, NAC and/or CGA administration one week before and after the Cp injection significantly downregulated the Bax/BCL2 ratio to about 1.12-fold for the NAC group, 1.3-fold for the CGA group, and 1.15-fold for the combined treatment group compared to the Cp-treated rats (Figure 5A). Caspase-3 renal expression was increased by Cp by about 1.6 folds compared to the control group (Figure 5B,C). However, NAC and/or CGA administration corrected this increment and nearly normalized the proteins’ expression levels, as shown in Figure 5B,C. ## 3. Discussion Cp has been known for its crucial role in prolonging patients’ survival rates, particularly those with solid tumors. However, the discontinuation of therapy because of its massive nephrotoxicity has limited its clinical outcome for decades. Although scientists have been trying to alleviate this acute nephrotoxicity through enormous measures, no ideal nephroprotective agent has emerged, and the molecular mechanisms have not been fully elucidated [1,2,4,42]. Indeed, Ozkok and Edelstein [42] suggested that the deep studying of the pathogenesis of Cp-mediated AKI is vital to prevent such AKI and improve survival in cancer patients receiving Cp. Therefore, the present study was the first to elaborate on the potential role of TLR4/inflammasome signaling in the nephroprotection provided by NAC and CGA against Cp-induced acute nephrotoxicity in rats. The possible nephroprotective mechanisms of NAC and CGA were investigated, including their effects on oxidative and inflammatory status, as well as pyroptosis and apoptosis. In the present study, a single intraperitoneal injection of Cp (7 mg/kg) resulted in increased BUN, serum creatinine, and kidney indices. Cp also induced severe tubular degeneration, cystic dilations, and necrotic tubular segments, which were consistent with increased renal function indices and severe necrosis and inflammation of glomerular and tubular cells, as previously reported [43]. These findings confirm massive morphological damage and functional disablement in the kidney that leads to the accumulation of BUN and serum creatinine due to the inability of the kidney to clear nitrogenous substances [44]. Our findings confirmed those of previous studies [45,46]. The administration of NAC and CGA markedly hampered these nephrotoxic damages supporting their promising nephroprotective role, and our results were consistent with previous studies [37,47]. In addition to nephrotoxicity markers, the molecular mechanisms underlying NAC and CGA nephroprotection were fully investigated. There is undeniable evidence that oxidative stress, inflammation, and apoptosis are among the confirmed pathophysiological responses of Cp-induced nephrotoxicity. Cp induces extensive mitochondrial-ROS response in renal tissues and forms protein- and thiol-addition products, resulting in the depletion of antioxidant resistance [48,49]. The generated oxidants/antioxidants imbalance promotes cellular damage [49], oxidative stress-mediated apoptosis and inflammation [50,51]. In this study, the Cp-injected rats demonstrated deliberate oxidative stress in the kidneys indicated by a significant increase in lipid peroxidation along with the depletion of total antioxidants levels and reduced catalase activity. These findings were consistent with those reported by previous studies [27,51,52,53]. ROS can activate both inflammatory and apoptotic pathways. Studies have proven the contribution of the inflammatory signaling responses in the pathogenesis of Cp-mediated renal tubular injuries. Our current study confirmed the inflammatory response induced by Cp injection, evidenced by the upregulation of tissue expression of NF-κB, TNF-α, and IL-1β [54,55]. Moreover, our results proved that Cp-induced massive apoptosis in renal tissues, as indicated by a significant escalation in the expression of the executive caspase, caspase-3, and the ratio of the proapoptotic protein (Bax) to the antiapoptotic one (BCL2). Cp-induced apoptosis most probably linked to ROS-induced mitochondrial injury, which results in the activation of the intrinsic apoptotic pathway [51,56]. Cp-induced apoptosis may also be a consequence of increased TNF-α, which can trigger the extrinsic apoptotic pathway via activating the Death Receptors. Our findings were consistent with what was reported previously [55,57,58,59]. Accordingly, TNF-α inhibitors showed a promising role in alleviating Cp-induced renal damage [57,59]. The current results showed that both NAC and CGA substantially hampered the renal tubular inflammation and apoptosis indicated by a significant reduction in NF-κB, TNF-α level, caspase-3 expression, and Bax/BCL2 ratio compared to the Cp group. These results were in accordance with recent studies [60,61]. We suggest that NAC and CGA might mitigate the Cp-induced apoptotic response by rebalancing renal tubular cells’ oxidative stress status. The current study aimed to further elucidate the inflammatory and apoptotic molecular mechanisms involved in Cp-induced nephrotoxicity. Both TLR4 and inflammasomes were recently reported to play a role in Cp-induced nephrotoxicity [62,63]. However, their cross-talk role in the NAC- and CGA-nephroprotective activity was not previously studied. TLR4 and inflammasomes were cross-talked in other disease models, e.g., Alzheimer’s disease [64]. Therefore, we hypothesized in this study that both TLR4 and inflammasomes are upregulated and cross-talked in Cp-induced nephrotoxicity. They play a crucial role in NAC and CGA nephroprotection. Both TLR4 and inflammasomes are types of pattern recognition receptors (PRR) that are capable of recognizing molecules frequently found in pathogens (including bacteria and viruses) or molecules released by damaged cells; they are known as pathogen-associated molecular patterns (Pamps) and endogenous-associated molecular patterns (Damps). Once activated, they initiate pro-inflammatory responses required to eliminate infectious agents or induce cell death [64]. The inflamed necrotic tubules release damage-associated molecular pattern molecules [65], which bind to TLR4 and induce different inflammatory responses, through the NF-κB pathway [66]. This results in a marked elevation in the levels of TNF-α and pro-IL-1β. Eventually, the pro-IL-1β is activated by an NLRP3, a cytosolic PRR. Once the NLRP3 gets activated, it oligomerizes and subsequently binds to the pro-caspase-1 and the adapter protein (ASC), forming a large inflammasome complex. This, in turn, results in the activation of pro-caspase-1, as well as the cleavage of the pro-IL-1β into the active IL-1β [11,67]. Swanson et al. [ 67] have suggested that NLRP3 activation and subsequent IL-1β production require two fundamental regulatory steps: priming and activation. In the priming step, NF-κB triggers the upregulation of the expression of the inflammasome components (NLRP3, caspase-1, and pro-IL-1β) to the level required for the activation step. In this context, NF-κB activation can be induced through a TLR4 dependent pathway [64,67]. The activation of inflammasomes can occur in several ways, including increased Pamps or Damps; Damps include ROS, mitochondrial damage, calcium influx, or potassium efflux. IL-1β is secreted outside the cell, where it can bind to IL-1β receptors to induce further NF-κB expression and other inflammatory responses inside the cell [68]. Aiming to answer the central questions of this current study, the TLR4/NLRP3 signaling pathway was investigated using western blot analysis. This study confirmed the involvement of the TLR4/NLRP3/IL-1β signaling in Cp-induced renal tubular damage. This is evidenced by the upregulation of the upstream proteins’ renal expression involved in the inflammasomes pathway’s priming step, such as NF-κB, TLR4, and NLRP3. In addition, intense increments in IL-1β and caspase-1 renal expressions were detected following Cp injection, and these findings were consistent with previous studies [69,70]. Interestingly, NAC and CGA administration reversed the upregulation of the inflammasome signaling responses and were accompanied by the correction of renal function, as well as oxidative and inflammatory indices. Moreover, a simple correlation curve comparing TLR4 and NLPR3 expression in different groups demonstrated a probable strong correlation (R2 = 0.87) between TLR4 and NLPR3 expression, Figure 6. Our current study suggested that the suppression of the TLR4/NLRP3-signaling pathway is one of the mechanisms incorporated in the nephroprotection of NAC and CGA against Cp-induced renal insults. Furthermore, few studies investigated the role of the caspase-1/GSDMD pathway in Cp-induced pyroptosis and inflammation-mediated programmed cell death [17]. Recently, GSDMD was identified as a critical mediator of pyroptosis. Active caspase-1 cleaves GSDMD within a linker between its N-terminal and C-terminal domains. After cleavage, the N-terminal field forms pores in the cell membrane to cause pyroptosis. In the current study, the protein expression of GSDMD in kidney tissues was examined in the Cp-injected rats, and the results proved that the upregulation of both caspase-1 was associated with the upregulation of GSDMD renal expression. Cp-induced pyroptosis was found to play a role in both Cp-cytotoxic effects [71,72], as well as its associated nephrotoxicity [13,17], cardiotoxicity [73], and cochlear toxicity [74]. In harmony, in the current study, Cp induced a significant increase in GSDMD compared to the control group. The results are in agreement with that reported before [13,28]. Treatment with NAC and/or CGA significantly inhibited GSDMD. For the first time, this study suggests that the crosstalk of TLR4 and NLRP3 might be implicated in Cp-induced nephrotoxicity, with the consequent activation of caspase-1/GSDMD. Additionally, NAC and CGA could protect kidney tissues from Cp-induced inflammatory mediated cell-death through the suppression of TLR4/NLRP3/caspase-1/GSDMD signaling and thus inhibiting pyroptosis, as well as apoptosis. The limitation and future recommendation based on this study is the need for studying the effect of NAC and CGA on Cp-induced cytotoxicity. The data is limited regarding NAC [75,76], and CGA [77,78], and they are not conclusive. ## 4.1.1. Drugs and Chemicals Cisplatin [Onco-Tain®] was purchased from Hospira, UK, LTD. Chlorogenic acid hemihydrate (ab120973) was obtained from Abcam Co. (Cambridge, MA, USA). N-acetylcysteine was purchased from Sigma (St. Louis, MO, USA). Tris buffer, non-fat dry milk, bovine serum albumin, and RIPA buffer were obtained from Sigma (St. Louis, MO, USA). Other chemicals were of the highest grade commercially available. ## 4.1.2. Animals In this study, we followed the ethical guidelines of the Faculty of Pharmacy, King Saud University, Saudi Arabia. Ethical approval was issued form King Saud Scientific Research Ethics Committee (IRB number: KSU-SE-20-52). Male Wistar rats (150–200 g), 10 weeks old in average, were obtained from the animal house of the Faculty of Pharmacy, King Saud University, Riyadh, Saudi Arabia, and were acclimated for one week before experimentation in standard conditions: air-conditioned atmosphere, 25 °C, and 12-h light and dark alternative cycles. In addition, rats were provided with a standard diet and water ad libitum. According to the legal guidelines, standard diet pellets contained not less than $20\%$ protein, $5\%$ fiber, $3.5\%$ fat, and $6.5\%$ ash, as well as a vitamin mixture. ## 4.2.1. Experiment Design and Sample Collection Forty rats were randomly assigned into five groups, with eight animals per group ($$n = 8$$) and treated for 14 days as follows: Group 1 (control group)—rats were given only vehicle (distilled water) daily by oral gavage for 14 days. Group 2 (Cp group)—rats were given vehicle daily by oral gavage for 14 days and injected with a single injection of Cp (7 mg/kg, i.p.,) on Day 7 to induce the nephrotoxicity. Group 3 (NAC group)—rats received a daily dose of NAC (250 mg/kg, p.o.) for 14 days and were injected with a single injection of Cp (7 mg/kg, i.p.) on Day 7. Groups 4 (CGA group)—rats received a daily dose of CGA (20 mg/kg, p.o.) for 14 days and were injected with a single injection of Cp (7 mg/kg, i.p.) on Day 7. Group 5 (NAC + CGA group)—rats received a daily dose of NAC (250 mg/kg, p.o.) and CGA (20 mg/kg, p.o.) for 14 days and were injected with a single injection of Cp (7 mg/kg, i.p.) on Day 7. Doses of Cp [79], NAC [50], and CGA [80] were chosen according to previous studies. The animals’ weights were recorded on the first and seventh days of the experiment to calculate the dose. At the end of the experiment, rats were weighed then sacrificed after carbon dioxide-mediated anesthesia. Blood from the different groups was collected from the trunk, left to stand for 45 min, and then centrifuged at 1000× g for 15 min. Sera was stored in 2 mL Eppendorf tubes, divided into aliquots, and kept at −80 °C for biochemical analysis. Kidney tissues were dissected, washed with ice-cold phosphate-buffered saline, and weighted. Part of the kidney was homogenized using a tissue homogenizer (Omni International Inc., Kennesaw, GA, USA) and centrifuged at 10,000 rpm at 4 °C for 15 min. Then, the supernatants were stored at −80 °C until they were used to assess the oxidative stress, inflammatory, and apoptotic markers. Another part of the kidney was kept in liquid nitrogen for western blot analysis. Furthermore, histopathological examination was done on kidney samples obtained from the different treatment groups and treated immediately with $10\%$ formaldehyde. ## 4.2.2. Assessment of Nephrotoxicity Indices The kidney index of the different treatment groups was calculated according to the following equation: (kidney weight/body weight) × 100 [81,82]. In addition, serum levels of creatinine [83] and blood urea nitrogen (BUN) [84] were assessed according to the previously described methods using the commercially available kits (United Diagnostics Industry, Dammam, Saudi Arabia). The manufacturer’s instructions were followed in the steps of the assay. ## 4.2.3. Assessment of Histopathological Changes For light microscopy, kidney tissues were kept in $10\%$ formalin (pH 7.4) for 24 h before preparing specimens in molten paraffin blocks. Then, five micron-thick sections were cut and mounted using a rotary microtome. These sections were stained with hematoxylin and eosin (H and E) to detect kidney tubular damage. Histopathological samples were scored in a blinded manner from six non-overlapping microscopic fields (400× nearly 79,000 square micrometers, using Leica Biosystems) per a tissue section of a sample, then scored by an experienced histologist using the system adopted from El-Nabarawy et al., 2020 and Moon et al., 2022 as follows: (Nil) for any sample with no abnormal cellularity, (+) if there were minor focal lesions seen in less than $50\%$ of samples, (++) if more than $50\%$ of samples showed mild lesions focally, (+++) for moderate diffused lesions in 75–$100\%$ of samples, and (++++) if total samples examined had severe diffused lesions [85,86]. ## 4.2.4. Assessment of Oxidative Stress Markers in Renal Tissues The effect of Cp, NAC, and CGA on the oxidative stress markers in kidney tissues was evaluated. The renal level of total antioxidants was measured according to the previously described method by Ellman [87] using the Total Antioxidants Assay Kit (BioSource International Inc., Camarillo, CA, USA). In addition, lipid peroxidation was assessed using malondialdehyde assay kit (Biodiagnostic Co., Giza, Egypt) that measures the level of thiobarbituric acid-reactive substances (TBARS), and data were finally expressed as malondialdehyde equivalents [88]. Glutathione peroxidase (GPx) and Catalase enzyme activities were also measured in using a commercially available kit (Biodiagnostic Co., Giza, Egypt). ## 4.2.5. Assessment of Renal Expression of Inflammatory Markers (NF-κB, TNF-α) Using ELISA According to the manufacturer’s instructions, the expression of NF-κB and TNF-α in tissue homogenates of the different treatment groups was measured using specific assay ELISA Kit (BioSource International Inc., Camarillo, CA, USA). ## 4.2.6. Assessment of Renal Expression of Apoptotic Markers (Bax/Bcl-2 Ratio) Using ELISA The commercially available ELISA kits (BioSource International Inc., Camarillo, CA, USA) were used to measure the renal expressions of Bax and Bcl-2 and expressed as Bax/Bcl-2 ratio. The procedure was done according to the manufacturer’s instructions. ## 4.2.7. Assessment of Protein Content We measured protein contents in kidney homogenates according to the previously described method by Bradford [89] using bovine serum albumin as a standard. ## 4.2.8. Western Blot Analysis of TLR4, NF-κB, NLRP3, Caspase-1, IL-1β, Caspase-3, and GSDMD Protein Expression in Kidney Tissues Kidney specimens from each group were subjected to western blot analysis preparation and protein extraction. Then, protein concentrations were determined using the Direct Detect quantification assay technique. Protein extraction and quantification were fully described previously [90]. Separated protein gels were transferred to polyvinylidene difluoride (PVDF) membranes (0.2 μm, Immun-Blot®, Bio-Rad, Hercules, CA, USA). Membranes were incubated (for 24–48 h) at 4 °C in the primary antibodies diluted as described in tris buffer saline with tween (TBST buffer) with a ratio of 1:1000 for TLR4 (ab22048), NF-KB p65 (ab16502), NLPR-3 (ab263899), caspase-1 (ab179515), IL-1β (ab254360), Caspase-3 ”Asp175” (C.S9661), and GSDMD (ab219800), while anti-GAPDH (ab8245) was used as a housekeeping loading control antibody. The intensities of different protein bands were quantified densitometrically using the Image J software (NIH Image, Bethesda, MD, USA) and normalized against loading control (GAPDH). ## 4.3. Statistics We used GraphPad Prism software version 5 (ISI® software, San Diego, CA, USA) to analyze and present graphs. Data are represented as means ± SEM. One-way ANOVA was used to compare multiple groups statistically, followed by the Tukey–Kramer post-hoc test. 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--- title: Mitochondrial Metabolism and EV Cargo of Endothelial Cells Is Affected in Presence of EVs Derived from MSCs on Which HIF Is Activated authors: - Federica Zanotti - Ilaria Zanolla - Martina Trentini - Elena Tiengo - Tommaso Pusceddu - Danilo Licastro - Margherita Degasperi - Sara Leo - Elena Tremoli - Letizia Ferroni - Barbara Zavan journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10055915 doi: 10.3390/ijms24066002 license: CC BY 4.0 --- # Mitochondrial Metabolism and EV Cargo of Endothelial Cells Is Affected in Presence of EVs Derived from MSCs on Which HIF Is Activated ## Abstract Small extracellular vesicles (sEVs) derived from mesenchymal stem cells (MSCs) have attracted growing interest as a possible novel therapeutic agent for the management of different cardiovascular diseases (CVDs). Hypoxia significantly enhances the secretion of angiogenic mediators from MSCs as well as sEVs. The iron-chelating deferoxamine mesylate (DFO) is a stabilizer of hypoxia-inducible factor 1 and consequently used as a substitute for environmental hypoxia. The improved regenerative potential of DFO-treated MSCs has been attributed to the increased release of angiogenic factors, but whether this effect is also mediated by the secreted sEVs has not yet been investigated. In this study, we treated adipose-derived stem cells (ASCs) with a nontoxic dose of DFO to harvest sEVs (DFO-sEVs). Human umbilical vein endothelial cells (HUVECs) treated with DFO-sEVs underwent mRNA sequencing and miRNA profiling of sEV cargo (HUVEC-sEVs). The transcriptomes revealed the upregulation of mitochondrial genes linked to oxidative phosphorylation. Functional enrichment analysis on miRNAs of HUVEC-sEVs showed a connection with the signaling pathways of cell proliferation and angiogenesis. In conclusion, mesenchymal cells treated with DFO release sEVs that induce in the recipient endothelial cells molecular pathways and biological processes strongly linked to proliferation and angiogenesis. ## 1. Introduction Stem-cell-based therapies have recently gained popularity as a promising approach to support regenerative processes. Among mesenchymal stem cells (MSCs), adipose stem cells (ASCs) are very attractive due to the simple method of harvesting them and the remarkably high cell yield [1]. Nevertheless, some impediments still limit the clinical translation of cell-based therapies. For this reason, research is increasingly focused on the paracrine mediators, the small extracellular vesicles (sEVs), which work between MSCs and target cells. sEVs are vesicles of endosomal origin with sizes in the range of 30–150 nm that bring RNAs, proteins, and lipids to recipient cells and thus have an important role in intercellular communication [2,3]. sEVs have regenerative attributes like parenteral cells, and these may exceed the unwanted effects associated with stem cell transplantation. In fact, sEVs have a lower possibility of immune rejection, and they are more stable and storable [4,5]. sEVs have been isolated from numerous sources of MSCs, and their regenerative properties have been investigated. For instance, sEVs obtained from bone marrow showed a prevalent effect on cell proliferation and viability, sEVs from dental-pulp-derived MSCs showed distinct transcriptomic signatures of neurogenesis, while sEVs from adipose tissue showed a significantly improved ability to promote endothelial cell migration and angiogenesis [6,7]. Recently, MSC-derived sEVs have been attracting interest as a possible novel therapeutic agent for the management of different cardiovascular diseases (CVDs). Moreover, sEVs secreted by MSCs have shown cytoprotection, the stimulation of angiogenesis, and the modulation of macrophage infiltration in peripheral arterial diseases, atherosclerosis, and myocardial infarct [8,9,10]. In vivo, MSCs reside in niches in low-oxygen conditions, and conversely, in vitro culture conditions are often at atmospheric oxygen tensions [11]. However, it has been observed that hypoxic culture condition could significantly enhance the secretion of angiogenic mediators from MSCs [12,13]. Likewise, sEVs derived from MSCs preconditioned by hypoxia promote the angiogenesis, proliferation, and migration of endothelial cells in vivo and in vitro [14]. Moreover, the overexpression of the hypoxia-inducible factor 1α (HIF-1α) in MSCs improves angiogenesis in endothelial cells by the release of Jagged1-carrying sEVs [15]. Furthermore, iron-chelating deferoxamine mesylate (DFO) can be added to the culture medium as a useful substitute for environmental hypoxia. Various studies have proved that compared with hypoxia, the hypoxia mimetic agent could also induce related hypoxic genes. De facto, DFO is a prolyl-4 hydroxylase inhibitor that stabilizes HIF-1α under normoxic conditions through the inhibition of the prolyl hydroxylases enzyme, which targets the HIF-1 protein through degradation. Since hypoxic conditioning enhances the regenerative potential of ASCs through the upregulation of the transcription factor HIF-1α, the iron-chelating DFO can be used to mimic the increase in HIF-1α expression [16]. The improved regenerative potential of DFO-treated ASCs has been attributed to the increased release of angiogenic factors [17]; however, whether this effect is also mediated by the secreted sEVs has not yet been investigated. The aim of this study is to investigate the biological effects of sEVs released from DFO-treated ASCs (DFO-sEVs) on human umbilical vein endothelial cells (HUVECs). Transcriptome sequencing and miRNA profiling of the sEV cargo (sEV-miRNA) of treated HUVECs have shown improvements in mitochondrial oxidative phosphorylation, as well as signaling pathways related to cell proliferation and angiogenesis. ## 2.1. DFO-sEVs Production and HUVEC Treatment Human ASCs were treated with 100 μM DFO to stabilize HIF-1 under normoxic conditions [18,19]. In particular, commercially available hASCs were seeded in a six-well plate at a density of 4 × 105 hASCs/well in a complete medium. The following day, cells were washed twice with phosphate-buffered saline (PBS, EuroClone) and then incubated with 100 μM deferoxamine mesylate (DFO; Thermo Fisher Scientific, Waltham, MA, USA) in EV-depleted DMEM overnight. After the overnight treatment, the conditional medium was harvested and the DFO-sEVs were isolated through a technology based on Norgen’s proprietary resin, which allows the purification of intact extracellular vesicles [20]. The DFO-sEVs appeared like rounded structures below 100 nm in the transmission electron microscopy (TEM) (Figure 1a). Tunable resistive pulse sensing (RPS) analysis confirmed the dimension of the vesicles: mean diameter of 90 ± 30.9 nm and mode of 73 nm. The particle size distribution of D10, D50, and D90 was 67 nm, 82 nm, and 121 nm, respectively. The average concentration was 1.33 × 109 particles/mL (Figure 1b). Moreover, the DFO-SEVs were positive for superficial markers CD81 and CD63, as shown in Figure 1c. Purified DFO-sEVs were added to HUVEC cultures for 24 h, and sEV internalization was detected through observation under confocal microscopy. In Figure 1d, HUVECs incubated with PKH67 green fluorescent DFO-sEVs (left) are compared with cells incubated with the negative control (right), i.e., probe-labeled PBS. The red areas show a 2× magnification of the green areas. After remaining for 72 h in an EV-depleted medium, the total RNA was isolated from HUVECs and the conditioned medium was harvested for sEV (HUVEC-sEVs) recovery. ## 2.2. RNA Sequencing of HUVECs Treated with DFO-sEVs Total RNA extracted from DFO-sEV-treated HUVECs and untreated HUVECs were sequenced, and ingenuity pathway analysis (IPA) was performed on differentially expressed genes (DEGs). Fourteen genes were significantly upregulated in HUVECs treated with DFO-sEVs (Figure 2a), including genes encoding mitochondrially encoded NADH dehydrogenase (MT-ND1, MT-ND2, MT-ND4, MT-ND5, and MT-ND4L), mitochondrially encoded cytochrome c oxidase (MT-CO1, MT-CO2, and MT-CO3), mitochondrially encoded cytochrome b (MT-CYB), mitochondrially encoded ATP synthase 6 (MT-ATP6), dynein cytoplasmic 1 heavy chain 1 (DYNC1H1), and heparan sulfate proteoglycan 2 (HSPG2). IPA canonical pathways analysis revealed that these genes are associated with three biological pathways: granzyme A signaling, the sirtuin signaling pathway, and oxidative phosphorylation (Figure 2b). The granzyme A and sirtuin pathways were significantly downregulated in HUVECs treated with DFO-sEVs (Z-score of −2.236; −log (p-value) of 9.43 and 10.1, respectively); on the contrary, oxidative phosphorylation was significantly upregulated (Z-score of +3.162; −log (p-value) of 21). A functional enrichment analysis of the 14 significant genes was performed using the FunRich database for the categories cellular component, biological pathway, and biological process (Figure 3). The significant upregulated genes were mostly mitochondrial components, as shown in the pie chart (Figure 3a). The enriched biological pathways were respiratory electron transport, ATP synthesis by chemiosmotic coupling, heat production by uncoupling proteins, the citric acid cycle and respiratory electron transport, the respiratory electron transport, and metabolism (Figure 3b). Three biological processes resulted in enriched cell growth and maintenance, metabolism, and energy (Figure 3c). Moreover, HUVECs treated with DFO-sEVs showed amplified mitochondrial membrane potential compared to untreated cells, as displayed by the microscopy analysis with a fluorescent probe that accumulates in the mitochondria in a membrane-potential-dependent manner (Figure 4). ## 2.3. sEV-miRNA Expression Profiling of HUVECs Treated with DFO-sEVs HUVEC-sEVs were isolated as previously described [20]. The TEM image and tunable resistive pulse sensing analysis showed the distinctive features of sEVs: bilayer cup-shaped membrane structure as a result of dehydration during sample preparation (Figure 5a), and particle size dimension roughly of 70–130 nm (Figure 5b). Illumina sequencing and IPA analysis were performed on sEV-miRNAs of treated HUVECs. Among the identified 89 miRNAs (Figure 6a), 18 miRNAs showed a significant fold-regulation value (upregulation cut-off > 2; down regulation cut-off < −2) (Figure 6b). Precisely, 11 miRNAs resulted in upregulation, while 7 resulted in downregulation. The functional enrichment analysis of upregulated and downregulated miRNAs was performed using the miRNet software Vs2 and Reactome biological pathway database (Figure 7). Figure 7a shows the 10 main target genes of the 11 upregulated miRNAs, such as MDM4, NOTCH2, VEGFA, CCND1, and TGFBR3. These target genes encoding proteins and receptors are related to several biological pathways of NOTCH, VEGFR, and HIF (Figure 7b). The ten main target genes of the seven downregulated miRNAs (Figure 7c) were related to cell proliferation and cell cycle progression (Figure 7d). ## 3. Discussion sEVs derived from MSCs grown under hypoxic conditions can induce desirable biological effects on receiver cells, such as improvements in proliferation and migration [14]. These effects appear to be related to hypoxia, which induces HIF-1α mRNA expression via the PI3K/AKT pathway and the activation of NFκB [21]. Gonzalez-King and colleagues reported that the overexpression of HIF-1α in dental-pulp-derived MSCs improves angiogenesis in endothelial cells by the release of Jagged1-carrying exosomes [15]. In the present study, instead, ASCs were treated with the iron-chelating DFO to induce an increase in the HIF-1 expression. Basically, DFO stabilizes HIF-1 under normoxic conditions through the inhibition of the prolyl hydroxylases enzyme, which targets HIF-1 protein through degradation [16]. Therefore, the increase in HIF-1α expression was not induced through gene overexpression, nor through the maintenance of the mesenchymal cells in hypoxic conditions, but through the treatment with a molecule that stabilizes HIF-1 protein by blocking its degradation. After overnight treatment with DFO, the conditioned culture medium was collected to isolate sEVs by precipitation. Single-particle characterization and quantitation were performed, including imaging by electron microscopy, particle tracking techniques, and flow cytometry, following the guidelines of Minimal Information for Studies of Extracellular Vesicles [22] (Figure 1). With TEM, EVs appeared with a diameter of less than 100 nm and with the typical bilayer cup-shaped membrane structure, due to dehydration during sample preparation [23]. Particle size, particle size distribution, and particle concentration were measured through tunable resistive pulse sensing (RPS) with a qNano device. Specifically, the particle mean diameter was 90 nm and the mode was 73 nm. The particle size distribution of D10, D50, and D90 was 67 nm, 82 nm, and 121 nm, respectively. Previously, Connor et al. [ 24] analyzed EVs with a qNano device, defining the particles with the average size of 92 nm as small EVs. Moreover, the presence of sEV markers was analyzed by the classical flow cytometry of bead-captured EVs [25]. Polystyrene beads (4.5 μm diameter) coated with a primary monoclonal antibody specific for the CD63 or CD81 membrane antigen were incubated overnight with DFO-sEVs. Then, the bead-bound sEVs were stained with a fluorescent-conjugated antibody for CD63 or CD81. Overall, the particles isolated from DFO-treated MSCs could be defined as small EVs because they were bilipid membrane vesicles with a mean diameter of 90 nm and positive to sEV markers CD81 and CD63 (Figure 1). DFO-sEVs were used to treat the recipient endothelial cells prior to transcriptome sequencing and SEV-miRNA profiling (Figure 1d). The transcriptome of HUVECs treated with DFO-sEVs was analyzed through IPA. Fourteen genes were significantly upregulated. Among the upregulated genes, 12 were mitochondrial genes related to the oxidative phosphorylation, including MT-ND2, MT-ND1, MT-RNR2, MT-ND4L, MT-CYB, MT-ND5, MT-ATP6, MT-ND4, MT-CO1, MT-CO3, MT-CO2, and MTATP6P1. The last two genes were DYNC1H1 and HSPG2 (Figure 2a). DYNC1H1 encodes cytoplasmic dynein that acts as a motor for the intracellular retrograde motility of vesicles and organelles along microtubules. It is reported that the loss of function of this gene causes a significant decrease in cell viability and cell proliferative ability [26]. The HSPG2 gene encodes “Perlecan”, the proteoglycan key component of the vascular extracellular matrix, which is able to maintain the endothelial barrier function [27]. Overall, the expression profile of DFO-sEV-treated HUVECs shows enhanced mitochondrial activity and the overexpression of proactive genes that could lead to the proliferation, development, and preservation of the extracellular matrix integrity. IPA was performed to identify the canonical pathways that are most significant to the transcriptome sequencing outcome and to categorize upregulated genes. Three canonical pathways resulted in significant predictions: the granzyme A signaling and sirtuin signaling pathway resulted in an inactivated profile; in contrast, oxidative phosphorylation showed an activated profile (Figure 2b). Granzyme A signaling induces a caspase-independent cell death pathway [28], whereas sirtuin signaling can induce aging [29]. Oxidative phosphorylation is the metabolic process that leads to ATP production inside cells [30]. The canonical pathways identified by IPA highlighted a possible positive effect of DFO-sEVs on HUVECs, as they increased the expression of genes involved in oxidative phosphorylation and therefore the production of energy; however, the treatment with DFO-sEVs reduced the expression of genes connected to cell death and aging. These observations were proved through functional analysis using the FunRich software and through mitochondrial membrane potential detection. The FunRich enrichment showed improvement in mitochondrial respiratory electron transport and ATP production, cell growth, and maintenance (Figure 3). Therefore, the canonical pathway of IPA and the FunRich analysis agreed in the identification of the same pathway, namely, mitochondrial respiration. Furthermore, the mitochondrial membrane potential was assessed with a probe that accumulates in the mitochondria in a membrane-potential-dependent manner. The microscopy analysis confirmed that the mitochondrial increased activation for DFO-sEV-treated HUVECs compared to untreated cells (Figure 4). The data on HUVEC responses to DFO-sEVs were implemented by sequencing the miRNA content in sEVs derived from the treated endothelial cells. The IPA on sEV-miRNAs revealed 11 upregulated and 7 downregulated miRNAs in the treated HUVECs compared to untreated cells (Figure 6). The eleven upregulated miRNAs (hsa-let-7d-5p, hsa-mir-107, hsa-mir-143-3p, hsa-mir-191-5p, hsa-mir-196b-5p, hsa-mir-23b-3p, hsa-mir-27a-3p, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-423-3p, and hsa-mir-9-5p) have pro-angiogenesis and pro-proliferative action on cells, as reported in the literature. For instance, mir-9-5p can induce enhancement in angiogenesis, proliferation, and migration and prevent apoptosis in endothelial progenitor cells [31]. The upregulation of mir-107, mir-143-3p, mir-23b-3p, and mir-27a-3p has also been reported to have a pro-angiogenic function [32,33,34]. The let-7d-5p and mir-196b-5p miRNAs have proliferation-inducing functions [35,36]. In contrast, the seven downregulated miRNAs (hsa-miR-10a-5p, hsa-miR-10b-5p, hsa-miR-148a-3p, hsa-miR-151a-3p, hsa-miR-196a-5p, hsa-miR-29c-3p, and hsa-miR-654-3p), if expressed, have anti-proliferative, anti-angiogenetic, and pro-inflammatory action [37,38,39,40,41,42]. It can be assumed that the downregulation of these miRNAs in HUVEC-sEVs serves to promote proliferation and prevent inflammation in the recipient cells. The enrichment analysis of the significantly upregulated and downregulated miRNAs was performed using the miRNet software (Figure 7). The functional enrichment of the ten main target genes of upregulated miRNAs with the Reactome database highlighted a link with the NOTCH, VEGFR, and HIF signaling pathways, namely, pathways related to proliferation and angiogenesis [43]. The functional enrichment on the ten main target genes of downregulated miRNAs revealed enrichment of the biological pathways that control the cell cycle progression and AKT/PI3K signaling [44]. In the present in vitro study, the effects of DFO on ADSCs were investigated, with a particular focus on the messages conveyed by the sEVs released after the treatment. In particular, human endothelial cells were incubated with sEVs derived from DFO-treated mesenchymal cells to evaluate the alteration in cellular transcriptomes and miRNA cargo in sEVs. The transcriptomic analysis revealed the downregulation of genes connected to cell death and aging and the upregulation of mitochondrial genes linked to oxidative phosphorylation. The potentiating effect of the treatment with DFO-sEVs on endothelial cell mitochondria was also highlighted by an increase in the mitochondrial membrane potential. Moreover, the functional enrichment analysis of the sEV-miRNAs released from the endothelial cells showed a connection with the signaling pathways related to cell proliferation and angiogenesis. In conclusion, adipose-derived mesenchymal cells treated with iron-chelating deferoxamine release small extracellular vesicles that induce in the recipient endothelial cells molecular pathways and biological processes strongly linked to energy storage, proliferation, and angiogenesis. ## 4.1. Adipose Stem Cell Treatment and SEV Characterization Human adipose stem cells (ASCs; purchased from ScienCell Research Laboratories, Inc., Carlsbad, CA, USA) were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM, EuroClone, Milano, Italy) supplemented with $10\%$ fetal bovine serum (FBS, EuroClone, Milano, Italy) and $1\%$ Antibiotic–Antimycotic (Thermo Fisher Scientific, Waltham, MA, USA) until the experiments were conducted. In a 6-well plate, 4 × 105 hASCs/well (passages between 2 and 4) were seeded in the complete medium. The following day, the cells were washed twice with phosphate-buffered saline (PBS, EuroClone) and then incubated with 100 μM deferoxamine mesylate (DFO; Thermo Fisher Scientific, Waltham, MA, USA) in EV-depleted DMEM overnight. The sEVs (DFO-sEVs) were isolated from the conditioned medium of DFO-treated hASCs using a Cell Culture Media Exosome Purification Kit (Norgen Biotek Corp., Thorold, ON, Canada) according to the manufacturer’s instructions. For transmission electron microscopy (TEM), the sEVs were fixed in a $2\%$ glutaraldehyde solution in phosphate buffer (ratio 1:1). The sEVs were then deposited, rinsed, and stained with heavy metal compounds onto a gridded slide according to the standard protocols. The slide was visualized with a TEM Zeiss EM 910 instrument (Zeiss, Oberkochen, Germany). The distribution size and diameter of the sEVs were analyzed with a qNano platform (iZON Science, Oxford, UK). The analyses were performed with NP150 nanopores and CPC200 calibration particles at 20 mbar pressure. The results were analyzed with the Izon control suite v3.4. For flow cytometry, CD81-positive and CD63-positive sEVs were isolated with Exosome-Human CD81 Flow Detection (Thermo Fisher Scientific, Waltham, MA, USA) and Exosome-Human CD63 Isolation/Detection Reagent (Thermo Fisher Scientific, Waltham, MA, USA), respectively. Briefly, 100 μL of an sEV suspension was incubated with 20 μL of CD81 or CD63 magnetic beads at 4 °C overnight. Bead-bound sEVs were washed twice with an Assay Buffer ($0.1\%$ BSA in PBS), and then labeled with 20 μL of mouse anti-human CD81-PE monoclonal antibody (BD Pharmingen™, BD Biosciences, San Jose, CA, USA) or 5 μL of mouse anti-human CD63-PE monoclonal antibody (eBioscience, San Diego, CA, USA). After incubation in an orbital shaker at 1000 rpm for 1 h, the bead-bound sEVs were washed twice with an Assay Buffer. Negative control was performed by staining PBS (vehicle) instead of the sEVs. Flow cytometric detection was performed with an Attune™ N × T Acoustic Focusing Cytometer (Life Technologies, Carlsbad, CA, USA), and the data were analyzed with the Attune N × T Software version 2.5 (Life Technologies). ## 4.2. Endothelial Cell Treatment and Analyses Human umbilical vein endothelial cells (HUVECs; Thermo Fisher Scientific, MA, USA) were cultured in an EBMTM-2 basal medium (Lonza, Basel, Switzerland) completed with EGMTM-2 SingleQuotsTM Supplements (Lonza). Into 6-well plates, HUVECs (passages between 2 and 4) at 2 × 105 cells/well were seeded. After 24 h, 500 μL of DFO-sEVs at a concentration of 1.33 × 109 particles/mL or an equal volume of PBS was added to the culture medium for 24 h. After 72 h of resting, the total RNA was isolated from the HUVECs using the Total RNA purification Plus kit (Norgen Biotek, Thorold, ON, Canada) according to the manufacturer’s instructions. sEV-miRNA was isolated from the conditional medium using Cell Culture Media Exosome Purification and an RNA Isolation Mini Kit (Norgen Biotek, Thorold, ON, Canada) according to the manufacturer’s instructions. For internalization detection, the DFO-sEVs were stained with PKH67 (PKH67 Green Fluorescent, Sigma-Aldrich) for 20 min at 37 °C. An equal volume of PBS without sEVs was labeled with the green fluorescent probe and used as the negative control. Excess unincorporated dye was removed from the labeled solutions by using Exosome Spin Columns (MW 3000) (Thermo Fisher Scientific), following the manufacturer’s instructions. Then, HUVECs were incubated with the labeled sEVs for 24 h. After washing with PBS, the nuclei were stained with Hoechst 33342 (ThermoFisher Scientific) for 10 min. The cells were observed with a laser scanning confocal microscopy system (Nikon A1 confocal microscope, Nikon Corporation, Tokyo, Japan) equipped with a 60× objective. The zoomed-in insets in Figure 1d were produced with Fiji Vs4 software. For the mitochondrial membrane potential, the cells were incubated with MitoTracker Red CMXRos (Thermo Fisher Scientific) for 30 min at 37 °C. After washing, the cells were immediately observed on a Nikon LiveScan Swept Field Confocal Microscope (SFC) Eclipse Ti equipped with NIS-Elements microscope imaging software and on a confocal laser scanning Olympus FV3000 microscope both equipped with a 63X oil immersion objective (N.A. 1.4). The red signal colocalization rate was evaluated using the JACOP colocalization counter available in the Fiji software (ImageJ). For each condition, the signal was also determined by manually counting the fluorescent puncta. For each ROI, the Manders’ parameter was calculated. For each condition, five replicates were observed, and four measurements were performed on each replicate. ## 4.3. Sequencing and Data Analysis mRNA sequencing and miRNA profiling were carried out by Area Science Park (ASP, Trieste, Italy) with Illumina sequencing. The total RNA was evaluated using NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA) and Agilent Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA). Libraries were created with 1 μg of the total RNA with the TruSeq Sample Preparation RNA Kit (Illumina Inc., San Diego, CA, USA) according to the manufacturer’s protocol. All libraries were quantified with the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) on a Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). RNA sequencing was realized on a Novaseq 6000 sequencer (Illumina Inc., San Diego, CA, USA) according to the manufacturer’s protocol. FASTQ files were output with the Illumina BCLFASTQ v2.20 software. All raw files’ quality was verified with FASTQC software V4 (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc; accessed on 15 October 2022), and low-quality sequences were discarded from the analysis. Selected reads were aligned onto the complete human genome using Splices Transcripts Alignment to the Reference algorithm STAR version 2.7.3 using hg38 Genome Assembly and Genecode.v35 as the gene definition. The resulting mapped reads were included as the input for the feature count functions of the Rsubread packages and were used as gene counts for differential expression analysis using the Deseq2 package. Reads comparison was performed between DFO-sEV-treated HUVECs and untreated HUVECs. Differentially expressed genes (DEGs) were selected for log 2 (FR) < −1 or >1 and p-value < 0.05. MiRNA-Seq libraries were prepared using the QIAseq miRNA Library Kit (QIAGEN; Hilden, Germany) and sequenced using Novaseq 6000 (Illumina; San Diego, CA, USA) in the 2 × 150 paired-end mode. Identification of miRNAs in the samples was performed using the QIAseq miRNA-NGS data analysis software V5 considering Single Read as the read type and Read 1 Cycles 75 as the read cycles. All the datasets from RNA sequencing and miRNA sequencing were analyzed with the Qiagen Ingenuity Pathway Analysis (IPA) software. For the RNA sequencing analysis, IPA categorized all DEGs in canonical pathways. IPA can make a prediction on possible diseases and functions, which were ranked based on their significance (p-value) and predicted state of activation/inhibition (z-Score). The Z-score value was set with cut-off < −2 or >+2. RNA sequencing was used to perform functional, biological pathway, biological process, and cellular component enrichment with the FunRich software [45], while MiRNet was used to analyze functional enrichment for miRNA sequencing. miRNet provided miRNA target gene data that were collected from four well-annotated miRTareBase v8.0 databases. 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--- title: Mucosa-Associated Oscillospira sp. Is Related to Intestinal Stricture and Post-Operative Disease Course in Crohn’s Disease authors: - Shukai Zhan - Caiguang Liu - Jixin Meng - Ren Mao - Tong Tu - Jianming Lin - Minhu Chen - Zhirong Zeng - Xiaojun Zhuang journal: Microorganisms year: 2023 pmcid: PMC10055919 doi: 10.3390/microorganisms11030794 license: CC BY 4.0 --- # Mucosa-Associated Oscillospira sp. Is Related to Intestinal Stricture and Post-Operative Disease Course in Crohn’s Disease ## Abstract Intestinal stricture remains one of the most intractable complications in Crohn’s disease (CD), and the involved mechanisms are poorly understood. Accumulating evidence suggests that the gut microbiota contributes to the pathogenesis of intestinal fibrosis. In this study, we investigated specific mucosa-associated microbiota related to intestinal strictures and their role in predicting postoperative disease course. Twenty CD patients who had undergone operative treatments were enrolled and followed up. Intestinal mucosa and full-thickness sections from stenotic and non-stenotic sites were sterilely collected. DNA extraction and bacterial 16s rRNA gene sequencing were conducted. Radiological and histological evaluations were performed to assess fibrosis. Microbial alpha diversity was significantly decreased in stenotic sites ($$p \leq 0.009$$). At the genus level, Lactobacillus, Oscillospira, Subdoligranulum, Hydrogenophaga, Clostridium and Allobaculum were decreased in stenotic segments ($p \leq 0.1$). The difference in Oscillospira sp. ( stenotic vs. non-stenotic) was negatively correlated with the erythrocyte sedimentation rate (correlation coefficient (CC) −0.432, $$p \leq 0.057$$) and white blood cell count (CC −0.392, $$p \leq 0.087$$) and positively correlated with serum free fatty acids (CC 0.575, $p \leq 0.05$). This difference was negatively associated with intestinal fibrosis evaluated by imagological and histological methods (CC −0.511 and −0.653, $p \leq 0.05$). Furthermore, CD patients with a higher abundance of Oscillospira sp. in the residual intestine might experience longer remission periods ($p \leq 0.05$). The mucosa-associated microbiota varied between stenotic and non-stenotic sites in CD. Most notably, Oscillospira sp. was negatively correlated with intestinal fibrosis and postoperative disease course. It could be a promising biomarker to predict post-operative disease recurrence and a microbial-based therapeutic target. ## 1. Introduction Crohn’s Disease (CD), one of the main subtypes of inflammatory bowel disease (IBD), is a lifelong relapsing and remitting inflammatory disease with increasing incidence and prevalence around the world [1]. More than $80\%$ of patients are diagnosed in their youth, usually with terminal ileal and colonic involvement. A series of gastrointestinal and extraintestinal symptoms can present in the disease course of CD, leading to poor quality of life. With the development of intestinal inflammation and disease progression, intestinal fibrosis occurs over time, which ultimately results in multiple complications (intestinal stenosis, obstruction, or perforation), potentially requiring intestinal resection. Despite several biological agents showing efficacy in intestinal inflammation alleviation, effective anti-fibrotic therapies for intestinal stricture have yet to be developed [2]. Approximately 30–$50\%$ of patients eventually undergo fibrosis-associated surgeries 10 or more years following CD diagnosis, and some patients suffer from postoperative recurrence within a short period of time [3]. Nowadays, innovative therapies for the prevention or reversal of intestinal strictures have become an urgent challenge in the field of CD management. Although a variety of genetic, immunologic, and environmental factors are considered to be jointly involved in intestinal stricture formation in CD, the exact pathogenetic mechanisms of fibrosis and stricture formation required further study. The gut microbiota, as an important environmental factor, has attracted increasing attention in recent years [4]. In our previous review, we summarized the causative and preventive effects of certain gut microbiota and metabolic products on intestinal fibrosis via inflammation regulation, fibroblast activation or differentiation, extracellular matrix formation, and so on. However, these data mainly come from animal experiments; reliable evidence from patients with CD is still very sparse [5]. Furthermore, most investigators have used fecal samples, which might be affected by various factors and fail to reveal the precise gut microbiota characteristics of particular sites [6]. Surgical excision is the primary therapy for CD patients with intestinal stricture, and these patients can experience different disease courses after the operation. Some patients might suffer from postoperative recurrence within a short period of time [7]. Previous studies have reported that intestinal microbiota dysbiosis is associated with postoperative infections and disease recurrence [8,9]. However, the results have been inconsistent and microbial predictors for postoperative courses have rarely been explored. To facilitate the use of the gut microbiota in improving the diagnosis and treatment of postoperative CD, it is imperative to elucidate the bacteria that are associated with disease recurrence and to evaluate whether these microbial factors can predict postoperative CD recurrence. Among the thousands of millions of intestinal bacteria, Oscillospira sp., a Firmicute from the Ruminococcaceae family, has received increasing attention. It is a type of Gram-positive bacteria with a slow growth rate, as calculated by the number of transfer RNA genes. No subordinate species could be successfully cultured for a long time [10,11]. However, with the development of next-generation sequencing for bacterial 16S rRNA genes, samples from the gut and feces were tested and Oscillospira sp. was found to account for a high proportion of the intestinal microbiota. This indicated Oscillospira sp. might play an important role in maintaining microbial flora balance and human health [12]. Oscillospira sp. has been detected frequently in recent research, with involvements in leanness, gallstones, diabetes, and Parkinson’s disease [13,14,15,16]. Notably, its abundance showed a negative correlation with a series of inflammatory diseases, such as non-alcoholic steatohepatitis [17]. However, the relationships between Oscillospira sp. And inflammatory bowel diseases have only rarely been explored. In this study, the main aim was to portray the mucosa-associated microbial characteristics of intestinal stricture in CD via a paired comparison between stenotic and non-stenotic sites. Furthermore, we aimed to identify the predictive value of recurrence-related microbiota in post-operative CD. The bacteria involved in intestinal stricture and postoperative disease recurrence might represent a promising precision treatment for CD patients undergoing ileocaecal resection. ## 2.1. Study Population and Data Collection This study was conducted between March 2021 and September 2022 and approved by the institutional ethics committee of the First Affiliated Hospital of Sun Yat-sen University. A total of 20 consecutive patients with CD who underwent intestinal resection were enrolled, and CD diagnosis was based on clinical history, symptoms, and/or colonoscopic findings. The inclusion criteria were as follows: [1] age ≥ 14 years; [2] ileal or ileocolonic involvement; [3] patients with intestinal stricture who received ileocolonic resections in the absence of dysplasia or cancer. The exclusion criteria were as follows: [1] had antibiotic treatment during the previous 3 months or >2 weeks after surgery; [2] any other disease or condition that might interfere with the study assessments (as judged by the investigator); [3] pregnant or lactating women. This study complied with the Declaration of Helsinki, and informed consent was obtained from all participants. Clinical data of patients with CD were obtained from the electronic information system including demographic, clinical, laboratory, endoscopic, radiological and pathological features. All patients were followed up, and a colonoscopy was performed to assess the endoscopic recurrence according to the Rutgeerts score, about 6–12 months after surgery. According to previous research, patients were identified as experiencing postoperative disease recurrence if their CDAI score was ≥200 or there was an increase of ≥70 points from baseline (clinical recurrence), or their Rutgeerts score was ≥ i2 (endoscopic recurrence) [18]. ## 2.2. Sample Collection Mucosal samples were sterilely collected from the surgical specimens of the stenotic and non-stenotic sites (incisal edge). For each location sampled, one biopsy was collected and fixed in formalin for standard histopathology at the sampling institution for pathology diagnosis. Additional biopsies were taken for microbial sequencing and saved at –80 °C until processing [19]. ## 2.3. Radiological Evaluation According to the approaches of previous studies, the normalized magnetization transfer (MT) ratio was applied to evaluate intestinal fibrosis by utilizing patients’ regular magnetic resonance imaging results. MT ratio pseudo-color maps were generated via an in-house Matlab script (Math Works; Natick, MA, USA) [20]. Different colors represented various extents of fibrosis in different kinds of tissues. The bowel segment with the most thickened intestinal wall was identified through evaluation by an experienced radiologist. After that, another radiologist who was blinded to the patients’ information drew regions of interest (ROIs) on the designated segment and muscles to measure the MT ratio. For a segment of full-thickness bowel wall, three ROIs of various sizes were drawn along the border of the intestinal wall. The MT ratio was calculated as follows: MT ratio (%) = [1 − (Msat/M0)] × 100. Therein, the Msat and M0 are the signal intensities acquired with and without the off-resonance pre-pulse saturation, respectively [21]. Then, the average MT ratio of three ROIs in the bowel wall and muscles was recorded separately. Finally, the MT ratio of the affected bowel wall was divided by that of the muscle on the same image to calculate the normalized MT ratio [22]. ## 2.4. Histological Evaluation According to standard procedures, the formalin-fixed mucosal tissues were embedded in paraffin and sectioned, and then hematoxylin-eosin staining (HE) and Masson’s trichrome staining were carried out. All slides were independently reviewed by two expert pathologists at our institution. In addition, the fibrosis score and space volume expansion were assessed according to the reported criteria. The areas of fibroblast proliferation mixed with collagen and other matrix deposition, forming scar-like fibrous tissue, were separately evaluated in each layer under four views of ten times magnification (including the mucosa, submucosa, muscularis propria and subserosal adventitia) and scored from 0 to 3 according to the extent of fibrosis (none, <$33\%$, 33~$66\%$, >$66\%$). The space volume expansion of each layer was evaluated and scored by comparing the stenotic intestinal segment to the paired non-stenotic segment (none, <2 times, 2~3 times, >3 times) [23]. ## 2.5. DNA Extraction and 16s rRNA Gene Sequencing Total genomic DNA samples were extracted from mucosal samples with an OMEGA Soil DNA Kit (OmegaBio-Tek, Norcross, GA, USA), following the manufacturer’s instructions. After that, the V3–V4 region of bacterial 16S rRNA genes was amplified via polymerase chain reaction (PCR). The forward primer was 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer was 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Sample-specific 7-bp barcodes were incorporated into the primers for multiplex sequencing. As a result, the PCR components contained 5 μL of buffer (5×), 0.25 μL of Fast pfu DNA Polymerase (5 U/μL), 2 μL (2.5 mM) of dNTPs, 1 μL (10 uM) of each forward and reverse primer, 1 μL of DNA Template, and 14.75 μL of ddH2O. The procedure and indexes of thermal cycling were as follows: initial denaturation at 98 °C for 5 min; followed by 25 cycles consisting of denaturation at 98 °C for 30 s, annealing at 53 °C for 30 s, and extension at 72 °C for 45 s; final extension of 5 min at 72 °C. PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified via at Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA), after which they were pooled and multiplexed at equal concentrations. Raw pair-end 2 × 250 bp sequencing was performed through the Illlumina NovaSeq platform (Illumina, San Diego, CA, USA) with the NovaSeq 6000 SP Reagent Kit at Personal Biotechnology Co., Ltd. (Shanghai, China) [9]. ## 2.6. Bioinformatics and Statistical Analysis Statistical analysis was performed with SPSS 20 (IBM, Armonk, NY, USA) and R version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria), and the microbiome bioinformatics were performed with QIIME2 [24]. In the matter of microbiome bioinformatics, raw sequence data were demultiplexed, and then quality filtered, denoised, and merged using the DADA2 plugin [25]. Non-singleton amplicon sequence variants (ASVs) were aligned with MAFFT and rarefied to ensure further analysis at the same level for every sample [26]. ASV-level alpha diversity metrics (Shannon, Simpson) were estimated [27,28]. Beta diversity analysis was also performed (Jaccard distance) and visualized via principal coordinate analysis (PCoA) [29]. Taxonomy was assigned to ASVs using the classify-sklearn naive Bayes taxonomy classifier against the SILVA Release 132 Database. Random forest was applied to discover the most distinguishing identifiable components between different groups at every taxonomic level [30]. Normally distributed continuous variables were presented as means ± SE, and the non-normally distributed variables were presented as medians and ranges. Categorical variables were presented as frequencies. Paired t-test was performed to explore the difference between samples from different sites in every patient for those variables that fit a normal distribution, such as the comparisons of Shannon or Simpson indexes. If variables were non-normally distributed, the paired Wilcoxon signed-rank test was used, such as when analyzing the relative abundance of bacteria. The Spearman rank correlation test was applied to analyze the degree of correlation between bacteria abundance and indexes of the severity of illness. Remission maintenance in the postoperative time period was estimated by the Kaplan-Meier method, and the results were visualized by Kaplan-Meier curves. Unless otherwise noted, two-tailed statistical significance was established if p ≤ 0.05. ## 3.1. Patient Characteristics Twenty consecutive patients with CD who underwent resection for intestinal stenosis were enrolled in this study. The average age was around 40 years old, and most of the patients ($60\%$) were male. The medium disease course was 30 months (ranging from 3 to 204 months). The included patients received diverse therapies before surgery, including immunosuppressants ($35\%$), biologics ($40\%$), and more. Two CD patients ($10\%$) were treatment-naive. All patients presented ileal stenosis and received ileocecal resection. Mucosal biopsies were collected from the resected tissue at the time of surgery. Additionally, sections from adjacent tissue corresponding to the origin of the biopsies for microbial analyses were histologically defined as stenotic and non-stenotic by a clinical pathologist. The clinical characteristics are summarized in Table 1. ## 3.2. Mucosa-Associated Microbiota in the Stenotic Sites Differed from the Non-Stenotic Sites A total of 20 paired intestinal mucous samples from stenotic and non-stenotic sites were collected for microbiome bioinformatics analysis. A medium sequencing read depth of 63,153 reads per sample (ranging from 39,459 to 79,835), including 8680 ASVs, was identified. Moreover, the dilution curve showed that all samples were sequenced with sufficient depth (Figure 1A). After rarefaction, 37,486 reads per sample and 8320 ASVs were randomly selected for further analysis. At the taxonomy level, 299 phyla, 602 classes, 971 orders, 1572 families, and 1986 genera were identified (Figure 1B). In terms of the microbial composition, the alpha diversity of mucosa-associated microbiota was decreased in the stenotic sites when compared to non-stenotic sites (Shannon index: 3.27 ± 1.23 vs. 3.90 ± 1.22, paired t-test $$p \leq 0.009$$; Simpson index: 0.72 ± 1.15 vs. 0.79 ± 0.11, paired t-test $$p \leq 0.032$$) (Figure 1C). However, beta diversity, assessed by Principal Co-ordinates Analysis, showed that mucosa-associated microbiota from these two groups were virtually indistinguishable (Figure 1D). Relative abundances of different bacteria in all mucosal samples were analyzed according to their different taxonomy levels, and the result was shown in Supplementary Materials Figure S1. In order to discover the bacteria with the greatest difference between the two groups, the random forest approach was applied to analyze distinctions at different taxonomic levels. Acidobacteria, Proteobacteria, Firmicutes, Bacteroides, Actinobacteria, Fusobacteria, Synergistetes, Verrucomicrobia, Thermi, and TM7 were the ten most distinguishing phyla between stenotic sites and non-stenotic sites. In addition, a significantly lower relative abundance of Acidobacteria, Proteobacteria, Firmicutes, and Fusobacteria was found in stenotic segments ($p \leq 0.1$). Similar analyses were also conducted at other taxonomic levels, and the results are shown in Figure 2A and Figure S2. At the genus level, Lactobacillus, Oscillospira, Subdoligranulum, Hydrogenophaga, Clostridium and Allobaculum were significantly decreased in the stenotic sites compared to the non-stenotic sites from the same patient ($p \leq 0.1$), indicating these microbes might be involved in the pathogenesis of CD and need further research (Figure 2B). Most notably, the taxonomic framework showed Allobaculum, Clostridium, Lactobacillus, Oscillospira, and Subdoligranulum belonged to the family (Lachnospiraceae, Lactobacillaceae and Ruminococcaceae), order (Clostridiales and Lactobacillales), class (Clostridia and Bacilli), and phylum (Firmicutes) levels with significantly different abundances as well (Figure 2C). ## 3.3. Mucosa-Associated Oscillospira sp. Was Negatively Associated with Intestinal Inflammation and Stenosis Considering that the relative abundance of Lactobacillus, Oscillospira sp., Subdoligranulum, Hydrogenophaga, Clostridium, and Allobaculum was lower in the intestinal stenotic sites, we hypothesized that these alterations might be related to disease activity and intestinal stricture to some extent. To test this hypothesis, the abundance variation was compared between the non-stenotic sites and the stenotic sites for every patient, and a correlation analysis was carried out between gut microbiota and inflammatory indicators (Figure 3A and Supplementary Materials Table S1). As shown in Figure 3B, the altered abundance of Clostridium (abundance in stenotic site—abundance in non-stenotic site) was negatively correlated with the erythrocyte sedimentation rate (ESR) (correlation coefficient −0.493, $p \leq 0.05$) and white blood cells (WBC) (correlation coefficient −0.621, $p \leq 0.05$). The same tendency could be found in the genus Oscillospira sp. ( ESR: correlation coefficient −0.432, $$p \leq 0.057$$; WBC: correlation coefficient −0.392, $$p \leq 0.087$$), but a positive correlation was identified between Oscillospira sp. and the concentration of serum free fatty acids (FFA) (correlation coefficient 0.575, $p \leq 0.05$). Furthermore, the correlation between gut microbiota alterations and the degree of intestinal fibrosis was also explored. Our research team verified that the normalized magnetization transfer (MT) ratio of the bowel wall, measured by magnetic resonance enterography, was a credible and noninvasive marker to estimate intestinal fibrosis in CD. Simply put, the normalized MT ratio is determined by the MT ratio of the bowel wall to the MT ratio of skeletal muscle in the same MR image (Figure 4A) [20]. We found that the abundance difference of genus Oscillospira sp. ( abundance in a stenotic site minus the abundance in a non-stenotic site) was negatively associated with the normalized MT ratio of stenotic intestinal segments (correlation coefficient −0.511, $p \leq 0.05$) (Figure 4B and Supplementary Materials Table S1). In addition, a similar correlation analysis was performed considering histopathological characteristics and the six distinguishing genera. For stenotic intestinal segments, the degree of fibrosis and space volume expansion were separately assessed in every layer (Figure 4C). As expected, the abundance difference of the genus Oscillospira sp. was negatively correlated with the total fibrosis score (correlation coefficient −0.653, $p \leq 0.05$). Regarding the specific intestinal layers, significantly negative correlations mainly occurred on the fibrosis scores of the submucosa (correlation coefficient −0.564, $p \leq 0.05$) and muscularis propria (correlation coefficient −0.465, $p \leq 0.05$). With respect to space volume expansion, we learned that the genus Oscillospira sp. was negatively associated with the expansion score of the muscularis propria layer (correlation coefficient −0.520, $p \leq 0.05$) (Figure 4D). ## 3.4. Mucosa-Associated Oscillospira sp. in the Residual Intestine Contributed to the Maintenance of Disease Remission Given that the genus Oscillospira sp. was identified as the most distinguishing microbe between the stenotic sites and non-stenotic sites, we considered the relationship between this genus and postoperative disease recurrence. We measured the relative abundance of the genus Oscillospira sp. in non-stenotic sites and in the residual intestine. According to the relative abundance of Oscillospira sp., patients with CD were equally divided into two groups: the low abundance group, containing nine patients with a relative abundance of Oscillospira sp. ranging from 0 to $0.021\%$; and the high abundance group, containing other eleven patients with a relative abundance ranging from $0.0381\%$ to $4.6\%$. All patients were followed up to evaluate their postoperative course. Detailed information on bacterial abundance and follow-up data are summarized in Table 2. As shown in Figure 5, CD patients in the high abundance group were inclined to maintain disease remission for a longer period of time when compared to the low abundance group ($$p \leq 0.0426$$). ## 4. Discussion Microbial characteristics of the intestinal mucosa of a cohort of CD patients undergoing fibrosis-associated surgeries were identified in this study, and we found that some specific microbes might be involved in intestinal stricture. Paired comparison between stenotic and non-stenotic sites in the same patient avoided confounding factors such as individual lifestyle, dietary patterns, and drug consumption. In addition, mucosal colonization microbiota allowed us to identify the specific bacteria involved in the pathophysiology of CD. We concluded that the bacterial diversity of mucosa-associated microbiota in the stenotic segment was decreased, especially the relative abundance of Lactobacillus, Oscillospira, Subdoligranulum, Hydrogenophaga and Clostridium. Moreover, we discovered the genus Oscillospira sp. was negatively correlated with the degree of intestinal stricture and associated with the maintenance of disease remission during the postoperative time period. Although Oscillospira sp. has rarely been researched in CD, a meta-analysis reported its abundance was decreased in patients with CD, and it was negatively correlated with intestinal fibrogenesis in another animal study [31,32]. Here, we first searched on GMrepo, a curated database of human gut metagenomes (https://gmrepo.humangut.info/home (accessed on 1 October 2022)) and identified significantly decreased Oscillospira sp. in patients with CD when compared to the healthy controls (Supplementary Materials Figure S3) [33]. Moreover, we further ascertained that the reduction of Oscillospira sp. in the stenotic sites was more severe, indicating sustained Oscillospira sp. loss with the disease progress of CD. In addition, results from our study showed the decreased degree of Oscillospira sp. in stenotic sites was negatively correlated with the concentration of serum-free fatty acids, and positively correlated with intestinal fibrosis and some inflammatory biomarkers (ESR, WBC). To our knowledge, this was the first study to detect a negative correlation between Oscillospira sp. abundance and the degree of intestinal stricture/fibrosis in a CD patient cohort. The underlying mechanism of intestinal inflammation alleviation and fibrosis improvement might depend on the metabolite butyrate. The complete butyrate-kinase mediated pathway was identified in Oscillospira sp. via gene sequence information, indicating this germ might be a prospective butyrate producer and able to utilize gluconate originating from the host [34]. Oscillospira sp. ’s butyrate production ability and the anti-inflammatory/epithelial protective role of butyrate have been comprehensively studied, and it has been confirmed that butyrate significantly prevents neutrophils from producing proinflammatory cytokines and chemokines [35,36]. It suppresses neutrophil migration and decreases the formation of neutrophil extracellular traps [37]. In terms of the fibrosis mechanism, butyrate was shown to suppress the extracellular matrix gene expression and decrease the abundance of alpha-smooth muscle actin and collagen in a model of human intestinal organoids [38]. More importantly, Oscillospira sp. was identified to be associated with the maintenance of post-operative remission in our study. Operation-treated patients with a decreased abundance of Oscillospira sp. in stenotic sites or residual intestinal segments had a higher risk of disease recurrence. CD is an autoimmune disease, and relapsing and remitting inflammation persists for the patient’s whole life. It is well known that persistent and overwhelming inflammation plays a vital role in post-operative recurrence [39]. As mentioned above, Oscillospira sp. has anti-inflammatory properties through its production of butyrate. Decreased abundance of Oscillospira sp. in residual intestinal segments might indicate more intense inflammation in the post-operative period and lead to relapsing. In that case, active follow-up and examination of these patients are recommended, to ensure that they benefit from treatment. In this single-center study, the number of patients was small and we lacked validation of independent cohorts, but comparisons of paired samples helped to reduce selection bias. Consequently, further studies in Oscillospira sp. culturation are of great significance, and more detailed biological functions of Oscillospira sp. should be confirmed. Deeper verification of Oscillospira sp. ’s effects on inflammation alleviation and intestinal stricture is required. In brief, the findings from this research suggest a novel microbial-based target for disease management and therapy in CD. Supplementation of this promising microbe in post-operative CD may be a strategy to prevent or treat disease recurrence. ## 5. Conclusions The mucosa-associated microbiota in the stenotic sites was different from that in the non-stenotic sites. In addition, the distinguishing microorganisms were interrelated at different taxonomic levels. The genus Oscillospira sp. was negatively correlated with the degree of intestinal fibrosis and positively correlated with prolonged time of disease remission after surgery. Additionally, Oscillospira sp. also showed a positive correlation with the concentration of serum-free fatty acids and a negative association with serum inflammatory biomarkers. Oscillospira sp. regulates inflammatory and fibrosis processes as a butyrate producer. In general, our study indicated Oscillospira sp. could help to monitor the disease course and predict postoperative disease recurrence. Further studies on Oscillospira sp. culturation and biological functions are urgently needed, and its exact mechanisms in CD are of great significance. ## References 1. Cushing K., Higgins P.D.R.. Management of Crohn Disease: A Review. JAMA (2021) 325 69-80. DOI: 10.1001/jama.2020.18936 2. Lin S.N., Mao R., Qian C., Bettenworth D., Wang J., Li J., Bruining D.H., Jairath V., Feagan B.G., Chen M.H.. 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--- title: Habitual Tea Consumption Increases the Incidence of Metabolic Syndrome in Middle-Aged and Older Individuals authors: - Shasha Yu - Bo Wang - Guangxiao Li - Xiaofan Guo - Hongmei Yang - Yingxian Sun journal: Nutrients year: 2023 pmcid: PMC10055940 doi: 10.3390/nu15061448 license: CC BY 4.0 --- # Habitual Tea Consumption Increases the Incidence of Metabolic Syndrome in Middle-Aged and Older Individuals ## Abstract In middle-aged and elderly individuals, the relationship between tea consumption and incident metabolic syndrome (MetS) is still unclear. Therefore, this study intends to figure out the relationship between tea-drinking frequency and MetS in rural middle-aged and older Chinese residents. In the Northeast China Rural Cardiovascular Health Study, 3632 middle-aged or older individuals (mean age 57 ± 8, $55.2\%$ men) without MetS were included at baseline during 2012–2013 and were followed up on between 2015–2017. Participants showing differential tea consumption frequency were divided into the following classes: non-habitual tea drinkers, occasional tea drinkers, 1–2 times/day drinkers, and ≥3 times/day drinkers. Data showed that non-habitual tea drinking was more common among women. The frequency of tea consumption was higher in ethnic groups other than Han and among singles, as well as in concurrent smokers and drinkers and individuals with primary or lower educational status. The increasing tea consumption was in line with baseline elevations in body mass index, systolic and diastolic blood pressure, high-density lipoprotein cholesterol (HDL-C), and AST/ALT ratio. Multivariate logistic regression analysis confirmed that occasional tea drinking increased the incidence of low HDL-C [OR ($95\%$ CI): 1.268 (1.015, 1.584)], high waist circumference [OR ($95\%$ CI): 1.336 (1.102, 1.621)], and MetS [OR ($95\%$ CI): 1.284 (1.050, 1.570)]. In addition, 1–2 times/day tea drinking increased the cumulative incidence of high TG [OR ($95\%$ CI): 1.296 (1.040, 1.616)], high waist circumference [OR ($95\%$ CI): 1.296 (1.044, 1.609)] and MetS [OR ($95\%$ CI): 1.376 (1.030, 1.760)]. We demonstrated that regular tea consumption is correlated with a greater incidence of metabolic disorders and MetS. Our findings may help clarify the contradictory association reported between tea drinking and MetS development in middle-aged and older residents of rural China. ## 1. Introduction The adverse effects of a combination of cardiovascular risk factors associated with metabolic syndrome (MetS) on the cardiovascular system are higher than those of the sum of the individual risk factors [1]. MetS encompasses insulin resistance, hypertension, dyslipidemia, and abdominal obesity [2]. Long-term studies have correlated MetS with an increased frequency of cardiovascular events and cardiovascular mortality. However, the prevalence of MetS has markedly increased in recent years. In China, the prevalence rate of MetS in 2001 was $13.7\%$, which increased to $24.2\%$ in 2010–2012 [3,4]. According to the Adults Treatment Panel III criteria, the International Diabetes Federation criteria, and a harmonized definition, the prevalence rate of MetS in the overall rural population increased to $41.3\%$, $34.2\%$, and $44.1\%$, respectively, in 2017–2018 [5]. These prevalence rates were higher than those estimated by another study ($20.5\%$) performed in the rural areas of China [6]. In China, the proportion of aging individuals has markedly increased, which is referred to as the silver tsunami [7]. According to the statistics from the Seventh National Population Census in 2020, the proportion of individuals aged > 60 years increased from $13.26\%$ in 2010 to $18.70\%$ in 2020, while that of individuals aged > 65 years increased from $8.9\%$ in 2010 to $13.5\%$ in 2020. As the proportion of middle-aged and elderly individuals in the population has increased, the risk factors in various age groups must be quantified to mitigate and manage MetS. The Chinese were aware of the health-promoting and therapeutic benefits of tea 4000–5000 years ago. The health benefits of tea have been recorded in old medical texts, such as Shen Nong’s Herbal Classic. Tea has become one of the most consumed beverages in China, especially by middle-aged and elderly people. Previous studies have reported that frequent consumption of green tea exerts protective effects against cardiovascular diseases and metabolic disorders [8,9,10]. Additionally, various studies have reported that tea exerts several beneficial effects, such as antioxidant, anti-inflammatory, antibacterial, anticarcinogenic, antihypertensive, neuroprotective, cholesterol-lowering, and thermogenic effects [11,12]. The consumption of tea (Camelia sinensis L., green or black tea) is reported to decrease the risk of developing hypertension, dyslipidemia, diabetes, obesity, and MetS [13,14,15]. However, some studies have revealed a strong correlation between regular green tea consumption and an increased risk of developing MetS [16]. Therefore, the correlation between MetS and tea is controversial. Additionally, previous studies evaluating the effects of tea have only enrolled elderly participants. As both middle-aged and elderly subjects are keen on tea consumption, the correlation between tea consumption and MetS among middle-aged and elderly Chinese adults must be elucidated. Moreover, the effects of tea on subjects from rural areas have not been previously evaluated. Socioeconomic status (low annual income and educational status) and lifestyle habits (increased physical activity) are different among the inhabitants of rural areas, which may also affect the potential correlation between tea consumption and MetS. Previously, we demonstrated that village doctor-led interventions, including improving health knowledge, recommending healthy life habits, and monitoring the blood pressure (BP) of participants, markedly improved BP management among Chinese rural populations [17]. Understanding whether habitual tea consumption should be recommended to rural middle-aged and elderly individuals as an effective complementary non-pharmacological therapeutic approach for metabolic diseases is valuable and must be studied. Therefore, this study aimed to elucidate the correlation between tea consumption frequency and MetS in middle-aged and elderly residents of rural China to examine the effects of tea consumption on patients with metabolic disorders. ## 2.1. Study Design and Participants A community-based prospective cohort research termed the Northeast China Rural Cardiovascular Health Study (NCRCHS) was conducted in rural Northeast China. The specific sample techniques and admittance requirements have previously been described in good detail [16]. Multi-stage, stratified, and random cluster sampling were used in the study design. From three respectively directional areas of Liaoning Province, Dawa from the eastern area, Zhangwu from the southern area, and Liaoyang from the northern area were selected in the first stage. A town from each area was chosen randomly for the following phase (a total of three towns). The final step involved selecting 8–10 rural communities at random from each town. There was a total of 26 rural villages. Each village’s eligible permanent residents (35 years of age or over) were sent an invitation to participate in the research. Pregnant women, people with malignant tumors, and people with mental illnesses were ruled out from the study [18]. The current study was certified by China Medical University’s ethics committee (Shenyang, China AF-SDP-07-1, 0-01). The baseline data for our study were based on the 2012–2013 survey results, and a total of 3988 participants who did not have MetS (older than 45 years) were enrolled. They were divided into two groups according to age: a middle-age category (between 45 and 60 years) and an elderly category (older than 60 years). The cohort was followed up from 2015 to 2017 and the median follow-up was 4.66 years. As a result of missing crucial data during the follow-up period, 356 participants were excluded. Eventually, 3632 participants were enrolled, and detailed data were gathered from them. ## 2.2. Study Variables In light clothing and wearing no shoes, subjects’ height and weight were recorded. Utilizing non-elastic tape, the waist’s circumference was measured at the umbilicus. Body mass index (BMI) was calculated using the following equation: weight kgheight2 m. An electronic standardized automated manometer (HEM-907; Omron, Tokyo, Japan) was used to measure participants’ blood pressure three times while they were seated after at least five minutes of rest. Participants who had been fasting for at least 12 h had their blood drawn in the morning. Low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), creatinine and uric acid were examined enzymatically. An interview utilizing a standardized questionnaire was performed to gather comprehensive data on characteristics, lifestyle and dietary components, and medical history at the baseline. Tea consumption habit was divided into the following four groups: non-habitual tea drinkers, occasional tea drinkers, 1–2 times/day drinkers, and ≥3 times/day drinkers. To assess their dietary habits, participants were asked to recollect specifics of their food consumption during the previous year. Details on the typical weekly consumption of various foods were also requested on the questionnaire. The stated intake was roughly expressed in terms of grams per week. The following scale was used to evaluate vegetable consumption: rarely = 3, less than 1000 $g = 2$, more than 1000 $g = 1$, and more than 2000 $g = 0.$ Meat consumption, including red meat, fish, and poultry, was evaluated using the following scale: rarely = 0, less than 250 $g = 1$, more than 250 $g = 2$, and more than 500 $g = 3.$ A distinct diet score was assigned to each individual (meat consumption score plus vegetable consumption score, which ranges from 0–6). Lower diet scores implied adherence to the Chinese diet, while higher scores, which represented a greater meat intake and a lower vegetable intake, revealed more adherence to a Westernized diet. The same formulas for generating a diet were also employed in the ATTICA study [19]. Physical activity included occupational and leisure time physical activity. A detailed description of the methods for assessing physical activity has been presented elsewhere [20]. Participants self-reported their occupational physical activity according to the following three categories: light was physically very easy, sitting office work, e.g., secretary; moderate was work including standing and walking, e.g., store assistant; and active was work including walking and lifting, or heavy manual labour, e.g., industrial work, farm work. Self-reported leisure-time physical activity was classified into three categories: low was defined as almost completely inactive, e.g., reading, watching TV, or doing some minor physical activity but not of moderate or high level; moderate was doing some physical activity more than four hours a week, e.g., walking, cycling, etc.; and high was performing vigorous physical activity more than three hours a week, e.g., running, jogging, skiing, or regular exercise in competitive sports several times a week. Occupational and leisure-time physical activities were merged and regrouped into three categories: low was defined as subjects who reported light levels of both occupational and leisure-time physical activity; moderate was defined as subjects who reported moderate or high level of either occupational or leisure-time physical activity; and high was defined as subjects who reported a moderate or high level of both occupational and leisure-time physical activity. MetS was defined following the unifying criteria set by the consensus of some major organizations in 2009 [21]. The coexistence of any three of the following listed five risk factors confirms metabolic syndrome diagnosis. 1. Raised blood pressure (therapy with antihypertensives in a patient with a history of hypertension is an alternative indicator). 2. High fasting glucose (an alternate indicator is medication used to treat high glucose): 100 mg/dL. 3. Reduced HDL-C levels (drug therapy for reduced HDL-C is an alternative indicator): less than 40 mg/dL (1.0 mmol/L) in men; less than 50 mg/dL (1.3 mmol/L) in women. 4. Men’s elevated waist circumference is more than 90 cm; women’s elevated waist circumference is more than 80 cm (Asians, Japanese, South and Central Americans). 5. Triglyceride elevation: 150 mg/dL (1.7 mmol/L) (drug treatment for triglyceride elevation is an alternative indication). ## 2.3. Statistical Analysis Descriptive statistics were calculated for all the variables. Categorical variables were reported as numbers and percentages. Continuous variables were reported as mean values and standard deviations. Differences among categories were evaluated using t-test, ANOVA, non-parameter test, or the χ2-test as needed. After controlling for potential confounders, logistic regression analysis was conducted in order to estimate odds ratios (ORs) and $95\%$ confidence intervals (CIs) for the related factors of MetS. Software SPSS version 20.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analyses, and p values under 0.05 were regarded as statistically significant. ## 3. Results The baseline characteristics of the enrolled participants according to sex are listed in Table 1. At baseline, the average age of the study cohort was 57.04 years. The proportion of females in the study cohort was $44.7\%$. Current smokers and alcohol consumers constituted $40.7\%$ and $26.9\%$, respectively, of the study cohort. The number of current smokers and alcohol consumers among males was markedly higher than that among females. The proportion of married individuals among females ($99.6\%$) was higher than that among males ($98.4\%$). The frequency of primary and lower education among females ($62.6\%$) was higher than that among males ($47.3\%$). The duration of sleep varied between males and females. Females from rural areas tended to have a short sleep duration. The number of individuals with an annual income of >20,000 CNY/year among females ($30.2\%$) was higher than that among males ($28.6\%$). Additionally, the number of individuals involved in low levels of physical activity among females ($41.6\%$) was higher than that among males ($28.3\%$). The frequency of individuals with a diet score of >3 among females ($40.2\%$) was higher than that among males ($55.9\%$). This indicates that the frequency of individuals with western dietary habits among males was higher than that among females. Additionally, the levels of systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), estimated glomerular filtration rate (eGFR), body mass index (BMI), and uric acid were upregulated in males, whereas those of high-density lipoprotein-cholesterol (HDL-C) and aspartate transaminase (AST)/alanine transaminase (ALT) ratio were downregulated at baseline. Compared with those in females, the levels of SBP, FPG, DBP, BMI, and uric acid were higher and the levels of HDL-C, AST/ALT ratio, and eGFR were lower in males during follow-up. The cumulative incidence of MetS in the study cohort was $24.0\%$ ($22.2\%$ and $25.8\%$ in males and females, respectively; $$p \leq 0.203$$). Table 2 shows the baseline characteristics of the study cohort according to the frequency of tea consumption. The frequency of non-habitual tea consumption among females was higher than that among males. Compared with that in the Han ethnicity ($4.8\%$), the frequency of tea consumption was higher in ethnic groups other than Han ($10.5\%$). The increased numbers of individuals with a diet score of >3 and smoking and alcohol consumption habits were consistent with the increased frequency of tea consumption. Furthermore, the frequency of tea consumption was high among unmarried participants. Participants with increased tea consumption frequency were less likely to have a primary and lower education. The frequency of tea consumption was inversely and significantly correlated with the sleep duration of ≤7 h/day. Similarly, the proportion of participants with an annual income of >20,000 CNY/year was the highest among those who consume tea ≥ 3 times/day. The incidence rates of low physical activity among participants with increased frequency of tea consumption was lower than that among non-habitual tea drinkers. The increased frequency of tea consumption was consistent with the increased levels of SBP, DBP, BMI, HDL-C, and AST/ALT ratio at baseline. The levels of triglyceride (TG), uric acid, and eGFR at baseline were different from those during follow-up. The SBP, DBP, and BMI levels during follow-up and baseline were positively correlated with tea consumption frequency. Figure 1A shows the tea consumption frequency of the participants ($61.2\%$, $19.5\%$, $16.4\%$, and $2.9\%$ of the participants were non-habitual tea consumers, occasional tea consumers, consumed tea 1–2 times/day, and consumed tea ≥ 3 times/day, respectively). The frequency of metabolic diseases according to tea consumption is shown in Figure 1B. The cumulative incidence of high BP and TG significantly varied depending on the frequency of tea consumption. As shown in Figure 2, multivariate logistic regression analysis after adjusting for potential confounders revealed that occasional tea consumption increased the incidence of low HDL-C [odds ratio (OR) = 1.268; $95\%$ confidence interval (CI) = 1.015–1.584], high waist circumference [OR = 1.336; $95\%$ CI = 1.102–1.621], and MetS [OR = 1.284; $95\%$ CI = 1.050–1.570]. Additionally, tea consumption 1–2 times/day increased the cumulative incidence of high TG [OR = 1.296; $95\%$ CI = 1.040–1.616], high waist circumference [OR 1.296; $95\%$ CI = 1.044–1.609], and MetS [OR = 1.376; $95\%$ CI = 1.030–1.760]. The study participants were subdivided into two categories according to age. As shown in Figure 3, occasional tea consumption was associated with an increased risk of developing MetS [OR = 1.510; $95\%$ CI = 1.184–1.926], high waist circumference [OR = 1.372; $95\%$ CI = 1.088–1.731], low HDL-C [OR = 1.345; $95\%$ CI = 1.035–1.748], and high TG [OR = 1.445; $95\%$ CI = 1.139–1.834] among the middle-aged group. Among the elderly group, tea consumption 1–2 times/day was correlated with an increased risk of developing MetS [OR = 1.565; $95\%$ CI = 1.089–2.248] and high waist circumference [OR = 1.559; $95\%$ CI = 1.075–2.261]. ## 4. Discussion This prospective study demonstrated that the risk of developing MetS among participants who occasionally consumed tea or consumed tea 1–2 times/day was higher than that among participants who non-habitually consumed tea. Additionally, the frequency of tea consumption was positively correlated with the levels of waist circumference and inversely correlated with the levels of HDL-C. These results revealed the contradicting effects of tea consumption on the metabolic health of rural middle-aged and elderly individuals. In this study, habitual tea consumption was correlated with an increased risk of developing MetS. This is not consistent with the findings of several previous studies, which reported that tea consumption exerted beneficial effects on MetS and metabolic disorders [22,23]. Vernarelli et al. reported that Camellia sinensis consumption was inversely correlated with weight gain and other markers of MetS in the general population of the United States [24]. Similarly, a study conducted in a rural community in Taiwan reported that partially fermented tea consumption was inversely correlated with the incidence of MetS among elderly males, especially among those who consumed ≥240 mL of tea per day [23]. A cross-sectional study with adults from the United States revealed that the consumption of hot (brewed) tea rather than iced tea was inversely correlated with MetS [24]. In addition to findings from individual studies, systematic reviews and meta-analyses examining more than 26 randomized controlled trials (RCTs) revealed that tea consumption can alleviate the symptoms of MetS [25,26]. A systemic review of the results of eight studies demonstrated that frequent tea consumption exerts health-promoting effects and alleviates various metabolic disorders [14]. Daily consumption of green tea was demonstrated to exert beneficial health effects. Green tea is reported to inhibit lipid emulsification, suppress adipocyte differentiation, promote thermogenesis, and decrease appetite, improving systemic metabolism and decreasing fat mass [27]. Additionally, other molecules, such as theaflavins, catechins and their metabolites, and polyphenols have been implicated in the protective functions of tea against several metabolic disorders, including MetS, cardiovascular diseases, diabetes, and obesity [28]. Tea can also effectively reduce appetite, food consumption, and nutrient absorption in the gastrointestinal system, as well as alter fat metabolism [29]. In contrast to the positive impacts mentioned above, one study in England reported that the daily consumption of green tea along with high-dose caffeine did not exert therapeutic effects on metabolic disorders within 12 weeks [30]. The reasons for these conflicting results are unknown and may be related to the differential physiological and dietary conditions of different populations. According to one theory, tea extracts increase the serum uric acid levels in healthy individuals but downregulate their levels in individuals with hyperuricemia [31]. A systematic review and meta-analysis demonstrated that green tea consumption was positively correlated with the serum uric acid levels [32]. Additionally, increased uric acid levels were independently correlated with a high risk of incident MetS in the general population [33,34,35]. The potential underlying mechanism may involve uric acid-induced mitochondrial oxidative stress, which can alter AKT phosphorylation and modulate endothelial function. Moreover, uric acid is involved in the activation of the renin-angiotensin system [36,37,38,39]. According to a speculative hypothesis, the constituents of tea are complex, and some unknown constituents can upregulate the serum uric acid levels. Another possible reason may involve the dual effect of polyphenols on the serum uric acid levels. Tea extracts exert adverse effects on serum uric acid levels in healthy individuals but exert beneficial effects in individuals with hyperuricemia [31]. The findings of this study indicate that elderly and middle-aged participants who frequently consumed tea, especially those who consumed tea 1–2 times/day, were at risk to develop MetS. One study indicated that the probability of developing MetS was high among the elderly who frequently consume green tea [16]. These deleterious effects of tea consumption may be attributed to the increased excretion of urinary oxalic acid, which leads to the formation of renal stones and results in a burden on circulation [40]. However, information on tea consumption was obtained based on the recall by participants, which may have led to misclassification and information bias. This further complicates the elucidation of the correlation between tea consumption and MetS [16]. Moreover, most of these studies that confirmed the beneficial effects of tea on metabolic disorders involved participants who regularly and persistently consumed green tea or green tea extracts for ≥8–12 weeks [41,42]. Except for the regular and persistent consumption of tea or tea extracts, the amount of tea ingested should be warranted too. Recent RCTs demonstrated that the consumption of 458–886 mg of green tea catechins daily for 90 days decreased body fat in moderately overweight Chinese participants [43]. However, a recent RCT involving obese Caucasian females reported that the supplementation of 200 mg of green tea extract per day for 12 weeks did not affect various parameters, including BMI, energy and fat metabolism, insulin sensitivity, total or low-density lipoprotein (LDL)-cholesterol levels [44]. The use of small amounts of green tea extracts may not be sufficient to exert beneficial effects. Similar to the correlation between frequent tea consumption and MetS, some studies reported that the consumption of oolong and green tea increased the risk of developing type 2 diabetes (T2D) in Asian participants [45,46]. The beneficial effects of tea consumption on T2D were attributed to pesticide residues, which is supported by several epidemiological and experimental studies [46,47,48,49]. The quantity of tea consumption was correlated with the serum concentration of total organochlorine pesticides [50]. Some studies have reported a negative correlation between tea consumption and BP. For example, the consumption of green tea decoction was associated with a mild increase in BP and a marked decrease in heart rate in 76 females enrolled in an observational study [51]. Moreover, in a controversial trial with 29 elderly individuals, the consumption of green tea before lunch markedly increased SBP and DBP without affecting heart rate. Green tea was initially used to treat postprandial hypotension in elderly patients [52]. To explain tea-induced BP elevation, large amounts of green tea intake were speculated to be associated with an increased risk of developing hyperhomocysteinemia (HHcy). HHcy is demonstrated to be an independent risk factor for hypertension among residents of rural China [53,54]. The potential underlying mechanisms may involve green-tea-induced arteriolar constriction, renal dysfunction, and sodium reabsorption [55]. In addition to increasing the risk of hypertension, HHcy increases the risk of dyslipidemia. This can be partially explained by HHcy-associated hypomethylation, which results in lipid accumulation and the downregulation of the synthesis of phosphatidylcholine that is required for very low-density lipoprotein assembly and homeostasis [56]. Low HDL-C levels are attributed to the homocysteine-induced inhibition of enzymes or molecules involved in HDL particle assembly [57]. Previous studies have elucidated the correlation between HHcy, metabolic disorders, and MetS. However, the underlying mechanisms have not been elucidated. Several possible speculations have been proposed to offer some mechanistic insights. HHcy is associated with various pathological effects, including vascular damage, cytotoxicity [58], neuronal apoptosis [59], oxidative stress-induced DNA damage [60], alterations in DNA methylation [61,62], and endothelial nitric oxide production [63]. The role of homocysteine in the development of metabolic disease is unclear. Furthermore, we suggest that the contradictory findings may be attributed to the relatively high prevalence of drinking and smoking among habitual tea consumers. In China, tea consumption is a social event, and tea is the preferred drink among friends after alcohol consumption to relieve the discomforts associated with alcohol consumption. Similarly, the prevalence of smoking is high among Chinese tea consumers. These factors contributed to the increased rates of concurrent smoking and drinking among individuals consuming tea. Therefore, it must be verified if tea consumers in the study cohort constituted a small group of unhealthy subjects with a relatively high rate of smoking and alcohol consumption. These findings cannot be widely extrapolated to other populations because the study cohort was not representative. Further studies are needed to confirm the findings of this study. At baseline, the levels of SBP, DBP, BMI, and uric acid in habitual tea consumers were significantly higher than those in non-habitual tea consumers (Table 1). The combination of an unhealthy lifestyle (e.g., smoking cigarettes, drinking alcohol, and lack of leisure time exercise) and poor health status of tea drinkers relative to non-drinkers also supports this positive association [64,65,66]. In addition to poor lifestyle habits, such as smoking and drinking, dietary patterns may also be crucial factors regulating the development of MetS. Previous studies have demonstrated that the consumption of a vegetable-rich diet is associated with a decreased risk of developing metabolic disorders [67,68]. Moreover, prospective studies have reported an inverse correlation between chronic disease and plant protein intake. In contrast, the consumption of animal protein was positively correlated with the risk of developing hypertension, diabetes, and heart disease [20,69,70]. Potential mechanisms underlying these correlations have been listed below. First, the predominant amino acids vary between animal proteins and vegetable proteins. Glycine, which is predominant in animal proteins, increases BP and is associated with the risk of developing diabetes [71,72,73]. Second, the high sodium content of processed meats may be associated with hypertension and the risk of developing coronary heart disease [74,75]. In addition to high contents of sodium, nitrites in processed meats, which can be converted into nitrosamines, are toxic to pancreatic beta cells and increase the risk of diabetes in animals [76,77]. The blood nitrite concentrations can induce endothelial dysfunction [78] and insulin resistance [79] in humans. Third, advanced glycation end-products [80] or elevated inflammatory mediators [81] and gamma-glutamyltransferase [82] associated with high red meat intake can increase the risk of developing diabetes and coronary heart disease [83,84,85,86,87,88,89,90]. Finally, dietary iron, especially the heme-iron from red meat, is positively associated with diabetes, myocardial infarction, and fatal coronary heart disease [91,92,93,94,95,96,97]. Iron is responsible for catalyzing several cellular reactions, leading to the production of reactive oxygen species and induction of oxidative stress, which damages the pancreatic beta cells, increases the risk of developing diabetes, and exacerbates myocardial damage [98,99,100,101]. This study demonstrated that the increase in the frequency of tea consumption from non-habitual tea consumption to tea consumption for ≥3 times/day, western dietary habits, and increased meat consumption may affect the correlation between tea consumption and MetS. However, diet patterns were not significant factors after accounting for potential confounders. This may be related to the simplistic analysis of a diet pattern that only included the consumption of meat and vegetables. This study did not categorize animal protein into specific types because not all foods high in animal protein were equal. Red meat is positively correlated with metabolic risk factors, whereas fish and dairy are inversely correlated [102]. Further studies are needed to elucidate the relevance of dietary habits in the correlation between tea consumption and MetS. Moreover, this study demonstrated that tea consumption was associated with MetS-related parameters (high TG, low HDL-C, and increased waist circumference). Xu et al. conducted a meta-analysis and reported that the consumption of green tea decreases the levels of LDL-C and total cholesterol but not those of HDL-C or TGs [103]. However, one systemic review of 14 unique RCTs reported that short-term (2–24 weeks) tea consumption did not significantly affect lipids in healthy or at-risk adults [104]. Recently, Huang et al. demonstrated that tea consumption delayed the aging-related decline in HDL concentrations after a six-year follow-up [19]. In this study, information on the tea consumption frequency in the past two weeks was obtained based on recall by the participants. This may have resulted in bias and consequently affected the conclusion of the study. Previous studies have indicated that the consumption of ≥3–4 cups of tea (600–900 mg tea catechins) alleviated MetS and decreased the risk of developing diabetes and cardiovascular diseases [13]. Therefore, the frequency of tea consumption may not be precise to establish the effect of tea on metabolic disorders. Several studies have demonstrated that tea consumption is frequently associated with decreased waist circumference [105]. These discrepant results may be due to the positive correlation between baseline BMI and tea consumption frequency. Tea consumption with meals decreases the absorption of non-heme iron, as tea contains tannins [106]. Black tea is reported to reduce the bioavailability of non-heme iron (Fe) by approximately 79–$94\%$. Similarly, green tea catechins have a strong affinity for Fe because their infusions significantly reduce the bioavailability of Fe from food [11]. A recent quantitative meta-analysis examining 26 cross-sectional and case-control studies revealed that iron deficiency is significantly associated with overweight and obesity. This partially explains the reason for the positive correlation between tea consumption frequency and the risk of developing abdominal obesity [107]. Tea consumption is believed to be an effective habit with beneficial effects on hypertension, diabetes, dyslipidemia, obesity, and other metabolic disorders. Tea consumption is prevalent throughout China irrespective of rural or urban areas. However, the results of this study revealed that tea consumption increased the risk of MetS. This may be because of the increased prevalence of accompanying unhealthy lifestyle habits, such as smoking, drinking, and the increased levels of other parameters at baseline that increase the susceptibility to metabolic disorders. Therefore, non-pharmacological strategies must be carefully assessed before their application in rural inhabitants, especially middle-aged and elderly individuals. Only middle-aged and geriatric Chinese individuals residing in Northeast China were eligible to participate in this study, which contributed to the inverse correlation between the incidence of MetS and the frequency of habitual tea consumption determined in this study. Therefore, the findings of this study may not be applicable to other populations with different demographics. Additionally, participants in this study were enrolled from rural Northeast China. This study cohort is not representative of the general population of China. Elderly individuals who frequently consumed tea in this study were a small group of non-healthy subjects with a high frequency of current smoking and alcohol consumption habits. Therefore, this is a non-representative group, which may lead to misclassification and information bias and may have distorted the effect estimates of tea consumption on MetS. Some bias was expected in the assessment because tea consumption was assessed based on self-reporting rather than through direct measurement. The potential presence of residual and unmeasured confounding factors, such as chronic illnesses, medication use, and malnutrition may increase the probability of developing MetS. Thus, the correlation reported in this study may not be accurate even though conditions were addressed for various potential confounders. This study only evaluated the frequency of tea consumption. However, information on the method used for tea preparation, the container used for tea consumption, the addition of sugar to tea, and the amount of tea consumed was not obtained. Thus, the description of the tea consumption behavior may be inaccurate. Fifth, the type of tea consumed may exert differential effects on metabolic disorders. Green tea is known to irritate the gastrointestinal tract, especially when ingested in large amounts on an empty stomach. Black tea is a gentle drink (less unpleasant) but its health benefits are limited. The composition and health benefits of dark tea, which is regarded as a mild drink, must be comprehensively investigated in the future. This study did not assess the specific types of tea consumed by the participants, which can affect the correlation between tea consumption and MetS. Additionally, comprehensive data on the intake of diets, such as dairy products, whole grains, fruits, or nuts were not available in this study. However, multivariate logistic regression analysis was performed after adjusting for some diet-related factors, including vegetable and meat consumption scores, which may partially mitigate the confounding effect of diet. ## 5. Conclusions Our research concluded that among middle-aged and elderly rural Chinese participants, occasionally drinking and 1–2 times/day drinking tea is linked to a higher risk of developing MetS. Additional research is required to clarify the paradoxical association between tea consumption and MetS described in the literature and to identify potential mechanisms underlying the adverse effects of tea consumption. ## References 1. Yao F., Bo Y., Zhao L., Li Y., Ju L., Fang H., Piao W., Yu D., Lao X.. Prevalence and Influencing Factors of Metabolic Syndrome among Adults in China from 2015 to 2017. Nutrients (2021) 13. DOI: 10.3390/nu13124475 2. Özmen M., Yersal Ö., Öztürk S., Soysal D., Köseeoğlu M.. Prevalence of the metabolic syndrome in rheumatoid arthritis. 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--- title: Thermogenic Capacity of Human Supraclavicular Brown Fat and Cold-Stimulated Brain Glucose Metabolism authors: - Mueez U-Din - Eleni Rebelos - Teemu Saari - Tarja Niemi - Katharina Kuellmer - Olli Eskola - Tobias Fromme - Johan Rajander - Markku Taittonen - Martin Klingenspor - Pirjo Nuutila - Lauri Nummenmaa - Kirsi A. Virtanen journal: Metabolites year: 2023 pmcid: PMC10055954 doi: 10.3390/metabo13030387 license: CC BY 4.0 --- # Thermogenic Capacity of Human Supraclavicular Brown Fat and Cold-Stimulated Brain Glucose Metabolism ## Abstract Human brain metabolism is susceptible to temperature changes. It has been suggested that the supraclavicular brown adipose tissue (BAT) protects the brain from these fluctuations by regulating heat production through the presence of uncoupling protein 1 (UCP-1). It remains unsolved whether inter-individual variation in the expression of UCP-1, which represents the thermogenic capacity of the supraclavicular BAT, is linked with brain metabolism during cold stress. Ten healthy human participants underwent 18F-FDG PET scanning of the brain under cold stimulus to determine brain glucose uptake (BGU). On a separate day, an excision biopsy of the supraclavicular fat—the fat proximal to the carotid arteries supplying the brain with warm blood—was performed to determine the mRNA expression of the thermogenic protein UCP-1. Expression of UCP-1 in supraclavicular BAT was directly related to the whole brain glucose uptake rate determined under cold stimulation (rho = 0.71, $$p \leq 0.03$$). In sub-compartmental brain analysis, UCP-1 expression in supraclavicular BAT was directly related to cold-stimulated glucose uptake rates in the hypothalamus, medulla, midbrain, limbic system, frontal lobe, occipital lobe, and parietal lobe (all rho ≥ 0.67, $p \leq 0.05$). These relationships were independent of body mass index and age. When analysing gene expressions of BAT secretome, we found a positive correlation between cold-stimulated BGU and DIO2. These findings provide evidence of functional links between brain metabolism under cold stimulation and UCP-1 and DIO2 expressions in BAT in humans. More research is needed to evaluate the importance of these findings in clinical outcomes, for instance, in examining the supporting role of BAT in cognitive functions under cold stress. ## 1. Introduction The maintenance of bodily temperatures is necessary for the optimal performance of systemic metabolic processes for the endothermic animal species. The brain, being the master regulator of all bodily functions, also requires an optimal working temperature, and the metabolism of the brain changes linearly with the changes in intracranial temperatures, with a 6–$8\%$ change in every 1-degree Celsius change [1,2]. The neural activity also fluctuates with the changing intracranial temperatures in numerous mammals, including cats, monkeys, and rats [3,4,5]. Brain temperature is dependent on both the heat loss and the local heat production and the temperature of the arterial blood supplied to it, while the latter is found to be the major determinant in endothermic animals [6,7]. The local heat production in the brain depends on the rate of substrate utilisation and oxygen consumption, while the blood is supplied via the internal carotid arteries and vertebral arteries, which both arise from common carotid arteries. The location of these arteries, supplying the blood to the brain, is in proximity to the brown adipose tissue (BAT) depots in the neck and supraclavicular region. BAT, predominantly a thermogenic organ, plays a role in regulating body temperature, as evident from the fact that it is hypermetabolic under cold stress [8,9]. The supraclavicular location of BAT has been speculated to be of functional relevance for the brain, but so far, no data exist that may corroborate this hypothesis. BAT produces heat due to the presence of a unique protein, uncoupling protein-1 (UCP-1), present in the inner mitochondrial membrane. The extent of expression of this protein in BAT likely represents its heat-generating capacity; since it has been seen that UCP-1 is the only protein able to translocate protons via the inner membrane of mitochondria [10]. BAT metabolism is heterogenous in various populations [11], and previously, we have found that the metabolism of BAT is blunted in obesity [12,13]. Thus, it is unknown how inter-individual variation in BAT metabolic capacity may influence brain metabolism, particularly during hypothermic situations. Based on this, it is reasonable to hypothesise that BAT thermogenic capacity, as determined via UCP-1, is linked to brain metabolism under mild cold stress in healthy humans. We have previously shown that the supraclavicular BAT glucose uptake (GU) rate is directly related to the brain glucose uptake (BGU) rate during cold stress [14], suggesting that the brain plays a role in regulating BAT metabolism. Given BAT’s possible role in endocrine signalling [15], the association of BAT’s heat-generating capacity or its secretome to the brain metabolism have not been evaluated. Therefore, in this study, as an extension of previous work, we examined the mRNA expression of the heat-producing protein UCP-1 in BAT, as well as those linked to BAT secretome profile [15] and investigated their links with cold-stimulated brain glucose metabolism. ## 2.1. Human Study Participants and Study Design Ten volunteers were recruited from Turku, Finland, via electronic and newspaper advertisements. All subjects were healthy as determined by laboratory tests, a 2-h oral glucose tolerance test, electrocardiography, blood pressure measurements and medical history. A hyperinsulinemic-euglycemic clamp technique was also used to measure whole-body insulin sensitivity (M-value). The anthropometric characteristics of the study participants have been given in Table 1. Written informed consent was obtained from all subjects prior to inclusion. The study protocol was approved by the Ethics committee of the hospital district of Southwest Finland. The study was carried out according to the principles of the Declaration of Helsinki and GCP guidelines. ## 2.2. Brain PET Imaging and Analysis Positron emission tomography (PET) was performed under fasting conditions. Before PET imaging, study participants spent 2 h in a room with an ambient temperature of 17 °C. During the imaging session, the participants were placed supine in the PET-CT scanner and the right foot of the participants was immersed in cold water (8 °C) in intervals of 5 min (5 min in water and 5 min out of water—Figure 1). Dynamic brain PET-image acquisition (5 × 3 min frames) took place ~90 min after a bolus injection of (18F) 2-fluoro-2-deoxy-D-glucose (18F-FDG). For emission data acquisition, GE Discovery VCT (General Electric Medical Systems, Milwaukee, WI, USA) and ECAT EXACT HR+ scanners (Siemens/CTI, Knoxville, TN, USA) were used. The PET data were corrected for photon attenuation, physical decay, dead time, scatter, and random coincidences, and PET images were reconstructed with a matrix size of 128 × 128 using iterative reconstructions. Voxel-based mapping of BGU was performed with SPM8 (http://www.fil.ion.ucl.ac.uk/spm/). Parametric BGU images were calculated voxel by voxel. The dynamic brain PET images were summed and normalized into MNI standard space; subsequently, these parameters were applied to the parametric BGU images. Spatially normalized BGU images were smoothed with a 12-mm full width at a half-maximum Gaussian kernel. Linear regressions were performed in SPM to evaluate correlations between BGU and single regressors (BAT UCP-1 expression, BAT secretomes gene expression, and BAT-probability score). A statistical threshold of $p \leq 0.05$ with false discovery rate correction at cluster levels was applied in all analyses. BGU values were also generated and extracted for the following regions of interest: frontal cortex, parietal cortex, temporal cortex, occipital cortex, limbic lobe, pons, sub lobar regions, hypothalamus, and cerebellum using the WFU Pickatlas tool (Wake Forest University Health Sciences Center, Winston-Salem, NC 27157, USA). ## 2.3. Adipose Tissue Biopsies BAT and WAT tissue samples were collected from participants under local lidocaine-epinephrine anaesthesia. The site of the supraclavicular BAT biopsy was determined from imaging data, and subcutaneous WAT was obtained from the same opening. An experienced plastic surgeon carried out this procedure. Immediately after being collected, the samples were snap-frozen in liquid nitrogen. The tissue samples were acquired at room temperature. RNA isolation and next-generation sequencing were performed as previously described [16]. ## 2.4. Statistical Analyses Statistical analyses were performed using IBM SPSS statistics (version 28.0.1.0), and the graphical plots and heatmaps were created using Graphpad Prism (version 6.01) and R-studio (version 1.2.5042), respectively. For paired sample comparison (gene expression in BAT vs. WAT), a paired t-test was used for normally distributed datasets, and where datasets did not show a normal distribution, a Wilcoxon rank-sum test was used. In correlation analyses, Spearman’s correlation test was used. Brain analysis at the voxel and cluster level was performed with statistical parametric mapping (SPM) running on Matlab for windows (version 9.1.0; Math Works, Natick, MA, USA). A p-value of <0.05 was considered significant. ## 3.1. Supraclavicular BAT UCP-1 Expression and Cold-Stimulated Brain Glucose Uptake The whole-brain GU rate under cold stress ranged from 29.6 to 44.3 µmol 100 g−1 min−1 (mean ± SD: 36.3 ± 5.1 µmol 100 g−1 min−1, median: 34.6 µmol 100 g−1 min−1), showing a certain degree of inter-individual variability in a healthy population. The GU in the whole brain and individual brain compartments have been shown in Figure 2A. The whole brain GU, as well as GU in sub-compartments in the brain, were not related to BMI or age in Spearman’s correlation analysis (BMI: rho = −0.38, $$p \leq 0.33$$; age: rho = −0.21, $$p \leq 0.56$$). The gene expression of UCP-1 was significantly higher in the supraclavicular fat depot (BAT) compared to neck subcutaneous white adipose tissue (WAT), confirming the presence of thermogenic adipose tissue in the supraclavicular depot (Figure 2B). The expression of the UCP-1 gene in the supraclavicular region was found to be directly related to whole-brain GU (Figure 2C,D), as well as to the cold stimulated GU of the frontal lobe, parietal lobe, limbic lobe, occipital lobe, hypothalamus, midbrain, and medulla (Figure 2D). There was a tendency toward significance for the cold-stimulated temporal lobe GU (Figure 2D). There was no significant relationship with cold-stimulated GU of the sub lobar region, pons, and cerebellum (Figure 2D). The relationships between BGU and BAT expression of UCP-1 remained significant once adjusted for either BMI or age in nonparametric partial correlation analysis (Table 2). There was no relationship between the expression of UCP-1 in subcutaneous WAT and BGU. ## 3.2. Supraclavicular BAT Secretome Genes, BAT-Probability Score and Brain Glucose Uptake BAT has been suggested to have endocrine effects on several tissues via secretory factors; we investigated the expression of genes that may hold relevance to brain metabolism and investigated their relationship to cold-stimulated brain glucose metabolism. We examined the expressions of the genes encoding Bone Morphogenetic Protein 8b (BMP8B), Nerve growth factor (NGF), Neurotrophin-3 (NTF3), Type II iodothyronine deiodinase (DIO2), Interleukin 6 (IL-6) and S100 calcium-binding protein B (S100B) in both supraclavicular BAT and subcutaneous neck WAT. Further, we also calculated the BAT-probability score using the ProFAT analysis tool [17] based on the transcriptional profile of the BAT samples from the supraclavicular region. The expression of BMP8B and DIO2 was significantly higher in BAT as compared to WAT, while no significant differences were observed in the expression of NRG4, S100B, NGF, NTF3 and IL6. The expression of DIO2 was directly related to the whole brain GU (rho = 0.66, $$p \leq 0.038$$), as well as to the GU of several sub-compartments of the brain, including the hypothalamus, medulla, midbrain, limbic lobe, frontal lobe, and parietal lobe (Figure 3A,B). There were no significant relationships between the expression of these genes in WAT and BGU. BAT-probability score was also significantly related to whole-BGU (Figure 3A,C) and to the GU of several sub-compartments of the brain, including the hypothalamus, medulla, midbrain, sub lobar, frontal lobe, limbic lobe, occipital lobe, temporal lobe and parietal lobe (Figure 3A). ## 4. Discussion The main findings of the present study are that BGU during cold stress correlates with the expression of UCP-1 in supraclavicular BAT, showing a physiological connection between the brain and BAT. Moreover, when assessing the BAT secretome genes, we found that the expression of the gene encoding iodothyronine deiodinase 2 (DIO2) was also associated with BGU. Also, the BAT-probability score, based on the transcriptional profile of excised BAT from the supraclavicular region, was found to be strongly linked to cold-stimulated BGU. These results require elucidation in the context of previous findings on this topic. In our previous publication by Orava et al. [ 2014], we have shown that cold stimulation increases BGU, and it correlates positively with cold-stimulated BAT GU [14]. Thus, in the present investigation, we pursued to assess whether BAT’s thermogenic capacity, as indicated by the expression of UCP-1, is also associated with BGU. Indeed, we found a direct relationship between cold-stimulated BGU and UCP-1, suggesting that not only are the two tissues metabolically linked during cold stress [14] but also that brain metabolism under mild cold stress is linked with BAT capacity to generate heat in healthy adults. The brain areas significantly associated with UCP-1 expression of BAT are shown in Figure 2C and are listed in Table 2, including the frontal, parietal, limbic, and occipital lobes, as well as the hypothalamus, midbrain, and medulla. The brain operates at a slightly warmer temperature than the rest of the body; Soukup et al. [ 2004] show that the temperature of deeper brain regions, on average, is approximately 1 °C higher than the rest of the body [18]. Physiological fluctuations in the temperature range of 2–4 °C have been observed in the brains of various endotherms [4,19]. Variations in brain temperature affect the metabolic rates of the brain through multiple mechanisms [20]. As the brain temperature is predominantly dependent on the temperature of the carotid blood supply [7], the functional importance of BAT in providing the brain with warm blood has been long speculated. Our results showing a direct link between BAT thermogenic capacity and BGU in cold conditions could be taken as evidence of the critical role of BAT in this physiological regulation. The hypothalamus, specifically the preoptic anterior hypothalamus, is considered the coordinating integration centre for thermoregulation [21,22,23]. Although quantifying hypothalamic metabolism with PET is difficult [24], our data suggest a significant role of the hypothalamus in the BAT-brain connection but also reveal that several other brain regions are involved as well. Indeed, important nodes of the thermoregulation network are also considered the periaqueductal grey in the midbrain and the nucleus raphe pallidus in the medulla [25], with midbrain and medulla GU rates in the present study being significantly associated with BAT UCP-1 expression. Whether cortical regions also participate in thermoregulation is less clear. However, since 18F-FDG-PET indexes brain substrate metabolism rather than neuronal firings, the positive association between BAT UCP-1 expression and cortical GU rates may indicate either the participation of cortical regions in the thermoregulatory control or that the metabolic rates in the cortical region, as in other regions, are linked to the extent of heat generating ability of BAT during cold stimulation. The relationship between the expression of DIO2 in BAT and cold-stimulated BGU is also of significant interest. DIO2 catalyses the transformation of the pre-hormone thyroxine (T4) to 3,5,3′-triiodothyronine (T3). In humans, DIO2 is expressed in the brain, BAT, uterus, placenta, thyroid gland, and heart. Under normal conditions, the primary circulating thyroid hormone is T4, and DIO2 contributes largely to circulating T3 and fulfills the intracellular needs of thyroid hormones. Considering the location of BAT is in close proximity to the brain, and the finding of a positive link between BAT expression of DIO2 and BGU, it is tempting to speculate that the increased expression of DIO2 in BAT may serve to provide T3 to the brain, along with the warm blood (Figure 4). This mechanism could be essential for supporting the T3 needs of the brain during cold stress [26] via circulation [27,28] or providing thermogenic feedback communication to the brain [29]. Previous studies have shown that T3 mediates glucose metabolism in brain astrocytes, as the primary cultures of rodents devoid of L-T3 have low rates of 2-deoxyglucose uptake [30], and when astrocytes are replenished with L-T3 the 2-deoxyglucose uptake is enhanced, increasing the binding sites of the glucose transporter [31]. As 18F-FDG uptake in the brain is mainly driven by astrocytes rather than neurons [32], it is possible that systemic DIO2 expression plays a role in regulating BGU, particularly in the challenging situation of mitigating hypothermia. However, it cannot be ruled out that T3 generated by BAT could be merely to support UCP-1 activation [33,34,35], or it may also act as a communication feedback loop between BAT and the brain to regulate thermogenesis. In the same context, López et al. [ 2010] have noticed that central administration of T3 in rats leads to the activation of SNS and BAT. Also, in humans, we have noticed that hyperthyroidism leads to upregulated BAT metabolism [36]. The plausibility of the co-occurrence of these mechanisms also exists. We also found a link between BAT gene expressions, BAT-probability score, and cold-stimulated glucose uptake of the brain, further strengthening the hypothesis of a metabolic link from the supraclavicular BAT to the brain. Previously, our institute, in collaboration with Klingenspor’s laboratory, has concretely shown that the gut hormone secretin activates BAT and induces satiation via BAT-to-Brain communication [37,38]. This communication between the BAT and brain to induce satiation likely happens through BAT’s heat generation in the postprandial state [16]. Our data suggest that BAT-to-brain communication may also occur during other physiological conditions, i.e., cold. These findings support the hypothesized functional BAT-brain axis, where BAT may communicate to the brain via heat production or via secreted factors. In this context, BAT may exert a positive influence to support glucose metabolism in the brain during cold stress, in line with our previous observations that BAT protects the brain from cognitive degeneration associated with cardiometabolic risk [39]. Our results have further implications, where data suggests that brain cognitive performance is adversely affected by frequently changing air temperatures [40], implying that the ability to maintain a stable brain temperature may have a link to cognitive performance. In a similar standpoint, and in the context of our findings, it can be speculated that healthy human adults with the capacity to actively regulate brain temperatures via active BAT depots may have better cognitive ability in cold environments. These findings may find applications in soldiers, divers, fighter jet pilots, astronauts, and other professionals who need to make intelligent decisions in fluctuating cold weather environments. The recruitment and activation of brown adipose tissue via BAT-selective pharmacological agents can be a meaningful approach for enhancing cognitive performance under cold stress; future studies are needed to systematically test this hypothesis. The novelty of this study consists of the combination of human brain imaging and human BAT excision biopsy measurements to correlate brain metabolism with UCP-1 expression and BAT’s secretome genes. Nevertheless, our study is not without limitations. The study is cross-sectional; hence, the causal links between BAT thermogenic capacity and BGU cannot be determined. Additionally, due to ethical constraints, the tissue excision from the supraclavicular fat depot was done without exposing the study participants to cold, limiting us from assessing the expression of genes under cold stress. Further, due to a lack of technological advancement in the robust measurements of intracranial temperatures, it remains unclear in humans whether glucose metabolism in the brain is associated with intracranial temperatures. Future studies in humans should focus on measuring intracranial brain temperature and brain substrate metabolism simultaneously to fully understand how variations in brain temperature affect the uptake and metabolism of different nutrients. However, the results shown here align with other published literature, and they advance our current understanding of the role BAT thermogenic function has in aiding brain metabolism in adult humans. Finally, hypothalamic GU values should be considered with caution since the assessment of tiny brain regions is hampered by the spatial resolution of the PET (6–8 mm) [24]. ## 5. Conclusions Altogether, our results reinforce the hypothesis that supraclavicular BAT in adult humans provides physiological assistance to the brain metabolism during cold stress. The support of the BAT to the brain metabolism is via its heat-generating properties and likely also through its secretome. Future studies are required that may elucidate whether or not adult humans with a substantial amount of BAT have a superior cognitive performance during cold stress. ## References 1. Lanier W.L.. Cerebral Metabolic Rate and Hypothermia: Their Relationship with Ischemic Neurologic Injury. J. Neurosurg. Anesthesiol. (1995) 7 216-221. DOI: 10.1097/00008506-199507000-00021 2. Brooks V.B.. Study of Brain Function by Local, Reversible Cooling. Rev. Physiol. Biochem. Pharmacol. (1983) 95 1-109. DOI: 10.1007/BFB0034097 3. Delgado J.M., Hanai T.. Intracerebral Temperatures in Free-Moving Cats. Am. J. Physiol. (1966) 211 755-769. DOI: 10.1152/ajplegacy.1966.211.3.755 4. Serota H.M., Gerard R.W.. 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--- title: 'MaestraNatura Reveals Its Effectiveness in Acquiring Nutritional Knowledge and Skills: Bridging the Gap between Girls and Boys from Primary School' authors: - Rosaria Varì - Annalisa Silenzi - Antonio d’Amore - Alice Catena - Roberta Masella - Beatrice Scazzocchio journal: Nutrients year: 2023 pmcid: PMC10055962 doi: 10.3390/nu15061357 license: CC BY 4.0 --- # MaestraNatura Reveals Its Effectiveness in Acquiring Nutritional Knowledge and Skills: Bridging the Gap between Girls and Boys from Primary School ## Abstract MaestraNatura (MN) is a nutrition education program developed to both enhance awareness about the importance of healthy eating behaviour and skills on food and nutrition in primary school students. The level of knowledge about food and nutritional issues was assessed by a questionnaire administered to 256 students (9–10 years old) attending the last class of primary school and was compared with that of a control group of 98 students frequenting the same schools that received traditional nutrition education based on curricular science lessons and one frontal lesson conducted by an expert nutritionist. The results indicated that students in the MN program showed a higher percentage of correct responses to the questionnaire when compared with the control group (76 ± 15.4 vs. 59 ± 17.7; $p \leq 0.001$). Furthermore, the students attending the MN program were requested to organise a weekly menu before (T0) and when finished (T1) the MN program. The results evidenced an overall significant improvement in the score obtained at T1 with respect to those at T0 ($p \leq 0.001$), indicating a strong improvement in the ability to translate the theoretical concepts of nutrition guidelines in practice. In addition, the analysis revealed a gender gap between boys and girls, with boys showing a worse score at T0 that was ameliorated after the completion of the program ($p \leq 0.001$). Overall, MN program is effective in improving nutrition knowledge amongst 9–10-year-old students. Furthermore, students showed an increased ability to organise a weekly dietary plan after completing the MN program, a result which also bridged gender gaps. Thus, preventive nutrition education strategies specifically addressed to boys and girls, and involving both the school and family, are needed to make children aware of the importance of a healthy lifestyle and to correct inadequate eating habits. ## 1. Introduction Childhood and adolescence are crucial times to lay the foundations of health in adult life; in particular, childhood is a period of biological and social change [1] in which eating behaviour often becomes unhealthy [2]. Excess weight in this phase of life represents an important challenge for global public health [3]. Childhood obesity, indeed, increases the risks of obesity in adult life [4] and those of developing type 2 diabetes, hypertension, dyslipidaemia [5], and CHD [6,7,8]. In this perspective, nutrition knowledge is recognised as an important factor able to promote healthy food choices, reversing the rise in the prevalence of overweight and obesity and their clinical consequences. However, numerous studies carried out mainly in adult people analysed the factors that influence the selection and consumption of food, highlighting that in Western societies sex-related biological or psychological factors as well as gender-related socioeconomic and cultural aspects can strongly influence food choices [9,10]. Women tend to select healthier food and are much more concerned than men with choosing appropriate food and healthy nutritional behaviours important for maintaining a good physical condition [9,10]. Thus, men should benefit from specific nutrition interventions considering the gender gap, which puts them at a disadvantage [10,11]. The urgent need to prevent or reverse the course of childhood obesity has led to the significant growth in research addressed at designing interventions to favour health-promoting dietary choices and to discourage those adversely affecting health. In this regard, increasing knowledge of food and nutritional issues appears as a necessary basis for the improvement of eating behaviours [9,11]. Due to the reciprocal relationship between health and education, schools are among the most effective settings for health promotion in terms of influencing the adolescents’ dietary habits [12,13]. Ideally, a school-based intervention should be carried out during school curriculum hours or an after-school program in order to reduce the attrition of participants [14]. In addition, in the school context, a holistic approach to health promotion could be established, allowing the involvement of families and communities to reinforce health messages outside the school environment [15]. Various interventions have been carried out so far in the school setting; however, only few of them have evaluated the real effectiveness in improving nutrition knowledge and dietary habits among children [14,16]. Furthermore, most of the interventions consisted of few and short lessons which were not organised in a structured education program and generally without planning practical activities or the involvement of parents [17,18]. The MaestraNatura Program (MNP) is an innovative nutrition education program developed by Istituto Superiore di Sanità to guide teachers, parents, and children attending primary and first-level secondary schools through an 8-year learning path [19,20]. The program has several innovative points. First of all, the didactic activities span the entire scholastic year and require the active participation of students in experimental activities and the involvement of parents in practical applications. Then, the understanding of the principles of dietary guidelines for healthy eating represents the endpoint, instead of the starting point, to be reached after gaining knowledge about nutritional facts. Finally, the MNP takes advantage of a web platform that makes it potentially spread and easily adapted everywhere, allowing the standardisation of the intervention [19,20]. The present study was aimed at investigating whether the MNP didactic path specifically developed for the students in their last class of primary school (9–10 years old) was able to favour the increase of knowledge about food and nutrition and related skills in young children. ## 2.1. Participants Twenty fifth-year classes of primary public schools were enrolled in the study. The schools were located in the north (5 classes), centre (9 classes), and south (6 classes) of Italy, in both small and large towns. A total number of 354 students (181 girls, 173 boys), aged 9–10 years, participated in the study. In each school, a MN group and a CO group were organised so that the socio-cultural and economic characteristics of the students were as homogeneous as possible (Figure 1). ## 2.2. Ethical Aspects Parents signed the informed consent to allow the participation of their children in the MNP as required by the Italian law regarding ethical and legal (personal data protection) aspects. The objectives of the study and the required activities were explained to teachers and parents in meetings and leaflets before the start of the didactic activities. The study was approved by the ethics committee of Istituto Superiore di Sanità (AOO-ISS 26.04.21 n.0015951) [20]. ## 2.3. Procedure The CO group attended curricular lessons (two 1 h lessons) about scientific topics related to nutrition and one frontal lesson (2 h) conducted by an expert nutritionist which focused on food groups, different meanings of food and nutrients, and the food pyramid [21]. The MN group took part in all the theoretical and practical activities planned by the MN educational path “Why do we have to eat?” that included three PowerPoint presentations (“Why do we have to eat?”, “ Discover the egg”, and “Discover the milk”) and several experiments aimed at increasing nutrition knowledge, thus promoting awareness of the importance of a balanced and varied diet (“What’s in egg?”, “ What’s in milk?”, “ What food group does it belong to?”, “ Plan a weekly menu” [19]). In addition, the learning path included a “how to cook” section, reporting recipes to cook at home together with parents in order to promote interaction between them and encourage children to taste new food, especially vegetables. The MNP didactic activities spanned the entire school year. It was possible to download all the contents from the MN web platform, which is divided into different areas specifically addressed to teachers, parents, and students. To compare the level of knowledge obtained through the two different learning approaches, CO and MN groups were required to fill in the multiple-choice questionnaire “What do you know about food and nutrition?” within one week from the end of the activities. Furthermore, each student from the MN group was asked to compile a weekly food plan (WFP), both before starting any activity related to nutrition issues (T0) and at the end of the didactic path (T1). In this way, each student had their own starting level (T0) and any improvements due to participation in the program were compared to the basic level. The task consisted of the construction of a weekly menu that included breakfast, a morning snack, lunch, an afternoon snack, and dinner for each day of the week. The total score was calculated by counting the number of breakfasts, servings of fruit, vegetables, fish, cereals, and legumes. From this score, points were subtracted for the incorrect use of protein-rich food. By comparing the scores totalised at T0 and T1 from each student it was possible to assess the improvement, if there was any, in children’s performance in terms of translating their acquired knowledge into the arrangements of daily meals. ## 2.4. Statistical Analysis Quantitative variables were analysed by means, standard deviation, medians, and ranges, while categorical variables by absolute and percent frequencies. With respect to the questionnaire, for any item the answer given by a single child was categorised as correct (1 out of 4 possible answers) or incorrect (3 out of 4 possible answers). Considering the proportion of children giving the correct answers, for any single item the difference between CO and MN groups was assessed by the Fisher’s exact probability test because of the expected presence of low frequencies in many items. The difference between the CO and MN groups was significant when the Fisher’s exact probability test was $p \leq 0.05.$ In addition, for all children, we computed the average proportion of items receiving correct answers on the whole questionnaire, that is, a sort of global correctness index (GCI) of answers to the questionnaire. Differences between the two groups with respect to the GCI were assessed by Student’s t test. To compare the WFP organised by the MN group at the beginning and at the end of the didactic activities, the scores obtained were analysed by Student’s t test. p values < 0.05 were considered significant. All statistical analyses were performed by STATA 16.0. ## 3.1. Evaluation of the Improvement in Knowledge Obtained by the “Why Do We Have to Eat?” Path with Respect to a ‘Traditional’ Nutrition Education Intervention The questionnaires filled by two hundred and forty-three students (corresponding to about the $80\%$ of the total students enrolled) were collected and analysed. The data revealed that MN group showed better performance in answering the administered questions compared with the CO group ($p \leq 0.001$); differences between boys and girls were not evident. On average, the percentage of correct answers was significantly higher in the MN group compared with the CO group (mean = 76.21 SD = 15.36 vs. mean = 59.25, SD = 17.68; $p \leq 0.001$) (Figure 2). MN group students were able to answer most of the items correctly, showing a significant difference compared with the CO group (Table 1). It is worth noting that when it was not possible to observe significant differences between the groups, both of them answered correctly, probably because the questions dealt with everyday life concepts with which the students were already familiar. ## 3.2. Weekly Food Plan Organisation The students, after finishing the MN program, showed an improved ability in planning their weekly menus. The scores obtained by the students at T1, indeed, were significantly higher with respect to those at T0 ($p \leq 0.001$) (Table 2). By analysing the individual scores obtained for the servings of fruit, vegetables, and fish added to the plan separately, we found that all the students had significant increases in these categories at T1 ($p \leq 0.001$, $p \leq 0.001$, $$p \leq 0.047$$, respectively), serving as evidence of their understanding and learning the right consumption frequencies for those foods reported by the guidelines for healthy nutrition (Table 2). By considering the data distribution in urban areas, the most evident effect of the efficacy of the MNP was found in small towns (Table 2). It is worth noting that students from large cities exhibited a starting level significantly higher than those from small ones ($p \leq 0.001$), but this difference was largely resolved and even completely removed at the end of the didactic path ($p \leq 0.001$). The data indicated that students from the small towns significantly improved their skills by planning the right number of servings of fruit, vegetables, fish, cereals and legumes in all parts of their weekly menus (Table 2). Moreover, the data disaggregated for sex showed that boys started from a lower basic level of knowledge at T0 with respect to the girls ($$p \leq 0.029$$) (Table 3), but they reached a remarkable significant improvement at the end of the path, even if the difference with the girls remained statistically significant ($$p \leq 0.018$$) (Table 3). Of note, the differences observed between boys and girls were also found in the small towns but disappeared in the large ones. ## 4. Discussion The present study showed the effectiveness of the innovative nutrition education intervention program MaestraNatura to increase knowledge and skills about food and nutrition in a sample of students, 9–10 years old, from the fifth class of primary school. Nutritional knowledge has been suggested to possibly play a role in the adoption of healthier food habits [22,23]. Healthy lifestyles are fundamental to fight obesity and overweight, which are principal challenges for the healthcare systems and serve as the main risk factors for the onset of non-communicable diseases [3,24]. The improvement of knowledge is considered a necessary step to increase the population’s health literacy and, especially, food literacy, being proposed as effective tools in improving the adherence to adequate dietary habits [25], as also indicated in the “White Paper on nutrition-, overweight-, and obesity-related health issues”, published in 2008 by the European Commission [26]. However, increasing knowledge, although necessary, might not be enough to promote effective and long-lasting effects on dietary habits without a concomitant acquisition of skills that enable people to translate the theory in practice, i.e., to implement the food pyramid principles into their daily diets [20,27,28]. This aspect was highlighted in a recent systematic review that pointed out how nutrition education interventions based on practical activities are more likely to be successful in improving diet quality among children [29]. Therefore, a meaningful way to improve health literacy and prevent improper eating behaviour might be to design effective educational intervention programs aimed at ameliorating not only knowledge, but especially attitude and practices related to food and nutrition in primary school [28]. Our results showed that the primary school students who underwent the nutrition education program MNP had more knowledge about nutrition, as assessed by the knowledge questionnaire, than those attending traditional programs, as was previously proven in middle school students [20]. It is interesting to note that these young students from primary school achieved a rate of correct answers even better than that achieved by older students from middle school [20]. This clearly indicates that educational interventions should be performed as early as possible to provide better results and an effective improvement in food and nutrition knowledge. In fact, eating behaviours start to be acquired early in life and, when established, they are rarely changed; it is thus required to intervene as soon as possible to promote the adoption of healthy lifestyles and balanced diets [30]. As regards the improvement in awareness and skills regarding nutritional facts and correct eating habits, the MNP was proven to be able to improve the transfer of the recommendations reported in nutrition guidelines to practical scenarios, most likely also thanks to the practical activities the MNP proposed to the students. In the present paper, the MNP showed its effectiveness on students that initially had worse nutrition-related scores in planning their weekly menus; these were mainly students from rural areas. However, after attending the MNP, they achieved a level of understanding comparable to that of students living in large cities. Interestingly, these findings are in line with previous studies on the influence of urbanisation on eating habits which indicate that city size is predictive of food behaviour [31,32]. This may happen because of a different level of interest and awareness about the relationship between diet and health and/or a stronger adherence to ‘traditional’ habits not always in line with healthy nutrition. The young students’ demonstrated ability to organise their weekly food plans was greater with respect to the students from middle school; specifically, primary school students, although starting from a worse initial level, overturned this situation and greatly improved their ability at the end of the didactic path [20]. The limitations of this study are as follows. We lacked a control group to carry out the WFP task, so we were only able to show the improvement in skills within the MN group, and were missing knowledge assessments in the two groups of students before and after the specific interventions. More studies will be needed to substantiate the improvement in knowledge and skills conferred by the MNP. Finally, it is worth noting that boys, regardless of the geographical areas considered, had a lower level of basic knowledge and less familiarity with nutrition than girls; however, they reached an adequate level of learning and scored good rankings in planning their weekly menus after the completion of the MN didactic activities. The different background and competence between the genders are in accordance with previous studies carried out in adult people [33] that showed a different attitude towards food and nutrition in men and women, highlighting that women have generally a more positive attitude towards healthy food than men, as well as a greater awareness about the influence of nutrition on health [34,35]. Our findings may suggest that sex and gender could influence dietary behaviour as early as childhood. This represents an interesting field of research that deserves further and deeper studies specifically addressed at identifying the main sex/gender-driven factors affecting food choices and consumption. ## 5. Conclusions Overall, our data demonstrated that the MNP is effective in improving nutrition knowledge amongst 9–10-year-old students. Furthermore, students, after completing the MNP, showed an increased ability to organise a weekly dietary plan. 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--- title: A Micro-In-Macro Gastroretentive System for the Delivery of Narrow-Absorption Window Drugs authors: - Mershen Govender - Thankhoe A. Rants’o - Yahya E. Choonara journal: Polymers year: 2023 pmcid: PMC10055986 doi: 10.3390/polym15061385 license: CC BY 4.0 --- # A Micro-In-Macro Gastroretentive System for the Delivery of Narrow-Absorption Window Drugs ## Abstract A micro-in-macro gastroretentive and gastrofloatable drug delivery system (MGDDS), loaded with the model-drug ciprofloxacin, was developed in this study to address the limitations commonly experienced in narrow-absorption window (NAW) drug delivery. The MGDDS, which consists of microparticles loaded in a gastrofloatable macroparticle (gastrosphere) was designed to modify the release of ciprofloxacin, allowing for an increased drug absorption via the gastrointestinal tract. The prepared inner microparticles (1–4 µm) were formed by crosslinking chitosan (CHT) and Eudragit® RL 30D (EUD), with the outer gastrospheres prepared from alginate (ALG), pectin (PEC), poly(acrylic acid) (PAA) and poly(lactic-co-glycolic) acid (PLGA). An experimental design was utilized to optimize the prepared microparticles prior to Fourier Transition Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM) and in vitro drug release studies. Additionally, the in vivo analysis of the MGDDS, employing a Large White Pig model and molecular modeling of the ciprofloxacin-polymer interactions, were performed. The FTIR results determined that the crosslinking of the respective polymers in the microparticle and gastrosphere was achieved, with the SEM analysis detailing the size of the microparticles formed and the porous nature of the MGDDS, which is essential for drug release. The in vivo drug release analysis results further displayed a more controlled ciprofloxacin release profile over 24 h and a greater bioavailability for the MGDDS when compared to the marketed immediate-release ciprofloxacin product. Overall, the developed system successfully delivered ciprofloxacin in a control-release manner and enhanced its absorption, thereby displaying the potential of the system to be used in the delivery of other NAW drugs. ## 1. Introduction Oral dosing of medicine is the most common and preferred method of administration, although many drugs possess certain characteristics which lead to complex dosing regimens, often resulting in decreased patient compliance [1,2,3,4]. A narrow absorption window (NAW) is such a characteristic and is possessed by many commonly used drugs, including metformin, riboflavin, levodopa and ciprofloxacin. These drugs are primarily absorbed in the proximal area of the small intestine, with minimal to no absorption taking place further down the GIT. This is usually caused by the physicochemical properties of the drugs, such as limited solubility or instability in the alkaline pH of the small intestine [5,6,7]. Drugs with a NAW usually require multiple dosing with very high drug concentrations to achieve therapeutic drug levels, with severe side effects resultantly occurring. For example, the antidiabetic drug metformin, has about only $50\%$ oral bioavailability and a half-life of up to 3 h, necessitating a high dose of 500 mg twice daily or 850 mg once a day. This standard dose can be titrated up to 3 g per day, depending on the patient’s response. The adverse effects resulting from these high doses include diarrhea and anorexia or more serious effects, such as lactic acidosis [8]. Advanced drug delivery systems that prolong the gastric retention of drugs, allowing for controlled release, is therefore a viable option to overcome the concerns of NAW drug delivery. Multiple methods have been investigated to promote gastric retention with the most common approaches being: (i) low density systems, which float on the surface of the gastric fluid; (ii) mucoadhesive systems, which adhere to the lining of the stomach; and (iii) swelling and expanding systems, which increase in size and inhibit passage through the pyloric sphincter. Additionally, a few important design factors must be taken into consideration when formulating a drug delivery system that is intended to have an increased gastric residence time. These include particulate density (to allow for buoyancy in the gastric media), size (units with a diameter of more than 7.5 mm are reported to have a greater gastric-residence time), geometry (tetrahedron and ring-shaped systems have an increased gastric-residence time when compared to other shapes) and the number of units (multi-unit systems are more ideal for gastric retention than single unit systems) [9,10]. For these reasons, multi-unit systems conforming to these requirements are more commonly utilized for gastroretentive drug delivery with these systems being further advantageous in having a greater predictability, a decreased potential of causing localized damage to mucosal linings and a lower risk of dose-dumping [11,12]. In the development of such a system, the use of a multi-component platform incorporating microparticles in a macroparticle has the potential to exhibit the ideal characteristics for gastric retention, such as gastro-flotation (flotation in gastric media due to its lower density) and gastro-adherence, in addition to the ideal properties of density, size and shape, as described above [13]. This study therefore provides for the development of a microparticle-in-macroparticle, gastrofloatable and gastroretentive platform for the delivery of NAW drugs (MGDDS). Ciprofloxacin, a broad-spectrum antibiotic with a NAW, has been used as the model-drug for the development of this system [14]. Ciprofloxacin is a weak base and has increased solubility and stability in the acidic environment of the stomach; a common property of NAW drugs [15]. The prepared system functions by retaining the ciprofloxacin-loaded units in the gastric media, slowly releasing the model drug into the gastric contents, thereby allowing for enhanced absorption in the proximal intestine. The MGDDS was developed using chitosan (CHT) and Eudragit® RL 30D (EUD), for their gelling and controlled-release properties, prepared into microparticles, which were thereafter incorporated into an ionic crosslinked gastrosphere for the controlled release of ciprofloxacin over 12 h. The gastrospheres were prepared from alginate (ALG), pectin (PEC), poly(acrylic acid) (PAA) and poly(lactic-co-glycolic) acid (PLGA). ALG and PEC were chosen for their gelling and gastrofloatable properties and their crosslinking potential with calcium ions, with PAA and PLGA utilized for their controlled release, gastroretentive and biodegradation properties [16,17,18,19]. The in vitro characterization of the developed platform was undertaken using FTIR and SEM prior to in vivo evaluation in a Large White Pig model. Additionally, molecular modeling of the drug-polymer interaction was undertaken. A schematic diagram summarizing the gastroretentive and drug release properties of the MGDDS is provided in Figure 1. In this figure, the microparticles containing ciprofloxacin incorporated into the gastrofloatable gastrospheres, as well as the subsequent release of ciprofloxacin into the gastric media is depicted. ## 2.1. Materials Chitosan (medium molecular weight), ciprofloxacin and sodium tripolyphosphate (TPP) were purchased from Sigma-Aldrich (Sigma-Aldrich Chemie, Steinheim, Germany); alginate (Protanal LF $\frac{10}{60}$; Mw 89,000) was purchased from FMC BioPolymer (Drammen, Norway); pectin (Classic CU 701; Mw ≈ 50,000) was purchased from Herbstreith and Fox (Neuenbürg, Germany); poly(acrylic acid) (Carbopol 974P NF; Mw ≈ 3 × 109) was purchased from Noveon (Cleveland, OH, USA); poly(lactic-co-glycolic) acid (Resomer RG 858 S; Mw 190,000–240,000) was purchased from Boehringer Ingelheim (Ingelheim, Germany); and calcium hydroxide was purchased from BDH Chemicals Ltd. (Poole, UK). Eudragit® RL 30D (Mw ≈ 32,000) was received as a gift from Röhm Pharma Polymers (Darmstadt, Germany). All other reagents used were of analytical grade and were employed as purchased, without further purification. ## 2.2. Preparation of the Ciprofloxacin-Loaded Microparticles The ciprofloxacin-loaded microparticles were formulated by preparing a homogenous solution of ciprofloxacin (50 mg), CHT and EUD (the amounts of which were determined by the experimental design), prior to aerosolization into a 500 mL beaker containing $6\%$ w/v crosslinker TPP [13]. Before use, the CHT was dissolved in acetic acid ($1\%$ w/v), with the EUD dispersed in distilled water at room temperature using a magnetic stirrer (300 rpm). Aerosolization was achieved by spraying the prepared polymeric solution through a fluid bed drier nozzle (Mini Lab Coater, Umang Pharmatech, Maharashtra, India) at a constant rate of 5 mL/min, a nozzle height of 20 cm above the collection vessel and a 0.1 MPa air pressure. After aerosolization, the prepared microparticles were left at room temperature for 30 min prior to collection through centrifugation. The microparticles were thereafter washed with distilled water and lyophilized at −60 °C for 24 h at 25 mmtor. ## 2.2.1. In Silico Analysis of the Ciprofloxacin-Polymer Interactions The Materials Science platform of Schrödinger software (version 2018-2) was used to assess the molecular interactions of ciprofloxacin and the CHT-EUD polymers. The 3D structure of the CHT was retrieved from Mol-Instincts (CT1078683894). This was then cross-linked with the Eudragit RL using a platform that allows for the formation of 3D structures, a 3D Builder tool; the complex was then subsequently refined by the protein preparation wizard [20,21]. In order to create a final CHT-EUD polymer, the prepared complex was submitted to the Polymer Builder tool with the standard settings, including the generation of an amorphous polymer under OPLS3e forcefield at a cut-off temperature of 300 K for the Boltzmann constant, the van der Waals clash scale factor of 0.5 and a density of 0.5 g/cm3 [22,23]. To prepare the polymer for drug loading, it was minimized and turned into a sphere by the Nanoparticle Builder at a standard cut-off radius of 5 Å. Next, to determine the interactions of the ciprofloxacin with this polymeric carrier system, the disordered structure of the CHT-EUD nanosphere was generated and ciprofloxacin was immersed as a substrate using a rigorous Disordered System Builder under the OPLS3e forcefield. The final complex was then assessed for crystal pose and intermolecular interactions [24,25,26]. ## 2.2.2. Construction of the Experimental Design A face-centered central composite experimental design was utilized to ascertain the optimal formulation for the delivery of ciprofloxacin over 12 h. For the experimental design Chitosan (0.5–$1.5\%$ w/v) and Eudragit® RL100 30D (0.5–$2\%$ w/v) were selected as the independent formulation variables with a Mean Dissolution at 12 h (MD12), drug entrapment efficiency (DEE) and microparticle yield (MPY) selected as the design responses. The experimental analyses were performed on statistically derived formulations composed of various combinations of CHT and EUD as highlighted in Table 1. All formulations were prepared at room temperature, as previously described. A statistical model incorporating interactive and polynomial terms was utilized to evaluate the responses. ## 2.2.3. Determination of the MPY and DEE The MPY was determined by comparing the actual particle yield weight achieved after synthesis to the theoretical yield from the individual formulation components (Equation [1]) with the DEE calculated by dissolving microparticles (50 mg) in simulated human gastric fluid (SHGF) (100 mL; pH 1.2; 37 °C) for 24 h and determining the ciprofloxacin composition through UV spectroscopy (CE 3021, Cecil Instruments, Cambridge, UK) at 280 nm (Equation [2]): [1]MPY= Actual Amount of MicroparticlesTheoretical Amount of Microparticles×100 [2]DEE= Amount of Encapsulated CiprofloxacinTheoretical Amount of Ciprofloxacin×100 ## 2.2.4. In Vitro Drug Release Analysis The in vitro drug release analysis of the ciprofloxacin-loaded microparticles ($$n = 3$$) was conducted employing a USP II dissolution apparatus (Erweka DT 700, Heusenstamm, Germany) set at 50 rpm. For the analysis, 50 mg of microparticles were placed in 200 mL SHGF. The samples (5 mL) were extracted at predetermined time intervals, filtered and analyzed by UV spectroscopy. An equal volume of fresh SHGF was replaced after each sample extraction. Fractional Drug Release (FDR) and MD12 for each design formulation was thereafter calculated. The FDR was calculated using Equation [3]:[3]FDR= Amount of Ciprofloxacin ReleasedAmount of Total Dosed Ciprofloxacin ## 2.2.5. Constraint Optimization of the Formulation Responses A model-independent approach (Minitab® V15, Minitab Inc., State College, PA, USA) was used to optimize the ciprofloxacin-loaded microparticles for a maximum DEE and MPY, and an MD12 value of 34.833, conforming to zero-order kinetics over 12 h. The optimized system was formulated and analyzed for its DEE, MPY and MD12 values as previously described. Dissolution modelling on the optimized microparticle system was additionally undertaken using the Zero Order, First Order, Higuchi, Hixson–Crowell and Korsmeyer–Peppas models [27,28]. ## 2.3. Preparation of the MGDDS Platform The optimized lyophilized microparticles, containing 250 mg of ciprofloxacin were uniformly dispersed and crosslinked in a homogenous polymeric gastrosphere solution (composed of a mixture of $1\%$ w/v ALG, $1\%$ w/v PEC, $2\%$ w/v PAA and $2\%$ w/v PLGA) using calcium hydroxide ($2\%$ w/v) as the crosslinking solution. The prepared drug-loaded microparticle-entrapped gastrospheres (MGDDS) were thereafter cured for a further 30 min prior to filtering, collection, washing and lyophilization, as previously described. ## Surface Morphology The SEM analysis was carried out on the optimized microparticles and the MGDDS using a Phenom™ scanning electron microscope (FEI Company, Hillsboro, OR, USA). Prior to the analysis, the samples were gold-sputter coated (SPI Module™ Sputter Coater, SPI Supplies, West Chester, PA, USA) for 90 s (18 mA). ## Fourier Transmission Infrared Spectroscopy The structural characterization of the prepared drug-free microparticles, gastrospheres and their native polymers was undertaken using a Spectrum 2000 FTIR spectrometer with a MIRTGS detector (PerkinElmer Spectrum 100, Beaconsfield, UK) to determine the properties of the cross-linked microparticles, as well as the chemical nature of the polymers used prior to and after the formulation process. All samples were analyzed at a resolution of 4 cm−1 for 16 scans over wave numbers from 4000–400 cm−1. ## Swelling Potential The calculation of the swelling potential of the prepared gastrospheres was achieved by immersing pre-weighed 50 gastrospheres in 100 mL SHGF. The solution was thereafter placed in an orbital shaker incubator rotating at 35 rpm at 37 °C for 12 h. The gastrospheres were thereafter blotted with filter paper to remove any excess SHGF and weighed. The swelling of the gastrospheres was calculated using Equation [4] [29]:[4]Swelling= Hydrated MassDry mass×100 ## 2.4. In Vivo Analysis Healthy female Large White pigs (35 ± 0.5 kg; $$n = 5$$) were used for the in vivo evaluation of the ciprofloxacin-loaded gastrospheres and the comparator 250 mg immediate-release marketed product. The study was divided into two groups with Group 1 administered with the ciprofloxacin-loaded gastrospheres (containing 250 mg ciprofloxacin in a hard gelatin capsule) and Group 2 with the marketed ciprofloxacin product via a gastric tube. The marketed product is routinely administered as a twice daily dose. A washout period of one day was maintained between dosing, with blood samples (10 mL) removed via a surgically implanted intra-jugular catheter, at 0, 2, 4, 6, 8, 10, 12, 16, 20 and 24 h after administration of the respective system. All blood samples were analyzed using the method by Pearce et al. [ 30] with modifications. Briefly, a Waters Acquity® UPLC system (Waters Corp., Milford, MA, USA) equipped with an Acquity® UPLC BEH shield Reverse Phase C18 column (2.1 mm × 100 mm I.D. 1.7 µm) set at 25 °C, a detection wavelength of 280 nm, a 5 min run time and a 5 µL injection volume was used. Prior to the analysis, plasma (1 mL) was precipitated with acetonitrile (0.2 mL), diluted with deionized water, and centrifuged (3200 rpm) for 15 min. The supernatant was thereafter removed and injected into a Waters Oasis HLB 3cc cartridge conditioned with deionized water (1 mL) and methanol (1 mL). The samples were thereafter washed with deionized water (1 mL) and $10\%$ methanol (1 mL) with ciprofloxacin eluted with 1 mL of acetonitrile:ammonia/ammonium buffer (1:1). The mobile phase consisted of both the buffer and acetonitrile under gradient conditions (0.0 min: 90:10 (Buffer:ACN); 0.5 min: 80:20; 3.0 min: 90:10) with ranitidine used as the internal standard. ## Pharmacokinetic Modelling Pharmacokinetic modelling was achieved using PKSolver to undertake a non-compartmental and compartmental analysis of the in vivo plasma ciprofloxacin levels and thus determine the AUC and plasma half-life of ciprofloxacin from the MGDDS and commercial comparator product [31]. ## 3.1. Analysis of the Ciprofloxacin Interactions with CHT-EUD Polymer through Molecular Modelling The CHT-EUD polymer virtual synthesis using molecular modelling technology was initiated on a 3D workspace known as a 3D Builder. The CHT and EUD were crosslinked by the 3D Builder tool to form a co-polymer that was further polymerized under the Polymer Builder panel. The Nanoparticle Builder subsequently generated a nanosphere from the final polymer showing the positively charged Eudragit RL head and the CHT tail (Figure 2) in agreement with the literature [32,33]. The central composite of the ciprofloxacin-loaded CHT-EUD carrier system was generated from the Disordered System Builder using the nanospheres with ciprofloxacin immersed as a substrate. This showed favorable interactions of ciprofloxacin with the CHT-EUD carrier system where the terminal carboxyl group of the drug formed hydrogen bond interactions with the hydroxyl groups of the carrier polymer (Figure 3). To further identify the polymer interaction site as well as the polymer configuration in the composite, the two monomers involved in the drug interaction were selected for analysis (Figure 4). This revealed that CHT-EUD interacted with the ciprofloxacin at the crosslink of the co-polymer mainly with the terminal glucosamine moiety of the CHT. Interestingly, this interaction pulled the crosslinking portion inward which then provided for the outward facing of the EUD, exposing the positively charged amine groups to the surface. This is the best configuration since the positively charged EUD is known to promote the cellular uptake of the drug through the ionic interactions with the negatively charged cellular membranes [34,35]. Moreover, the rest of the ciprofloxacin structure interacted with the polymers through the weak van der Waals forces. The balanced hydrogen bonding with the weaker van der Waals forces provide for the controlled release properties of the drugs [35], suggesting that the ciprofloxacin release will be sufficiently controlled. ## 3.2. Analysis of the Central Composite Experimental Design The MD12 values (as displayed in Table 2) noted values for the respective formulation of between 25.36 and 34.91 h, displaying the varying potential of the microparticle system to control the release of ciprofloxacin. It was also noted from these results that the EUD composition in the microparticle system appeared to have a greater influence on the in vitro ciprofloxacin release when compared to CHT. This was further depicted in the response surface plot (Figure 5a), where the respective trend in the MD12 values, in response to the varying CHT and EUD concentrations, is depicted. The highest MD12 was realized at a moderate EUD concentration ($1.25\%$ w/v) and decreased in the higher or lower ranges. Similarly, MD12 initially increased with an increase in the CHT concentration; however, this trend was reversed when CHT reached $2.0\%$. These results were attributed to the microparticles not being able to swell enough at the lower ranges of EUD to permit the passage of the drug through the pores, while at the higher concentrations the swelling capability was inhibited or restricted by the rigid crosslinked CHT structure. It was also noted that for any given amount of EUD and TPP, an increase in the CHT concentration resulted in an increased MD12. This could also be attributed to the enhanced complexation of CHT with EUD and the increased intermolecular crosslinks with TPP. The DEE was determined to be within a range of $50\%$ to $73\%$ for all the design formulations (Table 2). The evaluation of the influence of CHT and EUD on DEE (as provided in the response surface plot in Figure 5b) noted that at lower CHT concentrations, DEE was higher. Additionally, at lower concentrations of EUD (0.5 to $1\%$ w/v) and at an increased concentration ($2\%$ w/v), a decreased DEE was seen. A similar trend was also noted at high concentrations of CHT. This result was attributed to a loose network at low concentrations of EUD, potentially due to drug leaching during the formulation process. Furthermore, at higher EUD concentrations, the polymer network formed may become too dense, inhibiting drug entrapment. The MPY for the design formulations was relatively high (77 to $92\%$), with only one formulation (Formulation 13) deviating from this range at $65\%$ (Table 2). The influence of CHT and EUD on MPY is depicted in Figure 5c, where at low CHT concentrations MPY was low, with a higher MPY at increased CHT concentrations. Additionally, an increase in the EUD composition resulted in a decrease in MPY. The decreased MPY can be explained by the increase in viscosity of the polymer solution because of the increased CHT concentration, with a viscous solution more resistant to fragmentation into small droplets. ## Response Optimization The response optimization, targeting an MD12 value of 34.883, a maximum DEE and MPY indicated that the optimized formulation was obtained at $1.5\%$ CHT and $1.0741\%$ EUD. The optimized formulations were determined to have a desirability of >0.9 with the actual vs. predicted responses being ≥$90\%$. ## 3.3.1. Surface Morphology Characterization SEM imaging of the optimized microparticles and the MGDDS (as depicted in Figure 6) was performed to determine the size of the prepared microparticles, as well as the surface characteristics of the microparticles and the MGDDS. The evaluation of the optimized microparticles (Figure 6a) determined that consistently smooth-edged spherical particulates of between 1–4 µm were formed with no distinct surface pores. Additionally, for the MGDDS SEM image (Figure 6b), air bubbles or voids within the gastrosphere structure were seen, with embedded microparticles visible on the surface. The porous nature of the surface of the gastrospheres is essential for the hydration of the system to allow for drug release, while the air bubbles provide the buoyancy expected of the gastro-retentive systems and are required for their functionality. The voids seen were due to the lyophilization process, whereby water crystals were removed from the system through sublimation. The surface morphology of the gastrosphere also detailed a solid surface with numerous pores, correlating with the data seen in the experimental design. ## 3.3.2. FTIR Analysis of MGDDS The evaluation of the FTIR spectra of the microparticle, the gastrosphere and its component polymers (as provided in Figure 7) revealed that the NH stretching vibrations (at wavenumbers 3300–3500 cm−1) were found in the native CHT and EUD compounds, which were still noted in the prepared microparticles [37]. Additionally, C=O vibrations were seen in the native EUD spectra (between wavenumbers 1700 and 1900 cm−1) and again in the evaluation of the microparticle formulation; however, with a decreased intensity [38]. The other C-H bands (at wavenumbers 2800–2950 cm−1; 1355–1395 cm−1; 1405–1465 cm−1 and 1430–1470 cm−1) were noted to be present in all the spectra. The C=O ester (at the 1730 cm−1 peak) seen in the native EUD spectra, was, however, significantly diminished in the microparticle spectra with a new peak formed at 1531 cm−1. This result is consistent with the previous research on polymer systems consisting of CHT and EUD [39,40]. The noted diminishing of the esterified carboxyl group bands can also be attributed to the amount of CHT present, with the CHT concentration in the microparticle system being greater than the EUD [40]. It was also noted that the strong bands at ±3200 cm−1 observed for both the microparticle and gastrosphere systems, were still present after the formulation of the MGDDS. The results of this analysis therefore confirmed that the chemical nature of the native polymers was maintained during the production process and that there was no chemical interaction due to the incorporation of the microparticles into the gastrosphere system. This result is of importance as changes to the chemical structure can affect the functionality and safety of the polymer system. ## 3.3.3. In Vitro Drug Release The ciprofloxacin release profile from the MGDDS (Figure 8), which shows the FDR of ciprofloxacin over the 12 h test period, depicted the controlled release of drug with a 0.092 FDR ($9.2\%$) achieved after 1 h, a 0.23 FDR ($23.0\%$) after 2 h, rising to a 0.52 FDR ($52.7\%$) after 4 h. While controlled in nature, this result can be attributed to the initial rate of swelling allowing for a rapid drug release. The rate of release then begins to decrease with an FDR of 0.832 ($83.2\%$) after 8 h with a final FDR of 0.99 ($99.0\%$) achieved after 12 h. This result can be attributed to the decreased amount of drug present in the matrix after 6 h, causing a decrease in the rate of release. The analysis of the swelling potential of the prepared gastrospheres detailed a $445\%$ (±$32\%$) increase in mass after exposure to SHGF. This substantial increase in mass due to hydration is in correlation with the in vitro release data which reflected an initial rapid release prior to an FDR of 0.99 achieved after 12 h. The dissolution modeling of the release data, as provided in Table 3, further revealed that the optimized microparticle-loaded gastrosphere system best conformed to the Hixson–Crowell model ($R = 0.988$); however, a good linearity was also achieved when modelled with the Korsmeyer–Peppas model ($R = 0.971$; N value = 0.716). Through the use of these models, it can be conferred that a uniformity in release was achieved through hydration of the prepared gastrosphere system and that diffusion was a primary mechanism of release from the MGDDS. ## 3.4. In Vivo Analysis of MGDDS The in vivo drug release profiles of the MGDDS and the commercial comparator product (as depicted in Figure 9) displayed a high plasma level of ciprofloxacin from the MGDDS within a shorter period rising to a higher Cmax (2.25 µg/mL at Tmax 5.08 h) when compared to the commercial product (Cmax of 2.0 µg/mL at Tmax 4.0 h). The $12.5\%$ increase in Cmax noted can also be attributed to the enhanced absorption from the MGDDS. This increase however may be potentially substantial when delivering highly potent drugs, which will require further optimization of the MGDDS system. Additionally, the AUC0–12 of the MGDDS system was calculated to be 42.46 μg.h/mL while the commercial product had an AUC0–12 of 33.21 μg.h/mL. This detailed the greater residence time of ciprofloxacin within the cardiovascular system in addition to a greater bioavailability. It was also determined that the drug release was sustained, and the absorption controlled from the MGDDS, while with the commercial product, the ciprofloxacin concentrations decreased steadily after reaching the peak concentration. This was again seen upon the compartmental analysis and calculation of the t$\frac{1}{2}$, which for the gastrosphere system was 37.70 h with the commercial product having a t$\frac{1}{2}$ of 11.39 h. Overall, these results indicate that the MGDDS platform portrayed a superior bioavailability and drug residence when compared to the commercial product. ## 4. Discussion The NAW drugs are known for their erratic and often decreased absorption profiles due to the limited capacity of the intestinal tract to absorb these molecules. Through the use of gastrofloatable systems, that conform to preferred densities, sizes and shapes, the controlled release of a drug can be achieved to allow for a more effective absorption process, overcoming this biopharmaceutical concern. Gastrofloatable systems, or formulations with a lower density than gastric fluid are the most practical and researched platforms for the delivery of NAW drugs due to their predictability and uncomplicated formulation processes [41]. Gastrofloatable systems are categorized into two subtypes of platforms: effervescent and non-effervescent floating systems, with the non-effervescent system based on the utilization of highly swellable or gel-forming polymers to form hydrodynamically balanced systems (HBS). The use of HBS allows for a predictable release of loaded drugs through the appropriate control of the formulation matrix. This however is not always the case with the release kinetics of the dosed drug being dependent upon the floating properties of the system and vice versa [41,42]. In this study, it was shown that optimization of the polymer-based microparticles, which are thereafter incorporated in gastrofloatable gastrospheres can effectively control the delivery of NAW drugs, such as ciprofloxacin, enhancing its absorption when compared to a conventional immediate-release formulation. Previous research on gastroretentive systems for the delivery of the NAW drug metformin has also been undertaken using similar polymers with in vitro release data displaying a controlled release of the loaded drug in simulated gastrointestinal media [13,43]. During the optimization of the microparticles prepared in this study, which were composed of CHT and EUD, it was noted that a DEE of between $50.83\%$ and $73.24\%$ and Mean Dissolution Time values of 25.36 and 34.91 h was achieved over the various experimental design formulations, highlighting the potential of the microparticles to be statistically optimized to varying drug release profiles. This is of significance as other NAW drugs could potentially be included in microparticles comprising of CHT and EUD for a controlled release. The SEM imaging additionally displayed uniform microparticles of an ideal size and shape, as well as gastrospheres with a porous surface, which is a property required for adequate hydration in gastrofloatable systems and air bubbles and voids allowing for buoyancy in the gastric media. A simulation using molecular modelling additionally unveiled that the ciprofloxacin-loaded CHT-EUD assumed a conformation that placed the positive charge on the outer surface. This suggested an increased cellular uptake of ciprofloxacin and its subsequent bioavailability, resulting from the ionic interactions of the delivery system with the negatively charged cellular membranes [34,35]. Additionally, the mixed hydrogen bonding and hydrophobic interactions potentially contributed to the controlled release profile of the ciprofloxacin from the CHT-EUD-based delivery system. The gastrosphere system, incorporating the optimized microparticle system, was further noted in vitro to release the ciprofloxacin in a controlled-release manner over the 12 h test period, with the in vivo studies displaying a higher Cmax and a greater plasma concentration after 24 h when compared to an immediate-release comparator product. The future applications of the MGDDS would therefore be for the delivery of other NAW drugs and for a comparison to marketed controlled-release formulations. The results of this study however have shown the superior release and absorption profiles of ciprofloxacin when utilizing a micro-in-macro gastrofloatable system, highlighting the potential for further optimization and functionalization of such systems. ## 5. Conclusions In this study, a novel microparticle-entrapped gastrosphere designed to deliver ciprofloxacin in a gastric-retentive manner was prepared, optimized and analyzed for its structural, morphological and drug release properties. The optimized system displayed a controlled in vitro ciprofloxacin release over 12 h with the in vivo results determining that the blood plasma concentrations of ciprofloxacin were much higher and remained more constant when compared to the marketed comparator product. 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--- title: BCL-XL Overexpression Protects Pancreatic β-Cells against Cytokine- and Palmitate-Induced Apoptosis authors: - Atenea A. Perez-Serna - Reinaldo S. Dos Santos - Cristina Ripoll - Angel Nadal - Decio L. Eizirik - Laura Marroqui journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056015 doi: 10.3390/ijms24065657 license: CC BY 4.0 --- # BCL-XL Overexpression Protects Pancreatic β-Cells against Cytokine- and Palmitate-Induced Apoptosis ## Abstract Diabetes is a chronic disease that affects glucose metabolism, either by autoimmune-driven β-cell loss or by the progressive loss of β-cell function, due to continued metabolic stresses. Although both α- and β-cells are exposed to the same stressors, such as proinflammatory cytokines and saturated free fatty acids (e.g., palmitate), only α-cells survive. We previously reported that the abundant expression of BCL-XL, an anti-apoptotic member of the BCL-2 family of proteins, is part of the α-cell defense mechanism against palmitate-induced cell death. Here, we investigated whether BCL-XL overexpression could protect β-cells against the apoptosis induced by proinflammatory and metabolic insults. For this purpose, BCL-XL was overexpressed in two β-cell lines—namely, rat insulinoma-derived INS-1E and human insulin-producing EndoC-βH1 cells—using adenoviral vectors. We observed that the BCL-XL overexpression in INS-1E cells was slightly reduced in intracellular Ca2+ responses and glucose-stimulated insulin secretion, whereas these effects were not observed in the human EndoC-βH1 cells. In INS-1E cells, BCL-XL overexpression partially decreased cytokine- and palmitate-induced β-cell apoptosis (around $40\%$ protection). On the other hand, the overexpression of BCL-XL markedly protected EndoC-βH1 cells against the apoptosis triggered by these insults (>$80\%$ protection). Analysis of the expression of endoplasmic reticulum (ER) stress markers suggests that resistance to the cytokine and palmitate conferred by BCL-XL overexpression might be, at least in part, due to the alleviation of ER stress. Altogether, our data indicate that BCL-XL plays a dual role in β-cells, participating both in cellular processes related to β-cell physiology and in fostering survival against pro-apoptotic insults. ## 1. Introduction Type 1 and type 2 diabetes are characterized by β-cell dysfunction and demise. In type 1 diabetes, β-cell apoptosis results from an autoimmune attack, where β-cells are exposed to proinflammatory cytokines that are released by infiltrating immune cells. Type 2 diabetes, on the other hand, is marked by metabolic-stress-mediated loss of functional β-cell mass, in which high levels of free fatty acids (e.g., palmitate) impair β-cell function and survival [1,2]. The BCL-2 family of proteins plays a critical role in pancreatic β-cell survival, with the balance between anti-apoptotic and pro-apoptotic BCL-2 proteins defining whether the cell will either follow the mitochondrial pathway of apoptosis or survive [3]. BCL-XL, a member of the BCL-2 anti-apoptotic proteins, can bind to and sequester BH3-only activators (i.e., Puma, Bim, and tBid), which will prevent BAX- and BAK-mediated mitochondrial apoptosis [4]. In addition, BCL-XL also prevents mitochondrial outer membrane permeabilization by retrotranslocating the pro-apoptotic protein BAX from the mitochondria into the cytosol [5]. Pro-apoptotic insults, such as proinflammatory cytokines and palmitate, can either directly or indirectly regulate β-cell BCL-XL expression. For instance, tumor necrosis factor-α down-regulates BCL-XL at both mRNA and protein levels [6], while IFNγ plus IL-1β [6,7], or palmitate [8], induce the expression of the BH3-only sensitizer DP5—which, in turn, binds to and represses BCL-XL, thereby favoring apoptosis. BCL-XL promotes the survival of differentiating pancreatic progenitors from human-induced pluripotent stem cells [9]. Moreover, BCL-XL is key for the protection of β-cells against apoptotic stimuli, such as proinflammatory cytokines [6,10,11,12,13] and the endoplasmic reticulum (ER) stress inducer thapsigargin [11,14]. We have previously shown that pancreatic α-cells express much higher levels of BCL-XL than β-cells, which may contribute to α-cell resistance to palmitate. In fact, BCL-XL silencing sensitized α-cells to palmitate-induced apoptosis [15]. Due to its potential relevance to β-cell viability, two previous studies overexpressed BCL-XL with the aim of improving islet cell survival. The first showed that mouse islets overexpressing BCL-XL in β-cells were protected from thapsigargin-induced cell death [14]. Later, Holohan et al. showed that overexpression of BCL-XL in rat insulinoma cells (RIN-r) prevented the apoptosis induced by cytokines [10]. However, these prior studies were performed in rodents, and, to our knowledge, there have been no similar studies conducted on human models (e.g., human islets or β-cell lines). This is a crucial unmet need, as human islets/β-cells show major differences in their responses to stresses when compared to rodent islets/β-cells [16,17,18]. Against this background, our main objective in the present study was to achieve a step forward and to investigate whether BCL-XL overexpression could also protect human β-cells against the apoptosis induced by proinflammatory (i.e., IFNγ plus IL-1β) or metabolic (i.e., palmitate) insults. For this purpose, we overexpressed BCL-XL in two β-cell lines; namely, the rat insulinoma-derived INS-1E and the human EndoC-βH1 cells. ## 2.1. BCL-XL Overexpression in Rat and Human β-Cell Models As it is known that adenoviral vectors can have a dose-dependent negative effect on rat islet cell survival, regardless of the gene that is overexpressed [19], we infected INS-1E cells with different multiplicities of infection (MOIs) and assessed cell viability 48 h from infection. Compared to adLUC-infected cells, BCL-XL expression was 4- (MOI 0.5) to 20-fold (MOI 10) higher in adBCL-XL-infected INS-1E cells (Figure 1a,b). No significant changes in apoptosis rate were observed upon infection with either adLUC or adBCL-XL (Figure 1c), suggesting that neither the adenoviral vectors nor the MOIs used induced cytotoxicity. Evaluation of BCL-XL expression by immunofluorescence showed that infection with MOI 5 adBCL-XL induced a 7- and 5-fold increase in BCL-XL expression in INS-1E and EndoC-βH1 cells, respectively (Figure 1d,e). ## 2.2. Modulation of Intracellular Ca2+ Signals and Glucose-Stimulated Insulin Secretion by BCL-XL BCL-XL has been shown to influence Ca2+ homeostasis in different cell types, including β-cells [14,20,21]. We tested whether BCL-XL overexpression affects intracellular Ca2+ oscillatory responses in rat and human β-cells by registering variations in intracellular Ca2+ concentration in response to a stimulatory glucose concentration (20 mM), followed by an extracellular depolarizing stimulus (30 mM KCl) (Figure 2a,b,f,g, Supplementary Figure S1). Non-stimulatory glucose concentrations were established at 3 mM for INS-1E cells and 0 mM for EndoC-βH1 cells. While BCL-XL overexpression induced a $5\%$ reduction in Ca2+ oscillatory response to 20 mM glucose in INS-1E cells (Figure 2c), no changes were observed in human EndoC-βH1 cells overexpressing BCL-XL (Figure 2h). Other parameters related to intracellular Ca2+ concentration, such as Ca2+ peak amplitude upon KCl-induced depolarization (Figure 2d,i) and the ratio F340/F380 under non-stimulatory conditions (Figure 2e,j), were not altered by BCL-XL overexpression in both cell lines. These results indicate that the role of BCL-XL in regulating Ca2+ response to glucose in β-cells may be species-dependent, with a mild effect in rat but not human β-cells. As changes in cytosolic Ca2+ concentrations play a key role in the control of insulin release from pancreatic β-cells [22,23], we determined whether BCL-XL overexpression changes β-cell function under basal conditions by assessing glucose-stimulated insulin secretion. In rat INS-1E cells, BCL-XL overexpression reduced insulin secretion in response to high glucose or in high glucose plus forskolin (an adenylate cyclase activator) by nearly $20\%$ and $30\%$, respectively (Figure 3a,b). Insulin content remained unchanged (Figure 3c). Divergent from our data in INS-1E cells, insulin secretion in response to high glucose or high glucose plus IBMX (a phosphodiesterase inhibitor) was not modified by BCL-XL overexpression in EndoC-βH1 cells (Figure 3d,e). As in INS-1E cells, insulin content was not affected by BCL-XL overexpression (Figure 3f). These findings suggest that, in INS-1E cells overexpressing BCL-XL, the reduction in the Ca2+ oscillatory response under stimulatory conditions (i.e., 20 mM glucose) may result in the impairment of glucose-stimulated insulin secretion. The mRNA expression of key genes for the maintenance of β-cell function and phenotype, namely Ins1/INS, Mafa/MAFA, and Pdx1/PDX1, did not change upon BCL-XL overexpression in INS-1E cells (Figure 3g–i) and EndoC-βH1 cells (Figure 3j–l). ## 2.3. BCL-XL Overexpression Protects β-Cells against Cytokine- and Palmitate-Induced Apoptosis BCL-XL is known as an anti-apoptotic protein [4]. We next investigated whether BCL-XL overexpression would protect rodent and human β-cells against the apoptosis induced by proinflammatory (a mix of the cytokines IFNγ and IL-1β) or metabolic (palmitate) stimuli (Figure 4 and Figure 5). For this, we assessed cell viability using the DNA-binding dyes HO/PI upon treatment for 24 h (INS-1E cells) or 48 h (EndoC-βH1 cells). In rat INS-1E cells, β-cell death was induced by either cytokines (Figure 4a,c,e) or palmitate (Figure 4b,d,f). Furthermore, they were partially abrogated by BCL-XL overexpression, reaching about $40\%$ protection in both conditions. Remarkably, human EndoC-βH1 cells overexpressing BCL-XL showed an apoptosis rate that was nearly $80\%$ lower upon cytokine treatment than those found with the adLUC-infected cells (Figure 5a,c,e). Likewise, BCL-XL overexpression completely protected EndoC-βH1 cells from palmitate-induced apoptosis (Figure 5b,d,f). These data reinforce the role played by BCL-XL in human β-cell protection against different pro-apoptotic stimuli. ## 2.4. BCL-XL Overexpression Alleviates Cytokine- and Palmitate-Induced ER Stress in Human but Not in Rat β-Cells As ER stress is a common mediator for β-cell apoptosis in both type 1 and type 2 diabetes [2], we assessed whether the protection achieved by BCL-XL overexpression is related to an alleviation of ER stress. For this purpose, we measured the expression of three key ER stress markers known to be induced in β-cells by cytokines [18] or palmitate [24]—namely, Chop/CHOP, Bip/BIP, and Xbp1s/XBP1s—in INS-1E (Figure 6) and EndoC-βH1 cells (Figure 7). Of note, these genes are, respectively, regulated by the three ER stress transducers PERK, ATF6, and IRE1α [25]. Overall, following proinflammatory (Figure 6a,c,e) and metabolic insults (Figure 6b,d,f), the stress-induced expression of Chop, Bip, and Xbp1s was found to be similar between the adLUC-infected and adBCL-XL-infected INS-1E cells. In EndoC-βH1 cells, however, BCL-XL overexpression led to a decrease in the expression of all of the ER stress markers cited above. In cytokine-treated cells, BIP and XBP1s levels decreased by 25–$30\%$, while CHOP expression had a modest, non-significant $10\%$ reduction (Figure 7a,c,e). A 40–$50\%$ reduction in the expression of all three ER stress genes was observed in BCL-XL-overexpressing cells when they were exposed to palmitate (Figure 7b,d,f). The overexpression of BCL-XL did not change Chop/CHOP’s, Bip/BIP’s, and Xbp1s/XBP1s’ mRNA expression in basal conditions (Figure 6 and Figure 7). ## 3. Discussion In the present work, we show that BCL-XL overexpression protects both human and rodent β-cells against inflammatory- and metabolic stress-induced apoptosis. Depending on the species studied, the overexpression of BCL-XL differently affects intracellular Ca2+ oscillations and insulin secretion, as well as the expression of cytokine- and palmitate-induced ER stress markers in β-cells. BCL-XL is an anti-apoptotic member of the BCL-2 family of proteins whose main function is to inhibit the mitochondrial outer membrane permeabilization by directly interacting with pro-apoptotic members of the BCL-2 family, including tBid and BAX [4]. However, the non-canonical roles of BCL-XL have been progressively unveiled in different cell types, indicating that this BCL-2 protein may also be involved in the control of other cellular processes, such as cell cycle and senescence [26,27], mitochondrial bioenergetics [28,29,30], and autophagy [31,32]. Beyond its role in the control of apoptosis, BCL-XL has been implicated in other aspects of β-cell physiology, such as Ca2+ homeostasis [14,21,33] and insulin secretion [14,21]. BCL-XL inhibition by two inhibitors, namely C6 and YC137, elicited Ca2+ fluctuations that resembled Ca2+ responses to glucose in human and mouse islet cells, as well as in the mouse MIN6 cell line. Similarly, islet cells from a β-cell-specific BCL-XL knockout mouse showed a significantly greater Ca2+ response induced by glucose [21]. While BCL-XL inhibition increased the intracellular Ca2+ signals in β-cells, BCL-XL overexpression reduced the intracellular Ca2+ responses to glucose in the islets from transgenic mice overexpressing very high levels (i.e., an over 10-fold increase) of human BCL-XL under the rat insulin promoter [14] and in rat INS-1E β-cells (present data). In the presence of substimulatory glucose concentrations, Luciani et al. reported that BCL2/BCL-XL antagonism acutely induced insulin secretion. Interestingly, the islets from the β-cell-specific BCL-XL knockout mice only displayed a modest, non-significant tendency toward increase in glucose-stimulated insulin release [21]. On the other hand, glucose-stimulated insulin secretion was reduced by $60\%$ in islets from BCL-XL transgenic mice when compared with wild-type islets [14]. In addition, Pax4-induced BCL-XL expression in rat islets also resulted in the attenuation of insulin release in response to glucose [34]. Once again, our results in INS-1E cells concurred with the literature, as we observed that the overexpression of BCL-XL decreased the insulin secretion that was stimulated by glucose and by forskolin. On this note, we did not detect changes in the intracellular Ca2+ oscillations or glucose-stimulated insulin secretion when BCL-XL was overexpressed in human β-cells. First, it seems unlikely that this lack of effect is due to the overexpression of the rat BCL-XL in human EndoC-βH1 cells. This is because, as discussed above, in the mouse islets overexpressing human BCL-XL, altered intracellular Ca2+ signals and insulin secretion were presented [14]. Alternatively, it is possible that a certain level of BCL-XL hyperexpression is necessary to induce alterations in Ca2+ homeostasis and β-cell secretory function. Zhou et al. observed that in mice in which BCL-XL expression was augmented, only 2- to 3-fold presented normal glucose tolerance and ex vivo glucose-stimulated insulin secretion. Conversely, animals in which BCL-XL expression surpassed a 10-fold increase displayed severe glucose intolerance, as well as impaired ex vivo insulin secretion and intracellular Ca2+ responses [14]. The fact that we observed a 2.5- to 5-fold induction in BCL-XL protein levels in EndoC-βH1 cells and a 15- to 20-fold in INS-1E cells supports the hypothesis of a threshold for BCL-XL overexpression. BCL-XL plays a key role in β-cell survival and its silencing, either with small-interfering RNAs or with the pharmacological inhibition with small molecules that induced apoptosis under basal conditions in different β-cell models, such as in rat and mouse cell lines [6,8,12,13,21], primary rat and mouse islets/β-cells [12,21], and dispersed human islets [12,21]. BCL-XL expression increases during pancreatic specification from human pluripotent stem cells, which coincides with a decrease in cell death. Moreover, pancreatic progenitors, wherein BCL-XL was knocked down or chemically inhibited, showed increased cell death [9]. BCL-XL inhibition also sensitizes pancreatic cells to a wide range of deleterious stimuli, including thapsigargin [11], high concentrations of ribose [33], and proinflammatory cytokines [11,12]. In pancreatic α-cells, which are naturally protected from the apoptosis triggered by palmitate-induced, in part due to higher BCL-XL expression than in β-cells, BCL-XL knockdown did not induce apoptosis under basal conditions, but sensitized α-cells to palmitate. Interestingly, the rate of palmitate-induced apoptosis in BCL-XL-deficient α-cells was comparable to the rate observed in β-cells treated with palmitate [15]. In a mirror image of these findings, BCL-XL overexpression blunted stress-induced apoptosis in β-cells. First, Zhou et al. reported that the islets from transgenic mice overexpressing human BCL-XL were protected against thapsigargin-induced apoptosis [14]. Afterward, two other studies showed that BCL-XL prevents cell death upon exposure to a mix of proinflammatory cytokines [10,34]. In the present study, we not only confirm the anti-apoptotic role of BCL-XL against cytokine-triggered apoptosis, but also show, for the first time, that BCL-XL overexpression protects rat and human β-cells against the deleterious effects of palmitate. From a mechanistic perspective, this is an interesting finding. The pathogenesis of type 1 and type 2 diabetes is fundamentally different, where proinflammatory cytokines and palmitate trigger different biological processes and signaling pathways that will eventually impact β-cell dysfunction (immune-mediated vs. metabolic) and cell fate (massive vs. mild-to-moderate β-cell loss) in both forms of the disease [1,2]. However, despite inducing different pathways, cytokines and palmitate activate pro-apoptotic BH3-only proteins that repress BCL-XL, including DP5 and Bad [3]. In our β-cell models, it seems that an excess of BCL-XL overcomes the cytokine- or palmitate-induced activation of these BH3-only sensitizers, thereby leading to protection against apoptosis. Due to its dual subcellular localization, both at the mitochondria and the ER, BCL-XL has been implicated in the protection from ER stress through different mechanisms, such as the sequestration of Bim, which prevents ER stress-induced Bim translocation to the ER [35,36]. This is in addition to the maintenance of ER membrane permeability to ER luminal proteins (e.g., BiP) [37]. In β-cells, previous studies have suggested that BCL-XL is a key player in controlling ER stress-induced apoptosis, as BCL-XL levels are critical to blunt thapsigargin-triggered apoptosis [11,14]. As prolonged ER stress contributes to both cytokine- and palmitate-induced β-cell apoptosis [25], we investigated whether BCL-XL-mediated protection against these stressors was associated with the alleviation of ER stress in rodent and human β-cells. Upon exposure of rat INS-1E cells to cytokines or palmitate, the increased mRNA expression of the ER stress markers Bip, Chop, and Xbp1s was not altered by BCL-XL overexpression. On the other hand, the cytokine- and palmitate-induced mRNA expression of BIP, CHOP, and XBP1s was mostly abrogated by BCL-XL overexpression in human EndoC-βH1 cells. Similar to our findings in human β-cells, mouse embryonic fibroblasts where BCL-XL was targeted to the ER presented decreased the expression of several ER stress markers, such XBP1s, CHOP, and ATF4, following thapsigargin insult—which may have conferred resistance to thapsigargin-induced cell death [38]. In this case, BCL-XL at the ER negatively regulated the inositol 1,4,5-trisphosphate receptor (IP3R)-mediated Ca2+ release from this organelle, which prevented ER Ca2+ depletion and ER stress [38]. Of note, previous studies reported that BCL-XL is mainly localized in the mitochondria, with minimal association with ER (i.e., a localization 6-fold higher in the mitochondria when compared to ER) in rodent β-cells [12,21]. Taken together, these findings suggest that the regulation of the ER stress response by BCL-XL may differ between rat and human β-cells. While it is possible that BCL-XL subcellular localization and its involvement in Ca2+ homeostasis might play a role in this regulation, further investigation will be needed to elucidate how BCL-XL modulates cytokine- and palmitate-induced ER stress in human but not in rat β-cells. Although ER stress alleviation may account for the protection seen in BCL-XL-overexpressing human β-cells, it is likely that this protective effect also comes from the classical role played by BCL-XL as the gatekeepers of life and death. As the balance between anti- and pro-apoptotic BCL-2 proteins is key to define cell survival or death, BCL-XL overexpression may be just shifting the balance in favor of survival when cells are exposed to stressful conditions. This canonical, pro-survival role of BCL-XL would explain why rat β-cells overexpressing BCL-XL were partially protected from cytokine- and palmitate-induced apoptosis independently of the lack of effects that occurred following the ER stress response. Consistent with our data in β-cells, studies in other cell types have reported that BCL-XL overexpression protects from a myriad of stressors, such as viral infections [39], hydrogen peroxide [40], and lead exposure [41]. Based on our in vitro data, the next step would be to examine whether BCL-XL overexpression could prevent diabetes in animal models (e.g., NOD and db/db mice, respectively models for type 1 and type 2 diabetes). For this purpose, an interesting approach would be to use an adeno-associated virus(AAV)-based gene delivery system [42,43,44,45] to enhance BCL-XL expression specifically in β-cells of NOD and db/db mice, as well as to better evaluate the diabetes progression in these animals. In this case, gene therapy using AAV would require careful evaluation of the safety issues related to the expression of an anti-apoptotic protein in the long term. Among these safety issues, special attention should be given to increased susceptibility to viral infections and possible effects on tumorigenesis [39,46,47,48]. Collectively, our work adds to the evidence indicating that BCL-XL plays a dual role in β-cells, participating both in cellular processes related to β-cell physiology (e.g., Ca2+ homeostasis and insulin secretion) and survival. Moreover, it supports the notion that, depending on the amount of BCL-XL protein overexpressed, it is possible to protect β-cells against pro-apoptotic insults, without impairing β-cell function. This balanced scenario speaks in favor of the possibility of using BCL-XL as a therapeutic target for the treatment of diabetes. Importantly, we show for the first time that BCL-XL overexpression protects human β-cells without compromising insulin release, which sets the stage for the use of β-cell-specific BCL-XL overexpression as a potential therapeutic strategy in order to prevent β-cell loss during diabetes development. ## 4.1. Culture of EndoC-βH1 and INS-1E Cells The human EndoC-βH1 β-cell line (research resource identifier (RRID):CVCL_L909, Univercell-Biosolutions, France) was grown attached to Matrigel/fibronectin-coated plates or glass coverslips as described before [49]. Cells were cultured in DMEM, containing 5.6 mM glucose, 10 mM nicotinamide, 5.5 μg/mL transferrin, 50 μM 2-mercaptoethanol, 6.7 ng/mL selenite, $2\%$ BSA fatty acid free, 100 U/mL penicillin, and 100 μg/mL streptomycin. The rat INS-1E β-cell line (RRID:CVCL_0351, provided by Dr. C. Wollheim, Department of Cell Physiology and Metabolism, University of Geneva, Geneva, Switzerland) was cultured in RPMI 1640 GlutaMAX-I, containing 10 mM HEPES, 1 mM sodium pyruvate, 50 μM 2-mercaptoethanol, $5\%$ FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin [50,51]. Both cell lines were kept at 37 °C in a humidified atmosphere of $5\%$ CO2. ## 4.2. BCL-XL Overexpression BCL-XL was overexpressed in INS-1E and EndoC-βH1 cells using a recombinant adenoviral vector containing the rat BCL2L1 gene (GeneBank: NM_001033672; SIRION Biotech, Gräfelfing, Germany). An adenovirus encoding Luciferase (adLUC; Ad-CMV-Luc2, SIRION Biotech, Gräfelfing, Germany) was used as a control. Infection with recombinant adenoviruses, adLUC or adBCL-XL, was performed as described elsewhere [52] and in different MOIs, i.e., the number of viruses that infect individual cells were used as indicated. Upon infection, cells were allowed to recover for 48 h before treatments. ## 4.3. Cell Treatments Proinflammatory cytokine concentrations were used according to previously established dose–response experiments that were conducted in human and rodent cells [53,54,55]. The following concentrations were used: recombinant human IL-1β (R&D Systems, Cat. No. 201-LB/CF, Abingdon, UK) at 10 or 50 U/mL for INS-1E and EndoC-βH1 cells, respectively; recombinant human IFNγ (PeproTech, Cat. No. 300-02-250UG, Cranbury, NJ, USA) at 1000 U/mL for EndoC-βH1 cells; and recombinant rat IFNγ (R&D Systems, Cat. No. 585-IF) at 100 U/mL for INS-1E cells. For treatments in EndoC-βH1 cells, $2\%$ FBS was added to the culture medium. Palmitate was prepared as described elsewhere [56]. Cells were treated with vehicle (ethanol) or 0.5 mM palmitate that were precomplexed to $0.67\%$ BSA fatty acid free and $1\%$ FBS. For EndoC-βH1 cells, treatment was performed in DMEM/Ham’s F12 (1:1, vol/vol) with supplements as described before [49,57]. ## 4.4. Western Blot Analysis and Immunofluorescence Cells were washed with cold PBS and lysed in Laemmli buffer. Immunoblot analysis was performed as described before [58] using the monoclonal rabbit anti-BCL-XL (1:1000) and the monoclonal mouse anti-α-tubulin (1:1000) antibodies. Densitometry analysis was performed with Image Lab software (version 4.1, Bio-Rad Laboratories, Madrid, Spain). Immunofluorescence was performed as previously described [59,60]. Cells were incubated overnight with a monoclonal rabbit anti-BCL-XL antibody (1:200). Alexa Fluor 568 polyclonal goat anti-mouse IgG (1:500) was applied for 1 h. Upon nuclei staining with Hoechst 33342, coverslips were mounted with a fluorescent mounting medium (DAKO) and the immunofluorescence was observed with an inverted fluorescence microscope (Nikon Eclipse TE-2000-V) equipped with a digital camera (Nikon DMX 1200C) and a Cool led PE-300 wheel as the fluorophore excitor. Images were acquired at ×20 magnification and analyzed using the open-source FIJI software (version 2.0). See Supplementary Table S1 for more information about the antibodies used therein. ## 4.5. mRNA Extraction and Real-Time PCR Poly(A)+ mRNA extraction was performed using Dynabeads mRNA DIRECT kit (Invitrogen, Madrid, Spain), following the manufacturer’s instructions. The High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) was used to synthesize cDNA. Quantitative PCR was performed using the CFX96 Real Time System (Bio-Rad Laboratories, Madrid, Spain) [61]. The CFX Manager Version 1.6 (Bio-Rad Laboratories, Madrid, Spain) was used to analyze the values, which were expressed as relative expression in respect of control values (2−ΔΔCt) [62] using Gapdh and β-actin as the housekeeping genes for rat and human samples, respectively. Normalization was performed as follows: expression values were corrected by the housekeeping genes (i.e., Gapdh or β-actin, depending on the species). Afterwards, these values were normalized by the means of values obtained in control samples (considered as 1). In Figure 3, we normalized via the adLUC-infected cells, while in Figure 6 and Figure 7, values were normalized by the adLUC-infected cells treated with either cytokines or palmitate. The primers used herein are listed in the Supplementary Table S2. ## 4.6. Intracellular Ca2+ Analysis For intracellular Ca2+ measurements, cells were loaded with 2 ng/mL Fura-2AM (Invitrogen) for 1 h at room temperature in a humidified atmosphere. Fluorescence recordings were performed as described before [61]. Coverslips were perfused at a constant rate with a Krebs-Ringer solution containing 141 mM NaCl, 5.5 mM KCl, 1 mM MgCl2, 2 mM CaCl2, 20 mM HEPES, and pH 7.4. Basal fluorescence was determined in the absence of stimulus (3 mM glucose for INS-1E cells and 0 mM glucose for EndoC-βH1 cells) for 5 min before perfusing a solution with a stimulatory glucose concentration (20 mM glucose for both INS-1E and EndoC-βH1 cells). Fluorescence was determined for 15 min after each stimulus. Glucose-independent cell depolarization was elicited with high extracellular 30 mM KCl solution for 5 min, which was used as a positive control. Intracellular Ca2+ oscillations were recorded using an inverted fluorescence microscope (Zeiss Axiovert 200, Jana, Germany) equipped with a polychromator (TILL Photonics) to ensure 340- and 380-nm wavelength emission. Data were acquired with a Hamamatsu EMC9100 digital camera every 2.5 s and plotted with Aquacosmos version 2.6 (Hamamatsu Photonics, Massy, Francia). Ca2+ entry was assessed as an increase in the ratio of fluorescence at 340 and 380 nm (F340/F380) and analyzed as previously described [63] using GraphPad Prism version 7.0 (GraphPad Software, La Jolla, CA, USA). Intracellular Cas2+ variations were analyzed as changes in the area under the curve (AUC). ## 4.7. Glucose-Stimulated Insulin Secretion Glucose-stimulated insulin secretion in INS-1E and EndoC-βH1 cells was performed as previously described [51,64]. Briefly: before glucose stimulation, INS-1E cells were preincubated for 1 h in glucose-free RPMI 1640 GlutaMAX-I medium and then for 30 min in a Krebs-Ringer solution (as described in [50]). For the EndoC-βH1 cell line, cells were preincubated for 1 h in Krebs-Ringer buffer (115 mM NaCl, 5 mM KCl, 1 mM MgCl2, 1 mM CaCl2, 24 mM NaHCO3, 10 mM HEPES, pH 7.4, and $0.1\%$ BSA). At the end of this incubation, cells were sequentially stimulated with low glucose (1.7 or 0 mM), high glucose (17 or 20 mM) and then high glucose + forskolin (INS-1E cells) or high glucose + IBMX (EndoC-βH1 cells) for 30 min (INS-1E cells) or 1 h (EndoC-βH1 cells) (each stimulation). After each stimulatory period, the incubation medium was collected, placed onto ice and centrifuged at 700× g, 5 min at 4 °C. The supernatant was transferred into a fresh tube and stored at −80 °C until required for insulin measurements. For insulin content, cells were washed in cold PBS and then lysed in either an acid-ethanol solution ($95\%$ ethanol + $5\%$ HCl) (INS-1E cells) or cell lysis solution (137 mM NaCl, $1\%$ Glycerol, $0.1\%$ Triton X100, 2 mM EGTA, 20 mM Tris pH 8.0 and protease inhibitor cocktail) (EndoC-βH1 cells). Insulin secretion and content were measured using a rat or a human insulin ELISA kit (Mercodia, Uppsala, Sweden), following the manufacturer’s instructions. Forskolin and IBMX (Sigma-Aldrich, Barcelona, Spain) were prepared by dissolution in DMSO and used at concentrations of 10 μM and 50 μM, respectively. ## 4.8. Assessment of Cell Viability Cell viability was assessed by fluorescence microscopy using the DNA-binding dyes Hoechst 33342 (HO) and propidium iodide (PI), as previously described [50,65]. A minimum of 600 cells was counted for each experimental condition. Viability was evaluated by two independent observers, one of whom was unaware of sample identity. Agreement in results between observers was >$90\%$. This method has been extensively validated and correlates closely to other methods to measure apoptosis, including electron microscopy, cytochrome c release, cleaved caspases 3 and 9, BAX activation, as well as the determinations of histone-complexed DNA fragments by ELISA, MTT assay, and caspase $\frac{3}{7}$ activity [7,12,24,64,66,67,68,69,70]. ## 4.9. 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--- title: 'Photoplethysmography Driven Hypertension Identification: A Pilot Study' authors: - Liangwen Yan - Mingsen Wei - Sijung Hu - Bo Sheng journal: Sensors (Basel, Switzerland) year: 2023 pmcid: PMC10056023 doi: 10.3390/s23063359 license: CC BY 4.0 --- # Photoplethysmography Driven Hypertension Identification: A Pilot Study ## Abstract To prevent and diagnose hypertension early, there has been a growing demand to identify its states that align with patients. This pilot study aims to research how a non-invasive method using photoplethysmographic (PPG) signals works together with deep learning algorithms. A portable PPG acquisition device (Max30101 photonic sensor) was utilized to [1] capture PPG signals and [2] wirelessly transmit data sets. In contrast to traditional feature engineering machine learning classification schemes, this study preprocessed raw data and applied a deep learning algorithm (LSTM-Attention) directly to extract deeper correlations between these raw datasets. The Long Short-Term Memory (LSTM) model underlying a gate mechanism and memory unit enables it to handle long sequence data more effectively, avoiding gradient disappearance and possessing the ability to solve long-term dependencies. To enhance the correlation between distant sampling points, an attention mechanism was introduced to capture more data change features than a separate LSTM model. A protocol with 15 healthy volunteers and 15 hypertension patients was implemented to obtain these datasets. The processed result demonstrates that the proposed model could present satisfactory performance (accuracy: 0.991; precision: 0.989; recall: 0.993; F1-score: 0.991). The model we proposed also demonstrated superior performance compared to related studies. The outcome indicates the proposed method could effectively diagnose and identify hypertension; thus, a paradigm to cost-effectively screen hypertension could rapidly be established using wearable smart devices. ## 1. Introduction In recent years, the incidence and mortality rates of cardiovascular disease worldwide have continued to rise, with the age of onset advancing [1]. By 2020, the total number of cardiovascular disease patients in China had reached 330 million, and the out-of-hospital mortality rate was significantly higher than the in-hospital mortality rate [2]. Hypertension, an independent risk factor for cardiovascular and cerebrovascular diseases, has a substantial impact on cardiovascular health [3]. Research has shown that there are 245 million hypertensive patients in the Chinese population aged 18 and above. The total cardiovascular and cerebrovascular event risk of the population with normal high blood pressure increased by $37.0\%$, while the risk of ischemic stroke increased by $56.0\%$. The “Chinese Clinical Practice Guidelines for Hypertension” have also lowered the diagnostic threshold for hypertension by 10 mmHg, aimed at enabling timely treatment for patients. Early detection of hidden hypertension risks has significant implications for preventing cardiovascular and cerebrovascular diseases. Blood pressure refers to the lateral pressure that flowing blood in blood vessels exerts on the unit area of the blood vessel wall. It is generally divided into two types: SBP (Systolic Blood Pressure) and DBP (Diastolic Blood Pressure) [4], measured in millimeters of mercury. While arterial intubation is the international gold standard for blood pressure detection [5], this technology requires invasive procedures and is unsuitable for daily blood pressure measurement. Currently, mercury or electronic sphygmomanometers are commonly used for blood pressure measurement in homes and communities [6], but there are several limitations to these devices, such as the need for special instruments, cumbersome operation, inconvenient portability, and high equipment costs. These factors may discourage patients from continuing blood pressure detection, even though regular monitoring is crucial in helping patients better manage blood pressure and avoid serious cardiovascular complications [7]. People with hypertension are more susceptible to emotional problems, such as clinical depression [8,9], anxiety, and greater stress [10], which can contribute to changes in blood pressure [11]. However, traditional blood pressure monitoring methods do not capture these risks. In recent decades, non-invasive, continuous, and sleeveless alternatives for assessing blood pressure have gained increasing attention, most of which are based on the analysis of physiological signals. Researchers have explored different methods to evaluate blood pressure, such as calculating PTT (pulse pave transmit time) using ECG (electrocardiograph) and PPG (photoplethysmography) signals [12] and using simultaneous measurements of PPG from the toes and fingertips to determine PTT [13]. These methods require the synchronous acquisition of two sensors, and the measurement process is complex. Therefore, blood pressure detection based on a single PPG signal has become a new research focus. Many researchers have used machine learning to extract relevant features from PPG signals to estimate blood pressure. For instance, Monte-Moreno [14] extracted time-domain and frequency-domain features from PPG waveforms and estimated blood pressure using a random forest algorithm. Nour et al. [ 15] adopted four machine learning methods to classify PPG signals in hypertensive patients and concluded that the best methods for classifying hypertension types were the Decision Tree and Random Forest classifiers. Although these artificial feature extraction methods are highly subjective, the PPG signal can drift or distort under the influence of illumination, motion, or equipment, making feature extraction challenging and less accurate [16]. Deep learning methods can extract more abstract and complex features from input signals. For instance, Tjahjadi et al. [ 17] extracted time-domain information and used BiLSTM to classify blood pressure with a remarkable $97.33\%$ accuracy rate for hypertension. Liang et al. [ 18] converted PPG signals into RGB images and applied convolutional neural networks to evaluate hypertension, achieving F1-scores of $80.52\%$, $92.55\%$, and $82.95\%$ for normal blood pressure, prehypertension, and hypertension, respectively. Wu et al. [ 19] employed continuous wavelet transform to convert the original PPG signal into a pixel scalogram, and then trained and validated their CNN model with a $90\%$ accuracy rate. However, the feature extraction and deep learning approach may not fully exploit the advantages of neural networks, and the method of converting a one-dimensional PPG signal into an image may increase the signal dimension and prolong the model training time. Given that PPG signals are periodic and non-stationary, it is crucial to enhance the attention and connection between the signals and the early-stage signals for long-term blood pressure detection and evaluation. The study aims to explore a hypertension recognition method by the means of PPG signals to provide an alternative and cost-effective clinical monitoring tool. Thus, a bespoke data acquisition system has been developed to collect PPG signals. Furthermore, a Philips DB12 dataset as a reference was hired to evaluate the performance and accuracy of the bespoke data acquisition device. A model named LSTM for hypertension identification based on the attention mechanism was established to use raw data as input to avoid the tedious process of feature extraction. To verify and evaluate the performance, the performance of different models on the same dataset was applied to compare their metrics such as accuracy, precision, recall, F1-score, and operating efficiency. ## 2.1. Bespoke Data Acquisition Device and Its System To obtain high-quality raw PPG signals, we have developed a complete PPG signal acquisition system, as illustrated in Figure 1. The system is mainly divided into four modules: the PPG photoelectric sensor module, power supply module, control module, and communication module. The main objective of this system is to acquire, transmit, display, and store PPG signals. The photoelectric sensor, as the primary data source, has a direct impact on the accuracy and efficiency of subsequent data processing. In this study, we used the MAX30101 reflective photoelectric pulse sensor manufactured by Maxim Integrated (San Jose, CA, USA). This sensor has three LEDs, an adjustable constant current source drive, and a programmable sampling frequency. Additionally, the module is designed with low power consumption and high output data capacity, which can efficiently meet various sampling requirements. For data transmission, we used an ATK-ESP8266 as the Wi-Fi module, which supports standard IEEE802.11b/g/n protocols and the TCP/IP protocol stack and provides complete Wi-Fi functionality. The microcontroller selected for the system is the high-performance 32-bit MCU-STM32F103C8T6, designed with an ARM Cortex-M3 core. The CPU frequency of this microcontroller can reach up to 72 MHz, and its performance and chip resources are sufficient for PPG signal collection and forwarding. Figure 2 shows the PPG acquisition equipment developed in this study, and Table 1 summarizes its performance. To confirm the performance and precision of the experimental equipment, the heart rate measurement displayed by the PHILIPS DB12 blood oxygen monitor (Amsterdam, The Netherlands) was utilized as a benchmark for comparison before experimenting, as presented in Table 2. The heart rate data that was calculated from the PPG signal that was collected and processed had an error margin of less than $2\%$ when compared to the heart rate that was recorded by the DB12 blood oxygen monitor, thus guaranteeing the precision of the data acquisition. ## 2.2. Data Acquisition The participants were divided into two groups: the hypertension group and the healthy group. The hypertension group comprised 15 campus faculty and staff members who were diagnosed with hypertension. Similarly, 15 faculty and staff members without hypertension on campus were selected as the healthy group during the same period. In total, there were 30 subjects, consisting of 16 males and 14 females, with an age range of 50 ± 10 years. The basic information of all volunteers, including age, gender, BMI (Body Mass Index), and SBP, are presented in Table 3. Meanwhile, the PPG signals of the patients were collected in a controlled environment where the temperature of the room was set to 23 °C, and the finger temperature was maintained at 32 °C. The participants were instructed to remain still while their right index finger PPG signal was collected for 30 min, with one collection in the morning, one in the afternoon, and one in the evening, resulting in three samples per person, each with a sampling frequency of 100 Hz. The data collection process is depicted in Figure 3. Diagnostic criteria: volunteers with systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg (1 mmHg = 0.133 kPa) or those with a confirmed diagnosis of hypertension and were then taking hypertension medication were included in the hypertension group. Exclusion criteria: various acute, infectious, and contagious diseases; individuals with impaired visceral function; and those with moderate or above stenosis of the limb arteries were excluded from the study. The protocol for this study strictly adheres to the Helsinki Declaration [20], and the volunteers who agreed to participate in this study signed an informed consent form and obtained approval through the ethics committee of Shanghai University. ## 2.3. Data Preprocessing PPG is a non-stationary random signal that is characterized by low amplitude, low frequency, and susceptibility to interference. The quality of pulse signal acquisition can be affected by noise interference, mainly including baseline drift and high-frequency noise [21]. Baseline drift can be caused by respiration and body movement during signal acquisition, which leads to distortion of the PPG signal waveform. Therefore, data preprocessing is required before model training. In this study, the collected PPG raw signal was first filtered using a Butterworth digital filter to remove noise interference, followed by baseline drift removal using cubic spline interpolation. The data preprocessing flowchart is shown in Figure 4. ## 2.3.1. Butterworth Filter The Butterworth filter is an electronic filter characterized by a maximally flat frequency response curve within the passband, without any ripples, and gradually decreasing to zero within the stopband. On the Bode plot of amplitude versus logarithmic angular frequency, the amplitude decreases gradually with increasing frequency, tending towards negative infinity, starting from a certain boundary frequency [22]. The squared magnitude response of the Butterworth filter is expressed by Equation [1]:[1]Hjω2=11+ωωc2N N represents the filter order. The larger the N, the greater the approximation of the passband and stopband, and the steeper the transition. ωc is the cutoff frequency of the filter. The pulse wave frequency of healthy adults typically ranged from 0.1–20 Hz, with most of the invalid energy concentrated near the DC component. This energy accounted for approximately $95\%$ of the total energy of the waveform and reflected the characteristics of the heart pressure wave [23]. Meanwhile, the respiratory frequency of healthy adults was generally around 0.15–0.5 Hz. Therefore, the cut-off frequency of the filter was set at 0.6 Hz, and the second-order Butterworth filter was adopted to eliminate low-frequency noise caused by various factors, including baseline drift from different finger pressure and respiration. Finally, we smoothed the original signal using the smooth function. As shown in Figure 5, most of the baseline drift was removed by the Butterworth digital filter, and the distribution of waveform is more concentrated near the central axis. The amplitude spectrum diagram shows that the PPG frequency distribution is more focused within 10 Hz, with three distinct frequency peaks, indicating the filtering out of low-frequency noise. This processing successfully restores the original human PPG waveform, providing a solid foundation for the subsequent feature extraction of PPG. ## 2.3.2. Cubic Spline Interpolation To better extract accurate feature information, this study uses cubic spline interpolation to remove residual offset components in the signal processed, as mentioned earlier [24]. Cubic spline interpolation uses a smooth curve that can pass through a predetermined fixed point. The basic principle is to connect the points between each other using a cubic curve Pix, and Pix, Pi′x, and Pi″x are continuous at each boundary point. Because the second derivatives are continuous at all points, adjacent piecewise cubic polynomials have better coupling and a uniform curvature, making the curve smoother. The definition equation of the cubic spline can be expressed by Equation [2]. [ 2]Pix^=ai+bix^−xi +cix^−xi2+dix^−xi3 The baseline drift of the PPG signal is mainly caused by the fact that the trough positions of each cycle are mostly different from the same baseline. Therefore, a fitting curve can be constructed by taking the corresponding positions of trough points of each two cycles as nodes and fitting them with a spline function. By subtracting the fitting curve sequence from the PPG signal sequence, an un-offset PPG signal can be obtained. As shown in Figure 6, the curve fitted by cubic spline interpolation can accurately represent the current waveform offset, and the troughs of the PPG waveform after removing the offset are all placed at the same height. This establishes the benchmark for feature extraction and selection. It is only after eliminating noise and offset that relevant physiological information in the human body can be accurately displayed, which is also important for cleaning datasets in machine learning and deep learning. This is a noteworthy task in the model training process. ## 2.4. LSTM-Attention Model This section mainly introduces the LSTM-Attention model in four parts. The first part is the structure of the LSTM-Attention model. The second part describes the specific parameters of each network layer. The third part is the training of the model. The fourth part is the evaluation metrics of the model. ## 2.4.1. Model Architecture The proposed model comprises convolutional layers, LSTM network layers, and attention layers. It features one input node in the input layer and predicts a sequence of the next 1000-time steps in the output layer, taking filtered and denoised PPG signals as input. To extract deeper PPG features, the model employs three convolutional kernels with a stride of 40 in the first convolutional layer, expanding the feature dimension. Next, it maps the expanded features to 5 LSTM modules to generate feature maps. LSTM allows associating information from previous and subsequent time steps, enabling the model to predict the output at the next time step based on the previous input, thus anticipating future changes. Then, the information processed by the LSTM module feeds into the attention mechanism module. This module analyses the signal’s key and non-key regions and assigns different weights to signals in different regions, placing greater emphasis on details that require attention and enhancing data analysis accuracy. Finally, the model integrates feature information through the second convolutional kernel, reduces data dimensionality through the Flatten layer, conducts full connection via the Dense layer, and obtains the classification result through the Softmax function. The specific network structure is illustrated in Figure 7. ## LSTM Block Deep learning leverages hierarchical network structures to perform high-level abstraction on data. By learning from vast amounts of data, it extracts key features and computes nonlinear patterns. In the hypertension recognition model, the deep learning algorithm learns from the input pulse wave data, automatically extracting features from it. By avoiding the need for manual feature extraction for specific feature types, the model can resist interference and generalize better. Pulse wave data is a type of one-dimensional time-series data, and deep learning algorithms have shown promising results in processing such data [25]. Time-series data points are related to each other across different time points, and RNN (Recurrent Neural Network) structures are designed to capture such correlations and extract relevant features. RNN connects the previous and current nodes of the input data, allowing the calculation of the current node to depend on both the current input and the output of the previous time step. This approach enables the network to capture nonlinear relationships that match the time-series data pattern. However, RNN suffers from a long-term dependency problem, where the memory of the neuron for the input information at earlier time steps decreases as the network depth increases, leading to the significant influence of the final input [26]. This problem can cause gradient disappearance or explosion during the optimization stage. Therefore, in this experiment, we use the LSTM network structure to optimize the standard RNN model. Figure 8 illustrates the schematic diagram of the LSTM structure. The LSTM architecture effectively addresses the issues of gradient vanishing and exploding that often arise when processing time-series data with traditional RNNs. In contrast to the neural structure of traditional RNNs, LSTM incorporates three “gates” [27] that optimize its architecture. The “gate” structure comprises a Sigmoid operation, a sum operation, and a multiplication operation, with the output of the Sigmoid function ranging between 0 and 1. This output can be thought of as the amount of output information obtained after processing the input information, leading to the metaphorical reference of a “gate” structure. This “gate” structure enables neural nodes to selectively process the received information, thereby screening out key features. In LSTM, there are three types of gates: the forget gate, the input gate, and the output gate. The forget gate receives input from the current time step xt, the previous output ht−1, and the previous cell state Ct−1. The forget gate selectively “forgets” some of the information in the previous cell state Ct−1, based on the current input. The activation Equation for the forget gate (where b and W are value vectors, and σ is the sigmoid function) is:[3]ft=σWf×xt,ht−1,Ct−1+bf×Ct−1 After discarding irrelevant information through the forget gate, the neuron node uses the input gate to obtain new information at this time and update its state Ct based on xt, Ct−1, ht−1, and the activation function tanh. [ 4]it=σWi×xt,ht−1,Ct−1+bi [5]Ct=ft+it×tanhWc×xt,ht−1,Ct−1+bc Based on the new state Ct of the neuron, the LSTM structure can obtain the output of the current neuron through the output gate according to ht−1, xt, and the new state Ct. [ 6]ot=σWo×xt,ht−1,Ct+bo [7]ht=tanhCt×ot The output of a recurrent neural network can take different forms such as the corresponding judgment result for each input at every time step, a prediction of the next data point based on the learned temporal patterns, or a holistic judgment of the entire input sequence. For hypertension recognition in this study, the LSTM network is used to identify hypertension in a given data segment, and the output of the network represents a judgment of whether the data segment is likely to be in a hypertensive state. ## Attention Block The attention mechanism, proposed by Bahdanau in 2014 [28], mimics the image-processing mechanism unique to the human brain. This method selectively allocates attention according to needs, improving the efficiency of information processing in computers. Attention mechanisms are now widely used in various deep-learning fields, such as machine translation [29], image processing [30], and text classification [31], among others. Typically, attention mechanisms are based on the Encoder–Decoder framework, as shown in Figure 9. The model maps a variable-length input X=x1,x2,…,xn to a variable-length output Y=y1,y2,…,ym. The Encoder transforms the variable-length input sequence X into an intermediate semantic representation C via a non-linear transformation: C=fx1,x2,…,xn. The Decoder’s task is to predict and generate the output yi at the time i, based on the intermediate semantic representation C and the previously generated y1,y2,…,yi−1:yi=gy1,y2,…,yi−1, C, where f() and g() are both non-linear transformation functions. The attention mechanism proposed by Bahdanau et al. can address the issue of the lack of discriminability in the input sequence X for traditional Encoder–Decoder frameworks. The model framework is shown in Figure 10. St−1 is the hidden state of the decoder at time t − 1, yt is the target value, Ct is the context vector, and then the hidden state at time t is given by Equation [8]. [ 8]St=fSt−1,yt−1,Ct Ct depends on the hidden layer representation of the input sequence on the encoding side and can be represented, as shown in Equation [9], through weighted processing. [ 9]Ct=∑$j = 1$Tαt,jhj hj refers to the hidden vector of the jth value on the encoder side. It contains information about the entire input sequence but focuses on the surrounding parts of the jth value. T is the length of the input side. αt,j represents the attention allocation coefficient of the jth value on the encoder side to the tth value on the decoder side, and the sum of the probability values of αt,j is 1. The calculation formula for αt,j is shown in Equations [10] and [11]. [ 10]αt,j=expαt,j∑$j = 1$Texpαt,j [11]αt,j=aSt−1,hj αt,j represents an alignment model that measures the alignment/influence between the value at position j in the Encoder and the value at position t in the Decoder. Typically, the alignment model is parameterized as a feedforward neural network and trained jointly with the rest of the system. In the hypertension recognition experiment, local spatial features of the photoplethysmography signal were obtained through convolutional layers, and temporal feature representations of the PPG signal were further obtained through the LSTM network. However, these components did not effectively capture the correlation between current and past/future sampling points of the PPG signal, which can be detrimental for long-term PPG signal recognition tasks. To address this issue and improve the correlation between distant sampling points, an attention mechanism was introduced in this experiment. This mechanism selectively allocates different attention resources to sampling points with varying levels of importance. By incorporating the attention mechanism into the model, the spatial and temporal features of the PPG signal can be obtained, and the correlation between the current sampling point and the surrounding context points can also be considered. As a result, the model can allocate more attention resources to important areas, improving the accuracy of hypertension classification recognition. ## 2.4.2. Model Parameters After performing filtering and denoising on the data of all 30 volunteers using the preprocessing method described above, the PPG signal was sampled every 10 s and taken as one-dimensional data for training the model. Setting the learning rate is crucial in algorithm modeling. A low learning rate improves the reliability of the trained network, but it slows down the network parameter update speed and extends the training time. Conversely, a high learning rate may hinder network convergence and result in poor training effects. Additionally, a proper batch size setting can help the model determine the direction of gradient descent, which can improve the degree and speed of model optimization. To avoid overfitting during the model calculation process, the training process also introduced the dropout strategy. Table 4 shows the parameter settings of the LSTM model used in this research. ## 2.4.3. Model Training In this experiment, $70\%$ of the data was used for training, $20\%$ for testing, and $10\%$ for validation. The LSTM-Attention recurrent network was trained, and the trained LSTM-Attention network was used to classify the test set. The model training was repeated multiple times using different random seeds to ensure the randomness of the data. Finally, the average of all predicted values was taken as the final result. The Keras deep learning framework [32] was used to establish and train the model. ## 2.4.4. Performance Evaluation The model’s performance was evaluated by calculating the probability distribution of the dataset being hypertensive, as well as the overall accuracy, precision, recall, and F1-score. The calculation method for accuracy is shown in Equation [12]:[12]Accuracy=TP+TNTP+TN+FP+FN The calculation method for precision is shown in Equation [13]:[13]Precision=TPTP+FP The calculation method for recall is shown in Equation [14]:[14]Recall=TPTP+FN The calculation method for F1-score is shown in Equation [15]:[15]F1=2×Precision×RecallPrecision+Recall where TP, TN, FP, and FN correspond to the relationships shown in Table 5. During the training process, the loss function is the cross-entropy loss function, which is calculated using Equation [16]:[16]Lθ=−1N∑$i = 1$N∑$j = 1$myilogPyi=j\|xi To further evaluate the performance of the proposed LSTM-Attention model for hypertension recognition, it was compared with other models, including LSTM (without attention mechanism), BiLSTM, SVM (Support Vector Machine), and KNN (K Nearest Neighbors), on the same dataset. For the LSTM and BiLSTM models, the training samples were the same as those used for the LSTM-Attention model. However, the traditional SVM and KNN models utilized a feature extraction method that involved manually extracting features before training. Specifically, 20-dimensional features were extracted in the time and frequency domains for each sample data, which consisted of PPG signals recorded every 10 s, as shown in Table 6. With the increasing role of neural networks in the field of artificial intelligence, verifying their credibility has become a focus of attention, including their availability, reliability, robustness, and interpretability, among other factors [33]. In this experiment, a noise interference method was used to assess the robustness of the model, where unprocessed PPG test data was used to evaluate the model previously trained to verify its ability to resist interference. Furthermore, we used the data reservation method to assess the reliability of the LSTM-Attention model. We randomly selected and retained the data of one healthy volunteer and one patient with hypertension. The data of the remaining 28 volunteers were used as the training set to train the model, and the data of the two reserved volunteers were used as the test set to verify whether the model exhibited overfitting. ## 3. Results Based on the self-collected dataset, $70\%$ of the pre-processed data was used to train the LSTM-Attention model. The confusion matrix on the test set is shown in Figure 11a. In the robustness verification experiment, a portion of unprocessed original PPG signals was used as the test set to verify the model, and the confusion matrix is shown in Figure 11b. In the reliability verification experiment, data from two volunteers were reserved for testing the model, and the confusion matrix is shown in Figure 11c. Based on the confusion matrix, the recognition accuracy of the LSTM-Attention hypertension classification model was calculated as $99.1\%$, precision as $98.9\%$, recall as $99.3\%$, and F1-score as $99.1\%$. For the robustness verification, the classification accuracy of the unprocessed PPG data by the model was found to be $95.58\%$, precision as $95.89\%$, recall as $95.24\%$, and F1-score as $95.56\%$. For the reliability verification, the classification accuracy of the new data by the model was found to be $93.38\%$, precision as $94.22\%$, recall as $92.44\%$, and F1-score as $93.32\%$. The Accuracy and Loss curves for training and validation of the LSTM-Attention model are shown in Figure 12. As the number of iterations increases, the accuracy of the model gradually increases and reaches its highest value; the loss of the model gradually decreases and eventually converges to a stable value. The classification results of different models on the same dataset are shown in Table 7. The LSTM model with the attention mechanism achieved the highest accuracy, reaching $99.1\%$, which is $3.8\%$ higher than the accuracy of the regular LSTM model. The classification performance of the BiLSTM model was slightly higher than that of the LSTM model, reaching $95.8\%$. In contrast, the training and classification performance of SVM was the worst, with an accuracy of $90.4\%$, while KNN had a slightly higher accuracy than SVM, reaching $94.6\%$. Table 8 displays the training time required for one epoch by different algorithm models. The LSTM model completes one epoch of training in 6.5 s, while the BiLSTM model takes 8.6 s. In comparison, the LSTM-Attention model requires 10.3 s for one epoch of training, indicating that the introduction of the attention mechanism increases the model’s complexity and requires a longer training time. Nevertheless, considering the improvement in the final accuracy, increasing the complexity of the model is necessary. ## 4. Discussion The main findings of this study are as follows: [1] The PPG signal acquisition system designed and developed based on STM32 meets the experimental requirements and has the advantages of portability, low cost, and high accuracy. [ 2] The proposed LSTM-Attention hypertension recognition model has higher accuracy than the conventional RNN models. Compared with traditional machine learning methods, the proposed model avoids the cumbersome process of feature extraction and can better capture the correlation between PPG sequence information, enhance feature representation capability, and improve performance. ## 4.1. Performance of LSTM-Attention Model The proposed LSTM-Attention model for hypertension recognition in this research achieved an outstanding accuracy of $99.1\%$, surpassing that of the conventional LSTM model at $95.3\%$ and the BiLSTM model at $95.8\%$. The unidirectional LSTM model fell short of highlighting the connection between the current sampling point of the PPG signal and its historical and future information, making it unsuitable for long-term PPG signal recognition tasks. On the other hand, the LSTM model with an attention mechanism could allocate varying attention resources to sampling points of different importance levels, leading to a stronger feature expression ability. Furthermore, the LSTM-Attention model outperformed other models in terms of precision and recall evaluation. Nevertheless, as precision and recall mutually restrict each other, it is challenging to determine, objectively, which model is of higher quality. Hence, a more comprehensive evaluation index, the F1-score, is necessary for comparison. The F1-score of the LSTM-Attention model was $99.1\%$, which was still higher than the LSTM’s $95.2\%$ and BiLSTM’s $95.7\%$. In the formal verification experiment, the classification accuracy of the LSTM-Attention model for the PPG data without preprocessing reached $95.58\%$. Although the accuracy slightly decreased when tested on noisy data, it still achieved a good recognition rate, indicating that the model had good robustness. When recognizing unfamiliar data, the accuracy rate reached $93.38\%$. Given the limited size of the dataset used in this experiment, such a fitting effect can be considered as meeting the requirements. Traditional machine learning methods have been extensively studied in PPG [34,35,36,37], and this study also compares SVM and KNN classification models with the LSTM-Attention model. Unlike neural networks, which can directly train on raw signals, feature extraction is required before training these two models. In this study, PPG data was sampled at 10 s intervals, and 20 features in the time and frequency domains were extracted for each sample. The experimental results showed that the highest accuracy of SVM was $90.4\%$, and the highest accuracy of KNN was $94.6\%$, both of which were inferior to the LSTM-Attention model in evaluation metrics such as accuracy and precision. During the feature extraction process, it was found that the position of the reflected wave was difficult to locate or may disappear due to the influence of the elasticity of the vessel wall in hypertensive patients, leading to missing feature points and the need to discard such data, which reduced the overall data volume. Artificially extracted features have limited dimensions and make it difficult to discover correlations between features, which may overlook important features in identification and classification. The use of neural network methods can automatically obtain features and their correlations, avoiding the tedious work of manual feature extraction and enhancing the feature expression ability. In terms of model efficiency, the LSTM model required 6.5 s for one round of training, while the BiLSTM model required 8.6 s. On the other hand, the LSTM model with an attention mechanism took 10.3 s for one round of training. When the sample size was small, there was little difference in the running time of the three models. However, as the sample size increased, the running time of the LSTM network with the attention mechanism significantly increased due to the attention mechanism’s considerable time consumption in identifying the focus and non-focus regions. This may slightly affect performance, but accuracy is crucial in hypertension recognition. Moreover, future hypertension recognition is likely to be more focused on personal applications with a limited sample size, where this algorithm’s advantages become more significant. We compared our method with the results of previous studies, as shown in Table 9. These studies were focused on categorizing hypertension and non-hypertension using PPG signals. In the first study [38], 10 manually extracted features from PPG signals were used, and AdaBoost and KNN classifiers were employed as machine learning models. In the third [17] and fourth [18] studies, the scalogram and spectrogram of the PPG original waveform were used as input to the models, and the F1-scores obtained on the CNNs and BLSTM models reached $92.55\%$ and $97.39\%$, respectively, which already outperformed traditional machine learning solutions. In contrast, the LSTM-Attention hypertension recognition model proposed in this study used raw data and neural networks to fully extract the deep features of PPG signals. The introduction of an attention mechanism strengthened the model’s feature expression ability in the long term, further improving its performance. The experimental results showed that our model achieved an accuracy of $99.1\%$, outperforming previous studies. ## 4.2. Prospect of Hypertension Identification As mentioned in the introduction, PPG signals contain rich cardiovascular and cerebrovascular pathological information, which has led to an increasing number of studies using PPG for disease diagnosis. The integration of PPG technology into commonly used wearable devices, such as smart bracelets and watches, presents a promising opportunity for convenient and reliable early diagnosis of hypertension [39]. The PPG signal acquisition device designed in this study is lightweight and portable, as demonstrated in Table 2, with comparable accuracy to existing devices. The collected PPG data can be directly used for hypertension identification research. With the continuous advancement of microelectronic technologies, more advanced chips with greater computational power can be used in PPG devices, enabling the diagnosis of high blood pressure on wearable devices such as smart bracelets. This innovation could revolutionize traditional blood pressure measurements, allowing users to monitor their blood pressure health in real-time and receive alerts when their blood pressure is unhealthy, potentially preventing cardiovascular diseases. With the rapid development and widespread use of wearables, this research has the potential to benefit a larger population. ## 4.3. Limitations and Future Work Several limitations may affect the experimental results. First, the number of volunteers recruited was not sufficient, and PPG data for all blood pressure values were not obtained. Although we considered the balance of age and gender, the limited amount of data may limit the generalizability of the model. Additionally, while PPG signals are easy to collect, they may have a poor signal-to-noise ratio and are susceptible to interference, which could have affected the data quality and affected the experimental results. Moreover, we only used collected red light PPG data for research, and more research is needed on the multidimensional and diversified fusion of data. To overcome these limitations, future work will involve recruiting more volunteers with different blood pressure values, monitoring the frequency drift of PPG signals to enhance the robustness of the dataset [16], and conducting multidimensional feature fusion research using PPG signals from multiple light sources. This will help extract hidden features in PPG signals from other wavelength light sources and enhance the reliability of the model. ## 5. Conclusions This research designed a PPG signal acquisition device for hypertension recognition and proposed an LSTM-Attention hypertension recognition model based on the attention mechanism. The introduction of an attention mechanism improved the feature expression ability of the model, thereby improving the recognition accuracy. Experimental results showed that the accuracy of the LSTM-Attention hypertension recognition model reached $99.1\%$. Compared with other machine learning evaluation models, this model demonstrated excellent performance, ranking first in precision, accuracy, and F1-score indicators. Although there was some sacrifice in model complexity, the overall performance was improved. In summary, the proposed LSTM-Attention hypertension recognition model has significant advantages and has the potential to be used for hypertension assessment in home and community environments. 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--- title: Piceatannol Prevents Obesity and Fat Accumulation Caused by Estrogen Deficiency in Female Mice by Promoting Lipolysis authors: - Kotoko Arisawa - Miyuki Kaneko - Ayumi Matsuoka - Natsuki Ozawa - Rie Kawawa - Tomoko Ishikawa - Ikuyo Ichi - Yoko Fujiwara journal: Nutrients year: 2023 pmcid: PMC10056039 doi: 10.3390/nu15061374 license: CC BY 4.0 --- # Piceatannol Prevents Obesity and Fat Accumulation Caused by Estrogen Deficiency in Female Mice by Promoting Lipolysis ## Abstract Postmenopausal women have a higher susceptibility to obesity and chronic disease. Piceatannol (PIC), a natural analog of resveratrol, was reported to inhibit adipogenesis and to have an antiobesity effect. In this study, PIC’s effect on postmenopausal obesity and the mechanism of its action were investigated. C57BL/6J female mice were divided into four groups and half of them were ovariectomized (OVX). Both OVX and sham-operated mice were fed a high-fat diet (HFD) with and without the addition of $0.25\%$ of PIC for 12 weeks. The abdominal visceral fat volume was higher in the OVX mice than the sham-operated mice, and PIC significantly decreased the fat volume only in the OVX mice. Unexpectedly, expression levels of adipogenesis-related proteins in white adipose tissue (WAT) were suppressed in the OVX mice, and PIC did not affect lipogenesis in either the OVX or sham-operated mice. Regarding the expression of proteins associated with lipolysis, PIC activated the phosphorylation of hormone-sensitive lipase much more in the OVX mice, but it did not affect the expression of adipose triglyceride lipase. PIC also tended to induce the expression of uncoupled protein 1 in brown adipose tissue (BAT). These results suggest that by promoting lipolysis in WAT and deconjugation in BAT, PIC is a potential agent to inhibit fat accumulation caused by menopause. ## 1. Introduction Postmenopausal women are prone to obesity resulting from estrogen deficiency [1,2]. Menopausal changes tend to occur in fat deposition rather than in body weight gain and cause visceral fat accumulation, whereas premenopausal women accrue subcutaneous fat [3,4]. Estrogen deficiency is known to lead to many metabolic changes, such as overeating as a result of resistance to leptin sensitivity [5,6], low metabolic expenditure [3], and a decrease in spontaneous activity [6]. Excessive accumulation of visceral fat is known to increase the risks associated with metabolic syndromes, such as cardiovascular diseases, atherosclerosis, hypertension, and type 2 diabetes [7,8,9,10]. Therefore, suppressing the excessive accumulation of visceral fat is important for postmenopausal women’s health. Piceatannol (3,5,3′,4′-tetrahydroxystilbene, PIC) is a natural analog of resveratrol (RSV) [11], one of the widely studied polyphenols that have preventive effects on the development of obesity-related diseases [12,13,14]. PIC is found in the seeds of passion fruit, grapes, and many other fruits [15,16,17] and is known as a specific inhibitor of spleen tyrosinase kinase (Syk) [18]. Previous studies have identified many biological activities of PIC, including antioxidant [19], anticancer [20], and anti-inflammatory effects [11,21,22]. Especially, PIC is reported to inhibit adipogenesis of 3T3-L1 cells [12], mesenchymal stem cells [23], and cells from human visceral adipose tissue [24]. The inhibitory effect of PIC on adipocyte differentiation is reportedly mediated by the modulation of mitotic clonal expansion and insulin signaling in the early stage of differentiation [12,23,24]. In vivo, PIC has also been shown to reduce adipose tissue weight in male C57BL/6 mice fed a high-fat diet (HFD) and to suppress obesity complications in Zucker rats [13,14]. However, it is not clear whether PIC has a beneficial effect on obesity in postmenopausal women. Sex-specific disparities are commonly observed in animal models of obesity, often with a greater degree of severity in males [25]. The C57BL/6J mouse strain is widely used in obesity research and has been shown to develop obesity in male mice when fed an HFD. In contrast, female C57BL/6J mice exhibit greater resilience to the obesogenic effects of an HFD [26]. However, when subjected to ovariectomy, ERα knockout, or aromatase knockout, these female mice can develop obesity when fed an HFD [27,28,29]. Hormone replacement therapy has been found to suppress obesity resulting from estrogen deficiency [30]. These findings suggest that sex hormones play a protective role against obesity in female mice, and these mouse models are being utilized to investigate the mechanisms underlying postmenopausal obesity. Stilbene compounds, such as RSV and PIC, have similar molecular structures to 17 beta-estradiol [31], suggesting the possibility that they could act as estrogen replacement therapy. Hence, our study evaluated the effect of PIC on obesity induced by estrogen deficiency using ovariectomized (OVX) mice. Our findings indicate that PIC reduces fat accumulation in OVX mice by promoting lipolysis. ## 2.1. Materials Piceatannol was obtained from Morinaga Research Institute (Tokyo, Japan) in 2017. Its concentration was measured using HPLC analysis as previously described [32]. ## 2.2. Animals and Experimental Design The Animal Ethics Committee of Ochanomizu University approved the study (No. 18009, 1 May 2018; and 20028, 15 October 2020). Six-week-old female C57BL/6J mice were purchased from CLEA Japan, Inc. (Tokyo, Japan) and housed in cages with a maximum of five mice per cage. The mice were maintained in a room with controlled temperature (21–25 °C) and humidity (40–$60\%$) with a 12 h dark/light cycle under artificial lighting. During acclimation, the mice were fed a regular chow diet (CLEA Rodent Diet CE-2, CLEA Japan, Inc.) and had free access to water. The design of the experiment is shown in Figure S1. After a 7-day acclimation period, the mice were divided into 2 groups based on their body weight and were ovariectomized (OVX) or sham-operated (sham) ($$n = 15$$ and 12 for the OVX and sham groups, respectively). The surgery was performed under deep anesthesia following intraperitoneal injection of a 3-mixed solution (0.1 mL/10 g B.W.) containing Domitor® (0.3 mL; Nippon Zenyaku Kogyo Co, Ltd., Fukushima, Japan), midazolam (0.8 mL; Fuji Pharma Co., Ltd., Toyama, Japan), Vetorphale® (1.0 mL; Meiji Seika Pharma Co, Ltd., Tokyo, Japan), and physiological saline (7.9 mL). For the OVX group, the anterior uterine horns were excised to remove the ovaries, while in the sham-operated controls, the two ovaries were visualized but not removed [27,33]. The control diet used in this study was based on the AIN-93M diet and had a high-fat content (HFD, $45\%$ fat, $20\%$ protein, and $35\%$ carbohydrate). The experimental group was fed the same HFD but with the addition of $0.25\%$ PIC. Detailed information on the composition of the experimental diets used in this study is described in Table S1. After one week of either sham or OVX surgery, the mice were assigned randomly to one of four groups and placed on either the control or experimental diet. The sham and OVX groups were each divided into an HFD-alone group and an HFD containing $0.25\%$ PIC group (sham, sham + PIC, OVX, and OVX + PIC). Throughout the experimental period, all mice were provided with unlimited access to water and diets. After 12 weeks, all mice were euthanized following administration of pentobarbital sodium (Somnopentyl; Kyoritsu Seiyaku Corporation, Tokyo, Japan) into the abdominal cavity. Under anesthesia, blood was collected from the axillary vein, and tissues were harvested and stored at −80 °C until further analysis. Mice that did not show uterine atrophy upon dissection after the 12-week experiment were excluded from the results. The experimental period was determined based on our unpublished previous studies and was the period during which obesity due to the HFD was reliably observed in the OVX group compared to the sham group. ## 2.3. Glucose Tolerance Test The glucose tolerance test was performed 11 weeks after the start of the experimental diet. After 16 h fasting period, fasting blood glucose concentration was measured from the tail using FreeStyle Precision Neo/FS precision blood glucose test strips (Abbott Japan LLC, Tokyo, Japan). Subsequently, an intraperitoneal injection of a $10\%$ glucose solution was administered at a dose of 1 mg/g of body weight. After administering glucose, blood glucose levels were evaluated at 15, 30, 60, and 120 min. ## 2.4. Biochemical Parameters of Blood Blood was collected from the axillary vein of the mice under anesthesia at the time of dissection, mixed with a small amount of heparin sodium (5000 units/5 mL; Mochida Pharmaceutical Co., Ltd., Tokyo, Japan), and placed on ice. After centrifugation at 1200× g for 15 min, the resulting supernatant plasma was collected. Plasma total cholesterol and free fatty acid concentrations were determined with an enzymatic colorimetric assay, entrusted to Oriental Yeast Co., Ltd. (Tokyo, Japan). Plasma triglyceride concentrations were measured using enzymatic methods (Triglyceride E-Test Wako; FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan). ## 2.5. Micro X-ray Computed Tomography (CT) Analysis of Fat Accumulation To assess fat deposits, the abdominal region between the first and fifth lumbar vertebrae of the mouse was analyzed using micro-X-ray CT (CosmoScan FX; Rigaku, Tokyo, Japan). Images were acquired with a tube voltage of 90 kV, tube current of 88 μA, 120 μm slice thickness, and total scan time of 2 min [34,35]. Following scanning, the visceral and subcutaneous fat mass were calculated using 3-dimensional image analysis software (Analyze 12.0; AnalyzeDirect, Overland Park, KS, USA). The abdominal muscular wall was used to separate the visceral fat (inside of the abdominal wall) from the subcutaneous fat (outside of the abdominal wall) [36]. These analyses were performed at 4, 8, and 12 weeks after the start of the experimental diet. ## 2.6. Western Blotting Proteins were extracted from the tissues using RIPA buffer (R0278; Sigma-Aldrich, St. Louis, MO, USA) supplemented with protease inhibitor cocktail (P8340; Sigma-Aldrich) and phosphatase inhibitor cocktails (P5726 and P0044; Sigma-Aldrich). Protein concentrations were determined using the BCA assay (FUJIFILM Wako Pure Chemical Corporation). Equivalent amounts of proteins were separated by electrophoresis on $8\%$ SDS-PAGE and subsequently transferred to Immobilon PVDF membranes (Bio-Rad Laboratories, Inc., Hercules, CA, USA). The membranes were treated with blocking reagent ($5\%$ w/v nonfat dry milk in Tris-buffered saline containing $0.1\%$ Tween-20) for 1 h at room temperature. After that, the membranes were incubated overnight at 4 °C with the following primary antibodies (all from Cell Signaling Technology, Danvers, MA, USA): rabbit antiphospho-HSL (Ser563; CST#4139), rabbit anti-HSL (CST#18381), rabbit anti-ATGL (CST#2439), rabbit anti-p-ACC (Thr 172; CST#2535), rabbit anti-ACC (CST#3662), rabbit anti-FAS (CST#3189), and mouse anti-β-actin (CST#8457). The membranes were then incubated with peroxidase-conjugated antirabbit antibody (Jackson ImmunoResearch Laboratories, West Grove, PA, USA; 111-035-144) or antimouse secondary antibody (Jackson ImmunoResearch Laboratories; 115-005-003). Immunoreactivity was detected using ECL Prime western blotting detection reagents (Cytiva, Tokyo, Japan) with β-Actin serving as the loading control. ## 2.7. Quantitative Real-Time PCR (qPCR) Total RNA was extracted from frozen tissues using TRIzol reagent (ThermoFisher Scientific, Cleveland, OH, USA) and reverse transcribed into cDNA with ReverTra Ace® qPCR RT Master Mix (TOYOBO CO., LTD., Osaka, Japan). Gene expression levels were assessed by real-time PCR on the StepOnePlus Real-Time PCR System (Thermo Fisher Scientific) with SYBR Green (THUNDERBIRD® SYBR qPCR Mix, TOYOBO). β-actin mRNA served as the invariant control. The primer sequences used are as follows: β-actin (forward, 5′-ACT-ATT-GGC-AAC-GAG-CGG-TT-3′; reverse, 5′-ATG-GAT-GCC-ACA-GGA-TTC-CA-3′), UCP1 (forward, 5′-GGA-TGG-TGA-ACC-CGA-CAA-CT-3′; reverse, 5′-GAT-CTG-AAG-GCG-GAC-TTT-GG-3′), PGC1α (forward, 5′-AAG-TGT-GGA-ACT-CTC-TGG-AAC-TG-3′; reverse, 5′-GGG-TTA-TCT-TGG-TTG-GCT-TTA-TG-3′) and SIRT1 (forward, 5′-ATG-ACG-CTG-TGG-CAG-ATT-GTT-3′; reverse, 5′-CCG-CAA-GGC-GAG-CAT-AGA-T-3′). The thermal cycling conditions employed in this study consisted of an initial denaturation step at 95 °C for 10 min followed by 40 cycles at 95 °C for 15 s and 60 °C for 1 min. ## 2.8. Statistical Analysis All data were reported as the mean ± standard error (M ± SE). Statistical analysis was performed using Microsoft Excel for Mac version 16.70 and one-way ANOVA with GraphPad Prism 8 software (GraphPad Software Inc., San Diego, CA, USA) followed by the Tukey–Kramer’s test. A p-value below 0.05 was considered statistically significant. ## 3.1. Effect of Piceatannol on Body and Tissue Weight in HFD-Fed Ovariectomized C57BL/6J Mice In this study, acclimated seven-week-old C57BL/6J mice were subjected to OVX or sham surgery. Following 1 week of acclimation, the mice were divided into 2 groups randomly to ensure that the sham and OVX groups had the same baseline body weight, after which they were fed a $45\%$ high-fat diet (HFD) or the same HFD but with the addition of $0.25\%$ piceatannol (PIC). The sham-HFD, sham-PIC, OVX-HFD, and OVX-PIC groups contained five, six, six, and six mice, respectively. Figure 1a shows the results of the weekly weight measurements. At the end of the experiment, the OVX-HFD group was approximately $30\%$ heavier than the sham-HFD group, indicating that OVX increases obesity, as seen in previous reports [27]. On the other hand, the OVX-PIC group had a significant reduction in weight compared to the OVX group. However, there was no notable difference between the sham + PIC and sham groups. The results indicated that PIC had a selective effect on OVX-induced obesity and did not reduce the body weight of female mice who were not obese. Food intake among the four groups did not differ significantly, as shown in Figure 1b. White adipose tissue (WAT), brown adipose tissue (BAT), and liver weights are shown in Figure 1c. OVX significantly increased WAT weight, and PIC suppressed the increase; in BAT and the liver, there was no increase in weight by OVX. However, in BAT, the PIC-supplemented groups among both the sham and OVX mice showed a decreased weight. Browning with fat accumulation in the tissue was observed in the BAT of sham-HFD and OVX-HFD. These findings propose that PIC supplementation reduces OVX-induced weight gain by decreasing the weight of adipose tissue. ## 3.2. Effects of PIC on Blood Biochemical Parameters in Ovariectomized Mice The OVX + HFD group had significantly elevated serum total cholesterol levels compared to the sham + HFD group, but PIC supplementation significantly suppressed this effect (Figure 2a). Plasma triglyceride and nonesterified fatty acid levels in any group differed significantly from one another (Figure 2b,c). None of the groups displayed a substantially elevated blood glucose level during the oral glucose tolerance test, and the administration of PIC had no influence on blood glucose or insulin levels (Figure 2d,e). The PIC-supplemented groups had lower areas under the blood glucose curve (AUC) compared with the HFD groups, although this distinction was not statistically significant (Figure 2f,g). ## 3.3. Effects of PIC on Visceral and Subcutaneous Fat Accumulation HFD-Fed Ovariectomized Mice Micro-CT is noninvasive and provides high-resolution, 3D imaging of adipose tissue, enabling continuous analysis throughout a study period. The visceral and subcutaneous fat volumes of each group of mice at 4, 8, and 12 weeks, as analyzed by micro-CT, are shown in Figure 3a and Figure 3b, respectively. Fat accumulation in the OVX group was seen as early as 4 weeks and continued until 12 weeks. In each period, PIC suppressed the OVX-induced increase in fat accumulation. On the other hand, there was no significant difference between the sham + HFD and sham + PIC groups. These results suggest that PIC contributes to the suppression of obesity by inhibiting both visceral and subcutaneous fat accumulation, which was increased in OVX mice. ## 3.4. Effects of PIC on the Expression of Proteins Involved in Lipid Metabolism The expression of proteins implicated in the metabolic processes of lipid synthesis and lipolysis in ovarian adipose tissue was evaluated by western blotting (Figure 4a,b). Even though the OVX-HFD and OVX-PIC groups showed significantly higher fat accumulation than the sham groups, protein expression levels of fatty acid synthase (FAS) and acetyl CoA carboxylase (ACC), which are associated with fat synthesis, were significantly reduced. Activated ACC catalyzes the production of malonyl-CoA; ACC is inactivated by phosphorylation. PIC did not promote ACC phosphorylation, suggesting that it does not inhibit fat synthesis. We also assessed the evaluated levels of hormone-sensitive lipase (HSL) and adipose triglyceride lipase (ATGL), both of which are essential for the hydrolysis of triglyceride. The results showed that OVX surgery and PIC treatment did not affect the expression levels of the two lipases. However, HSL phosphorylation was decreased in the OVX-HFD group compared to the sham-HFD group, and PIC supplementation significantly improved that reduction. Phosphorylation of the serine residue (Ser563) of HSL enhances its activity and promotes lipolysis [37]. In the BAT, phospho-HSL was not increased by PIC treatment (Figure 4c). However, in terms of gene expression related to heat production, the addition of PIC had a tendency to increase UCP1 expression in the OVX group (Figure 4d). UCP1 enhances energy expenditure by promoting deconjugation in the mitochondrial inner membrane [38]. Gene expression levels of SIRT1 and PGC1α, which are regulators of UCP1, tended to increase with the addition of PIC, although no significant differences were observed. Thus, our results suggest that PIC promotes deconjugation in the BAT of postmenopausal obesity models via the SIRT1/PGC1α/UCP1 pathway [39,40]. These results suggest that PIC has antiobesity effects in OVX mice by enhancing lipolysis in WAT and energy expenditure in BAT. ## 4. Discussion One of the important public health concerns is the increase in overweightness and obesity in menopausal women. It is known that the decreased level of estrogen is the major cause of increased obesity among menopausal women [3]. During menopause, women experience a rapid decline in ovarian function, which leads to a decrease in estrogen production. This decrease in estrogen is associated with increased overall body fat, particularly visceral fat [4]. Moreover, estrogen deficiency is reported to aggravate metabolic dysfunction and cause susceptibility to metabolic syndrome, type 2 diabetes, and cardiovascular diseases [41,42,43]. PIC, a natural polyphenolic stilbene, has been reported to exhibit various beneficial effects, such as anti-inflammatory, antioxidant, and antiproliferative activities [11]. Recent studies suggest that PIC has both in vitro and in vivo antiobesity effects. In the 3T3-L1 cell model, PIC has been shown to reduce triglyceride accumulation by inhibiting the transcription factors C/EBPβ, C/EBPα, and PPARγ [12]. In vivo studies have shown that in male C57BL/6 mice induced to become obese by an HFD, PIC inhibited weight gain; reduced blood glucose, total cholesterol, LDL, and triglyceride concentrations; and inhibited visceral fat accumulation [14]. In women, however, it was not clear whether PIC is effective for treating obesity resulting from menopause. Therefore, we examined the antiobesity effects of PIC on estrogen deficiency-induced obesity. In this study, ovariectomized (OVX) female mice were used as an estrogen-deficient obesity model that mimics menopausal women [44]. The results showed weight gain and fat accumulation in the OVX group compared to the sham group. Notably, PIC significantly suppressed OVX-induced weight gain and reduced WAT weight. The distribution of adipose tissues differs between males and females. Males have a higher tendency to accrue fat in the visceral depot, while premenopausal females accumulate more subcutaneous fat. The tendency to deposit subcutaneous fat rather than visceral fat protects females from the harmful consequences of obesity and metabolic syndrome [5]. After menopause, the level of circulating estrogen decreases, causing a shift in the pattern of fat distribution from subcutaneous to visceral, which leads to an increase in the risk of metabolic disorders similar to that of males [4]. In the present study, PIC significantly suppressed both subcutaneous and visceral fat in the OVX group, suggesting that PIC is effective in suppressing visceral fat caused by the estrogen deficiency seen in postmenopausal women. Furthermore, micro-CT scan observations revealed that PIC had a significant antiobesity effect on OVX-induced obesity from the fourth week of the experiment, suggesting that it may also suppress early fat accumulation. In a previous examination of PIC’s inhibitory effect on HFD-induced obesity in male C57BL/6 mice, PIC showed an antiobesity effect with higher levels of pACC and lower levels of FAS suppressing fat synthesis [14]. In the estrogen deficiency-induced obese mice used in this study, OVX surgery rather reduced fat synthesis by pACC/FAS, and PIC did not suppress the synthesis, whereas it did increase phosphorylation of HSL, the rate-limiting enzyme for lipolysis. Taken together, our results suggest that PIC suppressed fat accumulation under estrogen depletion in WAT by a completely different mechanism than that shown in previous studies. HSL is the enzyme that hydrolyzes intracellular triglyceride and diacylglycerol to fatty acids and glycerol and is activated by phosphorylation of a serine residue (Ser563), which is transferred from the cytosol to the surface of lipid droplets [45,46]. In general, when noradrenaline binds to β-adrenergic receptors (βAR), intracellular signaling pathways, such as adenylate cyclase-cAMP-protein kinase A, are activated and downstream hormone-sensitive lipases are activated to degrade neutral lipids [47]. However, it has not been reported that PIC binds and activates βARs. On the other hand, it has been reported that the cAMP/PKA pathway is one of the signaling pathways downstream of the G protein-coupled estrogen receptor (GPER) [48]. Although GPER has an estrogen-responsive sequence, it differs from the nuclear estrogen receptors ERα and ERβ in that it exerts effects through the production of second messengers rather than through transcriptional regulation by nuclear translocation [49]. Previous studies have proposed that estrogen’s role in reducing obesity may be linked to the activation of GPER. It has been reported that PIC, due to its structural similarity to diethylstilbestrol, a synthetic estrogenic agent, can act as a phytoestrogen compound [31]. The estrogen-like structure of PIC may have ameliorated estrogen-deficient obesity by increasing phospho-HSL through GPER-mediated signaling. The addition of PIC did not decrease ATGL protein, another lipase that hydrolyzes triglyceride. Although PIC reportedly reduces ATGL expression in adipocytes via upregulation of the autophagy-lysosome pathway [50], it is possible that PIC would not have affected this pathway in WAT under the conditions of this study. Obesity is caused by excessive TG accumulation in adipocytes as well as by decreased energy expenditure and increased food intake, but in this study, there was no difference in food intake among the four groups. The decreased expression of energy expenditure-related genes in adipose tissue has been proposed as a potential factor in obesity induced by ovariectomy [27]. Mitochondrial deconjugation is a factor that increases adipocyte energy expenditure, and UCP1 promotes mitochondrial inner membrane deconjugation [38]. In this experiment, the addition of PIC tended to increase the gene expression of UCP1 in BAT. This suggests that PIC promotes deconjugation by increasing the expression of UCP1. Gene expression levels of UCP1 are regulated by SIRT1 and PGC1α, and UCP1 transcription is promoted when PGC1α is in an active state by deacetylation by SIRT1. In the present study, SIRT1/PGC1α showed a tendency to increase gene expression by PIC. In a previous report, increased gene expression of UCP1 and decreased levels of acetylation of PGC1α in BAT were observed when RSV, a PIC analog, was administered to obese mice on an HFD [39]. It has also been reported that the addition of PIC to human monocyte-derived cells increases both gene and protein expression levels of SIRT1 [51]. Figure 2a shows that PIC made no difference in nonesterified fatty acids in the blood, but it is possible that the fatty acids degraded in WAT were supplied to UCP1 in BAT, increasing energy expenditure. PGC-1α is a transcriptional coactivator that also regulates cellular energy production by promoting mitochondrial biogenesis and oxidative phosphorylation gene expression [52]. In humans, decreased PGC-1α expression is associated with obesity through reduced expression of oxidative phosphorylation genes and decreased muscle mitochondrial activity [53]. Although the increase in PGC-1α gene expression by PIC in BAT was not statistically significant in this study, future studies should further evaluate the involvement of PGC-1α-mediated enhancement of energy production in the antiobesity effects of PIC. It has also been reported that PIC has anti-inflammatory effects: In cocultures of 3T3-L1 and RAW cells, PIC prevented fat accumulation in 3T3-L1 adipocytes by suppressing IL-6 and TNFα produced by RAW macrophage cells [21]. Thus, the possibility that PIC’s anti-inflammatory effects may also be involved in the suppression of obesity should be investigated in future studies. Estrogen replacement therapy (ERT) is one of the treatments for postmenopausal obesity. It is known that estradiol replacement in postmenopausal model OVX rats suppresses obesity [54,55]. Estrogen replacement has also been shown to suppress obesity during menopause in human clinical studies [56]. However, the use of ERT needs to be limited because its use increases the risk of developing breast and uterine cancers; thus, there is a need to explore new treatment options [57]. The present results suggest that PIC is a useful compound for the treatment of postmenopausal obesity caused by estrogen deficiency. ## 5. Conclusions Our findings indicate that PIC prevents OVX-induced obesity in female C57BL/6J mice. The effects of PIC are due at least in part to lipolysis associated with the activation of phospho-HSL in WAT and the modulation of the SIRT1/PGC1α/UCP1 pathway in BAT. The present findings are an important step in inhibiting fat accumulation under the state of estrogen deficiency in menopausal women. ## References 1. Clegg D.J.. Minireview: The Year in Review of Estrogen Regulation of Metabolism. Mol. Endocrinol. (2012) 26 1957-1960. DOI: 10.1210/me.2012-1284 2. Leeners B., Geary N., Tobler P.N., Asarian L.. Ovarian Hormones and Obesity. Hum. Reprod. 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--- title: Metformin Suppresses Thioacetamide-Induced Chronic Kidney Disease in Association with the Upregulation of AMPK and Downregulation of Oxidative Stress and Inflammation as Well as Dyslipidemia and Hypertension authors: - Mohammad Y. Alshahrani - Hasnaa A. Ebrahim - Saeed M. Alqahtani - Nervana M. Bayoumy - Samaa S. Kamar - Asmaa M. ShamsEldeen - Mohamed A. Haidara - Bahjat Al-Ani - Alia Albawardi journal: Molecules year: 2023 pmcid: PMC10056045 doi: 10.3390/molecules28062756 license: CC BY 4.0 --- # Metformin Suppresses Thioacetamide-Induced Chronic Kidney Disease in Association with the Upregulation of AMPK and Downregulation of Oxidative Stress and Inflammation as Well as Dyslipidemia and Hypertension ## Abstract Toxic chemicals such as carbon tetrachloride and thioacetamide (TAA) are reported to induce hepato-nephrotoxicity. The potential protective outcome of the antidiabetic and pleiotropic drug metformin against TAA-induced chronic kidney disease in association with the modulation of AMP-activated protein kinase (AMPK), oxidative stress, inflammation, dyslipidemia, and systemic hypertension has not been investigated before. Therefore, 200 mg/kg TAA was injected (via the intraperitoneal route) in a model group of rats twice a week starting at week 3 for 8 weeks. The control rats were injected with the vehicle for the same period. The metformin-treated group received 200 mg/kg metformin daily for 10 weeks, beginning week 1, and received TAA injections with dosage and timing similar to those of the model group. All rats were culled at week 10. It was observed that TAA induced substantial renal injury, as demonstrated by significant kidney tissue damage and fibrosis, as well as augmented blood and kidney tissue levels of urea, creatinine, inflammation, oxidative stress, dyslipidemia, tissue inhibitor of metalloproteinases-1 (TIMP-1), and hypertension. TAA nephrotoxicity substantially inhibited the renal expression of phosphorylated AMPK. All these markers were significantly protected by metformin administration. In addition, a link between kidney fibrosis and these parameters was observed. Thus, metformin provides profound protection against TAA-induced kidney damage and fibrosis associated with the augmentation of the tissue protective enzyme AMPK and inhibition of oxidative stress, inflammation, the profibrogenic gene TIMP-1, dyslipidemia, and hypertension for a period of 10 weeks in rats. ## 1. Introduction The toxic effects of certain chemicals used in industries and laboratories, such as carbon tetrachloride, mercury, and thioacetamide (TAA), on the human body are well documented [1,2]. TAA, an organosulfur compound, is a severe hepato-nephrotoxic agent that has been reported to induce (i) liver fibrosis and cirrhosis in rats associated with the augmentation of biomarkers of liver damage such as ALT, AST, gamma-glutamyl transferase, alkaline phosphatase, and bilirubin [3]; (ii) kidney damage in mice associated with the upregulation of reactive oxygen species (ROS), programmed cell death (apoptosis), and tissue collagen deposition (fibrosis) via different cell signaling pathways [4]; and (iii) liver cancer due to the increased hepatic tissue levels of ROS that progressively lead to the damage of liver DNA and the development of hepatocellular carcinoma and cholangiocarcinoma in rodent models [5]. All observed TAA-induced pathology is dependent on the exposure time of the body to this toxic compound. For example, acute liver injury developed in rats after a single injection of TAA that caused tissue necrosis and severe leukocytes infiltration after 6–60 h; however, liver tissue levels of the tissue necrosis biomarkers, inducible nitric oxide synthase (iNOS) and nuclear factor-kB (NF-kB), peaked 60 min after TAA injection [6]. Additionally, deleterious effects of TAA were observed 7 days post TAA injection, as demonstrated by liver tissue lesions, inflammatory cytokines, and activation of NF-kB as well as the decrease in hepatic antioxidant levels [7]. Furthermore, hepatic fibrosis and cirrhosis were induced in animals following TAA injections for 6 to 10 weeks [8], whereas hepatocellular carcinoma nodules appeared in mice and rats 50 to 70 weeks after TAA treatment [5]. Chronic kidney disease (CKD) imposes a great challenge to the health system because it can lead to kidney failure and is associated with high treatment costs including kidney transplantation [9,10]. Screening for CKD should be efficiently implemented to save lives and reduce the financial burden on the healthcare systems because early detection and intervention reduce the morbidity and mortality rates from CKD [10]. Kidney injury ranges from renal insufficiency to end-stage renal disease and represents kidney response to a diversity of insults such as chemicals, toxins, bacterial infections, metabolic diseases, and autoimmune diseases [11,12]. Industrial toxicants such as carbon tetrachloride and TAA are reported to induce the pathology of renal disease via oxidative stress and inflammation pathways, causing kidney tissue damage and elevation of biomarkers of kidney injury [13,14,15]. Chronic kidney disease induced by carbon tetrachloride was associated with interstitial fibrosis, glomerular damage, and infiltration of inflammatory cells [13]. TAA augmented biomarkers of inflammation and oxidative stress in kidney tissue as well as induced blood levels of urea, creatinine, and creatine kinase [14]. Metformin is a pleiotropic medicine that has multiple beneficial effects on humans and animals, in addition to its hypoglycemic uses. For example, (i) metformin helps to treat women with polycystic ovary syndrome (PCOS), which is characterized by increased insulin resistance. Metformin induced ovulation in nonobese women with PCOS better than the first-line drug for anovulatory infertility treatment, clomiphene [16]; (ii) metformin is widely used in clinical and research studies for cardiovascular and liver protection [17,18]. Metformin has been suggested to reduce body weight, inhibit the deposition of fats in blood vessels (atherosclerosis), improve hemostatic function and immune cell performance, and protect against nonalcoholic steatohepatitis-induced hepatocellular carcinoma [17,18]; (iii) metformin also prevents apoptosis and cellular deterioration with age (senescence) in intervertebral disc degeneration via autophagy stimulation through the activation of AMPK, which ameliorate the degeneration of discs in vivo [19]; and (iv) metformin ameliorates several types of kidney diseases such as autosomal dominant polycystic kidney disease and acute kidney injury; reduces mortality in patients with CKD, diabetic nephropathy [20,21], and gentamicin-induced nephrotoxicity via reducing mitochondrial ROS and hence improves mitochondrial homeostasis [22] in patients with stable chronic renal impairment [23]; and, finally, metformin was reported to decrease the risk of death in patients with kidney cancers and localized and metastatic renal cell carcinoma [24]. Therefore, this study examined if metformin can protect against TAA-induced kidney injury and fibrosis in rats using physiological and biochemical methods, basic and special histology staining, immunoblotting, and real-time PCR (to assess the relative gene expression) to test the proposed working hypothesis. ## 2.1. Metformin Protects against TAA-Induced Kidney Injury TAA is a known hepato-nephrotoxic agent [25]. We assessed kidney injury induced by TAA at the end of the experiment with and without metformin incorporation. H&E images of kidney sections (Figure 1A) prepared from the control group revealed normal histomorphology that showed renal parenchyma without pathologic changes, as demonstrated by normal Malpighian renal corpuscle with glomerular tuft capillary and narrow Bowman’s capsule, and normal proximal (Px) and distal (Ds) convoluted tubules. The tubular epithelium displayed an acidophilic cytoplasm and vesicular nuclei with a prominent nucleolus. In contrast, TAA intoxication (model group) caused kidney injury as revealed by (i) a deformed glomerular shape with wide glomerular capillaries (arrow) and Bowman’s capsule (asterisk); and (ii) tubular injury showing dilated proximal renal tubules, an attenuated epithelium (arrowheads), apical cytoplasmic loss, reduced brush border, necrotic luminal debris, and interstitial edema. Metformin treatment (treated group) substantially but not completely protected against TAA-induced kidney injury. It displayed partial normalization of histomorphology and regenerative changes in tubules, and some Malpighian renal corpuscles showed dilated glomerular capillaries (arrow). Quantitative analysis of the Bowman’s spaces (also known as urinary space or capsular space) of Bowman’s capsule showed a significant ($p \leq 0.0001$) reduction in the dilatation of the Bowman’s spaces (Figure 1B). TAA also augmented blood levels of the kidney injury biomarkers urea (Figure 1C) and creatinine (Figure 1D), which were significantly ($p \leq 0.0001$) decreased by metformin treatment (Met + TAA) to values still significant (p ≤ 0.0073) compared with those of the control rats. ## 2.2. Metformin Protects against TAA-Modulated Kidney Levels of AMPK, Oxidative Stress, and Inflammation Oxidative stress and inflammation are well-known processes involved in the pathogenesis of kidney injury [26], and the mechanism of metformin action is reported to occur via activating the tissue-protective enzyme AMPK [27]. In light of our findings of TAA-induced kidney damage that was protected by metformin, we assessed the kidney tissue levels of phospho-AMPK (Figure 2A,B), total AMPK (Figure 2A,C), MDA (Figure 2D), SOD (Figure 2E), and TNF-α (Figure 2F) as well as blood levels of MDA, hsCRP, and TNF-α (data not shown) in all rats at the end of the experiment. Compared with the control rats, TAA caused a profound decrease in the expression of the active form of the enzyme AMPK (p-AMPK, but not the total AMPK) and SOD, and augmentation of MDA, TNF-α, IL-6 (data not shown), and hsCRP (data not shown) levels, which were effectively but not completely protected by metformin (Met + TAA). ## 2.3. Metformin Is Associated with the Protection against Kidney Fibrosis Induced by TAA We tested the hypothesis that metformin can protect against TAA-induced kidney fibrosis. The levels of TIMP-1 gene expression (the profibrogenic biomarker) and collagen fiber deposition (fibrosis) in kidney tissues were assessed at the end of the experiment in all animal groups using a quantitative real-time polymerase chain reaction (qPCR) technique and a special histological staining method with Sirius red (Figure 3). The model group of rats (TAA) showed an increase in TIMP-1 relative gene expression, which was effectively ($p \leq 0.0001$) inhibited by metformin (Figure 3A). Compared with a minimal collagen staining in kidney sections prepared from the control rats (Figure 3B) that revealed a fine deposition of collagen in the basement membrane of the renal corpuscle (arrow) and the convoluted tubules (arrowhead), kidney tissue sections of the TAA-intoxicated rats (Figure 3C) depicted substantially thick collagen deposition in the basement membranes (arrow), in the interstitium (star), and around blood vessels (Bv). Metformin treatment (Met + TAA) of the experimental group for 10 weeks significantly ($p \leq 0.0001$) but not completely protected against TAA-induced fibrosis (Figure 3D–F). ## 2.4. Metformin Protects against TAA-Induced Dyslipidemia and Systemic Arterial Pressure TAA is known to induce lipid dysregulation [28,29], dyslipidemia, and fibrosis-induced hypertension [29,30]. Therefore, the extent of the inhibition of dyslipidemia and hypertension by metformin against TAA-modulated blood lipids and systemic arterial pressure was evaluated in all rats at the end of the experiment. TAA caused a substantial increase in the blood levels of TG (Figure 4A), cholesterol (Figure 4B), vLDL-C (Figure 4C); systolic blood pressure (SBP) (Figure 4E); and diastolic blood pressure (DBP) (Figure 4F), which were significantly (p ≤ 0.0077) decreased by metformin treatment (Met + TAA) to levels still significant (p ≤ 0.0217) compared with those of the control group of rats for all these parameters. This meant a partial inhibition by metformin. On the other hand, metformin treatment (Met + TAA) significantly ($p \leq 0.0001$) augmented the HDL-C blood levels that were ameliorated by TAA (Figure 4D). ## 2.5. Correlation between Kidney Fibrosis Score and Biomarkers of Kidney Injury We determined the correlation between kidney fibrosis score and phosphorylated AMPK, oxidative stress (MDA), inflammation (TNF-α), renal injury biomarkers (urea and creatinine), dyslipidemia (TG), and systemic hypertension (SBP). This links the pathology of TAA kidney intoxication with the biomarkers of kidney injury, and it further supports the pleiotropic effects of metformin. The kidney fibrosis score displayed a significant ($p \leq 0.0001$) negative correlation with phosphorylated AMPK (r = −0.975) (Figure 5A, and a positive correlation with MDA ($r = 0.955$) (Figure 5B), TNF-α ($r = 0.954$) (Figure 5C), kidney tissue injury (assessed as increase in Bowman’s capsule space) ($r = 0.963$) (Figure 5D), urea ($r = 0.874$) (Figure 5E), creatinine ($r = 0.922$) (Figure 5F), TG ($r = 0.832$) (Figure 5G), and SBP ($r = 0.905$) (Figure 5H). ## 3. Discussion This article investigated the induction of chronic kidney disease (CKD) with the hepato-nephrotoxic agent TAA with and without the incorporation of the antidiabetic drug metformin in a rat model of the disease. We modeled this disease to test the hypothesis that metformin can ameliorate kidney injury and fibrosis induced by TAA, associated with the augmentation of phosphorylated AMPK and inhibition of biomarkers of inflammation, oxidative stress, dyslipidemia, and hypertension (Figure 6). Here, we report that the induction of CKD by TAA caused, after 10 weeks, profound renal cortical kidney damage and fibrosis in kidney tissues harvested from the model group of rats. This was associated with the inhibition of kidney tissue levels of phosphorylated AMPK and the antioxidant SOD and augmentation of the oxidative stress biomarker (MDA), inflammation biomarkers (TNF-α), TIMP-1, urea, creatinine, and lipid profile, as well as systemic arterial pressure, which appeared to be protected by metformin (Figure 1, Figure 2, Figure 3 and Figure 4). In addition, using the data obtained from the three animal groups, a significant correlation was observed between the renal fibrosis score and CKD biomarkers (Figure 5), which further confirms that metformin is a beneficial pleiotropic medicine to treat TAA-induced CKD. Therefore, our data support our working hypothesis mentioned above. Elevated levels of kidney injury biomarkers such as inflammation, oxidative stress, urea, and creatinine are reported in many kidney diseases such as diabetic nephropathy [31,32], renal vasculitis [33], nephrotoxicity induced by carbon tetrachloride [15], nephrotoxicity induced by paracetamol overdose [26], and kidney injury induced by TAA ($0.3\%$ dissolved in drinking water) given to baby rats (4 weeks old; 70–80 g weight) for a period of two weeks [14]. These reports are in agreement with our data presented in this study, as shown in Figure 1 and Figure 2. In addition, our data that point to the induction of renal cortical injury in rats by TAA after 10 weeks, depicted in Figure 1, are in agreement with those obtained from previous work on carbon-tetrachloride- or TAA-induced glomeruli changes with endothelial cell swelling in rats after 3 months [34]. Furthermore, our data that point to the inhibition of kidney damage and fibrosis associated with the upregulation of phosphorylated AMPK and inhibition of biomarkers of kidney injury, inflammation, oxidative stress, TIMP-1, and hyperlipidemia by metformin are congruous with those of previous reports [35,36], which demonstrated that (i) metformin inhibited stroke damage induced by transient occlusion to the middle cerebral artery in nondiabetic mice with chronic kidney disease associated with the upregulation of AMPK and downregulation of apoptosis, as well as increased neurone survival; (ii) metformin ameliorated acute kidney injury and chronic kidney disease; (iii) metformin decreased renal cell carcinoma, renal fibrosis, podocyte loss and apoptosis of mesangial cells, and protected renal tubular cells from the adverse effects of the inflammation; and (iv) the protective property of metformin in kidney disease is associated with the activation of the AMPK cell signaling and other pathways such as inhibiting the oxidative stress, endoplasmic reticulum stress, inflammation, lipotoxicity, and antifibrotic effects. However, clinical trial studies showed that metformin is associated with lactic acidosis when the estimated glomerular filtration rate (eGFR) reached below 30 mL/min/1.73 m2, and this may lead to renal imperilment, which requires discontinued treatment with metformin [37,38]. Lowered blood pressure by metformin in nondiabetic models was previously reported in (i) a mouse model of angiotensin-II-induced hypertension treated daily with metformin (300 mg/kg body weight) in association with the augmentation of p-AMPK and phospho-endothelial nitric oxide synthase (p-eNOS) and inhibition of the oxidative stress enzyme nicotinamide adenine dinucleotide phosphate (NADPH) oxidase in mesenteric resistance arteries [39]; (ii) spontaneously hypertensive rats treated with 350 to 500 mg/kg per day metformin; however, metformin treatment did not affect the control normotensive WKY rats [40]; (iii) nondiabetic patients with obesity or with impaired glucose tolerance gathered from 4113 participants in a meta-analysis of randomized controlled trials [41]; (iv) a mouse model of pre-eclampsia induced by a high-fat diet in pregnant mice treated daily with metformin (20 mg/kg). Metformin is also associated with the inhibition of proteinuria and improved fetal and placental weights [42]. ( v) A rat model of carbon tetrachloride-induced liver cirrhosis or common bile duct ligation-induced liver cirrhosis treated daily with metformin (300 mg/kg) caused a decrease in portal pressure and hepatic vascular resistance as well as inhibition of liver fibrosis, profibrosis biomarker alpha-smooth muscle actin (α-SMA), hepatic inflammation, and oxidative and nitrosative stress [43]. These reports are in agreement with our data shown in Figure 1, Figure 2, Figure 3 and Figure 4. In summary, our data demonstrate that the induction of CKD associated with the modulation of p-AMPK, inflammation, oxidative stress, profibrosis and fibrosis, dyslipidemia, and systemic hypertension by the hepato-renal toxic compound TAA appear to be protected by metformin for a period of 10 weeks in rats. In addition, we demonstrated a link between renal fibrosis and the above-mentioned parameters, with metformin showing useful renal pleiotropic effects. Thus, these findings may be translated into clinical therapy. ## 4.1. Animals Rats (albino male rats; 170–200 g) were kept in a clean animal room inside an animal facility with a controlled temperature of 22 ± 2 °C and 50 ± $10\%$ relative humidity. They were held in cages with 12 h light/dark cycles and permitted unrestricted access to water and food. All experiments were accomplished in accordance with the agreed guidelines mentioned in detail in the Institutional Review Board Statement section. ## 4.2. Experimental Design A total of 24 rats were divided equally into three groups ($$n = 8$$ rats per group) after a one-week acclimatization period. To induce kidney injury via TAA intoxication, the model group of rats (TAA) was injected with TAA (200 mg/kg, i.p.) twice a week for 8 weeks [8], whereas control rats were injected with the vehicle. To assess the effects of metformin on TAA-induced kidney damage, a second cohort of rats (Met + TAA) was pretreated with 200 mg/kg metformin from the first day, until being culled in week 10. This group received TAA injections similar to the model group from week 3 to week 10. Rats were then anesthetized at the end of the experiment using sodium thiopentone at 40 mg/kg body weight, and blood was collected using cardiac puncture into plain tubes for serum separation and sodium-citrate-containing tubes for plasma separation. Following that, animals were sacrificed and kidneys were harvested. ## 4.3. Measurements of hsCRP, ALT, TNF-α, MDA, Urea, Creatinine, Triglyceride, Cholesterol, vLDL-C, and HDL-C ELISA kits for the determination of blood and kidney tissue levels of highly sensitive C-reactive protein (hsCRP, ASSAYPRO, St. Charles, MO, USA), ALT (Randox Laboratories, Crumlin, UK), tumor necrosis factor-alpha (TNF-α, Abcam, Cambridge, UK), and lipid peroxidation measured as malondialdehyde (MDA, Cyaman Chemical, Ann Arbor, MI, USA) were used according to the manufacturer’s instructions. Blood urea and creatinine were measured using colorimetric methods (BioAssay System, Hayward, CA, USA). Serum levels of triglycerides (TGs), cholesterol, very-low-density lipoprotein (vLDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured using commercial kits supplied by SPINREACT, Coloma, Girona, Spain. ## 4.4. Determination of Arterial Blood Pressure Systolic and diastolic blood pressure (SBP and DBP, respectively) were measured in conscious rats using a BP monitor (LE 5001, LETICIA scientific Instruments, Barcelona, Spain), as previously reported [29]. The animals involved in the study were warmed at 28 °C for half an hour in a heating cabinet (Ugo Basile, Gemonio, VA, Italy), a process that helped the detection of the tail artery pulse. Through a miniaturized cuff, the tail was passed, and a tail-cuff sensor was attached to an amplifier (LE 5001, LETICIA scientific Instruments, Barcelona, Spain). The cuff was joined to a tail-cuff sphygmomanometer, blood pressure was recorded on a chart, and the average of three readings was taken. ## 4.5. Histological Analysis Kidney tissues were fixed in formal saline ($10\%$) and paraffin blocks were prepared using standard procedure. Sections of 5 μm thickness were stained with hematoxylin and eosin (H&E) for basic staining histological analysis [44]. To quantify the deposition of collagen fibers in kidney tissues, Sirius red staining was performed. Following dewaxing and rehydration of kidney tissue sections, slides were incubated with $0.1\%$ Sirius red (Sigma-Aldrich, Gillingham, UK) overnight, dipped in 0.01 M hydrochloric acid, and dehydrated with serial ethanol concentrations without water. Determination of collagen deposition (area percentage) in sections stained with Sirius red and Bowman’s capsule space (μm) was evaluated in 10 nonoverlapping fields for each group using a Leica Qwin 500 C image analyzer (Cambridge, UK). Data were summarized as means ± SD and compared using ANOVAs followed by a Tukey test. p-values < 0.05 were considered statistically significant. Calculations were made using SPSS software, version 19. ## 4.6. AMPK Western Blotting Analysis Proteins (20 μg per sample) extracted from kidney tissues were immunoblotted as mentioned previously [45]. Membranes were probed at 4 °C overnight with the primary antibodies anti-AMPK-phospho-Thr172, anti-AMPK, and beta-actin (Cell Signaling Technology, Danvers, MA, USA). Proteins were made visible with an ECL detection kit (Merck Life Science, Gillingham, Dorset, UK). Relative expression was resolved using image analysis software to obtain the intensity of the target protein bands with regard to a control sample after normalization on Chemi Doc MP imager by β-actin. ## 4.7. Kidney Tissue Inhibitor of Metalloproteinases-1(TIMP-1) Gene Expression Using Real-Time PCR (qPCR) Total RNA was prepared from the kidney tissue of rats using an RNeasy Mini Kit (Qiagen Pty, Victoria, Australia), and the RNA (1 μg) was reverse-transcribed with a complementary DNA (cDNA) synthesis kit (Fermentas, Waltham, MA, USA). cDNA samples and standards (triplicates) were amplified in Master Mix containing SYBR green (Thermo Fisher Scientific Inc., Waltham, MA, USA) with primers specific for TIMP-1(sense, 5′-GGT TCC CTG GCA TAA TCT GA-3′; antisense, 5′-GTC ATC GAG ACC CCA AGG TA-3′) or the housekeeping gene, β-actin, using the standard qPCR technique [39]. ## 4.8. Statistical Analysis The data are expressed as mean ± standard deviation (SD). Data were processed and analyzed using the SPSS version 10.0 (SPSS, Inc., Chicago, IL, USA). One-way ANOVA was performed followed by Tukey’s post hoc test. 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--- title: Efficacy Ozone Therapy in Reducing Periodontal Disease authors: - Giulia Tetè - Teresa D’Amicantonio - Elisabetta Polizzi journal: Materials year: 2023 pmcid: PMC10056049 doi: 10.3390/ma16062375 license: CC BY 4.0 --- # Efficacy Ozone Therapy in Reducing Periodontal Disease ## Abstract The aim of this study is to highlight the properties of ozone as an aid to non-surgical therapy compared to non-surgical therapy alone. This study included thirty Caucasian patients (eighteen women and twelve men) aged between 35 and 65, recruited at the Oral Hygiene and Prevention Center of the Dental Clinic of the Vita-Salute San Raffaele University, at the San Raffaele hospital in Milan. The periodontal probing was recorded with a PC-PUNC 15 manual probe (Hu Friedy) at time 0; the scaling and root planing session was performed at T1 with or without the aid of ozone therapy, and then, the patients were re-evaluated at one month (T2), three months (T3), and six months (T4). The results obtained show that there are not statistically significant differences in terms of reduction in clinical periodontal indices such as plaque, bleeding, and pocket depth between the two groups. Therefore, treatment with ozoral gel would not seem to improve non-surgical periodontal therapy alone. However, clinical periodontal indices significantly improved in patients treated with non-surgical therapy and ozone gel. From this point of view, ozone gel can be used as an aid to non-surgical therapy due to its excellent characteristics, in particular, its powerful virucidal action. ## 1. Introduction Periodontitis is a multifactorial disease which is born from an alteration of the balance between host, environment, and immune system [1]. The progression of the lesion is certainly mediated by the attack of bacteria that colonise the tooth surface, the gingival margin, and the subgingival environment in a susceptible host [2,3]. At the peak of this pathology, there is the loss of dental elements, which, together with other factors such as the increase in average age and associated comorbidities, has led to a growing need for rehabilitation of edentulous areas and a consequent advancement of minimally invasive procedures [4,5,6,7,8,9,10,11,12]. In the case of implant-supported fixed prostheses, the corresponding phenomenon of periodontitis is peri-implantitis; however, as has been demonstrated by several Authors [13,14,15,16,17,18,19,20], the correct pre- and post-surgical treatment of patients, combined with constant monitoring and inclusion in a periodic professional hygiene maintenance program, could reduce the incidence of this phenomenon and, consequently, of implant failure. It follows that the patient who presents pathological periodontal clinical indices must be supported by the professional with a precise and accurate diagnosis, an oral hygiene session with motivation and good home hygiene. While the prevention of periodontal diseases is based on microbial agents that inhibit bacterial plaque through two mechanisms, the former provides an inhibition of the primitive plaque, inhibiting bacterial proliferation before division, while the latter has a bactericidal action [21]. Research in recent years has focused precisely on this topic, finding the best antimicrobial to prevent the destruction of periodontal tissues. The literature agrees on the antimicrobial action of chlorhexidine, considered the gold standard of anti-plaque chemical agents, above all for its high substantivity. However, chlorhexidine has side effects such as peeling of the mucous membrane, impaired wound healing, pigmentation, and taste impairment [22]. Some authors point out that several strategies have been employed to accelerate tissue regeneration using bioactive molecules. In recent years, platelet concentrates derived from the patient’s own blood have been used as a regenerative strategy; some authors tested a new liquid platelet formulation prepared without the use of anti-coagulants (injectable platelet-rich fibrin, i-PRF) that was compared to the gold standard, platelet-rich plasma (PRP) with gingival fibroblasts cultured on smooth, rough titanium implant surfaces. Laboratory analyses aimed to observe the proliferation of molecules as well as the expression of platelet-derived growth factor (PDGF), transforming growth factor-β (TGF-β), collagen1 (COL1) and fibronectin (FN). The results show that i-PRF had a highly positive influence on cells in terms of proliferation. Therefore, derivatives without anti-coagulants will assume a key role in translational research [23]. Several studies focus on the need to create biomaterials without anticoagulants because of their side effects [24]. Some authors, on the other hand, have emphasised chitosan as a useful biomaterial in many dental fields including periodontal regeneration, due to its positive effects and its versatility and ease of use [25]. Some authors, on the other hand, have emphasised chitosan as a useful biomaterial in many dental fields including periodontal regeneration, due to its positive effects and its versatility and ease of use, for example, Muzzarelli et al. reported its use in the treatment of 52 periodontal defects with great results due to the molecule’s very architecture [26]. The pandemic caused by the SARS-CoV-2 virus has expanded scientific knowledge around the world. In our sector, it has emerged that good oral health prevents the onset of viral infections. As a result, researchers tested commercially anti-plaque microbial agents for their effectiveness against viruses and particularly against SARS-CoV-2. Several studies have shown that chlorhexidine is not totally effective against viruses; for this reason, ECDC indications and national ministerial guidelines suggest a rinse with chlorhexidine followed by another with hydrogen peroxide before starting the dental procedures [27,28]. A valid alternative to chlorhexidine could be ozone; it is considered a powerful antimicrobial, especially in its ability to counteract viral reduplication by inhibiting the activity of reverse transcriptase, a part of the viral protein synthesis. The application of ozone in dentistry is the result of its chemical–physical properties such as immunostimulant, analgesic, anti-hypoxic, detoxifying, antimicrobial, bioenergetic, and biosynthetic action [29,30,31]. Therefore, given the commitment and attention to safety protocols to counter the spread of the SARS-CoV-2 virus, our study aims to highlight the properties of ozone as an aid to non-surgical therapy compared to non-surgical therapy alone, proposing itself as a valid alternative to other microbial agents [32]. ## 2.1. Population and Study Design This study included thirty Caucasian patients (eighteen women and twelve men) aged between 35 and 65, recruited at the Oral Hygiene and Prevention Center of the Dental Clinic of the Vita-Salute San Raffaele University, at the San Raffaele hospital in Milan and who met the inclusion criteria described below. The periodontal probing was recorded with a PC-PUNC 15 manual probe (Hu Friedy) at time 0, scaling and root planing session was performed at T1 with or without the aid of ozone therapy and then the patients were re-evaluated at one month (phase T2), three months (phase T3), and six months (phase T4). Inclusion criteria:pathological clinical periodontal indices (BOP ³$20\%$ PI ³$20\%$ PPD ³4 mm);presence of at least 20 teeth;absence of periodontal treatment (non-surgical therapy) in the last 12 months. Exclusion criteria:treatment with drugs (antibiotics, anti-inflammatories);smokers (if former smokers must have stopped for at least 5 years);alcoholics (>150 mL of wine/day, >200 mL of beer/day, any spirits);chronic viral infections;cardiovascular diseases (heart attack, stroke, arterial hypertension, claudication);neurodegenerative diseases;diabetes mellitus;presumed or certain pregnancy;medical conditions or medical history requiring antibiotic prophylaxis prior to periodontal treatment. ## 2.2. Study Procedures The selected patients received information and instructions on the methods and aims of the study. The selection was made based on the inclusion and exclusion criteria described above and those who agreed to participate had to sign an informed consent. The study population consisted of patients belonging to the Oral Hygiene and Prevention Center of the Dental Clinic of the Vita-Salute San Raffaele University, at the San Raffaele hospital in Milan. During T0, and therefore in the first visit, the patient’s personal data, medical history, general state of health, and the use of any drugs were recorded. Furthermore, periodontal clinical indices were detected, i.e., PD > 4 mm, bleeding on probing (BOP), and positive plaque index (PI) (>$20\%$). The enrolled patients were randomised into two groups: a control group (GC) which included periodontal patients undergoing SRP (scaling and root planing), and a test group (GT), which included periodontal patients who were invited to use ozonated gel (Ozoral) at the end of non-surgical periodontal therapy. At phase T0: The two groups were included in the following protocol: a first visit which allowed the two groups to be randomised. Both were asked to complete and sign the anamnesis; then, periodontal probing and clinical parameters were performed (PPD = pocket depth, BOP = bleeding index, PI = plaque index, mobility, and furcation involvement). At the end of the first investigation, the two groups were randomised. The PC-PUNC 15 probe (Hu Friedy) was used for the detection of clinical periodontal indices. In addition, an erythrosine-based chromatic plaque detector (mira-2-tohager) was used to detect the plaque index (O’s Leary plaque index). At phase T1: All patients (test group and control group) underwent root planing with quadrant scaling (under anaesthesia, if necessary) and both groups received proper home hygiene (IOD) instruction. In addition, mechanical instruments (ultrasound with A, P, PS tips), manual instruments (Gracey courettes $\frac{7}{8}$–$\frac{11}{12}$–$\frac{13}{14}$), and scalers (204 S) were used for the hygiene session, while rubber pads and nupro paste (Cleanic) were used for the polishing procedure. At phase T2: One month after the hygiene session, reassessment for PI-BOP-PPD, presence of mobility and furcation involvement was performed. The PC-PUNC 15 probe (Hu Friedy) was used for the detection of clinical periodontal indices. At phase T3: Three months after the hygiene session, the reassessment for PI-BOP-PPD, presence of mobility and furcation involvement was performed. The PC-PUNC 15 probe (Hu Friedy) was used for the detection of clinical periodontal indices. At phase T4: Three months after the hygiene session, the reassessment for PI-BOP-PPD, presence of mobility and furcation involvement was performed. The PC-PUNC 15 probe (Hu Friedy) was used for the detection of clinical periodontal indices. For the control group:an oral hygiene session was also performed with supra/subgingival debridement, the patient was motivated with the correct home oral hygiene instructions (IOD) and the recommendation not to use any home antiseptic device after the session. For the test group:An oral hygiene session was performed with supra/subgingival debridement with application of ozonated oil during and at the end of the session, using disposable syringes. The patient was motivated with the correct home oral hygiene instructions (IOD) and the recommendation to use ozone oil for four weeks. ## 3. Results Our study led to results recorded during re-evaluations at phase T2 (one month from SRP), phase T3 (three months from SRP), and phase T4 (six months from SRP). The first parameter analysed was the plaque index: clinically there was a decrease over time especially in the test group (with ozone), as can be seen from graph 1. The decrease in the control group (without ozone) was statistically significant $$p \leq 0.03$$, while in the test group (with ozone) the decrease tends towards significant $$p \leq 0.06.$$ However, no statistically significant differences were found between the two groups $$p \leq 0.62$$ (Figure 1). The trend of BI over time (from baseline time to six months) was also considered in our study. Graph 2 shows that there was a reduction in both BI groups, higher in the group treated with ozone. Statistically, the control group had a significant trend $$p \leq 0.03$$, while the decrease in the BI of the test group tends to be significant $$p \leq 0.06.$$ Observing the values of the two groups, from the initial screening to six months, it emerges that the ozone gel has a greater influence in decreasing bleeding than the control group, $$p \leq 0.05$$ (Figure 2). The third parameter analysed is the probing depth (PD). The graph shows that there is a decrease in PD in both groups tending towards significant $$p \leq 0.06.$$ Furthermore, it seems that in the test group, and therefore with the aid of ozone, there is a much higher decrease than in the control but not confirmed by the statistical analysis $$p \leq 0.07$$ (Figure 3). ## 4. Discussion The results obtained show that there are no statistically significant differences in terms of reduction in clinical periodontal indices such as plaque, bleeding, and pocket depth. Between the two groups, therefore, treatment with ozoral gel would not seem to improve non-surgical periodontal therapy alone. However, clinical periodontal indices significantly improved in patients treated with non-surgical therapy and ozone gel. In this regard, the literature underlines that the topical application of ozone gel can improve clinical periodontal parameters. In 2014, Shoukheba saw that the results of ozone irrigation showed improvement of all clinical parameters in the ozone group, which was maintained up to six months, except BOP up to three months [33]. As far as the plaque index is concerned, we did not find significant differences between the test group and the control group, results which confirm the relevant literature; in fact, Tasdemir et al. state that, in a randomised clinical study on 36 patients, there were no statistically significant differences between the two groups treated with and without ozone in terms of plaque indices [34]. Although not statistically significant, an improvement in the bleeding index was found in the ozone-treated test group, a datum confirmed by the literature which indicates improvements in the groups treated with traditional therapy and adjuvants such as ozone therapy and chlorhexidine [35]. Yilmaz achieved statistically significant results in terms of reduction in periodontal clinical indices in patients treated with laser (YAG) and patients treated with topical application of gaseous ozone [36]. Ozone therapy is widely used in medicine and in many branches of dentistry; there is little scientific evidence of its effective use in endodontics and oral surgery. However, it is a growing market and its few risks versus many benefits make it a great aid for non-surgical therapy [37]. Some authors, exploiting the intrinsic characteristics of ozone, have tested it in the disinfection of different materials used for dental impressions. Through an analysis conducted in vitro, the ability to disinfect most commercially available impression materials was tested [38]. In the test group, therefore with the aid of ozone, a much higher decrease was recorded than in the control, but not confirmed by the statistical analysis $$p \leq 0.07$$ (Figrue 3) in line with the literature that confirms, for example at two and four months an improvement in pocket depth in the groups treated with traditional therapy and with ozone therapy [39]. In medicine, ozone is widely used for its antimicrobial and antioxidant properties and for biostimulation in the healing of chronic, non-healing or ischemic wounds through various compositions. For topical application, transcutaneous administration of 03 is used if we are talking about external wounds; if we are talking about muscular disorders, through ionised water; and finally, through gels for disorders of the oral cavity. It is also used as an adjuvant to surgical therapy insufflation and or suspension as medicinal oils in the treatment of osteonecrosis of the jaws [40]. To date, a different aspect than clinical efficacy is fundamental. Given the SARS-CoV-2 pandemic, national and international guidelines have been drawn up in the dental world for the safety of patients and operators themselves (our guidelines). Oral hygiene was the dental sector that aroused the greatest concern given the production of aerosols and droplets with the scaler and the use of powders; however, it has been demonstrated that manual therapy, therefore not generating aerosols, achieves the same results as mechanical therapy, through a correct learning curve on the part of the operator [41]. With the advent of the SARS-CoV-2 pandemic, numerous disinfectants have been tested that allow the complete disinfection of surfaces not only from bacteria but also from viruses. Among these, ozone has also been tested. In fact, some authors have proposed an in vitro study in which ozone at different concentrations was tested with the coronavirus family and viral infection decreased by $95\%$ after exposure to ozone for 20 min at 1000 ppmv, 30 min at 100 ppmv and about 40 min at 30 ppmv against the coronavirus family. The results therefore underline that the anti-viral capacity of ozone combined with hydrophilicity favoured a positive surface disinfection result especially on brass, copper and nickel. Overall, this study demonstrates the potential use of ozone gas disinfection to combat the COVID-19 epidemic [42]. In vivo animal studies were conducted to test the toxicity of ozone. Obviously taking into account the limitations of the different anatomy of the first airways, it has been shown that inhalation of ozone causes toxicity mainly at the level of type one cells of the airways, less toxicity to type two cells, and slight biochemical and physiological changes have also been found. However, ozone appears to be a mild mutagen and does not particularly create chromosomal abnormalities. Finally, it can be said from the results obtained that there is a predominantly qualitative but not quantitative difference between species (human–animal), so it can be used to test quantitative toxicity from animals to humans [43]. Other authors point out that the additional use of Xanthan to chlorhexidine gel promoted a greater reduction in PD and an increase in CAL than SRP alone. These results were accompanied by better microbiological and biochemical results when the use of Xan-CHX gel was added to SRP, particularly up to 3 months after treatment [44]. Therefore, in the intermediate period of the pandemic, researchers focused on all possible aids to simple non-surgical mechanical or manual therapy to reduce the risk of aerosol production and still achieve good results. For example, in the literature, chlorhexidine—which is used for the patient before procedures as a rinse, together with povidone iodine, or as an adjunct for non-surgical therapy (ref. chlorhexidine) or laser therapy—shows a significant reduction in clinical indices in patients with periodontal disease [45,46,47]. These aids to normal therapies are not only useful for the SARS-CoV-2 virus but also for all those patients with chronic viral, autoimmune, or cardiac diseases, and therefore, for all patients considered today “fragili” [48]. ## 5. 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--- title: 'Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study' authors: - Antonino Maniaci - Paolo Marco Riela - Giannicola Iannella - Jerome Rene Lechien - Ignazio La Mantia - Marco De Vincentiis - Giovanni Cammaroto - Christian Calvo-Henriquez - Milena Di Luca - Carlos Chiesa Estomba - Alberto Maria Saibene - Isabella Pollicina - Giovanna Stilo - Paola Di Mauro - Angelo Cannavicci - Rodolfo Lugo - Giuseppe Magliulo - Antonio Greco - Annalisa Pace - Giuseppe Meccariello - Salvatore Cocuzza - Claudio Vicini journal: Life year: 2023 pmcid: PMC10056063 doi: 10.3390/life13030702 license: CC BY 4.0 --- # Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study ## Abstract Objectives: To evaluate the role of clinical scores assessing the risk of disease severity in patients with clinical suspicion of obstructive sleep apnea syndrome (OSA). The hypothesis was tested by applying artificial intelligence (AI) to demonstrate its effectiveness in distinguishing between mild–moderate OSA and severe OSA risk. Methods: A support vector machine model (SVM) was developed from the samples included in the analysis ($$n = 498$$), and they were split into $75\%$ for training ($$n = 373$$) with the remaining for testing ($$n = 125$$). Two diagnostic thresholds were selected for OSA severity: mild to moderate (apnea–hypopnea index (AHI) ≥ 5 events/h and AHI < 30 events/h) and severe (AHI ≥ 30 events/h). The algorithms were trained and tested to predict OSA patient severity. Results: The sensitivity and specificity for the SVM model were 0.93 and 0.80 with an accuracy of 0.86; instead, the logistic regression full mode reported a value of 0.74 and 0.63, respectively, with an accuracy of 0.68. After backward stepwise elimination for features selection, the reduced logistic regression model demonstrated a sensitivity and specificity of 0.79 and 0.56, respectively, and an accuracy of 0.67. Conclusion: Artificial intelligence could be applied to patients with symptoms related to OSA to identify individuals with a severe OSA risk with clinical-based algorithms in the OSA framework. ## 1. Introduction Obstructive sleep apnea (OSA) is a respiratory disorder characterized by the partial or total collapse of the upper airways with intermittent hypoxia, a chronic systemic inflammatory state, and an increased cardiovascular risk [1]. OSA is also associated with comorbidities, such as metabolic syndrome and olfactory or neurodegenerative disorders. Although OSA has a very high prevalence worldwide, up to $50\%$ of the general population, subjects are often unaware of the disorder, and the diagnosis is performed when the associated comorbidities have already developed [2]. The strong connection between the severity of obstructive apnea identified through standard diagnostic tests, such as nocturnal polysomnography or pulse oximetry, and the associated cardiovascular or neurodegenerative risks are established in the literature [3,4]. Conversely, the different clinical features and scoring systems, such as age, BMI, the anatomical scores of palatal collapses, or validated questionnaires, although significantly correlating with pathology, do not possess adequate sensitivity or specificity to substitute instrumental diagnostics tools [5]. Recently, Sutherland et al. analyzed in a patient-level meta-analysis the relationship between craniofacial morphology and weight loss with sleep apnea severity [6]. The authors demonstrated a correlation between weight and AHI changes (rs = 0.3, $$p \leq 0.002$$) and an increased maxilla–mandible relationship angle related to AHI improvement (β [$95\%$ CI] −1.7 [−2.9, −0.5], $$p \leq 0.004$$) at linear regression. In this regard, clinical prediction decision-making algorithms that use clinical information, objective scores, and easy-to-execute subjective questionnaires could be useful strategies for predicting OSA [7]. Maranate et al. in 2015 proposed a prioritization process of clinical risk factors of the severity of OSA using the analytical hierarchy process to select the patients with the greatest need for an in-depth diagnostic study [8]. The authors developed an algorithm used on 1042 suspected OSA patients who had undergone diagnostic PSG study. Moreover, 42 variables of disease severity were identified, with an overall sensitivity/specificity of the model for severe, moderate, and mild of $92.32\%$/$91.76\%$, $89.52\%$/$88.18\%$, and $91.08\%$/$84.58\%$, respectively. Artificial intelligence (AI) is rapidly gaining importance in medicine, opening promising new perspectives [6,7,8]. Through a deep learning system, different types of data can be exploited, such as clinical scores, questionnaires, or diagnostics [2,4,5]. Tsuiki et al. in 2021 developed a deep convolutional neural network (DCNN) using data from lateral cephalometric radiographs of 1389 subjects and tested on $10\%$ of the enrolled sample ($$n = 131$$) [7]. The DCNN, as well as manual cephalometric analyses, significantly predicted the presence of severe OSA, with a full image sensitivity/specificity of $\frac{0.87}{0.82}$ (χ2 = 62.5, $p \leq 0.01$), demonstrating promising prospects for AI in the triage of OSAS. Nevertheless, these forecasting tools’ predictive performances and features differ significantly between literature studies, limiting their generalizability and practicality. The aim of our study was to evaluate the usefulness of the application of the SVM algorithm predicting the severity of OSA through clinical parameters, subjective questionnaires, anatomical scores, and associated comorbidities. ## 2.1. Study Design and Data Collection Guidelines on strengthening the reporting of observational studies in epidemiology (STROBE) were followed [9]. We carried out a multicentric retrospective study conducted at the Ear, Nose, and Throat Unit (ENT) of our hospitals from 1 January 2010 to 31 December 2021 (Figure 1). Participants aged ≥ 18 years who were referred to our units for sleep respiratory disorders were enrolled and subjected to clinical diagnostic evaluation and consequently phenotyped. A full clinical history of symptoms, such as morning headache and decreased libido, validated questionnaires (ESS), and anatomical or endoscopic scores were collected (Table 1) (Supplementary File S1–S3) [10,11,12,13,14,15,16,17,18,19,20,21]. All the participants underwent a sleep study that was carried out in an unattended way by means of a Polymesam Unattended Device 8-channel and then reviewed and scored by the same expert in sleep medicine according to the American Academy of Sleep Medicine (AASM) Guidelines [3]. We selected two diagnostic thresholds for OSA: mild to moderate (AHI ≥ 5 events/h and AHI < 30 events/h) and severe (AHI ≥ 30 events/h), according to the latest AASM guidelines on OSA management [3]. The algorithm was trained and tested using the two established threshold parameters to predict OSA patients’ severity. ## 2.2. Statistical Analysis Standard descriptive statistics were used, reporting mean and standard deviation for continuous variables and percentages for categorical ones. The independent t-test was performed for normally distributed values, while the Mann–Whitney U test was used for abnormally distributed values. The chi-square test was performed to test the observed and expected data differences. A value of $p \leq 0.05$ was deemed to be statistically significant. All analyses were performed using the Social Sciences Statistical Program (IBM SPSS Statistics for Windows, IBM Corp. Released 2017, Version 25.0 Armonk, NY, USA: IBM Corp). ## 2.3. Stratification Process To perform the stratification of participants, we selected among independent variables assessed for disease severity the AHI cutoff according to the AASM guidelines to define disease severity. Thus, we stratified patients into a mild–moderate OSA group when the AHI value was ≤30; instead, a severe OSA group was defined when a value of AHI > 30 was found. Other variables identified were instead introduced into the predictive models as independent variables and converted into a binary value according to the respective cutoff definition. Consequently, the splitting process of training and testing data divided the sample ($$n = 498$$) into two different homogeneous datasets. The training group included $75\%$ ($$n = 373$$) of participants while the remaining $25\%$ ($$n = 125$$) were used for testing. The performance of the logistic regression and SVM classifier models was tested according to the two AHI thresholds mild/moderate (AHI ≤ 30) and severe (AHI > 30). The study protocol was approved by the Ethical Committee and was conducted in accordance with the declaration of Helsinki. We explored the data to improve their quality, handling missing or removing duplicate values, managing the existence of outliers or anomalies (data points differing substantially from the rest of the data), converting invalid or bad formatted values. ## 2.4. Logistic Regression Model Logistic regression predictive models were used to classify patients and evaluate the performance of predictors. We used receiver operating characteristic (ROC) curves to assess the ability of the logistic regression models to identify patients with mild/moderate or severe AHI. Results were reported in terms of area under the curve (AUC) and $95\%$ confidence interval ($95\%$ CI). A first multivariate logistic regression model was used to evaluate the model accuracy in an outcome prediction with the complete set of variables (full model). A second multivariate logistic model was the result of a backward stepwise elimination for selecting features and eliminating the ones that did not have a significant effect on the dependent variable or prediction of outcome to find a reduced model that best explained the data. We first worked on patients with a complete assessment of the following information: Age, gender, BMI, familiarity with OSAS, hypertension, cardiovascular disorders, diabetes, dyslipidemia, COPD, anxiety/depression, septoturbinoplasty, tonsillectomy, snoring, choking, morning headache, decreased libido, ESS, septal deviation, internal valve collapse, external valve collapse, lower turbinate hypertrophy, adenoid hypertrophy, Friedman tonsils score, Mallampati score, Friedman palate score, palate phenotype according to Woodson classification, endoscopic lingual tonsils score, retropalatial Mueller maneuver, retrolingual Mueller maneuver, panting test, retrognathia, and upper jaw contraction. All tests were performed at a significance level α = 0.05. After backward elimination, the reduced model included the following features: Age, gender, BMI, diabetes, anxiety/depression, choking, and septal deviation. We consequently evaluated the multicollinearity of the logistic regression model due to the different features included. Therefore, a stepwise approach was performed in which previously removed features were individually reintroduced into the model to assess the risk of overfitting and the influence of individual values in the model. ## 2.5. SVM Model The same training/test set and features from the full logistic regression were later used to develop the SVM model. Several datasets are not linearly separable even in a feature space, not satisfying all the constraints in the minimization problem of SVM. To fill this gap, Slack variables are introduced to allow certain constraints to be violated. By choosing very large slack variable values, we could find a degenerate solution that would lead to model overfitting [22]. To penalize the assignment of too large slack variables, the penalty is introduced in the classification objective:εi, indicates slack variables, one for each datapoint i, to allow certain constraints to be violated. C, indicates a tuning parameter that controls the trade-off between the penalty of slack variables εi and the optimization of the margin. High values of C penalize slack variables leading to a hard margin, whereas low values of C lead to a soft margin, which is a bigger corridor that allows certain training points inside at the expense of misclassifying some of them. In particular, the C parameter sets the confidence interval range of the learning model. The radial basis fFunction (RBF) kernel function expression on two samples, x∧x^′, is defined as K (x,x^′) = exp(-γ\|(\|x-x^′\|)\|^2) where \|(\|x-x^′\|)\|^2 is the squared Euclidean distance between the two feature vectors, and γ is a free parameter. The RBF can be applied to a dataset by choosing two parameters, C and γ. The classifier performance of SVM depends on the choice of these two parameters. A grid search method was used to find the optimal parameters of the RBF for SVM. This method considered m values in C and n values in γ, according to the M × N combination of C and γ, by training different SVMs using K-fold cross-validation. Here, we used a grid search on a 5-fold cross-validation to optimize accuracy. Thus, we selected as optimal parameters for the RBF kernel in the SVM model a γ value of 0.5 and C of 100. Consequently, a Shapley plot (SHAP) was calculated. SHAP, which stands for Shapley Additive exPlanations, is an interpretability method based on Shapley values, a solution concept in cooperative game theory named in honor of Lloyd Shapley, who introduced it in 1951. SHAP was consequently applied to explain individual predictions of any machine learning (ML) model. The explanation model is represented by a linear model—an additive feature attribution method—or just the summation of present features in the coalition game. After the generation of Shapley plots, each predictor contribution to the SVM model output was described in terms of the SHAP average value. The independent variables were thus ranked in descending order of importance. Instead, the violin plot generated shows the impact of a value associated with higher or lower prediction and positive or negative correlation on the X-axis. The color correlates with the average feature value at the plot position: Red areas represent highly valued features while blue areas are low. The violin plot also shows the outliers drawn as points. The analyses were conducted using Python 3.6.9 with Statsmodel 0.10.2, Scikit-learn 1.0.2, and Shap 0.40 libraries. Shapley plots show the contribution of each predictor to the SVM model output in terms of SHAP value. The variables were ranked by importance in descending order and the color represents the average feature value at that position. The violin plot shows the average medium SHAP value of each independent variable; the violin plot shows the impact of a value associated with higher or lower prediction and positive or negative correlation. The violin plot also shows the outliers drawn as points. ## 2.6. Models Test Analysis Comparison of the accuracy of the diagnostic tests performed was analyzed by an analysis of the areas under the receiver operating characteristic (ROC) curves. Models with the same characteristics and the same training/test set were analyzed through a Z-test to evaluate a statistical difference. The Z-test is a parametric statistical test used to evaluate whether the mean of a given distribution differs significantly from a hypothesized value. ## 2.7. Reporting Completeness of Machine Learning Study We evaluated the reporting completeness of this research study referring to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis; www.tripod-statement.org, accessed on 8 February 2023) checklist for prediction model validation (accessed on 6 February 2023). This statement contains a 20-item checklist, for a total of 31 items, with all sub-items included. The checklist contains questions about the title, abstract, background, methods, results, discussion, Supplementary Material, and funding information. Each included item received a score of “1” for adherence and a score of “0” for non-adherence. Multiple items (items 1, 2, 3a, 4b, 5a, 6a, 7a, 7b, 9, 10a, 10b, 10d, 13a, 13b, 14a, 15a, 16, 17, 20, and 22) in the TRIPOD analysis were derived from several sub-items (the sub-items for each number can be found in www.tripod-statement.org (accessed on 7 June 2021)). The results of each TRIPOD item for each paper and the level of reporting adherence for each TRIPOD item were documented systematically in a spreadsheet. We thus obtained a TRIPOD adherence score of 93,$45\%$ ($\frac{29}{31}$ items) by dividing the sum of TRIPOD items adhered to by the entire number of applicable TRIPOD items in the study. ## 3.1. Patients Features After selection, a total of 498 participants, with an age of 50.96 ± 12.15 years, were included in the study, of which $\frac{427}{498}$ ($87.76\%$) were male vs. $\frac{61}{498}$ ($12.24\%$) female (Table 1). The mean BMI was 27.32 ± 4.02 kg/cm. A mean AHI value of 37.21 ± 23.24 events/h and a mean oxygen desaturation index (ODI) of 35.37 ± 24.79 events/h were reported. An overall $\frac{220}{498}$ ($44.17\%$) of the participants were identified as mild to moderate OSA (AHI < 30 events/h), while $\frac{278}{498}$ ($55.82\%$) of the participants as severe OSA (AHI threshold ≥ 30 events/h). Subsequently, the participants were divided into two homogeneous training ($$n = 373$$) and test ($$n = 125$$) groups, homogeneous for the independent variables included in the analysis (Figure 1). ## 3.2. Logistic Regression Analysis, Full and Reduced Models Through the traditional statistical analysis, the full model demonstrated a ROC curve with an AUC of 0.73 ($95\%$ CI = 0.64–0.82) and 0.68 accuracy (Figure 2a). The sensitivity and specificity of the regression to distinguish among OSA severity were, respectively, $\frac{0.74}{0.63.}$ The consequent features selection using the backward stepwise elimination demonstrated a reduced logistic regression model with a sensitivity/specificity of $\frac{0.79}{0.56}$, an accuracy of 0.67, and an AUC of 0.69 ($95\%$ CI = 0.6–0.78) (Figure 2b). The assessment of the multicollinearity of the model by re-introducing the removed features one-by-one did not demonstrate a statistically significant difference in model performance. ## 3.3. SVM Model Performance and ROC Curve Analysis The SVM algorithm was adopted to discriminate OSA severity among participants. Through the algorithm, all the overall performance scores as accuracy (number of correct predictions/total number of predictions), ROC AUC (area under the curve), sensitivity (true positive rate), and specificity (true negative rate) were improved. In particular, the SVM model demonstrated a recall and precision score of 0.80 and 0.93, respectively, to assess mild to moderate OSA; instead, the outcomes were 0.93 and 0.81 to identify severe OSA (Table 2). To avoid bias within the dataset, we evaluated the model score using a 10-fold stratified cross-validation strategy, obtaining an average accuracy score of 0.87 ($95\%$ CI = [0.83, 0.91]). The result of the stratified cross-validation is in line with the accuracy score of the model trained on $75\%$ of the dataset and tested on the remaining $25\%$. The algorithm provided a ROC with an AUC of 0.92 ($95\%$ confidence interval = 0.87–0.97) (Figure 3). The clinical variables included in the model and the Shapley plot consequently generated showed the contribution of each predictor to the SVM model, as summarized in Figure 4a,b. Among the independent variable predictors of OSAS severity assessed, dyslipidemia (0.0623), choking (0.0588), diabetes (0.0576), mood disorders (0.0486), and familiarity for OSAS (0.0452) demonstrated a higher average impact on model output magnitude in terms of an absolute mean SHAP value. The ROC AUC comparison between the complete logistic regression model (AUC = 0.73) and the SVM model (AUC = 0.92) through the Z-test confirmed a statistically significant difference among the two models ($p \leq 0$ 0.001). Instead, the ROC AUC comparison between full and reduced logistic regression model was not significant ($$p \leq 0.541$$). ## 4. Discussion Our study demonstrated how artificial intelligence can be useful in assessing patients with OSA-related symptoms and determining the risk of disease severity with clinic-based algorithms. We reported a sensitivity and specificity significantly higher for the SVM model than classical logistic regression. Indeed, as reported in our results through the SVM model, it is possible to stratify OSA patients according to the severity with a higher accuracy of 0.86 compared with the full logistic regression mode accuracy of 0.68. ## 4.1. Diagnostic and Therapeutic Role The use of AI technology in clinical practice is an emerging and debated topic, both for its possible diagnostic and therapeutic implications. Through ML, it is possible to exploit the multiple variables of easy and rapid extraction, such as a patient’s clinical history and anthropometric or demographic characteristics, to facilitate the identification of otherwise complex pathologies [6]. The field of breathing sleep disorders could also benefit from improving ML Technology, using both its application in early diagnosis and the analysis of predictive factors of response to medical or surgical treatment [7,8,23]. Kim et al. in 2021, through a preoperative machine learning-based clinical mode, improved the prediction of the therapeutic sleep surgery outcome with higher accuracy for the gradient boosting model than the logistic models [24]. The application of the algorithm elaborating several parameters collected at baseline visits between adherent and non-adherent groups demonstrated a sensitivity of $68.6\%$ and an AUC of $72.9\%$ through the vector machine model. ## 4.2. Diagnostic Application of AI Sleep breathing disorders lend themselves well to the diagnostic application of AI as they are often strongly correlated with elevated BMI, and cardiovascular, metabolic, or central nervous system disorders. Holfinger et al. assessed the diagnostic performance of OSA machine learning prediction tools using readily available data, such as age, sex, BMI, and race, and compared the efficacy with a STOP-BANG-based model [23]. The authors included a wide cohort of 17,448 subjects in a retrospective study, demonstrating that AUCs ($95\%$ CI) of the kernel support vector machine (0.66 [0.65–0.67]) were significantly higher than logistic regression ones (0.61 [0.60–0.62]). Machine learning-derived algorithms may also improve and simplify the widespread identification of OSA in the pediatric population, providing better diagnostic performance than logistic regression with patient-reported symptoms [8,24,25,26]. Gutiérrez-Tobal et al. performed a systematic review of studies assessing the reliability of a machine learning-based method implementation of OSA detection in clinical practice [25]. The authors retrieved 90 studies involving 4767 different pediatric subjects and demonstrated an improved ML diagnostic performance on OSA severity criteria (sensitivity = 0.652; specificity = 0.931; and AUC = 0.940). However, an important aspect that has not been analyzed on the advantages of artificial intelligence in OSA is its possible use in identifying OSA pathology concerning the healthy population, determining which patients are at risk, and optimizing the use of diagnostic resources, such as polysomnography [6,7]. Our analysis confirmed the superiority of vector models using SVM models in determining disease severity in patients with OSA compared to traditional logistic regression models. In fact, we found a significantly greater sensitivity/specificity of $\frac{0.93}{0.80}$, and an accuracy of 0.86 for the SVM than the logistic regression full mode. It is well-known in the literature that OSA is associated with obesity and cardiovascular and cerebrovascular disorders. Several mechanisms occur in patients with OSA, including a chronic inflammatory state, intermittent hypoxia, and even alterations of their lipid profile, probably due to a reduction in androgen levels. Moreover, OSA severity and lipid-related comorbidities, such as atherosclerosis, possess a well-known correlation. ## 4.3. OSA Risk Factors and Comorbidities Our SVM model demonstrated dyslipidemia as the highest average SHAP feature value (0.0623) for OSA severity among clinical variables included in the diagnostic algorithm. Conversely, its predictive role was not confirmed in the logistic regression full model analysis ($$p \leq 0.123$$). Although OSA is commonly associated with craniofacial anomalies and palatal or base tongue disorders validated as upper airway obstruction sites by DISE, no research study has evaluated the predictive role of such features using a machine learning algorithm. Craniofacial variables represent noteworthy risk factors for OSA. The risk of OSA in adult subjects with altered craniofacial anatomy on lateral cephalograms is well-known in the literature [6]. Among the variables with significant heterogeneity, the position and length of the mandible (BNS: −1.49° and Go-Me: −5.66 mm), the area of the tongue (T: 366.51 mm2), and the soft palate (UV: 125, 02 mm2), and upper airway length (UAL: 5.39 mm) were identified as strongly correlating with the presence of OSA. Our SVM analysis demonstrated a higher average SHAP feature value for the palate phenotype (0.0308), Friedman tonsils score (0.0363), and palate score (0.0623) for OSA severity than other clinical features assessed in the model. However, the predictive role in the logistic regression full model analysis was not significant for the palate phenotype ($$p \leq 0.148$$), while Friedman’s tonsils score ($$p \leq 0.041$$) and Friedman’s palate score ($$p \leq 0.038$$) were statistically significant. A systematic review of the prevalence of OSA in an Asian population reported interesting data on highly related features, such as gender, older age, BMI increase, and arterial hypertension, that significantly correlated with the onset of sleep breathing disorders and severity [4]. The difference in gender prevalence rates was also supported by our analysis, with gender, age, and hypertension showing a SHAP value of 0.0435, 0.0336, and 0.0408, respectively. Conversely, in the logistic regression full model, the predictive role in the analysis was significant for age ($$p \leq 0.017$$) and gender ($$p \leq 0.019$$), while not for hypertension ($$p \leq 0.649$$). ## 4.4. Study’s Limitations Prediction tools that determine OSA risk include variables, such as patient-reported symptoms through symptom questionnaires; however, there are often unavailable or inaccurate data from large groups of individuals with multicenter studies using electronic medical records that are not similar across institutions. Although the machine learning model demonstrated significant AUC and adequate sensitivity and specificity, our study’s main shortcoming was the reduced sample with which the model was trained. In fact, it is known how artificial intelligence models become proportionally more effective as new subjects are added, with a refinement of the predictive algorithm. A consequence of this limitation is that in our model, highly significant features in the literature, such as BMI, Mueller maneuver, ESS, or cardiovascular disorders, do not take on a dimension in the model that reflects clinical severity. 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--- title: Design, Synthesis, Characterization, and Evaluation of the Anti-HT-29 Colorectal Cell Line Activity of Novel 8-Oxyquinolinate-Platinum(II)-Loaded Nanostructured Lipid Carriers Targeted with Riboflavin authors: - Tugce Boztepe - Sebastián Scioli-Montoto - Rocio C. Gambaro - María Esperanza Ruiz - Silvia Cabrera - José Alemán - Germán A. Islan - Guillermo R. Castro - Ignacio E. León journal: Pharmaceutics year: 2023 pmcid: PMC10056074 doi: 10.3390/pharmaceutics15031021 license: CC BY 4.0 --- # Design, Synthesis, Characterization, and Evaluation of the Anti-HT-29 Colorectal Cell Line Activity of Novel 8-Oxyquinolinate-Platinum(II)-Loaded Nanostructured Lipid Carriers Targeted with Riboflavin ## Abstract Colorectal cancer is occasionally called colon or rectal cancer, depending on where cancer begins to form, and is the second leading cause of cancer death among both men and women. The platinum-based [PtCl(8-O-quinolinate)(dmso)] (8-QO-Pt) compound has demonstrated encouraging anticancer activity. Three different systems of 8-QO-Pt-encapsulated nanostructured lipid carriers (NLCs) with riboflavin (RFV) were investigated. NLCs of myristyl myristate were synthesized by ultrasonication in the presence of RFV. RFV-decorated nanoparticles displayed a spherical shape and a narrow size dispersion in the range of 144–175 nm mean particle diameter. The 8-QO-Pt-loaded formulations of NLC/RFV with more than $70\%$ encapsulation efficiency showed sustained in vitro release for 24 h. Cytotoxicity, cell uptake, and apoptosis were evaluated in the HT-29 human colorectal adenocarcinoma cell line. The results revealed that 8-QO-Pt-loaded formulations of NLC/RFV showed higher cytotoxicity than the free 8-QO-Pt compound at 5.0 µM. All three systems exhibited different levels of cellular internalization. Moreover, the hemotoxicity assay showed the safety profile of the formulations (less than $3.7\%$). Taken together, RFV-targeted NLC systems for drug delivery have been investigated for the first time in our study and the results are promising for the future of chemotherapy in colon cancer treatment. ## 1. Introduction Colorectal cancer (CRC) is the third most common type of cancer globally, after lung and breast cancers (1.93 million new cases in 2020), and the second most deadly [1]. While overweight or obesity, certain types of diets, physical inactivity, and alcohol and tobacco use are shown as risk factors, CRC is inherited in only $5\%$ of the cases [2]. Metallodrugs are a class of anticancer agents mostly used in the treatment of several pathologies including diabetes, neurodegenerative diseases, and cancer [3,4,5,6]. The most successful metallodrugs are cisplatin, carboplatin, and oxaliplatin, widely used in the treatment of several tumors and various kinds of cancer including colorectal, ovarian, cervical, testicular, lung, head, and neck cancers [7,8]. Despite their effective clinical applications, long-term use can cause drug resistance, dose-limiting side effects, and undesirable side effects in non-target tissues and organs [9]. In previous work, 8-oxyquinolinate-platinum(II) [PtCl(8-O-quinolinate)(dmso)] (8-QO-Pt) exhibited higher antitumor activity compared to cisplatin, generating neither resistance nor side effects in an osteosarcoma model in mice [10]. To overcome the drawbacks of platinum drugs, different drug delivery systems have been developed [9,11]. Nanosystems can enhance the bioavailability of these drugs by increasing the residence time in the bloodstream and protecting them from degradation while preventing possible damage to healthy tissue and delivering the drug in a controlled manner. Therefore, potential side effects and resistance to anticancer drugs may be averted [12]. Nanostructured lipid carriers (NLCs) are second-generation solid lipid nanoparticles (SLNs) that provide improved drug loading capacity and stability [13]. They are synthesized by the mixing of a solid lipid phase with a liquid lipid (at room temperature), and the further addition of a surfactant that acts as a dispersant and stabilizing agent [14]. Drug delivery systems can be roughly classified as passive or active. Passive drug delivery is allowed by the enhanced permeability and retention effect (EPR) that is characterized by the higher blood capillary permeability in the tumor tissue with a much lesser return of the fluids to the lymphatic circulation [15]. However, passive drug delivery based on the EPR effect has limitations. A dense fibrotic microenvironment can cause inhibition of the deep internalization of nanosystems [16]. On the other hand, active targeting delivery is based on a system that releases the drug into a targeted area [17]. The integration of active targeting ligands into the nanosystems has been sought to increase nanoparticle accretion at the tumor site [18]. Antibodies, antibody fragments, peptides, proteins, aptamers, and receptor ligands can be utilized to create an active drug delivery system [15]. In particular, numerous targeted nanosystems for delivering platinum-based drugs have been reported [9]. Riboflavin (RFV) or vitamin B2 is a water-soluble molecule (XlogP3 = −1.5) available in many foods and used as a common dietary supplement. RFV participates in the energetic and respiratory metabolism of cells through the synthesis of the two major flavin coenzymes (i.e., flavin adenine dinucleotide and flavin mononucleotide) [19]. Previous research has shown that RFV transporters and the riboflavin carrier protein show up-regulation in various tumor types such as breast, prostate, and hepatocellular carcinoma [20,21], as well as in some colon carcinoma cells [19]. Considering these results, RFV could be an efficient targeting ligand for tumor-specific drug delivery. To date, several different nanodelivery systems have been functionalized with RFV, such as liposomes [20], polymer conjugates [22], and telodendrimers [23], to actively target tumor cells. The present work aimed to design, synthesize, characterize, and evaluate in vitro antitumoral activity of a new RFV-functionalized 8-QO-Pt-loaded NLC for the treatment of colon cancer. Three formulations, which differ in the phase in which RFV was incorporated during the nanoparticle synthesis processes, were prepared. The nanosystem characterization included dynamic light scattering (DLS) and transmission electron microscopy (TEM) analysis. In addition, drug encapsulation efficiency, drug release and modeling, cell viability, cellular uptake, apoptosis, and hemotoxicity analyses of the nanoparticles were carried out. To the best of our knowledge, this is the first report of RFV-targeted NLCs for the delivery of a platinum compound. ## 2.1. Materials Myristyl myristate (MM) and capric triglyceride lipids were kindly donated by Croda (Martinez, Argentina). Poloxamer 188, riboflavin, and 3,3′-dioctadecyloxacarbocyanine perchlorate (DiOC18) were purchased from Sigma-Aldrich (Buenos Aires, Argentina). Dulbecco’s modified Eagle’s medium (DMEM) and TrypLE™ were purchased from Gibco (Gaithersburg, MD, USA). Fetal bovine serum (FBS) was bought from Internegocios S.A. (Mercedes, Argentina). Annexin V, Fluorescein isothiocyanate (FITC), propidium iodide (PI), and tetrazolium salt MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium-bromide) were supplied by Invitrogen Co. (Buenos Aires, Argentina). Other reagents were of analytical or HPLC grade from available commercial sources and used as received from Merck (Darmstadt, Germany) or a similar brand. ## 2.2.1. Preparation of the 8-QO-Pt The 8-QO-Pt was synthesized and characterized according to Martín Santos et al. [ 24], obtaining a yellow-orange solid ($78\%$ yield). 1H NMR (300 MHz, CD2Cl2) δ: 9.40 (dd, $J = 10.7$, 1.1 Hz, 1H), 8.37 (dd, $J = 8.3$, 1.0 Hz, 1H), 7.57–7.40 (m, 2H), 7.05–7.02 (m, 2H), 3.62 (s, 6H). ## 2.2.2. Preparation of the Formulations The 8-QO-Pt-compound-loaded (+) formulations of NLC/RFV were synthesized by the ultrasonication technique [25]. Different ratios of RFV were added to the lipidic or the aqueous phases according to Table 1. For the first formulation (R1-8-QO-Pt-NLC), 400 mg of myristyl myristate (MM) ($2.0\%$, w/v) was melted in a water bath at 70 °C and mixed with 2.0 mg of the 8-QO-Pt compound, previously dissolved in 200 μL capric triglyceride ($1.0\%$, v/v). Then, 20 mL of a hot aqueous solution with 10 mg of RFV ($0.05\%$, w/v) and Poloxamer 188 ($4.5\%$, w/v) was added to the lipid phase. Immediately, the mixture was ultrasonicated at $65\%$ amplitude for 30 min using a 6 mm titanium tip probe (ultrasonic processor, Cole Parmer, USA, 130 W). After sonication, the formulation was cooled down at room temperature and stored at 4 °C for further studies. On the other hand, in the second formulation (R2-8-QO-Pt-NLC), the RVF (10 mg) was incorporated directly into the lipid phase, whereas in the third one (R3-8-QO-Pt-NLC), 5 mg of RVF was incorporated in the aqueous phase and 5 mg of RFV into the lipid phase before mixing. ## 2.2.3. Measurement of Encapsulation Efficiency The encapsulation efficiency (EE, %) of 8-QO-Pt in the nanosystems was determined by the indirect method according to Equation [1]. Briefly, a volume of 500 µL of each formulation was transferred to a cutoff centrifugal filter (MWCO 10,000, Microcon®, Millipore, MA, USA) and centrifuged at 12,000× g for 30 min. The 8-QO-Pt in the filtrate was measured by HPLC (see Section 2.2.6). The EEs of the formulations were calculated as follows:EE (%) = (wi − wfd)/wi × 100 [1] where wi is the initial amount of 8-QO-Pt compound added to the formulation, and wfd is the amount of non-encapsulated 8-QO-Pt compound in the filtrate after the ultrafiltration process. ## 2.2.4. Particle Size, Polydispersity Index, Zeta Potential, and Transmission Electron Microscopy The average diameter and particle size distribution of NLCs were measured by dynamic light scattering (Nano ZS Zetasizer, Malvern Instruments Corp, UK) at 25 °C in polystyrene cuvettes with a thickness of 10 mm. Measurements were carried out in 10 mm path length capillary cells, using deionized water (Milli-Q®, Millipore, MA, USA). The polydispersity Index (PdI) values and the zeta potentials (ζ) were also determined. All the measurements were performed in triplicate to obtain the mean value. TEM analysis was conducted using a Jeol-1200 EX II-TEM microscope (Jeol, MA, USA). First, the NLC formulations were diluted 10 times with ultrapure Milli-Q® water, and 10 μL of the sample was spread onto a collodion-coated Cu grid (400-mesh). Liquid excess was taken out with filter paper, and for contrast enhancement, a drop of phosphotungstic acid was added to the samples. ## 2.2.5. In Vitro Drug Release Assay In vitro drug release assay was performed using Float-A-Lyzer®G2 dialysis devices (MWCO: 100 kD). The dialysis devices were previously soaked in $10\%$ ethanol for 10 min and then left in distilled water for 20 min. Next, the devices were filled with 2.0 mL of each formulation and immersed in 15 mL of $30\%$ isopropyl alcohol solution at 37 °C, with continuous shaking at 200 rpm [26,27]. For the free drug release, 2.5 mg of 8-QO-Pt was dissolved in 25 mL of the release medium and 2.0 mL of the solution was transferred to the dialysis device. Then, 200 μL samples were withdrawn at regular intervals for 24 h, and drug concentration was measured by HPLC. ## 2.2.6. HPLC Analysis Chromatographic analysis was performed using HPLC (Gilson SAS, Villiers-Le-Bel, France) with UV-VIS detection and a Zorbax Eclipse XDB-C18 (150 mm × 4.6 mm, 5 μm, Agilent Technologies Inc., Santa Clara, CA, USA) column as the stationary phase. The mobile phase consisted of a mixture of methanol and water (55:45). The system was operated isocratically at a 0.6 mL/min flow rate and the detection was performed at 262 nm. Samples were diluted with mobile phase and centrifuged (15,000× g for 5 min at 4 °C) before their injection (20 μL). The linearity, precision, and specificity of the method were validated over the range of expected concentrations (0.25–50.00 μg/mL). ## 2.2.7. Cell Cytotoxicity Assay The human colon carcinoma cell line (HT-29) was purchased from ATCC (HTB-38™) and cultured in DMEM (Gibco, Invitrogen Corporation, USA) supplemented with $10\%$ FBS (Internegocios, Buenos Aires, Argentina) and antibiotics (100 U/mL penicillin and 100 μg/mL streptomycin; Gibco, Invitrogen Corporation, USA) at 37 °C and under a $5\%$ CO2 atmosphere. Cell cytotoxicity assay was performed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) reagent [28]. Cells were seeded in a 96-multiwell plate and grown at 37 °C for 24 h. For the cytotoxicity assay, cells were treated with different concentrations (2.5, 5.0, 7.5, and 10.0 µM) of free 8-QO-Pt, R1-8-QO-Pt-NLC, R2-8-QO-Pt-NLC, and R3-8-QO-Pt-NLC in serum-free DMEM for 24 h. After this treatment, the cells were washed with PBS and incubated with 0.5 mg/mL MTT in supplemented DMEM for 3 h. Next, the absorbance was read spectrophotometrically in a microplate reader (multiplate reader multiscan FC, Thermo Scientific, MA, USA) at 570 nm. Cell viability was expressed as the percentage (%) of the untreated control value ($100\%$ survival). ## 2.2.8. Cellular Uptake Assay The cellular uptake assay was performed using empty (−) formulations of NLC/RFV (R1-NLC, R2-NLC, and R3-NLC) loaded with the green fluorescent probe DiOC18 (λabs/λem = $\frac{484}{501}$ nm) by adding 1.0 mg of DiOC18 to the lipid phase at 70 °C. The DiOC18-labeled formulations were synthesized by ultrasonication as mentioned above. The DiOC18 was $100\%$ incorporated into the NLCs. Flow cytometry was utilized to evaluate the internalization of the labeled nanoparticles into the HT-29 cells. Cells were seeded on a 12-well plate to be allowed to attach to the bottom surfaces of the wells for 24 h. Later, the cells were treated with 2.5 µM of the formulations for 24 h. After 24 h of treatment, the cells were washed with PBS and treated with 300 μL of trypLE until they were unattached from the surface. Next, 600 μL of the serum-containing medium was placed in each well and the cells were transferred to Eppendorf tubes and centrifuged at 2500× g at 4 °C for 5 min. Supernatants were separated carefully and the pellets were washed with PBS once. Later, the pellets were dispersed in 200 μL of PBS. Fluorescence was analyzed by FACSCalibur (Becton Dickinson, Franklin Lakes, NJ, USA), and the values were read by FlowJo 7.6 software. ## 2.2.9. Apoptosis Assay Early and late apoptotic cells were determined by annexin V-FITC and PI staining. The cells were seeded on a 12-well plate for 24 h and treated with two different concentrations (2.5 and 5.0 µM) of the formulations for 24 h. After the treatment and for the staining, the cells were washed with PBS and diluted with 1X binding buffer, Annexin V-FITC, and PI, and incubated at room temperature for 20 min before the analysis. Cells were collected using flow cytometry (BD FACSCalibur™) and the results were analyzed using FlowJo 7.6 software. For each analysis, 10,000 counts gated on an FSC vs. SSC dot plot were recorded. Four subpopulations were defined in the dot plot: the undamaged vital (FITC−/PI−), the vital mechanically damaged (FITC−/PI+), the early apoptotic (FITC+/PI−), and the late apoptotic (FITC+/PI+) subpopulations. ## 2.2.10. Hemotoxicity Assay Venous blood was obtained from healthy donors after written informed consent and collected in heparinized tubes. Then, whole blood was diluted in a six-well plate with culture medium Ham F12 with $10\%$ FBS in a final volume of 2.0 mL. Each culture was subjected to different treatments with free 8-QO-Pt, free RFV, their combinations (8-QO-Pt and RFV), and 8-QO-Pt-loaded or -unloaded formulations of NLC/RFV according to each specific experimental design, and kept in the culture at 37 °C with $5\%$ CO2 for 24 h and 48 h. The released hemoglobin was quantified by the absorbance read at 540 nm in a TECAN-infinite M200 Pro spectrophotometer to determine the percentage of lysed erythrocytes. Total hemolysis ($100\%$) was achieved by incorporating Triton X-100 into the medium, while the physiological solution was used as a negative control. ## 2.2.11. Statistical Analysis All experiments were carried out with a minimum of three independent replicates. Comparisons of the means were performed by analysis of variance (ANOVA) with a significance level of $5.0\%$ (α = 0.05) followed by Fisher’s least significant difference test. ## 3.1. Formulation Development and Nanoparticle Morphology All the formulations were prepared following a procedure previously reported by our group [25,29]. The lipid matrices were composed of myristyl myristate (MM, solid lipid), capric triglyceride (liquid lipid), the surfactant Poloxamer 188, and the RFV phase, and the content was changed according to the formulation. The formulations under study were: R1-8-QO-Pt-NLC containing 10 mg of RFV in the aqueous phase; R2-8-QO-Pt-NLC, containing 10 mg of RFV in the lipid phase; and finally, R3-8-QO-Pt-NLC containing 5 mg of RFV in the aqueous phase and 5 mg of RFV in the lipid phase. The preparations were made before the mixing and ultrasonication. All three 8-QO-Pt-loaded formulations showed an EE(%) higher than $70\%$, that is, 74.6 ± 1.7, 79.4 ± 1.1, and 79.5 ± 2.3 for R1-8-QO-Pt-NLC, R2-8-QO-Pt-NLC, and R3-8-QO-Pt-NLC, respectively. The mean diameter, PdI, and Z-pot (ζ) values of the formulations (with and without the drug) are listed in Table 2. All the formulations showed a narrow size distribution with average particle sizes from 144.50 ± 2.04 to 175.00 ± 1.18 nm. RFV did not significantly affect the particle size of 8-QO-Pt-loaded (+) formulations of NLC/RFV. The particle sizes were substantially monodisperse, with PdI values around 0.2, except for R2-NLC, which was higher than the optimal maximal limit of 0.3 [30,31]. The ζ value is a measure of the effective electric charge on the surface of the nanoparticles and is also one of the indicators of the stability of the colloidal systems [32]. The 8-QO-Pt-loaded (+) formulations exhibited lower negative ζ than the controls. The ζ values tend to decrease in the presence of a platinum compound in the formulations [33]. TEM was used to evaluate the morphology and size distribution of the nanoparticles. TEM images showed that the nanoparticles had a spherical shape. Moreover, the incorporation of 8-QO-Pt and RFV did not influence the morphology of nanoparticles (Figure 1). ## 3.2. In Vitro Drug Release Assay In vitro release studies of 8-QO-Pt from the NLC were carried out by the dialysis method using $30\%$ isopropyl alcohol solution as the release medium. An amount of 2 mL of each formulation was transferred into the Float-A-Lyzer® G2 dialysis device and the release of 8-QO-Pt from the nanoparticles was analyzed as a function of time. The in vitro release experiments could predict the stability of the formulations in terms of drug delivery once they reached physiological environments. Several studies have investigated the relationship between formulation stability and release profile in order to select the best nanoparticulate systems to be potentially administered [34,35,36]. According to the results shown in Figure 2, it was observed that the R2-8-QO-Pt-NLC and R3-8-QO-Pt-NLC formulations showed instability as soon as they were exposed to the release medium since NLC started releasing 8-QO-Pt as fast as the free drug. On the other side, the R1-8-QO-Pt-NLC exhibited a more controlled release profile. However, it is important to mention that the in vivo release profile of NLC will be modulated by interactions with different molecules from the bloodstream, which may form a “protein corona” that could delay the release of the cargo molecules [37]. All the formulations were able to effectively release the drug in the assayed conditions. In particular, R1-8-QO-Pt-NLC reached $62\%$ of drug release after 24 h, while R2-8-QO-Pt-NLC and R3-8-QO-Pt-NLC showed a release profile similar to the free drug, reaching over $80\%$ of the content released at that time. Since R1-8-QO-Pt-NLC was the only formulation with a significantly different release than the control, the DDSolver Excel add-in program [38] was used to fit different kinetic models that could provide some insights about the release behavior. The analysis showed that for the R1-8-QO-Pt-NLC formulation, the Baker and Lonsdale model best fitted the data, with an R2adj of 0.9364. This model, derived from Higuchi’s model, is generally used to describe the controlled release of drugs from spherical matrices, and diffusion and degradation are the main factors responsible for the release mechanism [39]. This type of device comprises a dispersion of the solid drug through the rate-controlling medium (i.e., the matrix). For low levels of drug loading, as in this case, the release involves the dissolution of the drug in the matrix followed by diffusion to the surface of the device. Therefore, the release profile is determined by the initial loading, the chemical nature of the matrix, and its geometry [40]. ## 3.3. Cell Cytotoxicity Assay The in vitro cytotoxic activity against the HT-29 colon carcinoma cell line was investigated by MTT assay. Figure 3 presents the cell viability (%) with 2.5, 5.0, 7.5, and 10.0 µM concentrations of the free 8-QO-Pt compound and 8-QO-Pt-loaded (+) formulations of NLC/RFV after 24 h treatment. The results revealed that both the free and loaded 8-QO-Pt (in all three formulations), displayed a dose-dependent cytotoxicity profile against HT-29 cells while the formulations without 8-QO-Pt did not present any toxicity. The cell viability (%) was reduced to 70.7 ± $4.7\%$ (free 8-QO-Pt), 59.7 ± $2.3\%$ (R1-8-QO-Pt-NLC), 78.3 ± $5.6\%$ (R2-8-QO-Pt-NLC), and 91.2 ± $4.8\%$ (R3-8-QO-Pt-NLC) for 2.5 µM; 67.5 ± $6.3\%$, 17.7 ± $2.8\%$, 31.8 ± $4.4\%$, and 51.4 ± $4.7\%$ for 5.0 µM; 29.0 ± $4.6\%$, 8.7 ± $0.1\%$, 11.1 ± $0.8\%$, and 16.1 ± $2.7\%$ for 7.5 µM; and 12.9 ± $1.0\%$, 8.6 ± $0.3\%$, 10.8 ± $4.5\%$, and 9.8 ± $2.4\%$ for 10 µM, respectively. Even though the three formulations contained the same amount of RFV (10 mg in total), the incorporation of RFV into distinct phases affected cell viability, particularly at low doses (2.5 and 5.0 µM). All the RFV-targeted 8-QO-Pt-loaded formulations demonstrated a higher cytotoxic effect than the free 8-QO-Pt compound (except at 2.5 µM). Moreover, R1-8-QO-Pt-NLC seemed to be more effective, especially at 5.0 µM, showing higher antitumor activity compared to free 8-QO-Pt, R2-8-QO-Pt-NLC, and R3-8-QO-Pt-NLC (3.8-fold, 1.8-fold, and 2.9-fold, respectively). Moreover, Table 3 lists the IC50 values for the free 8-QO-Pt compound and the nanoformulations in the HT-29 cells. In our previous study, the 8-QO-Pt-NLC nanosystem without RFV functionalization did not show a superior cytotoxic effect in comparison to the free 8-QO-Pt in HT-29 cells [29]. These results reveal that the RFV functionalization of nanoparticles increases cytotoxicity by targeting delivery [23]. As a third-generation of platinum-based anticancer drug, oxaliplatin has been approved to treat CRC [9]. The IC50 value of oxaliplatin in the HT-29 cells was determined as 2 µM [4] and another study has shown a value of 5 µM after 48 h treatment [41]. It has been reported that the IC50 value of oxaliplatin-loaded solid lipid nanoparticles was found to be higher than in folic-acid-functionalized oxaliplatin-loaded SLN but was lower than in free oxaliplatin in the HT-29 cells [42]. Our findings demonstrate that the R1-8-QO-Pt-NLC presents a similar effective antiproliferative activity profile in the HT-29 colorectal cancer cell line as the reference drug currently used in the clinic (oxaliplatin). ## 3.4. Cellular Uptake Assay To assess the cellular internalization of the NLC/RFV nanoparticles, the fluorescent probe DiOC18 was incorporated into all empty (−) formulations. Then, HT-29 cells were treated with empty labeled nanoparticles at a concentration equivalent to 2.5 µM of 8-QO-Pt-loaded (+) formulations of NLC/RFV for 24 h (Figure 4). After the treatment, the fluorescent signals were detected by flow cytometry. According to the results, HT-29 cells were able to successfully internalize the nanoparticles. The mean ± standard deviation of cell uptake demonstrated that R1-8-QO-Pt-NLC showed the highest cellular uptake (89.5 ± $1.0\%$) in comparison to R2-8-QO-Pt-NLC (25.6 ± $1.1\%$) and R3-8-QO-Pt-NLC (47.7 ± $1.5\%$). In addition, the untreated condition (basal media) showed 2.0 ± $0.4\%$. These data could be correlated with the superior cytotoxic effect of R1-8-QO-Pt-NLC observed in Figure 3. ## 3.5. Apoptosis Assay While anticancer drugs cause cell apoptosis, some biochemical and morphological changes can be observed with the use of tracers. The cell membrane exposes phosphatidylserine residues on the outer surface that can be detected with the fluorescent dye Annexin V-FITC by fluorescence assays [43]. Therefore, HT-29 cells were stained with the Annexin V-FITC/PI apoptosis detection kit after 24 h of treatment with free 8-QO-Pt and R1-8-QO-Pt-NLC, at 2.5 µM and 5.0 µM. Figure 5 shows the percentages of vital (FITC−/PI−), early apoptotic (FITC+/PI−), late apoptotic (FITC+/PI+), and necrotic (FITC−/PI+) subpopulations in the dot plot. According to the results, the free 8-QO-Pt compound and R1-8-QO-Pt-NLC induced 16.4 ± $3.7\%$ and 20.8 ± $1.2\%$ (at 2.5 µM), and 22.5 ± $4.8\%$ and 37.9 ± $9.9\%$ (at 5 µM) of cells in late apoptosis (FITC+/PI+), respectively. The fact that R1-8-QO-Pt-NLC was able to generate more apoptosis can be explained by taking into consideration that this formulation was the one with the highest cell internalization ability, i.e., it can deliver the 8-QO-Pt drug more efficiently into the cells. In our previous study, untargeted 8-QO-Pt-NLC and free 8-QO-Pt showed very similar results in the apoptosis assay [29]. When the same formulation was functionalized with RFV, the antiproliferation effect of 8-QO-Pt increased due to the higher affinity of RFV as a targeting agent. It has been shown that the conjugation of RFV with some nanosystems such as ultrasmall iron oxide nanoparticles, polyethylene glycol polymers, dendrimers, and liposomes exhibited high affinity toward tumors in preclinical studies [44]. However, the previous RFV-conjugated systems displayed some disadvantages. Iron oxide nanoparticles are well-known promoters of free radicals that could damage the surrounding tissues [45]. PEG polymers were reported to produce retarded immune responses in several patients [46]. Dendrimers showed high tissue toxicity beyond G3 (i.e., third generation) [47], and liposome stability requires constant size and size distribution for a prolonged time which may cause the liposomes to be unable to effectively release their cargo into the targeted cells [48]. ## 3.6. Hemotoxicity Assay The hemocompatibility of drug delivery nanosystems becomes the first approach to determine potential cytotoxic effects after the interaction of the blood cells with nanoparticles [49]. Considering a possible systemic administration of platinum nanoparticles by the intravenous route, the effect of the nanoparticles and their components on whole blood was studied (Figure 6). The hemolysis process could be related to the nature of the particles, the presence of surfactants, or strong negative charges on the surface [50,51]. In the case of 8-QO-Pt, a dose-dependent hemolytic effect was observed but produced a hemolysis degree of less than $1.5\%$ at the highest tested concentration (5.0 µM) at 24 h and 48 h (Figure 6a). Higher hemotoxicity values for RFV were found, with hemolysis percentages around $2.0\%$, $3.0\%$, and $4.5\%$ for the 7.5, 15.0, and 30.0 µM concentrations, respectively. Combinations of 8-QO-Pt and RFV at different proportions showed that the hemotoxic effect of RVF overlapped with the cytotoxic effect of 8-QO-Pt with no additive and/or synergetic effects being observed. On the other hand, the hemolysis degree of 8-QO-Pt-loaded (+) and empty (−) formulations of NLC/RFV after exposure to human blood cells for 24 h and 48 h was determined (Figure 6b). The hemotoxicity decreased for the formulations containing 8-QO-Pt and RFV compared to combinations of free 8-QO-Pt and RFV. A dose–hemolytic effect response in the case of 8-QO-Pt-loaded NLCs and a reduced hemolysis percentage in the case of empty NLCs (less than $1.0\%$ in all cases) were observed. The R1-8-QO-Pt-NLC formulation showed hemolysis around $3.0\%$, R2-8-QO-Pt-NLC around $2.0\%$, and R3-8-QO-Pt-NLC around $3.7\%$ at the concentration of 5.0 µM. These results suggest that R2-8-QO-Pt-NLC is the safest and R3-8-QO-Pt-NLC the most hemotoxic formulation. Nevertheless, all the formulations are in an acceptable and low range of hemotoxicity (lower than $5\%$), which is the value considered as toxic according to the ISO/TR 7406 [52]. ## 4. Conclusions In summary, RFV ligand-targeted NLC particles have been revealed as potential tumor-specific drug delivery systems. The current study reports an efficient encapsulation strategy of 8-QO-Pt into three novel formulations of NLC/RFV, which differ in the way RFV was incorporated into them. DLS and TEM analysis showed that the nanoparticles had a spherical shape and small particle size with a narrow size distribution (144–175 nm). A sustained release of 8-QO-Pt from the NLC/RFV nanoparticles was observed which could reduce the possible adverse effects caused by systemic administration of the drug. The formulations exhibited a dose-dependent manner in the cytotoxicity assays. In particular, the antitumor effect of the R1-8-QO-Pt-NLC system was superior to that of the free 8-QO-Pt compound and the other tested nanosystems. On the other hand, the cellular internalization level of R1-8-QO-Pt-NLC was found to be higher than those of R2-8-QO-Pt-NLC and R3-8-QO-Pt-NLC. In comparison to the free drug, the apoptosis assay revealed that active targeting with RFV resulted in a great antiproliferative effect due to cancer cell selectivity. Moreover, the high hemocompatibility of the nanoparticles was proven by the hemotoxicity assay which is an advantage in the case of intravenous administration. This relevant result makes the R1-8-QO-Pt-NLC system a good candidate for further in vivo studies. Finally, RFV-targeted 8-QO-Pt-NLC systems for drug-controlled release have been developed and investigated in our study for the first time. The results are superior to previous RFV-targeted anticancer systems. 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--- title: Morphological and Functional Remodeling of the Ischemic Heart Correlates with Homocysteine Levels authors: - Attila Cziraki - Zoltan Nemeth - Sandor Szabados - Tamas Nagy - Márk Szántó - Csaba Nyakas - Akos Koller journal: Journal of Cardiovascular Development and Disease year: 2023 pmcid: PMC10056082 doi: 10.3390/jcdd10030122 license: CC BY 4.0 --- # Morphological and Functional Remodeling of the Ischemic Heart Correlates with Homocysteine Levels ## Abstract Background: Homocysteine (Hcy) is involved in various methylation processes, and its plasma level is increased in cardiac ischemia. Thus, we hypothesized that levels of homocysteine correlate with the morphological and functional remodeling of ischemic hearts. Thus, we aimed to measure the Hcy levels in the plasma and pericardial fluid (PF) and correlate them with morphological and functional changes in the ischemic hearts of humans. Methods: Concentration of total homocysteine (tHcy) and cardiac troponin-I (cTn-I) of plasma and PF were measured in patients undergoing coronary artery bypass graft (CABG) surgery ($$n = 14$$). Left-ventricular (LV) end-diastolic diameter (LVED), LV end-systolic diameter (LVES), right atrial, left atrial (LA) area, thickness of interventricular septum (IVS) and posterior wall, LV ejection fraction (LVEF), and right ventricular outflow tract end-diastolic area (RVOT EDA) of CABG and non-cardiac patients (NCP; $$n = 10$$) were determined by echocardiography, and LV mass was calculated (cLVM). Results: Positive correlations were found between Hcy levels of plasma and PF, tHcy levels and LVED, LVES and LA, and an inverse correlation was found between tHcy levels and LVEF. cLVM, IVS, and RVOT EDA were higher in CABG with elevated tHcy (>12 µM/L) compared to NCP. In addition, we found a higher cTn-I level in the PF compared to the plasma of CABG patients (0.08 ± 0.02 vs. 0.01 ± 0.003 ng/mL, $p \leq 0.001$), which was ~10 fold higher than the normal level. Conclusions: We propose that homocysteine is an important cardiac biomarker and may have an important role in the development of cardiac remodeling and dysfunction in chronic myocardial ischemia in humans. ## 1. Introduction Ischemic heart disease, including coronary artery disease, is the leading cause of death worldwide [1]. Impaired coronary blood supply develops due to narrowing and/or constriction of coronary vessels of different sizes [2]. Myocardial ischemia causes injuries to the myocardium and simultaneously initiates compensatory mechanisms in the injured tissues, increases de novo protein synthesis and connective/fibrotic tissues, leading to a structural remodeling and functional changes of the heart [3,4,5]. Cardiac remodeling involves several signaling mechanisms, including, among others, DNA methylation, nitric oxide/asymmetric dimethyl-arginine (NO/ADMA) pathway, endothelin-1, and angiotensin II [6,7,8,9]. Methionine, an essential amino acid, plays an important role by providing methyl groups for protein and DNA methylation pathways. It is converted to s-adenosylmethionine, a general methyl donor in the cells, which provides methyl groups in trans-methylation reactions, for example, to the synthesis of methylarginines. Homocysteine (Hcy) is a sulfur-containing amino acid, which is metabolized via re-methylation by converting Hcy back to methionine, and trans-sulfuration by converting Hcy to cysteine and taurine amino acids [10]. Although the normal plasma level of Hcy can be disputed, in healthy young subjects, values above 15 µM/L are considered to be high, and the optimal homocysteine levels are thought to be below 10–12 µM/L [11,12,13,14,15]. Despite earlier reports, as of today, there is still no consensus regarding the reference limits for plasma homocysteine levels. Studies focusing on various parts of the population suggest that the upper limit of 15 µmol/L is too high in normally nourished people without vitamin deficiencies. Additionally, it seems that each 5 µmol/L increase in Hcy level increases the risk of CHD (independently of other traditional risk factors) by about $20\%$ and that a continuum exists with the subsequent risk. Some reports suggest that a level around 6 µmol/L Hcy should be considered normal [16]. A systematic review and meta-analysis of homocysteine level and coronary heart disease incidence are presented in [16]. Thus, as Milani and Lavie posited, “homocysteine, however, remains an important field of study as an unconventional risk factor, one facet of a complex metabolic puzzle—a veritable Rubik’s cube—that promotes atherosclerosis” and cardiac dysfunction [17]. Indeed, elevated plasma levels of Hcy play an important role in the development of cardiovascular diseases, such as coronary artery disease and atherosclerosis, which may further exacerbate cardiac ischemia/infarction and fibrosis [18,19]. Supporting this idea, previous studies by Jacob et al. have shown that hyperhomocysteinemia leads to pathological ventricular hypertrophy in normotensive rats [20]. However, few if any data are available in humans on whether or not plasma (PL) and pericardial fluid (PF) Hcy correlates with ischemic cardiac remodeling. Cardiac ischemia in humans—in many cases—is resolved by successful coronary bypass graft surgery (CABG) [21]. Because from the ischemic/injured cardiac muscle cardiac troponin-I (cTn-I) is released and thus becomes elevated in the plasma, it is used as one of the biomarkers/indicators of ischemic insult to the cardiac muscle [22,23,24]. Interestingly, its level, like the level of Hcy in the PF, has not yet been measured. Based on the aforementioned facts, we hypothesize that the levels of Hcy in the plasma and pericardial fluid correlate with cardiac remodeling of the ischemic heart of patients undergoing CABG treatment. Thus, we collected samples of the plasma and PF of patients undergoing CABG surgery and measured their total Hcy and cTn-I concentrations and correlated them with the morphological and functional characteristics of their hearts. ## 2.1. Study Description and Clinical Characterization of the Patients In the present study, subjects were recruited at the Heart Institute of the University of Pécs Medical School, Hungary. This is a cross-sectional investigation of 14 subjects with coronary artery disease and 10 non-cardiac patients (NCP) as a control for echocardiographic measurements. Non-cardiac patients underwent physical examination with no cardiac surgery. All patients with coronary artery disease underwent elective CABG surgery. Age- and sex-matched subjects with mild to moderate arterial hypertension were selected for the study. Written informed consent was obtained from all individuals before participation in the study. The investigation and consent documents were approved by the Ethics Committee of the Medical School of the University of Pecs (RKEB-$\frac{4123}{20110}$). The investigation conforms to the principle outlined in the Declaration of Helsinki. Blood plasma and PF samples were collected from the patients after median sternotomy. ## 2.2. Measurements of tHcy and cTn-I Pericardial fluid samples were collected by pericardiocentesis, together with blood samples, simultaneously from CABG patients in heparinized vacutainer tubes during CABG surgery, and centrifuged at 3000 rpm for 10 min. Supernatants were then kept at –80 degrees for further use. cTn-I was measured by Microparticle Enzyme Immunoassay, and tHcy was measured by Fluorescence Polarization Immunoassay on an Abbott Axsym immunochemical automated analyzer (Abbott Diagnostics, Abbott Laboratories, Abbott Park, IL, USA), according to the manufacturer’s instructions [25,26]. ## 2.3. Echocardiography Measurements Morphological characteristics of CABG patients’ hearts were assessed with 2-D transthoracic echocardiography. Two-dimensional (2-D), M-mode, and Doppler echocardiography with automated border detection were carried out using Hewlett-Packard Sonos 5500 echocardiograph with a 2.5 MHz transducer (Hewlett-Packard, USA). Two-dimensional echocardiographic measurements were performed according to European guidelines. In order to minimize the variability and bias, echocardiographic measurements were carried out by the same cardiologist (blinded to the patient’s identity) with an expert license in transthoracic echocardiography. All images were recorded and analyzed offline. The following parameters were measured: left ventricular end-diastolic diameter (LVED), left ventricular end-systolic diameter (LVES), the thickness of interventricular septum (IVS) and posterior wall (PW), right ventricular outflow tract end-diastolic area (RVOT EDA), and right atrial (RA) and left atrial (LA) area. The biplane Simpson method using the end-diastolic and end-systolic apical 4- and 2- chamber views for estimation of LV volume and calculation of the ejection fraction (LVEF) [27] was applied. ## 2.4. Statistics and Calculations Correlations between tHcy levels and echocardiographic parameters were performed using Pearson’s correlation analysis. Because we found a very high correlation between PF and plasma values of Hcy for correlation analysis plasma and PF, tHcy levels (plasma + PF)/2 were averaged. Plasma and PF tHcy and cTn-I levels were compared with a two-tailed independent t-test. For comparison of echocardiographic parameters of CABG patients with non-cardiac patients (NCP), two-tailed independent t-test was applied. Left ventricular mass (LVM) was calculated using the American Society of Echocardiography (ASE) convention: VM = 0.8 (1.04 ([LVED + PW+ IVS]3- [LVED]3)) + 0.6 g, where PW is posterior wall thickness [28]. Statistically significant changes were considered at $p \leq 0.05.$ ## 3.1. Characteristics of Patients Descriptive statistics of the patients are summarized in Table 1, showing the major demographic and clinical characteristics, as well as concomitant risk factors and medications of patients. Six patients exhibited high blood pressure, and seven patients exhibited left-ventricular hypertrophy (LVH). The types and number of CABG operations were as follows: CABGx2: 1: CABGx3: 10; CABGx4: 3. Homocysteine levels in the plasma and PF were similar and showed positive correlation in CABG patients. We found that mean tHCy levels were similar in the PF and plasma (PF vs. plasma: 11.5 ± 1.26 μM/L vs. 13.4 ± 1.04 μM/L, $$p \leq 0.2628$$) (Figure 1A). In addition, we found a positive correlation between PF and plasma tHcy levels in this group ($r = 0.9$, $p \leq 0.0001$) (Figure 1B). ## 3.2. Echocardiographic Parameters of the Heart in CABG Patients According to previous a publication [11] the optimal level of Hcy is considered to be below 12 µM/L; thus, we grouped CABG patients with tHcy levels below or above 12 µM/L [11]. In Figure 2, representative echocardiographic images of a CABG patient with tHcy < 12 μM/L (A) and a CABG patient with tHcy > 12 μM/L Hcy (B) can be seen. We found that the LVED and LVES of CABG patients with tHcy higher than 12 µM/L were greater than that of lower tHcy. The LA of CABG patients with elevated tHcy is higher than that of CABG patients with lower tHcy. In CABG patients with elevated tHcy, the cLVM was higher than that of CABG patients with lower tHcy. The LVEF, PW, and RA were similar between the two groups (Table 2). ## 3.3. Correlation of Echocardiographic Parameters of the Heart with Homocysteine Levels in CABG Patients Analyzing correlations of homocysteine levels and echocardiographic parameters revealed meaningful information. We found that tHcy levels positively correlated with LVED ($r = 0.6$, $p \leq 0.01$) (Figure 3A), LVDS ($r = 0.7$, $p \leq 0.01$) (Figure 3B), and LA ($r = 0.7$, $p \leq 0.01$) (Figure 3C) and inversely correlated with LVEF ($r = 0.5$; $p \leq 0.05$) (Figure 3D). ## 3.4. Cardiac Troponin-I in Levels Are Increased in PF of CABG Patients We have found that troponin-I levels were significantly higher in the PF than the plasma of CABG patients indicating myocardium hypoxia and injury (PF vs. plasma: 0.08 ± 0.02 ng/mL vs. 0.01 ± 0.003 ng/mL, $p \leq 0.001$) (Figure 4). ## 4. Discussion The salient findings of the present study are as follows: [1] There is a positive correlation between plasma and pericardial fluid Hcy levels. [ 2] The echocardiographic parameters, namely, end-diastolic (LVED), end-systolic diameter of the left ventricle (LVDS), the left atrial (LA) area, calculated left ventricular mass (cLVM), right ventricular outflow tract end-diastolic area (RVOT EDA), and thickness of the interventricular septum (IVS) were significantly higher in CABG patients with tHcy above 12 μM/L compared to non-cardiac patients. [ 3] There are positive correlations between tHcy levels and the structural changes in LVED, as well as LVDS, LA, and an inverse correlation between tHcy levels and left-ventricular ejection fraction (LVEF) in CABG patients. [ 4] The level of cardiac troponin-I was significantly higher in pericardial fluid than plasma. ## 4.1. Cardiac Ischemia, Remodeling and Homocysteine CABG surgery is a widely used surgical solution for myocardial ischemia due to infarction or narrowing of the large coronary arteries. The persistent myocardial ischemia before CABG surgery causes injury to the myocardium, initiating simultaneous compensatory mechanisms that lead to changes in the size of cardiac chambers (Figure 2). Indeed, we found an increase in the end-diastolic and systolic diameter (Table 2). In addition, we also found an increase in the thickness of IVS, calculated LVM, and right ventricular outflow tract end-diastolic area (RVOT EDA) (Figure 2, Table 2). These findings suggest that there is a hypertrophic remodeling in this group of patients, which may correspond to the increased level of Hcy in the plasma and PF. This idea is supported by the close correlation between Hcy level and morphological changes in the present study (Figure 3) and that of previous animal and human studies, indicating that elevated plasma Hcy levels are associated with cardiac ischemia and remodeling [29,30]. Plasma tHcy level considered to be normal between 5–15 μmol/L, and its elevation can contribute to coronary artery disease in humans [11,12]. Higher levels of Hcy—in addition to remodeling—have functional consequences, as shown by the reduced ejection fraction, suggesting contractile dysfunction (Figure 3D). An interesting novel finding of the present study is that the tHcy levels of PF and plasma are similar, and there is a positive correlation between the plasma and PF tHcy levels (Figure 1), suggesting that homocysteine can freely diffuse between the coronary vessels and cardiac interstitial space, thereby reaching the pericardial space. This could be explained by the small size of the Hcy molecule and by the inflammation-induced increased permeability of the epicardium. ## 4.2. Presence of Cardiac Hypoxia in the Patient Group Studied Cardiac troponins have been found to have high sensitivity as indicators of myocardial injuries, such as in myocardial ischemia. Cardiac troponin level in the plasma is <0.004 ng/mL or <0.005 ng/mL [23]. In the present study, we found that cTn-I levels were approximately 10-fold higher than the normal level and in the PF were significantly higher than in the plasma (Figure 4). This indicates that the origin of PF cTn-I is the cardiac tissues, explaining the injury of the myocardium, and thus, it has a higher diagnostic value than that of plasma. The upper limit for high sensitivity cTn-I was reported to be <0.004 ng/mL or <0.005 ng/mL [23] in the plasma; however, cardiac troponin assays are regarded as a biomarker for detecting acute myocardial necrosis, but they may also be released in the absence of cardiac necrosis [24]. ## 4.3. Previous Findings with Homocysteine and the Heart The Framingham Heart Study showed that Hcy levels in the plasma of cardiac patients are positively related to changes of the left ventricular structure and function, such as left ventricular wall thickness [31]. In the present study, we found positive correlations between the chambers of the left side of the heart and PF tHcy levels in CABG patients (Figure 3). This suggests that Hcy may contribute to cardiac remodeling in the ischemic heart. This conclusion is supported by experimental findings in rat by Chen et al. [ 32]. Interestingly, we have previously found in rats with elevated homocysteine that increases in flow elicit constrictions of isolated arterioles, instead of dilation, which were attributed to enhanced production of reactive oxygen species known to decrease the bioavailability of nitric oxide (NO).The lack of NO could contribute to hypoxia and inflammation [30,33], and also, —being an antigrowth factor—could contribute to cardiac remodeling. ## 4.4. Pathomechanisms That May Contribute to Cardiac Remodeling and Contractile Dysfunction: Human Pericardial Fluid ADMA and Endothelin and Cardiac Ischemia Previously, we found elevated asymmetric dimethylarginine (ADMA) levels in cardiac ischemic and valve diseased patients, which showed positive correlation with indices of cardiac hypertrophy [6]. We have also shown that in the pericardial fluid, several biologically active substances are present, such as endothelin-1, the increased level of which contributes to pathological cardiac function [34]. In addition, cardiac ischemia can initiate inflammatory responses—mediated by immune cells and inflammatory cytokines—eliciting adaptation to hypoxic conditions by cellular hyperplasia or to cell death by apoptosis or necrosis. The underlying molecular signaling of higher Hcy level is likely to involve several parallel running events, such as cardiac ischemia, oxidative stress, and inflammation, leading to an increased level of ADMA levels known to enhance the growth factor angiotensin II by activating the tissue renin–angiotensin system’s [35] cell apoptosis and necrosis, and then initiating—under these conditions—the dysmethylation of proteins and genes, all of which are likely to be responsible for cardiac muscle remodeling and dysfunction [36]. ## 4.5. Hypoxia and Inflammation May Contribute to Cardiac Remodeling and Contractile Dysfunction Hypoxia and inflammation can initiate fibrotic processes in which the heart undergoes structural remodeling, with consequent functional changes [37]. The initial mechanism for cardiac fibrosis is the fibroblast-to-myofibroblast transition, in which cardiac fibroblasts become activated and converted into myofibroblasts [38]. Myofibroblasts are characterized by the expression of α-smooth muscle actin and increased production of collagens, as well as the capability to contract [39,40]. During the process of cardiac fibrosis, myofibroblasts secrete excessive collagens in their extracellular matrix (ECM) and finally undergo apoptosis resulting in irreversible fibrosis [40]. Inflammatory signals, such as transforming growth factor beta (TGF-β), activate cardiac fibroblasts, while non-coding RNA transcripts, such as microRNAs (miRNAs), mediate gene regulation during cell transition [41]. This process involves epigenetic mechanisms, such as DNA methylation, and post-translational protein modifications, which regulate the myofibroblast phenotype in fibrosis [42]. Simultaneously, post-translational protein modifications’ methylation processes are increased during cardiac remodeling, which are carried out by methyltransferases [43], for instance, protein-arginine methyltransferase-1 methylates arginine forming asymmetric dimethylarginine, by which the methyl donor S-adenosylmethionine is utilized, converting to S-adenosylhomocysteine. In this process, S-adenosylhomocysteine is converted back into S-adenosylmethionine or more likely into adenosine and Hcy. The sulfur-containing amino acid Hcy is a crucial player in trans-methylation processes [10]. All of these molecular pathomechanisms can lead to changes in cardiac substrate utilization [44], reduced contractility, and eventually heart failure. Supporting this idea, Okuyan et al. reported that NT-proBNP, hs-CRP, E/A ratio, and HbA1C were independently associated with hyperhomocysteinemia in a patient with diastolic heart failure. [ 45]. In Figure 5, we summarized some of the main mechanisms of action of homocysteine, which can lead to functional and morphological cardiac remodeling and microvascular constrictions due to the free movement of endothelin in the pericardial fluid/sac, reaching remote cardiac muscle areas. ## 4.6. Clinical Importance of the Present Findings Our data suggest that it is important to measure homocysteine level in patient populations with ischemic heart disease, because higher but still normal levels of homocysteine may represent risk factors for morphological and functional remodeling of the heart in this condition. On the basis of our findings in Figure 6 we are showing two hypothetical cardiac cycles curves indicating that in a condition of higher homocysteine level end-diastolic volume increases, contractility decreases, resulting in a reduced ejection fraction. The higher level of Hcy may be due to—among other factors—low vitamin B6, B12, and folate levels, which, however, can be corrected with appropriate therapy in most cases [46]. The normal range for Hcy (like many other parameters) usually refers to young (25-year-old) healthy individuals. However, in diseased conditions, the normal—or, as we can call, optimal—range of homocysteine may shift to higher or lower values/range. For example, it is thought that systemic blood pressure in diabetic patients should be lower than in non-diabetics [47]. Thus, it is possible that in cardiac ischemia, reducing Hcy levels below the “normal” range is beneficial to prevent cardiac remodeling and fibrosis. Since the level of Hcy can be influenced by appropriate treatment, it could be recommended to do so in ischemic heart disease. The pericardial fluid Hcy and cardiac troponin levels could be important biomarkers of cardiac ischemia when their plasma levels are still in the normal range. ## 4.7. Clinical Aspects Related to Medications It is of note that the long-term administration of glucose-lowering drugs such as metformin may attenuate cardiac hypertrophy and improve cardiac function, as suggested by clinical and animal studies [48,49,50]. However, studies have also shown that anti-diabetic drugs could increase plasma homocysteine levels [51]. In the present study, 6 out of 14 patients had diabetes and received anti-diabetic drugs (such as metformin or Levemir); however, the LVM values of these six patients were similar to those of patients with no anti-diabetic medication. We also found that the tHcy levels of patients with diabetes were similar to those with no diabetes, suggesting that these differences did not significantly affect the morphological findings in these groups of patients. Another important aspect is that plasma lipid levels may influence cardiac morphology and function. Indeed, a recent clinical study showed a positive correlation between higher LDL cholesterol and higher LV end-diastolic volume, as well as higher LVM [52]. Among our patients, only one had borderline high total cholesterol and high triglyceride level, and both the LVM and LVEF values of this patient were in the average range. Additionally, it is known that cholesterol-lowering drugs, e.g., statins, inhibit cardiac hypertrophy [53]. In the present study, all patients but one received statins, and half of the patients exhibited left-ventricular hypertrophy, but their other cardiac parameters were similar. Likewise, there was a consistent correlation between Hcy levels and the echocardiographic parameters, suggesting that statins were unlikely to have influenced the findings of the present study. Thus, cardiac morphology and function of patients included in the present study were unlikely to be affected by glucose-lowering drugs or plasma cholesterol levels. ## Limitations of the Study As in most human studies, there are no appropriate controls; thus we used findings from experimental (animal) studies to draw our conclusions regarding the underlying mechanisms. In this human study, we were limited by the amount of coronary bypass graft surgery (CABG) performed at our institution. Due to the great advances in percutaneous intervention (PCI), fewer patients need this type of open chest surgery. In addition, we were limited regarding the number of patients available in our clinic, which fall into the category of our investigation. Nevertheless, the homogeneity of patients allowed for the statistical analysis of data. For this reason, more data are necessary to support our conclusions regarding the role of homocysteine metabolism in cardiac remodeling due to long-lasting ischemia. In addition, it is of note that the anti-diabetic medication may influence the homocysteine levels of these CABG patients [54]. ## 5. Conclusions In conclusion, the findings of the present investigation suggest that in chronic ischemic cardiac patients—as indicated by the increased troponin level in the pericardial fluid—the higher levels of homocysteine (but still in the “normal range”) in the pericardial fluid and plasma contribute to the development of cardiac remodeling and contractile dysfunction. Thus, in this special patient population, plasma homocysteine levels should be measured, considered, and lowered with appropriate therapy. ## References 1. Nowbar A.N., Gitto M., Howard J.P., Francis D.P., Al-Lamee R.. 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--- title: 'Association between Meal Frequency and Type 2 Diabetes Mellitus in Rural Adults: A Large-Scale Cross-Sectional Study' authors: - Bota Baheti - Xiaotian Liu - Mu Wang - Caiyun Zhang - Xiaokang Dong - Ning Kang - Linlin Li - Xing Li - Songcheng Yu - Jian Hou - Zhenxing Mao - Chongjian Wang journal: Nutrients year: 2023 pmcid: PMC10056094 doi: 10.3390/nu15061348 license: CC BY 4.0 --- # Association between Meal Frequency and Type 2 Diabetes Mellitus in Rural Adults: A Large-Scale Cross-Sectional Study ## Abstract Diet frequency may potentially influence metabolic health. However, general population-based evidence on the association between meal frequency and type 2 diabetes mellitus (T2DM) remains limited and inconclusive. Thus, this study aimed to investigate the association between meal frequency and T2DM in resource limited area. A total of 29,405 qualified participants were enrolled from the Henan rural cohort study. Data on meal frequency were collected through a validated face-to-face questionnaire survey. Logistic regression models were utilized to explore the association between meal frequency and T2DM. Compared with 21 times per week meal frequency group, the adjusted odds ratios (ORs) and $95\%$ confidence intervals ($95\%$CIs) were 0.75 (0.58, 0.95) and 0.70 (0.54, 0.90) for 16–20 times/week group and 14–15 times/week group, respectively. For the analysis of the three meals, significant associations were only found between dinner frequency and T2DM. Compared with seven times per week dinner group, the ORs ($95\%$CIs) were 0.66 (0.42, 0.99) and 0.51 (0.29, 0.82) for the group with three to six times/week and zero to two times/week. Reduced meal frequency, especially dinner frequency, was associated with lower prevalence of T2DM, which suggests that an appropriate reduction in meal frequency per week may have a role in decreasing the risk of T2DM. ## 1. Introduction Type 2 diabetes mellitus (T2DM), one of the metabolic diseases featured by high blood glucose levels, has emerged as a prominent public health problem around the world. According to the International Diabetes Federation (IDF), the worldwide prevalence of diabetes has reached $10.5\%$ in 2021, meaning that 537 million adults living with diabetes, and the fraction may climb to 783 million by 2045 [1,2]. Diabetes imposed tremendous socioeconomic pressure on individuals and healthcare systems around the world. The global health expenditure caused by diabetes was estimated to be $966 billion United States dollars (USD) in 2021, a threefold increase from 15 years ago. Importantly, more than three-quarters of people with diabetes live in low and middle-income countries [3]. China, a representative of developing countries, still has large populations living in rural areas. Age-standardized prevalence of T2DM in rural areas of China has been estimated at $6.98\%$ and it has still been escalating. In contrast to urban populations, rural populations have a higher prevalence of T2DM, but lower rates of treatment and control owing to the lack of medical service resources and health care system coverage [4,5]. Therefore, identifying the potentially harmful behaviors and adopting convenient and effective interventions for preventing the incidence and development of diabetes in rural populations may significantly alleviate the national and family burden, and this also has considerable value for reducing the overall prevalence of diabetes. Apart from the traditional factors, diet behavior as a modifiable factor is of increasing attention due to its potential impact on human health, especially on diseases relating to metabolism [6]. Nevertheless, research on this topic has produced inconclusive results. A prospective study conducted on Chinese community residents showed that increasing diet frequency may reduce the risk of T2DM [7]. Another study of American health professional subjects indicated that lower eating frequency was associated with a higher risk of T2DM [8]. Besides, previous studies have suggested that smaller and more frequent meals in an isocaloric condition were beneficial in improving metabolic health among obese patients with prediabetes [9]. However, it has been argued that this benefit was limited, and it was worth noting that increasing diet frequency in free-living populations might induce excessive energy intake, while frequent high-calorie diets, especially high-energy dinners, might increase the risk of metabolic syndrome [10,11]. Gradually, concentration has shifted to reducing the frequency of meals and then extending the duration of fasting between two meals, which may promote metabolic health [12,13,14]. For example, a study on American professional women revealed that the risk of T2DM was higher among participants who consumed breakfast irregularly but with higher total diet frequency, compared to those who consumed breakfast regularly but with lower total diet frequency, when classifying participants according to their breakfast consumption and eating frequency [15]. In addition, another study of postmenopausal women from American clinical centers found that higher diet frequency may elevate the risk of T2DM [16]. Furthermore, there were also some epidemiological studies indicating that appropriate caloric restriction (CR) might be helpful for improving glycemic control [17] and cardiometabolic status [18], and this might prevent the majority of chronic diseases. Moreover, several animal and clinical studies have shown that intermittent fasting (IF), a special form of reducing diet frequency, could also confer benefits for many health conditions, including improvements in blood lipids, blood pressure, glucose homeostasis, insulin resistance, metabolic disorder, and even might help delay aging and extend the life span [19,20,21,22,23,24,25,26]. However, most of the above studies were experimental studies or mainly focused on developed countries and urban settings, the general population-based evidence of the association between meal frequency and T2DM in resource-limited areas is still limited. In view of the cultural diversity of food between Western countries and China, and the differences in economic development, education level, medical resources, lifestyle behaviors, food choices, and dietary patterns between urban and rural populations in China, it has great practical importance to explore the health effect of modifiable dietary factors in rural population from the resource-limited area in the context of the increasing burden of T2DM. Therefore, this study aimed to explore the association between meal frequency and T2DM in rural populations of the resource-limited area, and then it investigated whether these associations were different among those in different breakfast, lunch, dinner frequencies and other subgroups. ## 2.1. Study Population Participants in the study were recruited from the Henan Rural Cohort study (Registration number: ChiCTR-OOC-15006699). Detailed information on the methodology of the Henan Rural Cohort has been previously reported [27]. Briefly, 39,259 permanent populations between the ages of 18–79 years were recruited from five rural areas of Henan (Tongxu, Yuzhou, Suiping, Xinxiang, and Yima) via multistage stratified cluster sampling from July 2015 to September 2017. This cross-sectional study included the following exclusion criteria: [1] miss information on meal frequency ($$n = 9293$$); [2] lack of information on T2DM ($$n = 57$$); [3] type 1 diabetes mellitus ($$n = 4$$); [4] implausible meal frequency ($$n = 190$$). [ 5] participants with cancer ($$n = 292$$) and kidney failure ($$n = 18$$). Finally, a total of 29,405 subjects were enrolled in this current study, and details can be found in Figure S1. All of the participants supplied written informed consent, and the researchers followed the guidelines issued by the Declaration of Helsinki. ## 2.2. Assessment of Meal Frequency A validated standardized questionnaire was used to obtain information on meal frequency by well-trained investigators through a face-to face interview (Cronbach’s alpha coefficient, α = 0.729, intraclass correlation coefficients, ICC = 0.841) [27]. The definition of meal frequency in this questionnaire was based on customary or regular main meals that contained staple foods, and snacks that did not contain staple foods, such as beverages and chips, were not considered as a meal and were not collected and included in the analysis. Participants were required to answer the questions below: “In the past week, how many times did you normally eat breakfast, lunch, or dinner?” and then asked, “how many times did you normally eat breakfast, lunch, or dinner away from home each week?” Then, the total weekly meal frequency for each participant was calculated by adding the weekly frequency of breakfast, lunch, and dinner. The distribution of total weekly meal frequency in the analyzed sample ranged from 14–21 times/week. So, the total weekly meal frequency was divided into two groups of 21 times/week (normal meal frequency group) and 14–20 times/week (reduced meal frequency group). Afterwards, in order to explore whether this relationship would be enhanced with decreasing meal frequency, weekly meal frequency was further divided into three groups of 14–15 times/week, 16–20 times/week, and 21 times/week, and the trend test was performed with the highest group as the reference. In parallel, the frequency of breakfast, lunch, and dinner was categorized into seven times/week (normal meal frequency group) and zero to six times/week (reduced meal frequency group), then the breakfast, lunch, and dinner frequency were also classified into zero to two times/week, three to six times/week, and seven times/week in further analysis. ## 2.3. Definition of T2DM After overnight fasting for >8 h, blood samples were sampled, and fasting glucose (FBG) was measured with an automated biochemical analyzer (Cobas c501, Roche, Switzerland). Participants who met the following criteria were considered to have T2DM: FBG ≥ 7.0 mmol/L, or previously diagnosed with T2DM by a physician, and taking insulin/oral hypoglycemic agents during the last two weeks. ## 2.4. Covariates Estimate Dietary intake information on individuals was obtained by well trained staff through a 13-item validated food frequency questionnaire (FFQ) that has been shown to have good reproducibility and validity [28]. The FFQ covered the frequency and amount of 13 types of food consumed in the past year, namely, staple foods, livestock, poultry, fish, eggs, milk, fruits, vegetables, legumes, nuts, preserved products, grains, and animal oils. Then, the total energy intake of each participant was computed based on the China Food Composition Table 2004. Abundant vegetable and fruit intake were identified as consumption of more than 500 g of vegetable and fruit each day. Consumption of fat over 75 g each day was considered a high-fat diet. Consumption of salt over 2 g each day was classified as a high-salt diet. Eating away from home was considered as consuming meals prepared outside the home, and then weekly eating away from home frequency was computed by adding the frequency of breakfast, lunch, and dinner eating out each week. Non-dietary covariates were also collected by validated standardized questionnaires with good validity and reliability [27], including demographic variables (age and gender), social–economic status (educational level, average monthly income, and marital status), lifestyle covariates (smoking and drinking status, physical activity), and family history of T2DM. Briefly, this current study age was grouped into <60 years and ≥60 years. There were two levels of marital status: married/cohabiting and widowed/divorced/separated/single. Educational level was classified into those three grades: elementary school or below, junior high school, and senior high school or above. Average monthly income was classified into <500 RMB, 500-RMB, and 1000-RMB. According to drinking and smoking status, participants were divided into non-drinker, drinker, non-smoker, and smoker, respectively. According to the International Physical Activity Questionnaire (IPAQ), physical activity was categorized into three levels: low, moderate, and high. The basal metabolic rate and body weight of subjects was measured by OMRONV. BODY HBF-371 instrument followed the operational instructions. BMI was counted as weight (kg) divided by the square of height. All of the above measurements were carried out by well trained staff following standardized procedures. ## 2.5. Statistical Analysis For descriptive analysis of participants, continuous variables and categorical variables were expressed as mean ± standard deviation (SD) and quantity (proportion), respectively. Student’s t-test and chi-squared test were used to compare differences in continuous and categorical variables between T2DM and non-T2DM groups, respectively. Logistic regression analysis was utilized to estimate the association of meal frequency and T2DM risk by the ORs and $95\%$CIs. The three models were fitted as follows: Model 1 was unadjusted; Model 2 was only adjusted for age and gender; Model 3 was adjusted for age, gender, marital status, average monthly income, education level, smoking status, drinking status, physical activity, vegetables and fruits intake, high-fat diet, high-salt diet, BMI, total energy intake, basal metabolic rate, family history of T2DM, and weekly frequency of eating away from home. The estimated effect of each time reduction of meal frequency was explored with the highest meal frequency as the reference. Then, trend analysis was performed to estimate the association between each level reduction of meal frequency and T2DM by treating the categorical variables as continuous variables in the logistic regression. In addition, stratified analyses were performed in different gender, ages, BMI, smoking and drinking status, high-fat diet, high-salt diet, and vegetable and fruit intake. The highest categories—21 times/week (total meal frequency) and seven times/week (breakfast, lunch, dinner)—were considered as the reference groups in all analyses. All analyses were accomplished with Statistical Package for the Social Sciences version 21.0 (IBM-SPSS Inc., Armonk, NY, USA) and R software version 4.0.3. Two-tailed p values < 0.05 were considered statistically significant. ## 3.1. Basic Characteristics of Participants Table 1 depicted the basic characteristic of the subjects. A total of 29,405 participants, aged 55.48 ± 12.32 years old, were recruited in this study, including 12,022 ($40.88\%$) men and 17,383 ($59.12\%$) women. Of these participants, 2585 had T2DM with a crude prevalence rate of $8.79\%$. Subjects identified with T2DM are more likely to have lower family income, education levels, ratios of smoking, physical activity, vegetable or fruit intake, and high-fat diet, as well as higher levels of age, BMI, basal metabolic rate, and family history of diabetes and so on. What is more, the mean frequency of weekly total meal, breakfast, and dinner were higher for participants with T2DM than for non-T2DM (all $p \leq 0.05$). ## 3.2. Association between the Weekly Total Meal Frequency and T2DM The association between weekly total meal frequency and T2DM was presented in Table 2 and Supplemental Table S1. The findings showed a positive association between weekly total meal frequency and T2DM. Firstly, when the participants were classified into reduced meal frequency group (14–20 times/week) and regular meal frequency group (21 times/week), the adjusted OR ($95\%$CIs) was 0.73 (0.60, 0.87) for 14–20 times/week, compared with 21 times/week group. When the 29,405 participants were further divided into three groups, the ORs ($95\%$CIs) of 16–20 times/week and 14–15 times/week were 0.75 (0.58, 0.95) and 0.70 (0.54, 0.90) compared with the reference group (21 times/week) after controlling multiple variables in model 3. Besides, the adjusted OR ($95\%$CIs) for each time reduction of meal frequency was 0.95 (0.92, 0.98). Additionally, the adjusted OR ($95\%$CIs) for each level reduction in total meal frequency was 0.82 (0.73, 0.92), and the p value of the trend test was 0.001. ## 3.3. Association between the Weekly Frequency of Breakfast, Lunch, and Dinner and T2DM The impacts of the breakfast, lunch, and dinner frequency on T2DM were shown in Table 2 and Supplemental Table S1. However, with the exception of dinner, no significant association between the frequency of breakfast or lunch and T2DM was found. When the participants were classified into reduced dinner frequency group and regular dinner frequency group, the adjusted OR ($95\%$CIs) was 0.59 (0.42, 0.81) for zero to six times/week compared with those who consumed dinner seven times/week in model 3 (Supplemental Table S1). When further analysis was performed by dividing into three groups, the adjusted ORs ($95\%$CIs) were 0.66 (0.42, 0.99) and 0.51 (0.29, 0.82) for participants who consumed dinner three to six times/week and zero to two times/week compared with seven times/week, respectively. In addition, the ORs and $95\%$CIs for each time reduction of dinner frequency was 0.89 (0.83, 0.96), Furthermore, the ORs ($95\%$CIs) for each level reduction in the frequency of dinner was 0.70 (0.56, 0.87), and the p value for the trend test was 0.002 on the fully adjusted model. ## 3.4. Stratified Analysis Figure 1 was drawn based on Supplemental Tables S2 and S3. In stratified analysis, significant associations between weekly total meal frequency and T2DM were found among participants who were women, age ≥ 60 years old, BMI < 28 kg/m2, do not smoke or drink, having a non-high fat diet, having a non-high salt diet, and having non-abundant vegetable and fruit intake (Figure 1). To be specific, in model 3, the ORs and $95\%$CIs of 16–20 times/week and 14–15 times/week were 0.65 (0.45, 0.91) and 0.71 (0.49, 0.98) among women. Besides, the ORs and $95\%$CIs of 14–15 times/week was 0.60 (0.36, 0.94) among participants who were ≥60 years. Additionally, the ORs and $95\%$CIs was 0.46 (0.23, 0.84) for normal body mass index subjects with meal frequency of 14–15 times/week, the ORs and $95\%$CIs were 0.68 (0.45, 0.99) and 0.60 (0.38, 0.89) for overweight participants with meal frequency of 16–20 times/week and 14–15 times/week, respectively (Pinteraction = 0.027). Furthermore, the ORs and $95\%$CIs were 0.73 (0.54, 0.97) and 0.59 (0.42, 0.82) for non-smokers with meal frequency of 16–20 times/week and 14–15 times/week (Pinteraction = 0.016). Moreover, the ORs and $95\%$CIs were 0.65 (0.47, 0.88) and 0.71 (0.52, 0.95) for non-drinkers with meal frequency of 16–20 times/week and 14–15 times/week. The ORs and $95\%$CIs were 0.74 (0.55, 0.97) and 0.73 (0.54, 0.96) for non-high fat diet participants with meal frequency of 16–20 times/week and 14–15 times/week. Furthermore, the ORs and $95\%$CIs was 0.62 (0.45, 0.82) for non-high salt diet participants with meal frequency of 14–15 times/week and 0.71 (0.50, 0.98) for non-abundant vegetable and fruit intake participants with meal frequency of 14–15 times/week. Briefly, in the stratified analysis of weekly dinner frequency (Figure 1), significant associations were found in both men and women, age < 60 years old, BMI < 28 kg/m2, participants who do not smoke or drink, non-high fat diet or non-high salt diet consumers, abundant or non-abundant fruit and vegetable consumers. ## 4. Discussion This large-scale cross-sectional study explored the association between meal frequency and the risk of T2DM in rural adults from the resource-limited area. The results of this study indicated that a reduced meal frequency was associated with a lower prevalence of T2DM. In addition, as for the analysis of three meals, a reduced dinner frequency was significantly associated with a lower prevalence of T2DM. Furthermore, in stratified analyses, significant associations between meal frequency and T2DM were found among participants with relatively healthy lifestyles. The findings of this current study provide meaningful information for the primary prevention of T2DM through dietary frequency changes in resource-limited populations. Studies on diet frequency and T2DM risk have yielded inconsistent results. A follow-up study of American health professional men showed that lower eating frequency was associated with a higher risk of T2DM [8]. Besides, another study conducted on Chinese community residents showed that eating four meals a day was related to a lower risk of T2DM compared to eating three meals a day, while eating two meals a day showed no relationship with T2DM [7]. In addition, there was a randomized trial focusing on prediabetes patients, suggesting that increasing the frequency of eating under isocaloric conditions can provide a variety of metabolic benefits, including improved glucose metabolism [9]. However, it seems very difficult to maintain a constant total energy intake in free-living populations who self-reported diets, and even a higher frequency of eating could lead to a higher calorie intake. This was because frequent eating might increase the stimulation of food and result in more energy intake, thus making it difficult to control energy balance [29,30,31,32]. Furthermore, there was also a study pointing out that changing eating frequency had virtually no effect on glucose regulation parameters, but consumption of most calories in the evening of the day might be harmful to glycemic control [33]. All these discrepant findings are possibly due to different study populations, lifestyles behaviors, dietary habits, sample sizes, definitions of diet frequency, methods for assessing meal frequency, and different adjusted covariates. In this current study, we found a positive association between meal frequency and T2DM, and it was in alignment with some previous studies. For instance, a prospective study of older women reported that, compared with three times per day, relative risks and $95\%$ confidence intervals were 1.13 (1.00, 1.27) for participants who ate four to five times per day. Additionally, participants who ate breakfast irregularly, but with higher total eating frequency, were at a greater risk of T2DM (RR: 1.47, $95\%$ CI: 1.23, 1.75) compared with those who ate breakfast daily but with lower eating frequency (one to three times per day) [15]. Furthermore, another cohort study focused on postmenopausal women found that hazard rates and $95\%$ confidence intervals were 1.38 (1.03, 1.84) for subjects who consumed meals four times per day compared with one to three times per day [16]. Moreover, a study based on an Iranian population showed that those who have six meals per day are at a higher risk of diabetes than those who eat three meals per day (OR: 2.503, $95\%$ CI: 1.651, 3.793) [34]. Additionally, there were several studies that also revealed that plenty of health parameters could be improved by reducing energy intake, including glycemic control, β cell function, insulin resistance, lipid profiles, oxidative stress, and inflammation [19,21,24,35]. In this study, no significant association between breakfast or lunch frequency and T2DM was found. Similar to our results, a prospective study based on the frequency of breakfast and the risk of T2DM among community-dwelling older adults also did not observe significant associations [36]. Unfortunately, as for lunch frequency, the number of participants in the group of zero to two times/week and three to six times/week is small, so results on lunch frequency might be unreliable. Due to the insufficient sample size, we did not further explore the association between lunch frequency and T2DM. However, we found that reduced dinner frequency per week to prolong the duration of the overnight fast was significantly associated with a lower prevalence of T2DM in this study. Although few studies have examined the relationship between dinner frequency and T2DM, there was still some evidence that could support the viewpoints of this study. For instance, a randomized crossover study comparing the effect of two meals (only breakfast and lunch) versus six meals a day demonstrated that a low-energy diet pattern of two meals per day reduced fasting plasma glucose, glucagon, C-peptide, and hepatic fat content, and it improved insulin sensitivity [37]. Besides, some recent studies focusing on early time-restricted eating patterns (eTRE) illustrated that consuming food at earlier times of the day (dinner was not eaten) and extending the length of the night fast could produce metabolic health improvements, including weight loss, improved insulin sensitivity, fasting insulin, and reduced serum glucose excursions after glucose loading in healthy individuals, as well as subjects with prediabetes [38,39]. Moreover, previous studies also have shown that high energy intake at dinner was associated with an increased incidence of diabetes, as well as increased mortality from diabetes and cardiovascular disease and all-cause mortality in people with diabetes [40,41]. Taken all together, reducing energy intake at dinner may be beneficial for metabolic health to some extent. In stratified analysis, we found that this association was significant among subjects who had relatively healthy lifestyles. Several studies have shown that smoking and drinking were risk factors for diabetes [42,43]. Furthermore, high-fat diets and high-salt diets have been shown to affect the metabolism of glucose and lipids, impair the function of major metabolic organs, and subsequently increase the risk of diabetes [44,45]. Taken together, these poor lifestyles might weaken the beneficial effects of reduced diet frequency. Moreover, this study found no significant association between meal frequency and T2DM among participants with BMI ≥ 28 kg/m2. It might be that these populations had multiple adverse physical conditions, such as chronic inflammation, dyslipidemia, impaired postprandial metabolism, or insulin resistance, which could potentially diminish the benefits of reduced meal frequency [46,47]. Overall, there are several potential mechanisms that might explain this association. First, metabolic conversion from glucose to fatty acid-derived ketones not only provided ketones required by cells during fasting, but also elicited a highly coordinated systemic and cellular response that could enhance resistance to disease [48,49]. Second, intermittent fasting activated adaptive cellular stress response signaling pathways, thereby enhancing mitochondrial health, DNA repair, and autophagy [50,51,52,53]. Third, intermittent fasting promoted the production of brain-derived neurotrophic factors, which increased neuronal resistance to dysfunction and degeneration and regulated the glucose metabolism disorder caused by its deficiency [48,54]. Fourth, caloric restriction and reduced eating frequency could increase antioxidant activity and reduce oxidative stress, thus preventing many diseases caused by oxidative stress, including diabetes [22,38,55,56]. This present study has a couple of limitations needing attention. Firstly, this was a cross-sectional study, so, we could not ascertain the causal relationship between meal frequency and T2DM. Therefore, long-term longitudinal studies are needed to validate this association. Secondly, since meal frequency was obtained by asking each participant about diets in the past week through recall, there might be recall bias due to participants’ unclear recall of past diets. However, FFQ and the standardized questionnaire used in this current study were attested to previously have good reproducibility and validity in our previous research [28]. Thirdly, some details about eating habits were not fully obtained, for example, snacking behavior, the time of a meal, and the quality of foods consumed with each meal, all of which might influence the association in this current study [8,57,58]. However, in contrast to Western populations, Chinese middle-aged and elderly consumers usually eat three regular main meals per day and might rarely eat snacks. Therefore, three main meals may account for the majority of daily energy intake [59]. In addition, according to our survey, snacking behavior was relatively not popular in rural areas, and eating away from home was more prevalent than snacking behavior, so we collected and adjusted for the covariates of eating out frequency, high-fat diet, high-salt diet, vegetable and fruit intake status, basal metabolic rate, and total energy intake to minimize this confounding effect as far as possible. Finally, even though this study adjusted for many potential confounders, the effect of unmeasured residual confounders could not be completely ruled out. Therefore, further research on the effects of the above factors on T2DM is required in the future. ## 5. Conclusions In summary, meal frequency might be a potentially modifiable factor for T2DM, and a positive association was observed between meal frequency and T2DM. The current study suggested that appropriately reducing the meal frequency, especially the dinner frequency, may be beneficial in the prevention of T2DM. 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--- title: 'Internalization of the Western Standard of Beauty and Body Satisfaction: Evaluation Utilizing COPS and SATAQ-3 Questionnaires among Girls with Scoliosis' authors: - Jakub Glowacki - Joanna Latuszewska - Natalia Skowron - Ewa Misterska journal: Medicina year: 2023 pmcid: PMC10056108 doi: 10.3390/medicina59030581 license: CC BY 4.0 --- # Internalization of the Western Standard of Beauty and Body Satisfaction: Evaluation Utilizing COPS and SATAQ-3 Questionnaires among Girls with Scoliosis ## Abstract Background and Objectives: Patients with adolescent idiopathic scoliosis (AIS) more frequently present significant back-related body image disturbances compared with healthy controls. The study aimed to adapt two screening questionnaires: Sociocultural Attitudes Towards Appearance Questionnaire (SATAQ-3) and Cosmetic Procedure Screening Questionnaire (COPS), that could identify AIS patients, especially those threatened with body image disorders and might predict dissatisfaction with a desired-by-patients cosmetic result of treatment. Materials and Methods: In total, 34 AIS patients who undergo Cheneau brace treatment were asked to complete SATAQ-3 and COPS. Results: AIS patients presented a high level of internalization. Clinical and radiological factors that play a crucial role in the evaluation and decision process during brace treatment were not significantly associated with COPS and SATAQ-3 total scores. The SATAQ-3 total score and COPS results were also not related to sociodemographic parameters of the analyzed group. Conclusions: The presented study confirms the usefulness of the questionnaires, which aimed to isolate sociocultural risk factors of body image disorders in scoliosis patients as predictors of treatment dissatisfaction and worse compliance. ## 1. Introduction Current research shows that sociocultural pressure to be thin and attractive could be the source of negative feelings about the body [1,2]. The internalization of media ideals and cultural pressure could quickly turn to body dissatisfaction concerns [3,4]. We must bear in mind that in the process of internalization, the person adopts their own certain behavior which previously appeared only as a direct reaction to environmental stimulation [5]. This adoption of body image created by media push women/men to body hyperawareness, leading to body shame and social anxiety [6,7]. Among patients’ motivations to undergo plastic surgery procedures, thus an improvement in their appearance, enhancements in self-confidence, self-esteem, and social interactions are the most common. The vast majority of patients are satisfied with cosmetic surgery. However, a specific group of patients is not [8]. Dissatisfaction can occur due to a preexisting psychiatric condition disregarding clinical outcomes [9]. Adolescent idiopathic scoliosis is a common skeleton deformity with 2–$3\%$ prevalence. A three-dimensional deformity occurs in frontal (lateral bend), sagittal (the deformation of thoracic kyphosis and lumbar lordosis), and transverse (associated with the rotation and translation of vertebrae) planes [10]. The progression of scoliosis is more prevalent in girls. As a result, girls need non-surgical or surgical treatment much more often than boys [11]. The progression of idiopathic scoliosis could manifest itself through multiple body deformities, including scapular and rib prominence, uneven shoulders, and an asymmetric waistline. According to several authors, those deformities can significantly influence self-esteem and body image [12,13,14]. Expressly, Auerbach et al. demonstrated more significant back-related body image disturbances in patients with scoliosis compared to healthy controls [8]. Spinal fusion surgery with instrumentation often reduces severe curves and minimizes the risk of curve progression [10,11]. Unfortunately, the technical success of the surgery does not correspond with patient satisfaction regarding surgical outcomes. Patient contentment with the surgical result appears to be unrelated to the medical aspects of spinal fusion surgery. This includes fundamental issues such as the preservation of pulmonary function and prevention of early osteoarthritis [15]. It should be emphasized that results concerning the body image assessment seem apparently contradictory. It was revealed that body dissatisfaction is related to the individual’s disfigured shape and weight. However, improving body shape and weight loss does not necessarily decrease body dissatisfaction [16]. This discrepancy might be explained by the Allocentric Lock Hypothesis (ALH) [17]. It implies that a basic disturbance may cause the maintenance of body dissatisfaction in the way the body is remembered [18]. Patients may be locked into remembering their body negatively, of which is not updated by contrasting self-centered representations driven by perception [19]. Returning to the main thread, it is still unclear under which circumstances patients with AIS revalue the perception of scoliosis-related body deformation. It is also unclear how those patients experience body image after operative treatment and how this overestimation is characterized. Those findings suggest that patients with AIS might need support while changing their desired body shape to feel more optimistic about the cosmetic results of scoliosis treatment [18,19]. A preoperative assessment tool that helps to identify patients with underlying psychiatric issues, improper motivations, or unrealizable expectations may help avoid such situations by providing a reason for psychological referral. However, it would not replace standard clinical examination but could alert the medical staff for possible psychological consultation. The Sociocultural Attitudes Towards Appearance Questionnaire (SATAQ) measures the awareness of the cultural ideal of beauty for women and the level of acceptance and internalization of that ideal. The recent version, SATAQ-3, is one of the gold instruments used worldwide [20]. The SATAQ-3 has shown a relatively stable internal structure and boasts good indicators of reliability and validity when applied to Western women [20]. Another helpful questionnaire proven to be a valid, feasible, and reliable screening tool for patients is the Cosmetic Procedure Screening Questionnaire (COPS). It can be applied to cosmetic practice before performing any intervention. The scale has a high sensitivity for diagnosing Body Dysmorphic Disorder (BDD) in people likely to undergo a cosmetic procedure [21]. Therefore, our study aimed to adapt and analyze the psychometric properties of the Polish versions of COPS and SATAQ-3, which could identify AIS patients especially threatened with body image disorders and might predict dissatisfaction with a desired cosmetic result of brace treatment. The study represents a preliminary validation. ## 2.1. Study Design This was a prospective study. Patients were purposely assigned to undergo the Cheneau brace intervention and were recruited from an academic center, where brace treatment was implemented. The inclusion criteria were: female patients diagnosed with AIS with a Cobb’s angle between 20° and 40°, apical vertebra below T8, 10–14 years of age, and a Risser sign ≤2 at the beginning of brace treatment. The minimum follow-up of brace treatment was 3 months. According to Sponseller, patients with a high thoracic curve and a major curve apex above Th8 were excluded [22]—those patients could not be treated with the thoraco-lumbo-sarcal orthoses used in current study. The exclusion criteria were also mental disorders associated with developing a distorted body image (psychotic, dysmorphia). None of the patients had received any other spinal treatment before. Concerning the bracing protocol, all patients were initially prescribed full-time bracing. The data regarding brace-wearing time compliance were based on anamnesis with patients during the clinical examination every three months. To verify patient claims concerning the time of bracing, an additional interview was conducted with the parents. Regarding the study protocol, all the participants and their parents received thorough information on the aim of the study. They were assured of anonymity, following which they gave informed consent. Then, they were asked to fill out the study questionnaires twice at home, with a 2-day interval, and return them by mail. ## 2.2. Study Sample The study group consisted of 34 AIS females. Figure 1 presents the entire process of patient recruitment to the study and data collection. Thoracic scoliosis was identified in $55.8\%$ of the patients ($$n = 19$$), thoracolumbar scoliosis in $26.47\%$ ($$n = 9$$), and lumbar scoliosis in the remaining $17.64\%$ ($$n = 6$$). The additional information about the study group is contained in Table 1. ## 2.3. Cultural Adaptation of Original Versions of COPS and SATAQ-3 We adopted the SATAQ-3 and COPS to Polish cultural settings. The process of cultural adaptation of the questionnaires was compliant with the guidelines of the International Quality of Life Assessment (IQOLA) Project and comprises the translation procedures and analysis of psychometric properties of adapted questionnaires [23]. ## 2.3.1. Questionnaires The COPS screens for attribute(s) that the surveyed participants find unattractive, the characteristics of the cosmetic procedures, and the diagnostic criteria of BDD. Answers to questions are made by a Likert scale which helps the surveyed to quantify responses. The total score is calculated by summing questions from 2 to 10 (items 2, 3, and 5 are reversed). It ranges from 0 to 72 with a cut-off value of ≥40, indicating a high probability of having BDD [24]. Those individuals should be referred for further assessment. The scale is available online to download for free [25]. The aforementioned questionnaire is sensitive to changes and has good test–retest reliability, convergent validity, and strong internal consistency [24]. The SATAQ-3 is a 30-item scale with four theoretical subscales. The first subscale, with nine items, is Internalization-General and evaluates the impact of the general media related to TV, newspapers, and movies. The second subscale, with five items, is Internalization-Athlete and evaluates the internalization of gymnastic and sports models. The other subscales are Information, with nine items. It evaluates how far it is recognized that different media are considered significant sources of information about looks. The subscale Pressures, with seven items, evaluates individual feelings of pressure from display to media images and messages to modify one’s look [20]. The question format is a Likert-type scale ranging from 1 = completely disagree to 5 = completely agree. The total ranges from 30 to 150 with a cut-off value of ≥60, indicating a high probability of having BDD [20]. ## 2.3.2. Translation Procedure The expert committee cooperated with the researchers to translate COPS and SATAQ-3, using the forward–backward method. At first, two experts working separately translated the questionnaire into Polish. Polish was the native language of these translators. In the second stage, these translations were synthesized into one version by translators and authors of the project. Then, two native English speakers who were bilingual back-translated COPS and SATAQ-3 into English. These translators did not know the original English version. Finally, all translators, a clinical psychologist, a statistician, and orthopedic surgeons reviewed all the translations. A consensus concerning all the inconsistencies was reached, establishing the Polish version of the SATAQ-3 and COPS. Finally, study participants filled in Polish versions of COPS and SATAQ-3 twice. ## 2.3.3. Statistics To determine the psychometric properties of Polish versions of COPS and SATAQ-3, statistical analysis was performed. Descriptive statistics (mean, $95\%$ confidence intervals, range, and standard deviations) were utilized to describe the distribution of the results with respect to quantitative statistical features. With respect to qualitative features, we assigned percentages to the number of units belonging to the described categories of a given feature. In addition, the psychometric properties of the COPS and SATAQ-3 were determined by the distribution of scores and determination of the floor effect (% of patients with the minimum score) and ceiling effect (% of patients with the maximum score). Internal consistency was examined using Cronbach’s alpha coefficient. Cronbach’s alpha coefficient values were accepted as follows: excellent >0.80, adequate 0.70–0.79, and poor <0.70 [26]. To assess temporal stability, the test–retest method was used. The COPS and SATAQ-3, as pointed out above, were filled in twice and then the Intraclass Correlation Coefficient (ICC) was calculated. Discriminant validity was assessed by calculating the correlation between the total COPS and SATAQ-3, and the Cobb angle. In addition, the responsiveness of the COPS and SATAQ-3 were determined by the means of effect sizes, which were calculated for each measure by dividing the mean absolute change score by the standard deviation of the baseline score. The interpretation of the magnitude of the effect size was based on Cohen’s rule-of-thumb, whereby an effect size of 0.2–0.5 was considered as small, 0.5–0.8 as moderate, and over 0.8 or greater represented a large effect. Spearman’s rank-order correlation coefficients were used to evaluate the correlations between COPS and SATAQ-3 results and detailed the clinical and radiological characteristics of study participants, such as body mass index, age, Cobb angle, angle of trunk rotation, apical translation, and daily and monthly duration of brace wearing. Correlations were defined as strong—0.60, moderate—0.30–0.60, and weak—0.30, respectively. We adopted $$p \leq 0.05$$ as the border level of statistical significance; test results with a p-value exceeding this level were considered insignificant. Statistical calculations were performed by the means of Statistica software. ## 2.4. Ethical Considerations The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee (No. $\frac{1148}{12}$). ## 3.1. Outcome Measures–Scores Distribution Minimal values, maximal values, means with SDs, and quartiles are presented in Table 2. Interestingly, a COPS total score ≥ 40 points, indicating a high level of BDD, applied to $5.88\%$ of patients (one participant) in the test and $2.94\%$ of patients in the retest (one participant). However, a SATAQ-3 total score of over 60 points, indicating a high level of Internalization, applied to $67.64\%$ of patients in the test (23 participants) and $61.76\%$ of patients in the retest (21 participants). Table 3 presents the percentage of concern allocated to each feature according to COPS (from the pie chart regarding the 1st Feature to 5th Feature). ## 3.2. Psychometric Properties of SATAQ-3 and COPS Floor and ceiling effects were present in the test and retest (see Table 4). To assess internal consistency, we used Cronbach’s alpha. Coefficient values equaled 0.79 ($95\%$ CI 0.67–0.88) and 0.93 ($95\%$ CI 0.89–0.96) for COPS and SATAQ-3, respectively. Similarly, temporal stability (test–retest reliability) based on the Intraclass Correlation Coefficient (ICC) was excellent and equaled 0.98 ($95\%$ CI 0.97–0.99) and 0.98 ($95\%$ CI 0.96–0.99) for COPS and SATAQ-3 total scores, respectively. These values are comparable with the psychometric properties of the original English version [20,24]. We also estimated the item–total correlation of both COPS and SATAQ-3 (see Table 5 and Table 6). Only one item in both questionnaires (item No. 1 for COPS and item No. 9 for SATAQ-3) was below the value of 0.20. ## 3.3. Correlation Analysis by Sociodemographic Characteristics We assessed the relations between the sociodemographic characteristics of study participants. Age, place of residence, type of school, physical education, or recreational sports activity were not significantly associated with the SATAQ-3 total score. COPS results were also not related to sociodemographic parameters (for details, see Table 7). ## 3.4. Correlation Analysis by Clinical and Radiological Parameters Interestingly, correlations between clinical and radiological parameters that play the primary role in the evaluation and decision process during brace treatment were not significantly associated with COPS and SATAQ-3 total scores (see Table 8). ## 3.5. The Logistic Regression Analyses The multivariate model could not be applied due to the relatively small sample size ($$n = 34$$) for the SATAQ-3, and the small number of patients reporting high levels of BDD according to COPS. ## 4. Discussion The research has demonstrated that body image is a significant concern among girls and women [27]. Many female adolescents attempt to have a perfect body appearance. Unfortunately, if they do not achieve their ideal appearance, they could develop negative emotions, leading to stress and anxiety [3]. Additionally, the mass media play a crucial role in distributing these body image criteria [3,28]. Thus, the psychometric assessment of body image could play a significant role in the preliminary selection of those who should be referred to cognitive-behavior therapy [29,30]. Referring to those assessment tools, Thompson et al. recommended the utilization of SATAQ-3 because of its excellent psychometric properties, the availability of its translated versions in different languages, and its structure dimensions [20]. However, many researchers use this scale mainly to study the relationship between sociocultural appearance standards promoted in the media and the selected risk factors concerning eating disorders [31]. The first Polish attempts to use SATAQ-3 took place in the research of Izydorczyk and Lizynczyk [32]. So far, there has been no Polish standardization of a tool for measuring body image and physical appearance standards promoted by mass media. Additionally, COPS, recognized as a reliable screening tool, is sensitive to changes in patients under cognitive-behavior therapy. It may be used as an outcome measure after treatment to assess if there is any improvement in the symptoms of BDD [32]. However, it was found in our studies that in general, AIS patients perceived their appearance positively. These findings are similar to the results presented by Misterska et al. where as many as $60\%$ of the study sample would not change anything [12]. Interestingly, a COPS total score ≥40 points, indicating a higher level of BDD, applied to $5.88\%$ of participants and $2.94\%$ in the retest, which is above general population estimation, but still significantly lower than in cosmetic surgery patients [29,30]. As previously stated, until recently, there were few studies that examined the perception of deformities in scoliosis patients [33]. This is probably because it is difficult to objectively assess the self-image perceptions of scoliotic patients. Returning to the main thread, the percentage of concern allocated to each feature in the COPS questionnaire was mainly concentrated on scoliosis in general ($35.29\%$) and its elements, such as rib prominence or uneven waist. Those findings are consistent with the results derived from the study performed by Misterska et al., finding that $29\%$ of patients indicated rib prominence as the element of trunk deformity which is the most disturbing to them [14]. Interestingly, the COPS total score was correlated neither to radiological parameters nor to clinical data, such as age at diagnosis and brace time treatment. Therefore, according to Wilhelm et al., a thorough identification of those with a high probability of having BDD enables an early referral to a trained psychologist before attempting any medical intervention. Those people would most likely benefit more from cognitive-behavioral therapy [34]. The present research proves that the sociocultural appearance standards that are promoted in the mass media play an essential role in the studied group. In particular, the findings revealed that internalization of the Western standard of beauty and body satisfaction among adolescent girls with idiopathic scoliosis is neither correlated in regard to sociodemographics nor to clinical parameters. Moreover, our study revealed that radiological parameters, which play a crucial role in outcome measurements and potential further surgical treatment, are not correlated with COPS and SATAQ-3 scores. However, body image in scoliosis patients significantly affects appearance perception [14]. On the other hand, our study had limitations—we did not perform an objective trunk shape analysis. Only a standard, radiographic assessment was utilized. Meanwhile, we must bear in mind that radiographs do not provide direct information about individuals’ body shapes with AIS [35]. Since rib hump magnitude is not well correlated to radiological features, this would explain the poor correlation between cosmetic and radiological features, as indicated in our study. So, for example, the Formetric Surface Topography system (DIERS Medical Systems, Inc. Chicago, IL, USA) could be used in future studies to create a 3D reconstruction of the spine and trunk shape instead of the X-ray’s 2D depiction [36]. Surface topography has been used as a radiation-free assessment tool to assess the posterior deformity and changes associated with scoliosis over time [36,37]. This would help to determine how strong the correlation is between body image disturbances and objective trunk shape analysis in AIS females. D’Andrea et al. pointed out that deformities resulting from scoliosis, such as rib hump, trunk compensation, or waist asymmetry that affect appearance, become particularly important to patients in puberty [38]. Such deformities are an independent risk factor for developing self-image disturbances such as bulimia and anorexia nervosa [39]. According to studies designed by Fallstrom et al., only $8\%$ of conservatively [40] treated patients and only $27\%$ of surgically treated patients had a positive body image. Many studies have reported that dissatisfaction with appearance is more prevalent among adolescent girls than adolescent boys. Girls seem to be more interested in losing weight than boys, who have a greater willingness to increase muscle [40,41]. Risk analysis is critical in the determination of potential prevention. The role of mass media as a possible source of preferences for unrealistic attractiveness and predictor of body dissatisfaction is also widely documented [3]. Media influences have received a great deal of attention as the internalization of images and messages could be a causal risk factor [20]. Results from the current study indicate the potential importance of direct pressures generated by the media regarding appearance standards. In contrast to former studies, factors reflecting mass media as an essential source of information about attractiveness and internalization of an athletic ideal did not emerge in the current research [20,28]. The stronger endorsement of Internalization-General than Internalization-Athlete and Pressures was not consistent with the pattern shown among the American population [41]. However, our findings are consistent with Izydorczyk and Lizynczyk where the level of the Pressures and Internalization-General dominates over the intensity of other forms of sociocultural influences of the mass media on body image and physical appearance [32]. ## 5. Conclusions The presented study has shown that COPS and SATAQ-3 are tools with satisfactory statistical reliability. It confirmed the usefulness of the questionnaires in scientific research and adolescent screening. This procedure could indicate that patients who score high on COPS and SATAQ-3 are strongly determined to undergo AIS scoliosis treatment mostly due to aesthetical concerns. Moreover, further analyses and observations on the use of COPS and SATAQ-3 could also be conducted on various populations of Poles to enrich this tool with the development of norms for individual categories. ## References 1. Doran J., Lewis C.A.. Components of shame and eating disturbance among clinical and non-clinical populations. Eur. Eat. Disord. Rev. (2012.0) 20 265-270. DOI: 10.1002/erv.1142 2. Dakanalis A., Zanetti A.M., Riva G., Colmegna F., Volpato C., Madeddu F., Clerici M.. Male Body Dissatisfaction and Eating Disorder Symptomatology: Moderating Variables among Men. J. Health Psychol. 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--- title: The Effect of Cholesterol Efflux on Endothelial Dysfunction Caused by Oxidative Stress authors: - Hua Ye - Qian Liu - Yuanyuan Wang - Ximian Zhen - Nianlong Yan journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056126 doi: 10.3390/ijms24065939 license: CC BY 4.0 --- # The Effect of Cholesterol Efflux on Endothelial Dysfunction Caused by Oxidative Stress ## Abstract Endothelial dysfunction (ED) is the initiation of atherosclerosis (AS). Our previous studies have found that cholesterol metabolism and the Wnt/β-catenin pathway can affect endoplasmic reticulum stress (ER stress), which ultimately leads to ED. However, the effects of cholesterol efflux on ED, which are caused by oxidative stress and the correlation among ER stress, Wnt/β-catenin pathway, and cholesterol efflux, are not clear during ED. To uncover them, the expressions of liver X receptors (LXRα and LXRβ) and ATP-binding cassette protein A1 (ABCA1) and G1 (ABCG1) in HUVECs (human umbilical vein endothelial cells) were measured under oxidative stress. Moreover, HUVECs were treated with LXR-623 (LXR agonist), cholesterol, tunicamycin, and salinomycin alone or together. The results indicated that oxidative stress-induced ED could deregulate the expressions of LXRα and LXRβ and trigger the ER stress and Wnt/β-catenin pathway, resulting thereafter in the accumulation of cholesterol. Furthermore, similar results were shown after treatment with cholesterol; however, the activation of liver X receptor (LXR) could reverse these changes. Furthermore, other results demonstrated that tunicamycin-induced ER stress could stimulate the accumulation of cholesterol and the Wnt/β-catenin pathway, further leading to ED. Inversely, salinomycin could reverse the above effects by deregulating the Wnt/β-catenin pathway. Collectively, our results showed that cholesterol efflux is partly responsible for the oxidative stress-induced ED; in addition, ER stress, the Wnt/β-catenin pathway, and cholesterol metabolism can interact with each other to promote ED. ## 1. Introduction Atherosclerosis (AS), one of the main causes of mortality globally, is the primary factor of cardiovascular diseases and contributes to the abnormal functions of the heart, brain, and kidney and to peripheral vascular lesions [1,2,3,4]. Numerous studies have shown that endothelial dysfunction (ED) is a marker during the initial stage of AS, which can cause poor prognoses of vascular occlusion, plaque shedding, and rupture [5,6]. Moreover, ED is a dysfunctional phenomenon of endothelial cells under adverse stimulations such as oxidative stress [7,8,9]. Therefore, since oxidative stress is crucial to ED [10], in this study, the activated ED model of human umbilical vein endothelial cells (HUVECs) was established by treatment with H2O2 [11]. The endoplasmic reticulum (ER) is an organelle with functions such as protein folding, calcium ion storage, and the synthesis of lipids and carbohydrates [12], which also regulates cellular metabolism. Therefore, protein overload, calcium ion imbalance, oxidative stress, and other factors may cause physiological dysfunctions of the ER, which further induce unfolded protein responses (UPRs) to maintain ER homeostasis [13,14,15]. If the homeostasis cannot be restored, ER stress will be triggered [13,14,15]. Moreover, the vital role of the ER on ED has been confirmed by many studies. For instance, Matshediso Zachariah et al. found that the high concentrations of selenium in endothelial cells can trigger ER stress, which leads to ED, through the increasing of ROS [10]. Melatonin inhibited JNK/Mff signaling and ER stress and hence protected endothelial cells against ox-LDL-induced damage [16]. In addition, abnormal lipid metabolism has been verified to be significant to facilitate ER stress. Lipotoxicity can damage liver cells by inducing ER stress in non-alcoholic fatty liver [17,18,19,20], and high-fat diets in mice or ectopic lipid deposition of cells can activate ER stress in the liver and inhibit insulin receptor signaling to accelerate the development of steatohepatitis [21,22]. Furthermore, damaged cholesterol metabolism also can trigger ER stress and ED in the endothelial cells [23]. It has been shown that elevated total cholesterol and cholesterol crystals can increase arginases metabolism and impair nitric oxide signaling, further aggravating ED in gestational diabetes mellitus and early atherosclerosis [24,25]. Moreover, our previous study indicated that simvastatin, an inhibitor of HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase) in the biosynthesis of cholesterol, could alleviate oxidative stress-induced ED by inhibiting ER stress [26]. Moreover, SMS2 (sphingomyelin synthase 2) has been demonstrated to promote the intracellular accumulation of cholesterol, which results in ER stress and oxidative stress-induced ED [23]. To sum up, both ER stress and cholesterol metabolism are involved in the occurrence of ED. The Wnt/β-catenin pathway is another inducement to ED. For instance, increased serum leptin levels in chronic kidney patients can lead to ED via the MTA1-WNT/β-catenin pathway [27]. Under oxidative stress, inhibition of the Wnt/β-catenin pathway has been shown to mitigate ED [28]. Moreover, it has been discovered that simvastatin can suppress the Wnt/β-catenin pathway by downregulating intracellular cholesterol accumulation, further resulting in relieving ED [26]. Moreover, our previous research also found that SMS2 could augment oxidative stress-induced ED by enhancing the Wnt/β-catenin pathway [23]. Obviously, the Wnt/β-catenin pathway, ER stress, and cholesterol metabolism all participate in ED. However, the relationship among the above three and the effects of cholesterol efflux on oxidative stress-induced ED need to be further investigated. Thus, this research aimed to explore the role and possible mechanisms of cholesterol efflux on oxidative stress-induced ED in HUVECs. ## 2.1. Oxidative Stress Inhibited the Cholesterol Efflux of HUVECs Since both cholesterol metabolism and oxidative stress are associated with ED, HUVECs were treated with H2O2 to explore whether the oxidative stress could affect the cholesterol metabolism of HUVECs. The results (Figure 1A) showed that the expression of cholesterol efflux-related proteins ABCA1 and their transcription factor LXRα and LXRβ were significantly downregulated in the treatment group. Moreover, HMGCR participated in cholesterol synthesis and was significantly upregulated under treatment (Figure 1A,B). Furthermore, the result (Figure 1C) of filipin staining demonstrated that the level of intracellular cholesterol was increased with H2O2 treatment. These outcomes indicated that oxidative stress could facilitate the accumulation of intracellular cholesterol by reducing the efflux and increasing the synthesis of cholesterol. However, our previous studies showed that simvastatin could attenuate the oxidative stress-induced ED of HUVECs [26]; therefore, the influence of cholesterol efflux on ED was to be investigated later. ## 2.2. The Cholesterol Metabolism Affected the Cholesterol Accumulation in HUVECs To further explore the effects of cholesterol efflux on endothelial cells, the activated LXR model of HUVECs was established by treatment with LXR-623 (activated both LXRα and LXRβ). The results indicated that LXR was activated successfully, and the impacts of H2O2 could be inhibited by upregulating ABCA1 and ABCG1 in the treatment group (Figure 2A). Additionally, filipin staining suggested that the H2O2-induced accumulation of intracellular cholesterol could be reversed by the treatment of LXR-623 (Figure 2B). These results proved that the enhanced cholesterol efflux can alleviate the accumulation of cholesterol in HUVECs caused by oxidative stress. Moreover, cholesterol was directly added to detect the effects of cholesterol metabolism on the accumulation of cholesterol. Interestingly, the results (Figure 2C) found that the expression of ABCG1 was significantly downregulated, and the level of ABCA1 was upregulated. Moreover, the content of total intracellular cholesterol was elevated after treatment with cholesterol alone (Figure 2C,D). However, when under the treatment of both H2O2 and cholesterol, the variations of ABCA1, ABCG1, and total intracellular cholesterol were not significantly different from the results after being treated separately in cholesterol or H2O2 (Figure 2C,D). The outcomes showed that the cholesterol treatment alone can also induce the accumulation of cholesterol in HUVECs. However, there were no synergistic effects in the H2O2 + CHOL group. ## 2.3. Cholesterol Accumulation Can Enhance the Damage of HUVECs Induced by Oxidative Stress To explore endothelial cell damage after altering the cholesterol metabolism, the levels of LDH, NOS, SOD, and ROS in HUVECs were measured. The studies discovered that the contents of LDH and ROS were upregulated by the treatment of H2O2, and the extracellular LDH and intracellular ROS were decreased in the LXR-623-treated group (Figure 3A,D). Conversely, the levels of intracellular SOD and NOS showed the opposite trend (Figure 3B,C). These findings indicated that oxidative stress-induced HUVEC injury and cholesterol efflux can alleviate HUVECs cell damage caused by oxidative stress. Furthermore, the levels of extracellular LDH and intracellular ROS were upregulated, and intracellular SOD and NOS were downregulated in the cholesterol treated group, which was similar with the results of H2O2 treatment. However, there were no significant variations in LDH, ROS, SOD, and NOS under both treatments of H2O2 and cholesterol compared to H2O2 or cholesterol treatment alone (Figure 3E–H). To sum up, the accumulation of cholesterol by treating cholesterol directly can enhance the injury of HUVECs. ## 2.4. Cholesterol Accumulation under Oxidative Stress can Increase the Adhesion Ability of HUVECs The role of cholesterol accumulation on the adhesion capacity of HUVECs was investigated. The results of Western blotting showed that the levels of adhesion molecules ICAM-1(Intercellular cell adhesion molecular-1), VCAM-1(Vascular cell adhesion molecular-1), and MCP-1(Monocyte chemoattractant protein-1) were upregulated under H2O2-induced oxidative stress, which enhanced the adhesion capacity of HUVECs to THP-1 (Human myeloid leukemia mononuclear cells). Nevertheless, there were opposite results in the LXR-623 treated group (Figure 4A,B). Therefore, it was suggested that the H2O2-induced adhesion capacity of HUVECs was attenuated by the cholesterol effluxion. Consistent with the above results, the expressions of ICAM-1, VCAM-1, and MCP-1 were also upregulated through cholesterol treatment, resulting in the elevation of the adhesion capacity of HUVECs. However, with the treatment of both H2O2 and cholesterol, the variations of ICAM-1, VCAM-1, and MCP-1 were slightly downregulated compared with those detected with the H2O2 or cholesterol treatment alone, and similar results were also observed in the adhesion capacity of HUVECs to THP-1 (Figure 4C,D). In general, these data could show that the adhesion ability of HUVECs could be promoted by treating the cholesterol alone. ## 2.5. The Accumulation of Cholesterol in HUVECs Triggers ER Stress and Activates the Wnt/β-Catenin Pathway As our previous studies indicated, the Wnt/β-catenin pathway is closely related with ED [29], and ER stress can cause ED via activation of the Wnt/β-catenin pathway [23]. Therefore, the ER stress markers Glucose-regulated protein 78 (GRP78) and C/EBP homologous protein (CHOP), β-catenin, and p-β-catenin were detected. The results determined that the expressions of GRP78, CHOP, and β-catenin were upregulated, and p-β-catenin was downregulated in the treatment of H2O2, whereas LXR-623 reversed the above trends with the decreased expressions of GRP78, CHOP, and β-catenin and the increased level of p-β-catenin protein (Figure 5A,B). These findings demonstrated that the enhanced cholesterol efflux can suppress ER stress and the Wnt/β-catenin pathway caused by the accumulation of cholesterol under oxidative stress. Moreover, the expressions of GRP78, CHOP, and β-catenin were enhanced, and the expression of p-β-catenin was inhibited under the treatment of cholesterol alone. However, the results of treatment with H2O2 and cholesterol together were not significantly different compared with the treatment of H2O2 or cholesterol alone (Figure 5C,D). In general, it has been shown that both the ER stress and Wnt/β-catenin pathway can be triggered by treating cholesterol directly. ## 2.6. Wnt/β-Catenin Pathway Can Interact with ER Stress, and Both of Them Can Affect the Cholesterol Metabolism It has been confirmed that ER stress, the Wnt/β-catenin pathway, and cholesterol accumulation play critical roles in ED, and cholesterol accumulation can trigger ER stress and the Wnt/β-catenin pathway. Therefore, whether ER stress and the Wnt/β-catenin pathway can modulate the cholesterol accumulation was explored. HUVECs were treated with tunicamycin to stimulate ER stress, and salinomycin was used to inhibit the Wnt/β-catenin pathway. The results (Figure 6A) verified that the expressions of GRP78 and CHOP were upregulated under tunicamycin treatment and downregulated by salinomycin, respectively. In other words, the effects of tunicamycin on HUVECs were reversed by salinomycin (Figure 6A). The findings demonstrated that the inhibition of the Wnt/β-catenin pathway can suppress ER stress. Furthermore, the influences of ER stress on the Wnt/β-catenin pathway were determined later. The data indicated that tunicamycin could enhance the salinomycin-induced downregulation of β-catenin expression; moreover, p-β-catenin presented the opposite trend (Figure 6B). Hence, it can be suggested that enhanced ER stress can activate the Wnt/β-catenin pathway. Moreover, the influences of ER stress and the Wnt/β-catenin pathway on the cholesterol efflux revealed that tunicamycin-induced ER stress could downregulate the expressions of ABCA1 and ABCG1, which resulted in an increase in total intracellular cholesterol. Conversely, salinomycin, as an inhibitor of the Wnt/β-catenin pathway, could change over the tunicamycin-induced cholesterol accumulation by upregulating ABCA1 and ABCG1 expressions (Figure 6C,D). In summary, both ER stress and the Wnt/β-catenin pathway could promote intracellular cholesterol accumulation. ## 2.7. Inhibiting the Wnt/β-Catenin Pathway under Endoplasmic Reticulum Stress Can Decrease HUVECs Cell Damage and the Adhesion Capacity To further investigate the functions of ER stress and the Wnt/β-catenin pathway to ED, the relevant indicators of ED were determined through treatment with tunicamycin to stimulate ER stress and salinomycin to inhibit the Wnt/β-catenin pathway. The results suggested that extracellular LDH and intracellular ROS were elevated under the treatment of tunicamycin alone, which were reduced by salinomycin treatment (Figure 7A,D). Conversely, the expressions of intracellular SOD and NOS had the opposite trend (Figure 7B,C). These results indicated that the activation of ER stress and the Wnt/β-catenin pathway can induce HUVEC injury. Furthermore, the expressions of ICAM-1, VCAM-1, and MCP-1 and the adhesion capacity of HUVECs to THP-1 were upregulated under tunicamycin treatment, which were reversed under salinomycin treatment (Figure 7E,F). These outcomes proved that the ER stress and Wnt/β-catenin pathway can augment the HUVECs adhesion capacity. Salinomycin, a potent inhibitor of Wnt/β-catenin signaling, is able to block the phosphorylation of Wnt-induced LRP6 (Recombinant Low Density Lipoprotein Receptor Related Protein 6). Next, LRP6 was exclusively knocked down using shRNA interference to further explore the effects of Wnt/β-catenin signaling on the ER stress and cholesterol metabolism (Figure 8A). Interestingly, specific knockdown of LRP6 did obstruct Wnt/β-catenin signaling, ultimately resulting in decreased ER stress and cholesterol accumulation (Figure 8B–E). All in all, knocking down LRP6 had the same effects as salinomycin, that is, mitigating the effects of tunicamycin on endothelial cells, finally alleviating ER stress, and promoting cholesterol outflow. ## 3. Discussion In this research, we discovered that the optimum concentration of H2O2 was 500 μmol/L. However, Ransy et al. believed that this dose could release O2 from the catalase reaction, which was disproportionally high with regard to physiological oxygen concentration [30]. A high concentration of oxygen may cause other reactions in cells. However, in our studies, the damage levels of HUVECs were raised with the increase of H2O2 concentration (from 100 to 700μmol/L) for the secretion of LDH. Therefore, the mechanisms of HUVEC injury are very complicated, which may be related to the Fenton’s reaction, a large excess in antioxidant defense, etc. [ 30]. More importantly, in this concentration, we found that the expressions of protein related to cholesterol metabolism changed, caused by H2O2. Among them, the levels of cholesterol efflux-related proteins (ABCA1 and ABCG1) were significantly downregulated, and cholesterol synthesis-related protein HMGCR was upregulated (Figure 1). Furthermore, the expressions of LXRα and LXRβ were decreased, which are the transcript factors of ABCA1 and ABCG1. Obviously, all of these variations contributed to the accumulation of intracellular cholesterol (Figure 1) and partly indicated that the cholesterol efflux may participate in the oxidative stress-induced ED. In order to further verify the functions of cholesterol efflux in the oxidative stress induced-ED, the activity of LXR was elevated by LXR-623 under oxidative stress. Results showed that both the accumulation of cholesterol and ED were decreased (Figure 2, Figure 3 and Figure 4). These demonstrated that the enhanced cholesterol efflux with LXR-623 treatment can be attenuated by oxidative stress induced-ED, which directly reduced the accumulation of cholesterol by LXR. In other words, the accumulation of cholesterol may be one of the direct factors to induce ED (Figure 2, Figure 3 and Figure 4). In patients with coronary ED, Monette et al. found similar results to ours, which showed that the cholesterol efflux capacity was a strong, inversed predictor of ED [31]. Subsequently, to prove the above hypothesis, the content of cholesterol was increased by adding cholesterol directly in this study. These results indicated that enhancing the level of cholesterol caused the accumulation of cholesterol and ED, which was similar to the observations of the oxidative stress-induced ED; therefore, it was suggested that cholesterol accumulation is a possible main factor of ED (Figure 2, Figure 3 and Figure 4). Interestingly, when HUVECs were treated with cholesterol alone, the expressions of ABCA1 and ABCG1 had the opposite results and finally affected the accumulation of cholesterol (Figure 2C,D). These results showed the different functions of ABCA1 and ABCG1 in endothelial cells. Moreover, the treatment of both H2O2 and cholesterol did not acquire a synergistic effector superposition effect on enhancing ED and the adhesion capacity of HUVECs to THP-1. Contrarily, when the H2O2 + CHOL group was compared to the H2O2 or CHOL group alone, ED (LDH and NOS) and the expressions of ICAM-1, VCAM-1, and MCP-1 had no significant difference and showed slight downregulation. The reasons may be related to the feedback regulation, because cholesterol was treated earlier than H2O2 by about 2 h (Figure 4C,D). Additionally, our previous experiment revealed that the inhibition of HMGCR by simvastatin could suppress the synthesis and accumulation of intracellular cholesterol and ultimately alleviated ED in HUVECs, which affirmed that cholesterol metabolism is an important factor for ED once again [26]. Moreover, we also found that SR-B1 and LDLR were decreased under oxidative stress-induced ED, which might be regulated by the cholesterol accumulation and also illustrated that cholesterol metabolism is associated with oxidative stress-induced ED. Clearly, all of these studies indicated that cholesterol metabolism plays a crucial role in the process of ED. The influence of cholesterol metabolism on ED is possibly related to ER stress, which promotes ED [23,26]. The ER is involved in the biosynthesis and storage of cholesterol; therefore, the cholesterol metabolism also can stimulate ER stress. Jiansen Yan et al. discovered that ER stress could be activated with cholesterol treatment by stimulating mSREBP1 in IDD, inducing the pyroptosis in NP cells and ECM degradation [32]. Moreover, our previous study indicated that the activating LXRα, which promoted cholesterol efflux by augmenting ABCA1 and ABCG1 expressions, could significantly reverse tunicamycin-induced ER stress in hepatocytes and macrophages [20]. Moreover, cholesterol homeostasis was also revealed to modulate the epithelial–mesenchymal transition (EMT) by regulating the ER stress in breast cancer cells [33]. Furthermore, in the occurrence of ED and cardiovascular disturbances, ER stress is a critical mechanism of ED [23,26,34]. In HUVECs, we previously confirmed that simvastatin attenuated the accumulation of cholesterol, which mitigated ED by relieving ER stress. Conversely, SMS2 could promote the accumulation of intracellular cholesterol, which leads to ER stress and ED [23,26]. In this study, when the HUVECs were treated with cholesterol and LXR-623, the ER stress was positively correlated with cholesterol accumulation and ED (Figure 5). Although the H2O2 + CHOL group still did not acquire a synergistic effect or superposition effect on the ER stress, which showed the same cause as the above, these results suggested that ED affected by the cholesterol efflux may depend on ER stress (Figure 5). The Wnt/β-catenin pathway is one of the factors that can promote ED. In this study, we discovered that changing cholesterol metabolism could regulate the Wnt/β-catenin pathway, which was also positively correlated with cholesterol accumulation and ED (Figure 5). Mechanistically, firstly, the regulation of cholesterol metabolism to the Wnt/β-catenin signaling pathway depended on the LRP6, which is a coreceptor of Wnt and located on the lipid rafts [35,36]. Lipid rafts are functional areas on the cell membrane, the major components of which are sphingolipids and cholesterol [36]. Thus, cholesterol efflux can impact the transmembrane signaling of LRP6 and further affect the Wnt/β-catenin pathway via changing the cholesterol compositions in the lipid raft. Secondly, ER stress was proved to be involved in the regulation of the Wnt/β-catenin pathway. For example, research showed that ER stress can inhibit the Wnt/β-catenin pathway in cancers cells [37,38,39]. Therefore, it was verified that ER stress can block the Wnt/β-catenin pathway, causing cancer cell apoptosis [37,38]. Contrarily, when HUVECs were treated with tunicamycin to trigger ER stress, the results showed that ER stress can activate the Wnt/β-catenin pathway and promote cholesterol accumulation by downregulating ABCA1 and ABCG1 expressions (Figure 6). Apparently, the results [1] revealed that the cholesterol accumulation induced by ER stress was partly alleviated by cholesterol efflux, and [2] were contradictory to the studies in cancers cells, possibly due to cell types. As a result, the Wnt/β-catenin pathway has different effects on endothelial cells and cancer cells. Moreover, our results also indicated that ER stress could regulate cholesterol metabolism (Figure 6). In male zebrafish, Zhang et al. showed that cholesterol genes were involved in ER stress and upregulated cholesterol gene expressions to bring about hepatic lipid accumulation by MC-LR [40]. Mechanistically, sterol regulatory element-binding protein (SREBP), a major component related to the cholesterol metabolism, can be activated and regulated by ER stress [41]. Obviously, both our present and previous studies suggested that cholesterol metabolism can activate ER stress and the Wnt/β-catenin pathway, further accelerating the process of ED (Figure 5) [23,26]. On the contrary, ER stress can regulate the cholesterol metabolism and Wnt/β-catenin pathway as well. However, what needs further discovery is whether the Wnt/β-catenin pathway can modulate ER stress and cholesterol metabolism. Therefore, when we inhibited the Wnt/β-catenin pathway treated with salinomycin or knocked down the expression of LRP6, the outcomes revealed that both the ER stress and cholesterol accumulation were decreased, and the regulation of cholesterol was related to ABCA1 and ABCG1 (Figure 6). Interestingly, the combined treatment with tunicamycin and salinomycin (or knock down of the expression of LRP6) can antagonize the activity of each other to HUVECs (Figure 6, Figure 7 and Figure 8). It was obvious that [1] the Wnt/β-catenin pathway simultaneously can regulate ER stress and cholesterol efflux, and [2] the Wnt/β-catenin pathway and ER stress can affect each other. ## 4.1. Cell Culture HUVECs (Cell Bank of Type Culture Collection of the Chinese Academy of Sciences, Shanghai, China), within 30 generations, were cultured with DMEM medium (11995, Solaibio, Beijing, China), and the THP-1 cells (Cell Bank of Type Culture Collection of the Chinese Academy of Sciences, Shanghai, China) were incubated with RPMI-1640 medium (31800, Solaibio, Beijing, China). All media were filled with $10\%$ FBS, and all cell cultures were carried out in an environment at 37 °C containing $5\%$ CO2, H2O2 (500 μmol/L, Guangdong Hengjian Pharmaceutical Co., Ltd., Jiangmen, China), LXR-623 (5 μmol/L, HY10629, MedchemExpress, Shanghai, China), cholesterol (100 μmol/L, C8280, Solaibio, Beijing, China), tunicamycin (10 μmol/L, MB5419, Meilunbio, Dalian, China), and salinomycin (5 μmol/L, HY-15597, MedchemExpress, Shanghai, China). ## 4.2. Cell Transfection The LRP6 gene was cloned into the pLKO.1 puro vector, and further the resulting vector was transferred into TransStbl3 Chemically Competent Cells (CD521-01, TransGen Biotech, Beijing, China). The plasmid was extracted with an EasyPure HiPure Plasmid MiniPrep Kit (EM111-01, TransGen Biotech, China) according to the instructions. We cotransfected psPAX.2, pMD2.G and the target plasmid into 293 T cells. TurboFect (R0531, Thermo Scientific, China) was used to transfect cells according to the protocol. The lentivirus in the supernatant of the medium was concentrated after culturing for 48 h using Lentivirus Concentration Kit (FV101-01, TransGen, Beijing, China). Finally, the concentrated lentivirus was used to infect HUVECs at the confluency of $50\%$ for 48 h. Two pairs of shRNA were created. One of the LRP6 shRNA sequences was 5′-CCGATGCAATGGAGATGCAAA-3′, and the other was 5′-GATAGCCTTCAGTTAACTAAC-3′. ## 4.3. Filipin Staining HUVECs were seeded on 24-well plates and cultured into an incubator at a density of 2 × 103 per well. With cell confluency reaching about $60\%$, HUVECs were treated with drugs and cultured for 24 h. Afterwards, we discarded the medium and then washed the cells with PBS buffer. Then, after being fixed with $4\%$ paraformaldehyde at 37 °C for 35 min, HUVECs were rinsed three times with PBS buffer and further immersed in glycine (1.5 mg/mL) for 10 min to neutralize the remaining formaldehyde. Finally, after rinsing off the excess dye with PBS after dyeing with filipin solution (50 μg/mL, F31601, Harveybio, Beijing, China) for 2 h, we observed the fluorescence intensity using a LeicaDM1000 (magnification, 5×) and an Olympus FV3000 (magnification, 10×) and then calculated the average fluorescence intensity of all the signal regions of the entire image by Image J 1.53e software, excluding the black background area. ## 4.4. Assessment of ED by Measuring the Content of LDH, SOD, and NOS HUVECs were planted in 6-well plates and then cultured with drugs together for 24 h with the cell density attaining about $60\%$. The supernatant was taken for the determination of LDH activity by a lactate dehydrogenase (LDH) assay kit (A020-1-2, Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Furthermore, HUVECs homogenated with PBS buffer were taken for the determination of SOD and NOS activity. The superoxide dismutase (SOD) assay kit (A001-3-2, Nanjing Jiancheng Bioengineering Institute, Nanjing, China) was used to test SOD content, and the nitric oxide synthase (NOS) assay kit (A014-2-2, Nanjing Jiancheng Bioengineering Institute, Nanjing, China) was used to detect NOS content. All of the above experiments were measured strictly according to the corresponding instructions. ## 4.5. Measurement of ROS Contents The level of intracellular ROS was determined by the fluorescence probe 2,7-dichlorodi-hydrofluorescein diacetate (DCFH-DA). HUVECs were tiled in a 24-well plate with a density of 2 × 103 per well. A DCFH-DA working solution (15 μmol/L) was prepared with DCFH-DA stock solution (10 mmol/L, E-BC-K138-F, Elabscience, Wuhan, China) and buffer. With the medium aspirated carefully, the cells were rinsed once with buffer solution. Then, at about $70\%$ growth, the HUVECs and the DCFH-DA working solution were cultured together for 50 min. Finally, when HUVECs were rinsed three times with buffer solution, the fluorescence degree was observed using an Olympus IX71 fluorescence microscope (magnification,10×), and then the mean fluorescence intensities of all the signal regions of the entire picture were analyzed with Image J 1.53e software. ## 4.6. Measurement of Adhesion Ability of HUVECs to THP-1 Cells The HUVECs were incubated in 24-well plates. Furthermore, 5 × 104 THP-1 cells, labeled with BCECF/AM (B115503, Aladdin, Shanghai, China) for 1 h in the dark cell incubator, were then transferred into drug-treated HUVECs in 24-well plates when the HUVECs had grown to about $70\%$ and cultured for 1 h in the dark. Afterwards, the THP-1 cells and HUVECs were dyed with Hoechst33342 for 40 min after being removed from the previous medium. Finally, PBS buffer was added to rinsed cells three times to gently wash away the THP-1 cells that had not adhered. Images were taken with an Olympus inverted fluorescence microscope (magnification, ×4). The average number of THP-1s of three independent tests was calculated. ## 4.7. Western Blot The HUVECs were harvested with trypsin (T8150, Solaibio, Beijing, China) after being treated with drugs for 24 h. RIPA lysate (C1053, Applygen Technologies Inc., Beijing, China) was used to lyse cells, and then the total protein of supernatant was collected through centrifugation. The collected supernatant was boiled for 5 min after adding 1 × loading buffer (DL101-02, TransGen Biotech, Beijing, China) and then electrophoretically isolated on 8–$12\%$SDS–PAGE gel. Then we used $5\%$ skimmed milk to block the PVDF membrane for 1 h. Firstly, the PVDF membrane was incubated with the primary antibodies at 4 °C for 12 h and washed with 1 × TBST for 30 min. Next, the PVDF membrane was incubated with the secondary antibodies at 37 °C for 1 h, further washed with 1 × TBST for 30 min, with 10 min each time at 100 rpm/min. At last, ECL ultra-sensitive luminescent liquid was added on the PVDF film equably, which was imaged on the BIORAD gel imager. The antibodies are as follows: MCP-1 (WL02966, Wanlei, Shenyang, China), 1:500; CD106/VCAM-1 (WL02474, Wanlei, Shenyang, China), 1:500; GRP78 (66574-1-Ig, Proteintech, Hubei, China), 1:10000; GAPDH (HRP-60004, Proteintech, Hubei, China), 1:40000; β-catenin (17565-1-AP, Proteintech, Hubei, China), 1:4000; ICAM-1 (10831-1-AP, Proteintech, Hubei, China), 1:1000; ABCA1 (D155299, BBI, Shanghai, China), 1:500; ABCG1 (AP6529A, Abgent, San Diego, USA), 1:500; LXRα (D198471, BBI, Shanghai, China), 1:2000; LXRβ (A04523-2, BOSTER, Hubei, China), 1:1000; LRP6 (AP1191, ABclonal, Hubei, China), 1:1000; HMGCR (AP11955B, Abgent, San Diego, CA, USA), 1:1000 and p-β-catenin (DF2989, Affinity, Jiangsu, China), 1:2000. In these experiments, the protein bands normalized to GAPDH. ## 4.8. Statistical Analysis All data were first tested by the Shapiro–Wilk normality test using GraphPad Prism 6.0 software, and we found $p \leq 0.1$, suggesting that all date conformed to a normal distribution. Then, a Student’s t-test was performed to compare between two groups, and one-way analysis of variance (ANOVA) was used to analyze the differences between more than two groups. The fluorescence intensity analysis was processed by Image J 1.53e. All experiments were performed at least three times, with three biological replicates per experiment, and all data were expressed as mean ± standard deviation. $p \leq 0.05$ indicates there was a statistical difference. ## 5. Conclusions Our studies suggested that inhibiting the cholesterol efflux of HUVECs could partly induce the cholesterol accumulation by decreasing the activity of LXRs, which led to the oxidative stress-induced ED. Mechanically, both cholesterol accumulation and Wnt/β-catenin pathway in HUVECs can trigger the ER stress; conversely, ER stress can regulate cholesterol metabolism and the Wnt/β-catenin pathway as well. Moreover, cholesterol metabolism and Wnt/β-catenin pathway also can affect each other. 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--- title: Subacute Exposure to Low Pb Doses Promotes Oxidative Stress in the Kidneys and Copper Disturbances in the Liver of Male Rats authors: - Dragana Vukelić - Aleksandra Buha Djordjevic - Milena Anđelković - Evica Antonijević Miljaković - Katarina Baralić - Katarina Živančević - Petar Bulat - Jelena Radovanović - Danijela Đukić-Ćosić - Biljana Antonijević - Zorica Bulat journal: Toxics year: 2023 pmcid: PMC10056143 doi: 10.3390/toxics11030256 license: CC BY 4.0 --- # Subacute Exposure to Low Pb Doses Promotes Oxidative Stress in the Kidneys and Copper Disturbances in the Liver of Male Rats ## Abstract Recent data indicate that lead (Pb) can induce adverse effects even at low exposure levels. Moreover, the corresponding mechanisms of low Pb toxicity have not been well identified. In the liver and the kidneys, Pb was found to induce various toxic mechanisms leading to organ physiological disruption. Therefore, the purpose of the study was to simulate low-dose Pb exposure in an animal model with the aim of assessing oxidative status and essential element levels as the main mechanism of Pb toxicity in the liver and kidneys. Furthermore, dose–response modelling was performed in order to determine the benchmark dose (BMD). Forty-two male Wistar rats were divided into seven groups: one control group, and six groups treated for 28 days with 0.1, 0.5, 1, 3, 7, and 15 mg Pb/kg b.w./day, respectively. Oxidative status parameters (superoxide dismutase activity (SOD), superoxide anion radical (O2−), malondialdehyde (MDA), total sulfhydryl groups (SHG), and advanced oxidation protein products (AOPP)) and Pb, copper (Cu), zinc (Zn), manganese (Mn), and iron (Fe) levels were measured. Lowering Cu levels (BMD: 2.7 ng/kg b.w./day), raising AOPP levels (BMD: 0.25 µg/kg b.w./day) in the liver, and inhibiting SOD (BMD: 1.3 ng/kg b.w./day) in the kidneys appear to be the main mechanisms of Pb toxicity. The lowest BMD was derived for a decrease in Cu levels in liver, indicating that this effect is the most sensitive. ## 1. Introduction Lead (Pb) is one of the most persistent hazardous contaminants in the environment, posing a serious public health risk. It is a multi-organ toxin that affects almost all organs, including the brain, kidneys, liver, and reproductive organs [1,2,3]. Lead exposure primarily occurs through ingesting tainted food or water, or inhaling Pb-contaminated air [4,5]. After being absorbed, Pb is conjugated with glutathione in the liver and distributed between blood and tissues, and a small amount is excreted by the kidneys, so Pb builds up in various body tissues and harms a variety of macromolecules and organelles [6,7]. Epidemiological studies conducted on workers exposed to Pb indicate a connection between Pb exposure and the induction of certain liver enzymes, increased plasma cholesterol levels, disrupted glucose homeostasis, and thickening of the gallbladder wall [8,9]. Chronic exposure to high doses of Pb could cause permanent alterations in the kidneys, including interstitial fibrosis, tubular atrophy, glomerular sclerosis, and eventually renal failure [10,11]. On the other hand, the signs of chronic low-dose Pb poisoning in humans are usually modest, and many individuals remain asymptomatic. Furthermore, chronic low-dose Pb exposure in humans has also been linked to gout and hypertension development trough renal and nonrenal mechanisms [12]. In the liver and the kidneys, Pb was found to induce oxidative stress by inducing reactive oxygen species, leading to oxidative damage of crucial molecules, proteins, nucleic acids, and lipids [13]. Although Pb is not a Fenton’s metal, it can induce oxidative damage, indirectly elevating the level of free Fe which acts as a Fenton’s metal. As a divalent cation, its chemical configuration allows Pb to mimic essential cations in physiological processes, such as calcium (Ca2+), zinc (Zn2+), copper (Cu2+) and manganese (Mn2+) [14,15]. Most of them are essential for proper enzyme activity as cofactors or for regular cell membrane signal transduction [16]. The elevation of Fe in the blood occurs due to Pb displacement from the hemoglobin molecule (plumbenia). By mimicking and displacing essential cations which are important cofactors of antioxidant enzymes, Pb decreases their activity by increasing oxidative stress in the tissues. However, hepatotoxic and nephrotoxic mechanisms in the case of low, environmentally relevant doses of Pb are still not clear [8,17]. Lead’s toxic potential has been extensively studied for many years. During widespread use of Pb in industry, especially after 1970s, humans were exposed to high lead levels that were reflected in blood Pb levels (BLLs) of 100 µg/dL, or even higher [18,19]. Nowadays, after the ban of Pb use in the many products, the BLL in the non-occupationally exposed general population is usually lower than 5 µg/dL (set as reference level by the Center for Disease Control and Prevention) [20,21]. In order to derive the point of departure for hazardous chemicals, for regulatory purposes, a novel benchmark approach has been developed. It is an advanced method that uses software modelling for toxic dose–response analysis [22,23]. The purpose of the present study was to simulate a low-dose subacute Pb exposure scenario in animal model, with the aim of obtaining relevant BLLs for environmental exposure. After determination of hepatotoxic and nephrotoxic effects, the aim was to determine the benchmark dose for such effects, which might be useful in further human health risk assessment and safety evaluation of low-dose Pb exposure. ## 2.1. Chemicals All chemicals used for oxidative status analyses and metal analyses were p.a. quality, and were purchased from Sigma Aldrich, Germany or Scientific Fisher, Germany. Lead (II) acetate trihydrate (Pb (CH3CO2)2 • 3H2O), Alkaloid Skopje Macedonia, was used for making solutions for experimental animal treatment. ## 2.2. Animals and Experimental Study Design The study was conducted on forty-two male albino Wistar rats (six weeks old) purchased from Military Medical Academy, Belgrade. The rats were randomized in seven groups ($$n = 6$$) and acclimatized for one week in the animal room at the Faculty of Pharmacy, University of Belgrade, under relative humidity of 40–$60\%$, a temperature of 25 ± 3 °C, and a 12 h light–dark cycle. After acclimatization, six groups were treated with rising doses of Pb (0.1, 0.5, 1, 3, 7, 15 mg Pb/kg body weight (b.w.)/day), while the control group received distilled water only. Oral gavage was performed every morning for a period of 28 days. The doses were chosen to mimic environmentally realistic and low-subacute exposure to Pb with the aim of obtaining BLLs that have been reported in Pb-exposed human populations [18,24,25,26]. Twenty-four hours after the last dose, the rats were humanely sacrificed. The liver and the kidneys were taken, extensively washed (ice cold $0.9\%$ NaCl) and dry-weighed. The relative organ weight (%) was calculated as organ weight (g)/final body weight (g) × 100. The samples of organs were dissected and appropriately stored for oxidative status (−80 °C), and metal (−20 °C). The study was approved by the Ethical Committee on Animal Experimentation of the University of Belgrade Faculty of Pharmacy (No. 323-07-$\frac{11822}{2018}$-05), and was carried out in accordance with the United Kingdom Animal (Scientific Procedures) Act 1986 and the EU Directive $\frac{2010}{63}$/EU for animal experiments. ## 2.3. Metal Analyses The preparation of samples for metal analyses was carried out using a microwave-assisted digestion system (Milestone, Start D SK-10T, Milestone Srl, Sorisole, Italy). Weighted samples were digested using 7 mL concentrated HNO3 ($69\%$) and 1 mL concentrated H2O2 ($30\%$) under conditions suggested by the manufacturer. After digestion, the cooled samples were filled up to 25 mL with deionized water. The determination of Pb was performed using a graphite furnace atomic absorption method, while essential metals were determined using a flame atomic absorption method on an AAS GTA 120 graphite tube atomizer, 200 series AA, Agilent Technologies, Santa Clara, CA, USA. The accuracy of the analyses was checked using standard reference materials (SRM) whole blood Level 2 (SeronormTM, Sero, Billingstad, Norway) and 1577c—Bovine liver (LGS Standard, London, UK). ## 2.4. Oxidative Status Analyses Preparation of tissue samples for oxidative status analyses consisted of homogenization with cold 0.1 mol/L phosphate buffer (pH 7.4) in a 1:9 weight-to-volume ratio, using a T10 basic Ultra-Turrax homogenizer (IKA, Staufen, Germany). With the aim of obtaining post-mitochondrial supernatant, the homogenates were centrifuged at 800× g for 10 min and then at 9500× g for 20 min (+4 °C). All oxidative status parameters were determined in the post-mitochondrial supernatant. The rate of superoxide anion radical (O2−) formation was determined using the method described by Auclair and Voisin [27]. The Misra and Fridovich method [28] was used for superoxide dismutase activity (SOD) determination. The total oxidative status (TOS) was measured based on the Erel-optimized spectrophotometric method [29]. The malondialdehyde (MDA) concentration was determined using a spectrophotometric method with thiobarbituric acid, and the results are expressed as μmol/g protein [30]. Total sulfhydryl groups (SHG) were calculated using the spectrophotometric method with 5,5′-dithiobis-2-nitrobenzoic acid (DTNB), as described by Ellman [31]. The method published by Witko et al. [ 32] was used for determination of advanced oxidation protein products (AOPP). All parameters are expressed on protein levels that were measured using the Bradford method [33]. ## 2.5. Benchmark Dose Modeling PROAST 70.1 software (https://www.rivm.nl/en/proast, accessed on 22 August 2020) was used to conduct benchmark dose–response modeling (Dutch National Institute for Public 132 Health and the Environment, RIVM). For continuous data, a benchmark response (BMR) of $5\%$ was utilized, as recommended by the Scientific Committee of the European Food Safety Authority (EFSA) at a $90\%$ confidence level [23]. External dosages, BLL and Pb levels in liver and kidneys tissues were all examined as doses for all parameters obtained in the research. The benchmark dose interval (BMDI) was determined using the program, which included lower (BMDL) and upper (BMDU) BMD. The model-averaging method in PROAST software combines all available models into one, which is then applied in data processing [34]. ## 2.6. Statistical Analyses GraphPad Prism8 software (GraphPad Software Inc., San Diego, CA, USA) was used for statistical analyses and graph-making. If data passed a normality and homogeneity of variance check, the data were presented as mean and standard deviation (S.D.), and analyzed using a one-way ANOVA followed by Fisher’s LSD test. If not, the data were presented as median and ranges (minimum-maximum), and the Kruskal-Wallis test followed by the Mann–Whitney U test was used. The level of statistical significance was set at $p \leq 0.05.$ ## 3.1. Relative Organ Weight Relative liver and kidney weights are presented in Table 1. The results show that there are no statistically significant differences in the organ weight of rats between groups. ## 3.2. Lead and Essential Metal Status in Rats’ Liver and Kidneys Lead tends to accumulate in the rat liver and kidney (Figure 1). In the liver, statistically increased Pb levels were determined in the groups treated with 7 and 15 mg Pb/kg b.w./day compared to controls, and in the case of the highest Pb dose of 15 mg Pb/kg b.w./day, in all treated groups. In the case of the kidneys, a statistically significant increase in Pb levels compared to control was detected in groups treated with 3, 7, 15 mg Pb/kg b.w./day. In both organs, a dose-dependent increase in the bioaccumulation of Pb was observed. A trend of decreasing Cu levels in the liver, with the lowest value in the highest dose group, can be observed (Table 2). In the liver, no statistically significant difference was observed in values of other essential elements (Zn, Fe, Mn) in relation to the control value. In the kidneys, there were no statistically significant changes in essential elements compared to the control group. ## 3.3. Oxidative Status in Rats’ Liver and Kidney Tissues In the liver, an increasing trend in AOPP levels was observed in the treated groups of rats (Table 3). In the treated groups, induction of SOD enzyme activity was also observed, with the highest value in the group treated with the highest dose of 15 mg Pb/kg b.w./day. The level of MDA varied irregularly between groups. In all treated groups, inhibition of the renal enzyme’s SOD activity occurred (Table 3). A slight decrease in MDA levels was observed in some treated groups. Other oxidative stress parameters remained unchanged from the control. ## 3.4. Benchmark Dose Modelling In the liver, dose dependence for the Pb effects was observed for Cu with external dose–response, BMDL: 2.7 × 10−6 mg Pb/kg b.w./day (Figure 2a) and AOPP with external dose–response, BMDL: 0.00025 mg Pb/kg b.w./day (Figure 2b). In case of internal dose (BLL)-response, BMDL: 1.4 × 10−5 µg/dL (Figure 3) and internal dose (ng Pb/g liver)-response, BMDL: 2.4 × 10−6 ng Pb/g (Figure 4) was determined for the Cu decrease. The external dose–response relationship for the effects of Pb in the kidneys was detected for SOD activity (Figure 2C), with BMDL: 1.3× 10−6 mg Pb/kg b.w./day. For all other investigated parameters in the liver and the kidneys, the dose dependence was not obtained. ## 4. Discussion In the present study, we have examined the hepatotoxic and nephrotoxic effects of low Pb doses, reaching a BLL similar to general exposed population [35,36]. The six dose groups allowed ideal statistical conditions for benchmark dose analysis. Our data suggested that the Pb treatment did not affect liver and kidney weight. Lowering Cu levels in the liver (BMDL: 2.7 × 10−6 mg Pb/kg b.w./day), along with higher AOPP liver levels (BMDL: 0.00025 mg Pb/kg b.w./day), and inhibition of SOD (BMDL: 1.3 × 10−6 mg Pb/kg b.w./day) in the kidneys might be the most sensitive mechanisms of low-dose Pb toxicity in these organs. Lead exposure has been found to affect all organs, including the blood, liver, kidneys, heart and brain, affecting their physiological structure and function [2,35,37,38,39]. Effects on the liver and kidneys are key points in the evaluation of the safety of existing and new substances. The main parameters in the serum that are usually used for the evaluation of liver function are liver enzymes’ activity and lipid profile parameters [40]. In our previously published study, we have reported that low lead exposure led to dysregulation of lipid profile in subacutely exposed rats, while liver enzymes remained in normal ranges [35]. Both Cu excess and deficiency in the liver can induce clinical problems, since it is a component of various enzymes that are required for health and wellbeing. Liver Cu levels homeostasis are highly regulated, and normally neither a decrease nor an excess buildup of Cu occurs due to effective physiological regulation processes [41,42]. The liver regulates the elimination of acquired Cu from the body through bile. Along with Zn, Cu has high affinity for SHG of metallothionines. Furthermore, as a cofactor, Cu is crucial for the function of several enzymes: SOD, mitochondrial monoamine oxidase, tryptophon-2,3-dioxygenase, cytochrome c oxidase, ceruloplasmin, and hephaestin [41,42]. Lead has been shown to induce Cu deficiency in animal studies [43] and humans [44], while Cu supplementation in animal studies has been proven to have protective effects on Pb toxicity [45]. In the present study, low Pb doses induced an external and internal (BLLs and tissue Pb levels) dose-dependent decrease in Cu in the liver. The derived BMDs were: 2.7× 10−6 mg/kg b.w./day for an external dose, 1.4 × 10−5 µg/dL for an internal dose (BLL), and 2.4 × 10−6 ng/g Pb for an internal dose (ng Pb/g liver tissue). The decrease in Cu may probably affect all previously mentioned biological processes in the liver, possibly leading to disruption of liver function. A potential mechanism by which Pb decreases Cu could be competition between Pb and Cu for several biological process, including transport through membranes or competition for active seats in biologically active proteins. By altering ion homeostasis, Pb causes many of its harmful effects. This disruption happens when Pb replaces other metal ions such as iron, calcium, zinc, magnesium, selenium, and manganese [14,16]. Another important mechanism of Pb toxicity is the induction of oxidative damage in various tissues [13]. Our results have shown induction of SOD enzyme activity and dose-dependent increases in AOPP levels in the liver, with a derived BMDL of 0.00025 mg Pb/kg b.w./day indicating oxidative stress induction in the liver. Advanced oxidation protein products are dityrosine-containing cross-linked protein products that have been shown to be good indicators of protein oxidation. In 1996, AOPP were first identified in the plasma of chronic uremic patients as new oxidative stress indicators [32]. AOPP are transported by oxidized plasma proteins and bind to the high-density lipoprotein (HDL) scavenger receptor class B type I; thus, they are classified as HDL receptor antagonists [46]. The induction of liver SOD enzyme activity in the present study might be a protective mechanism on oxidative stress induction, keeping in mind that production of free radicals can stimulate antioxidant enzymes’ activity or inhibit it [47]. Similar to our results, Barregard et al. have shown (40 days, 10.39 mg Pb/kg b.w./day) an increase in SOD activity in the liver and a decrease in SOD activity in the kidneys of rats [11]. In studies wherein higher doses of Pb were tested, inhibition of SOD, catalase (CAT) and glutathione peroxidase (GPx) have been documented. For example, in the 5-day long study on intraperitoneal application of Pb-acetate to Wistar rats, a dose of 20 mg/kg b.w./day induced a decrease in the liver’s SOD activity [48]. Another animal study on Wistar rats (100 mg/kg b.w./day, per os, 60 days) also resulted in a decrease in SOD activity, CAT activity and glutathione levels, while MDA increased in the liver, compared to the untreated group [49]. Lead excretion from organisms and lead’s ability to be distributed and accumulated in the kidneys allows for direct toxic effects. Pb has been shown to induce kidney damage through several mechanisms, including general oxidative stress induction, essential cation interaction, inflammation, and induction of glomerular and tubular cell apoptosis. Some of its additional mechanisms are changes in renal gangliosides (plasma membrane lipids that play a role in the control of glomerular filtration), changes in renal vascular tone, and alterations in the renin–angiotensin–aldosterone hormonal system [8]. In our previous study, we showed that low subacute lead exposure did not significantly impact the serum nephrotoxicity markers of creatinine and urea levels [35]. A recent study by Kwon et al. suggested a new mechanism following Pb exposure erythrophagocytosis in renal tubular cells, which might greatly increase nephrotoxicity [17]. The observed data in our study have shown a dose-dependent decrease in renal SOD activity in Pb-treated animals, with derived BMDL: 1.3 × 10−6 mg Pb/kg b.w./day. SOD activity has been disrupted probably due to direct inhibition of SOD by Pb, or due to the lower capacity of SOD synthesis. The SOD enzyme plays a key role in oxidative stress resistance, and it is responsible for control of potential over O2− production in the cell. It catalyzes the reaction of O2− dismutation into H2O2 and oxygen [50]. Similar to our results, in the study on male Wistar rats, Pb-acetate (20 mg/kg, i.p.) induced a decrease in renal antioxidant enzyme activity (SOD, catalase, and glutathione peroxidase) after seven days of treatment [51], and inhibition of renal SOD was noticed in the study on Wistar rats treated with 22.5 mg/kg b.w Pb-nitrate over a period of 28 days [52]. The other investigated parameters of oxidative status in the kidneys did not change significantly. Moreover, essential elements levels did not change. The majority of Pb’s harmful effects are thought to manifest at a BLL of 5 µg/dL or lower. As a result, the Centers for Disease Control and Prevention (CDC) is contemplating decreasing the reference levels for BLL in children from 5 g/dL to 3 g/dL [18]. Our findings support this, keeping in mind that our computed BMDL results were in the range of micrograms Pb per kg b.w./day. In our previous studies, the lowest BMD for Pb for neurotoxic effects was 4.5 × 10−6 mg Pb/kg b.w./day for induction of TOS in the brain [2]. This value was also in the range of values obtained in the presented study. Furthermore, for the cardiotoxic effects, the lowest BMD was 2.2 × 10−6 mg Pb/kg b.w./day for increases of MDA levels in cardiac tissue, while in case of toxic effects on blood, the lowest BMD was, as in the study in the liver, for a decrease in Cu levels, 1.4 ng/kg b.w./day [35,39]. The obtained results for the decrease in Cu in liver are in correlation with our previous results in blood. Nonetheless, this is to be expected, given that liver is rich in blood and receives over one quarter of the heart’s blood rate, despite making up only few % of the body’s total weight. On the other hand, some epidemiological studies reported the BMD values of Pb for few toxicological endpoints; however, to the best of our knowledge, there are no published values considering hepatotoxic and nephrotoxic endpoints. In the study, performed in China, that included lead-acid battery workers, the authors derived a BMDL of 13.5 µg Pb/dL for a decrease in red blood cells’ concentration, 10.5 µg Pb/dL for a decrease in hemoglobin levels, based on hematological toxicity, and an even tougher threshold of 6.6 µg Pb/dL for micronuclei or 3.5 µg Pb/dL for telomere length, based on genotoxicity [53]. Our research group’s recent publication demonstrated a connection between lead and hormones’ action, indicating a positive association between BLLs and serum insulin levels, with derived BMDs 1.49 and 0.74 µg Pb/dL in males and females, respectively [54]. In our study, the observed internal BMDs (blood lead levels) are lower than the values from epidemiological studies; this strengthens the notion that the blood Pb threshold level might be very low. ## 5. Conclusions In conclusion, our results strongly suggest the involvement of Cu in low-dose Pb hepatotoxicity. The higher levels of AOPP in the liver also suggest oxidative stress as an important mechanism. In the kidneys, Pb was found to inhibit the SOD antioxidative enzyme activity, so the oxidative stress was found to be the main mechanism in low-dose Pb nephrotoxicity. Benchmark dose modelling showed a dose response for those parameters. The lowest BMD was derived for the decrease in Cu levels in liver, indicating this effect is the most sensitive. Our results might be useful in low-dose Pb exposure risk assessment, and strengthens the notion that the Pb threshold level for negative health effects might be very low. 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--- title: 'The Association of Prostate Cancer and Urinary Tract Infections: A New Perspective of Prostate Cancer Pathogenesis' authors: - Szu-Ying Pan - Wen-Chi Chen - Chi-Ping Huang - Chung Y. Hsu - Yi-Huei Chang journal: Medicina year: 2023 pmcid: PMC10056160 doi: 10.3390/medicina59030483 license: CC BY 4.0 --- # The Association of Prostate Cancer and Urinary Tract Infections: A New Perspective of Prostate Cancer Pathogenesis ## Abstract Background and objectives: Microbiota of the urinary tract may be associated with urinary tract malignancy, including prostate cancer. Materials and Methods: We retrospectively collected patients with newly diagnosed prostate cancer and subjects without prostate cancer from the National Health Insurance Research Database (NHIRD) in Taiwan between 1 January 2000 and 31 December 2016. A total of 5510 subjects were recruited and followed until the diagnosis of a primary outcome (urinary tract infection, pyelonephritis, cystitis, and prostatitis). Results: We found that the patients with prostate cancer had a significantly higher risk of urinary tract infections than those without prostate cancer. The adjusted hazard ratios for pyelonephritis, prostatitis, and cystitis were 2.30 ($95\%$ CI = 1.36–3.88), 2.04 ($95\%$ CI = 1.03–4.05), and 4.02 (95 % CI = 2.11–7.66), respectively. We clearly identified the sites of infection and associated comorbidities in the prostate cancer patients with urinary tract infections. In addition, we found that the patients receiving radiotherapy and androgen deprivation therapy had a lower risk of urinary tract infections than the patients in corresponding control groups. Conclusions: Our study suggests that an abnormal urine microbiome could potentially contribute to the development of prostate cancer through inflammation and immune dysregulation. Furthermore, an imbalanced microbiome may facilitate bacterial overgrowth in urine, leading to urinary tract infections. These findings have important implications for the diagnosis and treatment of prostate cancer. Further research is needed to better understand the role of the urine microbiome in prostate cancer pathogenesis and to identify potential microbiome-targeted therapies for the prevention and treatment of prostate cancer. ## 1. Introduction Prostate cancer is the most common genitourinary tract malignancy and the second most common cancer in men [1]. According to the Global Cancer Observatory (GLOBOCAN) study, there were 1,276,106 newly diagnosed cases of prostate cancer in 2018 [2]. However, this number had increased to 1,414,259 by 2020 [3]. Risk factors associated with the formation of prostate cancer include androgen, genetics, diet, smoking, family history, and ethnicity [4]. However, there is no strong evidence of effective preventive methods for prostate cancer [1], and, therefore, studying factors associated with prostate cancer remains important. Bacteria have been associated with the development of several cancer types, and in particular cancer of the genitourinary tract cancer. Possible mechanisms include inflammation or metabolic processes influenced by the microbiota [5]. Prostate cancer is the most interesting of these genitourinary tract malignancies, not only because of the diverse treatments and evolving technologies for the diagnosis [6] and management, but also because many factors remain unknown [7]. Bacteria may affect the formation and progression of malignancies through pathways, such as susceptibility, inflammatory cytokines, and host immunity [8]. The role of bacteria in the development of bladder cancer has been proposed [9], but the evidence was weak when pooling data from more high-quality reports in a review by Bayne et al. [ 10]. In this study, we aimed to investigate the association between urinary tract infections and prostate cancer using data from a nationwide population-based cohort database. In addition, we discuss the potential connection between urinary tract microbiota and prostate cancer. ## 2.1. Data Source Taiwan launched the National Health Insurance (NHI) program, a compulsory social insurance program, in 1995, and it currently provides healthcare for more than $99\%$ of the population. This study used data from the Longitudinal Generation Tracking Database (LGTD 2005), which contains the data of two million individuals randomly selected from the National Health Insurance Research Database (NHIRD). The NHIRD contains detailed information on healthcare utilization, including hospital admissions, outpatient visits, and prescription medications. In addition, diagnoses are recorded according to the International Classification of Diseases, Ninth & Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM). This study was approved by the Institutional Review Board of China Medical University Hospital Research Ethics Committee (CMUH109-REC2-031(CR-2)). ## 2.2. Study Population, Primary Outcomes and Covariates We identified patients with newly diagnosed prostate cancer in the NHIRD. The index date was defined as the date of diagnosis between 1 January 2000 and 31 December 2016. We also enrolled patients without prostate cancer as the non-prostate cancer cohort, and their index date was defined as a random date between 2000 and 2016, and the index year was the year of the index date. Both cohorts were matched at a 1:1 ratio by age and index year. We excluded female patients, male patients who were aged <20 years, and male patients who had a prior history of infection (including urinary tract infection, prostatitis, cystitis, and pyelonephritis) before the index date. We also excluded patients without data on sex and age. The primary outcomes were urinary tract infection, prostatitis, cystitis, and pyelonephritis. Comorbidities including hypertension, diabetes, hyperlipidemia, chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD) were recorded in both cohorts. In the patients with prostate cancer, data on radiotherapy, chemical therapy, surgery (radical prostatectomy), and androgen deprivation therapy were also recorded. A total of 5510 subjects were followed until the diagnosis of a primary outcome: loss to follow-up, death, or 31 December 2017. ## 2.3. Statistical Analysis Categorical data are presented as numbers and percentages, and continuous data are presented as means and standard errors. The patients were classified into two age groups for analysis: 20–64 years, and ≥65 years. The chi-square test and t test were used to analyze differences in categorical variables and continuous variables, respectively. The incidence rate of urinary tract infections (including prostatitis, cystitis, and pyelonephritis) was calculated. Univariable and multivariable Cox proportional hazard models were used to compute crude hazard ratios (HRs), adjusted HRs, and corresponding $95\%$ confidence intervals (Cis). The multivariable Cox proportional hazard model was adjusted for age, comorbidities, medications and treatment for prostate cancer. Kaplan–Meier survival curves were used to compare the cumulative incidence between the non-prostate cancer and prostate cancer cohorts, and the log-rank test was used to examine the differences. All statistical analyses and graphs were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA) and R studio (3.5.2). ## 3. Results The characteristics of the non-prostate cancer and prostate cancer cohorts are presented in Table 1. Overall, $81.3\%$ of the patients were over 64 years of age, and $18.7\%$ were aged 20–64 years. We analyzed the data collected from a total of 2755 patients in the non-prostate cancer cohort and 2755 patients in the prostate cancer cohort. Compared to the patients without prostate cancer, those with prostate cancer had higher rates of hypertension ($66.4\%$ versus $58.7\%$), diabetes ($25.9\%$ versus $24.7\%$), hyperlipidemia ($38.7\%$ versus $32.9\%$), COPD ($28.9\%$ versus $25.8\%$), and CKD ($15.5\%$ versus $12.4\%$), respectively. In addition, in the patients with prostate cancer, surgery was the most common treatment. Table 2 shows that the patients with prostate cancer were associated with urinary tract infections, prostatitis, cystitis, and pyelonephritis. In addition, the patients with prostate cancer had a significantly higher risk of urinary tract infections than those without prostate cancer (adjusted HR = 1.59, $95\%$ CI = 1.42–1.77). Compared to the patients in the 20–64 age group, those over 64 years of age had a higher risk of urinary tract infections (adjusted HR = 1.71, $95\%$ CI = 1.44–2.03). In addition, the patients with hypertension (adjusted HR = 1.26, $95\%$ CI = 1.11–1.42), COPD (adjusted HR = 1.19, $95\%$ CI = 1.06–1.34) and CKD (adjusted HR = 1.27, $95\%$ CI = 1.10–1.48) had a significantly higher risk of urinary tract infections compared with the corresponding groups. In contrast, the patients with hyperlipidemia had a lower risk of urinary tract infections compared to those without hyperlipidemia (adjusted HR = 0.84, $95\%$ CI = 0.74–0.95). Comparisons of the incidence of urinary tract infections, prostatitis, cystitis, and pyelonephritis between the patients with and without prostate cancer are shown in Table 3. The patients with prostate cancer had a significantly higher risk of urinary tract infections (adjusted HR = 1.58, $95\%$ CI = 1.41–1.76), prostatitis (adjusted HR = 2.04, $95\%$ CI = 1.03–4.05), cystitis (adjusted HR = 4.02, $95\%$ CI = 2.11–7.66), and pyelonephritis (adjusted HR = 2.30, $95\%$ CI = 1.36–3.88) than those without prostate cancer. The Kaplan–Meier survival curves of the cumulative incidence for the different outcomes are shown in Figure 1. The cumulative incidence of urinary tract infections, prostatitis, cystitis, and pyelonephritis were significantly higher in the patients with prostate cancer compared to those without prostate cancer. As shown in Table 4, the patients with prostate cancer had a significantly higher risk of urinary tract infections than those without prostate cancer, regardless of age and comorbidities (adjusted HRs > 1, p-values < 0.05). In addition, the younger patients (<65 years) were more likely to have urinary tract infections than the older groups (HR = 2.78 vs. 1.50). We further divided the patients into four groups based on follow-up time: <0.5, 0.5–1, 1–2, and ≥2 years. The results still showed that the patients with prostate cancer had a significantly higher risk of urinary tract infections than those without prostate cancer, regardless of follow-up time (adjusted HRs > 1, p-values < 0.05). We then examined the risk of urinary tract infections among the patients with prostate cancer according to their age, comorbidities, and treatments (Table 5). We found that the patients who received radiotherapy and androgen deprivation therapy had a significantly lower risk of urinary tract infections than those who did not receive these therapies (adjusted HRs < 1, p-values < 0.05). In addition, age ≥ 65 years, hypertension, diabetes, COPD, and CKD were risk factors for urinary tract infections. ## 4. Discussion Our results demonstrated that the patients with prostate cancer were significantly associated with urinary tract infections including prostatitis, cystitis, and pyelonephritis. Older age, hypertension, COPD, and CKD were also associated with an increased risk of urinary tract infections. Statins (HMG-CoA reductase inhibitors) have been reported to reduce the occurrence of urinary tract infections, as they reduce susceptibility to bacteria [11,12,13], which corresponds with our findings. Diabetes mellitus has been associated with increased frequency, severity, and likelihood of developing urinary tract infections [14,15]. However, in both the whole cohort (Table 2) and the prostate cancer group (Table 5), diabetes mellitus had no effect on the risk of urinary tract infections. Therefore, prostate cancer seems to be a more influential factor. To address the potential confounding effect of comorbidities, we constructed a non-prostate cancer cohort that was well-matched for age, sex, index year, and comorbidities, including hypertension, diabetes, hyperlipidemia, COPD, and CKD (see Table S1). With this newly selected non-prostate cancer, our analysis revealed that the associations between prostate cancer and urinary tract infections, prostatitis, cystitis, and pyelonephritis remained significant. These findings were consistent with the original data and are presented in Table S2. In summary, as shown in Table 4, prostate cancer is a more crucial determinant compared with other comorbidities. More importantly, for the younger prostate cancer patients (<65 years), cancer itself led to a nearly two-fold higher risk (HR:2.78 vs. 1.50) of developing a urinary tract infection. Previous evidence has demonstrated the vital role and relevance of inflammation in prostate carcinogenesis and tumor progression [16]. Escherichia coli and Enterococcus species have also been shown to be the main bacteria associated with prostate inflammation. In recent years, many studies have investigated the role of the microbiome as a biomarker of disease [17]. The potential relevance of the urinary tract microbiome on prostate cancer diagnosis and treatment has also attracted attention, and many studies have investigated this relationship. The findings of these studies have suggested that urine is not sterile and that micro-organisms represent a distinct flora in the urinary tract. In addition, differences in the microbiome between patients with and without urinary tract infections have been reported, suggesting that the microbiome may have an effect on the susceptibility to infection in prostate cancer patients [18]. With emerging technologies such as next-generation sequencing, microbiome signatures in patients with prostate cancer have been described [19]. Yu et al. investigated the microbiota in expressed prostate secretion (EPS) from patients with prostate cancer, and found a significantly increased number of bacteria than in patients with benign prostate hyperplasia [20]. Interestingly, the number of E-coli was decreased in urine but increased in EPS and seminal fluid. This may also imply that a bacteriological change plays a role in prostate cancer. Moreover, recent research has identified five types of bacteria (Anaerococcus, Peptoniphilus, Porphyromonas, Fenollaria, and Fusobacterium) that are linked to a more rapid progression of prostate cancer to an aggressive state [21]. Good control of the disease can also decrease the risk of urinary tract infections. Our findings also revealed that radiation therapy was a protective factor against urinary tract infections in the patients with prostate cancer. Tolani et al. analyzed 118 Nigerian patients with prostate disease of whom $22\%$ had prostate cancer, and found that the only risk factor for acute urinary tract infections was an indwelling catheter [22]. In addition, Tunio et al. reported that $16.6\%$ of localized prostate cancer patients under curative radiotherapy had positive urine cultures [23]. However, all of their patients had indwelling catheters for at least 4 weeks, and therefore the cause of urinary tract infections in these two series may be related to the placement of catheters. We did not find this association. Furthermore, irradiation is thought to cause local immunosuppression by inducing tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells [24]. We hypothesize that these processes lead to conditions of immunosuppression in the micro-environment, and this reduces inflammation in the prostate, further upregulating the urine microbiome. Changing the micro-organisms in urine may make it less likely for bacteria to cause urinary tract infections. Androgen deprivation therapy was shown to significantly reduce total prostate volume by $37.7\%$ and improve lower urinary tract symptoms in patients with prostate cancer [25,26]. Improved voiding can reduce the risk of urinary tract infections. Patients in the radical prostatectomy group also had a lower incidence of urinary tract infections, but the difference was not statistically significant. This may be because patients undergoing radical prostatectomy may receive post-operative antibiotics, and removal of the prostate gland may also improve voiding. However, the urine microorganisms were still unchanged. Androgen ablation results in hypoxia [27]. Intraprostatic hypoxia has been associated with the early recurrence of prostate cancer [28]. Recent research has identified five types of mostly anaerobic bacteria that are linked to a more rapid progression of prostate cancer. That is, androgen deprivation therapy can reduce the risk of urinary tract infection by the shrinkage of prostate volume, treating prostate cancer by blocking the hypothalamic-pituitary-gonadal axis [29], but its biochemical mechanism is associated with early disease recurrence and rapid progression. Patients with advanced prostate cancer may experience bladder outlet obstruction, which can cause urine reflux and increase the risk of upper urinary tract infections, such as cystitis and pyelonephritis. Our study showed that prostate cancer patients are more susceptible to urinary tract infections, including these types of infections, compared to the general population. These findings highlight the importance of close monitoring and early intervention for urinary tract infections in prostate cancer patients. In our study, patients without prostate cancer were confirmed to have undergone prostate biopsy, which is a common diagnostic procedure that can cause urinary tract infections. Previous studies have shown that fluoroquinolones are an effective antibiotic prophylaxis for patients undergoing prostate biopsy, particularly in males with diabetes mellitus, who are at higher risk for urinary tract infections [30]. In our cohorts, antibiotics are routinely prescribed to patients, with fluoroquinolones being the most commonly used antibiotic. A systematic review regarding prostate cancer synchronous/metachronous with colorectal cancer by Celentano et al. reported that it was probably due to a combination of genetic and environmental factors [31]. Due to prostate cancer frequently occurring in older men, sharing same environment may have contributed to it being synchronous with rectal cancer. Bacteria inoculation in the rectum and urethra may be the same species, but the evidence remains weak. Our results may contribute to this hypothesis. Our study has several limitations that should be considered. Firstly, we used data from a nationwide database that did not include information on laboratory findings, staging, possible risk factors, or the treatment course. Additionally, we lacked data on the patients’ personal histories, such as smoking or alcohol consumption. Secondly, the small number of patients and the predominantly Asian population are limitations of this study. Furthermore, the use of the International Classification of Diseases code to identify disease events may have resulted in an underestimation of the number of events for prostatitis, cystitis, and pyelonephritis. Despite these limitations, our findings revealed a tendency for urinary tract infections to affect both the upper and lower urinary tracts. ## 5. Conclusions In summary, this study used nationwide population data to investigate the relationship between prostate cancer and urinary tract infections. The study not only explored the connection between prostate cancer and urinary tract microbiota but also identified associated comorbidities and types of infection. The findings suggest that androgen deprivation and radiation therapy may impact the pathogenesis and prognosis of prostate cancer, which challenges the current belief that surgery and radiation plus androgen deprivation therapy are of equal importance. Additionally, prostate cancer is an influential factor associated with the risk of urinary tract infections, particularly in younger patients. Beyond cancer, anti-inflammatory agents may also help prevent urinary tract infections by altering the prostate microenvironment. Contrary to common belief, our findings suggest that urinary tract infections do not cause prostate cancer. Rather, an abnormal urine microbiome may increase the risk of urinary tract infections, which are associated with prostate cancer. Additionally, our study sheds light on the relationship between prostate cancer treatments and the occurrence of urinary tract infections. 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--- title: 'Urine Immunoglobin G Greater Than 2.45 mg/L Has a Correlation with the Onset and Progression of Diabetic Kidney Disease: A Retrospective Cohort Study' authors: - Cheng Meng - Jiujing Chen - Xiaoyue Sun - Shilin Guan - Hong Zhu - Yongzhang Qin - Jingyu Wang - Yongmei Li - Juhong Yang - Baocheng Chang journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056169 doi: 10.3390/jpm13030452 license: CC BY 4.0 --- # Urine Immunoglobin G Greater Than 2.45 mg/L Has a Correlation with the Onset and Progression of Diabetic Kidney Disease: A Retrospective Cohort Study ## Abstract Aim: To further assess the correlation between urine immunoglobin G (IgG) greater than 2.45 mg/L and the onset and progression of diabetic kidney disease (DKD). Methods: One thousand and thirty-five patients with type 2 diabetes mellitus (T2DM) were divided into two groups based on the baseline levels of 24 h urinary albumin excretion (24 h UAE): one group with 24 h UAE < 30 mg/24 h and one with 24 h UAE ≥ 30 mg/24 h. The groups were subdivided using baseline levels of urine IgG (≤2.45 mg/L and >2.45 mg/L; hereafter, the Low and High groups, respectively). We used logistic regression to assess the risk of urine IgG and it exceeding 2.45 mg/L. Kaplan–Meier curves were used to compare the onset and progression time of DKD. The receiver operating characteristic curve was used to test the predictive value of urine IgG exceeding 2.45 mg/L. Results: Urine IgG was an independent risk factor for the onset and progression of DKD. The rate and risk of DKD onset and progression at the end of follow-up increased significantly in the High group. The onset and progression time of DKD was earlier in the High group. Urine IgG exceeding 2.45 mg/L has a certain predictive value for DKD onset. Conclusions: Urine IgG exceeding 2.45 mg/L has a correlation with the onset and progression of DKD, and it also has a certain predictive value for DKD onset. ## 1. Introduction DKD has become the leading cause of chronic kidney disease (CKD) with the increasing incidence of diabetes [1]. Microalbuminuria is a well-known predictor of DKD worsening but not of DKD, because DKD is often present at early stages with elevated glomerular filtration without microalbuminuria [2]. It is often difficult to reverse DKD after structural changes to the kidney occur. Consequently, there is an urgent need for biomarkers that can accurately diagnose early-stage DKD. Lots of biomarkers related to DKD have been identified [3,4]. However, since most of them have not been clinically verified, their applicability in clinical settings is limited [5]. Recently, the association between IgG and kidney disease has attracted widespread attention. Research on IgG in nephropathy has shown that an increased IgG excretion rate appears to signify a decrease in the estimated glomerular filtration rate (eGFR) and an increase in segmental glomerulosclerosis and may be a sign of disease progression [6]. Increased urinary IgG reflects severe glomerular damage accompanied by greater proteinuria [7]. Studies have shown that urinary IgG can increase before the appearance of microalbuminuria [8]. In addition, a 5-year follow-up research of type 2 diabetics with normal albuminuria at baseline found that an increase in IgG had a good predictive value for microalbuminuria [9]. Additionally, our preliminary cross-sectional study found that urine IgG was a reliable predictor of DKD, with a cut-off point of 2.45 mg/L (the sensitivity was $80\%$, and the specificity was $70.2\%$) [5]. However, the correlation between urine IgG exceeding 2.45 mg/L and the onset and progression of DKD required further verification. As a validation cohort, this research was to further evaluate the correlation between them. ## 2.1. Study Objects The recruitment criteria were a T2DM diagnosis, 18 years or older, follow-up time greater than 24 months, 24 h UAE < 300 mg/24 h, and complete relevant laboratory indicators during hospitalisation. We used the diagnostic criteria and classification system adopted for diabetes mellitus by the World Health Organization in 1999 to identify diabetics [10]. eGFR was according to the 2012 clinical practise guidelines Kidney Disease: Improving Global Outcomes (KDIGO) and the Chronic Kidney Disease Epidemiology Collaboration formula [11,12]. Thus, 1190 patients admitted to Tianjin Medical University Chu Hsien-I Memorial Hospital for the treatments of diabetes from June 2014 to April 2021 were enrolled. Since kidney function may be affected by other kidney diseases, increased albuminuria, and hospitalisation opportunities, we eliminated patients with a history of primary nephrotic syndromes, chronic glomerulonephritis, lupus nephritis, urinary tract infection, acute kidney injury, urinary calculi, polycystic kidney disease, renal tubular injury, gout-associated nephropathy, hypertensive nephropathy, pyelonephritis, and interstitial nephropathy, such as gouty nephropathy. After eliminating 39 patients missing vital data, 1 type 1 diabetic participants, 1 patient with acute diabetic complications, 1 patient with tumours, 20 patients with follow-up less than 24 months, 50 patients affected by other kidney diseases with eGFR smaller than 60 mL/min/1.73 m2, and 43 patients with liver diseases, as a result, 1035 type 2 diabetics were incorporated into this study. The selection process is shown in Figure 1. All participants analysed in this study provided written informed consent. The study followed the principles of the Declaration of Helsinki and was reviewed and approved by the Medical Ethics Committee of Tianjin Medical University Chu Hsien-I Memorial Hospital. ## 2.2. Definition of Onset and Progression of DKD DKD is a chronic kidney disease peculiar to diabetes mellitus, containing albuminuria urine albumin to creatinine ratio (ACR) ≥ 30 mg/g, urinary albumin excretion rate (UAE) ≥ 30 mg/24 h or eGFR < 60 mL/min/1.73 m2 present for more than three months [13]. In this study, we excluded patients with baseline eGFR < 60 mL/min/1.73 m2 and/or 24 h UAE ≥ 300 mg/24 h. ## 2.2.1. Definition of the Onset of DKD Among patients with 24 h UAE < 30 mg/24 h at baseline, those with eGFR ≥ 60 mL/min/1.73 m2 and 24 h UAE < 30 mg/24 h at the end of follow-up were categorised as ‘no onset’, those with eGFR < 60 mL/min/1.73 m2 and 24 h UAE < 30 mg/24 h were categorised as ‘onset1’, those with eGFR ≥ 60 mL/min/1.73 m2 and 24 h UAE ≥ 30 mg/24 h were categorised as ‘onset2’, and those with eGFR < 60 mL/min/1.73 m2 and 24 h UAE ≥ 30 mg/24 h were categorised as ‘onset3’. ## 2.2.2. Definition of the Progression of DKD Among patients with baseline 24 h UAE between 30 and 300 mg/24 h, those with eGFR≥ 60 mL/min/1.73 m2 and 24 h UAE < 300 mg/24 h at the end of follow-up were categorised as ‘nonprogress’, those with eGFR < 60 mL/min/1.73 m2 and 24 h UAE < 300 mg/24 h were categorised as ‘progress1’, those with eGFR ≥ 60 mL/min/1.73 m2 and 24 h UAE ≥ 300 mg/24 h were categorised as ‘progress2’, and those with eGFR < 60 mL/min/1.73 m2 and 24 h UAE ≥ 300 mg/24 h were categorised as ‘progress3’. ## 2.3. Data Collection We collected data on sex, age, BMI, course of diabetes, blood pressure, and other demographic and clinical information by interviewing patients and validating their responses against their medical records. We carefully recorded medication and smoking history. All hospitalised patients had undergone ophthalmic professional examinations to diagnose diabetic retinopathy. All blood samples were collected after 12 h of fasting. We used the AU5800 automatic biochemical analyser to analyse serum uric acid (SUA), serum creatinine (Scr), and blood lipids. We used the HLC-723G8 HbA1c analyser to measure haemoglobin A1c (HbA1c). All subjects provided 10 mL samples of clean midstream morning urine. The urine specimens were evaluated for β2-microglobulin (β2MG), IgG, and retinol-binding protein (RBP) using a Cobas8000 modular analyser. We collected 24 h urine for 2 successive days and used the average value in order to test the 24 h UAE level. We tested all specimens in the Laboratory of Tianjin Medical University Chu Hsien-I Memorial Hospital. The ranges that the kit manufacturer gives for urine biomarkers are as follows: IgG, 0.0–17.5 mg/L; RBP, 0.0–0.7 mg/L; β2-MG, 0.0–0.3 mg/L. ## 2.4. Statistical Analysis SPSS statistical software commercial version 22.0 (IBM, Chicago, IL, USA) was used to analyse the data. Sample sizes were estimated according to the factors included in the model. We used analysis of variance (ANOVA) and logistic regression to analyse differences between test groups. The onset and progression of DKD were regarded as the dependent variables. We used the log rank method and Kaplan–Meier curves to compare differences in the onset and progression time of DKD between the urine IgG ≤ 2.45 mg/L group and the urine IgG > 2.45 mg/L group. The receiver operating characteristic curve was used to test the predictive value of urine IgG > 2.45 mg/L for the onset and progression of DKD. All statistical tests were two-tailed, and p-values less than 0.05 were considered significant. Descriptive statistics are shown as means and standard deviation (SD) or medians with interquartile ranges (IQR) of continuous variables and percentages for categorical variables. Quantitative data for normal and non-normal distribution were expressed as the mean ± standard deviation and median (first quartile, third quartile). We used independent-sample t-tests and nonparametric tests to analyse differences between groups for data with normal and non-normal distributions. ## 3.1. Baseline Features of Samples Among patients with baseline 24 h UAE < 30 mg/24 h, we found significant differences between the High group (urine IgG > 2.45 mg/L) and the Low group (urine IgG ≤ 2.45 mg/L) in BMI, SBP, GGT, TG, HDL, urine β2-MG, urine IgG, urine RBP, and 24 h UAE. It is noteworthy that 24 h UAE was obviously higher in the High group (urine IgG > 2.45 mg/L) than in the Low group (urine IgG ≤ 2.45 mg/L) ($p \leq 0.001$) (Table 1). Among patients with baseline 24 h UAE ≥ 30 mg/24 h, significant differences between groups were found in eGFR, sex, urine IgG, urine RBP, and 24 h UAE. It is noteworthy that 24 h UAE was obviously higher in the High group (urine IgG > 2.45 mg/L) than in the Low group (urine IgG ≤ 2.45 mg/L) ($p \leq 0.001$) (Table 1). ## 3.2. Rates of the Onset and Progression of DKD between Groups As the results (Supplementary Table S1) revealed statistical significance between urine IgG and the onset and progression of DKD, we further evaluated the correlation between the cut-off point 2.45 mg/L (found in our previous study [5]) and DKD. The rate and risk of DKD onset and progression at the end of follow-up increased significantly in the High group. Additionally, the rates of urine IgG > 2.45 mg/L were significantly higher in onset2 and onset3 than in no onset (Table 2 and Figure 2a), and it was significantly higher in progress2 than in no progress too (Table 2 and Figure 2b). ## 3.3. The Relationship between Urine IgG Greater Than 2.45 mg/L and the Onset and Progression of DKD Among patients with 24 h UAE < 30 mg/24 h, univariate regression revealed that the High group (urine IgG > 2.45 mg/L) had a significantly increased risk for onset2 and onset3 at the end of follow-up compared to the Low group (urine IgG ≤ 2.45 mg/L) (OR = 2.959, $95\%$ CI: 1.949–4.505; OR = 8.333, $95\%$ CI: 2.326–30.303, respectively). Furthermore, multiple regression revealed the same effects (OR = 2.617, $95\%$ CI: 1.623–4.219; OR = 14.706, $95\%$ CI: 2.188–100.000, respectively) (Table 3). Among patients with 24 h UAE ≥ 30 mg/24 h, univariate regression revealed that the High group (urine IgG > 2.45 mg/L) had a significantly higher risk for progress2 at the end of follow-up compared to the urine IgG ≤ 2.45 mg/L group (OR = 6.711, $95\%$ CI: 1.570–28.571). Multiple regression revealed the same effects (OR = 7.353, $95\%$ CI: 1.475–37.037). ( Table 3). ## 3.4. Kaplan–Meier Curves for DKD Onset and Progression Since urine IgG > 2.45 mg/L had a significant correlation with onset2, onset3, and progress2, we further explored the onset and progression time between them. Among patients with 24 h UAE < 30 mg/24 h, the onset (onset2 and 3) time in the High group (urine IgG > 2.45 mg/L) was significantly earlier than in the Low group (urine IgG ≤ 2.45 mg/L) (χ2 = 19.960, $p \leq 0.001$; χ2 = 12.717, $p \leq 0.001$, respectively) (Figure 3a,b). Among patients with 24 h UAE ≥ 30 mg/24 h, the progression (progress2) time in the High group (urine IgG > 2.45 mg/L) was also significantly earlier than in the Low group (urine IgG ≤ 2.45 mg/L) (χ2 = 8.618, $$p \leq 0.0033$$) (Figure 3c). ## 3.5. Receiver Operating Characteristic Curves for DKD Onset and Progression Since urine IgG > 2.45 mg/L had a significant correlation with onset2, onset3, and progress2, we further examined the predictive value of urine IgG > 2.45 mg/L to them. The area under the curves (AUC) and $95\%$ CI of onset2, onset3, and progress2 were 0.631(0.595– 0.667) (Figure 4a), 0.738 (0.701–0.772) (Figure 4b) and 0.579 (0.517–0.640) (Figure 4c), respectively. Thus, urine IgG > 2.45 mg/L had a certain predictive value for DKD onset, especially for onset3. ## 4. Discussion In the present study, we found that urine IgG was an independent risk factor for the onset and progression of DKD. Urine IgG greater than 2.45 mg/L had a correlation with the onset and progression of DKD, and it also had a certain predictive value for DKD onset. In addition, the onset and progression time of DKD were earlier in the High group (urine IgG > 2.45 mg/L). IgG, a marker of increased glomerular permeability, was significantly associated with urinary albumin. This indicates that the supposed greater glomerular permeability leads to increased excretion of IgG and albumin, the latter of which is typically excreted at lower levels [7]. Several studies have reported increases in IgG in normoalbuminuric diabetic patients [14,15,16]. In addition, five years follow-up research revealed that increased urine IgG excretion could predict microalbuminuria in patients with T2DM [9]. However, the cut-off point of urine IgG for predicting the onset and progression of DKD in type 2 diabetics was rarely explored. In our previous study, we found that urine IgG was an important predictor of DKD. The cut-off point after propensity score matching was 2.45 mg/L [5]. However, the correlation between urine IgG > 2.45 mg/L and the onset and progression of DKD needs to be further verified. In the present validation cohort study, we found that urine IgG was an independent risk factor for the onset and progression of DKD. Additionally, the rates of onset and progression of DKD were obviously increased in people with urine IgG greater than 2.45 mg/L at baseline, which provided direct evidence of a potential correlation between urine IgG greater than 2.45 mg/L and the onset and progression of DKD. Additionally, analysis of logistic regression revealed that the High group (urine IgG > 2.45 mg/L) was more likely to suffer the onset and progression of DKD at the end of follow-up than the Low group. This further validated the correlation of urine IgG levels greater than 2.45 mg/L with them. Early detection of urinary IgG appears somewhat effective for delaying the onset and progress of DKD. The receiver operating characteristic curve revealed that urine IgG greater than 2.45 mg/L had certain predictive value for the onset of DKD, especially for the simultaneous onset of abnormal 24 h UAE and eGFR and provided certain reference value for the early diagnosis of DKD. Thus, it may be an important clinical indicator. Our conclusion verified its predictive value to the onset of DKD, and further found that it also had significant correlation with the progression of DKD. It is worthy of further research to verify its predictive ability for the progression of DKD. In the present study, urine IgG greater than 2.45 mg/L was not correlated with the reduced eGFR with stationary 24 h UAE, which was consistent with our previous findings. Reduced eGFR with stationary 24 h UAE may be related to reasons other than diabetic kidney disease, such as fluctuations in blood pressure. Urinary IgG excretion was analysed as an indicator of pore size selective damage in the renal globules [17,18,19]. According to a previous study [20], the rate of urine IgG excretion may increase in conditions as follows: pore size selective damage of glomerulus and enhanced intraglomerular hydraulic pressure. Urine IgG may reflect variations in renal hemodynamics, with a greater sensitivity than microalbuminuria [9]. Given that hyperglycaemia increases the intraglomerular hydraulic pressure [21,22,23] in the early stages of diabetic nephropathy, parallel increases in urinary IgG probably reflect increased intraglomerular hydraulic pressure. Increased intraglomerular hydraulic pressure does not increase albuminuria [9]. Our results revealed that HbA1c was an independent predictor of microalbuminuria. Therefore, it was speculated that the hyperglycaemic environment caused by diabetes induces an increase in intraglomerular hydraulic pressure, leading to increased excretion of urine IgG prior to 24 h UAE. It suggests that, in a diabetic patient with normal kidney function, the changes in urinary IgG precedes microalbuminuria to reflect an abnormal renal function. Our results also indicated that haemoglobin A1c and age were also associated with the onset and progression of DKD. Many trials have shown that the strict management of blood glucose (HbA1c 6.5–$7.0\%$) can reduce the risk for DKD [24,25,26,27]. The earlier antihyperglycemic therapy starts, the better the prognostic benefits [28]. Jiang et al. reported that the risk for DKD increased by $17\%$ with a $1\%$ increase in the HbA1c levels [29]. In addition, increases in the urinary excretion of albumin and IgG induced by diabetes were more readily normalised by euglycemia [30,31]. Our results were consistent with previous research. This indicated that the onset and progression probability of DKD increased considerably with increases in glycosylated haemoglobin. In previous studies, the elderly were generally considered to be an adverse factor for DKD development [32]. Russo et al. found that the prevalence of DKD was higher in people with type 2 diabetes who are over 65 years of age [33]. Age-dependent changes in DKD morbidity and risk factors may be associated with age related hormonal changes [34]. Our results revealed same effects, suggesting that age was a factor in DKD onset that cannot be ignored. In conclusion, this retrospective study confirmed that urine IgG greater than 2.45 mg/L had a correlation with the onset and progression of DKD, especially with 24 h UAE, thus providing a new route to the diagnosis of early staged DKD. However, this study was finished in a single clinical centre, and the versatility of the sample was limited. Therefore, further multicentre studies are needed to assess the correlation. Personalised part: At present, the early diagnosis of diabetic kidney disease is mainly dependent on microalbuminuria, and the renal function changes, such as increased glomerular filtration rate, have occurred before the onset of the symptom. Therefore, more accurate and personalised prevention and diagnosis for early diabetic kidney disease patients are urgently needed. 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--- title: The Association of the Polymorphisms in the FUT8-Related Locus with the Plasma Glycosylation in Post-Traumatic Stress Disorder authors: - Lucija Tudor - Gordana Nedic Erjavec - Matea Nikolac Perkovic - Marcela Konjevod - Suzana Uzun - Oliver Kozumplik - Ninoslav Mimica - Gordan Lauc - Dubravka Svob Strac - Nela Pivac journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056189 doi: 10.3390/ijms24065706 license: CC BY 4.0 --- # The Association of the Polymorphisms in the FUT8-Related Locus with the Plasma Glycosylation in Post-Traumatic Stress Disorder ## Abstract The molecular underpinnings of post-traumatic stress disorder (PTSD) are still unclear due to the complex interactions of genetic, psychological, and environmental factors. Glycosylation is a common post-translational modification of proteins, and different pathophysiological states, such as inflammation, autoimmune diseases, and mental disorders including PTSD, show altered N-glycome. Fucosyltransferase 8 (FUT8) is the enzyme that catalyzes the addition of core fucose on glycoproteins, and mutations in the FUT8 gene are associated with defects in glycosylation and functional abnormalities. This is the first study that investigated the associations of plasma N-glycan levels with FUT8-related rs6573604, rs11621121, rs10483776, and rs4073416 polymorphisms and their haplotypes in 541 PTSD patients and control participants. The results demonstrated that the rs6573604 T allele was more frequent in the PTSD than in the control participants. Significant associations of plasma N-glycan levels with PTSD and FUT8-related polymorphisms were observed. We also detected associations of rs11621121 and rs10483776 polymorphisms and their haplotypes with plasma levels of specific N-glycan species in both the control and PTSD groups. In carriers of different rs6573604 and rs4073416 genotypes and alleles, differences in plasma N-glycan levels were only found in the control group. These molecular findings suggest a possible regulatory role of FUT8-related polymorphisms in glycosylation, the alternations of which could partially explain the development and clinical manifestation of PTSD. ## 1. Introduction Post-traumatic stress disorder (PTSD) is a severe trauma- and stress-related disorder with characteristic symptoms that span across different emotional, cognitive, and psychological domains [1,2]. It is often accompanied with severe mental and somatic comorbidities such as depression, alcohol and substance abuse, suicidal behavior, and cardiovascular and metabolic diseases, leading to a higher probability of adverse health outcomes and shorter life expectancy among affected individuals [1,2]. The heterogeneity of PTSD symptoms, a broad spectrum of affected molecular systems and circuits, and complex molecular interactions between the inherited and acquired factors that contribute to the risk and progression of PTSD represent the confounding elements in the identification and validation of PTSD biomarkers. Recent studies demonstrated the involvement of altered N-glycosylation in several psychiatric disorders [3,4,5,6], including PTSD [7,8], as well as in various somatic pathological and inflammatory states, such as cardiovascular, metabolic and pulmonary diseases, infection, autoimmune disorders, and cancer [9,10]. N-glycosylation is the most common co- and post-translational modification of proteins in the eukaryotic cells that involves the addition of sugar moieties, with N-acetylglucosamine (GlcNAc), N-acetylgalactosamine (GalNAc), galactose, mannose on a consensus asparagine-containing sequence, sialic acid and fucose representing the most frequent added sugars [11]. The diversity of sugar residues and their possible combinations and linkages affect the physio-chemical properties of the glycoproteins on a molecular level, which can result in their altered biological function [12]. For instance, the galactosylation of the immunoglobulin G (IgG)-attached N-glycans acts as a modulator of its inflammatory activity by affecting the complement-dependent cytotoxicity [9,13]. Moreover the α2,6-sialylation of the IgG is associated with an anti-inflammatory response [14,15], while a terminal hypersialylation in the tumor cells can affect leukocyte migration, metastasis, and tumor progression [16]. Fucosylation is a molecular process in which fucose from the donor molecule guanosine biphosphate fucose (GDP-Fuc) is added to the acceptor molecules, such as terminal galactose via the α1,2 bond or the subterminal and innermost GlcNAc via the α1,$\frac{3}{4}$ and the α-1,6 glycosidic bond, respectively [17]. While there are several fucosyltransferases (FUT3-7, FUT9-11) that catalyze the addition of fucose via the α1,$\frac{3}{4}$ linkage, resulting in the antennary fucosylated glycoproteins, fucosyltransferase 8 (FUT8) is the only enzyme in mammals with α1,6 fucosyltransferase activity, resulting in a formation of the core-fucosylated N-glycans [17]. Fucose-containing glycans are involved in blood antigen synthesis and transfusion reactions, leukocyte–endothelial adhesion mediated by selectin, and host–microbe interactions [18,19,20,21]. In addition, the core-fucosylated glycans, predominantly attached to the IgG, are strongly linked to metastasis [22], possibly by affecting antibody-dependent cellular cytotoxicity (ADCC) [23], programmed cell death protein 1 (PD-1) [24], and transforming growth factor β1 (TFG-β) receptor [9,25]. Several pathological states such as autoimmune disorders, cardiovascular diseases, and cancer, and neuropsychiatric disorders including PTSD, have been associated with altered fucosylation molecular patterns in humans, where the core-fucosylation is crucial in maintaining the homeostasis of the organism [9,20,21]. Bi-allelic mutations in the FUT8 gene, resulting in defective FUT8 α1,6 fucosyltransferase activity and the absence of the core-fucosylated N-glycans, lead to the development of the severe metabolic congenital disorder of glycosylation with defective fucosylation 1 (FUT8-CDG) in humans [26]. Moreover, in mice, the complete deletion of this gene is highly lethal and causes severe growth retardation, emphysema-like changes in the lungs, and schizophrenia-like symptoms [27], possibly by interfering with TGF-1 receptor activation, vascular endothelial cell growth factor receptor-2 (VEGF-2) expression, EGF receptor signaling, and integrin α3β1-mediated cell adhesion [26,28,29]. Genetic influence on glycosylation and specifically core-fucosylation is still not completely understood. Unlike protein synthesis, glycosylation is a non-template-driven molecular process regulated by various microenvironmental and intracellular changes [30]. However, glycoenzymes and other proteins included in glycan formation and modification are encoded in a genome and their availability, expression, and activity are regulated at the transcriptional, translational, or post-translational levels [30,31]. It is estimated that approximately $1\%$ of a genome encodes for glycoenzymes, although large variations in heritability were observed depending on the N-glycan structures [30,32]. Estimated IgG N-glycans heritability of >$50\%$ and total plasma N-glycan heritability ranging from 17–$74\%$ (average $60\%$) [32,33] have been reported. Studies of the first plasma glycome, GWAS [34], and the following replication, GWA [35,36], identified the association of several loci with the levels of plasma N-glycans, of which most were located in the FUT8, FUT6, and HNF1A gene regions. HNF1A is considered a major molecular regulator of fucosylation, possibly by regulating the expression of FUT8 and antennary fucosyltransferases (FUT3, FUT5, FUT6). The genetic and epigenetic associations of the HNF1A gene with levels of several highly branched and sialylated plasma N-glycans, as well as with the core- and antennary-fucosylated IgG N-glycans, were reported in recent studies [37,38]. Numerous single nucleotide polymorphisms (SNPs) were suggested to significantly affect the IgG and plasma N-glycan composition, among which the most prominent ones were located within or near the FUT8 locus and associated with the A2 and A2BG2 glycan levels, as well as with the core-fucosylated FA2G2 and FA3B1G1 N-glycans [34,35,36]. Therefore, this study aimed to investigate the possible association of the plasma N-glycan levels in patients with PTSD and in control participants, with four polymorphisms related to the FUT8 gene region (rs6573604, rs11621121, rs10483776, and rs4073416), which have shown a high genome-wide significance in glycosylation during previous GWA studies. ## 2. Results Genotype and allele frequencies of the rs6573604, rs11621121, rs10483776, and rs4073416 polymorphisms located in the FUT8 gene region were determined in a total sample of 541 participants. Minor allele frequencies (MAF) and corresponding Hardy–Weinberg equilibrium (HWE) were determined for each polymorphism and are represented in Table 1. The MAFs for rs6573604, rs11621121, rs10483776, and rs4073416 polymorphisms were $19.0\%$, $44.0\%$, $20.7\%$, and $41.6\%$, respectively, in accordance with the estimated MAFs in the European population, as reported in the Allele Frequency Aggregator (ALFA) database [39]. The genotype distributions of the rs11621121, rs10483776, and rs4073416 polymorphisms were in the expected HWE, while the distribution of the rs6573604 genotypes deviated from the HWE (Table 1). Haplotype analysis showed a weak linkage disequilibrium (LD) between all four tested polymorphisms (D′ × 100 = 24); however, the rs11621121 and rs10483776 polymorphisms were in a strong LD (D′ × 100 = 93) (Figure 1). Therefore, the haplotypes for the rs11621121 and rs10483776 polymorphism block were determined for each subject using an expectation–maximization algorithm. The most common was the TA haplotype, which was represented in more than half of the subjects ($55.5\%$), followed by the CA ($25.9\%$) and the CG ($21.6\%$) haplotypes. The rarest was the TG haplotype ($0.5\%$), which was excluded from further analysis due to its low frequency (<$1\%$). ## 2.1. Association of the FUT8-Related Polymorphisms with PTSD Differences in the distribution of the genotypes, alleles, and haplotypes of the rs6573604, rs11621121, rs10483776, and rs4073416 polymorphisms between the control participants and the patients with PTSD were determined using a χ2-test. There were no significant differences in the observed frequencies of the rs11621121, rs10483776, and rs4073416 genotypes and alleles (Table 2), nor the rs11621121–rs10483776 haplotypes between these two groups of participants (Table 3). However, the C allele of the rs6573604 polymorphism was more frequently present in the control participants ($$p \leq 0.017$$; $R = 1.6$) compared to the patients with PTSD, who had a higher prevalence of the T allele than the control participants (Table 2). ## 2.2. Differences in the N-Glycome between the PTSD and the Control Group Multiple linear regression was used to determine the effect of age, body mass index (BMI), and diagnosis on the levels of different plasma N-glycan species. The significant effect of age on the N-glycome has been previously recognized [7,33,40,41] and it was confirmed in our model as well. Specifically, age was a main predictor for the plasma levels of most N-glycans, as reported previously (full data available on request), while BMI did not contribute significantly to the levels of plasma N-glycans [8]. Therefore, the residuals obtained from fitting the linear model of each glycan peak depending on the age were used in a further statistical analysis to correct this effect [7]. As diagnosis was also a significant predictor of the several N-glycan levels in the plasma, we performed the Supervised Orthogonal Partial Least Square–Discriminant Analysis (OPLS-DA), with all age-corrected levels of plasma N-glycans listed as variables. OPLS-DA acquired variable importance in the projection (VIP) scores, and correlation coefficients values—p(corr) showed the intermediate correlation for several N-glycan peaks in discriminating between the control and PTSD groups (Figure 2, Supplementary Figure S1, Supplementary Table S2). Among the strongest associated N-glycans in this model were also those that we previously reported as being significantly altered between the patients with PTSD and the control participants [8]. Therefore, in this study, we have focused mainly on the association of the polymorphisms in the FUT8-associated region with the relative levels of the N-glycans in the plasma, but due to differences in the N-glycome between the enrolled diagnostic groups, we performed the analysis separately for the patients with PTSD and for the control participants. ## 2.3. Association of the FUT8-Related Polymorphisms with the N-Glycan Levels The level of each N-glycan peak was analyzed in the carriers of different genotypes (genetic model) and alleles (allelic model) of the rs6573604, rs11621121, rs10483776, and rs4073416 polymorphisms, as well as the rs11621121–rs10483776 haplotypes, separately for the control and the PTSD group. The significance level was corrected for the number of analyzed peaks using the False Discovery Rate (FDR) method (Benjamini–Hochberg). The N-glycan moieties whose levels differed significantly between the carriers of different genotypes or alleles are reported in Table 4 for the control group and in Table 5 for the PTSD group. The statistical data for all analyzed N-glycan peaks are available in Supplementary Tables S3–S5. The strongest link of the plasma N-glycan levels was observed with the rs11621121 and rs10483776 polymorphisms, and their haplotype block in both diagnostic groups, while the significant associations of the rs6573604 and rs4073416 polymorphisms with the plasma N-glycan levels were found only in the control group. Polymorphism rs6573604 was associated with the levels of the GP36 ($$p \leq 0.020$$), GP37 ($$p \leq 0.013$$), and GP38 ($$p \leq 0.007$$) glycans in the plasma of the healthy control subjects (Table 4, Supplementary Table S3). These three glycan peaks share the same tetra-antennary, galactosylated, and sialylated N-glycan structure (A4G4S4), but they differ in the type of linkage (α2,3 or α2,6-bond) by which sialic acid is attached to galactose (Supplementary Table S1). For the rs6573604 polymorphism, the TT homozygotes had the lowest plasma levels of the GP36, GP37, and GP38 glycans compared to the CT ($p \leq 0.001$, post hoc Dunn test) and the CC carriers ($p \leq 0.016$, post hoc Dunn test) (Figure 3). The association of the T allele with a lower abundance of these N-glycans was confirmed in the allelic model ($$p \leq 0.003$$ for GP36, $$p \leq 0.001$$ for GP37 and GP38) (Table 4, Supplementary Table S3). Although several N-glycan peaks showed a nominal association with the rs6573604 polymorphism in the patients with PTSD, of which the GP29 glycan was the most prominent, none of the N-glycan peaks reached significance after the correction for multiple testing (Supplementary Table S3). For the rs11621121 polymorphism, the TT homozygotes (and the T allele carriers) had significantly higher levels of the GP22 (FA2G2S2) glycan in plasma compared to the CC and the CT carriers ($p \leq 0.001$, post hoc Dunn test) or C allele carriers ($$p \leq 0.001$$), both in the control as well as in the PTSD group (Table 4 and Table 5, Figure 3). There were no other significant differences in the plasma N-glycan levels associated with this SNP in the control participants. However, the GP08 (A2G2) glycan levels were higher in the carriers of the CC genotype or the C allele, but only at a nominal significance level (Supplementary Table S3). The relative distribution of the GP16 (FA2G2S1), GP20 (A2G2S2), GP23 (FA2BG2S2), and GP31 (FA3G3S3) glycan levels differed significantly between the carriers of different rs11621121 genotypes or alleles in the PTSD group (Table 5, Supplementary Table S3). Specifically, the TT homozygotes and the T allele carriers had higher plasma levels of the GP16 and GP23 glycans compared to the PTSD patients carrying the CC and CT genotypes (post hoc Dunn $$p \leq 0.004$$ for GP16; $$p \leq 0.003$$ for GP23) or the C allele ($$p \leq 0.020$$ for GP16; $$p \leq 0.013$$ for GP23, Figure 3). A significant association of the GP20 and GP31 glycan levels in the plasma with the rs11621121 polymorphism was observed only in the allelic model, in which a lower abundance of the GP20 glycan levels ($$p \leq 0.039$$) and higher abundance of the GP31 glycan levels ($$p \leq 0.045$$) in the plasma were detected in the T allele carriers (Figure 4). In both the PTSD and control subjects, the rs10483776 polymorphism was associated with the GP22 (FA2G2S2) and GP31 (FA3G3S3) glycan levels in plasma (Table 4 and Table 5). The GP22 levels were significantly higher in the AA genotype carriers compared to the AG ($p \leq 0.001$, post hoc Dunn test) and the GA carriers ($$p \leq 0.041$$, post hoc Dunn test) in the control group, as well as higher in comparison to the PTSD patients who were heterozygous for this polymorphism ($$p \leq 0.010$$, post hoc Dunn test) (Figure 2). Similarly, the GP31 glycan plasma levels were the highest in the AA homozygotes and the lowest in the carriers of the rs10483776 GG genotype (Figure 3). Additional associations of the rs10483776 SNP were observed with the plasma GP29 (FA3G3S3) and GP34 (A4G4S3) glycan levels in the control participants in both the genetic and allelic model. Specifically, the AA homozygotes had higher levels of these N-glycans compared to heterozygotes ($$p \leq 0.011$$ for GP29; $p \leq 0.001$ for GP31, post hoc Dunn test) and GG genotype carriers ($$p \leq 0.004$$ for GP29; $$p \leq 0.011$$ for GP31, post hoc Dunn test). The GP16 (FA2G2S1) glycan levels in the plasma of PTSD patients were also significantly different between the carriers of different genotypes, but not alleles, where the rs10483776 heterozygotes had the lowest levels of the GP16 glycan compared to the AA ($p \leq 0.001$, post hoc Dunn test) and GG homozygotes ($$p \leq 0.041$$, post hoc Dunn test) (Table 4 and Table 5, Figure 3). The rs4073416 polymorphism was associated with the plasma GP08 (A2G2), GP14 (A2G2S1), and GP22 (A2G2S2) glycan levels in the control participants (Table 4). The TT homozygotes had higher levels of GP22 glycan levels in the plasma compared to the CC homozygotes and the CT carriers (post hoc Dunn $$p \leq 0.001$$). The association of the T allele with the higher plasma levels of the GP22 glycan was also confirmed in the allelic model ($$p \leq 0.016$$) (Table 4). In contrast, the association of the rs4073416 polymorphism with the levels of the GP08 and GP14 glycans in the plasma was only observed in the allelic model, in which the T allele carriers had lower plasma levels of these glycans, representing the non-fucosylated, biantennary N-glycans with lower sialylation levels (Figure 4). The control participants and the PTSD patients carrying the different rs11621121–rs10483776 haplotypes showed differences in the relative distribution of the GP22 and GP31 glycan levels in the plasma. However, the differences in the plasma GP29 and GP34 glycan levels were only observed in the control participants, and the GP16 and GP23 glycan levels in the plasma only differed in the patients with PTSD (Table 4 and Table 5). All four significant N-glycan peaks followed the same distribution pattern across the carriers of different haplotypes in the control group. The lowest plasma levels of the GP22, GP29, GP31, and GP34 glycan levels were associated with the CG haplotype compared to the TA ($p \leq 0.001$, post hoc Dunn test) and the CA haplotype carriers ($p \leq 0.010$, post hoc Dunn test), who did not differ significantly in the relative distribution of these N-glycan levels (Figure 5). In the PTSD group, the CG haplotype and the CA haplotype carriers had lower levels of the GP16, GP22, and GP23 glycan levels in the plasma compared to the TA haplotype carriers ($p \leq 0.001$). Moreover, the lowest plasma levels of the GP31 glycans were associated with the CG haplotype compared to the TA ($p \leq 0.001$) and the CA ($$p \leq 0.009$$) haplotypes of the PTSD patients (Figure 5). ## 3. Discussion This study is the first association study to analyze the molecular link between plasma N-glycan levels, different genetic polymorphisms located in the FUT8-linked region, and PTSD. Our study found a significant association of the plasma N-glycan levels with PTSD, as well as with the rs6573604, rs11621121, rs10483776, and rs4073416 polymorphisms. We detected the strongest association of the rs11621121 and rs10483776 polymorphisms, as well as their haplotype block with the plasma levels of the core-fucosylated bi- and tri-antennary N-glycan species in both the control and the PTSD groups. On the other hand, for the rs6573604 and rs4073416 polymorphisms, the differences in plasma N-glycan levels among the carriers of the different genotypes and alleles were only found in the control group. Moreover, a possible association between the T allele of the rs6573604 polymorphism and PTSD was detected, because this allele was more frequent in the patients with PTSD than in the control participants, who were more frequent carriers of the C allele. Significant differences in N-glycome between the patients with PTSD and the control participants, as we have reported previously [8], were reflected mainly in the elevated plasma levels of the tri- and tetra-antennary, highly galactosylated, and sialylated N-glycan structures and in the decreased plasma levels of the core-fucosylated biantennary N-glycans with the lower degree of sialylation, which are mainly derived from the IgG [42]. The presence of the core-fucose acts as a “safety switch” against the ADCC by significantly decreasing the affinity of the IgG to FcγRIIIA and FcγRIIIB receptors and exerting its anti-inflammatory effect [7,9,43,44,45]. The lower degree of the core-fucosylation in the IgG N-glycans, as well as the increased complexity and sialylation of the plasma N-glycans, which are seen in PTSD, are also observed in inflammation, autoimmune diseases, cancer [10,46,47], and other psychiatric disorders, such as schizophrenia [4] and major depressive disorder [5] in several human studies. To the best of our knowledge, there are no reported data yet on the FUT8-related polymorphisms associated with PTSD, while molecular relationships with other psychiatric disorders and neurodegenerative diseases are based on GWAS reports. The GWA studies linked several variants located in a close proximity or within the FUT8 gene region with schizophrenia [48,49], major depressive episodes [50], cognitive decline in Alzheimer’s disease [51], and multiple sclerosis [52], although any replication of these results is missing. However, FUT8 polymorphisms have been associated with the levels of high-density lipoprotein cholesterol (rs10483776) [53,54], hypertension [55], and chronic obstructive pulmonary disease [56,57], which are common comorbidities in PTSD [1]. In our study, the T allele of the rs6573604 polymorphism was associated with higher risk of PTSD, as well as with the lower plasma levels of tetra-antennary, tetra-galactosylated, and tetra-sialylated (A4G4S4) N-glycans in the control participants. Previous reports on the association of the rs6573604 polymorphism with N-glycan levels demonstrated a positive effect of the G allele on the A2 N-glycan levels in the male population, but this effect was not related to the levels of highly-branched N-glycans [35]. This is not surprising, since this type of N-glycan moieties usually has low heritability scores [32,33], and its almost exclusive source in plasma is acute-phase alpha-1-acid glycoprotein (AAG) [42], whose concentration in the serum rises considerably in response to inflammatory stimuli [58]. Highly sialylated tri- and tetra-antennary N-glycans, which exhibit the immunomodulatory function by regulating the complement system and the transport of lipophilic molecules [42,59], are the predominant N-glycans on AAG, and a detected increase in the levels of these N-glycans in plasma often reflects ongoing inflammation [60,61]. Although genetic influence on these processes is considered negligible, low heritability N-glycans are often influenced by epigenetic factors, such as changes in DNA methylation [62]. In particular, recent studies have shown a significant correlation between the methylation of the HNF1A gene and the abundance of the highly sialylated tri- and tetra-antennary N-glycans including the A4G4S4 glycan [37,38]. Since the rs6573604 polymorphism is located within the microRNA 4708 gene (MIR4708), in close proximity of the 5′ end of the FUT8 gene, it is possible that it affects the plasma levels of these N-glycans through molecular epigenetic mechanisms [63]. Additionally, we found several other associations between the rs11621121 and rs10483776 polymorphisms located in the regulatory and intron regions of the FUT8 gene, respectively, and plasma levels of N-glycans containing mostly the core-fucosylated, bi- and tri-antennary, galactosylated, and sialylated structures. The strongest association in both diagnostic groups was detected with plasma GP22 (FA2G2S2) and GP31 (FA3G3S3) glycan levels, which were increased in the individuals carrying the T allele of the rs11621121 polymorphism, the A allele of the rs10483776 polymorphism, or the TA rs11621121–rs10483776 haplotype in both the control and PTSD individuals. Other associations of the N-glycan levels in the plasma with the rs11621121 polymorphism were mostly noticeable in the PTSD group, where the carriers of the T allele had increased plasma levels of GP16 (FA2G2S1) and GP23 (FA2BG2S2) glycans, and lower plasma levels of the non-fucosylated GP20 (A2G2S2) glycan. In contrast, the effect of the rs10483776 polymorphism (the A allele) on the plasma N-glycan levels was only observed in the control group, and was associated with higher plasma concentrations of GP29 (FA3G3S3) and GP34 (A4G4S3) glycans. As for GP16 glycan, the effective allele of the rs10483776 polymorphism could not be distinguished, as the heterozygotes had the lowest plasma levels of this N-glycan in the PTSD group. These results are in agreement with previous GWAS that reported the negative effect of the rs10483776 G allele on the plasma levels of the N-glycans DG6, DG10, GP10, and C-FUC, which represent the core-fucosylated, di-galactosylated and sialylated N-glycans with two or three antennae [34,35,36]. For the rs11621121 polymorphism, the observed significant positive association of the C allele with the plasma levels of non-fucosylated A2G2S2 glycan, as well as its nominal association with the A2G2 glycan levels in the plasma, is consistent with the previous studies demonstrating a positive effect of the rs11621121 G allele on the levels of the bi-antennary N-glycans without core-fucose [34,35,36]. Furthermore, our study showed a significant association of the C allele of the rs4073416 polymorphism with lower plasma levels of GP22 glycan and higher plasma levels of GP08 (A2G2) and GP14 (A2G2S1) glycans, both representing the afucosylated, bi-antennary, and di-galactosylated N-glycans with lower sialylation levels, although these results were limited to the control group. Previous GWAS reported a positive association of the G allele with bi-antennary agalactosylated N-glycans (A2); however, this effect was only observed in women [33]. These inconsistent results could be explained by the already-known gender differences in the N-glycome [33], as only male subjects participated in our study. The high heritability of the core-fucosylated N-glycans, GP16 (FA2G2S1), GP22 (FA2G2S2), GP23 (FA2BG2S2), and GP31 (FA3G3S3), which were significantly associated with the FUT8 polymorphisms in our study, has been reported previously [32]. In contrast, the non-fucosylated GP20 (A2G2S2) and GP14 (A2G2S1) glycans, which are the most abundant N-glycans present in several plasma glycoproteins, exhibit low heritability scores [32]. This could be due to the fact that most of the core-fucosylated, bi- and tri-antennary structures show greater protein specificity than the non-fucosylated N-glycans, with the exception of GP08 (A2G2) glycan, which is only present in apolipoprotein B-100 [42]. The plasma levels of the FA2G2S1 and the FA2BG2S2 glycans derive almost entirely from the IgM and the IgG-A22 glycan, respectively [64]. Although the FA2G2S2 glycan is mainly found in immunoglobulins, it is not exclusively restricted to them, but may also be present in other plasma proteins involved in the immunological and antioxidant response, such as alpha-1-glycoprotein, haptoglobin, serotransferrin, and to a lesser extent in other proteins [42]. The primary source of FA3G3S3 glycan is vitronectin [42], a glycoprotein involved in cell adhesion, extracellular matrix binding, and blood coagulation [65], and its glycosylation pattern mainly includes non-fucosylated N-glycans, with the site-specific core-fucosylation being a possible indicator of malignant changes, such as hepatocellular carcinoma [66]. In the patients with PTSD, almost all significant associations of the tested polymorphisms were observed with the plasma levels of N-glycans that were core-fucosylated, whereas in the control group, we found an additional association of all tested polymorphisms with the plasma levels of several non-fucosylated N-glycans linked to the acute-phase proteins detected in various inflammatory states. In a recent pilot study that evaluated patients with PTSD due to civilian trauma using in vivo neuro 2D MR spectroscopy, an increase in two fucose-α(1–2)-glycans and the appearance of the substrate α-fucose in the brain was detected [67]. Findings from animal models have already shown that fucose-α(1–2)-glycans, observed at the synapse of the neurons, play a role in several neurological processes such as neuronal development and learning [68,69]. This may indicate the role of fucose in neuronal communication and signal transduction, which can contribute to the altered neurobiology and pathogenesis of PTSD [67]. In contrast, the hypersialylation of plasma proteins could contribute to increased inflammation. Specifically, it could modulate the platelet activation through the interaction of sialic acid with P- and E-selectins [5], or by protecting the acute-phase proteins from protease digestion and therefore maintaining their abundance in the bloodstream [4]. It is possible that the underlying chronic inflammation, which is considered a hallmark of PTSD symptomatology [70,71], conceals the potential genetic influence on the plasma levels of some of the investigated N-glycans, with different microenvironmental and epigenetic factors potentially contributing more extensively to their abundance and release in a non-homeostatic state, such as PTSD. As mentioned previously, altered glycosylation plays an important role in modulating the immune response mainly through lectins (galectins, selectins, siglecs, etc.), the carbohydrate-binding proteins that can be found free or expressed on the cell surface of many immune cells, such as NK and T-cells, B-cells, dendritic cells, and leukocytes, as well as in endothelial cells and platelets [72]. The complex interaction of glycan-containing motifs on different cell receptors is involved in microbial recognition and elimination, cell adhesion, antigen-specific immune response, tumor cell identification, and modulation of the immune cell function [10]. These findings have enabled the development of potential strategies for the treatment of different autoimmune diseases and carcinomas. For example, the gp120 glycoprotein expressed on the surface of the Human Immunodeficiency Virus 1 (HIV-1) envelope enables the virus to evade detection by the host immune system. Differential glycosylation of the gp120 glycoprotein can be used not only for the identification of different types of HIV-1 clades, but also for the prediction of vaccine treatment efficacy and the production of more specific and optimized vaccination regimens [73,74]. Moreover, the glycoengineering of antibodies and intravenous Ig with elevated galactosylation and α2,6-linked sialylation, which exhibits anti-inflammatory properties, could be used in the treatment of chronic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and inflammatory bowel disease (IBD), as this type of IgG glycoform is often decreased in the aforementioned diseases [10,13,15,75]. On the other hand, afucosylated monoclonal antibodies could be applied to treat certain types of cancer due to enhanced ADCC [76]. In a recent pilot study, differences in baseline plasma glycosylation patterns were found in female MDD patients depending on the efficacy of antidepressant treatment [5]. Since PTSD and depression share similar glycosylation patterns and therapeutic strategies, and depression is one of the major comorbidities in PTSD [1], this finding may have the potential to estimate the treatment response in PTSD patients. However, it is important to investigate the possible role of gender and levels of sex hormones on this effect, as they can affect the N-glycan concentrations and neurotransmitter metabolism [33,77]. High-resolution separation techniques, strict exclusion criteria, and adjustments for the effect of the age and multiple testing contribute to the strength of the study. Moreover, the inclusion of solely male, Caucasian participants of similar age in both diagnostic groups, the additional control for the effects of age, BMI, and smoking, and the exclusive focus on combat-related PTSD reduce the possible effect of these confounding variables on N-glycan levels and support the findings of this study. However, the relatively small sample size for a genetic study, biological parameters that may have been overlooked in this study, and still-unresolved molecular mechanisms by which plasma N-glycans influence the signaling pathways pose challenges to the unequivocal interpretation of the obtained results. Additional multidisciplinary experiments in animal models of PTSD, such as immunohistochemical and Western blot analysis for the determination of FUT8 expression in different brain areas, high-performance liquid chromatography (HPLC) for neurotransmitter studies, and positron emission tomography (PET) scans and functional magnetic resonance imaging (fMRI) of patients with PTSD, would strengthen the current findings. In addition, the interaction of these SNPs with the expression levels of the FUT8 gene and plasma N-glycome and the validation of our results in a larger number of participants, as well as in women, in individuals of different ethnicities, and in individuals exposed to different types of trauma, could provide more insight and overcome the limitations of this study. Nonetheless, this is the first study to report the molecular associations of FUT8-related genetic polymorphisms with the levels of the plasma N-glycans in a relatively homogeneous group of PTSD patients and control participants. The differentiation between the non-fucosylated and the core-fucosylated N-glycan levels by different alleles or haplotypes of the FUT8-related polymorphisms is consistent with the known biological role of FUT8, and it adds supporting evidence for the genetic effects on the core-fucosylation. Moreover, the variations in the plasma N-glycome could reflect the changes in the protein composition in the plasma, thus providing a better insight into the immunological and pathological state of the organism at the molecular level, using this relatively easily obtainable source of biomarkers, and revealing novel and personalized strategies for the treatment of PTSD. ## 4.1. Participants This study enrolled a total of 541 unrelated, Caucasian, male participants, of whom 295 were war veterans with PTSD with a median age of 55 (51; 61), and 246 were healthy control participants not exposed to war trauma within the same age range (median age of 55 (48; 62)). Participants were recruited at the University Psychiatric Hospital Vrapce, Zagreb, and diagnosed with current and chronic PTSD using the Structured Clinical Interview (SCID) based on the DSM-5 criteria [78] and the Clinician Administered PTSD Scale (CAPS) [79]. The majority of participants with PTSD had moderately severe PTSD symptoms (median CAPS scores of 86 (78; 88)), with a similar number and type of trauma (combat-related). All participants were evaluated using the same diagnostic instruments according to the DSM-5 criteria and the International Classification of Diseases (ICD-10) to exclude the possible presence of other psychiatric disorders, such as schizophrenia, bipolar disorder, adult attention deficit hyperactivity disorder (ADHD), substance and alcohol abuse, Alzheimer’s disease, somatic diseases leading to altered liver function, and current use of antihypertensive, antidiabetic, and lipid-lowering medications. The patients with PTSD did not receive psychopharmacological therapy in the 30 days before the blood collection. The study was approved by the Ethics Committee of the University Psychiatric Hospital Vrapce, Zagreb, and the Bioethics Committee of the Rudjer Boskovic Institute, Zagreb, Croatia. All participating subjects signed an informed consent form prior to the blood sampling, in accordance with the Helsinki Declaration [1975], revised in 2013. ## 4.2. Blood Processing The blood samples were collected in the morning using BD Vacutainer™ glass collection tubes (Becton, Dickinson and Company, Franklin Lakes, NY, USA) with acid citrate dextrose (ACD) anticoagulant, and were processed on the same day. Platelet-poor plasma used for the glycomic analysis was isolated using the series of centrifugation (3 min at 1811× g, followed by 15 min at 5031× g), as described previously [80], while DNA from the peripheral blood was isolated using a salting out method [81]. The plasma samples were immediately frozen and stored at −80 °C and DNA samples were stored at +4 °C until further analysis. ## 4.3. Determination of N-Glycan Levels in the Plasma The relative distribution of the N-glycan levels derived from the total plasma glycoproteins was determined using hydrophilic interaction high-performance liquid chromatography (HILIC), as described previously [82]. Briefly, the protein denaturation from the platelet-poor plasma was performed with $2\%$ (w/v) sodium dodecyl sulfate (SDS) (Invitrogen, Camarillo, CA, USA) for 10 min at 65 °C, followed by the addition of $4\%$ (v/v) Igepal CA630 (Sigma Aldrich, St. Louis, MO, USA). N-glycan release from the proteins was accomplished by adding 1.2 U of the PNGase F (Promega, San Luis Obispo, CA, USA), followed by overnight incubation at 37 °C. Following extraction, the N-glycans were fluorescently labeled with 2-aminobenzamide (2-AB) (Sigma Aldrich, St. Louis, MO, USA) after 2 h incubation at 65 °C. The separation of the fluorescently labeled plasma N-glycans was performed using HILIC with an Acquity Ultraperformance Liquid Chromatographic (UPLC) instrument (Waters, Milford, MA, USA) on a Waters BEH Glycan chromatography column (150 × 2.1 mm i.d., 1.7 μm BEH particles) at 25 °C with 100 mM ammonium formate as solvent A (pH 4.4) and acetonitrile as solvent B. A linear gradient of the solvent A (30–$47\%$) at a 0.56 mL/min flow rate for 23 min, and the fluorescence detector set with the excitation wavelength of 250 nm and the emission wavelength of 428 nm, were used to perform the runs. The retention times for individual N-glycans were converted to glucose units using an external standard of hydrolyzed and 2-AB-labeled glucose oligomers for calibration. The obtained chromatograms were separated into 39 chromatographic peaks for which the major N-glycan structures have been previously assigned [46,83] (Supplementary Table S1). The amount of the N-glycans present in each peak was expressed as a percentage of the total integrated chromatographic area using an automatic method with a traditional integration algorithm and with manual correction afterward to maintain the same intervals of integration for all samples. ## 4.4. Genotyping The FUT8 gene-related polymorphisms, rs6573604, rs11621121, rs10483776, and rs4073416, were determined with the TaqMan Genotyping Assay (Applied Biosystems, Foster City, CA, USA) using the Applied Biosystems R 7300 Real-Time PCR System, according to the manufacturer’s protocol. The genotyping was performed in 10 µL reaction volume, which contained around 20 ng of DNA with the thermocycler conditions for the TaqMan Genotyping Assays: 10 min at 95 °C (initial denaturation), 40 cycles of 95 °C for 15 s, and 60 °C for 1 min. ## 4.5. Statistical Analysis The N-glycan data obtained by UPLC were expressed as percentages of the total area under the curve, after normalization and batch correction, performed to remove the experimental variation in the measurements, as described previously [8]. R Statistics 3.5.1 software was used for the statistical analyses and figure preparation. Haploview 4.2 software [84] was used to determine HWE using the χ2-test and LD values between the rs6573604, rs11621121, rs10483776, and rs4073416 polymorphisms based on the confidence interval method [85]. For the haplotype blocks that were in the strong LD (D′ > 0.80), an expectation maximization algorithm integrated into the PLINK 1.07 software [86] was used to assign the most probable haplotype pair for each subject. The frequency of occurrence of the different genotypes, alleles, and haplotypes of the tested polymorphisms in the PTSD patients and control participants was evaluated with the χ2 test. Standardized residuals (R) were calculated to determine which parameter contributed the most to the significant differences between the groups [87]. A Kolmogorov–Smirnov test was used to assess the normality of the data distribution for each N-glycan peak. Since the data distribution deviated from normal in most cases, the results were expressed as the median and interquartile range (25th and 75th percentile). The results were presented with the box-plot diagrams, where the central box represented the interquartile range of the age-adjusted percentage of the total glycan peak area, the middle line represented the median, the vertical line extended from the minimum to the maximum value, while separate dots represented the outliers (values lying more than 1.5 box-lengths and less than 3 box-lengths outside of the box). Extreme values (more than 3 box-lengths outside of the box) were excluded from the analyses. Multiple linear regression was used to determine the effect of age, BMI, and diagnosis on plasma N-glycan levels. Since it is known that N-glycans are highly influenced by age [88], and age was a significant predictor in this model, fitting the linear model of each glycan peak depending on age and using the obtained residuals for further analysis was used to correct for this effect [7,8]. OPLS-DA with the age-corrected levels of the plasma N-glycans as variables, and obtained VIP scores and correlation coefficients values—p(corr) for each N-glycan, were used to demonstrate the differences in the N-glycome between the PTSD and control group [89] and all further analyses were performed separately in the control participants and the patients with PTSD. 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--- title: Volatile Constituents in Essential Oil from Leaves of Withania adpressa Coss. Ex Exhibit Potent Antioxidant and Antimicrobial Properties against Clinically-Relevant Pathogens authors: - Mohammed Bourhia - Abdullah A. Alyousef - Ghizlane Doumane - Hamza Saghrouchni - John P. Giesy - Lahcen Ouahmane - Fatiha EL Gueddari - Yazeed A. Al-Sheikh - Mourad A. M. Aboul-Soud journal: Molecules year: 2023 pmcid: PMC10056193 doi: 10.3390/molecules28062839 license: CC BY 4.0 --- # Volatile Constituents in Essential Oil from Leaves of Withania adpressa Coss. Ex Exhibit Potent Antioxidant and Antimicrobial Properties against Clinically-Relevant Pathogens ## Abstract Withania adpressa Coss. ex is a plant used in traditional medications. Antioxidant, antibacterial, and antifungal properties of the essential oil from leaves of Withania adpressa Coss ex. ( EOW) were investigated. EOW was extracted using a Clevenger apparatus, and its volatile compounds were characterized by GC-MS. Antioxidant potency was determined using DPPH, FRAP, and TAC assays. Antibacterial effects were determined vs. Escherichia coli, Klebsiella pneumonia, Staphylococcus aureus, and Streptococcus pneumonia; while its antifungal efficacy was determined vs. Candida albicans, Aspergillus flavus, Aspergillus niger, and Fusarium oxysporum using the disc diffusion and minimum inhibitory concentration bioassays. A chromatographic analysis showed that EOW contained eight phytochemical compounds constituting $99.14\%$ of the total mass of oil. Caryophyllene ($24.74\%$), Longifolene ($21.37\%$), δ-Cadinene ($19.08\%$), and Carene ($14.86\%$) were predominant compounds in EOW. The concentrations required to inhibit $50\%$ of free radical (IC50) values of antioxidant activities of EOW were 0.031 ± 0.006 mg/mL (DPPH), 0.011 ± 0.003 mg/mL (FRAP), and 846.25 ± 1.07 mg AAE/g (TAC). Inhibition zone diameters of EOW vs. bacteria were 18.11 ± 0.5 mm (E. coli), 17.10 ± 0.42 mm (S. aureus), 12.13 ± 0.31 mm (K. pneumoniae), and 11.09 ± 0.47 mm (S. pneumoniae), while MIC values were 51 ± 3, 47 ± 5, 46 ± 3 and 31 ± 1 µg/mL, respectively. Inhibition zone diameters of EOW vs. fungi were 31.32 ± 1.32, 29.00 ± 1.5, 27.63 ± 2.10, and 24.51 ± s1.07 mm for A. flavus, C. albicans, F. oxysporum, and A. niger, respectively. MIC values were 8.41 ± 0.40, 28.04 ± 0.26, 9.05 ± 0.76, and 22.26 ± 0.55 µg/mL, respectively. Importantly, the highest dose of EOW (1 mg/mL) showed negligible (~$5\%$) cytotoxicity against MCF-12, a normal human epithelial cell line derived from the mammary gland, thus underscoring its wide safety and selectivity against tested microbes. To sum it up, EOW has exhibited promising antioxidant and antimicrobial properties, which suggests potential to abrogate antibiotic resistance. ## 1. Introduction Natural products derived from herbs constitute an important source of therapeutic agents that have applications in folk pharmacopoeia [1]. The use of alternative medicine to treat illnesses is still encouraged by scientists and health organizations, despite the fact that modern pharmaceuticals have replaced several natural preparations historically used to manage diseases. [ 2]. Since some of the secondary plant metabolites used in modern medicine have been discovered through ethnobotanical investigations, empirical studies of various traditional plant preparations are useful for screening and selecting plants with medicinal activities [3]. For many years, medicinal and fragrant plants have been an important part of drug research and development. Chemicals produced by plants are used as therapeutics and raw materials for drug manufacturing and as models to design synthetic molecules used pharmacologically [4]. Because of the formation of free radicals during photosynthesis, antioxidant agents, including pigments like carotenoids, have been found in nearly all plants [5]. Free radicals play a major part in the development of tissue damage in people as they age or suffer from illnesses such as cancer, malaria, neurological disorders, and arteriosclerosis [6]. Antioxidants can prevent or mitigate certain diseases. Increasing attention has been paid to plant-derived antioxidant compounds because of their potential role in nutrition and health and sickness [7]. A wide variety of phytochemicals, such as phenolic or nitrogen-containing compounds, as well as carotenoids, are included in the category of naturally occurring antioxidants [8]. Human pathogenic microorganisms are a major source of disease and mortality worldwide. Even though in recent years the pharmaceutical industry has developed several new antibiotics, resistance to these synthetic antibiotics has increased to such a degree that now it is a health major issue globally [9]. The worldwide emergence of multidrug resistant (MDR) microbes is reducing the efficacies of currently available antibiotics and leading to major failures to treat diseases caused by these microbes [10]. Resistance of bacteria to chemically unrelated drugs is a growing health concern and can occur by over-expression of MDR efflux pumps [11]. Essential oils (EOs) are complex mixtures of molecules that have a low molecular weight and are extracted from plants by the process of steam distillation. Terpenoids and phenylpropanoids are the primary ingredients of EOs that give them their distinctive scent and biological activities [12]. Traditional systems of medicine across the globe use EOs to treat a wide range of health issues due to numerous medicinal properties attributed to these essential oils, including antibacterial, antioxidant, antifungal, anti-inflammatory, antimutagenic, antidiabetic, antiviral, antiprotozoal, spasmolytic, and anesthetic remedies [12,13]. While these properties have not changed much in the last several decades, new information has been gained on a few, most notably those related to antimicrobial modes of action [14]. The plant genus Withania has a long history of medicinal use, including the treatment of conditions, such as conjunctivitis, inflammation, stress, bronchitis, anxiety, neurological illnesses, ulcers, and liver disease [15]. The genus Withania possesses multiple pharmacological roles including anticholinesterase, analgesics, cures, immunomodulators, and antioxidants [16,17,18,19,20,21,22,23]. Withania adpressa Coss. ex (family Solanaceae), an herb commonly known by its English name of Winter Cherry, which grows in North Africa and in particular the Mediterranean basin [24], is known to exhibit pharmacological properties, including, antitumor, immunomodulatory, anticonvulsant, anti-inflammatory as well as anti-stress properties [25]. Few pharmacological properties of Withania adpressa Coss. ex have been investigated, and, therefore, the current study was undertaken to investigate the antioxidant, antibacterial, and antifungal properties of the essential oil from leaves of Withania adpressa Coss. ex. ## 2.1. Phytochemical Identification of EOW The yield of EOW obtained by hydro-distillation was $1.18\%$ of the total mass of leaves extracted and comprised eight compounds, including Caryophyllene ($20.26\%$), δ-Cadinene ($18.08\%$), Longifolene ($11.29\%$), and Carene ($8.29\%$), which were the most dominant (Figure 1, Table 1). ## 2.2. Antioxidant Activity In a dose-dependent way, EOW was able to reduce the free radicals. The IC-50, which was calculated using the DPPH radical scavenging potency, was 0.031 ± 0.006 mg EOW/mL (Figure 2). The IC-50 was similar to that of EOs extracted from leaves of W. frutescens L., but more significant than what was shown with positive controls, such as BHT, which exhibited an IC50 of 0.011 ± 0.009 mg/mL, and Quercetin with an IC50 of 0.014 ± 0.001 mg/mL. The results of the FRAP experiment further indicated that EOW had substantial antioxidant efficacy with an EC50 value of 0.011 ± 0.003 mg EOW/mL, which was significantly less (more potent) than that of the positive control BHT, which had an EC50 of 0.051 ± 0.001 mg BHT/mL, and Quercetin for which the EC50 was 0.064 ± 0.002 mg/mL. Similarly, the results of the TAC demonstrated significant antioxidant potency of EOW, which was 846.25 ± 1.07 mg AAE/g EOW. ## 2.3. Antibacterial Activity EOW exhibited significant antibacterial activity against four species of bacteria: namely, K. pneumonia, E. coli, S. aureus, and S. pneumoniae (Table 2). EOW resulted in relatively large zones of inhibition against E. coli and S. aureus with diameters of 18.11 and 17.10 mm, respectively (Figure 3). More moderate antibacterial activities against K. pneumoniae and S. pneumonia were observed. Antibacterial activity was significant ($p \leq 0.05$) compared with that of commercial antibiotics Kanamycin and Oxacillin used as positive controls, neither of which was effective (Table 2). MIC values for EOW against E. coli, K. pneumoniae, and S. pneumoniae, ranged from 31 to 51 µg EOW/mL, which was excellent compared with MICs of Kanamycin and Oxacillin, which were 14 ± 1 and 15 ± 1 µg/mL, respectively. ## 2.4. Antifungal Activity EOW exhibited significant antifungal activity with large zone diameters against all fungal species, which reached 31.32 mm (Table 3) against three mold strains and one strain of yeast (Figure 4). The potency of EOW as an antifungal agent was similar to that of the commercially available fungicide Fluconazole which exhibited a strong antifungal activity with an exclusion diameter exceeding 33 mm for all strains of fungi tested. Similarly, MICs for EOW against A. niger or C. albicans were 22.26 and 28.04 µg/mL, respectively, which was less important when compared with those recorded for A. flavus (8.41 ± 0.40) and F. oxysporum (9.05 ± 0.76). These values were significantly ($p \leq 0.05$) greater (less potent) than the commercial fungicide Fluconazole, which had MICs values against all strains of fungi tested (Table 3). ## 2.5. In Vitro Toxicity of EOW against Human Cells In order to investigate the safety and spectrum of WEO, the highest dose of 1 mg/mL was tested against MCF-12, normal human epithelial cells derived from the mammary gland. ( Methods section). The highest dose of EOW (1 mg/mL) showed negligible (~$5\%$) cytotoxicity against MCF-12 after 24 h incubation period (Supplementary Figure S1). This highlights its safety and selectivity against tested bacterial and fungal microbes. ## 3. Discussion The yield of EOW of $1.18\%$ on a mass basis, observed in this study for leaves of W. adpressa is reasonable when compared with that documented for another species in this genus, such as Withania frutescens, which had a yield of $0.28\%$ [26]. This yield can be interesting when compared with some plants that are used industrially as sources of EOs, including Garden Thyme (2–$2.75\%$), bitter orange, (0.5–$1\%$), Latin rosa (0.1–$0.35\%$), English lavender (0.80–$2.8\%$), sweet cumin (1–$3\%$), Salvia rosmarinus L. (1–$2.5\%$), Mentha balsamea (0.5–$1\%$), Menth suaveolens ($0.79\%$) and Menth arvensis (0.36–$1.36\%$). The chemical composition of EOW was different from those of EOs from other plants in the genus Withania, particularly W. frutescens L., which had greater proportions of Carvacrol ($31.870\%$), Thymol ($30.076\%$), and Camphor ($9.130\%$) [26]. However, several chemicals observed in EOW during this study were also observed in EOs of Withania coagulans, such as Caryophyllene ($15\%$), Longifolene ($12\%$), δ-Cadinene ($11.7\%$), and Carene ($11.3\%$) [27]. Relative proportions of chemicals in EOs are susceptible to variations due to edaphic and environmental factors [8]. The results of the DPPH, FRAP, and TAC bioassays demonstrating that EOW exhibited powerful antioxidant activity have been previously reported [28]. EOs of W. frutescens L. also exhibited significant antioxidant activities [28]. Results of recent studies have shown that the antioxidant capacities of herbal oils are sometimes greater than commercially available, synthetic antioxidants, including BHT and Quercetin [29]. Based on the chemical composition of EOW, the observed antioxidant potency may be attributed to the presence of the most common chemicals, all of which have been shown previously to have antioxidant potency. A review of pertinent literature revealed that Caryophyllene displayed strong antioxidant effects in both the DPPH and FRAP scavenging assays, with IC50 values of 1.25 ± 0.06 and 3.23 ± 0.07 µM for the DPPH and FRAP assays, respectively [30]. These findings are in line with the results of a previous study on the antioxidant efficacy of β-Caryophyllene [31]. In another study, Longifolene showed important scavenging activity vs. DPPH free radicals [32]. The fact that EOW showed substantial antibacterial action even at low doses vs. almost all strains is consistent with previously reported results [33] wherein it was stated that species of Withania, particularly EOs of W. frutescens, exhibited stronger antibacterial activity against S. aureus, S. pneumoniae, E. coli, and K. pneumoniae. Antimicrobial activities observed can be due to compounds detected in EOW by GC-MS, such as β-Caryophyllene, which is a sesquiterpene; Longifolene, which is a sesquiterpene hydrocarbon; δ-Cadinene, which is a sesquiterpene hydrocarbon; and 3-Carene, which is a monoterpene. These molecules are in the class of sesquiterpenes known to have antibacterial properties, particularly β-Caryophyllene, which has been reported to be active against causative agents of infections [30]. β-Caryophyllene has been shown to induce apoptosis through nuclear fragmentation and condensation pathways, including disturbance of mitochondrial membrane potential in cells [30]. Longifolene is a known antimicrobial compound [34]. δ-Cadinenes are a group of isomeric hydrocarbons, found in a wide variety of essential oil–producing plants. It has been reported that δ-Cadinene has a MIC value of 500 µg/mL vs. S. pneumonia [35]. 3-Carene is an antimicrobial compound that occurs in many plants and possesses ambiguous antibacterial mechanisms versus various pathogens. Antibacterial effects and mechanism of action of 3-Carene vs. the Gram-positive B. thermosphacta and Gram-negative P. fluorescens have been reported previously [36]. Microscopy analysis and the release of alkaline phosphatase revealed that 3-Carene causes damage to the morphology and wall structures of germs, which disrupts cellular processes and ultimately results in the death of bacteria [37]. The results of this study are consistent with those reported by other studies, which showed that constituents of EOs exhibit more activity collectively rather than individually, perhaps due to some synergistic mechanisms [38]. The fact that EOW showed promising antifungal activity against fungi like A. niger, C. albicans, F. oxysporum, and A. flavus is generally in line with what has been reported before, where EOs from W. adpressa weakly stopped Penicillium italicum from growing. [ 39]. Similarly, EOs of W. frutescens were found to be active vs. A. niger, C. albicans, F. oxysporum, and A. flavus [26]. Modes of antifungal action of EOs are multifaceted and dependent on absolute and relative proportions of compounds present [8]. Multiple studies show that phytochemicals included in essential oils may disrupt cell membranes and affect a wide range of cellular functions. Reduced membrane potentials, proton pump interruption, and exhaustion of ATP could all play a role in the antifungal activity observed [40]. It is possible for antifungal drugs to exert their effects by causing damage to the cell membrane rather than by impairing the metabolic processes of the fungus, which would ultimately result in the death of the fungus [41]. Fungicidal effects of EOs can be linked to monoterpenes, which have been speculated to act as solvents of cell membranes owing to their lipophilicity, by directly disintegrating the lipidic component of the plasma membrane of the microbe. Subsequently, this leads to an increased extracellular conductivity and leakage of cell constituents [41,42]. Owing to their natural origin, EOs have been reported to exhibit wider safety margins and negligible toxicity, relative to synthetic agents and drugs. Therefore, they have been classified by the U.S. Food and Drugs Administration (USFDA) as GRAS (Generally Recognized as Safe) [43,44]. ## 4.1. Chemicals Muller Hinton (MH) agar, Muller Hinton Broth (MHB), Peptone Glucose (YPG) agar, Peptone Glucose broth, Potato Dextrose Agar (PDA), and Potato Dextrose Broth (PDB) were used as culture media. For the disk tests, positive control inhibitors included Kanamycin (KAN), Oxacillin (OXA), and Fluconazole (FLU). All media are supplied by Biokar, Pantin, France. ## 4.2. Plant Material Withania adpressa Coss. ex was collected from the Moroccan Sahara (29.7509° N, 7.9756° W) in March 2021, at the peak of flowering. After being authenticated by a botanist, the plant was placed at the University (USMBA-Morocco) Herbarium under the reference A2/WDBF21. Next, leaves were washed with water and dried in a dark, ventilated space for seven days before extraction. ## 4.3. Extraction of Essential Oils Leaves were crushed using a blender to yield 200 g of powder. Next, the powder was soaked in 700 mL water prior to extraction of essential oil using a Clevenger for 2 h under boiling. Afterward, the obtained EOW was stored in the dark at 5 °C until further use. The dry weight of the plant matter was used to figure out the EOW yield in percentage [29,45]. ## 4.4. Characterization of Phytochemicals Identification and separation of volatile compounds in EOW were conducted using GC-MS. The analysis was carried out using a gas chromatograph (Agilent GC 6850) equipped with an injector connected to a mass spectrometer (Agilent 5973 with ion trap) with an electronic impact ionization mode (70 eV) with a scanning interval of one wave and a fragmentation order of 5 × 10 to 4.5 × 103 daltons. Separation of individual compounds was performed using a 30 m, DB-5 capillary column of 0.2500 mm internal diameter with a film thickness of 0.250 mm. Temperature-programmed gas chromatography was initially 40 °C for 2 min followed by 260 °C for 10 min, then increased by 20 °C/min to 280 °C, and finally held at 280 °C for 10 min. The injection temperature was 200 °C and the temperature of the interface was 300 °C. With a flow rate of 1.4 mL/min, helium served as a transport gas. For chemical analysis, hexane ($1\%$ v/v)) was used to dilute EOW (1 µL). The solvent delay was less than 4 min, and the total duration of the chromatographic (GC/MS) analysis of EOW was about 59.81 min. EOW volatile chemicals were identified by a comparison of their retention indices to those reported in the literature (namely, the Adams database) and confirmed by masses of their parent ions and the fragment grams. The retention indices for the n-alkanes used (C9–C40) were obtained by GC/MS under the same analytical circumstances as for the EOW. The formula for calculating the retention index was as follows [46]:KI=100n+100Trx−Tn/Trx+1−Tn) Tn represents the retention time of the n-alkane that comes before the target compound, and Trx + 1 represents the retention time of the n + 1 alkane that comes after the target peak. Each peak’s quantitative component was given a percentage [47]. ## 4.5. Antioxidant Activity In vitro antioxidant potency of EOW was conducted using three chemical assays: DPPH, FRAP, and TAC. ## 4.5.1. DPPH Free Radical Scavenging The DPPH test was carried out by mixing 100 µL of various concentrations of EOW, varying from 0.001 to 1 mg/mL, with 0.75 mL of freshly prepared $0.4\%$ DPPH solution. After 30 min of dark incubation, the absorbance was measured at 517.00 nm. BHT and quercetin were used as positive controls, while methanol was used as a negative control. The efficacy of EOW was evaluated by determining the concentration required to inhibit $50\%$ of DPPH free radicals (IC50) [29,48]. The percentages of DPPH radical inhibition were calculated (Equation [1]). Inhibition (%) = [(Ac − As)/Ac] × 100 [1] where Ac—Absorbance of the negative control; AS—Absorbance of the test sample. ## 4.5.2. Ferric Reducing Power Two-point fifty milliliters of potassium ferricyanide and phosphate buffer ($1\%$) were mixed with EOW (1 mg/mL) [49]. The mixture was then heated to 50 °C for 20 min. Afterward, 2.50 mL of trichloroacetic acid was put in the reaction medium before centrifugation. Finally, each concentration was thoroughly mixed with 2.50 mL of distilled water and $0.5\%$ FeCl3 before measuring the absorbance at 700 nm. In this experiment, a blank reagent with no sample was employed as a negative control, while BHT and quercetin were utilized as the reference standard. The efficacy was assessed by determining the concentration required to achieve a $50\%$ antioxidant effect (EC-50) using a calibration curve. ## 4.5.3. Total Antioxidant Capacity of EOW A volume of 0.1 mL EOW was poured into one milliliter of 1.2 N sulfuric acid, 28 mM Na3PO4, and 4.0 mM (NH4)2MoS4 [50]. After that, the solution was placed in a 95 °C water bath and incubated for 90 min before reading the absorbance at a wavelength of 695.00 nm. We utilized a reagent blank as a negative control, while positive controls included BHT and quercetin. The overall antioxidant efficacy of EOW is given as mg EAA/g. ## 4.6.1. Microbial Strains The antibacterial potency of EOW was tested on a total of eight different microorganisms, including four different strains of bacteria—namely, Escherichia coli, Klebsiella pneumonia pneumonia, Staphylococcus aureus, and Streptococcus pneumoniae; while Candida albicans, Aspergillus flavus, Aspergillus niger, and Fusarium oxysporum were selected for the antifungal test. ## 4.6.2. Microbial Suspension Preparation For the inoculation of bacterial and yeast suspensions, 2 to 3 colonies from a fresh culture of MH and YPG media were taken and suspended in $0.9\%$ NaCl solution. Thereafter, the optical density of the obtained suspensions was verified using a UV-visible spectrophotometer at wavelength λ = 625 nm. Consequently, the bacterial suspension was determined to be approximately 1-2 × 108 CFU/mL, while the fungal suspension was 1-5 × 106 CFU/mL [51]. Inocula for molds were prepared as described elsewhere [52]. Briefly, sporulation was obtained by culturing the fungal strains on a PDA medium at 28 °C for 7 days. Spores were collected by flooding the dishes with 5 mL of $0.05\%$ (v/v) Tween 20, using a sterile spreader. Spores in the suspension were counted using a hemocytometer (area: 0.0025 mm2; depth: 0.2 mm) with light microscopy before the suspension was adjusted to 106 conidia/mL in $0.9\%$ NaCl solution. Sporulation was achieved by growing the fungal strains on the PDA medium for seven days at a temperature of 28 °C. To collect the spores, a sterile spreader was used to cover the surface of the plates with 5 mL of Tween 20 diluted to a volumetric concentration of $0.05\%$. Before adjusting the concentration to e 106 conidia/mL in $0.9\%$ normal saline, the spores in the suspension were counted using a hemocytometer (area: 0.0025 mm2; depth: 0.2 mm) with light microscopy. ## 4.6.3. Disc Diffusion Method Sensitivities of microbial strains to EOW were determined using the disc diffusion method as described previously [53]. First, 1 mL of fresh microbial cultures was added to each Petri dish containing either an MHA, a YPG, or a PDA medium in a 90 mm Petri dish, and then the plates were allowed to stand for 10 min. After that, sterile discs with a diameter of 6 mm were impregnated with 5 microliters of EOW, or positive controls, such as Kanamycin (50 μg), Oxacillin (4 μg), and Fluconazole (500 mg), were deposed on surfaces of media in Petri dishes. Finally, inoculated Petri dishes were then kept in the dark at 30 °C for the fungal species and 37 °C for the bacterial species. For bacteria and fungi, the inhibitory rates were calculated 24 and 48 h after incubation in mm [18]. ## 4.6.4. Determination of Minimum Inhibitory Concentration (MIC) To determine MICs, the micro-dilution method was conducted as previously described [54]. First, EOW was diluted in $5\%$ DMSO, while a positive control, such as Kanamycin, Oxacillin, and Fluconazole, was diluted in YPG or MHB media; then 50 μL of each medium was poured into microplate wells. Following that, one hundred microliters from each fraction was poured into the first well. After that, a micro-dilution was performed by diluting the sample by a factor of 2 in each well, with the exception of the last well, which acted as the positive control for growth. Inoculation was accomplished by placing fifty microliters of the suspension into each well of the microplate, with the exception of the first well, which acted as a control for the absence of growth. The bacterial strains were kept in microplates for 24 h at 37 °C, while the yeast strain was kept in an incubator at 30 °C. After incubation, 20 μL of $1\%$ 2, 3, 5-triphenyl tetrazolium chloride (TTC) (Biokar, France) was added to microplate wells. After incubation, 20 µL TTC ($1\%$) was added to each well of the microplate. After two hours of incubation, the wells in which bacteria grew became pink due to the activity of dehydrogenases, but the wells in which bacteria did not grow remained colorless The MIC was determined to be the lowest concentration at which there was no pink hue. The antifungal activity of mold strains, expressed as MICs, was evaluated using the macro-dilution bioassay [52]. After diluting EOW in a PDB medium, tubes were inoculated with 100 μL of fresh fungal conidia previously adjusted to 106 conidia/mL. The tubes were incubated under agitation for 5 days at 27 °C. After incubation, the MIC was defined as the least concentration at which no growth of fungi was visible. Following the dilution of EOW in the PDB medium, the tubes were infected with 100 µL of fresh fungal conidia that had been adjusted to a concentration of 106 conidia/mL beforehand. At a temperature of 27 °C, the test tubes were kept in the incubator for five days. Following incubation, the MICs were determined to be the lowest concentrations at which there was no detectable fungal growth. ## 4.7. Cell Line, Culturing Condition and MTT Viability Assay MCF-12 (ATCC® CRL-10782™), a cell line derived from the human mammary gland (epithelial breast), was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). Dulbecco’s Modified Eagle Medium (DMEM)/high glucose supplemented with two mM L-glutamine, $10\%$ FBS, and $1\%$ penicillin/streptomycin was utilized to grow cells. Next, 80–$90\%$ confluent cultures were trypsinized and split to an optimal seeding ratio. Cells were cultured at 37 °C in $5\%$ CO2 in a humidified incubator. The highest dose of WEO of 1 mg/mL was prepared in DMSO, and its cytotoxicity against the MCF-12 cell line was conducted according to our previously published protocol using 3-[4,5-dimethylthiazol-2yl]-2.5-diphenylterazolium bromide (MTT) assay [26]. ## 4.8. Statistical Analyses Means and standard deviations of triplicate tests were used to represent the results of this study. Tests for normality and homogeneity of variance were carried out using Shapiro–Wilk and T-tests, respectively. Multiple comparisons were handled utilizing the ANOVA and Tukey’s HSD test as a post hoc analysis of the variance test. At p less than 0.05, the difference was considered significant. ## 5. Conclusions Results of the studies reported here on chemical composition, antioxidant, antibacterial, and antifungal properties of EOs from leaves of the Winer Cherry, Withania adpressa L., demonstrate their promising antioxidant and antimicrobial potential, which could render these EOs useful as natural medicinal products to deal with resistance to other antibiotics; however, potential toxicities of the EOs to humans and nontarget organisms need to be investigated prior to their pharmacological uses. Importantly, the highest dose of EOW (1 mg/mL) has shown negligible (~$5\%$) cytotoxicity against MCF-12, normal human epithelial cells derived from the mammary gland, thus underscoring its wide safety and selectivity against tested microbes. Our findings highlight the significance of employing EOW as a safe and selective alternative to synthetic antibiotics to override the frequently encountered antimicrobial resistance. ## References 1. Bellakhdar J.. 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--- title: Heterologous Expression, Purification and Characterization of an Alkalic Thermophilic β-Mannanase CcMan5C from Coprinopsis cinerea authors: - Songling Yan - Baiyun Duan - Cuicui Liu - Guiyou Liu - Liqin Kang - Lei Sun - Lin Yi - Zhenqing Zhang - Zhonghua Liu - Sheng Yuan journal: Journal of Fungi year: 2023 pmcid: PMC10056200 doi: 10.3390/jof9030378 license: CC BY 4.0 --- # Heterologous Expression, Purification and Characterization of an Alkalic Thermophilic β-Mannanase CcMan5C from Coprinopsis cinerea ## Abstract A endo-1,4-β-mannanase (CcMan5C) gene was cloned from Coprinopsis cinerea and heterologously expressed in Pichia pastoris, and the recombinant enzyme was purified by Ni-affinity chromatography and identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/TOF-MS). CcMan5C hydrolyzed only locust bean gum galactomannan (LBG) but not α-mannan from S. cerevisiae or Avicel cellulose, oat spelt xylan, or laminarin from Laminaria digitata. CcMan5C exhibited distinctive catalytic features that were different from previously reported β-mannanases. [ 1] CcMan5C is the first reported fungal β-mannase with an optimal alkalic pH of 8.0–9.0 for hydrolytic activity under assay conditions. [ 2] CcMan5C is the first reported alkalic fungal β-mannase with an optimal temperature of 70 °C for hydrolytic activity under assay conditions. [ 3] The organic solvents methanol, ethanol, isopropanol, and acetone at concentrations of $10\%$ or $20\%$ did not inhibit CcMan5C activity, while $10\%$ or $20\%$ isopropanol and acetone even enhanced CcMan5C activity by 9.20–$34.98\%$. Furthermore, CcMan5C tolerated detergents such as Tween 20 and Triton X-100, and its activity was even enhanced to 26.2–$45.6\%$ by $1\%$ or $10\%$ Tween 20 and Triton X-100. [ 4] CcMan5C solution or lyophilized CcMan5C exhibited unchanged activity and even increasing activity after being stored at −20 °C or −80 °C for 12 months and retained above $50\%$ activity after being stored at 4 °C for 12 months. These features make CcMan5C a suitable candidate for the detergent industry and paper and pulp industry. ## 1. Introduction Mannans are one kind of hemicellulose that is mainly present in specialized plant structures such as woods, tubers, seeds, beans, and fruits, as well as fungal and bacterial cell walls [1]. Mannans are divided into α-mannans and β-mannans. β-Mannans have a linear backbone of mannose residues joined by β-(1→4)-mannosidic linkages, and they are further classified into mannan, glucomannan, galactomannan, and galactoglucomannan according to carbohydrate substitutions in the backbone. Of these, galactomannan is the largest group and is composed of a β-(1→4)-linked D-mannan backbone with α-(1→6)-substitutions of D-galactose. The ratios of mannoses to galactoses in galactomannans from different resources vary as locust bean gum (~4:1), tara gum (~3:1), guar gum (~2:1), and fenugreek gum (~1:1) [1,2]. β-Mannans can be hydrolyzed to mannooligosaccharides (MOSs) with various degrees of polymerization (DP, 2–10) and mannose by endo-1,4-β-mannanases (EC 3.2.1.78) [2,3]. Recently, MOSs have been investigated for their in vitro antioxidant activity [4,5], prebiotic activity [3,6,7,8,9] and anticancer activity [4,10,11], and their in vitro and in vivo immunomodulatory activity [12,13,14,15], antiobesity [16,17,18], and antidiabetic activity [19,20,21]. Endo-1,4-β-mannanases attack the internal β-1,4-glycosidic linkages in the β-mannan backbone to release short β-1,4-mannooligosaccharides [22]. A variety of bacteria, fungi, plants, and animals can produce endo-1,4-β-mannanases [23,24,25]. Endo-1,4-β-mannanases are distributed mainly in glycoside hydrolase (GH) families 5 and 26, while some endo-1,4-β-mannanases also are found in GH113 and GH134 according to the Carbohydrate-Active Enzyme Database (http://www.cazy.org/, accessed on 14 March 2023) [26]. Family 5 comprises some bacterial β-mannanases and most eukaryotic β-mannanases, while the β-mannanases in family 26 are of bacterial origin with the exception of a few anaerobic fungi [23]. The primary structures of β-mannanases in different GH families are different but they share similar (b/a)8-barrel folds catalytic domain with two conserved glutamic acid residues, located in the middle of the catalytic core [24,26]. In addition, some β-mannanases contain carbohydrate binding domain(s) (CBD) and additional functional domain(s) [24]. Depending on varying resources and their different amino acid sequences, different endo-1,4-β-mannanases exhibit different catalytic features, resulting in the production of different MOSs from the same galactomannan, such as locust bean gum β-galactomannan (LBG) [4,27,28,29]. In addition, β-mannanases are also applicable and have found applications in different industries such as animal feed, food, biorefinery, textile, detergent, and paper, and pulp [22,23,24,25]. These different application processes require some specific β-1,4-mannanases with distinctive catalytic features. Microorganisms are the main sources of β-1,4-mannanases. It has been known that the optimum pH for the activity of most bacterial β-1,4-mannanases is in the neutral pH range [30], while some Bacillus sp. β-1,4-mannanases exhibit an alkalic pH for the most activity [31,32,33]. However, fungal endo-1,4-β-mannanases often have an optimal pH for activity in the acidic range [34,35]. Although some fungal endo-1,4-β-mannanases have been reported to tolerate alkalinity up to pH 8, no alkalic fungal endo-1,4-β-mannanase has been identified [22]. On the other hand, it has been reported that the litter-decomposing basidiomycete Coprinopsis cinerea cultivated on barley straw solid-state medium preferred the degradation of hemicellulose rather than cellulose, and the pH in barley straw medium was over 8.5 during 6 weeks of cultivation [36]. This study aims to find a new alkalic thermophilic endo-1,4-β-mannanase from C. cinerea for application in some industry fields with conditions of high pH and high temperature. ## 2.1. Strains and Culture Conditions The C. cinerea ATCC 56838 strain from the American Type Culture Collection (Manassas, VA, USA) was cultured for growth of fruiting bodies according to Zhang et al. [ 37]. Pichia pastoris GS115 from Invitrogen (San Diego, CA, USA) was cultivated for genetic manipulation according to the user manual provided by Invitrogen. ## 2.2. Chemicals Locust bean gum (galactomannan polysaccharides) from seeds of Ceratonia siliqua (LBG, Edinburgh, UK), α-mannan from Saccharomyces cerevisiae, laminarin from Laminaria digitata, and xylan from oat spelts were ordered from Sigma (St. Louis, MO, USA). Avicel cellulose was ordered from TCI (Tokyo, Japan). ## 2.3. Analysis of the Sequence and Structure of CcMan5C Protein sequences for all GH5_family β-mannanases in the CAZy database were retrieved from the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/, accessed on 30 September 2020). A phylogenetic tree was constructed with the MEGA7.0 software (Mega Limited, Auckland, New Zealand) using the neighbor-joining method. The conserved domain of CcMan5C was analyzed using the NCBI Conserved Domain Database. The three-dimensional structure of CcMan5C was predicted using the I-TASSER (Iterative Threading ASSEmbly Refinement, https://zhanglab.ccmb.med.umich.edu/I-TASSER/, accessed on 26 February 2023) [38]. ## 2.4. Cloning and Expression of CcMan5C Total RNA was extracted from fruiting bodies of C. cinerea ATCC 56838 with a Total RNA Extractor (TRIzol) (Sangon, Shanghai, China), and the total RNA was treated to remove genomic DNA with gDNA wiper Mix (Vazyme, Nanjing, China) and then used as a template to synthesize cDNA by qPCR with a HiScript® II qRT SuperMix II. ( Vazyme, Nanjing, China). The cDNA encoding the mature β-mannanase CcMan5C (mCcman5C) was amplified by PCR from the cDNA preparation with PrimeSTAR Mix (Takara, Kusatsu, Shiga, Japan) and a pair of primers, the forward primer with EcoRI cut-site (5′-AGAGAGGCTGAAGCTGAATTCGTAGGCCCTTGGGGCCAGT-3′) and the reverse primer with NotI cut-site (5′-TGTTCTAGAAAGCTGGCGGCCGCCCCGCGAGCCTTCATGG-3′). According to the user manual of Invitrogen (USA), after being digested, respectively, with EcoRI and NotI, the PCR product containing mCcman5C was mixed with the plasmid pPICZαA (Invitrogen, San Diego, CA, USA) and ligated into the expression plasmid pPICZαAmCcMan5C with a ClonExpress® Entry One Step Cloning Kit (Vazyme, Nanjing, Jiangsu, China). The plasmid pPICZαAmCcMan5C was linearized with PmeI and then electrophoretically transformed into P. pastoris spheroplasts. The positive transformants with pPICZαAmCcMan5C were selected on YPDS plates containing 100 μg mL−1 Zeocin (Invitrogen manual, San Diego, CA, USA) and then identified by colony PCR with 2 × Extaq Mix (Takara, Kusatsu, Shiga, Japan) and a pair of universal primers for the AOX1 insertion site (5′-GACTGGTTCCAATTGACAAGC-3′ and 5′-GCAAATGGCATTCTGACATCC-3′). The transformant cells with pPICZαAmCcMan5C were inoculated into 15 mL of BMGY medium (pH 6.0) in a 50-mL conical flask and cultivated overnight at 28 °C and 180 rpm (Invitrogen manual, San Diego, CA, USA). The cells were harvested by centrifugation and resuspended in 1 mL of autoclaved ddH2O, and then each 0.5 mL of the cell suspension was added to 100 mL of BMMY medium (pH 6.0) in a 500 mL conical flask for further cultivation for 5 days at 28 °C and 180 rpm (Invitrogen manual, San Diego, CA, USA). During cultivation, methanol at $0.5\%$ of the total volume was added to the culture medium at an interval of 24 h to induce CcMan5C expression. ## 2.5. Purification and Identification of Recombinant CcMan5C According to the user manual of Invitrogen (San Diego, CA, USA), the above culture medium was centrifuged at 4 °C, and the supernatant was collected and mixed with 2 × binding buffer and then loaded into a 10 mL ProteinIso® Ni-NTA Resin column (Transgen, Beijing, China). After washing with binding buffer, the proteins were eluted from the Ni-NTA resin with the following elution gradient: 0–30 min, isocratic $95\%$ binding buffer: $5\%$ elution buffer; 30–60 min, isocratic $90\%$ binding buffer: $10\%$ elution buffer; 60–90 min, isocratic $85\%$ binding buffer: $15\%$ elution buffer; 90–120 min, isocratic $70\%$ binding buffer: $30\%$ elution buffer; 120–160 min, isocratic $0\%$ binding buffer: $100\%$ elution buffer. The molecular size and purity of the recombinant CcMan5C protein eluted from the Ni-NTA resin were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) [39]. The protein amount was detected by the Bradford method [40]. The recombinant CcMan5C protein was identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/TOF-MS) at Sangon Biotech (Shanghai, China). ## 2.6. Analysis of Hydrolytic Activity For analysis of the hydrolytic activity, 100 μL of reaction mixture containing 0.25 μg mL−1 CcMan5C and 2.5 mg mL−1 LBG in 50 mM PBS buffer (pH 8.0) was incubated at 50 °C and 800 rpm for 10 min; then, 100 μL of 3,5-Dinitrosalicylic acid (DNS) reagent [41] was added to the reaction mixture. The reaction mixture was heated at 100 °C for 10 min, cooled to room temperature and centrifuged. The supernatant was measured for the absorbance at 520 nm on a SpectraMax M2 microplate reader (Molecular Devices, San Jose, CA, USA) for the amount of reducing sugar (mannose used as a standard) released from LBG by CcMan5C. One unit of enzyme activity was defined as the amount of CcMan5C required to release 1 μmol of reducing sugar from LBG per minute. For determination of the specificity of CcMan5C toward substrates, α-mannan, avicel, oat spelt xylan or laminarin was used, respectively, to replace LBG as a substrate to determine the hydrolytic activity as above. The analysis of the effects of pH, temperature, and LBG concentration on the hydrolytic activity of CcMan5C was performed as described above, except for the following changes. A 50 mM sodium acetate butter (pH 3.0–6.0), 50 mM potassium phosphate buffer (pH 6.0–8.0), or 50 mM Tris/HCl buffer (pH 8.0–9.0) was used instead of 50 mM potassium phosphate buffer (pH 8.0) in the reaction mixture for optimal pH for CcMan5C activity. The optimal temperature for CcMan5C activity was determined at various temperatures ranging from 20 °C to 80 °C. For analysis of the stability of CcMan5C under different pH and temperature conditions, the enzyme solution was first incubated at 20–80 °C, pH 8.0, or at 50 °C, pH 3.0–9.0 for 1 h and then mixed with LBG to react as described above. For analysis of the effect of the metal ions on the hydrolytic activity of CcMan5C, CcMan5C was first incubated with 1 mM metal ion salt or the metal ion chelator EDTA in 50 mM Tris-HCl (pH 7.0) at 50 °C for 1 h, and then mixed with 2.5 mg.mL−1 LBG and corresponding metal ion or EDTA in 50 mM Tris-HCl (pH 7.0) to react at 50 °C, 800 rpm for 10 min. The kinetic parameters of CcMan5C were determined using 0.25 mg.mL−1–5 mg.mL−1 LBG instead of 2.5 mg.mL−1 LBG. The Km and Vmax were determined by plotting the hydrolytic rate against the LBG concentration using OriginPro 8 SR0 (OriginLab Corporation, Northampton, MA, USA) [42]. For analysis of the effect of organic solvents or detergents on the hydrolytic activity of CcMan5C, $10\%$ or $20\%$ indicated organic solvents or $1\%$ or $10\%$ indicated detergents were added to the above reaction mixture. ## 2.7. Analysis of Stability of CcMan5C CcMan5C solution or lyophilized CcMan5C was stored at 4 °C, −20 °C, or −80 °C in a refrigerator or freezer for the indicated times and then was used to determine the retained activity compared with the original enzyme sample as described as above. ## 3.1. Gene Cloning, Recombinant Expression, Purification and Identification of Mannanase CcMan5C The gene sequence encoding endo-1,4-β-mannanase (Ccman5C) from C. cinerea okayama7 (#130) (GenBank accession number: XP_001830154.1) was obtained from the United States National Center for Biotechnology Information (NCBI) website (https://www.ncbi.nlm.nih.gov/, accessed on 30 September 2020). The Ccman5C cDNA consists of 1332 nucleic acids, encoding 443 amino acids and a stop codon (Figure 1). SignalP 5.0 server (http://www.cbs.dtu.dk/services/SignalP/, accessed on 9 October 2020) analysis predicted that the sequence of N-terminal amino acids 1–21 was a signal peptide. Conserved domain analysis at NCBI showed that CcMan5C has a COG3934 catalytic domain of endo-1,4-β-mannosidase and a fungal-type cellulose-binding domain (fCBD), belonging to GH5 family in the carbohydrate-active enzymes (CAZy) database. Amino acid sequence analysis indicated that CcMan5C showed $63.2\%$ identity with a reported GH5 endo-1,4-β-mannanase from basidiomycete Agaricus bisporus (CAB76904.1) (Figure S1) [43]. The deduced amino acid sequence of CcMan5C contained two predicted conserved catalytic residues (Glu257 and Glu375). Moreover, CcMan5C had more negatively charged residues (Glu and Asp) and less polar residues (Ser and Thr) compared with the nonalkaline thermophilic endo-1,4-β-mannanases from ascomycetes (Figure S1). The model of CcMan5C was predicted by the I-TASSER server based on the closest related reported crystal structure of GH5 β-1,4-mannanase PaMan5A from Podospora anserina (PDB entry 3ZIZ), which has $33.4\%$ identity with CcMan5C [44]. Although P. anserine PaMan5A does not contain fCBD domain, the structure of catalytic domain of CcMan5C is similar to that of PaMan5A, both of which show a (β/α)8-barrel fold structure belonging to COG3934 catalytic domain (Figure 1A). The two conserved catalytic glutamate residues, Glu-257 (acid-base catalytic residue) and Glu-375 (nucleophile residue), position in the active site groove of CcMan5C. A phylogenetic tree of CcMan5C and other reported GH family 5 mannanases from fungi, the bacterium Bacillus sp., the plant Arabidopsis thaliana, and the animal Mytilus edulis were produced with the MEGA program by the neighbor-joining method (Figure 1B), which indicated that CcMan5C from C. cinerea and endo-β-mannanases from the basidiomycetes A. bisporus and Phanerodontia chrysosporium form a group but are far from other fungal mannanases, especially distantly related bacterial, plant, and animal mannanases. The 64 bp–1329 bp cDNA fragment encoding mature endo-1,4-β-mannanase (mCcman5C) was amplified from the mRNA extract of C. cinerea ATCC 56838 fruiting bodies by RT-PCR, as described in the Methods section, and inserted into the plasmid pPICZαA to generate the expression plasmid pPICZαAmCcMan5C. The plasmid pPICZαAmCcMan5C was transformed into P. pastoris GS115 cells to heterologously express secreted recombinant CcMan5C with a 6 × His-tag at the C-terminus. After five days of cultivation under the introduction of methanol, the maximum endo-β-mannanase activity (40.18 U mL−1) against LBG in the culture medium was reached from the recombinant expression strain with pPICZαAmCcMan5C. The recombinant CcMan5C was purified from the culture medium by Ni-affinity column, the specific activity of the purified CcMan5C toward LBG was 437.2 U mg−1 protein, and the purification fold was 59.0, with a yield of $84.9\%$. The purified recombinant CcMan5C by Ni-affinity chromatography exhibited two bands of proteins, 66 kDa and 64 kDa, on SDS-PAGE (Figure 1C). MALDI-TOF/TOF-MS analysis showed that the two partial amino acid sequences of trypsinized protein fragments in each band were consistent with CcMan5C, so both protein bands were characterized as CcMan5C (Figure 1D,E). The heterogeneity of purified CcMan5C may be due to the glycosylation of the recombinant protein in yeast P. pastoris [45]. ## 3.2. Catalytic Features of CcMan5C As shown in Table 1, CcMan5C hydrolyzed only LBG but not α-mannan from S. cerevisiae or Avicel cellulose, oat spelt xylan, or laminarin from Laminaria digitata. The optimal pH for CcMan5C activity toward LBG was 8.0–9.0, and CcMan5C was stable in an alkaline range of pH 8.0–9.0, retaining over $90\%$ hydrolytic activity after 1 h of incubation in Tris-HCl buffer (pH 8.0–9.0) (Figure 2A). The optimal temperature for CcMan5C activity toward LBG was 70 °C, and CcMan5C was stable in a range from 20 to 50 °C, retaining over $80\%$ of its activity after 1 h of incubation at 20–50 °C (Figure 2B). Therefore, CcMan5C is an alkalic thermophilic β-1,4-mannanase. CcMan5C activity was not significantly affected by 1 mM concentration of Na+ or K+, while the metal ion chelator EDTA at 1 mM slightly inhibited the enzyme activity (Figure 2C). This could be due to K+ or Na+ concentration in the PBS buffer because the reaction mixture sufficiently satisfied the requirement for enzyme activity and chelating monovalent metal ions by EDTA lead to decrease in enzyme activity. Furthermore, the enzyme activity was inhibited in the presence of Mg2+, Ca2+, Cu2+, Co2+, Fe2+, Zn2+, Mn2+, Al3+, and Fe3+ at 1 mM, which might interfere with the interaction between CcMan5C and monovalent metal ions (Figure 2C). Different concentrations of LBG affected CcMan5C activity, and CcMan5C showed a Km of 1.23 mg mL−1 and Vmax of 531.77 μmol min−1 mg−1 for LBG as a substrate (Figure 2D). The organic solvents methanol, ethanol, isopropanol, and acetone at concentrations of $10\%$ or $20\%$ did not inhibit CcMan5C activity toward LBG; in contrast, $10\%$ or $20\%$ isopropanol and acetone even enhanced CcMan5C activity by 9.20–$34.98\%$ compared with the control lacking organic solvents (Figure 2E). This indicates that CcMan5C activity tolerated the presence of organic solvents and was even stimulated by some organic solvents. As shown in Figure 2F, $1\%$ or $10\%$ SDS inhibited CcMan5C activity, whereas $1\%$ or $10\%$ Tween-20 and Triton X-100 enhanced CcMan5C activity by $26.2\%$–$45.6\%$ compared with the control lacking detergents. This suggests that CcMan5C activity tolerated detergents such as Tween-20 and Triton X-100 and was even stimulated by Tween-20 and Triton X-100. ## 3.3. Storage Stability of CcMan5C As shown in Figure 3A,B, the CcMan5C solution stored at −20 °C or −80 °C in a freezer for the first three months showed a slight reduction in activity, while CcMan5C solution stored at −20 °C or −80 °C in a freezer for six months or even 12 months showed almost similar activity to the original enzyme solution. The lyophilized CcMan5C stored at −20 °C or −80 °C in a freezer for the first three months also showed a slight reduction in activity, whereas lyophilized CcMan5C stored at −20 °C or −80 °C in a freezer for six months showed $40\%$ and $60\%$ increased activity, respectively. The lyophilized CcMan5C stored at −20 °C or −80 °C in a freezer for 12 months still showed $28\%$ increased activity compared to the original enzyme. In addition, when CcMan5C solution or lyophilized CcMan5C was stored at 4 °C in a refrigerator for 12 months, they always retained more than $50\%$ of the activity compared with the original enzyme. These data indicated that CcMan5C possessed high storage stability. ## 4. Discussion This study explored a novel β-mannanase, CcMan5C, from the basidiomycete C. cinerea, which exhibited distinctive catalytic features that were different from previously reported β-mannases in the following ways. [ 1] CcMan5C was the first reported fungal β-mannase with an optimal alkalic pH of 8.0–9.0 for hydrolytic activity and retained over $90\%$ hydrolytic activity after 1 h of incubation at pH 7.0–9.0. To date, known fungal β-mannase showed an acidic optimal pH for activity [34,35], although some of the fungal endo-β-mannanases could tolerate alkalinity up to pH 8.0 [22]. For example, a β-mannanase Man5XZ7 from thermophilic fungus Thielavia arenaria XZ7 was optimally active at pH5.0, but it only had $25.6\%$ activity at pH8.0–9.0 [46]. [ 2] CcMan5C showed an optimal temperature of 70 °C for hydrolytic activity and retained over $80\%$ of its activity after 1 h of incubation at 50 °C. Microbial mannanases have been shown to work at different temperatures, ranging from 37 °C to 70 °C [23,24,25,31,32,47,48,49,50,51,52,53,54]. Several bacterial mannanases have displayed thermostable properties, rising up to 93 °C [23,24,25]. Some fungal acidic β-mannanases were optimally active at up to 75 °C or above [46,55,56,57,58,59]. CcMan5C is the first reported fungal alkalic β-mannanase with optimal temperature of 70 °C. [ 3] The organic solvents methanol, ethanol, isopropanol, and acetone at concentrations of $10\%$ or $20\%$ did not inhibit CcMan5C activity, while $10\%$ or $20\%$ isopropanol and acetone even enhanced CcMan5C activity by 9.20–$34.98\%$. Furthermore, CcMan5C tolerated detergents such as Tween 20 and Triton X-100, and its activity was even enhanced to 26.2–$45.6\%$ by $1\%$ or $10\%$ Tween 20 and Triton X-100. However, most microbial β-mannanases showed a reduction to a certain extent in activity by adding organic solvents such as methanol, ethanol, and acetone [6,48,50,51], except for an endo-1,4-β-mannonase from Bacillus pumilus GBSW19 that showed enhancing activity by adding low concentrations of $10\%$ isopropanol and acetone, as well as $20\%$ ethanol [47]. Some microbial β-mannanases showed an inhibition in activity by the addition of detergents such as SDS, Tween-20 and Triton X-100 [6,50], while others showed resistance to these detergents and even an increase in activity by the addition of SDS [32,52]. [ 4] Furthermore, CcMan5C solution or lyophilized CcMan5C exhibited high storage stability. After being stored at −20 °C or −80 °C in a freezer for 12 months, CcMan5C solution showed almost unchanged activity, and lyophilized CcMan5C even showed $28\%$ increased activity, whereas after being stored at 4 °C in a refrigerator for 12 months, both CcMan5C solution and lyophilized CcMan5C retained above $50\%$ activity compared with the original enzyme. Some reported β-mannanases may have one or several of the above advantages, but CcMan5C appears in comparison to be a promising choice to investigate further, e.g., for applications in the detergent industry and paper and pulp industry with their distinct requirements [22,23,24,25]. Zhao et al. reported a structural analysis of alkalic β-mannanase from alkaliphilic Bacillus sp. N16-5 and found that the alkalic β-mannanase had more abundant surface-accessible negatively charged residues (Glu and Asp) when compared with the nonalkalic counterparts [60]. In contrast, the number of polar residues (Ser and Thr) on the surface in the alkalic β-mannanase was less than that in nonalkalic ones. They suggested that protein surfaces rich in acidic residues probably play an essential role in maintaining protein function under alkaline conditions, while an excess of polar residues on the protein surface might disturb the local stability at higher pH due to their unstable physical–chemical properties. In this study, sequence alignment showed that CcMan5C had more negatively charged residues (Glu and Asp) and less polar residues (Ser and Thr) compared to the nonalkaline thermophilic endo-1,4-β-mannanases from ascomycetes, and these acid amino acids were distributed on the protein surfaces by 3D structural analysis (data not shown), which may be responsible for the alkaline tolerance of CcMan5C. Of course, a convinced conclusion needs a high-resolution crystallographic analysis in the future. It has been known that alkalic β-mannanases that show stability toward detergent components are used as stain removal boosters in certain laundry segments because β-mannanases hydrolyze different materials that contain β-mannan such as gums [22,23,24,25,32,52,59]. These β-mannan gums as thickeners or stabilizers are often used in barbecue sauce, ice cream, salad dressing, makeup, hair styling gels, shampoos, conditioners, and toothpaste. However, these gums, like a glue, stick easily soil particles and the fabric; therefore, it is difficult to remove it along with the dirt. β-mannanases can hydrolyze these gums, removing it from the fabric to prevent the dirt from sticking to the fabric. For application in the detergent industry, the enzyme should be thermostable, storable, and active in alkalic pH and detergents [22,23,24,25]. CcMan5C had an optimum temperature of 70 °C and an optimum pH of 8.0–9.0, showed an enhancing activity in the presence of Tween 20 and Triton X-100 ($10\%$), and retained above $50\%$ activity after one year of 4 °C storage. These features make CcMan5C a suitable candidate for the detergent industry. Another potent application of alkalic β-mannanases is its potential use in enzymatic bleaching of softwood pulps. Pulp pretreatment under alkaline conditions hydrolyzes hemicelluloses covalently bound to lignin to facilitate subsequent removal of lignin. However, alkaline treatment of wood pulps leads to an environmental pollution problem. β-Mannanase as an alternate method equally facilitates lignin removal in pulp bleaching [6,22,23,24,25,32,43,46,52,55,56,57,58,59]. Softwoods, from which the majority of pulps are derived, contain about 15-$20\%$ galactomannan [22,23,24,25]. β-Mannanases specific for galactomannan constituents would be excellent candidates for use in enzymatic bleaching of softwood pulps. Moreover, pulping is best carried out at elevated temperatures, and alkalic thermophilic β-mannanase CcMan5C may offer significant advantages over mesophilic β-mannanases [22,23,24,25]. ## 5. Conclusions Endo-1,4-β-mannanase CcMan5C from C. cinerea belongs to the GH5 family in the carbohydrate-active enzymes (CAZy) database and contains a COG3934 catalytic domain and a fCBD domain. A phylogenetic analysis indicates that CcMan5C from C. cinerea and endo-β-mannanases from the basidiomycetes A. bisporus and P. chrysosporium form a group but are far from other fungal mannanases, especially distantly related bacterial, plant, and animal mannanases. The purified recombinant CcMan5C exhibited specific hydrolytic activity toward galactomannan (LBG) but not α-mannan or Avicel cellulose, oat spelt xylan, or laminarin. CcMan5C had an optimal alkalic pH of 8.0–9.0 and an optimal temperature of 70 °C for hydrolytic activity under assay conditions. CcMan5C tolerated some organic solvents (methanol, ethanol, isopropanol, and acetone) at concentrations of $10\%$ or $20\%$ and some detergents (Tween 20 and Triton X-100) at concentrations of $1\%$ or $10\%$, and its activity was even enhanced by these organic solvents or detergents. CcMan5C solution or lyophilized CcMan5C also exhibited high storage stability at 4 °C, −20 °C, or −80 °C for 12 months. These distinctive features make CcMan5C a suitable candidate for application in the detergent industry and paper and pulp industry. ## References 1. Singh S., Singh G., Arya S.K.. Mannans: An overview of properties and application in food products. Int. J. Biol. Macromol. (2018) 119 79-95. 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--- title: Bioactive Extracts from Salicornia ramosissima J. Woods Biorefinery as a Source of Ingredients for High-Value Industries authors: - Laura Sini Sofia Hulkko - Rui Miranda Rocha - Riccardo Trentin - Malthe Fredsgaard - Tanmay Chaturvedi - Luísa Custódio - Mette Hedegaard Thomsen journal: Plants year: 2023 pmcid: PMC10056203 doi: 10.3390/plants12061251 license: CC BY 4.0 --- # Bioactive Extracts from Salicornia ramosissima J. Woods Biorefinery as a Source of Ingredients for High-Value Industries ## Abstract Salt-tolerant plants, also known as halophytes, could provide a novel source of feedstock for biorefineries. After harvesting fresh shoots for food, the lignified fraction of Salicornia ramosissima J. Woods could be used to produce bioactive botanical extracts for high-value industries such as nutraceuticals, cosmetics, and biopharmaceuticals. The residual fraction after extraction can be further used for bioenergy or lignocellulose-derived platform chemicals. This work analysed S. ramosissima from different sources and growth stages. After pre-processing and extractions, the obtained fractions were analysed for their contents of fatty acids, pigments, and total phenolics. Extracts were also evaluated for their in vitro antioxidant properties and inhibitory effect towards enzymes related to diabetes, hyperpigmentation, obesity, and neurogenerative diseases. The ethanol extract from the fibre residue and the water extract from completely lignified plants showed the highest concentration of phenolic compounds along with the highest antioxidant potential and enzyme-inhibitory properties. Hence, they should be further explored in the context of biorefinery. ## 1. Introduction Halophytes are salt-tolerant plants that thrive in saline environments and can be found in different locations, including seashores, marshes, and saline deserts. Several species have commercial uses in different areas, including food and cosmetics, and can be commercially cultivated in saline systems. Soil and water salinisation has increased the rate of agricultural land degradation worldwide, and therefore, the cultivation of halophytes is one of the key implementations to solve these issues as they could be used for bioremediation and valorisation of these marginal lands [1]. Salicornia ramosissima J. Woods (Amaranthaceae), commonly known as sea asparagus or glasswort, is an edible annual succulent halophyte present in saltmarshes from the Arctic to the Mediterranean region. The importance of S. ramosissima as a commercial vegetable is increasing due to its organoleptic properties, including crunchy texture, slightly salty taste, and nutritional and functional properties. It is considered a reliable substitute for salt (NaCl) and, therefore, a promising functional ingredient to prevent cardiovascular diseases with appropriate levels of protein, dietary fibre, and minerals [2,3,4,5]. S. ramosissima can be cultivated in salt-affected marginal lands or hydroponic and aquaponic systems using saline water, including seawater [6,7,8]. However, as the plant matures, it lignifies, making it unpalatable. Due to the high salt content accumulated in the plant matrix, it is appropriate for animal fodder only when blended with other feedstuffs [9,10]. Therefore, this woody residual fraction is often considered agricultural waste; however, it could be utilised as a feedstock for biorefinery to bring additional value to farmers and provide a way to maximise material valorisation. In this regard, two concepts can be applied, depending on the plant’s growth stage: green biorefinery from partly lignified plants or more traditional lignocellulose biorefinery from completely lignified plants after seed production. The green biorefinery approach, where the biomass is first fractionated to green juice and fibre residue, often targeting the production of biochemicals, feed products, and biofuels, has been previously tested for Salicornia sinus-persica, Salicornia bigelovii, Salicornia dolichostachya, and Salicornia europaea [11,12,13,14]. In multi-product biorefinery, the value-added compounds with high market value can be produced to improve the process’s overall feasibility before residual fractions are utilised for bioenergy. Due to adaptation to extreme environmental conditions and a high natural defence against predators and pests, Salicornia spp. produce high concentrations of bioactive secondary metabolites, such as phenolic acids, flavanols, flavones, and flavanones [3,15,16]. These metabolites have reported health-beneficial properties, including antioxidant, anti-inflammatory, and anti-diabetic activities [7,15,17,18,19]. Intake of these compounds can prevent the onset of different diseases, such as cancer, hypertension, and cardiovascular diseases [3,4,20]. Moreover, studies suggest that S. ramosissima extracts exhibit photoprotective effects against UV radiation [4,21] and protective effects against testicular toxicity [22]. Botanical extracts or bioactive compounds with different purities could be used in different commercial areas, including nutraceuticals, pharmaceuticals, and cosmetics [16,23,24]. These are high-value industries, and the market size of nutraceuticals is forecasted to reach USD 650.5 billion by 2030. The leading force behind this growth is the increased demand for dietary supplements and functional foods [25]. The natural skin care market is forecasted to reach USD 11.87 billion by 2030, and the demand for natural cosmetics has increased due to changes in consumer behaviour [26]. The interest in biopharmaceuticals is also driven by the trend of shifting from synthetic medicine to plant-derived drugs [27]. However, the existing literature considering the bioactivity and nutritional characteristics of S. ramosissima has been mostly focused on fresh food-grade plants [4,18,22,28]. The number of studies considering fully mature or fractionated Salicornia biomass for its exploitation for bioactive compounds is limited, and only a few studies have analysed the non-food waste fraction [29] or the effect of the growth stage on the concentration of bioactive compounds. One study showed that the content of flavonoids in S. herbacea increases as the plant matures [30]. After extraction and production of value-added compounds, residual fractions could be utilised to produce lignocellulose-derived biochemicals or bioenergy. Considering this, biogas and bioethanol production from Salicornia spp. fibres and juice have been previously assessed [11,31]. In this study, the green juice fractions and botanical extracts from partly and completely lignified S. ramosissima plants were analysed for their total contents of fatty acids, chlorophylls, carotenoids, and phenolic compounds. In vitro antioxidant activity was also evaluated using assays with different antioxidant mechanisms. The in vitro enzyme inhibition activity was measured against enzymes related to dementia and neurogenerative diseases (such as Alzheimer’s and Parkinson’s), diabetes, obesity, and skin issues (such as hyperpigmentation and acne). Based on the obtained results, possible applications for the extracts and potential of S. ramosissima waste fractions for halophyte-based biorefinery are discussed. ## 2.1. Raw Material Fresh, non-food grade, partly lignified S. ramosissima biomass was obtained from two different producers: Les Douceurs du Marais in France (FR) and Riasearch in Portugal (PT). S. ramosissima is native to these regions and is already produced (e.g., Les Douceurs du Marais) or collected from the wild for commercial purposes, and the locations have the potential for expansion of biosaline agriculture; thus, the potential for integrated halophyte-based biorefineries. In Les Douceurs du Marais, the plants were produced on an organic open-field farm in the marsh exposed to tidal changes on the west coast of France near La Turballe. Partly lignified shoots were harvested at the vegetative stage right after the food production period and the start of lignification in May 2020. The data from the closest weather station show that during the last month of cultivation, the average daily maximum and minimum temperatures were 18.6 °C and 9.8 °C, respectively, and the extreme maximum and minimum temperatures were 28.7 °C and 4.9 °C, respectively, and the total rainfall was 35.9 mm divided into 7 days with precipitation [32]. The plants obtain their water uptake from seawater (approximately 3.5 dS/m), but the salinity may vary depending on the occurrence of precipitation or droughts. In Riasearch, biomass was cultivated in a sandy soil bed in a greenhouse. During the growth phase, plants were irrigated two times a day with a mixture of brackish water aquaculture effluent, which also fertilised the plants, and freshwater to keep the soil salinity at approximately 1.2 dS/m. Additional light fixtures were not used, and during the months with high temperatures and UV radiation, partial removable shade structures were used to protect the plants from drying and premature lignification. On the coast of Central Region of Portugal, during the summer months, the lowest average daily minimum temperature is 15.1 °C (June), and the highest average daily maximum is 24.4 °C (August) [33]. The plants were germinated at the end of February 2020, and for partly lignified biomass, aerial parts were harvested after 26 weeks of cultivation. Dry, completely lignified, and woody S. ramosissima was also obtained from Riasearch. Plants were cultivated under the same conditions as those harvested at the earlier growth stage, but they were allowed to produce seeds before harvesting approximately 8 months after germination. After harvesting, the lignified plants were air-dried for approximately 5 weeks in mesh trays in shade before shipping. The plants were carefully handled after drying to avoid seed loss. The biomass batches and considered fractions and extracts are summarised in Figure 1. Fresh biomass batches were first fractionated into green juice and fibre residue fractions using a screw press. The green juice fractions were centrifuged at 4000 rpm for 20 min, filtered using a Whatman filter (GE Healthcare), and freeze-dried using a ModulyoD freeze dryer (Thermo Scientific, Waltham, MA, USA). The fibre residue was dried overnight at a 60 °C fan oven, homogenised using a knife-miller to achieve a particle size of less than 2 mm, and stored at room temperature (RT). The completely lignified plant material was first rinsed with water, dried at RT, and size-reduced into pieces of less than 2 cm using an agricultural straw shredder (AM55, J. N. Jensen og Sønner, Agerskov, Denmark). The dried, shredded biomass was stored at RT. The dry matter (DM) and ash concentration of the fractions were determined according to the protocols by the National Renewable Energy Laboratory (NREL) [34,35]. ## 2.2. Extract Preparation From the fibre fraction of partly lignified S. ramosissima, after screw press, botanical extracts were prepared using water, $70\%$ aqueous ethanol (EtOH), and n-hexane as solvents, and the traditional Soxhlet apparatus with 100 mL extraction chambers. The sample size was approximately 10 g for water and EtOH extractions and 5 g for n-hexane extraction. All extractions were run as parallel experiments. The extraction time was 8 h for water and 6 h for organic solvents. Excess solvent was removed using a rotary evaporator, and EtOH and water extracts were freeze-dried. As the fibre residue from partly lignified plants from Portugal was not available for study, only the biomass from France was used in the extractions. Completely lignified biomass was extracted with a pilot-scale Soxhlet using 25 L of demineralised water. The amount of biomass used was 2 kg with a dry matter content of $88.5\%$, and the extraction time was 8 h. It must be noted that the pilot-scale equipment does not have the same particle retention as the lab-scale Soxhlet, and in order to avoid the smallest particles in the extract phase, the shredded biomass was sifted through a 2 mm sieve. Only particles with a size more than 2 mm were used for the extraction. The obtained water extract was spray-dried using an inlet temperature of 130 °C and outlet temperature of 80 °C. All dried extracts were re-solubilised in the corresponding solvents at a final concentration of 10 mg/mL and used in the assays. ## 2.3.1. Determination of Fatty Acids The fatty acid (FA) profile was determined from the n-hexane extract of fresh S. ramosissima fibre residue. In transesterification, approximately 0.15 g of lipid extract was dissolved in 1 mL of 0.5 M sodium hydroxide in methanol (MeOH) at 90 °C. Samples were cooled to RT, 1 mL of boron trifluoride and 0.5 mL of hydroquinone solution were added, and samples were kept at 90 °C for another 5 min. For phase separation, 4 mL of saturated NaCl water solution and 3 mL of n-heptane were added. The non-polar fraction was recovered and analysed using gas chromatography (Clarus 500, Perkin Elmer, Waltham, MA, USA) with a capillary column (Elite-WAX, 30 m × 0.25 mm ID × 0.25 μm, Perkin Elmer). Helium was used as a carrier gas, and the temperature program was set to 1 min at 150 °C, heating 10 °C/min until reaching 240 °C, and 10 min at 240 °C. Mass spectrometry was used for the detection and quantification of fatty acids. ## 2.3.2. Determination of Total Phenolic Compounds The total phenolic contents (TPC) of the extracts, at the concentration of 10 mg/mL, were determined using the Folin-Ciocalteau assay described by Velioglu et al. [ 36] and adapted to 96-well plates. The plates were incubated for 90 min at RT, protected from light, and the absorbance was read at 650 nm using a microplate reader (EZ Read 400, Biochrom, Cambridge, United Kingdom). Results were expressed as the amount of gallic acid equivalents (GAE) in the dried extract using a calibration curve (R2 = 0.997). The total flavonoid content (TFC) was also estimated in the dried extract at a concentration of 10 mg/mL, using the method developed by Pirbalouti [37] adapted to 96-well plates. Aliquots of 50 µL of the samples were mixed with 50 µL of $2\%$ aluminium chloride in MeOH solution. The plates were incubated for 10 min at RT and read at 405 nm. Results were expressed as the amount of quercetin equivalent (QE) per gram of dried extract using a calibration curve (R2 = 0.999). Total condensed tannins (TCT) of dried extracts at 10 mg/ ml were determined using a method described by Li et al. [ 38] using p-dimethylaminocinnamaldehyde hydrochloric acid (DMACA-HCl). Briefly, 10 μL of extracts were mixed with 200 μL of $1\%$ DMACA in MeOH and 100 μL of $37\%$ HCl. Plates were incubated for 15 min at RT and read at 640 nm. Results were expressed as the amount of catechin equivalent (CE) using a calibration curve (R2 = 0.991). Total anthocyanidins (TAC) were measured as cyanin chloride equivalent (CCE) in the dried extract based on a calibration curve (R2 = 0.991). The method developed by Mazza et al. [ 39] was followed and adapted to 96-well microplates. In brief, aliquots of 20 µL of the samples at 10 mg/mL were mixed with 20 µL $95\%$ EtOH containing $0.1\%$ HCl and 160 µL 1 M HCl. The absorbance was read at 492 nm. ## 2.3.3. Determination of Photosynthetic Pigments The total concentrations of chlorophylls (CHL) and carotenoids (TCA) were determined from green juice fractions and EtOH extracts, as described by Lichtenthaler and Wellburn [40]. The absorbance was measured at 470 nm, 649 nm, and 665 nm using a UV-visible spectrophotometer (Genesys 50, Thermo Scientific, Waltham, MA, USA). The following equations [40] were used to calculate the concentration of pigments:CHL $a = 13.95$ × A665 − 6.88 × A649[1] CHL $b = 24.96$ × A649 − 7.32 × A665[2] TCA = (1000 × A470 −2.05 × CHL a − 114.8 × CHL b)/245[3] ## 2.4. In Vitro Antioxidant Activity Assays Antioxidant properties were tested in vitro using radical-based and metal-based assays. S. ramosissima extracts were first tested at a concentration of 10 mg/mL, and the absorbances were read using a microplate reader. Antioxidant activities were calculated as a percentage relative to a control sample. For the samples with activities more than $50\%$, a minimum of eight different concentrations were evaluated to calculate the half-maximal effective concentration (EC50). ## 2.4.1. Radical-Based Antioxidant Activity Radical scavenging activity was tested against 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and nitric oxide (NO). For all assays, 1 mg/mL gallic acid was used as the positive control. The DPPH assay was carried out using the method developed by Brand-Williams et al. [ 41] adapted to 96-well microplates by Moreno et al. [ 42]. A sample of 22 µL was mixed with 200 µL of 120 µM DPPH in EtOH solution. Samples were incubated for 30 min in the dark, and the absorbance was read at 492 nm. A protocol described by Re et al. [ 43] was used to determine the ABTS radical scavenging activity. The 7.4 mM ABTS solution was prepared by mixing 100 mL of ABTS with 100 mL of 2.6 mM potassium persulphate in the dark and RT and incubating overnight. The final ABTS solution was diluted with EtOH to obtain an absorbance of approximately 0.7 at 734 nm. In the ABTS assay, 10 µL of the extract was mixed with 190 µL of the final ABTS solution, and plates were incubated for 6 min in the dark and read at 650 nm. In the NO scavenging assay developed by Baliga et al. [ 44], aliquots of 50 µL of sample solution and 10 mM sodium nitroprusside were mixed, and plates were incubated for 90 min at RT. Afterwards, 50 µL of Griess reagent (Sigma-Aldrich, Lisbon, Portugal) was added, and the absorbance was read at 562 nm. ## 2.4.2. Metal-Based Antioxidant Activity In metal-based assays, iron chelating activity (ICA), copper chelating activity (CCA), and ferric reducing antioxidant power (FRAP) were tested. Positive control samples for metal-based assays were 1 mg/mL gallic acid for FRAP and 1 mg/mL ethylenediaminetetraacetic acid (EDTA) for chelating activity. All metal-based assays were carried out following Megías et al. [ 45], with minor modifications. In the ICA assay, 30 µL of sample solution was mixed with 200 µL of distilled H2O and 30 µL of $0.01\%$ aqueous FeCl2, the plates were incubated for 30 min, 12.5 µL of 40 mM aqueous ferrozine was added, the plates were further incubated for 10 min, and absorbance was read at 562 nm. For the CCA assay, a 30 µL sample was mixed with 200 µL of 50 mM sodium acetate buffer, 100 µL of $0.005\%$ aqueous CuSO4, and 6 µL of 4 mM aqueous pyrocatechol violet, and plates were immediately read at 620 nm. The FRAP assay was performed by mixing 50 µL of the sample with 50 µL of distilled H2O and 50 µL $1\%$ potassium ferrocyanide. The plates were incubated for 20 min at 50 °C oven, 50 µL of $10\%$ aqueous trichloroacetic acid and 10 µL of $0.1\%$ aqueous FeCl3 were added, the plates were incubated for another 10 min at RT, and read at 650 nm. ## 2.5. In Vitro Enzyme Inhibition Assays Enzyme inhibition activity of 10 mg/mL sample solutions was analysed in vitro using spectrophotometric methods adapted to 96-well plates. Drugs already on the market were used as a reference: acarbose (10 mg/mL, anti-diabetic drug), arbutin (1 mg/mL, tyrosinase inhibitor), galantamine (1 mg/mL, dementia treatment), and orlistat (1 mg/mL, drug used to support weight-loss). Results were expressed as the percentage of inhibition. The assay used for α-amylase inhibition was developed by Xiao et al. [ 46] based on the reaction between iodine solution and starch. Aliquots (40 µL) of sample, $0.1\%$ boiled potato starch suspension and 100 U/mL α-amylase in 0.1 M sodium phosphate buffer solution (pH 6.9) were mixed, plates were incubated at 37 °C for 10 min, 20 µL of 1 M HCl and 100 µL of iodine solution (5 mM I2 and 5 mM KI in distilled H2O) were added, and absorbance was read at 570 nm. Results were calculated using two negative control samples, one without an enzyme (blank, $100\%$ inhibition) for calculations and one with an enzyme for colour correction:α-Amylase inhibitory activity [%] = (A570 sample − A570 colour control)/A570 blank × $100\%$[4] Inhibitory activity against α-glucosidase Saccharomyces cerevisiae was determined as described by Custódio et al. [ 47]. An aliquot of 50 µL of sample solution was mixed with 100 µL of 1 U/mL α-glucosidase in phosphate buffer, and plates were incubated at 25 °C for 10 min. Afterwards, 50 µL of 5 mM p-nitrophenyl-α-d-glucopyranoside was added, plates were incubated for another 5 min at 25 °C, and absorbance was read at 405 nm. Tyrosinase inhibition activity assay was performed as described by Trentin et al. [ 48] by mixing 70 µL of sample solution with 30 µL of 333 U/mL fungal tyrosinase solution, incubating plates for 5 min in RT, adding 110 µL of substrate solution (2 mM L-tyrosine diluted in 25 mM potassium phosphate buffer, pH 6.5), and incubating plates for 45 min at RT before reading the absorbance at 405 nm. Inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) were analysed using a method developed by Ellman et al. [ 49]. A sample of 20 µL was mixed with 140 µL 0.02 M sodium phosphate buffer (pH 8.0) and 20 µL of 0.28 U/mL enzyme solution. Plates were incubated for 15 min at 25 °C, and 10 µL of acetylcholine iodide or butyrylcholine iodide substrate in 4 mg/mL buffer solution, and 20 µL of 5,5′-dithiobis-(2-nitrobenzoic acid) (Ellman’s reagent) in 1.2 mg/mL EtOH solution was added. Plates were incubated for another 15 min at 25 °C and read at 405 nm. The protocol used by McDougall et al. [ 50] was used to measure the porcine pancreatic lipase inhibition activity. A sample of 20 µL was mixed with 200 µL 100 mM Tris-HCl buffer (pH 8.2), 20 µL of 1 mg/mL enzyme solution, and 20 µL of substrate solution (5.1 mM 4-nitrophenyl dodecanoate in EtOH). Plates were incubated for 10 min at 37 °C, and absorbance was read at 405 nm. ## 2.6. Statistical Methods Results are given as mean values with standard deviation marked in brackets. For the extraction yields, FA, phenolic acids, HCA, and pigments, the samples were tested in triplicate ($$n = 3$$), while for phenolic compounds, antioxidant activity and enzyme inhibition activity, the samples were tested in sexuplicate ($$n = 6$$). One-way analysis of variance (ANOVA) combined with the Tukey honest significant difference (HSD) test was used to evaluate significant differences between the results, and significantly different results are denoted with different letters. EC50 values were determined for antioxidant activities using an online tool by AAT Bioquest Inc. [51]. ## 3.1. Extraction Yields Extraction yields and fractions considered in the study are presented in Table 1. The unfiltered juice fraction corresponded to more than $80\%$ of the fresh weight of partly lignified S. ramosissima from France and $66.7\%$ of biomass from Portugal. The biomass from Portugal was fractionated using a lab-scale single-auger juicer, whereas French biomass was juiced using a pilot-scale double-auger juicer with higher fractionation performance. The green juice fraction had a high ash content due to water-soluble salts accumulated in succulent plant tissue. The green juice obtained from the biomass from France exhibited especially high ash content, $81.8\%$, which could be explained by plants’ exposure to seawater flooding and more succulent texture compared to the more woody phenotype from Portugal. The results are aligned to those previously reported for other Salicornia spp., as Alassali et al. [ 11] found $61.1\%$ ash content of S. sinus-persica juice, and Christiansen et al. [ 12] reported more than $80\%$ of the total ash from the fresh S. bigelovii was recovered in the juice fraction after screw press. Changes in cultivation salinity do not only affect the ash content of plants but may also change the metabolism of sugars and lipids, which has been previously shown by Hulkko et al. [ 13] and Duan et al. [ 52] for S. europaea and Magni et al. [ 53] for Salicornia perennis. The DM from lignified plants had a lower ash content than fibre residue from partly lignified plants, but rinsing with water could have removed some of the salts from the biomass surface. The ash content of lignified S. ramosissima is also greatly lower than the $43.8\%$ ash content previously reported for S. bigelovii [54]. ## 3.2. Fatty Acid Profile The content of non-polar compounds in S. ramosissima fibre residue was found to be low ($1.13\%$). However, the lipid profile can be an important factor when biomass is considered for nutraceutical or feed application. The FA profile of the fibre residue of S. ramosissima is presented in Table 2. The total detected FA consisted of polyunsaturated fatty acids (PUFA, $58.2\%$), followed by saturated fatty acids (SFA, $41.0\%$), and monounsaturated fatty acids (MUFA, $1.3\%$). The predominant FA was linoleic acid ($34.5\%$), followed by palmitic acid ($30.9\%$). The obtained ω-6 and ω-3 FA ratio of 1.5 is interesting, as a ratio lower than 5 has been reported to contribute more to the anti-inflammatory state of PUFA, thereby reducing the risk of cardiovascular diseases, cancer, and autoimmune diseases [18]. Our results are consistent with those reported by Maciel et al. [ 55], who found the total SFA, MUFA and PUFA in chloroform extract from fresh S. ramosissima shoots to be $32.44\%$, $6.24\%$, and $61.32\%$, respectively. However, they reported a lower ratio of ω-6 and ω-3 FA (0.51), and the major difference was the amount of detected α-linolenic acid ($39.6\%$). Isca et al. [ 18] determined the whole lipophilic profile for n-hexane extract from vegetative S. ramosissima and found $31.27\%$ SFA and only $3.29\%$ of unsaturated FA in total. Barreira et al. [ 56] found that $60.8\%$ of the total FA in S. ramosissima was SFA, and the contents of arachidic acid ($8.6\%$), behenic acid ($10.0\%$), and lignoceric acid ($7.1\%$) were especially higher compared to results obtained in this study. The differences in the lipids profile may be explained by the different phenotypes and growth stages of S. ramosissima. However, no studies have been found to show the variations in the FA profile regarding the growth stage or place of origin of this species. Cultivation conditions also affect the FA profile, and additional irrigation has been shown to increase the content of MUFA and PUFA in S. ramosissima [18]. The biomass drying process has been shown to affect the FA composition of S. ramosissima, as freeze-dried samples exhibited a higher amount of PUFA compared to oven-dried samples [4]. ## 3.3. Bioactive Compounds The total amounts of compounds from different phenolic groups and the concentrations of pigments in the fractions are presented in Table 3. The EtOH extract from fibre residue and water extract from lignified plant material were the richest in terms of TPC, with contents of 41.06 mg GAE/g DM and 30.10 mg GAE/g DM, respectively. These results are higher than those previously reported for S. ramosissima by Lima et al. [ 28] for the acetone extract from 200 mM salinity-cultivated fresh shoots (12.9 mg GAE/g DM) and Silva et al. [ 3] for the water extract from wild-harvested plants (15.02 mg GAE/g DM). However, the study by Lopes et al. [ 57] reports that 74.1 mg GAE/g DM of TPC were found in the S. ramosissima acetone extract from wild-harvested plants. Obtained results are also higher than water and EtOH extracts from S. europaea [58]. No significant differences were observed in the content of TFC in extracts where TFC were detected. The concentration of TFC in extracts was lower than previously reported for Salicornia spp. extracted with EtOH or MeOH using conventional solid-liquid extraction, such as maceration [59]. TCT was also not detected in the study by Lopes et al. [ 57]. However, Lima et al. [ 28] reported relatively high concentrations of TCT (32.5 mg CE/g DM). Polyphenols found in Salicornia spp. contain a variety of compounds, such as phenolic and hydroxycinnamic acids and flavonoids, linked to several biological activities of the extracts, such as anti-inflammatory and antimicrobial effects [3,4,60]. Antioxidant and cardiovascular-protective properties have been linked to compounds previously found in S. ramosissima, such as derivates of the flavonoids quercetin, kaempferol, and rutin, and derivates of phenolic acids, such as chlorogenic acid, p-coumaric acid, and protocatechuic acid [60]. Many processing parameters can affect the concentration of bioactive secondary metabolites, such as the used extraction method and solvent used [3], as well as drying and storage conditions [4,61]. In addition, intra-specific variability, different plant growth stages [30], biomass fractions, and plants’ exposure to abiotic and biotic stresses also affect biomass composition and phytochemical concentration. For example, condensed tannins, also called proanthocyanidins, and other flavonoids have been associated with a protective effect on plants exposed to abiotic stresses such as intensive UV radiation, drought and cold temperatures [62]. High temperatures and waterlogging (flooded conditions) have also been reported to increase the amount of bioactive compounds, as exposure to these conditions includes the production of free radicals [15,53]. Significant differences in chemical composition can be observed between plants harvested even within the same region [53]. Duan et al. [ 52] showed S. europaea cultivated under increased salinity to be enriched in phenolic acids and flavonoids in roots and aerial parts and exhibiting upregulation in bioactive compounds, including protocatechuic acid, quercetin and kaempferol derivatives, p-coumaric acid, and ferulic acid. Cultivated plants have also been shown to have lower phenolics content than wild populations, likely due to more controlled cultivation [63]. These aspects must be taken into consideration when planning potential biorefinery processes to ensure they are robust enough to withstand variations. Despite the bright red colour of the juice from French plants, anthocyanins were not detected in the sample. The red colour was only observed in the juice from French plants, which could be due to their cultivation methods, could have produced some protective pigments in response to abiotic stresses. However, the concentration of the pigments may have been below the limit of detection, or the stability of the compounds has been compromised due to the neutral pH of the solvent [64]. Among sugar-free anthocyanidins, pelargonidin is known to have colours ranging from orange to red, whereas cyanidin has a strong magenta colour [62]. Thus, there may be a difference in the maximum absorbance of the compounds present in the sample and cyanin chloride used as a standard. Photosynthetic pigments were detected in the EtOH extract due to the more non-polar nature of these compounds. In the EtOH extract, the concentration of CHL a was higher compared to the juice fraction, with a CHL a/CHL b ratio of 2.3, whereas in the juice fractions from the French and Portuguese biomass, the corresponding ratios were 0.72 and 0.53, respectively. A similar ratio in Salicornia EtOH extract has been reported previously for S. neei [65]. The amount of total CHL in the EtOH extract was lower than that previously reported for S. ramosissima EtOH extracts by Barreira et al. [ 56] (21560 μg/g DM) but higher compared to S. brachiata (746.5 µg/g DM) and S. neei (233.3 µg/g DM) studied by Parida et al. [ 66] and De Sousa et al. [ 65], respectively. Salt stress may decrease the amount of CHL in the plants [65], which may explain the lower CHL content in the juice from French plants compared to Portuguese plants, as the plants cultivated in France were exposed to higher salinity. The concentration of total carotenoids in Salicornia spp. varies between different studies [16], and all obtained results lay within the range of reported results. Carotenoids are non-enzymatic antioxidants produced in response to different stressors [67]. The different results reported for the same species are strongly dependent on the conditions where the plants have grown, and light, temperature, and salinity variations are the main factors that may lead to carotenoid production. Carotenoids have been previously reported to have a key role in the salt-tolerance mechanisms of Amaranthaceae, and cultivation salinity has been shown to impact the pigment content of Salicornia spp. [ 65,68,69]. An efficient water-based extraction process with high phenolics yield would be desirable in a biorefinery targeting the production of bioactive compounds, as using solvents such as EtOH increases the capital expenditures and operational costs of the larger-scale facility. Biomass cultivation in a controlled environment, such as a hydroponic system, could provide a possibility to modify the growth conditions and environmental stresses to enhance the production of target compounds, such as phenolic acids and flavonoids. ## 3.4. Antioxidant Activity Salinity and other environmental stresses trigger oxidative reactions and the generation of reactive oxygen species, causing cellular damage and metabolic disorders in plants [7,70]. For halophytes, these stresses are more pronounced, and they have developed efficient antioxidant defence mechanisms to cope with extreme environmental stresses. Halophytes produce different classes of antioxidant compounds, such as phenols and carotenoids, which may have a synergic effect as radical scavengers. As seen in Figure 2, all fractions presented antioxidant activity in radical-based assays, and the water extract from the fibre residue exhibited the highest activity in a 10 mg/mL concentration. Compared to the antioxidant activity previously reported for S. ramosissima EtOH extract by Barreira et al. [ 56], the obtained extracts exhibited lower DPPH radical scavenging activity (IC50 5.69 mg/mL). The results for aqueous extracts are also higher than those reported by Faria et al. [ 71], who found the DPPH and ABTS radical scavenging activity of 10 mg/mL S. neei $80\%$ EtOH extract to be $37.1\%$ and $46.1\%$, respectively. Considering the water extract from the fibre residue, the EC50 value of ABTS radical scavenging activity was very similar to that reported by Lima et al. [ 28] for S. ramosissima acetone extract. The activity in the NO assay indicates that the extracts from S. ramosissima could have anti-inflammatory properties, as exposure to NO radicals has been directly linked to chronic inflammation [44]. Thus, the extracts could be a potential source of ingredients for dermo-cosmetics and biopharmaceuticals. Extracts from S. europaea and S. brachiata have been previously reported to have anti-inflammatory properties [72]. Chronic inflammation is involved in various diseases, including but not limited to cardiovascular diseases, diabetes, autoimmune and neurogenerative conditions, and chronic kidney disease [73]. There are no available results for the NO scavenging activity of EtOH extract due to the precipitation of the sample. The antioxidant activity was generally more pronounced in metal-based assays (Figure 3). Considering the FRAP assay, even though the differences between the results for water and EtOH extracts are non-significant, the EC50 values for water extracts are much lower. Therefore, the extracts can be considered more potent, indicating the presence of strong, water-soluble antioxidant compounds in the S. ramosissima biomass. Unfortunately, there are no results for CCA of the green juice from Portuguese biomass due to issues with the assay. The EC50 values of fibre residue water extract for FRAP, ICA, and CCA were also greatly lower than those reported by Barreira et al. [ 56] for S. ramosissima EtOH extract. For the fibre residue water extract, the obtained EC50 (4.91 mg/mL) of CCA is very similar to the one reported by Lima et al. [ 28] for S. ramosissima acetone extract. Achieving the same antioxidant activity with water extraction and reducing the use of toxic and flammable organic solvents is highly desirable considering large-scale industrial applications. Similarly, obtaining extracts from S. ramosissima residues with bioactivity comparable to extracts from food-grade plants highlights the biorefinery and valorisation potential of these residues. Overall, the green juice fractions exhibited the lowest bioactivity, whereas water and EtOH extracts from the fibre residue of French plants had the highest activity. Even though more mature plants have been shown to have higher concentrations of certain compounds with antioxidant activity [30], the extract from completely lignified Portuguese plants exhibited the highest activity only in ICA and FRAP assays. Therefore, the cultivation conditions could explain the higher antioxidant activity of French S. ramosissima. Plants grown in an open field have most likely been exposed to more abiotic stresses, such as high UV radiation and temperature difference, as well as higher cultivation salinity, leading to increased antioxidant production. On the contrary, S. ramosissima was grown in a more controlled greenhouse environment. The difference can also be observed in the juice fraction, as the juice obtained from French plants had higher antioxidant activity in all assays, except ABTS scavenging activity, compared to Portuguese phenotypes. The mixture of different bioactive compounds present in the specific biomass fraction or extract could also contribute differently to each antioxidant mechanism, affecting the observed activities. The high concentration of antioxidants produced by halophytes has made them interesting for functional food applications and improving the nutritional quality of everyday products such as bread or pasta [2,7,58]. ## 3.5. Enzyme Inhibitory Properties The enzyme inhibitory properties of green juices and extracts are presented in Table 4. Samples displayed different inhibitory activities against the different enzymes, and the EtOH extract with 10 mg/mL concentration exhibited moderate to high inhibition activity against enzymes related to diabetes (α-glucosidase), obesity and acne (lipase), hyperpigmentation (tyrosinase), and neurogenerative diseases (AChE). One treatment for diabetes mellitus is to limit glucose absorption using inhibitors against enzymes responsible for breaking the carbohydrates, such as α-glucosidase and α-amylase, and the potential of plant-derived extracts as inhibition agents has been investigated [74,75]. Some compounds linked to anti-diabetic properties are phenolics, flavonoids, and anthocyanidins [74]. The α-glucosidase inhibition activity of the EtOH extract ($68.63\%$) is high, especially when compared to acarbose with the same concentration ($85.61\%$) of 10 mg/mL. Raw water extracts, which still include a high amount of salt, had moderate α-glucosidase inhibition activity ($34.12\%$ and $8.93\%$ for extracts from fibre residue and completely lignified biomass, respectively), which could be improved by extract purification and increasing the concentration of phenolics. There was no significant difference in the α-glucosidase inhibition activity of water extracts from partly lignified and completely lignified biomass. Considering other Salicornia spp., flavonoids isorhamnetin 3-O-glucoside and quercetin 3-O-glucoside isolated from Korean S. herbacea (syn. S. europaea) EtOH extract have previously shown potential for blood sugar regulation by α-glucosidase inhibition [76]. Similarly, hydroxycinnamic acid trans-ferulic acid, also found in S. ramosissima, has also been reported to have anti-diabetic properties [60]. Hwang et al. [ 77] also reported α-glucosidase inhibition of $31.9\%$ using desalted $70\%$ aqueous EtOH extracts from fresh shoots of S. herbacea with a concentration of 0.5 mg/mL. For the EtOH extract from S. ramosissima fibre residue, the α-amylase inhibition activity was observed visually by the change in sample colour. However, due to precipitation in the sample, it was not possible to reliably measure the absorbance. Green juice fractions and water extracts showed only a very low inhibition of α-amylase. Obesity, resulting from excessive accumulation of fats, affects metabolic health and its increased prevalence has led to various public health concerns worldwide [78]. To address this issue, phytochemicals with lipase inhibitory properties have been studied as a means of reducing lipid absorption [78]. S. herbacea has been shown to have lipase inhibition activity in vitro, and its consumption reduced plasma triglyceride and cholesterol levels in an animal model [77,79]. In addition to their use as anti-obesity agents, lipase inhibitory phytochemicals have been investigated for their potential in treating acne, a common chronic disease, as bacteria related to acne produce lipase to break down triglycerides in sebum to free FA, which then causes skin inflammation [80]. EtOH extract from S. ramosissima fibres showed moderate ($41.74\%$) lipase inhibition at a concentration of 10 mg/mL. There was significant differences in the lipase inhibition activity of water extracts from fibre residue and completely lignified biomass. Together with potential anti-diabetic properties, the obtained results indicate that extracts from S. ramosissima could provide potential ingredients for nutraceutical and pharmaceutical applications targeting obesity and related lifestyle diseases. However, further investigation and testing are required to confirm the therapeutic properties. Tyrosinase inhibitors are investigated as skin-whitening agents for cosmetics and for treating hyperpigmentation, but they also have an important role in food and agricultural applications for preventing products oxidation [81]. In order to preserve the appearance and nutritional properties of fresh foodstuff such as cut fruits, safer natural anti-browning agents have been investigated to replace commonly used sulfiting agents [82]. S. ramosissima EtOH extract (10 mg/mL) exhibited high tyrosinase inhibition activity of $71.85\%$. Anti-tyrosinase activity of Salicornia spp. has been explored in several studies. According to Ahn et al. [ 83], the inhibition activity of more than $50\%$ was reached at a concentration of 60 mg/mL of S. bigelovii ethyl acetate extract, which is a much higher required concentration. However, the EtOH extract of S. europaea has shown $21.04\%$ activity with a concentration of 1 mg/mL [84]. Sung et al. [ 67] also found that even low concentrations (0.1 μg/mL) of water extract from S. herbacea inhibit tyrosinase (>$50\%$) and significantly reduce melanin synthesis in melanoma B16 cells. Copper is a cofactor of tyrosinase, and tyrosinase inhibition activity may be linked to the CCA of extracts [85]. However, no tyrosinase inhibition activity was observed in S. ramosissima fibre residue water extract, which exhibited the highest CCA in antioxidant assays. Considering dermo-cosmetic applications, S. ramosissima water extract supplemented cream has already been shown to reduce mechanically evoked itching (hyperkinesis), a condition related to atopic dermatitis, and regulate the skin barrier [85]. Additionally, a cream supplemented with a 3-methoxy-3-methyl-1-butanol extract from Japanese S. europaea has shown promising results for UVB-protective properties by improving the skin texture in areas exposed to the sun [21]. Therefore, tyrosinase inhibition activity, together with these effects, could indicate the potential for Salicornia extracts for use in dermo-cosmetics. However, further investigation is needed to evaluate the therapeutic properties and effects of the long-term use of extracts. Cholinesterase enzymes are responsible for the breaking down of acetylcholine and other choline esters, which function as neurotransmitters. Targeting these enzymes with inhibitory agents and increasing the levels of neurotransmitters has been seen as a potential symptomatic treatment for neurogenerative diseases [86,87]. The EtOH extract from fibre residue (10 mg/mL) exhibited high AChE inhibition activity ($68.42\%$), and green juice fractions showed low to moderate AChE and BuChE inhibition activities. Higher inhibition of cholinesterase enzymes was observed using green juice from Portuguese plants compared to French plants. Differences could be explained by phenotypic variations, such as different phytochemicals present in the fractions. However, a detailed metabolomic analysis would be needed to determine the different compounds present in the juice fractions. The studied water extracts did not exhibit cholinesterase inhibition activity. However, Pinto et al. [ 29] previously determined the AChE inhibition activity of 1 mg/mL water extract from lignified S. ramosissima to be $32.34\%$ using a commercial assay kit and showed the extract to be rich in caffeoylquinic acid derivatives. Besides intra-specific variation, the difference could be due to different extraction methods and potential compound degradation, as Pinto et al. [ 29] used maceration in lower temperatures and shorter resident time than in Soxhlet extraction. Karthivashan et al. [ 88] reported in vitro AChE inhibition activities of approx. $42\%$ and approx. $78\%$ for 1 mg/mL desalted EtOH extract (rich isorhamnetin and acanthoside B) and enzyme-digested wild-harvested Korean S. europaea, respectively, and showed significant suppression in AChE activity in the mice model. The desalted EtOH extract from S. europaea has also been tested on subjects complaining of memory dysfunction without dementia, but regardless of some positive results concerning the comprehension of spoken language function and Stroop test results, the study has its limitations due to the small number of subjects and short duration [89]. Phytochemicals, especially phenolics, and the potential synergistic effect of different compounds in plant extract matrices have been suggested to contribute to the neuroprotective properties of botanical extracts [29]. However, further investigation is needed to reveal the potential of botanical extracts as therapeutic agents. Considering biorefinery, value-added products targeted to the biopharmaceutical industry and cosmetics are desirable, as they often have a relatively high market value and can also be seen as some of the key applications of Salicornia species [72]. Full metabolomic profiling and further analysis considering the contribution of specific compounds to different bioactivity are still open for investigation. Several mixtures of phenolic compounds have shown synergistic effects [90], and these mechanisms are still unexplored to a great extent. If the botanical extracts from halophytes could be utilised as a matrix instead of purified isolated compounds, some costly downstream processing steps could be avoided. However, the phenotypical variation due to the biomass harvest stage and cultivation conditions, which could be observed when comparing Portuguese and French biomass, may cause challenges when the extracted matrix is produced for an application requiring high consistency, such as biopharmaceuticals. Generally, biorefinery processes must be designed to withstand some degree of variation in the raw material composition. ## 4. Conclusions Bioactive properties of different residue fractions from S. ramosissima biomasses were assessed. The FA profile showed a high amount of PUFA. The water extract from completely lignified biomass and EtOH extract from the fibre residue fraction had the highest concentration of phenolic compounds. Aqueous extracts exhibited high antioxidant activity comparable to extracts with organic solvents, making them interesting for industrial applications. EtOH extract from fibre residue had high and moderate inhibition activity against α-glucosidase and lipase, respectively, indicating the potential for nutraceuticals and biopharmaceutical applications targeting obesity and diabetes. Tyrosinase and lipase inhibition activities also make the extracts interesting for cosmetic applications. Since raw extracts were considered, bioactivity can be improved by purification and increasing the concentration of phytochemicals. Extracts from residual fractions obtained with non-toxic solvents exhibited bioactivities comparable to fresh S. ramosissima, which has increased interest as a nutrient-rich commercial vegetable and potential source of bioactive compounds. Overall, residue fractions from S. ramosissima could be a potential source of bioactive extracts, making it interesting to investigate possible biorefinery concepts further for maximum feedstock valorisation. ## References 1. EIP-AGRI Focus Group Soil Salinisation: Final Report 2. Lopes M., Cavaleiro C., Ramos F.. Sodium Reduction in Bread: A Role for Glasswort (. Compr. Rev. Food Sci. Food Saf. (2017.0) 16 1056-1071. DOI: 10.1111/1541-4337.12277 3. Silva A.M., Lago J.P., Pinto D., Moreira M.M., Grosso C., Cruz Fernandes V., Delerue-Matos C., Rodrigues F.. Salicornia ramosissima Bioactive Composition and Safety: Eco-Friendly Extractions Approach (Microwave-Assisted Extraction vs. Conventional Maceration). Appl. Sci. (2021.0) 11. 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--- title: Impact of Sarcopenia on Survival in Patients Treated with FOLFIRINOX in a First-Line Setting for Metastatic Pancreatic Carcinoma authors: - Lisa Lellouche - Maxime Barat - Anna Pellat - Juliette Leroux - Felix Corre - Rachel Hallit - Antoine Assaf - Catherine Brezault - Marion Dhooge - Philippe Soyer - Romain Coriat journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056206 doi: 10.3390/jcm12062211 license: CC BY 4.0 --- # Impact of Sarcopenia on Survival in Patients Treated with FOLFIRINOX in a First-Line Setting for Metastatic Pancreatic Carcinoma ## Abstract Sarcopenia, defined as decreased muscle mass and strength, can be evaluated by a computed tomography (CT) examination and might be associated with reduced survival in patients with carcinoma. The prognosis of patients with metastatic pancreatic carcinoma is poor. The FOLFIRINOX (a combination of 5-fluorouracil, irinotecan, and oxaliplatin) chemotherapy regimen is a validated first-line treatment option. We investigated the impact of sarcopenia on overall survival (OS) and progression-free survival (PFS) in patients with metastatic pancreatic carcinoma. Clinical data and CT examinations of patients treated with FOLFIRINOX were retrospectively reviewed. Sarcopenia was estimated using baseline CT examinations. Seventy-five patients were included. Forty-three ($57.3\%$) were classified as sarcopenic. The median OS of non-sarcopenic and sarcopenic patients were 15.6 and 14.1 months, respectively ($$p \leq 0.36$$). The median PFS was 10.3 in non-sarcopenic patients and 9.3 in sarcopenic patients ($$p \leq 0.83$$). No differences in toxicity of FOLFIRINOX were observed. There was a trend towards a higher probability of short-term death (within 4 months of diagnosis) in sarcopenic patients. In this study, the detection of sarcopenia failed to predict a longer OS or PFS in selected patients deemed eligible by a physician for triplet chemotherapy and receiving the FOLFIRINOX regimen in a first-line setting, confirming the major importance of a comprehensive patient assessment by physicians in selecting the best treatment option. ## 1. Introduction Pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030 [1]. While surgical resection is the only potentially curative treatment, only 15–$20\%$ of patients are candidates for surgery at diagnosis, because the majority of patients are diagnosed at a locally advanced stage of the metastatic stage of the disease [2]. Gemcitabine was first identified as the cornerstone of the treatment of patients with metastatic pancreatic carcinoma [3]. In 2011 and 2013, two large phase 3 trials pinpointed a survival benefit with FOLFIRINOX (5-fluorouracil, irinotecan, and oxaliplatin) and gemcitabine plus nab-paclitaxel in comparison to gemcitabine monotherapy [4,5]. These combinations are now considered as the two validated options in the first-line setting for patients with metastatic pancreatic cancer, pending a good performance status (PS) (i.e., Eastern Co-operative Oncology Group [ECOG] PS 0 or 1). Despite these treatment improvements, the prognosis of patients with metastatic pancreatic adenocarcinoma is still poor [6]. Sarcopenia, defined as the decrease in skeletal muscle mass and strength, is a component of cancer cachexia, which is characterized by a negative protein and energy balance, resulting from multiple factors, such as reduced food intake, inflammation, and excessive catabolism [7,8]. In clinical practice, the most commonly used method for skeletal muscle mass assessment is obtained using cross-sectional imaging at the level of the third lumbar vertebra (L3), using computed tomography (CT) [9,10]. Skeletal muscle index (SMI) cut-offs based on gender and body mass index (BMI) to classify sarcopenia have been published [11,12]. Sarcopenia was significantly associated with a shortened overall survival (OS) ($p \leq 0.001$) and a reduced cancer-specific survival (CSS) ($p \leq 0.001$) in a large meta-analysis including 7843 patients with solid tumors [13]. At the time of diagnosis, the prevalence of sarcopenia in patients with solid tumors was estimated to be around $40\%$ [12]. In pancreatic adenocarcinoma, the prevalence of sarcopenia ranges from 19 to $65\%$ [12,14,15]. Recently, a Japanese study identified a shortened OS in sarcopenic patients treated with FOLFIRINOX for advanced pancreatic carcinoma ($$p \leq 0.001$$) [16]. The aim of this study was to determine whether sarcopenia was associated with an unfavorable outcome in a Western population of patients with metastatic pancreatic cancer treated with FOLFIRINOX in a first-line setting. ## 2.1. Study Design and Objectives We performed a single-center, retrospective study in patients with metastatic pancreatic carcinoma treated with a modified FOLFIRINOX regimen in the first-line treatment, from January 2012 to December 2020 in our tertiary center. The primary endpoint of the study was OS, defined as the time from diagnosis to death (or last news if alive). Secondary endpoint was PFS, defined as the time from diagnosis to radiological progression. Our study received approval from our local institutional review board (AAA-2022-08011). ## 2.2. Patients and Treatment Patients were included in the study if they had a histologically proven diagnosis of metastatic pancreatic carcinoma and had received at least one cycle of a triplet chemotherapy with 5-fluorouracil, oxaliplatin, and irinotecan (FOLFIRINOX regimen). All patients received prophylactic growth factors to prevent severe neutropenia. Patients were excluded if they did not have CT examination within the 30 days before the treatment initiation, if they did not have follow-up with CT examination, or if they had undergone a surgical resection or local treatment of the primary tumor or metastasis after the diagnosis of metastatic pancreatic cancer. Patients presenting with mixed tumors, or neuroendocrine tumors were excluded. Patients with metachronous metastasis were included in the present study. ## 2.3. Toxicity Assessment Treatment toxicity was evaluated during medical visit by experienced physicians after four to six cycles of chemotherapy and at progression. All side-effects were graded according to the Common Terminology Criteria for Adverse Events version (CTCAE) version 4 [17]. ## 2.4. Anthropometric Measurement For each patient, weight and height were measured according to standard methods, and body mass index (BMI) was calculated. ## 2.5. Image Analysis Sarcopenia was assessed using CT examination at the time of diagnosis of metastatic pancreatic cancer. A radiologist with 10 years of experience in pancreatic imaging analyzed CT images at the third lumbar vertebra (L3) and identified skeletal muscles according to anatomic features and predefined thresholds of Hounsfield units (−29 to +150) (Figure 1) [11]. Skeletal muscle area (cm2) was normalized by height (m2), allowing calculation of the skeletal muscle index (SMI) (cm2/m2). To define sarcopenia, we used the threshold values previously determined by Martin et al. which were associated with poor survival in patients with solid tumors [11]. Patients were considered sarcopenic when the following values were observed: SMI < 43 cm2/m2 for men with BMI < 25 kg/m2, <53 cm2/m2 for men with BMI ≥ 25 kg/m2, and <41 cm2/m2 for women, regardless of BMI. Radiologic progression was defined using the Response Evaluation Criteria In Solid Tumors (RECIST 1.1) criteria [18]. ## 2.6. Statistical Analysis The normality of the distribution of quantitative variables was assessed using Shapiro–Wilk test. Quantitative variables were expressed as means ± standard deviations (SD) and ranges when normally distributed, or as medians and interquartile ranges (Q1 and Q3) when non-normally distributed [19]. Qualitative variables were expressed as raw numbers, proportions, and percentages. Comparison between patients with sarcopenia and patients without sarcopenia was performed using Student t-test for continuous variables or the Chi2 test for qualitative variables. Survival in patients with sarcopenia and in patients without sarcopenia was analyzed by the Kaplan–Meier method and compared using the log-rank test. A p-value < 0.05 was considered to indicate significant differences. Calculations were performed with NCSSC 2007 software (NCSS, Kaysville, UT, USA). ## 3.1. Patients One hundred and seventy patients with histologically proven metastatic pancreatic carcinoma were initially identified. Among them, 24 were excluded due to the lack of a CT examination at the time of diagnosis, surgical resection of the primary tumor or metastasis ($$n = 3$$), or exclusive supportive care ($$n = 15$$). One hundred and twenty-eight patients ($75.3\%$) received chemotherapy. Among them, 75 received a FOLFIRINOX regimen ($58.7\%$), 33 received FOLFOX ($25.8\%$), nine received gemcitabine plus nab-paclitaxel ($7\%$), eight received gemcitabine monotherapy ($6.2\%$), and three received FOLFIRI ($2.3\%$). The study flow-chart is displayed in Figure 2. We included 75 patients who received at least one cycle of FOLFIRINOX. There were 38 women ($50.7\%$) and 37 men ($49.3\%$), with a mean age of 64 ± 11.2 (SD) years (range: 34–85 years). The patients’ baseline characteristics are reported in Table 1. All patients had a pancreatic ductal adenocarcinoma (PDAC) or variants (acinar cell carcinoma, $$n = 2$$; adenosquamous carcinoma, $$n = 1$$; undifferentiated carcinoma with osteoclast-like giant cells, $$n = 2$$). Ten patients had a past history of cephalic duodenopancreatectomy ($$n = 4$$) or pancreatosplenectomy ($$n = 6$$). Forty-three patients ($57.3\%$) were identified as sarcopenic. ## 3.2. Toxicity In the overall population, $22.7\%$ of patients ($$n = 17$$) experienced grade $\frac{3}{4}$ hematological adverse events (Table 2). The most common grade $\frac{3}{4}$ hematological adverse event was neutropenia or febrile neutropenia ($12\%$) despite prophylactic treatment. Non-hematological grade $\frac{3}{4}$ adverse events occurred in $26.7\%$ of patients ($$n = 20$$), including diarrhea ($$n = 10$$), nausea ($$n = 5$$), vomiting ($$n = 3$$), and chemotherapy-induced neuropathy ($$n = 2$$). There were no significant differences regarding the adverse effects between sarcopenic and non-sarcopenic patients, except for anemia, which was significantly higher in non-sarcopenic patients. Oxaliplatin and irinotecan were discontinued in $44\%$ and $18.7\%$ of patients, respectively (Table 3). No significant differences were found in terms of treatment reduction or discontinuation between the two groups. ## 3.3. Survival The median number of cycles of FOLFIRINOX administrated was 10 (range: 1–58) in the entire cohort, with no difference between sarcopenic and non-sarcopenic patients (9 vs. 10, $$p \leq 0.83$$). There were no significant differences in terms of the median OS (15.6 versus 14.1 months; $95\%$ CI, 0.56–1.45; $$p \leq 0.36$$) or median PFS (10.3 vs. 9.3 months; $95\%$ CI, 0.65–1.89; $$p \leq 0.83$$) between the non- and the sarcopenic patients (Figure 3). There were numerically more patients in the sarcopenic group who had an early death (25.6 versus $9.4\%$), within 4 months of diagnosis of metastatic pancreatic carcinoma, although this did not reach a statistical significance ($$p \leq 0.07$$) (Table 4). Seventy-two percent of sarcopenic patients who had a short-term death did not have a radiologically proven disease progression. There was no difference between the two groups in the percentage of deaths within 12 months of diagnosis. At progression, $41.3\%$ ($$n = 31$$) of patients received second-line chemotherapy (Table 5), and $58.7\%$ ($$n = 44$$) received best supportive care. Sarcopenic patients received significantly less second-line chemotherapy than non-sarcopenic patients ($30.2\%$ vs. $56.3\%$, $$p \leq 0.02$$). The second-line treatment was gemcitabine monotherapy for 11 patients ($14.7\%$) and gemcitabine plus nab-paclitaxel for 20 patients ($26.7\%$). The median second OS (since the start of the second-line chemotherapy) for sarcopenic and non-sarcopenic patients was 12.3 months (5.9–16.6) in the patients receiving gemcitabine plus nab-paclitaxel, and 4.6 months (1.8–9.7) in the patients receiving gemcitabine monotherapy. ## 4. Discussion Our study evaluated the association between sarcopenia at baseline and survival in 75 patients with metastatic pancreatic cancer who received FOLFIRINOX as the first-line therapy. We found no significant association between sarcopenia at baseline and OS or PFS. These findings are inconsistent with previous reports from Kurita et al. [ 16], who showed that sarcopenia at the time of diagnosis was an independent poor prognosis factor in 82 patients with advanced pancreatic cancer. There might be several explanations for these conflicting results. First, nearly half of the patients included in the study by Kurita et al. had previously received systemic therapy for advanced pancreatic cancer, whereas our study included only chemotherapy-naïve patients. Therefore, patients included in our study might have had a better general condition. Secondly, as the study by Kurita et al. involved an Asian population, the cut-offs used for the diagnosis of sarcopenia (SMI < 45.3 cm2/m2 and 37.1 cm2/m2 for men and women, respectively) were different than ours (SMI < 43 cm2/m2 for males with BMI < 25 kg/m2, <53 cm2/m2 for males with BMI ≥ 25 kg/m2, and <41 cm2/m2 for women, regardless of BMI). One difficulty in studying the impact of sarcopenia in clinical practice is the lack of consensus regarding the SMI thresholds for diagnosis. We chose to use those reported by Martin et al. in a large cohort of 1473 patients with lung or gastrointestinal tumors [11], but only $9.9\%$ of the included patients had pancreatic carcinoma, the vast majority of them having a colon or rectum cancer. We might hypothesize that, because patients with pancreatic carcinoma suffer from cachexia more often than those with colon or rectal cancer, the SMI thresholds for the diagnosis of sarcopenia should be different. In another study including only obese patients with a lung or gastrointestinal cancer, Prado et al. found different sex-specific SMI cut-offs associated with mortality (52.4 cm2/m2 for men and 38.5 cm2/m2 for women) [9]. The narrative review by Bozzetti et al. reported that the cut-offs for defining sarcopenia ranged from 36 to 55 cm2/m2 in men [12]. In this study, we chose to include only patients who were treated with FOLFIRINOX, which is a validated first-line standard for patients with an ECOG PS of 0 or 1. As FOLFIRINOX is considered an aggressive regimen, it is recommended only for patients in good general condition based on the oncologist’s clinical assessment. In our center, of the 170 patients diagnosed with metastatic pancreatic cancer, only $44\%$ ultimately received FOLFIRINOX, with the remaining patients receiving 5-fluorouracil-based bichemotherapy ($21.2\%$), gemcitabine plus nab-paclitaxel ($5.3\%$), gemcitabine monotherapy ($4.7\%$), or exclusive support care ($8.9\%$). Of the 75 patients receiving FOLFIRINOX, 11 ($14.7\%$) and two ($2.7\%$) had a reported ECOG PS of 2 and 3, respectively. These results should be interpreted with caution, as the literature reports conflicting data regarding the reproducibility of the ECOG PS scale [20,21]. In these 75 patients deemed eligible to receive FOLFIRINOX based on the physician global assessment, sarcopenia was not a predictor of reduced PFS of OS. In our study, sarcopenic patients received significantly less second-line chemotherapy than non-sarcopenic patients, although there was no difference in the median OS between the two groups. In metastatic pancreatic carcinoma, there are no large prospective randomized studies of second-line chemotherapy after FOLFIRINOX failure, as most data are from retrospective studies. In a prospective cohort of 57 patients receiving gemcitabine plus nab-paclitaxel after FOLFIRINOX failure, Portal et al. [ 22] identified a median second OS (since the start of the second-line chemotherapy) of 8.8 months. In our study, the median second OS in sarcopenic and non-sarcopenic patients treated with gemcitabine plus-paclitaxel was 12.3 months. While sarcopenia at baseline was not a prognosis factor for OS or PFS in our study, there was a trend toward a higher proportion of early deaths (within 4 months of diagnosis of metastatic pancreatic carcinoma) in sarcopenic patients. This may argue for the early detection of sarcopenia in patients undergoing chemotherapy, to improve the overall management of patients and attempt to reverse skeletal muscle loss and cachexia. Recently, the APACaP trial randomized 313 patients with advanced pancreatic cancer, to chemotherapy or chemotherapy plus adapted physical activity (APA) [23]. In this trial, APA was shown to be feasible in patients with pancreatic carcinoma, and was associated with an improvement in several quality-of-life dimensions. Moreover, there was a tendency for a longer OS and PFS in the patients randomized to the APA arm, although this result did not reach a statistical difference. In a retrospective study including Japanese patients, Uemura et al. found that the baseline sarcopenia in patients with advanced pancreatic adenocarcinoma who received FOLFIRINOX was not associated with OS either [24]. However, these researchers did report the negative impact of an early decrease in skeletal muscle mass on the OS, which may indicate that, more than sarcopenia at diagnosis, maintaining muscle mass throughout treatment is an important factor for improving survival. Interestingly, the incidence of grade ≥ 3 adverse events was not significantly greater in patients with sarcopenia in our study. Anemia occurred surprisingly more often in patients without sarcopenia, but this should be interpreted with caution as we were unable to identify patients who underwent a blood transfusion or treatment with erythropoietin-stimulating agents. Various studies have reported an association between sarcopenia and chemotherapy toxicity [25,26,27]. More specifically, sarcopenic obesity has been associated with increased chemotherapy toxicity [27,28,29,30]. The administration of cytotoxic agents is usually determined by the body surface area (BSA), calculated from weight and height. It has been hypothesized that patients with obesity and sarcopenia would have a large BSA despite a low lean body mass. Therefore, sarcopenic obese patients would receive a high dose of chemotherapy despite a reduced volume of distribution [9]. We did not evaluate the impact of sarcopenic obesity on FOLFIRINOX tolerability in our study as we included only one obese sarcopenic patient. Our study has several limitations. First, it is a single-center study, which could have led to patient and treatment strategy selection bias. Second, it is a retrospective study with missing data, especially regarding the toxicity assessment. Finally, as discussed above, one of the major limitations to sarcopenia studies is the lack of consensus on the SMI threshold. To date, specific cut-offs for sarcopenia in patients with pancreatic cancer have not been reported in large studies or meta-analyses. ## 5. Conclusions Sarcopenia at the time of diagnosis does not affect OS, PFS, or chemotherapy toxicity in selected patients receiving FOLFIRINOX for metastatic pancreatic carcinoma. Isolated sarcopenia should not be an exclusion criterion for the triplet chemotherapy regimen in patients deemed eligible by a comprehensive physician assessment. However, our results show a trend toward early death in sarcopenic patients, which should advocate for the early reversion of skeletal muscle loss as part of the global management of patients with metastatic pancreatic carcinoma. ## References 1. Rahib L., Smith B.D., Aizenberg R., Rosenzweig A.B., Fleshman J.M., Matrisian L.M.. Projecting Cancer Incidence and Deaths to 2030: The Unexpected Burden of Thyroid, Liver, and Pancreas Cancers in the United States. Cancer Res. (2014) 74 2913-2921. DOI: 10.1158/0008-5472.CAN-14-0155 2. 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--- title: Comparative Analysis of Metabolic Variations, Antioxidant Profiles and Antimicrobial Activity of Salvia hispanica (Chia) Seed, Sprout, Leaf, Flower, Root and Herb Extracts authors: - Sara Motyka - Barbara Kusznierewicz - Halina Ekiert - Izabela Korona-Głowniak - Agnieszka Szopa journal: Molecules year: 2023 pmcid: PMC10056211 doi: 10.3390/molecules28062728 license: CC BY 4.0 --- # Comparative Analysis of Metabolic Variations, Antioxidant Profiles and Antimicrobial Activity of Salvia hispanica (Chia) Seed, Sprout, Leaf, Flower, Root and Herb Extracts ## Abstract The purpose of this study was to evaluate the phytochemical profiles of the seeds, sprouts, leaves, flowers, roots and herb of Salvia hispanica and to demonstrate their significant contribution to antioxidant and antimicrobial activities. Applied methods were: HPLC-DAD coupled with post-column derivatization with ABTS reagent, untargeted metabolomics performed by LC-Q-Orbitrap HRMS, and two-fold micro-dilution broth method, which involved suspending a solution of tested compounds dissolved in DMSO in Mueller–Hinton broth for bacteria or Mueller–Hinton broth with $2\%$ glucose for fungi. Metabolomic profiling using LC-Q-Orbitrap HRMS used in this study yielded the identification and preliminary characterization of one hundred fifteen compounds. The dominant class of compounds was terpenoids (31 compounds), followed by flavonoids (21 compounds), phenolic acids and derivatives (19 compounds), organic acids (16 compounds) and others (fatty acids, sugars and unidentified compounds). The organic and phenolic acids were the most abundant classes in terms of total peak area, with distribution depending on the plant raw materials obtained from S. hispanica. The main compound among this class for all types of extracts was rosmarinic acid which was proven to be the most abundant for antioxidant potential. All tested extracts exhibited considerable antibacterial and antifungal activity. The strongest bioactivity was found in leaf extracts, which presented bactericidal activity against Gram-positive bacteria (S. aureus, S. epidermidis, M. luteus and E. faecalis). The work represents the first compendium of knowledge comparing different S. hispanica plant raw materials in terms of the profile of biologically active metabolites and their contribution to antioxidant, antimicrobial and antifungal activity. ## 1. Introduction Thanks to the development of advanced analytical technologies, it is now possible to better characterize the complex composition of many plant raw materials, with a focus on understanding their biologically active compounds. In this context, a very interesting plant species commonly used in phytotherapy as well as in health nutrition is chia—Salvia hispanica L. (Lamiaceae) [1,2]. Currently, S. hispanica can be field cultivated or cultivated in greenhouse conditions in many regions [3,4,5]. However, S. hispanica is a neglected and underutilized plant (NUCS) [6]. The plant is now known for its health-promoting properties [6,7,8]. Furthermore, chia seeds are well known in Traditional Chinese Medicine (TCM) and were used by the Mayan and Aztec tribes as food and an ingredient in many herbal mixtures. However, no specific healing properties were attributed to them at the time [1,9]. Currently, the main raw material obtained from S. hispanica is chia seed (Salviae hispanicae semen) [10]. The chemical composition and biological activities of chia seeds have been well described in the scientific literature, and therefore, commercial interest in this raw material continues to increase [11,12,13,14]. In 2009, the European Food Safety Authority (EFSA) issued a positive opinion on chia seeds, making them safe for use in the food industry [15]. In Canada, the seeds and chia seed oil are classified as “natural health product ingredient” [16]. However, few scientific studies deal with the analysis and commercial use of other raw materials derived from this plant, namely sprouts, leaves, flowers, and whole herbs. The valuable chemical composition of chia seeds is crucial for their popular use in nutritional therapy for many diseases in our civilization. Chia seeds are valued in food production due to their high content of essential fatty acids such as α-linolenic acid and linoleic acid. In addition, the seeds are an essential source of plant protein and contain all essential amino acids (arginine, leucine, phenylalanine, lysine, valine, isoleucine, threonine, methionine, histidine and tryptophan). Chia seeds also contain high levels of dietary fiber, predominantly found in the water-insoluble fraction, as well as essential macro- and micronutrients [17,18]. Equally important is the content of biologically active compounds and antioxidants, which are responsible for the health-promoting potential of the seeds. Phenolic acids (gallic acid, ferulic acid, p-coumaric acid, caffeic acid), depsides (rosmarinic acid and chlorogenic acid), flavonols (kaempferol, quercetin, myricetin and rutoside), flavones (apigenin), isoflavones (daidzein, glycitin, genistein and glycitein) and flavan-3-ols (catechin, epicatechin) are present in predominant amounts [17,18,19,20]. Particularly noteworthy in the studies is the high content of rosmarinic acid, which has a broad spectrum of biological activity, including, in particular, antioxidant, anti-inflammatory and antibacterial activities [17,21,22]. Scientific studies conducted on in vitro cultures on human and animal models demonstrate the antioxidant [11,19,23,24,25,26], antidiabetic [27,28,29,30,31], hypotensive [13,30,32,33,34], hypolipemic [28,35] and hepatoprotective effects of chia seeds. On the other hand, interestingly, there are few papers dealing with phytochemical or biological studies of the other raw materials extracted from S. hispanica. Only some researchers focused on the sprouts and leaves of S. hispanica and proved that they could be an interesting plant resource [36,37,38,39,40,41,42]. It is known that chia leaves are a source of hydroxycinnamic acid and its derivatives, flavones (apigenin, luteolin, orientin, and vitexin), and flavonols (quercetin, kaempferol derivatives). In addition, studies on chia sprouts have shown that they are a source of proteins, minerals (especially calcium and magnesium), and vitamins (especially A, E, C, and the B group) [41,43]. In addition, sprouts contain a high concentration of unspecified plant polyphenols with strong antioxidant potential [44,45]. However, there are no scientific studies detailing and comparing the phytochemical profile and antioxidant and antimicrobial activity of chia flowers or herb. The current scientific literature on the different morphological parts of S. hispanica is very limited. Studies have comprehensively described the composition and biological activity of chia seeds, but knowledge of the sprouts, flowers, leaves, or herb has not been exhaustively described. Current research on the analysis of the leaves is only related to the chemical analysis of the essential oil and the content of some biologically active compounds. Few studies describe the antioxidant capacity of chia leaf extracts. The work aims to explore the scientific knowledge on the content of the main metabolites, focusing on the group of polyphenols present in different raw materials extracted from S. hispanica, as well as on their antioxidant, antimicrobial and antifungal activities. The work represents a comprehensive comparative analysis of S. hispanica plant material—seeds, sprouts, leaves, flowers, roots and herb. The study included phytochemical and qualitative analysis by the HPLC technique in conjunction with post-column derivatization and untargeted metabolomic analysis. Quantitative analysis of the content of the predominant phenolic compound—rosmarinic acid—was performed using HPLC-DAD. The study of antioxidant activity with the indication of the main compounds responsible for this activity was performed by post-column derivatization with ABTS reagent. The study compared the antioxidant potential of the tested extracts from different morphological parts of S. hispanica. In addition, a comparative study of antibacterial and antifungal activity was also carried out using the microdilution broth method. ## 2.1. Metabolomic Profiling Using LC-Q-Orbitrap HRMS The high-resolution, accurate mass via Orbitrap used in this study yielded the identification and preliminary characterization of one hundred fifteen compounds (Table 1). In Figure 1, the metabolite profiles as total ion chromatograms and heat maps with the signal intensity of individual analytes are reported. The largest class of compounds was terpenoids, with 31 compounds, followed by flavonoids (21 compounds), phenolic acids and derivatives (19 compounds), organic acids (16 compounds) and others such as fatty acids, sugars and unidentified compounds. The organic and phenolic acids were the most abundant classes in terms of total peak area with distribution depending on the type of part S. hispanica (Figure 1A). This class was represented mainly by hydroxycinnamic acids and their derivatives. The main compound among this class for all types of extracts was rosmarinic acid (peak 49) with pseudo-molecular ions at m/z 359.0769 (C21H17O11−) and fragmentation ions at m/z 197.0450 and m/z 161.0235 formed by cleavage of a caffeic acid and danshensu moieties. The deprotonated form of caffeic acid was also detected in compound 69, which was identified as salvianolic acid F ([M-H]¯ m/z 313.07142). The extracts obtained from leaves, flowers and herbs were additionally characterized by a high content of ferulic acid (peak 54) and caffeic acid (peak 32) with [M-H] − ions at m/z 193.0496 and m/z 179.0336, respectively. In the case of ferulic acid, the fragmentation ion at m/z 134.0379 formed after the loss of carbon dioxide, and the methyl radical was observed. The caffeic acid precursor ion also generated characteristic major fragments at m/z 135.0441 due to the loss of carbon dioxide. Seed and sprout extracts, on the other hand, were characterized by a high content of salviaflaside (peak 39) with pseudo-molecular ions at m/z 521.1298 (C24H25O13−). The parent ion of this rosmarinic acid glucoside produced fragment ions at m/z 359.0748 and m/z 161.0235, expected for rosmarinic acid and caffeic acid, respectively. The highest content of organic acids was observed for flower extracts. The major compounds were assigned as gluconic (peak 2), tartaric (peak 7), malic (peak 9), citric (peak 11) and isocitric acid [13]. Another class of phytochemicals detected in S. hispanica extracts was flavonoids. Most of the identified compounds belonging to this class have been assigned to flavones. In almost all extracts, the content of flavone with peak number 72 was the highest. Compound 72 gave the precursor ion [M-H]− at m/z 329.0663, indicating that its molecular formula was C17H14O7. It produced prominent fragment ions at m/z 299.0198 attributable to the loss of two methyl groups and m/z 271.0246 due to the further elimination of carbon monoxide. Therefore, this peak was identified as jaceosidin. Compound 70 yielded the base peak [M-H]− at m/z 269.04522. Precursor and product ions at m/z 117.0333 and 151.0027 confirmed that this compound is apigenin. The glucoside, rutinoside and glucuronide of apigenin were identified by the pseudomolecular peak ions at m/z 431.0981 (peak 38), 577.1561 (peak 42) and 445.07747 (peak 46), respectively, and aglycon ion in MS2 spectra formed after loss of glucoside (−162 amu), rutinoside (−308 amu) and glucuronide (−176 amu) moieties. Peaks 34, 41 and 57 were identified as luteolin glucoside, luteolin rutinoside and luteolin, respectively, based on the presence of the ion at m/z 285.0401 in MS2 or MS spectra. Compounds 40, 58 and 80 were identified as scutellarin, luteone glucoside and hispidulin, respectively. Compound 61, with the highest content in sprout extracts, gave a [M−H]− ion at m/z 345.06136 (C17H13O8−). The main fragment ion at m/z 315.0149 was attributable to the loss of two methyl groups. This compound was identified as hydroxyflavan–spinacetin. Another main group of phytochemicals present in S. hispanica extracts were terpenoids. The highest content of these compounds was found in flower extracts. The most abundant compound (peak 87) with quasimolecular ion at m/z 345.17046 (C20H25O5−) has a unique fragmentation pattern with fragmentation ions at m/z 331.1508 and 315.1597 that have been previously observed for rosmadial and hydroxyrosmadial [46]. Therefore, this compound was assigned as a rosmadial derivative. Peaks 85 and 96 with a [M−H]− ions at m/z 343.15480 were assigned to isomers of rosmadial (C20H23O5−). Their parent ion generated characteristic fragments at m/z 315.1601 and m/z 299.1653 via the loss of ethylene and carbon dioxide, respectively. In the extracts studied, especially those from sprouts, a high content of saccharides was also noted. Peaks 1 and 4 were tentatively identified as raffinose and sucrose, as they are often major transport sugars in salvia species [47]. In the case of extracts from leaves, flowers and herbs, the high content of compound 17 was also observed. Its precursor ion [M-H]− was found at m/z 271.08193, which indicates that its molecular formula is C12H15O7−. This compound was tentatively identified as arbutin. The presence of fatty acids was also observed in S. hispanica extracts. The two with the highest concentration are compounds 62 and 91, identified as trihydroxyoctadecadienoic acid (C18H31O5−) and dihydroxyoctadecadienoic acid (C18H31O4−), respectively. ## 2.2. Antioxidant Profiling by Post-Column Derivatization with ABTS Post-column derivatization of analytes with ABTS reagent was performed during HPLC analysis of extracts from different plant parts of S. hispanica. In the applied post-column derivatization, the principle action of the ABTS reagent is the same as in the case of spectrophotometric tests. The reduction reaction of the ABTS reagent leads to a significant shift in the visible UV spectrum, which results in a change in the absorption of the ABTS reagent (discoloration). Post-column introduction of the reagent into the on-line system and the presence of antioxidants in the eluate result in negative peaks in the chromatogram recorded at 734 nm (Figure 2A). The profiles obtained after derivatization indicated that several compounds identified in S. hispanica extracts exhibit antioxidant activity. The greatest contribution to the overall antioxidant activity was made by rosmarinic acid (peak 49). Its activity covered 26 to $49\%$ of the total antioxidant activity (Figure 2B). In the case of seed extract, additionally, other derivatives of hydroxycinnamic acid, such as salviaflaside (peak 39) and dehydroxyl-rosmarinic acid-glucoside [45], showed antiradical potential visible as negative peaks on profile. In extracts from leaves, flowers and herbs, the presence of caffeic acid (peak 32) also caused the reduction of the ABTS radical. Other phytochemicals showing visible antioxidant activity were gluconic acid (peak 2), arbutin (peak 17), danshensu (peak 19), caftaric acid (peak 24) and chlorogenic acid (peak 27). The flower extract was characterized by a slightly different antioxidant profile compared to the rest of the extracts. In this case, additional antioxidants with a short retention time were noted (peaks 10, 12, 14). They were probably derivatives belonging to the group of pyrimidines and purines. Recalculation of the area of negative peaks using the calibration curve of the standard antioxidant enabled the quantification of the total antioxidant activity of the extracts in an on-line system and its expression as Trolox equivalents (Figure 2B). The highest antioxidant activity was found in flower extracts. Lower activity by about $40\%$ and $50\%$ was shown by extracts from leaves and herbs, as well as extracts from seeds and sprouts, respectively. ## 2.3. Analysis of the Average Content of Rosmarinic Acid Performed Using DAD-UHPLC in Extracts from Seed, Sprout, Leaf, Flower and Herb of S. hispanica Analysis of the average content of rosmarinic acid in extracts from seeds, sprouts, leaves, flowers and herb of S. hispanica showed that all analyzed morphological parts have a high content of rosmarinic acid. Among all the analyzed parts of S. hispanica, the average content of rosmarinic acid was the highest in the leaves (198.53 mg/100 g DW). There was slightly less rosmarinic acid in the herb (185.12 mg/100 g DW). Rosmarinic acid was present in lower amounts in the flowers (149.45 mg/100 g DW), in the sprouts (134.27 mg/100 g DW) and in the least amount in the seeds (127.25 mg/100 g DW) (Table 2). ## 2.4. Antibacterial and Antifungal Activities The antibacterial and antifungal activities of the tested extracts are presented as the MICs, i.e., the lowest concentration of compound that inhibits visible growth of the microorganism and the MBCs, i.e., the lowest concentration that results in a ≥$99.9\%$ reduction of the microorganism inoculum upon subculture to a compound-free medium (Table 3). Vancomycin, ciprofloxacin and nystatin were used as the standard drugs. Tested extracts were more active against Gram-positive reference strains. Gram-negative bacteria tested showed 4–8 times higher MIC values in comparison to those for Gram-positive bacteria. The best bioactivity was indicated for leaves extract, which presented considerable bactericidal activity against Gram-positive bacteria (S. aureus, S. epidermidis, M. luteus and E. faecalis) counted by MBC/MIC index, which equals 1–4. A stronger inhibitory effect against Gram-negative reference strains was also presented by leaves extract. The leaves extract showed the best antimicrobial activity against two Gram-positive bacteria (S. epidermidis and S. aureus). More favorable antifungal activity of chia leaves was found for S. albicans (MIC = 5). The whole seeds extract exhibited greater activity against S. aureus, M. luteus and B. cereus compared to the ground seeds extract. However, ground seeds extract demonstrated greater activity against E. faecalis. The sprout extract showed the best effect and the lowest MIC against two Gram-positive bacteria (M. luteus and B. cereus). The results showed that the sprout extract exhibited the best antifungal activity against C. parapsilosis (MIC = 0.625.). The roots extract demonstrated the best antibacterial activity against M. luteus. Roots extract exhibited beneficial antifungal properties against C. parapsilosis and C. glabrata. The herb extract showed the least favorable antibacterial and antifungal activity. The antibacterial efficiency of tested extracts was in the order of leaves > sprouts > whole seeds > ground seeds > roots > herb. However, their activity was much lower compared to standard drugs routinely used in bacterial infection treatment. Not strong, but still better antifungal activity against Candida spp. reference strains were shown for sprout extract. ## 3. Discussion The conducted research is the first comparative analysis providing phytochemical profiling and connected with its antioxidant potential as well as antimicrobial properties of different raw materials obtained from S. hispanica. Rosmarinic acid was identified as the major compound responsible for antioxidant activity. The comparison of the quantity of this compound in relation to the ABTS reagent was shown. Furthermore, except for the antioxidant potential, the antimicrobial and antifungal properties were profiled for the first time while studying different S. hispanica raw materials. The chemical characterization revealed the presence of various groups of compounds in the extract from seeds, sprouts, leaves, flowers, and herb, mainly terpenoids (31 compounds), flavonoids (21 compounds), phenolic acids and derivatives (19 compounds), organic acids (16 compounds) and others (fatty acids, sugars, unidentified compounds). Chia seeds are the most recognized raw material obtained from S. hispanica, although they are not well phytochemically profiled. A few studies described the polyphenolic profile of chia seeds, but our results are more comprehensive. Rahman et al. [ 48] determined the polyphenolic profile and biological activity of chia methanolic seed extract. They identified only the total phenolic content using the out-of-date method of the Singleton and Rossi assay [1965]. They indicated rosmarinic acid, protocatechuic acid, p-hydroxybenzoic acid, p-coumaric acid, caffeic acid, and quercetin as the major components using HPLC-DAD-MS/MS method. The results of Oliveira-Alves et al. [ 49] in identifying the main phenolic compounds in methanolic chia seed extracts by LC-DAD-ESI-MS/MS methods mostly correspond with compounds identified in our study, too. The researchers confirmed the presence of phenolic acids (protocatechuic acid, p-hydroxybenzoic acid, cis-p-coumaric, cis and trans-caffeic acids, hydroxycoumaric acid, cis- and trans-ferulic acids, ellagic acid, rosmarinic acid), flavonoids (quercetin, quercetin-hexoside, kaempferol-hexoside, myricetin, apigenin, daidzein, rutin, genistein), and procyanidins (procyanidin dimer B 1, 2 and 3, procyanidin dimer A). Our results were consistent with those of Martinez-Cruz et al. [ 23] on $70\%$ methanol extracts with the UHPLC method in chia seeds. The researchers indicate the main compounds as rosmarinic acid, protocatechuic acid, caffeic acid, gallic acid and daidzein. The estimated amount of rosmarinic acid was 92.67 mg/100 g DW, 1.4 times lower than the amount obtained in our results for seeds. The antioxidant activity determined by the DPPH assay indicated the high antioxidant capacity (percentage of inhibition = $68.83\%$) of chia seeds, which was 2 times higher than estimated in our study. The presence of phenolic acids, especially rosmarinic acid, and other phenolic compounds from isoflavones and anthocyanins were supposed to be responsible for this activity. The results were also most similar to those of Abdel-Aty et al. [ 50], who determined polyphenols profile using HPLC analysis. They identified in chia seed extracts: phenolic acids (gallic acid, protocatechuic acid, p-hydroxybenzoic acid, chlorogenic acid, caffeic acid, syringic acid, ferulic acid, synapic acid, rosmarinic acid, and cinammic acid) and flavonoids (quercetin, apigenin, chrysin). The researchers also proved that the most abundant phenolic acid identified in seeds extract was rosmarinic acid (0.320 mg/g DW). Dib et al. [ 51] quantified the groups of phenolic compounds in the hydromethanol extract of chia seeds. It was proved that chia seeds had a high content of total phenols (19.06 mg GAE/g DW), which were mainly represented by flavonoids (12.3 mg CE/g DW) and tannins (8.32 mg catechin equivalents (CE)/g DW). Moreover, the researchers assessed the antioxidant properties of the studied extract using DPPH and FRAP assay. The results indicated that the extract showed the highest DPPH scavenging potential with an IC50 of 0.27 mg/mL and FRAP assay with an EC50 of 0.06 mg/mL. In our study, qualitative and quantitative analysis of the main polyphenolic compounds present in chia seed extract demonstrated that the quantitatively dominant phenolic compound responsible for antioxidant activity was rosmarinic acid. The antioxidant activity of chia seeds has also been confirmed by other scientists, who have shown that chia seed extracts were able to scavenge DPPH radicals [52,53,54,55,56]. Tepe et al. [ 57] examined the antioxidant activity of an ethanolic extract of chia seeds and claimed that polyphenols present in chia seeds significantly inhibited oxygen free radicals. The same results were obtained by Craig et al. [ 58], who proved that the presence of polyphenols in chia seeds protects them from oxidative degradation. All performed studies confirm that rosmarinic acid was the main compound detected and quantified in chia seeds [23]. Similarly, the compound identified by our team—danshensu, is a simple polyphenol (3-(3,4-dihydroxyphenyl)lactic acid) corresponding to the hydrated form of caffeic acid [59]. This compound is also described in plants in the Lamiaceae family [60,61]. Our study compared the quantitative content of rosmarinic acid in S. hispanica seed extracts with other literature data. In addition to the present study, rosmarinic acid was also quantified in S. hispanica seed extracts at two other centers. In the present experiment, the content of rosmarinic acid in the seed extracts was 34.98 mg/100 g DW. which was almost 2 times less than the content determined by Pellegrini et al. [ 62] and 2.65 times less than the content estimated by Martinez-Cruz et al. [ 23]. Moreover, our study also examined the antioxidant properties of chia seed extracts and determined the percentage effect of rosmarinic acid content on this activity (which was indicated at $34.33\%$). We proved that other derivatives of hydroxycinnamic acids, such as salviaflaside and dehydroxyl-rosmarinic acid-glucoside, also exhibited antiradical visible potential. Chia seeds are the main raw material obtained from S. hispanica, but according to current scientific studies, compared to seeds, chia sprouts could have better nutritional value and antioxidant capacity, making them a new promising plant raw material of potential medical and agri-food utilities [63,64]. Our studies showed the metabolite profile of chia sprout extracts for the first time. We identified new compounds that had never been identified by researchers before. In sprouts extract, we identified organic acids—gluconic acid, xylonic acid, threonic acid, tartaric acid, quinic acid, malic acid, citric acid, isocitric acid, homocitric acid, caftaric acid, salicylic acid, tuberonic acid hexoside, phenolic acids and their derivatives—dihydroxybenzoic acid hexoside, danshensu, neochlorogenic acid, caffeic acid, salviaflaside, rosmarinic acid, dehydroxyl-rosmarinic acid-glucoside, ferulic acid, salvianolic acid F, 4-hydroxybenzoic acid, methyl rosmarinate, feruoyl arabinose, rabdossin, caffeoyl glucose. Namely, sprout extracts were characterized by a high content of salviaflaside and jaceosidin. Moreover, compared to all extracts we analyzed, the extracts from sprouts contained high amounts of saccharides (mainly raffinose and sucrose). In our study, we proved that the main group of secondary metabolites of chia sprout extracts were derivatives of hydroxycinnamic acids. Organic acids and flavonoids occupy the next position. We have shown that the proportion (%) of rosmarinic acid in the antioxidant activity of sprout extracts determined using post-column derivatization of analytes with ABTS reagent was the highest compared to the other analyzed extracts from raw materials obtained from S. hispanica. The analysis showed that chia sprouts had the highest percentage content of rosmarinic acid, which contributed to antioxidant activity, at $49.5\%$. These results are innovative because there are no scientific studies analyzing the phytochemical profile and antioxidant properties of chia sprouts extract. Calvo-Lerma et al. [ 36] indicated that chia sprouts extract contains higher total polyphenol content than seeds (2.87 vs. 1.78 mg GA/g DW). Their determination of the total antioxidant activity using the DPPH assay showed higher results in sprouts in comparison to seed extracts (5.69 vs. 3.49 mg TX/g DW), which is consistent with the results obtained by our team. Abdel-Aty et al. [ 50] evaluated the effect of the germination process of S. hispanica seeds on total phenolic and flavonoid contents and antioxidant and antimicrobial properties. In the chia sprout methanolic ($80\%$) extracts with the HPLC method, 12 phenolic acids (gallic acid, protocatechuic acid, p-hydroxybenzoic acid, chlorogenic acid, caffeic acid, syringic acid, vanilic acid, ferulic acid, synapic acid, p-coumaric acid, rosmarinic acid, and cinammic acid) and 5 flavonoids (catechin, quercetin, apigenin, kaempferol, chrysin) were identified with concentrations ranging from 0.06 to 0.80 mg/g DW. Our results presented different data. We did not detect protocatechuic acid, vanilic acid, p-coumaric acid and cinammic acid in the sprout extracts. The researchers showed that the dominant phenolic compound found in chia sprout extracts was protocatechuic acid (0.50 mg/g DW), followed by rosmarinic acid (0.60 mg/g DW). In our study, we proved that rosmarinic acid was the abundant compound (134.27 mg/100 g DW) identified in sprout extracts. Previously, only a few scientific studies dealt with chia leaves. The results demonstrated the presence of fatty acids, flavonoids and essential oil. Therefore, most scientific research focuses on analyzing components isolated from chia leaf essential oil [65,66,67,68,69,70]. In our study, we showed that phenolic acids derivatives (dominant compounds: rosmarinic acid, ferulic acid, caffeic acid, 4-hydroxybenzoic acid), flavonoids (dominant compounds: vitexin, jaceosidin) and organic acids (dominant compounds: gluconic acid, tartaric acid, malic acid, citric acid, isocitric acid) were predominant in studied S. hispanica leaves methanolic extract. Among phenolic acids, the most abundant in leaves extract was rosmarinic acid. These results are in line with those obtained by Amato et al. [ 40], who analyzed the methanolic extracts of chia leaf using the HPLC-ESI-MS method. In the study conducted by us in the chia plant material, we identified hydroxycinnamic acids and their derivatives, especially flavonoids, mainly flavones, such as apigenin, luteolin, orientin, vitexin, jaceosidin and phenolic acids with dominant amounts of rosmarinic, ferulic, isocitric, caffeic acid and their derivatives. These compounds have been commonly found in other members of the genus Salvia before [71,72]. Similar results were obtained by Zúñiga-López et al. [ 73], who identified the phenolic composition of chia leaves using the UHPLC-HRMS method. However, these researchers identified only 18 bioactive compounds in the chia leaf extracts: organic acids (dominant compounds: protocatechuic acid, p-coumaric acid, quinic acid, sinapic acid), phenolic acids derivatives (dominant compounds: chlorogenic acid, rosmarinic acid, caffeic acid, ferulic acid) flavonoids (dominant compounds: orientin, acetyl orientin, vitexin, coumaroyl, luteolin-O-glucuronide, kaempferol, genistein, naringenin, salvianolic acid F isomer, and dimethyl quercetin). Most of the detected compounds overlap with those identified in our study, but in our study, we identified 115 compounds (Table 1). There are very limited studies on the correlation between metabolomic analyses and the antioxidant properties of chia leaves. In our study, analysis of antioxidant activity determined by post-column derivatization with ABTS reagent of the chia leaf extracts showed that rosmarinic acid present in the leaf extracts was the most abundant compound responsible for this activity. Amato et al. [ 40], for potential antioxidant measurements of S. hispanica leaf extracts, used another three assays: oxygen radical absorbance capacity (ORAC), ORAC-Fluorescein (ORAC-FLORAC-FL index values) and 2,2′-diphenyl-1-picrylhydrazyl (DPPH). They proved that the methanolic extract of chia leaves exhibited higher antioxidant activity and indicated that rosmarinic acid was the most reactive compound, which is equivalent to the results obtained in our study. In our study, we broadened the results, and we pointed out that not only was rosmarinic acid responsible for antioxidant potential, but also other phytochemicals, such as caffeic acid, gluconic acid, arbutin, danshensu, caftaric acid and chlorogenic acid. Moreover, for the first time, we performed a recalculation of the area of negative peaks using the calibration curve of the antioxidant standard, which allowed us to quantify the total antioxidant activity of the leaf extracts in the on-line system. As a result, quantification of the total antioxidant activity of the extract expressed as Trolox equivalents and the percentage of the total antioxidant activity of the rosmarinic acids and other antioxidants present in the extracts showed that the antioxidant activity for the leaf extract was about 40–$50\%$. The study conducted by our team for the first time assessed the main metabolites and evaluated the antioxidant activity of S. hispanica flower extracts. The main group of secondary metabolites found in the flower extracts were organic acids (dominant compounds: gluconic acid, tartaric acid, citric acid, isocitric acid), flavonoids (dominant compounds: apigenin, jaceosidin), hydroxycinnamic acids and their derivatives (dominant compounds: caffeic acid, rosmarinic acid, ferulic acid, salvianolic acid F), and terpenoids (dominant compounds: hydroxyrosmadial, carnosic acid isomer, rosmadial derivative, rosmadial isomer, rosmanol, carnosol isomer). We showed that the main compound identified in flower extracts was rosmarinic acid. The maximum content of rosmarinic acid was determined in the flower extracts and was equal to 369.09 mg/100 g DW. Comparative analysis of the percentage contribution of rosmarinic acid to the antioxidant activity of all tested extracts from S. hispanica raw materials showed that the flower extracts contribute the least to antioxidant activity—$26.3\%$. In our study, flower extracts demonstrated a slightly different antioxidant profile compared to the rest of the analyzed extracts. Probably the presence of derivatives belonging to the group of pyrimidines and purines was responsible for this activity. Our study proved that the S. hispanica herb metabolite profile was the most abundant in organic acids (dominant compounds: citric acid, isocitric acid), phenolic acids (dominant compounds: rosmarinic acid, ferulic acid, salvianolic acid F) followed by flavonoids (dominant compounds: apigenin rutinoside, apigenin, jaceosidin, hispidulin), terpenoids and saccharides (dominant compounds: hydroxyrosmadial, carnosic acid isomer, rosmadial derivative, rosmadial isomer). The quantitively dominant compound for herb was also rosmarinic acid. Formerly, only Dziadek et al. [ 42] investigated the phytochemical profile of S. hispanica herb extracts. They identified the polyphenol profile by HPLC analysis. They found in herb extracts only the following compounds: p-hydroxybenzoic acid, caffeic acid, chlorogenic acid, ferulic acid, gallic acid, p-coumaric acid, rosmarinic acid, synapic acid, syringic acid, vanillic acid, acacetin, apigenin, catechin, epicatechin, hesperidin, hispidulin, isorhamnetin, kaempferol, luteolin, myricetin, naringin, quercetin, rutin, carnosic acid, and carnosol. In our study, a greater number of compounds were identified. We did not confirm the presence of chlorogenic acid, gallic acid, p-coumaric acid, synapic acid, syringic acid, vanillic acid, catechin, epicatechin, isorhamnetin, myricetin, naringin, quercetin and rutin. Dziadek et al. [ 42] also determined the antioxidant power of S. hispanica herb extracts at 716.26 μmol TX/g DW. In our study, we demonstrated that rosmarinic acid contributes to this activity. The percentage contribution of rosmarinic acid to the antioxidant activity of herb extracts was $47.66\%$ (185.12 mg/100 g DW). Abou Zeid et al. [ 74] studied the aerial parts of S. hispanica by the UPLC-ESI-MS/MS technique. They identified significantly fewer compounds than in our study (37 compounds) from phenolic acids, flavonoids, tannins, diterpenoids, lignans and triterpenoids. The individual compounds: caffeic acid, rosmarinic acid, ferulic acid, orientin, vitexin, danshensu, carnosol, jaceosidin, syringetin and luteolin, are similar to these confirmed in the present study. Furthermore, Abou Zeid et al. performed the analysis of antioxidant properties by the DDPH method and demonstrated the significant potential of the ethyl acetate extracts of S. hispanica aerial parts (herb). There is limited research on the antibacterial and antifungal actives of S. hispanica seeds, sprouts, leaves, roots and herb extract. Our study showed that chia leaf extracts exhibited the highest antibacterial and antifungal activity compared to the other tested extracts. Chia leaf extracts showed stronger antibacterial activity against two Gram-positive bacteria (S. epidermidis and S. aureus). More favorable antifungal activity of chia leaves was found for S. albicans. The sprout extracts demonstrated the best effect and lowest MIC against two Gram-positive bacteria (M. luteus and B. cereus). The results proved that the sprout extracts showed the best antifungal activity against C. parapsilosis. The herb extracts showed the least favorable antibacterial and antifungal activity. The antibacterial efficacy of the tested extracts was in the order of leaves > sprouts > herb. However, their activity was significantly lower compared to that of standard drugs routinely used to treat bacterial infections. Abdel-Aty et al. [ 50] examined the antimicrobial activity of chia sprouts raw chia seed extracts against Gram-negative bacteria (E. coli O157-H7 ATCC 51,659, Salmonella typhi ATCC 15,566 and Pseudomonas aeruginosa NRRL B-272) and one Gram-positive bacterium (Staphylococcus aureus ATCC 13,565). The range of MIC for the chia sprout extracts was lower (0.40–0.65 mg/mL) in comparison to the dry chia seed extracts. In our study, the activity of the chia sprout extracts was a bit less (the MIC range value 0.625–10 mg/mL). The antimicrobial activity of chia protein hydrolysates obtained from seeds was studied by Coelho et al. [ 75]. The protein hydrolysates exhibited favorable inhibitory activity against S. aureus to a greater extent compared to E. coli, which is in line with our results confirming the more favorable antimicrobial activity of all analyzed extracts against S. aureus compared to E. coli. Güzel et al. [ 76] investigated the antibacterial and antifungal activity of ethanol extract of chia seeds against reference strains of S. aureus, B. subtilis, E. coli, A. baumannii, A. hydrophila, C. albicans, C. tropicalis and C. glabrata showing their higher activity compared to those presented in this study. According to the recent literature, the composition and content of key bioactive compounds in chia seeds can vary depending on external factors such as geographic origin, climatic conditions, agricultural practices, extraction procedures and antimicrobial activity procedures [9,77]. These factors may affect the efficacy of the extract under study and may result in different outcomes compared to other studies. In addition, Güzel et al. demonstrated that chia seed extract exhibited the highest antifungal activity against C. glabrata, but the result was not as high as with fluconazole (MIC values: 31.25 µg/mL and 3.90 µg/mL, respectively). In our study, we found significantly higher antifungal activity of the whole seed extract compared to the ground seed extract. The antimicrobial effect of chia seeds is likely due to their rich chemical composition. Chia seeds are a source of fatty acids, accounting for about $30\%$, which include linoleic acid (17–$26\%$) and linolenic acid (50–$57\%$). Chia seeds are also a source of vitamins, macronutrients and micronutrients [9,78]. The presence of numerous antioxidants in chia seeds, such as omega-3 fatty acids, may determine their antimicrobial properties. Chia seeds contain kaempferol and quercetin, which have scientifically proven antibacterial properties. It can be inferred that kaempferol binds to an enzyme in bacterial cells and blocks a process essential for bacterial function. Chia seeds also contain caffeic acid and p-coumaric acid, which have proven antimicrobial activity [79,80]. Moreover, adding chia seeds to food products can increase their microbiological stability and prevent contamination without additional preservatives. Numerous human pathogens have been scientifically proven to be experimentally sensitive to the inhibitory effects of phenolic acids, flavonoids, tannins and anthocyanins, especially against several specific strains of Gram-positive bacteria (S. aureus, L. monocytogenes, M. luteus, E. faecalis, C. botulinum and B. subtilis) and Gram-negative bacteria (E. coli, S. typhimurium, S. enterica, P. mirabilis, Y. enterocolityca, S. dysenteriae, S. flexneri, P. fluorescens, P. aeruginosa and V. cholerae) and the fungal pathogen C. albicans [69,76,79,81,82,83,84,85,86]. In our study, we examined for the first time the antibacterial and antifungal properties of the root extract of the S. hispanica plant. The roots extract demonstrated beneficial antifungal properties against C. parapsilosis and C. glabrata. ## 4.1. Materials and Chemicals Reagents of analytical, HPLC or MS grade, including acetonitrile, methanol, water, and formic acid, reagents for antioxidant profiling: 2,2′-azinobis(3 ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) and (±)-6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox) and rosmarinic acid standard were purchased from Sigma- Aldrich (St. Louis, MO, USA. Reference strains came from American Type Culture Collection (ATCC) (LGC Standards, Teddington, UK), Mueller–Hinton broth and agar were purchased from Oxoid Ltd. (Hampshire England); glucose, dimethyl sulfoxide (DMSO) were purchased from Avantor Performance Materials Poland S.A. (Gliwice, Poland); sterile physiological saline (BioMerieux, Craponne, France). ## 4.2. Plant Material The seeds from which the plant material for analysis was obtained were from Guatemala, obtained from KruKam Polska S.A. (Wodzisław Śląski, Poland). The cultivation of S. hispanica was carried out under greenhouse conditions in the Prof. Marian Koczwara Medicinal Plants Garden of the Faculty of Pharmacy of the Jagiellonian University Medical College (Cracow, Poland). The herb, leaves and flowers were harvested in August 2021 during the flowering and fruiting period of the plants. Sprout culture was performed in a PlantiCo brand germinator (Stare Babice, Poland). The sprouts were collected in August 2021. The plant material was dried by freeze-drying (Labconco freeze-dryer, Kanas City, MO, USA). ## 4.3. Preparation of Extracts For chromatographic analysis, the powdered lyophilizates (50 mg) were extracted with a methanol solution ($70\%$, 0.5 mL). The extraction was assisted by ultrasound (15 min). The extracts were centrifuged (13,000 rpm, 15 min), and the supernatants were collected. The extraction step was repeated for the solid residue with another portion of methanol ($70\%$, 0.5 mL). The combined supernatants (~1 mL) were subjected to chromatographic analysis. To prepare the extracts used for the analysis of antibacterial and antifungal properties, samples of dry powdered plant tissue were weighed at 4 g DW each. The material was extracted with methanol in a volume of 100 mL. Extraction was carried out in an ultrasonic bath model 3 times for 20 min each. The extracts were filtered through tissue paper strainers. The material was extracted using blotting paper, which was poured into crystallizers after draining. The material was left to evaporate for 3 days. After 3 days, the material was eluted with methanol, and the weighed extract was placed in 7 mL (16 × 66 mm) polypropylene tubes from Rymed Company (Dabrowa Gornicza, Poland). ## 4.4. Untargeted Metabolomics by LC-Q-Orbitrap HRMS The Salvia hispanica hydromethanolic extracts were investigated using a Dionex Ultimate 3000 UHPLC system (Thermo ScientificTM, Dionex, San Jose, CA, USA). Chromatography separations were performed using SynergiTM Hydro-RP A (150 × 4.5 mm, 4 µm, Phenomenex) column. Mobile phases A (water) and B (acetonitrile), both acidified with formic acid ($0.1\%$ v/v), were pumped at a flow rate of 0.8 mL/min1, according to the following gradient pattern: 0 min, $5\%$ B; 20 min, $50\%$ B; 25 min, $100\%$ B; 27 min, $100\%$ B and finally, the initial conditions were held for 8 min as a re-equilibration step. The injection volume was 4 μL. The chromatographic unit was coupled to a Q ExactiveTM Focus quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) with a heated electrospray ionization source (HESI II). The HESI parameters in negative polarity included: sheath gas flow rate, 35 arb; auxiliary gas flow rate, 15 arb; sweep gas flow rate, 3 arb; spray voltage, 2.5 kV; capillary temperature, 350 °C; S-lens RF level, 50; heater temperature, 300 °C. Full scan data in the negative mode was acquired at a resolving power of 70,000 FWHM; AGC target, 1e6; max IT, auto. A scan range of m/z 100–1200 was chosen for the compounds of interest. The parameters of data-dependent MS2 were as follows: resolution, 17,500; isolation window, 3.0 m/z; normalized collision energy, 30; AGC target, 1e6; max IT, auto. Mass calibration was performed once a week, in both positive and negative modes, using mixture containing n-butylamine, caffeine, Met-Arg-Phe-Ala (MRFA) and Ultramark 1621. Raw data from high-resolution mass spectrometry were elaborated with Compound Discoverer (v. 2.1, Thermo, Waltham, MA, USA). Major metabolite identification was based on accurate mass and mass fragmentation pattern spectra against MS-MS spectra of compounds available on customized database of different classes of phytochemicals created on the basis of literature data on the Salvia species and implemented in the software. Raw data from three experimental replicates and a blank sample were processed using a workflow presented in Kusznierewicz et al. [ 87]. ## 4.5. Antioxidant Profiling by Post-Colum Derivatization with ABTS Antioxidant profiles were obtained for S. hispanica hydromethanolic extracts using an HPLC-DAD system (Agilent Technologies, 1200 series, Waldbronn, Germany) coupled with a Pinnacle PCX Derivatization Instrument (Pickering Laboratories Inc.) and a UV-Vis detector (Agilent Technologies). The chromatographic column and conditions of chromatographic separation were the same as in the case of LC-HRMS analysis. The post-column derivatization with ABTS reagent was carried out according to Kusznierewicz et al. [ 88,89] with slight modification. Stream of methanolic ABTS solution (1 mM) was introduced to the eluate stream at a rate of 0.1 mL/min and then directed to the reaction loop (1 mL, 130 °C). Reduction of ABTS radical by extract components was monitored at 734 nm. The antioxidant activity of the major reducing analytes was quantified with the use of Trolox calibration curve and expressed as Trolox equivalents. The percentage contribution of the rosmarinic acid to the antioxidant activity of extracts was estimated on the assumption that $100\%$ is the sum of the negative peak areas integrated into chromatograms obtained after derivatization with ABTS. ## 4.6. Rosmarinic Acid Determination For quantitative determination of rosmarinic acid, the calibration curve was generated by integrating the areas of absorption peaks (330 nm) determined during HPLC-DAD analysis of serial dilutions of authentic standard. The chromatographic system, column and conditions of separation were the same as in the case of antioxidant profiling (Section 4.5). ## 4.7. Antibacterial and Antifungal Activities All extracts were screened for antibacterial and antifungal activities by two-fold micro-dilution broth method. Minimal inhibitory concentration (MIC) of tested compounds were evaluated for the panel of reference Gram-positive bacteria: Staphylococcus aureus ATCC 25923, S. aureus ATCC BAA-1707, Staphylococcus epidermidis ATCC 12228, Micrococcus luteus ATCC 10240, Bacillus cereus ATCC 10876 and Gram-negative bacteria: Salmonella Typhimurium ATCC 14028, Escherichia coli ATCC 25922, Proteus mirabilis ATCC 12453, Klebsiella pneumoniae ATCC 13883, Pseudomonas aeruginosa ATCC 9027 and yeasts: Candida albicans ATCC 102231, Candida parapsilosis ATCC 22019, Candida glabrata ATCC 90030. The procedure for conducting antimicrobial activity testing has been described in detail before [90]. Briefly, the solutions of tested compounds dissolved in dimethylosulfoxide (DMSO) were suspended in Mueller–Hinton broth for bacteria or Mueller–Hinton broth with $2\%$ glucose for fungi. Then the series of two-fold dilutions were carried out in the sterile 96-well polystyrene microtitrate plates (Nunc, Denmark), obtaining concentration range from 10 to 0.078 mg/mL in the appropriate medium. Simultaneously, the inocula of 24 h cultures of microorganisms in sterile physiological saline (0.5 McFarland standard density) were prepared and added to each well, obtaining final density of 5 × 105 CFU/mL for bacteria and 5 × 104 CFU/mL for yeasts; CFU—colony forming units. Proper positive (inoculum without tested compound) and negative (compound without inoculum) controls were added to each microplate. Vancomycin, ciprofloxacin and nystatin were used as the standard reference reagents. After incubation (35 °C, 24 h), the growth of microorganisms was measured spectrophotometrically at 600 nm (BioTEK ELx808, BioTek Instruments, Inc, Winooski, VT, USA). MICs were marked at the lowest dilution of extract without the growth of bacteria or fungi. Then, 5 µL of the suspension from each well, including controls, was subcultured on the agar plates in order to determine the minimal bactericidal concentration (MBC) or minimal fungicidal concentration (MFC). The plates were incubated at 35 °C for 24 h. The MBC/MFC was determined at the lowest concentration of extracts inhibiting the growth of microbes. MBC/MIC index was also calculated to show bacteriostatic or bactericidal effect of tested extracts. ## 5. Conclusions In the current literature, there are no scientific studies on the comparative analysis of different plant raw materials obtained from S. hispanica. In addition, studies on the antioxidant, antimicrobial and antifungal activities of extracts from different chia raw materials are severely limited. Our work is innovative because it conducts an in-depth characterization and analysis comparing the antioxidant, antimicrobial and antifungal properties of all morphological parts of S. hispanica. In this study, for the first time, the phytochemical profiling and comparative analysis of various morphological parts/organs of S. hispanica extracts was conducted. We showed that in S. hispanica raw materials, the largest class of compounds were terpenoids, followed by flavonoids, phenolic acids and derivatives, organic acids, and other compounds, such as fatty acids and sugars. Conducted analyses proved that organic and phenolic acids were the most abundant class of phytochemicals identified in studied extracts. Rosmarinic acid, belonging to the hydroxycinnamic acids group, was the quantitatively dominant compound found in all tested extracts. The greatest contribution to overall antioxidant activity was made by this compound. Rosmarinic acid contribution to total antioxidant activity was the highest for sprout, herb and leaf extracts (49.5, 47.7 and $47.1\%$, respectively) and the lowest for seed and flower extracts (34.3 and $26.3\%$, respectively). The contribution to the antioxidant activity of sprout, herb and leaf extracts was 144, 139 and 137 times stronger compared to seeds extract. The results of the antibacterial and antifungal activities of the tested extracts proved their higher activity against Gram-positive than Gram-negative reference strains. In Gram-negative bacteria, the tested extracts showed 4–8 times higher MIC values compared to those for Gram-positive bacteria. The antibacterial efficiency of tested extracts was in the order of leaves > sprouts > whole seeds > ground seeds > roots > herb. However, compared to the leaf and herb extracts, the sprout extracts showed better antifungal activity against Candida spp. reference strains. In conclusion, the results obtained in our study indicate that not only the seeds but also other morphological parts of S. hispanica may be a potential source of novel raw materials containing compounds with strong antioxidant, antimicrobial and antifungal potential. ## References 1. 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--- title: In Vitro Antiglycation and Methylglyoxal Trapping Effect of Peppermint Leaf (Mentha × piperita L.) and Its Polyphenols authors: - Izabela Fecka - Katarzyna Bednarska - Adam Kowalczyk journal: Molecules year: 2023 pmcid: PMC10056224 doi: 10.3390/molecules28062865 license: CC BY 4.0 --- # In Vitro Antiglycation and Methylglyoxal Trapping Effect of Peppermint Leaf (Mentha × piperita L.) and Its Polyphenols ## Abstract The most significant reactive α-dicarbonyl RCS involved in the pathomechanism of glycation and related diseases is methylglyoxal (MGO). Hyperglycemia promotes the generation of MGO and leads to the formation of advanced glycation end products (AGEs). Therefore, MGO trapping and glycation inhibition appear to be important therapeutic targets in prediabetes, diabetes, and in the early prevention of hyperglycemic complications. Peppermint leaf is commonly used as herbal tea, rich in polyphenols. Eriocitrin, its predominant component, in a double-blind, randomized controlled study reversed the prediabetic condition in patients. However, the antiglycation activity of this plant material and its polyphenols has not been characterized to date. Therefore, the aim of this study was to evaluate the ability of a peppermint leaf dry extract and its polyphenols to inhibit non-enzymatic protein glycation in a model with bovine serum albumin (BSA) and MGO as a glycation agent. Peppermint polyphenols were also evaluated for their potential to trap MGO in vitro, and the resulting adducts were analyzed by UHPLC-ESI-MS. To relate chemical composition to glycation inhibitory activity, the obtained peppermint extract was subjected to qualitative and quantitative analysis. The capability of peppermint leaf polyphenols to inhibit glycation (27.3–$77.2\%$) and form adducts with MGO was confirmed. In the case of flavone aglycones, mono- and di-adducts with MGO were observed, while eriodictyol and eriocitrin effectively produced only mono-adducts. Rosmarinic acid and luteolin-7-O-glycosides did not reveal this action. IC50 of the peppermint leaf dry extract was calculated at 2 mg/mL, equivalent to a concentration of 1.8 μM/mL of polyphenols, including ~1.4 μM/mL of flavonoids and ~0.4 μM/mL of phenolic acids. The contribution of the four major components to the anti-AGE activity of the extract was estimated at $86\%$, including eriocitrin $35.4\%$, rosmarinic acid $25.6\%$, luteolin-7-O-rutinoside $16.9\%$, luteolin-7-O-β-glucuronoside $8.1\%$, and others $14\%$. The effect of peppermint dry extract and polyphenols in inhibiting MGO-induced glycation in vitro was comparable to that of metformin used as a positive control. ## 1. Introduction Non-enzymatic glycation is a reaction between the carbonyl groups of reducing sugars or their derivatives such as reactive carbonyl species (RCS) and the amino, guanidino, or thiol groups of some biomolecules including peptides, proteins, lipoproteins, and nucleic acids. Several physiological products of sugar autooxidation are known, as well as intermediates from glucose and fructose metabolism characterized by the presence of two adjacent carbonyl groups (α-dicarbonyl compounds). These compounds are ketoaldehydes or dialdehydes and are currently called RCS due to their high reactivity. The most important ketoaldehyde involved in the pathomechanism of non-enzymatic glycation and related diseases is methylglyoxal (MGO). Long-term hyperglycemia promotes the production of MGO, and this significantly increases glycation, leading, among other things, to the formation of irreversible advanced glycation end products (AGEs). AGEs can bind to specific receptors (RAGE) in the cell membrane and consequently trigger the nuclear factor κB (NF-κB) signaling pathway, which induces inflammation and oxidative stress and causes cell damage. In patients with diabetes mellitus, blood proteins such as hemoglobin and serum albumin are particularly vulnerable to glycation. MGO-derived AGEs have been shown to play a pivotal role in the onset and progression of vascular complications of diabetics. Methylglyoxal thus appears to be a significant contributor to endothelial dysfunction by increasing oxidation, as well as inducing inflammation and apoptosis [1,2,3,4]. Therefore, MGO scavenging and glycation inhibition are now recognized as promising therapeutic targets in diabetes, pre-diabetes, and early prevention of hyperglycemic complications. Several substances of natural origin that can inhibit non-enzymatic protein glycation in the BSA-methylglyoxal and BSA-glucose systems are known. Such potential has been described, among others, for the polyphenols of rooibos, green and black tea [5,6]. Peppermint leaf is commonly used as herbal tea, rich in polyphenolic compounds. Its extracts have been shown not only to have a hypoglycemic effect but also to alleviate the symptoms of metabolic disorders in animal models [7,8,9]. Similar results were observed in a study involving young, healthy volunteers, in which peppermint leaf improved biochemical and anthropometric parameters that are cardiometabolic risk factors [10]. Additionally, eriocitrin, the major component of peppermint extracts, in a double-blind, randomized controlled study reversed the prediabetic condition in patients [11,12]. Peppermint (Mentha × piperita L.) is a hybrid of spearmint (Mentha spicata L.) and water mint (Mentha aquatica L.), and its leaves are used for both food and medicinal purposes [8,13]. The chemical composition of peppermint leaf is variable and depends on the geographic region from which it comes and the conditions of cultivation and storage. However, two main groups of active compounds can be differentiated—a volatile fraction including essential oil and a non-volatile fraction characterized by polyphenolic compounds [13,14,15,16,17]. The European Pharmacopoeia monograph for peppermint leaf dry extract (lat. Menthae piperitae folii extractum siccum) requires that it should contain not less than $0.5\%$ rosmarinic acid [16]. Furthermore, the non-volatile fraction contains eriocitrin (eriodictyol-7-O-rutinoside) as the predominant polyphenolic metabolite, other glycosides of flavanone and flavone, as well as oligomeric caffeic acid esters [14,15,17]. The diverse chemical composition of M. piperita determines its biological and therapeutic indications. The European Medicines Agency’s Committee on Herbal Medicinal Products (EMA/HMPC) states that peppermint leaf is a traditional herbal medicine used for the symptomatic relief of digestive disorders such as indigestion and flatulence [17,18]. Medicines containing peppermint leaf preparation are usually used in liquid or solid form. In addition to the above-mentioned EMA/HMPC recommendations, compounds in peppermint leaf also display antioxidant, anti-allergic, anti-inflammatory, nociceptive, antimicrobial, antiviral, spasmolytic, choleretic, chemopreventive, hepatoprotective, and renoprotective effects [17]. However, the anti-glycation activity of this plant material and its polyphenolic components has not been characterized to date. Therefore, the aim of our study was to evaluate the ability to inhibit the process of non-enzymatic protein glycation by a peppermint leaf dry extract and its major polyphenolic components in a model with bovine serum albumin and methylglyoxal as a glycation agent. Peppermint polyphenols were also evaluated for their potential to capture MGO in vitro, and the resulting adducts were analyzed by UHPLC-ESI-MS. The effect of individual extract components on the observed antiglycation properties was also investigated. ## 2. Results and Discussion Peppermint leaf is a popular non-caffeine alternative to green or black tea. It is also used therapeutically for common digestive ailments. Current research indicates that it may also be effective in certain metabolic disorders. There is evidence from an animal model supporting its potential to lower blood levels of glucose, triacylglycerols, cholesterol and LDL (low-density lipoprotein) [7,8,9]. The effect of peppermint leaf on the biochemical and anthropometric profile of healthy volunteers was studied by Barbalho et al. [ 10]. Their results confirmed the ability of this plant material to reduce glycemia, triacylglycerols, total cholesterol, LDL-c, GOT (glutamic-oxaloacetic transaminase) and GPT (glutamic-pyruvic transaminase), and to increase HDL-c (high-density lipoprotein cholesterol). They also noted a reduction in blood pressure and body mass index (BMI). Thus, regular consumption of peppermint leaf may be beneficial for cardiometabolic health. Among the pharmacologically active components of M. piperita are essential oil (monoterpenes) and polyphenols (flavonoids and phenolic acids). Menthol and menthone give peppermint preparations a specific refreshing taste. The constituents of the essential oil also exert a cholagogic effect, stimulating digestion and eliminating flatulence. However, in aqueous extracts, such as infusion and dry water extract, polyphenols are dominant and can play a pivotal role in the activity. The polyphenol profile of peppermint leaf, its water infusion and alcoholic tincture have been reported in some detail [14,15,18]. Their principal polyphenol is eriocitrin, accompanied by smaller amounts of glycosides of luteolin, apigenin, diosmetin, naringenin, eriodictyol and hesperetin. Another group comprises oligomeric caffeic acid esters such as rosmarinic and lithospermic acids, which are classified as phenolic acids. These compounds are known for their antioxidant properties [19,20]. Anti-glycation properties of some flavonoids and rosmarinic acid have also been confirmed [21,22,23,24]. Nevertheless, most flavanone and flavone glycosides have not been studied for this purpose. The antioxidant potential of peppermint leaf was established using various methods. These properties included iron (III) reduction, iron (II) chelation, DPPH radical scavenging, and the ability to inhibit phospholipid peroxidation catalyzed by iron (III)-ascorbate [17,19,20]. In addition, there is clinical evidence supporting the beneficial effects of eriocitrin on the health of prediabetic patients [11,12]. Therefore, we decided to investigate the potential of peppermint leaf extract and its individual polyphenols to inhibit glycation and MGO uptake. Metformin, a standard oral hypoglycemic and antidiabetic agent was used as the reference substance (positive control). ## 2.1. Polyphenolic Profile of Peppermint Leaf Dry Extract In the present study, a dry extract was obtained from a water infusion of peppermint leaf using the solid-phase extraction (SPE) method. SPE is one of the simplest yet most efficient and versatile techniques for concentrating samples and extracts. With properly selected parameters, it also allows partial purification of the concentrated material from substances of high polarity such as salts and sugars. The peppermint leaf dry extract, containing partially purified polyphenols, was subjected to compositional analysis by UHPLC-ESI-MS against authentic flavonoid and phenolic acid standards. The qualitative composition of polyphenols is shown in Table 1. It lists only the components that occurred at relatively high levels. The chemical composition of the prepared extract did not generally differ from other M. piperita preparations [14,15]. The chemical structures of the main identified compounds are in Figure 1. The content of the major components of the extract was determined using the HPLC-DAD method previously developed and validated for plant materials in the Lamiaceae family. This method has been used successfully in several studies [14,15]. A typical HPLC-DAD chromatogram of peppermint leaf dry extract (1.5 mg/mL, solution in $50\%$ aq. methanol) is provided in Figure 2. As expected, the dominant compound was eriodictyol-7-O-rutinoside, known as eriocitrin (285.4 mg/$g = 478.4$ μM/g). Luteolin-7-O-rutinoside (syn. scolymoside, 78.5 mg/$g = 132.1$ μM/g) and luteolin-7-O-β-glucuronoside (27.6 mg/$g = 59.6$ μM/g) were reported in substantially lower amounts (Table 2). These were followed by rosmarinic acid (57.8 mg/$g = 160.5$ μM/g), hesperetin-7-O-rutinoside (syn. hesperidin, 22.9 mg/$g = 37.4$ μM/g), lithospermic acid (8.3 mg/$g = 15.4$ μM/g), diosmetin-7-O-rutinoside (syn. diosmin, 4.7 mg/$g = 7.8$ μM/g), apigenin-7-O-rutinoside (syn. isorhoifolin, 3.4 mg/$g = 5.8$ μM/g) and naringenin-7-O-rutinoside (syn. narirutin, 1.2 mg/$g = 2.1$ μM/g), as well as luteolin-7-O-β-glucoside, eriodictyol, luteolin, caffeic acid, and others (below 1 mg/g). Flavanone and flavone 7-O-rutinosides were predominant components (396.1 mg/$g = 663.7$ μM/g). The aglycones accompanied their own glycosides in negligible amounts. There were a total of 491 mg (904 μM) polyphenols in 1 g of the dry extract, including flavonoids 424.4 mg (726 μM) and phenolic acids 66.6 mg (179 μM). More than half of the polyphenolic components consisted of eriocitrin (~$53\%$), with smaller percentages noted for luteolin glycosides and rosmarinic acid (~$21\%$ and $18\%$, respectively). The four major M. piperita compounds accounted for up to $92\%$ of the polyphenol sum. The prepared dry extract of peppermint leaf met the requirements of the European Pharmacopoeia monograph for a minimum content of rosmarinic acid. ## 2.2. Anti-Glycation Activity of Peppermint Leaf Dry Extract and Its Polyphenolic Components Excessive consumption of simple sugars, glucose and fructose, is one of the reasons for the epidemic of obesity, type 2 diabetes, metabolic syndrome, and cardiovascular disease. Non-enzymatic and enzymatic oxidation of monosaccharides leads to excessive production of RCS, carbonyl stress and related oxidative stress. The consequence is inflammation and malfunction of many tissues and organs, including the liver, kidneys, blood vessels and nervous tissue. These disorders mutually reinforce each other and the accompanying pathologies accumulate with increasing age, which is associated with the progression of cardiometabolic factors such as insulin resistance, impaired glucose tolerance, hyperglycemia, hyperlipidemia, hypercholesterolemia, hypertension and central adiposity [1,2,3,4,25,26,27,28,29,30,31,32,33,34,35]. Belonging to the physiological α-ketoaldehydes, methylglyoxal is a highly reactive metabolite known for its harmful effects. It interacts with the arginine, lysine, and cysteine residues of peptides, proteins, and lipoproteins, causing their post-translational modification, and leading to impaired function of many enzymes, peptide hormones, transport, defense, membrane, structural proteins, and others. MGO-derived modifications also facilitate the formation of cross-links of proteins that alter their higher-order structure and conformation. The process of glycation is long-term and proceeds in several steps. Initially, reversible N-substituted imines (Schiff bases) are formed, which are then converted into more stable irreversible ketoamines (Amadori products). These products undergo further conversions, including dehydration, oxidation and condensation. The resulting final compounds are called advanced glycation end products. AGEs accumulate especially on long-lived proteins such as serum albumin, collagen, lens α-crystallin and hemoglobin (Hb) [28,29,30,31,32]. Increased levels of MGO-derived AGEs have been found in the serum of diabetic patients. In addition, MGO concentrations in blood samples from patients with diabetes correlate positively with the duration of the disease. In laboratory tests, incubation of Hb with MGO resulted in the formation of hydroimidazolone derivatives on arginine residues. The dominant arginine adduct was reported to be Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1). The sites of modification were recognized to be arginine residue 31 in the α-chain, as well as arginine residues 30, 40, and 104 in the β-chain. Hb-MGO adducts presumably may play a critical role in inducing vascular endothelial cell injury [31]. An insulin-MGO adduct was also identified. Methylglyoxal modifies insulin by attaching to arginine residues at position 22 of the B chain. This insulin-MGO adduct reduces in vitro insulin-mediated glucose uptake, impairs autocrine control of insulin secretion, and decreases insulin clearance, leading to insulin resistance [33]. Serum albumin is also modified by MGO. After one day of incubation with MGO, 21 of the 23 arginine residues were irreversibly modified in BSA. In the same conditions, 19 of the 24 arginine residues were modified in human serum albumin (HSA). Likewise, many of the lysine residues in BSA and HSA have fused with MGO (23 of 59 and 11 of 59, respectively). MGO-modified albumin loses its antioxidant capacity and has reduced binding activity for some physiologically relevant compounds and drugs [34]. For these reasons, among others, AGEs, including MG-H1, are now considered risk factors not only for diabetic complications, but also for lifestyle-related diseases. Clinical conditions that can be induced by AGEs are micro- and macroangiopathies such as retinopathy, nephropathy, neuropathy, arterial stiffness, arteriosclerosis, cardiovascular disease, as well as hepatic steatosis, joint stiffness, senile cataracts and Alzheimer’s disease [1,2,3,4,32,35]. Along with HSA, BSA is the most commonly used in model studies of compounds with antiglycation, anti-AGE and anti-RCS potential. Figure 3 summarizes the results of the glycation inhibition test in the BSA-methylglyoxal model obtained in our study. Peppermint polyphenols and the extract were examined at a concentration of 1–1.5 μM/mL and 1.5–3 mg/mL, respectively. The greatest anti-AGE and anti-MGO effect was noted for luteolin (77.2 ± $7.8\%$) followed by apigenin (74.5 ± 0.6) and peppermint leaf dry extract (73.7 ± $1.3\%$). Nevertheless, these differences were not statistically significant. Rosmarinic acid (58.7 ± $10.2\%$), hesperetin (56.9 ± $4.4\%$), luteolin-7-O-β-glucuronoside (50.1 ± $7.1\%$), luteolin-7-O-rutinoside (47.0 ± $9.8\%$) and eriodictyol (43.0 ± $2.6\%$) showed an intermediate action. The statistically significant weakest antiglycation effects were observed for luteolin-7-O-β-glucoside (29.3 ± $6.1\%$) and eriocitrin (27.3 ± $3.9\%$). Under analogous conditions, the antidiabetic metformin inhibited glycation by $52.3\%$ ± $13.8\%$. The complete dry extract of peppermint leaf at a concentration of 3 mg/mL showed statistically significantly larger effects than each of its individual components, with the exception of flavone aglycones. However, aglycones were noted in the extract in very low amounts. IC50 of the peppermint leaf dry extract was calculated at 2 mg/mL, equivalent to a concentration of 1.8 μM/mL of polyphenols, including ~1.4 μM/mL of flavonoids and ~0.4 μM/mL of phenolic acids. Eriocitrin accounted for 1 μM/mL, luteolin glycosides 0.4 μM/mL, rosmarinic acid 0.3 μM/mL, and others were approximately 0.1 μM/mL. In the same conditions, the IC50 values of eriocitrin, luteolin-7-O-rutinoside, luteolin-7-O-β-glucuronoside and rosmarinic acid were found to be 2.7, 1.6, 1.5 and 1.3 μM/mL. For metformin, it was 1.4 μM/mL. The contribution of the four major components to the antiglycation activity of the peppermint leaf dry extract was estimated at $86\%$ (based on concentration in μM/mL and % inhibition calculated from regression equations), including eriocitrin $35.4\%$, rosmarinic acid $25.6\%$, luteolin-7-O-rutinoside $16.9\%$, luteolin-7-O-glucuronoside $8.1\%$, and others $14\%$. The above analyses indicate that the principal component of the extract with antiglycation activity is eriocitrin. The contribution of rosmarinic acid and luteolin glycosides was at a similar but lower level (~$25\%$). The additive effect of MGO trapping for a mixture of the flavonoids quercetin and phloretin was described by Shao et al. [ 21]. It is likely that a similar phenomenon occurs for peppermint leaf polyphenols. Flavonoid rutinosides, unfortunately, show much lower bioavailability than simple glycosides or flavonoid aglycones. However, it should be noted that the activity of aglycones released by intestinal biotransformation is much higher. Moreover, some of their human metabolites, e.g., 7-O-, 3′-O and 4′-O-glucuronosides of luteolin (syn. glucuronides) [36,37], may retain at least part of the activity. On the other hand, phenolic acids such as rosmarinic acid have a slightly higher bioavailability [38]. The effect of peppermint leaf polyphenols on glycation and MGO levels in the intestinal lumen and potential interactions with the microbiota should also be taken into consideration. The anti-AGE effect of polyphenols is related to their protective action against chemical modifications of substrates involved in the glycation reaction, such as binding to groups involved in the initiation of glycation or inhibiting the oxidation of simple sugars and ketoamines (Amadori products). A key component of this effect is the ability to capture glycation- or oxidation-initiating factors (RCS, ROS) [21,22,23,39]. Anti-MGO potential of luteolin-7-O-rutinoside, luteolin-7-O-β-glucuronoside, luteolin-7-O-β-glucoside and eriodictyol-7-O-rutinosides with regard to aglycones was lower, at 35–$62\%$ of their activity. This loss of effectiveness in inhibiting glycation shown by 7-O-substituted flavones and flavanones may be related to the partial or complete abolition of MGO-trapping capacity. Therefore, in another experiment, we tested the ability of peppermint polyphenols and extract to capture MGO. ## 2.3. MGO-Trapping Potential of Peppermint Leaf Polyphenols Several synthetic medicines and plant polyphenols are known to have the capacity for MGO scavenging [5,39,40,41]. However, only metformin is of practical therapeutic use in patients with type 2 diabetes and insulin resistance. The products of nucleophilic addition to the carbonyl group of methylglyoxal are compounds with attached MGO in the form of a side chain and/or heterocyclic ring. Using metformin as an example, these include imidazolinone and triazepinone derivatives [41]. Research by Bhuiyan et al. [ 42] on quercetin-MGO adducts shows that they can have a hemiacetal or hemiketal structure and feature a dihydrofuran ring fused to the flavonoid benzene ring. The presence of these new compounds in the reaction mixture is confirmed using chromatographic methods coupled to a mass detector (MS). Mono- and di-adducts of MGO with polyphenols are characterized by a pseudomolecular ion with an exact mass of 72.02 Da and 144.04 Da higher than the precursor ion, respectively [21,39,40,42]. With the help of UHPLC-ESI-MS, we examined the ability of M. piperita polyphenols to form adducts with MGO. The results from the trapping test are summarized in Table 3. An analogous set of peppermint polyphenols was used as in the glycation inhibition assay. The MGO-trapping ability of other flavones and flavanones such as vitexin (apigenin-8-C-β-glucoside), isovitexin (apigenin-8-C-β-glucoside), hesperidin, hesperetin, diosmin and diosmetin have been reported by our group previously [5,39]. Mono-adducts with MGO have been noted for all flavonoid aglycones, both flavones and flavanones. Apigenin, luteolin, and hesperetin each provided two mono-MGO isomers. Di-MGO adducts were also formed by reaction with luteolin, apigenin and hesperetin (one each). The results obtained for these compounds were consistent with the literature data [39,43]. In our previous study for hesperetin and diosmetin three MGO adducts each were observed, two mono-MGO and one di-MGO [39]. Their 7-O-rutinosides, however, showed different properties. Diosmin did not trap MGO under the test conditions, while hesperidin was the source of six isomeric mono-MGO adducts. Flavanones contain a chiral C-2 atom and can therefore exist as (2S)- and (2R)-enantiomers, e.g., in citrus (2S)-hesperidin is the main one. For eriodictyol and eriocitrin, which also occur in (2S)- and (2R)-configurations, we observed four mono-MGO adducts each, with pseudomolecular ions at m/z 359.08 Da and 667.19 Da, respectively. Among the mono-MGO-eriocitrins, isomer 3 (21.40 min, m/z 667.1873 Da) was the predominant one. Unexpectedly, luteolin-7-O-glycosides (rutinoside, glucuronoside and glucoside) did not show the ability to capture methylglyoxal, probably due to glycosylation of the hydroxyl group at the C-7 position of the benzene ring. No adducts with rosmarinic acid were confirmed either. Former studies have revealed that the phloroglucinol arrangement in the flavonoid framework is an essential element for MGO trapping [5,21,44]. Phloroglucinol tested under the same conditions inhibited by $60\%$ the production of MGO-induced AGEs with the formation of mono-, di- and tri-MGO adducts [5]. It is likely that for some polyphenols, the antiglycation effect is solely related to their antioxidant and transition metal ion chelating activity [23,24]. They may act as radical scavengers and/or metal chelators. The presence of reactive oxygen species (ROS) during the glycation process with trace amounts of metal ions has been proven. However, metal chelation seems to be less involved in the anti-AGE activity [45]. Polyphenols can also form complexes with serum albumin and block access to the glycation site. Interactions of BSA and HSA with polyphenols, including eriocitrin, luteolin and rosmarinic acid, were analyzed by spectroscopic and molecular docking methods [46,47,48]. Phenolic compounds induced conformational changes in the albumin protein, and the main forces involved in binding were hydrophobic interactions. To sum up, [1] non-methylated flavones demonstrated substantially higher antiglycation and MGO-trapping potential than non-methylated flavanones. The difference in anti-MGO activity of luteolin and eriodictyol, as well as luteolin-7-O-rutinoside and eriocitrin, was $44\%$ and $42\%$, respectively. Generally, the double bond between C-2 and C-3 enhances anti-AGE efficacy. [ 2] Glycosylation of the 7-hydroxyl group of flavones and flavanones substantially reduced activity (e.g., luteolin vs. luteolin-7-O-rutinoside by $39\%$, eriodictyol vs. eriocitrin by $37\%$, hesperetin vs. hesperidin by $9\%$). With regard to flavone 7-O-glycosides, there was complete abolition of MGO-trapping ability (7-O-β-glucoside, 7-O-β-glucuronoside and 7-O-rutinoside of luteolin, as well as diosmin). Identical results were obtained by Li et al. [ 49] in the BSA-glucose model for hesperetin, hesperidin, and hesperetin-7-O-glucoside (1 mM; the inhibitory rate was $56.7\%$, $45.8\%$ and $43.5\%$, respectively), as well as their enantiomers (2S, 2R). Interesting features were noted before for flavone C-glycosides (vitexin and isovitexin), which, despite glycosylation of one of the two positions important in the addition reaction (C-8 or C-6), retained the ability to take up MGO with the formation of two isomeric mono-MGO adducts [5]. It also turned out that [3] the methylation of hydroxyl groups in C-4′ of the phenyl ring of flavanones may alter their anti-MGO properties. The methylated counterpart captured two MGO molecules and more strongly inhibited MGO-induced glycation (hesperetin vs. eriodictyol by $24\%$). This effect may be related to a change in the stability of the flavanone heterocyclic ring (e.g., methoxyl at C-4′ is less prone to oxidation and semiquinone formation). A different outcome was observed with 4′-methoxyflavones; their potential against analogous derivatives of 4′-methoxyflavanones decreased (diosmetin vs. hesperetin by $45\%$, and diosmin vs. hesperidin by $20\%$) [40]. Similar conclusions were reached by Shao et al. [ 21] in a study on the potential of flavonoids to scavenge MGO, as well as by Matsuda et al. [ 45] from results obtained in the BSA-glucose model. A trapping test conducted on peppermint leaf extract confirmed the observations gained for its individual compounds. Since the predominant component of M. piperita was eriocitrin, and 7-O-glycosides of luteolin did not capture MGO, the main signals in the extract were four pseudomolecular ions derived from mono-MGO-eriodictyol isomers (Table 3). The possible heterocyclic structures of the mono- and di-methylglyoxal adducts of luteolin, apigenin, eriodictyol and eriocitrin are shown in Figure 4. Nucleophilic addition can result in the formation of isomers that differ in the site of MGO attachment in the benzene ring of the flavonoid (at the C-6 or C-8 position next to the free -OH groups involved in the reactions), as well as in the arrangement and spatial orientation of the functional groups (-OH, -CH3) in the newly formed 2,3-dihydrofuran heterocyclic ring. Depending on which MGO carbonyl (aldehyde or ketone) participates in the reaction, the adduct can be either a hemiacetal or a hemiketal. According to a structural study of quercetin-MGO adducts by Bhuiyan et al. [ 42], hemiacetal is most likely the dominant form. Figure 5 summarizes the schematic 2,3-dihydrofuran structures of hemiacetal and hemiketal. The subsequent Figure 6, Figure 7 and Figure 8 show the pseudomolecular ions of the mono-MGO and di-MGO adducts of luteolin, apigenin, and eriocitrin. Figures S1–S13 in supplementary data present MS spectra of luteolin, apigenin, and eriocitrin authentic standards and their MGO adducts. Eriocitrin is a 7-O-rutinoside of eriodictyol with a health-promoting effect, found mainly in peppermint leaves, and lemon and lime fruits [13,14,15,50]. It has been shown to have antioxidant, anti-inflammatory, lipid-lowering, hypoglycemic and nephroprotective action [11,12,19,51,52,53,54,55,56,57,58]. Eriocitrin reduces oxidative stress and systemic inflammation and improves lipid and glucose metabolism [11,12,51,52,53,54,55,56,57]. It also ameliorates diet-induced hepatic steatosis by activating mitochondrial biogenesis [58]. In our study, we have demonstrated its anti-MGO and anti-AGE effect. These broad biological properties of eriocitrin are of great interest, and the revealed mechanisms of its action may become a starting point for further in vivo experiments. ## 3.1. Chemicals Methanol, acetonitrile (LC gradient grade and LC-MS grade), water (LC-MS grade), DMSO, 98–$100\%$ formic acid, methylglyoxal (MGO, $40\%$ in water), and bovine serum albumin (BSA) were purchased from Merck–Sigma–Aldrich (Poland–Sigma-Aldrich, Poznań, Poland). Glacial acetic acid, NaCl, KCl, Na2HPO4, and KH2PO4 (reagent grade) were obtained from Chempur (Piekary Śląskie, Poland). All other reagnts, unless stated otherwise, were of analytical grade and purchased from Chempur. Water was glass-distilled and deionized. ## 3.2. Authentic Standards Eriodictyol (CAS No. 552-58-9), luteolin (CAS No. 491-70-3), luteolin-7-O-β-glucoside (CAS No. 5373-11-5), apigenin-7-O-rutinoside (CAS No. 552-57-8, syn. isorhoifolin), naringenin-7-O-rutinoside (CAS No. 14259-46-2, syn. narirutin), hesperetin-7-O-rutinoside (CAS No. 520-26-3, syn. hesperidin), diosmetin-7-O-rutinoside (CAS No. 520-27-4, syn. diosmin), caffeic acid (CAS No. 331-39-5), and rosmarinic acid (CAS No. 20283-92-5) were purchased from Extrasynthese (Genay, France). Metformin hydrochloride (CAS No. 1115-70-4) was purchased from Merck-Sigma-Aldrich (Darmstadt, Germany). Eriocitrin (Cas No. 13463-28-0) and luteolin-7-O-rutinoside (CAS No. 20633-84-5, syn. scolymoside) were isolated from M. piperita leaf, as described previously [59]. Luteolin-7-O-β-glucuronoside (CAS No. 29741-10-4, syn. luteolin-7-O-β-glucuronide) and lithospermic acid (CAS No. 28831-65-4) were isolated from Thymus serpyllum L. herb [60]. Stock solutions (1 mg/mL) for quantitative analysis were made by dissolving 2–5 mg of flavonoid in 2–5 mL of methanol. Working standard solutions in the range of 10–250 g/mL (6 measurement points for each pattern) were prepared by mixing with $50\%$ aq. methanol (v/v), filtered through hydrophilic Millex Syringe Filters, Durapore 0.45 μm and 0.22 μm (Merck-Sigma-Aldrich, Darmstadt, Germany) and stored at −20 °C. ## 3.3. Plant Material and Extract Preparation Mentha × piperita L. leaves (M. piperita) were bought from the herbal company KAWON (Gostyń, Poland), GMP and ISO 9002 certified. Peppermint leaf was powdered (10 g) (IKA A11B analytical mill; IKA Warsaw, Poland), added to boiling distilled water (500 mL), stirred, and after 30 min filtered through filter paper (Whatman No. 1, Little Chalfont, Buckinghamshire, UK). The peppermint water infusion was concentrated using the solid-phase extraction (SPE) method. The filtrate (400 mL) was acidified with formic acid (2 mL) and adsorbed onto an octadecyl column (2 × 10 cm, BAKERBOND Octadecyl 40 m Prep LC Packing, J.T. Baker, Phillipsburg, NJ, USA). The column was dried under vacuum (30 min) and the polyphenols were eluted with methanol (3 × 100 mL). Eluates were combined and concentrated at 40 °C (Rotavapor R-300, BÜCHI Labortechnik AG, Flawil, Switzerland). The concentrated eluates were allowed to dry, yielding a peppermint leaf dry extract. The extraction was carried out according to a previously described method [59,60]. ## 3.4. Chemical Composition of Peppermint Leaf Dry Extract The presence of flavonoids and phenolic acids in peppermint leaf dry extract was confirmed by the UHPLC-ESI-MS method described previously by Bodalska et al. [ 15]. The content of the dominant compounds was determined by HPLC-DAD. This method was developed and optimized previously by Fecka and Turek [14]. Peppermint leaf dry extract was separated in a Smartline system (Knauer, Berlin, Germany) equipped with a pump (Managare 5000), a dynamic mixing chamber (V7119-1), a DAD 2800 detector, a manual 6-port 2-channel injection valve (A1366) and a column thermostat (Jetstream Plus). The separation was performed on a Hypersil GOLD C18 column (250 × 4.6 mm, particle size 5 µm) with a C18 precolumn (10 × 4.6 mm, size 5 µm) (Thermo Fisher Scientific, Waltham, MA, USA). The mobile phases were (v/v): $5\%$ formic acid in water (solvent A), and $5\%$ formic acid in acetonitrile (solvent B). The following gradient program was used: $10\%$→$40\%$→$70\%$ B in A at time points of 0→25→30 min. Before each analysis, the column was washed with mobile phase B (10 min) and stabilized with eluent under initial conditions (5 min). The flow rate was 1.0 mL/min, the injection volume 20 μL. The thermostat was set at 20 °C. UV/Vis spectra were taken in the wavelength range of 200–600 nm, with steps of 2 nm. Flavanones were evaluated at 280 nm, flavones at 360 nm, and phenolic acids (caffeic acid derivatives) at 320 nm. Data were processed using EuroChrom for Windows Basic Edition V3.05 (V7568-5). The HPLC-DAD method was validated according to ICH guidelines for linearity, detection and quantification limits, intra- and inter-day precision. Calibration curves for the quantified compounds were determined from 6 measurement points, and double injections were performed for each concentration. The correlation coefficients of the calibration curves (r) used in the calculations were above 0.999 [14,15]. The content of individual polyphenols (mg/g dry extract) was determined using the external standard method based on the area of the corresponding peaks. Polyphenol content was then converted to micromolar concentrations. The contents of flavonoids, phenolic acids, and polyphenols and their percentages in the polyphenol fraction (non-volatile compounds) were also calculated. ## 3.5. Antiglycation Non-Enzymatic Assay (BSA-Methylglyoxal Model) For this purpose, 21.2 μM bovine serum albumin was incubated with 0.5 mM methylglyoxal and 1–1.5 mM of the test compound or peppermint leaf dry extract (1–3 mg/mL) in 100 mM PBS solution, pH 7.4, with $0.02\%$ sodium azide. The reaction mixture was shaken (50 rpm) at 37 °C, for 7 days, in closed vials without light. The fluorescent intensity of MGO-mediated AGEs formed during incubation was analyzed using a Synergy HTX Multi-Mode Microplate Reader (BioTek Instruments Inc., Winooski, VT, USA) at a wavelength of 360 nm for excitation (λex) and 460 nm for emission (λem). Data processing was performed using Gen5 Software (BioTek In-struments Inc., Winooski, VT, USA). Measurements from in vitro experiments were made in triplicate, and the percentage inhibition of AGE formation was calculated from the following equation:%Inhibition of MGO-mediated AGEs = {1 − [(FI1)/(FI0)]} × 100 where FI0 is mean fluorescence intensity of the blank sample and FI1 is the mean fluorescence intensity of the tested sample [5]. The percentage contribution of each component to the activity of the peppermint leaf dry extract was calculated from regression equations for the relationship between the concentration [μM] and % inhibition. These were then related to the overall effect exerted by the extract. ## 3.6. MGO Trapping and Adduct Analysis The direct MGO-trapping capacity of peppermint polyphenols was investigated according to the method of Shao et al. [ 21], with slight modifications. Briefly, 0.6 mM of freshly prepared MGO solution was incubated with 0.2 mM of an individual compound and 0.1 M PBS (pH 7.4) at 37 °C and shaken at 50 rpm for 1 h. The reaction was termined by adding 2.5 μL of glacial acetic acid and transferring samples to an ice water bath. Samples were then filtered through Millex hydrophilic syringe filters (Durapore 0.22 μm) and analyzed using UHPLC-ESI-MS (described below). ## 3.7. Analysis of the Polyphenol-MGO Adducts Adducts of peppermint polyphenols with MGO were analyzed by UHPLC-ESI-MS using a Thermo Scientific Dionex UltiMate 3000 UHPLC system (Thermo Fisher Scientific; Waltham, MA, USA) incorporated with a Compact ESI-QTOF-MS (Bruker Daltonics; Bremen, Germany), a quaternary pump (LPG-3400D) and an UltiMate 3000 RS autosampler (WPS-3000). The reaction mixtures were separated on a Kinetex C18 column (150 × 2.1 mm, particle size 2.6 μm) (Phenomenex; Torrance, CA, USA) at 40 °C (a temperature-controlled column compartment, TCC-3000). The mobile phases were (v/v): $0.1\%$ formic acid in water (solvent C), and $0.1\%$ formic acid in acetonitrile (solvent D). The following gradient program was used: $3\%$→$35\%$→$80\%$ D in C, at time points of 0→12→14 min. The system was then washed in mobile phase D (3 min) and stabilized with eluent under initial conditions before the next analysis (2 min). The flow rate was 0.3 mL/min, the injection volume 2.5 μL. Negative ion mode (ESI−) was used for data acquisition. Nitrogen was used as a nebulizing gas at 210 °C, 2.0 bar pressure, and flow 0.8 L/min. For internal calibration, sodium formate clusters (10 mM) were used. Additional operating conditions of the mass spectrometer were as follows: the capillary voltage was set at 5 kV, the collisional energy was 8.0 eV, and for the MS2 mode, it was 40 eV. Data were analyzed using Compass Data Analysis software (Bruker Daltonics; Bremen, Germany). ## 3.8. Statistical Analysis All data are presented as mean ± standard deviation (SD). Data were analyzed using the Shapiro-Wilk test to assess the normality of distribution, followed by one-way analysis of variance (ANOVA) with Tukey’s multiple comparison test using the GraphPad Prism 9 software, and p values equal to or less than 0.05 were considered significant. ## 4. Conclusions Reactive dicarbonyls, such as methylglyoxal, cause post-translational modifications of physiologically relevant bimoolecules, changing their structure and impairing function. The AGEs formed as a result of such activity then initiate many unfavorable processes associated with cardiometabolic disorders. The strategy of inhibiting glycation and removing MGO by capturing or preventing its formation appears to be a promising therapeutic direction. In this study, peppermint leaf dry extract and its polyphenols revealed the capability to inhibit MGO-induced glycation in vitro. Both flavonoids and rosmarinic acid showed significant antiglycation activity. However, only for flavones and flavanones was the potential to capture MGO confirmed. Among the compounds tested, luteolin and apigenin were the most active. Eriocitrin, the principal component of peppermint leaf, was less effective in inhibiting glycation. Nevertheless, due to its high content in the extract, its contribution to the effect was superior to other components. In addition, unlike 7-O-glycosides of luteolin, eriocitrin effectively trapped MGO with the formation of isomeric mono-MGO adducts. The effect of dry peppermint leaf extract and polyphenols in inhibiting MGO-induced glycation in vitro was comparable to that of metformin used as a positive control. Since eriocitrin is the predominant component of M. piperita with anti-AGE and MGO-trapping potential, and available clinical studies support its efficacy in reversing prediabetes, the use of peppermint leaf in this regard may be warranted. 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--- title: The Role of Rehabilitation in Arterial Function Properties of Convalescent COVID-19 Patients authors: - Maria Ioanna Gounaridi - Angelos Vontetsianos - Evangelos Oikonomou - Panagiotis Theofilis - Nikolaos Chynkiamis - Stamatios Lampsas - Artemis Anastasiou - Georgios Angelos Papamikroulis - Efstratios Katsianos - Konstantinos Kalogeras - Theodoros Pesiridis - Aikaterini Tsatsaragkou - Manolis Vavuranakis - Nikolaos Koulouris - Gerasimos Siasos journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056228 doi: 10.3390/jcm12062233 license: CC BY 4.0 --- # The Role of Rehabilitation in Arterial Function Properties of Convalescent COVID-19 Patients ## Abstract Coronavirus disease (COVID-19) is a respiratory disease, although arterial function involvement has been documented. We assess the impact of a post-acute COVID-19 rehabilitation program on endothelium-dependent vasodilation and arterial wall properties. We enrolled 60 convalescent patients from COVID-19 and one-month post-acute disease, who were randomized at a 1:1 ratio in a 3-month cardiopulmonary rehabilitation program (study group) or not (control group). Endothelium-dependent vasodilation was evaluated by flow-mediated dilation (FMD), and arterial wall properties were evaluated by carotid–femoral pulse wave velocity (cf-PWV) and augmentation index (AIx) at 1 month and at 4 months post-acute disease. FMD was significantly improved in both the study (6.2 ± $1.8\%$ vs. 8.6 ± $2.4\%$, $p \leq 0.001$) and control groups (5.9 ± $2.2\%$ vs. 6.6 ± $1.8\%$, $$p \leq 0.009$$), but the improvement was significantly higher in the study group (rehabilitation) ($p \leq 0.001$). PWV was improved in the study group (8.2 ± 1.3 m/s vs. 6.6 ± 1.0 m/s, $p \leq 0.001$) but not in the control group (8.9 ± 1.8 m/s vs. 8.8 ± 1.9 m/s, $$p \leq 0.74$$). Similarly, AIx was improved in the study group (25.9 ± $9.8\%$ vs. 21.1 ± $9.3\%$, $p \leq 0.001$) but not in the control group (27.6 ± $9.2\%$ vs. 26.2 ± 9.8 m/s, $$p \leq 0.15$$). Convalescent COVID-19 subjects of the study group (rehabilitation) with increased serum levels of circulating IL-6 had a greater reduction in FMD. Conclusively, a 3-month cardiopulmonary post-acute COVID-19 rehabilitation program improves recovery of endothelium-dependent vasodilation and arteriosclerosis. ## 1. Introduction The interaction between coronavirus disease (COVID-19) and the cardiovascular system is multifaceted and well documented, extending from direct or inflammatory injury to the myocardium to endothelial involvement and impairment [1,2,3]. Furthermore, microvascular dysfunction, atherosclerotic plaque progression and vulnerability as well as accelerated arteriosclerosis have been reported [2,4]. Several mechanisms have been implicated in this interplay between COVID-19 and arterial health, including the COVID-19-related inflammatory cascade, the interaction between the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein and angiotensin-converting enzyme 2 (ACE2), and the subsequently increased activity of angiotensin (Ang) II/Ang II receptor type 1 (AT1) [1,5,6]. Importantly, the effects of COVID-19 may extend beyond the acute phase of the disease with symptoms involving multiple organs or systems, and this situation describes post-COVID-19 syndrome [2,7,8]. Vascular function and atherosclerosis data show that up to 6 months post-acute COVID-19, subjects continue to present impairment of vascular function parameters, which may be implicated in a situation of accelerated atherosclerosis [9]. The multi-organ nature of post-COVID-19 syndrome requires multi-disciplinary attention [10]. Cardiopulmonary rehabilitation has been advocated to improve the functional status of patients with respiratory and cardiovascular diseases [11,12]. Moreover, rehabilitation in patients with chronic pulmonary disease may improve vascular function parameters [13]. Additionally, scarce evidence in post-COVID-19 patients suggests that endothelium-dependent vasodilation may be ameliorated after cardiopulmonary rehabilitation [14,15]. Therefore, we hypothesize that cardiopulmonary rehabilitation exerts beneficial effects in post-COVID-19 patients via improvement in arterial function properties. Accordingly, we evaluated the impact of a structured cardiopulmonary rehabilitation program on endothelium-dependent vasodilation and arterial wall properties of convalescent subjects from COVID-19 infection. ## 2.1. Study Population In this study, we prospectively enrolled 60 adult convalescent patients from COVID-19 infection one month post-acute disease. All patients were enrolled from January to September 2022 when the dominant COVID-19 variable in Greece was omicron (variant B.1.1.529). From the outpatient center, Cardiology Department of Preventive Cardiology, 60 subjects were enrolled and served as the control group. Convalescent COVID-19 subjects were randomized at a 1:1 ratio to participate or not in a rehabilitation program. From the study, we excluded patients (a) with end-stage renal disease; (b) with active malignancy; (c) recently hospitalized with another infection; (d) with active COVID-19 infection; and (e) unable/not willing to consent to participate. Individuals were evaluated monthly for COVID-19 re-infection via nasopharyngeal PCR swabs, with those testing positive being further excluded from the study. For the convalescent COVID-19 patients, all arterial functional parameters were evaluated at two times points. The first time (T0) was at one month following recovery from acute COVID-19, and the second time (T1) was 3 months later. During each study visit, all measurements were performed in the same order with blood samples drawn at the end of the study enrollment visit. The hospital’s ethics committee with collaboration from the Athens Medical School of the National and Kapodistrian University of Athens, Greece, approved the study (protocol number: $\frac{23464}{07}$-09-20), which was carried out following the Declaration of Helsinki [1989]. All subjects provided written consent after being thoroughly informed about the study’s aims and procedures. ## 2.2. Rehabilitation Program Patients in the rehabilitation group entered the program 1 month following recovery from acute COVID-19. All subjects randomized to the rehabilitation program participated in 8 outpatient rehabilitation sessions for a total of 1 month, consisting of 30 min interval aerobic exercise on a stationary bike at $100\%$ of peak work rate along with resistance exercises for the upper and lower limbs. Then, for the next 2 months, they followed a remote home-based program with 24 sessions consisting of 30 min walking with an individualized target of steps (recorded via a mobile app installed in the patient’s mobile phone) along with a specific set of resistance exercises for the upper and lower limbs using elastic bands. The assessors set new step targets based on the steps and the symptoms reported by the patient. If dyspnea and leg discomfort were both <4 on the Borg scale [16], the weekly target of steps was increased by 5–$10\%$, otherwise the target remained the same. Patients in the no rehabilitation group were advised to be physically active. ## 2.3. Flow-Mediated Dilation Flow-mediated dilation (FMD) in the brachial artery was used to evaluate endothelium-dependent vasodilation [17,18,19] at enrollment (T0) and three months later (T1). The FMD technique examines the endothelium-dependent dilation of the peripheral artery. This method was originally introduced in 1992 and essentially evaluates the production of nitric oxide (NO) by the endothelium. For the examination, the patient stays in supine position for 10–15 min, and the right brachial artery is located through Doppler ultrasound 5 cm above the antecubital fossa. In this study, the Vivid-e Ultrasound system (General Electric, Milwaukee, WI, USA) was used, with a 5.0–13.0 MHz (harmonics) linear array ultrasound transducer. After the brachial artery is located, its diameter is measured longitudinally at various points, and the average of the multiple measured diameters is calculated. Then, a cuff with an air chamber is placed on the forearm at a pressure 50 mmHg higher than the patients systolic blood pressure in order to induce ischemia. After 5 min, the cuff is released, resulting in hyperemia to the forearm and dilation of the brachial artery. During the hyperemia the brachial artery, the diameter is measured again every 15 s for 2 min, and the average is derived from multiple measurements along the vessel. The diameter is measured during the same phase of the cardiac cycle each time, as it is synchronized with ECG during the examination. All diameters were measured at the boundary of the media–adventitia interface. FMD was then calculated as the percent change of vessel diameter from rest to the maximum diameter after cuff release. All tests were performed by the same operator throughout the study. ## 2.4. Pulse Wave Velocity and Augmentation Index We evaluated arterial wall properties by measurement of pulse wave velocity (PWV) and augmentation index (AIx) at enrollment (T0) and three months later (T1). In this study, the SphygmoCor device (AtCor Medical) was used to measure carotid femoral pulse wave velocity (cf-PWV). PWV reflects the speed in which the arterial pressure waves travel along the aorta and great arteries. It can be calculated by dividing the distance between the two arterial recording points by the travel time of the pressure waves between them. Cf-PWV is generally accepted as the “gold standard” measurement in the evaluation of arterial stiffness and can be used to predict cardiovascular events. The measurement is usually achieved with a set of devices that can measure PWV and can also analyze the pulse pressure waveform [20,21,22,23]. In this study, all subjects were placed in a quiet room, with no temperature fluctuation, and they rested supine for at least 10 min. All measurements were performed at the right common carotid and femoral arteries. Patients were advised not to speak during measurements. The distance between the suprasternal notch and the femoral artery was measured in a straight line using a measuring tape as well as the distance from the carotid artery to the suprasternal notch, and then, the difference between the two was calculated [21]. The value of PWV was derived from the pulse transit time and the distance measured between the two recording sites. A simultaneous ECG was recorded in order to synchronize the pressure waves. All tests were performed by the same expert operator. Augmentation index (AIx) of the central (aortic) pressure waveform was calculated to evaluate wave reflections. A validated acquisition system (SphygmoCor, AtCor Medical, Sydney, NSW, Australia) was used to noninvasively capture and analyze arterial pulse through applanation tonometry. Higher values of AIx correlate with increased arterial stiffness. With increasing stiffness, there is faster propagation of the forward pulse wave as well as a faster reflected wave. This results in the reflected wave reaching the central aorta earlier and increases the pressure in late systole. Accordingly, the augmentation index = (amplified pressure/pulse pressure) rises. A correction of AIx based on a heart rate of 75 bpm is needed, as AIx can be influenced by the heart rate. Radial pressure is calibrated using the systolic and diastolic blood pressure measured with a sphygmomanometer in the brachial artery [24,25,26]. ## 2.5. Laboratory Measurements Blood samples were drawn at study enrollment (T0) for all participants. They were centrifuged and refrigerated at a temperature of −80 °C until assayed. A Luminex assay was used to measure the levels of interleukin-6 (IL-6), a well-established inflammatory cytokine, in patients’ serum. ( Thermo Fisher Scientific Inc., Waltham, MA, USA) [27]. ## 2.6. Statistical Analysis Regarding continuous variables, the Kolmogorov–Smirnov test and visual inspection of P-P plots were used to test for normality of distribution. Accordingly, they were presented as mean with standard deviation or median with 25th and 75th quartile (for normally or not normally distributed variables, respectively). Valid frequencies with percentage were used to present categorical variables. Differences in continuous variables across the groups were evaluated with the t-test or the Mann–Whitney U test, depending on the normality of their distribution. Differences in categorical variables across the groups were calculated by formation of contingency tables and performance of χ2 test. A repeated-measures (paired) t-test was executed to determine the overtime changes in vascular function markers in the intervention and the control group of convalescent COVID-19 individuals. To assess the interactions of the overtime changes in the examined variables according to the intervention group, a general linear model was applied. All reported p values were based on two-sided hypotheses. When the p value was less than 0.05, differences were considered statistically significant. Adherence to the remote home-based exercise rehabilitation program was calculated as the rate of the sessions completed by the patients over the total sessions. All statistical calculations were performed in SPSS software (version 27.0; SPSS Inc., Chicago, IL, USA). ## 3.1. Baseline Characteristics of the Study Population The entire study population consisted of 60 convalescent COVID-19 individuals and 60 healthy controls (Supplementary Table S1). There were no major differences in age, sex, or the prevalence of major cardiovascular risk factors across the two groups. The adherence rate to the remote home-based rehabilitation program was $85\%$. The main reasons that patients missed rehabilitation sessions were weather and health issues (pain, feeling unwell). The inflammatory burden, as assessed by circulating IL-6 levels, was similar in the two groups (convalescent COVID-19: 1.74 (0.64, 4.97) pg/mL vs. control: 1.31 (0.98, 2.70) pg/mL, $$p \leq 0.95$$). However, convalescent COVID-19 patients had impaired vascular function, as evidenced by reduced FMD (convalescent COVID-19: 6.1 (1.9)% vs. control: 7.4 (3.2)%, $$p \leq 0.02$$), increased PWV (convalescent COVID-19: 8.4 (1.6) m/s vs. control: 7.3 (0.8) m/s, $p \leq 0.001$) and increased AIx (convalescent COVID-19: 26.5 (9.5)% vs. control: 23.4 (8.5)%, $p \leq 0.08$). The baseline characteristics of the convalescent COVID-19 patients according to the performance of rehabilitation are presented in Table 1. The age and sex distribution were balanced across the two groups. Moreover, we did not observe any statistically significant differences concerning the presence of cardiovascular risk factors (BMI, history of DM, arterial hypertension, and dyslipidemia). Regarding the disease course in the acute phase, the individuals were evenly distributed. We did not detect considerable differences in the examined vascular function markers, namely FMD (rehabilitation: 6.2 (1.8)% vs. no rehabilitation: 5.9 (2.2)%, $$p \leq 0.57$$), PWV (rehabilitation: 8.2 (1.4) m/s vs. no rehabilitation: 8.8 (1.9) m/s, $$p \leq 0.10$$), and AIx (rehabilitation: 25.9 (9.8)% vs. no rehabilitation: 27.6 (9.2), $$p \leq 0.28$$). Circulating IL-6 levels did not differ significantly between the two groups (rehabilitation: 1.74 (0.64, 6.46) pg/mL vs. no rehabilitation: 2.03 (0.95, 2.98) pg/mL, $$p \leq 0.77$$). ## 3.2. The Impact of Rehabilitation on Vascular Function Recovery Post-COVID-19 The influence of rehabilitation on vascular function recovery is illustrated in Table 2. Both groups exhibited statistically significant improvements in FMD across the recovery period (Figure 1A). However, rehabilitation resulted in improvements of greater magnitude when compared to no rehabilitation (p for interaction <0.001). Regarding PWV, its remarkable lowering was only observed in the rehabilitation group, while it remained practically unchanged in the control group (T0: 8.9 (1.8) m/s vs. T1: 8.8 (1.9) m/s, $$p \leq 0.74$$) (Figure 1B). A significant interaction of PWV with the intervention was noted ($p \leq 0.001$). Finally, similar findings were seen with AIx, which was significantly diminished across the follow-up only in individuals undergoing rehabilitation (T0: 25.9 (9.8)% vs. T1: 21.1 (9.3), $p \leq 0.001$) (Figure 1C). Accordingly, we also confirmed a significant interaction of AIx with the intervention ($$p \leq 0.005$$). ## 3.3. Interleukin-6 and the Effect of Rehabilitation on Endothelium-Dependent Vasodilation in Post-COVID-19 We also assessed whether the inflammatory burden post-COVID-19 could be a determinant of rehabilitation effectiveness. To begin with, we stratified subjects that underwent rehabilitation according to the median circulating IL-6 concentration (1.74 pg/mL). As shown in Figure 2, we observed that individuals presenting with baseline IL-6 concentration above the median had a greater reduction in FMD (ΔFMD) (IL-6 ≥ 1.74 pg/mL: 3.18 (1.78)% vs. IL-6 < 1.74 pg/mL: 1.74 (0.68)%, $$p \leq 0.007$$), as well as percentage change in FMD (IL-6 ≥ 1.74 pg/mL: 57.0 (46.4)% vs. IL-6 < 1.74 pg/mL: 30.8 (13.8)%, $$p \leq 0.04$$). Convalescent COVID-19 patients who did not undergo rehabilitation did not exhibit significant changes in ΔFMD (IL-6 ≥ 1.74 pg/mL: 0.70 (0.40)% vs. IL-6 < 1.74 pg/mL: 0.82 (1.65)%, $$p \leq 0.86$$) and percentage change in FMD (IL-6 ≥ 1.74 pg/mL: 16.5 (15.2)% vs. IL-6 < 1.74 pg/mL: 23.4 (39.7)%, $$p \leq 0.68$$) according to IL-6 levels. ## 4. Discussion In this study, we confirmed the impaired vascular function properties of convalescent COVID-19 subjects compared to healthy individuals with similar risk factor profiles. Moreover, we noted a gradual recovery of both endothelium-dependent vasodilation and arterial stiffness over time from acute COVID-19. Importantly, we documented that a structured rehabilitation program may accelerate or improve the recovery of arterial function parameters. Additionally, we documented that convalescent COVID-19 subjects with systemic inflammation and increased serum levels of circulating IL-6 may benefit more in terms of endothelium-dependent vasodilation from a rehabilitation program. In patients with COVID-19, endothelial dysfunction and altered arterial wall properties have been described not only at the acute phase of the disease but also at the recovery phase and even six months post-acute COVID-19 [28,29,30,31,32]. Infection from SARS-CoV-2 may involve many organs and systems, with the inflammatory cascade and endothelial dysfunction as the substrate of several disease manifestations [1]. “Post-acute COVID-19” or ‘’long COVID-19’’ syndrome is characterized by persistent symptoms and/or long-term complications beyond 3 or 4 weeks from the onset of acute symptoms [1,28,33,34]. These patients share common symptoms such as fatigue, post-exertional malaise, muscle weakness, and cognitive dysfunction, impacting everyday functioning and quality of life [35,36]. The underlying mechanisms contributing to the pathophysiology of post-acute COVID-19 have not been fully elucidated but might include persistent immune and inflammatory activation and target organ damage as well as the expected sequelae of post-critical illness and long hospital stays [34]. We have previously reported that patients with long COVID-19 present impaired endothelium-dependent vasodilation compared to their counterparts without long-COVID-19 [9,37]. Endothelium-dependent vasodilation and impaired ventriculoarterial coupling [2] may contribute to the functional limitations of post-COVID-19, including multisystem deconditioning and subsequent exercise intolerance and muscle weakness [38,39]. ## 4.1. Arterial Stiffness in Convalescent COVID-19 Subjects Subjects with COVID-19 present increased arterial stiffness as measured by PWV and AIx compared to matched control subjects without infection from SARS-CoV-2 [2,40,41]. Moreover, increased arterial stiffness may last up to six months post-acute infection and may be associated with sympathetic drive and endothelial dysfunction [2,9]. Endothelial impairment at the post-acute COVID-19 phase may contribute to increased arterial stiffness. The role of systemic inflammation may also affect arterial function parameters [42,43]. Additionally, the role of sympathetic drive has been described in the impaired ventriculoarterial coupling of convalescent COVID-19 subjects [2], which may contribute to long COVID-19 symptoms, especially regarding functional status and fatigue. The sympathetic overdrive was documented in asymptomatic individuals during COVID-19 convalescence, through multiple non-invasive measures (low/high-frequency power ratio, mean standard deviation of normal-to-normal intervals, and root mean square of successive RR interval differences) [44]. The above-mentioned findings may be more pronounced in survivors of severe acute COVID-19 at convalescence [45]. A recently reported study by Zanoli et al. further illustrates the impact of COVID-19 on vascular status [46]. The investigators noted abnormalities in various arterial stiffness measures (aortic PWV, carotid Young’s elastic modulus and distensibility) 12–48 weeks after COVID-19 [46]. The degree of PWV impairment at convalescence was dependent on the inflammatory burden during the acute phase of the disease [46]. Despite improvement in arterial stiffness measures being noted during long-term follow-up, these do not normalize [46]. Future studies should determine the importance of such findings in the long-term cardiovascular prognosis of COVID-19 survivors who present persistently impaired arterial stiffness. ## 4.2. Cardiopulmonary Rehabilitation Cardiopulmonary rehabilitation has been supported for several decades as a way to provide comprehensive care and to improve the functional status of patients with respiratory and cardiovascular diseases. It helps improve exercise capacity and patient’s quality of life, and it prevents long-term complications [11,47]. Aerobic exercise has been shown to improve endothelial function, both in large arteries and in microcirculation. It can modify the blood flow pattern at arterial branch points, thus leading to less turbulent flow. This reinforces the expression of genes with atheroprotective properties, mainly the NO synthase (eNOS), thus leading to the existence of a vasodilatory and vasoprotective environment in the endothelium. Other vasoprotective factors such as PGI2 and EDHF also appear to increase, which may lead to the improved physiological actions of the endothelium from exercise. Improving autonomic tone and reducing inflammation and oxidative stress may all contribute to the positive effects of exercise on endothelial function and decrease arterial stiffness [12,48,49]. The improvement in endothelial function appears to be largely independent of the type of aerobic exercise, such as treadmill walking versus cycling, as well as continuous versus interval training [50,51]. Moreover, in patients with chronic obstructive pulmonary disease, rehabilitation may improve arterial stiffness parameters (i.e., PWV and AIx) [14,52,53]. Several studies have been published investigating the effects of various exercise programs on reducing symptoms associated with hospitalization following COVID-19 infection, but they have focused on the improvement of patient functional status, quality of life, and respiratory function [54,55,56]. Limited data exist on the role of rehabilitation on endothelial function of post-COVID-19 subjects [15,57]. Similar to our results, significant improvement in clinically evaluated endothelial function after multidisciplinary rehabilitation with a $71\%$ increase in FMD compared to baseline values was documented following rehabilitation; however, this study does not provide evidence on the additional impact of rehabilitation on the natural over-time recovery of arterial function [15]. The mechanisms underlying improvement of endothelial function following cardiac rehabilitation include increased shear stress and activation of endothelial NO synthase [58,59], improved anti-oxidant capacity [60] and regulation of immune system activation [61] and of inflammatory status [62]. Especially, in animal models, aerobic exercise training may modulate IL-6 expression [63]. Our findings show that patients with higher IL-6 levels at baseline present higher improvement of endothelial function following rehabilitation. In line with the benefits of cardiac rehabilitation, other techniques such as enhanced external counterpulsation may also positively influence the quality of life of affected individuals. As recently shown in a retrospective study, convalescent COVID-19 patients improved several indices associated with quality of life and exercise capacity after 15–35 treatment sessions [64]. It may also enhance cognitive function in affected individuals [65]. An ongoing randomized trial (NCT05668039) on the effectiveness of this method in improving long-COVID-19 fatigue is expected to provide further evidence. The results of our study should be interpreted with caution, since the relatively small sample size may limit its credibility. Moreover, the menstrual status and the examination of premenopausal female patients during various phases of the menstrual cycle could be confounding factors that were not taken into account. However, it is considered unlikely that this could affect the overall outcome of the study since the number of pre-menopausal women was limited. Additionally, the use of contraception was not recorded. Finally, we solely assessed endothelium-dependent vasodilation in this study with the use of FMD, and the effect of cardiopulmonary rehabilitation in other methods of endothelium-dependent vasodilation, as well as in endothelium-independent vasodilation, is unclear. ## 5. Conclusions Convalescent COVID-19 subjects present impaired endothelium-dependent vasodilation and increased arterial stiffness. A 3-month cardiopulmonary rehabilitation program in the post-acute COVID-19 phase improves recovery of endothelium-dependent vasodilation, arterial stiffness, and arterial reflected waves, especially in patients with high levels of IL-6 before the initiation of cardiac rehabilitation. These findings may shed light on the mechanisms underlying the beneficial effects of a cardiopulmonary rehabilitation program and on the selection of subjects with the higher possibility of improvement, in terms of arterial function parameters, following rehabilitation. ## References 1. Cenko E., Badimon L., Bugiardini R., Claeys M.J., De Luca G., de Wit C., Derumeaux G., Dorobantu M., Duncker D.J., Eringa E.C.. 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--- title: Study of Influence of Extraction Method on the Recovery Bioactive Compounds from Peel Avocado authors: - Emir Martínez-Gutiérrez journal: Molecules year: 2023 pmcid: PMC10056231 doi: 10.3390/molecules28062557 license: CC BY 4.0 --- # Study of Influence of Extraction Method on the Recovery Bioactive Compounds from Peel Avocado ## Abstract The avocado peel is a waste material from consumption avocado (Persea americana Mill.) with big biotechnology potential. The purpose of the present work was to study the influence of six extraction methods, maceration (M), maceration plus β-cyclodextrin (MβC), solid-state fermentation (SSF), sonication with water or ethanol, wet grinding (WG), wet grinding plus maceration (WGM), on the recovery of bioactive compounds from the avocado peel such as total phenols, epicatechin and chlorogenic acid. The results showed that the extraction method has a significant effect on the content of total phenols, the WGM method obtaining the highest value of total phenols (2143.1 mg GAE/100 g dry weight). Moreover, the results indicated that the extraction method had a significant effect on chlorogenic acid and epicatechin recovery, the WGM method obtaining the highest amount of epicatechin and chlorogenic acid, 181.7 and 244.3 mg/100 g dry matter, respectively. Additionally, the characterization of WGM extract was realized by UPLC-ESI-MS/MS and GC-MS. Thus, the WGM method allowed for obtaining good yields of recovery of phenolic compounds using an accessible technology and a more environment-friendly solvent. ## 1. Introduction Around the world, population growth has increased the food demand, and with it organic waste has augmented, generating major environmental, economic, and social problems such as pollution of water, soil or air, waste treatment, among others [1]. Such as the case of the avocado (Persea Americana Mill), which is a fruit originating from Central America and Mexico that grows in tropical and subtropical regions, whose production has doubled in the last 10 years. Only in Mexico, 2,393,849 tons were produced in 2020, Mexico being the principal producer in the world [2]. In this way, avocado production generates large amounts of organic residues such as seeds and peels, which could be taken advantage as generating value-added products and at the same time contributing to the decrease in waste. Only the avocado peel represents about $16\%$ weight of the fruit [3]. Thus, these residues (avocado peels) have been studied as a potential source of bioactive compounds or industrial interest compounds [3,4]. The bioactive compounds are defined as primary or secondary metabolites of the metabolism of plants such as phenolic compounds, among others [5]. In the case of avocado residues, the extracts obtained from the avocado peel were reported with higher contents of total phenols, flavonoids, and antioxidant capacity than the avocado seed extracts [3,6]. Moreover, epicatechin, chlorogenic acid, rutin, and quercetin were reported as compounds present in avocado peel extracts [3,4]. On the other hand, the elemental composition of the avocado peel (var. Hass) is the following: C ($49.83\%$), N ($0.97\%$), H ($5.71\%$), and O ($42.2\%$) [7]. Among applications that can have the products of the avocado peel are colorants, biopolymers, natural antioxidants, among others. However, even the information is scarce; several studies show insights about the use potential of the extract of the avocado peel with functional antioxidant properties and medicinal agents in diseases related to oxidative stress [6]. In this sense, epicatechin, a compound commonly found in the avocado peel extracts, was reported as such a compound with the possible therapeutic effect against diseases as diabetes and cancer [8]. Recently, the use of epicatechin and chlorogenic acid was observed to have an inhibitory effect on heterocyclic amines’ formation in charcoal roasted lamb meats [9]. In this way, avocado peel extract could be a product with big biotechnological potential. However, there is little information about the strategies used for the release of bioactive compounds from the plant’s raw materials [10]. Some methods used for the release of bioactive compounds include heat reflux extraction [11]; ultrasound-assisted extraction [12]; microwave-assisted extraction [13], among others. However, most of these extraction methods use organic solvents such as methanol, ethanol, chloroform, acetone, etc., which can have negative effects on the environment and increase health risks [14]. Additionally, the modern extraction techniques are expensive methods that require specialized equipment such as microwave-assisted extraction. Meanwhile, conventional methods such as maceration, decoction, hydrodistillation, among others, are still preferred from an industrial point view [1]. In addition, there is still a long way for the development of more efficient methods that maximize the obtaining of bioactive compounds and at same time are environmentally friendly. In this sense, the extraction of polyphenols using water as a solvent, which is a food grade solvent and has a lower environmental impact compared to methanol or acetone, is an alternative that has been used with good results [3]. The addition of compounds in aqueous solutions that improve the extraction and stability of compounds through the formation of complexes has also been explored, such as the case of β-cyclodextrin, which seems to have good potential [15]. Another alternative with potential for sustainability is solid state fermentation (SSF), which has been used in the pretreatment of agro-industrial waste to improve the release and recovery of bioactive compounds [10]. SSF is a biotechnological process where the substrate serves as support for the growth of microorganisms in the absence of a free flow of water and is mainly carried out by yeasts and fungi whose advantages are low energy requirements and high product formation yields [16]. Between them, the organism utilized in SSF is Saccharomyces cerevisiae, which has the ability to produce alcohol from lignocellulosic biomass and tolerance to high concentrations of alcohol, among others [1]. However, reports of its application using the avocado peel as a substrate is very scarce. Moreover, it would be desirable to explore the combination of different methods in order to improve the recovery of active compounds. Thus, the aim of this work was to evaluate the effect of method extraction on obtaining bioactive compounds (epicatechin and chlorogenic acid) from the avocado peel mainly used as a water solvent. ## 2.1. Total Phenol Content and Antioxidant Activity The bio-compounds present in the plants can be obtained through an extraction method. However, the chosen extraction method could have an influence on the compounds obtained and at the same time have an environmental effect. The avocado peel is generally considered a residue of the consumption of avocado, but, also a source of polyphenols, which have been related with antioxidant properties [7]. The compounds that are attributed antioxidant properties generally have the capacity to free-radical scavenging, inhibition of oxidizing enzymes, among others [17]. In this sense, in the present study, the effect of the extraction method on total phenol content from the avocado peel was evaluated; the results are shown in Table 1. Previously, the humidity percentage of the avocado peel was determined, obtaining a value of $70.0\%$ ± 0.2. Regarding the SSF method, the results only are shown at 14 days, because, in that day, the higher value was obtained (data not shown). The results showed that the extraction method had a significant effect (p ˂ 0.001) on the content of total phenols, the WGM method being the one which obtained the highest value of total phenols (2143.1 mg GAE/100 g dry matter), which is a combination of the WG and M methods. In fact, this value was 1.37 and 2.2 times higher than when the WG and M methods were employed separately, respectively. Thus, the combination of both methods increased the total phenol content. On the other hand, Morais et al. [ 18] reported 1252.31 mg GAE/100 g dry matter of the total phenol content from the dried avocado peel, using methanol as the solvent. Shi et al. [ 19] reported similar results (1254 mg GAE/100 g dry weight) using the Soxhlet apparatus and methanol; however, this technology was not considered environmentally friendly [20]. However, Del Castillo-Llamosas et al. [ 21] reported values of the total phenol content between 3130 to 4060 mg GAE/100 g dry avocado peel by means of autohydrolysis, but using a stainless-steel reactor sophisticated at high temperatures (140–180 °C). Thus, the method proposed in the present work allowed for obtaining good yields of total phenol content phenol using accessible technology and a more environment-friendly solvent. Additionally, the antiradical activities of extracts were determined by the DPPH method; the results showed that the highest DPPH inhibition was $43.8\%$ when the M method was used, followed by the WG method, and minor inhibition was when SSF was employed (Table 1). These results were similar to those reported by Kamaraj et al. [ 22], whose inhibition values were between 24.7–$60.1\%$, depending on the extract concentration employed. However, the WGM extract showed only $65\%$ of the inhibition observed by the M extract despite the fact that it was the extract with the highest total phenol content. This behavior in the DPPH assay could be due to the possibility that, during the chemical reaction, a steric hindrance of the extract components can occur that prevents the interaction with the DPPH, or other factors can affect the result, such as polarity, pigments, or concentration [23,24], and, therefore, to underestimate the value of the antiradical activity. So, when the concentrations of the extract components were increased, these could negatively affect the performance of the method, underestimating the value obtained. ## 2.2. HPLC Analysis and Compounds’ Identification by UPLC-ESI-MS/MS All avocado peel extracts were analyzed initially by HPLC. In this analysis, large differences were observed between the extracts as the number peaks, heights, and areas. To exemplify this, Figure 1 shows three chromatograms of avocado peel extracts (WGM, WG, and ES), chosen to have different heights (small, medium, high), noting that the WGM extract (Figure 1A) obtained higher heights in comparison with the other extracts. The same behavior was observed with the areas. Furthermore, the identification and quantification of epicatechin and chlorogenic acid were realized, on the basis of retention time, compared with authentic standards and using the calibration curves’ standard (Table 2); when the SSF method was used, these compounds were not detected in the extract. The results showed that there was a statistically significant difference (p ˂ 0.001) between the extraction method used and the content of chlorogenic acid obtained in the extract. The acid chlorogenic content with the smallest value was 7.2 ± 0.3 mg/100 g dry matter when the ES method was used, while the highest value was 244.3 mg/100 g dry matter when WGM was employed. Similarly, López-Cobo et al. [ 5] reported a concentration of chlorogenic acid of 189.89 mg/100 g dry weight in the avocado peel, but using hexane as the extraction solvent. With respect to epicatechin content, the highest value was 181.7 ± 31.4 mg/100 g dry weight when the WGM method was utilized; this value was up to eight times highest than when the WS method was used. Therefore, the extraction method had a significant effect (p ˂ 0.001) on the epicatechin concentration obtained. Figueroa et al. [ 25] reported a concentration of epicatechin $\frac{59}{100}$ g peel dm using a microwave-assisted extraction (MAE) and ethanol ($36\%$) as the solvent. In other work, 129.79 µg of epicatechin/100 g dry weight was obtained using methanol as the solvent and stirring for four hours [20]. These results indicate that it is possible to obtain a good yield using an environment-friendly solvent and an accessible method such as the WMG method, which combines the mechanical action and maceration a 70 °C. Subsequently, the extract obtained by the WMG method was analyzed by UPLC-ESI-MS-MS because this extract presented the greatest number of peaks and the highest areas of the target compounds (epicatechin and chlorogenic acid). The phenolic compounds were identified on the basis of mass spectra, retention time, comparison with authentic standards when available, and previously reported information; the results are shown in Table 3. Through this study, it was possible to identify 26 tentative compounds; among them were found phenolic acids such as benzoic acid, gentisic acid and vanillic acid, hydroxycinnamic acid esters (e.g., chlorogenic acid), flavonoids (e.g., catechin and epicatechin), among others. Some of these compounds have been related to different promising properties, such as the case of the hydroxybenzoic acids such as salicylic acid and gentisic acid, which were related with an effect that was pharmacologically active, such as decreasing oxidative stress and inflammation [32], while the quinic acid was related with properties such as antioxidant, antidiabetic, analgesic, among others [33]. Moreover, chlorogenic acid (3-caffeoylquinic acid) was identified; this compound has been related with roles such as antioxidant activity, cardioprotective activity, neuroprotective activity, anti-obesity, among others [34]. Between flavonoids, epicatechin was identified and was one of the most abundant compounds in the extract. Epicatechin is a secondary metabolite that shows antioxidant and anti-inflammatory activities, prevents diabetes, protects the nervous system, among others [35], whereas naringin presents anti-inflammatory and anticancer activities [36]. Thus, the extract of the avocado peel can be a source of polyphenols with a wide range of applications. Additionally, a compound labeled as unknown 2 was detected, and it was not possible to achieve an unambiguous identification, but, according to m/z (609.28) and reports of previously works, it could be multinoside A [25], quercetin-rhamnoside-hexoside [30] or kaempferol-dihexosa [23]; the rutin was discarded because the retention time was not the same as the authentical standard. ## 2.3. GC-MS Analysis An analytical characterization of compounds present in avocado peel extract (WGM) was carried out using GC-MS. The identification was realized on the basis of the retention time value and fragmentation patterns of the mass spectra. GC-MS results of the extract confirmed the presence of 38 compounds that were tentatively identified and others whose unambiguous identification was not possible (Table 4). Between these compounds are sugars such as glucose and fructose and sugars’ alcohols (polyalcohols) such as glycerol, pentitol, and glucitol, which could be used as sweeteners [37]. Moreover, malic acid was identified, which presented one of the highest peaks. Malic acid is a compound predominant in several fruits, and among their applications are their use as an acidulant and taste enhancer in several food products [38]. Too, epicatechin was identified, presenting one of the highest peaks, which, as mentioned before, presents antioxidant activity. Moreover, fatty acids such as octanoic and palmitic acid were identified. ## 2.4. Comparison of the Extraction Methods In the present work, six extraction methods for the recovery of bioactive compounds from the avocado peel were evaluated, focusing mainly on the recovery of epicatechin and chlorogenic acid and using water as the solvent, except for the sonication method in which ethanol was also evaluated. These methods included conventional methods such as maceration and more advanced technologies such as sonication. The latter is considered a “Green Food Processing” technology, whose focus is simplicity, energy efficiency, and economy [39]. The results indicated that, with the WGM method, it was possible to recover a greater amount of both chlorogenic acid and epicatechin, though, in the case of chlorogenic, a significative difference was not observed when WG and WGM were employed. However, in comparison with the other methods, the difference was bigger, for example, comparing the MGW with the WS method, when WGM was employed up to 9 and 8 times more chlorogenic acid and epicatechin were obtained, respectively. On the other hand, the addition of β-cyclodextrin did not improve the extraction, while, when the SSF method was used, it was not possible to detect chlorogenic acid or epicatechin under the evaluated conditions, so it would be desirable to explore other conditions in order to obtain better results. The WGM method combines two methods, wet grinding and maceration; both are conventional technologies that together managed to enhance the recovery of bioactive compounds. This indicates that conventional technologies still have great potential in the recovery of bioactive compounds and may even have better performance than new technologies, despite the fact that it was indicated that these technologies are not sufficient to extract the maximum number of materials [39], while new technologies were attributed an improvement in mass transfer, cell permeability, and high extraction yields [40]. Therefore, the effectiveness of the extraction depends on a large number of factors, including type of method, temperature, solvent, time, use of one or more methods, etc. Therefore, it is necessary to continue exploring different conditions and combinations of technologies in order to maximize the recovery of bioactive compounds. ## 3.1. Chemical and Reagents Folin-Ciocalteu’s phenol reagent, β-cyclodextrin, catechin, chlorogenic acid, (−)-epicatechin, gallic acid, and DDPH (2,2-diphenyl-1-picrylhydrazyl) were purchased from Sigma-Aldrich. All reagents used as standards in the UPLC-ESI-MS/MS analysis were HPLC grade (Sigma-Aldrich). Acetonitrile was HPLC grade, and all others reagents such as ethanol were ACS reagent grade and were purchased from J.T. Baker. ## 3.2. Microorganism S. cerevisiae (strain S288C) was selected for solid-state fermentation and was maintained on yeast potato dextrose agar (YPD) Petri plates at 4 °C. The yeast colonies were revived by transferring onto fresh liquid YPD medium and incubated at 28 °C before use. ## 3.3. Preparation of Feed Stock and Extraction Methods The avocado peel (var. Hass) was collected from markets, coffee shops, and homes. The avocado peels were rinsed and collocated on absorbent paper to remove the water excess and were cut in pieces of approximately 1 to 2 cm2. Subsequently, for the first four methods, the biomass was ground in a commercial food processor and sieved using a sieve of 2000 µm. The resultant avocado peel was placed in bags and stored in refrigeration (−20 °C). Moreover, the humidity content was carried out by weight difference; the sample was dried at 60 °C over 24 h. Various extraction methods were evaluated, which are described as a continuation: (a) 40 g of avocado peel plus 200 mL of distillated water were maintained at 70 °C with agitation over 1 h (called maceration (M)); (b) in the second method (called MβC), the anterior process with the addition of β-cyclodextrin ($1\%$ w/v) was used; (c) in the third method, the sonication was evaluated using water or ethanol as the solvent (called WS and ES, respectively); in this method, 1 g of the substrate and 10 mL of the solvent were mixed for 1 min in a vortex, and were subsequently sonicated over 15 min (frequency 40 KHz, power 150 W, mrc Ultrasonic Cleaner D150); (d) in the fourth method, the solid-state fermentation (SSF) was evaluated, which later is described (called SSF); (e) in the fifth method, after cutting the avocado peel to 1 or 2 cm2, this biomass (40 g), plus 200 mL of water, was ground using a commercial blender (FARBERWARE, 120 V, 60 Hz, 900 W) over 30 s (called: wet grind (WG)); (f) in the last method, the wet grind method plus a last maceration stage with agitation at 70 °C during 1 h was used (called WGM). All extracts were filtered, protected from the light, and stored at −20 °C. ## Solid-State Fermentation (SSF) For SSF, 250 mL Erlenmeyer flasks containing 40 g avocado peel were autoclaved at 121 °C for 15 min and after were cooled. The flasks were inoculated with 2.5 × 107 cell/g. After being gently mixed, the fermentations were conducted for 28 days at 28 °C. The experiments were realized by triplicate. A total of 1 g of sample was removed after 7, 14, 21, and 28 days and stored at −20 °C and protected from light. For extraction, 1 g of the sample was added to 10 mL of distillated water and shaken in vortex for 1 min. After, the sample was maintained in a water bath at 70 °C for 1 h, and finally these last two steps were repeated. The extracts were filtrated and stored at −20 °C. ## 3.4. Total Phenolic Content The total phenolic content was determined for the Folin-Ciocalteu method [41] with modifications: 100 µL of extract were mixed with 0.5 mL Folin-Ciocalteu reactive (0.2 N) and 0.4 mL of Na2CO3 ($7.5\%$). The mixture was incubated at room temperature in the dark over 30 min; the absorbance was measured at 760 nm. The total phenolic content was calculated through a standard curve of gallic acid; the result was expressed as gallic acid equivalents/100 mg dry matter. ## 3.5. Antioxidant Activity Radical scavenging activity was determined according to Brand-Williams et al. [ 42] using DPPH (1-1-diphenyl-2-picrilhdrazyl). A total of 30 µL of the sample plus 2 mL of DPPH solution were shaken in vortex and after kept at room temperature in the dark for 30 min. Absorbance was measured at 515 nm. Radical scavenging activity was expressed as the inhibition percentage and was determined using the following equation:% inhibition = ((Absorbance of blank − Absorbance of sample)/(Absorbance of blank)) × 100[1] ## 3.6. HPLC Analysis The determination and separation of compounds were realized by HPLC [43,44]. Previously, the samples were filtrated in the nylon membrane (0.45 µm). A total of 10 µL of the sample was automatically injected into an Agilent 1200 series (Agilent Technologies, Waldbronn, Germany) equipped with a UV-Vis detector with a diode array. A column YMC-ODS-AM C-18 (250 × 4.6 mm) was utilized, and the mobile phase was composed by acetic acid ($1\%$) in water (phase A) and ($100\%$) acetonitrile (phase B). The gradient elution was the following: 0 min, $0\%$ B; 5 min, $5\%$ B; 10 min, $10\%$ B; 30 min $15\%$ B; 40 min, $15\%$ B; 42 min, $0\%$ B. The flow was 1 mL/min with λ = 280 nm at 25 °C. Epicatechin and acid chlorogenic were identified by taking the retention time of the standards, and their concentration was determined by interpolation of the peaks’ area in the standard calibration curve. ## 3.7. UPLC-ESI-MS/MS Analysis The analysis of the WGM extract previously lyophilized was realized using an Ultra-High-Performance-Liquid Chromatography (Waters Corp., Milford, MA, USA) coupled to an Electrospray Ionization-Tandem Mass Spectrometry, Triple Quadrupole (Xevo, TQS, Waters Corp., Wexford, Ireland). The injection volume was 1 µL, and a column Acquity BEH C18 (50 mm ± 1.7 × 2.1 µm) was used for the separation. The temperatures of the column and the sample were 35 °C and 6 °C, respectively. The mobile phase was composed by formic acid (7.5 mM) in water (phase A) and $100\%$ acetonitrile (phase B). The gradient elution was as follows: 0 min $3\%$ B, 1.23 min $9\%$ B, 3.82 min $16\%$ B, 11.4 min $50\%$ B, 13.24 min $3\%$ B, 15 min $3\%$ B. The ionization of the sample was by electrospray, capillary voltage 2.25 kV, cone voltage 30 V, source temperature 150 °C, desolvation temperature 400 °C, cone gas rate 150 L/h, collision gas flow (0.15 mL/min), MS mode 5, MS/MS mode 20. ## 3.8. GC-MS Analysis The WGM sample was derivatized before GC-MS analysis. The sample derivatization was carried out as follows: about 2 mg of the lyophilized sample were weighed out and dissolved with 80 µL of methoxyamine hydrochloride (20 mg/mL in pyridine) and incubated for 90 min at 37 °C. Subsequently, 80 µL of N-methyltrifluoroacetamide (MBSTFA) plus $1\%$ trimethylchlorosilane (TMCS) were added and incubated for 30 min at 37 °C. One microliter of the sample was injected into the GC/MS system. The GC/MS system consisted of the gas chromatography coupled to mass spectrometry (Agilent 5977A/7890B GC–MS, Santa Clara, CA, USA) with an automatic autosampler (G4513A, Agilent). The HP5ms column (30 m × 250 µm × 0.25 µm) of the Agilent brand was used to carry out the separation of the analytes using helium ($99.9999\%$ pure) as the mobile phase. The equipment was calibrated with the Agilent brand perfluorotributylamine (PFTBA) standard. An untargeted analysis was performed with an acquisition range of 50 to 600 Da with an ionization energy of 70 eV. A scan rate of 3 scan/s with a 20 Hz digital scan was used. The specified parameters of the run were the following: inlet temperature 200 °C, quadrupole temperature 150 °C, source temperature 250 °C, flow rate 1 mL/min, standard injection, injection volume 1 µL, split (splitsses). The running method consisted of a ramp of 60 °C to 325 °C with increases of 10 °C/min and 37 min of run time. All reagents and solvents used were pure analytical grade. The spectral deconvolution and peak alignment were realized with the Mzmine2 software, and the identifications were realized using the National Institute of Standards and Technology (NIST) 2.0 spectral library. It was considered as a correct (putatively) identification when the match was higher than $70\%$ (R > $70\%$); values below this limit were marked as unknown or omitted. ## 3.9. Statistical Analysis One-way analysis, ANOVA, and Tukey’s were evaluated using SPSS. The p values ˂ 0.05 were considered as significant differences. ## 4. Conclusions In this study, bioactive compounds were obtained from the avocado peel by different extraction methods. The influence of the extraction method was evaluated by using total phenol content and epicatechin and chlorogenic acid content, where the method with the highest recovery of bioactive compounds was MGW. 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--- title: 'S100B Protein but Not 3-Nitrotyrosine Positively Correlates with Plasma Ammonia in Patients with Inherited Hyperammonemias: A New Promising Diagnostic Tool?' authors: - Anna Maria Czarnecka - Marta Obara-Michlewska - Dorota Wesół-Kucharska - Milena Greczan - Magdalena Kaczor - Janusz Książyk - Dariusz Rokicki - Magdalena Zielińska journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056255 doi: 10.3390/jcm12062411 license: CC BY 4.0 --- # S100B Protein but Not 3-Nitrotyrosine Positively Correlates with Plasma Ammonia in Patients with Inherited Hyperammonemias: A New Promising Diagnostic Tool? ## Abstract Individuals with inherited hyperammonemias often present developmental and intellectual deficiencies which are likely to be exaggerated by hyperammonemia episodes in long-term outcomes. In order to find a new, systemic marker common to the course of congenital hyperammonemias, we decided to measure the plasma level of S100 calcium-binding protein B (S100B), which is associated with cerebral impairment. Further, we analyzed three mechanistically diverged but linked with oxidative–nitrosative stress biochemical parameters: 3-nitrotyrosine (3-NT), a measure of plasma proteins’ nitration; advanced oxidation protein products (AOPP), a measure of protein oxidation; and glutathione peroxidase (GPx) activity, a measure of anti-oxidative enzymatic capacity. The plasma biomarkers listed above were determined for the first time in congenital hyperammonemia. Also, the level of pro- and anti-inflammatory mediators (i.e., IL-12, IL-6, IL-8, TNF-α, IL-1β, and IL-10) and chemokines (IP-10, MCP-1, MIG, and RANTES) were quantified. S100B was positively correlated with plasma ammonia level, while noticeable levels of circulating 3-NT in some of the patients’ plasma did not correlate with ammonia concentration. Overall, the linear correlation between ammonia and S100B but not standard oxidative stress-related markers offers a unique perspective for the future identification and monitoring of neurological deficits risk-linked with hyperammonemia episodes in patients with inherited hyperammonemias. The S100B measure may support the development of therapeutic targets and clinical monitoring in these disorders. ## 1. Introduction Ammonia, an end product of amino acids and protein metabolism, exerts neurotoxic effects when present in excess. In order to reduce this excess, ammonia is detoxified via the urea cycle, which consists of a chain of enzymatic reactions localized in periportal hepatocytes that parallels glutamine synthesis in the brain [1,2,3]. Mutations in any of the genes constituting the urea cycle, encoding six enzymes and two mitochondrial transporters, lead to urea cycle disorders (UCDs), termed ‘primary hyperammonemias’. The most common UCD is an X-linked deficiency in ornithine transcarbamylase (OTC), whereas autosomally recessive inherited deficiency in N-acetyl glutamate synthase (NAGS), carbamoyl synthase (CPS1), argininosuccinate synthetase (ASS), argininosuccinate lyase (ASL), or arginase 1 (AR1) are less frequent [4]. Secondary hyperammonemias accompany metabolic disorders, i.e., organic acidurias, where the urea cycle malfunction is a consequence of enzymatic aberrations disabling the synthesis of the urea cycle substrates [4,5]. The most representative of these are organic acidemias, e.g., methylmalonic, propionic, and isovaleric acidemia, wherein the hyperammonemia is a result of the inhibition of NAGS and/or CPS1 by intermediary metabolites (e.g., methylmalonic or propionic acid, isovalerylglycine) accumulating due to the enzymatic defect [4]. The neurotoxic effect of ammonia can result in growth retardation, psychomotor delay, and neurological impairment [6,7,8]. Importantly, repeated episodes of hyperammonemia might potentiate neurological dysfunction with an eventual fatal outcome if untreated [5,9]. The aforementioned situation is especially true for neonatal-onset diseases, wherein patients’ outcomes are uniformly poor, even with relatively early recognition and treatment [10,11,12]. However, the symptoms of hyperammonemia are often not specific enough to instantly point to it as a reason for a neurological decline. Furthermore, the specificity and validity of measured ammonium concentrations are often limited [13,14]. In order to find a new, systemic marker common to the course of congenital hyperammonemias, we decided, for the first time, to measure the plasma concentration of S100 calcium-binding protein B (S100B). Importantly, an S100B increase was previously found in astrocytes in vitro upon ammonia treatment [15] and in patients with hepatic encephalopathy (HE), mechanistically related to ammonia accumulation [16,17,18,19]. Promisingly, the S100B protein was reported as a peripherally detectable brain injury biomarker in acute brain injury, neuroinflammatory, neurodegenerative, and psychiatric disorders [20,21,22,23]. The molecular mechanisms of ammonia neurotoxicity include overlapping processes involving oxidative–nitrosative stress and inflammation pathways, as it has been frequently documented in preclinical and clinical studies of hyperammonemia-related HE [24], though it escaped detailed investigation in congenital hyperammonemias. Thus, we investigated whether markers of oxidative-nitrosative stress and inflammatory cascades are associated with the course of the abovementioned disease entities. Notably, the nitrosative stress-modified end-point metabolite, 3-nitrotyrosine (3-NT), the proposed biomarker of subclinical HE in cirrhotic patients, was measured. Furthermore, the oxidative stress-indicative markers, such as glutathione peroxidase (GPx) activity and advanced oxidation protein products (AOPP), were measured. Likewise, we assessed the levels of inflammatory cytokines and chemokines in plasma samples obtained from patients diagnosed with congenital hyperammonemias, specifically primary (ornithine transcarbamylase deficiency, citrullinemia type 1 and 2) and secondary (hyperinsulinism-hyperammonemia, methylmalonic acidemia, and propionic acidemia). ## 2.1. Patient Characteristics Eleven patients with diagnosed inborn errors of metabolism, classified as primary or secondary hyperammonemia, were included in the analysis (Table 1). Standard analytical measurements, including metabolites pattern, C-reactive protein (CRP) level, and liver enzyme activity, were conducted along with clinical verification of neurological status (Table 1). Patients remained under the care of the Department of Pediatrics, Nutrition, and Metabolic Diseases, Children’s Memorial Health Institute, Warsaw. All of the procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed written consent was obtained from all patients being included in the study or from their legal guardians. The study was approved by the Committee for Medical Research Ethics of the Children’s Memorial Health Institute in Warsaw (No: 41/KBE/2018). Five of the patients had ornithine transcarbamylase deficiency (OTCD). Three patients (Patients 1–3), who had a late-onset OTCD form, came from one family, each carrying the same mutation: c.622G>A in OTC. Patient 4 carries a c.802A>G mutation, also presenting late OTCD onset, whereas Patient 5 developed with early onset (at 4 weeks of age). At the moment of sampling, Patients 1–4 were metabolically compensated. Patient 6 suffers from methylmalonic aciduria (MMA) type mut0. The first symptoms appeared early, on the 3rd day of life, in the form of acute decompensation. Starting at 1.5 months of life, the patient was fed by PEG. Despite treatment with diet or temporarily with benzoate, ammonia was constantly elevated, up to 200 µg/mL. Normalization of ammonia was achieved later on with carglumic acid. Patient 7, 14 years of age, developed symptoms on the 6th day of life, followed by a diagnosis of propionic acidemia (PA). From the 3rd year of age, the patient was fed by PEG. Despite diet and benzoate treatment, the patient presented constant hyperammonemia (124–356 µmol/L). Patient 8 suffered from PA. The first symptoms appeared at the 6th month of age as acute metabolic decompensation. Hyperammonemia was constant (up to 197 µg/mL) to the moment of carglumic acid introduction, which allowed for ammonia normalization. Patient 9 was admitted at the age of 2 months because of neonatal cholestasis and liver insufficiency. Ammonia was elevated in the range of 109–261 µg/mL. NGS analysis revealed c.1453–2A>T variant in SLC25A13–citrin deficiency. Hypertransaminasemia and mild hyperammonemia persisted until the introduction of a low-carbohydrate diet. Patient 10 underwent severe metabolic decompensation with brain edema from the 2nd day of life, with a maximal ammonia concentration of 1316 µg/mL, persisting until sodium butyrate administration. In Patient 11, genetic investigations demonstrated de novo mutation in GLUD1 (c.1493C>T). Patient’s elevated ammonia concentration did not decrease with diazoxide or upon consumption of a diet with leucine restriction and did not cause symptoms of decompensation. ## 2.2. Samples All of the blood was collected at a single time point: at the time of the patient’s hospital admission. Plasma samples were collected from whole blood after centrifugation and aliquots were kept frozen at −80 °C until analysis. ## 2.3. Biochemical Analysis All patients were evaluated by routine laboratory tests, including liver functions, complete blood count, c-reactive protein (CRP), and plasma ammonia. The plasma concentration of certain amino acids—glutamate (GLU), glutamine (GLN), citrulline (CIT), ornithine (ORN), and arginine (ARG)—was determined by ion exchange chromatography followed by photometric detection after ninhydrine derivatization using the AminoTac JLC-500/V amino acid analyzer (JEOL, Tokyo, Japan). ## 2.3.1. Inflammatory Cytokines and Chemokines Selected inflammatory cytokines in plasma were determined using the Cytometric Bead Array Human Inflammatory Cytokines kit (#551811, BD Biosciences, Becton and Dickinson, San Diego, CA, USA), following the manufacturer’s instructions. The kit is designated for quantitative and simultaneous measurement of interleukin (IL)-6, IL-8, IL-10, IL-12, and tumor necrosis factor-alpha (TNF-α). The BD CBA Human Chemokine Kit was used to evaluate selected chemokine levels (pg/mL) (552990, BD Biosciences, Becton and Dickinson, San Diego, CA, USA). The kit is designated for the quantitative and simultaneous measurement of IL-8, RANTES, monokine induced by interferon γ (CXCL9/MIG), monocyte chemoattractant protein-1 (CCL2/MCP-1), and interferon γ-induced protein-10 (CXCL10/IP-10). Data acquisition (300 events for each cytokine and chemokine) was performed using a BD FACSCanto II flow cytometer with BD FACSDiva Software and FCAP Array software, version 3.0 (BD Biosciences, San Jose, CA, USA). ## 2.3.2. 3-Nitrotyrosine The plasma 3-nitrotyrosine (3-NT) was determined with the use of the OxiSelect™ Nitrotyrosine ELISA Kit (STA-305, Cell Biolabs Inc., San Diego, CA, USA). The procedure was based on a competitive enzyme-linked immunosorbent assay. Quantitative determination of 3-NT in plasma samples was performed in duplicates according to the manufacturer’s instructions. Briefly, 50 μL of plasma sample was added to each well of 96-well plates and was then shaken for 10 min on an orbital shaker. Fifty microliters of anti-tyrosine antibody were then added and incubated for one hour on an orbital shaker at room temperature. Then, each well was washed thoroughly three times with 250 μL wash buffer. After removing all wash buffer, 100 μL secondary antibody–enzyme conjugate was added to all wells and incubated for one hour at room temperature on an orbital shaker. Again, each well was washed thrice with 1× wash buffer. Substrate solution (100 μL) was added to each well and incubated for about 15 min, and as color developed, 100 μL of stop solution was added per well. The plate was read at 450 nm on a spectrophotometer using a microplate reader (SPECTROstar Nano, BMG Labtech, Ortenberg, Germany). A calibration curve was performed using nitrated bovine serum albumin, corresponding to 3-NT concentrations of 1.95–8000 nM. The kit has a nitrotyrosine-detection sensitivity range of 20 nM to 8.0 µM. ## 2.3.3. Advanced Oxidation Protein Products (AOPP) Advanced oxidation protein product (AOPP) levels were analyzed using the OxiSelect AOPP kit (STA-318, Cell Biolabs, San Diego, CA, USA) following the manufacturer’s protocol. Ten microliters of plasma, with a 200 µL final volume, were subjected to 10 µL of chloramine reaction initiator followed by 20 µL of stop solution. Absorbance was recorded at 340 nm using the spectrophotometric microplate reader (SPECTROstar Nano, BMG Labtech, Ortenberg, Germany). ## 2.3.4. Glutathione Peroxidase Activity Assay GPx activity was analyzed using the Glutathione Peroxidase Assay Kit (ab102530, Abcam, Cambridge, UK), according to the manufacturer’s instructions. In brief, plasma samples were depleted of all GSSG by incubating the sample with glutathione reductase (GR) and reduced glutathione (GSH) for 15 min. GPx activity was determined by adding cumene hydroperoxide and incubating for 0 and 5 min. The absorbance was determined at OD340. One unit of GPx activity was defined as the amount of enzyme that causes the oxidation of 1 µmol of NADPH to NADP+ under the assay kit conditions per minute at 25 °C. ## 2.3.5. S100 Calcium-Binding Protein B (S100B) According to the manufacturer’s recommendations, the S100B protein concentration in plasma was measured using the EZHS100B-33K kit from Millipore (Millipore, Billerica, MA, USA). In brief, the assay is a sandwich ELISA based, sequentially, on the capture of S100B molecules from samples in the wells of a microtiter plate coated by a pre-titered amount of anti-S100B monoclonal antibody; the binding of biotinylated anti-S100B polyclonal antibody to the captured molecules; the conjugation of horseradish peroxidase to the immobilized biotinylated antibodies; and the quantification of immobilized antibody–enzyme conjugates by monitoring horseradish peroxidase activity in the presence of the substrate 3,3′,5,5′-tetramethylbenzidine. The enzyme activity was measured spectrophotometrically by the increased absorbency at 450 nm (SPECTROstar Nano, BMG Labtech, Ortenberg, Germany), corrected from the absorbency at 590 nm after the acidification of formed products. The increase in absorbency is directly proportional to the amount of captured S100B in the unknown sample, derived by interpolation from a reference curve with standards of known concentrations of S100B. The kit has an S100B detection sensitivity range of 2.7–2000 pg/mL. All samples were analyzed in duplicate, and the average of the two was used for statistical analysis. ## 2.4. Statistics Statistical analysis was performed using GraphPad Prism 7.0 (GraphPad Software Inc., La Jolla, CA, USA). Linear regression analysis was performed in order to identify correlations between ammonia levels and other biochemical parameters. The solid line is the result of linear regression, showing the corresponding $95\%$ confidence bands of the line of best fit and the Pearson correlation coefficient (R2). All analyses were performed using a significance level of 0.05. ## 3. Results Parameters related to the clinical characteristics and applied treatment are shown in Table 1. Most of the pediatric patients (except Patients 5 and 9) presented neurological impairment, which could manifest in the form of delayed development (defined for individuals < 5 years of age), intellectual disability (defined for individuals > 5 years of age), or epilepsy. Also, one of the four adult patients (Patient 4) manifested symptoms of intellectual disability and psychiatric disturbances. The morphology of blood cells and the CRP protein level (0.04–0.33 mg/dL) remained within the normal range and did not indicate an ongoing infection in any of the included patients. The ASPAT and ALAT values were also within the reference range (26–55 and 24–78 U/L, respectively). A plasma ammonia level < 80 μg/mL was regarded as the norm, and in 7 patients (Patients 5 to 11), an increased level was observed (95–364 μg/mL). The plasma amino acid (GLN, GLU, CIT, ORN, and ARG) levels, assessed for a discriminative diagnosis of metabolic diseases, were within the normal range, except for increased CIT in Patient 9 and Patient 10 and increased ORN and ARG in Patient 10. The clinical outcome data for S100B, 3-NT, AOPP concentration, and GPx activity in plasma are presented in Table 2. A linear correlation ($r = 0.705$, considered significant when $p \leq 0.05$) of S100B protein (17.48–228 pg/mL range) with plasma ammonia concentration (57–364 μg/mL) was revealed (Figure 1A). Furthermore, 3-NT levels, ranging from 401.61 to 3439.41 nM, were elevated substantially in some patients but were unrelated to ammonia levels (Figure 1B). The concentrations of inflammatory cytokines and chemokines measured in plasma are presented in Table 3. The levels of cytokines (IL-12, TNF-α, IL-10, IL-6, IL-1β, and IL-8) and chemokines (IP-10, MCP-1, and MIG) were consistently low, except for Patients 5 and 6, who presented substantially increased levels of IL-12, IL-10, IL-6, IL-1β, IL-8, IP-10, and MCP-1 (Patient 5) or TNF-α, IP-10, MCP-1, and MIG (Patient 6). Contrary to other cytokines and chemokines, the absolute values of RANTES concentrations are characterized by relatively high variability within the group (964.85–54,423.4 pg/mL). Although the measured levels of selected cytokines for some patients varied markedly within the group, a characteristic pattern cannot be discerned. None of the cytokines and chemokines studied correlated with either the ammonia levels or the type of congenital hyperammonemia. There were neither significant increases in AOPP level (572.79–1324.14 μM range within the group) nor decreases in GPx activity (182.51–357.88 mU/mL) nor a significant correlation between the two parameters in the studied group of patients. However, a trend can be observed toward an inverse correlation between pro- and anti-oxidative markers’ concentrations and activity, AOPP, and GPx (Figure 2). ## 4. Discussion In the present study, we proposed two circulating metabolites, S100B and 3-NT, that may have diagnostic utility in patients with congenital hyperammonemias. We hypothesized that the determination of selected metabolites, along with the measurement of ammonia concentration in plasma, biochemical hallmarks defining hyperammonemia episode occurrence, may serve as circulating indicators of neurological decline and systemic oxidative–nitrosative stress, respectively. The study documented two key findings: (i) a positive correlation between ammonia and S100B levels; (ii) distinctive levels of 3-NT, uncorrelated to ammonia in the plasma of studied patients. Plasma samples were obtained from individuals diagnosed and followed at the Children’s Memorial Health Institute due to several congenital hyperammonemias. The occurrence of clinically and biochemically verified episodes of hyperammonemia in the cohort of 11 previously recruited and genetically characterized patients was documented as described in Section 2. According to the guidelines for pediatric hyperammonemia diagnosis, ammonia measurement, despite technical pitfalls, is consistently recommended as a discriminating factor for further and more specific, i.e., metabolic and genetic, evaluation. However, accurate assessment of plasma ammonia levels is challenging, since the final ammonia concentration is affected by many factors, including the site of blood specimen collection, the handling of the specimen, and the analytical method used [25]. It has been reported that no UCDs patient having had >300 μmol/L initially or >480 μmol/L peak plasma ammonia exhibited normal psychomotor development, underscoring the potential utility of new markers in terms of the neurological status of pediatric patients with congenital hyperammonemias [11]. It was also shown that ammonia concentrations may correlate with disease severity and predict mortality correlated with encephalopathy resolution [26,27]. In our study, the ammonia concentrations of recruited patients presented significantly elevated but variable values, and the correlative analysis did not fully cover patients’ neurological status. However, it is essential to note that in most patients, noticeable neurological deteriorations were observed and verified in the clinical evaluation (Table 1). Particular neuromarkers are derived from specialized cell types within the brain, e.g., neuron-specific enolase (NSE) present in neurons, S100B in astrocytes, and myelin basic protein in oligodendrocytes. It has to be acknowledged that many brain-derived proteins can escape into the peripheral circulation despite an intact blood–brain barrier (BBB) [28,29,30]. BBB, limits their size and quantity, determines the peripheral values indicating cell injury, and the severity of BBB impairment [31]. The S100B protein is stable and relatively unaffected by storage, changes in temperature, and freeze–thaw cycles. In contrast to NSE, it is not affected by hemolysis in the sample [32]. Unlike other brain biomarkers such as GFAP or NSE, S100B release is independent of cell death [32]. Presuming the above, measuring S100B plasma concentration offers a unique opportunity to associate patients’ clinical/neurological status with the routine measurement of analytes. S100B is an acidic, homodimeric protein present physiologically at low or undetectable concentrations in serum, and elevated S100B serum levels have been detected in several neuropathological conditions [33]. Significantly, its extracellular concentration determines its neuroprotective (nanomolar) or detrimental (micromolar) effects, respectively [34], the latter by inducing apoptosis, stimulating the release of proinflammatory cytokines or nitric oxide from astroglial cells, and contributing to oxidative stress [35,36,37,38]. It is worth noting that S100B exhibits features that are considered to be typical of inflammatory molecules, such as the activation of microglia or the stimulation of IL-6 and TNF-α secretion [34,39,40]. In our study, the concentration of the peripheral biomarker of the central nervous system (CNS) impairment, S100B protein, demonstrated a positive correlation with plasma ammonia levels ($p \leq 0.05$). To the best of our knowledge, no previous studies have investigated the possible correlation between S100B and ammonia in children with an inborn error of metabolism. Thus, our results, for the first time, support S100B determination as a promising non-invasive diagnostic tool for inherited hyperammonemias. Previously, it was demonstrated that serum S100B and IL-6 levels were associated with HE in pediatric patients suffering from acute liver failure, indicating that measuring these markers may be of benefit to the assessment of neurological injury, impacting clinical decisions [41]. There was no significant correlation between HE and a biomarker for neuronal injury, NSE. Similarly, another study demonstrated an S100B increase in a group of cirrhotic adults with stage 1 and 2 HE, while NSE was unaltered [33]. Elevated S100B has also been shown in patients with fulminant hepatic failure [42]. It was also demonstrated that high ammonia levels induce an increase in S100B release without alterations to its intracellular content [15]. Acute exposure to ammonia in astrocytes increases cell calcium [43]; therefore, this mechanism could be involved in S100B release induced by ammonia. A few limitations may temper the promise of S100B as a viable diagnostic and prognostic biomarker. S100B concentrations fluctuate at several developmental stages [44,45,46]. Bouvier et al. mapped out reference ranges of the serum levels of S100B from a large cohort of healthy children [47]. The higher concentrations of S100B found in children under three years old could be related to the permeability of the BBB, low renal excretion of S100B, and reflect the dynamic neurodevelopmental processes [47]. The baseline concentrations seemed to stabilize after the age of 20 years [31] and decrease with age [48]. Therefore, it is crucial to establish reference values in studies involving the measurement of blood S100B in pediatric and adolescent patients. We excluded S100B originating from the cells of non-neural tissues (i.e., adipocytes, chondrocytes, and bone marrow cells) [32] as not crucial in our study. The extensive study of 200 subjects revealed that extracranial sources of S100B do not affect serum levels in intact subjects [49]. Therefore, the diagnostic value of S100B and its negative predictive value in neurological diseases appear not to be compromised in the clinical setting [49]. Whether S100B increase may additionally exacerbate or compensate for CNS impairment in congenital hyperammonemias should be investigated. Since serum S100B levels may increase before neuronal cell death or a significant change in neurological function [49], it should also be confirmed whether it has predictive value for the progression and severity of underlying neurological impairment during an episode of metabolic decompensation. As was mentioned above, S100B features are typical of inflammatory molecules. We subsequently measured a standard systemic inflammation-related panel of cytokines and chemokines in this context. However, neither significant changes in the levels of pro-inflammatory cytokines and chemokines nor correlation with ammonia or patients’ clinical status and the type of congenital hyperammonemia were observed. In the studied group of patients, no changes in CRP levels were noted, and infections were ruled out by clinical assessment and blood tests. A significant correlation was observed between CRP levels and inflammatory gene markers in patients with subclinical HE [50], but lack of correlation was documented elsewhere [51]. Importantly, parallel measurements of CRP and interleukin 6 (Il-6) turned out to be insufficient to establish inflammation, as concluded from large-scale epidemiological studies and meta-analyses of mortality outcomes due to a variety of causes, including cancer, cardiovascular disease, and metabolic syndrome (e.g., [52,53,54]). Consequently, assays of more unambiguous inflammatory biomarkers (e.g., IL-1β and TNF-α), were suggested to be necessary [55]. Of note, pro- and anti-inflammatory cytokines, but not CRP, were inversely correlated with severity and neurological assessments in major depression [56]. The second marker utility verified in this study was the 3-NT, an oxidative–nitrosative stress end-product. For the first time, to our knowledge, 3-NT was measured in patients with congenital hyperammonemias. Our study failed to demonstrate a positive correlation between ammonia and 3-NT levels. However, and it should be clearly emphasized here, the concentration of 3-NT varied considerably between patients, with some reaching high levels within a studied group. Protein nitration is a common process that occurs under physiological conditions, and the conversion of tyrosine to 3-NT can have a deleterious effect on protein function, resulting in cell damage [57,58]. The determination of 3-NT concentration correlated with psychometric tests was used to identify patients with subclinical HE [59,60]. Additionally, a significant increase in 3-NT levels in protein has been associated with a wide range of diseases, such as asthma [61], cardiovascular diseases, diabetes mellitus [62], diseases associated with immunological reactions [63], neurological diseases, and psychiatric disorders [64]. Our results cannot unquestionably support the 3-NT predictive value; thus, measurement should be extended to a larger and more homogenous group of patients and be secured with validated reference values which, to our knowledge, are unknown for pediatric patients. Since the redox-based mechanism is associated with a variety of pathological events, including metabolic disorders with inborn hyperammonemias [65,66] and, direct measurement of reactive oxygen species levels with accuracy and precision is difficult due to their short lifespan and high reactivity, we employed further approaches to measure advanced oxidation protein products (AOPP) and glutathione peroxidase GPx, well-documented indicators of oxidative stress. To our knowledge, there has been no attempt to analyze this product in the plasma of congenital hyperammonemia patients, and AOPPs with advanced glycated end products were typically elevated in patients with renal complications, atherosclerosis, or diabetes mellitus [67,68]. The results documented unaltered plasma AOPP levels in the studied group. However, a negative result, cannot definitively rule out the AOPP value to determine the effects of oxidative stress. In the studied group of patients, there was also no marked decrease in GPx, nor a significant correlation with ammonia level. Since the previous estimation of reduced glutathione (GSH) levels in UCDs patients was proposed as a useful indicator of oxidative stress in line with considerably reduced GSH levels [69], the small sample size in our study limits both GPx and AOPPs’ conclusive value, and its utility as an oxidative stress indicator in studied patients should be extended in our future research. ## 5. Conclusions The management of congenital hyperammonemia is complicated by the lack of currently applied biomarkers offering clear predictive value. Assessing plasma ammonia level is challenging, and measurement might be affected by many factors; thus, the determination of plasma ammonia and S100B may serve as an additional tool in monitoring the neurological deficits risk-linked with hyperammonemia episodes in patients with inherited hyperammonemias. It is also advisable for future research to measure the 3-NT concentration in the peripheral bloodstream and verify whether it correlates with neurological decline. Nonetheless, it should be confirmed whether a proposed metabolite can also have a predictive value for the progression and severity of underlying neurological impairment during acute decompensation. ## References 1. Häussinger D.. Glutamine Metabolism in the Liver: Overview and Current Concepts. Metabolism (1989) 38 14-17. DOI: 10.1016/0026-0495(89)90133-9 2. 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--- title: Clinical Characteristics of Acute Kidney Injury Associated with Tropical Acute Febrile Illness authors: - Fardosa Dahir Omar - Weerapong Phumratanaprapin - Udomsak Silachamroon - Borimas Hanboonkunupakarn - Natthida Sriboonvorakul - Janjira Thaipadungpanit - Wirichada Pan-ngum journal: Tropical Medicine and Infectious Disease year: 2023 pmcid: PMC10056292 doi: 10.3390/tropicalmed8030147 license: CC BY 4.0 --- # Clinical Characteristics of Acute Kidney Injury Associated with Tropical Acute Febrile Illness ## Abstract Tropical acute febrile illness (TAFI) is one of the most frequent causes of acute kidney injury (AKI). The prevalence of AKI varies worldwide because there are limited reports available and different definitions are used. This retrospective study aimed to determine the prevalence, clinical characteristics, and outcomes of AKI associated with TAFI among patients. Patients with TAFI were classified into non-AKI and AKI cases based on the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Of 1019 patients with TAFI, 69 cases were classified as having AKI, a prevalence of $6.8\%$. Signs, symptoms, and laboratory results were significantly abnormal in the AKI group, including high-grade fever, dyspnea, leukocytosis, severe transaminitis, hypoalbuminemia, metabolic acidosis, and proteinuria. $20.3\%$ of AKI cases required dialysis and $18.8\%$ received inotropic drugs. Seven patients died, all of which were in the AKI group. Risk factors for TAFI-associated AKI were being male (adjusted odds ratio (AOR) 3.1; $95\%$ CI 1.3–7.4), respiratory failure (AOR 4.6 $95\%$ CI 1.5–14.1), hyperbilirubinemia (AOR 2.4; $95\%$ CI 1.1–4.9), and obesity (AOR 2.9; $95\%$ CI 1.4–6). We recommend clinicians investigate kidney function in patients with TAFI who have these risk factors to detect AKI in its early stages and offer appropriate management. ## 1. Introduction Fever, or acute febrile illness (AFI), is defined as an increase in body temperature caused by alterations in the hypothalamic thermoregulatory center [1]. There is a wide range of AFIs with different etiologies and induced by local outbreaks of disease, which can vary from region to region and country to country [2]. Tropical acute febrile illness (TAFI), which occurs in individuals living in tropical and subtropical regions, is defined as an AFI with a fever of duration less than 14 days. TAFI has no specific clinical signs and symptoms; most patients complain of fever, myalgia, arthralgia, vomiting, breathlessness, cough, chest pain, headache, rash, conjunctival congestion, or other symptoms. There are many infections that manifest as AFI in tropical countries, including dengue fever, typhoid fever, leptospirosis, rickettsia, influenza, and malaria [3,4]. In some AFI cases, patients also develop acute kidney injury (AKI). AKI is characterized by a rapid loss of the kidney’s excretory function, with or without oliguria, which commonly occurs over the course of hours to days. AKI is common in hospitalized patients, especially critically ill patients [5]. The pathophysiology of AKI in TAFI remains unclear. It is likely multifactorial and may differ based on infectious etiologies and their clinical presentation. Different hypotheses have been proposed, including direct injury to kidney tissue, immune mechanisms, hemolysis, cytoadherence of parasite-infected erythrocytes, intravascular coagulation, severe hyperpyrexia, vasculitis, and a secondary outcome of rhabdomyolysis [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. In 2016, the incidence of AKI in Asia was as follows: $31\%$ in Southeastern Asia, $19.4\%$ in Eastern Asia, $16.7\%$ in Western Asia, $9\%$ in Central Asia, and $7.5\%$ in Southern Asia [21]. The reported incidence of AKI varied based on the definition of AKI used and by ethnicity [22]. Mortality in AKI remains high globally, even in high-resource settings [23]. Poor health awareness and a lack of good diagnostic tests, limited resources, and poor sanitation are the major reasons for the differences in AKI outcomes between developing and developed countries [23]. AKI in the tropics is little known due to limited reporting and differences in the definitions of AKI used. Early diagnosis is essential to prevent AKI in patients with TAFI, which can be a common outcome of many infections, such as dengue, malaria, influenza, rickettsia, and leptospirosis. The present study aimed to determine the prevalence, clinical characteristics, and outcomes of AKI associated with tropical acute febrile illness. ## 2.1. Study Design This retrospective study received ethical approval from the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University, Thailand (Certificate No. MUTM 2019–018-01). The Ethics Committee waived the requirement for informed consent and the data were fully anonymized before analysis. We assessed the medical records of patients with TAFI who were admitted to the Hospital for Tropical Diseases, Faculty of Tropical Medicine, Mahidol University, Thailand, between January 2015 and December 2018. The international statistical classification of diseases and related health problems, 10th revision (ICD10) criteria were used to search for medical records of patients diagnosed with TAFI. The inclusion criteria were TAFI patients aged ≥ 18 years who had a history of fever < 2 weeks and a fever ≥ 37.5 °C during their first 24-h period of hospitalization. Patients with no serum creatinine result during hospitalization, missing clinical data in their medical records, non-specific or non-infectious causes of AFI, specific organ involvement with non-tropical diseases, or a fever of unknown origin were excluded from the study. The medical records of included cases were classified into AKI and non-AKI groups, based on the Kidney Disease Improving Global Outcomes (KDIGO) criteria [24]. A diagnosis of AKI was based solely on serum creatinine (SCr) results. Patient data, including both hard copies and electronic records, were reviewed on a case-by-case basis. Demographic, clinical, and laboratory data of eligible patients were obtained. Demographic details and clinical presentations were recorded upon hospital admission, while laboratory data were collected during a patient’s hospitalization. ## 2.2. Clinical Definitions AKI is a complex clinical disorder characterized by a rapid loss in the kidney’s excretory function, with or without oliguria, occuring over the course of a few hours to days. It is closely associated with severe morbidity and mortality [5]. According to the KDIGO criteria, AKI is defined as an increase in SCr of ≥0.3 mg/dL within 48 h or an increase of ≥1.5 times the baseline within 7 days. It is divided into: stage 1, SCr increase >0.3 mg/dL or SCr increase 1.5–1.9-times the baseline; stage 2, SCr increase 2–2.9-times the baseline; stage 3, SCr increase 3-times the baseline, or the initiation of renal replacement therapy (RRT) [25,26]. Patients’ SCr results on admission were used as the baseline for comparison of their kidney function during hospitalization. For patients who had a single SCr result, the baseline SCr was estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [27]. Other AKI criteria, including conventional, RIFLE and AKIN, were defined (see Supplementary File S1) and explored in this study. In this study, patients were observed for the presence of clinical conditions and their severity grading. These included hyperbilirubinemia, transaminitis, metabolic acidosis, proteinuria, hematuria, pyuria, severe thrombocytopenia, respiratory failure, multi-organ dysfunction (MOD), and obesity. Further details on the definition of these conditions can be found in Supplementary File S1. ## 2.3. Statistical Analysis The sample size required was 1013 patients, based on the estimated proportion of patients with TAFI of 0.54 (Nair et al., 2016), with a margin of error of 0.025 and alpha 0.05 [28,29]. The statistical analysis was performed using SPSS software (version 18) Chicago: SPSS Inc. (Chicago, IL, USA) Quantitative variables are given as medians with interquartile ranges (IQRs). For hypothesis testing, appropriate tests, including the Student’s t-test or the Mann-Whitney U test, were selected, depending on the data distribution. All categorical variables are presented as numbers and percentages. The chi-square test and Fisher’s exact test were used for group comparisons based on cell values in the tables. A p-value of 0.05 or less was considered to indicate statistical significance. The Receiver Operating Characteristic (ROC) curve was applied to evaluate the diagnostic ability of tests. The optimal cut-off value of a test was obtained by maximizing the area under the ROC curve, at which the sensitivity and specificity were then reported. ## 3. Results Overall, a total of 2056 medical records of patients diagnosed with TAFI were screened for the study, of which 1037 cases were excluded, leaving 1019 patients who were eligible for the study, as shown in Figure 1. Of the eligible cases, 603 ($59\%$) had just one SCr result. Therefore, their baseline SCr was estimated using the CKD-EPI equation. ## 3.1. Characteristics of Eligible Cases and Prevalence of AKI in Patients with TAFI In total, 950 cases were classified into the non-AKI group, with the remaining 69 cases classified into the AKI group. Of the latter cases, $\frac{41}{69}$ ($59.4\%$) had dengue, $\frac{14}{69}$ ($20.3\%$) had malaria, $\frac{11}{69}$ ($15.9\%$) had influenza, $\frac{2}{69}$ ($2.9\%$) had rickettsial illness (murine typhus), and $\frac{1}{69}$ ($1.4\%$) had leptospirosis; there were no patients with mixed infections (Table 1). The prevalence of AKI by KDIGO criteria, AKIN criteria, RIFLE criteria, and conventional criteria was $6.8\%$, $5.9\%$, $4.1\%$, and $4.0\%$, respectively. The proportion of AKI by KDIGO criteria in stages 1, 2, and 3 was $\frac{45}{69}$ ($65.2\%$), $\frac{8}{69}$ ($11.6\%$), and $\frac{16}{69}$ ($23.2\%$), respectively (Table 2). In comparison with the non-AKI group, the AKI group comprised mostly males ($\frac{50}{69}$, $72.5\%$, $$p \leq 0.004$$), with a female to male ratio of 1:2.6. The distribution of AKI by TAFI is shown in Table 1. Among dengue infections, $\frac{41}{767}$ ($5.3\%$) had AKI; among malaria infections, $\frac{14}{131}$ ($10.7\%$) had AKI; among influenza infections, $\frac{11}{106}$ ($10.4\%$) had AKI; among rickettsial infections, $\frac{2}{11}$ ($18.2\%$) had AKI; and $\frac{1}{2}$ ($50\%$) of leptospirosis infections had AKI. According to the WHO 2009 case definition for dengue, $\frac{15}{48}$ ($31.3\%$) of severe dengue cases were in the AKI group. There were 131 malaria cases, of which 14 ($10.7\%$) were in the AKI group: 10 with Plasmodium falciparum (Pf) malaria and 4 with Plasmodium vivax (Pv) malaria. Of the 11 ($10.4\%$) influenza cases in the AKI group, 9 had influenza A and 2 had influenza B. Lastly, $\frac{2}{2}$ ($100\%$) of rickettsial cases in the AKI group had murine typhus. The clinical characteristics of the patients with TAFI in the AKI and non-AKI groups are summarized in Table 3. All, except two, patients complained of fever on admission. Chills and headache were reported among half of all patients in both groups. Nausea/vomiting and other symptoms, including abdominal pain and cough, were around $30\%$ in both groups. Arthralgia was rarely detected, at less than $5\%$ in each group. Pallor and dyspnea were significantly more common among AKI cases than non-AKI cases, at $10.1\%$ and $14.5\%$, respectively. Myalgia was significantly lower in the AKI compared with the non-AKI group, i.e., $45\%$ vs. $66\%$, respectively. The major underlying diseases in patients with AKI were diabetes mellitus ($\frac{11}{69}$, $15.9\%$), chronic kidney disease ($\frac{6}{69}$, $8.7\%$), hepatitis B virus ($\frac{4}{69}$, $5.8\%$), human immunodeficiency virus ($\frac{5}{69}$, $7.2\%$), and asthma ($\frac{6}{69}$, $8.7\%$), with statistically significant differences between the AKI and non-AKI groups. Hypertension ($\frac{8}{69}$, $11.6\%$) and dyslipidemia ($\frac{21}{69}$, $30.4\%$) showed no statistically significant differences between the AKI and non-AKI groups. Laboratory test results from patient samples collected on admission are shown in Table 4. Statistically significant differences were observed between the AKI and non-AKI groups in serum sodium, serum potassium, serum bicarbonate, serum total bilirubin, urine specific gravity, WBCs, neutrophils, lymphocytes, atypical lymphocytes, RBCs, and AST. In the AKI group, $\frac{8}{69}$ ($11.6\%$) cases had serum creatinine > 3.0 mg/dL and presented with AKI stage 3 on admission. In AKI cases, $\frac{31}{58}$ ($53.4\%$) developed hyperbilirubinemia (TB > 1.2 mg/dL) on admission. There were $\frac{22}{56}$ ($39.3\%$) AKI cases with moderate to severe transaminitis. The median specific gravity of urine from patients in the AKI group was 1.020 (1.01–1.025; $p \leq 0.001$), which was significantly higher than in the non-AKI group. Proteinuria was significantly higher in the AKI group ($\frac{27}{53}$, $50.9\%$, $p \leq 0.001$). Urine sedimentation was higher in the AKI group, including hematuria ($\frac{11}{53}$, $20.8\%$) and pyuria ($\frac{8}{53}$, $15.1\%$), but this was not statistically significant ($p \leq 0.05$). ## 3.2. Complications and Outcomes of AKI in Patients with TAFI Complications present in cases of AKI included severe transaminitis ($\frac{13}{56}$, $23.2\%$) and hypoalbuminemia ($\frac{21}{58}$, $36.2\%$), which were significantly higher than in the non-AKI group ($p \leq 0.05$). Metabolic acidosis was present in $\frac{10}{69}$ ($14.5\%$) cases; $\frac{23}{69}$ ($33.3\%$) cases developed respiratory failure and required mechanical ventilation; $\frac{20}{69}$ ($28.9\%$) patients were admitted to the intensive care unit (ICU); $\frac{13}{69}$ ($18.8\%$) patients developed multi-organ dysfunction, and $\frac{6}{69}$ ($8.7\%$) had underlying CKD. All were significantly higher than in the non-AKI groups. Only severe thrombocytopenia ($\frac{29}{69}$, $42\%$) showed no significant difference. There were $\frac{60}{69}$ ($86.9\%$) patients with AKI who improved and recovered, while one patient did not improve. Another case was transferred to lung cancer treatment. There were $\frac{13}{69}$ ($18.8\%$) and $\frac{14}{69}$ ($20.3\%$) patients who received inotropic drugs (norepinephrine) and hemodialysis, respectively. Among patients with hemodialysis, $\frac{4}{14}$ ($28.6\%$) patients were on intermittent hemodialysis, $\frac{3}{14}$ ($21.4\%$) patients on sustained low-efficiency dialysis (SLED), and $\frac{7}{14}$ ($50\%$) patients on continuous renal replacement therapy (CRRT). There were $\frac{7}{69}$ ($10.1\%$) patients who died; all were in the AKI group. Most patients who died ($\frac{5}{7}$, $71.4\%$) were female. As shown in Table 5, $\frac{6}{7}$ ($85.7\%$) patients developed multi-organ dysfunction and all patients developed respiratory failure. All patients who died were classified as having severe dengue with AKI stage 3. The parameters with $5\%$ occurrence of cases and non-complete separation (perfect predictor) were further processed to the risk association analysis. ## 3.3. Factors Associated with AKI Univariate and multivariate analyses were performed to identify independent risk factors for AKI, quantified by the adjusted odds ratio (AOR), with $95\%$ CI. Categorical variables were assigned to the model, with entry at 0.05 and removal at 0.10, and were scored using “no” as the reference category. We found that male sex (AOR 3.1; $95\%$ CI 1.3–7.4), respiratory failure (AOR 4.6; $95\%$ CI 1.5–14.1), hyperbilirubinemia (AOR 2.4; $95\%$ CI 1.1–4.9), and obesity (AOR 2.9; $95\%$ CI 1.4–6) were risk factors associated with AKI (Table 6). Based on the common model discrimination method using the receiver operating characteristic curve (ROC), the optimal sensitivity and specificity obtained at the maximum area under the curve of 0.767 ($95\%$ CI 0.69–0.85) were $70\%$ and $72\%$, respectively. ## 4. Discussion This retrospective study assessed the prevalence and clinical characteristics of AKI in patients with TAFI. We observed a prevalence of AKI by KDIGO, AKIN, RIFLE, and conventional definitions of $6.8\%$, $5.9\%$, $4.1\%$, and $4.0\%$, respectively. Three studies of TAFI-associated AKI conducted in India between 2010 and 2018 reported a prevalence between 28–$54\%$ using either RIFLE or KDIGO criteria [4,30,31]. One study from Malaysia in 2017 reported TAFI-associated AKI with a prevalence of $41.1\%$, using KDIGO [32]. The wide range of reported AKI prevalence was due to the different AKI criteria being used, as well as the different etiology of AKI in these places. For the present study, we found six patients with underlying chronic kidney disease (CKD). There were seven underlying CKD cases in our study. However, there remained the possibility of other cases of undiagnosed CKD. CKD has previously been reported as a risk factor for developing AKI [33]. Similarly, many previous studies reported some underlying CKD cases which subsequently developed AKI [30,34]. In our study, we excluded approximately $34.8\%$ ($\frac{715}{2056}$) of the total screened medical records due to the absence of a creatinine investigation. The clinicians did not request SCr investigation in these patients because they did not have symptoms and signs of AKI, and the clinicians did not suspect AKI. A previous study experienced a similar rate of exclusion ($\frac{810}{2476}$, $32.7\%$) during screening due to an absence of SCr results [34]. The infections present in patients with TAFI-associated AKI in our study were dengue ($59.4\%$), malaria ($20.3\%$), influenza ($15.9\%$), rickettsia ($2.9\%$), and leptospirosis ($1.5\%$). The mechanisms of TAFI-associated AKI are complex. The alteration of kidney tubular function resulting from hemodynamic instability and hypotension, cytokine production, and immune complex deposition has been noted for the three major TAFI listed (dengue, malaria, and influenza) in this study [35,36,37]. Dengue shock syndrome (DSS) also increases the risk of AKI [35]. Hemodynamic derangement from parasitized red blood cells and platelets resulting in microvascular blockage (sequestration) is also a major mechanism in malarial AKI [38]. Note that in our study dengue was the largest TAFI group at $75.3\%$ ($\frac{767}{1019}$). Different studies have reported a high proportion of leptospirosis [4], scrub typhus, malaria, and HIV [30,39,40] among TAFI-associated AKI patients. The causes of TAFI associated with AKI were different from region to region or country to country due to infection cause and population demographics [22,30,41]. In our study, the proportion of patients with AKI in stages 1, 2, and 3 were $65.2\%$, $11.6\%$, and $23.2\%$, respectively. All cases of stage 3 AKI in our study were associated with severe dengue infection or falciparum malaria. Most of the patients with stage 1 AKI were diagnosed by laboratory investigations [4]; all deaths were in stage 3 AKI. A high-grade fever (>39 °C) was observed in $29\%$ of cases in the AKI group, which was significantly higher than the proportion of patients with high-grade fever in the non-AKI group. Pyrexia causes dehydration and is characteristic of AFI [4]. On admission, a significantly higher proportion of patients with dyspnea ($14.5\%$) and pallor ($10.1\%$) were detected in the AKI group in our study. Previous studies have reported that $20\%$ of AKI cases presenting with dyspnea, metabolic acidosis, and shock are significantly likely to occur with dengue-associated AKI [4,10,34]. There were $50.9\%$ of AKI cases with proteinuria, which was significantly higher than in the non-AKI group, while $20.8\%$ and $15.1\%$ of AKI cases had hematuria and pyuria, respectively, which was not significantly different compared with the non-AKI group. Proteinuria, hematuria, and pyuria have been reported to be associated with AKI elsewhere [42]. The median WBC increased significantly in the AKI group compared to the non-AKI group. Both leukocytosis and leukopenia can increase the risk of developing AKI in critical patients in the ICU [43]. Inflammation plays an important role in tubular cell damage during AKI, with neutrophils being an obvious factor in the inflammatory cascade [44,45]. Lymphocytes play a critical role in the immune inflammatory response, and the production of cytokines participates in the AKI process [46]. We also observed severe transaminitis ($23.2\%$), hypoalbuminemia ($36.2\%$), and hyperbilirubinemia ($53.4\%$) to be higher in the AKI group than in the non-AKI group. Patients who exhibited an increase in transaminitis, hyperbilirubinemia, and hypoalbuminemia were shown in a previous study to be more likely to develop AKI [47]. Hypoalbuminemia is a risk factor for AKI [48]. In this study, the AKI group had a higher proportion of hyponatremia ($53.8\%$) and metabolic acidosis ($14.5\%$) than the non-AKI group. A decline in GFR will cause the retention of waste products, aggravating electrolyte imbalance, including hyponatremia, hyperkalemia, and metabolic acidosis. Metabolic acidosis has been reported to be associated with AKI [49,50,51]. In our study, most of the patients in both the non-AKI and AKI groups recovered and had improved by the time they were discharged. However, all of the deaths we recorded occurred in cases of AKI ($10.1\%$). The etiology of their deaths was unknown, as no autopsies were performed. Among the deaths, $\frac{5}{7}$ cases ($71.4\%$) were female, while all patients who died developed respiratory failure, and $\frac{6}{7}$ cases ($85.7\%$) had multi-organ dysfunction. All these cases had severe dengue infection and stage 3 AKI. Among the death cases in our study, 5 out of 7 death cases were referred from another hospital with severe dengue, multi-organ dysfunction, and shock on presentation, and 4 out of 5 referred cases were female. In our study, the mortality rate in patients with TAFI was $\frac{7}{1019}$ ($0.69\%$). Other studies have reported mortality rates in all cases of AFI, irrespective of AKI, of $3\%$ and $12.3\%$ [4,30]. The variation in mortality rates observed in those studies could be explained by differences in the study populations, rates of ICU admission, underlying diseases, and the different criteria used for the classification of AKI in different studies. However, patients with AKI had significantly higher in-hospital mortality than non-AKI cases, regardless of the AKI definition used [33]. Mortality in the AKI group ($10.1\%$) in our study was significantly lower than the study of acute kidney injury in a tropical country by Daher et al., which reported a mortality of $62.8\%$ [39]. The low mortality rate of our study can be explained by the fact that it was conducted in a tertiary care hospital for tropical diseases in Thailand, where the medical care and services include high-quality ICU settings, the availability of extensive laboratory investigations, mechanical ventilator support, and renal replacement therapy. In the present study, $18.5\%$ and $20.3\%$ of AKI cases required inotropic drugs and dialysis, respectively. Among the dialysis cases, $21.4\%$ and $50\%$ of them were on SLED and CRRT mode, respectively, according to the hemodynamically unstable conditions. There were no cases in the non-AKI group that required inotropic drugs or dialysis. Previous studies have reported between $7.9\%$ and $44.8\%$ of patients with TAFI-associated AKI requiring dialysis [4,30,39,40,52,53]. In our study, patients in the AKI group had significantly longer hospital stays than patients in the non-AKI group. A similar result was observed in a previous study [34]. Similar to some previous studies, males showed a higher risk of developing AKI than females (AOR 3.1; $95\%$ CI 1.3–7.4) [54,55]. Many studies have shown that endoplasmic reticulum stress participates in the development of AKI in both animals and humans, and that the kidney of the male is more vulnerable to endoplasmic reticulum stress [56,57]. Additionally, testosterone has apoptotic and fibrotic effects that are aggravated by the release of TNF-α, and the generation of inflammation leading to AKI. Respiratory failure was shown to be another risk factor for AKI in the present study (AOR 4.6; $95\%$ CI 1.5–14.1). This same result has been noted previously [58]. The pathophysiology of AKI in respiratory failure is not completely understood, but studies have reported many mechanisms that participate in the aggravation of AKI during respiratory failure. For example, increasing intrathoracic pressures with poorly compliant lungs can reduce cardiac output, resulting in inadequate renal perfusion that aggravates AKI [59,60,61]. Hyperbilirubinemia was shown to be one of the risk factors for AKI in the present study (AOR 2.4; $95\%$ CI 1.1–4.9), which agrees with a previous report [62]. There has also been a report of severe hyperbilirubinemia and association of severe AKI in patients with cardiac surgery [63]. Bilirubin and bile salt cause direct damage to the tubular epithelium of the kidney [64]. Furthermore, high bilirubin levels would stimulate renal ischemic-reperfusion injury [65]. Obesity is another risk factor for AKI identified in the present study (AOR 2.9; $95\%$ CI 1.4–6); again, this is in agreement with the findings of previous studies [66,67]. There was evidence of a linear correlation between a higher BMI and a higher incidence of AKI [68]. The pathophysiology linking obesity and AKI is unclear. One explanation would be that obesity causes a change in renal hemodynamics that may lead to vulnerability to kidney injury [69]. Furthermore, inflammatory cytokine production from adipose tissue during acute illness has been shown to participate in the development of AKI [70]. Subsequently, the increase of intra-abdominal pressure and central venous pressure from obesity was proposed as another mechanism which increases AKI risk [71]. Lastly, the meta-analysis study of COVID-19 suggested that obesity increases severe clinical course, ICU admission, and death among COVID-19 patients [72]. The limitations of this study were as follows. First, we excluded around $50\%$ of screened medical records because of the exclusion criteria, such as no SCr investigation or an unconfirmed diagnosis of TAFI. Second, the diagnosis of AKI was based only on SCr criteria. ## 5. Conclusions The overall prevalence of TAFI-associated AKI was $6.8\%$. TAFI-associated AKI can lead to an increased mortality rate. The risk factors for TAFI-associated AKI were being male, having respiratory failure or hyperbilirubinemia, and being obese. The combination of these identified risk factors produced a predictive algorithm, which achieves a value of sensitivity ($70\%$) and specificity ($72\%$) similar to previous studies [73]. 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--- title: Aortic Root Replacement Surgery—A Center Experience with Biological Valve Prostheses authors: - Mohamed Salem - Maximilian Boehme - Christine Friedrich - Markus Ernst - Thomas Puehler - Georg Lutter - Felix Schoeneich - Assad Haneya - Jochen Cremer - Jan Schoettler journal: Journal of Cardiovascular Development and Disease year: 2023 pmcid: PMC10056309 doi: 10.3390/jcdd10030107 license: CC BY 4.0 --- # Aortic Root Replacement Surgery—A Center Experience with Biological Valve Prostheses ## Abstract Objective: Outcomes after surgical aortic root replacement using different valved conduits are rarely reported. The present study shows the experience of a single center with the use of the partially biological LABCOR (LC) conduit and the fully biological BioIntegral (BI) conduit. Special attention was paid to preoperative endocarditis. Methods: All 266 patients who underwent aortic root replacement by an LC conduit ($$n = 193$$) or a BI conduit ($$n = 73$$) between $\frac{01}{01}$/2014 and $\frac{31}{12}$/2020 were studied retrospectively. Dependency on an extracorporeal life support system preoperatively and congenital heart disease were exclusion criteria. For patients with ($$n = 67$$) and without ($$n = 199$$) preoperative endocarditis subanalyses were made. Results: Patients treated with a BI conduit were more likely to have diabetes mellitus (21.9 vs. $6.7\%$, $p \leq 0.001$), previous cardiac surgery (86.3 vs. $16.6\%$; $p \leq 0.001$), permanent pacemaker (21.9 vs. $2.1\%$; $p \leq 0.001$), and had a higher EuroSCORE II (14.9 vs. $4.1\%$; $p \leq 0.001$). The BI conduit was used more frequently for prosthetic endocarditis (75.3 vs. $3.6\%$; <0.001), and the LC conduit was used predominantly for ascending aortic aneurysms (80.3 vs. $41.1\%$; <0.001) and Stanford type A aortic dissections (24.9 vs. $9.6\%$; $$p \leq 0.006$$). The LC conduit was used more often for elective (61.7 vs. $47.9\%$; $$p \leq 0.043$$) and emergency (27.5 vs. $15.1\%$; $$p \leq 0$$–035) surgeries, and the BI conduit for urgent surgeries (37.0 vs. $10.9\%$; $p \leq 0.001$). Conduit sizes did not differ significantly, with a median of 25 mm in each case. Surgical times were longer in the BI group. In the LC group, coronary artery bypass grafting and proximal or total replacement of the aortic arch were combined more frequently, whereas in the BI group, partial replacement of the aortic arch were combined. In the BI group, ICU length of stay and duration of ventilation were longer, and rates of tracheostomy and atrioventricular block, pacemaker dependence, dialysis, and 30-day mortality were higher. Atrial fibrillation occurred more frequently in the LC group. Follow-up time was longer and rates of stroke and cardiac death were less frequent in the LC group. Postoperative echocardiographic findings at follow-up were not significantly different between conduits. Survival of LC patients was better than that of BI patients. In the subanalysis of patients with preoperative endocarditis, significant differences between the used conduits were found with respect to previous cardiac surgery, EuroSCORE II, aortic valve and prosthesis endocarditis, elective operation, duration of operation, and proximal aortic arch replacement. For patients without preoperative endocarditis, significant differences were observed concerning previous cardiac surgery, pacemaker implantation history, duration of procedure, and bypass time. The Kaplan–Meier curves for the subanalyses showed no significant differences between the used conduits. Conclusions: Both biological conduits studied here are equally suitable in principle for complete replacement of the aortic root in all aortic root pathologies. The BI conduit is often used in bail-out situations, especially in severe endocarditis, without being able to show a clinical advantage over the LC conduit in this context. ## 1. Introduction Diseases of the aortic root are diverse and sometimes, as in Marfan syndrome, genetically predisposed [1]. Typical pathologies include poststenotic aortic dilatation in the presence of aortic valve stenosis, basal aneurysm, often with concomitant aortic valve insufficiency, and aortic dissection extending to the aortic base, possibly with resulting aortic valve insufficiency [2]. In addition, complete aortic root calcification with aortic valve vitium, e.g., after thoracic radiation therapy, and extensive endocarditis of the aortic valve with involvement of the proximal ascending aorta are included. The indication for total aortic root replacement arises when both the aortic valve and the proximal ascending aorta need replacing for the reasons listed above. Total aortic root replacement was first described by Bentall in 1968 [3]. Reconstruction of the aortic root must be distinguished from aortic root replacement. The techniques according to David or Yacoub are available for this purpose [4,5]. Various industrially manufactured conduits are available for complete replacement of the aortic root. A conduit consists of a tubular prosthesis with an integrated aortic valve prosthesis. There are mechanical conduits that include a mechanical prosthetic heart valve and biological conduits with a biological valve. Increasingly, affected patients prefer the implantation of a biological conduit because they wish to avoid anticoagulant therapy [6]. In addition, from the point of view of cardiac surgery, various aspects favor the implantation of a biological rather than a mechanical conduit. Thus, in the context of endocarditis, in emergency situations, and in complex redo surgery procedures, biological conduits are often favored by surgeons [7]. Therefore, biological conduits are now predominantly used in many cardiac surgery centers. This trend does not seem to have a negative effect on early or late survival [2,8,9]. So far, it has not been sufficiently proven which biological conduit is advantageous with regard to the outcome. The aim of the present study is to report our experience with morbidity and mortality after biological aortic root replacement with a LABCOR conduit, which carries a biological heart valve but the remaining components are synthetic, and a BioIntegral conduit, which is completely made of biological tissue. Special attention was paid to patients with preoperative endocarditis. ## 2.1. Patients All patients who underwent aortic root replacement with a LABCOR or a BioIntegral conduit between $\frac{01}{01}$/2014 and $\frac{31}{12}$/2020 at the Department of Cardiovascular Surgery, University Hospital Schleswig-Holstein, Campus Kiel, Germany, were studied retrospectively. Patients with congenital heart disease and preoperative insertion of extra corporeal life support were excluded. All included patients had given written informed consent for research with patient data. The study was approved by the local ethics committee of Christian-Albrechts-Universität zu Kiel (D$\frac{463}{21}$). ## 2.2. Imaging Preoperative Diagnostics Almost all patients included in the study had received transthoracic echocardiographic assessment preoperatively in the case of an elective procedure. The Patients were examined by transesophageally echocardiography in cases of inadequate transthoracic echocardiography.. In the context of emergency surgery, patients received transesophageal echocardiography immediately after induction of anesthesia. The majority of patients received preoperative thoracic computed tomography, in most cases, with contrast agent. To diagnose or exclude coronary artery disease requiring treatment before surgery, every electively operated patient underwent left heart catheterization as standard. In individual cases, the need for aortic root replacement was determined intraoperatively in the course of other cardiac surgical procedures without specific preoperative diagnostics. ## 2.3. Surgical Technique After general anesthesia and endo-tracheal intubation, the chest was entered through a median sternotomy. Full heparin administration was followed by institution of cardiopulmonary bypass either through right atrial or through bicaval cannulation to the distal ascending aorta return. An arterial cannula would be advanced into the left ventricle through the right superior pulmonary vein in the presence of type A aortic dissection [10]. In cases of proximal, partial, or complete arch replacement, deep hypothermia was first induced, and the appropriate section of the aorta was then replaced in transient hypothermic circulatory arrest, with selective cerebral perfusion if necessary. In cases without aortic arch surgery, moderate hypothermia was applied after induction of heart fibrillation, followed by cross-clamping of the ascending aorta, and administration of ante- and usually retrograde cardioplegia infusion. In operations with simultaneous coronary surgery, the bypasses were first created in a typical manner. If the mitral valve required attention, we would consider starting with mitral valve surgery. Then, the aortic valve was visualized via a supracoronary approach. The valve leaflets were removed, and the aortic valve annulus was carefully decalcified if necessary. If endocarditis affected the aortic annulus, it would be reconstructed with bovine pericardium. The coronary artery ostia were excised in a circular manner and the non-coronary sinus was resected. After appropriate measurement, the selected conduit would be implanted in the usual manner with single sutures. This was followed by reanastomosis of the coronary vessels with the conduit. Finally, the conduit was anastomosed distally to the ascending aorta or, in case of previous arch replacement, to the tubular prosthesis placed distally through end-to-end. After echocardiographic exclusion of air bubbles in the left ventricle, the cross-clamp was removed and thus reperfusion was started. After rewarming of the patient and echocardiographic assessment of the surgical result, extracorporeal circulation was discontinued. With occasional exceptions for dissections, a graft inclusion was made using the native ascending aorta. This was followed by careful hemostasis, placement of chest drains and pacing wires, and finally wound closure in layers. ## 2.4. Data Collection Data were collected retrospectively. Pre-, intra- and postoperative variables were taken from the Hospital medical records. Follow-up was performed by mail. All data collected were documented in an anonymized form in an Excel spreadsheet. ## 2.5. Statistical Analyses First, the overall population was compared with respect to the two conduits. Subanalyses were then performed for the patients with and those without preoperative endocarditis. Characteristics of the patient groups were presented as the median with 25th and 75th percentiles. Non-normally distributed continuous data as well as ordinal data were compared using the Mann-Whitney U test. Normal distribution was assessed using the Kolmogorov–Smirnov test. Categorical data were summarized as absolute (n) and relative (%) frequencies and compared by Chi2-test or Fisher’s exact test as appropriate. Survival was calculated on right-censored data using Kaplan–Meier analyses and compared for differences between the groups using the log rank test. Pre- and intraoperative variables were assessed for their adjusted impact on early mortality by multivariable logistic regression separately (model 1 and 2). Subsequently, significant pre- and intraoperative risk factors were included in model 3, with a goodness of fit, described by Cox and Snell R-Squared, of 0.204 (Model 1), 0.154 (model 2), and 0.209 (model 3). Variables with less than eight events and EuroSCORE II (The European System for Cardiac Operative Risk Evaluation) were excluded from the multivariable analyses; the latter was excluded since it complicated the detection of single risk factors due to multicollinearity. All tests were performed as two-sided tests and a p-value of ≤0.05 was regarded as statistically significant. Data analysis was performed using IBM SPSS Statistics for Windows (Version 28.0). ## 3. Results Between $\frac{01}{01}$/2014 and $\frac{12}{31}$/2020, a total of 266 patients underwent full aortic root replacement at the Department of Cardiovascular Surgery at the University Hospital Schleswig-Holstein, Campus Kiel. A total of 73 patients received a BioIntegral conduit and 193 patients received a LABCOR conduit. A total of 67 patients were admitted with preexisting endocarditis and 199 patients without endocarditis. ## 3.1. Total Group Table 1 shows the baseline characteristics. Diabetes mellitus, renal insufficiency, prior cardiac surgery with median sternotomy, and pacemaker implantation were more frequently found in the BioIntegral group than in the LABCOR one. The median logistic EuroSCORE II was significantly higher in the BioIntegral collective than in the LABCOR collective ($15\%$ vs. $4\%$). Preoperative aortic root disease, aortic valve stenosis, and prosthetic valve endocarditis were significantly more common in the BioIntegral conduit group, whereas aortic valve regurgitation, Stanford type A aortic dissection, and aneurysm of the ascending aorta were significantly less (Table 2). The intraoperative variables showed that the BioIntegral conduit was implanted less frequently in elective aortic root replacement surgery but more frequently in urgent and emergency surgery. In the BioIntegral collective, operation times were significantly longer, the proportion of combined cardiac surgery was significantly lower, and simultaneous total aortic arch replacement, frozen elephant trunk implantation, or coronary artery bypass grafting were performed less frequently in addition to aortic root replacement. Partial aortic arch replacement occurred more frequently in the BioIntegral group. The conduit sizes used were nearly identical with a median of 25 mm in both groups (Table 3). During postoperative treatment, it was found that patients treated with a BioIntegral conduit required significantly longer treatment in the intensive care unit, with longer mechanically ventilation, as well as they were tracheotomized more frequently. In the BioIntegral group, atrioventricular block and the need for pacemaker implantation was more frequent postoperatively; however, postoperative atrial fibrillation was observed significantly less. The BioIntegral patients suffered from renal insufficiency requiring hemodialysis more often during the postoperative course. The 30-day mortality was significantly higher in the BioIntegral group ($26.0\%$ vs. $7.8\%$) (Table 4). Follow-up time was significantly lower in the BioIntegral group. Mediastinitis and cardiac causes of death were significantly more frequent in the BioIntegral patients. Mortality at the time of follow-up was not significantly different between the two groups, being $28.8\%$ in the BioIntegral group and $18.1\%$ in the LABCOR group (Table 5). Transvalvular pressure gradient, effective valve orifice area, global left ventricular function and rate of conduit valve insufficiency did not diverge significantly (Table 6). Figure 1 shows that the patients treated with a BioIntegral conduit had a significantly worse survival rate than those implanted with a LABCOR conduit. ## 3.2. Endocarditis Group In the subanalysis of patients with endocarditis, those who received a BioIntegral conduit were significantly more likely to have the condition after prior cardiac surgery with median sternotomy and their logistic EuroSCORE II was significantly higher at $16\%$ versus $9\%$ median (Table 1). Regarding aortic root pathologies, prosthetic endocarditis was significantly more frequent in the BioIntegral group at $100\%$ versus $58.3\%$, and endocarditis of the native aortic valve was significantly less frequent at $0\%$ versus $41.7\%$ (Table 2). BioIntegral patients in the endocarditis group underwent elective cardiac surgery significantly more often and the median duration of the operations was significantly longer. In the BioIntegral group, proximal or partial aortic arch replacement was performed significantly more often (Table 3). Regarding postoperative variables, including 30-day mortality, no significant differences were observed between the two conduits in endocarditis patients (Table 4). During the follow-up of endocarditis patients, subsequent prosthetic endocarditis occurred in $7.7\%$ of BioIntegral patients and in none of the LABCOR group, with no statistical difference (Table 5). None of the echocardiographic parameters measured during the follow-up of the endocarditis patients showed a significant difference depending on the implanted conduit (Table 6). As shown in Figure 2, patients receiving a BioIntegral conduit did not experience worse survival compared to those who received a LABCOR conduit. ## 3.3. Non-Endocarditis Group In the Non-Endocarditis group, patients who received a BioIntegral conduit were significantly more likely to have had prior cardiac surgery and they were significantly more likely to have a permanent pacemaker (Table 1). The pathologies of the aortic root were similar (Table 2). Median procedure duration and bypass time were significantly longer in the BioIntegral group (Table 3). Postoperative variables did not differ in any respect in this subanalysis (Table 4). No significant differences were seen with regard to the findings and echocardiographic parameters obtained at follow-up (Table 5 and Table 6). The survival curves of both prosthetic groups were comparable in the subanalysis of patients without preoperative endocarditis (Figure 3). ## 3.4. Adjusted Risk Factors The multivariable analysis revealed age, female gender, diabetes mellitus, non-paroxysmal atrial fibrillation, renal insufficiency, and emergency admission as preoperative risk factors, while intraoperatively age and female gender were also risk factors as well as endocarditis and bypass time. Combined pre- and intraoperative risk factors were age, female gender, endocarditis, diabetes mellitus, renal insufficiency, and emergency admission. The BioIntegral Conduit was not shown to be a risk factor either preoperatively or intraoperatively (Table 7). ## 4. Discussion At present, there are no literature data comparing the performance of the LABCOR and BioIntegral conduits in terms of perioperative variables and outcome in patients requiring full aortic root replacement. Therefore, our experience in 266 patients over a 5-year period may contribute to shed some lights on the subject since our initial report in 2018 of the first 33 patients treated with a BioIntegral conduit in our hospital [11]. The LABCOR conduit consists of a stented porcine valve prosthesis and a synthetic tubular graft, whilst the BioIntegral conduit is a bovine pericardial tube including a stentless porcine valve. As stated by Puehler et al., reconstructive surgery of the aortic root seems more popular compared to root replacement due to infection, bleeding, and reoperation rate [11]. Additionally, reconstruction of the aortic root is considered the gold standard in the case of valve anomalies [12,13,14]. The choice between both conduits in cases of aortic root replacement is the responsibility of the respective cardiac surgeon; however, there is an internal consensus to use the fully biological BioIntegral conduit in cases with endocarditis [9]. Homografts or autografts were not used for total aortic root replacement during the study period at our department [15]. The current analysis showed that the LABCOR conduit was used more frequently during the study period and that the patients who were treated with a BioIntegral conduit had a worse preoperative status, and they suffered from more complications during the early postoperative course and had a higher mortality rate. Echocardiographic findings were comparable at the time of follow-up between both conduits used, with no significant differences during the study period. Wendt et al. analyzed the mid-term hemodynamic performance of BioIntegral and BioValsalva conduits after aortic root replacement in a smaller cohort with 55 patients [16]. Like the LABCOR conduit, the BioValsalva conduit is composed of a porcine heart valve and a synthetic tube. The most important difference is that the BioValsalva conduit, in contrast to the LABCOR conduit, carries a stentless valve prosthesis. Wendt and colleagues did not observe significant differences between the BioIntegral and the BioValsalva conduit with regard to the hemodynamic measurements at follow-up. The pressure gradients for all valve sizes in their group treated with a BioIntegral conduit were unremarkable and similar to the transvalvular gradients determined in our BioIntegral group. In comparison to the work of Wendt and coworkers, both of our patient groups appeared significantly sicker and suffered from significantly higher mortality. For example, in our patients who were treated with a BioIntegral conduit, the median EuroSCORE II was $14.9\%$, the rate of previous cardiac surgery procedures was $86.3\%$, and 30-day mortality was $26.0\%$, which is higher than in patients of Wendt and coauthors who also received a BioIntegral conduit. Wendt and coworkers recorded a median EuroSCORE II of only $5.3\%$ and a low rate of previous cardiac surgery of $10.7\%$in their BioIntegral group. Correlating to the relatively low risk profile of their patients, the 30-day mortality in their BioIntegral group was just $3.6\%$. It is uncertain whether they provided their sicker patients with another conduit or possibly also with homografts or autografts. However, the discrepancy between the groups in Kiel and Essen can also be explained by the strikingly low rate of patients with existing endocarditis who underwent aortic root replacement during the period studied there. Wendt et al. treated only two patients ($3.6\%$) with preoperative endocarditis and both patients received a BioValsalva conduit. In our analysis, $25.2\%$ patients suffered from active infective endocarditis prior to total aortic root replacement and underwent subanalysis. Additionally, in this high-risk group, all patients received a BioIntegral or LABCOR conduit without exception. Comparability between the two groups was more favorable in this subanalysis, and preoperative morbidity was high in each case but was more homogeneously distributed than in the overall comparison between both groups. Nevertheless, the patients who received a BioIntegral conduit were still more complex, with $96.4\%$ having previous cardiac surgery via a median sternotomy and $100\%$ having preoperative prosthetic endocarditis after aortic valve or aortic root replacement or after transcatheter aortic valve implantation in individual cases. In the LABCOR group, the proportion of patients with prior surgery and preoperative prosthetic infection was still high, but significantly lower at $58.3\%$ each. Considering only cases with preexisting endocarditis, the BioIntegral conduit was not significantly different from the LABCOR conduit in terms of subsequent prosthetic endocarditis in our study, at $7.7\%$ versus $0\%$. Furthermore, no significant differences were observed in this subanalysis in either numerical echocardiographic values or dichotomous rates of conduit valve regurgitation during follow-up. In this high-risk population, Kaplan–Meier analysis did not show a significant survival advantage for either conduit. Our subanalysis on patients without preoperative endocarditis reported a better outcome than in the total or endocarditis groups, with no significant differences between the two conduits, although again the BioIntegral group was worse from baseline with significantly more prior cardiac surgeries and pacemaker implantation and longer operative and bypass times. In this high-risk endocarditis population, the 30-day mortality was $30.9\%$ for BioIntegral versus $16.7\%$ for LABCOR without a significant difference. A study from Heinz et al. on patients with severe destructive aortic root endocarditis showed that the Freestyle root replacement was used successfully with no technical complications in such cases. In view of this complex patient population, short- and long-term results make this conduit a reliable choice for treatment of this condition [17]. Their 30-day mortality was $19.4\%$ ($$n = 6$$) which is comparable to our LABCOR mortality of $16.7\%$. The actuarial survival at 5 and 10 years was $61.9\%$ and $54.2\%$, respectively. Freedom from death, reoperation for prostheses dysfunction, and recurrence of endocarditis as the composite end points at 5 and 10 years was $56.3\%$ and $53.1\%$, respectively. In summary, total aortic root replacement, especially as a reoperation, is one of the most complex cardiac surgical procedures. The outcome of operated patients depends mainly on concomitant diseases, but especially on preexisting endocarditis, irrespective of the conduit used. Although our results should be interpreted with caution due to the different risk profiles of both groups, the BioIntegral group was sicker in several aspects, and we conclude that even in bail-out situations, such as extensive aortic valve or prosthesis endocarditis, a purely biological conduit does not necessarily have to be implanted, but that the only partially biological LABCOR conduit can also be used in these situations without tangible disadvantages. ## 5. Limitations The studies were performed retrospectively. Thus, randomization was excluded and the proportion of patients who received a LABCOR conduit was significantly higher. The patients who were treated with a BioIntegral conduit during the period under study showed a significant morbidity in the overall comparison and the corresponding surgical procedures were significantly more complex due to the difficulty of the tissue dissection due to redo surgery. ## 6. Conclusions Both conduits with a biological valve prosthesis for complete aortic root replacement compared with each other showed a good overall performance. The fully biological BioIntegral Conduit is more often chosen in situations with pre-existing endocarditis due to concerns that synthetic materials may promote reinfection. However, valid arguments against the use of the semisynthetic LABCOR conduit even in patients with preoperative extensive endocarditis in the region of the aortic root were not found in our studies. ## References 1. Yun K.L.. Ascending aortic aneurysm and aortic root disease. Coron. Artery Dis. (2002) 13 79-84. DOI: 10.1097/00019501-200204000-00002 2. 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--- title: Beneficial Effects of Citrus Bergamia Polyphenolic Fraction on Saline Load-Induced Injury in Primary Cerebral Endothelial Cells from the Stroke-Prone Spontaneously Hypertensive Rat Model authors: - Rosita Stanzione - Maurizio Forte - Maria Cotugno - Francesca Oppedisano - Cristina Carresi - Simona Marchitti - Vincenzo Mollace - Massimo Volpe - Speranza Rubattu journal: Nutrients year: 2023 pmcid: PMC10056311 doi: 10.3390/nu15061334 license: CC BY 4.0 --- # Beneficial Effects of Citrus Bergamia Polyphenolic Fraction on Saline Load-Induced Injury in Primary Cerebral Endothelial Cells from the Stroke-Prone Spontaneously Hypertensive Rat Model ## Abstract High salt load is a known noxious stimulus for vascular cells and a risk factor for cardiovascular diseases in both animal models and humans. The stroke-prone spontaneously hypertensive rat (SHRSP) accelerates stroke predisposition upon high-salt dietary feeding. We previously demonstrated that high salt load causes severe injury in primary cerebral endothelial cells isolated from SHRSP. This cellular model offers a unique opportunity to test the impact of substances toward the mechanisms underlying high-salt-induced vascular damage. We tested the effects of a bergamot polyphenolic fraction (BPF) on high-salt-induced injury in SHRSP cerebral endothelial cells. Cells were exposed to 20 mM NaCl for 72 h either in the absence or the presence of BPF. As a result, we confirmed that high salt load increased cellular ROS level, reduced viability, impaired angiogenesis, and caused mitochondrial dysfunction with a significant increase in mitochondrial oxidative stress. The addition of BPF reduced oxidative stress, rescued cell viability and angiogenesis, and recovered mitochondrial function with a significant decrease in mitochondrial oxidative stress. In conclusion, BPF counteracts the key molecular mechanisms underlying high-salt-induced endothelial cell damage. This natural antioxidant substance may represent a valuable adjuvant to treat vascular disorders. ## 1. Introduction The stroke-prone spontaneously hypertensive rat (SHRSP) represents a suitable animal model for the dissection of the pathogenetic basis of cerebrovascular damage associated with hypertension [1]. The stroke phenotype is accelerated in this model by feeding with a high-salt/low-potassium Japanese-style diet (JD) [2,3], with renal damage preceding stroke occurrence [4]. The stroke-resistant spontaneously hypertensive rat (SHRSR), which represents the strict control of the SHRSP strain, does not develop vascular damage upon the same dietary treatment despite similar blood pressure levels [3]. A genetic linkage analysis demonstrated that few chromosomal loci contributed in a significant manner to the stroke phenotype variance between the two strains [3]. Subsequent investigations targeted to a chromosome 1 locus (STR1), explaining $20\%$ of the stroke phenotype variance, highlighted a key role of mitochondrial dysfunction in mediating the high-salt-favored vascular damage of JD-fed SHRSP [5]. In fact, in this experimental condition, a mitochondrial complex I deficiency is induced by inhibition of the Ndufc2 subunit expression, whose gene maps are at the peak of linkage within STR1. Ndufc2 is a fundamental subunit to allow for regular assembly and function of the complex I, with consequent regular activity of the oxidative phosphorylation [5]. Subsequent studies revealed that this molecular mechanism also severely alters mitochondrial structure and function in peripheral blood mononuclear cells of healthy subjects once exposed to either high salt or lipopolysaccharides [6]. Most importantly, a decrease in the Ndufc2 subunit also contributes to both juvenile ischemic stroke and myocardial infarction occurrence in humans [5,6,7,8]. Interestingly, isolated primary cerebral endothelial cells (ECs) [6] from SHRSP, once exposed to saline load, show a significant degree of mitochondrial dysfunction, dependent from a decrease in Ndufc2 subunit expression, with a consequent increase in oxidative stress, reduced viability, and increased necrosis [9,10]. Therefore, this in vitro model mimics the in vivo condition quite well and represents a suitable experimental tool for testing the effects of protective molecules in vitro. Vegetal substances are known for their beneficial vascular properties in both animal models and in humans due to their ability to counteract oxidative stress, inflammation, and mitochondrial dysfunction [11]. In this regard, our previous studies demonstrated the protective role of Brassica oleracea sprout extract, based on both anti-inflammatory and antioxidant actions, toward the high-salt-induced vascular injury of SHRSP [12,13]. The latter evidence, showing a remarkable decrease in both renal and cerebrovascular damage, further supported the significant adjuvant role of favorable nutritional components to combat cardiovascular and cerebrovascular diseases. Among the emerging substances of natural origin provided of cardiovascular beneficial properties, the bergamot polyphenolic fraction (BPF), that is, the extract of the bergamot fruit (Citrus bergamia), is attracting much attention. BPF reduces serum lipid level (low-density lipoprotein cholesterol and triglycerides) and improves metabolic parameters and endothelial function in both animal models and in humans [14,15,16,17,18,19,20,21]. At the cellular level, BPF shows anti-inflammatory and antioxidant properties and can improve mitochondrial bioenergetics, mitochondrial function, and cell metabolism [16,22,23,24]. Interestingly, the protective action of BPF was also related to its ability to restore autophagy [25,26], a process with a fundamental role in cellular, tissue, and organismal homeostasis since it selectively targets dysfunctional organelles and pathogenic proteins [27]. Evidence that BPF can counteract high-salt-favored vascular injury in the context of arterial hypertension is still lacking. On these premises, the goal of the present study was to test in vitro the potential beneficial impact of BPF toward high-salt-induced injury and the underlying molecular mechanisms in SHRSP cerebral ECs, as starting knowledge for subsequent in vivo investigations. ## 2.1. Preparation of the Bergamot Polyphenolic Fraction (BPF) This step was performed through a standardized and previously reported procedure [14]. In particular, the cultivations of Citrus bergamia Risso & Poiteau are present along the Ionian coast of Calabria, in a geographical area of about 90 km between Bianco and Reggio Calabria, Italy. After harvesting, the peeled fruits were squeezed to obtain the bergamot juice, from which the oily fraction was removed by stripping. Furthermore, the juice was clarified by ultrafiltration. At the end of this process, the clarified juice was eluted through a polystyrene resin column, using a KOH solution, to retain the polyphenolic compounds with a molecular weight between 300 and 600 Da. Incubation of the basic eluate on a rocking platform allowed for a reduction in the furocoumarin content, on which the shaking time depended. In the next step, this phytocomplex was neutralized by filtration on cationic resin at acid pH, and after drying under vacuum and mincing, it was transformed into powdered BPF. Analysis of the BPF powder by UHPLC-HRMS/MS determined that it consisted of $40\%$ flavonoids and $60\%$ carbohydrates, fatty acids, pectins, and maltodextrins. The flavonoid profile analyzed by high-resolution mass spectrometry (Orbitrap spectrometer) and HRMSMS (ddMS2, data-dependent MS/MS) includes neoeriocitrin, naringin and neohesperidin. In addition, the entire HMG family is present with bruteridin and melitidin together with flavonoids such as 6.8-di-C-glycosides [24,28,29]. The BPF used in the present study was prepared and characterized by polyphenol content a month before performing the experiments. ## 2.2. Cell Isolation and Culture Primary cerebral ECs were isolated from newborn SHRSP rat brains (1–3 days old) by enzymatic and mechanic digestions and subsequent positive selection using microbeads magnetically labeled with CD31 antibody (Miltenyi Biotec, Bergisch Gladbach, Germany). For the enzymatic and mechanic digestions, neural tissue from neonatal brains was dissociated into single-cell suspension by using the Neural Tissue Dissociation Kit (Miltenyi Biotec) and the gentle MACS Dissociator (Miltenyi) with a specific program for neonatal brain. Afterword, the positive selection of ECs with CD31 antibody (Endothelial Cell Isolation Kit, Miltenyi) was performed by a separation over a MACS column placed in the magnetic field of a MACS separator (Miltenyi). ECs were grown in DMEM/F12 medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with $5\%$ FBS (Euroclone Srl, Pero, Italy) and ECGS (Sigma Aldrich–Merck, Darmstadt, Germany) on gelatin-precoated dishes at 37 °C and $5\%$ CO2 in an humified incubator. Cells were used between passages 1–4 for all experiments, as previously reported [9]. Animal experiments for EC isolation were performed in accordance with the European Commission guidelines (Dlg $\frac{2010}{63}$/EU) and the protocol was approved by Italian Ministry of Health (protocol n.: $\frac{448}{2022}$-PR). ## 2.3. Immunostaining of Primary Cerebral ECs for CD31 The purity of ECs was confirmed by immunofluorescence for CD31, a transmembrane glycoprotein expressed by ECs. For this purpose, 2 × 104 cells were plated in an 8-chamber slide and fixed for 10 min with $4\%$ PFA, washed in PBS, blocked with $5\%$ goat normal horse serum (Vector Laboratories, Burlingame, CA, USA) and incubated overnight at 4 °C with anti-CD31 antibody (0AAF00819-Aviva System Biology, San Diego, CA, USA). Then, Alexa fluor 488 (Invitrogen Carlsbad, CA, USA) was used for detection in fluorescence. Cell nuclei were stained with Höechst reagent (Thermo Fisher Scientific). Images were randomly taken with a fluorescence microscope. ## 2.4. Cell Treatments Preliminary experiments testing different BPF concentrations (from 50 to 500 µg/mL) on cell viability identified 250 µg/mL as the appropriate concentration to perform all studies in our cellular model exposed to salt loading. The latter is a noxious stimulus previously used in our studies to mimic the in vivo exposure of SHRSP to JD as a stroke-promoting diet [9,10,30]. Therefore, to test the effects of BPF on cell proliferation, viability, oxidative stress, angiogenesis, wound healing, and mitochondrial function, ECs were exposed for 72 h to the following treatments: NaCl 20 mM, BPF alone (250 µg/mL), and NaCl + BPF (20 mM and 250 µg/mL, respectively). Both NaCl and BPF were diluted in the cell medium. All experiments were performed at least in triplicate. ## 2.5. Cell Viability To test cell proliferation and viability, we used the quantitative colorimetric MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) test (Sigma Aldrich–Merck). MTT is a yellow powder containing bromide, which in living cells is transformed, by an enzymatic scission, into an insoluble blue/purple precipitate, the formazan. To perform the assay, ECs were plated in a 96-multiwell plate at a density of 1 × 104 cells per well, and they were exposed to 20 mM NaCl for 72 h, as previously described [9,10]. After the treatment, ECs were incubated for 2–3 h with 10 µL of MTT reagent (5 mg/mL) in a 37 °C, $5\%$ CO2 incubator. Then, 100 μL of DMSO were added to each well, and the mixture was stirred well until the formazan was completely dissolved. Finally, the absorbance of the solubilized substrate was measured with a microplate reader (Biorad, Hercules, CA, USA) at a wavelength of 570 nm. ## 2.6. Cellular Reactive Oxygen Species (ROS) Measurement Cellular ROS were evaluated using the fluorescent probe 2′,7′-Dichlorofluorescein diacetate (DCFH-DA Sigma Aldrich). DCHF-DA is an apolar molecule that diffuses easily in cells, where by two successive enzymatic reactions, it is transformed into DCF, a highly fluorescent molecule that is emitted at a wavelength of 532 nm. The oxidation of DCHF to DCF occurs mainly by H2O2. Therefore, the fluorescence intensity is considered directly proportional to the quantity of H2O2 produced by the cells. In our experiments, ECs were treated with 200 µL of 10 μM DCFH-DA for 30 min at 37 °C in the darkness. Production of ROS was measured by a microplate reader (Berthold, Bad Wildbad, Germany) at an excitation wavelength of 485 nm and an emission wavelength of 530 nm. ## 2.7. Angiogenesis Assay The angiogenesis assay was performed by using a Matrigel matrix (Corning, by Sigma Aldrich–Merck). In the specific, 50 microliters of Matrigel matrix were added to each well of a 96-multiwell plate and allowed to solidify for 1 h at 37 °C. After treatment with NaCl and BPF, 1 × 104 ECs were plated on top of the Matrigel layer and incubated for 4 h. Images were taken with EVOS Cell Imaging Systems (Thermo Fisher Scientific) and the number of master junctions was quantified using a specific plugin “Angiogenesis analyzer” of ImageJ software (National Institutes of Health, Bethesda, MD, USA) [31]. ## 2.8. Wound-Healing Assay Cell migration was evaluated by conventional wound-healing assay. For this purpose, 5 × 105 cells were plated on each well of a 24-multiwell plate until confluence was reached. Then, ECs were incubated with 20 mM NaCl for 72 h either in the absence or in the presence of BPF, and the cell monolayers were damaged by manual scratching with a sterile yellow tip. Images were randomly collected at different time points using an inverted microscope (EVOS Cell Imaging Systems, Thermo Fisher Scientific). Percentage of wound closure was calculated according to the following formula: % wound closure = [Area of the original wound (t0) − Area of the actual wound (t 72 h)]/Area of original wound (t0) × 100. Wound area was calculated by ImageJ software (version 1.53t). ## 2.9. Assessment of Mitochondrial Membrane Potential Using JC-1 Staining To determine the mitochondrial membrane potential depolarization by JC-1 reagent (Thermo Fisher Scientific), 3 × 104 cells were plated on each well of a 24-multiwell plate, and later, they were exposed to 20 mM NaCl for 72 h, as previously described [9], either in the absence or in the presence of BPF in a humidified CO2 incubator. After 72 h, ECs were incubated with 10 μg/mL JC-1 at 37 °C for 20 min. After incubation, cells were washed twice with ice-cold PBS 1X. Finally, the images were taken with EVOS Cell Imaging Systems (Thermo Fisher Scientific). JC-1 is a cationic dye that exhibits potential-dependent accumulation in mitochondria, indicated by a fluorescence emission shift from green to red. Mitochondrial depolarization is indicated by a decrease in the red/green fluorescence intensity. The red-to-green fluorescence intensity ratio (R:G) was calculated by ImageJ software. ## 2.10. Assessment of Mitochondrial Function To assess mitochondrial function, we measured mitochondrial complex I activity (assessed as NAD+:NADH ratio) and ATP levels with two commercially available kits. To investigate the redox status of the ECs after 72 h of treatment, the concentrations of NADH and NAD+ were determined using an NAD+/NADH Assay Kit (ABCAM) in accordance with the manufacturer’s instructions. The absorbance values were acquired at 450 nm by a microplate reader (Berthold), and data were analyzed following the manufacturer’s protocol. ATP levels were assessed with an ATP colorimetric assay (ABCAM), which, by a series of enzymatic reactions, forms a product that is quantified at 570 nm using a microplate reader (Berthold). Finally, data were analyzed following the manufacturer’s protocol. Mitochondrial ROS level was evaluated by using the MitoSOX™ Mitochondrial Superoxide Indicators (Thermo Fisher Scientific) following the manufacturer’s instructions. The MitoSOX Red reagent specifically reacts with superoxide, but not with ROS and reactive nitrogen species (RNS). To perform this experiment, ECs were plated onto 8-well chambered cell culture slides (Corning by Thermo Fisher Scientific) and treated as reported above. After 72 h of high salt exposure, ECs were treated with MitoSOX Red (5 μM) for 30 min at room temperature and then washed. Cell nuclei were stained by Höechst reagent (Thermo Fisher Scientific). MitoSOX Red fluorescence and Höechst were acquired by a fluorescence microscope Axiophot2 (Zeiss, Oberkochen, Germany), and MitoSOX fluorescent signal was determined with Image J. ## 2.11. Statistical Analysis All values are expressed as mean ± standard error (SEM). Comparisons between the experimental groups were performed by one-way ANOVA followed by Bonferroni post hoc test. A p value of <0.05 was considered significant. GraphPad Prism (Ver 5.01 GraphPad Software, Inc., La Jolla, CA, USA) statistical software was used for the statistical analysis. ## 3. Results First of all, we checked and confirmed the purity of ECs extracted from the brain of neonatal SHRSP by immunofluorescence for CD31, an established marker of ECs. Results are shown in Figure 1. As previously shown [9,10,30], in the current set of studies, we confirmed that the saline load increased oxidative stress; reduced cell proliferation, viability, and migration; impaired angiogenesis, and induced mitochondrial dysfunction with an impairment of mitochondrial membrane potential, reduced complex I activity, and ATP synthesis in SHRSP primary cerebral ECs (Figure 2, Figure 3 and Figure 4). In the presence of BPF, cerebral ECs exposed to saline load for 72 h significantly reduced ROS production (Figure 2A) and rescued cell proliferation and viability (Figure 2B). Of note, BPF alone increased cell viability (Figure 2B). BPF rescued the angiogenetic property of ECs, as shown by the increased number of master junctions in cells exposed to the high-salt treatment in the presence of BPF (Figure 3A,B). Moreover, BPF prompted wound healing by favoring EC migration (Figure 3C,D). In fact, the wound appeared significantly covered by migrated cells upon the combined treatment with salt loading and BPF. We also tested the impact of BPF on the saline load-induced mitochondrial dysfunction. First, we confirmed that salt loading caused a significant impairment of the mitochondrial membrane potential, which is fundamental to preserve cell integrity. Then, we observed that BPF rescued the mitochondrial membrane potential (Figure 4A,B), as documented by the increased JC1 red-to-green fluorescence ratio. BPF also rescued both ATP synthesis and complex I activity in cerebral ECs under saline load (Figure 4C,D). Consistently, the mitochondrial ROS production decreased in ECs treated with both saline load and BPF (Figure 4E,F). The whole results of the present investigation indicate that BPF can antagonize the harmful effects induced by high salt load in cerebral ECs, obtained from the SHRSP rat model, on several parameters of cell survival and function. ## 4. Discussion In the present study we demonstrate that BPF, a vegetal extract obtained from the Citrus bergamia fruit, exerts a significant protective effect toward the high-salt-induced injury in primary cerebral ECs isolated from the brain of newborn SHRSP rats. In fact, BPF treatment was able to rescue relevant cellular processes, including proliferation, viability, migration, angiogenesis, and mitochondrial function, which are all compromised upon saline load in the SHRSP experimental model. The latter represents a well-characterized animal model of human disease, particularly for its ability to accelerate cerebrovascular events upon high-salt dietary feeding, which is also based on a genetic predisposition [2,3,4,5,9,12,13,32]. The endothelial dysfunction precedes stroke occurrence in the JD-fed SHRSP as well as in humans [33,34,35,36], an observation that further supports the valuable role of this model for studies on the human disease. Interestingly, as shown in previous studies, the SHRSP primary cerebral ECs exposed to saline load quite closely mimic the in vivo condition and represent a unique experimental tool to test in vitro the effects of protective substances toward the molecular mechanisms involved in the higher predisposition to the vascular damage of the strain [9,10,30]. Herein, based on the available evidence supporting the beneficial properties of BPF in other experimental contexts [14,37,38,39], we aimed to test the potential protective effect of BPF on the cellular parameters known to be compromised upon saline load in the stroke predisposition of the SHRSP strain. We paid particular attention to the ability of BPF to rescue mitochondrial functional parameters (such as mitochondrial membrane potential, ATP synthesis, and complex I activity). A severe mitochondrial dysfunction, dependent on a complex I deficiency, has been previously identified as one of the main pathogenic molecular mechanisms underlying the higher predisposition of the JD-fed SHRSP to vascular damage and its dramatic consequences [5]. In fact, a significant reduction in complex I Ndufc2 subunit expression was observed in the JD-fed SHRSP. Importantly, we have already demonstrated that the recovery of the mitochondrial dysfunction through the correction in complex I deficiency with nicotinamide administration delayed both renal and cerebrovascular damage occurrence in JD-fed SHRSP [9]. The primary cerebral ECs also show an impaired mitochondrial function once exposed to the saline load [9,10,30]. As a result of our present study, BPF exposure led to a significant increase in cell survival, a reduction in oxidative stress and an increase in endothelial cell tubular formation. Remarkably, mitochondrial function was improved with a significant decrease in mitochondrial oxidative stress production. Our current evidence is consistent with the results of previous investigations and strongly points to BPF as a valuable adjuvant substance to combat cardiovascular diseases. In this regard, an integrated therapeutic strategy, combining standard pharmacological treatments with natural protective substances of either vegetable or animal origin, can reduce the risk of common cardiovascular diseases such as stroke and ischemic heart disease. To support this concept and further validate the suitability of our rat model, we previously reported the significant protective effect of a Brassica oleracea sprout extract administration toward renal and cerebrovascular damage occurrence in JD-fed SHRSP [12]. Moreover, the administration of the natural disaccharide trehalose, present in different foods, led to a significant delay in renal damage and stroke occurrence in JD-fed SHRSP [40]. Among the natural available compounds, the bergamot polyphenolic fraction (BPF), extracted from the bergamot fruit (Citrus bergamia), a plant endemic to the Calabrian Ionian coast in Southern Italy, belongs to a class of molecules (the polyphenols) that are well known for their protective properties on human health. In particular, bergamot is rich in flavonoid glycosides, such as neoeriocitrin, neohesperidin, and naringin; and glycosylated polyphenols, such as bruteridin and melitidin [28]. Several studies have demonstrated the beneficial effects of polyphenols against widespread pathologies, including cardiovascular diseases, both in preclinical models and in humans [18]. Regarding stroke, a neuroprotective effect was reported even when polyphenols were administered after stroke induction, indicating that these molecules can also contribute to the recovery of patients suffering from stroke [18]. Contrasting effects were observed in schizophrenia [41,42]. The protective functions of BPF are mainly based on antioxidant, anti-inflammatory, lipid-lowering, and hypoglycemic effects of polyphenols [43]. Of note, polyphenols act as both reactive oxygen species scavengers and metal chelators [44]. Moreover, they activate transcription factors such as erythroid 2-related factor 2 (Nrf2), which are able to stimulate the expression of several antioxidant enzymes, including superoxide dismutase (SOD), heme oxygenase-1 (HO-1), catalase, glutathione reductase, and glutathione-S-transferase [45]. In addition, polyphenols exert an anti-inflammatory property that is based on their ability to modulate immune cell regulation, inflammatory gene expression, and the synthesis of inflammatory mediators [46]. In vitro studies revealed that BPF stimulated higher mitochondrial activity with increased ATP production from oxidative metabolism in both isolated mitochondria and porcine aortic endothelial cells [24]. In addition, BPF carried out its beneficial effect on the mitochondrial permeability transition pore (mPTP) phenomenon by desensitizing the pore opening [24], a known molecular mechanism causing cell damage [43]. In fact, BPF inhibited the Ca2+-activated F1FO-ATPase, therefore counteracting the opening and size of the mPTP with a final protective effect on mitochondrial dysfunction [24]. In the same cell line, the authors demonstrated that BPF counteracted the toxic effect of doxorubicin on cell viability and mitochondrial function [29]. Based on this in vitro study, BPF, by restoring the correct metabolic cellular functions, can behave as a positive agent toward the cardiovascular disorders resulting from the toxic action of doxorubicin. Moreover, the protective effects of BPF were reported in a few models of cardiovascular diseases. For instance, consistently with the above-mentioned in vitro data, BPF exerted an antioxidant cardioprotective effect in a rat model of doxorubicin-induced cardiac damage [26]. Interestingly, in this work, the protective action of BPF was related to its ability to restore autophagy. BPF administration in the hyperlipidemic Wistar rat induced a significant reduction in malondialdehyde and glutathione peroxidase serum levels, two known markers of oxidative stress [47]. Furthermore, in a rat model of angioplasty, the pretreatment with bergamot essential oil reduced smooth muscle cell proliferation and neointima formation. This effect was associated with reduced free radical formation and reduced expression of LOX-1, the receptor for oxidized low-density lipoprotein [48]. A human study performed in subjects with moderate hypercholesterolemia evaluated the effects of a Bergamot extract on cardiometabolic parameters, including plasma lipids, atherogenic lipoproteins, and subclinical atherosclerosis, within a relatively short time frame of six months. As a result, the bergamot extracts reduced plasma lipids and improved the lipoprotein profile. Remarkably, a reduced subclinical atherosclerosis (assessed as carotid intimal media thickness) was observed [49]. Altogether, the available evidence, along with the results of the present study, strongly support the role of bergamot toward vascular protection and its potential role as an adjuvant for the treatment of vascular disorders and related acute events. ## 5. Conclusions We provide the first evidence that BPF is a natural antioxidant able to counteract high-salt-induced injury in a suitable experimental tool, the primary cerebral endothelial cells obtained from the SHRSP model. The latter represents an optimal animal model of human disease, regarding the hypertensive target organ damage favored by high salt exposure. In our study, the treatment with BPF in the presence of high salt, a noxious stimulus, allowed for the recovery of all cellular vital parameters and turned off the key molecular mechanisms underlying endothelial cell damage and dysfunction. This in vitro study represents a fundamental basis for further in vivo investigations testing the impact of BPF toward cerebrovascular accidents. This vegetal substance, as part of the Mediterranean diet, may become an attractive and useful adjuvant to either prevent or treat vascular disorders, such as stroke, associated with hypertension. ## References 1. 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--- title: Antioxidant and Lipid-Lowering Effects of Buriti Oil (Mauritia flexuosa L.) Administered to Iron-Overloaded Rats authors: - Jailane de Souza Aquino - Kamila Sabino Batista - Gabriel Araujo-Silva - Darlan Coutinho dos Santos - Naira Josele Neves de Brito - Jorge A. López - João Andrade da Silva - Maria das Graças Almeida - Carla Guzmán Pincheira - Marciane Magnani - Débora C. Nepomuceno de Pontes Pessoa - Tânia L. Montenegro Stamford journal: Molecules year: 2023 pmcid: PMC10056315 doi: 10.3390/molecules28062585 license: CC BY 4.0 --- # Antioxidant and Lipid-Lowering Effects of Buriti Oil (Mauritia flexuosa L.) Administered to Iron-Overloaded Rats ## Abstract The indiscriminate use of oral ferrous sulfate (FeSO4) doses induces significant oxidative damage to health. However, carotene-rich foods such as buriti oil can help the endogenous antioxidant defense and still maintain other body functions. This study aimed to assess the effects of buriti oil intake in iron-overloaded rats by FeSO4 administration. Buriti oil has β-carotene (787.05 mg/kg), α-tocopherol (689.02 mg/kg), and a predominance of monounsaturated fatty acids (91.30 g/100 g). Wistar rats ($$n = 32$$) were subdivided into two control groups that were fed a diet containing either soybean or buriti oil; and two groups which received a high daily oral dose of FeSO4 (60 mg/kg body weight) and fed a diet containing either soybean (SFe) or buriti oil (Bfe). The somatic and hematological parameters, serum lipids, superoxide dismutase (SOD), and glutathione peroxidase (GPx) were determined after 17 days of iron overload. Somatic parameters were similar among groups. BFe showed a decrease in low-density lipoprotein ($38.43\%$) and hemoglobin ($7.51\%$); an increase in monocytes ($50.98\%$), SOD activity in serum ($87.16\%$), and liver ($645.50\%$) hepatic GPx ($1017.82\%$); and maintained serum GPx compared to SFe. Buriti oil showed systemic and hepatic antioxidant protection in iron-overloaded rats, which may be related to its high carotenoid, tocopherol, and fatty acid profile. ## 1. Introduction Iron plays a key role in various cellular processes (e.g., oxygen transport, cell proliferation, and catalytic reactions) [1]. Despite the important metabolic role of iron in the body, iron overload is responsible for several disorders, whether due to hereditary hemochromatosis or to indiscriminate supplementation, especially in children, pregnant women, nursing mothers, and athletes [2,3,4]. Iron overload can cause anemia and even chronic intoxication due to its accumulation in various organs, promoting oxidative stress and inflammatory responses and, consequently, damage at the cellular level [5,6]. Thus, the organism’s iron balance must be maintained by meticulous regulation of its intestinal absorption release from macrophages to satisfy metabolic or functional demands and prevent deleterious effects due to iron deficiency or excess [7]. An overloaded iron condition is an important oxidative damage inducer, favoring reactive oxygen species (ROS) production by the Fenton reaction [8]. ROS are continuously produced by normal cell metabolism, causing structural and molecular damage, contributing to aging and diseases (e.g., cancer and diabetes) [9]. At this point, iron overload may increase cellular oxidative stress [10,11,12], and its hepatic accumulation modifies the liver enzyme gene expression involved in lipid metabolism, increasing serum and tissue cholesterol [13,14]. On the other hand, this oxidative damage in animals is minimized by an efficient antioxidant system, such as enzymes (e.g., superoxide dismutase and glutathione peroxidase), and low-molecular-weight compounds such as glutathione and ascorbic acid, or nonpolar compounds such as carotenoids to scavenge ROS and defend cells and macromolecules from oxidative damage. Although ROS act as secondary messengers, a redox imbalance is established under certain metabolic, physiological, and pathological conditions, in which the antioxidant defense systems are insufficient to scavenge oxidative compounds [12,15]. So far, drugs for the treatment of excess iron cause many side effects, whether it be iron overload generated by storage-related diseases or excessive iron supplementation. Chelation therapy is the only method applied to iron excretion despite its adverse effects [16], which in turn has stimulated studies with vegetable foods, medicinal plants, and their phytocomposition, rich in antioxidant compounds to control the redox imbalance caused by iron [17,18,19,20,21]. Thus, natural products with significant content in β-carotene, tocopherols, and polyphenols are efficient sources of exogenous antioxidants in regulating iron overload and are potential candidates for developing herbal medicines and/or functional foods [15,22]. In this context, the oil extracted from the buriti fruit pulp (Mauritia flexuosa L.f.) is a remarkable natural product due to its high carotenoid, tocopherol, and monounsaturated fatty acid (MUFA) content [23,24,25,26,27]. The growing interest in buriti oil is due to biological activities demonstrated in silico, in vitro, and in vivo [28]. Constituents of buriti oil have been shown to act as major virus SARS-Coronavirus peptidase inhibitors [29] and have shown antimicrobial activity [30]. Furthermore, buriti oil modulated physical parameters and reflex maturation of the offspring of dams fed with this oil; improved the lipid profile; and increased serum, hepatic retinol, and tocopherol in young rats [23,26]. Based on this, the purpose of this study was to assess the buriti oil intake effect in iron-overloaded rats by evaluating somatic parameters, serum lipid and hematological profile, and superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities. ## 2.1. Oil Characterization The characterization of the oils and the antioxidant capacity are presented in Table 1 and Figure S1, respectively. Buriti oil showed important levels of β-carotene and tocopherol, in addition to a higher concentration of monounsaturated fatty acids (MUFAs) such as oleic and palmitoleic acids, and a lower concentration of saturated (SFA) and polyunsaturated (PUFA) fatty acids compared to soybean oil (p ≤ 0.001). ## 2.2. Evaluation of Somatic Parameters and Food Intake Orogastric administration of a high FeSO4 dose in the SFe rat group resulted in a lower dietary intake compared to the SC and BFe groups (p ≤ 0.05) (Figure 1A). However, no significant differences ($p \leq 0.05$) regarding body mass gain (Figure 1B) and somatic parameters were observed among the groups (Table 2). ## 2.3. Effect of Buriti Oil on Lipid Profile, Aminotransferases, and Hematological Parameters Iron overload induced damage to the lipid profile, such as a significant increase in TAG (Figure 2A), VLDL (Figure 2C), and aminotransferases concentrations (Figure 2F,G) being observed in the SFe and BFe groups in comparison to the SC and BC, respectively (p ≤ 0.05). This result can be supported by a strong and significant positive correlation between TAG and VLDL values ($r = 0.84$; p ≤ 0.001) (Figure 3 and Table S2). The SFe group displayed the highest LDL value (p ≤ 0.05) (Figure 2D) compared to the control group (SC) (p ≤ 0.05); however, buriti oil consumption reversed the increase in LDL caused by iron overload in the BFe group (Figure 2D). No significant difference was determined in the TC (Figure 2B) and HDL values (Figure 2E). A moderate positive correlation between TC and body weight ($r = 0.66$; p ≤ 0.001) was observed (Figure 3 and Table S2), and an inverse correlation between C 18:0 acid (or stearic acid) and LDL seric (r = −0.66), and ALT (r = −0.62) and AST (r = −0.63) was observed (p ≤ 0.001). Furthermore, there is a positive correlation of LDL values with ALT ($r = 0.70$) and AST ($r = 0.71$) values (p ≤ 0.001) (Figure 3 and Table S2). The administration of iron overload altered some parameters in the hematological profile (Figure 4), regardless of the type of oil administered, such as hematocrit (Figure 4B), WBCs (Figure 4E), and lymphocytes (Figure 4F). Furthermore, WBCs and lymphocytes had a strong and significant positive correlation ($r = 0.77$; p ≤ 0.001) (Figure 3 and Table S2). On the other hand, buriti oil consumption reduced hemoglobin (Figure 4C) and increased monocyte counts (Figure 4G) in BFe rats compared to SFe rats, although no significant difference was observed among control groups (SC and BC) ($p \leq 0.05$) (Figure 4G). There was no significant difference regarding red blood cells and platelets among any of the groups (Figure 4A and Figure 4D, respectively). ## 2.4. Effect of Buriti Oil on Antioxidant Enzyme Activity in Serum and Liver The administration of the high-FeSO4 dose decreased serum (Figure 5A) and liver SOD activity (Figure 5B) in the SFe group (p ≤ 0.05). On the other hand, intake of the diet containing buriti oil increased SOD enzymatic activity in the BC and BFe groups (p ≤ 0.05) (Figure 5A,B). The BFe group maintained a similar GPx serum activity level to the other groups ($p \leq 0.05$), while the SFe group exhibited the lowest GPx compared to the SC group (p ≤ 0.05) (Figure 5C). An expressive increase in hepatic GPx activity was observed in rat groups fed a diet containing buriti oil (BC and BFe) (p ≤ 0.05) (Figure 5D). Furthermore, hepatic SOD ($r = 0.61$) and serum GPx ($r = 0.62$) were positively correlated with blood VLDL levels (p ≤ 0.001) (Figure 3 and Table S2). ## 3. Discussion Our study demonstrated that buriti oil has important antioxidant activity in vitro, as well as bioactive compounds such as β-carotene, α-tocopherol, and MUFAs such as palmitoleic and oleic acids. Previous studies have demonstrated a similar nutritional characterization of this oil, which has a predominance of monounsaturated fatty acids, followed by saturated and polyunsaturated fatty acids [23,31]. Buriti oil has a higher concentration of β-carotene, α-tocopherol, and oleic acid compared to oils extracted from the mesocarp of other Brazilian palm trees (Arecaceae), such as bacaba (Oenocarpus bacaba), inajá (Maximiliana maripa), pupunha (Bactris gasipaes), tucumã (Astrocaryum vulgare), and buritirana (Mauritiella armata) [32,33,34,35]. Our results demonstrate that iron overload administration in rats increased TAG, VLDL, and LDL values, which were associated with decreased antioxidant defense mechanisms, mainly hepatic and serum antioxidant enzymes (SOD and GPx). Otherwise, we demonstrated that buriti oil intake attenuated iron-overload-induced changes in hematological parameters, lipid profile, and serum and hepatic antioxidant status in BFe rats. Regarding dietary intake, the SFe group consumed less diet than the other groups, probably due to the ferrous sulfate taste interference on the rat gustatory sensitivity to the standard diet [36]. Although the BFe rats were also treated with FeSO4, no decrease in food consumption was observed, which may indicate that the buriti oil presence in the diet promoted a palatable effect [37]. However, we demonstrated that the consumption of diets containing different lipid sources (buriti or soybean oil) did not change the somatic parameters in young adult rats, which was also observed by Aquino et al. [ 23,38] in rats in childhood and adolescence phases who were fed diets added with either soybean or buriti oil for 28 consecutive days. Iron overload also did not influence these parameters, probably due to the short time of high-FeSO4 dose administration. In addition, the consumption of a high-fat diet associated with iron overload seems to enhance changes compatible with the metabolic syndrome in mice [39], which in the long term can cause somatic changes. However, the diets administered to the rats in the present study are isolipidic, and they followed dietary recommendations for rodents, which may have also contributed to these results. Iron overload in the present study caused deleterious oxidative effects on lipid metabolism, increasing TAG and VLDL levels in the SFe and BFe groups. In addition, a significant positive correlation between these parameters was observed in our study. This is interesting due to the VLDL lipoprotein transporting triglycerides to the bloodstream and organs, which can promote the development of vascular lesions [40]. On the other hand, buriti oil reversed the increase in LDL levels in the BFe rats caused by iron overload, which was not observed in the SFe rats. Although stearic acid (C 18:0) showed an inverse correlation with LDL levels, as confirmed by previous studies [41,42], soybean and buriti oils showed similar concentrations of this fatty acid, which may indicate the effect of other compounds present in buriti oil, such as oleic acid, β-carotene, and α-tocopherol, in reducing LDL levels. Studies have shown that unsaturated fatty acid intake can act as a protective agent against physiological injuries due to anti-inflammatory and antioxidant effects [43,44,45]. MUFAs can interfere with lipid metabolism through some mechanisms, which can decrease the sterol storage, the regulation of cholesterol synthesis, cellular absorption of LDL, and fat oxidation [46]. Oleic acid is the majority fatty acid in buriti oil, and this MUFA plays an important role in the peroxisome-proliferator-activated receptor-α (PPAR-α) upregulation, which in turn modulates the uptake, transport, and oxidation of fatty acids in rats induced to iron overload via fatty acid transporters [47]. Furthermore, the high carotenoid and tocopherol contents in buriti oil may be responsible for minimizing lipoprotein oxidation and, consequently, lowering serum LDL levels. Studies have shown the β-carotene- and α-tocopherol-modulating roles in pathways related to oxidative stress and inflammation, indicating that its dietary supplementation improved the serum lipid profile in rats fed a cholesterol-rich diet [23,48,49,50,51,52]. Although no histopathological analysis of the liver and other organs was performed, serum aminotransferase levels (ALT and AST) are reliable indicators of the functional or structural alteration of liver cells [53]. ALT and AST are widely used as markers of liver injury and inflammation in clinical practice [54]. In this concern, iron overload caused an increase in the ALT and AST concentrations in the SFe and BFe groups compared to the SC and BC groups, respectively. Nevertheless, it was evidenced that buriti oil consumption attenuated the elevation of both aminotransferases in BFe rats fed buriti oil compared to animals in the SFe group. The current treatment of iron overload includes metal chelators, which can cause adverse effects. For example, deferoxamine, deferiprone, and deferasirox increase the risk of agranulocytosis and neutropenia, risk of gastrointestinal disturbances, and hepatic failure. These adverse effects of available iron chelators highlight the need for alternative pharmacological interventions [55], such as buriti oil, which showed protective effects against damage induced to iron overload in rats. We demonstrated that the buriti oil intake attenuated the toxicity promoted in rats after iron-overload treatment, according to an evaluation of the hematological parameter data (erythrocytes, leukocytes, and platelet counts). Elevated hematocrit and hemoglobin levels exhibited by the SFe group compared to the SC group may be considered indicative of injury from a high-FeSO4 dose. Nevertheless, the BFe group displayed a decrease in hemoglobin content, probably due to a regulatory buriti oil effect on hemoglobin synthesis [6]. The buriti oil β-carotene content may have promoted maintenance of hemoglobin levels. β-carotene regulates iron metabolism, and its anti-inflammatory effect upregulates erythropoietin expression and iron mobilization for erythropoiesis [15,56]. Concerning the action mode of buriti oil, evidence indicates that both β-carotene and oleic acid interfere with iron absorption and metabolism. Buriti oil displays a very complex bioactive chemical composition, with β-carotene and oleic acid as major components, which may explain, due to a possible synergistic action, the iron absorption attenuation and metabolism in FeSO4-treated animals. At this point, studies with oils and other natural products have reported their effectiveness as a synergistic interaction result, in which it is difficult to determine the compound responsible for a biological effect since the chemical complexity represents a great challenge for its identification. Indeed, studies have indicated the behavior of a mixture as a general activity due to the synergistic effect related to the structural interaction of compounds belonging to different classes [57,58]. Iron overload had no effect on platelet counts, although it did impact leukocyte counts in the SFe group. Furthermore, WBCs and lymphocytes had a significant positive correlation, with the increase in lymphocytes (the WBC subtype) occurring in response to the injury caused by excess FeSO4 administered to the rats, since lymphocytes are a first-line protective barrier against the deleterious effects of excess iron [59]. Iron metabolism regulation occurs at systemic and cellular levels by several coordinated mechanisms involving erythrocytes, monocytes/macrophages, enterocytes, and hepatocytes through signaling triggered by the hepcidin–ferroportin interaction [6,60]. Thus, the results of this study indicate that the FeSO4 overload administration may have disrupted the iron metabolism in rats, promoting an inflammatory response detected by a significant increase in lymphocyte and granulocyte counts. The decrease in the monocytes (macrophage precursors) in the SFe rat group can be associated with this cell migration to several organs as an inflammatory response to oxidative stress induced by iron overload in the animal body [60,61]. Excess iron content in the cell is harmful because it is involved in redox reactions, which induce oxidative stress. Iron is involved in the Fenton reaction by producing highly reactive hydroxyl radicals from hydrogen peroxide. The role of oxidative stress in the pathogenesis and progression of other diseases, including cardiovascular disorders, is well established. Otherwise, carotenoids (mainly β-carotene) are the main nonpolar antioxidants in cells, presenting in buriti oil in a relevant quantity, and showing in vivo chelator potential, antioxidant activity, and enzymatic-induced activity [62,63,64]. In our study, the elevation in oxidative stress in chronically iron-overloaded rats was associated with possible depletion of nonenzymatic as well as enzymatic (superoxide dismutase and glutathione peroxidase) antioxidants in the liver and blood. However, buriti oil administration significantly restored the activities of enzymatic antioxidants. Concerning the antioxidant enzyme, the SOD and GPx activity decrease in the SFe group indicates the deleterious effect of a high-FeSO4 dose administration. However, the results from the group fed a diet with buriti oil (BFe) suggest that the oil chemical composition was efficient to minimize the deleterious effect of the high-FeSO4 dose on antioxidant enzyme activity. This result agrees with studies in which GPx and SOD activity evaluation in animals and humans with a β-carotene- or α-tocopherol-supplemented diet indicated the expression and activity of these enzymes, probably because of the compensatory defense of oxidative stress [64,65,66,67]. The high contents of β-carotene and α-tocopherol quantified in buriti oil contribute to the significant in vitro antioxidant capacity, which in turn can attenuate oxidative damage with consequent tissue recovery and biomolecules. β-carotene can quench singlet oxygen, α-tocopherol reduces mitochondrial hydrogen peroxide release rate, and both react as ROS scavengers in a nonenzymatic manner to prevent or decrease oxidative damage to biomolecules and cells [50,65,66,67,68]. Furthermore, a previous study demonstrated the synergism action among oleic acid and α-tocopherol to prevent oxidative stress [47]. Regarding the liver and blood-serum antioxidant enzyme activity significant differences, this event may occur because the liver is affected earlier by changes in diet and iron overload compared to enzymes analyzed at the systemic level. Therefore, the differences observed in the present study between liver and serum GPx and SOD activities may be related to specific dietary changes and liver lipid composition. This is supported by studies suggesting that antioxidant enzyme patterns in rats fed different dietary lipid sources are tissue-specific, meaning that the fat type in the diet can modify the responses to oxidative stress in specific organs, such as the liver [69]. Furthermore, the hepatic microsomal fatty acid composition may also play a role in the oxidative stress response since different fat types have been shown to affect this lipid composition [70]. A previous study has evidenced significantly lower hepatic levels of SOD, catalase, and GPx and SOD activities in nonalcoholic fatty liver disease compared to serum levels of these enzymes, respectively [71]. These results suggest a particular liver tissue vulnerability to oxidative damage and the importance of monitoring serum and liver antioxidant enzyme activity to obtain a more comprehensive assessment of oxidative stress. Overall, these studies highlight the importance of considering tissue-specific differences in antioxidant enzyme activity and possible implications for assessing pathogenesis and treatments [70]. It is noteworthy that the positive correlation between hepatic GPx and serum SOD values with the VLDL values is a defense mechanism against the oxidation of this lipoprotein, since a high-FeSO4-dose treatment induces liver oxidative damage and, consequently, lipid dysregulation. Iron-mediated lipid peroxidation generates ROS as well as oxidized lipoproteins, such as VLDL, an LDL precursor, promoting inflammation and foam cell formation, which in turn are factors associated with atherosclerosis [6,72]. The glutathione peroxidase family (GPx 1, 2, 3, and 4) is broadly distributed in different mammalian tissues. One of these (GPx4) binds to membranes and is responsible for nonapoptotic cell death regulation or ferroptosis due to excess iron exposure [73,74]. Therefore, the marked hepatic GPx depletion in the SFe rat group is indicative of the decrease or failure in the antioxidant enzyme defense system and the ferroptosis activation. Overall, the results show the attenuation of the metabolic effects caused by iron overload in rats fed a buriti oil diet. Notwithstanding, further studies are required regarding changes in antioxidant defense and the immune system, as well as focusing on liver histological evaluations to clarify other mechanisms involved and the progression of damage caused by iron overload. ## 4.1. Materials Ripe buriti fruits (Mauritia flexuosa L.f.) were purchased at a popular market in Picos (PI, Brazil) for oil extraction and were identified and deposited (Nº 30567) by the Herbarium Graziela Barroso of the Universidade Federal do Piauí (UFPI). The diet ingredients were acquired from Rhoster® (São Paulo, SP, Brazil), and refined soybean oil (Soya®, São Paulo, Brazil) was acquired in local markets (João Pessoa, PB, Brazil). SOD and GPx activities were determined by RANSOD and RANSEL diagnostic kits (Randox Laboratories, County Antrim, UK), respectively. Diagnostic kits to assess serum lipid profile (triacylglycerides (TAG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL)) were purchased from Labtest Reagents (Belo Horizonte, MG, Brazil). Fluorophore membranes (0.5 μm) were purchased from Millipore (Billerica, MA, USA), while HPLC-grade solvents and other analytical-grade chemicals were supplied by Merck (Darmstadt, Germany). ## 4.2. Oil Samples Buriti oil was extracted from buriti pulp using a manual procedure. First, fruit pulp samples were suspended in distilled water, heated at 60 °C, and stirred for 30 min to extract the crude oil. Then, the crude oil was used in the refining process, followed by neutralization, washing, degumming, and drying steps, according to Aquino et al. [ 31]. The oil was neutralized with $5.0\%$ sodium hydroxide solution at $12\%$, concerning the mass of oil, at a temperature of 50 °C, under stirring for 30 min. Subsequently, the oil was centrifuged at 5000 rpm and then transferred to a separatory funnel where successive washes were performed. The washes were carried out with water at room temperature alternating with water at a temperature of 90–95 °C at intervals of 30 min each, stirring the contents of the funnel manually. At each washing step, the water was discarded and evaluated with acid-base indicators (phenolphthalein and bromothymol blue) to detect any trace of sodium hydroxide used in the neutralization. The washes were completed when alkalinity was no longer detected in the discarded water. Then, the oil was dried in a rotary evaporator at 60 °C, with vacuum pressure for 20 min, under slight agitation and cooled to room temperature [31]. After the refining process, buriti oil was analyzed for the following parameters: free fatty acids (0.29 ± $0.03\%$), acidity index (0.22 ± $0.03\%$), and peroxide index (6.89 ± 0.78 mEq/kg), which demonstrated that the oil of refined buriti is within normal standards for consumption [31]. The extracted oil was stored at 7–10 °C in amber glass bottles for subsequent experiments. ## 4.3. Oil Chemical Characterization The buriti oil and soybean oil were analyzed in triplicate by chromatographic methods to determine their respective fatty acids, as well as the β-carotene and α-tocopherol contents, according to Aquino et al. [ 23] (Table 1). Fatty acid methyl esters (FAMEs) were obtained according to the methodology described by Hartman and Lago [69]. The FAMEs were separated, identified, and quantified on a gas chromatograph (Varian 430, Walnut Creek, USA), coupled to a flame ionization detector and an SPTM -2560 capillary column (100 m × 0.25 mm and film thickness of 0.20 μm, Supelco, Bellefonte, PA, USA). Helium (1 mL/min) was used as a carrier gas, and hydrogen (30 mL/min) and synthetic air (300 mL/min) were used as auxiliary gases. The temperatures of the split:splitless injector and the detector were maintained at 240 °C and 250 °C, respectively, with an injection volume of 1.0 μL. The initial oven temperature was 100 °C, which was then increased by 2.5 °C/min to 245 °C and maintained for 30 min. The FAME retention times of each oil were compared with a mixture of standards containing 19 methyl esters (ME19–Kit, Fatty Acid Methyl Esters C4-C24, Supelco, Bellefonte, USA) to identify the fatty acids, with results quantified by normalization areas of methyl esters. The chromatograms were recorded in the GalaxieTM Chromatography Data System software program (Varian, Palo Alto, CA, USA). The β-carotene and α-tocopherol analyses were performed by HPLC (Shimadzu HPLC class 10) coupled with a diode detector (DAD) and a LiChrospher 100 RP C18 column (125 cm × 4 mm, 5 µm particle stationary phase, Merck, Darmstadt, Germany). For β-carotene, the oil sample was cold saponified with a $10\%$ KOH methanolic solution and the carotenoids extracted with petroleum ether. After washing to neutral pH, the extract solution volume was adjusted and filtered through PTFE 0.45 µm membrane to quantify the β-carotene content. A 200 µL sample was injected manually using methanol:chloroform (95:5) as a mobile phase, at a flow rate of 1 mL/min, monitored at 450 nm, with 13 min of retention time [23]. Next, 0.1 g of each oil was weighed, followed by dilution in 2 mL of 2-propanol, and filtration through 0.45 um PTFE filters for injection into the chromatograph for the tocopherol analysis. A Si 60 normal phase column of 125 × 4 mm internal diameter with 5 µm particles was used, with n-hexane:ethyl acetate:acetic acid (97.6:1.8:0.6, v/v/v) as mobile phase and a flow rate of 1.5 mL/min. Quantification of tocopherol was performed using wavelengths of 294 nm for excitation and 326 nm for emission, with external standardization and 3 min of retention time [23]. The buriti oil and soybean oil in vitro antioxidant potential was evaluated by the DPPH assay [23] (Figure S1), expressing the results as the percentage (%) of free radical scavenging and IC50 value as the oil concentration necessary to promote $50\%$ of DPPH scavenging. ## 4.4. Animals, Diet, and Induction of Iron Overload The study was carried in compliance with ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) [75]. All animal procedures performed with prior approval by the UFPE Ethical Committee of Animal Research (protocol 23076.001000.2010-29). Thirty-two [32] male Wistar rats (349 ± 15 g) were kept in cages at 22 ± 1 °C on a 12 h light/dark cycle (light on 07:00 p.m.) and at a relative humidity of 50–$55\%$. Rats were randomized and allocated into four groups: two control groups fed diet containing soybean (SC, $$n = 8$$) or buriti oil (BC, $$n = 8$$) and daily saline solution via gavage; and two groups fed diet containing soybean (SFe, $$n = 8$$) or buriti oil (BFe, $$n = 8$$) and which received a high daily oral dose of FeSO4 (60 mg/kg body weight). Diets were prepared weekly and supplied daily in sufficient quantity to ensure ad libitum intake during the 17-day experimental period. Seven grams of oil (buriti or soybean oil) was added for every 100 g of diet. Each rat group was fed with balanced pellet diets prepared according to the American Institute of Nutrition—AIN guidelines [76], as described in the Table S1. The soybean oil was added to the control diet since it is the nutritionally recommend lipid source for rodents according to the AIN guidelines [76]. Therefore, the use of another lipid source other than soybean oil, even with a fatty acid composition similar to that of buriti oil, would configure an evaluation of another experimental oil, unlike the proposed study design. Furthermore, soybean oil is one of the most consumed and widely studied oils worldwide and also for this reason it was selected for addition to the control diet [77]. Animal iron overload were induced by gavage of 2 mL of FeSO4 (60 mg/kg body weight) was administered by gavage [78], corresponding to approximately $\frac{1}{5}$ of lethal dose for rats (LD50) [79]. The study design is outlined in Figure 6. ## 4.5. Food Intake, Weight Monitoring and Somatic Parameters Rat body weights and food intake were evaluated every two days during the 17-day experimental period. Immediately before euthanasia, measurements were conducted as described by Novelli et al. [ 80] in anesthetized rats. The parameters evaluated were abdominal circumference (AC; immediately anterior to the hind leg), chest circumference (CC; immediately behind the foreleg), body length (BL; measured from their nose to the tail base), and body weight (BW). BL and BW were used to calculate body mass index BMI (BW (g)/length2 (cm2)) and the Lee index (LI) (cube root of BW (g)/BL (cm)). ## 4.6. Buriti oil Effects on Lipid Profile and Hematological Biochemical Parameters Rats were anesthetized 24 h after the last treatment by intraperitoneal injection of 1 mL of ketamine hydrochloride (75 mg/kg body weight) associated with 1 mL of xylazine hydrochloride (5 mg/kg body weight), submitted to laparotomy, and euthanized by puncture of the left ventricle. Blood samples were collected by cardiac puncture from each animal to determine the hemoglobin, hematocrit, red blood cells (RBCs), white blood cells (WBCs), platelets, monocytes, leukocytes, and granulocytes, using a cell counter (ABX micro 60, Tokyo, Japan). Serum was also obtained by centrifugation (1825× g for 10 min at 4 °C) to evaluate serum lipids. TAG, TC, HDL, and LDL levels were analyzed using commercial reagent kits according to the manufacturer’s instructions, and absorbance was determined using a LabMax 240 Premium automatic analyzer (Labtest, Belo Horizonte, Brazil) at 500 nm (TC), 600 nm (HDL and LDL), or 505 nm (TG). Very low density lipoprotein cholesterol (VLDL) values were determined using the previously described equation, as follows: VLDL = TG/5 [81]. Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were analyzed to assess hepatic function using commercial Labtest kits (Labtest Diagnóstica S.A.), according to the manufacturer’s instructions. ## 4.7. Antioxidant Activity Erythrocytes were separated from plasma and then washed three times with 100 mM of potassium phosphate buffer at pH 7.4 and centrifuged (825× g at 4 °C for 10 min). These erythrocytes (0.2 mL) were mixed with 1.8 mL of $0.4\%$ β-mercaptoethanol (v/v) and frozen (−20 °C for 20 min). The hemolysate was then centrifuged (8274× g at 4 °C for 40 min). Liver tissue samples were homogenized in 50 mM of cold potassium phosphate buffer at pH 7.0, using a Potter–Elvehjem homogenizer to obtain a $10\%$ (w/v) homogenate after centrifugation (8274× g at 4 °C for 4 min). Both hemolysate and homogenate supernatants were used to determine the GPx (E.C. 1.11.1.9) and SOD (E.C.1.15. 1.1) activities (oxidative stress markers), using commercial kits according to the manufacturer’s instructions. Spectrophotometric measures were performed in triplicate (Shimadzu UV-VIS model 1650-PC spectrophotometer, Tokyo, Japan) and SOD and GPx activities were expressed as IU/mg hemoglobin (Hb) and IU/mg protein for serum and liver, respectively. ## 4.8. Statistical Analysis A minimum statistical power of $80\%$ was calculated considering the sample size (32 animals divided into four groups, $$n = 8$$), a minimally detectable effect size of 1.0, and a significance level of 0.05 (α = 0.05). Parametric data were assessed using the Student’s t-test at a significance level of $5\%$ (p ≤ 0.05). The results were expressed as mean ± standard deviation (SD). Statistical analyses and graphic design were performed using Sigma Plot 12.5 software for Windows (Systat Software Inc., San Jose, CA, USA). Data pre-treatment with the autoscaling method and calculation of Pearson’s correlation coefficient (r) to measure the association between two variables were carried out in MetaboAnalyst v.5.0 program (Xia Lab, McGill University, Montreal, QC, Canada). ## 5. Conclusions In summary, this is the first study to indicate the protective effect of in vivo buriti oil consumption against physiological damage induced by an iron overload condition. As a result, buriti oil intake was able to attenuate the deleterious effects caused by the administration of a FeSO4 overload. 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--- title: Collagen Functionalization of Polymeric Electrospun Scaffolds to Improve Integration into Full-Thickness Wounds authors: - Aswathy Ravindran Girija - Xanthe Strudwick - Sivakumar Balasubramanian - Vivekanandan Palaninathan - Sakthikumar Dasappan Nair - Allison J. Cowin journal: Pharmaceutics year: 2023 pmcid: PMC10056316 doi: 10.3390/pharmaceutics15030880 license: CC BY 4.0 --- # Collagen Functionalization of Polymeric Electrospun Scaffolds to Improve Integration into Full-Thickness Wounds ## Abstract Background: Electrospun fibers are widely studied in regenerative medicine for their ability to mimic the extracellular matrix (ECM) and provide mechanical support. In vitro studies indicated that cell adhesion and migration is superior on smooth poly(L-lactic acid) (PLLA) electrospun scaffolds and porous scaffolds once biofunctionalized with collagen. Methods: The in vivo performance of PLLA scaffolds with modified topology and collagen biofunctionalization in full-thickness mouse wounds was assessed by cellular infiltration, wound closure and re-epithelialization and ECM deposition. Results: Early indications suggested unmodified, smooth PLLA scaffolds perform poorly, with limited cellular infiltration and matrix deposition around the scaffold, the largest wound area, a significantly larger panniculus gape, and lowest re-epithelialization; however, by day 14, no significant differences were observed. Collagen biofunctionalization may improve healing, as collagen-functionalized smooth scaffolds were smallest overall, and collagen-functionalized porous scaffolds were smaller than non-functionalized porous scaffolds; the highest re-epithelialization was observed in wounds treated with collagen-functionalized scaffolds. Conclusion: Our results suggest that limited incorporation of smooth PLLA scaffolds into the healing wound occurs, and that altering surface topology, particularly by utilizing collagen biofunctionalization, may improve healing. The differing performance of the unmodified scaffolds in the in vitro versus in vivo studies demonstrates the importance of preclinical testing. ## 1. Introduction Steadily turning into a major public health concern, the global skin and wound care market is expected to grow from USD 18.49 billion in 2017 to USD 25.98 billion by 2025 [1]. Wound dressings play a critical role in wound care management, primarily providing an environment that stimulates healing by preventing fluid loss and protecting against microbial contamination [2,3]. However, most current wound care dressings are not completely successful in promoting regeneration of skin. Therefore, various studies are presently aimed at the development of novel and efficient dressing materials that can accelerate the process of wound healing in various forms including pads, films, hydrogels, sponges, and micro and nanofibers. Amongst these forms of wound dressing, micro/nanofibers are remarkably attractive because they resemble the natural fibrous network structure of the extracellular matrix (ECM) [4,5] and function as a provisional support that facilitates the adherence and proliferation of cells to generate native ECM in the wound [6]. Several approaches have been established for the fabrication of biomaterial-based scaffolds as wound dressings, including solvent casting, freeze-drying, particulate leaching, rapid prototyping, and electrospinning [7,8]. The electrospinning technique in particular has the potential for developing scaffolds with tuneable diameter to resemble or mimic natural ECM, with a range of polymers commonly used in tissue engineering studies [9,10]. Utilising biodegradable and biocompatible polymers in combination with electrospinning enhances cellular interaction and growth due to their biocompatibility with wound tissue and blood. Parameters such as the scaffold architecture, mechanical characteristics, geometry, dimensions and surface topography and chemistry all can direct the behaviour of cells with respect to attachment, proliferation and differentiation to influence the complex cellular process involved in the formation of new tissue [11,12]. Thus, electrospun scaffolds have the potential to exhibit properties including an enhanced surface-area-to-volume ratio, better mechanical properties, improved porosity, and exceptional capability to deliver bioactive agents such as growth factors, antimicrobials, anti-inflammatory agents, drugs, or nanoparticles into the nanofibers, which in turn can promote skin regeneration and accelerate wound healing [13,14]. Collagen is the key component of the ECM of several tissues including the skin. During the process of wound healing, collagen not only provides mechanical support for tissue repair, but influences the synthesis of other ECM proteins and plays a vital role in regulating the inflammatory response via the release of inflammatory cytokines as well as growth factors that are involved in the remodelling of the ECM [15,16]. Cells interact with collagen and collagen stimulates the differentiation of cellular phenotypes during wound healing [17]. Owing to its beneficial properties, such as superior biocompatibility, reduced cytotoxicity and antigenicity, and ability to promote cell adhesion, proliferation and migration, collagen is widely incorporated into wound dressings [18]. Several collagen-based dressings in the form of films, gels, hydrogels, pads, powders, sheets, membranes, cellular matrices and nanofibers are available on the market and are also under research; these are based on pure collagen, blended with other polymers, or coated over other polymers [19,20]. Collagen modification or collagen-based electrospun scaffolds mimic the native ECM in the skin and stabilise cell and vascular components in the wound by decreasing the level of matrix metalloproteinases (MMP), which is imbalanced in deep or chronic wounds [15]. The porosity that arises during the process of electrospinning offers structural support for cells to develop new tissues in the wound [21]. Collagen-based electrospun scaffolds also have the ability to absorb large volumes of wound exudate and maintain moisture for the wound, thus promoting a superior wound-healing process [20,21,22,23]. While pure collagen-based scaffolds face the major limitations of a faster degradation rate and poor mechanical properties, blending collagen with other polymers (natural or synthetic) such as polyethylene oxide (PEO), PCL, poly (L-lactic acid) (PLLA), PVA, hyaluronic acid (HA), elastin (EL) and silk fibroin (SF) has significantly improved its mechanical properties and has been widely used in skin tissue engineering studies [24,25,26,27,28,29,30]. Collagen can be coated or grafted using different methods such as drop casting (adsorption) and cross linking, or by creating chemical bonding with activated carboxylic functional groups [31,32,33]. We have previously used a simple drop casting method to coat PLLA electrospun scaffolds with collagen, and have shown that the electrospun scaffolds with variation in surface topology and surface chemistry affected the cellular responses including adhesion, proliferation, differentiation, and migration of keratinocytes and fibroblasts, with collagen-modified scaffolds (both porous and smooth) producing confluent and uniform epidermal sheets of keratinocytes on one plane, with healthy fibroblasts populated within the scaffolds [34]. In this study, we investigated how our different electrospun PLLA fiber scaffolds integrate into wounds and act to support healing in vivo using a mouse full-thickness excisional wound model. We hypothesized that application of these scaffolds could dramatically improve the healing of deep skin wounds and assessed not only the integration and infiltration of cells into the scaffold, but also their effects on wound closure, re-epithelialization and extracellular matrix deposition. ## 2.1. Scaffold Preparation PLLA scaffolds were fabricated by an electrospinner (Nanon- O1A MECC Co. Ltd. Fukuda, Japan) with PLLA, (Mw of 80,000–100,000, Polyscience Inc., Warrington, PA, USA) [34]. Smooth fibers were fabricated by dissolving PLLA in 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) (Fujifilm Wako Pure Chemical Corporation, Osaka, Japan) to obtain a $14.5\%$ w/v of PLLA solution using magnetic stirring for 4 h; this was followed by 4 h rest at room temperature (RT). The electrospinning process was performed at RT, a flow rate of 0.5 mL/h and an applied voltage of 15 kV. A grounded plate placed 12 cm from the tip of the needle was used as a collector. Porous fibers were fabricated using $11.5\%$ PLLA solution with a binary solvent system of chloroform: dimethyl formamide (9:1) and stirred for 24 h. The solution was further allowed to rest for another 4 h before electrospinning. Electrospinning was performed at a potential difference of 15 kV with a flow rate of 2 mL/h. The grounded plate was placed 12 cm from the tip of the needle and was used as the collector. The air humidity and temperature conditions during the spinning process were about 40–$50\%$ and 25 °C, respectively. The mat from the collector was detached, dried to remove any residual solvents, and stored for further characterization studies. Smooth- or porous-surfaced polymeric scaffolds were subsequently functionalized by a simple drop casting method with $0.1\%$ collagen type I (Sigma Life Science, Darmstadt, Germany), as described in Aswathy et al., 2020 [31]. For collagen-modified scaffolds, both UV-sterilized smooth and porous PLLA scaffolds were soaked in $0.1\%$ collagen for 30 min prior to application. Cocultures of keratinocytes and fibroblasts upon the scaffolds were prepared as previously described [34], and the morphology of the original polymeric scaffolds (before and after collagen coating) and the adhered cells were examined after 5 days’ coculture by scanning electron microscopy (SEM) using a Zeiss Merlin FEG SEM following previously described methods [34]. ## 2.2. Excisional Wound Model The use of animals was approved by the University of South Australia’s Animal Ethics Committee (U$\frac{15}{20}$) following the Australian Code of Practice for the Care and the Use of Animals for Scientific Purposes. Power studies showed that a sample size of 6 (equal males vs. females for all polymeric electrospun scaffolds) would give $80\%$ power using a $5\%$ test level and a one-tailed test. Male and female BALB/c mice of 10 weeks old were procured from Animal Resources Centre, Perth, Australia. Following acclimatization for 5 days, mice were administered pre-operative analgesia (Buprenorphine 0.05 mg/kg) via subcutaneous (SC) injection 30 min prior to wounding. Anaesthesia was induced by isoflurane inhalation, and the dorsal side of mice was prepared by removing hair with an electric shaver followed by Veet depilatory cream; the area was then cleansed with sterile water and ethanol ($70\%$ vol/vol). Full-thickness excisional wounds were created on the midline (wounds at 1.5 cm from the base of the skull and 0.5 cm either side of the midline) using a 10 mm diameter sterile biopsy punch (Acu-Punch, Norwalk, CA, USA). UV-sterilized circular scaffolds of 10 mm were inserted into the wound to act as dermal scaffolds within the full-thickness excisional wound; these were held in place by Tegaderm dressings that had an additional square of Tegaderm the size of scaffold (10 mm) placed over the sticky side to prevent the Tegaderm sticking to the scaffold itself whilst providing covering and protection of the scaffold within the healing wound (Figure 1b). Post-operative analgesia (Buprenorphine 0.05 mg/kg) was administered subcutaneously just prior to the dark cycle on day 0 for pain relief. The mice were monitored daily, and the scaffold dressings were allowed to remain in place for the duration of the study, with the Tegaderm reapplied as required. The mice were humanely killed by CO2 inhalation at 7 or 14 days post wounding, and the wounds were assessed for healing as described below. ## 2.3. Macroscopic Wound Assessment Digital photographs were taken on day 0, 7 and 14 with a ruler aligned next to the wound. Image Pro Plus image analysis software (Media Cybernetics, Inc., Bethesda, MD, America) was used to measure the area and diameter across the midline of the wounds, which were calibrated against the 1 mm graduations of the ruler that was included in the frame of each image. Healing at day 7 and 14 calculated by normalization to the day 0 wound using the following formula: Measurement of initial wound−measurement of actual woundMeasurement of initial wound×$100\%$ High resolution images of the healed wound on day 14 were also obtained using the DermaScope unit of the DermaLab Combo (Cortex Technology) following the manufacturer’s instructions. Erythema within the wound area was also assessed using skin reflectance spectroscopy by the DermaLab Combo to determine the redness of vascularized or inflamed skin. To obtain the erythema measurement, the instrument was calibrated and the reading taken directly above the wound to obtain an open-ended index in CIELab colour values and normalized to an area of unwounded dorsal skin for each mouse. Transepidermal water loss (TEWL) as an indicator of restored skin barrier function was measured for 8 s per site using the Vapometer evaporimeter (Delfin Technologies, Kuopio, Finland), following the manufacturer’s guidelines. Using an 11 mm adaptor, the Vapometer was placed directly onto the wounded or adjacent unwounded dorsal skin, and in the wound to obtain the TEWL in g/m2/h. ## 2.4. Histological Assessment of Healing The entire wound at day 7 or 14, including the scaffold and adjacent normal skin, was excised from the back of the mice and fixed in a formaldehyde-buffered solution ($10\%$ Neutral Buffered Formalin, Sigma) and embedded in paraffin wax for histological analysis. Histological sections of 4 μm were stained with haematoxylin and eosin (H&E) or Masson trichrome using previously described methods [35], and images were acquired using an Olympus IX81 light microscope (Olympus, Tokyo, Japan). The wound width and panniculus gape were manually quantified using Image Pro Plus image analysis software. The percentage re-epithelialization (%) was calculated based on the total wound length (the area between the first hair follicle either side of the wound and above the break in the panniculus) at day 7, using the following formula: Re−epithelialized wound lengthTotal epithelialized+un−epithelialized wound length×$100\%$ Infiltration of cells into the scaffold within the wound was represented as a score from 0 to 5 (none to high) based on H&E staining and visualization of blue/purple nuclei within the scaffold. The images were then analyzed using cellSENS Microscope Imaging Software (Olympus) to quantify the number of cells per area by counting the nuclei within the scaffold. Matrix deposition was also given a score from 0 to 5 by monitoring the density of the extracellular matrix within the wound in each test group from H&E images. For quantitative morphometric analysis of collagen deposition, RGB images of Masson Trichrome-stained section were analyzed in Image J software (Version 1.32j, National Institutes of Health, Bethesda, MD, USA) using a macro written by Kennedy et al., 2006 [36], whereby the number of blue/green pixels indicating collagen with substantially greater (>$120\%$) blue than red intensity are attributed the new, grey scale amplitude = 1, leaving other pixels = with amplitude = 0. Both the centre of the wound (defined ROI of standardized size) and the total wound area were measured. ## 2.5. Statistical Analysis All data are displayed as mean ± standard error of mean (SEM). The one-way analysis of variance test was used to assess the statistical comparisons. A value of $p \leq 0.05$ is considered statistically significant. All statistical analysis was performed using GraphPad Prism version 8.0 (GraphPad, Sacramento, CA, USA) ## 3. Results and Discussion The morphology of smooth and porous PLLA electrospun fibers before and after collagen modification and subsequent coculture with keratinocytes and fibroblasts was assessed by SEM (Figure 1a). Unmodified smooth PLLA fibers exhibited random distribution (size ranging from 900–1200 nm). Unmodified porous fibers were in the range of 1000–1300 nm, with an average pore size of 50 nm. Collagen modification further altered the morphology and porosity of both smooth and porous fibers, and deposits of collagen can be seen as aggregates and ultrafine fibrous structures. When fibroblast cells were cultured on collagen-modified fibers, the cells exhibited spindle-like elongated patterns, whereas keratinocytes exhibited a uniform patch of cells with good cell–cell interactions. As discussed in our in vitro study [34], while the unmodified smooth scaffold displayed better cell adhesion than unmodified porous scaffolds, collagen-modified scaffolds (both porous and smooth) produced confluent and uniform epidermal sheets of keratinocytes on one plane, with healthy fibroblasts within the scaffolds. Collagen-modified electrospun smooth and porous PLLA scaffolds of 10 mm diameter and unmodified counterparts were applied to the mice on the day of injury; full-thickness excisional wounds beneath a Tegaderm dressing (Figure 1b) were used to assess their ability to improve wound healing. Wounds were imaged on the day of surgery prior to scaffold application (day 0) and again on days 7 and 14 post-surgery (Figure 2a). No signs of any infection or major inflammation in the wounds were observed when the electrospun PLLA scaffolds were applied to the wounds. The scaffolds remained visibly attached to the wound at day 7 but were not observed by day 14. The wounds in all the treatment groups (smooth PLLA, porous PLLA, collagen-modified smooth PLLA and collagen-modified porous PLLA electrospun scaffolds) were not well healed on day 7, while the wounds were completely closed on day 14. Representative photographs from all treatment groups are provided in Figure 2. Images from Dermascope provided high-resolution images of healed wounds (Figure 2b). Wounds were healed in all the treatment groups. The PLLA scaffolds treatment groups did not exhibit any scabs, and exhibited healing with minimal scarring evident. Quantification of the macroscopic wound area (Figure 3a) and diameter (Figure 3b) suggested at day 7 that smooth, unmodified scaffolds were the worst performing and unmodified porous scaffolds were the best; however, by day 14, the mice treated with unmodified porous scaffolds had the largest wound area and diameter, with no significant differences between any of the groups found. TEWL measurements were taken to evaluate barrier function during the treatment of excisional wounds with an electrospun scaffold as a dressing (Figure 3c). No significant differences in TEWL were observed, although at day 7, both the unmodified and collagen-modified porous scaffold treatment groups had slightly higher TEWL than the two smooth scaffold groups, with the unmodified smooth scaffold-treated wounds having the lowest water loss. By day 14, however, when all groups showed low TEWL—which is indicative of the newly reinstated skin barrier—the opposite trend was observed, with the two porous scaffolds now having the lowest TEWL compared to the two smooth scaffold groups. No significant differences in wound redness were found, with wounds treated with smooth, unmodified scaffolds having the lowest erythema at day 7, and the unmodified porous and collagen-modified smooth scaffolds having the highest; however, by day 14, the unmodified porous group had the highest and the two collagen-modified scaffold groups exhibited the lowest redness within the wound. In contrast to wound area, diameter and TEWL—which all reduced over time as healing progressed—the erythema scores increased overtime, indicating that the measure is representative of vascularization within the healing wound tissue increasing as the wounds healed. The histological evaluation of wound healing was subsequently performed on H&E-stained tissue sections from wounds of mice treated with all four PLLA-based scaffolds at day 7 and 14 (Figure 4a). Wound healing was evaluated using the Image Pro Plus software to determine the wound width (Figure 4b), panniculus gape (Figure 4c) and the percentage of re-epithelization (Figure 4d) at the centre of the wound. It was observed that at day 7, the new epidermal layer had not been formed yet in any group, and the wound was covered with electrospun scaffold dressing; however, the scaffold was no longer detectable in day 14 wounds. In previous studies, with our collagen-coated PLLA fibers, which had an average of 80 um (measured with a Nikon Digi micro MH-15M that has a 0–15 mm measuring range and a 0.01µm minimum readable value at an accuracy of 0.7 µm), we observed that in vitro degradation showed a weight loss of $17\%$ and $23\%$ in PBS at 37 deg for 15 and 30 days, respectively [37]. For this in vivo study, knowing that reepithelialisation of 10 mm excisions should be completed at around 14 days, we reduced the electrospinning time from 30 min to 10 min to reduce the thickness of fibers, now at around ~10–15 um. Furthermore, in vivo polymer degradation occurs much faster than in in vitro conditions due to oxidative degradation [38,39,40,41]. Therefore, considering our previous experience in vitro and our current results, the reduced thickness and the oxidative stress around the wound site may have led to the accelerated degradation of the scaffold, the manner of which may not be possible under ambient conditions and lower humidity. The absence of a neo-epidermis at day 7 indicates that the TEWL results at this timepoint are indicative of the scaffold’s ability to provide barrier function, with the day 14 results better indicating the ability of the scaffold to induce better restoration of the skin barrier. Once again, the early indications from all three histological parameters on day 7 suggested poorest healing following treatment with the unmodified smooth scaffold (with a significantly larger panniculus gape in this group compared to the unmodified porous scaffold on day 7). By day 14, however, healing was similar in this group to that of both the unmodified porous and the collagen-modified porous scaffolds, both of which had the highest epithelialization, suggesting that these wounds were more fully healed overall. The trends observed suggest that collagen coating impacts healing potential regardless of the surface topography of the original scaffold, which agrees with previous observations that synthetic scaffolds have limited cellular infiltration in their native form without modification [42]. Furthermore, several studies have established that the structural and functional regeneration of the skin upon any structural damage, such as large and deep wounds, depends largely on the regulation of the ECM deposition [43]. Indeed, it is the formation and deposition of collagen that is a primary signature of the later phases of wound healing, and is critical for the normal functioning of skin [44]. In our own previous in vitro study, we demonstrated that the addition of collagen to a porous scaffold resulted in the formation of a nanofibrillar structure [31], which might have further aided the cells to interact, proliferate and deposit ECM to heal the wounds. These in vitro studies showed good infiltration of fibroblasts into all scaffolds, and as such, we anticipated similar observation when scaffolds were used as dressings for excisional wounds in mice. Figure 5a depicts the representative image of the lowest score [1], demonstrating limited infiltration of cells and no matrix deposition, and the highest score [5], showing maximum cellular infiltration and ECM deposition. It was observed that not all the treatment groups exhibited infiltration of cells and supported deposition of ECM around the scaffold (Figure 5b), with histological assessments for scoring the cellular infiltration into the scaffold and deposition of extracellular matrix within the wound showing that both the unmodified porous scaffold and collagen-modified porous scaffold exhibited superior cellular infiltration and matrix deposition. Quantification of cell infiltration by nuclei count (Figure 5c) confirmed that the smallest number of cells were present within the smooth scaffolds, and that the collagen-modified scaffold had the highest density of cells. This may be co-connected to the topography of the scaffolds, owing to the roughness imparted by the porous scaffold itself and also to the nano-structured fibrillar structure arising from the collagen modification of the scaffold prior to application to the wound. The Masson trichrome collagen deposition results, wherein porous PLLA scaffold exhibited superior collagen deposition at day 7 and day 14 treatment, also support this observation. Masson’s Trichome staining, wherein collagen fibers, the primary ECM component, [44] are stained in blue/green colour; this was carried out to evaluate the collagen formation and distribution in the healing wound on day 7 and 14 (Figure 6a), where the relative intensity of blue staining corresponds to the amount of collagen deposited and indicates the progression of collagen synthesis and remodeling within the wound tissue (Figure 6b). Our results suggest that among the treatment groups, the porous PLLA scaffold had the most improved collagen synthesis both in center of the wound and throughout the total wound area on day 7 and day 14, and the rate of collagen fibers’ synthesis and deposition throughout the wound was lowest with thesmooth PLLA scaffold on both day 7 and 14. We anticipated collagen coating over the scaffold might trigger further collagen deposition; however, while both collagen-modified scaffold types had a slightly higher collagen deposition when compared to the smooth scaffold, the unmodified porous PLLA scaffold supported the greatest collagen deposition in the wounds. ## 4. Conclusions In this study, electrospun PLLA scaffolds of different topologies (porous and smooth) were fabricated and functionalized with one of the major ECM proteins, collagen type I, and employed as wound dressings in mouse excisional wounds. Our previous in vitro studies showed that collagen modification enhanced major cellular activities including cell adhesion, spreading, proliferation and migration when skin cells were grown on the scaffolds, suggesting that collagen-modified electrospun scaffolds had the potential be used as promising wound-dressing materials to support a variety of cells and accelerate wound healing. Early indications confirmed that unmodified, smooth PLLA scaffolds may perform the worst in vivo, with these wounds having consistent trends towards delayed healing, appearing to be the largest in area, having the greatest wound width (including a significantly larger panniculus gape), and the lowest re-epithelialization. By day 14 however, no significant differences in healing were observed between the scaffolds. Nevertheless, wounds treated with the unmodified, smooth PLLA scaffolds still appear to have the least functional healing, with the lowest collagen deposition within the wound site. Taken together, the trends we have observed suggest that the limited incorporation of smooth PLLA scaffolds into the healing wound may be overcome by altering surface topology or biofunctionalization, and that in general, collagen biofunctionalization of PLLA scaffolds with either smooth or porous topology offers the greatest improvement to healing, with the collagen-functionalized smooth surfaces having a slightly decreased wound area and width compared to all other scaffolds, and collagen-functionalized porous scaffolds showing a decreased wound area and width when compared to the non-functionalized porous scaffold. Similarly, re-epithelialization at 14 days post-wounding was also highest in wounds treated with collagen-functionalized scaffolds. 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