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title: 'Undifferentiated pleomorphic sarcoma of the breast with neoplastic fever:
case report and genomic characterization'
authors:
- Thilo Gambichler
- Kai Horny
- Thomas Mentzel
- Ingo Stricker
- Andrea Tannapfel
- Christina H. Scheel
- Bertold Behle
- Daniel R. Quast
- Yi-Pei Lee
- Markus Stücker
- Laura Susok
- Jürgen C. Becker
journal: Journal of Cancer Research and Clinical Oncology
year: 2022
pmcid: PMC10020307
doi: 10.1007/s00432-022-04000-6
license: CC BY 4.0
---
# Undifferentiated pleomorphic sarcoma of the breast with neoplastic fever: case report and genomic characterization
## Abstract
### Purpose
Primary breast sarcomas are extraordinary rare, in particular undifferentiated pleomorphic sarcoma (UPS). UPS with neoplastic fever (UPS-NF) of the breast has not been reported yet. Here, we present an extended UPS-NF of the breast including its comprehensive molecular workup.
### Methods
A 58-year-old female presented with general malaise, fever spikes, weight loss, and a massively swollen left breast. C-reactive protein and blood leucocytes were significantly increased. However, repeated blood cultures and smears were all sterile. Histopathology of the abscess-forming tumor revealed an undifferentiated malignancy with numerous of tumor giant cells as well as spindle-shaped cells with nuclear pleomorphism and hyperchromasia. Immunohistochemistry demonstrated partial, patchy desmin staining and weak heterogonous neuron-specific enolase immunoreactivity of tumor cells, but a focal staining for Melan-A.
### Results
Neither common melanoma driver mutations nor an ultraviolet mutational signature was detected by whole genome sequencing. Using FISH and RT-PCR we also excluded translocations characteristic for clear cell sarcoma. Thus, the diagnosis of inflammatory UPS-NF of the breast was considered highly probable. Despite a complete mastectomy, the tumor recurred after only three months. This recurrence was treated with a combination of ipilimumab and nivolumab based on the primary tumor’s TPS score for PD-L1 of $30\%$. After an initial response, however, the tumor was progressive again.
### Conclusion
We describe here the first case of UPS-NF of the breast, which shows great clinical and histopathologic resemblances to previously reported UPS-NF of other anatomic localizations.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00432-022-04000-6.
## Introduction
Primary sarcomas of the breast are extraordinary rare with < $1\%$ of all breast cancer cases, typically affecting patients aged 55–59 years. Sarcomas of the breast can be classified into histological subtypes as follows: fibroblastic sarcomas, liposarcomas, fibrosarcoma, pleomorphic sarcoma, leiomyosarcoma, rhabdomyosarcoma, and angiosarcoma. Clinically, patients usually present with an unilateral, rapidly growing breast mass, often accompanied by thin vulnerable breast skin and nipple discharge/necrosis (Wang et al. 2011, 2021; Sang et al. 2021; Komaei et al. 2019; Oh et al. 2004; Hashimoto et al. 2021; Kazama et al. 2019; Sajko et al. 2019). Undifferentiated pleomorphic sarcoma (UPS), formerly malignant fibrous histiocytoma, is a high-grade neoplasm, accounting for less than $5\%$ of all adult sarcomas (Wang et al. 2011).
UPS with fever of unknown origin (also known as neoplastic fever) is a specific subtype of UPS (Wang et al. 2011). UPS with neoplastic fever has some clinical features which differentiates it from other UPS subtypes. This UPS subtype can develop anywhere in the body, and its incidence rate is even lower than the ‘normal’ subtypes (Wang et al. 2011, 2021; Sang et al. 2021; Komaei et al. 2019; Oh et al. 2004; Hashimoto et al. 2021; Kazama et al. 2019; Sajko et al. 2019). However, UPS with neoplastic fever (UPS-NF) of the breast has not been reported yet. Here, we present the first case of UPS-NF of the breast which, in addition, we have analysed by whole-exome sequencing (WES).
## Clinical presentation
A 58-year-old woman presented with a 3-month history of progressive general malaise, fever spikes, weight loss, and increasing enlargement and discoloration of her left breast. One month earlier, she had noticed an asymptomatic lump deep in her left breast. On examination, there was a massively swollen left breast with violaceous flame-like erythema, shiny thinned skin, and necrosis of the nipple (Fig. 1a, d). C-reactive protein (CRP) was highly elevated with 236 mg/l (< 5). Leucocytes were increased with 16.7/nl (3.9–10.4). As sepsis due to a monstrous breast abscess was suspected, she was urgently admitted to the intensive care unit. However, repeated blood cultures and swabs were all sterile. Incisional biopsy revealed a strong discharge of necrotic tissue and pus-like fluid. Histology of a punch biopsy of this giant abscess-forming tumor revealed a malignancy with spindle-shaped cells, giant cells, and an abundance of necrosis. Provisionally, the diagnosis of a high-grade undifferentiated large-cell malignant neoplasm was made. Fig. 1Showing a massively swollen left breast with violaceous erythema, venectases, shiny thinned skin, and necrosis of the nipple in a patient with undifferentiated pleomorphic sarcoma accompanied by neoplastic fever (UPS-NF, a). Histologic examination of the completely excised breast tumor revealed a high-grade sarcoma composed of numerous tumor giant cells and atypical spindle-shaped tumor cells (× 40, b; × 100, c). Computed tomography image demonstrating a huge, partly necrotic tumor of the left breast in a patient with UPS-NF (d) Tumor markers in the blood, including S100B, CEA, CA-19–9, CA125, CA15-3, CA72-4, cyfra, synaptophysin, and chromogranin A, were not elevated. Only neuron-specific enolase (NSE) was slightly elevated with 20 µg/l (< 16.3). Initial broad systemic antibiotic therapy did not result in improvement of clinical symptoms and laboratory parameters. However, a few days after complete ablation of the left breast the patient’s condition improved dramatically and was accompanied by a prompt decrease in CRP levels to 10 mg/l.
## Histopathology
Histologic examination of the completely excised breast tumor revealed a high-grade sarcoma composed of numerous tumor giant cells and atypical spindle-shaped tumor cells with prominent nuclear pleomorphism (Fig. 1b, c). In some areas, the mitotic count based on PPH3-staining exceeded over 45 mitoses/mm2. Moreover, expansive areas of necrosis were observed. Initially, based on tumor morphology a variety of differential diagnoses were considered, i.e., clear cell sarcoma, pleomorphic rhabdomyosarcoma, metaplastic carcinoma, malignant phyllodes tumor, primary large-cell neuroendocrine carcinoma, and malignant melanoma of the breast. Consequently, immunohistochemistry (IHC) and WES was performed to differentiate these possibilities.
## Immunohistochemistry
IHC was performed for the following parameters, all of which were negative: CEA, EMA, smooth-muscle actin, CK7, CK$\frac{5}{6}$, CK20, TTF1, MNF116, BerEP4, S100B, SOX10, HMB45, CD10, CD99, CD56, synaptophysin, vimentin, CD68, FLI1, and the hormone receptors ER/PgR/Her2. In addition, focal staining for Melan-A, weak chromogranin A staining, partial, patchy desmin staining and weak heterogonous NSE immunoreactivity was detected in the tumor cells. Ki-67 immunoreactivity was very high with $30\%$ to $90\%$ positively stained tumor cells. The TPS score for PD-L1 was $30\%$. CD8-positivity was $45\%$ surround the tumor and $7.5\%$ within the tumor. Thus, IHC provided only weak hints towards the identity of the patient’s tumor, with primary melanoma of the breast or undifferentiated sarcoma the most likely differential diagnoses.
## WES and fluorescent in-situ hybridization (FISH)
To obtain insights into the genomic aberrations characterizing this undifferentiated tumor, WES and consequent bioinformatic analysis were performed as previously described (Horny et al. 2021). WES data are illustrated in Fig. 2 and Tab. 1suppl. Because of the observed Melan-A expression in IHC we researched for melanoma-specific genomic aberrations as high tumor mutational burden (TMB), UV-associated mutational signature and characteristic melanoma driver mutations. Since we have not sequenced a normal control, variants were filtered for SNPs by comparing variant allele frequency with the gnomAD genome database. With respect to TMB, we identified 952 variants (19.17 mutations per mega base pair) and no over-representation of C > T mutations indicating absence of UV-associated mutational signature (Fig. 2a). We further analysed the trinucleotide context frequency and fitted them to COSMIC v3.1 single base substitution (SBS) reference signatures (Fig. 2b). Both clearly show the absence of UV-associated mutational signatures (SBS7a-d). Common melanoma driver mutations in BRAF, NRAS, KIT, RB1 genes, were not detected, but one missense mutation in TP53 (dbSNP: rs876660825).Fig. 2Mutational analysis shows medium tumor mutational burden and absence of UV-associated characteristics. ( a) Variants per megabasepair (Mbp) colored by nonsynonymous (red shades) and synonymous mutations. ( b) Fraction of variants colored by transitions and transversions. ( c) Fraction of variants by transition and transversions broken down by trinucleotide context (x axis) and colors by mutation type. Relative contribution of single base substitution (SBS) signatures after fitting trinucleotide context frequency to reference mutational signatures from COSMIC v3.1 database Since genomic data were not suggestive of melanoma or any other specific entity, we focused our attention on sarcoma-specific genetic aberrations, particularly since clear cell sarcoma of soft tissue are often Melan-A positive, as well. To this end, we queried the presence of inter-chromosomal translocations EWSR1-ATF1 and EWSR1-CREB1 which are usually present within clear cell sarcoma of soft tissue. We checked the presence of chimerics reads and inter-chromosomal read-pairs in EWSR1, ATF1 and CREB1 with the integrative genome viewer (IGV) and could not find evidence for translocations. Further, these translocations were not detected using FISH and RT-PCR in several tumor specimens of the primary tumor and cutaneous relapses. Taken together, we can also exclude a malignant melanoma and clear cell sarcoma in the diagnosis.
## Diagnosis, treatment and follow-up
Eventually, a diagnosis of inflammatory UPS-NF was deemed most likely, given the lack of any indicative immunohistological or genomic findings suggesting other entities together with clinical characteristics. With respect to treatment, complete mastectomy was followed with staging diagnostics including lymph node ultrasound, thoracic and abdominal computed tomography (CT) as well as cranial magnetic resonance imaging. On this basis, the patient was declared tumor-free and no further treatment was planned. However, only three months after initial breast ablation, multiple tumor nodules recurred on the left thoracic wall and axillar region (Fig. 3a). Lactate dehydrogenase (LDH) was elevated at 370 U/l (135–214), but other tumor markers such as serum S100B remained within the normal range. CT scans confirmed pectoral, thoracic, and axillar tumor masses on the patient’s left side. Moreover, a new suspect lesion was detected in the upper lobe of the right lung. Since strong PD-L1 expression was detected in the primary tumor, the interdisciplinary tumor board recommended combined radiotherapy (15 × 2.7 Gy) and immunotherapy using immune checkpoint inhibitors (ICI). Consequently, a combined regimen of ipilimumab (3 mg/kg body weight) and nivolumab (1 mg/kg/body weight) was administered. However, following the first cycle of combined immunotherapy, the patient developed severe ketoacidosis due to autoimmune-induced diabetes mellitus, which required insulin treatment. Combined immunotherapy and radiotherapy were continued and after the third cycle, the skin metastases on the left thoracic side and axillar region had significantly regressed (Fig. 3b). CT scans showed that the previously noted lung lesion in the right upper lobe had not increased in size. However, new mediastinal lymph node metastases were detected. LDH levels were within the normal range with 149 U/l [135-214]. On the basis of this mixed response, the patient received a fourth cycle of combined immunotherapy and then continued nivolumab monotherapy every four weeks. Unfortunately, after two cycles of nivolumab monotherapy, skin lesions reappeared on the left chest wall. CT scans revealed that both pulmonal and mediastinal metastases had significantly progressed, although no other organ manifestations. However, there were no other organ manifestations. LDH levels remained in the upper normal range with 204 U/l [135-214]. Consequently, immunotherapy was discontinued and instead, chemotherapy planned. However, the patient declined chemotherapy and wished, instead, to seek alternative medicine treatments elsewhere. Three months later, the patients returned to our department with further progressed pulmonal metastases and a new cutaneous tumor mass on the chest. Again, she refused systemic treatment, but instead decided on sole excision of the cutaneous metastasis. Nonetheless, the patient died from her tumor 15 months after diagnosis. Fig. 3Showing a patient with a massive relapse of an undifferentiated pleomorphic sarcoma of the breast (a). Three cycles of combined immunotherapy and radiotherapy resulted in significant regression of the tumor masses (b)
## Discussion
This case was associated with extraordinary diagnostic challenges, since the patient presented with signs of sepsis and clinical findings highly suggestive of a monstrous breast abscess. After biopsy pointed to a malignancy, the lack of specific immunohistology markers or genomic signatures further complicated making an accurate diagnosis. In addition, neoplastic fever, which typically presents with elevated parameters of inflammation without any evidence for an infection, is a relatively rare event, accounting for less than $3\%$ of patients with fever of unknown origin (Sørensen et al. 2005). Wang et al. [ 2011] reported two patients with highly aggressive, histologically identical tumors presenting as solitary, tender, necrotising, masses located to the bone that were associated with fever, leukocytosis and, similarly to our case, no evidence for infection. Histopathology revealed large monomorphic epithelioid cells with vesicular nuclei and abundant eosinophilic cytoplasm surrounded by numerous neutrophils and eosinophils which formed sterile microabscesses. IHC as well as electron microscopy did not reveal any specific differentiation or tissue origin. In contrast to these findings, the tumor discussed here clearly showed polymorphic cell growth. Moreover, a recent retrospective study provided detailed information on seven UPS-NF patients (three males, four females) (Wang et al. 2021). In all patients, the primary lesions (diameter 4.8–18.0 cm) were located in the extremities and intermuscular space. Of note, neoadjuvant radiotherapy and chemotherapy did not relieve neoplastic fever, however, similar to the case presented here, extensive resection of the primary tumor resulted in prompt dissolution of neoplastic fever (Wang et al. 2011, 2021). Moreover, it was reported that patients experienced at least one postoperative recurrence and fatal pulmonary metastatic disease with six patients still alive three years after surgery (Wang et al. 2011). Apart from this retrospective study, undifferentiated embryonal liver sarcoma in childhood and undifferentiated sarcomas in other anatomic sites have been reported in conjunction with fever and other signs of inflammation (Hashimoto et al. 2021; Kazama et al. 2019; Sajko et al. 2019; Mass and Talmon 2018; Horny et al. 2021; Sørensen et al. 2005; He et al. 2014). In some cases, elevated levels of tumor-released cytokines have been observed as the likely cause of systemic inflammation (Kazama et al. 2019; Yanagisawa et al. 2015; Li et al. 2010). Taken together, we did not find a matching case of inflammatory UPS-NF, but there are cases of soft tissue sarcomas of different subtypes in other locations that presented with signs of systemic inflammation most likely linked to cytokine production of the primary tumor.
Similar to two patients with UPS-NF reported by Wang et al. [ 2021], we did not observe any characteristic immunoreactivity for any marker investigated. Remarkably, even broadly mesenchymal markers vimentin and CD68 were negative, a finding typically seen in undifferentiated pleomorphic sarcomas. Because of the focal Melan-A expression we initially considered primary melanoma of the breast parenchyma as a differential diagnosis, which is exceptionally rare with only 15 confirmed cases in the literature (Snashall et al. 2020). However, genomic analysis by WES largely excluded melanoma, as no melanoma-specific mutations and mutational signatures were detected. IHC and genomic data carry particular weight with respect to melanoma, since it was shown previously that even highly un- or transdifferentiated melanomas display characteristic markers and mutational profiles (Ferreira et al. 2022; Agaimy et al. 2016). Moreover, during the entire course of her disease the patient did not show abnormal S100B levels.
Indeed, genomic analysis by WES did not shed any light with respect to the subtype of the sarcoma described here. Since Melan-A expression also occurs in the exceedingly rare cases of clear cell sarcoma of the breast (Pollard et al. 1990; Ibrahim et al. 2018), we checked for the frequent translocations EWSR1-ATF1 and EWSR1-CREB1. We could not detect these translocations within the WES data and by FISH analysis. Thus, mostly through exclusion of other possibilities and based on clinical and morphological findings, we determined that the most accurate diagnosis for this case would be that of a primary breast UPS-NF that initially imposed as a massive breast abscess accompanied by systemic inflammation.
Upon recurrence of the primary tumor together with the appearance of a suspect lesion in the lung, the interdisciplinary tumor board recommended double immune checkpoint blockade together with radiotherapy. Indeed, given the lack of any other effective systemic therapies in soft tissue sarcomas and the general interest in immunotherapy, a few studies using ICI and other avenues of immunotherapy have been conducted (Ayodele and Abdul Razak 2020). There are many ongoing phase I/II studies particularly determining the effects of double immunotherapy in conjunction with radiotherapy, as our patient received. Moreover, IHC of the present case revealed strong PD-L1 staining in the primary tumor, lending further support for first-line immunotherapy upon recurrence.
Moreover, WES detected a relatively high TMB, which is emerging as a predictor for response to ICI (Hu et al. 2020). *In* general, it is also thought that PD-L1 positivity of tumor cells “hot” (CD8 +), inflammatory tumors, as was the case discussed here, respond better to immunotherapy (Ayodele and Abdul Razak 2020). Indeed, the patient appeared to at least achieve a mixed response under double immunotherapy, but unfortunately, progressed three months later. Here, it would have been highly desirable to obtain another tumor sample to assess tumor evolution both using IHC for PD-1 and at the molecular level by another round of WES. More studies aimed at understanding the dynamics of metastatic progression under immunotherapy are, of course, urgently needed, while, at the same time, remain particularly challenging for rare entities such as soft tissue sarcomas. D’Angelo et al. [ 2018] recently demonstrated that nivolumab alone does not warrant further study in an unselected sarcoma population given the limited efficacy. However, nivolumab combined with ipilimumab demonstrated promising efficacy in certain sarcoma subtypes (e.g., UPS), with a manageable safety profile comparable to current available treatment options. Nakata et al. [ 2021] also reported that sarcomas with a greater TMB, such as UPS, myxofibrosarcoma and dedifferentiated liposarcomas, may be good candidates for ICI. Another avenue to boost response to immunotherapy might lie in identifying rational combinations of immune- and targeted therapy. For example, amplification of genes coding for histone deacetylases was recently reported to occur in about $20\%$ of soft tissue sarcoma samples (Que et al. 2021). Consequently, combination treatment with a PD-1 and HDAC inhibitor proved effective in a preclinical model. However, for a patient as presented here, with no targetable genomic aberrations, achieving durable responses will remain to pose extraordinary challenges.
In conclusion, we described the first case of UPS-NF of the breast, which clinically and histopathologically resembled previously reported UPS-NF of other anatomic sites.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (CSV 1199 kb)
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---
title: 'Streptococcus anginosus: a stealthy villain in deep odontogenic abscesses'
authors:
- Jussi Furuholm
- Johanna Uittamo
- Niina Rautaporras
- Hanna Välimaa
- Johanna Snäll
journal: Odontology
year: 2022
pmcid: PMC10020309
doi: 10.1007/s10266-022-00763-z
license: CC BY 4.0
---
# Streptococcus anginosus: a stealthy villain in deep odontogenic abscesses
## Abstract
Odontogenic infections (OIs) occasionally spread to deep facial and neck tissues. Our study aimed to explore the role of *Streptococcus anginous* group (SAG) in these severe OIs. A retrospective study of patients aged ≥ 18 years who required hospital care for acute OI was conducted. We analysed data of OI microbial samples and recorded findings of SAG and other pathogens. These findings were compared with data regarding patients’ prehospital status and variables of infection severity. In total, 290 patients were included in the analyses. The most common ($49\%$) bacterial finding was SAG. Other common findings were *Streptococcus viridans* and Prevotella species, Parvimonas micra, and Fusobacterium nucleatum. Infection severity variables were strongly associated with SAG occurrence. Treatment in an intensive care unit was significantly more common in patients with SAG than in patients without SAG ($p \leq 0.001$). In addition, SAG patients expressed higher levels of C-reactive protein ($$p \leq 0.001$$) and white blood cell counts ($p \leq 0.001$), and their hospital stays were longer than those of non-SAG patients ($$p \leq 0.001$$). SAG is a typical finding in severe OIs. Clinical features of SAG-related OIs are more challenging than in other OIs. Early detection of SAG, followed by comprehensive infection care with prompt and careful surgical treatment, is necessary due to the aggressive behaviour of this dangerous pathogen.
## Introduction
Odontogenic infections (OIs) can spread deeper into the surrounding tissues, requiring hospitalization, and potentially posing a life-threatening risk [1–3]. Behind more severe deep neck infections lie certain predictors, such as psychiatric disorder, alcohol abuse, and diabetes mellitus [4–6], although previously healthy patients may require hospital care due to an OI, as well [2, 7]. Mandibular molars are the most common sources of severe OIs [8, 9], and these arise from apical periodontitis in particular [10]. In addition, deep infection may occur after tooth removal to treat acute or subacute local infection symptoms [11]. Early management of the dental infection focus by tooth removal or root canal treatment effectively reduces dispersion of the infection, however, occasionally, the pathogenic microbes invade deeper spaces, causing infection complications such as pneumonia, septicaemia, and endocarditis [12–14].
The complexity of the oral microbiome is well known, with over 500–700 bacterial species identified [15–17]. Both cariogenic and periodontal pathogens have been detected in heart valve specimens, suggesting a causative relationship between dental infections and cardiovascular diseases [18, 19]. OIs and dental infectious diseases in general are polymicrobial and often caused by anaerobic and facultative bacteria [20, 21]. Streptococcus viridans species, including *Streptococcus anginosus* group (SAG)—formerly and popularly known as *Streptococcus milleri* group, have been identified as the most frequent bacteria in head and neck infections of odontogenic origin, resulting in increasing concern for antibiotic resistance, particularly for penicillin and clindamycin [22–24]. This emphasizes the importance of timely local treatment of the dental focus [5, 25].
Members of the SAG—Streptococcus intermedius, Streptococcus constellatus, and Streptococcus anginosus—share common traits regarding clinical associations but differ slightly in their abscess formation capacity [26]. Recent and earlier findings suggest *Streptococcus anginosus* group as a key factor in odontogenic descending necrotizing mediastinitis, pulmonary infections, and brain abscesses [27–29], while the presence of SAG has also been reported in many other morbidities such as skin and soft tissue infections, genitourinary infections and liver abscesses [30, 31]. However, the SAG organisms are categorized as commensals in the oropharynx as well as in the gastrointestinal and genitourinary tracts [32].
The aim of our study was to clarify the occurrence of SAG in deep OIs and to evaluate the clinical features and severity of these infections in relation to OIs caused by other microbes. Our hypothesis was that infections caused by SAG are associated with more complicated clinical features.
## Study design
Electronic health records of all acute maxillofacial infection patients treated at the Töölö Hospital Emergency Department between the years 2015 and 2019 were retrospectively reviewed, and data for study variables were retrieved from electronic patient records of each patient.
## Inclusion and exclusion criteria
Patients aged ≥ 18 years who required treatment and hospital stay for acute and deep OI (i.e., abscess or cellulitis of facial or neck region of dental origin) were included in the present study. All infections were confirmed as odontogenic by oral and maxillofacial surgeons. Patients with infection of unknown origin or other than odontogenic focus as a reason for maxillofacial infection were excluded from the analyses. Patients without any microbial finding in bacterial culture were also excluded.
## Study variables
Occurrence of SAG and other microbiological findings were recorded based on the routine bacterial culture reports released from Helsinki University Hospital Laboratory Services (HUSLAB). Patient’s prehospital and infection severity variables were compared between patients with and without SAG findings.
Age, sex, current smoking, excess alcohol consumption or regular use of drugs, and history of immunosuppressive disease or medications, or both, and duration of symptoms prior to hospital care were considered in the analyses as prehospital variables. Excess alcohol consumption was considered to be ≥ 12 doses per week for women and ≥ 23 doses for men; one alcohol dose was 12 g of pure alcohol.
To evaluate infection severity, need for treatment in an intensive care unit (ICU), level of C-reactive protein (CRP), white blood cell (WBC) count and tympanic body temperature at hospital admission, length of hospital stay (LOHS) in an ICU and in hospital, and occurrence of a distant infection (i.e., different distant infections and infection complications) were analysed. Multivariate analyses were conducted for need for ICU treatment, LOHS, and CRP at hospital admission.
Additionally, types of specific infection complications and distant infections were reported.
## Statistical analysis
IBM SPSS for Macintosh (version 27.0, IBM Corp., Armonk, NY, USA) software package was used for statistical analyses. Categorical variables were cross-tabulated and analysed with Pearson’s Chi-squared test or Fisher’s exact test if expected values were below 5. Student’s t-test and Mann–Whitney U test were used to compare differences between study groups in continuous variables. Pairwise comparisons were performed as post hoc analyses for Pearson’s Chi-squared test using Z test and Dunn’s [1964] procedure for Kruskal–Wallis H test, both with a Bonferroni correction for multiple comparisons. Treatment in ICU, LOHS, and CRP level were separately selected as outcome variables for binomial logistic regression analysis; age, sex, current smoking, excess alcohol consumption or regular use of drugs, history of immunosuppressive disease or medication, or both, origin of infection in mandible, and SAG-positive microbial sample were selected as independent variables. LOHS and CRP level were dichotomized by group median, and age was categorized into tertiles. P values ˂ 0.05 were considered significant throughout the study.
## Results
Data from altogether 357 patients with OI were collected from the electronic patient records. Of these patients, a bacterial sample had been obtained and microbial growth reported for 290 patients, who subsequently formed the final study population.
In 194 subjects ($67\%$), the bacterial culture finding was a mixed finding of aerobic and anaerobic bacteria. Aerobic bacteria alone were reported in 60 cultures ($21\%$) and anaerobic bacteria alone in 55 cultures ($19\%$). The most common bacterial finding was SAG, which was found in $49\%$ of patients. In 123 cases ($42\%$), SAG was diagnosed with anaerobic bacteria. Other common findings were normal oral microbial flora ($48\%$) and anaerobic Gram-negative rods ($47\%$, Fig. 1). Among other common findings were *Streptococcus viridans* group and Prevotella species, Parvimonas micra, and Fusobacterium nucleatum. Fig. 1Frequencies of different bacterial species in 290 patients. * Excluding Gram-negative rods, Parvimonas micra, Prevotella species, Fusobacterium nucleatum. SAG *Streptococcus anginosus* group In only few samples, findings not representative of normal oral flora were discovered. Among these were the aerobic cocci betahaemolytic streptococci ($$n = 4$$; $1\%$) and *Staphylococcus aureus* ($$n = 4$$; $1\%$), aerobic Gram-negative rods *Klebsiella pneumoniae* ($$n = 3$$; $1\%$) and *Enterobacter cloacae* ($$n = 3$$; $1\%$), and anaerobic species of the *Bacteroides fragilis* group ($$n = 3$$; $1\%$).
Of prehospital variables, mandibular odontogenic infection focus was more strongly associated with SAG than focus in the maxilla ($$p \leq 0.012$$, Table 1). Additionally, there was a significant association between SAG and tooth removal before hospitalization ($$p \leq 0.041$$). Antibiotic treatment was administered to all but one patient. In most patients ($$n = 269$$, $93\%$), metronidazole was combined with cephalosporin or penicillin. Clindamycin was administered to 8 patients ($3\%$). In 35 patients ($12\%$), antibiotic treatment was altered during hospital stay according to clinical and microbiological findings. Table 1Associations between explanatory variables and patients with and without a positive *Streptococcus anginosus* group sampleNo. of patients with *Streptococcus anginosus* ($$n = 144$$)No. of patients without *Streptococcus anginosus* ($$n = 146$$)n% of nn% of np-valueEffect size if significantAge (years) Mean ± SD46.4 ± 18.9547.6 ± 16.750.598Median (min − max)46 (18–96) ≤ 46 ($$n = 147$$)775270480.347 > 46 ($$n = 143$$)67477653Sex Men ($$n = 174$$)824793530.291 Women ($$n = 116$$)62535447SmokingYes ($$n = 83$$)374546550.274No ($$n = 207$$)1075210048Heavy alcohol use Yes ($$n = 28$$)165712430.404 No ($$n = 262$$)1284913451Immunocompromised condition due to disease and/or medication Yes ($$n = 51$$)285523450.409 Diabetes ($$n = 24$$)145810420.375 No ($$n = 239$$)1164812652Site of infection Mandible ($$n = 256$$)13452122480.0120.148 Maxilla ($$n = 34$$)10292471Tooth removal before hospitalization Yes ($$n = 104$$)605844420.0410.120 No ($$n = 186$$)844510255p < 0.05 values are in bold Infection severity variables were strongly associated with SAG occurrence. ICU treatment was significantly more common in patients with SAG than in patients without SAG ($p \leq 0.001$). Compared with non-SAG patients, SAG patients expressed higher levels of CRP ($p \leq 0.001$) and WBC counts ($$p \leq 0.001$$), and their LOHS was longer ($$p \leq 0.001$$), as presented in Table 2.Table 2Associations between infection severity variables and patients with and without a positive *Streptococcus anginosus* group sampleNo. of patients with *Streptococcus anginosus* ($$n = 144$$)No. of patients without *Streptococcus anginosus* ($$n = 146$$)n% of nn% of np-valueEffect size if significantICU treatment Yes ($$n = 83$$)58702530 < 0.0010.256 No ($$n = 207$$)864112159CRP level at hospital admission (mg/L) Mean ± SD171.5 ± 99.53136.4 ± 80.52 < 0.0010.393 Median (min − max)134.5 (6–565) ≤ 134.5 ($$n = 145$$)624383570.0190.138 > 134.5 ($$n = 145$$)82576343WBC count at hospital admission (109/L) Mean ± SD14.4 ± 5.4712.5 ± 4.300.0010.380 Median (min − max)12.6 (1.3–35.9) ≤ 12.6 ($$n = 144$$)634481560.0460.117 > 12.6 ($$n = 146$$)81566544Tympanic temperature < 38.0 °C ($$n = 216$$)10348113520.252 ≥ 38.0 °C ($$n = 74$$)41553345Length of hospital stay (days) Frequency*$$n = 140$$$n = 144$ Mean ± SD4.4 ± 4.622.9 ± 2.680.0010.391 Median (min − max)2 (< 1–37) ≤ 2 ($$n = 147$$)58398961 < 0.0010.204 > 2 ($$n = 137$$)82605440*3 deceased and data missing for 3Infection complication or distant infection Yes ($$n = 22$$)1464836 No ($$n = 268$$)13048138520.172p < 0.05 values are in bold Distant infections and/or other infection complications occurred in 22 of 290 patients ($8\%$, Table 2). Complications were more common in patients with SAG than in other patients ($$n = 14$$, $64\%$ vs. $$n = 8$$, $36\%$ of infection complications), however, the difference was not statistically significant ($$p \leq 0.153$$). Bacterial blood culture sample was collected and analysed in 125 patients. Of the 12 patients with septicaemia, 10 were SAG-positive and two were SAG-negative ($$p \leq 0.042$$). Other distant infections and/or other infection complications in 13 patients were pneumonia ($$n = 10$$), endocarditis ($$n = 2$$), necrotizing fasciitis ($$n = 2$$), urosepsis ($$n = 1$$), and embolic renal infarction ($$n = 1$$); of these patients, 8 ($62\%$) were SAG-positive and 5 ($38\%$) SAG-negative ($$p \leq 0.354$$). Three of the patients died; all were SAG-positive.
According to binomial logistic regression analyses (Table 3), patients with a SAG-positive microbial sample were 3.4 times more likely to need treatment in an ICU ($p \leq 0.001$). Smoking (odds ratio, OR = 2.1, $$p \leq 0.024$$) and origin of OI in mandible (OR = 5.8, $$p \leq 0.021$$) added independently to the odds for need for ICU treatment. Odds for longer hospital stay were 2.3-fold ($p \leq 0.001$), for higher CRP values 1.9-fold ($p \leq 0.010$), and for higher than median WBC counts 1.7-fold ($$p \leq 0.0351$$) for SAG-positive patients relative to SAG-negative patients. Table 3Results of binomial logistic regression analysesOR$95\%$ CI for ORLowerUpper p-valueANeed for ICU treatment Age, lowest tertile (ref.)0.828 Age, middle tertile0.8220.4201.6090.568 Age, highest tertile0.8480.4261.6870.638 Sex, male1.5350.8432.7970.161 Smoking2.0711.0993.9040.024 Heavy alcohol use1.1020.4202.8920.844 Immunocompromised condition due to disease and/or medication0.6010.2691.3440.215 Origin of infection in mandible5.7591.30225.4810.021 SAG-positive3.3931.9076.037 < 0.001BHigher than median LOHS* Age, lowest tertile (ref.)0.859 Age, middle tertile0.9760.5421.7570.935 Age, highest tertile1.1450.6262.0960.660 Sex, male1.2510.7502.0880.391 Smoking1.0520.6021.8410.858 Heavy alcohol use0.9360.3862.2680.883 Immunocompromised condition due to disease and/or medication1.4050.7202.7420.319 Origin of infection in mandible1.1630.5412.4980.699 SAG-positive2.3021.4113.756 < 0.001 *$$n = 284$$, 3 deceased and data missing for 3CHigher than median CRP Age, lowest tertile (ref.)0.897 Age, middle tertile1.1160.6241.9940.712 Age, highest tertile0.9800.5441.7660.947 Sex, male1.4870.9002.4590.122 Smoking0.9240.5331.6010.778 Heavy alcohol use0.6660.2791.5910.361 Immunocompromised condition due to disease and/or medication0.7970.4151.5290.494 Origin of infection in mandible0.7730.3631.6430.503 SAG-positive1.8861.1653.0550.010DHigher than median WBC Age, lowest tertile (ref.)0.832 Age, middle tertile1.0060.5621.7980.985 Age, highest tertile0.8550.4741.5420.604 Sex, male1.3630.8272.2460.224 Smoking1.3250.7632.3010.318 Heavy alcohol use1.3910.5753.3650.464 Immunocompromised condition due to disease and/or medication0.6630.3431.2800.220 Origin of infection in mandible1.0680.5022.2710.865 SAG-positive1.6771.0372.7140.035p < 0.05 values are in boldLOHS length of hospital stay, OR odds ratio, CI confidence interval
## Discussion
We examined the occurrence of SAG in deep OIs and the clinical features and severity of these infections in relation to OIs caused by other microbes. Our hypothesis that infections caused by SAG are associated with more complicated clinical features was confirmed.
SAG was present in nearly half of the patients, and it was the most common bacterial finding. SAG-associated infections were significantly more complicated than those without SAG. Need for ICU treatment was significantly more likely for SAG-positive patients (OR = 3.4, $p \leq 0.001$). Higher levels of CRP, WBC, and LOHS were associated with SAG. These findings imply that SAG infections are complicated, severe, and life-threatening. The typical nature of SAG-associated OIs differs from OIs caused by other pathogens.
In principle, SAG organisms are classified as commensals. However, it is essential to emphasize the clinical importance of the aggressive behaviour of SAG in severe OIs. SAG bacteria are able to cause infections if they gain access through mucosa to sterile sites, i.e., the underlying tissue or blood, and can, therefore, be viewed as opportunistic pathogens. It has previously been shown that SAG-related OIs are linked to the most severe disease, with difficulties in swallowing and opening the mouth [33]. In spreading OIs, SAG behaviour is known to be typically complicated and abscess-forming [34]. Our findings confirm the clinical significance of this aggressive pathogen (Fig. 2). However, despite the clinically notable typical infection features, the true pathogenic potential of SAG remains to be elucidated [35]. A recent study by Ismail et al. [ 36] clarified SAG occurrence in different infections in children and adolescents and discovered that S. intermedius was more commonly present in head and neck infections than other SAG species. Thus, differences exist in infection locations and SAG species. Fig. 2A 40-year-old male smoker without underlying diseases had had toothache in the right lower jaw for 3 days before onset of significant swelling and fever (39.2 °C). At hospital admission, C-reactive protein level was 253 mg/L and white blood cell count was 18.2 109/L. The patient underwent three wide surgical interventions to subside the deep oral, facial, and neck infection. Bacterial culture from the pus sample showed *Streptococcus anginosus* and mixed anaerobic flora. Blood culture was also positive (anaerobic Gram-positive coccus and anaerobic Gram-negative rod). A. Dental panoramic tomography radiograph showed the right mandibular third molar with periapical lesion as the main infection focus. B. Computer tomography imaging at hospital admission demonstrated bilateral gas formation in neck tissues from the level of skull base to the upper mediastinal space at the right side. The airway was also deviated. C. Extensive neck approaches were required, including temporal and upper mediastinal drainages. Swelling and tightness in buccal and periorbital areas remained significant after the third surgery. The overall length of hospital stay was 32 days. The patient eventually recovered well from the infection. A written consent was obtained from the patient for use of the image A suggested novel trait in the pathogenicity of S. anginosus may be its ability to produce bacteriocin designated as Angicin, which enhances membrane permeabilization [37], inhibits the growth of closely related species [38], and enables compartmental abscess formation. Acid tolerance exhibited by the micro-organism is another factor that may facilitate chronic inflammation and abscess development [39]. Ardent fever, chills, and systemic toxaemia have been observed in severe SAG infections; upper airway obstructions and necrotizing pneumonia are possible outcomes of abscess formation [33, 40]. Our results show that in SAG-related OI patients the airways are more often compromised, the infection parameters reflect a more severe infection, and the treatment of these infections takes longer than in other OIs. The potentially fatal characteristics of SAG infections emphasize the importance of early detection of these OIs.
To identify SAG infections without unnecessary delay their clinical features should be recognized. These include rapid and aggressive progression. According to our study, high CRP and WBC level as well as fever at hospital admission are typical for SAG infections. Gas bubble formation in radiological imaging is indicative of SAG [27]. The surgeon should consider SAG at an early stage and treat these infections with precision. Sufficient surgical treatment with wide approaches and careful abscess drainage is necessary to halt the progression of SAG infection. SAG is typically identified in abscesses as part of polymicrobial infections with anaerobes. In our study, SAG was identified concomitantly with anaerobic bacteria in $85\%$ of the samples with SAG. This underscores the need for abscess drainage as a crucial component of successful treatment. Furthermore, the simultaneous removal of odontogenic infection focus is known to shorten LOHS compared with tooth extraction after initial infection treatment [25]. Compartmental, tissue-disrespectful progression is typical of SAG, thus, close monitoring of the patient and, if necessary, repeated evaluation of the abscess chambers is essential.
Compared with figures from previous reports [13, 41], infection complications and distant infections were relatively scarce, occurring in $8.3\%$ of patients. The most common of these were septicaemia and pneumonia, whereas necrotizing fasciitis and endocarditis were each observed in only two patients. The rare occurrence of the most severe infections highlights the typical pattern of OIs; even deep infections requiring intensive care are limited to the neck area if treatment is effective and started in time. On the other hand, SAG is often associated with infection complications and distant infections, especially with septicaemia. All three deceased patients here were SAG-positive. These findings underscore the life-threatening features of SAG in OI patients.
Our research shows a clear association between the site of infection and the presence of SAG; OIs originating from the mandible occurred significantly more often in conjunction with SAG than maxillary infections. *In* general, most severe OIs are known to originate more often from the lower molars [7, 9]. We also observed that tooth removal prior to hospitalization was significantly more common in patients with SAG than those without ($56\%$ vs. $44\%$, $$p \leq 0.041$$). The finding suggests that previous tissue damage may predispose to the development of severe SAG-associated OIs.
The importance of immune defense and previous diseases is also worth considering. Currently, however, there are no reports showing that microbial findings would be altered in severe OIs compared to milder infections according to patients’ systemic condition, although the proportion of patients with underlying systemic disease has been reported to have increased [2, 41]. Our earlier studies have shown that severe OIs occur most often in previously healthy adults [11, 42]. Dental procedures including extraction and root canal treatment disrupt the mucosal barrier allowing introduction of mucosal opportunistic bacteria to normally sterile tissue, are local oral risk factors for severe OIs [10, 11]. Furthermore, ineffective early treatment of OI may increase the risk of a severe OI [42]. The findings of the present study are in line with these previous results as immunosuppression was not associated with SAG finding (Table 1). Additionally, only $18\%$ of patients had underlying immunosuppressive medication or disease. In conclusion, the risk of these infections seems to be primarily associated with local factors such as dental treatment procedures, delayed or inadequate dental treatment, or increased systemic susceptibility to infection complications, rather than altered microbial flora in certain systemic conditions.
In addition to drainage and treatment of odontogenic focus, antimicrobial treatment is often required for successful treatment of purulent OIs. These infections are usually polymicrobial, consisting primarily of SAG or other Viridans streptococci and anaerobic bacteria. Other microbes are rare findings, as observed also in our study. Yet another important aspect to consider in choosing the antimicrobial is the local resistance situation. Fortunately, in Finland, SAG and other Viridans streptococci have remained fairly sensitive to penicillin, with only a 1–$5\%$ resistance rate, and oral anaerobes remain highly susceptible to metronidazole [43]. In contrast, resistance to clindamycin of oral streptococci has recently slightly increased and needs to be monitored. Therefore, the Finnish Current Care Guideline recommends primarily using penicillin for oral streptococci and metronidazole for anaerobic bacteria, which may be beta-lactamase producers [44]. Broader spectrum cephalosporins or clindamycin are recommended instead of penicillin in case of penicillin allergy. If metronidazole is contraindicated, clindamycin can be combined with penicillin to cover anaerobes, if needed. If the above combinations are contraindicated, broad-spectrum amoxicillin-clavulanic acid may be used as monotherapy.
For the retrospective study design, some of the patients was excluded from the study because of unavailable bacterial cultures. In addition, PCR testing to determine the species of bacteria is not performed in our unit routinely, thus, the determination of bacteria was based on culture only. Data of patients treated in primary or secondary health care was unavailable; our data were collected from hospitalized patients. A prospective study design would refine these parameters. New and effective treatments for SAG-related infections should be explored in future studies.
## Conclusions
SAG is a typical finding in severe OIs. Clinical features of SAG-related OIs are more aggressive than in other OIs. Early detection of SAG, followed by comprehensive infection care with prompt and careful surgical treatment, is essential due to the aggressive behaviour of this stealthy pathogen.
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title: Determinants of adherence to recommendations on physical activity, red and
processed meat intake, and body weight among lynch syndrome patients
authors:
- M Hoedjes
- A Vrieling
- L de Brauwer
- A Visser
- E Gómez García
- N Hoogerbrugge
- E Kampman
journal: Familial Cancer
year: 2022
pmcid: PMC10020312
doi: 10.1007/s10689-022-00315-y
license: CC BY 4.0
---
# Determinants of adherence to recommendations on physical activity, red and processed meat intake, and body weight among lynch syndrome patients
## Abstract
This study aimed to identify determinants of adherence to lifestyle and body weight recommendations for cancer prevention among Lynch Syndrome (LS) patients. Cross-sectional baseline data of LS patients participating in the Lifestyle & Lynch (LiLy) study was used to assess determinants of adherence to the World Cancer Research Fund cancer prevention recommendations on body weight, physical activity, and red and processed meat intake. Adherence and potential determinants of adherence were assessed using questionnaires. Multivariable logistic regression analyses were conducted to identify determinants of adherence. Of the 211 participants, $50.2\%$ adhered to the body weight recommendation, $78.7\%$ adhered to the physical activity recommendation, and $33.6\%$ adhered to the red and processed meat recommendation. Being younger and having a higher level of education were associated with adherence to the recommendation on body weight. Having knowledge about the recommendation was associated with adherence to the recommendations on physical activity and red and processed meat. Results confirm that knowledge about recommendations for cancer prevention is an important determinant for adherence and suggest that strategies to increase knowledge should be included in lifestyle promotion targeted at LS patients, along with behavior change techniques influencing other modifiable determinants.
## Introduction
Lynch syndrome (LS) is an inherited cancer syndrome characterized by a high hereditary risk of various cancers, primarily in the colorectum and the endometrium [1]. Worldwide, approximately 28,600 cases of LS are newly diagnosed each year[2]. LS is caused by a germline mutation in one of the mismatch repair (MMR) genes [2, 3]. LS patients have a risk of developing colorectal cancer (CRC) between 22 and $69\%$ up to age 70, as opposed to 1–$5\%$ in the general Western population [4–6]. Significant differences have been reported in cumulative cancer risk and risk of different cancer types according to MMR gene mutation type (MLH1, MSH2, MSH6 and PMS2) [7, 8]. The clinical phenotype of LS has been shown to vary between families, countries, and continents [8], suggesting the importance of the role of environmental and non-genetic factors, such as lifestyle-related factors [9], in the development of cancer [10, 11]. In addition, low penetrance genetic risk factors may be associated with the observed variety in cancer risk among LS patients [12]. The influence of lifestyle-related factors on CRC among LS patients appears to be comparable or even stronger as compared with the general population [11].
Studies investigating the association between lifestyle-related factors and the occurrence of sporadic cancer have shown that lower levels of physical activity and higher body fatness are associated with an increased risk of different types of cancer, including CRC and endometrium cancer [13]. Also, the intake of red and processed meat has been associated with an increased risk of sporadic CRC [13]. Among LS patients, lifestyle-related factors have also been associated with cancer risk. A recent systematic review has shown that early-adulthood overweight/obese weight status and adulthood weight gain may be associated with increased colorectal cancer risk in LS patients [14]. Moreover, a recent meta-analysis has shown that obesity has been associated with an increased risk for colorectal cancer, but only in men with LS [15]. Furthermore, reviews of the current literature among LS patients have shown that high fruit intake and physical activity have been associated with decreased colorectal cancer risk [14], whereas smoking and alcohol consumption have been associated with an increased colorectal cancer risk in LS patients [16].
Based on a large body of scientific evidence for these observed associations, the World Cancer Research Fund (WCRF) has issued lifestyle and body weight recommendations for cancer prevention [13]. Cancer survivors (i.e. those who have been diagnosed with cancer including those who have recovered) are also recommended to meet these lifestyle and body weight recommendations for cancer prevention. Meeting these recommendations has been associated with favorable health-outcomes, such as a higher health-related quality of life, and a decreased risk for type II diabetes mellitus, cardiovascular disease, second primary cancers, cancer recurrences, and mortality [17–20]. Current guidelines from the European Hereditary Tumour Group (EHTG) and European Society of Coloproctology (ESCP) advise health care providers to inform LS patients about the observed associations between lifestyle, body weight and the risk of cancer [16].
We previously found that adherence to WCRF lifestyle and body weight recommendations in LS patients is low and that providing WCRF health promotion materials increased awareness of and knowledge about WCRF recommendations, without increasing psychological distress. However, this did not affect adherence [21]. Little is known on how adherence to these recommendations can best be promoted. Insight into determinants of health behaviors among LS patients is needed to be able to identify what techniques and strategies can be used to achieve health behavior changes in this specific patient population. Apart from our previous qualitative study on determinants of adherence to lifestyle and body weight recommendations among LS patients [22], to our knowledge, no other study has examined determinants of adherence or health behavior change among LS patients. Data on non-changeable determinants associated with (non-)adherence (such as sociodemographic and certain health-related determinants specific to LS, including cancer diagnosis in personal history and years since LS diagnosis) provides insight into which LS patients specifically should be targeted to improve adherence. Data on changeable determinants associated with (non-)adherence provides insight into which modifiable determinants should be targeted for change and informs about what type of techniques or strategies can be used to positively influence these changeable determinants. Such changeable determinants relevant for LS patients include psychological determinants, such as cancer worry and symptoms of depression. These psychological determinants have been associated with unfavorable lifestyle behaviors in previous studies [23, 24]. Besides, behavior change concepts that are frequently included in theories and models of health behavior change are knowledge (about the recommendations) and awareness (of the influence of lifestyle-related factors on cancer risk) [25]. Knowledge and awareness have been shown to be determinants of health behavior in other populations [26, 27].
The aim of this cross-sectional study was to explore demographic, health-related, behavior change and psychological determinants for adherence to body weight, physical activity, and red and processed meat intake recommendations among LS patients, as these specific recommendations are relevant for LS-related types of cancer (CRC, endometrium) [13].
## Study design
This study uses cross-sectional, baseline data ($$n = 218$$) from the Lifestyle & Lynch (LiLy) study, a randomized controlled trial to test the effect of providing LS patients with WCRF-NL health promotion materials of the WCRF cancer prevention recommendations [21].
## Participants and procedure
The LiLy study recruited participants between April and September 2015 at Radboud University Medical Center and Maastricht University Medical Centre. LS patients aged between 18 and 65 years were eligible for participation if LS diagnoses was confirmed by a germline pathogenic variant in one of the MMR-genes (MLH1, MSH2, MSH6 or PMS2). LS patients were excluded from participation if they had insufficient understanding of the Dutch language or if they were participating in the GeoLynch study, a prospective cohort study among LS patients, to prevent interference between both studies [28]. Since only $4\%$ of eligible LS patients participated in the GeoLynch study, this is unlikely to have biased the results of this study. More information on the LiLy study can be found elsewhere [21].
After informed consent was obtained, eligible LS patients were asked to fill in the baseline questionnaire, which took approximately 45 min to complete. The medical ethical research committees of the Radboud University Medical Center and Maastricht University Medical Centre granted permission to perform this study.
## Adherence to the WCRF recommendations
For this study, adherence to the WCRF recommendations on physical activity, body weight, and red and processed meat intake were included. These recommendations were included as these are relevant for LS-related types of cancer (CRC, endometrium) [13] and the smallest group of each of these dichotomous outcome variables (adherence vs. non-adherence to these recommendations) was large enough to be able to be incorporated into the statistical analyses given the sample size ($$n = 211$$)[29].
## Body weight
Self-reported body weight and height were used to calculated Body Mass Index (BMI) (kg/m2). Participants were categorised into the following BMI categories: <18.5; 25-<30;18.5-<25; and > 30 kg/m2. The category 18.5–24.9 kg/m2 was considered as adherent to the body weight recommendation. The other categories were considered not to be adherent to the body weight recommendation.
## Physical activity
Adherence to the physical activity recommendation was assessed using the validated Short Questionnaire to Assess Health Enhancing Physical Activity (SQUASH) questionnaire, which contains questions about multiple activities referring to a normal week in the past month. Results were converted to time spent on light, moderate, and vigorous activities, which were then converted to activity scores [30]. When the number of moderate and vigorous exercise activities was at least 30 min a day, for a minimum of 5 days a week, patients were categorized as adherent to the physical activity recommendation.
## Red and processed meat
Intake of red and processed meat was measured with an adjusted version of a 40-item, validated Food Frequency Questionnaire (FFQ) specifically developed to assess adherence to the Dutch Guidelines for a healthy diet [31]. Items assessing red meat intake (grams per week) and processed meat intake (grams per week) during the last month were used to determine adherence to the recommendation on red and processed meat intake. When red meat intake was < 500 g/w, of which processed meat was < 3 g/d, participants were considered to adhere to the recommendation on red and processed meat intake.
## Demographic and health-related characteristics
Demographic characteristics were assessed using the baseline questionnaire, which included items on age, gender, marital status, and education. Clinical characteristics were assessed using the same questionnaire by items on personal and family cancer history, colon surgery (colectomy, hemicolectomy, colon resection), time since LS diagnosis, and smoking behaviour.
## Awareness
Awareness of the cancer risk factors as described in the WCRF/AICR recommendations for cancer prevention (referred to as awareness of the WCRF/AICR recommendations) was assessed using a question from the AICR Cancer Risk Awareness Survey: “Do the following factors have a significant influence on whether or not the average person develops cancer?”.
From the exposures that were mentioned in the entire Awareness questionnaire reflecting all recommendations, only the exposures related to the recommendations on body weight, insufficient physical activity, and red and processed meat intake were included for the current study. For each exposure, answer options were: “yes, a big influence”; “yes, a small influence”; “no”; and “I do not know”. Participants with correct answers, indicating that the exposures were of influence, were considered to be aware of the specific cancer risk factors while participants with answers “no” and “I do not know“ were considered to be unaware.
## Knowledge
Knowledge of the WCRF recommendations on body weight, physical activity, and red and processed meat intake was assessed using 3 multiple choice questions; 1 for each recommendation. These knowledge questions required more detailed content-specific knowledge about the recommendations. For example, the multiple choice question “*What is* the minimally recommended amount of time a day you should be spending on physical activity according to the recommendations for cancer prevention?”, assessed knowledge about the physical activity recommendation. The 5 answer options included: “A recommendation regarding physical activity and cancer risk does not exist”; “A minimum of 30 minutes physical activity per day of moderate intensity (meaning an increased breath and heart rate)”; “A minimum of 60 minutes physical activity per day of moderate intensity”; “A minimum of 90 minutes physical activity per day of moderate intensity”; “I don’t know”. Participants with correct answers were considered to have knowledge about the respective recommendation.
## Cancer risk perception
Cancer risk perception was assessed by two standardized questions. Participants were asked to express their perceived cancer risk in a percentage between 0 and 100. In addition they were asked to choose one out of 5 categories: ranging from a very low to a very high perceived cancer risk [32].
## Symptoms of depression
Symptoms of depression were measured by using the Dutch version of the Hospital Anxiety and Depression Scale (HADS) [33]. The HADS consists of 14 items assessing self-reported symptoms of anxiety (7 items) and depression (7 items) in the past week. Each item is scored on a 4-point Likert scale, ranging from 0 to 3, with higher scores indicating more symptoms. For the current study, only scores for symptoms of depression were used (because of the conceptual overlap with cancer worry). A total score can be calculated for symptoms of depression by adding up the scores on the 7 items. This total score ranges from 0 to 21, with higher scores indicating more symptoms [33].
## Cancer worry
Cancer worry was assessed using the Cancer Worry Scale (CWS), consisting of 8 items. The reliability and validity has shown to be good among breast and colorectal cancer survivors [34, 35]. The total score ranges between 8 and 32, with higher scores corresponding to more cancer worry.
## Statistical analyses
The population for analysis consisted of participants with complete baseline data. Participants with missing data on one or more of the variables included in the analyses were excluded from the analyses.
Means with standard deviations (SD) and frequency tables were used to describe potential socio-demographic, health-related, and psychological determinants. Since the variables ‘age’ and ‘time since LS diagnosis’ were not normally distributed, these variables were incorporated in the statistical analyses as categorical variables. Age was categorized into the following categories based on the observed data distribution: 21–43 years; 44–54 years; and 55–73 years. Time since LS diagnosis was categorized into the following categories: 0–2 years; 2–4 years; and 4–20 years.
First, univariable logistic regression analyses were conducted with adherence to one of the WCRF recommendations on body weight, physical activity, or red and processed meat intake as dependent dichotomous variable, and a single potential determinant as independent variable. The following potential demographic determinants were included as independent variables: gender (male, female); age (21–43, 44–54, and 55–73 years), education level (low, medium, high), and marital status (partner, no partner). The following potential health-related determinants were included: years since LS diagnosis (0–2 years, 2–4 years, and 4–20 years), colon surgery (yes, no), personal cancer history (yes, no), and smoking status (current, ex-, never smoker). The following potential psychological determinants were included: awareness (yes, no) and knowledge of the recommendations (yes, no), symptoms of depression (continuous), cancer worry (continuous), and cancer risk perception (< $50\%$, $50\%$, > $50\%$).
Subsequently, multivariable logistic regression analyses were conducted with adherence to each recommendation as dependent variable, and as independent variables all socio-demographic, health-related, and psychological characteristics that were found to be statistically significantly associated with adherence in the univariable logistic regression analyses.
Statistical analyses were performed using IBM SPSS Statistics for Windows, version 24. P-values < 0.05 were considered to be statistically significant.
## Results
Of the 218 LS patients who agreed to participate in the study, seven participants with missing data on one or more of the variables were excluded and 211 were included in the population for analysis. Participants were aged between 21 and 73 years (mean 48.2; SD 10.9), and $61.1\%$ was female ($$n = 129$$) (Table 1). The number of years since LS diagnosis ranged between 0 and 20 years (mean 3.7; SD 2.7). $18\%$ had had a type of colon surgery (colectomy $$n = 7$$, hemicolectomy $$n = 24$$, colon resection $$n = 7$$).
Table 1Demographic, health-related, behavior change and psychological characteristics in Lynch Syndrome patients ($$n = 211$$) Total ($$n = 211$$) N (%)* Cancer in personal history ($$n = 75$$) N(%)* No cancer in personal history ($$n = 136$$) N(%)* Demographic characteristics GenderFemaleMale129(61.1)82(38.9)45(60.0)30(40.0)84(61.8)52(38.2)Age at measurement21–43 years44–54 years55–73 years72(34.1)74(35.1)65(30.8)10(13.3)34(45.3)31(41.3)62(45.6)40(29.4)34(25.0)Educational levelLowMediumHigh20(9.5)107(50.7)84(39.8)7(9.3)43(57.3)25(33.3)13(9.6)64(47.1)59(43.4)PartnerYesNo187(88.6)24(11.4)9(12.0)66(88.0)15(11.0)121(89.0) Health-related characteristics Years since LS diagnosis0–2 years2–4 years4–20 years82(38.9)63(29.9)66(31.3)27(36.0)18(24.0)30(40.0)55(40.4)45(33.1)36(26.5)Colon surgeryNo colon surgeryColon surgery173(82.0)38(18.0)38(50.7)37(49.3)135(99.3)1(0.7)Smoking statusCurrent smokerEx-smokerNever smoker22(10.4)92(43.6)97(46.0)8(10.7)40(53.3)27(36.0)14(10.3)52(38.2)70(51.5) Behavior change and psychological characteristics KnowledgeWeight recommendationYesNo111(52.6)100(47.4)41(54.7)34(45.3)70(51.5)66(48.5)Physical activity recommendationYesNo136(64.5)75(35.5)50(66.7)25(33.3)86(63.2)50(36.8)Meat intake recommendationYesNo30(14.2)181(85.8)10(13.3)65(86.7)20(14.7)116(85.3)AwarenessInfluence of overweight on cancer riskYesNoInfluence of physical activity on cancer riskYesNo154(73.0)57(27.0)141(66.8)70(33.2)56(74.7)19(25.3)52(69.3)23(30.7)98(72.1)38(27.9)89(65.4)47(34.6)Influence of meat intake on cancer riskYesNo79(37.4)132(62.6)35(46.7)40(53.3)44(32.4)92(67.6)Symptoms of depression [Mean(SD)]2.78(3.13)3.4(3.4)2.4(3.0)Cancer worry [Mean(SD)]13.8(4.22)15.1(4.6)13.1(3.8)Cancer risk perception<$50\%$$50\%$>$50\%$71(33.6)51(24.2)89(42.2)24(32.0)16(21.3)35(46.7)47(34.6)35(25.7)54(39.7) *Unless otherwise specified; M = mean; SD = standard deviation; BMI = Body Mass Index The majority of participants were aware of the influence of or had knowledge about the recommendation on body weight ($73\%$ and $64.5\%$, respectively) and physical activity ($66.8\%$ and $64.5\%$, respectively) in relation to cancer risk. Much less participants were aware of the influence of or had knowledge about the recommendation on red and processed meat intake in relation to cancer risk ($37.4\%$ and 14,$2\%$, respectively).
Of the 211 participants, $35.5\%$ had a cancer diagnosis in their personal medical history ($$n = 75$$), of which 37 had been diagnosed with colorectal cancer, 17 with endometrium cancer, 4 with both colorectal and endometrium cancer, and 17 with other types of cancer. Compared with LS patients without a cancer diagnosis in their personal history, LS patients with a cancer diagnosis were older ($p \leq .000$), had more often had a type of colon surgery ($p \leq .000$), were more frequently aware of the influence of meat intake on cancer risk ($$p \leq .04$$), and had a higher mean score of depressive symptoms ($$p \leq .037$$) and cancer worry ($$p \leq .001$$). See Table 1.
## Adherence to the recommendations
Out of the 211 LS patients, $50.2\%$ adhered to the body weight recommendation, $78.7\%$ adhered to the physical activity recommendation, and $33.6\%$ adhered to the red and processed meat intake recommendation.
## Body weight recommendation
The univariable logistic regression analyses showed that age 44–54 vs. <44 years, medium and high vs. low educational level, and symptoms of depression were associated with adherence to the body weight recommendation (Table 2).
Table 2Demographic, health-related, behavior change and psychological characteristics of Lynch Syndrome patients ($$n = 211$$) and associations with adherence to the WCRF recommendation on body weight [1] Non-adherent $$n = 105$$ Adherent $$n = 106$$ Univariable [[2]] Multivariable [[3]] N(%) N(%) OR($95\%$CI) OR($95\%$CI) Demographic characteristics GenderFemaleMale59(56.2)46(43.8)70(66.0)36(34.0)1.52(0.87–2.65)1Age at measurement21–43 years44–54 years55–73 years28(26.7)43(41.0)34(32.4)44(41.5)31(29.2)31(29.2)1 0.46(0.24–0.89)* 0.58(0.29–1.14)1 0.48(0.24–0.94)* 0.86(0.41–1.79)Education levelLowMediumHigh16(15.2)56(53.3)33(31.4)4(3.8)51(48.1)51(48.1)1 3.64(1.14–11.6)* 6.18(1.90–20.1)** 1 4.55(1.34–15.5)* 6.41(1.83–22.5)** PartnerYesNo97(92.4)8(7.6)90(84.9)16(15.1)0.46(0.19–1.14)1 Health-related characteristics Years since LS diagnosis0–2 years2–4 years4–20 years35(33.3)33(31.4)37(35.2)47(44.3)30(28.3)29(27.4)10.68 (0.35–1.31)0.58(0.30–1.12)Colon surgeryNo surgerySurgery81(77.1)24(22.9)92(86.8)14(13.2)11.95(0.94–4.02)Cancer in personalhistoryYesNo44(41.9)61(58.1)31(29.2)75(70.8)0.57(0.32–1.01)1Smoking statusCurrent smokerEx-smokerNever smoker12(11.4)54(51.4)39(37.1)10(9.4)38(35.8)58(54.7)10.84(0.33–2.15)1.79(0.70–4.53) Behavior change and psychological characteristics KnowledgeYesNo55(52.4)50(47.6)50(47.2)56(52.8)1.02(0.59–1.75)1AwarenessYesNo76(72.4)29(27.6)78(73.6)28(26.4)1.06(0.58–1.95)1Symptoms of depression[Mean(SD)] [4]3.25(3.22)2.32(2.98) 0.91(0.83–0.99)* 0.94(0.85–1.03)Cancer worry [Mean(SD)] [5]14.4(4.42)13.3(3.95)0.94(0.88-1.00)Cancer risk perception<$50\%$$50\%$>$50\%$39(37.1)27(25.7)39(37.1)32(30.2)24(22.6)50(47.2)11.08(0.53–2.23)1.56(0.83–2.93)*$p \leq .05$, **$p \leq .01$, ***$p \leq .001$ 1 Body weight recommendation: Body Mass Index 18.5–24.9 kg/m 2 2 Odds ratios are derived from univariable logistic regression analyses with adherence to the weight recommendation (yes vs. no) as dependent variable and one sociodemographic, health-related or psychological characteristic as independent variable 3 Odds ratios are derived from a multivariable logistic regression analysis with adherence to the weight recommendation (yes vs. no) as dependent variable and all statistically significant ($p \leq .05$) sociodemographic, cancer-related, and health-related characteristics in the univariable logistic regression analyses as independent variables 4 Odds ratio per 1 unit increase in the depressive symptoms subscale of the Hospital Anxiety and Depression Scale 5 Odds ratio per 1 unit increase in the Cancer Worry Scale In the multivariable analyses, only age 44–54 vs. <44 years (OR 0.48, $95\%$ CI: 0.24–0.94) and medium (OR 4.55, $95\%$ CI: 1.34–15.5) and high (OR 6.41, $95\%$ CI: 1.83–22.5) vs. low educational level remained statistically significantly associated with adherence to the body weight recommendation.
## Physical activity recommendation
The univariable logistic regression analyses showed that age 55–73 vs. <44 years, ex-smoking vs. current smoking, and having vs. not having knowledge about the physical activity recommendation were associated with adherence to the physical activity recommendation (Table 3).
Table 3Demographic, health-related, behavior change and psychological characteristics of Lynch Syndrome patients ($$n = 211$$) and associations with adherence to the WCRF recommendation on physical activity [1] Non-adherent $$n = 45$$ Adherent $$n = 166$$ Univariable [[2]] Multivariable [[3]] N(%) N(%) OR($95\%$CI) OR($95\%$CI) Demographic characteristics GenderFemaleMale23(51.1)22(48.9)106(63.9)60(36.1)1.69(0.87–3.29)1Age at measurement21–43 years44–54 years55–73 years16(35.6)23(51.1)6(13.3)56(33.7)51(30.7)59(35.5)10.63(0.30–1.33) 2.81(1.03–7.69)* 10.54(0.25–1.19)2.44(0.85–6.97)Education levelLowMediumHigh3(6.7)22(48.9)20(44.4)17(10.2)85(51.2)64(38.6)10.68(0.18–2.54)0.57(0.15–2.13)PartnerYesNo42(93.3)3(6.7)145(87.3)21(12.7)0.49(0.14–1.73)1 Health-related characteristics Years since LS diagnosis0–2 years2–4 years4–20 years22(48.9)12(26.7)11(24.4)60(36.1)51(30.7)55(33.1)11.56(0.70–3.46)1.83(0.82–4.13)Colon surgeryNo surgerySurgery33(73.3)12(26.7)140(84.3)26(15.7)11.96(0.90–4.28)Cancer in personalhistoryYesNo20(44.4)25(55.6)55(33.1)111(66.9)0.62(0.32–1.21)1Smoking statusCurrent smokerEx-smokerNever smoker8(17.8)14(31.1)23(51.1)14(8.4)78(47.0)74(44.6)1 3.18(1.13-9.00)* 1.84(0.69–4.93)12.59(0.87–7.74)1.72(0.60–4.95) Behavior change and psychological characteristics KnowledgeYesNo23(51.1)22(48.9)113(68.1)53(31.9) 2.04(1.04–3.98)* 1 2.22(1.09–4.52)* 1AwarenessYesNo27(60.0)18(40.0)114(68.7)52(31.3)1.46(0.74–2.89)1Symptoms of depression [Mean(SD)] [4]3.42(2.86)2.61(3.19)0.93(0.84–1.02)Cancer worry [Mean(SD)] [5]13.9 (4.68)13.8(4.09)0.99(0.92–1.07)Cancer risk perception<$50\%$$50\%$>$50\%$14(31.1)13(28.9)18(40.0)57(34.3)38(22.9)71(42.8)10.72(0.30–1.70)0.97(0.44–2.12)*$p \leq .05$, **$p \leq .01$, ***$p \leq .001$ 1 Physical activity recommendation: moderate to vigorous activities for at least 30 min a day, for a minimum of 5 days a week 2 Odds ratios are derived from univariable logistic regression analyses with adherence to the physical activity recommendation as dependent variable and one sociodemographic, health-related or psychological characteristic as independent variable 3 Odds ratios are derived from a multivariable logistic regression analysis with adherence to the physical activity recommendation (yes vs. no) as dependent variable and all statistically significant ($p \leq .05$) sociodemographic, health-related, and psychological characteristics in the univariable logistic regression analyses as independent variables 4 Odds ratio per 1 unit increase in the depressive symptoms subscale of the Hospital Anxiety and Depression Scale 5 Odds ratio per 1 unit increase in the Cancer Worry Scale In the multivariable analyses, only having knowledge about the physical activity recommendation remained statistically significantly associated with adherence to this recommendation (OR 2.04, $95\%$ CI: 1.04; 3.98).
## Red and processed meat intake recommendation
The univariable logistic regression analyses showed that only having vs. not having knowledge about the red and processed meat intake recommendation was associated with adherence to the red and processed meat recommendation (Table 4; OR 2.62, $95\%$ CI: 1.19; 5.74).
Table 4Demographic, health-related, behavior change and psychological characteristics of Lynch Syndrome patients ($$n = 211$$) and associations with adherence to the WCRF recommendation on red and processed meat intake [1] Non-adherent $$n = 140$$ Adherent $$n = 71$$ Univariable [[2]] Multivariable [[3]] N(%) N(%) OR($95\%$CI) OR($95\%$CI) Demographic characteristics GenderFemaleMale82(58.6)58(41.4)47(66.2)24(33.8)1.39(0.76–2.51)1Age at measurement21–43 years44–54 years55–73 years48(34.3)49(35.0)43(30.7)24(33.8)25(35.2)22(31.0)11.02(0.51–2.03)1.02(0.50–2.08)Education levelLowMediumHigh14(10.0)75(53.6)51(36.4)6(8.5)32(45.1)33(46.5)10.99(0.35–2.82)1.51(0.53–4.32)PartnerYesNo121(86.4)19(13.6)66(93.0)5(7.0)2.07(0.74–5.81)1 Health-related characteristics Years since LS diagnosis0–2 years2–4 years4–20 years58(41.4)38(27.1)44(31.4)24(33.8)25(35.2)22(31.0)11.59(0.80–3.18)1.21(0.60–2.43)Colon surgeryNo surgerySurgery111(79.3)29(20.7)62(87.3)9(12.7)11.80(0.80–4.04)Cancer in personalhistoryYesNo55(39.3)85(60.7)20(28.2)51(71.8)0.61(0.33–1.13)1Smoking statusCurrent smokerEx-smokerNever smoker19(13.6)59(42.1)62(44.3)3(4.2)33(46.5)35(49.3)13.54(0.98–12.9)3.58(0.99–12.9) Behavior change and psychological characteristics KnowledgeYesNo14(10.0)126(90.0)16(22.5)55(77.5) 2.62(1.19–5.74)* 1 2.62(1.19–5.74)* 1AwarenessYesNo50(35.7)90(64.3)29(40.8)42(59.2)1.24(0.69–2.23)1Symptoms of depression [Mean(SD)] [4]2.87(3.13)2.61(3.16)0.97(0.89–1.07)Cancer worry [Mean(SD)] [5]14.1(4.52)13.2(3.48)0.94(0.88–1.01)Cancer risk perception<$50\%$$50\%$>$50\%$46(32.9)32(22.9)62(44.3)25(35.5)19(26.8)27(38.0)11.09(0.52–2.31)0.80(0.41–1.56)*$p \leq .05$, **$p \leq .01$, ***$p \leq .001$ 1 Meat intake recommendation: <500 g/w red meat, < 3 g/d processed meat 2 Odds ratios are derived from univariable logistic regression analyses with adherence to the WCRF red and processed meat intake recommendation (yes vs. no) as dependent variable and one sociodemographic, health-related or psychological characteristic as independent variable 3 The independent variable *Knowledge is* the only variable that was statistically significantly ($p \leq .05$) associated with adherence to the WCRF red and processed meat intake recommendation (yes vs. no) in the univariable logistic regression analyses 4 Odds ratio per 1 unit increase in the depressive symptoms subscale of the Hospital Anxiety and Depression Scale 5 Odds ratio per 1 unit increase in the Cancer Worry Scale
## Discussion
This first quantitative explorative study on determinants of adherence to WCRF lifestyle and body weight recommendations for cancer prevention in LS patients showed that knowledge about the recommendations was a statistically significant determinant of adherence to the lifestyle recommendations on physical activity and red and processed meat intake. Being younger and having a higher level of education were associated with adherence to the recommendation on body weight.
Adherence to the body weight recommendation among LS patients in the current study was comparable to adherence in the general Dutch population in which $50\%$ of those aged 18 and older adhered to the body weight recommendation [36]. As compared to an observational study in Dutch colorectal cancer survivors, adherence to the recommendations on body weight ($50\%$ vs. $34\%$), physical activity ($78.7\%$ vs. $73\%$), and red and processed meat ($33.6\%$ vs. $8\%$) was higher in the LS patients participating in the current study [37].
To our knowledge, no other studies have quantitatively investigated determinants of adherence to lifestyle and body weight recommendations in LS patients. The results of this first quantitative exploration of determinants of adherence are in accordance with our previous qualitative findings showing that having knowledge about the recommendations serves as a cue to action for adherence to lifestyle recommendations in LS patients [22]. Knowledge is incorporated as a determinant in multiple frequently used theories and models of health behavior change (e.g., the theory of planned behavior, the Health Belief Model, Social Cognitive Theory) [25]. In this study, knowledge was found to be a determinant of adherence to the recommendations on health behaviors (physical activity and red and processed meat intake), but not of adherence to the body weight recommendation. These findings may be explained by the theoretical proximity of the determinant knowledge to a certain health behavior (such as physical activity or red and processed meat intake) as opposed to an outcome of multiple lifestyle behaviors (body weight). Considering that adherence to the body weight recommendation is subject to adherence to recommendations on energy balancing behaviors (physical activity, sedentary behavior, and dietary intake), it seems plausible that knowledge is a more proximal determinant of health behaviors and a more distal determinant of adherence to the body weight recommendation (outcome of the health behaviors physical activity and diet quality). In other words, it makes sense that it’s more difficult to influence (the result of) multiple lifestyle behaviors just by increasing knowledge than it is to influence a single lifestyle behavior. Hence, this could explain our finding that knowledge was found to be a statistically significant determinant of the health behaviors physical activity and red and processed meat intake, but not for the outcome of health behaviors (body weight).
The observed association between adherence to the body weight recommendation and educational level is in line with previous research. A large Canadian cross-sectional study examining determinants of adherence to WCRF recommendations in the general population, also found that higher education attainment was associated with higher odds of adhering to the recommendation for body weight [38].
It should be noted that most of the potential determinants of adherence included in this study did not show a statistically significant association with adherence to recommendations on body weight, physical activity, and red and processed meat intake. Contrary to our expectations, having a cancer diagnosis in one’s personal medical history was not found to be statistically significantly associated with adherence. This seems to be in disagreement with the presumed window of opportunity for lifestyle change after a cancer diagnosis that has been described in the scientific literature on health behavior change after a cancer diagnosis [39]. In addition, time after LS diagnosis also was not found to be statistically significantly associated with adherence.
## Strengths and limitations
A strength of this first quantitative study on determinants of adherence to WCRF lifestyle recommendations for cancer prevention in LS patients is the relatively large sample size ($$n = 211$$) in relation to the number of LS patients (estimated 10-year prevalence of 3.316 in the Netherlands) [40, 41]. Other strengths include the extensive assessment of adherence to the recommendations and potential determinants and the use of widely-used validated questionnaires.
Several limitations should be considered when interpreting the results of this study. Our study sample consisted of LS patients who agreed to participate in a study about lifestyle and cancer risk (response rate $53\%$). LS patients who participated were more likely to be older, female, and to have had a previous diagnosis of cancer compared with those who did not participate. Therefore, our study sample may not be a representative sample of LS patients. In addition, our sample consisted of a relatively high proportion of highly educated individuals, which may limit the generalizability of our findings and may reflect an overestimation of the proportion of LS patients having knowledge about the recommendations. Furthermore, while interpreting our findings, it should be taken into account that adherence to lifestyle and body weight recommendations was assessed using self-report questionnaires, which may have led to over-reporting of healthy lifestyle behavior and under-reporting of body weight, particularly among individuals with overweight or obesity [42, 43]. Additionally, the sample size ($$n = 211$$) was too small to be able to enter all independent variables into one multivariable logistic regression analyses as the validated rule of thumb of a minimum of 10 participants per independent categorical variable in the smallest group would have been violated [44]. Therefore, only the independent variables that were statistically significantly associated with adherence were entered into the multivariable logistic regression analyses. It should also be noted that we did not distinguish between different MMR genes in our statistical analyses, while the cumulative cancer risk and the risk of different cancer types differs according to MMR gene mutation type [7]. Since we found that having been diagnosed with (any type of) cancer was not associated with adherence this is not expected to influence our results. Finally, it should be noted that there are many more possible determinants of health behavior change that we did not incorporate in this study that may have influenced adherence. Such possible determinants include for example social and environmental factors, which should be incorporated in future studies to provide a more comprehensive picture of the determinants of adherence to lifestyle recommendations in LS patients.
The results of this study confirm the importance of having knowledge about lifestyle recommendations and suggest that such knowledge should be promoted to achieve adherence. Our previous publication about the LiLy study has shown that knowledge about lifestyle recommendations can be increased by providing LS patients with WCRF-NL health promotion materials [21]. Health care providers involved in (follow-up) care for LS patients (such as genetic counsellors, clinical geneticists, gastro-enterologists, gynaecologists) could easily incorporate providing WCRF-NL health promotion materials during counselling or surveillance visits with LS patients. Informing LS patients about lifestyle-related factors (including the preventive use of aspirin [45]) and cancer risk is in line with current guidelines for LS patients [16]. Increasing knowledge, by providing health promotion materials or referring to online health education material (e.g., via the international and national websites of the WCRF such as www.wcrf.org), is an important first step to achieve adherence. When health care professionals provide these materials, this is in itself an additional behavior change technique (credible source) [46]. However, as our previous study and many others have shown, health behavior change is not likely to be achieved by solely providing information [21, 47]. Although information provision is an important first step towards health behavior change, typically, a combination of multiple behavior change techniques and strategies targeting a multitude of health behavior determinants is needed to achieve and maintain health behavior changes. Therefore, health care professionals treating LS patients could refer to other health care professionals specialized in health behavior change (such as a dietician, physical therapist, or a lifestyle coach). They could provide these additional behavior change techniques to achieve health behavior changes and to improve health outcomes in LS patients.
## Conclusion
The results of this first quantitative study on determinants of adherence to WCRF lifestyle and body weight recommendations among LS patients confirm that knowledge about the recommendation is an important determinant for adherence to the recommendations on physical activity and red and processed meat intake. Results suggest that strategies to increase knowledge (e.g., providing health education materials) should be included in lifestyle promotion targeted at LS patients, along with behavior change techniques influencing other modifiable determinants of adherence.
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|
---
title: Evaluation of Cadmium or Lead Exposure with Nannochloropsis oculata Mitigation
on Productive Performance, Biochemical, and Oxidative Stress Biomarkers in Barki
Rams
authors:
- Marwa A. Hassan
- Yasmina K. Mahmoud
- A. A. S. Elnabtiti
- A. S. El-Hawy
- Moharram Fouad El-Bassiony
- Heba M. A. Abdelrazek
journal: Biological Trace Element Research
year: 2022
pmcid: PMC10020321
doi: 10.1007/s12011-022-03318-z
license: CC BY 4.0
---
# Evaluation of Cadmium or Lead Exposure with Nannochloropsis oculata Mitigation on Productive Performance, Biochemical, and Oxidative Stress Biomarkers in Barki Rams
## Abstract
This study was designed to determine the lead or cadmium exposure of Barki rams and the beneficial role of *Nannochlorposis oculata* (N. oculata) $4\%$ as a feed supplement, as well as its mitigating role against these elements’ impacts concerning performance, biochemical markers of liver enzymes and kidney function, thyroid hormone activity, and oxidative stress markers. Six groups of 36 Barki rams (33.63 ± 1.29 kg) were divided into G1: which served as control; G2: was given $4\%$ dietary N. oculata; G3: was given oral 1 mg/kg cadmium chloride; G4: was given 5 mg/kg/day lead acetate; G5: was given oral 1 mg/kg cadmium chloride and $4\%$ dietary N. oculata, and G6: was given oral 5 mg/kg/day lead acetate and $4\%$ dietary N. oculata; and treatments were continued for 60 days. Cadmium and lead-exposed groups exhibited lower and weaker weight gain as well as feed conversion ratio, respectively, than the control and other groups. Additionally, levels of T3, T4, total proteins, albumin, and glutathione (GSH) were significantly reduced in both G3 and G4 compared to control. However, urea, creatinine, ALT, AST, total cholesterol, triglycerides, protein carbonyl content (PCC), and malondialdehyde (MDA) were significantly increased (P ≤ 0.05) in cadmium and lead-exposed groups. Dietary N. oculata ($4\%$) improves serum proteins, creatinine, urea, T4, and oxidative stress indicators as compared to the control group. Finally, $4\%$ dietary N. oculata greatly enhances the investigated parameters in terms of performance, thyroid hormones, serum biochemical, and antioxidant activity and may assist in reducing the endocrine disrupting effects of Pb and Cd.
## Introduction
Heavy metal toxicity is growing in developing countries as urbanization and industrialization proceed [1]. The most major environmental and industrial contaminants have been identified as lead (Pb) and cadmium (Cd). Their pollution coexists with humans and animals in a variety of situations [2]. Cadmium is among the most toxic minerals in the ecosystem to animals and humans [3, 4] with organ toxicity ranging from mild to severe and a long half-life of clearance [5], which is not essential to physiological and biochemical functions [4]. It occurs naturally with greater concentrations in Cd-rich soils such as shales, marine and lacustrine sediments, and phosphorites. Nevertheless, industrial and agricultural processes account for more than $90\%$ of Cd in the surface environment [6]. Cd exposure in farmed ruminants occurs due to industrial processing and intensive agricultural practices that pollute water, soil, forage, feed, and air [7]; also, it is a phosphate fertilizer pollutant [8]; therefore, it is introduced to the soil via routine farming practices [9]. The highest Cd concentration tolerated in animal diets is 0.5 mg kg.−1 [10]. Whenever animals consume substantial quantities of Cd, it can bioaccumulate for decades, resulting in subacute, acute, or chronic intoxication [4], causing significant damage to numerous organs such as the liver and kidney, as well as structural and physiological abnormalities [4, 10] Lead has a negative influence on animal health and production due to its inability to degrade and bio-accumulate over long periods of time [11], affecting all biological systems through exposure from water, food sources, and air [12]. Pb is recognized as a crucial ecological pollutant that has been linked to unintentional toxicity in domestic animals, most commonly in industrialized areas of the world [13, 14]. Pb intoxication in animals is also commonly caused by contaminated feed from industrial effluents, home wastes, fertilizers, pesticides, and mineral combinations [15, 16]. Furthermore, the feed may be polluted by vehicle oil, pastures close to Pb industrial facilities and battery factories, ash from oil-painted wood, lubricants from machines, disposed paint cans, plies [17, 18], and railings, walls, floors, drinkers, feeders, and storage facilities with Pb-containing paints that animals might indeed lick [17, 19]. Another source is meadows along road boundaries that have been polluted with high quantities of fumes emitted by gasoline automobiles since their fuel includes Pb tetraetileno [20]. Water pollution by hazardous heavy metals such as Pb has increased rapidly as a result of natural and industrial sources [21], and these metals have subsequently reached plants and animals, which have a significant impact on human health via the food chain [22]. Pb exposure even in a small dose for a long period causes clinicopathological alterations due to damage to the liver, kidney, endocrine system, and reproductive performance of animals [23, 24].
The gap between growing water demand and restricted water availability is Egypt’s most serious water resource management concern. The new land projects need large volumes of water, which can only be obtained by improving water irrigation efficiency on previously watered old lands, as well as reusing drainage water and purified wastewater [25]. El-Salam *Canal is* one of the five enormous irrigation projects under construction in Egypt’s Northern Sinai. It has an impact on animal health and productivity. The Egyptian government plans to restore an estimated 620,000 ac of desert along Sinai’s Mediterranean coast by redirecting considerable volumes of agricultural drainage water to newly reclaimed regions and mixing it in a 1:1 ratio with Nile water [26]. El-Salam canal (latitudes 32° 40′ to 44°, longitudes 31° 40′ to 16°) runs southeast towards Lake El-Manzala, then south to mingle with El-Serw drainage water in a 1:1 ratio, then east, then south to combine with Hadous drainage water, then east beneath Suez Canal to *Sinai peninsula* [27]. Nevertheless, water contamination, which is a major environmental problem, may emerge from this mixing of water [26]. Furthermore, it is polluted by a wide range of pollutants, including elevated levels of minerals, heavy metals, organic debris, pesticide and herbicide residues, and microbiological contamination [28]. Multiple investigations identified cadmium and lead contamination in the El-Salm canal at higher levels of 0.215 to 2.17 and 5.20 mg/l, respectively [25], and metal concentrations in water fluctuated between years (2015–2018) and were Cd (0.76–0.87), Pb (0.98–1.12) mg/l, respectively [29]. The most widespread ruminant livestock species, grazing sheep, is one of Egypt’s agricultural foundations because it can convert low-quality roughages into meat and milk for human use, in addition to generating wool and hide [30]. The most widespread ruminant livestock species, grazing sheep, is one of Egypt’s agricultural foundations because it can convert low-quality roughages into meat and milk for human use, in addition to generating wool and hide [31]. The suitability and availability of key macro and microelements from pastures influence grazing animal performance and health. Animals under this regime rely entirely on forages to meet all their nutritional requirements. Metals and metalloids, for example, are hazardous chemicals or compounds that accumulate throughout the food chain. Furthermore, their concentrations in the environment rise in response to increases in urban, agricultural, and industrial emissions. The extensive prevalence of some metal pollutants, notably Cd and Pb, allows them to enter the food chain, raising the likelihood of harmful effects on people and livestock [32]. Additionally, the latter authors found Cd and Pb (0.54–0.8 and 3.32 to 5.76 mg/l, respectively) in fodder grown in the East Qantara area, near the El-Salam canal.
Natural antioxidants used as nutritional supplements, including microalgae, may enhance not only the health and performance of animals but also their resilience to environmental stressors such as heat stress, poor housing conditions, and infections [33]. Microalgae have previously been reported as an alternative non-traditional protein source and nutritional supplement for animal and human nutrition, but commercial large-scale production began just a few decades ago [34]. Nannochloropsis species have grown in popularity as a source of lipids for biofuels and/or the synthesis of long-chain polyunsaturated fatty acids, notably eicosapentaenoicaci [35]. Commercialization of this alga is being pursued [36]. Nannochloropsis species are freshwater and marine microalgae that are linked to diatoms and brown algae [37], they have been utilized to make nutraceuticals and feed additives for decades [38]. Nannochloropsis sp. has been used as an aquaculture feed ingredient, providing a supply of omega-3 fatty acids [39]. Among the microalgae that should be included as a feed supplement, Nannochloropsis species should be prioritized due to their suitability for intensive cultivation and high concentration of PUFAs (particularly EPA), antioxidants, and certain vitamins [34]. Additionally, *Nannochloropsis oculata* (N. oculata) is a marine-water microalga that is a strong source of omega-3 fatty acids, notably eicosapentaenoic acid (EPA), which is used to make an omega-3 oil for use as a dietary supplement [40]. According to Altomonte et al. [ 41], ruminants are good models for feeding with microalgae since they can break down cell wall organisms that are typically not metabolized. Kholif et al. [ 42] concluded that microalgae in diets improved feed utilization, milk production and quality, productive performance, and meat quality in ruminants because of improved diet nutritive value, leading to improved feed utilization; conclusively, feeding Nubian goats on a diet containing N. oculata (5 and 10 g) improved milk production and the nutritive value of the diet.
In this respect, this study aimed to investigate the lead or cadmium exposure on Barki rams, as well as the beneficial role of N. oculata $4\%$ as a feed supplement and its moderating role against these elements’ impact. This was accomplished by monitoring their performance, biochemical indicators of liver enzymes and renal function, thyroid hormone activity, and oxidative stress markers.
## Materials and Methods
All the experimental methods were carried out by well-trained experts in conformity with the principles of Suez Canal University’s Animal Ethics Review Committee.
## Animals and Experimental Design
The experiment was carried out on 36 healthy Barki rams ($$n = 6$$/group), aged approximately 6 months and weighing a mean of 33.63 ± 1.29 kg, which were raised for 60 days. The animals were housed in freely ventilated semi-closed pens with partitions between groups throughout the experimental period, with water supplied by troughs and shade provided for sun protection at the sheepfold of a private farm near the El-Salam canal in Sahl Altina, East Qantra area, Ismailia, Egypt. Before commencing the experiment, feces samples were submitted for parasitological analysis to determine the health of the animals [43]. Clinical examination of the animals (body temperature, mucous membrane, respiratory rate, pulse rate, and ruminal motility of all animals) was monitored according to Kelly [44]. The control sheep were offered feed and water free from Cd and Pb. The animals were divided into 6 groups: Control (G1): animals received basal diet only; Nanno. group (G2): animals received basal diet with $4\%$ dietary N. oculata; Cd (G3): animals administrated Cd chloride (1 mg/kg/day); Pb (G4): animals administrated Pb acetate (5 mg/kg/day); Nanno + Cd (G5): animals administrated Cd chloride (1 mg/kg/day) and $4\%$ dietary N. oculata; and Nanno + Pd; animals administrated Pb acetate (5 mg/kg/day) and $4\%$ dietary N. oculata. Based on the levels detected in forage by Donia and Marwa [32], heavy metals were administrated to the experimental sheep, Cd (1 mg/kg/ day) [45] and Pb acetate (5 mg/kg/day) [46] orally for 60 days. The concentration of heavy metals was calculated by the following equation (Eq. [ 1]):\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ppm\;of\;element=\frac{Molecular\;weight\;of\;salts}{Molecular\;mass\;of\;the\;element}\times required\;Conc.of\;elements$$\end{document}ppmofelement=MolecularweightofsaltsMolecularmassoftheelement×requiredConc.ofelements Cadmium and lead ($99.99\%$ purity CdCl2 and lead (II) acetate trihydrate, Merck) were administered to the animals. By dissolving cadmium chloride (0.163 g) and lead acetate (0.915 g) (Eq. [ 1]) in 1 l of distilled water, a stock solution of 500 and 100 mg l−1 of Cd and Pb was prepared, with the final concentration reaching 100 × (100 and 500 mg/l for cadmium and lead, respectively). The desired concentration (1 and 5 mg l−1 for Cd and Pb, respectively) was then obtained by adding 0.1 ml of the stock solution to 9.9 ml of distilled water, then the 10 ml was administered to the animals orally onto the tongue using disposable plastic syringe with a rubber long nozzle and the fluid was readily swallowed by the rams. The control group received the same treatment as the other groups, but with distilled water instead of heavy metals, and was kept under the same conditions.
## Experimental Diet and Microalga
The basal diet was designed to suit the ram’s nutritional requirements while balancing body weight gain at a rate of 0.3 kg/day [47]. The composition of the basal diet is presented in Table 1. Diet was provided twice a day, in the morning and evening, with unrestricted access to water. On days 0 and 60 of the experiment, animals were weighed after fasting for 12 h prior to the morning feedings. The Algal Biotechnology Unit, Biological and Agricultural Research Division, National Research Centre, Dokki, Giza, Egypt, cultivated and retrieved *Nannochloropsis oculata* (NNO-1 UTEX Culture LB 2164). The algae meal was added to the concentrate in the mixer at the feed mill. The concentrate intake was calculated from feed offered and refused on daily basis. Before the experiment, Cd and Pb levels in the diet, water, and N. oculata were measured and found to be undetectable. Table 1Ingredients and calculated chemical composition of experimental dietItem%Analyzed chemical composition (% on DM basis)IngredientBerseem Hay14.65DM88.7Wheat straw4.88CP14.2Corn grains57.7TDN76.2Cotton seed meal13.07NDF22.8Soybean meal7.4ADF14.9Limestone0.81EE4.14Salt0.50Ca0.7Sodium bicarbonate0.51P0.4Ammonium chloride0.20Vitamin-mineral premix0.30DM dry matter; CP crude protein; TDN total digestible nutrient; NDF neutral detergent fiber; ADF acid detergent fiber, EE ether extract; Ca calcium; P phosphorus
## Growth Performance Parameters
Each group’s rams were weighted at the start (initial body weight) and end (final body weight) of the trial. Furthermore, overall weight growth (kg/head) and average daily gain (g/head/day) were estimated. Feed intake (g DM/head) was recorded daily, and the feed conversion ratio (FCR kg DM/kg gain) was determined to evaluate the performance of the rams [48].
All the animals tested in the different treatments, as well as the control group, demonstrated normal clinical parameters (data not shown). According to the statistics, the starting body weight was nearly identical. The experimental groups’ values of dry roughage intake, concentrate intake, and total DMI (g/kg BW) were all negligible (Table 2). Animals exposed to Cd or Pb had poor growth performance, as evidenced by a significant (P ≤ 0.05) decrease in weight gain and a significant (P ≤ 0.05) weak FCR when compared to other groups (Table 2). Adding N. oculata $4\%$ to the experimental diet of Pb and Cd intoxicated rams significantly (P ≤ 0.05) improved weight gain and FCR than Cd and Pb groups; however, final body weights of the latter groups were not non-significantly varied. The addition of N. oculata $4\%$ to diet in the control negative group (G2) exerted no influence on the final body weight and weight gain. Table 2Growth performance and feed conversion ratio among the treated groupsParametersBody weight changesDaily feed intake (g DM/head)Treatments ($$n = 6$$/group)Initial BW (kg)Final BW (kg)Total gain (kg/head)Average daily gain (g/head/day)Concentrate mixtureRoughagesTotal DM intake/head/dayFCR (kg DM/kg gain)Control34.5a ± 1.843.87a ± 1.679.37ab ± 0.28156.11ab ± 4.73877.33a ± 33.47658a ± 25.11535.33a ± 58.579.9d ± 0.55Nanno32.5a ± 3.3142.64a ± 3.1310.14a ± 0.5169.06a ± 8.37852.87a ± 62.62639.65a ± 46.961492.52a ± 109.588.99d ± 0.88Cd34.67a ± 4.5839.55a ± 3.944.89d ± 0.6681.44d ± 11.05791.07a ± 78.78593.3a ± 59.081384.37 a ± 137.8619.69ab ± 4.15Pb32.33a ± 3.4137.34a ± 3.035.01d ± 0.4983.5d ± 8.14746.87a ± 60.54560.15a ± 45.411307.02a ± 105.9516.92bc ± 2.81Nanno + Cd34.33a ± 4.0541.88a ± 3.947.54c ± 0.97125.72c ± 16.2837.53a ± 78.88628.15a ± 59.161465.68a ± 138.0412.93 cd ± 2.14Nanno + Pb33.42a ± 2.2941.1a ± 2.217.68bc ± 0.52128.06bc ± 8.7822a ± 44.13616.5a ± 33.11438.5a ± 77.2311.47 cd ± 0.9Differences between means within the same column having different superscript letters are statistically significant (P ≤ 0.05)BW body weight, DM dry matter, FCR feed conversion ratio
## Blood Sample Collection and Biochemical Parameters
At 60 days of the trial, blood samples of 10 ml were collected from the jugular vein into sterile vacutainer tubes guaranteed free of any trace of heavy metals to harvest serum for biochemical studies. Between the hours of 8 and 9 a.m., blood samples were obtained. After 30 min at ambient temperature, blood samples were centrifuged at 3000 rpm for 15 min, and the sera were stored at − 20 °C until analysis.
Serum triiodothyronine (T3) and thyroxin (T4) concentrations were determined by radioimmunoassay (RIA) [49]. Serum proteins (total, albumin, globulin, and A/G ratio) and serum globulin concentrations were calculated by the difference between total protein and albumin concentrations. alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum creatinine, urea levels, cholesterol, and triglycerides were measured by using UV/visible spectrophotometer; test procedures were performed as per the manufacturer’s instructions (Diamond Diagnostic, Egypt), according to the method described by Young and Friedman [50].
Cd and Pb levels in serum samples were determined using atomic absorption spectrophotometry (Thermo Electron Corporation, model S4AA sys. USA) at a wavelength of 228.8 nm and 283.3 nm, respectively [51].
## Oxidative Stress Markers
The changes in MDA levels in the serum samples as an endpoint of lipid peroxidation were calculated by detecting the absorbance of thiobarbituric acid reactive substances at 532 nm [52]. Glutathione (GSH) levels were determined by measuring absorbance at 412 nm [53]. Estimation of protein carbonyl content (PCC) was based on the reaction between 2,4-dinitrophenylhydrazine (DNPH) which was analyzed spectrophotometrically at an absorbance of 370 nm. The above-mentioned parameters were measured using a commercially available kit following the manufacturer’s instructions.
## Statistical analysis
The data were processed using the SPSS version 22 computer program (Inc., 1989–2013), and the results are displayed as means ± SE for each treatment, with a one-way ANOVA analysis of variance LSD test conducted to test for a significant difference between treatments at p ≤ 0.01. The principal component analysis (PCA) method published by Liu et al. [ 54] was used for factor analysis.
## Thyroid Hormones and Biochemical Parameters
Before exposure, mean blood Cd and Pb levels were below the detection limit (5 µg l−1) in all groups. Serum Cd levels in G3 and G5 (0.173 ± 0.02 and 0.076 ± 0.01 mg l−1, respectively) were significantly different (P ≤ 0.001). While serum Pb was identified at the following levels in G4 and G6, respectively, with a statistical variation (P ≤ 0.001) of 0.331 ± 0.02 and 0.160 ± 0.01 mg l−1. Cd and Pb were not detected in either G1 or G2 (Fig. 1).Fig. 1Serum levels of Cd and Pb among the exposed groups. Differences between means having different superscripts (capital letters for Cd and small letters for Pb) are statistically significant (P ≤ 0.001) Thyroid hormones (T3 and T4) results are shown in Table 3. The Cd and Pb exposure resulted in a significant (P ≤ 0.0001 and 0.01, respectively) reduction of T3 and T4 serum levels than control. Administration of dietary N. oculata $4\%$ to Cd- and Pb-exposed rams caused significant (P ≤ 0.0001) improvement of T3 and T4 levels when compared to Pb- and Cd-exposed rams. In regard to biochemical parameters, data shown in Table 3 demonstrate significant (P ≤ 0.05) increases in creatinine (mg dl−1), urea (mg dl−1), ALT (U L−1), AST (U L−1), cholesterol (mg dl−1), and triglycerides (TG) (mg/dl) in Cd- and Pb-intoxicated rams, as compared to control. On the other hand, significant (P ≤ 0.001) hypoproteinemia associated with hypoalbuminemia was observed in Cd- and Pb-administered groups, as compared to the control rams. N. oculata $4\%$ as a Cd or Pb toxicity mitigator. Treated animals with N. oculata $4\%$ in combination with Cd (G5) or Pb (G6) demonstrated a significant (P ≤ 0.05) improvement in all the later evaluated parameters when compared to rams exposed to heavy metal toxicity (G3 and G4). Concerning the effect of N. oculata $4\%$, as compared to the negative control group, non-significant changes were recorded in all examined parameters except T4, total protein, albumin, and globulin that were significantly increased than the control. Moreover, rams in G2 (Nanno $4\%$) had the highest significant (P ≤ 0.01) serum total protein, albumin, and globulin levels among the experimental groups. The levels of urea and creatinine were significantly (P ≤ 0.05) reduced in the N. oculata $4\%$ group (G2) than the control (G1).Table 3Thyroid hormones and biochemical parameters among the treated groupsTreatments ($$n = 6$$/group)ControlNannoCdPbNanno + CdNanno + PbSig. P ≤ T3 (ng/ml)0.91a ± 0.030.91a ± 0.020.41c ± 0.010.46c ± 0.030.71b ± 0.040.74b ± 0.030.0001T4 (ng/ml)9.08b ± 0.310.8a ± 0.453.99d ± 0.144.98d ± 0.37.03c ± 0.377.09c ± 0.340.01Creatinine (mg/dl)1.63c ± 0.071.32d ± 0.032.33a ± 0.152.07ab ± 0.211.82bc ± 0.051.59c ± 0.060.05Urea (mg/dl)21.04d ± 0.9917.74e ± 0.8249.22a ± 1.0544.66b ± 1.2832.4c ± 1.7429.4c ± 1.560.05ALT (U/l)19.15c ± 0.5518.28c ± 0.4834.48a ± 0.9132.78a ± 0.9427.08b ± 0.5825.13b ± 0.690.0001AST (U/l)31.28c ± 0.3429.8c ± 0.547.57a ± 0.9945.05a ± 1.2936.52b ± 0.9834.74b ± 1.220.01Cholesterol (mg/dl)45.58c ± 1.2739.11c ± 0.96100.09a ± 5.9596.67a ± 5.4666.68b ± 2.7970.2b ± 2.760.0001TG (mg/dl)32.23c ± 1.2426.89c ± 0.4871.73a ± 2.9468.21a ± 4.4849.13b ± 2.3448.52b ± 2.20.0001Total protein (g/dl)6.8b ± 0.117.9a ± 0.064.89d ± 0.134.94d ± 0.166.19c ± 0.166.17c ± 0.160.001Albumin (g/dl)4.66b ± 0.085.29a ± 0.072.71c ± 0.22.87c ± 0.194.31b ± 0.094.28b ± 0.10.0001Globulin (g/dl)2.14b ± 0.062.6a ± 0.072.18b ± 0.172.07b ± 0.171.88b ± 0.141.89b ± 0.150.01A/G ratio2.2a ± 0.0772.06a ± 0.0831.31b ± 0.1941.44b ± 0.1612.37a ± 0.2012.34a ± 0.20.01Differences between means within the same row having different superscript letters are statistically significant at P ≤ 0.0001, 0.01, 0.05
## Oxidative Stress
The administration of Cd (G3) and Pb (G4) to experimental rams resulted in a significant (P ≤ 0.05) increase in MDA (nmol/ml) and PCC (nmol/ml) serum contents while reduced (P ≤ 0.05) GSH (nmol/ml), as compared to the control rams (Fig. 2). The addition of dietary N. oculata $4\%$ to Cd and Pb intoxicated rams (G5 and G6) resulted in significant (P ≤ 0.05) amelioration of MDA, PCC, and GSH levels in comparison to Cd (G3)- and Pb (G4)-exposed groups. Concerning the effect of N. oculata $4\%$ as compared to the negative control group, non-significant changes were recorded in PCC, on the other hand, significant (P ≤ 0.05) differences in MDA and GSH were observed (Fig. 2).Fig. 2Oxidative stress markers among the treated groups. ( A) Malondialdehyde (MDA) nmol/ ml. ( B) Protein carbonyl content (PCC) nmol/ml. ( C) Glutathione (GSH) nmol/ml. Differences between means having different superscript letters are statistically significant (P ≤ 0.05)
## Principal Component Analysis
To clarify the corrective effects of N. oculata $4\%$ administration on Cd or Pb toxicity in an interactive manner, principal component analysis (linear correlation) was carried out on the former biomarkers (Fig. 3 A and B). Concerning the interactive effect with Cd or Pb toxicity, the parameters yielded three principal components (PC) that explained $87.2\%$ of the total variances (Fig. 3A). PC1 had positive loading with both Cd and Pb, which correlated in a strong loading with Urea > ALT > MDA > PCC > cholesterol > AST > TG > creatinine > FCR were reported. Algae, on the other hand, supported the following parameters: total protein, albumin, GSH, T3, T4, and weight gain, which were directly correlated with them in the form of strong loadings (Fig. 3B).Fig. 3Principal component analysis of cadmium or lead intoxication and N. oculata $4\%$ supplementation with growth performance, liver and kidney markers, biochemical parameters, and antioxidant markers in Barki rams. ( A) Component matrix: the explained variance % was 70.588, 9.105, and $6.333\%$, while cumulative % was 70.588, 79.693, and $86.026\%$ for components (PC1; PC2, and PC3, respectively). ( B) Component plot: represent the principal component 1 variable interaction
## Discussion
Rams were chosen as the experimental animals in this study because they are an excellent model for ruminants and the ease of blood collection. Sheep, on the other hand, are more likely to expose to heavy metals since they graze herbage so close to the ground. As a result, it is hard to eliminate heavy metal exposure, and supplementation with N. oculata $4\%$ instead may alleviate this impact in addition to its nutritional qualities. Furthermore, Altomonte et al. [ 41] hypothesized that ruminants would be appealing targets for this novel feedstuff since they can use non-protein nitrogen contained in algae and break down algal cell walls. Despite the potential benefits of using microalgae in ruminant nutrition, our current knowledge of the applications is limited. As a direct consequence, this study was conducted to investigate the reinforcing effect of N. oculata $4\%$ as a novel natural feed supplement on rams’ performance, thyroid hormones, and some biochemical and antioxidative parameters, as well as its mitigating role against these parameters. The primary drawback is the high manufacturing cost [55], which makes them an uncompetitive feed choice [42]. The scenario may be altered shortly as a result of technological advancement [55].
Herein, rams were given an oral daily low metallic salt to repeat a feed exposure to detect Cd and Pb concentrations in the blood without causing clinical intoxication. The specified dose of 1 mg Cd and 5 mg Pb kg−1 body weight (33.6 kg) is comparable to the contaminated ruminant feed dose of 22.4 mg Cd and 112 mg Pb kg−1, the dose for a daily consumption of 1.5 kg of forage in sheep. It is worth noting that the absorption of Pb as acetate and carbonate is minimal and may reach $10\%$ of intake due to the formation of insoluble complexes of lead in the gastrointestinal tract that are excreted with feces [56]. As a result, the predictable absorbable Cd and Pb dose lay within the tolerated concentrations in animal feed (0.5 Cd and 30 Pb mg kg−1), according to Liu [57]. Our analysis demonstrated that there were no external indicators of apparent toxicity in the exposed group. Clinical symptoms are not usually associated with blood element concentrations [56]. Furthermore, sheep may take up to 5 Pb mg kg−1 body weight for up to a year without showing any clinical signs [58]. Also, the lethal dose of Cd shows visible signs when the diet contains > 40 mg of Cd kg−1 of DM [58]. Cd toxicity is mostly determined by the organism’s mineral inflow, exposed dose, a chemical form of the metal, exposure time, species, and age [59].
According to the results of this study, Cd or Pb had a negative impact on sheep performance, which was confirmed by an inverse association between heavy metal administration and weight gain. This finding is consistent with the findings of Lane et al. [ 7] who found that some co-exposed cattle to Cd and Pb had the poor general condition. Concurrently, various publications have underlined Cd’s deleterious effects on growth rates in growing ruminants [60, 61]. Cd-exposed animals were recorded to exhibit a decrease in growth, weight gain, and food intake [10]. Pb intoxication is one of the most common types of toxicity in pastured animals [62], and its toxicity has been recorded in domestic animals; ruminants demonstrated greater settling and absorption of Pb in the reticulum; therefore, the absorbed Pb displaces some bivalent cations such as calcium and affects enzyme function [57].
The detected levels of Cd and Pb in the serum of the heavy metals’ corresponding exposed groups were only recorded after 60 days in low concentrations, which might be attributable to inadequate absorption of these elements from the gastrointestinal tract. Oral Cd and Pb absorption in the sheep is as low as $5\%$ and $1.3\%$, respectively [63, 64]. The normal Pb level in bovine blood is 0.05 to 0.25 mg l−1 [65]. Intoxicated animals’ Pb levels in the blood can be restored to normal, but it requires time. This duration may range between 68 and 266 days, according to Miranda et al. [ 66]. This difference in returning to normal blood Pb levels (˂ 0.050 mg l−1) could be due to variation in lead absorbed and its particle size [66].
Regarding the significant decrease in serum T3 and T4 concentrations due to exposure to both elements, thyroid dysfunction may be related to structural damage of thyroid follicular cells caused by Cd and Pb accumulation in the thyroid gland, resulting in subclinical hypothyroidism [45, 46]. Yoshizuka et al. [ 67] hypothesized that Cd accumulating in the mitochondria of thyroid follicular epithelial cells may disrupt oxidative phosphorylation of this organelle and that the loss of energy supply may have inhibited thyroid hormone synthesis and release. Similarly, Pb can imply a decrease in T4 production and/or secretion from thyroid follicular cells [45, 46]. T3 is the active form of thyroid hormone; nevertheless, it accounts for just $20\%$ of the hormone released; most of T3 is synthesized by the peripheral conversion of T4 to T3, whereas T4 accounts for more than $80\%$ of the hormone secreted [68]. The peripheral deiodination of T4 to T3, which occurs primarily in the liver, is dependent on the activity of 5′-monodeiodinase (5′-D) [69, 70]. Studies have also found that Cd and Pb interfere with thyroid function at both the glandular and peripheral levels by preventing the conversion of T4 to T3 [45, 46]. A significant decrease in thyroid hormones was reported after the dosage of Pb [71] in buffalo cows, meanwhile, Zongping et al. [ 72] observed an increase in thyroid hormones of the sheep had high blood Pb concentrations. Such change in thyroid hormones may be related to high dosage and long duration of exposure to Pb [24]. Similarly, hepatic pathology affects serum thyroid hormone concentrations as a result of the impacts on peripheral enzyme pathways [73]. As a result, a partial drop in serum T3 concentrations in Cd and Pb-treated sheep may be associated with hepatic dysfunction [45, 46].
The most plausible explanation for the increase in hepatic enzymatic activities and renal markers is that Cd and Pb have detrimental impacts on liver and kidney tissues, releasing intracellular enzymes into the bloodstream [2], as well as increased cellular metabolic rate, irritability, and liver damage [74]. High AST and ALT activities are associated with increased liver microsomal membrane fluidity, free radical production, and liver tissue alteration [75]. This is supported by the significantly positive loading of Cd and Pb with serum former parameters as shown in PCA. Badiei et al. [ 45] reported increased levels of ALT and AST in experimentally Pb-poisoned Iranian rams [76] and confirmed comparable results in the Merino sheep. Similar outcomes were observed after oral administration of Pb in goats [77] and sheep [78].
The liver is considered one of the body’s key metabolic organs, regulating and maintaining lipid homeostasis [79]. As a result, increasing blood lipid levels could be attributed to increased lipoprotein production or reduced lipoprotein clearance [80]. The current study revealed a significant increase in cholesterol (mg dl−1) and TG (mg dl−1) levels in serum samples of Cd-intoxicated sheep, which is consistent with the findings of Chowdhury et al. [ 81] who found a significant increase in rats treated with Cd chloride, as well as alterations in the lipid profile and total cholesterol in Cd-administered animals. As a sequence, the high serum lipid levels could be attributed to increased synthesis or impaired clearance of lipoproteins. This result could be explained by the impairment of liver function induced by an imbalance in the antioxidant defense system in Cd-intoxicated rats; the Cd toxic state lowered HDL synthesis in the liver [82]. Furthermore, Cd toxicity causes a variety of derangements in lipid metabolic and regulatory processes, which leads to dyslipidemia, the most common metabolic complication observed in heavy metal toxicity, which is characterized by distinct changes from a normal plasma lipid and lipoprotein profile [83]. A similar profile as in the Cd-exposed group was observed in the Pb-exposed group; however, decreased clearance of lipoproteins may occur as a result of changes in the cell-surface receptors for lipoprotein [84] or as a result of suppression of hepatic lipoprotein lipase activity [85]. Furthermore, Pb has been demonstrated to inhibit the activity of cytochrome P450 [86], which can restrict the production of bile acids, which is the major pathway for cholesterol removal from the body.
In the present study, the significant decrease in serum proteins of Cd- or Pb-exposed groups compared could be induced by several pathological processes caused by heavy metals, including plasma dissolution, renal damage, and protein elimination in the urine, a decrease in liver protein synthesis due to hepatic damage, and changes in hepatic blood flow and/or hemorrhage into the peritoneal cavity and intestine [24]. Prabu et al. [ 87] found that Cd-exposed rats had reduced plasma total protein, albumin, and globulin levels. Similarly, when lambs were exposed to various levels of Pb, similar outcomes were obtained [88].
Regarding serum oxidative stress markers in the present study, there was a positive significant correlation between Cd and Pb exposure and MDA level. A significant increase in serum MDA levels of Pb-exposed group is in accordance with those previously mentioned by Kanter et al. [ 89] and Bayoumi et al. [ 90]. Such elevation could be attributed to Pb-induced lipid peroxidation, and the strong positive correlations between serum MDA and Pb administration [91]. A significant reduction in GSH was observed in the Cd- and Pb-exposed groups, as well as a significant negative correlation between them. These findings are consistent with those documented by Oraby et al. [ 11]. This result augmented the existence of oxidative stress whereas, the oxidative-stress-caused damage of macromolecules other than lipids; as a result, ROS can damage multiple biological molecules [92], and indices of lipid peroxidation may never be adequate markers of cellular damage caused by oxidative stress [93]. For this reason, the protein carbonyl content (PCC) in blood was evaluated as a marker of oxidative protein damage [92], as it is universally recognized as a gold standard for identifying protein oxidation [94]. It is an irreversible oxidative protein modification that is assumed to be an early indicator of protein oxidative stress-related disorders [95]. Metal-catalyzed oxidation of lysin, proline, arginine, and threonine residues, direct oxidation of tryptophan, and reactive lipid peroxidation products of cysteine, histidine, and lysine can all result in the formation of protein carbonyls [95]. The current study’s findings of a negative correlation between serum albumin and PCC levels in the Cd- and Pb-exposed groups could support the well-established theory that a low serum albumin level indicates the presence of systemic inflammation and oxidative stress [96].
Herein, a $4\%$ N. oculata-supplemented group compared to a control group showed no effect on serum T3, ALT, AST, cholesterol, and TG. Furthermore, these findings indicate normal activity and low impacts on liver function, showing the superior safety of feeding N. oculata to rams. The results also show the unaffected release of triglyceride-rich lipoproteins into the lymphatic system, which is consistent with the results of unaffected daily bodyweight reduction. Furthermore, the prior results are consistent with those published by Kholif et al. [ 42]. Rams in the microalga-supplemented group had the lowest significant MDA content and the highest GSH level among the treated groups. *In* general, *Nannochloropsis is* a rich source of proteins and lipids with an excellent fatty acid profile; the consumption of eicosapentaenoic acid (EPA) and other polyunsaturated fatty acids (PUFAs) is the most essential element of value for this microalga, which is supplemented by a significant contribution of other anti-oxidant components with high biological activity, including polyphenols, carotenoids, and vitamins [34].
The role of N. oculata $4\%$ supplementation to mitigate the Cd or Pb exposure was pronounced, with decreasing serum levels of both elements and significant improvements in weight gain and FCR reported in these groups when compared to heavy metal-exposed groups. A possible explanation is that microalga has accelerated extracellular passive adsorption (biosorption) and slow intracellular positive dispersion and buildup (bioaccumulation) with heavy metals, in furthermore to cell polymeric substances like peptides and exopolysaccharides with uronic groups; the cell wall of microalga is primarily composed of polysaccharides (cellulose and alginate), lipids, and organic proteins, which provide many functional groups (such as amino, hydroxyl, carboxyl, phosphate, imidazole, sulfonate, thiol, and others) capable of binding heavy metals [97].
Considering that *Nannochloropsis is* a high source of omega-3 fatty acids, particularly eicosapentaenoic acid (EPA) [40], there may be a link between Nannochloropsis supplementation and thyroid hormone levels, as hypothesized by Makino et al. [ 98], who claims that administration of EPA-E prevents a decrease in thyroid hormone levels, as omega-3 (polyunsaturated fatty acid (PUFA), containing EPA) controls thyroid cell activity via two major processes: signal transduction channel modification by modifying membrane fatty acid composition; and fast, direct stimulation of gene transcription. Additionally, N. oculata $4\%$ supplementation, significantly increased both T3 and T4 in Cd- or Pb-exposed groups with microalga supplementation, as compared to the heavy metal exposed groups. The current study performed PCA to analyze the detailed interaction between thyroid hormones and liver enzymes, which demonstrated a highly significant inverse correlation; also, microalga exhibited a direct relationship with thyroid hormones and an inverse association with liver enzymes. Moreover, similarly, hepatic pathology affects serum thyroid hormone concentrations as a result of the impacts on peripheral enzyme pathways [73]. As a result, a partial drop in serum T3 concentrations in Cd- and Pb-treated sheep may be associated with hepatic dysfunction [45, 46]. Also, the correction of Cd or Pb exposure by N. oculata $4\%$ supplementation was supported by the positive correlation of microalga supplementation with serum protein parameters and the negative correlation with liver enzymes and kidney function markers; this finding is partially in agreement with Aboulthana et al. [ 99] and Nacer et al. [ 100] who stated that a diet supplemented with microalga N. gaditana and N. oculata provided good protection against renal dysfunction in diabetic rats because this alga has great potential to normalize the contents of serum uric acid, urea, and creatinine in rats with diabetes. Also, Nacer et al. [ 100] added that N. gaditana caused a reduction in the activity of AST and ALT enzymes, which indicated their hepatoprotective effect.
Likewise, N. oculata’s hypocholesterolemic action in heavy metal-exposed groups may be attributed to the inhibition of cholesterol absorption from the intestines [101] or suppression of oxidation and LDL-C uptake [102]. Also, N. oculata can change bile acid absorption and metabolism, or increase propionic acid generation as a result of the fermentation of the soluble fiber content in the algal residue with an increase in this short-chain fatty acid (SCFA), which also hindered hepatic cholesterol synthesis [103]. Furthermore, Markovits et al. [ 104] revealed that dietary fibers found in Nannochloropsis, particularly insoluble fibers, inhibit intestinal cholesterol absorption and have an anti-hypercholesterolemic effect [105]. It was also proven that N. gaditana can improve lipid metabolism [106].
MDA and PCC showed a significant decrease in Cd or Pb with microalga supplementation groups whereas GSH contents were significantly higher than those in heavy metal-intoxicated groups. Simultaneously, PCA analysis revealed that microalga supplementation was negatively correlated with MDA and highly correlated with the previously mentioned antioxidant markers, suggesting that the microalga ameliorate the disruption of anti-oxidative defense mechanisms, implying a potential therapeutic role. N. oculata contains a high content of -3 PUFAs (-linolenic, ALA, C18:3 3) [107] and eicosapentaenoic (EPA, C20:5 3) [108], indicating that PUFAs may significantly contribute to its antioxidant capacity. Furthermore, carotenoids from N. oculata have similar antioxidant activity [109].
## Conclusion
In conclusion, N. oculata as a feed supplement ($4\%$) improves renal activity (creatinine and urea), T4, and oxidative stress indicators as compared to the control group. The impact of Cd or Pb administration was observed in all the examined parameters, which were significantly different from the control. Animals exposed to Cd or Pb and supplemented with microalga significantly outperformed in the measured parameters than heavy metal-exposed groups. The administration of N. oculata ($4\%$) might help to mitigate the oxidative stress and endocrine disruptive induced by Pb and Cd exposure.
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|
---
title: Age-Related changes in the morphological features of medial column of the proximal
humerus in the Chinese population
authors:
- Zuhao Chang
- Zhengguo Zhu
- Wei Zhang
- Hua Chen
- Yujie Liu
- Peifu Tang
journal: Frontiers in Surgery
year: 2023
pmcid: PMC10020333
doi: 10.3389/fsurg.2023.1138620
license: CC BY 4.0
---
# Age-Related changes in the morphological features of medial column of the proximal humerus in the Chinese population
## Abstract
### Background
Age-related changes in the medial column (MC) of the proximal humerus have a major impact on fracture management; however, the changes in the morphological features remain unclear. This study aimed to investigate the age-related changes in the morphological features of MC and present the morphological grading.
### Methods
One hundred computed tomography (CT) images of the proximal humerus of 100 individuals (19–95 years) were retrospectively obtained. The individuals were categorized into five age groups to quantify the differences among different ages; the youngest group (18–44 years) served as the baseline group. Parameters of the morphological features were measured on CT images with multiplanar reconstruction based on an explicit definition of MC, including length, thickness, width, oblique thickness (DSM), humeral head diameter (DHM), and ratio (RSM) of DSM to DHM. The morphological grading of MC was presented based on the value of RSM deviating different standard deviations (SD) from the mean value in the baseline group.
### Results
Significant negative correlations were observed between age and the morphological parameters of MC (r ranged from −0.875 to −0.926; all $P \leq 0.05$), excluding DHM ($r = 0.081$, $$P \leq 0.422$$). Significant differences in the values of morphological feature parameters were detected among the five age groups (all $P \leq 0.001$). The highest mean values of morphological feature parameters were observed in the youngest group (18–44 years), which decreased gradually with increasing age until the lowest mean values were observed in the oldest group (≥90 years) (all $P \leq 0.05$). The morphological features of MC were categorized into three grades based on the value of RSM deviating 1.5 SD or 3 SD from the mean value in the baseline group.
### Conclusion
Our study shows that the parameter values of morphological features of MC decreased with increasing age. The morphological features of MC could be categorized into three grades. Our findings may provide a more comprehensive insight into age-related changes in the morphological features of MC that facilitate risk stratification and optimize the management of proximal humeral fractures.
## Introduction
The medial column (MC) is considered the osseous region of the posteromedial metaphysis of the proximal humerus (PH), which plays a crucial role in fracture management (1–3). MC reportedly develops remarkable changes with increasing age owing to the development of osteopenia or osteoporosis [4, 5]. These age-related changes in MC may be responsible for the increased risk of proximal humeral fractures (PHFs) and higher fracture severity [1, 4, 6, 7]. Furthermore, incompetent MC, i.e., fragile with impaired strength, due to advanced age is also associated with a higher risk of fracture complications (8–12). Conversely, elderly patients with severe osteopenia treated with integrity restoration of MC could achieve a satisfactory prognosis, similar to that of younger patients (13–16). Therefore, investigating the morphological features of MC and elucidating its age-related changes may be conducive to the management of PHFs. However, the specific definition of MC is inconsistent, and age-related changes in its morphological features remain unclear owing to a lack of studies.
The range of region of interest of MC varied in different studies, ranging between 20 and 40 mm below the humeral head based on subjective interpretations [4, 5, 17, 18]. The consequent conclusions may be limited given the subjective selection methods. Furthermore, microstructural assessments of the cortical or trabecular bone in the cadaveric bone have been used to analyze the age-related changes in MC; however, these might be insufficient to derive a comprehensive conclusion given the limited sample sizes and differences between ex and in vivo samples [4, 5, 19, 20]. A study on the in vivo imaging of postmenopausal females reported the regional differences in the cortical bone of MC; however, studies on individuals of other ages remain lacking [18]. Moreover, no morphological grading of MC based on its age-related changes—such as the Singh index used for the proximal femur—has been proposed (21–23). Therefore, this study aimed to investigate the age-related changes in the morphological features of MC using in vivo imaging based on an objective definition of MC and individuals with a broad age range.
## Study design
This is a cross-sectional study approved by the Ethics Committee of Chinese PLA General Hospital (No. S2021–021–01) and registered in Chinese Clinical Trial Register (ChiCTR2200059524). We retrospectively reviewed 681 CT images of PH of 628 adult individuals in the Picture Archiving and Communication Systems of our institution between December 2019 and December 2021. The demographic and clinical information was obtained from electronic medical records, and all personal records were anonymized prior to data analysis. Owing to the retrospective and anonymous nature of data collection, the requirement for informed consent was waived.
The CT images were obtained with a 256-slice multidetector CT scanner (Philips Healthcare, Amsterdam, Netherlands) and standardized protocol with high-resolution algorithm (120 KV, 0.675 mm slice thickness, 0.335 mm interlayer spacing, 0.8 mm reconstruction slice thickness, 1.0 mm reconstruction interlayer spacing, and 512 × 512-pixel matrix). For the acquisition of in vivo CT images of normal intact PHs, the CT images from individuals older than 18 years of age were included. Axial scanning ranged from more than 3 cm superior to the acromion to more than 3 cm inferior to the deltoid tuberosity. The exclusion criteria were as follows: [1] Prior fracture or surgery in PH; [2] bone diseases and skeletal abnormalities, including osteoarthritis, rheumatism, dysplasia, and deformity; [3] diagnosis of metabolic diseases or receiving treatment that could affect bone metabolism, such as Paget's disease and primary hyperparathyroidism; [4] diagnosis of cancer or other malignant diseases; and [5] history of smoking. Thus, 261 CT images of normal PHs from 261 individuals were obtained.
To further clarify and quantify the age-related changes in the morphological features of MC, age was categorized into five groups (24–26): Group I (18–44 years), Group II (45–59 years), Group III (60–74 years), Group IV (75–89 years), and Group V (≥90 years). Group I served as the baseline group. As the age distribution of individuals was skewed, stratified sampling was used to ensure equal sample sizes among the age groups to avoid the influence of different sample sizes among age strata on further morphological analysis. In total, 100 CT images from 100 individuals were included for analysis (20 individuals per stratum, including 10 women and 10 men; Figure 1). The mean age of individuals included in the study was 64.94 ± 21.22 years (ranges 19–95 years; 64.74 ± 21.22 years for women, and 65.14 ± 21.52 years for men).
**Figure 1:** *Flow chart of inclusion of computed tomography images of the proximal humerus. CT, computed tomography; PH: proximal humerus.*
## Definitions of parameters for morphological features of the mc
CT images with multiplanar reconstruction in this study were acquired using RadiAnt DICOM Viewer (version 4.6.5; Medixant, Poznan, Poland). Previous studies showed that the changes in cortical bone and trabecular bone both have an impact on the medial supporting role of MC, and the changes in trabecular bone might be an early sign of a decrease in the mechanical properties of MC [2, 4, 5, 18, 27]. Thus, MC was defined as a complex osseous region in the medial metaphysis in this study, comprising the endosteal longitudinal trabecular bone and adjacent cortical bone; the endocortical longitudinal trabecular region was used as a reference (Figure 2). The parameters of the morphological features of MC, defined according to previous studies, were as follows (Figure 2) [7, 28]: [1]The length of MC (DSI): The longest axial distance of the endocortical longitudinal trabecular region in the frontal reconstruction view, extending from the intersection of the endosteal surface of the trabecular bone and the epiphyseal line of the humeral head (point S) to the intersection of the endosteal surface of the trabecular bone and distal endocortical surface (point I).[2]The thickness of MC (DLM): The combined horizontal distance of endocortical longitudinal trabecular bone and cortical bone in the frontal reconstruction view, extending from the endosteal surface (point L) to the periosteal surface (point M) at the level of the inferior margin of the humeral head.[3]The width of MC (DAP): The distance between the intersections (point A, point P) of the endosteal surface of the endocortical longitudinal trabecular region and antero-posterior endocortical surfaces in the axial reconstruction view of the inferior margin of the humeral head.[4]The oblique thickness of MC (DSM): *The sum* of the distance between the endocortical longitudinal trabecular region and cortical bone contacting the epiphyseal line in the frontal reconstruction view, extending from point S to point M.[5]The diameter of the humeral head (DHM): The distance between the superior and inferior margins of the humeral head in the frontal reconstruction view, from point H to point M, which indicates the overall size of PH.[6]The ratio of MC (RSM): The ratio of DSM to DHM was calculated to facilitate the comparison of the age-related morphological changes of MC in individuals with different PH sizes.
**Figure 2:** *Measurement parameters of the morphological features of the medial column in the frontal and axial reconstruction view. FVR, frontal reconstruction view; ARV, axial reconstruction view.*
The measurements of parameters in all individuals were obtained independently by two orthopedic researchers (ZC and WZ) with experience in CT imaging analysis. The intraobserver reliability of the measurement parameters was assessed by repeating the measurements of parameters in all individuals twice randomly by the same researcher (ZC), at least 4 weeks apart. In addition, the measurements of parameters in all individuals were obtained independently by the second researcher (W.Z.) to assess the interobserver reliability of the measurements of parameters. All measurements of parameters in all individuals were obtained thrice, and the average values were taken for final analysis to avoid researcher bias.
The morphological features of MC were graded using RSM, as RSM was presented as a ratio that facilitates comparison of the morphological features of MC with different PH sizes. The morphological features of MC were graded based on the value of RSM deviating different standard deviations (SD) from the mean value of RSM in the baseline group (Group I), which was referred to as the morphological grading method of vertebral compressive fractures and the categorization method of bone mineral density [29, 30].
## Statistical analysis
The intra- and interobserver reliability of the morphological parameters were assessed using the intraclass correlation coefficient (ICC). The threshold for excellent correlation was set at 0.75 [31]. The Shapiro–Wilk test was used as the normality test of continuous variables. The correlation between age and morphological parameters was assessed using Spearman's correlation coefficient. Correlation strength was assessed to be strong at r > 0.7, moderate at 0.7 > r > 0.3, and weak at r > 0.3 [32]. The mean values of normally distributed continuous variables within the groups were compared using one-way analysis of variance, followed by the least-significant difference (LSD) post-hoc test for pairwise comparisons. Non-normal distribution data were analyzed using Kruskal–Wallis H test; Bonferroni correction was used in the pairwise comparisons. Categorical variables were presented as constituent ratios and analyzed using the χ2 test or Fisher's exact test. All statistical analyses were conducted using IBM SPSS Statistics 26.0 (IBM Corp., Armonk, NY, United States), with a P value < 0.05 indicating statistical significance.
A priori power analysis (α = 0.05, β = 0.2, 2-tailed) was performed using PASS (version 15.0.5; NCSS, Kaysville, United States) to achieve a medium to large correlation coefficient (ρ ≥ 0.3), which was expected for the correlation between the measured parameters and age. A minimum sample size of 84 individuals was required in the present study.
## Reliability of the measurement parameters of morphological features of MC
The intra- and interobserver reliability showed almost perfect agreement for all measurement parameters of the morphological features in the present study (ICC ranged from 0.814 to 0.952; all $P \leq 0.001$). Detailed results are presented in Table 1.
**Table 1**
| Parameter | Intraobserver reliability | Intraobserver reliability.1 | Interobserver Reliability | Interobserver Reliability.1 |
| --- | --- | --- | --- | --- |
| Parameter | ICC (95% CI) | P value | ICC (95% CI) | P value |
| DSI | 0.826 (0.786, 0.887) | <0.001 | 0.834 (0.790, 0.869) | < 0.001 |
| DLM | 0.905 (0.859, 0.936) | < 0.001 | 0.910 (0.869, 0.931) | <0.001 |
| DAP | 0.814 (0.756, 0.882) | <0.001 | 0.819 (0.777, 0.852) | <0.001 |
| DSM | 0.908 (0.866, 0.937) | <0.001 | 0.944 (0.918, 0.962) | <0.001 |
| DHM | 0.923 (0.888, 0.948) | <0.001 | 0.952 (0.928, 0.967) | <0.001 |
## Age-related changes in morphological features of MC
Significant negative correlations were observed between age and the value of most parameters (including DSI, DLM, DAP, DSM, and RSM [all $P \leq 0.001$], but not DHM [$$P \leq 0.422$$]). The parameter values of the morphological features for the study individuals, and correlations between age and parameters, are provided in Table 2 and Figure 3. Difference in morphological features of medial column in five age groups is showed in Figure 4. Comparisons among multiple groups revealed differences in the parameter values of the morphological features of MC among the groups (including DSI, DLM, DAP, DSM, and RSM, all $P \leq 0.001$; Table 2). No significant difference was observed in DHM among the groups ($$P \leq 0.921$$; Table 2). Pairwise comparisons revealed that the highest mean parameter values of the morphological features were observed in the youngest group (18–44 years), while the oldest group (≥90 years) had the lowest mean parameter values (including DSI, DLM, DAP, DSM, and RSM, all $P \leq 0.001$ [Group I vs. Group V]). The values and differences in the parameters of the morphological features among the age groups are shown in Table 3 and Figure 5.
**Figure 3:** *The correlations between age and the values of the parameters of the morphological features. (A) DSI; (B) DLM; (C) DAP; (D) DSM; (E) DHM; (F) RSM.* **Figure 4:** *Difference in morphological features of medial column for representative individuals of five different age groups in frontal reconstruction view. (A) Group I; (B) Group II; (C) Group III; (D) Group IV; (E) Group V.* **Figure 5:** *The values of the parameters of the morphological features of the medial column in different age groups. (A) DSI; (B) DLM; (C) DAP; (D) DSM; (E) DHM; (F) RSM.* TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3
## Morphological grading of MC
The highest mean value of RSM was observed in the baseline group (RSM = 51.17 ± $7.22\%$ in Group I), and it was set as the reference value for morphological grading. Between-grades comparisons were performed to select the effective thresholds, which could distinguish between the different morphological grades. The threshold values of grading were set at different times of SD below the mean value of RSM in the baseline group (from 0.5SD to 4SD with 0.5SD interval). Finally, 1.5SD and 3SD below the mean value of RSM in Group I (RSM = $40.34\%$, RSM = $29.51\%$) were selected as the thresholds for the morphological grading of MC. The specific morphological grading was illustrated as follows: [1] Grade I: RSM > Mean - 1.5SD (RSM >$40.34\%$); [2] Grade II: Mean - 1.5SD ≤ RSM ≤ Mean - 3SD ($29.51\%$ ≤ RSM ≤ $40.34\%$); and [3] Grade III: RSM < Mean - 3SD (RSM <$29.51\%$). There were significant differences in the value of morphological features parameters of MC among the three grades (including DSI, DLM, DAP, DSM, and RSM, all $P \leq 0.05$; Table 4), while no significant difference was observed regarding sex ($$P \leq 0.329$$; Table 4).
**Table 4**
| Unnamed: 0 | Grade I (n = 19) | Grade II (n = 35) | Grade III (n = 46) | P value |
| --- | --- | --- | --- | --- |
| Age (yr) | 33.37 ± 8.91 (29.07–37.66) | 59.17 ± 10.99 (55.40–62.95)§ | 82.37 ± 10.49 (79.26–85.48)§‖ | <0.001* |
| Sex (Female/male) | 11/8 | 14/21 | 25/21 | 0.329† |
| DSI (mm) | 29.40 (25.30, 36.40) | 17.10 (15.20, 20.40) | 9.82 (8.52, 10.88)§‖ | <0.001‡ |
| DLM (mm) | 16.20 (14.10, 18.80) | 11.80 (10.40, 13.00) | 7.19 (6.08, 8.47)§‖ | <0.001‡ |
| DAP (mm) | 17.70 (16.20, 22.00) | 14.80 (13.80, 16.40) | 10.15 (9.04, 11.60)§‖ | <0.001‡ |
| DSM (mm) | 22.50 (20.74, 27.60) | 16.64 (15.22, 18.31) | 10.11 (8.63, 11.77)§‖ | <0.001‡ |
| DHM (mm) | 44.68 ± 3.81 (42.85–46.52) | 45.87 ± 3.53 (44.66–47.08) | 44.05 ± 2.95 (43.18–44.93) | 0.071* |
| RSM (%) | 51.16 (47.49, 57.38) | 36.15 (33.12, 39.92) | 23.39 (20.22, 25.84)§‖ | <0.001‡ |
## Discussion
This study investigated the age-related changes in the morphological features of MC and presented morphological grading based on an objective definition of MC that covers a broader age range. We identified and quantified the age-related changes in the morphological features of MC with an explicit definition. The parameter values of the morphological features decreased in multiple dimensions with increasing age. The highest mean values of the parameters of the morphological features (DSI = 31.03 mm, DLM = 16.33 mm, DAP = 18.91 mm, DSM = 23.16 mm, RSM = $51.17\%$) were observed in young adults (18–44 years) and decreased incrementally with increasing age until the lowest mean values (DSI = 8.39 mm, DLM = 6.67 mm, DAP = 9.35 mm, DSM = 9.32 mm, RSM = $20.76\%$) were observed in individuals with advanced age (≥90 years). Additionally, a morphological grading of MC was presented based on the thresholds with 1.5 SD and 3 SD below the mean value of RSM of young adults (RSM = $40.34\%$, RSM = $29.51\%$; Group I: 18–44 years).
The importance of MC for PHF management is widely acknowledged; however, there is no consensus regarding the definition of MC. Sprecher et al. [ 4] and Wang et al. [ 18] defined MC as the trabecular or cortical region using the humeral head height as a reference. Helfen et al. [ 5] chose a certain range of high-resolution peripheral quantitative CT scans (150 sections). Russo et al. [ 17] selected 20–25 mm long medial metaphysis without elucidating the anatomical rationale. However, the definitions of MC in the aforementioned studies varied by using certain distances defined subjectively as the references rather than the morphological features of MC. Additionally, the cortical or trabecular bone alone may not fully account for age-related changes in MC, as MC is affected by cortical and trabecular bone loss, which may be an early sign of a decrease in the mechanical properties of MC [4, 5, 18, 27]. Therefore, using the endocortical longitudinal trabecular region as a reference, we defined MC as the osseous region in the medial metaphysis combining the endocortical trabecular bone and adjacent cortical bone, that is dynamic with age. This definition, which is explicit with a reasonable anatomical rationale, could comprehensively reflect the age-related changes in MC.
In the present study, all values of the parameter of the morphological features of MC decreased with increasing age. Compared with younger individuals (18–44 years), older individuals (≥60 years) exhibited more pronounced decrease in the parameter values of the morphological features, especially in the advanced age population (≥90 years). Our findings specifically quantify the age-related changes in MC regarding the morphological features and support a previous study that showed a considerable loss of cortical and trabecular bone of the metaphysis with increasing age [5]. This finding was also indirectly supported by a previous histomorphometric study, which showed that the bone density of the trabecular bone in the medial metaphysis decreased significantly in osteoporotic individuals. Additionally, we observed that parameter value decrease ‘of the morphological features tended to flatten after the age of 75 years. Similarly, Helfen et al. [ 5] showed that decreases in the values of the microstructural parameters of the metaphysis were not visible after the age of 80 years.
Morphological grading based on morphological feature changes could provide important references for fracture management. The Singh index—which describes the morphological changes in the trabecular bone of the femoral neck and head—has an important influence on risk stratification and prognosis prediction of fracture [21, 22, 33]. Previous studies have reported a lower Singh index common in patients with hip or subsequent contralateral fractures. Carow et al. [ 33] demonstrated that a Singh index ≤ 3 was a risk factor for in-hospital mortality (OR = 5.00). In this study, we presented morphological grading of MC which was modeled after the grading of bone mineral density and vertebral compressive fractures [29, 30], using the mean value of RSM of young adults (Group I: 18–44 years) as the reference. MC was categorized into three grades: < 1.5 SD (RSM > $40.34\%$), 1.5 SD – 3.0 SD ($29.51\%$ ≤ RSM ≤ $40.34\%$), and > 3 SD (RSM < $29.51\%$) below the mean value of RSM of young adults. Between-grades comparison verified the validation of the morphological grading preliminarily.
Our study has some limitations; first, the morphological grading presented in our study was based on the Chinese population with a limited sample size; thus, further validation is required to extend the validity of its results to other races. Second, the measurements of morphological parameters were obtained from the CT images acquired with only one CT scanner and scanning protocol. The clinical application of our method of measuring parameters needs to be verified using CT images acquired with different scanning protocols. However, this did not hinder the feasibility of the measurement method and comprehensive insight into MC in this study. Lastly, additional biomechanical and clinical studies are needed for further validation; however, this is beyond the scope of this study.
## Conclusion
This study presents the age-related changes in the morphological features of MC of PH and morphological grading, based on an explicit definition of MC. Our results show that the parameter values of the morphological features of MC decreased with increasing age. Thus, the morphological features of MC could be categorized into three grades. This study may provide a more comprehensive insight into age-related changes in the morphological features of MC that can facilitate risk stratification and optimize the management of PHFs.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
Owing to the retrospective and anonymous nature of this study, the requirement for informed consent was waived by the Ethics Committee of Chinese PLA General Hospital (No. S2021-021-01).
## Author contributions
ZC writing - review and editing, formal analysis, collection and analysis of the data, and study design. ZZ and WZ formal analysis, collection and analysis of the data, and study design. HC and YL designed the study, supervised and directed the study. PT supervised and directed the study. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
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|
---
title: Relationship between risk factors for impaired bone health and HR-pQCT in young
adults with type 1 diabetes
authors:
- Etienne B. Sochett
- Mary Dominicis
- Reza Vali
- Amer Shammas
- Yesmino Elia
- Rahim Moineddin
- Farid Mahmud
- Esther Assor
- Michelle Furman
- Steve K. Boyd
- Nina Lenherr-Taube
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10020337
doi: 10.3389/fendo.2023.1144137
license: CC BY 4.0
---
# Relationship between risk factors for impaired bone health and HR-pQCT in young adults with type 1 diabetes
## Abstract
### Objective
In type 1 diabetes, risk factors associated with impaired bone health contribute to increased risk of fracture. The aim of this study was to [1]: compare the high-resolution peripheral quantitative computed tomography (HR-pQCT) parameters of young adults with type 1 diabetes with those of healthy controls [2], identify sex differences, and [3] evaluate the association between diabetes and bone health risk factors, with HR-pQCT.
### Methods
This is a cross-sectional study in young Canadian adults with childhood onset type 1 diabetes. Z-scores were generated for HR-pQCT parameters using a large healthy control database. Diet, physical activity, BMI, hemoglobin A1C (A1C) and bone health measures were evaluated, and associations were analyzed using multivariate regression analysis.
### Results
Eighty-eight participants (age 21 ± 2.2 years; 40 males, 48 females, diabetes duration 13.9 ± 3.4 years) with type 1 diabetes were studied. Low trabecular thickness and elevated cortical geometry parameters were found suggesting impaired bone quality. There were no sex differences. Significant associations were found: Vitamin D (25(OH)D) with trabecular parameters with possible synergy with A1C, parathyroid hormone with cortical parameters, BMI with cortical bone and failure load, and diabetes duration with trabecular area.
### Conclusions
Our data suggests impairment of bone health as assessed by HR-pQCT in young adults with type 1 diabetes. Modifiable risk factors were associated with trabecular and cortical parameters. These findings imply that correction of vitamin D deficiency, prevention and treatment of secondary hyperparathyroidism, and optimization of metabolic control may reduce incident fractures.
## Introduction
In adults with longstanding type 1 diabetes, poor bone quality and increased fracture risk are documented [1, 2]. More recently, increased fracture risk was also reported in children with type 1 diabetes [3]. Metabolic control, severe hypoglycemia, and delayed puberty have all been implicated [4, 5], as have hyperinsulinemia, autoimmune inflammation, altered muscle function and vitamin D deficiency [6]. However, there remain significant gaps in the understanding of determinants and mechanisms underlying bone health impairment and increased fracture risk.
Skeletal health and future fracture risk in both children and adults are largely dependent on normal bone mass accrual in childhood and adolescence [7]. This process is dependent on several factors that may be jeopardized in individuals with type 1 diabetes such as nutrition, physical activity, and a normal hormonal and glycemic milieu [8]. Given that approximately $40\%$ of peak bone mass accrues in the 4 years surrounding peak height velocity [9] and significant changes in bone microarchitecture occur during puberty [10], the immediate post pubertal window provides an opportunity to better understand the net impact on bone health in the population who developed type 1 diabetes during childhood.
Bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA) in adults, children, and adolescents with type 1 diabetes has shown mildly reduced values when compared with healthy controls (11–13). However, a more recent study found no difference in BMD across the lifespan of indivduals with type 1 diabetes when compared with healthy controls [14], implying that BMD may not explain the increased fracture risk seen in this population.
High resolution peripheral quantitative computed tomography (HR-pQCT) is a diagnostic tool that assesses bone quality in terms of microarchitecture and volumetric BMD (vBMD) of both cortical and trabecular bone compartments, as well as an estimate of bone strength, which can independently predict fracture risk [15, 16]. Studies have identified parameters, such as total vBMD, trabecular vBMD, cortical thickness, trabecular thickness, trabecular number and trabecular separation as predictors of incident fragility fractures [16]. Similar data are not yet available for young adults.
Even so, there are a limited number of published studies using HR-pQCT in type 1 diabetes. Studies in both adult [17] and children [18, 19] with type 1 diabetes have shown similar findings of reduced trabecular bone geometry in the radius and tibia, where studies in children also show an increase in trabecular separation [18] and an increase in trabecular inhomogeneity [18, 19]. Decreased cortical bone geometry parameters have also been shown in both populations of patients with diabetes [17, 19, 20]. Reduced estimated bone strength (failure load) was observed in children with diabetes when compared with healthy controls [19, 20]. Lastly, skeletal microarchitecture was found to be altered in children with diabetes early in the course of disease and among those with higher average glycemia [20], with hemoglobin A1C (A1C) negatively correlated to bone microarchitecture parameters and estimated bone strength [19].
The aims of this study using HR-pQCT in young adults with type 1 diabetes were to [1]: compare the bone microarchitecture, volumetric BMD, bone geometry and strength of young adults with childhood onset type 1 diabetes with those of healthy controls [2], identify any sex-specific differences, and [3] determine whether diabetes duration, A1C, BMI, bone health related laboratory measures and physical activity were associated with HR-pQCT measures.
## Study population and study design
Eighty-eight young adults between the ages of 17 to 27 years with type 1 diabetes, who as adolescents had participated in the Adolescent Type I Diabetes Cardio-Renal Intervention Trial (AdDIT, EudraCT Number: 2007-001039-72, Trial Registration Number: ISRCTN91419926) [21] in Canada from 2009-2015, were recruited into a longitudinal, observational study evaluating cardio-renal-bone health in young adults with type 1 diabetes at the Hospital for Sick Children (SickKids) in Toronto, Canada. This report is a cross-sectional study of the bone health assessment completed from 2018-2019.
Inclusion criteria for this study were confirmed diagnosis of type 1 diabetes according to the Canadian Clinical Practice Guidelines, and participation in AdDIT [21]. Exclusion criteria were the presence of any significant medical or surgical disorders, such as eating disorders, untreated thyroid disease, celiac or Crohn’s disease; a history of delayed puberty; oligomenorrhea (fewer than 6 periods per year) or secondary amenorrhea; familial history of multiple fractures (> 3 long bone fractures); and other factors that might negatively impact bone health, including systemic glucocorticoid use, immobility, or BMI <18.5 kg/m2. Data was collected for diet, physical activity, and life-time fracture history for the purposes of identifying those participants who might have significant deviations from the reference range and who therefore would meet the exclusion criteria.
This study was approved by SickKids Research Ethics Board. All participants provided informed consent to participate in this study. The study was conducted in accordance with the Declaration of Helsinki.
## Demographic data and clinical characteristics
Demographic characteristics including age, sex, and ethnicity was collected from each participant. History of concomitant medications, smoking status, significant family and fracture history (collected by questionnaire), as well as diabetes details including disease duration, daily total insulin dose (units/kg/day), and route of administration were also collected. Height was measured using a wall-mounted stadiometer and weight was measured by an electronic scale. BMI was calculated using these measurements.
Pubertal status was previously assessed in AdDIT using a Tanner staging self-reported questionnaire [22] to confirm sexual maturity and the absence of delayed puberty. In 5 participants, it was unclear whether sexual maturity had been reached at the last AdDIT visit. In these 5 participants, normal hypothalamic-pituitary-gonadal axis was confirmed at study entry with measurement of LH, FSH, estradiol and testosterone respectively. Female participants were questioned regarding frequency of menstruation.
## Physical activity
Moderate to vigorous physical activity (MVPA) and electronic screen time for each participant was calculated using the Physical Activity Characteristics Questionnaire derived from the Canadian Health Measures Survey (CHMS) [23], and analyzed using Canadian Physical Activity Guidelines [24].
## Dietary assessment
Dietary intake for a single typical day was obtained at study entry. A trained research assistant (RA) collected this information using a structured and scripted telephone interview. Information pertaining to portion size estimates, recipes, brands, and food preparation methods were documented. To improve portion size estimates, a hand portion guide was provided to participants for reference and household measures and weights were commonly referred to when discussing the foods. Dietary intake collection from each participant was reviewed by a registered dietician (RD) which was then entered into the web-based Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24), Canadian Version [2018]. Foods not available on ASA24 were entered manually. Dietary calcium, vitamin D, and magnesium were calculated and reported as daily intake and % Dietary Reference Intakes (DRI) [25, 26].
## Biochemical investigations
Fasting blood and urine specimens were collected at baseline. The baseline visit occurred over a 12-15 month period. with no predominance of sample collection related to season.
% A1C, serum lipids, creatinine and urinary albumin, calcium and creatinine were measured in the Department of Pediatric Laboratory Medicine at SickKids using standard laboratory methods.
Early morning urine samples were collected during three consecutive days. Urinary albumin/creatinine ratio (ACR) was calculated from either the mean value of the three independent collections or from a blended sample.
Serum calcium (Ca2+), phosphate (PO4), and magnesium (Mg) were measured in the Department of Laboratory Medicine & Pathobiology, Toronto General Hospital, University Health Network in Toronto, Canada using standard laboratory measures. In the same laboratory, bone-specific alkaline phosphatase (bALP) was measured using ELISA plate reader on Molecular Devices SpectraMax Plus (using Tandem-R Ostase Immunoradiometric Assay) and serum cross-linked C-telopeptide (CTX) was measured using an immunoassay test on a Diagnostics e411 analyzer (Roche, *Elecsys beta* CrossLaps serum assay). 25-hydroxy vitamin D (25(OH)D), and intact parathyroid hormone (PTH) were measured using the Abbott Alinity i immunoassay system. CV for 25(OH)D: (20.20 nmol/L- $6.71\%$, 33.81 nmol/L- $5.57\%$, 83.33 nmol/L- $4.60\%$: CV for PTH: 4.4 pmol/L -$5.46\%$, 33.5 pmol/L- 4.97, 108.5 pmol/L- $5.05\%$). Determination of Vitamin D sufficiency, insufficiency and deficiency was based on the Global Consensus Recommendations on Prevention and Management of Nutritional Rickets [25].
## High-resolution peripheral quantitative computer tomography imaging
The non-dominant radius and tibia were scanned on a HR-pQCT scanner (XtremeCT II; Scanco Medical AG, Brüttisellen, Switzerland). The forearm and leg were immobilized in a carbon fiber cast. An anteroposterior scout projection of the scan site was acquired for positioning of the tomographic acquisition. A reference line was placed on the plateau of the distal radius or distal tibia. The scan started 9 mm and 22 mm for the radius and tibia, respectively, from the reference in the proximal direction, and spanned 10.2 mm in length. Images were reconstructed using an isotropic resolution of 60.7 μm [27], thus resulting in a stack of 168 parallel HR-pQCT [27, 28] slices. Total scan time was 2.0 min, with each acquisition resulting in effective dose of approximately 3 µSv. All scans were graded once by the same two technologists with regard to subject motion using the 5-level motion grading scale (best score as 1, worst score as 5) [27]. At the time of scanning, if motion artifacts with a score of three or more were observed, then as per recommendations, the scan was repeated. After repeat, no scans were graded level 4 (severe motion artifacts) or 5 (extreme motion artifacts), and therefore no scans were excluded.
## HR-pQCT image analysis
All scans were evaluated using the standard patient image evaluation protocol that was provided by the manufacturer, and previously described [29]. First, the periosteal contour was automatically derived and manually altered by the same two technologists when contours visually deviated from the periosteal boundary. The endocortical contour was automatically created using a series of automatic morphological operations to separate the trabecular and cortical volumes of interest resulting in the uncorrected contour (AUTO method). Then, when the contour visually deviated from the apparent endocortical margin, it was manually corrected (S-AUTO method).
Standard morphologic analysis was used to measure volumetric BMD for total (TtBMD; mg HA/cm3) and trabecular (TbBMD; mg HA/cm3) bone, as well as trabecular number (TbN; mm-1), separation (TbSp; mm), and thickness (TbTh; mm) [30]. An automated segmentation algorithm was used to obtain total and cortical cross-sectional areas (TtAr, CtAr, mm2), cortical volumetric BMD (CtBMD; mg HA/cm3), cortical thickness (CtTh; mm) and cortical porosity (CtPo; %) [29, 31].
FE analysis was applied to HR-pQCT images to estimate bone strength. As previously reported, FE meshes from the 3D HR-pQCT images were created using the voxel conversion approach. Then, uniaxial compression on each radius section was stimulated up to $0.7\%$ strain. A single homogenous tissue modulus of 8,748 MPa and a Poisson’s ratio of 0.3 were applied to all elements. A custom FE solver (FAIM, version 8.0, Numerics88 Solutions, Calgary, Canada) was used for this analysis [27].
HR-pQCT parameters expressed as Z-scores were generated from a normative HR-pQCT database covering both sexes across the adult lifespan at the distal radius and distal tibia (www.normative.ca) [28, 32]. The ethnicity distribution and BMI (our cohort: 24.7 kg/m2 (male) and 27.5 kg/m2 (female); normative cohort: 26.8 kg/m2 (male) and 26.1 kg/m2 (female)) was similar to the study cohort and their ages were well within the normative age range [32].
## Statistical analysis
Descriptive statistics were calculated for demographic, clinical, and laboratory test variables. Mean, standard deviation (SD), and range (minimum and maximum values) were provided for continuous variables. Frequencies and percentages were calculated for categorical variables. Participant HR-pQCT Z-scores were plotted on the HR-pQCT normative database to determine the distribution of the results relative to the normative dataset. In the absence of studies for HR-pQCT Z-scores that indicate a measure to be increased or decreased, we followed the recommendations provided by Peacock et al. [ 33]. This review suggested that when a primary outcome is continuous and the clinically meaningful difference is uncertain, statisticians and researchers may express the difference to be detected as a multiple of the SD. Using this standardised effect size approach, calculated as the difference in the means divided by the SD at baseline, a cut-off of ± 0.8 is described as a large effect size. Z-scores above +0.8 were therefore defined as high and Z-scores below -0.8 were defined as low in comparison with subjects in the normative database. Females and males were compared using the Wilcoxon unpaired two-sample t-test for continuous variables given that the data was not normally distributed. Pearson’s chi-square test was used for categorical variables.
The association between individual HR-pQCT Z-scores and BMI, type 1 diabetes duration, mean MVPA, mean 10-year A1C, 25(OH)D, PTH, serum calcium, CTX, and bALP were assessed using multivariable linear regression. These variables were selected from the literature as either known or likely to be associated with bone health. Variables not included in the multivariable regression association were tested using univariable regression. To investigate the association between BMI and cortical HR-pQCT parameters, analysis of variance (ANOVA) was used, with the cohort being divided in three groups: 18.5-24.9 kg/m2, 25.0-29.9 kg/m2, and > 30.0 kg/m2.
Results are reported as beta values, with p values < 0.01 considered significant, to account for multiple comparison. Results with p values > 0.01 and < 0.5 are provided in the text and in regression analysis tables to accomodate for the possibility of a type 1 error, but are not used in the interpretation of the results. All analyses were performed using SAS 9.4 software (SAS Institute Inc., Cary, N.C.)
## Clinical characteristics
The demographic and clinical characteristics of 88 young adults with type 1 diabetes (mean age: 21 ± 2.2 years; 40 males, 48 females) are shown in Table 1. Mean diabetes duration was 13.9 ± 3.4 years. Mean BMI was 26.2 ± 5.5 kg/m2,with $28\%$ of the cohort being either overweight ($33\%$ female, $22\%$ males) or obese $18\%$ ($22\%$ female, $12\%$ male). Mean BMI in females was significantly higher than in males (27.5 and 24.7, $$p \leq 0.03$$). The cohort was predominantly Caucasian and non-smokers. Mean ± SD % A1C from 8 visits over the preceding 10 years was 8.18 ± 1.03.
**Table 1**
| Unnamed: 0 | Study Population | Reference Range |
| --- | --- | --- |
| Number (N) | 88 | |
| Sex | | |
| Male | 40 (45.45%) | |
| Female | 48 (54.55%) | |
| Age (years) | 21.29 ± 2.16 | |
| Height (cm) | | |
| Male | 177.04 ± 8.11 | |
| Female | 164.37 ± 6.27 | |
| Weight (kg) | | |
| Male | 77.42 ± 13.60 | |
| Female | 74.51 ± 17.58 | |
| T1D Duration (years) | 13.91 ± 3.42 | |
| Daily Total Dose of Insulin (units/kg/day) | 0.82 ± 0.25 | |
| Insulin Regimen | | |
| Injection | 36 (40.91%) | |
| Pump | 52 (59.09%) | |
| HbA1c (%, mmol/mol) Study entry %mmol/mol > 10 years %mmol/mol | 8.20 ± 1.4666.11 ± 16.018.18 ± 1.0365.89 ± 11.24 | lt; 6%< 42mmol/mol |
| BMI (kg/m2) | | 18.5-24.925.0-29.9≥ 30 |
| Male | 24.70 ± 4.15 | 18.5-24.925.0-29.9≥ 30 |
| Normal | 22.25 ± 1.57 | 18.5-24.925.0-29.9≥ 30 |
| Overweight | 27.08 ± 1.36 | 18.5-24.925.0-29.9≥ 30 |
| Obese | 33.17 ± 2.65 | 18.5-24.925.0-29.9≥ 30 |
| Female | 27.52 ± 6.10 | 18.5-24.925.0-29.9≥ 30 |
| Normal | 22.36 ± 1.04 | 18.5-24.925.0-29.9≥ 30 |
| Overweight | 27.91 ± 1.12 | 18.5-24.925.0-29.9≥ 30 |
| Obese | 36.79 ± 4.53 | 18.5-24.925.0-29.9≥ 30 |
| Ethnicity | | |
| White | 62 (70.45%) | |
| Black | 7 (7.95%) | |
| Asian | 10 (11.36%) | |
| Other | 9 (10.23%) | |
| Smoking Status | | |
| No | 85 (96.59%) | |
| Yes | 3 (3.41%) | |
The cohort had experienced normal pubertal development, except for 5 subjects who had a mild delay in puberty. All were sexually mature at the time of study. Approximately $40\%$ had a history of one or more lifetime fractures, with most fractures being peripheral. Twenty-one participants experienced 1 fracture, 9 participants experienced 2 fractures, and 1 participant had 3 fractures.
No significant differences were identified between the males and females in age, ethnicity, diabetes duration, or % A1C. Total daily insulin dose was similar in males and females. Serum creatinine was within the reference range, although significantly higher in males than females (66.8 and 53.9 umol/l respectively, $$p \leq 0.001$$) (Table 2). Mean ± SD urinary ACR and eGFR were 1.38 ± 2.77 mg/mmol and 128.83 ± 11.0 ml/min/1.73 m2, respectively, for both male and female combined.
**Table 2**
| Variable | n (%) or mean ± SD | Reference Range |
| --- | --- | --- |
| Biochemical Measures | Biochemical Measures | Biochemical Measures |
| Total Number (N) | 88 | |
| Serum Ca2+ (mmol/L) | 2.38 ± 0.09 | 2.20-2.62 |
| Serum PO4 (mmol/L) | 1.39 ± 0.20 | 0.80-1.40 |
| Serum Mg (mmol/L) | 0.76 ± 0.06 | 0.70-1.10 |
| 25(OH)D (nmol/L) | 61.13 ± 34.08 | |
| Sufficient | 49 (55.68%) | >50 |
| Insufficient | 26 (29.55%) | 30-50 |
| Deficient | 13 (14.77%) | <30 |
| PTH (pmol/L) | 5.52 ± 1.92 | 2.0 – 9.4 |
| bALP (ug/L) | 22.19 ± 14.54 | |
| Male | 27.83 ± 18.87 | 8.2-32.8‡ |
| Female | 17.48 ± 6.80 | 5.9-30.5‡ |
| CTX (ng/mL) | 0.57 ± 0.31 | |
| Male | 0.70 ± 0.35 | 0.238-1.019 |
| Female | 0.45 ± 0.21 | 0.148-0.967 |
| Creatinine (µmol/L) | | |
| Male | 66.9 ± 10.61 | 51-89 |
| Female | 54.36 ± 8.17 | 40-69 |
| Urine Calcium : Creatinine Ratio | 0.20 ± 0.19 | <0.14 |
| Dietary Intake | Dietary Intake | Dietary Intake |
| Total Number (N) | 79 | |
| Daily Calcium Intake | | 163-1600* |
| Calcium (mg)† | 807.3 ± 680.62 | 163-1600* |
| % Calcium DRI | 80.7 ± 68.1% | 163-1600* |
| Daily Vitamin D Intake | | 4-645* |
| Vitamin D (IU) | 341.81 ± 548.32 | 4-645* |
| % Vitamin D DRI | 57.4 ± 91.9% | 4-645* |
| Daily Magnesium intake | | 70-984* |
| Magnesium (mg) | 312.38 ± 151.46 | 70-984* |
| % Magnesium DRI | 87.4 ± 42.5% | 70-984* |
Participants reported a mean of 55.8 ± 39.6 min/day of MVPA, which is higher than that of 31 minutes per day for 18-39-year-old healthy individuals reported in the 2017 Canadian Health Measures Survey [34]. A mean of 71.3 ± 36.1 minutes/day of screen time including use of computers, tablets, phones, and TV was recorded.
Daily dietary intake measures and related biochemical measures are presented in Table 2. The mean daily intake, range and DRI, respectively, for relevant nutrients were: calcium 807 mg (range 163-1600 mg), DRI $80.7\%$; vitamin D 341.8U (4-645 IU daily), DRI $57.4\%$; magnesium 312.4 mg (70–984), DRI $87.4\%$; phosphate 1288.6 mg daily (370-1815 mg) DRI $184.1\%$. Mean daily protein intake was 1.1 g/kg (0.31-3.7 kg; 21-270 g/day). Mean serum Ca2+ was within the reference range, while mean serum PO4 (1.39 ± 0.20 mmol/L) was at the upper end of the reference range. $44\%$ of the cohort was either vitamin D insufficient ($$n = 26$$) or deficient ($$n = 13$$). PTH levels were mainly within the reference range of 2.0-9.4 pmol/L. It is recognized that different immunodiagnostic ostease assays may provide different reference ranges for bALP. However, using the $95\%$ RI for bALP provided from the age- and sex-specific reference ranges of the immunodiagnostic assay [35], our subjects were well within this reference range. CTX levels were also within the reference range.
## HR-pQCT measures compared with a normative database
HR-pQCT Z-scores for tibia and radius are presented in Figure 1. At the radius, cortical BMD (Z-score + 2.02) and cortical thickness (Z-score +0.93) were high and total area (Z-score -0.87) and trabecular area (Z-score -0.97) were low, in comparison with healthy controls. Trabecular thickness (Z-score -0.64) and trabecular BMD (Z score -0.64) were at the lower end of the defined range. At the tibia, trabecular thickness (Z-score -0.85) was low. The estimated bone strength (failure load) was similar to healthy controls and no sex-specific differences were found in any HR-pQCT Z-scores. Raw data of HR-pQCT measures are summarized in Supplementary Table 1.
**Figure 1:** *HR-pQCT parameters compared to Normative for tibia (A) and radius (B).*
## ANOVA:cortical HR-pQCT measures and BMI
As demonstrated in Supplementary Table 3, BMI was found to be significantly associated with a number of cortical parameters: cortical area in both the radius ($$p \leq 0.0159$$) and tibia ($$p \leq 0.0001$$), both of which were in the defined Z-score normal range; cortical thickness in the tibia($$p \leq 0.0148$$), which is in defined normal Z-score range; and cortical porosity in the tibia ($$p \leq 0.0034$$), with a borderline high Z-score, as shown in Figure 1. BMI was not associated with cortical vBMD (high Z-score).
## Multivariate regression
The significant associations ($p \leq 0.01$) between HR-pQCT measures, expressed as Z-scores, and diabetes and bone health-related measures are summarized in Table 3 (radius) and Table 4 (tibia). BMI was significantly associated with cortical area and failure load for both tibia ($p \leq 0.001$ and $p \leq 0.001$), and radius ($$p \leq 0.002$$ and $p \leq 0.001$), respectively.
At the radius, PTH was negatively and significantly associated with cortical area and cortical vBMD ($$p \leq 0.002$$ and $$p \leq 0.004$$, respectively) and showed borderline significance with cortical thickness ($$p \leq 0.023$$) and failure load ($$p \leq 0.011$$), both directionally negative. At the tibia, there was borderline significance between PTH and total vBMD and cortical area (0.039 and 0.016, respectively, both directionally negative). To further explore a potential interaction between PTH and 25(OH)D that could help explain these findings, 3-dimensional plots were constructed for these parameters. 25(OH)D levels showed either minimal or no nonsignificant interaction with PTH.
At the tibia, 25(OH)D was negatively and significantly associated with trabecular separation ($$p \leq 0.002$$) and trabecular inhomogeniety standard deviation ($$p \leq 0.002$$), and positively and significantly associated with trabecular number ($$p \leq 0.003$$). There was a borderline significant association between mean 10-year A1C (8.18 +/- $1.03\%$) and trabecular BMD (positive), trabecular separation (negative), trabecular number (positive), and trabecular number standard deviation (negative). 3D plots showed a possible interaction between 25(OH)D and mean A1C on trabecular separation (see Supplementary Figure S1) whereby as the level of 25(OH)D increased, the level of trabecular separation and inhomogeneity of trabecular network decreased. This decline was steeper for higher levels of mean A1C, although not statistically significant. For both trabecular number and its standard deviation, low or high mean A1C had only a small or no modifying effect on these parameters caused by low 25(OH)D. At the radius, there was a borderline significant association between 25(OH)D and trabecular separation (negative), trabecular number (positive) and trabecular inhomogeneity standard deviation (negative).
At the tibia, diabetes duration was negatively and significantly associated with trabecular area ($$p \leq 0.005$$) and borderline significant with total area (negative). At the radius, there was a borderline significant association between diabetes duration total area (negative), trabecular area (negative) and cortical thickness (positive).
## Discussion
This cross-sectional study used HR-pQCT to compare parameters of bone microarchitecture and estimated bone strength of young adults with type 1 diabetes with those of a large normative database [32]. Young adults with type 1 diabetes were shown to have low trabecular and high cortical bone geometry parameters (volumetric BMD and cortical thickness). No sex specific differences in bone geometry parameters were found. Our data further show that diabetes duration and modifiable risk factors of bone health, specifically 25(OH)D, PTH, and BMI, are associated with HR-pQCT parameters.
There are limited data evaluating bone microarchitecture using HR-pQCT in patients with type 1 diabetes, especially in young adults. In this study, we show that these young adults have low trabecular thickness in the tibia and low trabecular thickness and trabecular area, and borderline low, vBMD in the radius. These trabecular bone geometry parameters are consistent with the findings of studies involving both adults [17] and children (18–20). Furthermore, high cortical bone geometry parameters (cortical vBMD and cortical thickness) were found in the radius. By contrast, other studies have shown a reduction in cortical parameters. However, direct comparison is not possible either because of differences in age (i.e., older adults or children/adolescents) or because of small control groups (17–20).
There is increasing concern that obesity may be associated with suboptimal bone strength. However, most published studies are cross-sectional with a lack of longitudinal data to support cross-sectional findings. In this study, $47\%$ of the cohort was either overweight ($$n = 25$$) or obese ($$n = 16$$) based on obesity guidelines of the Canadian Medical Association. BMI was found to be positively and significantly associated with cortical area and failure load, suggesting that BMI may have a positive impact on bone health. Our study found no association between BMI and increased cortical parameters, suggesting that obesity may not contribute to increases in cortical bone. These findings support the proposal that high cortical bone may be a compensatory response to low trabecular bone.
In the healthy population, increased cortical geometry (cortical vBMD and cortical thickness) would be expected to improve bone quality. However, in type 2 diabetes, BMD that is normal to increased is associated with increased fracture risk. Several bone-derived factors may be altered by hyperglycemia as advanced glycation end products negatively impact the extracellular matrix and bone strength. This may also be relevant to type 1 diabetes, negating any positive benefits from cortical size. Increased cortical porosity would likely reduce bone quality. Elevated cortical porosity has been described in patients with type 2 diabetes possibly associated with microangiopathy and obesity (36–39). In our cohort, cortical porosity at the tibia, although not associated with BMI, was borderline high. Bone strength as measured by failure load was normal, implying a balance between the factors promoting and preventing fractures in this population.
Vitamin D deficiency and secondary hyperparathyroidism are important in increased fracture risk [6]. Osteomalacia and rickets are clinical expressions of low levels of vitamin D, which increase the risk of fracture [25, 40]. Our study identified a high rate of insufficient/deficient vitamin D levels using the criteria of <$\frac{50}{30}$ nmol/l. These findings are similar to previous studies from our diabetes clinic in which a higher prevalence of vitamin D deficiency/insufficiency was found compared with healthy controls [41]. Lower 25(OH)D levels were found to be significantly associated at the tibia with increased trabecular separation and lower trabecular number and higher trabecular number standard deviation, a measure of inhomogeneity of trabeculae, indicating advancing trabecular disruption. These results are consistent with previous studies evaluating 25(OH)D level and HR-pQCT in non-diabetic populations. Cheung et al. [ 42], using HR-pQCT, found that in healthy girls, cortical area, cortical thickness, and trabecular thickness were significantly correlated with serum 25(OH)D levels. In boys, cortical area, cortical thickness, trabecular bone volume/total volume and trabecular separation were significantly correlated with serum 25(OH)D levels. On the other hand, Boyd et al. [ 43] showed that overall, there was no strong association between microarchitectural parameters and 25(OH)D levels in a cohort of older vitamin D sufficient people (age 55 ± 15 years). Nevertheless, trabecular BMD and trabecular thickness at the tibia were positively associated with 25(OH)D levels. Data in this study of young T1D adults show the association of lower 25(OH)D levels with trabecular measures at the tibia more than at the radius with no apparent impact on cortical bone. The significant associations found with trabecular separation, number and trabecular number standard deviation at the tibia supports the idea that low vitamin D levels may be contributing to the trabecular changes.
Despite the high rates of vitamin D insufficiency/deficiency in this study population ($44\%$), PTH levels were mainly within the reference range. While many studies have suggested that PTH levels start to increase at 25(OH)D levels below 70 nmol/L, individual patients with vitamin D insufficiency do not always have high PTH levels [44]. Consistent with PTH’s contribution to bone turnover and regulation of bone mass, studies examining high PTH levels on bone have shown that increased PTH levels impact both cortical and trabecular compartments as well as bone strength [45]. In this study we found, at the radius, a significant negative association between PTH levels and cortical bone mineral density ($p \leq 0.004$) and cortical area ($p \leq 0.002$). The significance of these novel and intriguing findings of an association between PTH levels, within the reference range, and HR-pQCT parameters are uncertain and not documented in the literature, requiring further study. However, the advent of secondary hyperparathyroidism with its known negative impact on cortical as well as trabecular bone, could likely disrupt the current compensated state of low trabecular and high cortical bone parameters.
Both A1C and severe hypoglycemia, have been implicated in an increased rate of fractures in type 1 diabetes, in both children and adults (1–6). Eckert et al. [ 46] demonstrated that even moderately poor metabolic control A1C, $8.4\%$ [8.3–8.5], to be associated with fractures in both of these patient populations. In this study, there was a only a borderline significant association between long-term A1C levels with various trabecular parameters in the tibia. A potential interaction between 25(OH)D levels and A1C, was explored using three-dimensional plots. This showed that for trabecular separation, the combination of low 25(OH)D and high A1C, resulted in higher trabecular separation, although statistical significance was not reached. Mean % A1C over the preceding 10 years was 8.18 ± 1.03, suggesting that improved metabolic control may mitigate the impact of hyperglycemia on bone.
Despite the importance of physical activity for bone health, there is limited data examining the physical activity-skeletal health relationship in T1D. There are only a small number of studies assessing exercise and HR-pQCT in healthy subjects and none addressing this question in type 1 diabetes. In a study of healthy young men, Nilsson et al. [ 47] found in weight bearing limbs that several trabecular parameters were associated with the degree of current mechanical loading related to type of present physical activity. This data suggest that physical activity in T1D subjects might be expected to have a positive benefit on trabecular microstructure in the tibia. A moderate increase in physical activity was found in our subjects compared with the recommendations for age, but no association between physical activity as measured by Physical Activity Characteristics Questionnaire, and trabecular measures was found. However this assessment was undertaken as an exploratory analysis. Given that the gold standard for measurement of physical activity, accelerometery which provides precise real-world data regarding intensity, duration and frequency of daily activity was not used in this study, it remains possible that exercise may mitigate some of the negative effects of diabetes on trabecular bone and further studies are required.
This study has limitations and strengths. Previous fracture history was acquired using an unvalidated questionnaire and not confirmed using X-ray reports. Sex steroid measurements were not obtained at time of study. Furthermore, both physical activity and diet were captured using a questionnaire, which although validated, did not capture the long-term impact of these potentially confounding variables. Additionally, the Z-scores were based on a reference population 18 years and older, and although our study population had a few 17 year olds, it is not expected that the reference data would be much, if at all, affected. Nonetheless, the strength of this cross-sectional study is that the cohort, which has been followed for over 10 years, is well characterized and the comparison of HR-pQCT measures with a normative data base to generate Z-scores that account for matching of age, sex, and skeletal site with a similar ethnicity distribution.
In summary, our data show early evidence of trabecular and cortical microarchitectural values indicative of impairment of bone health although failure load was normal, possibly due to compensation of the cortical density and thickness. Low levels of vitamin D were also found to be common and to be associated with trabecular parameters more in the tibia than the radius with a possible synergy with A1C. The lack of significant associations with a long-term A1C of 8.18 suggests that metabolic control at this level may mitigate the negative impact of hyperglycemia on bone. BMI appears to have a protective influence and not to explain those cortical parameters that were increased. Our findings suggest that correction of low vitamin D levels, monitoring and treatment of secondary hyperparathyroidism, and optimal metabolic control may reduce the risk of fractures in the long term as this population ages.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by The Hospital for Sick Children Research Ethics Board, REB#: 1000055749. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
ES contributed to the study design, literature search, data interpretation, writing the manuscript and critically reviewing the manuscript. MD contributed to data collection and writing the manuscript. RV contributed to data interpretation. AS contributed to data interpretation. YE contributed to data collection, design of tables, and reviewing/editing the manuscript. RM contributed to data analysis and interpretation. FM contributed to critically reviewing and editing the manuscript. EA contributed to the dietary data collection and data interpretation. MF contributed to data collection. SB contributed to data analysis, data interpretation and editing of the manuscript. NL-T contributed to the literature search, data interpretation, design of figure and writing of the manuscript. ES is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1144137/full#supplementary-material
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|
---
title: 'Research hotspots and frotiers of stem cells in stroke: A bibliometric analysis
from 2004 to 2022'
authors:
- Qi Zhang
- Yuting Zeng
- Shuqi Zheng
- Ling Chen
- Haining Liu
- Hui Chen
- Xiaofeng Zhang
- Jihua Zou
- Xiaoyan Zheng
- Yantong Wan
- Guozhi Huang
- Qing Zeng
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10020355
doi: 10.3389/fphar.2023.1111815
license: CC BY 4.0
---
# Research hotspots and frotiers of stem cells in stroke: A bibliometric analysis from 2004 to 2022
## Abstract
Background: *Stroke is* one of the leading causes of mortality and permanent disability worldwide. However, the current stroke treatment has a limited effect. Therefore, a new treatment is urgently needed. Stem cell therapy is a cutting-edge treatment for stroke patients. This study aimed to gain better understanding of global stem cell trends in stroke via a bibliometric analysis.
Methods: We used the Web of Science Core Collection to search pertinent articles about stem cells in stroke published between 2004 and 2022. Analysis was conducted using CiteSpace, VOSviewer, and the R package “bibliometrix” to identify publication outputs, countries/regions, institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords.
Results: A total of 6,703 publications were included in the bibliometric analysis. The total number of citations significantly and rapidly increased between 2004 and 2022, with the most pronounced growth pattern observed in the period of 2008–2009. In terms of authoritarian countries, the USA had the most publications among the countries. As for institutions and authors, the most prolific institution was the University of South Florida, followed by Oakland University and then Shanghai Jiao Tong University, and Chopp, M. and Borlongan, Cesario V, had the most output among the authors. Regarding the journals, Cell Transplantation had the highest publication, followed by Brain Research. As for references, “Mesenchymal stem cells as trophic mediators” was the most frequently cited [2,082], and the article entitled Neuronal replacement from endogenous precursors in the adult brain after stroke had the strongest burstiness (strength = 81.35). Emerging hot words in the past decade included “adhesion molecule,” “mesenchymal stromal cell,” “extracellular vesicle,” “pluripotent stem cells,” “signaling pathway,” “plasticity,” and “exosomes.” Conclusion: Between 2004 and 2022, the terms “neurogenesis,” “angiogenesis,” “mesenchymal stem cells,” “extracellular vesicle,” “exosomes,” “inflammation,” and “oxidative stress” have emerged as the hot research areas for research on stem cells in stroke. Although stem cells exert a number of positive effects, the main mechanisms for mitigating the damage caused by stroke are still unknown. Clinical challenges may include complicating factors that can affect the efficacy of stem cell therapy, which are worth a deep exploration.
## 1 Introduction
Stroke is currently the third major cause of adult disability and the second leading cause of mortality worldwide (Owolabi et al., 2022). Following a stroke, the brain may be damaged by neuronal apoptosis, oxidative stress, and cytotoxic cascade reactions (Kuriakose and Xiao, 2020). Stem cell therapy, an emerging treatment option for stroke, has the potential to improve neurological outcomes and functions by promoting neurogenesis, reducing oxidative stress, and decreasing cytotoxicity (Zhao et al., 2022). At present, many stem cell types have been shown to be effective in treating stroke, such as pluripotent stem cells (Duan et al., 2021), neural stem cells (NSCs) (Zhang et al., 2017), embryonic stem cells (Xia et al., 2021), and mesenchymal stem cells (MSCs) (Bedini et al., 2018). In addition, many studies have demonstrated the capacity of these stem cells for brain rewiring (Pluchino and Peruzzotti-Jametti, 2013), neoangiogenesis (Nam et al., 2015), inflammatory inhibition (He et al., 2021a), and nerve regeneration (Ould-Brahim et al., 2018). Scholars have published a plethora of basic research and clinical trials on stem cell therapy in stroke, but new and comprehensive quantitative evidence to support the direction and research hotspots in this field is limited. Thus, it is necessary to review the development of research on stem cells in stroke from 2004 to 2022 and to present an objective analysis based on data from publications as a foundation for future study.
Bibliometric analysis is a statistical method for forecasting knowledge structure and hotspots within a certain field of study through visual representations (Ninkov et al., 2022). By reading this kind of study, readers may be able to obtain quantitative information on how journals are distributed by nation, organization, author, and journal in a specific field (Zhang L. et al., 2022). Bibliometric analysis provides unambiguous insights into many medical areas (Kokol et al., 2021). However, bibliometric studies conducted in the field of stem cells in stroke are scarce. As a result of the dramatic increase in stem cell research and publications over the past several years, the necessity to integrate and renew research data in a bibliometric analysis on stem cells in stroke has arisen.
As a response to the paucity of quantitative analysis of research regarding stem cells in stroke, the present study acquired global scientific research on stem cells in stroke between 2004 and 2022 with quantitative information on the publication outputs, countries/regions, institutions, authors/co-cited authors, journals/co-cited journals, co-cited and burst references, keywords, and burst keywords. This study aimed to highlight hotspots for study in this area by synthesizing research direction and emergent themes from these investigations.
## 2.1 Search strategy and data acquisition
The Web of Science (WoS) contains 20,000 reputable academic publications that span 250 different fields worldwide (Zhong and Lin, 2022). Other academic researchers in the field of bibliography have used the WoS as the most trustworthy data source for data extraction in bibliometric analysis (Dong et al., 2022).
We conducted a comprehensive literature search using the Web of Science Core Collection (WoSCC) database from 1 January 2004 to 11 August 2022. To obtain as comprehensive and accurate results as possible, the search strategies we used were TS=(stroke OR apoplexy OR “cerebrovascular accident” OR “cerebral hemorrhage” OR hematencephalic OR encephalorrhagia OR “cerebral ischemia”) AND TS=(“Stem Cells” OR “Cell, Stem” OR “Cells, Stem” OR “Stem Cell” OR “Progenitor Cells” OR “Cell, Progenitor” OR “Cells, Progenitor” OR “Progenitor Cell” OR “Mother Cells” OR “Cell, Mother” OR “Cells, Mother” OR “Mother Cell” OR “Colony-Forming Unit” OR “Colony Forming Unit” OR “Colony-Forming Units” OR “Colony Forming Units”). Only articles and reviews were included. Furthermore, letters, commentaries, meeting abstracts, and other types of documents were excluded. Finally, 6,703 records were included for analysis. The specific literature screening process is presented in Figure 1.
**FIGURE 1:** *Flow chart of the screening process for research on stem cells in stroke.*
## 2.2 Data analysis
The original data downloaded from the WoSCC were firstly imported into Microsoft Excel 2016, and then two authors (QZh and YZ) independently screened the final included articles and collected all data from the final papers that were included, such as titles, authors, keywords, institutions, countries/regions, citations, journals, and publication dates. Subsequently, the processed data was imported to VOSviewer (version 1.6.15), CiteSpace (version 5.8), and R package “bibliometrix” for bibliometric analysis.
CiteSpace is a bibliometric software that enables the analysis and visualization of trends and patterns in a research area (Pan et al., 2018; J; Zhang and Lin, 2022). It also creates a knowledge map of connected fields, clearly presents the panoramic information of a particular knowledge field, and identifies the critical studies, hot research, tendency, and frontiers of a specific scientific field using a variety of dynamic network analysis techniques (Godfrey et al., 2018; Y; Chen, Lin, and Zhuang, 2022). CiteSpace was used in this study to conduct co-occurrence and cluster analyses of authors, research institutions, nations, and discipline features. The parameters of CiteSpace were set as follows: in the Time Slicing column time settings 2004.01–2022.08, each year is a time slice.
The Leiden University Center for Science and Technology Studies (CWTS) created VOSviewer, a software for creating and analyzing bibliometric networks (Netherlands). VOSviewer can extract bibliographic networks (co-authorship, co-occurrence, and citation-based) from bibliographic data (Lin, Chen, and Chen, 2020; Moral-Muñoz et al., 2020; Luo and Lin, 2021). In this study, co-occurrence and cluster analyses of authors, research institutions, countries, and discipline features were conducted using CiteSpace.
Bibliometrix (https://www.bibliometrix.org) is an open-source R package developed by Dr. Massimo Aria and Corrado Cuccurullo from Naples University in Italy. It is capable of conducting comprehensive bibliometric and scientometric analyses (Moral-Muñoz et al., 2020). In this study, bibliometrix was used to create a global distribution network of articles on stem cells in stroke and to analyze the thematic evolution of those publications (Aria and Cuccurullo, 2017).
## 3.1 Temporal trend of publication outputs
As can be seen from Figure 2, the histogram and curves exhibit two trends: the total number of papers published and citations per year. Both trends grow throughout time, illustrating the direction in which research in this area is moving. The number of citations significantly and rapidly increased between 2004 and 2021, suggesting that research on stem cells in stroke has attracted interest. From 2004 to 2007, the number of literature grew rapidly, and in 2009, it dramatically increased. However, the number of articles remained relatively stable from 2010 to 2017. The year 2020 had the most number of publications in recent years, which peaked at 515. Although the data for 2022 have not yet been completed, it is predicted that they will exhibit a moderate trend compared with those in the previous year.
**FIGURE 2:** *Trends of annual publications on research of stem cells in stroke. The data for 2022 is not complete.*
## 3.2 Contributions of countries/regions
As for the geographical distribution, 6,703 documents were published from 94 different countries and regions; Table 1 presents the top 10 countries/regions in this category. As can be seen from Figure 3B and Supplementary Figure S1, we also used VOSviewer for the visual analysis of countries or regions. The USA published the most papers (1,555papers, $25.61\%$), followed by China (738 papers, $12.15\%$) and then Japan (714papers, $11.76\%$), indicating that these three countries play a crucial role in this field. The quantity and connections among publications in each nation were then used to create a collaborative network (Figure 3A). A country collaboration analysis was conducted on the 30 countries with the highest number of publications in this area (Figure 3C). According to the total link strength, the top five countries/regions were the USA, China, Japan, Germany, and South Korea.
## 3.3 Contributions of institutions
In Table 2, the top 10 institutions with the highest productivity are ranked by their productivity. China and the USA were the only two countries where the highest productivity were located. The most prolific institution was the University of South Florida (164 publications, $2.70\%$), followed by the Oakland University (151 publications, $2.49\%$) and then the Shanghai Jiao Tong University (129 publications, $2.12\%$). The clustering analysis of institutions is presented in Figure 4A. A tight and continuous interaction between institutions can also be observed. Among them, the institutions with more collaborations were Oakland University, Henry Ford Hospital, and Henry Ford Health Science Center, followed by Sapporo Medical University and Yale University. In Figure 4B, we analyzed the data of articles published in the last 5 years using VOSviewer. For instance, Harvard University started research in the field earlier and had published significantly more articles in the past than it had recently. Contrarily, Capital Medical University entered the field later and has recently published a higher number of articles. As can be seen from the cluster analysis figure, the red and light blue circles indicate mainly Chinese institutions. Combined with the visual timeline, it can be seen that Chinese institutions are predominantly yellow, indicating that they entered the field late or have recently published a high number of articles.
Figure 4C presents the publication trend in this field by different institutions over time. The University of South Florida accounted for more than half publications from 2004 to 2006 and then gradually declined; however, the University of South Florida remained in the leading position, demonstrating the remarkable contributions of this institution to this sector. The rest of the institutions exhibited an upward trend in the publication quantity. Interestingly, Shanghai Jiao Tong University has made the most significant progress. Since 2018, Shanghai Jiao Tong University has, unsurprisingly, ranked first among the 10 institutions in terms of publication ratio. We also used CiteSpace for the visual analysis of institution clusters and marked them with keywords (Figure 4D). Consequently, the Johns Hopkins University, Massachusetts General Hospital, and National University of Singapore exhibited high centrality. The largest cluster in Supplementary Figure S1 was designated “ischemic stroke” (cluster #0), indicating that various institutions are most concerned with this word. It was followed by “clinical trials” (cluster #1), “primate” (cluster #2), and “extracellular matrix” (cluster #3), respectively. Other important clusters were “mesenchymal stem cell,” “neurological function,” and “hypothermia.”
## 3.4 Authors and Co-cited authors
Approximately 174 writers contributed to a total of 6,703 articles. The most prolific author was Chopp, M. who produced 160 articles ($2.63\%$), closely followed by Borlongan, Cesario, V who produced 143 publications ($2.35\%$). Three authors published 50 and more articles (Zhang Zheng Gang, Yang Guo-Yuan, and Kokaia Zaal), and five authors published 40 and more articles (Hermann Dirk M. Chen Jieli, Lindvall Olle, Sanberg P. R. and Kaneko Yuji) (Table 3). Information on co-authors and co-cited authors was also analyzed using VOSviewer (Figures 5A, B). Figure 5A shows that there are primarily two research teams involved in author collaboration, led by Chopp, M. and Borlongan, Cesario, V, who frequently and closely collaborate with other authors. Co-cited authors are those who have had two or more of their names concurrently mentioned in one or more subsequent articles and who are therefore considered to have a co-citation connection. A total of more than 1000 citations have been received by the top six authors among the top 10 co-cited writers (Table 3). The most frequently referenced author was Chen Jl. ( $$n = 2$$,214), followed by Jin, Kl ($$n = 1$$,473), Li, Y ($$n = 1$$,367), Zhang, RI ($$n = 1$$,313), Borlongan, Cv ($$n = 1$$,144), and Avidsson, A ($$n = 1$$,003).
## 3.5 Journals and Co-cited academic journals
We found that 252 journals published 6,703 papers regarding stem cells in stroke. As can be seen from Table 4, it is clear that the journal Cell Transplantation has the most papers (172, $2.83\%$), followed by Brain Research (160, $2.63\%$). Among the top 10 journals, Stroke (10.17) has the greatest impact factor (IF). The number of times the top 10 most co-cited journals are cited determines their influence. As presented in Table 4, the publication with the most citations is Stroke [19,776], indicating that it has a significant impact in this category, followed by the Journal of Neuroscience [14,061] and Proceedings of the National Academy of Sciences of the United States of America [11,769].
**TABLE 4**
| Rank | Journal | Count (%) | IF(JCR 2020) | JCR Quartile | Co-cited-journal | Citations | IF(JCR 2020).1 | JCR Quartile.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Cell Transplantation | 172 (2.83%) | 4.139 | Q3 | Stroke | 19776 | 10.17 | Q1 |
| 2 | Brain Research | 160 (2.63%) | 3.61 | Q3 | J Neurosci | 14061 | 6.709 | Q1 |
| 3 | Stroke | 158 (2.60%) | 10.17 | Q1 | P Natl Acad Sci Usa | 11769 | 12.779 | Q1 |
| 4 | Plos One | 147 (2.42%) | 3.752 | Q2 | J Cerebr Blood F Met | 10862 | 6.96 | Q1 |
| 5 | Journal Of Cerebral Blood Flow And Metabolism | 137 (2.26%) | 6.96 | Q1 | Brain Res | 8227 | 3.61 | Q3 |
| 6 | Neural Regeneration Research | 129 (2.12%) | 6.058 | Q2 | Nature | 7386 | 69.504 | Q1 |
| 7 | International Journal Of Molecular Sciences | 113 (1.86%) | 6.208 | Q1 | Plos One | 7295 | 3.752 | Q2 |
| 8 | Neuroscience | 103 (1.70%) | 3.708 | Q3 | Stem Cells | 6787 | 5.845 | Q1 |
| 9 | Experimental Neurology | 102 (1.68%) | 5.62 | Q2 | Exp Neurol | 6630 | 5.62 | Q2 |
| 10 | Stem Cell Research and Therapy | 95 (1.56%) | 8.098 | Q1 | Science | 6519 | 63.798 | Q1 |
Using VOSviewer, we conducted a visual analysis of the published journals and obtained details about journal collaboration through Figures 6A, B. We could see that the journals of Stroke, Archives of Physical Medicine and Rehabilitation, and Neurorehabilitation and Neural Repair had more times of co-citation and greater influence. We also conducted comparative analysis of the journals’ popularity, as presented in Figure 6C. Through this heat map, we can understand the change in the research direction and emphasis in this field and grasp the development trends. We found that in recent years, the popularity of NEUROSURGERY, CURRENT NEUROVASCULAR RESEARCH, and PANS had gradually decreased, whereas that of CELLS, INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, and FRONTIERS IN CELLULAR NEUROSCIENCE had gradually increased. One of the interesting things about STEM CELL REVIEWS AND REPORTS is that its popularity had declined year by year, but in the last 2 years, it had returned to its level in 2004. Furthermore, on the dual-map overlay of journal publishing research (Figure 6D), we found four citation paths (colors orange, pink, and green), demonstrating that the studies published in molecular/biology/genetics journals and health/nursing/medicine journals were mainly cited by the studies published in molecular/biology/immunology, medicine/medical/clinical, and neurology/sports/ophthalmology journals.
**FIGURE 6:** *(A) Co-journal clustering analysis. Each circle represents a journal, the size of the circle depends on the strength of the connection, the number of citations, and so on. And, the color of the circle on behalf of the cluster to which it belongs, different clusters are represented by different colors. (B) Co-cited-journal clustering analysis (C) Journals heat map on stem cells in stroke. Each line was compared with each other. The value in the box is the number of articles published in the journal divided by the number of articles published in the year, verbalizing the current year’s popularity of the journal, The redder the color verbalizes the hotter journal in the year, so it was called the heat map (D) The dual-map overlay of journal publishing research. Citing journals on the left and cited journals on the right, and the curve is the citation line, which completely shows the context of the citation, the more papers the journal publishes, the longer the vertical axis of the ellipse; the more authors they are, the longer the horizontal axis of the ellipse.*
## 3.6 Co-cited reference and reference bursts
The top 15 documents that were cited the most often out of the 6703 retrieved are listed in Table 5. Mesenchymal stem cells as trophic mediators was the most frequently cited [2,082], which is a review of studies on the applications of adult marrow-derived MCSs. It was followed by Concise review: Mesenchymal stem cells: Their phenotype, differentiation capacity, immunological features, and potential for homing [1,725] and then Adult mesenchymal stem cells for tissue engineering versus regenerative medicine [1,378].
**TABLE 5**
| Rank | Author | Article title | Source title | Cited | Year | DOI |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | Caplan, AI, et al. | Mesenchymal stem cells as trophic mediators | JOURNAL OF CELLULAR BIOCHEMISTRY | 2082 | 2006 | 10.1002/jcb.20886 |
| 2 | Chamberlain, G, et al. | Concise review: Mesenchymal stem cells: Their phenotype, differentiation capacity, immunological features, and potential for homing | STEM CELLS | 1725 | 2007 | 10.1634/stemcells.2007-0197 |
| 3 | Caplan, AI | Adult mesenchymal stem cells for tissue engineering versus regenerative medicine | JOURNAL OF CELLULAR PHYSIOLOGY | 1378 | 2007 | 10.1002/jcp.21200 |
| 4 | Moskowitz, MA, et al. | The Science of Stroke: Mechanisms in Search of Treatments | NEURON | 1303 | 2010 | 10.1016/j.neuron.2010.07.002 |
| 5 | Langhorne, P, et al. | Stroke Care 2 Stroke rehabilitation | LANCET | 1290 | 2011 | 10.1016/S0140-6736 (11)60325-5 |
| 6 | Schmidt-Lucke, C, et al. | Reduced number of circulating endothelial progenitor cells predicts future cardiovascular events - Proof of concept for the clinical importance of endogenous vascular repair | CIRCULATION | 915 | 2005 | 10.1161/CIRCULATIONAHA.104.504,340 |
| 7 | Imitola, J, et al. | Directed migration of neural stem cells to sites of CNS injury by the stromal cell-derived factor 1 alpha/CXC chemokine receptor 4 pathway | PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 851 | 2004 | 10.1073/pnas.0408258102 |
| 8 | Bang, OY, et al. | Autologous mesenchymal stem cell transplantation in stroke patients | ANNALS OF NEUROLOGY | 838 | 2005 | 10.1002/ana.20501 |
| 9 | Abrous, DN, et al. | Adult neurogenesis: From precursors to network and physiology | PHYSIOLOGICAL REVIEWS | 750 | 2005 | 10.1152/physrev.00055.2003 |
| 10 | Lalu, MM, et al. | Safety of Cell Therapy with Mesenchymal Stromal Cells (SafeCell): A Systematic Review and Meta-Analysis of Clinical Trials | PLOS ONE | 728 | 2012 | 10.1371/journal.pone.0047559 |
| 11 | Falk, E | Pathogenesis of atherosclerosis | JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY | 715 | 2006 | 10.1016/j.jacc. 2005.09.068 |
| 12 | Dutta, P, et al. | Myocardial infarction accelerates atherosclerosis | NATURE | 688 | 2012 | 10.1038/nature11260 |
| 13 | Amariglio, N, et al. | Donor-Derived Brain Tumor Following Neural Stem Cell Transplantation in an Ataxia Telangiectasia Patient | PLOS MEDICINE | 671 | 2009 | 10.1371/journal.pmed.1000029 |
| 14 | Ohab, JJ, et al. | A neurovascular niche for neurogenesis after stroke | JOURNAL OF NEUROSCIENCE | 661 | 2006 | 10.1523/JNEUROSCI.4323-06.2006 |
| 15 | Lochhead, JJ, et al. | Intranasal delivery of biologics to the central nervous system | ADVANCED DRUG DELIVERY REVIEWS | 628 | 2012 | 10.1016/j.addr. 2011.11.002 |
When two or more articles are simultaneously cited in the same article, the relationship between the simultaneously cited articles is referred to as co-citation. As presented in Figure 7A, we also used the CiteSpace clustering function to create a visual map to cluster the co-citation literature, and the collected literature was separated into 11 clusters via cluster analysis. Each cluster was intimately connected to the others and worked together in specific areas. The weighted mean silhouette and modularity Q were 0.8694 and 0.691, respectively, demonstrating the stability, believability, and persuasiveness of the clustering structure. Figure 7A illustrates the time dimension by the color of circle changing from purple to yellow, which also shows the change in the direction and concentration of the research. While the “hippocampus” cluster had received more attention in the past, researchers recently turned their attention to “exosomes,” “hydrogels,” and “ischemic stroke.” The name of the biggest cluster was “subventricular zone” (cluster #0), which was followed by “transplantation” (cluster #1), “ischemic stroke” (cluster #2), and “hydrogel” (cluster #3). The “hippocampus,” “neurogenesis,” “bone marrow stromal cells (BMSCs),” and “exosomes” clusters were other significant groups that may have represented a turning point in some sense.
**FIGURE 7:** *(A) Co-cited references related to stem cells in stroke. The circles represent the number of co-citations, the purple circle represents centrality, the thickness of connection indicates the cooperation degree, and the purple to yellow gradient represents the time from the past to the present. (B) Cluster view of references in stem cells in stroke research in recent 3 years. The larger the circle, the more times the corresponding paper has been cited in the last 3 years (C) CiteSpace visualization map of top 25 references with the strongest citation bursts involved in stem cells in stroke. The blue line represents the time interval. The blue line represents the time interval. The time in which a reference was found to have a burst is displayed by a red line, indicating the first year and the last year of the duration of the burst.*
As shown in Figure 7B, we created a visual map to cluster the cited literature over the last 3 years and divided them into 21 clusters through cluster analysis using CiteSpace. The weighted mean silhouette and modularity Q were 0.8729 and 0.7518, respectively, demonstrating the stability, believability, and persuasiveness of the clustering structure. The Figure 7B can help us get the latest research hotspots. The largest cluster was “cell treatment” (cluster #0), which was followed by “exosomes” (cluster #1), “biomaterials” (cluster #2), and “ischemic stroke” (cluster #3). Additional significant clusters were “adult neurogenesis,” “microglia,” “intracerebral hemorrhage,” and “pericytes.” Combined with Figure 7A, the “hippocampus” cluster has received more attention in the past, researchers have recently turned their attention to “exosomes,” “hydrogels,” and “ischemic stroke.” In Figure 7C, the top 25 references are listed in chronological order, which have the greatest burst intensity. References that receive several citations over a period of time are known as “citation burst” references. We set the time period in CiteSpace to 2004–2022 and still kept references with a burst termination date of 2022. Neuronal replacement from endogenous antecedents in the adult brain following stroke by Andreas et al. was published in Nature Medicine in 2002, and it had the strongest burstiness (strength = 81.35), occurring from 2004 to 2007 (Arvidsson et al., 2002). The advantages and disadvantages of the top 25 articles with the strongest citation bursts are summarized in Supplementary Table S1.
## 3.7 Key topics of research hotspots
The use of cluster analysis to cluster the included keywords and summarize the study subjects might be helpful for relevant researchers in identifying popular topics and assisting scholars in better understanding current scientific concerns. We used VOSviewer to cluster the keywords into eight, as presented in Figure 8A: origin and behavior of stem cells (red), pathophysiological process of stroke (green), treatment of stroke and application of stem cells (purple), effects of stem cell therapy after stroke (light blue), cells that make up the central nervous system and the pathophysiological changes (orange), other diseases associated with stem cell therapy (brown), exosomes and mechanism of action (yellow), others (navy blue).
**FIGURE 8:** *(A) Clustering of co-occurrence among keywords. The circles and labels in the figure constitute a unit, and the units of different colors constitute different clusters (B) CiteSpace visualization map of top 25 keywords with the strongest citation bursts involved in stem cells in stroke.*
Meanwhile, we performed a series of keyword burst detections. To evaluate the development of stem cells in stroke research, researchers used a method named “keyword burst detection,” which is the recognition of phrases that often occur in a certain period of time. Table 6 demonstrates that terms with a high frequency in this study, aside from “stroke” [1538], include “ischemia” [801], “neurogenesis” [660], “brain ischemia” [553], “mesenchymal stem cell” [549], and “neural stem” [541]. Prominent keywords were further analyzed. The top 25 keywords are listed Figure 8B in chronological order, which have the greatest burst intensity. It can be seen from the figure that the keywords that had exploded in the past 5 years mainly focused on “extracellular vesicles,” “inflammation regulation,” and “regulation of extracellular matrix.” A timeline analysis that visually depicts the research hotspots and development paths of stem cells in stroke at various stages from a temporal perspective is presented in Supplementary Figure S2. The keywords that have received extensive attention in this field appear early, for example, cerebral ischemia, regeneration, NSC, endothelial progenitor cells, apoptosis, and cell therapy. Emerging hot words in the past decade include adhesion molecule, mesenchymal stromal cell, extracellular vesicle, pluripotent stem cell, signaling pathway, plasticity, and exosomes.
**TABLE 6**
| Rank | Keyword | Occurrences | Total link strength | Rank.1 | Keyword.1 | Occurrences.1 | Total link strength.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | stroke | 1538 | 4098 | 11 | transplant | 306 | 934 |
| 2 | ischemia | 801 | 2025 | 12 | inflammation | 259 | 804 |
| 3 | neurogenesis | 660 | 1878 | 13 | endothelial progenitor cells (epcs) | 204 | 436 |
| 4 | brain ischemia | 553 | 1381 | 14 | apoptosis | 191 | 554 |
| 5 | mesenchymal stem cell | 549 | 1307 | 15 | stem cell | 185 | 561 |
| 6 | neural stem cell | 541 | 1447 | 16 | traumatic brain injury | 161 | 490 |
| 7 | stem cells | 464 | 1351 | 17 | neurodegenerative disorder | 156 | 489 |
| 8 | cell transplantation therapy | 461 | 1327 | 18 | microglia | 150 | 444 |
| 9 | angiogenesis | 387 | 1157 | 19 | astrocytes | 144 | 429 |
| 10 | neuronal protection | 382 | 1087 | 20 | blood-brain barrier (BBB) | 138 | 391 |
## 4.1 Global research trends of stem cells in stroke
This study aimed to conduct a bibliometric analysis of the last 18 years’ worth of research on stem cells in stroke. The citation count has been steadily increasing each year, with the 2008–2009 period showing the clearest growth pattern. Prior to 2008, an average of 150 papers per year were published in this field. From 2009 to 2022, the number of publications gradually increased, with an average of 350 papers published annually. The number of articles published in 2020 peaked at 515. These findings indicated that studies on stem cell therapy for stroke have gained increasing attention from researchers from all over the world in recent years, and the research area is currently in a steady developmental stage.
The top three authoritarian countries performing research on stem cells in stroke are the USA, China, and Japan. Four European nations, four Asian-Pacific nations, and two American nations make up the top ten nations. Furthermore, among the top 10 research institutions, five were American, and the remaining five were Chinese. We observed a close coordination between four nations, namely, Germany, Japan, China, and the United States. China also actively works with Japan, Canada, and England. There are some academic institutions that collaborate well with one another, such as Shanghai Jiao Tong University, Capital Medical University, and Harvard University. Although Oakland University ranked second in terms of paper publications, it was found to have limited collaborations with other universities. Such collaborations are detrimental for these universities in terms of long-term scholarly advancement. Consequently, it is indeed necessary that research organizations from many nations work closely together and communicate to collaboratively promote stem cell therapy for stroke.
As for the journals, those listed in Table 4 may be the core journals of the publication of research on stem cells in stroke. The most widely followed journal in this field of study is Cell Transplantation (IF = 4.139, Q3), with the majority of research on stem cell therapy published in this journal. Stroke (IF = 10.17, Q1) has the greatest IF, which also received the most citations (19,776 times). According to the Journal Citation Reports (2021 edition), five journals had IF values between 5 and 10 (Journal of Cerebral Blood Flow And Metabolism, International Journal Of Molecular Sciences, Experimental Neurology, Stem Cell Research and Therapy, and Neural Regeneration Research), four had an IF value between 3 and 5 (Cell Transplantation, Brain Research, Plos One, and Neuroscience), and no journal had an IF value below 3. These results indicated that most studies were published in these high-quality journals, and when researching on this topic, academics should concentrate on the content published in these journals. Most of the co-cited journals are high-impact Q1 journals, as can be seen from the list of co-cited journals. These journals support the investigation of stem cells in stroke and are undoubtedly of high quality. Furthermore, current research on stem cells in stroke is mostly published in journals pertaining to molecular biology, immunology, and medicine, followed by journals about neurology, sports, and ophthalmology. This indicates that basic research still constitutes the focus of the research and that the proportion of clinical studies is relatively low.
As for the authors, Chopp, M. (Oakland University, USA) is the most prolific, followed by Borlongan, Cesario, V (Stanford University, USA). They contributed to so many publications and were leaders in this field. Before 2015, Professor Chopp, M. and his team mainly focused on the role of stem cells in stroke, including BMSCs, neural progenitor cells, and human umbilical cord blood cells. In 2002, they found that after adult mouse suffered focal cerebral ischemia, endothelial progenitor cells generated from bone marrow contributed to brain neovascularization (Z. G. Zhang et al., 2002). Then in 2006, they demonstrated that intracarotid transplantation of BMSCs increased axon-myelin remodeling following stroke (L.H. Shen et al., 2006). Their experimental results in 2010 indicated that following a stroke in mice, MSC-mediated enhanced tPA activation in astrocytes encouraged neurite development (Xin et al., 2010). The next year, they published a study on how the production of astrocytic endogenous glial cell-generated neurotrophic factor was enhanced by BMSC implantation in the ischemic boundary area following stroke in adult rats (Shen et al., 2010). In the same year, they confirmed that the subventricular area has more progenitor cells dividing than normal due to the human umbilical cord tissue-derived cells (hUTCs) (Zhang et al., 2011) and had a neurorestorative effect (Ding et al., 2013). They proposed that the level of proinflammatory factors in the blood can be significantly reduced after hUTC transplantation (Bae et al., 2012). Furthermore, Chopp, M. discovered several chemicals that control stem cell migration, differentiation, and proliferation, such as specific miRNAs (X. S. Liu et al., 2013; Buller et al., 2012), atorvastatin (R. L. Zhang et al., 2012; J; Chen et al., 2008), angiopoietin 2 (X. S. Liu et al., 2009), and erythropoietin (L. Wang et al., 2008). These findings provided a solid theoretical basis for the rapid development of stem cells in stroke. Afterwards, Chopp, M. and his team shifted the attention to MSC-derived exosomes and described how they contribute to immunological reactivity, vascular remodeling, and brain regeneration during stroke recovery. In addition, they provided an overview of the potential and perspectives of stem cells in the fields of stroke and regenerative medicine, the links between stem cells and inflammatory factors for stroke rehabilitation, and the prospects of exosomes in the field of stroke. In the recent 2 years, Chopp et al. gradually focused on and studied the application of extracellular vesicles in neurological diseases, indicating that the extracellular vesicles may perhaps become a new research hotspot in this field.
The most commonly co-cited author is Chen, Jl. ( citation = 2,214), followed by Jin, K.L. (citation = 1,473), and Li, Y. (citation = 1,367). In 2006, Chen, Jl. investigated the effects of giving human BMSCs (hBMSCs) intravenously after intracerebral hemorrhage (ICH) in rats and found that doing so dramatically improves neurological function (Seyfried et al., 2006). This paper provided the basis for the clinical investigation of BMSCs in ICH. The next year, Chen, Jl. demonstrated neurological recovery in rats intravenously injected with hBMSCs 1 month following a stroke (Shen et al., 2007b) and published the first 1-year follow-up report of BMSC therapy in stroke rats (Shen et al., 2007a). This report proved that BMSCs have an effect on scarring reduction and cell proliferation increase. In 2013, Chen, Jl. concluded that while the effect of multiple injections did not outperform single-injection therapy in terms of functional outcomes and histological evaluations, it significantly improved long-term functional outcomes following stroke (Shehadah et al., 2013). In 2014, in a review article published in Progress in Neurobiology, Chen, Jl. described the application of stem cells from various cell origins in stroke as well as the restorative mechanisms, distribution methods, and imaging methodologies, which also covered the difficulties in stem cell therapy converting to clinical applications (X. Liu et al., 2014). The aforementioned studies generally focus on the mechanisms and therapeutic benefits of stem cell treatment for stroke, indicating that the field is still in the development stage and that additional basic and clinical translational research are still required. Clearly, the achievements of Chen, Jl. have laid a theoretical and experimental basis for research on stem cells in stroke.
A reference is deemed to be co-cited if it is cited in a number of different publications, in which case the co-cited references could be viewed as the foundation of the field’s study. To determine the research foundation for stem cells in stroke, we selected 15 references with the largest number of co-citations for this bibliometric analysis. Among the top 15 co-cited publications, two were written by Caplan et al., the first of which was the most often mentioned study and was published in the Journal of Cellular Biochemistry in 2006. This study firstly showed the trophic of the MSC-secreted bioactive molecules and summarized the application of the MSC trophic effect in injured tissues (Caplan and Dennis, 2006). The biological mechanisms of the in vivo functionality of MSCs during development and aging were outlined in another study (Caplan, 2007). The second most co-cited study was written by Chamberlain, G. et al., in 2011 and published in STEM CELLS. Their discovery that the movement of MSCs from the circulation into tissues may be facilitated by chemokine receptors and adhesion molecules attracted interest in terms of the function of stem cells in immunological regulation. Of the top 15 co-cited references (Abrous, Koehl, and Le Moal, 2005; Ohab et al., 2006), two were about the neural regeneration function of stem cells, outlining the molecular mechanisms between stem cells and neurogenesis and suggesting that stem cell therapy may be a therapeutic strategy for nervous system diseases. Three references (Schmidt-Lucke et al., 2005; Falk, 2006; Dutta et al., 2012) demonstrated that stem cells can repair blood vessels and promote neoangiogenesis. Overall, the biological function, transplantation, components, and targeted delivery of stem cells are the main subjects of discussion in the top 15 co-cited references, which represent the research foundation of stem cells in stroke.
## 4.2 Hotspots and frontiers
Citation bursts refer to references that have received a lot of recent citations from other scholars and highlight emerging themes within a specific research field. In accordance with the key research topics of the references with the strongest citation bursts (Figure 7C), the potential mechanism of directed migration and neurogenesis of NSCs as well as the therapeutic effects of stem cells in stroke are currently the main areas of study for research on stem cells in stroke. In addition to references with citation bursts, keywords can facilitate swift capturing of the distribution and development of hotspots in the realm of research on stem cells in stroke. Combining the citation bursts and keywords, we prepare to divide the hotspots and frontiers into two main research areas, namely, the mechanistic research hotspots and the clinical research hotspots, to discuss the distribution of their respective hot spots according to Table 6 and Figures 8A, B.
## 4.3.1 Stem cell and regeneration mechanism
As can be seen from Table 6, “neurogenesis,” “angiogenesis,” and “neural stem cells” are currently the focus of research in stem cell regeneration. Nerve and cerebrovascular regenerations are essential for recovery from stroke (Zhang et al., 2022). In 1992, Reynolds, Weiss, et al. isolated NSCs and fostered for the first time in the presence of epidermal growth factor, leading to large cell spheres they called “neurospheres” (Reynolds and Weiss, 1992). The discovery of NSCs provides a new promising therapy for nerve and vascular regenerations in stroke. Neuronal and glial cells originate from the common immature NSC, defined as self-renewing and multipotent cells that can differentiate into neurons, astrocytes, and oligodendrocytes (Reynolds and Weiss, 1992). NSCs were found to exist not only in the developing brain but also in the mature mammalian brain. Many studies have demonstrated that NSCs can replace lost neurons and restore connectivity in neuronal circuits, contributing to improved recovery from stroke and brain injury in rats (Daadi et al., 2009; Yokobori et al., 2019; Abeysinghe et al., 2015; Hou et al., 2017; G; Wang et al., 2020). Transplanted NSCs may prevent neuronal apoptosis, exert immunomodulatory effects both inside and outside the brain, and increase endogenous neuronal regeneration and angiogenesis (G.-L. Zhang, Zhu, and Wang, 2019; Horie, Hiu, and Nagata, 2015). Numerous studies have evaluated the therapeutic efficacy and safety of transplanted exogenous NSCs in preclinical animals with cerebral ischemic stroke (Daadi et al., 2009; Horie et al., 2011; Yokobori et al., 2019). However, due to the limited regeneration capacity of NSCs, the physiological environment is complex, which limits their repair effect. The current alternative approach to the use of NSCs is the use of inducible pluripotent stem cells or MSCs. Tobin and colleagues reported that both activated and naive MSCs induced complete behavioral recovery, reduced infarct volumes, and reduced microglial activation and IL-1β, TNF-α, and IL-6 levels in treated animals compared with vehicle-treated control stroke animals (Tobin et al., 2020). The angiopoietin expression and blood vessel density in ischemic brain tissue significantly increased after MSC transplantation (Zhang et al., 2022). MSC transplantation can promote neurogenesis mainly involving enhancement of endogenous neural cell proliferation and protection of newly grown cells from the pathogenic environment. A recent study demonstrated that MSC spheroid-loaded collagen hydrogels played a therapeutic role through three upregulated signals related to cell communication and upregulated the PI3K-Akt signaling pathway, which increased the expression of proteins related to neurogenesis and neuroprotection (He et al., 2021b). As MSCs can be used to promote the differentiation of NSCs into neurons by the production of different classes of trophic factors and anti-apoptotic molecules, future studies can focus on the development of MSC cell therapies associated with NSCs to facilitate nervous system recovery.
## 4.3.2 Antioxidant and anti-inflammatory mechanism
Research on “oxidative stress” and “inflammation” have gradually become popular in recent years, suggesting that scholars have investigated stem cells into a deeper level. “ Oxidative stress” and “inflammation” were among the top 25 keywords from 2020 to 2022 with the most citation spikes. Previous research demonstrated that oxidative stress and inflammation are two of the initial steps in the chain of events leading to cerebral ischemia injury, which disrupts various neuronal circuits (Rana and Singh, 2018; Chen H. et al., 2020; Chen S. et al., 2020). It has been commonly acknowledged that inflammation plays a major role in the development and course of the disease, as well as in recovery and wound healing following a stroke (Shekhar et al., 2018). Meanwhile, the potential of antioxidant therapies for stroke is suggested by the presence of induced oxidative modulatory pathway in the development of stroke (Pradeep et al., 2012). In recent years, it has been proven that stem cell therapy is a potentially successful treatment for inhibiting inflammation and oxidative stress following stroke (Lei et al., 2022). Researchers found that MSCs reduced the level of cellular oxidative stress and elevated the intracellular calcium and reactive oxygen species of neuronal cells when under stress from cerebral ischemia (K.-H. Chen et al., 2016; Alhazzani et al., 2018). In addition, emerging evidences have suggested that stem cells can increase the effectiveness of mitochondrial transfer to improve oxidative phosphorylation, lessen cellular oxidative stress levels and subsequently the brain damage cascade caused on by ischemic injury (Tseng et al., 2021; K; Liu et al., 2019). Based on the foregoing discussion, by lowering the degree of oxidative stress and transferring healthy mitochondria to damaged cells, stem cells engage their antioxidant ability in ischemic stroke. As for the inhibition of inflammation, recent research suggested that stem cells can regulate immune cell infiltration and polarization in ischemic brain to reduce neuroinflammation (Li et al., 2019; Yang et al., 2020). Interestingly, many studies reported no difference in or even worse efficacy of anti-inflammatory agents for stroke (Iosif et al., 2008; Tobin et al., 2014). However, some researchers used anti-inflammatory compounds to strengthen the anti-inflammatory effects of MSCs. For instance, some investigators found that MSCs from human umbilical cords that overexpress C–C motif chemokine ligand two or MSCs that have been activated with interferon-γ have a stronger anti-inflammatory ability in ischemic stroke compared with naive MSCs (Lee et al., 2020; Tobin et al., 2020). Collectively, oxidative stress and inflammation are both mechanisms of the main occurrence and development of stroke. Stem cells can fight oxidative damage, reduce inflammation, and alleviate aggravation of stroke. However, the mechanisms involves many pathophysiological processes and molecular pathways, which are needed to figure out, including the phenomenon caused by MSCs combined with anti-inflammatory agents, as described above. Therefore, in an attempt to understand the underlying mechanism, more research on mechanism is warranted.
## 4.3.3 Stem cells and extracellular vesicles
“Mesenchymal stem cells” are among the current study hotspots in terms of stem cell type selection. Among several stem cell types, MSCs have attracted the attention of numerous researchers. MSCs can be simply extracted from the bone marrow ($51\%$), umbilical cord ($17\%$), and adipose tissue ($11\%$) (Kabat et al., 2020). MSC therapy has been proven to reduce inflammation, encourage neurogenesis, and inhibit angiogenesis and apoptosis to improve neuronal defects, neural network reconstruction, and neurological functions (Zhu et al., 2019). Cui J. et al. found that MSCs can reduce neurological impairments and enhance axonal regeneration in rats with stroke (Cui et al., 2017). Furthermore, MSCs exert angiogenic effects based on secreted angiogenic factors by significantly enhancing vitality, motility, and network formation (König et al., 2015). The fact that neuroinflammation accelerated the development of brain injury is widely accepted. The method for controlling immune response may thereby decrease brain damage. Studies have demonstrated that stem cells can facilitate nerve repair either by boosting the protective effects of anti-inflammatory cytokines or performing immunomodulatory functions, such as neutrophil and microglia regulation (Jingli et al., 2022). For instance, following a stroke, MSCs can suppress microglial activation by upregulating growth factors and hypoxia-inducible factor-1-alpha while downregulating proinflammatory cytokines and chemokines (Yan et al., 2013). Current studies have verified that the neuroprotection of MSCs in stroke. Therefore, enhancing the therapeutic benefits and immunomodulatory capabilities of MSC may provide researchers with potential targets for their future studies.
From the stem cell derivatives used in the treatment of stroke, our data (Figure 8B) indicated that the latest research hotspots from 2019 to 2022 were “exosomes” and “extracellular vesicle.” Extracellular vesicles, which can transport a variety of cargos, including lipids, nucleic acids, and proteins, and are released from the cell surface into body fluid, help cells communicate with one another (Allan et al., 2020). One of the most currently appealing subcategories of extracellular vesicles is exosome (Lawson et al., 2016). In recent years, studies have suggested restrictions and potential hazards with stem cell therapy, such as tumorigenicity and the inability to successfully penetrate the blood-brain barrier (BBB) (Lukomska et al., 2019). Researchers also found that the use of exosomes generated from stem cells could be an alternative strategy to stem cell (Venkat, Chopp, and Chen, 2018; Cai et al., 2020). The nanoscale characteristics of exosomes allow themselves to efficiently disperse throughout the body and pass across the BBB. Furthermore, it can imitate the ability of stem cells and other supporting cells to regenerate. Stem cells release exosomes to communicate with other cells, when they detect alterations in the microenvironment, such as “inflammation” or “oxidative stress.” Exosomes have been demonstrated to be useful for the regulation of post-stroke inflammation, neurovascular remodeling, angiogenesis, neurogenesis, synaptic plasticity, and apoptosis and autophagy control (Seyedaghamiri et al., 2022). According to some authors, exosomes can be used as natural biomarkers to gage the seriousness of clinical manifestations of neurological diseases (Hong et al., 2019).
## 4.3.4 Clinical application prospect of stem cells in stroke
Basic science and animal models have laid the groundwork for advancing stem cell therapy for stroke in clinical setting. NSC transplantation has been performed mainly as a treatment for chronic phase of post-stroke. In a PISCES one clinical trial (Kalladka et al., 2016), the NSC transplantation to the patients within 6 months to 5 years after stroke showed that the embedded delivery of the NSC line CTX0E03 was safe and suggested improved neurological function. However, the study was conducted on only 11 men; thus, further study is needed that includes female patients and larger patient populations. In another PISCES two study that included adults aged over 40 years with significant upper-limb motor deficit 2–13 months following ischemic stroke, 23 patients underwent CTX cell transplantation, and their upper-limb functions improved at 3, 6, and 12 months (Muir et al., 2020). Subsequently, the PISCES-3 study recruited approximately 130 patients with moderate to severe functional disability from 6 to 24 months after the stroke. The primary outcome was an improvement in the modified Rankin scale (mRS) score at 6 months following surgery (Wechsler et al., 2018). However, due to the ethical issues related to the harvesting of fetal NSCs, as well as the limited number of donors, very few trials used NSCs. In addition, the clinical use of NSCs has several disadvantages, such as immunogenicity and the possibility of rejection of allogeneic human NSCs, which limits its application.
A large number of preclinical data have proven the feasibility of MSCs in the treatment of stroke, and clinical administration of stem cell therapy is expected. Most of the clinical trials have evaluated the efficacy and safety of MSCs for the treatment of stroke. MSCs, especially bone marrow-derived ones, are most widely used in clinical trials. In a randomized study of 30 patients with severe stroke, Bang et al. reported that MSCs improved the mRS and Barthel index scores within 1 year following stroke and exerted no adverse cell-related, serological, or imaging-defined effects (Bang et al., 2005). Honmou et al. reported that there were no major side events after the intravenous infusion of autologous BMSCs expanded in human serum into 12 participants 36–133 days post-stroke (Honmou et al., 2011). Overwhelming evidence supports the safety of the approach, although data on its efficacy are scarce or indicate only a transient improvement (Nistor-Cseppentö et al., 2022). Majority of the adverse events in these clinical trials were minor (Kvistad et al., 2022). For example, two minor side effects in a clinical trial ($$n = 57$$) using MSCs to treat stroke may be linked to venous internal position stimulation and urinary tract infection (Levy et al., 2019). However, some animal studies have demonstrated that MSCs may increase the risk of autoimmune disease and the onset of tumors (Djouad et al., 2003). This prompted the researchers to look for some countermeasures. Furthermore, most of the relevant clinical trials included only a few patients (n < 100), and large multicenter randomized controlled trials are absent; thus, further research is warranted to determine the efficacy and security of MSCs for stroke treatment and also to identify the optimal cell concentration, time, patient selection criteria (age, stroke subtype, and damage area), and combination therapy for routine clinical uses.
Most of the studies on exosomes have focused on animal and in vitro experiments. According to available information, a few clinical studies have used exosomes for stroke treatment. As of August 2022, only three clinical trials involving stroke patients were available on the public clinical trial database (https://clinicaltrials.gov/). One study evaluated improvement in patients suffering from acute ischemic stroke who were administered with allogenic MSC-derived exosomes. In a different study, the diagnostic value of blood extracellular vesicles was investigated in stroke patients undergoing rehabilitation. The final study aimed to determine how acupuncture-induced exosomes may help treat post-stroke dementia. In addition, a pilot randomized clinical trial suggested that local injection of exosomes produced by allogenic placenta MSCs is safe after a malignant middle cerebral artery infarction (Dehghani et al., 2022). These data suggest that exosome therapy is a novel promising strategy for stroke in clinical translational application. In conclusion, although the therapeutic effect, biosafety, kinetics, and biodistribution of exosomes still need to be thoroughly investigated, their capacity for regeneration and repair provides new possibilities for the treatment of stroke.
## 4.3 Advantages and limitations
This study has several advantages. First, based on research published from 2004 to 2022, this bibliometric analysis is the first investigation of patterns and contentious topics relating to stem cells in stroke. Second, we used three bibliometric methods simultaneously for the survey and analysis in this study, which significantly increased the likelihood that our data analysis process is impartial; VOSviewer and CiteSpace were extensively used in this study (C. Chen et al., 2012). This study also involved a thorough analysis of the number and growth tendency of annual publications; relationships among journals, authors, nations, and institutions; and various references, citations, and keywords.
This study also has limitations that need to be acknowledged. First, because this study only included articles in English, non-English writings may have been underrepresented. Second, only data obtained from the WoSCC database were used in this study; therefore, some pertinent studies from other databases may have been missed. Furthermore, insufficient data prevented full inclusion of articles in 2022.
## 5 Conclusion
This analysis may help researchers in identifying new trends and research hotspots for stem cells in stroke in the period of 2004–2022. The steadily increasing number of publications indicates that research on stem cells in stroke is becoming more and more important to academics worldwide. The top 3 countries with the most number of publications were China, the USA, and Japan. The journal, organization, and author with the most influence on were Cell Transplantation, Florida State University, and Chopp, M. respectively. The keywords that highlight recent hot topics about research on stem cells in stroke were “neurogenesis,” “angiogenesis,” “mesenchymal stem cells,” “oxidative stress,” “inflammation,” “exosomes,” and “extracellular vesicles,” which will probably become promising in the future. Notably, stem cells have numerous positive effects, such as neuroprotection, enhanced angiogenesis and neurogenesis, and diminished inflammatory and immunological responses; however, the main mechanisms for mitigating the damage caused by stroke are still unknown. Clinical challenges may include complicating factors, such as the effect of age, stroke subtype, and stroke severity, all of which can affect the efficacy of stem cell therapy. In the future, to successfully create a therapeutic scheme, all of complicating factors must be carefully taken into account.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
## Author contributions
QZe, GH, and YW: conception and design the logic, revising the article. QZh and YZ: study conduct and editing the article. SZ, JZ, and XyZ: data acquisition, analysis and interpretation. QZh, YZ, SZ, LC, HL, HC, and XfZ: writing the manuscript. QZh, YZ, and SZ contributed equally to this work and should be considered co-first authors. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1111815/full#supplementary-material
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|
---
title: Eldecalcitol prevented OVX-induced osteoporosis through inhibiting BMSCs senescence
by regulating the SIRT1-Nrf2 signal
authors:
- Yuying Kou
- Xing Rong
- Rong Tang
- Yuan Zhang
- Panpan Yang
- Hongrui Liu
- Wanli Ma
- Minqi Li
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10020367
doi: 10.3389/fphar.2023.1067085
license: CC BY 4.0
---
# Eldecalcitol prevented OVX-induced osteoporosis through inhibiting BMSCs senescence by regulating the SIRT1-Nrf2 signal
## Abstract
Background: Aging and oxidative stress are considered to be the proximal culprits of postmenopausal osteoporosis. Eldecalcitol (ED-71), a new active vitamin D derivative, has shown a good therapeutic effect on different types of osteoporosis, but the mechanism is unclear. This study focused on exploring whether ED-71 could prevent bone loss in postmenopausal osteoporosis by regulating the cell senescence of bone mesenchymal stem cells (BMSCs), and explaining its specific mechanism of action.
Materials and methods: An ovariectomized (OVX) rat model was established and 30 ng/kg ED-71 was administered orally once a day. The weight of rats was recorded regularly. Micro-computed tomography (CT) and histochemical staining were used to evaluate bone mass, histological parameters, and aging-related factors. Rat bone mesenchymal stem cells were extracted and cultivated in vitro. Aging cells were marked with senescence-associated β-gal (SA-β-gal) dyeing. The mRNA and protein levels of aging-related factors and SIRT1-Nrf2 signal were detected by RT-PCR, Western blot, and immunofluorescence staining. The reactive oxygen species (ROS) levels were detected by DCFH-DA staining.
Results: Compared with the Sham group, the bone volume of the ovariectomized group rats decreased while their weight increased significantly. ED-71 prevented bone loss and inhibited weight gain in ovariectomized rats. More importantly, although the expression of aging-related factors in the bone tissue increased in the ovariectomized group, the addition of ED-71 reversed changes in these factors. After extracting and in vitro culturing bone mesenchymal stem cells, the proportion of aging bone mesenchymal stem cells was higher in the ovariectomized group than in the Sham group, accompanied by a significant decrease in the osteogenic capacity. ED-71 significantly improved the bone mesenchymal stem cells senescence caused by ovariectomized. In addition, ED-71 increased the expression of SIRT1 and Nrf2 in ovariectomized rat bone mesenchymal stem cells. Inhibition of SIRT1 or Nrf2 decreased the inhibitory effect of ED-71 on bone mesenchymal stem cells senescence. ED-71 also showed a suppression effect on the reactive oxygen species level in bone mesenchymal stem cells.
Conclusion: Our results demonstrated that ED-71 could inhibit the cell senescence of bone mesenchymal stem cells in ovariectomized rats by regulating the SIRT1-Nrf2 signal, thereby preventing bone loss caused by osteoporosis.
## 1 Introduction
Osteoporosis, an increasingly severe social problem, is characterized by systemic damage to the bone mass, strength, and microstructure, increasing the tendency for fragility fracture (Rachner et al., 2011). Postmenopausal osteoporosis is characterized by insufficient estrogen secretion starting from the perimenopausal period, which leads to a more severe decrease in bone density and fracture risk in elderly women than in elderly men (Wei et al., 2021). During postmenopausal osteoporosis, osteoblasts and osteoclasts in bone tissue are affected and the balance of bone remodeling is destroyed, and a severe increase in bone absorption occurs, resulting in bone loss (Eriksen et al., 1990). However, recent studies have found that the inflammatory state caused by estrogen deficiency could damage the bone marrow environment and affect the physiological processes of cells (Weitzmann and Pacifici, 2006; Fischer and Haffner-Luntzer, 2022); among them, cell senescence has received increasing attention (Wu et al., 2020).
Cell senescence represents a permanent cell growth stagnation state (Salama et al., 2014), manifested by an arrested cell cycle, senescence-associated secretory phenotype (SASP), macromolecular injury and metabolic disorders (Sikora et al., 2021). Cell senescence is an important driving factor for aging and many diseases (Rossman et al., 2017; Sun et al., 2018; Song et al., 2020). The causes of cell senescence are considered to be aging, oxidative stress, accumulated damaged DNA molecules/mutations, and the shortening of the telomeres (Horvath and Raj, 2018; da Costa et al., 2016), but they aren’t completely clear. Studies have found that estrogen is closely related to cell senescence and age-related diseases. Estrogen is considered a key deciding factor in the aging of non-reproductive peripheral tissues, especially bone, skin, and brain (Emmerson and Hardman, 2012). Estrogen regulates the stemness and aging of bone mesenchymal stem cells (BMSCs) by inhibiting the ERβ-SATB2 pathway to prevent osteoporosis (Wu et al., 2018). Estrogen also regulates USP10 in osteoblasts and osteocytes, which accelerates P53 degradation, preventing cell senescence (Wei et al., 2021). A study has found that apoptosis and aging cells were increased in postmenopausal osteoporosis, and the expression levels of P53 and P16 is raised (Sui et al., 2016). Lack of P16 inhibits oxidative stress, osteocyte senescence, and osteoclastic bone resorption, stimulating osteogenesis and osteoblastic bone formation (Li et al., 2020). Therefore, the intervention of cell senescence might be an effective treatment for osteoporosis caused by the estrogen deficiency.
Adult stem cells are considered to be primarily responsible for cell function loss and senescence related to age (Yu et al., 2022). Mesenchymal stem cells (MSCs) originating from adult bone marrow stroma are the best-characterized mesoderm-derived stromal cells with multipotent differentiation capacity, which is related to the regeneration and stability of the tissue (Klimczak and Kozlowska, 2016). BMSCs, which mainly differentiate into osteoblasts and adipocytes (Kassem and Bianco, 2015), have the potential to treat a variety of diseases, including osteoporosis (OP), diabetes (DM), osteoarthritis (OA), myocardial infarction (MI), and Crohn’s disease (CD) (Liu et al., 2021). JAK suppression protects BMSCs and prevents them from aging, thereby preventing bone loss in ovariectomized (OVX) mice (Wu et al., 2020). Melatonin prevents estrogen deficiency-related bone loss by improving BMSCs resistance to cell senescence (Chen et al., 2022). Therefore, regulating BMSC senescence may be an important way to treat osteoporosis.
A study has shown that supplementation of vitamin D and calcium is recommended for patients with osteoporosis as a baseline treatment (Rachner et al., 2011). Vitamin D is synthesized in the skin or absorbed from the diet. In the liver, it is mainly transformed into 25-hydroxy-vitamin D by CYP2R1, and then it is transformed into 1, 25-dihydroxy-vitamin D, which has biological activity via CYP27B1 in the kidney (Goltzman, 2018). Active vitamin D combines with the vitamin D receptor (VDR), increasing the absorption of calcium in the intestine to maintain the calcium and phosphorus balance, or directly regulating bone metabolism by affecting the muscle and bones (Gunton et al., 2015; Anderson, 2017). It has also been found to play the role of anti-aging. 1, 25(OH)2D3 plays a role in preventing age-related osteoporosis by upregulating Ezh2 and inhibiting the senescence of BMSCs (Yang et al., 2020). However, few studies have explored the relationship between active vitamin D and cell senescence in postmenopausal osteoporosis. In recent years, the development and usage of active vitamin D analogs have become hot spots. Eldecalcitol (ED-71), a second-generation active vitamin D analog with a hydroxypropyloxy residue at the 2β position, has been approved in Japan for the clinical treatment of osteoporosis (Sakai et al., 2015). ED-71 provides a high combination with Vitamin D Binding Protein (DBP) (Kubodera et al., 2003) and a lower risk of persistent hypercalcemia (Matsumoto et al., 2005), which makes it a potential drug for osteoporosis treatment. Studies have found that ED-71 could significantly improve bone mineral density (BMD) in patients with postmenopausal osteoporosis (Iba et al., 2017), and reverse bone loss in OVX rats (de Freitas et al., 2011). ED-71 is more effective than Alfacalcidol in preventing vertebral and wrist fractures in patients with osteoporosis (Matsumoto et al., 2011). However, current research on the role of ED-71 in bone remodeling is mainly focused on the absorption of osteoclasts (Uchiyama et al., 2002; de Freitas et al., 2011). There is no research on whether it regulates cell senescence during the process of preventing osteoporosis.
Sirtuin1 (SIRT1) belongs to the family of NAD + -dependent deacetylases, and it is an important protective agent against oxidative stress and aging (Chen et al., 2020). Its activation prevents oxidative stress-induced endothelial senescence and dysfunction (Luo et al., 2021). In addition to histones, SIRT1 deacetylates non-histone substrates, including P53, FoxOs, PPAR-γ, and NF-κB (Nakagawa and Guarente, 2014). A study has found that 17β-estradiol could delay the cell senescence by upregulating SIRT1 (Han et al., 2013). Besides, Nuclear factor erythroid 2-related factor 2 (Nrf2) is a nuclear transcription factor that plays an indispensable role in inducing endogenous antioxidant enzymes against oxidative stress (Surh et al., 2008). Studies have shown that BMSCs senescence is accompanied by oxidative damage (Chen X. et al., 2019), and activation of Nrf2 is considered to inhibit cell senescence (Fang et al., 2018). SIRT1 can play the role of an upstream signal of Nrf2 (Xu et al., 2021; Lee et al., 2022). Studies have examined the relationship between vitamin D with SIRT1 and Nrf2. Vitamin D increases the osteogenic potential of BMSCs by raising SIRT1 (Borojević et al., 2022). Vitamin D is also found to regulate Nrf2 to inhibit oxidative stress and DNA damage in order to play a role in delaying senescence (Chen L. et al., 2019). These findings have increased our interest in the relationship between ED-71 and SIRT1-Nrf2 signal.
In this study, we explored the role of cell senescence during the occurrence of postmenopausal osteoporosis by establishing an OVX rat model. Next, we explored whether ED-71 improved the bone loss caused by OVX by regulating the senescence of BMSCs and explained its specific mechanism of action. Our research proposed a new mechanism of ED-71 to treat osteoporosis and provided a new idea for its clinical applications.
## 2.1 Animals and reagents
Twenty-four female Wistar rats were purchased from Jinan Pengyue Experimental Animal Breeding Co., Ltd. (Shandong, China). All animal experiments were approved by the Institutional Animal Care and Use Committee, School and Hospital of Stomatology, Shandong University (No. 20210912).
ED-71 was purchased from Chugai Pharmaceutical Co., Ltd. According to the manufacturer’s instructions, it was dissolved in absolute ethanol for storage. Before use, the solution was diluted to the corresponding concentration. The Anti-Osterix antibody (ab209484), Anti-P16 antibody (ab54210) and secondary antibodies (ab6721, ab102448, ab150081, and ab150119) were purchased from Abcam (Cambridge, United Kingdom). The Anti-Runx2 antibody (20700-1-AP), Anti-P53 antibody (60283-2-Ig), Anti-GAPDH antibody (10494-1-AP), Anti-SIRT1 antibody (60303-1-Ig) and Anti-Nrf2 antibody (16396-1-AP) were purchased from Proteintech (Chicago, United States). The Anti-β-gal antibody (bs-4631R) and Anti-Osteocalcin (OCN) antibody (bs-4917R) was purchased from Bioss (Beijing, China). The Anti-P16 antibody (A0262) was purchased from ABclonal (Wuhan, China). EX-527 (HY-15452) and ML-385 (HY-100523) were purchased from MedChemExpress (New Jersey, United States).
## 2.2 Establishment of an ovariectomized (OVX) rat model
Eight-week-old female Wistar rats were used to establish an OVX model. All animals were randomly divided into the following three groups ($$n = 8$$): Sham group, OVX group and OVX + ED-71 group. Further, $1\%$ pentobarbital sodium was used to anesthetize rats. After performing an incision on the back and separating the subcutaneous tissue and muscles, the oviducts were ligated and the ovaries were removed. Then the wound was sutured and antibiotics were provided for 3 days after the operation. ED-71 was given via the oral route at a concentration of 30 ng/kg once a day. The weight of rats was monitored weekly. After 8 weeks, part of the rats were euthanized through excessive anesthesia. The fresh bone tissues of rats were harvested for the isolation of BMSCs or stored at −80°C. Other rats were fixed with $4\%$ paraformaldehyde by cardiac perfusion. The femur was separated and decalcified in EDTA-2Na solution at 4°C. After dehydration in an ethanol gradient and transparent in xylene, the samples were embedded in paraffin and continuously cut into 5 μm thick slices.
## 2.3 Micro-computed tomography (CT) scan
After fixation with $4\%$ paraformaldehyde by cardiac perfusion, rat femurs were separated. Then, these tissues were scanned at a resolution of 14.8 μm, a voltage of 70 kvp, and a current of 200 mA. The three-dimensional images were reconstructed with a micro-CT analysis system (Scanco Medical, Switzerland). The data were obtained from the region of interest (ROI), which was along the long axis of the distal femur.
## 2.4 Hematoxylin and eosin (HE) staining
The paraffin sections were immersed in xylene for dewaxing and hydrated in descending gradient alcohol. Next, the sections were stained with hematoxylin for 15 min and washed with distilled water. Then they were stained with eosin for 7 min and washed again. Finally, sections were dehydrated and mounted. An optical microscope (Olympus BX-53, Tokyo, Japan) was used to observe and obtain the digital image. Image pro Plus 6.0 (IPP 6.0) software (Media Cybernetics, Silver Spring, MD, United States) was used for the quantitative analysis.
## 2.5 Masson staining
Masson trichrome staining was used to identify the regenerated ossification. After being dewaxed and hydrated, slices were stained with hematoxylin for 10 min and washed with distilled water. Next, the slices were immersed in a Ponceau S acid fuchsin solution for 7 min and differentiated in a phosphomolybdic acid solution for 4 min. Then they were moved to the aniline blue solution directly and stained for 1 min. Finally, the slices were washed, dehydrated, and mounted. Stained sections were observed with an optical microscope (Olympus BX-53, Tokyo, Japan) and digital images were obtained.
## 2.6 Immunohistochemical staining
After being immersed in xylene and hydrated in alcohol, the sections were treated with $0.3\%$ hydrogen peroxide for endogenous peroxidase inhibition, and then treated with $1\%$ bovine serum albumin (BSA) in PBS for 20 min for non-specific staining blocking. They were incubated overnight with primary antibodies at 4°C. After washing with PBS, they were incubated with the secondary antibody for 1 h at room temperature. Finally, visualization was achieved by 3, 3′-diaminobenzidine tetrahydrochloride (DAB). Counterstaining with methyl green was performed for all sections. An optical microscope (Olympus BX-53, Tokyo, Japan) was used to observe and take photographs. The Image pro Plus 6.0 (IPP 6.0) software was used to analyze the positive expression in all sections (optical density, OD).
## 2.7 Extraction and culture of BMSCs
After 8 weeks of model establishment, the rat tibia and femur were separated to isolate BMSCs. They were dissected in a sterile manner and washed with PBS containing $5\%$ penicillin and streptomycin. Then the proximal and distal ends were removed so that the bone marrow was exposed. The bone marrow was rinsed by a syringe with the α-modified Eagle’s medium (α-MEM) containing $20\%$ fetal bovine serum (FBS). After allowing it to stand for 5–7 days, the medium was replaced. When the adherent cells reached $80\%$–$90\%$ confluence, they were digested with $0.25\%$ trypsin-ethylene diamine tetra acetic acid. The third to fifth-generation cells were used.
## 2.8 Alkaline phosphatase (ALP) staining and alizarin red dyeing
The BMSCs were cultured in an osteogenic induction medium containing 0.1 μM dexamethasone, 10 mM β-glycerophosphate, and 0.05 mM ascorbic acid. The medium was replaced every 3 days. ALP staining was performed after osteogenic induction for 7 days. After being fixed for 20 min with $4\%$ paraformaldehyde, BMSCs were incubated with ALP solution for 25 min and washed with PBS. Alizarin red dyeing was performed after osteogenic induction for 21 days. The BMSCs were fixed with $4\%$ paraformaldehyde and stained with $1\%$ alizarin red staining solution (G1452, Solarbio, China). The PBS solution was used for washing. Finally, ALP-positive osteoblasts and calcium nodules were observed with an optical microscope (Olympus BX-53, Tokyo, Japan). Image pro Plus 6.0 (IPP 6.0) software was used to analyze the ALP staining positive rate. Alizarin red was isolated with cetylpyridinium chloride and detected using a spectrophotometer at 450 nm.
## 2.9 Oil red O staining
BMSCs were cultured in an adipogenic induction medium. According to the instructions of the reagent, the cells were cultivated alternately with induction liquid A and induction liquid B for 21 days. The BMSCs were fixed with $4\%$ paraformaldehyde and stained with the Oil Red O Solution (OILR-10001, OriCell, China). The formation of lipid droplets was observed by an optical microscope (Olympus BX-53, Tokyo, Japan).
## 2.10 Senescence β-galactosidase (β-gal) staining
Aging cells were stained with the Senescence β-Galactosidase Staining Kit (C0602, Beyotime, China). The cells were fixed for 15 min with a fixed solution and then washed 3 times with PBS. They were then incubated at 37°C overnight in the working dyeing fluid. Aging cells were observed under an optical microscope (Olympus BX-53, Tokyo, Japan), and the number of β-gal positive cells was counted.
## 2.11 Real-time polymerase chain reaction (PCR) analysis
The total RNA of tissues or cells was extracted by the RNAex Pro Reagent (Accurate Biology, Hunan, China), and cDNA was obtained by the Evo M-MLV reverse transcription kit (Accurate Biology, Hunan, China). Real-time PCR was performed with the SYBR Green PCR kit (Accurate Biology, Hunan, China). The relative expression levels of P16, P53, SIRT1, and Nrf2 were quantified using the 2−ΔΔCT method, and normalized with the GAPDH level. All quantitative real-time PCRs were performed using the Roche Light Cycler 96 Real-time PCR system (Roche, Sussex, United Kingdom), and all samples were run in triplicate. The primer sequences are shown in Table 1.
**TABLE 1**
| Gene | Forward | Reverse |
| --- | --- | --- |
| P16 | 5′-TGCGGTATTTGCGGTATCTACTCTC -3′ | 5′-GGCCTAACTTAGCGCTGCTTTG-3′ |
| P53 | 5′-GCCATCTACAAGAAGTCACAACAC-3′ | 5′-TGTCGTCCAGTACTCAGCATAC-3′ |
| SIRT1 | 5′-TGACGCCTTATCCTCTAGTTCCT-3′ | 5′-TCAGCATCATCTTCCAAGCCATT-3′ |
| Nrf2 | 5′-TTAAGCAGCATACAGCAGGACAT-3′ | 5′-GGACAGTGGTAGTCTCAGCCT-3′ |
| GAPDH | 5′-GACATGCCGCCTGGAGAAAC-3′ | 5′-AGCCCAGGATGCCCTTTAGT-3′ |
## 2.12 Western blot analysis
The proteins of tissues and cells were extracted with RIPA Lysis Buffer (Cwbio, Beijing, China) mixed with protease and phosphatase inhibitors at a ratio of 98:1:1. The protein concentration was measured by the BCA protein assay (Beyotime, Beijing, China). Then the proteins were diluted in $\frac{1}{4}$ volume of 5×SDS loading buffer and heated at 97°C for 5 min. Equal amounts of protein were separated in a $10\%$ SDS-PAGE and transferred onto a PVDF membrane. They were incubated overnight with the primary antibody at 4°C, and then incubated with the secondary antibody at room temperature for 1 h. Finally, the enhanced chemiluminescence reagent (B500024, Proteintech Group, United States) and ECL detection system (Amersham Imager 600, General Electric Company, United States) were used to measure the immunoreactive bands. Each experiment was performed in triplicate.
## 2.13 Immunofluorescence staining
BMSCs were fixed with $4\%$ paraformaldehyde, permeabilized with $0.5\%$ Triton X-100, and blocked with $5\%$ BSA in PBS. Then they were incubated with the primary antibody at 4°C overnight, and with the secondary antibody for 1 h at room temperature. The nuclei were stained by incubation with DAPI for 5 min. Finally, the cells were observed under a fluorescent microscope (DMi8 automated, Leica Microsystems CMS GmbH, Germany). The relative fluorescence intensity was measured by Image J software.
## 2.14 Measurement of the intracellular reactive oxygen species (ROS) level
Changes in the ROS level were detected by the ROS Assay Kit (Beyotime, China). After adding 200 μm H2O2 for 30 min, BMSCs were stained with 10 μM DCFH-DA for 30 min at 37°C. Then, they were washed with α-MEM without fetal bovine serum 3 times. Images were captured with a fluorescence microscope (DMi8 automated, Leica Microsystems CMS GmbH, Germany). Image J software was used to measure the relative fluorescence intensity.
## 2.15 Statistical analysis
All values were expressed as mean ± standard deviation (SD). Statistical analysis was performed using GraphPad Prism six software (San Diego, California, United States). The difference between the two groups was assessed by the Student’s t test, and one-way ANOVA was used for comparing multiple groups, with Fisher’s least significant difference (LSD) test for multiple comparisons. $p \leq 0.05$ was considered statistically significant.
## 3.1 ED-71 prevented OVX-induced osteoporosis in vivo
The OVX rat model was established to explore the preventive effect of ED-71 on postmenopausal osteoporosis. The weight change curve showed that the weight of all rats was increased with age. Compared to the Sham group, weight of OVX rats was increased significantly, while ED-71 reversed the weight gain caused by OVX (Figure 1A). Micro-CT observed the 2D- and 3D-images of the femur, and it was clearly noted that the bone volume in OVX rats decreased compared to the Sham group, and this decrease in bone volume was improved in the OVX + ED-71 group (Figure 1B). HE staining showed that compared to the Sham group, the femoral bone volume and the number of trabecular bones were significantly reduced in the OVX group, accompanied by the thickness became narrow and the distribution became discrete and irregular. There was also a significant increase in lipid droplets in the bone marrow cavity. In the OVX + ED-71 group, the number and thickness of trabecular bones increased, and the separation of the trabecular bone and lipid droplets decreased (Figure 1C, D). Masson staining showed that the regeneration of new bone in the OVX group was significantly reduced compared to the Sham group, while it was increased in the OVX + ED-71 group (Figure 1C). In addition, the expression levels of the aging-related factors P16 and β-gal in the femur of OVX rats were significantly upregulated, while ED-71 reduced this increase (Figures 2A, B). The mRNA expression levels of P16 and P53 in the bone tissue of rats in the OVX group were higher than in the Sham and OVX + ED-71 groups (Figure 2C). The protein levels of P16 and P53 in bone tissues also showed the same trend (Figures 2D, E), indicating that ED-71 inhibited the senescence of bone tissue cells under the OVX state. These results suggested that ED-71 inhibited OVX-induced weight gain, bone loss, and cell senescence.
**FIGURE 1:** *The effect of ED-71 preventing the bone loss of OVX rat. (A) The curve of the weigh change of rats in Sham, OVX, and OVX + ED-71 groups. (B) The representative 2D and 3D Micro-CT image of rat femur. Bar, 1 mm. (C) The HE staining and Masson staining images of rat femur. Bar, 1,000 μm or 500 μm. (D) Statistical analysis for the bone volume fraction (BV/TV), trabecular number, trabecular thickness and trabecular separation. All experiments were carried out at least 3 times, and data are expressed as mean ± SD. *p < 0.05. **p < 0.01. ***p < 0.001. GP, Growth Plate. TB, trabecular bone. BM, bone marrow.* **FIGURE 2:** *The effect of ED-71 on cell senescence in Sham, OVX, and OVX + ED-71 group. (A) The immunohistochemical staining image of P16 and β-gal in rat femur. Bar, 20 μm. (B) Statistical analysis of the immunohistochemistry results. (C) The mRNA expression of P16 and P53 in bone tissue were detected by RT-PCR. (D) The protein level of P16 and P53 in bone tissue were analyzed by Western blot. (E) The statistical analysis of Western blot results. All experiments were carried out at least 3 times, and data are expressed as mean ± SD. *p < 0.05. **p < 0.01. ***p < 0.001. TB, trabecular bone. BM, bone marrow.*
## 3.2 OVX accelerated the senescence of rat BMSCs
To further explore the role of cell senescence during ED-71 preventing osteoporosis, BMSCs from rats in the Sham and OVX groups were extracted and cultivated in vitro. Under the optical microscope, BMSCs showed a polygonal shape (Figure 3A). At the same time, BMSCs showed good potential for osteogenesis and adipogenesis in vitro (Figure 3A). ALP staining showed that after 7 days of osteogenic induction, ALP-positive osteoblasts in the OVX group were significantly reduced than that in the Sham group, prompting that the osteogenesis ability of OVX rat BMSCs was reduced (Figure 3B). Western blot also showed that the expressions of Runx2, OCN, and Osterix in BMSCs after 14 days of osteogenic induction were significantly decreased in the OVX group (Figures 3C, D). Alizarin red staining showed that the mineralization ability of BMSCs in OVX group was significantly decreased compared with that in Sham group (Figure 3E). More importantly, the OVX group had more β-gal positive cells than the Sham group (Figure 3F). Higher mRNA expression levels of P16, and P53 were observed in the OVX group (Figure 3G). The protein levels of P16 and P53 in the OVX group also increased (Figures 3H, I). Immunofluorescence staining of P16 and P53 showed the same trend (Figures 3J, K). These results suggested that aging cells in BMSCs of OVX rats were significantly increased compared to those in BMSCs of Sham rats.
**FIGURE 3:** *The osteogenesis ability and senescence level of rat BMSCs. (A) The morphology of rat BMSCs primary cells under the optical microscope, and the oil red O and alizarin red staining of BMSCs. Bar, 200 μm or 100 μm. (B) ALP staining of rat BMSCs in Sham and OVX group. Bar, 1,000 μm. (C) The protein level of Runx2, OCN, and Osterix in BMSCs after osteogenic induction were analyzed by Western blot. (D) The statistical analysis of Western blot results. (E) Alizarin red staining of rat BMSCs in Sham and OVX group. Bar, 200 μm. (F) The cytochemical staining of senescence-associated β-gal (SA-β-gal) and the statistical analysis. Bar, 200 μm. (G) The mRNA expression of P16, P19, and P53 in BMSCs were detected by RT-PCR. (H) The protein level of P16 and P53 in BMSCs were analyzed by Western blot. (I) The statistical analysis of Western blot results. (J) The immunofluorescence staining of P16 in rat BMSCs and the statistical analysis. Bar, 100 μm. (K) The immunofluorescence staining of P53 in rat BMSCs and the statistical analysis. Bar, 100 μm. All experiments were carried out at least 3 times, and data are expressed as mean ± SD. *p < 0.05. **p < 0.01. ***p < 0.001.*
## 3.3 ED-71 improved the cell senescence of OVX rat BMSCs
After adding 50 nM ED-71 to OVX rat BMSCs, RT-PCR showed that ED-71 could downregulate the mRNA expression levels of P16 and P53 in BMSCs (Figure 4A). Western blot also showed that the protein levels of β-gal, P16, and P53 in BMSCs were suppressed by the addition of ED-71 (Figures 4B, C). The results of β-gal staining showed that the number of aging cells was gradually decreased in the control, 5 nM ED-71, and 50 nM ED-71 groups, indicating that ED-71 significantly improved the cell senescence of rat BMSCs induced by OVX (Figures 4D, E). With respect to immunofluorescence staining of β-gal, the positive expression of β-gal was significantly reduced after adding 5 nM and 50 nM ED-71 (Figures 4F, G). Besides, the results of Western blot showed that ED-71 significantly increased the protein expression of Runx2, Osterix and OCN in BMSCs after 14 days of osteogenic induction, suggesting that ED-71 promoted the osteogenic differentiation of BMSCs (Figures 4H, I). Therefore, ED-71 could significantly inhibit cell senescence and promote osteogenic differentiation.
**FIGURE 4:** *The effect of ED-71 on senescence of BMSCs. (A) The mRNA expression of P16 and P53 in BMSCs with or without the existence of ED-71 were detected by RT-PCR. (B) The protein level of P16, P53, and β-gal in BMSCs were analyzed by Western blot. (C) The statistical analysis of Western blot results. (D) The cytochemical staining of SA-β-gal. Bar, 200 μm. (E) The statistical analysis of β-gal staining. (F) The immunofluorescence staining of β-gal. Bar, 500 μm. (G) The statistical analysis of the fluorescence intensity. (H) The protein level of Runx2, Osterix, and OCN in BMSCs after osteogenic induction were analyzed by Western blot. (I) The statistical analysis of Western blot results. All experiments were carried out at least 3 times, and data are expressed as mean ± SD. *p < 0.05. **p < 0.01. ***p < 0.001.*
## 3.4 ED-71 upregulated the SIRT1-Nrf2 signal in OVX rat BMSCs
Next, we further explored the specific mechanism of ED-71 for inhibiting the cell senescence of OVX rat BMSCs. RT-PCR showed that after adding 5 nM or 50 nM ED-71, the mRNA expression levels of SIRT1 and Nrf2 were increased significantly in BMSCs (Figure 5A). The results of Western blot showed the same trend (Figures 5B, C). SIRT1 and Nrf2 also showed stronger fluorescence in the ED-71 group, suggesting that their expression was upregulated (Figure 5D). To prove our discovery, SIRT1 inhibitor EX-527 and Nrf2 inhibitor ML-385 were used to block the expression of SIRT1 or Nrf2 respectively. Western blot showed that the addition of EX-527 significantly inhibited the promotion effect of ED-71 on the expression of SIRT1, and at the same time, it also inhibited the protein level of Nrf2. The use of ML-385 blocked the promotion effect of ED-71 on Nrf2. Simultaneously, the additions of EX-527 and ML-385 reversed the inhibitory effect of ED-71 on the expression of P16 and P53 (Figures 5E, F). The results of β-gal staining showed that the addition of ED-71 reduced the number of aging cells, while the use of SIRT1 and Nrf2 inhibitors reversed this effect (Figures 5G, H). In addition, after adding 200 μM H2O2 for 30 min, obvious accumulation of ROS occurred in BMSCs, and this ROS accumulation was reduced by the use of ED-71 (Figures 5I, J). These results proved that ED-71 could inhibit the cell senescence through the SIRT1-Nrf2 signal, and it could also protect the BMSCs from damage caused by oxidative stress.
**FIGURE 5:** *The effect of ED-71 on the SIRT1-NRF2 signal. (A) The mRNA expression of SIRT1 and Nrf2 in BMSCs with or without the existence of ED-71 were detected by RT-PCR. (B) The protein level of SIRT1 and Nrf2 in BMSCs were analyzed by Western blot. (C) The statistical analysis of Western blot results. (D) The immunofluorescence staining of SIRT1 and Nrf2 and the statistical analysis of the fluorescence intensity. Bar, 100 or 200 μm. (E) The protein level of P16, P53, SIRT1, and Nrf2 in BMSCs were analyzed by Western blot. (F) The statistical analysis of Western blot results. (G) The cytochemical staining of SA-β-gal. Bar, 200 μm. (H) The statistical analysis of β-gal staining. (I) The ROS levels in BMSCs was detected by DCFH-DA staining. Bar, 500 μm. (J) The statistical analysis of DCFH-DA staining. All experiments were carried out at least 3 times, and data are expressed as mean ± SD. *p < 0.05. **p < 0.01. ***p < 0.001.*
## 4 Discussion
In this study, we explored the effect of ED-71 on cell senescence during the process of preventing postmenopausal osteoporosis through in vivo and in vitro experiments. Our results showed that ED-71 might reduce bone loss in OVX rats by inhibiting the senescence of BMSCs. ED-71 could also protect the BMSCs from damage caused by oxidative stress. In addition, the inhibitory effect of ED-71 on cell senescence might have occurred through the SIRT1-Nrf2 signal (Figure 6). Our results provided a new direction for exploring the mechanism of action of ED-71, that is, cell senescence, an important pathological manifestation of postmenopausal osteoporosis, might be an effective target for ED-71 treatment.
**FIGURE 6:** *Schematic diagram: ED-71 could inhibit the cell senescence of BMSCs in OVX rats by inhibiting the oxidative stress through the SIRT1-Nrf2 signal (By Figdraw; www.figdraw.com).*
As already known, postmenopausal osteoporosis is caused by estrogen deficiency. Eight-week-old rats were used to establishing an OVX model to simulate this process, which referred to other studies (Wu et al., 2020; Chen et al., 2022). The results showed that the femoral bone mass was significantly decreased in OVX rats, accompanied by an increase in rat weight and fat in bone marrow. This confirmed that our model was successfully established. At the same time, treatment with ED-71 significantly increased the bone volume and the number of trabecular bones, as well as the regeneration of new bone, consistent with a previous report (de Freitas et al., 2011). Interestingly, we found that ED-71 could significantly reduce lipid droplets in the bone marrow of rats. It was suggested that ED-71 might reduce the adipogenic differentiation of BMSCs. Although in a previous study, we have also observed the possible lipid-lowering effect of ED-71 (Lu et al., 2022), whether it is early work or in this study, we have not explored this aspect. Therefore, this may be a direction we study next.
With the decrease in bone mass, the positive expression of aging-related factors increased in the femur area of OVX rats, including the cells of bone marrow and bone surface. A higher expression of aging-related factors was found in the extract of OVX rat bone tissue, which suggested that OVX rats showed a significant aging state. After adding ED-71, the cell senescence caused by OVX was significantly reduced. An increase in cell senescence in postmenopausal osteoporosis has been partially reported (Manolagas, 2010; Wu et al., 2020). Studies have also found that in various osteoporosis models, the senescence of BMSCs was increased and the expression levels of P16 and P53 were raised (Sui et al., 2016). Aging BMSCs show loss of pluripotency, and changes in its differentiation potential, and the dynamic balance between osteogenesis and adipogenesis (Liu et al., 2021). P16 located in the Ink4a gene is strictly controlled by members of the PRC family, and it is a key gene that induces aging (Aguilo et al., 2011). P53, a cell cycle regulatory factor, participates in the function of the cell cycle, apoptosis, and genome stability (Armesilla-Diaz et al., 2009). Senescence-associated acidicβ-galactosidase (SA-β-Gal) is a widely used marker of aging (Itahana et al., 2007; Mohamad Kamal et al., 2020). Next, we extracted the BMSCs from rats in the Sham and OVX groups, and we found that aging cells were significantly increased in BMSCs from OVX rats, which was accompanied by a decrease in osteogenesis. This is consistent with the reported research (Huang T. et al., 2020). More importantly, we found that ED-71 significantly improved the senescence of OVX rat BMSCs, which was proved by β-gal staining, RT-PCR, and Western blot. This further verified our in vivo discovery, i.e., regulatiing cell senescence by ED-71 may be an important way to prevent osteoporosis.
An increasing number of studies have shown that active vitamin D is closely related to aging. Evidence shows that the synthesis of vitamin D decreases with age, resulting in aging. Vitamin D participates in maintaining genome stability and telomere length, which is a direct decisive factor for cell senescence (Bima et al., 2021). Active vitamin D could inhibit cell senescence and apoptosis by inhibiting oxidative stress and DNA damage (Chen L. et al., 2019). Active vitamin D deficiency is found to accelerate male reproductive senescence (He et al., 2021). In addition, a study has found that active vitamin D improved age-related osteoporosis through the VDR-EZH2-P16/P19 signaling pathway (Yang et al., 2020). ED-71, a new type of active vitamin D analog, has a better application prospect than traditional vitamin D. Although our previous research has shown that ED-71 might participate in regulating the production of oxidative stress (Huang C. et al., 2020; Zhang et al., 2022), there is no evidence regarding whether ED-71 affects cell senescence. Our results showed that ED-71 could inhibit the senescence of cells induced by OVX, which filled the gap in research on this aspect.
In further research, we found that the addition of ED-71 might play a role in regulating SIRT1 and Nrf2. The inhibitory of SIRT1 and Nrf2 reversed the effect of ED-71 on senescence, which proved that ED-71 could inhibit the senescence of BMSCs by activating SIRT1 and Nrf2 signal. Some studies revealed the connection between SIRT1 and Nrf2. SIRT1 upregulated the downstream signaling pathway of Nrf2 by reducing the acetylation level of Nrf2, thereby improving myocardial ischemia/reperfusion injury (Xu et al., 2021). Galangin can exert anti-oxidant and anti-senescence effects through the SIRT1-PGC-1α/Nrf2 signal (Lee et al., 2022). Our results showed that in ED-71-induced inhibition of BMSC senescence, SIRT1 might act as an upstream signal to regulate Nrf2, which was consistent with previous research. Given the close connection between Nrf2 and oxidative stress, we also detected the effect of ED-71 on oxidative stress. It was consistent with our supposition that ED-71 improved the antioxidant capacity of BMSCs. Therefore, ED-71 might prevent the cell senescence of OVX rat BMSCs by inhibiting oxidative stress, although this requires further experiments to prove.
It is worth mentioning that, we also detected the differences in the osteogenic ability of BMSCs in Sham group and OVX group, and initially explored the effect of ED-71 on the osteogenic differentiation of BMSCs. The osteogenic differentiation ability of BMSCs in OVX group was reduced compared with Sham group, which was consistent with previous study (Huang T. et al., 2020). Although the link between cell senescence and osteogenic differentiation wasn’t verified in this study, it has been confirmed in previous studies. On the one hand, aging BMSCs showed weak osteogenic differentiation potential and enhanced lipogenic differentiation potential (Liu et al., 2015). P16 deletion could ameliorate OVX-induced decrease in osteogenic differentiation of BMSCs (Li et al., 2020). On the other hand, aging BMSCs also produce various tissue-aging stimulating factors, and these bioactive mediators are considered a component of the senescence-associated secretory phenotype (SASP). Molecules of the SASP are secreted into the bone microenvironment by senescent cells, which molecules inhibit the osteogenic differentiation of BMSCs (Xu et al., 2018). We also found that ED-71 could improve the osteogenic differentiation of OVX rat BMSCs. Some studies promoting osteogenic differentiation of BMSCs and improve osteoporosis by regulating cell senescence (Wu et al., 2020; Chen et al., 2022). SIRT1 and Nrf2 have also been found to be associated with the osteogenic differentiation of BMSCs (Wang et al., 2018; Fei et al., 2021). This suggested that ED-71 might regulate the osteogenic differentiation of OVX rat BMSCs by inhibiting cell senescence, which needs to be further verified in future studies.
Taken together, we found that ED-71 inhibited the senescence of OVX rats in vivo, and we explored the specific mechanism of ED-71 in regulating the SIRT1-Nrf2 signal in vitro. Our study provides a new direction for ED-71 during osteoporosis prevention.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC), School and Hospital of Stomatology, Shandong University.
## Author contributions
Conceptualization: YK and XR; methodology: YK, XR, RT, and YZ; formal analysis and investigation: YK, XR, and PY; writing—original draft preparation: YK and XR; writing—review and editing: YK; data curation: YK, XR, and RT; funding acquisition: ML and HL; resources: WM and ML; supervision: ML and PY; project administration: WM and ML; software: YK and XR; validation: YK and YZ; visualization: YK and XR.
## Conflict of interest
This study received funding from Chugai Pharma China Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. All authors declare no other competing interests.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Risk factors for pressure ulcer recurrence following surgical reconstruction:
A cross-sectional retrospective analysis'
authors:
- Yueh-Ju Tsai
- Cen-Hung Lin
- Yuan-Hao Yen
- Cheng-Chun Wu
- Carolina Carvajal
- Nicolas Flores Molte
- Pao-Yuan Lin
- Ching-Hua Hsieh
journal: Frontiers in Surgery
year: 2023
pmcid: PMC10020371
doi: 10.3389/fsurg.2023.970681
license: CC BY 4.0
---
# Risk factors for pressure ulcer recurrence following surgical reconstruction: A cross-sectional retrospective analysis
## Abstract
Many studies on the recurrence of pressure ulcers after surgical reconstruction have focused on surgical techniques and socioeconomic factors. Herein, we aimed to identify the risk factors of the associated comorbidities for pressure ulcer recurrence. We enrolled 147 patients who underwent pressure ulcer reconstruction and were followed up for more than three years. The recurrence of pressure ulcers was defined as recurrent pressure ulcers with stage $\frac{3}{4}$ pressure ulcers. We reviewed and analyzed systematic records of medical histories, including sex, age, associated comorbidities such as spinal cord injury (SCI), diabetes mellitus (DM), coronary artery disease, cerebral vascular accident, end-stage renal disease, scoliosis, dementia, Parkinson's disease, psychosis, autoimmune diseases, hip surgery, and locations of the primary pressure ulcer. Patients with recurrent pressure ulcers were younger than those without. Patients with SCI and scoliosis had higher odds, while those with Parkinson's disease had lower odds of recurrence of pressure ulcers than those without these comorbidities. Moreover, the decision tree algorithm identified that SCI, DM, and age < 34 years could be risk factor classifiers for predicting recurrent pressure ulcers. This study demonstrated that age and SCI are the two most important risk factors associated with recurrent pressure ulcers following surgical reconstruction.
## Introduction
The management of pressure ulcers is a significant challenge for healthcare professionals. Despite advances in information and technological progress for prevention, the recurrence of pressure ulcers is not rare (1–7). Many of these recurrent ulcers require prolonged time in wound care and even surgery management, both of which often result in costly procedures, lengthy hospitalizations, expensive dressing changes, and worsened quality of life for these people (8–13). To achieve successful surgical reconstruction, thorough preoperative wound care, patient compliance, control of comorbidities, professional postoperative support, and sufficient pressure relief are essential [14]. Krause and Broderick suggested that lifestyle, exercise, and diet are protective mechanisms against the recurrence of pressure ulcers [15]. Furthermore, lack of social support, inadequate pressure sore prevention knowledge [2, 5, 16, 17], unemployment, and residing in a nursing home [2, 12] have been considered important issues related to recurrence. Regarding demographic and medical factors, it has been reported that male sex, younger age, and a history of previous pressure sore surgery are associated with the recurrence of pressure ulcers [2, 12]. However, as many studies describing recurrence after reconstruction have focused on socioeconomic factors [2, 17, 18], education [18], marital status [19], and surgical techniques (20–26), available data on factors associated with recurrence following surgical repair of pressure ulcers are rather limited.
In this study, we aimed to identify the risk factors associated with comorbidities of pressure ulcer recurrence following surgical reconstruction. In addition, we adopted the decision tree method, which is a machine learning model composed of decision rules based on optimal feature cutoff values that split independent variables into different groups in a hierarchical manner to predict an outcome (27–29), to explore the variables that could be used to identify individuals at risk of pressure ulcer recurrence following surgical reconstruction.
## Materials and methods
This study was approved by the Institutional Review Board (IRB) of Chang Gung Memorial Hospital (approval number 201701802B0). The need for informed consent was waived according to IRB regulations because the study was designed for a retrospective analysis of the registered database. In this study, 147 bed-ridden patients who underwent reconstruction for pressure ulcers from 2007 to 2014 were enrolled and followed up for more than three years. The recurrence of pressure ulcers was defined as recurrent pressure ulcers with stage 3, full-thickness ulcer that might involve the subcutaneous fat, or stage 4, full-thickness ulcer with the involvement of the muscle or bone. The systematic records of medical histories, including sex, age, associated comorbidities such as spinal cord injury (SCI), diabetes mellitus (DM), coronary artery disease (CAD), cerebral vascular accident (CVA), end-stage renal disease (ESRD), scoliosis, dementia, Parkinson's disease, psychosis, autoimmune diseases, hip surgery, and locations of the primary pressure ulcer were reviewed. In this study, primary sacral pressure ulcers were treated with perforator flaps or rotation gluteal flaps, ischial pressure ulcers with muscle flaps (gluteal muscle or biceps femoris muscle) and skin flaps (rotation gluteal flap or posterior thigh flap), and trochanteric pressure ulcers with a gluteal rotation flap or pedicled anterolateral thigh flap.
## Statistical analysis
Statistical analyses were performed using IBM SPSS Statistics for Windows, version 20.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were obtained by calculating the mean and standard deviation for continuous variables and the relative frequencies for categorical variables. These groups were compared using the chi-squared test for categorical variables with odds ratios (OR) and $95\%$ confidence intervals (CIs). Student's t-test was used for the analysis of continuous variables. Statistical significance was set at p value < 0.05.
## Decision tree classifier
The decision tree classification model was established by classification and regression tree (CART) analysis [30, 31] using the rpart function in the rpart package in R based on the Gini impurity index. CART analysis was used to search for the split on each variable to partition the data into two groups: one group of mostly “1s” (people who had sustained recurrent pressure ulcers) and another group of mostly “0s” (people who did not have recurrent pressure ulcers). The CART model identified the best overall split by iteratively testing all possible splits and creating a specified number of nodes until a further reduction in node impurity became impossible or the specified stopping criteria were reached (32–34). In this study, the complexity parameter (α) of the “cost-complexity” pruning method is set to 0.001. The complexity parameter (α) indicated a measure of how much additional accuracy a split must add to the entire tree to warrant additional complexity. A confusion matrix was used to determine the performance of the decision tree model for the presence of recurrent pressure ulcers. Accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve were measured.
## Results
Of the enrolled 147 patients, 46 had recurrent pressure ulcers. As shown in Table 1, among these patients, SCI was the most common associated comorbidity ($$n = 31$$, $67.4\%$), followed by DM ($$n = 21$$, $45.7\%$), CVA ($$n = 8$$, $17.4\%$), and scoliosis ($$n = 8$$, $17.4\%$). Among the patients without recurrence, DM was the most common associated comorbidity ($$n = 39$$, $38.9\%$), followed by CVA ($$n = 30$$, $30.0\%$), SCI ($$n = 24$$, $23.8\%$), and Parkinson's disease ($$n = 16$$, $15.8\%$). Patients with recurrent pressure ulcers were significantly younger than those without recurrence (55.7 ± 17.4 vs. 62.8 ± 17.4, respectively, $$p \leq 0.023$$). Patients who had SCI and scoliosis had significantly higher odds of recurrence of pressure ulcers than those without (SCI, OR = 6.63, $95\%$ CI = 3.08–14.29; scoliosis, OR = 10.42, $95\%$ CI = 2.12–51.31). In contrast, those patients who had Parkinson's disease had significantly lower odds of recurrence of pressure ulcers than those without (OR = 0.12, $95\%$ CI = 0.02–0.92). There were no significant differences in sex, location of ulcer, and associated comorbidities, such as DM, CAD, CVA, ESRD, hip surgery, dementia, psychosis, and autoimmune diseases.
**Table 1**
| Unnamed: 0 | Recurrence (n = 46) | No recurrence (n = 101) | Unnamed: 3 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | | | OR | 95%CI |
| Male, n (%) | 29 (63.0%) | 50 (49.5%) | 1.27 | 0.89–2.43 |
| Spine cord injury (SCI), n (%) | 31 (67.4%) | 24 (23.8%) | 6.63* | 3.08–14.29 |
| Diabetes mellitus (DM), n (%) | 21 (45.7%) | 39 (38.9%) | 1.34 | 0.66–2.70 |
| Coronary artery disease (CAD), n (%) | 4 (8.7%) | 13 (12.9%) | 0.65 | 0.20–2.10 |
| Cerebral vascular accident (CVA), n (%) | 8 (17.4%) | 30 (30.0%) | 0.49 | 0.20–1.16 |
| End-stage renal disease (ESRD), n (%) | 1 (2.2%) | 11 (11.0%) | 0.18 | 0.02–1.45 |
| Scoliosis, n (%) | 8 (17.4%) | 2 (2.0%) | 10.42* | 2.12–51.31 |
| Hip surgery, n (%) | 3 (6.5%) | 7 (7.0%) | 0.94 | 0.23–3.80 |
| Dementia, n (%) | 4 (8.7%) | 8 (8.0%) | 1.11 | 0.32–3.88 |
| Parkinson's disease, n (%) | 1 (2.2%) | 16 (15.8%) | 0.12 | 0.02–0.92 |
| Psychosis, n (%) | 1 (2.2%) | 1 (1.0%) | 2.22 | 0.14–36.33 |
| Autoimmune diseases, n (%) | 1 (2.2%) | 3 (3.0%) | 0.73 | 0.07–7.17 |
| Locations | Locations | Locations | Locations | Locations |
| Ischium, n (%) | 27 (58.7%) | 22 (21.8%) | 5.1 | 2.4–10.8 |
| Hip, n (%) | 6 (13.0%) | 11 (10.9%) | 1.2 | 0.42–3.6 |
| Sacrum, n (%) | 13 (28.3%) | 64 (63.4%) | 0.22 | 0.10–0.47 |
| Others, n (%) | 0 (0.0%) | 4 (4.0%) | | |
According to the classification by the decision tree algorithm, three groups of patient characteristics (SCI, DM, age < 34 years) with a high risk of recurrent pressure ulcers were identified (Figure 1). The presence or absence of SCI in the DT model was identified as a variable for the initial split. Among patients with SCI, $69\%$ had recurrent pressure ulcers and $31\%$ did not. Among patients with SCI, the presence or absence of DM was identified as a variable in the second split. For this node, $80\%$ of patients with DM had recurrent pressure ulcers. An age of less than 34 years served as the third split for patients without DM. For this node, $58\%$ of patients aged < 34 years had recurrent pressure ulcers. With all variables in the model, the decision tree algorithm achieved an accuracy of $78.23\%$ (sensitivity of $65.22\%$ and specificity of $84.16\%$). The decision tree model had an AUC of 0.764 for predicting the recurrence of pressure ulcers (Figure 2).
**Figure 1:** *Illustration of decision tree model for predicting recurrence of pressure ulcers in the patients receiving reconstruction for pressure ulcer. Boxes denote the percentage of patients with discriminating variables from CART analysis. Patients with and without recurrence of pressure ulcers are indicated by the fractional number inside the right and left sides of the boxes, respectively.* **Figure 2:** *Illustration of AUCROC curves for the decision tree model.*
## Discussion
Authors should discuss the results and their interpretations from the perspective of previous studies and working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted. This study demonstrated that patients with recurrent pressure ulcers were significantly younger and had a higher rate of sustaining SCI and scoliosis but a lower rate of Parkinson's disease than patients without recurrence. In addition, the decision tree algorithm identified that SCI, DM, and age < 34 years could be used as risk factor classifiers for predicting recurrent pressure ulcers.
This study recognized SCI as a risk factor, either from conventional comparisons or decision tree algorithms. Patients with SCI had a 6.6 times higher risk of pressure ulcer recurrence following surgical reconstruction. SCI has been recognized as a major risk factor for the recurrence of pressure ulcers [35, 36], and the recurrence rate can even be as high as 48 to $56\%$ among these patients [5, 37]. Furthermore, although older age might be responsible for delayed wound healing and was suggested to be a risk factor for the occurrence of pressure ulcers, this study recognized that younger age is a risk factor for the recurrence of pressure ulcers following surgical reconstruction. This is in accordance with the observation from some studies that younger age is associated with the recurrence of pressure ulcers [2, 12]. One possible explanation is that pressure ulcer patients with SCI were generally much younger than those with other illnesses. Therefore, specific awareness is recommended for young and neurologically disabled patients following surgical treatment of pressure ulcers [5].
DM has been widely recognized as a risk factor for the development of pressure ulcers (38–40). However, in this study, there was no significant difference in the incidence of DM between the patients with and without pressure ulcer recurrence. We believe that the reason is that in the condition of multiple factors contributing to pressure ulcer recurrence, there were potential confounding factors in the analysis. Although individually weighted risk factors based on adequate statistical methods would be useful to assess the role of each risk factor in the development of pressure ulcers [41], this study is limited by its relatively small sample size for doing such work. In contrast, DM has been recognized by decision tree algorithms as a risk factor for the recurrence of pressure ulcers following surgical reconstruction. Machine learning methods may recognize a specific pattern to provide a useful classifier to make predictions for unseen data/objects [42, 43].
This study also demonstrated that patients with recurrent pressure ulcers were significantly younger and had a higher rate of sustaining scoliosis, but a lower rate of Parkinson's disease, than patients without recurrence. It had been reported that pelvic obliquity occurs secondary to scoliosis and results in increased instability of the hip on the high side and ischial decubitus ulcers on the low side [44]. In a study of 166 patients who underwent 252 flap procedures, in addition to young age and oblique pelvis, scoliosis was recognized as a factor related to recurrence [45]. The observed lower rate of Parkinson's disease in this study seemed to contradict the concept that the prevalence of pressure ulcers was markedly increased when Parkinson's coexisted [46] because the incidence of pressure ulcers is suggested to be inversely related to the amount of movement made during the night. Another large cohort study on more than 87,000 persons with pressure ulcers also revealed that Parkinson's disease was associated with the highest prevalence of pressure ulcers, although this study group did not include those patients following surgical reconstruction. In this study, the decision tree algorithm did not include scoliosis and Parkinson's disease as risk factor classifiers, which may be because of the sacrifice of pruning these relatively small numbers of patients in constructing a decision tree composed of a three-layer structure. Indeed, the reconstruction of more layers in the decision tree model may only increase the fair predictive power in this study (AUC of 0.764), and a decision tree model with too many layers or splits would make the model complex and difficult to use in the clinical setting.
The study was limited to a relatively small sample population to explore a disease influenced by multiple complex factors. Additional limitations of this study should be addressed. The first is selection bias associated with the retrospective study design. Second, socioeconomic factors and other potential factors such as nutritional status, being under- or overweight, anemia, vitamin deficiency, and arterial obstructive diseases were not analyzed or controlled in this study; therefore, some bias may exist. Third, the wound management, rehabilitation process, and activity may differ widely among these patients, which may have led to some bias in the analysis. Fourth, the recurrence of pressure ulcers was limited to those pressure ulcers with stage 3 or 4, because in such circumstances, a surgeon may need to determine whether to perform further reconstruction or allow the wound to heal secondarily. However, if the definition of pressure ulcer includes those with stage 1 and 2 pressure ulcers, the results may be different. Furthermore, the duration of each previous ulcer, the infectious status and pathology of the involved skin region, extension of previous ulcers, the type of spinal cord injury, and the bedridden time of the patients were unknown in this study, resulting in some potential bias in the comparison of the outcome. Whether the study results of bed-ridden patients in this study could be generalized to those who had different ambulatory status require further investigation. In addition, a longer follow-up time of more than three years, as performed in this study, may also impact the analysis of the results. Finally, the study was limited to a single center with a relatively small number of studied patient population, and patient injury characteristics may vary from those observed at other institutions, thereby limiting the generalizability of the findings.
## Conclusions
This study demonstrated that age and SCI were the two most important risk factors associated with recurrent pressure ulcers following surgical reconstruction.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Institutional Review Board (IRB) of Chang Gung Memorial Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
Conceptualization, P-YL. and C-HH; methodology, Y-HY; validation, C-CW; investigation, CC; resources, NFM; writing—original draft preparation, Y-JT; writing—review and editing, C-HL; supervision, P-YL. and C-HH. All authors contributed to the article and approved the submitted version.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: Hepatic gene expression profiles during fed–fasted–refed state in mice
authors:
- Nana Ji
- Liping Xiang
- Bing Zhou
- Yan Lu
- Min Zhang
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10020372
doi: 10.3389/fgene.2023.1145769
license: CC BY 4.0
---
# Hepatic gene expression profiles during fed–fasted–refed state in mice
## Abstract
Background: Regulation of nutrient status during fasting and refeeding plays an important role in maintaining metabolic homeostasis in the liver. Thus, we investigated the impact of the physiological Fed–Fast–Refed cycle on hepatic gene expression in nutrient-sensitive mice.
Methods: We performed transcriptomic analysis of liver samples in fed, fasted and refed groups of mice. Through mRNA-sequencing (RNA-Seq) and miRNA-Seq, we compared fasted and fed states (fasted versus fed cohort) as well as refed and fasted states (refed versus fasted cohort) to detect dynamic alterations of hepatic mRNA–miRNA expression during the fed–fasted–refed cycle.
Results: We found dozens of dysregulated mRNAs–miRNAs in the transition from fed to fasted and from fasted to refed states. Gene set enrichment analysis showed that gene expression of the two cohorts shared common pathways of regulation, especially for lipid and protein metabolism. We identified eight significant mRNA and three miRNA clusters that were up–downregulated or down–upregulated during the Fed–Fast–Refed cycle. A protein–protein interaction network of dysregulated mRNAs was constructed and clustered into 22 key modules. The regulation between miRNAs and target mRNAs was presented in a network. Up to 42 miRNA–mRNA-pathway pairs were identified to be involved in metabolism. In lipid metabolism, there were significant correlations between mmu-miR-296-5p and Cyp2u1 and between mmu-miR-novel-chr19_16777 and Acsl3.
Conclusion: Collectively, our data provide a valuable resource for the molecular characterization of the physiological Fed–Fast–Refed cycle in the liver.
## Introduction
Obesity has emerged as a global public health problem, and improvement of nutrient status and dietary interventions have been touted as potential remedies. To achieve resistance to environmental stresses and toxicity, fasting can bring cells and tissues into a protected state. It is reported that preoperative fasting can alleviate hepatic damage induced by ischemia/reperfusion injury (Amer et al., 2017). Nutrient status is regulated by highly variable molecular mechanisms and has an impact on metabolic homeostasis in the liver, particularly for glucose, lipid and energy metabolism (Koliaki and Roden, 2013; Jones, 2016). To gain a comprehensive understanding of molecular alterations in different nutrient statuses, we performed hepatic mRNA-sequencing (RNA-Seq) and miRNA-Seq during the physiological Fed–Fast–Refed cycle in mice.
Over the past decade, RNA-Seq has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs, which interrogate global gene expression changes at the transcriptional level (Stark et al., 2019; Hong et al., 2020). miRNAs are a family of post-transcriptional gene repressors and are associated with the regulation of gene expression in metabolism (Lu and Rothenberg, 2018; Agbu and Carthew, 2021). To date, several microarray profiling studies have been performed to investigate the Fed–Fast–Refed cycle. However, most transcriptomic studies in the liver have focused on a single aspect of the cycle, such as transition from fed to fasted or from fasted to refed states (Chi et al., 2020; Hwangbo et al., 2020; Wahl and LaRocca, 2021), which might be inadequate. Our study investigated the Fed–Fast–Refed cycle comprehensively and combined RNA-Seq with miRNA-Seq analysis.
We performed a systematic evaluation of hepatic genome-wide mRNA and miRNA expression through RNA-Seq and miRNA-Seq in mice in fed, fasted and refed states. We compared mRNA–miRNA expression during the transition from fed to fasted and from fasted to refed states. We analyzed alterations in mRNAs–miRNAs and related pathways in fasted versus fed and refed versus fasted cohorts. We detected significant mRNA and miRNA clusters that were upregulated and subsequently downregulated (up–down) or downregulated and subsequently upregulated (down–up) during the Fed–Fast–Refed cycle. A regulatory network including protein–protein interaction (PPI), miRNA–mRNA and miRNA–mRNA pathways was established for further analysis in the Fed–Fast–Refed cycle. We aimed to provide novel insights into the molecular characteristics of the physiological impact of the Fed–Fast–Refed cycle in the liver.
## Animal experiments
The animal procedures were approved by the Animal Experiment Ethics Committees of Shanghai Jiao Tong University School of Medicine. Wild-type male C57BL/6J mice aged 8 weeks were purchased from Shanghai Laboratory Animal Company (SLAC, Shanghai, China). All mice were housed at 21°C ± 1°C with a humidity of $55\%$ ± $10\%$ and 12-h light/12-h dark cycle in a specific-pathogen-free facility. After 2 weeks of acclimatization, mice were divided into three groups. The fasted group was fed a regular diet with subsequent fasting for 24 h. The refed group underwent 24 h fasting and was refed a fixed-calorie meal for 2 h. Mice were anesthetized with sodium pentobarbital (Nembutal, 80 mg/kg, i.p.) and killed during the fasted and refed states. Liver tissues were harvested and snap-frozen in liquid nitrogen for further analysis.
## RNA-Seq and data processing
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, United States). A total of 3 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, United States), and index codes were added to attribute sequences to each sample. Library quality was assessed by Agilent Bioanalyzer 2100 system. The sequencing libraries were sequenced on an Illumina Hiseq2500/X platform. For mRNA sequencing, cuffdiff software (part of cufflinks) was used to obtain FPKM as the expression profiles of mRNA and differentially expressed mRNAs were calculated based on log(FPKM+1) with $p \leq 0.05$ and |Fold Change|≥2 used as the cutoff values. For miRNA sequencing, limma R package (http://bioconductor.org/packages/release/bioc/html/limma.html) (Ritchie et al., 2015) was used to obtain scaled raw counts, and differentially expressed miRNAs were identified with $p \leq 0.05$ and |Fold Change|≥2 used as the cutoff values. Pearson coefficient r 2 values were calculated based on FPKM values and raw counts in RNA-Seq and miRNA-Seq, respectively. Principal component analysis (PCA) was performed by R ggfortify package (http://bioconductor.org/packages/release/bioc/html/ggfortify.html). Heatmaps were plotted by applying R pheatmap package (http://bioconductor.org/packages/release/bioc/html/pheatmap.html).
## Functional enrichment analysis
Gene Ontology (GO) annotation (Ashburner et al., 2000) includes three categories: biological process, cellular compartment and molecular function. Biological process of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (Kanehisa and Goto, 2000) was performed by DAVID (Sherman et al., 2022) online tool (https://david.ncifcrf.gov/tools.jsp) with dysregulated mRNAs. Gene set enrichment analysis (GSEA) (Subramanian et al., 2005) was performed based on the log2(Fold Change) of mRNA by R clusterProfiler package (Yu et al., 2012) (http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html). $p \leq 0.05$ and gene number in one term ≥2 was identified as significant enrichment.
## Expression trend analysis
We obtained the union set of dysregulated mRNAs–miRNAs in the fasted versus fed and refed versus fasted cohorts, which were considered as mRNAs–miRNAs pairs in the Fed–Fast–Refed cycle. Trend cluster analysis was performed with these mRNAs–miRNAs to explore expression trends in the Fed–Fast–Refed cycle based on the R Mfuzz package (Kumar and Futschik, 2007) (http://bioconductor.org/packages/release/bioc/html/Mfuzz.html). Membership ≥0.3 was used as the cutoff value. For each cluster, large membership values indicated that the genes were in accordance with the expression trend cluster. Next, clusters with similar expression trends were merged. We focused on trend clusters that were upregulated and subsequently downregulated (up–down) and downregulated and subsequently upregulated (down–up) in the Fed–Fast–Refed cycle.
We analyzed the union set of dysregulated mRNAs/miRNAs in the fasted versus fed and refed versus fasted cohorts, resulting in 1,579 mRNAs and 48 miRNAs (Supplementary Documents S3, S4). Based on these dysregulated mRNAs–miRNAs in the union set, trend cluster analysis was performed using R Mfuzz package to detect mRNAs–miRNAs expression trends in the Fed–Fast–Refed cycle, and 12 mRNA clusters were obtained (Figure 4A). Details are summarized in Supplementary Document S3. Member.ship≥0.3 was used as the cutoff value. In the Fed–Fast–Refed cycle, we identified eight significant mRNA clusters including three (1, 6 and 10; 170 mRNAs) that were upregulated with subsequent downregulation, and five significant mRNA clusters (2–5 and 11; 474 mRNAs) that were downregulated with subsequent upregulation. Six miRNA clusters were obtained (Figure 4B). Details are summarized in Supplementary Document S4. In the Fed–Fast–Refed cycle, we identified three significant miRNA clusters including two (1 and 2; 11 miRNAs) that were upregulated with subsequent downregulation, and cluster 4 that was downregulated and subsequently upregulated.
**FIGURE 4:** *Expression trend analysis. (A) The results of expression trend analysis based on the union set of dysregulated mRNAs in fasted versus fed and refed versus fasted cohorts. Twelve mRNA clusters were detected. With a member.ship ≥0.3 used as the cutoff value, we identified eight significant mRNA clusters in the Fed–Fast–Refed cycle; three were upregulated and subsequently downregulated (1, 6 and 10) and five were downregulated and subsequently upregulated (2–5 and 11). (B) Expression trend analysis based on the union set of dysregulated miRNAs in fasted versus fed and refed versus fasted cohorts. Six miRNA clusters were detected. With a member.ship ≥0.3 used as the cutoff value, we identified three significant miRNA clusters in the Fed–Fast–Refed cycle; two were upregulated and subsequently downregulated (1 and 2) and one was downregulated and subsequently upregulated (4).*
## PPI network analysis
Based on the STRING (http://www.string-db.org/) dataset (von Mering et al., 2003), we predicted proteins encoded by dysregulated genes up–downregulated or down–upregulated in the Fed–Fast–Refed cycle and created a PPI network, which was visualized with cytoscape (http://chianti.ucsd.edu/cytoscape-3.4.0//) (Nangraj et al., 2020). The PPI score was set as 0.7, which was considered as high confidence. CytoNCA (http://apps.cytoscape.org/apps/cytonca) was applied to detect hub proteins through ranking Degree Centrality. MCODE (http://apps.cytoscape.org/apps/mcode) (Bader and Hogue, 2003) was applied to calculate key modules in the PPI network (Degree Cutoff = 2, Node Score Cutoff = 0.2, K-core = 2 and Max. Depth = 100). KEGG pathway enrichment analysis of key modules was performed with R clusterProfiler package (http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) (Kanehisa and Goto, 2000). p.adjust<0.05 was identified as enrichment significant.
## miRNA–mRNA regulation network analysis
miRanda (http://www.mircorna.org/) was applied to predict potential target mRNAs of miRNA. A score ≥140 and energy ≤−20 were set as cutoff values. We focused on dysregulated mRNAs–miRNAs in the Fed–Fast–Refed cycle and obtained miRNA–mRNA pairs through prediction by miRanda. miRNA–mRNA pairs were selected to construct an miRNA–mRNA regulatory network.
## miRNA–mRNA-pathway regulation analysis
A Sankey diagram was established between miRNA–mRNA pairs and mRNA-pathway pairs involved in metabolism. Significant correlation with $p \leq 0.05$ between miRNA and mRNA expression was presented with scatter plots.
A Sankey diagram was established between miRNA–mRNA and mRNA pathways involved in metabolism (Figure 8A). Up to 42 miRNA–mRNA-pathway pairs were identified (Table 4). A scatter plot showed the miRNA–mRNA pairs that participated in lipid metabolism (Figures 8B–E). There was a negative correlation between expression of these mRNAs and miRNAs (Figures 8B–E). The correlation between mmu-miR-296-5p and Cyp2u1 and between mmu-miR-novel-chr19_16777 and Acsl3 was significant ($p \leq 0.05$) (Figures 8B, C).
## RNA-Seq and miRNA-Seq data validation
The mouse model for Fed–Fast–Refed cycle was constructed. As expected, blood glucose levels were reduced in the fasted state and increased in the refed state (Supplementary Figure S1A). Besides, expression levels of gluconeogenic (PEPCK and G6Pase) and lipogenic genes (SREBP-1c, Fasn, Scd1, and Acc1) confirmed that gluconeogenesis was induced by fasting and lipogenesis was increased by refeeding, respectively (Supplementary Figures S1B, S1C). Then, mouse hepatic genome-wide mRNA and miRNA expression was profiled using RNA-Sequencing and miRNA-Sequencing, respectively. Details of the study groups are listed in Table 1. The fed, fast and refed groups had three replicates each. mRNA–miRNA expression density plots in the fed, fasted and refed groups are presented in Supplementary Figures S2A, S2B. We demonstrated the reproducibility and reliability of mRNA–miRNA expression profiles. Correlation analysis showed that mRNA–miRNA expression reads were correlated well between different samples (Supplementary Figures S2C, S2D). Pearson correlation r 2 values between all samples in the three groups are shown in Supplementary Figures S2C, S2D. Box plots based on normalized mRNA–miRNA expression reads after batch-effect correction by interquartile range are shown in Figures 1A, B. PCA of mRNA–miRNA expression profiles showed that samples within each group were close, while samples between different groups were separated (Figures 1C, D). Hierarchical clustering analysis showed that samples in each group clustered together (Figures 1E, F). These data demonstrated that all of the RNA-Seq and miRNA-Seq results were reproducible and reliable for downstream analysis.
## Differential expression analysis of RNA-Seq and miRNA-Seq
To detect dynamic alterations of hepatic genome-wide mRNA and miRNA expression during the Fed–Fast–Refed cycle, comparisons were made between fasted and fed states (fasted versus fed cohort) as well as refed and fasted state (refed versus fasted cohort) using RNA-Seq and miRNA-Seq. $p \leq 0.05$ and fold change>2 were set as the threshold for identifying dysregulated mRNA and miRNAs. Initially, we investigated the fasted versus fed cohort. For RNA-Seq, the liver underwent dramatic changes in gene expression during transition from fed to fasted state, with a total of 874 differentially expressed genes (DEGs); of which, 291 were upregulated and 583 were downregulated (Figure 2A). The most significantly upregulated genes included those encoding Cyp4a14, Cyp4a10, Cyp4a31, and Tnnt2, while the most significantly downregulated genes included those encoding A2m and Serpina12 (Figure 2A). The top 100 significantly dysregulated genes in the fasted versus fed cohort are presented in the circle heatmap plot (Figure 2B). In addition, we undertook intersection analysis of our RNA-*Seq analysis* with a public dataset (GSE107787), in which mice were fasted 20 h. As a result, a total of 298 DEGs were found between both screening, of which 129 were upregulated and 169 were downregulated (Supplementary Document S1). For miRNA-Seq, we identified 25 dysregulated miRNAs in the liver of mice in the fasted state compared with fed state; of which, 20 were upregulated and five were downregulated (Figure 2C). These differentially expressed miRNAs are shown in the circle heatmap plot (Figure 2D).
**FIGURE 2:** *Differential expression analysis of RNA-Seq and miRNA-Seq datasets. (A,B) Hepatic DEGs during the transition from fed to fasted in mice. (A) Volcano plot of DEGs in the livers of mice in fasted compared with fed state. The top DEGs are labeled as indicated. (B) Heatmap showing expression patterns between top 100 DEGs in the liver of mice in fasted compared with fed state after batch-effect correction. (C,D) Hepatic dysregulated miRNAs during the transition from fed to fasted in mice. (C) Volcano plot of dysregulated miRNAs in the liver of mice in fasted compared with fed state. (D) Heatmap showing expression patterns between dysregulated miRNAs in the liver of mice in fasted compared with fed state after batch-effect correction. (E,F) Hepatic DEGs during the transition from fasted to refed state. (E) Volcano plot of DEGs in the liver of mice in refed compared with fasted state. (F) Heatmap showing expression patterns between top 100 DEGs in the liver of mice in refed compared with fasted state after batch-effect correction. (G,H) Hepatic dysregulated miRNAs during the transition from fasted to refed state. (G) Volcano plot of dysregulated miRNAs in the liver of mice in refed compared with fasted state. (H) Heatmap showing expression patterns between dysregulated miRNAs in the liver of mice in refed compared with fasted state after batch-effect correction.*
In the refed versus fasted cohort, for mRNA-Seq, 1,048 genes were differentially expressed in the refed state compared with fasted state; among which, 698 were upregulated and 350 were downregulated (Figure 2E). The most significantly upregulated genes included those encoding Nrep, Cyp2c69, and Derl3, while the most significantly downregulated genes included those encoding Mt2, Igfbp1, Saa2, Lcn2, and A2m (Figure 2E). The top 100 significantly dysregulated genes in the refed versus fasted cohort are presented in the circle heatmap plot (Figure 2F). We also undertook intersection analysis of our analysis with a public dataset (GSE137385), in which mice were refed 3 h with low-fat diet after fasting. As a result, a total of 157 DEGs were found between both screening, of which 87 were upregulated and 70 were downregulated (Supplementary Document S2). These comparisons would inform on which gene expression sets change robustly enough across platforms and somewhat differing experimental conditions. For miRNA-Seq, we identified 32 differentially expressed miRNAs in the liver of mice in the refed state compared with fasted state; of which, 10 were upregulated and 22 were downregulated (Figure 2G). These differentially expressed miRNAs are shown in the circle heatmap plot (Figure 2H). Numbers of differentially expressed mRNAs and miRNAs detected are summarized in Table 2.
**TABLE 2**
| Unnamed: 0 | Unnamed: 1 | mRNA | miRNA |
| --- | --- | --- | --- |
| Fasted versus fed | Up | 291 | 20 |
| | Down | 583 | 5 |
| | Total | 874 | 25 |
| Refed versus fasted | Up | 698 | 10 |
| | Down | 350 | 22 |
| | Total | 1048 | 32 |
## KEGG signaling enrichment analysis of mRNA expression profile based on GSEA
KEGG signaling enrichment analysis based on GSEA was performed with log2(Fold Change) of all mRNAs in the fasted versus fed and refed versus fasted cohorts. In the fasted versus fed cohort, GSEA demonstrated that mRNAs were mainly mapped to 66 KEGG pathways; of which, 29 showed a trend to be upregulated, while 37 showed a trend to be downregulated (Supplementary Tables S1, S2). In particular, pathways such as insulin resistance showed a trend to be upregulated (Figure 3A), and pathways including fatty acid biosynthesis, steroid biosynthesis, protein export and protein processing in the endoplasmic reticulum (ER) showed a trend to be downregulated (Figure 3A). The top 20 pathways according to p-value were listed in the ridge plot, which showed the distribution of log2(Fold Change) of genes enriched in each pathway (Figure 3B). The top 20 pathways included nine pathways in which enriched genes were mainly upregulated and 11 in which enriched genes were mainly downregulated (Figure 3B). In the refed versus fasted cohort, GSEA demonstrated that mRNAs were mainly mapped to 70 KEGG pathways, of which, 33 showed a trend to be upregulated, and 37 showed a trend to be downregulated (Supplementary Tables S3, S4). Pathways including fatty acid biosynthesis, steroid biosynthesis, protein export and protein processing in the ER showed a trend to be upregulated (Figure 3C), and insulin resistance showed a trend to be downregulated (Figure 3C). The pathway results in the refed versus fasted cohort were in contrast with those in the fasted versus fed cohort. The top 20 pathways according to p-value are listed in the ridge plot (Figure 3D). The top 20 pathways included 13 in which enriched genes were mainly upregulated and seven in which enriched genes were mainly downregulated (Figure 3D).
**FIGURE 3:** *KEGG signaling enrichment analysis of mRNA expression profile based on GSEA. (A,B) KEGG signaling enrichment analysis of mRNA expression profile in fasted versus fed cohort. (A) Selected GSEA results of mRNA expression profile in fasted versus fed cohort. Running enrichment score and ranked list are presented. (B) Ridge plot listed the top 20 pathways in fasted versus fed cohort. (C,D) KEGG signaling enrichment analysis of mRNA expression profile in refed versus fasted cohort. (C) Selected GSEA results of mRNA expression profile in refed versus fasted cohort. Running enrichment score and ranked list are presented. (D) Ridge plot listed the top 20 pathways in refed versus fasted cohort.*
## Functional enrichment of dysregulated mRNAs in significant mRNA clusters
We performed GO analysis and KEGG signaling enrichment analysis of dysregulated mRNAs in significant mRNA clusters. These mRNAs were down–up or up–down regulated in the Fed–Fast–Refed cycle. GO analysis was classified into three categories: biological process, cellular component and molecular function. We only focused on biological process. The top 20 biological process terms of significant mRNA clusters are plotted in Figure 5A. The down–up mRNA clusters in the Fed–Fast–Refed cycle were mainly enriched in response to ER stress, sterol biosynthetic process, protein N-linked glycosylation, and isoprenoid biosynthetic process. The up–down mRNA clusters in the Fed–Fast–Refed cycle were mainly enriched in cellular response to insulin stimulus, amino acid transport, and response to glucocorticoid. The details of GO analysis are summarized in Supplementary Tables S5, S6. The top 20 KEGG pathways of significant mRNA clusters are shown in Figure 5B. The down–up mRNA clusters in the Fed–Fast–Refed cycle were mainly mapped to protein processing in the ER, metabolic pathways, terpenoid backbone biosynthesis, and retinol metabolism. The up–down mRNA clusters in the Fed–Fast–Refed cycle were mainly mapped to the FoxO signaling pathway, transcriptional regulation in cancer, osteoclast differentiation, and the PI3K–Akt signaling pathway. The details of KEGG analysis are summarized in Supplementary Tables S7, S8.
**FIGURE 5:** *Functional enrichment of dysregulated mRNAs in significant mRNA clusters. (A) GO analysis of dysregulated mRNAs down–up/up–down in the Fed–Fast–Refed cycle. (B) KEGG signaling enrichment analysis of dysregulated mRNAs down–up/up–down in the Fed–Fast–Refed cycle.*
## PPI network of dysregulated mRNAs in significant mRNA clusters
Visualized by cytoscape, a PPI was constructed to predict interaction between proteins encoded by down–up and up–down mRNAs in the Fed–Fast–Refed cycle (Figure 6A). Based on the CytoNCA tool, hub genes were identified through ranking Degree Centrality. The top 20 genes encoded Hspa5, Stat1, Ddost, Cyp2c29, Hspa1b, Ugt2b1, Irf7, Ifit2, Cyp2c70, Cyp2c55, Ugt2b37, Igtp, Pdia4, Hyou1, Cyp4a12a, Cyp3a13, Cyp2c40, Aldh1a7, and Fasn. *These* genes might be pivotal for the Fed–Fast–Refed cycle. Up to 22 key modules clustered in the PPI network were generated using the MCODE tool (Table 3). KEGG pathway enrichment analysis of these key modules was performed (Supplementary Table S9). Importantly, these key modules were mapped to a series of KEGG pathways related to metabolism. Module 1 was enriched in steroid hormone biosynthesis, and linoleic acid and arachidonic acid metabolism (Figure 6B). Module 12 was enriched in drug metabolism by cytochrome P450, metabolism of xenobiotics by cytochrome P450, and tyrosine metabolism. Module 3 was enriched in terpenoid backbone and steroid biosynthesis. Module 9 was enriched in glycerolipid and glycerophospholipid metabolism and alcoholic liver disease.
**FIGURE 6:** *PPI network of dysregulated mRNAs in significant mRNA clusters. (A) PPI network predicted the interaction between proteins encoded by down-up/up-down mRNAs in the Fed–Fast–Refed cycle. The orange proteins encoded by dysregulated mRNAs were upregulated and subsequently downregulated in the Fed–Fast–Refed cycle. The green proteins encoded by dysregulated mRNAs were downregulated and subsequently upregulated in the Fed–Fast–Refed cycle. The node size shows the degree of connection. The grey line shows interaction between proteins encoded by these mRNAs. (B) Key modules were enriched in a series of KEGG pathways related to metabolism.* TABLE_PLACEHOLDER:TABLE 3
## miRNA–mRNA regulatory network
miRNAs regulate gene expression after binding with target mRNAs through inhibiting mRNA translation or initiating degradation (Chen et al., 2019). Therefore, potential targets of miRNA were predicted using the miRanda tool to explore the interaction between miRNAs and target mRNAs. In total, 415 miRNA–mRNA pairs were detected based on down–up and up–down mRNAs–miRNAs in the Fed–Fast–Refed cycle. A miRNA–mRNA regulatory network was characterized with 275 miRNA–mRNA pairs (Figure 7). Each up–down miRNA in the Fed–Fast–Refed cycle regulated dozens of down–up mRNAs. Also, down–up miRNAs in the Fed–Fast–Refed cycle regulated dozens of up–down mRNAs. The 415 and 275 miRNA–mRNA pairs are listed in Supplementary Document S5.
**FIGURE 7:** *miRNA–mRNA regulatory network. The miRNA–mRNA regulatory network was constructed with 275 miRNA–mRNA pairs in which the regulatory trend between miRNAs and mRNAs was in contrast. The pink triangle indicates miRNAs upregulated and subsequently downregulated in the Fed–Fast–Refed cycle. The blue inverted arrow indicates miRNAs downregulated and subsequently upregulated in the Fed–Fast–Refed cycle. The orange circle indicates mRNAs upregulated and subsequently downregulated in the Fed–Fast–Refed cycle. The green square indicates mRNAs downregulated and subsequently upregulated in the Fed–Fast–Refed cycle. The node size shows the degree of connection. The grey line shows regulatory interaction between miRNA and targeted mRNAs.*
## Discussion
To explore the molecular alterations underlying the physiological Fed–Fast–Refed cycle, we analyzed hepatic mRNA–miRNA expression in mice during the fed to fasted and fasted to refed transitions, based on RNA-Seq and miRNA-Seq. We observed 874 DEGs and 25 dysregulated miRNAs in the liver of mice in the fasted state compared with fed state. A total of 1,048 DEGs and 32 dysregulated miRNAs were captured in the liver of mice in the refed state compared with fasted state. mRNAs in the fasted versus fed and refed versus fasted cohorts were mainly mapped to 66 and 70 KEGG pathways, respectively. We detected three up–down mRNA clusters, five down–up mRNA clusters, two up–down miRNA clusters and one down–up miRNA cluster during the Fed–Fast–Refed cycle. In addition, we observed up to 22 key modules clustered in a PPI network of proteins encoded by down–up mRNAs and up–down mRNAs in the Fed–Fast–Refed cycle. With 275 miRNA–mRNA pairs in which the regulatory trend between miRNAs and mRNAs was in contrast, a miRNA–mRNA regulatory network was constructed. Up to 42 miRNA–mRNA-pathway pairs were identified between miRNA–mRNA- and mRNA-pathways involved in metabolism.
In the fasted versus fed cohort, the most significantly upregulated genes included those encoding Cyp4a14, Cyp4a10, Cyp4a31, and Tnnt2, while the most significantly downregulated genes included those encoding A2m and Serpina12. Serpina12 is an adipokine, that is associated with development of insulin resistance, obesity, and inflammation (Kurowska et al., 2021). Interestingly, Tnnt2, which encodes the cardiac isoform of troponin T, has been shown to regulate hypertrophic cardiomyopathy. Tnnt2-high and Tnnt2-low cardiomyocytes showed differential mitotic activity in response to intracellular glucose (Fajardo et al., 2021). Therefore, we speculate that Tnnt2 expression in the liver could also be regulated by changes in blood glucose levels during fed-fasting-refed cycling. However, its function in the hepatic metabolic regulation remains to be explored in the future studies. In the refed versus fasted cohort, the most significantly upregulated genes included those encoding Nrep, Cyp2c69 and Derl3, while the most significantly downregulated genes included those encoding Mt2, Igfbp1, Saa2, Lcn2, and A2m. Igfbp1 is an endogenous promoter of β-cell regeneration and reduces the risk of developing type 2 diabetes (Lu et al., 2016).
GSEA showed that gene expression in the two cohorts shared common pathways, especially for lipid and protein metabolism. In the transition from fed to fasted state and from fasted to refed state, insulin resistance showed a trend to be upregulated. Pathways including fatty acid biosynthesis, steroid biosynthesis, protein export and protein processing in the ER showed a trend to be downregulated during the fed to fasted transition. However, these pathways showed a trend to be upregulated during the fasted to refed transition.
GO analysis demonstrated that down–up mRNA clusters in the Fed–Fast–Refed cycle were mainly related to response to ER stress, sterol biosynthetic process, protein N-linked glycosylation, and isoprenoid biosynthetic process. The up–down mRNA clusters were mainly related to cellular response to insulin stimulus, amino acid transport, and response to glucocorticoid. KEGG pathway analysis found that down–up mRNA clusters in the Fed–Fast–Refed cycle were mainly mapped to protein processing in the ER, metabolic pathways, terpenoid backbone biosynthesis, and retinol metabolism. Up–down mRNA clusters in the Fed–Fast–Refed cycle were mainly mapped to the FoxO signaling pathway, transcriptional misregulation in cancer, osteoclast differentiation, and the PI3K–Akt signaling pathway.
*Hub* genes were identified through PPI network analysis of proteins encoded by down–up and up–down mRNAs in the Fed–Fast–Refed cycle. We found that the top 20 hub genes included those encoding Hspa5, Stat1, Ddost, Cyp2c29, Hspa1b, Ugt2b1, Irf7, Ifit2, Cyp2c70, Cyp2c55, Ugt2b37, Igtp, Pdia4, Hyou1, Cyp4a12a, Cyp3a13, Cyp2c40, Aldh1a7, and Fasn. These hub genes included several classes with distinct functions. For instance, Cyp2c29, Cyp2c70, Cyp2c55, Cyp4a12a, Cyp3a13, and Cyp2c40 are cytochrome P450 (CYP) enzymes are involved in the metabolism of drugs, steroids and carcinogens (Guengerich et al., 2016; Zhao et al., 2021). Hspa5 and Hspa1b encode proteins which localized in the ER lumen. Overexpression of Hspa5 on the cell membrane mediates the vast number of disordered proteins produced under stress (Wang et al., 2017). Ugt2b1 and Ugt2b37 belong to UDP glucuronosyltransferase 2 family. UDP glucuronosyltransferase prevents the accumulation of potentially toxic compounds and their subsequent bioactivation to more toxic intermediates (Grancharov et al., 2001; Rowland et al., 2013). Functional enrichment analysis confirmed that steroid biosynthesis, and drug metabolism by cytochrome P450 or other enzymes were enriched by key modules in the PPI network. Importantly, these key modules were also mapped to a series of KEGG pathways related to lipid metabolism, including steroid biosynthesis, and linoleic acid, arachidonic acid, glycerolipid and glycerophospholipid metabolism.
Through analysis of miRNA–mRNA-pathway regulation, we found that several miRNA–mRNA pairs participated in lipid metabolism. The target mRNA expression was negatively regulated by miRNA expression. There was a significant correlation between mmu-miR-296-5p and Cyp2u1 as well as between mmu-miR-novel-chr19_16777 and Acsl3. Cyp2u1 has been reported to mediate hydroxylation of arachidonic acid metabolism (Yu et al., 2019). ACSL3 is regarded as a novel GABARAPL2 interactor that links ufmylation and lipid droplet biogenesis (Eck et al., 2020).
There were several limitations to our study. First, only three replicates were involved in each group. Second, our findings were based on murine models. Third, to further explore the role and mechanisms of significant genes and miRNAs in the physiological Fed–Fast–Refed cycle, functional experiments should be carried out.
In conclusion, this study identified several novel potential mRNAs and miRNAs through expression trend analysis and regulation networks. These up–down and down–up mRNAs and miRNAs might be involved in lipid metabolism during the physiological Fed–Fast–Refed cycle.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number (s) can be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE225697.
## Ethics statement
The animal study was reviewed and approved by Animal Experiment Ethics Committees of Shanghai Jiao Tong University School of Medicine.
## Author contributions
YL and MZ designed the study. NJ and BZ performed the animal experiments. NJ and LX performed gene expression profile analysis.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1145769/full#supplementary-material
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|
---
title: Sorafenib increases cytochrome P450 lipid metabolites in patient with hepatocellular
carcinoma
authors:
- Can G. Leineweber
- Miriam Rabehl
- Anne Pietzner
- Nadine Rohwer
- Michael Rothe
- Maciej Pech
- Bruno Sangro
- Rohini Sharma
- Chris Verslype
- Bristi Basu
- Christian Sengel
- Jens Ricke
- Nils Helge Schebb
- Karsten-H. Weylandt
- Julia Benckert
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10020374
doi: 10.3389/fphar.2023.1124214
license: CC BY 4.0
---
# Sorafenib increases cytochrome P450 lipid metabolites in patient with hepatocellular carcinoma
## Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer death, and medical treatment options are limited. The multikinase inhibitor sorafenib was the first approved drug widely used for systemic therapy in advanced HCC. Sorafenib might affect polyunsaturated fatty acids (PUFA)-derived epoxygenated metabolite levels, as it is also a potent inhibitor of the soluble epoxide hydrolase (sEH), which catalyzes the conversion of cytochrome-P450 (CYP)-derived epoxide metabolites derived from PUFA, such as omega-6 arachidonic acid (AA) and omega-3 docosahexaenoic acid (DHA), into their corresponding dihydroxy metabolites. Experimental studies with AA-derived epoxyeicosatrienoic acids (EETs) have shown that they can promote tumor growth and metastasis, while DHA-derived 19,20-epoxydocosapentaenoic acid (19,20-EDP) was shown to have anti-tumor activity in mice. In this study, we found a significant increase in EET levels in 43 HCC patients treated with sorafenib and a trend towards increased levels of DHA-derived 19,20-EDP. We demonstrate that the effect of sorafenib on CYP- metabolites led to an increase of 19,20-EDP and its dihydroxy metabolite, whereas DHA plasma levels decreased under sorafenib treatment. These data indicate that specific supplementation with DHA could be used to increase levels of the epoxy compound 19,20-EDP with potential anti-tumor activity in HCC patients receiving sorafenib therapy.
## 1 Introduction
Liver cancer is a global issue, being the most common cancer and the leading cause of cancer death in transition countries. In 2020, almost 9,06,000 patients were diagnosed with liver cancer and over 8,30,000 deaths were documented worldwide. Hepatocellular carcinoma (HCC) has the highest prevalence among the different subtypes of liver cancer (Sung et al., 2021). Viral infections, more specifically hepatitis B and C virus (Datfar et al., 2021), lifestyle factors such as alcohol intake (Matsushita and Takaki, 2019), as well as type 2 diabetes and non-alcoholic fatty liver disease (NAFLD) (Estes et al., 2018) remain the leading risk factors, depending on the region considered. Hepatocellular carcinoma remains one of the most common causes of cancer death, especially in men, and has one of the lowest 5-year survival rates of all different cancer types (Siegel et al., 2022).
In addition to chronic inflammation, tissue remodeling and changes in cellular signaling are pathogenetic factors of carcinogenesis (Refolo et al., 2020). Interestingly, patients with a non-cirrhotic HCC, mostly caused by NAFLD, seem to show a more severe HCC histopathology on one hand but better overall survival on the other hand (Gawrieh et al., 2019).
Limited stages of HCC can be treated with locoregional procedures such as surgical (e.g., resection, transplantation) or radiological (e.g., transarterial chemoembolization, selective internal radiotherapy) intervention, prolonging survival for more than 5 years, depending on the underlying liver disease. Once the disease progresses, survival is compromised to approximately 1 year even with systemic therapy (Forner et al., 2018; Vogel et al., 2022). Systemic therapy options were shown to increase overall survival (OS) and progression-free survival (PFS). However, either applying combination therapies including immune checkpoint inhibitors such as atezolizumab plus bevacizumab (OS 13,4-19,2 months; PFS 6,9 months) (Finn et al., 2020) or tyrosine kinase inhibitors lenvatinib (OS 13,6 months; PFS 7,3 months) (Kudo et al., 2018) or sorafenib (OS 10,7-15,5 months; PFS 3,6-5,5) (Kelley et al., 2022) has not altered the severity or mortality of the disease so far (Vogel et al., 2022).
The first targeted and currently a widely used systemic therapy for HCC is the oral multikinase inhibitor sorafenib binding in an ATP-binding pocket to inhibit kinase function (Hwang et al., 2013), which predominantly inhibits angiogenesis via binding the vascular endothelial growth receptor (VEGFR). Furthermore, it targets the cell proliferation and differentiation via the rapidly accelerated fibrosarcoma (RAF) signaling pathway (Wilhelm et al., 2004) and the platelet-derived growth factor receptor-β (PDGFR-β) (Mody and Abou-Alfa, 2019) as well as the beneficial effects, particularly in cancer and its complications, which is likely due to the inhibition of nuclear factor kappa B (NF-κB) and production of the pro-resolution mediators at the molecular level (Freitas and Campos, 2019).
In addition to the known anti-angiogenic and anti-proliferative effects of sorafenib it has also been described to have effects on the soluble epoxide hydrolase (sEH) which showed similar anti-inflammatory effects as conventional sEH inhibitors in lipopolysaccharide-induced inflammation models in mice (Liu et al., 2009). Sorafenib is a potent inhibitor of sEH compared with conventional urea-based sEH inhibitors (Hwang et al., 2013). The sEH is expressed in numerous human tissues with the main distinction between microsomal epoxide hydrolase (mEH) and sEH (Morisseau and Hammock, 2013). The sEH metabolization is the dominant pathway in humans, so that it can be assumed that sEH inhibition has a stabilizing effect on endogenous epoxy metabolites in tissues (Spector and Kim, 2015).
The epoxidation of long-chain polyunsaturated fatty acids (LC-PUFAs), such as the omega-6 (n-6) PUFA arachidonic acid (C20:4n6, AA), as well as the omega-3 (n-3) PUFAs eicosapentaenoic acid (C20:5n3, EPA) and docosahexaenoic acid (C22:6n3, DHA) to epoxyeicosatrienoic acids (EETs), epoxyeicosatetraenoic acids (EEQs) and epoxydocosapentaenoic acids (EDPs), respectively, are catalyzed by the cytochrome P450 (CYP) epoxygenases (Figure 1). These epoxymetabolites are then further metabolized via sEH into their biologically less active corresponding dihydroxy metabolites (Zhang et al., 2014; Wang et al., 2018).
**FIGURE 1:** *CYP-dependent lipid metabolite formation from AA/DHA/EPA, and the potential effects of sorafenib.*
The role of epoxidized LC-PUFAs is well established in several biological processes, such as angiogenesis, inflammation, and tumor growth: In different animal models, it has been shown that sEH-inhibition has a positive influence on cardiovascular and liver abnormalities (Iyer et al., 2012), liver fibrosis and portal hypertension (Zhang et al., 2018), fatty liver (Yao et al., 2019) and non-alcoholic steatohepatitis (Wang et al., 2019). The EETs are known to affect blood pressure, inflammation, pain sensation, and regeneration (Arnold et al., 2010; Morisseau et al., 2014). However, a proangiogenic effect of 11,12-EET and 14,15-EET, the main EET regioisomers in mammals (Spector and Kim, 2015), has been described via the epidermal growth factor (EGF) and VEGF pathways (Zhang et al., 2014), which may explain the finding that EETs promote tumor growth. The n-6 AA-derived regioisomers 5,6-EET and 8,9-EET were found to increase cell proliferation and de novo vascularization (Yan et al., 2008), whereas 11,12-EET and 14,15-EET promote tumor angiogenesis through endothelial cell proliferation (Panigrahy et al., 2012; Zhang et al., 2013). An increase in 14,15-EET through sEH-inhibition led to increased tumor growth and metastasis through cell invasion in experimental studies (Panigrahy et al., 2012). In summary, through mechanisms of cell proliferation, de novo vascularization and endothelial proliferation several EET-regioisomers promote tumor growth and metastasis (Yan et al., 2008; Panigrahy et al., 2012; Zhang et al., 2013; Zhang et al., 2014). The sEH-inhibitory effect of sorafenib might thus carry clinically relevant consequences by increasing these pro-tumorigenic EET mediators.
In contrast, n-3 PUFA-derived regioisomers show anti-angiogenic effects both via the VEGF and FGF-2 pathway (Zhang et al., 2014). In a tumor mouse model, it was shown, that low dose sEH-inhibition led to an increase in n-3 DHA-derived 19,20-EDP and thereby reduced tumor angiogenesis and cell invasion and thus inhibition of tumor growth (Zhang et al., 2013). Furthermore, a protective effect in obesity and obesity-related comorbidities, such as fatty liver disease, of n-3 epoxy PUFA was found in animal models (López-Vicario et al., 2015). The beneficial effects of n-3 PUFA regarding to cancer and its complications are probably due to their anti-inflammatory and pro-resolution mediators (Freitas and Campos, 2019). In the context of abundant DHA, the sEH-inhibitory effect of sorafenib might thus lead to higher levels of 19,20-EDP, mediating anti-tumor effects.
Currently, n-6 PUFA are found in a ratio of approximately 20 times more than n-3 PUFA in the human diet (Harris, 2006); therefore humans have low n-3 PUFA tissue levels, and a shift of the competitive n-6 and n-3 PUFA metabolism towards n-6 PUFA derived lipid metabolites (Spector and Kim, 2015).
We therefore aimed to investigate levels of n-6 and n-3 PUFA in HCC patients, as well as n-6 PUFA- and n-3 PUFA-derived epoxide and corresponding dihydroxy compounds in HCC patients without and during sorafenib therapy. Based on the data of our pilot study (Leineweber et al., 2020), we hypothesized that sorafenib treatment, due to sEH-inhibitory and possibly CYP-modulating effects, might increase the presence of potentially tumor growth-suppressing DHA-derived EDPs, as well as of potentially tumor growth-promoting EETs.
## 2.1 Patients and blood sampling
The study population evaluated in this sub-analysis comprised patients within the palliative treatment arm of the randomized, controlled, multicenter phase II SORAMIC study, which evaluated sorafenib alone compared to selective internal radiation therapy (SIRT) combined with sorafenib on overall and progression-free survival in patients with advanced HCC (Ricke et al., 2019).
Patients were included in this analysis if they received study treatment in the palliative arm of SORAMIC and signed an informed consent form, so blood samples were collected and analyzed at baseline (BL) and at the first follow-up visit (FU) after approximately 7–9 weeks, and samples were stored for subsequent analyses at −80°C. Of the 424 randomized patients assigned to the palliative arm, we performed lipidomic analysis of 43 patients from the intention to treat (ITT) population, all of whom received sorafenib, characterized in terms of gender distribution, age, body mass index (BMI), presence of cirrhosis, liver function, biomarker, or tumor stage according to Barcelona Clinic Liver Cancer (BCLC) stage, except in the expression of the Child-Pugh points between 5 and $\frac{6}{7}$ ($p \leq 0.0453$) as shown in Table 1.
**TABLE 1**
| Characteristic | Total (n = 43) |
| --- | --- |
| Gender | |
| Female | 5 (12%) |
| Male | 38 (88%) |
| Age (years) | 66.97 ± 2.19 |
| BMI | 26.98 ± 0.99 |
| Liver disease | |
| Alcohol | 17 (40%) |
| Hepatitis B/C | 15 (35%) |
| Liver cirrhosis | 36 (84%) |
| Child Pugh | |
| 5 | 28 (65%) |
| 6/7 | 15 (35%) |
| BCLC | |
| B | 16 (37%) |
| C | 27 (63%) |
| Further disease classification | |
| Liver dominant disease | 41 (95%) |
| Portal vein invasion | 16 (37%) |
| Extrahepatic metastases | 8 (19%) |
| Bilirubin (µmol/L) at baseline | 15.91 ± 1.66 |
| Albumin (g/L) at baseline | 37.68 ± 1.42 |
| AlBi Score at baseline | −2.44 ± 0.13 |
| DeRitis quotient | 1.65 ± 0.26 |
| AFP | |
| <400 ng/mL | 25 (58%) |
| >400 ng/mL | 16 (37%) |
| Sorafenib: Daily dose (mg) | 410.82 ± 23.40 |
| Overall survival (months) | 12.04 ± 1.46 |
## 2.2 Sample preparation and GC
Plasma samples were analyzed for determination of fatty acids using the gas chromatography (GC) technology as described previously (Wang et al., 2022). 75 μL of EDTA plasma per sample was used for the GC preparation. Methylation and extraction of FAs were carried out on the basis of an established protocol (Kang and Wang, 2005). Briefly, frozen samples were thawed at room temperature. All samples were then mixed with 50 μL pentadecanoic acid (PDA, 1 mg/mL in ethanol, Merck Schuchardt OHG, Hohenbrunn, Germany) as internal standard, 500 μL borontrifluoride (BF3, Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany) in $14\%$ methanol (Merck KGaA, Darmstadt, Germany), and 500 μL n-hexane (Merck KGaA, Darmstadt, Germany) in glass vials and were tightly closed. After vortexing, samples were incubated for 60 min in a preheated block at 100°C. After cooling down to room temperature, the mixture was added to 750 μL water, vortexed, and extracted for 4 min. Then all samples were centrifuged for 5 min (RT, 3,500 rpm). From each sample, 100 μL of the upper n-hexane layer was transferred into a micro-insert (placed in a GC glass vial), tightly closed, and analyzed by GC.
GC was performed on a 7890B GC System (Agilent Technologies, Santa Clara, CA, United States) with an HP88 Column ($\frac{112}{8867}$, 60 m × 0.25 mm × 0.2 μm, Agilent Technologies, Santa Clara, CA, United States), with the following temperature gradient: 50°C–150°C with 20°C/min, 150°C–240°C with 6°C/min, and 240°C for 10 min (total run time 30 min). Nitrogen was used as carrier gas (constant flow 1 mL/min). 1 μL of each sample was injected into the injector (splitless injection, 280°C). The flame ionization detector (FID) analysis was performed at 250°C with the following gas flows: hydrogen 20 mL/min, air 400 mL/min, and make up (nitrogen) 25 mL/min. Methylated FAs in the samples were identified by comparing the retention times with those of known methylated FAs of the Supelco® 37 FAME MIX standard (CRM47885, Sigma Aldrich, Laramie, WY, United States) and a mix of single FAME standards [DPA, C22:5 n-3, AdA, C22:4 n-6 (Cayman Chemicals, Ann Arbor, MI, United States)]. Analysis and integration of the peaks were carried out with OpenLAB CDS ChemStation Edition (Agilent Technologies, Santa Clara, CA, United States). FA values are presented as percentage (Ramsay et al., 2018) of total FA content and absolute concentrations (µg/mL). For the study, 16 FAs were included as follows: myristic acid (C14:0), palmitic acid (C16:0), stearic acid (C18:0), arachidic acid (C20:0), behenic acid (C22:0), lignoceric acid (C24:0), palmitoleic acid (C16:1 n-7c), oleic acid (C18:1 n-9c), nervonic acid (C24:1 n-9), eicosapentaenoic acid (EPA, C20:5 n-3), docosapentaenoic acid (DPA, C22:5 n-3), docosahexaenoic acid (DHA, C22:6 n-3), linoleic acid (LA, C18: 2 n-6), dihomo-gamma- linolenic acid (DGLA, C20:3 n-6), arachidonic acid (AA, 20:4 n-6), and adrenic acid (AdA, C22:4 n-6).
## 2.3 Sample preparation and LC/ESI-MS/MS
Plasma samples were analyzed for epoxymetabolites using the LC/ESI-MS/MS lipidomics technology as described previously (Fischer et al., 2014). Lipid mediators and deuterated standards used in this study were purchased from Cayman Chemical (Ann Arbor, MI, United States). Materials used for solid phase extraction (SPE), such as sodium acetate, ethyl acetate, acetic acid, and n-hexane were obtained from Fisher Scientific (Loughborough, UK). Additionally, $99\%$ butylated hydroxytoluene (BHT, 2,6-di-tert-butyl-4-methylphenol) was obtained from Acros Organics (Geel, Belgium), and Bond Elute Certify II columns from Agilent Technologies (Santa Clara, CA, United States) were used. LC-MS solvents, such as methanol (Lichrosoly hypergrade) and acetonitrile (Lichrosoly hypergrade), were obtained from Merck (Darmstadt, Germany).
For sample preparation, an internal standard consisting of 14,15-DHET-d11, 15-HETE-d8, 20-HETE-d6, 8,9-EET-d11, 9,10-DiHOME-d4, 12,13-EpOME-d4, 13-HODE-d4 and LTB4-d4 (500 pg each) and ice-cold methanol containing BHT ($0.1\%$) was added to 200 mL plasma. After alkaline hydrolysis using 1 mmol sodium hydroxide the pH was adjusted with acetic acid and sodium acetate buffer containing $5\%$ v/v methanol at pH 6. After centrifugation, the obtained supernatant was added to SPE columns, which were preconditioned with 3 mL methanol, followed by 3 mL of 0.1 mol/L sodium acetate buffer containing $5\%$ methanol (pH 6). The SPE columns were then washed with 3 mL methanol/H2O ($\frac{50}{50}$, v/v). For elution, 2.0 mL of n-hexane:ethyl acetate (25:75) with $1\%$ acetic acid were used. The extraction was performed with a SUPELCO Visiprep manifold. The eluate was evaporated on a heating block at 40°C under a stream of nitrogen. The solid residue was resolved in 100 µL $60\%$ methanol in water.
The prepared samples were analyzed using an Agilent 1290 HPLC system with a binary pump, an autosampler, and a column thermostat with a Agilent Zorbax *Eclipse plus* C18 column 150 mm × 2.1 mm, 1.8 µm using a solvent system of aqueous acetic acid ($0.05\%$) and acetonitrile:methanol (50:50). The multiple step elution gradient started at $95\%$ aqueous phase, which was increased within 18 min–$98\%$ organic phase and held there for 10 min. The flow rate was set at 0.3 mL/min, and injection volume was 20 μL. The HPLC was coupled with an Agilent 6495 Triple Quad mass spectrometer with an electrospray ionization source. Analysis of lipid mediators was performed with the Multiple Reaction Monitoring in the negative mode, limit of quantitation (LOQ) was 0.01 ng/mL.
## 2.4 Statistical analysis
Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, United States). The comparison was made using the Wilcoxon matched-pairs signed-rank test. The correlation was made using linear regression. All values are presented as the mean ± standard error of the mean. Statistical significance was assumed when $p \leq 0.05.$ (* 0.01 ≤ $p \leq 0.05$; **0.001 ≤ $p \leq 0.01$; ***$p \leq 0.001$).
## 3 Results
Blood samples from a total of $$n = 43$$ HCC patients were analyzed in a paired fashion with blood taken without and undergoing 7–9 weeks sorafenib treatment. This is a sub-analysis of the well-characterized study population of the randomized controlled, multicenter phase II SORAMIC trial (Ricke et al., 2019). Patients from this study population, from which suitable amounts of blood samples were available for the analysis of fatty acids and their lipid metabolites before and during sorafenib treatment, were chosen for the analysis performed and presented here. The patients received sorafenib 200 mg twice a day for 1 week before increasing the dose to 400 mg twice a day. Based on disease progression and clinical condition, the sorafenib dose was escalated to 600–800 mg or reduced to 0–200 mg. The patients’ general characteristics are shown in Table 1.
## 3.1 N-6 and n-3 epoxides and dihydroxy metabolites are higher in patients undergoing sorafenib treatment
To investigate the effect of sorafenib treatment on the n-6 and n-3 PUFA epoxide formation, we measured the concentrations of the epoxymetabolites and corresponding dihydroxy metabolites by quantitative LC-ESI-MS/MS analysis in plasma samples of HCC patients without and during sorafenib therapy. As a result of sorafenib treatment, the levels of n-6 AA-derived epoxymetabolites 5,6-EET and 8,9-EET increased significantly. Levels of epoxymetabolites derived from the n-3 PUFAs tended to increase as well, but for DHA- and EPA-derived epoxides failed to reach significance (Figures 2A–C; Supplementary Figures S1A–C). The concentrations of EETs were higher compared to the n-3 PUFA-derived EEQs. EDP metabolite concentrations were approximately half of those observed for the EETs, while the concentrations of the EEQs were the lowest in this patient cohort.
**FIGURE 2:** *Effects on the concentrations of (A) AA-, (B) DHA-, and (C) EPA-derived epoxy-PUFA EETs, EDPs, and EEQs; and (D) AA-derived epoxy-PUFA plus dihydroxy-PUFA, (E) DHA-derived epoxy-PUFA plus dihydroxy-PUFA, and (F) EPA-derived epoxy-PUFA plus dihydroxy-PUFA in the plasma of n = 43 patients with hepatocellular carcinoma (HCC) without and undergoing sorafenib treatment (ng/mL ± standard error of the mean). Statistical differences were determined using the Wilcoxon signed-rank test (**p < 0.01; ***p < 0.001; ****p < 0.0001).*
The dihydroxy-PUFA products of the epoxy-PUFA formed via the sEH, respectively dihydroxyeicosatrienoic acids (DHETs) from EETs, dihydroxydocosapentaenoic acids (DiHDPAs) from EDPs and dihydroxyeicosatetraenoic acids (DiHETEs) from EEQs increased whilst on sorafenib treatment as well (Supplementary Table S1). When comparing absolute amounts of AA-, DHA- and EPA-derived epoxy-plus dihydroxy-PUFA significantly higher levels of metabolites derived from all three PUFAs were found (Figures 2D, E).
## 3.2 N-6 and n-3 fatty acid levels decrease during sorafenib treatment
To explore the presence of LC-PUFA, the fatty acid composition in plasma from patients with HCC was analyzed without and during sorafenib treatment by gas chromatography.
Interestingly, we found a decrease in the relative content of n-6 (AA) and n-3 (DHA) PUFAs, with an increasing n-6/n-3 ratio (Figure 3A; Supplementary Table S2).
**FIGURE 3:** *(A) Relative n-3 (docosahexaenoic acid, DHA; eicosapentaenoic acid, EPA) and n-6 (arachidonic acid, AA) PUFA levels in plasma from n = 43 patients with hepatocellular carcinoma (HCC) without and during sorafenib treatment individually, summarized and as a ratio. (B) Relative content of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) in plasma from n = 43 patients with HCC without and undergoing sorafenib treatment. Statistical differences were determined using the Wilcoxon signed-rank test (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).*
HCC patients without sorafenib had significantly higher levels of monounsaturated FAs (MUFA, $p \leq 0.001$)—comprising palmitoleic acid (C16:1 n-7c), oleic acid (C18:1 n-9c), and nervonic acid (C24:1 n-9)—and significantly lower levels of PUFAs ($p \leq 0.01$)—comprising EPA (C20:5 n-3), docosapentaenoic acid (DPA, C22:5 n-3), DHA (C22:6 n-3), linoleic acid (LA, C18: 2 n-6), dihomo-gamma-linolenic acid (DGLA, 20:3 n-6), AA (20:4 n-6), and adrenic acid (AdA, C22:4 n-6)—but there was no significant difference in the content of saturated fatty acids (SFA)—comprising myristic acid (C14:0), palmitic acid (C16:0), stearic acid (C18:0), arachidic acid (C20:0), behenic acid (C22:0), and lignoceric acid (C24:0)—(Figure 3B). With respect to specific fatty acids, HCC patients whilst on sorafenib treatment had a significantly lower relative content of the n-3 PUFA DHA ($p \leq 0.0001$), thus a significant decrease in DHA + EPA ($p \leq 0.05$), analogous to the HS-Omega-3 Index (von Schacky, 2020), and also a significant lower relative content of the n-6 PUFA AA ($p \leq 0.0001$).
## 3.3 N-6 and n-3 cytochrome P450 epoxy and dihydroxy product ratios do not support the hypothesis of increased sEH inhibition during sorafenib treatment
To determine whether the sEH inhibitory effect of sorafenib is detectable from the lipid metabolites assessed here, we analyzed whether sorafenib treatment increases plasma content of epoxymetabolites as compared to their dihydroxy products. As a marker for the enzyme activity in the CYP epoxygenase/sEH axis the plasma ratio of EET to DHET as characterization of the sEH inhibition has been used (Liu et al., 2009). We adapted this approach using the following equation for the AA-as well as the DHA- and EPA derived epoxy- and dihydroxy compounds: sEH−Activity=dihydroxy−PUFAepoxy−PUFA However, in contrast to lower ratios that would indicate lower sEH activity, we found higher dihydroxy/epoxy product ratios in the sorafenib treated patients (Figure 4). This does not support lower sEH activity in the sorafenib-treated patients. Interestingly, these higher ratios were only significant for the n-3 PUFA DHA- and EPA-derived metabolites.
**FIGURE 4:** *Ratio of n-6 and n-3 PUFA-derived dihydroxy to epoxy-PUFA as a marker for sEH activity in n = 43 patients with HCC without and undergoing sorafenib treatment (***p < 0.001).*
## 3.4 Metabolization of AA and EPA to their derived cytochrome P450 epoxy and dihydroxy products is similar, while metabolization of DHA to 19,20-EDP and 19,20-DiHDPA is markedly higher
In order to assess total CYP-epoxide and corresponding dihydroxy formation as a function of their respective substrate fatty acids we analyzed the epoxy and corresponding dihydroxy concentrations as a ratio with their respective substrate PUFA: CYP−products=epoxy+dihydroxy PUFAsPUFAs We found higher CYP product/PUFA ratios due to sorafenib treatment, providing evidence of increased presence of bioactive epoxy-PUFA from AA, EPA, and DHA in patients undergoing sorafenib treatment (Figures 5A–C). Furthermore, when analyzed as a ratio of DHA-derived CYP-products versus DHA as substrate, the 19,20-metabolites were found to be the predominant metabolites formed (Figure 5B).
**FIGURE 5:** *N-3 and n-6 PUFA-derived epoxides plus dihydroxy compounds as a marker for the presence of CYP metabolites in plasma from n = 43 patients with HCC without and undergoing sorafenib treatment. (A) Ratio of AA-derived products divided by AA plasma content, (B) ratio of DHA-derived products divided by DHA plasma content, (C) ratio of EPA-derived products divided by EPA plasma content (*p < 0.05, **p < 0.01; ***p < 0.001; ****p < 0.0001).*
## 4 Discussion
We found significant differences in plasma fatty acid composition in patients with HCC without sorafenib compared to during sorafenib treatment. Relative levels of AA and DHA were significantly lower during sorafenib treatment. Furthermore, we were able to demonstrate significantly higher EET levels and a trend towards increased n-3 CYP metabolites especially 19,20-EDP in this study population with HCC receiving sorafenib treatment.
When taking into account the different levels of the precursor n-3 PUFAs EPA and DHA we were able to establish that EPA is metabolized by CYP enzymes to a similar extent as AA, while DHA utilization was higher, leading to significantly increased levels of the 19,20-metabolites derived from DHA during sorafenib treatment (Figures 2E, 5B). Given that previous data from mouse models show inhibition of tumor angiogenesis and reduced cell invasion by increasing 19,20-EDP (Zhang et al., 2013) and to dampen and alleviate inflammation in the liver (López-Vicario et al., 2015), this could be a beneficial effect of sorafenib that could be harnessed in HCC therapy by supplementing DHA.
Generally, a beneficial role of n-3 PUFAs to dampen development of HCC is described both in animal models (Lim et al., 2009; Weylandt et al., 2011; Inoue-Yamauchi et al., 2017) and human observation studies (Sawada et al., 2012; Gao et al., 2015; Koh et al., 2016). This and the disbalance of the n-6/n-3 ratio in the Western Diet (Harris, 2006) suggests that supplementation of n-3 PUFAs could balance the n-6/n-3 ratio and may reduce tumor progression in HCC patients, regardless of sorafenib treatment. With all the described effects of n-3 PUFAs as receptor agonists, modulators of molecular signalling pathways and inflammatory responses, and data indicating that n-3 PUFA increase the efficacy of chemotherapies and consequently the overall survival of cancer patients, n-3 PUFAs can thus be considered as pharmaceutical nutrients (Bougnoux et al., 2009; Chagas et al., 2017; Paixão et al., 2017). However, in the population studied here the n-3 PUFA baseline was not associated with the overall survival (Supplementary Figure S2).
Prior studies showed that dietary increase of baseline n-3 PUFA concentrations can enhance formation of n-3 PUFA-derived CYP epoxy-PUFA (Fischer et al., 2014; Sarparast et al., 2020; Weylandt et al., 2022). Higher levels of n-3 PUFA may thus potentially increase anti-tumor n-3 PUFA-derived epoxymetabolites as well as decrease pro-tumor n-6 PUFA-derived metabolites (Zhang et al., 2014). Interestingly, in this study we found lower levels of DHA in patients treated with sorafenib (Figure 3), further supporting the concept to increase DHA in the daily diet in order to increase also levels of 19,20-EDP in HCC patients treated with sorafenib.
Many classes of currently used drugs can block or modify pathways of lipid mediator formation. Particularly well-established are non-steroidal anti-inflammatory drugs inhibiting the cyclooxygenase (COX) enzymes as well as numerous clinically well-established substances that modify (induce, inhibit) CYP enzymes and thereby modify lipid mediator formation. *In* general, by using quantitative LC-MS/MS oxylipin analysis in the context of established pharmacotherapy (pharmacolipidomics) as shown in this paper we hope to identify oxylipins that might be used to stratify and possibly also modify and improve response to treatments: *Experimental data* indicate strong biological effects of specific lipid mediators, particularly with regard to inflammation-dampening oxylipins from n-3 PUFA (n-3 IDOs) (Weylandt et al., 2022) in contrast to often inflammation-promoting oxylipins from n-6 PUFA (n-6 IPOs). As concentrations of these can be modified by changes in fatty acids substrates, as well as established drugs such as sorafenib, there could be a rationale for targeted modifications of the n-6/n-3 PUFA ratio in the diet in the context of established pharmacotherapy to harness these effects.
In systemic HCC therapy, the combination of immune checkpoint inhibitors, and VEGF pathway inhibitors such as sorafenib could promote an immune-permissive environment, thereby enhancing the response to immune therapeutic approaches. Immunotherapy is effective in only approximately $\frac{1}{3}$ of cancer patients, and targeting the TME to decrease tumor cell evasion is regarded as an opportunity to improve response to immunotherapy, as conversion of “cold” tumors to “hot” tumors with T cell infiltration is associated with a better response rate to cancer treatment (Bonaventura et al., 2019). TME-modifying properties of lipid mediator levels may therefore enhance antitumor effects by transforming the immune landscape. Aspirin dampens tissue inflammation via the metabolism of arachidonic acid in the COX enzymatic pathways and can reduce cellular growth in hepatocellular carcinoma (Tao et al., 2018; Refolo et al., 2020). Whether a CYP/sEH-dependent effect on lipid mediators—which could be modulated/enhanced by sorafenib as described here—could also play a role in the TME, remains a topic for future studies that are directly analyzing effects of n-3 IDO and n-6 IPO levels and formation, as well as immune cells in liver and liver tumor tissue.
In our data presented here, we were not able to discern an sEH inhibitory effect of sorafenib (Figure 4). Non-etheless we established significantly higher levels of CYP-derived epoxy and dihydroxy metabolites in patients undergoing sorafenib treatment. While previous results from one of us show that storage at −80°C should be sufficient to yield stable CYP derived oxylipin readings comparable to other oxylipins (Gladine et al., 2019; Koch et al., 2020), there might have been variations in the process of blood sampling and storage leading to changes in epoxy-PUFA levels. Another explanation would be that sorafenib may have more complex effects on PUFA-derived metabolites in humans, with increased formation of epoxy-PUFAs. We did not analyse expression of sEH directly, therefore there is a possibility of increased sEH expression, as described in an animal models of high fat diet induced liver disease (López-Vicario et al., 2015) which might compensate for an inhibitory effect of sorafenib. Interestingly, in our pilot study we found an increase of 8,9-EET, 11,12-EET and 14,15-EET levels in HCC patients treated with sorafenib (Leineweber et al., 2020) while we found a significant increase only of 5,6-EET and 8,9-EET levels here. We believe this might be due to the analytical limitations due to differences in sample taking and storage, and not a mechanistic difference in the effect observed. Indeed, we suggest to use the combined analysis of epoxides and dihydroxy compounds (as in Figures 2D–F) to assess epoxy metabolite formation. However future studies could better address this question by analyzing the samples under defined conditions and at different time intervals after blood drawing to measure the effects of these variations on epoxy- and dihydroxy-PUFA levels.
## 5 Conclusion
In this study, we investigated the effect of sorafenib treatment on PUFA formation and epoxy lipid mediator concentrations in peripheral blood plasma in a group of 43 HCC patients as a sub-analysis of the randomized, controlled, multicenter phase II SORAMIC study. We were able to demonstrate markedly increased epoxy plus dihydroxy PUFA concentrations in the peripheral blood of HCC patients undergoing sorafenib therapy. These results support previous findings that sorafenib treatment induces a change in epoxy-/dihydroxy-PUFA concentrations.
Given the anti-tumor effects described in experimental models for the n-3 PUFA-derived 19,20-EDP, these data further support the hypothesis that dietary n-3 PUFA supplementation in addition to sorafenib treatment could contribute anti-tumor effects due to n-3 epoxy-PUFA.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics committee Otto-von-Guericke University Magdeburg, Germany and Local Ethics committees. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conceptualization: CL, JR, NS, JB, and K-HW; Methodology: JR, JB, CL, NS, MRo, NR, AP, and K-HW; Analysis: CL, MRa, AP, NR, NS, MRo, and K-HW; Investigation: CL, MRa, AP, and K-HW; Resources: JR, JB, CL, AP, and MRo; Data curation: JR, JB, MP, BS, RS, CV, BB, CS, CL, and MRa; Writing—original draft preparation: CL; Writing—review and editing: JR, NS, JB, and K-HW; Visualization: CL, MRa, and K-HW; Supervision: JB and K-HW; Project administration: JR and K-HW. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1124214/full#supplementary-material
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|
---
title: HPLC-MS-MS quantification of short-chain fatty acids actively secreted by probiotic
strains
authors:
- Marco Calvigioni
- Andrea Bertolini
- Simone Codini
- Diletta Mazzantini
- Adelaide Panattoni
- Mariacristina Massimino
- Francesco Celandroni
- Riccardo Zucchi
- Alessandro Saba
- Emilia Ghelardi
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10020375
doi: 10.3389/fmicb.2023.1124144
license: CC BY 4.0
---
# HPLC-MS-MS quantification of short-chain fatty acids actively secreted by probiotic strains
## Abstract
### Introduction
Short-chain fatty acids (SCFAs) are the main by-products of microbial fermentations occurring in the human intestine and are directly involved in the host’s physiological balance. As impaired gut concentrations of acetic, propionic, and butyric acids are often associated with systemic disorders, the administration of SCFA-producing microorganisms has been suggested as attractive approach to solve symptoms related to SCFA deficiency.
### Methods
In this research, nine probiotic strains (*Bacillus clausii* NR, OC, SIN, and T, *Bacillus coagulans* ATCC 7050, Bifidobacterium breve DSM 16604, Limosilactobacillus reuteri DSM 17938, *Lacticaseibacillus rhamnosus* ATCC 53103, and *Saccharomyces boulardii* CNCM I-745) commonly included in commercial formulations were tested for their ability to secrete SCFAs by using an improved protocol in high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS-MS).
### Results
The developed method was highly sensitive and specific, showing excellent limits of detection and quantification of secreted SCFAs. All tested microorganisms were shown to secrete acetic acid, with only B. clausii and S. boulardii additionally able to produce propionic and butyric acids. Quantitative differences in the secretion of SCFAs were also evidenced.
### Discussion
The experimental approach described in this study may contribute to the characterization of probiotics as SCFA-producing organisms, a crucial stage toward their application to improve SCFA deficiency.
## Introduction
The human gut microbiota actively cooperates in maintaining physiological balance, in nutrient catabolism and absorption, as well as in the production of short-chain fatty acids (SCFAs) (LeBlanc et al., 2017). SCFAs are small carboxylic organic acids with a backbone of at most six carbon atoms. They represent the largest group of metabolic products obtained by microbial fermentation of dietary complex carbohydrates, otherwise undegradable by the human digestive system (Oliphant and Allen-Vercoe, 2019). Faecalibacterium prausnitzii, Eubacterium rectale, Akkermansia muciniphila, Clostridium spp., Bifidobacterium spp., Lactobacillus spp., and members belonging to families Ruminococcaceae, Lachnospiraceae, and Bacteroidaceae are the main actors able to produce SCFAs in the human gut as result of bacterial metabolism (Markowiak-Kopeć and Śliżewska, 2020).
The highest intestinal SCFA concentrations are reached by acetic, propionic, and butyric acids, which are known to exert countless beneficial effects and orchestrate several physiological functions (Morrison and Preston, 2016; Silva et al., 2020). Acetic acid is the structurally simplest fatty acid and can, therefore, be used as metabolic substrate in fatty acid biosynthesis and Krebs’ cycle (Okamoto et al., 2019). Besides being a major energy source for skeletal muscle (Okamoto et al., 2019), acetic acid is also involved in lipid metabolism of the liver and adipose tissue (Liu et al., 2019). The administration of acetate was reported to decrease food intake, body weight gain, blood cholesterol, and triglyceride levels (Liu et al., 2019). Propionic acid improves the barrier function and epithelial integrity in the gut, and impacts on glucose and lipid liver homeostasis (Langfeld et al., 2021). Previous studies reported the ability of propionic acid to indirectly modulate gene expression and cell metabolism through immunomodulatory mechanisms (Tan et al., 2014; Langfeld et al., 2021). Butyric acid is probably the most multifunctional SCFA, acting as energy source for colonic epithelial cells and promoting beta oxidation rather than glycolysis (Riviere et al., 2016). Enhancing the expression of tight-junction proteins, butyric acid facilitates the maintenance of the intestinal barrier integrity, thus hampering epithelial invasion by pathogenic microorganisms (Riviere et al., 2016). It has also been revealed to inhibit tumoral cell expansion and release of pro-inflammatory cytokines (Liu et al., 2021; Gheorghe et al., 2022), thus demonstrating anti-carcinogenic and anti-inflammatory properties. All the main SCFAs were found able to differently modulate appetite and energy intake by increasing colonic glucagon-like peptide-1 secretion (Christiansen et al., 2018), thus reducing food craving (Goswami et al., 2018). Moreover, they are also directly involved in the microbiota-gut-brain axis neuroendocrine signaling, brain physiology, and neuronal damage prevention (O’Riordan et al., 2022).
A correlation between lower intestinal SCFA concentrations and the exacerbation of different pathological conditions, such as inflammatory bowel diseases and neurological and neuropsychiatric disorders (e.g., autism, depression, Alzheimer and Parkinson’s diseases, multiple sclerosis), has been confirmed (Sun et al., 2017; Kong et al., 2018; Blaak et al., 2020; Tobin et al., 2021; O’Riordan et al., 2022). The administration of properly selected probiotic microorganisms able to counteract SCFA deficiency appeared as a promising novel approach to co-adjuvate the management of these conditions in humans and improve pathology-associated symptoms. For instance, psychobiotics producing SCFAs, neurotransmitters, and neuroendocrine hormones were revealed to provide wide health benefits to patients suffering from mental and neurological disorders (Cheng et al., 2021), thus pointing out SCFA production by probiotics as an additional beneficial feature in certain clinical conditions. Several studies demonstrated the ability of some probiotics to shift the gut microbiota composition toward higher abundances of SCFA-producing species (Markowiak-Kopeć and Śliżewska, 2020). However, probiotic strains themselves were scarcely tested for the direct production of SCFAs. Studies focusing on this aspect demonstrated that probiotic microorganisms were able to secrete acetic, propionic, and butyric acids in different amounts and modalities often dependent on the tested species or strain (Asarat et al., 2015; Kahouli et al., 2015; LeBlanc et al., 2017; Cheng et al., 2021). For this reason, an in-depth characterization of commercialized probiotics as concern SCFA production should be performed for an optimal targeted bacteriotherapy.
In this study, nine probiotic strains included in commercial formulations worldwide were tested for their ability to actively secrete acetic acid, propionic acid, and butyric acid and the amount of secreted SCFAs was quantified by using an optimized and sensitive protocol in high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS-MS).
## Materials and chemicals
Acetic acid (purity ≥$99.8\%$), propionic acid (≥$99.5\%$), propionic acid-2,3-13C2 (99 atom% 13C), and butyric acid (≥$99.5\%$) analytical standards, water LC-MS grade, acetonitrile (ACN) LC-MS grade, formic acid (FA, ≧ $98\%$), hydrochloric acid (ACS reagent, $37\%$ w/w), NaOH solution 1 M, diethyl-ether (≥$99\%$), 3-nitrophenylhydrazine (3NPH) hydrochloride, N-(3-dimethylaminopropyl)-N′-ethyl carbodiimide (EDC) hydrochloride, Bifidus selective medium (BSM), BSM supplement, and Sabouraud-$2\%$ dextrose agar (SDA) were bought from Merck KGaA (Darmstadt, Germany). Acetic acid 13C2 (99 atom% 13C) analytic standard was purchased from Cambridge Isotope Laboratories (Tewksbury, MA, USA). Brain Heart Infusion (BHI) agar was obtained from Biolife (Monza, Italy), while Trypticase Soy agar supplemented with $5\%$ horse blood (TSH) was from bioMérieux (Paris, France). De Man, Rogosa, and Sharpe (MRS) agar was acquired from Thermo Fisher Scientific (Waltham, MA, USA).
## Microbial strains and growth conditions
Nine microbial strains contained in worldwide commercialized, mono-species probiotic formulations were selected and tested in the present investigation. In particular, *Bacillus clausii* NR, OC, SIN, and T (isolated from Enterogermina, Sanofi, Paris, France, and declared on the product label), *Bacillus coagulans* ATCC 7050 (isolated from Lactò Più, Recordati, Milan, Italy, and declared on the product label), Bifidobacterium breve DSM 16604 (isolated from Neovaxitiol, IBSA Farmaceutici, Lodi, Italy, and declared on the product label), Limosilactobacillus reuteri DSM 17938 (isolated from Reuflor, Italchimici, Brescia, Italy, and declared on the product label with the old nomenclature Lactobacillus reuteri DSM 17938), *Lacticaseibacillus rhamnosus* ATCC 53103 (isolated from Dicoflor, Dicofarm, Roma, Italy, and declared on the product label with the old nomenclature *Lactobacillus rhamnosus* ATCC 53103), and *Saccharomyces boulardii* CNCM I-745 (isolated from Codex, Zambon, Bresso, Italy, and declared on the product label) were tested. All probiotic products were purchased from local pharmacies. B. clausii NR, OC, SIN, and T were isolated from Enterogermina as described in a previous work (Senesi et al., 2001). B. coagulans was seeded on TSH and plates were incubated at 37°C for 48 h. B. breve was propagated on BSM containing 0.116 g/L of BSM supplement and grown at 37°C for 48–72 h in anaerobic atmosphere, generated by using Thermo Scientific™ AnaeroGen™ Compact (Thermo Fisher Scientific, Waltham, MA, USA). L. reuteri and L. rhamnosus were streaked on MRS agar and plates were incubated at 37°C for 48 h in $5\%$ CO2-enriched atmosphere, generated by using Thermo Scientific™ CO2Gen™ Compact (Thermo Fisher Scientific, Waltham, MA, USA). S. boulardii was seeded on Sabouraud-$2\%$ dextrose agar and plates were incubated at 30°C for 48 h.
## Preparation of culture supernatants
Different liquid culture media (i.e., Luria Bertani broth, BHI and BHIG broths, RPMI 1640 medium) were firstly tested for microbial propagation. BHIG broth was selected as the most suitable culture medium in the end, as it allowed an optimal uniform replication of all microorganisms and contained amino acids and glucose, which are important substrates for SCFA biosynthesis (Louis and Flint, 2017). Thus, for each strain, a well-isolated colony was inoculated in 5 mL of BHIG broth. Suspensions were incubated overnight at 37°C. While B. clausii strains, B. coagulans, and S. boulardii were aerobically incubated, B. breve was incubated in anaerobic atmosphere and L. reuteri and L. rhamnosus in $5\%$ CO2-enriched atmosphere. Subsequently, 100 μL of microbial cultures were inoculated in 25 mL of fresh BHIG medium. Cultures were incubated at 37°C up to an optical density at 600 nm (OD600) of 1.8 and then centrifuged at 3,870 rcf for 20 min at 4°C. Supernatants were collected and filtered using 0.22 μm filters to completely remove microbial cells. Supernatants were produced three times in separate days and stored at −80°C until use.
## Sample preparation
Microbial supernatants underwent a liquid-liquid extraction procedure before HPLC-MS-MS analysis, as previously described by De Baere et al. [ 2013] with protocol modifications. Briefly, 200 μL of each sample were placed in a 2 mL tube and added with 10 μL of a 10 μg/mL internal standard mixture made of 13C2-acetic acid and 13C2-propionic acid. The former was used as the internal standard (IS) for the quantification of acetic acid, while the latter for both propionic and butyric acids. Samples were mixed and equilibrated at room temperature for 5 min. Thereafter, 20 μL of HCl $37\%$ w/w were added, samples were mixed for 15 sec, and extracted for 20 min by gently shaking in an orbital shaker, using 1 mL of diethyl-ether. After a centrifugation step of 5 min at 1,230 rcf, the organic phase was transferred to new tubes and 100 μL of NaOH 1 M were added. Samples were shaked again for 20 min and then centrifuged. The organic phase was removed and the aqueous phase containing SCFAs was added with 10 μL of HCl $37\%$ in order to obtain a pH value in the range 4–7. Actually, pH is one of the limiting conditions of the derivatization process that was carried out to change the analytes’ structure, thus improving chromatographic separation and enhancing instrumental sensitivity. It can be achieved as follows: 50 μL of each sample was added with 50 μL of HPLC-MS water and then derivatization was performed adding 50 μL of 3-nitrophenylhydrazine (3NPH) hydrochloride 200 mM and 50 μL of N-(3-dimethylaminopropyl)-N′-ethyl carbodiimide hydrochloride 120 mM. 3NPH was employed to convert SCFAs to their 3-nitrophenylhydrazine form, which had showed an excellent in-solution chemical stability (Han et al., 2015). Solutions were incubated at 40°C for 30 min in constant shaking. Afterward, the derivatization reaction was quenched adding 200 μL of $0.1\%$ formic acid, and derivatized samples were ready to be injected into the HPLC-MS-MS system for analysis. Quantification made use of calibration curves, prepared by serial dilution with water of stock standard solutions at the concentration of 1 μg/mL (for propionic and butyric acids) and 5 μg/mL (for acetic acid), to obtain concentrations of 1,000, 500, 250, 125, 62.5, 31.25, 15.63, 7.81, 3.90, and 1.95 ng/mL for acetic acid, and 200, 100, 50, 25, 12.5, 6.25, 3.13, 1.56, 0.78, and 0.39 ng/mL for propionic and butyric acids. Each calibration point (50 μL) was diluted with 50 μL of sterile BHIG, previously extracted according to the procedure used for samples, and added with a proportional amount of the IS mixture in order to achieve, in all calibrators, the same initial concentration of IS in the samples. The use of the sample matrix to build the calibration curves makes calibrators similar to the samples, providing more reliable and reproducible results. Derivatization and injection of calibration points were then performed as described above.
## HPLC-MS-MS analysis
The instrumental layout consisted in a 1,290 ultra high performance liquid chromatography (UHPLC) Infinity II system (Agilent, Santa Clara, CA, USA), including a binary pump, a column oven set at 40°C, and a thermostated autosampler, coupled to a QTRAP 6500 + LC-MS-MS (Sciex, Concord, ON, Canada), working as a triple quadrupole and equipped with an IonDrive™ Turbo V source (Sciex). Chromatographic separation was achieved by using a 110 Å, 2 × 50 mm, 3 μm particle size, Gemini C18 HPLC column (Phenomenex, Torrance, CA, USA) protected by a C18 SecurityGuard™ cartridge (Phenomenex) and using acetonitrile containing $0.1\%$ formic acid (solvent A, A) and water with $0.1\%$ formic acid (solvent B, B) as mobile phases. Gradient elution, with a 500 μL/min flow rate, was performed as follows: 0.0–0.3 min (A) $10\%$, 2.5–3.5 min (A) $20\%$, 3.6–4.5 min (A) $90\%$, 4.6–5.5 (A) $10\%$. Injection volume was set at 5 μL. System control, data acquisition, and data processing were performed using Sciex Analyst® software (version 1.7.2). A mass spectrometry selected reaction monitoring method was operated in negative ion mode. For each compound, after the optimization of declustering potential (DP, −80 V), collision energy, and collision exit potential (Supplementary Table 1), three transitions were considered in the analysis. Based on the highest signal/noise ratios, one of them was used as quantifier (Q) and the other two as qualifiers (q) (Supplementary Table 1). Further operative parameters were set as follows: gas source 1, 45 arbitrary units; gas source 2, 30 arbitrary units; ion spray voltage, −4.5 kV; source temperature, 500°C; curtain gas, 35 arbitrary units; collision gas, N2; operative pressure with collision gas on, 3 mPa; entrance potential, −10 V.
## Method validation
To evaluate the method ability to differentiate the molecules of interest from other possible components and interferents present in samples, specificity was checked by repeated injections of analytes into the system and their retention times were monitored. Linearity was evaluated within the calibration curve range built as aforementioned, while instrumental sensitivity was assessed by evaluating limits of analyte detection (LOD) and quantification (LOQ). Using the S-to-N Script tool of Sciex Analyst® software, concentrations providing a S/N ratio close to 3 and 10 were assumed as LOD and LOQ, respectively. Recovery and matrix effect were calculated as reported by Matuszewski et al. [ 2003]. Recovery was evaluated by comparing the peak areas of analytes added to blank BHIG broth before and after the extraction procedure, while the estimation of matrix effect was performed comparing the peak areas of the analytes added to water (A) and blank BHIG broth (B) previously subjected to the extraction process [(B/A) × 100]. Precision (%), expressed as relative standard deviation (RSD%), intra-day and inter-day accuracies, calculated with the formula [(measured concentration/nominal concentration spiked) × 100], were measured in blank samples spiked with three different concentration levels of analytes (5 ng/mL, 50 ng/mL, and 250 ng/mL for acetic acid, 1 ng/mL, 10 ng/mL, and 50 ng/mL for propionic and butyric acids). Stability of analytes as a result of a freeze-thaw cycle was evaluated, as well. Aliquots of freshly prepared samples, spiked at low, medium, and high concentrations (already used for the calculation of accuracies) were immediately injected and results were compared to those from a second aliquot of the same concentration frozen at −20°C and thawed at room temperature before the assay.
Traces and specific retention times of acetic, propionic, and butyric acids from standard solutions and samples were achieved as shown in Figure 1. Each compound exhibited three traces, each corresponding to a specific multiple reaction monitoring (MRM) transition: the one possessing the higher signal to noise ratio was used for quantification (quantifier, Q) of the analyte, while the others, which are representative of the specific analyte structure, confirmed its identity (qualifier, q). Retention time further confirmed the peak correspondence. These features demonstrated excellent specificity and sensitivity (LOD and LOQ values) of the HPLC-MS-MS method for the quantification of SCFAs. Their values are reported in Table 1 together with the recovery of the extraction process and contribution of matrix effect. Linearity resulted ≥0.9997 for each analyte. Methodological inter-day and intra-day accuracy was in the optimal range of 85–$115\%$, which is in compliance with the European Medicines Agency guidelines (European Medicines Agency, 2015). Precision, representing the closeness among repeated individual measurements of the analytes, was always < $7\%$. Re-analysis of samples after storage showed no degradation of analytes or their relative internal standards, thus confirming the stability of our protocol.
**FIGURE 1:** *Representative chromatograms of the analytes in a standard mixture (1 μg/mL acetic acid, 200 ng/mL propionic, and butyric acids) (A) and in one of the real samples (B). For each analyte, three multiple reaction monitoring (MRM) transitions were monitored: the one with the higher signal/noise ratio, which usually correspond to the most intense trace, was used for the quantification of the analyte (Q), while the other two transitions confirmed that the peak is attributable to the analyte (q). All MRM transitions are summarized in Supplementary Table 1.* TABLE_PLACEHOLDER:TABLE 1
## Statistical analysis
Data are expressed as the mean ± standard deviation. For each strain, three biological replicates with three technical replicates each were performed. All statistical analyses were performed with GraphPad Prism 8 (GraphPad Software Inc., USA). To separately infer statistically significant differences in the production of acetic, propionic, and butyric acids by probiotic microbes, the one-way ANOVA followed by Tukey’s test for multiple comparisons was applied for comparing the mean amount of SCFAs produced by each strain. Statistical significance was set at a P-value of < 0.05.
## Quantification of the SCFA amount in culture supernatants
Bacillus clausii, B. coagulans, B. breve, L. reuteri, L. rhamnosus, and S. boulardii supernatants collected from actively replicating cells were subjected to a HPLC-MS-MS analysis to determine the amount of acetic, propionic, and butyric acids secreted in the culture medium.
Regarding acetic acid (Figure 2A), B. clausii T and L. reuteri resulted the highest producers among the tested strains, secreting 602.00 ± 54.15 ng/mL and 644.33 ± 7.15 ng/mL, respectively. No differences were highlighted among the four B. clausii strains. Secretion of acetic acid from B. coagulans, L. rhamnosus, and S. boulardii was significantly lower compared to B. clausii NR (BC: $$P \leq 0.0023$$; LRh and SB: $$P \leq 0.0003$$), OC (BC: $$P \leq 0.0215$$; LRh: $$P \leq 0.0031$$; SB: $$P \leq 0.0029$$), SIN (BC: $$P \leq 0.0132$$; LRh: $$P \leq 0.0019$$; SB: $$P \leq 0.0018$$), T (BC: $$P \leq 0.0002$$; LRh and SB: $P \leq 0.0001$), B. breve (BC: $$P \leq 0.0042$$; LRh and SB: $$P \leq 0.0006$$), and L. reuteri ($P \leq 0.0001$).
**FIGURE 2:** *(A) Concentration of acetic acid (ng/mL) in the culture supernatants of B. clausii (NR, OC, SIN, and T), B. coagulans, B. breve, L. reuteri, L. rhamnosus, and S. boulardii. NR = B. clausii NR; OC = B. clausii OC; SIN = B. clausii SIN; T = B. clausii T; BC = B. coagulans; BB = B. breve; LRe = L. reuteri; LRh = L. rhamnosus; SB = S. boulardii. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. NR vs. BC**, NR vs. LRh***, NR vs. SB***, OC vs. BC*, OC vs. LRh**, OC vs. SB**, SIN vs. BC*, SIN vs. LRh**, SIN vs. SB**, T vs. BC***, T vs. LRh****, T vs. SB****, BC vs. BB**, BC vs. LRe****, BB vs. LRh***, BB vs. SB***, LRe vs. LRh****, LRe vs. SB****. (B) Concentration of propionic acid (ng/mL) in the culture supernatants of B. clausii (NR, OC, SIN, and T), B. coagulans, B. breve, L. reuteri, L. rhamnosus, and S. boulardii. NR vs. T*, OC vs. BC*, OC vs. BB*, OC vs. LRe*, OC vs. LRh*, SIN vs. T*, T vs. BC****, T vs. BB****, T vs. LRe****, T vs. LRh****, T vs. SB***. (C) Concentration of butyric acid (ng/mL) in the culture supernatants of B. clausii (NR, OC, SIN, and T), B. coagulans, B. breve, L. reuteri, L. rhamnosus, and S. boulardii. NR vs. BC***, NR vs. BB***, NR vs. LRe*, NR vs. LRh***, NR vs. SB**, OC vs. BC****, OC vs. BB****, OC vs. LRe***, OC vs. LRh****, OC vs. SB****, SIN vs. BC****, SIN vs. BB****, SIN vs. LRe**, SIN vs. LRh****, SIN vs. SB***, T vs. BC****, T vs. BB****, T vs. LRe***, T vs. LRh****, T vs. SB****.*
Propionic acid concentrations were found to be three orders of magnitude lower than acetic acid in the culture supernatants (Figure 2B). B. coagulans, B. breve, L. reuteri, and L. rhamnosus did not secrete propionic acid at all in our conditions. B. clausii T was able to produce the highest levels of propionic acid (1.21 ± 0.38 ng/mL), resulting significantly different from NR ($$P \leq 0.0374$$), SIN ($$P \leq 0.0112$$), and S. boulardii ($$P \leq 0.0007$$).
The levels of butyric acid were found to be slightly higher than those of propionic acid (Figure 2C). B. coagulans, B. breve, and L. rhamnosus were unable to secrete butyric acid, while all B. clausii strains showed a comparable secretion (NR: 2.72 ± 0.47 ng/mL; OC: 2.70 ± 0.31 ng/mL; SIN: 2.70 ± 0.06 ng/mL; T: 3.04 ± 0.25 ng/mL). A higher concentration of butyric acid was found in B. clausii NR, OC, SIN, and T culture supernatants compared to L. reuteri (NR: $$P \leq 0.0380$$; OC and T: $$P \leq 0.0002$$; SIN: $$P \leq 0.0012$$) and S. boulardii (NR: $$P \leq 0.0029$$; OC: $$P \leq 0.0001$$; SIN and T: $P \leq 0.0001$).
## Discussion
Short-chain fatty acids are well-known to play important roles in the promotion and maintenance of the health status and systemic homeostasis (Silva et al., 2020) and SCFA-producing probiotics have been proposed as possible implementation in the case of SCFA-deficiencies. Different SCFAs share some biological functions, but each SCFA also possesses peculiar properties and beneficial effects (Tan et al., 2014; Morrison and Preston, 2016; Riviere et al., 2016; Christiansen et al., 2018; Goswami et al., 2018; Liu et al., 2019, 2021; Okamoto et al., 2019; Silva et al., 2020; Langfeld et al., 2021; Gheorghe et al., 2022; O’Riordan et al., 2022). Therefore, the choice of a probiotic strain able to produce one or another SCFA should take into consideration the effects a particular acid displays on the host.
Different HPLC technologies have been used over years to quantify SCFAs (De Baere et al., 2013; Zheng et al., 2019; Chen et al., 2021), especially in studies where the intestinal microbiota was altered in association with various pathological conditions (Yamada et al., 2015; Garcia-Mantrana et al., 2018; Xue et al., 2019; Sowah et al., 2020; Soriano-Lerma et al., 2021). In our study, coupling tandem mass spectrometry to HPLC (HPLC-MS-MS) to quantify acetic, propionic, and butyric acids in microbial culture supernatants led to a very specific and sensitive detection, which is properly required when low analyte concentrations are present, as in the case of SCFAs (Marzo and Dal Bo, 2007; Leung and Fong, 2014).
The in vitro evaluation of SCFA secretion by probiotic strains can provide dissimilar results, even when the same microbial strain is tested, probably due to the culture conditions and media used for microbial growth (Asarat et al., 2015; LeBlanc et al., 2017). In fact, experimental protocols and environmental conditions influence the outcomes of analyses in terms of both quality and quantity of secreted SCFAs. For this reason, to guarantee a uniform microbial growth and obtain comparable results from the different tested strains, in this study a unique culture medium (i.e., BHIG) containing substantial concentrations of glucose and amino acids, which are the main essential substrates for SCFA synthesis, was selected and the same culture conditions were applied.
Among members of the Bacillus genus, B. clausii and B. coagulans have been used as probiotics for years considering the beneficial effects exerted in several gastrointestinal disorders (Lee et al., 2019; Shinde et al., 2020a,b). Since no evidence is present in the literature regarding their direct production of SCFAs, the present study highlights new metabolic features of these species. The ability of strains NR, OC, SIN, and T to secrete acetic, propionic, and butyric acids in our in vitro model suggests the potential of the B. clausii species to produce SCFAs. These compounds could contribute to the properties this species demonstrated as adjuvant treatment in several gastrointestinal dysfunctions (Ianiro et al., 2018; de Castro et al., 2019, 2020; Paparo et al., 2020; Plomer et al., 2020; Szajewska et al., 2020). B. coagulans strains are worldwide recognized as effective probiotics, and the role of B. coagulans SANK 70258 in ameliorating ulcerative colitis and leading the gut microbiota composition toward the enrichment in butyrate-producing bacteria has recently been shown (Sasaki et al., 2020). Herein, B. coagulans ATCC 7050 was proven to be able to secrete acetic acid, while propionic and butyric acids were not detected in its culture supernatant.
Bifidobacterium spp. have been demonstrated to confer many benefits to the human health, mainly when administered for pediatric pathologies, such as allergies, obesity, diarrhea, colic, and celiac disease (Bozzi Cionci et al., 2018). Different strains of B. breve are widely effective in preventing or ameliorating symptoms of several diseases, including Alzheimer’s disease (i.e., B. breve A1) and obesity-associated insulin sensitivity (i.e., B. breve BR03 and B632) by directly or indirectly modulating the local concentration of SCFAs (Kobayashi et al., 2017; Solito et al., 2021). In the present investigation, high concentrations of acetic acid were found in the culture supernatant of B. breve DSM 16604, which was never tested before for SCFA production. As expected, propionic and butyric acids were not detected, since the biosynthetic pathways for propionate and butyrate are not present in Bifidobacterium species (Ruiz-Aceituno et al., 2020).
Numerous lactobacilli are commonly administered as probiotics due to their beneficial properties (Zhang et al., 2018). Among lactobacilli, L. reuteri DSM 17938 is a well-characterized and largely commercialized probiotic microorganism, found to be suitable for the prevention and co-treatment of chronic constipation, colic, diarrhea, and gastroenteritis, especially in children (Gutiérrez-Castrellón et al., 2017; Kołodziej and Szajewska, 2019; Patro-Gołąb and Szajewska, 2019; Kubota et al., 2020). The ability of L. reuteri to produce SCFAs is a strain-dependent feature, as previously evidenced for L. reuteri NCIMB 11951, 701359, 701089, 702655, and 702656 (Kahouli et al., 2015). Herein, we demonstrated the ability of L. reuteri DSM 17938 to secrete large amounts of acetic acid and butyric acid to a lesser extent, thus suggesting a possible mechanism of action for reaching the health benefits associated to its administration. L. rhamnosus, whose persistence on the international market has lasted for more than 30 years due to its efficacy in managing several clinical conditions, is another bacterial species considered to have excellent probiotic properties (Capurso, 2019). L. rhamnosus strains were often demonstrated to be able to promote butyrogenesis and shape the gut microbiota with increased abundances of butyrate-producing bacteria (Lin et al., 2020). L. rhamnosus GG turned out to secrete acetic, propionic, and butyric acids in skim milk supplemented with prebiotics, as reported by Asarat et al. [ 2015], while another study showed the release of only propionic acid by this strain in MRS (LeBlanc et al., 2017). In this study, L. rhamnosus ATCC 53103 was able to secrete acetic acid in BHIG, but propionic and butyric acids were not detected in its culture supernatant. Our findings on B. breve, L. reuteri, and L. rhamnosus are in line with previous observations reporting Bifidobacterium and Lactobacillus species as mainly acetate producers (Cheng et al., 2021).
Several species of Saccharomyces are intrinsically able to determine an enrichment of SCFA-producing bacteria in the gut microbiota (Moré and Swidsinski, 2015; Offei et al., 2019), but only a few acidify the intestinal environment through the secretion of high levels of acetic acid themselves (Offei et al., 2019). Up to date, no information about secretion of propionic and butyric acid by S. boulardii is available in the literature. Although many efforts have been made on S. cerevisiae strains for enhancing the production of SCFAs by genetic engineering (Leber and Da Silva, 2014; Yu et al., 2016), a clear characterization of S. boulardii ability to secrete SCFAs is still lacking. S. boulardii CNCM I-745 was shown to release both acetic, propionic, and butyric acids in its culture supernatant, confirming its potential as regards SCFA metabolism.
Considering that the in vitro conditions used in this study for testing SCFA production are far from the actual environment and conditions found in the human intestine, further studies are needed to elucidate if the behaviors observed in vitro for the tested probiotic strains could also be observed in vivo. In fact, the presence of the gut microbiota, nutrients, pH, oxygen gradient, and many other factors could influence SCFA secretion by probiotics in vivo. Nevertheless, the in vitro analysis described in this study appears useful as a screening to evaluate the microbial potential to secrete SCFAs and help in the primary selection of promising SCFA-producing probiotic strains.
## Conclusion
The application of a novel sensitive HPLC-MS-MS protocol for the detection and quantification of SCFAs allowed us to establish that all the tested probiotic strains are able to actively secrete acetic acid and a part of them all the three main short-chain fatty acids in vitro. Although our study cannot exclude a different microbial behavior in vivo or in other in vitro conditions, we recommend that the production of SCFAs should be taken into consideration as key feature when next generation probiotics and psychobiotics are evaluated for their potential clinical effectiveness. An in-depth characterization of strains contained in probiotic formulations as regards SCFA secretion could be a novel aspect to consider in the probiotic research and contribute to the spread of more targeted and personalized bacteriotherapy strategies to promote human health and manage diseases.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
RZ, AS, and EG: conception and design of the study, validation, and formal analysis. MC, AB, SC, RZ, AS, and EG: methodology. MC, AB, SC, DM, AP, MM, and FC: investigation. MC and AB: writing—original draft preparation. MC, AB, SC, DM, AP, MM, FC, RZ, AS, and EG: writing—review and editing. EG: supervision. All authors read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1124144/full#supplementary-material
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|
---
title: Calcium carbonate-enriched pumpkin affects calcium status in ovariectomized
rats
authors:
- Natalia Wawrzyniak
- Anna Gramza-Michałowska
- Paweł Kurzawa
- Paweł Kołodziejski
- Joanna Suliburska
journal: Journal of Food Science and Technology
year: 2023
pmcid: PMC10020404
doi: 10.1007/s13197-023-05686-3
license: CC BY 4.0
---
# Calcium carbonate-enriched pumpkin affects calcium status in ovariectomized rats
## Abstract
Calcium carbonate (CaCO3)-enriched pumpkin may serve as a good source of calcium for patients diagnosed with osteoporosis. In this study, we aimed to determine the effect of CaCO3-enriched pumpkin on *Ca status* in ovariectomized rats. The study included 40 female Wistar rats divided into five groups ($$n = 8$$). One group was fed with a standard diet (control group), while the other four groups were ovariectomized and received a standard diet (control ovariectomized group), or a diet containing CaCO3-enriched pumpkin, alendronate, or both. The nutritional intervention lasted 12 weeks, and then the rats were euthanized. Tissue and blood samples were collected and assessed for the levels of total Ca, estradiol, parathyroid hormone, and procollagen type I N propeptide. In addition, a histological analysis was performed on femurs. The results of the study suggest that CaCO3-enriched pumpkin can increase Ca content in femurs and improve bone recovery in ovariectomized rats. Furthermore, enriched pumpkin contributes to Ca accumulation in the kidneys, and this effect is more pronounced in combination with alendronate.
## Introduction
Postmenopausal osteoporosis is a condition characterized by a reduction in bone mineral mass due to a decline in estrogen levels as a result of endocrine disruption of the ovaries (Black and Rosen 2016). It is mainly diagnosed using dual X-ray absorptiometry (Yong and Logan 2021), but serum bone markers are also considered one of the prognostic indicators to determine disease development or treatment effects (Kanis et al. 2020). Bone turnover markers can be divided into two groups: formation markers and resorption markers. The former includes PINP and OC, which are by-products of bone mineralization. PINP found in the serum is released during collagen formation, while PTH stimulates osteoblasts to release OC (Marcu et al. 2011; Greenblatt et al. 2017). Histopathological analysis of bone aids in understanding the bone structure and cellular changes occurring in the bone tissue, thereby confirming the presence or absence of osteoporotic changes. A fewer number of osteoblasts and osteocytes (forming cells) and an increased number of osteoclasts (resorbing cells) might indicate the presence of osteoporosis. Moreover, a higher ratio of fat bone marrow to bone marrow cellularity is a negative prognostic indicator of osteoporosis development (Marcu et al. 2011). The percentage of de novo-built bones is indicated by the percentage of woven bones. First, a woven bone (immature bone undergoing reconstruction) is formed from mesenchymal osteoblasts; the woven bone is then remodeled into a lamellar bone (mature bone that does not undergo transformation) from surface osteoblasts, a process common in the general population. However, the proportion of woven bone to lamellar bone varies among individuals (Shapiro and Wu 2019). The number of woven bones is generally high during recovery from injury or during growth in children, whereas in adults bone formation and resorption processes occur continuously and bones undergo standard transformations (Downey and Siegel 2006).
The diagnosis of osteoporosis should be followed by appropriate treatment to increase bone density and decrease bone turnover. Pharmacological treatment intended for osteoporosis involves the use of drugs that reduce bone resorption and/or accelerate bone formation, such as bisphosphonates (alendronate and risedronate), denosumab, and teriparatide, or hormone replacement (Gallagher and Tella 2014). However, these drugs can cause side effects when used for a long term; for example, the use of bisphosphonates for over two years can result in atypical bone fractures (Black and Rosen 2016), jaw necrosis (Shibahara 2019), or digestive disorders (Fadda et al. 2015). Therefore, the public health system is currently focusing on developing new approaches for the treatment and prevention of osteoporosis.
A diet containing adequate amounts of Ca with high bioavailability is essential for maintaining bone health, as Ca constitutes a large portion of bone mass (Weaver 2015). In addition to eliminating substances that can reduce Ca absorption (e.g. phytic or oxalic acid), components that increase Ca bioavailability (e.g. vitamin D and inulin) should be included adequately in the diet (Wawrzyniak and Suliburska 2021), in order to improve bone health. Endogenous factors regulating the metabolism of Ca are equally important. The concentration of Ca in the blood is regulated mainly by active vitamin D (1,25-dihydroxycholecalciferol), calcitonin, and PTH. PTH plays a major role in Ca regulation in the blood and bone turnover, which stimulates the release of Ca in bones and its reabsorption in kidneys. In addition, PTH stimulates the synthesis of vitamin D, which increases intestinal Ca uptake, and inhibits collagen synthesis by osteoblasts. Collagen is the organic matrix for minerals (including Ca) deposited in bone. On the contrary, calcitonin inhibits bone resorption, the activation of vitamin D, and Ca renal reabsorption. Therefore, the status of Ca in the body is influenced by the supply, bioavailability, and factors regulating its metabolism (Marcu et al. 2011; Greenblatt et al. 2017).
Epidemiological studies have shown that Ca deficiency is common worldwide, and there is a need to identify dietary sources with high bioavailable Ca (Balk et al. 2017). It has been found that Ca-enriched food products based on pumpkin can help overcome this challenge (Weaver and Liebman 2002). Pumpkin comprises compounds with high biological activity, such as carotenoids, including α-carotene, β-carotene, zeaxanthin, or lutein, which have a beneficial effect on the bone mineral status, reduce susceptibility to fractures, and prevent the progression of osteoporosis (Kulczynski and Gramza-Michałowska 2019). Moreover, pumpkin is easy to use in an osmotic dehydration process, which allows the enrichment of its tissues with Ca salts (Kulczyński et al. 2020). Enriched pumpkin contains inulin which increases Ca bioavailability(Bakirhan and Karabudak 2021), other ingredients that can improve bone health, including lutein (Takeda et al. 2017; Tominari et al. 2017) and β-cryptoxanthin, and a pigment that inhibits bone resorption and reduces oxidative stress (Yamaguchi 2012; Ozaki et al. 2015). Pumpkin has also been used in previous studies due to its low caloric content (average 26 kcal/100 g). Moreover, pumpkin exhibits cardioprotective and hypoglycemic properties, and is therefore recommended for diabetics and patients with arterial hypertension and obesity (Kulczynski and Gramza-Michałowska 2019).
According to the available data, the ingredients of Ca-enriched pumpkin can increase bone mineral density and thus reduce the risk of osteoporosis. Therefore, this study aimed to determine the effect of Ca-enriched pumpkin on Ca metabolism and status in ovariectomized rats.
## Materials and reagents
Pumpkins (Cucurbita maxima, yellow melon) were obtained from a domestic organic farm after seeking permission from the land owner. Experimental research and field studies, including the collection of plant materials, were conducted in accordance with relevant institutional, national, and international guidelines and legislation. Inulin and CaCO3 were purchased from Agnex (Białystok, Poland). Sucrose, rapeseed oil, dextrin, corn starch, and casein were obtained from Hortimex (Konin, Poland). Minerals and vitamins were procured from Sigma-Aldrich (Darmstadt, Germany). ELISA kits were purchased from SunRed (Shanghai, China).
## Osmotic dehydration
Pumpkins were enriched with CaCO3 by osmotic dehydration with inulin, an osmotically active substance. Then, they were cleaned and washed, and the inner part attached to the seeds was removed. Subsequently, the skin was peeled, and the pumpkins were cut into cubes (1 cm) and frozen for 24 h at − 18 °C. After freezing, a solution composed of inulin (125 g) and distilled water (125 ml) in a 1:1 ratio was prepared in small jars. CaCO3 was added to the prepared solution such that its content was $5\%$ of the solution. Next, 50 g of frozen pumpkin cubes (5:1) was added to 250 g of the hypertonic solution in the jars. The jars containing the pumpkin cubes were closed and shaken for 2 h at 50 °C in a water bath. Upon completion of osmotic dehydration, the solution formed above the pumpkin cubes was removed and the cubes were filtered. This process was repeated three times. Then, the pumpkin cubes were frozen at − 18 to − 28 °C for the next 24 h, and freeze-dried to 3.5–$5\%$ water content. A 100 g of the obtained lyophilizate contained 2390.8 ± 63.3 mg of Ca (Kulczyński et al. 2020) compared to nonenriched freeze-dried pumpkin, in which the Ca content was only 264.89 ± 0.59 mg/100 g (Kulczynski and Gramza-Michałowska 2019).
## Animals
Forty female Wistar rats aged 12 weeks were purchased from the University of Adam Mickiewicz in Poznań, Greater Poland Center for Advanced Technologies, Poland. The animals were allowed to acclimatize for 1 week and then housed individually in cages under a 12-h dark–light cycle. All animal experiments were carried out in accordance with the EU Directive $\frac{2010}{63}$/EU for animal experiments. Approval for the study was obtained from the Local Ethics Committee in Poznań (protocol number: $\frac{34}{2019}$). The reporting in the manuscript follows the recommendations in the ARRIVE guidelines.
## Experimental protocols
Throughout the experiment, the rats were fed with the standard AIN-93 M diet (Reeves and Suppl 1997). The animals were divided into five groups, with eight in each. At the beginning of the experiment, the body weight of the rats was measured and found to be similar. Four groups (32 rats) were ovariectomized. After a one-week recovery period, the rats were subjected to a 12-week nutritional intervention. Unmodified standard AIN-93 M diet was given to the control group (C) and to one of the ovariectomized groups (OVX_C), while the other three groups received a diet containing CaCO3-enriched pumpkin (OVX_P group), alendronate (OVX_B), or alendronate and CaCO3-enriched pumpkin (OVX_P_B group). The standard diet contained CaCO3 as a source of Ca. Figure 1 presents a schematic of the experiment.
Fig. 1Scheme of the study. C, control group receiving standard diet; OVX_C, ovariectomized group receiving standard diet; OVX_P, ovariectomized group receiving diet with pumpkin enriched with CaCO3; OVX_B, ovariectomized group receiving diet with alendronate; OVX_P_B, ovariectomized group receiving diet with pumpkin enriched with CaCO3 and alendronate The amount of enriched pumpkin added to the modified diet was such that the Ca content of the modified diet was the same as that of the standard diet. For the OVX_B and OVX_P_B groups, the amount of alendronate was adjusted weekly, ensuring that they received 3 mg/kg body weight. All animals were allowed ad libitum access to deionized water and feed. The intake by animals was recorded daily, and the body weight was measured weekly. At the end of the experiment, a body weight was measured, and then the rats were decapitated. Blood samples were collected and stored at − 80 °C. Serum was obtained by centrifuging the blood samples at 1200 × g for 10 min at 4 °C. The femurs, liver, kidneys, femoral muscles, spleen, and pancreas were isolated for analyses. The obtained tissues were washed with saline, weighed, and stored at − 80 °C. Hair was collected from the interscapular area.
## Diet analysis
The lipid content in the samples was determined using the Soxhlet method (PN-EN ISO 3947:2001; Soxtec System, Foss Tecator), while the protein content was determined using the Kjeldahl method (AOAC, 1995; Foss Tecator). The sample was completely burned in a muffle furnace to determine the ash content (AOAC, 2000). The carbohydrate content was calculated from the content of fat, protein, water, and ash. The total fiber fraction was measured using the enzymatic-gravimetric method (Dziedzic et al. 2012).
## Ca analysis in diets
To determine Ca content in diets, 1 g of each sample of diet was burned in a muffle furnace at 450 °C until mineralization. The samples were then dissolved in 1 mol/l nitric acid (Merck, Kenilworth, NJ, USA). Using flame atomic absorption spectrometry, the mineral content was determined after diluting the samples with appropriate amounts of LaCl3 ($0.5\%$) and deionized water (AAS-3, Carl Zeiss, Jena, Germany). The method was validated with an accuracy of $92\%$ using brown bread (BCR191, Sigma-Aldrich, St. Louis, MO, USA), a certified reference material. All diet samples were analyzed in triplicate.
## Ca analysis in tissues
To determine Ca content in tissues, the samples were mineralized in a microwave digestion system (Speedwave Xpert, Berghof, Eningen, Germany) with pure nitric acid (Merck, Kenilworth, NJ, USA). After digestion, the samples were mixed with deionized water and then diluted with LaCl3 ($0.5\%$). The concentration of minerals was determined by flame atomic absorption spectrometry (AAS-3, Carl Zeiss, Jena, Germany) at a wavelength of λ = 422.7 nm. The method was validated with an accuracy of $91\%$ using bovine liver (1577 C, Sigma-Aldrich, St. Louis, MO, USA), a certified reference material.
## Histological analysis
The resected femoral bones were fixed with $10\%$ buffered formalin for 24 h. Then, the specimens were decalcified in Osteodec bone marrow biopsy decalcifying solution for another 3 h. Subsequently, each specimen was routinely processed and embedded separately in paraffin blocks. Two-micrometer-thick sections were cut from the blocks (three sections for each tissue sample) and stained with hematoxylin and eosin. Each slide contained two femoral bone sections with the bone marrow content. The bone marrow of each bone was analyzed under a light microscope (Leica, Allendale, NJ, USA), and the content of adipose tissue in the bone marrow was assessed separately by two scientists under a high-power field (HPF; 400× magnification). The percentile amount of fat bone marrow in the bone marrow was estimated under a light microscope in five different HPF areas (400× magnification, area of 0.25 mm2), and the mean value was calculated. The number of osteoblasts, osteocytes, and osteoclasts was counted in each HPF area (400× magnification, area of 0.25 mm2). The percentile amount of woven bone was also estimated under a light microscope in five different HPF areas (400× magnification, area of 0.25mm2), and the mean percentile amount in the entire bone was calculated.
## Biochemical parameters
The serum concentrations of PTH, PINP, OC, and ES were determined by ELISA using commercial ELISA kits (SunRed, Shanghai, China) which are used to estimate the mentioned parameters in samples of rat serum, blood, and plasma, and in other related tissue liquids. The precision of the technique used was validated, and the intra-assay and inter-assay precision (CV (%) = SD/mean × 100) for the estimation of ES, PTH, PINP, and OC was found to be < $9\%$ and < $11\%$, respectively. The sensitivity of the method for each determined parameter was as follows: 3.112 ng/l for ES, 0.227 ng/dl for PTH, 0.325 ng/ml for PINP, and 0.523 ng/ml for OC. The analysis was carried out using an infinite F50 spectrometer (Tecan Group Ltd., Männedorf, Switzerland). The ELISA kits used were based on the principle of the dual antibody sandwich technique for the detection of parameters in rats’ materials.
## Statistical analysis
Statistical analysis was performed using the Statistica program (StatSoft, Tulsa, OK, USA). The normality of the distribution of the variables was determined using the Shapiro–Wilk test. Statistical differences between the analyzed groups were determined using a one-way analysis of variance with Tukey’s post hoc test. P-value < 0.05 was considered statistically significant. The results are presented as mean values ± standard deviation.
## Results
Table 1 shows the composition of the standard diet provided to groups C, OVX_C, and OVX_B and that of the diet with enriched pumpkin provided to groups OVX_P and OVX_P_B. The content of macronutrients and Ca was comparable in both diets.
Table 1Composition of diets (mean ± standard deviation)ComponentsDietsStandard (C/OVX_C/OVX_B groups)Enriched pumpkin (OVX_P/OVX_P_B groups)Caloric value (kcal/100 g)326.37 ± 4.48311.59 ± 3.29Carbohydrates (g/100 g)47.92 ± 0.6041.6 ± 2.06Fiber (g/100 g)23.04 ± 0.6023.92 ± 0.69Fat (g/100 g)3.76 ± 0.414.17 ± 0.38Protein (g/100 g)13.70 ± 0.2114.95 ± 1.6Ca (mg/g)5.63 ± 0.375.57 ± 0.38 C control group, OVX_C ovariectomized group, OVX_B ovariectomized group receiving alendronate; OVX_P ovariectomized group receiving pumpkin enriched with CaCO3, OVX_P_B ovariectomized group receiving pumpkin enriched with CaCO3 and alendronate; alendronate concentration:3 mg/kg body weight Ovariectomy causes changes in body composition and in estrogen levels. In this study, ovariectomized rats showed a significant increase ($P \leq 0.05$) in body mass (Table 2). However, modified diets did not significantly ($P \leq 0.05$) affect the weight of rats in the ovariectomized groups (Table 2). As expected, ovariectomy also caused a significant decrease ($P \leq 0.05$) in serum ES concentration in rats (Table 3). An analysis of the parameters of Ca metabolism was also performed in this study, and the results are presented in Table 3. It was observed that ovariectomy had no significant effect ($P \leq 0.05$) on the concentration of PINP, while the combination of alendronate and enriched pumpkin caused a significant increase ($P \leq 0.05$) in the level of this bone formation marker in comparison to the control group. Similarly, ovariectomy did not significantly ($P \leq 0.05$) influence affect the PTH levels in the serum of rats. However, the addition of enriched pumpkin and alendronate alone in the diet caused an increase in PTH levels in rats in comparison to the OVX_C group, but this effect was not observed in rats that received the diet containing both these substances (OVX_P_B group).
Table 2Daily intake and body mass in rats (mean ± standard deviation)ParameterGroupCOVX_COVX_POVX_BOVX_P_BDaily intake of diet (g) 24.98 ± 0.5425.17 ± 1.4923.74 ± 0.9724.76 ± 0.2824.19 ± 1.3Daily intake of Ca (mg) 140.53 ± 3.05141.61 ± 8.4133.94 ± 3.05142.39 ± 1.61136.19 ± 7.31Body mass (g) 338.59 ± 29.71a442.71 ± 29.63b430.31 ± 14.94b439.01 ± 29.07b424.05 ± 30.19bC control group, OVX_C ovariectomized group, OVX_P ovariectomized group receiving pumpkin enriched with CaCO3; OVX_B ovariectomized group receiving alendronate, OVX_P_B ovariectomized group receiving pumpkin enriched with CaCO3 and alendronate; alendronate concentration:3 mg/kg body weighta,bSignificant differences between groups ($p \leq 0.05$) Table 3Level of estradiol and parameters of Ca metabolism in serum of rats (mean ± standard deviation)ParameterGroupCOVX_COVX_POVX_BOVX_P_BES (ng/l) 49.88 ± 5.06b23.39 ± 5.55a22.03 ± 5.27a18.06 ± 2.65a18.79 ± 3.59aPINP (ng/ml) 3.55 ± 0.83a4.6 ± 0.86ab4.6 ± 0.5ab4.58 ± 1.03ab5.17 ± 1.03bPTH (ng/dl) 3.44 ± 0.48ab2.56 ± 0.48a3.54 ± 0.71b3.58 ± 0.74b3.12 ± 0.42abOC (ng/ml) 18.57 ± 3.816.28 ± 1.1717.56 ± 4.8618.5 ± 6.0516.39 ± 4.25 C control group, OVX_C ovariectomized group, OVX_B ovariectomized group receiving alendronate, OVX_P ovariectomized group receiving pumpkin enriched with CaCO3, OVX_P_B ovariectomized group receiving pumpkin enriched with CaCO3 and alendronate; alendronate concentration:3 mg/kg body weighta,b significant differences between groups ($p \leq 0.05$) To estimate the effect of modified diets on the *Ca status* in rats, the content of this element was estimated in the collected tissue samples and serum (Table 4). It was found that ovariectomy caused a significant reduction ($P \leq 0.05$) in Ca content in the femur. In turn, the addition of enriched pumpkin to the diet increased the femoral Ca concentration, and a similar effect was observed in the group that received the diet with alendronate. The addition of alendronate and enriched pumpkin (OVX_P_B group) in combination also caused an increase in Ca concentration in the femur; however, this effect was less pronounced than that observed with the use of enriched pumpkin (OVX_P group) and alendronate (OVX_B group) alone. Ovariectomy caused a significant reduction ($P \leq 0.05$) in Ca content in the heart. The concentration of Ca in the spleen was significantly lower ($P \leq 0.05$) in the OVX_B and OVX_P_B groups compared to the OVX_C group. In the OVX_P group, a significant decrease ($P \leq 0.05$) in the Ca level was observed in the liver in comparison to the OVX_C group. Ovariectomy had no effect on Ca content in muscles, while the addition of alendronate alone and in combination with enriched pumpkin to the diet caused a significant reduction ($P \leq 0.05$) in the muscle Ca content. The use of modified diets led to a significant increase ($P \leq 0.05$) in the Ca content in the kidneys (almost twofold in the OVX_P group, threefold in the OVX_B group, and fivefold in the OVX_P-B group). The diet containing both alendronate and enriched pumpkin promoted more Ca accumulation in the kidneys than the diet containing either of these components.
Table 4Ca content in serum and tissues (mean ± standard deviation)ParameterGroupCOVX_COVX_POVX_BOVX_P_BSerum (µg/ml) 132.65 ± 12.81121.3 ± 8.76112.94 ± 7.03115.9 ± 8.48124.43 ± 18.43Femur (mg/g dm) 239.28 ± 18.01b217.22 ± 8.16a279.17 ± 12.83c290.94 ± 15.55c256.96 ± 10.22bPancreas (µg/dm) 110.11 ± 12.19114.2 ± 11.27102.89 ± 18.8297.79 ± 10.97108.15 ± 11.98Hair (µg/g dm) 603.68 ± 170.1b463.87 ± 55.87ab369.4 ± 78.73a413.12 ± 99.01a451.46 ± 54.8aSpleen (µg/g dm) 527.47 ± 112.62c460.31 ± 98.7c425 ± 54.04bc347.39 ± 27.01ab289.1 ± 33.07aLiver (µg/g dm) 156.01 ± 9.01bc140.74 ± 11.84bc94.91 ± 12.37a134.84 ± 21.87b159.54 ± 15.44cHeart (µ/g dm) 118.55 ± 16.79b83.9 ± 8.14a80.98 ± 7.47a81.5 ± 11.38a72.41 ± 8.67aBrain (µg/g dm) 178.93 ± 27.45227.33 ± 85.69219.14 ± 81.91196.76 ± 86.43239.07 ± 85.09Muscle (µg/g dm) 47.4 ± 4.08c52.81 ± 11.46c42.9 ± 4.91bc34.37 ± 5.4ab25.25 ± 6.36aKidney (µg/g dm) 91.19 ± 11.26a79.36 ± 8.19a148.66 ± 29.68b257.4 ± 42.85c461.07 ± 40.67dC control group, OVX_C ovariectomized group, OVX_B ovariectomized group receiving alendronate, OVX_P ovariectomized group receiving pumpkin enriched with CaCO3, OVX_P_B ovariectomized group receiving pumpkin enriched with CaCO3 and alendronate; alendronate concentration:3 mg/kg body weight; dm, dry massa,b,c,dSignificant differences between groups ($p \leq 0.05$) To assess bone structure and bone health related to Ca metabolism, the study also included a histological analysis of the femur in rats (Table 5). The changes observed in the bone structure are presented in Figs. 2, 3, 4 and 5. It was found that ovariectomy did not affect the numbers of osteoblasts and osteocytes, but the addition of alendronate with or without enriched pumpkin to the diet caused a significant increase ($P \leq 0.05$) in these parameters in rats in comparison to the OVX_C group. Moreover, ovariectomy reduced the number of osteoclasts and increased fat bone marrow, but modified diets did not reverse this effect. Ovariectomy also caused an increase in the percentage of woven bone, but alendronate and enriched pumpkin, even when used alone, reversed this effect.
Table 5Parameters of the histological analysis of the femur (mean ± standard deviation)ParameterGroupCOVX_COVX_POVX_BOVX_P_BNumber of osteoblasts 10 ± 3.78a10.5 ± 5.13a16 ± 7.01ab20 ± 4.5b18.63 ± 5.93bNumber of osteocytes 38.75 ± 8.35a45 ± 8.86ab40.25 ± 11.85a57.5 ± 8.02bc61.38 ± 11.99cNumber of osteoclasts 0.88 ± 0.990 ± 00 ± 00.25 ± 0.460 ± 0Bone marrow fat (%) 8.13 ± 3.72a43.75 ± 10.61b46.25 ± 9.16b36.25 ± 5.18b38.75 ± 6.41bWoven bone (%) 8.13 ± 2.59a18.75 ± 8.35c10 ± 4.63ab11.25 ± 3.54ab16.88 ± 3.72bcC control group, OVX_C ovariectomized group, OVX_B ovariectomized group receiving alendronate, OVX_P ovariectomized group receiving pumpkin enriched with CaCO3, OVX_P_B ovariectomized group receiving pumpkin enriched with CaCO3 and alendronate, alendronate concentration:3 mg/kg body weighta,b,cSignificant differences between groups ($p \leq 0.05$) Fig. 2Differences between the number of osteoblasts: A few osteoblasts along the bones in the representative of the OVX_C group (H&E; 100×); B numerous clusters of osteoblasts arranged along the bones in the representative of the OVX_B group (H&E; 100×); C numerous clusters of osteoblasts arranged along the bones in the representative of the OVX_P_B group (H&E; 100×) Fig. 3Differences between the number of osteocytes: A few osteocytes in the bone in the representative of the C group (H&E; 200×); B average number of bone osteocytes in the representative of the OVX_C group (H&E; 200×); C large number of bone osteocytes in the representative of the OVX_B group (H&E; 200×) Fig. 4Differences between the amount of bone marrow femoral adipocytes: A low number of bone marrow femoral adipocytes in the representative of the C group (H&E; 400×); B several number of bone marrow femoral adipocytes in the representative of the OVX_C group (H&E; 400×)
Fig. 5Differences between the content of woven bones: A low content of woven bone in the representative of the C group (H&E; 200×); B high content of woven bone in the representative of the OVX_C group (H&E; 200×); C average content of woven bone in the representative of the OVX_P group (H&E; 200×); D average content of woven bone in the representative of the OVX_B group (H&E; 200×); E high content of woven bone in the representative of the OVX_P_B group (H&E; 400×) The study also analyzed the relationships between the examined parameters, and the results of the correlation analysis are presented in Table 6. A significant negative correlation ($P \leq 0.05$) was found between body mass and serum ES level (r = − 0.67) as well as between body mass and serum Ca concentration (r = − 0.51). Similarly, a negative correlation in Ca level was found between the kidney and the spleen (r = − 0.73), between the kidney and muscle (r = − 0.93), and between the femur and the pancreas (r = − 0.56). A negative correlation was also found between the Ca content in muscles and the P1NP level in serum (r = − 0.62). A positive correlation was observed between the PTH level in serum and the Ca level in the femur ($r = 0.64$).
Table 6Significant ($P \leq 0.05$) Pearson correlation coefficient (r)Parametersr Body mass (g) and ES (ng/l)–0.67Body mass (g) and Ca in serum (µg/ml)–0.51Ca in kidney (µg/g dm) and Ca in spleen (µg/g dm)–0.73Ca in kidney (µg/g dm) and Ca in muscle (µg/g dm)–0.93Ca in femur (mg/g dm) and Ca in pancreas (µg/g dm)–0.56PINP (ng/ml) and Ca in muscle (µg/g dm)–0.62PTH (ng/dl) and Ca in femur (mg/g dm)0.64dm, dry mass
## Discussion
The results of the study showed that CaCO3-enriched pumpkin increased bone Ca content to the same extent as alendronate. This is a valuable finding as it may indicate that Ca combination with pumpkin can prevent bone resorption and contribute to an increase in bone formation. Because the Ca content was comparable in the tested diets, some ingredients in pumpkin could have improved Ca bioavailability from enriched pumpkin, which contains large amounts of inulin, and Ca metabolism, which might affect bone structure. It has been shown that inulin can improve Ca bioavailability in the intestine and can stimulate the transport of active Ca ions to cells, probably by increasing the level of calbindin (a transport protein) (Nzeusseu et al. 2006; Bakirhan and Karabudak 2021). Furthermore, from this study, it seems that Ca ions are shifted between tissues and that these ions may accumulate in bones at the expense of other tissues (Li et al. 2019; He et al. 2020). This mechanism was partly confirmed by the inverse correlation observed between Ca content in the pancreas and femur. Moreover, pumpkin contains other ingredients such as carotenoids, zeaxanthin, lutein, which could prevent bone resorption in rats after ovariectomy (Yamaguchi 2012; Ozaki et al. 2015; Takeda et al. 2017; Tominari et al. 2017). However, the changes observed in Ca content in the femur are difficult to associate with PTH levels. Although the levels of PTH were not changed by ovariectomy, the addition of alendronate and enriched pumpkin to the diet contributed to an increase in this hormone. The results obtained for PTH concentration in ovariectomized groups were unexpected. It is challenging to directly explain the low PTH concentration observed in the OVX_C group and the relatively high concentrations observed in the OVX_P and OVX_B groups since the opposite relationship was expected. Additionally, it was surprising to find a positive correlation between femoral Ca concentrations and PTH concentrations. It appears that in the pumpkin and alendronate groups, the observed relationships are associated with the high accumulation of Ca in the kidneys. PTH stimulates Ca reabsorption in the kidneys and promotes its accumulation. The observed relationships undoubtedly have a multidirectional aspect. In ovariectomized rats with low estrogen levels, we expected adverse bone changes, but these rats were not Ca-deficient, and all diets had adequate amounts of Ca. As a result of changes in bones, we observed an increase in PTH, which influenced the kidneys by inhibiting Ca excretion, and Ca possibly was delivered to the bones via the action of other factors, such as ion shifts between tissues, or by biologically active substances of the drug or pumpkin components. Research suggests that the action of lycopene and carotenoids on bones is related to the activity of PTH (Burri et al. 2016). Moreover, obesity and increased bone marrow fat in the bones observed in ovariectomized rats might have an influence on the noticed changes in biochemical parameters. Other studies have shown a correlation between obesity and bone marrow fat and PTH activity (Rao et al. 2003; Fan et al. 2017). Weight gain in rats with low estrogen levels was expected, as was the increase in bone marrow fat, and this may possibly affect PTH levels in rats (Guasch et al. 2012), hence the lack of expected relationships between ovariectomy, bone Ca, and PTH. Unexpectedly, we did not observe significant changes ($P \leq 0.05$) in PINP and OC levels in ovariectomized groups. PINP and OC are nonspecific collagen proteins, mainly produced by osteoblasts, and their content in the blood can reflect the activity of osteoblasts (Guo et al. 2021). Although the number of osteoblasts increased in groups fed with diets containing pumpkin and alendronate alone and in combination, the relative increase in PINP level was only observed in OVX_P_B group, which may indicate an increased intensity of bone turnover due to the presence of two factors: bioactive ingredients of enriched pumpkin and alendronate. We also observed a link between the PINP level and changes in Ca in the body, as evidenced by the negative correlation between the PINP level and muscle Ca content. In the intervention groups after ovariectomy, Ca from the muscles was probably shifted to the bones and to the kidneys, as indicated by significant correlations ($P \leq 0.05$) between Ca content in these organs. Moreover, in ovariectomized rats, a significant decrease ($P \leq 0.05$) in Ca in the heart was observed, which might lead to problems with myocardial contractility. Other studies have confirmed that after ovariectomy, the sensitivity of Ca2+ myofilament is reduced, which leads to the release of Ca ions from the heart (Fares et al. 2013). An unexpected finding of this study is that the use of modified diets resulted in Ca accumulation in the kidneys. Unfortunately, no parameters of kidney functioning were analyzed, and histological analysis of the kidneys was not performed in this study. However, it can be assumed that Ca ions from other tissues were transported to the kidneys in the rats that received modified diets. In a study by Nijenhuis et al., a significant increase ($P \leq 0.05$) in the expression of TRPV5 (a protein responsible for the transport of Ca ions) was observed in bones following the administration of alendronate, while no such increase was observed in the kidneys and intestine (Nijenhuis et al. 2008). On the other hand, alendronate has been known to cause damage to the kidneys by forming Ca aggregates, which can lead to the formation of kidney stones or glomerulonephritis (Song and Maalouf 2000). Because the kidneys are responsible for the reabsorption of Ca, stones can restrict their filtration, resulting in hypercalciuria, and consequently, a decrease in Ca concentration in the blood (Han et al. 2019). Enriched pumpkin contains ingredients that can affect kidney functioning. Inulin, which is one such ingredient, can expose the kidneys to a high amount of floating Ca due to its ability to increase Ca excretion (Adolphi et al. 2009). Large amounts of vitamins A and E found in pumpkins can lead to glomerular hyperfiltration and ultimately affect the filtration ability of the kidneys (Kedishvili 2016; Parente Filho et al. 2020; Chen et al. 2021).
For a detailed interpretation of the results, it is also worth paying attention to the results of histological analysis. In this study, the histopathological analysis of the femurs revealed interesting facts regarding bone cells, fat bone marrow degeneration, and woven bone. Osteoblasts are bone cells formed from mesenchymal precursors and eventually differentiate into osteocytes. Both osteoblasts and adipocytes are derived from the same stem cells, and thus a large amount of adipose tissue is an indicator of a large number of osteoblasts (Kos-Kudła et al. 2019). In this study, we observed a high number of both these cell types in ovariectomized rats; however, the increase in these cells was statistically significant ($P \leq 0.05$) only in the groups that received alendronate-supplemented diet, which suggests that stimulation of osteoblast differentiation intensifies the bone-building process (Ma et al. 2018). Rats with a high amount of adipose tissue also have a high percentage of adipose tissue marrow, as has been confirmed by previous studies on humans (Horowitz et al. 2017; van der Eerden and van Wijnen 2017) and animals (Iwaniec and Turner 2013; Fan et al. 2015). An interesting observation from these studies is the increased percentage of woven bone (immature bone) in the remodeling phase (Shapiro and Wu 2019). Woven bone is formed very quickly and appears porous. The proportion of woven bone is generally high during growth and puberty. On the other hand, in adults, this bone constitutes about 5–$10\%$, while its higher share indicates structural overload or trauma, which is a temporary effect associated with the reconstruction of the lamellar (mature) bone (Hart et al. 2020). In this study, we observed that ovariectomy caused a significant increase ($P \leq 0.05$) in the percentage of woven bone, while the addition of enriched pumpkin and alendronate to the diet resulted in an opposite effect. Thus, it can be concluded that the reduction in estrogen levels led to the need for bone reconstruction in ovariectomized rats, as indicated by the increase in the percentage of woven bone in these animals. On the other hand, inulin and CaCO3 present in enriched pumpkin and alendronate accelerated bone reconstruction by increasing bone formation (discussed earlier), thus reducing the share of woven bone.
## Limitations
Due to several limitations of this study, some of the obtained results could not be highlighted here. Because rats’ urine was not collected in the study, we could not state whether its excretion increased with the accumulation of Ca in the kidneys. Other parameters related to bone metabolism, such as vitamins K and D, were not analyzed because only limited volume of serum was obtained from rats. The study also did not include a histological analysis of the kidneys, which could have been helpful in explaining the mechanism of Ca accumulation in this tissue. Furthermore, the study did not have a sham-operated control, and therefore the effect of sham surgery on rats was not analyzed; however, the results obtained in the ovariectomized group were compared with the nonoperated control group and the ovariectomized group fed with a standard diet. Unfortunately, we did not study the group with unenriched pumpkin, so we cannot determine what changes would occur in the rats’ organism if the pumpkin was not subjected to osmotic dehydration.
## Conclusion
CaCO3-enriched pumpkin can improve the concentration of Ca in the femur and bone recovery in ovariectomized rats, which is similar to the effect of alendronate. However, enriched pumpkin causes Ca accumulation in the kidneys, which is exacerbated when it is used in combination with alendronate. Further research is needed to elucidate the mechanism of calcium accumulation in the kidneys as a result of the consumption of calcium carbonate-enriched pumpkins.
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|
---
title: The clinical influence of nasal surgery on PAP compliance and optimal application
among OSA subjects uncomfortable with PAP device wear
authors:
- Hyunkyung Cha
- Heonjeong Oh
- Sun A Han
- Seo Young Kim
- Jeong Kyou Kim
- Hae Chan Park
- Doo Hee Han
- Dong-Young Kim
- Hyun Jik Kim
journal: Scientific Reports
year: 2023
pmcid: PMC10020433
doi: 10.1038/s41598-023-31588-7
license: CC BY 4.0
---
# The clinical influence of nasal surgery on PAP compliance and optimal application among OSA subjects uncomfortable with PAP device wear
## Abstract
This study aimed to evaluate the alteration of PAP compliance after nasal surgery and to determine the optimal indications of nasal surgery in obstructive sleep apnea (OSA) subjects. Among OSA subjects using PAP devices, 29 subjects who underwent septoturbinoplasty due to nasal obstruction were included and their pre- and postoperative medical and PAP records were reviewed retrospectively. Postoperative autoPAP usage data was further assessed by grouping the compliance (the percentage of days with usage ≥ 4 h) data (group 1: the good compliance group; group 2: the poor compliance group). The data showed that $56\%$ of subjects in group 1 complained of nasal obstruction as the only barrier to using a PAP device and about $89\%$ reported experiencing the efficacy of PAP usage. Both the mean and peak average PAP pressures were significantly reduced in group 1 following nasal surgery. Group 2 had multiple subjective problems that interfered with wearing a PAP device and reported a lack of experiencing the efficacy of PAP usage. Preoperative nasal cavity volume values were smaller and absolute blood eosinophil counts were significantly lower in group 1. The current data demonstrate that nasal surgery might increase the compliance of PAP device wear in OSA subjects who complained of only nasal obstruction as a barrier to wearing PAP and who had small nasal cavity volumes combined with allergic inflammation.
## Introduction
Obstructive sleep apnea (OSA) is a breathing disorder caused by the narrowing of the multi-level upper airway that interrupts normal ventilation during sleep1–4. The nose is the starting point of the airway system, and about 50–$70\%$ of all airway resistance is devoted to the nasal cavity2. If there is no pathological problem, people typically breathe through their nose while sleeping3. It is well known that nasal obstruction worsens the OSA, and its removal reduces OSA severity3,4.
Positive airway pressure (PAP) is the treatment of choice for moderate-to-severe OSA patients5. PAP therapy can relieve subjective symptoms and prevent life-threatening conditions5–10. However, compliance rate with PAP therapy can be low, with reported rates of 54–$75\%$11. The American Academy of Sleep Medicine has recommended that PAP therapy for adult OSA patients be initiated using either by autoPAP therapy or in-laboratory titration PAP therapy, based on the patient circumstances12. Its guidelines also emphasize the need to identify clinical factors that can reduce compliance rates and correct them to ensure adequate treatment and compliance with PAP therapy12,13.
The most frequent complaints of PAP non-compliance patients include an inconvenience associated wearing, chest discomfort, allergic reaction, mouth dryness, and difficulty in breathing during sleep14. Although automatic PAP (autoPAP) adjusts the pressure based on inhalation and works in a specific pressure range compared to continuous PAP, our previous data showed that about $9\%$ of OSA subjects with poor compliance complained of nasal obstruction as a main problem interfering with wearing the PAP device14,15. Another study also reported that autoPAP therapy intolerance according to nasal problems seems to account for approximately $12\%$ of cases16. The nasal obstruction seems to have a decisive effect on the reduction of the compliance rate if this problem is not overcome in the early stages of using a PAP17.
The presence of a narrow nasal cavity is believed to contribute to an increase in therapeutic PAP and subjective discomfort, ultimately leading to poor PAP compliance14,18. Furthermore, some reports state that sleep apnea surgery for PAP intolerant patients can make PAP a therapeutic option, and, in particular, PAP compliance may improve following nasal and upper airway surgery19–21. One study suggested that subjective nocturnal nasal obstruction is not a predictor of becoming a non-user of PAP within the first 2 years22. However, this study has also found that a small nasal cavity volume at baseline may be a determinant of non-compliance22. According to the Poiseuille’s law, airway resistance is critically determined to the fourth power of airway radius23. More, the Bernoulli principle implies that the increased airflow velocity by the nasal blockage decreases the static pressure in the pharyngeal airway and makes the pharyngeal wall to collapse as a downstream segment23,24. Altogether, the treatment for nasal pathology may be a crucial treatment strategy for patients with OSA. But still, the therapeutic effect of correction for nasal obstruction on the compliance of PAP remains controversial or rather confusing due to a lack of adequate objective outcome measurements11,15,25–27.
In the present study, we aimed to examine factors affecting compliance to autoPAP after surgical correction of nasal obstruction in OSA subjects and also sought to determine whether surgical correction of nasal obstruction would improve PAP compliance in OSA subjects with nasal pathologies based on PAP parameters.
## Ethical approval
The institutional review board (IRB) of Seoul National University Hospital approved this study (IRB No. 2206–085-1332). All methods were performed in accordance with the approved guidelines and the Declaration of Helsinki. All personal information was kept confidential as required. Informed consent was waived because of the retrospective nature of the study.
## Study design and subjects
A retrospective medical review of patients who were diagnosed with OSA and prescribed autoPAP between January 2017 and July 2021 at the Seoul National University Hospital was performed. Subjects complaining of snoring and apnea underwent endoscopic examination, polysomnography, and drug-induced sleep endoscopy (DISE). OSA was diagnosed if the apnea–hypopnea index (AHI) value was > 15 or > 5 with related symptoms or problems, including daytime sleepiness, declined cognitive function, mood disorder, hypertension, a history of infarction, and decreased O2 saturation of < $85\%$. Severity was classified according to the AHI; mild OSA was defined as an AHI value of 5–14, moderate OSA was defined as an AHI value of 15–30, and severe OSA was defined as an AHI value of > 30. AutoPAP therapy was prescribed for patients with OSA. The autoPAP usage records were reviewed once every 3 months. The prescribed autoPAP device was initially set in the range of 5–12 mmHg and adjusted according to the patient’s follow-up autoPAP usage reports. The patients who complained of nasal obstruction as a barrier to using a PAP device with a deviated nasal septum who underwent nasal surgery were included in this study. Patients with severe comorbid diseases (such as cancer), diseases that decrease O2 saturation (such as congestive heart failure and chronic obstructive pulmonary disease), and central sleep apnea who were prescribed alternative treatment to CPAP were excluded. Additionally, patients who underwent oropharynx sleep surgery and nasal surgery at the same time or had loss of follow-up were also excluded. The flowchart of study design is indicated in Supplementary Fig. 1.
## Surgical procedure
The indication for septoturbinoplasty was determined to be a grade 2 or higher deviated nasal septum with the complaint of nasal obstruction28. The volume reduction for both sides of the inferior turbinate were performed with a COBLATOR™ II surgery system (Smith & Nephew, London, UK). The autoPAP was worn again 1 week after the nasal surgery.
## Outcome assessment
Patients visited outpatient clinic once every 3 months to review the usage records of autoPAP. In order to check psychosocial barriers related to PAP usage, we asked two questions of all patients: [1] Have you experienced any efficacy while using PAP? [ 2] Have you encountered any difficulties or complications while using the PAP device? If any difficulties were reported, we also asked for the reasons.
AutoPAP usage data before and after the surgery were assessed. The pairwise comparison of the percentage of days with device usage, the average use time, the percentage of days with usage ≥ 4 h, the mean pressure, the peak average pressure, the average device pressure ≤ $90\%$ of the time, and AHI values before and after the surgery was performed.
Adequate PAP device compliance was defined as ≥ 4 h of administration for ≥ $70\%$ of the nights (according to the U.S. Center for Medicare and Medicaid Services criteria)29,30. Postoperative autoPAP usage data was further assessed by grouping the compliance data. Group 1 was defined as the good compliance group, in which patients used PAP for at least 4 h per day, on $70\%$ or more days, after surgery. Group 2 was defined as the poor compliance group, in which patients used PAP for at least 4 h per day, on fewer than $70\%$ of days, after surgery. ( Fig. 1).Figure 1The autoPAP compliance groups were classified according to the percentage of days spent using PAP for > 4 h/day after surgery. The cutoff value was $70\%$.
The values of minimal cross-sectional area (MCA) and nasal cavity volume (NCV) from acoustic rhinometry are indicated as smaller values among the measures from both nasal cavities. The diagnosis of allergic rhinitis (AR) was made based on the patient history and laboratory data, including skin prick test, and serum-specific immunoglobulin (Ig) E levels. Perennial Allergic Rhinitis (AR) refers to cases of rhinitis caused by allergies to perennial allergens, such as dust mites, mold, pet dander, and cockroaches. Seasonal AR, on the other hand, refers to cases of rhinitis caused by seasonal allergens like tree, grass, and weed pollen. The blood eosinophil count was analyzed based on the highest value measured.
## Statistical analysis
Continuous variables are presented as mean ± standard deviation values. Continuous variables were analyzed by the t test or Mann–Whitney U test (two-tailed) depending on normality. Categorical variables were assessed by a chi-squared test. Paired samples were assessed by the Wilcoxon signed-rank test (two-tailed). Multivariate logistic regression analysis was performed with stepwise selection method. Log transformation was used for no normality assumption independent variables in logistic regression. $P \leq 0.05$ was considered to be statistically significant. Missing values were pairwise deleted. All statistical analysis was performed using R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria) and GraphPad Prism (version 9.0; GraphPad Software Inc., La Jolla, CA, USA).
## Clinical characteristics of patients
Patient’s demographic characteristics are presented in Table 1. Twenty-nine OSA patients (All male; mean age 52.45 years) were included. Their Tonsil grade 0, I, II, III, or IV was observed in $7\%$, $62\%$, $28\%$, $3\%$, and $0\%$ of patients, respectively. Palatal grade I, II, III, or IV was found in $0\%$, $3\%$, $48\%$, and $48\%$ of patients, respectively. The mean preoperative AHI value was 50.19 ± 27.17/h, and the mean minimal SpO2 was 75 ± $11\%$. Nasal surgeries were all successful. All patients showed a significant improvement of nasal obstruction after septoturbinoplasty and patent nasal airway by intranasal endoscopic examination. Table 1Demographic data of the recruited OSA subjects. ParameterValuesAge (years)52.45 ± 10.57SexMale, 29 ($100\%$); female, 0 ($0\%$)BMI (kg/m2)28.57 ± 3.79Preoperative PSG AHI (/h)50.19 ± 27.17 Supine AHI (/h)67.51 ± 32.26 Non-supine AHI (/h)25.26 ± 27.61 RDI (/h)51.05 ± 26.95 Minimum SpO2 (%)75 ± 11 Severity (mild/moderate/severe)$\frac{3}{6}$/20Physical examination Tonsil grade (0/I/II/III/IV)$\frac{2}{18}$/$\frac{8}{1}$/0 Palatal grade (I/II/III/IV)$\frac{0}{1}$/$\frac{14}{14}$Comorbidity HTN$\frac{20}{29}$ ($69\%$) DM$\frac{6}{29}$ ($21\%$) AR$\frac{14}{28}$ ($50\%$)Lab Absolute eosinophil count (/µL)258.42 ± 246.56 MCA (cm2)0.49 ± 0.08 NCV (cm3)6.07 ± 1.06BMI, body mass index; PSG, polysomnography; AHI, Apnea–Hypopnea Index; RDI, respiratory disturbance index; SpO2, pulse oximeter oxygen saturation; HTN, hypertension; DM, diabetes mellitus; AR, allergic rhinitis; MCA, minimal cross-sectional area; NCV: nasal cavity volume. Variables were stated as numbers (%) or mean ± standard deviation values.
## AutoPAP use prior to and following nasal surgery
The changes in PAP parameters after nasal surgery are indicated in Table 2 and we did not find any significant change in all PAP parameters. The percentage of days with device usage pre-operation was 89 ± $13\%$ and that post-operation was 85 ± $20\%$ ($$P \leq 0.356$$). The average usage time of PAP was 329.24 ± 83.38 min prior to nasal surgery and 338.49 ± 66.09 min following surgery ($$P \leq 0.350$$). The percentages of days with usage for ≥ 4 h pre-operation and post-operation, respectively, were 74 ± $21\%$ and 73 ± $24\%$ ($$P \leq 0.979$$). The mean device pressure pre-operation was 801.18 ± 213.78 Pascal (Pa) in OSA subjects and 780.59 ± 179.46 Pa post-operation ($$P \leq 0.115$$). The peak average pressure pre-operation was 959.06 ± 202.01 Pa and post-operation was 937.49 ± 183.38 Pa ($$P \leq 0.137$$). The average device pressure ≤ $90\%$ of the time was 948.28 ± 191.22 pre-operation and 928.66 ± 176.51 post-operation ($$P \leq 0.120$$). The average AHI values pre-operation and post-operation were 4.48 ± 4.32 /h and 4.72 ± 3.50/h ($$P \leq 0.560$$). These results suggest that nasal surgery does not induce changes in objective PAP use time or PAP pressure in a simple comparison when OSA subjects using autoPAP devices complain of nasal obstruction. Table 2AutoPAP usage data in OSA subjects. ParameterPre-operationPost-operationPPercentage of days with device usage (%)89 ± 1385 ± 200.356Average usage (min)329.24 ± 83.38338.49 ± 66.090.350Percentage of days with usage ≥ 4 h (%)74 ± 2173 ± 240.979Mean pressure (Pa)801.18 ± 213.78780.59 ± 179.460.115Peak average pressure (Pa)959.06 ± 202.01937.49 ± 183.380.137Average device pressure ≤ $90\%$ of the time (Pa)948.28 ± 191.22928.66 ± 176.510.120Average AHI (/h)4.48 ± 4.324.72 ± 3.500.560AHI, Apnea–Hypopnea Index.
## Compliance with autoPAP therapy in OSA subjects with nasal surgery according to psychosocial barriers
Eighteen subjects were included into group 1. Of these, $56\%$ complained of nasal obstruction as their only barrier to using a PAP device (Table 3). In contrast, $44\%$ had multiple problems (including frequent business trips, difficulty lying on one’s side, oropharynx air leakage, discomfort with wearing a mask, and dry nose) that interfered with wearing a PAP device accompanied by nasal obstruction. About $89\%$ were reported to be satisfied with the efficacy of PAP therapy due to the improvement of cardiac arrhythmia, voice change, dry mouth, sputum, excessive daytime sleepiness, headache, and/or fatigue. However, $11\%$ in group 1 did not feel there were any meaningful changes in subjective symptoms when using their autoPAP device. Table 3Subjective factors related to interference with wearing a PAP device in OSA subjects. PAP usage barriersPatientsPGroup 1 ($$n = 18$$)Group 2 ($$n = 11$$)None other than nasal resistance10 ($56\%$)0 ($0\%$)0.008*Frequent business trips1 ($6\%$)2 ($18\%$)0.649Difficulty lying on one’s side3 ($17\%$)1 ($9\%$)0.985Oropharynx air leakage4 ($22\%$)2 ($18\%$)1.000Troublesome cleaning the device0 ($0\%$)1 ($9\%$)0.800Discomfort with wearing a mask1 ($6\%$)6 ($55\%$)0.011*Dry nose1 ($6\%$)0 ($0\%$)1.000Uncontrolled allergic rhinitis symptoms0 ($0\%$)4 ($36\%$)0.028*Rhinitis medicamentosa0 ($0\%$)1 ($9\%$)0.800A lack of experiencing efficacy2 ($11\%$)8 ($73\%$)0.003*PAP, positive airway pressure.*Statistically significant ($P \leq 0.05$).
Eleven subjects who were classified into group 2, and they reported multiple problems (including frequent business trips, difficulty lying on one’s side, oropharynx air leakage, troublesome cleaning the device, discomfort with wearing a mask, uncontrolled allergic rhinitis symptoms, and rhinitis medica mentosa) that interfered with wearing the PAP device (Table 3). None of these subjects reported nasal obstruction as the only obstacle to PAP usage. About $27\%$ were reported to be satisfied with the efficacy of PAP due to improvements in dry mouth, daytime sleepiness, and headache. Through comparative analysis between groups, we found that OSA subjects who reported only nasal obstruction as a barrier to PAP use were all group 1 patients. On the other hands, OSA patients in group 2 reported multiple complaints as barriers to PAP use ($$P \leq 0.008$$). The number of patients who experienced no usefulness of the PAP device was significantly higher in group 2 ($$P \leq 0.003$$). The numbers of patients feeling discomfort wearing a mask and uncontrolled AR symptoms were also significantly higher in group 2 ($$P \leq 0.011$$ and $$P \leq 0.028$$, respectively). Interestingly, the sleep quality was dramatically improved in 3 subjects who were classified into group 2. Among them, 2 subjects underwent postoperative PSG and 1 patient improved their AHI value from 70.6 to 25.0/h, while another improved their value from 43.3 to 16.7/h. Although we recommended these subjects should continue using PAP, despite nasal surgery being effective in improving their nasal pathologies, they were satisfied with the improvement in sleep quality and refused to use the PAP device anymore.
These results revealed that nasal surgery can be effective in OSA subjects who are uncomfortable wearing a PAP device due to only nasal obstruction, and the compliance rate after surgery was high. However, the nasal surgery did not have much of an effect in OSA subjects who complained of multiple problems.
## Comparison of PAP parameters between the PAP compliance groups
We evaluated the PAP parameters of OSA patients who had undergone nasal surgery, based on PAP compliance (Table 4). However, we excluded 2 patients who refused to wear the PAP device, as they had experienced marked improvement in their subjective symptoms and sleep parameters. Table 4AutoPAP usage data of the PAP compliance groups following nasal surgery. ParameterGroup 1 ($$n = 18$$)Group 2 ($$n = 9$$)Pre-operationPost-operationPPre-operationPost-operationPPercentage of days with device usage (%)94 ± 794 ± 60.75175 ± 1670 ± 250.688Average usage (min)351.96 ± 72.51369.76 ± 52.800.211277.50 ± 91.59288.31 ± 39.460.773Percentage of days with usage ≥ 4 h (%)80 ± 1987 ± 80.35358 ± 2046 ± 130.375Mean pressure (Pa)780.22 ± 236.68738.38 ± 183.790.013*876.99 ± 145.77907.82 ± 102.210.453Peak average pressure (Pa)938.45 ± 227.89898.75 ± 195.430.049*1048.09 ± 116.091062.80 ± 85.480.844Average device pressure ≤ $90\%$ of the time (Pa)920.67 ± 212.88891.83 ± 188.390.062˚1038.10 ± 102.291043.71 ± 74.010.688Average AHI (/h)4.56 ± 5.174.12 ± 3.760.9014.33 ± 2.355.73 ± 2.970.383AHI, Apnea–Hypopnea Index.*Statistically significant ($P \leq 0.05$).
Figure 2 indicates group 1 PAP parameters. The PAP records showed that the percent of days with PAP use before nasal surgery was 94 ± $7\%$ and that after surgery was 94 ± $6\%$, while the average time of PAP usage before surgery was 351.96 ± 72.51 min and that after the surgery was 369.76 ± 52.80 min. Interestingly, the mean PAP pressure value was 780.22 ± 236.68 Pa but decreased significantly to 738.38 ± 183.79 Pa after surgery ($$P \leq 0.013$$). The peak average pressure value was also significantly reduced from 938.45 ± 227.89 Pa to 898.75 ± 195.43 Pa after surgery ($$P \leq 0.049$$). In addition, the average device pressure ≤ $90\%$ of the time, which was 920.67 ± 212.88 Pa, changed to 891.83 ± 188.39 Pa after nasal surgery in group 1 ($$P \leq 0.062$$). We did not observe any significant alterations in PAP parameters, including the time of PAP use and PAP pressure, in group 2 depending on nasal surgery (Fig. 3 and Table 4). Based on these findings, we estimated that PAP device wear after nasal surgery had an effect on the surgical result in OSA subjects, and PAP pressure was actually less in these OSA subjects. Figure 2AutoPAP usage data in group 1. The average values of mean and peak average pressures (Pa) significantly changed after nasal surgery. ( a) Percentage of days with device usage (%). ( b) Average usage (min). ( c) Average AHI (/h). ( d) Mean pressure (Pa). ( e) Peak average pressure (Pa). ( f) Average device pressure ≤ $90\%$ of the time (Pa).Figure 3AutoPAP usage data in group 2. There were no significant changes in the AutoPAP values postoperatively. ( a) Percentage of days with device usage (%). ( b) Average usage (min). ( c) Average AHI (/h). ( d) Mean pressure (Pa). ( e) Peak average pressure (Pa). ( f) Average device pressure ≤ $90\%$ of the time (Pa).
## Differences in demographic factors among the PAP compliance groups
In order to identify potential factors affecting PAP compliance in conjunction with nasal obstruction, we compared demographic data, such as age, sex, BMI, preoperative PSG, physical examination findings, comorbidities, acoustic rhinometry, and blood eosinophil count between the PAP compliance groups (Table 5). We did not find any significant differences in age, BMI, PSG parameters, grade of oropharyngeal structures, or comorbid diseases (including AR) between groups. However, the mean preoperative nasal cavity volume of group 1 (5.74 ± 0.96 cm3) was significantly less than that of group 2 (6.53 ± 0.69 cm3), and absolute blood eosinophil count was also significantly lower in group 1 (182.06 ± 163.00/μL) than group 2 (427.40 ± 349.12/μL) ($$P \leq 0.045$$ and $$P \leq 0.030$$, respectively). The preoperative MCA of group 1 was relatively smaller (0.48 ± 0.09 cm2) than that of group 2 (0.52 ± 0.04 cm2) ($$P \leq 0.113$$). The multiple logistic regression model was applied using independent variables from Table 5. Results indicate that blood eosinophil count ($$P \leq 0.025$$) and nasal cavity volume ($$P \leq 0.055$$) are potential factors that may contribute to an increased compliance rate following nasal surgery. These findings are supported by the data presented in Fig. 4.Table 5Demographic differences between the PAP compliance groups. ParameterGroup 1 ($$n = 18$$)Group 2 ($$n = 9$$)PAge (years)54.33 ± 10.5648.33 ± 11.200.189BMI (kg/m2)27.58 ± 3.5330.01 ± 4.190.131Preoperative PSG AHI (/h)52.20 ± 28.1644.71 ± 28.250.631 Supine AHI (/h)67.45 ± 30.8467.83 ± 38.630.668 Non-supine AHI (/h)20.24 ± 20.2334.89 ± 39.120.657 Severity (mild/moderate/severe)$\frac{1}{5}$/$\frac{122}{1}$/60.325 RDI (/h)51.72 ± 24.4251.17 ± 33.060.980 Minimum SpO2 (%)74 ± 1274 ± 100.733 Average SpO2 (%)91 ± 790 ± 40.107Physical examination Tonsil grade (0/I/II/III/IV)$\frac{2}{12}$/$\frac{4}{0}$/$\frac{00}{5}$/$\frac{4}{0}$/00.347 Palatal grade (I/II/III/IV)$\frac{0}{0}$/$\frac{9}{90}$/$\frac{1}{4}$/40.354Comorbidity HTN12 ($67\%$)7 ($78\%$)0.551 DM4 ($22\%$)2 ($22\%$)1.000 AR7 ($41\%$)5 ($56\%$)0.484 Single/multiple Sensitizations$\frac{4}{31}$/40.198 Perennial/seasonal AR$\frac{4}{30}$/50.147Lab Absolute eosinophil count (/µL)182.06 ± 163.00427.40 ± 349.120.030* MCA (cm2)0.48 ± 0.090.52 ± 0.040.113 NCV (cm3)5.74 ± 0.966.53 ± 0.690.045*BMI, body mass index; PSG, polysomnography; AHI, Apnea–Hypopnea Index; RDI, respiratory disturbance index; SpO2, pulse oximeter oxygen saturation; HTN, hypertension; DM, diabetes mellitus; AR, allergic rhinitis; MCA, minimal cross-sectional area; NCV: nasal cavity volume. Variables are stated as numbers (%) or mean ± standard deviation values.*Statistically significant ($P \leq 0.05$).Figure 4Prediction of the compliance group by the multiple logistic regression model. The y-axis with a value of 0 refers group 1 and a value of 1 refers to group 2. T denotes Tonsil grade.
## Discussion
The narrow airway anatomy and the complaint of difficulty exhaling against high pressure are important factors affecting compliance with CPAP15,31,32. Our previous study also showed that correction of nasal pathologies and relief of nasal obstruction can improve sleep parameters in OSA subjects, leading to better sleep quality3. As a result, clinicians often consider method to improve nasal breathing in OSA patients who complain of nasal obstruction, including medication and nasal surgery, when PAP therapy is necessary to control their OSA-related symptoms33,34. Technological development in PAP devices are also aimed at reducing the mask pressure during expiration27. However, in our data, we did not observe a significant change in objective PAP use time or PAP pressure for OSA subjects using autoPAP devices after nasal surgery, which is consistent with previous studies using CPAP with a reduction of expiratory pressure27,35. Despite decades of research, it is noteworthy that there is no single factor that reliably predicts PAP compliance due to complicated barrier dynamics36. A combination of biomedical and psychological predictors has been found to have the best predictive power for explaining PAP compliance36.
Our data showed that surgical correction of nasal pathologies improved the PAP pressure and increased the rate of compliance with autoPAP therapy in OSA subjects who complained of nasal obstruction as the main barrier to PAP device use. Värendh et al.22 previously reported that subjective nasal obstruction is not a predictor of poor continuous PAP compliance within the first two years. However, they did not confirm the presence of any co-subjective barriers22. Poirier et al.34 reported increased PAP compliance after nasal surgery among PAP users with nasal obstruction as the sole stated non-compliance factor. However, OSA subjects with other concomitant barriers to PAP device use may not respond to nasal surgery and their compliance did not improve even after their nasal obstruction was completely resolved. Furthermore, if an OSA subject experienced high efficacy PAP use, the subject seemed to be more likely to show a good compliance after nasal surgery. Our data revealed that OSA subjects who complained of discomfort wearing a mask and a lack of PAP efficacy did not respond to nasal surgery and were included into group 2, even though their nasal obstruction had improved. For example, two patients had multiple barriers to using PAP (both complained of discomfort while wearing a mask) in group 2 following nasal surgery. Specifically, these two OSA subjects underwent nasal surgery, and their nasal breathing improved too much, accompanied by a markedly improved OSA severity and quality of sleep. As a result, they did not try to wear a PAP device after nasal surgery and were included into group 2. These cases show that, even if nasal surgery is successful enough to resolve the nasal obstruction and OSA subjects do not want to wear the PAP device anymore, it is important for clinicians to conduct close communication with OSA subjects to identify OSA-related problems and to provide medical advice about the necessity and efficacy of PAP therapy.
Considering the differences in demographic parameters between the PAP compliance groups, the significant reduction in PAP pressure exhibited in group 1 and the presence of smaller nasal cavity volumes and lower absolute blood eosinophil counts showed better compliance with PAP device use after nasal surgery. A previous study also showed a small nasal cavity volume as a determinant of becoming a non-user of PAP after 2 years22. Based on these findings, we believe that it is important to evaluate the patient's discomfort accurately and measure their nasal volume and blood eosinophil count before wearing the PAP device in order for nasal surgery to improve compliance with PAP therapy and have a positive effect on the use of the PAP device. Our previous data showed that high-grade septal deviation and inferior turbinate hypertrophy correlate with low PAP compliance and suggested additional therapeutic approaches according to various anatomical characteristics14. In addition, other studies suggested the presence of AR and severity of nasal obstruction lead to a significant difference in the success rate of nasal surgery, and nasal surgery also reduces the nasal resistance, Epworth sleepiness scale score, and PAP pressure31. In concert with our clinical data mentioned above, it could be inferred that, if the MCA and NCV were smaller, the reduction in autoPAP pressure after surgery and the degree of subjective satisfaction improvement would be greater. The complaint of uncontrolled AR symptoms and blood eosinophil counts were significantly higher in group 2 after nasal surgery. Therefore, the high blood eosinophil counts might also be a good parameter to use to predict compliance with PAP therapy in OSA subjects who complained of nasal obstruction, and an evaluation for allergic diseases would be needed prior to prescribing PAP therapy to OSA subjects. Our clinical data did not prove the exact correlation between good compliance and the systemic eosinophil count, but the peripheral blood eosinophil count is known to be significantly correlated with the tissue-infiltrating eosinophil count and type 2 disease (including chronic rhinosinusitis and asthma) symptom aggravation, promoting airway edema37–39. The volatility of upper airway mucosal edema due to high type 2 inflammation might be a possible explanation for the absence of a decrease in PAP pressure after surgery and the poor compliance rate.
This study adds value to the field by investigating the factors that contribute to the reduction of PAP compliance in OSA subjects with nasal obstruction. The study considers both psychological and biomechanical factors, and demonstrates the necessity of nasal surgery for improved PAP device usage and compliance in those subjects. However, our study has several limitations. First, this retrospective case series lacks objective postoperative testing data. Additional data from postoperative PSG, rhinometry, and validated questionnaire with detailed items would be valuable in order to objectify the results. Second, only male subjects were included by coincidence, which may affect the generalizability of our results and require cautious interpretation. A large-scale prospective investigation of PAP compliance related to nasal surgery still needs to be performed in future studies.
## Conclusion
Our clinical study indicates that nasal surgery can be a beneficial surgical option for patients with obstructive sleep apnea (OSA) who experience nasal obstruction as a primary barrier to using continuous positive airway pressure (PAP) devices. Specifically, our findings suggest that nasal surgery may improve compliance with PAP therapy and lead to better treatment outcomes for OSA subjects with small nasal cavity volume and minimal allergic inflammation. However, it is important to note that nasal surgery may not be effective for OSA subjects who have multiple barriers to PAP device use or do not find PAP therapy to be effective. Further research is needed to confirm these findings and understand the underlying mechanisms.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31588-7.
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|
---
title: Non-alcoholic fatty liver disease is associated with decreased bone mineral
density in upper Egyptian patients
authors:
- Amro M. Hassan
- Mustafa Ahmed Haridy
- Mohamed Z. Shoaeir
- Tarek M. Abdel-Aziz
- Mohamed Khairy Qura
- Eglal M. Kenawy
- Tarek Mohamed M. Mansour
- Sameh Salaheldin Elsayed
- Wael Esmat Ali
- Mona Mohamed Abdelmeguid
- Muhammad Abdel-Gawad
journal: Scientific Reports
year: 2023
pmcid: PMC10020438
doi: 10.1038/s41598-023-31256-w
license: CC BY 4.0
---
# Non-alcoholic fatty liver disease is associated with decreased bone mineral density in upper Egyptian patients
## Abstract
Nonalcoholic fatty liver disease (NAFLD) has been linked with a number of extra hepatic diseases and could be a potential risk factor of decreasing bone mineral density. To determine whether Upper Egyptian patients with NAFLD are at risk of developing osteoporosis. Cross sectional study was done on a total 100 individuals; 50 patients diagnosed with NAFLD (based on ultrasound imaging) crossed-matched with 50 individuals without NAFLD based on age, sex and body mass index. Bone mineral density, serum calcium and phosphorus levels, serum parathyroid hormone, serum vitamin D and fasting insulin level were assessed. Osteoporosis was prevalent in NAFLD patients versus to controls ($\frac{19}{50}$ vs. $\frac{0}{50}$; $P \leq 0.001$). There was significant decrease in bone mineral density in NAFLD patients than controls (− 2.29 ± 0.4 vs. − 1.53 ± 0.1; $P \leq 0.001$). There was a statistical significance decrease in serum vitamin D and calcium levels in NAFLD patients than controls. Furthermore, vitamin D levels in the NAFLD group was a predictor for osteoporosis (OR 0.614; $95\%$ CI 0.348–0.825). Patients with NAFLD tend to have a significant decrease in bone density, vitamin D, and serum calcium levels than controls.
## Introduction
Nonalcoholic fatty liver disease (NAFLD) has emerged as noninfectious liver disease affecting between 17 and $33\%$ in the general population and $75\%$ in obese and/or diabetic individuals worldwide1. NAFLD is not confined to adult but it occurs in children and adolescents. Prevalence report from Egypt estimates NAFLD among school children was $15.8\%$ in cross-sectional study2.
The pathological spectrum of NAFLD ranges from simple steatosis to nonalcoholic steatohepatitis (NASH) that ultimately may progress to fibrosis and cirrhosis which may be complicated by hepatocellular carcinoma3,4.
NAFLD has been linked with extra hepatic diseases as cardiomyopathy, cardiac arrhythmias, chronic kidney disease, type 2 diabetes mellitus (T2DM) and obstructive sleep apnea3,5,6. Moreover, NAFLD has been associated with increased risk of extra hepatic cancer including gastrointestinal, urinary, lung, breast, and gynecological cancers7.
Osteoporosis (OP) is a systemic skeletal disease characterized by low bone mineral density (BMD) and may be associated with pathological fracture8. In developed countries, OP affecting 9–$38\%$ of women and 1–$8\%$ of men aged > 50 years9.
Diagnosis of OP is based on dual-energy X-ray absorptiometry (DEXA)10. There are multiple sites that could be used to assess bone mineral density including hip, spine and wrist. In spite of these multiple sites, DEXA scanning of the hip or spine is validated by world health organization (WHO)11.
About 12–$55\%$ of patients with liver cirrhosis have imminent risk of vertebral fractures so patients with chronic liver disease need to be screened by DEXA scan for early detection of OP as vertebral fractures are usually asymptomatic12,13.
Several cross sectional studies have evaluated association between NAFLD and lower BMD. Unfortunately, the results of these studies were conflicting as some studies have identified a significant association between NAFLD and low BMD14,15 and other studies showed no significant associations between NAFLD and low BMD17,18.
Some Studies showed that NAFLD may contribute in the pathophysiology of bone demineralization and OP via production of multiple pro-inflammatory cytokines, tumor necrosis factor α (TNF-α), pro-oxidant mediators, bone-influencing molecules and/or via the direct effect on hepatic and insulin resistance12,19.
As association between NAFLD and low BMD is still a matter of controversy, so the study aimed to evaluate bone mineral density, serum calcium and vitamin D in patients with NAFLD to determine if patients who have NAFLD are at risk of developing OP.
## Patients and methods
This cross section study was done in Hepato-Gastroentrology and rheumatology departments, Al-Azhar Assiut University hospital, from February 2019 to December 2020 to determine if patients who have NAFLD are at risk of OP.
A total 100 individuals were enrolled in the study; 50 patients diagnosed by ultrasound to have NAFLD and 50 crossed matched individuals without NAFLD based on age, sex and BMI.
The study was approved by ethical committee of Al-Azhar Assiut faculty of medicine and an informed written consent was signed by every individual before being enrolled in the study. The study was conducted in accordance with ethical principles of the World Medical Association Declaration of Helsinki.
## Inclusion criteria
Any individual diagnosed by ultrasound to have (NAFLD) and aged ≥ 18 years.
## Exclusion criteria
Individual with any one of the following criteria were excluded from the study: [1] aged ≤ 18 years, [2] any liver disease that could lead to NAFLD such as viral hepatitis, autoimmune liver diseases, alcohol consumption, Wilson’s disease, hemochromatosis, [3] recent exposure to hepatotoxic drugs within 6 months or drugs containing or affecting vitamin D level, [4] diabetes mellitus, [5] renal disease [6] Pregnant or lactating women.
## Investigatory work-up
Eligible individuals (cases and controls) were admitted to Hepato-Gastroentrology and rheumatology departments, et al.-Azhar Assiut University Hospital and full history taking, clinical examination and BMI were assessed for every individual.
After midnight fasting, eligible individuals were assessed by the following laboratory tests and imaging studies: complete blood count (CBC), liver function tests (serum bilirubin, AST, ALT, albumin and INR), renal function test (urea, creatinine and serum uric acid), fasting blood sugar (FBS), cholesterol, triglycerides and erythrocyte sedimentation rate (ESR).
Serum calcium and phosphorus levels were assessed by colorimetric method using (spinreact, S.A.U., Spain) and (5010 chemistry photometer, Germany) and reference range for serum calcium is 8.0–10.5 mg/dl and 2.5–5 mg/dl for phosphorus.
Serum parathyroid hormone level was assessed by ELISA using (Human PTH ELISA Kit, Bioassay technology laboratory) produced by (Shanghai Korian Biotech Co., Ltd, China) and (Robonik ELISA plate reader, India).
Serum 25-OH Vitamin D3/D2 level was assessed by ELISA using (ORGENTEC Diagnostika GmbH, Germany) and (Robonik ELISA plate reader, India).
Fasting insulin level was assessed by ELISA using (human insulin enzyme immunoassay test Kit, prechek Bio Inc, USA) and (Robonik ELISA plate reader, India) and Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was calculated by using the following equation: HOMA-IR = fasting insulin (micro unite/ml) × fasting blood glucose (mmol/ml)/22.520,21.
Pelvi-abdominal ultrasound examination for eligible individuals was done after a midnight fasting using a B-mode convex probe US equipment (Esaote ID, CE0051; Technos, Genoa, Italy) with a 4.5–7 MHz to assess severity of fatty liver and NAFLD was graded according to echogenicty of the liver22 into Grade I: minimal diffuse increase in hepatic echogenicty in which the liver appears bright compared with the cortex of the kidney with normal visualization of diaphragm and borders of intrahepatic vessel, Grade II: moderate diffuse increase in hepatic echogenicty with slightly impaired visualization of diaphragm and intrahepatic vessels, Grade III: marked increase in hepatic echogenicty which obscures visualization of intrahepatic vessels and diaphragm.
Bone mineral density (BMD) at lumbar spine was measured by dual energy X-ray absorptiometry (DEXA) (DEXA scan lunar DPX-NT 2013 made in USA by General Electric) and according to the World Health Organization (WHO) criteria for BMD, OP in adult is defined by a T score less than -2.5 and osteopenia is defined by a T score between − 1 and − 2.511,23–25.
## Statistical analysis
The statistical analysis was performed using Windows 10 SPSS version 22 (IBM SPSS Inc., Chicago, Illinois, USA)) program. Normally distributed data were presented as mean ± standard deviation (SD) and categorical data were expressed as number and percentage. The Student’s t test was performed for continuous variables and categorical variables were compared by the chi-square (χ2) and Fisher's exact tests. Regression analysis was done to predict the independent associated factors that may affect BMD in patients with NAFLD. P value of < 0.05 was considered as statistical significance.
## Results
A total of 100 individuals were included in the study. The baseline and laboratory characteristics of individuals with NAFLD (cases) and individuals without NAFLD (controls) is shown in (Table 1).Table 1Baseline and Laboratory characteristics of studied groups. ParametersCases ($$n = 50$$) Mean ± SDControls ($$n = 50$$) Mean ± SDp valueAge (years) Mean + standard deviation (SD)45.40 ± 12.545.12 ± 11.50.907Male28 ($56\%$)30 ($60\%$)0.420Female22 ($44\%$)20 ($40\%$)Nonsmoker37 ($74\%$)38 ($76\%$)0.817Smoker13 ($26\%$)12 ($24\%$)Body mass index(BMI)27.85 ± 5.427.77 ± 5.50.941Hemoglobin concentration (g/dl)12.79 ± 1.212.71 ± 1.30.41White blood cells *103/µL38.08 ± 10.636.09 ± 11.40.37Red blood cells (106/mm3)4.91 ± 0.95.01 ± 1.00.9Platelet *103/µL270.91 ± 13.3288.46 ± 16.20.76Mean corpuscular volume (M CV) (fl)83.95 ± 9.083.78 ± 8.90.072Mean Corpuscular Haemoglobin (MCH) (pg)28.43 ± 4.127.65 ± 1.80.13Triglycerides (mg/dl)162.12 ± 12.3163.66 ± 10.40.929Total cholesterol (mg/dl)183.22 ± 8.4176.64 ± 7.50.560Erythrocyte sedimentation rate (ESR) (mm/hr)21.44 ± 1.822.60 ± 1.90.661Alanine transferase (IU/L)31.52 ± 2.523.62 ± 1.40.003Aspartate transferase (AST) (IU/L)30.58 ± 2.128.60 ± 2.30.252Albumin (g/L)4.17 ± 0.54.15 ± 0.50.868Total Protein (g/dl)7.17 ± 0.77.29 ± 0.70.382Total Bilirubin (mg/dl)0.64 ± 0.10.65 ± 0.10.929Direct Bilirubin (mg/dl)0.22 ± 0.10.21 ± 0.10.820International normalized ratio (INR)1.10 ± 0.11.11 ± 0.20.770Urea (mg/dl)34.22 ± 10.831.34 ± 10.50.179Creatinine (mg/dl)0.90 ± 0.10.82 ± 0.10.142Fasting blood sugar (FBG) (mg/dl)98.94 ± 16.397.14 ± 15.60.589Fasting Insulin (mIU/L)4.50 ± 0.44.03 ± 0.30.380HOMA-IR, median (range)0.96 (0.329–2.24)0.827 (0.19–3.26)0.326HOMA-IR homeostasis model assessment of insulin resistance.
Our study showed no significant difference between cases and controls regarding to age, sex, smoking, BMI and the baseline and laboratory characteristic of studied groups. Moreover, cases had elevated ALT more than controls with significant P-value 0.003 (Table 1).
Among 50 patients enrolled in the study, 41 ($82\%$) patients had grade I fatty liver and 9 ($18\%$) patients had grade ≥ II (Table 2).Table 2Ultrasound characteristics of studied groups. ParametersCases ($$n = 50$$) Mean ± SDControls ($$n = 50$$) Mean ± SDp valueUS /Fatty liver grade 00 ($0\%$)50 ($100\%$) < 0.001US /Fatty liver grade I41 ($82\%$)0 ($0\%$)US /Fatty liver grade ≥ II9 ($18\%$)0 ($0\%$)US ultrasound.
As regarded to factors affecting bone density, the study showed a significant difference in PTH, vitamin D, and serum calcium between cases and controls group (Table 3).Table 3Factors affecting bone density among studied groups. Factors affecting bone densityCases ($$n = 50$$) Mean ± SDControls ($$n = 50$$) Mean ± SDp valuePTH (pg/ml)56.27 ± 7.832.48 ± 9.10.002Vitamin D (ng/ml)28.94 ± 7.561.50 ± 14.1 < 0.001Serum Ca (mg/dl)8.38 ± 0.69.86 ± 0.7 < 0.001Serum P (mg/dl)4.74 ± 0.94.87 ± 0.8 = 0.414PTH parathyroid hormone, Ca calcium, P phosphorus.
DEXA scan results showed a significant difference between cases (− 2.29 ± 0.4) and controls (− 1.53 ± 0.1) with p value < 0.001 (Table 4).Table 4Dual energy X ray absorptiometry among studied groups. Cases ($$n = 50$$) Mean ± SDControls ($$n = 50$$) Mean ± SDp value DEXA Scan (BMD) T-score− 2.29 ± 0.4− 1.53 ± 0.1 < 0.001DEXA dual energy x-ray absorptiomet ry.
Our results showed a significant difference in bone density between cases and controls. Moreover, OP was more prevalent in cases than controls group with p value < 0.001. Among the NAFLD group, 19 patients had OP (16 of them had grade I fatty liver and 3 of them had grade ≥ II fatty liver), 28 patients had osteopenia (22 of them had grade I fatty liver and 6 had grade ≥ II fatty liver) 3 patients had normal bone density (Table 5).Table 5Relationship between degree of fatty liver and degree of bone density. VariableOsteoporosis ($$n = 19$$)Osteopenia ($$n = 68$$)Normal ($$n = 13$$)p valueControl0 ($0\%$)40 ($58.8\%$)10 ($76.9\%$) < 0.001NAFLD19 ($100\%$)28 ($41.2\%$)3 ($23.1\%$)Grade of fatty liver00 ($0\%$)40 ($58.8\%$)10 ($76.9\%$) < 0.001I16 ($84.2\%$)22 ($32.4\%$)3 ($23.1\%$) > I3 ($15.8\%$)6 ($8.8\%$)0 ($0\%$) In our study, we performed logistic regression model analysis to determine predictors of OP among the NAFLD group. We found among patients with NAFLD, serum vitamin D level was statistically significant predicts OP (OR 0.614; $95\%$ CI 0.348–0.825) ($P \leq 0.001$). Also female gender, BMI, bilirubin, serum calcium, serum phosphorus PTH, fasting insulin, and insulin resistance were statistically significant predictors of OP among the NAFLD group (Table 6).Table 6Predictors of Osteoporosis among the NAFLD Sample: Logistic Regression Model. Variable (per unit increase)UnivariateMultivariateOR ($95\%$ CI)P valueOR ($95\%$ CI)P valueAge/years1.023 (0.976–1.073)0.3441.013 (0.981–1.062) = 0.547Sex (Female)3.600 (1.086–7.932)0.0362.288 (1.147–6.655) = 0.018Smoker3.516 (1.102–9.446)0.0213.057 (1.066–5.126) = 0.031BMI1.185 (1.006–2.008)0.0482.258 (1.287–4.916) = 0.040Bilirubin (mg/dl)2.521 (1.033–8.361)0.044Serum Ca (mg/dl)0.549 (0.212–0.769)0.0190.180 (0.043–0.756) = 0.019Serum P (mg/dl)0.893 (0.529–0.994)0.0310.741 (0.425–0.921) = 0.044PTH (pg/ml)1.098 (1.008–1.120)0.0411.020 (1.002–1.039) = 0.029Fasting Insulin1.313 (1.060–1.627)0.0131.868 (1.218–2.866) = 0.004HOMA-IR3.056 (1.244–7.507)0.015Vit. D Level (ng/ml)0.614 (0.348–0.825) < 0.0010.268 (0.018–0.446) = 0.011OR odd ratio, CI confidence interval.
## Discussion
Large numbers of cross sectional studies have identified a significant association between NAFLD and low BMD, but this association remains a matter of controversy14,18. One meta-analysis showed no significant difference in BMD between patients with NAFLD and controls26. Another meta-analysis showed NAFLD was associated with osteoporotic fracture, but not associated with low BMD27. However, recent meta-analysis done by Mantovani et al. found that NAFLD was significantly associated with low BMD in children and adolescents28.
Our study showed a significant difference in bone density between patients with NAFLD and controls. Our results agree with Xia et al. who found that patients with NAFLD were significantly associated with low BMD29. Lee et al. reported a positive association between NAFLD and lumbar spine BMD in postmenopausal females17. Moreover, in Shen et al. cohort study showed that males and females patients with NAFLD were associated with increased risk of low BMD30.
Our results showed that osteoporosis was more prevalent in patients with NAFLD compared to controls with p value < 0.001. Moreover among NAFLD group, 19 patients had osteoporosis and 28 patients had osteopenia. Our result is supported by meta-analysis study done by Pan et al.31 who reported that prevalence and risk of OP or osteoporotic fracture was significantly associated with NAFLD group than in controls. Also, Loosen et al.32 reported that incidence of OP was significantly higher in the NAFLD patients ($6.4\%$) compared to controls ($5.1\%$) with p value < 0.001). Moreover, our study agrees with Pirgon et al.33 who indicated that NAFLD has a noxious effect on BMD in adolescents and was correlated with increased insulin resistance. Another cohort study performed by Chen et al.34 showed increase risk of OP 1.35 times in patients with NAFLD than individuals without NAFLD. Also Li et al.16 showed that NAFLD was significantly associated with history of osteoporotic fractures in middle-aged and elderly Chinese men.
Our results can be interpreted in light that factors affecting bone mineral density including serum vitamin D and serum calcium were significantly lower in our studied patients with NAFLD than controls. Our results agree with Targher et al. who reported a potential link between decreased serum vitamin D and low BMD in patients with NAFLD19. Moreover, many studies proved that patients with NAFLD have lower levels of serum vitamin D than controls which lead to decrease BMD and increase risk of fractures35–38. Our study showed that patients with NAFLD had increased level of PTH compared to controls which indicates that patients with NAFLD had low BMD and OP.
The association between hypovitaminosis D and NAFLD have been found in many diseases such as metabolic syndrome and obesity39,40. But in observational studies like our study, it is difficult to judge if NAFLD is a cause or result to hypovitaminosis D. Moreover, association between hypovitaminosis D and NAFLD could be accidental without actual relations between them. Consequently, the relationship between hypovitaminosis D and NAFLD needs prospective randomized controlled trials to compare development of NAFLD in patients with hypovitaminosis D versus healthy subjects and to assess effect of vitamin D supplementation on regression of NAFLD.
In our study, we have performed logistic regression model analysis to determine predictors of osteoporosis among patients with NAFLD. We found that serum vitamin D levels was a statistically significant predictor for OP among NAFLD patients (OR 0.614; $95\%$ CI 0.348–0.825) ($P \leq 0.001$).
Insulin resistance is considerable risk factor for NAFLD4,41 and insulin resistance and high fasting serum insulin may be associated with increased risk of low BMD42.
Although most people with NAFLD have metabolic dysfunction such as diabetes mellitus, but we excluded diabetic patients as diabetes mellitus deteriorates bone strength and increase susceptibility to bone fracture and this could affect results of our study43,44.
In our study, 10 patients had HOMA-IR more than 2, nine of them had decreased BMD (osteoporosis and osteopenia) and only one patient had significant insulin resistant (HOMA-IR more than 2.7). The patient who had HOMA-IR more than 2.7 also had osteopenia.
In our study logistic regression model analysis showed that fasting insulin and insulin resistance were statistical significant predictors of OP among the NAFLD group. Our result agrees Filip et al.8 who found that patients with NAFLD are associated with insulin resistance which is risk factors for low BMD.
## Conclusion
Our study showed significant decreased in bone density and osteoporosis in patients with NAFLD compared to controls. Also serum vitamin D and serum calcium were significantly deceased in patients with NAFLD while level of PTH was increased in patients with NAFLD than controls which indicate that patients with NAFLD have potential risk of developing low BMD and OP. Additional further prospective studies are needed to determine the relationship between NAFLD and risk of low BMD and OP.
## Study limitations and future recommendations
This study had some limitations. The first limitation was the small sample size so we recommend further studies with large sample size with different risk factors to determine the relationship between NAFLD and the risk of low BMD and osteoporosis. Second, ultrasound had been used to determine whether individuals had NAFLD or not and its degrees, instead of liver biopsy which is the gold standard in diagnosis of fatty liver. Third, minimal steatosis (NAFLD grade 1) might be missed by using ultrasound. Fourth, further studies are recommended to screen BMD in patients with NAFLD who have diabetes mellitus as most patients with NAFLD have metabolic dysfunction as diabetes mellitus or insulin resistance. Moreover, in this study we screened only lumbar spine to detect BMD in patients with NAFLD and individuals without NAFLD and we recommend further studies for screening BMD of other vulnerable sites of fracture such as neck of the femur.
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|
---
title: Elucidating the NB-UVB mechanism by comparing transcriptome alteration on the
edge and center of psoriatic plaques
authors:
- Suphagan Boonpethkaew
- Jitlada Meephansan
- Sasin Charoensuksira
- Onjira Jumlongpim
- Pattarin Tangtanatakul
- Jongkonnee Wongpiyabovorn
- Mayumi Komine
- Akimichi Morita
journal: Scientific Reports
year: 2023
pmcid: PMC10020439
doi: 10.1038/s41598-023-31610-y
license: CC BY 4.0
---
# Elucidating the NB-UVB mechanism by comparing transcriptome alteration on the edge and center of psoriatic plaques
## Abstract
Narrow band-ultraviolet B (NB-UVB) is an effective treatment for psoriasis. We aim to generate a potential mechanism of NB-UVB through comparing the transcriptomic profile before and after NB-UVB treatment between the peripheral edge of lesional skin (PE skin) and the center of lesional skin (CE skin) on the basis of molecular mechanisms of these two areas display different downstream functions. More than one-fourth of the NB-UVB-altered genes were found to be plaque-specific. Some of them were psoriasis signature genes that were downregulated by NB-UVB in, both, PE and CE skin (core alteration), such as IL36G, DEFB4A/B, S100A15, KRT16, and KRT6A. After NB-UVB treatment, the activity score of upstream cytokines, such as interferons, interleukin (IL)-6, IL-17, and IL-22 in pathogenesis decreased. In addition, NB-UVB could restore normal keratinization by upregulating LORICRIN and KRT2, particularly in the CE skin. Finally, we illustrated that NB-UVB is capable of suppressing molecules from the initiation to maintenance phase of plaque formation, thereby normalizing psoriatic plaques. This finding supports the usefulness of NB-UVB treatment in clinical practice and may help in the development of new treatment approaches in which NB-UVB treatment is included for patients with psoriasis or other inflammatory skin diseases.
## Introduction
Narrow-band ultraviolet-B (NB-UVB) is the first-line phototherapy for moderate to severe psoriasis (> $10\%$ of the body surface area). Among phototherapy modalities, NB-UVB efficacy is lower than that of oral psoralen plus UV-A (PUVA). However, it has higher patient tolerability and lower adverse events than those of PUVA1. NB-UVB treatment results in rapid lesion clearance, long remission intervals, and minimal acute adverse events2–4. Although novel biologics have a high rate of psoriasis area severity index (PASI)-75 accomplishment, NB-UVB treatment may be safer than these in conditions, such as pregnancy, children, and breast-feeding mothers, based on their long-term record1,5. NB-UVB treatment requires no laboratory monitoring and is more affordable than biologics1. Moreover, it can be administered in combination with topical steroids, topical vitamin D derivative, methotrexate, acitretin, or even biologics to achieve a high PASI reduction6–9. These factors still make NB-UVB a useful modality in clinical practice.
The NB-UVB mechanism is complex. Apoptosis of epidermal T cells appears to be one of the cytotoxic effects of UVB radiation in inflammatory dermatoses treatment. The key cytotoxic effect of UVB during plaque clearance in psoriasis is apoptosis of epidermal keratinocytes10. Furthermore, UVB damages nuclear DNA (a UVB’s chromophore), which suppresses DNA synthesis in psoriatic epidermal cells11. In addition to its suppressive effect on keratinocytes, NB-UVB reduces the number of inflammatory myeloid dendritic cells (mDCs) and interleukin (IL)-$\frac{17}{22}$-producing CD3+ T cells12. In contrast, it increases the number of peripheral blood T regulatory (T reg) cells with restoration of their function in patients with psoriasis3. NB-UVB suppresses type I and type II interferons (IFNs), and T helper (Th) 17 signaling pathways13. NB-UVB treatment suppresses serum levels of psoriasis-driving cytokines, such as tumor necrosis factor (TNF)-α, IL-8, IL-12, IL-17, IL-22, IL-23, and IL-34 and elevates the levels of IL-10, an anti-inflammatory cytokine, in patients with psoriasis14,15. Moreover, NB-UVB treatment significantly suppresses the mRNA levels of nuclear factor-kappa-B inhibitor zeta, serpin family B member 4 (SERPINB4), autophagy related 13, and cathepsin S in the edges of psoriatic plaques16 and may attenuate TLR-$\frac{3}{4}$/9 activity in psoriatic plaques17.
Previously, it has been proposed that the edges of plaques show more intense inflammation than that at the center, where plaque formation is initiated; however, the center of the plaques are more stable and bioinformatic analysis reveals higher activity of growth factors at the center than that at the edges18,19. These results indicate that the molecular mechanisms in the different areas of plaques vary. Hence, this study aimed to elucidate the new approach comparing the molecular changes after NB-UVB treatment inside the plaque's edge with those at the center using RNA-seq and a new bioinformatic analysis program. Our results elucidate the overall concept of NB-UVB mechanism along the chronological mechanism of plaque formation, explaining how NB-UVB is effective in clinical practice.
## Immune cell alteration is indicated inside the plaques after NB-UVB treatment
Transcriptome data were used to analyze the relative immune cell alterations after NB-UVB treatment inside the peripheral edges of lesional (PE) skin [before NB-UVB-treated PE (bPE) skin vs after NB-UVB-treated PE (aPE) skin] and the center of lesional skin CE skin [bCE skin vs aCE skin]. The results are shown in Fig. 1a. Activated CD4+ T cells and M1 macrophage were likely to show increased suppression in the PE skin compared to that in the CE skin. Activated natural killer (NK) cells were likely to be restored in, both, the PE and CE skin. Activated dendritic cells (DCs) tended to be more suppressed in the CE skin than that in the PE skin. However, except for activated CD4+ T cells, these findings were not statistically significant. Figure 1RNA-seq visualization. ( a) Relative abundance of immune cells comparing before and after NB-UVB treatment in the PE skin and CE skin. Immune cell deconvolution was performed using the LM22 gene signature matrix and CIBERSORTx algorithm. The graph shows the mean and standard deviation of relative abundance. Significance was analyzed using the LIMMA package in Bioconductor. * $p \leq 0.05.$ ( b) Venn diagram showing the intersection implicating the number of altered plaque-specific genes. ( c) A heatmap showing 20 NB-UVB-altered core genes. #, indicates the psoriasis signature genes. aCE skin After-treatment CE skin, aPE skin After-treatment PE skin, bCE skin Before-treatment CE skin, bPE skin Before-treatment PE skin, DEGs Differentially expressed genes, FC Fold change, NB-UVB Narrow band ultra violet B, UN skin Uninvolved skin.
## NB-UVB could reverse several psoriasis signature genes as a core alteration of the edge and center of plaques
Transcriptome data were analyzed into differentially expressed genes (DEGs) using a cut-off of p-value < 0.01 and │log2 fold change (FC)│ ≥ 1.5. Gene expression in psoriatic plaques was classified into the following two groups: untreated group (bPE/uninvolved (UN)—and bCE/UN skin-derived DEGs) and NB-UVB-treated groups (aPE/bPE—and aCE/bCE skin-derived DEGs). DEGs of both the groups were intersected to identify the alteration of plaque-specific genes. A Venn diagram shows intersection of both the groups (Fig. 1b). The intersection indicated that NB-UVB could not alter all DEGs in untreated plaques. Only 62 ($28.44\%$) and 65 ($27.89\%$) NB-UVB-altered DEGs were plaque-specific for the PE and CE skin, respectively. These NB-UVB-altered DEGs are listed in Supplementary Fig. S1. Among them, 20 DEGs were identical between the PE and CE skin, representing the core alteration DEGs (Fig. 1c). Based on transcriptomic analysis lituratures20,21, majority of the core alteration DEGs were psoriasis signature genes (genes that tend to be more specifically expressed in psoriasis than other skin diseases or play a well-known role in psoriasis) (e.g., IL36G, DEFB4A/B, S100A7A (S100A15), SERPINB3, SERPINB4, KRT16, KRT6A, DSC2, and GJB2).
## NB-UVB suppressed the inflammatory processes and restored the normal differentiation and proliferation of keratinocytes in psoriatic plaques
Further, we analyzed DEG-enriched biofunctions and canonical pathways of cytokines using QIAGEN’s Ingenuity Pathway Analysis (IPA) (Fig. 2a and supplementary Fig. S2). The activation z-score of functions and cytokine signaling pathways associated with psoriatic plaque development, such as chemotaxis, angiogenesis, vasculogenesis, epithelial tissue proliferation, connective tissue proliferation and growth, IL-17 signaling, and IL-6 signaling, inversely decreased with NB-UVB treatment in, both, PE and CE skin. In addition, we used 62 and 65 NB-UVB-altered plaque-specific DEGs of both PE and CE skin, respectively (supplementary Fig. S1) to analyze functions and pathway enrichments using Metascape (Fig. 2b–c). NB-UVB treatment suppressed plaque-specific DEGs of, both, the PE and CE skin which were enriched in function related to formation of the late cornified envelope (e.g., KRT6A, KRT16, which are keratin induced in inflammatory conditions) suggesting the suppression of inflammatory phenotype of keratinocytes and abnormal keratinization process in, both, the PE and CE skin. In addition, LORICRIN of, both, the PE and CE skin and KRT2 of CE skin, which are related to skin development and epithelial differentiation functions, were restored, reflecting the normalization of keratinocyte differentiation. NB-UVB-altered plaque-specific DEGs in each function and pathway are listed in supplementary Table S1.Figure 2Downstream function analysis. ( a) DEGs were analyzed for their downstream function and canonical pathway enrichment using QIAGEN’s IPA. The comparison among each set of DEGs is shown with a heatmap of activation z score. Orange, white, and blue colors represent activation, neutral, and inhibition, respectively. A dot is designated for │z score│ < 1. ( b–c) Function and pathway enrichment of NB-UVB-altered plaque-specific DEGs of PE and CE skin were analyzed using Metascape. Interesting functions are designated with *. aCE skin After NB-UVB-treated PE skin, aPE skin After NB-UVB-treated CE skin, bCE skin Before NB-UVB-treated center of lesional skin, DEG Differentially expressed gene, bPE skin Before NB-UVB-treated peripheral edge of lesional skin, UN skin Uninvolved skin.
## Core plaque-specific DEGs may be reversed either in PE or CE skin, with improvement in plaque phenotype
We selected some psoriasis-related downstream functions and signaling from downstream function analysis (Fig. 2) to identify the number of plaque-specific DEGs in each function that were reversed by NB-UVB (Fig. 3a). In addition, examples of the NB-UVB-reversed plaque-specific DEGs are shown in Fig. 3b. NB-UVB reversed a small number of plaque-specific DEGs in, both, the PE and CE skin. Some core plaque-specific DEGs may be affected either in PE or CE. skin. Figure 3Reversed plaque-specific DEGs after NB-UVB treatment. ( a) Dot Plot showing the number of reversed plaque-specific DEGs after NB-UVB treatment. ( b) Heatmap of Log2FC showing examples of genes in each related function and signaling. Red color represents upregulated DEGs and green represents downregulated DEGs. aCE skin After-treatment CE skin, aPE skin After-treatment PE skin, bCE skin Before-treatment CE skin, bPE skin Before-treatment PE skin, DEGs Differentially expressed genes, FC Fold change, NB-UVB Narrow band ultra violet B, UN skin Uninvolved skin.
## Inflammatory regulator activity was suppressed and anti-inflammatory regulator activity was restored by NB-UVB, affecting their downstream genes
Upstream regulators control their respective downstream molecules. Thus, we analyzed alterations in the regulator’s activity after NB-UVB treatment. Potential regulators with their activity z score of untreated plaques were analyzed from bPE/UN DEGs and bCE/UN DEGs and of NB-UVB-treated plaques were analyzed from aPE/bPE DEGs and aCE/bCE DEGs. The results are represented as a heatmap of activation z score (Fig. 4a–c). NB-UVB suppressed the activation z-score of type I IFNs (IFN-α, IFN-α2, and IFN-β) more on the PE skin, while that of Type II IFN (IFN-γ) more on the CE skin. In addition, IFN α and β receptor subunit 1 (IFN-αR1), which is essential in Type I signaling22, was suppressed in the PE skin. Apart from the inflammation-related molecules, anti-inflammatory molecules, such as IL-10 receptor subunit α (IL-10Rα)23 and Cytotoxic T-lymphocyte antigen 4 (CTLA-4)24,25, were restored after NB-UVB treatment. Figure 4Effect of NB-UVB on potential upstream regulators. ( a) Regulators that may be altered in both PE and CE skin. This cluster heatmap was created by “pheatmap” package in R software 1.0.12. ( b–c) Regulators that might be altered either in PE skin or CE skin. Upstream regulators were analyzed by the upstream analysis function of QIAGEN’s IPA with a p-value of overlap < 0.05. The results are presented as a heatmap of the activation z-score. * indicates the potential key regulators. CE skin Center of lesional skin, NB-UVB Narrow band ultra violet B, PE skin Peripheral edge of lesional skin.
## Mechanistic networks of NB-UVB were mediated through, both, plaque specific and non-plaque-specific DEGs, suppressing inflammation and abnormal growth
Finally, we proposed the network of molecular effects after NB-UVB treatment in PE and CE skin by correlating the upstream regulator molecules to DEGs and their downstream functions (Fig. 5a–b). The mechanistic network of NB-UVB on the CE skin was primarily mediated through plaque-specific DEGs, in contrast to that on the PE skin. Figure 5Potential networks for the molecular effect of NB-UVB. ( a) Molecular network of NB-UVB effect in the PE skin. ( b) Molecular network of NB-UVB effect in the CE skin. * indicates plaque-specific DEG. Regulator effect function of QIAGEN’s IPA was used to analyze the networks by overlapping the upstream regulators molecules to DEGs and their downstream functions. aCE skin After-treatment CE skin, aPE skin After-treatment PE skin, bCE skin Before-treatment CE skin, bPE skin Before-treatment PE skin, DEGs Differentially expressed genes, NB-UVB Narrow band ultra violet B.
## Discussion
NB-UVB is the standard treatment for moderate to severe psoriasis. Its mechanism has continuously been elucidated. To our knowledge, this is the first report using RNA-seq technology with an advanced bioinformatic program to summarize the molecular profile in normalized plaques and to propose the mechanism of NB-UVB treatment by comparing the molecular changes in the PE and CE skin. The profile is shown in Fig. 6a and is discussed as follows. Figure 6Molecular profile of normalized plaques and potential NB-UVB mechanism. ( a) Molecular profile of normalized plaques based on RNA-seq analysis. ( b) NB-UVB treatment suppressed the initial molecules, and molecules along the following cascade, resulting in the suppression of inflammation and abnormal proliferation. In addition, NB-UVB enhanced the normal differentiation/proliferation of keratinocytes and enhanced immunosuppressive function. Figure 6b was created by Adobe photoshop and BioRender.com. CE skin Center of lesional skin, NB-UVB Narrow band ultra violet B, PE skin Peripheral edge of lesional skin.
Comparative analysis before and after treatment revealed that NB-UVB could alter more than 200 genes in, both, the PE and CE skin. However, approximately a quarter of these altered genes were plaque-specific DEGs. Among these, only 20 DEGs were affected by NB-UVB in both PE and CE skin (core alteration). The core NB-UVB downregulated DEGs included a few psoriasis signature genes, such as IL36G, DEFB4A/B (coding human β defensin 2), S100A15, SERPINB4, KRT16, KRT6A, and DSC220. These downregulated genes suppressed chemotaxis, abnormal keratinization, and growth/proliferation of connective tissues in the plaques. In addition, some of these core NB-UVB-downregulated DEGs, such as IL36G and DEFB4A/B, were involved in IL-6 and IL-17 signaling (Fig. 3b), which are key cytokines in psoriasis pathogenesis. This might result in the attenuation of IL-6 and IL-17 signaling inside the treated plaques as well.
Proinflammatory IL-6 and TNF released by activated keratinocytes mature and activate mDCs to secrete IL-2326–29. Both IL-6 and IL-23 are cytokines in the microenvironment of Th17 differentiation28,30. In addition, Th22 needs IL-6 in the microenvironment for differentiation28. NB-UVB suppresses IL-6 mRNA with its protein secretion from peripheral blood monocytes of patients with psoriasis31,32 and could suppress IL-6 and TNF-α levels in suctioned blister fluid of treated plaques33. Thus, the suppression of activation z score for IL-6 signaling after NB-UVB treatment from PE to CE skin may have suppressed initial inflammation and the transitional step to maintenance phase.
IL-17A, also known as IL-17, is a key cytokine in the pathogenesis of psoriasis and Th17 is its primary source. Th17 is activated by mDC-derived IL-23 to release IL-17A. In addition to Th17, T cytotoxic (Tc)-17, γδ T cells, innate lymphoid cells, and NK T cells could also secrete IL-17A. IL-17A induces diverse proinflammatory cytokines (e.g., IL-1β, IL-6), antimicrobial peptides (AMPs) (e.g., DEFB4A, S100A9), and neutrophil chemokines (e.g., CXCL8 (IL-8)) from keratinocytes27,28,34–36. Our results revealed that DEFB4A, S100A9, and CXCL8 were downregulated by NB-UVB treatment and the activity z-score of IL-1β (a regulator in both PE and CE skin) was decreased by NB-UVB treatment. In addition to IL-17A, IL-17C, another member of the IL-17 cytokine family, was also detected in psoriatic plaques and plays an important role in pathogenesis; NB-UVB treatment was found to decrease its activity z score. IL-17C is encoded by IL17C, which is a psoriasis signature gene20. Unlike IL-17A, IL-17C is majorly secreted by keratinocytes and acts as a regulator in, both, PE and CE skin18,19. It induces the expression of other psoriasis signature genes, such as IL36G, S100A15, DEFB4A, S100A9, CXCL8, TNIP3 (coding TNF-α-induced-protein 3 or TNFAIP3), and LCN2 (by synergy of IL-17C with TNF-α) in keratinocytes18,20. This study revealed that NB-UVB treatment suppressed these genes, particularly IL36G, S100A15, and DEFB4A, which were the core alteration.
In addition, various upstream regulators, such as TNF, IFN-α/β/γ, and IL-22, were altered after NB-UVB treatment in, both, PE and CE skin. However, several regulators were altered either in the PE skin (e.g., suppression of epidermal growth factor and Nuclear Factor Kappa B and enhancement of IL-10Rα) or CE skin (e.g., suppression of NF-Kappa-B Inhibitor Zeta and enhancement of CTLA-4).
Type I and II IFN signaling pathways are suppressed in psoriatic plaques after NB-UVB treatment13. Interestingly, NB-UVB treatment showed higher suppression of type I IFN (IFN-α) expression in the PE skin while increased suppression of type II IFN (IFN-γ) expression was observed in the CE skin. In addition to IL-6 and TNF secreted by activated keratinocytes, IFN-α secreted by plasmacytoid DCs could also activate mDCs, resulting in IL-23 production27,29. Thus, IFN-α also plays a role in initial inflammation and acts as an upstream cytokine along the IL-23/IL-17 axis in the pathogenesis29. Before IL-23/IL-17 axis had been established as the central mechanism, the IL-12/IFN-γ axis was primarily considered critical in psoriasis29. IFN-γ (type II IFN) could be secreted by Th1, T cytotoxic 1(Tc1), NK cells, and NK T cells, but not by keratinocytes, like IFN-α. Notably, Th17 is also capable of co-secreting IFN-γ with IL-17, particularly when stimulated with IL-12. IFN-γ has been also considered as upstream cytokine of the IL-23/IL-17 axis, wherein it stimulates mDCs to secrete IL-2328,29,37. Although IFN-γ plays a potential role in initial inflammation, blocking the IL-12/IFN-γ axis has limited beneficial effects29. However, IFN-γ mRNA expression is decreased in NB-UVB-normalized plaques38. In addition, our results suggested that NB-UVB tended to suppress the activity z-score of IFN-γ more in the CE skin than that in the PE skin. Accordingly, IFN- γ may be involved in the initial step latter than IFN-α.
Although it is difficult to identify the first molecule being altered in the normalized plaques (Fig. 6a), NB-UVB has the potential to suppress various molecules and signaling along the chronological mechanism of psoriatic plaque development18,19 (Fig. 6b). These suppress, both, inflammation-and growth-related downstream functions in the PE and CE skin; however, downstream functions related to inflammation are more likely to be suppressed in the PE skin than that in the CE skin, and those related to growth are more likely to be suppressed in the CE skin than that in the PE skin, which is in line with the concept of chronological plaque development18,19. With more than $75\%$ staining of the proliferation marker protein Ki-67 of suprabasal/total epidermal, psoriasis is suggested over other psoriasiform dermatitis39. The study findings suggested that MKI67 coding this marker was downregulated by NB-UVB treatment in the CE skin. In addition, NB-UVB treatment decreased IL-22 regulator activity more in the CE skin than that in the PE skin. IL-22 is secreted by Th17, and exclusively by Th22 and Tc22. It enhances keratinocyte migration, increases epidermal thickness, and inhibits keratinocyte differentiation. Like IL-17, IL-22 also enhances inflammation by inducing various cytokines, chemokines, and AMPs29. Other regulators such as Rho-Associated Protein Kinase 2, which is involved in the differentiation and proliferation of keratinocytes40, and Forkhead Box Protein O1, which is involved in the motility of keratinocytes41, also show decreased activity z score by NB-UVB treatment in the CE skin.
In psoriatic plaques, keratinocyte stem cells are activated and proliferate rapidly, and the terminal differentiation process is incomplete. Levels of late differentiation markers, such as loricrin and filaggrin, and terminal differentiation markers, such as keratin 2 are decreased, while the levels of keratin 6 and keratin 16, which are expressed in activated keratinocytes, are increased37. Here, NB-UVB could downregulate KRT6A (coding keratin 6A, an isoform of keratin 6) and KRT16 and restore LORICRIN, both, in the PE and CE skin, and upregulated KRT2 in the CE skin. This suggested that NB-UVB normalized psoriatic plaques by promoting normal differentiation to complete terminal differentiation, particularly in the CE skin.
Regarding immune cells, the relative abundance of activated CD4+ T cells significantly decreased in the PE skin after NB-UVB treatment. Although RNA-seq analysis showed that the relative abundance of T regs in psoriatic plaques was unchanged after NB-UVB treatment, its function inside the plaques might be restored, as indicated by the increased activity z-scores of regulatory IL-10Rα and CTLA-4, after NB-UVB treatment in the PE and CE skin, respectively. CTLA-4, a co inhibitory molecule expressed on T regs, binds to specific molecules on antigen presenting cells to transmit inhibitory signals, thereby decreasing their ability to stimulate effector T cells24,25. Similar to programmed cell death protein 1 (PD-1), CTLA-4 also acts as an immune checkpoint that is a negative regulator of T cell immune function42. In a western diet-induced obese mouse model, anti-PD-1 intensely aggravated psoriasiform ear thickness43. In addition, IL-10R, expressed on T regs, is important for their immunoregulatory functions. In mouse models, IL-10R-deficient T regs fail to suppress Th17 response and fail to produce IL-1044 as well as T reg-specific IL-10Rα deficiency leads to spontaneous hyper-Th17 function23. Thus, the increased activity z-score of both CTLA-4 and IL-10Rα reflect the enhancement function of T regs, attenuating the Th17 response after NB-UVB treatment.
A drawback of this study is the limited number of study samples for RNA-seq analysis. A larger sample size for RNA-seq may provide a more precise gene profile. Analysis using techniques such as polymerase chain reaction, immunohistochemistry, or western blot of the potential molecules in the proposed mechanism may be targeted. Furthermore, cross-ethnic analysis could help identify different molecular mechanisms of pathogenesis and possible treatments in the future45–48.
In conclusion, NB-UVB normalizes psoriatic plaques by suppressing the expression of psoriasis signature genes, such as IL36G, DEFB4A/B, S100A15, SERPINB4, KRT16, and KRT6A in, both, the PE and CE skin. In addition, IL-6 and IL-17A signaling activity and the expression of various upstream molecules such as IFNs, IL-17C, and IL-22 were suppressed. In summary, we proposed that NB-UVB can potentially suppress molecules along the initial inflammatory stage and later maintenance stage during psoriatic plaque development. This discovery helps explain why NB-UVB is so effective in clinical practice and may help in the development of new therapeutic approaches in which NB-UVB treatment is included for patients with psoriasis or other inflammatory skin diseases.
## Patients
We recruited patients with psoriasis vulgaris (aged ≥ 18 years), from Thailand, with a PASI score ≥ 10 (moderate to severe). Patients who had been treated with topical agents (steroids, vitamin D analogues) within 2 weeks or had undergone systemic medication (cyclosporin, methotrexate, biologics) and phototherapy within 4 weeks of the date of tissue sampling were excluded. A total of three patients participated in this study. The patients were informed of all study protocols, and they provided written informed consent. A graphic summary of the method is provided in supplementary Fig. S3a. This study was conducted according the Declaration of Helsinki guidelines and was approved by the Human Ethics Committee of Thammasat University (No. MTU-EC-OO-6-$\frac{188}{65}$).
## Treatment
The patients were treated with NB-UVB 2–3 times for 12 weeks with a starting dose 200 mJ/cm2. This dose was gradually increased 10–$20\%$ from the initial dose until the minimal erythema dose was met. Side effects of NB-UVB treatment included erythema, tenderness, and burning sensation; these were monitored as shown in supplementary Fig. S3b. All patients exhibited more than $75\%$ reduction in the PASI score at 12 weeks (PASI 75). PASI scores before and after treatment are listed in supplementary Table S2.
## Tissue sampling
Lidocaine ($2\%$) was used for pain control. Full thickness tissue samples were collected using a 6-mm punch biopsy equipment. UN skin, 10 cm away from the edge, was biopsied before treatment. The PE and CE skin were biopsied from active lesion before treatment (first biopsy) and 7 days after treatment (second biopsy). To certify that the after-treatment biopsies were from the same area of preexisting lesions, the shape of the selected lesions was drawn on clear plastic sheaths, which were used as a reference for the second biopsy (Fig. S3C). All biopsies were preserved in RNAlater (ThermoFisher, AMBION, USA) at − 80 °C until further analysis.
## High-throughput next-generation sequencing
Based on previous RNA-seq profiling studies in human psoriasis skin, three samples were deemed to be adequate for analysis17,49. Therefore, the UN skin, bPE skin, aPE skin, bCE skin, and after aCE skin samples from three patients were used for RNA-seq. Total RNA was extracted from each sample (bPE, aPE, bCE, aCE, UN skin, $$n = 2$$) using the TRIzol Reagent (Ambion, Austin, TX, USA). Next-generation sequencing (NGS) was performed using Macrogen (Seoul, Korea). Briefly, the structural integrity and purity of RNA were analyzed using an Agilent 2100 Bioanalyzer (RNA integrity number > 7). Sequence libraries were constructed using the SMARTer Universal Low Input RNA Kit and TruSeq RNA Sample Prep Kit v2 (pair-end). NGS was performed using the NovaSeq 6000 S4 Reagent Kit on the NovaSeq 6000 System (Illumina Inc., San Diego, CA, USA) with 100-bp paired-end reads. The raw data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo) and are accessible through the GEO Series accession number GSE183732 and GSE186117 for UN skin, PE skin, CE skin transcripts before treatment and GSE179632 and GSE179731 for PE skin and CE skin transcript after treatment.
## Sequencing data analysis
Basic data analysis was conducted using Macrogen. Briefly, overall read qualities, total bases, total reads, % guanine-cytosine content, and basic statistics were calculated. Trimmed reads were mapped to the reference genome using HISAT2, a splice-aware aligner. The transcripts were assembled using StringTie with aligned reads. Expression profiles were repeated as read counts and normalized based on the transcript length and depth of coverage. The fragments per kilobase of transcript per million mapped reads value was used for normalization. Furthermore, we used the read counts to statistically analyze the expression profile with edgeR Bioconductor statistical library version 3.1350,51 on R Studio52.
## DEG analysis
The DEGs were determined according to p-value < 0.01 and FC ratio (|log2FC|) ≥ 1.5. QIAGEN’s IPA software (QIAGEN Redwood City, www.qiagen.com/ingenuity) was used as the “core analysis” of DEGs in aspects of disease and biofunction, canonical pathways, and upstream regulators. The Metascape online tool (https://metascape.org/gp/index.html) was additionally used to analyze functional enrichment.
## Graphic presentation
GraphPad prism 9.4.1 and Adobe photoshop 2021 were used to created graphical presentations. Figure 6 was created with BioRender.com and Adobe photoshop.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31610-y.
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|
---
title: Deduction of the operable design space of RP-HPLC technique for the simultaneous
estimation of metformin, pioglitazone, and glimepiride
authors:
- Aya A. Marie
- Sherin F. Hammad
- Mohamed M. Salim
- Mahmoud M. Elkhodary
- Amira H. Kamal
journal: Scientific Reports
year: 2023
pmcid: PMC10020468
doi: 10.1038/s41598-023-30051-x
license: CC BY 4.0
---
# Deduction of the operable design space of RP-HPLC technique for the simultaneous estimation of metformin, pioglitazone, and glimepiride
## Abstract
A reversed-phase RP-HPLC method was developed for the simultaneous determination of metformin hydrochloride (MET), pioglitazone (PIO), and glimepiride (GLM) in their combined dosage forms and spiked human plasma. Quality risk management principles for determining the critical method parameters (CMPs) and fractional factorial design were made to screen CMPs and subsequently, the Box–Behnken design was employed. The analytical Quality by Design (AQbD) paradigm was used to establish the method operable design region (MODR) for the developed method depended on understanding the quality target product profile (QTPP), analytical target profile (ATP), and risk assessment for different factors that affect the method performance to develop an accurate, precise, cost-effective, and environmentally benign method. The separation was carried out using a mobile phase composed of methanol: 0.05 M potassium dihydrogen phosphate buffer pH 3.7 with $0.05\%$ TEA (78:22, v/v). The flow rate was 1.2 mL/min. DAD detector was set at 227 nm. Linagliptin (LIN) was used as an internal standard. The proposed method was validated according to The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). The assay results obtained by using the developed method were statistically compared to those obtained by the reported HPLC method, and a satisfying agreement was observed.
## Introduction
Diabetes is one of the rapidly spreading health problems in Egypt with a substantial impact on morbidity, mortality, and health care problem1. The International Diabetes Federation (IDF) marks Egypt as The ninth-highest country in the world containing many diabetics.
Metformin (MET, Fig. S1a) is a biguanide, an oral antidiabetic drug2 for treating type-II diabetes3. It reduces glucose production from the liver and minimizes triglyceride and cholesterol levels3.
Pioglitazone (PIO, Fig. S1b) is a thiazolidinedione-type, also called "glitazones"4. Thiazolidinediones are Peroxisome Proliferator-Activated Receptor (PPAR-gamma) agonists, used for the treatment of diabetes type II. Pioglitazone is popular to be active in controlling glycemic by reducing insulin resistance4. It is used either in single or in a mixture of anti-diabetic medications. Adding PIO to MET and/or insulin secretagogues as part of triple oral therapy in patients with diabetes (type II) or case of binary drug failure is essential for reaching glycemic targets, improving β-cell function, and minimizing the risk factors involved in atherosclerosis5. PIO also improves glycated hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG)5.
Glimepiride (GLM) (Fig. S1c) is a long-acting oral anti-diabetic used for decreasing the sugar level in blood6. GLM is used only for the treatment of diabetes type II. GLM may be used with Insulin or other drugs to obtain improved control over the blood sugar levels6.
Different analytical approaches were reported for the estimation of MET7–11, PIO12–16, or GLM17–21 alone and in combinations (MET and PIO)22–27, (MET and GLM)28–30 and (PIO and GLM)31,32.
Tribet-1 and Tribet-2 tablets are composed of (500 mg MET, 15 mg PIO, and 1 mg GLM) and (500 mg MET, 15 mg PIO, and 2 mg GLM), respectively33. These agents are effective for patients who require multiple agents to lower their blood glucose levels.
Chromatographic methods are well known for their superiority in separating and quantifying components in their complex mixtures34. Hyphenated chromatographic methods have an add-on advantage of enhancing method sensitivity and selectivity by using advanced spectroscopic techniques to detect and quantify components35–37. Several analytical methods have been reported for the simultaneous analysis of this triple antidiabetic mixture, including RP-HPLC methods33,38–41, LC–MS–MS30, and HPTLC42.
The reported RP-HPLC methods33,38–41 had some drawbacks, including; questionable methodologies and/or lack of satisfactory validation parameters. The reported method33 missed some validation parameters (LOD and LOQ) as well as system suitability parameters (resolution), in addition to very low MET (NTP)33. Reported methods38,40 had high-speed separation and short run-time incompatible with reported resolution values38,40. Also, the reported method37 did not identify the full details of the regression analysis38. Reported methods33,38,39 did not focus on either greenness assessment or the biological sample applicability. In the reported method39, the peak of MET appeared before the plasma peak, and the retention time of the plasma peak in spiked samples did not match that of the blank plasma chromatogram. None of the reported methods33,38–41 used IS in their calculations.
AQbD is a risk-assessment based and systematic method intended to find and reduce the variability sources that may lead to poor robustness of the analytical method, and confirm that the method meets its intended performance requirements43. In the Analytical quality by design models (AQbD), the “design space” is based on the intended purpose of the developed analytical method that allows its performance with allowable changes.
The current AQbD approach depends on the study of quality target product profile (QTPP), analytical target profile (ATP), and risk assessment tool for factors or critical method parameters (CMPs) that affect the method performance43. The (ATP) was to establish and validate robust, sensitive, and green RP-HPLC technique. Determination of the (ATP), critical quality attributes (CQAs), and critical method parameters (CMPs) is one of the essential steps in developing methods. Ishikawa diagrams as risk assessment tools can help identify the impact of different CMPs on the CQAs.
Design of Experiments (DOE) uses multivariate statistical techniques with advantages, such as the decrease in the total number of experimental runs needed DOE permits the establishment of mathematical models used to assess the statistical significance of different effects among many trivial parameters to determine the vital few ones44.
This paper represents the first HPLC method for the simultaneous determination of cited drugs based on the merits of the AQbD technique during development and optimization. Thus, the proposed method outperforms previously reported methods for determining the studied triple mixture, particularly for GLM concentration in a difficult dosage form ratio. ( 1:15:500) (GLM: PIO: MET). The paper focused on specifying the domain of the experimental space, where tolerance interval criteria for the studied chromatographic parameters intersect to obtain the method operable design region (MODR). The technique can be accounted for extending the method applications in biological samples by adding the internal standard to the analyzed compounds.
## Materials and reagents
MET ($99.00\%$), LIN ($99.7\%$), PIO ($99.5\%$), and GLM ($99.7\%$). Excipients included microcrystalline hypromellose, cellulose, magnesium stearate, hydroxypropyl methylcellulose, pregelatinized starch, lactose monohydrate croscarmellose sodium, pregelatinized starch, and colloidal silicon dioxide. All the materials used in the experiment were gifts from Sigma for pharmaceutical industries (Moubarak Industrial Zone, Quesna-Menoufia-Egypt). Human plasma samples were kindly provided from the blood bank center of Tanta University Hospital after the required processes were done. All methods were carried out under relevant guidelines and regulations.
Egyptian markets dosage forms are Amaryl M $\frac{2}{500}$ (2 mg Glimepiride and 500 mg Metformin) with Batch Number: $\frac{2}{2024}$ from SANOFI AVENTIS-HANDOK Pharmaceuticals, Bioglita Plus ($\frac{15}{500}$) with Batch Number: 200061 produced by Al Andalous for Pharmaceutical Ind. (15 mg Pioglitazone and 500 mg Metformin) and Piompride $\frac{30}{4}$, Batch Number: 191294, AVERROES PHARMA-Egypt (30 mg Pioglitazone and 4 mg Glimepiride).
Methanol of HPLC grade was purchased from (Fisher, UK). Potassium dihydrogen phosphate obtained from (Inter. Trade Co., Japan). Orthophosphoric acid of analytical grade was purchased from (Sigma-Aldrich, Germany). TEA of HPLC grade was purchased from (Oxford Laboratory, UK).
## Apparatus and HPLC software
Dionex UltiMate 3000 RS system (Thermo Scientific™, Dionex™, Sunnyvale, CA, USA) with RS auto-sampler injector, RS diode array detector, quaternary RS pump, and thermostated RS column compartment. ChromeleonR 7.1 software is used for data acquisition. Vortex (A & E, UK) and Hettich Centrifuge (Tuttlingen, Germany). A HANNA pH-meter (USA). Design-Expert version 11 software used for Design of Experiments (DOE).
## Chromatographic conditions
The CMPs qualified from the screening design were tested at different levels using the Box–Behnken optimization design. The values of the CAA were used to assess optimum chromatographic conditions by a mathematical technique using Derringer’s desirability algorithm within the predetermined MODR using the levels that best achieve the tolerance interval criteria for the studied chromatographic parameters. Separation was carried out by using methanol:0.05 M potassium dihydrogen phosphate buffer containing $0.05\%$ triethylamine (78:22, v/v) as the mobile phase. The buffer pH was adjusted to pH 3.79 utilizing ortho-phosphoric acid. Detection at 227 nm using DAD. A 1.2 mL/min flow rate was used.
## Preparation of stock and working standard solutions
Stock solutions (1000 µg/mL) were prepared for the three drugs (MET, PIO, GLM) and for the internal standard (LIN) by weighing 100 mg of each, then transferred into four separate 100 mL volumetric flasks and diluted using methanol then stored at 4 °C in the refrigerator. Subsequently, suitable dilutions of each stock solution were made to prepare working standard solutions to obtain 50 µg/mL of MET, LIN, and PIO and 40 µg/mL of GLM using the mobile phase.
## Calibration in pure form
Different volumes of the previously prepared working standard solutions were transferred into separate 10 mL volumetric flasks with a constant volume of LIN (IS) (20 µL), and volumes were diluted using the mobile phase. Dilutions were made to attain solutions covering the dynamic working range 0.05–30.00 µg/mL PIO, 0.05–500.00 µg/mL MET, and 0.04–20.00 µg/mL GLM. 10 µL was injected from each solution, and the separation was made by using the previously mentioned separation conditions. The calibration curves were constructed by plotting the average peak area ratio to (0.1 µg/mL LIN) versus concentration, and the regression equations were computed.
## Calibration in spiked human plasma
Different concentrations were prepared in spiked human plasma by using (50 µg/mL) working standard solutions of the internal standard (LIN) and the considered anti-diabetic drugs. Construction of calibration curves was made by plotting the average peak area ratio to (0.10 µg/mL LIN) against the corresponding concentrations of drug in spiked human plasma samples covering the dynamic working range of 0.04–2.00 µg/mL GLM and 0.05–2.00 µg/mL MET and PIO.
## Preparation of human plasma
Before the analysis, the frozen human plasma sample was permitted to be thawed and equilibrated to room temperature for about 1 h. Using multipulse vortex at 2000 rpm, the thawed plasma was vortexed for 30 s to confirm the well and homogenous mixing of the sample’s contents. In a centrifuge tube, an aliquot of 100 µL of blank plasma, a different aliquot from 50 µg/mL working standard solution of each drug. Obtained solutions were completed by using methanol up to 5 mL and vortexed at 2000 rpm twice to mix for 30 s to ensure the protein precipitation. Obtained plasma samples solutions were centrifuged for 30 min at 4000 rpm. From each supernatant, 1 mL was taken into a 5 mL volumetric flask, and the solutions were diluted using mobile phase to 5 mL. A cellulose acetate syringe filter (0.45 μm) was then used to filter all prepared solutions. An aliquot of 10 µL was injected from each solution at the before-stated separation conditions.
## Preparation of laboratory-prepared tablet
Tribet 2 XR tablets contain (2 mg GLM, 15 mg PIO and 500 mg MET) per tablet33 are not available in the Egyptian markets. Simulated synthetic tablets were prepared and used for analysis, regarding to preparation of laboratory prepared tablet45. The formula per five tablets was designed by weighing 2.5 g MET, 75 mg PIO and 10 mg GLM with the following excipients: 614.4 mg microcrystalline cellulose, 35 mg magnesium stearate, 75 mg hypromellose, 75 mg hydroxypropyl methylcellulose, 568 mg pregelatinized starch, 740 mg lactose monohydrate, 45 mg croscarmellose sodium, and 10 mg colloidal silicon dioxide. In a 100 mL volumetric flask, a weight equivalent to one tablet was transferred and dissolved with 70 mL methanol. The obtained solution was sonicated for 20 min, cooled, and completed to the mark by using the same solvent. The obtained solution was filtered, and the residues were washed. Serial dilutions were made to prepare different concentrations of the three drugs.
## Preparation of Egyptian-marketed dosage forms
Ten tablets of Amaryl M $\frac{2}{500}$, Bioglita Plus, or Piompride Tablets were weighed, ground, and powdered in three separate mortars. Into separate 100 mL, volumetric flasks weight of powder equivalent to (500 mg MET and 2 mg GLM), (15 mg PIO and 500 mg MET) and (15 mg PIO and 2 mg GLM) were transferred and dissolved by using 75 mL methanol, respectively. The solutions were sonicated for 15 min, cooled, and completed up to the volume by using the same solvent. The solutions were filtered, then the residues were washed. Dilutions were made to achieve different concentrations of the two drugs through the three dosage forms.
## Analytical quality-by-design
The first step in the AQbD method was to determine the (QTPP) of the final pharmaceutical product, and then the (ATP) was identified based on the before-determined (QTPP). Subsequently, the determination of (CQAs) depends on initial trials and literature review.
## AQbD-based risk assessment using screening design
Risk analysis was performed to outline and determine the CQAs that might affect the method's efficiency and performance. Ishikawa diagram as a risk assessment tool can help define the impact of different critical method parameters CMPs on the CQAs46.
This paper aims to separate and analyze the three anti-diabetic drugs with optimum resolution and selectivity and minimum run time without interference from endogenous matrix compounds.
Fishbone or Ishikawa diagram was drawn to determine the significant parameters (CMPs) that affect the RP-HPLC method performance. Ishikawa diagram shows different factors that could be considered (column temperature, column length, flow rate, type of organic solvent, percentage of organic solvent, injection volume, detector, buffer type, buffer concentration, and buffer pH). Subsequently, preliminary trials were conducted to select the highly critical factors that would be included in the screening design (next step).
Five factors were qualified most prominently affecting the method performance (flow rate, percentage of methanol (%MeOH), column temperature, buffer pH, and buffer conc.).
Screening is a critical stage in AQbD to characterize the critical or significant factors before moving towards optimization design. Full factorial design for five factors of two levels for the screening phase will result in 25 = 32 experiments (huge number), so; fractional factorial design (FFD) with resolution V (25–1 = 16 experiments) was carried out to decrease the number of trials during the optimization and the development of an analytical method to characterize the influence of different CMP on the selected CQAs. The regression coefficients of the studied CMPs were determined by using a mathematical model obtained from the design consisting of main and possible interaction effects (Eq. 1) for each of the following: Five responses or (CQA): Resolution-1 between MET and LIN (Rs-1), capacity factor-1 of MET (K′1), Resolution-3 between PIO and GLM (Rs-3), capacity factor-4 of GLM (K′4) and MET asymmetry (assym-MET).1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Y}= {\upbeta }_{0}+ \sum_{\mathrm{i}=1}^{\mathrm{n}}{\upbeta }_{1}{\mathrm{X}}_{\mathrm{i}}+ \sum_{\mathrm{i}=1}^{\mathrm{n}}{\sum_{\mathrm{j}=\mathrm{i}+1}^{\mathrm{n}}\upbeta }{_\mathrm{ij}}{\mathrm{X}}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{j}}+\upvarepsilon$$\end{document}Y=β0+∑$i = 1$nβ1Xi+∑$i = 1$n∑j=i+1nβijXiXj+εwhere β0, βi, and βij represent the coefficients for each main and interaction effect, n is the number of CMPs, X is the examined factor, Y is the response measured, and ε represents the model residuals.
Risk assessment relied on the fishbone or Ishikawa diagram Fig. S2 that was constructed considering earlier scientific knowledge and preliminary trials. Preliminary studies were performed by trying different flow rates, columns, aqueous phase, proportions of the mobile phase, and organic modifiers. Based on the results of preliminary trials, there were some problems regarding peak asymmetry of (MET) and the resolution between (MET & LIN) and (PIO & GLM). The method's sensitivity to GLM of the lowest concentration in the analyzed dosage form was required to be considered. Peak asymmetry is strongly influenced by the pH of the buffer, column temperature, type, and organic modifier percent, while the flow rate could affect peaks resolution, shape, and area.
A list of the most critical parameters noted through the preliminary trials was used as factors or CMPs for the screening design (A: % MeOH, B: Flow rate, C: column temperature, D: buffer pH, and E: buffer concentration). The screening phase was based on five CQAs: Rs-1, Rs-3, K′-1, K′-4, and Asym-MET.
Due to the large variability of the analyzed triplet dosage form components and the binary ones (500 mg MET, 15 mg PIO, 2 mg GLM), a compromise was needed when selecting the detection wavelength using the DAD detector. The wavelength 227 nm was selected where GLM signal intensity was at the maximum. In contrast, MET signal intensity was low to allow the simultaneous detection of both drugs (MET & GLM) at this ratio. LIN was selected as IS of choice; the concentration 0.1 µg/mL was used for the plasma and 5 µg/mL for the separation and analysis in the pure form.
## AQbD method optimization with Box–Behnken design
The insignificant factors would be overlooked and kept constant during the optimization. The most favorable levels of CMPs obtained from the screening design were determined by further optimization utilizing response surface methodology. The three significant CMPs (buffer pH, flow rate, and % MeOH) were optimized by Box–Behnken Design with three levels to detect the most favorable levels of each parameter. The design was composed of a total of 17 experiments (5 centers + 12 non-center) to consider the experimental errors. The optimization procedure relied on Six CQAs named: resolution-1 (Rs-1: MET and LIN), capacity factor-1 (K′1), resolution-3 (Rs-3: PIO and GLM) & capacity factor-4 (K′4), number of theoretical plates of MET (NTP-MET) and GLM (NTP-GLM).
## Establishment of the method operable design region (MODR)
The MODR was determined based on the regression models and using the same software with an estimate of the probability of failure. All the criteria stated in the ATP within the design region are fulfilled.
Based on the CQAs tolerance interval (TI) with the suitable Sigma (S) and acceptable delta (d), the designated CQAs were predicted and plotted with the proportion of 0.90 (one-sided) and probability (α) = 0.05. The domain of the experimental space that intersects tolerance interval criteria (TI) was defined as the MODR of the established HPLC approach. Derringer’s desirability algorithm models applied to suggest the most optimum levels of each CMP depend on the definite optimization criteria.
## Analytical quality-by-design paradigm
QTPP determination was based on the delivery system, route of administration, dosage form type, and stability of studied drugs should be taken into consideration47,48. ATP was identified depending on the determined QTTP to obtain a more efficient RP-HPLC analytical technique able to identify and determine all APIs within an acceptable range (98–102)%, suitable retention times, symmetrical and sharp peaks, and reasonable specificity. The selection of (CQAs) was made depending on preliminary trials and a review of the literature.
## Analysis of the experimental screening results
The procedures that have been followed to determine the CMPs that significantly affected each response (CQA):Inspection of Pareto chart, half normal probability plots, then selection of the significant model terms. Inspection of fitting statistics (R2 and adjusted R2).ANOVA interpretation with Inspection of model residuals and factor significance. Prediction equation coefficients interpretation based on sign and magnitude. Inspection of the integrity of ANOVA diagnostics plots. Characterization of significant factor performance based on the developed model graphs.
A half-normal probability plot is a graphical tool that uses these ordered estimated effects to help assess which factors are essential and which are unimportant. It displays the absolute values of the standardized |effect| from largest to smallest. The estimated |effect| of an insignificant factor was assigned to those on or close to the zero line, while the estimated |effect| of an essential factor was assigned to the ones off the line. Subsequently, the confirmation by the magnitude of F-value and corresponding p-value from ANOVA results and prediction equation coefficients' magnitude and sign. The positive sign of each parameter coefficient indicated that an effect of the parameter favors the response, while a negative sign suggested an inverse relationship between the parameter and the response. Table S1 represents the experimental results of the 16 fractional factorial design screening experiments. ANOVA results calculated for each response, such as p-value along with estimated responses’ coefficients greater than 0.9, were presented in Table 1, and the following was concluded:Table 1Coefficients and ANOVA statistical analysis for the five studied factors of the screening design. InterceptABCDEADRs-12.828− 0.540− 0.0910.135p-values < 1.00E−042.00E−04 < 1.00E−04Rs-34.028− 0.593− 0.253− 1.5691.227p-values < 1.00E−040.002 < 1.00E−04 < 1.00E−04Assym-MET1.4190.1140.108− 0.054p-values4.00E−046.00E−043.78E−02K11.6420.021− 0.238p-values0.004 < 1.00E−04K-44.270− 1.368− 0.539− 0.438− 1.020p-values < 1.00E−044.00E−041.40E−03 < 1.00E−04Rs: resolution, Assym: asymmetry; K′: capacity factor; A: %MeOH; B: flow rate; c: buffer pH; buffer pH and E: buffer concentration. Resolution-1 between (MET & LIN) peaks (Rs-1):(Rs-1) was affected by three factors (A, C & D); MeOH% (A) had the most important and significant effect, while temperature (C) had the least effect. Increasing A and C decreased (Rs-1). ( Fig. 1a). On the contrary, decreasing (D) decreased (Rs-1).Figure 1Half Normal probability plots fractional factorial design (FFD).Resolution-3 between (PIO and GLM) peaks (Rs-3):(Rs-3) was strongly affected by the buffer pH (D), %MeOH (A), and column temperature (C) with the negative effect of all. So, increasing the buffer pH up to pH 5 led to a sharp decrease in the Rs-3 value. There was a factors interaction between factors (A&D) (Fig. 1b).Further characterization of CMPs impacted on each CQA was done by the inspection of interaction plots that are very helpful tool to qualify the important parameters and selecting the suitable constant levels for the excluded ones. By the inspection of interaction plot (A&D) (Fig. S3), the Rs-3 value higher than 2 could be achieved by buffer pH [3] with MeOH% of $78\%$.MET asymmetry (Assym-MET):MET asymmetry was positively affected by both %MeOH (A) and buffer pH (D). So, decreasing MeOH% (A) or buffer pH (D) led to a decrease in MET asymmetry. MET asymmetry was the only response that was affected by the buffer concentration (E) with a negative effect. So, to decrease the MET asymmetry value, the buffer concentration should be used at the high level of 0.05 M. Thus, a decision was made to keep the buffer concentration (E) constant at (0.05 M) during the method optimization step (Fig. 1c).Capacity Factor-1 (K′-1):(K′-1) was strongly and negatively affected by the flow rate (B). ( K′-1) also was positively affected by MeOH% (A) but to a very small extent compared to the effect of the flow rate (B) (Fig. 1d).Capacity Factor-4 (K′-4):K′-4 was negatively affected by %MeOH (A), buffer pH (D), flow rate (B), and temperature (C), with different magnitudes (Fig. 1e).After the Inspection of the screening design outcomes, the vital few factors to be optimized (A: MeOH %), (B: flow rate), and (D: buffer pH), were qualified for the optimization step due to their stronger effects. On the other hand, factors (E: buffer concentration) and (C: column temperature) were held constant at 0.05 M and 25 °C, respectively. A low-temperature setting will allow a greener separation procedure, and a high buffer concentration is essential to control MET peak asymmetry. The need for an optimization strategy arose as a result of the variable factor setting's requirement to improve each measured response individually, implying non-linearity, which is better described by using three-level response surface optimization designs.
## (MODR) and optimization via Box–Behnken design
The AQbD approach's purpose is to define and outline the (MODR) which is a multidimensional combination and interaction of input variables and process factors that have been established to ensure the method quality47. In other words, it’s the region of (CMPs) that meet the (CQAs). Using the DoE strategy, the initial knowledge space was explored, and MODR was determined where the criteria stated in the ATP are met at a definite risk level47.
A Box–Behnken design was chosen to assess the influences of the three qualified CMPs (%MeOH, flow rate, and buffer pH) on the selected CQAs (Table S2). By the screening phase, we noticed in run No. 9 (Table S1 and Fig. S4) that severe overlap of the last two peaks took place when using the upper levels of the five factors. Box–Behnken design with 17 runs (Table S2) was more suitable, because it avoids the combination of the upper levels of all factors simultaneously and that fitted our optimization purpose.
All the developed models were quadratic, and variables behaved nonlinearly; this can be indicated by higher-order terms (x2). Also, models displayed high adjusted R2 and R2 values of more than 0.9, as shown in (Table S2) and insignificant Lack-of-fit relative to pure error values, where all indicated good model fitting. By the Inspection of the obtained model coefficients (Table 2) and 3D response surfaces (Fig. 2a–f).(Rs-1) between (MET & LIN) peaks: Fig. 2a shows a decrease in (Rs-1) value observed upon decreasing % MeO. (Rs-1) values between (2.2–3) were achieved using % MeOH not less than $74\%$ with minimal effect of pH.(Rs-3) between (PIO & GLM) peaks: Fig. 2b shows a decrease in (Rs-3) values upon increasing pH and % MeOH. ( Rs-3) values between (2.2–3.5) were achieved using %MeOH between (74–77) % and pH between (3–4).Capacity Factor-1 (K′-1): Fig. 2c shows that lower (K′-1) values were not obtained by variations in % MeOH and pH. However, (K′-1) value was significantly affected by the flow rate adopted in the analysis. Capacity Factor-4 (K′-4): Fig. 2d shows a decrease in (K′-4) values upon increasing %MeOH and to slight extent at higher pH values. Minimal (K′-4) values were obtained using % MeOH closer to $78\%$ with slight effect when using pH between 3–5.NTPs (MET) and (GLM): Fig. 2e,f shows that higher NTPs of MET and GLM were achieved upon using pH values closer to 3 with minimal effect of % MeOH.Table 2Coefficients and ANOVA *Statistical analysis* for the three studied factors of the optimization design. InterceptABCABACBCA2B2C2Resolution 12.750− 0.616− 0.0580.0240.018− 0.035− 0.0180.0530.0560.108p-values < 1.00E−040.0010.0730.3090.0650.3100.0110.0092.00E−04Resolution 33.810− 1.303− 2.344p-values < 1.00E−04 < 1.00E−04K′ 11.6800.024− 0.2410.003− 0.008− 0.0054.91E−190.0010.026− 0.006p-values < 1.00E−04 < 1.00E−040.2960.0480.1551.0000.695 < 1.00E−040.080K′ 45.250− 1.364− 0.551− 0.7730.4250.392− 0.505p-values < 1.00E−04 < 1.00E−04 < 1.00E−040.0020.0036.00E−04NTP 1 (MET)2456.4004.250− 102.750− 142.7509.750− 20.250− 22.75028.42556.925212.425p-values0.719 < 1.00E−04 < 1.00E−040.5620.2470.1990.1120.008 < 1.00E−04NTP 4 (GLM)4370.200− 94.187− 221.125− 799.938− 22.250− 58.62577.50078.838110.212− 467.413p-values0.0211.00E−04 < 1.00E−040.5510.2840.0700.0870.029 < 1.00E−04K′: capacity factor; NTP: number of theoretical plates. Figure 2Response surfaces Box–Behnken design (BBD) for factor interaction.
Increasing the GLM (NTP) and selecting of the suitable detection wavelength were successful measures that led to increasing method sensitivity to GLM with a more symmetrical and sharper peak of GLM. The buffer pH has a quadratic effect on the NTPs of MET and GLM, and this effect can’t be determined with other one factor at the time (OFAT) methods. From the design results, the best buffer pH that maximizes the NTPs and gives reasonable Rs values and capacity factor were selected at (3.7). This pH value led to a change in the ionization of both drugs and maximized the NTPs based on the pKa values of MET and GLM, which are 11.5 and 6.2, respectively.
To summarize the results of the optimization, process, the most significant factor was the % MeOH (A), as it was affecting nearly all responses; in most cases, the MeOH% needed to be increased. Flow rate (B) strongly affects the capacity factors (MET-K′-1) and (GLM-K′-4). Using (1.2 mL/min) flow rate led to the minimum K′ for MET & GLM. Buffer pH (C) strongly affected the NTPs (MET & GLM) and the Rs-3 between PIO and GLM.
Optimization criteria would help select the optimum levels of different CMPs. To optimize the different CQAs for optimum method efficiency and performance for the analysis of the three drugs, the following criteria were depicted:To minimize Rs-1 and R-3 in range (2.2–3) and (2.2–3.5), respectively. To minimize K′-1 and K′-4 for fast elution and minimum run time. To maximize the NTPs for both MET and GLM.
Desirability plots Fig. S5a–c shows that to achieve the maximum desirability, the MeOH % should be as high as (76–78) % and pH should be between (3.5–4) as well as the flow rate should be at the maximum (1.2 mL/min).
The design region was generated by applying limitations (max & min) which were achieved (Rs-1) below 3.6, (Rs-3) below 4.5, (K′-1) below 1.98, (K′-4) below 5.5, NTP MET below 3000 and NTP GLM below 5500 with outcome proportion that achieves the (TI) of 0.9 (one-sided) as shown in Table S2.
The method operable design regions (overlay plots) illustrated in Fig. S5d–f showed that the optimum and best conditions could be obtained using higher MeOH ratio (76–78) %, pH should be between (3.6–5) and flow rate should be between (1.05–1.2) mL/min.
Using derringer’s desirability algorithm, 30 solutions resulted for the selected criteria; the optimum chromatographic parameters were proposed to be %MeOH(A) ($78\%$) as shown in Fig. 3a, flow rate(B) (1.2 mL/min) as shown in Fig. 3b and buffer pH (3.73) as shown in Fig. 3c with expected attribute values of Rs-1 (2.22) as shown in Fig. 3d, Rs-3 (3.14) as shown in Fig. 3e, K′-1 (1.48) as shown in Fig. 3f, K′-4 (3.78) as shown in Fig. 3g, NTP (MET) [2518] as shown in Fig. 3h and NTP (GLM):[4389] as shown in Fig. 3i with a desirability value of (0.552). Desirability plots are shown in Fig. S5d–f.Figure 3Solution ramps for optimum conditions (a–i).
These suggested optimum chromatographic conditions were verified and tested three times, and the mean of observed values were Rs-1 (2.28), Rs-3 (3.42), K′-1 (1.49), K′-4 (4.08), NTP (MET) [2568] and NTP (GLM): [4520]. The predicted values were compared with those observed ones to demonstrate model predictability. All the results were satisfactory, with low prediction errors.
Finally, 78:$22\%$ MeOH: Phosphate buffer 0.05 M containing (0.05 v/v % triethylamine) pH (3.73) was the optimum mobile phase. 25 °C column temperature, 1.2 mL/min flow rate, and the PDA detector were set at 227 nm to allow detection of the three considered drugs.
## Method validation
The proposed AQbD technique was validated regarding ICH guidelines49. The results of system suitability parameter values at optimum separation conditions are presented in Table S3.
## Linearity and range
The established RP-HPLC technique was used over the ranges of 0.05–30 µg/mL for PIO, 0.05–500 µg/mL for MET, and 0.04–20 µg/mL for GLM, as presented in Table 3.Table 3Regression parameters for estimation of MET, PIO and GLM in pure and tablet form using the developed method. DrugMETPIOGLMConcentration range (µg/mL)0.050–500.0000.050–30.0000.040–20.000r0.99990.99990.9999a− 0.0570.1150.198b2.9901.6772.292Sa0.0410.0140.010Sb0.0000.0180.001S(y/x)0.1160.0310.027LOD0.0450.0280.015LOQ0.1360.0850.044r: correlation coefficient; b: slope; a: intercept; Sb: standard deviation of slope; Sa: standard deviation of intercept; Sy/x: residual standard deviation; LOQ: limit of quantitation; LOD: limit of detection.
## Limit of detection and quantitation (LOD and LOQ)
The detection limit and the quantitation limit (LOD and LOQ) were determined by referring to Eqs. [ 2] and [3]; respectively, Table 3 shows LOD and LOQ.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{LOQ }} = { 1}0{\text{ S}}_{{\text{a}}} {\text{/b}}$$\end{document}LOQ=10Sa/b3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{LOD }} = { 3}.{\text{3 S}}_{{\text{a}}} {\text{/b}}$$\end{document}LOD=3.3 Sa/bwhere b is the slope of the calibration curve and *Sa is* the standard deviation of the y-intercept of regression lines.
## Accuracy
The accuracy of the proposed approach was determined by calculating the mean % recoveries at three different concentration levels (triplicate determination) for MET (500, 400 and 250) µg/mL, PIO (15, 12, and 7.5) µg/mL, and GLM (2, 1.6 and 1) µg/mL (Table S4).
## Intra-day precision
Intra-day precision was assessed by using three replicate analyses at three drug concentration levels on the same day. The (SD) and (% RSD) were calculated for the results of the analysis as presented in (Table S5).
## Inter-day precision
The same three concentration levels of each drug were analyzed in three replicates at different three successive days. The (SD) and (% RSD) were calculated for the results of the analysis as presented in (Table S5).
All results were less than 2, as presented in (Table S5) demonstrating that the technique was precise.
## Robustness
According to ICH guidelines49 the robustness of an analytical process is the ability of method performance to remain unaffected by small but deliberate changes. Defining MODR based on the AQbD approach aid in assessing the robustness and ruggedness of the analytical method before validation, as the MODR itself is the region in which the CMPs meet the CQAs.
Robustness studies of the established RP-HPLC using the AQbD technique were carried out by using the multivariate design-based approach to study the effect of simultaneous variation of the studied five factors (pH, methanol%, flow rate, temperature, and buffer concentration) on the selected responses. A regular two levels (− 1, + 1) factorial screening design of eight runs with five factors was used for robustness testing (Table S6) to study only the main effects of the proposed study parameters (where factor interactions are not common, and to reduce experimentation time) on HPLC method performance.
Small changes in studied factors were carried out. The inspection of pareto charts revealed that the effect of all the considered parameters (CMPs) were non-significant on the pre-selected responses (CQAs) this was confirmed by that all experimental t-values were lower than the critical t-values limit as shown in Fig. S6a–g. The results indicate good stability and chromatographic performance of the established approach to small deliberate changes in its (CMPs).
## Specificity
The specificity of the established approach was demonstrated by comparing the test results and chromatograms of simulated tablet solution containing all excipients expected to be present in the dosage form and solution containing biological matrices of plasma with that of a standard solution of pure drugs of the same concentrations at the optimum separation conditions as presented in (Fig. 4a,b).Figure 4Chromatograms for standard pure drugs (a), lab-prepared mixture (b), and spiked plasma (c).
## Greenness evaluation method
For the estimation of the greenness of an analytical technique analytical Eco-Scale approach was applied50. The sum of the total penalty points was calculated for the whole procedure. According to the calculated results, the validated approach has acceptable greenness with an analytical eco-scale score 73 (Table S7).
## Application to simulated prepared tablets
The validated approach was applied to simultaneously determine three different concentration levels of the three antidiabetic drugs in its laboratory-prepared tablets in the ratio (500:15:2) (MET:LIN:EMP). The %recovery, SD, and %RSD were calculated, and acceptable results were obtained (Table 4). The results of the validated approach for the three concentrations of the simulated prepared tablets were compared to those found by applying the published RP-HPLC technique using a t-test and F-test at a $95\%$ confidence level regarding accuracy and precision, respectively. The calculated values did not exceed the tabulated ones, demonstrating any significant difference between the reported and the proposed methods, as presented in (Table 4). Table 4Comparison between the assay of prepared tablets using the proposed HPLC method and reported method. Proposed methodReported methodDrugsMETPIOGLMMETPIOGLMMean (Ẋ)100.804100.09999.657100.20999.731100.398S$0.3890.3440.1151.3260.4321.260\%$RSD0.3860.3440.1151.3240.4331.255tcal0.7461.1531.014ttab2.770Fcal11.6281.5780.008Ftab19.000X: Mean of % recoveries; S.D: standard deviation; R.S.D: relative standard deviation; tcal: calculated t-value; ttab: tabulated t-value; Fcal: calculated F-value; Ftab: tabulated F-value.
## Application to Egyptian market products
The validated method was applied for the simultaneous determination of (MET & GLM) in Amaryl M $\frac{2}{500}$ tablets (2 mg Glimepiride and 500 mg Metformin) with Batch Number: $\frac{2}{2024}$, (PIO & MET) in Bioglita Plus Tablets ($\frac{15}{500}$) with Batch Number: 200061 and (PIO & GLM) in Piompride $\frac{30}{4}$ tablets, Batch Number: 191294, AVERROES PHARMA-Egypt (30 mg Pioglitazone and 4 mg Glimepiride). The %recovery, SD, and %RSD were calculated, and acceptable results were obtained (Table S8).
## Results of analysis in spiked plasma samples
The validated method was applied for the simultaneous quantitation of (MET, PIO & GLM) with 0.1 µg/mL LIN internal standard in spiked plasma samples, as presented in (Fig. 4c) Calibration curves were plotted covering the range of 0.05–2 µg/mL of MET & PIO and 0.04–2 µg/mL of GLM. Results are presented in Table 5.Table 5Regression parameters for estimation of MET, PIO and GLM in spiked human plasma using the developed method. DrugMETPIOGLMConcentration range (µg/mL)0.050–2.0000.050–2.0000.040–2.000r0.99990.99990.9999a0.0750.1500.184b2.6651.6142.312Sa0.0140.0090.012Sb0.0140.0100.014S(y/x)0.0220.0170.024LOD0.0170.0190.017LOQ0.0520.0580.051r: correlation coefficient; b: slope; a: intercept; Sb: standard deviation of slope; Sa: standard deviation of intercept; Sy/x: residual standard deviation; LOQ: limit of quantitation; LOD: limit of detection.
## Conclusion
This study describes a fast, sensitive, and green RP-HPLC method that was optimized and validated by using the AQbD paradigm for the identification and estimation of MET, PIO, and GLM simultaneously in their pure and laboratory-prepared tablet. The method was extended to determine the studied drugs in spiked plasma samples. A scientifically organized approach was followed in developing, optimizing, and even in validating the proposed method, where MODR was assessed to minimize the number of out-of-trend results by determination of the design region where all the stated chromatographic criteria were satisfied. Because of the high level of quality built into this RP-HPLC method, it is an excellent candidate for routine analysis in quality control labs and bioanalytical analyses. Moreover, the procedures and details mentioned in this paper can help peer experimenters build and interpret AQbD-built methods efficiently.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30051-x.
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|
---
title: Adapted clustering method for generic analysis of histological fibrosis staining
as an open source tool
authors:
- Anca Remes
- Marie Noormalal
- Nesrin Schmiedel
- Norbert Frey
- Derk Frank
- Oliver J. Müller
- Markus Graf
journal: Scientific Reports
year: 2023
pmcid: PMC10020481
doi: 10.1038/s41598-023-30196-9
license: CC BY 4.0
---
# Adapted clustering method for generic analysis of histological fibrosis staining as an open source tool
## Abstract
Pathological remodeling of the extracellular matrix is a hallmark of cardiovascular disease. Abnormal fibrosis causes cardiac dysfunction by reducing ejection fraction and impairing electrical conductance, leading to arrhythmias. Hence, accurate quantification of fibrosis deposition in histological sections is of extreme importance for preclinical and clinical studies. Current automatic tools do not perform well under variant conditions. Moreover, users do not have the option to evaluate data from staining methods of their choice according to their purpose. To overcome these challenges, we underline a novel machine learning-based tool (FibroSoft) and we show its feasibility in a model of cardiac hypertrophy and heart failure in mice. Our results demonstrate that FibroSoft can identify fibrosis in diseased myocardium and the obtained results are user-independent. In addition, the results acquired using our software strongly correlate to those obtained by Western blot analysis of collagen 1 expression. Additionally, we could show that this method can be used for Masson’s Trichrome and Picosirius Red stained histological images. The evaluation of our method also indicates that it can be used for any particular histology segmentation and quantification. In conclusion, our approach provides a powerful example of the feasibility of machine learning strategies to enable automatic analysis of histological images.
## Introduction
Despite recent progress in diagnostics and treatment options, cardiovascular disease remains a leading cause of death worldwide1. One of the hallmarks of cardiovascular complications is the activation of pro-fibrotic processes2. Extracellular matrix deposition is associated with ischemic heart disease3, diabetic cardiomyopathy4 and hypertension5, accounting for reduced ejection fraction due to tissue stiffening and abnormal electric conduction leading to arrhythmias6. Although development of fibrosis is initially a protective mechanism of wound healing and regeneration, increased fibroblast proliferation and collagen deposition in the chronic stage of the disease were proven to lead to impaired cardiac diastolic function6. Preclinical studies revealed various mediators and possible therapeutic targets to be involved in promoting myocardial fibrosis7,8. Interestingly, the degree of fibrosis generation was proven to be a prognostic of cardiovascular events in patients9. These observations underline the need to accurately and selectively quantify extracellular matrix deposition in animal models and patient biopsies in the context of cardiovascular disease.
One of the most widely used methods to determine the degree of extracellular matrix deposition is staining of frozen or paraffin-embedded sections with specific dyes, such as Masson’s Trichrome or Picosirius Red. Although several methods have been employed to semi automatically quantify acquired images, proper validation of these approaches in animal models of cardiac dysfunction has not yet been performed10–13.
Hence, here we describe a novel staining quantification tool (FibroSoft) that was developed specifically to fit the aforementioned needs. Hereby, it is intended to decrease the time and effort of the challenging quantification process, especially by separating background, healthy, and fibrotic tissue areas. For that purpose, our proposed software is applying a modified supervised k-means clustering algorithm14. We therefore demonstrate the composition, accuracy and performance of this approach and show its relevance in an animal model of cardiac hypertrophy and heart failure.
## Animal experiments
Animal experiments were approved by the local animal welfare committee in Schleswig–Holstein (Ministerium für Energiewende, Klimaschutz, Umwelt und Natur, permission number V312-7224.121–4). Mice were housed in pathogen-free, temperature and humidity-controlled environment, with ad libitum access to water and food. All efforts were undertaken to minimize animal suffering.
Transverse aortic constriction (TAC) was performed in 8 weeks old male C57BL/N 6 mice (Charles Rivers Laboratories) as previously described15. Twelve mice were used for each group, and no mortality was observed during the observation time. In brief, mice received intraperitoneal injection of Buprenorphine (0.1 mg/kg) for pain relief and were anesthetized with $2.5\%$ isoflurane in 0.2 L/min of oxygen by oral intubation with a 20-gauge (G) tube and ventilation (Harvard Apparatus) at 120 breaths per minute (0.2 mL tidal volume). First, a small incision was performed in the second intercostal space to expose the aortic arch. A ligation was next positioned with a Prolene 6–0 suture between the brachiocephalic and arteria subclavia. For the spacer, a 27-G needle (Ø 0.42 mm) was used. Finally, the chest was closed with a suture (Prolene 4–0). The sham operation was performed similarly, excluding the ligature of aortic arch.
Echocardiography was conducted 6 weeks after surgery (VisualSonics Vevo 1100 and MS400 cardiovascular probe,18–38 MHz). For assessment of cardiac function, left ventricular (LV) fractional shortening (FS) and ejection fraction (EF) were measured by B-mode long axis. Additionally, the following parameters were measured by M-mode short-axis on the level of papillary muscles: LV interventricular septal thickness (IVS), LV internal dimensions (LVID) and posterior wall (PW) thicknesses at diastole and systole (IVSd, LVIDd, PWd and IVSs, LVIDs, PWs). Mice were sacrificed by cervical dislocation and cardiac tissue was collected 6 weeks after surgery. A total number of 12 mice were used for analysis in each group. Mice were randomly assigned to each group.
## Histological staining and image acquisition
Fibrosis staining was performed in 7 µm-thick frozen or paraffin tissue sections. Picosirius Red staining was conducted according to standard protocols16. Masson’s Trichrome staining was performed according to the instructions provided by the manufacturer (Sigma-Aldrich). Specimens were imaged using a brightfield microscope using a 20 × objective (BZ-X800, Keyence).
## Western blot analysis
Tissue lysates were prepared according to standard protocols with slight modifications15. In brief, 1 mL of RIPA buffer containing $0.1\%$ sodiumdodecylsuphate (SDS) with phosphatase and protease inhibitors (Roche Diagnostics) was added to approximately 50 mg of heart tissue. The tissue was further homogenized and protein concentration was determined using DC assay (Bio-Rad). Tissue lysates containing 30 μg protein/well were further subjected to SDS-PAGE separation and proteins were transferred to PVDF membranes. Next, membranes were blocked with $5\%$ skimmed milk. Further, membranes were cut at 55 kDa and primary antibodies was applied separately on each section of the membrane (upper part: Collagen 1a1, Santa Cruz, sc-293182, dilution 1:500; lower part: β-actin, Thermo Fischer Scientific, PA1-183, dilution 1:10 000), and incubated at 4 °C for 16 h. After a series of washes in TBS-T, a corresponding HRP-labeled secondary antibody (Dianova, dilution 1:10 000) was next incubated for 1 h at room temperature. Chemoluminescence was detected by Pierce ECL Substrate and using a ChemiDoc Imaging system (BioRad). Ponceau staining (BioRad) was used to visualize protein transfer. Relative protein levels were determined by ImageJ (NIH, version 1.8.0_66).
## Immunohistochemistry
Immunohistochemistry was performed to assess the levels of fibronectin in cardiac tissue sections. Seven μm-thick frozen sections were subjected to fixation with a solution containing $4\%$ paraformaldehyde for 10 min, followed by blocking with $10\%$ goat serum (Thermo Fischer Scientific) containing $0.01\%$ Triton X-100 for 1 h at room temperature. Next, primary antibody (anti-fibronectin, Abcam, ab2413 1:400 in blocking buffer) was incubated overnight, in a humidified atmosphere. After a series of washes in PBS, secondary antibody conjugated with Alexa-546 (Thermo Fischer Scientific, 1:400) was added and incubated for 1 h at room temperature. Imaging was achieved using confocal microscopy (LSM 800, Zeiss), and mean fluorescence intensity was measured using ImageJ (NIH, version 1.8.0_66). Twelve images were analysed/experimental condition.
## Image processing and analysis
Our suggested approach and software implementation relies on pixel-wise tissue classification followed by quantifying the surface of specific tissue in comparison to a reference area (usually a cell). Here, the reference (cell) is defined as the part of the slice having background area removed. Pixel classification is done by applying a variation of the k-means clustering algorithm17. Colors are represented in HSV color space (hue, value, saturation instead of red, green, and blue intensities) that models color more closely to human vision. We use the HSV color space model to appropriately interpret boundaries and textures within the algorithm.
## Software architecture and design
FibroSoft is written in C++ using QT framework and deployed as an open-source software system with binaries available on current MacOS, and Microsoft Windows (64 bit) operating systems. Images are loaded as TIFF from a folder into a list to be processed. The user interface allows users to load, prepare, and monitor the analysis flow, however, in this section we focus on the implementation of our suggested k-means clustering approach. Therefore, analysis by modified multi-subclass k-means as described above is shown in pseudo code in Listing 1. The C++ implementation can be found in the open-source repository at sourceforge. This part of the code described is listed in staininganalysis.cpp.
Calculation is therefore moved into this specific class implementation in order that either the batch process or a single image can be clustered by the same algorithm.
The other code mainly consists of preparation and back-ground processing tasks, in order to make a more responsive user feeling. Results of batch processing can be exported to a comma separated value (.csv) file in order to postprocess within spread sheet calculation software tools or import it into statistical analysis programs.
## Development of the quantification tool and experimental setup
FibroSoft ensures a semi-automatic quantification of any histological staining which means its semi-automatic processing isn’t bound to handle specific color spectrums. A summary of the animal experimental design and software workflow is described in Fig. 1. Moreover, Fig. 2 shows the initial sample selection step for the semi-automatic supervised clustering approach to evaluate fibrosis to cell ratio for quantification. FibroSoft’s main graphical user interface is split into three tissue preview panes where the user can interactively select sample points (color references) for the entire classification process. Figure 2 exemplarily shows the selection process for fibrotic tissue sample points. This is done for all tissue types as well as the background (in order to remove background pixels). After this step is done and the process is started, tissue clusters are computed automatically by cumulating corresponding k-means clusters for each class (tissue type sample). Another option is available to let the results be determined fully automatically on behalf of apriori knowledge coded into the application depending on the underlying staining method. After loading images to be processed, users have the option to either manually initialize the supervised k-means initial cluster centers by taking sample points for each tissue type, or to use pre-set data for each specific staining protocols (e.g., Masson’s Trichrome, Picosirius Red). Moreover, previously selected/estimated clusters can be subsequently used to create new segmentation results on other images of the same dataset for the aforementioned three tissue types. Figure 3 shows the workflow including interactions and decisions by users in order to quantify data sets. Once an appropriate setting is found, it can be used as a general pre-set fitting for a specific dataset within the same session and enables processing the entire experiment dataset in a single batch/run. Hence, the comparability between slices within the same experiments and specific data sets is additionally maximized. Figure 1Description of the animal experiments, image analysis and software design. Figure 2Quantification tool with sample selection panel and one sample for each tissue selected. Figure 3Schematic representation of the workflow and analysis process within the quantification tool.
This semi-automatic machine learning-based fibrosis classification approach is based on a slightly adapted k-means clustering algorithm which uses the features hue, saturation, and value of the HSV color space information. The adaptation can be described as a single-step supervised k-means with subclasses, as for each class users can choose multiple sample inputs. After classification corresponding subclasses are unified accordingly to the classes to be predicted. According to the initial definition of the k-means clustering method, all pixels are assigned to their closest cluster centers of the corresponding classes.
Instead of recalculating the cluster center µ_j within the cluster C_j after adding further sample points, we rather add new sub-clusters C_(j, i) to the list (described in Formula 2 and 3 below). All sub-cluster results will in the end be cumulatively added to the main segmentation S_j for that specific class/label l_j after the classification step.
According to Formula 4, the corresponding cluster sets for each tissue segmentations are combined (united) after they have been successfully identified in the k-means execution. With this approach we don’t average the sample point selections rather we add the range of each individually selected sample color. Therefore, the clusters will not be averaged and reassigned and we expect a more accurate adaption of multiple selections made by the user (Formula 4).
## Statistical analysis
Statistical analysis was performed using R18 and GraphPad Prism 5. Graphs are presented as mean ± SD. Statistical significance was calculated by Mann Whitney U test. P values smaller than 0.05 were considered as significant.
## Informed consent
All methods were performed in accordance with the relevant guidelines and regulations. The study is reported in accordance with ARRIVE guidelines. Images were created with BioRender.com.
## Single step supervised k-means with subclasses retrieves more reliable results than applying standard k-means
Applying a standard k-means algorithm leads to repeatedly changing cluster centers around which the classified tissue will be arranged. Due to the fact that following histological processing some color ranges of different tissue types are hardly fully-automatically distinguishable, a slight shift within those cluster centres might lead to false results (as marked with arrows in Fig. 4A—algorithm a). A solution to this challenge is to use supervised cluster centers, whereby a sample point is given by the user as a reference. Moreover, restricting the algorithm to a single iteration of the k-means method will only cluster around those user-selected references. Figure 4Workflow showing k-means classification with subclasses. ( A) Standard k-means clustering results (a) compared to single step supervised k-means (b) with each of the three classes (1–3). Arrows depict partially falsely classified tissue, in a.1 healthy tissue is shown as background (yellow arrows), where background was falsely classified as healthy tissue in a.2. ( B) Supervised single step k-means with subclasses (= multiple samples for each tissue type) showing improved combined results on all tissue types (a) background pixels, (b) healthy tissue, and (c) fibrotic tissue.
As outlined in Fig. 4, b1 and b2, this method retrieves more accurate results than standard unsupervised k-means. However, there are many instances where too few pixels are identified as being healthy tissue and vice versa because of the usual iterative approach of this method. In order to solve this issue, we created a supervised modification that defines subclasses in such a way, that each tissue can be assigned to multiple subclasses which in the end are combined into the resulting class. Visual results and the complete working process are illustrated in Fig. 4B.
Due to the nature of k-means clustering, each pixel is assigned to only one result group. Therefore, Fig. 4B depicts the processing with multiple selections for each tissue assigned pixels in individual subsets which are then combined to retrieve the resulting final classifications a, b, and c.
## FibroSoft can effectively assess extracellular matrix deposition in TAC model of cardiac hypertrophy and heart failure
Next, we aimed to investigate whether our results obtained with the designed software correlate with Western blot analysis of collagen 1 expression in sham and TAC-subjected mice. As expected, TAC caused severe deterioration of heart function in mice, suggested by significantly decreased EF and increased LV mass, measured by echocardiography (Fig. 5A,B). In addition, we could detect a marked elevation of collagen 1 expression in myocardium following TAC as compared to respective controls (Fig. 5C,D).Figure 5The results obtained by using the FibroSoft software strongly correlate with the results of Western blot analysis of collagen 1 in TAC model of heart failure. ( A) Reduced cardiac function following TAC surgery in mice, measured by echocardiography. ( B) Statistical quantification of left ventricular mass in mice subjected to TAC. ( C, D) Analysis of collagen 1a1 expression in myocardium of mice by Western blot. Tubulin served as a loading control, proving equal loading of protein samples. Values were normalized to sham operated mice. ( E) Representative images showing Masson’s Trichrome staining of cardiac paraffin sections in TAC and sham operated mice. Blue areas represent extracellular matrix deposition, while red stained tissue accounts for heathy heart muscle. ( F) Statistical quantification of percentage fibrosis area in Masson’s Trichrome staining images, quantified by FibroSoft. ( G) Scatter plot showing in each sample relative protein level against the surface percentage (H) Illustrative images showing Sirius red staining of cardiac sections in sham and TAC mice. ( I) Quantification of percentage fibrosis area in histological sections subjected to Sirius red staining. ( J) Scatter plot analyzing correlation between Western blot analysis and FibroSoft in sections subjected to Sirius red staining. EF: ejection fraction, LV: left ventricle. ( $$n = 12$$ mice/group, 12 images analysed, ***$p \leq 0.001$, Mann–Whitney U test).
Further, we analysed the fibrotic tissue segmentation for Picosirius Red and Masson’s Trichrome staining using FibroSoft and compared it to the values obtained through Western blot analysis. We particularly focused our analyses on the left ventricle, since pressure overload induced by TAC induces left ventricular remodeling and dysfunction. As depicted in Fig. 5E,F,G,H,I,J and Table 1, the use of our designed software proves a significant difference between fibrosis levels in sham versus TAC cardiac left ventricular samples of a similar magnitude as Western blot analysis. Moreover, fibronectin levels followed the same pattern, as demonstrated by immunohistochemistry experiments (Supplementary Figure. 4). To compare the feasibility and performance of our software we analyzed histology staining of each mouse accordingly to its corresponding Western blot data set. Most importantly, the values obtained with the two independent methods strongly correlate, evaluated in individual mice (Fig. 5F,I, Table 2).Table 1Statistical analysis of differences between sham and TAC tissue. Analysisn shamn TACWPWestern blot11886.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.0005154$$\end{document}0.0005154Picosirius red2433790\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.064\cdot {10}^{-15}$$\end{document}1.064·10-15Masson’s trichrome2133693\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8.161\cdot {10}^{-10}$$\end{document}8.161·10-10Table 2Regression analysis of scatter plots presented in Fig. 5F,I.Picosirius red/Western blotMasson’s trichrome/Western blotR20.82760.6804p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.727\cdot {10}^{-08}$$\end{document}6.727·10-080.00052 Furthermore, as demonstrated in Supplementary Figure. 1, interindividual segmentation results and quantification were equally distributed between observers for both Masson’s Trichrome and Picosirius red staining. Therefore, we can conclude that FibroSoft shows robust behavior when managed by different users.
## Analysis of defined reference volume shows high accuracy of FibroSoft
In order to assess the accuracy of any newly created software one usually compares it against an existing gold standard. However, as there is no widely used method besides manually extracting tissue areas by applying various imaging techniques19 and as those manual analysis methods are very time-consuming and prone to subjectivity, we defined a reference slice combining randomly selected sets of samples from all our available microscopy images per tissue type as well as background. Then, we analyzed the performance of the software against this standard reference image expecting each tissue specific compartment (a, b, and c) to be coherently classified and masked by its type (i.e., execting rectangular shaped areas, Table 3).Table 3Volume comparison by tissue type. Tissue typeDefined surface (px)2Analyzed (FibroSoft) (px)2Dice coefficient between defined and analyzedBackground21,33621,4040.9948Healthy107,188107,1410.9972Fibrotic21,33621,3150.9876 For that purpose, this rectangular test image, displayed in Fig. 6A was analyzed using FibroSoft tool after specifying individual tissue associated subclasses as shown in Fig. 6B by the indexed squares. Results of this analysis show a ratio of $16.54\%$ between fibrosis and the entire cell (as fibrosis + healthy tissue). The defined ratio of our reference setup is expected to be $16.56\%$ (21,336 px$\frac{2}{128}$,835 px2).Figure 6Reference chart and results of accuracy analysis. ( A) Defined reference chart which combines randomly selected samples per tissue type and then arranged for accuracy evaluation: (a) healthy tissue, (b) fibrotic tissue samples, (c) background samples. ( B) Selected sample color points for reference tissue associated subclasses for our k-means algorithm. ( C) Resulting image masks of FibroSoft cluster analysis using supervised k-means with subclasses obtained from a run on the defined reference image; (a) identified as healthy tissue, (b) classified fibrotic tissue, and (c) background.
Moreover, Table 3 proves an accuracy of around $99\%$ measured by dice coefficient in all tissue types. Figure 6C depicts a clear and correct separation of the specified tissues by applying our supervised k-means with subclasses algorithm on the test image.
## Discussion
Here we underline a novel tool for semiautomatic analysis of cardiac fibrosis that allows quantification of both Picosirius Red and Masson’s Trichrome stained tissue, validated in a widely used murine model of cardiac hypertrophy transitioning to heart failure. In addition, we prove that FibroSoft is efficient not only in determining extracellular matrix deposition in heart tissue, but also in lung paraffin and frozen sections, and could further be applied on various tissue types and disease models.
Western blot analysis can accurately determine the pathological degree of collagen deposition; however it does not allow assessment of the location of extracellular matrix in pathological conditions. This is particularly important for cardiovascular diseases, in order to distinguish the types of fibrosis, namely perivascular, interstitial or replacement2, all leading to impaired heart function and high risk of arrhythmias. Therefore, it is critical that users can precisely quantify and analyse the areas of interest within the tissue section.
An utmost important result of our study is the strong correlation of collagen 1 content, quantified by Western blot, with the results of the designed software. Collagen 1 was demonstrated to be highly increased during the transition between compensated cardiac hypertrophy to heart failure, as well as in biopsies isolated from patients diagnosed with dilated cardiomyopathy20. Moreover, collagen 1 confers tissue rigidity, leading to impaired cardiac function in several models of cardiac dysfunction21. Although collagen is the most abundant extracellular matrix protein in the heart, abnormal deposition of fibronectin was proven to contribute to pressure overload-induced myocardial dysfunction22. Interestingly, inhibiting fibronectin polymerization was shown to be beneficial in a mouse model of cardiac dysfunction and might be translated into a therapeutic option for heart failure23. Our data demonstrate that fibronectin levels also correlate with the data analysed by FibroSoft, suggesting that not only collagen can be accurately measured through this method, but other molecular players with major role in fibrosis development in heart failure.
Another benefit of our software is that it enables the quantification of both Picosirius Red and Masson’s Trichrome-based histological stainings, allowing users to choose according to the tissue or disease of interest. For example, Picosirius red was proven to be more accurate in quantification of hepatic fibrosis in patients with hepatitis C24. Similarly, renal fibrosis, quantified by Picosirius red staining correlates stronger with kidney function than Masson’s Trichrome and collagen staining in renal biopsies25.
Furthermore, FibroSoft allows users to manually choose extracellular matrix color hue, which was shown to differ with the progression of disease and age due to changes in the 3D structure of collagen protein26,27.
In conclusion, we prove the feasibility of machine learning methods (here: supervised k-means with subclasses) to design a semi-automatic fibrosis software, focusing on Masson’s Trichrome and Picosirius Red staining methods, which improves the specificity and reduces human error compared to previously described methods in the field. Additionally, the resulting final classifications contain smoother differences due to multiple cluster centers per class.
Unlike other procedures, FibroSoft discriminates between background tissue without relying on any kind of specific threshold values or preprocessing steps. Thus, this software is suitable for the analysis of Masson’s Trichrome and Picosirius Red staining as well as other histological analyses whenever three classes need to be distinguished.
Moreover, we can demonstrate that FibroSoft can clearly distinguish between healthy and diseased myocardium in case of pressure overload-induced cardiac hypertrophy and heart failure.
To the best of our knowledge, other available tools don’t provide similar functionality or lack the proof of accurate performance. Furthermore, several Photoshop-based analysis methods do neither prove the accuracy of the segmented results, nor are suitable for other staining techniques13.
Therefore, this tool may become useful not only for basic research-based studies, but could be translated into a powerful tool for tissue biopsy analysis not only in cardiovascular complications, but other disorders affected by abnormal extracellular matrix deposition. Its simple user interface and straightforward parametrization approach as well as batch processing create an ease of use for image analysis in various settings, for example under individual staining, disease models, color-shifts and lighting conditions. Thus, it can be widely and easily applied to yet even unknown analysis tasks.
Moreover, first experiments with deep learning algorithms fed by data produced with FibroSoft have shown that a fully automatic classification system is at reach in order to remove complete interaction. Further research is required to translate this tool into a fully automatic fibrosis classification software.
## Conclusion
In conclusion, here we underline a novel approach to automatically determine the degree of extracellular matrix deposition in cardiac sections under pathological conditions. Further studies assessing the efficacy of the method in human biopsies are required to translate the approach to patient situation.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30196-9.
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|
---
title: 'Body mass index percentiles versus body composition assessments: Challenges
for disease risk classifications in children'
authors:
- Jody L. Clasey
- Elizabeth A. Easley
- Margaret O. Murphy
- Stefan G. Kiessling
- Arnold Stromberg
- Aric Schadler
- Hong Huang
- John A. Bauer
journal: Frontiers in Pediatrics
year: 2023
pmcid: PMC10020489
doi: 10.3389/fped.2023.1112920
license: CC BY 4.0
---
# Body mass index percentiles versus body composition assessments: Challenges for disease risk classifications in children
## Abstract
### Background
Identifying at-risk children with optimal specificity and sensitivity to allow for the appropriate intervention strategies to be implemented is crucial to improving the health and well-being of children. We determined relationships of body mass indexes for age and sex percentile (BMI%) classifications to actual body composition using validated and convenient methodologies and compared fat and non-fat mass estimates to normative cut-off reference values to determine guideline reliability. We hypothesized that we would achieve an improved ability to identify at-risk children using simple, non-invasive body composition and index measures.
### Methods
Cross-sectional study of a volunteer convenience sample of 1,064 (537 boys) young children comparing Body Fat Percentage (BF%), Fat Mass Index (FMI), Fat-Free Mass Index (FFMI), determined via rapid bioimpedance methods vs. BMI% in children. Comparisons determined among weight classifications and boys vs. girls.
### Results
Amongst all subjects BMI% was generally correlated to body composition measures and indexes but nearly one quarter of children in the low-risk classifications (healthy weight or overweight BMI%) had higher BF% and/or lower FFMI than recommended standards. Substantial evidence of higher than expected fatness and or sarcopenia was found relative to risk status. Inaccuracies were more common in girls than boys and girls were found to have consistently higher BF% at any BMI%.
### Conclusions
The population studied raises concerns regarding actual risks for children of healthy or overweight categorized BMI% since many had higher than expected BF% and potential sarcopenia. When body composition and FMI and FFMI are used in conjunction with BMI% improved sensitivity, and accuracy of identifying children who may benefit from appropriate interventions results. These additional measures could help guide clinical decision making in settings of disease-risks stratifications and interventions.
## Introduction
In recent decades the worldwide rate of obesity in children has risen strikingly, especially in settings of lower socioeconomic status (1–5). In 2018 nearly one in five children or adolescents in the US were considered obese, defined as a body mass index (BMI), at or above the 95th percentile of the sex-specific BMI-for-age growth charts [6]. Obesity can impact nearly all aspects of a child's life, including risks of several major morbidities (metabolic syndrome, diabetes, cardiovascular diseases, cancer), their psychological health, and their overall physical health (7–13). Childhood obesity is also the major predictor of adult obesity, leading to a continued and often lifetime burden of increased medical and social costs [14, 15]. This problem is a major public health concern, and a major driver of medical and social costs, with an estimated financial burden to the US of ∼$14 billion annually.
Overweight and obesity are terms used to describe excess of adiposity, or fatness, above the ideal for good health. Defining obesity severity during childhood requires suitable measures of body fat and appropriate cutoff ranges, especially for the goal of stratifying disease risks, managing clinical care or developing treatment or prevention strategies. This is a common challenge in most pediatric patient care settings, and although according to the Centers for Disease Control and Prevention (CDC) “BMI can be considered a practical alternative to direct measures of body fat”, it has not been highly successful as a tool in children [14, 16]. The use of BMI for age and sex percentile (BMI%) alone, or BMI z-score corrected for age and sex, was found to have only modest value in stratifying patients; this is especially true in cases of extreme obesity (15, 17–20). In 2014 Skinner et al. proposed extension obesity classifications to include three gradations, and others have developed similar strategies for improved assessments of individuals with extreme obesity and stratify morbidities risks [5]. Although at this time there is no singular defined protocol or universal guideline for assessing obesity status in children there is a clear need to enhance the clarity of which children are of substantial risk of disease (21–23). Equally important is the need for reliable and specific measures to aid in determining treatment effectiveness. While BMI% has often been shown to have generalized predictive value in identifying obese children when compared to BIA, total body dual energy absorptiometry scans and other body composition methodologies, the lack of ability to distinguish between over-fatness or low fat-free mass (particularly in children with a health BMI%) remains problematic (24–27). At this time the pediatric obesity problem is in need of better ways to define which children are of highest concern, and the primary reliance on BMI% may be suboptimal [21].
The goal of this study was to investigate relationships of obesity classification based on current guidelines relative to additional measures of body composition and indexes in a large cohort of school age children for risk stratification. This goal was enabled by a large set of data our institution has accrued via several outpatient and/or school-based child body composition assessments, which utilized a reliable and validated set of standardized testing procedures including bioimpedance analysis (BIA) methodologies. The central questions were: how do the current CDC and AAP guidelines for weight classifications compare to actual body composition measurements in a large cohort of school-aged children? And, are there any patterns of deviation amongst boys vs. girls of this age group?
## Subjects
Pediatric participants previously enrolled in several clinical research studies conducted in conjunction with the University of Kentucky Pediatric Exercise Physiology Laboratory (PEP Lab) were included in this study. Enrollment criteria included: age 5–11 years, no active health issues, and no known congenital or genetic abnormalities. Data were collected in a variety of settings, including a dedicated clinical research lab for pediatric exercise studies (PEP Lab), and several public elementary schools throughout the state of Kentucky. All instrumentation for body composition measurements and personnel involved in data collection were identical for each subject.
Subject recruitment involved advertisement of the study protocol and/or visitation to a regional public school for description of the research projects, typically days prior to an actual study day to obtain consent/assent. All participants' parents provided written consent and all participants provided verbal assent prior to participation in accordance with the policies, procedures, and approval of the University of Kentucky's Office of Research Integrity Institutional Review Board. No subjects were recruited based on their specific body composition and we consider the enrollment processes employed to be volunteer convenience sampling of our regional population of school age children within the catchment area for our Children's Hospital. We limited our study cohort to 1,064 White Non-Hispanic (WNH) children under the assumption that BMI% may be best suited in this group due to under sampling by the NHANES of other ethnic groups prior to 2011 [28], the greater frequency of participation by WNH children in our clinical research studies, the availability of a pediatric (5–11 years) specific BIA equation that was validated and cross-validated in WNH children only, and the availability of ethnic specific means ± SD for the Fat Mass (FMI) and Fat-free Mass (FFMI) indexes.
In accordance with AAP guidelines, each child was categorized by BMI% using standardized nomogram tools accounting for age and sex, as healthy weight (HW; BMI% >5th, <85th), overweight (OW; BMI% ≥85th <95th), Class I obesity (OB I; BMI% ≥95th <120th), Class II obesity (OB II; BMI% ≥120th <140th), or Class III obesity (OB III; BMI% ≥140th percentile [5, 23, 29].
## Age, anthropometric and body composition measurements
Age was calculated from the date of birth to the testing date. Body mass and standing height were determined using a calibrated digital scale (Escali XL200 Digital Scale; Minneapolis, MN) and a wall-fixed meter stick (Starrett MS-2; Melville, NY) while wearing light-weight clothing and no shoes; these were subsequently used to determine the BMI%. The total body Fat Mass (FM), fat-free mass (FFM), and body fat percentage (BF%) were determined using a tetrapolar bioelectrical impedance analyzer (BIA; Bodystat 4,000 Quadscan; Bodystat, Isle of Man, British Isles). These measures were completed twice (mean of the resulting impedance measures used for subsequent analyses) using previously reported standard procedures, the impedance frequency of 50 kHz, and a validated age and ethnic group specific BIA equation established at our institution [30]. In all cases the total time for collection of body composition data via these established processes was less than 10 min.
## Reference values for body composition normative comparisons
Body composition variables for each subject were compared to published norms and threshold values currently accepted as appropriate for age and sex [31]. We are using these values to represent the ideal or appropriate healthy cutoff values for comparative purposes. The FMI (kg/m2) and the FFMI (kg/m2) were determined for each child participant by dividing the fat and fat-free body composition mass measures by height in meters squared, and compared to reference measures reported in 2005 by Freedman and colleagues [32]. Specifically, these cutoff values were (lower to upper limits): BF%: boys $10\%$–$24\%$, girls $17\%$–$32\%$; FMI: boys: 0.5–6.5, girls 1.8–7.8; FFMI: boys 13.2–15.2; girls 12.7–14.7.
## Statistical analyses
Data were analyzed using JMP version 16.0 (SAS Incorporated, Cary, NC) and are presented as means ± standard error. Comparisons of categorical variables were made using Chi-Squared tests. Continuous responses over groups were compared by one-way ANOVA, with normality assumptions justified by the Central Limit Theorem. Regression analyses for variables that were not linearly related employed a quadratic equation including a sex effect. In all cases significance was ascribed at $p \leq 0.05.$
## Results
Physical characteristics and weight categories of the 1,064 study subjects are shown in Table 1. Average age, weight, height and BMI were not different between the boys ($$n = 537$$) and girls ($$n = 528$$) studied. $46\%$ of the boys and $40\%$ of the girls had BMI% classifying them as overweight or obese. Furthermore, $30\%$ of the boys and $24\%$ of the girls had BMI% classifying them as obese (Class I, II and III obesity combined). The distribution of the weight categories for boys vs. girls was not different between groups. More detailed physical characteristics are provided in Supplementary Table S1. The frequency distribution for the BF% and FMI compared to BMI-based classifications (above or below the 95th percentile for age and sex) are shown in Table 2 with resulting sensitivity and specificity of 0.948 and 0.780 for BF%, and 0.902 and 0.952 for the FMI.
Shown in Figure 1 are comparisons of body composition data for boy and girl subjects for weight categories of HW and OW. These two categories are typically considered “low disease risk” by published guidelines [29] in the absence of other clinical assessments or symptoms. Shown in Panels 1A, 1B, and 1C are BF%, FMI, and FFMI respectively. Also presented in each of the panels of Figure 1 are normative reference lines, or cut-points of abnormal values, for each variable based on age and sex [29, 32]. This allows visual comparisons of the study population relative to cut-off values and expected ranges of normal values. We have also provided information concerning our modeling of these associations in the Supplementary Tables S2A–C.
**Figure 1:** *Distribution of body composition and indexes measures for the healthy weight and overweight BMI%-based weight categories for boys and girls. (A) Solid red lines represent the referenced (27) optimal range body fat percentage for boys (>10% to <24%) and girls (>17% to <32%); (B) Solid red lines represent the referenced (28) mean ± 1SD for the Fat Mass Index for boys (3.5 ± 3 kg/m2) and girls (4.8 ± 3 kg/m2); (C) solid red lines represent the referenced (28) mean ± 1SD for the Fat Free Mass Index for boys (14.2 ± 1 kg/m2) and girls (13.7 ± 1 kg/m2).*
Figure 1A shows individual subject BF% for each sex and low-risk weight category respectively, with cut-points for this age, superimposed on the observed per-subject data for visualization. Of the group of boys in the HW category we observed 56 of 289 ($19.4\%$) to have BF% above expected upper limit of normal (i.e., >$24\%$). Boys in the OW group also were found to commonly exceed upper limit of normal BF% (58 of 90, or $64.4\%$, Panel A). Of the 314 girls in the HW group 12 were found to have BF% above upper limit of normal ($32\%$ for girls of this age). In contrast, a great fraction of above-BF% cutoff was observed for OW girls (46 of 85, or $54.1\%$ of subjects).
Figure 1B shows individual subject FMI for boys vs. girls in the two low-risk weight categories. Also shown are reference values and upper and lower limits of normal [32]. Nearly all of the subjects of the HW group were found to have FMI within the expected normal range. A greater incidence of above-normal range was observed in OW boys (20 of 89, $22.5\%$) and OW girls (16 of 85, $18.8\%$).
Figure 1C shows individual subject FFMI vs. reference values for boys and girls in the HW and OW groups. Notable patterns emerged in the HW category for both sexes, in that 144 of 289 boys ($49.8\%$) and 126 of 314 ($40.1\%$) girls in this weight category were found to have FFMI lower than the reference value cutoff suggesting a lesser fat-free mass than expected for a substantial portion of this HW group of children. A lesser incidence of lower than expected FFMI was observed in the OW group for either sex, and no significant differences in average FFMI was observed between the two weight classes, or between sexes.
Shown in Figure 2 are body composition values (BF%, FMI, FFMI) for children categorized as Obese, and stratified to classes I, II, and III in accordance with current guidelines. Reference ranges are also superimposed on each panel. The BF% was consistently above expected values for boys and girls at each Obesity category (Figure 2A) and average values were increased at each Obesity class. In many subjects BF% was found to be more than 2-fold above upper limits. This trend was more prevalent in the OB-I and OB-II groups, wherein BF% was over $50\%$ (this represented approximately 20 kg of fat per subject). FMI was also elevated at each Obesity class for boys and girls (Figure 2B), with no difference between sexes at each Obesity category. In the OB-I grouping 28 of a total 196 subjects ($14.3\%$) were found to have FMI within normal range (Figure 2B).
**Figure 2:** *Distribution of body composition and indexes measures for the obese BMI%-based weight categories for boys and girls. (A) Solid red lines represent the referenced (27) optimal range body fat percentage for boys (>10% to <24%) and girls (>17% to <32%); (B) Solid red lines represent the referenced (28) mean ± 1SD for the Fat Mass Index for boys (3.5 ± 3 kg/m2) and girls (4.8 ± 3 kg/m2); (C) Solid red lines represent the referenced (28) mean ± 1SD for the Fat Free Mass Index for boys (14.2 ± 1 kg/m2) and girls (13.7 ± 1 kg/m2).*
Figure 3 shows regression analysis and relationships of BMI% vs. determined body composition for each subject, with comparisons between boys and girls. Model fitting using a quadratic equation showed statistically significant differences between boys and girls, with a generally higher BF% and FMI at any BMI% for girls when compared to boys (Figure 3A). Regression analysis of FMI vs. BMI% also revealed a significant difference between boys and girls (Figure 3B). Similarly, regression analysis of FFMI vs. BMI% also revealed a significant difference between boys and girls (Figure 3C) however this difference was not as strong as the analyses for BF% and FMI vs. BMI%.
**Figure 3:** *Regression analysis and relationships of BMI percentiles versus body composition and indexes with comparisons between boys and girls. (A) Significant correlations for boys (r = 0.91), girls (r = 0.90) and the total group (r = 0.89); (B) Significant correlations for boys (r = 0.93), girls (r = 0.92) and the total group (0.92); (C) Significant correlations for boys (r = 0.82), girls (r = 0.76), and the total group (r = 0.80). Sex slope comparison were significant for Figures 3A (p = 0.0014) and 3B (p < 0.001) and 3C (p = 0.002).*
## Discussion
Childhood obesity has reached epidemic proportions in the US, with the fastest growth affecting younger age groups, and the increased prevalence of individuals with extreme obesity. Stratifying individual children, identifying those with highest disease risks, and development of prevention and/or treatment strategies have become of vital importance. The use of BMI in combination with large dataset normalizations can provide rough approximation of risks, but without better tools to define adiposity in early life it will be difficult to optimize strategies and evaluate treatment successes. While previous reports have indicated significant relationship between BMI% and BF% derived from BIA, using absolute and relative measures of body composition (both fat and fat-free masses) provide greater sensitivity for classification and evaluating treatment effectiveness [33, 34].
In this study we explored the potential value of BIA for capturing additional subject-specific data regarding a child's adipose status and investigated the relationships to BMI% classifications that have become commonly employed in pediatric clinical care. While more sophisticated methods may provide greater accuracy of body composition measures and indexes of children, BIA provides a safe, non-invasive, economical (cost and time; taking less than 1 min to perform) method of providing additional information in clinical and field settings if a properly validated and cross-validated BIA equation is available [30, 31]. Over one thousand children were studied with no adverse event occurrences and the processes employed were well tolerated in children of 5–11 years. The large cohort of children studied were primarily from the surrounding region of Kentucky Children's Hospital and from rural counties. We found $27\%$ of our total study cohort ($30\%$ of boys, and $24\%$ of girls) had BMI percentiles classified as obese, well-exceeding the most recently reported (2015–2016) national average for this age group (6–11 years, $18\%$ among all children, $20\%$ of boys, $16\%$ of girls) [28].
A goal of this investigation was to compare actual body composition variables to BMI% categories recommended by several agencies (CDC, AAP, others), as well as to compare subject-specific data to what are considered “appropriate” values in BF%, FMI, and FFMI based on reference publications. We considered the published values used as thresholds as reliable for identifying normal vs. abnormal body composition status [27, 32].
In examining the total cohort combined we found that as weight classification increased from HW and OW to OB I, II, and III adiposity increased (e.g., BF%, and FMI increased, see Figures 1, 2). These trends are perhaps expected and suggest that BMI% stratifications can identify the most extreme cases of adiposity. However, we found surprisingly high levels of fatness in the HW and OW groups, which are typically considered “low-risk”. For example, a large proportion of children, especially boys ($19.4\%$), in the HW group had BF% above expected, and this was even greater in OW group ($64\%$ for boys and $54.1\%$ girls). Whether these children are truly “low-risk” remains of question and it is possible that the BMI-based methods alone are insensitive in identifying such cases.
An additional finding in the low-risk categories was potentially important evidence of low FFMI in the HW and (to a lesser extent) OW children. It should be recognized that OB individuals often have greater fat-free mass and FFMI compared to their HW counterparts due to increased bone mineral content and muscle mass as a result of greater mechanical loading stimuli of higher body mass (35–37). Others have described “obesity related sarcopenia” and our data suggest a sarcopenic state in children that are classified as HW using standard weight classifications based on BMI%. This low FFMI may be related to the rural and young population we studied, which has been characterized as increasingly sedentary and with little access to structured physical activity [38]. It is generally accepted that children with sarcopenia may experience deleterious long-term developmental and clinical outcomes (28, 39–44). Further investigation of this apparent phenomenon of sarcopenia, and its impact on disease risks during childhood, are clearly warranted.
We also observed differences in these relationships for boys vs. girls since at any BMI% level girls had a higher level of BF%, and a higher FMI (Figure 3). Others have documented sex-based differences in the incidence of obesity reporting that boys have a higher prevalence of obesity than girls [45, 46], however it should be noted that these sex differences were based on BMI% alone, and not based on BF% or FMI where the mean measures (and risk classification cutpoints) are greater for girls. Oobesity risks as well as disease outcomes and the life course and experiences of obese girls vs. boys are known to be different [47, 48]. Better understanding of the mechanisms and consequences of sex-based body composition differences in early life may lead to more specific intervention and/or prevention strategies.
Mechanistic studies have shown that the primary concerning element of obesity is increased adipose tissue mass. This tissue thereby drives several physiological and biochemical adaptations that affect numerous systems. Increased vascular resistance, alterations in glucose metabolism and related hormonal systems, and enhancement of inflammatory pathways are all recognized outcomes of excess adipose tissue mass in humans of various age. Of note is that in more than half of the children, age ∼10 years, studied herein estimated fat mass was greater than 15 kg, and $20\%$–$60\%$ of total body weight. Furthermore, sarcopenia is recognized as a separate mechanistic driver of disease via reduced skeletal muscle mass and reduced insulin-sensitized glucose disposition. Thus, excess adipose tissue, decreased muscle mass, and/or their combination are important factors in obesity driven disease and better characterizing these changes in early life are needed. Prospective studies to define adipose tissue mass with respect to physiological status, disease occurrence, and/or biomarkers of disease risks are clearly warranted, as are serial measurements during child growth and development.
Some weaknesses of this study exist. For example, we focused on WNH subjects (see Methods for rationale of this focus) and the findings we present may be non-representative of other groups. In addition, we did not perform maturational assessments for our child participants and can only speculate that the majority of the participants were prepubescent. Since it has long been recognized that pubertal stage alters the densities and the proportions of the components of fat-free mass [49], the accumulation of total body fat and fat distribution [50], and may have an impact on metabolism and insulin sensitivity [51], this study limitation warrants consideration. We also focused on the specific use of BIA as the sole method of body composition assessment; our investigative team has much experience in these methods and we are a proponent of this approach since it is fast, reliable, and has potential to fit into regular pediatric clinical care workflow and can be developed as part of a specialized clinic (ongoing study). It also has an advantage of screening at schools and other sites and is convenient for use in very young children. Other methodologies and broader trials of such tools for patient care are worthy of further investigation.
## Conclusions
In summary, the childhood obesity pandemic has relied heavily on BMI-based assessments of child body status, and even with more recent adaptations using normalized percentages this approach may be inaccurately categorizing some children. The population studied showed that some children have higher fat mass, lesser muscle mass, or both, even in the categories of HW and OW; they may be less healthy than the categorical methods can identify alone and this inaccuracy may be more common in girls than boys. Incorporation of body composition assessments (especially easy and reliable and noninvasive methods like BIA) could enhance the sensitivity and specificity of risks stratifications.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by University of Kentucky Institutional Review Board (Medical). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin and verbal assent from each child participant.
## Author contributions
JLC and EAE: conceptualized and designed the study, coordinated and supervised data collection, drafted the initial manuscript, and reviewed and revised the manuscript. MOM, SGK, HH and JAB: conceptualized and designed the study, reviewed and revised the manuscript, and critically reviewed the manuscript for important intellectual content. AS and AS: were responsible for designing the analyses and interpretation of the statistical findings, and reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fped.2023.1112920/full#supplementary-material.
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|
---
title: Effect of Chinese herbal medicine therapy on risks of all-cause mortality,
infections, parasites, and circulatory-related mortality in HIV/AIDS patients with
neurological diseases
authors:
- Jian-Shiun Chiou
- Chen-Hsing Chou
- Mao-Wang Ho
- Ni Tien
- Wen-Miin Liang
- Mu-Lin Chiu
- Fuu-Jen Tsai
- Yang-Chang Wu
- I-Ching Chou
- Hsing-Fang Lu
- Ting-Hsu Lin
- Chiu-Chu Liao
- Shao-Mei Huang
- Te-Mao Li
- Ying-Ju Lin
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10020503
doi: 10.3389/fphar.2023.1097862
license: CC BY 4.0
---
# Effect of Chinese herbal medicine therapy on risks of all-cause mortality, infections, parasites, and circulatory-related mortality in HIV/AIDS patients with neurological diseases
## Abstract
Introduction: Long-term living with human immunodeficiency virus (HIV) and/or antiretroviral therapy (ART) is associated with various adverse effects, including neurocognitive impairment. Heterogeneous neurocognitive impairment remains an important issue, affecting between 15–$65\%$ of human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS) patients and resulting in work performance, safety, and health-related outcomes that have a heavy economic burden.
Methods: We identified 1,209 HIV/AIDS patients with neurological diseases during 2010–2017. The Kaplan–Meier method, log-rank test, and Cox proportional hazards model were used to analyze 308 CHM users and 901 non-CHM users within this population. Major CHM clusters were determined using association rule mining and network analysis.
Results and Discussion: Results showed that CHM users had a $70\%$ lower risk of all-cause mortality (adjusted hazard ratio (aHR) = 0.30, $95\%$ confidence interval (CI):0.16–0.58, $p \leq 0.001$) ($$p \leq 0.0007$$, log-rank test). Furthermore, CHM users had an $86\%$ lower risk of infections, parasites, and circulatory-related mortality (aHR = 0.14, $95\%$ confidence interval (CI):0.04–0.46, $$p \leq 0.001$$) ($$p \leq 0.0010$$, log-rank test). Association rule mining and network analysis showed that two CHM clusters were important for patients with neurological diseases. In the first CHM cluster, Huang Qin (HQ; root of *Scutellaria baicalensis* Georgi), Gan Cao (GC; root of *Glycyrrhiza uralensis* Fisch.), Huang Lian (HL; root of *Coptis chinensis* Franch.), Jie Geng (JG; root of *Platycodon grandiflorus* (Jacq.) A.DC.), and Huang Bai (HB; bark of Phellodendron amurense Rupr.) were identified as important CHMs. Among them, the strongest connection strength was identified between the HL and HQ. In the second CHM cluster, Suan-Zao-Ren-Tang (SZRT) and Ye Jiao Teng (YJT; stem of *Polygonum multiflorum* Thunb.) were identified as important CHMs with the strongest connection strength. CHMs may thus be effective in treating HIV/AIDS patients with neurological diseases, and future clinical trials are essential for the prevention of neurological dysfunction in the population.
## 1 Introduction
Human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS) is a chronic, yet manageable disease that is commonly treated using combinatorial antiretroviral therapy (ART), also known as highly active antiretroviral therapy (HAART) (Barbier et al., 2020). In the era of combinatorial ART, patients with HIV/AIDS have shown prolonged life expectancy, delayed disease progression, and lower all-cause mortality (Antiretroviral Therapy Cohort, 2017; Lu et al., 2018). The long-term use of ART and living with HIV/AIDS are associated with numerous adverse effects, including hyperlipidemia (Tsai F. J. et al., 2017), cardiovascular disease (Dorjee et al., 2017), loss of bone density (Hoy et al., 2017; Chiu et al., 2021b), and neurocognitive impairment (Yuan and Kaul, 2021).
Neurocognitive impairment includes central nervous system (CNS) infections, cognitive disorders, vasculopathy, and peripheral neuropathy (Tsai Y. T. et al., 2017), affecting patients in many ways, including intellectual dysfunction, poor memory and thinking skills, behavioral problems, and difficulty in performing daily activities (Gorman et al., 2009). The prevalence of neurocognitive impairment ranges between $15\%$ and $65\%$ owing to cohort characteristics and heterogeneous HIV-related neurocognitive diseases (Trujillo et al., 1995; Joska et al., 2011; Dai et al., 2014). The pathological mechanism between ART and/or HIV and neurocognitive impairment remains unclear, but it is probably due to the interactions between ART and HIV in the CNS (Brew et al., 1997; Wright et al., 2010; Shikuma et al., 2012; Sharma, 2021; Ruhanya et al., 2022; Wallace, 2022). Neurocognitive impairment is associated with HIV virus-induced neurotoxicity and immune suppression in the CNS (Brew et al., 1997; Ruhanya et al., 2022; Wallace, 2022) and is also associated with ART-related neurotoxicity (Sharma, 2021), persistent low-grade chronic inflammation, immune reactivation, low-level viral replication in the CNS, and aging-related comorbidities (Wright et al., 2010; Shikuma et al., 2012). Patients receiving ART with higher CPE scores are indicated to be at a higher risk of neurocognitive impairment (Marra et al., 2009; Caniglia et al., 2014).
Chinese herbal medicine (CHM) has been used in patients with HIV/AIDS-related diseases (Tsai F. J. et al., 2017; Tsai et al., 2018; Sun et al., 2019; Ho et al., 2021; Jiang et al., 2022). CHMs and related natural compounds have been reported to have anti-cognitive, anti-neuroinflammatory, and anti-HIV activities (Han et al., 2011; Cheng et al., 2015; Esposito et al., 2016; Zhao et al., 2016; Lee et al., 2017; Adebiyi et al., 2018; Long F. Y. et al., 2018; Zhang et al., 2018; Zeng et al., 2019; Qu et al., 2021; Dong et al., 2022; Xu et al., 2022; Xue et al., 2022). These studies encourage the investigation of whether CHM could improve survival in patients with neurological diseases HIV/AIDS as a complementary therapy to conventional medicine. Therefore, we evaluated the effect of CHM treatment on all-cause mortality and infections, parasites, and circulatory-related mortality in HIV/AIDS patients with neurological diseases in Taiwan using a population-based nationwide database.
## 2.1 Study subjects
This longitudinal retrospective cohort study was conducted between 2008 and 2019 using the Taiwan National Health Insurance Research Database (NHIRD). Patients in the NHIRD were anonymized, and 20,355 patients with HIV/AIDS were identified with at least one inpatient or three outpatient visits within 1 year, as determined by the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) codes for 042-044 between 2010 and 2017 (Figure 1). Patients were further classified based on whether they had neurological diseases also with at least one inpatient or three outpatient visits within 1 year: 1) CNS infections: the ICD-9-CM codes: 013, 047, 053, 094, 200, 320, 321, 322, 323, 054.3, 054.4, 114.2, 130.0, 321.0, 003.21, 098.82, 112.83, and 115.91; 2) cognitive disorders: the ICD-9-CM codes: 290, 293, 294, 332, 345, 348.1, 348.3, and 780.3; 3) vasculopathy: the ICD-9-CM codes: 325, 430, 431, 432, 433, 434, 435, 436, and 437; and 4) peripheral neuropathy: the ICD-9-CM codes: 350, 351, 353, 354, 355, 356, 357, and 358) (Tsai Y. T. et al., 2017). Of these categories, the following types of patients were excluded: 1) neurological diseases diagnosed prior to HIV/AIDS diagnosis ($$n = 2$$,152); 2) missing age or sex information ($$n = 21$$); 3) patients with <14 days’ cumulative prescription of CHM in 1 year after neurological disease diagnosis ($$n = 1$$,587); and 4) any malignancy during the study period ($$n = 287$$) (Figure 1). According to the frequency distribution of neurological disease subtypes among patients with HIV/AIDS, we observed that more than $75\%$ patients belong to the CNS infections and cognitive disorders (Supplementary Figure S3). Also, for these patients, more than $75\%$ of the inpatient and outpatient visits belongs to the clinics including Infectious Disease, International Medicine, Emergency Medicine, and Psychiatry (Supplementary Table S7).
**FIGURE 1:** *Flowchart for the enrollment of CHM and non-CHM users in HIV/AIDS patients with neurological diseases. Abbreviations: CHM, Chinese herbal medicine.*
After exclusion, a total of 1,209 patients with neurological diseases were included, including 901 non-CHM and 308 CHM users (Figure 1). Patients who received >14 days of cumulative CHM prescription in the first year after the onset of their neurological diseases were defined as CHM users (Figure 1). The date after the 14 days’ cumulative prescription of CHM was designated the index date. Non-CHM users were determined to be those without any CHM treatment during the study period ($$n = 901$$). Both groups were matched for sex, age, Charlson comorbidity index (CCI) score, and duration between HIV/AIDS and neurological diseases at a ratio of 1:2 using the propensity score matching method to reduce potential confounders (Table 1). Finally, 266 CHM and 532 non-CHM users were identified (Figures 1, 2; Table 1). Comorbidities were determined before HIV/AIDS diagnosis (Table 1). The cumulative defined daily doses (DDDs) of ART drugs were obtained during the index duration (Table 1). The index duration indicates the date of first diagnosis of HIV/AIDS and the date of first diagnosis of neurological diseases. The approval number (CMUH107-REC3-074(CR1)) was provided by China Medical University Hospital.
## 2.2 Prescription patterns, network analysis, and association rule mining for CHM
Traditional Chinese medicine (TCM) is part of the regular healthcare system in Taiwan. In this study, CHMs were produced by Good Manufacturing Practice pharmaceutical companies and used by traditional Chinese medicine doctors for treatment. CHMs have two forms, i.e., single herb and herbal formula. The leaf, stem, root, or flower of a plant, as well as the organ of an insect, animal, or mineral source, could be used as a single herb, while herbal formulas comprise more than two single herbs. The CHM prescription pattern is shown in Supplementary Table S1 for Taiwanese HIV/AIDS patients with neurological diseases.
Association rule mining was conducted as previously described (Wang et al., 2020; Wu et al., 2020; Chiu et al., 2021a; Chen et al., 2021; Chou et al., 2021; Ho et al., 2021; Chiu et al., 2022), using the SAS software (version 9.4; SAS Institute, Cary, NC, United States). The connection strength between the two types of CHMs was calculated using lift values, confidence, and support for the co-prescription of CHM_X and CHM_Y (Table 4). The lift value is confidence (CHM_X → CHM_Y) (%)/p (Y) (%) or confidence (CHM_X → CHM_Y) (%)/p (X) (%). The lift value is the ratio of the observed support to the expected support, when X and Y are independent variables. A lift value greater than one suggests a strong association between CHM_X and CHM_Y, indicating that the association between the two CHMs is dependent. The confidence value is the conditional probability of receiving CHM_Y among those who already received CHM_X, which is calculated as follows (frequency of CHM_X and CHM_Y/frequency of CHM_X) × $100\%$. The confidence value (CHM_X → CHM_Y; %) is an indicator of how often CHM_Y appeared in calculations that contained CHM_X. Support value is the joint possibility of receiving both CHM_X and CHM_Y, which is calculated by (frequency of CHM_X and CHM_Y/total number of prescriptions) × $100\%$. Support value (X, %) is a measure of whether an association between CHM_X and CHM_Y occurred by chance.
Network analysis was performed as described previously (Wang et al., 2020; Wu et al., 2020; Chiu et al., 2021a; Chen et al., 2021; Chou et al., 2021; Ho et al., 2021; Chiu et al., 2022) (Figure 4). The green circle indicates a single herb, while the red circle indicates a herbal formula. A larger circle size was associated with a higher prescription frequency of CHM (Supplementary Tables S1, S2), and line size and color between CHM_X and CHM_Y represent the connection strength. The thicker line shows a higher support value between the CHMs (Table 4), while a darker line indicates a higher lift value (Table 4). Cytoscape software was used to analyze all the data (https://cytoscape.org/, version 3.7.0).
## 2.3 Statistical analysis
Categorical data, including age, sex, and Charlson comorbidity numbers, are presented as numbers (percentages) (Table 1). Categorical and continuous data were calculated using the chi-square test and unpaired Student’s t-test, respectively. The CCI score, cumulative DDDs of ART drugs, and index duration were presented as continuous data. The risks of all-cause mortality and infections, parasites, and circulatory-related mortality were estimated using univariate and multivariate Cox proportional hazard models (Tables 2, 3, Supplementary Tables S3, S5, S6, and S10). Adjusted factors included sex, age, Charlson comorbidity, and CHM use. The cumulative incidence of mortality between CHM and non-CHM users was estimated using the Kaplan-Meier method and log-rank test (Figure 3; Supplementary Figures S1, S2). SAS analysis was performed using the statistical software (version 9.4; SAS Institute, Cary, NC, United States).
## 3.1 Basic characteristics
The basic characteristics of patients with neurological diseases among the HIV/AIDS patients in Taiwan are presented (Table 1). For the subjects in this study, 308 CHM users and 901 non-CHM users were included. CCI score and age were significantly different between CHM and non-CHM users ($p \leq 0.05$; Table 1). To avoid confounding effects, propensity score matching was used to match the two groups at a 1:2 ratio for the duration between HIV/AIDS and neurological diseases, CCI score, sex, and age. After matching, there were no background differences between the 266 CHM users and 532 non-CHM users (matched subjects) ($p \leq 0.05$; Table 1). The CHM users received CHM therapies during the study period (Supplementary Table S4). There were 266 CHM users who were treated with CHMs and 532 patients who were not treated with any CHM during the study period. Furthermore, a separate percentage of malignancies in the two groups was shown in Supplementary Table S8. As shown there was no reported HIV/AIDS related malignancy including Kaposi’s sarcoma, lymphoma, and invasive cervical cancer during the study period ($p \leq 0.05$).
## 3.2 All-cause mortality
The cumulative incidence of all-cause mortality between CHM and non-CHM users was estimated using the Kaplan–Meier survival model (Figure 3A). The cumulative incidence of all-cause mortality between CHM and non-users was significantly different ($$p \leq 0.0007$$). Compared with non-CHM users, CHM users showed a significantly decreased cumulative incidence of all-cause mortality.
For HIV/AIDS patients with neurological diseases, the risk of all-cause mortality was estimated using univariate and multivariate Cox proportional hazards models (Table 2). The crude hazard ratios (cHR) showed differences in sex, age, comorbidities, and CHM use ($p \leq 0.05$). Patients aged >40 years were found to be at a higher risk of all-cause mortality than those aged <30 years (crude hazard ratio (cHR):4.48, $95\%$ confidence interval (CI): 2.64–7.60, $p \leq 0.001$). Females were at a higher risk of all-cause mortality than males (cHR, 2.98; $95\%$ CI: 1.40–6.32, $$p \leq 0.005$$). CHM users had a lower risk of all-cause mortality than non-CHM users (cHR: 0.38, $95\%$ CI: 0.21–0.66, $p \leq 0.001$). Patients with > three Charlson comorbidities were at a higher risk of all-cause mortality than those without any comorbidities (cHR: 3.90, $95\%$ CI: 2.00–7.64, $p \leq 0.001$). Patients with one or two Charlson comorbidities were at a higher risk of all-cause mortality than those with no comorbidities (cHR, 1.80; $95\%$ CI: 1.05–3.10, $$p \leq 0.033$$).
The adjusted hazard ratios (aHR) also showed differences in sex, age, and CHM use ($p \leq 0.05$) (Table 2). CCI score, CHM use, sex, and age were adjusted for in this model. Patients aged >40 years had a greater risk of all-cause mortality than those aged <30 years (adjusted HR (aHR), 3.79; $95\%$ CI: 2.12–6.76, $p \leq 0.001$). Females had a greater risk of all-cause mortality than males (aHR: 2.80, $95\%$ CI: 1.24–6.34, $$p \leq 0.013$$). CHM users had a lower risk of all-cause mortality than non-CHM users (aHR, 0.30; $95\%$ CI: 0.16–0.58, $p \leq 0.001$). Furthermore, we observed that CHM users still had a lower risk of all-cause mortality than non-CHM users after considering the adjusted factors, age, sex, Charlson comorbidity number, and the interval (between the diagnostic date of neurological diseases and the index date) (adjusted HR:0.31, $95\%$ CI: 0.16–0.59, $p \leq 0.001$) (Supplementary Table S3). Frequency of CHM use was associated with a reduced risk of overall mortality in patients with neurological diseases among HIV/AIDS (Supplementary Figures S1, S2) (Supplementary Tables S5, S6). For the sensitivity test, we observed that CHM users still had a lower risk of all-cause mortality than non-CHM users in patients with the CNS infections (Supplementary Figure S4: log rank $$p \leq 0.0009$$) (Supplementary Table S10: adjusted HR: 0.10, $95\%$ CI: 0.03–0.39, $p \leq 0.001$).
## 3.3 Infections, parasites, and circulatory system-related mortality
In addition to all-cause mortality, we classified deaths according to the cause of death information into categories using ICD-9-CM and ICD-10-CM codes (Eyawo et al., 2017; Long L. C. et al., 2018). In this study, the causes of death were grouped into the following categories: infections and parasites, circulatory, endocrine, nutritional, and metabolic diseases, viral hepatitis, and liver diseases, respiratory, genitourinary system, neoplasms, and other causes, which includes all causes of death not listed in the above mentioned categories (Supplementary Table S9). Supplementary Table S9 lists the ICD-9-CM and ICD-10-CM codes associated with the causes of death categories. Among the causes of death in HIV/AIDS patients with neurological diseases, approximately $51\%$ of patients had infections, parasites, and circulatory-related mortality (Figure 3B) (Supplementary Table S9). Patients with other causes of mortality were excluded from the final analysis when evaluating infections, parasites, and circulatory-related mortality.
The cumulative incidence of infections, parasites, and circulatory-related mortality between CHM and non-CHM users was calculated using a Kaplan–Meier survival plot (Figure 3B). The cumulative incidences of infections, parasites, and circulatory-related mortality between CHM and non-users were significantly different ($$p \leq 0.0010$$). Compared to non-CHM users, CHM users had a significantly decreased cumulative incidence of infections, parasites, and circulatory-related mortality.
The cHR and aHR were calculated for the risk of infections, parasites, and circulatory-related mortality (Table 3). cHRs showed differences in comorbidities, CHM use, sex, and age ($p \leq 0.05$). Patients aged >40 years were at a greater risk than those aged <30 years (cHR:8.09, $95\%$ confidence interval (CI): 3.57–18.32, $p \leq 0.001$). Females were at a greater risk than males (cHR: 3.69, $95\%$ CI: 1.42–9.57, $$p \leq 0.007$$). CHM users were at a lower risk than non-CHM users (cHR: 0.21, $95\%$ CI: 0.08–0.58, $$p \leq 0.003$$). Patients with more than three Charlson comorbidities were at a greater risk than those without any comorbidities (cHR, 6.13; $95\%$ CI: 2.59–14.54, $p \leq 0.001$). Patients with one or two Charlson comorbidities were at a greater risk than those with no comorbidities (cHR: 2.19, $95\%$ CI: 1.05–4.59, $$p \leq 0.037$$).
The aHRs showed differences in CHM use, sex, and age ($p \leq 0.05$) (Table 3). CCI score, CHM use, sex, and age were adjusted for in this model. Patients aged >40 years had a greater risk than those aged <30 years (aHR: 6.57, $95\%$ CI: 2.79–15.46, $p \leq 0.001$). Females were at a greater risk than males (aHR: 3.80, $95\%$ CI: 1.34–10.76, $$p \leq 0.012$$). CHM users had a lower risk than non-CHM users (aHR, 0.14; $95\%$ CI: 0.04–0.46, $$p \leq 0.001$$).
## 3.4 CHM prescription pattern
According to prescription frequency, the most frequently used herbal formulas and single herbs were found in patients with neurological diseases among the HIV/AIDS patients in Taiwan (Supplementary Table S1). Among herbal formulas, Long-Dan-Xie-Gan-Tang (LDXGT) was the most commonly used herbal formula (prescription frequency: 459). The second, third, and fourth formulas were Ban-Xia-Xie-Xin-Tang (BXXXT) (prescription frequency: 407), Ge-Gen-Tang (GGT) (prescription frequency: 390), and Suan-Zao-Ren-Tang (SZRT) (prescription frequency: 280), respectively. The most frequently used single herb was Huang Qin (HQ; root of *Scutellaria baicalensis* Georgi) (prescription frequency: 557), followed by Gan Cao (GC; root of *Glycyrrhiza uralensis* Fisch.) ( prescription frequency: 540), Da Huang (root and rhizome of DaH; *Rheum palmatum* L.) (prescription frequency: 528), and Jie Geng (JG; root of *Platycodon grandiflorus* (Jacq.) A.DC.) ( prescription frequency: 466).
Association rule analysis was applied to explore the strongest associated CHM pairs for HIV/AIDS patients with neurological diseases (Table 4). According to prescription frequency, support, and lift values of CHM pairs, the most commonly used co-prescriptions of CHM pairs were listed: Huang Lian (HL; root of *Coptis chinensis* Franch.) → Huang Qin (HQ; root of S. baicalensis Georgi) (first co-occurrence frequency:145, support: $1.97\%$, confidence: $36.34\%$, lift: 4.81), followed by Suan-Zao-Ren-Tang (SZRT) → Ye Jiao Teng (YJT; stem of *Polygonum multiflorum* Thunb.) ( second co-occurrence frequency: 96, support: $1.30\%$, confidence: $34.29\%$, lift: 7.81), and Gan Cao (GC; root of G. uralensis Fisch.) → Huang Qin (HQ; root of S. baicalensis Georgi) (third co-occurrence frequency: 91, support: $1.23\%$, confidence: $16.85\%$, lift: 2.23).
**TABLE 4**
| CHM products (LHS, X) | Chinese name | Frequency of prescriptions of X product | Dosage of X product | Unnamed: 4 | CHM products (RHS, Y) | Chinese name.1 | Frequency of prescriptions of Y product | Dosage of Y product | Frequency of prescriptions of X and Y products | Support (X) (%) | Confidence (X → Y) (%) | Lift |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Huang Lian (HL) | 黃連 | 399 | 2575 | → | Huang Qin (HQ) | 黃芩 | 557 | 6917 | 145 | 1.97 | 36.34 | 4.81 |
| Suan-Zao-Ren-Tang (SZRT) | 酸棗仁湯 | 280 | 23189 | → | Ye Jiao Teng (YJT) | 夜交藤 | 324 | 2161 | 96 | 1.3 | 34.29 | 7.81 |
| Gan Cao (GC) | 甘草 | 540 | 3938 | → | Huang Qin (HQ) | 黃芩 | 557 | 6917 | 91 | 1.23 | 16.85 | 2.23 |
| Jie Geng (JG) | 桔梗 | 466 | 3294 | → | Gan Cao (GC) | 甘草 | 540 | 3938 | 91 | 1.23 | 19.53 | 2.67 |
| Huang Bai (HB) | 黃柏 | 280 | 1969 | → | Huang Lian (HL) | 黃連 | 399 | 2575 | 88 | 1.19 | 31.43 | 5.81 |
The CHM pairs described above were used to construct network analysis using the Cytoscape software (Figure 4). In this study, 266 patients received 7,376 CHM prescriptions during the study period (Table 4). Among HIV/AIDS patients, two CHM clusters were important for patients with neurological diseases.
**FIGURE 4:** *Network analysis for CHM prescription pattern in HIV/AIDS patients with neurological diseases. Single herb is presented as a green circle; herbal formula is displayed as a red circle. Bigger circle size shows the higher prescription frequency of CHM. The connection strength is shown as the line size and line color between CHM_X and CHM_Y. Thicker line represents the higher support value between CHMs. Darker line shows higher lift value. Abbreviations: CHM, Chinese herbal medicine; SZRT, Suan-Zao-Ren-Tang; YJT, Ye Jiao Teng; HQ, Huang Qin; HL, Huang Lian; GC: Gan Cao; JG: Jie Geng; HB: Huang Bai.*
In the first CHM cluster, HQ (root of S. baicalensis Georgi) and JG (root of P. grandiflorus (Jacq.) A.DC.), HL (root of C. chinensis Franch.), Huang Bai (HB; bark of Phellodendron amurense Rupr.), and GC (root of G. uralensis Fisch.) were important CHMs. Among this cluster, the core CHMs were identified as the HL (root of C. chinensis Franch.), and HQ (root of S. baicalensis Georgi) due to the most commonly used CHM pair with the strongest connection strength within this cluster. According to the Taiwan Herbal Pharmacopeia (4th Edition English Version, the Ministry of Health and Welfare, Taiwan) (https://dep.mohw.gov.tw/docmap/lp-759-108.html), the CHM effects of HL and HQ were to clear heat and dry dampness, purge fire and detoxicate, and induce diuresis to alleviate edema. Furthermore, the SymMap website (http://www.symmap.org/) was also used to explore the relationship among single herbs, modern medicine symptoms, and neurological diseases (Supplementary Figure S5). As shown in Supplementary Figure S5, in the first CHM cluster, HQ and HL targeted more modern medicine symptoms that belongs to these neurological diseases.
In the second CHM cluster, SZRT and YJT (stem of P. multiflorum Thunb.) were important CHMs with the strongest connection strength. Therefore, in this cluster, the core CHMs were SZRT and YJT due to the most commonly used CHM pair with the strongest connection strength within this cluster. Also, according to the Taiwan Herbal Pharmacopeia (4th Edition English Version, the Ministry of Health and Welfare, Taiwan) (https://dep.mohw.gov.tw/docmap/lp-759-108.html), the CHM effects of SZRT and YJT were to nourish the heart to tranquilize, relieve sweating, generate fluid, and to nourish the heart to tranquilize, dispel wind to free collateral vessels. Furthermore, the SymMap website (http://www.symmap.org/) was also used to explore the relationship among single herbs, modern medicine symptoms, and neurological diseases (Supplementary Figure S6). As shown in Supplementary Figure S6, in the second CHM cluster, both of SZRT and YJT targeted more modern medicine symptoms that belongs to these neurological diseases.
## 4 Discussion
Neurocognitive impairment remains an important issue in HIV/AIDS patients, even in the era of combinatorial ART (Mothobi and Brew, 2012; Yuan and Kaul, 2021). We evaluated the effectiveness of CHMs on the risk of mortality in HIV/AIDS patients with neurological diseases in Taiwan. We found that CHM users had a decreased risk of mortality, including all-cause mortality, infection, parasites, and circulatory-related mortalities. Frequency of CHM use was associated with a reduced risk of overall mortality in patients with neurological diseases among HIV/AIDS. We observed that CHM users still had a lower risk of all-cause mortality than non-CHM users in patients with the CNS infections. Two CHM clusters were identified. The first CHM cluster, HQ (root of S. baicalensis Georgi), JG (root of P. grandiflorus (Jacq.) A.DC.), HB (bark of P. amurense Rupr.), HL (root of C. chinensis Franch.), and GC (root of G. uralensis Fisch.) were important CHMs. In the second CHM cluster, SZRT and YJT (stem of P. multiflorum Thunb.) were found to be important CHMs. This study suggests that CHM use shows lower risks of all-cause mortality and infections, parasites, and circulatory-related mortality in patients with neurological diseases among HIV/AIDS patients in Taiwan.
HIV infection impairs the immune system and increases the risk of opportunistic infections in infected individuals (Luo et al., 2016; Gangcuangco et al., 2017; Lee et al., 2018). Opportunistic infections also contribute significantly to increased morbidity and mortality among these patients (Xiao et al., 2013; Chanto and Kiertiburanakul, 2020). The introduction of Combination antiretroviral therapy (ART) has led to a significant decrease in the occurrence of opportunistic infections among these patients (Palella et al., 2006; Mirani et al., 2015). Furthermore, studies have shown that $15\%$–$65\%$ of HIV/AIDS patients suffer from neurocognitive impairment (Trujillo et al., 1995; Joska et al., 2011; Dai et al., 2014). HIV virus-induced neurotoxicity, immune suppression in the CNS, ART-related neurotoxicity, or persistent low-grade chronic inflammation may lead to neurological impairment (Brew et al., 1997; Sharma, 2021; Ruhanya et al., 2022; Wallace, 2022) (Wright et al., 2010; Shikuma et al., 2012). In the present study, we have included the opportunistic infection and the cumulative DDDs of ART drug information between our CHM and non-CHM users and we found that these two groups exhibited similar characteristics. We also found that patients with neurological diseases among HIV/AIDS and CHM users had decreased risks of all-cause mortality and infections, parasites, and circulatory-related mortality. Furthermore, our sensitivity test showed that CHM users still had a lower risk of all-cause mortality than non-CHM users in patients with the CNS infections. Studies have shown that CHMs and associated natural compounds may be beneficial for neurological impairment in HIV/AIDS through anti-cognitive decline via the promotion of blood circulation and attenuation of oxidative stress, as well as anti-neuroinflammation and anti-HIV (Han et al., 2011; Cheng et al., 2015; Lin et al., 2015; Esposito et al., 2016; Zhao et al., 2016; Lee et al., 2017; Adebiyi et al., 2018; Long F. Y. et al., 2018; Zhang et al., 2018; Zeng et al., 2019; Qu et al., 2021; Dong et al., 2022; Xu et al., 2022; Xue et al., 2022).
Among herbal formulas, Long-Dan-Xie-Gan-Tang (LDXGT) was the most commonly used herbal formula (prescription frequency: 459). The second, third, and fourth formulas were Ban-Xia-Xie-Xin-Tang (BXXXT) (prescription frequency: 407), Ge-Gen-Tang (GGT) (prescription frequency: 390), and Suan-Zao-Ren-Tang (SZRT) (prescription frequency: 280), respectively. LDXGT is a traditional Chinese herbal formula that is recorded in the ancient Chinese medical text called Yi-Fang-Ji-Jie (Collection of Prescriptions with Notes). It contains 10 Chinese herbs and has a wide range of uses, including the treatment of various types of infectious and inflammatory disorders (Xiong et al., 2018; Fan et al., 2020). In addition, it is also effective and safe against insomnia (Fan et al., 2020). Long Dan Cao (*Gentiana scabra* Bunge) is one herb of LDXGT that shows anti-inflammatory activity, relieves pain, and decreases postherpetic neuralgia in herpes zoster (Wang et al., 2017). Gentianine is a natural compound of Long Dan Cao (root and rhizome of G. scabra Bunge) that exhibits anti-ischemic stroke and anti-inflammatory activities (Wang et al., 2021). Baicalein, a natural flavone found in the root of Huang Qin (root of S. baicalensis Georgi, a Chinese herb of LDXGT), has antioxidant, anti-neuroinflammatory, and anti-cognitive effects (Jin et al., 2019; Shi et al., 2021). Zhi Zi (G. jasminoides J.Ellis) is also one of the aforementioned 10 Chinese herbs of LDXGT that exhibits anti-neuroinflammatory and anti-cognitive impairment effects in cerebral ischemia/reperfusion and Alzheimer’s disease animal models (Liu et al., 2021; Zang et al., 2021; Zang et al., 2022). Crocetin is a natural compound of Zhi Zi (ripe fruit of G. jasminoides J.Ellis), which protects neurons against microglial activation (Zang et al., 2022). Vanillic acid is a natural flavone found in the roots of Dang Gui (*Angelica sinensis* (Oliv.). Diels, a Chinese herb of LDXGT, have antioxidant, anti-neuroinflammatory, and anti-cognitive effects (Singh et al., 2015). The Gan Cao (root of G. uralensis Fisch.) is another one of the 10 Chinese herbs of LDXGT that exhibits antioxidant and anti-cognitive impairment activities (Ahn et al., 2006).
BXXXT is also a traditional Chinese herbal formula that is recorded in the ancient Chinese medical text called Shang-Han-Lun (The Treatise on Febrile Diseases). It contains seven Chinese herbs and has been used to treat various disorders including gastrointestinal inflammation, metabolic diseases, and depression (Yu et al., 2020; Xia et al., 2022). Zhi Ban Xia (*Pinellia ternata* (Thunb.) Makino) is one herb of BXXXT that promotes sleep by increasing rapid eye movement (REM) sleep (Lin et al., 2019). Gan Jiang (Zingiber officinale Roscoe) is one herb of BXXXT that improves cognitive function (Saenghong et al., 2012; Lim et al., 2014). The Gan Cao (root of G. uralensis Fisch.) and Huang Qin (root of S. baicalensis Georgi), previously reported in LDXGT, were also present in BXXXT. Berberine, one of the main bioactive components of Huang Lian (root of C. chinensis Franch., a Chinese herb of BXXXT), has anti-diabetes-related cognitive impairment and anti-cognitive deficiency effects (Durairajan et al., 2012; Hao et al., 2022).
GGT (also called Ge-Gen decoction) is a traditional Chinese herbal formula that is recorded in the ancient Chinese medical text called Shang-Han-Lun (The Treatise on Febrile Diseases) (Chen et al., 2009). It contains seven Chinese herbs and exhibits anti-depression and anti-inflammatory activities (Qin et al., 2019; Chiao et al., 2020). Puerarin, a natural compound of Ge Gen (Radix Puerariae, a Chinese herb of GGT), has antioxidant, anti-anxiety, and anti-cognitive effects (Huang et al., 2019; Chiao et al., 2020; Zhu et al., 2021). Ephedrine, a natural compound of Ma Huang (Ephedrae herba; a Chinese herb of GGT), has anti-HIV latency activity (Murakami et al., 2008; Panaampon et al., 2019). The Gan Cao (root of G. uralensis Fisch.) and Sheng Jiang (Z. officinale Roscoe), previously reported in BXXXT, were also present in GGT.
SZRT and YJT (P. multiflorum Thunb.) are important CHMs with the strongest connection strength found in this study. SZRT (also called Sansoninto) is a traditional Chinese herbal formula that is recorded in the ancient Chinese medical text called Jin-Gui-Yao-Lue (synopsis of prescriptions of the golden chamber). It contains five Chinese herbs and has been widely used therapeutically for major depressive, anxiety, and sleep disorders (Chen et al., 2011; Yeh et al., 2011; Lee et al., 2013; Xie et al., 2013; Chen et al., 2015; Hu et al., 2015; Ni et al., 2015; Ni et al., 2019; Chen et al., 2021). Jujuboside A and jujuboside B, natural compounds of Suan Zao Ren (*Ziziphus jujuba* Mill.; a Chinese herb of SZRT), have neuroprotective, blood circulation-promoting, and anti-cognitive effects (Seo et al., 2013; Zare-Zardini et al., 2013; Liu et al., 2014; Zhao et al., 2016; Shergis et al., 2017; Zhang et al., 2018; Huang et al., 2019; Zhu et al., 2021). Ye Jiao Teng (YJT) and its related natural compounds (2, 3, 5, and 4′-tetrahydoxystilbene-2-O-β-D-glucoside, emodin, and beta-sitosterol) show antioxidant and anti-cognitive activities (Chan et al., 2003; Um et al., 2006; Lee et al., 2017; Adebiyi et al., 2018; Zeng et al., 2019).
The first CHM cluster included five Chinese herbs: Huang Lian (root of C. chinensis Franch), Huang Qin (root of S. baicalensis Georgi), Jie Geng (root of P. grandiflorus (Jacq.) A.DC.), and Gan Cao (root of G. uralensis Fisch.), Huang Bai (bark of P. amurense Rupr.). Huang Qin (root of S. baicalensis Georgi), Gan Cao (root of G. uralensis Fisch.). Huang Lian (root of C. chinensis Franch) has also been previously reported in LDXGT, BXXXT, and/or GGT. Jie Geng (root of P. grandiflorus (Jacq.) A.DC.) contains platycodin D, which promotes cognitive functions (Kim et al., 2017). Huang Bai (bark of P. amurense Rupr.) contains cortex *Phellodendri amurensis* and exhibits anti-inflammatory activity (Park et al., 2007; Choi et al., 2014).
The limitations of this study are the lack of information on lifestyle, occupation, education, and laboratory tests in the database. However, we observed that CHM may lower the risks of all-cause mortality and infections, parasites, and circulatory-related mortality in patients with neurological diseases, and may be beneficial for functional studies and randomized controlled trials (RCTs) in neurocognitive protection in the future. These CHMs require large-scale RCTs in HIV/AIDS patients with neurological diseases to confirm their safety and relative effectiveness and to plot their interactions during regular treatments in these patients.
Among HIV/AIDS patients with neurological diseases, CHM users showed a better survival rate. Based on network analysis and association rule mining, the two CHM clusters were identified as potential CHMs for these patients. Further studies are required to validate the efficacy and safety of CHMs in these patients. The mechanism of the interactions between the natural compounds of CHMs also requires further investigation.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: Only citizens of the Republic of China who fulfill the requirements of conducting research projects are eligible to apply for the National Health Insurance Research Database (NHIRD). The use of NHIRD is limited to research purposes only. Applicants must follow the Computer-Processed Personal Data Protection Law (http://www.winklerpartners.com/?p=987) and related regulations of National Health Insurance Administration and NHRI (National Health Research Institutes), and an agreement must be signed by the applicant and his/her supervisor upon application submission. All applications are reviewed for approval of data release. Requests to access these datasets should be directed to Y-JL, yjlin.kath@gmail.com.
## Ethics statement
The approval number (CMUH107-REC3-074(CR1)) was provided by China Medical University Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
Y-JL, T-ML, and J-SC wrote the manuscript and interpreted data. J-SC, C-HC, M-WH, NT, W-ML, M-LC, F-JT, Y-CW, I-CC, H-FL, T-HL, C-CL, and S-MH collected, assembled, and analyzed the data. T-ML, W-ML, F-JT, and Y-JL provided study materials. J-SC and Y-JL designed, and conceived the study, and later amended the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1097862/full#supplementary-material
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|
---
title: Expression of cannabinoid (CB1 and CB2) and cannabinoid-related receptors (TRPV1,
GPR55, and PPARα) in the synovial membrane of the horse metacarpophalangeal joint
authors:
- Rodrigo Zamith Cunha
- Augusta Zannoni
- Giulia Salamanca
- Margherita De Silva
- Riccardo Rinnovati
- Alessandro Gramenzi
- Monica Forni
- Roberto Chiocchetti
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10020506
doi: 10.3389/fvets.2023.1045030
license: CC BY 4.0
---
# Expression of cannabinoid (CB1 and CB2) and cannabinoid-related receptors (TRPV1, GPR55, and PPARα) in the synovial membrane of the horse metacarpophalangeal joint
## Abstract
### Background
The metacarpophalangeal joint undergoes enormous loading during locomotion and can therefore often become inflamed, potentially resulting in osteoarthritis (OA). There are studies indicating that the endocannabinoid system (ECS) modulates synovium homeostasis, and could be a promising target for OA therapy. Some cannabinoid receptors, which modulate proliferative and secretory responses in joint inflammation, have been functionally identified in human and animal synovial cells.
### Objective
To characterize the cellular distribution of the cannabinoid receptors 1 (CB1R) and 2 (CB2R), and the cannabinoid-related receptors transient receptor potential vanilloid type 1 (TRPV1), G protein-related receptor 55 (GPR55) and peroxisome proliferator-activated receptor alpha (PPARα) in the synovial membrane of the metacarpophalangeal joint of the horse.
### Animals
The dorsal synovial membranes of 14 equine metacarpophalangeal joints were collected post-mortem from an abattoir.
### Materials and methods
The dorsal synovial membranes of 14 equine metacarpophalangeal joints were collected post-mortem from an abattoir. The expression of the CB1R, CB2R, TRPV1, GPR55, and PPARα in synovial tissues was studied using qualitative and quantitative immunofluorescence, and quantitative real-time reverse transcriptase PCR (qRT-PCR). Macrophage-like (MLS) and fibroblast-like (FLS) synoviocytes were identified by means of antibodies directed against IBA1 and vimentin, respectively.
### Results
Both the mRNA and protein expression of the CB2R, TRPV1, GPR55, and PPARα were found in the synoviocytes and blood vessels of the metacarpophalangeal joints. The synoviocytes expressed the mRNA and protein of the CB1R in some of the horses investigated, but not in all.
### Conclusions and clinical importance
Given the expression of the CB1R, CB2R, TRPV1, GPR55, and PPARα in the synovial elements of the metacarpophalangeal joint, these findings encouraged the development of new studies supporting the use of molecules acting on these receptors to reduce the inflammation during joint inflammation in the horse.
## Introduction
The metacarpophalangeal joint is a high mobility structure which undergoes enormous loading during locomotion and jumping in the horse [1], so much so that it is the most commonly reported joint affected by traumatic and degenerative lesions in equine athletes [2] and results in lameness in thoroughbred racehorses [3, 4]. Currently, there is no specific cure for joint disease, and the multimodal pharmacological treatment does not act on the cause of joint inflammation but is aimed at slowing its progression, minimizing/reducing pain, and increasing function and performance [5]. In recent decades, some molecules have given encouraging results for the treatment of osteoarthritis, even if it is difficult to draw definitive conclusions [6]. Therefore, there is a need for improving the understanding of the pathophysiology and mechanisms of joint pain in order to develop safe and effective drugs to alleviate symptoms in horses with synovitis and osteoarthritis (OA) [7]. Joint inflammation can affect cartilage, bone and the synovial membrane within the joint [8]. However, regardless of which intra-articular tissue type is first affected, the synovial membrane seems to modulate and reinforce the inflammatory responses of the joints [9, 10]. Hence, the synovial membrane is key to enhancing the understanding of the pathophysiological processes within the synovial joint.
The wall of the joint capsule is composed of two distinct layers: the external and thick fibrous layer (stratum fibrosum), and the inner and thin synovial membrane (synovium). The cells of the intimal lining of the synovium secrete the fluid into the joint cavity (synovial fluid), remove debris and are involved in the production of cytokines/molecules which may modulate the joint inflammation (9–13). Two types of synoviocytes lining the luminal side of the joint capsule have been described in depth in humans and animals [14, 15], including horses [11, 13, 16, 17]: [1] macrophage-like synoviocytes (MLS), also known as type A synoviocytes, and [2] fibroblast-like synoviocytes (FLS), also known as type B synoviocytes. Embedded in a thin layer of connective tissue rich in fenestrated capillaries, the synoviocytes produce and control the synovial fluid. Fibroblast-like synoviocytes, the dominating cell-type in the synovial intima, produce hyaluronic acid and other lubricating synovial additives of the synovial fluid, and also matrix components (such as collagens, proteoglycans and laminin) and degrading enzymes (such as matrix metalloproteinases [MMPs] and other proteases) [18]. In cultured human FLS, it has been shown that these cells organize a basement membrane-like extracellular matrix, capable of supporting monocyte survival and compaction into the lining [19]. A more recent study has shown that fibroblasts might also provide anchorage to the MLS and are also a source of key survival factors of the MLS [20].
Although FLS morphologically differ from the other fibroblasts, these cells may express the typical fibroblast markers vimentin [21] or, uniquely in the horse, the neuronal marker Protein Gene Product 9.5. [ 16]. However, due to the specific functions of the FLS in the synovial lining [22], there are only a few reports of selective markers of FLS, differentiating them from other musculoskeletal fibroblasts [13].
Macrophage-like synoviocytes are macrophages not derived from bone-marrow immune cells (monocytes) but derived from cells which disperse into the tissues during embryonic development and are resident in the joint [23]. Macrophage-like synoviocytes may be distributed unevenly adjacent to the joint lumen [11] or, as has recently been described in mice, may congregate to form an internal immunological barrier at the synovial lining which physically seclude the joint [24, 25]. General resident macrophage-markers, such as CD11b, CD14, CD68, and CD206, may be expressed by horse MLS [26].
The strong need to develop a treatment for synovial inflammation, cartilage degeneration, and bone deformation has led to research regarding the involvement of the immunomodulatory endocannabinoid system (ECS) in the development of OA (12, 27–32). The involvement of the ECS in immunocytes and macrophages, as well as in regulatory actions on sensory nociceptors to ameliorate pain in OA, has been described [33]. The ECS consists of endocannabinoid molecules involved in signaling processes, along with G-protein-coupled receptors (GPCRs) and enzymes associated with ligand biosynthesis, activation and degradation [33]. Endocannabinoids and endocannabinoid-like lipid mediators, such as palmitoylethanolamide (PEA) [34], the phytocannabinoids derived from Cannabis sativa, such as Δ-9-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabigerol, cannabichromene, and cannabinol [35, 36], and the synthetic cannabinoids all act on canonical cannabinoid-1 (CB1R) and−2 (CB2R) receptors. They also act on cannabinoid-related receptors, such as the transient receptor potential (TRP) channels, the G protein-coupled receptors (GPCRs), the nuclear peroxisome proliferator-activated receptors (PPARs), and the serotonin receptors (12, 35, 37–39).
There are studies showing that the activation of CB1R and CB2R, which are expressed in human, mouse, and horse synoviocytes (28, 40–44), can induce potent anti-inflammatory effects and modulate arthritic disease [31, 43, 45]. The TRP vanilloid 1 (TRPV1) ion channel, which is expressed in human and rat synoviocytes [41, 46], might also be a possible target for treating joint diseases [46].
There are no studies which have reported the expression of G protein-coupled receptor 55 (GPR55) or PPARα in synoviocytes. However, GPR55 has been localized in human chondrocytes, osteoclasts and osteoblasts [47, 48] and there are studies indicating that PPARα agonists may exert beneficial effects on OA due to their anti-inflammatory effects [49, 50].
Given the aforementioned data obtained in other species, it is conceivable that the receptors of the endocannabinoid system could be expressed in the horse synovial membrane and represent a pharmacological target for the treatment of joint diseases. Currently, only a few reports have been published regarding the cannabinoid and cannabinoid-related receptors of the horse joint synovium [4, 44].
Thus, the current study was designed to identify the mRNA of Cnr1, Cnr2, TRPV1, GPR55, and PPARA and to immunohistochemically localize these receptors in the synovial membrane of the equine metacarpophalangeal joint.
## Animals
The metacarpophalangeal joints of 14 healthy horses (9 females and 5 males), ranging from 2 to 20 years of age (mean: 12 years; SD ± 6.5), which were slaughtered for consumption were collected from the thoracic limbs post-mortem. The breeds included 1 Avelignese, 1 Italian thoroughbred, and 12 half-breeds. The distal forelimbs were removed at the carpal joint to obtain the metacarpophalangeal joints.
A complete cell blood count (CBC) and routine serum biochemical analyses were carried out using blood samples taken at the time of exsanguination. The horses, which did not show lameness of either the thoracic or the pelvic limbs, were considered to be healthy on the basis of a summary clinical visit prior to slaughter, normal results of the CBC count and routine serum biochemical analyses. In addition, the presence of OA or other pathological conditions were excluded by post-mortem gross and histological evaluation.
According to Directive $\frac{2010}{63}$/EU of the European Parliament and of the Council of 22 September 2010 regarding the protection of animals used for scientific purposes, Italian legislation (D. Lgs. n. $\frac{26}{2014}$) does not require any approval by competent authorities or ethics committees as this study did not influence any therapeutic decisions.
## RNA isolation and reverse transcription
Total RNA extraction was performed using TRI Reagent (Molecular Research Center In, Cincinnati, OH, USA) and a NucleoSpin RNA II kit (Macherey-Nagel GmbH & Co. KG, Düren, Germany) according to the manufacturer's instructions. Dorsal synovial membranes, collected from eight horses, were homogenized in TRI Reagent (50 mg/ml) with IKA T10 Basic Ultra-Turrax; 200 μL of chloroform were subsequently added to the suspension which was then mixed well. After incubation at room temperature (RT) (10 min), the samples were centrifuged (12,000 × g for 10 min) and the aqueous phase recovered. An equal volume of $70\%$ ethanol was added, and the RNA containing phase was applied to the NucleoSpin RNA Column and cleaned in the further steps of the protocol. Finally, 60 μl of molecular biology water was applied into the column membrane, centrifuged, and RNA was eluted into a new Eppendorf-type tube. After nanospectrophotometric quantification (DeNovix, DeNovix Inc. Wilmington, DE USA), the total RNA (500 ng) was reverse transcribed to cDNA using 5X iScript RT Supermix (Bio-Rad Laboratories Inc., Hercules, CA, USA) in a final volume of 20 μl.
## Quantitative real-time PCR (RT-PCR) gene expression analysis
To evaluate gene expression profiles, quantitative real-time PCR (qPCR) was carried out in a CFX96 thermal cycler (Bio-Rad Laboratories Inc.) using SYBR green detection (Cat.172-5121, Bio-Rad laboratories Inc) for target genes. Specific primers for the horse were designed (Beacon Designer 2.07, Premier Biosoft International, Palo Alto, CA, USA) using the target genes for Cnr1 (Cannabinoid receptor 1), Cnr2 (Cannabinoid receptor 2), GPR55, PPARA, and TRPV1 (Table 1). Regarding the reference genes, GAPDH (Glyceraldehyde-3-phosphate dehydrogenase), HPRT (Hypoxanthine phosphoribosyltransferase 1) and ACTB (Actin B) were selected on horse sequences as previously reported [51]. All the amplification reactions were carried out in 20 μl and analyzed in duplicate; the reaction contained 10 μl of iTaq Universal SYBR Green Supermix (Cat.172-5121, Bio-Rad laboratories Inc.), 0.8 μl of the forward and reverse primers (5 μM each) of each target gene, 1.5 μl cDNA, and 7.7 μl of water. The real-time procedure included an initial denaturation period of 3 min at 95°C, 40 cycles at 95°C for 15 s, and 60°C for 30 s, followed by a melting step with ramping from 55 to 95°C at a rate of 0.5°C/10 s. To validate the primers chosen, extraction and qPCR from a positive control (equine amygdala) were also performed. The specificity of the amplified PCR products was confirmed by agarose gel electrophoresis and melting curve analysis. The relative expressions of the interest genes (IG) were normalized based on the geometric mean of the three reference genes (RG) [52]. The relative mRNA expression of the genes tested was evaluated as using the ΔCt method with ΔCt = (Ct geometric mean RG – Ct IG), which directly correlated with the expression level.
**Table 1**
| Gene | Unnamed: 1 | Primer sequence (5′->3′) | PCR size (bp) | Accession number | References |
| --- | --- | --- | --- | --- | --- |
| Cnr1 | F | AACCTACCTGATGTTCTGGATTGG | 147.0 | NM_001257151.1 | Present study |
| Cnr1 | R | GATGTGTGGATGATGATGCTCTTC | | | |
| Cnr2 | F | CTCCTGTTCATTGCCATCCTCTTCTCTG | 114.0 | NM_001257179.1 | Present study |
| Cnr2 | R | CTGCCTGTCTTGGTCCTGGTGTTC | | | |
| GPR55 | F | CCGCCTTCTCCTCCTTCCTCTCAG | 118.0 | XM_023642534.1 | Present study |
| GPR55 | R | TCACTCCTCCACACCCATTTCTACCC | | | |
| PPARA | F | CATTGGCGAGGACAGTTGCGGAAG | 182.0 | NM_001242553.1 | Present study |
| PPARA | R | CGATGTTCAATGCTGTGCTGGAAGATTC | | | |
| TRPV1 | F | ACCTGTGTCGCTTCATGTTTGTCTACC | 105.0 | XM_014727972.2 | Present study |
| TRPV1 | R | ATTCAGCCAGCACGGAGTCATTCTTC | | | |
## Immunofluorescence
Dorsal synovial membrane specimens (~2 cm × 1 cm) were dissected with a scalpel and fixed for 48 hours at 4°C in $4\%$ paraformaldehyde in phosphate buffer (0.1 M, pH 7.2), subsequently rinsed in phosphate-buffered saline (PBS; 0.15 M NaCl in 0.01 M sodium phosphate buffer, pH 7.2) and stored at 4°C in PBS containing $30\%$ sucrose and sodium azide ($0.1\%$). The following day, the tissues were transferred to a mixture of PBS−$30\%$ sucrose–azide and Optimal Cutting Temperature (OCT) compound (94-4583, Sakura Finetek Europe, Alphen aan den Rijn, The Netherlands) at a ratio of 1:1 for an additional 24 h before being embedded in $100\%$ OCT in Cryomold® (94-4566, Sakura Finetek Europe). The samples were prepared by freezing the tissues in isopentane cooled in liquid nitrogen. Cryosections (14 μm thick) of synovial membrane were cut on a cryostat (MC5000, Histo-Line Laboratories, Pantigliate, Italy), and mounted on polylysinated slides (HL26765, Histo-Line Laboratories).
The cryosections were hydrated in PBS and processed for immunostaining. To block non-specific bindings, the sections were incubated in a solution containing $20\%$ normal donkey serum (Colorado Serum Co., Denver, CO, USA), $0.5\%$ Triton X- 100 (Sigma Aldrich, Milan, Italy, Europe), and bovine serum albumin—BSA ($1\%$) in PBS for 1 hour at RT (22–25°C). The cryosections were incubated in a humid chamber overnight at RT with the anti-CB1R, -CB2R, -TRPV1, -GPR55, and PPARα antibodies (single immunostaining) or with a cocktail of primary antibodies (double immunostaining) (Table 2) diluted in $1.8\%$ NaCl in 0.01 M PBS containing $0.1\%$ sodium azide. After washing in PBS (3 × 10 min), the sections were incubated for 1 h at RT in a humid chamber with the secondary antibodies (Table 3) diluted in PBS. The cryosections were then washed in PBS (3 × 10 min) and mounted in buffered glycerol at pH 8.6 with 4′,6-diamidino-2-phenylindole–DAPI (Santa Cruz Biotechnology, Santa Cruz, CA, USA). To identify macrophages and fibroblasts, the anti-ionized calcium binding adapter molecule 1 (IBA1) [53] and the anti-vimentin (Clone V9) [42] antibodies were used, respectively.
It is necessary to point out that the identification of the two types of synoviocytes of the equine metacarpophalangeal joints is by no means a simple matter. In fact, there are some articles which testify to the fact that the horse FLS can be very similar to the MLS, from a morphological point of view [11, 16, 17]. For this reason, two markers which should be selective for fibroblasts (vimentin) and for macrophages (IBA1) were used.
## Antibodies anti-cannabinoid receptors
The rabbit anti-CB1R antibody utilized in the present study had already been tested using Western blot (WB) analysis on horse tissues [54].
The rabbit anti-CB2R antibody (PA1-744) utilized in the present study had already been tested with Western blot (WB) analysis on horse tissues [55]. In the current study, another anti-CB2R antibody, raised in mice (sc-293188), was used, the specificity of which has not yet been tested on horse tissues; however, both the mouse and rabbit anti-CB2R antibodies were tested using a double-staining protocol and were co-localized in horse tissues (Supplementary Figure 1).
## Antibodies anti-cannabinoid-related receptors TRPV1, GPR55, and PPARalpha
The specificity of the anti-TRPV1 antibody had been tested by the research group using Western blot analysis on horse tissue [53]. In addition, the specificity of the anti-TRPV1 antibody had previously been tested using WB analysis on rat tissues [56].
The immunogen used to obtain the anti-GPR55 antibody was the synthetic 20 amino acid peptide from the third cytoplasmic domain of Human GPR55 in amino acids 200–250. The homology between the full amino acid sequences of the horse and human GPR55 was $80\%$, and the correspondence with the specific sequence of the immunogen was $78\%$. This antibody, which has recently been used in horse sensory neurons [53], had previously been tested on rat and dog dorsal root ganglia (DRG) using immunofluorescence [57] and on mice tissues using WB analysis [58]. However, the WB analysis had not been carried out on horse tissue.
The specificity of the primary anti-PPARα antibody had been tested using WB analysis on horse tissue [54]. In addition, the antibody utilized had also recently been tested on rat tissue [57] as the anti- PPARα antibody reacts with rat tissue, as stated by the antibody supplier. The same anti-PPARα antibody has recently been used in horse tissues [53, 59].
## Marker for macrophages (MLS) and fibroblasts (FLS)
The goat anti-IBA1 antibody, recently used on horse tissue [53], was directed against a peptide having the sequence C-TGPPAKKAISELP, from the C Terminus of the porcine IBA1 sequence. Horse and porcine IBA1 molecules share $92.3\%$ identity (https://www.uniprot.org/), and it is plausible that the antibody used can also recognize IBA1 in the horse.
The mouse anti-vimentin antibody (Clone V9) had already been used to label fibroblasts in the horse skin [60].
## Specificity of the secondary antibody
The specificity of the secondary antibodies was tested by applying them on the sections after omitting the primary antibodies. No stained cells were detected after omitting the primary antibodies.
## Quantitative analysis
Quantitative analysis of the intensity of the expression of cannabinoid and cannabinoid-related receptors in the synovial intimal layer was carried out on 12 horses.
For each animal, and each receptor, two randomly selected images of the synovial membrane (50 μm-thick and 100 μm-wide; 5,000 μm2 area) were acquired (high magnification, ×400), using the same exposure time for all the images. The 50 μm-thick of synovial membrane encompassed the intimal synoviocytes and a minimal amount of underlying subintimal blood vessels, infiltrating cells and fibroblasts [61]. In each image the signal intensity was analyzed using ImageJ software (Image J, version 1.52t, National Institutes of Health, Bethesda, MD, USA) [62] by standardized thresholds for brightness and contrast were determined empirically and applied to all images. The signal intensity was finally obtained using the Color histogram (gMEAN) tool of the software.
## Statistical methods
For each receptor the mean of the two values/case of signal intensity in the 12 horses were evaluated and compared. Statistical analysis was carried out using GraphPad Prism software (version 8.3, La Jolla, CA). The normality distribution of the data was assessed using the Shapiro-Wilk test. Comparisons between groups were performed with one way ANOVA Tukey's multiple comparisons test. A P-value ≤ 0.05 was considered significant.
## Fluorescence microscopy
The preparations were examined, by the same observer on a Nikon Eclipse Ni microscope (Nikon Instruments Europe BV, Amsterdam, The Netherlands, Europe) equipped with the appropriate filter cubes. The images were recorded with a DS-Qi1Nc digital camera and NIS Elements software BR 4.20.01 (Mountain View, Ottawa, ON, Canada). Slight contrast and brightness adjustments were made using Corel Photograph Paint whereas the figure panels were prepared using Corel Draw (Mountain View).
## qPCR for Cnr1, Cnr2, GPR55, PPARA and TRPV1
Quantitative PCR data demonstrated that Cnr2, GPR55, PPARA, and TRPV1 were detected in all the equine synovial samples ($$n = 8$$) while the transcript for Cnr1 was detectable in only four synovial samples ($50\%$). As reported in Figure 1, the level of gene expression was different in the synovial samples having a greater expression of PPARA.
**Figure 1:** *Gene expression of Cnr1, Cnr2, GPR55, PPARA, and TRPV1 in equine sinovial membranes. The results are presented as ΔCt = (Ct Mean RG – Ct IG). Symbols indicate individual animals. For each gene, mean ± SD are indicated by horizontal bars. Different letters indicate statistically significant differences (p < 0.05, Kruskal–Wallis test, Dunn's Multiple Comparison post-hoc test).*
## Vimentin and IBA1 distribution and expression analysis
Vimentin appears to be an excellent marker for identifying the morphology of the cells lining the synovial membrane of the horse joint. Bright vimentin immunoreactivity (vimentin-IR) was mainly expressed by FLS, which were recognizable owing to their long and thin processes extending toward the joint cavity. In some portions of the synovial intima, these processes exhibited a densely arranged plexus on the surface. Co-localization studies have indicated that a proportion of moderate-to-bright vimentin immunoreactive cells (MLS) also expressed IBA1-IR (Figures 2A–D). In the subintima, the IBA1 immunoreactive macrophages showed faint or moderate vimentin-IR.
**Figure 2:** *Photomicrographs of the cryosections of the synovial membrane of a horse metacarpophalangeal joint showing vimentin (B) and IBA1 (C) immunoreactivity. The white arrows indicate the DAPI (Blue) labeled nuclei (A) of some macrophage-like synoviocytes lining the synovial intima which co-expressed moderate IBA1 (Red) and bright vimentin (Green) immunoreactivity. The open arrows indicate subintimal macrophages, which were IBA1 immunoreactive and vimentin negative. (D) Merged image (Orange). Scale bar = 50 μm.*
## CB1R distribution and expression analysis
Faint CB1R-IR was expressed by the cytoplasm of the synoviocytes; however, the CB1R-IR was detectable in only $\frac{10}{14}$ (71 %) horses. Co-localization studies have indicated that vimentin immunoreactive FLS (Figures 3A–D) and IBA1 immunoreactive MLS (Figures 3E–H) expressed CB1R-IR. Cannabinoid receptor 1 was not expressed by blood vessels and fibroblasts.
**Figure 3:** *Photomicrographs of the cryosections of the synovial membrane of a horse metacarpophalangeal joint showing cannabinoid receptor type 1 (CB1) immunoreactivity in synoviocytes. (A–D) The arrows indicate the DAPI (Blue) labeled nuclei (A) of cells resembling fibroblast-like synoviocytes co-expressing faint CB1 (Green) receptor immunoreactivity (B) and bright vimentin (Red) (C) immunoreactivity. (D) Merged image (Orange). (E–H) The arrows indicate the DAPI (Blue) labeled nuclei (E) of round macrophage-like synoviocytes co-expressing faint CB1 (Green) receptor (F) and IBA1 (Red) (G) immunoreactivity. (H) Merged image (Orange). Scale bar = 50 μm.*
## CB2R distribution and expression analysis
Cannabinoid receptor 2 immunoreactivity was expressed by synoviocytes, blood vessels and fibroblasts. Bright cytoplasmic CB2R-IR was observed in oval and elongated FLS and in round-shaped IBA1 immunoreactive MLS (Figures 4A–D). Co-localization with the anti-CB1R antibody showed that synoviocytes expressed both receptors in those horses in which CB1R-IR was detectable (Supplementary Figure 2). The vascular endothelial and smooth muscle cells showed bright and moderate CB2R-IR, respectively (Data not shown). Moderate CB2R-IR was also expressed by cells, likely fibroblasts, distributed in the sublining layer.
**Figure 4:** *Photomicrographs of the cryosections of the synovial membrane of a horse metacarpophalangeal joint showing cannabinoid receptor type 2 (CB2) (B) and IBA1 (C) immunoreactivity. The white arrows indicate the DAPI (Blue) labeled nuclei (A) of some round macrophage-like synoviocytes lining the joint cavity which co-expressed IBA1 (Red) and bright CB2 (Green) receptor immunoreactivity. The open arrows indicate two cells expressing CB2 receptor immunoreactivity (likely fibroblast-like synoviocytes) which were IBA1 negative. (D) Merged image (Orange). Scale bar = 50 μm.*
## TRPV1 distribution and expression analysis
Transient receptor potential vanilloid 1 immunoreactivity was expressed by synoviocytes, blood vessels and fibroblasts. Bright TRPV1-IR was mainly expressed by the cell membrane and cytoplasm of FLS and, in particular, also by their long “dendritic” processes which extended irregularly toward the luminal surface of the synovial membrane (Figures 5A–D). Co-localization between TRPV1 and IBA1 showed that TRPV1-IR was also expressed by the cell membrane and cytoplasm of MLS (Figures 5A–D). In some portions of the synovial intima, oval-shaped synovial cells, expressing moderate-to-bright TRPV1-IR, appeared to be the prevalent cells, and were aligned and organized in such a way as to form an epithelium-like monolayer with the appearance of a barrier, resembling the cellular organization recently described in the rat synovial membrane [24, 25] (Figures 6A–C). In other portions of the membrane, however, the “elongated” FLS seemed to prevail in the most superficial layer (Figures 5A–D). The endothelial cells of the capillaries adjacent to the joint lumen and arteries of the stratum fibrosum showed moderate cytoplasmic TRPV1-IR. The vascular smooth muscle cells also showed moderate TRPV1-IR (Figures 6D–F).
**Figure 5:** *Photomicrographs of the cryosections of the synovial membrane of a horse metacarpophalangeal joint showing transient receptor potential vanilloid 1 (TRPV1) (B) and IBA1 (C) immunoreactivity. Both of the two cell types lining the synovial intima, i.e., the fibroblast-like synoviocytes (FLS) and the macrophage-like synoviocytes (MLS), showed bright TRPV1 (Green) immunoreactivity. The TRPV1 immunolabeling was also evident in the elongated cellular process of the FLS extending through the joint cavity. The white arrows indicate the DAPI (Blue) labeled nuclei (A) of two IBA1 (Red) immunoreactive MLS co-expressing bright TRPV1 (B) and moderate IBA1 (C) immunoreactivity. The open arrows indicate some round or elongated FLS which were TRPV1 immunoreactive and IBA1 negative. The small open arrows indicate the DAPI labeled nuclei of the endothelial cells showing moderate TRPV1 immunoreactivity. (D) Merged image (Orange). Scale bar = 50 μm.* **Figure 6:** *Photomicrographs of the cryosections of the synovial membrane of a horse metacarpophalangeal joint showing transient receptor potential vanilloid 1 (TRPV1) (B, E), immunoreactivity in synoviocytes (A–C), and fibroblast and vascular cells (D–F). (A–C) The white arrows indicate the DAPI (Blue) labeled nuclei of some round synoviocytes expressing bright TRPV1 (Green) immunoreactivity. The open arrows indicate subintimal cells (likely fibroblasts) showing faint-to-moderate TRPV1 immunoreactivity. (D–F) The open arrows indicate the DAPI (Blue) labeled nuclei of some cells of the interstitial connective tissues of the synovial membrane (close to the subintima) expressing moderate-to-bright TRPV1 (Green) immunoreactivity. The open arrows and the small open arrows indicate the DAPI labeled nuclei of the vascular smooth muscle cells and endothelial cells, respectively, expressing moderate TRPV1 immunoreactivity. (C, F) Merged images. Scale bar = 50 μm.*
## GPR55 distribution and expression analysis
G protein-coupled receptor 55 immunoreactivity was expressed by the cytoplasm of synoviocytes and endothelial cells. In particular, faint-to-moderate GPR55-IR was mainly expressed by vimentin immunoreactive FLS showing elongated processes (Figures 7A–D). Only a few IBA1 immunoreactive MLS showed faint-to-moderate GPR55 (Figures 7E–H). Vascular endothelial cells and smooth muscle cells showed moderate GPR55-IR, respectively (Figures 7A–H). In the subintima, bright GPR55-IR was expressed by unidentified perivascular round-shaped cells (likely lymphocytes) which did not have IBA1-IR (Supplementary Figure 3).
**Figure 7:** *Photomicrographs of the cryosections of the synovial membrane of a horse metacarpophalangeal joint showing G protein-related receptor 55 (GPR55) immunoreactivity in synoviocytes and vascular cells. (A–D) The white arrows indicate the DAPI (Blue) labeled nuclei (A) of elongated cells resembling fibroblast-like synoviocytes co-expressing moderate GPR55 (Green) (B) and bright vimentin (Red) (C) immunoreactivity. The small white arrows indicate the DAPI labeled nuclei of the endothelial cells of the subintima blood vessels showing moderate GPR55 immunoreactivity which was also expressed by the vascular smooth muscle cells (open arrow). (E–H) The white arrows indicate the DAPI (Blue) labeled nuclei of macrophage-like synoviocytes co-expressing faint-to-moderate GPR55 (Green) (F) and bright IBA1 (Red) immunoreactivity (G). The open arrow indicates the nuclei of a vascular smooth muscle cell expressing moderate GPR55 immunoreactivity. (D, H) Merged images (orange). Scale bar = 50 μm.*
## PPARα distribution and expression analysis
Peroxisome proliferator-activated receptor alpha immunoreactivity was expressed by synoviocytes, blood vessels and fibroblasts. The pattern of weak-to-moderate PPARα immunoreactivity was unusual as it appeared to be continuous, granular and indistinct immulolabeling of the cytoplasm of the upper portions/processes of the cellular elements facing the joint lumen (Figures 8A–D). The co-localization with the anti-vimentin antibody showed that the PPARα immunoreactive synoviocytes were most likely FLS (Figures 8E–H). No IBA1 immunoreactive synovial cells showed PPARα-IR (data not shown). Endothelial cells, as well as the smooth muscle cells of the blood vessels showed moderate cytoplasmic PPARα-IR; however, the PPARα-IR was more appreciable in large vessels (data not shown).
**Figure 8:** *Photomicrographs of the cryosections of the synovial membrane of a horse metacarpophalangeal joint showing peroxisome proliferator-activated receptor alpha (PPARα) immunoreactivity in synoviocytes (A–H). (A–C) The white arrows indicate the DAPI (Blue) labeled nuclei of synoviocytes brightly immunolabelled with the anti-vimentin (Red) antibody which expressed faint-to-moderate PPARα (Green) immunoreactivity. It is possible to see the indistinct PPARα immunostaining of the upper portions of the cells lining the joint cavity. (E–H) The figures show the longitudinal sections of two villi of the synovial membrane in which the arrows indicate the DAPI (Blue) labeled nuclei (E) of the cells, likely fibroblast-like synoviocytes and fibroblasts, co-expressing faint-to-moderate PPARα-(Green) (F) and bright vimentin-(Red) (G) immunoreactivity. (D, H) Merged images (Orange). Scale bar = 50 μm.*
Figure 9 shows the quantification of the intensity of the expression of CB1R, CB2R, GPR55, PPARα, and TRPV1 in the synovial membrane of the equine metacarpophalangeal joints.
**Figure 9:** *Quantification of the intensity of the expression of CB1R, CB2R, GPR55, PPARα, and TRPV1 in the synovial membrane of metacarpophalangeal joints of 12 horses. Data are represented as Mean ± SD and were analyzed using One-way ANOVA multiple comparisons test. *P < 0.05 and **P < 0.01.*
Figure 10 shows the graphical representation of the distribution of the CB1R, CB2R, TPRV1, GPR55, and PPARα in the different cellular elements of the equine metacarpophalangeal synovial membrane.
**Figure 10:** *Graphical representation of the distribution of the cannabinoid receptors 1 (CB1R) and 2 (CB2R) and the cannabinoid-related receptors transient receptor potential vanilloid 1 (TRPV1), G protein-coupled receptor 55 (GPR55) and nuclear peroxisome proliferator-activated receptor alpha (PPARα) in the different cellular elements of the synovial membrane of the equine metacarpophalangeal joint. Fibroblast-like synoviocytes (FLS), identified with an anti-vimentin antibody, expressed CB1R, CB2R, TRPV1, GPR55, and PPARα immunoreactivity. Macrophage-like synoviocytes (MLS), identified with an anti-IBA1 antibody, expressed CB1R, CB2R, TRPV1, and GPR55 immunoreactivity.*
## Discussion
Arthropathies can be a significant source of pain in horses, and finding new therapeutic treatments to alleviate the pain is of paramount importance [63]. It is known that cannabis-based drugs have therapeutic potential in inflammatory diseases, including OA and rheumatoid arthritis (RA), as demonstrated by pre-clinical and clinical studies in animals and humans [28, 64]. Interest in this type of molecule in horses has also recently been evidenced by a prospective, randomized, controlled study which attempted to determine the plasma pharmacokinetics, short-term safety, and synovial fluid levels of CBD following oral administration in horses [65].
Therefore, the localization of CB1R, CB2R, TRPV1, GPR55, and PPARα in the synovial FLS and MLS of the metacarpophalangeal joint of the horse is an encouraging finding.
Fibroblast-like synoviocytes are highly specialized mesenchymal cells found in the intimal lining layer of the synovium of diarthrodial joints. In a healthy joint, the FLS form a thin porous barrier at the interface between the sublining and the synovial fluid space [66]. Fibroblast-like synoviocytes are pivotal cells in both joint maintenance and integrity, and in the inflammatory response/pathogenesis of arthritis [10, 67]. The role of the FLS has also been highlighted in the pathogenesis of RA [15, 29, 68, 69]. It has been recognized that, even in horses, FLS participate in the pathogenesis of joint disease by producing proinflammatory cytokines and cartilage-degrading mediators [70, 71]. In horses with naturally occurring and experimentally induced OA and septic arthritis, increased levels of inflammatory components, such as leukocytes, interleukin (IL)-1β, IL-6, tumor necrosis factor α (TNF-α), and matrix metalloproteinases, has been demonstrated [72, 73]. In RA, it has been shown that FLS become active upon stimulation by inflammatory cytokines released by macrophage-like synoviocytes (and T-lymphocytes) and secrete matrix metalloproteases (MMP), causing joint destruction [69].
Macrophages derive from two main cellular lineages; one lineage arises from bone-marrow-derived monocytes and the other is derived from cells which disperse into the tissues during embryonic development [23]. The tissue-resident macrophages have distinctive gene-expression profiles which depend on the particular tissue in which they reside [25].
The three joint macrophage populations, i.e., the lining MLS, the sublining macrophages and the interstitial macrophages, differ in their origins and functions [74]. In the healthy synovium, macrophages are predominantly monocyte-independent [20, 24, 74]. The proliferation of macrophages harbored in the sublining connective tissue gives rise to both the MLS and the interstitial macrophages [24]. In both mice and humans, lining MLS seem to be highly phagocytic and anti-inflammatory [74]. In joint inflammation, the synovium also contains macrophages originating from recruited monocytes which produce pro-inflammatory cytokines and release molecules with the possibility of attracting lymphocytes which additionally propagate inflammation.
To add to the complexity, macrophages exist as various subsets, some of which are pro-inflammatory (M1) whereas others are anti-inflammatory and favor tissue repair (M2) [75, 76]. Undoubtedly, in synovial inflammation and arthritis, monocytes and macrophages play a central role, promoting the onset and the progression of joint inflammation [74]. In a recent study regarding the horse synovial membrane, M1 and M2 macrophages were characterized in normal and inflammed joints [26]. It appears evident that, given the central role of macrophages in OA, a clinical approach targeting activated macrophages at an earlier stage of OA may serve to inhibit or slow the progression of disease [77].
In the current study, all the macrophage populations expressed IBA1-IR; in addition, also MLS, and sublining and interstitial macrophages expressed vimentin-IR with its stronger immunolabeling expressed by the MLS. Vimentin, which is the main intermediate filament protein in mesenchymal cells (such as epithelial cells and fibroblasts), has already been observed in rat [78] and human FLS [79]. However, it has been reported that vimentin could also be expressed in the mononuclear phagocyte system [80]; in particular, vimentin manifests enhanced fluorescence in activated macrophages [81]. In the current study, only MLS showed bright vimentin-IR, evidence which suggested an activated state of the lining macrophages.
## Cnr1, Cnr2, GPR55, TRPV1, and PPARA gene expression in synoviocytes
To date, the gene expression has been reported in the equine synovial membrane only for TRPV1 [4]. The present study confirmed the expression of TRPV1 and also demonstrated the expression of Cnr1, Cnr2, GPR55, and PPARA, according to the Authors' protein data. However, Cnr1 were not expressed in all the horses.
## CB1R, CB2R, GPR55, TRPV1, and PPARα immunoreactivity in synoviocytes
Cannabinoid receptor 1, which is usually expressed by the neurons, also in horses [54, 82], has been identified in human and mouse synoviocytes [28, 31, 40]. Cannabinoid receptor 1 has also been identified in synoviocytes of the horse [44] in which it was co-expressed with CB2R; the Authors were not able to identify the synovial cell types expressing CB1R-IR. Comparing the results of the current study with those described by Miagkoff et al. [ 43], some differences should be noted. The first difference is related to the notable expression of CB1R-IR in synoviocytes (greater when compared to CB2R-IR) noted by Miagkoff et al. [ 44]. In the present study, the intensity of CB1R-IR was much lower than that of CB2R-IR; this evidence was also supported by the quantitative data of the mRNA Cnr1 (although not necessary, a correlation between mRNA and protein expression exists). The difference between the results of the present study and that of Miagkoff et al. [ 44] did not lie in the use of different anti-cannabinoid receptor antibodies since the same anti-CB1R and -CB2R antibodies were used for both the studies. Instead, a plausible reason for this discrepancy in the results could be the use of sections of paraffin-embedded tissues which may often create a background in immunofluorescence reactions. Unlike what was observed in the Miagkoff et al. study [44], no CB1R-nuclear immunolabeling was observed in the current study. To avoid any tissue background, which might be an interference in the reading of weak receptor immunostaining, cryosections of the synovial membrane were used in the present study.
Cannabinoid receptor 2 is mainly expressed by the immune cells [53], and its activation is usually associated with a decrease in both immune cell function and cytokine release [83]. Cannabinoid receptor 2 has been identified in human, mouse, rat, and horse synoviocytes (41–43).
Richardson et al. [ 28] identified CB2R (RNA and protein) in the FLS of healthy human patients, and patients with OA and AR. It has been shown that, in mouse and human joints, CB2R expression is up-regulated by proinflammatory mediators and injuries, and that its activation plays a key role in regulating inflammatory signaling in macrophages and FLS and suppresses the production of proinflammatory cytokines [42, 45, 84].
In the current study, CB2R-IR was brightly expressed in both FLS and MLS, suggesting a functional role of the endocannabinoid receptor system in horse joints. The evidence that targeting the CB2R in murine MLS and human FLS may be responsible for potent anti-inflammatory effects [45] could allow cautious speculation that the horse intra-articular ECS could be a promising therapeutic target for blocking pathological inflammation.
The TRP vanilloid 1 (TRPV1) ion channel is usually expressed by nociceptors of mammals [57, 85], including horses [53]. However, TRPV1 is also expressed in various non-neuronal tissues, such as rat [41] and human [46] synoviocytes. Cells in synovial compartments can be exposed to low pH conditions after inflammation, infection, or injury. An acid sensing receptor (TRPV1) has been identified on synovial cells which are responsive to a low pH (pH 5.5–7.0) [46]; TRPV1 is also activated by heat (>43°C) and capsaicin [86]. In joint inflammation, the synovial compartments can also be exposed to thermal (>43°C), chemical, and osmotic modifications which can activate the TRPV1 membrane sensors which respond by activating calcium and sodium fluxes.
A number of studies have indicated that the TRPV1, which seems to mediate the calcium dependent proliferative and secretory responses of the synoviocytes in the event of joint inflammation, might be a possible and valuable target for treating joint diseases [46, 87], even in the horse [4]. It has been shown that the TRP channels are functionally expressed in human synoviocytes and may play a critical role in adaptive or pathological changes in articular surfaces during arthritic inflammation, in particular in the response of the synoviocytes to the inflammatory mediator TNF-α [46]. This evidence seems to have some therapeutic relevance, given that an in vitro study showed that the synovial cells from arthritic animals spontaneously produced large amounts of TNF-α [88].
The finding of TRPV1-IR in the FLS of the horse is consistent with those obtained in humans [46, 89], rats [41], and mice [31]. The evidence of TRPV1-IR in MLS (and sublining macrophages) of the horse is also consistent with what has already been observed in human MLS [89]. Gene expression and immunohistochemical data strictly correlate and integrate with the recent observations of Braucke et al. [ 4] who identified and quantified the TRPV1 mRNA and the TRPV1 protein level in the metacarpo/metatarsophalangeal joints of the horse, and observed a higher expression of TRPV1 in samples from joints with pathology.
It has been shown that TRPV1 inhibits M1 macrophage polarization in the synovium and attenuates the progression of OA in a rat model of OA [90]. In addition, Engler et al. [ 91] showed that stimulation of the cultured synovial fibroblasts of OA and RA in human patients with capsaicin (TRPV1 agonist) led to the increased expression of IL-6 mRNA and IL-6 protein, and that IL-6 protein expression could be antagonized with capsazepine (a TRPV1 antagonist). Therefore, TRPV1 may play a role in non-neuronal mechanisms which could modulate nociception in symptomatic OA and RA patients.
Vanilloid receptor 1 (VR1 or TRPV1) is desensitized by endovanilloids, endocannabinoids (anandamide), endocannabinoid-like molecules [92, 93] and phytocannabinoids, such as CBD [38, 94] which shows anti-nociceptive, analgesic, and anti-inflammatory effects [35, 95]. The importance of the endocannabinoid signaling acting on TRPV1 has been highlighted by different OA studies in which it has been shown that synovial fibroblasts express several receptors involved in endocannabinoid action, and that endocannabinoid anandamide (AEA) reduces IL-6, IL-8, and TNF-α production by mixed synoviocytes [31].
Studies involving phytocannabinnoids showed that CBD, targeting synovial fibroblasts under inflammatory conditions, demonstrated anti-inflammatory effects on arthritis [96]. Cannabidiol may exert its anti-inflammatory and protective effects via TRPV1 receptors, as shown in the in vitro LPS-stimulated murine macrophage cell line [97]. In mice, it has been shown that synovial cells treated with CBD produced significantly less TNFα in culture and that CBD suppressed clinical signs of the disease without obvious side effects during chronic treatment [27]. Since CBD binds to several other receptors (TRPA1, GPR55, PPAR gamma, serotonin receptors, etc.), its mode of action remains elusive. However, CBD reduces IL-6/IL-8/MMP-3 production of RA synovial fibroblasts [96].
Not only phytocannabinoids but also the synthetic cannabinoid WIN55,212-2 mesylate (WIN) demonstrated strong anti-inflammatory effects in monocytes and synovial fibroblasts via a TRPV1 (and TRPA1) dependent pathway [12].
G protein-coupled receptor 55 (GPR55), which is considered to be the third cannabinoid receptor, has been identified in the sensory neurons of different species, including dogs, rats [57] and horses [53], and in canine inflammatory cells [58]. In addition, GPR55 has also been localized in human chondrocytes [48], osteoclasts and osteoblasts [47], and seems to be associated with bone remodeling and vascular homeostasis [98]. To the best of the Authors' knowledge, no data are available regarding the expression of GPR55 in synoviocytes and subintimal synovial cells. The expression of GPR55-IR has recently been shown in the macrophages harbored within the horse dorsal root ganglia [53]. A study on rodents has shown that the peripheral activation of GPR55 can reduce mechanosensitivity in the event of joint inflammation [99]. However, it is not clear whether this effect was exerted only at the level of the peripheral and central nervous system or also locally, at the level of the synovial cells.
In the present study, GPR55-IR has been demonstrated in both FLS, MLS, subintimal macrophages, and unidentified inflammatory/immunitary cells, suggesting an active role of the receptor in synovial membrane homeostasis and immunity. Cannabidiol, which acts as a GPR55 antagonist, should be able to reduce the migration of macrophages, as shown in mice [100].
Peroxisome proliferator-activated receptor alpha seems to have a role in sensory modulation due to its expression in the sensory neurons of animals, including horses [54].
Peroxisome proliferator-activated receptor alpha can be expressed by different cells of innate immunity, including monocytes and macrophages [101]. A number of studies have documented the anti-inflammatory consequences of PPARα activation in human and murine macrophages [102, 103]. Ligands of PPAR-α have been shown to regulate inflammatory responses [104] so much so that, in PPAR-α deficient mice, abnormally prolonged responses to different inflammatory stimuli have been noted [105]. The endogenous and exogenous PPAR-alpha ligands reduce the degree of macrophage inflammation caused by LPS/IFN-gamma stimulation [106].
There are studies indicating that PPARα agonists may exert beneficial effects on OA due to their anti-inflammatory effects [49]. Fenofibrate, a PPAR-alpha ligand, has been shown to inhibit the development of arthritis in a rat model of human RA by reducing cytokine production (IL-6, IL-8 and granulocyte monocyte colony-stimulating factor) from FLS [107].
There is extensive documentation regarding the anti-inflammatory, analgesic, immunomodulatory and neuroprotective effects of the endocannabinoid-like lipid mediator PEA, also for joint health and pain modulation [108, 109]. Palmitoylethanolamide exerts its analgesic and anti-inflammatory effects primarily by activating the PPAR-α; however, binding to PPAR-α, PEA triggers TRPV1 channel activation, providing another mode of action in which PEA interacts with the endocannabinoid and endovanilloid systems [110].
## CB1R, CB2R, GPR55, TRPV1, and PPARα immunoreactivity in synovial blood vessels
Endothelial cells and smooth muscle cells were positive for analyzed markers. However, since among cells surrounding endothelial cells there are not only smooth muscle cells but also pericytes as well as adventitial cells [111, 112] and no specific markers for these cellular cytotypes have been used, it cannot be excluded that cannabinoid and cannabinoid-related receptors may also be expressed by other vascular cells.
The principal functions of the endothelium are to promote smooth muscle cell relaxation and arterial dilation, control vascular permeability, exert an antithrombotic effect, and regulate angiogenesis [113].
The angiogenesis may exacerbate OA pain, and the upregulated angiogenic factors and the molecules produced by vascular cells may also stimulate nerve growth [7, 114]. In addition to the role they play regarding pain, neuropeptides released by stimulated nerve endings are involved in vasodilation, inflammation (by producing proinflammatory cytokines and by activating inflammatory infiltrating cells), and synoviocyte proliferation and activation [115, 116]. During joint diseases, the proliferation of endothelial cells and their morphological differentiation to form tubes accompanies extracellular matrix degradation which facilitates the tissutal invasion of inflammatory cells and is perpetuated by various mediators (2, 116–118). Therefore, angiogenesis and matrix degradation may be interesting/key targets to counteract the progression and chronicity of joint inflammation and degeneration.
Cannabinoids are hypotensive and vasodilator molecules which can exert their effects by acting on the vascular smooth muscle cells and/or endothelial cells [119].
Cannabinoid receptor 1 has been observed in both vascular cellular elements [120] in which it exerts vasodilatatory effects. However, in the present study, any CB1R-IR was observed in the capillaries or larger blood vessels of the horse synovium; this finding was also in contrast to the data published by Miagkoff et al. [ 44].
In the present study, the expression of CB2R-IR by vascular endothelial and smooth muscle cells was described. The Expression of CB2R-IR has previously been observed in the vascular endothelial cells of humans and animals [57, 121, 122], including horses [44].
The expression of CB2R-IR in blood vessels may functionally be in relation to the data observed in normal joint of rats in which it has been shown that the CB2R agonist JWH133 caused hyperemia via a CB2R and TRPV1 mechanism, and that, during acute and chronic inflammation, this vasodilatatory response was significantly attenuated [122]. Rajesh et al. [ 123], by investigating the effect of CB2R receptor agonists on TNFα-induced proliferation, migration and signal transduction in the smooth muscle cells of human coronary arteries, observed that CB2R agonists decreased vascular smooth muscle proliferation and migration. Although for the most part, hypothetically and totally not demonstrated, the Authors cannot exclude that CB2R agonists might also reduce the angiogenesis and inflammation in an inflamed horse joint.
In the current study, TRPV1-IR was observed in both the endothelial and smooth muscle cells of the synovial blood vessels, a finding consistent with that obtained in humans and other animals (124–127).
The notable anti-angiogenic activities of cannabinoid compounds, which have mainly been tested in tumor experiments, are carried out directly, inhibiting vascular endothelial cell migration and survival, and decreasing the expression of proangiogenic factors [35, 128]. It has been shown that CBD may inhibit angiogenesis by the down-modulation of several angiogenesis-related molecules [117]. Cannabinoids may act on different receptors to obtain their effect; however, the expression of TRPV1-IR in the endothelial cells of the horse synovial membrane is relevant as it is known that TRPV1 promotes endothelial cell proliferation and network-formation by means of the cellular uptake of the endocannabinoid anandamide [127]. Therefore, CBD, which stimulates and desensitizes TRPV1, may potentially contrast angiogenesis in horse joint inflammation.
The endothelium exerts a profound relaxing effect on the underlying smooth muscle cells; nitric oxide (NO) is a well-characterized vasoactive substance produced by the endothelium which diffuses to and relaxes the smooth muscle, causing arterial dilation [129]. It has been shown that CBD causes vasorelaxation of the human mesenteric arteries via activation of the CB1 and TRPV1 channels, and it is endothelium- and nitric oxide-dependent [129].
G protein-coupled receptor 55 was observed in the blood vessels of the horse joint, a finding which is consistent with that of Xu et al. [ 130] who identified GPR55 in the endothelium of human and mouse aortas, and of Daly et al. [ 131] who located GPR55 in the endothelium of mouse blood vessels. The first evidence of the functional role of GPR55 was obtained in the vascular system in which it was shown to regulate systemic vascular resistance and angiogenesis [98, 132]. Scientific evidence indicates that the agonists of GPR55 can elicit either vasoconstriction or vasorelaxation [133]. Recent studies involving humans have indicated that L-α-lysophosphatidylinositol, a GPR55 agonist, induced endothelium-dependent vasorelaxation in the pulmonary arteries [134] and mediated ovarian carcinoma cell-induced angiogenesis [135]. Due to the antagonist effect of CBD on GPR55, it is reasonable to consider, although in a purely speculative way, that CBD might reduce angiogenesis and vasorelaxation of the blood vessels of the horse joint via GPR55.
Also the expression of PPARα-IR was observed in the endothelial cells of the horse joint, as has already been described in the blood vessels of the cervical DRG [53]. The anti-proliferative and anti-angiogenic properties of PPARα in endothelial cells have been demonstrated in a variety of in vitro and in vivo models [104]. Taken together, these findings lead us to hypothesize that the analgesic and anti-inflammatory properties of this receptor, as previously described in other species, are also present in the horse [38, 136].
The present study demonstrated that, in the equine synovial membrane in healthy joints, the mRNA of Cnr1, Cnr2, TRPV1, GPR55, and PPARA was present, according to the protein results. Moreover, the mRNA results of TRPV1 were consistent with a previous study regarding equine articular tissue [4]. To the best of the Authors' knowledge, no data have been reported on equine articular tissue regarding the expression of the other receptors described in this paper.
## Limitation
There are some limitations which should be taken into consideration when interpreting the results of this study. It cannot be ruled out that some factors could potentially alter the CB1R, CB2R, TRPV1, GPR55, and PPARα expression in tissues, such as the unknown underlying pathological conditions of the horses in the study or the medications received. In addition, the limited number of horses considered in the current study, the reduced representation of male to female horses, as well as adult and young horses, represent another limitation of the study.
## Conclusion
The present study was the first study to demonstrate the mRNA presence and the protein cellular distribution of the cannabinoid receptors (CB1 and CB2) and three cannabinoid-related receptors (TRPV1, GPR55, and PPARα) in the horse synovial tissues of the metacarpophalangeal joint of the horse. Cannabinoid receptor 1 was identified in FLS and MLS, although it was not expressed in all the horses. Cannabinoid receptor 2, TRPV1 and GPR55 were identified in FLS, MLS, and blood vessels, while PPARα-IR was identified in FLS and blood vessels. Due to their cellular localization, these receptors may be the target of many drugs (endocannabinoids and endocannabinoid-related molecules, non-psychoactive phytocannabinoids, synthetic cannabinoids and several agonist and antagonist drugs) which could potentially be utilized to improve inflammation and pain in horses with joint diseases. These results should hopefully encourage the development of new molecular and preclinical studies supporting the use of molecules already tested and used in humans and animals which could potentially reduce the joint inflammation in horses with joint diseases. Comparison of the data of the current study with the data obtained from the synovial tissues of horses with metacarpophalangeal joint disease could be of interest to verify whether mRNA of Cnr1, Cnr2, TRPV1, GPR55, and PPARA, and the immunoreactivity for the same receptors are up- or down-regulated during joint disease.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
Ethical review and approval was not required for the animal study because the metacarpophalangeal joints of horses slaughtered for consumption were collected post-mortem. According to Directive $\frac{2010}{63}$/EU of the European Parliament and of the Council of 22 September 2010 regarding the protection of animals used for scientific purposes, the Italian legislation (D. Lgs. no. $\frac{26}{2014}$) does not require any approval by competent authorities or ethics committees because this study did not influence any therapeutic decisions.
## Author contributions
RC, RZC, RR, and AG contributed to the study design. The mRNA analysis was carried out by AZ and MF. The immunohistochemical experiments were carried out by RZC, MDS, and GS. Acquisition of data and drafting of the manuscript was done by RC. All authors interpreted the data. All authors contributed to the study execution and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2023.1045030/full#supplementary-material
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|
---
title: Homology modeling, virtual screening, molecular docking, and dynamics studies
for discovering Staphylococcus epidermidis FtsZ inhibitors
authors:
- Divya Vemula
- Dhanashri Ramesh Maddi
- Vasundhra Bhandari
journal: Frontiers in Molecular Biosciences
year: 2023
pmcid: PMC10020519
doi: 10.3389/fmolb.2023.1087676
license: CC BY 4.0
---
# Homology modeling, virtual screening, molecular docking, and dynamics studies for discovering Staphylococcus epidermidis FtsZ inhibitors
## Abstract
Staphylococcus epidermidis is the most common cause of medical device-associated infections and is an opportunistic biofilm former. Among hospitalized patients, S. epidermidis infections are the most prevalent, and resistant to most antibiotics. In order to overcome this resistance, it is imperative to treat the infection at a cellular level. The present study aims to identify inhibitors of the prokaryotic cell division protein FtsZ a widely conserved component of bacterial cytokinesis. Two substrate binding sites are present on the FtsZ protein; the nucleotide-binding domain and the inter-domain binding sites. Molecular modeling was used to identify potential inhibitors against the binding sites of the FtsZ protein. One hundred thirty-eight chemical entities were virtually screened for the binding sites and revealed ten molecules, each with good binding affinities (docking score range −9.549 to −4.290 kcal/mol) compared to the reference control drug, i.e., Dacomitinib (−4.450 kcal/mol) and PC190723 (−4.694 kcal/mol) at nucleotide and inter-domain binding sites respectively. These top 10 hits were further analyzed for their ADMET properties and molecular dynamics simulations. The Chloro-derivative of GTP, naphthalene-1,3-diyl bis(3,4,5-trihydroxybenzoate), Guanosine triphosphate (GTP), morpholine and methylpiperazine derivative of GTP were identified as the lead molecules for nucleotide binding site whereas for inter-domain binding site, 1-(((amino(iminio)methyl)amino)methyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium, and Chlorogenic acidwere identified as lead molecules. Molecular dynamics simulation and post MM/GBSA analysis of the complexes revealed good protein-ligand stability predicting them as potential inhibitors of FtsZ (Figure 1). Thus, identified FtsZ inhibitors are a promising lead compounds for S. epidermidis related infections.
## Introduction
Antibiotic resistance is a global issue associated with high morbidity and mortality (Akova, 2016). Multidrug-resistant bacteria and significant bacterial infections exhibit alarming rates of emergence and resistance to standard antibiotics. Currently, there are no viable preventative measures or effective medicines, and only a limited number of new antibiotics are developing, making the fight against bacterial infections even more challenging. Innovation is necessary until new treatment alternatives and antimicrobial therapies are developed (Chellat et al., 2016). Focusing on new targets or crucial mechanisms for identifying potential treatment is essential. We focused on cell division, a fundamental and vital process. Binary fission is a standard process in bacteria to produce offspring. Filamenting temperature-sensitive mutant Z (FtsZ) acts as a pacemaker for the formation of divisomes (macromolecular protein complexes that mediate the distinct and unique phases of bacterial cell division) during cytokinesis by assembling protofilaments to form the FtsZ-ring (also known as the Z-ring) at the site of potential division (Silber et al., 2020).
Staphylococcus epidermidis, belonging to the staphylococci family, has been identified as a significant contributor of nosocomial infection and recognized as an important opportunistic pathogen. ( Widerström, 2016). Currently, its rate of nosocomial infections is on par with that of Staphylococcus aureus, one of its more dangerous kin (National Nosocomial Infections Surveillance System, 2004). S. epidermidis and other coagulase-negative staphylococci were mainly found responsible for causing medical device-associated infections (Kleinschmidt et al., 2015). These species are highly contagious among prosthetic valves, cardiac devices, central lines, catheters, and IV drug use patients. In addition, neonates are found to be highly susceptible to them (Cheung and Otto, 2010). Approximately $20\%$–$30\%$ of orthopaedic device-related infections (ODRIs) (Trampuz and Zimmerli, 2006; Montanaro et al., 2011; Moriarty et al., 2016) are caused by S. epidermidis, and in late-developing infections, the incidence may potentially reach $50\%$ (Schafer et al., 2008). When studying the clinical course and outcome of staphylococcal ODRIs in older patients, Morgenstern et al. were able to demonstrate that S. epidermidis was linked to extended infections and had a lower cure rate ($75\%$) than S. aureus ($84\%$) (Morgenstern et al., 2016).
FtsZ is a crucial component of the cytoskeletal protein complex in bacterial cytokinesis (Erickson et al., 2010). Current technologies in the discovery of antibiotics have identified compounds that directly interact with the crucial cell division protein FtsZ, disturbing the dynamics and operation of the cell division machinery, or degrading FtsZ, damaging the structural integrity (Silber et al., 2020). As a result of prokaryotes’ significant protein conservation, FtsZ is present in various pathogens, such as Escherichia coli, Staphylococcus aureus, Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasma pneumoniae, Helicobacter pylori, Treponema pallidum, Neisseria meningitidis, Rickettsia prowazekii, Campylobacter jejuni, Shigella, and Salmonella (Chalker, A.F. and Lunsford, R.D., 2002).
FtsZ mainly consists of two binding sites: a nucleotide-binding site and an inter-domain binding site (Casiraghi et al., 2020) which includes a C-terminal tail (CTT) and C-terminal variable region (CTV) connected by a central helix (Oliva et al., 2007). In the nucleotide-binding site, GTP hydrolysis causes the protofilament to break down, weakening the protein-nucleotide connection, and ultimately preventing cell division. As the nucleotide-binding site is highly conserved among wide range of bacterial species, it became a crucial target for developing broad-spectrum antibacterial agents (Du and Lutkenhaus, 2019). Another functional site of FtsZ, located in a substantial cleft between the C-terminal domain and the H7 helix, is the inter-domain binding site. Various bacterial species have different cleft sizes, amino acid residue counts, and conservation rates. For instance, the inter-domain cleft is less conserved in Gram-negative bacteria than in Gram-positive bacteria. In accordance with the H7 helix’s curvature, the size of the interdomain cleft differs between bacterial species. FtsZ’s enzymatic domain has been shown to function as a self-activating GTPase (De Boer et al., 1992; RayChaudhuri, D. and Park, J.T., 1992). Similar to S. aureus, the FtsZ of S. epidermidis comprises two globular subdomains, the N- and C-terminal subdomains, which are connected by a synergy loop (T7 loop) and the H7 helix, which forms the centre of the structure. The N-terminal subdomain (residues 13–173) has a nucleotide-binding pocket i.e. nucleotide binding domain. Most likely, the C-terminal subdomain (residues 223–310) acts as a GTPase activating subdomain i.e., inter-domain binding site (Matsui et al., 2014). So, targeting an Inter-domain binding site of FtsZ can help design or develop target-specific drugs. Hence, we have screened compounds against the nucleotide and an inter-domain binding site of the FtsZ utilising molecular modeling methods.
## Materials and methods
The basic workflow of finding potential FtsZ inhibitors in S. epidermidis was discussed in Figure 1 and the mechanism of action of FtsZ inhibitor was explained in Figure 2.
**FIGURE 1:** *Computational screening workflow for identifying FtsZ inhibitors in S. epidermidis.* **FIGURE 2:** *The mechanism of action of FtsZ inhibitors - i) Formation of Z ring in absence of FtsZ inhibitor resulting in bacterial cell division; ii) mislocation of FtsZ in presence of inhibitor resulting in elongation of filaments into rod shaped cells thereby causing cell division arrest.*
## Conservation of FtsZ
The conservation of FtsZ protein was experimentally determined by performing BLAST analysis of whole UNIPROT database sequences. The default parameters like E-Threshold as 10, and the Auto-BLOSSUM 62 matrix were used. A pairwise sequence alignment of the two FtsZ sequences from S. epidermidis and S. aureus was performed using *Clustal omega* to identify the regions of similarity which indicates the structural, functional, and evolutionary relationship between the two sequences.
## Target preparation
The 3D structure of the S. epidermidis FtsZ protein was predicted using the Swiss-Model server because the detailed structural information of the crystallized structure is unavailable in PDB as the literature report of its PDB entry (4M8I) is not published. The protein sequence was retrieved from the Uniprot database (Uniprot ID: Q5HQ06). The template was selected based on the parameters observed in the BLAST findings by mainly focusing on the sequence similarity, resolution, and experimental technique used to determine the structure. ( Choudhary et al., 2020). The target protein’s predicted 3D structure was validated using the Ramachandran (RC) Plot. The 3D protein structure was visualized using Maestro 13.1. After that, the built model was pre-processed using the protein preparation wizard of Schrodinger-suite 2022, which refine the protein structure for docking by setting bond orders, filling in missing loops, adding hydrogens, and deleting water molecules that are more than 3Å distances from the protein (Sahayarayan et al., 2021). After the H-bond assignment, H-bonds were optimized using PROPKA tool. The binding sites of the FtsZ protein were anticipated using the Site Map tool of the Schrodinger-suite 2022 which predicted best five sites for a given protein entry.
The receptor grid was generated using the “Receptor grid generation” panel from the glide module of Schrodinger software by preserving the grid’s default settings and size. A receptor grid was generated for both the nucleotide and inter-domain binding sites.
Using the Swiss model server, FtsZ protein homology modeling was carried out by choosing a template, PDB ID: 4M8I (Organism: *Staphylococcus epidermidis* RP62A) with $100\%$ sequence identity and a Global Model Quality Estimation (GMQE) of $0.81\%$. The target protein’s 3D model was validated by the Ramachandran plot using the Swiss Model’s structure evaluation. ( Figure 4). The 3D protein structure is visualized using Maestro 13.1). The active site for the nucleotide binding region was determined by using a co-crystallized ligand present in the template that we used for homology modeling followed by supplying the X, Y, and Z coordinates as -18.5, −9.81, and 19.98 Å, respectively, and retaining the other parameters at their default values, the receptor grid was generated at the nucleotide-binding site of FtsZ. The inter-domain binding site was predicted using the sitemap tool of Schrodinger-suite 2022. The top-ranked site showed a site score of interdomain binding site, volume = 116.620 Å3, hydrophilic score = 0.781, and hydrophobic score = 0.746. This predicted site contained Gln192, Gln195, Gly196, Asp199, Leu200, Val203, Leu209, Ile228, Leu261, Asn263, Ile264, Thr265, Val297, Asn299, Leu302, Val307, Thr309, and Ile311 residues. Similarly, the site coordinates X = −0.31, $Y = 13.26$, and $Z = 24.61$Å were provided to generate a receptor grid at the inter-domain binding site with the default settings for the remaining parameters.
**FIGURE 4:** *Ramachandran Plot of homology modeled FtsZ protein, dots indicate the amino acid residues of FtsZ protein. The residues present in dark green, light green and light grey regions of the plot represent allowed, favourable and disallowed regions of the plot, respectively. Most amino acids of the modeled protein are present in the allowed region of Ramachandran plot.*
## Ligand preparation
The literature review obtained about 138 reported natural, semi-synthetic, and synthetic FtsZ inhibitors of various other pathogenic bacterial species. The experimentally proved, 138 natural, semi-synthetic, and synthetic FtsZ inhibitors of various other pathogenic bacterial species were obtained (Tripathy and Sahu, 2019). Among these ligands, the chemical structures of a few were obtained from PubChem, and the remainder were sketched using maestro 13.1’s 2D Sketcher. To prepare ligands, these structures were loaded into Schrodinger Workspace. The Ligprep module of Schrodinger-suite 2022 was used to prepare the ligands by optimizing their geometrical features and generating ionization states for the compounds to achieve the necessary pH of 7.0 ± 2.0.
## Molecular docking
Using the “ligand docking” panel of Schrodinger software, the prepared ligands were docked against both the nucleotide and inter-domain binding sites of the FtsZ protein. In order to generate hits from the ligand dataset, ligand docking was first carried out using the high throughput virtual screening (HTVS) approach with the precision mode set to HTVS, followed by standard precision (SP), and extra precision (XP). While taking the docking score into account, the Epik state penalties of the ligands were modified. ( Kapusta et al., 2021). The docking validation is accomplished by redocking co-crystal ligands to their specific binding site of the receptor protein (El-Far et al., 2020). Dacomitinib (S2727), used as a control drug to assess the binding affinities and free energy of the proposed inhibitors, is a promising FtsZ inhibitor that Du et al. identified using in vivo and in vitro bioassays. while PC190723 (Elsen et al., 2012) served as the inter-domain binding site control drug. Instead of suppressing FtsZ filament assembly and condensation, PC190723 (difluoro-benzamide derivative) induces it (Andreu et al., 2010), causing FtsZ to assemble into delocalized cellular foci as opposed to the Z-ring (Adams et al., 2011).
## Prime MM/GBSA analysis
The binding free energies of the top 10 docked complexes (nucleotide-binding site and inter-domain binding site) were determined using the Prime MM/GBSA module of Schrodinger-suite (Muthumanickam et al., 2022; Ramachandran et al., 2022). The equation used for calculating free energy is as follows ΔGbind=Gcomplex−(Gprotein+Gligand) The Gcomplex indicates complex energy, Gprotein indicates receptor energy, and Gligand indicates the unbound ligand energy.
## Prediction of ADME properties
The Qikprop module of Schrodinger’s suite 2022 was used to forecast the pharmacokinetic features, also known as absorption, distribution, metabolism, and excretion, of the top 10 hit compounds (Guo et al., 2016). This module provides information on the drug-like characteristics of the given ligands, such as molecular weight (mol MW), the number of hydrogen bond acceptors and donors (accptHB), solubility (QPlogS), Octanol/water Partition coefficient (QPlogPo/w), the percentage of oral absorption, apparent Caco-2 cell permeability (QPPCaco), and the brain/blood partition coefficient (QPlogBB).
## Toxicity prediction
ProTox—II was used to calculate the toxicity of the hit compounds. This programme delivers data based on chemical similarities, fingerprint propensities, etc. The ProTox-II employs machine learning models to determine the toxicity class, LD 50 values, organ toxicity, and toxicity endpoints, including hepatotoxicity, carcinogenicity, mutagenicity, immunogenicity, etc. Using a system that is universally accepted, toxicity classifications for chemicals are defined (GHS) (Banerjee et al., 2018). LD50 values are given in [mg/kg]:Class I: If ingested, deadly (LD50 ≤ 5)Class II: if ingested, deadly (5 < LD50 ≤ 50)Class III: poisonous if ingested (50 < LD50 ≤ 300)Class IV: Dangerous if ingested (300 < LD50 ≤ 2000)Class V: May cause injury if ingested (2000 < LD50 ≤ 5000)Class VI: Non-toxic (LD50 > 5000)
## Molecular dynamics (MD) simulation
The molecular dynamics (MD) simulation of the top docking scored protein-ligand complex was performed using the desmond module of Schrodinger suite 2022 for 100 ns. The simulation system was built using a system builder task where a simple point charge water (SPC) model was used as a solvent system, and periodic boundary condition (PBC) was applied using an orthorhombic boundary box. The system was neutralized by adding counter ions like Na+ and Cl−. The OPLS4 force field was used for energy minimization of molecular dynamics system. For the simulation of the build system it was loaded on to workspace, molecular dynamics simulation task was performed for 100 ns with a trajectory recording interval of 100 ps while the system was equilibrated with NPT ensemble system with pressure 1.01 bar and temperature 300 k, respectively. Finally, RMSD, RMSF, protein-ligand interaction and ligand properties were used for analyzing molecular dynamics simulation results (Kaushik et al., 2018).
## Post MM/GBSA analysis
Using the Prime MM/GBSA module of the Schrodinger, the post MM/GBSA analysis was carried out for the complexes at the 100 ns time frame of the molecular dynamics simulation to determine the binding free energies.
As stated in Table 9, the molecular dynamics simulation was generated with a 100 ns frame time interval for the purpose of analyzing the binding free energy calculation following MM/GBSA. The findings revealed that, on average, complexes (Compound A, B, C, and D) at nucleotide binding sites had better binding free energies at post simulation period (−97.15, −57.11, −73.07, and −42.02 kcal/mol) compared to pre-MM/GBSA complexes, but the complexes (Compound 2 and 3) at inter-domain binding sites had comparable binding free energies (−45.49 and −45.38 kcal/mol) compared to pre-MM/GBSA free energies. As there is less variation among the pre and post MM/GBSA energies of complexes at the inter-domain site, they can be considered as strong binders (Table 10). It was observed that for the binding of compounds at both binding sites, the MM/GBSA dG bind coulomb, and MM/GBSA dG bind solv GB energies majorly contributed to the binding energies of the complexes.
## Conservation of FtsZ protein
With the aid of BLAST, the entire Uniprot database was aligned using the reference sequence of the FtsZ protein from S. epidermidis (Uniprot ID: Q5HQ06). This produced 250 hits, with the sequence identities ranging from $100\%$ to $65.9\%$, indicating that they are the best matches or homologs present in different bacterial species such as Staphylococcus species, Macrococcus caseolyticus, Abyssicoccus albus, Bacillus sp. ( Supplementary Figure S5) The FtsZ protein of S. epidermidis has shown a high sequence similarity about $92.39\%$ identity with that of its kin i.e., S. aureus (Figure 3).
**FIGURE 3:** *Sequence alignment of ftsz sequence (Uniprot ID: Q5HQ06 and P0A031) of Staphylococcus epidermidis and Staphylococcus aureus, respectively. Blue colour outlined box represents the N-terminal subdomain while Red colour outlined box indicates C-terminal subdomains of FtsZ protein with 100 percent similarity between these sequences.*
## Molecular docking at nucleotide-binding site
Molecular docking was performed sequentially using the Schrodinger’s Glide module in three modes: HTVS, SP, and XP. It is crucial to know the binding affinity since docking was used to explore molecules that might inhibit the FtsZ protein. This research article discusses the outcomes of glide-XP docking since the extra precision (XP) mode generates precise results and uses the chemscore scoring program to evaluate the docked complex. The top 10 ligand structures are displayed in Table 1, along with their docking data and receptor-ligand interactions.
**TABLE 1**
| SI No. | Ligand | Structure | Docking score (kcal/mol) | Interacting amino acids |
| --- | --- | --- | --- | --- |
| 1 | Compound A | | −9.549 | Hydrogen Bond- Gly21, Gly22, Asn25, Arg29, Ala71, Ala73, Gly108, Thr109, Gly110, Thr133, Glu139, Asn166 |
| 1 | Compound A | ((2R,3S,4R,5R)-5-(2-amino-8-chloro-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate | −9.549 | Pi-Pi Stacking- Phe183 |
| 2 | Compound B | | −9.539 | Hydrogen Bond- Gly104, Gly108, Thr109, Gly110, Asn166, Asp187 |
| 2 | Compound B | naphthalene-1,3-diyl | −9.539 | Hydrogen Bond- Gly104, Gly108, Thr109, Gly110, Asn166, Asp187 |
| 2 | Compound B | bis(3,4,5-trihydroxybenzoate) | −9.539 | Pi-Cation - Arg143 |
| 3 | Compound C | | −9.530 | Hydrogen Bond- |
| 3 | Compound C | Guanosine-5′-triphosphate (GTP) | −9.530 | Gly21, Gly22, Asn25, Arg29, Ala71, Gly108, Thr109, Thr133, Glu139, Arg143, Asn166 |
| 3 | Compound C | Guanosine-5′-triphosphate (GTP) | −9.530 | Pi-Pi Stacking- Phe183 |
| 4 | Compound D | | −9.276 | Hydrogen Bond- Gly21, Arg29, Ala71, Gly72, Ala73, Met105, Gly108, Thr109, Gly110, Thr133, Arg143 |
| 4 | Compound D | ((2R,3S,4R,5R)-5-(2-amino-8-morpholino-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate | −9.276 | Hydrogen Bond- Gly21, Arg29, Ala71, Gly72, Ala73, Met105, Gly108, Thr109, Gly110, Thr133, Arg143 |
| 5 | Compound E | | −9.244 | Hydrogen Bond- Gly21, Gly22, Ala71, Ala73, Met105, Gly108, Thr109, Arg143 |
| 5 | Compound E | ((2R,3S,4R,5R)-5-(2-amino-8-(4-methylpiperazin-1-ium-1-yl)-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate | −9.244 | Hydrogen Bond- Gly21, Gly22, Ala71, Ala73, Met105, Gly108, Thr109, Arg143 |
| 6 | Compound F | | −8.902 | Hydrogen Bond- Gly22, Asn25, Arg29, Ala71, Ala73, Gly108, Thr109, Thr133, Glu139, Arg143, Asn166 |
| 6 | Compound F | ((2R,3S,4R,5R)-5-(2-amino-8-methyl-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl hydrogen triphosphate | −8.902 | Hydrogen Bond- Gly22, Asn25, Arg29, Ala71, Ala73, Gly108, Thr109, Thr133, Glu139, Arg143, Asn166 |
| 7 | Compound G | | −8.900 | Hydrogen Bond- Gly21, Gly22, Ala71, Ala73, Met105, Gly108, Thr109, Arg143 |
| 7 | Compound G | ((2R,3S,4R,5R)-5-(2-amino-8-(4-methylpiperazin-1-ium-1-yl)-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate | −8.900 | Hydrogen Bond- Gly21, Gly22, Ala71, Ala73, Met105, Gly108, Thr109, Arg143 |
| 8 | Compound H | | −8.886 | Hydrogen Bond- Gly21, Gly22, Asn25, Arg29, Gly108, Thr109, Gly110, Thr133, Glu139, Arg143, Asn166 |
| 8 | Compound H | ((2R,3S,4S,5R)-5-(2-amino-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl diphosphate | −8.886 | Hydrogen Bond- Gly21, Gly22, Asn25, Arg29, Gly108, Thr109, Gly110, Thr133, Glu139, Arg143, Asn166 |
| 8 | Compound H | ((2R,3S,4S,5R)-5-(2-amino-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl diphosphate | −8.886 | Pi-Pi Stacking- Phe183 |
| 9 | Compound I | | −8.837 | Hydrogen Bond- Gly22, Asn25, Arg29, Gly108, Thr109, Gly110, Glu139, Arg143, Asn166 |
| 9 | Compound I | ((2R,3S,4R,5R)-5-(2-amino-6-oxo-8-(pyrrolidin-1-yl)-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate | −8.837 | Salt Bridge- Arg143 |
| 9 | Compound I | ((2R,3S,4R,5R)-5-(2-amino-6-oxo-8-(pyrrolidin-1-yl)-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate | −8.837 | Pi-Pi Stacking- Phe183 |
| 10 | Compound J | | −8.789 | Hydrogen Bond- Gly22, Asn25, Arg29, Ala71, Ala73, Gly108, Thr109, Glu139, Arg143, Asn166 |
| 10 | Compound J | ((2R,3S,4R,5R)-5-(2-amino-8-methyl-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate | −8.789 | Pi-Pi Stacking- Phe183 |
| 11 | Control drug | | −4.450 | Hydrogen Bond- Gly22, Thr109, Arg143 |
| 11 | Control drug | Dacomitinib | −4.450 | Salt Bridge- Glu139 |
Out of 138 screened molecules, Compound A (2R,3S,4R,5R)-5-(2-amino-8-chloro-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl triphosphate) i. e, chloro derivative of GTP had the highest docking score of −9.549 kcal/mol compared to Compound I ((2R,3S,4S,5R)-5-(2-amino-6-oxo-1,6-dihydro-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl diphosphate i.e., GDP molecule, a co-crystallized ligand which was found in the template protein, with a docking score of −8.886 kcal/mol. Following analysis of the 2D interaction diagram of the top four ligand-protein complex, it was found that while the ring structure of the GTP derivative produced a Pi-Pi stacking interaction with the aromatic amino acid Phenylalanine (Phe) 183 of active site, phosphate groups interacted with the majority of amino acids by forging hydrogen bonds Figure 5. ( Figure 5A). Compound B produced hydrogen bonds and pi-cation interactions with the amino acids Gly104, Gly108, Thr109, Gly110, Asn166, Asp187, and Arg143, respectively (Figure 5B). It was observed that the compound C have shown similar interactions as that of compound A. (Figure 5C). The Compound D has interacted with Gly21, Arg29, Ala71, Gly72, Ala73, Met105, Gly108, Thr109, Gly110, Thr133, and Arg143 residues via hydrogen bonding (Figure 5D). whereas the compound E formed hydrogen bonding interaction with Gly21, Gly22, Ala71, Ala73, Met105, Gly108, Thr109, and Arg143 residues (Figure 5E). in the nucleotide binding site. The hydrogen bond interactions with Gly108 was found common in both GTP and non GTP derivatives which can be assumed as a crucial interaction for inhibiting FtsZ activity. The control drug i.e., Dacomitinib has interacted with Gly22, Thr109, Arg143 via hydrogen bonding and Salt Bridge with Glu139 of FtsZ’s nucleotide binding site. The docking results were validated by redocking the co-crystallized ligand (GDP) in the template protein (PDB—4M8I) to its nucleotide binding site. The co-crystal ligands’ original and docked confirmations were compared, and the computed root means square deviation (RMSD) between them was less than 2 Å i.e. 0.4302 Å (Figure 6).
**FIGURE 5:** *2-D interaction diagrams of top five docking score ligand-protein complexes at nucleotide binding site. The purple, green and red arrows represents hydrogen bonding, Pi-Pi stacking and Pi-cation interactions between ligands and FtsZ protein, (1) Compound A [-9.549 kcal/mol], (2) Compound B [-9.539 kcal/mol], (3) Compound C [-9.530 kcal/mol], (4) Compound D [-9.276 kcal/mol], (5) Compound E [-9.244 kcal/mol].* **FIGURE 6:** *Validation of the molecular docking procedure used by using the before docking (green) and after docking (red) poses of co-crystallized ligand (GDP). Both the poses of GDP overlaps almost exactly with an RMSD of 0.4302, showing the validity of our docking method and the accuracy of all docking scores.*
## Molecular docking at inter-domain binding site
Small molecules bind to the region between the N and C-terminal guanosine triphosphate (GTP) binding subdomains i.e., the inter-domain binding site of FtsZ, reducing its capacity to function allosterically, which eventually prevents bacterial division. However, the lack of adequate chemical tools to develop a binding screen against this region has hindered the search for FtsZ antibacterial inhibitors. ( Huecas et al., 2021).
At the FtsZ protein’s inter-domain binding site, ligand docking was conducted using the ligand molecules taken from the literature. The top 10 outputs’ binding affinities range was −7.243 to −4.290 kcal/mol (Table 2). According to the findings of this study, the ligands interact with the active residues Asp199, Glu301, Asn263, Thr265, Glu192, Thr309, and Gly196 through interactions that entail both salt bridges and hydrogen bonds.
**TABLE 2**
| S.No | Ligands | Structure | Docking score (kcal/mol) | Interaction |
| --- | --- | --- | --- | --- |
| 1 | Compound 1 | 1-(2-((amino(iminio)methyl)amino)ethyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium | -7.243 | Salt Bridge- Asp199 |
| 1 | Compound 1 | 1-(2-((amino(iminio)methyl)amino)ethyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium | -7.243 | Hydrogen Bond- Glu301 |
| 2 | Compound 2 | 1-(((amino(iminio)methyl)amino)methyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium | -6.518 | Salt Bridge- Asp199 |
| 2 | Compound 2 | 1-(((amino(iminio)methyl)amino)methyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium | -6.518 | Hydrogen Bond- Glu301 |
| 3 | Compound 3 | Chlorogenic Acid | -6.355 | Salt Bridge- Arg191 |
| 3 | Compound 3 | Chlorogenic Acid | -6.355 | Hydrogen Bond- Asn263, Thr265 |
| 4 | Compound 4 | (Z)-2-(2-(5-fluoro-2-methyl-1-(4-(methylthio)benzylidene)-1H-inden-3-yl)acetamido)-N,N-dimethylethan-1-aminium | -5.376 | Hydrogen Bond - Asp199 |
| 5 | Compound 5 | Curcumin | -5.273 | Hydrogen Bond- Arg191, Glu192, Asn263 |
| 6 | Compound 6 | Caffeic Acid | -5.097 | Hydrogen Bond- Thr309 |
| 7 | Compound 7 | Epirubicin | -5.067 | Salt Bridge- Asp199, Thr309, Gly196 |
| 7 | Compound 7 | Epirubicin | -5.067 | Hydrogen Bond- Glu301 |
| 8 | Compound 8 | Daphnetin | -5.057 | Hydrogen Bond- Thr309 |
| 9 | Compound 9 | Phellodenol A | -4.408 | Hydrogen Bond- Asn263, Thr265, Val307 |
| 10 | Compound 10 | (E)-2-(2-(1-(benzo[d]thiazol-2-ylmethylene)-5-fluoro-2-methyl-1H-inden-3-yl)acetamido)-N,N-diethylethan-1-aminium | -4.29 | Salt Bridge & Hydrogen Bond- Asp199 |
| 11 | Control drug (PC190723) | 3-((6-chlorothiazolo[5,4-b]pyridin-2-yl)methoxy)-2,6-difluorobenzamide | -4.694 | Halogen Bond –Asn25, Arg29 |
| 11 | Control drug (PC190723) | 3-((6-chlorothiazolo[5,4-b]pyridin-2-yl)methoxy)-2,6-difluorobenzamide | -4.694 | Hydrogen Bond- Gly108, Thr109, Thr133, Asn166 |
The two-dimensional interaction diagram of the ligand-protein complex at this binding site is shown in (Figure 7). While Glu301 formed hydrogen connections with Compounds 1 and 2, Asp199 altered the salt bridge (Figures 7A,B). Compound 3 (chlorogenic acid) formed hydrogen bonds with Asn263, Thr265, and Asp199 (Figure 7C). Compound 4 has shown only hydrogen bond interaction with Asp199 (Figure 7D). Compound 5 has interacted with Arg191, Glu192, Asn263 residues via hydrogen bonding (Figure 7E). The interaction with Asp199 via hydrogen bonding or through salt bridge was found to be common among all the hits. This indicates that the interaction with Asp199 may be essential for the compounds to exhibit FtsZ inhibitory activity.
**FIGURE 7:** *2-D interaction diagrams of top five ligand-protein complexes at inter-domain binding site, the interaction in purple colour represents hydrogen bonding, while blue-red line depicts salt bridge interaction between ligand and protein residues, (A) Compound 1 [-7.243 kcal/mol], (B) Compound 2 [-6.518 kcal/mol], (C) Compound 3 [-6.355 kcal/mol], (D) Compound 4 [-5.376 kcal/mol], (E) Compound 5 [-5.273 kcal/mol].*
## Prime MM/GBSA analysis of docked complexes at nucleotide binding site
The binding free energies of the top five docked complexes were calculated using the MM/GBSA approach. The results revealed that the binding free energy of FtsZ’s nucleotide-binding site with compounds A, B, C, D and E were found to be −41.92, −58.09, −65.68, −34.80, and −26.90 kcal/mol, respectively (Table 3). According to the total binding free energies, the hit molecules may form a potent interaction within the binding region of the selected target, thereby inhibiting enzyme activity.
**TABLE 3**
| Compound ID | MM/GBSA dG bind (kcal/mol) | MM/GBSA dG bind coulomb (kcal/mol) | MM/GBSA dG bind covalent (kcal/mol) | MMGBSA dG bind Hbond (kcal/mol) | MMGBSA dG bind Vdw (kcal/mol) | MMGBSA dG bind packing (kcal/mol) | MMGBSA dG bind solv GB (kcal/mol) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Compound A | −41.92 | 44.80 | 11.48 | −14.19 | −43.39 | −3.24 | −31.89 |
| Compound B | −58.09 | −45.60 | 6.9 | −5.07 | −45.17 | −0.74 | 46.59 |
| Compound C | −65.68 | 20.61 | 3.01 | −13.72 | −41.78 | −2.80 | −25.83 |
| Compound D | −34.80 | 53.73 | 10.56 | −12.60 | −53.21 | −3.21 | −25.56 |
| Compound E | −26.90 | 64.86 | 9.37 | −9.90 | −57.21 | −1.54 | −22.30 |
| Dacomitinib | −28.01 | −11.85 | 8.83 | −4.04 | −43.86 | −0.98 | 35.49 |
## Prime MM/GBSA analysis of docked complexes at inter-domain binding site
The binding free energies of the top five docked complexes were calculated using the MM/GBSA approach. The results revealed that the binding free energy of FtsZ’s nucleotide-binding site with compounds 1, 2, 3, 4, and 5 was found to be −49.14, −48.19, −19.67, −28.92, and −40.51 kcal/mol, respectively (Table 4). These hits may generate a strong interaction within the binding region of the chosen target, according to the total binding free energies, which would impede enzyme activity.
**TABLE 4**
| Compound ID | MM/GBSAdG bind (kcal/mol) | MM/GBSA dG bind coulomb (kcal/mol) | MM/GBSA dG bind covalent (kcal/mol) | MM/GBSA dG bind Hbond (kcal/mol) | MM/GBSA dG bind Vdw (kcal/mol) | MM/GBSA dG bind packing (kcal/mol) | MM/GBSA dG bind solv GB (kcal/mol) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Compound 1 | −49.14 | −339.49 | 4.71 | −1.93 | −34.07 | −3.74 | 340.74 |
| Compound 2 | −48.19 | −335.37 | 2.56 | −1.47 | −33.56 | −3.71 | 338.33 |
| Compound 3 | −19.67 | 124.94 | 7.49 | −2.22 | −30.16 | −0.03 | -108.22 |
| Compound 4 | −28.92 | −84.45 | 1.22 | −0.83 | −31.90 | −1.57 | 102.28 |
| Compound 5 | −40.51 | −22.87 | 3.83 | −2.60 | −35.15 | −0.31 | 32.22 |
| PC190723 | −36.51 | −29.75 | 2.16 | −2.86 | −33.58 | −1.78 | 38.15 |
## ADME/T prediction of top hits
ADME parameters were predicted for the top 5 hits of the nucleotide-binding site (Table 5). It was noticed that although most of the successful compounds were predominantly nucleotide analogues, which have shown excellent results, their expected oral absorption percentage in humans was low. The top 10 hit molecules were anticipated to be non-toxic compounds, as determined by Protox II (Table 6).
Following docking to the inter-domain binding site of FtsZ, the top 5 hits were predicted for their ADME properties (Table 7). The majority of the compounds have shown encouraging findings, and it was found that the projected values fall within the acceptable range. Although none of the compounds shows organ toxicity, Protox II predicted toxicity endpoints like immunotoxicity. ( Table 8).
## Molecular dynamics simulation
To investigate the real-time dynamics and conformational stability of a protein upon binding to a specific ligand, 100 ns of MD simulations were performed on the five best-docked compounds, (A, B, C, D, and E) at nucleotide binding site and (compound 1, 2, 3, 4, and 5) at inter-domain binding site. Our analysis of simulation interaction diagrams (SIDs) for the 100-ns SPC water model-based simulations provided a better understanding of Protein RMSD, Ligand RMSD, Protein RMSF, Ligand RMSF, Protein-Ligand contacts, and ligand characteristics were analyzed, and a simulation interaction diagram was constructed to assess the stabilities of the protein-ligand complexes.
## RMSD of top five hits at nucleotide binding site
The RMSD is generally used to calculate protein backbone (Cα, C, and N) deviation during the 100 ns simulation period. The FtsZ bound to compound A (Chloro derivative of GTP) showed a protein RMSD of 1.6–3.2 Å and ligand RMSD of 1.1–2.0 Å. Convergence was observed between the two plots around the initial 0–35 ns, which indicates the FtsZ-Chloro derivative of GTP complex’s stability. Additionally, for ligand, slight fluctuation in RMSD, was observed between 35–45 ns of trajectory; however, it remained consistent after that with RMSD of 0.6–1.8 Å Figure 8 (Figure 8A). In case of Compound B (naphthalene-1,3-diyl bis(3,4,5-trihydroxybenzoate)-FtsZ complex, the protein and ligand RMSD plots slightly fluctuated during the initial 0–20 ns and remained converged throughout the simulative period with RMSD of 1.8–5.6 Å which can be inferred as a stable complex (Figure 8B). The compound C-FtsZ complex MD trajectory of 100 ns indicates that the complex tends to be stable during simulation with a RMSD of less than 3 Å. This indicates that the FtsZ- Guanosine triphosphate (GTP) complex did not lead to much conformational changes in the dynamics environment (Figure 8C). The ligand RMSD of compound D bound to FtsZ was found to be in the range of 2.0–2.4 Å, and the protein’s RMSD lay in the range of 1.8–3.2 Å. Initially, the slight fluctuation was observed in the trajectory at 0–15 ns time frame followed by the convergence of both the RMSDs till 60 ns, a slight change in order was observed at 70–100 ns, which falls in the acceptable range (Figure 8D). This ultimately indicates the FtsZ-morpholine derivative of GTP complex’s stability. The FtsZ protein’s RMSD was 1.8–7.8 Å, whereas compound E’s RMSD was 1.1–8.0 Å (methylpiperazine derivative of GTP). Even though the RMSD plots of the ligand and the protein converged for a certain period, after 70 ns, the complex was entirely out of the binding pocket when the simulation trajectory was observed. As the ligand was found to show significant variations in their RMSD, compound E is considered an unstable molecule (Figure 8E).
**FIGURE 8:** *RMSD plot of FtsZ protein in complex with Compounds at nucleotide binding site during period of MD simulation, Blue line in graph shows protein RMSD in form of C alpha chain of protein and pink lines represents Lig fit Prot RMSD, protein RMSD values are on left y-axis while lig fit prot RMSD are on right Y-axis, the X-axis shows time period of MD simulation in nanoseconds. (1) RMSD of Compound A, (2) RMSD of Compound B, (3) RMSD of Compound C, (4) RMSD of Compound D, and (5) RMSD of Compound E.*
## RMSD of top five hits at inter-domain binding site
The RMSD of the protein in the presence of compound 1 [1-(2-((amino(iminio)methyl)amino)ethyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium] was found to be 1.8–10.5 Å whereas the ligand RMSD was in the range of 0.8–12 Å (Figure 9 (Figure 9A). Variation in both protein and ligand RMSDs was recorded until the end of the simulation, indicating that the ligand diffused away from the binding and complex is unstable. The compound 2 RMSD plot converged well with the FtsZ protein backbone for the 100 ns simulation period with a RMSD of 2.3–4.2 Å indicating ligand’s tight binding to the protein’s binding pocket and hence a stable complex (Figure 9B). In the case of compound 3 [chlorogenic acid], the RMSDs of the FtsZ protein and ligand were found fluctuating for the initial 0–10ns, but were found to show consistent stability with a ligand RMSD of less than 3.6–4.8 Å and protein RMSD was in the range of 2.9–6.2 Å (Figure 9C). The protein RMSD was found to be in the range of <3 Å in the case of both compounds 4 [(Z)-2-(2-(5-fluoro-2-methyl-1-(4-(methylthio)benzylidene)-1H-inden-3-yl)acetamido)-N,N-dimethylethan-1-aminium] and compound 5 [Curcumin], but the ligand RMSDs was found to be 0.9–10.5 Å and 0.5–10 Å respectively. Due to the large deviation among the protein and ligand RMSDs, the compound 4-FtsZ complex was found to be unstable throughout the 100ns simulation (Figure 9D) and when the simulation trajectory was observed, the ligand protruded away after 20 ns from the binding pocket indicating poor stability with FtsZ. The compound 5-FtsZ complex was unstable during the 100 ns simulation due to a substantial discrepancy between the protein and ligand RMSDs (Figure 9E).
**FIGURE 9:** *RMSD plot of FtsZ protein in complex with Compounds at inter-domain binding site during period of MD simulation, Blue line in graph shows protein RMSD in form of C alpha chain of protein and pink lines represents Lig fit Prot RMSD, protein RMSD values are on left y-axis while lig fit prot RMSD are on right Y-axis, the X-axis shows time frame of MD simulation trajectory in nanoseconds. (A) RMSD of Compound 1 (B) RMSD of Compound 2, (C) RMSD of Compound 3 (D) RMSD of Compound 4, and (E) RMSD of Compound 5.*
Based on the RMSDs of complexes, the stable complexes were considered for further analysis of simulation parameters like RMSF of protein and ligand, protein-ligand contacts and changes in the ligand properties during simulation.
## Protein and ligand RMSF at nucleotide binding site
Each protein residue’s flexibility and mobility are represented by the RMSF value. Greater RMSF values suggest more flexibility throughout the MD simulation, while a lower RMSF value affects the system’s stability. The protein’s RMSF in the presence of compound A was found to be 0.5–2.0 Å Figure 10 (Figure 10A). Similarly, compound A did not show any major fluctuations in their RMSF, while element number 10 of the ligand (involved in making hydrogen bond interactions with the active site) exhibited small fluctuation, which is under the acceptable range, retains the stability of Compound A-FtsZ complex (Figure 10B). In the presence of compound B, the backbone residues demonstrated a substantially lower than permitted amount of fluctuation and was determined to be 0.8–2.4 Å Figure 11 (Figure 11A). The fluctuation was only observed at ligand’s element number 5, which is not engaged in making any contact with the active site (Figure 11B). According to the protein RMSF assessment results, the residues did not exhibit any observable flexibility in the presence of compound C, which ranged from 0.5 to 1.8 Å Figure 12 (Figure 12A). The ligand-protein complex may not result in any structural variation as the ligand’s RMSF was within the acceptable range (Figure 12B). In the presence of compound D, the protein’s RMSF was found to be 0.6 to 1.8 Å was the observed RMSF of protein which falls under the acceptable range and indicates the protein’s stability Figure 13 (Figure 13A). Variations in the ligand’s elements 36 and 37, which do not interact with the active site, may not lead to structural variability in the ligand-protein complex (Figure 13B).
**FIGURE 10:** *RMSF plot of Ftsz Protein where the active site residues are on X-axis, root mean square fluctuation (RMSF) values on Y-axis, the green bar indicate point of contact of respective amino acid residues at nucleotide binding site with the following ligands (Ai) Compound A, (Bi) Compound B, (Ci) Compound C, and (Di) Compound D; Ligand RMSF plots of the compounds (Aii) Compound A, (Bii) Compound B, (Cii) Compound C, and (Dii) Compound D at nucleotide binding site. The pink line in the plot shows RMSF of ligand, the X-axis indicates residues of ligand and Y-axis shows value of RMSF.* **FIGURE 11:** *RMSF plot of Ftsz Protein where the active site residues are on X-axis, root mean square fluctuation (RMSF) values on Y-axis, the green bar indicate point of contact of respective amino acid residues at inter-domain binding site with the following ligands (Ai) Compound 2, and (Bi) Compound 3; Ligand RMSF plots of the compounds (Aii) Compound 2, (Bii) Compound 3 at inter-domain binding site. The pink line in the plot shows RMSF of ligand, the X-axis indicates residues of ligand and Y-axis shows value of RMSF.* **FIGURE 12:** *Protein-ligand Contacts histogram shows ligand interaction with amino acids at nucleotide binding site, purple for hydrophobic interaction, green for hydrogen bond, pink for ionic interaction whereas blue for water bridge, X-axis indicate amino acid residues while Y-axis shows Interaction fraction (A) Compound A, (B) Compound B, (C) Compound C, and (D) Compound D.* **FIGURE 13:** *Protein-ligand Contacts histogram shows ligand interaction with amino acids at inter-domain binding site, purple for hydrophobic interaction, green for hydrogen bond, pink for ionic interaction whereas blue for water bridge, X-axis indicate amino acid residues while Y-axis shows Interaction fraction (A) Compound 2, and (B) Compound 3.*
## Protein and ligand RMSF at inter-domain binding site
The protein’s RMSF showed fewer fluctuations i.e., 1.2 and 2.4 Å in the presence of compound 2, indicating protein stability (Supplementary Figure S1A). There is slight fluctuation observed in the ligand’s RMSF among the elements 18 and 19, which are involved in forming hydrogen bonds with the active site in the ligand (Supplementary Figure S1B). This fluctuation may not affect the structural variability of the Compound 2-FtsZ complex as the observed RMSF was within the acceptable range. The RMSF of protein backbone residues in the inter-domain binding site vary from 1.9–2.0 Å, which is under the acceptable range (Supplementary Figure S2A), and the RMSF for Compound 3 was found to be 1.5–3.0 Å where the element numbers 5 and 6 of the ligand, which are not engaged in forming contacts with the active site, have no impact on the structural variation of the Compound 3-FtsZ complex (Supplementary Figure S2B).
## Protein-ligand contacts at nucleotide binding site
During the simulation phase, the atomic-level interaction information is critical for predicting the binding affinity of the protein and ligand. During the 100 ns simulation, intermolecular interactions between protein and ligand molecules, such as hydrogen bonds, ionic interactions, hydrophobic contact, and the salt bridge, were thoroughly investigated for binding analysis. The compound A was in contact with 34 amino acid residues in the nucleotide-binding domain, forming crucial interactions like hydrogen bonds, water bridges, ionic bonds and hydrophobic bonds (Figure 10C). 32 amino acid residues of the nucleotide-binding site came into contact with compound B during the simulation, creating critical interactions like hydrogen bonds, water bridges, ionic bonds, and hydrophobic bonds, most of which are hydrogen bond interactions (Figure 11C). Throughout the simulation period, 30 amino acid residues in the nucleotide-binding site came into contact with compound C, forming crucial interactions such as hydrogen bonds, water bridges, and hydrophobic bonds (Figure 12C). 40 amino acid residues in the nucleotide-binding domain interacted with chemical D, forming important interactions such as hydrogen bonds, water bridges, ionic bonds, and hydrophobic bonds (Figure 13C).
## Protein-ligand contacts at inter-domain binding site
During the course of a 100 ns simulation, 16 amino acid residues in the inter-domain binding site made contact with compound 2, forming significant interactions such as hydrogen bonds, water bridges, ionic bonds, and hydrophobic bonds, the majority of which are hydrogen bond interactions (Supplementary Figure S1C). Compound 3 generated significant interactions with 25 amino acid residues of FtsZ, including hydrogen bonds, water bridges, ionic bonds, and hydrophobic bonds, among others, the water bridges and hydrogen bonds making most of these interactions (Supplementary Figure S2C).
## Changes in the ligand properties during simulation
To assess the stability of the lead molecules in the nucleotide and inter-domain binding sites of the FtsZ, the five molecular characteristics of ligand (ligand RMSD, the radius of gyration [rGyr], Molecular surface area [MolSA], solvent accessible surface area [SASA], and polar surface area [PSA]) were also investigated.
## Ligand RMSD
“Ligand RMSD” refers to a ligand’s root mean square deviation from the reference conformation. Typically, the beginning frame is used as the reference and is treated as time $t = 0.$ At the nucleotide-binding site, the RMSD of compound A was found below 0.75 Å (Figure 14A), whereas compound B was found between 1.2—1.8 Å (Figure 14B). Compounds C and D show RMSD between 0.8 (Figure 14C) and 1.2 Å, respectively (Figure 14D). In compounds 2 and 3 at the inter-domain binding site, the ligand RMSD was 0.3–0.9 Å (Figure 14E) and less than 1.5 Å (Figure 14F). All the molecules have shown the ligand RMSD value <2 Å indicating good ligand stabilities.
**FIGURE 14:** *Change in Ligand RMSD’s during 100 ns simulation at nucleotide binding site (A) Compound A, (B) Compound B, (C) Compound C, and (D) Compound D; at inter-domain binding site (E) Compound 2, and (F) Compound 3.*
## Radius of gyration
The radius of gyration is used to evaluate the ‘extendedness’ of a ligand and is comparable to its principal moment of inertia; the radius of gyration of compound A stayed constant during the 100 ns simulation and was found to be less than 5.0 Å indicating that the active site of a protein does not undergo any large conformational changes. The compound B’s rGyr was found to be 5.5 Å following 0–5 ns of stability without any significant conformational changes and retained the protein-ligand complex’s compactness. Compounds C and D, rGyr was found in the range 4.75–5.0 Å and 4.8–5.0 Å, which indicates the protein’s compactness during the simulation period. At the inter-domain binding site of FtsZ, the rGyr of Compound 2 was discovered to be steady and to lie between 4.64—4.72, Å Whereas compound 3, rGyr was found to be less than 4.6 Å after 45 ns of stabilization period because of protein-ligand complex compactness.
## Molecular surface area (MolSA)
The compound A was found to be polar based on its MolSA, comparable to the van der Waals surface area, and was found to be 376–384 Å2. Because the MolSA of compound B was found to be 396–400 Å2, it can be assumed as a polar molecule. The compound C was found to be polar due to its MolSA ranging between 365 and 375 Å2. The compound D is polar, as evidenced by the MolSA value, which ranges from 420 to 440 Å2. At the inter-domain binding site, for compound 2, the MolSA value lies between 400 and 405 Å2, indicating its polarity. Compound 3 is polar, as evidenced by the MolSA, which was found to be 308–312 Å2.
## Solvent-accessible surface area (SASA)
The SASA is the area of a molecule that the water molecules can access. The compound A has SASA in the range of 80–120 Å2. Higher SASA scores indicate that more of the molecule protrudes into the water, whereas lower scores indicate that the molecule is buried within the binding site. The SASA of compound B was determined to be 100–150 Å2, with an initial variation detected in the 0–5 ns time frame of MD simulation, indicating that the molecule is buried within the binding site. The compound C and D, SASA were between 80 and 120 Å2 and 120 to 180 Å2 respectively, indicating that more of the molecule is extending out into the water, which means good SASA.
## Polar surface area (PSA)
The PSA is the solvent-accessible surface area solely contributed by oxygen and nitrogen atoms. Due to the presence of oxygen and nitrogen atoms in compound A, its PSA was observed between 540–550 Å2. The ligands with PSA >140 Å2 indicate good oral and intestinal absorption (Supplementary Figure S4A). Compound B may exhibit good oral and intestinal absorption with a PSA of 360–372 Å2 due to oxygen atoms in its structure (Supplementary Figure S4B). Compound C’s PSA of 540–555 Å2, signifying favourable oral and intestinal absorption, was established by the compound’s quantity of nitrogen and oxygen atoms (Supplementary Figure S4C). Despite initial fluctuations at 0–5 ns, Compound D’s PSA of 520–540 Å2 was maintained until the 100 ns simulation, indicating that the amount of nitrogen and oxygen atoms in the compound determined its favourable oral and intestinal absorption (Supplementary Figure S4D). At the inter-domain binding site, compound 2’s PSA of 152–160 Å2 persisted through the 100 ns simulation, showing that the compound’s nitrogen and oxygen content led to the compound’s successful oral and intestinal absorption (Supplementary Figure S4E). The molecular polar surface area of compound 3 (Supplementary Figure S4F) was found to be 330–345 Å2 as it contains only oxygen atoms but not nitrogen atoms in its structure which generally corresponds to the accessibility towards the solvents present in the binding site.
The Compounds A, B, C, and D were potential lead molecules binding at the nucleotide-binding site, whereas compound 2 and 3 were considered potential inhibitors for the inter-domain binding sites. These lead compounds were further subjected to post MM/GBSA analysis to understand their binding strengths.
## Conclusion
Our research sought to identify a potential treatment for S. epidermidis infection and looked at FtsZ inhibitors previously examined among other pathogenic species found in the literature.
Virtual screening methods showed ten molecules as best hits, further evaluated by analyzing their ligand interactions and binding affinity. The shortlisted five compounds based on binding free energy analysis were further taken up for additional in silico investigations like ADME/T prediction, molecular dynamics simulations, and post MM/GBSA analysis. Based on all the results obtained, we conclude that, chloro-derivative of GTP (Compound A), naphthalene-1,3-diyl bis(3,4,5-trihydroxybenzoate (Compound B), Guanosine triphosphate (GTP) (Compound C), morpholine derivative of GTP (Compound D), and 1-(((amino(iminio)methyl)amino)methyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium (Compound 2), and Chlorogenic acid (Compound 3) can act as potential inhibitors of FtsZ protein interrupting cell division mechanism thereby limiting the growth of S. epidermidis. The current study will be very helpful for future research to develop targeted therapeutics to combat infection. To establish their status as novel compounds against S. epidermidis, the identified inhibitors will be expanded through experimental research in the future.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Author contributions
DV- Performed the experimental analysis, Organized the data, and wrote the manuscript. DM- Edited the manuscript. VB- Conception and design of the study, Edited manuscript and Final draft approval.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2023.1087676/full#supplementary-material
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|
---
title: ImmuneScore of eight-gene signature predicts prognosis and survival in patients
with endometrial cancer
authors:
- Jiahui Gu
- Zihao Wang
- B. O. Wang
- Xiaoxin Ma
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10020521
doi: 10.3389/fonc.2023.1097015
license: CC BY 4.0
---
# ImmuneScore of eight-gene signature predicts prognosis and survival in patients with endometrial cancer
## Abstract
### Background
Endometrial cancer (EC) is a common gynecological cancer worldwide and the sixth most common female malignant tumor. A large number of studies conducted through database mining have identified many biomarkers that may be related to survival and prognosis. However, the predictive ability of single-gene biomarkers is not sufficiently accurate. In recent years, tumors have been shown to interact closely with their microenvironment, and tumor-infiltrating immune cells in the tumor microenvironment were associated with therapeutic effects. Furthermore, sequencing technology has evolved and allowed the identification of genetic signatures that may improve prediction results. The purpose of this research was to discover the Cancer Genome Atlas (TCGA) data to evaluate new genetic features that can predict the prognosis of EC.
### Methods
mRNA expression profiling was analyzed in patients with EC identified in the TCGA database ($$n = 530$$). Differentially expressed genes at different stages of EC were screened using the immune cell enrichment score (ImmuneScore). Univariate and multivariate Cox regression analyses was applied to evaluate genes significantly related to overall survival and establish the prognostic risk parameter formula. Kaplan–Meier survival curves and the logarithmic rank method were applied to verify the importance of risk parameters for the prognostic forecast. The accuracy of survival prediction was confirmed using the nomogram and Receiver Operating Characteristic (ROC) curve analysis. The mRNA expression of eight genes were measured by qRT-PCR. According to COX and HR values, NBAT1, a representative gene among 8 genes, was selected for CCK-8 assay, colony formation assay and transwell invasion assay to verify the effect on survival.
### Results
Eight related genes (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, UNQ6494, KLRB1, and PRAC1) were discovered to be significantly associated with the overall survival rate. According to these eight-gene signatures, 530 patients with EC were assigned to high- and low-risk subgroups. The prognostic capability of the eight-gene signature was not influenced by other elements.
### Conclusions
Eight related gene markers were identified using ImmuneScore, which could predict prognosis and survival in patients with EC. These findings provide a basis for understanding the application of biological information in tumors and identifying the poor prognosis of EC.
## Introduction
In recent years, due to the extension of life expectancy and increase in the overall prevalence of obesity and metabolic syndrome, the incidence and mortality of endometrial cancer (EC) compared to other cancers have been continuously increasing [1]. There were 382,069 new estimated cases and 89,929 estimated deaths caused by this disease in 2018 [2]. EC usually occurs in postmenopausal women and only about $4\%$ of patients are under 40 years of age [3]. It is predicted that by 2025, the number of new cases and deaths will increase by $20.3\%$ and $17.4\%$, respectively [2]. Although most patients can be diagnosed early, some are already in the advanced stage of the disease at the time of diagnosis. Moreover, patients at identical stages can also show different responses to the same treatment and different prognoses. The mortality of EC is directly related to the poor prognostic factors that drive tumor recurrence [4]. Therefore, the discovery of effective biomarkers is important to assess prognosis and identify patients at high risk for EC.
An increasing number of studies have shown the significance of tumor microenvironment (TME) in tumor progression. Synergistic interaction between cancer cells and their support cells contributes to the malignant phenotype of cancer, for example, continuous diffusion, anti-apoptosis, and evasion of immune surveillance. Therefore, TME has a significant impact on the treatment effect and clinical outcomes in cancer patients [5, 6]. The main structural parts of TME are permanent stromal cells and recruited immune cells. However, the role of stromal cells in tumor angiogenesis and extracellular matrix remodeling is not completely understood [7]. Some research has concentrated on the influence of immune cells in the TME on tumor growth and spread. An increasing number of studies have shown that tumor-infiltrating immune cells (TICs) in the TME are promising indicators of therapeutic effects [8].
With the advancement of high-throughput sequencing technology, investigators have set up genome databases of many diseases to understand genomic changes more systematically and clearly. Through database mining, scientists have found several biomarkers that may be related to prognosis in patients with cancer [9, 10]. However, the predictive ability of single-gene biomarkers is still not sufficiently accurate. Studies have shown that evaluating genetic characteristics involving multiple genes can improve prognosis [11, 12]. The polygenic prognostic characteristics of primary tumor biopsies have a guiding role in treatment. There are reports wherein the impact of multigene signatures in EC has been studied to assess prognosis and identify potential patients at high risk for EC [13, 14].
To identify biomarkers, differential gene expression analysis usually involves comparing expression levels in genes between groups and focusing on the genes whose expression levels are significantly regulated. As an emerging method, ImmuneScore can determine the difference in survival rate in patients with EC between different disease stages and finally obtain the best gene combination. This is important for tumor prognosis and survival assessment [15].
We identified new genetic characteristics that predict the prognosis of EC. We explored the Cancer Genome Atlas (TCGA) data and selected the relevant genes using ImmuneScore. Furthermore, we applied mRNA expression data from TCGA to survey and draw marker genomes in 530 patients with EC. We identified 99 mRNAs significantly related to immune cells and established a risk profile of eight genes to effectively predict the prognosis in EC patients. The risk factors obtained through ImmuneScore can independently assess the prognosis in high-risk patients and identify and verify new genetic features and biomarkers.
## Patient clinical data and mRNA expression dataset
We collected clinical data and mRNA expression profiles of EC patients from TCGA (https://cancergenome.nih.gov/) [16]. The research included clinical data from 530 patients with the following parameters: matching age, stage, grade, radiation therapy, neoplasm cancer status, residual tumor, body mass index (BMI), percentage of tumor invasion, new events, and peritoneal wash (Table 1).
**Table 1**
| Clinical pathological parameters | N(n=Excluded due to patients with missing information) | % |
| --- | --- | --- |
| Age | Age | Age |
| ≥66 | 262 | 49.6 |
| <66 | 266(2) | 50.4 |
| Neoplasm cancer status | Neoplasm cancer status | Neoplasm cancer status |
| With tumor | 76 | 15.4 |
| Tumor free | 418(36) | 84.6 |
| Residual tumor | Residual tumor | Residual tumor |
| No | 365 | 83.0 |
| Yes | 75(90) | 17.0 |
| Stage | Stage | Stage |
| I-II | 381 | 71.9 |
| III-IV | 149(0) | 28.1 |
| New event | New event | New event |
| No | 470 | 88.7 |
| Yes | 60(0) | 11.3 |
| Grade | Grade | Grade |
| G1-2 | 216 | 40.8 |
| G3-4 | 314(0) | 59.2 |
| Radiation therapy | Radiation therapy | Radiation therapy |
| No | 483 | 91.1 |
| Yes | 47(0) | 8.9 |
| BMI | BMI | BMI |
| ≥28 | 340 | 68.0 |
| <28 | 160(30) | 32.0 |
| Percent tumor invasion | Percent tumor invasion | Percent tumor invasion |
| No | 414 | 90.6 |
| Yes | 43(73) | 9.4 |
| Peritoneal wash | Peritoneal wash | Peritoneal wash |
| With tumor | 57 | 14.2 |
| Tumor free | 344(129) | 85.8 |
## Immune cell enrichment score (ImmuneScore)
We used ImmuneScore to evaluate the difference in survival rates among the EC patient groups at different stages, and thereafter assigned them as high- and low-risk groups. Used the ESTIMATE package to calculate the ImmuneScore(proportion of immune component), StromalScore(proportion of stromal component) and ESTIMATEScore(sum of the above two scores) of the EC samples. The higher the score, Represented the higher proportion of the corresponding components (immune, stromal, and tumor purity) in TME. Then, We used the edgeR algorithm (http://bioconductor.org) for preliminary screening to generate differentially expressed genes. EdgeR is a bioconductor software package used to examine the differential expression of replicated count data. Next, we used the least absolute shrinkage and selection operator (LASSO) model to select statistically significant prognostic markers from the differentially expressed genes. We analyzed the expression standards of 24,991 mRNAs in EC specimens and neighboring non-cancerous tissues. Last, we used the normalized P-value ($P \leq 0.05$) to determine the function for subsequent analyses.
## Data screening and risk-parameter calculation
Log2 transformation was applied to normalize the expression profile of each mRNA. Univariate Cox regression analysis was applied to determine genes correlated with overall survival (OS), and multivariate Cox regression analysis was applied to identify genes associated with prognosis and obtain their coefficients. The selected mRNAs were then assigned as risk type (hazard ratio, HR > 1) and protective type (0 < HR < 1). By linear combination of the filtered gene expression value (weighted by its coefficient), we constructed the following hazard parameter formula: hazard parameter = ∑ (βn × gene n expression). Applying the median hazard parameter as a cut-off value, 530 patients were assigned to high- and low-risk subgroups.
## Quantitative real-time -PCR samples and patients
A total of 20 EC tissues and 20 normal endometrial tissues were obtained from patients in the Department of Gynecology and Obstetrics of Shengjing Hospital affiliated with the China Medical University. Normal tissues were taken from patients who underwent hysterectomy for unrelated diseases of the endometrium. All patients gave informed consent. This study was approved by the Ethics Committee of Shengjing Hospital affiliated with the China Medical University. The histological diagnosis and staging were evaluated by experienced pathologists according to the International Federation of Gynecology and Obstetrics (FIGO) 2009 staging system. None of the patients received systemic treatment before surgery. Data for endometrial cancer patients are presented in Supplementary Table S1.
## RNA extraction and qRT-PCR
TRIzol reagent (Vazyme, Nanjing, China) was used to extract total RNA from the tissue. PrimeScript RT-polymerase (Vazyme) was used for reverse transcription to obtain cDNAs corresponding to the target mRNAs. qRT-PCR was performed using SYBR-Green Premix (Vazyme) and specific PCR primers (Sangon Biotech Co., Ltd, Shanghai, China). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control. Primer sequences are shown in Supplementary Table S2. The 2−ΔΔCt method was used to calculate the relative fold-changes in mRNA expression.
## Transfection of cells
SiRNA sequences targeting NBAT1, and their respective negative control (NC) counterparts were purchased from GenePharma (Shanghai, China). According to the manufacturer’s instructions, Lipofectamine 3000 (Invitrogen) was used to transfect cells with siRNA for the following experiments. Sequences of siRNA are listed in Supplementary Table S3.
## Cell culture
Ishikawa cells and HEC-1A cells were cultured with RPMI 1640 (Gibco, Carlsbad, CA, USA). A $10\%$ fetal bovine serum (FBS) (Gibco) and $1\%$ penicillin–streptomycin was added to the medium of the cells. All cells were cultured in a humidified incubator at 37°C with $5\%$ CO2.
## Colony formation assay
To explore the effects of NBAT1 expression on cell proliferation, cells (1000/well) transfected with NC-siRNA or siRNA, and without si-RNA as a blank[-] group were added to each well of 6-well culture plates and incubated for two weeks. Cells were stained with $0.1\%$ crystal violet. Finally, the number of colonies was counted by light microscopy.
## CCK−8 assay
Ishikawa cells and HEC-1A cells were seeded in 96-well plates, CCK-8 reagent (10 µL) (Dojindo, Japan) was added to each well, and then incubated at 37°C with $5\%$ CO2 for 3 h. The microplate reader was used to measure OD450 values of eachwell at 0h, 24h, 48h, and 72h after treatment.
## Cell invasion assay
Transwell chambers (Corning, NY, USA) with a pore size of 8μm were used to detect cell invasion. Cells were placed into the upper chamber with 200μl serum-free medium and the chambers were precoated with Matrigel solution (BD, Franklin Lakes, NJ, USA). The lower chamber contained $10\%$FBS medium. After incubation for 24h,invaded cells on the lower membrane surface were fixed with $4\%$ paraformaldehyde and stained with $0.1\%$ crystal violet.
## Statistical analysis
We applied Kaplan–Meier (K–M) survival curves and the log-rank means to evaluate the importance of the hazard parameters. We performed multivariate Cox regression and data lamination analyses to examine whether the risk parameters were individual clinical characteristics, containing age, stage, grade, radiation therapy, neoplasm cancer status, residual tumor, BMI, percentage of tumor invasion, new events, and peritoneal wash, which were used as covariates. Statistical significance was established at $P \leq 0.05.$ *Statistical analysis* was performed using GraphPad Prism7 software (GraphPad, Inc., La Jolla, CA, USA) and SPSS software (version 20.0; SPSS, Inc., Chicago, IL, USA). The nomogram was constructed to evaluate prediction accuracy and recognition ability. The ROC curve (area under the curve [AUC]) was further applied to evaluate the discriminative ability of the nomogram [17, 18](Figure 1).
**Figure 1:** *Flowchart of the article.*
## Evaluation of survival rates in EC patients at different stages using ImmuneScore
We obtained the clinical characteristics of 530 patients with EC, along with the related 24,991 mRNA expression datasets from the TCGA database. Kaplan-Meier survival analysis was performed on ImmuneScore, StromalScore and ESTIMATEScore after they were generated. A higher ImmuneScore or StromalScore indicated a greater proportion of immune or stromal components in the TME. ESTIMATEScore was the sum of ImmuneScore and StromalScore, which represented tumor purity and represented the comprehensive ratio of the two components in TME. As shown in Figure 2A, the proportion of immune components was positively correlated with OS, while StromalScore and ESTIMATEScore were not significantly correlated with OS. These results implied that the immune components of TME were more suitable to indicate the prognosis of EC patients. According to the median ImmuneScore, 530 EC specimens were assigned to high and low-risk groups (Figure 2B).
**Figure 2:** *(A) Comparison of survival rates of ImmuneScore, StromalScore and ESTIMATEScore in different stages of endometrial carcinoma (a: ImmuneScore in different stages of endometrial carcinoma; b: StromalScore in different stages of endometrial carcinoma; c: ESTIMATEScore in different stages of endometrial carcinoma; d: comparison of survival rates of ImmuneScore; e: comparison of survival rates of StromalScore; f: comparison of survival rates of ESTIMATEScore) (B) ImmuneScore screens high-group and low-group differential genes.*
## Identification of mRNAs associated with survival
First, we screened out the differentially expressed ImmuneScore-related genes using the random forest algorithm, and obtained 19 best genes ($P \leq 0.05$) (Figure 3). Then, The least absolute shrinkage and selection operator (LASSO) regression (iteration equal 1000), univariate and multivariate Cox regression analyses were used to further verify the correlation between the 19 mRNA expression profiles and patient survival rates, and to clarify the best mRNA associations using the stepwise cleaning method. As shown in Table 2, eight mRNAs (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, UNQ6494, KLRB1, and PRAC1) were verified. After filtering, six mRNAs were classified as risky (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, and PRAC1) with HR>1 related to poorer survival, and two as protective types (UNQ6494 and KLRB1) with HR<1 related to better survival (Table 2).
**Figure 3:** *LASSO regression (iteration equal 1000) screening for survival-related mRNAs.* TABLE_PLACEHOLDER:Table 2
## Verification of TCGA expression using qRT-PCR
We detected notable changes in the expressions of eight mRNAs from 20 EC tissues and 20 normal endometrial tissues by qRT-PCR. We performed the unpaired t-test to assess the variation in mRNA expression of the two groups. The results showed that NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, and PRAC1 were up-regulated, whereas UNQ6494 and KLRB1 were down-regulated in EC tissues as compared with that in normal endometrial tissue (Figure 4). The changes verified by qRT-PCR in mRNA expression levels in the 20 patients with EC were identical to the predicted changes obtained from bioinformatics analysis, confirming the significance and accuracy of these results.
**Figure 4:** *Expression of eight mRNAs in endometrial cancer tissues and normal tissues.*
## Construction of an eight-mRNA signature to forecast patient prognosis
We linearly integrated the expression values of the selected genes and the values of these genes weighted by the coefficients obtained from the multivariate Cox regression analysis. We derived the following formula to evaluate the prognosis: Risk parameters = 0.0127 × expression of NBAT1 + 0.0005 × expression of GFRA4 + 0.0003 × expression of PTPRT + 0.0014 × expression of DLX4 + 0.0108 × expression of RANBP3L + 0.0058 × expression of PRAC1 − 0.0296 × expression of UNQ6494 − 0.0035 × expression of KLRB1. We computed the parameters in all patients and assigned hazard parameters to them. We ranked the patients in ascending order according to the parameters and used the median to divide them into high- and low-risk subgroups (Figure 5A). The life span of each patient is shown in Figure 5B. The mortality rate in patients in the high-risk parameter group was higher, whereas the survival rate in patients in the low-risk parameter group was better. Furthermore, the heat map shows the expression profiles of eight mRNAs (Figure 5C). The expression levels of risky-type mRNAs (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, and PRAC1) were higher in the high-risk group than in the low-risk group. In comparison, The expression levels of protective-type mRNA (UNQ6494 and KLRB1) were lower in the high-risk group than in the low-risk group.
**Figure 5:** *Eight mRNA signatures related to riskScore predict OS in endometrial cancer patients (A) Distribution of mRNA riskScore for each patient (the red line represents high risk, the blue line represents low risk) (B) Survival time (years) of EC patients in ascending order of riskScore (the red dots represent dead patients, the blue dots represent surviving patients) (C) A heatmap of eight genes expression profile.*
## Risk parameters obtained from the eight-mRNA signature as single-handed prognostic indicators
We compared the prognostic significance of the risk parameters and clinicopathological parameters using univariate and multivariate analyses (Table 1). Specimens with good clinical data were chosen for the analysis. The median age of the 528 patients with EC was 66 years. The median BMI of the 500 patients with EC was 28. Of the 530 patients, 60 ($11.3\%$) had new events during the follow-up period and 47 ($8.9\%$) were treated with radiation therapy. Of the 440 patients, 75 ($17.0\%$) had residual tumors. Of the 494 patients, 76 ($15.4\%$) had a tumor in neoplasm cancer status. Of the 457 patients, 43 ($9.4\%$) had tumor invasion. Of the 401 patients, 57 ($14.2\%$) had a tumor in peritoneal wash. Of the 530 patients, 216 ($40.8\%$) had grade 1-2 tumors, and the remaining 314 ($59.2\%$) had grade 3-4 tumors. In addition, among these patients, 381 ($71.9\%$) were in stage I-II, and 149 ($28.1\%$) were in stage III–IV. Based on the above data, we used the risk score, age, neoplasm cancer status, stage, grade, residual tumor, new tumor events, and percent tumor invasion as single-handed prognostic symbols, because these factors showed noticeable discrepancies in univariate and multivariate analyses (Table 3). Notably, the risk score showed a significant prognostic value ($P \leq 0.05$) (HR = 1.054).
**Table 3**
| Clinical feature | Number | Univariate analysisHR 95%CL of HR P-value | Multivariate analysisHR 95%CL of HR P-value |
| --- | --- | --- | --- |
| RiskScore(High‐risk/Low‐risk) | 274/256 | 1.070 1.052-1.089 0.00 | 1.054 1.029-1.079 0.00 |
| Age (≥ 66/< 66) | 262/266 | 1.667 1.080-2.571 0.02 | 1.309 0.775-2.209 0.31 |
| Stage (I-II/III–IV) | 381/149 | 2.013 1.662-2.438 0.00 | 1.404 1.055-1.869 0.02 |
| Grade (G1-2/G3–4) | 216/314 | 2.593 1.804-3.725 0.00 | 1.684 1.092-2.596 0.02 |
| Residual tumor (yes/no) | 75/365 | 2.888 1.784-4.674 0.00 | 0.788 0.415-1.496 0.47 |
| New tumor event(yes/no) | 60/470 | 4.451 2.863-6.921 0.00 | 2.010 1.120-3.606 0.02 |
| Neoplasm cancer status(with tumor/tumor free) | 76/418 | 8.860 5.704-13.761 0.00 | 3.079 1.514-6.262 0.00 |
| Percent tumor invasion (yes/no) | 43/414 | 1.008 1.001-1.015 0.02 | 1.006 0.998-1.015 0.12 |
## K–M survival estimation to verify eight-mRNA signatures for prognosis prediction
Survival estimates of K-M and logarithmic tests showed that patients in the high-risk group had a poorer prognosis (Figure 6A). Univariate Cox regression analysis of OS identified some clinicopathological parameters that could predict EC survival, such as age, grade, stage, residual tumor, new event, neoplasm cancer status, BMI, radiation therapy, peritoneal wash, and percentage of tumor invasion. We then used K–M survival assessment to verify the conclusions obtained. These conclusions gave identical results for patients above 66 years old, with G3-4 tumors, stage III–IV disease, neoplasm cancer status, tumor recurrence (new event), tumor invasion, and with poor prognosis of residual tumor (Figure 6B). Concurrently, based on the results of the multivariate model, a nomogram was constructed that combined clinical parameters. Based on established prognostic factors, it could provide a clinically useful quantitative method for predicting the probability of survival at 1, 3, and 5 years in patients with EC (Figure 7A). The analysis of the ROC curve showed the prediction accuracy of the nomogram in the test and validation cohorts (1-, 3-, and 5-year AUC) (Figure 7B). These results proved the accuracy of the analysis.
**Figure 6:** *Kaplan–Meier survival analysis of EC patients in the TCGA data set (A) K–M survival curve of high/low-risk EC patients (B) Clinical features, including age, stage, grade, residual tumor, new event, neoplasm cancer status, and percent tumor invasion, predict patient survival.* **Figure 7:** *(A) Nomogram (for OS) that integrated the clinicopathologic risk factors. To calculate the probability of status, the points identified on the scale for all the variables are summed up and a vertical line was drawn from the total points scale to the probability scale.(Stage: 1 means FIGO stage1, 2means FIGO stage2, 3 means FIGO stage3, 4 means FIGO stage4. Grade: 1 means well-differentiated, 2 means moderately differentiated, 3 means poorly differentiated) (B) ROC curves showing the predictive accuracy (1-, 3-, 5-year AUC) of the nomogram for OS in testing and validation cohorts. (*P<0.05, **P<0.01, ***P<0.001).*
## Cellular functional experimental validation of NBAT1
To further verify the roles of the 8 genes in EC, we selected NBAT1, the most representative gene among the 8 genes, according to COX and HR values. The expression of NBAT1 was knocked down in Ishikawa cells and HEC-1A cells, and the knockdown effect of NBAT1 was verified by PCR in Supplementary Table S4. Knockdown of NBAT1 inhibited the proliferation (Figures 8A, B) and invasion (Figure 8C) of ECs. The accuracy of these results was further confirmed by cellular functional experiments, which verified that the expression of NBAT1 was identical to the predicted changes obtained from the bioinformatics analysis.
**Figure 8:** *NBAT1 regulates the biological behavior of Ishikawa cell line and HEC-1A cell line. (A, B) CCK-8 and colony formation assays were used to assess the proliferative effect of NBAT1 (C) Effect of NBAT1 on invasion assessed using the Transwell assay. (**P<0.01, ***P<0.001).*
## Discussion
Accurate risk stratification and long-term prognostic prediction are essential for the correct selection of treatment modes for patients with EC. Integrating multiple independent prognostic variables into a single formula can significantly improve the prognostic ability [19]. An increasing number of studies have shown that genes can influence tumor progression by regulating the cell cycle, thereby providing candidates for targeted therapy. Therefore, the identification of prognostic EC biomarkers is essential to improve preoperative and postoperative risk assessments and guide treatment decisions. The stratification system for EC is based on a few molecules, mainly clinical and pathological parameters, but this system remains inaccurate. In this study, we constructed and verified ImmuneScore to compare survival rates in patients with EC at different stages to improve their prognosis prediction.
Recently, immune profiling studies have taken a leading position in cancer research. Several studies based on ImmuneScores have been published to describe the immune landscape and provide independent prognostic models for the survival of patients with several types of solid tumors, including gastric and liver cancers (19–21). In addition, previous data have also shown that specific immune cells were closely related to treatment response to therapies (such as chemotherapy and immune-modulating therapies) [22]. However, previous studies have established many molecular signatures (including genes, microRNAs, lncRNAs, and epigenetic biomarkers) to predict long-term survival in cancer patients [20, 23, 24]. These features have not been widely used in clinical practice due to variability in gene sequencing measurements, inconsistent testing platforms, and the requirement for specialized analysis. In this study, we used ImmuneScore combined with the edgeR algorithm and the LASSO model, as well as the nomogram and ROC curve verification, which might be widely used in clinical practice.
The molecular classification of TCGA is expected to provide additional prognostic information; therefore, it is expected to improve the ESMO-ESGO-ESTRO risk stratification system. Studies conducted in large cohorts of patients, especially those conducted using TCGA (other cohorts), Vancouver, and PORTEC groups (25–29) have verified their prognostic relevance and pointed out that they will benefit from this classification system. In particular, it was reported that $7\%$ of patients diagnosed with cancer with good prognosis (EC Grade 1) but with copy number polymer diagnosis were now classified into the poor prognosis group [27]. In contrast, all patients with POLE-hypermutation tumors (6 –$13\%$ of all EC tumors) are now considered good prognostic tumors regardless of the status of other prognostic factors (such as histological grade or FIGO stage).
The fast progress of high-throughput gene sequencing technique has laid the basis for large-scale biological data study [30]. All the genomic data is screened from a single specimen to distinguish fresh diagnostic, prognostic, or pharmacological biomarkers [31]. The combination of biomarkers provides discriminative power higher than molecular tests based on a single marker. Furthermore, as observed in a study by Yang et al., integrating molecular biomarkers with clinicopathological characteristics may be the easiest strategy to develop more sensitive and specific tests [32]. In this study, we used ImmuneScore to determine the difference in survival rates in patients with EC at different stages, combined with edgeR to screen out the differential genes, and obtained the best gene combination using the LASSO model.
In recent studies, new prognostic markers of gene expression levels or mutations have been constructed by applying microarray and RNA sequencing data. The Cox proportional hazards regression model was applied to identify and validate these markers [33, 34]. In this study, we identified 19 gene combinations using the LASSO model. We chose advanced features to screen for genes associated with sufferer survival forecast, instead of widespread exploration. Univariate and multivariate Cox regression analyses were used to clarify the prognostic significance of these eight-gene combinations in patients with EC. Compared to currently known prognostic evaluation indicators, this selected hazard contour may be a better targeted approach and a more powerful prognostic evaluation to predict positive clinical results.
Tumor immunotherapy is now receiving more and more attention and is recognized as a new and effective method for cancer treatment, and good clinical responses have been observed in some relapsed and refractory cases (35–37). Immune checkpoint inhibitors (ICIs), cancer vaccines, adoptive cell transfer (ACT), and lymphocyte-promoting cytokines are the main immunotherapy approaches, while immunotherapy targeting different EC subtypes (especially POLE and MSI-H) has also gradually attracted attention [38]. As endometrial cancer pathogenesis is further elucidated, more and more evidence shows that a large number of immune cells and cytokines can be seen in endometrial cancer tissue, which can stimulate endogenous antitumor immune responses. Compared with other gynecologic malignancies, endometrial cancer is most likely to benefit from immunotherapy (39–41). Certain immune environment signature parameters are often associated with ImmuneScores and can assess prognosis in other cancer types. Therefore, these characteristic parameters can be effective prognostic factors before and after treatment and can be used as predictive immune parameters in planning appropriate interventional treatment [42]. The ImmuneScore provides a reliable estimate for predicting the recurrence risk of EC patients.
In this study, bioinformatic methods were applied to discover the characteristics and clinical significance of mRNA hazard factors and to explore a new method to discover potential prognostic markers. We applied the EC dataset in TCGA to screen concerning genes through ImmuneScore, compared the data of different stages using EC tissue specimen data, and classified them using high- and low-risk ImmuneScores. For patients with low-risk parameters, K-M survival estimates showed a beneficial prognosis. We demonstrated the effects of 8 genes on EC prognosis and survival by qRT-PCR experiments and cellular experiments. At the same time, the discovery and calculation of hazard parameters for EC patients has great clinical significance. Due to the lack of data on metastasis and recurrence in EC patients in the TCGA database, one limitation of our study is that we could only apply the OS parameter to evaluate the prognosis in these patients. A second limitation was that all specimens were retrospectively obtained from the TCGA database. Therefore, our results need to be verified in a larger prospective cohort study. Furthermore, in the stratified analysis, the risk parameters in all subgroups could predict the prognosis in EC patients, except for the subgroup aged <66 years. The cause of this discrepancy remains unclear and needs further in-depth research.
Realizing the clinical implementation of biomarkers is another important discussion. Once verified, the biomarker should ideally be transferred to a standardized, economical, simple, and fast analysis platform, and should be prospectively verified in accordance with all regulatory requirements to become an in vitro diagnostic test. However, no relevant genetic markers have been established to predict the prognosis in patients with EC. Using bioinformatics methods, we clarified the genetic characteristics related to ImmuneScore (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, UNQ6494, KLRB1, and PRAC1), and proved their prognostic value in EC. At the same time, we also made some comparisons with existing literature, such as Jiang et al. [ 43] and Liu et al. [ 44]. The method we used showed better performance and stability, and ImmuneScore could be used for the prognosis of EC patients, providing reliable estimates, which highlights the good predictive performance of our eight-gene signature.
Recent studies have shown that TME also played a role in tumor occurrence and progression. Discovering latent therapeutic targets that can help shape TME and accelerate the transition of TME from tumor-friendly to tumor-suppressed state has great benefits. Many studies have shown the significance of the immune microenvironment in tumorigenesis. In particular, the rate of immune and stromal compositions in TME is closely related to tumor progression, such as invasion and metastasis [45]. These consequences highlight the importance of discovering the interplay between tumor cells and immune cells, which provides new insights for the development of more valid therapy options. The type of immunity may have a significant impact on individualized follow-up and adapted treatment decisions after surgery.
## Conclusion
We obtained an eight-gene signature risk profile that can predict the prognosis in patients with EC using ImmuneScore, and higher risk parameters were associated with a poor prognosis. This signature can be used as a classification tool in clinical practice. These findings provide the strategy for accurate identification of patients with EC with a poor prognosis.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.
## Ethics statement
All patients gave informed consent. This study was approved by the Ethics Committee of Shengjing Hospital affiliated with the China Medical University.
## Author contributions
JG participated in the design, methodology, data interpretation, and analysis for the work; carried out the statistical analyses; and drafted the manuscript. ZW participated in the methodology, data interpretation, and analysis of the work. BW carried out the statistical analyses. XM designed the study; participated in data interpretation, analysis for the work, and methodology. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1097015/full#supplementary-material
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|
---
title: 'Relationship between body composition and upper limb physical fitness among
Chinese students: 4-Year longitudinal follow-up and experimental study'
authors:
- Qingmei Wang
- Junwei Qian
- Haoran Pan
- Qianqian Ju
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10020539
doi: 10.3389/fphys.2023.1104018
license: CC BY 4.0
---
# Relationship between body composition and upper limb physical fitness among Chinese students: 4-Year longitudinal follow-up and experimental study
## Abstract
Background: Recently, students’ fitness has been declining, and high physical fitness level is crucial in establishing optimal physical/mental health and academic performance. The purpose of this study was to explore the relationship between body composition and upper limb physical fitness and the specific aspects of low physical fitness level in Chinese students. Exploring the development and impact factors for upper limb physical fitness can provide a theoretical basis for the health management strategy of students.
Methods: Study 1 collected data from 183 male students over 4 years and used Hierarchical Linear Model (HLM) to explore the quadratic predictive role of body composition on upper limb physical fitness. To further explored which aspects of upper limb physical fitness were affected by body composition, study 2 conducted an experimental investigation among 42 male students, comparing different kinds of upper limb physical fitness within two different body composition groups.
Results: Studies found [1] from 2015 to 2018, students’ Body-Mass-Index (BMI) showed an upward trend, and BMI differences were significant from year to year. While the upper limb physical fitness showed a downward trend. There were significant differences in the number of pull-outs between 2015 and 2016, 2015 and 2017, and 2015 and 2018. [ 2] The quadratic term of BMI could predict the upper body physical fitness in the same year and the following year. That is, when BMI was medium, the upper body fitness of the same year and the following year was the best. [ 3] Chinese students with excellent body composition had greater grip strength, drape height and anaerobic power than those with average body composition.
Conclusion: In recent years, male students’ BMI has been increasing, and the upper body physical fitness has been decreasing. Furthermore, body composition can predict the upper body mass in the same year and the second year, and male students with better body composition also had greater grip strength, drape height and anaerobic power in their upper limbs.
## Background
Physical fitness is one of the important signs of health (Granger et al., 2017). Relative to students with lower physical fitness, students with higher physical fitness have more opportunity to participate in sport activities (Marttinen et al., 2018), lower risk of health problems (Kao et al., 2020), better cognitive function (Reigal et al., 2020) and higher quality of life (Marker et al., 2018; Bermejo-Cantarero et al., 2021).
However, the situation of students’ physical fitness is not optimistic. The trend of physical fitness among Chinese students was declining over the past years (Tian et al., 2016). Similar results have been found in other countries (Marttinen et al., 2018; Masanovic, et al., 2020a; Farooq et al., 2020). For example, Masanovic, et al., 2020a found a constant decline in strength and endurance through a systematic review among students from 14 countries. Furthermore, the reasons for the decline included not only weight increase (Zhang et al., 2018) and lifestyle changes (like increase in physical inactivity and screen time, Masanovic, et al., 2020a), but also governments’ policies regarding physical activity (Costa et al., 2017). In China, the government issued relevant policies to improve the physical fitness for young adults. For example, the government revised the “National Physical Health Standards for Students (Revised in 2014)” (http://sports.upc.edu.cn/$\frac{2020}{0629}$/c7653a307672/page.htm) to increase the proportion of physical fitness tests in the total score. Also, they issued “Healthy China 2030” and “China’s Education Modernization 2035” strategy to emphasize the importance of promoting the physical development for Chinese children and young adults.
For Chinese college students, after entering college, they invest less in physical education courses compared with other courses, so their participation in sports activities decreases with the increase of college grades (Ting et al., 2021). Here, we aim to investigate the development tendency of college students’ physical fitness and its influencing factor, improving our understanding of the situation of college students’ physical fitness. Considering previous studies have found a decline in physical fitness among students with their ages (Masanovic, et al., 2020b), we expected physical fitness to decline as college grades increase, and hypothesis 1a was proposed.
H1a:Students’ physical fitness gradually declined during the first and fourth years of college. Recently, with the worldwide prevalence of obesity, body composition (such as Body-Mass-Index, BMI, calculated with individuals’ weight and height) has attracted more and more attention (Tomiyama, 2019). For example, a study published in Lancet (Wang et al., 2021) found the average BMI and obesity of Chinese adults have been steadily increasing from 2004 to 2018. Studies also found BMI was negatively correlated with muscle function (Bollinger, 2017) and physical fitness (Bulbrook et al., 2021). The development of upper limb strength may be limited for both the overweight people with large BMI, and the underweight people with low BMI because of lower muscle mass. Lopes et al. [ 2019] also found a quadratic relationship between BMI and physical fitness in Brazilian youth. However, in China, the non-linear relationship between body composition and physical fitness was not verified, and the casual relationship was also unclear. To explore the casual effect of body composition on upper limb physical fitness, we proposed hypothesis 1b.
H1b:The degree of healthier body composition would facilitate upper body physical fitness. To evaluate hypotheses 1a and hypotheses 1b, through exploring the developmental trajectory of students’ physical fitness and the specific impact of body composition on physical fitness, study 1 conducted a longitudinal follow-up survey for 4 years (2015–2018) among male students from several universities in Beijing. The performance of pull-up was used as objective indicators of physical fitness, and BMI was measured as a body composition index. Analysis of variance (ANOVA) and Chi-square test were used to investigate the developmental tendency of upper limb physical fitness, and regression analysis and Hierarchical Linear Model (HLM) were further used to explore the predictive relationship between BMI and pull-up performances with 4-year follow-up data. After exploring the predicted relationship between body composition and upper limb physical fitness, we examine whether the effect remained when body composition was measured more accurately, and what aspects of upper limb physical fitness were affected by body composition. For more accurate body composition measurement, Study 2 used GE dual-energy X-ray absorptiometry to measure body composition, and the measurements of body fat percentage and lean body mass percentage were better than BMI, which was often used in previous studies (Pan et al., 2021; Wang et al., 2021), because the relation between two measurements and the health degree was monotonic. In this way, we could easily interpret lower body fat percentage and higher lean body mass percentage as the reflections of more health. To measure upper limb physical fitness more comprehensively, we selected 3 different indices, including 1) Handgrip strength, 2) upper body strength with Power Slap Test (Draper et al., 2011), and 3) anaerobic power with Wingate anaerobic test (Patton et al., 1985). Physical activity (e.g., pull-ups) is beneficial for improvement of upper body power performance (Sas-Nowosielski and Kandzia, 2020). With single-factor design, the independent variable of this study was body composition groups (athlete group vs. non-athlete group), and the dependent variables were 3 kinds of upper limb physical fitness. Hypothesis 2 was proposed.
H2:Body composition would be positively correlated with the upper limb physical fitness in grip strength, upper body strength and anaerobic power of upper limbs.
## Experimental procedure
In study 1, we accessed the data from Department of Physical Education in Peking University. Male freshmen enrolled in a university in Beijing in 2015 was recruited, and provided informed consent for all participants before participating. Pull-up and BMI data were recorded during mid-April or late October of each year during the study period from 2015 to 2018. We matched 332 student data according to their ID, excluded students who had not participated in the measurement for more than one year, and the data analysis included 183 of them. According to standard *Chinese criteria* (Liu et al., 2022), for 183 participants (16.31 < BMI< 42.61 kg/m2), 20 of them was thin (BMI <18.5), 122 was in normal weight (18.5 < BMI <23.9), 32 was overweight (24 < BMI <27.9), and 9 was obesity (BMI >28). We could not access the data for female because the universities provided data for our studies (Beijing, China) did not measure their pull-up performance.
Study 2 was conducted from September to October 2018, and 42 young male students from universities in Beijing were included and analyzed. The participants in study 2 were independent from those in Study 1. Among them, 21 students whose sports level reached grade two or above were randomly selected as the excellent physical composition students (Athlete Group), and their sports training years were 2.42 (±0.95) years on average. Meanwhile, 21 students whose sports level did not reach grade were randomly selected as the common body composition students (Non-athlete Group). The grade about sports level would be evaluated only after students participate in municipal and above level sport competitions and reaches the standard of General Administration of Sports in China (https://www.sport.gov.cn/n20001280/n20067662/n20067740/c23624534/content.html).
The results of the manipulation check showed that there were significant differences in body composition (body fat percentage and lean body weight percentage) between two groups, and the body fat percentage to lean body weight percentage of the athlete group were significantly lower than those of the non-athlete group (ps≤0.001), and the selection of the two groups of participants is effective (as shown in Table 1). All students had adequate sleep, no strenuous exercise, no metal graft in the body in 24 h before the test, and no upper limb injury in the last 6 months.
**TABLE 1**
| Body composition measurement | Athlete group (95% CI) | Non-athlete group (95% CI) | F | p | Cohen’s d |
| --- | --- | --- | --- | --- | --- |
| Body fat percentage (%) | 8.49 ± 2.61 (7.30, 9.68) | 12.94 ± 4.62 (10.60, 14.79) | 14.892 | <0.001 | −1.186 |
| Lean body mass percentage (%) | 0.925 ± 0.026 (0.903, 0.927) | 0.873 ± 0.046 (0.852, 0.894) | 13.317 | <0.001 | 1.392 |
## Ethics approval and consent to participate
Study was conducted according to the guidelines of the declaration of Helsinki, and have been approved by the Committee for Protecting Human and Animal Subjects at School of Psychological and Cognitive Sciences in Peking University. Before participating in the study [2015], all participants were informed of the project contents, risks and benefits, and could withdraw at any time. The informed consents were obtained from all participants.
Study 2 was conducted according to the guidelines of the declaration of Helsinki, and have been approved by the Committee for Protecting Human and Animal Subjects at School of Psychological and Cognitive Sciences in Peking University (the approval number is #2018-10-01). Before participating in this study, all participants in each study were informed of the project contents, risks and benefits, and could withdraw at any time. The informed consents were obtained from all participants.
## Measurements
Numbers and grades of pull-ups The upper limb physical fitness was measured by numbers and grades of pull-ups. Number of pull-ups were measured in a Beijing university stadium using a pull-up machine from Siboyou Company. The tester jumps up and grabs the bar with both hands, arms hanging straight at shoulder width, and body still. Then, both arms pull up at the same time, pull up to the lower chin over the upper edge of the bar, the machine issued a drip slowly lowered the body to restore static, then one test completed (As shown in Supplementary Figure S1). The grades of pull-ups are divided according to Chinese National Physical Health Standards for Students: for freshmen and sophomores, 0–9 pull-ups are considered as fail, 10–14 as pass, 15–16 as good, and 17 or more as excellent. For juniors and seniors, 0–10 pull-ups are considered fail, 11–15 pass, 16–17 good, and 18 or more excellent.
Body-Mass-Index (BMI) Body composition was measured by BMI. BMI value was calculated based on the “weight (kg)/height (m)^2”. Height and weight were tested in a university stadium in Beijing using a machine from Siboyou Company. The participant stands barefoot on the instrument with the dipstick resting on their heels, sacrum, and shoulders, eyes straight ahead. After standing on the instrument and keeping still, the height and weight will be recorded and displayed automatically.
Body composition Body composition was measured by total body fat percentage and total lean body mass percentage. We scanned participants’ bodies using GE dual-energy X-ray absorptiometry. The body scan was carried out with medium speed and medium aim, and the combination of sector scan and dot beam scan. The DXA model was GE Lunar Prodigy, and the analysis software version was enCORE10.50.086. After scanning and analysis, the total body fat percentage and total lean body mass percentage would be calculated.
Upper Limb Physical fitness We included grip strength test, upper power and upper limb anaerobic power measurement for upper limb physical fitness.[1] Grip strength Participants were tested on their hand grip strength. Participants stood upright with their arms hanging down naturally. They held a grip meter with the palm inward and the dial outward tilted at 45°. Verbal encouragement was given to participants during the test. We measured grip strength on each side 3 times and recorded the best result.[2] Upper body strength Power Slap Test was used to test upper body power performance (Draper et al., 2011; Sas-Nowosielski and Kandzia, 2020). We selected a vertical rock wall that was perpendicular to the ground and protruding, and installed two holding points of the same specifications on the rock wall. The holding points were 2.4 m above the ground and the distance between the two holding points was 45 cm (as shown in Supplementary Figure S2). The scale ruler was stuck in the middle of the holding points. During the test, participants grasped the fulcrum with both hands to make their bodies hang naturally. After stabilizing, they attempted their best to pull up and touch the scale with their hands and recorded the height distance. They repeated the test 3 times with 2-min intervals for rest. The best result would be counted into the statistics. We ruled out the effect of wingspan on test scores by defining the Power Slap Index as the high dangling touch score divided by wingspan.[3] Anaerobic power of upper limbs Wingate anaerobic test was used (Patton et al., 1985). The test instrument was Monark 891E upper limb anaerobic power meter, and the resistance coefficient was set to 0.05 kP/kg (Kilopond ·kg-1BW). The test was conducted in a sitting position, with the seat adjusted so that the participant’s shoulders were parallel to the power generator axle. During the measurement, participants accelerated for 4 s under non-resistance load and then loaded resistance. After that, participants shook the crank at the fastest speed for 30 s. During this period, bicycle speed and number of turns were recorded every 5 s to calculate the output power.
## Data analyses
R 4.1.0 and Mplus 8.3 were used for data analysis. The R software is used for data preprocessing. ANOVA, Chi-square test, and regression analysis were used for the pull-up and BMI results, and Hierarchical Linear Models were conducted with Mplus to explore the quadratic relationship between BMI and the following year’s pull-up performance.
IBM SPSS 24.0 were used for data analysis. ANOVA was conducted, and the effect size of Cohen’s d representative data was calculated, where 0.5 or above was considered as medium effect and 0.8 or above was considered as high efficiency (Gignac & Szodorai, 2016).
## Pull-up performance declined and BMI increased year by year
This study analyzed the number of students’ pull-ups and found that since 2015, the number of students’ pull-ups has shown a downward tendency. ANOVA was used to examine the number of pull-ups and found the main effect of years was significant (F [180, 3] = 7.057, $p \leq .001$, η 2 = 0.037). Among them, there were significant differences between the numbers in 2015 and 2016 ($$p \leq 0.005$$), 2015 and 2017 ($$p \leq 0.031$$), 2015 and 2018 ($p \leq 0.001$). More details shown in Figure 1.
**FIGURE 1:** *Change tendency of pull-ups from 2015 to 2018 (A). Line chart for number of pull-ups, (B). Bar chart for grades of pull-ups and numbers of students.*
Besides, in the analysis of students’ pull-up grades, more than $60\%$ of the students failed during the 4-year period, and the number of failed students increased with the increase of years. With Chi-square test, we found no significant difference in 4-year’ pull-up performance (χ ^2 [9] = 13.451, $$p \leq 0.143$$).
BMI of students was also analyzed, and it was found that BMI was increasing year by year. ANOVA for BMI showed that the main effect of years was significant (F [180, 3] = 45.418, $p \leq .001$, η 2 = 0.200), and the difference between each years was significant (ps≤0.027). More details shown in Figure 2.
## The quadratic BMI predicted the number of pull-ups
In order to further explore the factors influencing the number of pull-ups, this study used regression analysis to explore the relationship between BMI and pull-ups. The results showed that the quadratic BMI significantly predicted the number of pull-ups in the same year (β = −0.337, R^2 = 0.118, $p \leq .001$).
Further, HLM was used to explore the prediction effect of quadratic BMI on the number of pull-ups at t +1 from the individual level from 2015 to 2018 (see Figure 3 for details). The study established a two-level linear model (level 2: individual, level 1: time point of measurement), and all variables were averaged by groups. The results showed that the model fit well (AIC = 3169.933, BIC = 3191.418, χ ^2(df) = 38.616[2], RMSEA = 0.000, SRMR = 0.001, CFI = 1.000, TLI = 1.000). Quadratic BMI significantly predicted the number of pull-ups in the following year (β = −0.577, SE = 0.059, $95\%$CI = [-0.707, −0.406]), while BMI had no significant predictive effect (β = 0.029, SE = 0.046, $95\%$CI = [-0.088, 0.147]). That is, the number of pull-ups in the following year was greatest when BMI was in the middle range.
**FIGURE 2:** *Line chart of BMI change tendency from 2015 to 2018.* **FIGURE 3:** *Hierarchical Linear Model for quadratic BMI predicting the number of pull-ups in the following year.*
## Manipulation check
ANOVA analysis was used for manipulation check. The analysis found that the body fat percentage of the athlete group was significantly lower than that of the non-athlete group ($p \leq 0.001$), and the lean body weight percentage was significantly higher than that of the non-athlete group ($p \leq 0.001$), that is, there were significant differences in the body composition of the two groups, as shown in Table 1.
## Physical fitness between two groups with different body composition
The results showed that the handgrip strength score and index of the athlete group were significantly higher than that of the non-athlete group (ps < 0.019), and the score and index of Power Slap Test were significantly higher than that of the non-athlete group (ps < 0.001). The anaerobic performances of the upper limbs were also different for the two groups (ps < 0.018), as shown in Table 2. Among them, the most significant difference was in score and index for Power Slap Test (Cohen’s $d = 3.318$, 3.052), followed by average power/body weight in upper limb anaerobic performances (Cohen’s $d = 1.718$). The least difference was the power decay rate of the anaerobic power of the upper limbs (Cohen’s d = −0.662).
**TABLE 2**
| Indicators | Indicators.1 | Athlete group (95% CI) | Non-athlete group (95% CI) | F | p | Cohen’s d |
| --- | --- | --- | --- | --- | --- | --- |
| Handgrip Strength | Left handgrip Strength | 0.72 ± 0.09 (0.68, 0.77) | 0.65 ± 0.08 (0.62, 0.69) | 5.952 | 0.019 | 0.822 |
| Handgrip Strength | Right handgrip Strength | 0.78 ± 0.13 (0.72, 0.83) | 0.68 ± 0.09 (0.65, 0.72) | 8.062 | 0.007 | 0.894 |
| Handgrip Strength | Index for Dominant hand | 0.78 ± 0.12 (0.73, 0.84) | 0.70 ± 0.08 (0.66, 0.73) | 7.315 | 0.010 | 0.784 |
| Handgrip Strength | Index for Non-dominant hand | 0.72 ± 0.10 (0.67, 0.76) | 0.65 ± 0.08 (0.61, 0.68) | 6.419 | 0.015 | 0.773 |
| Upper Body Strength | Score (cm) | 100.76 ± 4.95 (98.5, 103.0) | 68.59 ± 14.06 (62.2, 74.8) | 98.23 | <0.001 | 3.052 |
| Upper Body Strength | Index (Score/Wingspan) | 0.60 ± 0.03 (0.58, 0.61) | 0.40 ± 0.08 (0.36, 0.44) | 95.82 | <0.001 | 3.31 |
| Anaerobic Power of Upper Limbs | Peak Power (W) | 466.58 ± 62.21 (438.3, 494.9) | 417.45 ± 66.58 (387.2, 447.8) | 6.105 | 0.018 | 0.763 |
| Anaerobic Power of Upper Limbs | Average Power(W) | 374.49 ± 43.06 (354.9, 394.1) | 309.04 ± 58.66 (282.3, 335.7) | 16.988 | <0.001 | 1.272 |
| Anaerobic Power of Upper Limbs | Minimum Power(W) | 272.14 ± 34.32 (256.5, 287.8) | 217.17 ± 52.08 (193.5, 240.9) | 16.314 | <0.001 | 1.246 |
| Anaerobic Power of Upper Limbs | Peak Power/Weight (W·kg−1BW) | 7.61 ± 1.00 (7.15, 8.06) | 6.52 ± 0.89 (6.12, 6.93) | 13.834 | 0.001 | 1.151 |
| Anaerobic Power of Upper Limbs | Index (Average Power/Weight) (W·kg−1BW) | 6.09 ± 0.58 (5.83, 6.36) | 4.83 ± 0.86 (4.44, 5.22) | 31.345 | <0.001 | 1.718 |
| Anaerobic Power of Upper Limbs | Power decay rate (%) | 41.24 ± 6.59 (38.2, 44.2) | 47.59 ± 11.85 (42.2, 53.0) | 4.598 | 0.04 | −0.662 |
## Discussion
The relationship between body composition and upper limb physical fitness is investigated by both the 4-year longitudinal survey (study 1) and the factorial experimental study (study 2). Study 1 analyzed BMI and pull-up performance data of male university students from 2015 to 2018 using Hierarchical Linear Model. The results showed that both the number of pull-ups and their grades showed a declined development tendency, while BMI showed an increased tendency, which supports Hypothesis 1a. The quadratic BMI could predict the number of pull-ups in the following year, suggesting that healthier body composition improved better upper physical fitness, which supports Hypothesis 1b. In study 2, two groups of participants had different body composition (measured by high/low total body fat percentage and total lean body mass percentage) respectively, and their body composition was measured by grip strength, upper body strength and anaerobic power of upper limbs. Healthier body composition was associated with higher upper limb physical fitness, which supports Hypothesis 2.
These results are consistent with previous studies. A study found that the average BMI of the Chinese population increased as the dietary patterns of urban and rural residents in China changed and their physical activity decreased (Wang et al., 2021). A systematic review also found that the strength and endurance of students in 14 countries were declining and that physical strength of students in China increased from 1985 to 1995 and declined between 1995 and 2014 (Masanovic, et al., 2020b). Other studies also found that the increase in body weight is one of the main reasons for the decline in physical fitness (Tittlbach et al., 2017; Bulbrook et al., 2021), even during the COVID-19 pandemic (Basterfield et al., 2022).
This phenomenon may be due to lifestyle changes. Studies have found that students are experiencing changing lifestyles (Masanovic, et al., 2020b), like a sedentary lifestyle (Morales-Demori et al., 2016) with the lack of physical activity and the increasing of screen time (Venckunas et al., 2017). Studies in China also found the decline in fitness level was caused by lifestyle changes, such as prolonged media use and increased consumption of fast food (Ao et al., 2019; Dong et al., 2019). These results suggest unhealthy exercise habits (lack of exercise, long sedentary time, etc.) and diet structure (unhealthy food intake) may be important factors affecting body composition and physical fitness.
The results of study 2 not only replicating study 1’s result that body composition contributed to the physical fitness of upper limbs, but also indicated that body composition impacted three aspects of upper limb physical fitness (grip strength, drape height and anaerobic power). Finger strength and shoulder strap strength were evaluated by the handgrip strength test (Laffaye et al., 2014), the explosive force of the shoulder and back of the upper limbs was measured by Power Slap Test (Draper et al., 2011), and the endurance of the upper muscles was measured by anaerobic power (Lovell et al., 2013). If students want to improve their upper body strength with low body composition, in addition to reducing BMI and improving body composition, they can also design relevant training programs through these aspects to help students develop their upper body quality. In the future, self-service, personalized and diversified online training programs can be considered so that more students can learn and train through online videos.
For individual students, potential suggestions could include: pay attention to the overall improvement of upper body strength quality, not only to improve the pull-up test results but also to train around the whole elements of upper body strength quality, including maximum strength, strength endurance, explosive power, and coordination with the lumbar abdominal muscle group. In addition to the above training, lifestyle can also be considered (Ao et al., 2019; Dong et al., 2019). For example, a number of studies have found the transfer between a healthy diet and physical exercise; that is, improving healthy diet can also promote individual physical exercise (Duan et al., 2017; Liang et al., 2019). Meanwhile, in parenting, caregivers should be urged to increase their companionship with their children, especially in sports activities, and reduce their own and their children’s use of electronic media, which can further increase children’s sports activities and improve their overall physical and mental health.
For universities, in the content of physical education curriculum and extracurricular exercise, educators could increase exercises related to upper body strength, and the training content of upper body strength could be organic and unified, rich and diverse. For example, more attention and encouragement would be given to students whose physical fitness is reduced after entering college. Meanwhile, it is suggested that universities could set up more rich and interesting physical courses about improving upper limbs (like rock climbing classes), to improve students’ motivation in exercising upper limbs. Moreover, universities could give awards and gifts to students who have more sports activities about upper limbs.
There are some limitations to this study, and there are many potential directions for further research in the future. First, this study only included male college students. Although females’ pull-up performance is not measured in China at present, upper body strength is still an important factor of physical fitness for female individuals (Venckunas et al., 2017). Considering the presence of gender differences in BMI and physical fitness development (Zhang et al., 2018), future studies could include female individuals and investigate the similarities and differences of these results. Secondly, study 1 used pull-ups performances for the upper limb body quality. For this concern, study 2 further used 3 different tests for upper limb multidimensional indexes. In addition to a wider variety of upper limb physical fitness indicators, future studies can also include core and lower limb physical fitness and psychological measures (Masanovic, et al., 2020b), and further explore the developmental trajectory of overall physical and mental health among young adults. Lastly, future research can design tailored electronic and digital intervention programs for physical fitness (Watson et al., 2017), identify target populations for interventions, and promote the development of children and young adults’ physical and mental health.
## Conclusion
This study highlights the quadratic predictive effect of body composition on upper limb physical fitness. The results suggest that physical fitness gradually declines after male students entering college, and bad body composition has a negative impact on many aspects of physical fitness, like grip strength, drape height, and anaerobic power. Results from our studies help society and educators to understand why students’ physical fitness continues to decline and provide a theoretical basis for future targeted intervention studies. Future researchers should pay more attention to students’ upper limb physical fitness. Through physical exercise, students’ upper arm muscle strength, upper arm shoulder, back explosive power and upper limb muscle endurance, and others upper limb physical fitness should be strengthened. Meanwhile, to enhance students’ body composition and physical fitness, future research could focus on healthy diets, parent-child relationship, and integrated design of physical education curriculum, for further promotion of students’ overall development of the physical and mental health.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Committee for Protecting Human and Animal Subjects at School of Psychological and Cognitive Sciences in Peking University (the approval number is #2018-10-01). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
QW and JQ contributed to the study design and data collecting. QJ and HP analyzed the data and drafted the manuscript. JQ and QJ took charge of writing and reply to reviewers’ comments. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2023.1104018/full#supplementary-material
## Abbreviations
HLM, Hierarchical Linear Model; BMI, Body-Mass-Index.
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|
---
title: Synthesis and structure–activity relationship studies of benzimidazole-thioquinoline
derivatives as α-glucosidase inhibitors
authors:
- Sara Moghadam Farid
- Milad Noori
- Mohammad Nazari Montazer
- Minoo Khalili Ghomi
- Marjan Mollazadeh
- Navid Dastyafteh
- Cambyz Irajie
- Kamiar Zomorodian
- Seyedeh Sara Mirfazli
- Somayeh Mojtabavi
- Mohammad Ali Faramarzi
- Bagher Larijani
- Aida Iraji
- Mohammad Mahdavi
journal: Scientific Reports
year: 2023
pmcid: PMC10020548
doi: 10.1038/s41598-023-31080-2
license: CC BY 4.0
---
# Synthesis and structure–activity relationship studies of benzimidazole-thioquinoline derivatives as α-glucosidase inhibitors
## Abstract
In this article, different s-substituted benzimidazole-thioquinoline derivatives were designed, synthesized, and evaluated for their possible α-glucosidase inhibitory activities. The most active compound in this series, 6j ($X = 4$-bromobenzyl) exhibited significant potency with an IC50 value of 28.0 ± 0.6 µM compared to acarbose as the positive control with an IC50 value of 750.0 µM. The kinetic study showed a competitive inhibition pattern against α-glucosidase for the 6j derivative. Also, the molecular dynamic simulations were performed to determine key interactions between compounds and the targeted enzyme. The in silico pharmacodynamics and ADMET properties were executed to illustrate the druggability of the novel derivatives. *In* general, it can be concluded that these derivatives can serve as promising leads to the design of potential α-glucosidase inhibitors.
## Introduction
Diabetes is a metabolic disorder characterized by prolonged high blood sugar levels (hyperglycemia) which are associated with complications such as heart, kidney, and nervous system diseases as well as leg amputation and blindness1,2. According to the World Health Organization around 422 million people suffered from diabetes in 2014 and this number is predicted to reach 642 million by 20403. Among different types of diabetes, about $90\%$ of cases are type 2 diabetes (T2D)4. Current therapeutic approaches to target T2D include dipeptidyl peptidase-IV (DPP-IV) inhibitors5, glucagon-like peptide-1 (GLP-1) agonists6, and α-glucosidase inhibitors7.
α-glucosidase (EC 3.2.1.20) is a key carbohydrate hydrolase enzyme that regulates blood glucose levels by hydrolyzing 1,4-α-glucopyranosidic of oligosaccharide and disaccharide to produce monosaccharides and as a result, the level of glucose in the body increase8,9. The primary structure of lysosomal a-glucosidase has 952 amino acids with an apparent molecular mass of 110 kDa. Based on the sequence similarity and the mechanism of binding, Trp-516 and Asp-518 are demonstrated to be critical for catalytic functions10. It was shown that inhibition of α-glucosidase decreases carbohydrate digestion and glucose absorption, therefore, stabilizing blood glucose levels and preventing hyperglycemia7. Acarbose, (the first approved inhibitor), voglibose (discontinue), and miglitol (the first pseudo-monosaccharide inhibitor), were approved drugs as α-glucosidase inhibitors which reduce postprandial glucose11. However, low efficiency and unexpected adverse effects such as flatulence, diarrhea, and stomachache limited their clinical application. As a result, numerous efforts have been carried out to find and develop new a-glucosidase inhibitors from diverse sources, such as natural products and chemical synthetic compounds12.
Heterocycle-based α-glucosidase inhibitors have gained attention in the last few years including benzofuran13, xanthones14, imidazole15, benzothiazole16, isatin17, imidazopyridines18 triazole19 as well benzimidazole20,21 and quinolone.
Quinoline has been proven to be a very effective pharmacophore as α-glucosidase inhibitors capable of providing hits or leads with easy synthetic protocol and structural diversity which makes ideal structure in anti-diabetic drug discovery. Quinoline-2-carboxylic acid (Compound A, Fig. 1) framework showed IC50 values of 9.1 ± 2.3 µg/mL22. Furthermore, substituted quinolines were reported to possess anti-α-glucosidase inhibition effects. By way of illustration, oxadiazole-quinoline (Compound B) has shown potent α-glucosidase inhibition activity (IC50 = 2.60 to 102.12 μM) concerning that of the standard acarbose (IC50 = 38.25 ± 0.12 μM)23, In 2019 anther set of quinoline derivatives (Compound C) to target α-glucosidase were synthesized. In vitro assessments demonstrated IC50 values in the range of 6.20 to > 50 µM24. A series of quinolone- bis(indolyl)methane hybrids bearing a wide range of functional groups (Compound D) were synthesized as α-glucosidase inhibitors. Most of them showed significant α-glucosidase inhibitory activity compared to acarbose (IC50 = 154.7 ± 1.9 μM)25.Figure 1Design of novel α-glucosidase inhibitors (6a–r).
Also, benzimidazole pharmacophore is well known for its α-glucosidase inhibitory activities with strong interactions with the active site21. It was identified as anti-α-glucosidase agents via the random screening of the in-house compound library26. The follow-up optimization of hit E resulted in a series 2-phenyl-1H-benzo[d]imidazole derivatives (compound F). The kinetic study of F exhibited non-competitive inhibition with no cytotoxicity against LO2 cells27. Zawawi and coworkers prepared twenty-six analogs of benzimidazole derivatives (compound G) with IC50 values ranging from 8.40—12.49 μM which showed potency greater than standard acarbose (IC50 = 774.5 ± 1.94)28. The high potency of benzimidazole was also confirmed in the previous studies (compound H)28,29.
Regarding that, the α-glucosidase inhibitory activity is affected by combining the quinoline and benzimidazole moieties in one molecule and inspired by these results aiming to develop more effective α-glucosidase inhibitors, novel series of benzimidazole-thioquinoline hybrids were designed. Also, it was assumed that sulfur atoms might provide special interactions with critical residues of the enzyme binding site. All derivatives were synthesized and evaluated for α-glucosidase inhibition to identify lead molecules. The structure–activity relationships (SARs), molecular dynamic simulations (in silico), as well as kinetic assessments were also performed.
## Chemistry
The synthetic route to target compounds 6a–r is represented in Fig. 2. First, commercially available N,N-dimethylformamide [1] was reacted with phosphoryl chloride at 0 °C then phenyl-acetamide was added dropwise, and the mixture was stirred at 80 °C for 12 h to afford compound 3. The crude product was purified by recrystallization in ethanol. Sodium sulfide was added to 2-chloroquinoline-3-carbaldehyde (compound 3) in DMF and was stirred at room temperature for 2 h leading to the formation of 3-formyl-2-mercaptoquinoline [4]. Compound 5 was synthesized by the reaction of commercially available o-phenylenediamine with compound 4 in the presence of the catalytic amount of Na2S2O5 under reflux conditions in DMF for 2 h. Finally, different substituted 6a–r were synthesized by the reaction of different alkyl or aryl halides with compound 5 in DMF at 50 °C for 12 h. The crude products were purified by recrystallization in ethanol. The structures of purified products were confirmed by IR, 1H -NMR, 13C -NMR, and elemental analysis (Supplementary files 1, 2).Figure 2Synthesis of compounds 6a–r.
## Evaluation of α-glucosidase inhibitory activity and structure–activity relationships
In vitro α-glucosidase inhibitory activity of synthesized compounds, 6a–r was performed compared with acarbose as the reference inhibitor. The results of the anti-α-glucosidase assay were presented in Table 1 in terms of IC50. In this series, all compounds had promising inhibition against α-glucosidase with IC50 values ranging from 28.0 to 663.7 µM compared with a positive control with an IC50 value of 750.0 µM.Table 1α-glucosidase inhibitory activity of 6a–rCompoundsXIC50 ± SD (µM)a6aBenzyl153.7 ± 0.96b2-Fluorobenzyl187.9 ± 2.46c3-Fluorobenzyl76.7 ± 0.76d4-Fluorobenzyl80.9 ± 1.16e2-Chlorobenzyl663.7 ± 1.26f3-Chlorobenzyl48.2 ± 0.46g4-Chlorobenzyl96.6 ± 0.16h2-Bromobenzyl133.5 ± 1.36i3-Bromobenzyl65.5 ± 2.06j4-Bromobenzyl28.0 ± 0.66k3,4-Dichlorobenzyl99. 4 ± 0.76l2-Methylbenzyl195.7 ± 0.66m3-Methylbenzyl158.4 ± 2.46n4-Methylbenzyl116.6 ± 0.56o2,3-Dimethylbenzyl126.9 ± 0.56p4-Nitrobenzyl89.2 ± 1.36q4-Methoxybenzyl67.3 ± 0.86rEthyl300.7 ± 2.0Acarbose–750.0 ± 5.0aData represented in terms of mean ± SD.
The unsubstituted benzyl derivative 6a showed a considerable inhibitory effect against α-glucosidase with IC50 values of 153.7 μM. Different moieties were introduced at different positions of the benzyl pendant to investigate the effect of substitution on the phenyl ring. First, the inhibitory effect of halogen groups was evaluated. The introduction of a meta fluorine (6c) or para fluorine (6d) group on the benzyl ring improved the activity compared to the unsubstituted one, and there is no significant between the meta (6c) and para-substituted (6d) fluorine groups. However, the ortho fluorine substitution (6b) deteriorated the potency.
Next, Cl was substituted at the various position of the phenyl pendant. 3-chlorobenzyl (6f., IC50 = 48.2 μM) exhibited significant inhibitory activity in comparison with 6g ($X = 4$-chlorobenzyl, IC50 = 96.6 μM), 6k ($X = 3$,4-dichlorobenzyl, IC50 = 99.4 μM) and 6e ($X = 2$-chlorobenzyl, IC50 = 663.7 μM).
The 4-bromobenzyl derivative (6j, IC50 = 28.0 μM) was the most promising α-glucosidase inhibitor of this series, with around a 30-fold improvement in the potency compared with positive control. Similar to the previous sets, ortho-bromine substitution (6h) was inferior to the potency.
Subsequently, the inhibitory effect of electron-donating groups was assessed. The introduction of a 2-methyl group (6l) slightly reduced the activity (IC50 = 195.7 μM) compared to an unsubstantiated one (6a). However, this compound still demonstrated better activity compared to acarbose as the positive control. Similar to previous derivatives, the replacement of the position from ortho (6l) to meta (6m) or para (6n) empowers the potency to the IC50 value of 158.4 and 116.6 µM, respectively. It seems that the optimum position of the electron-withdrawing group was the para position. Furthermore, methyl multi-substitutions also disclosed improvement in the activity compared to unsubstituted derivative (6a).
With the promising results on the α-glucosidase inhibitory activity of different substitutions, the hydrogen bond interacting motifs were also synthesized. 6p ($X = 4$-nitrobenzyl) and 6q ($X = 4$-methoxybenzyl) demonstrated good activity with IC50 values of 89.2 and 67.3 μM, respectively. Based on enzymatic inhibitory activity, 6r containing ethyl fragment with IC50 = 300.7 μM deteriorated the activity compared to all aromatic substituted groups except 6e. It seems that aliphatic substitutions are not favorable. Also, it was understood that ortho substitution and aliphatic moiety on the benzyl ring reduced the inhibitory potencies, which could be due to the steric hinder at this position.
## Enzyme kinetic studies
According to Fig. 3, the Lineweaver–Burk plot showed that the Km gradually increased and Vmax remained unchanged with increasing inhibitor concentration indicating a competitive inhibition. The results show that 6j bound to the active site on the enzyme and competed with the substrate for binding to the active site. Furthermore, the plot of the *Km versus* different concentrations of inhibitor gave an estimate of the inhibition constant, Ki of 28.1 µM (Fig. 4).Figure 3The Lineweaver–Burk plot in the absence and presence of different concentrations of 6j. Figure 4The secondary plot between Km and various concentrations of 6j.
The mode of inhibition of the most active compound (6h), identified with the lowest IC50, was investigated against an α-glucosidase activity with different concentrations of p-nitrophenyl α-D-glucopyranoside (1–16 mM) as substrate in the absence and presence of 6h at different concentrations (0, 7, 14, and 28 µM). A Lineweaver–Burk plot was generated to identify the type of inhibition and the Michaelis–Menten constant (Km) value was determined from the plot between the reciprocal of the substrate concentration (1/[S]) and reciprocal of enzyme rate (1/V) over various inhibitor concentrations. The experimental inhibitor constant (Ki) value was constructed by secondary plots of the inhibitor concentration [I] versus Km43,44.
## Docking Study
Molecular docking was analyzed in order to gain an understanding of the binding mechanism of fluorine substituted derivatives which is less bulky compared with bromine substituted derivatives with bulkier moiety. First, the molecular docking validation was performed to dock acarbose as a native ligand inside the α-glucosidase and the alignment of the best pose of acarbose in the active site of the enzyme and crystallographic ligand recorded an RMSD value less than 2 Å confirming the accuracy of docking. Then, the docking procedures were applied to all synthesized derivatives. It was reported that Glu276, His348, and Asp349 play critical roles in the catalytic mechanism of in α-glucosidase. The detailed interactions of all derivatives are presented in Table 2. As can be seen, 6j exhibited the best value with a GlideScore of -8.08 and participated in critical interactions with Asp349 and Asp408 categorized as essential residues of the binding site. Also, some studies exhibited the important residues of Asp 214, Glu 276, Arg 312, Asp 408, and Arg 439 within the enzyme's binding site30–32. The other derivatives 6h and 6i showed values of -6.55 and -6.85 GlideScore. Although 6b recorded the second top GlideScore value (-7.95), it exhibited low potency in the biological assessments. A closer lock at this interaction reveals that benzimidazole moiety participates in unfavorable interactions with Phe157. Also, 6c and 6d bearing 3-flurobenzy and 4-flurobenzyl exhibited unfavorable interactions through benzimidazole moieties. Table 2The predicted binding energy of all derivatives with the desired enzyme. CompoundGlideScoreMoietyResidueType of interaction6b-7.95BenzimidazolePhe157H-boundBenzimidazolePhe157One bad interactionBenzimidazoleLys239Pi-pi stackingBenzimidazoleArg312H-bound6c-5.81BenzimidazoleTyr71Pi-pi stackingBenzimidazoleTyr177Pi-pi stackingBenzimidazoleAsp214Three bad interactionsQuinolineArg312Pi-cation3-FluorobenzylTyr313Pi-pi stackingBenzimidazoleAsp349H-boundBenzimidazoleArg439Pi-cation6d-6.16BenzimidazoleTyr71Pi-pi stacking3-FluorobenzylPhe157Pi-pi stackingBenzimidazolePhe177Pi-pi stackingBenzimidazoleArg439Two bad interactionsBenzimidazoleArg439Pi-cationBenzimidazoleArg439Pi-cation6h-6.55BenzimidazolePhe157Pi-pi stackingBenzimidazolePhe157Pi-pi stackingBenzimidazolePhe157one bad interactionBenzimidazoleHis279Pi-cationQuinolinePhe311Pi-pi stackingQuinolineArg312Pi-cation6i-6.85BenzimidazolePhe157H-boundBenzimidazoleLys239Pi-pi stackingBenzimidazoleArg312H-bound4-bromobenzylGln350Halogen interaction6j-8.08BenzimidazoleTyr71Pi-pi stackingQuinolineHis279Pi-pi stackingQuinolinePhe300Pi-pi stackingQuinolinePhe300Pi-pi stackingBenzimidazoleAsp349H-bound4-BromobenzylAsp408Halogen interaction
## Molecular dynamic simulations
Considering that the α-glucosidase x-ray crystallographic structure of S. cerevisiae is unavailable, the in silico study was performed using the homology-modeled enzyme previously reported in our articles33. The overall architecture of this enzyme is similar to the human intestinal α-glucosidase enzyme. According to our in silico evaluations (Fig. 5), the active site pocket of the enzyme consists of a functional site lid (blue- residues 305–315), the back wall helix (Teal -residues 425–437), and two β-sheet loops demonstrated in the green and yellow cartoon (residues 150 -160 and 250 -260).Figure 5The structure of modeled enzyme active site in complex with compound 6j consisted of active site lide (blue), back wall helix (cyan), distal β-loop (yellow), and proximal β-loop (red).
The stability of the protein–ligand complex trajectories was assessed with the enzymes’ backbone Root Mean Square Deviation (RMSD) during the 100 ns MD simulation. The RMSD comparison of apo-enzyme alongside the enzyme in complex with acarbose as natural ligand and compound 6j as the most potent inhibitor is demonstrated in Fig. 6. The RMSD value of α-glucosidase-acarbose complex stabilized after 10 ns with the range of (1.25 Å). It remained in the same situation with fewer fluctuations till the end of the simulation. On the other hand, the α-glucosidase enzyme took longer to stabilize (about 20 ns) and had higher values of fluctuation until the end of the simulation. The RMSD plot of α-glucosidase with 6h had more fluctuations than the latter complex, the complex stabilized after 5 ns around the (1.00 Å) until the end of the simulation with an average RMSD value of 2 Å. The overall RMSD values of both complexes didn’t seem to have a significant difference which can be contributed to the low steric hindrance and high flexibility of compound 6j. However, the RMSD of apo-enzyme had a significant difference with complexes which can be justified by the absence of any potent ligand. Figure 6The RMSD values of the α-glucosidase apo-enzyme (green), acarbose in complex with the α-glucosidase enzyme (red), and compound 6j in complex with the α-glucosidase enzyme (blue).
The Root Mean Square Fluctuations (RMSF) of Cα atoms from both complexes and the α-glucosidase revealed the detailed mechanism of the ligand interaction with the enzyme. Upon the binding of ligands to the α-glucosidase, residues movement decrease due to non-bonding interactions between the ligand and the enzyme34. The most important residues of the active site, including the functional site lid, the back-wall helix, and two β-sheet loops, mostly had a smaller RMSF value than the acarbose (Fig. 7).Figure 7The RMSF values of α-glucosidase, acarbose- α-glucosidase and compound 6j in complex with α-glucosidase.
The interactions of compound 6j with the active site pocket of the enzyme, which has been present in more than $20\%$ of the duration of simulations, are demonstrated in Fig. 8. His239 made a major H-bond interaction with the nitrogen atom of the quinoline system, two pi–stacking interactions was observed between the active side lid residues Phe311 and Tyr313. From the proximal β loop of the enzyme residues Phe157, Lys155 and Ser156 were found to have pi-pi staking, pi-stacking, and H-bond interactions, respectively. Conversely, acarbose showed multiple H-bond interactions with Ser244, His245, Ser281, His289, Ser156, and Asn412. Other interactions included charged interaction with Glu276 and double water bridged interactions with Ser244 and Glu304.Figure 8(a) 2D presentation of compound 6j interactions with the active site of the enzyme (b) 2D presentation of acarbose interactions with the active site of the enzyme.
## ADME-Toxicity profiles and physicochemical properties
The physicochemical properties and pharmacokinetic profile of the new benzimidazole-thioquinoline hybrid were calculated as part of preclinical drug development studies35. The intestine is usually the primary site for orally administered drugs, and a value of more than $30\%$ is considered good absorption. As can be seen in Table 3, the good human intestinal absorption of all compounds caused fast absorption from the intestine to the bloodstream. The steady-state volume of distribution (VDss) is the theoretical volume, and the higher the VD, the more of a drug is distributed in tissue rather than plasma. Log VDss < -0.15 is considered low, while log VDss > 0.45 categorize as high. All derivatives showed moderate VDss value with steady distribution in blood. The cytochrome P450’s are responsible for the metabolism of many drugs and an important detoxification enzyme in the body. The drug-metabolizing enzymes most studied are the cytochrome P450 superfamily with different isozymes, 2D6, 3A4, 1A2, 2C19, 2C9, 2D6, and 3A4 which cover a wide range of chemical structures in drug metabolism and distribution. The differences in these isoforms comebacks to the site of expression, and type of drug to be detoxified, which comes back to the structure of the enzyme and its sequences36–38. Results of Table 3 showed that the molecules are likely to be metabolized by 3A4, which affect the pharmacokinetics of these drugs. P450 3A4 (abbreviated CYP3A4), mainly found in the liver and the intestine, metabolizing broad substrate from most therapeutic categories and many endogenous substances. On the other hand, CYP2D6 is primarily expressed in the liver and central nervous system and is involved in antipsychotic metabolism. Cytochrome P450 inhibitors can increase the bioavailability of drugs with a high first-pass metabolism, and the inhibition of CYP450 isoform can result in the accumulation of parent drug concentrations. Table 3ADMET prediction of the synthesized derivatives as α-glucosidase inhibitors. AbsorptionDistributionMetabolismExcretionToxicityHuman Intestinal Absorption (% absorbed)VDss (logL/Kg)2D63A41A22C192C92D63A4Total Clearance (log mL/min/kg)Oral rat acute toxicity (mol/kg)SubstrateInhibitor6a84.4160.051NoYesYesYesYesYesYes1.0512.446b83.650.047NoYesYesYesYesYesYes0.9562.446c83.650.047NoYesYesYesYesYesYes0.992.446d83.650.047NoYesYesYesYesYesYes1.0062.446e82.7550.051NoYesYesYesYesYesYes0.9612.446f.82.7550.051NoYesYesYesYesYesYes0.9612.446g82.7550.051NoYesYesYesYesYesYes0.9612.446h82.6880.051NoYesYesYesYesYesYes1.0472.446i82.6880.051NoYesYesYesYesYesYes0.9332.446j82.6880.051NoYesYesYesYesYesYes0.8962.446k81.0940.051NoYesYesYesYesYesYes1.0842.446l84.2140.051NoYesYesYesYesYesYes1.0352.446m84.2140.051NoYesYesYesYesYesYes1.0352.446n84.2140.051NoYesYesYesYesYesYes1.0352.446o84.0110.054NoYesYesYesYesYesYes1.1322.446p82.0910.085NoYesYesYesYesNoYes0.7782.486q84.4130.049NoYesYesYesYesYesYes1.082.446r84.0110.043YesNoYesYesYesYesNo1.0372.42Acarbose4.172-0.836NoNoNoNoNoNoNo0.4282.45 Total clearance is a combination of hepatic and renal clearance expressed in the log (ml/min/kg). Low renal clearance is defined as ≤ 0.1 mL/min/kg, moderate as > 0.1 to < 1 mL/min/kg, and high as 1 > mL/min/kg39. In most cases, synthesized compounds showed moderate total clearance. Oral rat acute toxicity (LD50) is the amount of material given all at once that causes the death of $50\%$ (one-half) of a group of test animals. The LD50 is one way to measure a material's short-term poisoning potential (acute toxicity) compounds, and a value less than 0.5 is categorized as high toxic demonestrated LD50 value in the range of 2.42 to 2.48 mol/kg.
Also the physicochemical and molecular properties from the SwissADME website were presented in Table 440. Lipinski’s rule of five is a valid method to evaluate the drug-likeness criteria of compounds (lipophilicity ≤ 5, molecular weight ≤ 500, hydrogen bond donor (HBD) ≤ 5 (OH and NH groups), and hydrogen bond acceptor (HBA) ≤ 10 (N and O atoms). As seen in Table 4, all derivatives have acceptable molecular weight, number of rotatable bonds, number of H-bond acceptors, and number of H-bond donors. However, there is the validation of lipophilicity optimum range of all compounds except 6r. Table 4Drug-likeness properties of derivatives. CompoundMWNum. rotatable bondsNum. H-bond acceptorsNum. H-bond donorsLog P6a367.4774326.0716b385.4674316.216c385.4674316.216d385.4674316.216e401.9224316.726f401.9224316.726g401.9224316.726h446.3734316.836i446.3734316.836j446.3734316.836k436.3674317.386l381.5044316.386m381.5044316.386n381.5044316.386o395.534316.696p412.4745515.976q397.5034316.696r305.4063314.89
## Conclusion
Following our interest in the rational design of α-glucosidase inhibitors; herein, a series of benzimidazole-thioquinolines were designed and synthesized. All derivatives demonstrated promising glucosidase activity inhibitory activities with IC50 values of 28.0–663.7 μM compared with the reference compound, acarbose (IC50 = 750.0 μM). The SAR data revealed mostly any substitution at the para position is favorable regardless of the type of inhibition. Compound 6j (IC50 = 28.0 ± 0.6 μM) as the most potent inhibitor revealed competitive inhibition patterns in the kinetic experiments. Molecular docking studies justify the designing strategy as benzimidazole recorded H-bound interaction with Asp616 and the linker participate in pi-sulfur interaction with the binding site. Noteworthy the least active compound exhibited unfavorable interaction which justifies its low potency. MD studies showed that 6j-enzyme were stable during the simulation time and participated in pronounced interaction with the α-glucosidase active site through several H-bound interactions. Computed physicochemical and ADMET properties exhibited the druggability of the developed derivatives. These findings will be prominent for the structural design of a-glucosidase inhibitors in the development of novel anti-diabetic agents.
## 3-(1H-benzo[d]imidazol-2-yl)-2-(benzylthio) quinoline (6a)
IR (ν,cm-1): 3376, 1642, 1627, 1450, 1580. 1HNMR (300 MHz, DMSO-d6) δ: 13.00 (brs, 1H, NH), 8.69 (s, 1H, H4 quinoline), 8.11–7.78 (m, 8H, Ar), 7.64–7.57 (m, 3H, Ar), 7.26–7.24 (m, 2H, Ar), 4.64 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 158.2, 149.8, 149.0, 148.1, 147.7, 137.9, 132.7, 132.0, 129.7,128.8, 127.8, 126.1, 124.7, 124.3, 124.0, 117.0, 35.0. Anal. Calcd for C23H17N3S (367.47): C, 75.18; H, 4.66; N, 11.44. Found: C, 75.25; H, 4.56; N, $11.40\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((2-fluorobenzyl)thio)quinoline (6b)
IR (ν,cm-1): 3385, 1645, 1630, 1460, 1570. 1HNMR (300 MHz, DMSO-d6) δ: 13.14 (s, 1H, NH), 8.43 (s, 1H, H4 quinoline), 8.04 (d, 1H, $J = 9$ Hz, H6 quinoline), 7.94 (d, 1H, $J = 8.8$ Hz, H8 quinoline),7.90 (t, 1H, $J = 8.8$ Hz, H7 quinoline), 7.86–7.81 (m, 3H, Ar), 7.80–7.75 (m, 1H, Ar), 7.68–7.60 (m, 3H, Ar), 7.42 (s, 1H, Ar,7.23–7.11 (m, 2H, Ar), 4.55 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 157.54163.4 (d, CF, 1JCF = 284.5 Hz), 149.29, 147.09, 136.76, 133.06, 131.54, 128.53, 127.87, 126, 125.94, 125.25, 123.60, 122.91, 116.69, 115.96. Anal. Calcd for C23H16FN3S (385.46): C, 71.67; H, 4.18, N, 10.90. Found: C, 71.78; H, 4.24, N, $19.82\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((3-fluorobenzyl)thio)quinoline (6c)
IR (ν,cm-1): 3380, 1647, 1625, 1462, 1580. 1HNMR (300 MHz, DMSO-d6) δ: 12.99 (s, 1H, NH), 8.67 (s, 1H, H4 quinoline), 8.03–7.99 (m, 2H, H6,8 quinoline), 7.81 (t, 1H, $J = 9$ Hz, H7 quinoline), 7.70 (brs, 1H, Ar), 7.57 (t, 1H, $J = 9$ Hz, Ar), 7.32–7.25 (m, 5H, Ar, H9 quinoline), 6.99 (t, 1H, $J = 9$ Hz, Ar), 4.54 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6)δ 157.92(d, CF, 1JCF = 289.5 Hz), 148.95, 147.08, 143.99, 139.19, 136.66, 135.05, 132.07, 131.64, 128.79, 126.77, 125.09, 123.76, 123.16, 122.42, 119.79, 112.03, 36.63.. Anal. Calcd for C23H16FN3S (385.46): C, 71.67; H, 4.18; N, 10.90. Found: C, 71.58; H, 4.09; N, $10.98\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((4-fluorobenzyl)thio)quinoline (6d)
IR (ν,cm-1): 3380, 1649, 1630, 1455, 1579. 1HNMR (300 MHz, DMSO-d6) δ: 12.91 (s, 1H, NH), 8.67 (s, 1H, H4 quinoline), 8.04–7.97 (m, 2H, H6,8 quinoline), 7.81 (t, 1H, $J = 9$ Hz, H7 quinoline), 7.64–7.50 (m, 5H, H9 quinoline,Ar), 7.24 (brs, 2H, Ar), 7.07 (t, 2H, $J = 9$ Hz, Ar), 4.96 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 162.5 (d, CF, 1JCF = 289.5 Hz), 158.9, 149.9, 148.2, 138.0, 136.2, 132.8, 136.2, 132.8, 132.6, 129.8, 128.8, 126.1, 124.5, 124.0, 116.6, 116.2, 34.8. Anal. Calcd for C23H16FN3S (385.46): C, 71.67; H, 4.18; N, 10.90. Found: C, 71.58; H, 4.10; N, $10.99\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((2-chlorobenzyl)thio)quinoline (6e)
IR (ν,cm-1): 3369, 1645, 1630, 1450, 1580. 1HNMR (300 MHz, DMSO-d6) δ: 13.01 (s, 1H, NH), 8.69 (s, 1H, H4 quinoline), 8.05 (d, 1H, $J = 9$ Hz, H6 quinoline), 7.81 (t, 3H, $J = 9$ Hz, Ar), 7.74–7.39 (m, 5H, Ar), 7.26–7.22 (m, 4H, Ar), 4.64 (brs, 1H, CH2), 4.66 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 158.7, 150.0, 148.2, 145.0, 138.0, 137.1, 136.0, 134.9, 133.1, 132.7, 130.8, 130.4, 129.7, 128.8, 128.6, 127.7, 126.1, 124.6, 124.5, 123.3, 120.8, 112.7, 33.7. Anal. Calcd for C23H16ClN3S (401.91): C, 68.73; H, 4.01; N, 10.46;Found: C, 68.67; H, 3.91; N, $10.35\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((3-chlorobenzyl)thio)quinoline (6f)
IR (ν,cm-1): 3372, 1640, 1625, 1454, 1585. 1HNMR (300 MHz, DMSO-d6) δ: 13.06 (s, 1H, NH), 8.73 (s, 1H, H4 quinoline), 8.06–8.01 (m, 2H, H6,8 quinoline), 7.85 (t, 1H, $J = 9$ Hz, H7 quinoline), 7.78–7.74 (m, 1H, Ar), 7.63–7.59 (m, 2H, Ar), 7.54–7.50 (m, 1H, Ar), 7.42 (s, 1H, Ar), 7.33–7.24 (m, 15H, Ar), 4.56 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 157.6, 148.9, 147.1, 141.9, 137.0, 133.1, 131.7, 131.0, 130.5, 129.7, 129.1, 128.8, 128.6, 127.7, 127.2, 126.8, 125.1, 123.4, 122.3, 119.7, 112.0, 34.0. Anal. Calcd for C23H16ClN3S (401.91): C, 68.73; H, 4.01, N, 10.46. Found: C, 68.65; H, 4.08, N, $10.40\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((4-chlorobenzyl)thio)quinoline (6g)
IR (ν,cm-1): 3372, 1640, 1625, 1454, 1585. 1HNMR (300 MHz, DMSO-d6) δ: 12.97 (s, 1H, NH), 8.66 (s, 1H, H4 quinoline), 8.03–7.50 (m, 9H, Ar), 7.31–7.23 (m, 3H, Ar), 4.52 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 155.3, 148.2, 146.7, 145.9, 141.9, 139.3, 138.0, 132.7, 129.8, 129.6, 128.8, 127.7, 126.1, 124.6, 123.3, 120.7, 112.9, 34.9. Anal. Calcd for C23H16ClN3S (401.91): C, 68.73; H, 4.01; N, 10.46. Found: C, 68.65; H, 4.08, N, $10.39\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((2-bromobenzyl)thio)quinoline (6h)
IR (ν,cm-1): 3380, 1645, 1635, 1452, 1577. 1HNMR (300 MHz, DMSO-d6) δ: 13.00 (s, 1H, NH), 8.69 (s, 1H, H4 quinoline), 8.07–7.97 (m, 2H, $J = 9$ Hz, H6,8 quinoline), 7.81 (t, 1H, $J = 9$ Hz, H7 quinoline), 7.73–7.58 (m, 5H, Ar, H9 quinoline), 7.32–7.13 (m, 4H, Ar), 4.66 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 158.7, 149.9, 148.2, 138.7, 138.0, 134.1, 133.2, 132.7, 130.6, 129.8, 129.2, 128.8, 127.8, 126.1, 125.8, 124.4, 124.0, 36.4. Anal. Calcd for C23H16BrN3S (446.36): C, 61.89; H, 3.61; N, 9.41. Found: C, 61.80; H, 3.51; N, $9.52\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((3-bromobenzyl)thio)quinoline (6i)
IR (ν,cm-1): 3384, 1655, 1620, 1459, 1588. 1HNMR (300 MHz, DMSO-d6) δ: 13.06 (s, 1H, NH), 8.73 (s, 1H, H4 quinoline), 8.04 (d, 1H, $J = 8.9$ Hz, H6 quinoline), 7.98 (d, 1H, $J = 8.7$ Hz, H8 quinoline), 7.86 (t, 1H, $J = 9$ Hz, H7 quinoline), 7.70–7.62 (m, 3H, Ar), 0.757 (t, 1H, $J = 9$ Hz, Ar), 7.30–7.25 (m, 3H, Ar, 7.13–7.11 (m, 2H, Ar), 4.55 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 157.90, 149.10, 147.78, 147.07, 143.04, 141.29, 141.06, 140.15, 136.64, 136.22, 133.61, 131.90, 131.57, 130.94, 128.77, 127.63, 126.74, 125.4, 123.33, 123.25, 123.02, 36.67. Anal. Calcd for C23H16BrN3S (446.37): C, 61.89; H, 3.61, N, 9.41. Found: C, 61.78; H, 3.58, N, $9.55\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((4-bromobenzyl)thio)quinoline (6j)
IR (ν,cm-1): 3374, 1645, 1629, 1454, 1580. 1HNMR (300 MHz, DMSO-d6) δ: 12.96 (s, 1H, NH), 8.67 (s, 1H, H4 quinoline), 8.00–7.23 (m, 12H, Ar), 4.50 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 158.0, 149.0, 148.0, 139.7, 137.9, 133.0, 132.5, 129.7, 128.8, 127.7, 126.1, 124.6, 123.3, 120.7, 113.0, 34.9. Anal. Calcd for C23H16BrN3S (446.36): C, 61.89; H, 3.61; N, 9.41. Found: C, 61.77; H, 3.54; N, $9.49\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((3,4-dichlorobenzyl)thio)quinoline (6k)
IR (ν,cm-1): 3376, 1645, 1630, 1452, 1580. 1HNMR (300 MHz, DMSO-d6) δ: 13.00 (s, 1H, NH), 8.89 (s, 1H, H4 quinoline), 8.05–7.97 (m, 2H, H6,8 quinoline), 7.85–7.81 (m, 2H, Ar), 7.72 (d, 1H, $J = 9$ Hz, H7 quinoline), 7.49–7.26 (m, 4H, Ar), 7.30–7.21 (m, 2H, Ar), 4.52 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 158.4, 149.1, 146.7, 145.9, 144.9, 141.9, 138.0, 133.9, 132.8, 132.7, 131.9, 131.7, 131.2, 129.8, 128.7, 127.8, 126.1, 124.7, 123.3, 120.7, 112.9, 34.4. Anal. Calcd for C23H15Cl2N3S (436.36): C, 63.31; H, 3.46; N, 9.63. Found: C, 63.26; H, 3.38; N, $9.69\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((2-methylbenzyl)thio)quinoline (6l)
IR (ν,cm-1): 3372, 1650, 1633, 1455, 1585. 1HNMR (300 MHz, DMSO-d6) δ: 8.04 (s, 1H, H4 quinoline), 8.04–7.98 (m, 2H, H6,8 quinoline), 7.81 (t, 1H, $J = 9$ Hz, H7 quinoline), 7.69–7.44 (m, 4H, Ar, H9 quinoline), 7.22–7.09 (m, 5H, Ar), 4.53 (s, 2H, CH2), 2.37 (s, 3H, CH3). 13CNMR (75 MHz, DMSO-d6) δ: 159.3, 149.9, 148.3, 138.0, 136.9, 132.6, 131.7, 129.8, 128.7, 127.7, 127.4, 126.1, 124.6, 123.3, 120.7, 112.9, 34.1, 20.4. Anal. Calcd for C24H19N3S (381.49): C, 75.56; H, 5.02; N, 11.01. Found: C, 75.47; H, 5.11; N, $10.92\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((3-methylbenzyl)thio)quinoline (6m)
IR (ν,cm-1): 3382, 1674, 1635, 1465, 1575. 1HNMR (300 MHz, DMSO-d6) δ: 12.78 (s, 1H, NH), 8.45 (s, 1H, H4 quinoline), 8.04–801 (m, 2H, H6,8 quinoline), 7.99 (t, 1H, $J = 8.8$ Hz, H7 quinoline), 7.84–7.80 (m, 2H, Ar), 7.68–7.62 (m, 2H, Ar), 7.60 (1H, $J = 8.7$ Hz,Ar), 7.01–6.94 (m, 3H, Ar), 4.51 (s, 2H, CH2)), 2.20 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 157.99, 137.39, 136.66, 131.59,128.81, 127.77, 127.34, 126.78, 125.16, 123.78, 123.39, 35.75, 20.59. Anal. Calcd for C24H19N3S (381.13): C, 75.56; H, 5.02, N, 11.01. Found: C, 75.80; H, 5.18, N, $11.21\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((4-methylbenzyl)thio)quinoline (6n)
IR (ν,cm-1): 3378, 1670, 1648, 1465, 1585. 1HNMR (300 MHz, DMSO-d6) δ: 13.14 (s, 1H, NH), 8.77 (s, 1H, H4 quinoline), 8.02 (d, 1H, $J = 8.7$ Hz, H5 quinoline), 8.00 (d, 1H, $J = 8.5$ Hz, H8 quinoline) 7.90–7.88 (m, 2H, Ar), 7. 7.78 (d, 1H, $J = 8.2$ Hz, 2H, Ar), 7.68–7.66 (m, 1H, Ar), 7.60 (1H, $J = 8.1$ Hz,Ar), 7.37 (d, 1H, $J = 8.1$ Hz, 2H, Ar), 7.35–7.28 (m, 2H, Ar), 4.76 (s, 2H, CH2)), 2.07 (s, 2H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 157.98, 148.99, 147.18, 143.01, 137.38, 136.86, 136.65, 131.58, 128.81, 127.76, 127.33, 126.78, 125.63, 125.14, 123.77, 123.38,35.72, 20.60. Anal. Calcd for C24H19N3S (381.13): C, 75.56; H, 5.02, N, 11.01. Found: C, 75.74; H, 5.09, N, $11.17\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((2,3-dimethylbenzyl)thio)quinoline (6o)
IR (ν,cm-1): 3384, 1658, 1635, 1458, 1575. 1HNMR (300 MHz, DMSO-d6) δ: 13.15 (s, 1H, NH), 8.79 (s, 1H, H4 quinoline), 8.04(d, 1H, $J = 8.9$ Hz, H6 quinoline), -7.96 (d, 1H, $J = 8.8$ Hz, H8 quinoline), 7.94 (t,1H, $J = 8.8$ Hz Ar), 7.82 (t, 1H, $J = 9$ Hz, Ar), 7.58–7.30 (m, 4H, Ar), 7.14–7.11 (m, 2H, Ar),, 2.29 (s, 2H, CH3), 2.20 (s, 2H, CH3). 13CNMR (75 MHz, DMSO-d6) δ: 157.98, 149.00, 147.19, 137.38, 136.87, 136.66, 131.60, 131.58, 128.81, 127.77, 127.38, 126.77, 125.62, 125.15, 123.78, 123.39, 35.74, 20.59, 14.49. Anal. Calcd for C25H21N3S (395.15): C, 75.92; H, 5.35; N, 10.62. Found: C, 75.87.26; H, 5.38; N, $10.69\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((4-nitrobenzyl)thio)quinoline (6p)
IR (ν,cm-1): 3374, 1639, 1630, 1448, 1582. 1HNMR (300 MHz, DMSO-d6) δ: 13.00 (s, 1H, NH), 8.66 (s, 1H, H4 quinoline), 8.11–7.90 (m, 3H, Ar), 7.83–7.65 (m, 3H, Ar), 7.60–7.45 (m, 3H, Ar), 7.28–7.20 (m, 4H, Ar), 4.64 (brs, 1H, CH2), 4.54 (brs, 1H, CH2). 13CNMR (75 MHz, DMSO-d6) δ: 159.0, 158.2, 149.9, 149.1, 148.2, 144.9, 139.8, 137.9, 136.0, 132.6, 132.6, 132.0, 130.8, 129.7, 128.8, 128.3, 127.8, 127.7, 126.1, 124.7, 123.3, 120.7, 112.9, 35.8. Anal. Calcd for C23H16N4O2S (412.46): C, 66.97; H, 3.91; N, 13.58. Found: C, 66.88; H, 3.80; N, $13.65\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-((4-methoxybenzyl)thio)quinoline (6q)
IR (ν,cm-1): 3372, 1645, 1633, 1454, 1585. 1HNMR (300 MHz, DMSO-d6) δ: 12.97 (s, 1H, NH), 8.67 (s, 1H, H4 quinoline), 8.04–7.98 (m, 2H, H6,8 quinoline), 7.84–7.69 (m, 2H, Ar), 7.61–7.55 (m, 2H, Ar), 7.26–7.14 (m, 3H, Ar), 7.08–7.03 (m, 2H, Ar), 6.74 (d, 1H, $J = 9$ Hz, Ar), 4.51 (s, 2H, CH2), 3.67 (s, 3H, CH3). 13CNMR (75 MHz, DMSO-d6) δ: 160.6, 149.9, 148.2, 144.9, 141.3, 138.0, 136.0, 132.6, 130.8, 129.8, 127.7, 126.1, 124.6, 123.3, 123.1, 120.7, 116.4, 113.9, 112.9, 56.4, 35.7. Anal. Calcd for C24H19N3OS (397.49): C, 72.52; H, 4.82; N, 10.57. Found: C, 72.47; H, 4.74; N, $10.65\%$.
## 3-(1H-benzo[d]imidazol-2-yl)-2-(ethylthio)quinoline (6r)
IR (ν,cm-1): 3377, 1645, 1630, 1452, 1588. 1HNMR (300 MHz, DMSO-d6) δ: 13.00 (s, 1H, NH), 8.66 (s, 1H, H4 quinoline), 8.01 (d, 1H, $J = 9$ Hz, H6 quinoline), 7.96 (d, 1H, $J = 9$ Hz, H9 quinoline), 7.81 (t, 1H, $J = 9$ Hz, H7 quinoline), 7.76 (d, 1H, Ar), 7.61–7.57 (m, 2H, Ar), 7.30–7.25 (m, 2H, Ar), 3.29 (q, 2H, $J = 6$ Hz, CH2), 1.36 (t, 3H, $J = 6$ Hz, CH3). 13CNMR (75 MHz, DMSO-d6) δ: 158.6, 149.1, 147.5, 144.0, 137.1, 135.0, 131.5, 128.8, 127.8, 126.5, 124.1, 123.5, 122.3, 119.7, 112.0, 24.7, 14.5. Anal. Calcd for C18H15N3S (305.40): C, 70.79; H, 4.95; N, 13.76. Found: C, 70.69; H, 4.86; N, $13.85\%$.
## In vitro α-glucosidase inhibition assay
α-Glucosidase enzyme (EC3.2.1.20, Saccharomyces cerevisiae, 20 U/mg) and substrate (p-nitrophenyl glucopyranoside) were purchased from Sigma-Aldrich. 1 mg of α-glucosidase was dissolved in potassium phosphate buffer (50 mM, pH = 6.8) to obtain the initial activity of 1 U ml–1. Then, 20 µl of this enzyme solution was incubated with 135 µl of potassium phosphate buffer and 20 µl of test compound at various concentrations in DMSO. Therefore, the final concentration of the enzyme was about 0.1 U ml–1. After 10 min incubation at 37 °C, 25 µl of the substrate at a final concentration of 4 mM was added to the mixture and allowed to incubate at 37 °C for 20 min. Then, the change in absorbance was measured at 405 nm spectroscopically. DMSO ($10\%$ final concentration) as control and acarbose as the standard inhibitor were used41,42.
DMSO as control ($10\%$ final concentration) and acarbose as the standard drug were used. The percentage of inhibition for each entry was calculated by using the following formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\% \,{\text{Inhibition}} = [({\text{Abs}}\,{\text{control}} - {\text{Abs}}\,{\text{sample}})/{\text{Abs}}\,{\text{control}}] \times 100$$\end{document}%Inhibition=[(Abscontrol-Abssample)/Abscontrol]×100IC50 values were obtained from the nonlinear regression curve using the Logit method.
## Molecular docking
To perform the molecular docking studies, the Maestro Molecular Modeling platform (version 10.5) by Schrödinger, L.L.C. was used. The homology model structure of a-glucosidase was obtained according to the previously reported procedure. The protein was then prepared using a protein preparation wizard. PROPKA assigned H-bonds at pH: 7.4. To prepare the ligands, the 2D structures of the ligands were drawn in ChemDraw (ver. 16) and converted into SDF files, which were used further by the ligprep module. Ligands were prepared by OPLS_2005 force field using EPIK. The grid box was generated for each binding site using entries with a box size of 25 A, all derivatives were docked on binding sites using induced-fit docking, reporting 10 poses per ligand to form the final complex.
## Molecular dynamics simulations
MD simulations were conducted using the desmond operator of Schrodingers suit maestro. To build the system for MD simulation, the protein–ligand complexes were solvated with SPC explicit water molecules and placed in the center of an orthorhombic box in the periodic boundary condition42. The system’s charge was neutralized by adding Na+ and Cl- to simulate the real cellular ionic concentrations. The MD simulations protocol involved minimization, pre-production, and finally production MD simulation steps. In the minimization procedure, the entire system was allowed to relax for 2500 steps by the steepest descent approach. Then the temperature of the system was raised from 0 to 300 K with a small force constant on the enzyme to restrict any drastic changes. MD simulations were performed via NPT (constant number of atoms; constant pressure, i.e., 1.01325 bar; and constant temperature, i.e., 300 K) ensemble. The Nose–Hoover chain method was used as the default thermostat with 1.0 ps interval and Martyna-Tobias-Klein as the default barostat with 2.0 ps interval by applying an isotropic coupling style. Long-range electrostatic forces were calculated based on the particle-mesh-based Ewald approach with the cutoff radius for Columbia forces set to 9.0 Å. Finally, the system was subjected to produce MD simulations for each protein–ligand complex. The dynamic behavior and structural changes of the systems were analyzed by the calculation of the RMSD and RMSF45.
## In silico pharmacokinetic properties of synthesized compounds
Prediction of the molecular properties of the synthesized compounds was performed using the online servers such as SwissADME and pkCSM so that the structure of each molecule were uploaded and the physicochemical and drug-likeness properties were reported.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2. The online version contains supplementary material available at 10.1038/s41598-023-31080-2.
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|
---
title: Mitochondrial Lon-induced mitophagy benefits hypoxic resistance via Ca2+-dependent
FUNDC1 phosphorylation at the ER-mitochondria interface
authors:
- Ananth Ponneri Babuharisankar
- Cheng-Liang Kuo
- Han-Yu Chou
- Vidhya Tangeda
- Chi-Chen Fan
- Chung-Hsing Chen
- Yung-Hsi Kao
- Alan Yueh-Luen Lee
journal: Cell Death & Disease
year: 2023
pmcid: PMC10020552
doi: 10.1038/s41419-023-05723-1
license: CC BY 4.0
---
# Mitochondrial Lon-induced mitophagy benefits hypoxic resistance via Ca2+-dependent FUNDC1 phosphorylation at the ER-mitochondria interface
## Abstract
During hypoxia, FUNDC1 acts as a mitophagy receptor and accumulates at the ER (endoplasmic reticulum)-mitochondria contact sites (EMC), also called mitochondria-associated membranes (MAM). In mitophagy, the ULK1 complex phosphorylates FUNDC1(S17) at the EMC site. However, how mitochondria sense the stress and send the signal from the inside to the outside of mitochondria to trigger mitophagy is still unclear. Mitochondrial Lon was reported to be localized at the EMC under stress although the function remained unknown. In this study, we explored the mechanism of how mitochondrial sensors of hypoxia trigger and stabilize the FUNDC1-ULK1 complex by Lon in the EMC for cell survival and cancer progression. We demonstrated that *Lon is* accumulated in the EMC and associated with FUNDC1-ULK1 complex to induce mitophagy via chaperone activity under hypoxia. Intriguingly, we found that Lon-induced mitophagy is through binding with mitochondrial Na+/Ca2+ exchanger (NCLX) to promote FUNDC1-ULK1-mediated mitophagy at the EMC site in vitro and in vivo. Accordingly, our findings highlight a novel mechanism responsible for mitophagy initiation under hypoxia by chaperone Lon in mitochondria through the interaction with FUNDC1-ULK1 complex at the EMC site. These findings provide a direct correlation between Lon and mitophagy on cell survival and cancer progression.
## Introduction
Autophagy, a critical degradation mechanism of various intracellular components through the formation of double-bilayered membrane vesicles called autophagosomes and its fusion with lysosomes is necessary for maintaining the cellular homeostasis. Macroautophagy, a kind of autophagy, is important for its selective degradation and vital for the cell survival function through autophagosomes formation around specific cellular organelles or molecular components, including mitochondria, endoplasmic reticulum (ER), Peroxisome, Golgi and pathogen [1, 2]. Mitochondrial autophagy (mitophagy) removes damaged and surplus mitochondria, which plays a vital role in mitochondrial quality control. Mitochondria are the essential components for energy metabolic pathways and cell survival; they are dynamically regulated by the mitochondrial fission and fusion. Thus, quality control of mitochondria is important for cell survival under both physiological and stress/pathological conditions like starvation, oxidative stress, and hypoxia, whereas its dysfunction was reported to cause several serious diseases like neurodegeneration, diabetes, cardiovascular disorders, and cancer [3–9].
The initiation of autophagy/mitophagy is mediated by the ULK1 (Unc-51 like kinase), a serine/threonine-specific protein kinase, and its associated protein complex with HORMA domain-containing autophagy-related protein 13 (ATG13), scaffold FIP200 (RB1CC1), and autophagy-related protein 101 (ATG101) in mammalian cells [10, 11]. The upstream regulation of ULK1 by AMPK/mTOR has been extensively studied indicating ULK1 significance on deciding the on/off autophagy status. Phosphorylation of ULK1 complex and subsequent phosphorylation of Vps34-ATG14-Beclin1 complex by ULK1 are important for the phagophore nucleation formation through the ER cradle associated omegasomes [12–15]. In response to hypoxia, FUNDC1, a mitochondrial outer membrane protein, acts as a mitochondrial receptor to mediate hypoxia-induced mitophagy [16]. FUNDC1 accumulates at the ER-mitochondria contact sites (EMC), also called mitochondria-associated membranes (MAM), in response to hypoxia, recruiting Drp1 to ensure hypoxia-induced mitochondrial fission [17] and subsequently interacting with LC3 to complete mitophagy. The ER-mitochondria contact sites have been demonstrated to be involved in autophagy, Ca2+ transport, and lipid metabolism, signifying the fact that organelle communication is important for the cellular bioenergetics. The control of cytosolic Ca2+ oscillations by NCLX was associated with many pathological conditions including cancer. The sustained intracellular Ca2+ levels in cancer cells can activate the AMPK-ULK1 phosphorylation and FUNDC1 signaling during hypoxia for mitophagy [18, 19], highlights the significance of calcium-mitophagy coordination in regulating the cell proliferation during various stress conditions.
Mitochondrial *Lon is* a multi-function and stress-induced protein and where uses its protease, ATPase, DNA binding [20–22], and chaperone activities [23–25] to participate in the protein quality control and stress response pathways in mitochondria [26–28]. Lon is upregulated under hypoxia and oxidative stress [29, 30] and generates ROS to promote multifarious important cellular processes in tumorigenesis [29, 31]. Lon utilizes chaperone activity to stabilize its clients, mitochondrial p53 [23], mtHsp60-Hsp70 complex [24], PYCR1 [31], and mitochondrial Na+/Ca2+ exchanger (NCLX) [32], which is involved in the cell survival upon ROS stress. Intriguingly, a recent paper found that the translocation of Lon to the EMC was observed when cells was treated with Efavirinez, a non-nucleoside reverse transcriptase inhibitor [33], which causes both ER and mitochondrial stress [34]. However, the functional significance of Lon in the EMC and where how Lon promotes hypoxia-induced mitophagy has been remained poorly understood.
The present study aimed at delineating the mechanism responsible for the role of mitochondrial Lon in the EMC and in the initiation of mitophagy under hypoxia. Here, we report that chaperone Lon physically interacts with NCLX and FUNDC1-ULK1 complex. These interactions further enhance the ULK1 stability and its kinase activity to phosphorylate its interacting partners ATG13 (Serine 355), Beclin1 (Serine 15), and FUNDC1 (Serine 17) respectively. Accordingly, all these events responsible for mitophagy initiation are elucidated by the translocation of Lon from the matrix to the EMC region upon hypoxia stress, which provides the triggering mechanism from inside mitochondria to outside contacts. These findings define an important function of chaperone Lon that integrates calcium-mitophagy coordination, and it governs the function of mitochondrial Lon in cell survival and drug resistance upon hypoxia-induced mitophagy providing a future choice of cancer therapy for patients.
## Cell culture and cell treatment
HCT-15 colon cancer cells were cultured in medium containing Roswell Park Memorial Institute (RPMI) (GIBCO, New York, NY, USA) and oral cancer cells like OEC-M1 and HSC-3, cells were cultured in medium containing Dulbecco’s modified Eagle’s essential medium (DMEM) (GIBCO, New York, NY, USA), supplemented with $10\%$ Fetal Bovine Serum (FBS) (FBS qualified; Invitrogen) and penicillin/streptomycin (50 U/mL; sigma, St. Louis, MO, USA) in a 37 °C humidified incubator with $5\%$ CO2.
## Reagents and Antibodies
Antibodies to human Lon were produced as described previously [35]. The following primary antibodies were used in this study: Autophagy Induction Antibody Sampler Kit containing anti-ULK1 monoclonal antibody, anti-p-ULK1 S555, anti-ATG13, anti-p-ATG13 S355, anti-FIP200, anti-ATG101. Anti-NFkB anti-LC3B, anti-LAMP1, anti-VDAC, anti-Calnexin, anti-Tubulin, and ULK1 antibody sampler Kit containing anti-ATG14, anti-p-ATG14 S29, anti-Beclin1, anti-p-Beclin1 S15, were purchased from Cell signaling technology. Anti-FACL4, anti-GAPDH, and anti-β-actin were purchased from Gentex also the Gentex helped in producing in-house antibody anti-FUNDC1 and anti-p-FUNDC1 S17 which were raised in rabbit and purified. Anti-HSP60 and anti-Aconitase2 from santa cruz. Anti-myc and and anti-Flag were obtained from Merck Millipore. Anti-HIF1α was obtained from Life science bio. Anti-ULK1 for immuofluorescence studies was obtained from Santa cruz. Cobalt chloride (CoCl2), SBI-0206965, Bafilomycin A1 and N-Acetyl Cysteine (NAC) were purchased from sigma, dissolved in DMSO and stored at −20 °C.
## Patients and clinical sample
Tissue specimens of 92 patients with oral squamous cell carcinoma (OSCC) were used for immunohistochemistry (IHC) analysis based on the availability of archival human tissue blocks from diagnostic resection specimens in the Departments of Pathology at Mackay Memorial Hospital, Taipei, Taiwan with approval from the Institutional Review Board (IRB numbers 15MMHIS046 and 17MMHIS085). The main clinical characteristics of the 92 patients selected for this study are shown in a previous study [23]. All experiments were performed in accordance with relevant guidelines and regulations.
## Western blotting
The cells were harvested by trypsinization and lysed with NETN buffer (20 mM Tris (pH 8.0), 1 mM EDTA, 150 mM NaCl, $0.5\%$ nonidet P-40 (NP-40)) containing protease inhibitor cocktail (Roche, Mannheim, Germany). The cell lysates were then centrifuged at 10,000×g at 4 °C to obtain solubilized cellular proteins. Protein was quantified with a bicinchoninic acid protein assay (Pierce, Rockford, IL, USA) according to the manufacturer’s instructions. Proteins were separated by $8\%$ or $10\%$ or $12\%$ SDS-PAGE and electro-transfered to a polyvinylidene fluoride membrane. Target proteins were detected by incubating with the indicated primary antibodies, followed by the corresponding HRP-conjugated secondary antibodies. Immunoreactive bands were detected with Immobilon Western Chemiluminescent HRP Substrate (Millipore).
## Immunoprecipitation
HCT-15 cells seeded in 10 cm dish were co-transfected with specific constructs to overexpress proteins of interest for 24–48 h. NETN (150 mM NaCl, 1 mM EDTA, 20 mM Tris-Cl (pH 8.0), $0.5\%$ NP-40) lysis buffer containing protease and phosphatase inhibitors (1.0 mM sodium orthovanadate, 50 μM sodium fluoride). Samples were incubated on ice with intermittent agitation by pipetting for 30 min. Beads were equilibrated using bead resuspension buffer (150 mM Tris-buffered Saline, 50 mM NaCl, $0.25\%$ Tween). Protein lysates were precleared by centrifugation at 4 °C for 10 min at 15,000×g. Clarified lysates were incubated with specific equilibrated beads for various periods at 4 °C. Beads were then washed with ice-cold Wash buffer (150 mM Tris-buffered Saline, 50 mM NaCl) 3–5 times. Clarified lysates were first incubated with antibodies specified in figure legends for 24 h at 4 °C. A/G beads (Pierce) were equilibrated with resuspension buffer then incubated with antibody-lysate mix for 1 h at 4 °C. Beads was washed as above. Bound samples were eluted using 4X sample buffer. Samples were processed for immunoblotting for examining the binding partners.
## Subcellular fractionation
Previously the detailed protocol for the EMC/MAM fractionation has been described (Wieckowski et al.; [ 36]). In brief, 10 number of HCT-15 confluent plates (about 15-cm diameter) were collected and resuspended in ice-cold IB cells-1 buffer containing 225 mM mannitol, 75 mM sucrose, 0.1 mM EGTA, and 30 mM Tris–HCl pH 7.4. After, gentle and slow homogenization of resuspended cells by slow stokes about 200 times using the dounce homogenizer and homogenized extracts were subjected for centrifugation at 600×g for 5 min at 4 °C. Repeat the centrifugation with the collected supernatant until no traces of pellet is seen (PNS). The collected supernatants were transferred to a multiple 1.5 mL centrifuge tubes and centrifuge at 7000×g for 10 min at 4 °C to separate the crude mitochondrial pellet and supernatant containing cytosol and ER. Repeat the centrifugation with the collected supernatant until no traces of pellet to avoid the EMC/MAM and mitochondrial contamination. Further ultracentrifugation of the collected supernatant at 100,000×g for 1 h isolates the ER (pellet) and cytosol (supernatant). The crude mitochondrial pellet was resuspended gently in 2 ml of ice-cold mitochondrial resuspension buffer (MRB) containing 250 mM mannitol, 5 mM HEPES pH 7.4, and 0.5 mM EGTA which added on top of 8 ml of *Percoll medium* in an ultracentrifuge tube. MRB solution was then layered gently on top of the mitochondrial suspension to fill the centrifuge tube. At final step, centrifugation was carried out at 95,000×g for 60 min at 4 °C to isolate the EMC/MAM and pure mitochondria and to obtain pure EMC/MAM the collected supernatant was further ultracentrifuged at 100,000×g for 1 h.
## Immunofluorescence microscopy
Cells seeded in 6-well plate (corning) were treated as indicated in figure legends. After treatment, cells were rinsed in PBS and fixed with $4\%$ paraformaldehyde at RT for 10 min. Cells were washed with PBS. For immunostaining, cells were then permeabilized with methanol for 10 min, and blocked with $1\%$ BSA in TBS or PBS for 1 h min at RT. After incubation, cells were supplemented with antibodies (1:1000) overnight at 4 °C. Cells were then washed with PBS 3 times and incubated with Alexa 488 or 546, 405-conjugated anti-mouse or anti-rabbit secondary antibodies (Invitrogen). Finally, coverslips were mounted by Pro-Long® Gold Antifade Reagent with DAPI (Invitrogen, Carlsbad, CA) for room temperature 10 min. Fluorescent Cell images were captured with Olympus DP70 upright microscope. LC3B counting was done using cell counter macros for FIJI software (semi-automatic quantification).
For confocal imaging of mitophagy assay using mito-QC, HCT-15 cells were transiently transfected with mito-QC to express fluorescent tagged mcherry and GFP proteins under CoCl2 or Lon expressed cells. After treatments, cells were fixed, permeabilized, blocked and treated with corresponding primary and secondary antibodies as above and washed 3 times with PBS prior to image analysis. The cell treatment with anti-LAMP1 antibody to study the overlapping with mCherry signals. Also, the Confocal imaging were performed for the method indicated in figure legends (Fig. 5C–E). Images were taken using a ×63 oil objective on an SP5II Leica confocal microscope and the images were processed using LASX software/canvasX16 and Imaris (version 9.5 -Bit plane mode -3D surface construction) software. Images were digitally altered within linear parameters, with minimal adjustments to levels and linear contrast applied to all images.
## Transmission electron microscopy
The experiments were performed as previously described by reported protocols. Cells after respective antibodies treatment were fixed in $2\%$ glutaraldehyde in 0.1 M phosphate buffer, and a $2\%$ phosphotungstic acid solution (pH 7.0) was used for negative staining. Negative staining was used for the single-droplet negative staining technique on continuous and holey carbon support films. All transmission electron microscopy (TEM) procedures were performed by Bio Materials Analysis Technology (Bio MA-Tek, Taiwan).
## Calcium assay
Calcium assay was performed as described previously [32]. In brief, to measure cytosol or mitochondrial calcium, OEC-M1 cells were treated with CoCl2 (18 h) or transfected with Vector, Lon, shNCLX (48 h) in presence or absence of CGP37157 (10 µM - 4 h). After incubation, cells were washed using HBSS with Ca2+ and Mg2+ for three times and incubated with cytosolic calcium probe Fura-2 AM (1 µM) (molecular probes) for 30 mins at room temp. After incubation, cells were washed thrice with HBSS buffer and imaged under Leica microscope. Cells were imaged under 20X lens and captured using CCD camera every 0.5 s. Excitation was done at 340 and 380 nm alternatively and emission at 510 nm. Basal calcium levels were measured in presence of buffer using ATP agonizts to measure calcium. Analysis was done using LASX software and data was expressed as the ratio of $\frac{380}{340}$ subtracted from background ROI. Cells were imaged under 40 × 1.5 numerical aperture and a CCD camera was used to capture the images every 2 sec. In case of mitochondrial calcium, cells will be transducted with a lentiviral plasmid mt-lar-GECO for 24 h and replace medium before transfection or drug treatment. ( Ca2+) mito were measured by mt-lar-GECO with excitation $\frac{560}{40}$ nm and emission $\frac{645}{75}$ nm. First basal levels were measured in presence of HBSS buffer for 60 sec ATP agonist was added and made further readings. ( Ca2 +) mito changes were quantified as (F-F0)/F0 where F is fluorescence intensity at each time point and F0 is the average fluorescence intensity of basal calcium. Analysis was done using LASX software by choosing ROI. For cytosolic calcium, data expressed as the ratio of $\frac{488}{380}$ subtracted from background ROI.
## Cell viability assay
The HCT-15 cells were seeded in 96-well plates and were treated with CoCl2 or cells transiently expressing Lon. After the treatment time the respective cell conditions were treated with SBI-0206965 (20 μM) or BafilomycinA1 (100 nM) for 12–24 h. Later, MTS reagent about 120 μl was added to each condition mentioned in the figure legend and incubated for 90 min. The absorbance was read at 490 nm and the results were quantified using Graphpad prism 9.0.
## Apoptosis assay
Apoptosis was analyzed by caspase 3-dependent NucView® 488 caspase 3 assay kit (#30029 - Biotium, Inc. San Francisco, USA). NucView® 488 Caspase-3 substrate is a novel cell membrane-permeable fluorogenic caspase substrate designed for detecting caspase-3 activity within live cells in real time. The cells were seeded in 12 well plates and were treated with CoCl2 (200 μM) and pCDNA3-myc-Lon for 18 and 36 h respectively. After incubation the cells were subjected for treatment with SBI-0206965 and BafilomycinA1 in both CoCl2 treated/ Lon expressed cells. Finally, the NucView® 488 Caspase-3 substrate was added to all the treatment/no treatment conditions and incubated according to manufacturer’s instructions. The apoptosis-based fluorescence image were captured using Olympus DP80 inverted microscope using 40X objective.
## Immunohistochemistry, IHC
IHC was performed as described previously [24, 29].
## Statistical analysis
For statistics, cells were randomly selected to calculate the number of MAM, gold particles, and colocalizations. Assays for characterizing cell phenotypes were analyzed by Student’s t‐test, and correlations between groups were calculated using Pearson’s test for colocalization experiments. P‐values < 0.01 were deemed statistically significant. All statistical data were calculated with GraphPad Prism software.
## Hypoxia induces ULK1-related autophagy and mitochondrial Lon in cancer cells
During hypoxia-induced mitophagy, FUNDC1-ULK1 plays a critical role in regulating other autophagy-related proteins to mediate mitophagy [37, 38]. To explore the mechanism responsible for Lon in the initiation of mitophagy under hypoxia, the level of hypoxia-inducible factor-1α (HIF-1α), Lon, ULK1, and ULK1 downstream autophagy proteins was examined under physiological hypoxia condition. As expected [30], the level of HIF-1α, Lon, ULK1, and ULK1 downstream autophagy proteins, ULK1-S555 phosphorylation, ATG13, and FIP200, was increased under hypoxia exposure or CoCl2 treatment in HCT-15 colorectal cancer cells and in FADU, HSC-3 and OEC-M1 oral cancer cells (Figs. 1A and S1A, B). CoCl2 treatment is a well-established chemical hypoxia approach because CoCl2 alleviates HIF-1α degradation [39–41]. The results further showed that hypoxia activated Lon and the initiation of autophagy in a time- (Figs. 1B, C and S1C) and dose-dependent manner (Fig. S1D). We found that HIF-1α-Lon expression and the initiation proteins of autophagy, ULK1, ULK1-S555 phosphorylation, ATG13, and FIP200, were activated under hypoxia (Figs. 1B and S1C); the chronological activation of autophagy was showed by the HIF-1α-Lon-ULK1 axis (Fig. 1C). We also found that hypoxia treatment causes the phosphorylation of ULK1 downstream targets, ATG14 Serine 29 and Beclin1 Serine 15 (Fig. S1E, F), and the phosphorylation were reduced by the treatment of SBI-0206965, a potent ULK1 kinase inhibitor (Fig. S2A, B), which is supported by the recruitment of ATG14 and Beclin1 to the autophagosome initiation complex by ULK1 activation [42, 43].Fig. 1Hypoxia induces ULK1-related autophagy and mitochondrial Lon in cancer cells. A HCT-15 cells were exposed to hypoxia ($1\%$ O2) for 24 h or CoCl2 (200 µM) for 16 h and the collected lysates were immunoblotted against the mitophagy signaling using indicated antibodies. GAPDH as the loading control. B HCT-15 cells were exposed to hypoxia ($1\%$ O2) for the indicated time points and the protein levels of ULK1 signaling and LC3B were obtained after immunoblotting. Cell lysates were analyzed by immunoblotting using indicated antibodies. GAPDH as the loading control. C HCT-15 cells were treated with CoCl2 (200 μM) for different time points (0–48 h). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. D, E Hypoxia mimic induces lysosome-mediated autophagy. D Hypoxia mimic-induced lysosome-mediated autophagy were verified by immunofluorescence. HCT-15 cells were treated with or without CoCl2 (200 μM for 18 h) and with BafilomycinA1 (100 nM for 6 h) or not. The cells were fixed and immunostained by LC3B (green) and anti-LAMP1 (red) antibodies. DNA was stained with DAPI (blue). Scale bars = 50 μm. E Hypoxia mimic-induced lysosome-mediated autophagy were quantified by LC3B puncta/cell detection according to the immunofluorescence data in D. LC3B puncta/cell was quantified by selecting more than 50 cells per condition. $$n = 3$$ biological replicates. The error bars shown in the panel represent the standard deviation from three independent experiments. *** $p \leq 0.001.$ F, G Hypoxia mimic induces lysosome-mediated mitophagy. F Hypoxia mimic-induced lysosome-mediated mitophagy were verified by confocal immunofluorescence. HCT-15 cells transiently expressing mito-QC were treated with or without CoCl2 (200 μM for 18 h) and with BafilomycinA1 (100 nM for 6 h) or not. The cells were fixed and immunostained by GFP (mitochondria, green), mCherry (mitolysosome, red), and anti-LAMP1 (lysosome, blue) antibodies. Scale bars = 10 μm. G Hypoxia mimic-induced lysosome-mediated mitophagy were quantified by mcherry signals according to the immunofluorescence data in F. $$n = 3$$ biological replicates. The error bars shown in the panel represent the standard deviation from three independent experiments. *** $p \leq 0.001.$
Since mitophagy is induced by impaired mitochondria under stress and related to mitochondrial dynamics by fission or fusion [44, 45], we also found that the phosphorylation of Drp1 at serine 616 for mitochondrial fission was increased first and then decreased whereas fusion (p-DRP1 serine 637) was increased later under hypoxia (Fig. S2C). These supported the ULK1 activation for the selective mitophagy response under hypoxia stress. To confirm that hypoxia-induced ULK1 activation is involved in the selective autophagic response under hypoxic stress, the autophagic assay was performed using bafilomycinA1 (BafA1) treatment, a potent inhibitor of autophagosome and lysosome fusion (Fig. 1D–G). The results showed that LC3B-II levels were increased under hypoxia treatment and were dramatically increased in threefold upon BafA1 treatment (Fig. 1D, E). To quantify the mitophagy flux under hypoxia, we transiently transfected mito-QC (a tandem GFP-mCherry fusion reporter [46]) in cancer cells before hypoxia treatment. To validate that the mCherry-only puncta are mitolysosomes, a degraded mitochondrial component within lysosomes, we measured the co-localization signals of mCherry puncta and LAMP1, a lysosomal marker protein. The results showed that mCherry entry into lysosomes was inhibited by BafA1 treatment. We found that number of mitolysosomes (mCherry-only in lysosomes) was threefold increased under hypoxia treatment compared with the control, and more than $80\%$ of mCherry puncta were inhibited in LAMP-positive lysosomes upon BafA1 treatment (Fig. 1F, G). These data concluded that hypoxia induces the activation of mitophagy and activates Lon upregulation and the ULK1 downstream autophagy signaling concurrently.
## Mitochondrial Lon is significant for ULK1-induced mitophagy under hypoxia
To explore the role of mitochondrial Lon in regulation of autophagy, we first examined the expression level of ULK1 and its autophagy complex proteins when cells were transfected with the plasmid pCDNA3-Lon or Lon-shRNA (Fig. 2). We found that the initiation proteins of autophagy were increased and ULK1 downstream target proteins were significantly activated upon Lon overexpression whereas the initiation proteins of autophagy were inhibited in the Lon-shRNA HCT-15 cells (Fig. 2A, B) and in OEC-M1 oral cancer cells (Fig. S3A). To confirm ULK1 is important for Lon-induced mitophagy, we treated the cells with the inhibitor SBI-0206965. We found that hypoxia- and Lon-induced autophagy signaling is inhibited by ULK1 inhibitor (Fig. 2C, D). Similar results were observed in Lon-induced activation of ULK1 targets (Fig. S3B). Since mitochondrial Lon has been proved to induce ROS production [31], we confirmed that hypoxia- and Lon-induced autophagy signaling was inhibited by NAC, a ROS scavenger (Fig. S3C, D). These data certainly suggested the crucial involvement of Lon in the regulation of ULK1 function in the early stage of autophagosome biogenesis. Fig. 2Mitochondrial *Lon is* required for ULK1-mediated mitophagy under hypoxia. A, B HCT-15 cells were transiently transfected with the plasmids encoding Lon or Lon-shRNA. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. C HCT-15 cells transfected with the plasmids encoding Lon or empty were treated with SBI-0206965 (20 μM for 6 h) or not. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. D HCT-15 cells were treated with CoCl2 (200 μM for 18 h) or not in the presence or absence of SBI-0206965 (20 μM for 6 h). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. E, F Mitochondrial Lon induces lysosome-mediated autophagy. E Mitochondrial Lon-induced lysosome-mediated autophagy were verified by immunofluorescence. HCT-15 cells were transfected with the plasmids encoding Lon or empty and treated with BafilomycinA1 (100 nM for 6 h) or not. The cells were fixed and immunostained by RFP-LC3B (red) and anti-LAMP1 (green) antibodies. Scale bars = 50 μm. F Mitochondrial Lon-induced lysosome-mediated autophagy were quantified by LC3B puncta/cell detection according to the immunofluorescence data in E. LC3B puncta/cell was quantified by selecting more than 50 cells per condition. $$n = 3$$ biological replicates. The error bars shown in the panel represent the standard deviation from three independent experiments. *** $p \leq 0.001.$ G, H Mitochondrial Lon induces lysosome-mediated mitophagy. G Mitochondrial Lon-induced lysosome-mediated mitophagy were verified by confocal immunofluorescence. HCT-15 cells transiently expressing mito-QC were transfected with the plasmids encoding Lon or empty and treated with BafilomycinA1 (100 nM for 6 h) or not. The cells were fixed and immunostained by GFP (mitochondria, green), mCherry (mitolysosome, red), and anti-LAMP1 (lysosome, blue) antibodies. Scale bars = 10 μm. H Mitochondrial Lon-induced lysosome-mediated mitophagy were quantified by mcherry signals according to the immunofluorescence data in F. $$n = 3$$ biological replicates. The error bars shown in the panel represent the standard deviation from three independent experiments. *** $p \leq 0.001.$
To confirm that Lon-induced ULK1 activation is involved in autophagic flux, the autophagic assay was performed using bafilomycinA1 (BafA1) treatment, a potent inhibitor of autophagosome and lysosome fusion. The results showed that LC3B-II levels and RFP-LC3 puncta were increased under Lon overexpression and were dramatically increased upon BafA1 treatment (Figs. 2E, F and S3E). Similarly, mitophagic flux quantification was performed under condition of Lon overexpression using mito-QC reporter and LAMP1 co-localization. As expected, we observed that mCherry puncta in LAMP1 positive lysosomes were increased upon Lon upregulation and inhibited upon BafA1 treatment (Fig. 2G, H). Altogether, these findings indicate that *Lon is* involved in the early event of mitophagy signaling and may stabilize specific ULK1 kinase complex for the mitophagy turnover.
## Mitochondrial Lon chaperone activity contributes to the stability of ULK1 complex for the mitophagy activation
To confirm mitochondrial Lon influences the stability of ULK1 complex and its kinase activity for the mitophagy activation, we overexpressed Lon in cells with pCDNA3-Lon plasmid. Mitochondrial Lon protein level was increased with simultaneous increase in ULK1/FIP200/ATG101 complex (Fig. 3A). This depicts that Lon chaperone function may influence the ULK1 stability and its kinase-dependent function of mitophagy. To establish the role of Lon chaperone activity in the ULK1-dependent mitophagy, we overexpressed myc-Lon-WT, myc-Lon-K529R, or myc-Lon-S855A in cells where empty vector used as a control. The K529R mutant removed the conserved lysine residue in the Walker A motif of the ATPase domain [47] whereas the S855A mutant removed the catalytic serine in the protease domain [21]. The results indicated that the protein level of ULK1 and its complex were significantly increased upon WT Lon expression but decreased upon the LonK529R mutant overexpression. The protease mutant LonS855A has shown no significant changes compared with the WT overexpression (Fig. 3B). To confirm the role of Lon chaperone activity in the hypoxia-induced mitophagy, we performed the rescue experiment using the LonK529R mutant and CoCl2 treatment. Consistently, hypoxia treatment and Lon overexpression induced an increase in ULK1/FIP200/ATG101 complex and the activation of autophagy (Fig. 3C). We found that the LonK529R mutant suppressed the phosphorylation of ULK1 and the stability of ULK1 complex. However, hypoxia treatment restored the protein level and activation of ULK1 complex in LonK529R-overexpressed cells (Fig. 3C). Hsp60, a mitochondrial chaperone, was used as a positive control that is a member of Lon-mtHsp60-mtHSP70 complex [23, 24]. Similar results were observed in the activation of ULK1 downstream molecules for phagophore initiation, ATG14 and Beclin1 (Fig. 3D). In addition, we confirmed that the activation of ULK1 complex could be rescued upon overexpression of either Flag-ULK1 (Fig. S4A) or myc-Lon (Fig. S4B) expression upon the LonK529R mutant overexpression. Since the p32/C1QBP stabilizes the ULK1 against proteasome-mediated degradation [48], we tried to examine whether chaperone Lon protects the ULK1 complex from proteasome degradation. Thus, we used MG132, a well-known proteasome inhibitor, and cycloheximide (CHX), a protein synthesis inhibitor, to treat the Lon-shRNA expressing cells. The results showed that Lon-shRNA treatment resulted in depletion of the ULK1 complex; however, MG132 treatment rescued the protein level of the ULK1 complex in the Lon-shRNA expressing cells (Fig. 3E). To exclude the effect of MG132 on Lon protein level, we treated CHX in the Lon-shRNA expressing cells. The CHX experiment showed that Lon-shRNA further reduced the protein level of the ULK1 complex under the CHX treatment (Fig. 3F) and Lon overexpression rescued the protein level of the ULK1 complex under the same condition (Fig. S4C). These experiments exclude the effect of the treatment on Lon protein level and confirm the interaction between Lon and the ULK1 complex, which conclude that Lon maintains the stability of ULK1 complex. Collectively, these data ensure the specificity and involvement of Lon chaperone activity in stabilizing the ULK1 complex for the mitophagy activation. Fig. 3Mitochondrial Lon chaperone activity contributes to the stability of ULK1 complex for the mitophagy activation. A HCT-15 cells were transfected with the plasmids encoding pcDNA3-Lon in different concentrations (0.5–5 μg). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. B HCT-15 cells were transfected with the plasmids encoding myc-Lon, myc-LonK529R (ATPase mutant), or myc-LonS855A (protease mutant). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. C, D HCT-15 cells were transfected with the plasmids encoding empty, myc-Lon, or myc-LonK529R in the presence or absence of CoCl2 treatment (200 μM for 18 h). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. E HCT-15 cells transfected with the plasmids encoding Lon-shRNA or empty were treated with or without MG132 (10 μM for 6 h). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. F HCT-15 cells transfected with the plasmids encoding Lon-shRNA or empty vector were treated with or without Cycloheximide (50 µg/mL) for the indicated time course. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control.
## Lon and FUNDC1-ULK1 complex accumulate at ER-mitochondria tethering sites upon hypoxia
The ULK1 complex has been reported the function of omegasome formation during initiation of autophagy [49] and further translocation of ULK1 complexes that binds FUNDC1 in the EMC region [50–52]. The EMC plays a significant role in the autophagosome biogenesis during autophagy/mitophagy. In addition, mitochondrial Lon was identified as a novel EMC protein despite the detail function still remains unknown [33]. We thus speculated that, under hypoxia, the EMC is the important region for interaction between Lon and FUNDC1-ULK1 complex to initiate the mitophagy. Firstly, to confirm the accumulation of Lon and ULK1 complex in the EMC under hypoxia, we analyzed the subcellular distribution of Lon and ULK1 complex in both normal and hypoxia treated cells by Percoll density-gradient centrifugation. Different fractions were identified using the following organelle markers: VDAC1 (mitochondria and EMC/MAM), calnexin, FACL4 (ER and EMC/MAM), and Tubulin (Cytosol) [53, 54]. We found that both ULK1 and Lon accumulates at the EMC sites in response to hypoxia, although some amount of Lon can be also detected in the EMC under normoxia condition (Fig. 4A). This suggests the possibility that Lon and ULK1 complex association is established strictly in the EMC under hypoxia. To further prove the translocation of Lon to the EMC under hypoxia, we repeated the experiment of subcellular fractionation using the cells overexpressing myc-Lon and the empty vector as a control. Similarly, the accumulation of Lon was significantly increased in the EMC fraction upon Lon overexpression only, and the accumulation of ULK1 complex only in the EMC fraction was substantially increased upon Lon overexpression (Fig. 4B). To validate the interaction between Lon and ULK1 in the EMC under hypoxia, we performed the co-localization experiment by immunofluorescence microscopy imaging to show the association of Lon with ULK1, TOMM20 (mitochondria), SERCA-2 (an ER transporter), and FACL4 (ER and EMC). The results confirmed that mitochondrial Lon can co-localize with outer membrane protein TOMM20 (Fig. 4C). We then found that mitochondrial *Lon is* also able to co-localize with SERCA-2 under hypoxia (Fig. 4C), suggesting that the accumulation of Lon exits in the EMC site under hypoxia. We next validated the interaction between TOMM20/Lon and ULK1 in the ER (Fig. 4D) and the interaction between SERCA-2 and FACL4 (Fig. 4E) under hypoxia. This observation was confirmed by the interaction between TOMM20/Lon and FACL4 under hypoxia (Fig. 4E). These findings reveal the association of Lon and ULK1 at the EMC sites in response to hypoxia. To confirm this, we used the gold-labeled immunostaining TEM to detect Lon localization under Lon overexpression and hypoxia. We observed that gold-labeled Lon was detected in the mitochondria (WT and Lon, i, Fig. 4F) and ER (ii, Fig. 4F) and cytosol (iii, Fig. 4F) upon hypoxia. Consistently, the Lon was detected at the ER-mitochondria contact sites (Fig. 4G), and the EMC sites were increased (Fig. 4H) under both Lon overexpression and hypoxia treatment. Altogether, our results supported the idea that Lon accumulation in the EMC under hypoxia is important for the interaction and recruitment of the FUNDC1-ULK1 complex for the mitophagy initiation. Fig. 4Lon and ULK1 complex accumulates at ER-mitochondria tethering sites in response to hypoxia. A, B Lon and ULK1 complex accumulates at ER-mitochondria contact (EMC) sites under hypoxia. HCT-15 cells treated with or without CoCl2 (200 μM for 18 h) (A) or transfected with the plasmids encoding myc-Lon (B) were used to perform subcellular fractionation experiment. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. Mito mitochondria, MAM mitochondria associated membranes, ER endoplasmic reticulum, cyto cytosol, PNS post nuclear supernatant. C–E Accumulation of Lon and ULK1 at the EMC/MAM was verified by confocal immunofluorescence. HCT-15 cells were transfected with the plasmids encoding Lon or treated with CoCl2 (200 μM for 18 h) or not. The cells were fixed and immunostained by anti-Lon (green) (C), anti-ULK1 (green) (D), anti-FACL4 (ER and MAM, green) (E), anti-SERCA-2 (ER, blue), and anti-TOMM20 (mitochondria, red) antibodies. Scale bars = 10 μm. F–H Localizaton of Lon at the EMC/MAM was verified by transmission electron microscopy (TEM). HCT-15 cells were treated with CoCl2 (200 μM for 18 h). The cells were fixed and and immunostained by anti-Lon and immunogold labeling antibodies. The immunogold electron micrographs showed Lon in (i) damaged mitochondria (M), (ii) ER around Nucleus (N), (iii) Cytosol (F) and the ER-mitochondria tethering sites (G). ER: endoplasmic reticulum. Scale bars: 100 nm. Quantitation of the percentage of ER adjacent to mitochondria in both CoCl2 and Lon expressed HCT-15 cells and compared with control (H). The percentage was normalized by total ER and mitochondrial perimeter ($$n = 36$$ field for each condition). $$n = 3$$ biological replicates. The error bars shown in the panel represent the standard deviation from three independent experiments. *** $p \leq 0.001.$
## Mitochondrial Lon interacts with and stabilizes FUNDC1-ULK1 complex under hypoxia
The chaperone function of Lon may be associated with the binding to ULK1 and its interacting proteins. To prove this, we performed co-immunoprecipitation (co-IP) experiments with either Lon or ULK1 under hypoxia treatment. The results revealed that endogenous Lon associates with endogenous ULK1 and its complex, ATG13 and FIP200 (Figs. 5A and S5A). Moreover, we found that endogenous Lon was immunoprecipitated by ULK1 antibody upon transfection of Flag-ULK1, and ATG13 and FIP200 were considered as the positive control (Fig. 5B). Consistently, the ULK1 complex was associated with myc-Lon using anti-myc antibody upon co-transfection of Flag-ULK1 and myc-Lon (Fig. 5C), and myc-Lon was immunoprecipitated by using anti-Flag antibody (Fig. S5B). Next, we examined whether the chaperone activity of *Lon is* critical for the association between Lon and the ULK1 complex. Hence, we individually transfected myc-Lon or myc-LonK529R into cells and the ULK1 complex was further immunoprecipitated with anti-myc antibody. We found that the endogenous ULK1 complex proteins were immunoprecipitated by myc-Lon, whereas the interaction between Lon and ULK1 complex was significantly abolished using transfection of myc-LonK529R (Fig. 5D). These data clearly show that ATPase activity of mitochondrial *Lon is* required for the association with the ULK1 complex. Consistently, the immunofluorescence results showed that colocalization between Lon and ULK1 was enhanced in both hypoxia treatment (Fig. 5E) and Lon overexpression (Fig. 5F) but the localization was strictly inhibited upon treatment with SBI-0206965, the inhibitor of ULK1 kinase (Fig. 5E, F). These data indicate that ATPase activity of mitochondrial chaperone *Lon is* required for the association with the ULK1 complex, and the ULK1 kinase activity further contributes the stability of the association. Fig. 5Mitochondrial Lon interacts with and stabilizes ULK1 complex under hypoxia. A–D Mitochondrial Lon interacts with ULK1 shown by co-immunoprecipitation. A HCT-15 cells were treated with CoCl2 followed by co-immunoprecipitation with anti-ULK1. Whole cell lysates from HCT-15 cells treated with CoCl2 (200 μM for 18 h) were immunoprecipitated with anti-ULK1 antibodies. The immunoprecipitation complex was analyzed by Western blotting using the indicated antibodies. IP, immunoprecipitation. B Whole cell lysates from HCT-15 cells transfected with the plasmids encoding Flag-ULK1 or vector were immunoprecipitated with anti-ULK1 antibodies. The immunoprecipitation complex was analyzed by Western blotting using the indicated antibodies. C Whole cell lysates from HCT-15 cells transfected with the plasmids encoding myc-Lon and Flag-ULK1 were immunoprecipitated with anti-myc antibodies. The immunoprecipitation complex was analyzed by Western blotting using the indicated antibodies. D Whole cell lysates from HCT-15 cells transfected with the plasmids encoding myc-Lon or myc-LonK529R were immunoprecipitated with anti-myc antibodies. The immunoprecipitation complex was analyzed by Western blotting using the indicated antibodies. E, F Mitochondrial Lon interacts with ULK1 shown by immunofluorescence. E The interaction of Lon with ULK1 was enhanced by ULK1 activity under hypoxia. HCT-15 cells treated with or without CoCl2 (200 μM for 18 h) in the presence or absence of SBI-0206965 (20 μM for 6 h) were immunostained by anti-ULK1 (green) and anti-Lon (red) following image capturing by immunofluorescence microscopy. DAPI was used for nuclear staining. Scale bars, 50 μm (n = >50 cells/condition and 3 biological replicates). F The interaction of Lon with ULK1 was enhanced by ULK1 activity. HCT-15 cells transfected with the plasmids encoding Lon or empty in the presence or absence of SBI-0206965 (20 μM for 6 h) were immunostained by anti-ULK1 (green) and anti-Lon (red) following image capturing by immunofluorescence microscopy. DAPI was used for nuclear staining. Scale bars, 50 μm (n = >50 cells/condition and 3 biological replicates).
We further examined the presence of FUNDC1 and p-FUNDC1-S17 in mitochondria and the EMC by analyzing the subcellular fractions in cells. The results indicated that both FUNDC1 and p-FUNDC1-S17 were accumulated in the EMC fraction under Lon overexpression than the vector control (Fig. 6A). To further examine the function of Lon-ULK1-FUNDC1 complex in mitophagy under hypoxia, we detected the interaction between FUNDC1-S17 and LC3B by the immunoprecipitation experiment using anti-LC3B antibody. The results confirmed that Lon shows a strong interaction with ULK1-FUNDC1-S17-LC3B complex upon hypoxia treatment. The interaction between FUNDC1-S17 and LC3B was significantly abolished upon the treatment of ULK1 inhibitor SBI-0206965 under the same condition (Fig. 6B). Consistently, the interaction between FUNDC1-S17 and LC3B was significantly abolished upon SBI-0206965 treatment under Lon overexpression (Fig. 6C). FUNDC1 and LAMP1 were used as positive controls. Collectively, these data indicate that Lon interaction is involved in the FUNDC1-ULK1-dependent mitophagy at the EMC upon hypoxic condition. Fig. 6FUNDC1-Ser17 phosphorylation by ULK1 kinase at the EMC/MAM is important for the Lon-induced mitophagy. A HCT-15 cells transfected with the plasmids encoding myc-Lon were used to perform subcellular fractionation experiment. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. Mito mitochondria, MAM mitochondria associated membranes, ER endoplasmic reticulum, cyto cytosol, PNS post nuclear supernatant. B HCT-15 cells were treated with CoCl2 (200 μM for 18 h) or not in the presence or absence of SBI-0206965 (20 μM for 6 h). Whole cell lysates from the treated HCT-15 cells were immunoprecipitated with anti-LC3B antibodies. The immunoprecipitation complex was analyzed by Western blotting using the indicated antibodies. C HCT-15 cells transfected with the plasmids encoding Lon or empty vector were treated with SBI-0206965 (20 μM for 6 h) or not. Whole cell lysates from the treated HCT-15 cells were immunoprecipitated with anti-LC3B antibodies. The immunoprecipitation complex was analyzed by Western blotting using the indicated antibodies. D HCT-15 cells were treated with or without CoCl2 (200 μM for 18 h) or hypoxia exposure ($1\%$ O2). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. E HCT-15 cells were transfected with the plasmids encoding Lon or Lon-shRNA. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. F HCT-15 cells transfected with the plasmids encoding Lon or empty were treated with SBI-0206965 (20 μM for 6 h) or not. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. G HCT-15 cells were transfected with the plasmids encoding myc-Lon, myc-LonK529R (ATPase mutant), or myc-LonS855A (protease mutant). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. H HCT-15 cells were transfected with the plasmids encoding empty, myc-Lon, or myc-LonK529R in the presence or absence of CoCl2 treatment (200 μM for 18 h). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. I *Immunohistochemical analysis* of p-FUNDC1-S17 expression in OSCC patients. Representative immunohistochemical staining of p-FUNDC1-S17 was performed using paraffin-embedded sections of OSCC. The representative intensity of immunostaining was classified as four levels: negative staining intensity [0] and positive staining intensity, including weak (1+), median (2+), and strong (3+) staining. Microscopic magnification, ×400. Scale bar, 50 μm.
## FUNDC1-S17 phosphorylation by ULK1 kinase is important for the Lon-induced mitophagy
FUNDC1 (FUN14 domain-containing protein-1), an outer mitochondrial membrane protein, is a parkin-independent hypoxia-specific mitophagy receptor that binds to LC3 [16, 55, 56]. ULK1 kinase phosphorylates FUNDC1 at Serine 17 and promotes the interaction between FUNDC1 and LC3 in response to hypoxia [57]. We next investigated the role of mitochondrial Lon in the mitophagy induction through enhancing the FUNDC1 phosphorylation by ULK1 under hypoxia. The results found that p-FUNDC1-S17 was increased along with ULK1 activity under hypoxia (Figs. 6D and S3F). We further evaluated the role of Lon in the regulation of p-FUNDC1-S17 phosphorylation. We found that Lon upregulation induces extensive increase in protein and phosphorylation level of FUNDC1-S17 whereas the effect has been diminished upon knockdown of Lon (Fig. 6E), suggesting the significance of Lon in regulating the mitophagy through ULK1. We confirmed that the mitophagy induced by hypoxia and Lon was inhibited the treatment of SBI-0206965, an ULK1 kinase inhibitor (Figs. 6F and S3G). We then examined the role of Lon chaperone activity in the p-FUNDC1-S17-dependent mitophagy. Consistently, Lon increased the total FUNDC1 and the phosphorylation status of FUNDC1-S17 and its downstream of the LC3B activation, but not in the ATPase mutant LonK529R (Fig. 6G). The protease mutant of Lon (Lon-S855A) still showed a significant increase in both FUNDC1/FUNDC1-Ser17 phosphorylation and LC3B. Furthermore, the inhibition of FUNDC1-S17 phosphorylation and LC3B II level by the LonK529R mutant could be rescued by hypoxia treatment (Fig. 6H), suggesting that the chaperone property of *Lon is* involved in the FUNDC1-dependent mitophagy upon hypoxic condition. These data suggest that ATPase activity of *Lon is* important for the binding to FUNDC1 and the ULK1 stability, and kinase activity of ULK1 in mitophagy under hypoxia. Collectively, these data indicate that the chaperone activity of *Lon is* involved in the FUNDC1-ULK1-dependent mitophagy at the EMC upon hypoxic condition.
To associate the clinical significance of Lon-induced mitophagy in cancer progression, we examined whether the FUNDC1-S17 phosphorylation regulated by *Lon is* clinically relevant in cancer, for example oral cancer. The expression pattern of Lon and p-FUNDC1-S17 in 92 samples of tumor tissues from OSCC patients was determined by Immunohistochemistry (IHC) analysis. The clinicopathological characteristics of the patients in this study are as described in our previous results [23]. Representative samples defined negative, weak, median, and strong staining of Snail are shown (IHC level 0 to 3+, Fig. 6I). The association between Lon and p-FUNDC1-S17 expression in OSCC tissues was tested in the contingency table using Fisher’s exact test. The result showed that p-FUNDC1-S17 expression showed a significant correlation with Lon expression ($$P \leq 0.01063$$, Table 1). Consistently, the correlation between Lon and p-FUNDC1-S17 expression is statistically significant by Spearman’s rank test ($$P \leq 0.00239$$, Table 1). Taken together, these data indicate a direct correlation between the axis of Lon-ULK1 and mitophagy on cell survival and cancer progression under hypoxia condition in the tumor microenvironment. Table 1The contingency table shows a positive association between Lon and p-FUNDC1-S17, based on 92 OSCC patients with Lon/p-FUNDC1-S17 protein staining. LonFisher, PSpearman’sNone/weakMedianStrongRank correlation (ρ, P)p-FUNDC1 (S17)None/Weak2918220.010630.313, 0.00239Median368––Strong006––
## Mitochondrial Lon in the EMC depends on the interaction with NCLX to increase the cytosolic calcium levels and activate FUNDC1-ULK1 mitophagy under hypoxia
We then tried to find out the mechanism of Lon translocation to EMC to interact the FUNDC1-ULK1 complex for the mitophagy initiation under hypoxia. The cytosolic Ca2+ was reported to activate the autophagy signaling, and the low energy activation of AMPK/ULK1-mediated autophagy was repressed after mitochondrial calcium accumulation through MCU overexpression [58]. In addition, we recently proved that mitochondrial Lon was associated with NCLX to activate cytosolic calcium-dependent PYK2-SRC-STAT3 signaling contributing to the cisplatin resistance [32]. We speculated that Lon translocation to interact the FUNDC1-ULK1 complex is through the interaction of Lon with NCLX for accumulating in the MAM and inducing the cytosolic calcium release. To prove this, we first monitored the cytosol and mitochondrial calcium levels under CoCl2 treatment and Lon overexpression using Fura-2 AM dye and mt-lar-GECO, respectively. The cytosolic calcium levels were significantly increased and mitochondrial calcium levels were decreased upon CoCl2 treatment and Lon overexpression (Fig. 7A, B). Indeed, the treatment of NCLX inhibitor, CGP37157, or shNCLX largely reversed the change in mitochondrial and cytosolic calcium levels induced by CoCl2 treatment and Lon overexpression (Fig. 7A, B). To confirm the regulation of autophagy by mitochondrial calcium, both CoCl2 treatment and Lon overexpression in OEC-M1 cells consistently activated the Lon-ULK1-FUNDC1-mediated mitophagy pathway, whereas NCLX inhibition significantly impaired the mitophagy activation (Fig. 7C, D). Lon and NCLX significantly increased the Lon-ULK1-FUNDC1 signaling whereas CGP37157 treatment and shNCLX abolished the Lon-ULK1-FUNDC1 activation (Fig. 7D). We then examined whether NCLX activity is important for the Lon accumulation in EMC to regulate ULK1-FUNDC1-mediated mitophagy. In consistent with Fig. 4A, B, MAM fraction was enriched with Lon and ULK1 complex in OEC-M1 cells including p-FUNDC1-S17 under both CoCl2 treatment and Lon overexpression; however, CGP37157 treatment strictly inhibited the Lon and ULK1 complex accumulation in theEMC which signifies the calcium role in regulating the mitophagy (Fig. 7E, F). These data were further validated through immunohistochemical staining of ULK1, p-FUNDC1 S17, FUNDC1, LC3B, and Bcl-2 in Lon-overexpressed OEC-M1 tumors from mice in the presence or absence of CGP37157 (Fig. 8G). Collectively, these data show the significance of NCLX activity in regulating the Lon-ULK1-FUNDC1-mediated mitophagy in the EMC.Fig. 7Lon binds with mitochondrial Na+/Ca2+ exchanger to promote FUNDC1-ULK-mediated mitophagy in the EMC/MAM site. A, B Ca2+mito (mt-lar-GECO) and Ca2+cyto (Fura-2 AM) were measured by live-cell microscopy in OEC-M1 treated with CoCl2 (300 µM-18h) or transfected with Lon in presence or absence of CGP37157 (10 µM-4h). ATP (100 µM) was used as an agonist and further measured and analyzed ($$n = 3$$). C OEC-M1 cells were treated with CoCl2 (300 µM-18 h) in the presence or absence of CGP37157 (10 µM-4 h). Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. D OEC-M1 cells were transfected with Vector, Lon, NCLX (48 h) in the presence or absence of CGP37157 (10 µM-4h) and shNCLX transfected OEC-M1 cells were co-transfected with NCLX and Lon plasmids and incubated for 48 h. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. E, F Lon and ULK1 complex accumulation at EMC/MAM sites were abolished upon NCLX inhibition under hypoxia. OEC-M1 cells transfected with the plasmids encoding myc-Lon (E) or treated with CoCl2 (200 μM for 18 h) (F) in presence or absence of CGP37157 (10 µM-8h) were used to perform subcellular fractionation experiments. Cell lysates were analyzed by immunoblotting using the indicated antibodies. GAPDH as the loading control. Mito mitochondria, EMC ER-mitochondria contact sites, MAM mitochondria associated membranes, WCL whole cell lysate. Fig. 8Lon-ROS-ULK1-FUNDC1 axis induced mitophagy benefits cell survival and tumorigenesis in vitro and in vivo. A–C HCT-15 cells were treated with CoCl2 (200 μM for 18 h) or not in the presence or absence of SBI-0206965 (20 μM for 24 h). The MTS assay for cell viability (A), fluorescence-based cleaved Caspase 3 apoptosis assay (B), and western blotting analysis (C) were performed. Immunoblots were obtained using the indicated antibodies. Scale bar, 100 μm (n = >50 cells/condition and 3 biological replicates). D–F HCT-15 cells were treated with CoCl2 (200 μM for 18 h) or not in the presence or absence of Bafilomycin A1 (100 nM for 24 h). The MTS assay for cell viability (D), fluorescence-based cleaved Caspase 3 apoptosis assay (E), and Western blotting analysis (F) were performed. Immunoblots were obtained using the indicated antibodies. Scale bar, 100 μm (n = >50 cells/condition and 3 biological replicates). G *Immunohistochemical analysis* of ULK1, p-FUNDC1 S17, FUNDC1, LC3B, and Bcl-2 expression in Lon expressed OEC-M1 tumor generated in BALB/C Nu mice treated with or without CGP37157. Representative immunohistochemical staining of respective targets was performed using paraffin-embedded sections of tumors collected. Microscopic magnification, ×400. Scale bar, 50 μm. H Scheme of the interaction between mitochondrial Lon and ULK1 complex at the EMC/MAM promotes mitophagy under hypoxia by stabilizing FUNDC1-ULK1 complex that depends on mitochondrial Na+/Ca2+ exchanger (NCLX). Upon hypoxia, Lon promotes FUNDC1-ULK1-mediated mitophagy at the EMC/MAM site, which is dependent on the binding with mitochondrial Na+/Ca2+ exchanger (NCLX). This interaction stabilized the FUNDC1-ULK1 at the EMC and initiated the mitophagy through the regulation of Ca2+ levels between mitochondria and cytosol. Lon-ULK1 phosphorylates FUNDC1 at S17, and Lon-ULK1-FUNDC1 axis promotes mitophagosome-lysosome fusion. EMC ER-mitochondria contact sites, MAM mitochondria associated membranes. The scheme of this study was created with BioRender.com.
## Lon-ROS-ULK1-FUNDC1 axis induced mitophagy benefits cell survival under hypoxia
Mitophagy as a regulator of mitochondria quality control is involved in regulation of mitochondria-mediated cell death. To address whether mitophagy induction by hypoxia/Lon augments cell survival in cancer cells, we determined the cell viability of cancer cells treated with the ULK1 inhibitor SBI-0206965 or lysosomal H + ATPase inhibitor bafilomycin A1 (BafA1). We first found that hypoxia treatment decreases the cell viability in a minimal range about below $10\%$ compared with the control without treatment (Fig. 8A), which was in consistent with previous report [59]. However, inhibition of ULK1 by SBI-0206965 treatment made cells more susceptible to hypoxia treatment and exacerbated a decline in cell viability (Fig. 8A). The mechanism of lower cell viability upon ULK1 kinase inhibition was mediated by caspase 3-dependent apoptosis shown by fluorescence-based cleaved caspase 3 assay and immunoblotting (Fig. 8B, C), which were consistent with previous reports [60, 61]. Similarly, we confirmed the finding that the mechanism of low cell viability upon ULK1 kinase inhibition was mediated by caspase 3-dependent apoptosis under Lon overexpression. We found that Lon overexpression promotes cell viability by decreasing the cleaved caspase-3 and increasing the Bcl-2 expression (Fig. S6A–C), which is consistent with our previous reports [23, 24, 29]. This inhibition of apoptosis by Lon overexpression was reversed by SBI-0206965 treatment, an ULK1 inhibitor (Fig. S6A–C). To understand the mitophagy is critical for cell survival, we tested the cell susceptibility upon BafA1 treatment under hypoxia treatment. The results showed that BafA1 treatment caused cells to be more susceptible to hypoxia treatment and exacerbated a decline in cell viability (Fig. 8D). In contrast, however, we found that although the cell susceptibility was increased after BafA1 treatment, the mechanism of lower cell viability was not mediated by caspase 3-dependent apoptosis (Fig. 8E, F). The similar results showed that Lon-induced cell survival through the FUNDC1-S17 phosphorylation and mitophagy and this increase in survival by Lon overexpression was reversed by BafA1 treatment (Figs. S3E and S6D, E); however, the mechanism by BafA1 was not mediated by caspase 3-dependent apoptosis (Fig. S6E). These data suggested that cell survival through mitophagy maybe involved in different types of cell death including caspase-dependent and -independent mechanisms, which is supported by the finding of the BafA1-induced caspase-independent cell death could be due to the upregulation of PUMA [62]. These data were further validated by in vivo study through immunohistochemical staining of ULK1, p-FUNDC1 S17, FUNDC1, LC3B, and Bcl-2 in the mice bearing Lon-overexpressed OEC-M1 tumors in the presence or absence of CGP37157 (Fig. 8G).
## Discussion
This study demonstrates that mitochondrial Lon, a matrix-resident stress protein, is an EMC protein that accumulates at EMC sites upon hypoxia, allowing for its interaction with ULK1 kinase complex and phosphorylation of downstream FUNDC1-S17. Phosphorylation of FUNDC1-S17 at the EMC sites by ULK1 is necessary for the FUNDC1 binding to LC3 that triggers mitophagy (Fig. 8H). Our findings explore the role of mitochondrial Lon in mitophagy in response to hypoxia or ROS, which facilitates hypoxia-induced drug resistance in cancer therapy.
The maintenance of mitochondrial quality depends on the mitophagy upon stress condition that is shared with several signaling components in every step of autophagic process. The intriguing issue is to identify the factors responsible for triggering the mitophagy from the matrix inside of mitochondria in response to hypoxia. On the other hand, although mitochondrial Lon has been identified as an MAM (ER-mitochondria contact sites, EMC) protein under stress [33], the real physiological function of mitochondrial Lon remains still unclear. In the present study, our findings extend the recent study to establish a chaperone role of mitochondrial Lon in regulating autophagosome formation at the EMC site during mitophagy. We provide the evidence that mitochondrial matrix protease Lon interacts with ULK1 kinase complex at EMC sites to drive mitophagy upon hypoxia. The ULK1 kinase, one of the upstream components of autophagy, is important for recruitment and phosphorylation of its complex partners, ATG13/FIP200/ATG101. This complex was highlighted for the stability and kinase activity of ULK1 for the functional macroautophagy/selective autophagy [13]. The complex stability is affected when ULK1 is repressed by certain energy sensors like mTOR during nutrient-replete status [19, 63]. On the other hand, p32/C1QBP was reported to stabilize the ULK1 to promote its kinase activity for macroautophagy under starvation [48]. However, how the ULK1 kinase stability is improved to overcome repression to adapt the mitophagy under hypoxia is still not fully clear. In agreement with this scenario, the present study extends the recent study [33] to aim at a chaperone role of mitochondrial Lon in regulating the ULK1 complex at autophagosome formation for mitophagy during hypoxia. Through this study we have provided the evidence about the translocation of mitochondrial Lon to EMC and further promoting the interaction with the ULK1 complex, which is imperative to how signaling from the inside mitochondria to trigger the mitophagy involved in clearance of damaged mitochondria during hypoxic stress.
During hypoxia, Lon translocates from mitochondria to EMC to interact with FUNDC1-ULK1 and its complex partners and allow them enriched in the EMC, which further activates ULK1 activity to phosphorylate FUNDC1 at serine 17. This promotes the FUNDC1 to recognize and bind with LC3 that promotes the mitophagosome fusion with lysosomes. FUNDC1 acts as a key hypoxia-induced mitophagy receptor and is tightly regulated by posttranslational modification. At the early stage of hypoxia, FUNDC1-mediated mitophagy activity is inhibited by its phosphorylation by Src/CK2 kinase and ubiquitination-mediated degradation by MARCH5 [64], thus protecting mitochondria from unnecessary degradation. As hypoxia progress, the interaction of FUNDC1 with Src/CK2 gradually decreases and FUNDC1 begins accumulating at ER-mitochondria contact sites, allowing an abundance of FUNDC1 at the MAM stabilized by USP19 for hypoxia-induced mitochondrial division [65]. We also found that the phosphorylation of Drp1 at serine 616 for mitochondrial fission was increased first and then decreased whereas fusion (p-DRP1 serine 637) was increased later (Fig. S4C). Our results were consistent with the recent report on DRP1-mediated mitochondrial fission showing “eat me signal” by marking the damaged mitochondria under hypoxia [66].
Molecular chaperone Lon plays important roles in promoting cell survival and tumor growth under oxidative and hypoxic stress [22–24, 31]. We revealed, for the first time, the mechanism of how mitochondrial Lon regulates mitophagy and cell survival in the EMC. A growing list of proteins have been identified as the EMC constituents, but how *Lon is* recruited and functions during stress situations is still not known. The mechanisms involved in the EMC assembly are still not fully understood, which limits our knowledge of how signal transduction from mitochondria triggers the interaction between the two organelles. Most importantly, *Lon is* involved in the inter-organellar crosstalk between the ER and mitochondria as a EMC protein itself. The scenarios for the mechanism underlying Lon enhanced cell survival and mitophagy through binding with ULK1 complex are proposed. ULK1 kinase stability or its phosphorylation status determines its involvement in autophagy regulation or mitophagy towards cancer progression [67]; ULK1 was identified to be associated with the poor prognosis of patients with lung cancer [68]. Our data showed that ULK1 is phosphorylated and activated by AMPK at Serine 555 in response to hypoxia, which are similar to previous reports [69, 70]. The stability of ULK1 kinase upon CoCl2 treatment and Lon upregulation and its downstream activation of mitophagy was strictly increased. However, the treatment of proteasome inhibitor MG132 and NAC blocked the stabilization of ULK1 by Lon, indicating that Lon prevents ULK1 from proteolytic destruction and Lon- induced ROS might have a crosstalk role in the ubiquitination/deubiquitylation process of ULK1. Through our data, we brought a new dimension in the regulation of ULK1 signaling maintenance by chaperone Lon upregulation upon mitophagy demand. Chaperone *Lon is* evolving to be recognized for its cyto-protective role in cancer, which is supported by the observation that ULK1 association with the ATPase domain of *Lon is* important for its maintenance and kinase activity for mitophagy and cell survival under hypoxia. In the future, the mechanism of how mitochondrial Lon facilitates the demand changes in increased mitochondrial ROS, altered Ca2+ flux, or altered lipid biosynthesis for signaling to induce morphological changes at the EMC sites is worth further investigating upon the physiological stress demands during cancer therapy.
In conclusion, our findings reveal that under hypoxia, the chaperone property of mitochondrial Lon integrates mitochondrial quality maintenance and mitophagy at the interface of the EMC for cell survival of cancer through the Lon-ROS-ULK1-FUNDC1 axis. Targeting the chaperone activity of Lon and EMC function may be the new strategy in cancer therapy.
## Supplementary information
Supplementary material checklist The online version contains supplementary material available at 10.1038/s41419-023-05723-1.
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|
---
title: Predicting EGFR mutational status from pathology images using a real-world
dataset
authors:
- James J. Pao
- Mikayla Biggs
- Daniel Duncan
- Douglas I. Lin
- Richard Davis
- Richard S. P. Huang
- Donna Ferguson
- Tyler Janovitz
- Matthew C. Hiemenz
- Nathanial R. Eddy
- Erik Lehnert
- Moran N. Cabili
- Garrett M. Frampton
- Priti S. Hegde
- Lee A. Albacker
journal: Scientific Reports
year: 2023
pmcid: PMC10020556
doi: 10.1038/s41598-023-31284-6
license: CC BY 4.0
---
# Predicting EGFR mutational status from pathology images using a real-world dataset
## Abstract
Treatment of non-small cell lung cancer is increasingly biomarker driven with multiple genomic alterations, including those in the epidermal growth factor receptor (EGFR) gene, that benefit from targeted therapies. We developed a set of algorithms to assess EGFR status and morphology using a real-world advanced lung adenocarcinoma cohort of 2099 patients with hematoxylin and eosin (H&E) images exhibiting high morphological diversity and low tumor content relative to public datasets. The best performing EGFR algorithm was attention-based and achieved an area under the curve (AUC) of 0.870, a negative predictive value (NPV) of 0.954 and a positive predictive value (PPV) of 0.410 in a validation cohort reflecting the $15\%$ prevalence of EGFR mutations in lung adenocarcinoma. The attention model outperformed a heuristic-based model focused exclusively on tumor regions, and we show that although the attention model also extracts signal primarily from tumor morphology, it extracts additional signal from non-tumor tissue regions. Further analysis of high-attention regions by pathologists showed associations of predicted EGFR negativity with solid growth patterns and higher peritumoral immune presence. This algorithm highlights the potential of deep learning tools to provide instantaneous rule-out screening for biomarker alterations and may help prioritize the use of scarce tissue for biomarker testing.
## Introduction
Genomic-guided therapeutic choices are increasingly used in the management of advanced non-small cell lung cancer (NSCLC)1. Therapies requiring diagnostic testing include single-agent immunotherapy and kinase inhibitors targeting EGFR and ALK in the first-line and KRAS G12C, MET, and NTRK targeted therapies in the second-line2. Although multiplex diagnostic approaches such as next-generation sequencing are becoming more common, many labs perform testing for relevant biomarkers separately. As tissue acquired for testing is often limited and the number of diagnostics increases, care should be taken to prevent tissue exhaustion so that all appropriate clinical options may be determined3. One potential opportunity to mitigate this challenge is by leveraging machine learning with digital pathology.
Machine learning, and in particular deep learning, has recently gained broad traction across an expanse of medical domains, with its use showing promise in aiding diagnostics and biomarker discovery in applications relating to ophthalmology, heart disease, cancer care and more4–11. There is especially impactful opportunity within cancer care to leverage the immense data generated through clinical practice, including omics from sequencing technologies and gigapixel digital pathology scans. One such opportunity lays with the emerging sub-field of digital pathology, which investigates the rich trove of information present within high resolution scans of hematoxylin and eosin (H&E) stains alongside other stains such as immunohistochemistry stains. H&E stains are inexpensive and ubiquitous tissue specimen stains used during the pathology workflow that allow pathologists to better examine tumor morphologies and determine the diagnosis of the tumor12. Machine learning and deep learning models applied to digital scans of H&E-stained tissue slides have shown significant promise in enhancing a variety of aspects in cancer-care, including aiding in cancer diagnoses, improving operational efficiencies, and directly providing molecular insights.
In 2016, Wang et al. showed that deep learning could detect metastatic breast cancer in lymph node biopsies with high performance, and suggested value in computer-aided approaches augmenting the pathology workflow, with pathologist-computer combined methods achieving 0.995 AUC on the cancer detection task13. Following soon afterwards, Coudray et al. showed that deep learning could classify cancer subtypes effectively and, even more promisingly, could also predict gene alterations directly from lung adenocarcinoma H&E images, achieving 0.733 to 0.856 AUC for mutations in STK11, EGFR, FAT1, SETBP1, KRAS and TP5314. In 2018, Ilse et al. proposed that histopathology problems could be effectively formulated as multiple-instance problems, which many studies have applied to address a range of histopathology problems15. Campanella et al. showed that multiple-instance approaches could achieve clinical-level performance on predicting prostate and other cancers and, from a biomarker perspective, Naik et al. showed that such approaches could predict estrogen receptor status from breast cancers with high performance (0.92 AUC)16,17.
Despite these advances, clinical adoption of machine learning in digital pathology has been slow. This may be partially due to a lack of clinically relevant datasets for research, with many research models trained on homogenous datasets with high tumor purity14,18. For example, The Cancer Genome Atlas’s requirement of $60\%$ tumor purity in most diseases is in stark contrast to more real-world settings where tumor purities of $20\%$ are common19. Further limiting clinical use is the challenge of interpreting predictions made by deep learning models, making it difficult to ensure that given models are relevant and accurate for specific clinical samples.
Here we demonstrate that attention-based multiple-instance learning can predict EGFR mutational status in advanced metastatic lung adenocarcinoma samples directly from H&E images with state-of-the-art performance on real-world datasets, where many samples have less than $50\%$ tumor content. Through a combination of tissue morphology classification models and pathologist review we show that although tumor regions contain the most signal for EGFR, the attention-based model also considers relevant outlier instances from other tissue types such as immune or stromal features when predicting EGFR mutational status. With additional analysis via association rules mining, we demonstrate a process wherein morphology models and pathologist expertise can be leveraged to biologically verify end-to-end biomarker predictions by evaluating associated feature combinations, allowing for better model interpretation when supporting clinical decisions.
## Results
We investigated the ability of different modeling approaches to predict EGFR mutational status in lung adenocarcinoma resections (see “Methods” for details). First, to set a modeling baseline, we trained a weakly-supervised model that predicts EGFR mutational status using all tissue patches from a slide, irrespective of morphology, and aggregates those patch predictions to generate a slide-level output. Second, we trained a two-stage model. Stage one was a convolutional neural network, which classified patch morphology into categories of tumor, immune foci, stroma, necrosis, or normal tissue (Fig. 1a). Stage two models use only tissue patches classified from a single group (Fig. 1b). Last, we trained a multiple-instance learning model that achieves state-of-the-art performance for predicting EGFR mutational status in lung adenocarcinoma resections and investigated the features learned by the model (Fig. 1c).Figure 1Various models to characterize and predict EGFR mutational status from specimens with highly diverse tissue morphologies. ( a) A deep learning tissue morphology model that produces broad patch-level classifications for all patches from a slide. ( b) A deep learning modeling approach to predict EGFR mutational status at a slide-level by utilizing patches belonging to a specific predicted tissue group only and aggregating patch predictions for the final output. ( c) A multiple-instance learning approach that utilizes an attention mechanism, allowing for machine-intuition to automatically weigh patches of interest when directly predicting EGFR mutational status for the slide using a bag of patches. ( d) Confusion matrix for the patch-level tissue morphology classifier that categorizes each patch given its predominant tissue morphology. ( e) The distribution of tissue morphologies exhibited across the real-world lung adenocarcinoma cohort ($$n = 2099$$) as classified by the tissue morphology model.
## Real-world lung adenocarcinoma samples exhibit high morphological diversity
Our experimental dataset was comprised of 2099 lung adenocarcinoma resections from advanced or metastatic cancer patients whose specimens were submitted to Foundation Medicine for genomic profiling. Of the 2099 resections, 716 ($34\%$) were EGFR mutated (see “Methods”). The remainder of the dataset consisted of non-EGFR driver mutated specimens (e.g. KRAS or ALK) or driver wild-type specimens.
To evaluate the extent of morphological diversity within the samples comprising our dataset, we used pathologist annotations of tissue types to train a deep learning model that classifies tissue patches (512 × 512 pixels at 20 × magnification, resized to 448 × 448 pixels) into one of five tissue morphology groups: tumor, immune foci, stroma, necrosis, and normal. These predictions capture the predominant tissue type within a patch, but multiple cell types are likely present. The model effectively discriminated tumor patches from other tissue types, achieving a validation f1-score of 0.961 ($\frac{4504}{4658}$). Performance across all groups was high as shown by the f1-scores for stroma (0.943; $\frac{2106}{2233}$), immune (0.897; $\frac{262}{292}$), and necrotic tissue (0.986; $\frac{1276}{1294}$) (Fig. 1d). Normal tissue had the lowest f1-score of 0.727 ($\frac{570}{784}$) with 0.093 ($\frac{35}{376}$) of normal patches predicted as stroma and 0.138 ($\frac{52}{376}$) predicted as tumor.
By applying this tissue morphology classification model to all tissue slides, we determined that most slides have a high fractional area of non-tumor tissue types (Fig. 1e). Across the dataset, the median tumor fraction by patch area was 0.364 with an interquartile range of 0.290. Notably, the patch area of normal tissue had a median fraction of 0.394 with an interquartile range of 0.307, higher than that of the tumor group. There was an appreciable presence of stroma, with a median fraction of 0.133 and an interquartile range of 0.141. Immune and necrosis patches were present with median fractions of 0.025 and 0.002 and interquartile ranges of 0.042 and 0.011, respectively. Thus, relative to many research datasets like The Cancer Genome Atlas, our tissue slides exhibit high morphological diversity and low tumor content.
## Human-intuition models help isolate predictive signal when modeling morphologically-diverse real-world data
To provide a baseline model for predicting EGFR mutational status from our dataset, we trained a weakly-supervised patch-level classification model using ResNet50 as a backbone with five-fold cross-validation. The weakly-supervised models obtained an AUC of 0.792 ± 0.029 when aggregating patch predictions by the slide average and an AUC of 0.784 ± 0.026 when aggregating using the median.
Since EGFR is a tumor cell-intrinsic driver alteration, we hypothesized that tumor regions would contain most of the classification signal. We tested this hypothesis by developing a two-stage approach, first separating patches into the five tissue morphology types and then training separate deep learning classifiers to predict EGFR status using only patches from one of the morphology types. Each of these five morphology-selective models were trained with five-fold cross-validation.
The tumor patch-based models achieved an AUC of 0.831 ± 0.011 when aggregating using average patch prediction and an AUC of 0.828 ± 0.009 when aggregating using median patch prediction, which was better than the weakly-supervised models (Fig. 2a,b; $$p \leq 0.033$$).Figure 2Performance for deep learning models to predict tissue morphology and EGFR mutational status. ( a) Comparison of all deep learning models for predicting EGFR status from H&E images. ( b,c) Cross-validated receiver operator characteristic curve for each cross-fold of (b) the tumor-only two-stage EGFR model and (c) the MIL model.
In comparison, the cross-validated AUCs by mean aggregation for the immune (0.712 ± 0.039), stroma (0.673 ± 0.005), normal (0.666 ± 0.022), and necrosis (0.544 ± 0.034) based models were significantly worse than the tumor-based models and the weakly-supervised models (Fig. 2a; $$p \leq 0.012$$, $$p \leq 4.62$$e−5, $$p \leq 1.30$$e−4, $$p \leq 4.083$$e−6). We conclude that when training patch-based models, tumor regions contain the highest signal for EGFR classification and that excluding non-tumor regions from consideration reduces noise and increases performance.
## Multiple-instance learning model using attention mechanism improves EGFR predictive ability
We next assessed an attention-based multiple-instance learning (MIL) model to determine whether EGFR prediction performance could be further improved using machine-intuition alone. Bags of patches were randomly sampled from each slide during training and the entire bag was given the specimen-level EGFR status as the label. Through the attention mechanism, the model learned without human guidance how to weigh different patches within each bag when predicting for specimen-level mutational status. The AUC achieved by the MIL models with five-fold cross-validation was 0.870 ± 0.014, which was significantly higher than the tumor-only models (Fig. 2a,c; $$p \leq 0.002$$). The models also achieved an NPV of 0.954 ± 0.024 and a PPV of 0.41 ± 0.081 at a binary classification threshold of 0.5. If only slides with high-confidence predictions (defined as < 0.25 for wild-type call and > 0.75 for mutant call) were considered, the NPV was 0.970 ± 0.017 and the PPV was 0.527 ± 0.088. Thus, attention-based models outperformed the human-guided tumor-only models.
We next investigated regions with high attention scores from the MIL models to better understand what features the MIL models learned. We sampled 100 patches per bag for 100 validation slides and assessed model attention by tissue morphology (Fig. 3a–c). We found that the median attention score was highest for tumor patches at 0.013 with a maximum score of 0.038. Tumor patches received the highest attention when assessing all patches, median per slide, or maximum per slide attention. As a group, the immune patches were second with a median attention score of 0.009 and a maximum of 0.035. The median attention given to normal patches, stroma patches, and necrosis patches were 0.007, 0.006, and 0.002 with corresponding maximum attention scores of 0.033, 0.031, and 0.022, respectively. The tissue morphology classification of patches also allowed pathologists to quickly assess high-attention outlier patches for noteworthy visual features (Fig. 3d,e). In Fig. 3d, an EGFR true positive exemplar is presented. High attention was given to tumor and stroma patches. Patches I-V had a predominant acinar pattern and hobnail cytology, with low peritumoral and intratumoral immune fractions, ranging from 0.1 to 0.2. Patch IV had a low presence of necrotic tissue and patch VI was predicted as stroma by the tissue-morphology model, and pathologists confirmed this patch was fibrosis. In Fig. 3e, an EGFR true negative exemplar is presented. High attention was given to tumor patches and some immune patches. Patches I–II showed an acinar/lepidic pattern with hobnail cytology and intratumoral lymphoid aggregates. Patches III–VI were predicted to be tumor or immune foci by the tissue-morphology model. Pathologists confirmed high peritumoral and intratumoral immune fraction, ranging from 0.2 to 0.7, for these patches. Inflammation was noticeably present as well in patch IV. From these data we conclude that the MIL models learned to give high attention to tumor regions but likely boosted performance by also giving high attention to additional patterns that aid in classification such as immune infiltrates in EGFR negative samples. Figure 3Bags from slides with high-confidence predictions assessed by the MIL model, with attention weights extracted for each patch within the bags. ( a) All patch, (b) median per slide, and (c) maximum per slide attention weights for EGFR prediction as separated by predicted tissue morphology from (left column) 50 EGFR mutant slides, (center column) 50 EGFR wild-type slides, and (right column) 100 slides combined. ( d) EGFR TP exemplar with attention weights from bag of 250 patches. The six highest attention patches are shown (I-VI). ( e) EGFR TN exemplar with attention weights from bag of 250 patches. The six highest attention patches are shown (I-VI).
Pathologists also reviewed the top-25 highest attention patches in each of 49 randomly sampled bags for which the MIL models produced high confidence predictions. Bags predicted to be EGFR mutant had a lower standard deviation of tumor nuclei fraction across the highest-attention patches (Supplementary Table 1; $$p \leq 0.028$$, Pearson’s r: − 0.317). Bags predicted to be EGFR mutant also had higher minimum tumor nuclei fraction ($$p \leq 0.037$$, Pearson’s r: 0.301) and lower maximum peritumoral immune fraction ($$p \leq 0.041$$, Pearson’s r: − 0.297). Pathologists also assigned tumor architectural patterns to high attention patches. The overall mode of predominant tumor architectural patterns exhibited a statistically significant difference between EGFR mutant and wild-type slides (Fig. 4a; $$p \leq 0.035$$; Chi-squared test). More bags were predicted to be wild-type than EGFR mutant when the predominant architectural pattern was solid (Fig. 4a; $$p \leq 0.013$$).Figure 4Overall bag characteristics of high-attention patches for categorical variables for 49 pathologist reviewed bags. ( a) Predominant architectural pattern of high-attention patches, determined by patch mode, by predicted status. p-value from a Chi-squared test of the overall distribution. ( b) Minor architectural pattern of high-attention patches. ( c) Cytology for high-attention patches, determined by patch mode. ( d) Non-neoplastic qualities present in high-attention patches, as determined by patch mode.
There were also several trends in the data that are suggestive of known associations with EGFR mutational status that did not reach statistical significance. When considering overall architecture, bags that were predominantly lepidic or papillary were predicted as EGFR mutant five times more often than EGFR wild-type (Fig. 4a). In contrast, bags that predominantly possessed the solid architecture were predicted as EGFR wild-type seven times more often than mutant. When the predominant architecture was mucinous, it was twice as likely that the bag would be predicted as EGFR wild-type. There was no strong enrichment (ratio < 2.0) in prediction status of either type for predominantly acinar bags. All bags with any micropapillary content (predominant or minor) were predicted as EGFR mutant specimens (Fig. 4a,b). The directionality of preference for predicted status when considering acinar, lepidic, papillary, mucinous for the minor architectures were similar to the preference in the predominant architecture, but the solid minor architectural pattern did not see the same strength of preference for EGFR mutant predictions compared to instances where the solid architecture was the predominant pattern. From a cytology perspective, bags with columnar or hobnail as the most common cell type across the high-attention patches were more likely (> 1.5) to be predicted as mutant (Fig. 4c). Mucinous and sarcomatoid cytologies were more likely to be predicted as wild-type. From an overall tumor-feature perspective, our MIL models tended to predict lepidic and papillary patterns as EGFR mutant and any mucinous characteristic (architecture and cytology) as EGFR wild-type (Fig. 4c). For non-neoplastic qualities, slides with inflammation were more frequently predicted as EGFR wild-type (Fig. 4d). Generally, there were no categorical characteristics (aside from the micropapillary pattern) that perfectly separated specimens by predicted status, possibly suggesting that the models consider the various characteristics within each bag in combination.
To examine the relevance of the patch characterization in a combinatorial manner, we performed association rules mining21 to determine item-sets of interest using the categorical variables (Supplementary Table 2). Each bag’s overall characterization was determined via the category mode for the reviewed patches in the bag. The highest-lift item-sets for predicted wild-type status as a consequent included: {inflammation, hobnail cytology, solid minor architectural pattern}, {inflammation, acinar predominant architectural pattern, hobnail cytology}, {acinar predominant architectural pattern, hobnail cytology, solid minor architectural pattern} and {acinar predominant architectural pattern, inflammation, hobnail cytology, solid minor architectural pattern}, each with a lift of 2.097. In contrast, the highest-lift item-sets for predicted EGFR mutated status included: {fibrosis, lepidic minor architectural pattern, hobnail cytology} and {fibrosis, acinar predominant architectural pattern, hobnail cytology}, both with a lift of 1.92. In total, the EGFR prediction algorithm recapitulated several known morphological and cytological associations with EGFR status and these features can be tested on a per sample basis by analyzing highly attended regions manually or via tissue morphology/cytology classification algorithms.
## Discussion
As barriers to clinical adoption of digital tools are reduced, the development of machine learning models to augment and support established processes is highly desirable. However, models trained on research datasets that are dissimilar to real-world data may have difficulty generalizing in a clinical setting, where the incoming sample distribution may not align well with the training data. With this in mind, we developed machine learning models that predict EGFR mutational status on real-world H&E lung adenocarcinoma images with high morphological diversity and show the potential for use as screening algorithms with high NPV. We demonstrate that state-of-the-art performance for predicting EGFR can be achieved by using attention-based models that evaluate a full range of tissue morphologies, outperforming our tumor-only models as well as those shown in prior literature (0.825–0.831 AUC)14. Additionally, attention-based models do not require expensive manual annotation or guidance to train. Finally, we show that biological verification of attention-based end-to-end models can be performed by combining assessment approaches such as morphological profiling, item-set analysis, and pathology review, potentially increasing accuracy in a clinical setting.
The ability to directly assess the attention distribution of MIL models also allows an opportunity to investigate learned patterns regarding tumor biology and the tumor microenvironment (TME) when predicting EGFR mutational status. Various studies have shown that certain tumor architectural patterns and cytological features are correlated with EGFR mutated tumors in lung adenocarcinoma. For example, Sun et al. showed that acinar and lepidic architectural patterns, sometimes in mixed combination, are associated with EGFR mutations in NSCLC22. Other studies show that micropapillary or papillary patterns, with any presence of the lepidic pattern, are good indicators of EGFR mutation23,24. Associations of hobnail cytology with EGFR mutated samples have also been observed25. In line with these findings, our MIL models differentiate mutational status using the predominant architectural pattern (Fig. 4a), and appear to capture a relationship between EGFR mutations and the lepidic pattern coupled with hobnail cytology (Supplementary Table 2). It should be mentioned that the hobnail cytology is also present within high-lift wild-type prediction sets; however, the lepidic pattern is present only within high-lift predicted mutant sets while the solid and mucinous patterns are present only in the high-lift predicted wild-type sets. It is noteworthy that our models do not learn a sole architectural pattern, cytology, or non-neoplastic quality as the lone discriminator for predicting EGFR status.
The attention distribution of MIL algorithms also has the potential to allow for quality control tests in a clinical setting. One quality control method would be to simply have pathologists review highly attended patches and ensure they had characteristics of the EGFR mutant or wild-type call. A more automated approach would be to use trained morphology, growth pattern, and cytology algorithms to analyze highly attended patches. For example, an EGFR mutated prediction in a sample with predominantly solid architecture could be flagged and reviewed manually. If a pathologist then confirms that an EGFR mutation is unlikely given the specimen morphology, the specimen can be prioritized for genomic testing. Furthermore, utilizing multiple observations (or algorithms) to assess whether a particular diagnostic result is consistent with all the available evidence is similar to how pathologists assess cases in practice.
Beyond tumor-associated features, it has also been suggested that immune response and non-neoplastic components within the TME may be relevant when examining the effect of mutations upon linked biological pathways. Dong et al. showed that EGFR mutated NSCLC specimens possess significantly less T cell infiltration and lower immunogenicity than wild-type specimens26. In another study, Lin et al. suggest that TME immune response may be influenced by the EGFR mutation via manipulation of complex signaling pathways, leading to a reduction in the expression of the major histocompatibility complex and consequently lowered activation levels of CD8 + T cells27. Our MIL models appear to learn this trend of lowered immune response within the TME of EGFR mutated specimens, in part indicated by the significantly higher maximum peritumoral immune fraction (Supplementary Fig. 1c; $$p \leq 0.041$$) across high-attention patches for specimens strongly predicted to be wild-type. Additionally, inflammation is present within three out of four of the highest-lift item-sets for EGFR wild-type predictions, while it is absent from the highest-lift item-sets for EGFR mutant predictions.
Finally, the ability to examine the attention given by MIL models may allow exploration of other less obvious elements within the TME that could help elucidate the biological understanding of EGFR mutations. In two of the highest-lift item-sets for predicted EGFR mutant status, fibrosis is present alongside the tumor-related features. This inclusion of fibrosis is less expected than the inclusion of tumor features but may also suggest interesting interactions within the TME. Many studies now suggest that stroma and stromal elements may play far more than a passive role within TMEs and may have direct effects on tumorigenesis. For example, cancer-associated fibroblasts within desmoplastic stroma may help promote tumor invasion and metastasis, oncogenic angiogenesis, and immune evasion28. One change within the TME possibly affected by activated fibroblasts is the assisted generation and structuring of the extracellular matrix, which may influence tumor growth and cell motility29,30. Additionally, stroma may play a role in immune evasion by acting as a physical barrier to T cell infiltration31. The inclusion of fibrosis as a relevant feature may indicate the ability of machine learning models to recognize, without human guidance, patterns involving tissue regions that may be orthogonal to tumor-specific features.
Our experiments show that machine learning models enabled with self-directed intuition such as attention-based MIL models can predict EGFR mutational status, and potentially other biomarkers, from morphologically-diverse real-world tissue specimens without human intervention. The ability to rely upon machine-intuition to extract meaningful features could enable low-effort signal-searching experiments at scale, as well as provide a means to investigate machine-discovered patterns within the phenotype that may be biologically informative. It is encouraging from an interpretability standpoint that models intended to assist in clinical decision-making recapitulate expected results, such as finding tumor regions most predictive for genomic alteration signal, but also that such models may be capable of determining patterns and interactions within phenotypic features in ways that elevate performance beyond methods relying solely upon human intuition. In a clinical setting, these screening algorithms could provide rapid genomic insights regarding a patient specimen, which can then be checked by a combination of more interpretable models as well as pathologist visual examination. Any low-confidence predictions or samples flagged by pathologists could then be selected for further genomic testing.
We do note that the samples used within this study were limited to lung adenocarcinoma resections that were extracted from lung tissue sites only. Oftentimes in clinical practice, a majority of specimens are acquired as needle core biopsies from a variety of tissues outside of and including the lungs. To increase the clinical utility of pre-screening algorithms such as those described in our study, approaches should be developed to integrate both resection samples and needle core biopsy samples to enable optimal coverage of the clinical patient population. Since needle core biopsies offer much less tissue than resections for analysis, thoughtful modeling approaches to reconcile this difference will be needed. In the future, we hope to expand model performance to cover needle core samples across a variety of tissue extraction sites and to evaluate model transferability on additional external clinical datasets.
## Conclusions
We developed a multiple-instance model to predict EGFR mutational status in lung adenocarcinoma samples with diverse tissue morphologies, achieving an AUC of 0.870 with an NPV of 0.954 and a PPV of 0.410. By using a combination of tissue morphology classification models and expert pathologist review of high-attention patches to assess signal distribution, we found that our model learns to consider both tumor morphology as well as non-tumor morphologies when predicting EGFR mutational status. Our model’s performance as evaluated on validation sets reflecting the real-world prevalence of EGFR mutations in lung adenocarcinoma suggests utility as a rule-out screening tool that could provide rapid genomic insights regarding a patient specimen.
## Dataset
The dataset used in this study consists of lung adenocarcinoma resection H&E whole slide image scans acquired from specimens submitted to Foundation Medicine for genomic profiling. All data was de-identified following a de-identification protocol that was externally approved according to the Health Insurance Portability and Accountability Act Expert Determination Process. All images within this dataset were scanned at 20 × magnification. This image dataset was generated from 2099 tissue specimens from 2099 individual patients. 716 of the specimens were determined by genomic sequencing to be EGFR short-variant mutant specimens. Of the remaining specimens, 85 were ALK mutated, 93 BRAF mutated, 81 ERBB2 mutated, 606 KRAS mutated, 76 MET mutated, 35 RET mutated, 18 ROS1 mutated, and 389 were lung driver wild-type.
Five-fold cross-validation was performed to evaluate model performance and consistency. For ground-truth, all slides used the specimen-level mutational statuses as determined by FMI’s next-generation sequencing tests. The training/validation split for all experiments was $\frac{0.8}{0.2}$ for EGFR mutant slides. The real-world prevalence of EGFR short variant mutations is approximately $15\%$ in NSCLC, and thus represents a minority class for which class-imbalanced modeling was a consideration. As the data available at FMI contained a relatively large number of EGFR mutated lung adenocarcinoma specimens, we chose to forgo any minority class balancing techniques such as minority over-sampling or minority class weight penalization and instead chose to perform majority under-sampling, randomly selecting an equal number of slides that were not EGFR mutated to balance the EGFR mutated slides in the training set. For the validation sets, we selected enough slides that were not EGFR mutated so that the percentage of EGFR mutated slides in the validation was $15\%$, reflecting the real-world prevalence. By doing so, we aimed to simplify the training process while still allowing for an evaluation of the model against a validation dataset that more closely represented a real-world setting. As a result, each training set had 1146 slides and each validation set had 953 slides.
## Model architecture
The tissue morphology classifier was structured primarily using a trainable feature extractor (ResNet5032 without the top-layer). The feature extractor was followed by a global average pooling layer, which is then connected to a 5-dimensional fully-connected layer with softmax activation to predict the tissue type classification.
The weakly-supervised EGFR prediction model consisted of a trainable feature extractor, followed by a global average pooling layer, a dropout layer of 0.3, and a final 1-dimensional output layer with a sigmoid activation to predicted EGFR status at patch-level. The specimen-level prediction was made by aggregating patch-level predictions from the given slide. Each morphology-restricted patch-level EGFR classifier used the same architecture as the weakly-supervised model. The feature extractor backbone for all models was ResNet50.
The attention-based multiple-instance learning model was built using ResNet50 without the top-layer and with an added global average pooling layer to serve as a trainable feature extractor. Following the feature extractor was an attention-mechanism consisting of two fully-connected layers (512-dimensional, 256-dimensional) to reduce the embedding dimensionality. The reduced embeddings were then passed to a 256-dimensional fully-connected layer followed by another 1-dimensional fully-connected layer. The output is then transposed and all patches within a multiple-instance bag are passed through a softmax activation which fractionally weighs the attention for each patch within the bag. The reduced embeddings are then weighed using the softmax attention weights to generate the slide-level weighted embedding. A final fully-connected layer processes the slide-level weighted embedding and uses the sigmoid activation to predict the specimen-level EGFR status.
## Tissue morphology annotation procedure
In order to train a model to profile tissue types within the lung adenocarcinoma specimen set, pathologists performed non-exhaustive region annotations on a selection of lung adenocarcinoma slides for the tissue morphologies: tumor, normal lung tissue, stroma, immune foci, and necrosis. These annotations were performed to capture large representative regions for each of the groups, but since the tumor microenvironment is highly complex there are likely elements belonging to other groups within patches extracted from a particular morphology annotation.
The tissue morphology classifier was then trained to predict the tissue morphology for patches extracted from the region annotations. These annotations were deliberately chosen to maximize the variety within the morphological groups. For example, when annotating tumor regions, an effort was made to find and annotate the different lung adenocarcinoma histological subtype groups (lepidic, acinar, micropapillary, papillary, mucinous, and solid). Similarly, the scope for normal lung tissue annotations was also broad and included different sections of alveolar tissue and cartilage.
## Training procedure
To train the tissue morphology model, we generated a patch dataset by extracting non-overlapping patches directly from the pathologist-annotated regions-of-interest for the five chosen morphological classes (tumor, immune, normal, stroma, necrosis). The selection criteria for a morphology patch to be extracted was that each patch needed a pixel fraction of at least 0.75 to be from within an annotated region. We chose to extract these patches at 512 × 512 pixels at 20 × magnification, as this was determined via pathologist guidance to be adequate in receptive field for capturing the signal for the tissue morphology prediction task. We did not extract patches at a larger receptive field for this task in order to maximize the number of patches we could generate from the limited annotations we possessed. For training the tissue morphology model we resized the 512 × 512 patches to 448 × 448 patches before inputting into the model. We found that this resizing step allowed for larger batches during training and reduced the overall training time by $25\%$ while maintaining a validation concordance greater than $95\%$ between a morphology classifier using 448 × 448 inputs and a classifier using 512 × 512 inputs.
The patch morphology classification task was structured as a multi-class classification task. For simplicity, assume a single input instead of a batch. The morphology patch classifier takes a tissue patch as its input and classifies it into one of K categories. The final output layer for the morphology classifier is a fully connected layer of K dimensions and the activation function is the softmax function:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$a\left(z \right)_{i} = \frac{{e^{{z_{i} }} }}{{\mathop \sum \nolimits_{$j = 1$}^{K} e^{{z_{j} }} }}$$\end{document}azi=ezi∑$j = 1$Kezjwhere\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z = w^{T} x + b$$\end{document}z=wTx+band where x is the input to the fully-connected output layer, w is the weight matrix of the output layer, and b is the bias term for the output layer. The loss that we optimize for is the categorical cross-entropy loss:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l = - \mathop \sum \limits_{$j = 1$}^{K} \hat{y}_{j} log\left({a\left(z \right)_{j} } \right)$$\end{document}l=-∑$j = 1$Ky^jlogazjwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{y}_{j}$$\end{document}y^j is the target value of the j-th class for the sample. The morphology model was trained for 15 epochs and the model weights corresponding to the highest validation accuracy were used to morphologically profile the full resection set ($$n = 2099$$) at a patch level.
To train the EGFR mutation prediction models, we generated a patch dataset by exhaustively extracting non-overlapping tissue patches from all resection slides. We first performed tissue masking on down-sampled images for every slide in the resection cohort. The masking was performed on down-sampled images in the interest of computational efficiency. The tissue masking approach consisted of a colorspace transformation of RGB to HSV to allow for color separation of tissue from background and artifacts. Processing of the mask to remove small holes and objects was then performed. Following this, we iterated through the coordinates of the tissue mask to extract patches for the mutation classification dataset. Patches for use in the mutation classification task were kept if the tissue pixel fraction, as determined by the masked pixels, was at least 0.2 for a given patch. No further patch selection criteria was applied. For the EGFR mutation prediction task, we extracted patches at 1024 × 1024 pixels at 20 × magnification because we anticipated that the mutation classification task would require a broader view of the tumor microenvironment.
In order to perform tissue morphological profiling on the full mutation prediction patch dataset, we needed to reconcile the difference in extraction size of these patches (1024 × 1024) with the input size for the tissue morphology classification model (512 × 512 resized to 448 × 448). We did so by center-cropping each 1024 × 1024 patch to 512 × 512, resizing to 448 × 448, performing the morphology classification and then applying that classification of the center crop to the entire patch (Supplementary Fig. 2a). The tissue morphology classifications of these 1024 × 1024 patches were then used to select the appropriate patches for the two-stage models and to help analyze the MIL model’s learned attention after training.
For the training inputs of the EGFR mutation prediction models, we resized the raw 1024 × 1024 pixel patches to 224 × 224 pixel patches to allow each bag in the MIL formulation to include more patches, as limited by GPU memory, so that the MIL model would be allowed a more holistic view of each slide. To maintain consistency for the EGFR mutation classification task, we used 224 × 224 patch inputs (downsized from 1024 × 1024) for all EGFR mutation prediction models, including the weakly-supervised model, all two-stage models, and the MIL model (Supplementary Fig. 2b).
The EGFR mutation prediction task is a binary classification task where either given a single patch (in a setup where each patch has a label, such as in the mutation classification portion of the 2-stage models) or given a bag of patches (in the MIL formulation, where the bag as a whole has a label but patches individually do not) we predict from an H&E whether a gene mutation is present within a specimen. The final layer for each model is a fully-connected with a one-dimensional output and the activation function used is the sigmoid function:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left(z \right) = \frac{1}{{1 + e^{ - z} }}$$\end{document}gz=11+e-zwhere again\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z = w^{T} x + b$$\end{document}z=wTx+b The loss that we optimize for is the binary cross-entropy loss:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l = - (\hat{y} log\left({g\left(z \right)} \right) + \left({1 - \hat{y}} \right)log\left({1 - g\left(z \right) } \right)$$\end{document}l=-(y^loggz+1-y^log1-gzwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{y}$$\end{document}y^ is the target value for the input sample. The batch loss is aggregated across each input sample within the batch by either summing or averaging the losses, and gradient descent is performed to update the model parameters. The weakly-supervised model and the two-stage models were trained for a maximum of 200 epochs with early-stopping conditioned on validation AUC. The MIL models were trained for 200 epochs with 40 patches per bag during each training pass. All models in the study were trained using the TensorFlow33 framework. The Adam34 optimizer was used with a learning rate of 1e−5.
Additionally, when running inference on the validation slides, we found that performance was notably better if batch normalization layers used batch statistics for normalization instead of using the exponentially decaying running mean and variance tracked during training. As each training step involved processing bags from at most one or two slides due to GPU memory constraints, we found that generalizability to the validation set suffered if using the standard momentum-based training statistics for batch normalization, as each batch processed would not be a sampling from the overall cohort population but rather from a very limited number of slides. If each slide during validation is processed individually, then the patch instances within each batch are drawn from the same slide and thus the instance interdependence in the batch formulation is non-arbitrary. This is analogous to vision applications extracting multiple regions-of-interest from a single image and composing those regions-of-interest into a batch to utilize batch statistics for inference35.
## Pathologist review of high-attention patches from high-confidence bags
To examine the attention learned by our MIL models and to better understand what features were relevant for predicting EGFR mutant versus wild-type specimens, expert pathologists evaluated high-attention patches for bags confidently predicted to be mutant or wild-type. For 49 validation slides, we sampled 250 patches per bag and passed each bag through the trained MIL models. The patches within each bag were then ordered by descending attention weight. The top-25 highest-attention patches for each of the 49 bags were provided to pathologists for analysis, resulting in a total of 1225 patches being reviewed.
Pathologists scored each patch for a set of numerical variables and then further reviewed each patch for categorical characteristics. The numerical variables were tumor nuclei fraction, necrosis fraction, peritumoral immune fraction, and intratumoral immune fraction. Tumor nuclei fraction was determined as the fraction of tumor nuclei relative to all nuclei present within a patch. Necrosis fraction was determined as the fraction of the patch area containing necrotic tissue. The peritumoral immune fraction was determined as the fraction of tumor edges that had noticeable immune cell response, such as lymphocytes aggregating at or within the tumor boundary. The intratumoral immune fraction was determined as the fraction of tumor tissue within a patch that had noticeable immune infiltration, such as lymphocytes dispersed throughout a tumor mass or nest.
For the review of categorical variables, pathologists examined each patch for the tumor’s predominant architectural pattern, minor architectural pattern, cytology, and any notable non-neoplastic quality. The possible predominant and minor architectural patterns were acinar, lepidic, papillary, micropapillary, mucinous, and solid. The possible cytology types were hobnail, columnar, mucinous, sarcomatoid, anaplastic, large cell, small cell, or other. Non-neoplastic qualities included fibrosis, pneumonia, inflammation, or other.
In order to evaluate overall bag characteristics relative to the model’s mutant predictions versus wild-type predictions, we generated summary statistics and overall characteristics from the pathologist review of the high-attention patches. To determine each bag’s overall numerical statistics, we calculated the mean, standard deviation, minimum, and maximum of the numerical scores provided by pathologists across the top-25 high attention patches from that bag. To determine the bag’s overall categorical characteristics, we aggregated the patch reviews across the top-attention patches by taking the mode. Thus, for each bag we had an overall summary of patch scores and categorical labels for the high-attention patches, which we could then compare based on the model’s predicted EGFR mutation status.
Significance between the predicted mutant and predicted wild-type slides with respect to the numerical variables was tested using the two-way T-test. False discovery rate correction was additionally applied to generate q-values from T-test p-values. No comparisons were significant after false discovery rate correction. Categorical comparisons were completed using the Chi-square test, at an overall bag level. Finally, association rules mining21 was performed by treating each overall categorical value determined for the bags as items, with predicted EGFR status as the consequent item-set.
## Ethics approval and consent to participate
Approval for this study, including a waiver of informed consent and a Health Insurance Portability and Accountability Act waiver of authorization, was obtained from the Western Institutional Review Board (Protocol No. 20152817).
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31284-6.
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---
title: Domain adaptation for supervised integration of scRNA-seq data
authors:
- Yutong Sun
- Peng Qiu
journal: Communications Biology
year: 2023
pmcid: PMC10020569
doi: 10.1038/s42003-023-04668-7
license: CC BY 4.0
---
# Domain adaptation for supervised integration of scRNA-seq data
## Abstract
Large-scale scRNA-seq studies typically generate data in batches, which often induce nontrivial batch effects that need to be corrected. Given the global efforts for building cell atlases and the increasing number of annotated scRNA-seq datasets accumulated, we propose a supervised strategy for scRNA-seq data integration called SIDA (Supervised Integration using Domain Adaptation), which uses the cell type annotations to guide the integration of diverse batches. The supervised strategy is based on domain adaptation that was initially proposed in the computer vision field. We demonstrate that SIDA is able to generate comprehensive reference datasets that lead to improved accuracy in automated cell type mapping analyses.
A supervised strategy for scRNA-seq data integration uses domain adaption to generate comprehensive reference datasets to improve accuracy in automated cell type mapping.
## Introduction
Given the increasing use of scRNA-seq and the global efforts for building cell atlases in various biological contexts, large amounts of scRNA-seq data are generated and accumulated. Large-scale scRNA-seq studies typically generate data in batches, where samples are processed at different time points, handled by different personnel and labs, prepared and sequenced by different technology platforms, which are all potential causes of batch effects1. It is well-documented that batch effect is often strong in scRNA-seq, making it challenging to effectively integrate multiple scRNA-seq batches around a common biological theme (e.g., tissues, organs) into a single comprehensive atlas that fully captures the heterogeneity of the biological theme2. In the literature, a majority of the existing scRNA-seq batch integration methods address batch effects in an unsupervised manner, aligning the distributions of cells across different batches. A few examples of popular methods include canonical correlation analysis (SeuratV23), mutual nearest neighbor approach (MNN Correct4 and fastMNN5, Scanorama6, BBKNN7), nonnegative matrix factorization (LIGER8), and variational autoencoder (scVI9, scGen10). These unsupervised methods assume that many cell types are shared among the datasets to be integrated and run into the risk of aligning distinct cell types when the assumption does not hold.
Since many scRNA-seq datasets come with clustering analysis and cell type annotations performed by the researchers who generated the data, there is an opportunity to perform supervised data integration, using the cell type annotations to inform the data integration. By construction, supervised integration should outperform unsupervised approaches because the cell type annotations can be used to encourage cells with the same annotations across different batches to overlap and encourage cells with different annotations to be separated. A few supervised integration algorithms have been proposed recently. scAlign11 is an integration algorithm that provides a fully supervised option called scAlign+, which trains a deep neural encoder that incorporates cell type labels to map functionally similar cells to the same coordinates in a latent representation space. LAmbDA12 trains a classifier that maps cell type labels and removes batch effects by constructing a label mask that determines the known relationships between the cell type labels of two batches. SMNN13 and iSMNN14 perform batch effect correction via supervised mutual nearest neighbor detection. In this study, we are interested in developing a supervised integration algorithm that can compete with existing state-of-art integration algorithms for scRNA-seq data. In computer science, one way to implement supervised integration is supervised domain adaptation. *In* general, domain adaptation is to leverage information to a target domain from a different but related source domain, where the domains can be different batches in the context of scRNA-seq data integration.
The discussion of unsupervised and supervised approaches for scRNA-seq data integration is distinct from that in the context of automated cell type mapping, which is a related computational problem. Automated cell type mapping is typically supervised, where machine learning models are constructed from either prior knowledge of cell type marker genes15 or previously annotated scRNA-seq reference datasets16–19 and then applied to annotate cells in newly generated query datasets. The machine learning models could be based on invariant similarity metrics such as correlations16, tree-based classifiers15,17, or nearest neighbor classifiers18,19, etc. The nearest neighbor approach for cell type mapping is sometimes referred to as cell type label transfer and is typically implemented by unsupervised integration of reference and query data without considering cell type annotations in the reference data, followed by supervised classification that uses cell type annotations of the reference data to label cells in the query data. Therefore, although a majority of existing cell type mapping algorithms are supervised, supervised approaches for scRNA-seq data integration are less explored in the existing literature.
In this paper, we developed a supervised scRNA-seq data integration algorithm using a domain adaptation deep neural network called SIDA (Supervised Integration using Domain Adaptation). Given multiple scRNA-seq batches to be integrated, we implemented the Siamese Network20 to learn a shared embedding space that integrates multiple batches. The learning objective is a combination of contrastive semantic alignment loss and classification loss. We compared SIDA with three unsupervised scRNA-seq data integration algorithms in a recent benchmark study1, including SeuratV321 and Harmony22, which ranked highest in the benchmarking study, as well as limma23, which ranked relatively lowly in the benchmarking study. In addition to the unsupervised algorithms, we also compared SIDA with two supervised integration algorithms, scAlign+11 and LAmbDA12. Since scAlign+ also provides an unsupervised option (scAlign), we included both the supervised and unsupervised implementations of scAlign for completeness. According to the evaluation metric in ref. 24, SIDA provided significantly improved performance over both the existing unsupervised and supervised algorithms. Intuitively, the improved performance of supervised integration over unsupervised integration was expected because the supervised approach used additional information on cell type labels to inform the integration. However, among the three supervised integration algorithms, SIDA achieved overall remarkable improvement compared to the unsupervised integration algorithms. The improvement of SIDA over the best unsupervised algorithm was larger than the range of performance among the unsupervised algorithms, suggesting that scRNA-seq data integration should be performed in a supervised fashion whenever possible. To further demonstrate the utility of SIDA, we evaluated the integrated data in terms of its ability to serve as reference data for automated cell type mapping algorithms. We showed that SIDA generated more comprehensive references that led to improved cell type mapping accuracy for new datasets.
## SIDA framework
To achieve supervised integration, we propose to use a domain adaptation deep learning network architecture, which is able to incorporate cell type labels to inform data integration. As shown in Fig. 1, this network architecture takes training pairs generated by cells from different batches as input and passes the input cells through two identical network branches, “g” with shared weights, projecting the cells into a common embedding space. The network and weights are trained to optimize the classification and contrastive semantic alignment loss, which includes a semantic alignment loss that minimizes the distance between cells from different batches but of the same cell type, a separation loss that maximizes the distance between cells from different domains and cell types, and a classification loss encourages high classification accuracy, and hence further maximizing the distance between cells of different cell types, which further facilitate the integration process and the clustering of different cell types. Given a scRNA-seq data collection of multiple batches along with cell type labels of the cells, we sample cell pairs from the batches to train the proposed domain adaptation deep learning network, which is able to produce an embedding space where the batch effect is minimized based on both the distribution of the data and the cell type labels. Details of the design are described in the “Methods” section. Fig. 1Overview of the SIDA algorithm.a Generating cell pairs for training network model. b Domain adaptation network structure. c Classification and contrastive semantic alignment loss. Training pairs of cells from different batches are fed into a convolution network “g” and projected to a shared embedding space to optimize semantic alignment loss and separation loss. The embedded data are fed into a classification network “h” to optimize the classification loss.
## Data collection for evaluation
We evaluate SIDA on five collections of scRNA-seq data in the contexts of the pancreas, PBMC, gut, pancreatic islet, and hematopoietic stem cells (HSCs). Each collection contains multiple datasets, which we consider batches. The pancreas data collection consists of five batches of human pancreatic cells, including Baron25, Mutaro26, Segerstolpe27, Wang28, and Xin1,29. In total, there are 15 different cell types across the batches, among which four cell types appear in all batches, and four cell types appear in only one of the batches. The PBMC data collection consists of five batches of peripheral blood mononuclear cells, including control, stim30, PBMC3k31, 10x \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${3}^{{\prime} }$$\end{document}3′, and 10x \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${5}^{{\prime} }$$\end{document}5′1,32. These batches contain a total of 11 different cell types, with 5 appearing in all batches and 1 being batch specific. The gut data collection consists of four batches, Bigaeva33, Huang34, Parikh35, and Wang36. These four batches contain 11 different cell types in total, among which 3 cell types appear in all batches, and 1 cell type is batch specific. The pancreatic islet data collection consists of four batches of human pancreatic islet cells, including CEL-Seq, CEL-Seq2, Fluidigm C1, and Smart-Seq211,26. These four batches contain 13 different cell types in total. Since all these 13 cell types appear across all four batches, this pancreatic islet data collection does not contain any batch-specific cell type. The HSCs data collection consists of two batches of hematopoietic stem cells11. These two batches contain three different cell types in total, which appear across both batches, meaning that the HSCs data collection does not contain batch-specific cell type. Detailed references to these data collections and individual datasets are provided in Supplementary Note 1 and Supplementary Table 1.
## SIDA leads to improved batch mixing and cell type separation
We applied SIDA, four unsupervised integration methods (SeuratV321, Harmony22, limma23, scAlign11) and two supervised integration methods (scAlign+11, LAmbDA12) to three data collections (pancreas, PBMC, gut), generating integrated versions for each data collection separately. The integrated datasets are evaluated in terms of both batch mixing and cell type separation. We use a k-nearest neighbor-based approach to define positive rate and true positive rate, which quantify batch mixing and cell type separation24. We also examined evaluation metrics used in a recent benchmark paper for scRNA-seq data integration1, including k-nearest neighbor batch-effect test (kBET), local inverse Simpson’s index (LISI), average silhouette width (ASW), and adjusted rand index (ARI). Details of these evaluation metrics are described in the “Methods” section.
For the pancreas data collection, the integration results are shown in the tSNE visualizations in Fig. 2a, b, colored by cell types and batch labels. Seurat and Harmony successfully mixed the different batches, as shown in the second and third columns in Fig. 2b. However, when colored by cell type labels, the second and third columns of Fig. 2a show that Seurat and Harmony improperly aligned some of the distinct cell types in different batches, e.g., stellate and mesenchymal, acinar and ductal. From the fourth, fifth, and sixth columns of Fig. 2a, b, we can observe that Limma, scAlign, and scAlign+ performed poorly, where the same cell type in different batches did not align and mix with each other. LAmbDA successfully aggregated the same cell type and mixed the different batches. However, the last column in Fig. 2b shows that LAmbDA did not separate different cell types properly. As shown in the first column of Fig. 2a, b, SIDA was able to correctly align corresponding cell types across batches and separate different cell types. As a quantitative comparison of the three supervised and the four unsupervised algorithms on the pancreas data collection, Fig. 3a shows six evaluation metrics for each algorithm. Among the six metrics, SIDA achieved the highest performance for four metrics, the second highest for kBET, and the third highest for LISI. This quantitative evaluation shows that SIDA achieved better cell type separation and batch mixing compared to the four unsupervised and the two supervised methods, which is consistent with the visualization results in Fig. 2a, b.Fig. 2tSNE visualization of SIDA, four unsupervised algorithms (Seurat, Harmony, Limma, scAlign) and two supervised algorithms (scAlign+ and LAmbDA) applied to three data collections.a, b Integration of pancreas data collection colored by cell types and batch labels; c, d Integration of PBMC data collection colored by cell types and batch labels; e, f Integration of gut data collection colored by cell types and batch labels. Fig. 3Comparing supervised and unsupervised integration algorithms using six quantitative evaluation metrics for batch mixing and cell type separation.a Evaluation based on the pancreas data collections; b Evaluation based on the PBMC data collection; c Evaluation based on the gut data collection.
For the PBMC data collection, the integration results are shown in the tSNE visualization in Fig. 2c, d, colored by cell types and batch labels. The first column of Fig. 2c, d shows that SIDA performed well on this more difficult PBMC data collection, achieving proper mixing of different batches. Based on the second and third columns of Fig. 2c, d, Seurat and Harmony mixed the different batches, but Seurat and Harmony improperly aligned two similar cell types: CD4 T and CD8 T. Based on the fourth, fifth, and sixth columns of Fig. 2c, d, we observe that Limma, scAlign, and scAlign+ failed to properly integrate the PBMC data collection, which is consistent with their performance in the pancreas data collection. From the last column of Fig. 2c, d, we can observe that LAmbDA did not separate different cell types properly. As a quantitative comparison of the supervised and unsupervised integration algorithms in the PBMC data collection, Fig. 3b shows the six evaluation metrics for each algorithm in the PBMC data collection. SIDA achieved the highest performances for four metrics and the second highest for kBET and LISI, among which improvement in the true positive rate was the most significant.
Integration results for the gut data collection are shown in the tSNE visualization in Fig. 2e, f, colored by cell types and batch labels. As shown in the second to sixth columns of Fig. 2f, Seurat, Harmony, Limma, scAlign, and scAlign+ did not effectively mix the batches and, therefore, did not properly align corresponding cell types in different batches, as shown in Fig. 2e. LAmbDA successfully mixed the four different batches as shown in the last column of Fig. 2f. However, when colored by cell type labels, the last column of Fig. 2e shows that LambDA improperly aligned different cell types. In contrast, tSNE visualization of SIDA showed desirable batch mixing, alignment of corresponding cell types in different batches, as well as separation among different cell types. The performance difference shown in the tSNE visualizations was also reflected in the quantitative comparison shown in Fig. 3c, where SIDA consistently achieved the highest performance for five metrics and the second highest for kBET. Moreover, according to all six metrics, except for the positive rate, the improvement of SIDA over the best unsupervised algorithm was larger than the range of performance among the four unsupervised algorithms.
In addition to the tSNE plots in Fig. 2, we also visualized the integration results using UMAP shown in Supplementary Note 3 and Supplementary Fig. 1, where the observations and interpretations are highly consistent with the tSNE visualizations. Since both tSNE and UMAP are nonlinear dimension reduction tools to visualize high-dimensional distributions in two-dimensional space, the numerical values and range axes of tSNE and UMAP plots are not interpretable. Therefore, we removed the axis labeling of tSNE and UMAP plots, following the practice in a previous benchmarking paper for scRNA-seq data integration1. The numerical values of the quantitative metrics in Fig. 3 are summarized in Table 1. Comparing the evaluation metrics across the three data collections, we observed that all integration algorithms performed well on the pancreas data collection, whereas the integration performance was slightly lower in the PBMC data collection and the lowest in the gut data collection. Therefore, it seemed that the pancreas, PBMC, and gut data collections were progressively more and more challenging to integrate. It was encouraging to observe that SIDA achieved more pronounced performance improvement over existing unsupervised and supervised algorithms in the PBMC and gut data collections that were relatively more challenging to integrate. Table 1Comparing supervised and unsupervised integration algorithms using six quantitative evaluation metrics for batch mixing and cell type separation. Positive rateTrue positive ratekBETLISIASWARIPancreasSIDA0.950.360.690.420.680.97Seurat0.870.260.570.520.570.95Harmony0.840.20.650.310.560.88Limma0.870.050.5400.510.75scAlign0.610.060.590.240.510.54scAlign+0.590.010.590.010.540.54LAmbDA0.280.260.930.510.460.35PBMCSIDA0.850.550.770.750.680.96Seurat0.80.250.740.580.590.77Harmony0.760.110.510.540.560.82Limma0.700.010.030.540.59scAlign0.470.0030.160.170.540.61scAlign+0.460.0030.150.110.530.52LAmbDA0.150.130.930.650.500.56GutSIDA0.890.780.870.760.680.93Seurat0.610.130.110.390.530.44Harmony0.620.130.20.170.510.31Limma0.70.090.0100.530.31scAlign0.370.0080.130.060.510.43scAlign+0.380.010.150.060.490.27LAmbDA0.320.320.920.680.490.26Bold indicates the best performance for each metric in each data collection.
## Comparison between SIDA and supervised scAlign+
To further demonstrate the strength of SIDA, we performed an additional comparison with scAlign, which provides both unsupervised (scAlign) and supervised (scAlign+) options11. We performed the comparison on the pancreas islet and the HSCs data collections, which were used in scAlign’s tutorial demonstrations (https://github.com/quon-titative-biology/scAlign). We examined these data collections to make sure that we were able to faithfully reproduce the integration results in scAlign’s tutorial demonstrations, which would ensure a fair comparison with SIDA. For completeness, our comparison included both unsupervised and supervised options of scAlign. Figure 4a, b shows the tSNE visualizations of integration results of the HSCs data collection, colored by cell types and batch labels. Since the HSCs data collection consists of only two batches and all three cell types in the data collection appeared in both batches, it presented a relatively simple data integration challenge. Based on the tSNE visualizations in Fig. 4a, b, all three algorithms achieved decent integration performance on this HSCs data collection, aligning shared cell types across the two batches, among which SIDA and scAlign+ more significantly separated different cell types. UMAP visualizations shown in Supplementary Fig. 2 provided the same observation and interpretation. In Fig. 4c, a comparison based on the six quantitative metrics showed that SIDA achieved the best performance in most metrics except for LISI, which shows the effectiveness of SIDA over scAlign and scAlign+. The second and third columns of Fig. 4a–c show that supervised scAlign+ achieved significantly improved performance compared to the unsupervised scAlign, which is consistent with the intuition that supervised integration is able to improve batch mixing and cell type separation in scRNA-seq data integration. Fig. 4Comparing SIDA, scAlign, and scAlign+ on HSCs data collection.a tSNE visualization of SIDA, scAlign+, and scAlign colored by cell types; b tSNE visualization of SIDA, scAlign+, and scAlign colored by batch labels; c Evaluation metrics based on the HSCs data collection.
Figure 5a, b shows the tSNE visualizations of integration results of the pancreatic islet data collection, colored by cell types and batch labels. Figure 5b shows that all three algorithms were able to generate embedding spaces where cells in various batches were mixed together. In the first panel of Fig. 5a colored by cell types, SIDA successfully delineated various cell types in the data. However, the remaining two panels of Fig. 5a show that scAlign and scAlign+ were not as effective in properly separating distinct cell types. For example, the ductal cell type was split into two islands far away from each other in the embedding space. For alpha, beta, and delta cell types, cells were co-located in close proximity but separated in multiple small islands, where the distance between islands corresponding to different cell types could be smaller than the distance between islands corresponding to the same cell type. UMAP visualizations of this comparison shown in Supplementary Fig. 3 provided the same observation and interpretation. The integration performance in terms of batch mixing and cell type separation is also reflected in the quantitative comparison shown in Fig. 5c. Interestingly, Fig. 5c shows that supervised scAlign+ achieved minimal improvement over unsupervised scAlign when integrating this pancreatic islet data collection, which was a relatively more difficult integration challenge that involved multiple batches with a nontrivial number of cell types. Meanwhile, SIDA consistently achieved significant improvements over scAlign and scAlign+ across all six quantitative evaluation metrics, which indicates the effectiveness and robustness of SIDA.Fig. 5Comparing SIDA, scAlign+, and scAlign on pancreas islet data collection.a tSNE visualization of SIDA, scAlign+, and scAlign colored by cell types; b tSNE visualization of SIDA, scAlign+, and scAlign colored by batch labels; c Evaluation metrics based on the pancreas islet data collection.
## SIDA improves the accuracy of automated cell type mapping
To demonstrate the utility of SIDA in terms of cell type annotation, we applied a leave-one-out strategy to each data collection. For a given data collection, we first left out one batch and integrated the remaining batches using either SIDA or an existing integration algorithm. We then performed automated cell type mapping to predict the cell type labels of the left-out batch using the integrated data as a reference. The resulting cell type mapping accuracy was used to evaluate which integration algorithm was able to build a more comprehensive reference that led to better performance in cell type annotation of left-out data that was not used to generate the integrated data.
For an integrated dataset to serve as the reference in automated cell type mapping, the integrated data in the low-dimensional embedding space was insufficient. Instead, we needed to convert the integrated data from the low-dimensional embedding space back to the original high-dimensional gene space. To achieve this, we picked one of the batches in the integrated data as the target space, applied the Mutual Nearest Neighbors strategy in Seurat to find anchors between the picked batch and the other batches in the low-dimensional embedding space, and used weighted differences of the anchors in the original gene space to convert the integrated low-dimensional data to the original high-dimensional gene space, so that the integrated data in high-dimensional gene space resembled the picked batch. When converting the integrated low-dimensional space to the high-dimensional gene space, we could pick any of the integrated batches as the target space; therefore, one integration algorithm produced several integrated versions of integrated data, and the number of versions was the same as the number of batches that were integrated. *After* generating an integrated dataset using one integration algorithm with one choice for the target space, the integrated dataset was considered as the reference data for cell type mapping, and the left-out batch was considered as the query data. We applied the cell mapping pipeline in scanpy37, which first selected high variable genes and then used a PCA-based function to predict the cell type labels for the query cells based on the reference data. Figure 6 shows the results of the cell type mapping. Fig. 6Evaluating supervised and unsupervised integration algorithms using cell type mapping and leave-one-out strategy. Each heatmap shows the cell type mapping accuracies computed by leaving one batch out of a data collection to serve as the query data. Inside one heatmap, each element corresponds to a particular choice of reference data. Elements in the first row of a heatmap represent cell type mapping accuracies when individual batches were separately used as reference data. In the second row, the reference data were generated by SIDA results converted to different choices of target space. In the remaining rows, the reference data were generated by the existing unsupervised and supervised integration algorithms, with results converted to different choices of target space. a Cell type mapping accuracies in the pancreas data collection. Each heatmap corresponds to one left-out batch. b Cell type mapping accuracies in the PBMC data collection. c Cell type mapping accuracies in the gut data collection.
Cell type mapping in the pancreas data collection is relatively simple. The first row of the heatmaps in Fig. 6a shows cell type mapping accuracies between each pair of individual batches in the pancreas data collection. When Wang or Xin served as the reference, the accuracies were lower compared to cases where the other three batches served as the reference. This variation in performance was expected because certain batches may not be sufficiently comprehensive to serve as the reference for cell type mapping. In the remaining rows of the heatmaps in Fig. 6a, we generated integrated data by different algorithms and converted the data to different choices of target space. Based on the average of each row across all heatmaps in Fig. 6a, we observed that all three supervised integration algorithms showed similar performance, around 6–$8\%$ improvement in cell mapping accuracy compared to individual batches serving as reference. Among the unsupervised integration methods, scAlign achieved a $6\%$ improvement, whereas the other three only achieved a 1–$2\%$ improvement. Such improvement was more noticeable for target spaces defined by Wang and Xin. Therefore, starting with a dataset that was a poor reference by itself, integrating other datasets into this poor reference could significantly improve the performance of cell type mapping. This is consistent with the general intuition that proper data integration may lead to more comprehensive atlases that serve as better references to represent cellular distributions and heterogeneity.
Figure 6b shows the cell type mapping results in the leave-one-out analysis of the PBMC data collection. Based on the average of each row across all heatmaps in Fig. 6b, we observed that SIDA and existing supervised integration algorithms (scAlign+ and LAmbDA) showed similar performance compared to individual batches as reference, ranging between 89 and $91\%$, whereas the average performance of the four unsupervised integration algorithms (Seurat, Harmony, Limma, and scAlign) ranged between 89 and $90\%$. This result provided an example that data integration was not always necessary for cell type mapping.
Cell type mapping results in the gut data collection showed very interesting variation. As shown in the last heatmap in Fig. 6c, it seemed very challenging to predict cell types for one of the batches (Wang). When the other three batches served as the left-out query data, cell type mapping was able to achieve decent performance depending on the choice of reference. Based on the average of each row across all heatmaps in Fig. 6c, the average performance of individual batches as reference was $67\%$, the average performance of SIDA integration as reference was $77\%$, and the average performances of the other supervised integration algorithms, scAlign+ and LAmbDA, were 67 and $74\%$. The average performances of the four unsupervised integration algorithms, scAlign, Seurat, Harmony, and Limma, were $68\%$, $63\%$, $60\%$, and $69\%$, respectively. As described in the previous section on integration metrics, Limma, scAlign, and scAlign+ did not mix the batches in the gut data collection, and therefore, integrated data based on these three integration methods led to similar cell type mapping accuracy compared to individual batches as reference. Although Seurat and Harmony outperformed Limma, scAlign, and scAlign+ according to the integration metrics, the batch mixing achieved by these two algorithms was at the cost of improper alignment of some of the different cell types, which negatively impacted cell type mapping accuracy when Seurat and Harmony’s integrated data were used as references. This result showed that evaluations based on integration metrics and cell type mapping could provide complementary perspectives of data integration performance. In this gut data collection, SIDA integration as reference data led to an average of $77\%$ accuracy in cell type mapping, which achieved the highest performance improvement over individual batches as reference.
## Discussion
In this paper, we propose a supervised integration strategy for scRNA-seq data called SIDA. The key idea is to use cell type labels of individual datasets to inform the integration when cell type labels are available in the datasets to be integrated. The supervised integration is achieved using a deep neural network optimized with a Classification and Contrastive Semantic Alignment loss function to encourage the alignment of the same cell types across datasets and the separation of different cell types. When integrating scRNA-seq datasets that do not have cell type labels, SIDA is not applicable. However, when such cell type labels are available, SIDA is able to achieve better batch mixing and cell type separation, as well as improved accuracy in cell type mapping of new datasets. As global efforts of cell atlases progress, an increasing number of scRNA-seq are being accumulated, along with analysis results and cell type annotations. SIDA can be useful in any analysis that aims to summarize multiple previously analyzed datasets into larger and more comprehensive atlases.
To evaluate SIDA, we compared it with existing unsupervised and supervised integration algorithms. We applied two approaches that probed orthogonal perspectives of the integration performance. One approach was based on quantitative metrics that were previously used to benchmark unsupervised integration algorithms (i.e., positive rate, true positive rate, kBET, LISI, ASW, and ARI). These metrics were designed to quantify batch mixing and cell type separation in the embedding space. We observed that SIDA led to improved scores in almost all metrics across pancreas, PBMC, and gut data collections. When comparing with the existing supervised integration algorithm scAlign+ on the two data collections (HSCs and pancreas islet) provided in its tutorial documentation, we observed that SIDA also led to improved scores in the majority of evaluation metrics except for LISI in the HSCs data collection. The robustness of SIDA over scAlign+ was highlighted when integrating the pancreas islet data collection, which involved multiple batches with a relatively large number of cell types. The other approach was based on performance in cell type mapping, which explicitly quantified the utility of the integrated data in cell type interpretation of new data. We observed that supervised and unsupervised integration achieved similar performance in the pancreas data collection but showed moderate to large improvements in the PBMC and gut data collections. It was encouraging that SIDA showed improved performance based on both evaluation approaches.
Implementation of one of the evaluation metrics, ARI, involves cell clustering in the integrated embedding space followed by a comparison of the resulting clusters and the known cell type labels. This clustering step requires a pre-specified definition of the number of clusters k. In our implementation of ARI, we set k equal to the number of known cell types. This may not be the optimal choice because there is no guarantee that the k resulting clusters will align with the k cell types even if the integration result is perfect. However, setting k larger also does not guarantee that the clustering results would capture all the known cell types, especially relatively rare cell types. Given the fact that ARI is sensitive to the number of clusters and penalizes over-clustering, we decided to follow the practice of ARI calculation in published benchmarking analysis for data integration, setting k to be the same as the number of known cell types.
The preprocessing of SIDA involves principle component analysis to reduce the space of high variable genes down to the first 50 PCs, which serves as the input to the SIDA network. The choice of working with the PCs was largely driven by computational complexity. If the top 2000 high variable genes served as the input space, the SIDA network would include a substantially larger number of parameters, leading to significantly increased computational cost. As a separate note, when we generated the PCA space to integrate multiple batches in a data collection, we performed PCA on each batch separately. As a result, the first 50 PCs from various batches typically did not align with each other. This actually represented a more challenging situation compared to using highly variable genes where the features in different batches are the same.
The training process of SIDA involves the sampling of a subset of cells from various batches to form training pairs for SIDA to learn the differences of corresponding cell types across different batches. It is important to evaluate the robustness and reproducibility of SIDA with respect to the stochasticity involved in random sampling. We tested SIDA’s consistency by applying it to the pancreas, PBMC, and gut data collections multiple times with different random seeds (see Supplementary Note 4: Evaluation of robustness and reproducibility of SIDA). In Supplementary Figs. 4 and 5, we observed low variation in the evaluation metrics and highly stable tSNE visualization of the embedding space with respect to random sampling, both indicating SIDA’s robustness and reproducibility.
Since scRNA-seq data integration often aims to create comprehensive atlases that include a large number of cells, computational efficiency is an important consideration. We examined the running time of SIDA, four unsupervised algorithms (Seurat, Harmony, Limma, scAlign), and two supervised algorithms (scAlign+ and LAmbDA) across three data collections (pancreas, PBMC, and gut). The result is summarized in Supplementary Table 2 and Supplementary Note 2. Algorithms without deep learning strategy (Seurat, Harmony, and Limma) were computationally much cheaper than the other four deep-learning-based algorithms (SIDA, scAlign, scAlign+, and LAmbDA). Among all the algorithms, SIDA achieved the best integration performance and required the longest computing time. This result represents a trade-off between performance and computational cost.
Our current deep learning network for supervised integration provides integrated data in a low-dimensional embedding space, which is not able to directly serve as the reference data for cell type mapping. In order to perform cell type mapping, we apply the Mutual Nearest Neighbor strategy to convert the integrated low-dimensional embedding space to the original high-dimensional gene space, where we need to choose one of the original datasets as the target space. One future direction is to expand our deep learning network to include an encoder-decoder module which is trained to map the low-dimensional embedding space back to the high-dimensional gene space. This will lead to an end-to-end supervised integration method specifically optimized for automated cell type mapping applications.
## Data preprocessing
The first step of data preprocessing is to consolidate cell type annotations in the batches to be integrated because cell type annotations in different batches may have different terminologies at different levels of granularity (e.g., different abbreviations or naming conventions or different levels of details of cell types and subtypes). To apply SIDA, we manually consolidate the cell type labels across different scRNA-seq batches to be integrated. We use the following rules to consolidate the cell type annotations. [ 1] We unify cell type annotations to be the most general level across the batches to be integrated. For example, if monocytes in one batch are annotated as “CD14 monocytes” or “CD16 monocytes” while monocytes in another batch are annotated just as “monocytes”, we convert both “CD14 monocytes” and “CD16 monocytes” annotations in the first batch to “monocytes”. [ 2] We unify different abbreviations and spellings of the same cell type. For example, annotations of dendritic cells may be “Dendritic Cells” in one batch, “DCs” in another batch, and “DC” in a third batch. We update the annotations so that the dendritic cells in all batches are annotated with an identical name.
The second step of data preprocessing is gene selection and dimension reduction. We filter out the nonoverlapping genes across the batches to be integrated. We then apply library size normalization and log transformation to the raw data. After that, we apply PCA to each batch respectively, and keep the first 50 PCs in each batch. The subsequent domain adaptation neural network operates in the space of the first 50 PCs instead of the space of high variable genes, which reduces the size of the neural network and makes the computational complexity tractable.
## Domain adaptation network
We propose a deep domain adaptation neural network called SIDA to achieve supervised integration. The network architecture is shown in Fig. 1b. The preprocessed low-dimensional data (50 dimensions after PCA) is fed into the network as input. The network is composed of a Siamese network and a classification network. First, the input data is fed into the Siamese network, which has two shared-weight identical branches, “g”, the first branch is for source domain data, and the second is for the target domain data. Here, the source and target domains are different batches to be integrated. “ g” is a convolutional network for feature extraction, which is trained to map each batch into a common low-dimensional embedding space. To further facilitate the integration of multiple batches, a two-layer feed-forward classification network “h” is included, appended after the first branch (source domain branch). The Siamese network takes a pair of cells from two different batches for training. The two cells in the training pair are passed through the two shared-weight branches and thus are mapped into a common embedding space. As shown in Fig. 1a, training pairs are drawn from different batches in a rotated fashion. For example, if there are 3 batches to be integrated, cell pairs are generated by randomly drawing from batch 1 and 2, batch 1 and 3, batch 2 and 3, batch 2 and 1, batch 3 and 1, batch 3 and 2, and then rotating back to batch 1 and 2. Such a rotated fashion allows all batches to serve as the source domain of the Siamese network with respect to another batch as the target domain, which ensures that the network “g” is able to properly align cells from all possible pairs of batches to be integrated. Although the classification network “h” is only appended after the source domain branch, the rotated fashion of the training cell pairs enables “h” to be trained for all cell types in all batches to be integrated. This is especially important when there exist cell types that are unique to one of the batches to be integrated. When creating training cell pairs in the analyses shown in the “Results” section, we randomly selected 400 cells per cell type for each batch in the pancreas data collection, 800 cells per cell type for each batch in PBMC and gut data collections, and 250 cells per cell type for each batch in pancreas islet and HSCs data collections. We apply the Classification and Contrastive Semantic Alignment loss (Fig. 1c) to train the whole network.
The Classification and Contrastive Semantic Alignment loss function is composed of two separate loss functions: a contrastive semantic alignment loss and a classification loss. The Contrastive Semantic Alignment loss is applied to the output of network “g”. The Contrastive Semantic Alignment loss function contains two components: a semantic alignment loss LSA and a separation loss LS. Intuitively, the semantic alignment loss LSA minimizes the distance between cells from different batches domains with the same cell type label, which encourages the alignment of cells of the same cell type across batches. The separation loss LS maximizes the distance between cells with different cell type labels, which encourages the separation of cells of different cell types. More specifically, given two cells in a training pair from source and target batches (XS and Xt), if they are of the same cell type label, minimizing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\bf{L}}}}}}}}}_{{{{{{{{\rm{SA}}}}}}}}}({{{{{{{\rm{g}}}}}}}})=\mathop{\sum }\nolimits_{$a = 1$}^{C}\frac{1}{2}\parallel g({X}_{a}^{s})-g({X}_{a}^{t})\parallel$$\end{document}LSA(g)=∑$a = 1$C12∥g(Xas)−g(Xat)∥ will encourage \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{a}^{S}$$\end{document}XaS and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{a}^{t}$$\end{document}Xat to be close to each other in the embedding space, and if they are of different cell type labels, minimizing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\bf{L}}}}}}}}}_{{{{{{{{\rm{S}}}}}}}}}({{{{{{{\rm{g}}}}}}}})={\sum }_{a,b| a}\frac{1}{2}max{(0,m-\parallel g({X}_{a}^{s})-g({X}_{b}^{t})\parallel)}^{2}$$\end{document}LS(g)=∑a,b∣a12max(0,m−∥g(Xas)−g(Xbt)∥)2 will encourage \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{{{{{{{{\rm{a}}}}}}}}}^{{{{{{{{\rm{S}}}}}}}}}$$\end{document}XaS and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{{{{{{{{\rm{b}}}}}}}}}^{{{{{{{{\rm{t}}}}}}}}}$$\end{document}Xbt to be far away from each other, where C is the number of cell types and m is the fixed margin that specifies the separability in the embedding space. The classification loss LC(f) = E[l(f(Xs), Y)] is applied to train “h”, which is a standard cross entropy loss. The classification-based training process further encourages the separation of different cell types and the aggregation of the same cell type, including cell types that appear in multiple batches, as well as batch-specific cell types. The output of the second feature extraction layer of “h” is the final integrated embedding space.
## Execution of pancreas, PBMC, and gut data collections
We applied SIDA, four unsupervised integration methods (SeuratV321, Harmony22, limma23, scAlign11) and two supervised integration methods (scAlign+11, LAmbDA12) to three data collections (pancreas, PBMC, gut), generating integrated versions for each data collection separately. The integrated datasets are evaluated in terms of both batch mixing and cell type separation. We use a k-nearest neighbor-based approach to define positive rate and true positive rate, which quantify batch mixing and cell type separation24. We also examined evaluation metrics used in a recent benchmark paper for scRNA-seq data integration1, including k-nearest neighbor batch-effect test (kBET), local inverse Simpson’s index (LISI), average silhouette width (ASW), and adjusted rand index (ARI).
## Execution of pancreatic islet and HSCs data collections
We performed an additional comparison with scAlign, which provides both unsupervised (scAlign) and supervised (scAlign+) options11. We performed the comparison on the pancreas islet and HSCs data collections which were used in scAlign’s tutorial demonstrations (https://github.com/quon-titative-biology/scAlign). We decided to use these data collections to make sure that we properly reproduced the results in the tutorial demonstrations, which ensures a fair comparison with SIDA. For completeness, our comparison included both unsupervised and supervised options of scAlign. We use a k-nearest neighbor-based approach to define positive rate and true positive rate, which quantify batch mixing and cell type separation24. We also examined evaluation metrics used in a recent benchmark paper for scRNA-seq data integration1, including k-nearest neighbor batch-effect test (kBET), local inverse Simpson’s index (LISI), average silhouette width (ASW), and adjusted rand index (ARI).
## Execution of automated cell type mapping
We applied a leave-one-out strategy to each data collection. For a given data collection, we first left out one batch and integrated the remaining batches using either SIDA or an existing integration algorithm. We then performed automated cell type mapping to predict the cell type labels of the left-out batch using the integrated data as reference.
We first converted the integrated data from the low-dimensional embedding space back to the original high-dimensional gene space. To achieve this, we picked one of the batches in the integrated data as the target space, applied the Mutual Nearest Neighbors strategy in Seurat to find anchors between the picked batch and the other batches in the low-dimensional embedding space, and used weighted differences of the anchors in the original gene space to convert the integrated low-dimensional data to the original high-dimensional gene space, so that the integrated data in high-dimensional gene space resembled the picked batch. When converting the integrated low-dimensional space to the high-dimensional gene space, we could pick any of the integrated batches as the target space; therefore, one integration algorithm produced several integrated versions of integrated data, and the number of versions was the same as the number of batches that were integrated.
*After* generating an integrated dataset using one integration algorithm with one choice of target space, the integrated dataset was considered as reference data for cell type mapping, and the left-out batch was considered as query data. We applied the cell mapping pipeline in scanpy37, which first selected high variable genes and then used a PCA-based function to predict the cell type labels for the query cells based on the reference data.
## Evaluation metrics
To evaluate the performance of the data integration, we use a k-nearest neighbor-based approach to quantify both batch mixing and cell type separation24. We also examined evaluation metrics used in a recent benchmark paper for scRNA-seq data integration1, including k-nearest neighbor batch-effect test (kBET), local inverse Simpson’s index (LISI), average silhouette width (ASW), and adjusted rand index (ARI).
To quantify both batch mixing and cell type separation, we used the metric in ref. 24 based on the k-nearest neighbors (kNNs) of cells. First, we classify all cells into ‘positive’ and ‘negative’ cells. ‘ Positive’ cells are those surrounded mostly by cells from the same cell type. In our analysis, one cell is classified as ‘positive’ if at least $95\%$ of its k-nearest neighbors are of the same cell type, and k is set as 50. Then, the ‘positive’ cells are further classified into ‘true positive’ and ‘false positive’ cells. ‘ True positive’ cells are those surrounded by appropriate proportions of cells from different batches. A ‘positive’ cell is classified as ‘true positive’ if the batch distribution of its neighborhood is consistent with the global batch distribution. The three-sigma rule is used to measure the consistency of distribution. For a ‘positive cell’ of a certain cell type, assume the number of cells of this cell type in the n batches are N1, N2, …, Nn, and therefore, the distribution of this cell type’s cells across the batches is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}_{i}=\frac{{N}_{i}}{\mathop{\sum }\nolimits_{$j = 1$}^{n}{N}_{j}},$i = 1$,2,\ldots,n$$\end{document}pi=Ni∑$j = 1$nNj,$i = 1$,2,…,n. For the ‘positive cell’, we focus on its $k = 50$ neighbors and denote the number of neighbors from the batches as M1, M2, …, Mn. If the batches are well mixed and integrated, we expect the distribution of Mi to be within three standard deviations around the distribution of pi. More specifically, mi should be in the range of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[max(0,k{p}_{i}-3\sqrt{k{p}_{i}(1-{p}_{i})}),k{p}_{i}+3\sqrt{k{p}_{i}(1-{p}_{i})}]$$\end{document}[max(0,kpi−3kpi(1−pi)),kpi+3kpi(1−pi)] for all $i = 1$, 2, …, n. The percentage of ‘positive’ cells and the percentage of ‘true positive’ cells serve as metrics to quantify integration performance.
kBET measures batch mixing at the local level, which compares the kNN local distribution against global distribution using Pearson’s χ2 test. First, a k-nearest neighbor graph is constructed based on the integrated embedding space. Then, $10\%$ of the cells are chosen, and the batch distribution of the nearest neighbors of each chosen cell is compared with the global distribution of the batches using the χ2-test. If the local distribution is sufficiently similar to the global distribution, the χ2 test does not reject the null hypothesis that there is a good batch mixture around the chosen cell. The rejection rate ranges from 0 to 1. Here, we use (1-rejection rate) as the final kBET value, and a kBET value close to 1 signifies the batches are well mixed.
LISI measures the effective diversity of local distributions, which can be applied to quantify both cell type separation and batch mixing. First, LISI selects the nearest neighbors based on the local distribution of distance with a fixed perplexity. Then, it computes the inverse Simpson’s index for the diversity of selected neighbors, which reflects how many different types are in a neighborhood and how evenly distributed the population of each type is. For a given neighborhood, the formula to calculate inverse Simpson’s index is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/\mathop{\sum }\nolimits_{$b = 1$}^{B}p(b)$$\end{document}1/∑$b = 1$Bp(b). The probabilities p(b), $b = 1$, 2, …, B here refer to the batch probabilities in the local neighborhood distributions described above. When the type in LISI is defined by batch, the resulting score (iLISI) quantifies batch mixing, and a higher iLISI value indicates better batch mixing. When the type in LISI is defined by cell type, the resulting score (cLISI) quantifies cell type separation, and a lower cLISI value indicates better cell type separation. The harmonic mean of cLISI and iLISI is computed to combine the evaluations for cell type separation and batch mixing into an overall score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\bf{F}}}}}}}}}_{{1}_{{{{{{{{\rm{LISI}}}}}}}}}}=\frac{2(1-{{{{{{{\rm{cLISI}}}}}}}})({{{{{{{\rm{iLISI}}}}}}}})}{1-{{{{{{{\rm{cLISI}}}}}}}}+{{{{{{{\rm{iLISI}}}}}}}}}$$\end{document}F1LISI=2(1−cLISI)(iLISI)1−cLISI+iLISI.
ASW uses the average silhouette score to quantify cell type separation and batch mixing. For one data point, its silhouette score is computed by subtracting its average distance to other members in the same cluster from its average distance to all members of the nearest neighboring cluster and then dividing by the larger of the two values. The resulting score ranges from −1 to 1, where a higher value indicates that the data point fits well in its cluster. When the distances are computed in the integrated embedding space, and the clusters are defined by cell types, the ASW is denoted as ASWcelltype, with a higher value indicating cell clusters are well separated in the embedding space. When the distances are computed in the integrated embedding space, and the clusters are defined by batch labels, the ASW is denoted as ASWbatch, with a lower value indicating batches are well mixed in the embedding space. The harmonic mean of the two ASW values is used to combine them into an overall score: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\rm{F}}}}}}}}}_{{1}_{{{{{{{{\rm{ASW}}}}}}}}}}=\frac{2(1-{{{{{{{{\rm{ASW}}}}}}}}}_{{{{{{{{\rm{batch}}}}}}}}})({{{{{{{{\rm{ASW}}}}}}}}}_{{{{{{{{\rm{celltype}}}}}}}}})}{1-{{{{{{{{\rm{ASW}}}}}}}}}_{{{{{{{{\rm{batch}}}}}}}}}+{{{{{{{{\rm{ASW}}}}}}}}}_{{{{{{{{\rm{celltype}}}}}}}}}}$$\end{document}F1ASW=2(1−ASWbatch)(ASWcelltype)1−ASWbatch+ASWcelltype.
ARI measures the agreement between two sets of cluster labels, which can be applied to quantify both cell type separation and batch mixing. First, k-means is applied to cluster cells in the integrated embedding space and generates predicted clustering labels, where k is the number of unique cell types in the batches to be integrated. Then, the ARI between the k-means predicted cluster labels and the true cell type labels is calculated and denoted as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\rm{ARI}}}}}}}}}_{{{{{{{{{\rm{cell}}}}}}}}}_{{{{{{{{\rm{type}}}}}}}}}}$$\end{document}ARIcelltype, where a higher value corresponds to better cell type separation. A second ARI value between the k-means predicted cluster labels and the batch labels is calculated and denoted as ARIbatch, where a lower value corresponds to better batch mixing. The harmonic mean of the two ARI values is used to combine the two aspects into an overall score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\rm{F}}}}}}}}}_{{1}_{{{{{{{{\rm{ARI}}}}}}}}}}=\frac{2(1-{{{{{{{{\rm{ARI}}}}}}}}}_{{{{{{{{\rm{batch}}}}}}}}})({{{{{{{{\rm{ARI}}}}}}}}}_{{{{{{{{\rm{celltype}}}}}}}}})}{1-{{{{{{{{\rm{ARI}}}}}}}}}_{{{{{{{{\rm{batch}}}}}}}}}+{{{{{{{{\rm{ARI}}}}}}}}}_{{{{{{{{\rm{celltype}}}}}}}}}}$$\end{document}F1ARI=2(1−ARIbatch)(ARIcelltype)1−ARIbatch+ARIcelltype.
## Statistics and reproducibility
The statistical tests used in this study were performed using R 4.2.1 or Python 3.7, and details of statistical analyses are described in the “Methods” section. We have provided the reproducibility evaluation in Supplementary Note 4: Evaluation of robustness and reproducibility of SIDA.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Peer Review File Supplementary Information Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04668-7.
## Peer review information
Communications Biology thanks Travis Johnson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Gene Chong. Peer reviewer reports are available.
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|
---
title: Bio-efficacy of aluminum phosphide and cypermethrin against some physiological
and biochemical aspects of Chrysomya megacephala maggots
authors:
- Mahran Tony
- Mahmoud Ashry
- Mohammad M. A. Tanani
- Abdelbaset M. A. Abdelreheem
- Mohammad R. K. Abdel-Samad
journal: Scientific Reports
year: 2023
pmcid: PMC10020570
doi: 10.1038/s41598-023-31349-6
license: CC BY 4.0
---
# Bio-efficacy of aluminum phosphide and cypermethrin against some physiological and biochemical aspects of Chrysomya megacephala maggots
## Abstract
Carrion flies play a significant role in forensic entomotoxicology, where they are employed as alternative samples when traditional samples are unavailable. In situations of poisoned death, these toxins disrupt insect development and affect forensic entomology analyses. So, forensic entomotoxicologists must be aware of this impact. The present study aimed to determine the effects of aluminum phosphide (AlP) and cypermethrin (CP) on the biochemical parameters and antioxidant enzymes of the third instar of *Chrysomya megacephala* maggots. C. megacephala was reared on normal and poisoned rabbit carcasses with aluminum phosphide and cypermethrin. The third larval instar of C. megacephala was studied using by spectrophotometer for detection of total protein, (TP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total antioxidant capacity (TAC), superoxide dismutase (SOD), glutathione s-transferase (GST), catalase (CAT) and malondialdehyde (MDA). The results indicated to significantly decrease of TP, TAC, SOD, GST and CAT and increase of AST, ALT and MDA in the maggots reared on the poisoned carcasses with AlP or CP compared with control group. In conclusion, the tested insecticides brought about a decrease antioxidant enzyme activity and increase of MDA could be involved in free radicals in C. megacephala larvae leading to oxidative stress by these insecticidal components.
## Introduction
The oriental latrine fly, Chrysomya megacephala, a member of the family Calliphoridae, is a necrophagous dipteran, one of the dominant flies of forensics, thus used in forensic investigation1. This species is widely distributed over the world, and causing accidental myiasis2. The potential use of blowfly maggots as indicators for detecting toxins and drugs in decomposing carcasses have been widely demonstrated3.
The insect life cycle stage that feeds on the cadaver is a potential reservoir of undigested flesh from the corpse, because, in some circumstances, the flesh from the corpse can retain some types of toxins that had been consumed by the victim before death and which may even have been the cause of death, these toxins may be recovered by analyzing the insects4. Several researchers5,6 detected several toxins, drugs, and pesticides in different species of fly larvae that fed on intoxicated corpses.
Pesticides are chemical substances that are used to eradicate pests such as insects, rodents, fungus, and unwelcome plants (weeds). Pyrethroid pesticides are widely used in insect control owing to their low toxicity in mammals and relatively low environmental impact7,8. Pyrethroid are effective against a broad spectrum of insects, even in low doses and the lethal action on insects involves peripheral and central neurons9,10. Certain examples of pyrethroids used for insect control are cypermethrin, permethrin, and deltamethrin11. Despite they beneficial effects, they unmanaged and repeated applications result in unexpected consequences in non-target organisms12. Cypermethrin for example, a class II pyrethroid pesticides, has toxic effects on nervous system13 as well as on the functions of other systems and organs including such as liver14,15.
Aluminum Phosphide (AlP) is another pesticide that is used for protecting crops during repository and hipping. Accidental and suicidal human toxicity with AlP in most cases ends with death16. In insects, pesticides cause oxidative stress and lipid peroxidation17. Peroxidation and enzymatic activity levels are crucial in estimating toxin stress18.
There have been insufficient previous studies on the maggots of C. megacephala that fed on intoxicated carcasses. The aim of this study was to evaluate the effects of aluminum phosphide and cypermethrin on some metabolic parameters and antioxidant enzymes of the last instar maggots of Chrysomya megacephala.
## Total protein quantification (TP)
Protein synthesis is necessary for the maintenance of body growth and reproduction. Many factors had been implicated in the control of protein synthesis. According to the data assorted in (Fig. 1a). AlP and CP caused remarkable decrease in the total protein level of larvae forced to eat on treated carcasses compared to the total protein in larvae of the control carcasses. Recorded total protein was 3.02 ± 0.32, 3.45 ± 0.39 and 6.1 ± 0.20 g/dL in the larvae of AlP, CP and control groups, respectively. Figure 1Impact of aluminum phosphide (AlP) and cypermethrin (CP) pesticides on the biochemicals parameters of Chrysomya megacephala. ( a) total protein (TP); (b) aspartate aminotransferase (AST); (c) alanine aminotransferase (ALT); (d) total Antioxidant Capacity; (e) superoxide dismutase (SOD); (f) glutathione S- transferase (GST); (g) catalase (CAT); (h) total lipid peroxidation (MDA). * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$; ns not significant.
## Aspartate aminotransferase (AST)
As shown in Fig. 1b. Significant increase in the AST level was detected in the maggots reared on AlP carcasses compared to the maggots on the control carcasses. The AST for the AlP and control groups was 69.01.9 and 33.72.6 U/L, respectively. In the same context, CP caused a steady increase in AST level. Where the AST level in the maggots reared on CP carcasses reaching 53.72.5 U/L.
## Alanine aminotransferase (ALT)
As clearly shown in Fig. 1c, maggots fed on AlP and CP carcasses had a significant increase in ALT levels when compared to maggots grown on control carcasses. The maggots in AlP and CP and control groups recorded ALTs of 55.1 ± 2.9, 45.4 ± 2.7 and 28.5 ± 1.6 U/L, respectively.
## Total antioxidant capacity (TAC)
The obtained data showed significant decrease in the TAC level for the maggots fed on AlP carcasses (TAC: 0.49 ± 0.14 mmol/L) and CP carcasses (TAC: 0.89 ± 0.10 mmol/L) compared with the TAC level for the maggots fed on control carcasses (TAC: 1.60 ± 0.10 mmol/L) as illustrated in Fig. 1d.
## Superoxide dismutase (SOD)
Superoxide dismutase (SOD) levels in the maggots fed on AlP and CP carcasses were recorded 5.7 ± 0.35 and 6.04 ± 0.53 U/g, respectively. These levels were significantly lower than the SOD level for maggots fed on control carcasses (8.70 ± 0.25 U/g), as shown in Fig. 1e.
## Glutathione S- transferase activity (GST)
As illustrated in Fig. 1f, the obtained data showed that the maggots fed on AlP and CP carcasses showed a significant decrease in the glutathione S- transferase (GST) level that was 0.13 ± 0.03 and 0.22 ± 0.03 U/g, respectively. This decrease is significance decrease with compared with the GST level (0.61 ± 0.09 U/g) for maggots in the control group.
## Catalase (CAT)
The results showed no significance differences between the catalase (CAT) levels of maggots in all groups (Fig. 1g). CAT level was 0.94 ± 0.26, 0.53 ± 0.19 and 0.60 ± 0.16 (U/g) for maggots fed on control, AlP and CP carcasses, respectively.
### MDA)
The disturbance of lipid content of maggots fed AlP and CP carcasses was varied according to the potency of insecticides as well as the developmental age. As presented in Fig. 1h, The maggots that fed on control, AlP and CP carcasses showed 4.20 ± 0.40, 9.60 ± 0.92 and 7.80 ± 0.62, respectively in the level of MDA. Significant increase was recorded in the level of MDA in the maggots that fed on AlP and CP compared to MDA level in the maggots in control group. At the same time, no significance difference detected between the MDA levels in maggots of AlP and CP groups.
## Discussion
Pesticides come in over a thousand distinct varieties and are applied all over the world. Pesticides are used in agriculture to kill pests that harm crops and in public health to kill disease-carrying vectors like mosquitoes19. In agriculture sector, Pesticides are widely used and are an effective and cost-effective strategy to improve agricultural output quality and quantity20. Pesticides such as pyrethroid were used on a variety of vegetable crops21. They may also be used to combat cockroaches22, mosquitoes23, fleas, and termites24 in households as well as other buildings25.
Another low-cost, effective and widely used pesticide is aluminum phosphide (AlP). However, among agricultural pesticides, it is currently one of the most prevalent causes of poisoning26. Several incidents of persistent poisoning or mortality has been documented and proved among agricultural workers exposed to various types of pesticides in developing nations. These incidents mostly resulted from use/misuse of pesticides27.
In cases of poisoned death, these poisons disrupt insect development (such as Carrion flies) and have an impact on forensic entomology investigations (especially when traditional samples of victim are unavailable). In light of this, forensic entomotoxicologists need to be aware of this effect.
Generally, a significant biochemical effect was induced on maggots after fed on treated carcasses by AlP and CP. In the current investigation, the TP content decreased significantly in the maggots of treated groups compared to control group. While the activity of AST and ALT was increased in the treated maggots compared to control. These changes have been supported by previous studies that showed decrease in TP and increase in ALT and AST activities28–30. Proteins are biological macromolecules made up of one or more long chains of amino acids. Protein synthesis is required for growth and reproduction. Proteins are required for cell division and to govern chemical reactions throughout the cell metabolic process31.
Protein content may be attributed to the disenchantment of protein biosynthesis by amino acids and the reduction in protein could be attributed to changes in protein and free amino acid metabolism and their synthesis32,33. In addition, the observed decrease in proteins could be attributed in part to the damaging effect of insecticides as cypermethrin on larvae tissue cells, as confirmed by the increase in activities of AST, ALT for treated groups than control34. Also, Kinnear et al.35 suggested that decreased protein levels were due to decreased synthesis of new proteins by the fat body, haemolymph and other tissues of the *Calliphora stygia* larvae.
Similar literatures are in harmony with these results. Total protein levels in Culex fatigans were decreased by λ-cyhalothrin36. The level of total proteins was decreased in *Bacterocera zonata* pupae resulted of larvae treated with Beticol, Biosad, Elsan, Lufox, Mani, Match and Radiant during all tested periods as compared to control37. Total protein content in Culex pipiens declined while total lipids increased gradually with proceeded the generations38.
Detoxification enzymes in insects are important tools in defense mechanisms against toxins and xenobiotics. They play valuable roles in upholding the normal physiological status39. Detoxifying enzymes have been found to respond against insecticides, or compounds exhibiting insecticidal activities. The detoxifying enzymes were found to counter against insecticides activities29. Induction of detoxification metabolic system plays an important role in insect's detoxification mechanism40. In addition, decreasing ACP activity might be due to the reduced phosphorus liberation for energy metabolism and decreased rate of metabolism, as well as decreased rate of transport of metabolites41.
In our results, observed an increase in lipid peroxidation (MDA) and also in, AlP and CP exposure to the larvae diminished the activities of enzymatic antioxidants, superoxide dismutase (SOD), TAC, catalase (CAT) and glutathione S transferase (GST), increasing of Oxidative enzyme malondialdhide (MDA) that indicates to lipid peroxidation. Enzymatic and non-enzymatic systems preserve the antioxidant status, these defense systems become decreased during oxidative stress, leading a metabolic derangement due to an imbalance caused by excessive generation and diminished antioxidant capacity followed by larvae cell injury and inflammation42,43. Also, the antioxidant enzymes such as SOD, CAT and GST play a vital role in relief and prevent tissue toxicity and inflammation44,45, a higher effect observed in larvae treated with AlP, where the latter enzymes showed a moderate decrease in their levels regarding CP group may be attributed to its inhibition effect on free radical formation or through scavenging the well-formed radicals and it was deficiency might be associated with development of treated larvae. These findings are consistent with Gheldof et al.46, who stated that antioxidant analysis of the different honey fractions revealed that the water soluble fraction contained the majority of the antioxidant components, including gluconic acid, ascorbic acid, hydroxymethylfuraldehyde, and the enzymes glucose oxidase, catalase, and peroxidase.
Our results in contrast with other study that showed that increase of antioxidant enzyme activity during the harmful effect of dichlorvos, the SOD enzyme plays a critical role in ROS defence47. Previous research found that activation of SOD activity is the primary response of lepidopteran insects to organophosphate poisoning and other dietary prooxidants48,49.
In our study, CAT activity was decreased as compare to control, the decrease of CAT activity, may be due to the fact that CAT is known to be inhibited by superoxide anion buildup during degradation activities, which may be caused by insecticide and increased production of free radicals may lead to depletion or inactivation of CAT enzyme, and this due to high level of superoxide radical generation during oxidative stress in the acute stage of C. megacephala42,47,50.
GST activity in C. megacepala larva after treatment showed a significant decrease changes in treated groups compared with control these results were in agreement with Linares et al.44 and Doğru-Abbasoğlu et al.51, due to is involved in the inactivation of toxic lipid peroxidation products accumulated during destructive processes caused by this insecticides, according to the above results, AlP and CP increased free radicals (MDA) and decreased antioxidant defense in the larval haemolymph, hence, the viability of larvae decreased, the developed yield in larvae stages rearing decreased also. On the other hand, GST plays an important role on the biotransformation of both xenobiotic and endogenous substances. Therefore, its inhibition and induction has been used as a biochemical marker of exposure to xenobiotics with electrophilic centers52,53.
The pattern of the motor signs after AlP and CP administration is strongly suggestive of CNS toxicity54. Activities of SOD, CAT, GSH, and MDA levels in the larvae reflect the oxidative status and the haemolymph enzymes like AST and ALT, represent the functional status of the larvae55. Chemical-induced cellular alteration varies from simple increase of metabolism to death of cell and the increase or decrease of enzyme activity is related to the intensity of cellular damage56.
Therefore, increase of transaminase activity along with the decrease of activity of free radical scavengers may be the consequence of AlP and CP induced pathological changes of the larvae and the decreased CAT and SOD activities and increased MDA level in the larvae as well as increased AST and ALT levels suggest that insecticides causes damage in larvae tissues which may be through free radicals, in oxidative stress producing depletion of the activity of CAT, SOD, and the glycogen level, and increased level of MDA leading to tissues necrosis57. And in acute toxicity study, single dose of AlP and CP in increased the levels of MDA, AST and ALT and decreased the activities of TAC, SOD, GST and CAT level in treated larvae.
In conclusion, the both AlP and CP can affect third-instar maggots of C. megacephala. Antioxidant enzyme marker such as, TAC, SOD, GST and CAT and oxidative stress of (MDA) were involved in the free radical to these toxin exposure in maggots of C. megacephala and disturbances in the development of insect survived of treatments and they might be involved in integrated pest management strategies.
## Pesticides
Aluminum phosphide tablets (Shijiazhuang Awiner Biotech Co., Ltd) and cypermethrin (cypermethrin CP, > $99\%$ pure, Gharda Chemicals Ltd. Mumbai) were used.
## Flies’ origin and laboratory colony
Rotten chicken viscera organs were employed as bait to catch adult *Chrysomya megacephala* flies58. C. megacephala was taxonomically classified using taxonomic keys59. According to earlier researches60,61, the flies were raised in a controlled laboratory conditions with mild adjustments. Breifly, the flies were housed in hardwood cages (40 × 40 × 40 cm3) that were provided with the necessary meal at a temperature of 25 ± 2 °C, relative humidity of 60 ± $10\%$, and a light–dark cycle of 12:12 h.
## Animal model
This experiment was carried out on nine mature male domestic rabbits weighing approximately 1.5 kg. The rabbits were housed in adequate cast steel cages for 15 days for acclimation at 25 ± 2 °C, 12:12 h light–dark cycle, and 60 ± $10\%$ relative humidity. Across the experiment, all rabbits were kept in equivalent conditions of temperature, light, noise, and ventilation, and they were fed the same meal. Rabbit housing, procedures, and care principles were all carried out in accordance with the laboratory animal care and use recommendations62. The authors followed the ARRIVE standards.
## Experimental technique
Post acclimatization, the rabbits were randomly divided into three equal groups. 1st group represent the control group, in this group the rabbits were given distilled water. In 2nd group, the rabbits were treated with AlP (32.8 mg AlP/kg rabbit body weight)63,64. While the rabbits of 3rd group were treated with CP (1.4 mL/kg rabbit body weight)65. Carcasses of rabbits were moved into plastic boxes (25 × 10 × 15 cm3) that placed in the cages of flies to allowing the flies colonizing of the carcasses.
## Preparation of samples for analysis
The 3rd larval instars from each treated and control groups were collected then washed with saline, dried. About 100 mg from each collected larvae were homogenized in 1 mL of phosphate buffer. The samples were then centrifugated for 15 min at 14,000 r.p.m at 4 °C. The supernatant was stored at − 80 °C until analysis.
## Assessment of metabolic aspects
Total protein was evaluated by the method of Mæhre et al.66 and Zheng et al.67. Aspartate transaminase (AST) and alanine transaminase (ALT) were determined as described by Reitman and Frankel68, while glutathione-S-transferase enzyme (GST) activity was measured by methods of Pabst et al.69. Estimation of catalase (CAT) was done by method of Lück70.
Moreover, superoxide dismutase (SOD) was measured by methods of Misra and Fridovich43. Total antioxidant capacity and malondialdehyde (MDA) were estimated according to the methods of Koracevic et al.71 and Placer et al.72, respectively.
## Statistical analysis
The data were reported as the mean ± standard error of the mean (S.E.M). GraphPad Prism version 8.0.0 (for Windows, GraphPad Software, San Diego, California, USA) was utilized to conduct statistical analysis using one-way Analysis of Variance (ANOVA). $p \leq 0.05$ was considered significant.
## Ethical approval
Accepted in ethical committee at Faculty of Science (Assuit), Al-Azhar University. Certificate reference number: AZHAR$\frac{10}{2022.}$
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---
title: Intergenerational impact of dietary protein restriction in dairy ewes on epigenetic
marks in the perirenal fat of their suckling lambs
authors:
- Pablo A. S. Fonseca
- Aroa Suárez-Vega
- Rocio Pelayo
- Hector Marina
- María Alonso-García
- Beatriz Gutiérrez-Gil
- Juan-José Arranz
journal: Scientific Reports
year: 2023
pmcid: PMC10020577
doi: 10.1038/s41598-023-31546-3
license: CC BY 4.0
---
# Intergenerational impact of dietary protein restriction in dairy ewes on epigenetic marks in the perirenal fat of their suckling lambs
## Abstract
In sheep, nutrition during the prepubertal stage is essential for growth performance and mammary gland development. However, the potential effects of nutrient restriction in a prepuberal stage over the progeny still need to be better understood. Here, the intergenerational effect of maternal protein restriction at prepubertal age (2 months of age) on methylation patterns was evaluated in the perirenal fat of Assaf suckling lambs. In total, 17 lambs from ewes subjected to dietary protein restriction (NPR group, $44\%$ less protein) and 17 lambs from control ewes (C group) were analyzed. These lambs were ranked based on their carcass proportion of perirenal and cavitary fat and classified into HighPCF and LowPCF groups. The perirenal tissue from 4 NPR-LowPCF, 4 NPR-HighPCF, 4 C-LowPCF, and 4 C-HighPCF lambs was subjected to whole-genome bisulfite sequencing and differentially methylated regions (DMRs) were identified. Among other relevant processes, these DMRs were mapped in genes responsible for regulating the transition of brown to white adipose tissue and nonshivering thermoregulation, which might be associated with better adaptation/survival of lambs in the perinatal stage. The current study provides important biological insights about the intergenerational effect on the methylation pattern of an NPR in replacement ewes.
## Introduction
Among sheep products, the meat of suckling lambs stands out as a high-quality product valuable in Mediterranean countries due to its tenderness, low-fat, pale pink color and moisture1. There is a protected geographical indication (PGI) known as ‘Lechazo de Castilla y León’ related to dairy sheep production in the northwest region of Spain [Commission Regulation (EC) No $\frac{2107}{1999}$]. The animals which belong to this PGI are fed exclusively on sheep's milk and slaughtered before 35 days of age, at 9–12 kg of body weight. Consequently, in dairy flocks, suckling lambs are an important economic input. Suckling lambs have a rumen that is still not completely active; therefore, they can be considered functional monogastric organisms2. As a consequence, the composition of adipose depots in lambs is highly influenced by the diet they consume, for example, the milk and/or supplementary feedstuffs3–9. Therefore, the diet composition is a crucial factor for the proper development of the animal, resulting in a high-quality product that meets consumer demands. *In* general, feeding-related management decisions are responsible for the major cost in animal production systems, accounting for up to $75\%$ of all variable costs in a herd, with protein corresponding to a high proportion of these costs10–12. In addition, the supply of protein for animal feed in Europe relies mainly on importing soybean from subtropical regions. This process might disturb the nutrient cycle due to the geographical separation of soybean cultivation and livestock production as the manure produced by the animals fed with the imported soybean will not be available to fertilize the land used for the plantation13. Additionally, the recent logistic crisis worldwide has drastically increased the prices of traditional sources of proteins, such as soybeans. Consequently, protein intake in flocks is a key point in the control of the sustainability of the production chain. Taken together with the market price volatility and availability of feed components, the feeding strategies are under constant pressure for changes and adaptations. A previous study by our group assessed whether nutritional protein restriction (NPR) performed at prepubertal age in Spanish Assaf ewe lambs would affect economically important traits14. Interestingly, no effect of the NPR was observed on the milk production potential and the somatic cell counts of the animals in response to an inflammatory challenge of the mammary gland, suggesting the possibility of reduction of protein intake in the diet of replacement ewe lambs without a negative impact on their production traits as adult ewes.
Fetal developmental programming is a consequence of maternal stimulus in a sensitive period of intrauterine development and results in permanent effects on the structure, physiology and metabolism of the fetus15. Maternal nutrition is a major environmental factor among the different stimuli capable of affecting fetal development. Different from suckling lambs, adult sheep have a fully functional rumen. Consequently, they can use all nitrogen (protein and nonprotein nitrogen) compounds as a feed source. In the rumen, the dietary rumen degradable nitrogen is converted mainly into ammonia, providing a proper environment for microbial fermentation and the flow of microbial amino acids. In Assaf rams, feed with a low protein concentration significantly affected scrotal circumference and body weight gain 11 weeks post-weaning16. In female sheep, protein restriction during the gestation period is associated with altered developmental patterns of the endocrine (response to chemical compounds such as noradrenaline and acetylcholine) and cardiovascular (vascular function) systems alongside changes in the growth pattern of the offspring17,18. Additionally, maternal nutrient restriction may result in small gestational age offspring with reduced muscle mass19 caused by reduced myofiber number and/or impaired hypertrophy20. At the gene expression level, the feed of dams during pregnancy with diets composed of fiber plus protein plus fat resulted in differences (> 200 differentially expressed genes in all the comparison groups) in the gene expression levels in the perirenal fat, subcutaneous fat and longissimus dorsi muscle of sheep fetuses21. In addition, the timing of nutrient restriction is well established as a disturbing factor of adipose tissue development22. In dairy sheep, nutrition during the prepubertal stage is an important factor for growth performance, mammary gland development and milk production in the later stages of development23,24. However, there is an evident lack of studies investigating the intergenerational impact of nutrient restriction during the prepuberal stage of dairy ewes.
Epigenetic modifications, which are molecular processes that do not change the DNA sequence and alter genome activity, can be responsible for these lifelong consequences on the progeny of parents subjected to feed restrictions25. Different molecular processes can be responsible for epigenetic alterations, such as RNA methylation, noncoding RNA expression, histone modification and DNA methylation26–29. The intergenerational transfer of maternal effects through DNA methylation is a relevant hypothesis that has the potential to help in the understanding of the biological processes associated with the effect of maternal stress on progeny. The effect on the DNA methylation profile of the offspring from a mother subjected to environmental stress, including nutritional restriction, has previously been described in different species30–35. More specifically, maternal nutrient restrictions are linked with persistent and intergenerational metabolic disturbances with effects on the health and survival of the offspring36–38. In suckling lambs, the perirenal and cavitary fat (PCF) represents a notable proportion of the total fat in the body, approximately $11\%$ in suckling lambs from the Lechazo de Castilla y León protected geographical indication (PGI)39. In the first weeks of postnatal life, PCF is a very important component of the metabolism of lambs. The PCF transits from predominantly brown adipose tissue (BAT) in the first 4 days of life to predominantly white adipose tissue (WAT) at approximately 14 days of life40. However, in Assaf sheep, some studies from our research group confirmed the presence of brown adipocytes in the perirenal fat of suckling lambs slaughtered between 17 and 23 days of life41. The amount of BAT in the PCF is related to nonshivering thermogenesis, which is an important process for neonatal lamb survival42,43, and this transition from BAT to WAT reflects the necessity of the organism to move from thermoregulation in the first days of life to growth and homeostasis in the next stages of life. Interestingly, important components of the biological machinery responsible for nonshivering thermogenesis, such as UCP1 (a biomarker of BAT), are associated with metabolic inefficiency42. Consequently, different levels of PCF in suckling lambs might be associated with different responses to NPR. It is important to highlight that, to our knowledge, no studies in dairy sheep have evaluated the intergenerational consequences of an NPR performed in prepubertal replacement ewes over the offspring genome methylation markers.
Based on the preceding information, the main objectives of the current study were: [1] to investigate differential methylation patterns across the genome between the progeny from ewes subjected to an NPR at prepubertal age and progeny from control ewes; [2] to evaluate the effects on the methylation pattern across the genome, considering the interaction between the protein restriction in the ewes and the perirenal fat content in the progeny; and [3] to estimate the isolated impact of divergent perirenal fat content on the methylation pattern across the genome of Assaf suckling lambs.
## Effects of carcass internal fat depot content and age across groups
The age and half-carcass percentage of PCF for all 34 male lambs born are available in Supplementary Table 1. The descriptive statistics for the same traits for the 16 samples assigned to the four groups defined based on the two considered factors (NPR-LowPCF, NPR-HighPCF, C-LowPCF, and C-HighPCF) are shown in Table 1. The Anderson–Darling normality test indicated that there was no relevant deviation from normality for age in days (P value = 0.640) and PCF (P value = 0.019). The exploratory analysis of age in days and PCF suggested differences between the groups from the nutritional challenge and fat content (Supplementary Fig. 1). Significant differences were observed for the percentage of perirenal fat between the HighPCF and LowPCF fat groups regardless of the nutritional challenge groups (P value = 6.85 × 10–8) and between the HighPCF and LowPCF fat groups within the nutritional challenge groups C (P value = 0.004) and NPR (P value = 0.0003). Therefore, the results suggest an effective grouping of animals in the high-fat and low-fat groups, allowing a potential detection of differential methylation between these groups. Only the comparison between the HighPCF and LowPCF fat groups, disregarding the nutritional challenge grouping, resulted in a significant difference for age in days (P value = 0.022). Therefore, the identification of differential methylation patterns between the NutChal and FatGroup groups was performed, including the age of the animal as a fixed effect. Table 1Summary statistics (mean ± standard deviation) for the half-carcass percentage of perirenal and cavitary fat (PCF) and age (in days) for the lambs from the nutritional challenge and fat groups. Nutritional protein restriction (NPR) and control (C) groupsFat groupAge (in days)Percentage of perirenal and cavitary fatNPRCNPRCHighPCF28.00 ± 4.7628.75 ± 6.343.25 ± 0.103.23 ± 0.47LowPCF22.25 ± 5.3122.00 ± 4.081.67 ± 0.251.65 ± 0.16C lambs which were born from control dams; NPR lambs which were born from nutritional protein restriction dams; HighPCF lambs which were assigned to the high PCF group; LowPCF lambs which were assigned to the low PCF group.
## Differentially methylated regions identified for the multifactorial design
The mapping statistics for the reads obtained in the WGBS are shown in Supplementary Table 2. An average mapping of 70.10 ± $2.55\%$ was obtained, with values ranging from 65.56 to $75.58\%$. The mean percentage of methylated sites in the CG, CHG and CHH contexts (where H is A, C, or T) was 71.36 ± $0.5\%$, 1.40 ± $0.03\%$, and 1.47 ± $0.05\%$ for the NPR group, respectively. Thus, the mean percentage of methylated sites for the CG, CHG and CHH contexts for the control group were 71.49 ± $0.36\%$, 1.42 ± $0.02\%$, and 1.49 ± $0.03\%$, respectively. Therefore, similar proportions of methylation in all three contexts were observed between the NPR and C groups.
The number of DMLs and DMRs for each term from the multifactorial model tested per methylation context are shown in Fig. 1. Except for the NutChal term, DMRs were identified only in the CG context, with 61 (in 53 genes), 76 (in 58 genes) and 46 (in 41 genes) DMRs identified for NutChal, FatGroups and NutChal * FatGroups, respectively (Supplementary Table 3). In addition, two DMRs were identified in the CHH context for the NutChal term. Interestingly, no DMRs were shared among all the terms (based on DMR start and end coordinates), while five genes harboring different DMRs were annotated and shared among the three terms (Fig. 1). The five shared genes were ANXA2R (annexin A2 receptor), LOC121816055, TRNAG-GCC_6 (tRNA-Gly), LOC114112700 (translation initiation factor IF-2-like), and LOC121818805 (Table 2). In total, 24 unique DMRs were mapped in the regions harboring these five genes, with seven for ANXA2R, eight for LOC121816055, two for TRNAG-GCC_6, four for LOC114112700, and three for LOC121818805. The only shared DMR (NutChal * FatGroup and FatGroup) was mapped in the coordinates 1:112,885,986–112,886,049, harboring the TRNAG-GCC_6. Interestingly, only the DMRs identified in the TRNAG-GCC_6 gene were annotated in a promoter region among these 24 DMRs. Figure 1Bar plot showing the number of differentially methylated loci (A) and regions (B) identified in each nucleotide context (CG in red, CHG in green and CHH in blue) for the three terms evaluated in the multifactorial model (NuChal, FatGroup and NutChal * FatGroup). Venn diagram showing the number of differentially methylated regions (C) and genes harboring differentially methylated regions (D) among NuChal (ref), FatGroup (blue) and NutChal * FatGroup (purple).Table 2Genes harboring differentially methylated regions and shared among all the three terms for the multifactorial model (Nutchal * FatGroup, NutChal, and FatGroup).CoordinateGeneLengthnCGTermContextGenomic context16:31,783,099–31,783,205ANXA2R1078Nutchal * FatGroupCGIntergenic16:31,790,384–31,790,436ANXA2R539Nutchal * FatGroupCGIntergenic16:31,727,079–31,727,262ANXA2R1845Nutchal * FatGroupCGIntergenic16:31,731,294–31,731,346ANXA2R535Nutchal * FatGroupCGIntergenic16:31,787,826–31,787,932ANXA2R1075Nutchal * FatGroupCGIntergenic2:250,153,162–250,153,236LOC114112700754Nutchal * FatGroupCGIntergenic1:120,946,293–120,946,367LOC121816055755Nutchal * FatGroupCGIntergenic2:249,907,223–249,907,543LOC1218188053214Nutchal * FatGroupCGIntergenic1:112,885,986–112,886,049TRNAG-GCC_6645Nutchal * FatGroupCGPromoter16:31,807,558–31,807,655ANXA2R988NutChalCGIntergenic2:250,164,900–250,164,966LOC114112700677NutChalCGIntergenic2:250,145,061–250,145,120LOC114112700605NutChalCHHIntergenic1:120,986,211–120,987,063LOC1218160558539NutChalCGIntergenic1:120,963,799–120,963,849LOC121816055515NutChalCGIntergenic1:120,985,694–120,985,753LOC121816055604NutChalCGIntergenic1:120,958,079–120,958,147LOC121816055694NutChalCGIntergenic2:249,927,235–249,927,294LOC121818805606NutChalCGPromoter/Exon1:112,885,986–112,886,049TRNAG-GCC_6645NutChalCGPromoter16:31,757,401–31,757,485ANXA2R855FatGroupCGIntergenic2:250,147,524–250,147,587LOC114112700644FatGroupCGIntergenic1:120,980,087–120,980,955LOC12181605586911FatGroupCGIntergenic1:120,962,054–120,962,116LOC121816055639FatGroupCGIntergenic1:120,953,964–120,954,026LOC121816055638FatGroupCGIntergenic2:249,916,688–249,916,840LOC12181880515313FatGroupCGIntergenic1:112,885,845–112,885,903TRNAG-GCC_6596FatGroupCGPromoter
## Discriminant analysis between nutritional challenge and fat groups and functional interpretation of DMRs
The PLS-DA using the methylation means simultaneously within the DMRs identified for all the terms from the multifactorial model resulted in poor discrimination of the samples from the nutritional challenge and high-fat groups (Fig. 2A). However, the exclusion of the DMRs exclusive to the FatGroup term resulted in perfect clustering (AUC = 1, P value = 0.004) for all the sample groups in the second principal component (Fig. 2B). In addition, the PLS-DA using the methylation levels within DMRs identified for the NutChal and Fat terms individually resulted in a perfect clustering of NPR versus C and HighPCF vs LowPCF groups, respectively (Fig. 2C,D). The mean methylation level for all DMRs used in the PLS-DA is available in Supplementary Table 4. Figure 2 shows the evaluation of the top 10 absolute loading values obtained in each discriminant analysis. All the loading vectors obtained in the PLS-DA for the DMRs evaluated are available in Supplementary Table 5.Figure 2Results of partial least squares discriminant analysis (PLS-DA) and the top 10 loading vectors for the mean methylation levels within the identified differentially methylated regions (DMRs). ( A) *Discriminant analysis* using the mean methylation level within the DMRs identified for the three terms of the multifactorial model (NutChal * FatGroup, NutChal, and FatGroup) to classify the four groups from the multifactorial model: lambs from control ewes with a low percentage of perirenal and cavitary fat (C-LowPCF in orange), lambs from control ewes with a high percentage of perirenal and cavitary fat (C-HighPCF in blue), lambs from nutritional challenge ewes with a low percentage of perirenal and cavitary fat (NPR-LowPCF in green), and lambs from nutritional challenge ewes with a high percentage of perirenal and cavitary fat (NPR-High in gray). ( B) *Discriminant analysis* using the mean methylation level within the DMRs identified for only for the NutChal * FatGroup and NutChal terms to classify the four groups from the multifactorial model (C-LowPCF in orange, C-HighPCF in blue, NPR-LowPCF in green, and NPR-HighPCF in gray). ( C) *Discriminant analysis* using the mean methylation level within the DMRs identified for the NutChal term terms to classify the NPR and C groups. ( D) *Discriminant analysis* using the mean methylation level within the DMRs identified for the FatGroup term terms to classify the HighPCF and LowPCF perirenal and cavitary fat groups.
The results of the QTL annotation for the DMRs identified in each term from the multifactorial model are shown in Supplementary Table 6. All enriched traits among the annotated QTLs within the coordinates of the DMRs identified for the NutChal * FatGroup term were associated with the QTL types of “Production” (body length, body height and chest girth) and “Meat and Carcass” (muscle depth at third lumbar). Two enriched traits were identified for the DMRs obtained for the NutChal term, milk fat yield (180 d) and total lambs born. Only one trait was enriched for the DMRs identified for the FatGroup, the muscle depth at the third lumbar, which belongs to the “Meat and Carcass” QTL type (Table 3). Interestingly, the same DMR (18:58,058,339–58,058,484) was mapped in the region associated with the muscle depth at the third lumbar for both NutChal * FatGroup and FatGroup terms. Table 3Enrichment results for the quantitative trait loci annotated within the genomic coordinates for the differentially methylated regions for all three terms of the multifactorial model (Nutchal * FatGroup, NutChal, and FatGroup).TraitNumber annotated QTLsNumber of QTLs in the databaseP-valueFDRQTL typeTermBody length592.00 × 10–083.01 × 10–07ProductionNutChal * FatGroupBody height5179.20 × 10–076.90 × 10–06ProductionNutChal * FatGroupChest girth5245.96 × 10–062.98 × 10–05ProductionNutChal * FatGroupMuscle depth at 3rd lumbar2150.0130.032Meat and CarcassNutChal * FatGroupMilk fat yield {180d}42760.0070.014MilkNutChalTotal lambs born31730.0130.018ReproductionNutChalMuscle depth at 3rd lumbar2150.0040.037Meat and CarcassFatGroup The GO terms annotated for the genes associated with the DMRs identified for each term of the multifactorial model are available in Supplementary Table 7. Enriched terms were obtained only for the genes associated with DMRs from NutChal * FatGroup (16 GO terms) and NutChal (one GO term) terms (Table 4). *In* general, the enriched GO terms identified for the NutChal * FatGroup interaction were associated with plasma membrane structure (RASA3 and MTSS2), actin cytoskeleton (SHROOM2, SYNE3 and MTSS2), lyase activity (CA1 and CA5A), ligand-gated cation channel activity (RASA3 and SHROOM2), and one-carbon metabolic process (CA1 and CA5A). The only GO-enriched term identified for the DMRs identified for the FatGroup term was “phosphoric diester hydrolase activity” (supported by the PLCG1 and PLCXD1 genes).Table 4Enrichment results for the gene ontology annotated for the genes harboring differentially methylated regions for all three terms of the multifactorial model (Nutchal * FatGroup, NutChal, and FatGroup).DescriptionOntologyP-valueFDRGenesTermIntrinsic component of the cytoplasmic side of the plasma membraneCC1.29 × 10–050.0006RASA3, MTSS2NutChal * FatGroupCarbonate dehydratase activityMF4.17 × 10–050.0025CA1, CA5ANutChal * FatGroupCortical actin cytoskeletonCC0.00090.0202SHROOM2, MTSS2NutChal * FatGroupCortical cytoskeletonCC0.00160.0202SHROOM2, MTSS2NutChal * FatGroupCytoplasmic side of plasma membraneCC0.00390.0246RASA3, MTSS2NutChal * FatGroupCytoplasmic side of membraneCC0.00500.0246RASA3, MTSS2NutChal * FatGroupHydro-lyase activityMF0.00090.0272CA1, CA5ANutChal * FatGroupCarbon–oxygen lyase activityMF0.00140.0272CA1, CA5ANutChal * FatGroupLigand-gated cation channel activityMF0.00290.0272RASA3, SHROOM2NutChal * FatGroupActin bindingMF0.00320.0272SHROOM2, SYNE3, MTSS2NutChal * FatGroupLigand-gated ion channel activityMF0.00450.0272RASA3, SHROOM2NutChal * FatGroupLigand-gated channel activityMF0.00450.0272RASA3, SHROOM2NutChal * FatGroupOne-carbon metabolic processBP0.00030.0402CA1, CA5ANutChal * FatGroupLyase activityMF0.00800.0422CA1, CA5ANutChal * FatGroupActin filament bindingMF0.00960.0445SHROOM2, SYNE3NutChal * FatGroupCell cortexCC0.01250.0461SHROOM2, MTSS2NutChal * FatGroupPhosphoric diester hydrolase activityMF0.00230.0421PLCG1, PLCXD1NutChal Despite the limited number of enriched terms, which could be expected due to the limited number of DMRs and associated genes identified, interesting functional profiles were obtained when the complete list of GO terms was analyzed. The network composed of genes and GO terms created for the genes annotated in each of the three terms suggested that several genes were connected through similar biological mechanisms.
*The* genes harboring DMRs identified for the interaction term of the multifactorial model (NutChal * FatGroup) were allocated into two principal clusters (Fig. 3). The largest cluster was composed of nine genes (G3BP1, RASA3, MTSS2, SHROOM2, SYNE3, COL14A1, SS18L2, ULK4 and CAMTA1), while the second cluster was composed of CA1 and CA5A. These clusters reflect the groups identified in the functional clustering of GO terms (Fig. 3B). The first cluster of genes was associated with the following functional groups of GO terms: “GTPase ion cytoplasmic channel”, “cortical actin cytoskeleton binding”, “coregulator neuron morphogenesis development”, and “molecular protein-macromolecule adaptor activity”. The functional grouping of GO terms associated with the second cluster indicated an association with the activity of “carbon–oxygen carbonate lyase dehydratase”. Figure 3Functional analysis for the genes harboring differentially methylated regions for the interaction term (NutChal * FatGroup) of the multifactorial model. ( A) Network composed of genes (gray circles) and gene ontology (GO) terms (yellow circles) showing the functional connection between the genes harboring DMRs identified for the interaction term (NutChal * FatGroup). ( B) Functional grouping tree diagram for the annotated GO terms. Each color in the dendrogram represents a functional group obtained after estimating the Jaccard correlation coefficient. The area of the circles represents the number of genes assigned to each GO term, and the color of the circle indicates the P value estimated for each GO term.
Only the genes PLCG1 and PLCXD1 shared GO terms among the genes identified harboring the DMRs for the NutChal term in the multifactorial model. *These* genes shared the GO terms phosphoric ester hydrolase activity and phosphoric diester hydrolase activity (Supplementary Fig. 2). However, the functional grouping (including GO terms associated with only one gene) for the genes harboring the DMRs identified for the NutChal term suggested an association with interesting biological functions (Supplementary Fig. 2). Among these functional groups, it is interesting to highlight the “histone biosynthetic compensation chromosome” (PCGF3 and A4GALT) and “glycolytic through fructose-6-phosphate glucose-6-phosphate” (ADPGK) terms.
A single network was created for the genes harboring DMRs that were identified for the FatGroup (Fig. 4). This network was composed of 15 genes, from which 3 were identified in the network for the NutChal * FatGroup term (RASA3, SHROOM2 and SYNE3), two were the genes interacting in the network for the NutChal term (PLCXD1 and PLCG1), and 11 were exclusively identified for the FatGroup term (MKS1, SLC2A6, TMEM144, TMEM192, PER2, NSG2, IGF2R, TYMS, MAD2L1, SETD7 and RTRAF). The functional grouping of the GO terms associated with the genes harboring DMRs that were identified for the FatGroup suggested activity over interesting biological processes (Fig. 4). The first functional group identified was “acid endosome network metabolic”, from which it is relevant to highlight liver development (IGF2R and TYMS), hepaticobiliary system development (MKS1, IGF2R and TYMS), animal organ regeneration (IGF2R and TYMS), gland development (IGF2R and TYMS) and methylation (SETD7 and TYMS). The second functional group identified was “modification by protein conjugation”, which was composed of interesting GO terms, such as rhythmic process (PER2 and TYMS), circadian rhythm (PER2 and TYMS), histone modification (SETD7 and PER2), regulation of translation (PER2 and TYMS), negative regulation of protein ubiquitination (MAD2L1 and PER2), and negative regulation of transferase activity (MAD2L1 and RTRAF). The “actin establishment envelope filament” functional group is composed of GO terms such as actin filament binding (SHROOM2 and SYNE3), the establishment of organelle localization (MAD2L1, SHROOM2 and SYNE3) and nuclear envelope (MAD2L1, IGF2R and SYNE3). The “carbohydrate generation anion energy” functional group is composed of GO terms such as carbohydrate transmembrane transporter activity (SLC2A6 and TMEM144), generation of precursor metabolites and energy (SLC2A6 and PER2), organic anion transport (SLC2A6 and PER2), and lytic vacuole membrane (TMEM19 and SLC2A6). The last functional group identified was “channel calcium cation cytosol”, from which it is interesting to highlight the negative regulation of the sequestration of calcium ions (PLCG1 and RASA3), phosphoric ester hydrolase activity (PLCG1 and PLCXD1), and ligand-gated ion channel activity (RASA3 and SHROOM2).Figure 4Functional analysis for the genes harboring differentially methylated regions for the FatGroup term of the multifactorial model. ( A) Network composed of genes (gray circles) and gene ontology (GO) terms (yellow circles) showing the functional connection between the genes harboring DMRs identified for the FatGroup term. ( B) Functional grouping tree diagram for the annotated GO terms. Each color in the dendrogram represents a functional group obtained after estimating the Jaccard correlation coefficient. The area of the circles represents the number of genes assigned to each GO term, and the color of the circle indicates the P value estimated for each GO term.
## Discussion
Increased attention to the intergenerational effects of maternal stress in the progeny has been observed for livestock species44. In cattle, the intergenerational effects of mastitis infection, heat stress and maternal metabolism have previously been evaluated45–48. In sheep, the multigenerational and intergenerational effects of maternal overnutrition were evaluated over leptin surge and metabolic syndrome, respectively49–52. In addition, the effect of parental diet on the progeny was described and confirmed in several species, such as humans, rodents and model organisms (D. melanogaster and C. elegans)35,53. However, to the best of our knowledge, there is no evaluation of the intergenerational effect of dietary protein restriction on methylation markers across the sheep genome. The intergenerational transfer of maternal effects through DNA methylation can help us to understand the biological processes involved in the response to protein restriction challenges. Therefore, in the current study, the effect of NPR in the prepuberal stage of Assaf ewes was evaluated regarding the impact over methylation marks across the genome of their suckling lambs. Here, DMRs were identified in functional candidate genes associated with relevant biological processes, which may help to better understand the effects in the offspring of ewes subjected to NPR in the prepuberal stage. Additionally, an interesting effect on the methylation marks regarding the interaction between the nutritional status of the ewes (NPR or C) and the level of PFC fat (high and low) in the lambs was observed.
Perirenal fat is the major fat deposit in suckling lambs, accounting for ~ $11\%$ of total body fat (the rest of the fat is distributed across the subcutaneous, intermuscular and pelvic depots) and ~ $2\%$ of total carcass weight in the Lechazo de Castilla y Leon PGI39. This tissue is very important for newborns due to its nonshivering thermogenesis ability and passes through an intensive transition from BAT to WAT54,55. This transition reflects the necessity of the organism to move from thermoregulation in the first days of life to growth and homeostasis in the later days of life40,41. Consequently, fine regulation of this tissue must be present to provide proper physiological conditions for survival and growth. Therefore, the study of methylation markers in the perirenal fat of suckling lambs is an interesting point to be scrutinized to better understand the biological processes involved with the intergenerational impact of dietary protein restrictions.
Despite being limited, the number of DMRs identified in the current study is in concordance with the number of DMRs identified in similar studies. For example, in an intergenerational study about the effect of methionine supplementation (a dietary methyl donor), 216 DMRs were identified in the sperm of F0 rams to produce the F1 generation51. However, it is important to highlight that here, a more stringent threshold (FDR < 0.05) was applied when compared to the threshold established in the abovementioned study (P value < 0.1). Consequently, the threshold reinforces the detection power of the experimental design and statistical model applied here.
## Genes shared among the different terms analyzed
Despite the absence of common DMRs among the three evaluated terms, five common genes harboring different DMRs were identified. Two of these genes were uncharacterized loci (LOC121818805 and LOC121816055) encoding lncRNAs, and one was a LOC predicted as a translation initiation factor IF-2-like (LOC114112700). There is no functional information associated with any of these tree genes. ANXA2R encodes a receptor of annexin-2, a calcium-dependent protein that plays several roles in hematopoiesis, osteoclastic activation and osteoblast mineralization56–58. The fifth gene common among all three terms was TRNAG-GCC_6, which encodes a tRNA for glycine. The DMRs identified for this gene were mapped in the promoter region for all three terms. Glycine is a nonessential amino acid that may directly impact fetal development if major fetal requirements are not met59. Interestingly, during pregnancy in rats, supplementation with glycine rectifies the vascular dysfunction induced by protein restriction60. Additionally, an intergenerational effect of increased systolic blood pressure caused by a maternal low-protein diet was reversible through supplementation with glycine in rats61. In addition, in rats, protein restriction in dietary isocaloric diets resulted in higher levels of glycine in the liver, skeletal muscle, and kidney62. Consequently, these results indicate an important role of glycine in response to the effects caused by dietary protein maternal restriction, suggesting a potential functional role of the identified DMRs.
## Genes harboring DMRs identified for the Nutchal * FatGroup interaction term
The DMRs identified for the interaction term (Nutchal * FatGroup) were mapped, more than randomly expected (enriched), in regions previously associated with body composition traits in sheep (body length, body height, chest girth, and muscle depth at the third lumbar). Two DMRs were associated with these traits, which were mapped in the following genomic coordinates: 11:50,031,219–50,031,384 18:58,058,339–58,058,484. The first DMR was mapped in intronic or exonic regions (depending on the transcript isoform) in a CG context of the genes LOC121820642 and SYNE3, respectively. LOC121820642 is a lncRNA with no available functional information. SYNE3, also called Nesprin-3, is a member of a family of nuclear transmembrane proteins that binds to the cytoskeletal linker protein plectin, helping to regulate endothelial cell morphology and perinuclear cytoskeletal architecture63,64. Other members of the nesprin family (nesprin-1 and nesprin-2) are associated with muscle development and myogenesis65,66. The major functional group observed in the analysis performed using the Jaccard coefficient similarity matrix for the genes harboring DMRs identified for the NuChal * FatGroup term was “GTPase ion cytoplasmic channel”. *The* genes RASA3, G3BP1, MTSS2 and SHROOM2 were associated with this functional group. Among these genes, RASA3 encodes Ras p21 protein activator 3, a stimulator of GTPase activity of Ras p21 and was previously identified as a GTPase-activating protein modulating Ras activity during normal brain development67. Interestingly, the Ras p21 protein is involved in the progression of fetal brown adipocytes through the S phase of the cell cycle in rats68. Additionally, Ras signaling regulates the differentiation of brown adipocytes and UCP1 expression69. A key function of UCP1 in the thermogenesis of brown adipocyte tissue is highlighted by the fast abundance decrease of its mRNA levels after birth, following the BAT decrease, acting in a protective manner to increase the survival rates in the neonatal period43,70,71. Consequently, UCP1 is considered a classical biomarker for BAT. Indeed, the percentage of multilocular adipocytes in Assaf sheep is followed by the expression of UCP1, as recently demonstrated by our research group41. It is important to highlight that in the current study, for the interaction term, the DMR mapped on RASA3 was located in the promoter region. Among the other genes harboring DMRs identified exclusively for the interaction term (Nutchal * FatGroup), COL14A1 is involved in the development of muscle and preadipocytes72. The expression and polymorphisms (near or within the genomic coordinates) of COL14A1 were associated with fat content in pigs and cattle73–76. In addition, COL14A1 is mapped in regions of the cattle genome previously identified as signature selections for growth efficiency77,78. The second cluster of genes, including CA1 and CA5A, was associated with one-carbon metabolism. Interestingly, methylation in DNA occurs as an output of one-carbon metabolism through the metabolic pathway responsible for the utilization of dietary methyl donors, usually obtained from folate transfer of methyl groups obtained from methionine79.
## Genes harboring DMRs identified for the Nutchal term
*The* genes harboring DMRs identified exclusively for the NutChal term from the multifactorial model and sharing GO terms, PLCG1 and PLCXD1, were associated with phosphoric ester hydrolase activity and phosphoric diester hydrolase activity. Interestingly, the same GO terms were identified as enriched in the analysis of differentially expressed genes in the subcutaneous fat from the progeny of ewes fed with dried corn distillers grains (fiber plus protein plus fat) when compared with the progeny of ewes fed with alfalfa haylage (fiber) or corn (starch)21.
The analysis of the DMRs identified exclusively for the NutChal term was supposed to pinpoint candidate genes associated with biological functions not related to the differential fat content present within the NutChal and control groups. The major functional group identified for this term was the “histone biosynthetic compensation chromosome”. This functional group was related to biological processes involved in the regulation of gene expression by genome imprinting, histone ubiquitination, and neuroepithelial and odontoblast differentiation. In total, 53 genes harboring DMRs identified for the Nuchal term were annotated, of which 33 were uncharacterized loci. Consequently, the functional interpretation of these genes is difficult to apply. Among the other 20 genes, five codifying tRNAs were annotated (TRNAV-CAC_4, TRNAG-GCC_6, TRNAS-GGA_58, TRNAS-GGA_161, and TRNAW-CCA_86), which suggests an impact of protein restriction on the general availability and/or use of amino acids in the progeny. Maternal protein restriction was previously associated with a low level of circulating amino acids during intrauterine growth80. In addition, low amino acid levels were observed in newborns affected by intrauterine growth restriction caused by maternal protein restriction81. Consequently, this reinforces the potential role of these methylation marks as a response to protein restriction and subsequent amino acid availability.
## Genes harboring DMRs identified for the FatGroup term
In total, 11 genes were annotated exclusively for the DMRs identified for the FatGroup term (MKS1, SLC2A6, TMEM144, TMEM192, PER2, NSG2, IGF2R, TYMS, MAD2L1, SETD7 and RTRAF). The analysis of biological functions to which these genes are enrolled might be useful to understand epigenetic differences observed between the sheep with divergent fat content, excluding the effect of the nutritional challenge or control groups.
A gene identified exclusively for the FatGroup term associated with adipocytes was IGF2R, which has a DMR mapped in the promoter, exonic or intronic regions (depending on the transcript). In cell culture, the knockdown of IGF2R affects the survival of brown adipocyte precursor cells negatively and reduces brown adipogenesis82. Interestingly, IGF2R is mapped in a cluster of genes with differential imprinting patterns between the maternal and paternal DNA in the progeny. The paternally expressed transcripts act as enhancers of prenatal growth, and the maternally expressed transcripts act as inhibitors of prenatal growth in mice83. Additionally, in pigs, IGF2R was identified as differentially expressed between pig breeds (Songliao black and Landrace) with extreme levels of backfat (high and low), with a higher expression in the low backfat breed (Landrace)84. In addition, IGF2R mutations are associated with perinatal lethality and overgrowth85,86. Consequently, these results reinforce the potential effect of the DMRs mapped within IGF2R on differential fat content and survival rate in Assaf suckling lambs.
Another functionally relevant gene for differential fat deposition was the PER2 (period 2) gene, which has a DMR mapped in one of its introns. The PER2 gene plays an important role in the regulation of the circadian clock, generating the circadian rhythm in the central nervous system and peripheral organs87. Interestingly, circadian rhythms are associated with the control of lipid metabolism88. More specifically, PER2 is responsible for directly regulating PPARγ, a nuclear receptor that plays crucial roles in adipogenesis, the inflammatory response and insulin sensitivity89–91. This regulation occurs through the repression of PPARγ by blocking target promoters and transcription factors92. Additionally, mice deficient for PER2 were observed to show a drastic reduction in total triacylglycerol and nonesterified fatty acids92. Interestingly, in sheep, the suppression of melatonin by exposure to constant light resulted in increased basal lipolysis with overexpression of adipogenic/thermogenic and circadian clock genes (including PER2 and PPARγ)93. In addition, in the same experiment, the weight of BAT was half of the weight observed in the newborns of ewes supplemented with melatonin93. These results corroborate the physiological importance of the circadian clock to the regulation of BAT94. In cattle, the silencing of PER2 was associated with suppressing lipid synthesis in the mammary gland through the regulation of SREBF1 and PPARγ95. The results obtained here first suggest a potential impact of methylation markers on PER2 over differential fat deposition in sheep.
The intergenerational effects of nutrient protein restriction during the prepubertal stage of replacement ewes, especially protein restriction, on methylation markers across the genome of livestock species are poorly evaluated. In the current study, the effects of protein restriction performed in replacement ewe lambs and the interaction with the amount of fat in the perirenal and cavitary deposits of their progeny were evaluated using WGBS from perirenal tissue. The results obtained here suggest that differential methylation is caused in suckling lambs by maternal protein restriction at prepubertal age. In addition, a multifactorial model was employed to identify DMRs for the interaction between the nutritional challenge and divergent fat level deposition groups and individually for the nutritional challenge and divergent fat deposition groups. The PLS-DA analysis confirmed that these DMRs could perfectly classify those groups. The functional analyses of the genes harboring these DMRs suggested their involvement in relevant biological processes. The DMRs identified individually for nutritional challenge groups were mapped in several tRNAs from different amino acids, suggesting a relationship between a dietary protein restriction of replacement ewe lambs and the availability of amino acids in their progeny. In addition, the genes harboring DMRs identified for the interaction between the nutritional challenge performed here and the divergent fat deposition groups were associated with the development and differentiation of adipocytes, especially those that characterize BAT. Similarly, the DMRs identified individually for the divergent fat deposition groups were also associated with the regulation of adipocytes and BAT. However, this regulation seems to occur by a different mechanism, where the regulation of the circadian rhythm is involved. In light of the above, the current study provides important biological insights into the effect of protein restriction in replacement ewes on the resulting methylation pattern in their future progeny. These results, consequently, may help pinpoint potential candidate genes and biological processes involved with different phenotypes regarding fat deposition and feed utilization.
## Ethical approval
All procedures involving animals in this study were performed in accordance to Spanish regulations regarding the protection of animals used for experimental and other scientific proposes (Royal Decree $\frac{53}{2013}$), under the supervision of the Ethical and Animal Welfare Committee of the University of León to after the approval of the competent body, Junta de Castilla y León. The nutritional challenge described in this work (ewes) was approved by the Ethics Committee of the Instituto de Ganadería de Montaña (IGM, CSIC-ULE) in León (Spain) (Reference $\frac{100102}{2018}$-1).
Regarding their suckling lambs, the management in a commercial farm, the transport and sacrifice were performed following Spanish and EU legislation [Spanish Laws $\frac{32}{2007}$, $\frac{6}{2013}$ and RD $\frac{37}{2014}$; Council Regulation (EC) $\frac{199}{2009}$]. According to the Research Ethics Committee of the University of León, formal ethical approval was not necessary for this case. In addition, the experimental design and the analysis performed in the current study are in accordance with ARRIVE guidelines96.
## Nutritional protein restriction experiment for ewes and selection of lambs with divergent internal fat deposition levels
Initially, 40 Assaf female lambs (2 months of age) were acquired from one flock in the northwest region of Castilla y León (Spain) and transported to the facilities of the Instituto de Ganadería de Montaña (IGM, CSIC-ULE) in León. All the animals were fed a standard diet for replacement ewe lambs providing $16\%$ crude protein until three months old, and subsequently, they were divided into two groups. These two groups were composed of 20 nutritionally challenged (NPR) and 20 control (C) animals to evaluate the impact of a feed restriction challenge due to a trade market problem and a shortage of concentrate inputs. Over 64 days, the C ewes received the standard diet mentioned above, while the NPR ewes received the same diet without soybean meal ($44\%$ reduction in protein intake). The complete description of the standard and NPR diets can be found at Supplementary Table 897. After that period, the two groups received the same diet (standard diet) according to their growing needs and physiological status. At ten months of age, the ewes were artificially inseminated and subjected to standard periodic veterinary treatments (e.g., vaccines and anthelmintic treatments) and routine monitoring of their health status.
This study initially considered a total of 34 male lambs born in the same lambing season (January and February 2020) from the ewes included in the NPR experiment. These lambs were kept with the dams during the first four to eight hours of life to suck the first colostrum. Subsequently, the lambs were fed with milk replacer powder ad libitum using a milk replacer machine until slaughter. The lambs were slaughtered at a local slaughterhouse when the market weight was reached, with an average age of 24.85 ± 5.25 days (range of 16–37 days) and an average carcass weight of 2.87 ± 0.3 kg (range of 2.36–3.80 kg). At slaughter, perirenal adipose tissue was collected from each of the sacrificed lambs. The tissue samples were frozen at − 20 °C until DNA extraction.
With the aim of considering in our study the potential interaction of the effects of the nutritional challenge of the dams and the internal fat depot levels of the lambs, the percentage of PCF was measured in the half carcasses of all 34 slaughtered lambs. The age and PCF values for all 34 male lambs born are available in Supplementary Table 1. Based on the ranked distribution of the PCF values within the NPR/C groups, we selected the four animals with the highest (H) and lowest (L) PCF values born from the NPR and C dams. Consequently, a total of 16 Assaf suckling lambs were selected for further analyses related to the identification of methylation marks between the NPR and C lambs (4 NPR-LowPCF, 4 NPR-HighPCF, 4 C-LowPCF, and 4 C-HighPCF). Descriptive statistical analysis for the PCF trait in relation to the NPR and C groups was performed for these 16 selected animals using the R 4.2.0 statistical environment98. Additionally, as the animals were slaughtered at different ages, we evaluated the effect of age on the PCF trait. Initially, the Anderson–Darling normality test was applied to identify potential normality deviations. After this step, a test of variance equality was applied, and a t-test was chosen to test the differences in age and percentage of perirenal and cavitary fat across the groups. Significant differences between groups were defined using a threshold of P value < 0.05.
## DNA extraction and whole-genome bisulfite sequencing analysis
The perirenal fat from the 16 selected animals was used for DNA extraction using the QuickGene DNA Tissue Kit (Autogen, MA, USA), which is based on protein removal by protease K following the manufacturer’s instructions. The whole-genome bisulfite sequencing (WGBS) protocol applied to these samples was previously described by reference99. Briefly, the samples were used for paired-end (150 bp) library construction on Novogene in Cambridge (UK), and libraries were sequenced on an Illumina NovaSeq 6000, with a minimum coverage depth of 20X for each sample. The raw datasets derived from the sequencing are available at the European Nucleotide Archive (ENA) repository with accession number PRJEB56595.
## Methylation calling and identification of differentially methylated regions
The WGBS raw reads generated for the 16 samples of perirenal fat tissue under analysis were subjected to quality control using FastQC100.(Andrews, 2015),Subsequently, the reads were trimmed based on quality scores (Phred < 20), the adapters were removed, and short reads (< 20 bp) were filtered using the default options of Trim Galore software (version 0.6.5)101.
The trimmed reads were aligned to the reference genome (Oar_Ram_v2.0), indexed by BowTie2102, using Bsseeker2 software by the Python script bs_seeker2-align.py with the default options. The alignment output files were sorted by position using Samtools software (version 1.15.1)103, and the duplicated reads were removed using Picard software (version 2.25) (https://broadinstitute.github.io/picard/). Finally, the methylation call procedure was performed using the Python script bs_seeker2-call_methylation.py from Bsseeker2 using the default options.
The differentially methylated loci (DMLs) and DMRs were identified using the R package DSS104. Only methylated sites with ten or more reads mapped within the region were included in the analysis. First, a simple average algorithm for smoothing was used to estimate the mean methylation levels. A multifactorial design was applied to identify the DMLs using a model that included the NPR and control groups, the age of the animals (in days) and an interaction between the nutritional challenge and fat groups (NutChal * FatGroups) as terms. Subsequently, DMRs were detected based on regions harboring statistically significant methylated sites based on the following criteria: False-Discovery Rate (FDR) adjusted P value < 0.05 for the methylated site, minimum length (50 bp), minimum number of methylated sites (50 bp), and percentage of methylated sites being significant in the region (0.5). Additionally, the DMRs mapped in regions less than 50 bp from each other were merged into a single DMR.
## Partial least squares discriminant analysis between nutritional challenge and fat groups
The R package mixOmics105 was used to perform a partial least squares discriminant analysis (PLS-DA) to evaluate the potential of the identified DMRs to discriminate between the groups from the Nutchal and FatGroups effects. First, for each term from the multifactorial model used to identify the DMRs (NutChal, FatGroups, and NutChal * FatGroups), the average methylation level for the DMLs within each DMR was calculated. The discriminant potential was evaluated through the area under the curve (AUC) values and graphically plotting the two first principal components with centroids defining the groups.
## Annotation of gene ontology terms, metabolic pathways and QTLs associated with the differentially methylated regions
*The* genes harboring the identified DMR were annotated with the R package genomation106 using the ovine reference genome Oar_Ram_v2.0. The R packages ClusterProfiler107 and enrichplot were used for Gene Ontology (GO) term annotation, graphic representation and functional grouping. GO terms and KEGG pathways108,109 were considered enriched when the FDR was < 0.05, and a minimum of two genes were assigned to the respective term. The function pairwise_termsim() from enrichplot was used to calculate the Jaccard correlation coefficient among GO terms. After this step, GO terms shared between at least two genes were functionally grouped using the Jaccard correlation matrix to identify classes of terms that were closely related. For the functional grouping, the GO terms were included to estimate the Jaccard correlation matrix, disregarding the enrichment status. In addition, the GO terms shared between at least two genes were used to create networks linking different genes through the GO terms shared using the cnetplot() function.
The GALLO R package110 was used to annotate the colocation of the genes harboring DMRs with quantitative trait loci (QTL) using the SheepQTLdb from Animal QTLdb111 and considering a 250 kb interval downstream and upstream. The gff file from SheepQTLdb (Oar_Ram_v2.0) was edited to remove QTLs with lengths higher than 1 Mb. This approach was chosen to avoid the annotation of QTLs, which cover large extensions of the chromosomes, usually identified by linkage methods and using low-density microsatellite marker maps. In addition, a QTL enrichment analysis was performed using the qtl_enrich() function from the GALLO package. Similar to GO terms and KEGG pathways, QTLs were considered enriched when the FDR was < 0.05 and more than one QTL was described in the SheepQTLdb.
A flowchart summarizing the methodology applied in the current study is available in Supplementary Fig. 3.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31546-3.
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|
---
title: Quantitative flow ratio-guided versus angiography-guided operation for valve
disease accompanying coronary heart disease
authors:
- Wenlong Yan
- Yangyang Wang
- Xin Zheng
- Pengfei Guo
- Sumin Yang
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10020583
doi: 10.3389/fcvm.2023.1076049
license: CC BY 4.0
---
# Quantitative flow ratio-guided versus angiography-guided operation for valve disease accompanying coronary heart disease
## Abstract
### Background
Valve replacement combined with coronary artery bypass graft (CABG) operation (VR + CABG) is usually associated with higher mortality and complication rates. Currently, angiography remains the most commonly used approach to guide CABG. The aim of this study is to investigate whether a quantitative flow ratio (QFR)-guided strategy can improve the clinical outcomes of VR + CABG.
### Methods
Patients ($$n = 536$$) treated by VR + CABG between January 2018 and December 2021 were retrospectively assessed. In 116 patients, all lesions were revascularized entirely based on QFR (the QFR-guided group), whereas in 420 patients, all lesions were revascularized entirely based on angiography (the angiography-guided group). To minimize selection bias between the 2 groups, propensity score matching was performed at a ratio of 1:2. The primary endpoint of the study was the rate of major adverse cardiac and cerebrovascular events (MACCE) at 1-year, which was defined as a composite of cardiac mortality, myocardial infarction (MI), any repeat revascularization, and stroke.
### Results
No statistically significant differences were observed in the baseline clinical characteristics between the QFR-guided and angiography-guided groups after propensity score matching. The mean age of all patients was 66.2 years [standard deviation (SD) = 8.3], 370 ($69\%$) were men, the mean body-mass index of the population was 24.8 kg/m2 (SD = 4.5), 129 ($24\%$) had diabetes, and 229 ($43\%$) had angina symptoms. When compared with the angiography-guided group, the QFR-guided group had a significantly shorter operative time (323 ± 60 min vs. 343 ± 71 min, $$P \leq 0.010$$), extra corporal circulation time (137 ± 38 min vs. 155 ± 62 min, $$P \leq 0.004$$), clamp time (73 ± 19 min vs. 81 ± 18 min, $P \leq 0.001$), and less intraoperative bleeding volume (640 ± 148 ml vs. 682 ± 166 ml, $$P \leq 0.022$$). Compared with the angiography-guided group, the 1-year MACCE was significantly lower in the QFR-guided group ($6.9\%$ vs. $14.7\%$, $$P \leq 0.036$$, hazard ratio = 0.455, $95\%$ confidence interval: 0.211–0.982).
### Conclusion
Our results raise the hypothesis that among patients who undergo VR + CABG, QFR-guided strategy is associated with optimized surgical procedure and a superior clinical outcome, as evidenced by a lower rate of MACCE at 1-year compared with conventional angiography-guided strategy.
## Introduction
Valve combined coronary artery bypass graft (CABG) operation still accounts for a significant proportion of adult cardiac surgery according to the Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database (ACSD) in 2021 [1]. Particularly, patients receiving valve replacement (VR) + CABG exhibit clinical features that generally place them at a higher risk than patients receiving valve operations or CABG alone. Whether or which lesions should be revascularized intraoperatively remains controversial for such patients. Presently, visual assessment based on coronary angiography is the main method to guide CABG, however, coronary angiography can only identify lesions with anatomic narrow and cannot assess the physiological impact of lesions on the myocardium they dominate. The physiological assessment of coronary artery has been recommended to evaluate the severity of coronary stenosis [2]. These recommendations are primarily based on the excellent performance of fractional flow reserve (FFR) in randomized controlled trials guiding percutaneous coronary intervention (PCI) (3–5). Although some relevant guidelines have strongly recommended the use of physiological assessment based on pressure wires to assess moderate stenosis [2], it is largely underutilized in practice because of the long operation time, potential complications of pressure wires, and the side-effects of pharmacological agents. The calculation of quantitative flow ratio (QFR) is mainly based on three-dimensional (3D) coronary models derived from invasive coronary angiography and fast computational fluid dynamics, which enables the online FFR estimation without using any pressure guidewire and vasodilator drugs [6, 7]. Previous studies have demonstrated that online QFR have satisfactory feasibility and accuracy in evaluating the hemodynamics of vessel stenosis when compared with FFR [8, 9]. Therefore, in this study, we investigated whether a QFR-guided lesions selection strategy can improve the clinical outcomes of VR + CABG.
## Study design
We retrospectively analyzed adults (≥18 years, $$n = 566$$) who underwent VR + CABG at the Affiliated Hospital of Qingdao University from January 2018 to December 2021 (Figure 1). Patients ($$n = 6$$) who declined to participate and those ($$n = 24$$) with emergency operation, renal insufficiency, hepatic insufficiency, recent myocardial infarction (MI) (<30 days), or former heart surgery were all excluded. The included population was then assigned to QFR-guided or angiography-guided groups. The patients ($$n = 116$$) were assigned to the QFR-guided group if all lesions of QFR ≤ 0.8 were revascularized and QFR > 0.8 were deferred. The patients ($$n = 420$$) were assigned to the angiography-guided group if all lesions were revascularized entirely based on angiography.
**Figure 1:** *Study flow chart. VR + CABG, heart valve surgical replacement combined with coronary artery bypass graft; QFR, quantitative flow ratio.*
The present study was approved by the local ethics committee, and the requirement of individual consent for this retrospective analysis was waived.
## Coronary angiography
The puncture point for coronary angiography was the radial or femoral artery, the diameter of the puncture sheath was 5-F or 6-F, and the images were obtained using the x-ray systems (Allura X per FD20, Philips; Innova IGS520, GE) at ≥15 frames/s. The preoperative anticoagulation strategy for coronary angiography was intravenous heparin at 100 IU/kg.
## QFR analysis
QFR was performed on any lesions with a visual reference vessel ≥1.5 mm in diameter. For each lesion, at least two angiographic images with a difference of >25° in the projection angle were transmitted to the Angio Plus system (Pulse Medical Imaging Technology, Shanghai, China) for QFR calculation. The analyst manually placed markers at the proximal and distal locations of the detected vessel, and the system automatically outlined the contours of the detected vessel. If the traced vessel trajectory deviated from the normal lumen, additional markers were placed, or the vessel outline was manually edited. The quantitative coronary angiography mainly reported reference vessel diameters, minimal lumen diameter, minimal lumen area, and percent diameter/area stenosis. An artificial intelligence-assisted computing software (Angio Plus Core, Pulse Medical Imaging Technology, Shanghai, China) combined vascular image information from multiple angles with estimated vessel flow to obtain a 3D-QFR. QFR evaluation was performed later off-line by a blinded core laboratory (Pulse Medical Imaging Technology, Shanghai, China). Each patient's QFR was independently interpreted by two observers. The observers were blinded to all clinical information except for the diagnosis. If the QFR conclusions of these two observers were inconsistent, a third observer participated.
## Surgical technique
The operation type, graft type, and specific procedures were conducted at the discretion of the surgeon. The grafts used for CABG included the internal mammary artery, radial artery, and saphenous vein. The replacement valves were Carpentier-Edwards PERIMOUNT Plus Pericardial Bioprosthesis (Edwards Lifesciences, Irving, CA) and St. Jude Medical Regent Mechanical Heart Valve (St. Paul, MN).
## Study endpoints
The primary endpoint of the study was the rate of major adverse cardiac and cerebrovascular events (MACCE) at 1-year postoperatively, which was defined as a composite of cardiac mortality, MI, any repeat revascularization, and stroke. Secondary endpoints were cardiac mortality, MI, any repeat revascularization, stroke, worsening in the NYHA class of ≥1, rehospitalization for heart failure and valve reoperation at 1-year postoperatively. MI was defined as described previously [10].
## Sample size and power calculation
The primary purpose of this study was to assess MACCE after 1 year in patients who underwent QFR-guided VR + CABG versus angiography-guided VR + CABG. We estimated the MACCE rate of $6.1\%$ after 1 year in the QFR-guided group, as well as a MACCE rate of $15.2\%$ after 1 year in the angiography-guided group. These rates of MACCE were based on the data from the study by Bowdish et al. [ 11] and the results of our center. We estimated a minimum sample size of 116 patients in the QFR-guided group and 232 in the angiography-guided group, based on a 2-sided Chi-square test with an alpha level of 0.05 and a statistical power of 0.80.
## Statistical analysis
All statistical analyses were performed using the R version 4.2.1, and the two-tailed probability values <0.05 were considered statistically significant. Continuous variables with a normal distribution were described as the mean ± standard deviation (SD), and differences between these variables were compared by Student's t-test. The median and interquartile ranges were calculated to describe the continuous variables that did not conform to a normal distribution, and the differences between these variables were compared by the Mann–Whitney U test. Categorical variables were described as frequencies and percentages, and the differences between these variables were compared by the Pearson's χ2 test or Fisher's exact test. MACCE and its constituent events were compared by Cox proportional hazards analysis, and Kaplan–*Meier analysis* was performed to present the primary and secondary endpoints at 1-year postoperatively. To minimize any bias between the 2 groups, propensity score matching at a ratio of 1:2 was utilized to compare clinical outcomes from patients in the 2 groups. The patients in the QFR-guided group were matched to the angiography-guided group by all the preoperative variables in the Table 1. The nearest neighbor method was applied with a caliper of 0.2, and the balance after matching was evaluated with standardized mean differences (SMD).
**Table 1**
| Unnamed: 0 | QFR-guided (n = 116) | Angio-guided (n = 420) | P value |
| --- | --- | --- | --- |
| Age (years) | 67.1 ± 8.0 | 65.3 ± 7.8 | 0.029 |
| Male | 85 (73%) | 285 (68%) | 0.264 |
| BMI (kg/m2) | 25.0 ± 4.5 | 24.6 ± 4.3 | 0.38 |
| Hypertension | 40 (34%) | 168 (40%) | 0.28 |
| Hypercholesterolemia | 35 (30%) | 138 (33%) | 0.584 |
| Diabetes mellitus | 31 (27%) | 98 (23%) | 0.421 |
| Previous MI | 20 (17%) | 63 (15%) | 0.555 |
| Smoking history | 58 (50%) | 193 (46%) | 0.439 |
| Cerebrovascular diseases | 9 (7.8%) | 28 (6.7%) | 0.681 |
| ACS | 6 (5.2%) | 19 (4.5%) | 0.769 |
| CCS classification | | | 0.704 |
| No angina | 71 (61%) | 236 (56%) | |
| I | 19 (16%) | 84 (20%) | |
| II | 22 (19%) | 75 (18%) | |
| III | 3 (3%) | 21 (5%) | |
| IV | 1 (1%) | 4 (1%) | |
| LVEF, % | | | 0.786 |
| <35 | 6 (5%) | 25 (6%) | |
| 35–50 | 29 (25%) | 116 (28%) | |
| >50 | 81 (70%) | 279 (66%) | |
| LVEDD, mm | 52.5 ± 7.26 | 51.6 ± 8.14 | 0.282 |
| SYNTAX score | 24.4 ± 7.66 | 24.7 ± 7.91 | 0.716 |
| Valve disease type | | | 0.74 |
| Aortic valve disease | 61 (53%) | 235 (56%) | |
| Mitral valve disease | 41 (35%) | 143 (34%) | |
| Aortic + mitral valve disease | 14 (12%) | 42 (10%) | |
## Baseline characteristics
From January 2018 to December 2021, a total of 536 patients’ clinical data were collected, of which 116 patients fully met the QFR guidance criteria and were included in the QFR guidance group and 420 patients were included in the angiography guidance group. The details about the clinical characteristics of the patients are summarized in Table 1. There was statistically significant difference in the ages between the two groups. The baseline differences were balanced by propensity matching between the two groups (Table 2) and the distribution of propensity scores is presented in Figure 2. The mean age of all patients was 66.2 years (SD: 8.3) and 370 ($69\%$) were men. The mean BMI of the population was 24.8 kg/m2 (SD: 4.5), 129 ($24\%$) had diabetes, and 229 ($43\%$) exhibited angina symptoms.
**Figure 2:** *Distribution of the propensity scores.* TABLE_PLACEHOLDER:Table 2
## Operative characteristics
The operative outcomes of the patients are summarized in Table 3. Of 348 patients, 188 ($54.0\%$) underwent mitral VR + CABG (MVR + CABG), 120 ($34.5\%$) underwent aortic VR + CABG (AVR + CABG), and 40 ($11.5\%$) underwent MVR + AVR + CABG (DVR + CABG). The number of anastomoses per patient was statistically different between the two groups (1.8 ± 0.9 vs. 2.1 ± 1.2, $$P \leq 0.018$$). When compared with the angiography-guided group, the QFR-guided group showed a significantly shorter operative time (323 ± 60 min vs. 343 ± 71 min, $$P \leq 0.010$$), extra corporal circulation time (137 ± 38 min vs. 155 ± 62 min, $$P \leq 0.004$$), clamp time (73 ± 19 min vs. 81 ± 18 min, $P \leq 0.001$), and less intraoperative bleeding volume (640 ± 148 ml vs. 682 ± 166 ml, $$P \leq 0.022$$).
**Table 3**
| Unnamed: 0 | QFR-guided (n = 116) | Angio-guided (n = 232) | P value |
| --- | --- | --- | --- |
| Operation methods | | | 0.956 |
| MVR + CABG | 61 (53%) | 127 (55%) | |
| AVR + CABG | 41 (35%) | 79 (34%) | |
| DVR + CABG | 14 (12%) | 26 (11%) | |
| Anastomoses per patient | 1.8 ± 0.9 | 2.1 ± 1.2 | 0.018 |
| Grafted coronary arteries | | | 0.389 |
| LAD grafted | 78 (37%) | 139 (33%) | |
| Diagonals grafted | 25 (12%) | 63 (15%) | |
| CX grafted | 50 (24%) | 88 (21%) | |
| RCA grafted | 56 (27%) | 130 (31%) | |
| Operative time, min | 323 ± 60 | 343 ± 71 | 0.010 |
| ECC time, min | 137 ± 38 | 155 ± 62 | 0.004 |
| Clamp time, min | 73 ± 19 | 81 ± 18 | <0.001 |
| Hospital stay time, day | 24 ± 5.0 | 25 ± 6.1 | 0.128 |
| RBC transfusion, units | 2.6 ± 1.8 | 2.8 ± 1.7 | 0.311 |
| Intraoperative bleeding volume | 640 ± 148 | 682 ± 166 | 0.022 |
## Clinical outcomes
We obtained the clinical outcome data for all patients via outpatient and telephonic follow-up (Table 4, Figures 3, 4). The composite primary endpoint occurred within 1 year in 8 of the 116 patients in the QFR-guided group and in 34 of the 232 patients in the angiography-guided group ($6.9\%$ vs. $14.7\%$, $$P \leq 0.036$$, HR = 0.455, $95\%$ CI [0.211–0.982]). Kaplan-*Meier analysis* (Figure 3) showed that MACCE was significantly lower in the QFR-guided group than in the angiography-guided group. There were no significant difference between the QFR-guided group and the angiography-guided group in terms of the rates of cardiac mortality ($2.6\%$ vs. $4.7\%$, $$P \leq 0.500$$, HR = 0.530, $95\%$ CI [0.148–1.898]), MI ($2.6\%$ vs. $5.2\%$, $$P \leq 0.263$$, HR = 0.477, $95\%$ CI [0.135–1.690]), any repeat revascularization ($3.4\%$ vs. $4.7\%$, $$P \leq 0.576$$, HR = 0.694, $95\%$ CI [0.211–2.181]), stroke ($1.7\%$ vs. $2.6\%$, $$P \leq 0.899$$, HR = 0.650, $95\%$ CI [0.131–3.221]), worsening in NYHA class of ≥1 ($4.3\%$ vs. $4.7\%$, $$P \leq 0.856$$, HR = 0.915, $95\%$ CI [0.318–2.634]), rehospitalization for heart failure ($9.5\%$ vs. $10.3\%$, $$P \leq 0.801$$, HR = 0.917, $95\%$ CI [0.449–1.871]), and valve reoperation ($2.6\%$ vs. $2.2\%$, $$P \leq 0.899$$, HR = 1.203, $95\%$ CI [0.288–5.035]).
**Figure 3:** *Kaplan–Meier curves for the primary endpoint.* **Figure 4:** *Kaplan–Meier curves for the secondary endpoints. (A) Kaplan–Meier curves for cardiac mortality. (B) Kaplan–Meier curves for MI. (C) Kaplan–Meier curves for any repeat revascularization. (D) Kaplan–Meier curves for stroke.* TABLE_PLACEHOLDER:Table 4
## Discussion
QFR is a new method for estimating FFR, which uses 3D coronary artery reconstruction and computational fluid dynamics from angiography, and reflects the ratio of coronary pressure distal to the stenosis to aortic pressure under the condition of maximal myocardial hyperemia [6, 9]. Recent studies have demonstrated that physiology assessment-guided lesion selection strategy improve the clinical outcomes when compared with angiography-guided strategy in patients with coronary artery disease undergoing PCI or CABG (12–15). Valve combined with CABG operation is usually associated with a higher mortality and complication rates, and the prognosis of patients is worse than that of patients undergoing valve or CABG operation alone [16, 17]. This is the first study to report that QFR-guided VR + CABG reduced MACCE at 1-year significantly and optimized the surgical procedure compared with conventional angiography-guided strategy.
QFR assessment was performed on all lesions with a visual reference vessel diameter ≥1.5 mm. Notably, angiographic and hemodynamic assessments were inconsistent in more than one-third of the patients with intermediate coronary lesions [18]. In our study, this difference resulted in less average number of anastomoses in the QFR-guided group than in the angiography group (1.8 ± 0.9 vs. 2.1 ± 1.2, $$P \leq 0.018$$). This result is consistent with those of most of the previous studies [12, 19]. At the same time, we observed that the QFR-guided group had shorter operative time, extra corporal circulation (ECC) time and clamp time when compared with the angiography-guided group. This observation may be related to the following results: first, the QFR-guided surgical strategy reduced the average number of anastomoses, thereby simplifying the surgical procedure and shortening the related time. Second, our surgical procedure was CABG followed by VR operation, and functional complete revascularization guided by QFR may be more accurate [20], with myocardial cardioplegia perfusion through the bridging vessels, and a shorter time to induce cardiac arrest. Third, functionally complete revascularization leads to better intraoperative myocardial protection and less myocardial damage; therefore, cardiac resuscitation is smooth and the time is short [21].
The practical implication is that QFR can identify lesions that require revascularization and those that can be safely delayed, thereby reducing the incidence of early and late myocardial infarction without increasing ischemia-driven revascularization procedures during the 1-year follow-up compared with that in angiography-guided lesion selection. Other studies have also confirmed that the patency of bypass grafts with functional revascularization is significantly higher than that of bypass grafts with non-functional revascularization [22, 23]. In our study, MI and repeat revascularization were lower in the QFR-guided group than in the angiography-guided group, albeit there was no significant difference between the two groups. The possible reason for this is that our follow-up time was short. In both the groups, the increased rates of MI and repeat revascularization could be observed in the later follow-up period, which may be related to our selection of graft materials. The preferred strategy involved the routine use of the left internal mammary artery to the left anterior descending coronary artery and segments of the saphenous vein to the remaining coronary arteries requiring revascularization; therefore, the saphenous vein accounts for a relatively high proportion, resulting in: first, the biological characteristics of saphenous vein promote a high bridging vessel occlusion rate. With the extension of the follow-up time, the bridging vessels related to meaningless revascularization can result in occlusion. Second, saphenous vein anastomosed to the coronary artery with functionally insignificant stenoses might accelerate the atherogenesis process of the native vessels. These two points may have led the increased rate of late MI and repeat revascularization in our study. Moreover, we analyzed that the average number of anastomoses in the angiography-guided group was higher than that in the QFR-guided group, but the primary endpoint of MACCE was significantly lower in the QFR-guided group ($6.9\%$ vs. $14.7\%$, $$P \leq 0.036$$, HR = 0.455, $95\%$ CI [0.211–0.982]). The specific reasons for this warrant further analyses. Based on the present results, the possible reason for this could be that the angiography-guided group performed more meaningless revascularization, which did not bring benefits to the patients during operation. However, increased operative time, ECC time, and clamp time may increase the perioperative cardiac mortality and stroke.
## Limitation
The study has some limitations that must be acknowledged. The main limitation of the study was its retrospective and observational design; therefore, we cannot rule out selection bias, confounding of indications, and underreporting of events. Second, the accuracy of QFR measurement depends on the technique and quality of angiographic acquisition, and retrospective studies cannot control the quality of angiography. Next-generation QFR systems will require only a single projection and incorporate more automated processes, which will further reduce the analysis variability and time expenditure [24]. Third, the follow-up time was relatively short.
## Conclusion
The present results raise a hypothesis that among patients who undergo VR + CABG, QFR-guided strategy is associated with a lower rate of MACCE after 1 year when compared to conventional angiography-guided strategy. Meanwhile, QFR-guided strategy can optimize the operative procedure, including reducing the operative time, extra corporal circulation time, clamp time and intraoperative bleeding volume. Further studies of high-quality randomized controlled trials with larger sample size and long-term follow-up are needed.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by The Ethic committee of the Affiliated Hospital of Qingdao University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
WY: conceptualization, investigation, methodology, project administration, visualization, writing—original draft, writing—review and editing. YW: formal analysis, methodology, writing—review and editing. XZ: data curation, investigation, software. PG: investigation. SY: conceptualization, methodology, resources, supervision, validation, writing—review and editing. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Ectopic expression of Nav1.7 in spinal dorsal horn neurons induced by NGF contributes
to neuropathic pain in a mouse spinal cord injury model
authors:
- Yan Fu
- Liting Sun
- Fengting Zhu
- Wei Xia
- Ting Wen
- Ruilong Xia
- Xin Yu
- Dan Xu
- Changgeng Peng
journal: Frontiers in Molecular Neuroscience
year: 2023
pmcid: PMC10020601
doi: 10.3389/fnmol.2023.1091096
license: CC BY 4.0
---
# Ectopic expression of Nav1.7 in spinal dorsal horn neurons induced by NGF contributes to neuropathic pain in a mouse spinal cord injury model
## Abstract
Neuropathic pain (NP) induced by spinal cord injury (SCI) often causes long-term disturbance for patients, but the mechanisms behind remains unclear. Here, our study showed SCI-induced ectopic expression of Nav1.7 in abundant neurons located in deep and superficial laminae layers of the spinal dorsal horn (SDH) and upregulation of Nav1.7 expression in dorsal root ganglion (DRG) neurons in mice. Pharmacologic studies demonstrated that the efficacy of the blood–brain-barrier (BBB) permeable Nav1.7 inhibitor GNE-0439 for attenuation of NP in SCI mice was significantly better than that of the BBB non-permeable Nav1.7 inhibitor PF-05089771. Moreover, more than $20\%$ of Nav1.7-expressing SDH neurons in SCI mice were activated to express FOS when there were no external stimuli, suggesting that the ectopic expression of Nav1.7 made SDH neurons hypersensitive and Nav1.7-expressing SDH neurons participated in central sensitization and in spontaneous pain and/or walking-evoked mechanical pain. Further investigation showed that NGF, a strong activator of Nav1.7 expression, and its downstream JUN were upregulated after SCI in SDH neurons with similar distribution patterns and in DRG neurons too. In conclusion, our findings showed that the upregulation of Nav1.7 was induced by SCI in both SDH and DRG neurons through increased expression of NGF/JUN, and the inhibition of Nav1.7 in both peripheral and spinal neurons alleviated mechanical pain in SCI mice. These data suggest that BBB permeable Nav1.7 blockers might relieve NP in patients with SCI and that blocking the upregulation of Nav1.7 in the early stage of SCI via selective inhibition of the downstream signaling pathways of NGF or Nav1.7-targeted RNA drugs could be a strategy for therapy of SCI-induced NP.
## Introduction
NP following SCI is a debilitating and distressing condition leading to sleep disturbances and depression (Burke et al., 2017; Widerström-Noga, 2017). The prevalence of NP following SCI is about $38\%$–$70\%$ (Werhagen et al., 2007; Burke et al., 2017; Kim et al., 2020), and the severe pain in SCI patients is primarily spontaneous due to central sensitization (Shiao and Lee-Kubli, 2018). NP induced by SCI is hard to manage because the complicated mechanisms behind it have not been fully uncovered yet.
Voltage-gated sodium channels (VGSCs) play critical roles in pain sensation and conduction, and are potential targets for pain relief. The first-generation unspecific VGSCs inhibitors lidocaine and mexiletine are local anesthetics used in clinics but have side effects in the brain and heart due to their inhibition of Nav1.1, Nav1.2, and Nav1.5 (Eijkelkamp et al., 2012). Therefore, the idea to develop second-generation sodium channel blockers with Nav-subtype selectivity was raised. Nav1.8 (SCN10A, also named PN3 and SNS), a member of the VGSC family, was first identified to be a Tetrodotoxin-resistant sodium channel and primarily expressed in the small neurons of rat DRG by Akopian et al. [ 1996] and Sangameswaran et al. [ 1996]. Functional studies showed that Nav1.8 knockout mice have elevated mechanical pain thresholds to noxious pressure and also have deficits in inflammatory and visceral pain, but not in neuropathic pain (Akopian et al., 1999; Laird et al., 2002). Moreover, Nav1.8 gain-of-function mutations were found in patients with painful small-fiber neuropathy or with lower mechanical pain sensitivity (Faber et al., 2012; Duan et al., 2016; Han et al., 2018).
Nav1.7 (SCN9A, also named PN1), another member of VGSCs, was originally found in mice by Beckers et al. [ 1996] and Kozak and Sangameswaran [1996] and found to be principally expressed in peripheral neurons (Sangameswaran et al., 1997; Toledo-Aral et al., 1997). Lai et al. [ 2000] reported that knocking down the expression of Nav1.7 using Scn9a antisense in DRG neurons alleviated neuropathic pain. Following this, it was found that gain-of-function mutations of SCN9A caused inherited erythromelalgia, idiopathic small-fiber neuropathies, and spontaneous pain (Cummins et al., 2004; Yang et al., 2004; Faber et al., 2012; Xue et al., 2022). Knockout of Scn9a in DRG neurons of mice attenuated mechanical pain, inflammatory pain, and certain types of heat pain (Nassar et al., 2004; Minett et al., 2012), and the loss of function mutation of SCN9A leads to congenital insensitivity to pain in humans (Cox et al., 2006).
Given the strong clinical relevance of Nav1.7 and Nav1.8 in neuropathic pain, second-generation sodium channel blockers selectively targeting Nav1.7 or Nav1.8 have been developed since 2009, but none of them have yet achieved efficient effects in the attenuation of neuropathic pain in clinical trials. We previously found that Nav1.7 and Nav1.8 were upregulated in SDH neurons of peripheral nerve-injured mice and contributed to NP (Sun et al., 2021). It is unknown whether Nav1.7 and Nav1.8 is also ectopically expressed in SDH neurons to participate in NP following SCI. Here we show that SCI also activated ectopic expression of Nav1.7 which led to the sensitization of SDH neurons and contributed to NP.
## Animals
Adult (8–11 weeks) C57BL/6N mice(Vital River, Beijing)were fed with food and water ad libitum and housed five per cage, at 21°C, $50\%$ humidity, on a 12 h light:12 h dark schedule in the standard animal facility in accordance with the guidelines of Tongji University. All animal work was conducted under ethical permission from the Tongji University ethical review panel.
## Measurement of mechanical threshold
Before measuring the mechanical threshold, the mice were placed on a metal mesh and covered with transparent plexiglass, allowing them to acclimate for 30 min. Mechanical threshold was measured according to the previously described procedure (Sun et al., 2021). Briefly, the paw-withdrawal threshold of the ipsilateral hind paws of sham and SCI mice was measured with a set of calibrated monofilaments (von Frey hairs) in order of increasing forces from 0.008 g to 2 g; the force which caused paw withdrawal for three times during continuous stimulation was recorded as the paw-withdrawal threshold. Each monofilament was applied for a maximum of five times. The response ratio of paw withdrawal was the ratio of the number of times the animal withdrew the paw to the total number of measurements.
## Surgery
Spinal cord injury (SCI) surgery was performed under isoflurane-induced anesthesia according to the protocol previously described (Sun et al., 2020) with a modification. Briefly, lumbar spinal segment L5 was exposed by laminectomy at vertebral level T13 (Figure 1A). Under a dissecting microscope, a small cut was made to the dura and arachnoid membrane followed by a defined lesion with a 26G needle (0.3 mm depth) unilaterally to the left dorsal horn, avoiding the dorsal root entry zone. Finally, skin was closed with 5–0 Nylon sutures. The surgical sham procedure was the same as SCI procedures except for injury of the dura, arachnoid membrane, and spinal cord. Mechanical pain threshold was measured at day 0 pre-surgery, and day 3, day 5, and day 8 after surgery. The mice with paw-withdrawal thresholds ≤0.4 g on day 8 after surgery were designated into the SCI group with NP and used for later experiment.
**Figure 1:** *Neuropathic pain developed in SCI mice. (A) The SCI mouse model was generated by punching a hole in left L5 segment of spinal cord under vertebra thoracic segment 13 (T13) using a 26G needle. Injury site is marked with a red circle. (B) Hematoxylin and Eosin (H&E) staining of spinal cord sections showed that the injury track only presented in SCI mice, not in naive nor sham mice. (C) The paw-withdrawal threshold of the ipsilateral side of sham mice pre-surgery and three, five, and eight days post operation (DPO), n = 6. (D) The paw-withdrawal threshold of the ipsilateral side of SCI mice significantly dropped at 3 DPO and further decreased by 8 DPO. Baseline 1.3 ± 0.30 g; Day 3, 0.8 ± 0.35 g; Day 5, 0.7 ± 0.28 g; Day 8, 0.3 ± 0.12 g, n = 7. * p < 0.05, ** p < 0.01, **** p < 0.0001, unpaired t-test or one-way ANOVA. n.s. not significant. Scale bar = 100 μm.*
## Drug treatments
From day 8 to day 11 after surgery, Nav1.7 channel blockers including PF-05089771 (Tocris, #5931, 2 mg/kg, and 4 mg/kg) and GNE-0439 (ProbeChem, # PC-62325, 10 μg/kg, 20 ug/kg, and 30 ug/kg) were administrated individually via IP injection into adult C57BL/6 SCI mice and mechanical pain threshold was tested at 30- and 60-min post drug administration. The voltage-gated calcium channel blocker Gabapentin (Sigma, #G154, 50 mg/kg) was administrated via IP injection into SCI mice and mechanical threshold was tested at 30- and 60-min post drug administration. Each drug test had a vehicle (saline or $20\%$ DMSO) group and pre-treatment threshold was firstly measured each day.
## Western blot
On day 12 after surgery, mice were anaesthetized and perfused using saline via left ventricle, and then L4-6 DRGs, L4-6 SDH, or L5 SDH from SCI mice, sham mice, and control mice were dissected and were immediately lysed with RIPA lysis buffer (Beyotime, China). The lysates were centrifuged at 4°C and 12,000 g for 10 min to pellet remaining cells and the cellular debris. About 20 μg DRG protein or 40 μg SDH protein from each animal were separated by SDS-PAGE in 10 and $6\%$ (3:4) mixed cast gels and transferred onto PVDF membranes (Merck, USA). After 1h-blocking with $5\%$ BSA in PBS, the membranes were incubated with primary antibody at 4 degree for overnight. Primary antibodies included: rabbit antibodies against Nav1.2 (Alomone labs, ASC-022, 1:100), NGF (Abcam, ab52918, 1:1,000), Phospho-c-JUN (Cell signaling technology, #9261, 1:1,000), and GAPDH (Proteintech, 60004-1-Ig, 1:3,000), and mouse antibodies against Nav1.7 (Abcam, ab85015, 1:800) and Nav1.8 (NeuroMab, 75–166, USA, 1:1,000). After washing three times in TBST, the membranes were further incubated with horseradish peroxidase (HRP)-conjugated horse anti-mouse secondary antibodies (Cell signaling technology, 7076S, 1:3,000), or HRP-conjugated goat anti-rabbit secondary antibodies (Cell signaling technology, 7074S, 1:3,000) for 1 h at room temperature. The proteins were visualized by chemiluminescent method using the ECL and detected using Western blot imaging machine (CLiNX, China). The intensity of each protein band was normalized to the intensity of GAPDH band to get the relative expression level of the interested protein.
## Immunostaining
On day 12, mice were anaesthetized and perfused via left ventricle using saline, followed by $4\%$ PFA, and then L4-6 DRGs, L4-6 SDH, or L5 SDH from SCI mice, sham mice, and control mice were dissected and were immersed in $4\%$ PFA overnight. They were then soaked in $15\%$ sucrose for 24 h and $30\%$ sucrose for 24 h; afterwards, tissues were embedded in OCT. DRG and spinal cord tissues were sectioned in pieces of 14 μm thickness using a cryostat, and immunostaining was performed as previously described (Peng et al., 2017). The spinal cord sections and dorsal root ganglia sections were incubated overnight (2 days for Nav1.7 antibodies) at 4°C with primary antibodies, including rabbit antibodies against Nav1.7 (Proteintech Group, 20257-1-AP, 1:200), NGF (Abcam, ab52918, 1:300), and Phospho-c-JUN (Cell signaling technology, #9261, 1:300). The sections were further incubated with IgG Alexa Fluor 488 (Invitrogen, 1:1,000) and IgG Alexa Fluor 555 (Invitrogen, 1:1,000) secondary antibodies for 1.5 h at room temperature. According to the manufacturer’s instructions, we first used tyramide signal amplification (TSA) kits (PerkinElmer, NEL701A001KT, 1:50) to probe Nav1.7 (Proteintech Group, 20257-1-AP, 1:3,000) on the spinal cord sections, then incubated the rabbit antibodies against FOS (Abcam, ab190289, 1:1,000). The sections were further incubated with IgG Alexa Fluor 555 (Invitrogen, 1:1,000). Finally, the sections were counterstained with DAPI (Sigma, MBD0015, 1:10,000). Fluorescent images were captured by confocal laser scanning microscope (LSM780 or LSM880, Carl Zeiss, Oberkochen, Germany).
## Statistical analysis
All quantitative data were presented as the mean ± standard deviation (SD). The data were analyzed using GraphPad Prism 9.0.0 software (GraphPad Software Inc., CA, USA), *The data* collected from von Frey test, protein level quantification, and immunofluorescence staining cell number were analyzed by unpaired t-test or one-way ANOVA, and the response ratios of paw withdrawal of SCI mice were analyzed by two-way ANOVA. A value of * $p \leq 0.05$ was considered as statistically significant.
## Upregulation of Nav1.7 in DRG of SCI mice
To investigate whether the expression levels of sodium channels changed in SDH and DRG after SCI, we generated SCI mouse models by making a restricted narrow lesion on the unilateral dorsal horn of lumbar segment 5 of the spinal cord while keeping the dorsal column and ventral horn intact (Figures 1A,B; Sun et al., 2020). Mechanical pain developed in the ipsilateral paws of SCI mice three days post operation (DPO), and the threshold of mechanical pain further lowered from three to eight DPO, but the paw-withdrawal threshold of sham mice did not significantly change after the sham operation (Figures 1C,D). These results indicated that the SCI model was successfully generated and NP was developed in these SCI mice.
We then examined the expression level of Nav1.2 (SCN2A), Nav1.8 (SCN10A), and Nav1.7 (SCN9A) in SDH, and the result of the Western blot showed that none of the expression levels of Nav1.2, Nav1.8, or Nav1.7 in L4-6 segment SDH of SCI mice was significantly changed when compared to that in naive mice (Figures 2A–C, uncropped image of membrane showed in Supplementary Figure S1). However, these results showed an upregulation trend of Nav1.7 (1.1 ± 0.18) and Nav1.2 (1.1 ± 0.39) in SDH of the SCI mice (Figures 2A–C). It is possible that spinal cord injuries might affect the expression of Nav1.7 and Nav1.2 in DRG, so we detected the expression levels of Nav1.7 and Nav1.2 in DRG using Western blot. The results showed that Nav1.7 was significantly upregulated 1.5-fold in DRG of SCI mice when compared to that in naive mice (Figure 2D, uncropped image of membrane showed in Supplementary Figure S1), but the expression of Nav1.2 was undetectable in the DRG of both SCI and naive mice (data not shown).
**Figure 2:** *Expression of voltage-gated sodium channels in spinal dorsal horn and DRG of SCI mice at 12 DPO. (A) Western blot showed that the expression level of Nav1.2 was not significantly upregulated in SDH of SCI mice when compared to that in naive mice. (B) Western blot demonstrated that there was no significant difference in the expression level of Nav1.8 in SDH between SCI mice and naive mice. (C) The expression level of Nav1.7 in SDH of naive and SCI mice measured by Western-blot assay. (D) The expression level of Nav1.7 in DRG significantly increased 1.5-fold (1.5 ± 0.32) in SCI mice compared to that in naive mice. Data are shown as Mean ± SD, * p < 0.05, n = 4, unpaired t-test. n.s. not significant. Scale bar = 100 μm.*
## Ectopic expression of Nav1.7 in SDH neurons in SCI mice
We previously found that sham operations (cutting open the skin of the leg and destroying the peripheral nerve of DRG neurons in the skin) altered the expression Scn9a mRNA in SDH. To decipher the contributions of spinal injuries and sham operations, which include peripheral injuries in the back skin and drilling a hole in vertebrate to NP, we employed sham group mice and found that there was an increase of Nav1.7 expression in L4-6 DRG of sham mice (1.1-fold), but no statistical significance when compared to that in naive mice; the expression of Nav1.7 in L4-6 DRG was significantly upregulated 1.3-fold more in SCI mice than that in naive mice (Figures 3A,B, uncropped image of membrane showed in Supplementary Figure S2). These data indicated that spinal cord injuries indeed contributed to the upregulation of Nav1.7 in DRG although the sham operation of SCI surgery also mildly enhanced the expression of Nav1.7 in L4-6 DRG.
**Figure 3:** *Upregulation of Nav1.7 in DRG and SDH of SCI mice. (A) The expression level of Nav1.7 in DRG and SDH of naive, sham, and SCI mice was measured by Western blot. (B) Quantitative analysis of duplicate Western-blot membranes showed that compared to naive mice, the expression level of Nav1.7 increased significantly in DRG of SCI mice (1.3 ± 0.10), and an increasing trend in expression level of Nav1.7 was also observed in sham mice (1.1 ± 0.19). The expression level of Nav1.7 in SDH of SCI and sham mice was 2.2-fold and 1.5-fold higher than that in naive mice, respectively, (2.2 ± 1.50, 1.5 ± 1.03). (C) Immunostaining for Nav1.7 (Green) on spinal section derived from naive, sham, and SCI mice. Arrows point to Nav1.7 positive neurons, DAPI is counterstaining for nuclei. Inset is the high magnification view of the boxed area. (D) The number of neurons expressing Nav1.7 in laminae I-VI of naive, sham, and SCI mice. # represents the comparison between SCI and sham，* represents the comparison between naïve and SCI or sham. (E) Immunostaining for Nav1.7 (Green) on DRG section derived from naive, sham, and SCI mice demonstrated the upregulation of Nav1.7 in DRG neurons in SCI and sham mice when compared to that in naive mice. Inset is the high magnification view of the boxed area. Data are shown as Mean ± SD, n = 3. * p < 0.05, ** p < 0.01, *** p < 0.001, ##
p < 0.01, ###
p < 0.001, unpaired t-test or one way ANOVA. n.s. not significant. Scale bar = 100 μm.*
As the damaged area of the spinal cord was small due to only one minor lesion being made in our model, in order to measure the change of gene expression in injured tissue we then dissected injury-site-contained L5 segment SDHs only and excluded the L4 and L6 segments of SDH which were further from the injury site. The Western-blot results showed that the expression level of Nav1.7 was increased 1.5-fold and 2.2-fold in SDH of sham and SCI mice, respectively, although it was not statistically significant (Figures 3A,B). In order to understand if SDH neurons in SCI mice gained expression of Nav1.7, we performed immunostaining for Nav1.7 and found that the number of Nav1.7-expressing neurons significantly increased 2.9-fold and 12.9-fold in SDH of sham (15 ± 3.1 cells per section) and SCI (64 ± 4.9 cells per section) mice, respectively, when compared to that in naive mice (5 ± 1.7 cells per section) (Figures 3C,D). The increased Nav1.7-expressing neurons were distributed from laminae I-VI in SCI mice (Figure 3D). Immunostaining on DRG sections also showed upregulation of Nav1.7 in DRG neurons of SCI mice and sham mice when compared to that in naive mice (Figure 3E).
## Blockers of Nav1.7 alleviated mechanic pain of SCI mice
In order to confirm if the upregulation of Nav1.7 in DRG contributed to NP induced by SCI, we employed the blood–brain-barrier (BBB) non-permeable blocker of Nav1.7, PF-05089771(McDonnell et al., 2018). Intraperitoneal injection of 2 mg/kg PF-05089771 significantly alleviated mechanical pain when compared to vehicle (Figure 4A), and 4 mg/kg of PF-05089771 did not increase the efficacy of pain relief in SCI mice (Figure 4A). These data indicated that the upregulation of Nav1.7 in DRG indeed participated in mechanical allodynia. To investigate if the ectopic expression of Nav1.7 in SDH neurons contributed to NP in SCI mice, we employed another Nav1.7 blocker GNE-0439 (Chernov-Rogan et al., 2018) which is able to penetrate BBB (data not shown). GNE-0439 also attenuated mechanical pain in SCI mice significantly at a dose range from 10 to 30 μg/kg, with maximal efficacy at a dose of 20 μg/kg, but the effects of GNE-0439 declined 30 to 60 min post drug administration (Figure 4B). Intraperitoneal injection of 50 mg/kg Gabapentin also significantly relieved mechanical pain in SCI mice (Figure 4C). However, Gabapentin at this dose caused sedation and movement disability in one out of seven tested mice (data not shown). Although there was not a statistically significant difference, the maximal efficacy of GNE-0439 and Gabapentin in the SCI mice was slightly better than that of PF-05089771 (PF-05089771 0.4 ± 0.20 g, GNE-0439 0.5 ± 0.28 g, Gabapentin 0.5 ± 0.36 g) (Figure 4D). GNE-0439 had equivalent efficacy of pain relief as Gabapentin, but no sedation side effect. We further compared the response ratio of SCI mice to mechanical pressing stimulus from 0.07 g Von Frey to 1.0 g Von Frey after administration of Nav1.7 blockers, and found that GNE-0439 showed better effects to alleviate mechanical pain than PF-05089771 did (Figure 4E). These data suggested that the upregulation of Nav1.7 in DRG and SDH may contribute to NP in SCI mice.
**Figure 4:** *Blockers of Nav1.7-alleviated mechanical pain in SCI mice. (A) Nav1.7 blocker PF-05089771 significantly relieved mechanical pain of SCI mice at doses of both 2 mg/kg and 4 mg/kg (Vehicle 0.03 ± 0.08 g, 2 mg/kg 30 min 0.2 ± 0.13 g, 4 mg/kg 30 min 0.3 ± 0.16 g, 2 mg/kg 60 min 0.4 ± 0.19 g, and 4 mg/kg 60 min 0.4 ± 0.20) g. △PWT = post-drug PWT - pre-drug PWT. (B) Nav1.7 blocker GNE-0439 significantly alleviated mechanical pain of SCI mice at doses from 10 μg/kg to 30 μg/kg (10 μg/kg 30 min 0.3 ± 0.15 g, 20 μg/kg 30 min 0.5 ± 0.28 g, 30 μg/kg 30 min 0.2 ± 0.11 g, 10 μg/kg 60 min 0.2 ± 0.18 g, 20 μg/kg 60 min 0.3 ± 0.20 g, and 30 μg/kg 60 min 0.2 ± 0.08 g). (C) Gabapentin significantly reduced mechanical pain of SCI mice at dose of 50 mg/kg (30 min 0.5 ± 0.36 g and 60 min 0.5 ± 0.30 g). (D) The efficacy of GNE-0439 to relieve mechanical pain was equivalent to Gabapentin, and the efficacy of GNE-0439 and Gabapentin was slightly better than PF-05089771, but not statistically significant (PF-05089771 0.4 ± 0.20 g, GNE-0439 0.5 ± 0.28 g, and Gabapentin 0.5 ± 0.36 g). (E) Response ratio of paw withdrawal of SCI mice administrated neither PF-05089771 (blue line) or GNE-0439 (red line) to stimulus from 0.07 g Von Frey monofilament to 1.0 g Von Frey monofilament. Data are shown as Mean ± SD, n = 7. *p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, unpaired t-test or one-way ANOVA, or two-way ANOVA. n.s. not significant.*
## Nav1.7-expressed SDH neurons involved in NP
Nav1.7 plays an important role in determination of action potential threshold (Bennett et al., 2019), and we previously found that mild mechanical pressing caused mechanical allodynia and activated FOS expression in SDH neurons with ectopic expression of Nav1.7 in Gad2CreERT2/+; miR-96flox/flox mice which had ablation of miR-96 in GAD2 neurons in SDH, but not in DRG (Sun et al., 2021). To investigate if SDH neurons with ectopic expression of Nav1.7 in SCI mice participated in pain conductions, we performed double immunostaining for Nav1.7 and FOS, which is a marker indicating the activation of neurons. The results showed the number of activated neurons with FOS expression increased significantly more in SCI mice than that in sham and naive mice (Figures 5A–M). Moreover, the number of Nav1.7/FOS double-positive neurons was also significantly elevated in laminae III–V of SCI mice (Figures 5D–F,N). Sham mice and naive mice had only a few and no neurons expressing both Nav1.7 and FOS in SDH, respectively. Further analysis showed that more than one quarter (26 ± $8.2\%$) of Nav1.7-expressing SDH neurons were activated in SCI mice which had mechanical stimuli mainly from walking in their home cages (Figures 5O,P). These data together suggested that SCI mice had mechanical pain during walking in their home cages and that ectopic expression of Nav1.7 made neurons in deep laminae layers hyperactive and participatory in the conduction of mechanical pain.
**Figure 5:** *Activation of Nav1.7+ SDH neurons in SCI mice. (A–L) Double immunostaining for FOS (Red) and Nav1.7 (Green) on spinal section derived from naive (A, D, G, J), sham (B, E, H, K), and SCI (C, F, I, L) mice showed more FOS-positive neurons and more FOS/Nav1.7 double-positive neurons in deep laminae layers of SCI mice. Inset is the high magnification view of the boxed area. Scale bar 100 μm. (M–O) The number of FOS+
(M), Nav1.7+/FOS+
(N), and Nav1.7+
(O) SDH neurons in laminae I–VI of home-caged naive mice, sham, and SCI mice. The symbol # represents the comparison between SCI and sham, and the symbol * represents the comparison between naive and SCI or sham. (P) The ratio of Nav1.7+/FOS+ SDH neurons to total Nav1.7+ SDH neurons in naive (0%), sham (3 ± 4.8%), and SCI (26 ± 8.2%) mice. Data are shown as Mean ± SD, n = 3. #
p < 0.05, ##
p < 0.01,*p < 0.05, ** p < 0.01, ***p < 0.001, unpaired t-test or one way ANOVA. Scale bar = 100 μm.*
## SCI induced the elevation of NGF and phosphorylated-JUN in DRG and SDH mice
It was reported that the expression of Nav1.7 in DRG could be induced by nerve growth factor (NGF) (Toledo-Aral et al., 1997; Liu et al., 2021), so we examined whether the expression of NGF was upregulated after SCI. The results of the Western blot showed that the expression level of NGF increased 1.7-fold and 1.2-fold in DRG and SDH, respectively, in SCI mice when compared to that in naive mice (Figures 6A,B, uncropped image of membrane showed in Supplementary Figure S3), although it was not statistically significant. We then detected the expression of NGF on a cellular level by immunostaining and found that the number of NGF-expressing SDH neurons significantly increased in laminae IV–VI of SCI mice when compared to those in both sham and naive mice (Figures 6C,D). The immunostaining also demonstrated that the number of neurons expressing high levels of NGF (NGFH+) was upregulated in L4-6 DRG of SCI and sham mice when compared to that in naive mice (Figures 6E,F), which was in line with previous reports that the expression of NGF in DRG was induced by injuries of the skin and muscle (Liu et al., 2021). We then asked how NGF activated transcription of Scn9a. It is known that NGF can activate MAPK/ERK signaling pathways in DRG neurons (Obata et al., 2004) and that MAPK/ERK activates JUN (Raitano et al., 1995). We found that there are predicted binding sites of JUN and FOS on the promoter of Scn9a 1, so it is possible that NGF induces the upregulation of Nav1.7 through MAPK/ERK/JUN/FOS. Therefore, we examined the expression of phosphorylated JUN by Western blot and immunostaining. The result of Western blot showed that the expression levels of phosphorylated JUN was not significantly increased in DRG and SDH of SCI mice when compared to that in naive mice (Figures 7A,B, and uncropped image of membrane showed in Supplementary Figure S4). However, the number of phosphorylated JUN-expressing SDH neurons significantly increased in laminae I–VI of SCI mice when compared to naive mice, and SCI mice had more phosphorylated JUN-expressing SDH neurons on laminae IV than sham mice (Figures 7C–O). The immunostaining also demonstrated that the number of neurons expressing high levels of phosphorylated JUN (Phos-JUN H+) was upregulated in L4-6 DRG of SCI mice when compared to that in naive and sham mice (Figures 7P–V). Moreover, phosphorylated JUN was found in the nucleus of DRG and SDH neurons of SCI mice (Figures 7N,U). These data suggest that NGF induced Nav1.7 expression via transcription factor JUN in both SDH and DRG after spinal cord injury.
**Figure 6:** *Induced expression of NGF in DRG and SDH of mice after SCI. (A) The expression level of NGF in DRG and SDH of naive, sham, and SCI mice was measured by Western blot. (B) Quantitative analysis of duplicate Western-blot membranes showed that there was an increase trend in the expression level of NGF in DRG and SDH in SCI and sham mice when compared to naive mice (sham DRG 1.5 ± 0.34, SCI DRG 1.7 ± 0.52; sham SDH 1.1 ± 0.11, SCI SDH 1.2 ± 0.32). (C) Immunostaining for NGF (Green) on spinal cord section derived from naive, sham, and SCI mice. Arrows point to NGF positive neurons. Inset is the high magnification view of the boxed area. (D) The number of neurons expressing NGF in laminae I-VI of naive, sham, and SCI. # represents the comparison between SCI and sham, * represents the comparison between naive and SCI or sham. (E, F) Immunostaining for NGF (Green) on DRG section derived from naive, sham, and SCI mice (E) and the number of neurons expressing high levels of NGF in DRG neurons in naive, sham, and SCI mice. Inset is the high magnification view of the boxed area. Data are shown as Mean ± SD, n = 3. ## p < 0.01, ###p < 0.001, *p < 0.05, **p < 0.01, ***p < 0.001,unpaired t-test or one way ANOVA. n.s. not significant. Scale bar = 100 μm.* **Figure 7:** *Increase of phosphorylated JUN in SDH and DRG of SCI mice. (A). The expression level of phosphorylated JUN in DRG and SDH of naive, sham, and SCI mice was measured by Western blot. (B) Quantitative analysis of the expression of phosphorylated JUN in DRG and SDH in naive, sham, and SCI (sham DRG 1.0 ± 0.26, SCI DRG 1.1 ± 0.40; sham SDH 1.1 ± 0. 48, SCI SDH 1.2 ± 0.50). (C–N) Immunostaining for phosphorylated JUN (green) and DAPI (red) staining on spinal cord sections derived from naive (C–F), sham (G–J), and SCI mice (K–N). (E-F, I–J) and (M–N) are the high magnification views of the boxed area in (C,D), (G,H), and (K, L), respectively. The inset (right) in (N), which is the high magnification view of the boxed area in (N) (left), shows phosphorylated JUN in the nucleus. (O) The number of neurons expressing phosphorylated JUN in laminae I–VI of naive, sham and SCI mice. (P–U) Immunostaining for phosphorylated JUN (green) and DAPI (red) staining on DRG sections derived from naive (P, S), sham (Q, T), and SCI (R, U) mice. (S–U) are the high magnification views of the boxed area in (P–R), respectively. The inset (right) in (U), which is the high magnification view of the boxed area in (U, left) shows phosphorylated JUN in the nucleus. (V) The number of neurons expressing phosphorylated JUN in DRG of naive, sham, and SCI mice. # represents the comparison between SCI and sham. * represents the comparison between naive and SCI or sham. Data are shown as Mean ± SD, n = 3. #p < 0.05, *p < 0.05, **p < 0.01, unpaired t-test or one-way ANOVA. n.s. not significant. Scale bar = 100 μm.*
## Discussion
Voltage-gated sodium channel Nav1.7 plays a vital role in physiological and pathological pain. Here, we demonstrated that SCI induced upregulation of NGF and JUN in both SDH and DRG neurons, subsequently increased the expression of Nav1.7 in SDH and DRG neurons to contribute to NP, and Nav1.7 selective blockers significantly attenuated NP in SCI mice (Figure 8).
**Figure 8:** *Schematic view of mechanism underlying SCI-induced NP. (A). SCI induced the upregulation of NGF, and consequently increased phosphorylated JUN and the upregulation of Nav1.7 in SDH and DRG neurons of mice. (B). Nav1.7 selective blockers attenuated SCI-induced NP in mice through inhibiting the activity of Nav1.7 in both the spinal cord and DRG.*
Under physiological conditions, Nav1.7 is primary expressed in the peripheral nerve system (Toledo-Aral et al., 1997); here, we reported that Nav1.7 was induced to be expressed ectopically in SDH neurons located in both the superficial and deep layers surrounding the damaged spinal area in SCI mice (Figures 3C,D). It is known that neurons in deep layers (laminae III–VI) may be involved in pain conduction (Yu et al., 2011; Peng et al., 2017; Zain and Bonin, 2019), and the ectopic expression of Nav1.7 leads to hypersensitivity and hyperexcitability of SDH neurons in deep (III–V) and superficial layers (I–II) (Sun et al., 2021). The majority of Nav1.7-expressing neurons are located in laminae III–VI and more than $20\%$ of these Nav1.7-expressing neurons were activated to express FOS in the SCI mice which had mechanical stimuli mainly from walking. This suggested that ectopic expression of Nav1.7 in laminae III–VI neurons participated in the conduction of mechanical and/or spontaneous pain. These data and our previous findings that peripheral nerve injury and knockout of miR-96 led to ectopic expression of Nav1.7 in SDH neurons (Sun et al., 2021) together suggested that Nav1.7 was also expressed in SDH neurons and contributed to central sensitization and NP under pathological conditions, such as peripheral and spinal neuropathy.
Nav1.7 blockers were able to attenuate mechanical pain in the SCI mice, and GNE-0439 obtained an efficacy of pain relief equivalent to Gabapentin, but without the side effects. Moreover, in line with the increase of Nav1.7 expression in SDH neurons, BBB-permeable Nav1.7 blocker GNE-0439 relieved mechanical pain better than BBB non-permeable Nav1.7 blocker PF-05089771 (Figures 4D,E). However, the efficacy of GNE-0439 in SCI mice quickly dropped from 30 min after drug administration to 60 min after drug administration, suggesting that the half-life of GNE-0439 in mouse is short and its efficacy could be improved by increasing its metabolic stability and/or using a sustained-release tablet form. These pharmacological data suggest that BBB-permeable Nav1.7 blocker might efficiently attenuate NP in patients with SCI.
It was previously reported that primary DRG neuron culture treated with NGF upregulated the expression of Nav1.7 and increased the distribution of Nav1.7 in axon growth cone which was believed to play an important role for axon projection (Toledo-Aral et al., 1997); it is also known that SCI induced the expression of NGF mRNA and protein in both spinal cord and DRG neurons (Bakhit et al., 1991; Krenz and Weaver, 2000; Brown et al., 2004, 2007), so upregulation of NGF in DRG neurons found in the SCI mice was possible to help DRG neurons to regenerate axon projection through axon growth cone-located Nav1.7. The distribution patterns of Nav1.7-expressing SDH neurons (laminae I-VI) and NGF-expressing SDH neurons (majority in laminae IV-VI) overlapped, surrounding the damage area and with the majority of them located in deep layers (Laminae III–VI) (Figures 3C, 6C); this suggested that NGF was autocrine and/or paracrine to activate the expression of Nav1.7 in SDH neurons. Therefore, NGF induced by injury played contradictory roles in SCI. On one hand, it could promote the repairment of the spinal cord and survival of both spinal neurons and DRG neurons (Rich et al., 1989; Kim et al., 1996; Ljungberg et al., 1999); on the other hand, NGF caused central sensitization and peripheral hyperexcitability via increasing expression of Nav1.7 in both SDH and DRG neurons (Figure 3). Anti-NGF treatment or blocking NGF signaling pathways could reduce SCI-induced NP and osteoarthritic pain in animal models (Christensen and Hulsebosch, 1997; Krenz et al., 1999; Gwak et al., 2003; Hirose et al., 2016; Eitner et al., 2017), but in the meanwhile could also delay the reparation of the spinal cord. Our data showed that transcription factor JUN, downstream of NGF signaling pathways (Yu et al., 2011; Zain and Bonin, 2019) was increased in laminae I-VI of SCI mice in a similar pattern as Nav1.7 (Figure 7), and we found that there are predicted binding sites of JUN and FOS on the promoter of Scn9a 2; together, these suggest that NGF induces upregulation of Nav1.7 through MAPK/ERK/JUN. One choice to eliminate the negative role of NGF is to block its activation of Nav1.7, which could be approached by inhibiting the downstream MAPK signaling pathways, but retaining the downstream PKC and NF-KB signaling pathways which promote survival, or could be approached by Nav1.7-targeted siRNAs (Lai et al., 2000) or miRNAs, such as miR-96 (Sun et al., 2021).
In conclusion, our data demonstrated that the upregulation of Nav1.7 was induced by SCI in both SDH and DRG neurons through increased expression of NGF and its downstream transcription factor JUN, and the inhibition of Nav1.7 in both peripheral and spinal neurons was better than the inhibition of peripheral Nav1.7 only in the alleviation of mechanical pain in SCI mice. These data suggest that BBB permeable Nav1.7 blocker might relieve NP in patients with SCI.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The animal study was reviewed and approved by Tongji University ethical review panel.
## Author contributions
CP: conceptualization and writing – original draft. YF, LS, FZ, WX, TW, and RL: methodology. YF and CP: investigation and visualization. CP and LS: funding acquisition. DX and CP: project administration and supervision. LS, XY, and DX: writing – review and editing. All authors contributed to the article and approved the submitted version.
## Funding
Changgeng *Peng is* supported by the National Natural Science Foundation of China [32070977, 51971236, 31871063] and National Major Science and Technology Projects of China (2018ZX09733001-006-005). Liting *Sun is* supported by the National Natural Science Foundation of China [82101320].
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2023.1091096/full#supplementary-material
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|
---
title: Characterization of a novel potency endpoint for the evaluation of immune checkpoint
blockade in humanized mice
authors:
- Alba Matas-Céspedes
- Jean-Martin Lapointe
- Matthew J. Elder
- Gareth J. Browne
- Simon J. Dovedi
- Lolke de Haan
- Shaun Maguire
- Richard Stebbings
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10020612
doi: 10.3389/fimmu.2023.1107848
license: CC BY 4.0
---
# Characterization of a novel potency endpoint for the evaluation of immune checkpoint blockade in humanized mice
## Abstract
### Introduction
Humanized mice are emerging as valuable models to experimentally evaluate the impact of different immunotherapeutics on the human immune system. These immunodeficient mice are engrafted with human cells or tissues, that then mimic the human immune system, offering an alternative and potentially more predictive preclinical model. Immunodeficient NSG mice engrafted with human CD34+ cord blood stem cells develop human T cells educated against murine MHC. However, autoimmune graft versus host disease (GvHD), mediated by T cells, typically develops 1 year post engraftment.
### Methods
Here, we have used the development of GvHD in NSG mice, using donors with HLA alleles predisposed to autoimmunity (psoriasis) to weight in favor of GvHD, as an endpoint to evaluate the relative potency of monoclonal and BiSpecific antibodies targeting PD-1 and CTLA-4 to break immune tolerance.
### Results
We found that treatment with either a combination of anti-PD-1 & anti-CTLA-4 mAbs or a quadrivalent anti-PD-1/CTLA-4 BiSpecific (MEDI8500), had enhanced potency compared to treatment with anti-PD-1 or anti-CTLA-4 monotherapies, increasing T cell activity both in vitro and in vivo. This resulted in accelerated development of GvHD and shorter survival of the humanized mice in these treatment groups commensurate with their on target activity.
### Discussion
Our findings demonstrate the potential of humanized mouse models for preclinical evaluation of different immunotherapies and combinations, using acceleration of GvHD development as a surrogate of aggravated antigenic T-cell response against host.
## Introduction
Immunotherapy has emerged in recent years as a novel approach for treatment of cancer through modulation of the patient’s immune system. Antibodies targeting immune checkpoint inhibitors such as programmed cell death-1 (PD-1) its ligand (PD-L1), or cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) have greatly contributed to advances in cancer treatment in diverse tumour types (1–4). Regulation of immune responses through PD-1 and CTLA-4 is mediated by non-redundant mechanisms [5]. While both are negative signals for T cell activation, CTLA-4 is expressed exclusively on T cells and regulates the amount of CD28 co-stimulatory signalling a T cell receives during both activation and also at later stages of differentiation [5, 6]. By contrast PD-1 is more broadly expressed and regulates T cells at later stages of immune activation in peripheral tissues or at the tumour site [5]. Though recent evidence points to an overlap in the PD-1 and CTLA-4 pathways, given the novel finding that CD80, a ligand for CTLA-4, can dimerise in cis with PD-L1 [7]. Hence, combined blockade of both immune checkpoints could lead to additive or synergistic effects and improve the response rate to these therapies compared to their use as monotherapies. In this line, clinical data demonstrates the impact of combined therapy with increases in overall survival observed in patients with different solid tumours (8–10).
Syngeneic mouse models are relevant for the study of immunotherapies as they consist of immunocompetent mice engrafted with tumour tissues derived from the same genetic background and can help to understand the immune response after immunotherapy treatment. However, a number of limitations complicate the translational value of these models for humans [11]. First, mouse cancer cell lines are limited and do not carry many of the relevant mutations associated with clinical disease. Second, surrogate antibodies, which are functionally equivalent to the therapeutic antibody candidate but binding to the target ortholog expressed in mice, are sometimes difficult to be generated and usually have affinities and binding domains that may not closely resemble the biology of the candidate drug, rendering the data of limited use to understand the pharmacology of the drug on the immune system [11, 12].
Immunodeficient mice engrafted with a functional human immune system are emerging as useful models to evaluate some human immune responses in different pre-clinical scenarios, however they have a number of caveats given their incomplete human immune reconstitution (13–16). The most common immunocompromised strain of mice used for humanization is the NOD-scid IL2Rγnull (NSG) [17, 18]. These mice can be engrafted with either PBMCs [13] or human haematopoietic stem cells (HSCs), isolated from umbilical cord blood (UCB) [19], bone marrow (BM) [20] or foetal liver [21]. This latter model leads to the generation of a diverse repertoire of human immune cells in the mice, including B, T, NK cells, monocytes and dendritic cells, and humanization is improved if newborn mice are used instead of adult mice [22]. It typically takes 16 weeks to obtain stable human cell engraftment in mice, and they can survive for more than one year without developing T cells-mediated graft-versus-host disease (GvHD). In these humanized mice, injected stem cells are educated against the murine major histocompatibility complex (MHC) during their differentiation into T cells, making them tolerant to the mouse environment, compared to the PBMC model [13], with higher thresholds for their activation [23, 24]. The use of human HSCs cells with certain human leucocyte antigen (HLA) types associated with development of autoimmune disorders such as psoriasis or rheumatoid arthritis has been previously reported to accelerate the development of GvHD in this model [25, 26].
In this study, we first compared the in vitro biological functionality of different immune checkpoint inhibitors: anti-PD-1 monoclonal antibody (mAb), anti-CTLA-4 mAb, a combination of anti-PD-1 & anti-CTLA-4 mAbs and MEDI8500 that is bivalent for both targets [27] but has reduced affinity to the CTLA-4 receptor. For in vivo comparisons, we used UCB-derived CD34+ HSCs from donors with HLA alleles predisposed to psoriasis to humanize newborn NSG mice, in order to accelerate development of GvHD, as previously described [25, 26]. In these mice we compared the ability of anti-PD-1 and anti-CTLA-4 mAbs as single agents, in combination, and MEDI8500 to break tolerance to the mouse environment, using the development of GvHD lesions as a surrogate to evaluate aggravated antigenic T cell response and as an endpoint to determine relative potency. We demonstrate that both the combination of anti-PD-1 & anti-CTLA-4 mAbs and MEDI8500, show enhanced potency compared to anti-PD-1 or anti-CTLA-4 monotherapy both in vitro and in vivo. This study provides further evidence for the utility of humanized mice for the evaluation of comparative potency of different immunotherapeutics and combinations.
## Antibodies
Anti-PD-1 LO115 is a fully human IgG1 mAb with an affinity for human PD-1 of KD = 0.81nM. Anti-CTLA-4 TM is a fully human IgG1 mAb with an affinity for human CTLA-4 of KD = 0.42nM. Anti-PD-1/CTLA-4 BiS 5 (MEDI8500) is a bivalent BiSpecific antibody where the Fab domains are specific for PD-1, and where the scFv domains are specific for CTLA-4 and are tethered to the CH3 domain. In the BiSpecific format the affinity for human PD-1 is KD = 1.23nM and human CTLA-4 is KD = 1.22nM (Supplementary Figure 1). All antibodies were generated by AstraZeneca.
## PD-1 reporter assay
CHO K1 OKT3-CD14 (low) hB7H1 (high) cl 2 cells (expressing anti-CD3 and PD-L1) were seeded at 0.6 × 104 cells in RPMI1640 GlutaMAX™ medium (Gibco, Paisley, UK) supplemented with $10\%$ FBS, in 384-well tissue culture-treated plates and incubated for 18 hours at 37°C. Supernatants were removed and fresh culture medium added with 1.5 × 104 Jurkat NFAT Luc2 PD1 clone 3L-B9 cells (expressing PD-1 and possessing NFAT promoter driven luciferase expression) supplemented with 2.31 nM anti-CD28 antibody (BD Biosciences, Oxford, UK). Single agents anti-PD-1 and anti-CTLA-4 were added to this co-culture in a 3-fold dose titration from 0.005 nM to 300 nM. MEDI8500 was added in a 3-fold dose titration from 0.016 nM to 1000 nM. Co-cultures were incubated for 5 hours and 40 minutes at 37°C followed by 20 minutes at room temperature. Steady-Glo Buffer (Promega, Southampton, UK) was added to tissue culture wells for 10 minutes to lyse cells and luminescence was detected using the using the ultra-sensitive luminescence settings on an Envision spectrophotometer (Perkin Elmer, Seer Green, UK).
## CTLA-4 reporter assay
2 × 104 Raji cells (expressing CD80 and CD86) and 8 × 104 Jurkat CTLA4 IL2 luc2 cells (expressing CTLA-4 and possessing IL-2 promoter driven luciferase expression) were seeded in RPMI1640 GlutaMAX™ medium supplemented with $10\%$ FBS and $1\%$ non-essential amino acids), in 96-well tissue culture-treated plates supplemented with 2.5 µg/mL anti-CD3 (eBioscience, UK). Single agents anti-PD-1 and anti-CTLA-4 were added to this co-culture in a 3-fold dose titration from 0.008 nM to 500 nM. MEDI8500 was added in a 3-fold dose titration from 0.160 nM to 1111 nM. Co-cultures were incubated for 6 hours at 37°C. Steady-Glo Buffer was added to tissue culture wells for 10 minutes to lyse cells and luminescence was detected using the ultra-sensitive luminescence settings on an Envision spectrophotometer.
## Human anti-CD3/SEB assay
Human PBMCs were isolated and re-suspended in assay medium, RPMI1640 GlutaMAX™ medium supplemented with $10\%$ FBS and $1\%$ penicillin-streptomycin and a total of 2 × 105 PBMCs/well were added in triplicates to a 96-well flat-bottomed tissue culture-treated plate, pre-coated for 2 hours at 37°C with 0.5 µg/mL mouse anti-human CD3 antibody (Invitrogen, Paisley, UK), then Staphylococcal enterotoxin B (SEB) (Sigma-Aldrich, St. Louis, MO, USA) was added to a final concentration of 100 ng/mL. Next, anti-PD-1, anti-CTLA-4, MEDI8500, or a combination of anti-PD-1 + anti-CTLA-4 monotherapies were added in a 4-fold dose titration from 400 nM. Cell cultures were incubated at 37°C with $5\%$ CO2 for 3 days, supernatants were harvested, and IL-2 secretion was evaluated by ELISA (R&D Systems, Minneapolis, MN, USA).
## Mice
Human CD34+ haematopoietic stem cell-engrafted NSG™ females (huNSG) were purchased from The Jackson Laboratory (Sacramento, CA, USA) with specific HLA types predisposed to autoimmunity (Table 1). Mice were housed in individually ventilated cages under SPF conditions and used at ~19 weeks of age. Animals were checked daily for morbidity and mortality. At the time of routine monitoring, the animals were checked for any effects of treatments on normal behaviour such as mobility, visual estimation of food and water consumption, body weight gain/loss, eye/hair matting and any other abnormal observations. All study procedures were conducted following an approved IACUC protocol and Crown Bioscience San Diego Standard Operating Procedures.
**Table 1**
| Donor | 2315 | 2340 | 5263 | 5437 | 5443 | 5445 | 5468 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| HLA-A | A*03:02 | A*23:01 | A*02:01 | A*01:03 | A*02:01 | A*02:01 | A*01:01 |
| HLA-A | A*24:02 | A*30:04 | A*26:01 | A*33:03 | A*30:01 | A*02:01 | A*68:01 |
| HLA-B | B*51:01 | B*44:03 | B*27:05 | B*13:02 | B*13:02 | B*07:02 | B*18:01 |
| HLA-B | B*57:01 | B*53:01 | B*35:01 | B*39:24 | B*13:02 | B*35:01 | B*35:02 |
| HLA-C | C*04:01 | C*06:02 | C*02:02 | C*06:02 | C*06:02 | C*04:01 | C*04:01 |
| HLA-C | C*06:02 | C*07:01 | C*04:01 | C*07:01 | C*06:02 | C*07:02 | C*07:01 |
| HLA-DRB | DRB1*04:01 | DRB1*07:01 | DRB1*04:04 | DRB1*11:02 | DRB1*07:01 | DRB1*11:01 | DRB1*08:01 |
| HLA-DRB | DRB1*07:01 | DRB1*13:04 | DRB1*12:01 | DRB1*12:01 | DRB1*07:01 | DRB1*13:05 | DRB1*13:01 |
| HLA-DRB | DRB4*01:03 | DRB3*02:02 | DRB3*02:02 | DRB3*02:02 | DRB4*01:03 | DRB3*02:02 | DRB3*01:01 |
| HLA-DRB | DRB4*01:03N | DRB4*01:01 | DRB4*01:03 | DRB3*02:02 | DRB4*01:03 | DRB3*02:02 | Blank |
| HLA-DQ | DQB1*03:01 | DQB1*02:02 | DQB1*03:01 | DQB1*03:01 | DQB1*02:02 | DQB1*03:01 | DQB1*04:02 |
| HLA-DQ | DQB1*03:03 | DQB1*03:01 | DQB1*03:02 | DQB1*05:01 | DQB1*02:02 | DQB1*03:01 | DQB1*06:03 |
| HLA-DQ | DQA1*02:01 | DQA1*02:01 | DQA1*03:01 | DQA1*01:04 | DQA1*02:01 | DQA1*05:05 | DQA1*01:03 |
| HLA-DQ | DQA1*03:03 | DQA1*05:05 | DQA1*05:05 | DQA1*05:05 | DQA1*02:01 | DQA1*05:05 | DQA1*04:01 |
| HLA-DP | DPB1*04:01G | DPB1*13:01G | DPB1*04:01G | DPB1*01:01G | DPB1*04:01G | DPB1*02:01G | DPB1*02:01G |
| HLA-DP | DPB1*04:01G | DPB1*17:01G | DPB1*04:01G | DPB1*04:02G | DPB1*20:01G | DPB1*09:01G | DPB1*04:01G |
| HLA-DP | DPA1*01:03 | DPA1*02:01 | DPA1*01:03 | DPA1*02:02 | DPA1*01:03 | DPA1*01:03 | DPA1*01:03 |
| HLA-DP | DPA1*01:03 | DPA1*02:01 | DPA1*01:03 | DPA1*03:01 | DPA1*01:03 | DPA1*02:01 | DPA1*01:03 |
## In vivo study design
Two different in vivo studies were run using the same design. Before grouping and treatment all animals were weighed. The grouping was performed by using StudyDirector™ software (Studylog Systems, Inc. CA, USA). One optimal randomization design (generated by Matched distribution) that showed minimal group to group variation was selected for group allocation. Animals within a single HLA cohort were evenly distributed throughout the groups. Randomization was based on body weight and donor HLA type. Prior to dosing, cheek bleeds were collected from each animal for flow cytometry to detect the baseline immune cell population prior to treatment.
The data presented is a compilation of two different studies. There were 5 treatment groups in total: vehicle ($$n = 35$$), anti-PD-1 ($$n = 15$$), anti-CTLA-4 ($$n = 15$$), combination of anti-PD-1 + anti-CTLA-4 ($$n = 20$$) and MEDI8500 ($$n = 35$$). Mice received 3 mg/kg of the corresponding single agent or MEDI8500, or a combination of 3mg/kg + 3 mg/kg in the anti-PD-1 + anti-CTLA-4 combination group subcutaneously every 3 days, followed by termination on day 46 (Supplementary Figure 2). Body weight and clinical observations were made 3 times a week. Signs and clinical observations of GvHD were established as follows: Grade 0 = Normal; Grade 1 (mild) = 5-$10\%$ weight loss, mildly decreased activity, hunching only at rest and/or mild ruffling; Grade 2 (moderate) = 10-$20\%$ weight loss, moderately decreased activity, hunching only at rest and/or moderate alopecia; Grade 3 (severe) = >$20\%$ weight loss, stationary until stimulated, impaired movement and/or severe ruffling.
Animals were terminated for humane reasons if body weight loss was > $20\%$ over a period of 72 hours. Upon termination, blood was collected for plasma processing and flow cytometric analysis. Lungs, liver and half of the spleen were collected for histology and immunohistochemistry. The other half of the spleen was processed for flow cytometric analysis. For any animals found dead, tissues were not taken for analysis.
## Histopathology and immunohistochemistry
Samples of liver, lung and spleen were fixed in $10\%$ neutral-buffered formalin, and processed to paraffin blocks using routine methods. Previous observations (unpublished) had shown that these organs were the most consistently and strongly affected in animals that developed GvHD. Tissues were sectioned at 4 μm thickness and stained with hematoxylin and eosin (H&E), or with immunohistochemistry using a rabbit monoclonal antibody specific for human CD45 (D9M81, Cell Signaling Technology, Leiden, The Netherlands) at a 0.05 µg/mL dilution, on an automated Leica Bond-RX immunostainer (Leica Biosystems, Wetzlar, Germany), using DAB as a chromogen. This antibody was demonstrated not to cross-react with mouse CD45 in wild-type mouse lymphoid tissues (unpublished observations).
For histopathology evaluation, H&E stained sections were examined by a board-certified veterinary pathologist with experience in mouse pathology, and evaluated for inflammatory/immune changes. Changes that were considered by the pathologist to be a consequence of GvHD (rather than background/incidental changes) were characterised and scored for severity on a subjective scale of 1 to 4, depending on the extent of the change thoughout the tissue. A total GvHD score [0-12] was calculated by adding the lesion scores from each tissue (if more than one change was observed per tissue, only the highest lesion score was used for that tissue).
For immunohistochemistry evaluation, sections stained for huCD45 were digitally scanned at 20x magnification, using an Aperio scanner (Leica Biosystems). The extent of huCD45 cell infiltration in the tissues was quantified using Halo image analysis software (Indica Labs, Albuquerque, NM, USA). Briefly, the tissue region of interest (ROI) was annotated by the pathologist, targeting the maximal amount of tissue, but excluding large tissue artifacts (debris, folds, etc…) and extraneous tissue. Positive huCD45 staining was quantified, using the Halo Area Quantification algorithm (v.2.1.3), adapted to the staining characteristics of the study. An area-based detection was considered more representative than cell-based quantification, because the intensity of huCD45 staining and the tendency of huCD45-positive cells to form dense clusters made single-cell recognition difficult. The huCD45-positive area was reported as percentage of total tissue area (excluding clear spaces).
## Flow cytometry
Flow cytometric analysis was conducted on whole blood from cheek bleeds prior to dosing, and also on blood and spleen from all mice taken at termination, except for those found dead. Upon collection of blood samples, red blood cells (RBC) were lysed first with 1x RBC Lysis Buffer (Invitrogen, Waltham, MA, USA), followed by mouse Fc blocking with TruStain FcX™ (anti-mouse CD$\frac{16}{32}$, BioLegend, San Diego, CA, USA) and human Fc blocking with Human TruStain FcX™ (BioLegend) prior to the staining. The spleens were smashed through PBS pre-wet 70-μM cell strainers, followed by RBC lysis and Fc blocking before staining. Single cells were stained with the following anti-human antibodies obtained from BioLegend: CD45 (clone 2D1), CD3 (clone HIT3a), CD8 (clone SK1), CD4 (clone RPA-T4), CD154 (CD40L) (clone 24-31) and CD134 (OX40) (clone Ber-ACT35). Stained samples were run on a BD LSRFortessa™ flow cytometer (BD Biosciences, San Jose, CA, USA). Data were analysed with the Kaluza Analysis Software (Beckman Coulter, Brea, CA, USA). Human cells were phenotyped following the gating strategy presented in Supplementary Figure 3.
## Statistical analysis
Data analysis was conducted using GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA). Survival curves were represented with Kaplan-Meier plots and statistical differences calculated using the Mantel-Cox test. A Mixed-effects analysis followed by Sidak’s multiple comparisons test was used to evaluate statistical differences between pre-treatment and post-treatment groups. A one-way ANOVA followed by Tukey’s multiple comparison was used to evaluate statistical differences between treatment and control groups. Differences in incidence of histologic GvHD lesions between groups were evaluated with chi-square tests.
## In vitro functionality of monoclonal antibodies and MEDI8500 targeting PD-1 and CTLA-4
We performed two separate reporter assays to evaluate the in vitro potency of the different antibodies blocking both pathways: PD-1/PD-L1 and CTLA-4/CD80&86. In the PD-1 reporter assay, we observed that the potency for human PD-1 reporter blockade of anti-PD-1 mAb as single agent (EC50 = 0.92 nM) was similar to that of MEDI8500 (EC50 = 1.30 nM) (Figure 1A). In the CTLA-4 reporter assay, the potency for human CTLA-4 receptor blockade of anti-CTLA-4 mAb as single agent (EC50 = 3.57 nM) was ~ 4-fold higher compared to that of MEDI8500 (EC50 = 12.61 nM) (Figure 1B).
**Figure 1:** *Increased in vitro potency with antibody combination and MEDI8500 compared to monotherapies (A) Bioluminescence levels in a PD-1/PD-L1 blockade bioassay after 5 hours and 40 minutes of incubation with increasing doses (3-fold) of either anti-PD-1, anti-CTLA-4 or MEDI8500. (B) Bioluminescence levels in a CTLA-4 blockade bioassay after 6 hours of incubation with increasing doses (3-fold) of either anti-PD-1, anti-CTLA-4 or MEDI8500. Data are represented as Mean ± SD of duplicates in each point. (C) Levels of IL-2 secretion by human PBMCs after 72h of in vitro treatment with increasing doses (4-fold) of either anti-PD-1, anti-CTLA-4, combination of anti-PD-1 + anti-CTLA-4 or MEDI8500, on an anti-huCD3 + SEB stimulation assay. Data are represented as Mean ± SEM for 4 independent donors. A two-way ANOVA followed by Tukey’s multiple comparison was used to evaluate statistical differences between antibody combination or MEDI8500 and single agents at each concentration; statistically significant differences are noted when *P < 0.05.*
Moreover, treatment of primary PBMCs with MEDI8500 significantly increased the levels of IL-2 secreted by PBMCs at all concentrations after 72 hours, compared to the respective single agents (Figure 1C). Similarly, the combination of anti-PD-1 + anti-CTLA-4 mAbs significantly raised the levels of IL-2 secreted at different concentrations compared to the single agents, but to a lesser extent than MEDI8500. No significant differences were observed in the level of IL-2 secretion between MEDI8500 and combination treatment (Figure 1C).
## In vivo functionality of monoclonal antibodies and MEDI8500 targeting PD-1 and CTLA-4
Acceleration of GvHD in huNSG mice after immunotherapy treatment was used as an endpoint to evaluate the relative potency of monoclonal & BiSpecific antibodies targeting PD-1 and CTLA-4. Most animals treated with the single agents did not develop symptoms of GvHD (Grade 0) or had mild signs of rough coats and hunching (Grade 0-1) between day 24-46, and the majority survived until the end of the study (12 out of 15 in the anti-PD-1 mAb group and 14 out of 15 in the anti-CTLA-4 mAb group). However, survival was significantly decreased in the anti-PD-1 group compared to vehicle (Figure 2A). In the mice that received the anti-PD-1 + anti-CTLA-4 combination significantly reduced survival was noted compared to the control group, and when compared to the anti-PD-1 or anti-CTLA-4 single agent groups (Figure 2A), with some animals terminated as soon as 11 days after the start of treatment due to the onset of Grade 3 adverse signs, including moderate rough coats and hunching, in addition to weight loss (Supplementary Figure 4), and just 6 out of 20 mice ($30\%$) surviving until the end of the study with mild (Grade 0-1) or moderate (Grade 2) symptoms of GvHD. Similarly, mice in the MEDI8500 group showed a significantly decreased survival compared to the control animals, and animals receiving anti-PD-1 or anti-CTLA-4 as single agents, with mice terminated from day 19 onwards due to development of Grade 3 GvHD phenotype, including moderate rough coats, hunching and emaciation. Just 11 out of 35 mice ($31\%$) survived until the end of the study with mild (Grade 0-1) or moderate (Grade 2) symptoms of GvHD. There was no significant difference in survival between the antibody combination and MEDI8500 groups.
**Figure 2:** *Acceleration of GvHD with antibody combination and MEDI8500 compared to monotherapies (A) Kaplan-Meier plot showing overall survival in the different groups of treatment. The control (n=35) and MEDI8500 (n=35) groups include data from two independent experiments, while the data for the anti-PD-1 (n=15), anti-CTLA-4 (n=15) and antibody combination (n=20) groups are from a single experiment. (B) Survival curves in the antibody combination group depending on the HLA type of the donor. Each donor showed was used to reconstitute 4-6 mice per treatment group. (C) Survival curves in MEDI8500 group depending on the HLA type of the donor. Each donor showed was used to reconstitute 4-6 mice per treatment group. The Mantel-cox test was used to assess statistical differences between groups; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.*
We observed differences in survival rate after treatment between some of the donor HLA types in both the antibody combination (Figure 2B) and MEDI8500 group (Figure 2C). Mice reconstituted with cells from donor 5443 showed a significant reduction in overall survival after treatment with either the antibody combination or MEDI8500, with all mice terminated due to adverse GvHD signs Grade 3 between days 16-24 after the start of treatment in the antibody combination group (Figure 2B) or after 16-37 days in MEDI8500 group (Figure 2C). Donor 5443 was unusual, in that this donor was homozygous for a total of 7 out of 9 HLA alleles: HLA-B, HLA-C, HLA-DRB1, DRB4, DQA1, DQB1 and DPA1, with specific HLA alleles more predisposed to autoimmunity (Table 1). By contrast, the other donors were more heterozygous, with at most 4 homozygous alleles, and mice reconstituted with stem cells from more heterozygous donors survived significantly longer with both treatment regimens (Figures 2B, C).
## Expansion of T cells in humanized mice after administration of PD-1 and CTLA-4 targeting antibodies
Analysis of the percentage of huCD3+ T cell population in dissociated spleen at termination revealed significant T cell expansion in the antibody combination (> $60\%$) and MEDI8500 (> $55\%$) groups compared to the control group (~ $20\%$) and the single agent groups (~ $30\%$) (Figure 3A). Similarly, the percentages of huCD4+ and huCD8+ T cells were significantly increased solely in the antibody combination and MEDI8500 groups (huCD4+ > $35\%$; huCD8+ ~ $18\%$) compared to the control (~ $10\%$) and single agent groups (Figures 3B, C).
**Figure 3:** *Significant expansion of T cells with antibody combination and MEDI8500 compared to monotherapies. Flow cytometry analysis of the percentages of (A) huCD3+ T cells, (B) huCD4+ T cells and (C) huCD8+ T cells in spleen of mice at sacrifice. Data are represented as percentage within huCD45+ population for each individual mouse and as Mean ± SD of all mice in each treatment group. Mice numbers are the same as in
Figure 2A
. A one-way ANOVA followed by Tukey’s multiple comparison was used to evaluate statistical differences between treatment and control groups; ****P < 0.0001.*
Likewise, a significant increase on the percentage of huCD3+ T cell population was found in blood at termination in the different groups after treatment compared to the pre-treatment levels (Supplementary Figure 5A). This rise in huCD3+ T cells corresponded with a significant increase of circulating huCD4+ and huCD8+ T cells in the antibody combination and MEDI8500 groups after treatment (huCD4+ > $50\%$; huCD8+ > $20\%$), when compared to the control and anti-PD-1 groups (Supplementary Figures 5B, C). The percentage of huCD4+ T cells was also significantly higher in the anti-CTLA-4 single agent group (> $35\%$) after treatment (Supplementary Figure 5B), but not the percentage of huCD8+ T cells (Supplementary Figure 5C).
## Activation of T cells in humanized mice after administration of PD-1 and CTLA-4 targeting antibodies
We evaluated the expression levels of different activation markers on human T cells circulating in blood in the different groups of mice at sacrifice. We observed that T cells from mice from the antibody combination and MEDI8500 groups showed a significantly increased percentage of OX40+ (~ $13\%$ and ~ $11\%$, respectively) and CD40L+ (~ $18\%$ and ~ $14\%$) huCD4+ T cells compared to the control group (Figure 4). Similarly, we found a significant increase of huCD8+ T cells expressing these markers in the antibody combination and MEDI8500 groups (OX40+ ~ $4\%$ and ~ $3\%$, respectively; CD40L+ ~ $4\%$ and ~ $3\%$, respectively) when compared to control, but the extent of increase in the percentage of cells expressing these markers was lower on huCD8+ T cells than on huCD4+ T cells (Figure 4).
**Figure 4:** *Significant activation of T cells with antibody combination and MEDI8500 compared to monotherapies. Percentage of huCD4+ and huCD8+ T cells expressing T cell activation markers in terminal blood of mice in the different groups of treatment. Data were analysed by flow cytometry and represented as mean of all mice in each treatment group. Mice numbers are the same as in
Figure 2A
. A Mixed-effects analysis followed by Dunnett’s multiple comparisons test was used to evaluate statistical differences between control and treated groups. A significant difference (***P < 0.001) was noted in the antibody combination and MEDI8500 groups compared to the control group in the percentage of huCD4+OX40+ T cells, huCD8+OX40+ T cells and huCD8+CD40L+ T cells. *P < 0.05 was noted in the antibody combination and MEDI8500 groups compared to the control group in the percentage of huCD4+CD40L+ T cells.*
## Highest incidence of GvHD lesions after antibody combination or MEDI8500 in humanized mice
Sections of liver, lung and spleen stained with H&E were examined to detect inflammatory changes consistent with GvHD (Figure 5). In the liver (Figure 5A), inflammatory changes consisted mostly of infiltration of the portal spaces by immune cells, predominantly lymphocytes but frequently accompanied by macrophages. This infiltrate often breached the portal limiting plate to extend into the lobular parenchyma, and was occasionally associated with the presence of single necrotic hepatocytes within the infiltrate. This immune infiltrate was also occasionally observed perivascularly, around centrolobular veins, as well as in random foci in the lobular parenchyma. In the more severe cases the immune infiltrate was very abundant, with miliary foci throughout the tissue. In some cases the infiltrate had a distinctly granulomatous component, with prominent macrophages and multinucleated giant cells.
**Figure 5:** *Greater GvHD lesion score with antibody combination and MEDI8500 compared to monotherapies. (A) Representative images of GvHD lesions in tissues: (1) lung, with vascular intimal, perivascular and interstitial lymphocytic infiltration (20x magnification) (2) Liver, with portal infiltration with lymphocytes and macrophages extending to the lobular parenchyma (20x magnification) (3) spleen, with expansion of white pulp by lymphocytes, and small macrophage nodules in the red pulp (10x magnification) (4) spleen, with diffuse granulomatous infiltration with coalescing macrophage nodules (12x magnification) (B) Histopathologic scoring of GvHD lesions, shown as mean ± SD per treatment group. Total score is sum of liver, lung and spleen scores. Mean total scores are calculated either for the whole group (including animals without any lesions), or excluding the animals without lesions. a significant difference (*P < 0.05) with vehicle, b significant difference (*P < 0.05) with vehicle, anti-PD-1 and anti-CTLA-4, c significant difference (*P < 0.05) with vehicle and anti-PD-1, d significant difference (*P < 0.05) with vehicle and anti-CTLA-4 (C) huCD45 positive cell infiltration in liver, lung and spleen, expressed as proportion of total tissue area. **P<0.01, ***P<0.001, ****P<0.0001.*
In the lung (Figure 5A), the observed inflammatory changes consisted predominantly of perivascular immune cell infiltrates around small and medium sized vessels, sometimes associated with leukocyte margination in the vascular lumen and focal intimal immune cell infiltration. The immune cells were predominantly lymphocytes, with the macrophage component observed in the liver not as prominent in the lung lesion. The alveolar parenchyma around the vessels with perivascular infiltrates often contained small foci of interstitial immune cell infiltration. In the more severe cases, the perivascular infiltrates formed thick cuffs, with the intima markedly thickened by immune cells, and the surrounding parenchyma showing significant interstitial infiltration.
In the spleen (Figure 5A), the changes were variable. Expansion of the periarteriolar white pulp areas by a dense population of immune cells, mostly lymphocytes, was frequently observed, but in some cases significant numbers of macrophages were also present. Another frequently observed change was infiltration of the red pulp area with small nodules of macrophages and occasional multinucleate giant cells; in more severe cases this infiltration became almost diffuse throughout the red pulp, significantly expanding the spleen, and associated with areas of necrosis. Often, a significant and variable degree of extra-medullary hematopoiesis (EMH) was observed. This is a common observation in mouse spleen, and this was not interpreted as associated with GvHD. However, individual variability in EMH likely had a confounding effect on relative huCD45+ area measurements.
The incidence of mice with GvHD lesions in at least one tissue and the severity score of GvHD lesions per group are shown in Figure 5B. The incidence of histologic lesions was clearly higher than the incidence of pre-mortem clinical signs. Overall, there were more frequent GvHD lesions in the anti-PD-1 and anti-CTLA-4 groups than in the vehicle group, and more frequent lesions in the antibody combination and MEDI8500 groups than in the vehicle and monotherapy groups. A similar pattern was noted in the total lesion severity scores, with the antibody combination and MEDI8500 groups showing higher mean scores than the monotherapy groups, and the latter groups showing higher severity scores than the vehicle group. This overall pattern was preserved when animals without lesions were excluded. This indicates that, when present, lesions tended to be more severe in the antibody combination and MEDI8500 groups than in the monotherapy and vehicle groups. In the monotherapy groups, when present, lesions were more severe in the anti-PD-1 group than in the vehicle group, but there was no statistically significant difference in lesion severity between the CTLA-4 group and vehicle. Finally, there were no differences in lesion incidence or overall severity between the antibody combination and MEDI8500 groups. Potential effect of donors on the incidence and severity of GvHD lesions was difficult to reliably assess, because of the small number of animals per donor within each treatment group, and the individual variability in GvHD lesion scores.
Immunohistochemical staining of liver, lung and spleen section with a huCD45-specific antibody was carried out in an attempt to quantify GvHD-linked immune cell infiltration, and compare these results to histopathologic evaluation (Figure 5C and Supplementary Figure 6). Significant increases in huCD45 staining were noted in the livers of some treatment groups, mainly the antibody combination and MEDI8500 groups which showed significantly higher huCD45 infiltration than the vehicle and monotherapy groups. In the lung, the anti-CTLA-4, antibody combination and MEDI8500 groups showed higher huCD45 staining than the vehicle group, with MEDI8500 group showing the most marked increase in huCD45 staining. These results tended to mirror the results from the GvHD lesion scores, although GvHD lesion scores tended to show more statistically significant differences between groups than huCD45 staining (Supplementary Table 1). In the spleen, some differences in huCD45 staining were noted between groups, but these did not correlate with GvHD lesion severity, and therefore huCD45 staining was not helpful in quantifying GvHD lesions in the spleen (Supplementary Figure 6).
## Discussion
In this study we have analysed the in vitro functionality of the immune checkpoint inhibitors anti-PD-1 and anti-CTLA-4 antibodies as either single agents or in combination, along with MEDI8500, which is an anti-PD-1/CTLA-4 BiSpecific antibody. PD-1 and CTLA-4 are immune checkpoint inhibitors that provide negative signals for T cell activation after interaction with their ligands, helping to maintain immune tolerance [5]. In the clinic, when anti-PD-1 and anti-CTLA-4 mAbs are administered as monotherapy, patients who respond can have significantly increased survival. However, only a proportion of patients respond to these monotherapies [28], hence there was rationale for combining PD-1 and CTLA-4 inhibitors, as it could lead to synergistic increases in T cell activation and patient response rate [28]. The combination of anti-PD-1 and anti-CTLA-4 mAbs has shown improved responses in the clinic and a sustained survival benefit, compared to those in the monotherapy groups (8–10, 29–31), and is approved for different types of solid cancers [32]. However these combinations can lead to an increase in immune related adverse events. Development of an anti-PD-1/CTLA-4 BiSpecific antibody is a novel approach aiming to release the full potential of the combination into a single molecule. MEDI8500 has a similar potency against human PD-1 as the anti-PD-1 mAb, but it has a lower affinity for the CTLA-4 receptor and a 4-fold lower potency than the anti-CTLA-4 mAb. The first-in-class anti-PD-1/CTLA-4 bispecific to be recently approved for the treatment of advanced cervical cancer has been Cadonilimab [33], which is a quadrivalent bispecific with similar format to MEDI8500. Similarly, MEDI5752, which is a monovalent anti-PD-1/CTLA-4 BiSpecific [34], is currently under evaluation in patients and durable responses have been seen across diverse tumour types (35–37). In this line, our in vitro data demonstrate that both the anti-PD-1 and anti-CTLA-4 combination and MEDI8500 induced increased activity on primary immune cells compared to the single agents treatment.
We next evaluated the in vivo potency of anti-PD-1 and anti-CTLA-4 mAbs as monotherapies, combination and MEDI8500 in non tumour-bearing humanized mice, using GvHD development as a potential predictor of toxicity and potency of these immunotherapies and combinations. Our model used mice reconstituted with CD34+ HSCs cells derived from donors with HLA alleles predisposed to autoimmune disorders. The use of HLA donors predisposed to autoimmunity has previously been described for studies of the pathogenesis of GvHD [25, 26]. For our purposes, use of such donors was designed to increase the incidence of GvHD after treatment with immunotherapies and potentially shorten the study duration. However, for other study purposes these HLA type donors should be avoided in order to lengthen the survival of the humanized mice.
As expected, we observed a significantly lower survival rate due to accelerated development of GvHD in the checkpoint inhibitor treated groups compared to the controls, consistent with enhanced immune activation in humanized mice treated with these agents. The anti-PD-1 + anti-CTLA-4 combination and MEDI8500 groups showed lower survival, and accelerated onset of clinical GvHD compared to vehicle and to the single agents anti-PD-1 and anti-CTLA-4, and generally higher GvHD lesion levels compared to the single agent treated mice.
Certain HLA types are more likely to be susceptible to autoimmune disorders, viral infections or development of cancer [26, 38]. Patients with autoimmune disorders treated with immune checkpoint inhibitors may show aggravation of their disease, more severely when given in combination [39]. In our study, there was evidence of an effect of the donor HLA type on the onset and severity of GvHD reactions. Specifically, mice reconstituted with cells from one homozygous HLA type donor (D5443) appeared to have lower survival rate and acceleration of GvHD development compared with mice reconstituted with HLA donors with higher heterozygosity, in response to anti-PD-1 + anti-CTLA-4 combination and MEDI8500.
Flow cytometric analysis of circulating human lymphocyte profiles in the mice revealed a significant increase in the percentage of huCD3+ T cells in all groups, including the controls. This increase in controls was likely due to a baseline immune activation associated with incipient development of GvHD, as was seen histologically in several of the control animals. The choice of human cell donors predisposed to autoimmunity would be expected to lead to quicker development of GvHD reactions even in the absence of immune stimulatory drugs, and therefore this should be carefully considered when interpreting data from this model; effect of administered drugs should be interpreted in light of the baseline immune activation present. This was the case in this study, where we saw a significant increase in the percentage of huCD3+, huCD4+ and huCD8+ T cells in groups treated with either the anti-PD-1 + anti-CTLA-4 combination or MEDI8500, compared to the control or anti-PD-1 or anti-CTLA-4 monotherapy groups. PD-1 and CTLA-4 act at different stages of T cell activation, thus, combined blockade of both immune checkpoint inhibitors has synergistic effects on T cell activation and proliferation, as aforementioned [28]. The increase in percentage of circulating human lymphocytes has been reported previously after treatment of humanized mice with either anti-PD-1 or anti-CTLA-4 mAbs [40, 41], and it likely reflects an increased production of these cells as a response to induction of GvHD reactions in various tissues by PD-1 and CTLA-4 blockade. Furthermore, it was also associated with an increase in T cell activation. We used 2 different cell markers associated with T cell activation: OX40, which is not constitutively expressed on resting naïve T cells and expressed after 24 to 72 hours following activation [42]; and CD40L, which is predominantly expressed by activated huCD4+ T cells shortly after T cell activation [43]. We observed a significant increase in activated T cells in terminal blood with antibody combination and MEDI8500 treatments, compared to vehicle and monotherapy groups.
Histologic evaluation of liver, lung and spleen confirmed the presence of GvHD type lesions in these mice. This evaluation not only revealed lesions in mice culled prematurely due to adverse clinical signs, but also in mice that survived until the end of the study. However mice that were culled prematurely showed more severe lesions, an increased GvHD score and greater influx of huCD45+ based on IHC analysis.
Although some of the control group animals had GvHD lesions, these were mild, and not associated with adverse clinical signs or body weight loss (Supplementary Figure 4). This early development of GvHD was likely related to the use of donors predisposed to autoimmunity, and there was some evidence for donor-related differences in the incidence of lesions. The presence of such lesions underscores the need for careful study design when using such a model, with a need to use multiple donors and distribute donors evenly throughout the treatment groups to avoid confounding effects of variable degrees of donor predisposition to GvHD.
The histopathological lesions observed in these mice were consistent with those previously reported for humanized mice [14, 44] and with some of the changes reported for chronic human GvHD [45, 46], although overall the mouse lesions tended to be less variable than the fairly broad spectrum of changes reported in humans. This is not surprising, considering the consistent and well-controlled methodology, limited genetic variability of the host mice, and use of a limited pool of donors in this model compared to the myriad possible variations in the human population. One frequent feature of the mouse lesions was the presence of a significant, often abundant, macrophage component within the immune cell infiltrate of the liver or spleen; in some cases macrophages were the dominant immune population, forming granulomatous nodular infiltrates. This has also been previously reported in this model [14], but it is unclear why this cell population becomes stimulated to this extent, and why it is prominent in some mice and not others. There did not appear to be an association of this observation with specific treatments or donors.
In order to be able to compare different treatments based on histopathology, we evaluated the incidence of GvHD lesions per group, and developed a semi-quantitative histopathologic scoring system based on lesion severity. Both incidence and severity showed clear differences between groups, with the severity score providing more granularity to the analysis, and better differentiation between groups. When considering the total severity score (combined scores for the liver, lung and spleen), significant differences were evident between the anti-PD-1 or anti-CTLA-4 monotherapy groups and the control group, as well as between the antibody combination or MEDI8500 groups and the monotherapy and vehicle groups. This confirmed the additive immune-stimulatory activity of anti-PD-1 and and-CTLA-4, when combined as single agents or as a BiSpecific. We could not detect clear differences in activity between the antibody combination and MEDI8500 treatment.
Immunohistochemical staining for huCD45 was applied to the liver, lung and spleen, with image analysis quantitation of positive cells, in an effort to develop a quantitative tool for GvHD evaluation. This proved a useful way to obtain more quantitative and objective data on human immune cell infiltration in liver and lung, with huCD45 positivity generally mirrorring the GvHD lesion scores; however it proved to be somewhat less sensitive than histopathologic evaluation in identifying the early, milder lesions. This is most likely because immune cell infiltration in GvHD lesions must attain a threshold in order to be detectable above of the normal baseline level of huCD45 cells in tissues, which are either circulating through the vasculature or resident tissue immune cells. In line with this notion, liver and lung huCD45 staining in the antibody combination and MEDI8500 groups, which had higher lesion severity scores, clearly was significantly increased when compared to controls and monotherapy groups, whilst huCD45 staining in monotherapy groups were generally not high enough to show statistically significant differences from controls, despite presence of mild histopathologic lesions. In the spleen, huCD45 staining did not consistently detect GvHD lesions. This was likely due to the fact that human cells administered to these mice would be expected to home in to normal lymphoid tissue sites like the spleen, and therefore a large proportion of cells within the spleen of reconstituted mice would normally be huCD45+ in the absence of any GvHD reaction.
In summary, evaluation of mice with a humanized immune system showed it was a useful model for comparing the in vivo potency and safety profile of PD-1 and CTLA-4 based immuno-oncology therapeutics. Analysis of blood and tissue responses provided quantitative and semi-quantitative data with sufficient granularity to differentiate the potency of different treatment modalities. Induction of GvHD in these mice was a consistent endpoint for demonstration of immunotherapeutic potency. This aggravated antigenic T-cell response against host due to checkpoint blockade is a surrogate of the antigenic T-cell response that can be potent in targeting cancer cells or any other foreign antigens. Histopathologic evaluation of liver, lung and spleen was the most sensitive indicator of GvHD, compared to huCD45 IHC or clinical observations. Administration of anti-PD-1 or anti-CTLA-4 as single agents induced a significant degree of immune stimulation, but significantly less than a combination of the two or MEDI8500. However, the results in the monotherapy groups do not completely correlate with the safety profile of anti-CTLA-4 mAbs in the clinic, where these therapies have much more severe immune mediated adverse reactions than anti-PD-1 mAbs when given as monotherapies [47]. Taken together, the use of humanized mice may be a useful platform to evaluate the function of human immunotherapies, where no other preclinical models exist. These models may also provide information on the potential for immune related adverse reactions with immunotherapies combination, but like all animal models, this model has limitations which must be understood and improved upon to increase their translational predictivity to the clinical situation.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors.
## Ethics statement
The animal study was reviewed and approved by AstraZeneca ethics review.
## Author contributions
RS, SM and LH contributed to conception and design of the in vivo study. J-ML performed the histology evaluation. ME and GB performed the in vitro experiments. SD surpevised the in vitro experiments and project development. AM-C performed data analysis and interpretation. AM-C wrote the first draft of the manuscript. J-ML and RS wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
## Conflict of interest
AM-C, J-ML, ME, GB, SD, LH, SM, and RS report being AstraZeneca employees during the conduct of the submitted work. LH is currently an employee at ADC therapeutics.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1107848/full#supplementary-material
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|
---
title: Letrozole cotreatment improves the follicular output rate in high-body-mass-index
women with polycystic ovary syndrome undergoing IVF treatment
authors:
- Yali Liu
- Jiaying Lin
- Xi Shen
- Qianqian Zhu
- Yanping Kuang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10020617
doi: 10.3389/fendo.2023.1072170
license: CC BY 4.0
---
# Letrozole cotreatment improves the follicular output rate in high-body-mass-index women with polycystic ovary syndrome undergoing IVF treatment
## Abstract
### Background
Women who have polycystic ovary syndrome (PCOS) with high body mass index (BMI) typically have an attenuated ovarian response and decreased follicular size, which are linked to unfavourable clinical outcomes following in vitro fertilization (IVF) therapy. The follicular output rate (FORT), a qualitative indicator of follicular response, seems to be positively linked to the clinical outcomes of IVF. Progestin-primed ovarian stimulation (PPOS) has become an alternative to gonadotropin-releasing hormone (GnRH) analogues to inhibit the premature luteinizing hormone (LH) surge. As letrozole (LE) shows promise in enhancing ovarian response, we compared PPOS with and without LE for PCOS in high BMI women with a focus on the FORT and associated clinical and pregnancy outcomes.
### Methods
For the recruited 1508 women, ten variables including AFC; age; basal sex hormone level; BMI; infertility type; period of infertility and number of previous IVF attempts were chosen in the propensity score matching (PSM) model to match 1374 women who taken the MPA+ hMG protocol with 134 women who received the MPA+ hMG+ LE treatment at a 1:1 ratio. FORT was selected as the primary outcome measure. The number of oocytes retrieved, viable embryos, hMG dosage, duration, oocyte maturity rate, fertilization rate, and implantation rate were established as secondary outcomes.
### Results
FORT was substantially elevated in the MPA+hMG+LE group compared with the MPA+hMG group ($61\%$ [$35\%$, $86\%$] vs. $40\%$ [$25\%$, $60\%$], $P \leq .001$). Interestingly, the LE cotreatment group had a considerably lower mature oocyte rate despite having a similar number of mature oocytes and embryos recovered. The average hMG dosages and durations in the study group were similar to those in the control group. The implantation rate in the study group was numerically higher but without statistic significant than that in the control groups ($43.15\%$ ($\frac{107}{248}$) vs. $38.59\%$ ($\frac{115}{298}$), OR 1.008, $95\%$ CI 0.901-1.127; $P \leq .05$).
### Conclusion
The effect of LE combined with PPOS on FORT is better than the effect of the standard PPOS treatment in women with PCOS and a high BMI, but there is no substantially beneficial impact on pregnancy outcomes or the cycle features of COS, including consumption of hMG.
## Introduction
Polycystic ovary syndrome (PCOS) is a prevailing form of endocrinopathy that affects women of reproductive age [1]. The rates of hyperandrogenism, obesity, and primary infertility have increased dramatically among women with PCOS over the last decade, resulting in a more severe phenotype among this population [2]. Infertile women with PCOS may be treated with in vitro fertilization (IVF), laparoscopic ovarian surgery, and behavioural, and pharmaceutical interventions (including gonadotropins, metformin, aromatase inhibitors, and clomiphene citrate (CC)) [3]. IVF is regarded as a third-line therapy and is often used in cases in which tubal factors and male factors exist [3].
As an alternative to standard GnRH analogues, progestin-primed ovarian stimulation (PPOS) by administering human menopausal gonadotropin (hMG) and medroxyprogesterone acetate (MPA) simultaneously from the early follicular phase successfully inhibits the oestradiol (E2)-induced LH surge [4]. The PPOS protocol could achieve similar numbers of oocytes, viable embryos, and pregnancy outcomes [5]; moreover, it is more patient-friendly, as it can further reduce the injection burden compare with the conventional GnRH analogue protocol [6].
Previous studies found that increased body mass index (BMI) is probably linked to an increased risk of insufficient follicle development as well as an increased follicle-stimulating hormone (FSH) requirement in the process of ovarian stimulation for IVF [7, 8] or dysregulation of meiotic spindle formation [9] and, consequently, developmental ability [10]. Preliminary data showed that in normo-cycling women, the ratio of the preovulatory follicle count (PFC) to the antral follicle count (AFC), widely recognised as the follicular output rate (FORT), is positively linked to IVF outcomes [11, 12] and is regarded as a qualitative indicator of the follicular response. For women with PCOS, especially those who are obese, letrozole (LE) is recommended as a first-line treatment option for the induction of ovulation [13]. LE can improve the follicular response to FSH by elevating intrafollicular androgen levels and reducing circulating oestrogen concentrations [14]. At present, LE is extensively utilised as an adjunct for IVF treatment [15]. Therefore, the current retrospective cohort study was conducted to evaluate the impact of combining LE with the PPOS protocol on FORT, as well as the features of the frozen embryo transfer (FET) cycle and oocyte pick-up cycle in high-BMI women with PCOS receiving IVF treatment.
## Patients and study setting
The research protocol for this study was approved by the Shanghai Ninth People’s Hospital Ethics Committee (Institutional Review Board). Women with PCOS who completed IVF/ICSI cycles between January 2017 and September 2022 were recruited to the control group (hMG+MPA) and the study group (hMG+MPA+LE). For patients who received more than one cycle of COS within this time frame, only the first cycle was considered to prevent repeated inclusion. The patients satisfied the following conditions: 1. BMI between 25 and 37 kg/m2; 2. basal FSH level < 10 mIU/ml; 3. Age between 21 and 40 years; and 4. at most 1 previous cycle with no available embryo. According to the 2003 *Rotterdam consensus* [16], at least two of the following symptoms were required for women to be diagnosed with PCOS. 1) oligo- and/or anovulation; 2) ultrasonography appearance of polycystic ovaries; or 3) biochemical and/or clinical indicators of hyperandrogenism. Ultrasonography was unnecessary in cases where both hyperandrogenism and oligo- or anovulation existed. Reproductive, metabolic, and psychological factors were all considered in the assessment and management of the condition once diagnosed with PCOS. The exclusion criteria included the presence of the following disorders: hyperandrogenaemia and ovulatory dysfunction due to other aetiologies, such as thyroid disease, hyperprolactinaemia, androgen-secreting tumours, and congenital adrenal hyperplasia. Women who were currently receiving treatment for a clinical condition, for example diabetes or high blood pressure, were also eliminated. Figure 1 depicts the study process.
**Figure 1:** *Flow chart of the study. IVF, In vitro fertilization; ICSI, Intracytoplasmic sperm injection; FET, Frozen embryo transfer; LE, Letrozole; MPA, Medroxyprogesterone acetate; hMG, Human menopausal gonadotropin.*
## Controlled ovarian stimulation
Patients were given 150-225 IU/d hMG intramuscularly (Anhui Fengyuan Pharmaceutical Co., Ltd.) and 4 mg/d MPA orally (Shanghai Xinyi Pharmaceutical Co., Ltd.) from the third day of the menstrual cycle (MC3) until the trigger day. The study group was given oral LE (Jiangsu Hengrui Pharmaceutical Co., Ltd., 2.5 mg/day) starting on MC3 and continued this treatment for 5 days. Our research objects were women who have PCOS with high BMI, the median number of AFC was 20 and the mean BMI was 28 kg/m2. Except the number of AFC and BMI, the basal FSH value also been suggested as one of the influencing factors to select the initial Gn doses in IVF/ICSI treatment [17, 18]. Women with basal FSH <7 mIU/ml were administered hMG 225 IU/day, for individuals with mildly increased basal FSH (7-10 mIU/ml) were administered hMG at a beginning dosage of 150 IU/day. From MC8 forwards, hMG dosages were modified for both groups every 2–4 days. If there were more than 20 follicles with a diameter >10 mm, we decreased the dosage of hMG from 225 to 150 IU. And if the FSH level was lower than 10 mIU/ml, we would add 75IU to the original dose. The hMG dose was adjust every 2–4 days according to the above principles.
When the dominant follicle reached a size exceeding 20 mm or more than three follicles reached a size exceeding 18 mm, 2000 IU or 5000 IU human chorionic gonadotropin (Lizhu Pharmaceutical Trading Co., China) in combination with 0.1 mg triptorelin (Decapeptyl; Ferring Pharmaceuticals, Germany) was employed to trigger the final phase of oocyte maturation. However, or 5000 IU human chorionic gonadotropin (hCG (500 IU hCG combined with 0.2 mg triptorelin or only 0.2 mg triptorelin was used if the patient was at risk of developing hyperstimulation. Under transvaginal ultrasound (TVS) guidance, oocytes were retrieved 34-38 hours after triggering. Aspiration was performed on all follicles measuring >10 mm in diameter [4].
Oocytes were then fertilised using conventional IVF or intracytoplasmic sperm injection (ICSI), depending on the quality of the semen and the success rate of previous fertilization attempts [19]. The quantity and distribution of blastomeres, as well as the extent of fragmentation, were measured in the embryos. Within three days following oocyte retrieval, high-quality embryos (grade-1 and grade-2 6-cell embryos and above) were vitrified and frozen using the procedures stipulated by Cummins et al. [ 4, 20]. Low-quality embryos were subjected to culture for a longer period, whereas blastocysts that were well-formed were frozen on day 5 or 6 [4].
## Measurement of hormones
On MC3, MC8, MC10-12, the day of the trigger and the day following the trigger, serum levels of LH, FSH, E2, and P4 were measured. Chemiluminescence (Abbott Biologicals B.V., the Netherlands) was used to analyse the hormone levels. The maximum E2 value that could be measured was 5000 pg/ml. Samples with an E2 concentration over 5000 pg/ml were recorded at a value of 5000 pg/ml. The sensitivity thresholds were as follows: P4 0.1 ng/ml; E2, 10 pg/ml; LH, 0.09 mIU/ml; and FSH, 0.06 mIU/ml.
## Preparation of endometrium and frozen embryo transfer
Following the procedures outlined previously [4, 21], the preparation of endometrium and FET were scheduled in the second cycle following oocyte retrieval. LE was initially prescribed for mild stimulation of the endometrium as our data showed superiority of this protocol over hormone replacement therapy (HRT) [22]. Those who had trouble conceiving after undergoing mild stimulation cycles or who had a history of an abnormally thin endometrium (≤ 6 mm) were then subjected to HRT. The women who participated in the mild stimulation cycle received 2.5 or 5 mg of LE for 5 days, starting at MC3. Ovulation was induced by injecting urine hCG (5000 IU) under the following conditions: dominant follicle diameter ≥17 mm, endometrium lining ≥8 mm, P4 level ≤1 ng/ml, and E2 level ≥150 pg/ml. Two or three days later, progesterone was started, and then five days or seven days after ovulation induction, abdominal ultrasound was used to guide the transfer of day-3 embryos or day-7 blastocysts. We used the “freeze-all” strategy and there was a waiting period between oocyte retrieval and embryo transfer. Our recruitment period was from January 2017 to September 2022 and patients were followed up to January 9, 2023.
## Outcome measures
In this investigation, FORT was used as the primary outcome. The number of retrieved oocytes and viable embryos, the oocyte retrieval rate, the oocyte maturity and fertilization rates, the hMG dosage and duration, and the implantation rate were established as secondary outcomes. FORT was computed by using the ratio between the number of preovulatory follicles (PFCs) on hCG day × 100 and the number of AFCs at baseline. A prior study [12] concluded that only follicles between 16 and 22 mm in diameter should be included in the computation of FORT, to establish small antral follicles that responded best to FSH. The rate of oocyte retrieval was computed by dividing the total number of ruptured follicles by the sum of recovered oocytes. The rate of oocyte maturation was computed by dividing the sum of mature oocytes by the sum of all retrieved oocytes; To determine the fertilization rate, we divided the sum of fertilized oocytes by the sum of mature oocytes; the sum of fertilized oocytes was divided by cleaved embryos to obtain the cleavage rate. The rate of cycle cancellation was calculated as the sum of the number of patients whose oocyte retrieval resulted in zero viable embryos. At 4 weeks following FET, ultrasound detection of a gestational sac with or without foetal heart activity indicated the diagnosis of clinical pregnancy. The clinical pregnancy rate was determined by dividing the total number of clinical pregnancies by the sum of FET cycles. The implantation rate was computed by dividing the sum of embryo transfers by the total number of gestational sacs. The miscarriage rate was determined by the percentage of pregnancies that ended early due to therapeutic or spontaneous abortions.
## Statistical analysis
To account for inherent disparities in the baseline characteristics of the two groups, we developed a propensity score matching (PSM) model. Ten variables were chosen for use in the propensity score estimation, including, AFC; age; basal levels of P4, E2, LH, and FSH; BMI; infertility type (primary or secondary); period of infertility and number of previous IVF attempts (0, 1–2 or ≥3). We used the nearest neighbour random matching technique to match patients receiving MPA+hMG+LE protocol with those receiving MPA+hMG treatment in a 1: 1 ratio. PSM was completed utilising R software (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria).
The mean ± standard deviation (SD) and Student’s t test were used to display and evaluate normally distributed continuous data. The median [25th percentile, 75th percentile] and Mann–Whitney U tests were used to express and assess nonnormally distributed continuous data. Categorical data are displayed as n (percentage) and compared with Fisher’s exact test or Pearson’s chi-squared test. The SPSS statistical package (version 24, SPSS Inc.) was utilised for data analysis. Two-sided $P \leq .05$ was the criterion for statistical significance.
As a patient may have more than one FET cycle, we used the generalised estimating equation (GEE) modelling to reduce the potential bias of repeated cycles and compared the different treatment protocols with the odds ratio (OR) and corresponding $95\%$ confidence interval (CI). $P \leq .05$ was the criterion for statistical significance. Variables as the clinical pregnancy rate, implantation rate, miscarriage rate, ectopic pregnancy rate and live birth rate were included in the regression equation.
## Patient features
The study’s flow chart is summarised in Figure 1. Specifically, we enrolled 1792 high-BMI (BMI>25) women with PCOS who were candidates for assisted reproductive technology treatment in our clinical centre between January 2017 and September 2022. Based on the criteria outlined in the Methods and Materials, 340 cycles were eliminated. For the remaining 1508 women, we used the nearest neighbour random matching method to match 1374 women who received the MPA+hMG+LE protocol with 134 women who received the MPA+hMG treatment at a 1:1 ratio. The baseline and outcome parameters before PSM are presented in Table 1. Although our patients were recruited from January 2017 to September 2022, but the number of patients enrolled in 2022 only accounted for less than $7\%$ of the total number due to the COVID-19 epidemic in China. Women who received the MPA+hMG+LE protocol were concentrated in the year of 2020 and 2021, while those received the MPA+hMG treatment were evenly distributed during the enrolment period. Our follow-up time was up to January 9, 2023, hence $93\%$ of the patients had at least one year of follow-up after recruitment. Recruitment trend of patients in different years before and after PSM are provided in Supplemental Figure 1. All patients postmatching finished their oocyte retrieval cycles and were successful in obtaining oocytes (ranging from 1 to 52), however, 21 of these patients did not have any viable embryos. In addition, 222 of the remaining 247 patients finished 356 FET cycles. Postmatching analysis showed no significant variations in any baseline characteristics across the groups (all $P \leq .05$) (Table 1).
**Table 1**
| Parameters | Pre-match | Pre-match.1 | P value | Post-match | Post-match.1 | P value.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Parameters | Study group: | Control group: | P value | Study group: | Control group: | P value |
| Parameters | hMG+MPA+LE (n=134) | hMG+MPA (n=1374) | P value | hMG+MPA+LE (n=134) | hMG+MPA (n=134) | P value |
| Age (years) | 32.43 ± 3.58 | 35.36 ± 4.08 | <0.001 | 32.43 ± 3.58 | 32.35 ± 3.7 | 0.854 |
| Duration of infertility (years) | 4.04 ± 2.33 | 4.2 ± 2.42 | 0.52 | 3.7 ± 2.7 | 3.93 ± 2.91 | 0.501 |
| Primary infertility, n (%) | 64.93% (87/134) | 22.56% (310/1374) | <0.001 | 64.93% (87/134) | 62.69% (84/134) | 0.703 |
| Indication, n (%) | | | <0.001 | | | 0.884 |
| Male factor | 14.93% (20/134) | 19.65% (270/1374) | | 14.93% (20/134) | 13.43% (18/134) | |
| Tubal factor | 65.67% (88/134) | 50.15% (689/1374) | | 65.67% (88/134) | 67.91% (91/134) | |
| Combination of factors | 8.21%(11/134) | 21.11% (290/1374) | | 8.21% (11/134) | 9.70%(13/134) | |
| Unknown factor | 11.19% (15/134) | 9.1% (125/1374) | | 11.19% (15/134) | 8.96%(12/134) | |
| Previous IVF failure | Previous IVF failure | | <0.001 | | | 0.905 |
| 0 | 88.80% (119/134) | 65.28% (897/1374) | | 88.80% (119/134) | 90.30% (121/134) | |
| 1–2 | 8.96%12/134) | 21.83% (300/1374) | | 8.96% (12/134) | 7.46%(10/134) | |
| > 3 | 2.24%(3/134) | 12.88% (177/1374) | | 2.24% (3/134) | 2.24%(3/134) | |
| BMI | 28.44 ± 2.65 | 30.12 ± 90.97 | <0.001 | 28.44 ± 2.65 | 28.18 ± 2.58 | 0.411 |
| Basal hormone concentrations | Basal hormone concentrations | Basal hormone concentrations | Basal hormone concentrations | Basal hormone concentrations | Basal hormone concentrations | Basal hormone concentrations |
| FSH (IU/L) | 5.23 ± 1.28 | 5.23 ± 1.38 | 0.768 | 5.23 ± 1.28 | 5.26 ± 1.5 | 0.848 |
| LH (IU/L) | 4.12 ± 2.18 | 4.01 ± 2.43 | 0.132 | 4.24 ± 2.6 | 4.07 ± 2.29 | 0.583 |
| E2 (pg/ml) | 33.34 ± 11.79 | 32.26 ± 12.56 | 0.253 | 33.34 ± 11.79 | 33.25 ± 12.35 | 0.991 |
| P (ng/ml) | 0.23 ± 0.1 | 0.23 ± 0.11 | 0.682 | 0.23 ± 0.13 | 0.22 ± 0.13 | 0.217 |
| AFC | 19.64 ± 6.99 | 17.82 ± 6.49 | 0.002 | 20 [15,22] | 20 [16,20.25] | 0.774 |
## Ovarian stimulation, follicle development, and oocyte performance
The study and control groups exhibited similar numbers of retrieved oocytes (13.5 [8.75, 20.25] vs. 15 [9, 21], $P \leq .05$) and viable embryos (5 [2, 8] vs. 5 [2.75, 8], $P \leq .05$). Neither the hMG doses nor the period of ovarian stimulation were significantly different between the two groups ($P \leq .05$). The sums of follicles with diameters of 10-12 mm and 12-14 mm were comparable across the two groups. The study group had a substantial reduction in the sum of follicles with a diameter of 14-16 mm (2 [0, 6] vs. 3 [1, 6], $P \leq .05$) but significantly higher number of follicles larger than 16 mm when compared with the control group (10.5 [7, 18] vs. 8 [4, 11], $P \leq .05$) (Table 2). Consistent with the above results, FORT was substantially elevated in the study group compared with the control group ($61\%$ [$35\%$, $86\%$] vs. $40\%$ [$25\%$, $60\%$], $P \leq .01$). Although the mature oocyte rate was meaningfully reduced in the LE cotreatment group ($81\%$ ± $18\%$ vs. $86\%$ ± $12\%$, $P \leq .05$), the oocyte retrieval rate was similar in the two groups. Additionally, the number of aspirated follicles and mature oocytes were not significantly different. Of the 134 women receiving LE+MPA+HMG treatment, 3 had no mature oocytes, 3 failed fertilization, and 5 failed cleavage. Of the 134 women who underwent the MPA+HMG protocol, 2 had no mature oocytes, 4 failed fertilization, and 4 failed cleavage. The cycle cancellation rates for unviable embryos that were not significantly different between the two groups ($8.2\%$ ($\frac{11}{134}$) vs. $7.5\%$ ($\frac{10}{134}$), $P \leq .05$) (Table 2).
**Table 2**
| Unnamed: 0 | Study group: hMG+MPA+LE (n=134) | Control group: hMG+MPA (n=134) | P value |
| --- | --- | --- | --- |
| hMG dose (IU) | 2370.15 ± 738.98 | 2392.16 ± 789.52 | 0.841 |
| hMG duration (d) | 9.31 ± 1.86 | 9.63 ± 2.4 | 0.391 |
| hCG Dose on trigger day (IU) | 2082.09 ± 1255.36 | 2221.64 ± 1637.68 | 0.989 |
| GnRHa Dose on trigger day (mg) | 0.14 ± .05 | 0.12 ± 0.04 | 0.0 |
| 10-12-mm follicles on hCG day (n) | 2 [0,4] | 2 [0,5] | 0.143 |
| 12-14-mm follicles on hCG day (n) | 2 [0,5.25] | 3 [1,6] | 0.278 |
| 14-16-mm follicles on hCG day (n) | 2 [0,6] | 3 [1,6] | 0.033 |
| > 16-mm follicles on hCG day (n) | 10.5 [7,18] | 8 [4,11] | 0.0 |
| FORT (%) | 61 [35,86] | 40 [25,60] | 0.0 |
| Punctured follicles (n) | 18 [12,29] | 20 [13,27.25] | 0.555 |
| Oocyte retrieved (n) | 13.5 [8.75,20.25] | 15 [9,21] | 0.777 |
| Mature oocytes (n) | 11 [7,16] | 12.5 [7,17] | 0.149 |
| Fertilized oocytes (n) | 9 [5,13] | 10 [6,14] | 0.29 |
| Cleaved embryos (n) | 9 [5,12.25] | 9.5 [5.75,13] | 0.31 |
| High-quality embryos (n) | 3 [2,6] | 4 [2,7] | 0.37 |
| Blastocyst embryos (n) | 1 [0,3] | 1 [0,2] | 0.178 |
| All cryopreserved embryos (n) | 5 [2,8] | 5 [2.75,8] | 0.756 |
| Oocyte retrieval rate (%) | 75% ± 19% | 74% ± 22% | 0.904 |
| Mature oocyte rate (%) | 81% ± 18% | 86% ± 12% | 0.04 |
| Fertilization rate (%) | 79% ± 18% | 79% ± 16% | 0.749 |
| Cleavage rate (%) | 96% ± 11% | 97% ± 7% | 0.907 |
| Cycle cancellation rate (%) | 8.2% (11/134) | 7.5% (10/134) | 0.82 |
## Profiles of hormones during treatment
Figure 2 depicts the endocrine dynamics that occurred in response to ovarian stimulation, including those of P4, E2, LH, and FSH. After the hMG injection, FSH levels spiked considerably 5 days later and then remained constant until the trigger day. Rapid elevation of FSH to over 15 mIU/ml was observed following the dual trigger. No remarkable differences were observed in FSH levels between the two groups at any time point (Figure 2A).
**Figure 2:** *The dynamic changes in hormones during ovarian stimulation in the two groups. (A). Serum FSH levels in the two groups during COS. (B). Serum LH concentration in the two groups during COS. (C). Serum E2 level in the two groups during COS. (D) Serum P levels in the two groups during the COS. The solid red lines represent the study group (hMG+MPA+LE) and the solid heavy blue lines represent the control group (hMG+MPA). The asterisks denote significant changes in hormone levels at the indicated time points (*P <.05, **P <.01).*
LH remained low in both groups throughout the COS. There was a declining trend in LH levels in the control group. On the other hand, the LH concentration in the study group was rather stable for the initial five days. During COS, LH levels at MC8, MC9-11 and the trigger day were remarkably higher in the study group than in the control group($P \leq .01$). Neither group had any cases of premature LH surge (Figure 2B).
As several follicles matured, there was a continuous rise in serum E2. Due to the use of LE, the oestrogen values in the study group were lower than those in the control group during COS, and the variation was statistically significant at all time points ($P \leq .01$) (Figure 2C).
P4 levels in both groups gradually increased during ovulation stimulation. Additionally, the P4 levels in the study group were substantially elevated compared with those in the control group at MC9-11($P \leq .01$) (Figure 2D).
## Outcomes of pregnancies following FET procedures
There were 247 women with viable embryos that developed successfully. A total of 546 embryos were thawed, and all ($100\%$) were viable after the thawing procedure. Ultimately, 356 FET cycles were completed by 222 women. In the LE cotreatment group, 103 women finished 163 FET cycles in total: 63 women had accomplished one FET cycle, 26 women had completed two FET cycles, 14 women had accomplished more than or equal to three FET cycles. Whereas 119 women in the PPOS group finished 193 FET cycles: including 65 women with one FET cycle, 38 women with two FET cycles, 16 women finished greater than or equal to three FET cycles. In total, $86\%$ of patients in both groups had fewer than three FET cycles. However, 46 women failed to start their FET cycles for numerous reasons in the two groups. There was no significant difference on the stage and number of embryos transferred per cycle between the two groups, and the neonatal status between the two groups were similar ($P \leq .05$) (Table 3).
**Table 3**
| Variable | Study group: | Control group: | Unnamed: 3 | Unnamed: 4 | P value |
| --- | --- | --- | --- | --- | --- |
| Variable | hMG+MPA+LE | hMG+MPA | | | P value |
| Patients (n) | 103 | 119 | | | |
| FET cycles (n) | 163 | 193 | | | |
| Thawed embryos (n) | 248 | 298 | | | |
| Viable embryos after thawed (n) | 248 | 298 | | | |
| The number of embryos per transfer (n) | 1.53 ± 0.50 | 1.54 ± 0.50 | | | 0.758 |
| Indication, n (%) | | | | | 0.497 |
| cleavage-stage embryo | 56.85% (141/248) | 59.73% (178/298) | | | |
| blastocyst embryo | 43.15% (107/248) | 40.27% (120/298) | | | |
| Endometrial preparation n (%) | Endometrial preparation n (%) | | | | 0.605 |
| Mild stimulation | 63.80% (104/163) | 61.14% (118/193) | | | |
| Hormone therapy | 36.20% (59/163) | 38.86% (75/193) | | | |
| Endometrial thickness (mm) | 10.44 ± 2.56 | 10.09 ± 2.04 | | | 0.249 |
| Newborn | Newborn | Newborn | Newborn | Newborn | Newborn |
| Single birth (n) | 47 | 56 | | | |
| Single birthweight (g) | 3399.57 ± 565.19 | 3304.17 ± 531.81 | | | 0.403 |
| Twin birth (n) | 11 | 9 | | | |
| Twin birthweight (g) | 2535 ± 392.22 | 2310 ± 495.71 | | | 0.117 |
| Variable adjusted in GEE models | Variable adjusted in GEE models | | OR | 95% CI | |
| Clinical pregnancy rate per transfer (%) | 53.37% (87/163) | 52.85% (102/193) | 1.008 | 0.901-1.127 | 0.891 |
| Implantation rate (%) | 43.15% (107/248) | 38.59% (115/298) | 1.065 | 0.918-1.235 | 0.405 |
| Miscarriage rate (%) | 13.79% (12/87) | 15.69% (16/102) | 0.982 | 0.891-1.083 | 0.714 |
| Ectopic pregnancy rate (%) | 1.15% (1/87) | 1.96% (2/102) | 0.987 | 0.95-1.026 | 0.514 |
| Live birth rate per cycle (%) | 35.58% (58/163) | 34.20% (66/193) | 0.991 | 0.838-1.171 | 0.913 |
| Live birth rate per patient (%) | 56.31% (58/103) | 60.55% (66/119) | 0.991 | 0.838-1.171 | 0.913 |
Our results showed that after controlling for the potential bias of repeated cycles from one patient, the different treatment protocols in the LE cotreatment group and the PPOS group were not associated with significant differences in the indicators as implantation rates ($43.15\%$ ($\frac{107}{248}$) vs. $38.59\%$ ($\frac{115}{298}$), OR 1.008, $95\%$ CI 0.901-1.127; $P \leq .05$), clinical pregnancy rate, miscarriage rate, and live births rate ($P \leq .05$). ( Table 3). All pregnancy data were followed up until January 9, 2023 (Table 3).
## Discussion
This research showed that LE may play a role as an adjuvant medicine to enhance the FORT of the PPOS regimen in PCOS patients with a high BMI. Nevertheless, the number of retrieved oocytes and mature oocytes, the dose of gonadotropin consumption and gonadotropin days, and the number of embryos in the combined group were similar to those in the group that received the PPOS protocol alone. The implantation rate was elevated in the combined group compared with the PPOS group, but the difference was not significant, and the rates of clinical pregnancies, miscarriages and live births were comparable between the two groups.
Increased BMI in PCOS is linked to elevated androgen levels, which could block dominant follicle development and cause follicular degeneration [23]. In our study, the FORT in the LE cotreatment group was significantly greater than that in the control group. The higher FORT after LE combination therapy may be the result of endocrine alterations. Coadministration of LE causes an acute hypoestrogenic condition, which relieves the hypothalamic-pituitary axis of oestrogenic negative responses and enhance gonadotropins production [24], these changes may explain the increased follicle diameter at oocyte retrieval time in the LE cotreatment group. We found that the number of larger follicles (>16 mm) was substantially elevated in the study group compared with the control group. After diameter deviation, increased LH levels tend to promote dominant follicle selection and enhance the development of dominant follicles [25, 26]. Notably, the dose of GnRH-a for triggering was markedly enhanced and the dose of hCG was decreased without statistical difference in the LE cotreatment group compared with the PPOS-only group. An increased number of large preovulatory follicles was observed in the LE cotreatment group, suggesting that women in that group had a higher likelihood of receiving a single GnRHa trigger rather than a dual trigger. The number of oocytes retrieved and the oocyte retrieval rate were similar between the two groups, indicating that the ovulation trigger method in the present study did not affect oocyte retrieval.
Although LH values were meaningfully increased in letrozole cotreatment group compared with the standard PPOS group, FSH levels were similar between the two group. Women with PCOS would have a partial pituitary desensitization and relative decline of FSH responsiveness [27, 28] which might owe to the hyperactive GnRH neurons [29]. Higher LH values in the study group might induced by LE through blocking oestrogen production [24]. Progestogen was one of the precursors of estrogens and transformed into estrogens by aromatase [30]. LE, an aromatase inhibitor, can inhibit the production of estrogens, thereby reducing estrogens level and accumulating progesterone [15, 24, 30]. Hence, lower serum E2 levels and higher P levels were observed after cotreatment with LE in the PPOS protocol.
Notably, oocyte maturity rates were significantly decreased in the LE cotreatment group compared with the PPOS-only group, although mature oocyte yields were comparable. The influence of LE on oocyte maturity remains controversial in the literature (31–33). LE, an aromatase inhibitor, is usually used in patients who need fertility preservation, such as those with breast cancer, to reduce oestrogen levels [33]. Our research is consistent with Quinn’s study showing that GnRH antagonist protocol cotreatment with LE in breast cancer patients decreased the oocyte maturity rate [33]. LE coadministration decreased oestrogen levels and resulted in the accumulation of progesterone, 17α-progesterone and testosterone [15, 24, 30], These changes in the endocrine microenvironment affect meiotic maturation probably by inhibiting oocyte cytoplasmic maturation, contributing to reduced oocyte maturation. There is also evidence that LE does not increase the risk of spindle assembly and preimplantation developmental arrest [34]. Hu’s study of mouse oocytes showed that the antral space formed earlier if they were cultured in the presence of aromatase inhibitor, while the oocyte competency was not reduced [35]. In the current retrospective study, lead follicles were trigger at diameters of 18 mm, leading to lower oocyte maturity rates in the LE cotreatment group. Hence, Oktay [32] suggested that instead of triggering lead follicles at diameters of 17–18 mm, they should trigger at 19.5–20.5 mm under LE-containing stimulation, as LE cotreatment requires a different trigger criterion. In the present research, the lower oocyte maturity rates in the LE cotreatment group did not affect the number of mature oocytes as the number of large follicles on the trigger day was significantly higher. Prospective randomised controlled studies with different trigger criteria for the PPOS- LE cotreatment protocol are needed in the future.
In our present study, FORT didn’t convert into significant higher implantation rate here. Although previous studies illustrated that patients with an elevated FORT can achieve improved clinical outcomes [36] and good pregnancy outcomes [37] in IVF cycles and that embryos generated from oocytes from the dominant follicle group could have enhanced implantation potential since they are less fragmented [38, 39], opposing studies have linked an abundance of dominant follicles to impaired oocyte growth performance and a poor pregnancy rate, since excessive follicular development during ovarian stimulation might cause oocyte overmaturation, leading to unsuccessful pregnancies [40]. We have showed that oocyte maturity rates were significantly decreased in the LE cotreatment group compared with the PPOS-only group in the previous statement, and LE-containing stimulation should trigger lead follicles at larger diameters than the original standard [32]. Therefore, we speculated that although the proportion of FORT increased significantly after adding LE in PPOS protocol, its oocyte development potential did not increase significantly. However, this needs to be confirmed by large-scale prospective randomized controlled studies.
A higher BMI has been linked to a greater need for gonadotrophins during stimulation [7]. PCOS may be accompanied by an altered ovarian response to gonadotropins, which would reduce early ovarian responsiveness compared with that in ovulatory controls [41]. FORT is an objective method for determining the real effect of external FSH on follicles since it is not impaired by preexisting antral follicle population size [11]. Although concurrent LE administration enhanced FORT in the PPOS protocol, no significant difference in gonadotropin consumption was observed. This was surprising given that previous studies have suggested that LE lowers the overall gonadotropin intake for ovarian stimulation [24, 42, 43]. In contrast to low responders, high responders with PCOS exhibit FSH receptor upregulation in their follicular granulosa cells [44], which could explain why LE did not work to lower the overall gonadotropin dosage necessary for ovarian stimulation in hyperresponders [45].
Our study’s retrospective nature and limited sample size are notable drawbacks. As a retrospective study, there existed selectivity bias in this research. Most Chinese patients with PCOS ($80\%$) are within the healthy weight range [2]. Additionally, our data were acquired from a single centre; as a result, it was challenging to gather sufficient patient data to detect statistically significant variations. There is also the possibility of unmeasured or unidentified confounders in this retrospective analysis, which might result in less-than-perfect matching and weaken the reliability of the results. In addition, despite having embryos of high quality, some patients were unable to finish their FET cycles for a variety of reasons. Thus, the present research should be considered a preliminary effort, and the development of additional evidence requires further by more prospective studies to validate the impact of combine LE with PPOS for women with PCOS women and a high BMI. Further research on the application of LE in women with PCOS and different BMIs is needed to help physicians to develop individualised treatment plans for each patient.
## Conclusion
Our findings show that the addition of LE to the PPOS regimen increased FORT. In patients with PCOS and high BMI who were receiving IVF therapy, there was not a statistically favourable impact of LE addition on the cycle parameters of COS, including hMG consumption, or on pregnancy outcomes.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors.
## Ethics statement
The studies involving human participants were reviewed and approved by The Ethics Committee of Shanghai Ninth People’s Hospital (Institutional Review Board) (Number: SH9H-2021-T294–1). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
YK and QZ conceived and direct this study. XS and JL contribute to data collection and analysis. YL dedicated to draft and revise the manuscript. All authors have made a corresponding contribution to this article. The authors have nothing to declare on the final manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1072170/full#supplementary-material
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|
---
title: 'Association between endotypes of prematurity and pharmacological closure of
patent ductus arteriosus: A systematic review and meta-analysis'
authors:
- Gema E Gonzalez-Luis
- Moreyba Borges-Lujan
- Eduardo Villamor
journal: Frontiers in Pediatrics
year: 2023
pmcid: PMC10020634
doi: 10.3389/fped.2023.1078506
license: CC BY 4.0
---
# Association between endotypes of prematurity and pharmacological closure of patent ductus arteriosus: A systematic review and meta-analysis
## Abstract
### Introduction
Endotypes leading to very and extremely preterm birth are clustered into two groups: infection/inflammation and dysfunctional placentation. We conducted a systematic review of observational studies exploring the association between these two endotypes and the pharmacological closure of patent ductus arteriosus (PDA) induced by cyclooxygenase (COX) inhibitors. Chorioamnionitis represented the infectious-inflammatory endotype, while dysfunctional placentation proxies were hypertensive disorders of pregnancy (HDP) and small for gestational age (SGA) or intrauterine growth restriction.
### Methods
PubMed/Medline and Embase databases were searched. The random-effects odds ratio (OR) and $95\%$ confidence interval (CI) were calculated for each association. We included 30 studies (12,639 infants).
### Results
Meta-analysis showed a significant association between exposure to HDP and increased rate of pharmacological closure of PDA (17 studies, OR 1.41, $95\%$ CI 1.10–1.81, $$p \leq 0.006$$). In contrast, neither chorioamnionitis (13 studies, OR 0.75, $95\%$ CI 0.47–1.18, $$p \leq 0.211$$) nor SGA (17 studies, OR 1.20, $95\%$ CI 0.96–1.50, $$p \leq 0.115$$) were significantly associated with the response to therapy. Subgroup analyses showed that the higher response to COX inhibitors in the HDP group was significant for indomethacin (OR 1.568, $95\%$ CI 1.147–2.141, $$p \leq 0.005$$) but not for ibuprofen (OR 1.107, $95\%$ CI 0.248–4.392, $$p \leq 0.894$$) or for the studies using both drugs (OR 1.280, $95\%$ CI 0.935–1.751, $$p \leq 0.124$$). However, meta-regression showed that this difference between the drugs was not statistically significant ($$p \leq 0.404$$).
### Discussion/Conclusion
Our data suggest that the pathologic condition that triggers prematurity may alter the response to pharmacological treatment of PDA. The DA of infants exposed to HDP appears to be more responsive to COX inhibitors.
## Introduction
The ductus arteriosus (DA) is a fetal vessel connecting the pulmonary artery to the descending aorta, which is of major functional importance for the integrity of the fetal circulation (1–6). Morphological and functional maturation of the DA during fetal life prepares the vessel for functional and then anatomical postnatal closure. This closure is a key event in the transition to extrauterine life. However, when preterm birth interrupts physiological maturation, infants are exposed to patent ductus DA (PDA) due to underdeveloped ductal closure mechanisms (1–6).
PDA in very and extremely preterm infants (i.e., with gestational age less than 32 weeks) is a persistent dilemma for neonatal medicine, as well as a potential morbidity and mortality contributor when hemodynamically significant [1, 7, 8]. Although the therapeutic approach has changed and continues to change over the years, the classical pharmacological treatment of PDA is based on cyclooxygenase (COX) inhibitors, such as indomethacin and ibuprofen (9–12). Paracetamol has been added to these drugs in recent years [9].
Low gestational age (GA) is the main risk factor for both failure of spontaneous and pharmacologic closure of the DA (2, 13–15) but there is a growing recognition that the pathologic conditions that trigger preterm birth may play a relevant role in in the incidence of PDA, as well as in the response to COX inhibitors (16–20). The two main pathophysiologic pathways, or endotypes, leading to very/extremely preterm birth are 1) infection/inflammation and 2) dysfunctional placentation (21–24). Previous studies by our group and other investigators have systematically reviewed the association between these two endotypes and a number of complications of prematurity including PDA (15, 22, 25–31). In these meta-analyses, the infectious-inflammatory endotype was represented by chorioamnionitis, while the placental dysfunction endotype was represented by hypertensive disorders of pregnancy (HDP) and fetal growth restriction (15, 22, 25–31).
Individual studies suggest that prenatal conditions such as chorioamnionitis [16, 17] or HDP (18–20) may affect the therapeutic response to COX inhibitors. However, these findings have not been systematically reviewed. Our aim in the present study is to fill this gap in the literature by conducting a systematic review and meta-analysis on the association between endotype of prematurity and pharmacological closure of PDA in very and extremely preterm infants.
## Methods
The methodology of the present study is based on that used in our previous meta-analyses on the association of several risk factors and the incidence of PDA and/or the response to pharmacological treatment of PDA (26, 27, 32–34). The study was performed and reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and meta-analysis of observational studies in epidemiology (MOOSE) guidelines [15]. Review protocol was registered in the PROSPERO international register of systematic reviews (ID = CRD42018095509). The Population, Exposure, Comparison and Outcome (PECO) question was: Do very/extremely preterm infants (P) exposed to chorioamnionitis, HDP, or growth restriction during pregnancy (E) have a different rate of PDA closure in response to treatment with COX inhibitors (O) than infants with no history of exposure (C)?
## Sources and search strategy
A comprehensive literature search was undertaken using the PubMed and EMBASE databases. The search terms involved various combinations of the following key words: ductus arteriosus, patent ductus arteriosus, PDA, Patency of the Ductus Arteriosus, Ductus Botalli, treatment, pharmacologic(al) closure, indomethacin, ibuprofen, paracetamol, acetaminophen, cyclooxygenase, COX, chorioamnionitis, intrauterine infection, intrauterine inflammation, antenatal infection, antenatal inflammation, preeclampsia, IUGR, growth restriction, growth retardation, restricted growth, fetal growth, fetus growth, placental dysfunction, placental insufficiency, chronic hypoxia, chronic hypoxemia, small for gestational age, small for date, SGA, gestational hypertension, maternal hypertension, hellp syndrome, hypertensive disorders, toxemia, hypertensive disorders of pregnancy. No language limit was applied. The literature search was updated up to March 2022. Narrative reviews, systematic reviews, case reports, letters, editorials, and commentaries were excluded, but read to identify potential additional studies. Additional strategies to identify studies included manual review of reference lists from key articles that fulfilled our eligibility criteria, use of “related articles” feature in PubMed, and use of the “cited by” tool in Web of Science and Google scholar.
## Study selection and definitions
Studies were included if they had a prospective or retrospective design, examined infants with GA below 32 weeks and reported primary data that could be used to measure the association between pharmacological closure of PDA and exposure to chorioamnionitis (clinical or histological), HDP (including pregnancy-induced hypertension, preeclampsia and eclampsia) or fetal growth restriction. As we did in our previous meta-analyses, we accepted small for gestational age (SGA) as a proxy for fetal growth restriction [22, 27]. Regarding response to drug treatment, when a study reported on several treatment courses, only the final response was taken into account. To identify relevant studies, two reviewers (GG-L and MB-L) independently screened the results of the searches and applied inclusion criteria using a structured form. Discrepancies were resolved by the third reviewer (EV).
## Data extraction and assessment of study quality
Two investigators (GG-L and MB-L) extracted data on study design, demographics, and response to treatment. Another investigator (EV) checked the data extraction for completeness and accuracy. Methodological quality was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies [15]. This scale assigns a maximum of 9 points (4 for selection, 2 for comparability, and 3 for outcome). NOS scores ≥ 7 were considered high-quality studies (low risk of bias), and scores of 5 to 6 denoted moderate quality (moderate risk of bias) [15].
## Statistical analysis
Studies were combined and analyzed using comprehensive meta-analysis V3.0 software (Biostat Inc., Englewood, NJ, USA). The odds ratio (OR) with $95\%$ confidence interval (CI) was calculated with a random-effects model and subgroups were combined with a mixed-effects model [35]. Statistical heterogeneity was assessed by Cochran's Q statistic and by the I2 statistic. I2 was interpreted on the basis of Higgins and Thompson criteria, where $25\%$, $50\%$, and $75\%$ correspond to low, moderate, and high heterogeneity, respectively [36]. Potential sources of heterogeneity were assessed through subgroup analysis and/or random effects (method of moments) univariate meta-regression analysis as previously described [16, 17]. For both categorical and continuous covariates, the R2 analog, defined as the total between-study variance explained by the moderator, was calculated based on the meta-regression matrix. Predefined sources of heterogeneity included the following characteristics of cohorts: definition of exposure (clinical or histological chorioamnionitis, HDP definition, and growth restriction or SGA definition), mean or median GA, median year of birth, and drug used for PDA treatment. We used the Egger's regression test and funnel plots to assess publication bias. Subgroup analyses, meta-regression, and publication bias assessment were performed only when there were at least ten studies in the meta-analysis. A probability value of less than 0.05 (0.10 for heterogeneity) was considered statistically significant.
## Description of studies and quality assessment
The flow diagram of the search process is shown in Supplementary Figure S1. Of 964 potentially relevant studies, 30 (including 12,639 infants) were included (13, 16–20, 37–60). Their characteristics are summarized in Supplementary Table S1. Focusing on exposure, 13 studies provided data on chorioamnionitis, 17 on HDP, and 17 on SGA. We found no studies that evaluated IUGR (i.e., assessment of growth during fetal period). Nineteen studies reported on indomethacin (16–20, 37–50), five on ibuprofen (51–55) and in six studies both drugs were used (13, 56–60). The quality score of each study according to the Newcastle-Ottawa *Scale is* depicted in Supplementary Table S1. All studies received at least 7 points indicating a low risk of bias.
## Meta-analysis
Meta-analysis could not demonstrate a significant association between chorioamnionitis and response to pharmacological treatment of PDA (OR 0.746, $95\%$ CI 0.472–1.181, $$p \leq 0.211$$) (Figure 1). This lack of significant effect of chorioamnionitis was observed for both clinical (OR 0.838, $95\%$ CI 0.492–1.428, $$p \leq 0.516$$) and histological chorioamnionitis (OR 0.536, $95\%$ CI 0.217–1.319, $$p \leq 0.175$$). Meta-regression showed that the difference in effect size between clinical and histological chorioamnionitis was not statistically significant ($$p \leq 0.400$$). In contrast, meta-analysis showed a significant association between exposure to HDP and pharmacological closure of PDA (OR 1.413, $95\%$ CI 1.102–1.811, $$p \leq 0.006$$) (Figure 2). Subgroup analysis showed that this significant association was maintained in the any HDP group (OR 1.392, $95\%$ CI 1.004–1.931, $$p \leq 0.006$$) but not in the preeclampsia group (OR 1.441, $95\%$ CI 0.984–2.109, $$p \leq 0.061$$). However, meta-regression showed that the difference in effect size between any HDP and preeclampsia was not statistically significant ($$p \leq 0.962$$). Finally, the meta-analysis could not demonstrate a significant association between being SGA and response to pharmacological treatment of PDA (OR 1.197, $95\%$ CI 0.957–1.497, $$p \leq 0.115$$) (Figure 3). This lack of significant association was consistent for all SGA definitions (Figure 3). Meta-regression showed that the difference in effect size between the different SGA definitions was not statistically significant ($$p \leq 0.852$$). Neither visual inspection nor Egger's test suggested the presence of publication or selection bias for none of the three meta-analyses (Supplementary Figure S2).
**Figure 1:** *Meta-analysis on the association between chorioamnionitis and pharmacological closure of patent ductus arteriosus.* **Figure 2:** *Meta-analysis on the association between hypertensive disorders of pregnancy (HDP) and pharmacological closure of patent ductus arteriosus.* **Figure 3:** *Meta-analysis on the association between being small for gestational age (SGA) and pharmacological closure of patent ductus arteriosus.*
## Subgroup analysis and meta-regression
In addition to the subgroup analysis based on the definition of the different exposures (Figures 1–3), we conducted a second analysis based on the drug used to treat PDA. As shown in Table 1 and Supplementary Figure S3, the association between HDP and pharmacologic closure of PDA was significant only for indomethacin but not for ibuprofen or for studies using either indomethacin or ibuprofen. However, meta-regression showed that this difference between the drugs was not statistically significant ($$P \leq 404$$, Supplementary Figure S3). In the chorioamnionitis and SGA meta-analyses, no significant association with any of the drugs was detected (Table 1, Supplementary Figure S3).
**Table 1**
| Meta-analysis | Drug | K | OR | 95% CI | 95% CI.1 | P | Heterogeneity | Heterogeneity.1 | Meta-regressionP-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Meta-analysis | Drug | K | OR | Lower limit | Upper limit | P | I 2 | P | Meta-regressionP-value |
| Hypertensive disorders of pregnancy | Any | 4 | 1.280 | 0.935 | 1.751 | 0.124 | 0.0 | 0.460 | 0.404 |
| Hypertensive disorders of pregnancy | Ibuprofen | 3 | 1.107 | 0.248 | 4.932 | 0.894 | 48.4 | 0.144 | 0.404 |
| Hypertensive disorders of pregnancy | Indomethacin | 10 | 1.568 | 1.147 | 2.141 | 0.005 | 24.4 | 0.219 | 0.404 |
| Chorioamnionitis | Any | 2 | 0.991 | 0.755 | 1.299 | 0.945 | 0.0 | 0.417 | 0.673 |
| Chorioamnionitis | Ibuprofen | 3 | 0.627 | 0.306 | 1.284 | 0.202 | 0.0 | 0.462 | 0.673 |
| Chorioamnionitis | Indomethacin | 8 | 0.725 | 0.399 | 1.319 | 0.292 | 72.7 | 0.001 | 0.673 |
| Small for gestational age | Any | 5 | 1.082 | 0.844 | 1.388 | 0.533 | 0.0 | 0.923 | 0.148 |
| Small for gestational age | Ibuprofen | 1 | 2.286 | 0.169 | 30.959 | 0.534 | 0.0 | 1.000 | 0.148 |
| Small for gestational age | Indomethacin | 11 | 1.372 | 0.914 | 2.059 | 0.127 | 48.4 | 0.036 | 0.148 |
We also analyzed by meta-regression how the median year of the cohort (Table 2, Supplementary Figure S4) or the gestational age of the included infants (Table 2, Supplementary Figure S5) could influence the effect size of the different meta-analyses. The only significant finding from these meta-regressions (Table 2, Supplementary Figure S4) was the correlation between the median year of the cohort and the effect size of the association between SGA and drug response ($$p \leq 0.008$$). As the cohort became more modern, the effect size of the association significantly decreased (Table 2, Supplementary Figure S4).
**Table 2**
| Meta-analysis | Covariate | K | Coefficient | 95% CI | 95% CI.1 | P-value | R2 analog |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Meta-analysis | Covariate | | | Lower limit | Upper limit | P-value | |
| Chorioamnionitis | GA cohort (weeks) | 13.0 | 0.478 | −0.125 | 1.081 | 0.120 | 0.0 |
| Chorioamnionitis | Median year of cohort | 13.0 | −0.035 | −0.130 | 0.060 | 0.469 | 0.0 |
| Hypertensive disorders of pregnancy | GA cohort (weeks) | 17.0 | 0.027 | −0.270 | 0.324 | 0.859 | 0.0 |
| Hypertensive disorders of pregnancy | Median year of cohort | 17.0 | −0.037 | −0.076 | 0.002 | 0.063 | 0.61 |
| Small for gestational age | GA cohort (weeks) | 17.0 | −0.095 | −0.264 | 0.074 | 0.269 | 0.0 |
| Small for gestational age | Median year of cohort | 17.0 | −0.049 | −0.093 | −0.004 | 0.032 | 0.71 |
## Discussion
To the best of our knowledge, the present study is the first systematic review and meta-analysis examining the effect of endotype of prematurity on the response to pharmacological treatment of PDA in very or extremely preterm infants. We analyzed three pathological conditions (chorioamnionitis, HDP, and SGA) and observed that HDP was associated with a higher rate of pharmacologic closure of PDA. Moreover, subgroup analysis showed that the association between HDP and response to treatment was particularly significant for indomethacin. However, the relatively small number of studies and the moderate degree of heterogeneity that was present in some of the meta-analyses limit our results.
As mentioned in the introduction, the different endotypes of prematurity are not only the trigger for preterm birth but also induce a different pathophysiological environment for the development of fetal organs and systems. Thus, the infectious-inflammatory endotype is characterized by the development of a systemic inflammatory response with elevated cytokine levels [21, 23, 24]. In turn, the placental dysfunction endotype is characterized by chronic hypoxia and imbalance of pro- and anti-angiogenic factors (21–24). Both pathophysiological pathways could plausibly affect the normal development of the DA resulting in delay or failure of its spontaneous or pharmacological closure.
Peri- and postnatal infection is classically considered a key risk factor for PDA (16, 17, 61–64). Two previous meta-analyses showed a significant association between chorioamnionitis and risk of developing PDA in very preterm infants [26, 31]. However, a significant proportion of the risk appears to be associated with the lower GA of infants with chorioamnionitis compared to those not exposed to the insult [26]. Although intrauterine infection is associated with increased COX expression and prostaglandin production [17, 61, 63, 65], the present meta-analysis could not demonstrate an association between chorioamnionitis and response to COX inhibitors. Nevertheless, the low number of studies reporting on histological chorioamnionitis limits our results. Moreover, histological chorioamnionitis have been dichotomized without taking into account that there are different grades of the condition [66, 67]. In our previous meta-analysis on the association between chorioamnionitis and risk of developing PDA, we could perform a sub-analysis comparing fetal inflammation (i.e., funisitis) with maternal inflammation (i.e., chorioamnionitis without funisitis). We did not observe an increased risk of PDA in the funisitis group [26]. Unfortunately, for the present analysis we could only find three studies that reported on histological chorioamnionitis [17, 55, 59] and, of these, only one reported data on funisitis [55]. The data from that single study do not suggest that funisitis significantly increases the risk of non-response to pharmacological treatment of PDA (OR 0.46, $95\%$ CI 0.14–1.52) [55].
The only antenatal pathology for which we have found an association with pharmacological closure of the DA was HDP. That HDP may affect the incidence of PDA has been demonstrated in several cohort studies (68–70). However, a recent meta-analysis, including eight studies, found no significant association between HDP and the risk of developing PDA [15]. There are several potential explanations for the higher rate of spontaneous and pharmacologic ductal closure in preterm infants with intrauterine exposure to HDP. Women with HDP are likely to deliver before the onset of natural labor and therefore the production of prostaglandins is lower than that of pregnant women in labor [71]. In addition, preterm infants born due to HDP tend to have higher gestational ages than those born due to chorioamnionitis [22]. This leads to higher clinical stability and lower incidence of respiratory complications [22, 23]. Nevertheless, preterm birth is by definition a pathological condition. Therefore, there is no “healthy control group” to compare with. The positive effects of HDP on pharmacological closure of the DA may reflect not the direct action of HDP but the absence, or at least attenuated presence, of an infectious process. Although placental dysfunction in preeclampsia is accompanied by an inflammatory response with release of cytokines such as tumor necrosis factor-α and interleukin-6 [72, 73], the levels of proinflammatory mediators are lower than those in chorioamnionitis [74, 75].
Subgroup analysis showed that the association between HDP and higher rate of pharmacologic ductal closure was particularly significant for indomethacin. However, meta-regression showed that the difference between the drugs was not statistically significant. Interestingly, Louis et al. reported that the rate of ductal closure induced by prophylactic indomethacin was significantly higher in the offspring of HDP mothers [76]. With our present results, we can only speculate on the potential mechanisms responsible for this difference between indomethacin and ibuprofen. A Cochrane meta-analysis concluded that ibuprofen was as effective as indomethacin for PDA closure, whereas the former reduced the risk of necrotizing enterocolitis and transient renal insufficiency, when compared with indomethacin [77]. The choice of indomethacin or ibuprofen to treat PDA is highly variable among neonatologists and is often influenced by non-clinical factors such as difficulties with drug supply (10–12). Nevertheless, there are pharmacokinetic, pharmacodynamic and pharmacogenetic differences between ibuprofen and indomethacin that may account for a different response in particular subgroups of preterm infants (78–80).
The third condition in which we have analyzed pharmacological ductal closure is fetal growth restriction. It should be noted that these results were limited because all included studies reported data on SGA and not IUGR. Although the terms SGA and IUGR are often used synonymously, SGA is a statistical definition based on BW, with the 10th percentile as the most frequently used cut-off (81–83). Therefore, the term SGA also encompasses constitutionally small infants without growth restriction (81–83). On the other hand, infants with pathologic growth restriction may have a BW above the 10th percentile (81–83). In addition, prenatal congenital infections may account for a percentage of cases of IUGR and preterm SGA infants are more prone to postnatal infection than their GA-matched appropriately grown controls [84, 85]. Therefore, infection/inflammation may also be present in SGA infants.
In a recent meta-analysis, we investigated the association between SGA/IUGR and incidence of PDA. Although we observed a negative association, i.e., the rate of any PDA was lower in the growth-restricted group, this association only involved the subgroup in which SGA was defined using the 10th percentile threshold [27]. Moreover, when examining the subgroup of studies that used a definition for growth restriction that went beyond BW for GA (i.e., assessment of fetal growth or presence of abnormal Doppler), meta-analysis could not find a significant association with PDA [27]. In addition, we could not find a significant association between SGA/IUGR and the development of hemodynamically significant PDA [27]. In the present meta-analysis, we could only find studies focusing on SGA infants, mostly defined by the 10th percentile cutoff. Although very limited by this fact and the low number of studies, our data do not suggest that being SGA affects the rate of pharmacological closure of PDA.
It may be noteworthy that of the two proxies we used to represent placental dysfunction (HDP and SGA), only HDP were significantly associated with pharmacological closure of PDA. First argument that can be used to explain this difference is that not all HDP induce growth retardation and not all SGA infants are smaller because of HDP. In previous meta-analyses, we have shown that the positive association between HDP and SGA is very strong (OR 4.70, $95\%$ CI 3.57–6.17), whereas the associations between chorioamnionitis and HDP (OR 0.11, $95\%$ CI 0.08-0.16) or SGA (OR 0.35, $95\%$ CI 0.24–0.51) were strongly negative [22]. Thus, although HDP and SGA are strongly associated, there is a proportion of infants who do not present with both disorders or even who present chorioamnionitis as well. In two previous meta-analyses, we used these same proxies (HDP and SGA) to investigate the association between placental dysfunction and mortality [86] as well as between placental dysfunction and BPD [22]. In the second case, the SGA group showed a positive association with BPD, while HDP was not significantly associated with the condition [22]. Interestingly, when we analyzed the association between endotype of prematurity and mortality, we observed that the SGA group had a higher risk of mortality (OR 1.68, $95\%$ CI 1.38–2.04) while HDP had a “protective” effect on the mortality of very preterm infants (OR 0.74, $95\%$ CI 0.64–0.86) [86]. Meta-analysis also showed a positive mortality odds for chorioamnionitis (OR 1.43, $95\%$ CI 1.25–1.62) [86]. Taken together, these data suggest that when HDP-induced placental dysfunction reaches a degree of severity sufficient to affect fetal growth, the risk of worsening prematurity outcome is increased. On the contrary, when HDP is not accompanied by growth retardation, the prognosis is more favorable and even a certain “protective” effect is observed when compared with chorioamnionitis-induced prematurity.
Neonatology is a medical specialty in constant evolution and this is also reflected in the diagnostic and therapeutic approach to PDA. Neonatologists have been debating for decades what is a hemodynamically significant PDA, what are the health consequences of the presence of a ductal shunt for the preterm newborn and when and how PDA should be treated [1, 7, 8]. As a result of this debate, there has been a transition from advocating that all PDA should be treated, with an intermediate step in which only targeted group were treated, to a current trend of therapeutic nihilism. That is, the consideration of PDA in very preterm infants as a “physiological” condition whose treatment produces more harm than benefit [1, 7, 8]. This may influence our results as the number of infants treated has probably decreased with the passing of time. We have analyzed by meta-regression whether the association between endotype of prematurity and pharmacological closure of the DA has changed over the years. We have found that as cohorts become more contemporary, the response to COX inhibitors in SGA infants tends to decrease. The course of time does not seem to have affected pharmacological ductal closure in infants exposed to chorioamnionitis or HDP. However, it should be noted that our meta-regression analysis is based on few studies. The minimum number of trials per covariate in meta-regression analyses required to minimize the risk of overfitting is unknown but it has been suggested a minimum of 10 studies per examined covariate [87, 88]. The meta-regressions presented here include 15–16 studies and are therefore just above the minimum recommended threshold. Therefore, they have mainly an exploratory and hypothesis-generating value.
In conclusion, our data suggest that the pathological condition that triggers prematurity may alter not only the incidence of PDA but also the response to pharmacological treatment. Our meta-analysis further supports the growing evidence that not all preterm infants are identical even if they are of the same gestational age. Therefore, one size fits all is no longer an appropriate approach to perinatal medicine (23, 89–91). Personalized medicine requires an adequate characterization of endotypes and clinical phenotypes so that each infant can receive the therapeutic approach best suited to his or her individual characteristics. Finally, very recent evidence from a randomized controlled trial suggests that expectant management of PDA in extremely preterm infants was non-inferior to ibuprofen treatment with respect to moderate-to severe bronchopulmonary dysplasia, necrotizing enterocolitis, or mortality [92]. Further research is warranted to elucidate whether the etiology or endotype of prematurity affects the outcome differently when PDA is managed with a non-pharmacological approach.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
## Author contributions
G G-L and M B-L selected studies for inclusion, collected data, contributed to the statistical analysis and interpretation of the results, collaborated in the preparation of the graphs and tables, and reviewed and revised the manuscript. EV conceptualized and designed the study, performed the search, supervised data collection, planned and performed the statistical analysis, and wrote the drafts of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fped.2023.1078506/full#supplementary-material.
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|
---
title: Causal association between sleep traits and the risk of coronary artery disease
in patients with diabetes
authors:
- Mengyun Tian
- Hongchuang Ma
- Jiaxi Shen
- Teng Hu
- Hanbin Cui
- Ning Huangfu
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10020648
doi: 10.3389/fcvm.2023.1132281
license: CC BY 4.0
---
# Causal association between sleep traits and the risk of coronary artery disease in patients with diabetes
## Abstract
### Background and aims
The association between sleep traits and coronary artery disease (CAD) in patients with diabetes has been reported in previous observational studies. However, whether these potential relationships are causal remains unclear. We aim to assess the causal relationship between sleep traits and CAD in diabetic.
### Methods
Genetic instrumental variables associated with five sleep-related traits (insomnia, sleep duration, ease of getting up, morningness and snoring) were extracted from corresponding genome-wide association studies (GWAS). The associations of genetic variants with CAD were based on 15,666 individuals with diabetes (3,968 CAD cases and 11,696 controls). The primary analysis was derived using the inverse variance weighting method. Further sensitivity analysis was conducted to confirm the robustness and consistency of the main results.
### Results
Genetic liability to insomnia was significantly related to the increased risk of CAD in individuals with diabetes [odds ratio (OR): 1.163; $95\%$ CI: 1.072–1.254; $$p \leq 0.001$$]. Suggestive evidence was found for the borderline associations between both sleep duration (OR: 0.629; $95\%$ CI: 0.380–1.042, $$p \leq 0.072$$) and snoring (OR: 1.010, $95\%$ CI: 1.000–1.020, $$p \leq 0.050$$) with CAD risk. However, no consistent evidence was found for the association between ease of getting up and morningness with the risk of CAD in diabetic. Similar results can be verified in most sensitivity analyses.
### Conclusions
We provide consistent evidence for the causal effect of insomnia on the increased risk of CAD in individuals with diabetes. The management of sleep health should be emphasized to prevent CAD in diabetic patients.
## Introduction
Cardiovascular disease (CVD) is the leading cause of global mortality and a principle contributor to disability [1]. Meanwhile, CVD remains a major cause of death among patients with diabetes [2]. The latest diabetes management guidelines published by ESC/ESAD elevated the status of cardiovascular risk in the management of type 2 diabetes mellitus (T2DM) [3]. Several clinical studies have been published on cardiovascular risk management in adults with T2DM, involving lifestyle, blood pressure, blood glucose, cholesterol management and sleep in primary and secondary prevention of CVD (4–8).
Sleep disorders are increasingly prevalent modifiable risk factors for CVD [9]. Plenty of epidemiological studies suggested that sleep duration was associated with an increased risk of CVD events and higher mortality risk (10–14). A recent prospective study including 18,876 patients with T2DM reported that short and long sleep durations were both independently associated with the increased incidence and mortality of CVD [15]. Another study based on 36,058 Korean new-onset T2DM patients indicated that sleep disturbance was significantly associated with an increased risk of CVD mortality [16]. Besides, a cross-sectional study with small samples found that the effect of poor sleep on the risk of CVD in patients with T2DM may be mediated by some inflammatory factors [17]. Involvement of the endothelial function, autonomic nervous system, regulation of metabolism and inflammation have been proposed as possible mechanistic linked between sleep disorders and CVD [18]. Some studies also suggested that sleep disorders contributed to the risk of T2DM by affecting insulin production (19–22). However, the causality between these associations remains unclear, especially in individuals with diabetes.
Mendelian randomization (MR) uses genetic variation as a natural experiment to study the causal relationship between potentially modifiable risk factors and health outcomes [23]. As alleles follow random assignment during gamete formation, the estimations would not be affected by confounding factors and reverse causality compared to observational studies [24]. Several previous studies have provided genetic evidence for a causal association of insomnia and sleep duration with increased risk of CAD in general population [13, 25]. However, the causal association pattern in the diabetics remains unclear. Besides, the causal effect of other sleep characteristics, such as ease of getting up, morningness (being a morning person rather than an evening person) and snoring needs to be further investigated. As the sleep traits are driven by genetic risk, the MR study could provide long-term stable genetic evidence, which is not affected by lifestyle factors.
In the current study, we conducted a comprehensive MR study to evaluate the causal associations between five sleep traits and genetic susceptibility to CAD in individuals with diabetes (Table 1).
**Table 1**
| Trait | Data source | Sample size (case/controls) | Population |
| --- | --- | --- | --- |
| Insomnia | UKB,23andMe | 397,959/933,051 | European |
| Sleep duration | UKB | 446118 | European |
| Easy to get up | UKB | 385949 | European |
| Morningness | UKB,23andMe | 432835 | European |
| Snoring | UKB | 359916 | European |
| CAD in patients with diabetes | UKB | 3,968/11,698 | European |
## Study design
The study design overview was shown in Figure 1. The single nucleotide polymorphisms (SNPs) identified as genetic instrumental variables (IVs) for sleep traits depend on the following three core assumptions: (I) *The* genetic variation should be strongly related to sleep traits, (II) genetic variation is not influenced by potential confounding factors, as well as (III) genetic variation should only be related to the risk of CAD in diabetic patients through sleep traits [26]. Ethical review approval and informed consent were obtained from participants for all included original studies.
**Figure 1:** *Design of the Mendelian randomization study. Three core assumptions were as follows: Relevance assumption, the genetic IVs must be associated with sleep traits; Independence assumption, IVs should not be associated with confounders; Exclusion restriction, IVs must influence CAD in patients with diabetes only via sleep traits. IVs, instrumental variables; Easy to get up, ease of getting up in the morning; CAD, coronary artery disease.*
## Insomnia
Genetic association with insomnia was obtained from the largest GWAS to date including 1,331,010 individuals of European ancestry [27]. Insomnia was measured by a standardized question: “Do you often fall asleep late at night or wake up at midnight?”. There were four answer options: “never/rarely”, “sometimes”, “usually”, or “prefer not to answer”. Among them, the participants who answered “usually” were defined as suffering from insomnia, while the participants who answered “never or rarely” and “sometimes” were included in the control group.
In total, 208 SNPs were identified and used as the genetic IVs for insomnia. *The* genetic liability to insomnia in diabetic patients showed a significant association with an increased risk of CAD in the IVW analysis (OR: 1.163; $95\%$ CI: 1.072–1.254, $$p \leq 0.001$$; Figure 2). The main results remained consistent in the sensitivity analysis using the weighted median and maximum likelihood method (Supplementary Table S7). MR-Egger regression analysis showed no overall pleiotropy or heterogeneity between insomnia and CAD in diabetic patients ($p \leq 0.05$) (Table 2). In addition, scatter plots also showed that there was no directional polymorphism on CAD in diabetic patients (Figure 3). The leave-one-out sensitivity analysis indicated that the causal association was not greatly driven by any single SNP (Supplementary Figure S1).
**Figure 2:** *Mendelian randomization estimates of genetically predicted sleep disorders on coronary artery disease in patients with diabetes. Easy to get up, ease of getting up in the morning; OR, odds ratio; CI, confidence interval; IVW, inverse-variance weighted.* **Figure 3:** *Scatter plot of the association between insomnia and the risk of CAD in patients with diabetes. Each dot indicates a SNP; each line indicates the estimate of association between insomnia and the risk of CAD in patients with diabetes using corresponding methods.* TABLE_PLACEHOLDER:Table 2
## Sleep duration
The sleep duration GWAS analysis identified 78 independent SNPs associated with sleep duration ($p \leq 5$ × 10−8), involving 446,118 individuals of European ancestry [28]. The sleep duration was determined in the form of self-report. Subjects were asked “How long do you sleep every 24 h, including naps”, and the answers increased in hours. Subjects who responded to extreme values (less than 3 h or more than 18 h) or who reported using any sleep-regulating medication were excluded. Sleep duration was examined continuously.
## Ease of getting up in the morning
The ease of getting up was investigated in a study including 1,331,010 participants by asking: “On an average day, how easy do you find getting up in the morning?”. The possible responses included “not at all easy”, “not very easy”, “fairly easy” and “very easy”. The ease of getting up was divided into four categories and examined as a continuous scale. The corresponding GWAS extracted 70 independent pilot SNPs located in 62 different genomic loci [27].
## Morningness
Morningness was evaluated by asking: “Do you consider yourself to be?”. There were five possible responses “*Definitely a* ‘morning’ person”, “*More a* ‘morning’ than ‘evening’ person”, “More an ‘evening’ than a ‘morning’ person”, “Definitely an ‘evening’ person”, and “Do not know”. The corresponding GWAS identified 274 independent pilot SNPs located in 207 different genomic loci [27].
## Snoring
Snoring was evaluated based on asking: “Does your partner or a close relative or friend complain about your snoring?”. People would reply with “yes” or “no”. A total of 3,59,916 subjects were included in the genome-wide analysis of snoring. The corresponding GWAS analysis revealed 3,416 GWS SNPs ($p \leq 5$ × 10−8), resulting in the identification of 42 SNPs associated with snoring, which were located in 36 different genomic loci [27].
## CAD in patients with diabetes
The summary statistical data of CAD in diabetic were obtained from the latest GWAS [29]. That study was conducted on the basis of the UK Biobank cohorts in 2018, including 15,666 European individuals with diabetes (3,968 CAD cases and 11,696 non-cases). The diabetes and CAD were defined based on linked data from hospital admissions and death registries, and verbal health interview (see Supplementary Methods). The average age at visit was 62.7 ± 5.6 and 60.2 ± 7.0 for CAD group and non-CAD group, respectively. $74.0\%$ of CAD group and $60.2\%$ of non-CAD group were male. In the CAD group, 268 ($6.8\%$) individuals were with type I diabetes, while in the non-CAD group 945 ($8.1\%$) individuals were with type I diabetes. Linkage disequilibrium (LD) testing was performed based on 1,000 genomes LD reference panel of Europeans only [30].
## Statistical analysis
The inverse variance weighting (IVW) method was applied to evaluate the influence of genetically predicted sleep traits on the risk of CAD in diabetic populations in the main analysis [31]. The Wald estimator was used to generate a causal estimate for each SNP and the standard error was obtained using the Delta method. The overall effect value was then obtained by combining these estimates [32].
Sensitivity analysis was performed using the maximum likelihood method, the weighted median method, the MR-Egger method, and the Mendelian Randomization Pleiotropy Residual Sum and Outlier (MR-PRESSO) method to evaluate the robustness of the results. The weighted median method allows accurate calculation of causal association effects, even when less than $50\%$ of the genetic variation violates the core assumptions [33]. The MR-PRESSO can exclude specific outliers to obtain estimates closer to the true value and detect horizontal gene pleiotropy [34]. The MR-Egger intercept test was used to evaluate possible horizontal pleiotropic effects and to visually examine potential multidirectional effects by generating funnel plots [35]. The causal association between exposure and health outcomes was described by using scatter plots. Subsequently, the leave-one-out sensitivity analyses were performed to assess whether the casual relationship was dramatically driven by any single SNP. All the statistical analyses were conducted using the TwoSampleMR and MR-PRESSO packages in the R software (Version 4.3.1).
## Other sleep traits
With the IVW analysis, the current study found suggestive evidence for the borderline associations between both sleep duration (OR: 0.629; $95\%$ CI: 0.380–1.042, $$p \leq 0.072$$; Figure 2) and snoring (OR: 1.010, $95\%$ CI: 1.000–1.020, $$p \leq 0.050$$) with CAD risk in individuals with diabetes, which needed to be further investigated. Horizontal pleiotropy was not detected using MR-Egger intercept testing (Supplementary Table S7). However, no consistent evidence was found for the causal associations between ease of getting up (OR: 0.853; $95\%$ CI: 0.111–1.595; $$p \leq 0.675$$) and morningness (OR: 1.050; $95\%$ CI: 0.800–1.299; $$p \leq 0.704$$) with the risk of CAD in diabetic patients. Besides, in the MR-Egger analysis, no pleiotropy was detected between ease of getting up and morningness with CAD risk (Supplementary Table S7). The association patterns kept consistent and robust in the other three statistical models (Supplementary Table S7).
## Discussion
In this study, we investigated the causal effects of five sleep traits including insomnia, sleep duration, ease of getting up, morningness and snoring on CAD in patients with diabetes. Genetic liability to insomnia was closely related to the increased CAD risk in individuals with diabetes. Besides, suggestive evidence was found for the borderline associations between both sleep duration and snoring with the risk of CAD, which needed to be further investigated. However, no valid evidence for the causal effects of morningness and ease of getting up was found.
In regards to the directionality of the observed effects, we found a significant association between insomnia and increased CAD risk in patients with diabetes. Besides, the results suggested the borderline associations between sleep duration with reduced CAD risk and snoring with increased CAD risk, which were in line with previous observational studies. The easy to get up showed a trend of protective factors, however no consistent evidence was found. There were large differences in variation between the ORs of different sleep traits, partly due to the difference between binary variable (e.g., insomnia) and continuous variable (e.g., sleep duration). Besides, the causal effects of morningness and snoring on CAD risk may be relatively limited, compared to insomnia.
In the recent years, increasing observational evidence supported that insomnia was an important risk factor for the progression of CVD. Previous studies reported that people with significant insomnia symptoms have a $41\%$–$55\%$ increased risk of myocardial infarction and coronary heart disease, as well as a higher risk of cardiovascular and cerebrovascular related death. Suzanne M.Bertisch et al. found that insomnia or poor sleep quality combined with frequent short sleep increased the incidence of CVD events by $29\%$ compared with controls in a propensity matching model [36]. A large population cohort study showed that short or long sleep duration, insomnia and snoring were all related to an increased risk of CVD in a multivariate model [14]. In a prospective cohort study involving 4,07,500 individuals, the incidence of CVD was significantly associated with a $7\%$, $26\%$, and $20\%$ increased risk of snoring, insomnia, and narcolepsy, respectively [37].
Shorter sleep duration was independently associated with increased risk of subclinical multiple atherosclerosis [38]. Among patients younger than 40 years of age, insomniacs had a higher risk of T2DM than controls (HR: 1.31; $95\%$ CI: 1.14–1.55) [39]. Insomnia symptoms may lead to an increase in glycated hemoglobin levels, suggesting a causal link between insomnia and T2DM [40]. In addition, short and long sleep duration would increase the risk of T2DM [41]. Another prospective cohort study also indicated that insomnia was associated with a higher risk of T2DM [42]. And a meta-analysis of 13 prospective trials demonstrated that insomnia would increase the risk of CVD [43].
The pathophysiological basis and mechanisms underlying the association of insomnia with CVD have not been fully clarified. Possible mechanisms may include dysregulation of the hypothalamic-pituitary axis (HPA) [44], dysregulation of the autonomic nervous system [45], systemic inflammatory activation [46], and acceleration of atherosclerosis [47]. First, sleep was linked to haematopoiesis and atherosclerosis in mice, and sleep fragmentation would lead to more Ly-6Chigh monocytes, larger atherosclerotic lesions and less hypocretin, which controls myelopoiesis. Second, insomnia may affect the endothelial function of coronary arteries through the autonomic nervous system, thereby accelerating coronary atherosclerosis [48]. Third, sleep disturbance could induce increased NF-κB, proinflammatory cytokine production, and systemic inflammation through activation of β-adrenergic signaling pathways [49]. Forth, sleep disorders can also lead to the occurrence of CAD by affecting the endocrine mechanism, inhibiting the metabolism of blood lipids and the regulation of blood glucose [50].
The main strength was the study design using multiple SNPs as genetic IVs for sleep characteristics, which minimized confounding and reverse causality. In the current study, sleep traits were driven by genetic risk and not modifiable, thus providing stable genetic evidence, which was not affected by lifestyle factors. In addition, as appropriate genetic IVs were screened by a large amount of genetic associations, the summary of data guaranteed the estimation of causal effect on the basis of the large sample. Besides, we evaluated the causal effect of five sleep traits on CAD in individuals with diabetes using a comprehensive analysis, which could greatly expand the scope of our discovery.
Several limitations of the current study should be pointed out. First, the selection of genetic instruments was based on the GWAS without hypothesis, which may lack a comprehensive understanding of the potential association mechanism between genetic variation and diseases. Second, it was impossible to completely exclude the potential effects of pleiotropy, thus the results may be affected. Third, our findings could not be generalized to other populations, as only European participants were included. Therefore, further studies were needed to investigate the causal association pattern in other populations. Forth, all the sleep traits were obtained through subjective description of patients, so it was difficult to avoid misclassification. Besides, due to lack of individual-level genetic data, stratification analysis was not available in the current study to assess the gender and/or age differences in the association between sleep and CAD risk. Likewise, we failed to perform the analysis on if the insomnia treatments influence the association with CAD, which needed to be further investigated.
## Conclusion
In conclusion, we provided consistent evidence for the causal effect of insomnia on the increased CAD risk in diabetic patients. The borderline associations between both sleep duration and snoring with the risk of CAD needed to be further investigated. Increased attention should be paid to sleep health and better prevention of sleep disorder, which may reduce the risk of CAD in diabetic patients.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
## Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MT, HM, TH, and HC: made contributions to the design of the study. MT, HM, JS and HC: made contributions to the obtaining, analysis, or explanation of data for the study. MT: drafted the manuscript with critical revisions from JS and NH. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1132281/full#supplementary-material.
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|
---
title: Nutrition literacy differs based on demographics among University students
in Bengbu, China
authors:
- Tianjing Gao
- Ying Duan
- Qi Qi
- Guangju Mo
- Siyue Han
- Huaqing Liu
- Min Zhang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10020653
doi: 10.3389/fpubh.2023.1113211
license: CC BY 4.0
---
# Nutrition literacy differs based on demographics among University students in Bengbu, China
## Abstract
### Background
Nutrition literacy (NL) encompasses the knowledge and skills that inform individuals' food choices. This cross-sectional study explored factors associated with NL among Chinese university students in Bengbu, China.
### Methods
A cross-sectional survey was carried out. Two thousand one hundred thirty-three university students were selected by stratified cluster sampling. A 43-item NL questionnaire was used to assess NL. Binary logistic regression was used to determine odds ratios (ORs) along with $95\%$ confidence intervals (CIs) for NL and to test the interaction effects of multiple factors on total NL and its six dimensions.
### Results
Of these participants, 1,399 ($65.6\%$) were women and 734 ($34.4\%$) were men. Students who were from urban areas (OR = 1.36, $95\%$ CI: 1.08–1.72), were living with both parents (OR = 1.30, $95\%$ CI: 1.02–1.65), and had high academic performance (OR = 1.85, $95\%$ CI: 1.34–2.57) were more likely to report higher NL levels than did other students. The ORs for NL (OR = 1.60, $95\%$ CI: 1.06–2.41), nutrition knowledge (OR = 1.51, $95\%$ CI: 1.00–2.26), obtaining skills (OR = 1.76, $95\%$ CI: 1.16–2.65), and critical skills (OR = 1.59, $95\%$ CI: 1.05–2.39) were higher for medical students who had received nutrition education than for other students. The ORs for NL (OR = 2.42, $95\%$ CI: 1.21–4.84), nutrition understanding (OR = 2.59, $95\%$ CI: 1.28–5.25), and interactive skills (OR = 2.06, $95\%$ CI: 1.04–4.08) were higher for only-child students and those with a monthly expenditure of >¥1500.
### Conclusions
NL of university students differed in terms of place of origin, living arrangement, nutrition education, academic performance, and household income, and the findings imply that universities should have all students take a basic nutrition course to improve their NL.
## 1. Introduction
The period of university study may be influential in the establishment of long-term dietary patterns and may thus influence the risk of chronic diseases [1]. The transition from high school to university, known as emergent adulthood, is a vulnerable period that is frequently characterized by weight gain; therefore, it is a critical period for prevention and intervention in relation to dietary patterns [2, 3]. This period of transition is typically characterized by leaving home for the first time, adapting to a new environment, developing new friendships and social networks, and having greater independence in overall decision-making [4, 5]. An individual's dietary attitudes and behaviors during the period of university study can profoundly influence their adult lifestyle habits and thus influence the risk of obesity and related comorbidities such as diabetes and heart disease [6]. University students constitute a vulnerable group for poor dietary intake, insufficient physical activity, and sedentary behavior [7]. Young people are usually prone to adopt unhealthy dietary habits [8]. They exhibit dietary restraint, low intake of fruit and vegetables, and high intake of energy-dense nutrient-poor foods such as takeaway foods and sugar-sweetened drinks [5, 9]. Factors influencing healthy eating among university students include individual factors (e.g., nutrition knowledge and education), social factors (e.g., social support from parents), and environmental factors (e.g., product prices and limited budgets) [10, 11].
To reduce the increasing prevalence of nutritional health problems, it is of great importance to increase the knowledge level of individuals and society about nutrition and to develop healthy nutrition skills and behaviors [12]. Nutrition literacy (NL), also known as “health literacy applied in the field of nutrition” [13], refers to individuals' competence in healthy eating. NL was defined as “the degree to which individuals have the capacity to obtain, process, and understand nutrition information and skills needed in order to make appropriate nutrition decisions” [14]. Studies present nutrition literacy measurement instruments should be of multiple characteristics, e.g., different domains of cognition and skill, different dimensions of obtain, understand, analyze, appraise, and apply (15–17). However, existing NL instruments often assessed a certain characteristic of NL; moreover, they mainly focused on functional nutrition literacy [13] and rarely included interactive or critical nutrition literacy. Our previous study [18] developed a nutrition literacy measurement instrument with multiple characteristics which assess comprehensively NL for Chinese adults. Our NL instrument include two domains (cognition and skill), 3 levels of nutrition literacy (functional, interactive, and critical) and 6 dimensions of knowledge, understanding, obtaining skills, applying skills, interactive skills (the ability to act effectively to improve health and to communicate, provide, and apply relevant health information), and critical skills (the ability to critically assess and reflect on nutritional information or dietary advice in terms of personal nutritional needs) [13, 15]. The dimensions of nutrition knowledge and nutrition understanding represent the understanding of nutrition information and services; the dimension of obtaining skills represents the process of obtaining nutrition information and services; and the dimensions of applying skills, interactive skills, and critical skills represent the processing and application of nutrition information and services [18, 19]. Therefore, NL encompasses the crucial knowledge and skills that inform food choices [20]. NL emphasizes nutrition-related skills in which an individual should have to make wise decisions regarding dietary situations in daily life, it can be regarded as an imperative component of food education programs and important to promoting healthy eating behaviors [21]. A university campus with an adequate eating environment and adequate healthy eating campaigns could effectively improve healthy eating behaviors in university students [11]. Higher levels of NL were reported to be associated with healthier and higher-quality diets, which could in turn reduce the risk of diet-related chronic diseases [22].
A handful of studies have been conducted on NL among different subgroups of the population (i.e., adolescents, students, and adults) in Turkey [23, 24], Taiwan [25], Iran [26], US [27, 28], and Palestine [29]. Nevertheless, there is a lack of available evidence to investigate the NL of Chinese university students. Accordingly, the presented study investigated factors associated with NL and its six dimensions among university students in China. The findings of this study may inform the design of interventions for improving NL among university students.
## 2.1. Participants and procedure
This study involved a cross-sectional design and was conducted from April to June 2021 in Bengbu, China. Participants were recruited through stratified cluster sampling. Firstly, two universities (medical and non-medical) were selected by convenience sampling. Second, eight classes were randomly selected in each grade. And then all students (about 30 individuals) in these classes were asked to participate in the survey. An individual who was 18 years old and above was included in the survey if he or she willing to participate in it, but was excluded if he or she was unwilling to do it. The students were notified that participation was voluntary, and signed informed consent was obtained. This study was approved by the Ethics Committee of Bengbu Medical College. A total of 2,190 students completed the survey. After the exclusion of 57 ($2.7\%$) students who provided invalid responses, 2,133 students remained, and their responses were analyzed in this study.
## 2.2. NL assessment
A 43-item NL questionnaire (NL-43) [18], which was developed by experts in public health and nutrition education and promotion using the Delphi method, was used to assess the students' NL in the six dimensions containing nutrition knowledge (7 items), nutrition understanding (5 items), obtaining skills (5 items), applying skills (11 items), interactive skills (9 items), and critical skills (6 items). The Cronbach's alpha for the total scale of NL was 0.962 and the Cronbach's alpha for each dimensional scale ranged from 0.845 to 0.954. Each item is rated on a 5-point Likert-type scale (1 = strongly disagree, 2 = disagree, 3 = average, 4 = agree, 5 = strongly agree; or 1 = strongly non-conforming, 2 = non-conforming, 3 = average, 4 = conform, 5 = strongly conform), with a higher score indicating a higher NL level. The total score for the NL *Scale is* 215. NL and its six dimensions were dichotomised into low and high levels on the basis of their corresponding median scores (Supplementary Table 1). An individual was referred as high levels (coded as 1) in NL and its six dimensions if the score was above corresponding median score; otherwise, was referred as low levels (coded as 0).
## 2.3. Demographic information
This study obtained the students' demographic information, including their age (classified as 16–21 vs. 22–27 years), sex (male vs. female), major (medical vs. non-medical majors), grade (junior vs. senior), place of origin (rural vs. urban), only-child status (yes vs. no), living arrangement (living with both parents vs. living with a single parent, grandparents, or other). Acquisition of nutrition education was obtained through the question “Did you take any courses in nutrition at school?”; responses were “no” (coded as 1), “yes” (coded as 2). Parent education level was classified as elementary school or below (coded as 1), junior high school (coded as 2), high school or technical school (coded as 3), and college or university and above (coded as 4). Academic performance was obtained using the question “What was your grade point average in the last semester?”; responses were “ <70” as “poor,” “70–80” as “average,” and “≥80” as “good.” Household income per month was classified as <¥6,000 (coded as 1), ¥6,000–12,000 (coded as 2), and >¥12,000 (coded as 3). Expenditure per month was classified as <¥1,000 (coded as 1), ¥1,000–1,500 (coded as 2), and >¥1,500 (coded as 3).
## 2.4. Statistical analysis
All data were entered in duplicate into an Epi Data version 3.1 database (EpiData Association, Odense Denmark). Measurement data are presented herein as mean (M) ± standard deviation (SD). Descriptive statistics were used to determine the distributions of total NL and its six dimensions. Moreover, categorical variables are expressed herein as numbers and proportions, and such variables were compared across groups by using a chi-square test. Ddependant variable (NL) is dichotomous, and binary logistic regression was performed to determine the odds ratios (ORs) and the corresponding $95\%$ confidence intervals (CIs) for NL; it was also conducted to evaluate the interaction effects of multiple factors on total NL and its six dimensions. Some independent variables had three codes or above and were put into model as categorical. All statistical analyses were performed using SPSS 26.0 (IBM, Armonk, NY, USA). $P \leq 0.05$ (two-sided) was considered statistically significant.
## 3. Results
As presented in Table 1, this study included a total of 2,133 university students, and their age ranged from 16 to 27 years ($M = 20.91$; SD = 1.57). Of these students, $65.6\%$ were women and $34.4\%$ were men. Furthermore, $47.0\%$ of the participants were medical students, and $53.0\%$ were nonmedical students. Of the students, $49.9\%$ were seniors, $69.6\%$ were from rural areas, $29.2\%$ were the only child in the family, $83.8\%$ were living with both parents, $53.4\%$ had received nutrition education, $42.7\%$ had high academic performance, $9.8\%$ belonged to households with a monthly income of >¥12,000, and $25.1\%$ had a monthly expenditure of >¥1,500. Most of the students reported that their parents' education level was junior high school (with $46.7\%$ of fathers and $40.6\%$ of mothers attaining this education level).
**Table 1**
| Characteristics | n (%) | Nutrition literacy | χ2 | Knowledge | χ2.1 | Understanding | χ2.2 | Obtaining skills | χ2.3 | Applying skills | χ2.4 | Interactive skills | χ2.5 | Critical skills | χ2.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Age group (years) | | | 3.955 * | | 0.384 | | 14.891 *** | | 4.538 * | | 3.945 * | | 2.945 | | 9.530 ** |
| 16–21 | 1,428 (66.9) | 676 (47.3) | | 705 (49.4) | | 597 (41.8) | | 597 (41.8) | | 668 (46.8) | | 689 (48.2) | | 624 (43.7) | |
| 22–27 | 705 (33.1) | 366 (51.9) | | 338 (47.9) | | 357 (50.6) | | 329 (46.7) | | 362 (51.3) | | 368 (52.2) | | 358 (50.8) | |
| Sex | | | 0.002 | | 1.609 | | 4.725 * | | 0.242 | | 0.741 | | 0.327 | | 12.768 *** |
| Male | 734 (34.4) | 359 (48.9) | | 345 (47.0) | | 352 (48.0) | | 324 (44.1) | | 345 (47.0) | | 370 (50.4) | | 377 (51.4) | |
| Female | 1,399 (65.6) | 683 (48.8) | | 698 (49.9) | | 602 (43.0) | | 602 (43.0) | | 685 (49.0) | | 687 (49.1) | | 605 (43.2) | |
| Major | | | 14.595 *** | | 27.613 *** | | 67.840 *** | | 8.632 ** | | 0.021 | | 9.205 ** | | 17.625 *** |
| Medical | 1,003 (47.0) | 534 (53.2) | | 551 (54.9) | | 543 (54.1) | | 469 (46.8) | | 486 (48.5) | | 532 (53.0) | | 510 (50.8) | |
| Non-medical | 1,130 (53.0) | 508 (45.0) | | 492 (43.5) | | 411 (36.4) | | 457 (40.4) | | 544 (48.1) | | 525 (46.5) | | 472 (41.8) | |
| Grade | | | 5.160 * | | 0.056 | | 15.442 *** | | 3.080 | | 0.637 | | 1.416 | | 13.412 *** |
| Junior | 1,069 (50.1) | 496 (46.4) | | 520 (48.6) | | 433 (40.5) | | 444 (41.5) | | 507 (47.4) | | 516 (48.3) | | 450 (42.1) | |
| Senior | 1,064 (49.9) | 546 (51.3) | | 523 (49.2) | | 521 (49.0) | | 482 (45.3) | | 523 (49.2) | | 541 (50.8) | | 532 (50.0) | |
| Place of origin | | | 28.751 *** | | 11.264 ** | | 29.856 *** | | 14.605 *** | | 16.100 *** | | 18.253 *** | | 26.198 *** |
| Rural | 1,484 (69.6) | 668(45.0) | | 690(46.5) | | 606(40.8) | | 604(40.7) | | 674(45.4) | | 690(46.5) | | 629(42.4) | |
| Urban | 649 (30.4) | 374 (57.6) | | 353 (54.4) | | 348 (53.6) | | 322 (49.6) | | 356 (54.9) | | 367 (56.5) | | 353 (54.4) | |
| Only-child status | | | 12.867 *** | | 0.635 | | 12.124 *** | | 10.381 ** | | 9.392 ** | | 15.452 *** | | 18.630 *** |
| No | 1,510 (70.8) | 700 (46.4) | | 730 (48.3) | | 639 (42.3) | | 622 (41.2) | | 697 (46.2) | | 707 (46.8) | | 650 (43.0) | |
| Yes | 623 (29.2) | 342 (54.9) | | 313 (50.2) | | 315 (50.6) | | 304 (48.8) | | 333 (53.5) | | 350 (56.2) | | 332 (53.3) | |
| Living arrangement | | | 9.746 ** | | 8.442 ** | | 2.117 | | 1.634 | | 8.377 ** | | 8.620 ** | | 1.086 |
| Living with both parents | 1,788 (83.8) | 900 (50.3) | | 899 (50.3) | | 812 (45.4) | | 787 (44.0) | | 888 (49.7) | | 911 (51.0) | | 832 (46.5) | |
| Living with a single parent, grandparents, or other | 345 (16.2) | 142 (41.2) | | 144 (41.7) | | 142 (41.2) | | 139 (40.3) | | 142 (41.2) | | 146 (42.3) | | 150 (43.5) | |
| Father's education level | | | 16.011 ** | | 2.669 | | 31.381 *** | | 13.135 ** | | 12.835 ** | | 11.585 ** | | 21.812 *** |
| Elementary school or below | 371 (17.4) | 160 (43.1) | | 191 (51.5) | | 143 (38.5) | | 139 (37.5) | | 163 (43.9) | | 162 (43.7) | | 157 (42.3) | |
| Junior high school | 996 (46.7) | 468 (47.0) | | 469 (47.1) | | 419 (42.1) | | 425 (42.7) | | 459 (46.1) | | 484 (48.6) | | 423 (42.5) | |
| High school or technical school | 452 (21.2) | 236 (52.2) | | 226 (50.0) | | 210 (46.5) | | 202 (44.7) | | 236 (52.2) | | 236 (52.2) | | 228 (50.4) | |
| College or university and above | 314 (14.7) | 178 (56.7) | | 157 (50.0) | | 182 (58.0) | | 160 (51.0) | | 172 (54.8) | | 175 (55.7) | | 174 (55.4) | |
| Mother's education leve>p27mm | | | 22.738 *** | | 7.633 | | 28.920 *** | | 14.897 ** | | 13.817 ** | | 14.853 ** | | 23.617 *** |
| Elementary school or below | 727 (34.1) | 324 (44.6) | | 346 (47.6) | | 283 (38.9) | | 287 (39.5) | | 327 (45.0) | | 332 (45.7) | | 305 (42.0) | |
| Junior high school | 866 (40.6) | 408 (47.1) | | 410 (47.3) | | 384 (44.3) | | 375 (43.3) | | 407 (47.0) | | 421 (48.6) | | 386 (44.6) | |
| High school or technical school | 345 (16.2) | 194 (56.2) | | 192 (55.7) | | 173 (50.1) | | 158 (45.8) | | 184 (53.3) | | 191 (55.4) | | 174 (50.4) | |
| College or university and above | 195 (9.1) | 116 (59.5) | | 95 (48.7) | | 114 (58.5) | | 106 (54.4) | | 112 (57.4) | | 113 (57.9) | | 117 (60.0) | |
| Acquisition of nutrition education | | | 27.205 *** | | 14.290 *** | | 40.630 *** | | 5.168 * | | 9.493 ** | | 13.336 *** | | 30.174 *** |
| No | 995 (46.6) | 426 (42.8) | | 443 (44.5) | | 372 (37.4) | | 406 (40.8) | | 445 (44.7) | | 451 (45.3) | | 395 (39.7) | |
| Yes | 1,138 (53.4) | 616 (54.1) | | 600 (52.7) | | 582 (51.1) | | 520 (45.7) | | 585 (51.4) | | 606 (53.3) | | 587 (51.6) | |
| Academic performance | | | 28.284 *** | | 4.865 | | 14.844 ** | | 18.055 *** | | 25.425 *** | | 19.722 *** | | 10.060 ** |
| Poor | 193 (9.0) | 78 (40.4) | | 92 (47.7) | | 75 (38.9) | | 74 (38.3) | | 73 (37.8) | | 79 (40.9) | | 82 (42.5) | |
| Average | 1,030 (48.3) | 460 (44.7) | | 481 (46.7) | | 429 (41.7) | | 409 (39.7) | | 464 (45.0) | | 479 (46.5) | | 445 (43.2) | |
| Good | 910 (42.7) | 504 (55.4) | | 470 (51.6) | | 450 (49.5) | | 443 (48.7) | | 493 (54.2) | | 499 (54.8) | | 455 (50.0) | |
| Household income per month (RMB) | | | 11.989 ** | | 3.515 | | 21.291 *** | | 11.701 ** | | 9.478 ** | | 3.180 | | 5.373 |
| < 6,000 | 1,043 (48.9) | 487 (46.7) | | 501 (48.0) | | 433 (41.5) | | 439 (42.1) | | 490 (47.0) | | 501 (48.0) | | 463 (44.4) | |
| 6,000−12,000 | 881 (41.3) | 430 (48.8) | | 427 (48.5) | | 398 (45.2) | | 373 (42.3) | | 418 (47.4) | | 442 (50.2) | | 408 (46.3) | |
| >12,000 | 209 (9.8) | 125 (59.8) | | 115 (55.0) | | 123 (58.9) | | 114 (54.5) | | 122 (58.4) | | 114 (54.5) | | 111 (53.1) | |
| Expenditure per month (RMB) | | | 3.854 | | 2.962 | | 15.178 ** | | 10.714 ** | | 1.506 | | 1.924 | | 4.368 |
| < 1,000 | 275 (12.9) | 131 (47.6) | | 126 (45.8) | | 118 (42.9) | | 122(44.4) | | 141 (51.3) | | 134 (48.7) | | 125 (45.5) | |
| 1,000−1,500 | 1,323 (62.0) | 630 (47.6) | | 640 (48.4) | | 558 (42.2) | | 541 (40.9) | | 627 (47.4) | | 644 (48.7) | | 590 (44.6) | |
| >1,500 | 535 (25.1) | 281 (52.5) | | 277 (51.8) | | 278 (52.0) | | 263 (49.2) | | 262 (49.0) | | 279 (52.1) | | 267 (49.9) | |
The univariate analysis results revealed significant differences in total NL by age, major, grade, place of origin, only-child status, living arrangement, acquisition of nutrition education, academic performance, and household income per month (Table 1). Among the participants, medical students exhibited significantly higher total NL than did non-medical students. The results also indicated a significant relationship between total NL levels and parents' education level; specifically, students whose parents' education level was college or university and above exhibited the highest total NL level. Additionally, the levels of critical skills were significantly associated with sex. Specifically, male students had higher levels of critical skills than did female participants (51.4 and $43.2\%$, respectively).
Multiple logistic regression was performed to determine factors influencing total NL and its six dimensions (Table 2). The results revealed that students who were from urban areas (OR = 1.36, $95\%$ CI: 1.08–1.72), were living with both parents (OR = 1.30, $95\%$ CI: 1.02–1.65), received nutrition education (OR = 1.53, $95\%$ CI: 1.25–1.86), had high academic performance (OR = 1.85, $95\%$ CI: 1.34–2.57), and had a monthly household income of >¥12,000 (OR = 1.61, $95\%$ CI: 1.14–2.26) were more likely to report a higher level of total NL than did other students. Regarding the six dimensions of NL, the results indicated that female students (OR = 1.35, $95\%$ CI: 1.11–1.66) and medical students (OR = 1.56, $95\%$ CI: 1.27–1.92) were more likely to report a higher level of nutrition knowledge than did other students. Older students (OR = 1.33, $95\%$ CI: 1.05–1.68) were more likely to report a higher level of nutrition understanding than did other students. Students with high academic performance (OR = 1.52, $95\%$ CI: 1.10–2.11) and a monthly household income of >¥12,000 (OR = 1.41, $95\%$ CI: 1.01–1.97) were more likely to report a higher level of obtaining skills than did other students. In addition, students who were older (OR = 1.28, $95\%$ CI: 1.01–1.62) and were from urban areas (OR = 1.27, $95\%$ CI: 1.01–1.61) were more likely to report a higher level of applying skills than did other students. Students who were the only child in the family (OR = 1.29, $95\%$ CI: 1.03–1.60) were more likely to report a higher level of interactive skills than did other students. Finally, female students (OR = 0.80, $95\%$ CI: 0.66–0.98) were less likely to report a high level of critical skills than did male students.
**Table 2**
| Characteristics | Nutrition literacya | Knowledgea | Understandinga | Obtaining skillsa | Applying skillsa | Interactive skillsa | Critical skillsa |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Age group (ref. = 16–21) | Age group (ref. = 16–21) | Age group (ref. = 16–21) | Age group (ref. = 16–21) | Age group (ref. = 16–21) | Age group (ref. = 16–21) | Age group (ref. = 16–21) | Age group (ref. = 16–21) |
| 22–27 | 1.14 (0.90, 1.44) | 0.93 (0.74, 1.17) | 1.33 (1.05, 1.68)* | 1.21 (0.96, 1.53) | 1.28 (1.01, 1.62)* | 1.20 (0.95, 1.52) | 1.14 (0.90, 1.44) |
| Sex (ref. = Male) | Sex (ref. = Male) | Sex (ref. = Male) | Sex (ref. = Male) | Sex (ref. = Male) | Sex (ref. = Male) | Sex (ref. = Male) | Sex (ref. = Male) |
| Female | 1.15 (0.94, 1.41) | 1.35 (1.11, 1.66)** | 1.09 (0.88, 1.33) | 1.08 (0.88, 1.32) | 1.13 (0.93, 1.39) | 1.07 (0.88, 1.31) | 0.80 (0.66, 0.98)* |
| Major (ref.=Non-medical) | Major (ref.=Non-medical) | Major (ref.=Non-medical) | Major (ref.=Non-medical) | Major (ref.=Non-medical) | Major (ref.=Non-medical) | Major (ref.=Non-medical) | Major (ref.=Non-medical) |
| Medical | 1.11 (0.90, 1.36) | 1.56 (1.27, 1.92)*** | 1.72 (1.40, 2.13)*** | 1.16 (0.95, 1.43) | 0.81 (0.66, 1.00)* | 1.11 (0.90, 1.36) | 1.02 (0.83, 1.26) |
| Grade (ref. =Junior ) | Grade (ref. =Junior ) | Grade (ref. =Junior ) | Grade (ref. =Junior ) | Grade (ref. =Junior ) | Grade (ref. =Junior ) | Grade (ref. =Junior ) | Grade (ref. =Junior ) |
| Senior | 1.00 (0.80, 1.26) | 0.96 (0.77, 1.20) | 1.07 (0.85, 1.34) | 1.00 (0.80, 1.25) | 0.86 (0.68, 1.07) | 0.90 (0.72, 1.12) | 1.15 (0.92, 1.44) |
| Place of origin (ref. = Rural) | Place of origin (ref. = Rural) | Place of origin (ref. = Rural) | Place of origin (ref. = Rural) | Place of origin (ref. = Rural) | Place of origin (ref. = Rural) | Place of origin (ref. = Rural) | Place of origin (ref. = Rural) |
| Urban | 1.36 (1.08, 1.72)* | 1.32 (1.05, 1.67)* | 1.24 (0.98, 1.57) | 1.18 (0.94, 1.49) | 1.27 (1.01, 1.61)* | 1.22 (0.96, 1.53) | 1.28 (1.01, 1.61)* |
| Only-child status (ref. = No) | Only-child status (ref. = No) | Only-child status (ref. = No) | Only-child status (ref. = No) | Only-child status (ref. = No) | Only-child status (ref. = No) | Only-child status (ref. = No) | Only-child status (ref. = No) |
| Yes | 1.16 (0.93, 1.45) | 0.98 (0.79, 1.23) | 1.06 (0.85, 1.32) | 1.18 (0.95, 1.47) | 1.20 (0.96, 1.50) | 1.29 (1.03, 1.60)* | 1.15 (0.92, 1.43) |
| Living arrangement (ref. = Living with a single parent, grandparents, or other) | Living arrangement (ref. = Living with a single parent, grandparents, or other) | Living arrangement (ref. = Living with a single parent, grandparents, or other) | Living arrangement (ref. = Living with a single parent, grandparents, or other) | Living arrangement (ref. = Living with a single parent, grandparents, or other) | Living arrangement (ref. = Living with a single parent, grandparents, or other) | Living arrangement (ref. = Living with a single parent, grandparents, or other) | Living arrangement (ref. = Living with a single parent, grandparents, or other) |
| Living with both parents | 1.30 (1.02, 1.65)* | 1.35 (1.06, 1.71)* | 1.03 (0.80, 1.31) | 1.06 (0.83, 1.35) | 1.33 (1.04, 1.69)* | 1.30 (1.02, 1.65)* | 1.01 (0.79, 1.28) |
| Father's education level (ref. = Elementary school or below) | Father's education level (ref. = Elementary school or below) | Father's education level (ref. = Elementary school or below) | Father's education level (ref. = Elementary school or below) | Father's education level (ref. = Elementary school or below) | Father's education level (ref. = Elementary school or below) | Father's education level (ref. = Elementary school or below) | Father's education level (ref. = Elementary school or below) |
| Junior high school | 1.11 (0.86, 1.44) | 0.79 (0.62, 1.02) | 1.11 (0.86, 1.44) | 1.21 (0.94, 1.57) | 1.06 (0.82, 1.36) | 1.18 (0.92, 1.52) | 0.98 (0.76, 1.27) |
| High school or technical school | 1.09 (0.79, 1.50) | 0.72 (0.52, 0.98)* | 1.14 (0.82, 1.57) | 1.17 (0.85, 1.61) | 1.17 (0.85, 1.60) | 1.14 (0.83, 1.56) | 1.20 (0.88, 1.65) |
| College or university and above | 1.01 (0.68, 1.49) | 0.66 (0.44, 0.98)* | 1.40 (0.94, 2.09) | 1.19 (0.80, 1.76) | 1.08 (0.73, 1.60) | 1.06 (0.72, 1.57) | 1.07 (0.72, 1.58) |
| Mother's education level (ref. = Elementary school or below) | Mother's education level (ref. = Elementary school or below) | Mother's education level (ref. = Elementary school or below) | Mother's education level (ref. = Elementary school or below) | Mother's education level (ref. = Elementary school or below) | Mother's education level (ref. = Elementary school or below) | Mother's education level (ref. = Elementary school or below) | Mother's education level (ref. = Elementary school or below) |
| Junior high school | 1.03 (0.83, 1.27) | 1.00 (0.81, 1.24) | 1.20 (0.96, 1.49) | 1.09 (0.89, 1.35) | 1.01 (0.82, 1.25) | 1.04 (0.84, 1.28) | 1.07 (0.86, 1.32) |
| High school or technical school | 1.24 (0.91, 1.70) | 1.29 (0.94, 1.77) | 1.13 (0.82, 1.56) | 1.01 (0.74, 1.39) | 1.11 (0.81, 1.53) | 1.17 (0.86, 1.60) | 1.04 (0.76, 1.42) |
| College or university and above | 1.24 (0.81, 1.91) | 0.87 (0.56, 1.33) | 1.16 (0.75, 1.79) | 1.24 (0.81, 1.90) | 1.22 (0.79, 1.87) | 1.18 (0.77, 1.81) | 1.41 (0.92, 2.17) |
| Acquisition of nutrition education (ref. = No) | Acquisition of nutrition education (ref. = No) | Acquisition of nutrition education (ref. = No) | Acquisition of nutrition education (ref. = No) | Acquisition of nutrition education (ref. = No) | Acquisition of nutrition education (ref. = No) | Acquisition of nutrition education (ref. = No) | Acquisition of nutrition education (ref. = No) |
| Yes | 1.53 (1.25, 1.86)*** | 1.25 (1.03, 1.52)* | 1.36 (1.11, 1.66)* | 1.11 (0.91, 1.35) | 1.42 (1.17, 1.74)*** | 1.33 (1.09, 1.62)** | 1.50 (1.23, 1.82)*** |
| Academic performance (ref. = Poor) | Academic performance (ref. = Poor) | Academic performance (ref. = Poor) | Academic performance (ref. = Poor) | Academic performance (ref. = Poor) | Academic performance (ref. = Poor) | Academic performance (ref. = Poor) | Academic performance (ref. = Poor) |
| Average | 1.21 (0.88, 1.66) | 0.96 (0.70, 1.32) | 1.15 (0.83, 1.60) | 1.08 (0.78, 1.48) | 1.38 (1.00, 1.91)* | 1.28 (0.93, 1.76) | 1.07 (0.78, 1.48) |
| Good | 1.85 (1.34, 2.57)*** | 1.16 (0.84, 1.60) | 1.56 (1.12, 2.17)** | 1.52 (1.10, 2.11)* | 1.97 (1.42, 2.73)*** | 1.77 (1.28, 2.45)** | 1.43 (1.03, 1.97)* |
| Household income per month (RMB) (ref.= <6,000) | Household income per month (RMB) (ref.= <6,000) | Household income per month (RMB) (ref.= <6,000) | Household income per month (RMB) (ref.= <6,000) | Household income per month (RMB) (ref.= <6,000) | Household income per month (RMB) (ref.= <6,000) | Household income per month (RMB) (ref.= <6,000) | Household income per month (RMB) (ref.= <6,000) |
| 6,000-−12,000 | 1.08 (0.89, 1.31) | 1.01 (0.83, 1.23) | 1.13 (0.93, 1.38) | 0.97 (0.80, 1.18) | 1.05 (0.87, 1.28) | 1.06 (0.88, 1.29) | 1.04 (0.85, 1.26) |
| >12,000 | 1.61 (1.14, 2.26)** | 1.27 (0.91, 1.78) | 1.67 (1.18, 2.35)** | 1.41 (1.01, 1.97)* | 1.71 (1.22, 2.40)** | 1.20 (0.86, 1.68) | 1.20 (0.86, 1.68) |
| Expenditure per month (RMB) (ref.= <1,000) | Expenditure per month (RMB) (ref.= <1,000) | Expenditure per month (RMB) (ref.= <1,000) | Expenditure per month (RMB) (ref.= <1,000) | Expenditure per month (RMB) (ref.= <1,000) | Expenditure per month (RMB) (ref.= <1,000) | Expenditure per month (RMB) (ref.= <1,000) | Expenditure per month (RMB) (ref.= <1,000) |
| 1,000-−1,500 | 1.04 (0.79, 1.37) | 1.21 (0.92, 1.59) | 1.08 (0.82, 1.43) | 0.89 (0.68, 1.18) | 0.82 (0.62, 1.08) | 1.01 (0.77, 1.33) | 1.08 (0.82, 1.42) |
| >1,500 | 0.98 (0.70, 1.37) | 1.26 (0.90, 1.75) | 1.25 (0.89, 1.76) | 1.05 (0.76, 1.46) | 0.68 (0.48, 0.94)* | 0.97 (0.70, 1.35) | 1.11 (0.79, 1.54) |
The study also examined the interaction effects of multiple factors on total NL and its six dimensions (Table 3). The results revealed that the ORs for total NL (OR = 1.88, $95\%$ CI: 1.26–2.81), nutrition understanding (OR = 1.73, $95\%$ CI: 1.15–2.59), and critical skills (OR = 1.78, $95\%$ CI: 1.19–2.66) were higher for medical students who were women than for other students. Furthermore, the ORs for total NL (OR = 1.60, $95\%$ CI: 1.06–2.41), nutrition knowledge (OR = 1.51, $95\%$ CI: 1.00–2.26), nutrition understanding (OR = 1.85, $95\%$ CI: 1.22–2.80), obtaining skills (OR = 1.76, $95\%$ CI: 1.16–2.65), and critical skills (OR = 1.59, $95\%$ CI: 1.05–2.39) were higher for medical students who had received nutrition education than for other students. The ORs for total NL (OR = 2.42, $95\%$ CI: 1.21–4.84), nutrition knowledge (OR = 2.69, $95\%$ CI: 1.33–5.44), nutrition understanding (OR = 2.59, $95\%$ CI: 1.28–5.25), interactive skills (OR = 2.06, $95\%$ CI: 1.04–4.08), and critical skills (OR = 2.15, $95\%$ CI: 1.09–4.28) were higher for students who were the only child in the family and had a monthly expenditure of >¥1,500 than for other students.
**Table 3**
| Characteristics | Nutrition literacya | Knowledgea | Understandinga | Obtaining skillsa | Applying skillsa | Interactive skillsa | Critical skillsa |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Major × Sex | Major × Sex | Major × Sex | Major × Sex | Major × Sex | Major × Sex | Major × Sex | Major × Sex |
| Medical × Female | 1.88 1.26, 2.81)** | 0.75 (0.50, 1.12) | 1.73 (1.15, 2.59)** | 1.35 (0.90, 2.02) | 1.15 (0.77, 1.72) | 1.46 (0.98, 2.17) | 1.78 (1.19, 2.66)** |
| Major × Acquisition of nutrition education | Major × Acquisition of nutrition education | Major × Acquisition of nutrition education | Major × Acquisition of nutrition education | Major × Acquisition of nutrition education | Major × Acquisition of nutrition education | Major × Acquisition of nutrition education | Major × Acquisition of nutrition education |
| Medical × Yes | 1.60 (1.06, 2.41)* | 1.51 (1.00, 2.26)* | 1.85 (1.22, 2.80)** | 1.76 (1.16, 2.65)** | 1.09 (0.72, 1.64) | 1.29 (0.86, 1.93) | 1.59 (1.05, 2.39)* |
| Only-child status × Expenditure per month (RMB) | Only-child status × Expenditure per month (RMB) | Only-child status × Expenditure per month (RMB) | Only-child status × Expenditure per month (RMB) | Only-child status × Expenditure per month (RMB) | Only-child status × Expenditure per month (RMB) | Only-child status × Expenditure per month (RMB) | Only-child status × Expenditure per month (RMB) |
| Yes × 1,000-−1,500 | 2.21 (1.16, 4.21)* | 2.38 (1.23, 4.60)* | 2.01 (1.04, 3.89)* | 1.17 (0.62, 2.21) | 1.14 (0.60, 2.15) | 1.48 (0.79, 2.79) | 1.60 (0.85, 3.03) |
| Yes × >1,500 | 2.42 (1.21, 4.84)* | 2.69 (1.33, 5.44)** | 2.59 (1.28, 5.25)** | 1.79 (0.90, 3.53) | 1.04 (0.52, 2.06) | 2.06 (1.04, 4.08)* | 2.15 (1.09, 4.28)* |
## 4. Discussion
This study investigated factors associated with NL and its six dimensions among Chinese university students. The findings indicate that place of origin, living arrangement, acquisition of nutrition education, academic performance, and household income per month were independently associated with NL. This finding provides empirical evidence for designing nutrition literacy intervention strategies for practitioners, when implementing nutrition education in the future.
The application of food- and nutrition-related information acquired through a variety of media channels may be challenging for university students because of circumstances unique to university environments [19]. In addition, excessive and ambiguous nutrition information may lead to confusion among individuals with low levels of nutrition knowledge [23]. Therefore, the ability to exchange food- and nutrition-related information with family, peers, and experts or to extract information from different media channels, in addition to the quantity of such information, is crucial [13]. NL might be an important factor in determining healthy-eating behaviors during university [25]. Higher nutrition literacy, which is the immediate goal of nutrition education, would subsequently lead to higher diet quality [30]. Therefore, advancing nutrition literacy in the school setting is important to promote healthy eating and support long-term academic outcomes to reduce the burden of food-related diseases across the lifespan [31]. The present study demonstrated that older students were more likely to report higher levels of nutrition understanding and applying skills than did other students. A higher degree of cognitive abilities positively influences NL. Similarly, other factors such as practice, communication, media, and cognitive reserve may help increase NL in older students [32]. The present study also revealed that female students were more likely to report a higher level of nutrition knowledge than did male students. Attitudes toward reading and learning generally differed by sex [33]. Female students typically paid more attention to their dietary intake and were more likely to receive nutrition education than did male students [34]. However, our findings reveal that female students had lower levels of critical NL than did male students. Women reported difficulty in distinguishing between scientific and non-scientific information on nutrition. Moreover, women were influenced by dietary advice presented in the media and considered alternative medical advice to be credible [16].
Nutrition and dietary information sources are associated with adequate NL [24]. Students who received nutrition information at university, had taken nutrition-related courses, or had a strong demand for nutrition information exhibited superior NL [25]. This result also strengthens the need for nutrition education on college campuses [25]. The present study found that the ORs for total NL, nutrition knowledge, nutrition understanding, obtaining skills, and critical skills were higher for medical students who received nutrition education than for other students. The explanation for this finding is that in health science–related courses and professions—primarily nutrition and dietetics—topics such as nutrition, healthy eating, and correct eating habits are frequently addressed, as well as people who are accompanied by these professionals [35]. Of course, *It is* also associated with the number of nutrition courses and nutrition education contents. As NL is literacy focusing on nutrition-related information, medical students with more exposure to medical and health information in faculty courses perform better compared to other non-medical courses [36]. However, with the exception of students pursuing medicine-related majors, university students are unlikely to have access to nutrition education [21]. NL encompasses a set of knowledge and competencies that an individual develops over the course of their life, and it can be regarded as an outcome of nutrition health education [37]. Medical students who had received and applied more medical knowledge had more favorable objective conditions for acquiring nutrition knowledge and had a more comprehensive grasp of nutrition [38]. Moreover, our findings suggest that students with high academic performance were more likely to report a higher level of total NL, nutrition understanding, obtaining skills, applying skills, interactive skills, and critical skills. Such students have a strong self-learning awareness and learning ability and a higher degree of absorption and understanding of nutrition information; this provides them with a foundation for screening and obtaining nutrition information in daily life and for developing healthy habits [38].
Social factors may affect NL and food- and nutrition-related decisions [39]. This study revealed that students who were the only child in the family and had a high monthly expenditure reported higher NL, nutrition knowledge, nutrition understanding, interactive skills, and critical skills. In mainland China, only-child students are more likely to be from urban areas [40], and their families have higher monthly incomes [41]. In addition, the parents of only-child students have a higher socioeconomic status [42]. Under these superior family conditions, only-child students can receive more resources and support from their families, which can benefit their physical and psychological health [41]. Individual NL is developed through information exchange with experts, peers, parents, or caregivers, and it is influenced by the context [37]. Students from families with a lower socioeconomic status face barriers to developing a healthy diet; this is possibly because such families have fewer financial resources for the purchase of healthier foods or lack knowledge regarding nutritional recommendations for healthy eating [43]. Students' ability to obtain, interpret, and apply information about nutrition affected their healthy eating behaviors [12]. Our findings suggest that students from urban areas have a higher level of NL than those from rural areas; this is probably because rural students are affected by an underdeveloped economy, poor basic life outcomes, limited access to nutrition information, and low awareness of good eating habits. This demonstrates the importance of narrowing the gap between urban and rural nutrition services [34]. Our results reveal no association between parental education level and the total NL level of university students. Family members of senior high school students usually focus more on students' study, resulting in insufficient nutrition education at home [34]. Moreover, students who become independent in managing their diets are less affected by their parents from the time they enter university [25]. This study observed that students who were living with both parents reported a higher level of NL. The eating behavior of individuals were indeed affected by their NL levels [23], and a higher NL level was associated with healthier eating practices and lifestyle behaviors [44]. Two-parent families may have sufficient family functioning, which could lead to a higher family health status. By contrast, single-parent families may have fewer resources such as time, money, and social networks; this may lead to poor health outcomes [45].
This study has some limitations. First, it applied a cross-sectional design, which prevented the interpretation of the direction of the associations. Moreover, the results cannot be generalized to all students. Finally, this study used self-reported data, and discrepancies may have existed between the participants' subjective perceptions and actual practice, which may have led to errors in the interpretation of the results. Despite these limitations, this study adopted a rigorous research design, including valid and reliable measures and strict statistical procedures, to reduce possible research biases. Future research efforts need to investigate the association between nutrition literacy and nutrition-related diseases, and how to take targeted intervention to improve nutrition literacy.
## 5. Conclusions
This study revealed the NL of university students differed in terms of place of origin, living arrangement, nutrition education, academic performance, and household income. These findings imply that targeted intervention should consider the disparities of family resources and universities should have all students take a basic nutrition course to improve their NL.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The students were notified that participation was voluntary, and signed informed consent was obtained. This study was approved by the Ethics Committee of Bengbu Medical College.
## Author contributions
MZ and TG: conceptualization. HL, TG, and YD: methodology. HL, GM, and MZ: investigation and data management. TG: writing—original draft preparation. HL, YD, TG, and QQ: writing—review and editing. MZ, GM, and SH: supervision. HL, QQ, and SH: project administration. HL and MZ: funding acquisition. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1113211/full#supplementary-material
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|
---
title: 'Associations of the Healthy Eating Index-2010 with risk of all-cause and heart
disease mortality among adults with hypertension: Results from the National Health
and Nutrition Examination Survey 2007–2014'
authors:
- Yuhui Zhang
- Duanbin Li
- Haizhu Zhang
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10020655
doi: 10.3389/fnut.2023.1077896
license: CC BY 4.0
---
# Associations of the Healthy Eating Index-2010 with risk of all-cause and heart disease mortality among adults with hypertension: Results from the National Health and Nutrition Examination Survey 2007–2014
## Abstract
### Background
Studies regarding the impact of the Healthy Eating Index-2010 (HEI-2010) on the mortality of adults with hypertension are lacking.
### Objectives
This study aimed to prospectively explore the relationships between HEI-2010 and mortality from heart disease and all causes in adults with hypertension based on the National Health and Nutrition Examination Survey (NHANES), 2007–2014.
### Methods
This is a prospective cohort study including 6,690 adults with hypertension from NHANES (2007–2014). National Death *Index data* up to 31 December 2019 were used to determine the number of deaths due to heart disease and all other causes. We evaluated hazard ratios (HRs) and $95\%$ confidence intervals (CIs) using the Cox proportional hazards model.
### Results
A total of 1,259 deaths from all causes, including 338 due to heart disease, were documented over an average follow-up duration of 8.4 years. In comparison with the lowest quartile of HEI-2010 scores, multivariable-adjusted HRs ($95\%$ CIs) for all-cause mortality were 0.82 (0.70, 0.97), 0.78 (0.64, 0.95), and 0.68 (0.54, 0.85) for the second, third, and fourth quartiles of the HEI-2010 scores (P-trend < 0.001) and for heart disease mortality were 0.60 (0.44, 0.81), 0.59 (0.40, 0.89), and 0.53 (0.35, 0.80) (P-trend = 0.010). Each increment in natural-log-transformed HEI-2010 scores was linked to a $43\%$ reduction in the risk of all-cause mortality ($P \leq 0.001$) and a $55\%$ reduction in the risk of heart disease mortality ($$P \leq 0.003$$). Among the 12 components of HEI-2010, adherence to a higher intake of greens and beans, vegetables, total protein foods, seafood and plant proteins, and unsaturated fatty acids, as well as moderate consumption of empty calories, were related to a 21–$29\%$ lower risk of all-cause mortality.
### Conclusion
In the current study, there was a statistically significant inverse relationship between HEI-2010 and mortality from heart disease and all causes among adults with hypertension. Based on the findings, it may help guide the dietary intake for adults with hypertension.
## 1. Introduction
Hypertension is becoming a global public health challenge. Among people aged 30–79 years, the prevalence of hypertension doubled from 1990 to 2019 [1]. Elevated blood pressure (BP) has been recognized to be responsible for pathophysiological changes in the end organs of the brain (infarction and hemorrhage), the heart (myocardial ischemia and left ventricular hypertrophy, and heart failure), and the kidneys (renal sclerosis and proteinuria) [2]. The presence of hypertension increases the risk of cardiovascular disease (CVD) and stroke, leading to an increase in CVD and all-cause mortality [3, 4]. With the growing recognition of the major role diet plays in disease risk, identifying healthy dietary patterns that can prevent CVD and premature death in adults with hypertension is critical.
Dietary research is increasingly focusing on dietary patterns rather than single nutrients or food groups as dietary components are interrelated and consumed in combination. Healthy dietary patterns were characterized as diets low in saturated fat, added sugars, and sodium and high in vegetables, whole grains, fruits, lean protein, and low- and non-fat dairy [5]. The HEI, as one of the healthy dietary patterns, is a comprehensive measurement of dietary quality in line with the Dietary Guidelines for Americans (DGA) and is the basis for US government nutrition policy. Based on the DGA recommendations, which included adding fruits, vegetables, low-fat dairy products, and whole grains, as well as limiting added sugars, saturated fats, and refined grains, the HEI generated scores for each component and a total score showing the diet quality over multiple dietary dimensions [6]. Modeled on the 2010 DGA recommendations, the HEI-2010 included 12 components that were proven to be a reliable and valid measurement of dietary quality for Americans [7]. In two studies, HEI-2010 has been shown to have a reverse relationship with serum C-reactive protein (CRP), apolipoprotein B, and systolic blood pressure [8, 9]. In a cross-sectional research study involving 1036 women in Iran, the HEI-2010 was related to a lower metabolic syndrome risk [10]. Meanwhile, two other prospective cohort studies suggested that HEI-2010 was inversely correlated with CVD and all-cause mortality in multiethnic populations [11] or older adults [12]. Even with these advantages, among individuals with hypertension who often had unhealthy lifestyle factors [13], endothelial dysfunction, increased oxidative stress, vascular remodeling [14], pro-inflammatory release [15], and higher risk of developing heart disease and mortality, the health impacts of HEI-2010 on heart disease mortality remain unclear.
Therefore, the purposes of the present study were to explore the relationship of HEI-2010 and its components with mortality from heart disease and all causes in adults with hypertension based on NHANES.
## 2.1. Study population
With a nationally representative sample, NHANES estimates the nutritional and health status of the US civilian population. The survey was performed by the National Center for Health Statistics, and it collected information through personal structured interviews at home, health screenings at a mobile examination center (MEC), and laboratory examinations. Detailed information can be obtained elsewhere (https://www.cdc.gov/nchs/nhanes/index.htm).
Based on NHANES 2007–2014 surveys, 7914 participants with hypertension aged 20 years and older were included when complete dietary intakes of the 2-day dietary interviews were provided. Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg, physician-diagnosed hypertension, or consuming anti-hypertensive medicine. In the final analysis, 6,690 participants with hypertension were included after excluding those who reported pregnancy ($$n = 20$$), cancer ($$n = 1$$,200) or those with no follow-up data ($$n = 4$$) (Supplementary Figure 1).
## 2.2. HEI-2010 scores
HEI-2010 was calculated to indicate diet quality from two 24-h dietary recollection data collected, one of which was a face-to-face survey conducted at the MEC by trained interviewers, followed by a telephone follow-up 3–10 days later to obtain a more complete picture of the usual dietary intake of the US population. Average dietary intake data were used for analysis only when participants completed two 24-h recalls. A total of 12 components make up HEI-2010, with nine adequacy components (total vegetables, whole fruits, total fruits, whole grains, greens and beans, total dairy, seafood and plant proteins, total protein foods, and fatty acid ratio) and three moderation components (sodium, empty calories, and refined grains). Supplementary Table 1 illustrates HEI-2010 components along with their point values and scoring criteria [6]. The HEI 2010 scores consist of 12 dietary component scores, which add up to a total score of 100. For adequacy components, the highest score was awarded for intake at or above the criteria. As for the moderation components, the highest score was awarded for intake at or below the criteria. A proportional score is assigned to intakes between the lowest and highest criteria [6]. For each of the 12 HEI-2010 components, participants were considered compliant if they obtained the highest component score; otherwise, they were classified as non-compliant. Thus, the compliant participants had the highest score of 5, 10, or 20, while non-compliant participants scored lower than this, with higher scores indicating closer adherence to the dietary guidelines.
## 2.3. Ascertainment of mortality
We assessed mortality over the follow-up period based on National Death Index records through 31 December 2019 (https://www.cdc.gov/nchs/data-linkage/mortality-public.htm). The codes of the International Classification of Diseases, Tenth Revision (ICD-10) were used to classify the causes of death. Death due to heart disease included rheumatic heart disease, hypertensive heart and renal disease, ischemic heart disease, and heart failure (ICD10 codes I00–I09, I11, I13, and I20–I51). Other causes of death included chronic lower respiratory diseases (J40–J47), malignant neoplasms (C00–C97), cerebrovascular diseases (I60–I69), influenza and pneumonia (J09–J18), Alzheimer's disease (G30), diabetes mellitus (E10–E14), nephritis, nephrotic syndrome and nephrosis (N00–N07, N17–N19, N25–N27), accidents (unintentional injuries) (V01–X59, Y85–Y86), and all other causes (residual).
## 2.4. Assessment of covariates
Sociodemographic variables included age, sex (male and female), ethnicity (non-Hispanic white, non-Hispanic Black, Mexican American, and other races), and education (below high school, high school, and above high school), which were assessed during the interview. Corresponding questionnaires and MEC obtained data on body mass index (BMI), alcohol consumption, smoking, dietary intake, recreational activity, anti-hypertensive medicine use, blood pressure level, and presence of hyperlipidemia, diabetes, and CVD. Never smokers were defined as those who smoked <100 cigarettes in their lifetime. Those who smoked >100 cigarettes and no longer smoke were considered former smokers, and those who smoked >100 cigarettes in their lifetime and still smoke some days or every day were considered current smokers. Drinking status was grouped into nondrinker, low-to-moderate drinker (<3 drinks/day in men and <2 drinks/day in women), or heavy drinker (≥3 drinks/day in men and ≥2 drinks/day in women). The recreational activity was categorized into three groups: inactive (no recreational physical activity), moderately active (moderate recreational physical activity), or vigorously active (vigorous recreational physical activity). Based on the physical activity questionnaire, recreational activities leading to a slight increase in heart rate or breath such as bicycling, brisk walking, golf, or swimming for at least 10 continuous min were considered moderate activity, and recreational activities leading to great increases in heart rate or breath such as running or basketball for at least 10 continuous min were considered vigorous activity.
Diabetes is defined as fasting blood glucose ≥7.0 mmol/L, plasma glucose levels 2 h after meals ≥11.1 mmol/L, physician-diagnosed diabetes, use of hypoglycemic medications, or glycosylated hemoglobin (HbA1c) ≥$6.5\%$ [16]. Participants with total cholesterol levels ≥240 mg/dL, fasting triglyceride ≥150 mg/dL, high-density lipoprotein (HDL) cholesterol <40 mg/dL, low-density lipoprotein (LDL) cholesterol ≥100 mg/dL, and a history of taking lipid-lowering medications were regarded as hyperlipidemia. Based on self-reported information, CVD (yes/no) and anti-hypertensive medicine use (yes/no) were defined. Three and sometimes four BP determinations (systolic and diastolic) are taken in the MEC and during home examinations on all eligible individuals using a mercury sphygmomanometer. SBP average and DBP average represent blood pressure results that were reported to the examinee. A detailed information can be obtained elsewhere (https://wwwn.cdc.gov/Nchs/Nhanes/2001-2002/BPX_B.htm#Quality_Assurance_&_Quality_Control). In our study, the SBP average and DBP average represent blood pressure results. We categorized participants into two groups based on their blood pressure levels: SBP/DBP <$\frac{160}{100}$ mmHg (either SBP <160 mmHg and/or DBP <100 mmHg) and SBP/DBP ≥ $\frac{160}{100}$ mmHg (either SBP ≥ 160 mmHg and/or DBP ≥ 100 mmHg). Furthermore, strict laboratory analyses, including measurement of estimated glomerular filtration rate (eGFR) and total cholesterol levels at baseline, were conducted.
## 2.5. Statistical analysis
All analyses were conducted using sample weights, strata, and primary sampling units to obtain accurate national estimates. Continuous variables were expressed as mean (SE) and categorical variables as numbers (percentages). HEI-2010 total scores were divided into quartiles, and the difference between the four groups was compared by one-way ANOVA tests (continuous variables with normal distribution) and χ2 test (categorical variables). We estimated HRs and $95\%$ CIs for heart disease and all-cause mortality based on quartiles of HEI-2010 scores using the Cox proportional hazards model. Person-time is referred to the period between the NHANES interview date and the date of death or the end of the follow-up (31 December 2019). We fitted three statistical models. Model 1 was adjusted for age (continuous), sex (male or female), and ethnicity (non-Hispanic white, non-Hispanic Black, Mexican American, and other race). Model 2 was adjusted for education (below high school, high school, and above high school), BMI (continuous), smoking status (never smoker, former smoker, and current smoker), drinking status (nondrinker, low-to-moderate drinker, and heavy drinker), recreational activity (inactive, moderately active, and vigorously active), and total energy intakes (in quartiles). Model 3 was further adjusted for blood pressure level (SBP/DBP ≥ $\frac{160}{100}$ mmHg or SBP/DBP <$\frac{160}{100}$ mmHg), anti-hypertensive medicine use (yes or no), hyperlipidemia (yes or no), diabetes (yes or no), and CVD (yes or no). To analyze the linear trend, each category was assigned a median value as a continuous variable. Multiple imputations were conducted to minimize the reduction in sample size resulting from missing covariates.
To investigate dose-response associations between HEI-2010 scores and mortality, we used a restricted cubic spline regression model with four knots at the 5th, 35th, 65th, and 95th percentiles of the HEI-2010 scores, excluding the most extreme $5\%$ values to reduce the potential influence of outliers. The likelihood ratio test was used for testing non-linearity. Furthermore, stratified analyses were performed to assess whether the relationship of HEI-2010 scores with all-cause mortality differed by age (<60 and ≥60 years), sex (men and women), ethnicity (non-Hispanic white and others), BMI (<30 and ≥30), drinking status (non-drinker and drinker), smoking status (never smoker and former/current smoker), recreational activity (inactive group and active group), blood pressure level (SBP/DBP ≥ $\frac{160}{100}$ mmHg or SBP/DBP <$\frac{160}{100}$ mmHg), anti-hypertensive medicine use (yes or no), hyperlipidemia (yes or no), diabetes (yes or no), and CVD (yes or no). The P-value of the product term between continuous HEI-2010 scores and stratified variables was calculated to assess the significance of the interaction.
To determine whether statistically significant correlations were ascribed to specific components of the HEI-2010, we further assessed the correlation between the HEI-2010 components and all-cause mortality adjusted for all covariates. We classified the component scores by selecting appropriate cutoff points according to the overall sample score distribution or restricted cubic spline model. To better show the percentage contribution of the dietary components to the maximum possible score, we created radar plots.
We also conducted several sensitivity analyses to assess the robustness of our findings. First, we excluded participants who died within the first 2 years of follow-up ($$n = 199$$) to minimize the potential reverse causation bias. Second, we further adjusted for individual foods or nutrients, including intakes of fiber, total fat, cholesterol, vitamin A, vitamin C, and vitamin E (all in quartiles). Third, we further adjusted for other biomarkers, including total cholesterol levels and eGFR (all in quartiles). Finally, we also assessed the associations of HEI-2010 scores with cerebrovascular deaths and cancer deaths.
We performed all analyses with R version 4.2.0. A two-sided P-value of < 0.05 was considered statistically significant.
## 3.1. Participants characteristics
The baseline characteristics according to quartiles of HEI-2010 scores are summarized in Table 1. Out of 6,690 participants with hypertension (mean age, 55.91 years; $51.9\%$ women), the median (interquartile range) HEI-2010 score was 53.9 (44.1, 64.1). Individuals with higher HEI-2010 scores were older, were more likely to be women, tended to have higher educational levels, engaged in more recreational activities, never smoked, and were less likely to be obese.
**Table 1**
| Characteristics | HEI-2010 scores | HEI-2010 scores.1 | HEI-2010 scores.2 | HEI-2010 scores.3 | HEI-2010 scores.4 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| | Total | | | | | P value |
| Range | | 14.0–44.1 | 44.1–53.9 | 53.9–64.1 | 64.1–98.8 | |
| Patients, n | 6690 | 1668 | 1682 | 1667 | 1673 | |
| Age, years | 55.91 ± 0.25 | 50.61 ± 0.45 | 55.36 ± 0.52 | 57.89 ± 0.48 | 59.93 ± 0.42 | <0.001 |
| Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | <0.001 |
| Women | 3,471 (51.90) | 751 (45.03) | 851 (48.92) | 881 (51.96) | 988 (59.87) | |
| Men | 3,219 (48.10) | 917 (54.97) | 831 (51.08) | 786 (48.04) | 685 (40.13) | |
| Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | <0.001 |
| Non-Hispanic white | 2,922 (43.70) | 762 (66.41) | 769 (70.30) | 690 (69.51) | 701 (69.18) | |
| Non-Hispanic Black | 1,862 (27.80) | 535 (18.61) | 474 (15.07) | 449 (13.93) | 404 (12.43) | |
| Mexican American | 824 (12.30) | 185 (6.53) | 191 (5.59) | 258 (7.30) | 190 (5.10) | |
| Other race | 1,082 (16.20) | 186 (8.45) | 248 (9.05) | 270 (9.26) | 378 (13.28) | |
| BMI, kg m2 | 31.18 ± 0.13 | 32.08 ± 0.19 | 31.50 ± 0.27 | 31.12 ± 0.22 | 29.95 ± 0.19 | <0.001 |
| Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | <0.001 |
| Below high school | 1,926 (28.80) | 538 (23.75) | 493 (20.63) | 499 (20.29) | 396 (15.28) | |
| High school | 1,685 (25.20) | 478 (30.00) | 461 (27.72) | 405 (24.08) | 341 (19.96) | |
| Above high school | 3,079 (46.00) | 652 (46.25) | 728 (51.65) | 763 (55.63) | 936 (64.76) | |
| Recreational activity, n (%) | Recreational activity, n (%) | Recreational activity, n (%) | Recreational activity, n (%) | Recreational activity, n (%) | Recreational activity, n (%) | <0.001 |
| Inactive | 4,060 (60.70) | 1,142 (64.72) | 1,077 (58.82) | 997 (54.90) | 844 (45.90) | |
| Moderately active | 1,819 (27.20) | 371 (25.68) | 425 (28.68) | 462 (30.26) | 561 (34.10) | |
| Vigorously active | 811 (12.10) | 155 (9.60) | 180 (12.50) | 208 (14.84) | 268 (20.00) | |
| Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | <0.001 |
| Never smoker | 3,452 (51.60) | 708 (45.40) | 825 (49.85) | 909 (53.09) | 1,010 (57.65) | |
| Former smoker | 1,966 (29.40) | 420 (23.30) | 494 (29.50) | 529 (33.43) | 523 (33.87) | |
| Current smoker | 1,272 (19.00) | 540 (31.29) | 363 (20.65) | 229 (13.48) | 140 (8.48) | |
| Drinking status, n (%) | Drinking status, n (%) | Drinking status, n (%) | Drinking status, n (%) | Drinking status, n (%) | Drinking status, n (%) | <0.001 |
| Non-drinker | 2,671 (39.90) | 650 (35.02) | 639 (31.44) | 686 (32.50) | 696 (33.49) | |
| Low-to-moderate drinker | 2,996 (44.80) | 646 (41.91) | 741 (49.40) | 789 (54.82) | 820 (57.53) | |
| Heavy drinker | 1,023 (15.30) | 372 (23.07) | 302 (19.16) | 192 (12.68) | 157 (8.97) | |
| Total energy intakes, n | Total energy intakes, n | Total energy intakes, n | Total energy intakes, n | Total energy intakes, n | Total energy intakes, n | <0.001 |
| Q1 (<2,702) | 1,673 (25.00) | 349 (15.45) | 381 (17.67) | 446 (21.24) | 497 (24.43) | |
| Q2 (2,702–3,591) | 1,671 (25.00) | 332 (19.24) | 421 (25.33) | 449 (26.13) | 469 (27.20) | |
| Q3 (3,591–4,644) | 1,673 (25.00) | 413 (26.67) | 441 (26.06) | 397 (26.23) | 422 (28.66) | |
| Q4 (≥4,644) | 1,673 (25.00) | 574 (38.64) | 439 (30.94) | 375 (26.39) | 285 (19.72) | |
| Blood pressure level, n (%) | Blood pressure level, n (%) | Blood pressure level, n (%) | Blood pressure level, n (%) | Blood pressure level, n (%) | Blood pressure level, n (%) | 0.630 |
| SBP/DBP <160/100 mmHg | 5,976 (89.30) | 1,493 (90.97) | 1,511 (91.64) | 1,469 (90.48) | 1,503 (91.64) | |
| SBP/DBP ≥ 160/100 mmHg | 714 (10.70) | 175 (9.03) | 171 (8.36) | 198 (9.52) | 170 (8.36) | |
| Anti-hypertensive medicine use, n (%) | Anti-hypertensive medicine use, n (%) | Anti-hypertensive medicine use, n (%) | Anti-hypertensive medicine use, n (%) | Anti-hypertensive medicine use, n (%) | Anti-hypertensive medicine use, n (%) | 0.100 |
| No | 5,579 (83.40) | 1,428 (86.04) | 1,402 (83.99) | 1,374 (83.50) | 1,375 (81.77) | |
| Yes | 1,111 (16.60) | 240 (13.96) | 280 (16.01) | 293 (16.50) | 298 (18.23) | |
| CVD, n (%) | 1,280 (19.10) | 330 (15.68) | 315 (15.95) | 340 (17.69) | 295 (15.82) | 0.580 |
| Diabetes, n (%) | 2,079 (31.10) | 441 (21.39) | 503 (24.60) | 589 (27.50) | 546 (25.72) | 0.010 |
| Hyperlipidemia, n (%) | 5,359 (80.10) | 1,322 (81.09) | 1,321 (78.73) | 1,379 (83.82) | 1,337 (80.62) | 0.080 |
## 3.2. HEI-2010 scores and mortality
During an average follow-up of 8.4 years, 1259 deaths from all causes were documented, including 338 heart disease deaths. The relationship between HEI-2010 scores with heart disease and all-cause mortality is presented in Table 2. After multivariate adjustment, higher HEI-2010 scores were linked to lower heart disease and all-cause mortality. In comparison with the lowest quartile of HEI-2010 scores, multivariable-adjusted HRs ($95\%$ CIs) for all-cause mortality were 0.82 (0.70, 0.97), 0.78 (0.64, 0.95), and 0.68 (0.54, 0.85) for the second, third, and fourth quartiles of the HEI-2010 scores (P-trend < 0.001), as for heart disease mortality were 0.60 (0.44, 0.81), 0.59 (0.40, 0.89), and 0.53 (0.35, 0.80) (P-trend = 0.010). The curve associations of HEI-2010 scores (range: 32.2–78.6) with all-cause mortality (non-linear $$P \leq 0.899$$) and heart disease mortality (non-linear $$P \leq 0.455$$) were described based on the restricted cubic spline models (Figure 1). Each increment in natural-log-transformed HEI-2010 scores was linked to a $43\%$ reduction in the risk of all-cause mortality ($P \leq 0.001$) and a $55\%$ reduction in the risk of heart disease mortality ($$P \leq 0.003$$).
## 3.3. Subgroup analysis
Consistent findings were found between HEI-2010 scores and all-cause mortality when stratifying the analysis by age (≤ 60 and >60 y), sex (male and female), ethnicity (non-Hispanic white and others), education (below/high school and above high school), BMI (<30 and ≥30), drinking status (non-drinker and drinker), smoking status (never smoker and former/current smoker), recreational activity (inactive and active), hyperlipidemia (no or yes), diabetes (no or yes), CVD (no or yes), and blood pressure level (SBP/DBP ≥ $\frac{160}{100}$ mmHg or SBP/DBP <$\frac{160}{100}$ mmHg) (Table 3). Whereas, when the analysis was stratified by anti-hypertensive medicine use (yes or no), the subgroup dataset analyses were all statistically significant (P-trend <0.05), but the direction was not consistent across subgroups (Table 3). No significant interactions were found between HEI-2010 scores and strata variables (all P for interaction > 0.05), except for smoking (P for interaction= 0.022). Stronger inverse relationship between HEI-2010 scores and all-cause mortality in the hypertension population was observed in adults who never smoked.
**Table 3**
| Characteristic | HEI-2010 scores HR (95% CI) a | HEI-2010 scores HR (95% CI) a.1 | HEI-2010 scores HR (95% CI) a.2 | HEI-2010 scores HR (95% CI) a.3 | P-trend | P-interactionb |
| --- | --- | --- | --- | --- | --- | --- |
| | Q1 14.0–44.1 | Q2 44.1–53.9 | Q3 53.9–64.1 | Q4 64.1–98.8 | | |
| Age, y | Age, y | Age, y | Age, y | Age, y | Age, y | 0.649 |
| <60 (n = 3,205) | 1.00 | 0.90 (0.59, 1.37) | 0.83 (0.52, 1.33) | 0.78 (0.44, 1.39) | 0.338 | |
| ≥60 (n = 3,485) | 1.00 | 0.83 (0.68, 1.00) | 0.83 (0.65, 1.06) | 0.73 (0.57, 0.93) | 0.034 | |
| Sex | Sex | Sex | Sex | Sex | Sex | 0.09 |
| Women (n = 3,471) | 1.00 | 0.96 (0.73, 1.26) | 0.82 (0.61, 1.10) | 0.81 (0.58, 1.14) | 0.161 | |
| Men (n = 3,219) | 1.00 | 0.75 (0.57, 0.98) | 0.78 (0.62, 1.00) | 0.59 (0.42, 0.83) | 0.002 | |
| Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | 0.961 |
| Non-Hispanic white (n = 2,922) | 1.00 | 0.83 (0.68, 1.03) | 0.76 (0.60, 0.97) | 0.66 (0.49, 0.88) | 0.003 | |
| Others (n = 3,768) | 1.00 | 0.76 (0.58, 0.98) | 0.78 (0.57, 1.07) | 0.69 (0.50, 0.94) | 0.040 | |
| Education | Education | Education | Education | Education | Education | 0.617 |
| Below/high school (n = 3,611) | 1.00 | 0.81 (0.66, 1.00) | 0.87 (0.66, 1.13) | 0.81 (0.62, 1.06) | 0.216 | |
| Above high school (n = 3,039) | 1.00 | 0.88 (0.62, 1.23) | 0.68 (0.46, 1.01) | 0.56 (0.39, 0.82) | 0.001 | |
| BMI, kg/m2 | BMI, kg/m2 | BMI, kg/m2 | BMI, kg/m2 | BMI, kg/m2 | BMI, kg/m2 | 0.793 |
| <30 (n = 3,319) | 1.00 | 0.97 (0.74, 1.27) | 0.86 (0.64, 1.16) | 0.70 (0.52, 0.93) | 0.011 | |
| ≥30 (n = 3,371) | 1.00 | 0.72 (0.56, 0.93) | 0.66 (0.49, 0.88) | 0.70 (0.49, 1.00) | 0.043 | |
| Recreational activity | Recreational activity | Recreational activity | Recreational activity | Recreational activity | Recreational activity | 0.126 |
| Inactive (n = 4,060) | 1.00 | 0.87 (0.72, 1.04) | 0.87 (0.70, 1.01) | 0.75 (0.59, 0.96) | 0.025 | |
| Active (n = 2,630) | 1.00 | 0.69 (0.46, 1.03) | 0.51 (0.34, 0.77) | 0.50 (0.32, 0.77) | 0.002 | |
| Drinking status | Drinking status | Drinking status | Drinking status | Drinking status | Drinking status | 0.66 |
| Non-drinker (n = 2,671) | 1.00 | 0.69 (0.54, 0.88) | 0.70 (0.55, 0.88) | 0.64 (0.49, 0.84) | 0.003 | |
| Drinker (n = 4,019) | 1.00 | 0.97 (0.72, 1.31) | 0.86 (0.62, 1.18) | 0.67 (0.48, 0.92) | 0.006 | |
| Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | 0.022 |
| Never (n = 3,452) | 1.00 | 0.61 (0.46, 0.82) | 0.70 (0.54, 0.92) | 0.59 (0.43, 0.81) | 0.0139 | |
| Former/Current (n = 3,238) | 1.00 | 0.93 (0.75, 1.16) | 0.76 (0.59, 0.97) | 0.62 (0.46, 0.84) | <0.001 | |
| Hyperlipidemia | Hyperlipidemia | Hyperlipidemia | Hyperlipidemia | Hyperlipidemia | Hyperlipidemia | 0.132 |
| No (n = 1,331) | 1.00 | 0.56 (0.34, 0.94) | 0.82 (0.51, 1.31) | 0.50 (0.30, 0.85) | 0.036 | |
| Yes (n = 5,359) | 1.00 | 0.87 (0.69, 1.09) | 0.75 (0.59, 0.96) | 0.71 (0.54, 0.93) | 0.007 | |
| Diabetes | Diabetes | Diabetes | Diabetes | Diabetes | Diabetes | 0.444 |
| No (n = 4,611) | 1.00 | 0.78 (0.61, 0.98) | 0.72 (0.55, 0.94) | 0.64 (0.48, 0.87) | 0.007 | |
| Yes (n = 2,079) | 1.00 | 0.93 (0.65, 1.31) | 0.90 (0.67, 1.20) | 0.75 (0.51, 1.10) | 0.100 | |
| CVD | CVD | CVD | CVD | CVD | CVD | 0.138 |
| No (n = 5,410) | 1.00 | 0.81 (0.66, 1.00) | 0.68 (0.54, 0.85) | 0.62 (0.47, 0.80) | <0.001 | |
| Yes (n = 1,280) | 1.00 | 0.86 (0.62, 1.17) | 1.02 (0.75, 1.39) | 0.84 (0.60, 1.19) | 0.516 | |
| Blood pressure level | Blood pressure level | Blood pressure level | Blood pressure level | Blood pressure level | Blood pressure level | 0.91 |
| SBP/DBP <160/100 (n = 5,976) | 1.00 | 0.81 (0.67, 0.98) | 0.79 (0.64, 0.98) | 0.71 (0.56, 0.91) | 0.007 | |
| SBP/DBP ≥ 160/100 (n = 714) | 1.00 | 0.88 (0.47, 1.67) | 0.71 (0.42, 1.22) | 0.50 (0.28, 0.91) | 0.011 | |
| Anti-hypertensive drug use | Anti-hypertensive drug use | Anti-hypertensive drug use | Anti-hypertensive drug use | Anti-hypertensive drug use | Anti-hypertensive drug use | 0.664 |
| No (n = 5,579) | 1.00 | 0.91 (0.72, 1.15) | 0.84 (0.67, 1.06) | 0.72 (0.54, 0.95) | 0.012 | |
| Yes (n = 1,111) | 1.00 | 0.47 (0.29, 0.76) | 0.52 (0.34, 0.80) | 0.72 (0.54, 0.95) | 0.035 | |
## 3.4. HEI-2010 component scores and mortality
There were significant differences in the weighted proportions of participants who obtained the maximum component score by the HEI-2010 component (Table 4), which we presented as a radar plot (Figure 2). The proportion of participants receiving the highest component score for each component was over $60\%$ for total protein foods, 20–$40\%$ for total seafood and plant proteins, greens and beans, vegetables, refined grains, total fruits, and whole fruits; 10–$20\%$ for dairy, unsaturated fatty acids, and empty calories; and <$10\%$ for sodium and whole grains. When the components of HEI-2010 were evaluated (Table 4), higher component scores were linked to a lower all-cause mortality risk in all three multivariate models for six of the 12 components: seafood and plant protein, total protein foods, greens and beans, total vegetables, unsaturated fatty acids, and empty calories, reducing the risk of all-cause mortality by 21–$29\%$. For instance, participants consuming at least 2.5 oz per 1,000 calories a day of total protein foods reduced their risk of death from all causes by $25\%$ based on the results in Model 3. Other HEI-2010 components not shown to be linked to all-cause mortality were whole grains, refined grains, dairy, sodium, whole fruit, and total fruit.
## 3.5. Sensitivity analyses
In sensitivity analyses, the inverse relationship of HEI-2010 scores with heart disease and all-cause mortality remained largely unchanged after excluding the participants who died within the first 2 years of follow-up ($$n = 199$$) (Supplementary Table 2). After additional adjustments for dietary intakes of cholesterol, fiber, total fat, vitamin E, vitamin A, vitamin C, and biomarkers of serum total cholesterol, eGFR, the results remained largely unchanged (Supplementary Table 3). Finally, HEI-2010 scores were not related to cerebrovascular mortality and cancer mortality (Supplementary Table 4).
## 4. Discussion
To date, it is the first research to examine the relationship of the HEI-2010 and its components with mortality from heart disease and all causes in adults with hypertension. We observed that higher HEI-2010 scores were linked to lower heart disease mortality and all-cause mortality risk independent of various factors, which included lifestyle and dietary factors, anti-hypertensive medicine use, and blood pressure levels. We demonstrated the robustness of our findings through stratified analyses and sensitivity analyses. According to our study, among the 12 components of HEI-2010, a higher intake of greens and beans, seafood and plant proteins, total protein foods, vegetables, and unsaturated fatty acids, as well as moderate consumption of empty calories, were linked to a lower risk of all-cause mortality.
Previous studies including some meta-analysis studies have investigated the associations of healthy dietary patterns with cardiovascular and all-cause mortality and reached consistent findings with ours. They indicated that the inverse relationships of healthy dietary patterns with cardiovascular and all-cause mortality were statistically significant in both general populations and other people (11, 12, 17–21). For instance, according to a linear dose-response meta-analysis, each 5-point increment in compliance with Dietary Approaches to Stop Hypertension (DASH) significantly reduced the all-cause mortality risk (assessed in 13 cohort studies, 9 publications, including 1,240,308 participants) and cardiovascular mortality (assessed in 12 cohorts, 9 publications, including 1,314,675 participants) for $5\%$ (6–$4\%$) and $4\%$ (5–$2\%$), respectively [20]. In addition, in a prospective multiethnic cohort study, each healthy dietary pattern including HEI-2010, the Alternative Healthy Eating Index (AHEI-2010), DASH, and the Alternate Mediterranean Diet was linked to reduced risk of deaths from cardiovascular disease and all causes [11]. The same results were also seen in two other prospective cohort studies that recruited older adults [12] and postmenopausal women in the United States (US) [18], respectively. One possible explanation for the consistent findings is that despite there being multiple dietary pattern index scores, they tend to converge in preventing major chronic diseases, such as diabetes [22, 23] and cardiovascular disease [24, 25], as they are derived from many of the same core principles emphasizing vegetables, whole grains, plant-based proteins, and fruits, food combinations that are primarily rich in antioxidants and anti-inflammatory nutrients. A systematic review with 16 observational and 13 interventional studies indicated an inverse relationship between healthy dietary patterns with oxidative stress and pro-inflammatory biomarkers [26]. In addition, healthy dietary patterns may improve lipid metabolism, blood pressure, and endothelial function, and has anti-oxidative and anti-inflammatory properties [25, 27].
However, among adults with hypertension who tend to have endothelial dysfunction, increased oxidative stress, pro-inflammatory release, insensitivity to vasodilators, arterial vascular smooth stiffening [14, 15], and with a higher incidence of CVD and all-cause mortality, it is still not well known whether HEI-2010 has a long-term effect on mortality in this specific population. Although previous reviews and studies have reported beneficial effects on blood pressure control with HEI-2010 [8]. Based on a nationally representative American adult sample with a long follow-up duration, the current study found inverse relationships between HEI-2010 and mortality from heart disease and all causes in adults with hypertension. Our results were consistent with a study performed on male diabetic physicians, where diabetes is characterized by increased pro-inflammatory and oxidative status. In this prospective cohort study of 1,163 male physicians with diabetes only, an inverse relationship between the AHEI-2010 score and all-cause mortality was found (HR = 0.59; $95\%$ CI 0.44, 0.79) [21]. A further analysis of the 12 individual components of HEI-2010 was conducted to determine possible dietary components that might affect all-cause mortality. It found that a higher intake of greens and beans, seafood and plant protein, total vegetables, total protein foods, unsaturated fatty acids, and moderate consumption of empty calories were associated with a 21–$29\%$ reduction in the all-cause mortality risk in adults with hypertension. The results can be explained as follows.
First, vegetables are rich in different nutrients and bioactive compounds, such as phytochemicals, vitamins, minerals, and fibers, which have cardioprotective effects, including anti-inflammation, anti-oxidation, anti-platelet properties, regulate blood pressure and lipid metabolism, improve endothelial function, and reduce myocardial injury [28, 29]. As reported in one review, nitrate, which is rich in vegetables, is now considered a critical bioactive phytochemical with cardioprotective performances, increasing nitrogen oxide (NO) and other nitrogen oxides via the nitrate-nitrite-nitric oxide pathway to improve endothelial function, lower blood pressure, regulate arterial stiffness, reduce ischemia-reperfusion injury, modulate blood flow, and anti-platelet aggregation [30]. In addition, vegetables are a significant source of potassium, and higher potassium intake was related to lower blood pressure, especially a high potassium/sodium ratio [31]. Third, green vegetable soya bean is rich in carbohydrates, omega-3 fatty acids, protein, fiber, and a variety of micronutrients providing nutrients for humans, where steroids 7, saponins 2, alkaloids 6, and isoflavones 5 are found, exhibiting anti-oxidative and anti-inflammatory properties to some extent [32, 33]. Fourth, the fatty acids ratio is expressed as monounsaturated and polyunsaturated fatty acids (PUFA) to saturated fatty acids. Omega-3 PUFA has been reported to reduce cardiovascular disease risk by regulating lipid metabolism and platelet aggregation [34]. Also, seafood is a major source of omega-3 PUFA, which has been discovered to provide anti-inflammatory effects [35] and regulate blood lipids [36]. Fifth, plant protein consumption has been found to lower the levels of total cholesterol, LDL cholesterol, and blood pressure [37, 38]. Finally, in the HEI-2010, empty calories are those derived from added sugars and solid fats [7], there has been an association between solid fat intake as well as added sugar intake with mortality in previous studies [39, 40]. Excessive added sugars intake is associated with elevated triglyceride levels [41] and inflammatory markers [42], which are crucial determinants in the pathogenesis of CVD. Therefore, it is necessary to limit the intake of added sugar. Through the mechanisms described earlier, the HEI-2010 may improve cardiovascular conditions and reduce all-cause mortality in adults with hypertension.
Furthermore, in adults with hypertension, HEI-2010 seemed to have a stronger protective effect against mortality events in non-smokers than in smokers. The finding may be due to the fact that smoking increases cardiovascular mortality and morbidity, which is supported by epidemiological studies [43, 44]. When the analysis was stratified by anti-hypertensive medicine use (yes or no), the subgroup dataset analyses were all statistically significant, but the direction was not consistent across subgroups. The most common reason may be that there was an interaction between subgroup factors or between the observed subgroup factors with unknown factors. Positive results found in subgroup analyses have an extremely high probability of being false positive due to the fact that the subgroup analyses may not maintain randomization within subgroups, have an insufficient sample size, and low degree of certainty. Therefore, the essence of the evidence is a result of an observational study, which needs to be interpreted with caution and applied carefully to guide the work of clinical practice.
This study has several strengths. First, it is the first study to assess the relationship between the HEI-2010 and mortality from heart disease and all causes in adults with hypertension based on NHANES. Second, this study used data from NHANES, which collected and reported data using standardized procedures and strict quality assurance. Third, we were able to generalize our results to the US adult population by utilizing a broad, nationally representative database to estimate diet quality. Finally, modeled upon the 2010 DGA recommendations, the HEI-2010 is a reliable and valid measurement of dietary quality for Americans.
There are also some potential limitations to the current study. First, the current study did not have detailed information on the severity of hypertension, although the results did not change significantly after further adjustment of blood pressure levels and anti-hypertensive medicine use. In addition, as some missing data in the database, we cannot include all potentially significant variables, for example, more than $50\%$ of the data for CRP and LDL cholesterol were missing.
## 5. Conclusion
According to this prospective cohort study of adults with hypertension, we observed an inverse association between HEI-2010 and mortality from heart disease and all causes. Based on the findings, it may be helpful to guide the dietary intake of adults with hypertension. Further studies are needed to support these results.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.
## Ethics statement
The studies involving human participants were reviewed and approved by National Center for Health Statistics (NCHS) Research Ethics Review Board. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
Study design: YZ, DL, and HZ. Data collection and analysis: YZ and DL. Writing the manuscript: YZ. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1077896/full#supplementary-material
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|
---
title: RhoA rescues cardiac senescence by regulating Parkin-mediated mitophagy
authors:
- Joanne Ern Chi Soh
- Akio Shimizu
- Md Rasel Molla
- Dimitar P. Zankov
- Le Kim Chi Nguyen
- Mahbubur Rahman Khan
- Wondwossen Wale Tesega
- Si Chen
- Misa Tojo
- Yoshito Ito
- Akira Sato
- Masahito Hitosugi
- Shigeru Miyagawa
- Hisakazu Ogita
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC10020657
doi: 10.1016/j.jbc.2023.102993
license: CC BY 4.0
---
# RhoA rescues cardiac senescence by regulating Parkin-mediated mitophagy
## Body
Heart failure is a multifaceted disease with a complex etiology. It remains a major public health problem and is the leading cause of death worldwide, with high morbidity and mortality rates [1, 2, 3, 4]. Heart failure is a chronic pathophysiological state in which the heart muscle is unable to pump an adequate supply of blood to the whole body due to progressive loss of myocardial contractile function over time [5]. Despite medical advances, the prognosis of patients with heart failure remains poor [6], and current therapeutic approaches seem palliative as the underlying mechanisms contributing to heart failure are still not fully addressed.
The heart is a highly metabolic organ in which mitochondrial dynamics are precisely regulated to ensure optimal mitochondrial function [7, 8]. Given the high energetic demand of the heart, age-related defects in mitochondrial bioenergetics can have detrimental effects on normal cardiac pumping. Accumulation of dysfunctional mitochondria is associated with suppression of mitophagy [9, 10], leading to a defect in mitochondrial quality control. Mitophagy is an evolutionarily conserved mechanism that plays a crucial role in the mitochondrial quality control [11]. It enables the degradation of damaged and superfluous mitochondria in response to cardiac stress, including senescence.
RhoA is a small GTPase that regulates diverse cellular events, including actin cytoskeleton organization, cell adhesion, migration, invasion, apoptosis, extracellular matrix remodeling, and smooth muscle contractility [12, 13]. RhoA is ubiquitously expressed in almost all tissue and cell types, including cardiomyocytes. RhoA signaling plays a pivotal role in processes leading to cardiovascular diseases, such as pulmonary hypertension, vasospastic angina, and heart failure [14, 15]. Thus, RhoA function in the heart remains an interesting focus among molecular cardiologists as well as biologists. However, the understanding of the molecular signaling of RhoA in the heart is still incomplete.
In this study, we found that cardiomyocyte-specific RhoA conditional knockout (cKO) mice had a significantly shorter lifespan with features of early senescence, severely impaired cardiac function, and build-up of many structurally disorganized and enlarged mitochondria compared with control mice. These phenotypes suggest a causative link between cardiac aging and mitochondrial dysfunction with regard to RhoA signaling. We further revealed the molecular mechanisms of cardiac RhoA in regulating mitochondrial dynamics, which may protect the heart from senescence-mediated dysfunction.
## Abstract
Heart failure is one of the leading causes of death worldwide. RhoA, a small GTPase, governs actin dynamics in various tissue and cell types, including cardiomyocytes; however, its involvement in cardiac function has not been fully elucidated. Here, we generated cardiomyocyte-specific RhoA conditional knockout (cKO) mice, which demonstrated a significantly shorter lifespan with left ventricular dilation and severely impaired ejection fraction. We found that the cardiac tissues of the cKO mice exhibited structural disorganization with fibrosis and also exhibited enhanced senescence compared with control mice. In addition, we show that cardiomyocyte mitochondria were structurally abnormal in the aged cKO hearts. Clearance of damaged mitochondria by mitophagy was remarkably inhibited in both cKO cardiomyocytes and RhoA-knockdown HL-1 cultured cardiomyocytes. In RhoA-depleted cardiomyocytes, we reveal that the expression of Parkin, an E3 ubiquitin ligase that plays a crucial role in mitophagy, was reduced, and expression of N-Myc, a negative regulator of Parkin, was increased. We further reveal that the RhoA–Rho kinase axis induced N-Myc phosphorylation, which led to N-Myc degradation and Parkin upregulation. Re-expression of Parkin in RhoA-depleted cardiomyocytes restored mitophagy, reduced mitochondrial damage, attenuated cardiomyocyte senescence, and rescued cardiac function both in vitro and in vivo. Finally, we found that patients with idiopathic dilated cardiomyopathy without causal mutations for dilated cardiomyopathy showed reduced cardiac expression of RhoA and Parkin. These results suggest that RhoA promotes Parkin-mediated mitophagy as an indispensable mechanism contributing to cardioprotection in the aging heart.
## Deterioration of cardiac function and early death in RhoA cKO mice
RhoA cKO mice were healthy at birth with normal growth. The mice were similar in weight, and no obvious phenotypic abnormalities were observed at around 10 weeks after birth, compared with the littermate control mice (Fig. 1A). However, after 10 weeks of age, the body weight of RhoA cKO mice did not increase further and was significantly lower than that of control mice. RhoA expression was confirmed to be absent in cardiomyocytes from RhoA cKO mice (Fig. 1B). Strikingly, RhoA cKO mice experienced early death from around 30 weeks of age compared with control mice (Fig. 1C). To investigate the cause of early death in RhoA cKO mice, we assessed the cardiac function of these mice by echocardiography. The left ventricular ejection fraction of RhoA cKO mice was initially normal after birth, but it decreased significantly with age (Fig. 1, D and E). In addition to the lower left ventricular ejection fraction (LVEF), LV dilatation and increased LV mass without LV wall thickening during the experimental period were observed in the RhoA cKO hearts compared with the control hearts (Fig. 1, F–H), suggesting age-dependent cardiomyopathy caused by loss of RhoA in the heart. We also measured heart rate (HR) and blood pressure (BP). HR was similar between RhoA cKO mice and control mice, while RhoA cKO mice exhibited an age-dependent lower systolic BP than control mice (Fig. 1, I and J). Collectively, these results indicate the severe low cardiac output condition and an accelerated transition to heart failure in RhoA cKO mice, resulting in a shorter lifespan. Figure 1Shorter lifespan and impaired cardiac function in RhoA cKO mice. A, body weight of 9-, 18-, and 45-week-old mice. B, immunostaining for RhoA in the heart at 9 weeks after birth. F-actin and nuclei were counterstained with phalloidin and DAPI, respectively. Scale bar: 20 μm. C, Kaplan–Meier survival curve of mice. The number of mice (n) in each group at every 10 weeks is indicated below the graph. D, echocardiographic images of control and RhoA cKO mice at the indicated time points. E–H, echocardiographic analyses of left ventricular ejection fraction (LVEF; E), LV end-diastolic diameter (LVDd; F), LV mass (G), and LV posterior wall thickness at end-diastole (LVPWd; H) in mice at 9, 13, 21, 29, 37, 45, and 53 weeks after birth. I and J, heart rate (HR; I) and systolic blood pressure (BP; J) measured by plethysmographic tail-cuff method in 9-, 21-, and 45-week-old mice. The data in each graph are shown as the mean ± SD. In (A) and (E–J) two-way ANOVA and one-way ANOVA were applied to compare the data between groups and weeks, respectively, and in (C), the data were analyzed by Kaplan–Meier method. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, and ∗∗∗$p \leq 0.001$ versus control; †$p \leq 0.05$, ††$p \leq 0.01$, and †††$p \leq 0.001$ versus week 9. cKO, conditional knockout.
## Accelerated aging and fibrosis in the RhoA cKO heart
To explore how cardiac function rapidly declined with aging in RhoA cKO mice, we examined the progression of cardiac senescence using several markers because cardiac senescence impairs cardiac function [16, 17]. Cellular senescence markers, including p16, p21, and senescence-associated β-galactosidase, were more highly detected in the RhoA cKO hearts than the control hearts (Fig. 2, A–F). Consistent with these results, the histological analysis by hematoxylin and eosin (H-E) staining revealed severe myocardial pathology, including increased myofiber disarray and interstitial space in LV of aged RhoA cKO mice (Fig. 2G). The RhoA cKO hearts also exhibited significantly augmented LV fibrosis (Fig. 2, H and I). In agreement with echocardiography, these results suggest that cardiac RhoA depletion accelerates cardiac aging and induces cardiac structural changes with abnormally increased fibrosis. Figure 2Accelerated cardiac aging in RhoA cKO mice. A, C, and E, immunostaining for the senescence markers p16 (A), p21 (C), and senescence-associated β-galactosidase (SAβ-Gal; E) in the heart. Nuclei were counterstained with DAPI. Scale bars: 20 μm. B, D, and F, summary graphs of the percentage of positive area for each marker analyzed in (A, C, and E), respectively. G, H-E staining of the heart at the indicated time points. Scale bar: 50 μm. H, Picro-sirius red staining of the heart for the detection of fibrosis at the indicated time points. Scale bar: 50 μm. I, summary graph of the percentage of cardiac fibrosis. The data in each graph are shown as the mean ± SD. One-way ANOVA was applied to compare the data between groups in B, D, F, and I. ∗∗∗$p \leq 0.001$ versus control; †††$p \leq 0.001$ versus week 18. cKO, conditional knockout; H-E, hematoxylin and eosin.
## Mitochondrial dysfunction and mitophagy dysregulation in the RhoA cKO heart
Mitochondrial dynamics in the heart are closely related to aging [7], and abnormal mitochondrial dynamics result in an insufficient energy supply in the heart, suppressing cardiac function [8, 10]. Thus, we examined the morphology of the mitochondria in the heart by transmission electron microscopy (TEM). The mitochondria in the RhoA cKO hearts were severely damaged by aging, which occurred in parallel with the accumulation of many swollen and fragmented mitochondria with cristae disruption (Fig. 3A). We also found that the expression of ATP5A, a subunit of the mitochondrial ATP synthase, decreased in the heart of RhoA cKO mice compared with control mice (Fig. 3, B–E), validating the functionally defective mitochondria that resulted from RhoA knockout. Next, to examine the effect of RhoA on mitochondrial function in in vitro experiments, RhoA expression was knocked down in HL-1 cardiomyocytes. When two siRNAs for RhoA were transfected into HL-1 cells to check their efficiency for RhoA inhibition, siRhoA #2 significantly reduced RhoA expression, while siRhoA #1 did not. Thus, siRhoA #2 was used for further experiments (Fig. 3, F–H). Similar to the RhoA cKO hearts, ATP5A expression was significantly reduced in RhoA-knockdown HL-1 cardiomyocytes (Fig. 3, I–L).Figure 3Accumulation of dysfunctional mitochondria and mitophagy dysregulation in the RhoA cKO hearts. A, TEM images of the heart at the indicated time points. Arrowheads indicate swollen and severely damaged mitochondria. Scale bar: 1 μm. B and D, immunostaining (B) and Western blotting (D) for ATP5A in the mouse heart samples at the indicated time points. Nuclei were counterstained with DAPI (B), and GAPDH was blotted as the loading control (D). Scale bar in (B): 20 μm. C and E, summary graphs of the percentage of ATP5A-positive area (C) and the ATP5A/GAPDH band ratio (E) examined in (B and D), respectively. F, qPCR analysis of *Rhoa* gene expression in HL-1 cells after transfection of scramble RNA (Scramble), siRhoA #1, or siRhoA #2. *Gapdh* gene expression was used as the control. G, Western blotting of RhoA in HL-1 cells. H, summary graph of the RhoA/GAPDH band ratio in (G). I and K, immunostaining (I) and Western blotting (K) for ATP5A in HL-1 cells. The plasma membrane was stained with wheat germ agglutinin (WGA) in (I). Scale bar in (I): 20 μm. J and L, summary graphs of the percentage of ATP5A-positive area (J) and the ATP5A/GAPDH band ratio (L) examined in (I and K), respectively. M, fluorescence images of mitophagy in viable HL-1 cells after CCCP induction. Nuclei were counterstained with Hoechst. Scale bar: 20 μm. N, summary graph of the percentage of mitophagy area. The data in each graph are shown as the mean ± SD. Comparisons of the data between groups were performed using one-way ANOVA (C and E) or t test (F, H, J, L, and N). ∗∗∗$p \leq 0.001$ versus control or scramble; †††$p \leq 0.001$ versus week 18. cKO, conditional knockout; TEM, transmission electron microscopy.
Mitochondrial function and homeostasis are mainly regulated by [1] mitophagy and [2] fission and fusion [18, 19]. Mitophagy is an important regulatory mechanism for clearing damaged mitochondria by proteasomal degradation. We first detected impaired mitophagy in siRhoA-treated HL-1 cells compared with scramble RNA-treated cells after exposure to carbonyl cyanide m-chlorophenyl hydrazone (Fig. 3, M and N). These results suggest defective mitophagy regulation in the absence of RhoA, leading to impaired removal and abnormal accumulation of damaged mitochondria in the heart in response to cardiac stress, such as aging. In contrast, the expression and phosphorylation of the mitochondrial fission marker Drp1 were not different between the RhoA cKO and control hearts or between RhoA-knockdown and control HL-1 cells (Fig. S1), suggesting that mitochondrial biogenesis in cardiomyocytes is normal, regardless of the absence of RhoA.
## Reduced Parkin expression and ubiquitinated mitochondrial proteins by loss of RhoA in cardiomyocytes
To delineate the mitochondrial abnormality and mitophagy dysregulation in cardiomyocytes with loss of RhoA, we focused on Parkin, an E3 ubiquitin (Ub) ligase, which mediates the ligation of Ub to the damaged mitochondria for proteasomal degradation [20]. The loss of RhoA resulted in the reduction of Parkin expression with a decrease in ubiquitinated mitochondrial proteins in the hearts of younger (18-week-old) and older (55-week-old) mice (Fig. 4, A–F). Similarly, Parkin expression and ubiquitinated mitochondrial proteins in HL-1 cells were suppressed by RhoA knockdown (Fig. 4, G–L). These data suggest that RhoA plays a role in the expression of Parkin in cardiomyocytes, which regulates ubiquitination of mitochondrial proteins. Figure 4Reduced Parkin expression in the RhoA cKO hearts. A, Western blotting for RhoA and Parkin in the heart at the indicated time points. GAPDH was blotted as the loading control. B, summary graph of the Parkin/GAPDH band ratio. C, immunostaining for Parkin in the hearts of 45-week-old mice. The plasma membrane and nuclei were counterstained with WGA and DAPI, respectively. Scale bar: 20 μm. D, summary graph of the percentage of Parkin-positive area. E, Western blotting for ubiquitin (Ub) in the mitochondrial fraction of the heart samples at the indicated time points. Tom20 was blotted as the loading control. F, summary graph of Ub bands density. A.U.: arbitrary unit. G and I, Western blotting (G) and immunostaining for Parkin (I) in HL-1 cardiomyocytes transfected with siRhoA and scramble RNA. H and J, summary graphs of the Parkin/GAPDH band ratio (H), and the percentage of Parkin-positive area (J) examined in (G and I), respectively. K, Western blotting for Ub in the mitochondrial fraction of HL-1 cells. Tom20 was blotted as the loading control. L, summary graph of Ub bands density. A.U.: arbitrary unit. The data in each graph are shown as the mean ± SD. Comparisons of the data between groups were performed using one-way ANOVA (B and F) or t test (D, H, K, and L). ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, and ∗∗∗$p \leq 0.001$ versus control or scramble. cKO, conditional knockout; WGA, wheat germ agglutinin.
Parkin is phosphorylated, and its function is regulated by PTEN-induced putative kinase 1 (PINK1) [21]. We then examined PINK1 expression in the presence and absence of RhoA in the mouse heart. PINK1 protein expression was almost identical between control and RhoA cKO hearts and was not different between young (18-week-old) and old (53-week-old) mice (Fig. S2, A and B). Similar to this, the gene expression of Park6, encoding PINK1, as well as the protein expression of PINK1 was not changed by RhoA knockdown in HL-1 cardiomyocytes, as shown by quantitative PCR and Western blotting (Fig. S2, C–E). Immunostaining of HL-1 cells also showed that the PINK1-positive area after siRhoA transfection was equal to that after scramble RNA transfection (Fig. S2, F and G). Thus, RhoA does not seem to affect PINK1 expression in the heart.
## RhoA-mediated N-Myc–Parkin pathway regulation
N-*Myc is* a negative transcription factor for the *Parkin* gene expression [22], and the expression of N-*Myc is* reduced by phosphorylation-dependent degradation [23]. We determined the endogenous expression of N-Myc in both the mouse heart and HL-1 cells. By depletion of RhoA, the expression of N-Myc was increased (Fig. 5, A–F), together with a remarkable reduction of its phosphorylation (Fig. 5, A–D), indicating an inverse correlation between N-Myc and Parkin expressions. To further examine how RhoA regulates N-Myc phosphorylation, we focused on Rho kinase (ROCK), which is an effector of RhoA [14]. ROCK in HL-1 cells was confirmed to be inhibited by treatment with a ROCK inhibitor Y-27632 (Fig. 5G). In the presence of Y-27632, N-Myc phosphorylation was decreased, and N-Myc expression was increased, resulting in the reduction of Parkin and ATP5A expressions (Fig. 5, H and I).Figure 5RhoA-mediated Parkin expression in cardiomyocytes. A and C, Western blotting for N-Myc and phosphorylated N-Myc (P-N-Myc) in the hearts of 45-week-old mice (A) and HL-1 cardiomyocytes (C). GAPDH was blotted as the loading control. B and D, summary graphs of the N-Myc/GAPDH and P-N-Myc/GAPDH band ratios examined in (A and C), respectively. E, immunostaining for N-Myc in HL-1 cells. The plasma membrane and nuclei were counterstained with WGA and DAPI, respectively. Scale bar: 20 μm. F, summary graph of the percentage of N-Myc-positive area. G, ROCK kinase assay in HL-1 cells with or without a ROCK inhibitor Y-27632 for 1 h. H, Western blotting for the indicated molecules in HL-1 cells treated with or without Y-27632 for 1 h. I, summary graphs of the ratio for the band density of each molecule to that of GAPDH, which was examined in (H). J, Western blotting for the indicated molecules in HL-1 cells transfected with siRhoA, siRhoA+siN-Myc, or scramble RNA as the control. K, summary graphs of the Parkin/GAPDH and ATP5A/GAPDH band ratios. L, immunostaining for Parkin in HL-1 cells. Scale bar: 20 μm. M, summary graph of the percentage of Parkin-positive area in (L). N, Western blotting for Ub in the mitochondrial fraction of HL-1 cells. Tom20 was blotted as the loading control. O, summary graph of Ub bands density. A.U.: arbitrary unit. P, fluorescence images of mitophagy in viable cultured HL-1 cells after CCCP induction. Nuclei were counterstained with Hoechst. Scale bar: 20 μm. Q, summary graph of the percentage of mitophagy area. The data in each graph are shown as the mean ± SD. In (C, E, J, L, N, and P), HL-1 cells were used for experiments at 48 h after siRNA transfection. Comparisons of the data between groups were performed using t test (B, D, F, and H) or one-way ANOVA (J, L, O, and Q). ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, and ∗∗∗$p \leq 0.001$ versus control or scramble; ††$p \leq 0.01$ and †††$p \leq 0.001$ versus siRhoA. CCCP, carbonyl cyanide m-chlorophenyl hydrazone; ROCK, Rho kinase; Ub, ubiquitin; WGA, wheat germ agglutinin.
We next examined whether N-Myc deletion in RhoA-deficient cardiomyocytes could rescue Parkin expression. When N-Myc was knocked down by transfection of siN-Myc in RhoA-knockdown HL-1 cells, the reduced expression of Parkin in RhoA-knockdown cells was restored (Fig. 5, J–M). Similarly, the attenuated ATP5A expression in RhoA-knockdown HL-1 cells was recovered by the additional N-Myc knockdown (Fig. 5, J and K). The ubiquitination of mitochondrial proteins was also recovered by siN-Myc transfection in RhoA-knockdown HL-1 cells (Fig. 5, N and O). Furthermore, immunofluorescent microscopy confirmed the recovery of mitophagy by the additional N-Myc knockdown (Fig. 5, P and Q). Taken together, our findings suggest that N-Myc functions downstream of RhoA as a negative regulator of Parkin expression and that N-Myc expression is inhibited by the RhoA–ROCK-mediated N-Myc phosphorylation, leading to the sufficient Parkin expression for maintenance of cardiomyocyte mitophagy.
## Restoration of mitophagy and cardiac function by supplementation of Parkin expression in the RhoA-depleted cardiomyocytes
To demonstrate the essential effects of Parkin on the rescue of mitophagy and cardiac function in the RhoA cKO hearts and RhoA-knockdown cardiomyocytes, we used the adeno-associated virus (AAV) serotype 6 gene transfer system to introduce the *Parkin* gene in cardiomyocytes. First, we infected AAV-Parkin-T2A-green fluorescent protein (GFP) and control AAV-GFP into HL-1 cells and examined how the infection increased Parkin expression. AAV-Parkin-T2A-GFP infection recovered the siRhoA-mediated decrease in Parkin expression back to the basal level (Fig. 6, A and B). Similarly, AAV-Parkin-T2A-GFP restored mitochondrial protein ubiquitination and mitophagy (Fig. 6, C–F). In the fluorescence microscopy, we found that all of the HL-1 cells were infected with AAV-Parkin-T2A-GFP or AAV-GFP as monitored by GFP fluorescence, although the level of GFP fluorescence was variable in each cell (Fig. 6E).Figure 6Restoration of mitochondrial function in RhoA-knockdown HL-1 cardiomyocytes by AAV-Parkin infection. A, Western blotting for Parkin in HL-1 cardiomyocytes infected with AAV-Parkin-T2A-GFP or AAV-GFP as the control. GAPDH was blotted as the loading control. B, summary graph of the Parkin/GAPDH band ratio. C, Western blotting for Ub in the mitochondrial fraction of HL-1 cells. Tom20 was blotted as the loading control. D, summary graph of Ub bands density. A.U.: arbitrary unit. E, fluorescence images of mitophagy in viable cultured HL-1 cells after CCCP induction. Nuclei were counterstained with Hoechst. Scale bar: 20 μm. F, summary graph of the percentage of mitophagy area. The data in each graph are shown as the mean ± SD. One-way ANOVA was used to compare the data between groups. ∗∗$p \leq 0.01$ and ∗∗∗$p \leq 0.001$ versus Scramble; †††$p \leq 0.001$ versus AAV-GFP. AAV, adeno-associated virus; CCCP, carbonyl cyanide m-chlorophenyl hydrazone; GFP, green fluorescent protein.
Next, we examined the in vivo function of AAV-Parkin-T2A-GFP in RhoA cKO mice by intravenous injection of AAV through the tail vein. When AAV-Parkin-T2A-GFP was administered in control mice to assess the in vivo efficiency of the AAV-mediated *Parkin* gene transfer, Parkin expression was increased in the heart compared with the administration of AAV-GFP (Fig. S3, A and B). In contrast, the increase was not observed in the brain (Fig. S3A), confirming adequate gene transfer by the AAV serotype 6 system. Four weeks after AAV infection in mice, cardiac function was unchanged, as evaluated by LVEF and hemodynamics, such as HR and systolic BP (Fig. S3C). This suggests that *Parkin* gene was safely transferred by AAV in vivo. Because Parkin expression in the heart was restored for approximately 25 weeks after AAV-Parkin-T2A-GFP administration in RhoA cKO mice (Fig. S3D), we injected AAV in RhoA cKO mice twice (10 and 32 weeks after birth) for a total 1-year (53 weeks) observation period. After the injection of AAV-Parkin-T2A-GFP, the deterioration of LVEF was attenuated, and the lifespan was prolonged compared with mice injected with AAV-GFP (Fig. 7, A and B). Mice were sacrificed at around 55 weeks after birth. The heart was enlarged in RhoA cKO mice injected with control AAV-GFP due to heart failure, which was clearly recovered by AAV-Parkin-T2A-GFP injection (Fig. 7, C and D). Similarly, the lung weight, which was also increased by heart failure-induced pulmonary edema in RhoA cKO mice, was reduced by AAV-Parkin-T2A-GFP injection (Fig. 7D). The treatment maintained the expression of Parkin in the RhoA cKO hearts, and the results were comparable to those of the control hearts (Fig. 7, E–H). H-E staining showed an improvement of the severe myocardial damage in the RhoA cKO hearts after AAV-Parkin-T2A-GFP injection (Fig. 7I). The increased cardiac fibrosis in the RhoA cKO hearts was also attenuated by the injection (Fig. 7, J and K).Figure 7Improved cardiac function and increased lifespan in RhoA cKO mice after intravenous administration of AAV-Parkin. A, LVEF analyzed by echocardiography in 9-, 13-, 21-, 29-, 37-, 45-, and 53-week-old mice. AAV-Parkin-T2A-GFP or AAV-GFP as the control was injected into RhoA cKO mice through the tail vein twice (10 and 32 weeks after birth). B, Kaplan–Meier survival curve of RhoA cKO mice injected twice with AAV-Parkin-T2A-GFP or AAV-GFP. The number of mice (n) in each group at every 10 weeks is indicated below the graph. C, external appearance of the hearts extracted from 53-week-old mice. Scale bar: 5 mm. D, heart and lung weight in 53-week-old mice, which was normalized to tibia length. E, Western blotting for Parkin and GFP in the hearts of 53-week-old mice. GAPDH was blotted as the loading control. F, summary graph of the Parkin/GAPDH band ratio. G, immunostaining for Parkin in the hearts of 53-week-old mice. Nuclei were counterstained with DAPI. Scale bar: 20 μm. H, summary graph of the percentage of Parkin-positive area. I and J, H-E staining and Picro-sirius red staining of the hearts from 53-week-old mice. Scale bars: 50 μm. K, summary graph of the percentage of cardiac fibrosis examined in (J). The data in each graph are shown as the mean ± SD. In (A), two-way ANOVA and one-way ANOVA were applied to compare the data between groups and weeks, respectively, and in (C), the data were analyzed using the Kaplan–Meier method. One-way ANOVA (D, F, and H) or t test (K) was used to compare the data between groups. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, and ∗∗∗$p \leq 0.001$ versus AAV-GFP; †$p \leq 0.05$, ††$p \leq 0.01$, and †††$p \leq 0.001$ versus Week 9; §§§$p \leq 0.001$ versus control. AAV, adeno-associated virus; cKO, Conditional knockout; GFP, green fluorescent protein; H-E, hematoxylin and eosin; LVEF, Left ventricular ejection fraction.
Further ultrastructural analysis using TEM revealed a remarkable reduction of damaged mitochondria in the RhoA cKO hearts after treatment with AAV-Parkin-T2A-GFP (Fig. 8A). This was justified by the restored expression of ATP5A and the increase in mitochondrial protein ubiquitination in the AAV-Parkin-T2A-GFP-treated RhoA cKO hearts (Fig. 8, B–G). Cellular senescence in RhoA cKO cardiomyocytes was also suppressed by AAV-Parkin-T2A-GFP treatment (Fig. 8, H and I). These findings suggest that Parkin, as the downstream molecule of RhoA, could compensate for RhoA deficiency by maintaining mitochondrial homeostasis, resulting in the prevention of age-related acceleration of cardiac dysfunction and heart failure in the absence of RhoA.Figure 8Clearance of damaged mitochondria and improvement of earlier senescence in RhoA cKO mice after AAV-Parkin injection. A, TEM images of the hearts from 53-week-old mice after intravenous injection of AAV-Parkin-T2A-GFP or AAV-GFP twice (10 and 32 weeks after birth). Scale bar: 1 μm. B, Western blotting for ATP5A in the hearts from 53-week-old mice. GAPDH was blotted as the control. C, summary graph of the ATP5A/GAPDH band ratio examined in (B). D, co-immunostaining for ATP5A and Tom20 in the hearts from 53-week-old mice. Nuclei were counterstained with DAPI. Scale bar: 20 μm. E, summary graph of the percentage of ATP5A-positive area examined in (D). F, Western blotting for Ub in the mitochondrial fraction of the hearts from 53-week-old mice. Tom20 was blotted as the loading control. G, summary graph of Ub bands density. A.U.: arbitrary unit. H, Immunostaining for a senescence marker SAβ-Gal in the hearts from 53-week-old mice. Scale bar: 20 μm. I, summary graph of the percentage of SAβ-Gal-positive area. The data in each graph are shown as the mean ± SD. One-way ANOVA (C) or t test (E, G, and I) was used to compare the data between groups. ∗∗∗$p \leq 0.001$ versus AAV-GFP; §§§$p \leq 0.001$ versus control. AAV, adeno-associated virus; cKO, conditional knockout; GFP, green fluorescent protein; Ub, ubiquitin.
Considering the clinical implication of Parkin expression supplementation in patients with heart failure caused by reduction or loss of RhoA, we additionally examined the effect of the *Parkin* gene transfer on RhoA cKO mice when cardiac function was mildly impaired. After AAV-Parkin-T2A-GFP was administered once in 30-week-old RhoA cKO mice, the impairment of LVEF tended to be prevented for 15 weeks after the administration (Fig. S4A), and the lifespan was significantly prolonged compared with after AAV-GFP administration (Fig. S4B). Parkin expression in 50-week-old mice after a single AAV-Parkin-T2A-GFP administration was higher than after AAV-GFP administration, whereas the expression was lower than after AAV-Parkin-T2A-GFP administration twice (Fig. S4, C–F). The histological analysis showed that dysregulation of myocardial tissue and the degree of fibrosis were similar between mice treated with AAV-Parkin-T2A-GFP and mice treated with AAV-GFP (Fig. S4, G–I). However, cardiac senescence determined by senescence-associated β-galactosidase and mitochondrial function evaluated by ATP5A expression were significantly improved after a single administration of AAV-Parkin-T2A-GFP compared with AAV-GFP administration (Fig. S4, J–O), suggesting the benefit of the *Parkin* gene transfer in RhoA cKO mice even after mild heart failure begins.
## Reduced RhoA expression in aged patients with idiopathic dilated cardiomyopathy
There is no definitive knowledge about the RhoA expression in aged patients who suffer from heart failure without known hereditary gene mutations. In this context, we examined the cardiac RhoA expression in the heart samples obtained from adult patients with severe heart failure caused by idiopathic dilated cardiomyopathy (DCM). The heart samples were obtained at the time of heart transplantation. The clinical characteristics of the patients are shown in Table 1. Because all patients underwent LV assist device (LVAD) implantation prior to heart transplantation, the data in Table 1 were obtained just before LVAD implantation. The average period between LVAD implantation and heart transplantation was 4.4 ± 0.9 years. Cardiac RhoA expression was significantly decreased in patients with idiopathic DCM compared with control subjects (average age: 38.5 ± 10.8 years; male/female (n): $\frac{12}{3}$) who died accidentally without cardiovascular diseases (Fig. 9, A–C). Concomitant with these results, the significant reduction of Parkin expression was observed in the hearts of patients with DCM (Fig. 9, A–C). These findings may validate our hypothesis that reduced RhoA expression in the heart attenuates Parkin expression, not only in mice but also in humans. In contrast to RhoA and Parkin expressions, PINK1 expression in the heart was almost equal between DCM patients and control subjects (Fig. 9, B and C). We confirmed the severe myocardial damage and fibrosis in patients with DCM by histological analysis (Fig. 9, D–F). TEM also detected an abundance of disrupted mitochondria in the hearts of patients with DCM compared with control subjects (Fig. 9G). Finally, we found the significant decrease in ATP5A expression in the hearts of patients with DCM (Fig. 9, H and I). These results support our conclusion that RhoA plays a role in cardiac mitochondrial function via Parkin and that the defect of RhoA expression results in mitophagy dysregulation, leading to accelerated cardiac senescence and heart failure. Table 1Clinical characteristics of DCM patientsTotal (n)15Age (year)42.2 ± 11.6Male/Female (n)$\frac{11}{4}$BMI (kg/m2)21.9 ± 3.3Hemoglobin (g/dl)12.5 ± 1.6Blood serum sample data Albumin (g/dl)3.7 ± 0.7 LDH (IU/l)227 ± 49 T-Bil (mg/dl)1.4 ± 0.7 Creatinine (mg/dl)1.01 ± 0.19 BNP (pg/ml)881 ± 622Echocardiography LVDd (mm)74 ± 10 LAD (mm)54 ± 10 LVEF (%)16 ± 8The data are shown as the mean ± SD.Abbreviations: BMI, body mass index; BNP, brain natriuretic peptide; LDH, lactate dehydrogenase; T-Bil, total bilirubin. Figure 9Reduced RhoA and Parkin expressions and impaired mitochondrial function in patients with idiopathic DCM.A, qPCR analysis of gene expressions of RHOA and PARK2, which encodes Parkin, in the human heart. Control heart samples were obtained from subjects who died accidentally, and DCM heart samples were obtained at the time of heart transplantation. GAPDH gene expression was used as the control. B, Western blotting for RhoA, Parkin, and PINK1 in the human heart. GAPDH was blotted as the loading control. C, summary graphs of the band ratios of RhoA, Parkin, and PINK1 to GAPDH examined in (B). D and E, H-E staining and Picro-sirius red staining of the human heart. Scale bar: 100 μm. F, summary graph of the percentage of cardiac fibrosis examined in (E). G, TEM images of the human heart. White and yellow arrowheads indicate normal and disrupted mitochondria, respectively. Scale bar: 1 μm. H, Western blotting for ATP5A in the human heart. I, summary graphs of the ATP5A/GAPDH ratio examined in (H). The data in each graph are shown as the mean ± SD. Comparisons of the data between the groups were conducted using t test. ∗∗∗$p \leq 0.001$ versus control. DCM, dilated cardiomyopathy; H-E, hematoxylin and eosin; PINK1, PTEN-induced putative kinase 1.
## Discussion
This study provides an important insight into the function of RhoA in the aging heart, as well as the molecular mechanism by which RhoA regulates cardiac function through Parkin-mediated mitochondrial homeostasis. RhoA cKO mice showed earlier death from around 30 weeks of age and a dramatic reduction of LVEF with accelerated senescence and age-dependent cardiac fibrosis. Concomitant with the severe deterioration of cardiac function, we found that loss of RhoA in the heart induced excess accumulation of severely damaged mitochondria in cardiomyocytes. In patients with idiopathic DCM who had no hereditary gene mutations, both RhoA and Parkin expressions in the heart were markedly reduced, and the morphology of the cardiac mitochondria was disturbed. This suggests that RhoA has cardioprotective effects and is crucial for the maintenance of healthy mitochondria, resulting in the prevention of heart failure with aging.
In support of our findings, another research group has recently reported that in myocardial infarction, cardiac RhoA signaling plays a role in mitochondrial quality control by regulating the function and expression of Parkin and PINK1, a protein kinase that phosphorylates and activates Parkin [24]. In our study, we further revealed the mechanism of RhoA in the regulation of Parkin expression through N-Myc in cardiomyocytes. N-*Myc is* a member of the Myc family. It is a transcription factor that is critically involved in diverse physiological and pathological events, including neuronal development and tumor progression [25, 26]. This protein binds to the E-box motif at the Parkin transcription initiation site and transcriptionally inhibits Parkin expression in neuroblastoma cell lines [22]. In this study, we observed downregulation and upregulation of N-Myc expression in RhoA-intact and RhoA-depleted cardiomyocytes, respectively. N-Myc expression has also been shown to be suppressed by its phosphorylation and subsequent ubiquitination [23, 27]. GSK-3β was identified to be a kinase that phosphorylates N-Myc, but other kinases that contribute to N-Myc phosphorylation have not been well documented. Using a ROCK inhibitor Y-27632, we discovered that ROCK, which is an effector of RhoA, functions as a kinase that phosphorylates N-Myc to reduce its expression. Thus, we propose that the RhoA–ROCK axis negatively regulates N-Myc to maintain sufficient Parkin expression in cardiomyocytes.
As for ROCK, there are two isoforms ROCK1 and ROCK2, and the disruption of both ROCK isoforms has been reported to be cardioprotective by promoting autophagy and reducing cardiac fibrosis during aging [28]. ROCKs are well-known effectors of RhoA, while other proteins, such as mDia, also function downstream of RhoA. Inhibition of mDia in the heart markedly suppressed the cardiac function and induced heart failure [29]. In addition, a single deletion of ROCK2 in cardiomyocytes was profibrotic and reduced autophagy [28], suggesting that only ROCK1 deletion is favorable for cardiomyocytes and overwhelms the ROCK2 deletion-mediated deteriorative cardiac phenomena. Collectively, because RhoA regulates a variety of molecules including ROCKs, it might be reasonable that the phenomena observed in RhoA cKO mice are different from those in mice in which double cardiac ROCKs are ablated.
Mutations in the *Parkin* gene are intimately related to familial Parkinson’s disease (PD) [30]. PD is the common neurodegenerative disorder that involves loss of dopaminergic neurons in the substantia nigra [31, 32]. In addition to neuronal system dysfunction in PD, PD is associated with the risk of cardiovascular disease, including congestive heart failure [33]. Although the role of Parkin in the brain has been extensively studied [34, 35], the understanding of Parkin regulation in the aging heart downstream of RhoA remains elusive. Mitophagy is the system that clears the damaged mitochondria in various cell types, including cardiomyocytes, and is fundamental for constitutive mitochondrial housekeeping to maintain cardiac homeostasis and prevent heart failure [36]. Several reports have demonstrated the pathophysiological importance of mitophagy in the heart, in which Parkin exerts cardioprotection in response to ischemic stress [37, 38]. In addition, PINK1 contributes to the maintenance of cardiac function because PINK1 knockout mice develop LV dysfunction and pathological cardiac hypertrophy with impaired mitochondrial function [39]. Although the present study showed that loss of RhoA in cardiomyocytes attenuated Parkin expression, PINK1 expression was not changed. Moreover, the mitochondrial fission marker Drp1 and its phosphorylated form were not disturbed in the RhoA cKO hearts and RhoA-knockdown HL-1 cells. These findings suggest that RhoA specifically regulates Parkin in cardiomyocytes, independent of PINK1, and that it does not affect mitochondrial biogenesis.
Parkin is a cytosolic E3 Ub ligase that selectively ubiquitinates proteins located on dysfunctional mitochondria for mitophagy [40, 41]. To prevent unnecessary cell death, dysfunctional mitochondria, which are harmful to cells, should be cleared, and mitophagy is one of the systems responsible for this clearance. In our study, the hearts from 18-week-old RhoA cKO mice had normal mitochondria, while the hearts from 55-week-old RhoA cKO mice had swollen and disorganized mitochondria with broken cristae, which was quite different from the hearts from 55-week-old control mice that had morphologically normal mitochondria. Our data suggest that RhoA deficiency in the heart causes a defect in the clearance of dysregulated mitochondria due to reduced Parkin expression. Similar to RhoA cKO mice in the present study, young 12-week-old Parkin−/− mice had normal cardiac function under baseline conditions in a previous study. However, Parkin−/− mice were quite sensitive to the cardiac stress induced by myocardial infarction [42]. According to another previous study, mitochondrial DNA mutations in mice accelerated cardiac aging, and overexpression or deletion of Parkin in the mice did not rescue or worsen the cardiac phenotype [43]. These results differ from ours; however, different mouse models may demonstrate different degrees of mitochondrial damage and different regulatory mechanisms to compensate for the defect in mitophagy in the aged heart, which may affect the rate of transition to cardiomyopathy.
One advantage of our study is that we demonstrated the significant reduction of both RhoA and Parkin expressions in patients with DCM compared with normal subjects. Although it might be difficult to strictly identify which of RhoA or Parkin reduction is the primary and specific cause of DCM, it is possible to interpret that RhoA is involved in cardiac homeostasis cooperatively with Parkin. Mitochondrial morphology and function as well as mitophagy were disturbed in patients with DCM in the present study. Furthermore, several novel functions of RhoA, which are mediated by Parkin, were observed not only in the mouse heart but also in the human heart. To date, several gene mutations associated with heart failure have been listed [44, 45]. Loss or mutation of the RHOA and PARK2 genes can be added to the list in line with the findings from our and other research groups.
In conclusion, we showed the functional role of RhoA in regulating Parkin expression through ROCK and N-Myc and Parkin-dependent mitophagy for the clearance of damaged mitochondria in the heart, resulting in the maintenance of mitochondrial homeostasis and prevention of cardiac senescence (Fig. S5). Thus, we conclude that loss of RhoA in the heart induces heart failure due to early cardiac senescence and cardiomyopathy. Further understanding of RhoA signaling in the aged heart will help to develop future therapies for the prevention and treatment of heart failure.
## Generation of RhoA cKO mice
RhoA-floxed mice (RhoAfl/fl: C57BL/6 background), in which exon 3 of the *Rhoa* gene was flanked by loxP sites, were generated and used in our previous study [46]. The mice were then mated with C57BL/6 mice expressing Cre recombinase under the control of the α-myosin heavy chain promoter (Myh6-Cre; Jackson Laboratory) to generate cardiomyocyte-specific RhoA cKO mice. In the Myh6-Cre mice, Cre exerts its recombination activity specifically in cardiomyocytes but not in other tissues such as the liver, lung, skeletal muscle, and spleen [47], and the recombinase functions from embryonic day 9.5 [48]. Mice harboring RhoAfl/fl alleles alone were used as controls. The mice were housed in specific pathogen-free conditions at the Research Centre for Animal Life Science of Shiga University of Medical Science. All animal protocols were in accordance with institutional guidelines, including Animal Research Reporting of In Vivo Experiments (ARRIVE) guidelines, and were approved by the Animal Care and Use Committee of Shiga University of Medical Science (No. 2020-9-8).
## Human heart sample collection
All protocols using human heart samples were approved by the Research Ethics Committee of Osaka University Graduate School of Medicine and Shiga University of Medical Science and conformed to the principles of the Declaration of Helsinki. Heart tissues were obtained from [1] subjects who died accidentally without cardiovascular diseases and were sent to Division of Legal Medicine, Shiga University of Medical Science, for forensic autopsy and [2] patients with idiopathic DCM at the time of heart transplantation during the period of November 2017 through June 2021. All of the patients provided written informed consent for the use of heart tissues in this study.
## Cell culture, siRNA transfection, and plasmid transfection
HL-1 cells (a gift from Dr Ayako Takeuchi, Faculty of Medical Science, University of Fukui, Japan) were cultured in Claycomb Medium supplemented with $10\%$ fetal bovine serum, 2 mmol/l L-glutamine, 100 μg/ml penicillin/streptomycin, and 0.1 mmol/l norepinephrine (Nacalai Tesque) as previously described [49]. siRhoA, siN-Myc, and negative control (scramble) RNA were synthesized using the CUGA7 in vitro Transcription Kit (Nippon Gene). The siRNA sequences were as follows: siRhoA #1 (5′-GACAUGCUUGCUCAUAGUCUU-3′), siRhoA #2 (5′-GCAGAGAUAUGGCAAACAG-3′), siN-Myc (5′-GCUCUUGCGGCCAGUAUUA-3′), and scramble RNA (5′-CAGUCGCGUUUGCGACUGG-3′). HL-1 cells were plated 24 h prior to siRNA transfection (2 μmol/l each), which was introduced using Lipofectamine RNAiMAX reagent (Invitrogen). After 48 h of siRNA transfection, HL-1 cells were used for experiments. The duration of the treatment with 10 μmol/l Y-27632 was 1 h. As for the transfection of plasmids into the cells, Lipofectamine 2000 reagent (Invitrogen) was used.
## Echocardiographic analysis
Echocardiography was performed on 9-, 13-, 21-, 29-, 37-, 45-, and 53-week-old mice. Mice were anesthetized with isoflurane/air mix (induced at $2\%$ and maintained at ∼$1\%$ by isoflurane). LV dimension and cardiac function were assessed by transthoracic ultrasonography using the Vevo 2100 system (VisualSonics Inc). Mouse hearts were imaged in the two-dimensional parasternal long-axis (B-mode) and short-axis (M-mode) for cardiac systole evaluation. All measurements of cardiac anatomic and functional parameters were as described previously [50].
## BP measurement
Arterial BP and HR of conscious mice were assessed using the noninvasive plethysmographic tail-cuff method (model BP-98-AL; Softron). Mice were weighed and warmed at 37 °C in a cylindrical thermostat supplemented on the BP-98-AL machine before and during the assessment. Measurements were taken at 2-min intervals, and an average of five BP and HR measurements was taken as the true BP and HR of each mouse, respectively.
## Histological analysis of the heart
Fresh mouse and human hearts were fixed with $4\%$ paraformaldehyde and $10\%$ formaldehyde, respectively, followed by embedding in paraffin blocks overnight. Otherwise, the hearts were frozen in water-soluble medium (Surgipath FCS22; Leica Biosystems) in liquid nitrogen. Formalin-fixed paraffin–embedded heart tissues were sectioned at 4-μm thickness using a microtome (Leica Biosystems). The frozen heart tissues were sectioned at a thickness of 10 μm using a cryostat (Leica CM1800; Leica Biosystems) at −20 °C. The sections were layered on poly-L-lysine-coated slides. The formalin-fixed paraffin–embedded heart sections were deparaffinized before being subjected to H-E or Picro-Sirius red staining [51].
## Confocal microscopy
Cells on poly-L-lysine-coated cover slides were fixed with $4\%$ paraformaldehyde and permeabilized with $0.2\%$ Triton X-100 in phosphate-buffered saline (PBS) for 30 min at 37 °C. Primary antibodies were applied in $2\%$ bovine serum albumin (BSA) plus $1\%$ or $3\%$ skimmed milk in PBS overnight, followed by a 1-h incubation with the fluorescent dye-labeled secondary antibody. The cells were imaged using the Leica SP8 X confocal microscope (Leica Microsystems). Similar staining techniques were performed on cross-sections of the frozen hearts. The percentage of positive area in the images was quantified using ImageJ software (National Institute of Health). After converting the composite fluorescent image (with three colors) into individual RGB images, the individual threshold level for each fluorescent marker was determined to generate the percentage of fluorescence positive area for each marker.
## TEM
Mouse and human hearts were fixed with $2.5\%$ glutaraldehyde in 0.1 mol/l cacodylate buffer; postfixed in $1\%$ osmium tetroxide; treated with $0.5\%$ tannic acid, and $1\%$ sodium sulfate; cleared in 2-hydroxypropyl methacrylate; and embedded in LX112 (Ladd Research). Sections were mounted on copper slot grids coated with parlodion and stained with uranyl acetate and lead citrate for examination on the H-7500 electron microscope (Hitachi High-Tech Corporation).
## Mitophagy assay
Viable cells were stained with 100 nmol/l Mitophagy Dye (Dojindo Laboratories) for 30 min and washed in Hank’s Hepes buffer solution. The attached cells were stimulated with 10 μmol/l carbonyl cyanide m-chlorophenyl hydrazone (Nacalai Tesque) for 24 h before observation, as described in the manufacturer’s protocol. Fluorescent images were obtained using the Leica SP8 X confocal microscope.
## Cellular senescence detection
Frozen sections were fixed with $4\%$ paraformaldehyde for 3 min and incubated with SPiDER-βGal solution (Dojindo Laboratories) at 37 °C for 30 min. After removing the solution, the sections were washed with PBS and mounted with mounting medium including DAPI (Vector Laboratories).
## Isolation of the mitochondrial fraction
Cells were washed in ice-cold PBS and resuspended in subcellular fractionation buffer containing 20 mmol/l Hepes (pH 7.4), 10 mmol/l KCl, 2 mmol/l MgCl2, 200 mmol/l sucrose, 1 mmol/l ethylenediaminetetraacetic acid, 1 mmol/l ethyleneglycol tetraacetic acid, 2 mmol/l phenylmethylsulfonyl fluoride (PMSF), and 1 mg/l leupeptin [52]. The hearts extracted from mice were washed with PBS, transferred into the subcellular fractionation buffer, and homogenized in the buffer with 15 strokes using the Potter-Elvehjem tissue homogenizer (DWK Life Sciences). Cell and heart samples were then passed through a 26-gauge needle attached to a 1-ml syringe ten times for lysis, followed by centrifugation at 800g at 4 °C for 5 min. The supernatant including the mitochondria was transferred into a new tube and centrifuged at 10,000g at 4 °C for 5 min. The pellets were resuspended in radioimmunoprecipitation assay buffer containing 50 mmol/l Tris-HCl (pH 7.5), 150 mmol/l NaCl, $0.5\%$ sodium deoxycholate, $0.1\%$ sodium dodecyl sulfate (SDS), $1\%$ Nonidet P-40, 1 mmol/l PMSF, and 1 μg/ml leupeptin to obtain the mitochondrial fraction.
## Western blotting
Mouse and human hearts were homogenized mechanically in radioimmunoprecipitation assay (RIPA) buffer containing 50 mmol/l Tris-HCl (pH7.5), 150 mmol/l NaCl, $0.5\%$ sodium deoxycholate, $0.1\%$ SDS, $1\%$ Nonidet P-40, 1 μg/ml aprotinin, 1 μg/ml leupeptin, 1 mmol/l PMSF, 5 mmol/l NaF, and 1 mmol/l Na3VO4. HL-1 cells were also lysed in RIPA buffer. The homogenates and lysates were centrifuged at 14,000 rpm for 15 min, and the supernatant was used for further analysis. After the protein concentration in the supernatant was measured by Quick Start Bradford (Bio-Rad Laboratories) using BSA standards, 10 μg of protein samples were separated by $10\%$ or $12\%$ SDS-polyacrylamide gel electrophoresis and transferred to a polyvinylidene difluoride membrane (Bio-Rad Laboratories). The membrane was then blocked for 1 h at room temperature in $5\%$ BSA or $5\%$ skimmed milk in Tris-buffered saline with Tween 20. The membrane was incubated with primary antibody overnight in $5\%$ skimmed milk at 4 °C, followed by incubation with horseradish peroxidase (HRP)-labeled secondary antibody (GE Healthcare) for 1 h in $5\%$ skimmed milk. The membrane was incubated with HRP substrate (Luminata Forte) for 5 min and observed on a luminescent image analyzer (Fusion Solo 6S Edge; Vilber Bio imaging). The band densities were analyzed using ImageJ software.
## Primary antibodies
The detailed information of the primary antibodies used in this study is summarized in Table S1.
## Secondary antibodies
We used the following secondary antibodies: Alexa Fluor 488 goat anti-mouse polyclonal secondary antibody (Cat. No: A-11001, Thermo Fisher Scientific; 1:1000 dilution), Alexa Fluor 488 goat anti-rabbit polyclonal secondary antibody (Cat. No: A-11008, Thermo Fisher Scientific; 1:1000 dilution), Alexa Fluor 555 goat anti-rabbit polyclonal secondary antibody (Cat. No: A27039, Thermo Fisher Scientific; 1:1000 dilution), wheat germ agglutinin conjugated with tetramethylrhodamine (Cat. No: W849, Thermo Fisher Scientific; 5 μg/ml solution), DAPI (Cat. No: NA065, Dojindo Molecular Technologies; 1 μg/ml solution), HRP-linked donkey anti-rabbit IgG secondary antibody (Cat. No: NA934V, GE Healthcare Life Sciences, 1:2000 dilution for Western blotting), and HRP-linked donkey anti-mouse IgG secondary antibody (Cat. No: NA931V, GE Healthcare Life Sciences; 1:2000 dilution for Western blotting).
## ROCK kinase assay
HL-1 cells (6 × 105 cells) were treated with or without 10 μmol/l Y-27632 for 1 h and were lysed in RIPA buffer. After centrifugation at 16,000g, the clear supernatant was applied to Cyclex Rho-kinase Assay kit (Medical & Biological Laboratories) for measurements of the kinase activity. The procedures were carried out according to the manufacturer’s instructions, and the optical absorbance was measured at 450 nm with MultiSkan JX (Thermo Fisher Scientific). The background-subtracted values were used for data presentation.
## RNA isolation and quantitative PCR
Total RNA was isolated from human heart samples using TRIzol RNA isolation reagent (Thermo Fisher Scientific). cDNA was synthesized by ReverTra Ace quantitative PCR RT Master Mix with gDNA Remover (Toyobo). After the reverse transcription, quantitative PCR was performed using LightCycler Instrument (Roche Diagnostics). The PCR data were analyzed using standard curve method, and human GAPDH mRNA expression level was used as the internal control. The primers for the gene amplifications were as follows: RHOA forward (5′-AGCCTGTGGAAGACATGCTT-3′), RHOA reverse (5′-TCAAACACTGTGGGCACATAC-3′), PARK2 forward (5′-CAAGACTCAATGATCGGCAG-3′), PARK2 reverse (5′- ACACACTCCTCTGCACCATA), GAPDH forward (5′-AGCCACATCGCTCAGACAC-3′), GAPDH reverse (5′-GCCCAATACGACCAAAATCC-3′).
## AAV serotype 6-mediated Parkin expression
Viral particles containing the AAV serotype 6 vector harboring the Parkin and EGFP genes linked with the T2A sequence (AAV-Parkin-T2A-GFP) driven by the cytomegalovirus promoter were generated using Vector Builder. The AAV serotype 6 vector carrying only the EGFP gene (AAV-GFP) was similarly generated and used as the control. For the recombinant AAVs manufacturing, the plasmid carrying the cDNA of Parkin-T2A-GFP or GFP was transfected into HEK293T packaging cells, together with Rep-cap plasmid and helper plasmid (Vector Builder) encoding adenovirus genes (E4, E2A, and VA) that mediate AAV replication. After a short incubation period, viral particles were harvested from the cell lysate and concentrated by polyethylene glycol precipitation. The viral particles were further purified and concentrated by cesium chloride gradient ultracentrifugation. For measurements of the AAVs titer, digital PCR-based approach was applied. Parkin and GFP expressions in HL-1 cells and mouse hearts were performed by infecting the above viral particles. For the administration of AAV into mice, 1 × 1011 viral particles of AAV-Parkin-T2A-GFP or AAV-GFP were intravenously injected through the mouse tail vein after mice were anesthetized with $2\%$ isoflurane. Following AAV serotype 6 injection, HL-1 cells, as well as mouse hearts and lungs, were harvested and isolated, respectively, at the appropriate time points for further analysis.
## Statistical analysis
All numerical values are shown as the mean ± standard deviation. All experiments were performed at least three times independently. Statistical differences between experimental groups were evaluated by two-tailed unpaired Student’s t test or one-way or two-way analysis of variance with Bonferroni’s multiple comparison test. The Kaplan–*Meier analysis* was conducted to evaluate the lifespan of mice in the two groups. The survival curves were compared using the log-rank test. For all analyses, $p \leq 0.05$ was considered statistically significant.
## Data availability
All of the data are contained within the article and are available from the corresponding author on reasonable request.
## Supporting information
This article contains supporting information.
Supporting Table S1 and Figures S1–S5
## Conflict of interest
The authors declare that they have no conflicts of interest with the contents of this article.
## Author contributions
J. E. C. S., Akio Shimizu, M. R. M., D. P. Z., M. T., Y. I., and H. O. data curation; J. E. C. S., Akio Shimizu, M. R. M., D. P. Z., and H. O. formal analysis; J. E. C. S., Akio Shimizu, M. R. M., D. P. Z., L. K. C. N., M. R. K., W. W. T., S. C., M. T., Y. I., and Akira Sato investigation; J. E. C. S., Akio Shimizu, M. R. M., and H. O. methodology; J. E. C. S. and H. O. visualization; J. E. C. S., Akio Shimizu, and H. O. writing–original draft; J. E. C. S., Akio Shimizu, M. H., S. M., and H. O. writing–review & editing; M. T., Y. I., M. H., and S. M. resources; Akio Shimizu, Akira Sato, and H. O. funding acquisition; H. O. conceptualization; H. O. supervision.
## Funding and additional information
This study was supported in part by Grants-in-aid for Scientific Research <KAKENHI> from $\frac{10.13039}{501100001691}$Japan Society for the Promotion of Science for Akio Shimizu [21K06854], Akira Sato [21K09419] and H. O [17K08627 and 20K08489]; $\frac{10.13039}{100007449}$Takeda Science Foundation, $\frac{10.13039}{100007428}$The Naito Foundation, $\frac{10.13039}{501100008667}$SENSHIN Medical Research Foundation, and $\frac{10.13039}{100008732}$Uehara Memorial Foundation for H. O.
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|
---
title: 'Understanding online health information seeking behavior of older adults:
A social cognitive perspective'
authors:
- Xiumei Ma
- Yunxing Liu
- Pengfei Zhang
- Rongtao Qi
- Fanbo Meng
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10020694
doi: 10.3389/fpubh.2023.1147789
license: CC BY 4.0
---
# Understanding online health information seeking behavior of older adults: A social cognitive perspective
## Abstract
### Introduction
Online health information seeking has been verified to play a crucial role in improving public health and has received close scholarly attention. However, the seeking behavior of older adults, especially the underlying mechanism through which they are motivated to seek health information online, remains unclear. This study addresses the issue by proposing a theoretical model leveraging social cognitive theory.
### Methods
IT self-efficacy and IT innovativeness were identified as personal factors and professional support and social support were identified as environmental factors. We conducted a survey that included 347 older people in China and examined the research hypotheses with a structural equation model.
### Results
IT self-efficacy and IT innovativeness facilitate older adults to seek health information online by increasing their perceived benefit of using the internet. Additionally, professional support and social support enhanced older adults' online seeking behavior by promoting their health awareness. We also found that perceived benefit displayed a stronger impact than health awareness on older adults' behavior related to searching for health information online.
### Conclusion
This study reveals that IT self-efficacy, IT innovativeness, professional support, and social support will promote older adults to seek health information online by enhancing their health awareness and perceived benefit. The findings of this study provide significant theoretical and practical implications.
## 1. Introduction
The rapid development of information technology has made the internet the most popular source of health information based on convenient access and quick response, and the number of people making use of the internet to search for health information has continued to grow. National surveys conducted in America, German, China and other countries indicated that a large proportion of internet users frequently searched for health information, totaling more than $50\%$ of the respondents in those countries [1]. Especially in the context of the COVID-19 epidemic, the internet has become the primary means as well as the best way to obtain health information.
Health information seeking behavior has been the subject of scholarly attention since the 1960's. Recently, the advent of the information technology age and internet growth have focused the spotlight on online information seeking concerning health-related topics. The term online health information seeking refers to individuals using the internet to search for find information about their health, risks, illnesses, and health-protective behaviors. Ease of access, immediacy, and diversity of information sources are all factors that have contributed to individuals choosing online information as their preferred source for health information [2]. In fact, empirical evidence demonstrates significant impacts of internet-based health information on individuals' physical and psychological health [3, 4].
In particular, scholars are paying increasing attention to older adults' behaviors related to seeking health information online (5–7). According to the World Health Organization, the population of adults over age 60 will reach 2.1 billion by 20501 *As a* result of the aging of the world's population overall, the tremendous growth of healthcare and health information needs has become a significant issue. Although seeking health information online is beneficial in addressing or solving health problems, its popularization among older adults faces many challenges due to these individuals' generally lower cognitive and technical abilities. For example, many older adults suffer a lack of technical skills and internet search skills that impedes them from using the internet to access information [8]. In addition, some research has indicated that older adults tend to rely on their cognitive ability and existing medical knowledge when seeking health information; however, their cognitive abilities have been shown to typically decline with age [9]. Therefore, identifying factors that can support older adults to search for health information online has major implications in both practice and research.
Although prior studies have made a great effort to identify the determinants of online health information seeking behavior (10–12), investigations into better ways to support and encourage older adults' search behavior remain scarce, and the underlying mechanisms are yet unclear. For example, some researchers identified such instrumental factors as information quality, trustworthiness, and utility of information as the dominant predictors of online health information seeking [1]. However, in the case of older people, obstacles to searching for information online include a having negative attitude about the internet, entertaining poor health beliefs, and suffering IT deficiencies and low support from others [5]. Thus, instead of concentrating on factors related to the health information itself, studies should pay more attention to the individual and environmental factors that directly affect older adults, such as individual cognition, IT resources, and external support. Furthermore, attaining deeper understanding of how to support older adults in searching for health information online requires exploring the mechanism through which these factors influence their health information seeking behavior.
This study addressed the above issues by drawing upon social cognitive theory to develop a theoretical research model. Two reasons support the choice of this theory for the current study. First, social cognitive theory has been widely used to explain and predict individuals' behavior and decision-making, especially in the contexts of health behaviors and information behaviors [13, 14]. Second, social cognitive theory illustrates how personal factors and environmental factors simultaneously influence individual behavior [15], which fits well with the current study's research objective. In this study, we conceptualized IT efficacy and IT innovativeness as observable personal factors and conceptualized social support and professional support as the environmental factors of interests. Next, we examined how both types of factors influenced the online search behavior of older adults seeking health information through increasing their perceived benefit of using internet and their health awareness.
This study makes several contributions to the field. For example, the findings provide a new understanding of how older adults seek for health information online from the perspective of social cognitive theory. In particular, this work is one of the first to empirically investigate how the targeted information seeking behavior is influenced by older adults' individual IT capacity and environmental support. In addition, this study clarifies the influencing mechanism through introducing the specific cognitions of perceived benefit and health awareness. This outcome fills the gap left by previous studies that focused primarily on exploring influencing factors while overlooking the underlying mechanism. Lastly, this study verifies specific personal factors and environmental factors affecting older adults based on their characteristics in the context of searching for health information online. This aspect of the current study thus complements the existing research while also providing practical suggestions on how to improve older adults' efforts on looking for health information online.
## 2.1. Online health information seeking of older adults
The development of information technology has caused the internet to become the main source for health information seeking [16]; as a result, many studies have focused on online health information seeking behavior. Scholars have used multiple perspectives to investigate this topic and verified its crucial role in healthcare. For example, Zhao and Zhang [17] found that health-seeking on social media could fill the demand for health information while also providing social and emotional support via peer-to-peer interaction.
Notably, the growth in the number of older adults and their high prevalence of health problems has attracted scholars' close attention to older adults' information seeking behavior. However, even though the positive results reported for online health information seeking, studies found that using the Internet to obtain health information is comparatively low among older adults [18]. For example, research found that older adults have less trust in the Internet source and present negative attitudes toward health information from Internet [19]. Moreover, older age and reduced cognitive abilities hinder older adults' access to the Internet and online health information [20]. In fact, a current study illustrated that older adults relied on medical personnel, family and friends, and health brochures rather than the Internet as main sources of health information [7]. Existing findings indicate that it is necessary to help older adults better utilize the Internet to search for health information.
Recent studies have made great efforts to explore the motivators facilitating online health information seeking behavior of older adults. For example, Oh and Lim [21] found that communication with medical professionals significantly accelerated use of the Internet by older people to search for health information. Weber et al. [ 7] found that older adults' seeking behavior is related to their lifestyle, where the Average Family Person and the Sociable Adventurer use the internet more often for health information. In addition, research has found that the health condition, especially a recent diagnosis of cancer, positively facilitated older people to seek health information on the Internet [22]. An empirical study by Zhu et al. [ 23] revealed that social support, and self-efficacy were necessary predictors of health information seeking for older adults with coronary heart disease.
However, although the current literature offers insight into factors influencing online health information seeking of older adults, few of the prior studies have yet clarified the influence mechanism [24]. Improving the online seeking behavior of older people is more difficult than that of younger people because of cognitive limitations, low electronic health literacy, and negative attitudes toward technology [6]. Thus, in addition to exploring the influencing factors, understanding the mechanism of influencing factors can fundamentally provide evidence for effectively promoting the online health information seeking behavior of older adults. According to previous findings, older adults' IT-related capabilities and support from their external environment are key factors affecting their behavior to search for health information online [25, 26]. Consequently, this study aimed to deeply revel how personal IT-related factors and environmental factors might influence older adults' behavior in terms of searching for health information online.
## 2.2. Social cognitive theory
Social cognitive theory, a classical theory that finds its basis in social learning theory, has been widely used to explain individual actions [27]. This theory can be referred to as ternary reciprocal determinism; in other words, individual behaviors are determined by the interaction of three factors: person, environment, and behavior [28]. In addition, these three factors can influence each other, and any two factors can influence the third factor [28]. Since Bandura originally proposed the social cognitive theory, it has received ongoing examination with a focus on various individual behaviors.
The growing prominence of health issues has led to the argument that social cognitive theory should be used to achieve a healthy society [29, 30]. In fact, social cognitive theory has been one of the most influential theories on health behavior [31]. In particular, the key construct of social cognitive theory, self-efficacy, has been incorporated into most health behavior theories [32]. Social cognitive theory addresses the environmental determinants of health as well as personal determinants and has been widely applied in the study of older adults' health behaviors and health management. For instance, based on the social cognitive theory, Borhaninejad, Iranpour [33] found that self-efficacy, social support, outcome expectations, and outcome expectancy significantly predicted diabetes self-care behaviors among the older people. In a similar vein, Zhang et al. [ 34] used social cognitive theory to investigate the impact of information communication technology usage on older adults' loneliness; in their findings, the authors identified the crucial role of self-efficacy and health awareness. Social cognitive theory was also successfully applied in predicting respiratory infection prevention among older adults [35].
Existing research suggests that social cognitive theory is suitable for the study of health behavior of older adults. Although many studies have incorporated social cognitive theory, few scholars have used social cognitive theory to explore older adults' online health information seeking. In contrast, this study focused on the determinants of older adults' behaviors related to searching for health information online, giving additional attention to individual differences and environmental uncertainties. Consequently, this paper addresses the identified research gap by proposing a research model based on social cognitive theory to understand how older adults approach seeking for health information online.
## 3. Research model and hypotheses development
Based on the social cognitive theory, this research took personal factors (self-efficacy and IT innovativeness) and contextual factors (social support and professional support) as antecedent variables in developing a research model to verify how personal and environmental factors influence older adults' online information seeking via perceived benefit and health awareness. In addition, gender, age, education level, and the existence of chronic disease were included as control variables. An illustration of the proposed research model appears in Figure 1.
**Figure 1:** *Theoretical research model.*
## 3.1. Personal factors
Self-efficacy, widely recognized as a critical factor that affects individual behavior, generally refers to the determination and belief that individuals can complete an action under specific circumstances and can also refer to an individual's assessment of self-ability [36, 37]. The current study refers to IT self-efficacy as older adults' judgment of their ability to use information technology to locate health-related information. Older people with high IT self-efficacy are likely to experience smooth, enjoyable internet interaction and will probably obtain positive outcomes [38]. Some previous studies have confirmed that self-efficacy can positively affect users' perceived value [39, 40]. In the context of obtaining health information from an online source, older adults who have mastered the necessary skills to use information technology tend to access valuable health information easily and are more likely to perceive that using the Internet to seek health information is beneficial. Thus, our first hypothesis is based on the assumption that IT self-efficacy will positively affect older adults' perceived benefits.
H1: IT self-efficacy is positively related to perceived benefit.
As another consideration, innovativeness generally refers to the degree to which a person prefers to use new technologies, products, or services [41]. IT innovativeness can be defined as the willingness of an individual to try out any new information technology [42]. For the purposes of this study, IT innovativeness as a personal trait represents older adults' tendency to focus on and accept new information technologies or new IT functions. Prior findings suggest that individuals with higher innovativeness are able to cope with a higher level of uncertainty [43]. Thus, it is reasonable to expect that a high level of IT innovativeness leads to positive experiences and outcomes from using IT. Previous research has also noted that personal innovativeness has a strong positive effect on perceived ease of use and perceived benefit [41, 44, 45]. Similarly, older people who are more willing to accept and use new information technologies will probably perceive greater benefit during the health information seeking process. These observations form the basis for our second hypothesis, as follows: H2: IT innovativeness is positively related to perceived benefit.
Perceived benefit refers to consumers' confidence that they can improve their circumstances by using certain products or services [46]. In this research, perceived benefit specifically refers to older adults' perception of positive consequences by using information technologies or the Internet to seek health information. Perceived benefit is usually regarded as relative advantages, which have the capacity to meet individuals' needs or wants and further positively influence their behavior [47]. Thus, it is reasonable to predict that when individuals perceive beneficial outcomes from certain behaviors, they are more likely to continue the behavior. Several previous studies have provided empirical evidence of perceived benefit significantly facilitating user behavior [48, 49]. In this study, when older adults perceive that using the Internet to seek health information can meet their needs conveniently and in a timely way, their seeking behavior is likely to be encouraged. Accordingly, we proposed the following in our third hypothesis: H3: Perceived benefit is positively related to online health information seeking.
## 3.2. Environmental factors
Social support has become an essential predictor of online health information seeking [23, 50]. Social support refers to people's access to various resources provided by others through interpersonal communication, including support concerning information, assistance, and comfort [51]. In this study, we specifically define social support as resources and support from family, friends, and other non-professional social peers. Scholars have widely verified that social support exerts a significant impact on individuals' health attitudes and decisions. For example, individuals will be more aware of making healthier lifestyle decisions when they receive social support through interpersonal communication [52]. In addition, Choi [53] discovered that social support, such as the care of family members, will encourage individuals to actively participate in their own health maintenance. Health awareness is the consciousness to maintain one's health; thus, it is reasonable that social support is positively related to health awareness, leading to the formulation of the study's fourth hypothesis, as follows.
H4: Social support is positively related to health awareness.
Professional support means that individuals obtain help from professionals who have received professional training or education in medicine and healthcare [54]. In this study, it distinguishes social support and refers to support and help from medical professionals. Since health professionals are required to master qualified and effective health knowledge, they play a crucial role in helping individuals establish positive health views, change health behaviors, and attain improved health outcomes [55, 56]. When individuals receive professional support from doctors, their health problems are likely to be effectively resolved. Especially in the case of older adults, communication with health professionals enables them to access reliable medical knowledge and update their understanding of health issues, which will likely enhance their health awareness. Based on these ideas, we proposed the following: H5: Professional support is positively related to health awareness.
Health awareness is generally used to measure the readiness of individuals to take health actions [57]. People with higher health awareness tend to be more active and pay more attention to health information than their peers [58, 59]. Previous studies have identified health awareness as a driver leading to healthy lifestyle change and able to affect health-related behaviors [60, 61]. In addition, some scholars have suggested that the higher the level of an individual's health awareness, the more concerned the individual will be about his or her health, motivating the person to further engage in health-promoting behaviors [62]. For the purposes of this study, online health information seeking is regarded as a kind of health behavior. Thus, it is reasonable to argue that people with higher health awareness will more actively seek health information. This argument supports the following proposal: H6: Health awareness is positively related to online health information seeking.
## 4.1. Data collection
To test the research model, we collected data via a survey that targeted to older adults who had experience using information technology to seek health information. Because the survey was conducted in mainland China, we employed the backward translation method to translate the questionnaire into the Chinese language. Before data collection began, a pilot test was conducted to ensure that the measurement would be clear and understandable to participants. According to the pilot test results, along with comments and feedback from the interviewees, we modified some descriptions and wording in the questionnaire to make it easier to understand while maintaining the original meaning.
Before surveying, we submitted the application to the university and received approval from the academic board. We then distributed the modified questionnaires to older people in some residential communities in Northeast China. We worked with neighborhood committees who assisted us in recruiting participants, instructing participants to fill in the questionnaire, and collecting the responses. Before joining the study, all participants were informed the purpose of the survey and voluntarily choose whether to participate. To ensure the accuracy and validity, interviewees in the polit test were excluded. Once they agreed to participate, participants were given a paper questionnaire, along with a research staff who explains precautions and assists in filling it. The survey was anonymous, and participants were assured that the data collected will be kept confidential and used only for academic research, which encourages participants to answer the questionnaire as truthfully as possible. The survey was conducted on-site, where participants were rewarded with two eggs after completing the questionnaire. Completed questionnaires were collected and sent back directly to our research team for quality review and data analysis.
Out of 500 questionnaires that were distributed, 405 questionnaires were obtained after removing incomplete responses, rendering an $81\%$ response rate. Since the objective of this study concerned the older segment of the population, respondents who were younger than 55 years old were excluded. The rationale is that the legal retirement age for females in *China is* 55, which is also widely identified as the age of older adults in numerous studies (63–65). Additionally, the questionnaire began with the screening question, “Have you experience in seeking health information using information technology or on the Internet?” *If a* respondent answered, “No,” the questionnaire was considered invalid. To further improve the validity of the questionnaires, we eliminated questionnaires that repeated more than 75 percent of the answers. A final total of 347 valid questionnaires was obtained for further analysis. We compared the demographics such as age, gender, and education between first 100 and last 100 respondents and found no significant differences, indicating that non-response bias was not a factor in this study. Table 1 presents the demographics information of the respondents.
**Table 1**
| Variables | Category | Frequency | Percentage (%) |
| --- | --- | --- | --- |
| Gender | Male | 126 | 36.3 |
| | Female | 221 | 63.7 |
| Age | 55–60 | 40 | 11.5 |
| | 61–65 | 78 | 22.5 |
| | 66–70 | 89 | 25.6 |
| | 70–75 | 100 | 28.8 |
| | 76–80 | 36 | 10.4 |
| | Over 80 | 4 | 1.2 |
| Education level | Middle school and below | 41 | 11.8 |
| | High school | 185 | 53.5 |
| | College | 85 | 24.5 |
| | Bachelor's degree and above | 36 | 10.4 |
| Chronic disease | Yes | 214 | 61.7 |
| | No | 133 | 38.3 |
## 4.2. Measurement
All measures of constructs in this study were adapted from previous studies and were appropriately modified to fit the current research context. Specifically, online health information seeking (OHIS) was measured with three items adapted from Cao et al. [ 66]. Perceived benefit (PB) refers to older adults' perceived benefit of using IT to seek health information, which was measured with three items adapted from Al-Debei et al. [ 47]. In addition, health awareness (HA) was measured with items adapted from Guo et al. [ 67], reflecting the health concerns and consciousness of the older adults. IT self-efficacy (ITS) and IT innovativeness (ITI), two constructs representing the personal IT resources of the study participants, were measured with items adapted from Thatcher and Perrewe [68] and Zhang et al. [ 69], respectively. Social support (SS) refers to the support that the participants received from family, friends, and social networks, which was measured with items adopted from Zimet et al. [ 70], while professional support (PS) refers to support from doctors and other medical professionals and was measured with items from Rosland et al. [ 71]. All measurement items are specifically listed in the Table A1. Seven-point Likert scales were employed, ranging from 1 (strongly disagree) to 7 (strongly agree).
## 5. Data analysis and results
In this study, we used structural equation modeling (SEM) with the partial least squares (PLS) algorithm to analyze the collected data and evaluate the research model. PLS-SEM is relatively robust in survey data analysis while considered more suitable for testing models with small sample sizes [72]; therefore, this method was deemed suitable for this study. SmartPLS 3.2 software was employed as our analytic tool. Following two-step procedures, we first examined the measurement model to ensure its reliability and validity, then examined the structural model to confirm the hypothesized relationships.
## 5.1. Measurement model
To assess the measurement model, we examined the reliability, convergent validity, and discriminant validity of our constructs. The reliability of constructs was assessed by checking whether composite reliability and Cronbach's alpha were higher than the threshold of 0.7. As shown in Table 2, composite reliability and Cronbach's alpha of all constructs were >0.7, indicating good reliability [73]. Convergent validity was assessed by the item loadings and the average variance extracted (AVE) from expected constructs, which needed to be higher than 0.7 and 0.5, respectively [73]. Table 3 reveals that all item loadings of constructs were >0.7; meanwhile, Table 2 shows that all AVE values were >0.5, thereby suggesting good convergent validity. Two approaches were employed to assess the discriminant validity. First, we compared whether the square root of AVE for a construct was greater than the correlation coefficients between the expected construct and other constructs. As shown in Table 2, all constructs satisfied the criterion. Second, we compared whether the item loadings of a construct were higher than the cross-loadings, which was verified by the results presented in Table 3. These results indicated that constructs in this study had good discriminant validities [74, 75].
To further test the potential problem of multi-collinearity for constructs, we calculated variance inflation factor (VIF) values. According to the results, VIF values for all constructs ranged from 1.059 to 2.052, less than the suggested criteria threshold of 3.3 [76]. Thus, multi-collinearity was not an issue in this study. In addition, to test for common method bias, we used Harman's single factor test to examine whether a single component accounted for most of the variance [77]. The results indicated that the most variance explained by one factor was $36.8\%$, which was lower than the $50\%$ threshold, thus indicating that common method bias is not a concern.
## 5.2. Structural model
Figure 2 depicts the structural model results. For personal factors, both IT self-efficacy (β = 0.285, $t = 4.621$, $p \leq 0.001$) and IT innovativeness (β = 0.508, $t = 9.168$, $p \leq 0.001$) had a positive significant effect on perceived benefit, supporting H1 and H2. The results demonstrate that perceived benefit (β = 0.589, $t = 13.373$, $p \leq 0.001$) significantly promoted online health information seeking behavior, supporting H3. In addition, for environmental factors, social support (β = 0.361, $t = 6.811$, $p \leq 0.001$) and professional support (β = 0.242, $t = 4.284$, $p \leq 0.001$) were verified to positively impact health awareness, supporting H4 and H5, respectively. Meanwhile, health awareness (β = 0.177, $t = 3.726$, $p \leq 0.001$) showed a positive effect on online health information seeking. Overall, the structural model explained $45.5\%$ of the variance in online health information seeking, along with $54.9\%$ of the variance in perceived benefit and $23.7\%$ of the variance in health awareness. Lastly, among the control variables, gender and chronic disease revealed a positive effect on online information seeking, indicating that female older adults and older adults with chronic disease were more likely to seek health information online.
**Figure 2:** *Structural model results. ***p < 0.001, **p < 0.01.*
## 5.3. Post-hoc analysis
The structural model results indicated that perceived benefit and health awareness simultaneously determined online health information seeking; specifically, perceived benefit was influenced by IT self-efficacy and IT innovativeness, while health awareness was influenced by social support and professional support. To further reveal the underlying influence mechanism, we went on to examine whether mediating effects existed in the research model. Employing the PROCESS, a widely used tool developed by Hayes [78] to estimate models with mediators, we tested the mediation effect of perceived benefit and health awareness. As presented in Table 4, both perceived benefit and health awareness demonstrated significant partial mediating effects. Specifically, the effects of IT self-efficacy and IT innovativeness on online health information seeking were partially mediated by perceived benefit. Similarly, the effects of social support and professional support on online health information seeking were partially mediated by health awareness.
**Table 4**
| Mediator | Path | Indirect effect (95%) | Indirect effect (95%).1 | Indirect effect (95%).2 | Direct effect (95%) | Direct effect (95%).1 | Direct effect (95%).2 | Results |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Size | LLCI | ULCI | Size | LLCI | ULCI | |
| PB | ITS->PB->OHIS | 0.186 | 0.1090 | 0.2625 | 0.463 | 0.3558 | 0.5592 | Patrial mediating |
| | ITI->PB->OHIS | 0.215 | 0.1189 | 0.3069 | 0.399 | 0.2887 | 0.5090 | Partial mediating |
| HA | SS->HA->OHIS | 0.067 | 0.0201 | 0.1144 | 0.315 | 0.2107 | 0.4195 | Partial mediating |
| | PS->HA->OHIS | 0.038 | 0.0067 | 0.0481 | 0.474 | 0.4933 | 0.6564 | Partial mediating |
Furthermore, the results for the structural model visually demonstrated that the influence path coefficient (β = 0.589) between perceived benefit and online health information seeking was greater than the path coefficient (β = 0.177) between health awareness and online health information seeking. We further statistically verified whether there were differences in the effects of perceived benefit and health awareness on online health information seeking. Using the approach proposed by Keil et al. [ 79], we discovered that the difference in path coefficients between perceived benefit and health awareness on online health information seeking was significant ($t = 116.384$). Thus, perceived benefit was shown to play a more important role than health awareness in promoting older adults to seek health information online. Similarly, we also compared the effects of IT self-efficacy and IT innovativeness on perceived benefit, as well as the effects of social support and professional support on health awareness. The results in Table 5 indicate that IT innovativeness exerted a stronger impact than IT self-efficacy, while social support exhibited a stronger impact than professional support.
**Table 5**
| DV | Path | Path coefficient | T value | Conclusion |
| --- | --- | --- | --- | --- |
| OHIS | βPB−>OHIS vs. βHA−>OHIS | 0.589*** vs. 0.177*** | 116.384*** | βPB−>OHIS > βHA−>OHIS |
| PB | βITS−>PB vs. βITI−>PB | 0.285*** vs. 0.508*** | 50.135*** | βITS−>PB < βITI−>PB |
| HA | βSS>HA vs. βPS−>HA | 0.361*** vs. 0.242*** | 4.755*** | βSS>HA > βPS−>HA |
## 6.1. Key findings
Drawing upon social cognitive theory, this study investigated the effects of personal and environmental factors on the online behavior of older adults seeking health information and uncovered the influencing mechanism. The results elicit several key findings. For example, the study findings verified that IT self-efficacy and IT innovativeness are two crucial personal factors for older adults in promoting their online behavior when seeking health information; in particular, IT innovativeness was identified as having a stronger impact. This finding is in line with practice and previous studies that have emphasized the importance of IT capacity and resources in older adults' behavior related to searching for health-related information online [8].
This study also confirmed two significant environmental factors: social support and professional support. Our results indicated that both factors showed positive impacts whereby the effect of social support was stronger, demonstrating that support from doctors, families, and friends can encourage older adults to actively seek health information. This observation complements the findings of previous studies that less communication with professionals and families leads to online health information seeking [21, 80]. We empirically found that support from professionals and families was significantly positively related to older adults' online health information seeking.
Our results also validate the direct effect and mediating role of perceived benefit and health awareness for older adults. Specifically, when such individuals believe that using IT is beneficial and experience a high level of health awareness, they tend to seek health information online. In particular, the perceived benefit of using IT to seek health information revealed a much stronger impact on older adults' behavior than health awareness. Furthermore, the effects of IT self-efficacy and IT innovativeness on online health information seeking were partially mediated by perceived benefit, while the effects of social support and professional support on online health information seeking were partially mediated by health awareness.
## 6.2. Theoretical implications
This study contributes to the field by raising several theoretical implications. First, this study enriches the research on online health information seeking through investigating older adults' online health information seeking behavior. Although a few scholars previously sought to understand older adults' attitudes toward searching for health information online [80, 81], they mainly explored and summarized the factors influencing older adults' behavior while neglecting to interpret how these factors motivated their subjects to seek health information online. As far as we know, our study is one of the first to address this issue. In particular, due to older adults' characteristics, their means of obtaining health information is usually passive when compared to young people [82]. Therefore, clarifying the mechanism underlying older adults' online health information seeking can shed light on how to effectively facilitate this process for them while, at the same time, deepening the scholarly understanding of this issue.
Second, this study empirically confirms the antecedents of online health information seeking by contextualizing older adults' specific drivers. Drawing on social cognitive theory, we integrally examined antecedents from personal and environmental perspectives. Although factors such as IT self-efficacy and social support have been identified as playing significant roles in determining online health information seeking [10, 66], this study takes a further step by empirically verifying their effects on older adults' behavior. Based on the framework of social cognitive theory, we also authenticate the significant role of IT innovativeness and professional support. Furthermore, we clarify the differential impacts of antecedents by comparing their effects, revealing the underlying influence paths. Thus, this study not only comprehensively highlights the impacts of different determinants but also provides new understanding and suggests directions for future research.
Third, this study contributes to social cognitive theory by introducing it in the online health information seeking context and validating the mediation role of perceived benefit and health awareness. Although social cognitive theory has been widely applied in studies examining health behaviors [83, 84], to our knowledge, no other scholars have previously investigated online health information seeking from the perspective of social cognitive theory. This study fills a gap in the literature by providing a deeper understanding of how older adults' personal factors and environmental factors comprehensively influence how they search for health information online. Furthermore, this study certifies that older adults' cognitions and perceptions (i.e., perceived benefit and health awareness) significantly mediate the impacts of personal factors and environmental factors on behavior. In this regard, this study enriches the previous understanding of social cognitive theory by revealing the influence mechanism of personal and environmental factors.
## 6.3. Practical implications
The findings of this study lead to some practical suggestions to aid the public, especially older adults, in actively seeking health information online. For example, results illustrated that both perceived benefit and health awareness significantly enhance older adults' search behavior, suggesting managers and organizations should take corresponding measures to improve older adults' evaluation of information technology and promote their health awareness. Accordingly, to enhance perceived benefit, health information technology service providers are encouraged to develop and optimize health information seeking functions to improve user-friendliness, such as increasing front size, simplifying search interface, and adding guidance-related notes for older adults. These tips will improve user-friendliness and are likely to increase older adults' perceived benefit and further promote online health information seeking. In addition, since health awareness is positively related to health information seeking behavior, organizations and governments are encouraged to organize health lectures and strengthen health publicity to improve older adults' health awareness.
Moreover, IT self-efficacy has been found to play a significant role in older adults' perceived benefit and online health information seeking, thus practitioners should address the issue of improving older adults' IT-related abilities. Along these lines, health information technology companies are advised to set up a special department for older adult users and send staff to train such individuals on how to effectively use the Internet to obtain health information. Furthermore, since IT innovativeness significantly increases perceived benefit and further promotes information seeking, we recommend that information technology designers should develop exploratory features for older adults to improve their innovativeness. For example, developing exploratory games and displaying them on login screens to encourage older adults act in a more innovative way.
Lastly, results in this study demonstrated that social support and professional support significantly enhance older adults' health awareness and further facilitate their efforts to find relevant health information. Therefore, we strongly suggest that healthcare workers and people surrounding older adults (e.g., family and friends) should provide more support in terms of their health management. According to our findings, a family doctor is necessary for older adults, allowing them to obtain professional information and medical support on a regular basis. Similarly, since social support was shown to have a stronger positive impact on older adults' health awareness, we suggest that families and friends offer more help and care to older adults, for example, keeping an eye on their health and discussing health issues with them regularly.
## 6.4. Limitations and future research
Although several notable theoretical and practical implications emerged from the study findings, some limitations should be addressed, which lead to suggestions for future research. For example, this study used a cross-sectional survey to examine the research model. Although tests were conducted to verify that the results were not affected by common method bias and multicollinearity, future research is strongly recommended using mixed methods and a longitudinal design to confirm the causal relationships. Furthermore, the research was conducted on the Chinese mainland, potentially limiting the generality of the research results. In particular, older Chinese adults prefer collectivism in which social support is more likely to play significant roles. Future research should therefore take cross-cultural issues into account to reach more interesting findings. Lastly, drawing on social cognitive theory, we captured IT self-efficacy and IT innovativeness as personal factors and incorporated professional support and social support as environmental factors. However, technology characteristics, such as the user-friendliness of information technology, may also play significant roles and are worth investigating in future research.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethical Committee of the Harbin Institute of Technology (N.2021-10 dated on 8th of November 2021). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XM and PZ: conceptualization, methodology, and writing. YL and RQ: methodology and writing. FM: review, editing, and supervision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Visit-to-visit variability in triglyceride-glucose index and diabetes: A 9-year
prospective study in the Kailuan Study'
authors:
- Xianxuan Wang
- Yanjuan Chen
- Zegui Huang
- Zefeng Cai
- Xinran Yu
- Zekai Chen
- Linyao Li
- Guanzhi Chen
- Kuangyi Wu
- Huancong Zheng
- Shouling Wu
- Youren Chen
journal: Frontiers in Endocrinology
year: 2022
pmcid: PMC10020697
doi: 10.3389/fendo.2022.1054741
license: CC BY 4.0
---
# Visit-to-visit variability in triglyceride-glucose index and diabetes: A 9-year prospective study in the Kailuan Study
## Abstract
### Instruction/Aims
It is unknown whether variability in the triglyceride-glucose index (TyG-index) is associated with the risk of diabetes. Here, we sought to characterize the relationship between TyG-index variability and incident diabetes.
### Methods
We performed a prospective study of 48,013 participants in the Kailuan Study who did not have diabetes. The TyG-index was calculated as ln [triglyceride (TG, mg/dL) concentration × fasting blood glucose concentration (FBG, mg/dL)/2]. The TyG-index variability was assessed using the standard deviation (SD) of three TyG-index values that were calculated during $\frac{2006}{07}$, $\frac{2008}{09}$, and $\frac{2010}{11.}$ We used the Cox proportional hazard models to analyze the effect of TyG-index variability on incident diabetes.
### Results
A total of 4,055 participants were newly diagnosed with diabetes during the study period of 8.95 years ($95\%$ confidence interval (CI) 8.48–9.29 years). After adjustment for confounding factors, participants in the highest and second-highest quartiles had significantly higher risks of new-onset diabetes versus the lowest quartile, with hazard ratios ($95\%$ CIs) of 1.18 (1.08–1.29) and 1.13 (1.03–1.24), respectively (P trend< 0.05). These higher risks remained after further adjustment for the baseline TyG-index.
### Conclusions
A substantial fluctuation in TyG-index is associated with a higher risk of diabetes in the Chinese population, implying that it is important to maintain a normal and consistent TyG-index.
## Introduction
Owing to socioeconomic advances and rising standards of living, the prevalence of diabetes mellitus in China has risen sharply over the past four decades, from $0.67\%$ in 1980 to $12.8\%$ in 2018 [1, 2]. There were 140.9 million people in China with diabetes in 2019, and in 2045, the number is predicted to reach 174.4 million [3]. Furthermore, diabetes is a risk factor for cardiocerebrovascular events, renal dysfunction, and overall morality (4–7), which have a major impact on society and the economy. Insulin resistance is a key pathogenetic feature of diabetes [8, 9], which is characterized by various metabolic disorders, including hyperglycemia and hypertriglyceridemia [10]. Thus, it is essential to identify and control insulin resistance early to prevent diabetes.
The assessment of insulin resistance in the clinical setting is challenging because the gold standard method of the euglycemic clamp is expensive and relatively complex [11]. Instead, the triglyceride-glucose (TyG) index, which is the product of the fasting blood glucose (FBG) and the fasting triglyceride (TG) concentration, has become established as reliable surrogate marker of insulin resistance [12, 13]. Several studies have shown a link between a high TyG-index and diabetes (14–16). Furthermore, cohort studies conducted in European, Korean, and Chinese populations have revealed that a high TyG-index level is also associated with subsequent incident cardiovascular disease (CVD) (17–19). Although most previous studies of this index considered single measurements, it can be affected by several factors, such as age, diet, and exercise [20]. The variability of the TyG index can reflect the long-term level of fluctuation [21]. Therefore, in the present study, we aimed to test the hypothesis that high TyG-index variability is associated with the risk of diabetes-related outcomes in the Chinese population.
## Study sample
We studied data from the Kailuan Study, an ongoing prospective cohort study [22]. This comprised information regarding 101,150 individuals who were enrolled to participate in a biennial questionnaire-based interview, which covered their demographic characteristics, medical history, and lifestyle; to undergo clinical examinations; and to undergo the measurement of laboratory parameters between 2006 and 2007. For the present study, the participants were required to have undergone two consecutive medical examinations during $\frac{2008}{09}$ and $\frac{2010}{11}$ to be eligible. Participants were excluded if they had diabetes in or prior to 2010, or if their FBG or TG data were missing for any of the examinations. After the application of these criteria, 48,013 participants remained for enrollment in the present study (Figure 1). The first survey, during $\frac{2006}{07}$, was defined as the baseline survey, and the third survey ($\frac{2010}{11}$) as the starting point of the follow-up period.
**Figure 1:** *Flow chart for the inclusion of participants in the study.*
All the participants gave their written informed consent and the study protocol was approved by the Ethics Committee of the Kailuan General Hospital (approval number: 2006-05).
## TyG index and the calculation of TyG index variability
The TyG index was calculated as ln [TG (mg/dL) × FBG (mg/dL)/2] [23]. TyG index variability was defined as the intra-individual variability of the TyG index, calculated using data collected during the three physical examinations. Four indices of variability were used: [1] standard deviation (SD): SD = 1n-1∑$i = 1$n(xi−x¯)2; [2] coefficient of variation (CV): CV = (SD/mean × $100\%$); [3] variation independent of the mean (VIM) [24, 25]: VIM = SD/meanχ, where “mean” is the average of the mean TyG index values for the participants, and χ is derived from non-linear regression analysis in the PROC NLIN procedure of the SAS package (SAS Institute Inc., Cary, NC, USA);
[4] average real variability (ARV) [21]: ARV=1N−1∑$K = 1$N−1|ValueK+1−Valuek|.; and [5] Slope of the TyG index change: regression lines were created using the three sets of TyG index data, and the slope of this regression line represented the overall trend in TyG index. This was used as an index of the long-term change in the TyG index. In the present study, a slope of the change in TyG index > 0 indicated overall positive variation, and a slope ≤0 indicated overall negative variation.
As previously described [26, 27], we placed the participants into four groups according to quartiles of the baseline SD of the TyG index: a Q1 group,<0.18; a Q2 group, 0.18–0.30; a Q3 group, 0.30–0.44; and a Q4 group, ≥0.44.
## Outcome events
The outcome of the present study was new-onset diabetes, which has been defined previously in detail [28]. Briefly, diabetes [29] was defined using an FBG of ≥7.0 mmol/L, the use of glucose-lowering drugs, or a self-reported history of diabetes. Participants were followed from their third examination, during $\frac{2010}{11}$, to the first of the date on which diabetes was first diagnosed, the date of death or December 31, 2019.
## Assessment of covariates
The demographic data (e.g., age, sex, and educational background), lifestyle (smoking, alcohol consumption, and physical activity habits) and medical history (hypertension and diabetes) of the participants were collected using questionnaires completed at face-to-face interviews. BMI was calculated as body mass (kg) divided by the square of height (m). Height, body mass, and blood pressure were measured by trained physicians using a standardized protocol.
Participants were instructed to visit the testing site in the morning after at least 8 hours of fasting and blood samples were collected from a cephalic vein by a trained laboratory technician. An automatic biochemical analyzer (7600-020, Hitachi, Tokyo, Japan) was used to measure the FBG, TG, low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), and high-sensitivity C-reactive protein (hs-CRP) concentrations. A current smoker was defined as someone who had smoked a mean of ≥ 1 cigarette per day during the preceding year, and participants were categorized as non-smokers or current smokers. An alcohol consumer was defined as someone who drank a mean of ≥ 100 mL of alcohol per day for at least the preceding year, and participants were categorized as non-drinkers or current drinkers. Participants were categorized as undertaking physical exercise if they performed exercise ≥ 3 times per week for ≥ 30 min on each occasion [30]. Education was classified as high school or above vs. below high school level. Hypertension [31] was defined as a blood pressure ≥$\frac{140}{90}$ mmHg, the use of antihypertensive medication, or a self-reported history of hypertension.
## Statistical analysis
Normally distributed, continuous data are expressed as mean ± standard deviation (x̅ ± s) and non-normally distributed data as median ($25\%$, $75\%$ percentile), and were analyzed using one-way ANOVA or the Kruskal-Wallis rank sum test, respectively. Categorical data are expressed as absolute number and percentage and were analyzed using the chi-square test.
We used the Kaplan–Meier method to calculate the cumulative incidence of the primary outcome in each group and then compared the groups using the log-rank test. We also used univariate and multivariate Cox regression models to identify potential risk factors for diabetes. The relationship between TyG index variability and diabetes was characterized using Cox proportional hazards regression models. In model 1, we adjusted for age (continuous) and sex (categorical) at baseline. In model 2, we further adjusted for LDL-C (continuous), HDL-C (continuous), hs-CRP (continuous), BMI (continuous), smoking status (categorical, yes/no), alcohol consumption status (categorical, yes/no), physical exercise habits (categorical, yes/no), educational level (categorical), hypertension (categorical, yes/no), and the use of lipid-lowering medication (categorical, yes/no) at the start of the follow-up period. In model 3, we further adjusted for the TyG index at baseline.
We further conducted stratified analyses by the sex, age, and slope of the change in the TyG index of the participants. Several sensitivity analyses were conducted as follows: [1] after the exclusion of participants in whom diabetes developed within the first year of follow-up; [2] after the exclusion of participants who were taking lipid-lowering or antihypertensive medication; [3] after the exclusion of participants with a TG concentration ≥ 2.3 mmol/L at baseline; [4] adjusting for the baseline TG and FBG concentrations and without the inclusion of the baseline TyG-index; and [5] using other indices of TyG-index variability (ARV, CV, and VIM) instead of SD. We also repeated the analyses using Cox proportional hazards models. A two-sided $P \leq 0.05$ was considered to be statistically significant. We used SAS (version 9.4, SAS Institute Inc.) for the statistical analyses.
## Baseline characteristics of the study sample
A total of 48,013 participants were selected for the study. Their mean age was 48.78 ± 12.05 years and 36,356 ($75.72\%$) were male. Compared with the Q1 group, the Q2 and Q3 groups had much higher BMI, SBP, DBP, TG, FBG, hs-CRP; and had higher prevalences of smoking, drinking, and hypertension ($P \leq 0.01$; Table 1).
**Table 1**
| Unnamed: 0 | Total | Q1 | Q2 | Q3 | Q4 | P |
| --- | --- | --- | --- | --- | --- | --- |
| Participants | 48013 | 12003 | 12003 | 12004 | 12003 | |
| Age(years) | 48.78 ± 12.05 | 50.09 ± 12.31 | 49.36 ± 12.09 | 48.85 ± 12.05 | 46.82 ± 11.51 | <.01 |
| Male, N (%) | 36356 (75.72) | 8805(73.36) | 8978(74.80) | 8989 (74.88) | 9584 (79.85) | <.01 |
| BMI (kg/m2) | 24.84 ± 3.12 | 24.76 ± 3.15 | 24.81 ± 3.18 | 24.87 ± 3.15 | 24.92 ± 3.00 | <.01 |
| SBP (mmHg) | 128.55 ± 16.75 | 128.36 ± 16.93 | 128.44 ± 16.91 | 128.66 ± 16.92 | 128.73 ± 16.22 | 0.07 |
| DBP (mmHg) | 83.19 ± 9.18 | 82.82 ± 9.15 | 82.94 ± 9.15 | 83.25 ± 9.25 | 83.77 ± 9.15 | <.01 |
| HDL-C (mmol/L) | 1.54 ± 0.32 | 1.55 ± 0.32 | 1.55 ± 0.32 | 1.54 ± 0.31 | 1.53 ± 0.32 | <.01 |
| LDL-C (mmol/L) | 2.48 ± 0.63 | 2.50 ± 0.63 | 2.50 ± 0.63 | 2.48 ± 0.64 | 2.44 ± 0.63 | <.01 |
| FBG (mmol/L) | 5.24 ± 0.52 | 5.09 ± 0.32 | 5.14 ± 0.60 | 5.35 ± 0.38 | 5.49 ± 0.47 | <.01 |
| TG (mmol/L) | 1.30 (0.96-1.87) | 1.21(0.88-1.58) | 1.27(0.95-1.81) | 1.32 (1.01–1.75) | 1.69 (1.14-2.48) | <0.01 |
| Hs-CRP (mg/L) | 1.42 (0.76-2.83) | 1.37 (0.73-2.70) | 1.40 (0.76-2.71) | 1.43 (0.77-2.92) | 1.46 (0.78-3.06) | <0.01 |
| TyG index2006 | 8.55 ± 0.63 | 8.49 ± 0.50 | 8.52 ± 0.54 | 8.52 ± 0.60 | 8.71 ± 0.77 | <.01 |
| TyG index2008 | 8.57 ± 0.62 | 8.51 ± 0.50 | 8.49 ± 0.53 | 8.56 ± 0.60 | 8.69 ± 0.80 | <.01 |
| TyG index2010 | 8.61 ± 0.61 | 8.50 ± 0.51 | 8.56 ± 0.53 | 8.64 ± 0.59 | 8.90 ± 0.75 | <.01 |
| Smoking, N (%) | 18360 (38.24) | 4296.0 (35.79) | 4423 (36.85) | 4680 (38.99) | 4961 (41.33) | <.01 |
| Drinking, N (%) | 16957 (35.32) | 3948 (32.89) | 4101 (34.17) | 4188 (34.89) | 4720 (39.32) | <.01 |
| Physical activity, N (%) | 6906 (14.38) | 1944 (16.20) | 1817 (15.14) | 1697 (14.14) | 1448 (12.06) | <.01 |
| Hypertension, N (%) | 22072 (45.97) | 5391 (44.91) | 5444 (45.36) | 5563 (46.34) | 5674 (47.27) | <.01 |
| Antihypertensive drugs, N (%) | 6944 (14.46) | 1654 (13.78) | 1715 (14.29) | 1738 (14.48) | 1837 (15.30) | <.01 |
| Lipid-lowering drugs, N (%) | 728 (1.52) | 186 (1.55) | 178 (1.48) | 165 (1.37) | 199 (1.66) | 0.33 |
| High school or above, N (%) | 6217 (12.95) | 1599 (13.32) | 1606 (13.38) | 1583 (13.19) | 1429 (11.91) | <.01 |
## Results of the univariate and multivariate Cox regression analyses to identify risk factors for diabetes
Univariate Cox proportional-hazards regression showed that TyG index variability, age, sex, SBP, DBP, TyG-index, LDL-C, HDL-C, hs-CRP, BMI, smoking status, educational level, physical activity habits, hypertension, and the use of lipid-lowering drugs were significantly associated with diabetes ($P \leq 0.05$, Table 2).
**Table 2**
| Unnamed: 0 | Univariate Cox regression analyses | Univariate Cox regression analyses.1 | Multivariate Cox regression analyses | Multivariate Cox regression analyses.1 |
| --- | --- | --- | --- | --- |
| | HR (95%CI) | P value | HR (95%CI) | P value |
| TyG index variability | 1.08 (1.07,1.10) | <0.01 | 1.06 (1.03,1.09) | <0.01 |
| Age | 1.01 (1.01,1.02) | <0.01 | 1.01 (1.00,1.01) | <0.01 |
| Gender | 1.22 (1.13,1.32) | <0.01 | 0.98 (0.90,1.07) | 0.68 |
| BMI | 1.16 (1.15,1.17) | <0.01 | 1.12 (1.10,1.13) | <0.01 |
| SBP | 1.04 (1.04,1.05) | <0.01 | / | / |
| DBP | 1.06 (1.05,1.06) | <0.01 | / | / |
| HDL-C | 0.63 (0.56,0.70) | <0.01 | 0.82 (0.74,0.92) | <0.01 |
| LDL-C | 1.24 (1.19,1.30) | <0.01 | 1.11 (1.06,1.16) | <0.01 |
| hs-CRP | 1.03 (1.02,1.03) | <0.01 | 1.01 (1.00,1.02) | <0.01 |
| TyG index2006 | 1.95 (1.87,2.05) | <0.01 | 1.82 (1.72,1.91) | <0.01 |
| Current smoking | 1.06 (1.00,1.13) | <0.01 | 1.03 (0.95,1.11) | 0.50 |
| Current drinker | 1.02 (0.95,1.08) | 0.12 | 0.96 (0.89,1.04) | 0.31 |
| Physical activity | 0.94 (0.90,0.98) | <0.01 | 0.94 (0.86,1.03) | 0.17 |
| Hypertension | 1.90 (1.79,2.02) | <0.01 | 1.29 (1.21,1.38) | <0.01 |
| education | 0.74 (0.69,0.78) | <0.01 | 0.80 (0.75,0.85) | <0.01 |
| Lipid-lowering drugs | 1.85 (1.53,2.24) | <0.01 | 1.22 (1.00,1.48) | 0.04 |
## Relationship between TyG-index variability and incident diabetes
During the mean follow-up period of 8.95 years ($95\%$ confidence interval (CI) 8.48–9.29 years), 4,055 ($8.45\%$) of the participants developed diabetes. The incidence of diabetes increased with increasing TyG-index variability quartile, from 8.80 in Q1 to 11.70 per 1,000 person-years in Q4 (Tables 2, 3). Figure 2 shows that the participants in Q4 had a higher cumulative incidence of diabetes than those in Q1 (log-rank test, $P \leq 0.01$). Tables 2, 3 shows the risk of incident diabetes according to the category of TyG-index variability, and the hazard ratio (HR) ($95\%$ CI) for Q4 versus Q1 was 1.34 (1.23–1.47) after adjustment for potential confounding factors. This association remained even after adjustment for the baseline TyG-index (HR 1.18, $95\%$ CI 1.08–1.29). Each 1-SD increase in the SD of TyG-index variability was associated with a $4\%$ higher risk of diabetes (HR 1.04, $95\%$ CI, 1.01–1.07). In addition, similar results were obtained when the variability in the TyG-index was assessed using the ARV, CV, and VIM (Figure 3).
**Figure 2:** *Kaplan-Meier incidence rate of diabetes by TyG-index variability (SD).* **Figure 3:** **Sensitivity analysis* of the association of TyG index variability with incident diabetes according to other indices of TyG-index variability (Average Real Variability, Coefficient of Variation, Variability Independent of the Mean) replacing Standard Deviation the in all the models. Model adjusted for age, sex, LDL-C, HDL-C, hs-CRP, BMI, smoking status, alcohol consumption status, physical exercise habits, educational level, hypertension, the use of lipid-lowering drugs, and TyG index.* TABLE_PLACEHOLDER:Table 3
## Results of the stratified and sensitivity analyses
Table 4 shows the results of the stratified analyses. *In* general, high TyG-index variability (group Q4) was significantly associated with a higher risk of diabetes across the various stratified groups. There were no significant effects of age, sex, or the slope of the change in the TyG index on the association between TyG-index variation and incident diabetes.
**Table 4**
| Unnamed: 0 | Age (P for interaction 0.33) | Age (P for interaction 0.33).1 | Sex (P for interaction 0.16) | Sex (P for interaction 0.16).1 | TyG index change slop(P for interaction 0.74) | TyG index change slop(P for interaction 0.74).1 |
| --- | --- | --- | --- | --- | --- | --- |
| | <45 years | ≥ 45 years | Female | Male | >0 | ≤0 |
| Quartiles | Quartiles | Quartiles | Quartiles | | | |
| Q1 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Q2 | 1.09 (0.92,1.30) | 1.02 (0.93,1.12) | 1.07 (0.88,1.30) | 1.03 (0.93,1.15) | 0.93 (0.81,1.05) | 1.16 (1.01,1.32) |
| Q3 | 1.23 (1.06,1.50) | 1.07 (0.96,1.19) | 1.18 (0.98,1.43) | 1.11 (1.01,1.24) | 0.98 (0.94,1.01) | 1.28 (1.13,1.46) |
| Q4 | 1.25 (1.06,1.46) | 1.11 (1.00,1.24) | 1.36 (1.12,1.65) | 1.14 (1.02,1.25) | 1.06 (1.01,1.11) | 1.50 (1.32,1.70) |
| P for trend | <0.01 | 0.04 | 0.02 | <0.01 | <0.01 | <0.01 |
With respect to the sensitivity analyses, the results of excluding outcome events occurring within the first year of follow-up, individuals taking lipid-lowering or antihypertensive medication, or individuals with TG ≥ 2.3 mmol/L at baseline were consistent with the results of the principal analysis. Because the SD may depend upon the mean value for each person, we also reanalyzed the data using other indices of TyG-index variability (ARV, CV, and VIM) in place of SD, but the findings were unaffected (Table 5).
**Table 5**
| Unnamed: 0 | Sensitivity analysis 1 | Sensitivity analysis 2 | Sensitivity analysis 3 | Sensitivity analysis 4 |
| --- | --- | --- | --- | --- |
| Q1 | 1.00 | 1.00 | 1.00 | 1.00 |
| Q2 | 1.04 (0.95,1.14) | 1.02 (0.93,1.12) | 1.11 (0.98,1.26) | 1.03 (0.94,1.13) |
| Q3 | 1.13 (1.03,1.24) | 1.13 (1.03,1.24) | 1.17 (1.03,1.33) | 1.13 (1.03,1.23) |
| Q4 | 1.18 (1.08,1.29) | 1.19 (1.06,1.28) | 1.42 (1.25,1.62) | 1.22 (1.12,1.34) |
| P for Trend | <0.0001 | 0.0002 | <0.0001 | <0.0001 |
## Discussion
In the present study, we have shown that high variability in the TyG-index is an independent risk factor for incident diabetes, even in individuals who are not taking antihypertensive or lipid-lowering medication and do not have a TG concentration ≥ 2.3 mmol/L by means of a longitudinal cohort study. Several previous studies have evaluated the relationship of a single TyG-index value with diabetes in the general population (32–36). For example, a 9-year follow-up study showed that individuals with the highest TyG indexes were at a 2.30-fold higher risk of developing diabetes [37]. In the China Health and Retirement Longitudinal Study, which involved 3.4 years of follow-up, every 1-SD increase in TyG index was associated with a $22\%$ increase in the risk of developing diabetes (HR 1.22, $95\%$ CI 1.14–1.31) in Chinese people of 45 years or above [36]. In addition, TyG-index is positively associated with CVD in patients with diabetes [38]. The results of the present study extend these findings by showing that visit-to-visit fluctuation in TyG-index is positively associated with the incidence of diabetes in the general population, independent of conventional risk factors for diabetes and the baseline TyG index. This implies that both the absolute value and the fluctuation in the TyG-index influence the risk of incident diabetes in the general population.
We have previously shown that the risk of diabetes is lower after antihypertensive and lipid-lowering therapy [39, 40]. Therefore, we repeated the analysis after excluding individuals who were taking antihypertensive or lipid-lowering drugs, but this did not affect the findings. In addition, because metabolic abnormalities, including a high circulating TG concentration, increase the risk of diabetes [41], we excluded participants with TG ≥ 2.3 mmol/L, but the results obtained were similar. Therefore, our findings emphasize the importance of regular monitoring and the maintenance of an appropriate TyG-index to prevent diabetes in the general population, even in individuals who are not taking antihypertensive or lipid-lowering medication and in those who do not have a TG concentration ≥ 2.3 mmol/L.
Although the mechanism linking high TyG-index variability with the development of diabetes has not been identified, there are several possible candidates. First, TyG is an index created using the fasting TG concentration and FBG [17, 23]; therefore, high variability in TyG may be derived from large fluctuations in serum TG and/or FBG, which are associated with vascular endothelial cell dysfunction, oxidative stress, and inflammation (42–45), all of which are key pathophysiological features of diabetes [46]. In addition, β-cell dysfunction is a key defect in the pathogenesis of diabetes [47], and aberrant glucose and lipid metabolism can lead to the apoptosis of β cells [48], which causes a deterioration of glycemic control and ultimately the development of diabetes.
The strengths of the present study include that it represents the first assessment of the relationship between the fluctuation in TyG index between clinic visits and the risk of developing diabetes, performed using data from a large, prospective cohort study. However, the study also had some limitations. First, we did not distinguish type 1 and type 2 diabetes mellitus in the present study. However, the Chinese diabetes guidelines state that type 2 diabetes currently accounts for $95\%$ of all cases of diabetes [29] and that type 2 diabetes is more common in older people. Given that the mean age of the study participants was 48.78 years, the present findings are likely to be largely representative of the risk type 2 diabetes. Second, the observational design of the study prevents the confirmation of a causal relationship between the variability in TyG index and diabetes. However, when we excluded individuals who developed diabetes within a year, the results were similar. Third, we did not assess the changes in blood glucose using other methods, such as the measurement of glycated hemoglobin or continuous blood glucose monitoring. Fourth, despite adjusting for potential risk factors for cardiovascular disease, because the study was an observational cohort study, other sources of residual or unmeasured confounding may still have existed, such as differences in diet.
In conclusion, we have shown that TyG-index variability is an independent risk factor for new-onset diabetes, which implies that TyG-index should be maintained to prevent the development of diabetes.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the corresponding author, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Kailuan General Hospital Ethics Committee (approval number: 2006-05). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XW, and YaC wrote the main manuscript text and conceived and designed the study. ZH analyzed the data. ZCa, and XY carried out literature search. ZCh and GC were responsible for developing the first draft of the manuscript. LL, KW, and HZ were responsible for developing the second draft of the manuscript. SW and YoC performed the manuscript review. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by the National Natural Science Foundation of China (No. 81870312).
## Acknowledgments
We thank all the survey teams of the Kailuan study group for their contribution and the study participants who contributed their information.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Emodin ameliorates renal injury and fibrosis via regulating the miR-490-3p/HMGA2
axis
authors:
- Liulin Wang
- Xuerui Wang
- Gang Li
- Shanshan Zhou
- Rui Wang
- Qi Long
- Min Wang
- Liang Li
- Hai Huang
- Yuanming Ba
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10020706
doi: 10.3389/fphar.2023.1042093
license: CC BY 4.0
---
# Emodin ameliorates renal injury and fibrosis via regulating the miR-490-3p/HMGA2 axis
## Abstract
Renal fibrosis is a major pathological feature of chronic kidney disease (CKD). While emodin is reported to elicit anti-fibrotic effects on renal injury, little is known about its effects on microRNA (miRNA)-modulated mechanisms in renal fibrosis. In this study, we established a unilateral ureteral obstruction (UUO) model and a transforming growth factor (TGF)-β1-induced normal rat renal tubular epithelial cell line (NRK-52E) model to investigate the protective effects of emodin on renal fibrosis and its miRNA/target gene mechanisms. Dual-luciferase assay was performed to confirm the direct binding of miRNA and target genes in HEK293 cells. Results showed that oral administration of emodin significantly ameliorated the loss of body weight and the increase in physicochemical parameters, including serum uric acid, creatinine, and urea nitrogen in UUO mice. Inflammatory cytokines, including tumor necrosis factor-α, monocyte chemoattractant protein-1, and interleukin (IL)-1β, but not IL-6, were down-regulated by emodin administration. Emodin decreased the expression levels of TGF-β1 and fibrotic-related proteins, including alpha-smooth muscle actin, Collagen IV, and Fibronectin, and increased the expression of E-cadherin. Furthermore, miR-490-3p was decreased in UUO mice and negatively correlated with increased expression of high migration protein A2 (HMGA2). We further confirmed HMGA2 was the target of miR-490-3p. Transfection of miR-490-3p mimics decreased, while transfection of miR-490-3p inhibitors increased fibrotic-related proteins and HMGA2 expression levels in TGF-β1-induced NRK-52E cells. Furthermore, transfection of miR-490-3p mimics enhanced the anti-fibrotic effects of emodin, while transfection of miR-490-3p inhibitors abolished the protective effects of emodin. Thus, as a novel target of emodin that prevents renal fibrosis in the HMGA2-dependent signaling pathway, miR-490-3p has potential implications in CKD pathology.
## 1 Introduction
Chronic kidney disease (CKD) involves sustained damage of the renal parenchyma and chronic deterioration of renal function (Akchurin, 2019). The global burden of CKD is a major public health problem, especially in high-income countries (Gaitonde et al., 2017), with approximately $10\%$ of adults worldwide affected by some form of CKD, resulting in 12 million deaths each year (Xie et al., 2018; Collaboration, 2020). Although there are many known causes of CKD, including diabetes mellitus, glomerulonephritis, and cystic kidney disease, the etiology of CKD remains incompletely understood (Kalantar-Zadeh et al., 2021). At present, CKD treatment primarily involves slowing disease progression and preventing complications by managing modifiable risk factors, such as diabetes and blood pressure, and initiating nephroprotective drug regimens in the early stages of disease (Curtis and Komenda, 2020). Thus, the discovery of novel therapeutic agents that can prevent or delay the progression of disease will contribute to CKD treatment.
Increasing evidence suggests that renal fibrosis is a common final stage of CKD (Klinkhammer et al., 2017). Similar to wound healing, renal fibrosis is a dynamic process with early beneficial effects on injury, but can lead to persistent pathological fibrosis, glomerulosclerosis, tubular atrophy and dilatation, and tubulointerstitial fibrosis (Boor et al., 2010; Djudjaj and Boor, 2019). Over the past 2 decades, microRNAs (miRNAs) have been implicated in fibrosis of various organs, and their role in renal fibrosis has attracted widespread attention, with new therapeutic strategies targeting miRNAs showing considerable promise (Van der Hauwaert et al., 2015; Fan et al., 2020). Transforming growth factor (TGF)-β is a key driver of renal fibrosis and is involved in the dynamic pathophysiological processes leading to CKD and end-stage renal disease (ESRD) (Ma and Meng, 2019). Recent evidence indicates that multiple miRNAs are involved in renal fibrosis through the TGF-β pathway (Zhong et al., 2013; Sun et al., 2018; Sun et al., 2021). For example, MiR-490-3p is known to play a key regulatory role in silica-induced pulmonary fibrosis (Cheng et al., 2021). Studies have also found that miR-490 is decreased in unilateral ureteral obstruction (UUO) model mice (Chung et al., 2010; Qin et al., 2011), and elevated in the urine of active segmental glomerulonephritis patients (Zhang et al., 2014). However, while miR-490 appears to play a crucial role in the pathogenesis of CKD, little is known regarding its functional role.
Emodin is a naturally occurring anthraquinone derivative and active ingredient of rhubarb (Liu et al., 2016), with diverse biological and pharmacological properties, including anticancer, anti-inflammatory, antioxidant, antibacterial, antiviral, anti-diabetic, immunosuppressive, and renoprotective activities (Dong et al., 2016). Emodin is reported to suppress renal fibrosis via multiple mechanisms (Ma et al., 2018; Yang et al., 2020; Liu et al., 2021), and is involved in the regulation of miRNAs in several diseases (Xiang et al., 2017; Cao et al., 2019; Xie et al., 2019). We previously reported that a stewed rhubarb decoction mitigated chronic renal failure progression in mice (Wang et al., 2022). However, further experimental research is needed to explore the potential fibrotic effects of emodin on CKD. Here, we hypothesized that miR-490-3p may be involved in the pathological process of renal fibrosis in CKD and that emodin may exert anti-fibrotic effects on CKD via regulation of miR-490-3p.
## 2.1 Animals
Male C57BL/6 mice (age 8–10 weeks, weight 20–24 g) were purchased from Beijing Huafukang Biotechnology Co., Ltd. (Beijing, China). The mice were maintained in an environmentally controlled rearing room (temperature: 22°C ± 2°C, humidity: $50\%$ ± $5\%$, 12-h light/dark cycle) for 1 week of adaptive feeding, with distilled water and sterilized food freely available. The experimental protocols used in this study were approved by the Ethics Committee for Animal Experimentation of the Hubei Provincial Hospital of Traditional Chinese Medicine (NO. 2019008) and were conducted according to the Guidelines for Animal Experimentation of the Hubei Provincial Hospital of Traditional Chinese Medicine.
## 2.2 UUO model
The UUO mouse model was established as described previously (Zhou et al., 2020). Briefly, mice were anesthetized with pentobarbital sodium (40 mg/kg body weight) by an intraperitoneal injection. The left ureter was isolated through a median incision and ligated at two points with 5–0 silk. Mice in the sham-operated control group received the same operation, except the ureter was not ligated or cut.
## 2.3 Treatment protocols
Emodin (Shanghai Yuanye Bio-Technology Co., Ltd. China) was dissolved in pyrogen-free saline and sodium carboxymethyl cellulose (Beijing ITA Biotechnology Co., Ltd., China). Daily oral emodin treatment was started 2 h after UUO surgery for 14 continuous days, with 10 mg/kg, 20 mg/kg, and 40 mg/kg considered as low (EM-L), medium (EM-M), and high (EM-H) dosages. Losartan (LST, Merck Sharp and Dohme., Ltd., United States) was dissolved in pyrogen-free saline as a positive control. Daily oral LST treatment (10 mg/kg) was started 2 h after UUO surgery for 14 continuous days. All sham and UUO mice were treated with the same volume of saline and sodium carboxymethyl cellulose as the vehicle for treatment.
## 2.4 Cell culture and treatment
The normal rat renal tubular epithelial cell line NRK-52E was purchased from American Type Culture Collection (ATCC, Manassas, United States), and routinely cultured in Dulbecco’s modified Eagle’s medium (DMEM, Wisent, Nanjing, China) containing $10\%$ fetal bovine serum (Gibco, Gaithersburg, MD, United States), 100 U/ml penicillin (Gibco, Carlsbad, CA, United States), and 100 μg/mL streptomycin (Gibco, Carlsbad, CA, United States) at 37°C with $5\%$ CO2. In the first experiment, NRK-52E cells were pretreated with serum-free medium for 24 h, then exposed to 40 μM or 80 μM emodin (Shanghai Yuanye Bio-Technology Co., Ltd., China) for 24 h. In the second experiment, NRK-52E cells were pretreated with serum-free medium for 24 h, then exposed to 10 ng/mL TGF-β1 (R&D Systems, MN, United States) with or without 40 μM or 80 μM emodin for 24 h. In the third experiment, HEK293 cells were transfected with miR-490-3p mimics, miR-490-3p inhibitors, and negative controls (NCs), then treated with or without 10 ng/mL TGF-β1 for 24 h. In the fouth experiment, NRK-52E cells transfected with miR-490-3p mimics, miR-490-3p inhibitors, and NCs were treated with or without 80 μM emodin for 24 h, then exposed to 10 ng/mL TGF-β1 for 24 h. All control groups were treated with the same volume of vehicle.
## 2.5 Transfection of miRNA mimics and inhibitors
The miR-490-3p mimics (5'-CAACCUGGAGGACUCCAUGCUG-3'), inhibitors (5'-AUUCGUCCUCCUGACCAUGGUC-3'), and NCs were obtained from Biomics Biotech (Nantong, China). The NRK-52E cells were transfected with 50 nM miR-490-3p mimics or inhibitors, and corresponding NCs at the same concentration, using Liposome 2000 reagent (Invitrogen, United States) according to the manufacturer’s instructions. Cells were collected 24 h after transfection for further experimental analysis.
## 2.6 Luciferase reporter assay
HEK293 cells were co-transfected with NCs or miR-490-3p mimics (50 nM), CMV-Renilla, HMGA2 promoter-luciferase using Lipofectamine 2000 reagent (Invitrogen, United States) following the manufacturer’s protocols. The plasmids were constructed and provided by Hunan Fenghui Biotechnology Co., Ltd. (Cat. BR082). Luciferase activities were measured 48 h after transfection using dual-luciferase assay kits according to the manufacturer’s protocols (Promega, United States).
## 2.7 Cell counting Kit-8 (CCK-8) assay
The cytotoxicity of emodin was determined by CCK-8 assay. Cells were seeded into 96-well plates. According to the instructions of the CCK-8 kit (Beyotime, Shanghai, China), each well was supplied with 10 μL of CCK-8 after treatment, then incubated at 37°C for 4 h. Absorbance was recorded at 450 nm, and experiments were performed in parallel and in triplicate.
## 2.8 Physiological and biochemical parameters
Body weights were recorded on day 14 after UUO surgery. Plasma creatinine, urea, and uric acid levels were detected using a creatinine colorimetric kit, urea colorimetric kit, and uric acid colorimetric kit (Bohu Biotechnology Co., Ltd. Shanghai, China), respectively, according to the manufacturer’s instructions.
## 2.9 Histological examination
Renal tissues were fixed in $4\%$ formaldehyde, dehydrated, embedded, and sectioned at 5 μm thickness. Renal sections were stained with hematoxylin and eosin (H&E) or Masson’s trichrome staining (Solarbio Life Sciences, Beijing, China) according to the manufacturer’s instructions. Sections were observed and scored in a blinded manner. Tubular injury scores based on morphological damage (epithelial necrosis, tubular necrotic debris, and tubular dilatation) were quantified in three or four sections and 10–12 regions per kidney, with a score range of 0–4 (0: $0\%$; 1: <$25\%$; 2: $26\%$–$50\%$; 3: $51\%$–$75\%$; 4: ≥$76\%$ of renal tubular injury) (Liu N. et al., 2015; Ren et al., 2021). All positive signals from the histological images (at least three areas per sample) were quantified using ImageJ v1.8.0 software (National Institutes of Health, Bethesda, MD, United States).
## 2.10 Enzyme-linked immunosorbent assay (ELISA)
Mouse blood was collected from the abdominal aorta 14 days after UUO surgery and centrifuged at 3,000 r/min for 15 min. Serum was collected and stored in a −80°C refrigerator for testing. The levels of interleukin (IL)-1β, tumor necrosis factor-α (TNF-α), IL-6, and monocyte chemoattractant protein-1 (MCP-1) were detected using ELISA kits (ABclonal Biotechnology Co., Ltd. China) according to the manufacturer’s protocols.
## 2.11 Western blot analysis
Total proteins were isolated from kidney tissues and cells using RIPA lysis and extraction buffer (Biyuntian, Shanghai, China). Protein content was determined using BCA protein assay reagent (Thermo, Rockford, IL, United States). The proteins were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes. The membranes were blocked in $5\%$ skim milk in phosphate-buffered saline-Tween at room temperature for 1 h and probed with specific antibodies overnight at 4°C. Primary antibodies included alpha-smooth muscle actin (α-SMA) (1:1,000, Abcam, United Kingdom), Collagen IV (1:1,000, Abcam, United Kingdom), Fibronectin (1:1,000, Abcam, United Kingdom), E-cadherin (1:1,000, Abcam, UK), TGF-β1 (1:1,000, Abcam, United Kingdom), HMGA2 (1:1,000, Invitrogen, United States), and β-actin (1:1,000, Boster, China). Horseradish peroxidase-conjugated secondary antibodies (Sigma, United States) were used for detection of specific proteins for an additional 60 min β-actin was used as the loading control. After a final wash with tris-buffer physiological saline (TBST), immunoreactive bands were detected using an Odyssey Imaging Analyzer (LAS-4000; Toyobo Engineering, Osaka, Japan). Signal intensity was measured using ImageJ and normalized to β-actin signal intensity.
## 2.12 RNA extraction and real-time polymerase chain reaction (Real-time PCR)
Total RNA from mouse renal tissues, HEK293 cells, and NRK-52E cells was extracted using Trizol Reagent (Invitrogen) according to the manufacturer’s instructions. For miRNA, total RNA was reverse transcribed into cDNA using a microRNA First-Strand cDNA Synthesis Kit (Sangong Biotech, Shanghai, China). For mRNA, cDNA was obtained using the GoScript Reverse Transcription System Kit (Promega, Madison, WI, United States). GAPDH and small nuclear U6 were used as endogenous controls for mRNA and miRNA levels respectively. Gene expression levels were analyzed by Real-time PCR performed using 2 × SYBR master mix (Takara, Otsu, Shiga, Japan) and a BioRad iCycler iQ5 (BioRad, Hercules, CA, United States). GAPDH and U6 were used as the endogenous controls for measurement of mRNA expression level and miRNA expression analysis, relative mRNA expressions were compared with normalized Sham group. All samples were run in duplicate. Primer sequences are listed in Table 1.
**TABLE 1**
| Name | Sequences |
| --- | --- |
| miR-490-3p | Forward: 5'-AACACGTGCAACCTGGAGGAC-3' |
| miR-490-3p | Reverse: 5'-ATCCAGTGCAGGGTCCGAGG-3’ |
| U6 | Forward: 5′-GCTTCGGCAGCACATATACTAAAAT-3′ |
| U6 | Reverse: 5′-GCTTCGGCAGCACATATACTAAAAT-3′ |
| mus-HMGA2 | Forward: 5'-CCTAAGAGACCCAGAGGAAGA-3' |
| mus-HMGA2 | Reverse: 5'- CGACTTGTTGTGGCCATTTC-3' |
| homo-HMGA2 | Forward: 5'- GTCCCTCTAAAGCAGCTCAAA-3' |
| homo-HMGA2 | Reverse: 5'- TGAGCAGGCTTCTTCTGAAC-3' |
| rattus-HMGA2 | Forward: 5'- CTGGACGTCCGGTGTTGGT-3’ |
| rattus-HMGA2 | Reverse: 5’-AACACCTTTCGGGAGACGGG-3’ |
| mus-MCP-1 | Forward: 5'-TTAAAAACCTGGATCGGAACCAA-3' |
| mus-MCP-1 | Reverse: 5'-GCATTAGCTTCAGATTTACGGGT-3' |
| mus-TNFα | Forward: 5'-ACCCTCACACTCAGATCATCTTC-3' |
| mus-TNFα | Reverse: 5'-TGGTGGTTTGCTACGACGT-3' |
| mus-IL-6 | Forward: 5'-ACAAAGCCAGAGTCCTTCAGAGA-3' |
| mus-IL-6 | Reverse: 5'-CTGTTAGGAGAGCATTGGAAATTG-3' |
| mus-IL-1β | Forward: 5'-TGGCAACTGTTCCTG-3' |
| mus-IL-1β | Reverse: 5'-GGAAGCAGCCCTTCATCTTT -3' |
| mus-GAPDH | Forward: 5'- TGTGTCCGTCGTGGATCTGA-3' |
| mus-GAPDH | Reverse: 5'- CCTGCTTCACCACCTTCTTGAT-3' |
| homo-GAPDH | Forward: 5'- CTGCCAACGTGTCAGTGGTG-3' |
| homo-GAPDH | Reverse: 5'- GTCGCTGTTGAAGTCAGAGGAG-3' |
| rattus-GAPDH | Forward:5’-ACAGTCCATGCCATCACTGCC-3’ |
| rattus-GAPDH | Reverse:5’-GCCTGCTTCACCACCTTCTTG-3’ |
## 2.13 Statistical analysis
All experimental data were analyzed using SPSS v20.0 and expressed as mean ± standard deviation (x ± s). One-way analysis of variance (ANOVA) was used for comparisons between multiple groups, and $p \leq 0.05$ was considered statistically significant. All data in the manuscript were analyzed by an investigator blind to the experimental groups.
## 3.1 Emodin ameliorates the physiological, biochemical and pathological damage in UUO mice
As shown in Figure 1, after 14 days of treatment, significant differences in body weight and creatinine, urea nitrogen, and uric acid levels were observed between the UUO and Sham groups. Data showed that body weight in the UUO group was significantly lower compared to the Sham group, while body weight in the EM-H group was significantly higher compared to the UUO group (Figure 1A). In addition, EM-L, EM-M, and EM-H treatment reduced creatinine, urea nitrogen, and uric acid levels to varying degrees in the UUO mice (Figures 1B–D), with emodin at 40 mg/kg showing the strongest modulatory effect.
**FIGURE 1:** *Emodin ameliorates the physiological, biochemical and pathological damage in UUO mice. Mice were randomly divided into Sham, UUO, EM-L, EM-M, EM-H, and LST groups. Mice were administrated with dosages of 10 mg/kg, 20 mg/kg, 40 mg/kg emodin, or 10 mg/kg Losartan started 2 h after UUO surgery, daily, orally, for 14 continuous days. (A) Body weight, (B) levels of uric acid, (C) levels of creatinine, and (D) levels of urea nitrogen were detected at day 14. Representative pictures of renal (E) H&E and (G) masson staining. Scale bar = 200 μm. (F) Tubule injury index score and (H) masson trichromatic positive area were calculated at day 14. Data were shown as mean ± SD (n = 10 per group). *compared with Sham,
&
compared with UUO, *p < 0.05, **p < 0.01,***p < 0.001 of each symbol, respectively.
&
p < 0.05,
&&
p < 0.01,
&&&
p < 0.001 of each symbol, respectively. UUO, unilateral ureteral obstruction; EM-L, emodin low dosage; EM-M, the emodin medium dosage, EM-H, emodin high dosage; LST, Losartan.*
We used H&E and Masson’s trichrome staining to evaluate renal damage and fibrosis. Glomeruli and tubules in the Sham group were structurally normal, with closely aligned tubules. However, kidneys in the UUO mice exhibited severe structural disorder, inflammatory cell infiltration, tubular atrophy, and necrosis (Figures 1E,F). Based on Masson’s trichrome staining, banded interstitial fibrosis and collagen fiber hyperplasia were observed in the UUO mice (Figures 1G,H). In contrast, treatment with emodin and LST alleviated these pathological changes in the UUO mice (Figures 1E–H).
## 3.2 Emodin reduces inflammatory cytokines release in UUO mice
To further investigate the effects of emodin, we used Real-time PCR and ELISA to detect the inflammatory factors TNF-α, MCP-1, IL-6, and IL-1β in UUO mice. Compared with the Sham group, inflammatory factors in the UUO group increased significantly, after emodin intervention, TNF-α, MCP-1 and IL-1β decreased significantly in the UUO mice, while IL-6 showed a non-significant decrease (Figures 2A–F). These data indicated that emodin can reduce inflammation in UUO mice.
**FIGURE 2:** *Emodin reduces inflammatory cytokines release in UUO mice. Mice were randomly divided into Sham, UUO, EM-L, EM-M, EM-H and LST groups. Mice were administrated with dosages of 10 mg/kg, 20 mg/kg, 40 mg/kg emodin, or 10 mg/kg Losartan started 2 h after UUO surgery, daily, orally, for 14 continuous days. Gene levels of (A) TNF-α, (B) MCP-1, (C) IL-6, and (D) IL-1β in renal tissues were determined by Real-time PCR. Serum levels of (E) TNF-α, (F) MCP-1, (G) IL-6, and (H) IL-1β were determined by ELISA. Data were shown as mean ± SD (n = 10 per group). *compared with Sham,
&
compared with UUO, *p < 0.05, **p < 0.01,***p < 0.001 of each symbol, respectively.
&
p < 0.05,
&&
p < 0.01,
&&&
p < 0.001 of each symbol, respectively. UUO, unilateral ureteral obstruction; EM-L, emodin low dosage; EM-M, emodin medium dosage, EM-H, emodin high dosage; LST, Losartan; TNF-α, tumor necrosis factor-α; MCP-1, monocyte chemoattractant protein-1; IL-6, interleukin-6; IL-1β, interleukin-1β.*
## 3.3 Emodin regulates fibrotic related protein expressions in UUO mice
We used western blot analysis to fibrotic related protein expressions. The protein expression levels of Fbronectin, α-SMA, Collagen IV, and TGF-β1 increased, while that of E-cadherin decreased significantly in the UUO mice. In contrast, emodin decreased Fbronectin, α-SMA, Collagen IV, and TGF-β1 expression and increased E-cadherin expression (Figures 3A,B), suggesting that emodin can reduce fibrosis in UUO mice.
**FIGURE 3:** *Emodin regulates fibrotic related protein expressions in UUO mice. Mice were randomly divided into Sham, UUO, EM-L, EM-M, EM-H and LST groups. Mice were administrated with dosages of 10 mg/kg, 20 mg/kg, 40 mg/kg emodin, or 10 mg/kg Losartan started 2 h after UUO surgery, daily, orally, for 14 continuous days. Representative blots of Fibronectin, α-SMA, Collagen IV,TGF-β1, E-cadherin and β-actin in the renal tissues, and (B) statistical analyses of relative protein expressions. Data were shown as mean ± SD (n = 10 per group).
*
p < 0.05 vs. Sham,
&
p < 0.05 vs. UUO. UUO, unilateral ureteral obstruction; EM-L, emodin low dosage; EM-M, emodin medium dosage, EM-H, emodin high dosage; LST, Losartan. TGF-β1, transforming growth factor-β1; α-SMA, alpha-smooth muscle protein.*
## 3.4 Decreased miR-490-3p expression and increased HMGA2 expression are negatively correlated in UUO mice
We compared the endogenous expression levels of miR-490-3p and HMGA2 in the kidney tissues of male C57BL/6 mice in the Sham and UUO groups by Real-time PCR. As shown in Figure 4A, miR-490-3p expression was significantly lower in the kidneys of the UUO group than the matched Sham group. However, HMGA2 mRNA expression was increased (Figure 4B), and miR-490-3p expression was negatively correlated with HMGA2 expression (Figure 4C), further suggesting that miR-490-3p and HMG2A may be associated with pathological injury in UUO mice.
**FIGURE 4:** *Decreased miR-490-3p expression and increased HMGA2 expression are negatively correlated in UUO mice. Gene Levels of (A) miR-490-3p, and (B) HMGA2 were detected by Real-Time PCR. (C) A negative correlation were found between miR-490-3p and HMGA2 mRNA expressins in the renal tissues of Sham and UUO mice (Spearman correlation analysis, r = −0.9614, p < 0.0001). Data were shown as mean ± SD (n = 10 per group).
**
p < 0.01 vs. Sham,
***
p < 0.001 vs. Sham. UUO, unilateral ureteral obstruction; HMGA2, high mobility protein A2.*
## 3.5 Emodin reversed the fibrosis in TGF-β1- induced NRK-52E cells
To investigate the effects of emodin on NRK-52E cell fibrosis, we first investigated the effects of emodin on NRK-52E cell viability using CCK-8. We found that different concentrations of emodin (40 and 80 μM) had no effect on cell viability (Figure 5A). As shown in Figure 5B, cell viability increased significantly in the TGF-β1 group, and decreased in the TGF-β1-induced NRK-52E cells in the emodin group (80 μM).
**FIGURE 5:** *Emodin reversed the fibrosis in TGF-β1- induced NRK-52E cells. CCK-8 assay were performed in emodin-treated NRK-52E cells (A) without or (B) with TGF-β1 stimulation. The gene levels of (C) miR-490-3p and (D) HMGA2 were evaluated by Real-Time PCR. (E) Representative blots of Fibronectin, α-SMA, Collagen IV, E-cadherin, and HMGA2 in NRK-52E cells, and (F) statistical analyses of relative protein expressions. Data were shown as mean ± SD (n = 6). *compared with control, &compared with TGF-β1. *p < 0.05, **p < 0.01 of each symbol, respectively.
&
p < 0.05,
&&
p < 0.01 of each symbol, respectively. TGF-β1, transforming growth factor-β1; HMGA2, high mobility protein A2; α-SMA, alpha-smooth muscle protein.*
We next investigated the effects of emodin on renal fibrosis in vitro using western blot analysis to detect fibrosis-related protein levels. Compared to the TGF-β1 group, Fibronectin, α-SMA, Collagen IV decreased, while epithelial marker E-cadherin increased after emodin intervention (Figures 5E,F), consistent with the in vivo results, with the strongest effect observed in the high emodin dose group.
## 3.6 Emodin affects expression of miR-490-3p and HMGA2 in NRK-52E cells
The expression levels of miR-490-3p in NRK-52E cells treated with TGF-β1 and/or emodin were detected by Real-time PCR, and the protein and mRNA expression levels of HMGA2 were detected by western blot analysis and Real-time PCR. TGF-β1 treatment significantly down-regulated the expression of miR-490-3p mRNA in the NRK-52E cells (Figure 5C), but up-regulated HMGA2 protein and mRNA expression (Figures 5D–F). Compared with the TGF-β1 group, the expression level of miR-490-3p was significantly increased, while the expression level of HMGA2 was decreased in the NRK-52E cells after emodin intervention, with higher doses showing greater effects (Figures 5C–F). These results suggest that emodin can reverse the TGF-β1-induced decrease in miR-490-3p expression in NRK-52E cells.
## 3.7 MiR-490-3p/HMGA2 axis are involved in TGF-β1-induced fibrotic effect in NRK-52E cells
To verify whether HMGA2 is a potential target of miR-490-3p. We performed dual-luciferase reporter experiments. The HEK293 cells were transfected with miR-490-3p mimics and NCs. As shown in Figure 6A, the miR-490-3p mimic markedly inhibited luciferase activity of the HMGA2-5’UTR reporter, and Real-time PCR showed that, compared to the NC group, HMGA2 was down-regulated after miR-490-3p overexpression (Figure 6B). These results suggest that HMGA2 was targeted by miR-490-3p.
**FIGURE 6:** *MiR-490-3p/HMGA2 axis are involved in TGF-β1-induced fibrotic effect in NRK-52E cells. HEK293 cells were co-transfected with a NC or miR-490-3p mimic. After 48 h, (A) dual luciferase reporter assay and (B) Real-time PCR were performed in control, miR-490-3p NC, and miR-490-3p mimic groups to evaluate the luciferase activities and gene level of HMGA2. NRK-52E cells were transfected with miR-490 mimic, NC, or inhibitor for 48 h, and then treated with or without TGF-β1 for 24 h. The gene levels of (C) miR-490-3p were evaluated by Real-Time PCR. (D) and (F), the protein expressions of Fibronectin, α-SMA, Collagen IV, E-cadherin, and HMGA2 in miR-490-3p overexpression and inhibition experiment, respectively. (E) and (G), statistical analyses of relative protein expression in each group. Data were shown as mean ± SD (n = 6). *compared with control, &compared with TGF-β1. *p < 0.05, ***p < 0.001.
&
p < 0.05. TGF-β1, transforming growth factor-β1; HMGA2, high mobility protein A2; α-SMA, alpha-smooth muscle protein.*
We further investigated the effects of miR-490-3p overexpression and inhibition on HMGA2 and TGF-β1-induced fibrosis in NRK-52E cells. Transfection efficiency was determined by Real-time PCR. As shown in Figure 6C, miR-490-3p expression was increased in the mimic group and decreased in the inhibitor group compared with the NC, indicating that the cells were successfully transfected with miR-490-3p mimic or inhibitor. Western blot analysis showed that TGF-β1 stimulation significantly up-regulated the expression levels of Fibronectin, α-SMA, and Collagen IV, but decreased the expression level of E-cadherin in the NRK-52E cells (Figures 6D–G). Compared with the TGF-β1 group, the miR-490-3p mimic group showed a decrease in the expression levels of Fibronectin, α-SMA, Collagen IV, and HMGA2 proteins but an increase in the expression level of E-cadherin protein (Figures 6D,E), while the miR-490-3p inhibitor group showed an increase in the expression levels of Fibronectin, α-SMA, Collagen IV, and HMGA2 proteins but a decrease in the expression of E-cadherin protein (Figures 6F,G). These data suggest that miR-490-3p is a potent regulator of HMGA2 involved in the regulation of fibrotic effects on NRK-52E cells.
## 3.8 Emodin reduces renal fibrosis by regulating miR-490-3p/HMGA2 axis
We further studied the regulatory role of miR-490-3p and HMGA2 in the anti-renal fibrotic effects of emodin. Results showed that TGF-β1 stimulation up-regulated Fibronectin, α-SMA, Collagen IV, and HMGA2 protein expression and decreased E-cadherin protein expression in the NRK-52E cells. Emodin treatment significantly alleviated the increase in Fibronectin, α-SMA, Collagen IV, and HMGA2 proteins and decline in E-cadherin protein. Compared with the TGF-β1 +EM + NC group, the TGF-β1 + EM + miR-490-3p mimic group showed a decrease in the Fibronectin, α-SMA, Collagen IV and HMGA2 protein levels and an increase in the E-cadherin protein level, while the TGF-β1 + EM + miR-490-3p inhibitor group showed an increase in the Fibronectin, α-SMA, Collagen IV, and HMGA2 protein levels and a decrease in the E-cadherin protein level (Figure 7). These results suggest that miR-490-3p mimics enhance the protective effects of emodin on TGF-β1-induced NRK-52E cells, while miR-490-3p inhibitors eliminate these protective effects. Thus, emodin may inhibit the HMGA2-dependent signaling pathway and reverse renal fibrosis by up-regulating miR-490-3p expression in NRK-52E cells.
**FIGURE 7:** *Emodin reduces renal fibrosis by regulating miR-490-3p/HMGA2 axis. MiR-490-3p mimic, miR-490-3p inhibitor, and NC groups of NRK-52E cells were transfected for 48 h, and then treated with or without emodin for 24 h, and finally exposed to TGF-β1 for 24 h. (A) Representative blots of Fibronectin, α-SMA, Collagen IV,TGF-β1, and E-cadherin in each group, and (B) statistical analyses of relative protein expression. Data were shown as mean ± SD (n = 6). *compared with control, #compared with TGF-β1, &compared with TGF-β1+EM + NC. *p < 0.05,
&
p < 0.05, #
p < 0.05. TGF-β1, transforming growth factor-β1; EM, emodin; HMGA2, high mobility protein A2; α-SMA, alpha-smooth muscle protein.*
## 4 Discussion
Renal fibrosis is known as the major pathological mechanism of CKD (Nogueira et al., 2017). Rhubarb, a traditional herbal medicine, is widely used in China to treat CKD (Wang et al., 2012). As the active ingredient in rhubarb, emodin exhibits anti-renal fibrosis activity through a variety of ways. Ma et al. reported that emodin ameliorates renal fibrosis by down-regulating TGF-β1 and Smurf two expression (Ma et al., 2018). Liu et al. reported that emodin alleviates epithelial-to-mesenchymal transition (EMT) by activating bone morphogenic protein (BMP)-7-mediated autophagy in renal fibrosis (Liu et al., 2021). Consistently, our research confirmed that emodin is an anti-renal fibrosis agent. Briefly, using a UUO mouse model, we found that emodin increased body weight, decreased blood creatinine, urea nitrogen, and uric acid levels, reduced renal pathological damage, and attenuated renal tissue inflammation and fibrosis in UUO mice. In vitro, using TGF-β1-induced NRK-52E cells, we found that miR-490-3p targeting HMGA2 was involved in renal fibrosis, and further showed that emodin via regulation of miR-490-3p/HMGA2 and ameliorates renal fibrosis.
The role of inflammation in the pathogenesis and progression of CKD has been recognized since the late 1990s. Recent studies have also confirmed that inflammation and the inflammatory response can alter or interfere with intrarenal microcirculatory regulation and perfusion distribution and can induce renal damage and promote disease progression. Persistent, low-grade inflammation is now considered a distinguishing feature of CKD and is associated with all-cause mortality in patients (Lv et al., 2018a; Mihai et al., 2018). Hirudin is reported to suppress fibrosis in renal tissues and renal tubular epithelial cells by inhibiting inflammation (Xie et al., 2020). Therefore, treating or preventing underlying inflammation may improve CKD prognosis. The anti-inflammatory effects of emodin have been confirmed in various disease studies. Notably, emodin has been shown to significantly down-regulate inflammatory factors, such as TNF-α, MCP-1, IL-1β, and IL-6, via regulation of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and p38 mitogen-activated protein kinase (MAPK) pathways (Zhu et al., 2013; Xia et al., 2019; Zang et al., 2020). In our study, emodin was selective for the regulation of inflammatory factors in the UUO model, down-regulating TNF-α, MCP-1, and IL-1β, but with no significant effect on IL-6. The result is different from previous results reported for IL-6 regulation by emodin used in other models, it may be due to the different pathophysiological processes of the models.
Fibrosis is a result of tissue repair and becomes dysregulated after many types of tissue damage (Rockey et al., 2015). The fact that changes in fibrosis are linked to various diseases in various organ systems, including heart, kidneys, liver, skin or any other body organ. A variety of cellular and molecular signaling mechanisms are involved in the pathogenesis of fibrosis (Alqudah et al., 2020). Overproduction of cytokines, chemokines, growth factors, extracellular matrix proteins, and loss of normal organ structure and function are common features of fibrosis in organs (Weiskirchen et al., 2019). The ability of fibrosis to resolve may depend on the organ involved, the nature of the injury stimulus, and host-specific factors. The liver stands out from all other tissues with its strong regenerative capacity. In metabolic liver disease, lifestyle changes and bariatric surgery can cause histological fibrosis to regress (Vilar-Gomez et al., 2015). The cardiac parenchymal cells are muscle cells (cardiomyocytes) displaying a very limited regenerative capacity (Zeisberg and Kalluri, 2013). Some researchs on diabetic cardiomyopathy that controlling autophagy and lowering cardiomyocyte apoptosis via a variety of routes can prevent cardiomyocyte fibrosis (Zhang et al., 2021; Xue et al., 2022). The kidney has a complex structure, and renal fibrosis is manifested by the presence of glomerulosclerosis, vascular sclerosis and tubulointerstitial fibrosis. Because existing knowledge about the progression of renal fibrosis is extremely complex, preventing or even eliminating renal fibrosis is difficult (Nogueira et al., 2017). TGF-β is considered as one of the common master switches for the induction of the fibrotic program during chronic phases of inflammatory diseases in many organs and tissue. Many evidence suggests that miRNAs are both downstream effectors of TGF-β-dependent renal fibrosis and upstream regulators of TGF-β-dependent signaling pathways (Lv et al., 2018b). Therefore, the regulation of miRNA expression may be as a promising therapeutic strategy for the treatment of CKD.
Various studies have confirmed that HMGA2 is a key factor in the development of EMT. Notably, TGF-β1 is a potent fibrogenic factor that can mediate EMT through the regulatory effects of HMGA2 (Hills and Squires, 2010; Iwano, 2010; Wang et al., 2016)and mediate the development of renal fibrosis through different signaling pathways (Hills and Squires, 2010; Iwano, 2010). EMT is an important mechanism of renal fibrosis (Lovisa et al., 2016). Although the role of EMT in renal fibrosis has been questioned (Kriz et al., 2011), recent evidence suggests that EMT in renal tubular epithelial cells promotes renal fibrosis (Grande et al., 2015; Lovisa et al., 2015) and inhibition of EMT improves renal fibrosis (Sun et al., 2018; Wang et al., 2020; Nagavally et al., 2021). Accumulating evidence also suggests that specific miRNAs play important roles in CKD progression, and that miR-490-3p targeting HMGA2 is implicated in the metastasis and proliferation of multiple tumors (Liu W. et al., 2015; Zhang et al., 2019). Previous sequencing results showed that miR-490 was the most significant down-regulated miRNAs after UUO surgery (Chung et al., 2010; Qin et al., 2011). Our pre-experiment verified the regulatory effects of emodin on the top five miRNAs, including miR-490-3p, miR-137-3p, miR-208-3p, miR-429-3p, and miR-200a-3p, which reported to be significantly decreased in UUO mice (data not shown), and found that emodin had the best regulatory effects on elevating the expression of miR-490-3p. In this study, we found that miR-490-3p was negatively correlated with HMGA2 expression in UUO mice. Using dual luciferase reporter gene assay, we also verified that miR-490-3p exhibited a targeting relationship with HMGA2. Further experiments found that miR-490-3p mimic-inhibited fibrosis in NRK-52E cells was reversed by silencing the expression of HMGA2, and miR-490 inhibitor promoted fibrosis in NRK-52E cells by overexpressing HMGA2. These results suggest that miR-490-3p inhibits fibrosis in NRK-52E cells by down-regulating HMGA2-mediated expression. MiR-490 is also known to have tumor suppressive effects in many cancer types, preventing cancer cells from acquiring a mesenchymal phenotype by modulating EMT, thus inhibiting proliferation and metastasis (Vinchure and Kulshreshtha, 2021). MiR-490-3p is also involved in silicon-induced pulmonary fibrosis by targeting TGFBR1 modulators (Cheng et al., 2021). These findings support our results suggesting that miR-490-3p exerts a protective effect on renal fibrosis. Finally, to identify the underlying molecular mechanism, we detected the effects of emodin on the miR-490-3p/HMGA2 signaling pathway. Results indicated that the miR-490-3p mimic enhanced the protective effects, while the miR-490-3p inhibitor abrogated the protective effects of emodin on TGF-β1-induced NRK-52E cell fibrosis. These findings suggest that emodin alleviates renal fibrosis by up-regulating miR-490-3p. However, limitations of the present study should be considered, the downstream signaling pathways need to be further evaluated in future studies.
In conclusion, emodin increased miR-490-3p expression, inhibited HMGA2 expression, and blocked TGF-β1-induced fibrosis, thereby exerting significant nephroprotective effects and ameliorating UUO-induced renal injury in CKD mice. Therefore, miR-490-3p may be a new drug target for emodin to prevent HMGA2-dependent signaling pathway fibrosis, with potential implications in CKD pathology.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by Ethics Committee for Animal Experimentation of the Hubei Provincial Hospital of Traditional Chinese Medicine.
## Author contributions
LW, XW, and YB conceived, designed the study, and wrote the manuscript, which as approved by all the authors. XW and LW carried out the UUO surgery, drug treatment, and the collection of samples. GL and RW histologic examination, physiological and biochemical parameters. LW, XW, SZ, MW, and HH carried out elisa evaluation, CCK-8 assay, transfection of miRNA mimics and inhibitors and real-time PCR. LW and QL performed data analysis.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Glucose oxidase as an alternative to antibiotic growth promoters improves the
immunity function, antioxidative status, and cecal microbiota environment in white-feathered
broilers
authors:
- Wenyu Zhao
- Yuan Huang
- Na Cui
- Ruiguo Wang
- Zhiming Xiao
- Xiaoou Su
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10020722
doi: 10.3389/fmicb.2023.1100465
license: CC BY 4.0
---
# Glucose oxidase as an alternative to antibiotic growth promoters improves the immunity function, antioxidative status, and cecal microbiota environment in white-feathered broilers
## Abstract
This study aimed to demonstrate the effects of glucose oxidase (GOD) on broilers as a potential antibiotic substitute. A total of four hundred twenty 1-day-old male Cobb500 broilers were randomly assigned into five dietary treatments, each with six replicates (12 chicks per replicate). The treatments included two control groups (a basal diet and a basal diet with 50 mg/kg aureomycin) and three GOD-additive groups involving three different concentrations of GOD. Analysis after the t-test showed that, on day 21, the feed:gain ratio significantly decreased in the 1,200 U/kg GOD-supplied group (GOD1200) compared to the antibiotic group (Ant). The same effect was also observed in GOD1200 during days 22–42 and in the 600 U/kg GOD-supplied group (GOD600) when compared to the control group (Ctr). The serum tests indicated that, on day 21, the TGF-β cytokine was significantly decreased in both GOD600 and GOD1200 when compared with Ctr. A decrease in malondialdehyde and an increase in superoxide dismutase in GOD1200 were observed, which is similar to the effects seen in Ant. On day 42, the D-lactate and glutathione peroxidase activity changed remarkably in GOD1200 and surpassed Ant. Furthermore, GOD upregulated the expression of the jejunal barrier genes (MUC-2 and ZO-1) in two phases relative to Ctr. In the aureomycin-supplied group, the secretory immunoglobulin A significantly decreased in the jejunum at 42 days. Changes in microbial genera were also discovered in the cecum by sequencing 16S rRNA genes at 42 days. The biomarkers for GOD supplementation were identified as Colidextribacter, Oscillibacter, Flavonifractor, Oscillospira, and Shuttleworthia. Except for Shuttleworthia, all the abovementioned genera were n-butyrate producers known for imparting their various benefits to broilers. The PICRUSt prediction of microbial communities revealed 11 pathways that were enriched in both the control and GOD-supplied groups. GOD1200 accounted for an increased number of metabolic pathways, demonstrating their potential in aiding nutrient absorption and digestion. In conclusion, a diet containing GOD can be beneficial to broiler health, particularly at a GOD concentration of 1,200 U/kg. The improved feed conversion ratio, immunity, antioxidative capacity, and intestinal condition demonstrated that GOD could be a valuable alternative to antibiotics in broiler breeding.
## Introduction
Antibiotics have been playing a vital role in commercial poultry production since their first use in the 1940s (Castanon, 2007). They greatly improve the production efficiency of the breeding industry and satisfy the increasing human demand for animal-derived foods. However, their continued use has had negative impacts. The issue of antibiotic resistance has attracted a great amount of attention because of the existing and potential threats antibiotics pose to public health (Laxminarayan et al., 2014). Many countries, such as those in the European Union and the USA, started to restrict or even forbid the use of antibiotics in industrial-scale animal production (Marshall and Levy, 2011). From 1 January 2020, the Ministry of Agriculture of China also issued regulations prohibiting the use of any growth-promoting antibiotics. Thus, it is essential to find effective in-feed antibiotic alternatives in animal production without compromising human health. Accordingly, novel products, including plant essential oils (Brenes and Roura, 2010), probiotics (Kabir et al., 2005), organic acids (Adil et al., 2010), antibacterial peptides (Wang et al., 2016), and feed enzymes (Askelson et al., 2018), were investigated.
Glucose oxidase (GOD), as one of the feed enzymes, could specifically catalyze the oxidation of β-D-glucose to gluconic acid and hydrogen peroxide (Bankar et al., 2009).
This enzyme has been gradually accepted by the feed industry due to several verified advantages, including growth promotion, feed quality improvement, intestinal health regulation, and toxic reaction reduction, with non-toxic, low-residue characteristics (Dang et al., 2021, 2022; Hoque et al., 2022; Sun et al., 2022). In animals, particularly poultry, intestinal barrier and microbiota compositions are critical, since they are closely related to the immune system and health (Robinson et al., 2015; Awad et al., 2017; Pandit et al., 2018). The gastrointestinal tract's microbiota flora is linked to “intestinal” or “non-intestinal” functions ranging from nutrient absorption to immune response and even the gut–brain axis (Gao et al., 2017; Borda-Molina et al., 2018). Therefore, animal nutrition research mainly focuses on the host gut which correlates with optimal health and productivity. GOD has been known to help animals avoid intestinal dysfunction or other gut problems based on its reaction mechanism (Qu and Liu, 2021). The GOD-catalyzed glucose products can act on the gut of broilers, gluconic acid can produce the short-chain fatty acids (SCFAs) and further create a weakly acidic intestinal tract environment (Mortensen et al., 1988; Biagi et al., 2006), and hydrogen peroxide can participate in the oxidative stress response and regulate gut microbiota through its bactericidal and antimicrobial properties (Vatansever et al., 2013; Belambri et al., 2018). Though some researchers recently elucidated the effects of GOD with a sequencing-based technique (Wu et al., 2020; Meng et al., 2021), it is still ambiguous how GOD improves gut health and immunity function and why antibiotics can be replaced by it in broiler production (Liang et al., 2022). Some voids, such as how the additive, defense function, and growth performance interact with each other, still remain.
Therefore, this study aimed to determine the impact of glucose oxidase on the growth performance, immunity, antioxidative stage, and intestinal function of white-feathered broilers and attempted to explain it from the perspective of intestinal microorganisms. These findings may contribute to expanding the knowledge concerning the application of glucose oxidase. Furthermore, the comparison between the GOD and aureomycin-supplemented groups can further illustrate the role of GOD in feed as a substitute for antibiotic growth promoters (AGPs).
## Birds, diet, and management
A total of four hundred twenty 1-day-old male Cobb500 white-feathered broiler chicks obtained from Beijing Poultry Breeding Co., Ltd. were randomly assigned into five dietary treatments, each in six replicates (12 chicks/replicate) by cage-rearing, and the original average weight of every replicate had no remarkable difference. The control group (Ctr) was fed with a basal diet formulated to meet the nutrient requirements of poultry as per the National Research Council 1994, and other treatment groups were based on the basal diet with the addition of various feed additives. The antibiotic group (Ant) was supplied with 50 mg/kg aureomycin (Chia Tai Co., Ltd., Henan, China). Different concentrations of GODs (300, 600, and 1,200 U per kilogram of diet) were determined from the doses recommended by the manufacturer (VTR Bio-tech Co., Ltd., Zhuhai, Guangdong, China) and from massive references for their effective applications in the poultry industry, and named as GOD300, GOD600, and GOD1200. Table 1 details the diet compositions and nutrient contents of the basal diet for the entire study's starting (day 0–21) and growing (day 21–42) phases. All the chickens were exposed to incandescent light for a 24-h photoperiod instead of daylight, and the birds were allowed ad libitum access to drinking water from nipple drinkers. The diets for the chickens were mash feed for the first 12 days and then gradually transited to pellet diets. For temperature, ventilation, and other types of ventilation management for the birds in this research, one is referred to the guidelines for raising meat-type broilers (National Technical Committee for Animal Agriculture Standardization, 2005). Feed consumption and body weight were recorded every week and the mortality of the birds was checked daily. These data were used to calculate the feed intake, body weight gain, and feed conversion ratio.
**Table 1**
| Items | Contents (%) | Contents (%).1 |
| --- | --- | --- |
| | 1–21 d of age | 22–42 d of age |
| Ingredients | Ingredients | Ingredients |
| Corn | 56.59 | 59.96 |
| Soybean meal | 25.95 | 20.00 |
| Cottonseed meal | 4.50 | 4.42 |
| Corn gluten meal | 4.00 | 5.00 |
| Wheat middling | 2.00 | 2.00 |
| Soybean oil | 2.49 | 4.50 |
| Calcium hydrogen phosphate | 1.82 | 1.58 |
| Limestone | 1.35 | 1.27 |
| Salt | 0.35 | 0.35 |
| L-Lysine-HCl | 0.35 | 0.35 |
| DL-Methionine | 0.23 | 0.21 |
| Threonine | 0.05 | 0.04 |
| Mineral Premixa | 0.20 | 0.20 |
| Vitamin Premixb | 0.02 | 0.02 |
| Choline chloride | 0.10 | 0.10 |
| Total | 100.00 | 100.00 |
| Nutrients | Nutrients | Nutrients |
| ME (kcal/kg) | 2980.00 | 3160.00 |
| Crude protein | 21.95 | 19.95 |
| Ca | 1.00 | 0.90 |
| Non-phytate P | 0.45 | 0.40 |
| Lysine | 1.30 | 1.15 |
| Methionine | 0.58 | 0.54 |
| Methionine + cystine | 0.94 | 0.87 |
| Threonine | 0.84 | 0.75 |
| Tryptophan | 0.23 | 0.20 |
## Sample collection
One chicken with an average weight from each replicate was chosen for sample collection after a 12-h fast at the end of the two phases (days 21 and 42). The blood samples were drawn from the wing vein and dropped into tubes without anticoagulants. The serum used in further research was received after the blood samples were centrifuged at 3,500 × g for 10 min (4°C) and stored at −80°C. Birds were killed and shortly dissected after collecting blood samples. Immune organs (the thymus, the spleen, and the bursa of Fabricius) were taken out from the dead body individually, following the rinsing, blotting, and weighting procedures. The segments (~2 cm) in the middle of the jejunum were collected, washed with physiological saline, and then dropped into $10\%$ neutral-buffered formalin for immobilization. Meanwhile, the remaining segments of the jejunum were gently scraped to sample the mucous membrane, snap-frozen in liquid nitrogen, and stored at −80°C for gene expression analysis. Then, under the condition of being germ-free, the cecal part of the bird was gathered. Its contents were speedily squeezed into sterile cryopreservation tubes and then stored in liquid nitrogen as described previously.
## Biochemical index and enzyme activity analysis
Biochemical index and enzyme activity were measured after the collected, frozen serum samples finished the two-step gradient thawing. Alanine aminotransferase (ALT), aspartate transaminase (AST), total protein (TP), alkaline phosphatase (ALP), and urea were all determined by a Cobas 6000 automatic biochemical analyzer (Roche Diagnostics Co., Ltd., Shanghai, China). The enzyme activities of glutathione peroxidase (GSH-Px), diamine oxidase (DAO), and also the malonaldehyde (MDA) concentration of serum were measured by colorimetric methods with a T9CS+ spectrophotometer (Purkinje General Instrument Co., Ltd., Beijing, China). A microplate reader detected the total antioxidant capacity (T-AOC) and superoxide dismutase (SOD). All the antioxidant indexes aforementioned were conducted according to the manufacturer's instructions.
## ELISA
Transforming growth factor-β (TGF-β), D-lactate (D-Lac), diamine oxidase (DAO), and 8-hydroxy-2′-deoxyguanosine (8-OH-dG) in the serum were measured through enzyme-linked immunosorbent assay kits (Nanjing Jiancheng Institute of Bioengineering, Nanjing, China).
## Immunohistochemical observations of jejunal secretory immunoglobulin A
Jejunum samples fixed in $10\%$ neutral-buffered formalin for over 24 h were embedded in paraffin. The 4-μm tissue slices were prepared by Leica RM2255 (Leica Biosystems, Wetzlar, Germany). Afterward, dewaxing and dehydration of the samples were executed and $3\%$ H2O2 was used to remove the endogenous peroxidase activity in slices. Next, the primary antibody (SouthernBiotech, Birmingham, AL, USA) and secondary antibody (Thermo Fisher Scientific, Fremont, CA, USA) were applied for incubation of the samples accordingly, the former left overnight at 4°C and the latter for 10 min at room temperature, along with the color reaction visualized by the DAB chromogen. The SIgA-positive cells were stained prominently brown in contrast to the surrounding tissue, which was counterstained for identifying host cells. Finally, the slides were observed under the microscope (Olympus Corporation, Tokyo, Japan).
## Total RNA extraction and gene expression in the jejunum
The total RNA was extracted from collected jejunal mucosa using the RNA Easy Fast Tissue Kit (Tiangen Biotech Co., Ltd., Beijing, China) following the standard operating procedure. Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose–ethidium bromide electrophoresis were applied to determine the concentration, purity, and integrity of the RNA. The synthesis of complementary DNA (cDNA) and further real-time PCRs in duplicate were all performed with the One Step TB Green® PrimeScriptTM RT-PCR Kit II (TaKaRa, Dalian, China) and the ABI 7500 Fast Real-Time PCR system (Applied Biosystems, Waltham, MA, USA). The information on primer sequences of Claudin-1, Occludin, ZO-1, MUC-2, and β-actin is given in Table 2. The eukaryotic reference gene β-actin was used to normalize the relative gene quantification by the 2−ΔΔCt method (Livak and Schmittgen, 2001).
**Table 2**
| Gene name | Primers (5′ to 3 ′ ) | Primers (5′ to 3 ′ ).1 |
| --- | --- | --- |
| | Forward | Reverse |
| ZO-1 | CTTCAGGTGTTTCTCTTCCTCCTC | CTGTGGTTTCATGGCTGGATC |
| Occludin | GCAGATGTCCAGCGGTTACTAC | CGAAGAAGCAGATGAGGCAGAG |
| Claudin-1 | ACAACATCGTGACGGCCCA | CCCGTCACAGCAACAAACAC |
| MUC-2 | AGGAATGGGCTGCAAGAGAC | GTGACATCAGGGCACACAGA |
| β-actin | GAGAAATTGTGCGTGACATCA | CCTGAACCTCTCATTGCCA |
## 16srRNA amplification and illumina sequences
The cecal contents at the end of 42 day were extracted using the E.Z.N.A.® Stool DNA Kit (Omega Bio-tek, Norcross, GA, USA) for microbial DNA as per the manufacturer's instructions. The DNA extract's quality was checked similarly to that of RNA. Hypervariable region V3–V4 of the bacterial 16S rRNA gene was amplified by the primer pair 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) with an ABI GeneAmp® 9700 PCR thermocycler (Applied Biosystems, Waltham, MA, USA). The specific program for the PCR amplification of 16srRNA was conducted as follows: denaturation at 95°C for 3 min, then followed by 27 cycles at 95°C for 30 s, annealing at 55°C for 30 s, then extension at 72°C for 45 s, and a final extension at 72°C for 10 min, and the final temperature was 4°C. The PCR was conducted in triplicate with a 20-μL mixture for one. The mixture was composed of 4 μL of 5 × FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL each of forward and reverse primers (5 μM), 0.4 μL of FastPfu DNA Polymerase, 10 ng of template DNA, and the ddH2O. Two percent agarose gel and the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) were used to finish the extraction and purification of the PCR product, and they were then quantified by a Quantus™ Fluorometer (Promega, Madison, WI, USA).
Purified amplicons were pooled in equal amounts and paired-end sequenced (2 × 300 bp). All the analysis was finished by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) in accordance with the standard protocol. Finally, the raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: SRP392472).
The in-house Perl script was used to demultiplex the raw FASTQ files that were quality-filtered by fastp version 0.19.6 and merged by flash version 1.2.7 with the following criteria later: (i) The bases whose quality value is below 20 bp at the end of the reads are filtered. A 50-bp window is set and the back-end bases under 20 of the average quality value are cut off, the reads containing N bases are filtered, and the quality control value should be below 50 bp; (ii) paired reads are merged into one sequence with the relationship of overlap between the PE reads. Furthermore, the length of the overlap should be longer than 10 bp; (iii) the overlap region of the spliced sequence with the allowable mismatch rate is screened out to be higher than 0.2; and (iv) the barcode and primers at both ends of the sequence are used to distinguish the samples and adjust their direction. The barcode should have no mismatched primers, and the maximum primer mismatch number is 2.
## Statistical analysis
The data were analyzed by SPSS 25.0 (SPSS Inc., Chicago, IL, USA). The Shapiro–Wilk test was initially used to assess the normality of data. The differences between samples were evaluated using the one-way analysis of variance (ANOVA) and Duncan's multiple comparisons test. Each control group was pairwise compared with GOD300, GOD600, and GOD1200 using the t-tests to assess the growth performance indexes. A tendency toward significance was considered at 0.05 ≤ $P \leq 0.1$, and the statistical significance was stated based on the value of P of < 0.05 (Granato et al., 2014).
For microbiota profiling, the processed effective reads were clustered into operational taxonomic units (OTUs) using UPARSE 7.1 (http://drive5.com/uparse/) with $97\%$ sequence similarity. Each 16S rRNA gene sequence was analyzed by the RDP Classifier algorithm (http://rdp.cme.msu.edu/) at different taxonomic levels and then against the Silva (SSU128) 16S rRNA database using a confidence threshold of $70\%$.
Rarefaction curves and α-diversity indices were calculated by Mothur v1.30.1 (Schloss et al., 2009). The similarity among the microbial communities in different samples was determined by the principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity using the Vegan v2.5-3 package. The species composition was obtained based on the taxonomic analysis. Analysis of similarities (ANOSIM) was applied to assess the significance of the microbial community differences among various treatments. The Kruskal–Wallis H test and the Wilcoxon rank-sum test were employed to explore the differences in the relative abundance of bacteria among multiple groups and then between every two groups, respectively.
Each OUT representative sequence's taxonomy level was analyzed by an RDP Classifier version 2.2 (Wang et al., 2007) using the confidence threshold of 0.7. The PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) software was used to predict the microbiome function, based on these OUT sequences. All the data aforementioned were analyzed on the platform of Majorbio I-Sanger Cloud Platform (www.i-sanger.com).
## Growth performance and the immune organ indexes
There were no significant differences in the growth performance indexes after multiple comparisons in five treatments. The specific indexes, including average daily feed intake (ADFI), average daily-weight gain (ADG), and feed:gain ratio (F:G), are shown in Table 3. Furthermore, the t-test result in Supplementary Figure S1 indicated that, during 0–21 days, the antibiotic supplement showed a significantly higher ADFI value ($P \leq 0.05$) and an F:G value ($P \leq 0.05$) than GOD1200. For days 22–42, the F:G showed a significant increase in the basal diet group when compared to GOD600 ($P \leq 0.05$) or GOD1200 ($P \leq 0.05$). Additionally, GOD and the antibiotic-supplemented groups have no significant effect ($P \leq 0.05$) on the immune organ indexes (organ weight:body weight) during the experimental periods (Table 4).
## Biochemical, cytokine, and antioxidant parameters in serum
The relevant parameters tested in serum are shown in Table 5. For biochemical parameters, ALP was significantly higher in GOD300 than in Ctr ($P \leq 0.05$) on day 21. However, no effect was observed for ALT, AST, TP, and urea ($P \leq 0.1$). For cytokines, the GOD supplementation gave rise to significant differences in TGF-β in its moderate and higher dosage groups ($P \leq 0.05$) on day 21, and the effect was also exerted in the indicator of D-Lac in GOD1200 on day 42 ($P \leq 0.05$) (Table 6). There were no significant differences in the activity of DAO in both stages. For the antioxidant parameters (Table 7), GOD1200 significantly increased the SOD activity ($P \leq 0.05$) and showed a trend toward a lower level of MDA content (0.05 ≤ $P \leq 0.1$) compared with Ctr on day 21. Moreover, it was noted that the GSH-Px activity was extremely significant ($P \leq 0.01$) at the growth anaphase of broiler in GOD1200, and no differences were found among other GOD treatment groups and the two control groups ($P \leq 0.05$).
## Jejunal secretory immunoglobulin A
The distribution of SIgA in the jejunum and the proportion of positive cell ratio on day 42 are given in Figure 1. The SIgA-positive cells were prominently stained brown compared with the surrounding tissues. Although no significant differences were observed in the basal diet and GOD-supplied groups, the positive cell ratio in aureomycin-supplied group was remarkably decreased among the whole treatments ($P \leq 0.05$).
**Figure 1:** *The effect of GOD-supplemented dietary on SIgA distribution in the jejunum of broilers on day 42 was examined by immunohistochemistry. SIgA-positive cells were stained prominently brown. The pictures were observed at 100× magnification (n = 6) (A). The ratio of positive cells was calculated and analyzed among the five treatment groups (B) (P < 0.05). Ctr, negative control fed with the basal diets; Ant, positive control fed with the basal diets added 50 mg/kg aureomycin; GOD300, GOD600, and GOD1200, the basal diets supplied with 300, 600, and 1,200 U/kg glucose oxidase, respectively. a,bMeans within a row with no common superscripts are significantly different (P < 0.05). Ctr, negative control group fed with the basal diets; Ant, positive control group fed with the basal diets added 50 mg/kg aureomycin.*
## Gene expressions in the jejunum related to intestinal tight junctions
Figure 2A shows that, compared with Ctr, GOD300, and GOD1200 showed a significant increase in the content of Mucin-2 (MUC-2) ($P \leq 0.05$) and the effect was the same as that of Ant during the first growth stage of broilers (0–21 days). There were no notable differences in the extra three jejunal junction protein genes (ZO-1, Claudin-1, and Occludin) during this period ($P \leq 0.05$). During the late growth stage (Figure 2B), we found that the relative mRNA expression of ZO-1 upregulated apparently in GOD600 and GOD1200 compared with Ctr or Ant ($P \leq 0.05$). However, no other significant differences were found in the expression of Claudin-1, MUC-2, and Occludin.
**Figure 2:** *The effect of GOD-supplemented dietary on the relative mRNA expression of jejunal junction protein genes and the mucin gene of broilers during both days 0–21 (A) and 22–42 (B) growth phases. a,bMeans within a row with no common superscripts are significantly different (n = 6, P < 0.05). Ctr, negative control fed with the basal diets; Ant, positive control fed with the basal diets added 50 mg/kg aureomycin; GOD300, GOD600, and GOD1200, the basal diets supplied with 300, 600, and 1,200 U/kg glucose oxidase, respectively. a,bMeans within a row with no common superscripts are significantly different (P < 0.05). Ctr, negative control group fed with the basal diets; Ant, positive control group fed with the basal diets added 50 mg/kg aureomycin.*
## Microbiota analysis by 16SrDNA
After filtering, an average of 50,736 reads per sample was obtained. The rarefaction curves are plotted in Supplementary Figure S2 to provide the complete evidence of adequate sequencing depth. As the result showed, every sample reached the plateau indicating an adequate sampling depth. The α-diversity of intestinal microbiota was analyzed using the indices of Shannon, Simpson, ACE, and Chao 1. The result in Table 8. The β-diversity analysis was performed to compare the overall microbial profiles and obtain the results shown in Supplementary Figure S3 without a notable difference ($P \leq 0.05$).
**Table 8**
| Variable | Ctr | Ant | GOD300 | GOD600 | GOD1200 | SEM | P-value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Shannon index | 4.12 | 3.95 | 4.06 | 4.22 | 4.15 | 0.051 | 0.645 |
| Simpson index | 0.06 | 0.07 | 0.06 | 0.05 | 0.05 | 0.012 | 0.677 |
| ACE index | 479.84 | 480.83 | 488.62 | 485.16 | 491.68 | 5.485 | 0.953 |
| Chao 1 index | 487.36 | 484.55 | 497.91 | 490.66 | 498.19 | 5.772 | 0.899 |
To assess the role of the GOD in feed, the taxonomic compositions of cecal microbes were compared at phyla and genus levels among the treatments (Figure 3). At the phylum level, it could be concluded from the relative abundance maps that Firmicutes, Bacteroidota, and Cyanobacteria are the main phyla in the cecal bacterial community of broilers. Firmicutes constituted nearly $63.32\%$ of the whole sequences followed by ~$35\%$ Bacteroidota and ~$0.68\%$ Cyanobacteria. When compared to the positive control group, broiler-fed GOD had a higher relative abundance of Firmicutes and a lower relative abundance of Bacteroidetes. At the genus level, the distribution shown in Figure 3B was summarized to show that 28 dominant genera (Alistipes, g_norank_f_norank_o_Clostridia_UCG-014, Lacto bacillus, Ruminococus_torques_group, Barnesiella, norank_f_norank_o_Clostridia_vadinBB60_group, Lachnoclostridium, unclassified_f_Lachnospiraceae, Eisenbergiella, Faecalibacterium, Blautia, Bacteroides, Christensenellaceae_R-7_group, norank_f_Ruminococcaceae, Odoribacter, Subdoligranulum, Butyricicoccus, norank_f_Eubacterium_coprostanoligenes_group, norank_f_norank_o_RF39, norank_f_Barnesiellaceae, unclassified_f_Oscillospiraceae, unclassified_f_Ruminococcaceae, norank_f_Oscillospiraceae, Colidextribacter, Phascolarctobacterium, UCG-005, NK4A214_group, and norank_f_norank_o_Gastranaerophilales.) changed among these groups after various supplementations were added to the basal diet. Specifically, after performing the Kruskal–Wallis H test, five significantly differential bacteria ($P \leq 0.05$) were noticed at the genus level in the multiple-group comparisons test (Figure 4A). They were Colidextribacter, Oscillibacter, Shuttleworthia, Flavonifractor, and Oscillospira. Subsequently, the Wilcoxon rank-sum tests were employed for each of the five bacteria to implement a pairwise comparison, and the results can be seen in Figures 4B–F. Compared with the basal diet group, GOD600 and GOD1200 showed a significant increase in the relative abundance of Oscillospira ($P \leq 0.05$). The abundance of Colidextribacter was enriched significantly in GOD600 as well ($P \leq 0.01$). More and varying degrees of effects were noticed when GOD-supplied groups were compared with Ant. All three GOD-supplemented diets caused a significant increase in the relative abundance ($P \leq 0.05$) of Oscillibacter. GOD600 produced significant ($P \leq 0.05$) and even extreme differences ($P \leq 0.01$) in the genera of Flavonifractor and Colidextribacter, respectively. A noticeable increase in Colidextribacter also took place in GOD1200 in broilers' cecal microbiota ($P \leq 0.05$). However, in contrast to the aforementioned conclusions, we noticed that Shuttleworthia exhibited different trends in the other four genera. The control group (negative and positive types) brought about a significant increase ($P \leq 0.05$) in this genus regardless of whether it was compared with GOD600 or GOD1200. There was an extreme increase in Ant when compared with GOD600 ($P \leq 0.01$). Moreover, GOD300, which exerted a few impacts on the other four genera, significantly raised the content of this genus compared with GOD600 ($P \leq 0.05$). Additionally, except for the comparisons of control groups and the additive-supplied groups, we also noticed that the abundance of Flavonifractor and *Oscillibacter* genera in the basal diet group was significantly higher than that in the antibiotic control group.
**Figure 3:** *Compositions of cecal microbiota identified under different concentrations of GOD and the antibiotic-supplied diet on day 42 of the broilers at phylum (A) and genus levels (B) (n = 6, P < 0.05). Ctr, negative control fed with the basal diets; Ant, positive control fed with the basal diets added 50 mg/kg aureomycin; GOD300, GOD600, and GOD1200, the basal diets supplied with 300, 600, and 1,200 U/kg glucose oxidase, respectively. Ctr, negative control group fed with the basal diets; Ant, positive control group fed with the basal diets added 50 mg/kg aureomycin.* **Figure 4:** *Five significantly differential bacteria (P < 0.05) of the cecal microbiota under different concentrations of GOD and the antibiotic-supplied diet on day 42 of the broilers at the genus level. (n = 6, P < 0.05) among multiple comparisons (A) and pairwise comparisons for each one (B–F). (B) for Colidextribacter, (C) for Oscillibacter, (D) for Flavonifractor, (E) for Oscillospira, and (F) for Shuttleworthia. Ctr, negative control fed with the basal diets; Ant, positive control fed with the basal diets added 50 mg/kg aureomycin; GOD300, GOD600, and GOD1200, the basal diets supplied with 300, 600, and 1,200 U/kg glucose oxidase, respectively. Ctr, negative control group fed with the basal diets; Ant, positive control group fed with the basal diets added 50 mg/kg aureomycin.*
## Functional potential of cecal microbiota composition
All alterations in the presumptive function were evaluated using PICRUSt2 in the cecal microbiota at 42 days of the broilers. There were 43 pathways predicted at level 2 of the KEGG pathways. However, no significant differences were found among them, and specific information was not shown here. Furthermore, it was found that some pathways were differentially enriched at level 3 among groups, and 289 KEGG categories were identified in total. We conducted six pairwise comparisons between each of the control groups and each concentration of the GOD-supplied groups, that is, Ctr vs. GOD300, Ctr vs. GOD600, Ctr vs. GOD1200, and Ant vs. GOD300, Ant vs. GOD600, Ant vs. GOD1200, respectively. The results revealed in Figure 5 illustrated that, compared with the basal diet (i.e., negative control group), the GOD treatments significantly influenced the abundance of six functional pathways. Meanwhile, the abundance shifted remarkably in seven functional pathways when this comparison was made between the antibiotic-supplied group (i.e., positive control group) and the GOD treatments.
**Figure 5:** *The mean proportion in predicted pathways grouped into level 3 functional categories of the cecal microbiota in various treatments on day 42 (n = 6, P < 0.05). Ctr, negative control fed with the basal diets; Ant, positive control fed with the basal diets added 50 mg/kg aureomycin; GOD300, GOD600, and GOD1200, the basal diets supplied with 300, 600, and 1,200 U/kg glucose oxidase, respectively. *Means there are significant differences in the enrichment of this pathway between the two groups. Ctr, negative control group fed with the basal diets; Ant, positive control group fed with the basal diets added 50 mg/kg aureomycin.*
Specifically, the negative control group showed a significantly larger abundance of “Riboflavin metabolism” and “Proximal tubule bicarbonate reclamation” pathways against GOD600 ($P \leq 0.05$). As for the “ABC transporters” and “Insect hormone biosynthesis” pathways, GOD1200 exerted a significant influence on making them decrease ($P \leq 0.05$). In addition, the pathways of “Proximal tubule bicarbonate reclamation,” “Protein digestion and absorption,” and “Protein processing in endoplasmic reticulum” were all differentially enriched in GOD treatment groups ($P \leq 0.05$). After the comparison between positive control and GOD groups, we observed that the former had a significantly lower abundance of several functional pathways ($P \leq 0.05$), including “Carbon fixation pathways in prokaryotes,” “Glycine, serine and threonine metabolism,” “Lysine degradation,” “Benzoate degradation,” and “Protein processing in endoplasmic reticulum.” Whereas, a significantly lower abundance of “Secondary bile acid biosynthesis” and “Riboflavin metabolism” existed in GOD ($P \leq 0.05$). The details about how different concentrations of GOD interacted with both control groups are shown in Figure 5.
## Growth performance, immune organ indexes, and serum test results
Based on the aforementioned results, the antibiotic group showcased a relatively higher feed intake and a low weight gain against GOD1200 during the stage of starting phase (1–21 days). A few research types showed this tendency of the antibiotic (Khan et al., 2012; Elagib et al., 2013). However, it implied that the GOD1200 supplement could relatively improve the feed conversion efficiency. For the same index, GOD600 and GOD1200 exerted a better efficiency than the basal diet group during the growing phase (22–42 days). These could serve as evidence for GOD in the role of a growth promoter. The F:G ratio were significantly affected by the dietary GOD supplied, especially in GOD1200, indicating that the dietary GOD-supplied could help cut costs and increase farming efficiency. Nevertheless, no other differences in growth indexes were observed, and we inferred that there were specific conditions that caused the disparity. A better feeding environment, various dosages of additives as well as the broiler's breed could result in this disparity (Hashemipour et al., 2013; Ahiwe et al., 2021). The unchanged relative weights of immune organs implied that additives or antibiotics may act to cause no significant shift in the immune organs under certain circumstances. It can be further conjectured that the three levels of GOD have no adverse effect on the development of the immune organ of the white-feathered broilers during their growth status.
Except for the relative weights of organs, some serum tests, such as biochemical, cytokines, and antioxidant indexes, could reflect the condition of the immune cells and the health level of the host medically as well. Several indexes changed significantly in the present study. A significant reduction in ALP, whose activity in blood was considered an essential indicator for assessing the adequacy of phosphorus (Li et al., 2020), was found in GOD300 on day 21 compared with the control group. Some experiments showed that the lower activity of ALP stood for a healthy broiler status (Skalicka et al., 2000), whereas we cannot draw a conclusion from this index alone. The immunity indicator, TGF-β cytokine, significantly decreased when GOD (GOD600 or GOD1200) was compared with the basal diet group. Cytokines are mainly synthesized and secreted from various immune cells in the form of polypeptides or glycoproteins. Their existence can mediate the interaction between cells that perform various biological functions (Haddad, 2002). As a member of them, TGF-β is regarded as a considerable enforcer of immune homeostasis and tolerance, especially for regulating inflammatory processes (Batlle and Massagué, 2019; Fasina and Lillehoj, 2019) with a dual effect (Pickup et al., 2017; David and Massagué, 2018). The broiler growth stage of 1–21 days is typically associated with an immature immune system (Song et al., 2021). Based on the significant TGF-β difference on day 21, we inferred that GOD- and Ant-supplied diets gave broilers a better ability toward stress stimulation at a specifically tested concentration. It could be further verified by the remarkable MDA increase and the SOD decrease in the basal diet group at 21 days, which was consistent with the former reports (Wang et al., 2018).
With the characteristic of high lipid content, broilers are easily induced to produce ROS (Bai et al., 2017). As for the results of this study, a higher content of MDA in Ctr represented attenuated antioxidant protection when the ROS increased (Yang et al., 2008). Correspondingly, SOD and GSH-Px, the two enzymes, were used to remove excess ROS (Ko et al., 2004) and showed a significant positive advantage in GOD1200. Meanwhile, the high GOD supplement exerted a similar and even better effect in contrast to the aureomycin-added diet on days 21 and 42, respectively. The changes in antioxidant parameters illustrated that the addition of GOD could reduce lipid peroxidation for broilers to a certain extent. In animals, the endogenous antioxidant defense system and immune system rely on external sources (Pamplona and Costantini, 2011). Both systems can facilitate the development of a robust antioxidant capacity. Herein, our results initially identified that the GOD could improve broilers' immune and antioxidant capacity, thereby further improving their health status.
## Immunohistochemical result and the gene expression of tight junctions in the jejunum
The gut is the most significant immune organ undertaking both the tolerance to dietary antigens and the immune defense for broilers. The organized gut-associated lymphoid tissues (GALT) in birds generate efficient responses with secretory IgA (Fagarasan et al., 2009), known as SIgA, to maintain mucosal homeostasis (Lammers et al., 2010; Curtis, 2017). For this experiment, the SIgA content showed a conspicuous decrease in the aureomycin-added group compared with all other groups. The reduction in SIgA could influence the pro-inflammatory downregulation ability of the host (Boullier et al., 2009), indicating that, after 42 days of feeding, the antibiotic did not give an advantage to the immunologic barrier. The results proved that GOD might be an advantageous alternative to antibiotics. Alongside the immunologic barrier, the mechanical barrier is the other primary component of intestinal mucosal immunity (Reynolds et al., 1996). Some studies reported that the intestinal mechanical barrier of chickens could be affected by the factor of dietary components' alteration (Fasina et al., 2006; Ma et al., 2021). In this study, the high-level GOD supplement increased the relative abundance of MUC-2 and ZO-1. These two are the major components of adherence junctions (AJs) and tight junctions (TJs), respectively, which act as the crucial parts of the physical gut barrier (Ballard et al., 1995; Anderson et al., 2012). It was reported that MUC-2 exerted a major role in protecting the intestinal epithelium in preventing infection and maintaining the integrity of the intestinal mucosal barrier (McGuckin et al., 2011). As a member of the tight junctions, the ZO-1 is negatively correlated with intestinal permeability (Alhotan et al., 2021). The results of this study indicated that the additive supplied could enhance the intestinal physical barrier of broilers. This inference can also be supported by the D-Lac's change in serum in this study, as the D-*Lac is* used to detect intestinal permeability and is an indirect indicator of the intestinal barrier (Fukudome et al., 2014; Wang J. et al., 2022). Taken together, GOD at a certain concentration can facilitate the integrity of the intestinal epithelium and enhance the mucosal immune capacity of broilers, thereby promoting their healthier living conditions.
## Intestinal microbiota comparison in the cecal contents and the functional prediction result
As the chief functional part in the distal intestine, the cecum has received increasing attention for its importance in chicken metabolism since it contains a vast majority of gut bacteria and has a significant fermentation ability with a lower passage rate (Pourabedin and Zhao, 2015). The cecal microbiota of chickens can influence the host health and productivity by regulating nutrient absorption and metabolism, immune response, and pathogen invasion (Stanley et al., 2014; Huang et al., 2018). There are a few reports on the main site of GOD's action in the intestinal segments. To the best of our knowledge, these reports include its biochemical features of oxygen consumption, gluconic acid production, and negative effects on certain pathogenic bacteria in broilers' gut (Liang et al., 2022). Therefore, to better understand and complement the current mechanism of GOD supplement in broilers and to evaluate its application effects at different concentrations, shifts in the cecal microbiota among five groups were observed in this study.
First, we noticed that the results of the data analysis for α-diversity were not statistically significant after a 42-day feeding. These results are similar to the intestinal diversity results reported after certain additives were added to the feed (Ma et al., 2021; Liu C. et al., 2022). However, it is possible that rare numbers found in a small population could make a significant difference for the host (Shang et al., 2018). Therefore, the sequences were further analyzed at phylum and genus levels to identify the cecal differential bacteria. Similar to previous studies, Firmicutes, Bacteroidota, and Cyanobacteria are the dominant phyla in the broiler cecal bacterial community (Mohd Shaufi et al., 2015; Dai et al., 2021; Segura-Wang et al., 2021).
At the genus level, Colidextribacter, Oscillibacter, Flavonifractor, Oscillospira, and Shuttleworthia emerged after multiple comparisons and were identified as biomarkers to distinguish the groups for whether supplied with GOD. The former four genera showed a significant increase in GOD600 and GOD1200 compared with control groups. The first one, Colidextribacter, has been reported to promote the production of short-chain fatty acids (SCFAs) (Oakley et al., 2014; Wang Q. et al., 2022) and inosine (Lee et al., 2020; Guo et al., 2021). Studies on chickens illustrated that SCFAs can reduce inflammation in the intestines (Wu et al., 2016). For example, butyrate can repress cell invasion in pathogenicity island caused by Salmonella, a pathogen of concern to the global poultry industry (Gantois et al., 2006), and the microbiota-derived butyrate could also effectively ameliorate certain immune system diseases (He et al., 2020). Correspondingly, substantial evidence highlighted that inosine has broad anti-inflammatory and immunomodulatory properties (Haskó et al., 2000, 2004; da Rocha Lapa et al., 2012). The second noticed genus, Oscillibacter, whose clade was regarded as a potential n-butyrate producer (Gophna et al., 2017; Contreras-Dávila et al., 2021), was placed in the family Ruminococcaceae. In recent studies, this family showed a high correlation with the increase in bodyweight and tight junction protein expression for birds (Dai et al., 2021; Farkas et al., 2022). Oscillibacter is considered a potentially beneficial microbe as it plays a crucial role in sugar fermentation (Ze et al., 2012) and starch degradation (Kim et al., 2014) and is positively associated with feed efficiency for broilers (Liu J. et al., 2021). One of its species, Oscillibacter ruminantium, was found to be negatively correlated to Salmonellac (Pedroso et al., 2021) after the investigation of chickens' cecal content. Moreover, Oscillibacter was also demonstrated to reduce blood triglyceride concentration and the negative reaction to stress for research on humans (Jiang et al., 2015; Tong et al., 2020; Liu X. M. et al., 2022). Flavonifractor is also a butyrate-producing producer (Meng et al., 2019). It was positively correlated with ADG and could improve the growth performance of broilers (Zhang et al., 2021). Some of its clades were the key to catalyzing and initiating flavonoid metabolism. Currently, many studies on the flavonoid showed its marked effects on improving growth performance and the antioxidant capacity of broilers (Kamboh and Zhu, 2013). In light of the predominance of GOD in this study and the positive correlation between this genus and GOD in the cecum, we speculated that the combination of flavonoid and glucose oxidase might exert better anti-inflammatory, bacteriostatic, and immunity enhancement effects on animals as a novel feed additive (Wang et al., 2020; Yang G. et al., 2021). Along with Oscillibacter, the *Oscillospira genus* belongs to the family of Ruminococcaceae and is also regarded as a short-chain fatty acid butyrate producer. Accordingly, it could downregulate the expression of genes encoding pro-inflammatory cytokines and prevent inflammation in the host (Cushing et al., 2015; Gophna et al., 2017). Ruminococcaceae have been reported to be highly positively correlated with the gene expression of ZO-1 (Dai et al., 2021). This can be confirmed in this study as the Oscillibacter and *Oscillospira* genera in GOD (GOD600 and GOD1200) were significantly higher than those in control groups, and the trend in ZO-1 expression was the same between GOD and control groups. Moreover, based on human research, the significant Oscillospira decrease was associated with obesity-related chronic inflammatory and metabolic diseases (Yang J. et al., 2021). Hence, it may become a next-generation probiotic candidate for symptom relief in broilers. The genus of Shuttleworthia showed a noticeable increase in control groups, especially for GOD at the concentration of 600 or 1,200 U/kg. Compared with the former four genera, the tendency was the opposite. Combined with other parameters tested in the present study, although there were some benefits found in this genus for animals, we conjectured that its negative effects played a leading role in the control groups (Liu Y. et al., 2021). These negative effects might be attenuated with the addition of glucose oxidase. *The* genera above all belong to the anaerobic genus, which could confirm that the GOD additive is conducive to creating a better anaerobic condition for the proliferation of beneficial bacteria into the gut. In addition, gluconic acid was rarely absorbed in the small intestine and primarily fermented by specific bacteria to produce SCFAs in the cecum (Biagi et al., 2006; Huyghebaert et al., 2011), which can be further verified. Furthermore, analysis of characteristic genera also provided a profound interpretation of the phenomenon of a higher F:G index in GOD.
According to the predicted functional profiles analyzed by PICRUSt, the microorganisms of broilers' cecum were mainly enriched in functions of metabolism, organismal systems, environmental information processing, and genetic information processing, of which the pathways associated with metabolism occupied the majority. Clearer differences were observed in level 3 KEGG pathways.
The metabolic pathways, such as riboflavin metabolism, insect hormone biosynthesis, and secondary bile acid biosynthesis, displayed varying degrees of benefits for broilers. The riboflavin metabolism was reported to be related to mitochondria-mediated apoptosis, and the flavoprotein participating in this metabolism may contribute to superoxide production along with mitochondrial energy metabolism (Balasubramaniam and Yaplito-Lee, 2020; Liao et al., 2021). It implied that this metabolism might play a part in oxidative stress. In our study, GOD supplementation of 600 U/kg significantly decreased this metabolic pathway in both control groups, indicating that the broilers were found to thrive in better living conditions after the GOD supply. The enrichment in the basal diet group of “insect hormone biosynthesis” may demonstrate that GOD1200 played a role in parasite removal, which contributed to a better intestinal condition and immune capacity for broilers. Moreover, the secondary bile acid biosynthesis enriched in Ant could provide a reasonable explanation for the phenomenon that the kind of biosynthesis always occurs in specific microbiota after antibiotic therapy to fight against pathogenic bacteria (Buffie et al., 2015; Koenigsknecht et al., 2015), similar to *Clostridium difficile* (Rupnik et al., 2009; Buffie et al., 2012). Notably, there were more differences in metabolic function pathways when comparing Ant and GOD. Among them, the amino acid, the energy, and the xenobiotic biodegradation and metabolism pathways were more abundant in the cecal flora in the medium and high concentration GOD. Combined with other indexes of the advantages displayed in this study, it was speculated that the aforementioned pathways mainly took effect in promoting nutrient absorption, digestion, and resistance to external disturbances. This could also be supported by significant enrichment of “protein digestion and absorption” in GOD compared with the basal diet group.
Meanwhile, the pathway of “proximal tubule bicarbonate reclamation” was reported to play an important role in maintaining the acid–base balance in host organisms (Guo et al., 2014). Since the acid–base balance can be easily disturbed by internal and external factors, such as diet, environmental conditions, and metabolism (Anrewaju et al., 2007), we inferred that the enrichment of certain genes in response to GOD (GOD600 and GOD1200) treatment may provide broilers with increased resistance to various stresses. This is supported by our findings from the antioxidant analysis in this study.
Furthermore, two pathways involved in information processing drew our attention: “protein processing in endoplasmic reticulum” and “ABC transporters.” Previous research showed that the pathway of “protein processing in endoplasmic reticulum” can have different effects on chickens depending on the conditions they are exposed to. For example, the pathway might be enriched and participate in the apoptotic process in response to toxic substances (Sun et al., 2021) or environmental changes (Srikanth et al., 2019), however, it can also be downregulated in response to the infection by parasites (Li et al., 2019). A complicated process must exist in a broiler's body in response to various conditions. The results of our study suggest that this pathway may have a positive effect on broilers treated with GOD1200, even though further research is needed for validation. Similarly, the “ABC transporters” accounting for a high abundance in the negative control group have been shown to play a role in both multidrug resistance and nutrition uptake (Rice et al., 2014; Hofmann et al., 2019). Based on the results aforementioned, we inferred that the 1,200 U/kg GOD-supplied group may help to reduce the microbial resistance in contrast to the control group.
Overall, evidence for all the pathways illustrated that specific GOD supplementations may have a beneficial effect on the health of birds, particularly in GOD1200, whereas the antibiotic additive may not. Even though the predictive tool of PICRUSt is well-used, it cannot confirm the functional capabilities of the metagenome with absolute certainty. Herein, further research is needed to validate our findings.
## Conclusion
In conclusion, our research showed that a diet supplemented with GOD resulted in a higher feed conversion efficiency and enhanced the internal body environment of broilers, and the concentration of 1,200 U/kg could be the recommended dosage based on the overall results. Unlike AGPs, which can disrupt the integrity of small intestinal epithelium and microbiota, GOD provides a non-pharmacological manner for strengthening the immunologic barrier and maintaining a healthy intestinal microecology. These findings deepen our understanding of the potential benefits of GOD as a feed additive and highlight its potential as a safe and effective substitute for AGPs as a growth promoter in poultry production.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
## Ethics statement
The animal study was reviewed and approved by Animal Care and Use Committee of the Feed Research Institute of the Chinese Academy of Agricultural Science.
## Author contributions
WZ, RW, and ZX conceived and designed the experiments. WZ, YH, and NC performed the animal experiments. WZ analyzed the data and wrote the manuscript. XS supervised and provided continuous guidance for the experiments. All authors discussed the results and reviewed the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1100465/full#supplementary-material
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|
---
title: 'Adenosine Receptors Expression in Human Retina and
Choroid with Age-related Macular Degeneration'
authors:
- Collin P. Goebel
- Yong-Seok Song
- Ismail S. Zaitoun
- Shoujian Wang
- Heather A. D. Potter
- Christine M. Sorenson
- Nader Sheibani
journal: Journal of Ophthalmic & Vision Research
year: 2023
pmcid: PMC10020792
doi: 10.18502/jovr.v18i1.12725
license: CC BY 4.0
---
# Adenosine Receptors Expression in Human Retina and
Choroid with Age-related Macular Degeneration
## Abstract
### Purpose
Adenosine signaling modulates ocular inflammatory processes, and its antagonism mitigates neovascularization in both newborns and preclinical models of ocular neovascularization including age-related macular degeneration (AMD). The adenosine receptor expression patterns have not been well characterized in the human retina and choroid.
### Methods
Here we examined the expression of adenosine receptor subtypes within the retina and choroid of human donor eyes with and without AMD. Antibodies specifically targeting adenosine receptor subtypes A1, A2A, A2B, and A3 were used to assess their expression patterns. Quantitative real-time PCR analysis was used to confirm gene expression of these receptors within the normal human retina and choroid.
### Results
We found that all four receptor subtypes were expressed in several layers of the retina, and within the retinal pigment epithelium and choroid. The expression of A1 receptors was more prominent in the inner and outer plexiform layers, where microglia normally reside, and supported by RNA expression in the retina. A2A and A2B showed similar expression patterns with prominent expression in the vasculature and retinal pigment epithelium. No dramatic differences in expression of these receptors were observed in eyes from patients with dry or wet AMD compared to control, with the exception A3 receptors. Eyes with dry AMD lost expression of A3 in the photoreceptor outer segments compared with eyes from control or wet AMD.
### Conclusion
The ocular presence of adenosine receptors is consistent with their proposed role in modulation of inflammation in both the retina and choroid, and their potential targeting for AMD treatment.
## INTRODUCTION
Age-related macular degeneration (AMD) is an inflammatory driven neurodegenerative disorder that develops in the elderly due to a combination of genetics, the environment, and other factors. It contributes to substantial irreversible central vision loss in the industrialized countries and was present in an estimated $6.5\%$ of all American adults over the age of 40 as of 2011.[1] AMD is characterized by the deposition of cellular debris, called drusen between the retina and choroid, and divided into the categories of wet and dry AMD.[2] In patients with wet AMD, subretinal neovascularization occurs, which may lead to edema and hemorrhage. In contrast, dry AMD is characterized by drusen and retinal degeneration without neovascularization or hemorrhage. Currently, treatment for AMD, especially dry AMD, is very limited. Antibodies against vascular endothelial growth factor (VEGF) improve visual outcomes in most patients with wet AMD.[3,4] In addition, antioxidant vitamins and minerals slow down the progression of moderate or severe dry AMD but fail to prevent the development of moderate AMD from mild AMD.[5] Thus, these therapeutics are best for preventing progression in patients who have already developed moderate or severe disease and are unable to halt or reverse the disease process.
To develop better therapeutic targets for patients with AMD, a more complete understanding of its pathophysiology is required. The dysfunction and death of retinal pigment epithelial (RPE) cells and degeneration of photoreceptors are observed in AMD. However, the detailed cellular and molecular mechanisms driving this degeneration needs further investigation. Several hypotheses have been proposed, including accumulation of toxins, dysfunction of mitochondria, and damage from reactive oxygen species in the RPE cells and choroidal vasculature.[5,6,7,8] Recently, immune and inflammatory regulatory pathways have been proposed to be the central components in the pathophysiology of AMD, and preclinical models have demonstrated complement and IgG deposition in the RPE and choroid of mice with AMD.[5,8,9,10] Recent investigations indicate the importance of adenosine receptors in modulation of ocular inflammatory processes. Adenosine elicits its effects through its G-protein coupled receptors: A1, A2A, A2B, and A3.[11] Adenosine receptor A1 activation inhibits calcium influx-induced release of neurotransmitters in the central nervous system (CNS) under hypoxic conditions, creating a neuroprotective effect.[12] Adenosine receptor A3 engagement has both pro- and anti-inflammatory properties throughout the body, including the lung, cardiac, and gastrointestinal systems.[10] Blockade of the adenosine A2A receptor in microglial cells reduces inflammatory responses and photoreceptor cell loss in cultured human cells. Furthermore, adenosine receptors A2A and A2B expression are upregulated by hypoxia-inducible factor during hypoxic conditions and inflammation in the eye,[13,14] and their antagonism blocks ischemia-mediated retinal neovascularization.[15] Thus, the process of inflammation and angiogenesis in dry and wet AMD could be linked with adenosine receptor signaling. However, the function of these receptors in the RPE and choroid, and their potential activity in pathophysiology of AMD, needs further evaluation.
The importance of adenosine receptor signaling pathways in ocular inflammatory and neovascular diseases has been further supported by studies of caffeine, an adenosine receptor antagonist, in both preclinical models and humans.[16] Maugeri and colleagues found evidence that caffeine decreases the permeability of the RPE layer and thus may inhibit the development of macular edema.[17] In addition, caffeine administered to infants born prematurely for apnea diminishes the severity of retinopathy of prematurity.[18] We recently showed that caffeine is efficacious in mitigating choroidal neovascularization in a preclinical model of wet AMD.[19] However, the identity of adenosine receptor(s) involved in these activities remains unknown, and results have yet to be verified in humans. Here we assessed the presence of specific adenosine receptors in the retina and choroid of human donor eye samples from control and patients with wet and dry AMD. These studies, to the best of our knowledge, are the first to demonstrate the presence of specific adenosine receptors in the human retina and choroid and examine whether their expression pattern is altered under AMD conditions.
## Human Donor Eyes and Other Materials
Deidentified human ocular samples were from the Lion Gift of Sight (St. Paul, MN). The eyes were collected by written consent from donors or donors' family for medical research as delineated by the Declaration of Helsinki. We were provided with a list of 28 potential donor samples with histological evaluations, of which 13 were control eyes and 15 were eyes with AMD. Eyes from two donors with wet AMD, two donors with dry AMD, and two donors with no AMD were selected by the help of our ocular pathologist from the available samples. Each experimental group contained samples matched by age and gender, and all samples included the macula. Presumptive diagnoses were confirmed histologically. Anti-ADORA1 (55026-1-AP, Proteintech, Rosemont, IL), anti-ADORA2A (PA1-042), anti-ADORA2B (PA5-72850), and anti-ADORA3 (PA5-36350) were obtained from Thermo Fisher Scientific (Carlsbad, CA). Anti-collagen IV antibody was from Southern Biotech (1340-01; Birmingham, AL). Cy5-labeled anti-goat [705-175-147] and Cy2-labelled anti-rabbit [305-225-045] were obtained from Jackson ImmunoResearch Laboratories (West Grove, PA).
## Antibody Staining and Microscopic Analysis of Eye Sections
Four paraffin sections, taken from each donor eye, were placed on glass slides. Sections were washed with xylene four times for five min. This was followed by two washes in $100\%$ and $95\%$ ethanol for 10 min, and the pure water for 5 min. Slides were then heated in a citrate solution (H-3300, Vector Laboratories, Burlingame, CA) for 11 min to retrieve epitopes. For each set of samples, slides were then stained overnight with 750 μ L ADORA1, ADORA2A, ADORA2B, and ADORA3 primary antibodies, diluted in blocking buffer (1:500; PBS with $1\%$ bovine serum albumin, $0.2\%$ skim milk powder, and $0.3\%$ Triton-X100). Diluted Anti-collagen IV antibody (1:500) was added to each sample to target vasculature in the samples, and DAPI diluted 1:1000 was added to each sample to visualize the cellular nuclei of the retina and choroid. The slides were then rinsed with PBS buffer three times for 5 min, and 750 μ L of appropriate secondary antibodies (diluted 1:500 in PBS blocking buffer) were added to each sample. Slides were incubated at room temperature for 4 h allowing the visualization of collagen in the vasculature and adenosine receptor expression.
Following staining with primary and secondary antibodies, the expression of the A1, 2A, 2B, and A3 receptors in donor eyes with wet AMD, dry AMD, or no AMD were compared using fluorescence microscopy. Light intensity and exposure time were standardized for each group of slides under the microscope. Photographs were taken of fluorescence emission patterns for the adenosine receptor, collagen IV, and DAPI located within the macula and underlying choroid. The fluorescence intensity in each sample was then compared to determine the predominant location of each adenosine receptor in the retina and choroid, as well as look for differences in adenosine receptor expression between wet and dry AMD compared to control.
## RNA Isolation and Quantitative PCR (qPCR) Analysis
The retina and RPE/choroid were dissected from at least three non-diseased human eyes of similar age (male and female) and cut into smaller pieces in cold PBS. The tissue samples were snap frozen in liquid nitrogen and stored at –80ºC for RNA preparation. Tissue samples (50–100 mg) were dissolved in 1 mL of Trizol reagent (Invitrogen, San Diego, CA). Total RNA was extracted using RNeasy mini kit as recommended (Qiagen, Valencia, CA). Complementary DNA was prepared using 1 μg of total RNA and the RNA to cDNA EcoDry Premix (TaKaRa, Mountain View, CA) and diluted 1:10. qPCR was conducted in triplicates using a Mastercycler Realplex (Eppendorf, Enfield, CT) and TB-Green qPCR Premix (TaKaRa). The cycles for amplification were 95ºC for 2 min; 40 cycles of amplification (95ºC for 15 s, 60ºC for 40 s); and dissociation curve step (95ºC for 15 s, 60ºC for 15 s, 95ºC for 15 s). The relative fluorescent units (RFUs) at a threshold fluorescence value (Ct) were used for linear regression line and assessment of nanograms of DNA. The target gene expression levels were determined by comparing the RFU at the Ct to the standard curve and normalized by the housekeeping gene ribosomal protein L13α (RPL13A). The primer sequences used in this study are listed in Table 1. Each sample was run in triplicates.
## Statistical Analysis
Differences between the expression level of ADORA in the retina and RPE/Choroid were evaluated using t-tests and GraphPad Prism version 8 (GraphPad Software, La Jolla, CA). $P \leq 0.05$ was considered significant. Data are the mean ± standard deviation.
**Figure 1:** *Expression ofADORA1 in retinal and choroidal cross sections. (A) ADORA1, (B) collagen IV, and (C) merged images and DAPI staining of eye sections with no AMD (Control). (D) A higher magnification of C. (E) ADORA1, (F) collagen IV, and (G) merged images and DAPI staining of eye sections with dry AMD. (H) A higher magnification of G. (I) ADORA1, (J) collagen IV, and (K) merged images and DAPI staining of eye sections with wet AMD. (L) Higher magnification of K. Scale bar = 400 µm (A, B, C, E, F, G, I, J, and K) and 200 µm (D, H, and L).
V, vitreous; C, choroid.* **Figure 2:** *Expression ofADORA2A in retinal and choroidal cross sections. (A) ADORA2A, (B) collagen IV, (C) merged images and DAPI staining of eye sections with no AMD (Control). (D) A higher magnification of C. (E) ADORA2A, (F) collagen IV, and (G) merged images and DAPI staining of eye sections with dry AMD. (H) A higher magnification of G. (I) ADORA2A, (J) collagen IV, and (K) merged images and DAPI staining of eye sections with wet AMD. (L) A higher magnification of K. Scale bar = 400 µm (A, B, C, E, F, G, I, J, and K) and 200 µm (D, H, and L).
V, vitreous; C, choroid.* **Figure 3:** *Expression ofADORA2B in retinal and choroidal cross sections. (A) ADORA2B, (B) collagen IV, and (C) merged images and DAPI staining of eye sections with no AMD (Control). (D) A higher magnification of C. (E) ADORA2B, (F) collagen IV, (G) merged images and DAPI staining of eye sections with dry AMD. (H) A higher magnification of G. (I) ADORA2B, (J) collagen IV, and (K) merged images and DAPI staining of eye sections with wet AMD. (L) A higher magnification of K. Scale bar = 400 µm (A, B, C, E, F, G, I, J, and K) and 200 µm (D, H, and L).
V, vitreous; C, choroid.* **Figure 4:** *Expression ofADORA3 in retinal and choroidal cross sections. (A) ADORA3, (B) collagen IV, and (C) merged images and DAPI staining of eye sections with no AMD (Control). (D) Higher magnification of C. (E) ADORA3, (F) collagen IV, and (G) merged images and DAPI staining of eye sections with dry AMD. (H) higher magnification of F. (I) ADORA3, (J) collagen IV, and (K) merged images and DAPI staining of eye sections with wet AMD. (L) Higher magnification of K. Scale bar = 400 µm (A, B, C, E, F, G, I, J, and K) and 200 µm (D, H, and L).
V, vitreous; C, choroid.* **Figure 5:** *Quantitative PCR results demonstrating relative mRNA expression of adenosine receptor subtypes in a human eye sample without AMD. Blue bars display mRNA expression for each receptor in the retina, and orange bars display mRNA expression for each receptor in the choroid. ADORA1 and ADORA2B showed significantly higher levels in the retina, while ADORA2A and ADORA3 showed significantly higher levels in the choroid. *$P \leq 0.05$, $$n = 3$.$* TABLE_PLACEHOLDER:Table 1
## Adenosine Receptors Expression in Retina and Choroid
Each eye section selected for fluorescent staining was from a patient between the ages of 76 and 100 years old at the time of death. One male and one female sample was chosen for each of the AMD and control groups. Each sample was preserved within 28 h of the patient's death. All samples demonstrated successful staining with each of the adenosine receptor antibodies. Furthermore, vascular staining with collagen IV antibody and nuclear staining with DAPI were performed for each of the samples.
Adenosine receptor A1 demonstrated expression throughout the retina. A1 expression was particularly prominent in the outer plexiform layer (OPL), inner photoreceptor layer, and inner plexiform layer (IPL) of the retina. Retinal pigment epithelium (RPE) was also positive. No noticeable differences in choroidal vascular or retinal expression of the A1 were appreciated between patients with AMD compared to control patients regardless of sex. Representative images of A1 receptor staining in the retina and choroid are shown in Figure 1.
Receptor A2A primarily demonstrated expression within the retinal and choroidal vasculature, with a modest expression within the outer (ONL) and inner nuclear layers (INLs). The expression of the A2A was also detected in the RPE and was similar in patients with AMD compared to control eyes. However, there did appear to be a modest decrease in A2A receptor expression in retinal and choroidal vasculature in samples from patients with wet and dry AMD. There did not appear to be a dramatic difference in receptor A2A expression between patients with wet AMD compared with dry AMD [Figure 2].
Receptor A2B demonstrated expression throughout the retina in all samples, particularly the ganglion cell layer, ONL, and INL, and RPE. Receptor A2B was also strongly expressed in the retinal and choroidal vasculature in all samples. However, there was no dramatic difference in A2B receptor staining in the retina, retinal vasculature, or choroidal vasculature of patients with AMD compared with control eyes. Representative images of retinal and choroidal staining with antibodies to the A2B receptor are shown in Figure 3.
Receptor A3 was expressed primarily within the ganglion cell layer, INL, and ONL of the retina, and RPE. There was also some A3 receptor expression in the retinal and choroidal vasculature of each sample. A dramatic differences in A3 staining was observed in dry AMD samples compared with control and wet AMD samples. The dry AMD samples lost the expression of A3 receptor in the photoreceptor outer segments, which was prominently present in control and wet AMD samples. However, no additional differences in the intensity of staining were observed in retina or choroid in patients with AMD compared to those with no AMD. Representative images of A3 receptor staining are shown in Figure 4.
## Adenosine Receptors mRNA Expression in Retina and RPE/Choroid Tissues
Quantitative PCR of cDNA prepared from the retinal and choroidal/RPE tissues from normal human eyes demonstrated notable expression of adenosine receptors within both the retina and choroid. Adenosine receptor A1 displayed gene expression primarily within the retina, with little to no choroidal expression. This was consistent with predominant immunostaining of A1 receptor in IPL and OPL, where microglia are normally residing. We previously showed predominant expression of A1 receptor in mouse microglia and retinal vascular cells.[19] Receptors A2A, A2B, and A3 displayed gene expression within both the retina and choroid. Although A1 receptor expression was significantly lower, the expression of A2A and A3 were significantly higher in the choroid/RPE compared with the retina, as we previously reported in human and mouse tissue samples.[19] The average relative expression of each adenosine receptor in both the retina and choroid/RPE is shown in Figure 5.
## DISCUSSION
The role of adenosine receptors in inflammatory pathways, as well as prior clinical and preclinical studies of their antagonism with caffeine suggest that these receptors may play important roles in the development of neurodegenerative diseases such as AMD. However, previous literature has not sufficiently examined the distribution of the adenosine receptor subtypes in the human retina and choroid. Furthermore, this is the first study to compare the expression patterns of adenosine receptors in the eyes of patients with and without AMD.
Collectively, our qPCR and antibody staining experiments demonstrated that adenosine receptors are widely expressed throughout the human eye and are present within both the human retina and choroid. Antibody staining suggested that receptors A1, A2A, A2B, and A3 are widely expressed in multiple layers of the human retina, providing further support for the importance of these receptors in the human ocular homeostasis and pathophysiology of AMD. A recent study involving zebrafish studied the expression of all four adenosine receptor subtypes, reporting A2A and A2B receptors in both the inner and outer plexiform and nuclear layers, and the ganglion cell layer.[20] Our results demonstrated similar distribution of the A2B receptors throughout the retina, but we primarily observed the A2A receptors expressed within the vasculature. Much like our results, A3 receptors were primarily in the inner and ONLs and A1 receptors were primarily in the inner and OPLs.[20] Therefore, our results indicate some overlap with the preclinical models mapping the expression of adenosine receptors in the retina, but we did find differences in human retinal expression compared to animal models.
Prior human studies have examined the location of adenosine A1 receptors in the retina of humans and other mammals. Like our results, these studies demonstrated A1 expression in both the inner and outer retina, with expression in the inner plexiform, ganglion cell, inner nuclear, and photoreceptor layers.[21,22] A separate study demonstrated the presence of A2 receptors within the human RPE but did not attempt to map the A2 receptors throughout the retina.[23] In addition to the human neuroretina, receptors A2A, A2B, and A3 were strongly expressed within the retinal and choroidal vasculature. Studies have suggested that the loss of the inner choroidal vascular layer is associated with the development of AMD and likely to occur due to inflammation within the choroid.[24] Our observation of adenosine receptors within the choroidal vasculature and their involvement in inflammation suggests they may also have a role in hallmark AMD changes within the choroid.
We hypothesized that altered A2A expression in patients with wet and dry AMD could contribute to pathophysiology of AMD. Prior studies have suggested that A2A receptor stimulation is pro-inflammatory, and antagonism of the A2A receptor can prevent neovascularization in the retina.[13,14][15] Interestingly, our results suggest there may be a modest decrease in the expression of the A2A receptor in patients with AMD disease process, which requires further verification in future studies. Therefore, a change in the A2A receptor levels in the human retina and choroid may disrupt normal signaling in the eye potentially contributing to pathogenesis of AMD.
A previous study in our lab examined the effect of caffeine, an adenosine receptors antagonist, and istradefylline, a specific A2A receptor antagonist, on choroidal neovascularization after laser-induced rupture of Bruch's membrane. These studies demonstrated that antagonism of adenosine receptors, particularly A2A, was successful in inhibiting choroidal neovascularization. We also demonstrated that caffeine inhibits the migration of retinal and choroidal endothelial cells.[19] Thus, the results of the current study suggesting that the A2A receptor may have altered expression in human eyes with AMD fits well with prior findings. Together these results suggest a potential role for adenosine receptor antagonism in preventing changes associated with AMD.
In our previous study we noted variable and limited expression of A3 receptor in retina and choroid/RPE tissues from mouse eyes.[19] However, here we noted significant expression of A3 receptor in human eye sections with predominant expression in the choroid/RPE. The immune staining of the eye sections from dry AMD patients lacked A3 staining in the photoreceptor outer segments, which was predominantly present in eye sections from control and wet AMD patients. Thus, downregulation of A3 receptor expression may specifically contribute to loss of photoreceptor cells in dry AMD and awaits future studies of its significance in pathophysiology of dry AMD.
Overall, this study suggests that adenosine receptors are present throughout the retina and choroidal vasculature and supports the potential role of adenosine as a key signaling molecule and inflammatory mediator in pathophysiology of AMD. Furthermore, there may be changes in the levels of adenosine receptor A2A and A3 expression in patients who have AMD. However, the number of samples evaluated here were limited and awaits further confirmation of these results using additional samples in future studies as more suitable samples become available. We propose it is possible that the adenosine receptors contribute to the development of both wet and dry AMD and are suitable candidates to be targeted in the ongoing search for AMD therapeutics in future studies.
## Ethical Considerations
The human eyes were obtained from Lions Gift of Sight (formerly known as Minnesota Lions Eye Bank, Saint Paul, MN) with the written consent of the donor or the donor's family for use in medical research in accordance with the Declaration of Helsinki. Lions Gift of *Sight is* licensed by the Eye Bank Association of America (accreditation #0015204) and accredited by the FDA (FDA Established Identifier 3000718528). Donor tissue is considered pathological specimens and is therefore exempt from the process of Institutional Review Board approval.
## Acknowledgements
The authors would like to thank the Lion Gift of Sight (St. Paul, MN) personnel for obtaining the eyes and preparing the tissues for immunostaining. They are also thankful to the donors and their families for their valuable contributions to the research.
## Financial Support and Sponsorship
This work and/or the investigator(s) were supported by an unrestricted award from Research to Prevent Blindness to the Department of Ophthalmology and Visual Sciences, Retina Research Foundation, RRF/Daniel M. Albert chair, and by National Institutes of Health grants P30 EY016665, R01 EY026078, EY030076, EY032543, and HL158073. CPG was recipient of a VitreoRetinal Surgery Foundation research award, Edina, MN.
## Conflicts of Interest
The authors declare no conflict of interest.
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|
---
title: Development of a risk score model for the prediction of patients needing percutaneous
coronary intervention
authors:
- Xi Wang
- Yuping Lin
- Feng Wang
journal: Journal of Clinical Laboratory Analysis
year: 2023
pmcid: PMC10020842
doi: 10.1002/jcla.24849
license: CC BY 4.0
---
# Development of a risk score model for the prediction of patients needing percutaneous coronary intervention
## Abstract
This nomogram predicts the probability of needing PCI in patients with coronary heart disease using 12 indicators. To use the nomogram, the patient variables are positioned on the corresponding axes, a line is drawn to obtain a score, and the p value corresponding to the summed total score is the predicted probability. ( as marked in red in the Image, the total score for this patient is 698, corresponding to a p‐value of 0.985). The graphs are marked with “*” for $p \leq 0.05$, “**” for $p \leq 0.01$, and “***” for $p \leq 0.001$, indicating its significant effect on the predicted results.
### Background
The incidence of coronary heart disease (CHD) is increasing worldwide. The need for percutaneous coronary intervention (PCI) is determined by coronary angiography (CAG). As coronary angiography is an invasive and risky test for patients, it will be of great help to develop a predicting model for the assessment of the probability of PCI in patients with CHD using the test indexes and clinical characteristics.
### Methods
A total of 454 patients with CHD were admitted to the cardiovascular medicine department of a hospital from January 2016 to December 2021, including 286 patients who underwent CAG and were treated with PCI, and 168 patients who only underwent CAG to confirm the diagnosis of CHD were set as the control group. Clinical data and laboratory indexes were collected. According to the clinical symptoms and the examination signs, the patients in the PCI therapy group were further split into three subgroups: chronic coronary syndrome (CCS), unstable angina pectoris (UAP), and acute myocardial infarction (AMI). The significant indicators were extracted by comparing the differences among the groups. A nomogram was drawn based on the logistic regression model, and predicted probabilities were performed using R software (version 4.1.3).
### Results
Twelve risk factors were selected by regression analysis; the nomogram was successfully constructed to predict the probability of needing PCI in patients with CHD. The calibration curve shows that the predicted probability is in good agreement with the actual probability (C‐index = 0.84, $95\%$ CI = 0.79–0.89). According to the results of the fitted model, the ROC curve was plotted, and the area under the curve was 0.801. Among the three subgroups of the treatment group, 17 indexes were statistically different, and the results of the univariable and multivariable logistic regression analysis revealed that cTnI and ALB were the two most important independent impact factors.
### Conclusion
cTnI and ALB are independent factors for the classification of CHD. A nomogram with 12 risk factors can be used to predict the probability of requiring PCI in patients with suspected CHD, which provided a favorable and discriminative model for clinical diagnosis and treatment.
## INTRODUCTION
Coronary heart disease (CHD), one of the leading causes of death worldwide, is defined as atherosclerosis, thromboembolism, or spasm of the blood vessels supplying blood and oxygen to the myocardial cells, causing luminal narrowing or even occlusion, which in turn leads to myocardial ischemia and hypoxia or even necrosis. 1 The incidence of CHD has been increasing recently and has been getting more common among young individuals. According to the China Cardiovascular Health and Disease Report 2020, cardiovascular disease deaths accounted for the first cause of total deaths among urban and rural residents in China in 2018, with 330 million cardiovascular patients, including about 11.39 million with CHD. The prevalence and mortality of CHD are continuously rising, and seriously threaten human health. 2 The fast‐paced life, intense work pressure, dietary habits, obesity, and erratic lifestyle are closely related to CHD. 3, 4, 5, 6 Pharmacologic therapy is fundamental for the stabilization or abatement of coronary atherosclerotic plaques and preventing diseases such as coronary thrombosis, acute myocardial infarction, and sudden cardiac arrest. Revascularization by percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) can further reduce angina, improve life quality, and increase infarct‐free survival. 7 However, many studies have shown that in most patients with stable angina, the benefits of coronary revascularization are limited to improving the quality of life rather than reducing cardiovascular events. 8 Coronary angiography is a gold standard for evaluating the degree of coronary artery stenosis in coronary atherosclerotic heart disease. However, it is a risky and invasive procedure for patients. Therefore, in this study, we reviewed the clinical characteristics and laboratory indexes of 454 patients, analyzed the relevant impact factors, and developed a model to predict whether PCI is required, hence exploring a new model to identify CHD requiring PCI economically and feasibly, which will reduce unnecessary medical consumables and alleviate the burden on the healthcare system.
## Study design and participants selection
From January 2016 to December 2021, a total of 454 patients with CHD were admitted to the cardiovascular medicine department. Of the collected patients, 286 underwent CAG and were treated with PCI, and the remained 168 underwent CAG confirmed the diagnosis of CHD only were put into the control group. According to the clinical symptoms and examination results, the patients in the therapy group were split into three subgroups: 115 patients with chronic coronary syndrome (CCS), 68 patients with unstable angina pectoris (UAP), and 103 patients with acute myocardial infarction (AMI). The 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes served as the foundation for the grouping criteria. 9 The inclusion criteria of current study are CDH diagnosed by CAG, the criteria for intervention with PCI were performed according to the Chinese Guidelines for Percutaneous Coronary Intervention [2016]: I. Stable coronary heart disease (SCAD) should be based on the degree of coronary stenosis as a decision basis for whether to intervene, with direct intervention when the lumen stenosis is ≥$90\%$; when the lumen stenosis is <$90\%$, only corresponding intervention if there is evidence of ischemia or a flow reserve fraction (FFR) ≤0.8. II. Patients with non‐ST‐segment elevation acute coronary syndrome (NSTE‐ACS) without ECG ST‐segment elevation are recommended for the first time to have high‐sensitivity troponin testing as their early diagnosis (I, A). III. Emergency coronary angiography should be performed in very high‐risk NSTE‐ ACS patients (<2 h) (I, C); Early CAG is recommended in high‐risk patients, and the choice of an invasive intervention strategy is based on the lesion (<24 h) (I, A). 10 Exclusion criteria: I. Previous history of coronary stenting or coronary artery bypass surgery; II. Severe heart failure and ventricular arrhythmias; III. Malignancy or history of major surgery. The study protocol was reviewed and approved by the Ethics Committee of Ningbo Medical Center Li Huili Hospital (NO. KY2022PJ188).
## Data collection
General clinical data of all patients were recorded, including age, sex, history of hypertension, history of diabetes mellitus, smoking history, and alcohol consumption. After admission, patients underwent echocardiography, and their ejection fraction was recorded. Laboratory tests of routine blood, biochemistry, glycosylated hemoglobin, cardiac markers, and coagulation function were recorded and compiled.
## Test method
Routine blood tests were performed using the Sysmex XN‐9000 hematology analyzer. Biochemical indexes were detected using the Siemens ADVIA2400 biochemical analyzer with reagents from Ningbo PREB Biotechnology Company. Glycosylated hemoglobin was detected using the Bio‐Rad D‐100 instrument. High‐sensitivity troponin‐I, creatine kinase, and creatine kinase isoenzyme were detected by Johnson & Johnson VITRO5600 dry biochemical immunoassay machine. The coagulation function was tested using Wolfen ACL‐TOP700 automatic hemagglutination instrument.
## Statistical analysis
SPSS 25.0 statistical software was used for data processing and analysis. The measurement data conformed to normal distribution or approximately normal distribution were expressed as x ± s, and the independent sample t test was used for comparison, Analysis of variance (ANOVA) was used for comparison among the three subgroups, and the chi‐square test was used before ANOVA, and the F test was used when the variance was the same, and the Welch test was used when the variance was different; skewed distribution was expressed as M (Q R), and rank‐sum test was used for comparison; count data were expressed as the number of cases, and chi‐square test was used for comparison between groups. Univariate and multifactorial analyses were performed to analyze the differences of each index in each group of patients, and then the statistically significant indexes in the analysis results as well as other meaningful indexes were included in the logistic regression analysis, the fitted model was obtained after stepwise regression using R4.1.3 software, and 12 predictors were obtained and plotted in nomogram. The probability of patients with suspected CHD requiring PCI could be obtained by the nomogram scores. $p \leq 0.05$ was considered a statistically significant difference.
## Comparison of general clinical data between the PCI treatment group and the control group
Male and Diabetic patients account for a higher proportion of PCI cases. There was no statistically significant difference in age, smoking history, drinking history, and hypertension history between the two groups (all p values >0.05). As shown in Table 1, the proportion of male and diabetic patients was significantly higher in the PCI treatment group than in the control group (all p values <0.05).
**TABLE 1**
| Variables | Treatment group | Control group | t/χ 2 | p value |
| --- | --- | --- | --- | --- |
| Age (years, x ± s) | 61.27 ± 11.18 | 62.85 ± 11.05 | 1.465 | 0.935 |
| Gender/(male/female) | 207/79 | 104/64 | 5.38 | 0.02 |
| Smoking/(positive/negative) | 133/153 | 64/104 | 3.046 | 0.081 |
| Drinking/(positive/negative) | 89/197 | 49/119 | 0.191 | 0.662 |
| Hypertension (positive/negative) | 182/104 | 111/57 | 0.274 | 0.601 |
| Diabetes/(positive/negative) | 76/210 | 22/146 | 11.358 | 0.001 |
## Comparison of testing indexes between two groups of patients
Twenty‐four testing indexes show significant difference between the treated and control groups before coronary angiography. The examination items and laboratory indices from patients without missing indices. The statistically significant differences were found in ejection fraction, creatine kinase, creatine kinase isoenzyme, high‐sensitivity troponin I, glucose, glycosylated hemoglobin, fructosamine, alanine aminotransferase, aspartate aminotransferase, lipoprotein a, total bile acids, lactate dehydrogenase, high‐sensitive C‐reactive protein, white blood cell count, absolute neutrophil count, absolute monocyte count, fibrinogen, normal prothrombin time, red blood cell count, ferritin, unconjugated iron, potassium, and creatinine (all p values <0.05). For the data high‐sensitivity troponin I levels, a skewed distribution and string data types exist, so the data were converted to a categorical variable, that is, 0–0.034 was normal and >0.034 was high, as shown in Table 2.
**TABLE 2**
| Variables | Treatment group (286) | Control group (168) | p value |
| --- | --- | --- | --- |
| EF | 0.64 (0.06) | 0.66 (0.07) | 0.011 |
| CK/(U/L) | 89 (82) | 82 (48) | 0.042 |
| CKMB/(U/L) | 14.8 (10.9) | 12.65 (6.9) | 0.001 |
| hs‐cTnI/(normal/high value) | 142/133 | 117/15 | 0.0 |
| GLU/(mmol/L) | 6.15 ± 2.40 | 5.18 ± 1.35 | 0.0 |
| GHbA1/(%) | 8.55 ± 1.57 | 7.9 ± 0.90 | 0.0 |
| GHbA1c/(%) | 6.34 ± 1.36 | 5.82 ± 0.75 | 0.0 |
| GSP/(mmol/L) | 1.99 ± 0.56 | 1.85 ± 0.33 | 0.0 |
| ALT/(U/L) | 23 (21) | 20 (16) | 0.0 |
| AST/(U/L) | 23 (19) | 20 (7) | 0.0 |
| AST/ALT | 1.64 ± 1.40 | 1.11 ± 0.45 | 0.0 |
| LPa/(g/L) | 0.17 (0.25) | 0.13 (0.26) | 0.029 |
| TBA/(μmol/L) | 5.33 ± 4.55 | 6.98 ± 6.48 | 0.025 |
| LDH/(U/L) | 188 (82) | 170 (37) | 0.0 |
| hsCRP/(mg/L) | 9.52 ± 21.15 | 3.16 ± 8.25 | 0.0 |
| WBC/(x109/L) | 7.30 ± 2.75 | 6.29 ± 1.91 | 0.0 |
| NE#/(x109/L) | 4.70 ± 2.48 | 3.78 ± 1.69 | 0.0 |
| MO#/(x109/L) | 0.55 ± 0.24 | 0.49 ± 0.17 | 0.0 |
| FIB/(g/L) | 4.29 ± 1.21 | 3.77 ± 0.78 | 0.0 |
| NPT/(s) | 11.2 ± 0.10 | 11.31 ± 0.15 | 0.003 |
| FIB/ALB | 0.11 ± 0.04 | 0.09 ± 0.02 | 0.0 |
| RBC/(x1012/L) | 4.54 ± 0.60 | 4.46 ± 0.49 | 0.012 |
| FER/(μg/L) | 219.5 ± 224 | 163.4 ± 176.9 | 0.0 |
| UIBC/(μmol/L) | RBC/(x1012/L) | 35.83 ± 9.97 | 0.012 |
| K/(mmol/L) | 3.94 ± 0.41 | 3.96 ± 0.32 | 0.029 |
| CREA/(μmol/L) | 70.85 (23) | 67.5 (22.6) | 0.029 |
## Comparison of clinical data of the three subgroups of the treatment group
Among the three subgroups (CCS, UAP, and AMI), gender and age showed statistically significant difference between the CCS and AMI groups ($p \leq 0.05$), with lower proportion of males in the CCS group and younger patients in the AMI group (Figure 1). This result indicates that older patients are more likely to have CCS and younger patients are more likely to have AMI.
**FIGURE 1:** *Comparative results of clinical data of patients between the three subgroups of the treatment group.*
## Comparison of laboratory indicators in the three subgroups of the treatment group
The laboratory indicators such as ejection fraction, creatine kinase, creatine kinase‐MB isoenzyme, troponin I, alanine aminotransferase, aspartate aminotransferase, lactic dehydrogenase, hypersensitive‐C‐reactive protein, leucocyte count, neutrophil count percentage, LDL cholesterol levels, apolipoprotein B, fasting glucose, ferritin, and albumin showed statistically significant among the three subgroups ($p \leq 0.05$, Figure 2). Ejection fraction and serum albumin were lower in the AMI group than CCS group, and all other test indexes were higher in the AMI group than in CCS and UAP.
**FIGURE 2:** *Comparison of test indicators among the three subgroups of patients in the treatment group.*
## Univariate and multifactorial binary logistic regression analysis
The indicators with significant differences among the aforementioned indicators were used as independent variables. Whether PCI was performed was set as the dependent variable. With univariate logistic regression analysis, previously proved significant indicators showing the severity of CHD but were found insignificant here, such as smoking, alcohol consumption, hypertension, homocysteine, and low‐density lipoprotein, were included in the binary logistic regress. After stepwise regression, 12 predictors were obtained, namely history of diabetes, history of alcohol consumption, potassium, lipoprotein a, fibrinogen, alanine aminotransferase, white blood cell count, fructosamine, lactate dehydrogenase, high‐sensitivity troponin I, absolute neutrophil value, and glucose, and the results of the binary logistic regression analysis were visualized (Figure 3). Among them, the history of diabetes mellitus, high‐sensitivity troponin I, fructosamine, glucose, absolute neutrophil value, and fibrinogen may be independent influencing factors, that is, $p \leq 0.05.$
**FIGURE 3:** *Results of binary logistic regression analysis.*
## Univariate and multifactorial multivariate logistic regression analysis
The indicators with significant differences in the univariate analysis were subjected to univariate logistic regression analysis and multivariate logistic regression analysis, and the results are shown in Table 3. The model fit was good at $p \leq 0.05$, and the comparison between the CCS and AMI groups showed significance between the two groups in age, cTnI, and ALB.
**TABLE 3**
| Group | Unnamed: 1 | Std. Error | Wald | Sig. | Exp(B) | 95% CI for Exp(B) | 95% CI for Exp(B).1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Group | | Std. Error | Wald | Sig. | Exp(B) | Lower bound | Upper bound |
| CCS | Intercept | 4.371 | 0.365 | 0.546 | | | |
| CCS | Age | 0.023 | 4.391 | 0.036 | 1.05 | 1.003 | 1.098 |
| CCS | EF | 3.611 | 0.062 | 0.804 | 2.455 | 0.002 | 2908.384 |
| CCS | CK | 0.005 | 1.671 | 0.196 | 1.006 | 0.997 | 1.015 |
| CCS | CKMB | 0.041 | 1.952 | 0.162 | 0.945 | 0.872 | 1.023 |
| CCS | cTnI | 0.394 | 4.301 | 0.038 | 0.442 | 0.204 | 0.956 |
| CCS | ALT | 0.019 | 0.117 | 0.733 | 1.007 | 0.969 | 1.046 |
| CCS | AST | 0.031 | 1.623 | 0.203 | 0.961 | 0.905 | 1.021 |
| CCS | LDH | 0.006 | 2.611 | 0.106 | 0.991 | 0.98 | 1.002 |
| CCS | LDL‐C | 0.659 | 1.007 | 0.316 | 0.516 | 0.142 | 1.879 |
| CCS | APOB | 2.312 | 0.119 | 0.73 | 2.222 | 0.024 | 206.243 |
| CCS | hs‐CRP | 0.017 | 0.244 | 0.621 | 1.009 | 0.975 | 1.044 |
| CCS | WBC | 0.124 | 1.36 | 0.243 | 0.866 | 0.679 | 1.103 |
| CCS | GR% | 0.025 | 1.122 | 0.289 | 0.974 | 0.929 | 1.022 |
| CCS | GLU | 0.104 | 0.441 | 0.506 | 0.933 | 0.76 | 1.145 |
| CCS | FIB | 0.248 | 1.045 | 0.307 | 1.288 | 0.793 | 2.093 |
| CCS | ALB | 0.073 | 3.987 | 0.046 | 1.158 | 1.003 | 1.336 |
| CCS | Gender | 0.501 | 0.435 | 0.509 | 1.392 | 0.521 | 3.72 |
| UAP | Intercept | 4.582 | 0.477 | 0.49 | | | |
| UAP | Age | 0.023 | 0.13 | 0.719 | 1.008 | 0.965 | 1.054 |
| UAP | EF | 4.079 | 2.346 | 0.126 | 516.666 | 0.174 | 1531533.07 |
| UAP | CK | 0.004 | 0.35 | 0.554 | 1.002 | 0.995 | 1.009 |
| UAP | CKMB | 0.036 | 0.053 | 0.817 | 0.992 | 0.924 | 1.065 |
| UAP | cTnI | 0.109 | 1.899 | 0.168 | 0.86 | 0.694 | 1.066 |
| UAP | ALT | 0.019 | 0.467 | 0.494 | 0.987 | 0.952 | 1.024 |
| UAP | AST | 0.02 | 0.188 | 0.665 | 1.009 | 0.97 | 1.049 |
| UAP | LDH | 0.006 | 5.469 | 0.019 | 0.987 | 0.976 | 0.998 |
| UAP | LDL‐C | 0.68 | 3.587 | 0.058 | 0.276 | 0.073 | 1.046 |
| UAP | APOB | 2.37 | 1.954 | 0.162 | 27.461 | 0.264 | 2858.886 |
| UAP | hs‐CRP | 0.021 | 0.304 | 0.581 | 0.988 | 0.948 | 1.031 |
| UAP | WBC | 0.132 | 1.834 | 0.176 | 0.837 | 0.646 | 1.083 |
| UAP | GR% | 0.026 | 1.483 | 0.223 | 0.969 | 0.921 | 1.019 |
| UAP | GLU | 0.114 | 1.119 | 0.29 | 0.886 | 0.709 | 1.108 |
| UAP | FIB | 0.26 | 2.437 | 0.119 | 1.5 | 0.902 | 2.494 |
| UAP | ALB | 0.073 | 2.321 | 0.128 | 1.118 | 0.969 | 1.291 |
| UAP | Gender | 0.533 | 0.009 | 0.924 | 0.95 | 0.335 | 2.7 |
## Nomogram drawing and verification
Based on the binary logistic regression analysis model, a nomogram was drawn (Figure 4), and the scores of each index could be obtained and summed to obtain the p‐value corresponding to the total score, which is the predicted probability of needing PCI in patients with suspected CHD. The calibration curve (Figure 5) was found to be in general agreement with the reference curve, indicating that the predicted probability of occurrence was in good agreement with the actual probability of occurrence. The uncorrected C‐index was 0.84 ($95\%$ CI: 0.79, 0.89) and the corrected C‐index was 0.80, indicating good accuracy of the prediction model (C‐index 0.50–0.70 is average accuracy, 0.71–0.90 is good accuracy, and above 0.90 is excellent accuracy), and the results of the goodness‐of‐fit test showed that $$p \leq 0.372$$ > 0.05, indicating a good fit. The ROC curve was plotted according to the fitted model results (Figure 6), and the area under the curve was calculated to be 0.801, indicating that the accuracy of the prediction model was good.
**FIGURE 4:** *Nomogram for predicting the probability of requiring PCI in patients with coronary heart disease.* **FIGURE 5:** *Calibration curve of the prediction model.* **FIGURE 6:** *ROC curves of the prediction efficiency of the new model and individual indicators.*
## DISCUSSION
Coronary heart disease is a common cardiovascular disease, and the initial diagnosis of CHD can be determined by clinical symptoms, electrocardiogram, and biomarker of myocardial injury. However, CAG is required to confirm the diagnosis of CHD and the degree of coronary stenosis to determine whether the patient needs to undergo PCI. As known, CAG is an invasive procedure, and patients may be allergic to the contrast agent, which will cause severe hypersensitivity reactions and can be life‐threatening. 11 Many scholars have explored the influencing factors related to the degree of stenosis in patients with CHD, suggesting that the history of diabetes, blood glucose level, white blood cell count, serum troponin, fibrinogen level, fibrinogen to albumin ratio, BNP level, total bilirubin, and uric acid level were independent factors on the severity of coronary artery lesions. 12, 13, 14, 15, 16, 17, 18 However, to date, there is still a lack of non‐invasive and efficient methods to predict the need for PCI in patients with coronary artery disease. In this study, we reviewed the clinical characteristics and laboratory indices of 454 patients with suspected CHD, analyzed possible influencing factors, and developed predictive models based on the need for PCI. Our results showed that the diabetes history, and levels of troponin I, fructosamine, glucose, and absolute neutrophil values were independent PCI markers.
Diabetes is clinically referred to as an equivalent risk for CHD, indicating that diabetes plays an important role in the development of cardiovascular disease. 19 Numerous studies have confirmed that diabetes mellitus is an important risk factor for CHD. First, elevated blood glucose is a major determinant of arterial stiffness, and chronic hyperglycemia is associated with the accumulation of advanced glycosylation end products (AGEs), which contribute to atherosclerosis and increase the severity of coronary artery lesions. Secondly, oxidative stress exacerbates macrovascular damage in diabetic patients, namely by inducing the production of reactive oxygen species (ROS), which subsequently damages the endothelial system. 11 Hyperglycemia in diabetic patients also promotes protein kinase C activation and diacylglycerol production, both accelerate the development of atherosclerosis by promoting inflammatory mediators and smooth muscle cell recruitment. It has been shown that the risk of developing CHD in diabetes is highest at any age and is mainly associated with insulin resistance, type 2 diabetes, and metabolic syndrome. 20 The results of this study also suggest that the history of diabetes and fasting glucose levels, fructosamine are good predictors for PCI in patients with CHD and are the main influencing factors of the severity of coronary artery disease.
Inflammatory responses occur throughout the pathophysiology of the development of coronary atherosclerotic heart disease. Leukocyte counts are positively correlated with the risk of CHD. 13 Neutrophils are the most abundant circulating leukocytes in the body and play an important role in the inflammatory response. The underlying mechanisms are as follows: [1] neutrophils promote the deleterious effects of TLR2 activation, and this activation leads to persistent local endothelial damage and surface erosion‐associated thrombosis, followed by endothelial cell death or shedding; [2] neutrophil recruitment, thereby expanding, maintaining, and propagating local processes which ultimately lead to endothelial injury and local thrombosis. 21 The current study suggests that neutrophil count is an important factor in the development process of coronary stenosis and is of great value in the prediction of patients to undergo PCI.
The development of CHD is accompanied by varying degrees of myocardial damage. The measurement of cardiac troponin concentrations has become a central component in the assessment of patients with acute and chronic cardiovascular disease. And studies have shown that the release of troponin is entirely due to irreversible cell death. Troponin can be released into the bloodstream in large amounts by oxidoreductase and protein kinase A phosphorylation, which in turn aggravates the degree of myocardial cell damage, creating a vicious circle and stimulating further progression. 12 In particular, high‐sensitivity troponin I is useful in assessing patients with mild myocardial injury and is a significant predictor for PCI.
Among the three subgroups of the treatment group, ejection fraction, creatine kinase, creatine kinase‐MB isoenzyme, troponin I, alanine aminotransferase, aspartate aminotransferase, lactic dehydrogenase, hypersensitive‐C‐reactive protein, leukocyte count, neutrophil count percentage, LDL cholesterol levels, apolipoprotein B, fasting glucose, ferritin, and albumin basically showed trend changes in the progression of coronary artery disease patients from stable to unstable and then to myocardial infarction. The CCS group and AMI group showed significant differences. Hakan Duman et al. 22 found that higher CRP levels, lower serum albumin levels, higher CAR, higher neutrophil levels, and troponin I levels were independent predictors of increased thrombotic burden. This study also concluded that lower blood albumin levels and higher cTnI levels were significant predictors of myocardial infarction in individuals.
In conclusion, we found that the history of diabetes mellitus, high‐sensitivity troponin I, fructosamine, glucose, absolute neutrophil value, and Fibrinogen were independent factors for predicting PCI. A nomogram using 12 predictors can be used to predict the probability of requiring PCI in patients with suspected CHD, and the accuracy and goodness of fit of our model were verified. Previous studies have shown that hypertension, smoking, and homocysteine are risk factors for CHD, but in this study no statistically significant differences were found between these indicators in patients who underwent PCI or not. We think that these indicators may play an important role in the process of triggering CHD but have little significance for whether to perform PCI; it may also be caused by the relatively small sample size and geographically base of this study, which may generate a possibility of bias, and in the future, we need to enlarge the sample size with multiple centers, and use a machine learning models to improve the prediction efficiency to further improve the predictive ability of PCI for CHD.
## AUTHOR CONTRIBUTIONS
XW, YPL, and FW jointly designed this study and reviewed and revised the article. XW and YPL collected clinical data from CHD patients. XW and FW further collated and preliminarily analyzed the data and conducted statistical analysis and drew the figures and tables of the whole article. XW and YPL wrote the results section of the article, while XW wrote the rest of the article. All authors read and approved the final article.
## FUNDING STATEMENT
No funding.
## CONFLICT OF INTEREST STATEMENT
The authors declare that they have no competing interests.
## CONSENT FOR PUBLICATION
All the participants gave consent for direct quotes from their interviews to be published in this manuscript.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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|
---
title: 'Promoting Self-management and Patient Activation Through eHealth: Protocol
for a Systematic Literature Review and Meta-analysis'
journal: JMIR Research Protocols
year: 2023
pmcid: PMC10020897
doi: 10.2196/38758
license: CC BY 4.0
---
# Promoting Self-management and Patient Activation Through eHealth: Protocol for a Systematic Literature Review and Meta-analysis
## Abstract
### Background
Major advances in different cancer treatment modalities have been made, and people are now living longer with cancer. However, patients with cancer experience a range of physical and psychological symptoms during and beyond cancer treatment. New models of care are needed to combat this rising challenge. A growing body of evidence supports the effectiveness of eHealth interventions in the delivery of supportive care to people living with the complexities of chronic health conditions. However, reviews on the effects of eHealth interventions are scarce in the field of cancer-supportive care, particularly for interventions with the aim of empowering patients to manage cancer treatment–related symptoms. For this reason, this protocol has been developed to guide a systematic review and meta-analysis to assess the effectiveness of eHealth interventions for supporting patients with cancer in managing cancer-related symptoms.
### Objective
This systematic review with meta-analysis is conducted with the aim of identifying eHealth-based self-management intervention studies for adult patients with cancer and evaluating the efficacy of eHealth-based self-management tools and platforms in order to synthesize the empirical evidence on self-management and patient activation through eHealth.
### Methods
A systematic review with meta-analysis and methodological critique of randomized controlled trials is conducted following Cochrane Collaboration methods. Multiple data sources are used to identify all potential research sources for inclusion in the systematic review: [1] electronic databases such as MEDLINE, [2] forward reference searching, and [3] gray literature. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for conducting the review were followed. The PICOS (Population, Interventions, Comparators, Outcomes, and Study Design) framework is used to identify relevant studies.
### Results
The literature search yielded 10,202 publications. The title and abstract screening were completed in May 2022. Data will be summarized, and if possible, meta-analyses will be performed. It is expected to finalize this review by Winter 2023.
### Conclusions
The results of this systematic review will provide the latest data on leveraging eHealth interventions and offering effective and sustainable eHealth care, both of which have the potential to improve quality and efficiency in cancer-related symptoms.
### Trial Registration
PROSPERO 325582; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=325582
### International Registered Report Identifier (IRRID)
DERR1-$\frac{10.2196}{38758}$
## Introduction
The incidence of cancer is rising, and it is estimated that by 2040, globally, more than 28 million people will experience cancer as new cancer cases [1]. It is expected that nearly half of Canadians will develop cancer in their lifetimes [2]. The main goal of a cancer treatment program is to cure or considerably prolong the life of patients and to ensure the best possible quality of life for cancer survivors [3]. Major advances in different cancer treatment modalities (ie, surgery, chemotherapy, radiotherapy, hormonal therapy, and biological response modifiers) have been made, and people are now living longer with cancer than they were in the past [3-5]. However, patients with cancer experience from a range of physical and psychological symptoms during their cancer journey. These symptoms are either directly related to the adverse effects of cancer or arise from the different types of treatments and may range from mild and temporary to severe, chronic, and life-threatening [6]. Moreover, symptoms impact daily physical function and can lead to or exacerbate psychological distress and worse health-related quality of life [7,8].
Globally, there is recognition that patients benefit from being actively engaged in their own health [9]. Active engagement of patients is considered critical to minimize the consequences of disease in daily living, support a better quality of life [10], and reduce health care costs [9].
eHealth interventions could potentially enhance the clinical, organizational, and relational aspects of care by integrating patient databases for individualized treatment and real-time decision support. Moreover, it has been reported that electronic technology, by identifying decision support, care coordination, and continuity of care, could improve cancer care delivery [11]. This approach can empower patients to manage their symptoms, improve patient-professional interactions, prevent unplanned hospital admissions, and reduce health care costs [12,13]. Additionally, for nurses, working with innovations such as mobile health (mHealth) in practice is becoming essential as it may facilitate the provision of quality care [14].
Even though there is empirical evidence that substantiates the role of eHealth interventions in the delivery of supportive care to people living with the complexities of chronic health conditions [15-19], the effects of eHealth interventions specifically designed for supporting patients with cancer to manage cancer-related symptoms and the effects on outcomes, that is, symptom burden, are less clear [20,21].
Reviews on the effects of eHealth interventions are scarce in the field of cancer supportive care, particularly for interventions to increase patient activation and empower patients to self-manage cancer-related symptoms. There is substantial evidence that patients who have the appropriate information and skills are more likely to engage actively in their care and effectively manage the consequences of treatment, including their physical and psychological symptoms [13,22]. Furthermore, information on how these interventions were planned and carried out and who benefited from these approaches is still required [23].
We propose to conduct a systematic review with meta-analysis and methodological critique of the literature to answer the following PICO (Population, Intervention, Comparison, Outcomes) research question: *What is* the efficacy in cancer populations (population: any phase of cancer, treatment, survivorship, palliative, and end of life care) of eHealth interventions (intervention) compared to usual care or other active intervention (comparison) on symptom severity, psychological distress, self-management behaviors, health outcomes, and health use (emergency department use, unplanned visits to the health care provider, hospitalization, patient activation, and patient empowerment; outcomes).
This review aims to explore usage and effectiveness of eHealth interventions designed to support patients with cancer in managing cancer-related symptoms and the effects on outcomes. The findings could inform and promote evidence-informed oncology practice for eHealth interventions targeted at cancer and advance science in the field.
## Overview
This systematic review to identify randomized controlled trials (RCTs) and meta-analyses follows methods as specified by the Cochrane Collaboration [24] and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (see Figure 1) [25]. A broad search to identify trial evidence for eHealth interventions to empower cancer patients to manage their symptoms, increase patient activation, and improve patient-professional interactions will be conducted. We posed the following specific research questions.
**Figure 1:** *CONSORT (Consolidated Standards of Reporting Trials) chart: self-management and patient activation through eHealth review.*
## Primary Research Question
Are eHealth interventions (or programs) effective in reducing the physical or psychological effects of cancer and its treatment or improving other health outcomes (ie, function, health-related quality of life, health use, or costs) compared with usual care or other active treatment?
## Secondary Research Questions
Does effectiveness (effect sizes [ESs]) of eHealth interventions (or programs) differ by patient or disease characteristics (age, race or ethnicity, education, cancer type, stage or phase in trajectory, treatment modality, or other antecedent personality variables such as optimism or trait anxiety), that is, effect modifiers?
Does effectiveness (ESs) of eHealth interventions (or programs) differ based on intervention design (delivery setting) or methods (eHealth-based self-management tools and platforms), training and qualities of the interventionist, intervention components, length of the intervention, or other potential mediators, that is, adherence to the intervention?
## Literature Search Strategy
The search strategy was developed with assistance from a library information specialist. A computerized search of electronic databases will be conducted from January 2000 until January 2022 as follows: The Cochrane Central Registry of Controlled Trials (CENTRAL), Cochrane Library and Trials Registry and the Database of Abstracts of Reviews of Effectiveness (DARE), MEDLINE (2000 to January 2022), Embase (2000 to January 2022), CINAHL (2000 to January 2022), PSYCHINFO (2000 to January 2022), and CancerLit (2000 to January 2022). In addition, the gray literature databases, EAGLE (2000 to January 2022), openSIGLE (2000 to January 2022), and PsychEXTRA (2000 to January 2022) will be searched. The reasons for limiting the literature search from 2000 onwards are the development of internet technology and the use of internet-based support programs in the delivery of supportive care over the past 22 years [26].
## Search Terms
The search for eligible studies will include search terms: self-manage* or “self-manage*” or self-car* or “self-care*”, behave* or cognitive* or train* or instruct* or patient education or “patient education” or “management plan*” or “management program* (AND) ehealth* or mHealth* or “mobile Health*” or Telehealth* (AND) terms for neoplasms, cancers, or cancer symptoms (fatigue, nausea or vomiting, pain, depression, anxiety, insomnia) and sensitive search terms for identifying randomized trials as subject headings specified by Cochrane [27]. The initial search strategy will be developed in MEDLINE (Multimedia Appendix 1) and will be adapted for all other databases.
## Types of Studies
Eligible studies will be identified based on the inclusion or exclusion criteria described below.
## Inclusion Criteria
The following inclusion criteria were used:
## Exclusion Criteria
The following exclusion criteria were used:
## Primary Outcomes
We will be studying the following primary outcomes:
## Secondary Outcomes
We will be studying the following secondary outcomes:
## Selection of Studies
Studies will be selected using Covidence software based on a review of the title, keywords, and abstract and coded using the following criteria: [1] include: an RCT, with a focus on cancer patient activation or self-management; [2] exclude: no self-management focus. Selecting studies includes these steps: [1] using a reference management software to merge search results and remove duplicates; [2] examine all titles initially to remove articles that are clearly not eHealth, followed by an abstract review (if there is any uncertainty, the abstract is included for a full text review); and [3] retrieve the full study reports and assess compliance with eligibility criteria independently by 2 reviewers. Agreement will be examined using interrater agreement (<75 nonagreement); disagreements will be resolved by consensus or in consultation with a third reviewer. Authors| will clarify study eligibility criteria or missing data results if necessary. Interventions with more than 1 article will also be retrieved and reviewed to complement the data abstraction and quality assessment of the study (Multimedia Appendix 2).
## Data Abstraction and Management
Data will be abstracted using a data abstraction form developed for the review based on Cochrane methods. Data abstraction is independently assessed by 2 reviewers with reliability of coding assessed by computing Kappa, or percentage agreement, for categorical data and the intraclass correlation for continuous data. If any aspect of the study design and conduct is unclear, the study authors will be contacted to complete data abstraction. Two other review authors will check a random sample of the abstractions. Disagreements will be resolved by discussion, with arbitration by a third author if necessary, following an independent review of the study report in question. The data abstraction form will be pretested on a minimum of 5 studies. The abstracted data will include categories as per Cochrane: [1] source and setting; [2] methods; [3] participants; [4] experimental interventions (extent to which specific intervention components delivered as described [adherence]; number, length, and frequency of implementation of intervention components; and characteristics of the interventionists); [5] control treatment; [6] analysis; [7] adverse events; [8] outcome measures; [9] results; [10] conclusions of study authors; and [11] miscellaneous, that is, funding sources.
## Assessment of Study Quality
A quality assessment will be performed by 2 review authors and checked by another author. A methodological quality assessment of studies will be conducted based on an adapted version of the Cochrane Collaboration Back Review *Group criteria* [28], which were previously used in other systematic reviews of internet-based interventions [29,30]. The *Cochrane criteria* was modified to better suit the type of examined studies: specification of eligibility criteria, randomized groups, treatment allocation concealed, groups similar at baseline, explicit description of interventions, description of compliance, description of dropout and comparison with completers, long-term follow-up (>3 months after postintervention assessment), timing of outcome assessment comparable, sample size described with power calculation, intention-to-treat analyses, and point estimates and measures of variability. The quality score could range from 0 to 12 points. For each study, all criteria will be scored as yes, no, or unclear, resulting in a maximum quality score of 12. In line with other researchers [29-31], studies obtaining at least two-thirds of the total score (ie, ≥8 points) will be considered high quality. Studies scoring 4 to 7 points will be rated as moderate quality, and studies scoring lower than 4 points will be rated as low quality.
Authors will be contacted with 2 reminders to complete missing data. Reviewers will be blinded to the authors of study reports. For each of these potential sources of bias, a judgment of yes (low risk of bias), no (high risk of bias), or unclear is assigned to each study (number of yeses is the single score or study). A summary table of the risk of bias across studies will be developed for reporting purposes.
## Data Analysis and ES Calculation
Outcomes will be analyzed as continuous or dichotomous variables depending on data reporting using standard statistical techniques. For continuous data (ie, symptom severity), a standardized mean difference with $95\%$ CIs will be calculated as appropriate to facilitate comparison between intervention and controls with correction for differences in the direction of the scale. If reported as medians with ranges, means and SDs will be calculated [32]. For dichotomous outcomes, a relative risk ratio with $95\%$ CIs is calculated.
## Assessment of Heterogeneity
As per Cochrane, clinical heterogeneity (variability in the participants, interventions, and outcomes) [33,34] will be examined using the “I” squared statistic [35]. Random effects meta-analysis will be used if heterogeneity across studies cannot be explained; otherwise, a fixed-effects model will be used [33,36].
## Subgroup Analysis
The following subgroup analysis will be conducted if the number of studies available for the analysis is adequate (10 studies for each characteristic modeled by participant characteristics and intervention components) as described below and based on the research questions posed:
## Measurement of Treatment Effects and ES Calculation
A summary of findings table will be completed to synthesize the reporting of common primary outcomes: physical effects (function and symptoms), psychological effects (depression, anxiety, and health distress), and secondary outcomes (quality of life, health care use, and satisfaction) using the GRADEpro software. ES computations will be calculated using Hedges g. [37]. Where g cannot be computed directly from means and SDs based on the source paper, it will be computed indirectly from the available test statistics, for example, t, based on Rosenthal [38]. The estimates of g will be corrected for small-sample bias [37]. Given that outcomes could be differentially effective over different dimensions, particularly for symptoms, separate analyses for comparison, that is, physical symptoms (pain, fatigue, nausea or vomiting, insomnia), psychological symptoms (depression, anxiety, health distress), and separately for quality of life and use of health care services, will be conducted. For studies where a primary outcome is possible for the main analysis, this will require the identification of a primary outcome.
## Sensitivity Analysis
A sensitivity analysis will be conducted to evaluate the robustness of the meta-analysis, that is, the effects of methodological quality on study outcomes, by assessing for associations between individual items in the methodological quality checklist and the study outcomes. When data can be pooled, sensitivity analysis will be conducted by pooling the “yes” versus “no” responses to risk. When the data cannot be pooled, the sensitivity analysis will be performed using a chi-square analysis as per Cochrane.
## Results
The literature search and data collection started in October 2017, after making a work plan to design and run the systematic search strategies in databases and the timeframe for delivery of the search results with the Library and Information Services within the University Health Network. However, to provide a comprehensive snapshot of knowledge since the time of incorporation of data from studies identified during the first search, a second literature search was conducted in February 2022 to ensure new studies were included and increase the validity of the review. The literature search yielded a total of 10,202 publications. Data will be summarized, and if possible, meta-analyses will be performed to evaluate the effectiveness of eHealth interventions on the outcomes. Results are expected to be published in winter 2023.
## Discussion
Recent literature has highlighted the utility of eHealth interventions with promising outcomes in cancer care, although mixed and inconclusive results were also presented [39]. The main contributions to this review will be the following: the use and effectiveness of eHealth interventions for supporting patients with cancer in managing cancer-related symptoms; identifying the key implications for better design, integration, and implementation that may have important effects on intervention outcomes; and a discussion, based on the data synthesized, on current gaps and limitations to inform better research toward all phases of development and evaluation of these interventions. Therefore, we will provide essential information for developing and implementing these interventions into clinical practice by providing recommendations based on the current best available evidence. The evaluation of the interventions might be limited by explicitly reporting the interventions. Any modifications or revisions made to the protocol will be presented in the final reports.
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|
---
title: 'Critical Criteria and Countermeasures for Mobile Health Developers to Ensure
Mobile Health Privacy and Security: Mixed Methods Study'
journal: JMIR mHealth and uHealth
year: 2023
pmcid: PMC10020905
doi: 10.2196/39055
license: CC BY 4.0
---
# Critical Criteria and Countermeasures for Mobile Health Developers to Ensure Mobile Health Privacy and Security: Mixed Methods Study
## Abstract
### Background
Despite the importance of the privacy and confidentiality of patients’ information, mobile health (mHealth) apps can raise the risk of violating users’ privacy and confidentiality. Research has shown that many apps provide an insecure infrastructure and that security is not a priority for developers.
### Objective
This study aims to develop and validate a comprehensive tool to be considered by developers for assessing the security and privacy of mHealth apps.
### Methods
A literature search was performed to identify papers on app development, and those papers reporting criteria for the security and privacy of mHealth were assessed. The criteria were extracted using content analysis and presented to experts. An expert panel was held for determining the categories and subcategories of the criteria according to meaning, repetition, and overlap; impact scores were also measured. Quantitative and qualitative methods were used for validating the criteria. The validity and reliability of the instrument were calculated to present an assessment instrument.
### Results
The search strategy identified 8190 papers, of which 33 ($0.4\%$) were deemed eligible. A total of 218 criteria were extracted based on the literature search; of these, 119 ($54.6\%$) criteria were removed as duplicates and 10 ($4.6\%$) were deemed irrelevant to the security or privacy of mHealth apps. The remaining 89 ($40.8\%$) criteria were presented to the expert panel. After calculating impact scores, the content validity ratio (CVR), and the content validity index (CVI), 63 ($70.8\%$) criteria were confirmed. The mean CVR and CVI of the instrument were 0.72 and 0.86, respectively. The criteria were grouped into 8 categories: authentication and authorization, access management, security, data storage, integrity, encryption and decryption, privacy, and privacy policy content.
### Conclusions
The proposed comprehensive criteria can be used as a guide for app designers, developers, and even researchers. The criteria and the countermeasures presented in this study can be considered to improve the privacy and security of mHealth apps before releasing the apps into the market. Regulators are recommended to consider an established standard using such criteria for the accreditation process, since the available self-certification of developers is not reliable enough.
## Introduction
More than 5.19 billion people now use mobile phones, which indicates that mobile phones form an important part of daily life worldwide [1]. Mobile phone features, including mobility, instantaneous availability, and direct communication, have changed the provision of health care services. These features introduce mobile health (mHealth). Of about 2 million smartphone apps available in app stores, 318,000 are health apps [2]. According to a World Health Organization report [3], the penetration of mHealth, with promising results, in low- and middle-income countries would be even more.
mHealth has improved the patient care status through the provision of health care anytime and anywhere [4]. Even in recent years, the integration of mHealth and wireless technologies has provided clinicians with an opportunity to collect real-time data via wearable sensors [5]. Health information is deemed sensitive, and its protection is of significance. Nevertheless, smartphones are vulnerable to a wide range of security threats [6]. Moreover, electronic transmission of information has brought about concerns about its privacy and security. A national survey showed that 1 of the common reasons for people not having downloaded health apps is concern about apps gathering their data [7,8]. The privacy and confidentiality of information, as a human right, have long been considered in law and regulations. Well-known examples are the Health Insurance Portability and Accountability Act (HIPAA) rules, the General Data Protection Regulation (GDPR), and the Common Rule [9-11]. The terms “security,” “privacy” and “confidentiality” are all separate yet connected concepts that need to be addressed. The National Committee for Vital and Health Statistics [12] defines and distinguishes these concepts as follows: Despite the importance of the privacy and confidentiality of patients’ information, studies report that mHealth apps may share the information with third parties, which raises the risk of violating patients’ privacy and confidentiality [13-15]. Dehling et al [16] evaluated the information security and privacy of 24,405 health-related apps and revealed that most apps request access to sensitive information. Robillard et al [17] reported that most of the apps do not include privacy policies and terms of the agreement. Moreover, it has been shown that many apps provide an insecure infrastructure and security is not a priority for the developers [18]. Similar studies emphasize assessing mHealth apps for the privacy, security, and confidentiality of information to minimize the associated risks [16,19,20].
Criteria have been proposed in previous studies for assessing mHealth apps. Benjumea et al [21] proposed a novel scale to assess the privacy policy of mHealth apps. However, the scale considers only specific items associated with the privacy policy content based on the GDPR rather than considering security and privacy in general. Another study [22] also proposed a heuristic evaluation approach to assessing the privacy of mHealth apps, but that is a time-consuming approach because heuristics require a close reading of the privacy policy. Another study proposed a security-testing method for Android mHealth apps designed based on a threat analysis, considering probable attack scenarios and vulnerabilities associated with the domain [18]. They assessed security using novel dynamic and static analysis testing methods that were expensive to perform. Benjumea et al [23] conducted a scoping review on studies exploring privacy issues in mHealth apps. Finding that most studies assess the apps based on heterogeneous criteria, Benjumea et al [23] emphasized the importance of developing a scale based on more objective criteria for evaluating privacy issues. In addition, the mHealth field faces a variety of legal and cultural differences over privacy between nations, so it needs a comprehensive tool for assessing both privacy and security issues [24]. Thus, developing a comprehensive tool assessing both privacy and security sounds necessary. This study aims to develop and validate a comprehensive tool to be considered by developers for assessing both the security and the privacy of mHealth apps targeting patients.
## Study Design
This study was conducted to answer the following question: What security and privacy criteria should be considered when developing or assessing mHealth apps targeting patients based on 3 main phases: item generation, tool development, and tool evaluation? These main phases [25] were performed based on 4 steps: [1] identifying criteria associated with mHealth apps’ security/privacy according to a literature search (item generation); [2] conducting an expert panel for determining the categories and subcategories according to meaning, repetition, and overlap (tool development); [3] testing the validity of the instrument (tool evaluation); and [4] testing the reliability of the instrument (tool evaluation).
## Stage 1: Literature Review
An unstructured literature search was performed to identify papers on app development, assessment, security, or privacy that reported criteria for the security and privacy of mHealth. PubMed, Scopus, Web of Science, and Cochrane were searched for English language papers published until December 15, 2021, without a time limitation. The search strategy (Multimedia Appendix 1) included a combination of 4 keywords: (“mobile device” OR “mobile phone” OR smartphone OR “smart Phone” OR mHealth OR “mobile health”) AND (App OR apps OR application*) AND (security OR privacy OR confidentiality OR cybersecurity) AND (guideline* OR standard* OR criteria OR risk* OR assess* OR evaluat* OR measure).
The HIPAA and GDPR websites were searched for relevant criteria. After removing duplicate papers, the titles and abstracts of the studies were screened for inclusion. The full text of potentially relevant papers was investigated based on study objectives. Studies substantially focusing on security or privacy, not just mentioning them in passing, and stating clear criteria for assessing the privacy/security of mHealth apps were included. Studies evaluating the privacy or security of mHealth apps were also included to specify the criteria used for evaluation. Papers proposing a secure architecture, investigating technical solutions for mHealth apps (eg, access control, authentication approaches, encryption methods), presenting technical solutions for connecting mHealth apps to cloud computing or the internet of things devices or conducted on wearable devices without connecting to a mobile device, and discussing mobile phone access to electronic health records were excluded. Papers focusing on mHealth apps targeting users other than patients, focusing on app quality or determining functional requirements, and examining user experiences were also excluded. The criteria were extracted using content analysis.
## Stage 2: Expert Panel
The list of primary criteria extracted through the literature search was presented to a focus group including 2 health information technology (HIT) specialists, 2 medical informatics specialists, and 1 software and IT specialist. The focus group discussion consisted of 4 major steps: designing research, collecting data, analyzing, and reporting results through a moderated interaction [26]. The experts discussed and categorized the criteria and decided over their inclusion or exclusion based on the relevancy, clarity, importance, comprehensiveness, and overlap with other included criteria, and they determined subcategories based on meaning, repetition, and overlap. This method can have a high level of validity due to the interaction among experts that confirms, reinforces, or rejects the individual respondents’ contributions. The criteria extracted through the focus group discussion were used in the next stage.
## Stage 3: Testing the Validity of the Instrument
Quantitative and qualitative methods were used for validating the instrument. To validate the instrument based on the qualitative approach, face validity was checked through face-to-face interviews by 8 HIT specialists and 5 software and IT experts. The inclusion criteria for the experts included specialists in HIT, IT, or software, with a master’s degree in science or higher, with at least 1-year work experience in software security, network security, health information security, or mobile app development. The criteria were modified based on the experts’ comments.
To validate the instrument quantitatively, the impact score was calculated for each criterion. The impact score determines inappropriate criteria. Thus, the criteria were evaluated based on a 5-point Likert scale ranging from 5 (very important) to 1 (not at all important). The impact score for each criterion was calculated as follows: Impact score = Frequency (%) × Importance Content validity was evaluated by 16 other IT ($$n = 8$$, $50\%$) and software ($$n = 8$$, $50\%$) experts, of whom 3 ($18.8\%$) experts did not participate. Thus, to make sure the most essential criteria for the study objective were chosen, the content validity ratio (CVR) was measured. The CVR was calculated based on the following formula: According to the Lawshe table, if the number of experts in the panel is 13, the minimal acceptable CVR is 0.54.
In addition, to ensure the relevancy and clarity of each criterion, the content validity index (CVI) was measured. Thus, the 13 experts also completed a 4-point scale based on relevance, clarity, and simplicity for the criteria. The CVI was calculated using the following formula: *The criteria* were included in the final assessment tool if the CVI was ≥0.79 [27,28]. If the CVI was between 0.70 and 0.79, it needed to be calculated after the criteria were revised by the experts. Criteria with a CVI of <0.70 were removed.
## Stage 4: Testing Reliability
To assess the reliability of the final tool, the hypertensive self-care app developed in our previous study [29] was selected. The app needs to record a variety of personal information. In total, 30 experts in HIT, medical informatics, IT, and software assessed the reliability of the instrument. The instrument was distributed among these experts twice in a 2-month interval. They were asked to assess the privacy and security of the self-care app using the criteria provided in the checklist. After collecting expert opinions about the self-care app, the data were analyzed using the Cronbach α.
## Ethical Considerations
The research was conducted according to the principles stated by the Vice-Chancellorship for Research Affairs of Shiraz University of Medical Science and approved by the Ethics Review Board of the Vice-Chancellorship for Research Affairs of Shiraz University of Medical Science (ethical code IR.SUMS.REC.1397.500).
## Study Selection
The search strategy retrieved 10,092 papers, of which 1902 ($18.8\%$) were duplicates. Of the 8190 ($81.2\%$) remaining papers, 8072 ($98.6\%$) were irrelevant. To retrieve the greatest number of possible relevant papers, our search strategy included smartphone or mobile devices as a synonym for mHealth (“mobile device” OR “mobile phone” OR smartphone OR “smart Phone” OR mHealth OR “mobile health”); this resulted in retrieving papers basically irrelevant to the health discipline, in addition to those relevant to the health discipline—for example, studies associated with payment/banking/commercial apps were also retrieved in the primary result. In total, 33 ($0.4\%$) studies were deemed eligible for inclusion in the research (Figure 1). The characteristics of the included studies [13,14,16,18-20,24,30-56] are presented in Multimedia Appendix 2.
A total of 218 criteria were extracted based on the literature search; of these, 119 ($54.6\%$) were removed as duplicates (showing the same idea) and 10 ($4.6\%$) were deemed irrelevant to the security or privacy of mHealth apps. The remaining 89 ($40.8\%$) criteria were presented to the expert panel. As shown in Figure 2, 63 ($70.8\%$) criteria were confirmed at last.
The mean CVR of the total instrument was 0.72, while the mean CVI was 0.86. Multimedia Appendix 3 shows the complete list of removed criteria in the different phases of the study.
Finally, to measure the reliability of the instrument, the experts were asked to assess the hypertensive self-care app using the instrument. When measuring the reliability of the instrument, 18 ($28.6\%$) of the 63 criteria received the lowest and the highest score of the *Likert spectrum* (“not at all” and “completely”) equally. Since the variance of equal data was 0, these 18 criteria did not automatically enter for calculating the Cronbach α value. Thus, the test was performed with 45 ($71.4\%$) criteria. The Cronbach α value was 0.89.
The 63 criteria were grouped into 8 categories: authentication and authorization ($$n = 8$$, $12.7\%$), access management ($$n = 6$$, $9.5\%$), security ($$n = 13$$, $20.6\%$), data storage ($$n = 4$$, $6.3\%$), integrity ($$n = 2$$, $3.2\%$), encryption and decryption ($$n = 5$$, $9.5\%$), privacy policy ($$n = 15$$, $23.8\%$), and privacy policy content ($$n = 10$$, $15.9\%$); see Textbox 1.
**Figure 1:** *Flow diagram of study selection. EHR: electronic health record.* **Figure 2:** *Flowchart of criteria determination. CVI: content validity index; CVR: content validity ratio.*
## Principal Findings
In this study, we developed an instrument for assessing the security and privacy of mHealth apps. The criteria proposed in this tool were classified into 8 categories: authentication and authorization, access management, security, data storage, integrity, encryption and decryption, privacy, and privacy policy. These criteria can be considered by mHealth app developers to improve the privacy and security of their apps before releasing them into the market.
## Authentication and Authorization
The criteria in the tool suggest implementing rigorous authentication and authorization techniques. More time and effort should be devoted to preventing unauthorized access to personal health information. The developers are asked to provide a unique master ID and a secret key identity for users to control role-based access and verify users’ activities according to the defined identity and roles. Authentication via a fingerprint or a personal identification number is necessary for internal storage, internal cache, external storage, and databases [57]. Audit trails should be in place to track logs, protect data, and identify which user’s health data was handled and by whom. Each user should be able to create, change, and protect their passwords. The developers should make sure the passwords are strong enough and are changed periodically, because there are tools that produce 1014 guesses in an hour to find the correct password [58]. There are some strategies to be used by developers to make sure passwords are secure; these include enforcing password complexity; making passwords unviewable, even to the app administrator; and locking a user’s account after a determined number of consecutive unsuccessful log-in attempts. System-generated passwords can be strong, but they do not guarantee memorability. Using Optiwords8 passwords [59], based on the picture superiority effect on the mobile phone keyboards, guarantees the security of passwords, while keeping them usable and memorable as a result.
## Access Management
mHealth app developers need to define access controls for their team members as well as users. For those apps providing health care provider–patient communication, granting access to specific app functions should be based on predetermined and confirmed roles and attributes. Patients should be users allowed to control the access level of their health information by third parties. Greene et al [60] proposed the ShareHealth framework, which provides cryptographically enforced access to data. The framework takes advantage of combining a robust cryptographic scheme, hash chains (to control access by data time), and attribute-based encryption (to control access by data type). Rectification, deleting, or blocking of data should be facilitated for users [53].
## Security
Some mHealth apps use connections for several purposes, including fetching mail, sending analytics data, or checking for updates. To protect the authenticity, confidentiality, and integrity of the connection, developers are encouraged to use an up-to-date version of the Transport Layer Security protocol and its predecessor, the Secure Socket Layer (SSL) [54]. SSL protocols provide an encrypted link that connects a server and a client and makes sure the transmitted data remain impossible to read and are kept private; however, if the coding is not strong enough, hackers would be able to interpret health data during transmission [44]. There should be a functionality of remote control of data to securely transfer, retrieve, or completely erase health information if the mobile phone is stolen/lost [35]. However, it is safer to store data on users’ own devices rather than on the app company’s servers [13]. Some apps use external devices, such as cameras, sensors, or payment apps, to improve their functionality, but this endangers users’ confidentiality through attacks, such as external-device misbonding [48]. Moreover, using cookies can jeopardize user privacy especially those used for data analysis by third parties [14]. Users should be able to manipulate their profile or delete it completely when they stop using an app [31].
## Encryption and Decryption
Bhanot and Hans [61] compared various encryption algorithms based on different criteria, such as cryptography type, key management, keys number, and bit numbers used in a key. They found that elliptic-curve cryptography and blowfish encryption algorithms are the best, providing higher security levels as well as faster encryption speeds, which is required for mobile devices due to less power consumption [61]. Security measures, such as wired equivalent privacy, which is used to provide security to mobile devices, are vulnerable to hackers [62,63]. Thus, developers are required to perform a security risk analysis to determine vulnerabilities at each stage of design and implementation throughout testing and use. Arora et al [64] suggest using a “red team” for risk analysis. Red team experts are charged with hacking cyber systems in order to detect weaknesses.
## Privacy
Papageorgiou et al [49] found that although many of the studied apps ask for dangerous permissions (eg, read/write external storage, access camera, location, and contacts), they do not follow well-known regulations, such as HIPAA. Developers are required to collect data as much as they need to provide their services, so they are required to provide reasons for permissions they ask for, the type of data they collect, and how the data will be used by them or third parties, including insurance companies, government institutions, or even research centers [18,38]. Third-party usage of health data can bring about privacy intrusions, such as loss of insurance coverage or higher insurance premiums [65]. Complying with regulations and which country these regulations belong to is also important because when enforcing privacy rights, the regulations may differ from the users’ own country [13]. Users’ records should be stored in incognito forms, which are anonymized and unidentifiable; if anonymization is not possible, users should be informed [40].
All mHealth apps need to provide a transparent, precise, and well-readable privacy policy statement or a link to the complete privacy policy. Procedures for refusing data sharing, consequences of not providing/sharing data, procedures for changing the terms of the policy, procedures for editing or deleting data held by developers/third parties, procedures for complaints, and procedures for handling data for vulnerable users are subsets of “user rights” a privacy policy should contain. In addition, a data retention policy, data ownership, date of the policy, and next reviews should be contained as “administrative details” of the privacy policy. Users' access to their health information is another right. A systematic review [66] indicated that patients’ access to their health information has a positive impact. A similar study [21] proposed a 14-criteria scale for assessment of a privacy policy based on the GDPR. Although the items by proposed Benjumea et al [21] overlap our proposed criteria (some with different words but similar concepts), they include 5 items not included in our tool; 2 items are “legal basis for processing” and “legitimate interests from controller” that imply the bases for the processing determined by the GDPR. This may be similar to the criteria associated with permission/consent and how users’ data will be processed/used, which are considered in our tool in general. Another item is “transfers to non-EU countries,” which sounds similar to the “regulation the mHealth app comply with and the country (as general, not only European ones) that the regulation belongs to” also considered in our tool. The fourth item is “obligation to provide personal data,” which can be considered as a subset of “user rights” [34] (existent among our criteria). As mentioned earlier, users need to be informed about the consequences of not providing their information. The last item is “existence of automated decision-making or profiling,” which is not included in our tool. It also worth to note that the criteria proposed in our study are general criteria for assessing both privacy and security classified into 8 categories. We tried to determine a comprehensive list of criteria, but we also faced a restriction to limit our criteria to general important aspects of privacy and security, because including a large number of criteria makes it difficult for assessors to consider all of them and this may result in rejection of the tool. That is why we tried to use general concepts that cover more specific criteria (eg, user rights) or merge some criteria into a single one (eg, administrative details).
## Limitations
In this study, a list of criteria was proposed using published papers. A limitation of this study is conducting an unstructured literature search, due to which we missed some related papers. However, to the best of our knowledge, many of the criteria included in our study overlap those that were not included. Another limitation is the large number of included criteria, which may make it difficult for assessors to consider all of them; however, we tried to limit our criteria to important ones to make them more applicable, and we also used general concepts that cover more specific criteria (eg, user rights) or merged some items into a single one (eg, administrative details). Another limitation is the difficulty in assessing some criteria—for example, app compliance with regulations may not be clearly stated in the app. It is recommended that future studies verify the proposed criteria using mobile apps. However, they should be considered in conjunction with other assessment strategies, such as risk analysis, data leakage detection, and continuous revision accordingly. Moreover, this study focused on the security and privacy challenges of mHealth apps, but there are other important challenges, such as interoperability. Thus, it is recommended that future studies combine both aspects to obtain not only a secure system but also an interoperable one, because mHealth apps communicate with a variety of sources.
## Conclusion
With the evolution in the health field through smartphones and mHealth apps, privacy and security challenges need to be addressed. The proposed comprehensive criteria can be used as a quick guide for app designers, developers, regulators, and even researchers. The criteria and the countermeasures presented in this study can be considered to improve the privacy and security of an mHealth app before releasing it into the market. Regulators are recommended to consider an established standard using such criteria for the accreditation process, since the available self-certification of developers is not reliable enough.
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|
---
title: 'Evaluating the Feasibility, Acceptability, and Preliminary Efficacy of SupportMoms-Uganda,
an mHealth-Based Patient-Centered Social Support Intervention to Improve the Use
of Maternity Services Among Pregnant Women in Rural Southwestern Uganda: Randomized
Controlled Trial'
journal: JMIR Formative Research
year: 2023
pmcid: PMC10020914
doi: 10.2196/36619
license: CC BY 4.0
---
# Evaluating the Feasibility, Acceptability, and Preliminary Efficacy of SupportMoms-Uganda, an mHealth-Based Patient-Centered Social Support Intervention to Improve the Use of Maternity Services Among Pregnant Women in Rural Southwestern Uganda: Randomized Controlled Trial
## Abstract
### Background
SMS text messaging and other mobile health (mHealth) interventions may improve knowledge transfer, strengthen access to social support (SS), and promote positive health behaviors among women in the perinatal period. However, few mHealth apps have been taken to scale in sub-Saharan Africa.
### Objective
We evaluated the feasibility, acceptability, and preliminary efficacy of a novel, mHealth-based, and patient-centered messaging app designed using behavioral science frameworks to promote maternity service use among pregnant women in Uganda.
### Methods
We performed a pilot randomized controlled trial between August 2020 and May 2021 at a referral hospital in Southwestern Uganda. We included 120 adult pregnant women enrolled in a 1:1:1 ratio to receive routine antenatal care (ANC; control), scheduled SMS text or audio messages from a novel messaging prototype (scheduled messaging [SM]), and SM plus SMS text messaging reminders to 2 participant-identified social supporters (SS). Participants completed face-to-face surveys at enrollment and in the postpartum period. The primary outcomes were feasibility and acceptability of the messaging prototype. Other outcomes included ANC attendance, skilled delivery, and SS. We conducted qualitative exit interviews with 15 women from each intervention arm to explore the intervention mechanisms. Quantitative and qualitative data were analyzed using STATA and NVivo, respectively.
### Results
More than $85\%$ and $75\%$ of participants received ≥$85\%$ of the intended SMS text messages or voice calls, respectively. More than $85\%$ of the intended messages were received within 1 hour of the expected time; $18\%$ ($\frac{7}{40}$) of women experienced network issues for both intervention groups. Over $90\%$ ($\frac{36}{40}$) of the intervention participants found this app useful, easy to use, engaging, and compatible and strongly recommended it to others; $70\%$ ($\frac{28}{40}$), $78\%$ ($\frac{31}{40}$), and $98\%$ ($\frac{39}{40}$; $$P \leq .04$$) of women in the control, SM, and SS arms, respectively, had a skilled delivery. Half ($\frac{20}{40}$), $83\%$ ($\frac{33}{40}$), and all ($\frac{40}{40}$; $$P \leq .001$$) of the women in the control, SM, and SS arms attended ≥4 ANC visits, respectively. Women in the SS arm reported the highest support (median 3.4, IQR 2.8-3.6; $$P \leq .02$$); <$20\%$ ($\frac{8}{40}$; $$P \leq .002$$) missed any scheduled ANC visit owing to lack of transportation. Qualitative data showed that women liked the app; they were able to comprehend ANC and skilled delivery benefits and easily share and discuss tailored information with their significant others, who in turn committed to providing them the needed support to prepare and seek help.
### Conclusions
We demonstrated that developing a novel patient-centered and tailored messaging app that leverages SS networks and relationships is a feasible, acceptable, and useful approach to communicate important targeted health-related information and support pregnant women in rural Southwestern Uganda to use available maternity care services. Further evaluation of maternal-fetal outcomes and integration of this intervention into routine care is needed.
### Trial Registration
ClinicalTrials.gov NCT04313348; https://clinicaltrials.gov/ct2/show/NCT04313348
## Introduction
Antenatal care (ANC) is a mainstay for preventing maternal and perinatal morbidity and mortality, promoting the detection and treatment of prenatal complications, and identifying women at high risk to ensure delivery in skilled settings [1,2], but the use of these services in Uganda remains low. For example, only $58\%$ of expectant mothers attend at least 4 ANC visits (of the recommended 8 by the World Health Organization) and only $70\%$ of women deliver with a skilled attendant [3]. Consequently, Uganda has the highest maternal mortality (360 per 100,000 women) and child perinatal mortality rates (41 deaths per 1000 births) worldwide [3].
SMS text messaging and other mobile health (mHealth) interventions have been proposed to promote positive health behaviors and strengthen informed decision-making in women in the perinatal period [4-6]. Such interventions are hypothesized to improve outcomes through knowledge transfer and strengthened access to social support (SS). For example, mHealth interventions in pregnant women have been shown to increase ANC attendance [7,8], institutional delivery [9,10] and vaccination rates [4,10]. mHealth interventions that bolster SS can also improve pregnancy experiences by decreasing anxiety and depression [11-14], while increasing perinatal bonding [13] and communication [14]. These benefits are believed to be mediated through the promotion of family structure, partner involvement, and social networks, which in turn foster financial and emotional coping mechanisms to enable women to overcome socioeconomic and physical barriers to target outcomes such as food insecurity and transportation [14-17].
However, few mHealth apps for maternal care have been taken to scale in sub-Saharan Africa (SSA), where the contextual factors that drive successful interventions differ [18] but the public health impact of such interventions is likely to be the greatest. Some studies have hypothesized that the underutilization of behavioral science theory in intervention design contributes to the lack of successful interventions at scale [4,6]. Few apps incorporate end-user designs or iterative development. The Healthcare Utilization Model (HUM) highlights three dynamics that predict health care service use including [1] predisposing factors (eg, marital status, birth order, knowledge gap, and health beliefs), [2] enabling factors (eg, SS, community participation, information access, respectful patient-centered care, income, travel or waiting time, accessibility of ANC, and delivery services), and [3] perceived or evaluated needs (eg, the state and perceptions of current health or pregnancy and perceived benefits or threats) [19-25]. Although used to explain health-seeking behaviors in resource-rich countries [26,27], few studies have examined the HUM framework in low- and middle-income countries. Furthermore, there are no theory-informed mHealth interventions targeted at improving the use of maternity services by promoting SS.
We previously reported our iterative app development activities, including stakeholder interviews, content development, app design, and testing [28]. We now report the results of a pilot study to evaluate the feasibility, acceptability, and preliminary efficacy of this novel mHealth-based, patient-centered, and audio-based SMS text messaging app (SupportMoms-Uganda) that draws upon HUM concepts, mHealth technologies, and SS to communicate targeted health-related information and promote the use of maternity services by pregnant women in rural Southwestern Uganda.
## Study Design
We conducted a 3-arm interventional study among pregnant women in Uganda to evaluate SupportMoms-Uganda, an mHealth app incorporating appropriate end-user intervention design characteristics, including SS network engagement through SMS text messaging notifications; motivators such as tailored, automated SMS text messaging; or voice call health information messaging to facilitate the uptake and use of maternity care services [28]. Scheduled SMS text messaging reminders were also incorporated as part of the intervention as a stimulus, prompt, or cue to take action. We used the behavioral change technique taxonomy [29,30] to identify and characterize the key components of this app aimed at communicating information on the benefits of nutrition, exercise, attending ANC, skilled delivery, partner involvement, birth preparedness, and monitoring danger signs. The app was designed using an end-user iterative approach to refine user-driven message content tailored to women’s needs and preferences. This trial was registered at ClinicalTrials.gov (NCT04313348).
## Study Participants
Two types of participants were enrolled in this study: [1] study participants, comprising pregnant women with a gestational age of £20 weeks (determined by the last menstrual period); and [2] their nominated social supporters. Eligible participants included [1] adults aged ≥18 years living in Mbarara district (within 20 km of the antenatal clinic), [2] having access to a mobile phone for personal use with reliable cellular phone reception, [3] being able to provide informed consent, and [4] willing to identify at least 2 social supporters or identified as a social supporter. We excluded women with known high-risk pregnancies at the time of enrollment, including hypertension; history of gestational diabetes and preeclampsia; or other severe birth complications because they could already be motivated to engage in ANC, and it would be unethical to enroll in the control group.
## Study Setting
This study was conducted at Mbarara Regional Referral Hospital, located approximately 290 km southwest of Uganda’s capital, Kampala. The hospital receives over 30,000 women attending routine ANC annually, including uncomplicated and high-risk pregnancies, and conducts over 12,000 deliveries annually. Maternity services, including delivery, are largely provided free of charge through public hospitals and health centers.
## Recruitment and Enrollment of Study Participants
Participants were screened for eligibility by a study nurse in the antenatal clinic and referred to a research assistant for enrollment or referred from village health teams [31]. Consenting participants were asked to identify at least 2 individuals from their existing SS network with whom they have had good, stable, and long-term relationships and believed they would be available to support them during the pregnancy and study follow-up period. Social supporters of at least 18 years of age, living within the same parish as the participant, who owned a cell phone for personal use with reliable cellular phone reception, and who knew the study participant’s pregnancy status were also eligible to enroll. Potential social supporters were excluded from the study if they were unable to use SMS text messaging or unwilling to receive SMS text message notifications. Potential social supporters were contacted during the first 2 weeks preceding participant enrollment to ensure an ongoing relationship at the time of their enrollment. The social supporters were then invited to participate in the study, consented, and enrolled. They were informed that they would receive weekly SMS text message notifications regarding the study participant’s next scheduled obstetric review during pregnancy and the postpartum period. No specific instructions or recommendations guiding social supporters on how to respond to SMS text message reminders were provided because the intervention was designed to build on existing supportive relationships among study participants. All participants provided written informed consent, or for those who could not write, a thumbprint was made on the consent form, as approved by the ethics committees. The study was conducted in a private space, and the data were coded and anonymous in accordance with the Declaration of Helsinki.
## Randomization and Blinding
Before study initiation, a study biostatistician digitally generated a random list used to determine arm assignment for study participants in block sizes of 20. Study participants were randomized equally in a 1:1:1 ratio to the control, scheduled messaging (SM), or SS arms. Once eligibility was established and participants consented to the study, a number was allocated by taking the next in a series of similar prior labeled opaque envelopes provided by the study coordinator to conceal group allocation. Research assistants were blinded to the study hypothesis as well as group allocation and were only informed of the arm assignment at the time of participant enrollment. Data were collected electronically. The data analyst was blinded to the group allocated to different study participants.
## Intervention Arms
Participants were screened for eligibility, randomized, and enrolled between August 2020 and May 2021 to one of three arms: [1] the standard routine care arm, [2] scheduled SMS text messaging arm, and [3] SS engagement arm. The standard routine care arm included routine information given to pregnant women at the maternity centers during ANC visits by clinic staff and midwives as per the Uganda’s Ministry of Health guidelines [32]. The scheduled SMS text messaging arm included automated health education SMS text messages or audio messages; a weekly SMS text message reminder about upcoming ANC appointments; and expected date of delivery at their preferred time, language, and day of the week. The content of the SMS text message reminders was customized and determined by participants at enrollment. If the participant had no preference, we used the message, “*This is* your ANC visit reminder, encouraging you to attend on [expected ANC visit date].” The SS engagement arm was similar to the scheduled SMS text messaging arm, with the addition of sending SMS text message reminders to the 2 participant-identified social supporters. Social supporters were also able to personalize the SMS text message content at enrollment; the default message read as follows: “We appreciate you being consistently close to your friend XX who is pregnant, we are reminding you of her upcoming antenatal visits on the date indicated on her card.” No additional health information was provided to the social supporters. Messages were sent as preferred in the local language Runyankole or English.
## Study Procedures
Participants in the intervention arms received SMS text messaging reminders, plus message content and information developed as part of the SupportMoms-Uganda app. The participants obtained instructions on how to use the app to retrieve or receive information. The times and lengths of the individual sessions were recorded and transmitted to the server. The phone served as a gateway to display and visualize the intended message content in the form of voice or text. Once reception or visualization was complete, data were transmitted from the gateway device to a secure web-based session and logged out to enable submission of data to the server for review via password access of any device that can access the web. Delays during periods of inadequate cellular reception were stored for later transmission. All study participants were given solar chargers and were reminded to charge their phones as needed during enrollment. App reception was considered a proxy for accessing information to alter existing predisposing factors (such as negative health beliefs) that could enable and improve the perceived need to seek care.
## Data Collection
All data collection was performed in Runyankole. Quantitative questionnaire data were collected from both study participants and their social supporters at enrollment on the following topics: sociodemographic characteristics, health, comorbidities and outcomes [33], food insecurity [34], SS [35], reproductive health history, and perceptions of pregnancy and delivery [36]. Reports of SS received by study participants did not specify the source as it could occur from outside the dyad studied here. Women were followed up for at least 6 months and, at exit (within 2-4 weeks following delivery), a survey was administered to assess the ability of participants to receive and understand SMS text messages or voice calls, technology usefulness, engagement, and acceptance. Exit questionnaires on feasibility and acceptability were developed using the Unified Theory of Acceptance and Use of Technology model [37]. Quantitative data were collected using a web-based database developed on SupportMoms-Uganda and ComCare platforms to improve data completeness, management, and quality control monitoring. A transport refund of US $3 was provided for each visit.
In addition, 30 face-to-face, in-depth exit interviews (15 from each intervention arm) were conducted to explore the patterns of SS and mechanisms of the intervention effect. Participants were purposively selected to ensure a range of prenatal and perinatal outcomes, types of SS reported, and intervention influence. These interviews were carried out within 2 to 4 weeks after delivery in a private place, lasted an hour, and were audio recorded. Interview guides were developed based on the observed quantitative results on technology acceptance. To maximize data quality, we asked interviewees to describe actual experiences and events whenever possible. Interview topics included [1] experiences with voice and SMS text messages received, including the most or least useful reminders, and communication between social supporters and study participants, including the things they talked about or did together; [2] acceptance and challenges, including mobile phone ease of use; their ability to understand, request, or receive support or guidance as needed; messaging problems experienced with the intervention; usefulness; and intention to use in future; [3] consequences, including changes or lack of changes resulting from the use of messages; and [4] comparisons and attitude, including differences, similarities, and attitude across the messaging types and suggested changes.
## Study Outcomes
Our primary outcome measures were [1] the feasibility of SupportMoms-Uganda app prototype, assessed by the number of received calls or SMS text messages and ability of participants to read or listen to and understand messages; [2] the acceptability of SupportMoms-Uganda app, measured using the Technology Acceptance Model to assess ease of use, motivation, social influence, perceived control, attitude toward use of the technology, and its usefulness; [3] average number of ANC visit attendance; and [4] proportion of births attended by a skilled provider. Other secondary outcomes include mode of delivery, maternal complications or need for resuscitation, birth weight, stillbirths, intrauterine fetal deaths, maternal deaths, interaction with a social supporter, and overall reported SS received by the study participant during pregnancy to improve her pregnancy experience and ANC visit attendance. SS was defined as [1] enabling the study participant to reach the clinic or hospital through monetary support, direct transportation, or taking care of daily activities while they are absent or [2] motivating the study participant to go for scheduled and necessary prenatal checkups, reviews, and skilled births, including addressing cognitive and behavioral barriers, such as food insecurity, depression, and alcohol use.
## Sample Size Estimation and Data Analysis
We determined our sample size for a 3-armed pilot randomized controlled trial intended to identify unforeseen app uptake and use problems among eligible pregnant women in Uganda. Using the rule of thumb [38,39], we calculated the sample size needed to identify $2.5\%$ of social and technical problems that may arise among SupportMoms-Uganda users, with a $95\%$ CI of 120 participants.
## Quantitative Data Analysis
We used summary statistics to compare the health-related and sociodemographic data of study participants between arms; data specific to social supporters will be presented elsewhere. We also assessed the technical function of the intervention using the following statistics: number of successful calls, SMS text messages delivered and received by the participant over the number of SMS text messages or calls anticipated per protocol, number and type of technical problems encountered, number of SMS text message notifications sent to social supporters, actual messaging reception and use, number of women using the SMS text message response and interactive message feature, messages coming within 1 hour of expected time, reminder or notification, and total ANC reviews. We assessed acceptability by describing technology expectancy, skills, facilitating conditions, acceptance, and engagement as per the Unified Theory of Acceptance and Use of Technology model. Although not powered to detect significant differences, we compared technology and maternal health outcomes among the 3 study arms to explore group differences using 1-way ANOVA. Study participant’s SS was divided into instrumental (physical and economic) and emotional (emotional and informational) support. The Household Food Insecurity Access Scale was calculated as recommended [40], and the median score was considered the cut-off for food insecurity. Instrumental SS and food insecurity were described because of the low-resource nature of this setting, which may impede the ability to provide physical support despite the intention to do so. Data analysis was conducted using STATA (version 13; StataCorp).
## Qualitative Data Analysis
Transcripts were generated from audio-recorded interviews. Qualitative data were coded using NVivo (version 12.0; QSR International) data management software. Coded data were iteratively reviewed and sorted to identify repeated themes (topics) arising from the data. Themes were generated using inductive content analysis [41]. Data analysis was performed jointly by ECA and JNN. Both JNN and ECA double-coded 5 sampled transcripts, yielding a Cohen κ statistic of 0.852. Together with EA, we resolved the coding disagreements to ensure consistency in the codebook. The content consisted of descriptive labels that defined and specified each theme’s meaning, along with illustrative quotes taken from the qualitative interviews.
## Ethics Approval
We formed an independent committee involving a biostatistician and clinicians with expertise in health service use and obstetric care. This committee, together with the community Advisory Board at Mbarara University, monitored participant confidentiality, data quality, implementation, outcomes, and potential harms. This study was reviewed and approved by the Mbarara University of Science and Technology Institutional Ethics Review Committee (registration number $\frac{13}{09}$-18) and the Uganda National Council for Science and Technology, Kampala, Uganda (registration number SS 4809). Permission to conduct the study was obtained from district and local community leaders.
## Participant Characteristics
Of the 161 women screened for eligibility from July 12 to September 20, 2020, a total of $74.5\%$ ($$n = 120$$) of women were eligible, and all participants consented to participate in the study. A total of 120 women were randomized equally into 3 study arms: control, scheduled SMS text messaging, and SS. All participants completed the study procedures. Their demographic and clinical characteristics were similar across the 3 study groups (Table 1).
**Table 1**
| Characteristics | Characteristics.1 | Control (n=40) | Scheduled messages (n=40) | Social support (n=40) |
| --- | --- | --- | --- | --- |
| Age (years), mean (SD) | Age (years), mean (SD) | 27.5 (4.3) | 26.9 (3.8) | 28.1 (3.5) |
| Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) |
| | Married | 25 (63) | 27 (68) | 25 (63) |
| | Single or separated | 15 (38) | 13 (33) | 15 (38) |
| Educational attainment (primary), n (%) | Educational attainment (primary), n (%) | 14 (35) | 16 (40) | 17 (43) |
| Parity, n (%) | Parity, n (%) | Parity, n (%) | Parity, n (%) | Parity, n (%) |
| | 0 | 14 (35) | 12 (30) | 16 (40) |
| | 1 | 9 (23) | 10 (25) | 11 (28) |
| | 2 | 6 (15) | 5 (13) | 7 (18) |
| | ≥3 | 11 (28) | 13 (33) | 6 (15) |
| Current pregnancy is planned, n (%) | Current pregnancy is planned, n (%) | 27 (68) | 30 (75) | 28 (70) |
| History of still birth, n (%) | History of still birth, n (%) | 4 (10) | 4 (10) | 3 (8) |
| Anyone in household aware of the pregnancy, n (%) | Anyone in household aware of the pregnancy, n (%) | 37 (93) | 36 (90) | 36 (90) |
| Household income (Ugandan Shilling >150,000 [approximately US $42]), n (%) | Household income (Ugandan Shilling >150,000 [approximately US $42]), n (%) | 25 (63) | 23 (58) | 20 (50) |
| Social support scorea, median (IQR) | Social support scorea, median (IQR) | 2.4 (2.1-2.8) | 2.8 (2.3-3.1) | 2.5 (2.2-3.0) |
| Food insecure (HFIASb >8), n (%) | Food insecure (HFIASb >8), n (%) | 11 (28) | 13 (33) | 11 (28) |
| History of complications in pregnancies, n (%) | History of complications in pregnancies, n (%) | 6 (15) | 5 (13) | 6 (15) |
| Mobile telecom service providerc, n (%) | Mobile telecom service providerc, n (%) | Mobile telecom service providerc, n (%) | Mobile telecom service providerc, n (%) | Mobile telecom service providerc, n (%) |
| | MTN (Mobile Telephone Network-Uganda) | 30 (75) | 28 (70) | 30 (75) |
| | Airtel-Uganda | 31 (78) | 33 (83) | 30 (75) |
| Preferred time to receiving messages, n (%) | Preferred time to receiving messages, n (%) | Preferred time to receiving messages, n (%) | Preferred time to receiving messages, n (%) | Preferred time to receiving messages, n (%) |
| | Sunrise to midday | 9 (23) | 10 (25) | 13 (33) |
| | Midday to sunset | 6 (15) | 5 (13) | 5 (13) |
| | Sunset to midnight | 5 (13) | 7 (18) | 10 (25) |
| | Anytime | 20 (50) | 18 (45) | 12 (30) |
| Preferred days of the week for messaging, n (%) | Preferred days of the week for messaging, n (%) | Preferred days of the week for messaging, n (%) | Preferred days of the week for messaging, n (%) | Preferred days of the week for messaging, n (%) |
| | Weekdays | 11 (28) | 9 (23) | 8 (20) |
| | Any day or no preference | 29 (73) | 31 (78) | 32 (80) |
## Primary Finding 1: Feasibility
At least 1 cell phone was reported in a household for both the scheduled SMS text messaging and SS arms (median 2, IQR 1-3), and $20\%$ ($\frac{8}{40}$) reported smartphones (Table 2). More than $70\%$ of the women in both groups owned a personal cell phone, and all women in both groups were able to operate the phone for either SMS text messages or voice calls. More than $85\%$ of automated informational SMS text messages, >$70\%$ of automated audio messages, and >$80\%$ of SMS text message reminders were successfully sent throughout the study period for both intervention arms. At least $85\%$ and $75\%$ of participants received a minimum of $85\%$ of the intended SMS text messages or voice calls, respectively. All participants received at least $65\%$ and $60\%$ of the intended SMS text messages or voice calls, respectively, for either group. At least $85\%$ of all participants used the interactive or response-messaging feature of the app. The messaging interactive feature was rated good by at least $90\%$ of the participants in both intervention groups. More than $85\%$ of all messages were received within 1 hour of the expected time, with less than $20\%$ of participants in both intervention groups reporting network issues as a reason for missing or delayed calls or messages. Using a scale of 1 to 5, all participants were able to hear calls clearly in both intervention groups, with on-call engagement lasting an average of 1.5 minutes. Confidentiality was ranked as the least important feature of a cell phone on SMS text messaging; clarity and language of the message was ranked as important for both groups.
**Table 2**
| Characteristics | Characteristics.1 | Scheduled messaging arm (n=40) | Social support arm (n=40) |
| --- | --- | --- | --- |
| Number of accessible mobile phonesa | Number of accessible mobile phonesa | Number of accessible mobile phonesa | Number of accessible mobile phonesa |
| | Household, median (IQR) | 2 (1-3) | 2 (1-3) |
| | Neighborhood, median (IQR) | 3 (2-4) | 3 (2-5) |
| | Smartphones, n (%) | 8 (20) | 8 (20) |
| Owns a personal cell phone, n (%) | Owns a personal cell phone, n (%) | 30 (75) | 31 (78) |
| Ability to operate phone for SMS text messaging or voice call, n (%; 95% CI) | Ability to operate phone for SMS text messaging or voice call, n (%; 95% CI) | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| Total number of messages sent automatically, n (%; 95% CI) | Total number of messages sent automatically, n (%; 95% CI) | Total number of messages sent automatically, n (%; 95% CI) | Total number of messages sent automatically, n (%; 95% CI) |
| | Informational SMS text messaging | 2683 (86.02; 84.80-87.22) | 2702 (86.71; 85.53-87.90) |
| | Audio messages | 752 (72.3; 69.5-75.0) | 783 (75.3; 72.6-77.9) |
| | Participant SMS text messaging reminders | 646 (80.8; 77.8-83.4) | 1312 (82; 80.01-83.86) |
| | Supporter SMS text messaging reminders | N/Ab | 671 (83.9; 81.1-86.4) |
| Number of planned messages received, n (%; 95% CI) | Number of planned messages received, n (%; 95% CI) | Number of planned messages received, n (%; 95% CI) | Number of planned messages received, n (%; 95% CI) |
| | ≥85% SMS text messages | 34 (85; 70.2-94.3) | 36 (90; 76.3-97.2) |
| | ≥85% voice calls | 30 (75; 58.8-87.3) | 33 (83; 67.2-92.7) |
| | ≥65% SMS text messages | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | ≥60% voice calls | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| Messages came within 1 hour of expected time, n (%; 95% CI) | Messages came within 1 hour of expected time, n (%; 95% CI) | 34 (85; 70.2-94.3) | 35 (88; 73.2-95.8) |
| Response messaging feature, n (%; 95% CI) | Response messaging feature, n (%; 95% CI) | Response messaging feature, n (%; 95% CI) | Response messaging feature, n (%; 95% CI) |
| | Used the response feature | 34 (85; 70.2-94.3) | 35 (88; 73.2-95.8) |
| | Rate response feature >3 | 36 (90; 76.3-97.2) | 37 (93; 79.6-98.4) |
| Reasons for missed or delayed messagesa, n (%; 95% CI) | Reasons for missed or delayed messagesa, n (%; 95% CI) | Reasons for missed or delayed messagesa, n (%; 95% CI) | Reasons for missed or delayed messagesa, n (%; 95% CI) |
| | Sometimes turn off phone deliberately | 6 (15; 5.7-29.8) | 4 (10; 2.7-23.7) |
| | Network issues | 7 (18; 7.3-32.8) | 5 (13; 4.2-26.8) |
| | Battery or phone charging issues | 3 (8; 1.6-20.4) | 1 (3; 0.1-13.2) |
| | Lost phone or phone functionality | 6 (15; 5.7-29.8) | 3 (8; 1.6-20.4) |
| Rate ability to hear voice message clearly >3, n (%; 95% CI) | Rate ability to hear voice message clearly >3, n (%; 95% CI) | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| Average length of on-call engagement (minutes), mean (SD) | Average length of on-call engagement (minutes), mean (SD) | 1.5 (0.3) | 1.5 (0.2) |
| Ranking important features of messagingc, n (%; 95% CI) | Ranking important features of messagingc, n (%; 95% CI) | Ranking important features of messagingc, n (%; 95% CI) | Ranking important features of messagingc, n (%; 95% CI) |
| | Confidentiality: least important | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Clarity of message: most important | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Language: most important | 36 (90; 76.3-97.2) | 36 (90; 76.3-97.2) |
## Primary Finding 2: Acceptability
As shown in Table 3, >$90\%$ of app users found it generally acceptable and helpful. All participants found the messaging app useful, motivating, and improved their involvement in health matters that concern them. Nearly all participants found the messaging app clear and easy to use and the content of the messaging program easy to understand. All participants liked the messaging program and found it fun and interesting; nearly all women obtained support from people who influenced their behavior or those around them to use the messaging program. More than $80\%$ of women did not need additional resources to use the messaging program for both the SM ($\frac{32}{40}$, $80\%$) and SS ($\frac{37}{40}$, $93\%$) groups. All women had the knowledge necessary to use the messaging program, and none reported incompatibility of the program with their existing messaging programs on their phones. Nearly all women reported to have had enough skills to operate the phone for all SMS text message or voice calls. None reported anxiety or apprehension, fear, intimidation, or hesitation to use the messaging program. All women intended, predicted, and planned to use the messaging program in the future and would definitely recommend it to others. Using a scale of 1 to 5, at least $90\%$ of the participants rated SMS text messaging and voice messages as highly relevant for both groups. None of the women found the scheduled SMS text messages or voice calls bothersome but engaging. Compared with routine calls or SMS text messages, >$95\%$ of participants in both groups read SMS text messages or received voice calls in the app whenever they saw them all the time. All interviewed participants found the messaging program convenient. Additional details are presented in Table 3.
**Table 3**
| Unnamed: 0 | Unnamed: 1 | Scheduled messaging (n=40), n (%; 97.5% CI) | Social support (n=40), n (%; 97.5% CI) |
| --- | --- | --- | --- |
| Performance expectancy | Performance expectancy | Performance expectancy | Performance expectancy |
| | I find the messaging program useful in my pregnancy journey | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Using the messaging program motivates me to attend to my ANCa visits seriously | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Using the messaging program increases my involvement in health matters that concern me | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Rateb usefulness of the messaging program ≥4 | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| Effort expectancy | Effort expectancy | Effort expectancy | Effort expectancy |
| | My interaction with the program is clear and understandable | 39 (98; 86.8-99.9) | 40 (100; 91.2-100) |
| | Messaging program was easy to use | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Learning to operate the program was easy for me | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Rateb ability to understand the message content >3 | 39 (98; 86.8-99.9) | 38 (95; 83.1-99.4) |
| Attitude toward the messaging program | Attitude toward the messaging program | Attitude toward the messaging program | Attitude toward the messaging program |
| | Working with the messaging program is fun | 36 (90; 76.3-97.2) | 40 (100; 91.2-100) |
| | I like working with the messaging program | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| Social influence | Social influence | Social influence | Social influence |
| | People who influence my behavior think that I should use the messaging program | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | People around me have supported use of messaging program | 36 (90; 76.3-97.2) | 40 (100; 91.2-100) |
| Facilitating conditions | Facilitating conditions | Facilitating conditions | Facilitating conditions |
| | I have the resources necessary to use messaging program | 32 (80; 64.4-91.0) | 37 (93; 79.6-98.4) |
| | I have the knowledge necessary to use program | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Program not compatible with other messaging programs I use. | 0 (0; 0-8.8) | 0 (0; 0-8.8) |
| Self-efficacy | Self-efficacy | Self-efficacy | Self-efficacy |
| | I could complete a job or task with no one to tell me what to do | 39 (98; 86.8-99.9) | 40 (100; 91.2-100) |
| Anxiety | Anxiety | Anxiety | Anxiety |
| | I feel apprehensive about using the messaging | 0 (0; 0-8.8) | 0 (0; 0-8.8) |
| Behavioral intention to use messaging program | Behavioral intention to use messaging program | Behavioral intention to use messaging program | Behavioral intention to use messaging program |
| | I intend to use the program in future | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | I plan to use the program in future | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | I definitely recommend it to others to use | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| | Rateb relevance of the messages >3 | 39 (98; 86.8-99.9) | 40 (100; 91.2-100 |
| Technology engagement or fatigue (compared with the traditional SMS text messages or voice calls) | Technology engagement or fatigue (compared with the traditional SMS text messages or voice calls) | Technology engagement or fatigue (compared with the traditional SMS text messages or voice calls) | Technology engagement or fatigue (compared with the traditional SMS text messages or voice calls) |
| | I found the study messages bothersome | 0 (0; 0-8.8) | 0 (0; 0-8.8) |
| | I found the voice messages engaging | 36 (90; 76.3-97.2) | 40 (100; 91.2-100) |
| | I found the SMS messages engaging | 32 (80; 64.4-91.0) | 40 (100; 91.2-100) |
| | I always receive these calls whenever I see them | 38 (95; 83.1-99.4) | 40 (100; 91.2-100) |
| | I read these SMS whenever I see them: all the time | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
| Rateb convenience of the messaging program >3 | Rateb convenience of the messaging program >3 | 40 (100; 91.2-100) | 40 (100; 91.2-100) |
## Preliminary Birth Outcome Data by Arm
In total, $88\%$ ($\frac{35}{40}$), $90\%$ ($\frac{36}{40}$), and $88\%$ ($\frac{35}{40}$) of women delivered vaginally in the control, scheduled SMS text messaging, and SS arms, respectively (Table 3); $70\%$ ($\frac{28}{40}$), $78\%$ ($\frac{31}{40}$), and $98\%$ ($\frac{39}{40}$; $$P \leq .04$$) of women in the control, SM, and SS arms, respectively, had a skilled delivery; $50\%$ ($\frac{20}{40}$), $83\%$ ($\frac{33}{40}$), and $100\%$ ($\frac{40}{40}$; $$P \leq .001$$) of women in the control, SM, and SS arms attended ≥4 ANC visits, respectively. Although we noted fewer maternal complications or needs for neonatal resuscitations, stillbirths, or intrauterine fetal deaths at delivery reported by women in the intervention groups than in the control group, the differences were not significant ($$P \leq .18$$). The types and extent of SS as reported by the study participants varied. Women in the SS arm reported the highest SS (median 3.4, IQR 2.8-3.6) compared with 2.8 (IQR 2.6-3.2) and 2.4 (IQR 2.2-2.8; $$P \leq .02$$) in the SM and control arms, respectively. More women in the SS group also communicated and interacted at least once a week with their social supporters about ANC and pregnancy needs ($\frac{31}{40}$, $78\%$; $$P \leq .002$$) compared with $40\%$ ($\frac{16}{40}$) and $35\%$ ($\frac{14}{40}$) in the SM or control arms, respectively. More study participants in the control arm ($\frac{16}{40}$, $40\%$; $$P \leq .01$$) did not interact with their social supporters about ANC and pregnancy needs compared with the other 2 intervention groups. Fewer participants missed scheduled ANC appointments in the SS group ($\frac{8}{40}$, $20\%$; $$P \leq .002$$) because they could not afford transport compared with women in the SM arm ($\frac{23}{40}$, $58\%$) and control arm ($\frac{27}{40}$, $68\%$; Table 4).
**Table 4**
| Outcomes | Outcomes.1 | Control arm (n=40) | Scheduled messaging arm (n=40) | Social support arm (n=40) | P value |
| --- | --- | --- | --- | --- | --- |
| Spontaneous vaginal delivery, n (%) | Spontaneous vaginal delivery, n (%) | 35 (88) | 36 (90) | 35 (88) | .82 |
| Skilled delivery, n (%) | Skilled delivery, n (%) | 28 (70) | 31 (78) | 39 (98) | .04 |
| Attended ≥4 ANCa visits, n (%) | Attended ≥4 ANCa visits, n (%) | 20 (50) | 33 (83) | 40 (100) | .001 |
| Maternal complications or need for resuscitation, n (%) | Maternal complications or need for resuscitation, n (%) | 8 (20) | 5 (13) | 3 (8) | .18 |
| Birth weight <2.5 kg, n (%) | Birth weight <2.5 kg, n (%) | 9 (23) | 6 (15) | 6 (15) | .42 |
| Stillbirths or intrauterine fetal death at delivery, n (%) | Stillbirths or intrauterine fetal death at delivery, n (%) | 3 (7) | 0 (0) | 0 (0) | .18 |
| Reported maternal deaths, n (%) | Reported maternal deaths, n (%) | 0 (0) | 0 (0) | 0 (0) | N/Ab |
| Overall reported social support, median (range) | Overall reported social support, median (range) | 2.4 (2.2-2.8) | 2.8 (2.6-3.2) | 3.4 (2.8-3.6) | .02 |
| Participant interaction with a social supporter about ANC and pregnancy needs, n (%) | Participant interaction with a social supporter about ANC and pregnancy needs, n (%) | Participant interaction with a social supporter about ANC and pregnancy needs, n (%) | Participant interaction with a social supporter about ANC and pregnancy needs, n (%) | Participant interaction with a social supporter about ANC and pregnancy needs, n (%) | Participant interaction with a social supporter about ANC and pregnancy needs, n (%) |
| | At least once a week | 14 (36) | 16 (40) | 31 (78) | .002 |
| | More than a week to a month | 10 (25) | 17 (43) | 9 (23) | .17 |
| | Never | 16 (40) | 7 (18) | 0 (0) | .01 |
| Missed a scheduled appointment, n (%) | Missed a scheduled appointment, n (%) | Missed a scheduled appointment, n (%) | Missed a scheduled appointment, n (%) | Missed a scheduled appointment, n (%) | Missed a scheduled appointment, n (%) |
| | Could not afford transport | 27 (68) | 23 (58) | 8 (20) | .04 |
| | Could not miss work | 4 (10) | 3 (8) | 0 (0) | .18 |
| | Could not get someone to leave family or children | 7 (18) | 4 (10) | 1 (3) | .08 |
## Overview
Of the 30 women interviewed, 20 ($67\%$) had had a skilled delivery, 25 ($83\%$) had attended at least 4 ANC visits, and 22 ($73\%$) reported moderate to high SS. All non–control group participants received at least $50\%$ of the planned SMS text messages or voice messages. Technology expectancy, acceptance, and engagement was dynamic across both intervention groups. Women reported different motivations, goals, likes, needs, and expectations while using or engaging in the messaging program that was customized and automated to deliver tailored audio messages or SMS text messages. From the qualitative data, all women described the intervention as useful, actionable, and easy to use; the tailored health information helped them to learn, internalize, and comprehend ANC and skilled delivery benefits, strengthening their informed decision-making as they were reportedly able to easily share and discuss information with their significant others, who in turn committed to providing them the needed support to prepare and seek help. Women identified 5 important app attributes that enabled them to use the program continuously. Women reported that they were able to [1] receive and understand messages easily and independently; [2] receive trusted and actionable information sent directly on mobile phones, which helped women pay more attention; [3] appreciate scheduled, personalized, and precautionary messages delivered in a friendly tone; [4] obtain complementary educational support for sharing with their friends and partners or for future reference; and [5] engage partners and social networks for needed support.
## Receiving and Understanding Messages Independently
Women found the app familiar and easy to use. They reported receiving voice calls or SMS text messages on their or significant others’ phone devices effortlessly without added cost or skill. Because phones are familiar and already integrated into daily routines, women often use this technology. The expectations of using familiar devices also eased anxiety, apprehension, hesitation, or intimidation about the technology used for this messaging program. Their familiarity with cell phones improved their interaction with the messaging app, improved their understanding, and improved their behavioral intention to use the messaging program continuously and in the future:
## Trusted and Actionable Information Sent Directly on Mobile Phones Helped Women Pay More Attention
Women reported that interacting with the app messages prompted them to take action as they were able to obtain trusted and credible information sent directly on their mobile phones. Women relied on information from the app to process and gain knowledge, as well as make informed decisions that would help them work through certain set maternity goals, such as delivering a healthy baby, having a safe skilled delivery, and attending scheduled ANC visits. Women also indicated that they were able to obtain useful and actionable information on health, instructions about different preferred topics, or how to perform safe motherhood behavior, as well as information on health consequences or regrets of poor health-seeking behavior, what to do, or where to seek care or redress to prepare and solve their identified problem, which kept them attentive and motivated. Routine information from a trusted source also helped women build confidence in the app and stay alert in reviewing their progress toward individual birth goals as they continuously interacted with their partners and health care providers for redress or follow-up on ANC monitoring visits:
## Scheduled, Personalized, and Precautionary Messages Delivered in a Friendly Tone
App messages were preferred and expected at certain times of the day and week. Women reported that this interface helped them not to miss calls or texts unnecessarily or waste time waiting around expecting the messages. Unlike the random, redundant, and unsolicited messages routinely sent to their phones in different numbers by telecom companies, these scheduled messages from a known sender helped women plan and engage with the expected messages. These schedules helped women stay expectant, light, and excited to receive messages addressed to them. Customization with individual names, plus tailoring of these messages to their needs and demands, made the messages relevant, making women feel a sense of comfort and value. Women also felt included and understood by the app callers and promoters: Women also described the app messages as precautionary and friendly and that such straightforward messages encouraged, motivated, and prompted them to take or plan actions, such as attending scheduled visits, seeking financing to prepare, or seeking skilled delivery. Information cues such as danger signs during pregnancy, communicated in a friendly tone, were said to help women appreciate their risks and keep them interested. Women also seemed to build more trusting relationships with their health care providers, as they engaged with the messaging app and continuously understood birthing procedures or processes through these cautionary messages. This continuous engagement helped women seek formal maternity care services that facilitated more useful one-on-one information transfers and support. Notably, the delivery of these messages was reported to be friendlier and more responsive to their needs compared with routine group health education experiences at public health facilities:
## Continuous and Complementary Educational Support for Sharing or Future Reference
Women described the app as a good and ongoing way of obtaining information that they stored on their phone for future reference or sharing with friends within their social networks. Some women described this continuous and customized messaging approach as continuity of care and the needed confidence in the intervention as a “birth companion” that helped them learn, keep motivated, and monitor their progress in time. Sharing this information with others was also reported to improve interaction and engagement with the app and others, such as women, and reviewed and shared knowledge. The ability to receive, understand, store, and share information with peers, spouses, and significant others from a credible source was seen as a more important factor than the actual provision of messages, which reportedly empowered women through call back, repeat, or other app interaction features:
## Engagement of Partners and Social Networks for Needed Support
Active engagement of women and their social supporters through SMS text messaging reminders or their phones to access important, targeted health information during pregnancy helped women mobilize the needed company and resources to access maternity services: fairly or adequately preparing for birth. Women reported that partner engagement in the messaging program improved their involvement and communication as they sought to understand their risk or schedule and offered the necessary physical, financial, and emotional help to seek care in time before complications occurred. The approach to independently consent to these social supporters in the study seemed crucial to reinforce technology trust and confidence. The active involvement of preferred social supporters in the messaging program was reported to improve their physical interaction about pregnancy needs as well as the quality of women’s pregnancy and birthing choices and experiences:
## Principal Findings
We assessed the feasibility, acceptability, and preliminary effectiveness of a novel, patient-centered, and audio-based SMS text messaging app to support women in using maternity care services in rural Southwest Uganda. We observed high intervention acceptability and feasibility, with >$80\%$ of women receiving ≥$85\%$ of the intended messages within 1 hour. More than $90\%$ of the women found this intervention useful, easy to use, interesting, appropriate, engaging, and compatible and would strongly recommend it to others. Nearly all women ($\frac{39}{40}$, $98\%$) in the SS arm had a skilled delivery compared with $78\%$ ($\frac{31}{40}$) and $70\%$ ($\frac{28}{40}$) in the SM and control groups, respectively. All women whose social supporters were engaged in the app attended ≥4 ANC visits, compared with $83\%$ ($\frac{33}{40}$) and $50\%$ ($\frac{20}{40}$) of women in SM and routine ANC, respectively. More study participants in the control arm ($\frac{16}{40}$, $40\%$; $$P \leq .01$$) did not interact with their social supporters about ANC and pregnancy needs compared with the other 2 intervention groups. Fewer women in the SS arm ($\frac{8}{40}$, $20\%$; $$P \leq .002$$) missed any visits owing to lack of transportation, compared with $58\%$ ($\frac{23}{40}$) and $68\%$ ($\frac{27}{40}$) of women in the SM and routine care arms, respectively. Women in the SS arm reported improved SS (3.4, IQR 2.8-3.6) compared with 2.8 (IQR 2.6-3.2) and 2.4 (IQR 2.2-2.8; $$P \leq .02$$) in the SM and control arms, respectively. The interactive messaging feature was rated highly by >$90\%$ of participants in both intervention groups. Our screen-to-eligible ($\frac{120}{161}$, $74.5\%$) and eligible-to-enroll ($\frac{120}{120}$, $100\%$) ratios were very high, suggesting promise or potential for wide reach. None of the participants were lost to follow-up. Our pilot data support the first mHealth app developed in the SSA setting by the SSA team to leverage existing social networks to support SSA women with promising findings.
In the qualitative interviews, all women described the intervention as useful, actionable, and easy to use; it helped them learn, cope, prepare, and take action within a friendly, trusted, and familiar environment. Scheduled, customized, and precautionary messages delivered in a friendly tone at preferred times of the week were valued as motivating and encouraging. The app was reported to provide complementary educational support for future reference or for sharing among their social networks. Women expressed that tailored health information helped them to learn, internalize, and comprehend ANC and skilled delivery benefits. This strengthened their informed decision-making, as they were reportedly able to easily share and discuss information with their significant others, who in turn committed to providing them the needed support to prepare and seek help. Women also expressed that involvement of their significant others within a friendly, trusted, and familiar environment helped them to mobilize needed support during pregnancy. Involving both health care providers and end users in characterizing, developing, and formulating the mHealth intervention allowed tailoring the intervention to their preferences. We incorporated women’s expectations, experiences, perceptions, and choices of a familiar mHealth-based technology that would benefit and support them in seeking professional maternity care within their local communities long-term, subject to the standard limitations of mobile phone ownership, type, and network challenges in the region.
Prior studies have reported improved engagement, acceptability, and use of programs that have been developed using a patient-centered approach, where mHealth interventions aim to address barriers to health care use through a multipronged approach by [1] teaching positive health behaviors and addressing specific health concerns (predisposing factors), [2] empowering and strengthening informed decision-making (enabling factors), and [3] improving the perceived need for the use of available services [4-6,18,42-46]. Such novel mHealth interventions help individuals internalize the benefits of health services and strengthen informed decision-making, especially when provided with adequate and relevant information on the promoted behavior to reduce the risk of morbidity and mortality, support healthier lifestyles, empower, and enable individuals to address specific health concerns or seek help [47]. This patient-centered mHealth intervention offered women complementary support through mobile phones as a health communication tool to bridge information gaps and provide continuity of care through tailored and targeted messaging.
Many women in Uganda are largely dependent on their significant others for economic provision, which together with the existing gender and traditional norms and beliefs, limits women’s ability and freedom to make family or health decisions to seek skilled care [31,48]. Knowledge gaps majorly influenced women’s past and future decisions to not attend ANC and pursue unskilled home births [31,48]. In line with previous studies [4-6,17,42,47,49,50], our ongoing and directed engagement and support at individual and family or societal levels were observed to have meaningfully or significantly improved individual risk internalization, partner involvement, pregnancy experience, perceived need, and informed decision-making to attend scheduled ANC visits and deliver in the presence of a health care provider. Similar directed and customized mobile phone–based interventions have previously been observed to motivate and inspire women, as well as offer individual or family SS [11-14,51], cues to action [52], or a source to challenge and debunk societal negative beliefs influencing access and use of health care [53].
With increasing and changing demands, tastes, trends, and preferences, users need relevant, appealing, and unique approaches and not the one-size-fits-all approach. The SupportMoms-Uganda app used theoretical models to develop appropriate and high-yield intervention design characteristics, such as an easy-to-use interface; use of familiar technology; SS network engagement through automated SMS text message notifications; motivators such as tailored SMS text messaging, voice information messaging, or customized reminders; and key factors that jointly improve participant experience and facilitate the use and retention of the messaging program. Women reported that this program was relevant and useful when personalized to fit their needs and demands. Our data also showed that our multifaceted mobile app designed using a behavioral model improved the use of maternity services, especially among women in the SS group who continuously shared their experiences concerning their milestones, concerns, challenges, and goal attainments with their significant others. Such ongoing sharing and feedback experiences that involve health care providers and significant others toward the attainment of set goals and targets have been documented to motivate app users [54]. In line with previous studies, characterizing the key components of an intervention, tailoring, and personalizing the information for end users improves engagement, ownership, motivation, and use of the intervention [29,30,54]. Previous studies have found that the SMS text message language, medium of message delivery, experience with similar technology, phone type, and characteristics are critical in designing and delivering a culturally appropriate mHealth program [18,44]. The engagement of social networks through SMS text message reminders has been documented to motivate individuals toward positive health behaviors and provide active SS to access health services [4-6,17,42]. Scheduled SMS text message reminders (1-way SMS text message sent on a fixed schedule, such as daily and weekly) and telephone apps have also been said to work as support, incentive, or enablers, especially when provided with accurate and relevant information on the promoted behavior [47]. Scheduled and automated messages help avoid technology fatigue, unnecessary repetition, and burdensomeness, making the messaging intervention an acceptable tool for delivering health promotion content [55]. SMS text messaging and voice calls for pregnant women have also been documented to increase singular maternal and child health outcomes, such as ANC attendance [7,8], institutional delivery [9,10], and vaccination rates [4,10] in low- and middle-income countries.
Our study had several strengths. Our app integrated maternal health epidemiology, well-established behavioral change theories, and qualitative research methods to characterize and consider key components of a patient-centered messaging app to improve maternity care use, making our findings more grounded, meaningful, acceptable, and generalizable in similar settings. We used both qualitative and quantitative methods to investigate the synergistic impact of a combination of novel mHealth interventions comprising SMS text message reminders and health information and leveraging SS to empower and motivate women to access perinatal care and improve maternal child health in the region. We used a stepped multidisciplinary approach that improved technology ownership, inclusiveness, confidence, and uptake to improve maternity service use among the targeted end users. Our research findings provide preliminary data that can be used to perform power calculations for a phase 3 definitive randomized controlled trial to evaluate the effectiveness of the SupportMoms-Uganda app or intervention compared with routine care in improving maternal and child health outcomes in Uganda.
Our study has some limitations. Many people in Uganda are transient in searching for stable work or new settlements [17], including during pregnancy. Some participants changed or lost their mobile phones or had inaccessible phones because of network issues. In addition, travel was restricted during the COVID-19 pandemic lockdowns, which might have affected the travel and attendance of ANC. However, these effects were distributed across all 3 arms. We leveraged our previous clinical research experience to maximize retention in care through the enrollment and engagement of alternative contacts in their social networks. We also actively explained the study purpose, schedule, and expectations at the time of enrollment and updated the residence and phone details at each follow-up visit to minimize lost to follow-up. We used appropriate means of contact based on participant preferences and information on the best telephone network for the time of the day to call or send text messages. Although we emphasized that participation in this study was voluntary, no eligible participant declined or withdrew from the study. Our study setting includes mainly persons from a less affluent or educated background and fewer smartphone users, limiting internet access despite improved internet penetration through local mobile phone companies. As such, the messaging content and delivery medium was developed to suit most phone types and characteristics for similar settings, and thus findings or the approach might not be generalizable to settings where literacy, the internet, or smartphone use is high.
## Conclusions
Our study contributes to a greater understanding of the characteristics and complexity of mHealth messaging apps that leverage SS networks and relationships to influence the uptake and use of maternity services. We demonstrate that developing a novel, patient-centered, and customized audio-based SMS messaging app is a feasible approach to communicate important targeted health-related information and support rural pregnant women in Southwestern Uganda to attend scheduled ANC visits and deliver them in the presence of a skilled health care provider. We also demonstrated that developing a useful and appropriate patient-centered, audio-based SMS text messaging app is widely acceptable in Southwestern Uganda to support women in accessing timely and credible health-related information through targeted and customized mHealth messaging approaches sent directly to individual mobile phones. We observed that involving end users gave women an opportunity to develop a tailored app according to their needs, preferences, and demands, an approach that was seen to improve technology engagement, as well as the uptake and use of available maternity care services. Women liked the app and described it as useful and easy to use, helping them learn, cope, prepare, and take action in a friendly atmosphere. Further evaluation of the effectiveness and integration of this mHealth-based SS intervention into routine care as a novel approach to improving maternal-perinatal health outcomes in *Uganda is* needed.
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|
---
title: Are Nonalcoholic Fatty Liver Disease and Bone Mineral Density Associated? —
A Cross‐Sectional Study Using Liver Biopsy and Dual‐Energy X‐Ray Absorptiometry
authors:
- Stinus Gadegaard Hansen
- Charlotte Wilhelmina Wernberg
- Lea Ladegaard Grønkjær
- Birgitte Gade Jacobsen
- Tina Di Caterino
- Aleksander Krag
- Claus Bogh Juhl
- Mette Munk Lauridsen
- Vikram V. Shanbhogue
journal: JBMR Plus
year: 2023
pmcid: PMC10020916
doi: 10.1002/jbm4.10714
license: CC BY 4.0
---
# Are Nonalcoholic Fatty Liver Disease and Bone Mineral Density Associated? — A Cross‐Sectional Study Using Liver Biopsy and Dual‐Energy X‐Ray Absorptiometry
## ABSTRACT
There is controversy regarding the association between nonalcoholic fatty liver disease (NAFLD) and osteoporosis. Our study aim was to assess bone mineral density (BMD) in patients with biopsy‐proven NAFLD and examine if the severity of NAFLD affects BMD. A total of 147 adult women ($$n = 108$$) and men ($$n = 39$$) aged 18–76 years (mean ± standard deviation [SD] age 45.3 ± 12.5) were recruited in this cross‐sectional study and underwent a liver biopsy and dual‐energy X‐ray absorptiometry (DXA). NAFLD activity score (NAS) based on the degree of steatosis, lobular inflammation and hepatocellular ballooning was used to assess NAFLD severity. The majority of subjects, $53\%$, had steatosis, $25\%$ had nonalcoholic steatohepatitis (NASH) whereas $23\%$ served as control subjects with no evidence of NAFLD. There were no significant differences in the lumbar spine (1.09 ± 0.12, 1.11 ± 0.18, and 1.12 ± 0.15 g/cm2, $$p \leq 0.69$$, in controls, steatosis, and NASH, respectively) or hip BMD (1.10 ± 0.15, 1.12 ± 0.13, and 1.09 ± 0.13 g/cm2, $$p \leq 0.48$$, in controls, steatosis, and NASH, respectively) between the groups. Adjusting for age, gender, body mass index, and diabetes in multiple regression models did not alter the results. There was no correlation between NAS and neither lumbar spine BMD ($r = 0.06$, $$p \leq 0.471$$), nor hip BMD (r = −0.03, $$p \leq 0.716$$). In conclusion, BMD was similar across the spectrum of NAFLD in both genders and not related to the severity of the underlying histological lesions, suggesting that neither steatosis nor NASH exerts a detrimental effect on BMD in these relatively young patients. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
## Introduction
Nonalcoholic fatty liver disease (NAFLD) is the most common liver disorder in Western countries encompassing a spectrum of liver damage ranging from steatosis (increased liver fat without inflammation) and nonalcoholic steatohepatitis (NASH; increased liver fat with inflammation and hepatocellular injury) when no other causes for secondary hepatic fat accumulation are present. Although the pathogenesis of NAFLD has not been fully elucidated, insulin resistance has been implicated as the key mechanism leading to hepatic steatosis with additional oxidative injury and an inflammatory cascade believed to play integral roles in the necroinflammatory component of steatohepatitis.[1] *Osteoporosis is* a common disease characterized by bone loss leading to low bone mass, microarchitectural disruption, and increased skeletal fragility predisposing to an increased risk of fractures. Chronic inflammation has been purported as a risk factor for osteoporosis. Indeed, cytokine activation in chronic inflammatory states such as rheumatoid arthritis, systemic lupus erythematosus, inflammatory bowel disease, primary biliary cirrhosis and chronic viral hepatitis has been associated with low bone mass and osteoporosis.[2] In line with this theory, it has been hypothesized that NASH, a chronic inflammatory state, may be associated with low bone mass. In a study of 102 adult patients with NAFLD, Purnak and colleagues[3] found that in comparison to healthy control subjects, presence of NASH but not simple steatosis, was associated with low bone mineral density (BMD). Several other studies have found a negative association between steatosis and BMD in men and women,[4] postmenopausal women,[5] and obese children,[6, 7] and a negative association between NASH and BMD in obese children.[6] However, the evidence till date has been far from convincing, with some studies reporting a higher lumbar BMD in patients with NASH.[8] *In a* recent meta‐analysis including 1276 participants, 638 patients with NAFLD, Upala and colleagues[9] found no significant differences in BMD between patients with NAFLD and control subjects.
One of the main limitations fueling the controversy between the association of NAFLD and BMD is that most studies till date have based the diagnosis of NAFLD on ultrasonographic evidence of hepatic steatosis, with an elevated alanine aminotransferase (ALT) serving as a surrogate marker for the presence of NASH. As already alluded to, NAFLD encompasses a spectrum of distinct conditions with variable severity, with liver biopsy remaining the gold standard to distinguish simple steatosis from NASH with inflammation in addition to grading of fibrosis.
Our aim was to assess BMD in patients with biopsy‐proven NAFLD and examine if the severity of NAFLD in obese subjects affects BMD. Based on previous literature we hypothesized NAFLD will be associated with a low BMD.
## Patients
This cross‐sectional study was conducted at University Hospital, Southwest Jutland between August 2018 and December 2021 after ethical approval from the Regional Health Ethics Committee of Southern Denmark (ID S‐20170210). All participants provided written informed consent and the study was performed according to the guidelines from the Declaration of Helsinki. All participants had a body mass index (BMI) ≥35 kg/m2 and were accordingly at high risk of NAFLD. None of the participants reported an alcohol consumption above 84 g or 168 g per week for women and men, respectively, or the use of hepatotoxic medications. A questionnaire was used to assess details of medical history including menopausal status in women, diagnosis of diabetes, other obesity related conditions, and medications.
## Liver biopsy and histological analyses
Liver biopsies were obtained during the morning hours on fasting patients using a Menghini suction needle. Histological assessment was performed by a single pathologist (TC) using NAS (NAS‐CRN) for steatosis (0–3), ballooning (0–2), lobular inflammation (0–3), and *Kleiner fibrosis* grading for fibrosis (0–4).[10] Accordingly, NAS range from 0 to 8, and *Kleiner fibrosis* stage range from 0 to 4, where 4 is liver cirrhosis.
## Anthropometrics
Body weight and height was measured in indoor clothing without shoes to the nearest 0.1 kg using a scale (Tanita) and to the nearest 0.1 cm on a wall‐mounted stadiometer, respectively. A measuring tape was used to measure hip and waist circumferences to the nearest centimeter.
## BMD
Dual‐energy X‐ray absorptiometry (DXA) (Hologic Horizon, Waltham, MA, USA) was used to measure areal BMD (aBMD) at the lumbar spine (L1–L4) and total hip region. The measurements were obtained no further than a fortnight form the liver biopsy. Lumbar spine BMD was not available in two subjects.
There were no significant differences in the lumbar spine (1.09 ± 0.12, 1.11 ± 0.18, and 1.12 ± 0.15 g/cm2, $$p \leq 0.69$$, in controls, steatosis and NASH, respectively) or total hip BMD (1.10 ± 0.15, 1.12 ± 0.13, and 1.09 ± 0.13 g/cm2, $$p \leq 0.48$$, in controls, steatosis, and NASH, respectively) measures between the groups (Figs. 1 and 2). Adjusting for age, gender, BMI, and diabetes in multiple regression models did not change the results (Tables 2 and 3).
**Fig. 1:** *Box‐and‐whisker plots depicting the median, 25th to 75th percentiles, and 5th and 95th percentiles of lumbar spine BMD in the three groups.* **Fig. 2:** *Box‐and‐whisker plots depicting the median, 25th to 75th percentiles, and 5th and 95th percentiles of total hip BMD in the three groups.* TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3
## Blood samples
A venous blood sample was obtained after an overnight fast and analyzed at the Clinical Biochemical Department at UHSD. Ferritin, 25‐hydroxy vitamin D, glycated hemoglobin A1c (HbA1c), insulin, glucose, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were measured using standardized assays. The homeostatic model assessment for insulin resistance (HOMA‐IR2) was calculated from fasting glucose and insulin using the Oxford HOMA calculator (www.dtu.ox.ac.uk/homacalculator). Ferritin was not available in one and insulin in two subjects.
## Statistical analyses
Statistical analysis was performed using SPSS statistical package version 28.0.1.0 (IBM SPSS Statistics; IBM Corp, Armonk, NewYork, USA). Normality was evaluated using normal probability plots and confirmed objectively using the Shapiro‐Wilk test. All data are expressed as mean ± SD, median (interquartile range [IQR]) or numbers as appropriate. Comparisons between groups were done using chi‐square test for categorical variables and analysis of covariance or independent‐samples median test for normally or non‐normally distributed continuous variables, respectively. Multiple linear regression analyses with lumbar spine BMD or total hip BMD as dependent variables and groups (categorized as 0 control subjects without NAFLD, 1 steatosis, 2 NASH) as independent variable was used to assess differences between the groups adjusted for age, gender, diabetes (0 no diabetes, 1 diabetes) and BMI. Histograms and normal probability plots of residuals were used to check model assumptions. BMI and waist hip ratio were assessed separately in the model to avoid multicollinearity. Bivariate Pearson correlation (for normally distributed data) or Spearman's rank correlation (for non‐normally distributed data) were used to assess the strength and direction of the relationship between lumbar spine/hip BMD, NAS, ferritin, HOMA‐IR, and HbA1c. All tests were two‐tailed and p values <0.05 were considered statistically significant.
## Results
A total of 147 adult women ($$n = 108$$) and men ($$n = 39$$) aged 18–76 years (mean 45.3 ± 12.5 years) were recruited and underwent a liver biopsy, DXA scan, and the relevant biochemical investigations. Although $23\%$ served as control subjects with no evidence of NAFLD, $53\%$, had steatosis and $25\%$ had NASH (Table 1). Subjects with steatosis and NASH tended to be older than the control group. There were more women compared to men in all the three groups ($$p \leq 0.02$$). Although less than a quarter in the control group and up to two‐thirds of the women in the steatosis and NASH groups were postmenopausal, this was not statistically significant ($$p \leq 0.11$$). The prevalence of diabetes, although similar between the steatosis and NASH groups ($23\%$ versus $38\%$), was significantly higher than in the control group where none of the subjects had diabetes ($p \leq 0.001$). Although subjects in all three groups had similar BMI, subjects with steatosis and NASH had significantly higher waist hip ratio than control subjects ($p \leq 0.001$).
**Table 1**
| Characteristic | Control | Steatosis | NASH | p |
| --- | --- | --- | --- | --- |
| Number, n (%) | 32 (21.7) | 78 (53.1) | 37 (25.2) | |
| Age (years), mean ± SD | 40.5 ± 11.0 | 46.8 ± 13.0 | 46.4 ± 11.8 | 0.05 |
| Gender (F/M), n | 28/4 | 50/28 | 30/7 | 0.02 |
| Menopausal status (pre/post), n | 23/5 | 30/20 | 17/11 | 0.11 |
| Current smoking yes/no, n | 2/30 | 14/64 | 5/32 | 0.63 |
| BMI (kg/m2), mean ± SD | 42.9 ± 5.2 | 42.8 ± 5.7 | 42.6 ± 5.3 | 0.96 |
| Waist hip ratio, mean ± SD | 0.84 ± 0.09 | 0.93 ± 0.11 | 0.91 ± 0.11 | <0.001 a |
| Diabetes, n (%) | 0 | 18 (23.1) | 14 (37.8) | <0.001 b |
| Hypertension, n (%) | 9 (28.1) | 33 (42.3) | 16 (43.2) | 0.38 |
| HOMA‐IR2, median (IQR) | 2.3 (1.9) | 3.2 (2.0) | 4.4 (4.7) | <0.001 c |
| Vitamin D (nmol/L) (50–160 nmol/L), mean ± SD | 63.6 ± 27.6 | 56.0 ± 25.3 | 59.8 ± 23.1 | 0.36 |
| Ferritin (μg/L) (15–180 μg/L), median (IQR) | 79 (75) | 88 (98) | 116 (115) | 0.12 |
| HbA1c (mmol/mol) (<48 mmol/mol), median (IQR) | 35 (5) | 39 (7) | 41 (17) | 0.004 d |
| ALT (U/L) (10–45 U/L), mean ± SD | 28 ± 22 | 41 ± 24 | 61 ± 27 | <0.001 e |
Subjects with steatosis and NASH had significantly higher ALT, HOMA‐IR2, and HbA1c values than the control subjects ($p \leq 0.001$, $p \leq 0.001$, and $$p \leq 0.004$$, respectively) whereas there was no significant difference in 25‐hydroxy vitamin D or ferritin levels between the three groups.
## Correlations
We found that HOMA‐IR correlated positively with hip BMD ($r = 0.17$, $$p \leq 0.042$$), whereas there was a positive trend between HbA1c and lumbar spine BMD ($r = 0.15$, $$p \leq 0.073$$). Although there was no correlation between NAS and neither lumbar spine BMD ($r = 0.06$, $$p \leq 0.471$$), nor hip BMD (r = −0.03, $$p \leq 0.716$$), there was a strong positive correlation between NAS and HOMA‐IR ($r = 0.45$, p ≤ 0.001) and ALT ($r = 0.63$, p ≤ 0.001). There was no correlation between ferritin and lumbar spine BMD ($r = 0.13$, $$p \leq 0.116$$), hip BMD (r = −0.01, $$p \leq 0.956$$), between HbA1c and hip BMD ($r = 0.08$, $$p \leq 0.309$$), and between HOMA‐IR and lumbar spine BMD ($r = 0.06$, $$p \leq 0.491$$).
## Discussion
To the best of our knowledge, this is the largest study assessing bone mass in adult obese subjects with histologically proven NAFLD. We found no significant difference in BMD, neither at the lumbar spine nor at the hip, in subjects with steatosis and NASH in comparison to obese controls, in spite of accounting for effects of age, gender, diabetes status, and BMI. Further, there was no association between the severity of the histopathological lesions in NAFLD and BMD. In addition, we also found a robust correlation between HOMA‐IR2, a surrogate estimate for insulin resistance, and NAFLD severity scores indicating increasing insulin resistance in subjects with more severe NAFLD.
Our findings are in contrast to the biopsy proven NAFLD study in adult subjects by Kaya and colleagues[8] that also assessed the association with BMD. In this small study of 38 patients with histological evidence of NASH and 42 healthy control subjects, the authors reported a higher lumbar spine BMD but not femoral BMD in patients with NASH. They purported that the elevated vitamin D levels seen in patients with NASH in comparison to healthy controls was the main responsible factor for the differences in BMD.[8] Although there was no difference in vitamin D between the groups in our study, we had a preponderance of women (almost $64\%$) as opposed to the aforementioned study[8] where $66\%$ of the study cohort were men. Though we neither found any difference in the overall results when adjusting for gender nor in subgroup analysis only in men (data not shown), there was an unequal distribution of men in the various groups in our study with only $10\%$ men in the group without evidence of NAFLD, making it challenging to make reliable comparisons with our study. In a more recent study of 6634 adult subjects where liver ultrasonography was used to detect fatty changes in the lever, Lee and colleagues[11] reported a sex‐specific association between hepatic steatosis and BMD, with a negative association in men but a positive association in women. Whether there is a gender predilection and if this is related to the differences between men and women in bone structure and strength, body fat composition or sex hormone levels is uncertain.
Higher BMD as reported by the aforementioned study by Kaya and colleagues[8] would suggest that men with NASH have better bone health and theoretically lower fracture risk. However, in a cross‐sectional study of 7797 Chinese adults aged 40 years and above, 2352 of whom had NAFLD diagnosed by hepatic ultrasonography, Li and colleagues[12] reported that the presence of steatosis was significantly associated with osteoporotic fractures in men but not women, independently of other potential risk factors such as age, smoking, physical activity, BMI, presence of diabetes, and steroid use. In a recent register‐based retrospective cohort study of 50,689 adult patients with an International Classification of Diseases and Related Health Problems, 10th Revision (ICD‐10) diagnosis of NAFLD, Loosen and colleagues[13] reported that during a follow‐up of 10 years, the incidence of osteoporosis as well as osteoporotic and non‐osteoporotic fractures was significantly higher in patients with NAFLD. Although this association was seen in both genders, it was stronger in women compared to men. In a recent population‐based cohort study using national registry, Wester and colleagues[14] found a marginally higher incidence of all fractures but not osteoporotic fractures in persons with NAFLD in comparison to the general population. The potential link between NAFLD, osteoporosis and fracture risk remain unclarified, and the available evidence is weakened by limitations related to accurate diagnosis of NAFLD and confounding factors related to gender and comorbidities. In addition, the accuracy of BMD assessment with DXA declines with increasing BMI, which is of importance in our patient population where all of those with NAFLD are overweight or obese.[15] We found no significant correlation between the histological severity of hepatic steatosis, necroinflammation and fibrosis and lumbar spine or hip BMD. Although the scarcity of studies assessing BMD and histologically diagnosed NAFLD limits comparisons, evidence from other studies using liver ultrasonography present conflicting results. Lee and colleagues[11] reported that mild steatosis had no effect on BMD, but more advanced steatosis had a detrimental impact on femoral neck BMD in men but a positive effect on lumbar BMD in postmenopausal women. Purnak and colleagues[3] found no significant relationship between the degree of hepatic steatosis and BMD, but lower BMD in a subgroup of patients assumed to have NASH based on the presence of elevated ALT and high‐sensitivity C‐reactive protein. Liver biopsy remains the gold standard in NAFLD patients for grading the severity of steatohepatitis based on the increasing degree of steatosis, hepatocyte ballooning, and degree of inflammatory foci. However, the inflammatory component extends across the spectrum of steatosis and NASH, limiting the utility of systemic inflammatory markers in predicting the severity of NAFLD.
We found higher levels of insulin resistance with increasing severity of NAFLD. The pathophysiology of NAFLD is multifactorial and not fully elucidated but the most widely supported theory implicates insulin resistance and cytokine activation leading to a chronic inflammatory state as key mechanisms leading to NASH.[16] *There is* some controversy regarding the effect of insulin on bone. Although some in vitro studies indicate that insulin stimulates bone formation[17] and bone resorption,[18] clinical studies across a wide range of hyperinsulinemic states such as polycystic ovarian syndrome, impaired glucose tolerance, and type 2 diabetes feature the consistent finding of high BMD.[19, 20, 21] Thus, the higher prevalence of type 2 diabetes in patients with NAFLD in comparison to control subjects and tendency toward higher prevalence in patients with NASH than in those with steatosis in our study could have potentially impacted/mitigated differences in BMD between the groups. On the other hand, the important role of dysregulated inflammatory signaling pathways in the development and progression of NAFLD had been demonstrated, potentially linking NAFLD to osteoporosis.[22] Chronic inflammation leads to increased bone resorption and suppressed bone formation and is increasingly recognized as a risk factor for osteoporosis.[23] Thus, it can be speculated that detrimental effects on the skeleton due to the chronic inflammatory state are counteracted to a large extent, by the hyperinsulinemia secondary to insulin resistance.
Although we used histology to assess the severity of NAFLD in a fairly large cohort of women and men, certain limitations need to be addressed. First, the cross‐sectional nature of the study does not allow for addressing the casualty of findings. Second, unequal numbers in the groups and between the genders precludes any firm conclusions on the specific effects in each sex. Third, we used DXA to assess BMD and as alluded to above, this may limit the assessment of bone mass in those with obesity compared to measurement using three‐dimensional techniques such as computed tomography.[15] The mean BMI of participants in our study was 42 kg/m2 and although there was no difference between BMI across the three groups this may have affected measurement of BMD and the ability to explore the association with NAFLD.
In conclusion, BMD was similar across the spectrum of NAFLD in both genders. Although there was no correlation between the severity of the underlying histological lesions and bone mass, subjects with more severe liver disease were increasingly insulin resistant and that could partly account for preserved BMD. In conjunction with recent epidemiologic data suggesting no increased risk of osteoporotic fractures in patients with NAFLD in comparison to the background population,[14] our findings suggest that screening for osteoporosis in these relatively young patients may not be warranted.
## Peer Review
The peer review history for this article is available at https://publons.com/publon/10.1002/jbm4.10714.
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|
---
title: Advanced Glycation End Products and Bone Metabolism in Patients with Chronic
Kidney Disease
authors:
- Kélcia R. S. Quadros
- Noemi A. V. Roza
- Renata A. França
- André B. A. Esteves
- Joaquim Barreto
- Wagner V. Dominguez
- Luzia N. S. Furukawa
- Jacqueline Teixeira Caramori
- Andrei C. Sposito
- Rodrigo Bueno de Oliveira
journal: JBMR Plus
year: 2023
pmcid: PMC10020922
doi: 10.1002/jbm4.10727
license: CC BY 4.0
---
# Advanced Glycation End Products and Bone Metabolism in Patients with Chronic Kidney Disease
## ABSTRACT
Advanced glycation end products (AGEs) accumulation may be involved in the progression of CKD‐bone disorders. We sought to determine the relationship between AGEs measured in the blood, skin, and bone with histomorphometry parameters, bone protein, gene expression, and serum biomarkers of bone metabolism in patients with CKD stages 3 to 5D patients. Serum levels of AGEs were estimated by pentosidine, glycated hemoglobin (A1c), and N‐carboxymethyl lysine (CML). The accumulation of AGEs in the skin was estimated from skin autofluorescence (SAF). Bone AGEs accumulation and multiligand receptor for AGEs (RAGEs) expression were evaluated by immunohistochemistry; bone samples were used to evaluate protein and gene expression and histomorphometric analysis. Data are from 86 patients (age: 51 ± 13 years; 60 [$70\%$] on dialysis). Median serum levels of pentosidine, CML, A1c, and SAF were 71.6 pmol/mL, 15.2 ng/mL, $5.4\%$, and 3.05 arbitrary units, respectively. AGEs covered $3.92\%$ of trabecular bone and $5.42\%$ of the cortical bone surface, whereas RAGEs were expressed in $0.7\%$ and $0.83\%$ of trabecular and cortical bone surfaces, respectively. AGEs accumulation in bone was inversely related to serum receptor activator of NF‐κB ligand/parathyroid hormone (PTH) ratio (R = −0.25; $$p \leq 0.03$$), and RAGE expression was negatively related to serum tartrate‐resistant acid phosphatase‐5b/PTH (R = −0.31; $$p \leq 0.01$$). Patients with higher AGEs accumulation presented decreased bone protein expression (sclerostin [1.96 (0.11–40.3) vs. 89.3 (2.88–401) ng/mg; $$p \leq 0.004$$]; Dickkopf‐related protein 1 [0.064 (0.03–0.46) vs. 1.36 (0.39–5.87) ng/mg; $$p \leq 0.0001$$]; FGF‐23 [1.07 (0.4–32.6) vs. 44.1 (6–162) ng/mg; $$p \leq 0.01$$]; and osteoprotegerin [0.16 (0.08–2.4) vs. 6.5 (1.1–23.7) ng/mg; $$p \leq 0.001$$]), upregulation of the p53 gene, and downregulation of Dickkopf‐1 gene expression. Patients with high serum A1c levels presented greater cortical porosity and Mlt and reduced osteoblast surface/bone surface, eroded surface/bone surface, osteoclast surface/bone surface, mineral apposition rate, and adjusted area. Cortical thickness was negatively correlated with serum A1c (R = −0.28; $$p \leq 0.02$$) and pentosidine levels (R = −0.27; $$p \leq 0.02$$). AGEs accumulation in the bone of CKD patients was related to decreased bone protein expression, gene expression changes, and increased skeletal resistance to PTH; A1c and pentosidine levels were related to decreased cortical thickness; and A1c levels were related to increased cortical porosity and Mlt. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
## Introduction
Mineral and bone disorder (MBD) is a major complication of chronic kidney disease (CKD) and causes systemic effects, resulting in cardiovascular disease, bone fractures, and increased mortality.[1, 2, 3, 4, 5] MBD pathophysiology is related to the accumulation of many uremic toxins, such as phosphate and parathormone.[6, 7] Advanced glycation end products (AGEs) constitute one group of uremic toxins, whose effect on bone metabolism in CKD patients is poorly understood.[8, 9, 10] AGEs represent a heterogeneous group of molecules, constituted by nonenzymatic glycation reactions reducing sugars, amino acids, lipids, or DNA. These AGEs molecules accumulate with the CKD progression and activate intracellular signals through nonspecific, specific receptors (RAGEs) and non‐receptor‐mediated mechanisms, leading to increased production of reactive oxygen species and inflammatory cytokines.[10, 11] At the cellular level, AGEs are related to the decreased differentiation and proliferation of osteoblasts, osteoclasts, and mesenchymal stem cell apoptosis. AGEs also affect matrix protein production and lead to collagen cross‐linking activity alterations.[12, 13, 14] Some studies have identified a link between AGEs, osteoporosis, and bone fractures in clinical settings.[15, 16, 17]
However, evidence of the effects in CKD is scarce, and the underlying mechanisms are not fully understood. In a rat model of renal osteodystrophy induced by adenine, Aoki et al. observed a greater accumulation of AGEs in peritrabecular osteoblasts and suppressed expression of runt‐related transcription factor 2 (RUNX2), alkaline phosphatase, secreted phosphoprotein‐1, and lysyl oxidase mRNA levels than in normal animals. The authors suggest that these findings represent suppression of osteoblast differentiation and function.[9] Chen et al. tested the effects of AGEs lowering drug ALT‐711 in the aorta and bone. They observed bone AGEs content reduction without any improvement in bone mechanics.[10] In humans, Mitome et al. observed a significant presence of pentosidine in bone from patients on dialysis. The pentosidine concentration was inversely related to the bone formation rate and volume.[18] Together, these studies reveal the need to deepen knowledge of the effects of AGEs on bone metabolism to generate new hypotheses to better understand their contribution to the pathophysiology of CKD‐MBD. For this reason, we performed an extensive examination of the impact of AGEs in bone from patients with CKD at different stages and treatments. Our working hypothesis is that CKD is associated with the accumulation of AGEs, which in turn results in the dysfunction of bone metabolism, expressed by bone morphological alterations, reduced protein synthesis, and changes in gene expression. Our primary aim was to identify and quantify the accumulation of AGEs and RAGEs in bone and AGEs in the blood (serum pentosidine, carboxymethyl lysine, and glycated hemoglobin levels) and skin and then study the relations between AGEs accumulation, bone histology, protein and gene expression, and serum markers of bone metabolism.
## Study design and patient selection
Eighty‐six patients at different CKD stages were enrolled in this observational and double‐center study from February 2016 to November 2017. Patients were recruited from the Nephrology Department's outpatient clinics at the Hospital de Clínicas of the State University of Campinas (UNICAMP) and São Paulo State University (UNESP) and divided into the following CKD subgroups: CKD stages 3–5 nondialysis on conservative management but still without a clinical indication for dialysis ($$n = 26$$), hemodialysis (HD, $$n = 32$$), and peritoneal dialysis (PD, $$n = 28$$). Patients were selected for convenience, sequentially, and according to the established inclusion and exclusion criteria. They were not part of another study, and all tests performed were part of the research protocol provided for this study.
The inclusion criteria were age over 18 years, in CKD stages 3 to 5D according to Kidney Disease Outcomes Quality (KDIGO),[19] and, specifically for patients under HD/PD, to be under these treatments for at least 3 months. The CKD‐EPI equation estimated the glomerular filtration rate.[20] Patients in the HD subgroup were on chronic HD treatment three times weekly, 4 hours/session, using high‐flux and high‐efficiency polysulfone dialyzers. Patients in the PD subgroup were on automated PD ($$n = 18$$) or continuous ambulatory PD ($$n = 10$$).
Exclusion criteria were the presence of chronic inflammatory disease, primary hyperparathyroidism, kidney transplantation, acute cardiovascular event in the 3 months before screening for inclusion, cognitive impairment, cancer, HIV, and clinical instability; 12 ($46\%$) patients in the CKD 3–5 nondialysis subgroup were classified as having CKD stage 3, 11 ($42\%$) stage 4, and three ($12\%$) stage 5 nondialysis. Written informed consent was obtained from all patients; the local ethics committee approved the study protocol under numbers CAAE 38108314.6.0000.5404‐45943115.9.0000.5404‐45777015.5.0000.5404, and the clinical and research activities being reported are consistent with the Declaration of Helsinki.
## Measurement of AGEs levels by skin autofluorescence (AGE‐sAF)
AGE skin deposition was evaluated by SAF using the AGE‐Reader™ (DiagOptics BV, Groningen, the Netherlands). This device measures fluorescence emitted from the skin influenced by the deposition of AGEs, and it calculates the ratio between emitted and reflected excitation light. The measurements were in triplicate on the ventral side of the forearm. Areas with arterial–venous fistulas, scars, and tattoos were avoided. The mean values were used for all statistical analyses and AGEs levels in skin expressed as arbitrary units (AU).
## Measurement of serum AGE levels
In accordance with the manufacturer's instructions, serum pentosidine and N‐carboxymethyl lysine levels were determined by enzyme‐linked immunosorbent assay (ELISA) (pentosidine, kit provided by Cusabio Biotech Co. Ltd.; N‐carboxymethyl lysine, kit supplied by Blue Gene Biotech Co.). The detection ranges of the pentosidine and N‐carboxymethyl lysine kits were 25–2000 pmol/mL and 5–100 ng/mL, respectively.
## Biochemical analysis
Serum intact parathyroid hormone (PTH) levels (reference range: 15–65 pg/mL) were measured using a chemiluminescence assay (Liaison N‐tact PTH CLIAR, Diasorin, Stillwater, USA). Serum 25‐hydroxyvitamin D levels (reference range: 30–100 ng/dL) were measured using a chemiluminescence method. Alkaline phosphatase (reference range: 30–120 IU/L) was measured using a kinetic colorimetric test (Beckman Coulter OSR6104, California, USA). Serum calcium and phosphate levels and a general serum biochemistry profile were assayed by standard autoanalyzer techniques in an on‐site biochemistry laboratory (Modular IIPR system, Roche Diagnostics, Basel, Switzerland). Serum tartrate‐resistant acid phosphatase 5b (TRACP‐5b) levels were determined using an ELISA kit (MicroVue, Quidel, Santa Clara, CA, USA), normal range: 1.2–6.7 U/L; sclerostin was assessed by Teco Sclerostin EIA Kit (enzyme‐linked immunosorbent assay; Teco Medical Group, Sissach, Switzerland, reference range: 0.2–0.6 ng/mL). Intact fibroblast growth factor 23 (FGF‐23), Dickkopf‐related protein 1 (DKK1), and receptor activator of nuclear factor kappa‐B ligand (RANKL) were measured using a multiplex assay kit (Merck Millipore, Darmstadt, Germany). Plasma specimens were prepared for analysis utilizing a multiplex assay kit (Milliplex Human Bone Magnetic Bead Panel, EMD Millipore Corp., Massachusetts, USA) according to specific protocols provided by the company. Blood samples were collected on a previously scheduled date for patients in the CKD 3–5 nondialysis and PD groups. Blood samples of hemodialysis patients were collected immediately before the week's second session.
## Bone biopsy
Bone biopsy samples were obtained from the right or left iliac crests through a trephine for bone biopsy (diameter = 7 mm), adapted to an electrical drill (dewalt™ and Rochester bone trephine™, USA). A double labeling tetracycline course for 3 days (20 mg/kg/d), with a 10‐day interval, was used. The biopsy was performed 3–5 days after the last tetracycline dose. A bone fragment was divided into three parts for histomorphometry, immunohistochemistry, and molecular biology studies. The undecalcified bone fragments were submitted to histological processing and analysis. Bone histomorphometry was performed through a semiautomatic method (Osteomeasure™, Osteometrics, Atlanta, GA, USA). The static, dynamic, and structural histomorphometric indices were reported using international nomenclature.[21]
## Immunohistochemistry and quantification of accumulation of AGEs and RAGEs expression
Immunohistochemical quantification of the accumulation of AGEs and RAGEs expression in bone was performed by adapting a method previously reported by Gomes.[22] In brief, two adjacent 5‐μm sections of bone tissue were placed side by side on each slide. Bone sections were deacrylated in a 1:1 mixture of xylene and chloroform for 30 minutes, rehydrated in graded alcohol solutions, submitted to a quick semidecalcification with $1\%$ acetic acid for 10 minutes, and rinsed twice with distilled water. Endogenous peroxidase activity was inhibited by a mixture of $3\%$ hydrogen peroxide in methanol for 30 minutes, followed by two water washes. The samples were incubated with protein block (DakoCytomation, California, USA) to block nonspecific binding. Sections were incubated overnight at 4°C in a humidified chamber using the primary rabbit polyclonal antibodies anti‐AGE (ab23722, Abcam, Cambridge, UK) (dilution 1:5000) and anti‐RAGE (16346‐1‐AP, Proteintech Group, Manchester, UK) (dilution 1:200). After incubation with the secondary antibody, the slides were incubated with the avidin/biotin HRP complex. The revelation was developed with a Vector kit (DAB Substrate Kit, Vector Laboratories, Burlingame, USA) (1 drop DAB +1 mL of the substrate). The sections were rinsed in distilled water and counterstained with Mayer's hemalum solution (Merck KGaA, Darmstadt, Germany). Negative controls were performed by omitting the primary antibody. For analysis, the images were captured using an Olympus BX53 photomicroscope (Olympus Corp., Tokyo, Japan) integrated into a computer and analyzed using Image‐Pro Premier® software (Media Cybernetics, Rockville, USA). The entire extent of the trabecular and cortical bone tissue regions were photographed with final magnifications of ×100 and ×400, and through the software, the areas of interest imunostained for anti‐AGEs and anti‐RAGEs were automatically quantified through the creation of a macro capable of identifying in each pixel of the image the shade of the brown color defined as being representative of positive immunostaining, in relation to the negative control. The immunopositivity was expressed as a percentage of the total software‐classified areas.
## Quantification of bone protein by multiplex
Protein lysates were extracted from bone samples. The bone contents of sclerostin, osteocalcin, DKK1, and FGF‐23 were measured with a multiplex assay kit (Human Bone Magnetic Bead Panel, Milliplex, EMD Millipore Corp., Darmstadt, Germany) based on Luminex™ xMAP technology according to the manufacturer's instructions.
## Gene expression
After total RNA extraction from bone sample using trizol, a NanoDrop 1000 spectrophotometer (Thermo Scientific) was used to determine the total RNA amount. cDNA was synthesized from total RNA by reverse transcriptase (Improm‐II Reverse Transcriptase, Promega Corp., Madison, WI, USA) using a thermocycler (DNA Engine, MJ Research, Massachusetts, USA).
Gene expression was determined from the cDNA through quantitative PCR using SYBR Green (Rotor‐Gene SYBR Green PCR kit, Qiagen, Hilden, Germany) and a Rotor‐Gene Q thermocycler (Qiagen, Hilden, Germany). *The* genes analyzed were SOST (AF_331844.1), RANKL (NM_003701.3), OPG (U94332), β‐catenin (X_87838.1), FGF‐23 (NM_020638.2), p53 (NM_001276760), DKK (NM_012242.4), Osterix (AF477981), ALP‐1 (J04948.1), collagen 1 (D21337.1), BGLAP (NM_199173), and the reference gene GAPDH (glyceraldehyde 3‐phosphate dehydrogenase‐NM_002046.4) with their respective primers designed from IDT (Integrated DNA Technologies, Coralville, USA). Gene expression was calculated by the ΔΔCt method of relative quantification. Values are expressed as a multiple (fold) of the expression compared to the value of the calibrator. The statistical analysis between groups was performed using REST™ software (Qiagen, Hilgen, Germany).
## Statistical analysis
The continuous variables are reported as the mean ± SD or medians and interquartile intervals. Categorical data are reported as frequencies and percentages. Comparisons between the continuous variables, skewed data, and categorical variables were performed using the Student's t test, the Mann–Whitney test, and the chi‐square test. To detect associations between AGEs accumulation and changes in bone metabolism, the median value of AGEs accumulation parameters (glycated hemoglobin, N‐carboxymethyl lysine, pentosidine, SAF, and AGEs/RAGEs accumulation/expression in bone) was used for the purpose of comparison. Spearman's coefficient test provided the significance of the correlation between noncontinuous variables. Statistical analyses were performed using SPSS 22.0 (SPSS, Chicago, IL, USA). A two‐sided p‐value <0.05 was considered statistically significant.
## General findings and accumulation of AGEs in serum, skin, and bone
We considered data from 86 individuals for analysis; this was a population with a mean age of 51 ± 13 years; 48 ($56\%$) were male, 41 ($48\%$) were Caucasian, and 16 ($19\%$) had type 2 diabetes. All participants had CKD; 32 ($37\%$) were on HD, 28 ($33\%$) were on PD, and 26 ($30\%$) were on conservative management, displaying an estimated glomerular filtration rate of 26.9 (17.2–34.5) mL/min/1.73 m2. The baseline characteristics of the study population are summarized in Table 1. Clinical, demographic, and biochemistry findings of all CKD populations and subgroups are summarized in supplementary data (Table S1).
**Table 1**
| N = 86 | Unnamed: 1 |
| --- | --- |
| Age (years) | 51 ± 13 |
| Male (N, %) | 48 (56) |
| Caucasian (N, %) | 41 (48) |
| Etiology of chronic kidney disease (N, %) | |
| Hypertension | 23 (27) |
| Chronic glomerulonephritis | 16 (19) |
| Diabetes mellitus | 9 (10) |
| Dialysis vintage (months) | 21 (10–44) |
| Body mass index (kg/m2) | 26 ± 4.8 |
| Hemoglobin (g/dL) | 12.1 (11–13.6) |
| Albumin (g/dL) | 3.7 (3.3–4.0) |
| Total calcium (mg/dL) | 8.9 ± 0.8 |
| Phosphate (mg/dL) | 5 ± 1.6 |
| 25‐vitamin D (ng/mL) | 28.1 (20.8–34.5) |
| FGF‐23 (ng/mL) | 1570 (273–6499) |
| Sclerostin (ng/mL) | 1.46 (0.94–2.19) |
| Alkaline phosphatase (IU/mL) | 90 (71–112) |
| Parathormone (pg/mL) | 228 (117–439) |
| RANKL (pg/mL) | 0.19 (0.01–0.75) |
| TRACP‐5b (U/L) | 5.1 (3.3–7.7) |
AGEs were detected in blood, skin, and bone in all patients. No correlation was found between measurements of AGEs in blood, skin, and bone.
In serum, the median levels of pentosidine, N‐carboxymethyl lysine, and glycated hemoglobin were 71.6 (44.2–121.2) pmol/mL, 15.2 (9.7–32.4) ng/mL, and $5.4\%$ (5–$6.1\%$), respectively. In skin, the median value of SAF was 3.05 (2.5–3.4) AU and was positively correlated with age ($R = 0.55$; $$p \leq 0.0001$$) and dialysis vintage ($R = 0.30$; $$p \leq 0.04$$).
In bone, accumulation of AGEs and expression of RAGEs were detected in both trabecular and cortical surfaces in all patients. AGEs in trabecular and cortical bone covered $3.92\%$ (1.6–$15.3\%$) and $5.42\%$ (3–$12.1\%$) of its surface, respectively. RAGE expression in trabecular and cortical bone covered $0.7\%$ ($0.13\%$–$2.88\%$) and $0.83\%$ (0.2–$2.3\%$) of its surface, respectively (Fig. 1A–H). Of note, AGEs accumulation seems to demonstrate affinity with osteocytes (Fig. 1, detail D).
**Fig. 1:** *Trabecular and cortical bone AGEs accumulation and RAGEs expression in patients with CKD (AGEs accumulation (A–D); RAGEs expression (E–H).*
AGEs in trabecular bone were positively correlated with AGEs in cortical bone ($R = 0.77$; $$p \leq 0.0001$$) and dialysis vintage ($R = 0.31$; $$p \leq 0.03$$) and negatively correlated with the serum RANKL/PTH ratio (R = −0.25; $$p \leq 0.03$$).
RAGEs expression in trabecular bone was positively correlated with RAGEs expression in cortical bone ($R = 0.76$; $$p \leq 0.0001$$), dialysis vintage ($R = 0.49$; $$p \leq 0.03$$), phosphate ($R = 0.26$; $$p \leq 0.03$$), and parathormone ($R = 0.40$; $$p \leq 0.001$$) and negatively correlated with serum glycated hemoglobin levels (R = −0.26; $$p \leq 0.03$$) and the TRACP‐5b/PTH ratio (R = −0.31; $$p \leq 0.01$$).
## Serum and skin AGEs levels: associations with bone morphology and metabolism
Patients presenting high serum glycated hemoglobin levels displayed greater cortical porosity [1.9 (1.2–3.1) vs. 1.18 (0.47–2.22); $$p \leq 0.02$$], mineralization lag time [24.8 (18–54.2) vs. 19.1 (9.8–34.7); $$p \leq 0.03$$], reduced osteoblast surface/bone surface [1.7 (1–3.6) vs. 3.6 (1.4–6.7); $$p \leq 0.04$$], eroded surface/bone surface [2.4 (1.5–3.9) vs. 4.9 (3.2–7.9); $$p \leq 0.0001$$], osteoclast surface/bone surface [0.1 (0.04–0.18) vs. 0.29 (0.11–0.52); $$p \leq 0.0001$$], mineral apposition rate [0.59 (0.47–0.71) vs. 0.78 (0.48–0.98); $$p \leq 0.02$$] and adjusted apposition rate/bone area [0.26 (0.12–0.42) vs. 0.38 (0.25–0.68); $$p \leq 0.009$$]. Cortical thickness was negatively correlated with serum glycated hemoglobin (R = −0.28; $$p \leq 0.02$$) and with pentosidine levels (R = −0.27; $$p \leq 0.02$$). No differences were observed in bone parameters according to median levels of N‐carboxymethyl lysine.
No differences in bone protein expression were observed according to serum AGEs levels. Bone histomorphometric parameters, bone protein, and gene expression were similar among groups defined by the median skin AGEs accumulation.
## Bone AGEs accumulation and RAGEs expression: associations with bone morphology and metabolism
Bone histology was similar among groups defined by the median AGEs accumulation in trabecular bone (Table 2). Patients with high levels of AGEs in trabecular bone had decreased bone levels of sclerostin [1.96 (0.11–40.3) vs. 89.3 (2.88–401) ng/mg; $$p \leq 0.004$$], DKK1 [0.064 (0.03–0.46) vs. 1.36 (0.39–5.87) ng/mg; $$p \leq 0.0001$$], FGF‐23 [1.07 (0.4–32.6) vs. 44.1 (6–162) ng/mg; $$p \leq 0.01$$] and osteoprotegerin [0.16 (0.08–2.4) vs. 6.5 (1.1–23.7) ng/mg; $$p \leq 0.001$$] compared with patients presenting low trabecular bone AGEs levels (Table 3). Above‐median trabecular bone AGEs accumulation upregulated the p53 gene and downregulated DKK1 gene expression. Comparisons of bone gene expression according to median of accumulation of AGEs in skin, trabecular bone and expression of RAGEs in trabecular bone are summarized in supplementary data (Table S2).
Patients above the median of trabecular RAGEs expression had increased bone volume/tissue volume [$26.2\%$ (19.2–$30.7\%$) vs. $18.9\%$ (15.4–$24.4\%$); $$p \leq 0.002$$], trabecular thickness [135 (125–154) vs. 118 (107–136) μm; $$p \leq 0.003$$], trabecular number [1.83 (1.5–2.1) vs. 1.5 (1.4–1.9) mm/mm; $$p \leq 0.04$$], and fibrosis volume/bone volume [$0.04\%$ (0.11–$0.29\%$) vs. $0.02\%$ (0.003–$0.05\%$); $$p \leq 0.04$$], and decreased trabecular separation [398 (316–531) vs. 531 (410–595) μm; $$p \leq 0.02$$] (Table 2).
Bone proteins and gene expression did not reveal differences according to the median trabecular bone RAGEs expression (Table 3). Patients above the median of trabecular bone RAGEs expression had increased serum levels of parathormone (277 [152–416] vs. 206 [71–456] pg/mL; $$p \leq 0.01$$) and FGF‐23 (1431 [400–7319] vs. 1120 [123–9985] ng/mL; $$p \leq 0.003$$).
## Treatment regimen
To evaluate differences across treatment regimens regarding AGEs levels, we compared individuals on HD, PD, and conservative treatment. Only a few differences were noted between the treatment regimens.
N‐Carboxymethyl lysine levels were higher in PD patients than HD and conservative patients (25 [12–52] vs. 13 [9–22] and 10 [9–25] ng/mL, respectively; $$p \leq 0.009$$). Serum glycated hemoglobin levels were higher in HD than in PD and conservative treatment ($5.9\%$ [5.5–$6.7\%$] vs. $5.7\%$ [5.1–$6.2\%$] vs $5.2\%$ [5–$5.4\%$], respectively; $$p \leq 0.0001$$). RAGEs expression in trabecular bone was higher in HD than in conservative treatment (1.21 [0.23–4.67] vs. 0.18 [0.06–1.44]; $$p \leq 0.030$$), whereas no differences across groups were reported for cortical bone. No difference was found among groups regarding serum pentosidine levels, AGEs in skin, AGEs in bone surface, and RAGEs expression in cortical bone.
## Discussion
Our study confirmed that bone AGEs accumulation occurred in patients with CKD and might be an early event in the CKD course since no significant differences in their levels were noted across distinct CKD stages or treatments. The AGEs accumulation in bone was related to decreased bone protein expression and changes in gene expression. The relationship observed between bone AGEs accumulation and a decreased serum RANKL/PTH ratio suggests that AGEs contribute to skeletal resistance to the actions of PTH. Although bone and skin AGEs accumulation were not related to significant histological changes, serum pentosidine and glycated hemoglobin levels were related to decreased cortical thickness; glycated hemoglobin levels were related to increased cortical porosity and mineralization lag time. Trabecular bone RAGEs expression was related to better structural bone parameters.
As far as we know, this is the first study to reveal bone AGEs accumulation along CKD stages and treatments and its impact on histology, bone protein, and gene expression. In animals with CKD, Aoki et al. detected AGEs in peritrabecular osteoblasts by immunohistochemistry and western blot techniques.[9] In humans with CKD, pentosidine‐induced cross‐links by HPLC were detected in the bones of 21 patients under HD and one in PD. The authors observed a negative correlation between pentosidine in bone and bone‐formation rate (BFR)/bone volume (BV) and MAR, but the number of subjects in this analysis was limited to 10.[18] Our findings reveal a relation between bone AGEs accumulation and a marked reduction in key bone protein expression, namely, osteoprotegerin, FGF‐23, sclerostin, and DKK1. Osteoprotegerin, also known as osteoclastogenesis inhibitory factor, is expressed by osteoblasts and plays a central role in regulating bone mass. Osteocytes and osteoblasts mainly secrete FGF‐23 in bone, and studies have shown that FGF‐23 overexpression or suppression is associated with defects in skeletal mineralization.[23, 24, 25] Sclerostin and DKK1 are Wnt/β‐catenin pathway inhibitors and are considered negative regulators of bone mass. Sclerostin is produced mainly by osteocytes, while DKK1 is mainly produced by osteoblasts.[26, 27] *It is* worth highlighting that bone AGEs accumulation was detected mainly around osteocytes and osteoblasts (Fig. 1D–H). The reduction in proteins synthesized by osteocytes and osteoblasts maybe reflects the important dysfunction of these cells due, at least partially, to AGEs accumulation.[12, 13] The reduction in bone protein expression, such as FGF‐23 and sclerostin, was observed based on intragroup comparison according to the median AGEs accumulation in trabecular bone. However, we do not know whether there is a certain cut‐off level related to the reduction expression of these proteins after which the circulating levels of these molecules would be affected. As expected in CKD patients, in our study we observed elevated serum levels of FGF‐23 and sclerostin, regardless of the level of AGEs accumulation in trabecular bone or protein expression.
*Bone* gene expression and regulation are complex processes, and scientific reports about AGEs and bone genes in patients with CKD is scarce.[28, 29, 30, 31, 32] In our study, we observed that AGEs may upregulate p53 and downregulate DKK1 gene expression in patients who presented above‐median trabecular bone AGEs accumulation.
p53 is a well‐known tumor suppressor that promotes cell cycle arrest, programmed cell death, and cell senescence and acts as a transcriptional repressor.[29] Verma et al. observed that AGEs impair the autophagy process in p53‐negative cells and then promote apoptosis via regulation of NF‐κB. The authors claim that p53 acts antagonistically to prevent this impairment.[30] *It is* plausible to think that this mechanism may explain our cohort's observed upregulation of the p53 gene.
In contrast to our findings about AGEs‐mediated downregulation of DKK1 gene expression, Li et al. observed an AGEs‐induced inhibition of the Wnt/β‐catenin pathway in vitro.[31] Notsu et al. demonstrated that incubating osteocyte‐like cells with AGEs increased the sclerostin‐producing gene Sost transcription in a dose‐dependent manner.[32] Both studies were performed under controlled conditions, while our data were from patients. This contrasting finding probably occurs because regulation of bone metabolism depends on several factors in complex systems. Other factors that alter sclerostin or DKK1, such as parathormone, FGF‐23, or even AGEs, may play a role.[33, 34] Tominaga et al. examined factors related to bone responsiveness to PTH in patients undergoing chronic hemodialysis. They proposed the TRACP‐5b/intact PTH (iPTH) ratio as an index that reflects bone responsiveness to PTH.[35] In our cohort, we observed that patients with an AGEs accumulation and RAGEs expression in trabecular bone above the median presented decreased RANKL/PTH and TRACP‐5b/PTH ratios, respectively. These findings suggest that AGEs could be another factor for skeletal resistance to PTH in patients with CKD.
Our study found no correlation between serum, skin, and bone levels of AGEs. This finding agrees with previous observations; in human body tissues, organs and structures seem to present different affinities for AGEs accumulation, either by constitution itself as due to metabolism particularities. For example, the proteins in the human eye are highly susceptible to the formation of AGEs, which accumulate at a higher rate in diseases such as cataracts. As bone turnover is lower than other tissues, some authors hypothesized that bone was potentially more susceptible to AGEs accumulation and effects. AGEs measurement in serum samples remains a challenge because of the lack of standardized methods and because circulating AGEs may not accurately reflect their accumulation in body tissues, which results from long‐term exposure. Skin measurement using SAF attenuates this effect, since AGEs accumulation in the skin may be more closely related to AGEs deposition in the bone, yet the differences affect the correlation between these events in the intracellular synthesis of AGEs that vary across tissues.[36, 37, 38, 39] Our results showed that serum pentosidine and glycated hemoglobin levels were related to decreased cortical thickness, increased cortical porosity, and mineralization lag time. We observed that glycated hemoglobin affected cortical bone differently than pentosidine. Previous studies in animals and humans showed direct relationships between glycated hemoglobin and cortical microarchitecture alteration.[40, 41] However, Sroga et al. observed differences in the progression of bone pathologies related to protein glycation by different sugars: in vitro glycation of bone using glucose leads to the formation of lower levels of AGEs, whereas ribosylation appears to support a pathway toward pentosidine formation.[42] This observation suggests differential actions of different AGEs in cortical bone.
This study had limitations. It was an observational and, essentially, descriptive study able to generate new hypotheses. The conclusions suggest relationships between bone AGEs accumulation and a decrease in essential bone proteins, changes in bone gene expression, potential increased skeletal resistance to PTH, and negative effects of serum AGEs on cortical bone. To overcome the lack of a control group, we attempted to perform statistical analyses through intragroup comparisons of AGEs accumulation parameters according to their respective medians; since there is no standardization of serum pentosidine and CML levels measurements at CKD setting, as well comparisons with more accurate methods such as HPLC, the results on these parameters must be interpreted with caution; we cannot exclude the possibility that the lack of correlation between AGEs accumulation in trabecular bone and BFR/bone surface (BS) was modified by serum PTH levels due to potential sample selection bias. Further interventional studies are required to confirm the mechanisms involved in these associations.
Our study also had some strengths. Most importantly, (i) it demonstrated, using bone biopsy, that AGEs and RAGEs accumulate in the bone of CKD subjects; (ii) a myriad of morphofunctional and genetic parameters was analyzed, providing insightful data on the mechanisms involved in renal osteodystrophy; and (iii) skin, serum, and bone AGEs levels were evaluated, and their associations with bone metabolism impairment were explored.
## Conclusions
We demonstrated that bone accumulation of AGEs occurs in patients with CKD and might affect the metabolism of this tissue. A possible mechanism would be a reduction in the synthesis of essential bone proteins and changes in gene expression. Cortical bone seems to be affected by different serum AGEs. The mechanisms behind these interactions, including differential effects according to AGEs types, should be explored in specific studies. Reducing AGEs accumulation may be a therapeutic target that would modify the progression of skeletal disorders in patients with CKD.[10] Pharmacological studies or dietary approaches should be proposed to test the effects of lowering AGEs on outcomes involving skeletal disease caused by CKD.
## Author Contributions
Kélcia R. S. Quadros: Conceptualization; data curation; formal analysis; investigation; writing – original draft; writing – review and editing. Noemí A. V. Roza: Data curation; formal analysis; methodology; writing – review and editing. Renata A. França: Investigation; writing – review and editing. André B. A. Esteves: Investigation; writing – review and editing. Joaquim Barreto: Formal analysis; methodology; writing – original draft; writing – review and editing. Wagner V. Domingues: Formal analysis; methodology; writing – review and editing. Luzia N. S. Furukawa: Formal analysis; methodology; writing – review and editing. Jacqueline Teixeira Caramori: Investigation; writing – review and editing. Andrei C. Sposito: Conceptualization; writing – review and editing. Rodrigo Bueno de Oliveira: Conceptualization; data curation; formal analysis; funding acquisition; project administration; resources; supervision; writing – original draft; writing – review and editing.
## Conflict of Interest
The authors declare there are no competing financial interests.
## Peer Review
The peer review history for this article is available at https://publons.com/publon/10.1002/jbm4.10727.
## Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon request.
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|
---
title: Unique metabolism and protein expression signature in human decidual NK cells
authors:
- Ping Wang
- Tingting Liang
- Heqin Zhan
- Mingming Zhu
- Mingming Wu
- Lili Qian
- Ying Zhou
- Fang Ni
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10020942
doi: 10.3389/fimmu.2023.1136652
license: CC BY 4.0
---
# Unique metabolism and protein expression signature in human decidual NK cells
## Abstract
Human decidual natural killer (dNK) cells are a unique type of tissue-resident NK cells at the maternal-fetal interface. dNK cells are likely to have pivotal roles during pregnancy, including in maternal-fetal immune tolerance, trophoblast invasion, and fetal development. However, detailed insights into these cells are still lacking. In this study, we performed metabolomic and proteomic analyses on human NK cells derived from decidua and peripheral blood. We found that 77 metabolites were significantly changed in dNK cells. Notably, compared to peripheral blood NK (pNK) cells, 29 metabolites involved in glycerophospholipid and glutathione metabolism were significantly decreased in dNK cells. Moreover, we found that 394 proteins were differentially expressed in dNK cells. Pathway analyses and network enrichment analyses identified 110 differentially expressed proteins involved in focal adhesion, cytoskeleton remodeling, oxidoreductase activity, and fatty acid metabolism in dNK cells. The integrated proteomic and metabolomic analyses revealed significant downregulation in glutathione metabolism in dNK cells compared to pNK cells. Our data indicate that human dNK cells have unique metabolism and protein-expression features, likely regulating their function in pregnancy and immunity.
## Introduction
Natural killer (NK) cells are innate lymphocytes that can instantly eliminate stressed, infected, transformed, or allogeneic cells without being exposed to them first [1]. As the first identified subset of innate lymphoid cells (ILCs), the cytotoxic capacity of NK cells and their capacity to generate cytokines in response to stimulation are their most distinguishing features [2, 3]. In addition, NK cells influence other immune cells and are vital in resistance to intracellular bacterial infections, malignancies, and viruses [2, 4, 5]. Human NK cell phenotypic characteristics are determined by the expression of CD56 and the absence of CD3, and they can be further subdivided into two major subsets based on the density of CD56 and CD16 expression: CD56dimCD16+ and CD56brightCD16- [6]. CD56dimCD16+ cells are more cytotoxic, whereas CD56brightCD16- cells are more specialized for immunoregulation and have more immature functions [4, 7, 8]. These subsets differ in phenotype, function, and tissue localization [6, 9, 10].
Recent studies have provided insights into tissue-resident NK cells (trNK), which are found in many tissues including the liver, lung, lymph nodes, kidney, and uterus [3, 11, 12]. Uterine decidual NK (dNK) cells are critical during pregnancy, functioning in formation of the fetal-maternal interface, remodeling of the maternal spiral arteries, and promoting fetal development (13–16). In early human pregnancy, the hallmark of the decidua is the abundance of NK cells, which constitute ~$70\%$ of decidual lymphocytes [13]. These dNK cells express high levels of CD56, CD49a, and both inhibitory receptors and immature surface signature molecules [13]. In contrast, human peripheral blood NK (pNK) cells express high levels of CD16, and are frequently thought to be more inherently cytotoxic [6], with significant activity in anti-virus and anti-tumor immunity [5, 17, 18]. Despite emerging characterization of phenotypic and functional differences between dNK and pNK cells (14, 19–21), the mechanism behind these differences in human NK cell function is still largely unknown.
Over the past decade, numerous advances in “-omics”-scale technology, such as microarray technology, RNA-sequencing (RNA-seq), and assay for transposase-accessible chromatin using sequencing (ATAC-Seq), have enriched our understanding of the biology of NK cells (22–25). Additionally, single-cell analysis has revealed three primary subgroups of dNK cells, showing cell type-specific activities and intercellular communication at the maternal-fetal interface (26–28). Our group has also used microRNA microarray technology and single cell RNA-seq to uncover the molecular basis of the different phenotypes and functions of human NK cell subsets [29, 30]. Although some additional genes or factors have been identified that contribute to the key aspects of human NK cells, our understanding of the molecular basis of the phenotypes and functions of human NK cells is incomplete.
In this work, we applied metabolomics and proteomics to analyze the metabolome and proteome of human dNK and pNK cells. Metabolomics analysis showed significant changes in metabolic pathways and that glycerophospholipid metabolism was downregulated in dNK cells. Integrative proteomics and metabolomics analysis revealed the disequilibrium of redox in dNK cells. Correlation analyses showed that metabolites have strong correlations with NK functions. Our data revealed global internal metabolic alterations between pNK and dNK cells. Our study provides a new perspective on NK cell phenotype, metabolism, and functional network and is a valuable resource enriching our understanding of metabolic alterations in NK cell subsets.
## Samples
All decidual samples from healthy donors undergoing elective abortion in the first trimester between 6 and 12 weeks of gestation were obtained at Anhui Provincial Hospital, Hefei. Peripheral blood samples were collected from age-matched non-pregnant healthy women. This study was approved by the Medical Ethics Committee (No. 2022KY063) of the First Affiliated Hospital of the University of Science and Technology of China. All donors supplied informed consent.
## Isolation of human NK cells from decidua and peripheral blood
The cells were processed within 4 h of collection. Peripheral blood samples were diluted 1:2 in PBS. Mononuclear cells were isolated by Ficoll-Hypaque centrifugation using standard procedures [29]. CD3−CD56+ NK cells were isolated from peripheral blood mononuclear cells with the MACS isolation system according to the manufacturer’s instructions (Miltenyi Biotec). Decidual NK cells were isolated from decidual samples as previously described [29, 31]. The cell purity was determined to be >$95\%$ by post-purification FACS analysis.
## Proteomics analysis and data processing
Frozen samples were transferred into low protein binding tubes (1.5 ml Eppendorf) and lysed with 300 µL lysis buffer supplemented with 1 mM PMSF with sonication. After sonication, the samples were centrifuged at 15,000 g for 15 min to remove insoluble particles. Protein concentration was determined by BCA and aliquoted for storage at -80°C. The filter-aided sample preparation (FASP) approach was used to decrease and trypsinize the isolated proteins [32]. The digested peptides were desalted by the C18-Reverse-Phase SPE Column.
All analyses were performed using a Q-Exactive mass spectrometer (Thermo, USA) equipped with a Nanospray Flex source (Thermo, USA). Samples were loaded and separated in a C18 column (15 cm × 75 µm) on an EASY-nLCTM 1200 system (Thermo, USA). The flow rate was 300 nL/min and the linear gradient was 90 min (0-55 min, $8\%$ B; 55-79 min, $30\%$ B; 79-80 min, $50\%$ B; 80-90 min, $100\%$ B; mobile phase $A = 0.1$% FA in water and $B = 80$% ACN/$0.1\%$ FA in water). Full MS scans were acquired in the mass range of 300 -1,600 m/z with a mass resolution of 70,000 and the AGC target value was set at 1e6. The ten most intense peaks in MS were fragmented with higher-energy collisional dissociation (HCD) and analyzed by MS/MS. MS/MS spectra were obtained with a resolution of 17,500 with an AGC target of 2e5 and a max injection time of 80 ms. Q-E dynamic exclusion was set for 15.0 s and run under positive mode.
Peak lists were generated from raw data files and searched against the Uniprot Human Protein Database using MaxQuant (Version 1.3.0.5). All peptides and proteins were filtered with false discovery rate (FDR) below 0.01. Label-free protein quantification was carried out using LFQ intensities by MaxQuant 1.5.2.8, and overlapped proteins between replicates were used for the following analysis.
## Metabolomics analysis and data processing
A 250 μL mixture of methanol and water ($\frac{7}{3}$, vol/vol) was added to each sample. QC samples were prepared by mixing aliquots of all samples for a pooled sample. An Acquity UHPLC system (Waters Corporation, Milford, USA) coupled with an AB SCIEX Triple TOF 5600 System (AB SCIEX, Framingham, MA) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. An Acquity UPLC BEH C18 column (1.7 μm, 2.1 × 100 mm) was employed in both positive and negative modes. The binary gradient elution system consisted of (A) water (containing $0.1\%$ formic acid, v/v) and (B) acetonitrile (containing $0.1\%$ formic acid, v/v), and separation was achieved using the following gradient: 0 min, $5\%$ B; 2 min, $20\%$ B; 4 min, $60\%$ B; 11 min, $100\%$ B; 13 min, $100\%$ B; 13.5 min, $5\%$ B and 14.5 min, $5\%$ B. The flow rate was 0.4 mL/min and the column temperature was 45°C. All samples were kept at 4°C during analysis. The injection volume was 5 μL. Data acquisition was performed in full scan mode (m/z ranges from 70 to 1,000) combined with IDA mode. Parameters of mass spectrometry were as follows: ion source temperature, 550°C (+) and 550°C (−); ion spray voltage, 5,500 V (+) and 4,500 V (−); curtain gas of 35 PSI; declustering potential, 100 V (+) and −100 V (−); collision energy, 10 eV (+) and −10 eV (−); and interface heater temperature, 550°C (+) and 600°C (−). For IDA analysis, the range of m/z was set as 50–1,000, and the collision energy was 30 eV. Peak lists were generated from raw data files and searched against the Uniprot Human Protein Database using MaxQuant (Version 1.5.2.8).
## Data analysis
The Gene Ontology (GO) processes and kyoto encyclopedia of genes and genomes (KEGG) pathways of proteomics data were enriched using the Metascape web-based platform [33]. KEGG pathways of metabolomics data were enriched by MetaboAnalyst 5.0 [34]. The principal component (PC) contribution plots were generated using the factoextra (v.1.0.7) package and only the top 25 contributions were displayed in the contribution plot. R statistical software (v.4.1.2) was used for the correlation analysis. The P value of the correlation coefficient was computed by the corPvalueStudent function with the WGCNA (v.1.71) package. Networks were created by Cytoscape (v.3.9.1) software [35].
## Flow cytometry
Cells were stained with the following human mAbs purchasing from Biolegend for FACS: anti-CD45 conjugated with fluorescein isothiocyanate (FITC), anti-CD3 conjugated with allophycocyanin-Cy7 (APC-Cy7), and anti-CD56 conjugated with phycoerythrin (PE). CellROX Deep Red Reagent was purchased from Invitrogen as reactive oxygen species (ROS) dye. Hoechst stain was purchased from Beyotime. FACS staining was performed according to the manufacturer’s instructions. The data were analyzed using FlowJo software (Version 10).
## Differential metabolites between dNK and pNK cells
We conducted metabolomics analysis on pNK cells from blood ($$n = 4$$) and dNK cells from humans in the first trimester of pregnancy ($$n = 4$$) to assess metabolic differences between dNK and pNK cells (Figure 1). The orthogonal partial least squares discrimination analysis (OPLS-DA) clearly distinguished dNK from pNK cells (Figure 2A). Metabolomics detected 77 differential metabolites (variable importance in projection (VIP) > 1, p value < 0.05) and showed that differential metabolites were mainly composed of lipids, organic acids, nucleosides, and benzenoids (Figure 2B). We found 17 metabolites that were upregulated (fold change > 1) and 60 metabolites that were downregulated (fold change < 1) (Figure 2C).
**Figure 1:** *Workflow of proteomics and metabolomics. Summary for the analysis of blood and decidual samples by mass cytometry.* **Figure 2:** *Metabolomics profile of dNK and pNK cells. (A) OPLS-DA score plots of metabolomics data. Each symbol represents one donor (n=4). (B) Pie chart showing differential metabolites of different classes. Different colors indicate different classes. (C) Volcano diagrams showing all of the identified metabolites from metabolomics data. The x-axis and y-axis are based on the fold change (FC) and p-values, respectively. Each dot represents a metabolite. Significantly upregulated, downregulated (VIP > 1, p value < 0.05, FC > 1 or < 1), and unchanged DEPs are colored in red, blue, and gray, respectively. The horizontal line denotes a p-value cutoff of 0.05. (D) Bubble plot of log2(fold change) in abundance of metabolite species in dNK relative to pNK cells (ctrl). Values are shown as log2(fold change) relative to pNK cells. Each dot represents a metabolite species. Color-coded per metabolite class. Dot size indicates significance. The horizontal line denotes FC of 1.*
To illustrate alterations in different classes of metabolites, we displayed all metabolites in a bubble plot (Figure 2D). The bubble plot revealed that the majority of elevated metabolites were found in lipids and lipid-like compounds, particularly fatty acyls and glycerophospholipids, pointing to complex alterations in lipids between pNK and dNK cells (Figure 2D). Together, these findings shed light on the differences in metabolite levels between pNK and dNK cells.
## Glycerophospholipid metabolism is downregulated in dNK compared to pNK cells
To characterize the differential metabolic pathways between dNK and pNK cells, we performed pathway enrichment analysis of the differential metabolites and observed that glycerophospholipid metabolism, glutathione metabolism, purine metabolism, glycerolipid metabolism, and glycosylphosphatidylinositol (GPI)-anchor biosynthesis were all significantly changed in dNK cells (Figure 3A). Glycerolphospholipids, which included 24 differential metabolites, were the most abundantly altered class of metabolites so we analyzed glycerophospholipid subgroups to further determine the alterations. The radar map shows counts of upregulated/downregulated metabolites in these subgroups (Figure 3B). Most subgroups had more downregulated metabolites in dNK cells compared to pNK cells except phosphatidic acid (PA). For metabolites contained in each subgroup, phosphatidylcholine (PC), lysophosphatidylcholine (LPC), phosphatidyl-ethanolamine (PE), and lysophosphatidylethanolamine (LPE) were significantly reduced in dNK cells and phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidylglycerol (PG) showed a trend of reduction (Figure 3C), indicating downregulation of glycerophospholipid metabolism in dNK from pNK cells.
**Figure 3:** *Metabolic pathways change in dNK cells compared to pNK cells. (A) KEGG metabolic pathway impact analysis using Metaboanalyst 5.0. (B) Radar diagram of subclasses in glycerophospholipids. (C) Relative intensity of glycerophospholipid related metabolites in dNK and pNK cells. Statistical analyses were performed using the Mann–Whitney U-test. The box plots show the median and 25th and 75th percentiles, with whiskers indicating maximal and minimal values. (D) Lipid fatty acid (FA) chain statistics of glycerophospholipid subclasses. The x-axis presents the number of FA double-bonds and the y-axis presents the number of FA chain carbon. Fold changes are labeled in the circle. The FA chain analysis was conducted using http://www.lintwebomics.info/.*
A previous study showed that PS exposure was proportional to the degree of NK cell activation during the NK cell activation process [36], suggesting that the reduced cytotoxicity may be linked to the decreased content of glycerophospholipids in dNK cells. In addition to the increase of PA, individual lipids in PG, PC, and PI, such as PG (16:$\frac{0}{0}$:0), PC (16:$\frac{0}{3}$:1(2E)), and PI (16:$\frac{0}{16}$:0) were significantly upregulated in dNK cells (Supplementary Figure 1). Plotting chain carbon and double-bonds showed that upregulated glycerophospholipids tended to have fewer double bonds and downregulated glycerophospholipids might have more fatty acid chain carbons in dNK cells (Figure 3D). This finding demonstrated that the level of unsaturation of glycerophospholipids was dramatically reduced, implying that dNK cell membrane fluidity may be affected [37]. In summary, these results indicate considerable alterations of complex lipids between dNK and pNK cells.
## The metabolic network changed between dNK and pNK cells
Strong correlations among metabolites suggest that the metabolites have similar roles and belong to related metabolic networks [38]. To find potential co-regulatory relationships of differential metabolites, we performed Spearman correlation analysis and constructed a correlation network for all metabolites. In total, $85\%$ of differential metabolites were significantly correlated with each other (Figure 4A). Notably, most differential metabolites had strong positive correlations with a threshold of absolute correlation coefficient greater than 0.95 (Figure 4B). There was a strong correlation between metabolites in the same metabolic pathway such as glycerophospholipid metabolism (Figure 4B). Furthermore, different pathways such as glutathione metabolism and purine metabolism seemed to have a significant positive correlation (Figures 4A, B). Overall, our survey demonstrated that the metabolic alterations between dNK and pNK cells are highly coordinated.
**Figure 4:** *Metabolic network analysis of NK cells. (A) Heatmap of Spearman correlation coefficients of differential metabolites between dNK and pNK cells. Only paired metabolites with p-value < 0.05 and absolute correlation coefficient greater than 0.6 were colored. (B) Spearman correlation networks of differential metabolites with absolute correlation coefficients greater than 0.95. Solid lines represent positive correlations and dashed lines represent negative correlations. (C) Schema of metabolic pathways with select metabolites. Color corresponds to the log2 FC between dNK and pNK (ctrl) cells. Gray nodes represent metabolites that were not detected and borders are color-coded by statistical significance. CDP-DAG, Diacyl glycerol; IMP, inosine monophosphate; GMP, guanosine monophosphate; FAICAR, 1-(5’-Phosphoribosyl)-5-formamido-4-imidazolecarboxamide.*
To further explore the comprehensive metabolic alterations between dNK and pNK cells, we plotted the schema of metabolic pathways (Figure 4C). Previous research has validated that glycolysis and oxidative phosphorylation (OXPHOS) are crucial to immune metabolism in NK cells (39–41). Our data demonstrated that glycolysis and the tricarboxylic acid cycle (TCA) cycle have a slight disturbance, indicating that glycolysis and OXPHOS are not vital in metabolic distinctions between pNK and dNK cells (Figure 4C). In glycerophospholipid metabolism, PA significantly accumulated in dNK cells (Figure 4C). Most metabolites of glycerophospholipid metabolism significantly decreased and affected pyruvate metabolism, leading to significant downregulation of the downstream glutathione pathway (Figure 4C). In addition, decreased NADPH could not donate enough electrons to reduce glutathione (GSH) from glutathione disulfide (GSSG), implying a redox balance shift in dNK cells (Figure 4C). Sphingomyelin in sphingolipid metabolism showed a significant increase in dNK cells (Figure 4C), consistent with a previous report showing that sphingolipid metabolism is a crucial signal for decidualization [42]. Taken together, these results highlight interrelated metabolic network modifications in dNK cells compared to pNK cells.
## Proteomics analysis of dNK and pNK
The above results indicated that metabolism changed significantly between dNK and pNK cells, so we also collected normal dNK and pNK cells and performed proteomics to identify the protein alterations. In total, 2,724 proteins were found and 394 were identified as differentially expressed proteins (DEPs, p-value < 0.05, fold change > 3 or fold change < $\frac{1}{3}$) (Supplementary Figure 2A). We found 232 upregulated DEPs (fold change > 3) and 162 downregulated DEPs (fold change < $\frac{1}{3}$) in dNK cells compared to pNK cells (Supplementary Figures 2A, B). Principal component analysis (PCA) showed significant variance in proteomes between dNK and pNK cells, which had a separation in PC1 of $98.3\%$ (Figure 5A). We focused on PC1 and displayed the top 25 DEPs that contributed to it (Figure 5B). Furthermore, Gene Ontology (GO) enrichment analysis of the DEPs contributing to PC1 (loading > 0.95 or loading < -0.95) showed that DEPs related to oxidoreductase activity were enriched (Figure 5C), suggesting that redox equilibria have shifted in dNK cells. In addition, for all GO-enriched DEPs the term oxidoreductase activity was upregulated (Supplementary Figure 2C). Some enriched GO terms (supramolecular fiber organization, GTPase regulator activity and actin binding) displayed alterations in the cytoskeleton (Supplementary Figure 2C), consistent with previous research showing that dNK cells failed to polarize their microtube to the synapse in contrast to pNK cells [43].
**Figure 5:** *Proteomics analysis of dNK and pNK cells. (A) Principal component analysis (PCA) of dNK and pNK (ctrl) cells. Each symbol represents one donor (n=3). (B) Top 25 differentially expressed proteins (DEPs) mostly contributing to PC1. (C) GO enrichment of DEPs contributing to PC1, |loading| > 0.95. (D) KEGG enrichment of all DEPs. (E) PPI analysis of DEPs. MCODE was used to calculate the score of each DEP. The size of each circle represents the score.*
To investigate modified molecular pathways in dNK and pNK cells, we performed KEGG enrichment analysis of all DEPs. NK cell mediated cytotoxicity was the most significantly downregulated pathway in dNK cells (Figure 5D). Pathway protein processing in the endoplasmic reticulum had notable upregulation because of active cytokine production in dNK cells (Figure 5D). Additionally, leukocyte transendothelial migration appeared to be downregulated because dNK cells are tissue-resident (Figure 5D). Apart from the well-known proteins PRF1, CXCR4, HSPA2, etc., most DEPs involved in those three pathways have enormous potential to be investigated in NK cells in the future (Supplementary Figure 2D). The most surprising aspect of the KEGG enrichment analysis was DEPs enriched in a set of metabolic pathways, including arginine and proline metabolism, biosynthesis of amino acids, fatty acid metabolism, glutathione metabolism, and glycolysis/gluconeogenesis (Figure 5D). Additionally, 110 DEPs were involved in focal adhesion, cytoskeleton remodeling, oxidoreductase activity, and fatty acid metabolism based on pathway enrichment analysis (Figure 5D, Supplementary Figure 2C). Next, we plotted protein-protein interaction (PPI) networks to find interactions between the NK cell mediated cytotoxicity pathway and metabolic pathways (Figure 5E). The PPI network showed that ANPEP and TFRC had strong connections with DEPs in the NK cell mediated cytotoxicity pathway, indicating that metabolism of fatty acids and glutathione influenced NK immune function (Figure 5E). Overall, our results indicated various proteome profiles that revealed phenotypic alterations in pNK and dNK cells.
## Integrated metabolomic and proteomic analysis revealed downregulation of glutathione metabolism in dNK compared to pNK cells
To understand the comprehensive inner network alterations between dNK and pNK cells, we combined metabolomic and proteomic datasets. The amounts of metabolites altered more than just proteins in glycerophospholipid metabolism, suggesting that upstream metabolites affect more than proteins (Figure 6A). Upregulation of GPD2 might result in production of PA, resulting in significant accumulation of PA (Figure 6A). Apart from this, glutathione metabolism was altered both in protein and metabolite levels (Figure 6A). Increased GSH might enhance cytotoxic functions in NK cells (44–46). We found that lactoylglutathione considerably decreased despite little change in glycolysis or pyruvate metabolism, suggesting that the glutathione source may be limited (Figures 5C, 6A). Glutathione peroxidase 1 (GPX1) was increased in dNK cells, suggesting a compensatory elevation for inner redox environment disruption (Figure 6A).
**Figure 6:** *Integration of metabolomics and proteomics of dNK and pNK cells. (A) Schema of metabolic pathways (glycolysis/gluconeogenesis, glutathione and glycerophospholipid metabolism) with select metabolites and proteins. Metabolites or proteins significantly upregulated, downregulated, and unchanged were colored in red, blue, and black, respectively. Gray nodes represent metabolites that were not detected. The chord diagrams of all differential metabolites and reactive oxygen species metabolic process (B), and natural killer cell mediated cytotoxicity (E). Spearman correlation coefficients are used as links. (C) Relative intensity of metabolites involved in glutathione metabolism in pNK and dNK cells. Statistical analyses were performed by Mann–Whitney U-test. The box plots show the median and 25th and 75th percentiles, with whiskers indicating maximal and minimal values. (D) Expression of ROS in dNK and pNK cells. FlowJo was used to plot histogram (left) and GraphPad prism was used for statistical analysis (right). Statistical analyses were performed by two-tailed unpaired Student’s t-test and data were presented as mean ± sd.*
As mentioned above, redox equilibrium might shift in dNK compared to pNK cells. We further explored the correlation between selected DEPs and all differential metabolites. Proteins in the reactive oxygen species (ROS) metabolic process exhibited a negative correlation with common metabolites including glutathione metabolism, implying that proteins in oxidation-reduction (redox) reactions increased activity related to downregulated metabolites (Figure 6B). Metabolic reactions are inseparable from redox reactions [47]. GSH and GSSG is the major redox pair in cells [48], and the ratio of GSH/GSSG is often used as an indicator of the cellular redox state [49]. A notable aspect of our results was that glutathione metabolism was significantly downregulated, which included five downregulated metabolites (Figure 3A). Both GSH and GSSG significantly decreased and there was also a downregulated trend in the ratio of GSH/GSSG (Figure 6C), indicating alterations in amounts of glutathione metabolism and an imbalance in the conversion of GSH and GSSH. These findings pointed to significant modifications in the redox metabolic pathways between dNK and pNK cells. ROS is the kernel of redox systems and GSH is the major antioxidant for eliminating ROS [44]. Therefore, it is reasonable to speculate that dNK has more ROS than pNK cells. To validate this speculation, we detected the ROS contents in dNK and pNK cells using FACS. The data showed that ROS in dNK cells were significantly higher than in pNK cells (Figure 6D).
To demonstrate that altered metabolites are interrelated with NK cell functions, we also plotted the correlation chord between proteins in NK cell mediated cytotoxicity and common differential metabolites (Figure 6E). The chord chart revealed that cytotoxicity was proportional to metabolite alteration (Figure 6E), implying that low cytotoxicity was due to the reduced level of whole cellular metabolites. LCK and PRF1, which were shown to affect cytotoxicity in NK cells, were both significantly downregulated, consistent with the reduced trend of whole metabolite alteration in dNK cells (Supplementary Figure 2D). Together, our metabolomics and proteomics data highlight that the inner redox state may affect dNK cell functions.
## Discussion
NK cell immunology has rapidly developed in recent decades and research on it is elucidating NK cell functional fates. Although classical NK phenotypes have indisputably served to identify diverse NK subsets, the phenotypes do not explicitly indicate NK cell functions since phenotypes and functions do not have a one-to-one correspondence [50]. In recent years, immunologists have rediscovered the critical role of metabolism in immune cell functions. Recent studies have shown that distinct metabolic features drive NK cell functional potential, which may serve as a reliable way to identify functional fates. Our study provides detailed and exhaustive data regarding the metabolism and protein signatures of dNK cells by using metabolomics and proteomics with independent verification, revealing comprehensive metabolic features. We identified dramatic alteration of glycerophospholipids of dNK cells. Moreover, integrated metabolomics and proteomics data demonstrated the redox disequilibrium and the increased level of ROS in dNK cells. Finally, our data suggested novel perspectives into the metabolic mechanisms of divergent NK cell functions.
Researchers have found that immune cells modify their metabolism to perform various functions (51–54). NK cells modify their metabolic pathways to fulfill certain energy and biosynthetic requirements for various cell functions (55–57). The anti-tumor and antiviral activities of NK cells are compromised by the inhibition of glycolysis and OXPHOS [39, 58, 59]. Glutamine withdrawal can suppress IFNγ production and cytotoxicity of NK cells by regulating cMyc [60]. Additionally, lipids in the microenvironment appear to change NK metabolism and impair NK cell activities [61, 62]. The immunometabolism of pNK cells has been broadly investigated, but the metabolic pathways in tissue-resident NK cells and how cellular metabolism impact their function have not been thoroughly studied. It has been reported that NK cells from peripheral blood differ from liver- and spleen-resident NK cells in the expression profile of nutrient transporters Glut1, CD98 and CD71, consistent with a cell-adaptation to the different nutritional environment in these compartments [63]. As for uterus-resident NK cells, Vento-Tormo et al. [ 26] reported that there are three major subsets of uterine dNK cells (dNK1, dNK2 and dNK3) and predicted that CD39+ dNK1 subset can be primed metabolically through increased expression of glycolytic enzymes. Recently, Strunz et al. [ 64] identified that KIR+CD39+uterine NK cells presented with increased mitochondrial mass and membrane potential. Here, we performed metabolomics to investigate divergent metabolic pathways in dNK cells distinct from pNK cells. We noticed that the most abundant classes of differential metabolites were lipids and lipid-like molecules. Some studies have shown that lipid metabolism is crucial in coordinating NK cell immunosuppression and impaired under pathological conditions (e.g., obesity and cancer) [65]. Our data showed clear reduction of lipids in dNK cells, which seems contrary to earlier findings showing that excessive lipids impaired innate immunity [61]. Research on T cells revealed that activated T cells upregulate lipid synthesis and inhibition of fatty acid synthase (FASN) preventing cell death of activated CD4+T effector cells [66, 67]. Combined with proteomics, a possible explanation for decreased lipids in dNK cells might be that overexpression of FASN (Supplementary Figure 2D) inhibits the activation of NK cells. Furthermore, upregulation of the fatty acid degradation pathway may result in decreased lipids in dNK cells.
Glycerophospholipids are nearly the most abundant substances in mammal cellular membranes, and have critical roles in signaling and regulation [68]. Glycerophospholipids with long and saturated hydrophobic tails influence membrane fluidity, while polyunsaturated lipids could reduce membrane bending rigidity to promote deformation [37]. Here, we reported the alteration of glycerophospholipid metabolism in different NK cell subsets. Total PC, LPC, PE, LPE, PG, LPI, and PS were all significantly downregulated in dNK cells, with only a few metabolites such as PA, PG(16:$\frac{0}{0}$:0), PC(16:$\frac{0}{3}$:1(2E)), and PI(16:$\frac{0}{16}$:0) significantly upregulated. The chain carbon and double-bond analysis emphasized high saturation of glycerophospholipids in dNK cells, suggesting possible difficulty with synapse formation. Moreover, the downregulation of PS might also inhibit dNK cell functions due to the scrambling of PS exposure attenuated NK cell activation [36]. Apart from that, PA had an unexpected upregulation in the glycerophospholipids pathway. The reason for this is not clear but it may be due to the unknown block from PA to CDP-DAG. Integrated analysis of proteomics and metabolomics reflected that glycerophospholipid metabolism might have a positive impact on NK cell cytotoxicity. Further work is required to validate the correlation between glycerophospholipids and cytotoxicity in NK cells.
It is commonly accepted that pNK and dNK cells have differential expression of CD56 and CD16 [11, 13]. Using high-resolution microarray analyses, Wang et al. found that most dNK cells were immature types with the CD56brightCD16-T-bet- phenotype, and most of the pNK cells had the CD56dimCD16+T-bet+ mature phenotype [23]. Consistent with the literature, our data showed that CD56 was highly expressed and CD16 and T-bet were significantly decreased in dNK cells compared to pNK cells (Supplementary Figure 1B). It is worth noting that KEGG enrichment analysis exhibited substantial alterations in some interesting pathways, including focal adhesion, leukocyte transendothelial migration, and chemokine signaling pathway. The downregulation of proteins associated with transendothelial migration and chemokine signal on dNK cells might be because dNK cells, as tissue-resident cells, do not need to cross through the endothelial cells to function like pNK cells. Tight interactions with cells in the decidual microenvironment promoted high expression of focal adhesion proteins on dNK cells.
Our data suggested unexpected alteration of the cytoskeleton and upregulation of protein biosynthesis in dNK cells, which seems to be related to lower cytotoxicity and higher cytokine secretion. Prior studies have noted that immature synapses, which cannot normally release cytotoxic granules, were a physiological mechanism of suppressed cytotoxicity in dNK cells [43]. This also accords with our results, which showed prominent enrichment of supramolecular fiber organization and a higher level of cytotoxic granules (granulysin and granzyme A). Furthermore, our findings suggest that the reduction of actin binding did not match the high level of supramolecular fiber organization, providing a reason why synapse maturation was prevented.
Notably, our study described the discrepancy of ROS concentration in the different NK subsets and explored the relationship between redox and cytotoxicity in NK cells. Integrated proteomics and metabolomics demonstrated significant downregulation in glutathione metabolism in dNK cells compared to pNK cells. Although the ratio of GSH/GSSG showed a tendency to decline, the GSH system and the conversion balance between GSH and GSSG exhibited overall downregulation. Downregulation of GSH might impair NK cell functions while high GSH increases cytotoxic functions [45, 46]. Since the GSH system is an important antioxidant, we validated the unusually higher ROS concentration in dNK cells, suggesting that redox equilibrium shifted in dNK cells compared to pNK cells. This finding is in line with previous reports that tumor-infiltrating NK cells were impaired by high ROS levels in the tumor microenvironment (69–71). The observed increase in ROS concentration in dNK cells could be attributed to the hypoxic environment in decidua (57, 72–74). Previous studies showed that upregulation of TRX-1 helps GSH maintain a reduced state even though the protection is limited [44]. Our proteomics data showed that TXN (TRX-1, data not shown) had nearly a 6-fold increase in dNK cells, indicating a struggle with oxidative stress in dNK cells. Further research should be undertaken to determine the direct relationship between redox and the immunological characteristics of dNK cells.
The interpretation of our findings may be affected by some limitations of our study. First, the range of samples was relatively small and the pregnancy dNK cells and the pNK cell counterparts were not from the same donor. Although it is tempting to reveal comprehensive metabolic alterations between dNK and pNK cells, we cannot rule out the possibility that there is some contribution of immune-metabolic adaptations in pregnancy. Second, untargeted metabolomics cannot provide detailed modification for a specific class of metabolites thus limiting full comprehension of precise metabolic alterations. Third, we did not detect activated NK cells and thus did not compare activated pNK and dNK cells.
Overall, our study identified global metabolic alterations between dNK and pNK cells. Integrated analysis of metabolomics and proteomics revealed the coordinated network of metabolism and phenotype related to NK cell functions. Our data provide a new perspective on dNK immunomodulation, which could be explored in further research. This could be very helpful in creating novel therapies or techniques to ward off specific diseases.
## Data availability statement
The datasets presented in this article are not readily available because the data also forms part of a larger ongoing study. Requests to access the datasets should be directed to FN, fangni@ustc.edu.cn.
## Ethics statement
The studies involving human participants were reviewed and approved by First Affiliated Hospital of the University of Science and Technology of China. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
FN, PW, and TL designed the experiments. PW, and TL performed the experiments and data analysis with the help from HZ, MZ, MW, LQ, and YZ. HZ and PW collected blood and decidual samples. FN, PW, and TL wrote the manuscript. FN was responsible for the supervision and project administration. All authors discussed, edited, and approved the final version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1136652/full#supplementary-material
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|
---
title: 'Aβ8-20 Fragment as an Anti-Fibrillogenic
and Neuroprotective Agent: Advancing toward Efficient Alzheimer’s
Disease Treatment'
authors:
- Stefania Zimbone
- Maria Laura Giuffrida
- Giuseppina Sabatino
- Giuseppe Di Natale
- Rita Tosto
- Grazia M. L. Consoli
- Danilo Milardi
- Giuseppe Pappalardo
- Michele F.M. Sciacca
journal: ACS Chemical Neuroscience
year: 2023
pmcid: PMC10020970
doi: 10.1021/acschemneuro.2c00720
license: CC BY 4.0
---
# Aβ8-20 Fragment as an Anti-Fibrillogenic
and Neuroprotective Agent: Advancing toward Efficient Alzheimer’s
Disease Treatment
## Abstract
Alzheimer’s disease (AD) is the most common cause of dementia, characterized by a spectrum of symptoms associated with memory loss and cognitive decline with deleterious consequences in everyday life. The lack of specific drugs for the treatment and/or prevention of this pathology makes AD an ever-increasing economic and social emergency. Oligomeric species of amyloid-beta (Aβ) are recognized as the primary cause responsible for synaptic dysfunction and neuronal degeneration, playing a crucial role in the onset of the pathology. Several studies have been focusing on the use of small molecules and peptides targeting oligomeric species to prevent Aβ aggregation and toxicity. Among them, peptide fragments derived from the primary sequence of Aβ have also been used to exploit any eventual recognition abilities toward the full-length Aβ parent peptide. Here, we test the Aβ8-20 fragment which contains the self-recognizing Lys-Leu-Val-Phe-Phe sequence and lacks Arg 5 and Asp 7 and the main part of the C-terminus, key points involved in the aggregation pathway and stabilization of the fibrillary structure of Aβ. In particular, by combining chemical and biological techniques, we show that Aβ8-20 does not undergo random coil to β sheet conformational transition, does not form amyloid fibrils by itself, and is not toxic for neuronal cells. Moreover, we demonstrate that Aβ8-20 mainly interacts with the 4–11 region of Aβ1-42 and inhibits the formation of toxic oligomeric species and Aβ fibrils. Finally, our data show that Aβ8-20 protects neuron-like cells from Aβ1-42 oligomer toxicity. We propose Aβ8-20 as a promising drug candidate for the treatment of AD.
## Introduction
Alzheimer’s disease (AD) is considered the most common form of dementia in the elderly population.1 *Dementia is* a significant contributor to loss of independence, disability, and care home placement and represents one of the costliest long-term pathologies to society, with $85\%$ of costs related to family or social care.2 According to data from the World Alzheimer Report, over 46.8 million people were affected by dementia worldwide in 2015, with a prevision of a doubling of this number in the next 20 years.2−4 To date, despite the high interest of the scientific community, the etiopathogenesis of the disease is not fully understood, and no drugs are still available for the treatment despite the large number of clinical trials5 and the intense research activity on synthetic and/or natural compounds.6−11 AD is characterized by the appearance in the hippocampal region of the brain12 of two kinds of proteinaceous deposits: (i) one mainly constituted by fibrillar aggregates of phosphorylated tau protein inside neuronal cells, called neurofibrillary tangles,13 and (ii) one in the extracellular space, called amyloid plaques, mainly constituted by fibrillar aggregates of amyloid beta protein (Aβ).14 Amyloid plaque composition was demonstrated to be a complex mixture with the presence of 100s of proteins (∼500) and several non-proteinaceous components.15−17 Aβ is the final product of the cleavage of amyloid precursor protein (APP)18 operated by the sequential action of β and γ secretases. Although Aβs spanning 34 to 50 amino acid length are the most common, there are even shorter Aβ isoforms (Aβ1–$\frac{17}{18}$/$\frac{19}{20}$) that depend on γ-secretase, but the precise mechanism of their generation is unknown.19−21 It was proposed that an abnormally high concentration of Aβ1-40 and/or Aβ1-42, the two most common isoforms, could result in aggregation into a β-sheet-rich structure, the starting point of Aβ fibrillogenesis.22,23 Aβ aggregation is a complex mechanism which starts with the formation of oligomeric species, suggested to be the most toxic species for cells,24−31 which undergo conformational reorganization into protofibrils and fibrils. Monomeric and fibrillar forms have been demonstrated to be, respectively, protective32−34 and mostly inert35,36 for neuronal cells.
Many studies focused on the primary sequence of Aβ, trying to shed light on the role played by a single amino acid.37 *Although a* high amount of data is available, the literature is often contradictory. It has been shown that the mainly hydrophobic C-terminal region of Aβ plays a pivotal role in controlling Aβ structure stability and self-assembly.38−41 On the contrary, it is generally accepted that the N-terminal region of Aβ, Aβ1-16, is not able to aggregate and is not cytotoxic.42 Nevertheless, it was also shown that under particular conditions, Aβ1-16 can aggregate and form cytotoxic species containing β-turns.43 Moreover, the N-terminal region of Aβ was demonstrated to control the aggregation rate and fibrillar stability of amyloid fibers.44 In particular, residues Arg5, Asp7, and Ser8 were found to form important inter-molecular contacts stabilizing the overall fibril structure of three-fold symmetry.45 Aβ undergoes several post-translational modifications,19 including the formation of truncated species as the result of physiological enzymatic cleavage.46−49 Many truncated forms of Aβ have been identified in blood plasma samples and human cerebrospinal fluids of AD patients.50 Recently, Abedin et al. reported a structural and aggregation propensity study of seven Aβ fragments with the aim of identifying the region of Aβ which is able to inhibit fibrillogenesis.51 Generally, truncated forms of Aβ are of particular interest since they are known to affect the Aβ aggregation rate. Short Aβ fragments known as β-sheet breakers are able to recognize and tie to the same regions of the parent amyloid peptide, effectively inhibiting accumulation or promoting disaggregation of pre-existing fibrillar amyloids.52−58 In particular, β-sheet breakers Aβ17-21 and Aβ16-20 (KLVFF) have been shown to significantly inhibit amyloidogenic aggregation in vitro.59−63 Unfortunately, these peptide-based systems have a remarkable tendency to self-aggregate and short circulatory half-lives. For this reason, the inclusion of charged residues at the N-terminus could be thought of as a valuable strategy to enhance bioavailability. Here, we explore the ability of the Aβ fragment SGYEVHHQKLVFF (Aβ8-20) to prevent the aggregation and toxicity of Aβ1-40 and Aβ1-42. The choice of this peptide, which has not yet been found in vivo, arises from two main reasons:(i)the absence of Arg5 and Asp7 is important to prevent the stabilization of the fibril structure.45 Moreover, it is known that angiotensin-converting enzyme is a candidate enzyme for the formation of the 8-x Aβ species,64 although so far, there are no in vivo data supporting this pathway;(ii)the cleavage in position 20 removes a major part of the Aβ C-terminus region which is known to play an important role in the aggregation process. Moreover, it encompasses the Lys-Leu-Val-Phe-Phe (KLVFF) sequence which was demonstrated to recognize the analogous sequence in the full-length protein.65 Indeed, several studies were performed on KLVFF alone or embedded in the peptide sequence,6,52,66,67 indicating the ability of the KLVFF sequence to both recognize and prevent Aβ aggregation.
We used chemical and biochemical techniques to fully evaluate the behavior and the properties of Aβ8-20. We show that Aβ8-20 invariably maintains a random coil conformation and is not able to form amyloid aggregates by itself. Interestingly, Aβ8-20 suppresses the Aβ1-40 and Aβ1-42 random coil to β-sheet conformational transition and completely prevents their ability to form amyloid aggregates. We also demonstrate, through a combination of mass spectrometry and dot blot assay, that this peptide hampers the formation of Aβ1-42 oligomeric species, which are considered the most toxic species, probably by interacting in the 4–11 region of the protein. Finally, we show that Aβ8-20, which is not toxic per sé, protects neuronal-like cells from Aβ1-42 toxicity. Overall, our data indicate Aβ8-20 as a good candidate for the prevention of cell damage induced by Aβ in AD and help us improve our knowledge of the mechanism underlying the detrimental action of oligomeric and/or prefibrillar Aβ species.
## Aβ8-20 Adopts a Stable Random Coil Conformation
and Does Not Form Amyloid Aggregates
Several fragments of Aβ have been shown to undergo amyloidogenic aggregation.51,68 Although Aβ8-20 lacks the aggregation-prone C-terminus region and residues in the N-terminus responsible for amyloid fiber stabilization, we could not rule out the possibility that this fragment may form amyloid aggregates. To evaluate the aggregation properties of Aβ8-20, we initially performed a well-known thioflavin T (ThT) assay. Aβ8-20 in buffer solution (10 mM MOPS buffer, 100 mM NaCl, pH 7.4) does not aggregate over a time length of 48 h (Figure 1a, blue curve).
**Figure 1:** *(a) Amyloid aggregation
measured by the ThT assay of 10 μM
Aβ8-20 (blue curve), Aβ8-20:Cu2+ 1:1 (gray curve), Aβ8-20:Cu2+ 2:1 (orange curve), and Aβ8-20:Cu2+ 1:2 (yellow curve). (b) Secondary structure measured
by CD of 10 μM Aβ8-20 at t = 0 (blue curve), t = 24 h (orange curve), t = 48 h (gray curve), and t = 96 h (yellow
curve). All the experiments were performed in 10 mM MOPS buffer and
100 mM NaCl, pH 7.4. ThT curves are the average of three independent
experiments.*
To further test the amyloidogenic properties of the peptide, we performed ThT experiments also under more complex fibrillogenic conditions. Interestingly, also the presence of the Cu2+ ion, which is well known to strongly modulate the aggregation of Aβ depending on the ion/protein ratio,69−73 in sub-stoichiometric (Figure 1a, orange curve), stoichiometric (Figure 1a, gray curve), and over-stoichiometric (Figure 1a, yellow curve) ratios does not induce any aggregation of Aβ8-20.
Circular dichroism (CD) experiments, performed in a time range of 96 h (Figure 1b), reveal that Aβ8-20 adopts a random coil conformation over time. Interestingly, our results differ from those obtained by Abedin et al. for the Aβ11-20 fragment which shows, despite the high sequence homology, a high propensity to form a β-sheet-rich structure.51 The absence of any significant secondary structure transition, typical of amyloid fiber formation, supports well ThT results, although it is not possible to exclude the formation of amorphous aggregates. However, the stable intensity of ellipticity measured by CD experiments suggests that Aβ8-20 does not form any insoluble aggregates over time.
## Aβ8-20 Prevents Aggregation and Random
Coil to β-Sheet Transition of Both Aβ1-40 and Aβ1-42
Aggregation of both Aβ1-40 and Aβ1-42 peptides in buffer solution shows the typical sigmoidal ThT curve (Figure 2a black curve and Figure 2b black curve). CD spectra show, over time, a random coil to β-sheet secondary structure transition (Figure 2a inset and Figure 2b inset). Aβ1-42, which is considered the most toxic species,74,75 forms amyloid fibers faster than Aβ1-40, which, in turn, is the most abundant species in vivo76−79 but is considered less toxic. Noteworthy, the presence of Aβ8-20 in a 1:1 concentration ratio completely suppresses Aβ1-40 aggregation (Figure 2a red curve) and prevents the random coil to β-sheet transition (Figure 2a inset) after 24 h incubation. A similar effect was observed for samples containing Aβ1-42 (Figure 2b, black curve). Aβ1-42 in the presence of Aβ8-20 shows only a small residual increase in the ThT signal (Figure 2b, red curve). Interestingly, CD spectra (Figure 2b, inset) reveal a mixture of the random coil and β-sheet structure at $t = 0$ which evolves over 24 h into a random coil/α-helix. Thus, our data suggest that Aβ8-20 could interfere with the aggregation process of both Aβ1-40 and Aβ1-42 already at a 1:1 molar ratio, suggesting a higher efficiency than, for example, that of Aβ11-20.51
**Figure 2:** *(a) Amyloid aggregation measured by ThT assay of 10 μM Aβ1-40 (black curve) and Aβ1-40:Aβ8-20 1:1 (red curve). Inset: CD spectra
of 10 μM Aβ1-40 at t = 0 (black curve) and t = 24 h (blue curve) and
Aβ1-40:Aβ8-20 1:1
at t = 0 (red curve) and t = 24
h (green curve). (b) Amyloid aggregation measured by ThT assay of
10 μM Aβ1-42 (black curve) and Aβ1-42:Aβ8-20 1:1 (red curve).
Inset: CD spectra of 10 μM Aβ1-42 at t = 0 (black curve) and t = 24 h (blue
curve) and Aβ1-42:Aβ8-20 1:1 at t = 0 (red curve) and t = 24 h (green curve). All the experiments were performed in 10 mM
MOPS buffer and 100 mM NaCl, pH 7.4. ThT curves are the average of
three independent experiments, and the single traces and error bar
are reported in the Supporting Information (Figure S3).*
To confirm ThT results, we acquired transmission electron microscopy (TEM) images for samples containing Aβ1-42 100 μM alone (Figure 3a) and Aβ1-42 100 μM: Aβ8-20 100 μM (Figure 3b) after 96 h incubation at 37 °C. β1-42 showed the classical fiber network, while the presence of the Aβ8-20 fragment almost completely inhibited the fiber formation of Aβ1-42 which were not detected over the entire surface of the grid. This result confirms what we observed by ThT experiments.
**Figure 3:** *TEM image of (a) Aβ1-42 100 μM after
96 h incubation and (b) Aβ1-42 100 μM:
Aβ8-20 100 μM after 96 h incubation.*
## Aβ8-20 Reduces the Dimension of Aβ1-40 Soluble Aggregates
To evaluate the effect of the presence of Aβ8-20 on the size distribution of soluble species of Aβ, we resorted to dynamic light scattering (DLS) measurements. We chose the Aβ1-40 isoform since it is known to aggregate more slowly than Aβ1-42, giving us enough time to evaluate the dimension of soluble aggregated species. Thus, the growth of the Aβ1-40 aggregates was monitored in the absence and in the presence of Aβ8-20. Data collected at $t = 0$, 24 h, and 6 days as the analysis of the intensity (%) of scattering objects are reported in Figure 4. The analysis indicated that freshly prepared samples of Aβ1-40 and Aβ8-20 form structures with mean hydrodynamic diameter around 50 and 60 nm, respectively (Figure 4a, red and black curves, respectively). After 24 h, in the sample containing only Aβ1-40, aggregation phenomena generated a population of larger aggregates with size centered at 1505 nm ($64\%$) in addition to a population centered at 190.9 nm ($36\%$), whereas only a population centered at 116 nm was observed in the presence of Aβ8-20 (Figure 4b). After 6 days, two main populations with mean hydrodynamic diameter centered at 1663 ($60\%$) and 102.7 nm ($40\%$) and at 850.3 ($33\%$) and 188.7 nm ($67\%$) were observed for Aβ1-40 alone and in the presence of Aβ8-20, respectively (Figure 4c). The data collected clearly indicated the reduction of the dimension of Aβ1-40 soluble aggregates in the presence of the Aβ8-20 fragment.
**Figure 4:** *Intensity-weighted hydrodynamic diameter distribution for (a) 10
μM Aβ1-40 and Aβ8-20 at t = 0 and (b) 10 μM Aβ1-40 (red line) and Aβ1-40:Aβ8-20 1:1 concentration ratio (black line) at t = 24
h and (c) at t = 6 days. Curves are the average of
three independent experiments. Measures were performed at 37 °C
in 10 mM phosphate buffer.*
## Aβ8-20 Hampers Aβ1-42 Oligomer Formation
Since Aβ1-42 oligomers have been demonstrated to be the main species responsible for Aβ toxicity in vitro and in vivo,24−27 we resorted to matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) to investigate the effect of the Aβ8-20 peptide on the formation of Aβ1-42 oligomers. Indeed, MALDI-MS can acquire the m/z values of peptides and proteins predominately in the singly charge state, enabling a direct indication of the mass of Aβ1-42 oligomers. In particular, the m/z values reported in the mass spectra acquired by MALDI-MS give a direct indication of the mass of peptides and proteins, revealing the monomeric/multimeric composition of Aβ samples.80−82 Nevertheless, a drawback of the MALDI approach in the characterization of multimeric forms of Aβ1-42 was the use of organic solvent acetonitrile (ACN) during sample preparation. In particular, the ACN/H2O (1:1, v/v) solvent mixture, needed for both matrix dissolution and rapid evaporation of the solvent after the deposition of the sample on target plates (see the Materials and Methods section), prevents the hydrophobic interactions within oligomers affecting the oligomer composition.83 Therefore, we carried out MALDI experiments at Aβ1-42 concentrations higher (100 μM) than those generally used in MALDI investigations (5–10 μM) to aid the formation of high-molecular weight (MW) oligomers84 and prevent the complete dissolution of oligomers during deposition of the sample (Aβ/matrix mixture) on target plates.
An Aβ1-42 sample was analyzed by MALDI-time of flight (TOF) after 2, 4, 6, 8, and 24 h of incubation at 37 °C. The mass spectra acquired (Figure 5a) were compared with those recorded when an equimolar amount of the Aβ8-20 peptide was added to the Aβ1-42 sample solution (Figure 5b).
**Figure 5:** *MALDI-MS spectra acquired
in the linear mode (m/z range =
7000–20,000) at different incubation
times of (a) Aβ1-42 (c =
100 μM) in PBS buffer (5 mM) pH 7.8 and (b) Aβ1-42/Aβ8-20 in PBS buffer (5 mM) pH = 7.8 (cAβ = cAβ8-20 = 100 μM).*
The MALDI-TOF spectrum recorded after 8 h of incubation showed (Figure 5a) the formation of a series of signals corresponding to dimeric {[(Aβ1-42)2 + H]+m/$z = 9029$} and trimeric {[(Aβ1-42)3 + H]+m/$z = 13540$} oligomers (Figure 5a). These signals could be related to the formation of high-MW oligomers that were partially disrupted when the Aβ1-42 sample was mixed with matrix solution. Interestingly, the mass spectrum of the sample containing the Aβ1-42/Aβ8-20 mixture, recorded after 8 h of incubation (Figure 5b), showed a clear reduction of the signal’s intensity corresponding to the Aβ1-42 oligomers. The m/z signal corresponding to the Aβ1-42 dimer can be observed only in the mass spectrum acquired after 24 h of incubation.
These findings are in keeping with the results observed in ThT experiments and support the hypothesis that Aβ8-20 may interfere with Aβ aggregation by means of the formation of a noncovalent adduct with the amyloid peptide.
To further confirm the interaction between Aβ8-20 and Aβ1-42 and investigate oligomer formation, we performed gel electrophoresis after incubation of freshly prepared Aβ1-42 in the presence or in the absence of Aβ8-20 for 48 h at 4 °C. We tested three different molar ratios of Aβ1-42/Aβ8-20 (1:1; 1:5; and 1:10), and after incubation, each sample was characterized for its composition of Aβ aggregates. The samples were loaded onto a polyacrylamide gel and transferred onto a nitrocellulose membrane (Figure 6). As expected, we found that Aβ1-42 alone aggregates into small oligomers ranging from 8 to 16 kDa, representing dimers, trimers, and tetramers. In the presence of Aβ8-20, the signal observed was less intense when incubated at the molar ratio of 1:1 and became very weak at 1:5 and 1:10. These data suggest the ability of Aβ8-20 to bind the full-length peptide, inhibiting its aberrant aggregation by hampering oligomer formation.
**Figure 6:** *Representative western blot of Aβ oligomers prepared
in the
presence or absence of Aβ8-20. Samples were
separated onto a 4–12% bis·tris SDS-PAGE gel and blotted
with anti-Aβ N-terminal 1–16 mouse monoclonal antibody
6E10 (1:500).*
## Aβ8-20 Interacts with the N-Terminal
Region of Aβ1-42
Noteworthily, it is clear from Figure 6 that samples containing Aβ1-42/Aβ8-20 in molar ratios 1:5 and 1:10 not only show a decrease in the intensity of trimer and tetramer signals, but also monomer bands seem to disappear. To better investigate this unexpected result, we spotted the co-incubated samples Aβ1-42/Aβ8-20 at the three different molar ratios (1:1; 1:5; 1:10) onto a nitrocellulose membrane that we probed with the 6E10 antibody (Figure 7a). We used Aβ1-42 oligomers and freshly prepared monomers as controls. Even in this case, we found that when Aβ8-20 was incubated with Aβ1-42 at more than the 1:1 molar ratio, a clear signal decrease was evident. Controls confirmed that the antibody was working properly, revealing the presence of Aβ in both incubated and freshly spotted samples.
**Figure 7:** *Dot blot
analysis of Aβ oligomers prepared in the presence
or absence of Aβ8-20. Samples were spotted
after 48 h incubation at 4 °C under gentle rotation. Membranes
were blotted with the following antibodies: anti-Aβ N-terminal
1–16 mouse monoclonal antibody 6E10 (1:100); anti-Aβ
17–24 mouse monoclonal antibody 4G8 (1:100); or anti-oligomer
A11 rabbit polyclonal antibody (1:100).*
As for electrophoresis data, these results suggest that the presence of the peptide strongly modulates Aβ1-42 self-assembly. To prove this, we used a different antibody, anti-Aβ 4G8 (Figure 7b), which is reported to react to amino acid residues 17–24 with the epitope lying within amino acids 18–22 of β-amyloid (VFFAE). Targeting a different epitope led to a different signal pattern in which we detected a clear staining even in the case of co-incubated samples, revealing that the lack of 6E10 signals previously observed could be due to the presence of the small peptide Aβ8-20 along the Aβ1-42 reactive sequence.
6E10 is, in fact, directed against amino acids 1–16 of the Aβ sequence. During the incubation time, the binding of Aβ8-20 to Aβ1-42 could hinder the interaction between the antibody and its target sequence (Scheme 1).
**Scheme 1:** *Schematic Representation
of Anti-Aβ Antibody Interaction with
Aβ1-42 in the Presence of the Aβ8-20 Fragment*
We finally used the anti-oligomer antibody A11 to assess the conformation of each spotted sample and the ability of Aβ8-20 to effectively interfere with Aβ1-42 assembly. As expected, A11 strongly reacted with the Aβ incubated alone and very slightly with Aβ monomers freshly spotted. The increasing presence of the Aβ8-20 fragment during the incubation time leads to a decrease in the antibody signal underlying the reduction of the oligomeric species formed.
On the basis of these findings, we moved onto limited proteolysis experiments to better clarify the site of interaction between Aβ8-20 and Aβ1-42. Indeed, these interactions could occur at the peptide bonds involved in the proteolytic cleavage affecting, in turn, enzyme’s accessibility to the cleavage sites.
To this scope, we used α-chymotrypsin enzyme that selectively catalyzes the hydrolysis of peptide bonds at the C-terminal side of tyrosine, phenylalanine, tryptophan, and leucine residues. We analyzed by MALDI-TOF the Aβ1-42 peptide fragments generated at the initial stage, namely, after 10 min of α-chymotrypsin digestion, where the hydrolysis rate is higher, and small differences in the peptide interactions with Aβ1-42 would be more pronounced, as observed in our previous studies.6 The identified peptide fragments are indicated in Scheme 2. A quite similar proteolytic pattern was observed in the MALDI-TOF spectrum of Aβ1-42 digested in the presence of the Aβ8-20 peptide.
**Scheme 2:** *Aβ1-42 Proteolytic Pattern after 10 min of
α-Chymotrypsin DigestionAβ1-42 (10 μM) in PBS buffer (5 mM) pH 7.8 and an enzyme/substrate
ratio of 1:200 w/w.*
Despite the low reproducibility of MALDI measurements, a comparative analysis of the signal intensity averaged over 15 replicate measurements (Figure 8) revealed some reasonable differences. In particular, the reduction of signal intensity of the peaks assigned to the peptide fragments Aβ5-42 and Aβ11-42, when the Aβ8-20 peptide was added to the Aβ1-42 sample solution (Figure 8, orange bar), indicates a lower hydrolysis rate at the cleavage sites of Phe4 and Tyr10. This may suggest a lower accessibility of these cleavage sites. Interestingly, the CD spectra of Aβ1-42 in the presence of Aβ8-20 show a mixture of the random coil and β-sheet structure at $t = 0$ h (Figure 2b inset, red curve) which evolves over 24 h into a random coil/α-helix conformation (Figure 2b inset, orange curve). The structuring effects within the polypeptide backbone can alter peptide chain flexibility of the Aβ1-42 N-terminal domain, affecting the cleavage of the peptide bonds by a protease.
**Figure 8:** *Signal intensities of
digestion fragments of Aβ1-42 protein in Aβ1-42 (blue bar) and Aβ1-42/Aβ8-20 samples (orange
bar), after 10 min of α-chymotrypsin digestion.*
## Aβ8-20 Prevents Aβ1-42 Toxicity in Differentiated SH-SY5Y Cells
To have a functional readout of the data, we investigated the protective activity of Aβ8-20 both per sé and toward the toxicity of Aβ1-42 oligomers by using the well-known viability test, 3-(4 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. We used a neuronal-like model obtained by the neuroblastoma cell line, SH-SY5Y, fully differentiated with all-trans-retinoic acid (RA). Aβ8-20 did not show any toxic activity. Even at higher concentrations and after 48 h exposure, Aβ8-20 was not significantly toxic for the cell, whose viability was comparable to that of controls (Figure 9).
**Figure 9:** *MTT assay of fully differentiated SH-SY5Y cells
treated for 48
h with increasing concentrations of Aβ8-20 (2, 5, and 10 μM). Bars represent means ± SEM of three
independent experiments with n = 3 each.*
The ability of Aβ8-20 to prevent oligomer toxicity was tested by incubating Aβ1-42 for 48 h at 4 °C alone or in combination with Aβ8-20, added at the molar ratios of 1:1; 1:5; and 1:10, previously used for dot blot analysis. After incubation, cells were exposed to oligomers at the final concentration of 2 μM, and the resulting toxicity was compared to the effects of the other co-incubated solutions (Figure 10). As expected, after 48 h of treatments, oligomers were toxic, affecting the cell viability by approximately a $30\%$ reduction. Unlike the lower (2 μM) concentration, both 1:5 and 1:10, corresponding, respectively, to 10 and 20 μM Aβ8-20, were able to counteract oligomer toxicity.
**Figure 10:** *MTT assay of fully differentiated SH-SY5Y cells treated
for 48
h with Aβ oligomers prepared in the presence or absence of different
molar ratios of Aβ8-20 (1:1; 1:5; and 1:10).
Samples were incubated at 4 °C under gentle rotation for 48 h.
Bars represent means ± SEM of three independent experiments with n = 3 each. ***P < 0.001 vs Ctrl by
one-way ANOVA + Tukey test and # <0.001 vs Aβ1-42 by one-way ANOVA + Tukey test.*
## Conclusions
Over the past years, the anti-aggregating properties of several natural compounds, synthetic derivatives, and peptides have attracted the attention of the scientific community as potential drugs for the treatment of AD. Furthermore, the growing evidence supporting a role for Aβ in neuronal physiology32,34,85 has highlighted the need to find novel potential drugs capable of blocking the progression of the disease and preserving the natural functions of Aβ, which are normally lost following its self-assembly. Although difficult due to their transient nature, oligomers represent the main targets to be addressed, while the activity of Aβ is still a matter of investigation and only partially clarified. To date, none of the proposed molecules have been successfully proven to halt and/or prevent the pathology.
In this paper, we present data about a newly synthesized fragment of Aβ, encompassing residues 8–20 (Aβ8-20), which has shown promising features to be considered in AD therapy.
Aβ8-20 freshly solubilized has a hydrodynamic diameter of ∼60 nm (Figure 4a) and assumes a random coil conformation which does not evolve into a β-sheet-rich structure over time (Figure 1b) according to the absence of any amyloid structure as evidenced by ThT assay under several conditions (Figure 1a). Thus, as expected, the lack of the hydrophobic C-terminus and residues Arg5 and Asp7 involved in fiber stabilization in the N-terminal region of the protein hampers the formation of the amyloid structure. Notably, this peptide is not toxic toward cells (Figure 9) up to a concentration of 10 μM. Aβ8-20 almost completely inhibits fiber formation of both Aβ1-40 and Aβ1-42 (Figure 2a,b, respectively, and Figure 3) which mostly remains in a random coil conformation over time (Figure 2a inset and Figure 2b inset, respectively). Moreover, the presence of Aβ8-20 significantly reduces the dimension of soluble aggregated species of Aβ1-40 over time (Figure 4b,c), suggesting a strong modulation of oligomer formation. This hypothesis was corroborated by MALDI experiments combined with dot blot analysis which clearly show that Aβ8-20 efficiently reduced the formation of the Aβ1-42 dimer and trimer species (Figure 5) in a concentration-dependent way (Figure 6). The presence of the self-recognizing KLVFF sequence suggests that Aβ8-20/Aβ1-42 interaction should occur in the N-terminal region as confirmed by the dot blot analysis (Figure 7) which shows that the interaction of Aβ8-20 with Aβ1-42 hinders the link between the 6E10 antibody and its target sequence (1–16) on Aβ. Limited proteolysis experiments confirm these data, indicating the 4–11 region as the most involved in the interaction (Figure 8). Finally, Aβ8-20 proved to completely protect, in a dose-dependent way, SH-SY5Y cells from the toxicity of Aβ1-42 oligomers (Figure 10).
The whole of our results clearly indicates Aβ8-20 as an interesting Aβ fragment that combines the required β-sheet breaker activity with promising features such as the lack of toxicity on neuronal cultures and the effective protective properties against oligomer-mediated cellular death.
These properties make the fragment a good candidate for a potential disease-modifying drug in AD therapy, encouraging a more in-depth study on the biological and molecular features of the peptide.
## Reagents
Aβ 1–40 (Aβ1-40) and Aβ 1–42 (Aβ1-42) were purchased from Bachem (Bubendorf, Switzerland) with a purity of ≥$95\%$. Amyloid fragments 8–20 (Aβ8-20) were synthesized and purified in our laboratory with a purity of ≥$98\%$. ThT, ascorbic acid, NaCl, and all other salts were purchased from Sigma-Aldrich (St. Louis, MO, USA). The MALDI matrices 3,5-dimethoxy-4- hydroxycinnamic acid (sinapinic acid, SIN) and α-cyano-4-hydroxycinnamic acid (α-CHCA) were purchased from Sciex and used without further purification. Bovine serum albumin, immunoglobulin G, and peptide mass standard calibration kits were purchased from Sciex. ACN and trifluoroacetic acid (TFA) (mass spectrometry-grade) were purchased from Fisher Scientific. Alpha-chymotrypsin was purchased from Sigma-Aldrich. All aqueous solutions were prepared using a Barnstead NanoPure system with a 0.2 mm membrane filter (Thermo Scientific).
## Amyloid Fragment 8–20 Synthesis
The Aβ 8–20 (Aβ8-20) were synthesized by a fully automated microwave-assisted solid-phase peptide synthesis following the Fmoc/tBu strategy on a liberty peptide synthesizer (CEM) starting from Rink amide AM resin (substitution 0.59 mmol/g). After the resin swelling in dimethylformamide (DMF), all orthogonally protected Fmoc amino acids were introduced according to the following N,N′-Diisopropylcarbodiimide (DIC)/Oxyma activation method consisting of [1] Fmoc deprotections ($20\%$ piperidine in DMF); [2] washes (3×) with DMF; [3] couplings with protected amino acids (5 equiv, 0.2 M in DMF), Oxyma pure (5 equiv, 1 M in DMF), and DIC (5 equiv, 0.5 M in DMF) prepared in separate bottles; and [4] washes (3×) with DMF. The following instrumental conditions were used for each coupling cycle: (a) 220 W, 65 °C, 30 s and (b) 25 W, 90 °C, 90 s. The instrumental conditions used for the deprotection cycle were (a) 220 W, 70 °C, 30 s and (b) 25 W, 75 °C, 30 s. After the last Fmoc deprotection, N-terminal acetylation was carried out with Ac2O (100 μL/200 mg of resin) in DMF (2 × 10 min). The cleavage of the peptide from the resin, with concomitant deprotection of acid-labile amino acid side-chains, was achieved by treatment of peptide–resin with TFA/Triisopropylhydrosilane/H2O (95:2.5:2.5 v/v/v, 10 mL) for 2.5 h at room temperature and with magnetic stirring. The resin was filtered, and the crude peptide was recovered by precipitation with freshly distilled diisopropyl ether. The purification of crude Aβ8-20 was carried out by preparative reversed-phase high-performance liquid chromatography (RP-HPLC) using a SHIMADZU LC-20A chromatography system equipped with an SPD-M20A photodiode array detector with detection at 222 and 254 nm. A Jupiter 10u Proteo C12 250 × 21.2 mm (90 Å pore size, AXIA Packed) column was used. The peptides were eluted at a flow rate of 10 mL/min according to the following protocol: from 0 to 3 min, isocratic conditions in $90\%$ solvent A (H2O containing $0.1\%$ TFA) followed by a 3 min linear gradient from 10 to $45\%$ B (CH3CN containing $0.1\%$ TFA) and then a 4 min linear gradient from 45 to $50\%$ B and finally 5 min isocratic conditions in $50\%$ B. Fractions containing the desired product were collected and lyophilized. The purity of the peptide was checked by analytical RP-HPLC using a Phenomenex Kinetex XB- C18 analytical column (pore size: 100 A, particle size: 5 μm, column length: 250 mm, and internal diameter: 4,60 mm). A linear gradient of ACN (containing $0.1\%$ TFA) and water (containing $0.1\%$ TFA) (90:10 water–acetonitrile to 0:100 water–acetonitrile over 15 min and at a flow rate of 1 mL min–1) was used. Sample identity was confirmed by MALDI-MS. Calculated mass 1632.81; observed: [M + H]+ = 1632.96; [M + Na]+ = 1653.87; [M + K]+ = 1670.85.
## Peptide Preparation
To prevent the presence of any preformed aggregates, Aβ1-40, Aβ1-42, and Aβ8-20 were initially dissolved in hexafluoroisopropanol (HFIP) at a concentration of 1 mg/mL and then lyophilized overnight. To be used for the experiments, the lyophilized powder was initially dissolved in 1 mM NaOH to obtain a stock solution with a final concentration of 100 μM. Each stock solution was used immediately after preparation by diluting it in the opportune buffer solution to reach the concentration needed for experiments.
## Thioflavin T Assay
Kinetics of Aβ1-40, Aβ1-42, and Aβ8-20 fiber formation was measured using ThT assay. Samples were prepared by diluting, in 3-(N-morpholino)propanesulfonic acid (MOPS) buffer, stock solution of Aβ8-20, Aβ1-40, Aβ1-42, or a combination to reach the final concentration. Copper, where present, was added from a stock solution at the indicated concentration. ThT was then added to a final concentration of 20 μM. Experiments were carried out in Corning 96-well non-binding surface plates. Time traces were recorded using a Varioskan (Thermo Fisher, Walham, MA) plate reader using a λex of 440 nm and a λem of 485 nm at 37 °C, shaking the samples for 10 s before each read. All ThT curves represent the average of three independent experiments.
## Circular Dichroism
CD spectra were acquired using a J-810 spectrometer (Jasco, Japan) under a constant flow of N2 at room temperature. The CD spectra were recorded for Aβ1-40 and Aβ1-42 (10 μM) monomers in the absence and presence of Aβ8-20 (10 μM) in the 1:1 molar ratio. The lyophilized samples were dissolved in 1 mM NaOH and then diluted to obtain a concentration of 10 μM for Aβ alone and the mixture. The CD measurements were carried out in aqueous solution (1 × 10–3 M MOPS buffer and 0.05 M NaF). A 0.5 cm path length quartz cuvette was used to acquire the far-UV CD spectra (190–260 nm), at a scan speed of 50 nm/min. 10 scans were collected. The measurements were performed in triplicate. The CD intensities were expressed as θ(mdeg).
## Transmission Electron Microscopy
Samples were prepared by incubating at 37 °C for 96 h 100 μL of phosphate buffer solutions 10 mM and 100 mM NaCL, pH 7.4 containing Aβ1-42 100 μM or Aβ1-42 100 μM: Aβ8-20 100 μM. After incubation, 3 μL of each sample was deposited onto a copper grid and allowed to adsorb for 5 min before the grid was rinsed with H2O twice. Samples were dried overnight and then stained with uranyl acetate. TEM micrographs were acquired using JEOL JEM 2010F using a 2 K × 2 K Gatan ORIUS camera. Samples have been observed in low magnification using the image formed inside the transmitted beam on the focal plane. This method is useful in low-magnification detection of weak objects, without any change in the focus configuration of the microscope. By switching between the out-of-focus image in the focal plane and the in-focus image in the image plane, it is possible to scan a large area of the grid with enhanced detection capability and easily come back to the normal image configuration.
## Dynamic Light Scattering
DLS measurements were carried out on a Zetasizer NanoZS90 Malvern Instrument (UK) equipped with a 633 nm laser at a scattering angle of 90° and at 37 °C. The samples of Aβ1-40 and Aβ8-20 (5 μM) were prepared under the same experimental conditions as those described above. Each measurement was performed three times.
## Mass Spectrometry
MALDI mass spectra were obtained using a 5800 MALDI-TOF/TOF mass spectrometer (Sciex) equipped with an automated single-plate sample-loading system, 1 kHz OptiBeam On-Axis Laser Nd/YAG 349 nm wavelength, delayed extraction (DE), two acceleration regions, a two-stage reflector mirror, and a 1000 MHz digitizer. The instrument was operated in the reflectron mode (m/range: 800–5000) and linear mid-molecular weight mode (m/range: 7000–20,000). When operating in the linear mode, the instrument’s acquisition parameters were set to optimize detection sensitivity of Aβ oligomers. In particular, the lowest possible laser intensity was used to minimize dissociation and enable the detection of Aβ1-42 oligomers. Moreover, the detection of high-MW oligomers was hindered by the presence of the monomers. Therefore, mass spectra were acquired starting at higher m/z values to enhance the sensitivity for the large-MW species. DE was applied, and the delay time was set according to the MW of the analytes to optimize resolution of their molecular ion. Mass spectra were acquired by averaging 300 to 600 shots. Sinapinic acid and α-CHCA were prepared by dissolving 10 mg of matrices in 1 mL of $50\%$ ACNe in $0.05\%$ TFA and 1 mL of $30\%$ ACN in $0.1\%$ TFA, respectively. Standard kits were used to calibrate the mass scale of the MALDI mass spectrometer. The peptide mass standard kit includes des-Arg1-Bradikynin, angiotensin I, Glu1-Fibrinopeptide B, adrenocorticotropic hormone (ACTH) (clip 1–17), ACTH (clip 18–39), and ACTH (clip 7–38), and it was used to cover a mass range of 800 to 4000 Da. Bovine insulin, E. coli thioredoxin, and horse apomyoglobin were used to cover a mass range from 4000 to 20,000 Da. Aβ1-42 samples were monomerized to remove any preformed aggregates using the procedure described above. Stock 1 solutions of Aβ1-42 and Aβ8-20 were prepared by dissolving 0.1 mg of each lyophilized peptide in HFIP (stock 1 = 1.5 mM). An opportune amount of each stock solution was diluted in phosphate buffer solution (5 mM, pH 7.8) to a concentration of 50 μM (dtock 2) and mixed to obtain stock solutions to be used for the experiments. The Aβ1-42 sample and the equimolar mixtures of Aβ1-42/Aβ8-20 were prepared from stock 2 solutions and stock 1 solution for a final concentration of 5 μM for limited proteolysis experiments and 100 μM for oligomer experiments, respectively. For limited proteolysis experiments, a fresh stock of α-chymotrypsin (1.0 mg/mL) was made with HCl (1 × 10–3 mol dm–3), and then, an appropriate volume of the enzyme stock solution was added to Aβ1-42 and Aβ1-42/Aβ8-20 samples for a final enzyme/substrate ratio of 1:200 w/w. Solutions were incubated at 25 °C for 10 min. For MALDI-TOF measurements, samples were analyzed using the dried-droplet preparation methods. In particular, 1 to 2 μL of the sample and 1 to 2 μL of matrix solution were mixed into a 0.5 mL tube, and 1 μL of this mixture was deposited on a stainless steel 384-well plate. The mixture samples on the plate were dried by evaporation of the solvent at room temperature till a thin microcrystalline layer of the sample/matrix occurred. All the samples were spotted in three different wells of the plate (triplicate), and five mass spectra were recorded for every spot. MS data were imported into freely available open-source software mMass (http://www.mmass.org). Mass spectra acquired for each sample (15 spectra) were averaged, and monoisotopic peaks were automatically picked. Theoretical m/z values of Aβ1-42, Aβ8-20, and peptides resulting from in silico digestion of amyloid protein were compared with the m/z values assigned to experimental mass spectra. Peptides matching successfully, within a tolerance of 0.05 Da, were annotated. Moreover, mass spectra were exported as a peak list and processed using Excel (Microsoft) software to evaluate the $95\%$ confidence interval of each signal intensity assigned.
## Dot Blot Analysis
The Aβ1-42 sample (100 μM) was incubated for 48 h at 4 °C under gentle rotation in the presence or absence of different molar ratios of Aβ8-20 (1:1; 1:5; and 1:10). Then, samples were spotted onto a nitrocellulose membrane. The membrane was blocked with Odyssey blocking buffer (LiCor, Biosciences) at room temperature for 1 h. After blocking, the membrane was probed overnight at 4 °C and with gentle shaking with the following antibodies: anti-Aβ N-terminal 1–16 mouse monoclonal antibody 6E10 (1:100) (BioLegend), anti-Aβ 17–24 mouse monoclonal antibody 4G8 (1:100) (BioLegend), or anti-oligomer A11 rabbit polyclonal antibody (1:100) (Invitrogen, Thermo Fisher). Finally, the membrane was repeatedly washed and exposed to the anti-mouse antibody labeled with the IRDye secondary antibody (1:20.000 Li-Cor Biosciences) for 45 min at room temperature. Hybridization signals were detected with the Odyssey CLx Infrared Imaging System (LI-COR Biosciences).
## Western Blot Analysis
Aβ1-42 (100 μM) alone and in combination with different molar ratios of Aβ8-20 (1:1; 1:5; and 1:10) was incubated at 4 °C for 48 h to form Aβ oligomers. After incubation, the amount and size of Aβ aggregates were determined by Western blot analysis. A volume of 25 μL of each unheated sample was loaded onto a precast Bis-Tris gel (Bolt 4–$12\%$, Life Technologies) with 2-morpholin-4-yl ethanesulfonic acid. Samples were transferred onto a nitrocellulose membrane (0.2 mm, Hybond ECL, Amersham Italia) by using a wet transfer unit Mini Blot Module (Life Technologies). Membranes were blocked in Odyssey blocking buffer (Li-COR Biosciences) and incubated at 4 °C overnight with anti-Aβ N-terminal 1–16 mouse monoclonal antibody 6E10 (1:500) (BioLegend). A secondary goat anti-mouse antibody labeled with infrared dye (1:20.000) was used at room temperature for 45 min. Hybridization signals were detected with the Odyssey CLx infrared imaging system (LI-COR Biosciences, Lincoln, NE).
## Cell Culture and MTT Assay
The neuroblastoma cell line, SH-SY5Y, was maintained in Dulbecco’s modified Eagle’s medium (DMEM)-F12 (Gibco, Thermo Fisher) supplemented with $10\%$ heat-inactivated (HI) fetal calf serum (Gibco, Thermo Fisher), 100 mg/mL penicillin and streptomycin (Gibco, Thermo Fisher), and 2 mM l-glutamine at 37 °C and $5\%$ CO2. Two weeks before experiments, 5 × 103 cells were plated on 96-well plates in DMEM-F12 with $5\%$ HI fetal calf serum. The percentage of serum was gradually decreased until it was $1\%$ of the total. All-trans-RA (Sigma), 5 μM, was used to promote neuronal differentiation, and medium-containing RA was changed every 3 days. Fully differentiated SH-SY5Y cells were treated with increasing concentrations of Aβ8-20 (2, 5, and 10 μM). After 48 h treatment, cultures were incubated with MTT (5 mg/mL) for 2 h at 37 °C and then lysed with dimethyl sulfoxide (DMSO), and the formazan production was evaluated in a plate reader through the absorbance at 570 nm.
## Anti-Oligomerization Activity
To prepare Aβ1-42 oligomers, 1 mg of Aβ1-42 (HFIP-treated) was first dissolved in 5 mM DMSO. A solution of 100 μM Aβ1-42 in ice-cold DMEM F-12 without phenol red was prepared and allowed to oligomerize for 48 h at 4 °C according to the Lambert protocol86 with some modifications as previously described.32 To evaluate the ability of Aβ8-20 to inhibit toxic Aβ oligomerization, Aβ1-42 was incubated in the presence or absence of different molar ratios of Aβ8-20 (1:1; 1:5; and 1:10). After 48 h incubation at 4 °C under gentle rotation, Aβ1-42/Aβ8-20 samples were applied to the differentiated SH-SY5Y cells at the final concentration of 2 μM Aβ1-42. The ability of Aβ8-20 to prevent Aβ oligomer formation and toxicity was evaluated by measuring cell viability after 48 h treatment by MTT assay.
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|
---
title: Prediction and prevention of preeclampsia
authors:
- Fernando Maia Peixoto-Filho
- Fabricio da Silva Costa
- Sergio Kobayashi
- Patricia El Beitune
- Adriana Gualda Garrido
- Anselmo Verlangieri Carmo
- Guilherme de Castro Rezende
- Heron Werner Junior
- Joffre Amin Junior
- Jorge Roberto Di Tommaso Leão
- Luciano Marcondes Machado Nardozza
- Luiz Eduardo Machado
- Manoel Alfredo Curvelo Sarno
- Pedro Pires Ferreira Neto
- Eduardo Becker Júnior
journal: RBGO Gynecology & Obstetrics
year: 2023
pmcid: PMC10021002
doi: 10.1055/s-0043-1763495
license: CC BY 4.0
---
# Prediction and prevention of preeclampsia
## Background
Preeclampsia (PE) is an important cause of maternal and perinatal mortality worldwide. It represents 10-$15\%$ of direct maternal deaths and $99\%$ of these deaths occur in low-income countries. 1 A systematic review by Abalos et al. in 2013, 2 showed an incidence ranging from $1.2\%$ to $4.2\%$ for PE and $0.1\%$ to $2.7\%$ for eclampsia. The highest rates were identified in regions of lower socioeconomic development. In Brazil, the incidence of PE ranges from $1.5\%$ to $7\%$, 2 3 that of preterm PE is $2\%$ 3 and of eclampsia is $0.6\%$. 2 However, these statistics may be underestimated and vary according to the region studied.
Although the pathogenesis of PE remains unknown, the most accepted theory suggests a two-stage process. In the first stage, there would be a superficial invasion of the trophoblast, resulting in inadequate remodeling of the spiral arteries, which would lead to the second stage that involves the maternal response to endothelial dysfunction and an imbalance between angiogenic and antiangiogenic factors, resulting in the clinical features of this condition. 4 5 6 Although the placenta plays an essential role in the development of PE, evidence suggests that the maternal cardiovascular system contributes significantly to the disorder. 7 According to the International Society for the Study of Hypertension in Pregnancy (ISSHP), PE is defined as systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure of ≥90 mmHg on at least two occasions, measured at four-hour intervals in previously normotensive women, and is accompanied by one or more of the following new-onset conditions after 20 weeks' gestation: [1] proteinuria, [2] evidence of other maternal organ dysfunction, or [3] uteroplacental dysfunction. With regard to classification, PE can still be subclassified into: [1] early PE (delivery < 34+0 weeks' gestation); [2] preterm PE (delivery < 37+0 weeks' gestation); [3] late-onset PE (delivery ≥ 34+0 weeks' gestation); [4] full-term PE (delivery ≥ 37+0 weeks' gestation). 8 The capacity of screening tests, the management and maternal and perinatal mortality will vary according to this classification. It is important to identify women at higher risk for PE so they can receive preventive measures and greater maternal and fetal surveillance during pregnancy. 6
## What are the parameters and strategies for predicting preeclampsia?
The screening strategies for PE described in the literature vary according to the parameters used, the pre-test risk, the stratification of the result and the gestational age at which the screening is performed. However, there is consensus in the literature that no single-parameter screening test has shown to adjust the preexisting maternal risk of PE with sufficient specificity and sensitivity for clinical use. As with screening for aneuploidies, the best screening strategies for PE involve several parameters in combination. 9 Next, we describe the main factors used in these algorithms, alone and in combination.
## Maternal characteristics
The use of information from maternal pathological history and gestational history in the assessment of risk for PE offers a reasonable performance and is still proposed in some national guidelines. The Institute for Health Care and Clinical Excellence (NICE) PE screening guidelines were investigated in a prospective study, 10 describing the possibility of a detection rate of $90\%$ for preterm PE and $89\%$ for term PE, at the expense of a $64.1\%$ false-positive rate. The authors demonstrate that these same factors combined in an algorithm derived from multivariate analysis produce a detection rate of $37\%$ for early-onset PE and $28.9\%$ for late-onset PE, and a $5\%$ false-positive rate. The limitations of using maternal factors alone to predict PE in primigravidae were well illustrated in the prospective multicenter SCOPE study in which an algorithm was developed; it detected $37\%$ rate of PE for a $10\%$ false-positive rate and $61\%$ for a $25\%$ false-positive rate. 11
## Biomarkers
A wide range of potential biomarkers for PE has been identified in the maternal circulation, reflecting the complex pathogenesis of this condition. 12 However, no biomarker has demonstrated sufficient predictive value to be of clinical utility if used alone. 13 Instead, they appear to be more valuable in combination with other parameters.
## Mean blood pressure
Mean arterial pressure (MAP) is calculated by dividing the sum of systolic blood pressure with twice the diastolic blood pressure divided by three. A prospective study of 5,590 women with singleton pregnancies identified that a combination of maternal risk factors and MAP measured at 11-14 weeks' gestation was more predictive of PE than its use alone. 14 *In this* study, the combination of maternal history and PAM identified $62.5\%$ of PE cases at a $10\%$ false positive rate. The combination of these two factors is currently the basis of virtually all PE screening strategies.
## Doppler velocimetry of the uterine arteries
The abnormal placentation that characterizes PE is associated with increased resistance in the uteroplacental circulation. Based on this premise, the analysis of uterine artery Doppler velocimetry in the risk assessment for PE has been extensively studied, initially in the second trimester and later in early pregnancy. Doppler velocimetry evidence of this resistance includes a qualitative and quantitative assessment of flow. In the qualitative assessment, a protodiastolic notch is observed in the waveform. Quantitative assessment demonstrates the increase in the pulsatility index (PI) of this vessel. 15 Current risk calculation algorithms preferentially use quantitative assessment because the PI value is a continuous variable objectively measured. 16 The ability to predict PE using uterine artery Doppler velocimetry is quite limited, and the performance of this parameter is better in the second trimester and in the identification of early-onset PE. First-trimester uterine artery Doppler sensitivity in predicting PE was $26\%$ ($95\%$ confidence interval [CI]: 24-29) and specificity was $91\%$ ($95\%$ CI: 91-91) in a meta-analysis involving 11 studies. 17 Studies have suggested that uterine artery Doppler may be more predictive if performed sequentially in the first and second trimester. 18 However, such an approach would prevent the timely early initiation of prophylaxis.
## Biochemical markers
Several biochemical markers have been described in the prediction of PE, but only two (placental growth factor [PlGF] and pregnancy-associated plasma protein A [PAPP-A]) have shown some discriminatory power and have been used. The PlGF is a glycosylated dimeric glycoprotein secreted by trophoblast cells and part of the angiogenic vascular endothelial growth factor (VEGF) family. This isolated biomarker has a detection rate of $55\%$ and $33\%$ for the identification of early- and late-onset PE, respectively for a false-positive rate of $10\%$. 19 The PAPP-A is an insulin-like growth factor binding protein of the metalloproteinase secreted by the syncytiotrophoblast that plays an important role in placental growth and development. A maternal concentration of PAPP-A below the 5 th percentile is associated with the risk of developing PE, with a detection rate of $16\%$ and a false-positive rate of $8\%$. 20
## Multiparametric tests
A systematic review evaluating PE screening models indicated that among 16 models validated in four studies, only five (four first trimester models and one second trimester model) were considered to have statistically acceptable discriminatory characteristics. 21 The use of a multivariate logistic regression algorithm, a combination of maternal factors, MAP, uterine artery PI, maternal serum PAPP-A and PlGF at 11-13 weeks' gestation allowed the detection of rates of $93\%$ and $36\%$ for the prediction of early- and late-onset PE, respectively, for $5\%$ false positives. 22 23 The largest study to date on the development of the first-trimester combined test using the concurrent risk model was reported by Tan et al. 24 *In this* study, from a 1 in 100 risk cutoff for PE in white women, the positive screening rate was $10\%$ and the detection rates of preterm and full-term PE were $69\%$ and $40\%$, respectively.
## Validation of models in the Brazilian population
The Fetal Medicine Foundation (FMF) prediction models were prospectively evaluated in several countries, with similar results, including Brazil, 25 and were recently approved by the International Federation of Gynecology and Obstetrics (FIGO) in the screening of PE. 26 A study conducted in Brazil using the FMF model based on maternal characteristics and PAM showed a detection rate of $67\%$ of preterm PE cases, at a false positive rate of $10\%$, a positive predictive value of $17\%$ and negative predictive value of $99\%$. 3 The performance of universal screening is important, always using a risk calculation model, but the parameters adopted will depend on the availability of each service.
## When is ASA indicated for the prevention of preeclampsia?
Using the FMF combined screening algorithm, the ASPRE study proposed a risk cutoff of 1:100 to define the high-risk group, which led to a detection rate of $77\%$ for a positive screening rate of $11\%$. 27
## Is the use of ASA safe in pregnancy?
The use of ASA during pregnancy appears safe for both the mother and the fetus. Treatment with ASA did not show an increased risk of congenital malformations and had no negative effect on fetal development or bleeding complications in the neonatal period. 35 36 37 Despite side effects such as minor vaginal bleeding and gastrointestinal symptoms, which occur in approximately $10\%$ of users, there is no evidence of an increased risk of major maternal bleeding or association with placental abruption. 27 Concerns about premature closure of the fetal ductus arteriosus have never been confirmed. However, there is a lack of data on possible side effects and long-term outcomes when ASA is prescribed on a large scale to low-risk patients. 27
## When to start ASA for patients at high risk for preeclampsia?
Most trials using ASA to prevent placental complications started treatment at or after 12 weeks' gestation. There is current convincing evidence that the strongest reduction in premature PE is achieved with initiation of therapy before 16 weeks' gestation. 38 However, the incidence of PE can still be positively influenced when ASA is started only after 16 weeks' gestation and given its safety profile, high-risk women who present for antenatal care after 16 weeks may still benefit from prophylaxis. Note that this aspect has been controversially discussed in the literature, and the maximum prophylactic effect seems to occur when ASA is started early. 39
## What is the optimal dose of ASA to prevent preeclampsia?
The most commonly evaluated daily doses of ASA range from 60 to 162 mg. However, in vitro and in vivo studies have shown that the optimal dose is ≥ 100 mg per day. 38 40 It also appears that there is a clear dose-dependent effect. In a study published by Caron et al., 41 at a daily dose of 81 mg, 121 mg, and 162 mg, $30\%$, $10\%$, and $5\%$ of subjects were classified as non-responders, respectively. Therefore, doses below 100 mg should be avoided, 27 although direct comparisons of different dose regimens in randomized trials are not available. In Brazil, ASA at a dose of 100 mg is widely available and inexpensive, hence an interesting option is the use of one and a half ASA pill to prevent PE in our country. It is important to emphasize the need to discard the residual portion of the tablet, as its use in the following day is not supported in the literature.
## When should patients stop taking ASA?
In most RCTs and meta-analyses, a significant increase in major bleeding complications has not been found and in the absence of other anticoagulants, neuraxial blockade is not contraindicated. 27 42 The ASPRE study discontinued ASA use at 36 weeks' gestation, but treatment until delivery is considered safe. There are no studies evaluating if stopping prophylaxis at an earlier gestational age would have similar efficacy.
## What to do with patients at high risk for preeclampsia who report a known allergy to ASA?
In patients with a known urticarial allergic reaction to ASA or other contraindications such as bleeding disorders or severe asthma, ASA should not be used. Patients at high risk for PE who cannot take ASA may benefit from calcium supplementation or LMWH in specific cases. These interventions should be considered on a case-by-case basis after appropriate counseling and risk-benefit assessment.
## Final considerations
Preeclampsia is a condition that results in high maternal and perinatal morbidity and mortality worldwide, with a more severe impact on developing countries such as Brazil. Considering the availability of efficient tools for early screening and low-cost prophylaxis, we recommend: [1] universal screening of PE in the first trimester using a risk calculation model; [2] use of ASA at a dose ≥ 100 mg for PE prophylaxis in patients with high-risk screening.
National Commission Specialized in ultrasound in GO of the Brazilian Federation of Gynecology and Obstetrics Associations (Febrasgo) President: Eduardo Becker Júnior Vice-president:
Heron Werner Júnior Secretary: Sergio Kobayashi Members:
Adriana Gualda Garrido Anselmo Verlangieri Carmo *Fabricio da* Silva Costa Fernando Maia Peixoto Filho
Guilherme de Castro Rezende Joffre Amim Junior Jorge Roberto Di Tommaso Leao Luciano Marcondes Machado Nardozza
Luiz Eduardo Machado Manoel Alfredo Curvelo Sarno Patricia El Beitune Pedro Pires Ferreira Neto
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18. Napolitano R, Melchiorre K, Arcangeli T, Dias T, Bhide A, Thilaganathan B. **Screening for pre-eclampsia by using changes in uterine artery Doppler indices with advancing gestation**. *Prenat Diagn* (2012) **32** 180-4. DOI: 10.1002/pd.2930
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24. Tan M Y, Syngelaki A, Poon L C, Rolnik D L, O'Gorman N, Delgado J L. **Screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks' gestation**. *Ultrasound Obstet Gynecol* (2018) **52** 186-95. DOI: 10.1002/uog.19112
25. Lobo G A, Nowak P M, Panigassi A P, Lima A I, Araujo Júnior E, Nardozza L M. **Validation of Fetal Medicine Foundation algorithm for prediction of pre-eclampsia in the first trimester in an unselected Brazilian population**. *J Matern Fetal Neonat Med* (2019) **32** 286-92. DOI: 10.1080/14767058.2017.1378332
26. Poon L C, Shennan A, Hyett J A, Kapur A, Hadar E, Divakar H. **The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: a pragmatic guide for first-trimester screening and prevention**. *Int J Gynaecol Obstet* (2019) **145** 1-33. DOI: 10.1002/ijgo.12802
27. Rolnik D, Wright D, Poon L C, O'Gorman N, Syngelaki A, de Paco Matallana C. **Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia**. *N Engl J Med* (2017) **377** 613-22. DOI: 10.1056/NEJMoa1704559
28. Davenport M H, Ruchat S M, Poitras V J, Jaramillo Garcia A, Gray C E, Barrowman N. **Prenatal exercise for the prevention of gestational diabetes mellitus and hypertensive disorders of pregnancy: a systematic review and meta-analysis**. *Br J Sports Med* (2018) **52** 1367-75. DOI: 10.1136/bjsports-2018-099355
29. Grobman W A, Rice M M, Reddy U M, Tita A T, Silver R M, Mallett G. **Labor induction versus expectant management in low-risk nulliparous women**. *N Engl J Med* (2018) **379** 513-23. DOI: 10.1056/NEJMoa1800566
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42. Askie L M, Duley L, Henderson-Smart D J, Stewart L A. **PARIS Collaborative Group. Antiplatelet agents for prevention of pre-eclampsia: a meta-analysis of individual patient data**. *Lancet* (2007) **369** 1791-8. DOI: 10.1016/S0140-6736(07)60712-0
|
---
title: 'Identifying Plasma and Urinary Biomarkers of Fermented
Food Intake and Their Associations with Cardiometabolic Health in
a Dutch Observational Cohort'
authors:
- 'Katherine
J. Li'
- Kathryn J. Burton-Pimentel
- Elske M. Brouwer-Brolsma
- Carola Blaser
- René Badertscher
- Grégory Pimentel
- Reto Portmann
- Edith J. M. Feskens
- Guy Vergères
journal: Journal of Agricultural and Food Chemistry
year: 2023
pmcid: PMC10021015
doi: 10.1021/acs.jafc.2c05669
license: CC BY 4.0
---
# Identifying Plasma and Urinary Biomarkers of Fermented
Food Intake and Their Associations with Cardiometabolic Health in
a Dutch Observational Cohort
## Abstract
Identification of food intake biomarkers (FIBs) for fermented foods could help improve their dietary assessment and clarify their associations with cardiometabolic health. We aimed to identify novel FIBs for fermented foods in the plasma and urine metabolomes of 246 free-living Dutch adults using nontargeted LC–MS and GC–MS. Furthermore, associations between identified metabolites and several cardiometabolic risk factors were explored. In total, 37 metabolites were identified corresponding to the intakes of coffee, wine, and beer (none were identified for cocoa, bread, cheese, or yoghurt intake). While some of these metabolites appeared to originate from raw food (e.g., niacin and trigonelline for coffee), others overlapped different fermented foods (e.g., 4-hydroxybenzeneacetic acid for both wine and beer). In addition, several fermentation-dependent metabolites were identified (erythritol and citramalate). Associations between these identified metabolites with cardiometabolic parameters were weak and inconclusive. Further evaluation is warranted to confirm their relationships with cardiometabolic disease risk.
## Introduction
Accurate dietary assessment is crucial for detecting potential associations between diet and health. To date, many epidemiological studies still predominantly rely on self-reported dietary assessment methods, such as food frequency questionnaires (FFQ) and 24 h food recalls, which heavily depend on the memory and dedication of the participants.1,2 As such, they are prone to multiple sources of measurement errors such as underreporting, inaccurate portion size estimation, and imprecision of food composition databases. Such measurement errors can reduce study power and miss detecting potential associations and may also lead to spurious findings.3,4 Additionally, to capture the increasing diversity and complexity of modern diets, self-report methods require extensive food lists, which is burdensome for both participants and researchers. To address these limitations, food intake biomarkers (FIBs) have emerged as a more objective measure of dietary intake. Depending on their specificity, FIBs can be single compounds or a multimarker panel consisting of a combination of different compounds.5 Recent advances in nutritional metabolomics have led to the identification of numerous candidate FIBs linked to the ingestion of a food, food group, or a dietary pattern.3,6 However, FIBs for many foods in the diet have yet to be explored and validated—including fermented foods.
Fermented foods have been consumed since the beginning of human civilization and comprise up to $40\%$ of the human diet.7,8 The fermentation process not only improves the shelf life and organoleptic qualities of food, but it can also impart novel nutritional qualities that could improve human health.9,10 A number of dietary intervention and epidemiological studies have suggested that the consumption of fermented foods positively affects cardiometabolic health, including weight maintenance, glucose metabolism, and cardiovascular health,9,11−14 but the evidence is inconclusive. Thus, identification and validation of FIBs for fermented foods could improve the accuracy of dietary assessment and support further studies in obtaining more conclusive diet–health associations. Additionally, FIBs could also help elucidate the mechanisms of action that underpin the purported health benefits of fermented foods.
We previously conducted a systematic review of FIBs of fermented foods consumed worldwide and found several candidate FIBs at the food level, food group level, and/or fermentation level for several fermented foods, including wine, beer, bread, cocoa, coffee, postfermented tea, fermented soy, cheese, and yoghurt.15 The majority of these FIBs were identified in postprandial studies with a small number of participants, and their relevance needs to be explored in free-living populations with complex, uncontrolled diets.16 In the current work, we aimed to identify further FIBs of fermented foods consumed in The Netherlands by analyzing the plasma and urine metabolomes of a Dutch adult cohort using LC–MS and GC–MS. By utilizing a larger, free-living population, we expected the FIBs that emerge would be considered to be the most powerful and reliable indicators of habitual fermented food intake. In addition, we examined associations between the identified FIBs and several cardiometabolic risk parameters and composite risk scores.
## Study Population
The Nutrition *Questionnaires plus* (NQplus) study is a prospective cohort study of 2048 Dutch men and women (20 to 70 years) with the aim to gather extensive data on participant demographics, anthropometrics, lifestyle, medical history, and cardiometabolic health outcomes.17,18 Participants were recruited between June 2011 and February 2013. All measurements were performed according to a standardized protocol by trained research personnel. The study was approved by the ethical committee of Wageningen University and Research (protocol number NL34775.081.10) and conducted in agreement with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to the start of the study.
Metabolomics analyses were performed on a subcohort of NQplus participants ($$n = 531$$; $$n = 485$$ with plasma samples and $$n = 492$$ with urine samples) (herein referred to as the “metabolomics subcohort”). These participants were initially selected based on having a biosample collected within 14 days of completing either a FFQ or a 24 h recall. The FFQ was preferred over the 24 h recalls since it reflects more precisely the intake on any given day and is less sensitive to fluctuations in daily intake. Thus, for the selection of the most discriminant metabolites for identification, we focused the analyses on $$n = 246$$ unique participants who had a biosample collected within 14 days of completing a FFQ ($$n = 228$$ with plasma samples, and $$n = 216$$ with urine samples) (herein referred to as the “identification subcohort”, which is contained within the “metabolomics subcohort”). This criterion ensured that biosample collection occurred within the FFQ reference period of 1 month. To explore the stability of the FIBs with increasing time between biosample collection and FFQ completion, additional correlation analyses were conducted among participants with biosample collection within ±30 days ($$n = 273$$), ±90 days ($$n = 354$$), and ±180 days ($$n = 501$$) of completing the FFQ, as well as within all 531 participants in the metabolomics subcohort.
## Food Frequency Questionnaire and Levels of Fermented Food Intake
A detailed description of the validated, self-administered, semiquantitative 216-item FFQ used to assess habitual dietary intake has been reported previously.17,18 In, participants completed the FFQ online and answered questions relating to frequency by selecting 1 of 10 frequency categories ranging from “never” to “6–7 days per week”. Portion sizes were estimated using commonly used household measures. Total food intake (in g/d) was determined by multiplying consumption frequency by portion size as defined in the Dutch food composition tables [2011].19 A total of 39 food items were classified as fermented, using criteria described previously8 (Table S1). Most of the fermented foods and food groups in the FFQ have already been judged to have a good agreement with the intakes reported in 24 h recalls.8 Only fermented foods and food groups that achieved “adequate” to “good” agreement in the validation study8 (which is important for determining the reliability of self-reported intakes) and were consumed by at least a third of the population (which is important for the detection of potential FIBs in biosamples and selection of the most relevant FIBs) were included in the current analyses. These included fermented beverages (coffee, beer, and wine), fermented cereals/grains (white bread and whole-grain bread), fermented dairy (cheese and yoghurt), and cocoa-based products.
To facilitate the selection of FIBs that reflect the absolute dry weight of the different fermented foods considered within the fermented food groups (beverages, cereals/grains, cocoa-based products, and dairy), we further calculated the g dry matter/day intakes for each fermented food by subtracting the water weight of each food (in g/day) from the total intake (in g/day) (water weight determined from the Dutch food composition tables). Subsequently, energy adjustment was performed on all individual fermented foods as well as fermented food groups using the commonly used residual method.20 All energy-adjusted fermented food intakes (in g/day and g dry matter/day) were then divided into tertiles representing the low (T1), mid (T2), and high (T3) levels of intake.
## Cardiometabolic
Health Parameters
Ten cardiometabolic health parameters collected at the baseline were included in the current analysis.18 Height was determined using a stadiometer (SECA, Germany, nearest 0.1 cm), and weight was determined using a digital weighing scale (SECA, nearest 0.1 kg). The BMI was calculated by dividing weight (in kg) by height (in m2). Waist circumference was measured twice using a nonflexible measuring tape (SECA 201, nearest 0.5 cm) and averaged. Enzymatic methods21 were applied to assess fasting plasma glucose, total cholesterol, HDL cholesterol, and serum triglycerides using a Dimension Vista 1500 automated analyzer (Siemens, Erlangen, Germany) or Roche Modular P800 chemistry analyzer (Roche Diagnostics, Indianapolis, USA). Plasma LDL cholesterol was calculated with the Friedewald equation.22 Hemoglobin A1c (HbA1c) concentrations in whole blood were determined by HPLC using an ADAMS A1c HA-8160 analyzer (A. Menarini Diagnostics). Systolic and diastolic blood pressure were measured using a digital blood pressure monitor (IntelliSense HEM-907, Omron Healthcare, USA); the first measurement was discarded, and the remaining (up to 6) measurements were averaged. Participants were classified as having hypertension, suboptimal cholesterol, or type II diabetes based on the cutoffs and definitions described in relevant guidelines of the European Society of Cardiology/European Atherosclerosis Society (ESC/EAS)23−25 and having metabolic syndrome based on the harmonized guidelines of the International Diabetes Federation (IDF) et al.26 Two composite risk scores were also determined as previously described,27 consisting of a continuous metabolic syndrome (MetS) score using summed age- and sex-adjusted standardized residuals (z-scores) of individual MetS parameters28−30 and the European Systematic COronary Risk Evaluation (SCORE)31,32 evaluating 10 year risk of fatal cardiovascular disease.
## Covariates
All covariate data relevant to the current work (age, sex, education level, smoking status, physical activity, alcohol consumption, and dietary intake) were collected via questionnaires.18 For educational level, participants with no education or primary/lower vocational education were classified under “low”, participants who completed lower secondary or intermediate vocational education were classified under “intermediate”, and participants who completed higher secondary or higher vocational education, or university, were classified under “high”. A “smoker” was defined as a current smoker and former smoker who quit >35 years old and a “nonsmoker” as never smoker and former smoker who quit <35 years old.33 Information on the participants’ usual physical activity over the past 4 weeks was obtained using the validated Activity Questionnaire for Adults and Adolescents (AQuAA), which provides the time spent on sedentary-, light-, moderate-, and vigorous-intensity activities in min/week.34 Intake levels of alcohol and different foods were assessed by a FFQ, as described above.
## LC–MS Metabolomics Analysis
EDTA plasma and 24 h urine samples collected for NQplus were used for metabolomics analyses. All samples were thawed on ice and kept at 4 °C during analysis. Prior to LC–MS analysis, phospholipids were removed from plasma samples to limit ion suppression using a Phree filter (Phenomenex Inc., Torrance, CA). Urine samples were normalized based on the specific gravity as determined by the refractive index (refractometer RE40, Mettler Toledo, Switzerland), as described previously.35,36 LC–MS metabolomics analysis was performed using an UltiMate 3000 RS UPLC system (Thermo Fisher Scientific, Waltham, MA) with a Waters Acquity UPLC HSS T3 column (length 150 mm, diameter 2.1 mm, and particle size 1.8 μm), coupled with a maXis 4G+ quadrupole time-of-flight mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany). A gradient was run from $5\%$ to $95\%$ of mobile phase A within 15 min at 0.4 mL/min. Mobile phase A consisted of Milli-Q water with $0.1\%$ formic acid, and mobile phase B consisted of acetonitrile with $0.1\%$ formic acid. The column was heated to 35 °C with a postcolumn cooler set to 25 °C. The resulting system pressure was ∼600 bar, dependent on the actual composition of the mobile phase at the specific time. The mass spectrometer ESI was operated in positive ion mode, and spectra were recorded from 75 to 1500 m/z. Collision-induced dissociation was performed using energies from 20 to 70 eV. 5 μL of filtered plasma or normalized urine from each sample was injected once in a randomized sequence. Quality control (QC) pools were prepared from plasma or urine samples by mixing all samples of each sample type at equal volumes. QC samples were injected at five sample intervals for signal drift correction. Blanks (consisting of ultrafiltered LC–MS-grade water) were also injected at the beginning and end of each batch for the detection of contaminants.
Progenesis QI (v.2.3.6198.24128, NonLinear Dynamics Ltd., Newcastle upon Tyne, United Kingdom) was used for retention time correction, peak-picking, deconvolution, adducts annotation, and normalization (default automatic sensitivity and without minimum peak width). All solvents and reagents were purchased from Sigma-Aldrich Chemie GmbH (Buchs, Switzerland).
## GC–MS
Metabolomics Analysis
Plasma and urine samples were prepared for GC–MS analysis as described previously for serum37 and urine.38 Specifically, for each 100 μL plasma sample, 50 μL of an internal standard solution (labeled d-sucrose, 13C12, $98\%$, Cambridge Isotope Laboratories, Inc., Cambridge, UK, c ≈ 0.16 mg/mL in water) was added, followed by precipitation with 300 μL of cold methanol, centrifugation, transfer of supernatant (370 μL), and drying using a vacuum centrifuge. Urine samples were normalized prior to analysis using the refractive index methods described above for the LC–MS analysis. For each 100 μL urine sample, 50 μL of an internal standard solution (labeled d-sucrose) was added and dried using a vacuum centrifuge. The plasma and urine samples further underwent a two-step derivatization (methoximation with O-methylhydroxylamine hydrochloride followed by silylation with N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA)) and were analyzed by GC–MS 7890B/MS5977A (Agilent Technologies, Santa Clara, CA, US) with a CombiPAL autosampler (CTC-Analytics AG, Zwingen, Switzerland) and a DB-5 ms fused silica capillary column (60 m, 0.25 mm i.d., 0.25 μm film thickness, Agilent Technologies, Basel, Switzerland). The samples were injected using a multimode injector according to the following temperature program: initially 90 °C, a heating rate of 900 °C/min until 280 °C, held for 5 min and cooled at a rate of −30 °C/min, and maintained at 250 °C. The oven program was as follows: initial temperature 70 °C for 2 min, increase up to 160 °C at a rate of 5 °C/min, increase to 300 °C at a rate of 10 °C/min, which was held for 36 min, equilibration time 1 min. The MS detection mass ranged from 28.5 to 600 Da, the MS source temperature was 230 °C, and the MS Quad temperature was 150 °C. Electron ionization was performed with 70 eV. Each batch was initiated by three injections of QC samples for equilibration and after every fifth plasma sample, a fresh QC was injected. At the start and end of each batch, a blank sample (Milli-Q water) was included. QC samples and blank samples underwent the same sample preparation as plasma samples.
Agilent data files acquired from GC–MS analysis were deconvoluted and converted into CEF files using Agilent MasshunterProfinder (Agilent Technologies, Santa Clara, US). Data files were further processed in Agilent Mass Profiler Professional (Agilent Technologies, Santa Clara, U.S.) to perform alignment and compound identification. Features with retention time before 10 min (reagents region) were removed. All markers selected based on deconvoluted data were further evaluated using a targeted approach in order to optimize integration. Using RI, quantifier and qualifier ion retrieved from deconvoluted data, the suggested markers were analyzed in MassHunter Quantitative Analysis (Agilent Technologies, Santa Clara, US). The peak integration was checked in each sample individually. Responses from the quantifier ion of marker compounds were normalized with the response of the quantifier ion of internal standard [labeled d-sucrose (ion 220)].
## Metabolomics
Data Preprocessing
The dataset was corrected to account for signal drift and reduced via multiple filtering steps to remove features with poor repeatability and potential contaminants (Figure 1). Principal component analyses (PCAs) of the QCs for both LC–MS and GC–MS present the relative stability of the analysis (Figure S1). For LC–MS, the QC-based robust locally estimated scatterplot smoothing signal correction method was applied for signal drift correction39 using R (v.3.6.3).40 Features resulting from LC–MS analysis were removed if they had poor repeatability (detected in less than one-third of samples), a relative standard deviation > $30\%$ in the QC samples, and a median in the QC samples that was <3 times higher than the median calculated for the blanks. For GC–MS, features detected in less than one-third of samples were removed (features that had high levels in blanks or originated from the GC column were removed after identification to ensure all features captured during automatic detection are retained and further inspected for relevance). Exploratory analyses were performed, and metabolomics sample outliers, defined as observations clearly falling outside Hotelling’s T2 tolerance eclipse ($95\%$ confidence interval) in the PCA score plot, were identified and excluded ($$n = 23$$ LC–MS plasma, $$n = 3$$ LC–MS urine, and $$n = 4$$ GC–MS plasma, $$n = 3$$ GC–MS urine) (Figure S2).
**Figure 1:** *GC–MS and LC–MS
metabolomics workflow for feature
filtration and outlier removal.*
## Selection of Discriminant Metabolites by Univariate and Multivariate
Statistics
We performed several complementary univariate and multivariate statistical tests to select and confirm the most consistent features to proceed with metabolite identification.41 Differences in levels of features by tertiles of intake for fermented foods and groups (T1, T2, and T3) were assessed by a Kruskal–Wallis test followed by a posthoc Conover-Iman pairwise comparison test. An additional step was conducted to select features with higher levels in higher tertiles compared to lower tertiles (i.e., a median of T3 > T1, T3 > T2, and T2 > T1). To determine the strength and direction of the associations between fermented food intakes and features, nonparameteric Spearman’s rank correlation coefficients (rs) were calculated; significant correlations with rs > 0.20 were selected for further analysis. For all univariate statistical tests, p-values were adjusted for the false discovery rate (FDR) using the method of Benjamini and Hochberg,42 and FDR-adjusted p ≤ 0.05 was set as the significance threshold.
Two multivariate tests were also conducted to further unveil and confirm features that discriminate between tertiles of fermented food intake. Partial least-square discriminant analysis (PLS-DA) was performed to identify features that differentiate the lowest and highest tertiles of intake for each fermented food or food group (SIMCA-P software v.15.0; Umetrics). The dataset was scaled using the unit variance (UV) method. The quality and validity of the models were evaluated by the goodness-of-fit parameter (R2Y > 0.5), the predictive ability parameter (Q2 > 0.2), and permutation tests with 999 random permutations to exclude any random separation of the sample groups.43 Permutation plots (correlations of the observed and permuted data on the X-axis against the R2Y and Q2 on the Y-axis) for all models were visually interpreted as follows: the model was considered to be well guarded against overfitting if the Q2 values of the permuted dataset were lower than the Q2 value of the actual dataset to the observed dataset. Finally, the most discriminant features from these models were selected based on variable importance in projection (VIP) scores (VIP > 1 as a cutoff value). Second, we used random forests to model these data and further select the most discriminant features between T1, T2, and T3 of fermented food or food group intake, using the randomForest package.44 The dataset was split into training (0.75) and test (0.25) datasets. For tuning the random forest, the number of trees ranged from 500 to 800 and the node sizes from 1 to 10. The “mtry” parameter was set to x (0.01, 0.05, 0.15, 0.25, 0.333, and 0.4), where x is the number of features considered for the model. We then implemented a full Cartesian grid search to choose the best model using the out-of-bag estimates generated from the random forest model. The results and variable importance from this step were further subjected to permutation testing using the “altmann” method ($$n = 500$$)45 applied in the ranger package.46 For features selected from multivariate analysis, the Wilcoxon test (for two comparisons) or Kruskal–Wallis test followed by a posthoc Conover-Iman pairwise comparison test (for three comparisons) was also conducted (non-FDR-adjusted p ≤ 0.05) as a separate validation test of the features selected from these models.
Given the large number of significant features revealed across complementary univariate and multivariate tests (586 plasma and 151 urinary metabolites from GC–MS and 110 plasma and 4473 urinary metabolites from LC–MS; data not shown in tables), those significant in at least two of the four statistical tests were prioritized and selected for identification. For urinary features measured by LC–MS, a large number of features remained significant; thus, an additional criterion of Spearman’s FDR p-value ≤ 1 × 10–10 had to be applied to select a number of features that could feasibly be identified. A summary of the significant features across at least two of the four statistical tests (and prioritized for identification) are provided in Table S2. Aside from PLS-DA, all analyses were performed in R (v.3.6.3).40
## Metabolite Identification
For LC–MS, the Human Metabolome Database,47 the MassBank of North America,48 the National Institute of Standards and Technology database (NIST v17), and METLIN49 were used to screen the identity of metabolites with a 10 ppm mass accuracy threshold. Identity suggestions from databases were then screened based on the chemical and biological relevance of each suggested metabolite identification (as provided on HMDB and/or through a search of the compound name on PubMed and Google) and confirmed by MS fragmentation data (where available). Pure analytical standards were then purchased for the tentatively identified and most biologically plausible compounds and injected at two concentrations in sample QCs and in the solvent. For GC–MS, the Golm Metabolome Database50 and NIST v17 were used to screen the identity of compounds, and an internal database of internal standards was used to confirm the metabolite identification. In the case that stereoisomeric forms of selected discriminating features were identified, the peak with a higher response was further evaluated. The list of standard suppliers is provided in Table S3. For both LC–MS and GC–MS, the level of identification of each discriminant metabolite is defined according to the Metabolomics Standards Initiative (MSI) recommendations,51 as follows: level 1, compounds identified by comparison to a pure reference standard based on spectral data (LC: molecular weight with a 10 ppm accuracy threshold, fragmentation pattern when available, isotopic distribution, and retention time with a $10\%$ accuracy threshold; GC: based on spectral data and retention indices (RIs) with a $5\%$ accuracy threshold and $10\%$ for very large peaks); level 2, based on spectral data but without chemical standards (LC: fragmentation pattern match to library spectral data of at least two major peaks; GC: library match factor >$80\%$); level 3, putatively characterized compound classes; and level 4, unknown compound. Details of the identification features of metabolites analyzed from GC–MS (37 plasma and 75 urinary metabolites) and LC–MS (13 plasma and 89 urinary metabolites) are presented in Tables S4 and S5, respectively. The metabolites corresponded to the intakes of total fermented beverages (FBs) (number of metabolites: 112), wine [89], coffee [72], beer [17], white bread [9], total fermented cereals/grains (FCG) [1], total fermented dairy (FD) [1], cheese [1], and cocoa [1] (none for whole-grain bread or yoghurt).
## Associations between Identified Metabolites
and CMD Risk Parameters
Participant characteristics are reported as number (percentages), mean (standard deviation) for normally distributed variables, or medians (interquartile range) for skewed variables. Multivariable adjusted linear regression and restricted cubic spline regression were used to evaluate the associations between the identified metabolites and CMD risk factors. CMD risk parameters acting as dependent variables that were not normally distributed were log-transformed, which included: BMI, plasma HbA1c, plasma glucose, serum triglycerides, and SCORE. All variables were normalized by z-scores prior to analysis to allow comparability across associations. Analyses were performed unadjusted (model 0), adjusted for age (years) and sex (male, female) (model 1) + physical activity (minutes/week), smoking (smoker/non-smoker), and education level (high, intermediate, low) (model 2) + dietary factors (g/day) (model 3). For associations with continuous MetS, which already takes into account age and sex, analyses were performed unadjusted (model 0) and fully adjusted for smoking, physical activity, education, and dietary factors (model 3). For associations with SCORE, which already takes into account age, sex, and smoking status, analyses were performed unadjusted (model 0) and fully adjusted for physical activity, education, and dietary factors (model 3). Dietary factors included in the fully adjusted models included those indicated in the literature to be important for CMD risk in addition to those significantly correlated with the identified metabolites and included vegetables, fruits, alcohol, meat, and confectionary/desserts. All analyses were performed in R (Version 3.6.3).52 For all models, the level of significance was set at p ≤ 0.05. To account for multiple comparisons, FDR-adjusted p-values are also presented.
## Characteristics
of the Population
The characteristics of the participants in the metabolomics and identification subcohorts are presented in Table 1. The median age of the participants was ∼58 years, and the majority were highly educated (>$60\%$) and nonsmokers (>$69\%$). No significant differences were observed in background demographics between the two subcohorts. Among the dietary factors, participants in the identification subcohort had significantly higher intakes of total energy, fat, sodium, beer, soft drinks, and egg products compared to participants in the metabolomics subcohort but with a similar interquartile range (significant differences were also observed for tea intake but medians were comparable) (p ≤ 0.05). Among cardiometabolic parameters, participants in the identification subcohort have a slightly larger waist circumference, higher systolic blood pressure, and lower plasma HDL-cholesterol than participants in the metabolomics subcohort. However, although significant, the differences observed are relatively minor and do not pertain to the broader indicators of health linked to each measure (e.g., BMI, hypertension, and suboptimal cholesterol). The distribution of participant risk for continuous MetS and SCORE is presented in Figure S3.
**Table 1**
| characteristic | metabolomics subcohort (n = 531) | identification subcohort (n = 246) | p-value |
| --- | --- | --- | --- |
| Demographics | Demographics | Demographics | Demographics |
| age, years | 57 (46–63) | 58 (46–65) | 0.26 |
| education, n (%) | | | 0.73 |
| low | 37 (7) | 19 (8) | |
| intermediate | 148 (28) | 77 (31) | |
| high | 344 (65) | 149 (61) | |
| smoking status, n (%) | | | 0.20 |
| smoker | 118 (26) | 70 (31) | |
| nonsmoker | 343 (74) | 159 (69) | |
| physical activity, min/week | 2136 ± 1093 | 2043 ± 1046 | 0.37 |
| supplement use, n (%) | 0.8 ± 1.2 | 0.7 ± 1.2 | 0.58 |
| Dietary Factors | Dietary Factors | Dietary Factors | Dietary Factors |
| total energy intake, kcal/day | 2128 ± 499 | 2220 ± 530 | 0.02* |
| Macronutrients | Macronutrients | Macronutrients | Macronutrients |
| fat, g/day (En %) | 84 ± 25 (36%) | 90 ± 27 (36%) | 0.01* |
| carbohydrates, g/day (En %) | 230 ± 60 (43%) | 237 ± 63 (43%) | 0.17 |
| protein, g/day (En %) | 77 ± 18 (14%) | 80 ± 18 (14%) | 0.06 |
| fiber, g/day | 25 ± 7 | 25 ± 7 | 0.80 |
| sodium, mg/day | 2261 ± 653 | 2375 ± 711 | 0.03* |
| Fermented Foods and Groups | Fermented Foods and Groups | Fermented Foods and Groups | Fermented Foods and Groups |
| total fermented beverages, g/day | 592 (324–799) | 629 (406–865) | 0.26 |
| coffee, g/day | 406 (174–638) | 406 (196–638) | 0.48 |
| wine, g/day | 25 (4–87) | 20 (0–80) | 0.31 |
| beer, g/day | 9 (0–79) | 20 (0–118) | 0.04* |
| total fermented cereals/grains, g/day | 130 (88–166) | 133 (88–170) | 0.41 |
| whole-grain bread, g/day | 80 (47–112) | 77 (41–114) | 0.51 |
| white bread, g/day | 2 (0–8) | 2 (0–10) | 0.24 |
| cocoa, g/day | 4 (1–8) | 4 (1–8) | 0.80 |
| total fermented dairy, g/day | 152 (76–245) | 151 (69–240) | 0.79 |
| cheese, g/day | 25 (13–42) | 28 (14–46) | 0.24 |
| yoghurt, g/day | 89 (29–139) | 82 (21–139) | 0.30 |
| Other Foods and Groups | Other Foods and Groups | Other Foods and Groups | Other Foods and Groups |
| tea, g/day | 174 (67–406) | 174 (67–406) | 0.04* |
| alcoholic drinks, g/day | 81 (18–207) | 108 (19–245) | 0.26 |
| soft drinks, g/day | 5 (0–42) | 13 (0–54) | 0.04* |
| fruits, g/day | 217 (86–238) | 166 (81–233) | 0.10 |
| vegetables, g/day | 150 (97–204) | 140 (94–196) | 0.11 |
| potatoes, g/day | 67 (37–87) | 67 (37–87) | 0.29 |
| legumes, g/day | 38 (19–79) | 38 (22–79) | 0.89 |
| meat products, g/day | 72 (46–98) | 79 (54–105) | 0.053 |
| eggs and egg products, g/day | 9 (7–18) | 14 (7–18) | 0.03* |
| fish, g/day | 11 (6–16) | 11 (6–16) | 0.72 |
| nuts and seeds, g/day | 13 (6–25) | 13 (6–26) | 0.71 |
| sauces, spreads, and cooking fats, g/day | 41 (28–54) | 42 (30–57) | 0.21 |
| salty and processed snack foods, g/day | 35 (16–59) | 37 (20–64) | 0.16 |
| sugary confectionary and desserts, g/day | 70 (47–104) | 78 (50–113) | 0.09 |
| Cardiometabolic Factors | Cardiometabolic Factors | Cardiometabolic Factors | Cardiometabolic Factors |
| BMI, kg/m2 | 25.1 (22.9–27.2) | 25.5 (23.2–28.0) | 0.12 |
| BMI category, n (%) | | | 0.61 |
| underweight (<18.5 kg/m2) | 4 (1) | 2 (1) | |
| normal weight (18.5–24.9 kg/m2) | 249 (7) | 103 (42) | |
| overweight or obese (≥25–29.9 kg/m2) | 278 (52) | 141 (57) | |
| waist circumference, cm | 91 ± 12 | 93 ± 12 | 0.04* |
| diastolic blood pressure, mm Hg | 73.7 ± 10.4 | 74.5 ± 10.8 | 0.38 |
| systolic blood pressure, mm Hg | 125.5 ± 16.0 | 128.7 ± 16.6 | 0.01* |
| hypertension, n (%) | | | 0.42 |
| hypertensionb | 109 (20.6) | 62 (25.2) | |
| normal or optimal | 421 (79.4) | 184 (74.8) | |
| hypertension treatment, n (%) | | | 0.95 |
| being treated with medication and/or diet | 69 (13.0) | 36 (14.6) | |
| not being treated | 462 (87.0) | 210 (85.4) | |
| plasma total cholesterol, mmol/L | 5.4 ± 1.0 | 5.3 ± 1.0 | 0.15 |
| plasma LDL cholesterol, mmol/L | 3.3 ± 0.9 | 3.2 ± 0.9 | 0.63 |
| plasma HDL cholesterol, mmol/L | 1.6 ± 0.4 | 1.5 ± 0.4 | 0.01* |
| serum triglycerides, mmol/L | 1.0 (0.7–1.4) | 1.0 (0.7–1.4) | 0.74 |
| suboptimal cholesterol, n (%) | 398 (75.0) | 182 (74.0) | 0.84 |
| high cholesterol treatment, n (%) | | | 0.76 |
| being treated with medication and/or diet | 56 (10.5) | 23 (9.3) | |
| not being treated | 475 (89.5) | 223 (90.7) | |
| HbA1c, mmol/mol | 35.5 (34.0–38.0) | 35.8 (34.0–38.0) | 0.97 |
| fasting glucose, mmol/L | 5.4 (5.1–5.8) | 5.3 (5.0–5.8) | 0.11 |
| diabetes, n (%) | 13 (2.4) | 6 (2.4) | 0.98 |
| diabetes treatment, n (%) | | | 0.77 |
| being treated with medication and/or diet | 15 (2.8) | 5 (2.0) | |
| not being treated | 516 (97.2) | 241 (98.0) | |
| metabolic syndrome, n (%) | 67 (12.6) | 33 (13.4) | 0.85 |
| SCORE, n (%) | | | 0.25 |
| ≥15% | 6 (1.3) | 6 (2.6) | |
| 10–14% | 17 (3.7) | 14 (6.2) | |
| 5–9% | 73 (16.0) | 42 (18.5) | |
| 1–4% | 211 (46.2) | 98 (43.2) | |
| <1% | 150 (32.8) | 67 (29.5) | |
## Intake Levels of Different Fermented Foods
The levels of intake of fermented foods in the identification subcohort (mean and tertiles) are presented in Table 2 (in both absolute g/day and g dry matter/day). Out of the fermented food groups evaluated, the highest intake on a g/day basis was total FB followed by total FD, while the highest mean intake of foods on a g dry/matter per day was total FCG. Out of individual fermented foods, coffee had the highest intake among all other fermented foods on a g/day basis (466 g/day) but the lowest intake on a g dry matter/day basis (similar trends were observed for wine and beer). Conversely, intakes of cocoa remained the same regardless of g/day or g dry matter/day (similar trends were observed for white and whole-grain bread, and cheese).
**Table 2**
| Unnamed: 0 | energy-adjusted intakes (g/day) | energy-adjusted intakes (g/day).1 | energy-adjusted intakes (g/day).2 | energy-adjusted intakes (g/day).3 | energy-adjusted intakes (g dry matter/day) | energy-adjusted intakes (g dry matter/day).1 | energy-adjusted intakes (g dry matter/day).2 | energy-adjusted intakes (g dry matter/day).3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| food group | mean ± SD | T1 (n = 82) | T2 (n = 82) | T3 (n = 82) | mean ± SD | T1 (n = 82) | T2 (n = 82) | T3 (n = 82) |
| total FB | 638 ± 398 | 264 (124, 378) | 615 (551, 679) | 978 (857, 1205) | 22 ± 20 | 6 (3, 8) | 17 (14, 21) | 37 (30, 51) |
| coffee | 466 ± 297 | 142 (63, 238) | 453 (418, 510) | 691 (640, 903) | 5 ± 3 | 1 (1, 2) | 5 (4, 5) | 7 (6, 9) |
| beer | 112 ± 202 | –14 (−32, 11) | 48 (37, 71) | 208 (136, 374) | 9 ± 16 | –1 (−3, 1) | 4 (3, 6) | 17 (11, 30) |
| wine | 61 ± 89 | 3 (−7, 7) | 25 (16, 38) | 130 (92, 187) | 8 ± 12 | 0 (−1, 1) | 4 (2, 5) | 17 (12, 27) |
| total FCG | 134 ± 60 | 79 (61, 94) | 132 (115, 144) | 182 (168, 219) | 85 ± 37 | 52 (38, 61) | 85 (74, 91) | 116 (105, 137) |
| white bread | 8 ± 13 | –1 (−2, 1) | 4 (3, 5) | 17 (10, 30) | 5 ± 8 | 0 (−1, 1) | 3 (2, 3) | 11 (7, 19) |
| whole-grain bread | 82 ± 56 | 29 (12, 42) | 77 (68, 88) | 131 (114, 149) | 52 ± 35 | 20 (8, 27) | 49 (43, 55) | 83 (72, 93) |
| cocoa | 6 ± 9 | 1 (0, 1) | 3 (3, 4) | 10 (8, 17) | 6 ± 9 | 1 (0, 1) | 3 (2, 4) | 10 (8, 17) |
| tTotal FD | 170 ± 121 | 55 (33, 69) | 142 (126, 173) | 286 (238, 342) | 35 ± 19 | 16 (11, 21) | 32 (28, 38) | 54 (47, 63) |
| cheese | 34 ± 26 | 12 (7, 17) | 27 (23, 32) | 56 (47, 73) | 19 ± 15 | 7 (4, 10) | 16 (13, 18) | 31 (27, 42) |
| yoghurt | 89 ± 79 | 7 (0, 21) | 82 (59, 96) | 139 (139, 193) | 11 ± 10 | 1 (0, 3) | 11 (8, 13) | 20 (17, 24) |
## Biomarkers Identified for Fermented Food Intake
A total of 12 plasma metabolites and 27 urinary metabolites were identified. An overview of the candidate FIBs identified for various fermented foods and food groups, along with their platforms and biosamples of detection, are presented in Table 3 (plasma) and Table 4 (urine). The majority of the identified metabolites corresponded to the intakes of total FB (7 plasma and 19 urine), which encompasses coffee (3 plasma and 9 urine), wine (3 plasma and 10 urine), and beer (1 plasma and 6 urine). One urinary metabolite identified was discriminant for the intakes of total FCG, and one plasma metabolite was for white bread. However, metabolites discriminant for the intakes of whole-grain bread, cocoa, total FD, cheese, and yoghurt could not be identified.
A closer examination revealed that several of the metabolites (plasma dodecanoic acid, urinary d-psicose, glycine, d-gluconate, m-cresol, d-fucitol, and 2-keto-l-gluconic acid) were negatively associated with the fermented foods and food groups indicated (based on Spearman’s correlations, Table S6) but contributed to the discrimination of the intake of these fermented foods based on statistical significance in multivariate tests that do not distinguish between features that are at a higher abundance in a higher tertile intake group (e.g., PLS-DA and random forest). Thus, these metabolites may not be suitable for reflecting fermented food intake and thus are not further discussed as FIBs. However, they may still be important biomarkers in revealing the metabolic effects of consuming these fermented foods.
Several of the identified FIBs overlapped across several fermented foods. Specifically, urinary 2,3-dihydroxybutanoic acid, ethyl α-d-glucopyranoside, and 4-hydroxybenzeacetic acid were discriminant for the intake of total FB, wine, and beer. Several other urinary metabolites also appeared to overlap between two fermented food groups, including 2,3-dihydroxypropyl phosphoric acid and d-lactose (total FB, beer), catechol, furoylglycine, niacin, and 3-deoxy-d-ribo-hexonic acid γ-lactone (total FB, coffee), as well as erythritol, tartaric acid, and arabinofuranose (total FB, wine). However, in each case, the significance of the total FB group could be driven by the significance of the individual beverages in this group. Similar overlaps were observed in plasma for l-cysteine (total FB, beer), xylitol (total FB, wine), and quinate (total FB, coffee). One metabolite identified for wine intake (erythritol) was identified in both plasma and urine.
## Stability of the Identified
Biomarkers
Spearman’s correlations between the identified FIBs for fermented foods across different times between biosample collection and dietary assessment with the FFQ are presented in Table S6. Almost all correlations observed for the identification cohort (FFQ ± 14 d) remained significant with increasing time between biosample collection and FFQ completion (FFQ ± 30 d, 90 d, 180 d, and all FFQ), with only slight attenuations when the time between biosample collection and FFQ completion increased. The strongest correlations were observed between self-reported coffee intake and a series of FIBs, including plasma quinate, urinary niacin, furoylglycine, methyluric acid, and dimethyluric acid (rs ≥ 0.4, p ≤ 0.05). For wine, the strongest correlations included urinary tartaric acid and arabinofuranose (rs ∼ 0.4), and for beer, the strongest correlation observed was ethyl α-d-glucopyranoside (rs ∼ 0.27) (p ≤ 0.05). These correlations were also largely echoed between these metabolites and the intake of total FB. For total FCG, a significant moderate correlation was observed between self-reported intake and urinary glyceryl–glycoside ether in the identification cohort (rs ∼ 0.37), but the correlation attenuated in the full metabolomics cohort (rs < 0.3) (p ≤ 0.05). Conversely, correlations for intakes of cocoa, total FD, cheese, yoghurt, white bread, and whole-grain bread and their potential FIBs were either weak or nonexistent.
## Associations
between Identified Biomarkers and Cardiometabolic
Health Parameters
The results of all associations between the identified biomarkers and CMD risk factors are presented in Table S7. In the fully adjusted model, 21 metabolites were positively associated and 11 were negatively associated with CMD risk parameters (unadjusted p ≤ 0.05). After adjusting for multiple comparisons, 11 associations remained significant, including between plasma glutamic acid and urinary 2,3-dihydroxypropyl phosphoric acid with BMI (standardized (Std.) β = 0.28, R2 = 0.32; Std. β = 2.2 × 10–7, R2 = 0.30, respectively) and waist circumference (Std. β = 1.73 × 10–7, R2 = 0.50; Std. β = 0.28, R2 = 0.48, respectively) (FDR p ≤ 0.05). Additional FDR-adjusted significant associations were observed between plasma xylitol (Std. β = 2.20 × 10–7, R2 = 0.26), glutamic acid (Std. β = 0.31, R2 = 0.30), and trigonelline (Std. β = 0.34, R2 = 0.28), as well as urinary niacin (Std. β = 2.55 × 10–7, R2 = 0.32), furoylglycine (Std. β = 8.94 × 10–8, R2 = 0.29), and methyluric acid (Std. β = 0.34, R2 = 0.28), with SCORE (FDR p ≤ 0.05). A negative association was observed between plasma cinnamoylglycine with HbA1c (Std. β = −0.27, R2 = 0.36, FDR p ≤ 0.05). No FDR-adjusted significant associations were observed between metabolites with plasma lipids, glucose, or blood pressure.
## FIBs Identified
for the Habitual Intake of Individual Fermented
Foods
In the current work, we aimed to identify FIBs for fermented foods consumed in the habitual Dutch adult diet, which included coffee, wine, beer, whole-grain bread, white bread, cheese, yoghurt, and cocoa. A total of 12 plasma and 27 urinary metabolites were identified at level 1 or 2 from nontargeted GC–MS and LC–MS analyses, the majority of which corresponded to the intakes of coffee, wine, and beer (no metabolites were identified for cocoa, white bread, whole-grain bread, cheese, and yoghurt intake). These fermented foods were also coincidentally those with the highest intakes (in g/day) in the Dutch adult diet and span a wide range of intakes, which is conducive for the selection of discriminant metabolites. Several of the most promising FIBs identified for these foods were also previously captured by other nontargeted and targeted studies. For instance, plasma/serum quinate and trigonelline, as well as urinary niacin, furoylglycine, catechol, and methyluric and dimethyluric acids, have been previously reported as candidate FIBs of habitual coffee intake.55−66 Out of the metabolites identified for wine intake, hydroxy(iso)butyric acid has been previously detected in serum after long-term (>4 weeks) wine intake,67 tartaric acid in urine following acute wine intake,68,69 and urinary 4-hydroxybenzeneacetic acid in urine following both acute and long-term (>4 weeks) wine intake.67,70−73 The detection of these previously identified FIBs in our free-living population further supports their status as reliable indicators of the habitual intake of these fermented foods.
Additionally, several metabolites were identified for coffee, wine, and beer intake which have not been previously reported. For instance, we found urinary 3-deoxy-d-ribo-hexonic acid γ-lactone to be discriminant for coffee intake. This compound is a degradative product of glucose produced during the Maillard reaction,74 which could have formed during coffee brewing. For wine intake, plasma xylitol, plasma/urinary erythritol, and urinary glucuronic acid, citramalate, 2,3-dihydroxybutanoic acid, arabinofuranose, and ethyl α-d-glucopyranoside were identified as potential FIBs. Furthermore, for beer intake, plasma l-cysteine, urinary 2,3-dihydroxypropyl phosphoric acid, 4-hydroxybenzeneacetic acid, d-lactose, 2,3-dihydroxybutanoic acid, and ethyl α-d-glucopyranoside were identified. While not detected previously in biofluids, almost all of these metabolites have been detected or quantified in the associated foods themselves. Erythritol (a natural sugar alcohol) has been previously detected in multiple fermented foods, including wine, beer, sake, coffee, cheese, and soy sauce.56,75−77 Interestingly, erythritol can be produced by microorganisms (e.g., Penicillium sp. used in the ripening of cheese).15,77 Similarly, citramalate (a microbial metabolite that is found to be a byproduct of Saccharomyces, Propionibacterium acnes, and Aspergillus niger) has been detected in red wine.78,79 The detection of these metabolites in the plasma and urine metabolomes of free-living individuals consuming these fermented foods indicates that “fermentation-dependent” metabolites could act as powerful complementary FIBs (in addition to other FIBs originating from the raw food substrate) to improve the accuracy of dietary assessment of fermented foods in future studies. Thus, further validation studies are required in order to confirm the robustness and reliability of these newly identified FIBs (e.g., from the “identification cohort”) in a separate population (e.g., “validation cohort”).
One major challenge to the validation of single FIBs relates to their nonspecificity for a particular food. Indeed, the vast majority of metabolites identified for the intake of coffee, wine, and beer as described above have also been detected in biofluids following the consumption of other foods. For instance, plasma/serum quinic acid and urinary furoylglycine have also been identified for habitual cocoa intake,80,81 while methyluric and dimethyluric acids, being caffeine metabolites, have naturally also been identified for the intake of caffeinated foods (i.e., cocoa and tea).81,82 The phenolic 4-hydroxybenzeneacetic acid corresponding to wine and beer intake has also been detected in urine after acute bread intake83 and in serum after long-term coffee intake.56 Furthermore, tartaric acid, a fairly specific FIB for wine intake, has also been identified in urine following acute and long-term bread intake83 as well as acute beer intake.84 The limitations of using these single metabolites as FIBs could be circumvented by developing reliable multimarker panels.5 On the other hand, for fermented foods, nonspecific markers shared between different foods could also be useful for indicating common raw materials, fermentation processes (e.g., lactic, acetic, alcoholic, or alkaline fermentation), and/or fermentation with common microorganisms (e.g., lactic acid bacteria, with yeast). This work could be further extended by pathway analyses for the identified compounds, which could aid greatly in understanding their relationships to other metabolites and to human health.
## FIBs Identified
for the Intake of Groups of Fermented Foods
In the current work, we also explored using dry matter as a novel method to unify individual fermented foods with similar qualities into fermented food groups. Several groups were generated: total FB (comprising coffee, wine, and beer), total FCG (whole-grain and white bread), and total FD (cheese and yoghurt). By far, the largest number of identified metabolites were discriminant for the intake of total FB; however, the significance of the majority of these metabolites appeared to be largely driven by the individual beverages under this group. A few metabolites (plasma 2-hydroxybutyric acid, trans-aconitic acid, l-phenylalanine, and isoleucine; urinary guaiacol, 3-hydroxyhippuric acid, and 3,4-dihydroxyhydrocinnamic acid) appeared to be uniquely discriminant for total FBs. One metabolite was identified for total FCG intake (urinary glyceryl–glycoside ether). This metabolite has not been identified as a FIB previously and needs to be validated in further studies. No metabolites were identified for the intakes of cocoa or total FD, which could be due to the low or inconsistent intake of these foods (in the case of cocoa), or the discriminant metabolites being also of endogenous origin and thus influenced more heavily by human metabolism (in the case of fermented dairy).
While this is the first study to identify FIBs in the context of fermented food groups, this is also an area in need of further development. We formed the fermented food groups based on the groups for which the FFQ was previously validated for.8,15 Evidently, there could be other strategies to group fermented foods, which could reveal different sets of FIBs. For instance, fermented foods could be grouped based on a common fermentation process (e.g., lactic fermented foods and yeast-fermented foods), which may further reveal fermentation-dependent FIBs. Unfortunately, we did not have access to information on the fermenting microorganisms in order to group fermented foods according to this strategy. In addition, while we did not consider a total fermented food group in the current study, a total intake group might be worth exploring in future which would be highly relevant to examine the health impacts of a dietary pattern of fermented foods.
## Methodological Considerations for the Identification
and Stability
of the Biomarkers
Although the primary aim of this study was to identify FIBs for the habitual intake of fermented foods, this work also contributes several methodological insights. First, to comprehensively capture the metabolome for FIB identification, we analyzed two biosamples (plasma and urine) using two analytical platforms (GC–MS and LC–MS). The 24 h urine samples were anticipated to better capture FIBs than plasma collected under fasting conditions, as depending on the speed of metabolism, the metabolite may not be detected even several hours after ingestion in plasma. Indeed, a larger number of urinary metabolites were significant and identified. Still, the number of significant urinary metabolites likely represented a smaller fraction of the total significant (and biologically relevant) metabolites in urine but which are present at relatively low concentrations due to dilution (a necessary step to ensure metabolites are measured within the linear range of the MS instruments). In addition, there could be differences in the metabolism of different metabolites that influence the detection of potential FIBs (i.e., not all metabolites are eliminated in urine). A combination of these factors may explain why only one identified metabolite (erythritol for wine intake) overlapped between plasma and urine.
We also exploited a combination of univariate and multivariate statistical tests to identify the most discriminant FIBs—a strategy that has been explored by an increasing number of groups.85−87 While the results of univariate and multivariate analyses are not always congruent, the use of both statistical approaches can generate complementary sets of FIBs. However, the results should be interpreted within the statistical framework from which they have been generated.88 Additionally, we ran our analyses in positive mode to make use of the optimized settings by which the widest range of metabolites are expected to ionize and thus be detected. However, we recognize that running the same samples through negative mode (which was not possible due to time and economic constraints) could have been beneficial for expanding the detection of different metabolites and aid in metabolite identification.
Additionally, we investigated the stability of the identified FIBs with increasing time frames between biosample collection and completion of self-reported dietary assessment. This was a necessary analysis since the biosample collection did not occur at the same time as the dietary assessment and could be a source of variability. Importantly, we observed excellent stability (correlation coefficient and significance were maintained) with increasing time from biosample collection to the FFQ completion (within 14 d, 30 d, 180 d, and all) for almost all of the identified FIBs. The driving force for this stability could be the larger numbers of participants in longer time frames (affording more statistical power). Moreover, these results could indicate that the FFQ used to collect information on self-reported fermented food intake in this study is fairly robust and/or that the diets of this population are very stable.
## Associations between Identified FIBs and
CMD Risk Parameters
We further examined associations between the identified FIBs with several CMD risk factors as a preliminary analysis to unravel the complex relationships between fermented food consumption and cardiometabolic health. Of the 39 metabolites identified, 7 were positively and 1 was negatively significantly associated with CMD risk factors after adjustment for multiple comparisons. All associations were weak, which may be attributed to the relatively healthy study population that may not have provided the gradient of CMD risk required to observe a large effect size. Thus, these associations need to be confirmed in larger, prospective cohorts or populations with a more distinctive divide between low and high CMD risk. Nonetheless, some of the associations we found have been reported in the literature. For instance, plasma glutamic acid has been positively associated with obesity, particularly with metabolically unhealthy obese phenotypes.89 Other associations are more contested. In some studies, the consumption of non-nutritive sweeteners (which includes xylitol) has been shown to increase weight and waist circumference, as well as the incidence of obesity, hypertension, metabolic syndrome, type II diabetes, and cardiovascular events.90 However, several recent systematic reviews have also revealed that the use of non-nutritive sweeteners (instead of sugar) reduces energy intake as well as body weight.91,92 In all studies, the distinct effects of xylitol (compared to other non-nutritive sweeteners) as well as the underlying mechanisms behind these associations have yet to be verified. Similarly, while we observed significant positive associations between urinary niacin and plasma trigonelline with overall CMD risk (SCORE), this is not in line with the literature for specific CMD risk factors. Niacin is used as pharmacotherapy to prevent cardiovascular disease by lowering cholesterol levels in blood.93 In mechanistic studies, trigonelline has been shown to improve insulin sensitivity by interfering with NADPH oxidase gene expression of pathways and mitochondrial electron chain transport.94 Given these conflicting findings, further studies in larger, prospective cohorts are needed to clarify these associations and examine whether they play an intermediate role in CMD.
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|
---
title: 'Triggered Polymersome
Fusion'
authors:
- Stephen D. P. Fielden
- Matthew J. Derry
- 'Alisha
J. Miller'
- Paul D. Topham
- Rachel K. O’Reilly
journal: Journal of the American Chemical Society
year: 2023
pmcid: PMC10021019
doi: 10.1021/jacs.2c13049
license: CC BY 4.0
---
# Triggered Polymersome
Fusion
## Abstract
The contents of biological cells are retained within compartments formed of phospholipid membranes. The movement of material within and between cells is often mediated by the fusion of phospholipid membranes, which allows mixing of contents or excretion of material into the surrounding environment. Biological membrane fusion is a highly regulated process that is catalyzed by proteins and often triggered by cellular signaling. In contrast, the controlled fusion of polymer-based membranes is largely unexplored, despite the potential application of this process in nanomedicine, smart materials, and reagent trafficking. Here, we demonstrate triggered polymersome fusion. Out-of-equilibrium polymersomes were formed by ring-opening metathesis polymerization-induced self-assembly and persist until a specific chemical signal (pH change) triggers their fusion. Characterization of polymersomes was performed by a variety of techniques, including dynamic light scattering, dry-state/cryogenic-transmission electron microscopy, and small-angle X-ray scattering (SAXS). The fusion process was followed by time-resolved SAXS analysis. Developing elementary methods of communication between polymersomes, such as fusion, will prove essential for emulating life-like behaviors in synthetic nanotechnology.
## Introduction
Phospholipid membrane-delineated compartments are present in biological cells to control the diffusion of material and allow incompatible processes to occur simultaneously.1,2 The fusion of phospholipid membranes results in the merging of compartments and is therefore a fundamental mechanism for controlling the movement of material within and between cells.3−7 Many regulated biological signaling processes, such as immune responses,8 hormone release,9 and nerve propagation,10 are coordinated by phospholipid membrane fusion. This is possible because the fusion of phospholipid membranes is not normally spontaneous; it carries a large energetic barrier due to the need to increase phospholipid membrane curvature/tension and overcome charge repulsion from approaching lipid headgroups.11 This means that fusion only occurs when additional biological machinery is employed, permitting regulation of the process. Specifically, phospholipid membrane fusion is catalyzed over a two-step process by a family of membrane-bound proteins, situated on the two phospholipid membranes undergoing fusion.12−16 These proteins, termed SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors), first form complementary interactions with each other that tether two phospholipid membranes in a metastable state. While this also provides the driving force required to overcome the energetic barrier (Figure 1a), a further stimulus is then often required to trigger the collapse of this intermediate and thus promote phospholipid membrane fusion. Directing membrane fusion through a two-step process permits rapid and coordinated fusion in response to triggering.17,18
**Figure 1:** *(a)
Triggered biological membrane fusion proceeds over two steps.
First, membranes are brought into close contact by the complementary
interactions of SNARE proteins to generate a metastable state (with
respect to the final fused product). Next, an influx of Ca2+ promotes protein rearrangement, which triggers fusion of the two
membranes. (b) In this work, polymersomes are produced in a persistent
metastable state. They then undergo rapid fusion on application of
a trigger. Relative thicknesses of layers in polymersome cartoons
are not to scale.*
The need for a trigger also allows membrane fusion to occur in response to environmental changes, thus allowing it to be coupled to other cellular processes. Such regulated fusion therefore provides an important mechanism for mediating cellular communication. For example, when a nerve signal reaches a synapse, an influx of Ca2+ triggers the exocytosis (i.e., fusion with the outer cell membrane) of synaptic vesicles, resulting in the secretion of neurotransmitters and signal propagation.19−21 The Ca2+ trigger functions by lowering the activation energy to fusion. Scientists have applied this biological machinery to direct the fusion of liposomes, artificial vesicles formed of phospholipids.22 It has also been possible to use synthetic chemistry to direct liposome fusion via molecular recognition as a driving force.23−26 While phospholipid membrane fusion is a key process found in biological cells, the fusion of polymersomes remains an underexplored process.27−31 Of the limited number of examples of polymersome fusion,32−45 none occur via a triggered two-step mechanism as seen in biological signaling. *The* generation of a metastable intermediate that remains kinetically inert until a trigger is present is essential for developing responsive and/or stepwise fusion sequences. This biomimetic approach to polymer membrane fusion would find a wide array of applications, such as sequence control over reagent trafficking and catalysis. These could not be effectively coordinated using previously reported methods of spontaneous polymersome fusion.
Here, we demonstrate a polymersome system that undergoes fusion due to the action of a trigger (Figure 1b). This method exploits the ability to form polymer nanoparticles with high membrane curvature in an out-of-equilibrium state. This is achieved using ring-opening metathesis polymerization-induced self-assembly (ROMPISA) of substituted norbornene monomers.39,46−54 ROMP has previously been used55,56 to access a variety of nanoscopic assemblies, such as bottlebrushes,57 dendrimers,58 porous materials,59 and complex phase separated networks.60 *In aqueous* ROMPISA, rapid polymerization and self-assembly occur simultaneously.61,62 *As a* hydrophilic macroinitiator, derived of P(NB-PEG) and G3, is chain extended using hydrophobic NB-MEG, the resulting amphiphilic diblock copolymer self-assembles in aqueous solution.
Initially, self-assembly results in the formation of small spherical polymersomes. The hydrophobic portions of the constituent polymer chains adopt a rod-like conformation to give a glassy membrane.63,64 Continued polymerization of NB-MEG after polymersome self-assembly initially serves to drive the system away from thermodynamic equilibrium. This is because the polymersomes are unable to undergo further morphological transformation to accommodate the growing polymer chains, meaning that the free energy released from polymerization becomes stored as membrane tension rather than being dissipated.65−67 The release of this tension provides a driving force for fusion, circumventing the need for additional machinery to impart local membrane deformations.68 In contrast, lipid-based membranes, as found in biological systems, behave with fluid-like behavior. This is because the constituent lipid chains have a low molecular weight that precludes chain entanglement, permitting the resultant membranes to rapidly rearrange to minimize curvature or in response to a shear force. This means that lipid membranes cannot store free energy as tension or persist in a out-of-equilibrium anisotropic morphology. This necessitates the presence of SNARE proteins to induce the unfavorable membrane geometries required for fusion. Conversely, polymersomes generally possess a significantly greater bending rigidity and lysis tension than lipid vesicles, meaning that they can be subjected to stretching forces without membrane rupture.11 It was previously found that as NB-MEG polymerization proceeds further, the membrane tension reaches a critical value that causes the isotropic polymersomes to spontaneously fuse together to produce linear tube-like particles (tubesomes).69−71 This results in uncontrolled release of stored free energy over the course of several minutes, until polymerization is complete.39 The fusion process displays step-growth kinetics; short tubesomes initially form that increase in length as the NB-MEG degree of polymerization (DP) increases, and further fusion occurs. Here, we show that the incorporation of stimuli-responsive monomers into ROMPISA polymers permits temporal control over fusion. The on-demand release of free energy stored in the polymersomes is triggered by a pH change, which alters the chemical structure of the membrane corona and consequently lowers the energetic barrier to fusion. This process is rapid (the onset of anisotropy occurs within 5 s), meaning that the fusion of particles is coordinated rather than stochastic. The development of synthetic analogues to triggered biological membrane fusion permits temporal control over the dynamics between polymer nanoparticles. ROMPISA provides the ideal platform for this as it occurs under mild reaction conditions (room temperature, air atmosphere, and aqueous solution).
## Results and Discussion
Previous work has shown that the fusion of ROMPISA polymersomes is inhibited at pH 2 when the corona is formed of P(NB-amine·H)+.39 *Such a* corona adopts a coil-like conformation in an aqueous environment and provides a barrier to fusion through a combination of charge repulsion and steric hindrance.64,72 We therefore reasoned that forming a corona from both NB-PEG and NB-amine would produce polymersomes that do not fuse at pH 2 but would fuse at higher pH when NB-amine becomes deprotonated. This is because NB-amine is not hydrophilic at high pH, causing a reduction in corona bulk and removing charge repulsion. The change in hydrophilicity of a short P(NB-amine)5 block upon deprotonation can be determined in silico using our reported method that normalizes the partition coefficient, Log Poct, of an oligomer by the solvent-accessible surface area, SA (Log Poct/SA).50,73 We previously determined the Log P/SA of protonated P(NB-amine·H)+5 to be approximately −0.003 Å–2 (i.e., hydrophilic). The calculated value for deprotonated P(NB-amine)5 is +0.004 (see the Supporting Information, Section S2 for details). This indicates that such a polymer block would indeed be corona forming at low pH and core forming at high pH. If a change in pH occurred quickly, then a “frozen” glassy membrane core could not rearrange into a more stable state before inelastic collisions between particles promotes the loss of membrane tension via fusion.
To explore whether triggered polymersome fusion was possible, the pH-responsive amphiphilic block copolymer, P1x, P(NB-PEG)11-b-P(NB-amine)5-b-P(NB-MEG)x, containing a corona formed from discrete blocks of NB-PEG with NB-amine and a core of NB-MEG (DP = x) was accessed via ROMPISA (Supporting Information, Section S4). This was performed analogously to a previously reported method of polymersome synthesis.39 A second polymer, P2x, P(NB-PEG)11-r-P(NB-amine)5-b-P(NB-MEG)x, containing a corona formed of a random copolymer of NB-amine and NB-MEG was also investigated (Supporting Information, Section S5) to determine whether the corona microstructure had an effect on the fusion process (Figure 2). This is the first investigation of the effect on the ROMPISA process of altering the microstructure of a corona block containing multiple monomers. To form nano-objects by ROMPISA, the corona was first synthesized by reacting the G3 initiator with the hydrophilic monomers (sequentially, first, NB-PEG and then NB-amine, for P1x or simultaneously for P2x) in tetrahydrofuran (THF). The resulting water-soluble macroinitiator was then chain-extended upon addition to a solution of NB-MEG dissolved in 100 mM phosphate buffer adjusted to pH 2 (PB2), to give a final solvent composition of THF/PB2 of 1:9 v/v. Consumption of NB-MEG was complete within 30 min, as judged by proton nuclear magnetic resonance (1H NMR) to give a 1 wt % polymersome dispersion. When a NB-MEG DP of 200 ($x = 200$) was targeted, analysis by gel permeation chromatography (GPC; Supporting Information, Figures S2 and S9) indicated controlled polymerization to give both P1200 and P2200 with low dispersity (Đ ≤ 1.1). These polymers both self-assembled to give narrowly disperse (polydispersity, PD < 0.10) spherical nano-objects, as determined by dry state/cryo-TEM (Figure 2) and DLS (Supporting Information, Figures S3 and S10). P1200 and P2200 particles had number-average diameters of 36 and 37 nm, respectively, as determined by dry-state TEM (Supporting Information, Figures S4 and S11). DLS measurement gave a Zavg of 49 nm for P1200 and 47 nm for P2200.
**Figure 2:** *TEM analysis of P1x and P2x nano-object products when NB-MEG DP = 200 (unfused) or 300 (fused). P1x particles contain a corona formed of a diblock
copolymer (blue and green layers in cartoon). P2x particles contain a corona formed of a random
copolymer (aquamarine layer in cartoon). Hydrophobic layers in cartoons
are red. Dry-state TEM samples were stained with 1% uranyl acetate
solution prior to imaging.*
Lengthening the core block further increased membrane tension to a level that overcame the threshold for spontaneous fusion to occur.39 *When a* NB-MEG DP of 300 was targeted, control over polymerization was retained (Đ ≤ 1.1), but the corresponding P1300 and P2300 particles spontaneously fused to give short tubes (Figure 2) with number-average lengths (as determined by dry-state TEM; Supporting Information, Figures S6 and S12) of 97 and 81 nm, respectively (Zavg = 98 and 80 nm, respectively, as measured by DLS; Supporting Information, Figures S3 and S10) As both P1300 and P2300 particles underwent spontaneous fusion at a similar core DP, it was concluded that the nature of the corona microstructure did not strongly influence the outcome of uncontrolled fusion at pH 2.
Triggered fusion was therefore optimized using particles formed from P1200 or P2200 because these did not undergo spontaneous fusion at pH 2 but should still possess significant membrane tension (Supporting Information, Section S6). In other words, P1200 or P2200 particles formed at pH 2 are metastable structures, similar to an untriggered but tethered SNARE complex. Addition of three volume equivalents of an aqueous NaOH solution (100 mM + 10 vol % THF) to P1200 and P2200 particles (suspended in 100 mM PB2 + 10 vol % THF) switched the pH from 2 to 12 (Figure 3). Triggered fusion was observed only to occur at pH 12 or above due to the high basicity of the amine side chains. For both particles, switching to pH 12 resulted in a rapid increase (<5 s) in turbidity. Analysis of the resultant particles by DLS (Supporting Information, Figure S15) showed both an increase in particle size (Zavg = 166 nm for P1200 particles and 303 nm for P2200 particles) and polydispersity (PD = 0.18 for P1200 particles and 0.24 for P2200 particles), indicating an increase in particle size and size distribution, as expected for fusion.39 Analysis by dry-state and cryo-TEM showed morphological changes for both samples—discrete, tube-like fused particles (mean length = 99 nm by dry-state TEM; Supporting Information, Figure S16) were formed with P1200 (Figure 3a), and large (>0.5 μm diameter) aggregates of fused particles (Supporting Information, Figure S17) were observed for P2200 (Figure 3b). Maintaining 10 vol % THF upon addition of NaOH was crucial for fusion to occur; if aqueous NaOH containing no THF was added, then no change in morphology of P1200 occurred. THF presumably acts as a plasticizer and facilitates chain rearrangement upon fusion.74
**Figure 3:** *Triggered polymersome
fusion by a pH switch. Continuous phase at
pH 2: 90 vol % 100 mM PB2 + 10 vol % THF. Continuous phase at pH 12:
90 vol % (25 mM PB2 + 75 mM NaOH) + 10 vol % THF. TEM analysis (dry-state
and cryo-) of (a) fused P1200 particles and
(b) fused and aggregated P2200 particles.
(c) Structure and attempted fusion of P3200, which contains pH unresponsive P(NB-NR4) rather than P(NB-amine). Dry-state TEM samples were
stained with 1% uranyl acetate solution prior to imaging.*
We attribute the varying fusion behavior between P1200 and P2200 to the difference in the corona microstructure after deprotonation of P(NB-amine·H). For P1200, the hydrophobic P(NB-amine)5 block can become buried within the membrane core at pH 12. However, this is not possible for P2200 because the NB-amine units are randomly distributed throughout the corona block. Therefore, upon deprotonation, the corona of P2200 contains interspersed hydrophobic patches, which presumably promote higher order aggregation during the fusion process. Similar “patchy” behavior has been previously observed to control aggregation of both synthetic nanoparticles75,76 and proteins.77 Analysis of dry P1200 and P2200 by differential scanning calorimetry (Supporting Information, Figure S20) allowed Tg values to be determined for each polymer. The Tg of P1200 (83 °C) is 8 °C higher than for P2200 (75 °C), suggesting that the latter has greater chain mobility. This greater mobility may also allow fused P2200 particles to form extended aggregates upon pH triggering. Further studies therefore focused on P1200 particles as these fused to give discrete structures, simplifying the analysis of the fusion process.
To demonstrate that triggered fusion is an irreversible process, fused P1200 particles at pH 12 were reacidified back to pH 2. No change in morphology, including fission to reform isotropic particles, was observed by TEM (Supporting Information, Figure S22). This indicates that fusion is irreversible, and fused particles are more thermodynamically stable than unfused particles at both pH values. The pH trigger controls the kinetics of fusion (i.e., the energy maximum), rather than altering the favored morphology (i.e., the energy minimum). If these transformations were instead occurring under thermodynamic control, then reversible morphological changes would be expected on pH toggling.41 Despite residing in a metastable state, no change in the morphology of unfused P1200 particles was observed by TEM for at least 3 months at pH 2 (Supporting Information, Figure S5). We propose that the glassy dynamics of the membrane core hinder chain rearrangement; instead, the tension within the membrane can be harnessed as an energy source to drive fusion when a trigger is applied. No significant change is observed by GPC analysis of P1200 before and after fusion (Supporting Information, Figures S2 and S14), further evidencing that the driving force for fusion is the release of tension, rather than a change in the polymer backbone structure.
It was also important to prove that the pH trigger acted as a specific signal for particle fusion by deprotonating the P(NB-amine·H)+ units. It was also possible that a pH switch may instead trigger fusion due to an extrinsic change in reaction conditions (e.g., due to a change in salt composition, on dilution, or upon agitation). To rule this out, a polymer analogous to P1200 that was formed using quaternary ammonium monomer NB-NR4, rather than NB-amine, was synthesized by ROMPISA at pH 2 (Supporting Information, Section S7). This polymer, P3200, P(NB-PEG)11-b-P(NB-NR4)5-b-P(NB-MEG)200, self-assembled to give particles similar to those formed from P1200, as judged by DLS (Zavg = 57 nm; Supporting Information, Figure S24) and dry-state TEM (number average diameter = 42 nm; Supporting Information, Figure S25). Upon switching the pH from 2 to 12, only a small change in mean particle length (to 46 nm; Supporting Information, Figure S27) was observed by dry-state TEM, with a narrow distribution (from 42 ± 9 nm at pH 2 to 46 ± 10 nm at pH 12) of particle lengths being retained. This confirms that a specific pH-responsive functional group is indeed required to facilitate triggering of fusion. This is reminiscent of fusion catalyzed by SNARE proteins, where programmed interactions between SNARE pairs direct the fusion of two specified membranes.16 This therefore opens the possibility of using a variety of orthogonal stimuli (e.g., redox switching and host–guest recognition) to choose which polymersomes fuse together within a mixture.
Further complementary analysis of fused and unfused P1200 particles was obtained by small-angle X-ray scattering (SAXS; Supporting Information, Section S10). As for previous SAXS analysis of polymersomes obtained by ROMPISA, data obtained for unfused particles at pH 2 were best fitted using a spherical micelle model78 (i.e., with a solid polymer core) rather than a vesicle model79 (Figure 4a). This is due to the thick membrane being of the order of the overall particle radius, and thus, the model is insensitive to such a small lumen (a few nanometers in diameter).50 The overall volume-weighted mean diameter of these particles was determined by SAXS to be 46 nm, which lies between the number-weighted diameter measured by TEM (36 nm) and intensity-weighted diameter determined by DLS analysis (49 nm). The mean aggregation number, Nagg, was found to be 306.80
**Figure 4:** *Mechanistic
analysis of fusion of P1200 particles by SAXS.
(a) Static SAXS data of unfused P1200 particles
fitted to a spherical micelle model (red
line). (b) Static SAXS data of fused P1200 particles fitted to a cylindrical micelle model (red line). (c)
Evolution of cylinder length over time during in situ SAXS analysis. The dashed gray line corresponds to the cylinder
length from modeling of the static measurement in part (b). Line added
to guide the eye. (d) Evolution of cylinder width over time during in situ SAXS analysis. The dashed gray line corresponds
to the cylinder width from modeling of the static measurement in part
(b). Line added to guide the eye. (e) Evolution in volume fraction
of spheres [unfused particles] and cylinders [fused particles] during in situ SAXS analysis. Values obtained from weighting of
models to give the best fit. (f) Proposed two-step fusion mechanism
with dry-state TEM images of particles quenched after 5 s and 5 min
at pH 12. Dry-state TEM samples were stained with 1% uranyl acetate
solution prior to imaging.*
The SAXS data of fused P1200 particles at pH 12 fitted best to a cylindrical micelle model (Figure 4b). The mean length of particles was 132 nm, and the mean width was 46.6 nm. The mean aggregation number, Nagg, was found to be 1900, implying that, on average, a tube is formed of six fused particles.
Due to the fast onset of fusion (solution turbidity increased within seconds of application of the pH trigger), the process could not be followed by conventional light scattering techniques, such as DLS analysis. Fusion of P1200 was instead probed by time-resolved SAXS analysis using a stopped-flow apparatus to initiate fusion while monitoring using a synchrotron light source.80−84 *This is* the first example of studying particles formed by ROMPISA using time-resolved SAXS. Pre-formed P1200 in PB2 was rapidly mixed with NaOH/THF solution and flowed into the measurement capillary within 10 ms. Scattering data from the resultant solution was acquired at 1 s intervals (500 ms exposure) for 10 min. No significant change in scattering was observed after this time. The first scattering pattern (taken 1 s after mixing) and the pattern taken at 5 min matched well with static SAXS patterns of unfused and fused P1200 particles, respectively (Supporting Information, Figures S37 and S38), where fusion had been initiated by standard pipette mixing. This confirms that the in situ SAXS experiment proceeded analogously to conventional laboratory experiments and thus provides an accurate picture of the fusion process.
Time-resolved SAXS data between 1 and 300 s were fitted to a linear combination of the spherical micelle and a cylindrical micelle model previously employed for the static data (see the Supporting Information). Parameters for the unfused spherical particles present at pH 2 were not modified when accounting for their presence on fitting data from the time-resolved experiment: the dimensions of these initial particles remained unchanged throughout the experiment, in line with TEM analysis. Satisfactory fits were obtained for all SAXS patterns using this approach, and it was deduced that the cylinders formed increased in both length (Figure 4c) and width over time (Figure 4d). The volume fraction of cylinders also increased with time (Figure 4e), reflecting the progression of the fusion process. Importantly, the final dimensions of the cylindrical particles (mean length = 133 nm, mean width = 46.8 nm) are in close agreement with those obtained from the static measurement (mean length = 132 nm, mean width = 46.6 nm).
The cylinder length evolved more quickly than cylinder width, which implies a two-step mechanism for fusion: (i) spherical polymersomes rapidly combine to give elongated particles; (ii) these then undergo a slower structural rearrangement to give the final fused product. Presumably, the second step occurs as polymer chains rearrange to minimize the particle surface area.64 To further probe the fusion mechanism, two aliquots of P1200 particles undergoing fusion at pH 12 were quenched back to pH 2 after 5 s and 5 min (Supporting Information, Section S8). The aliquots exhibited near-identical DLS data (Supporting Information, Figure S28). Smooth tubes were observed by TEM imaging of particles quenched after 5 min, suggesting that fusion was complete (Figure 4f). However, segmented tubes were observed by TEM imaging of particles quenched after 5 s, suggesting incomplete fusion. This provides further evidence that fusion occurs via rapid adhesion of spherical polymersomes followed by a slower structural rearrangement.
Finally, the fusion of polymers containing two different pH-responsive monomers was studied. Using a combination of monomers with differing pKa values should allow fusion to be controlled over a series of triggers, giving greater temporal control of the process. A tetrablock copolymer P4200, P(NB-PEG)11-b-P(NB-amine)2.5-b-P(NB-Py)2.5-b-P(NB-MEG)200 (Figure 5 and Supporting Information, Section S9), for which half of the NB-amine content of P1200 had been replaced with pyridine containing monomer NB-Py, was synthesized via ROMPISA at pH 2. Protonated P(NB-Py·H)+ should exhibit a pKa several units lower than P(NB-amine·H)+, meaning that it will be deprotonated at a lower pH value.85
**Figure 5:** *Multistep triggered fusion with tetrablock copolymer P4200 containing two pH-responsive blocks. The
first trigger
(switching pH from 2 to 7) deprotonates the P(NB-Py)
block, resulting in partial fusion. The second trigger deprotonates
the P(NB-amine) block, resulting in complete fusion.
Dry-state TEM samples were stained with 1% uranyl acetate solution
prior to imaging.*
Particles formed from P4200 had similar dimensions to P1200 particles—with an average length of 43 nm, as judged by dry-state TEM (Supporting Information, Figure S33). A small degree of spontaneous fusion occurred at pH 2, showing that P(NB-Py·H)+ provided a lower barrier to fusion than P(NB-amine·H)+. The first trigger for fusion was adjusting the pH from 2 to 7, which resulted in an increased degree of particle fusion, causing the average particle length to increase to 55 nm (Supporting Information, Figure S35). The second trigger further increased the pH to 12, which prompted complete fusion to give particles with an average length of 127 nm (Supporting Information, Figure S36). Thus, the release of membrane tension could be achieved over multiple steps. By incorporating a broader range of responsive monomers, it should be possible to trigger a series of polymersome fusion using combinations of different stimuli.
## Conclusions
The triggered fusion of polymersomes synthesized by ROMPISA has been demonstrated. By identifying suitable polymer structures, it was possible to synthesize unfused polymersomes in an out-of-equilibrium state. The free energy contained as membrane tension in these particles was used to drive particle fusion when triggered by a pH change. The microstructure of the corona was crucial for determining fusion outcome and allowed release of membrane tension to occur over time in a stepwise manner. The process described herein mimics protein-assisted triggering of phospholipid-based membranes found in biological cells, where two membranes are held in an out-of-equilibrium state until a trigger promotes their fusion. This process is essential for intercellular communication and the regulation of many biological phenomena. The trigger mechanism permits temporal control over the fusion process. This in turn allows membrane fusion to regulate and be integrated with metabolic sequences. This work provides an important advance in the development of such temporally controlled processes in non-biological media.
The use of kinetic control to generate out-of-equilibrium states is being increasingly exploited to endow chemical systems with emergent functions, such as directional motion,86−88 transient assembly,89−92 and concentration gradient formation.93−96 However, the application of this approach to nanoscale systems derived from covalent polymers remains largely unexplored.66,67,97−103 Here, we demonstrate that assembled glassy polymers can form an out-of-equilibrium assembly that can be used as a transient store of energy. This would not be possible if polymer assembly occurred under thermodynamic control. Controlled dissipation of excess free energy permitted temporal control over polymersome morphology. The wide variety of reported assemblies derived from covalent polymers suggests that there is much to be gained by further investigating how they can be used to produce functional nanotechnology that is formed in the out-of-equilibrium regime. Future work will focus on controlling the selectivity of fusion. Release of free energy could be used to overcome the entropic penalty that arises when fusion is targeted between two polymersome populations (i.e., crossed fusion).104,105 The work presented here therefore lays the foundation for developing networks of out-of-equilibrium polymersomes capable of communicating via fundamental processes such as particle fusion.106,107 *It is* clear that developing new mechanisms for temporally controlling the dynamics of polymersomes is essential to advance this area.
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---
title: Revisiting the role of IL-1 signaling in the development of apical periodontitis
authors:
- Kento Tazawa
- Mariane Maffei Azuma Presse
- Hisako Furusho
- Philip Stashenko
- Hajime Sasaki
journal: Frontiers in dental medicine
year: 2022
pmcid: PMC10021022
doi: 10.3389/fdmed.2022.985558
license: CC BY 4.0
---
# Revisiting the role of IL-1 signaling in the development of apical periodontitis
## Abstract
Apical periodontitis (AP) develops as a result of an immune response to pulpal bacterial infection, and various cytokines are involved in the pathogenesis of AP, with Interleukin (IL)-1 being considered a key cytokine. The role of IL-1 in the pathogenesis of AP has been well studied. It is known that IL-1 expression in periapical lesions correlates closely with the development of AP. IL-1 is a potent bone-resorptive cytokine that induces osteoclast formation and activation. Hence, inhibiting its signaling with IL-1 receptor antagonist (IL-1RA) results in a reduction in periapical lesion size. On the other hand, IL-1 is also a central cytokine that combats bacterial infection by activating innate immune responses. Therefore, a complete loss of IL-1 signaling leads to a failure to limit bacterial dissemination and consequently exacerbates AP. In vivo, IL-1 expression is tightly regulated and its signaling is modulated to optimize the immune response. Obesity causes systemic low-grade chronic inflammation and increases the risk of cardiovascular, renal, and other disorders. In experimentally induced AP, obesity significantly increases periapical bone loss, albeit the underlying mechanism remains unclear. Recent technological innovations have enabled more comprehensive and detailed analyses than previously, leading to new insights into the role of IL-1RA in regulating IL-1 signaling, and modulating apical lesion progression in obesity. In this review, we provide a brief overview of the function of IL-1 in AP development, with special emphasis on the latest findings in normal weight and obese states.
## Introduction
Apical periodontitis (AP) involves chronic inflammation and alveolar bone loss. Kakehashi et al. demonstrated for the first time that AP is caused by pulpal infection. Rats maintained in a conventional microbial environment developed pulp necrosis and periapical inflammation after pulp exposure; *In a* germ-free environment, the pulps remained vital without periapical bone destruction, and dentin bridges formed over the exposed pulp, demonstrating the capacity for tissue regeneration in the absence of infection [1].
In response to infection, complexly mixed immune cells migrate to the infected site. First neutrophils infiltrate, a followed by monocytes/macrophages, and subsequently by lymphocytes [T, B, and natural killer (NK) cells] [2, 3]. These cells play critical roles in innate and adaptive immunity. Innate immunity comprises nonspecific responses that do not require prior sensitization to an antigen. Phagocytes are key to innate responses; neutrophils and macrophages engulf bacteria, and NK cells eliminate infected cells. Innate cells also produce inflammatory cytokines, which mediate immune and connective tissue cell activity (4–6). To eliminate pathogens and establish immune memory, the adaptive response activates antigen-specific CD4 + helper and CD8 + cytotoxic T cells, as well as B cells and plasma cells that produce antibodies [7, 8]. The innate immune system lastly eliminate bacteria, apoptotic/dead cells, and debris. These responses are precisely regulated by the complex cytokine network.
Cytokines thus primarily protect the pulp and periodontal tissue from infection; however, cytokine-activated immune and inflammatory responses induce tissue destruction, particularly bone resorption [9, 10]. Regarding bone resorption, Horton et al. firstly reported that immune cells can influence osteoclast activity in 1972. Osteoclast-activating factor (OAF), a powerful stimulator of osteoclastic bone resorption, was released from human peripheral blood leukocytes stimulated by the mitogen phytohemagglutinin, or by antigenic material present in human dental plaque [11]. In 1985, OAF was subsequently purified to homogeneity and sequenced, and shown to be identical to interleukin-1-beta (IL-1β). It was later shown that macrophage-derived IL-1 is a prominent mediator in developing bone destructive periapical lesions (12–15). These and other basic studies on the interactions between the immune system and bone following pulpal infections have been important in establishing the field of osteoimmunology. These basic studies have provided a rationale of clinical research on IL-1/IL-1 signaling in AP and foundation for interpreting their outcomes (16–22).
Obesity is one of the most prevalent non-communicable diseases and predisposes to various disorders, including hypertension, type 2 diabetes mellitus (DM), dyslipidemia, and coronary heart disease [23, 24]. The increased morbidity associated with obesity is a worldwide public health issue [25]. Besides, obese people are more susceptible to infections than their non-obese counterparts as well to developing serious complications from common infections [26]. AP is one of the most prevalent oral infectious diseases. In DM subjects, where obesity is the greatest risk factor, the success of root canal treatment is decreased, in teeth with AP [27, 28]. Moreover, studies in the rodent diet-induced obesity (DIO) model have revealed that obesity promotes the progression and severity of experimental AP (29–31). However, the underlying mechanism(s) by which obesity alters the immune response in AP remain unclear.
As the background for future basic and clinical research, this mini review aims first to reaffirm the role of IL-1 signaling in the development of AP in the lean state, and then to provide new insights into the possible mechanisms underlying the expansion of periapical bone destruction associated with obesity, based on the latest experimental findings.
## IL-1 signaling is the central pathway in periapical lesion development
The IL-1 family comprises 11 cytokines: 7 pro-inflammatory mediators (IL-1α, IL-1β, IL-18, IL-33, IL-36α, IL-36β, and IL-36γ), and 4 anti-inflammatory cytokines [IL-1 receptor antagonist (RA), IL-36RA IL-37, and IL-38] [32]. Each family member binds to a specific primary receptor which combines with co-receptors to transduce pro-inflammatory or anti-inflammatory activity. The primary receptors include IL-1 receptor type 1 (IL-1R1), IL-1R2, IL-1R4, IL-1R5, and IL-1R6. The co-receptors include IL-1R3, IL-1R7, IL-1R8, IL-1R9, and IL-1R10 [32, 33]. IL-1α, IL-1β, and IL-1RA are the primary members that regulate the progression of periapical lesions, and their roles have been well studied. In contrast, the role of the other family members in the development of AP has not been systematically evaluated.
IL-1α and IL-1β are encoded by IL1A and IL1B respectively in humans [34]. Both isoforms bind to IL-1R1 and show similar biologic activities, including immune cell activation [33, 35]. IL-1 is also closely involved in both bone formation [36] and resorption [12, 15]. IL-1 inhibits nodule formation by osteoblasts in a dose-dependent manner [36]. IL-1 strongly promotes osteoclast differentiation indirectly by inducing the expression of receptor activator of NF-κB ligand (RANKL; *Tumor necrosis* factor ligand superfamily member 11) in osteoblasts [37]. IL-1 directly induces the fusion of mononuclear pre-fusion osteoclasts and enhances osteoclast function (resorption pit-forming activity) (38–40). Moreover, activation of NF-κB promoted by IL-1 prolongs osteoclast survival [41, 42]. However, IL-1α and IL-1β differ in several ways. First, species differences are found in their expression in periapical lesions. In rodent lesions, the predominant isoform is IL-1α rather than IL-1β [43, 44]. In contrast, the protein level of IL-1β in human periapical exudate is double that of IL-1α [45]. Furthermore, the bone resorption potency of IL-1β is 13-fold that of IL-1α in a rat assay system [10]. Second, the expression level after root canal treatment is different. Following treatment, the level of IL-1β in the periapical exudates decreased, while the level of IL-1α increased. This suggests that IL-1α and IL-1β may play different biological roles in the healing process [45, 46]. In this regard, a finding that bacteria-induced IL-1β and IL-1RI-myeloid differentiation factor 88 (MyD88) signaling are necessary and sufficient for efficient wound healing and tissue regeneration [47] is interesting. Third, the IL-1β cannot bind to IL-1R1 unless it is cleaved into its biologically-active mature form. Conversely, IL-1α precursor can bind to and activate the IL-1 receptor without proteolysis [48].
The expression level of IL-1 positively correlates to the extension of bone destruction and severity of AP. IL-1α mRNA and protein expression was identified in murine periapical lesions from the early stage of development, with increased levels found on day 7 after pulp infection [43, 44, 49]. Higher levels of IL-1α and IL-1β were detected in human periapical lesions with severe inflammation than mild inflammation [50, 51]. In periapical lesions, IL-1 is produced by various cells, including macrophages, fibroblasts, polymorphonuclear leukocytes, endothelial cells, osteoblasts, and osteoclasts in response to infection [44, 49]. Among these cells, macrophages are the major source of IL-1. Macrophage-derived IL-1 plays a critical role in the periapical immunity. IL-1β and IL-1α are respectively 1000- and 75-fold more potent in stimulating bone resorption than TNFα or TNFβ (lymphotoxin) respectively in vitro [10]. Besides, IL-1 neutralization significantly reduced bone resorptive activity in extracts from periapical tissue explants, whereas TNF-α neutralization had no effect [13, 15].
These studies focused on the bone-destructive effects of IL-1, but IL-1 also protects the host early after bacterial challenge. Antibody-mediated neutralization of both IL-1α and IL-1β leads to a failure to contain pulpal infection in male but not female mice, resulting in orofacial abscesses and sepsis [52]. Ovariectomized mice also developed sepsis, but were protected by an estrogen implant. Accordingly, IL-1 signaling synergizes with estrogen signaling to prime phagocytic cells for enhanced anti-microbial activity resulting in infection localization. IL-1R1 deficient mice identically showed severe bone destruction and sepsis after pulpal infection [53, 54]. Taken together, a severe deficiency of IL-1 signaling leads to poor infection control, dissemination of infection, and elevated bone destruction.
Subsequent studies using IL-1RA have confirmed the correlation between IL-1 and bone resorption. IL-1RA, produced by macrophages and monocytes [55], competitively blocks the action of IL-1. IL-1RA binds to IL-1R1 with equal or greater affinity than IL-1α and IL-1β but does not activate downstream signaling [34, 55, 56]. IL-1RA has a significant impact by suppressing periapical lesion development. Stashenko et al. demonstrated a 14-day IL-1RA treatment inhibited lesion development by approximately $60\%$ [57]. Maintaining IL-1 and IL-1RA in balance prevents excess inflammation and bone destruction. Once this balance is upset, inflammation and tissue damage may deteriorate [58]. To block IL-1-mediated bone resorption ex vivo, rat fetal long bones and mouse newborn calvariae require approximately 10-fold and 100–1000-fold IL-1RA to IL-1, respectively [59]. In periapical lesions, the level of IL-1RA is more abundant than IL-1 (mean IL-1RA: IL-1β ratio = 128: 7). Interestingly, exudates from symptomatic human lesions contained a significantly lower ratio of IL-1RA to IL-1β than exudates from asymptomatic human lesions [22]. Taken together, the local balance of IL-1 and IL-1RA is crucially important in the periapical lesion development.
## The cytokine network in periapical lesions centered on IL-1 signaling
Macrophages are major players involved in the cytokine network, and secrete various immunoregulatory mediators, including IL-1 [35, 60]. TNF-α is another pro-inflammatory cytokine expressed by macrophages [61] and increased in periapical lesions [44, 49]. TNF-α promotes IL-1 secretion from murine resident peritoneal macrophages in vitro [62] and increases osteoclastogenesis by upregulating RANKL [63, 64]. However as noted above, TNF-α itself is not much bone resorptive as IL-1 isoforms, and TNF-α deficient mice exhibited similar periapical lesion size to wild-type controls [65].
The role of type-1 T-helper (Th1) cytokines [Gamma interferon (IFN-γ), IL-12, IL-18] and Th2 cytokines (IL-4, IL-6, IL-10) on periapical bone destruction has also been evaluated. IFN-γ, IL-12, and IL-18 potentiate pro-inflammatory signaling (66–68) and their expression is increased in periapical lesions [43, 69, 70]. IFN-γ modulates macrophage-derived IL-1 expression, but its effect is not consistent. IFN-γ promotes secretion of IL-1 from LPS-stimulated human macrophages in vitro [71], whereas suppresses IL-1 in mouse RAW 264.7 macrophages [72]. IL-12 induces Th1 cell development, and IL-18, with IL-12, activates established-Th1 cells to produce IFN-γ. Thus, IL-12 and IL-18 are considered pro-inflammatory cytokines that facilitate type-1 responses [67, 73]. However, previous studies demonstrated that gene knockouts of IL-12, IL-18, and IFN-γ all exhibited similar lesion sizes as wild-type controls [65, 74]. Recombinant IL-12-infused wild-type mice also showed similar bone resorption to controls. The findings with IFN-γ were not confirmed in another study which reported that IFN-γ-deficient(−/−) mice presented with periapical lesions larger than those in wild-type animals [75]. The expression level of IL-1 in periapical lesions was unchanged in these mice [74]. Taken together, these results indicate that none of these cytokines has a non-redundant function in mediating periapical bone resorption.
IL-6, another macrophage-derived cytokine, was also detected in inflamed periapical tissue [76, 77]. Its expression was found to be transiently increased on day 14 after infection and decreased in the chronic phase [43]. IL-6 is a well-known pro-inflammatory cytokine, promoting bone resorption via osteoclastogenesis (78–80). Recent research has demonstrated that IL-6 also has anti-inflammatory effects by promoting macrophage IL-1RA secretion [81] and bone-forming effects by enhancing osteoblast differentiation (82–84). Previously, the protective role of IL-6 in periapical lesions was showed in vivo. Bone destruction was significantly increased in IL-6−/− mice versus in wild-type mice [69, 85]. IL-6 antibody-mediated neutralization also increased bone resorption compared to untreated controls. In IL-6−/− mice, increased bone resorption importantly correlated with osteoclast count and IL-1 expression in periapical lesions, and inversely with anti-inflammatory IL-10 expression [69].
Both IL-4 and IL-10 are increased in periapical lesions [69]. IL-4 is an anti-inflammatory cytokine playing pleiotropic roles in inflammation [86, 87]. IL-10, a potent anti-inflammatory cytokine produced by regulatory T cells (Treg), macrophages, dendritic cells, Th 2 cells, and Th1 cells, among other immune cells (88–90). However, IL-4 and IL-10 have different anti-inflammatory effects on macrophages. In macrophages stimulated by oral pathogens, recombinant IL-10 inhibited IL-1α production, whereas recombinant IL-4 had no significant suppressive effect [91]. Consistent with these in vitro findings, IL-10−/− mice exhibited significantly greater infection-stimulated bone resorption than wild-type mice, as well as markedly elevated IL-1 production in periapical inflammatory tissues [91]. In contrast, there was no difference in periapical lesion size between IL-4−/− and wild-type mice [75, 91].
IL-17 is a pleiotropic cytokine produced by Th17 cells that induces a myriad of pro-inflammatory mediators [92]. The expression of IL-17 was increased in infection-induced periapical lesions [65] and was significantly higher in symptomatic versus asymptomatic lesions [93]. IL-17 induces human macrophages to produce and secrete pro-inflammatory cytokines IL-1β and TNF-α in vitro [94]. IL-17A−/− mice were resistant to periapical lesions versus wild-type controls [65]. However, IL-17 receptor type A-deficient (IL-17RA−/−) mice conversely exhibited significantly increased bone destruction and inflammation. The expression of IL-1 was significantly upregulated in IL-17RA−/− lesions in vivo and IL-17RA−/− macrophages in vitro. The lesion size of IL-17RA−/− mice was decreased by IL-1β neutralization [95]. IL-17A utilizes two IL-17 receptors, and IL-17RA has four ligands [96], therefore, this system must be meticulously dissected to comprehend these data. Nevertheless, IL-17RA signaling likely plays a protective role in periapical lesions via IL-1 signaling and neutrophil priming.
Table 1 Summarizes the effect of cytokine or receptor deficiency/neutralization on periapical lesions. Although it is difficult to evaluate the effect of each cytokine because of their complex interactions [97], above reviewed experimental models suggest that anti-inflammatory cytokines such as IL-10 and, to a lesser extent, IL-6, are dominant and have non-redundant functions, compared to inflammatory cytokines in the immunomodulation of AP. In addition, the positive correlation between the IL-1 level and lesion size implies IL-1 is a principal cytokine in periapical lesion expansion and a useful biomarker for assessing inflammation.
## The impact of obesity and diabetes mellitus on periapical lesions
It is now widely accepted that obesity causes systemic low-grade chronic inflammation [98]. As noted above, obesity increases the risk of severe inflammation [26], and predisposes to the development of postoperative and nosocomial infections, as well as serious complications of common infections [98, 99]. Obesity also increases the risk for severe symptoms and poor prognosis in viral infections, including coronavirus disease 2019 [100]. In the oral cavity, obesity correlates with the prevalence and severity of periodontitis [101]. Deshpande et al. reported that obesity worsens all gingival index, probing depth, gingival recession, and clinical attachment levels than non-obese patients [102].
Diabetes, as an obesity complication, also has negative effects on AP. Diabetes decreases the success rate of endodontic treatment in teeth with AP preoperatively, and increases the risk of post-treatment tooth loss (27, 28, 103–105). According to previous in vivo rodent studies, obesity significantly increases bone destruction in experimentally-induced AP (29–31). As discussed in the following section, several potential mechanisms underlying obesity-induced inflammation have been proposed, but the actual mechanism is not yet fully understood.
## Potential mechanism of obesity-exacerbating periapical bone destruction
Many studies provide evidence that obesity alters immune responses. In obesity, macrophages significantly accumulate in the white adipose tissue [106, 107]; and the phenotype of accumulated macrophages possess a pro-inflammatory M1-polarized state, whereas resident macrophages in lean mice have a pro-resolving M2 phenotype (108–111). The M1-dominant adipose macrophages likely develop an inflammatory milieu [112]. The circulating levels of pro-inflammatory cytokines, including TNF-α, IL-6, and IL-1β was elevated in obese subjects [113, 114]. Chronic exposure to these cytokines potentially causes insulin resistance resulting in hyperglycemia [115, 116]. In addition, the serum levels of adipose tissue-derived cytokines, adipokines and adiponectin are also altered in the obese state. Obese adipose tissue increases inflammatory adipokines, including leptin, resistin, visfatin, IL-6, TNF-α, and monocyte chemoattractant protein-1, while decreasing anti-inflammatory adipokines, including adiponectin, omentin, IL-10, and IL-4. The dysregulation of adipokine production may alter cellular immune function and contribute to chronic low-grade inflammation and disease pathology (117–119). Obesity also increases the populations of activated CD4+ and CD8+ T cells in adipose tissue [120] and significantly reduces circulating Treg cells (121–123) which may sustain low-grade chronic inflammation. Furthermore, obesity induces thymic involution and convergent T cell repertoire, impairing impaired immune responses and increasing the risk and severity of infections [124].
As noted above, the effects of obesity on immune function are manifold. However, it remains unclear how obesity is associated with the expansion of periapical bone destruction. Therefore, our group examined possible pathways involved in bone loss in obesity using bulk-mRNA next-generation sequencing analysis. *Comprehensive* gene expression analysis revealed that, among a total 15,029 expressed genes, only 51 were differentially expressed in periapical lesions in DIO-B6 mice versus lean controls. Among them, Il1rn encoding IL-1RA was remarkably down-regulated (Log2 fold change = −1.18, False Discovery Rate (q-value) = 0.0002). At the same time, Il1a, but not Il1b, was also decreased (−0.994-fold, $q = 0.046$) [31]. These results suggest DIO impairs IL-1RA-dependent homeostatic suppression of IL-1 signaling, at least in the local environment.
Systemically, significantly increased IL-1 serum levels [114, 125] likely contribute to worsening of insulin resistance under obese conditions [116]. However, given the lack of significant changes in the expression of IL-1 signaling genes, including NF-κB, in AP [31], systemically increased IL-1 may have little effect on AP. Interestingly, IL-1RA serum levels are also elevated in obesity [126]. However, the concentration of IL-1RA is likely insufficient to block the effects of elevated IL-1. Indeed, administration of IL-1RA improves insulin sensitivity in animal models of obesity [116], suggesting IL-1RA-dependent homeostatic regulation of IL-1 signaling is not fully functional in obesity. We therefore examined if a decrease or loss of IL-1RA contributes to obesity-associated periapical inflammation by IL-1RA administration in infected DIO-B6 mice. Remarkably, periapical bone destruction was inhibited by $41.2\%$ by IL-1RA (Figure 1A, $p \leq 0.05$). Histological analysis revealed that IL-1RA-treated mice showed less inflammatory cell infiltration and well-developed fibrosis (Figure 1B). These results indicate that inflammation was down-regulated by IL-1RA, and that the lesion was composed mainly of mature granulation tissue compared to the immune granulomas in controls. Therefore, immunomodulation by IL-1RA is likely important for the control of AP, even in obesity.
Appropriate regulation of IL-1 signaling according to the host and infection status may lead to an optimal immune/inflammatory response in terms of timely onset/resolution and adequate host defense. In the first section, we explained that excessive IL-1 and its signaling cause exacerbation of AP in the non-obese state. At the same time, IL-1RA homeostatically regulates IL-1 signaling, suppressing excessive IL-1-mediated responses. In the second section, we described that obesity dysregulates IL-1RA-dependent homeostatic IL-1 signaling regulation and causes chronic elevation of inflammation, tissue destruction, and prolonged healing. Endodontic infection in DIO may exacerbate bone destruction in the long term via chronically elevating IL-1 signaling at a low level due to downregulation of Il1rn. However, the role of IL-1 signaling is diverse and complex. The impact of IL-1 signaling on both systemic and local conditions has not been fully understood. Thus, further studies are essential for the changes in IL-1 signaling associated with various systemic conditions, the underlying mechanisms, and infection-stimulated bone destruction.
## Funding
This study was supported in part by a grant from National Institute of Dental and Craniofacial Research/National Institutes of Health (R01DE024796 to HS).
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|
---
title: Intra-articular delivery of AAV vectors encoding PD-L1 attenuates joint inflammation
and tissue damage in a mouse model of rheumatoid arthritis
authors:
- Wenjun Li
- Junjiang Sun
- Susi Liu Feng
- Feng Wang
- Michael Z. Miao
- Eveline Y. Wu
- Shannon Wallet
- Richard Loeser
- Chengwen Li
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10021025
doi: 10.3389/fimmu.2023.1116084
license: CC BY 4.0
---
# Intra-articular delivery of AAV vectors encoding PD-L1 attenuates joint inflammation and tissue damage in a mouse model of rheumatoid arthritis
## Abstract
### Objective
Rheumatoid arthritis (RA) is the most common form of autoimmune inflammatory arthritis. Intra-articular gene delivery to block proinflammatory cytokines has been studied in pre-clinical models and human clinical trials. It has been demonstrated that the level of programmed death-ligand 1 (PD-L1) is associated with rheumatoid arthritis (RA). This study examined the therapeutic role of PD-L1 by intra-articular delivery via adeno-associated virus (AAV) vectors in the mouse collagen-induced arthritis (CIA) model.
### Methods
Mice were intra-articularly injected with AAV5 vectors encoding human PD-L1 on day 0 and immunized with bovine type II collagen to induce CIA simultaneously. On day 49 post AAV administration, joints were collected for histo-pathological and cytokine analysis. Additionally, the systemic impacts of intra-articular injection of AAV5/PD-L1 vectors were also studied. To study the therapeutic effect of PD-L1, AAV5/PD-L1 vectors were administered into the joints of RA mice on day 21.
### Results
After administration of AAV5/PD-L1 vectors, strong PD-L1 expression was detected in AAV transduced joints. Joints treated with PD-L1 at the time of arthritis induction exhibited significantly less swelling and improved histopathological scores when compared to untreated joints. Additionally, the infiltration of T cells and macrophages was decreased in joints of CIA mice that received AAV5/PD-L1 vectors ($P \leq 0.05$). The levels of pro-inflammatory cytokines, including IL-1, IL-6, IL-17 and TNFα, were lower in AAV5/PD-L1 treated than untreated joints ($P \leq 0.05$). Furthermore, the administration of AAV5/PD-L1 vectors into the joints of CIA mice did not impact serum cytokine levels and the antibody titers to type II collagen. Biodistribution of AAV vectors after intra-articular injection showed undetectable AAV genomes in other tissues except for a low level in the liver. Similar to the results of AAV5/PD-L1 vector administration on day 0, decreased joint swelling and lower histopathological damage were observed in joints treated with AAV5/PD-L1 vectors on day 21.
### Conclusion
The results from this study demonstrate that local AAV mediated PD-L1 gene delivery into the joints is able to prevent the development and block the progression of arthritis in CIA mice without impacting systemic immune responses. This study provides a novel strategy to effectively treat inflammatory joint diseases using local AAV gene therapy by interference with immune checkpoint pathways.
## Introduction
Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by skewed and dysregulated immune responses that affect multiple organs, particularly the joints. As one of the most common autoimmune disorders, the prevalence rate of RA is approximately $1\%$ worldwide [1], with higher risk of development in middle-aged women [2]. In addition to articular joints, RA can impact other tissues, such as blood vessels, heart, or lungs [3]. Due to progressive joint inflammation and damage, as well as extra-articular involvement, patients often suffer from pain, impaired mobility, and decreased life expectancy [4].
Currently, there is no perfect cure for RA and patients are treated with one or more disease-modifying antirheumatic drugs (DMARDs) and/or biologics such as cytokine inhibitors to inhibit immune and inflammatory responses. However, less than $30\%$ of patients have had robust responses to these drugs [2], and long-term remission is not achieved for many patients [5]. Additionally, these therapeutics can be expensive and have a number of side-effects related to their systemic delivery [6], in particular an increased risk of certain infections [7].
Systemic treatments have been widely applied for management of RA since it is a systemic disease. However, these treatment regimens are not always ideal. First, it is difficult to maintain a satisfactory drug concentration in specific sites where the disease is particularly active. This is supported by the fact that at least $10\%$ of patients with RA still end up with severe disabilities despite undergoing systemic treatments [3]. Second, systemic treatments require a much higher dose than local treatments, which results in increased adverse effects. Third, in some patients only one or a few joints may be active and therefore having an option of an effective intra-articular therapy would reduce the need for increasing the dose or changing of systemic therapies.
Even though RA as a systemic disease, demands systemic treatment for full recovery, intraarticular gene therapy, particularly with adeno-associated virus (AAV) vectors, has been explored to address the most severe problem in local sites. Due to the rapid clearance in the synovial space [8], traditional drug therapy has encountered problems with achieving a sustained and therapeutic concentration in affected joints with intra-articular injection and repeated administration of drugs is usually required [9]; Gene delivery to the synovium could induce sustained therapeutic gene expression locally within joints depending on the AAV serotypes used [10]. It has demonstrated that one-time administration of AAV vectors is able to achieve transgene expression in humans for as long as 10 years [11]. There have been several clinical trials using AAV vectors for intra-articular delivery [8], mostly targeting a single cytokine, such as IL-1Ra, IL-6, and TNFα [12, 13], but the benefit has been limited suggesting that other targets are needed.
It has been well known that immune cells infiltrate the joints in inflammatory arthritis, especially T cells and macrophages [14, 15]. These immune cells are able to secrete a variety of cytokine/chemokines in the joint [2]. In particular, the CD4+ T cell subsets, T helper type 1 cells (Th1), T helper type 17 cells (Th17) and regulatory T cells (Treg), are critical for RA initiation and development [16]. Therefore, targeting immune cells may represent a more ideal strategy to improve arthritis. It was reported that therapies to block T cell co-stimulation by targeting CD80/CD86-CD28 interactions were very effective in remediating both early and advanced disease [15, 17, 18]. Jun et al. applied anti-human DR5 antibody TRA-8 to deplete macrophages and decreased severity of arthritis [19]. These results indicate that the suppression of T cells and/or macrophages is critical for RA treatment.
PD1 is a well-known checkpoint protein for the maintenance of immune cell homeostasis [20]. PD1 is expressed on immune cells including T cells and macrophages (21–23). Its ligand PD-L1 is expressed ubiquitously on all cell types and is able to interact with PD1 on immune cells to influence the function of immune responses [15]. The binding of PD-L1 to PD1 on immune cells initiates the recruitment of Src Homology 2-Domain-Containing Tyrosine Phosphatase 1 and 2 (SHP-1/SHP-2) [24], that causes the dephosphorylation of signaling kinases such as CD3ζ, PKCθ and ZAP70 [24], and leads to the inactivation of the transcription factors such as STAT family and a global inhibition of immune cells [25]. Reports have shown that PD-L1 + cells were significantly altered in synovium tissue of untreated RA patients [26, 27], thus, increasing PD-L1 expression on immune cells from RA patients can be a potential strategy for RA treatment.
In this study, we explored the role of the PD1/PD-L1 pathway in the pathogenesis of arthritis in an RA mouse model. Our results showed that intra-articular injection of AAV vectors encoding PD-L1 was able to prevent the development and block the progression of arthritis in mice with collagen induced arthritis (CIA). The expression of PD-L1 in the joints alleviated knee joint swelling and inflammation by decreasing T cell/macrophage infiltration and proinflammatory cytokine production while maintaining a safe profile without significant systemic side effects.
## Cells and AAV vector production
HEK-293 cells were cultured in Dulbecco’s Modified Eagle Medium with $10\%$ fetal calf serum, 100 U ml−1 penicillin G and 100 μg ml−1 streptomycin in the 37°C incubator. Cells were routinely split 1:5 three times a week, when ~$90\%$ confluency was reached.
AAV5 vectors were produced by triple transfection in HEK-293 cells and purified using cesium chloride (CsCl) gradient ultracentrifugation. To quantify AAV titers, Real-time quantitative polymerase chain reaction (RT-qPCR) was carried out in a 10μL volume in 96-well plates using Fast SYBR Green Master Mix (Applied Biosystems, Foster City, California, USA) by detecting the AAV sequence ITR. Primers for the ITR (forward: 5’- AAC ATG CTA CGC AGA GAG GGA GTG G -3’, reverse: 5’-CAT GAG ACA AGG AAC CCC TAG TGA TGG AG-3’) were designed and synthesized (Gene Script, New Jersey, USA). All RT-qPCRs included 40 cycles and a melt-curve. Serial dilutions of AAV with known titers were used as standards. Alkaline gel electrophoresis was also applied to verify AAV vector genome integrity; SYPRO Ruby protein gel stain (Thermo Fisher, Waltham, Massachusetts, US) was used to ensure the capsids contained all the three proteins, VP1, VP2 and VP3.
## Construction of AAV cassette for PD-L1 protein expression
Due to the homology of human PD-L1 and mouse PD-L1, human PD-L1 was reported to bind to mouse PD1 [28]. Therefore, the human PD-L1 cDNA was synthesized and was cloned to pTR-CBh-GFP by substitution of the GFP transgene through the use of two restriction enzymes—Age I and NotI to generate pTR-CBh-PD-L1. The PD-L1 expression was driven by the CBh promoter that is able to induce a strong, long-term, and ubiquitous transgene expression [29]. A 6* His-tag (CACCATCACCATCACCAT) was fused to PD-L1 right before the stop codon. The insert sequences were verified by Sanger sequencing, and the intact ITRs in the PD-L1 plasmid were verified by SmaI restriction enzyme digestion.
## Western blot
The pTR-CBh-PD-L1 plasmid was transfected into a 6 well plate of HEK-293 cells. After 48h, the cell medium supernatant was collected, and the HEK-293 cells were then lysed for 30 minutes on ice in RIPA buffer (Thermo Fisher, Waltham, Massachusetts, US) and x100 cocktail protein inhibitor (Thermo Fisher, Waltham, Massachusetts, US), followed by centrifugation at 13,000rpm for 10min at 4°C to separate the cell lysates from the cell debris. The supernatant or cell lysates were mixed with 4x Laemmli Sample Buffer (Bio-Rad), boiled for 7min, and separated by gel electrophoresis in $10\%$ Mini-PROTEAN® TGX™ Protein Gels precast gels (Bio-Rad Watford, UK). Recombinant human PD-L1 protein with His tag was used as a positive control (abcam, Cambridge, UK). After gel electrophoresis, proteins were transferred on nitrocellulose membranes (Bio-Rad) using a Trans-Blot Turbo Transfer System (Bio-Rad). Membranes were first blocked using blocking buffer ($5\%$ milk powder in TBS-T) and subsequently stained with mouse anti-6* His tag (abcam, Cambridge, UK) or mouse anti-human PD-L1 primary antibody (abcam, Cambridge, UK) overnight in antibody buffer ($5\%$ BSA in TBS-T). The blots were then washed in TBS-T, incubated with the HRP conjugated secondary antibody for 1h, and washed in TBS-T again, before visualizing with ECL substrate (GE Healthcare, Chicago, IL, USA) and imaging in an AI600 Chemiluminescent Imager (GE, Buckinghamshire, UK).
For detection of phosphorylated PI3K and AKT expression in activated T cells, T cells were lysed by RIPA lysis buffer and cocktail proteinase inhibitor (x100) at 4°C for 30min. The cell lysate was collected by centrifugation at 12,000rpm for 10 minutes at 4°C. 10 μg cell lysate from T cells were loaded in SDS-PAGE to detect p-PI3K and p-AKT protein by western blot using a rabbit anti- p-PI3K antibody (abcam, Cambridge, UK) and mouse anti- p-AKT antibody (abcam, Cambridge, UK). GAPDH was utilized as an internal control for tissue samples.
To verify the PD-L1 protein expression levels in the tissues, the mouse knees and the livers were collected and homogenized on dry ice, then incubated with T-PER™ Tissue Protein Extraction Reagent (Thermo Fisher, Waltham, Massachusetts, US) and cocktail proteinase inhibitor (x100) at 4°C for 30min. The supernatant was collected by centrifugation at 12,000rpm for 10 minutes at 4°C. 20 μg of supernatant from the tissue lysates were loaded in SDS-PAGE to detect PD-L1 protein by western blot using a mouse anti-6*His tag antibody (abcam, Cambridge, UK). GAPDH was utilized as an internal control for tissue samples.
## T cell functional assays
PD-L1 protein was purified from a PD-L1-transfected HEK-293 cells. The supernatant and cell lysates were collected and PD-L1 protein was purified using a HisTrap column (Cytiva, MA, USA); recombinant PD-L1 protein (R&D, MN, USA) was used as the positive control. Pan T cells from spleen were filtered through a 70 μm cell strainer (Miltenyi, Germany) and extracted by a T cell isolation kit (Miltenyi Biotec, Germany). T cells were cultured in RPMI medium 1640 with 2 mM L-Glutamine, $10\%$ FBS, and 100 U/mL penicillin/streptomycin. T cells were stained with CellTrace Violet dye as indicated by the CellTrace™ Violet Cell Proliferation Kit (Thermo Fisher, Waltham, Massachusetts, US), then incubated with 2x105 anti-CD3/CD28 beads (Thermo Fisher, Waltham, Massachusetts, US) and 10U/ml IL-2 (R&D, MN, USA) with or without 5 μg/ml of purified PD-L1. Recombinant PD-L1 and PBS were used as positive and negative controls, respectively. T cells cultured with no anti-CD3/CD28 beads were also designed. After 72h, T cells were collected, and the percentage of proliferating cells from each group were determined by Attune Flow Cytometer (Thermo Fisher, Waltham, Massachusetts, US) with an emission of $\frac{405}{445}$nm. T cell apoptosis rate was also detected by anti-7AAD antibody (abcam, Cambridge, UK) with an emission of $\frac{550}{650}$nm.
## Collagen induced arthritis mouse model
All animal care and housing requirements were followed under the guidance of the National Institutes of Health Committee on the Care and Use of Laboratory Animals of the Institute of Laboratory Animal Resources, and all animal protocols were reviewed and approved by the Institutional Animal Care and Use Committee at the University of North Carolina at Chapel Hill. The CIA model was used to mimic the acute inflammation conditions of RA. DBA/1J mice were selected due to the higher incidence and severity of RA manifestations compared to other strains such as C57BL/6 mice [30]. Male DBA/1J mice at the age of 7-8 weeks were used.
For the primary immunization, bovine type II collagen (Chondrex, Woodinville, WA, USA) was dissolved with 0.01 N glacial acetic acid at a concentration of 2mg/ml, emulsified with complete Freund’s adjuvant (CFA) (Sigma-Aldrich, Burlington, MA, USA) using a “two-syringe” method for 1h on ice, and injected into the mouse [30]. The booster vaccination was administered 21 days later with bovine type II collagen mixed with incomplete Freund’s adjuvant (IFA) (Sigma-Aldrich, Burlington, MA, USA). Collagen was injected into mouse tail root for both immunizations.
## Animal study design
For the prophylactic treatment, AAV5/PD-L1 vectors were injected into the knee joints on day 0 before arthritis was initiated, on the same day as the primary immunization of the type II collagen; We also administered AAV5/luc vectors or PBS into knee joints as control. For the therapeutic treatment, AAV5/PD-L1 vectors were injected on day 21, on the same day as booster vaccination, with the inflammation already gradually induced in joints. Mice received intra-articular administration of self-complementary (sc) AAV5/PD-L1 driven by the CBh promoter at a dose of 1x1010 particles in the left knee joint. From our pilot study, histology staining showed AAV5/luc didn’t enhance or decrease the joint inflammation in CIA mice model, and AAV5/luc didn’t trigger inflammation in naïve mice joints. There was no significant difference between AAV5/luc and PBS group in pathological score of joints from both CIA mice and naïve mice (Figure S1). Therefore, mice treated with PBS in the contralateral knee joints were used as control. For detecting the cytokine level in serum, the mice from untreated group received intra-articular administration of PBS in both knee joints. The negative control group consisted of naïve mice.
## Joint size measurement
The knee joint size (mm) from left to right side was measured using a caliper (31–33) (Mitutoyo, Takatsu-ku, Kawasaki, Kanagawa) at week 0 before primary immunization and weeks 3, 4, 5, 6, and 7 after primary immunization. The joint size increase percentage was calculated by the diameter of joints at different time points divided by the diameter of baseline size on day 0.
## Tissue histopathology
At week 7 after collagen primary immunization, mice were sacrificed and the knee joints were collected by dissecting the femur and tibia 5 mm away from the knee joint. The harvested knees were fixed in $4\%$ paraformaldehyde, decalcified in $5\%$ trichloroacetic acid for 7 days, dehydrated, embedded in paraffin, and sectioned at 5 μm for hematoxylin and eosin (H&E) staining and immunohistochemistry staining (IHC). Histopathological evaluation was conducted by two independent observers blinded to experimental group, and at least 10 fields of view per joint were analyzed. The score was based on following change in four conditions: synovial hyperplasia [0-3], infiltration of leukocytes into the synovial membrane/joint space [0-3], pannus formation [0-3], and necrosis/erosion of cartilage [0-3] [34].
Spleens and livers were fixed in $4\%$ paraformaldehyde, dehydrated, embedded in paraffin, and sectioned at 5 μm for H&E staining.
To perform IHC, the sections were de-paraffinized, rehydrated, subjected to heat-induced antigen retrieval at 95°C for 20 min in 0.01 M sodium citrate, and cooled at room temperature for 25min. Subsequently, the samples were incubated in $3\%$ H2O2 in methanol for 20 min, blocked with $10\%$ normal goat serum for 1h, and incubated overnight at 4°C with following antibodies: rabbit anti-mouse CD68+ primary antibody (abcam, Cambridge, UK, $\frac{1}{150}$ dilution) for macrophages, rabbit anti-mouse CD206 primary antibody (abcam, Cambridge, UK, $\frac{1}{200}$ dilution), rabbit anti-mouse iNOS primary antibody (abcam, Cambridge, UK, $\frac{1}{150}$ dilution), rabbit anti-mouse CD3+ primary antibody (abcam, Cambridge, UK, $\frac{1}{150}$ dilution) for pan T cells, rabbit anti-IL-17 primary antibody (abcam, Cambridge, UK, $\frac{1}{200}$ dilution), and rabbit anti-FOXP3 primary antibody (abcam, Cambridge, UK, $\frac{1}{200}$ dilution), rabbit anti-6*His primary antibody (Thermo Fisher, Waltham, Massachusetts, US, $\frac{1}{500}$ dilution), rabbit anti-murine PD-L1 antibody (abcam, Cambridge, UK, $\frac{1}{500}$ dilution) and rabbit anti-human and murine PD-L1 antibody (Thermo Fisher, Waltham, Massachusetts, US, $\frac{1}{1000}$ dilution) for PD-L1 expression. Negative controls were treated with $10\%$ normal goat serum without the primary antibodies. Anti-rabbit antibody (abcam, Cambridge, UK, $\frac{1}{500}$ dilution) was used as secondary antibody, the color was developed using a Vectastain Elite ABC Kit (Vector Laboratories, Burlingame, CA, USA) and DAB Substrate Kit (Vector Laboratories, Burlingame, CA, USA). 5 knee joints were used in each group, and 10 fields of view per joint were evaluated. Positively stained cells in each field of view were counted by two blinded observers and averaged.
## Cytokine assay
To examine the cytokines in the knee joint and serum at week 7 post primary immunization of collagen, the mice were sacrificed, knee joints were harvested, and the muscles were peeled off as much as possible. The bony structures were then minced and put into 500µl of cold PBS overnight in a 4°C cold room. The mouse blood was also collected by retro-orbital bleeding method using non-heparinized micro-hematocrit capillary tubes (DWK Life Sciences, Millville, NJ, US) and set for 30 min at room temperature. The supernatant from the joints and sera were then collected by centrifugation at 3,000rpm for 10 min. The protein concentration was measured by BCA assay. Multiple cytokines in the knee joint homogenization, including IL-1, IL-6, IL17A, TNFα, and IL-10, were measured on a Luminex MAGPIX system (Luminex Corporation, Austin, TX, USA). Cytokine levels were expressed in picograms per milliliter (pg/ml). Levels below the detection limit were defined as 0 pg/ml for each cytokine. The cytokine levels per mg protein were calculated.
## AAV copy number
DNA from the heart, liver, spleen, lung, kidney, and knee were extracted using DNeasy Blood & Tissue Kits (Qiagen, Germantown, Maryland, USA). AAV copy numbers in the heart, liver, spleen, lung, kidney, serum and knee were then determined by qPCR as described in our previous studies [35].
## Liver function
Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in serum at week 7 after primary immunization were measured at a wavelength of 340nm using AST/ALT Assay kit (abcam, Cambridge, UK).
## Detection of collagen antibodies
50ng/μl type II collagen were coated with 100 μl coating buffer (BioLegend, San Diego, CA, US) on Corning Costar Brand 96-Well EIA/RIA Plate (Thermo Fisher, Waltham, Massachusetts, US) overnight. After washing and blocking with $1\%$ BSA buffer, mice sera or joint lysate (as mentioned in cytokine assay) were diluted from 1:10 to 1:107 with three times dilution and incubated for 2h at room temperature. After wash, HRP conjugated anti-mouse antibody (abcam, Cambridge, UK, $\frac{1}{10000}$ dilution) was added in each well for 1h at room temperature, then TMB substrate (abcam, Cambridge, UK) was added for 15 min and reactions stopped with stop solution (Thermo Fisher, Waltham, Massachusetts, US), the color intensity was analyzed at 450nm, and the antibody titer was determined by the OD value that was 3 times higher than that in naïve mice.
## Statistical analysis
GraphPad Prism 9 software was used for statistical analysis. Data are shown as mean ± SD, and the box and whisker plots were used for descriptive statistics. Differences among each group were determined by one-way ANOVA or student’s t test. Bonferroni and Sidak tests were used for multiple comparisons between groups. The significance level was set at 0.05. Based on the power analysis of our preliminary data using nQuery software, the power of mouse sample size was over $80\%$ at a significance level of 0.05.
## PD-L1 was expressed in HEK-293 cells
To verify the human PD-L1 (hPD-L1) protein expression in vitro, we cloned the pTR-CBh-PD-L1 plasmid (Figure 1A), in which the hPD-L1 transgene is driven by the truncated CBA promoter, and transfected the plasmid in HEK-293 cells; both supernatants and cell lysates were then collected on day 2. PD-L1 was detected at 33kDa in cell lysates but not in supernatants. No PD-L1 was detected in HEK-293 cells transfected with pTR-CBh-GFP (Figure 1B).
**Figure 1:** *PD-L1 gene expression and function in vitro. (A), Schematic diagram of the AAV/PD‐L1 cassette. (B), Western blot analysis for PD‐L1 protein expression. After transfection of pTR/CBh-PD-L1 or pTR/CBh-GFP into HEK-293 cell lines, 48h later, the supernatant and cell lysate were collected for PD-L1 expression by western blot with antibodies against either His tag or PD-L1. Data shown is the representative of 5 independent experiments. (C), T cell proliferation. Purified splenic T cells were stained with CellTrace Violet dye, then co-cultured with PBS, PD-L1 in the presence of anti-CD3/anti-CD28 for 72h. The proliferation of positively stained cells was analyzed with flow cytometry. Data shown is representative of 5 independent experiments. (D), Summary data of T cell proliferation. The result was normalized to T cell proliferation rate in the group with CD3/28+PBS (n=5). Mean values are shown with standard derivation. Data was analyzed using one-way ANOVA followed by Bonferroni multiple comparison test for comparisons. *, p < 0.05, ****, p < 0.001.*
## The effect of PD-L1 on T cell function inhibition
Next, PD-L1 protein was purified from transfected cells, and PD-L1 function was verified. Pan T cells were isolated and stained with CellTrace™ Violet, then activated and expanded with CD3/CD28 beads and IL-2, in the presence of PD-L1 protein. After 72h of T cell expansion, cells were resuspended in staining buffer and analyzed with flow cytometry. At the same threshold setting, the proliferation rates in the purified PD-L1 group and the positive control group (recombinant PD-L1 protein) were found to be similar without significant difference (25 ± $7.3\%$ and 25.2 ± $6.5\%$, respectively; $P \leq 0.05$). In contrast, the PBS group had a proliferation rate of 30.6 ± $9.9\%$ (Figures 1C, D), and T cells without CD$\frac{3}{28}$ activation (inactivated cells) had a proliferation rate of 2.2 ± $0.6\%$. We normalized the proliferation rate in each group to that of the PBS group (Figure 1D) and found that PD-L1 from both the positive control and the pTR-CBh-PD-L1 transfection significantly decreased T cell proliferation($P \leq 0.05$). To further confirm the role of PD-L1 from our construct in inhibition of T cell function, we examined one of the downstream signals—PI3K-AKT pathway in T cells. We have tested expression of phosphorylated PI3K and AKT by western blot in the T cells incubated with PD-L1 from our construct, after 48h, we found less expression of p-PI3K and p-AKT when compared to T cells without treatment (Figure S2). Additionally, we also examined the apoptosis of activated T cells after 72h incubation by flow cytometry using anti-7AAD antibody. Higher apoptosis was found in T cells treated with PD-L1 when compared to PBS (Figure S2). These results clearly support that PD-L1 from our construct is able to block activated T cell function.
## PD-L1 expression in AAV5/PD-L1 transduced mouse knee joint
To examine PD-L1 expression in vivo, AAV5/PD-L1 vector was injected into the left knee, and PBS was injected into the right knee of the same CIA mouse as a control (Figure 2A). At week 7 post AAV injection, we collected mouse knee joints and carried out a western blot to detect PD-L1 expression in knee joint lysates. PD-L1 was detected in the knee joint lysates of the AAV5/PD-L1 injected group, but not in the PBS group (Figure 2B). PD-L1 expression was further validated by IHC staining with anti-6* His tag. In AAV5/PD-L1 injected mice, approximately $15\%$ of synoviocytes and $12\%$ chodrocytes were detected as positive in the AAV5/PD-L1 group (Figures 2C, D) but not in PBS injected group. These findings indicate that locally injecting AAV/PDL1 vector into the joints induced PD-L1 expression.
**Figure 2:** *High PD-L1 expression in knee joints treated with AAV5/PD-L1 vectors. (A), Schematic diagram of animal experiment. On day 0, AAV5/PD-L1 vectors at a dose of 1x1010 particles were injected in CIA mouse model into the left knee, and PBS into the right knee of the same CIA mice. On the same day, type II collagen was injected into the mouse tail root as the primary immunization, followed by booster immunization on day 21. Joints were collected for PD-L1 detection on day 49. (B), Western blot of PD-L1 expression in knee. Knee joints from both left knee (AAV5/PD-L1 injection) and right knee (PBS) were lysed and the supernatant was collected for PD-L1 protein detection by western blot using mouse anti-6*His tag antibody. Data shown is representative of 3 independent experiments. (C), Immunohistochemistry staining for cells transduced by intra-articular injection of AAV5/PD-L1 vectors. Representative images are shown with anti-6* His positive cells in AAV5/PD-L1 treated mouse knees (left) and PBS treated mouse knees (right) (n=3, bar=20μm). The blue arrow indicated positively stained synoviocytes, the orange arrow indicated positively stained chondrocytes. (D), PD-L1 expression cell percentage in AAV5/PD-L1 treated joints. Three knee joints were analyzed in each group, the positive PD-L1 cells were counted from 10 fields of view and averaged. Each dot represents one mouse joint. The data is represented by mean and standard derivation. Data was analyzed by two-tailed paired student’s t test.*
## Prophylactic local injection of AAV5/PD-L1 vectors reduced knee joint swelling and clinical scores in RA mice
To investigate whether the local PD-L1 expression from AAV vector gene therapy was able to prevent knee joint inflammation, the well-established CIA mouse model was used. DBA/1J mice were treated with AAV5/PD-L1 vectors via intra-articular injection of 1x1010 particles in the left joint and PBS in the right joint on day 0. On the same day, type II collagen was injected into the tail root (Figure 3A). The booster of type II collagen was applied on day 21. At that time, the mice were already exhibiting some degree of swelling around the joint. The size of the joints with PBS and AAV5/luc treatment gradually increased to their peak by day 35 post primary collagen immunization and remained unchanged over the next two weeks. Compared to their size before primary collagen immunization, the size of joints injected with PBS had increased by 33.8 ± $6.9\%$ and joints injected with AAV5/luc had increased by 35.43 ± $6.2\%$ (Figure 3C). However, the joint swelling of the AAV5/PD-L1 treated group reached the peak one week earlier than that of the PBS group, and joint size had only increased by 6.2 ± $3.9\%$ on day 49 post AAV intra-articular injection, significantly reduced as compared to the PBS group in CIA mice ($P \leq 0.05$).
**Figure 3:** *Improved knee joint swelling and decreased histological change in joints treated with AAV5/PD-L1 vectors as prophylaxis. (A), Schematic diagram of animal experiment with intra-articular injection of AAV5/PD-L1 vectors as a prophylactic treatment, 1x1010 particles of AAV5/PD-L1 vectors were injected into the left knees of CIA mice on day 0, while PBS or AAV5/luc was injected into the right knees of the same mice. On the same day, the primary immunization with type II collagen was applied, and on day 21, the booster immunization was applied. Knee joint size was measured weekly. At week 7, joints were collected for histology and cytokine analysis, and other tissues, including sera, liver, spleen, heart, lung, and kidney, were also harvested to evaluate systemic impact of PD-L1 expressed in the joints. (B), joint histology analysis with H&E staining. Representative images of H&E staining from the CIA mouse knees injected with PBS, CIA mouse knees injected with AAV5/PD-L1, AAV5/luc or naïve mice at week 7 are shown (n=10, a-d, bar=200μm; e-h, bar=20μm). (C), knee joint swelling change with PD-L1 prophylactic treatment. The knee joint sizes at different time points were measured and compared to those on day 0. Data is shown as means ± SEM (n=10). ****, p < 0.001. (D), The knee joint histological score (n=10). Histopathological evaluation was performed and scored by two independent observers for the following changes: synovial hyperplasia, leukocyte infiltration, pannus formation, and cartilage necrosis/erosion. Each dot represents one mouse joint. Data is represented as means ± SEM. Data was analyzed using one-way ANOVA followed by Bonferroni multiple comparison test for group comparisons. **, p < 0.01, ****, p < 0.001. ns, not significant (p ≥ 0.05).*
At week 7, the mice were sacrificed. Knees were collected and sectioned for H&E staining, and the histopathological change was graded based on synovial hyperplasia, immune cell infiltration, pannus formation, and cartilage necrosis/erosion (Figure 3B). As shown in Figures 3B, D, AAV5/PD-L1-treated knees had a significantly lower histopathological score compared to that of the PBS and AAV5/luc group ($P \leq 0.05$); for comparison, the histopathological score of the untreated knees of naïve mice was 0.8± 0.4($P \leq 0.05$) (Figure 3D). These results implicate the prophylactic effect of PD-L1 expressed from AAV transduced synoviocytes on arthritis development in CIA mice.
## PD-L1 expression decreased T cell infiltration in the knee
T cells play a critical role in the initiation and progression of RA. T cells were stained with a CD3 antibody with brown color (Figure 4A) and T cells infiltrating the synovium of each joint were counted and averaged in 10 fields of view (x400) under the microscope. There was a remarkable decrease of T cell infiltration in AAV5/PD-L1 treated joints compared to those of the PBS group ($P \leq 0.05$). The T cell numbers were 79.8 ± 17.8, 22.8 ± 10.8 and 3.7 ± 1.5 in the synovium of the CIA+PBS, CIA+AAV5/PD-L1, and naïve joints, respectively (Figure 4B). We also further analyzed cells expressing IL-17 or FOXP3 (Figure 4A), and found that positive IL17 cells were ~4 fold higher in the CIA+PBS group than that in the CIA+AAV5/PD-L1 group (Figure 4B, 86.8 ± 19.7 vs 22 ± 8.2, $P \leq 0.05$). Positive FOXP 3 cells were ~2.5 fold higher in CIA+PBS group than CIA+AAV5/PD-L1 group (Figure 4B, 56.9 ± 12.9 vs 19.4 ± 7, $P \leq 0.05$). The same trend in positive cell percentages was observed as cell numbers (Figure 4C). The ratio of IL17+ cells to FOXP3+ cells was 1.8, 1.0, and 0.7 in the PBS, AAV5/PD-L1, and naïve groups, respectively. The ratio of IL17/FOXP3 was significantly higher in the PBS group than that in both the AAV5/PD-L1 group and the naïve group ($P \leq 0.05$), but no significant difference was observed between the AAV5/PD-L1 group and the naïve group ($P \leq 0.05$, Figure 4B).
**Figure 4:** *Infiltration of T-cells in the synovium was reduced in PD-L1 treated joints with AAV vectors for prophylactic treatment. (A), Immunohistochemistry staining for T cells. Representative images of in situ immunohistochemical visualization of T cells stained with antibodies against CD3, IL-17, FOXP3 (bar=20μm) is shown in CIA mouse knees treated with PBS, AAV5/PDL1, or naïve mice. B-C, Total number (B) and cell percentage (C) for CD3, IL-17 and FOXP3. The cells were counted in 10 fields of view and averaged in each joint. Each dot represents one mouse joint. Five knee joints were analyzed in each group. Mean values are shown with standard derivation (n=5). Data was analyzed using one-way ANOVA followed by Bonferroni multiple comparison test for group comparisons. *, p < 0.05, **, p < 0.01, ***, p < 0.005, ****, p < 0.001. ns, not significant (p ≥ 0.05).*
## PD-L1 expression decreased macrophage infiltration in the knee
To verify if PD-L1 decreased macrophage infiltration in mouse knees in the CIA model, macrophages were stained with CD68 antibody (Figure 5A), and the infiltration of macrophages in the synovium was quantified. The macrophage number in the PBS group was found to be ~5.5 fold higher than that of the AAV5/PD-L1 treated joints ($P \leq 0.05$) and ~20 fold higher than that of the naïve mice (Figure 5B, 85.8 ± 18, 15.6 ± 8.8, and 4.7 ± 1.5 respectively). Next, we investigated the infiltration of different subsets of macrophages using iNOS staining for M1 macrophages and CD206 antibody staining for M2 macrophages. As shown in Figure 5B, the number of iNOS+ cells in the PBS group was ~3 fold higher than that of the AAV5/PD-L1 group (99.4 ± 14.3 vs 32.8 ± 15.7, $P \leq 0.05$). Similarly, about 3 fold more CD206+ cells were detected in the PBS group than in the AAV5/PD-L1 group (Figure 5B, 92.6 ± 25.3 vs 27 ± 11.5, $P \leq 0.05$). There was a significant difference in the percentage of total macrophages as well as cells expressing iNOS or CD206 between the AAV5/PD-L1 group and PBS group, but no significant difference in cell percentages between AAV5/PD-L1 group and naïve mice (Figure 5C). Furthermore, we calculated the iNOS+/CD206+ ratio by dividing the positive iNOS cell number by the positive CD206 cell number, and found that the ratios were ~1.5, 1.2, and 0.9 in the PBS, AAV5/PD-L1 and naïve groups, respectively; there was no significant difference between the AAV5/PD-L1 group and the PBS group or between the AAV5/PD-L1 group and the naïve group($P \leq 0.05$, Figure 5B). However, a significant difference was found between the PBS group and the naïve group ($P \leq 0.05$, Figure 5B).
**Figure 5:** *Decreased infiltration of macrophages in the synovium with PD-L1 prophylactic treatment. (A), Immunohistochemistry staining for macrophage cells. Representative images of in situ immunohistochemical visualization of macrophages stained with antibodies against CD68, iNOS, CD206 (bar=20μm) are shown in CIA mouse knees. (B, C), Total number (B) and cell percentage (C) for CD68, iNOS and CD206. The macrophages were counted from 10 fields of view and averaged in each joint for five knee joints. Each dot represents one mouse joint. Mean values are shown with standard derivation (n=5: PBS and AAV5/PD-L1; n=3: naïve). Data was analyzed using one-way ANOVA followed by Bonferroni multiple comparison test for group comparisons. *, p < 0.05, ***, p < 0.005, ****, p < 0.001. ns, not significant (p ≥ 0.05).*
## AAV5/PD-L1 decreased inflammatory cytokine levels in the knee
Immune cell infiltration and activation induces production of proinflammatory cytokines/chemokines. We studied whether PD-L1 treatment in the joints impacted the production of cytokines. Five representative cytokines were analyzed in local joint lysates by a Luminex MAGPIX system. As shown in Figure 6, the levels of pro-inflammatory cytokines IL-1, IL-6, IL-17, and TNFα in the AAV5/PD-L1 treated knee joint tissue homogenization were 2~5 fold lower than those in the PBS groups ($P \leq 0.05$), while the level of inhibitory cytokine IL-10 was not different between AAV5/PD-L1 and PBS groups ($P \leq 0.05$). The cytokine levels in the knee joints of naïve mice were undetectable (data not shown). This result showed that local AAV5/PD-L1 treatment decreased the generation of proinflammatory cytokines in the knee joints of CIA mice.
**Figure 6:** *Reduced cytokines level in knee joints treated with AAV5/PD-L1 vectors. Knee joints were homogenized and the supernatant was collected for detection of cytokines IL-1, IL-6, IL17A, TNFα, and IL-10. Mean values are shown with standard derivation (n = 12: PBS; n = 7: AAV5/PD-L1). Data was analyzed using two-tailed unpaired student’s t test. *P < 0.05, **P < 0.01, ***, p < 0.005. SF, synovial fluid. ns, not significant (p ≥ 0.05).*
## AAV5/PD-L1 decreased mouse endogenous PD-L1 expression in the knee
It has been demonstrated that inflammation could up-regulate PD-L1 expression. For example, IFN-γ, IL-2, IL-17, IL-15, IL-4 and granulocyte-macrophage colony-stimulating factor (GM-CSF) have been considered potent inducers of PD-L1 expression [36, 37]. Therefore, we have performed the immunohistochemistry staining to detect PD-L1 expression in joints of CIA mice. When anti-murine PD-L1 antibody with cross-reactivity to human PD-L1 was used, total cells expressing PD-L1 were similar in joints regardless of PD-L1 treatment. However, when we applied the antibody only detecting mouse PD-L1, we found a decrease in mouse PD-L1 expression in cells in the joints treated with AAV5/PD-L1 vectors (Figure S3). The up-regulation of PD-L1 expression upon inflammation in arthritis is perhaps caused by the compensation feedback of host response to inflammation and then attenuates the disease. Collectively, these results indicate that early delivery of AAV vectors for PD-L1 expression could decrease joint inflammation and then reduce mouse endogenous PD-L1 expression in joints with arthritis.
## Intra-articular injection of AAV5/PD-L1 vectors did not induce PD-L1 expression in other tissues
Ideally, local PD-L1 application would induce minimal effects on systemic immune responses. In order to elucidate whether intra-articular injection of AAV5/PD-L1 vectors induce a significant systemic immune response modulation, we first studied AAV genome biodistribution using qPCR in different tissues including joints, liver, spleen, kidney, heart and serum. Besides high AAV genomes in the AAV5/PD-L1 treated knees with ~ 1.3 viral copy numbers per diploid genome, AAV genomes were also detected in the liver at levels of about 0.008 viral copy numbers per diploid genome, approximately 150-fold lower than that of the joints ($P \leq 0.05$). AAV genomes were undetectable in other tissues with less than 0.001 copy per diploid genome (Figure 7A). We further checked PD-L1 expression and found no detectable PD-L1 in the livers of CIA mice after intra-articular administration of AAV5/PD-L1 vectors (Figure 7B).
**Figure 7:** *No systemic side effects detected after intraarticular AAV5/PD-L1 injection. (A), AAV genome copy numbers. At week 7 after AAV injection, mice were euthanized and different tissues (liver, knee, heart, lung, spleen and kidney) were collected for AAV genome analysis using qPCR. DNA from the heart, liver, spleen, lung, kidney, and knees from mice injected with AAV5/PD-L1 were extracted using DNeasy Blood & Tissue Kits and then determined by qPCR. The data represent the mean and standard derivation (n=3). ****, p < 0.001. (B), Western blot of PD-L1 in liver lysates. 20 μg of supernatant from the liver lysates collected from mice injected with AAV5/PD-L1 and PBS were loaded in SDS-PAGE to detect PD-L1 protein by western blot using mouse anti-6*His tag antibody. Lane 1, recombinant PD-L1-his protein, lane 2, AAV5/PD-L1 treated mice liver, lane 3, PBS treated mice liver.*
## Intra-articular AAV5/PD-L1 treatment did not change cytokine levels in serum
To determine if joint PD-L1 treatment had a similar effect on cytokine levels in serum as in the joints, we further investigated cytokine levels in the blood serum from CIA mice treated with AAV5/PD-L1 or PBS, as well as in naïve mice. Higher cytokine levels were detected in CIA mice than in naïve mouse serum ($P \leq 0.05$) (Figure 8), but there was no significant difference in serum cytokine levels of CIA mice regardless of treatment with AAV5/PD-L1 vectors or not ($P \leq 0.05$). This result implies that intra-articular AAV5/PD-L1 treatment does not have an impact on the systemic cytokine profile in sera of CIA mice.
**Figure 8:** *The levels of multiple cytokines in mouse serum. Mouse sera were collected from both CIA mice that had been injected with or without AAV5/PD-L1. The concentrations for IL-1, IL-6, IL17A, TNFα, and IL-10 were measured. The mean values are shown with standard derivations. *P < 0.05, **P < 0.01, ***, p < 0.005, ****, p < 0.001. ns, not significant (p ≥ 0.05).*
## Intra-articular administration of AAV5/PD-L1 vectors did not impact the antibody titers against type II collagen
The CIA model was established by immunization of two doses of type II collagen antigen. We studied whether the local administration of AAV5/PD-L1 vectors influences the collagen specific antibody titers in sera and joints. In both the serum and knee joint lysate, similarly high titers of antibodies against type II collagen were generated in CIA mice treated with AAV5/PD-L1 and PBS, with a type II collagen antibody titer of ~3.3x105 per μl in serum and ~3.3 x104 per 1mg in knee joint lysate, but not in naïve mice (Table 1). These results suggest that local joint PD-L1 expression is unable to affect antibody production and local specific antibody titers in CIA mice.
**Table 1**
| Unnamed: 0 | CIA+PBS | CIA+AAV5/PD-L1 | Naïve |
| --- | --- | --- | --- |
| Antibody titer in serum | 3.3e5 | 3.3e5 | < 10 |
| Antibody titer in knee/mg protein | 3.3 e4 | 3.3 e4 | < 10 |
## Local joint PD-L1 expression did not impact general health conditions in CIA mice
After immunization with type II collagen, CIA mice manifested redness and swelling in paws without mobility change. After intra-articular administration of AAV5/PD-L1 vectors, we only observed a slight increase in body weight during the time of observation, without significant differences among groups (Figure S4). No death from the PD-L1 treatment was observed.
We also analyzed histological changes in the liver and spleen. Hepatocytes and splenocytes were normal in shape and arrangement with no macrovesicular steatosis, necrosis, or apoptosis, indicating no signs of inflammation, cellular damage, or carcinoma (Figure S5). At week 7 after AAV administration, the levels of AST and ALT in sera were within normal range in all mice, regardless if treated with AAV5/PD-L1 or not (Figure S6).
## Local joint injection of AAV5/PD-L1 vectors blocked the progression of arthritis in CIA mice
As shown above, we had demonstrated that administration of AAV5/PD-L1 was able to prevent arthritis development in the CIA model. Next, we addressed whether PD-L1 expressed from AAV transduced joints had the potential to block the progression of arthritis, since RA is a chronic disorder with intermittent remissions and flare ups. To execute the study, AAV5/PD-L1 vectors were injected in the left knee on day 21 post primary immunization of type II collagen in CIA mice instead of on day 0 (Figure 9A). Consistent with the results of AAV5/PD-L1 vectors injected on day 0, mice treated with intra-articular injection of AAV5/PD-L1 vectors on day 21 developed a ~4-fold reduction of swelling of the knee joint compared to the untreated knee joints by week 7 (Figure 9C, 10.8 ± 5.3 vs 34.9 ± 8.6, $P \leq 0.05$), as well as a ~2.5-fold decrease in histopathological scores (Figures 9B, D, 3.3 ± 1 vs 8.8 ± 1.4, $P \leq 0.05$). Again, the infiltration of T cells and macrophages experienced a 2~4-fold decrease in joints treated with AAV5/PD-L1 compared to those treated with PBS (Figure S7).
**Figure 9:** *Improved arthritis in the synovium of CIA mice treated with AAV5/PD-L1 vectors as a therapeutic regimen. (A), Schematic diagram of animal experiment. AAV5/PD-L1 at a dose of 1x1010 particles was intra-articularly injected on day 21 into the knees of CIA mice. Knee joint size and joint histopathological alteration were monitored. (B), Knee joint histology analysis with H&E staining. Representative images are shown (a-c, bar=200μm; d-f, bar=20μm). (C), The change in knee joint swelling. Data is shown as means ± SEM. *P < 0.05, ****, p < 0.001. (D), Knee joint histological scores. Mean values are shown with standard derivation (n=10). Data was analyzed using one-way ANOVA followed by Bonferroni multiple comparison test for group comparisons. **P < 0.01, ****, p < 0.001.*
## Discussion
RA is a chronic inflammatory disease primarily involving the joints. Mainstream therapies have been aimed at reducing synovial inflammation and pain, as well as preventing joint destruction by targeting pro-inflammatory cytokines or immune cells including B cells and T cells. However, over $50\%$ of patients lack adequate responses to current available immunomodulatory therapies. In this report, we investigated a novel gene therapy approach using AAV vectors to deliver PD-L1 into the knee joints, and demonstrated that a one-time intra-articular injection of AAV5/PD-L1 vectors was able to decrease knee joint swelling, block the infiltration of lymphocytes and macrophages, and decrease pro-inflammatory cytokines in joints without impacting systemic immune responses. The PD-L1 expressed from direct injection of AAV vectors into the joints was able to prevent both the development and progression of arthritis in CIA mice.
The broad impact of PD-L1 on immune cells suggests that the PD-1/PD-L1 pathway may be involved in multiple autoimmune diseases and so it has been explored for therapeutic purposes. Nasr et al. reported that the overexpression of PD-L1 in autologous hematopoietic stem and progenitor cells (HSPCs) reversed hyperglycemia in $100\%$ of NOD mice as a model of type 1 diabetes, with long-term effects observed in ∼$30\%$ of mice [38]. Similarly, Zhang, et al. showed that engineered PD-L1-expressing platelets were able to reverse type 1 diabetes (T1DM) [39]. There is evidence that PD-L1 heterogeneity is directly related to RA in some patients. The downregulation of PD-1 inhibitory signaling in RA might be due to increased levels of PD-1 in serum and decreased levels in synovial tissue, leading to fewer interactions between PD-L1 and PD-1 in RA synovial tissue [27]. Additionally, the availability of PD-L1 could be limited due to increased expression of CD80 and the binding of CD80 to PD-L1 in cis, therefore reducing the amount of functionally available PD-L1 able to access PD-1 on T cells [40]. These data all highlight that PD-L1 should be considered as a potential treatment target for a wide variety of autoimmune disorders [27].
Besides T cells, other immune cells, including macrophages, NK cells, B cells, and antigen presenting cells also express the PD-1 molecule [41]. It is well known that immune cells such as T cells and macrophages infiltrating joints play a major role in pathogenesis of arthritis. Theoretically, targeting these immune cells would reduce joint inflammation [42]. After treatment with AAV5/PD-L1 vectors, total T cells and macrophages as a whole, along with IL17+cells, CD206+cells, FOXP3+cells and iNOS+ cells, were all reduced in joint tissues. This result is consistent with findings that mice with CD4+ T cell or macrophage deficiencies developed less arthritis induced by collagen immunization [43, 44]. In the present study, the ratio of IL-17+ cells/FOXP3+ cells in PD-L1 treated joints also decreased compared to control; however, the ratio of iNOS+/CD206+ cells for the AAV5/PD-L1 treated group was similar to that of the group without PD-L1 treatment. These findings may indicate that the effect of PD-L1 treatment is mainly mediated by blocking total immune cell infiltration and shifting proinflammatory T cells to anti-inflammatory T cells, but not by shifting macrophages from proinflammatory to anti-inflammation states.
A current focus in RA therapy is cytokine inhibition, including TNF-α [45], IL-6 [46], and IL-1 [47]. However, TNF-α inhibitors showed approximately 30-$50\%$ efficacy in clinical reports, with less than $50\%$ of patients showing alleviation [6]. Similarly, high doses of the IL-6 inhibitor tocilizumab showed only $20\%$ improvement in ~$50\%$ patients [46] and expression of an IL-1R antagonism showed only a mild response, possibly due to other pathways such as synovial TLR ligands that bypass IL-1-dependent signaling [48]. Recently, one clinical trial reported that IFN-β provided via AAV gene delivery was insufficient for patients and the trial was paused [49].
Compared to published results from targeting single cytokines, our approach of targeting an immune checkpoint pathway showed much greater promise. An improvement of approximately $75\%$ was achieved in histopathology and clinical manifestations, with almost every mouse having some degree of positive response after AAV5/PD-L1 treatment. However, a complete response without joint swelling and histopathological change was not demonstrated. There are several possible reasons why our AAV-PD-L1 therapy did not result in complete recovery from inflammation. First, systemic proinflammatory cytokines may play a role in the pathogenesis of arthritis in RA. Local PD-L1 expression is capable of inhibiting immune cells from secreting cytokines in the joint but are unable to block cytokines in the blood that could enter the joints. This can also partially explain why proinflammatory cytokines were still detected in PD-L1 treated joints even though the immune cells had reduced to the level in joints of naïve mice. Second, as the dose of AAV vectors we used in this study was not optimal, increasing the AAV vector dose may transduce more cells in the joint and improve the PD-L1 effect. In this study, only $15\%$ of cells in the joints were transduced when 1x1010 particles of AAV5 vectors were administered. Third, collagen specific antibodies circulate in the blood and will ultimately penetrate the joint barrier and enter the joint synovia to interact with joint synoviocytes, inducing joint inflammation and damage. Although local joint PD-L1 expression decreased the level of immune cells in the joints, it did not block antibody production; as shown in our result, similar levels of antibodies against collagen were found in both the blood and joints of CIA mice regardless of whether PD-L1 treatment was applied or not.
To enhance PD-L1 function, several strategies can be explored. First, AAV vector modification, including capsid engineering, optimized PD-L1 sequence, or the development of stronger promoters, can be used to enhance AAV transduction [50]. Second, AAV vectors encoding soluble PD-L1 can be designed, which allows PD-L1 to enter the joint fluid broadly without being restricted by cell type, as AAV vectors encoding wild type PD-L1 with a transmembrane domain can only be expressed on a limited array of cells. Lastly, PD-L1 could be combined with other approaches to block antibodies in joints to enhance therapeutic effects, such as immunoglobulin cleavage enzymes IdeS or IdeZ [51].
Local joint gene delivery of therapeutic proteins has been actively explored for RA in clinical trials in recent years. A limitation of intra-articular therapy for RA is that only one or a few joints could be practically treated. However, a recent study demonstrated that joint inflammation in RA patients tends to recur in the same joints over time [52] and so those specific joints could be targeted. Additionally, children with certain forms of inflammatory arthritis may only have a mono or oligoarticular synovitis that could be amendable with IA therapy [53]. An advantage to the present approach is that AAV transduction is able to induce persistent transgene expression and so intra-articular administration of AAV5/PD-L1 is a feasible strategy and will likely benefit RA patients in the long term.
Overexpression of PD-L1 in other tissues could cause unwanted side effects. For example, PD-L1 in the liver might cause immune suppression, which could potentially facilitate infections and even liver tumorigenesis. To decrease the systemic impacts, AAV5 was used as the viral vector to deliver PD-L1, as this serotype showed a good transduction efficiency in the knee joint, transduced both synoviocytes and chondrocytes [54, 55], and demonstrated limited liver transduction [54, 55] when compared to other serotypes of AAV. Consistent with the results from previous reports using AAV5 [56], PD-L1 expression was exclusively detected in AAV5 vector-transduced joints. Even though PD-L1 effectively blocked immune cell infiltration and decreased the cytokines in the knee joint, it didn’t impact systemic immunity, because the cytokine levels and collagen antibody titers in serum were not altered. We also see the swollen paws and high arthritis scores in untreated joint of one leg, but low arthritis scores in the AAV/PD-L1 treated knees with the swollen paws of another leg. This further supports that the therapeutic effect of AAV5/PD-L1 injection was confined within the local joints. In addition, mice treated with AAV5/PD-L1 in joints didn’t show any abnormalities in other tissues, such as liver disfunction. These results all indicate a good safety profile of intra-articular injection of AAV5/PD-L1 vectors for arthritis treatment.
In this study, both prophylaxis and therapeutic treatment regimens were investigated to mimic two different scenarios: gene therapy before RA development and during early phase of RA. For the prophylactic treatment, instead of injecting AAV after the development of acute inflammation, AAV was injected at the same time as CIA induction. This decision was related to the characteristics of RA as a chronic autoimmune disease with both flare-ups and remissions; if the patient is able to get preventative treatment during remission, it may prevent them from reexperiencing acute inflammation. For the therapeutic treatment, AAV was injected after CIA induction, the rationale being that most patients will seek long-term treatment only after symptoms are developed or during the progression. Though persistent expression of PD-L1 after AAV delivery is favorable for the treatment of a chronic disease like RA, there is still concern about overexpression of PD-L1 causing long-term immune response suppression once the arthritis is in remission. However, we have followed up with wild type mice injected with AAV5/PD-L1 into the knees for over 1 year, and have not observed any significant adverse events (data not shown). This may be attributed to the knee joint anatomy, which is an enclosed environment with very few exposures to bacteria and other microbes; this may help in avoiding infection even when local immunity is suppressed. For future study, in order to regulate PD-L1 expression in different inflammatory conditions and avoid side effects related from long-term overexpression of PD-L1, inflammation inducible promoters might be considered to regulate transgene expression in matching disease activity.
In conclusion, this study laid a foundation for potential clinical applications of PD-L1-based therapies. PD-L1 have successfully attenuated inflammation and improved the pathology with decreased lymph cells and cytokine levels in the CIA joints. The precise mechanisms of PD-L1 need to be further investigated. It is promising that one-time injections can potentially induce robust transgene expression in local joints and provide long-term relief of inflammation in RA patients. This may reduce the amount of systemic therapies needed for treatment while improving levels of chronic joint pain and swelling, and eventually, the joint destruction and disability associated with RA. In this study, we only investigated the role of PD-L1 via AAV gene delivery in preventing RA development and blocking the progression from early phase of RA. Further studies will be focused on enhancing PD-L1 expression and secretion, optimizing AAV vectors for maximal efficiency, and investigating the long-term efficiency of PD-L1 in blocking the flare-ups or established joint inflammation.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by Institutional Animal Care and Use Committee at the University of North Carolina at Chapel Hill (UNC IACUC ID: 21-233.0).
## Author contributions
CL, RL, EW and SW contributed to conception and design of the study. WL, FW and MM organized the database. WL performed the statistical analysis. WL, JS and SF did the animal operations. WL wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
WL, JS, RL and CL are inventors on the patent application related to this work. CL is a cofounder of Bedrock Therapeutics, Nabgen, GeneVentiv and Astro, Inc. He has licensed patents by UNC and has received royalties from these startups and Asklepios Biopharmaceutical.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1116084/full#supplementary-material
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|
---
title: Implementation of the Diabetes Prevention Program in Georgia Cooperative Extension
According to RE-AIM and the Consolidated Framework for Implementation Research
authors:
- Hannah K. Wilson
- Caroline Wieler
- Darci L. Bell
- Ajit P. Bhattarai
- Isaura M. Castillo-Hernandez
- Ewan R. Williams
- Ellen M. Evans
- Alison C. Berg
journal: Prevention Science
year: 2023
pmcid: PMC10021035
doi: 10.1007/s11121-023-01518-0
license: CC BY 4.0
---
# Implementation of the Diabetes Prevention Program in Georgia Cooperative Extension According to RE-AIM and the Consolidated Framework for Implementation Research
## Abstract
Increased dissemination of the CDC’s Diabetes Prevention Program (DPP) is imperative to reduce type 2 diabetes. Due to its nationwide reach and mission to improve health, Cooperative Extension (Extension) is poised to be a sustainable DPP delivery system. However, research evaluating DPP implementation in Extension remains scant. Extension professionals delivered the DPP in a single-arm hybrid type II effectiveness-implementation study. Semi-structured interviews with Extension professionals were conducted at three time points. The Consolidated Framework for Implementation Research (CFIR) guided interview coding and analysis. Constructs were rated for magnitude and valence and evaluated as facilitators or barriers of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) outcomes. The program reached 119 participants, was adopted by $92\%$ ($$n = 12$$/13) of trained Extension professionals and was implemented according to CDC standards: all programs exceeded the minimum 22-session requirement (26 ± 2 sessions). The program was effective in achieving weight loss (5.0 ± $5.2\%$) and physical activity (179 ± 122 min/week) goals. At post-intervention, eight professionals ($67\%$) had begun or planned to maintain the intervention within the next 6 months. Several facilitators were identified, including Extension leadership structure, organizational compatibility, and technical assistance calls. Limited time to recruit participants was the primary barrier. Positive RE-AIM outcomes, facilitated by contextual factors, indicate *Extension is* an effective and sustainable DPP delivery system. Extension and other DPP implementers should plan strategies that promote communication, the program’s evidence-base, recruitment time, and resource access. Researchers should explore DPP implementation in real-world settings to determine overall and setting-specific best practices, promote intervention uptake, and reduce diabetes.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11121-023-01518-0.
## Introduction
With 96 million American adults living with prediabetes and 5–$10\%$ of these individuals developing type 2 diabetes mellitus (T2DM) each year, increased dissemination of evidence-based T2DM prevention interventions is imperative (Glechner et al., 2018). The Centers for Disease Control and Prevention’s (CDC) National Diabetes Prevention Program (DPP) aims to increase screening for and detection of prediabetes and T2DM and to increase dissemination of and access to the diabetes prevention. The DPP is a 12-month lifestyle change intervention designed to reduce T2DM risk through diet, exercise, and lifestyle changes (Knowler et al., 2002, 2009). DPP clinical trials resulted in reduced rates of T2DM up to $58\%$ in individuals with prediabetes (Allaire et al., 2020; Knowler et al., 2002, 2009). Since the initial DPP clinical trial, nearly 20 years of translational research has demonstrated similar results can be achieved using trained lay leaders in a variety of settings if critical components are upheld (i.e., use of approved curriculum, program duration, frequency of sessions) (Centers for Disease Control and Prevention, 2018; Ali et al., 2012).
Cooperative Extension (here forth, “Extension”), with its over 100-year history of providing health education interventions, presence in almost every county in every state, and trained personnel (i.e., state-level Extension leaders with health program implementation expertise: Extension Specialists), is poised as an effective platform for DPP dissemination and implementation (Franz & Townson, 2008; Molgaard, 1997). In the state of Georgia, there are 159 counties, with 57 having a county-based Extension professional that specializes in health and wellness (University of Georgia, 2023). At present, 31 Extension organizations, representing 17 U.S. states, are CDC-recognized DPP providers (CDC, 2023). While this number is growing, this is far fewer than the potential 50 states and additional U.S. territories that could be DPP providers.
While CDC is tracking overall effectiveness of the DPP among CDC-recognized providers (Ely et al., 2017), little is known about context-specific effectiveness and implementation and which organization types are uniquely positioned to succeed in effective and sustainable DPP delivery. Damschroder et al. ( 2017a) published a rigorous evaluation of DPP implementation in the Veterans Health Administration (VA) system and revealed that the DPP’s strong evidence base and committed leadership facilitated VA implementation with time as a primary implementation barrier. Beyond this important work in the VA system, a paucity of data exists exploring implementation of the DPP adhering to CDC standards in various community contexts, especially Extension (Nicole et al., 2021; CDC, 2018). Additional implementation research targeting translation of the DPP to Extension and other delivery systems is imperative to help reduce T2DM (Whittemore, 2011). The authors conducted a 12-month hybrid type 2 effectiveness-implementation study of the DPP in Georgia Extension from January 2020-March 2021 using a single-arm, multi-site design (Curran et al., 2022; Damschroder et al., 2017a; Swindle et al., 2019).
## Purpose and Objectives
The study purpose was to rigorously evaluate community translation of the DPP by Georgia Extension according to CDC standards using the 2016 Prevent T2 curriculum (CDC, 2018). To encourage comparability across translational studies of evidence-based programs and to meet the call for greater use of the Consolidated Framework for Implementation Research (CFIR) in implementation science, the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework and the CFIR were selected to evaluate DPP implementation and effectiveness (Glasgow et al., 2019; King et al., 2019; Kirk et al., 2016; CFIR Research Team-Center for Clinical Management Research, 2019; Swindle et al., 2019; Varsi et al., 2015). Moreover, to encourage comparability across DPP implementation contexts, we modeled our work after Damschroder et al. ’s research on the DPP implemented in the VA, the only comparable literature to date (Damschroder et al., 2015; Damschroderet al., 2017a). Thus, our specific objectives were to [1] evaluate implementation of the DPP in Georgia Extension using RE-AIM, and [2] identify implementation barriers and facilitators that influenced RE-AIM outcomes using the CFIR.
## DPP Participants
DPP participants were overweight or obese adults (18–75 years) with or at high risk for prediabetes and without any major comorbidities or physical disabilities ($$n = 88$$). Program participants were recruited via word of mouth, physician referrals, new or existing community collaborations/relationships, local employers, flyers, newspaper ads, radio announcements, social media advertisements, and in-person informational sessions. All DPP participants were recruited to participate in the research study by Extension professionals at initial program sessions or via email or phone after registering to participate in the DPP. All 13 Extension professionals implementing the DPP in 2020 were recruited to participate in the implementation study during regular technical assistance calls and via email. All methods and procedures were approved by the University’s Institutional Review Board on Human Subjects. All participants provided informed consent. Study components are reported according to the Standards for Reporting Implementation Studies (StaRI) checklist (Pinnock et al., 2017).
## Implementer Training
In October 2019, 13 Georgia Extension professionals with health and wellness specialty attended the CDC-approved Emory University Diabetes Training and Technical Assistance Center two-day DPP lifestyle coach training that is required for individuals implementing the DPP (Emory University, 2021). Researchers led a third training day on Extension context-specific DPP implementation, including data reporting, research measures, and standards required to obtain CDC recognition as a DPP provider (weight loss, PA, attendance, session length and frequency, total program length) (CDC, 2018).
Following the training, technical assistance calls with Extension professionals were held weekly by the Extension Specialist and graduate research assistant, then decreased to every other week in mid-June 2020 per Extension professionals’ recommendation. Technical assistance calls covered program support resources available from the Diabetes Training and Technical Assistance Center (Emory University, 2021) and the research team, updates from the research team, and time to discuss and explore intervention and/or research study successes and challenges (e.g., recruitment, implementation questions, participant barriers).
## DPP Implementation
Twelve Extension professionals began implementing the in-person DPP in January 2020 to March 2020 in thirteen Georgia counties (7 metropolitan and 6 nonmetropolitan counties) (U.S. Economic Research Service, 2020). One Extension professional delivered programs in two counties. In June 2020, one Extension professional resigned and the 13th trained Extension professional not implementing a DPP at the start of the study took over that cohort. Upon the onset of the COVID-19 pandemic in March 2020, all DPPs transitioned from in-person to distance learning formats (12 groups via Zoom Web Conferencing, and one group via FreeConferenceCall.com®) for the remainder of the 12-month program (FreeConferenceCall.com, 2021; Zoom Video Communications, 2021). Conversion to distance learning followed the CDC’s guidance on transition to distance-learning modes in response to COVID-19 (CDC, 2021). Distance delivery support was provided by the research team during technical assistance calls, one-on-one technology support, and additional materials accessible through the university’s learning management system, and from the Diabetes Training and Technical Assistance Center and CDC webinars available to all DPP providers.
## Implementation Evaluation
The implementation evaluation utilized quantitative program participant outcome data, DPP program reports, and qualitative semi-structured interviews with DPP implementers (Extension professionals) to identify implementation barriers and facilitators according to the CFIR and their influence on RE-AIM outcome domains.
The RE-AIM framework was used to evaluate quantitative implementation outcomes and effectiveness (Glasgow et al., 2019). Reach (R) was calculated as the number of eligible individuals who participated (attended at least one session) compared to the number of interested participants (124 that attended informational sessions only). Effectiveness (E) was measured per CDC intervention goals as participant percent weight loss (≥ $5\%$ of initial body weight) and physical activity (150 min per week of moderate activity). Adoption (A) was measured as the number of trained Extension professionals who implemented the program. Implementation (I) was assessed by fidelity to important program components (program and session duration, frequency and number of sessions) according to reports completed by Extension professionals following each session (Table 1). Maintenance (M) was defined as the number of Extension professionals that had already begun another DPP at the end of the present study or had a start date for a new DPP planned within the next 6 months. Table 1RE-AIM outcomes of the DPP in Extension and select associated implementation barriers and facilitatorsRE-AIM domainOutcomesCFIR and additional implementation constructsFacilitatorBarrierMixedReach• 124 individuals screened• 119 eligible for program• 88 eligible for research• External change agents• Structural characteristics• TimeEffectiveness• $46.7\%$ of participants met $5\%$ weight loss goal (M ± SD: $5.2\%$ ± $5.0\%$)• $56.7\%$ met PA goal (M ± SD: 179 ± 122 min/week)• Evidence strength & quality• Relative advantage• Compatibility• Organizational incentives & rewards• Goals & feedback• Participant receptivity• ComplexityAdoption• 12 out of 13 ($92\%$) of Extension professionals trained immediately adopted• Intervention source• Evidence strength & quality• Patient needs & resources• Implementation climate• Tension for change• Organizational incentives & rewards• Leadership engagement• Opinion leaders• Formally appointed internal implementation leaders• Champions• Cost• Structural CharacteristicsImplementation• Avg sessions implemented: 26 (range: 25–27)• Evidence strength & quality• Relative advantage• Networks & communications• Implementation climate• Tension for change• Organizational incentives & rewards• Goals & feedback• Learning climate• Readiness for implementation• Leadership engagement• Access to knowledge & information• Individual identification with organization• Other personal attributes• Opinion leaders• Formally appointed internal implementation leaders• Champions• External change agents• Implementation strategy• Agent networks• Complexity• Cost• Time• COVID• Structural CharacteristicsMaintenance• 5 Extension professionals started 6 new DPP cohorts (virtual)• 1 Extension professional had started 1 new in-person DPP cohort• 2 Extension professionals planned for 2 new in-person cohort; these were implemented as planned after the conclusion of the study• Evidence strength & quality• Relative advantage• Implementation climate• Compatibility• Organizational incentives & rewards• Learning climate• Leadership engagement• Access to knowledge & information• Individual stage of change• Formally appointed internal implementation leaders• Implementation strategy• Agent networksRE-AIM Reach, Effectiveness, Adoption, Implementation, and Maintenance, CFIR Consolidated Framework for Implementation Research The CFIR guided qualitative evaluation of the implementation process. A trained graduate student interviewed each Extension professional three times over the course of the intervention: [1] baseline–within 4 weeks of session 1 of the program, [2] midpoint–after completing the first 6 months of the program (minimum of 16 weekly sessions), and [3] post intervention–within 4 weeks of completing the second 6 months of the program (minimum of 6 monthly sessions). Thus, interviews totaled 36 across the entire intervention with 12 per timepoint (baseline, midpoint, and post-intervention). Semi-structured interview guides were adapted from Damschroder et al. [ 2015], informed by all five CFIR domains, and included open-ended and select scaled (1–5) questions to evaluate Extension professionals’ experiences implementing the DPP in Georgia Extension over time. Interviews were audio recorded via Zoom Web Conferencing (Zoom Video Communications, 2021) and transcribed via a third-party transcriptionist (Rev.com, 2021).
## Data Analysis
Descriptive statistics were calculated for quantitative RE-AIM components. IBM SPSS version 27 was used for all quantitative data analysis (IBM, 2020).
Qualitative interview coding was primarily deductive, guided by the CFIR constructs, but allowed for inductive coding when the data did not fit the CFIR constructs. ATLAS.ti version 9 was used as a tool for qualitative coding and analyses (ATLAS.ti, 2019). Transcripts were reviewed by five analysts in pairs using a consensual qualitative approach (Damschroder et al., 2015, 2017a, b; Swindle et al., 2019). One researcher served as one of the two coders on all transcripts to provide consistency. Analyst pairs coded each transcript independently then met to review all codes, discuss, and reach consensus on any discrepancies.
Each construct within each transcript was then rated for its influence on implementation, using similar methods to (Damschroder & Lowery, 2013). Constructs were assigned both a valence (+ or –) and a magnitude (1 or 2) to indicate the direction and strength of influence on implementation, respectively. Ratings of “0” indicated a neutral influence on implementation, “X” a mixed influence, and “*” a slight influence in the direction specified (e.g., 1+*). For constructs missing within a transcript, “M” was assigned.
Following visual inspection of the data, constructs were identified as having “strong” influences on implementation if ratings were consistently positive (+, facilitator) or negative (–, barrier) AND at least $25\%$ ($$n = 3$$) of transcripts had a +2 or –2 rating. Constructs were considered to have “weak” influences if ratings were consistently positive (+) or negative (–) OR at least $25\%$ ($$n = 3$$) of transcripts had a +2 or -2 rating for the construct. Magnitude and valence of all CFIR constructs and identification of those manifesting as “strong” or “weak” influencers of implementation are included in Supplemental File 1.
CFIR constructs that were determined to be “weak” and “strong” influencers of implementation were also assessed for how often the occurred (i.e., were coded together) with the RE-AIM domains to inform implementation barriers and facilitators to achieving each RE-AIM outcome (Supplemental File 2). Researchers coded each quote within each transcript with the RE-AIM domain(s) being described. CFIR constructs discussed more than $50\%$ of the time in respect to a single RE-AIM domain are highlighted in the results.
## Results
A summary of the RE-AIM outcomes is presented in Table 1. Table 2 shows the RE-AIM domains, along with selected constructs that co-occurred at least $50\%$ of the time with the RE-AIM domain. Table 2 also includes representative quotes to provide evidence of the construct as described by the Extension professionals (Tong et al., 2007). The following sections discuss the RE-AIM outcomes and select CFIR and researcher-developed constructs that manifested as strong implementation barriers (–) and facilitators (+). Note that the “barrier” and “facilitator” language throughout is used to describe Extension professionals’ discussion of the construct, not to describe a cause-effect relationship between the construct and outcomes. Constructs are designated in italics throughout. Table 2Select CFIR constructs that acted as barriers and facilitators to RE-AIM domains in the DPPRE-AIM domainConstructBarrier or facilitatorRepresentative quoteReachExternal change agentsFacilitator (strong)“Having the Wellness Coordinator…was extremely instrumental because…she's privy to everyone's blood tests, high-risk issues…So she's able to parlay that information into getting them in the pipeline” (Extension professional J)Structural characteristicsMixed (weak)“the hospital…I already had a working relationship with…I've already proven why Extension programs work, so just bringing in this list, ‘Hey, I have another resource.’” ( Extension professional G)EffectivenessParticipant receptivityFacilitator (weak)“seeing the bonding among the participants…that’s a huge accomplishment because some of my participants…really don’t get out, they’re older. That increases their quality of life…not just their health.” ( Extension professional K)“the consistent attendance…even considering we went away from in person…the fact that people are still …actively participating in the groups would show a success.” ( Extension professional B)AdoptionPatient needs and resourcesFacilitator (weak)“I'm just doing it for the health of the people in my community. ”(Extension professional B)Intervention sourceFacilitator (strong)“if it wasn't for…[state Extension Specialist], I wouldn't have known the program was out there so I would have never implemented the program…” (Extension professional E)“it being a CDC program. I mean, that comes with a lot of clout.” ( Extension Professional A)Tension for changeFacilitator (weak)“We've done a lot of programs on managing chronic disease, being on that preventative side is important.” ( Extension professional H)Evidence strength and qualityFacilitator (strong)“The evidence…the wide scope of it…how far reaching it was…[I]was like, ‘Oh my goodness, this is definitely something that all Extension offices should be offering because of the positive impact that it has made nationwide.’" ( Extension professional D)“I'm about prevention rather than treatment, and I do believe that with healthy eating and regular physical activity you can prevent a lot of things'm (Extension professional F)CompatibilityFacilitator (strong)“I think it aligns extremely well [with the Extension mission] because we’re taking research-based programming and this is one of the best research-based diabetes programs that there has been.” ( Extension professional A)CostBarrier (weak)“[The state Extension office covering the costs of training and printing] was huge because I certainly couldn’t have come up with that kind of money out of my [county] account.” ( Extension professional J)ImplementationNetworks and communicationsFacilitator (strong)“it [the technical assistance calls] helps me to be more accountable and make sure that I am thorough in what it is I'm giving you, what you're asking for." ( Extension professional D)Learning climateFacilitator (weak)“You guys are very approachable and…offer questions and concerns about the program…yourself. So, we don’t feel like we’re just barking at the wrong tree.” ( Extension professional B)Goals and feedbackFacilitator (weak)“there were clear goals set out there which makes it easy to know how and what needs to be done from my end to implementhe (Extension professional G)Leadership engagementFacilitator (strong)“I think it speaks very highly of [DPP coordinator] and [Specialist], because you are implementing the program…you have firsthand some of those same challenges that we have… It's not just the knowledge from research or the curriculum, you actually have firsthand knowledge.” ( Extension professional K)Formally appointed internal implementation leadersFacilitator (strong)“The planning it took…this is where…having a Specialist…or having [a DPP coordinator] and the Specialist kind of helps.” ( Extension professional G)Opinion leadersFacilitator (strong)ChampionsFacilitator (strong)Access to knowledge and informationFacilitator (weak)“for something like this, I think anytime you can have [a registered dietitian] involved…you're definitely meeting a baseline need.” ( Extension professional J)Individual identification with organizationMixed (weak)“being a CEC [County Extension Coordinator, administrative role], there's just always something. I don't know that I ever feel like, on a daily basis, I'm doing everything I could do to support my programing as effectively as I would like to. ”“no matter what happens, I'm going to stay in touch with them and support them because I feel like I've made an obligation.” ( Extension professional J)Evidence strength and qualityFacilitator (strong)“I believe that it has been done and implemented for long enough that there is strong evidence that by following the program the way it's written, it does have value.” ( Extension professional I)Knowledge and beliefs about the interventionMixed (strong)“I find it fascinating that there's really no recipes……in general, I'm surprised…that we really don't talk about some of the nuts and bolts of nutrition a little bit more.” ( Extension professional J)“the idea is that we spend at least one hour a week sitting with these folks and we've talked to them about fitness breaks…the lesson on fitness breaks incorporates getting up and moving. But none of the other lessons, I don't believe has a fitness break worked into it.” ( Extension professional B)ComplexityBarrier (weak)“starting to do the food logs and get feedback is going to be a little bit challenging.” ( Extension professional A)MaintenanceLearning climateFacilitator (strong)“I think that it’s something that…we’ve shown…successful…it…definitely falls in line with our mission…I think that it’s…definitely one that’s here to stay.” ( Extension professional E)Access to knowledge and informationFacilitator (weak)“there are things that we do need, so it’s not just give me the keys to the car.” ( Extension professional B)CFIR Consolidated Framework for Implementation Research, RE-AIM Reach, Effectiveness, Adoption, Implementation, and Maintenance
## Reach (R)
Of the 124 individuals that attended DPP informational sessions or otherwise expressed interest in participating in the DPP, five were ineligible due to T2DM diagnosis. The program reached 119 individuals ($96\%$), and $71\%$ ($$n = 88$$) were eligible and consented to participate in the research study (Table 1). Reach at each site ranged from 1 to 13 participants.
External change agents were community partners such as physicians, worksite wellness coordinators, and local news outlets (radio, newspaper, television) that, for most Extension professionals, promoted recruitment for the DPP. Extension professionals valued their community partners for promoting DPP reach.
Structural Characteristics of Extension acted as both barriers and facilitators of reach. Some Extension professionals discussed Extension’s established reputation as a health education provider and administrative structure, with county, district, and state leaders to provide programming support, including recruitment ideas, as DPP recruitment facilitators. However, others discussed poor visibility of Extension in some communities, particularly larger and more urban communities, as a recruitment barrier. Extension professionals noted that DPP reach would likely be limited to Georgia counties with Extension professionals who had a health and wellness assignment and that this ultimately impacted Extension’s reach and Readiness for Implementation.
Time was a primary barrier to reach. Extension professionals began recruiting in late October/early November for a January start date, with holidays presenting recruitment challenges. Most felt that at least 3 months and as many as 6 months may be needed to optimize reach.
## Effectiveness (E)
Mean weight loss (5.2 ± $5.0\%$) and physical activity (179 ± 122 min/week) exceeded the program goals. Nearly half ($46.7\%$) and more than half ($56.7\%$) of participants met the program weight and PA goals, respectively (Table 1).
Participant receptivity emerged as a subtheme of patient needs and resources to capture discussion around participants’ perception of DPP effectiveness and receptivity to/satisfaction with the DPP. Extension professionals discussed outcomes that they perceived to be additional indicators of program effectiveness, including self-reported reductions in hemoglobin A1c, and reduction or elimination of medications. Some Extension professionals spoke to the dynamic of their group, “bonding,” attendance, and retention as measures of DPP effectiveness.
Overall, COVID-19 impeded implementation, including effectiveness. Further descriptions of the perceived influence of the COVID-19 pandemic on DPP participants’ health behaviors are reported elsewhere (Wilson et al., 2022).
## Adoption (A)
Of the 13 Extension professionals trained to deliver the DPP, 12 of 13 ($92\%$) adopted the program and began implementation in winter 2020 as intended (Table 1). The non-adopting Extension professional relocated shortly after the training and began a cohort in fall 2020, though not included in the present study.
Patient needs and resources was the primary facilitator of DPP adoption, as every professional cited high T2DM rates and a need for T2DM prevention interventions in their communities. The DPP’s published evidence strength and quality for reducing T2DM risk and the “clout” that came with CDC being the intervention source encouraged them to adopt the program.
Extension professionals frequently discussed the compatibility of the DPP with Extension’s mission and values of evidence-based health promotion programs. Moreover, several professionals indicated the DPP addressed the tension for change within Extension toward more prevention-focused, evidence-based programs with measurable outcomes and funding potential, like the insurance reimbursement potential for DPP providers who obtain CDC recognition.
Multilevel leadership engagement was also instrumental in adoption. Extension professionals identified engagement from the state Extension Specialist in nutrition and health (champion), their district- and county-level extension leaders (opinion leaders), and the two Extension professionals that implemented the DPP prior to this project as facilitators of adoption.
Cost and time were primary barriers to adoption. While grant funding covered costs for these cohorts, Extension professionals spoke to cost as a potential barrier to adoption by others following this implementation project. the known time commitment to implement the year-long DPP and its Complexity emerged as barriers to adoption. However, many professionals stated a willingness to overcome these barriers due to the potential results.
## Implementation (I)
Extension professionals implemented the DPP according to CDC standards, as evidenced by the average number of sessions [26], their length, and the minimum number exceeding the 22 minimum session requirement (Table 1).
Several features of the DPP and implementation strategies used facilitated implementation. First, while Extension professionals felt that the program could be improved through activities like recipe and physical activity demonstrations, their knowledge of the DPP’s evidence strength and quality and the need to implement it as intended to achieve the intervention goals positively influenced fidelity in implementation. The clear program goals and feedback from leaders also facilitated implementation. Extension professionals felt that the program effectiveness goals (weight loss, PA, attendance) and implementation goals (meeting CDC recognition standards) were clearly defined and progress toward these goals was communicated back to the Extension professionals by leadership.
Networks and communications between Extension professionals and state Extension leaders and implementation strategies, such as technical assistance calls, that facilitated communication were discussed extensively as facilitators of DPP Implementation. Extension’s Structural Characteristics supported communication and implementation. Leaders engaged through regular emails, calls, and texts with Extension professionals to answer questions, solve problems, and provide other support as necessary. Extension professionals also spoke to the value of access to knowledge and information in the form of leaders with nutrition expertise (Extension Specialist in nutrition and health is a registered dietitian nutritionist) and with first-hand experience delivering the DPP. One of the two Extension professionals who had implemented the DPP prior to this implementation project spoke to the value of this support compared to when they were implementing the DPP on their own.
The complexity of the intervention and time and resources to implement were the primary barriers to implementation. Four Extension professionals served as County Extension Coordinators, which is an administrative role. These additional administrative responsibilities limited time and available resources they had to devote to DPP implementation, especially during the COVID-19 pandemic. Extension professionals noted the complexity of the DPP made implementation difficult, particularly the year-long duration, providing feedback on participants’ food records at each session, and completing make-up sessions for participants who missed sessions.
Extension professionals generally felt that the COVID-19 pandemic negatively influenced implementation of the DPP. Detailed descriptions of the influence of the COVID-19 pandemic on DPP implementation are reported elsewhere (Wilson et al., 2022).
## Maintenance (M)
At the conclusion of the present study (April 2021), five Extension professionals had begun six new DPP cohorts (virtual), one Extension professional had started one new in-person DPP cohort, and two Extension professionals planned to start new in-person cohorts in Fall 2021 (Table 1). Both programs were implemented as planned.
Extension professionals spoke to the evidence strength and quality of the DPP and its compatibility with Extension’s mission as facilitators of maintenance of the DPP in Extension. Some spoke to the potential for Medicare reimbursement giving the DPP a higher relative advantage compared to existing Extension health behavior change programs. Lastly, Extension professionals spoke to the value of implementing an evidence-based program like the DPP for demonstrating local impact for their promotion process (organizational incentives and rewards).
Extension professionals spoke to the need for continued leadership engagement in the form of involvement and support of leaders as the DPP is maintained in Extension. In particular, they discussed the value of the regular technical assistance calls for maintaining implementation.
Extension professionals also spoke to the value of communicating with other Extension professionals implementing the program (agent networks) through these calls and other avenues for DPP maintenance. Several suggested a mentorship program for Extension professionals new to implementing the DPP as essential to further dissemination and maintenance in Georgia Extension.
## Implications for Public Health
The present study provides important insights into implementation barriers, facilitators, and outcomes of the DPP in the context of a U.S. state Extension organization. RE-AIM outcomes were comparable to other DPP implementations (e.g., Damschroder et al., 2017a) and several constructs described as influential to implementation were identified using the CFIR framework.
## RE-AIM Outcomes of DPP Implementation in Georgia Extension
Reach of the DPP was positive ($96\%$) compared to intended reach. Adoption was similar to Damschroder et al. ( 2017a) evaluation in a clinical setting, with a majority ($92\%$) of trained individuals adopting the program. Basic assessment of implementation fidelity indicated that the program was implemented as intended. Lastly, maintenance in Georgia *Extension is* promising, with 8 of the 12 ($67\%$) Extension professionals beginning or planning for another DPP cohort.
Barriers to and facilitators of reach identified through this trial can be utilized in future implementation to improve reach of the DPP in Georgia Extension. Effectiveness, adoption, implementation, and maintenance were respectable. Effectiveness measured by average weight loss and physical activity exceeded program goals and were similar to results of other research (Damschroder et al., 2017a, b; Ely et al., 2017; Gorczyca et al., 2022). All Extension professionals trained to implement the DPP eventually adopted the program, and a majority began another (and in some cases, multiple) DPP cohorts following conclusion of this study. These adoption and maintenance outcomes are noteworthy, considering their significance for T2DM prevention efforts in Georgia. Lastly, maintaining implementation fidelity in this community context of Extension, even during the COVID-19 pandemic, is of value not only for achieving the effectiveness outcomes observed in the present study, but also for informing DPP implementation fidelity in other community contexts where DPP adaptations are commonplace.
## CFIR Barriers and Facilitators Influencing RE-AIM Outcomes
This implementation evaluation identified several barriers and facilitators of DPP implementation in the context of Extension, some of which may be applicable to other community settings. Discussed facilitators of DPP reach included community partners and the rapport of Extension in communities, while limited time for recruitment was a barrier. These findings highlight the importance of Extension professionals having an established community presence and network of partners to promote recruitment. These findings are consistent with other studies using the CFIR to evaluate other community-based programs, including our own (King et al., 2019), reporting that programs with the highest referral rates were implemented by those who had strong community partner relationships (Damschroder et al., 2017a; King et al., 2019). In the case of the DPP, community partners help increase awareness of the program and clinical providers (physicians and nurses) can directly refer eligible patients. DPP dissemination and implementation efforts should consider allowing at least 3–4 months, and up to 6 months, for recruitment depending on the degree of implementers’ network in the community and existing referral structures. When medical providers are referring to the program, this time may be reduced depending on the provider’s volume of eligible participants.
The discussed role of goals and feedback in effectiveness highlights the importance of DPP leaders providing consistent feedback on cohorts’ progress and potential areas for improvement. Notably, the present study utilized the Data Analysis of Participants System to track participant attendance, weight, and physical activity data (Association of Diabetes Care and Education Specialists, 2021). Extension professionals highlighted the value of this system coupled with leadership feedback for keeping their cohort progressing toward program goals.
The DPP’s source and associated evidence base in CDC facilitated adoption, a finding consistent with the findings of Damschroder et al. ( 2017a). DPP marketing efforts to potential participants, community partners, and even potential program implementers should emphasize the DPP as a CDC, evidence-based program to improve buy-in. Extension professionals’ perceived “fit” of the DPP with Extension’s mission and programming, along with the present study’s high adoption rate, supports the value of Extension as a delivery system to increase dissemination of the DPP. When asked about increasing adoption of the DPP in other counties throughout the state, Extension professionals felt that success stories from the implementation pilot, the support provided for implementation, and the value of the DPP’s evidence base for building community rapport and Extension professionals’ impact statements would be incentives for adoption by other counties. Still, Extension professionals noted that adoption would be limited to counties with Extension professionals. With the number of county Extension professionals decreasing, considerations on how to maintain the strong adoption observed in the present study and how to promote reach throughout the state should be made in light of these realities. Delivery during the COVID-19 pandemic highlighted the value of virtual delivery for accessing residents in counties without county-based Extension professionals. Virtual delivery should be explored in the future to overcome potential adoption and reach barriers.
Extension leadership contributed to the professionals’ knowledge of the DPP and decision to adopt the program. Compared to other settings in which the DPP might be implemented, the support infrastructure of Extension further positions it to be a strong delivery system (Franz & Fahey, 2012; Franz & Townson, 2008; Franz et al., 2010). Most Extension organizations have a nutrition and/or health Extension Specialist that provides access to expertise in DPP-related content areas (Harden et al., 2019), administrative oversight, and implementation support. Still, depending on the Extension structure, some Extension Specialists are assigned to several programs and may have limited time to support a single, complex program like the DPP. This barrier is not specific to Extension, as Damschroder et al. ( 2017a) cited similar challenges in the VA context. Extension professionals discussed the need for a permanent DPP coordinator to assist the Extension Specialist to overcome this challenge. CDC does suggest that programs have an assigned DPP coordinator. In small organizations, this may be particularly challenging; but in larger organizations like Extension, a staff member or graduate student can be assigned to this role, as in the case of our study.
## Implementation Strategies Utilized
Implementation strategies, including technical assistance calls, created a positive learning climate that Extension professionals felt facilitated implementation. These results echo those reported by Damschroder et al., who also used bi-weekly meetings to provide pertinent updates and information and problem-solve issues (Damschroder et al., 2017a). Extension professionals also spoke to the value of the additional day of training held after the lifestyle coach training. Damschroder et al. also found leadership involvement and support to be one of the most important facilitators of DPP implementation in the VA context (Damschroder et al., 2017a). For multisite DPP delivery systems, additional training on implementation protocols specific to that delivery system may be beneficial for optimizing outcomes. Continued support from leaders in the form of consistent communication and continuing education were all cited as important components of implementation that would be important for maintenance as well as expansion of the DPP into other counties. These consistencies noted between the present and Damschroder et al., ( 2015, 2017a) studies indicate that the implementation strategies utilized in both (technical assistance calls, leadership involvement, training) may promote implementation outcomes across multiple contexts.
## Limitations and Strengths
The present study is not without limitations. Notably, no control or comparison group was included to allow for either comparison of implementation outcomes with and without the utilization of implementation strategies, or comparison of barriers and facilitators presented by the context of Extension compared to another context, limiting conclusions that can be made from the presented results. Still, comparisons to the most comparable literature to date (Damschroder et al., 2017a) have been made throughout. Many of the implementation strategies employed in this study involved state-level leadership support and training for Extension professionals. Withholding support and training from Extension professionals is not acceptable in the setting of Extension, making comparison of outcomes with and without these implementation strategies not feasible. Future studies should consider testing different implementation strategies side by side (e.g., one-on-one technical assistance verses group-based technical assistance) and/or comparison of implementation barriers and facilitators within and outside Extension.
In addition, the research team involved in data collection and analysis was heavily involved in supporting program implementation, potentially introducing researcher bias. However, the familiarity of the researchers with the implementation process offered a more comprehensive understanding of the topics discussed in interviews. Furthermore, three of the five data analysts were not involved in supporting implementation. Additionally, no objective measure of fidelity was included in the present study. Lastly, the number of counties/Extension professionals included in the present study was limited, compared to the total possible sample size in the state of Georgia. The initial sample was limited to meet financial constraints and assess initial feasibility in the pilot implementation study. Counties and Extension professionals from every region of the state, as well as both rural and urban counties, were included in an effort to increase the generalizability of the results.
There are also several strengths. This study is unique in its contribution to the literature by using standard frameworks (CFIR and RE-AIM) to rigorously evaluate implementation of an evidence-based program in a community setting that is well positioned to be an established DPP provider: Extension. Integration of the CFIR with RE-AIM also increases the translational value of this study, as the barriers and facilitators of RE-AIM identified using the CFIR in this study provide a foundation on which implementation strategies can be built to potentially enhance RE-AIM outcomes of the DPP in Extension and potentially other community contexts.
## Conclusions
Although freely available, the Diabetes Prevention *Program is* a complex intervention with many considerations for enhancing dissemination and implementation to reduce the public health burden of T2DM. Using the CFIR and RE-AIM frameworks, this study demonstrated similar reach, effectiveness, adoption and maintenance in Extension to DPP implementation in clinical contexts, and revealed Extension-system specific facilitators of RE-AIM outcomes. The supportive leadership structure, with state-level Extension Specialists and local community health educators (Extension professionals), compatible mission, access to content and implementation expertise, and established communication channels were discussed as benefits of this organizational structure. The strong Implementation, Adoption, and Maintenance observed in this study support the value of Extension as an effective and sustainable delivery system for the DPP. Future research should use similar methods to explore implementation in Extension and other contexts across the U.S. to further test the promising implementation strategies utilized in this study that promote communication and access to information, resources, and support to promote uptake and implementation of the DPP in Extension and beyond.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 58 KB)Supplementary file2 (DOCX 40 KB)
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|
---
title: 'Snacktivity™ to promote physical activity and reduce future risk of disease
in the population: protocol for a feasibility randomised controlled trial and nested
qualitative study'
authors:
- Amanda J. Daley
- Ryan A. Griffin
- Catherine A. Moakes
- James P. Sanders
- Magdalena Skrybant
- Natalie Ives
- Ben Maylor
- Sheila M. Greenfield
- Kajal Gokal
- Helen M. Parretti
- Stuart J. H. Biddle
- Colin Greaves
- Ralph Maddison
- Nanette Mutrie
- Dale W. Esliger
- Lauren Sherar
- Charlotte L. Edwardson
- Tom Yates
- Emma Frew
- Sarah Tearne
- Kate Jolly
journal: Pilot and Feasibility Studies
year: 2023
pmcid: PMC10021043
doi: 10.1186/s40814-023-01272-8
license: CC BY 4.0
---
# Snacktivity™ to promote physical activity and reduce future risk of disease in the population: protocol for a feasibility randomised controlled trial and nested qualitative study
## Abstract
### Background
Many people do not regularly participate in physical activity, which may negatively impact their health. Current physical activity guidelines are focused on promoting weekly accumulation of at least 150 min of moderate to vigorous intensity physical activity (MVPA). Whilst revised guidance now recognises the importance of making small changes to physical activity behaviour, guidance still focuses on adults needing to achieve at least 150 min of MVPA per week. An alternative ‘whole day’ approach that could motivate the public to be more physically active, is a concept called Snacktivity™. Instead of focusing on achieving 150 min per week of physical activity, for example 30 min of MVPA over 5 days, Snacktivity™ encourages the public to achieve this through small, but frequent, 2–5 min ‘snacks’ of MVPA throughout the whole day.
### Methods
The primary aim is to undertake a feasibility trial with nested qualitative interviews to assess the feasibility and acceptability of the Snacktivity™ intervention to inform the design of a subsequent phase III randomised trial. A two-arm randomised controlled feasibility trial aiming to recruit 80 inactive adults will be conducted. Recruitment will be from health and community settings and social media. Participants will be individually randomised (1:1 ratio) to receive either the Snacktivity™ intervention or usual care. The intervention will last 12 weeks with assessment of outcomes completed before and after the intervention in all participants. We are interested in whether the Snacktivity™ trial is appealing to participants (assessed by the recruitment rate) and if the Snacktivity™ intervention and trial methods are acceptable to participants (assessed by Snacktivity™/physical activity adherence and retention rates). The intervention will be delivered by health care providers within health care consultations or by researchers. Participants’ experiences of the trial and intervention, and health care providers’ views of delivering the intervention within health consultations will be explored.
### Discussion
The development of physical activity interventions that can be delivered at scale are needed. The findings from this study will inform the viability and design of a phase III trial to assess the effectiveness and cost-effectiveness of Snacktivity™ to increase physical activity.
### Trial registration
ISRCTN: 64851242.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40814-023-01272-8.
## Background
Many people do not regularly participate in physical activity, which may adversely affect their health [1–3]. The prevention of non-communicable diseases is a major worldwide public health goal and improving lifestyle behaviours is considered essential to reducing the financial and health burden of non-communicable diseases [4]. Current physical activity guidelines are focused on weekly accumulation of at least 150 min of moderate to vigorous intensity physical activity per week (MVPA) [1]. This recommendation is often promoted as 30 min of MVPA on at least five days per week. Revised guidance now also recognises the importance of making small changes to physical activity behaviour, and that any physical activity is better than none. However, guidance still focuses on the public needing to achieve a behavioural goal of at least 150 min of moderate intensity physical activity per week [1, 5, 6]. Guidance also advises that adults should complete muscle and strength based physical activity on at least two days per week, but very few adults (~ $20\%$) achieve this regularly [7]. The low levels of participation in physical activity in the population is concerning and there is no reason to assume the situation will improve unless acceptable and effective interventions are put in place. Guidelines themselves do not change behaviour, it is having the means and motivation to achieve them that matters for public health.
## Snacktivity™
An alternative approach to physical activity promotion that could motivate the public to be more physically active throughout the whole day, is a concept referred to here as Snacktivity™ [8]. Rather than focusing on promoting 150 min of physical activity per week (e.g. ~ 30 min per day over 5 days), Snacktivity™ focuses on encouraging small, but frequent, doses of regular MVPA throughout the whole day such that at least 150 min of MVPA is accumulated weekly. A physical activity ‘snack’ lasts between 2 to 5 min, and examples include walk-talk conversations, walking coffee breaks, using stairs instead of the lift, pacing whilst using the telephone, or parking the car a little further away and walking to the destination. Of relevance here, updated guidance from health agencies around the world have removed the need for adults to complete physical activity in bouts lasting 10 min or more [1]. Improved cardio-metabolic aerobic fitness has been reported from brief bouts of physical activity [9–13], with other studies reporting no difference in the rate of improvement in cardiovascular fitness between accumulated and continuous bouts of physical activity lasting the same total duration [14].
How people feel about participation in physical activity is an important predictor of whether they will continue to engage and adhere with the activity [15]. A Snacktivity™ approach may help to develop individuals’ confidence to be physically active, particularly those who are inactive, by encouraging people to ‘start small’. Psychological theories recognise that achieving small changes is important for developing task and self-regulatory self-efficacy, as well as habit formation [16, 17]. Simple actions may become habitual more quickly than complex ones, suggesting that integration of small physical activity changes, or ‘snacks’, within everyday routines may be more feasible than longer ones, for the population to initiate and then sustain [16]. Thus, over time Snacktivity™ may be a vehicle for more sustained engagement in physical activity [8, 17]. Our earlier work has shown that the Snacktivity™ approach to promoting physical activity was viewed positively by the public [18, 19].
A common reason for inactivity is a perceived lack of time and Snacktivity™ provides an opportunity to address this barrier because it only requires a small amount of time, and no preparation/planning or equipment is required [20]. Snacktivity™ can also be incorporated into usual daily routines (e.g. getting off the bus a stop early on the way to work and walking meetings), and may therefore be perceived as appealing and feasible to achieve, compared with aiming for larger changes in physical activity. Snacktivity™ also provides the opportunity to promote the importance of muscle strength-based activities since many of these lend themselves to Snacktivity™ (e.g. squats when brushing your teeth and calf raises while waiting for the kettle to boil).
An inverse dose–response relationship exists between physical activity and all-cause mortality, therefore for people who are inactive any increase in physical activity is important [21, 22]. Snacktivity™ could also be important because the relationship between physical activity and mortality is also characterised by a steep early slope meaning the greatest gains in health are experienced when moving people who are inactive to 2–3 metabolic equivalent hours (MET/h) per week (~ 30 min per week), than moving more active people to doing marginally more [21–23]. In addition to concerns about low levels of participation in the population, there is growing concern about the amount of time the public spend in sedentary behaviours. Adults spend approximately 60–$70\%$ of their waking hours sedentary (e.g. sitting) [24]. This is of concern because too much time spent sedentary has been associated with type 2 diabetes, cardiovascular disease (CVD), all-cause and CVD-related mortality [25, 26], and guidelines now include recommendations about reducing time spent sedentary. An important additional benefit of Snacktivity™ is that it encourages breaking up sitting time throughout the day, meaning that it has the potential to impact two health behaviours simultaneously.
## Aims and objectives
Whilst the idea that small bouts of physical activity may improve health is not new, it is not a message that has been highlighted to the public, in part, because of a lack of robust evidence in real world health settings. To date, no randomised controlled trial (RCT) has directly investigated whether Snacktivity™ increases participation in MVPA or the number of people meeting the recommended guidelines for physical activity. The primary aim of the Snacktivity™ research programme is to evaluate the clinical and cost effectiveness of the Snacktivity™ intervention for increasing and maintaining physical activity by undertaking a large multicentre RCT. However, first we need to undertake a randomised feasibility trial with nested qualitative interviews to assess the feasibility and acceptability of the Snacktivity™ intervention and the methods needed to conduct the phase III randomised trial. We are seeking to test a complex intervention and there are uncertainties, specifically regarding recruitment methods and rates, intervention adherence and participant retention, that need to be assessed before undertaking a large-scale RCT.
This feasibility trial aims to:Examine the recruitment rate to inform the planned phase III trial. Provide data to review and inform the sample size assumptions for the phase III trial. Assess the data collection methods, data completeness and retention rates. Investigate the feasibility and acceptability of the Snacktivity™ intervention to the public and health care providers (HCPs) delivering the intervention. Assess adherence to the Snacktivity™ interventionGather data to refine the Snacktivity™ intervention and assess intervention delivery fidelity. Examine the potential for intervention contamination, to inform the decision to use individual randomisation over a cluster RCT.Assess acceptability of the intervention training materials and procedures for HCPs.
## Trial design and setting
A two-arm, multi-centre, individually randomised controlled feasibility trial will be conducted with a target recruitment of 80 participants across the East and West Midlands, UK. See Fig. 1 for participant flow. Recruitment will be from National Health Service (NHS), social care, public health, primary care services, community settings, and via social media. The trial will be conducted in accordance with the United Kingdom (UK) Policy Framework for Health and Social Care Research, the applicable UK Statutory Instruments, (which include the Data Protection Act 2018) and the principles of Good Clinical Practice. The study is following protocol version 7.0 (27 May 2022). Birmingham Community Healthcare NHS Foundation Trust in England are the sponsor for this trial. A SPIRIT Figure shows the different data collection steps of the trial (Fig. 2) and a completed SPIRIT checklist is available as an additional file (Additional file 1).Fig. 1Participant flowFig. 2SPIRIT figure
## Recruitment of participants
*Participating* general practices and NHS Trusts will search their electronic records to identify patients who are ≥ 18 years and have a health care consultation booked during the recruitment phase of the trial. These individuals will be sent a consultation appointment letter (or reminder of the upcoming appointment), along with the trial invitation letter, participant information sheet (PIS), expression of interest form (EOI) and the General Practice Physical Activity Questionnaire (GPPAQ) for screening physical activity status [27]. Those classed as inactive/moderately inactive/moderately active according to the GPPAQ (based on questions 1, 2a, and 2b) will be contacted by telephone to complete eligibility screening with a member of the research team. If a general practice or NHS Trust service routinely uses, or wishes to use, Short Message Service text messages to send patients details of their scheduled appointments and/or to notify them about this study, a study invite link will be added to these text messages where the trial documents referred to above can be accessed by those interested in taking part.
Clinicians/HCPs can also raise the topic of the Snacktivity™ study in routine consultations and those interested in discussing the trial further will be asked to complete the EOI form and GPPAQ, which will be passed on to the research team and these potential participants will complete eligibility screening with the research team by telephone. Potential participants can also be invited to take part in the study via community settings, including social media. These participants will either be sent the recruitment packs in the post or asked to complete the study documents electronically via the study invitation link as detailed above.
For all routes of recruitment, potentially eligible participants will be contacted by telephone by a member of the research team to complete further and final eligibility screening (see below for the full list of criteria applied). If eligible, a baseline visit will be booked to collect assessment data. For participants recruited via a reminder letter this will take place prior to participants’ health/intervention consultation appointment.
## Consent processes and steps
Potential participants will provide consent for all parts of the screening process described above, as well as consent to participate in the trial. Written informed consent for participation in the trial will be obtained for each participant by a researcher at the baseline visit. Where face to face visits are not possible, participants will be asked to provide written informed consent either online/email or by post. Participants will be made aware at the beginning of the study that they can freely withdraw (discontinue participation) from the trial (or part of) at any time without giving a reason.
## Inclusion
Inactive, moderately inactive or moderately active (as measured by the GPPAQ) [27].Able to provide informed written consent. Aged ≥ 18 years. Own a mobile phone capable of hosting apps (Apple and Android).Agreement for their HCP to be notified of their involvement in the study (if applicable).
## Exclusion criteria
Unable to understand English sufficiently to complete the trial assessments. Women known to be pregnant or breast feeding.
## Ineligible participants
If following screening an individual is not eligible, their identifiable data and contact details will be deleted from the trial system and any paper documents received from them will be destroyed. Verbal consent will be requested to store anonymous research data, for example, reasons why they were not eligible, to help inform any future trial.
## Randomisation and blinding
Once eligibility is confirmed, consent obtained and baseline data collected, participants will be randomised at the level of the individual in a 1:1 ratio to either the Snacktivity™ intervention or usual care. We have chosen individual rather than cluster randomisation for the definitive trial, and therefore also for this feasibility trial. An individually randomised trial also allows us to recruit participants outside a healthcare service/setting. Whilst there is a risk of contamination in an individually RCT, we anticipate the risk of contamination to be low and this will be assessed. Randomisation will be performed via a secure web-based service provided by the Birmingham Clinical Trials Unit. A minimisation algorithm will be used to ensure balance in the treatment allocation over the following variables: route of recruitment (primary care, community health service, other); age (18–45, ≥ 46 years); and gender (male, female). A ‘random element’ will be included in the minimisation algorithm. Participants are not informed of their group allocation until all the baseline data has been collected.
## Blinding
Participants will not be blinded to the exact purpose of the trial. It is not possible to blind the data collector, as the same researcher may be needed to undertake both the baseline and follow-up visit to collect data. We do not believe this will introduce bias, as the aim of this trial is to assess the feasibility of undertaking a large phase III RCT, and these outcomes are not affected by knowledge of group allocation and the data relating to the feasibility outcomes are not collected during the baseline and follow up visits. Only secondary outcomes are collected by researcher. The trial treatment allocation will be posted directly to the participants HCPs (where applicable). Physical activity will be assessed using a blinded research grade wrist worn accelerometer in both groups.
## The Snacktivity™ intervention
Participants randomised to the Snacktivity™ intervention and the current guidance for physical activity provided at NHS, social care, primary care or public health consultations will be advised to accumulate their physical activity through Snacktivity™. Snacktivity™ is defined as participation in small, but frequent, doses of regular MVPA throughout the day such that at least 150 min of MVPA is accumulated weekly. A physical activity ‘snack’ lasts between 2 to 5 min. The behavioural goal is for participants to work towards achieving at least 30 min of Snacktivity™ per day. The Snacktivity™ intervention aims to promote participation in Snacktivity™, the usefulness of Snacktivity™, encourages regular self-monitoring of Snacktivity™ to achieve sustained Snacktivity™, goal setting for daily Snacktivity™, as well as action planning and implementation strategies for Snacktivity™. There are two main components within the Snacktivity™ intervention; health professionals raising awareness of, and encouraging Snacktivity™ with participants in their consultations, and the promotion of technology to support behaviour change and sustained engagement in Snacktivity™ (via the phone app called SnackApp™ and physical activity self-monitoring device. See later for details.
The Snacktivity™ intervention is based on self-regulation theory and the habit formation model [28, 29]. Our own work and other studies have shown self-regulation/self-monitoring to be an effective foundation strategy for health behaviour change [30–32]. Self-monitoring of Snacktivity™ may act as a reward for individuals who increase their physical activity behaviour, who are then provided with positive feedback from the monitoring process, thereby enhancing their motivation and reducing the potential for relapse. Frequent monitoring and reflection of Snacktivity™ progress may also improve self-efficacy for participation in both short and longer bouts of physical activity. Encouragement of self-monitoring and recording of Snacktivity™ is a simple concept for a health professional to advocate as a public health communication. It is simple for people to understand and implement. Trials have shown that participants can adhere to daily self-monitoring of physical activity [33]. An intervention logic model was developed to guide the content and nature of the intervention.
## Snacktivity™ consultation
Participants will receive standard guidance about the importance of physical activity and any behavioural change strategies usually adopted by HCPs, but they will be advised to accumulate their physical activity through Snacktivity™. The intervention will start with delivery of the Snacktivity™ message. HCPs will also briefly discuss the purpose of Snacktivity™, the hypothesis underpinning its principals, how it differs from standard physical activity advice, and provide examples of Snacktivity™. HCP’s will use a picture board that illustrates a range of activity snacks.
HCPs will promote the rationale for Snacktivity™ and the benefits of physical activity for health, give examples of Snacktivity™, explain implementation plans and action planning. HCPs will highlight to participants that Snacktivity™ will work best if they develop habits or routines and how participants can achieve this. The purpose of the physical activity monitor and SnackApp™ for facilitating self-monitoring of activity snacks will be discussed; use of this technology will be specifically encouraged. An intervention checklist will be completed by HCPs to aid delivery and provide a reminder prompt of areas that must be covered during consultations. We anticipate the delivery of the Snacktivity™ intervention to participants taking approximately 5 to 7 min. The role of the HCP is to simply raise the topic of Snacktivity™/physical activity, to signpost participants to the SnackApp™ for further advice and support, and to encourage participants to use their physical activity monitoring device to facilitate their engagement with Snacktivity™ and to obtain feedback.
If a participant is recruited via community settings, including third party organisations and social media, a researcher will call the participant and deliver the intervention over the telephone. The consultation will be delivered following the same protocol and intervention checklist as is being used by HCPs. The delivery of the intervention by both HCPs and researchers will be audio recorded to assess fidelity (only if the participant has consented to this).
## SnackApp™ and physical activity monitoring device
As part of the intervention participants receive access to the mobile phone application SnackApp™ and are provided with a physical activity monitoring device (Fitbit). Technology-based interventions offer some key advantages over traditional behaviour change interventions and have the potential to reach a large number of people at a relatively low cost and offers increased access to the public at a time and place that suits their preferences, including the ability to overcome the need to attend face-to-face sessions to receive the intervention. As a population approach, smartphone-based interventions are very attractive as $90\%$ of mobile phone users are in possession of their telephones 24 h per day. mHealth technology is applicable across the age and cultural spectrums and studies have shown that electronic devices that promote physical activity are acceptable [34, 35]. In earlier work to survey the views of the public about the Snacktivity™ concept, and the use of technology to support Snacktivity™, $90\%$ of 724 respondents were found to own a smartphone and $45\%$ used their phone to monitor their physical activity [17].
To facilitate habit formation the SnackApp™ will generate regular reminders and notifications for the intervention group to engage in Snacktivity™. Self-monitoring may be particularly relevant for developing Snacktivity™ habits because it may be more difficult for people to easily recall how many activity snacks they have achieved each day. The design and content of the SnackApp™ is based on previous work packages, some of the principals of existing apps, and our previous experiences of developing apps for promoting physical activity and lifestyle behaviours. Participants will receive free access to the SnackApp™ after their consultation, which, within its functions, will contain features common in digital health interventions. Within these functions, the SnackApp™:Automatically captures/monitors daily physical activity and inactive time via a wrist worn consumer monitor (Fitbit Versa 2 device). It provides measures of steps, activity level, inactivity and energy expenditure each minute. A companion SnackApp™ will be downloaded onto the clock face of the Versa to allow instantaneous feedback on Snacktivity™ progress throughout the intervention period. Classifies participants into an activity profile according to their active and inactive time. Provides physical activity prompts after user-defined inactive periods, encouraging regular active snacks throughout the day. Generates individualised motivational push notifications to participants’ mobile phones based on prior behaviour and supports goal setting (both automatic and user-defined).Provides individualised feedback related to goal achievement to encourage adherence and facilitate self-efficacy. Individualised feedback will be provided on the accumulated number of physical activity snacks completed and total minutes ‘activity snacking’ per day, progress towards meeting the recommended level of physical activity for health benefits (150 min of MVPA per week) and the total number of minutes of physical activity, MVPA and the number of steps each day. Enables access to educational content (text, static images, video, and audio content) regarding Snacktivity™, both externally hosted and supplied by the research team, including examples of activity snacks (with instructive photos and gifs)Encourages social support via a forum to facilitate a Snacktivity™ social community. To promote habit formation, users will be able to plan when to perform activity snacks.
## Training of HCPs/researchers to deliver the Snacktivity™ intervention
Those delivering the intervention will be trained by the research team to deliver the Snacktivity™ intervention following a standard protocol; we have developed a media-based training module that can be delivered face-to-face or remotely. We anticipate the training will take no more than 1 h given the involvement of HCPs/researchers is simple and brief. The training tools will include information on the importance of adhering to the study protocol, the research study procedures, trial design and ways of delivering the Snacktivity™ intervention. More specifically, the training aims to demonstrate different ways in which Snacktivity™ can be promoted by HCPs within the consultation (where appropriate). We have also developed video clips that show Snacktivity™ being delivered in GP practices, so that HCPs have an understanding of how the intervention might be delivered by them.
## Intervention fidelity and contamination
With the consent of participants, the delivery of the Snacktivity™ information will be audio-recorded to assess for fidelity against the intervention component checklist. Those delivering the intervention will be trained on the importance of delivering the correct information to participants according to their randomised group to minimise the possibility of contamination. The Fitbit device and SnackApp will only be available to participants randomised to the intervention group. A range of the strategies to reinforce intervention fidelity will be used. We will. Develop a standardised training resource. Train HCPs/researchers to deliver the intervention according to the protocol. Explain the intervention logic model to HCPs/researchers. Audio record all consultations and telephone calls with a researcher to assess whether the intervention is being delivered according to the intervention checklist (with participant consent).Check for intervention ‘receipt’ and enactment by checking whether participants attended their consultation and whether the physical activity self-monitoring device and SnackApp™ are activated.
## Comparator group
We are not proposing any change to standard care. The usual care group will therefore receive the normal physical activity advice and any typical behavioural change strategies adopted by HCPs within consultations. The usual care group will only receive the current guidance for physical activity (to achieve 150 min of MVPA) within their healthcare consultation or during the telephone call with a researcher, who will advise they work towards the accumulation of at least 150 min MVPA per week. Participants will also receive a leaflet. With the consent of participants, the delivery of the usual care information will be audio-recorded to assess fidelity and intervention contamination.
## Primary outcome
The primary outcome is the feasibility and acceptability of a subsequent phase III RCT according to pre-specified progression criteria. We are primarily interested in whether the Snacktivity™ intervention and trial are appealing to participants (assessed by the recruitment rate) and if the Snacktivity™ intervention and the evaluation methods are acceptable to participants (measured by Snacktivity™/physical activity adherence and retention rates). We also wish to assess the recruitment and randomisation processes, measure the extent of any intervention contamination, and use data collected in the feasibility trial to review the sample size assumptions for the phase III trial.
## Progression criteria for phase III trial and stop–go criteria
The decision of whether to continue to the phase III trial will be guided by the assessment of the data collected during the feasibility trial (both quantitative and qualitative). For the quantitative data (Table 1), the following pre-defined stop–go criteria will be used:Recruitment: defined as the number/percentage of people randomised against the recruitment target of 80 participants over 5 months. Snacktivity™ adherence: defined as the number of physical activity snacks achieved (defined as minimum of four bouts of MVPA lasting ≥ 2 min on average each day over 12 weeks) assessed by the Fitbit monitoring device (in the Snacktivity™ arm only).Physical activity adherence: defined as the proportion of participants who are accumulating a total weekly average of at least 105 min of MVPA (~ 15 min daily) (in the Snacktivity™ arm only).Attrition: defined as withdrawal from the trial and/or no follow-up data available. Table 1Traffic light criteriaGreenAt least $80\%$ of the target sample size is recruitedAt least $65\%$ of the intervention group are achieving Snacktivity™ adherenceAt least $60\%$ of the intervention group are achieving physical activity adherenceAttrition < $21\%$If all four criteria are met, we will proceed to the full trial with the protocol unchanged (unless there is a clear indication from the qualitative interviews and our experience that would improve the protocol)Amber50–$79\%$ of the target sample size is recruited45–$64\%$ of the intervention group are achieving Snacktivity™ adherence45–$59\%$ of the intervention group are achieving physical activity adherenceAttrition 21–$35\%$If one or more of our amber criteria are met, we will plan to adapt the protocol in light of the results of the feedback from the qualitative interviews and our experience to improve which ever criteria are not at the ‘green light’ level before proceeding to the full trialRed < $50\%$ of the target sample size is recruited < $45\%$ of the intervention group are achieving Snacktivity™ adherence < $45\%$ of the intervention group are achieving physical activity adherenceAttrition > $35\%$If one or more of these criteria are met, we would consider the current protocol not feasible and not progress to the phase III RCT with the current protocol
## Secondary outcomes
Data will be collected on outcomes that we plan to collect in the definitive RCT at baseline and follow-up (see Table 2). While this feasibility trial is not powered to detect meaningful differences in these outcomes, collecting this data means we can assess and ensure that there are no issues with the collection and completion of these measures in preparation for the phase III trial. We will calculate MVPA, total physical activity, light physical activity, sedentary time (i.e. inactive time during waking hours), and sleep (and other metrics that may become available through novel processing methods) using data collected from a wrist worn research grade blinded accelerometer on participants’ non-dominant wrist (Axivity AX3; Axivity, Newcastle, UK) for at least seven days in both groups. A self-reported wake and sleep times log will be completed during the same days the accelerometer is worn. Other data collected at baseline and follow-up in all participants include self-reported sedentary behaviours (using the Workforce Sitting Questionnaire (WSQ) [36] and the sedentary behaviour item from the International Physical Activity Questionnaire [37], lower limb muscle strength (Takei dynamometer squat position), weight, waist circumference, blood pressure, and depression/anxiety (Hospital Anxiety and Depression Scale) [38], Physical Activity Enjoyment Scale [39] and Exercise Self-efficacy Questionnaire [40]. The Self-Report Habit Index [41] and a checklist of popular activity snacks questionnaire (Snacktivity™ checklist) are completed at follow-up in the intervention group only. Participants’ experiences of the trial will be captured in single item exit questions at follow-up, which includes questions relating to contamination. Specifically, participants are asked whether they know anyone else taking part in the study, and if so, whether they discussed the study with them. Table 2Schedule of assessmentsVisitScreeningBaseline visit (− 7 + 14 days)Follow-up(12 weeks)(− or + 14 days)Expression of interestxPhysical activity status: General Practice Physical Activity Questionnaire) [27]xEligibility screening telephone callxPersonal identifiers and demographic informationxCurrent medicationsxSmoking historyxAlcohol consumptionxMobilityxDiseases/conditionsxxWrist worn accelerometer (worn for up to 8 days) (axivity)xxAnxiety and depression: HADS [38]xxHealthcare utilisationxHousehold income/compositionxProductivityxSedentary behaviours: Workforce Sitting Questionnaire [36] and sedentary behaviour item from the International Physical Activity Questionnaire [37]xxEnjoyment of physical activity: Physical Activity Enjoyment Scale [39]xxSelf-efficacy for exercise and Exercise Self-efficacy Questionnaire [40]xxHabit strength (Snacktivity™ group only): The Self-Report Habit Index [41]xSnackApp™ engagement analytics (Snacktivity™ group only)xHeightaxWeightxxBody mass index (BMI)xxWaist circumferencexxLower limb muscle strength (Takei dynamometer squat position)xxBlood pressurexxChecklist of popular snacks (paper copy, Snacktivity™ group only)xSemi-structured interviews (Snacktivity™ intervention group and health care providers)xxSingle item study feedback questionsxaTo allow for the calculation of BMI, height will be measured to the nearest 0.1 cm using SECA 213 stadiometers at baseline and follow-up We plan to conduct a cost effectiveness study in the subsequent phase III trial and the questionnaire items to be used to assess health care resource use and productivity will be piloted in this feasibility trial. The physical activity self-monitoring device (Fitbit) will provide the following data for participants in the intervention group; steps, distance, calories, bouted active minutes, inactive time, sleep and awake time, and wear time (through body sensor). The Snacktivity™ active minutes will be computed from Fitbit device measured METs and activity snacks.
## Data collection
At baseline participants wear the axivity accelerometer for at least seven days before randomisation and complete the baseline questionnaires (either using an online link or paper copy sent by post) before the baseline home visit. At follow-up, the accelerometer is posted to participants in advance of their follow up visit, and the questionnaires are sent to participants once the follow-up visit has been booked (online link or paper as described above). The study visits will be conducted face to face by a member of the research team at participants’ home, a community venue or at their GP practice and completed in line with Government COVID-19 guidance, including NHS infection control procedures. If in person data collection cannot be completed, assessments will take place remotely online or using video conferencing tools. All data collected from participants is stored in a secure password protected database. All participants will receive a £20 high street voucher at completion of follow-up. All data is collected according to a data management plan.
## Collection of SnackAppTM data
The Fitbit Versa 2 collects various measures of physical activity data and displays these data using a bespoke SnackAppTM clockface (see Fig. 3). The SnackAppTM clockface, which is set as default, provides immediate feedback on the number of activity snacks, ‘active minutes’ (i.e. MVPA) and the number of steps users have achieved as measured by Fitbit Versa 2 watch. The clockface on the Fitbit smartwatch downloads the data to a ‘companion app’ via Bluetooth low energy which sit within the Fitbit app environment. With an internet connection, the companion app uploads the data to the Snacktivity™ application programming interface (API) where they are stored in PostgreSQL databases hosted on a secure encrypted Google server. Engagement analytic from the SnackAppTM will be collected and defined as participants’ use and interactions with the SnackAppTM and Fitbit clockface. Fig. 3SnackAppTM clockface
## Adverse event reporting
There is no reason to assume that this trial will lead to an excess of adverse events. The intervention consists of HCPs/researchers promoting short bouts of MVPA within everyday life and use of a commercially available physical activity device to monitor activity, along with a mobile phone app to log movement, none of which are likely to create harm. Furthermore, the promotion of physical activity by health providers is already part of standard care and has been demonstrated as being low risk for all citizens in England as per the NHS Making Every Contact Count Campaign [42] without specific follow-up for adverse events. Therefore, no adverse events will be collected.
## Serious adverse events (SAE)
The research team at site (where applicable) will report all SAEs that are not defined as protocol exempt in an expedited manner. The following are ‘protocol exempt’ SAEs: events related to the participants pre-existing condition(s) (pre-existing conditions are medical conditions that existed before entering the trial, or for which they have already consulted medical advice, as identified on the baseline questionnaire (as per the diseases/conditions specified in Table 3)); and hospital visits for any elective procedures. Musculoskeletal and bone injuries/fractures, and trips and fall injuries are regarded as expected SAEs and are recorded on the follow-up case report forms. Table 3Protocol exempt SAEs relating to pre-existing conditionsCancerSarcopenia (loss of muscle strength)Type 1 diabetesChronic obstructive pulmonary diseaseType 2 diabetesAsthmaHigh cholesterolKidney diseaseHigh blood pressureBack pain resulting in time off workHeart disease, heart attack, angina, aneurysmRheumatoid arthritisStrokeOsteoarthritisDepression or anxietyNeurological condition (e.g. epilepsy, myalgic encephalomyelitis, or multiple sclerosisDementia or Alzheimer’s diseaseCOVID-19OsteoporosisFoot/ankle problem affecting patient’s mobilityObesity
## Statistical considerations and data analysis
The feasibility trial aims to recruit 80 participants over 5 months and the sample size is based on recommended sample sizes for feasibility and pilot trials [43]. This means that we will be able to estimate a Snacktivity™ adherence rate of $65\%$ to within a $95\%$ confidence interval of ± $14.8\%$ (based on $$n = 40$$); a physical activity adherence rate of $60\%$ to within a $95\%$ confidence interval of ± $15.2\%$ (based on $$n = 40$$); and an attrition rate of $20\%$ to within a $95\%$ confidence interval of ± $8.8\%$ (based on $$n = 80$$). Based on a response rate of between 1 and $2\%$, 4000 to 5000 people will need to be invited to participate in the trial to achieve the required number of participants.
A separate statistical analysis plan will provide a more comprehensive description of the planned statistical analyses. The data analysis for this feasibility trial will be mainly descriptive, and focus on confidence interval estimation, with no hypothesis testing performed and no p-values presented. A brief outline of the planned analyses in relation to the stop–go criteria for this feasibility trial is provided. Recruitment and attrition rates will be analysed by pooling the two randomised groups. Adherence (Snacktivity™ and physical activity) rates will be assessed for the Snacktivity™ group only. Progression criteria will be summarised as proportions and percentages with $95\%$ confidence intervals. We will collect data on the number of invitations sent, and aggregated data on the age and gender of invitees to compare with the trial participants.
## Interview study
To gain further insight into the process of intervention delivery and receipt and the Snacktivity™ intervention we will ask participants to complete semi structured interviews about their experiences of the Snacktivity™ intervention. Questions informed by self-regulation and the habit formation model will focus on how often and how easy activity snacking is to do, how many snacks are achievable, ideal timing for snacking, barriers to snacking, formation of habits and implementation strategies used. Feedback on the use of the SnackApp™ and the suggested activity snacks will also be collected. Questions will also be guided by the intervention logic model. We expect to complete 20–25 interviews which should allow for saturation to be reached, as recommended for this type of study [44]. Purposive sampling will enable inclusion of participants who reflect as many socio-demographic characteristics of the possible eligible population (e.g. age, gender, ethnicity, socio-economic status, general exercise behaviour), and display different levels of engagement and participation with Snacktivity to capture the range of views.
Interviews will be audio-recorded, transcribed verbatim and analysed using a framework approach [45]. As well as documenting individual and overall themes we will carry out theme comparison as appropriate, for example across socio-demographic characteristics and engagement level. Findings will be interpreted against and combined with other study data. This will help us to understand the intervention process and the participants’ experiences, allowing us to further refine the intervention as necessary [46].
HCPs delivering the intervention will also be interviewed to gain their views and feedback about delivering the Snacktivity™ intervention within their routine consultations. We anticipate conducting 12–15 interviews with a range of HCPs. Interviews will be recorded, transcribed and analysed. Data will be interpreted as described above by means of overall and individual themes, theme comparison and in the context of findings from participant interviews and other study findings. Data will be used to inform and maximise participants experiences of receiving the Snacktivity intervention and HCPs experience of delivering the intervention in the Phase III RCT.
## Trial oversight and management
The Trial Management Group, which includes two members of the patient advisory group, will meet regularly (approximately every month) to ensure successful implementation and delivery of the trial. They will monitor participant recruitment; any departure from the expected recruitment rate will be dealt with according to the specific issues that arise. A joint independent Trial Steering Committee and Data Monitoring Committee (TSC/DMC) has been created for the Snacktivity™ feasibility trial. The TSC/DMC will meet at least twice a year or as required depending on the needs of the trial. The joint TSC/DMC will provide overall oversight of the trial, including the practical aspects of the trial, as well as ensuring that the trial is run in a way which is both safe for the participants and provides appropriate feasibility data to the sponsor and investigators.
## Public and patient involvement
This trial is supported by a Public Advisory Group (PAG) that consists of 10 members of the public from a range of backgrounds with different attitudes towards physical activity. The PAG is facilitated by a public involvement Lead and PAG members contribute to the development of the Snacktivity intervention through attending quarterly meetings or completing tasks remotely (e.g. providing feedback on documentation). Progress updates will be provided in periods between meetings (either short online meetings or newsletter). The PAG will have input into the design and development of the phase III trial and will be invited to comment on all participant facing documents and strategies for recruitment. Two PAG members will be part of the Trial Management Group and one PAG member will be attend the Trial Steering Group meetings. All PAG members will be offered an honoraria for involvement, which aligns to the National Institute for Health and Care Research (NIHR) Centre for Engagement and Dissemination recommendations.
## Trial progress
The trial has so far recruited 72 participants who have completed their baseline assessment and follow-up is ongoing. Recruitment to the qualitative study is ongoing and expected to be completed in February 2023.
## Discussion
There is strong evidence that physical inactivity and high levels of sitting are associated with poorer health and mortality, yet the population has become less physically active. Interventions that can be delivered at scale to address these health behaviours are required. In previous research, we reported that the Snacktivity™ approach to promoting physical activity was viewed positively by the public [17, 18]. In this research, we are proposing that Snacktivity™ may be an alternative way of promoting participation in physical activity to the public and assessing whether a full-scale trial is feasible. While there might be advantages to the Snacktivity™ approach, there may also be disadvantages. Snacktivity™ may be disruptive to the day, it may be easily forgotten, or difficult to achieve MVPA in short bouts. It may also not impact health sufficiently, or it may be difficult for the public to think of ways to achieve Snacktivity™ or implement it into their everyday lives. Research needs to explore these possible issues and consider how any potential barriers to Snacktivity™ might be overcome and this trial will allow us to assess and understand any issues before embarking on a subsequent phase III trial. Using both quantitative and qualitative methods the results of this trial will provide robust evidence regarding the feasibility and acceptability of an alternative approach to promoting physical activity in the population.
## Supplementary Information
Additional file 1. SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents*.
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|
---
title: Systematic evaluation of membrane-camouflaged nanoparticles in neutralizing
Clostridium perfringens ε-toxin
authors:
- Jinglin Xu
- Dongxue Li
- Lin Kang
- Tingting Liu
- Jing Huang
- Jiaxin Li
- Jing Lv
- Jing Wang
- Shan Gao
- Yanwei Li
- Bing Yuan
- Baohua Zhao
- Jinglin Wang
- Wenwen Xin
journal: Journal of Nanobiotechnology
year: 2023
pmcid: PMC10021051
doi: 10.1186/s12951-023-01852-z
license: CC BY 4.0
---
# Systematic evaluation of membrane-camouflaged nanoparticles in neutralizing Clostridium perfringens ε-toxin
## Abstract
Clostridium perfringens ε-toxin (ETX) is the main toxin leading to enterotoxemia of sheep and goats and is classified as a potential biological weapon. In addition, no effective treatment drug is currently available in clinical practice for this toxin. We developed membrane-camouflaged nanoparticles (MNPs) with different membrane origins to neutralize ETX and protect the host from fatal ETX intoxication. We evaluated the safety and therapeutic efficacy of these MNPs in vitro and in vivo. Compared with membranes from karyocytes, such as Madin-Darby canine kidney (MDCK) cells and mouse neuroblastoma N2a cells (N2a cells), membrane from erythrocytes, which do not induce any immune response, are superior in safety. The protective ability of MNPs was evaluated by intravenous injection and lung delivery. We demonstrate that nebulized inhalation is as safe as intravenous injection and that both modalities can effectively protect mice against ETX. In particular, pulmonary delivery of nanoparticles more effectively treated the challenge of inhaled toxins than intravenously injected nanoparticles. Moreover, MNPs can alter the biological distribution of ETX among different organs in the body, and ETX was captured, neutralized and slowly delivered to the liver and spleen, where nanoparticles with ETX could be phagocytized and metabolized. This demonstrates how MNPs treat toxin infections in vivo. Finally, we injected the MNPs into mice in advance to find out whether MNPs can provide preventive protection, and the results showed that the long-cycle MNPs could provide at least a 3-day protection in mice. These findings demonstrate that MNPs provide safe and effective protection against ETX intoxication, provide new insights into membrane choices and delivery routes of nanoparticles, and new evidence of the ability of nanoparticles to provide preventive protection against infections.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12951-023-01852-z.
## Introduction
The bacterium *Clostridium perfringens* produces a remarkable seventeen exotoxins, and of these, ε-toxin (ETX) is by far the deadliest [1, 2]. ETX is synthesized by C. perfringens types B and D. This toxin is the main virulence factor of C. perfringens type D, which is responsible for enterotoxemia in sheep, goat, and, more rarely, cattle. Enterotoxemia is a rapidly fatal disease that causes important economic losses globally [3]. ETX is the most powerful known toxin after botulinum and tetanus toxins [4]. Therefore, ETX is regarded as potential threat to national security and the animal husbandry economy. Moreover, ETX is classified as a potential biological weapon in some countries and as a category B biological agent by the Centers for Disease Control and Prevention (CDC) of the United States [5, 6]. The animal kidney is the primary target organ for ETX, followed by the brain [7]. ETX causes congestion and edema when target organs accumulate large quantities of toxin, leading to the death of animals [8]. Although there have been few reports of human infection with ETX [7, 9], our previous studies showed that ETX can form pores on human mature red blood cells (RBC) and produce hemolytic effects in a short period of time [10]. Therefore, the threat of ETX to human health is real, and as yet, there is no effective treatment drug in clinical practice. These features of ETX make it impossible for us to ignore its potential as a human threat. Therefore, developing a new safe and efficient drug to treat ETX infection is high priority.
In recent years, the rapid development of nanotechnology has extended the application of nanoproducts, especially in the field of medical treatment [11–17]. Nanoparticle-based methods of diagnosing and treating diseases have certain advantages in efficacy and safety compared to traditional methods [18, 19]. Among them are membrane-camouflaged biomimetic approaches [20] that use naturally-derived cell membranes to directly endow nanoparticles with enhanced biological interface capabilities [19]. Nanoparticles wrapped with cell membranes essentially replicate the properties of membrane-derived cells [21], allowing these membrane-camouflaged nanoparticles (MNPs) to treat various injuries and diseases caused by pore-forming toxins [22]. Current studies focus more on nanoparticle design and fabrication, with limited evaluation of membrane choice, delivery route, and mechanism of detoxification, all of which are important for application of MNPs.
Given that ETX is a pore-forming toxin, we hypothesized that MNPs might be used to neutralize ETX. MDCK cells are highly sensitive to ETX [23, 24], and ETX is able to form pores on the membrane of red blood cells (RBCs) [10]. Thus, MDCK cells and RBCs were used as materials to prepare MNPs for neutralizing ETX. In this study, we designed two MNP types using different membranes to neutralize ETX, and systematically evaluated the safety and neutralization ability of these MNPs in vitro and in vivo. MNPs were fabricated by mechanically squeezing the extracted MDCK cell membranes and RBC membranes tightly onto the nanoparticles, as previously described [20] (Fig. 1). The MNPs were able to neutralize ETX in the blood of mice infected with ETX, reducing symptoms of poisoning and successfully treating mice (Fig. 1).Fig. 1Schematic illustration of the procedure used to prepare RBC membrane-camouflaged nanoparticles (RBC-NPs) or MDCK cell membrane-camouflaged nanoparticles (MDCK-NPs). Membrane-camouflaged NPs can neutralize ETX in mice infected with ETX
## Packaging of nanoparticles into cell membranes and characterization of MNPs
We prepared the 50:50 Poly (DL-lactide-co-glycolide) Carboxylate End Group (PLGA) nanoparticles, co-incubated them with purified cell membranes, and extruded the result from polycarbonate membranes to generate cell-membrane coated PLGA nanoparticles (Fig. 1). Transmission electron microscopy (TEM) was used to observe three types of nanoparticles: bare nanoparticles (bare NPs), coated with RBC cell membrane (RBC-NPs), and coated with MDCK cell membrane (MDCK-NPs) (Fig. 2A–C). All nanoparticles exhibited spherical structures. When RBC-NPs and MDCK-NPs are compared to bare NPs, it can be clearly seen that the surface is covered with a monolayer film. A Flow NanoAnalyzer was used to measure the diameter (DH) of the three kinds of nanoparticles; bare NPs were ~ 95 nm, and RBC-NPs and MDCK-NPs were ~ 123 nm and ~ 124 nm, respectively, or ~ 30 nm larger than bare NPs (Fig. 2D). Zeta potentials of the three nanoparticles measured using dynamic light scattering (DLS), indicated that the zeta potentials of RBC-NPs and MDCK-NPs were ~ 15 mV greater than bare NPs (Fig. 2E). To assess the dispersion of nanoparticles in liquids, the polydispersity index (PDI) of nanoparticles dispersed in PBS was measured; PDI was less than 0.2, indicating that the three nanoparticles have good dispersion (Fig. 2F). No change in PDI a week later indicated that nanoparticles can be stably dispersed in PBS over the short term (Fig. 2G).Fig. 2Characterization of nanoparticles. TEM images of A bare NPs, B RBC-NPs, and C MDCK-NPs. D Flow NanoAnalyzer measures of nanoparticle diameter (nm). E DLS measurements of zeta potential (Zeta, mV) of nanoparticles. F DLS measurements of PDI of nanoparticles. G DLS measurements of the changes in the PDI of the MNPs in PBS at 37 °C. Data are presented as the means ± SD
## In vitro evaluations of MNPs
Neutralization capacity in vitro is an important indicator of in vivo therapeutic efficacy. We therefore assessed the neutralization capacity of the two kinds of MNPs (RBC-NPs and MDCK-NPs) to recombinant ETX with Glutathione-S-transferase (GST) tags (GST-ETX) in vitro by 3-(4,5-Dimethylthiazol-2-yl)-5(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium inner salt (MTS) assay with cells. MDCK cells are the most sensitive cells to ETX and are commonly used in ETX cytotoxicity. ETX with different tags (GST and 6 × His) did not significantly differ in toxicities (Additional file 1: Fig. S1). Therefore, MDCK cells were used in all evaluations of GST-ETX and MNPs in vitro. The concentration of GST-ETX that killed $50\%$ of MDCK cells (denoted by CT50) was 0.8813 nM (Fig. 3A). When the toxin reached a concentration of 20 nM, maximum cell mortality was achieved. To investigate whether the three kinds of nanoparticles could damage MDCK cells, MDCK cells were treated with a series of concentrations of nanoparticles. A measure of nanoparticles as high as 2.4 mg showed no cytotoxicity to MDCK cells (Fig. 3B).Fig. 3In vitro evaluation of GST-ETX and MNPs. A In vitro toxicities of GST-ETX. CT50 values were calculated based on concentration–survival curves. B MDCK cells were exposed to increasing concentrations of nanoparticles for 1 h at 37 °C and survival of MDCK cells measured. C Cells were observed by confocal microscopy. ( Scale bar: 100 μm). D MDCK cells were exposed to increasing concentrations of nanoparticles and 20 nM of GST-ETX for 1 h at 37 °C. Survival of MDCK cells was measured. E A series of concentrations of GST-ETX were treated with two kinds of nanoparticles or PBS for 1 h at 37 °C. Toxicities of the mixtures were measured. F RBCs in $2.5\%$ solution were incubated with increasing concentrations of GST-ETX for 1 h at 37 °C. Relative hemolysis of RBCs was measured. G RBCs in $2.5\%$ solution were incubated with GST-ETX (5 μM) or $1\%$ TritonX-100 for 1 h at 37 °C. Hemolysis of RBCs caused by Triton X-100 is defined as $100\%$. Maximal extent of hemolysis induced by GST-ETX was measured. H RBCs in $2.5\%$ solution were incubated with GST-ETX (30 nM) and nanoparticles for 1 h at 37 °C. An assay of relative hemolysis of RBCs was performed. Data are presented as mean ± SD Cell death was examined using a confocal high-content imaging system (Fig. 3C, Additional file 1: Fig. S2). MDCK cells incubated with the two kinds of MNPs maintained a high survival rate under ETX challenge. MDCK cells were incubated with GST-ETX and a series of concentrations of MNPs. MNPs significantly reduced the toxicity of GST-ETX in a dose-dependent manner, with 2 mg MNPs nearly abolishing the toxicity of GST-ETX to MDCK cells (Fig. 3D). MDCK-NPs reduced the toxicity of GST-ETX more than RBC-NPs, suggesting that MDCK-NPs can neutralize GST-ETX faster. We next treated a series of concentrations of GST-ETX with MNPs, to assess the neutralization ability of MNPs mixed with toxin. Both MNPs neutralized ETX, reducing the toxicity of the mixture to MDCK cells (Fig. 3E). The two types of MNPs created mixtures that differed in CT50, with 2 mg of RBC-NPs neutralizing 3.793 pmol GST-ETX, and 2 mg of MDCK-NPs neutralizing 11.75 pmol GST-ETX. Thus, while both kinds of MNPs can effectively neutralize ETX, the MDCK-NPs neutralized more GST-ETX faster when cells were challenged by GST-ETX. Therefore, MDCK-NPs have a better performance against GST-ETX than RBC-NPs.
To verify the difference in sensitivity between RBCs and MDCK cells, we assessed the relative hemolysis damage in RBCs caused by GST-ETX. Hemolysis increased with the concentration of GST-ETX (Fig. 3F). The concentration of GST-ETX required to cause hemolysis in RBCs is close to 20 nM, and the maximum relative hemolysis was achieved at a toxin concentration of approximately 150 nM. In addition, RBC hemolysis by GST-ETX was not complete within 1 h (Fig. 3G). In contrast, 20 nM GST-ETX caused maximum cell mortality to MDCK cells within 1 h. This indicates that RBCs and MDCK cells differ in sensitivity to ETX, and this difference is reflected in the differences between the two kinds of MNPs. We estimated the concentration of 30 nM of GST-ETX that would cause $50\%$ relative hemolysis to verify the neutralization capacity of MNPs. We calculated that a complete neutralization of 30 nM GST-ETX 1 ml would require about 5.1 mg of MDCK-NPs and about 15.8 mg of RBC-NPs. The results of co-incubation with RBCs showed that MDCK-NPs could reduce the toxicity by $85\%$, and RBC-NPs could reduce the toxicity by $60\%$ (Fig. 3H). None of the three kinds of nanoparticles caused any damage to RBCs. This result is consistent with the difference in protective ability of the two MNPs in the MDCK cell protection experiment above. When MNPs and cells competitively bind to GST-ETX, MDCK-NPs can neutralize more toxin faster. This may be because there are more receptors on the membranes of MDCK cells, given that MDCK cells are more sensitive than RBCs to GST-ETX.
## In vivo safety assessment of MNPs
Safety assessment in vivo is an important precondition of in vivo therapeutic efficacy. We therefore assessed the safety of the three kinds of nanoparticles to mice in vivo. Following injection of one of the three kinds of the nanoparticles to three groups of mice intravenously, mice were observed for 7 consecutive days (Fig. 4A). Bare NPs and RBC-NPs were safe to animals, while MDCK-NPs caused death in half of the mice (Fig. 4B). We examined multiple blood parameters, including a comprehensive serum chemistry panel and blood cell counts. Cells count in mice injected with bare NPs and RBC-NPs were consistent with baseline levels, indicating these two nanoparticles did not cause any toxic or immune response (Fig. 4C). In contrast, mice injected with MDCK-NPs, displayed significant anomalies in white blood cell (WBC), eosinophil (EOS), basophil (BAS) and platelet (PLT) counts, indicating that MDCK cell membranes could trigger an immune response, which is presumably related to MDCK-NP-induced death in mice. In addition, we assessed blood markers of liver and kidney damage; alkaline phosphatase (ALP) [25] and glucose (GLU) in the blood are thought to reflect damage to the liver [26], while serum calcium (Ca), serum sodium (Na), and urea in blood are thought to reflect damage to the kidney [27–31]. However, these indicators were normal in all mice (Fig. 4D), indicating the short-term safety of MNPs. Fig. 4In vivo safety evaluations of the three kinds of the nanoparticles. A Schematic illustration of the experiments. Four groups of 8-week-old female BALB/c mice were injected with 100 μL PBS or PBS with bare NPs, with RBC-NPs or with MDCK-NPs ($2\%$ w/v) intravenously. B Survival curves of the mice in the next 7 days ($$n = 6$$). Five groups of 8-week-old female BALB/c mice, were injected intravenously with 100 μL PBS or PBS with nanoparticles ($2\%$ w/v) or N2a cell membrane (100 μL, approximately 5 × 10.7 cells). C Blood cell counts of the mice ($$n = 3$$). D Chemical composition analysis of serum ($$n = 3$$). E The concentration of IgE in plasma in the mice ($$n = 3$$). Data are presented as the mean ± SD. $p \leq 0.05$ (*), $p \leq 0.01$ (**), $p \leq 0.001$ (***), p ≥ 0.05 (ns) Immune response triggered by MDCK-NPs, which include increased EOS, BAS and decreased PLT, are presumed to be related to hypersensitivity [32–34]. To verify whether the anomalies of MDCK-NPs were related to hypersensitivity, we measured IgE in mouse serum. The results of serum IgE detection showed that IgE in the serum of mice injected with MDCK cells membrane was significantly increased (Fig. 4E), while no obvious IgE reaction was detected in the serum of the other mice. These results suggest that MDCK cell membranes induced strong hypersensitivity in mice, which is likely the main cause of death in mice injected with MDCK-NPs. MDCK cells originate from normal kidney cells of a cocker spaniel dog [35], which is distantly related to murine species, and we infer that the injection of membranes from a distantly related species are likely to induce strong immune response and hypersensitivity. To test our hypothesis, we injected membranes from N2a cells, which originate from a murine species. Membrane from N2a cells induced slight immune response but did not trigger hypersensitivity, in keeping with our hypothesis.
Given the unstable safety profile of MDCK-NPs, we abandoned the use of MDCK-NPs in further animal experiments, despite their excellent ability to compete and bind ETX.
## In vivo therapeutic efficacy evaluations of MNPs
After assessing neutralization capacity in vitro and the safety in vivo, we next assessed therapeutic efficacy of RBC-NPs in vivo. We first intravenously injected GST-ETX into mice of each group, and then 10 min later, we intravenously injected RBC-NPs or PBS into mice of the treatment and positive control group, respectively (Fig. 5A). The dose of 20 ng GST-ETX led to $100\%$ death of mice in the positive control group within 24 h (Additional file 1: Fig. S3), while mice given RBC-NPs had $100\%$ survival for 7 days (Fig. 5B). Histopathological analysis showed that the kidneys, brains, livers and lungs of mice in the positive control group had different degrees of damage, mainly manifested as congestion or edema; however, these symptoms were significantly relieved by RBC-NPs (Fig. 5C).Fig. 5In vivo therapeutic efficacy evaluations of the RBC-NPs. A Schematic illustration of the experiments. Three groups of mice were injected with 20 ng GST-ETX intravenously; 10 min later, mice in the treatment group were injected with 2 mg RBC-NPs intravenously and mice in the positive control group were injected with PBS intravenously. At the same time, mice in the negative control group were injected both times with PBS intravenously. B The survival curves of mice in the 7 days post-injection ($$n = 6$$). C Blood cell counts in the mice ($$n = 3$$). D Chemical composition analysis of serum ($$n = 3$$). E Major organs were stained with hematoxylin and eosin (H&E). Representative sections are shown for various organs of the mice in different groups (scale bar: 200 μm). Data are presented as mean ± SD. $p \leq 0.05$ (*), $p \leq 0.01$ (**), $p \leq 0.001$ (***), p ≥ 0.05 (ns) In another three groups of mice, 6 h after completion of the two injections, blood for analysis was done (Fig. 5A). Blood biochemical analysis revealed that, compared to the negative control group, WBC count in the blood of ETX-infected mice in the positive control group was significantly elevated $300\%$, and the change of WBC was caused mainly by $900\%$ elevation of neutrophils (NEU) (Fig. 5D). WBC count in the blood of mice treated with RBC-NPs was closer to that of the negative control group of mice. This indicates that RBC-NPs effectively attenuated the inflammatory response triggered in vivo by the ETX challenge [36]. In addition, we assessed blood markers of liver and kidney damage. The livers and kidneys of mice were damaged to varying degrees during the ETX challenge, and RBC-NPs can play a role in protecting these organs, which was consistent with the results of histopathology analysis (Fig. 5E, Additional file 1: Fig. S4). Thus, RBC-NPs were sufficiently able to competitively bind ETX in vivo to protect host organs from damage and can effectively treat GST-ETX infection in vivo.
## The interaction between RBC-NPs and ETX and its metabolism in vivo
We further investigated the interaction between in metabolisms of RBC-NPs and GST-ETX in vivo to verify how the RBC-NPs protect the host. We injected mice intravenously with fluorescently-labeled RBC-NPs and GST-ETX, and the distribution of radiant efficiency in blood and tissues reflected their interactions and metabolisms. To display the metabolism of ETX toxin and RBC-NPs in mouse tissues, the NIR dye DiOC18[7] (DiR) was used to label RBC-NPs (DiR-RNPs), the NIR dye cyanine 5.5 (Cy5.5) was used to label GST-ETX (Cy5.5-ETX). We intravenously injected Cy5.5-ETX into mice of each group and 10 min later, intravenously injected DiR-RNPs or PBS into mice of the treatment group and the positive control group, respectively. At several time points post-injection (after 5 min, 24 h, 48 h and 72 h), blood of random mice in each group was taken for quantitative analysis of fluorescence intensities and its major organs were taken at the same time for fluorescence images in vitro (Fig. 6A). The quantitative analysis of fluorescence intensities in blood showed that DiR-RNPs in the blood of mice in the treatment group decreased over time (Fig. 6B). It should be noted that Cy5.5-ETX in the blood of mice in the treatment group was not completely cleared after three days, but consistently decreased along with DiR-RNPs (Fig. 6C). Levels of Cy5.5-ETX in the blood of mice in the treatment group was lower than that of the positive control group all time points and was reduced to a very low level 24 h after injection. Thus, within 5 min of injection, RBC-NPs neutralized the majority ETX, preventing ETX from spreading in vivo. In addition, the RBC-NPs that neutralized ETX remained stable in the blood and did not release toxins into the blood again. Fig. 6Interaction and metabolism between RBC-NPs and GST-ETX in vivo. A Schematic illustration of the experiments. Two groups of mice were injected with 5 ng GST-ETX intravenously, and 10 min later, mice in the treatment group were injected with 2 mg DiR-RNPs intravenously and mice in the positive control group were injected with PBS intravenously. Blood and organs were analyzed at 5 min, 24 h, 48 h and 72 h in vitro. B The fluorescence level of the DiR in blood ($$n = 3$$). C The fluorescence level of Cy5.5 in blood ($$n = 3$$). D In vitro fluorescence images of DiR in liver and spleen, E radiant efficiency of the DiR in liver and spleen ($$n = 3$$). F In vitro fluorescence images of Cy5.5 in liver and spleen. G Radiant efficiency of Cy5.5 in liver and spleen ($$n = 3$$). The positive control group was injected with Cy5.5-ETX and PBS, the treatment group was injected with Cy5.5-ETX and DiR-NPs. Data are presented as the mean ± SD *Quantitative data* of fluorescence images in vitro of DiR in tissues indicated that DiR-RNPs were captured by the liver and spleen (Fig. 6D–E, Additional file 1: Figs. S5, S6). The Cy5.5 signal of mice in the positive control group suggested Cy5.5-ETX gradually decreased in the liver, and no fluorescence signal was observed in the spleen (Fig. 6F–G, Additional file 1: Figs. S7, S8). In the treatment group, Cy5.5-ETX was much higher in the liver and also had a high signal in the spleen. As with the fluorescence signal in blood of the treatment group, the distribution signal of GST-ETX in organs is consistent with that of RBC-NPs. This was especially evident in the spleen, in which GST-ETX was not detected in the spleen in the positive control group, but because RBC-NPs had been captured by the spleen, it was detected in the spleen of the treatment group. The difference is that the two fluorescence signals tend to decrease over time in the blood, while fluorescent signals in the spleen and liver show a tendency to stabilize or increase. The results show that as immune and detoxification organs of animals, the spleen and liver captured RBC-NPs that had neutralized GST-ETX from the blood. This is how RBC-NPs treat GST-ETX infection in vivo.
## In vivo therapeutic efficacy evaluations of nebulized pulmonary inhalation
Toxic aerosols can be used as weapons in terrorist attacks. For ETX, which was classified as a potential biological weapon, whether RBC-NPs can play a protective role in ETX challenges from pulmonary inhalation is of key import. We therefore assessed the therapeutic efficacy of the RBC-NPs during nebulized pulmonary inhalation of mice in vivo. Liquid aerosol devices were used to administer GST-ETX and RBC-NPs from the mouse trachea by quantitative nebulization, thereby simulating ETX aerosol challenges and lung drug delivery. The advantage of using liquid aerosol devices is that the drug can be evenly distributed into the lungs of mice (Additional file 1: Fig. S9) and ensures that each mouse receives the drug at the same distribution location and reduces the risk of pulmonary edema caused by lung administration. This not only allows us to simulate an ETX aerosol weapons challenge, but also minimizes possible adverse effects during lung delivery of RBC-NPs. We first administered 50 ng GST-ETX into the tracheas of mice in each group, and 10 min later, administered 2 mg RBC-NPs by trachea or intravenously to mice in the two treatment groups; PBS was administered by trachea to mice in the positive control group. Mice in the negative control group had PBS administered by tracheas each time (Fig. 7A). Mice were observed for 14 days post-infection. Introduction of 50 ng GST-ETX into mouse lungs resulted in $100\%$ mortality of the mice in the positive control group within 8 days (Additional file 1: Fig. S10). Treatment mice that had RBC-NPs introduced by aerosol into the lungs had $100\%$ survival (Fig. 7D). However, RBC-NPs given intravenously did not play a protective role to mice, mice in this group all died within 8 days. Fig. 7The in vivo therapeutic efficacy of RBC-NPs and metabolism of RBC-NPs and GST-ETX in lung. A Schematic illustration of the experiment. Four groups of mice were administered 50 ng GST-ETX via trachea; 10 min later, two treatment groups of mice were administered 2 mg RBC-NPs via trachea or intravenous, and a positive control group of mice was administered PBS via trachea. A negative control group of mice were administered PBS via trachea twice. B Schematic illustration of the experiment. Three groups of mice were administered 50 ng GST-ETX via trachea, 10 min later, treatment groups of mice were administered 2 mg RBC-NPs via trachea, and a positive control group of mice were administered PBS from tracheas. At the same time, a negative control group of mice were administered PBS via trachea twice. C Schematic illustration of metabolism experiment. Two groups of mice were administered 12.5 ng Cy5.5-ETX via trachea; 10 min later, the treatment group of mice were administered 2 mg DiR-RNPs via trachea, and the positive control group of mice were administered PBS from trachea. D Survival curves of mice over 14 days post-infection ($$n = 6$$). E, F Blood cell counts in mice ($$n = 3$$). G The chemical composition analysis of serum ($$n = 3$$). H Representative sections made from various organs of experimental mice, stained with H&E (scale bar: 200 μm). I In vitro fluorescence images of DiR in liver and spleen. J In vitro fluorescence images of Cy5.5 in liver and spleen. Data are presented as mean ± SD. $p \leq 0.05$ (*), $p \leq 0.01$ (**), $p \leq 0.001$ (***), p ≥ 0.05 (ns) In another groups of mice, blood samples were taken for analysis 6 h post-infection (Fig. 7A). Compared to the negative control group of mice, WBC counts in ETX-infected mice were elevated $300\%$. The WBC counts of mice treated with RBC-NPs in lungs were closer to the negative control group of mice, but the WBC counts of mice treated with RBC-NPs intravenously were closer to those of mice in the positive control group. The increase in WBCs was mainly caused by $600\%$ increases in NEU (Fig. 7E–F). This result showed that only RBC-NPs in lungs effectively attenuate the inflammatory response triggered in vivo by the ETX challenge in lungs. As before, we also tested blood markers of liver and kidney damage. The livers of mice were not damaged during the ETX challenge in lungs, but kidneys, the most sensitive organ to ETX, were slightly damaged with ETX challenge to lungs. Compared with injection of MNPs via veins, RBC-NPs in lungs appear to play a critical role in protecting kidneys of mice (Fig. 7G).
As with our experiment testing therapeutic efficacy injecting intravenously, we again dissected the main organs and stained tissue with H&E (Fig. 7B) for aerosol-challenged mice. Histopathological analysis showed that the brains of mice had no obvious pathological changes (Fig. 7H, Additional file 1: Fig. S11). The lungs of mice in the positive control group had serious damage, mainly manifested as congestion and edema, while the liver and kidneys of these mice had mild damage, mainly manifested as edema. However, these symptoms were significantly relieved by RBC-NPs in lungs.
To verify why pulmonary infection with ETX causes less damage to organs other than the lungs, and how RBC-NPs in lungs can protect mice, Cy5.5-ETX and DiR-RNPs were used to measure their interactions and metabolism in lungs (Fig. 7C). Quantitative data of fluorescence images in vitro of the DiR indicated that DiR-RNPs cannot escape the lungs (Fig. 7I). Quantitative data of fluorescence images in vitro of the Cy5.5 of the mice in the positive control group indicated that most Cy5.5-ETX remains in the lungs after 24 h, but after 48 h, some Cy5.5-ETX was detected in the livers and a smaller amount of Cy5.5-ETX was detected in the kidneys (Fig. 7J). This result showed why GST-ETX causes little damage to other organs when administered via the lung. However, for mice in the treatment group all Cy5.5-ETX was in the lungs and the distribution of the signal of GST-ETX was consistent with that of RBC-NPs. Thus, GST-ETX was completely neutralized in the lungs by RBC-NPs, preventing it from causing damage to the lungs and also preventing it from spreading beyond the lungs. This is how RBC-NPs protects the host against an ETX challenge to the lungs. This experiment demonstrated the efficacy of using RBC-NPs in lungs to treat of GST-ETX infection in lungs in vivo.
## Sustained protection of MNPs in vivo
Previous tissue in vitro imaging experiments had shown that RBC-NPs have a significantly longer circulation time than expected in vivo. This suggests that the stability of RBC-NPs can provides long-term protection in vivo. To test this, we delivered RBC-NPs to mice 1–3 days in advance of a toxin challenge via intravenous and aerosol, and then delivered GST-ETX to mice using the same delivery system. Mice were then observed for survival (Fig. 8A, C). RBC-NPs given intravenously 3 days in advance still provided protection for mice against ETX, and RBC-NPs in lungs 2 days in advance provided partial protection for mice (Fig. 8B, D). In other groups of mice, 6 h after completing toxin delivery, blood was taken for analysis (Fig. 8A, C). Blood biochemical analysis shows that, according to the two standards of WBC and NEU counts, appropriate early injection of RBC-NPs can provide protection to the host, although the protective effect decreases with time (Fig. 8E, F). This experiment demonstrated the efficacy of using RBC-NPs in advance to treat of GST-ETX infection in vivo. Fig. 8In vivo evaluation of projection provided by preinjected RBC-NPs. A Schematic illustration of the experiments. Three treatment groups of mice were injected with 2 mg RBC-NPs intravenously 24, 48, and 72 h prior to injection with 20 ng GST-ETX intravenously. The negative and positive control groups of mice were injected with PBS intravenously in the 72 h prior to toxin challenge, and mice were injected with 20 ng GST-ETX or PBS intravenously on the last day. B Survival curves of the mice for 7 days post-intravenous toxin challenge ($$n = 6$$). C Schematic illustration of the experiments. Three treatment groups of mice were injected with 2 mg RBC-NPs via aerosol into the lungs at 24, 48, and 72 h prior to aerosol challenge with 50 ng GST-ETX. The negative and positive control groups of mice had PBS via aerosol in the 72 h prior to aerosol lung exposure with 20 ng GST-ETX or PBS on the last day. D Survival curves of the mice for 7 days after aerosol toxin challenge ($$n = 6$$). E Blood cell counts of cells of mice injected intravenously ($$n = 3$$). F Blood cell counts of mice injected via aerosol into lungs ($$n = 3$$). Data are presented as mean ± SD. $p \leq 0.05$ (*), $p \leq 0.01$ (**), $p \leq 0.001$ (***), p ≥ 0.05 (ns)
## Discussion
As a potently toxic potential biowarfare or bioterrorism agent [1], ETX may represent a threat to human health, national security and animal husbandry economy for which no effective therapeutic drug is currently available. Inspired by the fact that ETX can form pores on cells membrane [3, 10] and based on the potential capabilities of the membrane-camouflaged biomimetic approach [22, 37], we explored whether MNPs might provide a medical countermeasure against the toxicity of ETX. Based on physical characteristics of MNPs, including ultra-small volume and higher specific surface area, as well as ability to interact with biomolecules and diffusion properties, it is expected that MNPs would exhibit stronger toxin binding ability than typical cells [38, 39]. As the results of in vitro evaluations of MNPs shown, MNPs have a more potent ability to bind to ETX than MDCK cells and human erythrocytes, indicating that MNPs has advantages over both cell lines and endogenous cells in binding to ETX. We hypothesize that MNPs can bind ETX faster than host cells in vivo. Subsequent experimental results confirmed our hypothesis, as RBC-NPs administered to mice can bind free ETX in the blood faster than the organs of the mice, resulting in significant reduction of inflammation and organ damage.
In addition, in this study, we assessed the neutralization performance and safety of MNPs against GST-ETX in vitro and in vivo, assessed the therapeutic efficacy of MNPs against GST-ETX delivered via different routes simultaneously and in advance in vivo, and verified the ways in which MNPs protect organs.
Membrane origins affect the neutralization capacity and safety of MNPs. Many cell lines that have been shown to be sensitive to ETX. However, the MDCK cell line is the most sensitive to ETX [40]. For animal RBCs, only human RBCs are sensitive to ETX [10]. Moreover, the morphological effects of ETX-induced hemolysis in RBCs resembles that in MDCK cells, but MDCK is much more sensitive to ETX. Therefore, we evaluated the neutralization capacity of MNPs camouflaged using these two unique cells membrane separately. MNPs replicate the ability of membrane-derived cells to bind to ETX, and likewise replicate the difference in sensitivity of the two kinds of cells to ETX; as such, MDCK-NPs are more effective than RBC-NPs at binding ETX [10]. However, the more capable MDCK-NPs performed worse in safety evaluations with mice. Comparing mice after being injected with RBC-NPs, MDCK-NPs and N2a cell membranes, we found that the source of the cells and the sophistication of cells affected the safety of MNPs. We demonstrated that three kinds of exogenous cell membranes do not cause liver or kidney damage in mice. However, because karyocytes have more complex cells membrane, MDCK cells membrane and N2a cells membrane cause a stronger immune response. This also reflects the advantages of human mature RBCs membrane. Karyocytes have more sophisticated biological functions than RBCs, and the biological characteristics of their membrane surface are also more sophisticated. In addition, other membrane proteins including C8-binding protein (C8bp), homologous restriction protein (HRP), decay-accelerating factor (DAF), membrane cofactor protein (MCP), complement receptor 1 (CR1), and CD59 on RBC surfaces fend off the attack by the complement system[41]. Thus, RBC-NPs have lower immunogenicity for reduced immune rejection of the host. For karyocytes such as MDCK cells and N2a cells, the more distantly related the membrane-derived cells are to the host, the more severe the immune response that is caused. MDCK cells line originate from normal kidney cells of a cocker spaniel dog, which is distantly related to murine species [35]; as a result, MDCK-NPs caused severer hypersensitivity reactions and led to the death of some mice. This suggests that the sophistication of the cell membrane is related to the in vivo safety of MNPs, and whether the membrane derived organism is homologous with the host will also influence in vivo safety. Hence, we suggest that the selection of cell membrane is also an important aspect for follow-up studies of MNPs. Under the premise that the desired therapeutic effect can be achieved, the cell membrane chosen should be as simple as possible. When a choice must be made between karyocytes, the cell membrane more closely related to the host is preferred.
MNPs alter the biological distribution of ETX among organs in the body. ETX was captured, neutralized and slowly delivered to the liver and spleen, where nanoparticles with ETX were phagocytized and metabolized. And the interaction between RBC-NPs and ETX, as well as metabolism of RBC-NPs and ETX complexes in vivo were verified for the first time in this study. This allowed us to visualize the process by which RBC-NPs treat ETX infection and protect organs in vivo. In our findings, RBC-NPs can neutralize toxins in the host within 5 min, and ETX and RBC-NPs remain combined in vivo, which ensures that RBC-NPs with neutralized ETX can be safely and stably be decomposed by liver and spleen. The liver and spleen are major a part of the mononuclear phagocyte system (MPS), and the majority of the injected nanoparticles are cleared from the bloodstream by cells of the MPS [42]. Biodistribution studies have shown this to be the case for all types of nanomaterials—micelles [43, 44], quantum dots [45, 46], gold nanoparticles [47, 48], and carbon nanotubes [49, 50]. Similarly, MNPs has the same distribution characteristics [20]. This phenomenon has been reported many times and is also the focus of research on nanoparticles in targeted therapy. However, for protecting host from fatal ETX challenge caused by intoxication with ETX, the function of MPS capture and decomposition can ensure that toxins are removed along with RBC-NPs, realizing the safe removal of toxins in the body. The residue after 24 h of most nanoparticles is negligible in the blood of mice [51–53]. Nonetheless, the RBC-NPs exhibit superior in vivo residence time [20]. In our study, RBC-NPs that neutralize toxins can achieve long circulation, and membrane-bound ETX on the RBC-NPs surface are stable. After 72 h of injection, the fluorescence level in the blood dropped by about $70\%$, while the fluorescence signal in the liver and kidney was relatively stable. Therefore, RBC-NPs bound to ETX in the blood are slowly captured by the MPS and continuously broken down, without a large accumulation in the MPS. This elucidates how the MNPs protect host organs in toxin challenges in vivo. Long circulation times that rely on RBC-NPs, can not only reduce the pressure on the MPS while ensuring the continuous decomposition of RBC-NPs and ETX, but also protects the host from an ETX challenge continuously, thereby prolonging the required injection cycle of RBC-NPs.
Pulmonary inhalation MNPs are as safe as intravenous injection MNPs [54]. We demonstrate that nebulized inhalation is a safe way to deliver MNPs into the lung. Both modalities can effectively protect mice against ETX. The blood markers of liver and kidney damage following lung delivery of GST-TEX were compared with intravenous injection GST-ETX and revealed differences in the degree of organ damage in mice after infection with toxin via different delivery methods]. Histopathological analysis showed that only the lungs of mice infected with ETX by aerosol had severe damage and that other organs had minor or no damage. Quantitative data of fluorescence images in vitro of the Cy5.5-ETX indicated that ETX cannot immediately escape the lungs, explaining the severe damage to the lungs by aerosol-infection with ETX. The fatal damage occurred in the lungs of mice, and lung delivery of MNPs can protect the lungs of mice faster and more directly than intravenous injection of MNPs. The results also indicate that pulmonary delivery of nanoparticles can more effectively treat the challenge of lung-inhaled toxins than intravenous injection of nanoparticles. This is important because toxin attacks by aerosols are likely in terrorist attacks. This is the first report of the use of RBC-NPs to neutralize inhaled toxins in the lung, and that this mode of infection is ineffectively treated by intravenous treatment. The results of this research can also provide an important reference for treating other aerosol-borne diseases, such as corona virus disease 2019 (COVID-19), which can cause lung damage and pulmonary anthrax, which can produce toxins in the lungs [55–58]. That RBC-NPs nebulized and delivered to the lungs can safely act as a protective effect has clear significance for studies of treatment in aerosol-borne diseases.
Our experiments also showed that, although RBC-NPs in the blood dropped rapidly on the first day, it remained detectable in the blood at 72 h and that RBC-NPs inhaled into the lungs remain present in the lungs for more than 72 h. The half-life of the fluorescence signal of RBC-NPs in the blood is close to 48 h, and the fluorescence signal of RBC-NPs in the lungs is not significantly reduced at 72 h. We verified that the delivery of RBC-NPs to the host in advance via vein or lung, can still provide protective effects to the host in the short term. Thus, RBC-NPs provide long-term protection that is difficult for chemical drugs to achieve. This is promising for treating ETX infection with RBC-NPs, in scenarios such as biochemical warfare; injecting susceptible people with RBC-NPs in advance or delivery of nebulized RBC-NPs to their lungs can continue to provide reliable protection for several days.
Based on the high efficiency and long-term protective performance of RBC-NPs, we suggest that MNPs have wide practicability in the treatment of infections. Toxins normally act on the plasma membrane or in cytoplasm of target cells, they must therefore interact with a membrane at some point [59]. Thus, by screening cell membranes and designing MNPs rationally, membrane-camouflaged biomimetic approaches have the potential to serve as a therapeutic platform for any toxin. To develop MNPs as nanomedicines, all that is required is understanding which cells are sensitive to the toxin, or the mechanism of action of the toxin. In the future, mixing cell membrane-camouflaged nanoparticles or mixing different MNPs could become a multifunctional and powerful therapeutic platform for treating toxins. Most importantly, this therapeutic platform can treat both acute toxin infections and provide long-term protection against toxins.
## Conclusion
In this study, a nanomedicine was developed based on membrane-camouflaged biomimetic approaches, to treat fatal infection caused by ETX. The safety and therapeutic efficacy of MNPs was assessed in vitro and in vivo, and was used as a reference for screening membranes of cells. RBC-NPs can neutralize ETX efficiently in vitro and in vivo. Only the membrane from RBCs is used to camouflage nanoparticles that protect mice from ETX infection, as the RBC membrane is superior in safety. Nebulized inhalation and intravenous injection with RBC-NPs both safety treat ETX infection in corresponding modes of infection. RBC-NPs injected in the vein can protect organs by altering the biological distribution of ETX. Inoculation with RBC-NPs in over a period up to 3 days in advance can provide protection for the host in a time-dependent manner. Thus, our experimental results suggest RBC-NPs provide a unique and feasible nanomedicine against ETX infection.
## Materials
The PLGA were purchased from LACTEL Absorbable Polymers (Brimingham, USA). The 100 nm polycarbonate membranes were purchased from Avanti Polar Lipids (Alabama, USA). The GST-ETX monoclonal antibody was developed previously by colleagues in our laboratory. MDCK cells were preserved previously by colleagues in our laboratory. Mouse neuroblastoma N2a cells were purchased from BeNa Culture Collection (Beijing, China). The MTS were purchased from Promega Corporation (Madison, USA). BABL/c mice of SPF grade were purchased from Sipeifu (Beijing, China). Cy5.5-antibody conjugation kits were purchased from Bioss (Beijing, China). DiR were purchased from Invitrogen (Carlsbad, USA).
## Preparation of MNPs
To begin with, PLGA nanoparticles with a diameter of about 100 nm were prepared by using the emulsion-evaporation method. Carboxy-terminated PLGA was dissolved in dichloromethane (DCM) at 0.67 dL/g and mixed thoroughly with polyvinyl alcohol (PVA) aqueous solution. The mixture was mixed into a homogeneous emulsified state by a Qsonica Q125 (Qsonica LLC, USA) sonicator and then stirred in fume hoods for 4 h at 25 °C by magnetic stirrers. After removing the DCM in the mixture, PLGA nanoparticles were obtained using low temperature ultrahigh speed centrifugation followed by washing three times using ultrapure water to remove the residual PVA. Finally, the PLGA nanoparticles suspension was freeze-dried to obtain dried PLGA nanoparticles.
RBCs were obtained by removing the upper plasma from 5 mL of whole blood that had been centrifuged at 1,000 × g at 4 °C for 10 min. Next, 1 mL of collected RBCs were washed three times using 1 × PBS and then resuspended in 2 mL of ultrapure water. Osmotic pressure was used to break the RBCs, and then broken erythrocyte membranes were washed three times using 1 × PBS to remove residual hemoglobin (HGB). Membranes were resuspended in 1 mL of 1 × PBS and mixed with 10 mg PLGA nanoparticles. The mixture was then extruded through 100 nm polycarbonate membranes to prepare RBC-NPs.
MDCK cells were cultured with Dulbecco's Modified Eagle Medium (DMEM) supplemented with $10\%$ fetal bovine serum (FBS). Using the same method as above for RBCs to extract membranes, membranes from 1 × 108 cells were resuspend in 1 mL of 1 × PBS and mixed with 10 mg PLGA nanoparticles. The mixture was then extruded through 100 nm polycarbonate membranes to prepare MDCK-NPs.
## Characterization of nanoparticles and MNPs
The Flow NanoAnalyzer (NanoFCM, China) was used to measure the diameter and size distribution of the nanoparticles at 0.01 mg/mL in ultrapure water. The Zetasizer Nano ZS90 (Malvern, UK) was used to measure the PDI and zeta potentials of the nanoparticles were determined at 0.01 mg/mL in ultrapure water and at 25 °C. Transmission electron microscopy images from the FEI Tecnai G2 F30 (FEI, US) were used to characterize morphology of the nanoparticles.
## Cell culture
MDCK cells and N2a cells were cultured in DMEM with $10\%$ serum at 37 °C in a $5\%$ CO2 atmosphere and $100\%$ humidity. The cells were used for the experiments within the first 20 passages.
## Cytotoxicity Assay
Cytotoxic activity was measured by MTS colorimetric assay with MDCK cells. MDCK cells were spread into 96-well plates at a cell density of 1 × 105 cells/mL and incubated at 37 °C for 24 h. The solution to be measured was added at a series of dilutions concentrations and incubated for 1 h at 37 °C in $5\%$ CO2 atmosphere. Next, the culture medium was removed, and plates washed with PBS three times. MTS was added to plate wells, incubated for 3 h, and then toxicity estimated by measuring absorbance at 492 nm. All experiments were conducted in triplicate.
## High-content Imaging
ImageXpress and MetaXpress (Molecular Devices, USA) were used to detect kinetic changes in MDCK cells. MDCK cells were incubated with GST-ETX, MDCK-NPs and an equal amount of 4′, 6-diamidino-2-phenylindole (DAPI) (5 μg/ mL) for 1 h, then the culture medium was removed, and cells washed with PBS three times to remove excess DAPI. Next, DAPI and propidium iodide (PI) were added into the MDCK cells’ culture medium and incubated for 3 h. After incubation, fluorescence signals were observed under the confocal microscope.
## Animals
BABL/c mice of SPF grade were further bred in the accredited animal facility of the Experiment Center of the Academy of Military Medical Sciences. Mice were used at 6–10 weeks of age. The number of animals in each group was set according to statistical verification and previous studies.
## Nanoparticles and GST-ETX Fluorescent staining
The Cy5.5-antibody conjugation kit (Beijing, China) and DiR (Carlsbad, USA) were used to label the nanoparticles and GST-ETX separately.
## In vitro imaging of major tissues
Dyed nanoparticles and ETX were injected into 6–8-week-old healthy female BABL/c mice. At 5 min, 24 h, 48 h, and 72 h post-injection, blood and major organ samples (liver, heart, spleen, lung, kidney and brain) were collected. Nanoparticles and GST-ETX distribution were detected by IVIS Spectrum (PerkinElmer, USA) imaging system.
## Enzyme-linked immunosorbent assay (ELISA)
We used the mouse IgE ELISA kit (Beyotime, China) to detect the IgE of mice, following the manufacturer’s instructions. All assays were performed in triplicate. Optical density was measured at a wavelength of 450 nm with a spectrophotometer.
## Histopathological analysis of samples
Samples were collected after injection with GST-ETX (800 ng/kg) and nanoparticles, then fixed in $4\%$ formaldehyde solution. The histopathological analysis of samples was performed by H&E staining.
## Statistical analysis
All data are expressed as mean ± SD. Statistical analysis was performed using GraphPad Prism. Statistical comparisons were performed using Student’s t-tests, one-way ANOVAs, or two-way ANOVAs. Statistical significance was indicated by p-values < 0.05 (*), < 0.01 (**), or < 0.001 (***); non-significant differences were indicated by p- values ≥ 0.05 (ns).
## Supplementary Information
Additional file 1: Systematic evaluation of membrane-camouflaged nanoparticles in neutralizing *Clostridium perfringens* ε-toxin. Figure S1. In vitro toxicities of recombinant ETX. ETX with different tags (GST and 6×His) did not significantly differ in toxicities ($$n = 3$$). Data are presented as the means ± SD. Figure S2. MDCK cells were exposed to 2 mg nanoparticles and 20 nM of GST-ETX for 1h at 37°C. The cells were observed by confocal microscopy. ( Scale bar: 1 mm). Figure S3. Four groups of eight-week-old female BALB/c mice, were injected with increasing dosages of GST-ETX in intravenous respectively. The survival curves of the mice in the next 7 days ($$n = 6$$). Figure S4. Representative sections made from various organs of experimental mice with intravenous injection, stained with H&E (scale bar: 2 mm). Figure S5. In vitro fluorescence images of DiR in organs of mice which injected intravenously with Cy5.5-ETX and PBS. Figure S6. In vitro fluorescence images of DiR in organs of mice which injected intravenously with Cy5.5-ETX and DiR-RNPs. Figure S8. In vitro fluorescence images of Cy5.5 in organs of mice which injected intravenously with Cy5.5-ETX and PBS. Figure S9. In vitro fluorescence images of Cy5.5 in organs of mice which injected intravenously with Cy5.5-ETX and DiR-RNPs. Figure S9. Real-time in vivo fluorescence images of mice after lung delivery or intravenous injection 10 min. ( A) Lung delivery DiR-RNPs, DiR-RNPs were evenly dispersed in lung of the mouse but did not escape the lung. Radiant efficiency exceeded 2×109 in the lung. ( B) Lung delivery PBS. ( C) Intravenous injection PBS. ( D) Intravenous injection DiR-RNPs, DiR-RNPs spread throughout the mouse by the bloodstream. The systemic radiation efficiency of mouse was generally low, and the maximum radiation efficiency did not reach 2×109. Figure S10. Four groups of eight-week-old female BALB/c mice, were introduced by aerosol into the lungs with increasing dosages of GST-ETX. The survival curves of the mice in the next 14 days ($$n = 6$$). Figure S11. Representative sections made from various organs of experimental mice with lung delivery, stained with H&E (scale bar: 2 mm).
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|
---
title: 'Impact of Socio-demographic Characteristics on Time in Outpatient
Cardiology Clinics: A Retrospective Analysis'
authors:
- Daniel McIntyre
- Simone Marschner
- Aravinda Thiagalingam
- David Pryce
- Clara K. Chow
journal: 'Inquiry: A Journal of Medical Care Organization, Provision and Financing'
year: 2023
pmcid: PMC10021097
doi: 10.1177/00469580231159491
license: CC BY 4.0
---
# Impact of Socio-demographic Characteristics on Time in Outpatient
Cardiology Clinics: A Retrospective Analysis
## Abstract
Inequitable access to health services influences health outcomes. Some studies have found patients of lower socio-economic status (SES) wait longer for surgery, but little data exist on access to outpatient services. This study analyzed patient-level data from outpatient public cardiology clinics and assessed whether low SES patients spend longer accessing ambulatory services. Retrospective analysis of cardiology clinic encounters across 3 public hospitals between 2014 and 2019 was undertaken. Data were linked to age, gender, Indigenous status, country of birth, language spoken at home, number of comorbidities, and postcode. A cox proportional hazards model was applied adjusting for visit type (new/follow up), clinic, and referral source. Higher hazard ratio (HR) indicates shorter clinic time. Overall, 22 367 patients were included (mean [SD] age 61.4 [15.2], 14 925 ($66.7\%$) male). Only 7823 ($35.0\%$) were born in Australia and 8452 ($37.8\%$) were in the lowest SES quintile. Median total clinic time was 84 min (IQR 58-130). Visit type, clinic, and referral source were associated with clinic time (R2 = 0.23, 0.35, 0.20). After adjusting for these variables, older patients spent longer in clinic (HR 0.94 [0.90-0.97]), though there was no difference according to SES (HR 1.02 [0.99-1.06]) or other variables of interest. Time spent attending an outpatient clinic is substantial, amplifying an already significant time burden faced by patients with chronic health conditions. SES was not associated with longer clinic time in our analysis. Time spent in clinics could be used more productively to optimize care, improve health outcomes and patient experience.
## Introduction
Time spent accessing healthcare is a key measure of service quality and strain.1,2 Elective surgery waiting times are the focus of most analyzes,2 and have increased in recent years.3 However, patients wait in multiple settings—in the community for primary care,4 specialist,5,6 and allied health7 appointments, and in waiting rooms in emergency8 and ambulatory clinics.9 Compared to elective surgery, these other waiting times are poorly characterized, providing clinicians and policy makers with an incomplete view of patient time burden across healthcare systems.
This burden is greatest for patients with multiple comorbid conditions, such as cardiovascular diseases, who require increased healthcare contact.10 *There is* international evidence that elective surgery waiting times are greater for patients of lower socio-economic status (SES).11-14 *This is* particularly concerning in single payer health systems where waiting time should be allocated according to clinical acuity, rather than ability to pay. However, there are few studies on patient time burden in other settings.
Particularly, there are a lack of data on time spent accessing ambulatory care and in outpatient clinic waiting rooms. Such time may seem less significant as an absolute, but cumulates with increasing healthcare contact and has an associated opportunity cost secondary to missed work hours, estimated at 15 cents per dollar spent on healthcare.15 The largest reports on waiting room time are from the USA and indicate a likely time of 20 to 40 min.9,16,17 Some studies suggest patients from lower socio-economic backgrounds wait longer in this setting as well. An analysis of 3787 responses to the American Time Use Survey by Ray et al18 found time accessing outpatient care was 123 min on average and significantly longer for Black and Hispanic patients, those with less education, and the unemployed. Oostrom et al19 analyzed 21 million outpatient office visits in the USA, finding publicly insured (Medicaid) patients were $20\%$ more likely than privately insured patients to wait longer than 20 min. A small 2022 analysis of 423 attendees to a public outpatient clinic in Ethiopia found those with lower educational attainment were more likely to have long waiting times than tertiary-educated participants (odds ratio 2.25 [$95\%$ CI 1.11, 4.58]).20 A study of 96 patients in a Nigerian outpatient department found women were more likely to experience waiting times of ≥180 min than men ($31.6\%$ vs $6.3\%$, respectively).21 While these data suggest a relationship may exist, to our knowledge, there are no studies comparing clinic time with SES in single-payer healthcare systems such as the UK, Canada, or Australia.
In this study, we present data from consecutive patients attending outpatient cardiology appointments across 3 public hospitals in Sydney, Australia between 2014 and 2019. We aim to describe the “clinic time” (difference between time arrived and time departed) and assess whether this is impacted by socio-demographic characteristics including SES, age, gender, number of comorbidities, country of birth, and language spoken at home.
## Setting and Study Population
We examined a consecutive patient-level data set of all public outpatient cardiology encounters across 3 hospitals within Western Sydney Local Health District (WSLHD) between July 2014 and December 2019. Clinics are consultant-led and staffed by junior doctors, training cardiologists, and nursing staff. Patients are referred by general practitioners, emergency departments, or other doctors and generally do not pay to access these clinics. WSLHD comprises 5 hospitals, 7 community health centers, and serves 946 000 residents in the western suburbs of Sydney.22 The population is diverse with $46.8\%$ of residents born overseas and $50.3\%$ speaking a language other than English. WSLHD also houses the largest Aboriginal and Torres Strait Islander population in Australia (approximately 13 000 persons).22
## Inclusion and Exclusion Criteria
All adult (>18) patients who accessed outpatient cardiology services in-person across WSLHD between July 2014 and December 2019 were included in the analysis.
Patients were excluded if their clinic time was not assessed. This was defined if clinic time data were missing, equal to 0, or if all patients within a clinic were allocated to a pre-specified time (eg, 30, 45, or 60 min). Extreme values were excluded with cut-offs of ≤20 min (the presumed time of a consultation only), or ≥240 min (the entire duration of a morning or afternoon clinic session) as these times were likely due to data entry error or unreliable clerical processes. Audio and inpatient consultations were excluded.
## Data Collection, Handling, and Definitions
The data were cleaned, de-identified and processed by the Business Analytics Service (BAS) at Westmead Hospital and passed to the Westmead Applied Research Center, University of Sydney, via a secure server. The data contained patient-level variables on age, gender, Indigenous status, country of birth, language spoken at home, number of comorbidities, and postcode. Data on country of birth, Indigenous status, and language spoken is obtained from all patients via self- report on presentation to hospital. Patient postcode was correlated with the 2016 socio-economic indexes for areas (SEIFA) Index of relative socio-economic disadvantage (IRSD) score. This score is derived from 2016 *Australian census* data and summarizes variables that indicate relative disadvantage. The lower the score, the higher proportion of disadvantaged people reside within the postcode of interest.23 IRSD deciles were applied to each patient for the final analysis. In addition, the data contained appointment-level information on time of day, visit type (new or follow up), referrer (emergency department or other), clinic type (arbitrarily categorized A-R for consultant and hospital anonymity), arrived time, and departed time.
Total clinic time was calculated by measuring the difference between time arrived and time departed. This is a convenience measure taken by administration staff as part of the normal clinic workflow.
## Statistical Analysis
Statistical analysis was undertaken using R statistical software (V3.6.1). All variables of interest were first interrogated visually to assess for normality of distribution. Means were calculated for normally distributed continuous variables, and medians for non-normal continuous variables. Categorical variables were presented as frequencies and percentages.
Initially, the proportion of patients waiting longer than the median clinic time in different demographic groups (Age ≥75 vs <75, IRSD ≤5 vs >5, ≥4 comorbidities vs <4, female vs male, Indigenous vs non-Indigenous, born in Australia vs born Overseas, and English vs other language spoken at home) was compared with a chi-squared test. A univariate unadjusted linear regression was then conducted on the above patient characteristics and clinic process measures (clinic, visit type (new/follow up), referrer, appointment year, and time of day) to determine variables associated with increased clinic time.
A cox proportional hazard model was then applied to identify patient-level predictors of increased time in clinic. The model outcome was the time the patient left clinic. A higher hazard ratio (HR) described greater chance of leaving clinic earlier and hence shorter total time in clinic. This analytic approach was selected due to the non-normal distribution of the time data and is similar to cox proportional hazard models applied to assess time to wound healing, where a higher HR corresponds to a better outcome.24 Multivariate models controlled for clinic, visit type, referral source, and the above demographic characteristics. Results of these models are presented as HRs with $95\%$ confidence interval (CIs). Further analysis was conducted to identify interactions between patient and clinic-level variables of interest. Finally, within-hospital and within-clinic (shorter wait versus longer wait) analysis was conducted to determine whether discrepancies could be accounted for by between-hospital and clinic differences.
## Results
Of 37 456 patients assessed for eligibility, 14 823 were excluded and 22 367 were included in the final analysis (Figure 1). Of these, 14 925 ($65.9\%$) were male and the mean age was 61.4 (SD 15.2) years. Only 7823 ($35.0\%$) were born in Australia, and 8452 ($37.8\%$) were in the lowest IRSD decile, indicating they resided in a postcode with a greater proportion of disadvantaged residents than $90\%$ of postcodes in Australia. A significant proportion of patients had >4 comorbidities ($40.4\%$). Cardiac risk factors and comorbid cardiac conditions were also relatively common (Table 1).
**Figure 1.:** *Inclusion/exclusion of patients for the final analysis.* TABLE_PLACEHOLDER:Table 1.
## Time Spent in Clinic
The median total time in clinic was 84 min (interquartile range 58-130). The distribution was flat across the years of observation, ranging from 69 min in 2014 to 101 min in 2017 (Figure 2).
**Figure 2.:** *Median [interquartile range] time in clinic according to appointment
year.*
## Process Measures as Predictors of Longer Time in Clinic
Clinic process measures were analyzed for their association with clinic time. New patients and those referred from the emergency department were the most likely to spend longer in clinic (median 120 and 125 min, respectively, Figure 2). There was significant variance between clinics (Table 2). Linear regression demonstrated low to moderate association between all process measures and clinic time besides year of appointment and time of day (Table 2). Visit type, clinic, and referral source account for $23.0\%$, $35.0\%$, and $20.0\%$ of the variance (R2) in clinic time, respectively.
**Table 2.**
| Unnamed: 0 | N (%) Total = 22 367 | Median (IQR) clinic time | Overall R2 | F-statistic | F-statistic P-value |
| --- | --- | --- | --- | --- | --- |
| Visit type | | | 0.23 | 749.0 | <0.01 |
| Follow-up | 15 283 (68.3) | 69 (54-110) | | | |
| New | 7084 (31.7) | 120 (86-159) | | | |
| Clinic | | | 0.35 | 213.0 | <0.01 |
| A | 429 (1.9) | 94 (65-129) | | | |
| B | 74 (0.3) | 83 (61-120) | | | |
| C | 17 (0.1) | 135 (80-158) | | | |
| D | 139 (0.6) | 97 (78-124) | | | |
| E | 6777 (30.3) | 58 (50-68) | | | |
| F | 249 (1.1) | 40 (29-58) | | | |
| G | 128 (0.6) | 45 (30-51) | | | |
| H | 12 (0.1) | 30 (30-35) | | | |
| I | 352 (1.6) | 42 (30-59) | | | |
| J | 343 (1.5) | 148 (106-186) | | | |
| K | 1408 (6.3) | 72 (53-105) | | | |
| L | 1296 (5.8) | 90 (60-137) | | | |
| M | 6946 (31.1) | 127 (92-169) | | | |
| N | 25 (0.1) | 80 (63-112) | | | |
| O | 11 (0.0) | 55 (49-70) | | | |
| P | 1104 (4.9) | 101 (74-136) | | | |
| Q | 1373 (6.1) | 107 (82-140) | | | |
| R | 1684 (7.5) | 102 (76-133) | | | |
| Referral source | | | 0.2 | 213.0 | <0.01 |
| Emergency department | 5185 (23.2) | 125 (91-168) | | | |
| Other | 17 182 (76.8) | 72 (55-116) | | | |
| Year | Year | Year | 0.03 | 1.68 | 0.2 |
| 2014 | 1380 (6.2) | 69 (59-97) | | | |
| 2015 | 3011 (13.5) | 74 (57-115) | | | |
| 2016 | 3945 (17.6) | 80 (58-120) | | | |
| 2017 | 4568 (20.4) | 101 (60-153) | | | |
| 2018 | 4680 (20.9) | 88 (58-132) | | | |
| 2019 | 4783(21.4) | 88 (58-135) | | | |
| Time of day | | | 0.004 | 271.0 | <0.01 |
| Morning | 16 253 (72.7) | 76 (55-130) | | | |
| Afternoon | 6114 (27.3) | 98 (72-130) | | | |
## Patient-Level Predictors of Time in Clinic
All patient-level variables were assessed for their correlation with clinic time in a multivariate cox proportional hazards model controlling for clinic, referral source and visit type. In the unadjusted model, low (IRSD ≤ 5th decile) SES patients spent less time in clinic than those of high (IRSD > 5th decile) SES (median 66 min vs 109 min, Figure 3). After adjustment, this was no longer significant (HR 1.02 [0.99-1.06]). Those older than 75 were less likely to leave the clinic (HR 0.94 [0.90-0.97). The relationship between all other sociodemographic characteristics did not reach significance after adjustment (Table 3).
**Figure 3.:** *Survival plot: Socio economic status (IRSD ≤5 vs >5) and time leaving
clinic. Unadjusted model.* TABLE_PLACEHOLDER:Table 3.
## Interaction Analysis of Demographic, Process Measures, and Socio-Economic
Status
Further analysis was performed assessing the interaction between SES, patient characteristics and clinic process measures. Those of lower SES spent less time in clinic irrespective of their age, gender, number of comorbidities, country of birth or language spoken at home. However, after adjustment for visit type, clinic, and referral source, there was no interaction between SES and any of the identified demographic variables (Supplemental Table 1). Patients of lower SES were more likely to attend follow-up appointments ($77.2\%$ vs $57.6\%$), clinics with short clinic time ($66.8\%$ vs $21.1\%$) and be referred from sources other than the emergency department, compared to patients of higher SES (Supplemental Table 1).
## Clinic and Hospital Sub Analysis
To assess for discrimination within hospitals and clinics, the association between socio-economic status and time in clinic was analyzed in a further cox proportional hazards model adjusted for clinic, referral source and visit type. Those of lower SES spent slightly less time in clinics in hospital C (57 min vs 60 min, HR 1.24 [1.13-1.37]), though there were no differences within other hospitals. Within short wait clinics, lower SES spent less time in clinic (59 min vs 71 min, HR 1.10 [1.05-1.17]). There was no difference according to SES in longer wait clinics (Supplemental Table 2).
## Discussion
This analysis of over 20 000 consecutive outpatient cardiology clinic encounters aimed to determine whether those of low SES were more likely to spend longer in clinic. After adjusting for visit type, clinic, and referral source, there was no difference in clinic time according to SES. Overall, $75\%$ of patients spent at least 1hour in clinic. One quarter spent more than 2 hours. Potential implications of these findings include consideration of a more productive use of this time in ambulatory clinics, such as implementing interventions during this time that can improve health literacy and may improve health outcomes and satisfaction with health services.25,26 The interaction between SES and time to accessing health services has been debated for over 20 years. Most data are derived from elective surgery waiting lists,13,27 and there is some evidence discrimination is reversing as new policies are introduced. Cooper et al28 analyzed elective surgery wait lists in 1997 to 2000, 2001 to 2004, and 2005 to 2007, finding the effect of SES on waiting time reduced over the period of observation and reversed for knee replacement and cataract repair in 2005 to 2007, such that the most deprived fifth waited less than the least deprived fifth. There are less studies of the Australian system, but most reports suggest discrimination. Johar et al29 studied 90 162 patients in New South Wales public hospitals, finding that more advantaged patients waited less for elective surgery at all quintiles of waiting time. Data from developing countries is also suggestive of discrimination in this setting. A 2017 analysis of 219 surgeries within an Indian teaching hospital found those living below the poverty line had threefold higher waiting times than those above the poverty line.30 However, data are very limited within developing countries, largely due to a lack of systematic reporting. For example, a recent international collaboration for systematic reporting of waiting times is limited to organization for economic co-operation and development (OECD) countries, which are almost exclusively high-income.31 The finding of no relation to SES for patients accessing public clinics in our study is reassuring and may be explained by several reasons. There are likely fewer opportunities for preferential treatment within waiting rooms (where patients are seen in the order they arrive) than elective surgery (where waiting time is determined by clinician priority allocation), which may explain the lack of association between SES and clinic time in our study.
The Australian system is private-public, where patients with insurance that anticipate a long wait time can opt-in for private hospital care. There is evidence this preferential service selection model explains elective surgery waiting time inequity in Australia,32 though more studies of waiting room time are needed. Many hospitals in Australia run large public outpatient services where patients generally do not pay out-of-pocket for services, which are the services analyzed here. However, higher SES patients are more likely to access privately billed clinics in the community and findings here may have limited applicability to these care settings. They do however suggest that the lack of relation to SES of time spent in public clinics found here may be because of the absence of per-patient payment and of classification based on public/private status.
Patients with cardiovascular disease are more likely to be older, Indigenous, of lower SES, live in rural areas and have comorbidities than the general population.33 Analysis of time in cardiology clinics provides an opportunity to assess for poorer outcomes among these patient populations. In our study, we found patients older than 75 were more likely to spend longer in cardiology clinics. This may be due to these patients having more complex care needs requiring a longer consultation with additional time to see other health professional, for example, nurses, allied health workers, social workers. Older patients may also be more likely to arrive early to clinic appointments, increasing the overall appointment time. Faiz and Kristoffersen34 collected data from 1353 outpatient neurology clinic appointments and found older patients were less likely to arrive late than younger patients (OR 0.74 [0.63-0.88]).
In our study, lower SES patients were more likely to attend follow-up appointments and clinics with shorter waits overall, both strong predictors of reduced total clinic time. Sub-analysis of these clinics found lower SES patients spent less time after adjusting for process measures. Importantly, our analysis did not delineate between consultation and waiting room time. It is possible that lower SES patients had shorter consult times, which was the primary driver for a shorter total clinic time. This is supported by an analysis of 70 758 GP consultations in Australia in 2001 to 2002, which found older patients of higher SES had longer consultation times.35 A 2020 qualitative analysis of 36 head and neck cancer appointments found lower SES patients were more passive in their care, engaging in less agenda setting and information seeking, potentially explaining shorter consultation times within this group.36 Further studies are needed to better define patient time burden while waiting, an indicator of poor care, from time spent with clinicians, likely an indicator of quality care.
The implications of “in-clinic” waiting times are different to those for elective surgery, specialist and primary care visits, where longer waiting time has been associated with poorer clinical outcomes.37-39 Increased time in ambulatory care has been linked to reduced care satisfaction,40 however the consequences are primarily economic – the opportunity cost of accessing healthcare. Increasing workforce casualization, where employees do not have access to sick leave, further compounds the economic cost of increased clinic time.41 These implications are greater for patients that require more contact with healthcare services.
## Addressing Patient Waiting Time—What Approaches Are Needed?
Several methods have been trialed to reduce the time patients spend accessing healthcare. In the emergency department, the introduction of 4-hour targets in the UK, Australia and other countries has seen significant reductions in waiting times.42 However, there may be diminishing returns from further reductions. Sullivan et al43 present an analysis of 12.5 million emergency department episodes of care, finding compliance with waiting time targets reduced in-hospital mortality. However as compliance increased past a critical point of $83\%$, the relationship was lost. Countries that lack a benchmark likely have even longer waiting times. A 2006 analysis of 675 patients at a public hospital in Barbados revealed a median 377 min length of stay, over 2 hours longer than targets in Australia and the UK.44 Despite some small studies in China,45 Singapore,46 and Korea,47 there is a paucity of research about interventions to address in-clinic waiting time. To our knowledge, there are no examples of such interventions within cardiology outpatient clinics.
Irrespective of between-group differences, this study underscores that time spent accessing healthcare is significant. This time could be better utilized to deliver health interventions that convert this from wasted to productive time. There is some literature suggesting waiting room interventions can improve patient knowledge, but a paucity of robustly designed studies to assess the efficacy of waiting room interventions on clinical outcomes.48,49 *Though a* focus on health outcomes is desirable, waiting room interventions could also target process outcomes such as patient satisfaction with care, total time in clinic or consultation time. Integrated delivery of tech-enabled interventions that begin in the waiting room, continue through the consultation and into the post-consultation period could contribute to a new paradigm of healthcare that values patient time whilst also increasing provider efficiency.50 There are several strengths and weaknesses to this study. We considered a consecutive sample of patients attending a single specialty within one local health district. This limited between-hospital and specialty heterogeneity, however provided limited view on waiting times in rural locations, other cities and specialties. Data were collected over 5 years, providing insight into longitudinal waiting time trends within our sample and were convenience based and likely less prone to bias than data collected by self-report or specifically measured for the monitoring of waiting time. The convenience nature of these data also limits generalizability. Approximately $40\%$ of encounters where data were incomplete or unreliable were excluded to minimize impact on findings (Figure 1). We did not have differential data on time spent with clinicians versus in waiting rooms and could not identify patients that left clinic without being seen by a doctor. We were unable to characterize the urgency of each patient’s clinic visit and cannot rule out an effect due to preferential treatment of higher acuity patients. A sample size calculation was also not performed in this study. All available data in the sample were analyzed. Finally, data were at the level of the encounter, not the patient. It is possible there are duplicate patients who attended clinics multiple times within the data set.
## Conclusions
Accessing healthcare presents a significant time burden for patients at all levels of the health system. In this analysis of 22 367 patients attending publicly funded outpatient cardiology clinic appointments over 6 years, older patients spent longer in clinic, but no difference for low SES or other demographically disadvantaged patients was identified. This is reassuring, however does not exclude the possibility of disparities. Further studies that are prospective and diverse in geographical, health service funding, and economic advantage at a country level are required. Ongoing monitoring of the health system with respect to performance and inequities is also important. Consideration should be given to the opportunistic delivery of interventions during this time to improve health engagement and outcomes.
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|
---
title: 'A Current Review of the Uses of Bioelectrical Impedance Analysis and
Bioelectrical Impedance Vector Analysis in Acute and Chronic Heart Failure
Patients: An Under-valued Resource?'
authors:
- Jenjiratchaya Thanapholsart
- Ehsan Khan
- Geraldine A. Lee
journal: Biological Research for Nursing
year: 2022
pmcid: PMC10021121
doi: 10.1177/10998004221132838
license: CC BY 4.0
---
# A Current Review of the Uses of Bioelectrical Impedance Analysis and
Bioelectrical Impedance Vector Analysis in Acute and Chronic Heart Failure
Patients: An Under-valued Resource?
## Abstract
### Background
There is a need to detect and prevent fluid overload and malnutrition in heart failure. Bioelectrical impedance analysis and bioelectrical impedance vector analysis are medical instruments that can advance heart failure management by generating values of body composition and body water, assisting clinicians to detect fluid and nutritional status. However, there is a lack of evidence to summarise how they have been used among heart failure patients.
### Method
A systematic search was conducted.
### Result
Two hundred and four papers were screened. Forty-eight papers were reviewed, and 46 papers were included in this review. The literature shows that bioelectrical impedance analysis and bioelectrical impedance vector analysis were mostly used to assess fluid and nutritional status, together with diagnostic and prognostic values. Contraindication of using BIA and implications for practice are also demonstrated.
### Conclusion
The findings suggest that bioelectrical impedance vector analysis is superior to bioelectrical impedance analysis when assessing hydration/nutritional status in heart failure. Assessing a patient using bioelectrical impedance analysis /bioelectrical impedance vector analysis, together with natriuretic peptide -heart failure biomarkers, increases the diagnostic accuracy of heart failure. Further studies are required to examine the cost effectiveness of using these instruments in clinical practice.
## Introduction
Heart failure is commonly associated with fluid overload. An assessment of fluid congestion is crucial in heart failure management as it determines disease prognosis, morbidity, and mortality (Arrigo et al., 2020). Bioelectrical impedance analysis (BIA) and bioelectrical impedance vector analysis (BIVA) is a non-invasive, affordable, quick, and tested method to accurately assess body composition and fluid status in clinical practice (Marra et al., 2019).
## Bioelectrical Impedance
BIA uses bioelectrical impedance, described as resistance to flow of alternating current (Khalil et al., 2014). Bioimpedance is a composite measure that includes resistance and reactance. Biologically, electrical resistance is inversely related to total body water (TBW; Di Somma et al., 2014b) and therefore as the TBW increases, such as in edema, resistance decreases. Conversely, reactance is primarily related to capacitance of the cell membrane, thus reporting body cell mass (Kyle et al., 2004a; Walter-Kroker et al., 2011). Therefore, an increase in total cell body mass results in an increase in reactance. Clinically, these measures are used to derive some useful body composition parameters including intracellular body water (ICW), extracellular body water (ECW), body fat mass, and fat free mass.
Based on frequency there are 2 types of BIA: the initial single 50 kHz frequency BIA (SF-BIA), and the more recent multiple frequency BIA (MF-BIA), 1 kHz–500 kHz (Kyle et al., 2004a). Using separate frequencies is beneficial as it allows for ECW and ICW assessment, because high frequencies allow penetration of cell membrane and assessment of ICW whereas low frequencies are not able to penetrate cell membranes (Marra et al., 2019) and therefore provide assessment of TBW. These assessments enable calculation of fat free mass (Haverkort et al., 2015), together with giving an estimation of interstitial fluid or oedema (Marra et al., 2019). Although using BIA has benefits in body composition assessment, there are limitations regarding the equation used to calculate these compositions, as the measurement is influenced by factors such as body shape abnormalities, races, extreme body mass index (Kyle et al., 2004a), and fluid imbalance (Haverkort et al., 2015).
A derivative of BIA, BIVA has been used to assess nutritional and fluid status by plotting a bivariate vector analysis of reactance and resistance standardised by height and overcome the limitations of BIA (Norman et al., 2012) (Figure 1A). Unlike BIA, BIVA does not rely on a regression equation or body weight to assess body composition as it uses raw impedance measurements (Castizo-Olier et al., 2018), thus, it can be used under diverse alterations of weight and fluid volume (Nwosu et al., 2019). To help understand the parameters measured by these techniques, some definitions are provided in Table 1.Figure 1.(A) BIVA ellipse indicates [1] volume overload in chronic heart failure is a bivariate vector falls outside $50\%$ ellipse (yellow), and in acute heart failure is a bivariate vector falls outside $75\%$ ellipse (red); [2] cachexia is identified when the bivariate vector falls outside $95\%$ ellipse at right lower quadrant. PA = arctan(reactance/resistance) × (180°/π). This PA is drawn to be 45° for illustrative purposes. ( B) Hydrograph indicates fluid status; fluid overload is when hydration index is over $74.3\%$.Table 1.BIA Parameters, Description and Normal Values. ParametersDescription and normal valuesEdema indexThe ratio of ECW to TBW are obtained from BIA (Lyons et al., 2017)*An edema* index >0.39 is clinically recognised as the threshold for fluid overload (Lyons et al., 2017)BIVAAn ellipse nomograph (Figure 1A) provides estimates of body composition (X axis) and fluid status (Y axis). The ellipse is generated by using means of bivariate vector of resistance and reactance standardised by height from a healthy population to calculate $95\%$ and $75\%$ confidence intervals (Figure 1A) (Piccoli et al., 1994)Phase angleAn arc tangent of reactance to resistance (Figure 1A) correlates to cellular characteristics, such as membrane integrity, cell mass and hydration (Stapel et al., 2018) and provides a composite measure for cellular health. Hydration indexA measure reports the degree of hydration: (Dehydration, normal hydration and hyper hydration) and is generated from BIVA (Valle et al., 2011) (Figure 1B). A normal hydration index is 72.7–$74.3\%$ which is equal to $50\%$ on the BIVA ellipse (Figure 1A) (Di Somma et al., 2014b)Fluid distributionAn impedance ratio measures at low (5 kHz) and high (200 kHz) frequencies ($\frac{200}{5}$ kHz), and this ratio might be utilised to demonstrate ICW and ECW compartments, respectively. Abnormal fluid distribution can be indicated when the value of $\frac{200}{5}$ kHz is ≥0.85 (Castillo-Martínez et al., 2020)
## Literature Review
A literature review was conducted to examine how BIA and BIVA are used in heart failure patients and whether it is useful in heart failure treatment and management. A systematic search was conducted to identify relevant studies related to the topic area via MEDLINE from 2002 to 19 April 2022 using search terms; ‘electric impedance’, ‘bioelectrical impedance vector analysis’, ‘BIVA’, ‘heart failure’. Inclusion and exclusion criteria to select papers were applied. Inclusion criteria were research paper in which the main aim of study is to examine whether using BIA and BIVA can benefit heart failure patients in the hospital setting. Exclusion criteria were studies using other types of bioimpedance as the main aim of the paper and studies using BIA to investigate the effect of a specific drug, non-human study.
One-hundred and ninety-seven studies were identified through the database, a shown in Figure 2 (Page et al., 2021). Seven papers were identified using citation searching. Therefore, two-hundred and four papers were screened in total. The papers were selected according to the inclusion and exclusion criteria, and therefore, 48 papers were fully reviewed, and 46 papers were included for analysis. The main themes of the uses of BIA and BIVA parameters are to facilitate diagnosis of heart failure, heart failure fluid assessment and management, predict prognosis, and assess nutritional status. The parameters used in each main theme are summarised in this review together with their limitations and contraindications, as well as implications for using these measures in clinical practice. Figure 2.PRISMA flow diagram.
## Diagnosis Heart Failure, Fluid Assessment and Management
BIA and BIVA have been used in acute and chronic heart failure to assess fluid status to diagnose and manage heart failure (Supplement 1).
## Diagnosis of Heart Failure
Biological markers, B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP), are used to identify the degree of heart failure (Ponikowski et al., 2016). However, accuracy of these markers can be questioned as levels can be affected by multiple factors, such as kidney disease (Gil Martínez et al., 2016), liver dysfunction, and anemia (McDonagh et al., 2021). As BIA can assess fluid status, it has a potential to diagnose heart failure. BIA assessed fluid status has been examined and compared against BNP and NT-proBNP to accurately diagnose heart failure using area under the curve (AUC) derived from receiver operating characteristic analysis (Génot et al., 2015; Gil Martínez et al., 2016). Using reactance alone to diagnose acute heart failure (AUC = 0.76) was inferior to BNP (AUC = 0.92) (Génot et al., 2015), while using resistance/height (AUC = 0.83, $95\%$ confidence interval (CI) 0.75–0.92), and reactance/height (AUC = 0.80, $95\%$ CI: 0.70–0.89) to diagnose acute heart failure was as good as using ultrasound of maximum and minimum inferior vena cava (AUC = 0.90, $95\%$ CI: 0.84–0.96 and AUC = 0.93, $95\%$ CI: 0.87–0.98) and NT-proBNP (AUC = 0.84, $95\%$ CI: 0.74–0.93) (Gil Martínez et al., 2016). Indeed, in multiple logistic regression analysis, the combination of BIA and BNP levels can be a strong predictor for the presence of acute decompensated heart failure (odds ratio: 40.1408, $95\%$ CI: 5.0456 to 319.3434, $$p \leq 0.0005$$; Parrinello et al., 2008).
Interestingly, the edema index (the ratio of ECW to TBW) showed the highest sensitivity and specificity of $78\%$ and $96\%$, respectively, compared to orthopnea (sensitivity and specificity: $28\%$ and $90\%$), pretibial edema ($72\%$ and $92\%$), pulmonary congestion ($68\%$ and $86\%$), and rales ($42\%$ and $90\%$) in detecting fluid congestion to help diagnose acute heart failure (Park et al., 2018). Also, a moderate correlation between high edema index at lower extremities and log BNP was reported ($r = 0.603$, $p \leq 0.001$) (Park et al., 2018).
These data suggest that BIA and BNP, NT-proBNP levels, provide similar accuracy of diagnosis, and they may be used interchangeably; however, the limitation of BIA when used in patients with unstable fluid status may lead to inaccurate results. This issue with BIA can be solved by using BIVA. The BIVA generated hydration index can improve diagnosis of acute heart failure when BNP levels are undecisive (100–400 pg/mL) and using BNP levels in conjunction with the hydration index, the diagnostic ability (AUC) in this regard was reported as 0.77 with $65.3\%$ sensitivity and $78.8\%$ specificity ($p \leq 0.0001$). This combination increased the net diagnosis of acute heart failure increased from $19\%$ ($$p \leq 0.016$$) to $77\%$ ($p \leq 0.001$) (Di Somma et al., 2014a). This suggests that BIVA in combination with BNP is superior to BIA in diagnosing heart failure.
## Fluid Assessment and Management
Fluid overload in acute heart failure produces a bivariate vector that fell outside of $75\%$ ellipse (Alves et al., 2015; Kammar-García et al., 2021; Massari et al., 2016) with $75\%$ sensitivity and $86\%$ specificity, as shown in Figure 1A. While in chronic heart failure, the cut-off point was a bivariate vector that falls outside of $50\%$ ellipse with $85\%$ sensitivity and $87\%$ specificity (Massari et al., 2016). It has been suggested that the combination of BIVA and BNP levels increases the ability to detect fluid overload in heart failure, improving treatment and prevent further complications (Di Somma et al., 2010, 2014a; Santarelli, Russo, Lalle, De Berardinis, Navarin et al., 2017a), such as worsening renal function (Valle et al., 2011).
A number of bioimpedance-related measures including edema index, ECW, PA, resistance, reactance, and hydration index have been used to identify fluid status and degree of fluid congestion. The edema index has been used to guide fluid removal in acute heart failure patients (Yamazoe et al., 2015) by directly guiding diuretic therapy as a 0.01 increase in normal edema index equates to a 1 Kg increase edematous fluid which needs to be removed (Yamazoe et al., 2015). Edema index was also used to define cardiorespiratory fitness and functional capacity. It was reported to be inversely related to peak VO2 (rho = −0.307, $$p \leq 0.009$$) and exercise time (rho = −0.314, $$p \leq 0.006$$) (Marawan et al., 2021). This may be explained because then edema index identified increased ECW causing lung congestion and consequent decreased exercise capacity. Also, there was a strong correlation between weight loss and ECW ($r = 0.766$, $p \leq 0.001$) (Sakaguchi et al., 2015). However, there are limitations of BIA that parameters generated can be affected by the fluctuation of fluid and differences in equations used, and therefore, using BIVA might be more useful to assess fluid status in heart failure patients as reported in the evidence.
PA is inversely related to fluid overload (Table 1). There is a weak negative correlation between ECW and PA (r = −0.367, p ≤ 0.0001) (Colin-Ramirez et al., 2006). PA was significantly lower in NYHA class III-IV than I-II in both systolic ($$p \leq 0.04$$) and diastolic heart failure ($$p \leq 0.01$$) (Castillo Martinez et al., 2007). The decreased PA, therefore, significantly was related to fluid overload ($p \leq 0.05$) (Colin-Ramirez et al., 2006) and high risk of acute decompensation (Gulatava et al., 2021). PA also gradually reflects a decrease in fluid volume. This has been seen with fluid loss following intensive diuretic therapy, where the mean PA increased from 3.61 ± 0.82 (hospitalisation) to 3.83 ± 0.74 (on discharge) (mean ± standard deviation (SD)), and the $95\%$ CI of this change was reported 0.15, 0.29; (De Ieso et al., 2021). PA can potentially identify nutritional status as well as hydration level (Gulatava et al., 2021); however, PA may be a better tool to assess fluid status (Scicchitano et al., 2020).
Furthermore, when hydration index is over $74.3\%$ ($50\%$ BIVA ellipse) (Di Somma et al., 2014b) (Figure 1B), this indicates hyper-hydration (Di Somma et al., 2014a; Génot et al., 2015; Valle et al., 2011). This hydration index can be used to guide diuretic treatment as the index decreases rapidly following fluid removal from admission to discharge (76.74 ± 4.0 vs. 74.4 ± 2.0 ($p \leq 0.0001$; Di Somma et al., 2010) and (82.8 ± 6 vs. 78.5 ± 6 ($p \leq 0.001$; mean ± SD; Santarelli, Russo, Lalle, De Berardinis, Vetrone et al., 2017b). This demonstrates its potential to monitor treatment effect, together with being a diagnostics tool. In summary, although BIA can be used to facilitate heart failure treatments and managements, BIVA and its derived measures seem to be more accurate values to manage and monitor heart failure than BIA.
## Using BIA and BIVA for Predicting Prognosis
Parameters calculated using BIA and BIVA, such as body compositions, edema index, hydration status, and PA can predict prognosis (Supplement 2). In chronic heart failure patients, those with a high lean body mass and body fat mass index had better 5-year clinical outcomes and better survival rates than those with low lean body mass ($89.3\%$ vs. $80.9\%$, $$p \leq 0.036$$) and body fat mass index ($90.2\%$ vs. $80.1\%$, $$p \leq 0.008$$) (Thomas et al., 2019). This phenomenon is known as the obesity paradox, which states that heart failure patients with obesity had better prognoses than heart failure patients who were normal weight and underweight, regardless of their ejection fraction status (heart failure with preserved ejection fraction, heart failure with reduced ejection fraction) (Carbone et al., 2019).
An increased edema index is associated with increased rates of all-cause mortality, urgent transplant, or insertion of ventricular assistant device (Lyons et al., 2017). Using the edema index combined with a multidisciplinary approach in acute heart failure patients can reduce rehospitalisation ($3.8\%$) compared to a control group ($18.9\%$) or a case management group ($13.2\%$, $$p \leq 0.03$$; Liu et al., 2012). Moreover, abnormal fluid distribution together with low grip strength in men was independently related to all-cause mortality (hazard ratio 2.8; $95\%$ CI: 1.25–6.4; $$p \leq 0.01$$), and this combination of parameters could suggest advanced heart failure regardless of gender (Castillo-Martínez et al., 2020). However, using BIA to examine prognostic values remain controversial. As Curbelo et al. [ 2019] reported, BIA parameters did not show prognostic values (Curbelo et al., 2019), and this might be due to the use of SF-BIA rather than MF-BIA and BIVA that probably affects the results due to the equation and its ability to penetrate cells. This, therefore, introduces the use of BIVA.
BIVA can also help predict cardiovascular events after discharge. Using a threshold hydration index level of $74.3\%$, acute heart failure patients with a higher hydration index had higher deaths and rehospitalisation rates than patients with a lower hydration index level (83.7 ± $7\%$ vs. 80 ± $7\%$, $p \leq 0.008$) (Di Somma et al., 2014a) and (82.2 ± 4.8 vs. 73.7 ± 2.0, $p \leq 0.0001$, mean ± SD; Villacorta et al., 2021). The mortality and readmission rates were higher in patients with hyper-hydration index (>$74.3\%$) than patients with normal hydration index (<$74.3\%$, and >$72.7\%$, Figure 1B) (3.28 and 3.83 per 10 persons-years vs. 1.43 and 2.68 per 10 persons-years, ($p \leq 0.05$; Núñez et al., 2016). Also, acute heart failure patients with the severe hyperhydration, hydration index $87.1\%$–$100\%$, had a longer length of stay in the hospital than those with normal hydration (9.04 days [IQR: 8.85–9.19 d] vs. 7.36 days [IQR: 7.34–7.39 d], $p \leq 0.05$; Massari et al., 2019). Furthermore, use of a combination of using BIVA parameters, BNP, hydration index, estimated plasma volume status, and BUN/creatinine ratio together, is a useful predictor of mortality risk (Massari et al., 2020).
PA was adversely associated to mortality rates as a PA was significantly lower in non-survivor group than survival group in acute heart failure (4.3 [IQR: 3.4–5.6] vs. 3 [IQR: 2.1–3.9], $p \leq 0.0001$ (Kammar-García et al., 2021); 6.3 ± 2.2 versus 5.08 ± 1.9, mean ± SD, $p \leq 0.038$ (Alves et al., 2016)). Additionally, the relative risk (RR) for the association with all-cause mortality in a group with lowest PA < 4.2 was reported (RR = 3.08, $95\%$ CI: 1.06–8.99) compared to the group with highest PA ≥ 5.7 (RR = 1) (Colín-Ramírez et al., 2012). Therefore, low PA can be used as a prognostic marker. Moreover, PA was used with galectin-3 levels, a biomarker representing cardiac fibrosis, to predict prognosis. A reduced PA and elevated galectin-3 levels significantly relates to hospitalisation at 60 days (AUC = 0.625, $$p \leq 0.003$$), 180 days (AUC = 0.545, $$p \leq 0.05$$) and 18 months (AUC = 0.620, $$p \leq 0.04$$) and mortality at all time points (1, 2, 3, 6, 12, 18 months) ($p \leq 0.005$) (De Berardinis et al., 2014). The benefit of using this combination of biomarkers help describe both degree of cardiac fibrosis/remodelling and fluid status. Therefore, parameters derived from BIVA -hydration index and PA-seems to be better prognostic markers than BIA.
## Nutritional Assessment Using Bioelectric Impedance Measures in Heart Failure
Patients
BIAs and BIVA have important roles in measuring body compositions among heart failure patients and identifying their nutritional status (Supplement 3). BIA frequency is important when assessing body compositions and there are 2 types of BIAs: SF-BIA and MF-BIA. MF-BIA has been used in heart failure patients due to the fact that multiple frequencies provides more accurate assessment of body water and therefore body cell mass, which improves accuracy of consequent anthropometric measurements (Liu et al., 2012). Hence, MF-BIA has also been used to assess body composition to identify malnutritional status; sarcopenia (Ogawa et al., 2020), and cardiac cachexia in heart failure patients (Castillo-Martínez et al., 2012; González-Islas et al., 2020; Hirose et al., 2020). There was a significantly negative correlation between parameters generated by MF-BIA -PA and reactance- and C-reactive protein level -inflammatory marker used to diagnose cachexia ($p \leq 0.01$). This might relate to the occurrence of cachexia (Sobieszek et al., 2019).
Compared to SF-BIA, MF-BIA accuracy was proven to be as good as dual-energy X-ray absorptiometry (DEXA) with no differences in mean (mean (standard deviation) of DEXA versus MF-BIA: body fat 28[6] versus 27[9]; fat mass 20[6] versus 20[9]; fat free mass 52[10] versus 53[11] (Alves et al., 2014). There were also strong correlations between determination of lean mass ($r = 0.95$), fat mass ($r = 0.96$) and body mass ($r = 0.84$) between MF-BIA and DEXA (Shah et al., 2021). However, it is suggested not to use them interchangeably due to mean differences of fat mass (mean difference −5.1 kg) and lean mass (mean difference 5.5 kg) in both methods (Shah et al., 2021). Thus, although MF-BIA is more accurate than SF-BIA and reported high correlation with DEXA, there was a wide limit of agreements for MF-BIA reported, which was believed to be due to a nonlinear distribution, leading to a need for an appropriate regression equation (Alves et al., 2014). Due to this reason and the limitations of BIA as previously mentioned, vectorial analysis of the BIA parameters (BIVA) should be considered and used in heart failure management.
BIVA has been utilised in heart failure patients to assess nutritional status (Figure 1A). A decreased PA suggests nutritional status anomalies, such as cachexia, sarcopenia and malnutrition in chronic heart failure patients (Castillo-Martínez et al., 2012; González-Islas et al., 2020; Hirose et al., 2020). A positive correlation between PA and body mass index was reported for males and females $r = 0.3310$ ($p \leq 0.0001$) and $r = 0.3115$ ($p \leq 0.001$), respectively (Hirose et al., 2020).
In conclusion, according to the evidence, BIVA seems to be more accurate to identify nutritional status in heart failure patients than BIAs due to the conditions of heart failure, such as abnormal fluid status, and inconsistency of findings when comparing BIA to DEXA that might result in inaccurate results. Although BIA and BIVA benefit heart failure assessment and management, safety concerns regarding using BIA and BIVA have been reported and examined to ensure their safety, such as interference of BIA to pacemaker’s function. The issues will be explored below.
## Contra-Indications of BIA in Heart failure Patients
Despite the potential advantages of BIA, there are some notable contra-indications in heart failure patients associated with cardiac implantable electronic devices (Cornier et al., 2011; NIHR, 2016). A potential for BIA to interfere with electrical current of pacemakers and defibrillators resulting in malfunction of the device, signal oversensing or stimulation inhibition, has been reported (Fabregat-Andrés et al., 2015; Kyle et al., 2004b). However, more recent studies tested the safety of BIA in heart failure patients and reported no interference with battery and functions of cardiac implantable electronic devices (CIED) and cardiac resynchronization therapy (Buch et al., 2012; Chabin et al., 2019; Fabregat-Andrés et al., 2015; Garlini et al., 2020; Meyer et al., 2017; Roehrich et al., 2020) (Supplement 4). Following this, some versions of BIA were shown to be safe to use, under manufacturer guidance.
## Implication for Practice
BIVA is more advantageous in heart failure screening, treatments, and management, including determining fluid and nutritional status than BIAs. BIVA and hydrograph have potential benefits as they can be used to identify chronic or acute heart failure, facilitate heart failure treatment by avoiding complications when adjusting diuretic treatment in acute settings and monitoring fluid status. Currently, the American Heart Association recommends BIVA to optimise fluid treatment to avoid cardiorenal syndrome (Rangaswami et al., 2019). The combination of using BIVA and serum BNP or NT-proBNP levels also increase capabilities to guide heart failure treatments and predict prognosis in heart failure patients.
Despite the benefits of bioimpedance measurements, safety concerns must be acknowledged. In cased where BIA measurement cannot be performed due to concerns regarding contraindications, an alternative method to measure anthropometry or assessing fluid status should be considered. In addition to safety concerns, it is worth noting that although the statistical significances were reported in the findings of this review, the effect size of some included studies might be small and therefore should be interpreted with caution considering clinical applicability. Furthermore, due to lack of consistency in reports of BIA/BIVA parameters leading to difficulties in combining analyses, future studies should report BIA/BIVA parameters, such as PA, edema index, hydration index, reactance/height and resistance/height, if applicable as these parameters tend to be accurate measurements that would further benefit heart failure management and research, particularly, in a systematic review and meta-analysis to further investigate on which parameters would comprehensively reflect conditions of heart failure patients.
## Conclusion
This review has demonstrated the uses of BIA and BIVA in acute and chronic heart failure patients. It also emphasises the importance of using BIA and BIVA to screen and detect for malnutrition, assess, and monitor fluid status to provide treatment, predict prognosis, including the safety concern of using BIA. Indeed, BIVA and its parameters, such as PA and hydration index, seem to be more superior than BIA in heart failure patients. The combinations of using BIVA and BNP/NT-proBNP increases the ability to detect heart failure and predict prognosis. However, further studies are required to examine the replacement of current practice by using BIVA, including cost effectiveness. Further work is needed on determining the effects of BIA on patients with CIED.
## ORCID iD
Jenjiratchaya Thanapholsart https://orcid.org/0000-0001-5754-3557
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|
---
title: 'How to Evaluate the Effectiveness of Health Promotion Actions
Developed Through Youth-Centered Participatory Action Research'
authors:
- Manou Anselma
- Teatske M. Altenburg
- Jos W. R. Twisk
- Xinhui Wang
- Mai J. M. Chinapaw
journal: Health Education & Behavior
year: 2021
pmcid: PMC10021122
doi: 10.1177/10901981211046533
license: CC BY 4.0
---
# How to Evaluate the Effectiveness of Health Promotion Actions
Developed Through Youth-Centered Participatory Action Research
## Abstract
Most actions targeting children’s health behaviors have limited involvement of children in the development, potentially contributing to disappointing effectiveness. Therefore, in the 3-year “Kids in Action” study, 9- to 12-year-old children from a lower-socioeconomic neighborhood were involved as coresearchers in the development, implementation, and evaluation of actions targeting health behaviors. The current study describes the controlled trial that evaluated the effects on children’s energy balance-related behaviors, physical fitness, and self-rated health, as well as experienced challenges and recommendations for future evaluations. Primary school children from the three highest grades of four intervention and four control schools were eligible for participation. Outcome measures assessed at baseline, and at 1- and 2-year follow-up were as follows: motor fitness by the MOPER test ($$n = 656$$, $$n = 485$$, $$n = 608$$, respectively), physical activity and sedentary behavior by accelerometry ($$n = 223$$, $$n = 149$$, $$n = 164$$, respectively), and consumption of sugar sweetened beverages and snacks and self-rated health by a questionnaire ($$n = 322$$, $$n = 281$$, $$n = 275$$, respectively). Mixed-model analyses were performed adjusted for clustering within schools and relevant confounders. Significant beneficial intervention effects were found on self-reported consumption of energy/sports drinks at T2 versus T0, and on total time and ≥5-minute bouts of moderate-to-vigorous physical activity at T1 versus T0. Significant adverse effects were found on “speed and agility” and “coordination and upper-limb speed.” No other significant effects were found. The inconsistent intervention effects may be explained by the dynamic cohort and suboptimal outcome measures. We advise future studies with a similar approach to apply alternative evaluation designs, such as the delayed baseline design.
## Impact Statement
That health behavior change is difficult is an understatement. Many interventions have been developed to improve children’s energy balance-related behaviors (EBRBs), but their effectiveness is mostly limited and of short duration. Participatory action research with children in which they cocreate actions may lead to more attractive, better tailored and thereby more effective interventions. The current study describes a controlled trial that evaluates a participatory action research together with 9- to 12-year-old children as coresearchers to develop, implement, and evaluate actions to improve children’s EBRBs. This article presents valuable lessons for designing future studies evaluating the effectiveness of health promotion actions cocreated by children in a participatory research process.
## Introduction
In the Netherlands, the number of children with overweight and obesity gradually declined in the past decade (Dutch Bureau for Statistics [Centraal Bureau voor de Statistiek–Dutch], 2019), also in the city of Amsterdam (City of Amsterdam, 2017). Despite this promising development, the number of children with overweight or obesity with a low-socioeconomic position (SEP; based on family income, household conditions, parental education, and occupation) or from a non-Western background remains high (City of Amsterdam, 2017; Franssen et al., 2015). These children are disproportionally affected by unhealthy behaviors and related health effects, for example, because healthy foods are not/cannot be prioritized, cultural habits, and limited finances (Anselma et al., 2018). Previous intervention studies showed that these groups are often not reached by existing health promotion programs (Bonevski et al., 2014; Craike et al., 2018), which could be due to unsuitable communication materials, communication channels, or divergent attitudes of academic researchers (Carroll et al., 2011; Harkins et al., 2010). Therefore, changing behaviors in children from low SEP environments remains a huge public health challenge.
EBRBs—that is, behaviors that effect energy intake or expenditure, such as physical activity, dietary behavior, and screen time—have been associated with overweight and obesity in children (Romieu et al., 2017; te Velde et al., 2012), with children from lower educated parents being more likely to engage in unhealthy EBRBs (Fernandez-Alvira et al., 2013). However, few interventions proved effective in improving EBRBs in children from low SEP environments, and those that are effective showed small effects (Anselma et al., 2020; Wijtzes et al., 2017). One explanation could be that intervention strategies are insufficiently tailored to children from low SEP environments and therefore the strategies do not match their personal and community’s context, culture, needs, and interests. Interventions that are specifically designed for, or even together with, children from these communities may better fit their needs and interests and thereby may be more effective (Anselma et al., 2020).
The “Kids in Action” study combined youth-centered participatory action research and intervention mapping, to structurally develop actions in collaboration with children from a low SEP neighborhood to improve their EBRBs (Anselma, Altenburg, Emke, et al., 2019). Participatory action research is increasingly being used in public health especially in so-called hard-to-reach communities, as this bottom-up approach could lead to, for example, a better understanding of the community, better tailored actions, positive community development, and empowerment (Anyon et al., 2018; Lems et al., 2020; Shamrova & Cummings, 2017). To improve EBRBs in children from low SEP environments and with that fight for health equity, the health promotion sector needs to adopt such approaches. Participatory action research with youth is a research approach in which children are trained as coresearchers and work side-by-side with researchers (Kellett, 2005; Langhout & Thomas, 2010; London et al., 2003). Children study their own environment and develop solutions for problems they identify. We combined this participatory approach with intervention mapping, which is a stepwise approach for identifying behavioral determinants and developing evidence-based strategies (Bartholomew Eldredge et al., 2016). We added intervention mapping to structure the action development process and stimulate use of evidence-based theoretical methods and strategies.
The process evaluation of the Kids in Action study showed that the cocreated actions were well received, both by the children and other community members (Anselma et al., 2020). Children and community partners mentioned that empowerment of children, who actively participated in the participatory action research, improved. Moreover, these children developed skills such as critical awareness and self-confidence as well as research skills. Community partners indicated that in children of the intervention schools awareness about EBRBs improved, but they questioned whether the actions also improved their actual behavior. Evidence for the effectiveness of applying participatory action research in the field of health promotion is currently lacking (Anyon et al., 2018; Jacquez et al., 2013). For example, effects of participatory developed actions on children’s EBRBs have rarely been evaluated in a controlled trial design. Challenges for the effect evaluation of participatory action research are, for example, that at the start it is unknown which specific behaviors will be targeted and therefore what optimal outcome measures are. Therefore, the current study describes the effect evaluation of the Kids in Action study on children’s dietary behavior, physical activity, sedentary behavior, physical fitness, and self-rated health, using a controlled design over the course of 3 years, and the experienced challenges and recommendation for future evaluations. Some challenges are highlighted in the methods sections, and elaborated on in the discussion.
## Kids in Action
The Medical Ethics Committee of the VU University Medical Center approved the study protocol (2016.366). Kids in Action was a 3-year participatory action research, taking place in a low SEP neighborhood in Amsterdam, The Netherlands. The neighborhood was characterized by high numbers of residents with a non-Western background ($50\%$; Municipality of Amsterdam, 2017a) and high numbers of childhood overweight with $30\%$ of the 10-year-olds having overweight or obesity in 2017–2018 (Municipal Health Services Amsterdam, n.d.). Moreover, in 2015–2016, $31\%$ of children younger than the age of 18 years grew up in a household with an income up to $110\%$ of the *Dutch minimum* standard and capital below the social welfare limit (Municipality of Amsterdam, 2017b). Participatory action research is mostly conducted in low SEP communities (Shamrova & Cummings, 2017), as these communities can benefit most from such an approach by becoming empowered, learning new skills, and developing actions suitable to their needs (Ozer, 2017). In Kids in Action, children participated in the development, implementation, and evaluation of actions, as explained in detail elsewhere (Anselma, Altenburg, Emke, et al., 2019). In Kids in Action, we collaborated with children through their schools. Schools were chosen as a setting because we wanted to collaborate with a diverse group of children that could benefit most from participating in our study. This would have been different when, for example, working together with a sports club (i.e., only children interested in sports) or after school day care (i.e., children of high-income families). Second, because of the close collaboration between the local municipality and the academic researchers, it was a setting that was well accessible. Third, schools gave us indirect access to parents and other community partners which could help the reach and impact of the developed actions.
In the first year of this study, the health needs of children were identified in a participatory needs assessment (Anselma et al., 2018). This needs assessment resulted in the focus on improving children’s physical activity and dietary behavior. In the first 2 years a participatory group was installed in each of the four intervention schools. These so called “Action Teams,” consisting of 6 to 8 children aged 9 to 12 years old and a facilitating academic researcher, developed, implemented, and evaluated actions (Anselma, Altenburg, Emke, et al., 2019). In the third year, one Action Team was established with representatives of three schools and they again worked together on new actions to be implemented in their neighborhood. Children could self-subscribe for the Action Teams and some children were specifically suggested by the teachers because teachers thought those children would like to participate and could miss school lessons. The meetings lasted 45 to 60 minutes and depending on the schools occurred during or after school hours. The meetings were semistructured, starting with a short game and introduction, and ending with a reflection and a game. For the content of the meetings, we followed a general outline based on the intervention mapping protocol, but we were flexible for whatever came to the table as not all children were always present due to, for example, birthday parties or children were distracted with something that happened during the day and wanted to share that. In the first few meetings, more time was spent on getting to know each other and learning research skills. Next, children conducted and analyzed their own research, intertwined with related skill development exercises. From the results of their research the Action Teams identified the most important problems and barriers children faced for engaging in healthy behaviors. The Action Teams came up with ideas for how to improve the situation, which were linked to and strengthened by evidence-based strategies identified by academic researchers. For the best ideas implementation plans were made together with relevant community partners, who helped implement the actions. The actions varied in reach (e.g., a one-time health stand at a sponsored run at one school, an Olympic sports event for four schools) and required resources (e.g., a few items for a health stand versus materials and finances for an Olympic sports event for 350 children). The implemented actions are depicted in Figure 1.
**Figure 1.:** *Timeline of implementation of cocreated actions.Note. The vertical length of the lines represent the
duration of the actions. (n) = number of schools
involved; (C) = community activity; blue = promoting healthy physical
activity; green = promoting healthy dietary behavior; black = promoting
healthy physical activity and dietary behavior.*
## Study Design
All four primary schools in the intervention neighborhood were approached by the local government and invited to participate as intervention schools. Within these intervention schools, the Actions Teams developed and implemented actions for children of the three highest grades of their school. As mentioned in Textbox 1, control schools were recruited from neighborhoods with inhabitants with similar socioeconomic characteristics. Schools in these neighborhoods were contacted ($$n = 22$$) until four schools agreed to participate. Control schools did not partake in the participatory design of action development, but only participated in the measurements. The four control schools and four intervention schools participated in three measurement waves as part of the controlled trial. The first wave took place throughout the school year 2016–2017 and was considered the baseline (T0). In the school year 2017–2018, measurements were conducted in March–April 2018 (T1). The last measurement wave was conducted in February–April 2019 (T2). Each year all children in the three highest grades of the schools were invited to participate in the measurements. This resulted in a dynamic cohort, where some children were invited for two or three measurements, others only for one (e.g., children in the highest grade in the first year of the study only participated once). Thus, the number of measurements varies per age group.
## Procedures
The Motor Performance (MOPER) fitness test was included to measure neuromotor fitness, a self-report questionnaire to assess self-perceived health, sports participation, outdoor play, sedentary behavior, consumption of sugar sweetened beverages and high-energy snacks, and accelerometers for physical activity and sedentary behavior. The MOPER fitness test was part of the school curriculum for children in the three highest grades and data were collected anonymously. Parents received an information letter from the physical education teacher. Attached was a refusal form to be signed and returned if they did not approve of MOPER fitness test results to be anonymously shared with the academic researchers. For the accelerometer and self-report questionnaire, each year children of the three highest grades received an information letter with attached an informed consent form that at least one parent had to sign to approve participation in the measurements. Parents could contact the academic researchers by phone or email in case they had questions or wanted more information. Because of the different consent procedures, the number of participants varied between the MOPER, questionnaire, and accelerometer, as depicted in Figure 2. We calculated that 240 children in the intervention group and 240 in the control group are needed to detect a difference of 0.15 SD in the aforementioned outcome variables (Anselma, Altenburg, & Chinapaw, 2019).
**Figure 2.:** *Flowchart of participants in the Kids in Action effect measurements.Note. Each year, children of Grades 6/7/8 participated
in the measurements, resulting in a different sample at each time
point.1Participants needed a minimum of 8 hours of wear time per day
on at least 4 days, including at least one weekend day, to be included
in the analyses.*
## MOPER Fitness Test
The MOPER fitness test consists of eight test items: 10 × 5 meter run, leg-lifting while laying down, plate-tapping, bent-arm hang, sit-and-reach, arm-pull, standing high jump, and a 6 minutes run test. The MOPER fitness test items have shown acceptable validity and reliability for estimating neuromotor fitness in 9- to 12-year-old children (Leyten, 1982). For practical reasons, the hand-grip test was used instead of the arm-pull test and the 6-minute run test was omitted. The hand-grip test has also shown acceptable validity for measuring children’s arm strength (Gasior et al., 2020). Seven test items were included measuring speed and agility, strength, flexibility, and coordination and upper-limb speed (see Supplemental 1). The MOPER fitness test was administered during physical education by the physical education teacher with assistance of academic researchers or sports instructors. The class was divided in seven groups who completed all test items in the same order. Tests were conducted barefoot to limit (dis)advantage of different footwear.
## Questionnaire
The questionnaire was developed based on validated items from the ENERGY-child questionnaire (Singh et al., 2011), the DOiT questionnaire (Janssen et al., 2014), and the EuroQol (Ravens-Sieberer et al., 2010), that covered identified determinants of overweight in the needs assessment (Anselma et al., 2018). The developed questionnaire consisted of nine sections: (a) Demographic and Family characteristics, (b) Soft drinks consumption, (c) Energy and sport drinks consumption, (d) Sweets consumption, (e) Snack consumption, (f) Playing outdoor, (g) Sports participation, (h) Screen viewing behavior, and (i) Perceived health (Anselma, Altenburg, & Chinapaw, 2019). Participants completed the questionnaire during school hours under the supervision of two trained academic researchers and the class teacher. Each section was explained by the academic researcher before that part of the questionnaire was collectively filled in. During the completion of the questionnaire, children were free to ask questions or withdraw from participation at any time. Children needed approximately 45 minutes to complete the questionnaire.
If possible, categorical variables were recoded into continuous variables. For example, the frequency of soda consumption was multiplied with the sum of number of glasses, cans, and bottles of soda consumed. Covariates were as follows: gender, birth country of parents, having younger/older siblings, living with both parents or otherwise, and speaking mainly Dutch at home or not.
## Accelerometer
Time spent in physical activity and sedentary behavior was assessed using the Actigraph accelerometer. Children were asked to wear the accelerometer on their right hip for seven consecutive days during waking hours, with the exception of water activities and heavy contact sports. The Actigraph was set on a sample frequency of 100 Hz and data were analyzed in 15-second epochs between 07.00 a.m. and 10.00 p.m. (Chinapaw et al., 2014). Nonwear time was defined as a period of at least 60 consecutive minutes of zero counts (Chinapaw et al., 2014). For inclusion in the data analysis, each participant needed at least 4 days with a minimum of 8 hours wear time per day, including at least one weekend day.
Accelerometer count data were processed using a custom-made program developed in R. A cut point of ≤25 counts per 15 seconds (counts/15-sec) was selected for sedentary behavior (Fischer et al., 2012; Trost et al., 2011), 26 to 573 counts/15-sec for light physical activity and ≥574 counts/15-sec for moderate-to-vigorous physical activity (MVPA; Evenson et al., 2008). A sedentary bout was defined as a period of at least 10 consecutive minutes <25 counts/15-sec. An MVPA bout was defined as a period of at least five consecutive minutes ≥574 counts/15-sec with $10\%$ tolerance allowed below the threshold and an absolute tolerance of three consecutive minutes.
## Analyses
Means (x̅) and standard deviations (SD) or medians (x~; in case of normally distributed variables) and interquartile ranges (25th–75th percentiles; in case of skewed variables) were calculated for descriptive purposes. For all regression analyses, the residuals of linear regression analyses were used to check the assumptions of normality and homoscedasticity. Linear mixed-model analyses with a four-level structure (i.e., repeated measures were clustered within children, children were clustered within classes and classes were clustered within schools) were used to examine the difference in the outcome variables between the control and the intervention group for the questionnaire and accelerometer data. For the MOPER fitness data, a three-level structure was used because the data were collected anonymously. Linear mixed-model analyses was applied as these analyses adequately deal with missing data (Twisk et al., 2018). There was a substantial amount of missing data by design in the present study because data was collected of children in Grades $\frac{6}{7}$/8 for 3 years, instead of following the same group of children for 3 years. The linear mixed-model analyses included time (represented by two dummy variables) and the interaction between group and time. The latter indicated the difference in outcome between the groups at the two follow-up moments (Twisk et al., 2018).
Analyses using MOPER fitness test data were adjusted for gender and age. Analyses using questionnaire and accelerometer data were adjusted for ethnicity and living with both parents. Analyses using the accelerometer data were further adjusted for wear time. For all analyses, betas and $95\%$ confidence intervals (CIs) were calculated. The statistical analyses were conducted using IBM SPSS Statistics 24.0.
In case assumptions of normality and homoscedasticity were not met, log-transformations were conducted. The variables “bent-arm hang” of the MOPER fitness test, “consumption of sodas” and “consumption of energy and sports drinks” of the questionnaire, and “MVPA accumulated in bouts ≥5 minutes” of the accelerometer, had a skewed distribution with an excess of zeros and were therefore analyzed using tobit mixed models analyses. Tobit mixed models analyses were performed in STATA (version 15).
## Results and Discussion
Figure 2 presents the flowchart of participants in the measurements. Supplemental 2 provides the characteristics of children participating in the MOPER fitness test. Participating children were equally divided across grades with a mean age of 10.6 years old and $47\%$ to $55\%$ of the participating children were girls. Supplemental 3 and Supplemental 4 present the characteristics of the subgroup of children who completed the questionnaire and had valid accelerometer data, respectively. More girls than boys participated in these measurements ($57\%$–$71\%$). In this subgroup, a substantial number of children had parents who were born in Morocco or Turkey ($27\%$–$41\%$), or in another country than the Netherlands ($29\%$–$38\%$). Most children spoke Dutch at home ($72\%$–$91\%$), lived with both parents ($67\%$–$85\%$) and had siblings ($84\%$–$93\%$).
Table 1 provides the results of the mixed model analyses. The actions had significant adverse effects on the “10 × 5 meter run” (β = 0.5 sec, $95\%$ CI [0.0, 1.0]) at T2 versus T0 and “plate-tapping” (β = 0.5 sec, $95\%$ CI [0.1, 0.9]) at T1 versus T0, the latter due to improved scores in the control group. We found a significant beneficial intervention effect on consumption of energy/sports drinks at T2 versus T0 (β = −1023.1 mL, $95\%$ CI [−1940.7, −105.5]) due to an increase in the control group. Based on the accelerometer data, the intervention had significant beneficial effects at T1 versus T0 on total MVPA (β = 9.5 min per day, $95\%$ CI [2.5, 16.5]) and MVPA in bouts (β = 2.0 minutes per day, $95\%$ CI [0.0, 3.9]). These effects were not present at T2 versus T0, due to an improvement in MVPA in the control group and a decline in the intervention group. No other significant effects were found.
**Table 1.**
| Unnamed: 0 | T1 vs. T0 | T2 vs. T0 |
| --- | --- | --- |
| MOPER a | MOPER a | MOPER a |
| Bent-arm hangb (s) ↑c | 1.5 [−0.7, 3.7] | −0.7 [−2.7, 1.2] |
| 10 x 5 meter run (s) ↓d | −0.4 [−0.9, 0.2] | 0.5 [0.0, 1.0]* |
| Leg-lifte (s) ↓ | 1.0 [1.0, 1.0] | 1.0 [1.0, 1.0] |
| Plate-tapping (s) ↓ | 0.5 [0.1, 0.9]* | 0.2 [−0.2, 0.6] |
| Sit-and-reach (cm) ↑ | 0.6 [−0.7, 2.0] | 1.1 [−0.1, 2.3] |
| Hand-grip strength (kg) ↑ | 0.3 [−0.6, 1.2] | 0.1 [−0.7, 1.0] |
| High-jump (cm) ↑ | 0.9 [−0.6, 2.3] | 0.6 [−0.8, 1.9] |
| Self-report f | Self-report f | Self-report f |
| Consumption sodab (mL/week) | −578.1 [−1798.0, 641.8] | −736.4 [−1910.9, 438.1] |
| Consumption energy/sports drinksb (mL/week) | −192.4 [−1156.7, 771.8] | −1023.1 [−1940.7, −105.5]* |
| Consumption candye (portions/week) | 1.0 [0.9, 1.1] | 1.0 [0.9, 1.1] |
| Consumption snackse (portions/week) | 0.9 [0.8, 1.1] | 1.0 [0.9, 1.1] |
| Active transport to school (min) | −1.0 [−2.4, 0.5] | −1.5 [−3.0, 0.0] |
| Outside play (min/day) | 3.6 [−15.0, 22.2] | −3.6 [−22.3, 15.1] |
| Sports participatione (min/day) | 1.0 [0.9, 1.1] | 1.0 [0.9, 1.1] |
| Watching TV/movies (min/day) | 6.1 [−15.4, 27.7] | −0.7 [−21.6, 20.3] |
| Gaming (min/day) | 19.8 [−6.1, 45.8] | −0.9 [−26.1, 24.3] |
| Self-rated health (scale 0–100) | 4.2 [−1.6, 9.9] | −1.4 [−6.9, 4.1] |
| Accelerometer g | Accelerometer g | Accelerometer g |
| Time spent sedentary (min/day) | −0.3 [−18.8, 18.2] | 18.4 [−0.2, 37.1] |
| Time spent in LPA (min/day) | −6.2 [−24.2, 11.7] | −5.3 [−22.4, 11.7] |
| Time spent in MVPA (min/day) | 9.5 [2.5, 16.5]** | −6.4 [−13.3, 0.5] |
| MVPA accumulated in bouts ≥5 minb (min/day) | 2.0 [0.0, 3.9]* | −1.0 [−2.9, 0.9] |
| Sedentary time accumulated in bouts ≥10 min (min/day) | −1.0 [−18.1, 16.2] | 3.5 [−12.7, 19.8] |
## Challenge 1 and Recommendations—Study Design
We recruited four control schools from neighborhoods with similar characteristics as the intervention schools. However, the control schools also had certain policies targeting healthy behaviors, possibly diluting intervention effects. The favorable intervention effects on the consumption of energy/sports drinks at T2 versus T0 resulted from an increase in the consumption of energy/sports drinks of children in the control group. Promotion of drinking water was implemented as part of “usual care” by community organizations and local government in both intervention and control neighborhoods and in most schools. Additionally, one of the cocreated actions promoted drinking water and raised awareness on sugar-sweetened beverages. These child-initiated actions within Kids in Action at intervention schools could have contributed to the stabilization of consumption of energy/sports drinks in the intervention group, versus an increase in the control group. This is supported by the process evaluation, which showed that Kids in Action stimulated organizations in the intervention neighborhood to prioritize healthy lifestyle policies (Anselma et al., 2020). Since we did not know the focus of actions at the start, we did not measure consumption of water (see Challenge 2).
We recommend future participatory studies to apply more flexible study designs to deal with some of the challenges such as finding suitable control schools, monitoring what policies are being implemented at those schools, and the varying sample throughout the study. An example of a more flexible design is the extended cohorts design, as in this design time point one of the study sample serves as a baseline for age-equivalent groups at following time points (Olweus, 2005).
## Challenge 2 and Recommendations—Measurements
An intricate and inevitable challenge of evaluating participatory studies is that beforehand it is unknown what behaviors will specifically be targeted by the developed actions (Anyon et al., 2018). Consequently, it is unknown at the start what specific outcome measures are optimal. For example, in the present study, a water policy was successfully implemented at one school, but water consumption was not measured. Also, the adverse effects on some fitness items are difficult to explain, but since no actions were developed that specifically targeted neuromotor fitness, these could be chance findings. Future participatory studies might add delayed baseline measurements to include measures of outcomes that were unknown at baseline. Additionally, process evaluations are of utmost importance to provide insight into the participatory process, community experiences and how these may have influenced the targeted health behaviors (Lindquist-Grantz & Abraczinskas, 2018).
It is difficult to compare the current study with previous studies as to the best of our knowledge there are no other participatory studies aimed at improving children’s EBRBs with a similar level of child participation throughout the development, implementation, and evaluation of actions, and including a controlled trial design. Looking more generally to previous studies evaluating interventions developed in participation with children or adolescents aiming to improve EBRBs, these interventions also showed small or inconsistent effects (Frerichs et al., 2016; Froberg et al., 2018; Verloigne et al., 2017), similar to interventions which did not include participatory methods (Kornet-van der Aa et al., 2017; Metcalf et al., 2012; Olstad et al., 2017). Participatory action research with children does show promising results in creating actions that adhere to children’s needs and interests, community engagement, improving children’s awareness of unhealthy behavior, and developing several valuable life skills (Anselma et al., 2020; Anyon et al., 2018; Shamrova & Cummings, 2017). Therefore, we hope that future studies aiming to improve children’s EBRBs apply the lessons learned from studies such as ours, and further examine how effectiveness of cocreated interventions in participatory action research can be properly evaluated and improved.
## Challenge 3 and Recommendations—Analyses
We want to acknowledge that the design of this study and the analyses have their limitations. As academic researchers, we are however obliged to use and report on the data that we have, as the participants have dedicated their time and efforts (Alley et al., 2015; World Medical Association, 2018). We looked for analyses that best fitted our data and chose linear mixed model analysis as this adequately handles missing data. For future participatory studies that want to include a controlled design, we have the following recommendations. First, it is recommended to clearly register the children who participate in actions and action development to enable including this in the analyses. For example, by registering attendees to sessions and events, retrospectively asking children their exposure/attendance/involvement or incorporating monitoring of dose/response in the process evaluation using a meaning for “dose” that fits the study (Rowbotham et al., 2019). This will help in gaining knowledge on the effectiveness on EBRBs of participatory approaches and the actions it produces. Our second recommendation is to ensure that you have considerable time and resources for recruitment of participants. We did not reach the required sample size, and were therefore underpowered for detecting intervention effects. Recruiting participants, especially in lower-socioeconomic areas, can be challenging, but it is not impossible when using the right approaches (Carroll et al., 2011; Harkins et al., 2010). For example, working together with local organizations who are already known by the children and their parents, using informal networks and develop recruitment materials together with the local community so that they match their cultures, interests, and their level of understanding. Last, we recommend academic researchers to be creative in working with their data. An example is to create hypotheses that match the data set and relate to the implemented actions, before analyzing the data. For example, in Kids in *Action a* water policy was implemented at one school, so a hypotheses would be that water consumption of children would have increased more at that school compared with the other intervention schools. This leads to more tailored analyses than just comparing intervention groups with control groups.
## Recommendations—Participatory Approach
In Kids in Action the focus was on the collaboration with children (Anselma, Altenburg, Emke, et al., 2019). This process was optimized by closely collaborating with schools, community organizations, and the local government. By developing and implementing actions with them, Kids in Action hoped to also reach changes in the system and local/organizational policies. However, not all partners were engaged in all phases of the project and most actions focused on the school and neighborhood environment and less on the home environment and parents (Anselma et al., 2020). Ecological models describe that when aiming to improve EBRBs in children, the system surrounding the child needs to be targeted (Davison & Birch, 2001; Lytle, 2009). We recommend future participatory studies to obtain a systems approach, involving important stakeholders on all system levels and thereby develop synergistic actions, and also evaluate their impact on different levels and with all involved stakeholders (Frerichs et al., 2016; Gates, 2016; Waterlander et al., 2020). Additionally, in participatory research with children, children decide to work on a topic that is relevant to them and that they want to address (London et al., 2003; Ozer, 2017). For children, this may mean that they do not wish to participate in all topics related to a healthy lifestyle and perhaps even decline some power. In Kids in Action, children mainly developed actions related to sports and play, and were less interested in developing actions to improve their dietary behavior. This could explain the favorable effects on total MVPA and MVPA in bouts (Anselma, Altenburg, Emke, et al., 2019). Future studies could discuss with the children which topics they would like to address themselves and which topics they rather leave to others (e.g., researchers, parents, and teachers). Therefore, we also recommend future studies to discuss with children their desired level of power sharing on each of the research topics, to make sure all topics are covered and children participate on the level of their choosing (Hart, 1992; Wong et al., 2010). Last, although the duration of Kids in Action was 3 school years few children could actively participate in the development, implementation, and evaluation of actions. In Kids in Action, we closely collaborated with 13 to 25 children per year, over the course of 3 years, and the majority of actions were developed and implemented in the second year (Anselma, Altenburg, Emke, et al., 2019; Anselma et al., 2020). Our process evaluation indicated that community partners put healthy behaviors and child participation higher on their agenda, that professionals from different organizations worked more closely together, that children’s awareness about healthy behaviors improved, as well as children’s empowerment (Anselma et al., 2020). However, it may take more time and participation of more children, parents, and other stakeholders for these improvements to result in detectible changes in EBRBs (Anselma et al., 2020; Moore et al., 2019). We recommend future studies to aim for structural changes in policy and practice, as we believe that participation of children in decision making and the cocreation of actions has many benefits and therefore should be embedded in for example the education of teachers and social workers.
## Strengths and Limitations
The current study has several strengths and limitations. A limitation of this study is the low participation rate in the self-report questionnaire and accelerometer data, limiting the power of our study sample. Additionally, the actions have reached a limited number of children while the evaluation also included children who did not participate in certain actions, for example, because an action was not implemented at their school. Relatedly, we did not register which children participated in which actions, so we did not have any information about the intervention dose received per child. Another limitation is that no valid and reliable questionnaires on the consumption of sugar-sweetened beverages and unhealthy snacks, and sports and outdoor play participation were available. So even though our questionnaire consisted of the most valid and reliable items from existing questionnaires, the questionnaire may have been inadequate in detecting subtle changes in EBRBs. Last, a limitation is the dynamic cohort, making it impossible to draw strong conclusions about the intervention effect. This is further impeded by the choice of one intervention school to withdraw from participation in the second year.
An important strength of this study is that it included a community approach, in which all primary schools in the community participated, as well as the local government and relevant stakeholders. A second strength is that this study assessed actual behavior change both in interventions and control schools, which rarely occurs in participatory action research (Anyon et al., 2018; Jacquez et al., 2013). Furthermore, intervention and control schools were similar regarding childhood overweight, ethnicity, and socioeconomic status. Future studies could consider a three-arm study adding a treatment arm where actions are developed and implemented top–down without child participation to examine the added effect of child participation.
## Conclusion
In the Kids in Action study, 9- to 12-year-old children cocreated actions to promote physical activity and healthy dietary behaviors in peers using a participatory approach. Despite positive findings on children’s empowerment and awareness of healthy behaviors observed in the process evaluation (Anselma et al., 2020), the current effect evaluation showed no consistent beneficial effects on children’s physical activity, sedentary behavior, dietary behavior, neuromotor fitness and self-perceived health. To obtain larger effects, we recommend future participatory action research to collaborate with more children and more intensively with school staff, families, and local organizations, trying to create effects in the larger system surrounding the child. To measure the effects more accurately, we recommend alternative evaluation designs such as the extended cohorts design. Additionally, we advocate for the value of process evaluations in participatory action research with youth, obtaining stakeholders’ experiences as well as including relevant effect measures from the stakeholder perspective.
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|
---
title: COVID-19 orphans—Global patterns associated with the hidden pandemic
authors:
- Callum Lowe
- Leli Rachmawati
- Alice Richardson
- Matthew Kelly
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021133
doi: 10.1371/journal.pgph.0000317
license: CC BY 4.0
---
# COVID-19 orphans—Global patterns associated with the hidden pandemic
## Abstract
Whilst the COVID-19 pandemic has caused significant mortality across the globe, many children have been orphaned due to the loss of their parents. Using the framework of an ecological analysis, we used estimates of total maternal/paternal orphans using an online COVID-19 orphanhood calculator to estimate the total orphans per COVID-19 death for 139 countries. Descriptive statistics were used to determine global patterns behind this risk of children being orphaned. Linear regression models were fitted to determine factors associated with this risk, and the association with vaccination coverage was calculated. We found that there is tremendous global variation in the risk that COVID-19 deaths will lead to orphaned children, and that this risk is higher in countries below median GDP per capita (1·56 orphans per deaths) compared to countries above (0·09 orphans per death). Poverty prevalence ($B = 2$·32, $p \leq 0$·01), GDP per capita (B = -0·23, $p \leq 0$·05), and a greater proportion of people with NCDs being reproductive aged ($B = 1$·46, $p \leq 0$·0001) were associated with this risk. There was a negative correlation between 2nd dose vaccination coverage and orphans per death ($p \leq 0$·05). The risk of children being orphaned per COVID-19 death, alongside fertility rate, is due to there being a greater share of COVID-19 deaths among younger persons. This is more likely in poorer countries and those where the age distribution for non-communicable diseases that elevate COVID-19 mortality risk are more uniform. Due to vaccine coverage inequity, more children will suffer the loss of their parents in poorer countries.
## Introduction
The COVID-19 pandemic has resulted in health, economic and social crises around the world, causing significant morbidity and mortality. As of December 10, there were 5,285,888 deaths from COVID-19 worldwide [1]. Most COVID-19 deaths are among adults and the elderly. Smith et al [2021] estimated the case fatality rate for children <18 years old in England to be two per million, [2] compared to almost one in four in Italy [3]. Particularly with the advent of the Omicron variant, coronavirus is extremely infectious, and can spread quickly to all members of a household. Coupled with the low risk of mortality among children, there is great possibility that children will survive COVID-19 infection while their parents or caregivers will not. As such, there exists a unique risk for COVID-19 orphans, the ‘hidden’ pandemic.
A modelling study by Hillis et al [2021] was the first to quantify the burden of orphans due to the COVID-19 pandemic [4]. The study took mortality and fertility rates by age and sex in 21 countries to model estimates of the number of children affected by the pandemic, either through the loss of one or both parents, caregivers, or custodial grandparents. The findings estimate that between March 1, 2020 and April 30, 2021, 1,134,000 children experienced one of the above deaths. This is roughly a third of the 3,322,107 deaths as of April 30 due to COVID-19, indicating the immense scale of this challenge [5]. Whilst little academic research has pertained to the issue of COVID-19 orphans, the issue has been documented in different countries globally, such as Peru [6] and Indonesia [7]. Field-interviews have uncovered the drastic consequences this experience can have on individuals and families, such as the reports of women needing to take care of as many as eight children due to parental death [6].
Whilst little research has investigated the distribution and effect of orphans due to the COVID-19 pandemic, this phenomenon has been observed in other pandemics. The 2014 Ebola Virus Disease outbreak in West Africa led to an estimated 9,600 orphans [8]. Indeed, orphanhood can have extreme long-term impacts on children’s lives, both in terms of physical and mental health [9]. Racial disparities exist in the impact of orphanhood in midlife [10]. Losing one or both parents can be extremely difficult for children; orphans might receive less love and attention and are more prone to behavioural and emotional problems [10]. These challenges will ultimately have an impact on social life and mental health. Orphans can experience problems with home life, classroom learning, and recreational activities, and are also susceptible to child labor demands [11]. An additional challenge from the COVID-19 pandemic is the shift to distance learning, which in turn will exacerbate the negative impact of being orphaned due to limited social interaction. Orphans experience a greater risk of disease and malnutrition [12] and sexual abuse, [13] and have higher rates of depression, posttraumatic stress disorder, suicidal thoughts, and anxiety [14]. These issues are bolstered in low- and middle-income countries, where social support services for orphaned children are both fewer and less extensive than high-income countries [15]. This is a unique risk in the COVID-19 pandemic, because coronavirus transmission is high on nearly every continent.
The findings of Hillis et al [2021] however suggest major variation in the burden of orphanhood worldwide after holding the number of COVID-19 deaths constant [4]. The study estimates the rate of orphaned children is 2·02 per death in Angola, compared with 0·13 in Australia–a 15-fold difference [4]. This indicates that the risk of a child being orphaned per death in *Angola is* 15 times higher than in Australia. Whilst fertility rate is a significant factor, influencing the number of children at risk, the difference in fertility rates between Angola (5·4) and Australia (1·7) for example [16] is insufficient to explain the 15-fold difference in the risk of children being orphaned per death. It may be possible that differences between countries such as poverty rate and the proportion of people with co-morbidities that are current parents of children are also responsible for this discrepancy.
Understanding the patterns of discrepancies between countries will shine light on the scarcely discussed issue of COVID-orphans in existing literature. Directing greater attention towards this issue will encourage an enhanced global effort to support and prevent the burden of further orphans. As such, we aim to address this gap in the literature by extending the work of Hillis et al [4] to investigate the patterns and factors associated with the risk of COVID-19 orphans globally.
## Methods
To determine patterns associated with, and the distribution of the risk of children being orphaned due to the COVID-19 pandemic, we conducted an ecological analysis at a global level. An ecological analysis was suitable as we aimed to look at global variation between countries. The basis of our analysis is that of the work of Hillis et al [2021], whereby estimates for the total number of children orphaned due to the COVID-19 pandemic were calculated [4]. This study estimated the total orphans caused by excess mortality during the COVID-19 pandemic using age and sex disaggregated mortality and fertility rates for 21 countries. Following this, a linear model with a log link for the number of orphans (caused by maternal/paternal, primary caregiver, and grandparent mortality) was fitted, and day-to-day estimates for orphans of each category in all countries was made available online [17]. For the purposes of our analysis, we only considered maternal/paternal orphans which comprise one of the most detrimental categories of orphanhood.
## Calculating orphans per death
To obtain estimates of the total number of maternal/paternal orphans for all countries, we used the online COVID-19 orphanhood calculator, developed as part of the study by Hillis et al [2021] to extract the estimated number of maternal/paternal orphans up to September 28, 2021 [4]. The details of this calculation are available in the supplementary material section of Hillis et al [2021] [4]. Briefly, maternal/paternal orphans were estimated by first estimating the number of children each adult of different age/sex groups would have in 2020. This was then aggregated into 5-year age bands and multiplied by the excess mortality rate to estimate total maternal/paternal orphans. The authors then adjusted for ‘double’ (maternal and paternal) orphans to obtain a reliable estimate. This calculation was performed for 21 countries and then extrapolated in the online COVID-19 orphanhood calculator using a logistic model relying on the high correlation between orphans per death and total fertility rate [4]. We then used this estimate of the total number of maternal/paternal orphans and then divided by the total cumulative number of COVID-19 deaths up to the same date in each country obtained from the Johns Hopkins Coronavirus Resource Center [18]. By dividing the estimated number of maternal/paternal orphans by the total number of COVID-19 deaths, we obtain the dependent variable used in analysis termed orphans per death (OPD). As the extrapolated estimates of the number of orphans for the countries not included in the study of Hillis et al [2021] were based on COVID-19 deaths only and not excess mortality, [4] we divided total estimated orphans by COVID-19 deaths only and not excess deaths.
## Calculating covariates
As we aimed to also look at potential variables that correlate with OPD, we collected country-level covariates based on the hypothesis that where a greater proportion of a countries’ deaths are among persons of reproductive age (i.e. more likely to be parents of children), then the risk of children being orphaned is greater. We conducted a literature search for co-morbidities that might elevate the risk of COVID-19 mortality among people of reproductive age. Non-communicable diseases (NCDs) identified were hypertensive heart disease (HHD), diabetes, obesity, and cardiovascular disease [19, 20]. We hypothesised that these conditions, alongside indicators of country level socio-economic status might correlate with OPD. The way in which NCDs might correlate with OPD is that in some countries, sufferers of these NCDs may be generally younger than in other countries and therefore more likely to be at the age of a child’s parent. We also included HIV and stroke; the former might correlate with OPD as HIV would be expected to elevate the risk of death (from any condition), and the latter likely correlates with the aforementioned NCDs.
We collected data on NCD prevalence using the Global Burden of Disease (GBD) Results Tool using data for 2019 estimates [21]. NCD prevalence was calculated as the proportion of all persons with the disease in a given country/province that were aged 15–49 years (reproductive age), as opposed to the prevalence in that age group. This decision was made because if such conditions were associated with greater risk of COVID-19 mortality, then it would be expected that in countries/provinces where a greater proportion of persons that suffer that particular condition are reproductive aged then the percentage of all COVID-19 mortality from persons in that age group would also increase. The following equation describes this calculation: NCDi=NumberofpersonswithNCDiaged15−49TotalnumberofpersonswithNCDi where i = HHD, diabetes, chronic kidney disease, HIV, stroke.
Thus, we assume that reproductive age (15–49 years) is a good proxy for the age of the majority of parents at the time of the pandemic. For obesity, data was not available on the breakdown of obesity prevalence by age group for each country in the GBD results. Instead, we used the crude obesity prevalence for each country using data from the Global Health Observatory [22].
Country-level indicators of socio-economic status used comprised the prevalence of poverty (obtained from the world population review [22]) and Gross Domestic Product (GDP) per capita (current $US) obtained from the World Bank [23]. These covariates were chosen because we hypothesize that a greater proportion of COVID-19 deaths attributed to younger persons would be associated with poor socio-economic status. Total fertility rate was obtained from the World Bank [15].
The proportion of each countries’ population that was reproductive aged (15–49 years) might be a potential confounder as younger populations may have more deaths among younger persons more likely to be a parent. As such, we collected this variable by calculating the proportion of the population aged 15–49 using UN World *Prospects data* [24].
Vaccination rate as of 1st December 2021 was tabulated using data from Our World in Data [25]. We extracted the percentage of each countries’ population that had received at least two doses of a COVID-19 vaccine.
## Statistical analysis
Countries included in analysis were those that had full data on all covariates and the dependent variable (OPD). As fertility rate directly linearly scales the risk of children being orphaned (where fertility rate doubles, the number of orphans per death would double), we computed the OPD divided by fertility rate for regression analysis, termed OPD adjusted. This variable can be thought of as ‘households’ or ‘family units’ affected by COVID-19 pandemic orphanhood; however, the fertility rate variable is a national average and so does not differentiate between individual household sizes.
The OPD values were ranked by country according to WHO region (Africa, Americas, Eastern Mediterranean, European, South-East Asia, and Western Pacific). Following this, countries were categorised as having a GDP per capita as below or above the median GDP per capita of countries in the analysis. Subsequently, the kernel density distribution of OPD was plotted by GDP category. Boxplots were also constructed in the same manner. Following this, simple bivariate linear regression models for all covariates with the OPD adjusted variable were created, and the correlation coefficient and p-value for regression were calculated.
Principal components analysis was conducted to group non-communicable disease variables with high multicollinearity into components. The component eigenvalues were plotted on a scree plot and the component explaining the most variance was extracted and scores created using the regression method. The Kaiser-Meyer-Olkin (KMO) test and Bartlett’s Sphericity Test (BST) were computed to assess the suitability of the principal component analysis [26]. A single component was extracted representing the NCD variables. A linear regression model was then fitted to identify significant predictors of adjusted OPD. A logit link for OPD adjusted was used. Dividing OPD by fertility rate acted as a means of controlling for the direct and non-differentiating effect of fertility rate on OPD, and led to a variable with values between 0 and 1. This then meant that the logit transformation could be computed, leading to an outcome on the range (-∞, ∞). A forward selection process was used to identify important variables based on the change in model R2 adjusted and visual inspection of residuals. Next, pairwise interactions were assessed for inclusion in a similar way. Finally, the correlation between vaccination rate and OPD was computed in a separate analysis.
Statistical analysis was performed in RStudio 3.5.1 [27].
## Results
A total of 139 countries had available data on all covariates and were included in this analysis. In Fig 1 we present the estimates of OPD in these countries by WHO region. The highest OPD rates were observed in Africa ranging between 1·5 and 2 except for 6 countries with values below 1·5. Large variation was observed in the South-East Asia region, with values close to 2 in Timor-Leste, but below 0.5 in all other countries. OPD rates were consistently below 0·25 in European countries except for Kyrgystan, Israel, Kazakhstan and Tajikistan, and varied widely in the Americas; the highest observed in Peru. Variation in Eastern Mediterranean countries was large; the highest was nearly 2 in Sudan and below 0·25 in Bahrain.
**Fig 1:** *Distribution of orphans per death by country.Countries organized by WHO region.*
In Fig 2 we present the kernel density function of OPD (left) and boxplot of OPD (right) stratified by GDP per capita category. Countries with a GDP per capita above the median were right-skewed with the majority having OPD values less than 0·5, whilst countries with a GDP per capita below median had a bimodal density distribution. As a result, the variation in higher income countries was extremely small in comparison to lower income countries, although a number of outlier values were present. The median OPD value for lower income countries (1·56) was 17 times greater than for higher income countries (0·09).
**Fig 2:** *Kernel density functions for OPD by GDP category (Left) and boxplots of OPD by GDP category (right).GDP categories calculated as countries with a GDP below the median value in the study data, and those countries with a GDP above the median value. Median value = US $12,939 per capita.*
Scatter plots with univariate linear regressions for OPD adjusted on each covariate are shown in Fig 3. Statistically significant associations were positive for % population reproductive aged ($R = 0$·21, $p \leq 0$·.05), poverty ($R = 0$·70, $p \leq 0$·0001), and the proportion of chronic kidney disease ($R = 0$·66, $p \leq 0$·.0001), diabetes ($R = 0$·71, $p \leq 0$·0001), HHD ($R = 0$·61, $p \leq 0.0001$) and stroke ($R = 0$·70, $p \leq 0$·0001) cases among people of reproductive age. The associations with obesity and GDP per capita were negative and statistically significant (R = -0·66, $p \leq 0$·0001 and R = -0·62, $p \leq 0$·0001 respectively).
**Fig 3:** *Simple univariate linear regression models for the correlation between covariates and OPD Adjusted (OPD divided by fertility rate).x-axis expressed as proportion (between 0 and 1) except for GDP per capita (expressed as $US). P-values calculated for regression model. Y-axis transformed to the logit scale. Note that the y-axis intervals are not evenly spaced. Orphans per death adjusted = orphans per death divided by national average total fertility rate. Solid lines represent regression prediction, grey bands represent 95% confidence interval. Chronic Kidney Disease, Diabetes Mellitus, HHD, HIV, and stroke measured as proportion of country population with condition that are aged 15–49 years. Obesity and poverty measured as proportion of population. GDP per capita measured as $US.*
The correlation matrix (left) and principal components analysis (right) for NCD variables is shown in Fig 4. There was generally strong positive correlation between NCD variables except for obesity where the correlation was negative with all other NCD variables. On the basis of this, all NCD variables except obesity were included in principal components analysis. The scree plot (right) indicated that the majority of variation ($86\%$) in the four entered NCD variables could be explained in a single component. This component can be thought of as a general variable indicative of greater proportion of NCDs occurring among younger people.
**Fig 4:** *Correlation matrix for covariates tabulated in the analysis (left) and principal components analysis results (right).Scree plot (right) for total variance explained for included variables in principal components analysis (CKD, Diabetes, Stroke, HHD). CKD, Chronic Kidney Disease. DM, Diabetes mellitus. HHD, Hypertensive Heart Disease, HIV, Human Immunodeficiency Virus. KMO, Kaiser-Meyer-Olkin test. BST, Bartlett’s Sphericity Test.*
Following principal components analysis, the first component was extracted and component scores were extracted using the regression method to compute the NCD principal component score variable. Subsequently, multiple linear regression models were fitted to OPD adjusted with a logit link (Table 1). NCD component scores, poverty and GDP per capita all had statistically significant associations with OPD adjusted at $p \leq 0$·0001 in univariate regressions. In the multiple regression model, these associations remained significant; NCD component score (β = 1·46, $95\%$ CI = 0·95, 1·97, $p \leq 0$·0001), poverty prevalence (β = 2·32, $95\%$ CI = 0·65, 3·99, $p \leq 0.01$), and GDP per capita (β = -0·23, $95\%$ CI = -0·41, -0·05, $p \leq 0$·05). The NCD component score was the only covariate to have an increased coefficient in the multiple regression model whilst poverty and GDP per capita coefficients reduced in magnitude.
**Table 1**
| Covariates (univariate models) | Estimate (SE) | 95% CI | p |
| --- | --- | --- | --- |
| NCD Principal component score | 1·18 (0·08) | 1·02, 1·34 | <0·0001 |
| Poverty | 5.41 (0.58) | 4·27, 6·55 | <0·0001 |
| GDP per capita | -0.56 (0·06) | -0·68, -0·44 | <0·0001 |
| Covariates (multiple model) | Estimate (SE) | 95% CI | p |
| NCD Principal component score | 1·46 (0·26) | 0·95, 1·97 | <0·0001 |
| Poverty | 2·32 (0·85) | 0·65, 3·99 | <0·01 |
| GDP per capita | -0·23 (0·09) | -0·41, -0·05 | <0·05 |
| Interaction terms | | | |
| NCD×Poverty | -1·54 (0·65) | -2·81, -0·27 | <0·05 |
| NCD×GDP | -0·12 (0·06) | -0·27, 0·00 | <0·05 |
| Poverty×GDP | 0·21 (0·48) | -0·73, 1·15 | 0·67 |
Finally, univariate associations between 2nd dose COVID-19 vaccination coverage and OPD by region are presented in Fig 5. There is a statistically significant negative association between vaccination coverage and OPD in all regions except for South-East Asia (R = -0·17, $$p \leq 0$$·63). This association was strongest in Eastern Mediterranean countries (R = -0·87, $p \leq 0$·0001), Africa (R = -0·77, $p \leq 0$·0001) and also Western Pacific (R = -0·75, $p \leq 0$·05). The association was weaker in the Americas (R = -0·42, $p \leq 0$·05). There was strong clustering in African countries with extreme high values of OPD and low values of vaccination coverage. European countries had significant clustering of OPD values below 0·25, despite large variation in 2nd dose vaccination coverage.
**Fig 5:** *Association between 2nd dose vaccination coverage and OPD by region.Correlation coefficient and p-value for univariate linear regression models displayed in text in main panels. Gray bands represent 95% confidence intervals. Vaccination data as of 1st December 2021.*
## Discussion
The COVID-19 pandemic so far has caused significant mortality around the world, but has also led to a ‘hidden pandemic’ of children orphaned by the loss of one or both parents. In this ecological analysis, we investigated the distributions of the risk of children being orphaned from COVID-19 deaths and found that this risk is significantly higher in lower income countries and those with greater prevalence of poverty. Furthermore, we found that in countries where a greater proportion of people suffering comorbidities associated with elevated COVID-19 mortality risk are reproductive aged, the risk of children being orphaned per death was greater.
A higher risk of children being orphaned per COVID-19 death is intrinsically linked to the age distribution of COVID-19 mortality. Flatter distributions will mean that a greater share of deaths are among younger persons which will lead to the death of more parents. Where the distribution is right skewed and deaths are predominantly among the elderly, few deaths among parents of young children will occur, and so the risk of children being orphaned per death is diminished.
We found that there was a statistically significant association between OPD and poverty prevalence as well as GDP per capita (Fig 3, Table 1). This result is consistent with the observation that COVID-19 age-mortality distributions are flatter among poorer countries [28]. One possible explanation for this association is that in poorer countries, access to healthcare and treatment is more limited than in richer counterparts. The number of hospital beds (per 100,000 population) is correlated with GDP, and therefore in poorer countries fewer hospital beds would be expected to correlate with more COVID-19 deaths across all age groups [28]. Thus, the proportion of deaths attributable to people of reproductive age would rise, and as such the risk of children being orphaned per death would also rise.
We also found that when a greater proportion of a countries’ population that suffer particular NCDs are reproductive aged there was a positive association with OPD (Fig 3, Table 1). This can be explained by the notion that when a greater share of diseases that elevate COVID-19 mortality risk are among persons of parenting age, then a greater share of COVID-19 deaths will be among this group whose death is more likely to lead to a child being orphaned. These findings are supported by the work of Nepomuceno et al [2020] who suggested that flatter age-mortality distributions in low- and middle-income countries are due to a greater proportion of chronic diseases among younger persons in these countries [29]. We found this to be true for hypertensive heart disease, chronic kidney disease, diabetes, and stroke (Fig 3). We did not find this association for obesity, however this may be due to tabulating obesity as overall national prevalence and not the proportion of obese people aged 15–49. We also found in post-hoc analysis that obesity prevalence correlated strongly with GDP, and therefore may be confounded subject to confounding.
It is a particular concern that the impact on children being orphaned due to the pandemic is likely to be greater in low and middle income countries, because the capacity of governments to manage orphaned children and facilitate social support might be lower than in higher income countries [9]. Orphaned children may face ongoing challenges with nutrition, schooling, financial support and psychosocial support [30].
These issues are further exacerbated by the fact that countries with a greater OPD rate tended to have a lower vaccination coverage. In countries where vaccination coverage is lower, COVID-19 associated mortality rates will be higher, and therefore the subsequent numbers of children orphaned will follow in a similar fashion. In more developed countries however, not only is the vaccination rate higher, but the risk of children being orphaned is also lower. Therefore, of the fewer deaths that will occur in these more developed countries, the risk that those deaths will lead to children being orphaned is reduced. These issues underscore the tragedy that is unequal distribution of COVID-19 vaccines across the globe. Our results highlight the need for vaccine equity across the globe. While many have argued for vaccine equity from the perspective of preventing mortality and reducing the risk of potentially more severe coronavirus variants, our results show that in addition to this, the prevention of children being orphaned will also be tied directly to even vaccination coverage.
This study is not without limitations. Our analysis relied on estimates of orphans produced from an extrapolation that was only based on 21 countries [4]. The extrapolation was only based off of fertility rate and total COVID-19 deaths and not each countries underlying mortality and fertility rate distribution. As such, we have regressed data that is not obtained empirically which therefore may lead to reduced reliability in the estimates of our associations. However, the extrapolation based off fertility rate in the aforementioned study relied on a high correlation (R2 = 0·93). As such, we recommend that the numerical estimates produced from our study are carefully interpreted, and large variation between countries is more valid than comparison of similar countries. Furthermore, we could not assess obesity in the units of the proportion of persons with obesity aged between 15–49 years, and so by reverting to country obesity prevalence we may not have captured this association as accurately as the other NCD variables. Under- or over-estimation of COVID-19 deaths in each country and variability in the systems to track mortality will directly affect the estimate of orphans per death, and as such our results are subject to variation due to the reporting of COVID-19 deaths. We only considered orphans due to the loss of parents in this analysis, and not from the loss of grandparents or other caregivers. However, our aim was to look at patterns associated with arguably the most severe form of orphanhood–the loss of a child’s parents. Future research could study whether the same variations occur when including these categories. In line with the ecological study framework, we were not able to capture differences between people within countries. Whilst we identified the risk of OPD to be higher in poorer countries, within many countries there is likely inequity in the risk of OPD whereby poorer communities within the same country are likely to be burdened by more COVID-19 orphans. We did not take into account excess mortality in the calculation of OPD as the extrapolated estimates of orphans did not consider excess mortality. Thus, there may exist other factors predicting the risk of children being orphaned due to non-COVID-19 deaths during the pandemic.
Our study serves as a reminder of the perpetual cycle of poverty experienced in poorer countries, furthered by the elevated risk of children being orphaned per COVID-19 death. Our findings underscore the need for uniform vaccination coverage across the globe, which will minimize the number of deaths among all demographics including parents, and therefore minimize the number of children becoming orphaned. Our findings also suggest that the proportion of persons with NCDs that are aged 15–49 years also correlates with OPD independently of poverty and GDP.
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|
---
title: Validation of MuLBSTA score to derive modified MuLB score as mortality risk
prediction in COVID-19 infection
authors:
- Richie George
- Asmita A. Mehta
- Tisa Paul
- Dipu T. Sathyapalan
- Nithya Haridas
- Akhilesh Kunoor
- Greeshma C. Ravindran
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021136
doi: 10.1371/journal.pgph.0000511
license: CC BY 4.0
---
# Validation of MuLBSTA score to derive modified MuLB score as mortality risk prediction in COVID-19 infection
## Abstract
COVID-19pandemic was started in December 2019. It has variable presentation from mild sore throat to severe respiratory distress. It is important to identify individuals who are likely to worsen. The Research question is how to identify patients with COVID-19 who are at high risk and to predict patient outcome based on a risk stratification model? We evaluated 251 patients with COVID-19 in this prospective inception study. We used a multi-variable Cox proportional hazards model to identify the independent prognostic risk factors and created a risk score model on the basis of available MuLBSTA score. The model was validated in an independent group of patients from October2020 to December 2021. We developed a combined risk score, the MuLBA score that included the following values and scores: Multi lobar infiltrates (negative0.254, 2), lymphopenia (lymphocytes of <0.8x109 /L, negative0.18,2), bacterial co- infection (negative, 0.306,3). In our MuLB scoring system, score of >8 was associated with high risk of mortality and <5 was at mild risk of mortality ($P \leq 0.001$). The interpretation was that The MuLB risk score model could help to predict survival in patients with severe COVID-19 infection and to guide further clinical research on risk-based treatment.
## Introduction
The novel corona virus disease 2019 (COVID-19) has affected the public across the globe and continue to be an important and urgent threat to global health. Since the outbreak in early December 2019 in the Hubei province of the People’s Republic of China, the number of patients confirmed to have the disease has exceeded 5,507,376 in more than 160 countries, and the number of people infected is 308,034,154 as of 10-1-22 [1] *Efficient diagnosis* and better predictors of prognosis in COVID-19 are needed to mitigate the burden on the healthcare system [2]. Prediction models comprising of clinical features or laboratory parameters that can estimate the risk of people poor outcome from the COVID-19 infection could assist medical staff to triage patients when allocating limited healthcare resources [3]. A variety of clinical prediction scores for community acquire pneumonia such as CURB-65 and PSI are widely used in the assessment [4], they remain not applicable in the setting of viral infection. There are other reported risk factors such as PO2/FiO2, lymphocyte count, and antigen-specific T cells used for predicting mortality and deciding on appropriate for influenza pneumonia. ( Viasus et al., 2016; Shi et al., 2017). Various models ranging from rule based scoring systems to advanced earning models have been proposed and published that are relevant for COVID-19 infection and have helped to save lives [3–7].
The “Multi-lobar infiltration, hypo lymphocytosis, bacterial co-infection, smoking history, hyper-tension and age score” abbreviated as the MuLBSTA score is one such proposed model which is likely to assist medical professional in arriving at a clinical decision [5]. The researchers Guo L, Wei D et al. was able to demonstrate that the MuLBSTA score could help in predicting the ninety-day mortality in viral pneumonia [6]. MuLBSTA scoring system was proven to be a good model for predicting mortality and risk stratification of the patients in a study by Rong Xu et al. Prognosis scores routinely used for CAP (PSI and CURB-65) were good predictors for mortality in patients with COVID-19 CAP but not for need of hospitalization or ICU admission. Study by Garcia Clement MM. et al. showed that MuLBSTA score was better at predicting the need for ICU admissions than other prognostic scores such as PSI‑PORT and CURB‑65 [7].
This study was aimed at validating MuLBSTA score [3, 4] in predicting 28- day mortality and prognosis in seriously ill COVID-19 patients.
## Aims and objectives
Primary objective of the current study was to identify and validate MuLBSTA score as clinical indicator tool that could predict the prognosis of patients with severe COVID-19 during admission in the study cohort. We also aimed to establish and validate MulBSTA score and to derive modified score if possible.
Secondary objective of the study was to identify other prognostic markers by studying the association of various laboratory parameters and the comorbidities with mortality in severe COVID 19 infection in a tertiary setting.
## Study methodology
This was an analytical cohort study to validate the role of MuLBSTA score in estimating 28-day mortality. The study was commenced after receiving an authorization certificate from both the scientific and ethical committee of Amrita Institute of Medical Sciences and Research Centre, Kochi. Informed written consent was taken from all patients before enrolling them in the study. The study included analysis of the clinical characteristics and laboratory parameters during the time of hospitalization between the survivor and the non-survivor groups. Patients admitted with severe COVID 19 infection between October 2020 and December 2021 were considered in our study. Based on the proportion of greater than or equal to 12 of MuLBSTA score in alive patients ($3.6\%$) and odds ratio of 22.29 observed in an earlier study on “MuLBSTA score in COVID-19 pneumonia and prediction of 14-day mortality risk “by Preetham and et al. [ 5] and with $80\%$ power and $95\%$ confidence the least sample size is fourteen each (survivors as well as non-survivors).
Study definitions: [8] COVID-19 illness was categorized as mild, moderate or severe as per following symptomatology.
Mild COVID-19 Illness: Individuals who have any of the various signs and symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhoea, loss of taste and smell) but who do not have shortness of breath, dyspnoea, or abnormal chest imaging.
Moderate COVID-19 Illness: Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have an oxygen saturation (SpO2) ≥$94\%$ on room air at sea level.
Demographics characteristics, history, clinical presentation laboratory findings, and radiological features were noted. The MuLBSTA score was calculated as showed in S1 Table [5, 6], and the patients were classified into survivors and non-survivor’s group. The MuLBSTA score and its association with mortality at 28 days thus determining its use as a prognostic tool was studied. Individual clinical parameters like Absolute lymphocyte count, Serum Albumin, Serum sodium, Platelet counts, Serum lactate dehydrogenase levels, Serum Ferritin, C reactive protein, D Dimer were taken into account along with the presence of co-morbidities like type 2 *Diabetes mellitus* (DM), chronic liver disease (CLD), chronic kidney disease (CKD), coronary artery disease (CAD), cerebrovascular accident (CVA), obstructive airway disease (OAD), hypothyroidism, obstructive sleep apnea (OSA) and its value as a prognostic marker was studied. The standard cut offs were applied as per the guidelines published by the Government of Kerala in managing the COVID pandemic as of August 2021 [7].
## Statistical analysis
Statistical analysis was done using IBM SPSS 20. ( SPSS Inc, Chicago, USA). For all the continuous variables, the results are given in Mean ± SD, and for categorical variables as percentage. We used the x2 test or Fisher exact test to compare the categorical variables between different groups and the Mann-Whitney U test to compare median differences between the two groups for continuous variables. Uni variate and multivariate Cox regression models served as the main statistical methods for identification of prognostic factors for 28-day mortality. All the variables found to have P value of <0.05 in Uni variate analysis were subjected to multivariate analysis. We used a multi variable logistic regression analysis model to identify independent prognostic risk factors and calculate their HR’s, $95\%$ CI, and beta regression coefficients. To estimate the overall survival time of samples, Kaplan Meier curves was used. The optimal cut-off value for MuLBSTA score was determined with the use of receiver operating characteristic curve analysis, then each continuous parameter was converted into a classification variable. For all analyses, statistical significance was calculated with the use of two-tailed probability values; $P \leq .05$ was considered significant.
## Results
During the study period 26,854 total hospital admissions occurred and among them 2,081 were COVID-19 related admissions. Out of them 251 had severe COIVD-19 infection requiring ICU or HDU admission and formed the study cohort (Fig 1). The mean age of the cohort was 61 ±14.3 years. The study comprised of 169($67.3\%$) men and 82 women ($32.7\%$). The mean overall survival status in days was 19.84 days with a $95\%$ confidence interval of 16.54 and 23.14 days.
**Fig 1:** *Showing flowchart of the study cohort.*
Uni-variate analysis of baseline demographics and co-morbidities among survivors and non survivors are shown in Table 1. Association of Hypothyroidism between the survivors and non-survivors’ groups were found to be statistically significant and noted to be a protective factor (OR: 0.298, CI: 0.108–0.817, p value 0.019) in our study population. Uni-variate analysis of laboratory parameters and its association with survival is shown in Table 2.
The variables which were showing P value <0.005 were subjected to multivariate analysis.
On multivariate analysis of the individual parameters in the scoring system, Multi lobar infiltrate (OR:3.620, CI:1.972–6.643, p-value <0.001), Absolute lymphocyte count (OR:2.684, CI:1.461–4.931, p-value:0.001), and Bacterial co infection (OR:6.531, CI: 2.877–14.825, p-value:<0.001) were found to be independent risk factors associated with mortality as shown in Table 3. Multivariate cox regression analysis after adjusting all the factors included in our study, showed that platelet counts<150000 (HR: 1.989, CI: 1.239–3.194, p-value: 0.004), Serum LDH ≥ 245(HR:1.908, CI: 1.004–3.579, p-value:0.049), and D Dimer ≥1 (HR:1.895, CI: 1.004–3.579, p-value:0.049) were found to be statistically significant predictors of mortality in COVID patients (Table 3).
**Table 3**
| Variable | B | Wald | HR (95% of CI HR) | p value |
| --- | --- | --- | --- | --- |
| Platelet count <150x109/L | 0.688 | 8.111 | 1.989(1.239–3.194) | 0.004 |
| Serum albumin < 3.5 g/dL | 0.418 | 2.693 | 1.519(0.922–2.503) | 0.101 |
| Serum LDH ≥ 245 | 0.646 | 3.891 | 1.908(1.004–3.625) | 0.049 |
| D Dimer ≥ 1 | 0.639 | 3.886 | 1.895(1.004–3.579) | 0.049 |
| Multi-lobar infiltrate (+) | 1.286 | 17.244 | 3.620(1.972–6.643) | <0.001 |
| ALC (≤0.8x 109/L) | 0.987 | 10.129 | 2.684 (1.461–4.931) | <0.001 |
| Bacterial coinfection (+) | 1.877 | 20.131 | 6.531(2.877–14.825) | <0.001 |
| Smoking (+) | 0.342 | 1.21 | 1.408(0.765–2.590) | 0.271 |
| Systemic hypertension (+) | 0.108 | 0.119 | 0.898(0.487–1.656) | 0.730 |
| Age (≥ 60 years) | 0.281 | 0.802 | 1.325(0.716–2.453) | 0.371 |
On multivariate analysis, Multi lobar infiltrate (OR:3.620, CI:1.972–6.643, p-value <0.001), Absolute lymphocyte count (OR:2.684, CI:1.461–4.931, p-value:0.001), and Bacterial co-infection (OR:6.531, CI: 2.877–14.825, p-value:<0.001) were found to be independent risk factors associated with mortality.
The area under the receiver operating characteristic curve (ROC) of MuLBSTA for predicting mortality at the time of admission was 0.799 (SE 0.028) as shown in Fig 2. The ROC analysis in our study revealed a cut off value for MuLBSTA score to be 8, with a sensitivity of $74.29\%$ and specificity of $73.97\%$ (Fig 2). The association between MuLBSTA score ≥ 8 (OR:8.211, CI: 4.630–14.560, p value <0.001) and mortality was found to be statistically significant with p value of <0.001. MuLBSTA score≥8 had a Positive Predictive value of $67.24\%$, Negative Predictive value of $80\%$ with an Accuracy of $74.10\%$ in our study.
**Fig 2:** *ROC of MuLBSTA score and mortality.*
The median (Q1, Q3) value of MuLBSTA score among survivors was 5 (2–8), and the median (Q1, Q3) value of MuLBSTA score among non-survivors was 11(7–15). The P value was found to be statistically significant (<0.001) as shown in Fig 3.
**Fig 3:** *MuLBSTA score among groups.*
Out of the 116 patients with MuLBSTA score ≥8, 78 expired ($67.2\%$) whereas, out of the 135 patients with MuLBSTA score < 8, 27 expired ($20\%$) during the study period. Kaplan Meier curve was plotted for MuLBSTA score of >8 and <8 days as shown in Fig 4. The median overall survival of patients with MuLBSTA score ≥ 8 was 14 days and in patients with < 8 was 22 days with $95\%$ CI of 12.387 and 15.613 days (P value of <0.001).
**Fig 4:** *Kaplan-Meier curve of comparison of MuLBSTA score of <8 with >8.*
## Validation of MuLBSTA score
For validation and deriving modified score linear regression analysis was done and the results are shown in Table 4. The score was modified as per beta coefficient and new scoring system was derived. The new MuLB score of 8 was associated with high risk of poor survival (S2 Table).
**Table 4**
| Variable | HR(95% CI) | P Value | Beta coefficient by current study | Existing score for MuLBSTA | New proposed scores |
| --- | --- | --- | --- | --- | --- |
| Multi lobar infiltrate | 4.3(0.07–0.02)- | <0.001 | -0.254 | 5 | 3 |
| Absolute Lymphocyte count <0.8x109/L | 3.29(0.300–0.075) | <0.001 | -0.187 | 4 | 2 |
| Bacterial Infection | 5.45(0.132–0.062) | <0.001 | -0.306 | 4 | 3 |
| Smoking history | 0.646(0.059–0.030) | 0.519 | -0.036 | 3 | 0 |
| Systemic hypertension | 0.129(0.101–0.116 | 0.897 | -0.007 | 2 | 0 |
| Age >60 years | 1.16(0.087–0.022) | 0.247 | -0.065 | 2 | 1 |
## Discussion
COVID-19 virus has thrown up a multitude of challenges since its first detection. One of the foremost challenges has been the identification of at-risk individuals and appropriate targeting of resources for effective management so as to limit the accompanying mortality. Even though the condition has many similarities with other viral pneumonia’s, we are yet to identify a definite tool to ascertain the disease’s outcome immediately upon infection. It may be noted that the association of various risk factors assists in better prediction of the worse outcomes [8].
Out of t 251 patients, 169 were men and 82 women with a mean age group of 61 years and the male gender was found to be a risk factor associated with mortality. It is well known fact that male gender is an independent risk factor of mortality as found in many studies earlier [8, 9].
Although, the virus infects people of all ages alike, poorer outcomes have been noted in older age groups, and especially those persons above sixty years of age. However, in our study age more than sixty years was not associated with mortality. Patients with smoking history were more prone to die and this must probably be because tobacco smoking likely alters the expression of “angiotensin converting enzyme receptors” making it more likely to be associated with mortality [10]. It is also pertinent to mention that the virus infected individuals are more likely to contract secondary bacterial or fungal co infections resulting in many individuals succumbing to the viral illness [11].
Most patients in our study with multi lobar infiltrates on the chest X-ray and/or lymphopenia during the time of admission had worse outcomes; the finding similar to previously published study [12].
It has been proven by various studies that DM, CKD, CLD, CAD and cancer are comorbidities associated with poor outcome in COVID-19 infection [13–22]. In current study, CLD (OR:3.836, CI:1.529–9.624, p value 0.004), CKD (OR:6.185, CI:2.410–15.875, p value <0.001), Cancer (OR:3.332, CI:1.380–8.049, p value 0.007), were found to be independent risk factors associated with mortality while the association of Systemic hypertension(OR:1.058(0.864–3.305), Type 2 DM (OR:1.379, CI:0.831–2.289, p-value:0.213), CAD (OR:1.488, CI:0.680–8.059, p value 0.240), OAD(OR:1.022, CI:0.449–2.325, p value 0.959), and CVA(OR:1.151, CI: 0.459–2.884, p value 0.765) were not found to be statistically significant. We do not have any explanation for the above finding other than that the present study included seriously ill patients with more severe disease and DM was just one of the variables.
Previous studies have shown that there is no impact of Hypothyroidism on COVID 19 mortality [23]. However, in the present study, hypothyroidism was found to be a protective factor (OR: 0.298, CI: 0.108–0.817, p value 0.019) against mortality. The results should be taken with caution as only 26 patients had hypothyroidism and more studies are needed to confirm our finding.
The laboratory parameters that were obtained at the time of admission were analyzed and it was observed that Serum Albumin <3.5g/dl (OR:7.611, CI:4.259–13.601, p value <0.001), CRP > 100 mg/mL (OR:3.819, CI:1.996–7.310, p value <0.001), LDH ≥245 units/Litre (OR:6.746, CI:3.364–13.525, p value <0.001), Serum Ferritin≥300 ng/mL (OR:8.397, CI:4.507–15.643, p value <0.001), D Dimer ≥1 mcg/mL (OR:12.176, CI:6.521–22.736, p value <0.001), Serum sodium<135 mmol/L (OR:3.378, CI:1.993–5.726, p value <0.001), Platelet count<150x 109/L (OR:5.013, CI:2.748–9.142, p value <0.001) were found to be independent risk factor associated with mortality. On multi-variable cox regression analysis after adjusting for all the factors that were obtained during the time of admission in our study, platelet counts<150109/L (HR: 1.989, CI: 1.239–3.194, p-value: 0.004), Serum LDH ≥ 245 units/Litre (HR:1.908, CI: 1.004–3.579, p-value:0.049), and D Dimer ≥1 mcg/mL (HR:1.895, CI: 1.004–3.579, p-value:0.049) were found to be statistically significant predictors of mortality in patients with COVID 19 infection. The finding was similar to other studies [24–33]. Hypoalbuminemia, elevated CRP, and elevated d-dimer and Ferritin are associated with adverse outcome among COVID-19 as previously shown in other studies [2, 24–34]. Many of the aforementioned factors have been taken into account in the MuLBSTA scoring system [34, 35]. The overall survival among the patients with a MuLBSTA score more than eight was significantly less in the current study.
MuLBSTA scoring system was proven to be a good model for predicting mortality and risk stratification of the patients in a study by Rong Xu et al. [ 36] Prognosis scores routinely used for CAP (PSI and CURB-65) were good predictors for mortality in patients with COVID-19 CAP but not for need of hospitalization or ICU admission. Study by Garcia Clement MM. et al. showed that MuLBSTA score was better at predicting the need for ICU admissions than other prognostic scores such as PSI‑PORT and CURB‑65 [35–37].
We also did validation of MuLBSTA score in the study cohort and derived the new score for each variable as shown in S2 Table. As per the new MuLB score, the score of 8 is associated with poor survival. Unlike the single-risk indicator found in previous studies, our scoring system combined a variety of clinical and laboratory indicators and comprehensively and accurately predicted patient’s survival. Out of the 116 patients with MuLBSTA score ≥8, 78 expired ($67.2\%$) whereas, out of the 135 patients with MuLBSTA score < 8, 27 expired ($20\%$) during the study period. However, the new score needs validation by other studies to determine the reproducibility of the results.
## Strength and limitations
The strength of our study was that we were able to include two hundred fifty-one patients which attributed to a good sample size.
The limitations of our study are that it was single center study and the intrinsic bias associated while collecting some data retrospectively could not be avoided. The MuLBSTA score was used to predict ninety-day mortality in all the previous studies and our study was limited to following up patients only for a period of 28 days. The laboratory values of patients who developed the viral infection as a part of hospital acquired infection was taken during the date of positivity and not at the time of admission. The new MuLB score is very promising but it needs further validation by other studies.
## Conclusion
Our study validated the MuLBSTA score with ROC analysis and score of ≥ 8 was dependable tool to assess 28-day mortality in seriously ill COVID-19 positive patients. We also derived the new MuLB score which was better score than existing score to identify the high risk COVID-19 patients. It was also noted that high levels of C-reactive protein, Serum Ferritin, Serum Lactate dehydrogenase, D Dimer and low levels of serum albumin, sodium, platelet and absolute lymphocyte counts were associated with increased mortality. Co -morbidities such as CKD, CLD and cancer were also associated with poor outcome. It may hence be concluded that such a tool may help primary care medical doctors in reallocating patients to centers with better care and facilities and as a consequent thereof even aid in containing the disease and its mortality rates.
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|
---
title: Contribution of genetic factors to high rates of neonatal hyperbilirubinaemia
on the Thailand-Myanmar border
authors:
- Germana Bancone
- Gornpan Gornsawun
- Pimnara Peerawaranun
- Penporn Penpitchaporn
- Moo Kho Paw
- Day Day Poe
- December Win
- Naw Cicelia
- Mavuto Mukaka
- Laypaw Archasuksan
- Laurence Thielemans
- Francois Nosten
- Nicholas J. White
- Rose McGready
- Verena I. Carrara
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021142
doi: 10.1371/journal.pgph.0000475
license: CC BY 4.0
---
# Contribution of genetic factors to high rates of neonatal hyperbilirubinaemia on the Thailand-Myanmar border
## Abstract
Very high unconjugated bilirubin plasma concentrations in neonates (neonatal hyperbilirubinaemia; NH) may cause neurologic damage (kernicterus). Both increased red blood cell turn-over and immaturity of hepatic glucuronidation contribute to neonatal hyperbilirubinaemia. The incidence of NH requiring phototherapy during the first week of life on the Thailand-Myanmar border is high (approximately $25\%$). On the Thailand-Myanmar border we investigated the contribution of genetic risk factors to high bilirubin levels in the first month of life in 1596 neonates enrolled in a prospective observational birth cohort study. Lower gestational age (<38 weeks), mutations in the genes encoding glucose-6-phosphate dehydrogenase (G6PD) and uridine 5′-diphospho-glucuronosyltransferase (UGT) 1A1 were identified as the main independent risk factors for NH in the first week, and for prolonged jaundice in the first month of life. Population attributable risks (PAR%) were $61.7\%$ for lower gestational age, $22.9\%$ for hemi or homozygous and $9.9\%$ for heterozygous G6PD deficiency respectively, and $6.3\%$ for UGT1A1*6 homozygosity. In neonates with an estimated gestational age ≥ 38 weeks, G6PD mutations contributed PARs of $38.1\%$ and $23.6\%$ for “early” (≤ 48 hours) and “late” (49–168 hours) NH respectively. For late NH, the PAR for UGT1A1*6 homozygosity was $7.7\%$. Maternal excess weight was also a significant risk factor for “early” NH while maternal mutations on the beta-globin gene, prolonged rupture of membranes, large haematomas and neonatal sepsis were risk factors for “late” NH. For prolonged jaundice during the first month of life, G6PD mutations and UGT1A1*6 mutation, together with lower gestational age at birth and presence of haematoma were significant risk factors. In this population, genetic factors contribute considerably to the high risk of NH. Diagnostic tools to identify G6PD deficiency at birth would facilitate early recognition of high risk cases.
## Introduction
Neonatal hyperbilirubinaemia (NH) is common. Although it is usually benign and resolves in the first week of life without treatment, sustained very high plasma concentrations of unconjugated bilirubin are neurotoxic and cause kernicterus [1]. Morbidity and mortality from severe NH occurs predominantly in resource-limited settings as a result of delays in diagnosis and treatment [2]. Permanent sequelae to the nervous system of surviving neonates cause substantial morbidity to the affected individual and difficulties for their family. Most low-income countries have little or no infrastructure for social and medical support of affected children [3]. Identifying newborns at risk of severe NH is important therefore to permit preventive steps.
Genetic factors predisposing to haemolysis or reduced bilirubin conjugation predispose to NH [4]. X-linked glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common human enzymopathy, with an allelic frequencies averaging 8–$10\%$ in tropical areas, but in some populations reaching over $30\%$ [5]. G6PD deficiency is expressed completely in the red cells of hemizygote males and homozygote females but, because of Lyonisation, heterozygotes have a range of phenotypic expression between deficient and normal. The increased risk of NH in G6PD deficient neonates probably results from the shortened erythrocyte lifespan, sometimes exacerbated by exposure to oxidising agents. Over 200 mutations causing reduced enzymatic activity have been described [6], affecting over 400 million people worldwide. Mahidol (487G>A), Viangchan (871G>A), Union (1360C>T), Canton (1376G>T) and Kaiping (1388G>A) are the most common variants found in the Greater Mekong Subregion [7]. These variants are historically classified as moderate to severe and can be associated with severe acute haemolysis upon exposure to oxidants.
The uridine diphosphate glucuronosyltransferase (UGT) enzymes are a superfamily of conjugating enzymes. UGT1A1 is the sole enzyme responsible for the metabolism of bilirubin. Reduced activity is associated with neonatal unconjugated hyperbilirubinemia, Gilbert’s syndrome, and both type I and type II Crigler-Najjar syndromes. Several mutations cause reduced activity in the UGT1A1 protein. In the promoter region, the UGT1A1*28 and UGT1A1*37 alleles have 7 and 8 repetitions of the (TA) box respectively which impair efficient transcription, resulting in >$70\%$ reduction in gene transcription [8–10]. In the coding region, the UGT1A1*6 allele (Arg71Gly; 211G>A; rs4148323) results in a critical reduction in enzymatic activity in both homozygotes ($32\%$ of normal) and heterozygotes ($60\%$ of normal [11]). The prevalence of UGT1A1*28 is around $30\%$ in Caucasians, between $40\%$ and $56\%$ in African Americans, and less than $15\%$ in Asian populations [12]. UGT1A1*6 has been found mostly in Asian population where its allele frequency ranges from $13\%$ to $23\%$ [13].
Haemoglobinopathies are also potential risk factors for NH, notably in neonates born from mothers carrying sickle cell [14], or thalassaemia genetic polymorphisms.
On the Thailand-Myanmar border NH requiring phototherapy is common. G6PD deficiency was identified as a major contributory factor a decade ago [15]. A further prospective birth cohort from the same site (ClinicalTrials.gov Identifier: NCT02361788) described the epidemiology of NH and confirmed increased the risk in G6PD deficient neonates [16]. The current analysis of the same cohort assessed the relative contributions of genetic traits including G6PD and UGT1A1 mutations and maternal abnormal haemoglobins to NH.
## Study
This prospective observational birth cohort study was conducted on the Thailand-Myanmar border in three SMRU clinics between January 2015 and May 2016 (ClinicalTrials.gov Identifier: NCT02361788). SMRU clinics serve a refugee and migrant population mainly comprising subjects of Sgaw Karen, Burman and Poe Karen ethnicities. Antenatal care (ANC) is provided free of charge. Estimation of gestation by ultrasound is routine, as are laboratory analyses including regular assessments of haematocrit concentration and malaria smear. In addition, a maternal complete blood count at the first ANC visit was performed together with a G6PD qualitative test and haemoglobin typing [17] during the study period. All live born neonates with estimated gestational age (EGA) ≥ 28 weeks were included if they were seen within 48 hours of life, or if they presented with jaundice within their first week of life. Clinical examination and laboratory tests were scheduled at defined time-points (see later) during the first week of life and weekly until one month of age [18]. Mothers were encouraged to bring their jaundiced or unwell neonates to the clinics any time in-between appointments for examination and treatment.
Total serum bilirubin (TSB) levels were used to define NH using EGA and neonatal age-adjusted treatment thresholds for phototherapy which followed NICE guidelines [19], e.g. newborns with EGA ≥38 weeks and a TSB of 260umol/L at 48 hours of life, would be diagnosed with NH and treated with phototherapy. Two types of bulbs for phototherapy were available: Phillips TL$\frac{20}{52}$ blue light bulbs of 400 to 500 nm wavelength and LED bulbs (peak wavelength 455 nm). The blue-light bulbs were either inserted into a wooden or a metallic framed cot; LED bulbs units were mobile and set directly above the baby cot. Phototherapy units delivered recommended minimal irradiance levels of 8–10 μW/cm2/nm; the distance between the light and the cot was adjustable in order to obtain, if necessary, intensive phototherapy (≥30 μW/cm2/nm, [20]). The first phototherapy units were set up in the clinics in 2009 [15] and by the time of this study they were well accepted by the mothers who could sleep near the cot, breastfeed, and care for their newborn.
For the analysis in neonates with EGA≥38 weeks, NH diagnosed within the first 48 hours of life was categorised as ‘early NH’, while NH occurring between 48-168h of life was defined as ‘late NH’. The 48h cut-off was based on the median duration stay in the postnatal ward following an uncomplicated delivery in this setting. NH was defined as severe if at least one TSB was on or above the NICE-defined exchange transfusion threshold. Care for each newborn with severe NH was based on clinical assessment and TSB trajectories after diagnosis; it also included discussion with the local Thai hospital (located approximately 1-hour drive from SMRU clinics) where exchange transfusion was available. By protocol, follow up measurements included TSB, haematocrit, and daily weight for 3 days and at day 7 on all newborns. Follow-up was deemed ‘complete’ if a minimum of three TSB measurements were available: I) one before or at 30h hours of life, II) a second ≤36h after the first, and III) a third between 5 and 7 days of life.
Neonates were then assessed weekly until one month of age. Each visit included a clinical examination, weighing and visual assessment of jaundice. Clinically apparent jaundice assessed at any follow-up visit in neonates older than 14 days was defined as prolonged jaundice. Onsite TSB levels were checked at each visit while direct and indirect bilirubin concentrations were measured at weeks 3 or 4.
## Laboratory evaluations
G6PD status was assessed initially on cord blood by the qualitative fluorescent spot test (FST, R&D Diagnostic, Greece). ABO and Rhesus blood grouping was performed using the agglutination method with anti-A, anti-B and anti-D sera (Plasmatec, UK). TSB and haematocrit measurements were performed in centrifuged capillary heel prick samples (3 min centrifugation at 10,000 rotations per minute). Haematocrit was estimated using a Hawksley micro-haematocrit reader. The sample were then used to assess total serum bilirubin photometrically using the Bilimeter2 or Bilimeter3 micro-bilirubinometers (Pfaff Medical GmbH, Germany). During the follow-up visits after three weeks of life, when clinically indicated, serum direct and indirect bilirubin measurements were assessed biochemically at an external accredited laboratory.
At the central haematology laboratory, newborns’ DNA was extracted using column kits (Favorgen Biotech Corp., Taiwan) from 200 μL of cord blood. G6PD genotyping for Mahidol (487G>A), the most common local variant, was performed on all samples; genotyping for the other 4 local G6PD variants, Union (1360C>T), Canton (1376G>T), Kaiping (1388G>A) and Chinese-4 (392G>T) was performed only on FST-deficient samples; established protocols were used [21, 22]. Since over $90\%$ of G6PD mutations in this population are Mahidol variant, for the statistical analyses all detected mutations were pooled; for the analyses of risk, hemizygote and homozygote genotypes were pooled. Genotyping for UGT1A1*6 (211G>A) and for TA repeats in the UGT1A1 gene promoter (UGT1A1*28, UGT1A1*26, UGT1A1*37) was adapted from published protocols and summarized in S1 Table. For the statistical analyses of risk, heterozygote and homozygote UGT1A1*28 genotypes were pooled together.
Haemoglobin typing of the mother was carried out by Capillary Electrophoresis using a Capillarys II (Sebia, France) on blood collected at the first ANC visit. Capillary Electrophoresis allows for diagnosis of Hb structural variants such as HbE, HbC, HbS (by appearance of retention peaks at specific elution times), presumed beta-thalassaemia carriage (by increased percentage of HbA2), and presumptive diagnosis of alpha-thalassaemia trait (by decreased percentage of HbA2). For the statistical analysis, women were classified based on the likely expected haematologic picture associated with the globin variant; normal women were grouped with carriers of presumptive alpha-thalassemia trait or HbE trait in the “Non-clinically significant haemoglobinopathies” group. Homozygous HbEE and women with beta-thalassaemia trait, and HbE/beta-thalassaemia were pooled in the “Haemoglobinopathies” group.
## Statistical analysis
The prospective observational cohort study that was used for analyses included 1,710 neonates. In order to evaluate the contributions of G6PD and UGT1A1 genotypes, and maternal abnormal haemoglobin types to the risk of NH in the first week and in prolonged jaundice, the analysis included variables related to the mother, the obstetric history, the neonate and the perinatal period previously identified in the same cohort [16]. These were maternal age, literacy, smoking, gravida, overweight (body mass index ≥27.5 mg/kg2 within 2 weeks of delivery [23]), pre-eclampsia or eclampsia for the mother; prolonged rupture of membranes, oxytocin infusion, delayed cord clamping for the obstetric history, gestational age, resuscitation, presence of haematoma, ethnicity, sex, size for gestational age, siblings with history of jaundice, use of naphthalene for storing clothes, G6PD deficiency by FST, potential ABO incompatibility (i.e. mother with blood group O and neonate with either A, B or AB), positive Coombs test for the neonate; and severe infection, weight loss >$7\%$, haematocrit level and polycythaemia for the clinical events within the first 24 hours of life.
For the neonates’ genotypes, allelic frequencies (p) were calculated as the total number of mutated alleles observed as a proportion of the total analysed; for G6PD mutations, males provide 1 allele per person and females provide 2. $95\%$ CI were calculated as 1.96 multiplied by the square root of [p (1-p)]/N where N was the total number of alleles analysed, where 1.96 is the standard normal z-value corresponding to the $95\%$ CI. Allelic frequencies were compared between ethnic groups using the Chi squared test. Neonates’ ethnicity (Sgaw Karen, Poe Karen, Burman, “Burmese Muslim” and others) was based on self-reported ethnicity of both parents and grandparents. People of Islamic faith self-identified as “Burmese Muslim” [17]. Ethnicity was reported as “mixed” when parents’ ethnicity differed.
A mixed effects Cox proportional hazard model that accounted for clustering by site was used to analyse risk factors for NH in the first week of life. Accounting for clustering was important because members of the same cluster (site) tend to have more correlated outcomes compared to members of a different cluster (site). Failure to account for these correlations tends to bias p-values downwards thereby increasing type I error. The hazard ratios (HRs) and the corresponding $95\%$ CIs from this model have been presented. Harrell’s C statistic was used for Cox regression model discrimination. Because neonates born earlier have an increased risk of NH and NICE guidelines for starting treatment propose lower thresholds with each gestational week below 38 weeks, analysis of “early” and “late” NH was carried out only on newborns with EGA≥38 weeks who would normally be discharged from clinics around two days of life. In order to assess the impact of the risk factors on neonatal hyperbilirubinaemia the Population Attributable risk (PAR) percentages have been used. The PAR percentages were calculated for all significant risk factors of the multivariable analysis as: PAR% = [prevalence of exposed x (AHR-1)] / (1 + [prevalence of exposed x (AHR-1)]) x 100. The $95\%$CIs of PAR% were calculated using the same formula whereby AHR is replaced by the lower and upper $95\%$ CI limits of AHR.
A mixed effects logistic model that accounted for clustering by site was used to analyse the risk of prolonged jaundice. The odds ratios (ORs) and the corresponding $95\%$ CIs from this model are presented. The PARs for the odds ratios were also calculated using the same formula as that for AHR using AOR instead of AHR.
A mixed effects negative binomial model that took into account clustering by site was used to analyse the duration of prolonged jaundice. The incidence rate ratios (IRRs) and the corresponding $95\%$ CIs from this model are reported. A mixed effects linear regression clustering by site was used to analyse interactions between G6PD and UGT1A1 genotypes on TSB levels. The slope and the corresponding $95\%$ CIs from this model are reported. Comparison of total and indirect levels of bilirubin at week 3 among different genotypes was analysed by ANOVA. All tests of significance were performed at $5\%$ level. Data were analysed using SPSS version 27 and Stata MP version 16.
## Ethics approval
The study was approved by Oxford Tropical Research Ethics Committee, UK (OxTREC 41–144), the Mahidol University Faculty of Tropical Medicine Ethical Committee, Thailand (TMEC 14–012) and the Tak Province Border Community Ethics Advisory Board (TCAB-08-13). Written informed consent was obtained from literate parents or guardians of the neonates; a thumbprint was obtained in the presence of a literate witness for illiterate parents.
## Results
The full cohort included 1,710 neonates (890 males and 820 females); a small percentage ($1.2\%$, $$n = 20$$) were twins and were excluded from the genetic analysis because related. Twins were also excluded from the risk analysis because they are often born smaller and earlier independently from their genetic background or other clinical factors. Of the remaining 1,690 neonates, there were 120 that could not be genotyped so a total of 1,570 neonates were analysed for distribution of genetic variants among ethnic groups. The study flow is represented in Fig 1. Among the 1,690, 420 with incomplete TSB follow-up were excluded from the risk factors analysis of NH. The remaining 1,270 neonates were assessed for NH, $96.5\%$ (1,$\frac{225}{1}$,270) of whom had an available genotype for G6PD and/or UGT1A1. Sub-analysis of early NH (within 48h of birth) and late NH (after 48h) in the first week of life was performed in a total of 1,124 neonates born with EGA≥ 38 weeks (1,087 with a genotype; 562 males and 525 females). Prolonged jaundiced was analysed in 1,596 newborns with at least one follow-up visit of the full cohort. Analysis of the duration of prolonged jaundice was performed on 1222 neonates with full follow-up until 35 days of life.
**Fig 1:** *Study flow.*
In total $24.3\%$ ($\frac{309}{1}$,270) neonates developed NH in the first week of life. Among the neonates with EGA≥38 weeks, $5.4\%$ ($\frac{61}{1}$,124) developed NH early (within 48 hours) and $11.4\%$ ($\frac{128}{1}$,124) developed NH late (49–168 hours). Among neonates with at least one follow-up until 35 days of life, $38.5\%$ ($\frac{615}{1}$,596) had prolonged jaundice.
## G6PD and UGT1A1 genotypes
Among the 802 males genotyped for G6PD mutations, 97 ($12.1\%$) were hemizygotes (91 Mahidol, 4 Canton and 2 Union mutations) and 7 other males were deficient by G6PD testing but none of the tested mutations were found. Among the 767 females genotyped, 10 ($1.3\%$) were homozygotes and 156 ($20.3\%$) were heterozygotes for the Mahidol mutation, and 1 had a deficient phenotype but no mutation was identified. The overall allelic frequency of all characterised G6PD deficient mutations was $11.6\%$.
Among the 1,570 neonates genotyped for the UGT1A1*6 allele, 47 were homozygotes and 440 were heterozygotes. The overall allelic frequency was $17.0\%$. Among the neonates genotyped for the UGT1A1 promoter [1,246], the allelic frequency of the TA7 repeat (UGT1A1*28) was $12.3\%$; there were 251 heterozygotes and 28 homozygotes. No TA8 repeat (UGT1A1*37) was observed in the population.
Results of genotyping by ethnic group are shown in Table 1 and Fig 2. There was a distinct association of genotypes with ethnic groups. G6PD deficient mutations were more common among newborns of Karen ethnicity ($13.5\%$) as compared to Burman ($9.2\%$, $$P \leq 0.011$$). The allelic frequency of the UGT1A1*6 mutation was significantly higher in Sgaw Karen ($21.0\%$) as compared to Burmans ($12.4\%$, $P \leq 0.001$) and was twice as high as in “Burmese Muslims” ($8.4\%$, $P \leq 0.001$). Poe Karen ($16.2\%$) also had a significantly higher allelic frequency of the of the UGT1A1*6 mutation compared to “Burmese Muslims” ($P \leq 0.020$). Allelic frequency of UGT1A1*28 had the opposite distribution, with a significantly higher frequency in “Burmese Muslims” ($28.1\%$) and Burmans ($16.2\%$) as compared to Sgaw Karen ($8.8\%$, $P \leq 0.001$) and Poe Karen ($9.2\%$, $P \leq 0.001$ for “Burmese Muslims” and $$P \leq 0.025$$ for Burmans).
**Fig 2:** *Allelic frequencies of analysed genetic traits by ethnicity.Bars indicate $95\%$CI.* TABLE_PLACEHOLDER:Table 1
## Analysis of risk factors for NH in the first week of life
Independent of maternal, obstetric and neonatal risk factors, G6PD deficiency hemizygotes or homozygotes had an adjusted Hazard Ratio (AHR) of 4.78 ($95\%$CI:3.35–6.84; $P \leq 0.001$) for developing NH in the first week of life compared to G6PD wild type genotypes (Table 2, and S2 Table). This confirmed the results obtained previously with the G6PD FST phenotypic screening test in the same cohort [16]. In addition, females heterozygous for G6PD deficient alleles had an AHR of 2.09 ($95\%$CI: 1.41–3.12; $P \leq 0.001$) for developing NH in the first week of life. Nearly all G6PD heterozygous neonates ($\frac{121}{123}$) had a “normal” phenotype assessed by the FST, and would therefore not be considered at risk of NH if a qualitative screening test only had been used. The overall PARs for G6PD hemi/homozygotes and heterozygotes compared to G6PD wild type genotype were $22.9\%$ and $9.9\%$ respectively.
**Table 2**
| Characteristics | Univariable analysis | Univariable analysis.1 | Multivariable analysisa | Multivariable analysisa.1 | PAR |
| --- | --- | --- | --- | --- | --- |
| Characteristics | HR (95% CI) | p-value | HR (95% CI) | p-value | % (95% CI) |
| Newborn genotyping | | | | | |
| G6PD (any mutation) | | | | | |
| WT | Reference | | Reference | | |
| Heterozygote | 1.76 (1.25, 2.47) | 0.001 | 2.09 (1.41, 3.12) | <0.001 | 9.9 (4.0–17.6)b |
| Hemi + Homozygote | 3.58 (2.63, 4.86) | <0.001 | 4.78 (3.35, 6.84) | <0.001 | 22.9 (15.6–31.4)b |
| UGT1A1*6 | | | | | |
| WT | Reference | | Reference | | |
| Heterozygote | 1.18 (0.91, 1.51) | 0.206 | 1.24 (0.92, 1.66) | 0.151 | |
| Homozygote | 2.50 (1.55, 4.01) | <0.001 | 3.22 (1.94, 5.37) | <0.001 | 6.3 (2.8–11.7) |
| UGT1A1*28 | | | | | |
| WT (TA6/6) | Reference | | Reference | | |
| Hetero and homozygote (TA6/7+ TA7/7) | 0.67 (0.48, 0.95) | 0.022 | 0.77 (0.53, 1.11) | 0.160 | |
| Maternal Characteristics | | | | | |
| Primigravida (Primipara) | 1.81 (1.45, 2.27) | <0.001 | 1.71 (1.30, 2.25) | <0.001 | 19.6 (9.3–30.0) |
| Obstetric characteristics | | | | | |
| Rupture of membranes ≥ 18h | 1.76 (1.21, 2.57) | 0.003 | 2.30 (1.50, 3.53) | <0.001 | 7.9 (3.2–14.3) |
| Neonatal Characteristics | | | | | |
| Gestational age (<38 weeks) | 12.6 (10.0, 15.8) | <0.001 | 15.0 (11.3, 20.0) | <0.001 | 61.7 (54.2–68.6) |
| Presence of haematoma | 2.24 (1.45, 3.46) | <0.001 | 1.98 (1.18, 3.32) | 0.010 | 3.8 (0.7–8.5) |
| Sgaw Karen ethnicity | 1.36 (1.04, 1.77) | 0.025 | 1.29 (0.95, 1.74) | 0.104 | |
| Potential ABO incompatibility | 1.32 (0.98, 1.76) | 0.068 | 1.30 (0.93, 1.82) | 0.131 | |
UGT1A1*6 homozygotes had an AHR of 3.22 ($95\%$CI:1.94–5.37; $P \leq 0.001$) contributing a PAR of $6.3\%$ for NH, but for heterozygotes the risk was not significantly increased; AHR 1.24 ($95\%$CI:0.92–1.66; $$P \leq 0.151$$). Those with the UGT1A1*28 allele had a non-significant reduced AHR of 0.77 ($95\%$CI:0.53–1.11;) when pooling heterozygous and homozygous genotypes compared to wild type genotype. Harrell’s C statistic for model discrimination indicated that $81.8\%$ of NH in the first week of life was explained by the analysed factors in the multivariable analysis.
## Severe NH
A total of 20 severe cases of neonatal jaundice in this cohort of 1,710 neonates have been described previously [16]. The current analysis of genotypes showed that a boy born at home who was seen at day 2, had fever, clinical signs of sepsis and died shortly afterwards, was hemizygous for Canton mutation (he tested G6PD deficient by FST). In two neonates who received exchange transfusion, one was a UGT1A1*6 heterozygous and G6PD**Mahidol hemizygous* boy and the other was a UGT1A1*6 heterozygous and G6PD**Mahidol heterozygous* girl.
Among the 1,270 neonates with full follow-up in the first week of life analysed here, 15 reached TSB levels above the exchange transfusion threshold; 5 in the group with EGA<38 weeks and 10 in the group with EGA≥38 weeks. Among the neonates with EGA≥38 weeks reaching the severe threshold, 3 out 5 males were G6PD*Mahidol hemizygotes (and tested deficient by FST at birth) and 3 out 5 females were G6PD*Mahidol heterozygote (and tested normal by FST); 6 neonates were heterozygote for the UGT1A1*6 allele. Overall, $\frac{9}{10}$ neonates with EGA≥38 weeks in the group of severe NH had at least one mutation in either the G6PD or UGT1A1 genes, but only 3 had been diagnosed earlier as having a risk factor. One female term neonate who was heterozygous for G6PD*Mahidol allele had clinical signs of sepsis and severe NH at the day 7 visit (TSB = 1,072 μmol/L) and was referred for exchange transfusion at the local Mae Sot Hospital. Despite receiving 3 exchange transfusions, she died the same day. She was diagnosed with possible ABO incompatibility.
## Analysis of risk factors of “early” and “late” NH in neonates with EGA≥38 weeks
A risk analysis for early and late NH was performed only on 1,124 neonates born with EGA≥38 weeks. Of those, $5.4\%$ ($\frac{61}{1}$,124) developed NH early (≤48 hours), and $11.4\%$ developed NH ($\frac{128}{1}$,124) late (49–168 hours), the remaining $83.2\%$ ($\frac{935}{1}$,124) did not develop NH. Their characteristics by group are presented in S3 Table.
## Risk factors for early NH
Primigravida and the mother being overweight were independently associated with a 2-fold increased risk of early NH while delayed cord clamping had a protective effect (Table 3 and S4 Table). All mutated G6PD genotypes were associated significantly with an increased risk of developing NH in the first 48 hours of life; G6PD heterozygotes had more than twice the risk of developing early NH as compared to wild type (AHR = 2.61, $95\%$CI: 1.18–5.77; $$P \leq 0.018$$), while G6PD hemi and homozygous neonates had >9-fold risk as compared to wild type (AHR = 9.18, $95\%$CI: 4.79–17.59; $P \leq 0.001$). Combined PAR ($95\%$CI) for mutated G6PD genotypes against wild type genotype was 38.1 (22.1–54.4) %. Mutations in the UGT1A1 gene had no impact on the development of early NH.
**Table 3**
| Characteristics | Univariable analysis | Univariable analysis.1 | Multivariable analysisa | Multivariable analysisa.1 | PAR |
| --- | --- | --- | --- | --- | --- |
| Characteristics | HR (95% CI) | p-value | HR (95% CI) | p-value | % (95% CI) |
| Newborn genotyping | | | | | |
| G6PD (any mutation) | | | | | |
| WT | Reference | | Reference | | |
| Heterozygote | 2.27 (1.05, 4.91) | 0.038 | 2.61 (1.18, 5.77) | 0.018 | 13.1 (1.7–30.8)b |
| Hemi + Homozygote | 8.75 (4.93, 15.54) | <0.001 | 9.18 (4.79, 17.59) | <0.001 | 34.8 (19.8–52.0)b |
| UGT1A1*6 | | | | | |
| WT | Reference | | Reference | | |
| Heterozygote | 0.92 (0.51, 1.66) | 0.781 | 1.17 (0.64, 2.17) | 0.608 | |
| Homozygote | 2.49 (0.77, 8.03) | 0.127 | 1.47 (0.43, 5.10) | 0.540 | |
| UGT1A1*28 | | | | | |
| WT (TA6/6) | Reference | | | | |
| Hetero and homozygote (TA6/7+ TA7/7) | 1.17 (0.63, 2.16) | 0.620 | | | |
| Maternal Characteristics | | | | | |
| Primigravida (Primipara) | 1.74 (1.05, 2.88) | 0.032 | 2.06 (1.10, 3.88) | 0.024 | 26.4 (3.3–49.3) |
| Overweight | 2.35 (1.41, 3.95) | 0.001 | 2.15 (1.20, 3.88) | 0.011 | 21.6 (4.6–40.9) |
| Obstetric characteristics | | | | | |
| Delayed cord clamping | 0.36 (0.20, 0.64) | <0.001 | 0.38 (0.17, 0.84) | 0.018 | |
## Risk factors for late NH
Maternal haemoglobinopathies, prolonged rupture of membranes, the presence of haematoma at birth, and neonatal sepsis in the first 24 hour of life were all independently associated with an increased risk of late NH (Table 4 and S5 Table). Mutations in the G6PD gene were associated with significant risk of late NH (AHR = 2.16, $95\%$ CI: 1.23–3.78; $$P \leq 0.007$$ for heterozygotes and AHR = 4.40, $95\%$ CI: 2.54–7.71; $P \leq 0.001$, for hemi and homozygotes) with combined PAR% ($95\%$CI) of 23.6 (12.6–36.1). Homozygotes for UGT1A1*6 had an almost 4-fold increase in risk of late NH (AHR = 3.77, $95\%$CI: 2.01–7.08; $P \leq 0.01$- PAR = $7.7\%$) while UGT1A1*28 in the promoter had a protective effect compared to G6PD wild type genotype (AHR = 0.40, $95\%$CI: 0.20–0.81; $$P \leq 0.011$$). When risk factors were compared in neonates developing NH between 72 hours and one week of life and those who did not develop NH in the first week of life (S6 Table), the UGT1A1*6 homozygotes had an even higher AHR of 7.78 ($95\%$CI: 3.68–16.47; $P \leq 0.001$).
**Table 4**
| Characteristics | Univariable analysis | Univariable analysis.1 | Multivariable analysisa: Model A | Multivariable analysisa: Model A.1 | PAR |
| --- | --- | --- | --- | --- | --- |
| Characteristics | HR (95% CI) | p-value | HR (95% CI) | p-value | % (95% CI) |
| Newborn genotyping | | | | | |
| G6PD (any mutation) | | | | | |
| WT | Reference | | Reference | | |
| Heterozygote | 2.07 (1.26, 3.41) | 0.004 | 2.16 (1.23, 3.78) | 0.007 | 10.2 (2.2–21.5)b |
| Hemi + Homozygote | 3.59 (2.20, 5.85) | <0.001 | 4.40 (2.51, 7.71) | <0.001 | 17.7 (8.7–29.8)b |
| UGT1A1*6 | | | | | |
| WT | Reference | | Reference | | |
| Heterozygote | 1.51 (1.03, 2.23) | 0.036 | 1.34 (0.87, 2.06) | 0.179 | |
| Homozygote | 4.46 (2.45, 8.11) | <0.001 | 3.77 (2.01, 7.08) | <0.001 | 7.7 (3.0–15.5)b |
| UGT1A1*28 | | | | | |
| WT (TA6/6) | Reference | | Reference | | |
| Hetero and homozygote (TA6/7+ TA7/7) | 0.34 (0.18, 0.65) | 0.001 | 0.40 (0.20, 0.81) | 0.011 | |
| Maternal Characteristics | | | | | |
| Haemoglobinopathies | 1.81 (1.10, 2.99) | 0.020 | 1.88 (1.09, 3.26) | 0.024 | 6.6 (0.7–15.3) |
| Obstetric characteristics | | | | | |
| Rupture of membranes ≥ 18h | 1.63 (0.90, 2.97) | 0.107 | 2.02 (1.07, 3.82) | 0.030 | 5.9 (0.4–14.7) |
| Neonatal Characteristics | | | | | |
| Presence of haematoma | 2.81 (1.52, 5.22) | 0.001 | 2.04 (1.01, 4.11) | 0.047 | 3.8 (0.0–10.5) |
| Clinical events | | | | | |
| Severe infection 0-24h | 2.05 (1.08, 3.92) | 0.029 | 2.29 (1.12, 4.67) | 0.023 | 5.4 (0.5–14.0) |
## Analysis of TSB levels
A comparison of TSB levels at 24h (±4h), 48h(±4h), 72h(±4h) and 168h(±4h) follow-up in neonates with EGA≥38 weeks who did not need phototherapy, or before they had received it, is shown in Fig 3 and S7 Table. While the physiologic increase in TSB levels between 24h and 48h was slightly more pronounced in neonates with G6PD mutations, TSB levels after 48 hours increased substantially in G6PD wild type neonates with homozygote UGT1A1*allele. Neonates with EGA≥38 weeks who are G6PD normal are usually considered low risk and tend to be discharged early from the clinic. There was no interaction between G6PD mutated and UGT1A1*6 homozygous genotypes on TSB levels over time (slope of regression ($95\%$CI): -8.8 (-38.7, 21.0); $$P \leq 0.56$$) although the number of neonates with both conditions was very small (4, 3, 2 and 1 G6PD mutated and UGT1A1*6 homozygotes per time point, S7 Table).
**Fig 3:** *Total serum bilirubin concentrations in neonates with EGA≥38 weeks: UGT1A1*6 genotype (homozygote vs non-homozygote) in G6PD hemi-homo/heterozygote group and wild type group.TSB = Total Serum Bilirubin; Boxes represent inter quartile ranges; middle horizontal lines are medians Red areas represent TSB values at which phototherapy (light red) and exchange transfusion (darker red) should be provided based on neonates’ age (NICE guideline). One data point (TSB = 1072μmol/L) at 168h in the right panel among UGT1A1*6 WT-heterozygote is not shown.*
## Analysis of risks factors for prolonged jaundice in the first month of life
A total of 1,596 neonates (185 with EGA<38 weeks and 1,411 with EGA≥38 weeks) with at least one follow-up visit after the first week of life were included in the analyses.
Overall, over a third ($\frac{615}{1}$,596, $38.5\%$) of the neonates followed up between 14 days and one month of life had visible jaundice at one follow-up visit; neonates with lower EGA (<38 weeks) were more likely to develop prolonged jaundice compared to neonates with EGA≥38 weeks ($\frac{107}{185}$ vs $\frac{507}{1}$,411, $p \leq 0.001$).
A large proportion of neonates with prolonged jaundice ($64.7\%$, $\frac{397}{614}$) did not have NH in the first week of life. Of the neonates who had NH in the first week of life, $60.6\%$ ($\frac{217}{358}$) had also prolonged jaundice.
Most neonates were exclusively breastfed, both among those who had prolonged jaundice ($\frac{94}{107}$, $87.9\%$ in EGA<38 weeks and $\frac{497}{507}$, $98.0\%$ in EGA≥38 weeks) and those who did not have it ($\frac{73}{78}$, $93.6\%$ in EGA<38 weeks and $\frac{890}{904}$, $98.5\%$ in EGA≥38 weeks).
Among jaundiced neonates, further clinical and laboratory investigations were done for 80 ($74.8\%$) neonates with EGA <38 weeks and 359 ($70.8\%$) neonates with EGA≥38 weeks. None of the neonates with prolonged jaundice were diagnosed with intra or extrahepatic disease.
EGA <38 weeks and presence of haematoma at birth were the only clinical factors which were significantly associated with an increased risk of developing prolonged jaundice (Table 5). Neonates with G6PD hemi and homozygous genotypes had more than 2-fold increased risk ($95\%$CI:1.3–3.2, $$p \leq 0.002$$) of having prolonged jaundice in their first month of life and those carriers of UGT1A1*6 both in heterozygosity and homozygosity had a risk of about 1.6 ($95\%$CI:1.2–2.0, $$p \leq 0.001$$) and 3.6 ($95\%$CI:1.8–6.9, $p \leq 0.001$) respectively. The proportion of newborns with prolonged jaundice at each follow-up visit according to G6PD and UGT1A1 genotypes is shown in S9 Table.
**Table 5**
| Characteristics | Neonates with prolonged Jaundice, n (%) (N = 615) | Neonates without prolonged Jaundice, n (%) (N = 981) | Univariable analysis | Univariable analysis.1 | Multivariable analysis a | Multivariable analysis a.1 | PAR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Characteristics | Neonates with prolonged Jaundice, n (%) (N = 615) | Neonates without prolonged Jaundice, n (%) (N = 981) | OR (95% CI) | p-value | OR (95% CI) | p-value | % (95% CI) |
| Newborn genotyping | | | | | | | |
| G6PD (any mutation) b | (N = 573) | (N = 914) | | | | | |
| WT | 465 (81) | 772 (84) | Reference | | Reference | | |
| Heterozygote | 56 (10) | 92 (10) | 1.00 (0.69, 1.44) | 0.998 | 1.00 (0.69, 1.46) | 0.982 | |
| Hemi + Homozygote | 52 (9) | 50 (6) | 1.93 (1.27, 2.95) | 0.002 | 2.06 (1.31, 3.24) | 0.002 | 6.8 (2.1–13.4) |
| UGT1A1*6 b | (N = 555) | (N = 914) | | | | | |
| WT | 356 (62) | 665 (73) | Reference | | Reference | | |
| Heterozygote | 187 (33) | 233 (25) | 1.61 (1.26, 2.05) | <0.001 | 1.57 (1.22, 2.02) | 0.001 | 14.0 (5.9–22.6) |
| Homozygote | 29 (5) | 16 (2) | 3.50 (1.84, 6.67) | <0.001 | 3.57 (1.84, 6.91) | <0.001 | 7.4 (2.5–15.5) |
| UGT1A1*28 | (N = 489) | (N = 713) | | | | | |
| WT (TA6/6) | 366 (75) | 567 (80) | Reference | | | | |
| Hetero and homozygote (TA6/7+ TA7/7) | 123 (25) | 146 (20) | 1.23 (0.92, 1.63) | 0.156 | | | |
| Maternal Characteristics | | | | | | | |
| Young maternal age (≤20 y) | 191 (31) | 245 (25) | 1.30 (1.03, 1.63) | 0.028 | 1.24 (0.96, 1.59) | 0.097 | |
| Illiterate (cannot read) | 200 (33) | 363 (37) | 0.87 (0.70, 1.08) | 0.214 | | | |
| Smoking | 53 (9) | 90 (9) | 0.93 (0.65, 1.35) | 0.718 | | | |
| Primigravida (Primipara) | 233 (38) | 321 (33) | 1.23 (0.99, 1.53) | 0.059 | | | |
| Overweight | 146/596 (25) | 237/967 (25) | 1.09 (0.85, 1.39) | 0.505 | | | |
| Pre-eclampsia or eclampsia | 15 (2) | 24 (2) | 1.08 (0.55, 2.11) | 0.833 | | | |
| Haemoglobinopathies | 42 (7) | 80 (8) | 0.88 (0.59, 1.31) | 0.535 | | | |
| Obstetric characteristics | | | | | | | |
| Rupture of membranes ≥ 18h | 45/564 (8) | 61/900 (7) | 1.08 (0.72, 1.64) | 0.698 | | | |
| Oxytocin infusion | 70/606 (12) | 108/958 (11) | 1.07 (0.77, 1.49) | 0.679 | | | |
| Delayed cord clamping | 494 (80) | 775 (79) | 1.03 (0.79, 1.33) | 0.836 | | | |
| Neonatal Characteristics | | | | | | | |
| Gestational age <38 weeks | 108 (18) | 77 (8) | 2.77 (2.00, 3.84) | <0.001 | 2.79 (1.95, 3.98) | <0.001 | 17.2 (9.9–25.7) |
| ≥38 weeks | 507 (82) | 904 (92) | Reference | | Reference | | |
| Resuscitation | 17/598 (3) | 30/942 (3) | 0.87 (0.47, 1.61) | 0.649 | | | |
| Presence of haematoma | 31/607 (5) | 28/ 968 (3) | 1.85 (1.08, 3.19) | 0.026 | 1.95 (1.10, 3.44) | 0.022 | 3.4 (0.4–8.3) |
| Sgaw Karen ethnicity | 249/599 (42) | 363/955 (38) | 1.33 (1.04, 1.70) | 0.021 | 1.20 (0.92, 1.56) | 0.187 | |
| Male sex | 354 (58) | 467 (48) | 1.50 (1.21, 1.85) | <0.001 | * | | |
| Small for gestational age | 119/610 (20) | 190/975 (20) | 0.96 (0.74, 1.25) | 0.754 | | | |
| Sibling with history of jaundice | 68 (11) | 107 (11) | 1.01 (0.73, 1.41) | 0.939 | | | |
| Use of naphthalene for storing the clothes | 33 (5) | 51 (5) | 0.88 (0.55, 1.40) | 0.590 | | | |
| G6PD deficiency (by FST) | 538 (9) | 57 (6) | 1.98 (1.33, 2.95) | 0.001 | * | | |
| Potential ABO incompatibility | 94 (15) | 146 (15) | 1.03 (0.77, 1.38) | 0.843 | | | |
| Positive Coombs test | 14/543 (3) | 32/869 (4) | 0.62 (0.32, 1.20) | 0.156 | | | |
| Clinical events | | | | | | | |
| Severe infection 0-24h | 30 (5) | 38 (4) | 1.34 (0.81, 2.23) | 0.258 | | | |
| Weight loss ≥7% at 24h [12-30h] of life | 13/610 (2) | 20/974 (2) | 0.87 (0.42, 1.80) | 0.714 | | | |
| HCT at 24h [12-30h] of life | mean (SD)58.1 (6.8) | mean (SD)57.8 (7.2) | 1.69 (0.31, 9.12)(Per 10-unit increment) | 0.540 | | | |
| Polycythaemia (HCT > = 70%) at 24 [12-30h] of life | 45 (7) | 73 (7) | 1.17 (0.78, 1.76) | 0.443 | | | |
Of the 1,596 neonates, the majority ($76.6\%$, 1,222) had a full follow-up with three completed visits at week 2, week 3 and one month of age. Among those with a full follow-up, 761 ($62.3\%$) never had visible yellow skin. The majority of neonates with prolonged jaundice were visibly jaundiced during the first 3 weeks ($\frac{231}{462}$) and one month ($\frac{166}{462}$) of life; only a small minority were jaundiced only at week 2 ($\frac{65}{462}$). Analysis of risk factors for duration of prolonged jaundice (Table 6) identified gestational age and positive Coomb’s test as significant factors in addition to the heterozygous and homozygous UGT1A1*6 genotypes (Incidence rate ratio[$95\%$CI] of 1.4 [1.1–1.7], $$p \leq 0.001$$ and 2.4[1.5–3.9], $p \leq 0.001$ respectively) and G6PD deficiency (hemi and homozygous genotypes, IRR[$95\%$CI] = 1.6[1.2–2.3], $$p \leq 0.003$$).
**Table 6**
| Characteristics | Univariable analysis | Univariable analysis.1 | Multivariable analysis a | Multivariable analysis a.1 |
| --- | --- | --- | --- | --- |
| Characteristics | IRR (95% CI) | p-value | IRR (95% CI) | p-value |
| Newborn genotyping | | | | |
| G6PD (any mutation) b | | | | |
| WT | Reference | | Reference | |
| Heterozygote | 0.99 (0.73, 1.33) | 0.929 | 0.95 (0.70, 1.30) | 0.766 |
| Hemi + Homozygote | 1.49 (1.09, 2.04) | 0.012 | 1.63 (1.18, 2.26) | 0.003 |
| UGT1A1*6 b | | | | |
| WT | Reference | | Reference | |
| Heterozygote | 1.39 (1.15, 1.69) | 0.001 | 1.40 (1.14, 1.71) | 0.001 |
| Homozygote | 2.22 (1.42, 3.47) | <0.001 | 2.42 (1.51, 3.86) | <0.001 |
| UGT1A1*28 | | | | |
| WT (TA6/6) | Reference | | | |
| Hetero and homozygote (TA6/7+ TA7/7) | 1.13 (0.90, 1.42) | 0.295 | | |
| Neonatal Characteristics | | | | |
| Gestational age | | | | |
| <38 weeks | 1.86 (1.47, 2.35) | <0.001 | 1.96 (1.52, 2.54) | <0.001 |
| ≥38 weeks | Reference | | Reference | |
| Sgaw Karen ethnicity | 1.32 (1.09, 1.61) | 0.005 | 1.22 (0.99, 1.51) | 0.065 |
| Positive Coombs test | 0.63 (0.37, 1.06) | 0.083 | 0.58 (0.34, 0.99) | 0.044 |
Total and indirect bilirubin levels were analysed in neonates who had visible yellow skin discoloration at the 3-weeks follow-up visit. Bilirubin analysis results and genotype were available in $87.5\%$ ($\frac{386}{441}$) of jaundiced neonates at that visit. Mean [SD] total and indirect bilirubin levels were 156.2[58.2] μmol/L and 138.8 [56.1] μmol/L in neonates with EGA <38 weeks with similar values in neonates with EGA>38 weeks; 144.6 [61.3] μmol/L and 128.4 [60.0] μmol/L respectively. Indirect bilirubin levels (i.e. unconjugated bilirubin) were similar among neonates with different G6PD genotypes but were significantly higher in 132 neonates with UGT1A1*6 heterozygous genotype (143.6 [60.9] μmol/L) and 23 neonates with homozygous genotype (182.5 [62.4] μmol/L) as compared with the 230 neonates with UGT1A1 wild type genotype (117.3 [53.8] μmol/L; $P \leq 0.001$). No differences were observed among neonates with different genotypes in the UGT1A1 promoter.
## Discussion
In this population living along the Thailand-Myanmar border low EGA was the main risk factor for NH in the first week of life (PAR[$95\%$CI] = 61.7 [54.2–68.6]%); Among neonates with EGA≥38 weeks, the analysed genetic risk factors had a combined PAR ($95\%$CI) of 38.1 (22.1–54.4) % for early NH and 34.9 (21.9–47.8) % for late NH. Currently available tests in most low-resource settings do not identify all neonates at risk, especially term neonates who are often discharged around two days of life. Identification of risk factors at birth for a “late” increase in bilirubinaemia levels is particularly important because these neonates may not be able to access required medical care or may access it too late.
G6PD deficiency was strongly associated with both early and late NH. Hemizygotes and homozygotes had adjusted risks (AHR) of more than 9 for early and more than 4 for late NH. G6PD heterozygotes also had an increased risk of more than twice that in the remaining population [25–27]. G6PD *Mahidol is* the main genotype accounting for nearly $90\%$ of G6PD deficiency. The allele frequency varies among the different ethnic groups but averages approximately $10\%$. Currently available qualitative point-of-care tests have moderate sensitivity for identifying deficient newborns ($8.7\%$ of deficient newborns missed) and cannot identify females with intermediate phenotypes [28]. In this series, $98\%$ ($\frac{121}{123}$) of G6PD heterozygous neonates were classified as phenotypically normal using the rapid FST screening test and could not be diagnosed as being at increased risk during post-natal care.
In this population, mutations in the bilirubin conjugating enzyme UGT1A1 were described for the first time. UGT1A1*6 allele was common (prevalence ranging from $12\%$ to $21\%$ in the major ethnic groups) and was associated specifically with late NH, which developed more commonly than early NH in term neonates. UGT1A1*6 homozygotes had a 3-fold increased risk of NH in the first week of life, in particular after 2–3 days of life. Increased risk of NH in UGT1A1*6 was first reported in Japan over 20 years ago [29] and has been observed elsewhere in East Asian countries (Taiwan [30]; Malaysia [31]; Thailand [32]), although in certain contexts the increased risk in neonates with the mutation was only observed in association with large neonatal body weight loss [33]. NH which develops after discharge from birth centres in newborns with higher EGA and no obvious risk factors represents a clinical challenge in low resource settings and, in particular, in migrant populations where access and medical follow-up cannot be provided easily [34]. Levels of TSB observed in neonates homozygous for UGT1A1*6 at 48h (i.e. roughly around the time of discharge) were only slightly elevated as compared to UGT1A1 wild type and would not have justified extended observation. Since reduced activity of UGT1A1 cannot be identified by a simple laboratory test, a genotyping test for the UGT1A1*6 mutation in either the expectant mother or the neonates at birth, may be warranted especially in the ethnic groups with the high allele frequencies. Parental education about signs of NH after discharge from hospital remains of paramount importance and is feasible in low-resource settings; while neonates with undiagnosed UGT1A1*6 mutations might be numerically few, their individual risk is high.
Increased number of TA repeats in the promoter (UGT1A1*28; a common cause of Gilbert’s syndrome) was found in $12\%$ of the newborns and was associated with lower risk of NH. Two meta-analyses conducted in 2015 and 2020 [35, 36] showed a large variability in risk of NH for each variant across the 34 included studies. Overall, the UGT1A1*6 allele was associated with a larger risk as compared to allele *28 (the latter found mostly in African populations). Studies in East Asia where both alleles were analysed in the same newborn population ((Malaysia [31]; Vietnam [37]; Taiwan [30] and China [38]) provided very similar results to those observed here. In these studies, UGT1A1*6 allele had a higher population frequency as compared to allele UGT1A1*28 and was associated with increased risk of NH as opposed to allele UGT1A1*28. This suggests different contributions to increased risk by different mutations on the UGT1A1 gene according to their prevalence.
A novel result was the impact of beta-thalassemia trait and HbE of the mother on the increased risk (AHR = 1.88, $95\%$CI 1.09–3.26) of developing late NH (after 48 hours of life). Hypothesising increased neonatal anaemia in these cases, we analysed haematocrits at 24 hours of life (S9 Table) but no differences in infants’ haematocrit values were observed.
Prolonged jaundice was common and mostly uncomplicated in this population of mainly breastfed neonates, an association described extensively in the literature since the 1960s [39]. There are different recommendations regarding the required investigations in case of prolonged neonatal jaundice [40, 41] in order to exclude potential treatable causes (including sepsis, urine tract infection, hypothyroidism, metabolic and liver disease- mainly congenital biliary atresia). In this study, in addition to lower EGA, mutations in the G6PD and UGT1A1 genes were the major risk factors for prolonged jaundice; total and especially indirect bilirubin levels were significantly elevated in neonates with the UGT1A1*6 allele. Commonly seen “breast milk jaundice” might indeed have a genetic component due to this trait [42].
The current analysis has some limitations. *Other* genetic traits not analysed here presumably contributed to increased risk. Other UGT1A1 variants associated to increased risk in Asian population [43] have not been investigated here. Variation in expression of the HMOX gene which encodes for heme oxygenase, the enzyme responsible for transformation of heme into biliverdin, has been shown to be associated with increased risk of NH [38]. Analysis of the gene promoter in the local adult Karen and Burman population, showed a high degree of polymorphism [44]. Mutations on the alpha-globin genes were not analysed in this cohort but are common in the population (around $25\%$ carrier; [17]) and might play a role on the onset of NH [45, 46].
## Future perspectives
In conclusion, the high risk of NH in the first week of life in this cohort of Karen and Burman neonates was mainly a result of lower EGA. This has many aetiologies but, in low resource settings, infections are an important and preventable cause [47]. For neonates with EGA≥38 weeks, some actionable risk factors were identified such as excess maternal weight gain [48], prolonged rupture of membranes, trauma at birth and neonatal sepsis. The analysis showed that delayed cord clamping, which is an inexpensive practice associated with multiple benefits for the newborn, also reduces risk of NH in this population with multiple risk factors [49]. Genetic risk factors, common in this population, play a large role in neonatal jaundice, including the severe forms, as seen in other low-resource settings [50]. Improved diagnostics are urgently needed and different screening strategies should be considered in populations with a high prevalence of these traits. Genotyping of expectant mothers might prove cost-effective in settings where high-throughput techniques are widely available. For example, this would be useful for ruling-out heterozygosity for UGT1A1*6 allele in the mother (necessary for homozygosity in newborn) or planning for extended monitoring of bilirubin levels after birth. In rural and low-resources settings, in lack of simple and cheap genetic tests for UGT1A1 (such as a LAMP-based PCR test), continued neonatal bilirubin monitoring (where possible) and education on signs of NH remain the only feasible approaches. For G6PD deficiency, easy to use quantitative point-of-care tests (in place of qualitative tests) able to identify both deficient and intermediate phenotypes would represent a cost-effective tool to provide appropriate care in male and female neonates at risk. One such test has been recently evaluated with good results in this setting among newborns (Bancone, in preparation) and few more are in the late stage of development.
## Transfer Alert
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|
---
title: 'Overweight and obesity in south central Uganda: A population-based study'
authors:
- Adeoluwa Ayoola
- Robert Ssekubugu
- Mary Kathryn Grabowski
- Joseph Ssekasanvu
- Godfrey Kigozi
- Aishat Mustapha
- Steven J. Reynolds
- Anna Mia Ekstrom
- Helena Nordenstedt
- Rocio Enriquez
- Ronald H. Gray
- Maria J. Wawer
- Joseph Kagaayi
- Wendy S. Post
- Larry W. Chang
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021145
doi: 10.1371/journal.pgph.0001051
license: CC BY 4.0
---
# Overweight and obesity in south central Uganda: A population-based study
## Abstract
Obesity is a rapidly growing global health challenge, but there are few population-level studies from non-urban settings in sub-Saharan Africa. We evaluated the prevalence of overweight (body mass index (BMI)>25 kg/m2), obesity (BMI>30 kg/m2), and associated factors using data from May 2018 to November 2020 from the Rakai Community Cohort Study, a population-based cohort of residents aged 15 to 49 living in forty-one fishing, trading, and agrarian communities in South Central Uganda. Modified Poisson regression was used to estimate adjusted prevalence risk ratios (PRR) and $95\%$ confidence intervals (CI) in 18,079 participants. The overall mean BMI was 22.9 kg/m2. Mean BMI was 21.5 kg/m2 and 24.1 kg/m2 for males and females, respectively. The prevalence of overweight and obesity were $22.8\%$ and $6.2\%$, respectively. Females had a higher probability of overweight/obesity (PRR: 4.11, CI: 2.98–5.68) than males. For female participants, increasing age, higher socioeconomic status, residing in a trading or fishing community (PRR: 1.25, CI 1.16–1.35 and PRR: 1.17, CI 1.10–1.25, respectively), being currently or previously married (PRR: 1.22, CI 1.07–1.40 and PRR: 1.16, CI 1.01–1.34, respectively), working in a bar/restaurant (PRR: 1.29, CI 1.17–1.45), trading/shopkeeping (PRR: 1.38, CI 1.29–1.48), and reporting alcohol use in the last year (PRR: 1.21, CI 1.10–1.33) were risk factors for overweight/obese. For male participants, increasing age, higher socioeconomic status, being currently married (PRR: 1.94, CI 1.50–2.50), residing in a fishing community (PRR: 1.68, CI 1.40–2.02), working in a bar/restaurant (PRR: 2.20, CI 1.10–4.40), trading/shopkeeping (PRR: 1.75, CI 1.45–2.11), or fishing (PRR: 1.32, CI 1.03–1.69) increased the probability of overweight/obesity. Non-Muslim participants, male smokers, and HIV-positive females had a lower probability of overweight/obese. The prevalence of overweight/obesity in non-urban *Ugandans is* substantial. Targeted interventions to high-risk subgroups in this population are needed.
## Introduction
Obesity is a rapidly growing global health challenge with grave health consequences [1–3]. Potential health complications include hypertension, diabetes, cardiovascular disease, and major cancers [4]. In Uganda, overweight and obesity rates have increased [5, 6], with one study showing a doubling of overweight/obesity rates between 1995 and 2011, from $8\%$ to $18\%$ [7, 8].
The growing prevalence of overweight and obesity in low- and middle- income countries has been associated with increased urbanization, access to high-caloric diets, and lower physical activity [3, 9, 10]. Predictors of risk of obesity in Sub-Saharan Africa from past literature include female sex [5, 6, 11, 12], living in urban areas [3, 5, 7], older age [5], higher education [3], higher income [3, 7], and being married [3]. In rural communities of Eastern Uganda, similar factors were associated with overweight/obesity [13]. One Ugandan study found a higher prevalence of abdominal fat in women, married or cohabiting people, and urban dwellers [7].
Most prior studies of overweight and obesity had small sample sizes, were not population-based, and did not assess certain sociodemographic factors needed for designing appropriate public health interventions, especially in rural communities, as the prevalence of cardiovascular disease increases in sub-Saharan Africa [14]. This study aimed to determine the prevalence of overweight and obesity in South Central Uganda and associated sociodemographic factors for being overweight or obese to facilitate policies and programs to address this issue in Uganda.
## Sample inclusion and criteria
The study used cross-sectional data from the Rakai Community Cohort Study (RCCS), collected from May 2018 to November 2020. RCCS is an open population-based, longitudinal cohort study that began initially in 1994 to study and address the growing HIV/AIDS epidemic in Rakai, Uganda. A full description of the RCCS design and data collection procedures has been described in previous literature [15, 16]. Briefly, RCCS holds informational community mobilization events and periodic censuses in the Rakai district, during which all households from the study communities are approached by the study team for recruitment. Eligible cohort participants are identified and enrolled. RCCS collects key health data of adult residents (aged 15 to 49 years) who provide written informed consent, using periodic interviews to assess demographics, sexual and health-seeking behaviors, and uptake of HIV prevention and treatment services. Pregnant women [992] and participants with incomplete data [62] were excluded from this analysis.
## Calculation of body mass index
Height was measured in centimeters on a flat surface, with participant shoes removed, using a stadiometer. Weight was measured in kilograms using a portable weighing scale (SECA scale, model 762 1019008) on a flat surface, with participants in light clothing and without shoes. BMI was calculated as weight divided by height squared (kg/m2) using the World Health Organization (WHO) defined standards of underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5 kg/m2–24.9 kg/m2), overweight (BMI 25.0 kg/m2–29.9 kg/m2) and obese (BMI ≥30.0 kg/m2), respectively [17].
## Covariates
Community type included agrarian, trading, and fishing communities [15]. Occupations for individual participants were categorized into the five groups according to the most common occupations: agriculture/housework, bar and restaurant work, fishing, trading/shopkeeping, and other (i.e., government/clerical/teaching, student, medical worker, military/police, hairdresser/salon owners, mechanics). Education level was categorized as none, primary, and secondary or above. Socioeconomic status (SES) was calculated using a household asset-based measure [18]. Marital status was categorized into “Never married,” “Currently married,” and “Previously married” (i.e., separated, divorced, or widowed). Past pregnancy was assessed with the question “Have you ever been pregnant (including current pregnancy)?”. HIV serostatus was determined using a rapid antigen test algorithm with confirmatory immunoassay testing [19]. Current antiretroviral therapy (ART) use was assessed using a list of ART medications.
Current smoking status (i.e., cigarettes, tobacco, or pipe smoking) was assessed, and alcohol use was determined in the past year, stratified by recency of use. Participants self-reported religion as Muslim or non-Muslim (i.e., None, Catholic, Protestant, Saved/Pentecostal, or Other).
## Analysis
Data analysis was performed using STATA/BE (version 17.0 2021, StataCorp LLC, College Station, Texas). The prevalence of overweight/obesity and obesity was calculated as the percentage of individuals with BMI ≥25 kg/m2 and BMI ≥30kg/m2, respectively. Regression analysis was performed separately for males and females. The prevalence of overweight and/or obesity was the primary outcome. Secondary outcomes included the prevalence of underweight and prevalence of obesity alone. Covariates, selected based on prior literature, included age, community type, SES, occupation, education level, marital status, religion, current smoking, HIV status, current ART, current and last alcohol use. Past pregnancy was a covariate for females.
The prevalence of overweight, obesity, and underweight were calculated as a percentage of the study population. To evaluate factors associated with underweight, overweight and obesity, we used a modified Poisson model with a log link function to estimate prevalence risk ratios (PRR) [20]. To estimate the association between sex and obesity, a combined model with both males and females was also fitted. This combined model estimated a coefficient for the sex and included interactions for significant covariates that differed between the final male and female stratified models, controlling for other covariates (see S1 File). Regression models estimated unadjusted PRRs and $95\%$ confidence intervals (CIs). Covariates with p-value <0.05 were included in the adjusted models. Multicollinearity in the final adjusted models was tested using the variable inflation factor (VIF). All VIFs were below 5, indicating acceptably low levels of collinearity. P-values <0.05 were considered statistically significant.
## Ethics statement
This study was approved by the Research and Ethics Committee of the Uganda Virus Research Institute (UVRI: GC/$\frac{127}{08}$/$\frac{12}{137}$), the Ugandan National Council for Science and Technology (UNCST HS 540), and the Johns Hopkins University School of Medicine Institutional Review Board (IRB00217467). Written informed consent was obtained from all adult participants and emancipated participants less than 18 years of age. For unemancipated participants less than 18 years of age, written informed consent was obtained from their parent/guardian and written informed assent was obtained from them.
## Participant characteristics
Table 1 presents the characteristics of the 18,062 participants included in the study. Fifty-one percent were female, the mean age was 30.1 years (standard deviation (SD): 0.07 years, range 15–49). About a third ($31\%$) of participants were ages 20–29 years, while $21.5\%$ were age 40–49 years old. Forty-six percent were from agrarian, $33\%$ from trading, and $21\%$ percent from fishing communities. The largest proportion of participants came from low SES households ($31.8\%$), were agricultural or houseworkers ($38.5\%$), currently married ($53.5\%$), had a primary education ($58.0\%$), and identified as non-Muslim ($86.3\%$). Most participants were never smokers ($92.5\%$) and HIV seronegative ($82.6\%$). Most had not used alcohol in over a year ($56.1\%$) and most of the women reported a past pregnancy ($80.3\%$).
**Table 1**
| CHARACTERISTIC | COUNT (PERCENT) |
| --- | --- |
| SEX | |
| MALE | 8,767 (48.5) |
| FEMALE | 9,295 (51.5) |
| AGE GROUP, YEARS | |
| 15–19 | 3,410 (18.9) |
| 20–29 | 5,535 (30.6) |
| 30–39 | 5,239 (29.0) |
| 40–49 | 3,878 (21.5) |
| MEAN +/- SD | 30.12 +/- 0.073 |
| COMMUNITY TYPE | |
| AGRARIAN | 8,314 (46.0) |
| FISHING | 3,826 (21.2) |
| TRADING | 5,922 (32.8) |
| SOCIOECONOMIC STATUS | |
| LOWEST | 5,744 (31.8) |
| LOW-MIDDLE | 4,236 (23.4) |
| HIGH-MIDDLE | 3,876 (21.5) |
| HIGHEST | 4,214 (23.3) |
| OCCUPATION | |
| AGRIC\HOUSEWORK | 6,945 (38.5) |
| BAR\RESTAURANT | 664 (3.7) |
| FISHING | 1,266 (7.0) |
| TRADE\SHOPKEEPER | 2,786 (15.4) |
| OTHER | 6,401 (35.4) |
| MARITAL STATUS | |
| NEVER | 5,255 (29.1) |
| CURRENTLY MARRIED | 9,668 (53.5) |
| PREVIOUSLY MARRIED | 3,139 (17.4) |
| EDUCATION | |
| NONE | 1,256 (6.95) |
| PRIMARY | 10,479 (58.0) |
| SECONDARY AND ABOVE | 6,327 (35.0) |
| RELIGION | |
| MUSLIM | 2,469 (13.7) |
| NON-MUSLIM | 15,593 (86.3) |
| PAST PREGNANCY | |
| YES | 7,463 (80.3) |
| NO | 1,832 (19.7) |
| TOTAL | 9295 |
| CURRENT SMOKER | |
| NO | 16,704 (92.5) |
| YES | 1,358 (7.5) |
| HIV SEROSTATUS | |
| NEGATIVE | 14,911 (82.6) |
| POSITIVE | 3,151 (17.4) |
| CURRENT ART USE | |
| YES | 2,654 (84.2) |
| NO | 497 (15.8) |
| TOTAL | 3151 |
| DRINKS ALCOHOL | |
| NO | 10,125 (56.1) |
| YES | 7,937 (43.9) |
| LAST ALCOHOL USE | |
| MORE THAN 12 MONTHS | 10,125 (56.1) |
| WITHIN THE LAST 12 MONTHS | 1,273 (7.0) |
| 1–4 WEEKS | 1,751 (9.7) |
| 0 DAYS–1 WEEK | 4,913 (27.2) |
| BMI | |
| UNDERWEIGHT | 1,625 (9.0) |
| NORMAL WEIGHT | 12,329 (68.3) |
| OVERWEIGHT | 2,984 (16.5) |
| OBESE | 1,123 (6.2) |
| TOTAL | 18,062 (100.0) |
## Prevalence of overweight and obesity
The overall mean BMI was 22.8 kg/m2 (SD 4.1 kg/m2). The mean for males was 21.5 kg/m2 (SD 2.9 kg/m2), while the mean for females was 24.1 kg/m2 (SD 4.6 kg/m2). Overall, $9\%$ of study participants were underweight, $68\%$ were normal weight, approximately $17\%$ were overweight, and $6\%$ were obese (Table 1). Therefore, $23\%$ of all participants were overweight or obese. Tables 2 and 3 present counts and percentage distributions of participants in each BMI category by sociodemographic characteristics. Approximately $24\%$ of females were overweight and $11\%$ were obese while approximately $9\%$ and $1\%$ of males were overweight and obese, respectively (Tables 2 and 3). The combined prevalence of overweight and obesity increased from $14\%$ in 15–19-year-old females to $42\%$ and $47\%$ in females aged 30–39 and 40–49 years, respectively. For males, the prevalence of overweight and obesity increased from $2\%$ in 15–19-year-old males to $15\%$ and $14\%$ in males aged 30–39 and 40–49 years, respectively.
## Factors associated with the probability of being overweight/obese and obese
All results are conditional on the other covariates in the model. Females had a higher probability of being overweight/obese (classified as BMI≥ 25 kg/m2) or obese, compared to males. Specifically, males had a $76\%$ lower probability of being overweight/obese (adjusted PRR: 0.24, CI: 0.18, 0.34) and $86\%$ lower probability of being obese (adjusted PRR: 0.14, CI: 0.18, 0.34) compared to females (Table 4). Males had a $75\%$ higher probability of being underweight (adjusted PRR: 1.75; CI: 1.56, 1.96) compared to females (Table 4).
**Table 4**
| BMI Category | Count (%) | Unadjusted PRR (95% CI) | Adjusted PRR (95% CI) | P-value |
| --- | --- | --- | --- | --- |
| Underweight | | | | |
| Females (Ref.) | 682 (6.5) | -- | -- | -- |
| Males | 1,025 (11.7) | 1.80 [1.64, 1.98] | 1.75, [1.57, 1.97] | <0.001 |
| Overweight | | | | |
| Females (Ref.) | 2,216 (23.8) | -- | -- | -- |
| Males | 773 (8.8) | 0.28 [0.27, 0.30] | 0.24, [0.18, 0.34] | <0.001 |
| Obesity | | | | |
| Females (Ref.) | 1,026 (11.0) | -- | -- | -- |
| Males | 98 (1.1) | 0.10 [0.08, 0.12] | 0.14, [0.08, 0.24] | <0.001 |
Tables 5–7 show the unadjusted and adjusted PRRs by participant characteristic for being overweight/obese, obese, and underweight. The probability of being overweight/obese or obese was higher with age for both males and females. Males in the 40–49 years age group were 4.91 times more likely to be overweight/obese compared to males in the 15–19 years age group (CI: 3.14, 7.68). Currently married individuals had a higher probability of being overweight/obese compared to never married individuals. Specifically, currently married males had a 1.94 times higher probability of being overweight/obese compared to never married males (CI: 1.50, 2.50). Currently married females also had a higher probability of being overweight/obese, as did previously married females. Female participants who were currently or previously married had a higher probability of obesity, but not male participants.
Males in fishing communities had a 1.68 times higher probability of being overweight/obese compared to males in agrarian communities (CI: 1.40, 2.02). Females residing in fishing and trading communities had a higher probability of being overweight/obese and obese compared to those residing in agrarian communities (adjusted PRR 1.25 and 1.17, respectively). As SES increased, the probability of being overweight/obese and obese became higher. Compared to the lowest SES, males in the highest SES had 2.52 times higher probability of being overweight/obese (CI: 2.06, 3.07). Although SES presented a higher probability of obesity for high-middle or high SES males, there was no difference in the probability of obesity alone for males in the low-middle SES group compared to those in low SES group (adjusted PRR: 1.62; CI: 0.78, 3.34). Females also had a higher probability of being overweight/obese and obese with increasing SES. Females in the highest SES group were 1.72 times more likely to be overweight/obese compared to females in the lowest SES group (CI: 1.59, 1.87). Females who worked in bar/restaurant jobs or in trading/shopkeeping had a higher probability of overweight/obesity and obesity. For males, occupations in fishing and trading/shopkeeping (but not bar/restaurant work) were associated with a higher probability of being overweight/obese, but not a higher probability of being obese alone. Females who reported alcohol intake in the past year had a higher probability of being overweight/obese. Additionally, females who reported alcohol use in the last week had a higher probability of obesity.
Male smokers had a lower probability of being overweight/obese, as did females who identified as non-Muslim. Females living with HIV had a lower probability of obesity compared to HIV negative participants. For females, having a past pregnancy was not associated with being overweight or obese. For all participants, current ART use had no significant associations with overweight or obesity.
## Factors associated with the probability of being underweight
Males had a higher prevalence of underweight, BMI <18.5 kg/m2, ($11.7\%$) compared to females ($6.5\%$). Smoking was associated with a higher probability of being underweight for male participants, with smokers being 1.91 times more likely to be underweight than nonsmokers (CI = 1.60, 2.27). Being from a fishing community, a higher SES group, currently married, or having trade/shopkeeping occupation were associated with lower probability of being underweight for both males and females. For females only, being from a trading community or being previously married were associated with lower probability of underweight. HIV status was not associated with being underweight for males or females.
## Discussion
Preventing and reducing the prevalence of overweight and obesity is an important public health issue. In this study, one of the largest to date in sub-Saharan Africa, the mean BMI was 22.9 kg/m2 and over $68\%$ of the population fell within the normal BMI category. However, we also observed substantial prevalence of overweight/obesity in this non-urban population-based cohort, comparable or higher than previous studies of overweight and obesity in rural and peri-urban communities in Uganda [13, 21].
Older age was consistently associated with a greater prevalence of overweight and obesity, consistent with past studies in Uganda [13, 21] and other sub-Saharan African countries [22–25]. Female sex was also associated with higher probability of overweight and obesity. Past literature hypothesized reasons for the sex disparity, including hormonal differences between men and women leading to increased fat accumulation for women, lower levels of physical activity [9, 26], and cultural norms, particularly in African countries [9, 27, 28]. In Uganda, and many communities in sub-Saharan Africa, a married woman’s weight may be seen as a reflection of her family’s wealth and status [29], and a larger body stature may be desired [30, 31]. For women, past pregnancy was not associated with a higher probability of overweight/obesity in the adjusted model, but being multiparous, although not captured by this study, may increase the probability of obesity [32].
Being currently married was found to be significantly associated with a greater probability of being overweight or obese compared with never married individuals, consistent with prior African studies [7, 25, 29]. One explanation for this is the social obligations hypothesis, which proposes that the social stability and spousal obligations to share meals may contribute to increased BMI for married individuals [33]. Further, males who were separated, divorced, or widowed did not experience a higher probability of overweight and/or obesity with marriage, which is consistent with the marriage market hypothesis that return to the “marriage market” may promote weight loss or a healthy BMI to attract a more favorable spouse, especially among men [33, 34].
Muslim religion was associated with higher probability of being overweight and/or obese compared to non-Muslims. Religiosity has been previously linked to higher BMI [23, 35]. Bharmal et al. proposed that religious individuals often attend religious gatherings that involve celebratory foods high in fats and sugar, engage in religious media practices (such as watching prayers or sermons at home) that provide easy access to snacks, and may hold a perception of prayer as physical activity due to the changes in body position [35].
In this study, the probability of being overweight or obese increased with SES, similar to past research in Uganda [13, 36, 37], sub-Saharan Africa [3, 24, 25, 38] and low- and middle-income countries [39]. This trend differs from that of high-income countries where the prevalence of overweight and obesity decreases with wealth [40]. Changes in the lifestyle of those with more resources may contribute to being overweight or obese [7, 13]. However, it is thought that as sub-Saharan Africa undergoes the nutritional transition fueled by urbanization and globalization [41, 42], consumption of calorie-dense but nutrition-poor foods will increase the prevalence of obesity in lower SES groups as well [22].
The lower physical activity required for the occupations associated with obesity in this study (fishing, bar/restaurant work, and trading/shopkeeping) may play a role in increasing BMI. These occupations and related communities (fishing and trading) are in more urbanized regions of the Rakai District and individuals may have more exposure to the effects of urbanization, which has previously been reported as a risk factor for obesity. Several studies have found that urbanization is associated with increased weight [10], partly due to easy access to unhealthy foods [13, 25] and decreased physical activity [38].
For women only, alcohol use in the past year increased the probability of overweight/obesity while reporting alcohol use in the last week increased the probability of obesity. Studies on the association between drinking and BMI are conflicting, with many reporting little or no correlation between the two [43, 44]. However, several studies have suggested that binge or heavy drinking, rather than frequent light to moderate alcohol intake may be associated with increased BMI [45] and increased likelihood of being obese [46, 47]. The amount of alcohol intake was not available in this study.
Current smoking was inversely associated with being overweight/obese and was the only covariate that was positively associated with being underweight for males. Several studies in sub-Saharan Africa have similarly found smoking to be inversely associated with obesity in adults [13, 25, 48]. One study reported no association [11]. Nicotine has been shown to suppress appetite [49] and provide a behavioral alternative to eating [50], leading to decreased food intake and weight loss [50]. Smokers often gain weight after smoking cessation [50].
Importantly, HIV positive status was not associated with being underweight for males or females, although HIV positivity was inversely associated with obesity for females. HIV positivity was historically associated with lower BMI due to the health issues and the emaciated state of late-stage AIDS, however advances in HIV treatment have made the disease a manageable chronic disease. It has been proposed that stigma against the thinness associated with HIV may have promoted a cultural preference for a larger stature in African countries [26, 51]. This may no longer be the case, especially among younger generations who appear to value maintaining a healthy body weight [52, 53]. Additionally, weight gain and obesity is often associated with the initiation of ART in HIV positive individuals [54]. ART use was not associated with a higher probability of being overweight/obese in this population, which has high ART coverage ($84\%$).
Past studies have reported higher education to be a risk factor for overweight/obesity in sub-Saharan Africa [23, 24, 29, 55], a trend that is opposite what is seen in high-income countries [56] and in South Africa [25]. It is proposed that educational attainment is associated with higher SES, leading to increased exposure to the risk factors for obesity. However, no significant association between education and the probability of being overweight or obese was found in this study.
## Limitations and strengths
This study has several limitations. First, BMI was used as a proxy for body fat, but is an imperfect assessment measurement due to differences in body composition and fat distribution. Secondly, there were limitations in the specificity of some covariates such as religion, occupation, and alcohol use. This prevented detailed characterization and analysis. Many variables, such as smoking or alcohol intake, were self-reported and may have been affected by social desirability bias or stigma. Additionally, the study has a primarily younger demographic, which limits its generalizability to older populations who may experience different associated factors. However, this study is one of the largest of its type to date, taking advantage of a large data set and conservative statistical analyses to provide valid findings with high power despite data limitations. Future studies could explore whether social and economic perceptions of body stature in rural Uganda affect the risk of obesity, what general awareness the population has of the risks of cardiovascular disease, and how awareness of this risk affects lifestyle choices.
## Implications and recommendations
This study found the prevalence of overweight and obesity in non-urban communities of South Central Uganda to be $22.8\%$ and $6.2\%$, respectively. Targeting healthy weight interventions to higher risk populations may be an effective way to effect change using interventions optimized to fit the unique needs of specific community members [57, 58]. Therefore, there is an opportunity to increase awareness of the benefits of maintaining a healthy body weight amongst groups with higher prevalence of obesity identified in this study. As shifts in access to calorie-dense foods, increased wealth, and adoption of sedentary lifestyles occur, it is important to continue wide-spread education on the importance of healthy weight control, the benefits of healthy eating, and the cardiovascular risks of obesity.
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|
---
title: Accessing HIV care may lead to earlier ascertainment of comorbidities in health
care clients in Khayelitsha, Cape Town
authors:
- Richard Osei-Yeboah
- Tsaone Tamuhla
- Olina Ngwenya
- Nicki Tiffin
journal: PLOS Global Public Health
year: 2021
pmcid: PMC10021146
doi: 10.1371/journal.pgph.0000031
license: CC BY 4.0
---
# Accessing HIV care may lead to earlier ascertainment of comorbidities in health care clients in Khayelitsha, Cape Town
## Abstract
Successful antiretroviral rollout in South Africa has greatly increased the health of the HIV-positive population, and morbidity and mortality in PLHIV can increasingly be attributed to comorbidities rather than HIV/AIDS directly. Understanding this disease burden can inform health care planning for a growing population of ageing PLHIV. Anonymized routine administrative health data were analysed for all adults who accessed public health care in 2016–2017 in Khayelitsha subdistrict (Cape Town, South Africa). Selected comorbidities and age of ascertainment for comorbidities were described for all HIV-positive and HIV-negative healthcare clients, as well as for a subset of women who accessed maternal care. There were 172 937 adult individuals with a median age of 37 (IQR:30–48) years in the virtual cohort, of whom $48\%$ [83 162] were HIV-positive. Median age of ascertainment for each comorbidity was lower in HIV-positive compared to HIV-negative healthcare clients, except in the case of tuberculosis. A subset of women who previously accessed maternal care, however, showed much smaller differences in the median age of comorbidity ascertainment between the group of HIV-positive and HIV-negative health care clients, except in the case of chronic kidney disease (CKD). Both HIV-positive individuals and women who link to maternal care undergo routine point-of-care screening for common diseases at younger ages, and this analysis suggests that this may lead to earlier diagnosis of common comorbidities in these groups. Exceptions include CKD, in which age of ascertainment appears lower in PLHIV than HIV-negative groups in all analyses suggesting that age of disease onset may indeed be earlier; and tuberculosis for which age of incidence has previously been shown to vary according to HIV status.
## Introduction
South Africa bears the highest burden of the human immunodeficiency virus (HIV) epidemic in sub-Saharan Africa (SSA) [1] where about 7.2 million people are affected [2], and is considered the current epicentre of the HIV epidemic [3]. Antiretroviral therapy (ART) became available in 2004 but prior to the evolution of guidelines to the current “test and treat” policy in South Africa, many people living with HIV (PLHIV) did not reach older ages due to high mortality. Over the last decade, South Africa has expanded the ART and HIV prevention campaign by investing about $1.1 billion annually to run what is considered the largest program worldwide [4]. In 2014, Boulle et al., reported a rapid decline in mortality among HIV-infected patients with an increased duration on ART in South Africa and emphasized that though the rates may not be comparable to developed countries initially, after four years on ART they approach the rates observed in high-income countries where HIV is now treated as a chronic infection [5]. A decline in the percentage of acquired immunodeficiency syndrome (AIDS)-related mortalities from $42.1\%$ in 2004 to $23.4\%$ in 2019 [2], and the rising general life expectancy [2, 5] demonstrate the success of this ART programme. The prevalence of HIV in the population aged >50 years increased from $7.1\%$ in 2012 to $12.5\%$ in 2017 in South Africa [6].
The success of the ART intervention has resulted in more older people living with HIV, with improvements in their general quality of life, but in this ageing population other comorbidities still negatively affect mortality rate and life course outcome [7]. In addition to ongoing environmental and behavioural exposures that affect the whole population, comorbidities in PLHIV may also include HIV-related conditions such as HIV-induced persistent immunodeficiency, inflammation, and increased toxicity from longer durations of ART use [8], or HIV nephropathy [9]. Ageing also affects the immune system and the production and function of T cells, and these effects are worsened by HIV [10]. Some drugs included in standard regimens may also increase the risks of some non-communicable diseases (NCDs) [11]. A low progress in the care continuum is reported for PLHIV plus cardiometabolic conditions [12], and a significant proportion of mortalities in PLHIV may result from co-infections and NCDs such as malignancies, diabetes mellitus, hypertension, lipid disorders, and vascular diseases.
Recent studies outside Africa suggest higher prevalence and earlier incidence of comorbidities in PLHIV, especially those older than 50 years [13, 14], but more research is needed in SSA to see if similar trends may be seen [15]. Studies in 2015 and 2017 reported risks of—and increased—hospital admissions of PLHIV for AIDS-defining illnesses, renal and cardiovascular-related issues in Cape Town and other parts of South Africa [16–18], and the observations of hypertension, congestive cardiac failure, cancer and diabetes among PLHIV in Zimbabwe [19] suggest a growing burden of HIV comorbidities. Given the expanding ageing population of PLHIV within SSA, we need to understand the health needs of this population group now and in the future, as well as identifying potential drivers of comorbidities that may provide avenues for future interventions. In light of the current COVID-19 pandemic, it is more important than ever to understand the background of comorbidities and co-infections in a population at high risk of COVID-19 [20].
In this study, we analyze routine health data collected from a variety of public healthcare facilities including primary health care clinics, district level and tertiary hospitals across the Western Cape Province in South Africa. These data are used to describe ascertained comorbidities in a healthcare-seeking population from Khayelitsha, a high-density urban district in Cape Town. The Competition Commission reported that in 2018, approximately 83 per cent of the South African population who were mostly without medical insurance relied on public healthcare facilities and private healthcare facilities served the remaining 17 per cent with medical insurance [21–23]. Govender et al., report that among low-income patients in South Africa, affordability and convenience account for the top reasons influencing healthcare-seeking behaviour in public facilities whilst the quality of care accounts for the key reason for private healthcare facilities, and further observed a cycling behaviour between public and private sector clinics [24]. The subdistrict Khayelitsha, where this cohort originates, has a generally low-income population where we anticipate the majority of residents will access public health facilities.
We describe the median age of ascertainment for comorbidities in PLHIV and also for HIV-negative health care clients. In order to better understand the relationship between age of ascertainment and likely access to screening for common comorbidities, we compare the age of ascertainment for comorbidities in the subset of women accessing maternal care under the assumption that all women accessing maternal care are likely to receive screening for comorbidities at a younger age regardless of their HIV or other health status.
## Ethics
Ethics approval was obtained from the Human Research Ethics Committee of the Faculty of Health Sciences, University of Cape Town (HREC ref: $\frac{482}{2019}$). A waiver for consent was granted because the data were anonymized and perturbed, and individuals could not be identified or re-identified from the data. A data access request was approved by the Health Impact Assessment Directorate at the Western Cape Department of Health, South Africa. There was no involvement of the public or patients because the data were accessed as an anonymized, perturbed dataset from routine data platforms without any interactions with individuals.
## Data source
The PHDC is a health information exchange facility that collates administrative health data for the Western Cape Province. Unique identifiers are used to link individuals to administrative health records [25], and facility visit, laboratory, and pharmacy data are updated daily for about 6.6 million people currently seeking care in public facilities in the Western Cape Province. Algorithms are used to infer disease episodes from combinations of pharmacy-dispensed drugs, laboratory test results, ICD-10 diagnosis codes, and facility encounter data. These algorithms are developed and tested in collaboration with clinicians who specialise in each condition. A data set was obtained from the Provincial Health Data Centre (PHDC), Western Cape Government Health Department, with longitudinal data ranging from 2007 to 2017. The median length of time for which individuals have available data is 8 years (IQR: 3.6–10 years). The study dataset was anonymised and perturbed prior to release, to prevent identification or re-identification of individuals. The electronic confirmation of disease diagnosis resulting from administrative health record linkage is referred to as “ascertainment” rather than diagnosis, as it is derived from the electronic records rather than from a diagnosis made by a clinician during consultation.
## Study population
All adults (≥18 years) who accessed public health facilities in the Khayelitsha subdistrict between 1 January 2016 and 31 December 2017, described as the ‘recruitment period’, were included in this study. Khayelitsha is a high-density, mixed informal/formal housing suburb in Cape Town, South Africa.
Descriptive statistics were generated for age, gender and burden of comorbidities in this study population. The comorbidities assessed were tuberculosis (TB)–using the age of ascertainment for first known episode, chronic obstructive pulmonary disease and/or asthma (COPD/Asthma), hypertension, diabetes, chronic kidney disease (CKD), cervical cancer, lung cancer, breast cancer, and mental health diagnoses. Cardiovascular disease was not included in this study because the PHDC algorithm to infer cardiovascular disease is not yet validated.
The age at ascertainment of each comorbidity in HIV-negative and HIV-positive subgroups of the total healthcare-seeking population was determined.
In addition to describing metrics for all healthcare seekers, age at ascertainment for each comorbidity was determined in a subset of all women who had ever accessed some form of pregnancy and/or maternal care. This subset was chosen to represent individuals who would have been linked to care independent of their HIV and general health status and are very likely to have undergone screening for common conditions as young adults. This subgroup was used to compare the age of ascertainment of comorbidities in HIV-positive and HIV-negative strata, in order to indicate whether earlier linkage to care might lead to earlier ascertainment of these comorbidities. The significance of difference between median ages at ascertainment was calculated using *Wilcoxon sum* ranked tests, and the significance of difference in proportions of comorbidities between PLHIV and HIV-negative groups in this subset was calculated using Fisher’s exact test.
Multivariate logistic regression was used to assess the likelihood of individuals seeking healthcare for each condition to also present with HIV and other comorbidities. Each comorbidity was independently assessed as an outcome/dependent variable with independent variables age, sex, HIV, and other comorbidities. This approach was used to accommodate known bias in the dataset.
Data analyses were done using R Software (version 3.6.0) and RStudio (version 1.1.447); Graphical representations of age distributions at start of recruitment period for HIV-negative and -positive population, sex, age at ascertainment of HIV, and comorbidities distributions by age, HIV status, and sex were generated using the ggplot2 package in RStudio version 1.1.447.
## Patient and public involvement
The participants in this study were healthcare seekers who visited public health facilities and generated at least one electronic health record. Retrospective data for this population spanned about 8 years. Inclusion in the study was restricted to healthcare clients who accessed care between 2016 and 2017 but included their complete retrospective data. The study questions were designed to explore the common comorbidities among these healthcare clients who seek care from public facilities. A waiver for participants’ consent was granted because the data were obtained directly from digital routine health data in the PHDC and were anonymized and perturbed to prevent re-identification of participants.
## Study population characteristics
The total study population was 172 937 healthcare seekers, with a median age of 37 years (IQR:30–48 years), of which 125 468 ($73\%$) were females. There were 83 162 HIV-positive individuals—$48\%$ of the total healthcare-seeking population. There were 59 164 HIV-positive females, representing $71\%$ of all PLHIV. There were 67 499 women with evidence of previous access to maternal care, more than half ($54\%$) of all female healthcare seekers. Of those who had accessed maternal care, 29 828 ($44.2\%$) were living with HIV. About $46.6\%$ of PLHIV were seeking care for the additional comorbidities investigated in this study compared to $61.5\%$ of individuals without HIV (Table 1).
**Table 1**
| Healthcare seeking population | Total | HIV- (%) | HIV+ (%) |
| --- | --- | --- | --- |
| Healthcare seeking population | 172 937 | 89 775 (52%) | 83 162 (48%) |
| Female | 125 468 (72.6%) | 66 304 (73.9%) | 59 164 (71.1%) |
| Has accessed maternal care | 67 499 (53.8% *) | 37 671 (55.8%**) | 29 828 (44.2%**) |
| No comorbidity | 78 990 (45.7%) | 34 575 (38.5%) | 44 415 (53.4%) |
| 1 comorbidity | 65 207 (37.7%) | 36 701 (40.9%) | 28 506 (34.3%) |
| 2 comorbidities | 21 514 (12.4%) | 13 674 (15.2%) | 7 840 (9.4%) |
| ≥3 comorbidities | 7 226 (4.2%) | 4 825 (5.4%) | 2 401 (2.9%) |
The age distribution assessed at the beginning of the recruitment period for females and males, as well as HIV-negative and HIV-positive healthcare seekers, shows a non-uniform distribution: there were more women than men in this cohort of healthcare seekers, with more HIV-positive individuals in the younger age groups. More women were living with HIV at younger ages than men and their HIV-positive status was ascertained at earlier ages than men (Fig 1).
**Fig 1:** *Age distributions at the beginning of the recruitment period.Age in years is shown for the beginning of the recruitment period, subgroups shown are HIV status and sex.*
## The burden of comorbidities and age of ascertainment in HIV-positive and HIV-negative healthcare seekers
The proportion of HIV-positive and HIV-negative individuals seeking care in public health facilities with the assessed comorbidities were: have ever had tuberculosis ($21.4\%$), chronic obstructive pulmonary diseases or asthma ($7.4\%$), hypertension ($26.4\%$), diabetes ($9.7\%$), chronic kidney disease ($2.4\%$), cervical cancer ($0.9\%$), lung cancer ($0.5\%$), breast cancer ($0.4\%$), and mental health conditions ($7.2\%$), (Table 2). The median age (IQR) of HIV ascertainment differs in females and males at 35 years (IQR:30–43 years) and 40 years (IQR: 34–47 years) respectively ($p \leq 0.001$). Except for tuberculosis, all comorbidities were ascertained earlier among people living with HIV, with large differences in the age of ascertainment seen between HIV-negative and HIV-positive healthcare seekers (Table 2).
**Table 2**
| Condition | Healthcare seeking population | Healthcare seeking population.1 | HIV-negativen = 89 775 | HIV-negativen = 89 775.1 | HIV-positiven = 83 162 | HIV-positiven = 83 162.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Condition | Count (%) n = 172 937 | Age at ascertainment (IQR) | Count (%) | Age at ascertainment (IQR) | Count (%) | Age at ascertainment (IQR) |
| Tuberculosis | 36 837 (21.3%) | 34 (27–42) | 11 298 (12.6%) | 33 (24–48) | 25 539 (30.7%) | 34 (28–41) |
| COPD/Asthma | 12 820 (7.4%) | 45 (32–56) | 8 477 (9.4%) | 50 (33–60) | 4 343 (5.2%) | 39 (32–48) |
| Hypertension | 45 691 (26.4%) | 49 (40–58) | 34 090 (38%) | 52 (43–60) | 11 601 (14%) | 43 (36–50) |
| Diabetes | 16 979 (9.8%) | 51 (41–59) | 13 561 (15.1%) | 52 (44–61) | 3 418 (4.1%) | 44 (36–51) |
| Chronic Kidney Disease | 4 179 (2.4%) | 57 (48–67) | 2 833 (3.2%) | 62 (55–71) | 1 346 (1.6%) | 46 (38–55) |
| Cervical Cancer* | 1 180 (0.9%) | 38 (32–47) | 294 (0.4%) | 52 (30–61) | 886 (1.5%) | 36 (31–42) |
| Lung Cancer | 784 (0.5%) | 47 (34–59) | 443 (0.5%) | 56 (40–65) | 341 (0.4%) | 39 (31–49) |
| Breast Cancer | 691 (0.4%) | 44 (33–54) | 458 (0.5%) | 47 (34–57) | 233 (0.3%) | 40 (33–46) |
| Mental Health Condition | 12 512 (7.2%) | 37 (27–50) | 8 279 (9.2%) | 39 (26–54) | 4 233 (5.1%) | 36 (29–45) |
Within the subset of women who have ever accessed maternal care, however, the differences in the median ages at ascertainment for each comorbidity were much smaller compared to the whole population of healthcare seekers, except in the case of chronic kidney disease where the median age of ascertainment was approximately 5.5 years earlier in HIV-positive women ($p \leq 0.001$), and tuberculosis where the median age of ascertainment was approximately 5 years later in HIV-positive women ($p \leq 0.001$) (Table 3). The percentage of HIV-negative and HIV-positive women presenting with each comorbidity in this subset are shown (Table 3). HIV-positive women were more likely to present with tuberculosis (OR:6.78, $95\%$ CI: 6.40, 7.18); CKD (OR:3.48, $95\%$ CI:2.67, 4.58); cervical cancer (OR:9.47, $95\%$ CI: 7.22,12.60); lung cancer (2.39, $95\%$ CI:1.65, 3.5) and mental health conditions (OR:1.41, $95\%$ CI:1.30, 1.53). They were less likely to present with diabetes (OR:0.69, $95\%$ CI:0.64, 0.75).
**Table 3**
| Condition | Women who accessed maternal care | Women who accessed maternal care.1 | HIV-negative women (n = 37 671) | HIV-negative women (n = 37 671).1 | HIV-positive women (n = 29 828) | HIV-positive women (n = 29 828).1 | OR (C.I)Comorbidity count | P-valueAscertainment age |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Condition | Count (%)n = 67 499 | Ascertainment age (IQR) | Count (%) | Ascertainment age (IQR) | Count (%) | Ascertainment age (IQR) | OR (C.I)Comorbidity count | P-valueAscertainment age |
| Tuberculosis | 8 416 (12.5%) | 29 (24–34) | 1 583 (4.2%) | 24 (21–30) | 6 833 (22.9%) | 29 (25–34) | 6.78 (6.40–7.18) | <0.001 |
| COPD/Asthma | 2 587 (3.8%) | 32 (36–38) | 1 225 (3.3%) | 31 (25–38) | 1 362 (4.6%) | 33 (28–38) | 1.42 (1.31–1.54) | <0.001 |
| Hypertension | 7 475 (11.1%) | 36 (30–41) | 4 202 (11.2%) | 36 (30–42) | 3 273 (11%) | 35 (30–40) | 0.98 (0.93–1.03) | 0.008 |
| Diabetes | 2 434 (3.6%) | 35 (29–41) | 1 565 (4.2%) | 36 (30–41) | 869 (2.9%) | 35 (29–40) | 0.69 (0.64–0.75) | 0.05 |
| Chronic Kidney Disease | 292 (0.43%) | 39 (33–45) | 78 (0.21%) | 43 (36–47) | 214 (0.72%) | 37.5 (32–44) | 3.48 (2.67–4.58) | <0.001 |
| Cervical Cancer | 504 (0.75%) | 33 (29–38) | 60 (0.16%) | 34 (31–39∙5) | 444 (1.5%) | 33 (29–38) | 9.47 (7.22–12.60) | 0.05 |
| Lung Cancer | 130 (0.19%) | 31 (26–37) | 45 (0.12%) | 31 (26–41) | 85 (0.28%) | 32 (26–36) | 2.39 (1.65–3.51) | 0.79 |
| Breast Cancer | 212 (0.31%) | 33.5 (27.8–41) | 120 (0.32%) | 30∙5 (26–41) | 92 (0.31%) | 35.5 (29–40) | 0.97 (0.73–1.28) | 0.05 |
| Mental Health Condition | 2 480 (3.7%) | 32 (26–38) | 1 178 (3.1%) | 31 (25–38) | 1 302 (4.4%) | 32 (27–38) | 1.41(1.30–1.53) | <0.001 |
## Distribution of ascertainment age for comorbidities in HIV-negative and HIV-positive healthcare seekers
The distributions of age at ascertainment for the comorbidities assayed are shown in Fig 2 for both HIV-negative and -positive groups and Fig 3 for PLHIV. Generally, in the HIV-positive healthcare seekers, all comorbidities are ascertained across a narrower range of ages, whilst ascertainment of comorbidities in HIV-negative healthcare seekers show a wider age range. There is a drop off in ascertainment of comorbidities in HIV-positive individuals at older ages.
**Fig 2:** *Age at ascertainment for comorbidities for HIV-positive and HIV-negative health care seekers.The absolute counts of comorbidities are shown, grouped by count range for optimal display. A. COPD/Asthma, CKD, and cervical cancer. B. Tuberculosis, hypertension, and diabetes. C. Lung cancer and breast cancer. D. Mental health condition.* **Fig 3:** *Age at ascertainment of comorbidities in HIV-positive healthcare seekers by sex.The absolute counts of comorbidities are shown, grouped by count range for optimal display. A. COPD/Asthma, CKD, and cervical cancer. B. Tuberculosis, hypertension, and diabetes. C. Lung cancer and breast cancer. D. Mental health condition.*
## HIV status of individuals presenting with common conditions and other comorbidities
Multivariate logistic regression analysis shows the likelihood of having HIV and other comorbidities in patients presenting with each condition. As expected, the odds of having all conditions increased with age, calculated per 5-year increments. In line with existing studies, within the whole study population, people who seek care for a first episode of TB are 2.74 ($95\%$ C.I:2.66, 2.81) times more likely to be HIV-positive than HIV-negative. People presenting with CKD are 1.67 ($95\%$ CI:1.54, 1.82) times likely to be HIV-positive and those presenting with cervical cancer are 4.90 ($95\%$ CI:4.22, 5.71) times more likely to be HIV-positive. The complete data are shown in S1 Table.
The subset of women who ever accessed maternal care was used to estimate the contribution of HIV to the likelihood of having each comorbidity when adjusting for age and the co-occurrence of other comorbidities simultaneously, using multivariate logistic regression analysis. This subset was analysed in order to ameliorate the impact of the bias in the composition of the whole study population, using access to maternal care as a proxy for linkage to care at a younger age without necessarily presenting with ill health. The results presented in Table 4, show independent analyses with each assayed comorbidity modelled as the outcome, and the contribution of HIV when adjusted for the other comorbidities. In particular, within the subset of those who have ever accessed maternal care, women who ever had tuberculosis are 6.24 ($95\%$ CI: 5.89, 6.61) times more likely than those without tuberculosis to also present with HIV; those with COPD/Asthma are 1.14 ($95\%$ CI:1.04, 1.24) and 1.76 ($95\%$ CI:1.58, 1.95) times more likely to have had HIV and TB, respectively; and maternal care seekers presenting with cervical cancer are 7.41 ($95\%$ CI:5.67, 9.7) times more likely to be HIV-positive compared to those without cervical cancer.
**Table 4**
| OUTCOMES | INDEPENDENT VARIABLES (OR [95% C.I]) | INDEPENDENT VARIABLES (OR [95% C.I]).1 | INDEPENDENT VARIABLES (OR [95% C.I]).2 | INDEPENDENT VARIABLES (OR [95% C.I]).3 | INDEPENDENT VARIABLES (OR [95% C.I]).4 | INDEPENDENT VARIABLES (OR [95% C.I]).5 | INDEPENDENT VARIABLES (OR [95% C.I]).6 | INDEPENDENT VARIABLES (OR [95% C.I]).7 | INDEPENDENT VARIABLES (OR [95% C.I]).8 | INDEPENDENT VARIABLES (OR [95% C.I]).9 | INDEPENDENT VARIABLES (OR [95% C.I]).10 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| OUTCOMES | HIV | TB | COPD/Asthma | Hypertension | Diabetes | CKD | Cervical cancer | Lung cancer | Breast cancer | Mental Health | Age5 yr. increment |
| Tuberculosis | 6.24(5.89,6.61) | - | 1.74(1.56,1.92) | 0.64(0.58,0.69) | 1.18(1.04,1.34) | 2.82(2.18,3.65) | 1.33(1.08,1.62) | 3.38(2.29,4.95) | 1.28(0.86,1.85) | 1.88(1.69,2.09) | 1.15(1.12,1.16) |
| COPD/Asthma | 1.14(1.04,1.24) | 1.76(1.58,1.95) | - | 1.80(1.62,2.00) | 1.14(0.96,1.35) | 1.55(1.07,2.19) | 0.97(0.65,1.38) | 1.65(0.85,2.90) | 1.27(0.73,2.06) | 2.02(1.74,2.33) | 1.20(1.18,1.24) |
| Hypertension | 0.83(0.79,0.88) | 0.65(0.59,0.70) | 1.78(1.60,1.98) | - | 3.70(3.37,4.07) | 3.32(2.52,4.36) | 1.02(0.80,1.30) | 1.12(0.66,1.82) | 1.46(1.02,2.06) | 1.68(1.50,1.88) | 1.85(1.82,1.89) |
| Diabetes | 0.56(0.51,0.61) | 1.26(1.10,1.43) | 1.08(0.91,1.29) | 3.66(3.32,4.03) | - | 1.98(1.42,2.71) | 1.32(0.91,1.87) | 0.72(0.25,1.63) | 0.61(0.30,1.10) | 1.33(1.12,1.58) | 1.53(1.50,1.58) |
| CKD | 2.74(1.86,3.30) | 3.01(2.33,3.90) | 1.57(1.08,2.23) | 3.48(2.66,4.54) | 2.25(1.62,3.08) | - | 1.91(1.00,3.32) | 1.74(2.79,5.78) | 0.24(0.01,1.26 | 1.81(1.24,2.57) | 1.70(1.57,1.85) |
| Cervical cancer | 7.41(5.67,9.7) | 1.33(1.09,1.63) | 0.95(0.64,1.36) | 1.03(0.80,1.31) | 1.31(0.90,1.85) | 1.52(0.80,2.66) | - | 3.42(1.30,7.42) | 1.10(0.32,2.79) | 1.77(1.28,2.39) | 1.63(1.53,1.73) |
| Lung cancer | 1.37(0.92,1.07) | 3.42(2.31,5.03) | 1.59(0.82,2.81) | 1.14(0.67,1.85) | 0.67(0.23,1.55) | 1.18(0.19,3.95) | 3.13(1.20,6.71) | - | 3.90(0.92,10.9) | 2.29(1.28,3.83) | 1.25(1.11,1.41) |
| Breast cancer | 0.73(0.54,1.09) | 1.41(0.95,2.04) | 1.28(0.74,2.08) | 1.47(1.03,2.08) | 0.62(0.31,1.11) | 0.34(0.12,1.55) | 1.36(0.41,3.320 | 3.86(0.92,10.9) | - | 5.61(3.96,7.80) | 1.42(1.30,1.56) |
| Mental Health Condition | 1.11(1.02,1.22) | 1.90(1.71,2.10) | 2.00(1.73,2.32) | 1.71(1.52,1.90) | 1.35(1.14,1.60) | 1.63(1.12,2.32) | 1.76(1.27,2.37) | 2.34(1.30,3.91) | 5.68(4.00,7.91) | - | 1.16(1.13,1.19) |
*In* general, hypertension, diabetes and CKD had increased odds of co-occurring, as expected (Table 4). When adjusting for these comorbidities, the impact of HIV could be more clearly determined. Individuals with hypertension from this subset are $17\%$ less likely to present with HIV (OR: 0.83, $95\%$ CI:0.79,0.88), those with diabetes are $44\%$ less likely to present with HIV (OR:0.56, $95\%$ CI:0.51,0.61), and those with CKD are 2.74 ($95\%$CI:1.86, 3.30) times more likely to present with HIV. There is no significant difference in HIV presentation between those with and without lung cancer or breast cancer. Finally, women who accessed maternal care and have mental health conditions are $11\%$ more likely to present with HIV (OR:1.11, $95\%$ CI: 1.02, 1.22) as well as other comorbidities compared to those without mental health conditions.
## Discussion
Our results reveal that PLHIV in Khayelitsha, Cape Town are seeking care for multiple chronic comorbidities in addition to co-infection with tuberculosis. Analysis of the healthcare client population in this study shows earlier ascertainment of most chronic comorbidities in PLHIV. Whilst this could be due to generally earlier incidence of comorbidities in the HIV-positive population, it could also reflect an earlier diagnosis of comorbidities in those with frequent access to health care and earlier screening due to HIV treatment visits: ascertainment of comorbidities might occur later in people who do not normally access health care frequently and therefore only receive a diagnosis when comorbidities are sufficiently advanced to present with symptoms. Statistical metrics were not used to directly compare the prevalence of comorbidities in HIV-positive and HIV-negative subsets of the overall study population–people seeking healthcare—due to the known bias in this dataset which is enriched for people who are already ill or have frequent healthcare-seeking behaviour due to existing chronic conditions such as HIV. Bias also results from young healthy women attending healthcare facilities for contraceptive or maternal health services, whilst young, healthy men seldom access health care services. *The* general populations of healthcare seekers who are HIV-positive and HIV-negative are not directly comparable, accordingly.
To further explore ascertainment of comorbidities in an unbiased subset of this population, we analysed data for a subset of women who have previously accessed maternal care, under the assumption they are likely to have been screened for common comorbidities such as hypertension, diabetes, and kidney disease during their pregnancy. This provided a proxy dataset for individuals who have been screened at a younger age even in the absence of known health conditions or symptoms. The much smaller differences in ages at ascertainment of most comorbidities among women seeking maternal care in both HIV-negative and HIV-positive groups that we identified suggest that frequent access to healthcare may result in earlier ascertainment of these comorbidities, rather than there being generally earlier incidence in the HIV-positive population. In all comorbidities assayed where significant differences were identified in the age of ascertainment of this group, the difference is in the range of only 1–2 years—with the notable exception of CKD which occurs an average of 5.5 years earlier in HIV positive women, in line with existing studies on HIV Nephropathy [26, 27]. For tuberculosis, the median age of ascertainment is approximately 5 years higher in PLHIV, and we believe this may reflect the difference in age distribution for tuberculosis incidence in HIV-negative and HIV-positive individuals: for those without HIV, tuberculosis risk is high in young adults but decreases rapidly at older ages [28] whereas tuberculosis risk in PLHIV remains elevated throughout adulthood, leading to a shift to an older median age of first tuberculosis ascertainment. We anticipate that data for cardiovascular disease (CVD) in this population may also show earlier occurrence in PLHIV, based on prior studies [29], and we will conduct a similar analysis for CVD when these data are available. Multivariate analysis shows that in women, having tuberculosis or cervical cancer is highly associated with being HIV-positive. Both arise from infectious agents and are classified as HIV-related conditions [30].
We recognize the bias in our dataset due to imbalances in the sectors of the population commonly seeking healthcare, and the exclusion of many healthy individuals (especially young men) who do not frequently attend healthcare facilities. Bias in the data means that direct comparisons of comorbidity prevalence could not be made between the total HIV-positive and HIV-negative study groups. Several sources of bias exist: healthy HIV-negative individuals without comorbidities are under-represented in people commonly seeking healthcare in public facilities; HIV-negative health care clients are likely to be seeking healthcare because they are ill with other conditions, so the HIV-negative group in the study population is enriched for other comorbidities; and individuals with conditions requiring frequent medication–especially HIV medication–are more likely to visit a facility during the recruitment period and subsequently be included in the dataset, so the study population is further enriched for HIV-positive individuals. In the subset analysis, women who have accessed maternal health services previously were selected to represent a subgroup of individuals who have accessed care and received screening for common comorbidities at a younger age, regardless of their HIV- or general health status, thus providing a less-biased subset for additional analyses. Whilst this analysis can address the bias resulting from differences in accessing health care services, some of the limitations of the maternal subset analysis include our inability to assess comorbidities occurring more commonly in men, or much older women. In addition, it is possible that pregnant women living with HIV may have more rigorous screening and antenatal care which may lead to more frequent ascertainment of existing conditions that for HIV-negative pregnant. As we collect more data about this cohort over time, we will also be able to analyse evolving comorbidity profiles as the maternal cohort ages.
Individuals visiting healthcare facilities who are not seeking care for HIV are more likely to be accessing care for one or more other comorbidities. This explains the high prevalence of these comorbidities in health care clients who are HIV-negative and does not accurately represent the prevalence of those comorbidities in the general HIV-negative population, many of whom may not be currently in active care.
For these reasons, we have not attempted to compare the estimated prevalence of comorbidities in PLHIV with those who are HIV-negative in this study population, and we did not use HIV status as an outcome for multivariate regression analysis. We have used the maternal subset as a proxy for a more balanced analysis, based on the assumption that women who access maternal health care do so at a relatively younger age regardless of their HIV status or other health conditions. Because of the time frame for which retrospective data are available, the subset who have ever accessed maternal care had a lower maximum age than the whole group (S1 Fig). The analysis of the maternal subset clearly cannot, however, be used to understand sex differences in the healthcare-seeking population.
Differences between female and male demographics of healthcare clients may also reflect to some extent contraceptive and maternal care access by women who are not experiencing health issues or poor health, and this group of health care clients contributes to the relatively high proportion of younger women without HIV presenting with no comorbidities in the data set. We do not see a similar proportion of young, healthy males reflected in this dataset, accordingly. Prior studies also suggest that women are more likely to have frequent healthcare-seeking behaviours than men which may also contribute to the higher numbers of women in this study [31], and frequent access to health care plays a pivotal role in the ascertainment of HIV among both younger and older women [32]. The later ascertainment of HIV among men compared to women could be a result of men only presenting to facilities when they are already ill [33], and health promotion and encouraging healthcare-seeking behaviour are key in ensuring early detection of HIV in this sector of the population [32].
A rapid fall-off in numbers of HIV-positive people aged over 60 years at recruitment is because prior to ART rollout in 2004, there was high mortality in PLHIV [34] and there are few people who were infected prior to 2004 and have now survived beyond 60 years of age. As the HIV-positive population now ages, however, the rising challenge of NCDs among ageing HIV-infected persons indicates that disease-specific care delivery for PLHIV may need to become more integrated and holistic to ensure that comorbidities in these patients receive the necessary attention.
## Conclusion
Ascertainment of comorbidities relies on screening, which is influenced by healthcare-seeking behaviours. Our analysis suggests that when women link to maternal care, or PLHIV link to HIV care, which both include point-of-care screening, they have earlier ascertainment of common conditions. This may be a more likely explanation for earlier age of ascertainment of comorbidities in PLHIV than in HIV-negative individuals, rather than earlier disease onset. If this holds true in the wider population, it would suggest that earlier screening, in general, could lead to earlier ascertainment of common comorbidities–in turn leading to earlier linkage to care and better patient outcomes. Our data also suggest that as PLHIV age, their comorbidities curve will also widen toward the older ages and share similarities with the distribution of comorbidities in HIV-negative healthcare seekers, increasing the burden on existing healthcare facilities. Careful planning can ensure that this ageing population has sufficient access to healthcare for HIV and comorbidities, into the future.
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|
---
title: 'Wealth and obesity in pre-adolescents and their guardians: A first step in
explaining non-communicable disease-related behaviour in two areas of Nairobi City
County'
authors:
- Sophie Ochola
- Noora Kanerva
- Lucy Joy Wachira
- George E. Owino
- Esther L. Anono
- Hanna M. Walsh
- Victor Okoth
- Maijaliisa Erkkola
- Nils Swindell
- Gareth Stratton
- Vincent Onywera
- Mikael Fogelholm
journal: PLOS Global Public Health
year: 2023
pmcid: PMC10021148
doi: 10.1371/journal.pgph.0000331
license: CC BY 4.0
---
# Wealth and obesity in pre-adolescents and their guardians: A first step in explaining non-communicable disease-related behaviour in two areas of Nairobi City County
## Abstract
The prevalence of non-communicable diseases is increasing in lower-middle-income countries as these countries transition to unhealthy lifestyles. The transition is mostly predominant in urban areas. We assessed the association between wealth and obesity in two sub-counties in Nairobi City County, Kenya, in the context of family and poverty. This cross-sectional study was conducted among of 9–14 years old pre-adolescents and their guardians living in low- (Embakasi) and middle-income (Langata) sub-counties. The sociodemographic characteristics were collected using a validated questionnaire. Weight, height, mid-upper arm circumference, and waist circumference were measured using standard approved protocols. Socioeconomic characteristics of the residential sites were accessed using Wealth Index, created by using Principal Component Analysis. Statistical analyses were done by analysis of variance (continuous variables, comparison of areas) and with logistic and linear regression models. A total of 149 households, response rate of $93\%$, participated, 72 from Embakasi and 77 from Langata. Most of the participants residing in Embakasi belonged to the lower income and education groups whereas participants residing in Langata belonged to the higher income and education groups. About $30\%$ of the pre-adolescent participants in Langata were overweight, compared to $6\%$ in Embakasi ($p \leq 0.001$). In contrast, the prevalence of adults (mostly mothers) with overweight and obesity was high ($65\%$) in both study areas. Wealth (β = 0.01; SE 0.0; $$p \leq 0.003$$) and income (β = 0.29; SE 0.11; $$p \leq 0.009$$) predicted higher BMI z-score in pre-adolescents. In, pre-adolescent overweight was already highly prevalent in the middle-income area, while the proportion of women with overweight/obesity was high in the low-income area. These results suggest that a lifestyle promoting obesity is high regardless of socioeconomic status and wealth in Kenya. This provides a strong justification for promoting healthy lifestyles across all socio-economic classes.
## Introduction
The contribution of non-communicable diseases (NCDs) to morbidity and mortality in lower middle-income countries (LMIC) is continuously increasing and at a faster rate than in high-income countries [1]. Hence, LMICs are facing a triple burden of NCDs, communicable diseases and undernutrition. In LMICs, the share for communicable diseases and infections was $27\%$ among the top 10 causes of death in 2015 and for NCDs the share was $63\%$ [2] whereas in upper-middle income and high-income countries, NCDs explained already more than $90\%$ of deaths related to top 10 causes.
Kenya has been recently classified as a LMIC by the World Bank [3] and it is undergoing a very rapid lifestyle transition. The existing data collected mainly before 2010 indicates that the country is reaching the point where unhealthy lifestyle that follows economic development and increase the risk of chronic diseases start to entrench unwanted health gaps between genders (more females) [4,5] and between living environments (more in urban than rural areas) [6,7]. In 2014, overweight and obesity among women was $33\%$ in total and positively related to both education and wealth [8]. Inadequate physical activity was more frequent among women than among men, it was high also in those with no education, but low among poor people [9]. In Nairobi City County, $21\%$ of children were overweight or obese, with higher rates among girls and those of higher socioeconomic status [10,11]. Fast and strong actions are needed to at least slow down the increasing prevalence of NCDs and widening of inequalities in Kenya. To plan better health policies and health promotion actions, a step also in science should be taken from describing the situation to better understanding of determinants of current health behaviour and how it could be changed.
The present paper is the first from a multidisciplinary research project aimed at collecting novel and important in-depth data on determinants of NCD-related lifestyles and risk factors among families (pre-adolescents and their guardians) residing in Nairobi City, Kenya, with special focus on comparing two socioeconomically different areas.
The overall aim of the study was to get insight on the role of poverty, education, gender, and place of residence as determinants of physical (in)activity, dietary quality and variability, and overweight/obesity. This opening paper analyses the associations between indicators of wealth and obesity in guardians and pre-adolescent children in a low- and middle-income areas in urban Nairobi and includes a description of the overall study protocol.
## Study design
This cross-sectional study was part of a 3-year collaborative project “The Kenya-Finland Education and Research Alliance (KENFIN-EDURA)” funded by the Finnish Ministry of Foreign Affairs through the Higher Education Institutions Institutional Cooperation Instrument (HEI ICI). The aim of this project was to improve education and teaching capacity at the Kenyatta University, Nairobi, with a focus on NCDs-related behavior, mainly diet and physical activity. The overall study was multi-disciplinary and it included several tasks. In this paper, we report methods and results from the socio-demographic indices, including wealth and anthropometric assessments. The study design in shown in S1 Text.
## Study sites
The study was carried out in Embakasi Central sub-county (Kayole South Ward) and Langata sub-county (Nairobi West Ward) within Nairobi County. When referring to these areas, we use “Embakasi” and “Langata”. These areas were selected since people who reside there are from low to middle socioeconomic status (SES) [12]. People in these SES groups are likely to be the most affected by a recent transition in health behaviour (towards unhealthy diet and inadequate physical activity) [12,13]. Furthermore, the pre-adolescents and their families living in these sub-counties have not been previously studied in such detail. The map of the study sites is shown in S1 Text.
## Study population and sample size
The study targeted families of low or middle SES with pre-adolescents in the age range of 9–14 years and their guardian(s), residing in the selected study regions in Nairobi County. The main inclusion criteria were that the family has at least one child aged from 9 to 14 years and at least one parent or guardian available, and who have been residents of Langata or Embakasi for at least 6 months before the study. Moreover, the family had to sign an informed consent, hence be a voluntarily participant. The study included the pre-adolescent and guardian(s) (mother and/or father, if both belonged to the family and regardless of whether they were biological parents or not). If there was more than one pre-adolescent in the target age range, the participating child was drawn randomly. Households with a 9–14 years old with documented chronic disease conditions, such as tuberculosis, impacting diet or who had any significant illness preventing participation were excluded from the study.
The quantitative part of the study was mainly descriptive, and the study had multiple outcome measures. However, one main outcome was childhood overweight/obesity which was used as the basis for power calculation. According to Broyles et al. [ 14], an expected difference in child BMI z-score between low- and middle-income SES in low-income countries is 0.5. When we use 1.0 as the SD, and α = 0.05 and power = $80\%$, a total 2-group sample size is $$n = 126$$ ($$n = 63$$ per group). To allow for non-response, we aimed to invite in total 160 households, i.e., 80 from each for the study area.
## Sampling technique
A multi-stage sampling technique was conducted to identify the households where data would be collected (Fig 3 in S1 Text). Embakasi represented low SES (partly an informal settlement) and Langata the middle SES [15]. Next, five villages were selected randomly from Kayole North Central Ward (from Embakasi) and 12 estates from Nairobi West Ward (from Langata). These wards were purposively selected from the sub-counties, since they are densely populated and would therefore provide adequate sample of the target population (pre-adolescents). The final stage of sampling involved the enumeration of households with a child in the age range 9–14 years from the selected villages and estates with the assistance of Community Health Volunteers (CHVs). In total 223 households were enumerated in Embakasi and 173 households in Langata. Simple random sampling technique was used to select 80 households from both sub-counties. The final sample sizes were 72 for Embakasi and 77 for Langata (S1 Text). There were only few families (8 in Embakasi and 3 in Langata) that did not want to take part and no data was collected from them.
## Data collection and processing
Field assistants visited the households twice approximately 8 days apart during April-June 2019. All data analysed in this paper were collected during the first visit: the pre-adolescents and guardians completed questionnaires, including questions on demographic and socioeconomic background. Moreover, the participants’ anthropometric measurements were taken. For each household, the assessments took in total about 4 h, divided on two occasions. The time-consuming and multiple assessments were a restriction for a much larger study sample.
## Socio-demographic characteristics
The interviewer-administered questionnaires were done in English or Kiswahili languages, depending on the preference of the family. The quantitative data was collected electronically by use of android phones/tablets using purposefully developed questionnaires developed on Open Data Kit (ODK) software [16], in a face-to-face interview during household visits.
A structured and validated questionnaire was used to collect demographic and socioeconomic characteristics, as well as housing conditions and ownership of assets [8]. This questionnaire was administered to guardians. The questionnaire included information on participants’ sex, age, marital status, education, income, occupation, form of employment, household expenditure on various items, the materials from which the household is constructed based on the questionnaire used in the Demographic and Health Survey in Kenya [8]. Wealth is often measured in terms of economic status and living standards of households. However, the income, expenditure and consumption data needed for calculating these can be challenging to measure accurately. For this reason, we chose to use the Wealth Index similarly to the Demographic and Health Surveys (DHS) [8] and World Food Programme (WFP) Surveys [17]. The calculation of the *Index is* explained in the statistical analyses section.
## Anthropometric measurements
Weight and height of pre-adolescents and their guardians were measured with minimal clothing on and shoes off, using a digital electronic scale (Seca Robust 813) to the nearest 0.1 kg and a stadiometer (Seca 217) to the nearest 0.1 cm. BMI-for-age z-scores for pre-adolescents were calculated, using WHO’s growth references [18]. For adults, BMI (kg/m2) was calculated by dividing weight for the square of height. Waist circumference (WC) was measured, using a waist circumference tape (Edtape for body measurements) to the nearest 0.1 cm around one’s body about halfway between the bottom of the lowest rib and the top of the hip bone, roughly in line with their belly button over light clothing or skin. The Mid-Upper Arm Circumference (MUAC) of the left upper arm was measured at the mid-point between the tip of the shoulder and the tip of the elbow using a non-elastic anthropometric measuring tape (to the nearest 0.1 cm). Each of the measurements was taken twice and an average calculated to ensure accuracy. For pre-adolescents, underweight was defined as BMI z-score < -2.0, overweight as BMI z-score >1.0 and obesity as BMI z-score >2.0. For thinness, we used MUAC values <18.5 cm and <16.0 cm, the latter indicating severe thinness. For adult underweight, overweight and obesity, we used international cut-offs of BMI <18.5 kg/m2, >24.9 kg/m2, and >29.9 kg/m2. Waist/abdominal obesity was defined as >88.0 cm for women and >102 cm for men. For adults, MUAC cut-offs for underweight were defined as <22.0 cm for women and <23.cm for men.
## Statistical analysis
Statistical analyses were done using SPSS (IBM SPSS Statistics version 25) and R software (version 3.6.3 for mac) [19]. Household characteristics were calculated for the total population and by study area as means and standard errors for continuous variables and as counts and percentages for categorical variables. Difference in anthropometric measurements between study areas was analyzed using analysis of variance or logistic regression. The continuous anthropometric measures (BMI, z-BMI score, MUAC and waist circumference) were treated as outcome variables and a binary variable indicating study area was used as independent variable in the analysis of variance. In the logistic regression, the outcome variables were binary variables for overweight (for adults coded as BMI >24.9 = 1, others = 0; for adolescents coded as BMI z-score >1.0 = 1, others = 0), obesity (for adults, coded as BMI >29.9 = 1, others = 0; for adolescents coded as BMI z-score >2.0 = 1, others = 0) and central obesity (coded as WC >88cm for women and WC>102cm for men = 1, others = 0). All analyses were adjusted of participants’ sex and age. Further, we analyzed the associations of wealth (categorical independent variable) and income (categorical independent variable) with BMI and BMI z-score (continuous outcome variables) by using a linear regression model, adjusted for age and sex. The same analyses with obesity and overweight as binary outcome variables were done by using a logistic regression model.
The Wealth Index was created according to the WFP VAM Guidance Paper [20]. Principal component analysis (PCA) (SPSS command FACTOR, Method: Principal components, Varimax-rotation) was applied to combine information on asset ownership and housing characteristics. Where necessary, variables were recoded into binary variables based on knowledge and insight of Kenyan researchers. Additional information related to the construction of the Wealth Index are presented in S2 Text.
First, the frequencies of the wealth indicators were explored in both areas. The indicators that existed in over $95\%$ or less than $5\%$ of the households were removed. These included: electricity, mobile phone, solar panel, table, sofa and bed. The Kaiser-Meyer-Olkin (KMO) test was used to determine the sampling adequacy of data and to ensure that the data were suitable to run a Factor Analysis. KMO values between 0.8 and 1 were deemed to indicate adequate sampling. The correlation test printed out with the PCA was used to evaluate whether correlations were too high meaning certain variables measure the same thing. This led to removing improved drinking water, motorcycle, improved wall material, radio, and cassette or CD player. The final wealth index had a KMO value of 0.871 and it explained $40\%$ of the total variation (S2 Text). The index included sanitation, floor material, television, refrigerator, chair, cupboard, wall clock, microwave, DVD player, electric or gas stove, kerosene stove, bicycle and car or truck. The wealth index was grouped by quintile classification.
## Ethical considerations
Ethical clearance was sought from the Kenyatta University Ethical Review Committee (KUERC) and a research permit from the National Commission for Science, Technology and Innovation (Ethical clearance number: PKU/946/I1002). Further clearance was sought from the Sub-County Health Management Teams (from Embakasi and Langata) and from the local administration (chiefs) for the areas where the study was to be conducted before its commencement. Eligible pre-adolescents along with their guardian(s) were given an informed consent form to sign to show willingness to participate in the study. The purpose of the study, the interviews to be done, the voluntary nature of participation, and the right to refuse to participate in any part of the study were also to be explained orally.
## Results
Altogether, 149 households ($93\%$ of those invited) participated in the study (Table 1). Seventy-two [72] families participated in Embakasi and 77 in Langata. Of the participating guardians, almost all were women, whereas from the 9–14 years old pre-adolescents, about half were girls. The pre-adolescents’ mean age (11 years) was similar, but guardians were younger in Embakasi compared to Langata (32 vs. 38 years). In both areas, the average household size was approximately five. In total, the biggest income group was participants earning 10,000–30,999 Ksh (about 77–237€) per month. Most participants residing in Embakasi belonged to this or lower income groups whereas majority of participants residing in Langata belonged to the highest income group (>51,000 Ksh per month), as expected. Most of the participants had completed primary or higher education. Most of those who had completed only primary education resided in Embakasi, whereas most of those who had completed tertiary education resided in Langata. The biggest difference occupation-wise was that casual jobs (employment on a need basis and the service can be terminated without notice), were more common in Embakasi. Marital status was similar between the two study areas.
**Table 1**
| Unnamed: 0 | Embakasi | Embakasi.1 | Langata | Langata.1 | Total | Total.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | Mean (SD) or N | % | Mean (SD) or N | % | Mean (SD) or N | % |
| Number of households | 72 | | 77 | | 149 | |
| Household size | 4.9 (1.4) | | 5.1 (1.6) | | 5 (1.5) | |
| All guardians | 72 | 100.0 | 76 | 100.0 | 148 | 100.0 |
| Women | 71 | 98.6 | 67 | 87.0 | 138 | 92.6 |
| Age, y | 31.9 (10.4) | | 38.4 (12.3) | | 35.3 (11.9) | |
| All pre-adolescents | 72 | 100.0 | 77 | 100.0 | 149 | 100.0 |
| Girls | 39 | 54.2 | 39 | 50.6 | 78 | 52.3 |
| Age, y | 11.1 (1.5) | | 11.1 (1.6) | | 11.1 (1.5) | |
| Income, ksh/month | | | | | | |
| < 10,000 ksh | 27 | 37.5 | 1 | 1.3 | 28 | 18.8 |
| 10,000–30,999 ksh | 37 | 51.4 | 17 | 22.1 | 54 | 36.2 |
| 31,000–50,999 ksh | 2 | 2.8 | 16 | 20.8 | 18 | 12.1 |
| >51,000 ksh | 3 | 4.2 | 41 | 53.2 | 44 | 29.5 |
| Education | | | | | | |
| | 2 | 2.8 | 1 | 1.3 | 3 | 2.0 |
| Incomplete primary | 14 | 19.4 | 4 | 5.2 | 18 | 12.1 |
| Primary | 26 | 36.1 | 5 | 6.5 | 31 | 20.8 |
| Incomplete secondary | 14 | 19.4 | 7 | 9.1 | 21 | 14.1 |
| Secondary | 12 | 16.7 | 15 | 19.5 | 27 | 18.1 |
| Tertiary | 4 | 5.6 | 45 | 58.4 | 49 | 32.9 |
| Occupation | | | | | | |
| Unemployed* | 17 | 23.6 | 30 | 39.0 | 47 | 31.5 |
| Employed | 20 | 27.8 | 19 | 24.7 | 39 | 26.2 |
| Casual | 21 | 29.2 | 4 | 5.2 | 25 | 16.8 |
| Business | 10 | 13.9 | 22 | 28.6 | 32 | 21.5 |
| Unknown | 4 | 5.6 | 2 | 9.5 | 6 | 4.0 |
| Marital status | | | | | | |
| Married or cohabiting | 55 | 76.4 | 53 | 68.8 | 108 | 72.5 |
| Single | 10 | 13.9 | 17 | 22.1 | 27 | 18.1 |
| Divorced or separated | 5 | 6.9 | 2 | 2.6 | 7 | 4.7 |
| Widow | 2 | 2.8 | 5 | 6.5 | 7 | 4.7 |
The ownership of different indicators (assets) by the Wealth Index fifths showed that car, microwave, and refrigerator ownership was very clearly concentrated to those in the highest fifth of wealth, while no one in the lowest fifth reported having any of these assets. Kerosene stove was the only asset with evident concentration on the lowest fifth of wealth. By visual inspection, the most even distribution through wealth classes was observed for electric or gas stove, chair and television.
Comparison of the Wealth Index and simple self-reported income classes suggest a strong relationship. Almost all with <10,000 Ksh/month income (about 77 euro) belonged to the lowest $40\%$ of Wealth Index (Fig 1). The distribution of families into the Wealth Index fifths in Embakasi and *Langata is* shown in Fig 2. As expected, in Embakasi, none of the participants belonged to the highest Wealth Index fifth whereas in Langata none of the participants belonged to the lowest Wealth Index.
**Fig 1:** *Distribution of income classes among individuals in fifths of wealth index, among 149 households from two sub-counties in Nairobi County, Kenya.Ksh = Kenyan shillings.* **Fig 2:** *Distribution of the wealth index (as fifths) in participants from Embakasi and Langata study areas, Nairobi County, Kenya.*
The anthropometric results are shown in Table 2. The prevalence of pre-adolescents with overweight and obesity was significantly higher in Langata. About $30\%$ of the pre-adolescent participants in Langata were classified as having at least overweight, whereas the respective number in Embakasi was only $6\%$. In striking contrast to the pre-adolescents, the prevalence of adults with overweight and obesity was high ($65\%$) and very similar in the two study areas. It should be noted that the adult results represent mostly mothers, whereas the sample for pre-adolescents was quite evenly distributed between girls and boys. The mean BMI in the 11 participating adult men was 24.6 (SD 3.5) and in women ($$n = 138$$) 28.5 (SD 6.1), suggesting even without a statistical verification a clear sex difference. We assessed potential undernourishment using mid upper-arm circumference (MUAC). There was a small difference between the two sub-counties, and this was apparently explained by higher number of pre-adolescents with mild undernourishment in Embakasi (Table 2).
**Table 2**
| Pre- adolescents | Total | Total.1 | Embakasi | Embakasi.1 | Langata | Langata.1 | Unnamed: 7 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | Mean | SD | Mean | SD | Mean | SD | P 1 |
| BMI, z-score | -0.08 | 1.50 | -0.43 | 1.25 | 0.24 | 1.64 | 0.007 |
| MUAC, cm | 20.8 | 3.6 | 20.0 | 2.7 | 21.6 | 4.1 | 0.005 |
| | n | % | n | % | n | % | |
| Overweight (z-score >1.0) | 26 | 17.7 | 4 | 5.7 | 22 | 29.3 | <0.001 |
| Obesity (z-score >2.0) | 11 | 7.5 | 3 | 4.3 | 8 | 10.7 | 0.16 |
| Underweight (z-score < -2.0) | 10 | 6.8 | 4 | 5.7 | 6 | 8 | 0.61 |
| MUAC < 18.5 cm | 32 | 21.8 | 22 | 31.4 | 10 | 13.3 | 0.008 |
| MUAC < 16.0 cm | 7 | 4.8 | 4 | 5.7 | 3 | 4 | 0.62 |
| Adults | | | | | | | |
| | Mean | SD | Mean | SD | Mean | SD | P |
| BMI, kg/m2 | 28.2 | 6.1 | 27.6 | 5.8 | 28.8 | 6.3 | 0.24 |
| Waist circumference, cm | 91.2 | 14.4 | 89.5 | 14.1 | 92.8 | 14.5 | 0.16 |
| MUAC, cm | 32.2 | 5.4 | 31.7 | 5.0 | 32.6 | 5.7 | 0.31 |
| | n | % | n | % | n | % | |
| Overweight, (>24.9 kg/m2) | 95 | 65.5 | 46 | 64.8 | 49 | 66.2 | 0.49 |
| Obesity, (>29.9 kg/m2) | 49 | 33.8 | 23 | 32.4 | 26 | 35.1 | 0.43 |
| Central obesity (women >88 cm, men >102 cm) | 79 | 55.6 | 38 | 53.5 | 41 | 57.7 | 0.42 |
| Underweight (<18.5 kg/m2) | 3 | 2.1 | 1 | 1.4 | 2 | 2.7 | 0.83 |
| MUAC women <22cm, men <23cm | 1 | 0.7 | 0 | 0 | 1 | 1.4 | NA2 |
In a linear regression model, adjusted for age and sex, the beta-coefficient for Wealth Index was 0.01 (SE 0.0; $$p \leq 0.003$$) and for income 0.29 (SE 0.11; $$p \leq 0.009$$). The same trend was also seen when using a logistic regression model to explore associations with Wealth Index / income and overweight (the highest or two highest classes had an OR statistically significantly different from reference (S1 Table), but not with obesity. The main problem in using income classes in the logistic regression was related to the very low number of cases in the two highest income classes and this led to spuriously high odds ratios. In contrast to the pre-adolescents, the analysis of wealth/income vs. obesity did not show any evident associations among adults (S1 Table).
## Discussion
The main result in our study was that pre-adolescent overweight was highly prevalent in a middle-income area in Nairobi County, while the proportion of women with overweight or obesity was high in both the low- and middle-income areas. One of the characteristics of the lifestyle transition in lower middle-income economies is that the higher SES dominance in NCDs and NCD-related behaviour start to disappear [21,22]. Based on these findings, our results showing a high prevalence of obesity and NCD-related lifestyles in quite low SES areas in adult women in urban Nairobi was according to our hypothesis. It is good to realize that although the two areas were very different from a SES viewpoint, they do not represent the poorest or richest areas in Nairobi.
This study is unique in that it targeted pre-adolescents (9–14 years old), a group on which there is limited information on nutrition in Kenya and yet this group contributes to a fairly large proportion of the population. Our findings provide important new data on obesity, as well as on determinants of NCD-related lifestyles and risk factors in urban Nairobi City County, although the rather limited number of households prevents us from an accurate estimation of prevalence. The comparison of low- and middle-income mother-adolescent pairs is novel in African context, and our data give a unique insight into urban residential areas with varying SES. These data are urgently needed to understand determinants of health and to plan health promotion interventions and programs. It is anticipated that the overall results of the present proposal can be extrapolated to other sub-Saharan African countries facing similar challenges and with comparable socioeconomic profiles and trajectories.
Comparison of the prevalence of pre-adolescent overweight and obesity between different studies is difficult, particularly because of different age-groups and similarity of the study areas. The prevalence of pre-adolescents with overweight/obesity in our sample had similar level, compared with findings from in Malawi and Benin, more than in Ghana and Cameroon, but less than in Djibouti and South Africa [23–26]. Out of these countries, *Malawi is* still a low-income economy, while Benin has just recently become a low-middle income economy, like Kenya. Also, Ghana and Cameroon are low-middle income economies, while South-*Africa is* a higher middle-income economy [3]. From this viewpoint, the prevalence of pre-adolescents with overweight and obesity in our sample was according to the expectations.
The critical question in our study, and around Sub-Saharan Africa, is whether overweight and obesity have become an issue also in the poorest segment of population [27]. We could not show this in our adolescent population, since the prevalence of both overweight and obesity was much higher in Langata, than in Embakasi. A similar SES-difference for younger children has been shown in South Africa [24] and Cameroon [26], but we are not aware of any studies using the same age-group as we did. Moreover, it should be noted that we did not compare high and low SES, but middle and low SES. We hypothesize that due to the activities of daily living, the adolescents in Langata are less physically active because of using motorized transport to school and other activities than those in Embakasi whom the majority walk to school. Further research should be conducted to investigate the similarity in the level of overweight and obesity among the women in low and middles SES in this study.
In contrast to children, we found a high and similar prevalence of mothers with overweight and or obesity in both study areas. Already more than 10 years ago, Ziraba et al. [ 28] showed that the proportion with obesity increased more rapidly in Sub-Saharan countries among those with lower education. The very few participating men had clearly lower BMI, compared to women, but the small number prevents any interpretations. However, it seems that cultural expectations and beliefs may contribute to the high level of overweight and obesity among women in many Sub-Saharan countries [29]. Hence, in terms of preventive health policies, women in Sub-Saharan urban areas may be particularly responsive to negative effects of the lifestyle transition, regardless of their living conditions and household wealth.
The strengths of this study include two very well selected sub-counties, which enabled us to study socio-economically very different areas, but still within the low-middle range of SES. We chose the areas and potential households purposefully, but the final sample was invited by using a random selection of households. Most of the families invited participated in the study. This gives us a reason to expect that the families that fulfilled our study criteria and were included do not differ from those who were not included. Hence, we believe they represented the sub-county despite a rather small number. We used several methods to assess both nutritional status and the economic situation of the households. Comparison of the Wealth Index and self-reported income class indicated very similar site-differences and associations with indicators of over- and under-nourishment. Since the Wealth *Index is* quite cumbersome to calculate, self-reported income may work almost equally well in lower middle-income economies, at least in urban settings. However, the classification should be much denser than we had, and should be planned to describe particularly the lower range better.
The main weakness in this study was a limited number of households, although we met the initial power calculation for the expected difference of BMI z-score [14]. The power for identifying dichotomous (e.g., overweight vs. normal weight) was not as good. We were not able to study a larger number of households, since the total number of assessments done with each household was large and in total took for two visits 4—5h per household. Still, the analyses done with the entire sample ($$n = 149$$) gave similar results, compared to those using site-specific analyses, giving confidence that our conclusions are valid even for the sub-county level with this sample size. Moreover, in the linear regression model, the beta-coefficient for Wealth Index against BMI was very small (0.01), albeit significant. Hence, the interpretation should be done with caution.
The study was carried out among households with pre-adolescents between 9–14 years of age and their guardians in Nairobi City County and, thus, the research findings can only be specifically generalised to areas with similar characteristics and pre-adolescents of the same age group. This is a cross-sectional study, and the survey was done during one season (April-June 2019) only. Effects of seasonality and the direction of observed associations between variables cannot be assessed, and naturally any evidence of causality cannot be assumed.
In conclusion, we studied two Nairobian sub-counties, Embakasi (low SES) and Langata (middle SES). The thorough assessment with Wealth Index and income levels showed remarkable differences in the household situations between these two sites. The prevalence of pre-adolescents with overweight and obesity was clearly higher in Embakasi, but no difference was seen among guardians (mostly mothers). Moreover, the number of guardians ($93\%$ were mothers) with overweight was high in both sites. Neither of the studies sub-counties represents true high-income areas. We can conclude that in Nairobi, pre-adolescent overweight was already highly prevalent in middle-income areas, while the proportion of women with overweight/obesity was high. These results suggest that a lifestyle promoting obesity is prevalent even in lower income areas in urban Kenya, and this should further be used as a strong justification for promoting healthy lifestyles across all SES classes.
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|
---
title: 'Integrated healthcare services for HIV, diabetes mellitus and hypertension
in selected health facilities in Kampala and Wakiso districts, Uganda: A qualitative
methods study'
authors:
- Dominic Bukenya
- Marie-Claire Van Hout
- Elizabeth H. Shayo
- Isaac Kitabye
- Brian Musenze Junior
- Joan Ritar Kasidi
- Josephine Birungi
- Shabbar Jaffar
- Janet Seeley
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021152
doi: 10.1371/journal.pgph.0000084
license: CC BY 4.0
---
# Integrated healthcare services for HIV, diabetes mellitus and hypertension in selected health facilities in Kampala and Wakiso districts, Uganda: A qualitative methods study
## Abstract
Health policies in Africa are shifting towards integrated care services for chronic conditions, but in parts of Africa robust evidence on effectiveness is limited. We assessed the integration of vertical health services for HIV, diabetes and hypertension provided in a feasibility study within five health facilities in Uganda. From November 2018 to January 2020, we conducted a series of three in-depth interviews with 31, 29 and 24 service users attending the integrated clinics within Kampala and Wakiso districts. Ten healthcare workers were interviewed twice during the same period. Interviews were conducted in Luganda, translated into English, and analysed thematically using the concepts of availability, affordability and acceptability. All participants reported shortages of diabetes and hypertension drugs and diagnostic equipment prior to the establishment of the integrated clinics. These shortages were mostly addressed in the integrated clinics through a drugs buffer. Integration did not affect the already good provision of anti-retroviral therapy. The cost of transport reduced because of fewer clinic visits after integration. Healthcare workers reported that the main cause of non-adherence among users with diabetes and hypertension was poverty. Participants with diabetes and hypertension reported they could not afford private clinical investigations or purchase drugs prior to the establishment of the integrated clinics. The strengthening of drug supply for non-communicable conditions in the integrated clinics was welcomed. Most participants observed that the integrated clinic reduced feelings of stigma for those living with HIV. Sharing the clinic afforded privacy about an individual’s condition, and users were comfortable with the waiting room sitting arrangement. We found that integrating non-communicable disease and HIV care had benefits for all users. Integrated care could be an effective model of care if service users have access to a reliable supply of basic medicines for both HIV and non-communicable disease conditions.
## Background
Non-communicable diseases (NCDs), specifically hypertension and diabetes, now account for much of the global disease burden. The population of sub-Saharan *Africa is* experiencing a burden of infections and NCDs [1], albeit in the midst of social, political and economic uncertainties which influence health, disease and mortality patterns [2]. In East Africa, for example, a WHO STEPS [3] survey in south west Uganda and north west Tanzania reported diabetes and HIV prevalence to be $4\%$ and $5\%$ respectively, while hypertension prevalence ranged between $25\%$ -$30\%$ [4]. Similarly, in Kenya the 2015 STEPS survey gave an age-standardised prevalence of hypertension of $24.5\%$ with diabetes at $2.4\%$ [5], and HIV prevalence in 2020 of $4.2\%$ among adults aged 15–49 years [6].
The increasing availability of, and access to, HIV care and anti-retroviral treatment (ART) has enabled people living with HIV to live healthy lives into older age in most of the world [7–9]. People living with HIV and who are on ART can experience a range of NCD comorbidities, partly as a result of living longer when people become more prone to a range of non-communicable diseases [10]. However, in addition, HIV infection can increase the risk of some conditions, including cancer, and some ART drugs can increase the likelihood of heart disease and diabetes [11–14]. Therefore, this population is a part of a growing cohort in need of NCD care, requiring NCD services to be integrated into the care provided by HIV programmes [4, 15–18].
The World Health Organization (WHO), advocates for integration of health service delivery as the most logical way to provide health care [19]. The WHO defines integration as: `the organization and management of the health services so that people get the care they need, when they need it in ways that are user friendly, achieve the desired results and provide value for money’ [19]. In line with the WHO integration, we defined and operationalised integration as: ‘a one stop centre’ providing health care to the community and accessible to people with any chronic condition. Integration requires a well-trained work force, drug supplies, diagnostic resources and clinic structural improvements to support the successful treatment of a range of chronic conditions [13, 20, 21].
An integrated chronic care model was introduced in South Africa in 2011 to provide HIV and other chronic condition care in combined clinics [22]. The findings of feasibility studies showed that the model reduced service duplication and the frequency of patient visits in selected primary health facilities in South Africa [23–25]. Similar improvements were also found in HIV clinic settings in both Malawi [26] and Nigeria [27]. The integration of NCD care for people living with HIV has been highlighted as a need in Nigeria, Zambia, Swaziland and Kenya [28–30] as well as in Uganda [31–33].
Integration not only requires the implementation of supportive policies but also training for healthcare workers and infrastructure development, to enable changes in care delivery in public health systems [34–36]. Such provision is required for people with a range of chronic conditions to enable them to access integrated care in a single location. In Uganda, health provision for HIV and NCDs is usually offered separately through condition-specific clinics, and NCD care is less well funded than HIV care [37]. In addition, there is still a limited understanding of service user and provider perceptions and experiences of accessing, receiving, and providing integrated HIV and NCD care with which to inform the establishment and scaling up of integrated services. To respond to this need, we conducted a longitudinal study, using qualitative methods, to examine the perceptions and experiences of receiving and providing integrated HIV/NCD care in selected health facilities in Kampala and Wakiso districts in Uganda. Ultimately, we sought to understand better the integration process from diverse perspectives and experiences in order to contribute to the growing body of knowledge on integrated health services in Africa.
## Study background
This study was nested within a larger feasibility study entitled Management Of Chronic Conditions in Africa (MOCCA) which from August 2018 to December 2019 was conducted in five health facilities (four government and one private not for profit clinic) in Kampala and Wakiso districts in Uganda. The aim of MOCCA was to develop and evaluate a model of integrated HIV, hypertension and diabetes care where all health facility services were to be provided in a single location as a ‘one stop centre’. To facilitate this, the participating health facilities established integrated HIV/NCD clinics. Thus, patients with any one or more of the target conditions–HIV, diabetes or hypertension–were managed in a single clinic. This meant that people presenting with HIV, diabetes or hypertension all had the same waiting areas, were seen by the same clinical staff and used the same counselling, laboratory and pharmacy services.
Healthcare workers at the participating facilities were trained to ensure provision of quality and standardised care for all three conditions in an integrated manner. This was supported by the provision of a buffer supply of diabetes and hypertension drugs and laboratory supplies in the event of drug shortages in the Ugandan Government pharmaceutical supply chain. The MOCCA study did not supply buffer drugs for HIV treatment because the supply of these drugs through the Government system was dependable. The study also facilitated the identical file colouring for patients with all conditions in the HIV/NCD integrated clinic.
## Conceptual framework
A number of theoretical and conceptual frameworks have been applied in studies of integrated care systems which focus on concepts such as satisfaction and acceptability [38–41]. We utilised the conceptual framework developed and described by Thiede, Akweongo [42] and McIntyre, Thiede [43] on access to health care, since `access’ was a prominent concept arising from the data. Thiede, Akweongo [42] describe access as `the opportunity to use health services, reflecting an understanding that there is a set of circumstances that allows for the use of appropriate health services’ (p.104). They proceed to divide access into three dimensions: availability (physical access); affordability (financial access) and acceptability (cultural access). Cleary, Birch [44] further developed this framework to examine access to ART in South Africa, and defined the three dimensions in the following way: availability as the `fit between a patient’ needs and the type, place and time of services provided; affordability is the `fit between the costs of utilising a service and the patient’s ability to pay the associated costs’; and acceptability as the fit between healthcare provider and the patient’ attitudes and expectations of each other (p.142). We summarise these dimensions in Fig 1 below:
**Fig 1:** *Dimensions of access.*
We used this framework to examine the dimensions of access to integrated HIV and NCD care in a Ugandan context.
## Study setting
The study took place in clinics at different levels in the Government system in Uganda. The five facilities included in the study were categorised as follows, and identified in this paper by letter: A and B: Health centre IVs which have an inpatient capacity of 50–100 inpatient beds overseen by a medical doctor and offering the following services: general minor surgery, laboratory investigations, diagnosis and management of uncomplicated acute and stable chronic conditions including diabetes, hypertension and HIV. At these centres people with stable chronic conditions including diabetes, hypertension and HIV are treated (patients with complicated conditions are referred to district hospitals). Both facilities are in Wakiso District.
C and D: Health centre IIIs which are run by a clinician or a mid-wife and offer the following the services: diagnosis and management of uncomplicated chronic conditions including diabetes, hypertension and HIV, while referring complicated cases to health centre IVs. Both health facilities are in Kampala District.
E: Private not for profit facility which specialised in HIV care delivery and the treatment of uncomplicated diabetes and/or hypertension experienced by people living with HIV who attended the facility. This facility is in Kampala District.
All health facilities offered HIV and NCD care either in standalone clinics or in an outpatient department prior to MOCCA. The facilities were selected purposely to represent different health facility levels and ownership with a mix of urban and peri-urban characteristics.
The Medical Research Council /Uganda Virus Research Institute (MRC/UVRI) and the London School of Hygiene and Tropical Medicine (LSHTM), Uganda Research Unit, in collaboration with the Liverpool School of Tropical Medicine (LSTM) and the Uganda Ministry of Health, designed and implemented the feasibility study.
## Ethical considerations
The study received ethical clearance from The AIDS Support Organisation (TASO) Research Ethics Committee and Uganda National Council for Science and Technology (UNCST) in Uganda (Reference TASO REC/$\frac{015}{18}$-UG-REC-009) and the Liverpool School of Tropical Medicine in the United Kingdom (LSTM REC 18.044).
Permission to conduct the study was also sought from the relevant district and health facility authorities. At each clinic the social science interviewers provided information about the qualitative methods study embedded in MOCCA and sought informed consent. All participants (both users and HCW) signed an informed consent form before enrolment.
## Sample selection
Study participants were selected from health facility service users (hereafter referred to as `users’) enrolled or receiving care from the HIV, hypertension and diabetes clinics and healthcare workers (doctors, medical assistants and nurses, hereafter referred to as HCW) at the participating facilities. Users were purposively recruited at enrolment into HIV/NCD integrated care study clinic during the months of March and April 2019. We aimed to recruit five users with either HIV, hypertension, diabetes or a combination of these conditions from each participating facility. Out of the five, we recruited two who had a combination of either HIV with hypertension or diabetes or had hypertension and diabetes. The three other users recruited were to have been diagnosed with one of the three conditions (HIV, diabetes or hypertension). The focus for recruitment was the condition and a combination of conditions rather than age or sex. We recruited/sampled additional individuals because we anticipated loss to follow up. By the end of the recruitment period, a total of 31 users had been recruited into the study. Two HCWs (nurses, other clinicians and in-charges of the clinics) were purposively selected from each site at the beginning of the study to take part in provider interviews. The study aimed to recruit HCWs engaged in HIV and NCD care delivery. In total ten HCWs were recruited. The person in charge of each facility supported the selection of user participants and the two HCWs from each facility.
## Data collection
Data collection was undertaken from November 2018 to February 2020. Service users were recruited sequentially and interviewed after their visit to the clinic. Users were interviewed prior to the establishment of the HIV/NCD integrated clinic (Phase 1: March–April 2019), two to eight weeks after enrolment (Phase 2: June-July 2019) and nine months after recruitment, at the end of the integrated clinic feasibility study (Phase 3: January–February 2020).
HCWs had two rounds of interviews. The first was carried out prior to the establishment of the integrated clinic between May–June 2019. The second round was November 2019-January 2020 at the end of the integrated clinic follow up time.
Interviews were used to investigate users’ perspectives prior to the establishment of the NCD integrated clinic, soon after its establishment and at the end of the feasibility study. The same technique explored HCWs’ perspective prior to establishment of the integrated clinic and at the end of feasibility study. User and HCW interviews were supported by clinic level observations.
Observations of day-to-day practice in each facility, to observe waiting time, room and service access procedures were undertaken three times: in November 2018 prior to the establishment of the integrated clinic at the participating sites, then in June 2019 and finally November 2019 (see S1 File).
At the first round of interviews, users were asked about the services they expected to be provided with at the HIV/NCD integrated clinic (S1 File–guides in English and Luganda). Subsequent interviews explored the experiences attending the HIV/NCD integrated clinic. In brief, the phase two and three interview guide covered: patient demographics, health conditions for which care was sought, health seeking since the HIV/NCD integrated care establishment, procedure followed while seeking integrated HIV/NCD care, quality of services received, comfort with the sitting arrangement at the HIV/NCD integrated clinic and appropriateness of the HIV/NCD integrated clinic care. Additional questions included: cost of accessing integrated HIV/NCD care, freedom of entry, discussion and movement around the health facility among others (see S1 File).
The HCW interview guide explored conditions treated at their health facilities, types of HIV/NCD services offered, what HCWs did individually and at an institutional level to ensure provision of user-friendly services. Other topics included: changes in the management of HIV, diabetes and hypertension in the previous six months, what had to be done to ensure user-friendly service provision at individual/institution levels. HCWs were also asked about patient attitudes and perceptions on the NCD integrated clinic (S1 File–guides in English only). The final HCW interview, explored changes introduced in the HIV and NCD care delivery in the previous six months, HCW thoughts and opinions on what would be the benefits and challenges of integrated care delivery; HCW satisfaction with the integrated care delivery; user views about integrated services, and what needed to be done to ensure increased demand for integrated HIV and NCD care (S1 File).
The clinic observations which documented changes at the clinic levels and were used to inform the implementing team of any adaptations required. Observations investigated how services were provided in practice in standalone clinics (pre-phase 1) and were used to identify strengths, weaknesses and lessons learnt from the integrated HIV/NCD clinic. Baseline observations investigated experiences of NCD care delivery in the standalone condition-specific clinics, while subsequent data collection covered the experiences of the integrated clinics.
## Data management
All interviews were conducted by trained and experienced social science interviewers in a private place at each clinic. All interviews were face to face and audio-recorded after obtaining consent from the study participant. Interviews with users were conducted in Luganda, the main local language in the study area while those with HCWs were conducted in English (see S1 File). Interview transcription was done verbatim and later translated into English for the user interview transcripts. Each interviewer proof-read their transcripts to ensure completeness and accuracy. The first author reviewed the transcripts throughout the course of the study to check interview content and format. Where gaps were identified in the transcripts, the audio recordings were listened to, to check the reason for the gap and to confirm the correctness of the transcription. Final transcripts were kept on a password protected study computer, and organised in a study folder with sub folders for each data category. The data were backed up on a secure server.
## Data analysis
The first author read all the transcripts several times to fully understand the material. Through this process, data patterns were observed and emerging themes were identified, shared, discussed and agreed with the MCH, EHS, SJ and JS. The first author grouped the emerging themes and patterns according to the constructs of the access conceptual framework described above to develop the coding framework. The coding framework was discussed and agreed upon by DB, MCH, EHS, IK, BMJ, JRK and JS. The first author manually coded all the data in a way that allowed comparison for differences and similarities at different data collection points using deductive and inductive processes. Illustrative quotes in the participants own words were extracted from the interviews and are presented in this paper. Data from users, healthcare providers and the observations were triangulated to increase the richness and trustworthiness of the findings. The entire analysis process involved back and forth discussions between DB, MCH and JS.
Our findings presented in this paper are grouped by the three dimensions of access: availability, affordability and acceptability. We have grouped the themes derived from the data during analysis under each of these headings, following the example of Cleary, Birch [44], having adapted their framework for our study (see Fig 2 below).
**Fig 2:** *Dimensions of access to integrated and non-integrated care services.*
## Results
Demographically. the 31 users had a mean age of 45 years (range of 23–72 years) and most (22 out of 31) were women. Slightly more than half were married, educated up to secondary level and earned a living through informal businesses. In terms of their health, most users had hypertension and diabetes (Table 1). HCWs had a mean age of 36 (range 28–51) years, most were women (six), married and educated up to tertiary level (Table 2).
We now present the findings on the three dimensions from our conceptual framework, availability, affordability and acceptability in turn, and divided between `before integration’ and `after integration’ for each dimension.
## Type of services available before the establishment of the integrated HIV/NCD clinics
All users reported that the health facilities provided HIV, diabetes and hypertension care prior to the establishment of the integrated HIV/NCD clinic. HIV care was similar in all the participating health facilities and included: pre and post-test counselling, anti-retroviral therapy initiation and drug refills, coupled with adherence counselling. Neither HCWs nor users reported experiencing HIV drug shortages or problems accessing diagnostic testing materials.
Although the participating public facilities provided diabetic and hypertensive care together in an NCD clinic, the NCD care provision was often subject to drug and/or reagent shortages coupled with missing or malfunctioning diagnostic equipment.
However, this was not the case in the private facility (E), where no user reported experiencing drug shortages, or laboratory reagents missing or malfunctioning diagnostic equipment for HIV or NCD conditions.
The users attending all health facilities prior to integration were familiar with the services available to them for their treatment needs.
Treatment services for the HIV and NCDs were in different places in the facility.
## HIV/NCD service availability after the establishment of the HIV/NCD integrated clini
All HCWs reported that the establishment of the integrated clinic boosted the availability of diabetes and hypertension care, noting that HIV care availability was already sufficient. They further explained that shortly before the establishment of the integrated clinics, their facilities were supplied with new functional diagnostic equipment for diabetes and hypertension, such as blood pressure machines and dip sticks. They added that a diabetes and hypertension drug buffer stock was also established to cover for drug shortages. These augmented services were, as we explain above, provided as part of MOCCA. It is not surprising therefore, that it was during the second round of interviews the HCWs reported improved diabetic and hypertensive drug availability. In Facility D two clinical staff, both women aged about 30 years, explained how the care provision had been transformed: Almost all diabetes and hypertension users at the public health facilities also reported that drug shortages of drugs for those conditions had become rare since the establishment of the integrated clinic.
However, as MOCCA was coming to an end (when the third round of interviews took place) there were a few users with diabetes and hypertension who reported that drug shortages had slowly started creeping back.
The participants who were accessing integrated care from facility E, the private not for profit facility, did not report experiencing any NCD drug shortages at any time during the follow up period. However, users of this clinic did report that the establishment of the integrated clinic marked the start of active screening of diabetes and hypertension among the people living with HIV attending that facility. Those users described how active screening for diabetes and hypertension for them accompanied the opening up of the facility to users accessing non- HIV-care.
## Challenges with types of service before establishment of integrated HIV/NCD clinic
As the users of facility E note above, prior to establishment of HIV/NCD integrated clinic, that facility provided limited diabetes and hypertension care to users in receipt of HIV care. Complicated diabetes and hypertension cases at that facility were referred to the neighbouring national referral hospital.
In the public health facilities, the HCWs reported during the first round of interviews that the availability of diabetic and hypertensive care was limited due to drug and diagnostic material shortages, and faulty or missing diagnostic equipment.
Users in the public health facilities reported that often, when they had sought care for diabetes and hypertension, they were given prescriptions and advised to go and buy the drugs from private pharmacies.
However, in one of the facilities (A), the HCWs had initiated a diabetes and hypertension club where users contributed some money towards their refill visits. The diabetes and hypertension users at this facility explained this money was used to buy themselves buffer drugs/testing materials to counter the shortages.
The HCW role was only to provide guidance on where to buy the drugs for the buffer stock financed and managed by the users themselves.
## Expenditure to reach health facility before the establishment of the HIV/NCD integrated clinic
Regardless of the disease condition, most users reported that prior to the establishment of the HIV/NCD integrated clinic their inability to afford transport to and from the health facilities hindered their access to care. The challenge of finding money to support a visit to the clinic was a recurrent theme throughout the study. This was unsurprising since most users continued to attend the facility they used prior to integration.
Some HCWs agreed that transport was a major challenge to accessing health care for all users in general and was not only limited to those coming for HIV, diabetes and hypertension care. Lack of transport was a common reason for people to miss drug refills and clinic appointments.
A small number of users reported no challenges with transport because they lived close to the facility where they were accessing care.
Integration reduced the number of visits users had to make to facilities (as we discuss further below), which did make accessing care more affordable overall, even if finding funds for each clinic visit remained challenging.
## Expenditure on drugs, diagnostics and consultations before establishment of the HIV/NCD integrated clinic
Prior to the establishment of the integrated clinic, diabetes and hypertension users reported an inability to buy diabetic and hypertensive drugs from private pharmacies. In addition to this, some diabetes users reported that they could not afford the private clinical investigations that they needed to have.
Almost all diabetes and hypertension care users attending the integrated clinic reported reduced requirements to purchase drugs from private pharmacies. This was observed during the second and third interviews.
The HCWs agreed that the integrated clinic had helped to make diabetic and hypertensive drugs available and affordable to the users through buffer stock supplies, although most expressed concerns about whether this situation would be sustainable.
As described earlier, the only exception was Facility A which operated a diabetes and hypertension patient club before establishing the integrated clinic, where they contributed money which helped to buy drugs in the event of a shortage.
## Expenditure on self-care before establishment of the HIV/NCD integrated clinic
A small number of HCWs reported that diabetes and hypertension users were non-adherent to the treatment before the HIV/NCD clinic integration, an observation they attributed to poverty and an inadequate knowledge about their conditions.
However, it was not only a matter of not being able to afford drugs. Some HCWs reported that hypertensive users were less adherent and skipped drug refill appointments.
Some HIV-care users reported being faced with a shortage of food, which sometimes affected their ability to adhere to treatment, because of drug side effects when they took their drugs on an empty stomach.
Given that the integrated clinic did not address food insecurity this situation did not change.
## Expenditure on self-care after the establishment of the HIV/NCD integrated clinic
Challenges caused by poverty continued in the second and third phases of data collection. Indeed, there was an increasing number of HIV and diabetes care users who reported needing particular types of food. These people explained that this was because they were advised to revise their dietary intake by the HCWs, which some could not afford to do (because the foods they were advised to take were more expensive than their usual diet).
## Users views on the acceptability of integrated care before HIV/NCD care integration
Prior to the establishment of the integrated clinic, there were a small number of diabetes and hypertension service users who perceived that the integrated clinic could potentially expose them to tuberculosis (TB) infection, especially if the HIV users were ill with various opportunistic infections. These users expressed the view that if people living with HIV had linked to care in good time, they would not be sick, and therefore would not pose a risk.
People with hypertension and diabetes did not openly express their stigma against people living with HIV sharing a clinic before integration occurred. Instead, their concerns focused on the risk to their own health from being close to people with contagious conditions.
## HIV/NCD integrated clinic acceptability after the establishment of the integrated clinic
The majority of the HCWs reported that the users’ knowledge about their conditions improved with the establishment of the integrated clinic. The HCWs attributed the increased user knowledge to the intensified joint HIV/NCD patient education sessions.
This increased knowledge also helped users to understand conditions others attending the facility were accessing treatment for.
## Impact of type of service on stigma after HIV/NCD care integration
Prior to HIV/NCD care integration, most of the people living with HIV accessing care reported that HIV-related stigma had greatly reduced in recent years. They further added that diabetes and hypertension were not stigmatised conditions.
Most HIV care users prior to the integration of HIV/NCD care explained that they enjoyed a good relationship amongst themselves which helped to minimise HIV related stigma.
No HCWs reported observing or expressing stigma towards the HIV and NCD care users prior to the establishment of the integrated clinic.
## Acceptability of HIV/NCD integrated care after HIV/NCD care integration
Some users reported that the integrated clinic was acceptable because it had helped to further reduce stigma, as it was hard to tell what condition a person was being treated for. They explained that the file colour was identical for all conditions (under MOCCA) and that each participant consulted the clinician privately, so the person’s reason for seeking treatment was kept confidential. The HCW responses showed agreement with this view.
However, in one facility, facility C, registration, health education and dispensing occurred in the same room. With such an arrangement users could overhear what may be being said to an individual coming for their appointment or collecting their drugs.
Despite claims of reduced HIV related stigma and efforts to reduce it further, some young HIV care users were uncomfortable queuing for the integrated services amongst older people because of the age differences associated with the three conditions.
## HCW-user relations before the HIV/NCD integrated clinic establishment
Most of the HIV and NCD users reported that they enjoyed good relationships with their HCWs prior to the establishment of the HIV/NCD integrated clinic. However, some users said that there were some HCWs who talked to them rudely and did not take time to explain things to them.
## HCW-patient relationship after the establishment of the HIV/NCD integrated clinic
Most of the users reported that improved HCW–patient relationships made the integrated clinic acceptable. They explained that HCWs who worked in the integrated clinic talked to them nicely and sometimes addressed them by name.
The users did observe, however, that the HCWs were tough on those who missed their refill appointments.
HCWs reported that they were trying their best to deliver user friendly health services in the integrated clinic. They explained that they offered health education to the integrated clinic attendees, they explained the procedure to access care, and they did this in a friendly way. They added that all this was an effort to make the integrated clinic acceptable to the participants.
## Efficiency of services of the HIV and NCD care before to HIV/NCD care integration
Almost all the users of the HIV and NCD care services reported that they appreciated the HCW and facility observation of the ‘first come, first serve’ principle prior to the HIV/NCD care integration. The users explained that it was important that they took turns to consult the HCW privately. HCW reported the same practice, and the importance of users taking turns.
## Efficiency of services after the establishment of the HIV/NCD integrated clinic
The users attributed the acceptability of the integrated HIV/NCD clinic to the continued observation of the `first come, first serve’ principle. The users explained that this continued practice, where someone who came later could not jump the queue, improved the acceptability of the integrated HIV/NCD clinics.
During the second and third round of interviews, most of the diabetes and hypertension users reported that the integrated clinic was acceptable to them as drugs/diagnostics shortages had become rare and the service was more efficient.
As noted above, changes were beginning to creep in as MOCCA was coming to an end. Some drugs were beginning to run out.
## Length of waiting time before the HIV/NCD integrated clinic establishment
Most users reported that they spent a lot of time at the health facility each time they went for either a review or a drug refill prior to the establishment of the HIV/NCD integrated clinic. The users explained that most delay was at the consultation and the dispensing sections of the care pathway. The users further attributed the long waiting time to the fact that HCWs were faced with many patients to attend to. The majority of the HCWs agreed with this view.
Some users attributed the long waiting time to the fact that some HCWs reported late for work so there was a backlog of people to be seen throughout the day.
## Length of waiting time after the establishment of the HIV/NCD integrated clinic
Most care users reported that with the introduction of the integrated clinic, their service waiting time had greatly reduced. They explained that users accessing care through the integrated clinic were often prioritised first when they went to the laboratory and at the dispensing window.
## Sitting arrangements before the HIV/NCD clinic integration
Almost all the users prior to the HIV/NCD integrated clinic establishment reported that their sitting arrangement was arranged by the HCWs, but within the designated area users were free to sit anywhere. They enjoyed sitting with their friends as they waited to be served. Among the things they discussed as they waited for the service were treatment side effects, adherence, dietary requirements, and service access challenges.
## Sitting arrangements after the establishment of the HIV/NCD integrated clinic
Most users reported that they were comfortable with the sitting arrangements under the integrated clinic. The users explained that although the HCWs organised these sitting arrangements, the people attending the facility were free to sit anywhere.
The users explained that with the freedom to sit anywhere, they were still able to sit with their friends. The observations by the interviewers in the clinics before and after integration confirmed this explanation. While the waiting area was integrated, sitting patterns did not change markedly because people continued to sit with their friends. These friends were usually people seeking care for the same condition they had sat with before the clinic was integrated.
## Number of visits to the clinic before HIV/NCD integration
Prior to the establishment of the integrated clinic, most users with multi-morbidities reported that they were making more frequent visits to the health facilities to seek care for a single condition per visit. The only exception was the users with multi-morbidities attending facility E where less severe hypertension and diabetes care needs were handled jointly with HIV. However, even from there the severe hypertension and diabetes care needs were referred to the nearby national referral hospital.
## Number of visits after the HIV/NCD clinic integration
In the second and third phases of data collection, all the users reported that the integrated clinic was acceptable to them because it had indeed helped reduce their number of visits. This saved them transport money and time.
The HCWs reported that while having a single treatment visit was a good practice for users, it made more work for them.
Overall, the integrated clinic model resulted in services being accessible to HIV, hypertensive and diabetic users alike. While integration was blamed for an increased workload by some HCWs, on balance care users and healthcare providers welcomed the integrated model.
## Discussion
Our findings show that the integrated HIV, diabetic and hypertensive care delivery model improved diabetic and hypertensive care availability without compromising the provision of HIV care. Costs of transport were reduced for users, who were able to access care for more than one condition at a single visit. Users remarked that everyone looked the same in the waiting area, so in most clinics it was impossible to distinguish people seeking HIV care from those accessing care for diabetes or hypertension. We also found that integration helped to make hypertension and diabetes care more affordable by ensuring drugs were available for those conditions, and users did not have to meet prescription costs themselves.
In MOCCA the improvement in drug and diagnostics supply and availability was key to the acceptability of the integrated service. However, providing integrated care could not eradicate all the costs related to treatment, such as the costs of transport, and also food which users said they needed to support treatment adherence, a finding corroborated in other studies [26, 45, 46]. Out of pocket expenses have been shown to be a significant in impeding access to and retention in NCD care in Africa. A study in eastern Uganda among users with an NCD showed that over $92\%$ of the diabetic users were unable to afford drugs purchases and this increased their rates of multi-morbidity [47]. Other research showed that NCD users in Uganda were found to be unable to afford the cost of care [48] and faced significant geographical constraints to accessing services [49, 50]. In South Africa, Malawi, Kenya and Uganda transport difficulties remained a key component not only in the integrated non-communicable diseases care but also for general healthcare access [7, 50–55]. We found in our study that even when the number of clinic visits was reduced, some people still struggled to meet the costs of transport to get to their appointments.
The challenges related to access, particularly costs, faced by users prior to MOCCA were not unusual in the region. There are multiple challenges in establishing integrated clinics–one stop shops–which provide both for people living with HIV, and people with an NCD who do not require HIV care: ensuring an NCD drug supply chain, establishing sustainable referral systems, ensuring adequately trained staff and providing monitoring across a range of different conditions [56–58]. In South Africa, a third of the hypertensive service users attending primary healthcare facilities could not access their prescribed drugs due to drug unavailability [59]. The provision of functional diagnostic equipment is needed to ensure that no users are asked to finance further investigations out of pocket or purchase drugs from private pharmacies [60]. Drug shortages and lack of diagnostic equipment negatively affected the service availability, with evidence from a number of studies showing that the provision of integrated care and management calls for a clear strategy to support a well organised health system in a supportive political environment for optimal outcomes [23, 48, 61–63]. As Kasaie, Weir [13] observe, for integration to be successful increased financial commitment is required to support and sustain services, particularly if they are to be delivered at scale. A review of healthcare policies in East Africa showed that in Uganda, Kenya, Tanzania and Rwanda, that while steps had been taken to change policy and practice to integrate NCD care into HIV care in the region, with some success, there were still challenges in sustaining integrated care [21].
Evidence from elsewhere has highlighted that a poor HCW-patient relationship, long queues, as well as stigma are barriers to integrated chronic disease care acceptability [23, 25, 50, 63, 64]. Our study findings on stigma corroborate those of research in South Africa where integrated care helped to reduce stigma among the people living with HIV [23, 50], although we found that this was not necessarily the case for younger people, who feared that they could be identified as someone with HIV because they thought they were too young to have an NCD.
Studies in rural Kenya and South Africa have reported that being dissatisfied with treatment was one of the reasons why hypertensive, diabetic and HIV service users skipped refill appointments [54, 61]. Ensuring quality care, is important to acceptability in any type of clinic. Where good care is provided, or where care is perceived to be of better quality to that provided prior to integration, users are less likely to be dissatisfied. A study in Malawi showed that NCD-HIV care integration increased NCD service users’ enrolment upon care integration resulting in similar retention levels for hypertension, diabetes and HIV as well as favourable clinical outcomes [65]. Other factors also play a part in maintaining access to care. Studies from rural Uganda and South Africa among hypertensive service users and HIV positive postpartum women respectively reported that having a supportive family and accessible health facilities promoted care acceptability [53, 55]; these are areas of support which go beyond the establishment of integrated clinics, but can be important in helping people stay in care.
## Strengths and limitations
The key strength of this study lies in the longitudinal design that allowed us to observe and document user and HCW perceptions and experiences of integrated clinics at different points. It is the first study of its kind in Uganda evaluating a one-stop clinic where people with any one or more of the chronic conditions (HIV, diabetes and hypertension) can attend. Another strength lies in our ability to triangulate perspectives of both those receiving integrated care and those providing care. We investigated the users and healthcare providers perceptions and experiences of an integrated care delivery model using multiple data sources which were triangulated across sources (users and providers) and methods (interview and observation). Other strengths include: the diverse range of health facility type and level and the different levels of integration at each of the facilities included in this study. Limitations centred on the difference across facilities in terms of the level and type of healthcare services offered and/or integration. Also, we recognise that MOCCA as a feasibility study provided drugs and diagnostic support which covered shortages in NCD drugs. We therefore do not know how available, affordable and acceptable the integrated HIV/NCD healthcare delivery model would be without this support.
## Conclusion
Our study shows that integrated healthcare services can save patients time, improve their access to services and increase their general health literacy. To be effective it also needs political commitment to primary healthcare; adequate and consistent drug, reagent and diagnostic equipment supplies, a fully supported healthcare workforce, and serious commitment to ensuring food security.
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|
---
title: 'Exploring the non-communicable disease (NCD) network of multi-morbid individuals
in India: A network analysis'
authors:
- Parul Puri
- Shri Kant Singh
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021153
doi: 10.1371/journal.pgph.0000512
license: CC BY 4.0
---
# Exploring the non-communicable disease (NCD) network of multi-morbid individuals in India: A network analysis
## Abstract
Nationally representative evidence discussing the interplay of non-communicable diseases (diseases) are scarce in India. Therefore, the present study aims to fill this research void by providing empirical evidence on disease networking using a large nationally representative cross-sectional sample segregated by gender among older adults in India. The analysis utilized data on 10,606 multimorbid women and 7,912 multimorbid men from the Longitudinal Ageing Study in India (LASI), 2017–18. Multimorbidity was defined as the co-occurrence of two or more diseases in an individual using a list of 16 self-reported diseases. Weighted networks were visualized to illustrates the complex relationships between the diseases using network analysis. The findings suggest that women possess a higher burden of multimorbidity than men. Hypertension, musculoskeletal disorder, gastrointestinal disorder, diabetes mellitus, and skin diseases were reported as the most recurrent diseases. ‘ Hypertension-musculoskeletal disorder’, ‘diabetes mellitus-hypertension’, ‘gastrointestinal disorders-hypertension’ and ‘gastrointestinal disorders- musculoskeletal disorder’ were recurrent disease combinations among the multimorbid individuals. The study generated compelling evidence to establish that there are statistically significant differences between the prevalence of diseases and how they interact with each other between women and men. These findings further accentuate that disease networks are slightly more complex among women. In totality, the study visualizes disease association, identifies the most influential diseases to the network, and those which acts as a bridge between other diseases, causing multimorbidity among the older adult population in India.
## Introduction
Epidemiological transition coupled with lifestyle and behavioral modifications has synergistically accelerated the global non-communicable disease (NCD) burden [1]. The World Health Organization (WHO) suggested that in 2016, 71 percent of global deaths were contributed by NCDs, and 77 percent of all NCD deaths occurred in low-and-middle-income countries (LMICs) [1–3]. This rising NCD burden accompanied with lifestyle and behavioral modifications has resulted in simultaneous occurrence of multiple health conditions in an individual, alias multimorbidity [4,5]. Multimorbidity, usually defined as the simultaneous occurrence of two or more chronic (long-term) conditions, is becoming fairly common in LMICs, with around 30 percent of the population affected with it [5,6].
India is no exception to the existing norm and is experiencing the burden of multimorbidity from past two decades [7]. Findings from Longitudinal Ageing Study in India, suggests that 18 percent of the individuals aged 45 years or older were affected with multimorbidity in 2018 [8]. Studies have highlighted linkages of multimorbidity with high healthcare utilization and expenditure [9], poor quality of life [10,11], low self-rated health [11,12], increased frailty [13], disability [11,12,14] and mortality [6]; this makes it a serious public health concern for the government of India [15]. Despite the deleterious implications the need for effective management of multimorbidity was given its due attention post COVID-19 pandemic [16]. However, till date the empirical shreds of evidence on multimorbidity remain inadequate at the national level [5,17,18]. Existing literature draws conclusions on the basis of the smaller sub-samples that are drawn on selected sub-groups of the population, primarily located at healthcare facilities [5,17–21]. In addition, the studies providing nationally representative evidence are based on lesser number of diseases and employ crude measures like chronic disease score to operationally define multimorbidity [7,19,22]. In totality, there is a dearth in the studies exploring the interplay between disease and thus, they are unable to encapsulate the linkages between diseases among the multimorbid population in the country.
The present study aims to decode the complexities of multiple non-communicable diseases among older adults (individuals aged 45 years or older) in India. The study provides empirical evidence on disease networking using a large nationally-representative sample on older adults. The study presents an in-depth comparative analysis, which provides an overview on most recurrent diseases and dyads (disease combinations). Furthermore, the study visualizes the disease networks and gives insights on the diseases which are most prominent (influential) to the network and which acts as a bridge between two diseases; and thus, can be treated as a precursor to multimorbidity.
## Data source and sampling design
The data employed for the present analysis is obtained from the Longitudinal Ageing Study in India (LASI), wave-1, 2017–18. LASI was conducted under the stewardship of the Ministry of Health and Family Welfare, Government of India [8]. LASI implemented a multi-stage stratified probability cluster sampling design to draw nationally representative data from 35 states/union territories (except Sikkim). For each state, LASI stratified the sampling design on the basis of residence, i.e., rural and urban. A three-stage sampling design was adopted for rural areas, whereas a four-stage sampling design was adopted for the urban areas.
In rural areas, stage one involved selection of a Primary Sampling Unit (PSU), this included selection of sub-districts (Tehsils/Talukas). In the second stage, a Secondary Sampling Unit (SSU) was selected, thus included selection of village. In the third stage, households were selected, from which eligible respondents were interviewed. Whereas in urban areas, stage one involved selection of a PSU, this included selection of sub-districts (Tehsils/Talukas). In the second stage, a SSU was selected, i.e., urban wards. Once an urban ward is selected a Census Enumeration Block (CEB) was selected in the third stage. Following which households were selected in the fourth stage.
For the selection of PSU, a sampling frame was chosen from the 2011 census. In rural areas, the sampling frame in the second stage was the villages in all selected sub-districts. The list of CEBs in each selected ward was the sampling frame in the third stage. To obtain the sampling frame for the selection of households from secondary sampling units (SSUs), a mapping and household listing operation was carried out in the sampled SSUs (i.e., villages in rural areas and CEBs in urban areas). All of the listed households in selected villages/CEBs formed the sampling frame for the selection of households. A detailed account of the survey design, sampling frame and sample size can be seen elsewhere [8].
## Ethical consideration
LASI received ethical approval from the Indian Council of Medical Research (ICMR) and Institutional Review Board held at International Institute for Population Sciences (IIPS), India. During the fieldwork, a catalogue containing the information on the purpose of the survey, confidentiality and safety of health assessment was provided to each eligible participant. In addition, separate written consent forms were administered at household and individual levels, in accordance with the Human Subject Protection [8]. There was no personal information included in LASI, and therefore ethical approval for open use of LASI data was not required.
## Study population
LASI contained de-identified data on 72,250 individuals above the age of 45 years and their spouses irrespective of age. From the retrieved dataset, information on 65,562 individuals above the age of 45 years were derived. Further, employing the operational definition multimorbidity, i.e., simultaneous occurrence of two or more chronic diseases, 18,518 multimorbid individuals (to be referred as individuals from now onwards) were identified. These 18,518 individuals included 10,606 multimorbid women (to be referred as women from now onwards) and 7,912 multimorbid men (to be referred as men from now onwards). A description of the sample selection is presented in Fig 1.
**Fig 1:** *Description of study sample, Longitudinal Aging Study in India, 2017–18.*
## Measures
LASI commissioned the question, “Has any health professional ever diagnosed you with the following chronic conditions or diseases?”. The study included a list of 16 non-communicable diseases, namely asthma (AS), cancer (CA), chronic bronchitis (CB), chronic heart disease (CHD), chronic obstructive pulmonary disease (COPD), chronic renal failure (CRF), diabetes mellitus (DM), gastrointestinal disorder (GD), high cholesterol (HC), hypertension (HYP), musculoskeletal disorder (MKS), neurological and psychiatric disorder (NPD), skin disease (SD), stroke (ST), thyroid disease (THY) and urinary incontinence (UI). All the aforementioned diseases were self-reported medical diagnoses and were classified into binary form: absent- ‘0’ and present- ‘1’. Owing to the social and biological differences between women and men, the current analysis was segregated by the gender of the individual.
## Preliminary analysis
The prevalence of selected diseases among the multimorbid population was reported. These findings were supplemented with chi-square p-values to identify the statistically significant difference between the disease burden among women and men. From the sixteen diseases included in the analysis, a total of 16C2 = 120 combinations (dyads) were explored; heat maps were generated to identify recurrent dyads for both women and men.
## Network analysis
The main statistical procedure employed in the study was a network analysis. A network essentially comprises of two components, namely nodes and edges. All sixteen diseases were represented by nodes (represented by circles), whereas edges (━) represented the association between two diseases. For each of the two components two separate input files were generated. Nodes file contained the information on disease prevalence, whereas edges file included information on disease associations. For constructing the edges file, all possible disease combinations were utilized, an array of age-adjusted binary logistic regression models were executed. Age-adjusted odds ratios along with p-values were reported for each dyad combination. Any specific association (dyad combination) was included in the final edge file, if the conditions below are satisfied: This was done to ensure that only statistically significant positive associations are selected for the final disease network. The size of the node represented the prevalence of the disease, whereas the edge thickness was proportional to the degree of association (age-adjusted odds ratio) between two diseases. Thus, bigger the node, higher the prevalence of the NCD, and thicker the edge, stronger is the association between the two diseases. The information from nodes and final edges file was utilized to create an edgelist, which served as an input to visualize the final disease network. The final disease network was a weighted undirected network as all the edges included in the study were of bi-directional nature.
## Network and node attributes
For the full network, four attributes were computed, namely number of nodes, number of edges, network diameter and network density. Number of nodes refers to the total number of network units which in present case is NCDs [24]. Whereas, the number of edges is the number of links (statistically significant and positive associations) connecting the nodes (NCDs) [24]. More number of nodes refers to a higher number of statistically significant and positive association, which depicts more complex disease networks. Network diameter refers to the maximum distance between any two nodes (diseases) in the network. It is an indicator of network cohesiveness, i.e., how united is a network. Diameter ranges between zero and one, where zero indicates no incohesive, whereas one indicates complete cohesiveness [23]. While network density measure the sparsity, and is defined as the number of actual connections divided by the potential connections in any network [23,24]. Higher network density represents, more complicated disease networks. In addition, positional features were computed for individual nodes (diseases). These include measures of centrality including degree (local measure), node closeness centrality (global measure) and node betweenness centrality were reported.
Degree (Ki) measures the connectivity of a node ‘i’. It represents how involved a specific node is in a network [23]. It is defined as: Ki=niin+niout Where, *Ki is* the degree for node ‘i’ niin is the number of ties directed inwards and
niout number of ties directed outwards Dense networks are highly connected networks where NCDs can quickly move across the networks. Therefore, denser the network, easier is the disease propagation. Node closeness centrality (or closeness centrality) represents the connectedness of a disease (node) to a network. It is a score (sum) which is constructed for each disease (node) on their closeness (shortest path) to all other nodes within the network. Higher the closeness centrality, more important position it holds in the disease network. In other words, that specific disease is connected with a greater number of NCDs in the networks [23,25,26].
Node betweenness centrality (or betweenness) represents the potential influence of a node (disease) on the distribution of other nodes (diseases). It is defined as the number of times a particular node (diseases) acted as a link in the shortest paths between other nodes (diseases) [23,25]. Thus, a disease with higher betweenness centrality means that it acts as a bridge between other diseases through common pathophysiological mechanism or shared risk factors. Thus, such diseases hold utmost importance in forming these networks.
All the estimates generated in the study were presented after suitable application of sampling weights provided by LASI, 2017–18 [8]. Analysis for identifying and visualizing disease network was conducted using in RStudio version 1.1.463 (R Studio, Inc.). The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies presented in S1 Table.
## Burden of individual non-communicable diseases among multimorbid older adults
The study included 18,518 multimorbid individuals, which included 10,606 ($56.9\%$) women and 7,912 ($43.1\%$) men. Table 1 provides the prevalence of selected chronic non-communicable diseases among the multimorbid population. The findings suggest that burden of asthma (p-value = 0.000), cancer (p-value = 0.005), chronic heart disease (p-value = 0.000), coronary obstructive pulmonary disease (p-value = 0.000), chronic renal failure (p-value = 0.000), diabetes mellitus (p-value = 0.000), hypertension (p-value = 0.000), musculoskeletal disorder (p-value = 0.000), neurological and psychiatric disorder (p-value = 0.002), skin disease (p-value = 0.000), stroke (p-value = 0.000), and thyroid disease (p-value = 0.000) were significantly different between women and men.
**Table 1**
| Non-communicable Diseases | Total(n = 18518) | Men(n = 7912) | Women(n = 10606) | p-value |
| --- | --- | --- | --- | --- |
| | n (%) | n (%) | n (%) | |
| Asthma (AS) | 1838 (12.28) | 909 (14.30) | 929 (10.75) | 0.0 |
| Cancer (CA) | 317 (1.64) | 111 (1.26) | 206 (1.93) | 0.005 |
| Chronic Bronchitis (CB) | 477 (3.47) | 216 (2.94) | 261 (3.87) | 0.253 |
| Chronic Heart Disease (CHD) | 2028 (11.46) | 1063 (12.96) | 965 (10.33) | 0.0 |
| Chronic Obstructive Pulmonary Disease (COPD) | 587 (4.05) | 303 (3.73) | 284 (4.28) | 0.0 |
| Chronic Renal Failure (CRF) | 395 (1.94) | 216 (2.54) | 179 (1.49) | 0.0 |
| Diabetes (DM) | 6716 (35.23) | 3071 (35.94) | 3645 (34.69) | 0.0 |
| Gastrointestinal Disorder (GD) | 7546 (38.74) | 3217 (39.27) | 4329 (38.34) | 0.83 |
| High Cholesterol (HC) | 2119 (7.01) | 882 (7.28) | 1237 (6.81) | 0.276 |
| Hypertension (HYP) | 12895 (67.85) | 5187 (62.80) | 7708 (71.68) | 0.0 |
| Musculoskeletal Disorder (MKS) | 6655 (39.38) | 2305 (32.41) | 4350 (44.67) | 0.0 |
| Neurological and Psychiatric Disorder (NPD) | 1160 (6.81) | 545 (7.20) | 615 (6.51) | 0.002 |
| Skin Diseases (SD) | 2245 (12.83) | 1050 (14.99) | 1195 (11.91) | 0.0 |
| Stroke (ST) | 1008 (5.63) | 604 (7.91) | 404 (3.91) | 0.0 |
| Thyroid Disease (TD) | 1577 (8.10) | 295 (3.82) | 1282 (11.34) | 0.0 |
| Urinary Incontinence (UI) | 1470 (8.59) | 642 (9.05) | 828 (8.23) | 0.444 |
For women, hypertension ($71.7\%$), musculoskeletal disorder ($44.7\%$), gastrointestinal disorder ($38.3\%$), diabetes mellitus ($34.7\%$), and skin disease ($11.9\%$) were five most prominently occurring NCDs, which is similar to that reported by the total population. For men, however, the diseases remained same, but their ranking was modified, i.e., hypertension ($62.8\%$), gastrointestinal disorder ($39.3\%$), diabetes mellitus ($35.9\%$), musculoskeletal disorder ($32.4\%$), and skin disease ($15.0\%$) were most commonly occurring diseases.
## Non-communicable disease combinations in multimorbid older adult population
Fig 2 represents two heat maps for multimorbid women and men, respectively. Heat maps depict the prevalence of all possible disease combinations (120 combinations for women and men each) among the multimorbid older adult population.
**Fig 2:** *Heat maps depicting non-communicable disease dyads prevalence for (A) women and (B) men aged 45 years and above, Longitudinal Ageing Study in India (LASI), 2017–18.*
Among women, 55 out of the 120 combinations had a prevalence greater than one percent. ‘ Hypertension-musculoskeletal disorder’ ($28.9\%$), ‘diabetes mellitus-hypertension’ ($27.8\%$), ‘gastrointestinal disorders-hypertension’ ($23.2\%$), ‘gastrointestinal disorders- musculoskeletal disorder’ ($14.9\%$) and ‘diabetes mellitus- musculoskeletal disorder’ ($11.5\%$) were five most recurrent disease combinations among the multimorbid women. Whereas, in men 52 out of the 120 combinations had a prevalence greater than one percent. ‘ Diabetes mellitus-hypertension’ ($27.8\%$), ‘gastrointestinal disorders-hypertension’ ($19.1\%$), ‘hypertension-musculoskeletal disorder’ ($14.5\%$), ‘gastrointestinal disorders- musculoskeletal disorder’ ($12.2\%$) and ‘chronic heart disease- musculoskeletal disorder’ ($9.3\%$) were five most recurrent disease combinations among the multimorbid men. Prevalence of all possible dyad combinations by gender can be seen in S2 Table.
## Non-communicable disease network among older adults
Fig 3 represents undirected network with positively and statistically significantly associated diseases (p-value<0.05, OR ≥1.2). Comparing the disease networks for women and men, both had equal number of nodes i.e., 16 diseases (network units). Initially, the total number of edges i.e., the number of disease association explored were 120 for each network, however, to ensure that only statistically significant positive associations are visualized, two inclusion criteria were employed, after which 35 and 25 associations were remaining among women and men, respectively (Table 2). Age-adjusted association (OR values) for all possible disease pairs by gender is presented in S3 Table. Women network had a greater diameter as compared to men; while both networks were equally sparse. In addition, full multimorbidity networks can be seen in S1 Fig.
**Fig 3:** *Multimorbidity networks for (A) women and (B) men aged 45 years and above, considering inclusion criterion for sixteen non-communicable diseases, Longitudinal Ageing Study in India (LASI), 2017–18.* TABLE_PLACEHOLDER:Table 2 Table 3 provides node specific centrality measures. Among women, the diseases with more connections in the networks were coronary obstructive pulmonary disease [9], chronic heart disease [7], chronic renal failure [7], chronic bronchitis [6], stroke [5], thyroid disease [5], and hypertension [5]. Considering the closeness centrality, coronary obstructive pulmonary disease ($2.7\%$), thyroid disease ($2.2\%$), diabetes mellitus ($2.2\%$), chronic renal failure ($2.1\%$), and neurological and psychiatric disorder ($2.1\%$) were placed at the best positions to impact the whole disease network quickest. Betweenness, which represents the number of times a specific node (disease) acted as a link between the shortest path between two other diseases; coronary obstructive pulmonary disease ($44.0\%$), urinary incontinence ($26.0\%$), gastrointestinal disorder ($14.0\%$), hypertension ($10.0\%$), and thyroid disorder ($9.5\%$) acted a bridge between two diseases.
**Table 3**
| Non-Communicable Diseases (NCDs) | Degree | Degree.1 | Closeness Centrality(in %) | Closeness Centrality(in %).1 | Betweenness Centrality (in %) | Betweenness Centrality (in %).1 |
| --- | --- | --- | --- | --- | --- | --- |
| Non-Communicable Diseases (NCDs) | Women | Men | Women | Men | Women | Men |
| Asthma (AS) | 2 | 2 | 1.50 | 1.19 | 0.00 | 0.00 |
| Cancer (CA) | 3 | 4 | 1.80 | 1.60 | 1.00 | 9.00 |
| Chronic Bronchitis (CB) | 6 | 6 | 1.91 | 1.60 | 5.00 | 20.00 |
| Chronic Heart Disease (CHD) | 7 | 2 | 2.00 | 0.98 | 3.00 | 0.00 |
| Coronary Obstructive Pulmonary Disease (COPD) | 9 | 3 | 2.66 | 1.13 | 44.00 | 0.00 |
| Chronic Renal Failure (CRF) | 7 | 5 | 2.12 | 1.52 | 8.00 | 12.00 |
| Diabetes (DM) | 4 | 2 | 2.18 | 1.20 | 6.00 | 0.00 |
| Gastrointestinal Disorder (GD) | 2 | 1 | 1.41 | 1.22 | 14.00 | 0.00 |
| High Cholesterol (HC) | 4 | 5 | 1.80 | 1.53 | 3.00 | 33.00 |
| Hypertension (HYP) | 5 | 3 | 1.91 | 1.20 | 10.00 | 1.00 |
| Musculoskeletal Disorder (MKS) | 3 | 2 | 2.03 | 1.34 | 6.00 | 0.00 |
| Neurological and Psychiatric Disorder (NPD) | 4 | 3 | 2.11 | 1.38 | 6.00 | 5.00 |
| Skin Diseases (SD) | 1 | 0 | 1.12 | 0.42 | 0.00 | 0.00 |
| Stroke (ST) | 5 | 2 | 1.99 | 1.39 | 6.00 | 1.00 |
| Thyroid Disease (TD) | 5 | 5 | 2.21 | 1.83 | 9.50 | 45.00 |
| Urinary Incontinence (UI) | 3 | 5 | 1.97 | 1.61 | 26.00 | 22.00 |
Among men, the diseases with more connections in the networks were chronic bronchitis [6], chronic renal failure [5], high cholesterol [5], thyroid disease [5], urinary incontinence [5], and cancer [4]. Considering the closeness centrality, thyroid disease ($1.8\%$), urinary incontinence ($1.6\%$), chronic bronchitis ($1.6\%$), cancer ($1.6\%$), and high cholesterol ($1.5\%$) were placed at the best positions to impact the whole disease network quickest. Thyroid diseases ($45.0\%$), high cholesterol ($33.0\%$), urinary incontinence ($22.0\%$), chronic bronchitis ($20.0\%$), and chronic renal failure ($12.0\%$) acted a bridge between two disease highest number of times.
## Discussion
The present study provides empirical evidence using a nationally representative data on 18,518 multimorbid older individuals (10,606 women and 7,912 men); from the first wave of the Longitudinal Ageing Study in India (LASI), 2017–18. The findings suggest that women ($56.9\%$) possess a higher burden of multimorbidity than men ($43.1\%$) in India. Existing literature in India, have reported a significantly higher burden of multimorbidity among women [7,27] as compared to men. The primary reasons for higher disease burden is increased life expectancy, which enables them to encounter more number of vital events in life, which have a direct or indirect relationship with expanding chronic disease burden [27].
Hypertension, musculoskeletal disorder, gastrointestinal disorder, and diabetes mellitus were reported as most recurrent NCDs for multimorbid women and men. These findings are in concordance with the studies based in South Asian countries, which suggest a preponderance hypertension [7,20,25,27], musculoskeletal disorders [20,27], gastrointestinal disorder [20,25,27], and diabetes [7,20,25]. ‘ Hypertension-musculoskeletal disorder’ and ‘diabetes mellitus-hypertension’ were recurrent disease combinations among the multimorbid women and men. Musculoskeletal disorders comprise of a wide range of diseases and conditions, most common include tendonitis, osteoarthritis and rheumatoid arthritis. While some studies report a negative association between osteoarthritis (a musculoskeletal disorder) and hypertension [28,29]; others report a positive association between hypertension and musculoskeletal complaints, like rheumatoid arthritis and musculoskeletal pain [30–32]. These studies suggest that musculoskeletal pain affect the Autonomic Nervous System (ANS) that controls the cardiovascular activities and regulates blood pressure levels and heart rate, and this might interfere with both diastolic and systolic blood pressure levels [31]. On the other hand, diabetes mellitus and hypertension are stated to have bi-directional relationship with shared risk factors. Also, literature suggest that diabetes can intensify age-specific blood pressure dysfunction [21,33].
The findings further emphasize that linkages between the diseases are much more complex in women as compared to men. This is because, on the basis of the inclusion criterion applied, a greater number of associations were remaining in women. In absence of comparable studies in India, we refer to evidence generated by Schäfer et al. [ 2014], which represents similar findings [25]. The study generated compelling evidence to establish that there are statistically significant differences between the prevalence of diseases and in the way, they interact with each other for women and men. For instance, in women, coronary obstructive pulmonary disease, thyroid disease, and diabetes mellitus were placed at the best positions to impact the whole disease network quickest; whereas, coronary obstructive pulmonary disease, along with urinary incontinence, and gastrointestinal disorder acted a bridge between two diseases majority of the times. In men however, thyroid disease was placed at the best positions to impact the whole disease network; whereas, thyroid disease and high cholesterol acted a bridge between two disease majority of the times.
## Strengths and limitations
The strength of our study lies in the use of a recently published large-scale nationally representative data on older adults in India. The study uses a list of sixteen NCDs (diseases), and employed a globally accepted definition of ‘simultaneous occurrence of two or more chronic diseases’ to measure multimorbidity. In addition, the use of network analysis, has provided a novel perspective on diseases interaction in India, which by far has been overlooked. The study utilized a rigorous inclusion criterion for selecting statistically significant positive associations which provides robustness to the findings. All the disease associations depicted in the study are sex-specific and are adjusted for age of the multimorbid individual.
The diseases included in the study are self-reported, which might lead to misclassification bias. Directed networks were not studied in the present analysis as age at diagnoses was not asked for all the diseases included in the study. To assess the generalizability of our study findings, it is essential to replicate our study using different data sets (if available) for older adult population in India.
## Implications of findings
Although in the strict medical viewpoint, NCDs are non-transmissible between individuals. However, owing to the deep-rooted linkages NCDs share with geographical and behavioral aspects, like temperature, altitude, precipitation, pollution levels, dietary influences, lifestyle modifications, exposure to risky health behaviors, non-compliance with medical regimes and avoidance towards health care [34]; it would not be an exaggeration to affirm that, each individual has the tendency and capacity to alter the behaviors of another individual, living in the vicinity; which, can be referred to as “neighborhood affect”.
Considering the aforementioned explanation, which acts as a catalyst for diseases with shared pathophysiologies and risk factors, NCDs are not rigorously non-transmissible. Despite this, existing evidence on multimorbidity in India, has by far been following a unidirectional approach, i.e., chronic disease score (CDS)-based approach [4,5,10], which fails to study disease linkages, and hence miss out vital information, which can be used in devising community-oriented treatment and management regimens.
Considering the social and biological differences, our study decodes this vital piece of information for the older women and men in India. The study visualizes the complicated relationships between diseases, and identifies the NCDs that are most influential to the network, and those which acts as a bridge between diseases. These findings can assist physicians in understanding the interplay between diseases and can be used for mending existing treatment strategies, reducing the likelihood of multimorbidity-related organ failure and polypharmacy. These findings can further be employed for the purpose of drug repurposing in India.
## Conclusion
Our study provides empirical evidence which establishes that multimorbidity is an emergent public health concern among older women and men in India. The findings indicate that issue of multiple morbidities (diseases) is much more complicated and is not yet decrypted in its full extent. The findings highlight most recurrent diseases, frequently occurring disease combinations, and their association among older adults in India. These findings may assist policymakers in drafting and issuing improved guidelines to effectively and adequately manage the chronic non-communicable disease progression and thus, combat the escalating burden of multimorbidity among older adults in India.
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|
---
title: 'Relation of incident chronic disease with changes in muscle function, mobility,
and self-reported health: Results from the Health and Retirement Study'
authors:
- James Davis
- Eunjung Lim
- Deborah A. Taira
- John Chen
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021154
doi: 10.1371/journal.pgph.0000283
license: CC BY 4.0
---
# Relation of incident chronic disease with changes in muscle function, mobility, and self-reported health: Results from the Health and Retirement Study
## Abstract
The primary objective was to learn the extent that muscle function, mobility, and self-reported health decline following incident diabetes, stroke, lung problem, and heart problems. A secondary objective was to measure subsequent recovery following the incident events. A longitudinal panel study of the natural history of four major chronic diseases using the Health and Retirement Study, a nationally representative sample of adults over age 50 years. People first interviewed from 1998–2004 were followed across five biannual exams. The study included 5,665 participants who reported not having diabetes, stroke, lung problems, and heart problems at their baseline interview. Their mean age was 57.3 years (SD = 6.0). They were followed for an average of 4.3 biannual interviews. Declines and subsequent recovery in self-reported health, muscle function, and mobility were examined graphically and modeled using negative binomial regression. The study also measured the incidence rates and prevalence of single and multiple chronic diseases across the follow-up years. Self-reported health and muscle function declined significantly following incident stroke, heart problems, lung problems, and multiple chronic diseases. Mobility declined significantly except following incident diabetes. Self-reported health improved following incident multiple chronic conditions, but recovery was limited compared to initial decline. Population prevalence after five follow-up waves reached $9.0\%$ for diabetes, $8.1\%$ for heart problems, $3.4\%$ for lung disease, $2.1\%$ for stroke, and $5.2\%$ for multiple chronic diseases. Significant declines in self-reported health, muscle function, and mobility occurred within two years of chronic disease incidence with only limited subsequent recovery. Incurring a second chronic disease further increased the declines. Early intervention following incident chronic disease seems warranted to prevent declines in strength, mobility, and perceptions of health.
## Introduction
Chronic diseases can lead to declines in physical health and a reduced perception of health. Patients who develop multiple chronic diseases face further risks of adverse outcomes that limit healthy aging [1, 2]. Chronic diseases may decrease function beyond their immediate adverse health effects. For example, studies report that having chronic obstructive pulmonary disease leads to impairments beyond the respiratory system [3–5]. Stroke can affect being able to walk without assistance and safe mobility in the home [6]. A Finland study reported cardiovascular disease and diabetes were long-term predictors of declines in muscle function and a heightened risk of mortality [7]. A study from Taiwan described a gradient effect of function with mortality [8]. Patients with declines in function coupled with multiple chronic diseases had the greatest mortality [8]. A mixed methods study of patients with multiple chronic diseases reported that three of the most bothersome chronic conditions were diabetes, heart failure, and lung problems based on their impact on function and quality of life from symptoms and activity limitations [9]. A British study reported chronic conditions and functional limitations were associated with being pushed from employment, rather than choosing to retire in good health [10].
Having existing chronic conditions can exacerbate the effects of incurring a chronic disease [11]. Patients with multiple chronic conditions often have reduced physical performance, and multiple diseases may increase declines in function with age [12]. One study described patients with congestive heart failure, diabetes, and respiratory problems as having worse physical component scores on the SF-36 over 4 years of follow-up, as did patients reporting four or more chronic conditions [13]. Physical component scores are based on eight scales of SF-36 and higher scores in physical component score indicates better physical health. A cross-sectional study reported patients with 4 or more chronic diseases had poorer physical function, increased disability and a lowered quality of life [14].
We know little about the natural history of health perceptions and function occur relative to incident chronic disease. Our study follows patients even before they incur diabetes, heart problems, lung problems, and stroke using data from the Health and Retirement study (HRS). The objective of this study was to learn how rapidly self-reported health, muscle function, and mobility decline after incident disease and to monitor if health and function recover. We hypothesized that participants would incur declines in function and self-reported health following incident chronic disease and that prior function may not be completely regained.
## Data source
The data came from the HRS, a long-term study of aging among a representative national sample of adults in the United States [15]. The HRS provides detailed economic and health data from a series of cohorts designed to cover aging since 1924 to the current time. Cohorts are re-interviewed every two years, which we termed “waves.” The HRS provides deidentified data that is freely available with registration from the HRS website. Because the data are deidentified, studies using the HRS data do not require Institutional Review Board approval.
## Study design
We designed the study as a longitudinal analysis of the influence of incident chronic diseases on self-reported health, muscle function, and mobility. The design focuses on incident disease and excluded patients diagnosed with diabetes, heart problems, stroke, or lung problems at their baseline interview. Participants were followed from their baseline exam until the end of their participation in the study. Participants were not dropped if they missed an interview. Some participants skipped interviews but returned at later exams. We classified participants as having the first disease that occurred if one developed and reclassified them as having multiple chronic conditions if they developed a second of the four chronic diseases.
## Study variables
The questions on chronic diseases were worded “has a doctor ever told you that you have ….” The conditions were assessed on later interviews to verify the incident disease. Disease would have occurred between the last negative interview and the next interview when the disease was reported. The HRS defined diabetes as having diabetes or high blood sugar; a heart problem as having a heart attack, coronary heart disease, angina, or congestive heart failure; a lung problem as chronic lung disease such as chronic bronchitis or emphysema but except asthma; and stroke as stroke or transient ischemic attack. Having multiple chronic conditions is considered as having at least two of the four chronic diseases—diabetes, heart problems, stroke, and lung problems.
The primary study outcomes were changes in self-reported health, mobility, and muscle function by time since incidence. Secondary outcomes were the incidence and prevalence of diabetes, heart problems, stroke lung problems, and multiple chronic conditions across five follow-up waves. The HRS chose measures suitable for participants with both high and low functional status. Subjective perceptions of health were considered important, regardless of whether they are accurate reflections of objective indicators [16]. The functional status measures were considered to summarize overall health status. Self-reported health had categories of excellent [1], very good [2], good [3], fair [4], and poor [5]. These categories were used for the regression model for self-reported health. Self-reported health has been shown to be associated with mortality [17–20]. Mobility was based on being unable to do the following five tasks: walking several blocks, walking one block, walking across the room, climbing several flights of stairs and climbing one flight of stairs. Large muscle function was based on being unable to do the following four tasks: walking one block, walking across the room, climbing one flight of stairs, and bathing. The total number of tasks for mobility (0–5) and large muscle function (0–4) were computed as outcomes. As example, a person able to do all the tasks for mobility or muscle function would get scores of zero. These total scores in regression models for mobility and large muscle function. As example, someone unable to do three of the mobility tasks would get a score of three, and someone unable to do two of the mobility tasks would get a score of 2. A person able to do all of the tasks for mobility or muscle function would get scores of zero. For self-reported health, scores were the category selected (e.g. poor health was scored as five).
## Study participants
The study sample was the participants first enrolled between 1998 and 2004 who, at their first interview, did not report having ever had diabetes, heart problems, lung problems or stroke. We selected these early years so that participants would have multiple follow-up exams. Interviews were conducted in 2004, 2006, 2008, 2010, 2012, and 2014. The study included members of the Children of Depression (CODA) cohort born 1924 to 1930 ($$n = 1432$$); members of the War Baby (WB) cohort born 1942 to 1947 ($$n = 1773$$), and members of the Early Baby Boomer (EBB) cohort born 1948 to 1953 ($$n = 2086$$). Smaller numbers were from the Health and Retirement cohort born 1931 to 1941 ($$n = 305$$) and the AHEAD cohort ($$n = 69$$) born before 1924. The years of recruitment of early cohorts overlapped by calendar time to provide a wide age range [21]. The cohorts provided a range of participant ages. Response rates tended to be the lowest in the first interview of the cohort and increase and stabilize thereafter [21]. The initial response rates and subsequent ranges of response rates are $80.4\%$ ($87.7\%$-$93.0\%$) for AHEAD, $81.6\%$ ($85.4\%$-$89.6\%$) for the Health and Retirement cohort, $75.3\%$-$85.5\%$-$87.7\%$) for EBB, $69.9\%$ ($87.0\%$-$90.9\%$) for WB, and $72.5\%$ ($88.7\%$-$92.3\%$) for CODA. Participants were kept in analyses for all interviews they attended prior to death. Participants were not dropped from analyses if they missed an interview. Some participants skipped interviews but returned at later exams, so they are not lost to follow-up.
## Statistical analysis
We analyzed data in three parts. First, we analyzed the baseline data on demographic variables and functional limitations. Second, we measured incidence and prevalence using data available from the five biannual waves. Third, we used multiple regression to study associations between chronic disease incidence and changes in self-reported health, muscle function, and mobility. Age groups (under age 55, 55–64, 65–74, and 75 years and older), sex, and body mass index (normal body mass, overweight, and obese) were adjusted in all regression analyses.
For the first and second parts, descriptive analyses included means and $95\%$ confidence intervals (CIs) for continuous variables and proportions with $95\%$ CIs for categorical variables. For the third part, we visualized the means of the primary outcomes graphically to understand the patterns of change in function. Changes in function were subsequently estimated as two linear slopes: one prior to incident disease and the other across subsequent waves. The statistical method used was piecewise linear models adjusted for age group, sex and body mass group [22]. Since the outcome variables are provided as counts (i.e., self-reported health score and difficulties with tasks for muscle function and mobility), negative binomial models were utilized controlling for age group, sex, and body mass index. The chronic conditions were those collected by the HRS and not included as outcomes (arthritis, cancer, and psychological problems. Arthritis has been found strongly associated with changes in physical function in older adults [23], cancer and cancer treatments may have an association [24], and psychological problems may have a bidirectional relationship [25].
Results for the change from before to after the incident wave are presented as percentage increases from baseline scores with $95\%$ confidence intervals (CIs) and results for the change across the subsequent follow-up waves are presented as percentage decreases per wave after the incident wave with $95\%$ CIs. All the analyses accounted for the complex survey design of the HRS using the baseline weight and the strata and primary sampling units of the study. All regression models were conducted using generalized estimating equations [26]. We performed analyses using SAS version 9.4 (SAS Institute, Inc., Cary, NC) and R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).
## Results
At their baseline exam the participants had a weighted mean age of 57.2 ± 6.0 years and weighted percentages of $48.15\%$ for female sex, $32.2\%$ for normal weight, $40.0\%$ for overweight, and $27.8\%$ for obesity. Of the three study outcomes weighted means were 2.66 ± 1.13, 0.96 ± 1.21, and 0.71 ± 1.16 for self-reported health, large muscle function, and mobility, respectively. Maximum scores were 5 for self-reported health and mobility, and 4 for muscle function.
Table 1 presents incidence and prevalence for the single and multiple chronic diseases across the follow-up waves. Incidence was less than two percent per year for each of the four of chronic diseases. Incidence only increased every interview for multiple chronic conditions reaching $1.9\%$ at wave 6. Prevalence in contrast to incidence increased every interview for all four chronic diseases and for multiple chronic conditions. At wave 6 diabetes and heart problems attained prevalences of $9.0\%$ and $8.1\%$, respectively. The prevalence of stroke, lung disease, and multiple chronic conditions reached $2.1\%$, $3.4\%$, and $5.2\%$ at wave 6, respectively. Eighty four percent included in the initial wave who had not reported one of the four conditions were know alive at the last follow-up wave.
**Table 1**
| Condition | Category | Wave 2 | Wave 3 | Wave 4 | Wave 5 | Wave 6 |
| --- | --- | --- | --- | --- | --- | --- |
| Diabetes | | | | | | |
| | Unweighted N | 102 | 192 | 256 | 333 | 379 |
| | Weighted incidence | 1.90% | 1.70% | 1.60% | 1.50% | 1.40% |
| | Weighted prevalence | 1.90% | 3.80% | 5.40% | 7.70% | 9.00% |
| Heart | | | | | | |
| | Unweighted N | 110 | 193 | 275 | 338 | 371 |
| | Weighted incidence | 1.80% | 1.40% | 1.20% | 1.10% | 1.00% |
| | Weighted prevalence | 1.80% | 3.30% | 5.20% | 7.00% | 8.10% |
| Stroke | | | | | | |
| | Unweighted N | 19 | 47 | 74 | 89 | 105 |
| | Weighted incidence | 0.30% | 0.20% | 0.20% | 0.10% | 0.10% |
| | Weighted prevalence | 0.30% | 0.90% | 1.40% | 1.70% | 2.10% |
| Lung | | | | | | |
| | Unweighted N | 56 | 94 | 123 | 136 | 193 |
| | Weighted incidence | 1.00% | 0.70% | 0.60% | 0.50% | 0.50% |
| | Weighted prevalence | 1.00% | 1.80% | 2.60% | 3.00% | 3.40% |
| Multiple chronic conditions | | | | | | |
| | Unweighted N | 19 | 72 | 121 | 184 | 241 |
| | Weighted incidence | 0.30% | 1.00% | 1.10% | 1.70% | 1.90% |
| | Weighted prevalence | 0.30% | 1.30% | 2.20% | 3.70% | 5.20% |
Fig 1 depicts the means ($95\%$ CIs) by chronic condition and wave for self-reported health, large muscle function, and mobility. Higher means correspond to poorer self-reported health, reduced muscle function, and decreased mobility. Across the study conditions, self-reported health has a notable “jump” in means from before-incidence to the first follow-up wave; thereafter, means remained constant or had slight declines. For muscle function and mobility, stroke, lung conditions and multiple chronic conditions had greater changes than diabetes and heart problems (shifts toward less strength and poorer mobility). Following the initial changes muscle function and mobility was not regained. Heart conditions and stroke showed modest improvements in self-reported health.
**Fig 1:** *Means of self-reported health, large muscle function, and mobility by chronic diseases.Error bars are 95% confidence intervals obtained from piece-wise linear regression models. Waves are follow-up intervals roughly two years apart. Patients initially free of the listed chronic conditions were followed from before incidence though five waves after incidence. Patients were analyzed as having single conditions until a second occurred. Subsequently, the patients were treated as having multiple chronic conditions.*
Results from the adjusted regression models had two coefficients: one for change from baseline to the first follow-up visit and the other to estimate linear slopes across the subsequent waves. Except for diabetes and mobility, the initial coefficients from the piece-wise models showed significantly higher scores representing poorer self-reported health and more difficulties related to large muscle function, and mobility (Table 2). The higher scores for the three outcome measures ranged from $10.6\%$ to $20.5\%$ for self-reported health, from $12.6\%$ to $41.5\%$ for large muscle function, and from $10.7\%$ to $101\%$ for mobility. Participants incurring multiple chronic conditions increased scores by $20.5\%$ for self-reported health, $35.9\%$ for muscle function, and $48.1\%$ for mobility. Only mobility for participants incurring multiple diseases showed significant improvement in scores after incident disease occurred (Table 3).
Table 4 gives the associations from the adjusted regression models of age group, sex, and body mass index group with self-reported health, large muscle function and mobility by diabetes, heart problems, lung problems, and stroke. Separate results are given for each of the five disease categories in columns labeled by the diseases. As an example, for diabetes participants age 75 years and older were estimated to have 0.26 higher scores on self-reported health compared to participants under age 55 years. A higher score represents poorer health. For diabetes, large muscle function was estimated as 0.77 units higher for participants age 75 years and older compared to participants under and 55 years, and mobility scores were estimated as 1.94 units higher. Table 4 provides estimates with $95\%$ confidence intervals. Across the five disease categories older age, female, and higher body mass index were associated with higher scores.
**Table 4**
| Outcomes | Predictors | Regression results by Major Chronic Disease | Regression results by Major Chronic Disease.1 | Regression results by Major Chronic Disease.2 | Regression results by Major Chronic Disease.3 | Regression results by Major Chronic Disease.4 |
| --- | --- | --- | --- | --- | --- | --- |
| Outcomes | Predictors | Diabetes | Heart problems | Lung problems | Stroke | Multiple chronic conditions |
| Self-reported health | age 55–59 | 0.07 (0.06, 0.09) | 0.07 (0.06, 0.09) | 0.08 (0.06, 0.09) | 0.08 (0.07, 0.09) | 0.08 (0.06, 0.09) |
| | age 65–74 | 0.13 (0.11, 0.15) | 0.13 (0.11, 0.15) | 0.14 (0.12, 0.16) | 0.14 (0.12, 0.16) | 0.13 (0.11, 0.15) |
| | age 75 and older | 0.26 (0.23, 0.29) | 0.25 (0.22, 0.27) | 0.26 (0.23, 0.29) | 0.26 (0.24, 0.29) | 0.25 (0.22, 0.27) |
| | Female | 0.03 (0.01, 0.05) | 0.03 (0.01, 0.05) | 0.03 (0.01, 0.05) | 0.03 (0.01, 0.05) | 0.03 (0.01, 0.05) |
| | Overweight | 0.05 (0.02, 0.07) | 0.05 (0.02, 0.07) | 0.05 (0.02, 0.07) | 0.05 (0.02, 0.08) | 0.05 (0.02, 0.07) |
| | Obese | 0.17 (0.14, 0.20) | 0.18 (0.15, 0.21) | 0.18 (0.15, 0.21) | 0.18 (0.15, 0.21) | 0.18 (0.15, 0.21) |
| Large muscle function | age 55–59 | 0.24 (0.20, 0.29) | 0.24 (0.19, 0.29) | 0.24 (0.20, 0.29) | 0.25 (0.20, 0.30) | 0.24 (0.19, 0.29) |
| | age 65–74 | 0.35 (0.26, 0.44) | 0.34 (0.25, 0.43) | 0.35 (0.26, 0.44) | 0.35 (0.27, 0.45) | 0.33 (0.24, 0.42) |
| | age 75 and older | 0.77 (0.65, 0.89) | 0.73 (0.62, 0.85) | 0.76 (0.65, 0.88) | 0.76 (0.64, 0.88) | 0.71 (0.60, 0.83) |
| | Female | 0.47 (0.38, 0.57) | 0.47 (0.38, 0.57) | 0.46 (0.37, 0.56) | 0.47 (0.38, 0.57) | 0.47 (0.38, 0.57) |
| | Overweight | 0.17 (0.08, 0.26) | 0.17 (0.08, 0.27) | 0.17 (0.08, 0.27) | 0.17 (0.08, 0.27) | 0.16 (0.07, 0.26) |
| | Obese | 0.62 (0.50, 0.75) | 0.63 (0.50, 0.76) | 0.63 (0.51, 0.77) | 0.63 (0.51, 0.77) | 0.60 (0.48, 0.74) |
| Mobility | age 55–59 | 0.40 (0.32, 0.48) | 0.38 (0.30, 0.46) | 0.38 (0.30, 0.46) | 0.40 (0.32, 0.48) | 0.37 (0.30, 0.45) |
| | age 65–74 | 0.70 (0.55, 0.86) | 0.67 (0.52, 0.83) | 0.68 (0.53, 0.84) | 0.71 (0.56, 0.87) | 0.62 (0.48, 0.77) |
| | age 75 and older | 1.94 (1.68, 2.22) | 1.81 (1.56, 2.09) | 1.87 (1.61, 2.14) | 1.91 (1.66, 2.20) | 1.73 (1.48, 1.99) |
| | Female | 0.65 (0.51, 0.81) | 0.66 (0.51, 0.82) | 0.64 (0.50, 0.80) | 0.65 (0.51, 0.82) | 0.66 (0.52, 0.82) |
| | Overweight | 0.22 (0.09, 0.37) | 0.22 (0.09, 0.37) | 0.23 (0.10, 0.38) | 1.12 (0.89, 1.38) | 0.21 (0.08, 0.36) |
| | Obese | 1.09 (0.86, 1.34) | 1.10 (0.88, 1.35) | 1.11 (0.89, 1.36) | 0.24 (0.10, 0.39) | 1.03 (0.82, 1.28) |
## Discussion
The results support the study hypothesis that declines in muscle function, mobility, and self-reported health occur following incident chronic disease and that prior function is not completely regained. We found that self-reported health and large muscle function declined by the next interview, most often held within about two years following incident diabetes, stroke, or heart or lung problems. When graphed, mean scores showed jumps toward worse function for all four study diseases, followed by a leveling off. Adjusted regression models supported that strength, mobility and self-reported health decline after incident disease. We were able to calculate with our study design both the incidence and prevalence of four major chronic diseases across time for older adult population without the diseases at baseline. Incidence increased $1\%$ to $2\%$ per wave for diabetes and heart problems and less than $1\%$ per wave for stroke and lung problems. After five biannual follow-up waves $2.1\%$ of participants had developed stroke, $3.4\%$ had lung problems, $8.1\%$ had heart problems, and $9.0\%$ of participants had developed diabetes. These results show the health risks facing initially healthy older adults.
In our study, incident chronic disease was strongly associated with self-reported health which has strong validation as a measure of adverse health [17–20]. Self-reported overall health is a widely used patient-reported outcome measure for population health surveillance. The strength of an overall self-reported measure is that it conveys not only the objective presence of disease but the patient’s perception of the impact of the disease on their overall health. Low self- reported health can predict major cardiovascular events [27]; and people reporting poor health face a greater risk of mortality compared to people reporting excellent health [18–20]. Hence, it may be important for health care providers to monitor self-reported health following chronic disease onset.
Our results found that both perceived health and functional decline follow chronic disease. The combination may have amplified the adverse effects [1]. Effects were not uniform across chronic diseases; mobility declined with incident heart and lung problems and following stroke, but not after incident diabetes. This result is consistent with a study by Fishman using propensity matching that found a weaker association between diabetes and mobility than earlier studies [28]. Fishman assumed that the smaller association was because of closer matching on covariates.
We found only minimal recovery in lost function once declines occurred. A slight rebound occurred in self-reported health following stroke and lung problems, but the rebounds were small compared to the declines. Muscle function and mobility are measures of health status and self-reported health, a risk indicator of mortality. The results emphasize the seriousness of the declines and raise the question of whether immediate interventions might mitigate the effects.
The incidence of having a second chronic condition increased every wave from $0.3\%$ at the first follow-up wave to $1.9\%$ at the fifth follow-up wave. Its prevalence reached $5.2\%$ at the fifth follow-up wave. Having multiple chronic conditions had strong associations with all of the outcomes in our analyses. Earlier research has shown that having multiple chronic conditions affects health outcomes greater than expected from the risks of individual diseases [11, 13, 14]. Multiple chronic conditions can include factors affecting declines beyond multiple chronic diseases. Wei et al. validated an index in the HRS based on 16 measures of function; the index was associated with both physical function and cognitive performance [29]. Given the complexity of patients with multiple chronic conditions, a summary index may be useful for clinical applications. Early attention to modifiable risk factors may prevent later disability [11–14]. Our results indicate intervention should begin shortly after chronic disease occurs.
In an HRS study Koroukian et al. hypothesized chronic conditions and functional limitations are part of a geriatric syndrome that tilts the balance toward increased patient burden, use of health care services, and costs [1]. Koroukian et al. analyzed eight conditions as measures of the geriatric syndrome (visual impairment, hearing impairment, moderate or severe depressive symptoms, low cognitive performance, persistent dizziness or light-headedness, and severe pain). Outcomes were fair/poor self-reported health and worsening reported health or death within 2-years. The results showed significant increases in prospective health status, major health decline, and mortality with multiple chronic conditions. A commentary by Mohammad on the article noted the importance of learning how much of the decline is preventable and the optimal approaches to prevent the declines [30]. In a follow-up article, Koroukian et al. used a classification tree and regression analysis to find combinations of measures that predicted self-reported health and mortality [27]. They concluded functional limitations and geriatric syndromes were the most prominent.
Social determinants such as isolation and food insecurity can further increase declines in health and functional limitations. Using data from the HRS, Bishop and Wang found food insecurity associated with the number of mobility limitations [31]. Patient self-perceptions of declines may affect their behavior and how they manage their chronic conditions [32–34]. Therefore, further studies are needed to explore the relationships, including mediation and moderation, among social determinants, chronic conditions, and functional limitations.
One strength of the HRS data is that questions on chronic diseases are repeated every interview and discrepancies in reported diseases get resolved. The HRS is a long-standing source of high-quality data on a nationally representative sample of older adults. Our study used a subset of the HRS population free from chronic diseases at baseline so that we could investigate incident disease. However, the following limitations should be noted. First, interviews were two years apart and outcomes were measured at variable times following incidence. Second, the results were controlled for age, sex, and body mass index, but not for health conditions or other social determinants. The results could be biased because of such omitted covariates. Third, the outcomes based on self-reported health and participants more likely to have declines following incident chronic disease may have under or overestimated their muscle function or mobility at their baseline exam leading to differential misclassification. Imprecision in measuring the outcomes by self-report could lead to non-differential misclassification. Forth, in fitting regression lines participant data was limited to interviews in which they participated.
Multiple chronic conditions were a mixed category including patients with different combinations of chronic disease. We did not have sufficient numbers to distinguish combinations of chronic diseases with the greatest adverse effects. Our study based chronic diseases and functional limitations on self-report and may differ from clinical assessments. Functional limitations, however, are based on 4 to 5 questions using validated instruments. Self-reported health is validated against mortality and outcomes of patients with heart disease [18–20]. In our study outcome assessments were at two-year intervals so shorter-term effects could not be analyzed. The change in self-reported health, muscle function, and mobility by the first two-years after incident chronic disease suggests shorter intervals would be useful to study. muscle function.
In conclusion, our study describes the natural history of four major chronic diseases in adults who had reached their fifties without incurring the chronic diseases. This study has significant public health implications. First, our findings suggest that within two years of incident diabetes, stroke, heart problems, or lung problems significant declines occurred in self-reported health, muscle function, and mobility. The only exception was diabetes and mobility. This suggests that patients may be need additional resources to mitigate or address these deficits, including physical therapy, social support, and ongoing monitoring. For low-income individuals or those with limited health literacy, this support may need to come from community health centers or other community outreach. Approaches to prevent declines should begin when chronic diseases are first identified. Second, we found that multiple chronic conditions heightened the declines. Hence, ambulatory care settings or health plans, including Medicare or Medicaid, may want to flag patients with onset of multiple chronic conditions for more intensive follow-up. Third, the fact that improvements across subsequent years were limited or non-existent emphasizes the importance of preventing the first incident events. Strengthening public health initiatives to lessen modifiable risk factors may prevent later disability.
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|
---
title: Changes in non-communicable diseases, diet and exercise in a rural Bangladesh
setting before and after the first wave of COVID-19
authors:
- Carina King
- Sanjit Kumer Shaha
- Joanna Morrison
- Naveed Ahmed
- Abdul Kuddus
- Malini Pires
- Tasmin Nahar
- Raduan Hossin
- Hassan Haghparast-Bidgoli
- A. K. Azad Khan
- Justine Davies
- Kishwar Azad
- Edward Fottrell
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021158
doi: 10.1371/journal.pgph.0001110
license: CC BY 4.0
---
# Changes in non-communicable diseases, diet and exercise in a rural Bangladesh setting before and after the first wave of COVID-19
## Abstract
Prevalence of non-communicable diseases (NCDs) is high in rural Bangladesh. Given the complex multi-directional relationships between NCDs, COVID-19 infections and control measures, exploring pandemic impacts in this context is important. We conducted two cross-sectional surveys of adults ≥30-years in rural Faridpur district, Bangladesh, in February to March 2020 (survey 1, pre-COVID-19), and January to March 2021 (survey 2, post-lockdown). A new random sample of participants was taken at each survey. Anthropometric measures included: blood pressure, weight, height, hip and waist circumference and fasting and 2-hour post-glucose load blood glucose. An interviewer-administered questionnaire included: socio-demographics; lifestyle and behavioural risk factors; care seeking; self-rated health, depression and anxiety assessments. Differences in NCDs, diet and exercise were compared between surveys using chi2 tests, logistic and linear regression; sub-group analyses by gender, age and socio-economic tertiles were conducted. We recruited 950 ($72.0\%$) participants in survey 1 and 1392 ($87.9\%$) in survey 2. The percentage of the population with hypertension increased significantly from $34.5\%$ ($95\%$ CI: 30.7, 38.5) to $41.5\%$ ($95\%$ CI: 38.2, 45.0; p-value = 0.011); the increase was more pronounced in men. Across all measures of self-reported health and mental health, there was a significant improvement between survey 1 and 2. For self-rated health, we observed a 10-point increase (71.3 vs 81.2, p-value = 0.005). Depression reduced from $15.3\%$ ($95\%$ CI: 8.4, 26.1) to $6.0\%$ ($95\%$ CI: 2.7, 12.6; p-value = 0.044) and generalised anxiety from $17.9\%$ ($95\%$ CI: 11.3, 27.3) to $4.0\%$ ($95\%$ CI: 2.0, 7.6; p-value<0.001). No changes in fasting blood glucose, diabetes status, BMI or abdominal obesity were observed. Our findings suggest both positive and negative health outcomes following COVID-19 lockdown in a rural Bangladeshi setting, with a concerning increase in hypertension. These findings need to be further contextualised, with prospective assessments of indirect effects on physical and mental health and care-seeking.
## Introduction
Globally, the prevalence of type-2 diabetes mellitus (T2DM) has been increasing and is one of the biggest health challenges, particularly in Asia [1]. The morbidity burden has been exacerbated by the COVID-19 pandemic, with uncontrolled hyperglycaemia and T2DM as significant risks for severe COVID-19 disease and mortality [2–4]. It has also been hypothesised that SARS-CoV-2 infections could trigger diabetes onset [5], and post-infection syndromes (i.e. long-COVID) include depression and persistent multi-organ impairment [6, 7]. Other common non-communicable diseases (NCDs), including obesity, hypertension and heart disease are also risks for COVID-19 mortality [8–10].
Non-pharmaceutical interventions which restrict movement and close contacts to try and contain COVID-19 transmission however could worsen NCD risks and management. For example, studies from Bangladesh have reported that older adults with NCDs experienced barriers in accessing care [11], and people living with diabetes were concerned about increased risks and consequences of COVID-19 infections [12]. Negative impacts on mental health, physical activity, diet and income have been reported in a range of South Asian contexts [13, 14], and the link between poorer mental health and diabetes increases the vulnerability of this group [15]. Therefore, improving T2DM management and reducing NCD risks may be effective in lessening the impact of COVID-19 in these populations [3, 16].
Bangladesh reported its first confirmed case of SARS-CoV-2 on the 8th March 2020, and entered into a national government declared lockdown from the 23rd March. The lockdown involved the closure of non-essential workplaces, schools, and places of worship and advice was to stay at home. The lockdown ended on 30th May 2020 but was followed by restrictions on mass gatherings which ended in September 2020 [17]. By the 1st January 2021 there had been 514,500 confirmed COVID-19 cases, and 7576 registered deaths, with the epicentre in Dhaka and cases confirmed in all districts [18]. The second wave began in March 2021 and a new lockdown was implemented on 5th April 2021.
In rural Bangladesh, where more than one third of adults have raised blood glucose levels and almost half have a raised blood pressure [19], understanding the direct and indirect impacts of the COVID-19 pandemic on NCDs is important. We therefore aimed to compare the prevalence of common NCDs (T2DM, hypertension, obesity, depression and anxiety) and measures of self-rated health, diet and exercise, immediately before the COVID-19 outbreak began and one year later. These provide a snapshot of potential indirect impacts of the pandemic.
## Methods
We conducted an opportunistic before-after study, using two cross-sectional surveys conducted from February to March 2020 (survey 1), and January to March 2021 (survey 2) in Faridpur District, Bangladesh. The surveys form the baseline for the D:Clare trial, a randomised controlled trial of a community-based participatory learning and action intervention to reduce T2DM (ISRCTN 42219712) [20]. The trial was interrupted in March 2020, and resumed in January 2021 with a new baseline survey, allowing us to compare population T2DM and other NCD prevalence and risks immediately before the pandemic was declared in Bangladesh, and one year later.
## Setting
The study is based in *Alfadanga upazilla* (sub-district), Faridpur district, with an approximate population of 120,000. The setting was purposefully selected for the D:Clare trial as it had not previously been exposed to the intervention, is less prone to flooding and is close to a field office [20]. Alfadanga is generally reflective of a ‘typical’ rural context, with an agricultural economy, and majority Bengali and Muslim population. The upazilla is divided in six unions, which we further divided into 12 clusters, and villages from all clusters were purposefully selected for inclusion on the basis of being typical of the setting. Government healthcare is provided at Union Health and Family Welfare Centres and at Community Clinics. In- and out-patient services are provided at the upazilla health complex, and tertiary care is provided at district hospitals and medical college hospitals, available in Faridpur town.
## Sampling
We followed a three-stage sampling procedure. Firstly, villages were purposefully selected from each of the 12 clusters using a ‘fried egg’ approach to minimise contamination between intervention and control clusters for the main D:Clare trial. We aimed for 800–1000 households in each cluster with eligible villages defined as: not sitting on a border with a neighbouring study cluster, not a major trading centre or administrative centre, and having a minimum of 50 households. The list of villages, and their estimated population sizes was derived from the 2011 Bangladesh census, and administrative maps. A sampling frame of all the households within the selected villages was then generated through our own study household census completed in November 2019. A simple random sample of households with at least one eligible adult was done, followed by the selection of a single eligible adult from sampled households, again using simple random sampling. Eligible adults were aged 30 years or older, resident in Alfadanga for at least 6-months, and not pregnant. At each survey, a new sample of households and individuals was generated, and some individuals may be sampled in both surveys by chance. The sample for the first survey was 1320 and 1584 for the second survey, according to the D:Clare trial which assumed a baseline prevalence of increased blood glucose of $40\%$ and power to detect a $30\%$ reduction [20].
## Data collection
Both surveys followed the same procedures. Local data collectors underwent 10 days training, including: consent, survey tools and taking anthropometric measurements, followed by one-week field piloting in a neighbouring upazilla. Data collectors worked in teams of two, with one female and one male data collector. Three field supervisors were responsible for observing and verifying data within each team at least every two days. The sampled individuals were informed of the study procedures the day before planned data collection by the study data collectors, requested to fast overnight, and asked to wear a light layer of loose clothing, which was worn for measurements. We planned two visits to each community, with the second seeking to recruit those missed in the first visit. We did not provide any monetary or non-monetary incentives for participation.
The data collectors conducted anthropometric measurements and administered a survey, adapted from the WHO Stepwise tool [21], and the 2014 Bangladesh Demographic and Health Survey [22]. This included questions on: socio-demographics; lifestyle and behavioural risk factors (diet, exercise, tobacco use); T2DM diagnosis, treatment, complications and care-seeking; hypertension and obesity diagnosis and other NCD care-seeking. Given the focus of the D:Clare Trial, more in-depth questions were asked about T2DM compared to other pre-existing NCDs. Depression and anxiety were assessed using the GAD-7 and PHQ-9 mental health screening tools amongst all participants [23, 24], the EQ-5D visual analogue scale used for self-rated health [25]. In survey 2 only, we asked about changes in recent care-seeking and avoidance of care.
For anthropometric data collection, temporary testing locations in a convenient central location in the village were set-up. Anthropometric measures included: height, weight, waist and hip circumference, blood pressure and fasting and 2-hour post-glucose load blood glucose. Blood pressure was measured twice, with a 5-minute interval between measurements using the Omron HBP 1100 Professional Blood Pressure Monitor (Kyoto, Japan). Blood glucose was measured from whole capillary blood from the finger with the OneTouch Verio Flex Glucometer (Lifescan IP Holdings, LLC), which was calibrated before data collection. Participants received a 75g glucose load dissolved in 300ml of water, and had a second blood glucose sample taken 2-hours later. People who self-reported diabetes were exempt from fasting and taking the glucose load, and instead had a single random blood glucose test. All data was collected using ODK Collect and was uploaded to a central server on a weekly basis for routine cleaning and checks.
## Analysis
Definitions of the categorical and binary indicators for impaired glucose tolerance and T2DM, hypertension, obesity and abdominal obesity, anxiety and depression are presented in Table 1. The primary analysis described these outcomes as proportions and $95\%$ confidence intervals and compared between the surveys using chi2 tests. We conducted a multivariable logistic regression to estimate the association of survey round (exposure) on the NCD outcomes, adjusted for socio-demographic factors (gender, age, socio-economic tertile, education, occupation and marital status). We used a complete case analysis due to low numbers of missing data. Secondary analysis of continuous outcomes of fasting glucose (mmol/l), systolic and diastolic blood pressure (mmHg), body mass index (BMI), waist-to-hip ratio, self-rated health (scale from 0–100), PHQ-9 and GAD-7 summarised the mean and standard deviation, and compared values in survey 1 and 2 using linear regression.
**Table 1**
| Variable | Unnamed: 1 | Definition |
| --- | --- | --- |
| Diabetes status [40] | Normal | Fasting plasma glucose <6.1mmol/l |
| Diabetes status [40] | Impaired Fasting Glucose | Fasting plasma glucose ≥6.1mmol/l to <7.0mmol/l |
| Diabetes status [40] | Impaired Fasting Glucose | and |
| Diabetes status [40] | Impaired Fasting Glucose | Two-hour post glucose load blood glucose of <7.8mmol/l |
| Diabetes status [40] | Impaired Glucose Tolerance | Fasting plasma glucose <7.0mmol/l |
| Diabetes status [40] | Impaired Glucose Tolerance | and |
| Diabetes status [40] | Impaired Glucose Tolerance | Two-hour post glucose load blood glucose of ≥7.8mmol/l to <11.1mmol/l |
| Diabetes status [40] | Type 2 Diabetes Mellitus | Fasting plasma glucose ≥7.0 mmol/l |
| Diabetes status [40] | Type 2 Diabetes Mellitus | Or |
| Diabetes status [40] | Type 2 Diabetes Mellitus | Two-hour post glucose load blood glucose of ≥11.1mmol/l |
| Diabetes status [40] | Type 2 Diabetes Mellitus | Or |
| Diabetes status [40] | Type 2 Diabetes Mellitus | Self-reported diagnosis of diabetes by a healthcare provider |
| Hypertension* | Hypertension* | Systolic blood pressure ≥ 140 mmHg |
| Hypertension* | Hypertension* | Or |
| Hypertension* | Hypertension* | Diastolic blood pressure ≥ 90 mmHg |
| Hypertension* | Hypertension* | Or |
| Hypertension* | Hypertension* | Self-reported diagnosis of hypertension by a healthcare provider |
| Obesity | Underweight | Body mass index <18.5 |
| Obesity | Normal | Body mass index ≥18.5 to <23 |
| Obesity | Overweight | Body mass index ≥23 to <28 |
| Obesity | Obese | Body mass index ≥28 |
| Abdominal obesity | Abdominal obesity | Waist to hip ratio ≥0.85 for woman and ≥0.9 for men |
| Anxiety [23] | Anxiety [23] | GAD7 score ≥10 |
| Depression [41] | Depression [41] | PHQ9 score ≥10 |
Descriptions of self-reported food consumption, exercise, NCD diagnosis awareness, T2DM treatment and complications, care-seeking and associated costs are presented. Exploratory sub-group analyses were done by gender, age and socio-economic tertiles, to check if the direction and magnitude of change were broadly similar across groups. Data was weighted to account for the sampling method (Appendix A in S1 File). Analyses were conducted using Stata SE14.
## Ethics
Written informed consent was obtained from all participants before data collection, or a thumb print for those unable to write. Ethical approvals were given by the University College London Research Ethics Committee (ref: $\frac{4199}{007}$) and the Ethical Review Committee of the Diabetic Association of Bangladesh (ref: BADAS-ERC/E/$\frac{19}{00276}$). Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in (S1 Questionnaire).
## Study participants
We recruited 950 ($72.0\%$) and 1392 ($87.9\%$) participants in the first and second surveys (Fig 1). In both surveys, non-responders were more commonly male ($55.2\%$ and $60.4\%$). Socio-demographic characteristics were similar across surveys, except for the proportion of currently married respondents ($92.6\%$ in survey 1 versus $86.8\%$ in survey 2)–Table 2.
**Fig 1:** *Participant recruitment diagram.* TABLE_PLACEHOLDER:Table 2
## Diabetes
The prevalence of T2DM and mean population fasting glucose was similar between the two surveys, and no significant differences were seen when adjusted for socio-demographic factor or explored by sub-group (Tables 3 and 4, Appendices B to D in S1 File). Overall half of participants with T2DM based on the glucose testing were aware of their status ($48.6\%$). While more diabetic participants were aware of their diagnosis in the first survey ($53.5\%$; $95\%$ CI: 47.8, 59.2), this was not statistically different from survey two ($45.3\%$; $95\%$ CI: 37.4, 53.5; p-value = 0.102). Additionally, there was no difference in time since diagnosis, with $14.9\%$ ($95\%$ CI: 8.1, 25.7) in survey 1 and $13.1\%$ ($95\%$ CI: 6.4, 24.8; p-value = 0.772) in survey 2 diagnosed in the prior 12-month period.
Of the whole population 196 people self-reported diabetes. They reported no differences in their history of diabetes-related complications ($76.3\%$ ($95\%$ CI: 66.0, 84.2) versus $72.4\%$ ($95\%$ CI: 54.6, 85.1); p-value = 0.656) or hospitalisation in the prior year ($4.1\%$ ($95\%$ CI: 1.0, 16.0) versus $3.3\%$ ($95\%$ CI: 1.2, 9.2); p-value = 0.807). In terms of self-care, blood glucose testing at least monthly decreased from $46.0\%$ ($95\%$ CI: 33.9, 58.5) to $30.5\%$ ($95\%$ CI: 23.4, 38.7; p-value<0.001), but the proportion of people with diabetes taking any medication increased non-significantly from $68.9\%$ ($95\%$ CI: 54.2, 80.6) to $73.5\%$ ($95\%$ CI: 63.8, 81.3; p-value = 0.552). The average amount spent in the prior 30-days on care-seeking for diabetes was significantly lower in survey two (mean: 888 Bangladeshi Taka (BDT); $95\%$ CI: 737, 1039) compared with survey one (mean: 1565 BDT; $95\%$ CI: 938, 2191; p-value = 0.040). In the second survey, $20.0\%$ ($$n = 22$$/117) of self-reported people with diabetes reported delaying or avoiding care in the prior 6-months; specifically, $3.2\%$ ($$n = 5$$/117) reported concerns about COVID-19 and lockdown restrictions as the reason.
## Hypertension and obesity
The percentage of people with hypertension significantly increased from $34.5\%$ in survey 1 to $41.5\%$ in survey 2 (Table 3), and this association was statistically significant when adjusted for socio-demographic factors in the multivariable regression (aOR: 1.31; $95\%$ CI: 1.04, 1.66 –Table 4). In the sub-group analysis, the increase in hypertension was more pronounced for men (+$9.9\%$, p-value = 0.012), than women (+$4.8\%$, p-value = 0.148)–Fig 2, Appendix B in S1 File, but was a similar percentage point increase amongst age and wealth sub-groups (Appendices C and D in S1 File). This increase was also reflected in the mean population systolic and diastolic measures. Amongst the participants who were hypertensive according to the blood pressure measurements, $48.2\%$ had been previously diagnosed, and this was lower in survey two ($43.2\%$; $95\%$ CI: 34.0, 53.0) compared to survey one ($57.3\%$; $95\%$ CI: 49.7, 64.6; p-value = 0.027).
**Fig 2:** *Systolic and diastolic blood pressure, by gender, age and wealth sub-groups.*
Overall there were no significant changes in either BMI or abdominal obesity categories. Women, the wealthiest tertile and younger respondents had higher rates of both measures than men or older persons, respectively. Of those with a BMI>23, $17.5\%$ had been previously told that they were overweight by a healthcare provider, with no change across survey one ($17.8\%$; $95\%$ CI: 10.0, 29.7) and survey two ($17.3\%$; $95\%$ CI: 10.6, 27.0; p-value = 0.928).
The average amount spent in the prior 30-days on care for non-T2DM NCDs was again lower in survey two (799 BDT; $95\%$ CI: 640, 959) compared to survey one (1966 BDT; $95\%$ CI: 724, 3208; p-value = 0.066). Amongst those with another self-reported NCD diagnosis, $5.3\%$ ($$n = 19$$/356) reported delaying or avoiding care in the prior 6-months due to concerns about COVID-19 or lockdown.
## Depression, anxiety and self-rated health
Across all three measures of self-reported health and mental health, there was a significant improvement between survey one and two (Fig 3). For self-rated health, assessed on a scale of 0–100, we observed a significant 10-point increase (71.3 vs 81.2, p-value = 0.005 –Table 3), and this was consistent across all sub-groups; the largest absolute increase was +14.6 points in the lowest wealth tertile group (Appendix D in S1 File). Depression was twice as high in the first survey, and generalised anxiety was 4-times higher–Table 3. The decreases in depression and anxiety were larger for women, who had lower self-rated health, and considerably higher rates of depression and anxiety in survey one than men (Appendix B in S1 File). The odds of anxiety were $83\%$ lower in survey 2 (aOR: 0.17; $95\%$ CI: 0.07, 0.41), and $72\%$ lower for depression (aOR: 0.28; $95\%$ CI: 0.10, 0.82)–Table 4.
**Fig 3:** *Change in self-reported health, GAD-7 an PHQ-9 between survey 1 and 2.*
## Diet and exercise
Diet and exercise variables are summarised in Table 5. Overall no significant difference in the hours of physical activity was seen between surveys. However, there was a significant decrease amongst men, from 14.3 hours ($95\%$ CI: 9.8, 18.8) to 9.2 hours ($95\%$ CI: 7.3, 11.1; p-value = 0.041). Dietary diversity and rice consumption was lower in survey two, while household consumption of oil and added sugar increased, and added salt and unhealthy snacks showed no change. These patterns were consistent in gender, age and wealth sub-groups.
**Table 5**
| Exercise (hrs per week) | Exercise (hrs per week).1 | Survey 1 | Survey 1.1 | Survey 1.2 | Survey 2 | Survey 2.1 | Survey 2.2 | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Exercise (hrs per week) | Exercise (hrs per week) | Mean | 95% CI | 95% CI | Mean | 95% CI | 95% CI | |
| Active work | Active work | 9.62 | (6.16, | 13.07) | 8.17 | (6.44, | 9.90) | 0.447 |
| Sport | Sport | 0.64 | (0.40, | 0.88) | 0.48 | (0.19, | 0.78) | 0.407 |
| Total active (work + sport) | Total active (work + sport) | 10.26 | (6.67, | 13.84) | 8.65 | (6.93, | 10.38) | 0.413 |
| Total sitting (daily) | Total sitting (daily) | 2.03 | (1.65, | 2.42) | 2.20 | (1.92, | 2.47) | 0.480 |
| Diet | Diet | Mean | 95% CI | 95% CI | Mean | 95% CI | 95% CI | |
| Dietary diversity* | Dietary diversity* | 7.89 | (7.46, | 8.31) | 6.90 | (6.59, | 7.20) | 0.001 |
| Rice (grams/day) | Rice (grams/day) | 1078.5 | (984.0, | 1173.1) | 785.7 | (737.1, | 834.2) | <0.001 |
| Bread (pieces/day) | Bread (pieces/day) | 0.59 | (0.45, | 0.74) | 0.60 | (0.47, | 0.73) | 0.983 |
| Oil (litres/mth) | Oil (litres/mth) | 4.45 | (4.27, | 4.63) | 4.78 | (4.62, | 4.95) | 0.010 |
| | | % | 95% CI | 95% CI | % | 95% CI | 95% CI | |
| Snack consumption** | Snack consumption** | 56.3% | (49.9, | 62.5) | 56.8% | (50.7, | 62.7) | 0.911 |
| Added salt | Every meal | 43.7% | (38.3, | 49.3) | 45.1% | (41.1, | 49.2) | 0.986 |
| Added salt | Most meals | 13.1% | (9.2, | 18.3) | 12.8% | (8.9, | 18.0) | |
| Added salt | Occasionally | 20.8% | (16.5, | 26.0) | 20.1% | (15.3, | 26.0) | |
| Added salt | Never | 22.4% | (17.2, | 28.6) | 22.0% | (17.2, | 27.7) | |
| Added sugar | | 70.7% | (62.7, | 77.6) | 60.0% | (55.8, | 63.9) | <0.001 |
| Added sugar | 1 teaspoon | 12.6% | (9.4, | 16.6) | 12.0% | (7.6, | 18.4) | |
| Added sugar | 2 teaspoons | 3.6% | (2.4, | 5.4) | 17.2% | (13.6, | 21.4) | |
| Added sugar | 3 or more teaspoons | 5.8% | (3.9, | 8.5) | 9.6% | (6.7, | 13.4) | |
| Added sugar | Not sure | 7.4% | (2.1, | 22.6) | 1.4% | (0.6, | 2.8) | |
## Discussion
In this opportunistic comparison of pre-COVID-19 and post-wave 1 lockdown population-level prevalence of NCDs and NCD risk factors in a rural Bangladesh setting, we observed a worsening in blood pressure. We did not see increases in blood glucose, diabetes, obesity or under-weight, and surprisingly, improvements across all self-reported indicators of health and mental health were observed one year on from the first COVID-19 cases in Bangladesh. Some differences in diet were seen, and less money spent on NCD care suggest negative economic impacts of the pandemic led to changes in self-care.
The increase in hypertension, and a decline in those who were aware of their diagnosis, from $57\%$ to $43\%$, suggests the pandemic may have worsened the diagnostic gap. This increase in hypertension was also more pronounced in men than women, and may be explained by the reduction in exercise, in combination with the decline in dietary quality (i.e. increased sugar and oil consumption and decreased dietary diversity). However, self-reported salt consumption did not change, despite being one of the best established risks for developing hypertension [26]. The absolute increase in systolic (6.84 mmHg) and diastolic (4.45 mmHg) population measures is a considerable cause for concern given strong associations with cardiovascular morbidity and mortality [27]. Therefore, it should be a priority to diagnose and treat missed hypertension cases, together with population-level risk reduction strategies.
Our finding of 2–4 times lower prevalence of depression and anxiety was unexpected and counters the common narrative around mental health and COVID-19. Additionally the 7–18 point increases in self-rated health in the second survey, given objective increases in hypertension (suggesting poorer health), also seems contradictory. Globally, the COVID-19 pandemic has been widely reported as worsening mental health, with an estimated $36.1\%$ increase in depression and $35.1\%$ increase in anxiety cases in the South Asian region [28]. Indeed, Bangladesh was estimated to have had one of the largest increases in poor mental health, but notably no primary data from the South Asian region was used in this modelled estimate. Studies from Bangladesh conducted during the lockdown period have reported high rates of depression in the general population ($27.8\%$-$34.1\%$) [14, 29–31]. However, two used online convenience sampling, making direct comparisons with our rural representative adult population challenging. The other key difference between our study and this literature is that our second survey occurred after restrictions had been lifted and before the second wave.
Appropriate published evidence to compare our findings with has been hard to find, and therefore we can only speculate on the reasons for improved mental health. One hypothesis is that a sense of relief that restrictions had been lifted, case numbers were low and study participants had survived meant participants reported more positively. This may especially hold true if communities trusted that restrictions had protected them, and therefore the lifting of lockdown reflects a safer situation. An online survey from New Zealand reported post-lockdown pride in coping and appreciation of family [32], and a UK study observed living in rural areas and with others were protective against lockdown loneliness [33]. Therefore, the return of family members from international and urban areas to their rural family homes during periods of restriction [34], may also have been a key contributing factor. A qualitative contextual understanding of this finding is needed.
The only methodological difference between the two surveys was five of the 12 data collectors being replaced, and it is possible that interviewers influenced participant’s respondents through the way they administered the questions. Considerable efforts were made during training to standardise the interview process, and both the GAD-7 and PHQ-9 have been used and validated for the Bangladeshi context [35, 36]. Given the results were consistent across sub-groups and all three measures, this suggests it is a genuine change. Our data should highlight the need for on-going assessments of mental health impacts, to understand care and support needs over the course of this complex pandemic situation.
We observed two possible financial coping strategies—reduced food consumption and reduced care-seeking. While respondents in the second survey did not commonly report delaying care-seeking due to COVID-19 concerns, they spent less money on care and people living with T2DM reduced their glucose testing. In this context, informal observations suggest the price of medications can be lower than going for routine blood glucose testing, and so as a financial coping mechanism it is plausible that people known to have diabetes shifted their management priorities. This is reflected in a study in which people with diabetes reported that they were more concerned about not being able to test ($26\%$) due to COVID-19 than access to medication ($18\%$), and a quarter felt their quality of care had declined [12]. Other studies from Bangladesh and India have also reported reduced care access [11, 13, 37]. However multiple barriers to accessing diabetes services were present before the pandemic, including cost [38, 39]. This short term shift in management, and challenges in care access, did not appear to lead to catastrophic health outcomes for known diabetics. However, this needs to be carefully monitored for possible longer term consequences.
While our study is strengthened by the large random population sample, and objective anthropometric measures, we had three key limitations. Firstly, the lower response rate in the first survey. We stopped field activities early due to the COVID-19 pandemic, and therefore had a higher proportion of sampled participants who were not recruited. The age and gender of non-responders was similar between survey 1 and 2, but the non-responders may differ in other socio-demographic factors which made them hard to recruit on our first visit their village. Secondly, we relied on self-reported measures of diet, exercise and care-seeking, depression and anxiety were classified using screening tools instead of clinical diagnosis and we considered self-reported clinical diagnoses of diabetes and hypertension as valid. These variables are therefore subject to recall and social-desirability biases and non-differential misclassification of outcomes; however, given our methodology was the same for both surveys we could expect these biases to be consistent. Third, the change in NCD prevalence may reflect regression to the mean, with the sample in survey 1 representing values further from the true population mean.
Amongst this representative rural adult population from Faridpur district, Bangladesh, it was apparent that several months after COVID-19 lockdown measures had been lifted, both negative and positive health changes were still present. Most notably, between the 12-month period from early 2020 to 2021 there had been a large population increase in systolic and diastolic blood pressure. The potential public health implications of this are substantial and need to be a priority for both preventive risk reduction and diagnosis and treatment of existing morbidities. However, on a positive note, consistent improvements in mental health suggest that the widely document negative impacts of lockdowns may not persist once restrictions are lifted. Further work into understanding this phenomenon is needed to support effective recovery.
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|
---
title: Chronic diseases and mortality among hospitalised COVID-19 patients at Bafoussam
Regional Hospital in the West region of Cameroon
authors:
- Imelda Sonia Nzinnou Mbiaketcha
- Collins Buh Nkum
- Ketina Hirma Tchio-Nighie
- Iliasou Njoudap Mfopou
- Francois Nguegoue Tchokouaha
- Jérôme Ateudjieu
journal: PLOS Global Public Health
year: 2023
pmcid: PMC10021159
doi: 10.1371/journal.pgph.0001572
license: CC BY 4.0
---
# Chronic diseases and mortality among hospitalised COVID-19 patients at Bafoussam Regional Hospital in the West region of Cameroon
## Abstract
Reducing mortality among COVID-19 cases is a major challenge for most health systems worldwide. Estimating the risk of preexisting comorbidities on COVID-19 mortality may promote the importance of targeting at-risk populations to improve survival through primary and secondary prevention. This study was conducted to explore the contribution of exposure to some chronic diseases on the mortality of COVID-19. This was a case control study. The data were collected from the records of all patients hospitalised at Bafoussam Regional Hospital (BRH) from March 2020 to December 2021. A grid was used to extract data on patient history, case management and outcome of hospitalised patients. We estimated the frequency of each common chronic disease and assessed the association between suffering from all and each chronic disease (Diabetes or/and Hypertension, immunodeficiency condition, obesity, tuberculosis, chronic kidney disease) and fatal outcome of hospitalised patients by estimating crude and adjusted odd ratios and their corresponding $95\%$ confidence intervals (CI) using time to symptom onset and hospital admission up to three days, age range 65 years and above, health professional worker and married status as confounder’s factors. Of 645 included patients, 120($20.23\%$) deaths were recorded. Among these 645 patients, 262($40.62\%$) were males, 128($19.84\%$) aged 65 years and above. The mean length of stay was 11.07. On admission, 204 ($31.62\%$) patients presented at least one chronic disease. The most common chronic disease were hypertension (HBP) 73($11.32\%$), followed by diabetes + HBP 62 ($9.61\%$), by diabetes 55($8.53\%$) and Immunodeficiency condition 14($2.17\%$). Diabetes and Diabetes + HBP were associated with a higher risk of death respectively aOR = 2.71[$95\%$CI = 1.19–6.18] and aOR = 2.07[$95\%$ CI = 1.01–4.23] but HBP did not significantly increased the risk of death. These results suggest that health authorities should prioritize these specific group to adopt primary and secondary preventive interventions against SARS-CoV-2 infection.
## Introduction
The COVID-19 pandemic has become a global public health threat, endangering the health and well-being of populations [1, 2]. The response to this pandemic requires the identification and control of factors that increase its attack and case fatality rates (CFR) [3, 4]. Since the onset of the COVID-19 pandemic, several studies have been conducted to identify the determinants of its transmission, morbidity, and mortality. Poverty, low level of education, fragility of the health system, overcrowding in urban areas and climate have been identified as factors that increase the risk of transmission of the virus [5–9]. COVID-19 patient with diabetes, obesity, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), hypertension, malignancies, Human Immunodeficiency (HIV) virus and other comorbidities may develop a life-threatening condition. Comorbidities lead the COVID-19 patient into an infectious vicious cycle and are substantially associated with significant morbidity and mortality. Comorbid individuals require vigilant preventive measures and careful management [10]. In the current state of knowledge COVID-19 infection in patients aged age 65 years and above, male, and non-communicable pathologies has been found to contribute in inducing complication of COVID-19 and to increase the probability of death [11–13]. Furthermore, some studies have reported that healthcare workers are more likely to develop COVID-19 [14, 15], to be hospitalised and to die [16] despite having pre-existing conditions.
These determinants necessary for effective monitoring of pandemic’s progress may vary across countries or settings and need to be considered to tailor the response to the realities of the pandemic in each setting.
In Cameroon, the epidemic began in March 2020. The response consisted of government measures such as closing all borders, closing bars and restaurants after 6:00 p.m., restricting people on public transport, closing schools and universities, and mandating the wearing of masks in public places [17]. These initial strict government measures were quickly relaxed [18, 19] to including the establishment of specialized centers for the management of COVID-19 cases, the decentralization of interventions, the integration of operational research, the availability of rapid diagnostic tests that improve screening capabilities, and the establishment of a national surveillance platform to track circulating SARS-CoV-2 have helped limit disease transmission. In addition, the implementation of a vaccination program since April 2021, despite public reluctance, would strengthen the control of the epidemic. Despite these primary and secondary preventive measures taken at individual and collective levels, COVID-19 is still characterized by a relatively high CFR of 1,$7\%$ in Cameroon as on the 31st December 2021 [20]. To the best of our knowledge, many studies have been conducted on the effects of comorbidities on the risk of death in COVID-19 in other countries [10–13] but none of them reflect the situation of Cameroon where the epidemiology of disease depends on the physical, financial and acceptability of health care services to populations [21, 22]. This lack of information in Cameroon explains why this issue was investigated in the present study.
This study was conducted to assess the distribution of chronic disease cases and deaths, among COVID-19 hospitalised and death patients at the Bafoussam Regional Hospital (BRH), the intermediate care level in one of most affected regions of Cameroon and to determine if any of these conditions increases the risk of mortality among COVID-19 patients.
The findings are expected to be shared with health personnel and authorities in charge and interested scientists to contribute to the review of policy and research needs for a better response to COVID-19 in Cameroon and other developing countries with similar parameters.
## Diabetes
Blood glucose level, at any time of the day, is higher than 2 g/l in the presence of symptoms such as polyuria, polydipsia, constant hunger, weight loss, visual impairment and fatigue; the fasting blood glucose level is ≥ 1.26 g/l, tested twice in the absence of symptoms. In order to confirm the result of the fasting blood glucose test, a second blood test is performed.
## HBP: Hypertension
(High blood pressure-HBP) is the permanent elevation of blood pressure (BP) above 140mm Hg for systolic pressure and 90 mm Hg for diastolic blood pressure.
## Immunodeficiency condition
Detection in blood sample of antibodies against human immunodeficiency virus (HIV).
## Obesity
Body mass index (BMI) ≥30, calculated as weight divided by the square of height, expressed in kg/m2.
## Tuberculosis
Presence of *Mycobacterium tuberculosis* determined by microscopic examination, bacteriological examination of the sputum, broncho alveolar lavage and/or tests of gene amplification.
## Chronic kidney disease
Is a long-standing and progressive deterioration of kidney function. The diagnosis is based on laboratory tests of the renal function using estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73 m2 or urine albumin to creatinine ratio [ACR] ≥ 30 mg/g).
COVID-19 (Coronavirus disease 2019): presence of SARS-CoV-2 virus detected by reverse transcription polymerase chain reaction (RT-PCR) or by a rapid diagnostic test (BIO RAD RDT COVID test kit).
## Ethical considerations
The present study is aim at providing information on factors that increase case fatality in hospitalised COVID-19 cases, which will allow targeting of primary and secondary interventions to help reduce case fatality in these groups. The protocol was evaluated and approved by the Cameroon National Ethics Committee for Human Health Research (N°$\frac{2022}{04}$/110/CE/CNERSH/SP). In addition, administrative authorization was obtained from the Director of BRH to collect data from the patient records of the COVID-19 case management center. The information collected was coded and anonymous, and access was restricted to researchers involved in the data collection.
## Study design
This was a case control study. Data were collected from the files of COVID-19 patients hospitalised at the BRH from March 2020 to December 2021. The descriptive part allocated to distribute chronic disease cases and deaths among COVID-19 hospitalised and death patients and case control component assessed the effect of presenting each chronic disease as a comorbidity on the risk of COVID-19 mortality.
The cases were the deceased patients and were matched to controls who were 02 recovered patients of the same sex. The exposure was defined as the presence of diabetes and/or HBP, immunodeficiency condition, obesity, tuberculosis, chronic kidney disease. The association between these exposures and the occurrence of COVID-19 death was estimated by calculating crude odd ratio (OR) and adjusted OR with age 65-year and above, married status, time from symptom onset to hospital admission of three days or more, and health professional.
## Study site and period
The study was conducted at the COVID-19 Case Management Center at the BRH, the best technically equipped hospital in the western region for the management of severe COVID-19. This reference center was selected and has, among other missions, the management of symptomatic and eligible COVID-19 cases referred by the 20 health Districts of the region since the beginning of the pandemic in March 2020. Data’s were collected from March 2020 to December 2021.
## Study population
Was included for the descriptive component of our study any patient who was tested positive for SARS-CoV-2 either by a reverse transcription polymerase chain reaction (RT-PCR) or a rapid diagnosis test (RDT) and was hospitalised at the COVID-19 case management unit at the BRH.
## Sampling and sample size
For the descriptive component, all 645 patients hospitalised at the BRH for the period of the study were included. For the case-control component, same-sex controls were randomly matched to cases. The sample size estimated was 120 cases and 240 controls, assuming a study power of $90\%$, level of significance of $5\%$, that the risk of death will be about $50\%$ and an expected odd ratio of at least 2, 5 between exposures and death. The estimation was guided by the WHO (World Health Organization) manual for sample size determination in health practices [23].
## Selection of cases (deaths)
We included patients who tested positive for SARS-CoV-2, were hospitalised and died from COVID-19 and excluded patients who were transferred, escaped, had undocumented exposure or treatment outcome or had incomplete data.
## Selection of controls (recovered)
We included patients who tested positive for SARS-CoV-2, were hospitalised and recovered of COVID-19 and excluded patients who were transferred, escaped, whose exposure or treatment outcome was not documented or whose data were incomplete.
Criteria for matching cases to controls: were randomly matched to 1 case 2 controls of same-sex and who recovered after hospitalisation.
## Data collection tools
Data were collected from patient records using a data collection grid in Microsoft Excel developed by the study team and pretested in a district hospital in the western region. The main variables collected were chronic diseases of hospitalised cases, admission symptoms, outcome (death or recovered) temporal location and socio-demographic characteristics.
## Data management
Data were reviewed after completing the data collection grid in Microsoft Excel to detect any omissions or confusing observations. These data were compared to the existing hospital database and any corrections were made. The data collected was processed daily and cross-cheeked by a second person to detect and process errors.
## Data analysis
For descriptive analyses, proportions and means were estimated. Crude and adjusted odds ratios were used to estimate the associations between the presence of diabetes and/or HBP, immunodeficiency condition, obesity, tuberculosis, chronic kidney disease and death of COVID-19. Chi-square tests were used to compare the characteristics of the case and control groups and the characteristics that differed were included as adjustments: health professional, age 65 years and above, married status and time from symptom onset to hospital admission for three days or more. Analyses were performed using IBM SPSS version 23 (IBM Corporation, IBM SPSS Statistics for Windows, Version 23.0. Armonk, New York.) and Microsoft 2019 Excel software (Microsoft Corporation, Microsoft Excel. 2019) and p-value < 0.05 were considered significant.
## Results
From the onset of the pandemic in March 2020 to December 2021, 645 COVID-19 patients were hospitalised at the COVID-19 Case Management Center at the BRH. Only 593 patients met the inclusion criteria, of which 120 ($20.23\%$) died and 473 ($79.76\%$) recovered. Of these 593, 360 were included in the case-control arm: 120 included as cases and 240 as controls. The number of patients included at each stage of the study are shown in Fig 1.
**Fig 1:** *Flow chart of the study.*
## Study population characteristics
A total of 645 patients were hospitalised at BRH from March 2020 to December 2021. The median age was 43 years (interquartile range 31–61). Of all hospitalised patients 262 ($40.62\%$) were male and 128(19, $84\%$) were aged 65 and above. Almost all patients were married: 591($91.63\%$). Health professional were represented as 85 ($13.18\%$) (Table 1).
**Table 1**
| Modalities | Frequency (N) | Proportion (%) |
| --- | --- | --- |
| Profession | | |
| Self-employment | 10.0 | 155.0 |
| Farmer/trader | 26.0 | 403.0 |
| Electro technician | 23.0 | 357.0 |
| Employee | 10.0 | 155.0 |
| Student | 41.0 | 636.0 |
| Civil servant | 56.0 | 868.0 |
| Housekeeper | 102.0 | 1581.0 |
| Not documented | 225.0 | 3488.0 |
| Health professional | 85.0 | 1318.0 |
| Religious | 5.0 | 78.0 |
| Pensioner | 62.0 | 961.0 |
| Sex | | |
| Female | 383.0 | 5938.0 |
| Male | 262.0 | 4062.0 |
| Marital status | | |
| Single | 37.0 | 574.0 |
| Married | 591.0 | 9163.0 |
| Not documented | 13.0 | 202.0 |
| Widow | 4.0 | 62.0 |
| Age range | | |
| [15–49] | 385.0 | 5969.0 |
| [50–64] | 132.0 | 2047.0 |
| ≥65 | 128.0 | 1984.0 |
The temporal distribution of COVID-19 confirmed cases admitted to BRH is shown Fig 2. Two peaks of absolute increase in cases are noted from May to June 2020 and from April to May 2021.
**Fig 2:** *Temporal distribution of COVID-19 confirmed cases: Hospitalised cases and deaths.March 2020-December 2021.*
The symptoms of COVID-19 confirmed cases on admission are shown in Fig 3. The most frequent symptoms were cough ($76.86\%$), fatigue ($75.97\%$) and dyspnea ($71.32\%$).
**Fig 3:** *Distribution of COVID-19 confirmed cases and deaths according to symptoms on admission.*
The most common comorbidities among COVID-19 confirmed patients hospitalised at BRH were diabetes, hypertension, diabetes + hypertension and HIV positivity Fig 4.
**Fig 4:** *Distribution of COVID-19 confirmed hospitalised cases and deaths according to comorbidities.*
## Characteristics of cases (Deaths) and controls (recovered) of COVID-19
A total of 360 patients were included in the case-control arm, 120 cases (deaths) and 240 controls (recoveries). The reported Chi-square shows that these characteristics are significantly different with respect to time to symptom onset and hospital admission up to three days, age range 65 years and above, health professional worker and married status. These characteristics being significantly different in cases and controls were considered confounding factors for this study. The comparison of socio-demographic characteristics of cases and controls are shows (Table 2).
**Table 2**
| Variables | Therapeutical evolution | Therapeutical evolution.1 | Therapeutical evolution.2 | Therapeutical evolution.3 |
| --- | --- | --- | --- | --- |
| Variables | Cases n (%) | Controls n (%) | Chi2 | p-value |
| Age range | | | | |
| ≥65 | 68(56,67) | 35(14,58) | 6936 | 0,000 * |
| <65 | 52(43,33) | 205(85,42) | | |
| Symptom admission delay | | | | |
| >3 | 87(73,11) | 99(41,6) | 3156 | 0,000 * |
| < = 3 | 32(26,89) | 139(58,4) | | |
| Marital status | | | | |
| Married | 110(96,49) | 209(88,56) | 599 | 0,014 * |
| Single/widow | 4(3,51) | 27(11,44) | | |
| Professions | | | | |
| Housekeeper | 27(23,08) | 31(17,03) | 546 | 019 |
| Health professional | 17(14,53) | 56(30,77) | 1017 | 0,001 * |
## Association between chronic diseases and mortality of COVID-19 cases
In univariate logistic regression of each of the chronic disease with the therapeutic outcome death/recovered, it was found that diabetes, diabetes +Hypertension, obesity and immunodeficiency condition were significantly associated with the occurrence of COVID-19 mortality. However, after adjustment of each of these variables, only diabetes, diabetes + Hypertension and immunodeficiency condition remained significantly associated with the occurrence of COVID-19 death: diabetes significantly increased the risk of dying from COVID-19 by 2.71, diabetes and Hypertension significantly increase the risk of dying from COVID-19 by 2.07 and immunodeficiency condition increases the risk of dying from COVID-19 by 7.21 (Table 3).
**Table 3**
| Variables | Variables.1 | Death (%) | Recovered (%) | Crude Association | Crude Association.1 | Crude Association.2 | Adjusted Association | Adjusted Association.1 | Adjusted Association.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Variables | Death (%) | Recovered (%) | cOR | 95%CI | p-Value | aOR | 95%CI | p-Value |
| Diabetes | Yes | 29(24,17) | 12(5,00) | 605 | 2,96–12,38 | 0,000* | 271 | 1,19–6,18 | 0,017* |
| Diabetes | No | 91(75,83) | 228(95,00) | 605 | 2,96–12,38 | 0,000* | 271 | 1,19–6,18 | 0,017* |
| Diabetes + HBP | Yes | 30(25,00) | 18(7,50) | 411 | 2,18–7,74 | 0,000* | 207 | 1,01–4,23 | 0,04* |
| Diabetes + HBP | No | 90(75,00) | 222(92,50) | 411 | 2,18–7,74 | 0,000* | 207 | 1,01–4,23 | 0,04* |
| Hypertension | Yes | 22(18,33) | 32(13,33) | 145 | 0,80–2,64 | 021 | | | |
| Hypertension | No | 98(81,67) | 208(86,67) | 145 | 0,80–2,64 | 021 | | | |
| immunodeficiency condition | Yes | 7(5,83) | 4(1,67) | 364 | 1,04–12,71 | 0,03* | 721 | 1,49–34,7 | 0,013* |
| immunodeficiency condition | No | 113(94,17) | 236(98,33) | 364 | 1,04–12,71 | 0,03* | 721 | 1,49–34,7 | 0,013* |
| Obesity | Yes | 6(5,00) | 3(1,25) | 415 | 1,02–16,09 | 0,03* | 534 | 0,92–30,8 | 006 |
| Obesity | No | 114(95,00) | 237(98,75) | 415 | 1,02–16,09 | 0,03* | 534 | 0,92–30,8 | 006 |
| Tuberculosis | Yes | 4(3,33) | 2(0,83) | 409 | 0,73–22,65 | 01 | | | |
| Tuberculosis | No | 116(96,67) | 238(99,17) | 409 | 0,73–22,65 | 01 | | | |
| Chronic Kidney Disease | Yes | 3(2,50) | 2(0,83) | 305 | 0,5–18,4 | 02 | | | |
| Chronic Kidney Disease | No | 117(97,50) | 238(99,17) | 305 | 0,5–18,4 | 02 | | | |
## Discussion
This study was conducted to assess the contribution of chronic disease on the mortality of COVID-19 hospitalised cases in a hospital offering an intermediate level of care in Cameroon.
A total of 120 ($20.23\%$) deaths were recorded among included hospitalised COVID-19 patients. Being diabetic, diabetics and hypertensive, or having immunodeficiency condition was found to be associated with a higher risk of death while being hypertensive did not significantly increase the risk of death among hospitalised COVID-19 patients.
The present study revealed a significant association between the presence of diabetes and COVID-19 mortality; crude OR = 6.05 [$95\%$ CI = 2.96–12.38] and adjusted OR = 2.71 [$95\%$ CI = 1.19–6.18]; this association was found both in a retrospective cohort study and in an analysis of global data from China in 2020 [24, 25] with a RR = 3.64 [$95\%$ CI = 1.09–12.21] and RR = 1.59 [$95\%$ CI = 1.03–2.45] respectively. Similar results were reported in other studies [26–30]. None of the data we have collected does not allow us to explain this association, but the plausible hypothesis could be that the vulnerability of these patients can be explained by the presence of comorbidities, notably cardiovascular disease [31]; and by a weakened immune response to the infection [32]. The data suggest that the increased disease severity observed in diabetes is likely due to a dysregulated immune response because of increased expression of ACE-2 (SARS-CoV-2 receptor) in diabetics; this may promote increased cellular binding to SARS-CoV-2. The virus is known to use ACE-2 receptors, which are found on the surface of host cells, to enter the cell [33]. High levels of ACE-2 receptors have been shown to be associated with diabetes, which may predispose diabetics to SARS-CoV-2 infection. A dysregulated immune response with increased ACE-2 receptors may lead to increased lung inflammation and lower insulin levels. The easy entry of the virus leads to a life-threatening situation for diabetic patients. Also, impaired T-cell function and high levels of interleukin-6 (IL-6) also play a decisive role in the development of COVID-19 disease in diabetics [34]. In addition, COVID-19 tends to progress in a high glucose environment, and fluctuations in blood glucose levels can compromise the immune system and make the viral infection more difficult to treat and longer lasting [35].
Although the sample size was small, this study was able to show that immunodeficiency condition was associated with a 3-fold increased risk of death from COVID-19: crude OR = 3.64 [$95\%$ CI = 1.04–12.71]. Till date, no available comparable study has been published in Cameroon. Our results are similar to those of a retrospective cohort study comparing the risk of death from COVID-19 in people living with and without HIV in the OpenSAFELY trial in England [36, 37], and different from those of other studies in other countries [37, 38]. People with immunodeficiency condition are at high risk of developing COVID-19 disease due to their weakened immune system.
Obesity was not a risk factor for COVID-19 in this study after adjustment for age 65 years and above, time from first symptom to hospital admission, health professional and married status (crude OR = 4.15 [$95\%$ CI = 1.02–16.09] and adjusted OR = 5.34 [$95\%$ CI = 0.92–30.08]); this result is similar to that reported in China [39]. This difference could be due to the difference in population, sample size and methodology. Furthermore, adjustment for intermediate factors may reduce the relationship between obesity and severity in COVID-19. Nevertheless, several other studies have found that a high body mass index (BMI) is a risk factor for the severity of COVID-19 [40, 41]. Indeed, obesity is generally associated with impaired lipid synthesis and clearance, which can trigger or aggravate inflammation and lung damage. It has been shown that for viral entry into the host cell, SARS-CoV-2 utilizes angiotensin converting enzyme 2 (ACE-2) receptors present on cells; diet-induced obesity showed a significant increase in ACE-2 expression in the lungs [42]. Further studies are needed to explore this relationship.
This study found no association between the presence of HBP (OR = 1.45, $95\%$ CI 0.80–2.64) and the occurrence of COVID-19 mortality. This trend varied between studies. A cohort study of electronic data from patients in England showed that HBP was not a risk factor for COVID-19 mortality after adjusting for age, sex, and ethnic group (OR = 0.89 $95\%$ CI = 0.85–0.93) [43]. Similarly, in Italy, after a multicenter cross-sectional study involving 26 hospitals contacted by the network of the Italian Society of HBP in 13 regions in 2020, it was shown that HBP was not associated with the occurrence of death from COVID-19 after adjustment for age and sex (p-value = 0.944) [44]. Some studies found no association between the presence of HBP and the occurrence of mortality to COVID-19. However, other studies reported a significant association between the two [11, 28, 30, 45–47], These results may be explained by the difference in the population, the difference in our sample sizes. The data suggest that the use of different antihypertensive treatments in the population plays an important role: the risk of developing a severe form of COVID-19 was lower in patients treated with ACE inhibitors [45, 48]. Additional large-scale studies are needed to explore the causal relationships underlying the observed associations between HBP and mortality in COVID-19 and controlling for possible confounders, including age and various morbid conditions.
Interpretation and use of these results must take into account limitations such as low completeness of data, lack of standardization of data in records, poor completion of records, small sample size and difficulties in deciphering data in records; the study was conducted at a single site with limited external validity; many patients who tested positive for COVID-19 and died on admission to the management service were not included in our study; Also, we considered infectious pulmonary diseases (tuberculosis) and not pulmonary diseases (COPD) as comorbidity. Further studies are needed in the African context to clarify the consistency of the results.
## Conclusion
The overall objective was to assess the contribution of chronic disease to COVID-19 mortality at BRH. Approximately $18.60\%$ of COVID-19 patients at that time died in this intermediate care hospital in Cameroon. Being diabetic, diabetics and hypertensive, or having immunodeficiency condition was found to be associated with a higher risk of death while being hypertensive did not significantly increase the risk of death among hospitalised COVID-19 patients. We can conclude that patients presenting some comorbidities are exposed to a relatively higher of risk of COVID-19 mortality.
This study allows to improve COVID-19 management, health professionals should better understand the chronic conditions that make patients more vulnerable to develop a severe complication and death due to COVID-19, to upgrade the management of COVID-19 patients with comorbidities, as opposed to patients without comorbidities, in order to control the danger of death. People with comorbidities should take vigorous preventive measures to protect themselves during the pandemic. The use of the COVID-19 vaccine would protect patients with comorbidities and they should be vaccinated as a priority. We recommend to the health authorities to prioritize diabetics, diabetics and hypertensive patients for primary and secondary prevention of COVID-19 cases, to validate appropriate therapeutic protocols to reduce morbidity and mortality; to improve and evaluate primary prevention; to the scientific Community we recommend to repeat the study prospectively with a large sample size to assess accurate associations between hypertension, obesity and morbidity-mortality in COVID-19.
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|
---
title: 'Prevalence of and factors associated with high atherogenic index among adults
in Nairobi urban informal settlements: The AWI-Gen study'
authors:
- David Wambui
- Shukri Mohamed
- Gershim Asiki
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021160
doi: 10.1371/journal.pgph.0000224
license: CC BY 4.0
---
# Prevalence of and factors associated with high atherogenic index among adults in Nairobi urban informal settlements: The AWI-Gen study
## Abstract
Dyslipidemia is an important cardiovascular disease predictor. Atherogenic index of plasma (AIP), a ratio of triglycerides (TG) to high density lipoprotein (HDL) cholesterol has been deemed to be more informative as a cardiovascular disease predictor compared to using any single predictor. The aim of this study was to explore the factors associated with elevated atherogenic index among people living in low-income urban areas of Nairobi, Kenya. Data used in this study were obtained from a cross-sectional population-based study with 2,003 participants conducted in Nairobi as part of the Africa Wits-INDEPTH Partnership for Genomic Research, AWI-Gen). Sociodemographic, behavioral, and clinical characteristics were collected from the participants. AIP was derived from the log of TG/HDL cholesterol and categorized into low risk (AIP<0.1), intermediate risk (AIP = 0.1–0.24) and high risk (AIP >0.24). Fifty-four percent ($54\%$) of the study participants were women and the mean age of participants enrolled in this study was 48.8 years. Twenty-nine percent ($29\%$) of study participants had high or medium atherogenic risk. Men, HIV patients, individuals with self-reported uncontrolled diabetes and obese individuals were at higher atherogenic risk. We have identified modifiable risk factors which can be addressed to reduce dyslipidemia in this population. Longitudinal studies may help to precisely determine how these factors relate with cardiovascular diseases.
## Introduction
Atherosclerotic cardiovascular diseases including ischemic heart disease, stroke and peripheral arterial disease present a significant contribution to global mortality [1]. In 2019, cardiovascular diseases accounted for 18.6 million deaths, a $17.1\%$ increase since 2010 [2]. Cardiovascular disease has for the last 15 years consistently remained the leading cause of death globally [3]. The burden is projected to increase over the next couple of years [4–6]. By 2030 there will be 23.6 million deaths from heart disease and stroke [4]. Atherosclerotic cardiovascular diseases risk factors range from environmental, behavioral and clinical [7] causes including dyslipidemia, physical inactivity, obesity and Type II diabetes [8]. Understanding the distribution of these risk factors is critical in designing prevention strategies for atherosclerotic cardiovascular diseases. Individual lipid profiles as single measures have been used widely by clinicians and researchers as risk predictors for atherosclerotic cardiovascular diseases [8,9]. However, AIP has been shown to be more informative compared to single lipid profiles and hence a better risk predictor [7,8,10]. AIP has also been shown to reflect the distribution of lipid particle sizes and significantly correlates with presence of other atherosclerosis risk factors [11–13]. AIP takes into account the balance between harmful and protective lipids and has been described as a better risk predictor [10,14]. AIP is derived from a logarithmic transformation of the ratio between TG and HDL [15,16]. High values of AIP are associated with increased atherosclerotic cardiovascular risk. Over the recent past, atherogenic index, a ratio of triglycerides and HDL cholesterol has become an important predictor of cardiovascular diseases [17]. This is because unlike single lipid profile predictors, atherogenic index is more informative and is therefore regarded as a powerful surrogate indicator for cardiovascular disease risk [17,18]. A number of studies have shown correlations between high atherogenic index and cardiovascular diseases [7,10,15,19]. For better characterization of atherosclerotic cardiovascular risk using AIP, threshold values have been defined such that AIP of less than 0.1 regarded as low risk, 0.1 to 0.24 as intermediate risk and AIP above 0.24 regarded as high risk [10].
As a measure of atherosclerotic cardiovascular risk, it is important to understand factors that contribute to an increase in AIP. Over the past two centuries, epidemiological transition driven by industrialization, urbanization, economic growth, and lifestyle changes has resulted to the double burden of disease among sub-Saharan African countries with an upward trend in noncommunicable diseases. These changes have resulted to a cluster of environmental, behavioral and clinical risk factors for cardiovascular disease including hyperlipidemia, tobacco smoking, diabetes mellitus, hypertension, obesity, physical inactivity, alcohol consumption and poor dietary habits [20]. An interplay of these environmental and behavioral factors may explain the reported high prevalence of dyslipidemia (15–$50\%$) among African adults [21]. It is projected that by 2030, cardiovascular diseases will be the leading cause of death overtaking infectious diseases [22].
These changes in environmental and behavioral factors among people in sub-Saharan Africa (SSA) could be a driver of increased atherosclerotic cardiovascular risk. A recent study reported a $50\%$ increase in age-adjusted cardiovascular disease associated mortality in the region over the past three decades [23]. In 2017, SSA reported close to one million cardiovascular-related deaths with ischemic heart disease and stroke accounting for over $70\%$ of these deaths [24]. In Kenya, a study conducted in the same population identified increased age and hypertension as significant drivers of CVD mortality in the population [25]. According to this study, addressing the identified risk factors would reduce the observed CVD mortality by up to $29\%$. This calls for better understanding of associated risk factors to better tailor preventative measures. The aim of this study was to explore the factors associated with elevated AIP among people living in low-income urban areas of Nairobi Kenya.
## Study design and data collection
Data were drawn from a cross-sectional population-based study conducted in Nairobi as part of the Africa Wits-INDEPTH Partnership for Genomic Research, AWI-Gen. The AWI-Gen study was conducted to estimate physiological, environmental, and genetic risk factors for cardio-metabolic diseases among individuals aged between 40 and 60 years. This study focused on 2,003 participants who were recruited from two urban slums (Viwandani and Korogocho) in Nairobi, Kenya. Data collection occurred between 2014 and 2016. Participants were randomly selected until the target number was attained. Participants were randomly selected from an existing population sample [26]. Pregnant women, people with physical impairments that would prevent measurement of blood pressure and anthropometric indices and recent immigrants (those with less than 10 years of residency) were excluded from the study. A geographical sampling frame covering two peri-urban Nairobi slums, Korogocho and Viwandani were used to ensure an approximately equal selection from both slums as well as to ensure approximate equal number of male and female participants. Detailed participant enrollment procedures and sample size determination are included in the AWI-Gen study protocol [26,27].
## Ethics statement
The study was approved by the Human Ethics Committee of the University of Witwatersrand (Protocol Number: M121029) and African Medical Research and Education Foundation (AMREF)—Health Ethics and Scientific Review Committee in Kenya (P114-2014). All participants were provided with a paper consent form (in English or translated to Swahili) that described the study, ethical use of data, samples storage for future use, participant confidentiality and protection from harm. A copy is attached in supplementary materials.
## Measurements and definitions
A paper questionnaire (attached as supplementary materials) was administered by trained field interviewers to collect sociodemographic and behavioral characteristics including gender, age, ethnicity, occupation, education level, marital status, diet, physical activity, tobacco smoking and alcohol use. Sociodemographic and behavioral characteristics were all self-reported. Physical activity was self-reported using the Global Physical Activity Questionnaire (GPAQ) [28]. Dietary factors were measured using the WHO Steps Instrument [29] to assess the consumption of fruits and vegetables. Tobacco and alcohol use were self-reported including all forms of tobacco use (chewing, snuffing, smoking) or any form of alcohol (locally brewed or purchased) consumed. Weight and height were measured using digital Physician Large Dial 200kg capacity scales (Kendon Medical) and a stadiometer ((Holtain, Crymych, Wales) respectively. A soft measuring tape was used to measure waist and hip circumferences. These measurements were used to determine body mass index (BMI) and waist/hip ratio.
Blood pressure (BP) measurements were taken using a validated Omron™ M10-IT blood pressure machine with appropriately sized cuffs. A total of three BP measurements were taken with five minutes break between each measurement. Measurements were taken while participants were seated with their arm resting on armrest or a desk and arm facing up. We used the mean of the last two measurements. Hypertension was defined as either having a systolic blood pressure ≥ 140mmHg and/or a diastolic blood pressure ≥90mmHg, and/or a self-report of previous diagnosis of hypertension by a healthcare provider, and/or currently taking hypertension medication.
Fasting blood glucose (FBG), cholesterol levels (TG, HDL) were measured using a Randox Plus clinical chemistry analyser (UK) using colorimetric assays [26]. Diabetes was defined as a FBG ≥7 mmol/L or a random glucose of ≥11 mmol/L or a self-report of previous diagnosis of diabetes by a healthcare provider, or currently on treatment for diabetes. Tuberculosis infection was defined as a self-reported case of newly diagnosed tuberculosis i.e., in the last 12 months. HIV status was defined as a laboratory confirmed HIV positive test or self-reported to having ever tested HIV positive.
A LOGIQ e ultrasound system (USS) (GE Healthcare, CT, USA) with a 2–5 MHz 3C-RS curved array transducer were used to determine carotid intima-media thickness (CIMT), visceral adipose tissue and subcutaneous adipose tissue thicknesses. To measure CIMT, two sternocleidomastoid muscles were used as landmarks and the area was scanned to find the common carotid artery. CIMT was measured for the posterior of both left and right common carotid artery. To calculate the CIMT, the cursor was placed at two points of the posterior wall of about 10mm segment of the artery. The starting point was 1 cm from the bulb of the common carotid artery. CIMT measurement was automatically captured by the instrument as the distance of the intima-media interface [26]. Detailed explanation of biomarkers and other measurement is explained elsewhere by Ali et al. 2018 [26]. Atherogenic index was derived from the log of TG/HDL cholesterol and categorized into low risk (AIP<0.1), intermediate risk (AIP = 0.1–0.24) and high risk (AIP >0.24) [13].
## Statistical analysis
Descriptive statistics included means and standard deviation (SD) for normally distributed continuous variables and median and interquartile range (IQR) for continuous variables that were not normally distributed. Categorical variables were described using proportions stratified by gender. For continuous variables, a student’s t-test was conducted to determine differences between males and females while a chi-square test was performed for categorical variables. We conducted a univariate ordered logistic regression to determine socio-demographic, behavioral, biological, and clinical factors associated with AIP. From the univariate analysis, factors with a p-value of ≤ 0.1 were selected for the hierarchical stepwise approach ordered multivariate regression model [30]. The first model included only socio-demographic characteristics. From this model, only factors with p value <0.1 were included in the second model in addition to behavioral factors selected from the univariate models. The third model included all significant factors from the first and second models in addition to biological and clinical factors. The final model included age even though it was not statistically significant because age is a well-known risk factor for atherosclerosis. Multicollinearity of covariates included in the final model was conducted using the variable inflation factor (VIF) test. None of the covariates had a VIF >10 and therefore it was concluded that there was no multicollinearity in the model. Bayesian Information criterion (BIC) was used to determine changes in the overall fit of the models. Statistical analysis was performed using Stata 15 (Stata Corp, College Station, TX).
## Characteristics of study participants
As shown in (Table 1), the study enrolled 2,003 participants:1,081 ($54\%$) women. The mean age of participants enrolled in this study was 48.8 years with a standard deviation of 0.13. The ethnic distribution showed that, $36\%$ were Kikuyu, $20\%$ Kamba, $16\%$ Luhya, $19\%$ were Luo and other ethnicities accounted for $9\%$ of the study participants. In terms of behavioral characteristics, $29\%$ reported tobacco use while $48\%$ reported alcohol use. Most ($64\%$) people reported no intake of sugary drinks. Although $93\%$ of participants reported moderate-vigorous physical activity, $55\%$ also reported that their work involves sedentary activities such as sitting or standing still. In terms of clinical characteristics, $7\%$ of the participants had diabetes mellitus. $12\%$ of participants were HIV positive, $12\%$ had TB, $27\%$ had hypertension and $18\%$ had high total cholesterol. About $26\%$ and $20\%$ of the participants were overweight or obese respectively by BMI estimates while, $54\%$ of participants had abnormal waist hip ratio (>0.95 cm for men and >0.80 cm for women) ($87\%$ among women and $16\%$ among men). Subcutaneous fat and visceral fat were not normally distributed, and they had a median of 1cm (IQR 1-2cm) and 5cm (IQR 4cm-6cm) respectively. The overall mean right CIMT was 0.58 mm (SD, 0.12) while the overall mean left CIMT was 0.60 mm (SD, 0.12).
**Table 1**
| Sociodemographic characteristics | Behavioral characteristics | Clinical/Biological characteristics |
| --- | --- | --- |
| Age | Tobacco use | Diabetes status |
| Ethnicity | Alcohol use | TB status |
| Marital status | Fruit/vegetable consumption | HIV status |
| Education | Vendor meals consumption | Hypertension status |
| Occupation | Sugary drinks consumption | Low density lipoprotein (LDL) |
| Wealth quintile | Sedentary work | BMI |
| | Physical activity | Subcutaneous fat |
| | | Visceral fatCIMT |
| | | Waist hip ratio |
## Prevalence of atherogenic index
Most people ($71\%$) had low atherogenic index. There were $7\%$ [144] and $22\%$ [443] of participants who had intermediate or high atherogenic index respectively (Fig 1). Men had a slightly higher proportion of high atherogenic index ($24\%$) compared to women ($20\%$). In terms of atherogenic risk distribution by age, those who were between 45 and 54 years accounted for $51\%$ of individuals with high atherogenic index.
**Fig 1:** *Atherogenic index among participants.*
## Factors associated with atherogenic index
Univariate analysis showed that sex, age, ethnicity, occupation, socioeconomic status, sedentary lifestyle, diabetes, total cholesterol, LDL, BMI, subcutaneous fat, visceral fat, and left CIMT were associated with AIP (Table 2). Men were 1.21 times more likely to have higher atherogenic indices compared to women. Participants aged 55 years or older were 1.39 times more likely to have higher atherogenic index compared to those who were younger than 45 years. The Luhya or Luo ethnicities were 0.65 and 0.47 times less likely to have higher atherogenic index respectively compared to those from Kamba ethnic group.
**Table 2**
| Characteristic | Characteristic.1 | Total | Female | Male | p-value |
| --- | --- | --- | --- | --- | --- |
| | | N = 2,003 | N = 1,081 | N = 922 | |
| Age (Years) | <45 | 564 (28%) | 309 (29%) | 255 (28%) | 0.036 |
| | 45–54 | 1,043 (52%) | 581 (54%) | 462 (50%) | 0.036 |
| | 55 + | 396 (20%) | 191 (18%) | 205 (22%) | 0.036 |
| Ethnicity | Kamba | 393 (20%) | 196 (18%) | 197 (21%) | <0.001 |
| | Kikuyu | 725 (36%) | 480 (44%) | 245 (27%) | <0.001 |
| | Luhya | 322 (16%) | 143 (13%) | 179 (19%) | <0.001 |
| | Luo | 374 (19%) | 159 (15%) | 215 (23%) | <0.001 |
| | Other | 189 (9%) | 103 (10%) | 86 (9%) | <0.001 |
| Marital Status | Married/Cohabitating | 1,332 (67%) | 492 (46%) | 840 (91%) | <0.001 |
| | Never Married/Divorced/Separated/Widowed | 670 (33%) | 588 (54%) | 82 (9%) | <0.001 |
| Education | No Education | 154 (8%) | 118 (11%) | 36 (4%) | <0.001 |
| | Primary Level | 1,151 (57%) | 682 (63%) | 469 (51%) | <0.001 |
| | Secondary + | 698 (35%) | 281 (26%) | 417 (45%) | <0.001 |
| Occupation | Self-employed | 946 (47%) | 632 (58%) | 314 (34%) | <0.001 |
| | Formal (Full/Part-time) | 312 (16%) | 69 (6%) | 243 (26%) | <0.001 |
| | Informal | 623 (31%) | 287 (27%) | 336 (37%) | <0.001 |
| | Unemployed | 119 (6%) | 93 (9%) | 26 (3%) | <0.001 |
| Tobacco use | No | 1,430 (71%) | 998 (92%) | 432 (47%) | <0.001 |
| | Yes | 573 (29%) | 83 (8%) | 490 (53%) | <0.001 |
| Alcohol use | No | 1,042 (52%) | 778 (72%) | 264 (29%) | <0.001 |
| | Yes | 960 (48%) | 303 (28%) | 657 (71%) | <0.001 |
| Wealth quintile | First quintile | 241 (12%) | 145 (13%) | 96 (10%) | <0.001 |
| | Second quintile | 451 (23%) | 275 (25%) | 176 (19%) | <0.001 |
| | Third quintile | 464 (23%) | 245 (23%) | 219 (24%) | <0.001 |
| | Fourth quintile | 405 (20%) | 226 (21%) | 179 (19%) | <0.001 |
| | Fifth quintile | 442 (22%) | 190 (18%) | 252 (27%) | <0.001 |
| Fruit or Vegetable consumption | <5 servings | 739 (37%) | 372 (34%) | 367 (40%) | 0.013 |
| | 5 + servings | 1,264 (63%) | 709 (66%) | 555 (60%) | 0.013 |
| Vendor meals consumption | | 1 (0–3) | 1 (0–2) | 2 (0–4) | <0.001 |
| Sugary drinks (No. of cups/bottles/cans) | 0 | 1276 (64%) | 749 (69%) | 527 (57%) | <0.001 |
| | 1 | 519 (26%) | 245 (23%) | 274 (30%) | <0.001 |
| | >1 | 208 (10%) | 87 (8%) | 121 (13%) | <0.001 |
| Work Sedentary | No | 901 (45%) | 466 (43%) | 435 (47%) | 0.068 |
| | Yes | 1,102 (55%) | 615 (57%) | 487 (53%) | 0.068 |
| Moderate—Vigorous PA | Inactive | 141 (7%) | 103 (10%) | 38 (4%) | <0.001 |
| | Active | 1,862 (93%) | 978 (90%) | 884 (96%) | <0.001 |
| Diabetic | No | 1,843 (93%) | 972 (91%) | 871 (95%) | <0.001 |
| | Yes | 143 (7%) | 99 (9%) | 44 (5%) | <0.001 |
| TB status | Negative | 1,784 (89%) | 964 (89%) | 820 (89%) | 0.86 |
| | Positive | 219 (11%) | 117 (11%) | 102 (11%) | 0.86 |
| HIV status | Negative | 1,536 (77%) | 821 (76%) | 715 (78%) | <0.001 |
| | Positive | 243 (12%) | 175 (16%) | 68 (7%) | <0.001 |
| | Not known | 224 (11%) | 85 (8%) | 139 (15%) | <0.001 |
| Hypertensive | No | 1,470 (73%) | 757 (70%) | 713 (77%) | <0.001 |
| | Yes | 533 (27%) | 324 (30%) | 209 (23%) | <0.001 |
| Cholesterol level | Desirable | 1,640 (82%) | 867 (80%) | 773 (84%) | 0.035 |
| | High | 363 (18%) | 214 (20%) | 149 (16%) | 0.035 |
| LDL | | 3.03 (1.14) | 3.03 (1.13) | 3.03 (1.16) | 0.89 |
| HDL | | 1.26 (0.47) | 1.25 (0.44) | 1.27 (0.50) | 0.25 |
| BMI | Underweight | 149 (7%) | 41 (4%) | 108 (12%) | <0.001 |
| | Normal | 943 (47%) | 360 (33%) | 583 (63%) | <0.001 |
| | Overweight | 513 (26%) | 333 (31%) | 180 (20%) | <0.001 |
| | Obese | 398 (20%) | 347 (32%) | 51 (6%) | <0.001 |
| Subcutaneous fat | | 1.57 (0.76) | 1.94 (0.73) | 1.14 (0.55) | <0.001 |
| Triglycerides | | 1.09 (0.70) | 1.07 (0.62) | 1.12 (0.78) | 0.04 |
| Visceral fat | | 4.98 (3.72–5.99) | 4.78 (3.59–5.81) | 5.22 (3.96–6.28) | <0.001 |
| Mean CIMT (Right) | | 0.58 (12) | 0.58 (0.12) | 0.57 (0.12) | 0.079 |
| Mean CIMT (Left) | | 0.60 (0.12) | 0.61 (0.12) | 0.60 (0.12) | 0.028 |
| Waist hip ratio | Normal | 913 (46%) | 140 (13%) | 773 (84%) | <0.001 |
| | Abnormal | 1,090 (54%) | 941 (87%) | 149 (16%) | <0.001 |
Higher *Socioeconomic status* (SES) as indicated by wealth quintiles was associated with higher odds of high atherogenic indices. Similarly, increased use of sugary drink was associated with higher odds of high atherogenic indices with those who reported drinking one sugary drink having 0.08 higher odds and those who reported drinking more than one sugary drink having 0.37 higher odds of high atherogenic index compared to those who reported not drinking sugary drinks. High levels of subcutaneous and visceral fat were associated with higher odds of high atherogenic index. Those with abnormal waist-hip-ratio were 1.64 more likely to have high atherogenic risk.
From the multivariate models, model 3 was selected as it had the lowest BIC statistic and therefore provided the best fit for the data. The model indicated that male participants were about three times more likely to have higher atherogenic index compared to females. Luhya and Luo ethnicities were $34\%$ and $54\%$ less likely to have higher atherogenic index compared to Kamba ethnicity. Participants with diabetes had higher odds (2.23) of having a high atherogenic index compared to those without diabetes. Participants with HIV were 1.83 more likely to have a higher atherogenic index compared to those who were HIV negative. Individuals with high level of LDL were $22\%$ more likely to have higher atherogenic index compared to those with desirable levels. Increased subcutaneous and visceral fat was associated with a high atherogenic index of 1.36 and 1.18 respectively. Lastly participants with abnormal waist hip ratio were 2.37 times more likely to be associated with a higher atherogenic index (Table 3). As shown in the hierarchical regression model (Table 4), sociodemographic characteristics only accounted for $2.4\%$ of observed variance (R2 = 0.0235). Behavioral factors seemed to not influence the observed variance of atherogenic risk and clinical factors contributed additional $4.9\%$ of the observed variance (ΔR2 = 0.0493).
## Discussion
In this study we estimated the prevalence of high atherogenic index as a marker of cardiovascular disease risk among older adults living in low-income urban areas of Nairobi Kenya. We further examined factors associated with high atherogenic index. The results revealed that, up to one fifth ($22\%$) of the study population had a high atherogenic index and $7\%$ were classified to have intermediate atherogenic risk index. While there was only one study that used AIP in the region, our study was consistent with the population-based cohort study conducted in rural Uganda [25]. The study reported $25\%$ of participants to have high atherogenic risk. Similar to our study, the study in Uganda found that males had the greater proportion of high atherogenic risk.
When we explored factors associated with high atherogenic indices we found men, and participants with diabetes, HIV infection, high LDL, high subcutaneous and visceral fat and high waist-hip ratio had higher odds of experiencing a high AIP. Ethnicity was observed to be significantly associated with lower atherogenic index among the Luhya and Luo ethnicities. While there are no studies that have explored ethnicity as a determinant for cardiovascular diseases in Kenya, differences in dietary habits may have contributed to these findings. While the Left CIMT was a statistically significant predictor of higher atherogenic index from the univariate analysis. Anatomical origin differences between the right and the left carotid arteries have been speculated to explain the observed differences. The origin differences could mean differences in flow intensities from the aortic arch [31]. Men were three times more likely to have higher atherogenic index compared to women. Similar findings were reported in a population study in rural Uganda where women were significantly less likely to have higher atherogenic index compared to men [25]. While there are studies that have found similar differences [25,32–34], the observed difference in this study could be due to behavioral characteristic such as tobacco and alcohol use that was observed to be more prevalent among males. These results highlight the need for targeted interventions for men in such settings.
Individuals with diabetes mellitus were twice as likely to have higher atherogenic index compared to those without this condition. These findings are consistent other study findings reporting correlations between type 2 diabetes and high atherogenic indices [35–37]. The proposed mechanism for the observed association is an increased oxidative stress and endothelia cell dysfunction [35]. Matsuzawa et al. [ 1995] observed that high concentrations of visceral fat could drive insulin resistance. Metabolism of visceral fat releases free fatty acids which gets into the portal circulation then into the liver where they can cause enhanced biosynthesis of lipids [38]. Insulin resistance on the other hand may induce hyperlipidemia and glucose intolerance which can lead to atherosclerosis [38].
HIV infection was found to increase the likelihood of high atherogenic index by 57 Although we did not have information on the use of Antiretroviral therapy (ART), the observed results could be due to use of ART as has been reported in other studies [39,40]. Although complex HIV regimens have caused significant repression of viral replication and have helped to increase patient survival times tremendously, the ART has been shown to induce morphological abnormalities such as redistribution of body fat (lipodystrophy) [41]. This is characterized by peripheral fat wasting, dorsocervical fat pad enlargement, increased visceral fat and breast hypertrophy among women [41]. Metabolic abnormalities such as hypertriglyceridemia and hypercholesterolemia have also been identified to result from ART use [42,43]. An alternative mechanism is the direct effect of HIV replication leading to altered lipid metabolism resulting from inflammation caused by HIV shown to be associated with higher atherogenic index [44].
Lastly, we found LDL, subcutaneous, visceral fat, and waist-hip ratio to be independently associated with higher atherogenic index. LDL cholesterol has been shown to cause fatty deposits in arteries which could result in blocking of blood and oxygen flow [45]. In this study, subcutaneous fat was shown to elevate the risk of cardiovascular disease outcome. Some studies have as well identified subcutaneous fat as a risk enhancing factor [46,47] while other studies have indicated that it could be protective against cardiovascular disease outcomes [48–50]. A study by Demerath et al. [ 51] may help explain these discrepancies. According to the study, different levels of visceral fat may result to a different association between subcutaneous fat and cardiovascular disease outcome. Visceral fat is metabolically active and secretes inflammatory markers such as adipocytokines [52,53], homeostasis and fibrinogen markers [54,55] and vascular endothelial growth factor [56]. These factors could play a role in cardiovascular risk manifestation among individuals. Waist-hip ratio is a marker for visceral obesity. A high waist-hip ratio is an indicator of deposition of fat around abdominal organs, blood vessels and other body organs such as the heart thus its association with a higher atherogenic index [57].
## Study strengths and limitations
The strength of this study is that it is the first study from an informal urban settlement in Kenya to estimate AIP and its determinants. Atherogenic index is not commonly used as an index in cardiovascular risk studies. There are not a lot of such studies that have been conducted to investigate the AIP profiles among African populations. In line with this, there was the lack of validated local cut-off points for AIP in the African population. This could lead to misclassification of individuals and therefore under or over estimations of their atherogenic indices. Lastly, this study was cross-sectional in nature thus casual associations cannot be made. Nonetheless, valuable information on the prevalence and factors associated with AIP was obtained which is useful in developing programs or interventions that will promote cardiovascular health.
## Conclusion and recommendation
This study is among the few studies from sub-Saharan Africa to highlight AIP prevalence and factors associated with high atherogenic risk in two urban informal settlements. The results indicate that men, individuals with diabetes, HIV patients and obese individuals are at higher risk of atherosclerosis. These risk factors can be addressed through targeted programs and interventions such as screening individuals during routine care for early detection of cardiovascular diseases, targeted education to increase awareness. Further research is also needed to explore other atherosclerosis risk factors this study did not assess. Longitudinal studies with the same study participants to assess cardiovascular disease outcomes related with AIP may improve the understanding of AIP risk profiles in the population. Similarly, studies with more precise measurements of behavioral characteristics such as alcohol intake and robust dietary assessment will improve the AIP risk profiling.
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|
---
title: 'Experiences of multimorbidity in urban and rural Malawi: An interview study
of burdens of treatment and lack of treatment'
authors:
- Edith F. Chikumbu
- Christopher Bunn
- Stephen Kasenda
- Albert Dube
- Enita Phiri-Makwakwa
- Bhautesh D. Jani
- Modu Jobe
- Sally Wyke
- Janet Seeley
- Amelia C. Crampin
- Frances S. Mair
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021162
doi: 10.1371/journal.pgph.0000139
license: CC BY 4.0
---
# Experiences of multimorbidity in urban and rural Malawi: An interview study of burdens of treatment and lack of treatment
## Abstract
Multimorbidity (presence of ≥2 long term conditions (LTCs)) is a growing global health challenge, yet we know little about the experiences of those living with multimorbidity in low- and middle-income countries (LMICs). We therefore explore: 1) experiences of men and women living with multimorbidity in urban and rural Malawi including their experiences of burden of treatment and 2) examine the utility of Normalization Process Theory (NPT) and Burden of Treatment Theory (BOTT) for structuring analytical accounts of these experiences. We conducted in depth, semi-structured interviews with 32 people in rural ($$n = 16$$) and urban settings ($$n = 16$$); 16 males, 16 females; 15 under 50 years; and 17 over 50 years. Data were analysed thematically and then conceptualised through the lens of NPT and BOTT. Key elements of burden of treatment identified included: coming to terms with and gaining an understanding of life with multimorbidity; dealing with resulting disruptions to family life; the work of seeking family and community support; navigating healthcare systems; coping with lack of continuity of care; enacting self-management advice; negotiating medical advice; appraising treatments; and importantly, dealing with the burden of lack of treatments/services. Poverty and inadequate healthcare provision constrained capacity to deal with treatment burden while supportive social and community networks were important enabling features. Greater access to health information/education would lessen treatment burden as would better resourced healthcare systems and improved standards of living. Our work demonstrates the utility of NPT and BOTT for aiding conceptualisation of treatment burden issues in LMICs but our findings highlight that ‘lack’ of access to treatments or services is an important additional burden which must be integrated in accounts of treatment burden in LMICs.
## Introduction
Multimorbidity, the co-occurrence of ≧2 long term conditions (LTCs) is a pressing global health problem [1], particularly in low- and middle-income countries (LMICs) [2]. Rising prevalence of chronic non-communicable diseases (NCDs) in LMIC is attributed to increasing urbanization, lifestyle changes, and advances in HIV treatment; [3] about $80\%$ of global NCD-related deaths occur in LMICs [4]. In sub-Saharan Africa (SSA), NCDs are experienced alongside infectious diseases such as HIV and Tuberculosis (TB) [5]. Deaths from NCDs in Africa are projected to overtake infectious diseases, maternal, perinatal and nutrition-related deaths by 2030 [6].
A recent scoping review of the epidemiology of multimorbidity in LMICs [2] showed multimorbidity is associated with older age [7–10] and female sex [7, 9, 11–13]. In high-income countries multimorbidity reduces quality of life, ability to be economically active, makes everyday tasks a continual struggle, and increases healthcare utilization and costs [14–17]. In LMICs, research has identified common challenges due to delays to care; access to medicines; increased costs as a result of referrals to tertiary care; and barriers to attending referral hospitals [18, 19].
Less is known about the experience of living with multimorbidity in SSA. Experiences of living with HIV and type 2 diabetes in South Africa have been explored [20] using a model of ‘Cumulative Complexity’ [21], which suggests that care of, and outcomes for, chronic illness are a result of a balance between workload demands (e.g. daily tasks of self-care, managing treatments, caring responsibilities and household tasks) and capacity to manage that workload (the extent of impairments, social and economic resources to help undertake the tasks and literacy). Patient workload was reported as high because of the time and effort required to attend multiple clinics, manage strict dietary requirements; pill burden; and the work of managing stigma. Capacity to manage this workload was reported as depending on a positive attitude, health literacy, family support, clinic support and availability of finances for recommended diets [20]. The paucity of comparable empirical work was noted.
In Malawi, the focus of this paper, epidemiology on multimorbidity is scarce. A single study offers a limited view on multimorbidity by assessing the prevalence of two or more of obesity, hypertension and diabetes in rural and urban settings, finding prevalence ranging from $1\%$ in rural men to $7\%$ in urban women [11]. These estimates are undoubtedly conservative, given high prevalence of HIV in these communities. Treatment for those living with chronic conditions in *Malawi is* delivered through government facilities which are free at point of delivery, but limited in provision [22], the Christian Health Association of Malawi and a range of private clinics, research organisations and non-governmental organisations.
Drawing on research in Malawi, we set out to contribute to the literature on treatment burden and experiences of multimorbidity in SSA through a theoretically informed account of living with multimorbidity, drawing on Normalization Process Theory (NPT) [23] and Burden of Treatment Theory (BoTT) [24]. NPT describes four aspects of treatment burden each of which involve work for the person with LTCs and which have been explored in a range of studies [24–26]: coherence (how people make sense of their condition(s) and treatment(s)); cognitive participation (how they interact with others to promote management of their condition); collective action (how they work individually and collaboratively to self-manage their condition in the context of their everyday lives) and reflexive monitoring (how they reflect on their efforts to care for themselves and adjust what they do in response).
BoTT incorporates and builds on NPT and cumulative complexity theory to take account of the factors that influence a person’s ability to self-manage and cope with any given level of treatment burden [24]. It offers a series of ‘generative principles’ for understanding the complex relational dynamics of the work of being a patient, the capacities patients have to manage their LTCs and interactions with healthcare systems. May et al. [ 24], who proposed BoTT, position patients as agents with capacities to manage LTCs which are potentiated or constrained by their social networks, controlled by the ways in which healthcare is organised and the opportunities accessible, available healthcare services offer. Patient capacities are seen as closely related to their functional performances–their bodily, material and cognitive capabilities–which are, again, potentiated or constrained by people in their social networks who provide support or whose actions inhibit capacity to self-manage. As patients work to care for their conditions, they utilise their capacities by making sense of the tasks they need to do to manage them, drawing on their social networks, enacting the tasks and then reflecting on the work they (and others) have performed. As well as offering an analytical approach to evaluating patient/healthcare interactions, May et al. sets out recommendations for interventions to improve health outcomes and patient experience, arguing that to improve patient capacity to undertake work requires strengthening collective capacity, social networks, and social capital [24]. We summarise BoTT in Fig 1.
**Fig 1:** *Conceptual map of burden of treatment theory adapted from May et al. 2014.*
NPT and BoTT seem to have the potential to support a generalizable understanding of multimorbidity in LMICs such as Malawi, but were developed in high-income settings and may not be either appropriate or relevant to LMICs such as Malawi, in that observed challenges for patients and their support community may differ at the system or individual level and the same may be true for factors that influence capacity. In this paper we aim to investigate: 1) how those living with multimorbidity in urban and rural Malawi describe their experiences of life with multimorbidity and particularly their experiences of burden of treatment; and 2) how useful NPT and BOTT are for structuring analytical accounts of these experiences, and whether these theoretical frameworks need any refinements or adaptation to increase their utility in a LMIC context.
## Methods
We used semi-structured in-depth interviews to generate rich narratives of participants’ experiences of living with multimorbidity which allowed us to better understand their experiences of burden of treatment. An interpretivist narrative approach was taken to enable participants and the interviewers to re-construct their journeys from diagnoses to present-day practices and experiences relating to life lived with multiple conditions. This approach has proven invaluable in past research on chronic illness and we sought to generate narratives of a similar nature and depth [27].
## Study setting, sample and recruitment
Participants with ≥2LTCs (diabetes, hypertension and HIV) who had granted permission for future contact were identified through searches of a database from a previous survey [11] and were recruited in Lilongwe and Karonga. Lilongwe is the capital city of Malawi and an important economic and transportation hub. Karonga is a rural district of Northern Region of Malawi where livelihoods are based on smallholder farming and fishing. This allowed comparison of experiences by area of settlement. As experiences of multi-morbidity may also differ by sex and age, purposive sampling sought to include males and females, and those aged both 50 and under and over 50.
Potential participants were telephoned and asked if they would accept a visit from a researcher who would like to introduce a new study to them. If they agreed, a researcher visited the home of the potential participant and presented the study in in participants’ everyday language vernacular language (Chichewa or Chitumbuka), using information sheets (also in the vernacular) and encouraged participants to ask questions. A vernacular consent form was then read through with the participant and discussed. Written consent or thumb-print consent witnessed by a person of the participant’s choosing was given by all participants.
## Data collection
After giving informed consent, participants were interviewed by a Malawian researcher in Chichewa or Chitumbuka to ensure the interview was conducted in participants’ everyday language and that this language was also spoken by the interviewer. The topic guide (S1 File) asked about the impact of multimorbidity on lives and routines, as well as support from family and community. Interviews were audio-recorded using digital recorders, stored and transferred using encrypted devices, transcribed verbatim by transcription assistants whose first language matched that of each interviewee, translated into English and anonymised for analysis. Researchers and interviewers held regular meetings to discuss responses to the topic guide and evolving study findings. No changes to the topic guide were made during data collection. Interviews began in May 2019 and were completed in August 2019.
## Data analysis
To understand how those living with multimorbidity in urban and rural Malawi describe their experiences, data were analysed thematically [28]. Researchers (EC, CB) familiarised themselves with a set of 8 transcripts, noting down impressions separately and generating initial codes which focused on the experience of multimorbidity. Secondly, they agreed on a coding frame which was applied to the full dataset. Thirdly, when the full dataset was coded, the researchers discussed how the coded data could be gathered under themes and then named the themes. Finally, the crosstab and matrix functions of NVivo 12 were used to allow exploration of patterns of difference in the thematic analysis by age, sex, and setting.
To consider how useful NPT and BOTT are for understanding accounts of multimorbidity in Malawi, we first mapped themes onto a previously described burden of treatment framework using four NPT constructs [24, 25] namely: making sense of multimorbidity (coherence); interacting with others (cognitive participation); enacting management strategies (collective action); and reflecting on management (reflexive monitoring). We assessed whether each theme was able to ‘fit’ one (or more) of the four domains or whether it fell outside the framework. Our analysis is presented using these four NPT domains. We also drew on the extended conceptual framework provided by BoTT to understand what influenced capacity to cope with any given level of treatment burden, to summarise the findings, and to identify any potential gaps in the theory. By producing an inductive thematic analysis of the data before mapping it onto the domains of NPT and BoTT, we sought to reduce the potential confirmation bias that would have arisen had we coded directly to the framework.
## Reflexivity
The researchers who conducted interviews were members of the communities in which the research took place. We believe that this enabled the building of rapport and trust, ensured culturally-specific references and framings were communicated and understood and provided interviewees with a familiar point of contact to support accountability. However, working with community researchers in the rural site may have introduced degrees of self-censorship in participant responses, for fear of potential breaches of confidence. We made this choice knowingly, calculating that the benefits of cultural familiarity outweighed the potential for participants to be overly guarded. The rich nature of the narratives we collected suggest this choice was justified. These concerns were not as great for the urban site, due to the significant size of the population.
The primary (EFC) and secondary (CB) analysts occupy very different social positions to both the interviewers and the interviewees, having access to greater economic, cultural and social capital and substantially different life experiences [29]. These characteristics undoubtedly limited the extent to which the analysts could fully apprehend the embodied experiences narrated by participants. However, both analysts have significant experience of working in these communities as researchers and, in EFC’s case, as a clinician. These experiences include observing and participating in clinics and consultations which interviewees spoke about, as well as having family members living with multimorbidity who seek care in the same system as the interviewees. These experiences helped both analysts achieve degrees of empathy and insight that their social positions would otherwise have limited.
## Ethical review and community consultation
The study and its procedures were reviewed and approved by the National Committee on Research in the Social Sciences and Humanities in Malawi (ref: P$\frac{.02}{19}$/350); and College of Social Sciences Ethics Committee, University of Glasgow (ref: 400180124). Leaders and members of the two communities in which the study took place supported the research and welcomed the opportunity for the experiences of community members living with multimorbidity to be amplified by researchers, expressing hope that the research would lead to improved healthcare in the future.
## Results
We recruited 32 people living with ≥2 LTCs, equally divided between the study sites and sexes, with 17 being at least 50 years old. Interviews lasted 25–144 minutes. The most common combination of conditions in the study sample was hypertension and type 2 diabetes [16], followed by hypertension and HIV [10], type 2 diabetes and HIV [4], and a combination of all three conditions [2].
Our thematic analysis produced 11 themes, 10 of which we judged to fit within the NPT framework (Table 1). The theme that we felt unable to allocate to the framework related to different kinds of ‘lack’ which our participants described. In presenting the analysis under the four NPT-related constructs we elaborate further consistency with key constructs in BoTT and summarise findings using a BoTT-based taxonomy in Table 2. Except where mentioned in the findings below, there were no differences between those living in different areas, different ages or between men and women.
## Making sense of multimorbidity (coherence)
In response to initial questions about how their LTCs started, participants offered accounts we organised as a theme coming to terms with and reflecting on life with multiple LTCs, consistent with the NPT theme coherence. Many of these accounts began with narratives that showed how participants explained the causes of their multiple LTCs. For example, a woman whose LTCs were identified at around the same time her child became sick saw that as the main explanation: When describing life with multimorbidity some participants recounted adapting to the reduced capacities of their bodies, for example by leaving jobs which involved heavy labour and starting a small business instead. Others spoke of the impact of multimorbidity on how they manage the future. For example, one of the younger men explained how his LTCs limited his agency and capacity to act in the world (using the Chichewa term word chopinga, which can translate as ‘hindrance’ or ‘challenge’) consistent with BoTT’s ‘mobilising capacity (agency)’ construct [24]: Among the accounts of life with multimorbidity provided, disruptions to life featured frequently, consistent with the concept of ‘functional performance’ outlined in BoTT [24]. In some instances, these disruptions were very limiting: *Our data* suggests that participants’ attempts to make sense of multimorbidity were focused on establishing causal narratives, the impacts multimorbidity has on their capacity to act in the world, their functional abilities, and on the disruptions it has brought to their lives.
## Engaging with others (cognitive participation)
From questions about how others helped or did not help with managing participants’ multimorbidity we identified three themes which are consistent with the NPT construct cognitive participation: family assistance, interactions in the workplace, and navigating the healthcare system, which all shaped capacity to carry out the work of life with multimorbidity.
The management of the three LTCs included in this study demands adjustments to lifestyle, with diet being particularly important for type 2 diabetes and hypertension. One participant described how to avoid spending more money and time in preparing separate meals his whole family adopted the diet he had been told to adopt to help manage his diabetes. They used mgaiwa, a whole grain maize flour instead of ufa woyera a de-husked and refined flour, to prepare the staple stiff porridge, nsima. Due to the differences in, the glycemic index, mgaiwa is recommended for diabetics. The family would prefer ufa woyera, making a sacrifice for the father to enhance his capacity to care for himself, consistent with the concept, taken from BoTT, of enacting delegated tasks, and the practical help offered in preparing the food [24].
Other forms of family assistance included providing money for transport to clinics, food supplies and money for medicines. Family members collected medicines too and reminded interviewees of their hospital appointments. Multiple accounts collected in our interviews described family members as a great source of encouragement and moral support. For example one of the younger women explained that her brothers travelled all the way from South Africa to encourage her, offering practical and emotional support, consistent with the line of argument in BoTT [24] that enabling social capital (through family support) can enhance capacity to self care: There was a difference in how women and men described the reactions of their communities and workplaces to their LTCs. Women’s accounts focussed on the response of community members, demonstrating that social networks can enhance a person’s capacity to self-manage their illnesses: Other women spoke about how the fear of the continued stigma faced by those living with HIV led to them keeping their diagnoses to themselves, suggesting how stigma can inhibit ‘building and maintaining relational networks’ identified as a key process enabling self-care in the BoTT framework [24].
Men’s accounts, on the other hand, focussed on response in the workplace. For example, stigma was also present in the account that one younger man gave of the mixed reactions he received from co-workers when he disclosed that he had diabetes: Another described how his workplace routines made it challenging for him to stick to his medication plan, particularly when working on jobs away from home. The workspace was a challenging place for younger men living with multimorbidity: some ‘structural resilience’ and ‘social capital’ is available to enhance capacity, but they are also subject to mockery and to unpredictable working patterns which present challenges for the ‘workability’ of self-care, reducing capacity [23, 24].
When participants described navigating the healthcare system a range of issues were discussed. A primary concern was waiting times: The disruption to the day that substantial waiting times cause was more commonly noted by younger and urban interviewees. These interviewees were of working age and had work, business or caring needs that they had to balance with their need for healthcare, compromising the ‘workability’ of interacting with the healthcare system [24]. The under-staffed and poorly-resourced health systems with which participants were trying to interact limited opportunities, increased treatment burden and diminished capacities to undertake the work of self-care [21].
Participants also spoke of many and routine shortages of medicines in the public health system. The shortages can also be understood of the lack of opportunity of health systems to support capacity to self-manage. While an urban male interviewee could resort to trying to buy medications from local pharmacies, a rural female interviewee who needed insulin injections could not get her medicine in the absence of public provision, significantly limiting her capacity to care for her diabetes.
Participants also spoke of seeing different staff each time they attended clinic. As one participant described: The lack of continuity of care might constrain patient capacity, with the interactional workability of consultations limited by social ritual or clinical details withheld due to a failure to establish trust [24]. Such accounts were more prevalent among urban participants. In the rural setting, few mentioned changes to the medical personnel they saw and when they did, it was not a topic of concern ultimately leading to higher levels of confidence in outcomes [24].
Interactions with family, community, work colleagues and healthcare providers play important roles in supporting or limiting the capacity of those living with multimorbidity to carry out the work of self-care. Through some of these relationships, substantial material and emotional support is obtained. Through others, stigma is applied, time is wasted, medicines are absent, and trust is limited by staff turnover.
## Enacting management strategies (collective action)
From questions about how participants managed their conditions we identified two themes: caring for the self and negotiating medical advice consistent with the NPT theme collective action. Many participants described in great detail the measures they took and challenges they faced in order to stay well.
Dietary changes were discussed extensively. For those living with hypertension, the need to reduce salt intake presented a challenge: This account, echoed by others highlighted how enacting dietary change could be a struggle but in this instance it was validated by an embodied feeling of positive change, which was subsequently backed up by clinical blood pressure measurements.
Other participants described caring for type 2 diabetes using a glucometer. One man described how he used the device: Following BoTT, this man demonstrated that he had the mental and physical capacity (functional performance) and financial resources (exploitable resources) to enable him to purchase both the glucometer and testing strips, combined with his socially acquired understanding of how to use it (‘social capital’ and ‘social skill’) enabling him to cope with adversity (loss of work), and sustain optimal blood glucose levels [24].
While these two examples of caring for the self present positive pictures of how our participants approached the work of self-care, this was not the case for all. A common response to life with multimorbidity, particularly from urban participants, was to express frustration with medications. The daily work of consuming multiple medications was recognised as ‘tiresome work’ and some expressed extreme reluctance to accept this workload for life, pointing to a rejection of what in the BoTT framework is seen as a ‘workability’ of ‘delegated tasks’ [24].
During the interviews, participants described negotiating medical advice. Specifically, the ways in which they critically appraise the self-management strategies they are asked to enact in relation to their life situations and priorities. For example, one woman who was diagnosed with HIV and hypertension while pregnant reframed the tasks she was asked to carry out in relation to her priorities, embodied feeling and assessment of whether tasks were feasible (considered their ‘interactional workability’ in the BoTT framework): Other participants described how they made sense of and responded to dietary recommendations that they received from clinicians. For example, for the man in this extract, retaining and understanding the advice he received was not a problem, but he felt that financial constraints rendered enacting the advice impossible (exploitable resources): Some accounts of enacting self-management strategies described how careful and considered their approaches to caring for the self were. Others noted the burdensome nature of life with multimorbidity, particularly in relation to polypharmacy. We also found that participants negotiated medical advice in relation to the resources available to them and their assessments of priority and likely outcomes.
## Reflecting on management (NPT reflexive monitoring)
As we have seen, some participants in this study appraise the work they do to care for their LTCs and make decisions based on this. We identified three themes which together are consistent with the NPT theme reflexive monitoring: reflections on treatment, choosing care providers and suggestions for improvements.
Participants commonly linked the introduction of treatment with loss of symptoms and increased functionality: Thus, consistent with BoTT, we can see that successful treatment of multimorbidity increases patient capacity, especially through improvements in physical wellbeing (functional performance), enabling them to continue to enact self-management strategies, grounded in confidence in the outcomes they have experienced [24].
A less common reflection on management related to choosing care providers and is closely linked to the stigma experienced by some participants when interacting with others. Being seen attending a clinic was a focal concern: This example demonstrates that participants can intentionally increase their burden of treatment to protect their social identities from the detriments of stigma, increasing the work of their social networks.
Finally, participants reflected on ways in which their treatments might be improved. One issue which was raised related to health education: Others spoke of receiving a 30 minute briefing at diagnosis, and then receiving no further informational input. In these ways, and in line with BoTT, participants highlighted the need for access to ‘informational resources’ to enhance their capacity for self-care [24].
A second group of suggestions for improvement related to material provision. These participants made suggestions such as: The participants draw attention to the problem of poverty and the importance of improving standards of living (better housing, better food and money) to promote physical wellbeing.
## Lack
Across all themes we identified common narratives of absences or lack in relation to life with multimorbidity were present. It is clear from the data already presented that people living with multimorbidity in Malawi do face treatment burdens, but participants also carry the burden of lack of treatment. In this section we explore this theme, before returning to how it relates to BoTT in the discussion.
A focal lack reported by many was appropriate food, particularly among those living with diabetes. There was a perceived inability to avoid carbohydrate heavy, fatty and processed foods because these are the less expensive foods and are easily available. As one woman noted: Another participant pointed to how lack of capacity in the health service directly shapes how he approaches his care: This combination of lack of staff and lack of medication (lack of opportunity of healthcare services to support capacity) led this patient to pursue unsupported management of his conditions.
Finally, some participants perceived a lack of specialist knowledge among the clinicians who managed some of their conditions. For example, in this example the participant suggested the lack of specialist care is a problem potentially affecting his confidence in the care offered which is consistent with BoTT in relation to confidence in outcomes (relational integration)): Participants highlighted key absences in their treatment emphasising that “lack” can be an important issue in SSA. Participants linked diabetes mortality in their communities to lack of funds and supply of appropriate food; they reflect on a healthcare system’s lack of capacity to meet their needs respond by self-treating without qualified supervision; and they perceive a lack of specialist care for a disease which is affecting growing numbers of their community.
## Discussion
These findings illuminate the multiple and intersecting aspects of burden of treatment which people living with multimorbidity in urban/rural Malawi experience in their daily lives.
We have shown that the key concepts of burden of treatment: gaining an understanding of life with multimorbidity (coherence); the engagement work of seeking family and community support (cognitive participation); enacting self-management advice (collective action); and appraising treatments (reflexive monitoring) identified through NPT and BOTT are described by people with multimorbidity in SSA. However, we have also shown that dealing with the burden of lack of treatments/services is a key issue.
Future work should therefore examine the processes through which this burden manifests in greater detail. We have shown a similar interplay between treatment burden and capacity issues in Malawi to that seen in HICs and that these issues can be usefully assessed and unpicked using both NPT and BoTT approaches, which highlight the social processes through which capacities and resources interact to produce greater or lesser experiences of burden. The work illustrates how poverty and inadequate healthcare and information provision in SSA constrained capacity to deal with treatment burden while supportive social and community networks were important enabling features.
Our findings resonate with other work in Malawi and SSA showing how people face challenges accessing care, encountered difficulty obtaining medicines from public hospitals and faced financial barriers to treatment, leading to delayed care and, most likely, poorer clinical outcomes [19]. Our findings reinforce research that suggests Malawi’s ‘Essential Health Package’ (the minimum government provision) continues to experience important gaps in provision [22, 30]. Our findings also overlap with work in South Africa which identified burdens relating to a lack of holistic care which took account of all of a person’s problems, the amount of time taken up by treatment, limited treatment choices and medication burden. Likewise, we also found that participants reported experiencing overcrowding in clinics, drug shortages and stigma [20]. Where our study goes beyond these contributions is in the accounts it offers of the sense-making work that our participants did to locate their multiple illnesses within their biographies [27], grapple with questions of identity and self [31, 32], and to narrate disruptions to their lives [33].
*The* generative principles of BoTT enabled us to bring out the links between patient capacity and workload, as mediated by social networks, healthcare systems and situated opportunities. It provided the conceptual toolkit to demonstrate how these domains structure action through feedback loops, for example, when a patient’s appraisal of long waiting times and drug shortages led to an un-supported approach to discharging the work of self-care. The theory also enabled us to highlight the importance of social networks and the resources which they can bring to lighten the burden of treatment, by adding to both capacity and resilience, as exemplified by the woman who received many community visitors who all brought her ‘a little something’. Finally, the concept of ‘functional performance’ was particularly useful for illuminating the positive effect successful treatment of multimorbidity can have by re-enabling participants to contribute to their social networks (e.g. through cultivation), (re-)building social ties which enable future social support and thus greater resilience/capacity to manage adverse consequences of their multiple illnesses.
While Burden of Treatment Theory does include the concept of ‘opportunity’ which addresses the issue of availability of services in a given context, this concept incorporates impacts on the people’s capacity to self-manage with a given level of treatment burden rather than explicitly identifying this as an additional cause of treatment burden. In addition, this concept of ‘opportunity’ does not address lack of access to medications which was a key issue identified here. Lack of access to good care is a burden in and of itself: to live in the knowledge that there are treatments and resources available that will improve your health, but that these are withheld from you for economic or spatial reasons [34, 35].
Importantly, none of the three measures of treatment burden developed thus far [36–38] explicitly measure lack of treatment as a component of burden of treatment. Indeed, there is an implicit assumption that medications and therapies are available, albeit perhaps difficult to access at times or in ways that suits people’s needs [26]. We would therefore suggest that measures of treatment burden in LMIC contexts will need to be adapted to measure the burden of “lack of treatment” as a specific domain and barometer of quality of care. In addition, this concept may also be important when assessing burden of treatment with socioeconomically disadvantaged populations more widely, especially in contexts where there is not access to universal health care provision, free at point of care. We think of this as the burden of lack of treatment, of living with the knowledge of what is missing in your treatment and propose to integrate this with BoTT (see Fig 2).
**Fig 2:** *Burden of treatment theory with the integration of the construct of lack of access to treatments or services.*
As policy makers in Malawi and the wider SSA region contemplate how to respond to the growing burden of multimorbidity associated with the rapid rise of NCDs, much can be learnt from the experiences of those who live with multimorbidity. Their lives involve significant additional work to remain well and to contribute to their families, communities and wider social networks. In many instances, this work is shared with those in wider social networks that increase patient resilience and capacity, enabling better navigation of healthcare systems. The priorities for the participants in this study relate to: better access to high quality care including with less waiting time and reliable provision of medicines; improved standards of living (housing, food, income) to allow the resources through which people can better self-manage; and greater access to informational resources. Attending to these needs would substantially reduce both the burdens of treatment and of lack of treatment experienced by those living with multimorbidity in Malawi and possibly SSA more widely.
## Strengths and limitations
Our study has made a contribution to what is a very limited literature, making comparisons difficult [20]. However, our contribution builds on this work and has highlighted the potential of theory-engaged work to illuminate the complex lived experiences of those with multimorbidity. While our study included people living with three different conditions in a variety of combinations, a broader sample that encompassed a greater number of conditions would have been desirable and would likely have given us insight into the implications of living with multimorbidity that involves combinations of conditions which receive less attention in SSA e.g. arthritis, epilepsy. Finally, while many interviewees gave critical accounts of the care they receive, not all did; it is possible that some feared repercussions from healthcare professionals and other authorities or that respect for those in professional positions tempered criticism of difficult experience.
## Conclusions
Our study contributes to the emerging literature on experiences of multimorbidity in SSA and LMIC countries more broadly and demonstrates the utility of NPT and BOTT for aiding conceptualisation of treatment burden issues in LMICs. However, our findings highlight that ‘lack’ of access to treatments or services is an important additional dimension of the concept of burden of treatment which merits further investigation and should be integrated into future measures of treatment burden in SSA and likely other LMICs. Our work has shown that greater access to health information and education would lessen treatment burden as would better resourced healthcare systems that provide more continuity. Poverty also constrained capacity to deal with treatment burden suggesting the need for improved standards of living as a key issue. Nonetheless, supportive social and community networks were important enabling features and policies to support these networks would be important.
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|
---
title: 'Identifying effective interventions to promote consumption of protein-rich
foods from lower ecological footprint sources: A systematic literature review'
authors:
- Rimante Ronto
- Golsa Saberi
- Gianna Maxi Leila Robbers
- Stephanie Godrich
- Mark Lawrence
- Shawn Somerset
- Jessica Fanzo
- Josephine Y. Chau
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021177
doi: 10.1371/journal.pgph.0000209
license: CC BY 4.0
---
# Identifying effective interventions to promote consumption of protein-rich foods from lower ecological footprint sources: A systematic literature review
## Abstract
Addressing overconsumption of protein-rich foods from high ecological footprint sources can have positive impacts on health such as reduction of non-communicable disease risk and protecting the natural environment. With the increased attention towards development of ecologically sustainable diets, this systematic review aimed to critically review literature on effectiveness of those interventions aiming to promote protein-rich foods from lower ecological footprint sources. Five electronic databases (Medline, Web of Science, Scopus, Embase and Global Health) were searched for articles published up to January 2021. Quantitative studies were eligible for inclusion if they reported on actual or intended consumption of protein-rich animal-derived and/or plant-based foods; purchase, or selection of meat/plant-based diet in real or virtual environments. We assessed 140 full-text articles for eligibility of which 51 were included in this review. The results were narratively synthesised. Included studies were categorised into individual level behaviour change interventions ($$n = 33$$) which included education, counselling and self-monitoring, and micro-environmental/structural behaviour change interventions ($$n = 18$$) which included menu manipulation, choice architecture and multicomponent approaches. Half of individual level interventions ($52\%$) aimed to reduce red/processed meat intake among people with current/past chronic conditions which reduced meat intake in the short term. The majority of micro-environmental studies focused on increasing plant-based diet in dining facilities, leading to positive dietary changes. These findings point to a clear gap in the current evidence base for interventions that promote plant-based diet in the general population.
## Introduction
Current global food systems are not environmentally sustainable [1,2]. Food systems account for 21–$37\%$ of anthropogenic greenhouse gas (GHG) emissions and agriculture production for $70\%$ of global freshwater withdrawals [3–5]. Dietary behaviours are both the result and driver of food systems [1]. Unhealthy dietary behaviours have significant impacts on human health, environmental sustainability and contribute to climate change [4]. In order to achieve positive outcomes for human health and the environment, diets that are both healthy and environmentally sustainable are needed.
In 2019, the Food and Agriculture Organization (FAO) of the United Nations and World Health Organization defined sustainable diets as “healthy dietary patterns that aim to promote optimal health and wellbeing and have minimal environmental pressure and impact. Sustainable healthy diets are equitable, affordable, accessible and culturally acceptable” [6]. The EAT-Lancet Commission stated that a “planetary healthy diet” consists largely of plant-based foods, low amounts of animal-derived foods (red and processed meat in particular) and little to no added sugars, refined grains, and ultra-processed foods [7]. Addressing overconsumption of animal-derived foods such as red and processed meat can have positive impact on health and environment. Cultivation of animal-derived foods, in aggregate, has a larger environmental impact compared with plant-based food alternatives [8]. Animal-derived foods require more water, more fossil fuels and generate substantially more greenhouse gasses than plant-based food equivalents [9]. Additionally, overconsumption of red meat has been linked to negative health outcomes such as cardiovascular diseases and colorectal cancer [10,11] whereas adequate consumption of fruit and vegetables have protective effects [12]. Processed animal-derived foods have been linked to growing rates of obesity [13] and place burden on natural resources [8].
Growing evidence demonstrates that population level dietary changes can improve health and environmental sustainability and also help in achieving the United Nation’s Sustainable Development Goals (SDGs) [14–19]. Changing dietary preferences and behaviours and food systems from animal-derived foods to plant-based diets will require a ‘Great Food Transformation’ [7]. There has been an increase in advocacy for and feasibility of harnessing the increasing interest in plant-based diets to influence large population-based public health nutrition interventions, for example Meatless Monday campaigns [20]. However, there is limited evidence on the effectiveness of plant-based dietary behaviour interventions in changing people’s behaviour. Therefore, this study aimed to critically review literature on interventions aiming to promote protein rich food intake from low ecological footprint sources to inform the design of larger population-based dietary interventions to achieve major shifts away from a reliance on animal-based foods.
## Materials and methods
The systematic literature review was planned and conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting (PRISMA) statement [21] and the protocol was registered on PROSPERO (Registration Number CRD42020178683). Details of each eligibility criteria are presented in Table 1. This systematic review included quantitative studies only published in peer review academic literature in English language, but it had no restrictions on the study design or year of publication (up until January 2021).
**Table 1**
| Unnamed: 0 | Inclusion | Exclusion |
| --- | --- | --- |
| Population | All except those ones listed in the exclusion criteria. | People diagnosed with clinical condition(s) for which it is required to consume specific amounts of red meat. |
| Intervention | Interventions aiming to reduce the demand for red/processed meat and to increase in plant-based proteins including micro-environmental structural (physical) changes. | Dietary interventions aiming to promote a general dietary pattern.Interventions with structural (physical) environment changes but with no evaluation. |
| Comparator | No- or minimal-intervention controls, pre-intervention baseline, or other eligible intervention(s). | - |
| Outcome | Objective or self-reported measures of demand for red/processed meat and/or plant-based protein, defined as actual or intended consumption, purchase, or selection of meat in real or virtual environments. | - |
## Search strategy
We searched the following databases: Medline, Web of Science, Scopus, Embase and Global Health. Initially, five primary concepts (meat, plant, food, intake and intervention) were adopted, in order to identify search terms (listed in Table 2). Subsequently, search strings were developed by the research team and with the help of research librarian. Two researchers (GS and GR) conducted the search independently through all databases. Then, potential articles were imported into Covidence (covidence.org) where duplicates were removed. The screening of search results was conducted and recorded using the PRISMA checklist, by two researchers (GS and GR) independently and in consultation with a third researcher (RR). First, two researchers (GS and GR) independently performed the title and abstract screening of all imported studies against inclusion and exclusion criteria. Where a consensus regarding the inclusion of a study between the first and a second researcher was not reached, it was resolved with two other researchers (RR and SG). Then, full-text versions were obtained for all studies identified to be suitable in the first stage of data screening and reviewed by two researchers (GS and GR) independently. The reference lists of all included studies were hand searched for relevant studies not identified in the first search strategy. Authors of identified studies and experts of the field were consulted, where further details were required. The PRISMA flow diagram was used to document the number of articles at each screening stage (see Fig 1).
**Fig 1:** *Flowchart of the literature search and review process.* TABLE_PLACEHOLDER:Table 2
## Data extraction and analysis
The following information was extracted from included articles: author(s), year of publication, country of study, title, location, study type (structural/individual), intervention year, intervention length, target audience, sample characteristics, aims, intervention design, behaviour change theory/framework used, eligibility, recruitment, demographic characteristics, measure/tool(s) used, outcomes measured, results, follow up period, follow up results. Three researchers (GS, GR and RR) tested the data extraction sheet by extracting data from $10\%$ of articles; minor disagreements were identified and discussed. Then, two researchers (GS and GR) extracted data from all included studies independently and then cross-checked all extracted data. Any disagreements were resolved in consultation with the third researcher (RR).
The risk and sources of potential bias of each included study were assessed by two researchers (GS and GR) independently by using the Effective Public Health Practice Project Quality Assessment Tool (EPHPP) [22]. This tool was developed to assess the quality of a diverse group of empirical studies. Each included study was assessed on study design, selection bias, confounders, blinding, data collection method, withdrawals and dropouts and assigned to either ‘strong’, ‘moderate’ or ‘weak’ category. Finally, the overall rating was determined based on these ratings as indicated in the assessment tool dictionary. Any disagreements were resolved with a third researcher (RR). Finally, all quantitative data were summarised.
## Search results and characteristics of included studies
The initial search strategy yielded 5043 studies from five databases. Of these studies, 141 were assessed for eligibility for full-text reviewing of which 90 were excluded due to not meeting the inclusion criteria. The remaining 51 studies formed the final sample for this review (see Fig 1). These studies were divided into two categories: individual level [23–55] and micro-environmental/structural level [56–73] studies. The summary of each study is provided in Tables 3 and 4.
Of the 33 individual level studies, there were 24 Randomised Controlled Trials (RCT) [23–46], two Non-randomised Controlled Trials (CT) [47,48] and seven used a pre-post study design [49–54]. The number of participants ranged from 7 to 48,835 and the participants’ age ranged from 6 months (infants) to 75 and older. Nine studies had close to $100\%$ female participants [24,25,38,42,43,46–48,52], and three studies had $100\%$ male participants [23,29,53]. In six studies, gender distribution was either even or the difference between them was less than $10\%$ [26,28,34,36,44,49]. In total, 30 studies aimed to reduce animal-derived foods (mainly red and processed meat intake) due to health concerns (cancer, overweight/obesity, high risk of developing Type 2 diabetes, ischemic heart disease), and only three studies considered both health and the environmental concerns in reducing animal-derived foods [23,26,27]. Half of the studies ($$n = 17$$) used behavioural change theories to guide their interventions. The Social Cognitive Theory (SCT) was the most frequently used theory [31,34,39,47,49,50,55], following by the Theory of Planned Behaviour (TPB) [26,27] and the Transtheoretical Model (TTM) [32,38].
Of the 51 included studies, 18 studies were categorised as micro-environmental/structural level studies which were conducted in high income countries. Micro-environmental/structural level studies refer to those studies which aimed to change immediate food environments in which people make food choices. They included nine RCTs [56–64], two CTs [65,66], three field experimental design studies [67,68,73], one quasi-experimental design study [69], and three pre-post design studies [70–72]. The number of participants ranged from 24 to 3,066 participants, and the participants’ age ranged from 12 to 75 years and older. In eight studies, there were no significant differences in gender ratio of the sample (less than $10\%$) [56–58,62,63,66,69,70] and six studies did not provide the sex differences between the participants [59,60,65,67,71,72]. Nearly all studies took place in dining facilities, for example restaurants, cafés or worksite canteens; one study selected a farm and a small community as their participants [70]. In total, eight studies focused on health concerns only when designing the interventions to reduce unsustainable protein intake [60–62,64,66,68,71,72], two studies were developed to address the environmental considerations [65,73], and nine studies considered both health and environmental concerns for reducing unsustainable protein intake [56–59,63–65,69,70]. Twelve studies reported using one or more behavioural change theories in their intervention design [56–59,61,62,65,68–71,73]. Nudge theory either on its own or in combination with other theories (e.g. choice architecture, TPB) was used most commonly [56,58,59,65,68,69,73].
## Study quality
The overall methodological quality for all included studies was ‘strong’ for 12 studies, ‘moderate’ for 28 studies and ‘weak’ for 11 studies. The quality assessment for each individual study on each individual criterion is provided in Table 5.
**Table 5**
| Author(s) (year), country | Selection bias | Design | Confounders | Blinding | Data collection methods | Withdrawals and drop-outs | Overall |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Amiot et al, 2018, Canada | 2 | 1 | 2.0 | 2 | 3 | 1.0 | 2 |
| Archarya et al. 2004, USA | 2 | 1 | 1.0 | 2 | 2 | 3.0 | 2 |
| Attwood et al. (2020), UK | 2 | 1 | 1.0 | 2 | 3 | 2.0 | 2 |
| Bacon et al. (2018a), UK | 2 | 1 | 3.0 | 2 | 2 | 1.0 | 2 |
| Bacon et al. (2018b), UK | 2 | 1 | 2.0 | 2 | 2 | 3.0 | 2 |
| Beresford et al., 2006, USA | 2 | 1 | 3.0 | 2 | 3 | 1.0 | 3 |
| Campbell-Arvai et al. (2014), USA | 1 | 1 | 3.0 | 2 | 2 | 1.0 | 2 |
| Carfora et al., 2017a, Italy | 2 | 1 | 3.0 | 2 | 2 | 2.0 | 2 |
| Carfora et al., 2017b, Italy | 2 | 1 | 3.0 | 2 | 2 | 1.0 | 2 |
| Carmody et al., 2008, USA | 2 | 1 | 3.0 | 2 | 3 | 3.0 | 3 |
| Celis-Morales et al., 2017, Ireland, The Netherland, Spain, Greece, UK, Poland and Germany | 1 | 1 | 3.0 | 2 | 2 | 2.0 | 2 |
| Craveiro et al (2019), Portugal | 2 | 2 | | 2 | 2 | 3.0 | 2 |
| Dalgard et al., 2001, Denmark | 3 | 1 | 3.0 | 2 | 3 | 1.0 | 3 |
| Delichatsios et al, 2001a, USA | 1 | 1 | 1.0 | 2 | 2 | 3.0 | 2 |
| Delichatsios et al, 2001b, USA | 3 | 1 | 2.0 | 2 | 2 | 1.0 | 2 |
| Dos Santos et al (2020), Denmark, France, Italy and UK | 2 | 1 | 2.0 | 2 | 3 | 3.0 | 3 |
| Emmons et al, 2005a, USA | 2 | 1 | 1.0 | 2 | 2 | 1.0 | 1 |
| Emmons et al- 2005b, USA | 2 | 1 | 1.0 | 2 | 2 | 1.0 | 1 |
| Flynn et al, 2013, USA | 2 | 2 | | 2 | 3 | 2.0 | 2 |
| Friis et al. (2017), Denmark | 2 | 1 | 2.0 | 2 | 2 | 2.0 | 1 |
| Gravert & Kurz (2019), Sweden | 2 | 1 | 3.0 | 2 | 3 | 3.0 | 3 |
| Grimmett, et al, 2015, UK | 2 | 1 | 3.0 | 2 | 2 | 2.0 | 2 |
| Hatami et al, 2018, Iran | 2 | 1 | 3.0 | 2 | 1 | 1.0 | 2 |
| Hawkes et al- 2012, Australia | 2 | 2 | | 2 | 1 | 3.0 | 2 |
| Hawkes et al, 2009, Australia | 2 | 2 | | 2 | 1 | 1.0 | 1 |
| Herbert et al. (1993), USA | 2 | 1 | 1.0 | 2 | 2 | 2.0 | 1 |
| Jaacks et al, 2014, USA | 2 | 1 | 1.0 | 2 | 2 | 3.0 | 2 |
| Johansen et al, 2009, Norway | 2 | 1 | 2.0 | 2 | 2 | 3.0 | 2 |
| James et al, 2015, Australia | 3 | 1 | 1.0 | 2 | 1 | 2.0 | 2 |
| Kongsbak et al. (2016), Denmark | 2 | 1 | 1.0 | 2 | 2 | 3.0 | 2 |
| Kurz (2018), Sweden | 2 | 1 | 2.0 | 2 | 2 | | 1 |
| Lee et al, 2018, China | 2 | 1 | 1.0 | 2 | 2 | 1.0 | 1 |
| Lessem et al- 2019, USA | 3 | 2 | | 2 | 1 | 1.0 | 2 |
| de Liz et al, 2018, Brazil | 2 | 1 | 3.0 | 2 | 2 | 2.0 | 2 |
| Maryuyama et al, 2017, Japan | 2 | 2 | | 2 | 2 | 1.0 | 1 |
| Matthews et al, 2019, Finland | 2 | 1 | 3.0 | 2 | 2 | 3.0 | 3 |
| McClain et al. (2013), USA | 2 | 1 | 1.0 | 2 | 1 | 3.0 | 2 |
| Merrill et al, 2009, USA | 2 | 1 | 1.0 | 2 | 1 | 1.0 | 1 |
| Polak et al. (2019), Israel | 2 | 1 | 1.0 | 3 | 2 | 3.0 | 3 |
| Prusaczyk et al. (2021), USA | 1 | 1 | 3.0 | 3 | 2 | 3.0 | 3 |
| Reinders et al. (2017), Netherlands | 2 | 1 | 1.0 | 2 | 3 | | 2 |
| Resnicow et al, 1992, USA | 1 | 2 | | 2 | 2 | 3.0 | 2 |
| Ring et al, 2019, USA | 2 | 2 | | 2 | 1 | 1.0 | 1 |
| Saffari et al, 2014, Iran | 2 | 1 | 1.0 | 2 | 3 | 1.0 | 2 |
| Sacerdote et al, 2005, Italy | 1 | 1 | 3.0 | 1 | 2 | 1.0 | 2 |
| Schiavon et al, 2014, Brazil | 2 | 1 | 2.0 | 2 | 2 | 2.0 | 1 |
| Shai et al, 2012, Israel | 2 | 1 | 3.0 | 2 | 3 | 1.0 | 3 |
| Sorensen et al. (2005), USA | 3 | 1 | 1.0 | 2 | 2 | 1.0 | 2 |
| Spees et al, 2016, USA | 2 | 2 | | 2 | 2 | 1.0 | 1 |
| Sperber et al. (1996), Israel | 2 | 2 | | 2 | 3 | 3.0 | 3 |
| Zuniga et al, 2018, USA | 1 | 1 | 1.0 | 2 | 3 | 1.0 | 2 |
## Educational interventions
Twelve studies (RCT = 8, Pre/post = 4) used educational approach to reduce red/processed meat intake and purchase behaviour which included tailored education, educational classes, workshops and courses. Among RCT studies, six found positive impacts on the reduction in red meat intake in IG in comparison to CG [25,29,36,43,45,46], follow up varied between 3 months to 6 years. Two RCTs found that daily consumption of red meat was reduced in the IG, but it was not significant in comparison to CG [38,39]. Among pre-post studies: one study found a significant decrease in processed meat intake from baseline to 6 weeks [49]; one study found decrease in meat servings per week by $86\%$ over 3 weeks period and significant increase in legume intake (pre-intervention 4.43 servings to 12.13 servings post-intervention) [52]; one study showed that intakes of red meat and poultry decreased significantly post intervention from baseline to 6 weeks [53]. Finally, one study found a decrease in purchases of meat (average dollars/week spent on meat) at baseline to 6 weeks [51].
## Counselling interventions
Eleven studies (RCT = 9, CT = 1, Pre/post = 1) used counselling approach to reduce red/processed meat intake ($$n = 10$$) and increase in soya intake ($$n = 1$$). These interventions included telephone and in person counselling sessions providing dietary advice. Among RCT studies, nine studies found positive impact on reduction in red/processed meat intake in IG in comparison to CG [24,28,30,33–35,40,42,44] and increase intake in soyabean products [24], follow up period varied between 4 weeks to 24 months. One CT study showed that the IG improved their adherence of red/processed meat intake to the guidelines over 12-month program [48]. Pre-post study found that processed meat intake decreased pre- to post- intervention (6 weeks) but no changes for red meat intake were observed [50].
## Self-monitoring interventions
Two studies used self-monitoring approach to reduce red meat intake. They were both RCTs and used daily text-messaging (SMS) approach to reduce red and processed meat intake. These studies urged participants to self-monitor meat intake and measured attitudes, intentions and anticipated regret [26,27]. It found positive impact on reduction in processed meat intake in IG in comparison to CG after one week [26], and red meat intake in IG in comparison to CG after two weeks [27].
## Multicomponent interventions
Eight studies (RCT = 5, CT = 1, Pre/post = 2) used multicomponent approach to reduce red/processed meat intake such as education and self-monitoring [23], education and counselling approaches [31,32,37,41,47,54,55]. Among RCTs, three studies found no significant intervention effect on red meat/processed meat [31,32,41] and two studies showed significant reductions in red meat intake at 4 weeks [23] and over 9 years [37]. Also, a CT study showed significant reductions in red/processed meat intake among women with breast cancer after 12 months [47]. Two pre-post studies showed significant changes in decreasing red/processed meat intake among students and cancer survivors [54,55].
Seven studies (RCT = 3, CT = 1, Pre/post = 3) used multicomponent approach to reduce red meat intake which included combination of education, labelling, policy, counselling and choice architecture. Of three RCTs, two studies found significant decrease in ground and processed meat intake [60] and high-fat meat intake [62]. One RCT showed that percentage of participants eating ≤3 servings per week of red meat did not differ between IG and CG over 18-months [64]. One CT study and three pre-/post- studies showed significant reduction in processed meat intake [66] and red meat intake [70–72].
## Menu manipulation interventions
Of 18 micro-environmental level studies, seven studies (RCT = 4, CT = 1, Exp = 3) used menu manipulation approach in order to reduce meat options or increase choice/sale of plant-based meal options [56–59,65,67,69,73]. Menu manipulation included different approaches such as adding attractive meat free choices on the menu, adding specific symbols specifying that less meat intake can save the environment, increasing the visibility of plant-based options, and describing the plant-based option as a ‘Dish of the day’. Of four RCTs, two did not show a difference on the choice of plant-based options in IG compared to CG [56,57], and two showed a positive impact on meat reduction behaviour by choosing more plant-based options in restaurants in IG compared to CG [58,59]. Also, a CT study showed increase in sales of plant-based lunches [65]. One experimental study revealed a significant changes on plant-based dish sales by changing the language to explain plant-based options on café/restaurant menus (e.g. replacing Meat-Free Breakfast with Garden Breakfast) [67]. However, the other experimental study found that the nudging strategy a ‘Dish of the day’ did not show a difference on the choice of the plant-based option among adolescents [69].
## Choice architecture interventions
Three studies used choice architecture approach (RCT = 2, Exp = 1) which included dining environment manipulation such as altering the serving sequence of plant-based and meat-based dishes [61], altering portion sizes of plant and meat-based foods [63] and using priming (environmental changes- adding green plants, herbs and green colour bowls), default (pre-portioned salad bowls) and perceived variety options (pre-mixed salad to increase the visual variety of vegetables) [68]. The manipulation of altering the serving sequence and default approach found no significant difference in selection of meat dishes between IG and CG [61,68]. However, the manipulation of altering portion sizes resulted in significant higher vegetable intake and lower meat intake in IG than CG [63] as well as using the priming and perceived variety conditions showed decrease in choosing meat-based options [68].
## Discussion
This systematic literature review explored the effectiveness of interventions which aimed to promote protein consumption from low ecological footprint sources (reduction in animal-derived proteins and increase in plant-based proteins). Most of individual level studies demonstrated reduction of animal-derived protein intake, mainly measured by reduction in red and/or processed meat intake with only a few studies measured increase intake in plant-based proteins such as legumes and soyabeans. Furthermore, $52\%$ of these studies ($$n = 17$$) targeted people with current or past chronic conditions such as cancer, diabetes and cardiovascular diseases. The majority of micro-environmental/structural level studies found positive dietary changes in reducing animal-derived protein intake mainly red meat and increase in plant-based protein (e.g. plant-based vegetarian dishes). The majority of these studies were conducted in dining facilities such as cafes, restaurants and canteens. Similar findings have been observed in a several systematic literature reviews which evaluated the effectiveness of interventions targeting to reduce demand for meat [74,75].
Educational, counselling and self-monitoring interventions are promising approaches to dietary behaviour change such as increase in environmentally sustainable protein intake which have been observed in other systematic literature reviews [76,77]. However, these approaches have been often used among at risk or highly motivated populations such as people with obesity or other chronic condition, cancer survivors, and may have limited success in changing behaviour among general population. In addition, research indicates that educational interventions on its own may not be sufficient to behaviour change in long term [77,78]. There is a need for further longitudinal studies to confirm that the reduction in animal-derived protein intake from high ecological footprint sources among healthy general population sustain over prolonged period of time.
The majority of our included interventions focused on reduction of red/processed meat intake mainly from a health perspective with only three studies emphasising the reduction of animal-derived foods due to negative impact on the environment. This is not a surprising finding as recent studies reported general population having low food literacy and limited understanding of food impact on the environment and often focus on changing their dietary behaviours such as reducing red/processed meat intake due to health reasons [79–82]. Therefore, there is a need to develop interventions aiming to educate general public on sustainable and healthy diets and evaluate how feasible these interventions are in reducing overconsumption of protein from high ecological footprint sources in order to reduce the negative impact it has on health and environment.
The findings from included micro-environmental behaviour change interventions showed that altering food environments using nudges or choice architecture can lead to positive dietary behaviour changes such as reduction in unsustainable protein intake/purchase and increase in plant-based meals which aligns with findings from previous studies [75,83]. Most promising approaches included altering portion sizes, menu manipulation by adding plant-based meal options and policy implementation; similar environmental approaches have been identified as successful in changing dietary behaviours among young adults [84]. In order to reduce the human behaviour towards more environmentally sustainable protein intake, it is important not only to change the supply but also the demand of unsustainable foods [78]. Interestingly, the most recent qualitative study indicated that young Australians were open to or interested in affordable meat alternatives such as plant-based meals and reported that often these options were not available or very limited when dining out (unpublished work). This indicates that people may be interested in changing their dietary behaviours to more sustainable and healthy but food environments need to be supportive in helping them to make informed food choices. Choice architecture, nudging strategies and policy implementation can be promising approaches to create enabling food environments and for changing dietary behaviour towards more sustainable diets. However, there is still a need to develop and test different strategies among more general population and settings to determine what motivates them in choosing more environmentally sustainable food options and if it leads to sustained behaviour change. In addition, there is a need to explore what would motivate food retailers to offer plant-based meal options and what the impact it may have on the food purchasing behaviour.
The main strength of this review is that a systematic approach was used and reported following the PRISMA guidelines to synthesise the evidence on the interventions aiming to promote protein intake from low ecological footprint sources. We included individual and micro-environmental level behaviour change interventions, which provides a more comprehensive picture on the effectiveness of interventions in changing people behaviour towards increase in environmentally sustainable protein intake. One limitation of this review is that most of the included studies have been conducted in high income countries and only a few studies were conducted in low- and middle-income countries (LMIC). This might be due to the fact that plant-based diet concept in high income countries has received increased attention in the last five years and LMICs have not prioritised it as a significant nutrition and environmental issue due to dealing with other diet related issues such as undernutrition and nutrient deficiencies. Research indicates that meat intake in LMIC has been associated with wealth as the rise in income has resulted in significant animal-derived food consumption in these countries [85]. Furthermore, most studies used self-reported measures to measure dietary behaviours which may increase biases [86]. Also, this review was limited to the literature published in English language and did not included articles published in grey literature, therefore it may be we missed some important research written in other languages. Finally, the majority of individual level behaviour change interventions included people who may be highly motivated to change their dietary behaviour such as cancer survivors, people at risk of developing chronic conditions, limiting the generalizability of the data to general population.
## Conclusions
The present review identified effective individual and micro-environmental behaviour change interventions which showed promising results in reducing protein intake from high ecological footprint sources. The findings suggest that individual behaviour change interventions such as education, counselling and self-monitoring interventions might be useful strategies to educate people to change their dietary behaviours to more sustainable ones. However, there is a need to test these strategies among the general population longitudinally. In addition, our findings showed that altering food environments using nudging and choice architecture approaches can achieve positive dietary changes but there is a need for development and evaluation of interventions in general settings (macro-environments) and explore motivations in sustainable food purchasing behaviours. Our findings inform future research for development and evaluation of interventions and strategies to encourage greater adoption of sustainable and healthy diets.
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|
---
title: 'Cultivating resilience and hope: A qualitative study of a pilot program using
patient navigators to assist men who have sex with men with retention in the HIV
care continuum in Uganda'
authors:
- Markus Larsson
- Arielle N’Diaye
- Richard Lusimbo
- Anette Agardh
journal: PLOS Global Public Health
year: 2023
pmcid: PMC10021195
doi: 10.1371/journal.pgph.0001475
license: CC BY 4.0
---
# Cultivating resilience and hope: A qualitative study of a pilot program using patient navigators to assist men who have sex with men with retention in the HIV care continuum in Uganda
## Abstract
In Uganda, due to the criminalization of same-sex sexual practices, men who have sex with men (MSM) experience barriers to accessing HIV care. To retain patients within the HIV Care Continuum, some health interventions have used patient navigators as an ancillary support service. To understand the potential care benefits of using patient navigators for marginalized populations experiencing challenges to HIV care and treatment access in a Ugandan context, this qualitative study explored the experiences of newly diagnosed MSM using patient navigators for ARV retention in care in Kampala. Additionally, to gain insight into the feasibility of patient navigator interventions, this study also aimed to understand the perspectives and experiences of patient navigators working with HIV positive MSM. Individual in-depth, semi structured interviews were conducted with 24 HIV positive MSM and four patient navigators that were part of a patient navigator pilot program from January 2019 –December 2020. Analysis was done using manifest and latent qualitative content analysis. Results showed that HIV positive MSM in Uganda experienced a variety of social, emotional, and financial challenges that placed them at risk for dropping off the HIV Care Continuum. Patient navigators provided HIV positive MSM with the skills, support, and resources necessary to overcome these challenges. Based on study results, we conclude that within the patient navigator pilot program, patient navigators improved MSM participants’ quality of life by helping them to achieve the HIV Care Continuum stages: diagnosis, linked to care, receiving HIV treatment, and retention in care. Study results suggest future research is needed on the psychosocial support needs of patient navigators, how the support needs of MSM change throughout their lifetime on the HIV Care Continuum, and how potential benefits of patient navigators may differ in rural Ugandan contexts.
## Introduction
The HIV Care *Continuum is* a model that describes HIV treatment and management in five stages: Diagnosis, Linked to Care, Receiving Treatment, Retainment in Care, and Achieving Viral Suppression [1–3]. Some studies have used it as a tool to investigate opportunities to better operationalize HIV specific health and social services [2, 3]. To retain patients within the HIV Care Continuum, some health systems have used patient navigators (PNs) as an ancillary support service [4, 5].
PNs are individuals tasked with linking patients to health and social services, minimizing barriers to care, ensuring patient adherence to treatment plans, increasing patient health literacy, and reducing patient fears [6–10]. Additionally, PNs provide a sense of continuity to patients as they navigate health and social service systems and have been effectively used to manage chronic conditions such as cancer, diabetes, cardiovascular disease, and HIV [7–12]. Existing scientific literature on PNs has identified examining patient experiences and intervention feasibility as areas for further study [11]. In contexts where same-sex sexual behavior is highly stigmatized, PNs have been used to both provide information about HIV and assist with gaining the skills needed for living with HIV. Set in Guatemala, Nigeria, and Kenya, these interventions successfully motivated patients to remain engaged in care, linked newly diagnosed patients to care, addressed antiretroviral therapy (ARV) adherence barriers, and provided emotional support [4, 5, 13].
In 2020, it was estimated that 24,100 men who have sex with men (MSM) were living in Uganda, with an HIV prevalence rate of $13.2\%$ [14]. Among MSM in Uganda, it has been estimated that $54\%$ were aware of their HIV status, and $66\%$ of MSM diagnosed with HIV were receiving ARVs [14]. However, due to the criminalization and stigmatization of same-sex sexual behavior, official demographic information on this population is scarce [15]. Moreover, due to the stigmatization and criminalization of same-sex sexual practices, HIV positive MSM in Uganda face many barriers that hinder their access to ARVs [16–18]. These barriers include stigma and homophobia, difficulty affording care, and low numbers of health workers sensitized to meet MSM specific health needs [17–21]. In healthcare settings, MSM often feel uncomfortable discussing their sexual practices with health workers and fear being denied treatment if health workers learn of their sexual history [19, 20]. For some MSM, internalized stigma and health worker discrimination lead them to completely forgo accessing health services [20]. These barriers together with high levels of geographic mobility among MSM have made necessities such as lubricants, ARVs, and treatments for sexually transmitted infections (STIs) difficult to access and retention in care challenging [19]. To better understand the potential care benefits of using PNs for marginalized populations experiencing challenges to HIV care and treatment access in a Ugandan context, this study aimed to explore the experiences of newly diagnosed MSM using PNs for ARV retention in care in Kampala. Additionally, to gain insight into the feasibility of PN interventions, this study also aimed to understand the perspectives and experiences of PNs working with HIV positive MSM.
## Study setting
This study was part of a PN pilot program that took place in Kampala, Uganda, from January 2019 to December 2020. The PN pilot program had the overall goals of 1) helping MSM newly diagnosed with HIV to make informed decisions about their healthcare seeking practices, and 2) aiding newly diagnosed MSM to understand how to navigate the healthcare system to access medication and treatment. Because this research study was part of the PN pilot program, everyone involved within the pilot was also part of the research study described in this article. A summary of the goals is presented in Table 1.
**Table 1**
| Research study aims | Patient navigator pilot program goals |
| --- | --- |
| To explore the experiences of newly diagnosed MSM using PNs for ARV retention in care in Kampala | Help MSM newly diagnosed with HIV make informed decisions about their healthcare seeking practices |
| To understand the perspectives and experiences of PNs working with HIV positive MSM | Aid newly diagnosed MSM to understand how to navigate health system to access medication and treatment |
## Study sample
PNs and MSM participants were recruited through purposive sampling for the PN pilot program and the study that took place within it. Eligible MSM participants were MSM over 18 years of age and newly diagnosed with HIV (one-month post diagnosis or less). Eligible PN participants were MSM over 18 years of age. For recruitment, the research team contacted an LGBTQ (Lesbian, Gay, Bisexual, Transgender, and Queer) umbrella organization (NGO) in Kampala that assisted with PN and MSM participant recruitment for both the PN pilot program and the study. In total, four PN participants were recruited, all engaged in different organizations working with LGBTQ populations. For their participation in the pilot program, PN participants were compensated with a stipend and received training by a medical professional associated with the research study. The training was conducted using existing WHO and UNAIDS protocols for explaining HIV and AIDS, treatment, stigma, and discrimination. This same organization then sourced potential MSM participants through their networks. Once a potential MSM participant fitting the selection criteria was identified, the organization informed them about the pilot program and the research study. In total 24 MSM participants were recruited.
Once data collection for the research study began, consent was obtained from all participants prior to beginning each interview. This recruitment protocol for both MSM and PN participants was considered appropriate given the sensitivity surrounding LGBTQ individuals in Uganda. Recruitment finished when it was agreed upon among the researchers that sufficient information power had been reached, according to the concept suggested by Malterud et al. [ 22].
## Data collection
Data was collected using individual in-depth interviews with two semi-structured interview guides (i.e., PN and MSM participants, respectively). The PN participant interview guide contained four parts: their motivation and ideals; their perceptions of pilot participants; their relationship with pilot participants; and their experiences within the patient navigator pilot program. The MSM participant interview guide contained three parts: information about their experience living with HIV; their individual relationship with their patient navigator; and their experience within the patient navigator pilot program. Further detail about these interview guides can be found in S1 and S2 Texts. Because the study aim strived to capture MSM participant experiences as they moved through different stages within the HIV Care Continuum, both MSM and PN participant interviews were conducted at two points in time (June 2019 and November 2019). These two points in time were chosen because it gave MSM participants six months to move to other stages within the HIV Care Continuum. The same interview guide was used on both occasions. Participation in two interviews was, however, optional. Interviews were conducted in person and in English with a Luganda speaking translator present. A total of eight interviews used an interpreter. In these interviews the interpreter was used to clarify questions posed by the interviewer in English that respondents did not fully understand. The first author (ML) conducted the interviews in June and November 2019, and the last author (AA) conducted the interviews in November 2019. Interviews were audio recorded and transcribed verbatim. The text sections in Luganda were translated by a member of the research team, fluent in both Luganda and English. The option to conduct the interview without an audio recorder was provided, but all respondents agreed to be recorded. Interviews occurred in a secure location within a private room. To protect respondent identities, no identifying data was collected during interviews. Additionally, all transcripts were anonymized prior to analysis and only members of the research team had access to audio recordings and interview transcripts.
## Data analysis
Transcripts were analyzed using manifest and latent qualitative content analysis with an inductive approach [23, 24]. Study data was analyzed from the perspective of pilot participants moving through the HIV Care Continuum. As a result, both June and November interviews were analyzed together. For the coding process, individual interviews were the units of analysis, and meaning units in the form of sentences and paragraphs were condensed and labeled with codes. Codes were grouped into subcategories and combined to form categories. The content of these categories was examined to identify emerging subthemes and themes. Analysis was initially conducted by one member of the research team (AN). Following this, the analytical model was examined by the other co-authors (AA, ML, RL) to obtain feedback and consensus on study findings.
## Researcher characteristics and reflexivity
Among the co-authors, two are Swedish and based in Sweden (AA, ML), one is American and based in the United States (AN), and one is Ugandan and based in Uganda (RL). All co-authors have previous knowledge and experience of working in the areas of HIV and key populations within a variety of Sub-Saharan African contexts.
This study was conducted in accordance with the Standards for Reporting Qualitative Research (SRQR) checklist [25]. As a result, steps were taken by the research team to reflexively examine how their positionality could potentially influence study outcomes. Because of his experience as a Ugandan working with key populations in Kampala, RL brought an insider perspective to this study’s design, analysis, and reporting processes. Since interviews were conducted by two non-Ugandan co-authors (AA, ML), having a Luganda speaking interpreter ensured that socio-linguistic nuances were mutually understood by both interviewers and respondents. Additionally, previous research experiences on the topics of HIV and key populations in Uganda had familiarized these two co-authors with the socio-cultural dynamics experienced by our study population. While conducting the initial analysis AN used a reflexive journal to describe analytical decisions, in addition to thoughts and questions that arose. The contents of this journal were discussed among the co-authors while examining the analytical model to mitigate the possibility of bias resulting from being an outsider to Ugandan socio-cultural dynamics. This reflexive process was also used when drafting and revising this manuscript.
## Ethical considerations
This study received approval from St. Francis Hospital Nsyamba REC: UG-REC-020 in Kampala, Uganda, and was performed in accordance with the principles of the Declaration of Helsinki [26]. Because this study involved a vulnerable research population, PN and MSM participants received thorough information about the voluntary and anonymous nature of their involvement. A representative from the collaborating partner organization that works with key populations met with prospective study respondents to explain the study purpose, potential risks, benefits, and their right to withdraw from the study at any time during the interview process or afterwards. None of the participants recruited chose to withdraw from this study. Information about the study and the terms of participation was also provided by the interviewer prior to beginning each interview. Respondents were also given the opportunity to ask questions on both occasions. In the event negative emotions arose during an interview, a member of the research team was available to connect all participants to a counsellor that would provide free of cost counselling. This information was also provided in the information letter.
## Results
39 interviews were conducted among 28 respondents (4 PN and 24 MSM participants), where the mean age among respondents was 29 years of age. 17 respondents (17 MSM participants) were interviewed once (either in June 2019 or in November 2019), and 11 respondents (4 PN and 7 MSM participants) were interviewed both in June 2019 and November 2019. In total, respondents ranged between 20–42 years of age. All respondents (both PN and MSM participants) self-identified as MSM; however some specifically identified as gay or bisexual. Respondent demographics are further described in Tables 2 and 3.
## Overarching theme: Cultivating resilience and hope against enduring trauma and stress
Data analysis resulted in one overarching theme, two sub-themes, seven categories, and 22 subcategories. The overarching theme Cultivating resilience and hope against enduring trauma and stress captures how the PN participants assisted MSM participants with developing their capacity to be resilient and remain hopeful in a social context where they are at risk for experiencing traumatic encounters and stress during their journey through the HIV Care Continuum. This theme illustrates how it is through strengthening these capacities—in tandem with the social and resource support network that the PN pilot program provides—that MSM participants are aided in remaining within the HIV Care Continuum despite the potential life challenges they may face. The overarching theme is supported by two subthemes: Providing refuge for the adjustment to a new normal and Sharing the weight of difficult realities. These subthemes are supported by seven categories and 22 subcategories that are described in the analytical model shown in Fig 1.
**Fig 1:** *Analytical model of subcategories, categories, subthemes, and themes.*
## Subtheme 1: Providing refuge for the adjustment to a new normal
Post diagnosis, the PN pilot program provided MSM participants with a space in which to acclimate to life with HIV. Prior to enrolling in the program, learning to live with HIV was a difficult and lonely process for MSM participants. However, after meeting their respective PNs, MSM participants were able to find community both with their PN and with people who also identified as gay/bisexual and living with HIV. This subtheme represents both how MSM participants were given a dedicated safe space to process life post-diagnosis, and why they felt this space was needed.
Difficulty navigating life after diagnosis. According to MSM participants, prior to participating in the PN pilot program, navigating life post-diagnosis was a difficult and lonesome experience. For them, diagnosis was a moment of crisis where they experienced feelings of despair. These feelings could be described as a spectrum of hopelessness, shock, malaise, being overwhelmed, confusion, and suicidality. Some participants mentioned having difficulty accepting their status, and others spoke of having difficulty accepting how they acquired HIV. For many MSM participants, diagnosis felt like it was the beginning of the end of their lives. Additionally, post-diagnosis, MSM participants reported feeling alone and completely lacking support. They mentioned feeling abandoned by others following their diagnosis, experiencing stress about how to move forward with their lives, and needing someone to be there for them.
MSM participants also disclosed feeling distressed whenever they thought about how they were going to continue living, as they now knew they were HIV positive. PN participants similarly stated that recently diagnosed MSM participants were especially fragile and mentioned trying to be there for them as they adjusted to life post-diagnosis.
Finding community. After joining the PN pilot program MSM participants experienced finding community. Through their PNs, MSM participants felt they had someone they could trust. Even though some MSM participants were initially shy and hesitant to open up to their PN, PN participants were eventually viewed by all MSM participants as someone they could call at any time. MSM participants described PN participants as someone with whom they could talk about all aspects of life. Among PN participants, knowing MSM participant needs, secrets, and maintaining confidentiality were viewed as core competencies of their role. MSM participants mentioned how PN participants were trusted because of their patience, discretion, and their ability to support MSM participants. It is important to note that this trust between PN and MSM participants was mutual, where PN participants often shared personal details such as their HIV status and sexual orientation with MSM participants. Overall, both MSM and PN participants reported having good relationships with one another.
Furthermore, many MSM participants perceived their PN as taking care of them like a family member and subsequently viewed them as a close friend or family member. PN participants shared a similar perception.
Moreover, MSM participants described the PN program as somewhere they could receive comfort and care. This occurred through activities such as receiving home visits from their PN, visiting their PN at their home, being given words of encouragement, receiving counselling, being told that they were not alone, and being told that having HIV was not the end of their life.
Almost all PN and MSM participants identified providing guidance and counselling as a core responsibility of PNs. According to MSM participants, counselling and encouragement played a large role in their adjustment process: “I felt really good. Because [my PN] counselled me and told me that you are not alone, many people are sick out there” (Participant 2). During counselling, MSM participants asked questions about how to live with HIV and expressed when they were feeling low. They described these activities as helping them to have a positive outlook on life and feeling like someone cared for them. Furthermore, from participating in the PN program MSM participants expressed discovering others who are also HIV positive and MSM. They articulated how PN participants created a support network of other HIV positive MSM in-person through one-on-one introductions and support groups, and online through WhatsApp groups. Through this network, MSM participants were able to form meaningful relationships by sharing their experiences of being HIV positive and LGBT. MSM participants found this network to be a source of encouragement and enjoyed meeting individuals among whom they could speak freely.
## Subtheme 2: Sharing the weight of difficult realities
While participating in the PN program, MSM participants experienced a variety of challenges. These included difficulties stemming from their sexual practices and HIV status, lacking basic material necessities, experiencing barriers to ARV adherence, and having limited access to treatment and health information.
To help MSM participants stay within the HIV Care Continuum, PN participants assisted them with overcoming these challenges. PN participants aided MSM participants with challenges relating to their sexual practices and HIV status by improving their quality of life. PN participants accomplished this by assisting with stigma management and creating safe spaces for HIV positive MSM. PN participants helped MSM participants overcome the challenges of lacking basic material necessities and barriers to ARV adherence by, for example, connecting MSM participants with individuals essential to solving their challenges and making resources accessible to participants.
Being LGBT and HIV positive is difficult. Both MSM and PN participants described being LGBT and HIV positive as a difficult experience. MSM participants feared stigmatization and recalled experiences of being doubly stigmatized for being HIV positive and MSM. MSM participants disclosed experiencing discrimination and being mistreated by health workers within hospital settings. It was because of this that some participants did not feel comfortable discussing their sexual practices with health workers.
MSM participants mentioned anonymity and privacy as important to them. They feared being seen by others at health facilities because not only did they face discrimination from general Ugandan society for their sexual practices, but also from the LGBT community for their HIV status. Both MSM and PN participants stated that being LGBT was not accepted within Ugandan society. MSM participants described fearing rejection from their families and being forced from home because of their sexuality. They also described feeling endangered, experiences of being outed on social media, and experiences of violence in reaction to their sexuality.
This fear of familial and societal rejection led many MSM participants to keep their HIV status secret. MSM participants feared what others would think about their status and feared that disclosure would lead to inquiries about their sexuality.
Working to improve quality of life for participants. PN participants sought to improve MSM participants’ quality of life by assisting with stigma management and by creating safe spaces for MSM living with HIV. One way in which PN participants helped MSM participants navigate stigma management was by guiding them with HIV status disclosure. This was done by providing counseling about HIV disclosure to participants and their families. PN participants also encouraged MSM participants to utilize health services and disclose their sexual practices to health workers they trusted. Additionally, PN participants reduced experiences of stigma by providing sensitivity training to health workers. Many MSM participants described waiting in line for long periods of time for ARVs as eliciting feelings of internalized stigma and fears of being stigmatized by health workers. In response to this, after consulting the PN participants, an LGBT organization created a voucher system for MSM participants to collect their ARVs without having to physically say that they are MSM. With health workers, PN participants often encountered the challenge of needing to conduct sensitivity trainings every few months due to frequent staff transfers between health facilities.
Overall, both PN and MSM participants voiced that assisting with stigma management and increasing participant comfort with health workers was seen as a core responsibility of PN participants.
To create safe spaces for MSM living with HIV, PN participants brought MSM participants to MSM friendly health facilities. At these facilities MSM participants felt comfortable speaking with health workers about their sexuality and did not experience discrimination because of their sexual practices.
According to MSM participants, many MSM friendly health facilities provided a variety of services and worked in partnership with LGBT friendly organizations.
Experiencing PN burnout and needing sustainability. For PN participants, the high level of financial and emotional demands from MSM participants was sometimes overwhelming. According to MSM participants, finding sustainable employment was challenging and the majority reported being unemployed or underemployed.
Many expressed difficulty affording things like food, housing, mobile phones, and transportation and reported participating in activities like asking others for money to support themselves or engaging in sex work. As described by one PN participant, “/…/ these are people who are needy, who are malnourished, who cannot even afford to buy a kilogram of rice” (PN 4). For some PN participants, these feelings were exacerbated by challenges they were experiencing in their personal lives.
Moreover, listening to MSM participants’ traumas and hardships occasionally caused PN participants to relive their own past traumatic experiences. Likewise, PN participants also mentioned how they felt upset when they were not able to help MSM participants overcome their challenges. To help cope, PN participants participated in counselling and debriefed with each other. But despite these resources, some PN participants wished they had more substantial psychosocial support to help cope with these challenges.
Experiencing barriers to medication adherence. Furthermore, MSM participants experienced a variety of barriers to medication adherence. For some, experiencing side effects created challenges with ARV adherence. Many MSM participants were fearful of taking medication without food and consequently chose not to take their ARVs until they were able to find food.
To help with this, PN participants provided participants with food assistance.
It is important to note that some MSM participants had trouble adhering to ARVs because of a general struggle to take medication regularly. They found the concept of being on medication for the rest of their lives as difficult to accept and something that caused them to lose hope.
Additionally, some MSM participants struggled with ARV adherence because they did not feel ready to start medication post diagnosis, they experienced inadequate counselling on ARVs at the hospital, or they felt that counsellors at health facilities were unavailable for drop-in inquiries. To help with these challenges, PN participants counselled MSM participants about the importance of taking ARVs and gave them the opportunity to ask questions.
Moreover, MSM participants struggled to take their ARVs because of additional medications they were taking for other illnesses they had. Lastly, MSM participants mentioned having difficulty taking their ARVs on time and frequently taking their ARVs late. This was attributed to challenges such as not being able to find transportation to collect their medication and preferring to take their medication in secret because they did not want others to know they were taking medication. As one MSM participant stated, “Yeah, I have to hide [my ARVs]. I cannot tell people. Because I normally take it in the night, no one can see me” (Participant 5). To help participants overcome these challenges, PN participants assisted MSM participants with collecting their medication and performed follow-ups to encourage medication adherence.
Furthermore, both MSM and PN participants mentioned that prior to participating in the PN pilot program, most MSM participants possessed minimal information about sexual health and life with HIV. They reported believing misinformation about life with HIV and receiving insufficient information about ARVs once diagnosed.
To combat misinformation, PN participants provided MSM participants with accurate sexual health information. As a result of this, MSM participants expressed having increased their knowledge about HIV and how to live with it. It is noteworthy that this information did not stay with MSM participants. Instead, MSM participants described sharing these lessons with others in their social networks.
Making support accessible. According to both PN and MSM participants, a key aspect of PN participants’ role is successfully connecting MSM participants with the right people and services to help them solve their challenges.
PN participants accomplished this through consistently checking-in with MSM participants and asking about their challenges: “I must call the participant every day to know about the participant’s situation, and we meet every Sunday in our support group” (PN 1). This also occurred through linking MSM participants with relevant health workers and services: “Sometimes I bring [a psychologist] to our meetings and she talks to my [participants] and colleagues” (PN 3). As a result of this support, MSM participants expressed feeling empowered and being given the skills needed to engage with health workers in the future.
According to both PN and MSM participants, the PN pilot program was integral in helping participants obtain essential material resources like food, transportation, and housing assistance.
As previously mentioned, MSM participants experienced a variety of challenges to ARV adherence. When assisting participants with ARV adherence, some PN participants believed it was crucial to provide extra emotional support to MSM participants beginning ARVs for the first time. They did this because they themselves knew firsthand from their own experiences how difficult beginning ARVs can be.
## Discussion
This study sought to explore the potential care benefits of a pilot program using patient navigators (PNs) to assist men who have sex with men (MSM) recently diagnosed with HIV in Kampala, Uganda. From the multiple ways that PN participants ensured that MSM participants attended medical appointments, adhered to their ARV treatment protocol, and received help for their general medical needs, our findings demonstrate that in a Ugandan context, PN participants were helpful in not only getting MSM participants to achieve the HIV Care Continuum stage of retention in care, but also effectively assisted MSM participants in reaching the other Care Continuum stages of diagnosis, linking participants to care, and receiving HIV medical care [1]. Although it is not known how many participants reached viral suppression while enrolled in the PN pilot program, PN participants illustrated via activities such as counselling and ARV adherence reminders that viral suppression was something that they were helping MSM participants to work towards. The emerging theme Cultivating resilience and hope against enduring trauma and stress highlights the barriers faced by HIV positive MSM and the important role played by the PN participants by giving MSM participants the tools needed to overcome future challenges they face as HIV positive MSM. Our findings also suggest that PN participants need psychosocial support to cope with participant expectations and demands.
## Experiencing a variety of barriers
In this study, MSM participants experienced a range of barriers to care, including HIV and LGBT related stigma, lacking money for necessities, difficulty finding sustainable work, struggling to take ARVs, fearing ARV side effects, and experiencing ARV side effects. These barriers were rooted in social, resource, or emotional challenges and had the potential of causing MSM participants to drop off the HIV Care Continuum. Except for LGBT related stigma, these barriers were not unique to MSM and have been similarly seen in other studies about the experiences of people living with HIV (PLHIV) in Uganda [17–20, 27].
As an intervention, PNs represent a type of peer support [28–30]. Previous studies on the benefits of peer support for PLHIV in the United States, United Kingdom, Uganda, Nigeria, Mozambique, Kenya, and South Africa found that peer support—when provided in conjunction with consistent medical care—yielded high rates of retention in care, when compared to solely providing in-clinic follow ups [28–30]. Our results support previous findings that PNs are effective in bridging gaps between different entities within a health system, connecting individuals to health services, improving health literacy, and reducing patient fears [6, 8, 9]. These study results are similar to previous research findings in that the PN pilot both engaged MSM participants within every facet of the Continuum of Care and offered protective factors against potential challenges to medication adherence through providing a social support network [8, 31]. In Uganda, among heterosexually identified PLHIV, it has been previously observed that these individuals both created and linked networks that consisted of family, friends, other PLHIV, religious leaders, neighbors, and health workers [31]. The social capital from these naturally occurring networks was found to provide protection factors against risks factors for ARV non-adherence by helping with food assistance, transportation costs, information about HIV, stigma management, status disclosure, medication shortages, and ARV adherence [31]. Our results build on this finding because societal stigma against LGBT individuals and the illegality of same-sex sexual practices prevent MSM from accessing these networks. MSM participants within our study were wary of others knowing their HIV status because they viewed it as inviting questions about how they acquired HIV and who their sexual partners were. Our findings suggest that PN participants via the PN pilot program created formal networks for MSM participants that proxied the informal familial-communal networks used by heterosexual PLHIV. Within these formalized networks, MSM participants were assisted with their social, emotional, and resource needs through attending MSM friendly clinics, visiting LGBT organizations, and engaging with familial-like networks that consisted of PN participants and other HIV positive MSM.
## Receiving the tools to overcome future challenges
Through PN participants, the health professionals they engaged with, and counselling sessions they participated in, MSM participants received information about how to live well with HIV. From participating in the PN pilot, MSM participants obtained skills like how to take their ARVs, how to refill their prescriptions, how to interact with LGBT sensitized health professionals, how to navigate the health system, and how to advocate for themselves within healthcare settings. Similar findings have been found in other PN interventions [4–9]. Additionally, MSM participants in the PN pilot program shared their newly gained information and skills with other MSM in their circles. These findings shed light on how PN programs could benefit other MSM communities in high discrimination settings. It is important to note that in these settings, it has been documented that HIV prevention and control efforts are often hindered due to inaccuracies and scarcity of MSM and HIV positive population data and difficulties reaching MSM individuals [5, 13, 32]. Based on our findings, it could be suggested that PN interventions in these settings are well suited to overcome these two challenges as they can use both HIV positive and negative MSM to provide information and services to other individuals in their community.
## PNs needing psychosocial support to cope patient demands
When interviewed, PN participants expressed needing psychosocial support to offset both the high level of expectations and demands placed on them by MSM participants, to process their traumas and challenges, and to manage negative emotions that arose when they were unable to meet participant needs. For some PN participants, hearing about HIV or sexuality related participant traumas and challenges caused them to relive similar experiences that they themselves had gone through previously. These findings illustrate the potential needs of PN participants working in high-stigma contexts where participants have limited access to social, economic, and emotional support. These findings corroborate previous observations by Li et al. [ 33] who found that among HIV positive HIV service providers in Canada, many individuals reported needing more psychosocial support services after encountering challenges related to the transition from HIV service recipient to HIV service provider [33]. Some of these challenges included losing access to support groups when support group members became their clients, losing access to counsellors who had now become their colleagues, and having limited options in peers they could confide in as a result of being part of a small community [33]. Moreover in similarity to our study, Li et al. [ 33] also found that service providers adapted to these challenges by confiding in colleagues who were also peers and service providers, or by confiding in mental health professionals if they had access to them [33]. It is important to note that even though not all the PN participants in our study were HIV positive, the findings of Li et al. [ 33] are applicable because PN participants in our study provided services for members of a marginalized community that they themselves were part of.
## Sustainability of PN programs
The PN pilot program discussed within this study took place from January 2019 to December 2020, where MSM participants received a variety of material, emotional, and social support from PN participants with the goal of helping them remain within the HIV Care Continuum. Although this pilot was successful in meeting this goal, further study is needed on the long-term sustainability and efficacy of PN interventions. This suggestion is similarly highlighted in a review of seven PN programs by Roland et al. [ 10], who found that participants were apprehensive about leaving their respective programs because they wanted to continue their relationship with their PNs, and because they still needed long term assistance with nonmedical support [10, 34, 35].
Additionally, we recommend further study on the long-term financial feasibility of PN programs. As previously mentioned, PN participants within our study were able to help MSM participants remain within the HIV Continuum of Care by providing material resources, emotional support, and social support at an individual level. Because this individualized approach to support was key to this pilot’s success, further research is recommended to understand the financial feasibility of this individualized approach for long term and larger scale programs. This recommendation was similarly made by Shade et al. [ 36], who in their review of 16 PN interventions across the United States found that among these interventions (all of them taking place over 11–12 months) most of their intervention costs went towards providing tailored support to patients at the individual level [36].
## Methodological considerations
A few important limitations exist within this study. First, even though Malterud’s concept of information power was used to decide when study recruitment finished, because this study contains the perspective of only four PN participants it is not possible to know if including additional PNs within the pilot program and research study would have further strengthened this study’s design [22]. Second, since June 2019 and November 2019 interviews were analyzed together, it was not possible to establish temporality and pinpoint exactly when participants moved to different stages within HIV Care Continuum, when participants became adjusted to life with HIV, and when in the HIV Care Continuum participants experienced challenges and setbacks. Moreover, since this study was set in Kampala, it is possible that study results might not be applicable to rural settings in Uganda where contextual differences may be present (e.g., less resources being available to MSM or PLHIV, and a lower population density of MSM or PLHIV).
This study also has several strengths. The analytical process allowed for multiple research team members to examine the analytical choices made, thus increasing the confirmability of study results. Moreover, the use of interpreters allowed interviewees to both clearly understand the questions posed to them and describe their experiences as accurately as possible. Lastly, research team members’ previous experience and knowledge regarding the topics of HIV, key populations, Uganda, and sexual and reproductive health and rights enabled a nuanced and informed approach to this study’s design, execution, and analysis [22].
## Conclusion
In Kampala, Uganda, men who have sex with men (MSM) living with HIV experience a variety of material, emotional, and social challenges that place them at risk for falling off the HIV Care Continuum. MSM living with HIV benefitted from patient navigators (PNs) via a PN pilot program by being provided with the support, skills, and resources needed to overcome these barriers. Among participants, the assistance from the PNs helped to cultivate the resilience needed to remain within the HIV Care Continuum. While participating in the PN pilot program, both PN and MSM participants reported positive experiences and occasional challenges. For PN participants, positive experiences included fulfillment from helping participants overcome obstacles and challenges, but they also reported feeling overwhelmed by the high number of MSM participant demands. For MSM participants, positive experiences included finding community among other HIV positive MSM but also challenges such as difficulty with ARV adherence. Based on our study results, further research is required on the psychosocial support needs of PNs and how the support needs of MSM participants change throughout their lifetime within the HIV Care Continuum. Additionally, study results also suggest that more research is needed on how the potential care benefits of using PNs might differ in rural contexts within Uganda.
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|
---
title: Exploring ways to respond to rising obesity and diabetes in the Caribbean using
a system dynamics model
authors:
- Leonor Guariguata
- Leandro Garcia
- Natasha Sobers
- Trevor S. Ferguson
- James Woodcock
- T. Alafia Samuels
- Cornelia Guell
- Nigel Unwin
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021196
doi: 10.1371/journal.pgph.0000436
license: CC BY 4.0
---
# Exploring ways to respond to rising obesity and diabetes in the Caribbean using a system dynamics model
## Abstract
Diabetes and obesity present a high and increasing burden of disease in the Caribbean that have failed to respond to prevention policies and interventions. These conditions are the result of a complex system of drivers and determinants that can make it difficult to predict the impact of interventions. In partnership with stakeholders, we developed a system dynamics simulation model to map the system driving diabetes and obesity prevalence in the Caribbean using Jamaica as a test case. The study aims to use the model to assess the magnitude changes necessary in physical activity and dietary intake to achieve global targets set by the WHO Global Action plan and to test scenarios for interventions to reduce the burden of diabetes and obesity. Continuing current trends in diet, physical activity, and demographics, the model predicts diabetes in Jamaican adults (20+ years) to rise from $12\%$ in 2018 to $15.4\%$ in 2030 and $20.9\%$ by 2050. For obesity, it predicts prevalence to rise from $28.6\%$ in 2018 to $32.1\%$ by 2030 and $39.2\%$ by 2050. The magnitude change necessary to achieve the global targets set by the World Health *Organization is* so great as to be unachievable. However, a combination of measures both upstream (including reducing the consumption of sugar sweetened beverages and ultra processed foods, increasing fruit and vegetable intake, and increasing moderate-to-vigorous activity) at the population level, and downstream (targeting people at high risk and with diabetes) can significantly reduce the future burden of diabetes and obesity in the region. No single intervention reduces the prevalence of these conditions as much as a combination of interventions. Thus, the findings of this model strongly support adopting a sustained and coordinated approach across various sectors to synergistically maximise the benefits of interventions.
## Introduction
Rising prevalence of obesity and type 2 diabetes have been of concern for decades [1, 2]. There have been a series of calls to action, including the WHO Global Action Plan for the Prevention and Control of NCDs 2013–2020 [3] with global targets set for no increase in obesity or diabetes by 2025 compared to 2010. The relationship between obesity and diabetes is well-established [4] and many of the trials designed to prevent or delay the onset of type 2 diabetes have set weight loss as the cornerstone of interventions, usually through a combination of calorie restriction and increased physical activity in those at highest risk of progressing to type 2 diabetes [5]. While these have been shown to be highly effective in controlled settings, they have not halted the inexorable increase in diabetes prevalence at the population level. The most likely cause for this failure is not because of the intervention design, but because the system driving obesity and subsequent diabetes is more complex than is represented in a controlled study or trial [6]. One way to characterise complex systems is through mapping of the feedbacks, delays, flows and accumulations (stocks) [7]. The complex nature of systems can make it hard to predict their behaviour and the effects on outcomes. In 2006, Jones et al. developed a system dynamics model for exploring the likely progression of the diabetes and obesity epidemics in the United States and tested scenarios for intervening in that system to reduce the burden of diabetes and its complications [8]. The model was developed using a complex systems approach engaging directly with key stakeholders to map and simulate the dynamics of the epidemic and led to the informed development and adoption of policies to target the epidemic [9].
The *Caribbean is* faced with a particularly high double burden of type 2 diabetes and obesity [10], driven in large part by an increasingly sedentary population [11] and unhealthy diets [12]. It is expected that this high burden will increase cases and deaths over the coming decades. As early as 2007, Caribbean policymakers recognized the importance of targeted and coordinated responses [13], but despite this the prevalence of obesity and diabetes has continued to rise [14]. As a result, the System Science in Caribbean Health project was developed with the overarching aim of using systems thinking and modelling to help inform, through engaging with stakeholders, coordinated interventions designed to curb the rise in obesity and type 2 diabetes [15]. The study also aims to enable more realistic targets and measures of success to be developed. We present a system dynamics model that is part of that project to simulate the burden of diabetes and its relationship with obesity, including inputs for diet and physical activity to help understand the possible trajectories of the epidemics, simulate the impact of changing determinants, and understand the magnitude of change needed to achieve at least a stabilisation in diabetes and obesity in adults in one exemplar Caribbean island, Jamaica.
## Ethics statement
The study was submitted for ethical review and approved by the University of the West Indies, Cave Hill Institutional Review board, and also approved by the Barbados Ministry of Health Research Ethics Committee.
In addition, ethical approval for the stakeholder consultations was obtained from the University of the West Indies, Mona, Jamaica, Ethics Committee; the University of Medicine and Health Sciences, St. Kitts and Nevis; and the Ministry of Health, Wellness, and the Environment, St. Vincent and the Grenadines with a waiver of informed consent from participants.
## Summary of the study
The model presented here is one component of a larger study [15] with multiple mixed methods components. The study used methods from system dynamics to engage stakeholders in the development of conceptual maps, or causal loop diagrams (CLDs) [16], to describe the drivers of rising obesity and diabetes prevalence in the Caribbean. Key stakeholders were identified and gathered from experts in the region representing a breadth of experience in healthcare, academia, civil society, government, intergovernmental organisations, food producers, retailers and distributors in the Caribbean. Experts in food science and physical activity were also included in the sessions and through consultations to inform the development of CLDs. Stakeholders participated in in-depth interviews and group model building (GMB) sessions where they were tasked with developing the conceptual maps, identifying feedback cycles, and elaborating potential systems-oriented interventions to change the trajectory of diabetes and obesity in the region. The details and outputs for these workshops have been previously described [17, 18]. Those diagrams and discussions form the basis for the development of the simulation model presented here.
## Study setting
Jamaica was selected as a test case for the simulation model. This was in part because it has a history of repeated population-based national cross-sectional health surveillance surveys [19–22] that provided data inputs as well as measures for calibration. It is the largest English speaking island in the Caribbean with an evidence base for noncommunicable diseases (NCDs) trends [1] and an engaged political profile for NCD prevention and management [23] that often serves as a model for other countries in the region [24]. Jamaica was considered by stakeholders to provide a case study relevant to other English speaking countries in the Caribbean.
## Model structure
System dynamics models are nonlinear time-continuous simulation models that use first-order differential equations and integrals that are connected using a causal structure[16]. The causal structure for the model was developed and informed by the outputs from the GMB sessions with key stakeholders in the region. The CLDs developed by stakeholders helped to inform the summary stock and flow diagram presented here (see Fig 1), and the full model diagrams presented in the S1 File. Some of the model structure adapts aspects of the model developed by Jones et al. [ 8] but, in following key stakeholder input, expands the connections for upstream determinants including diet and physical activity. It also includes differences in distribution of risk factors and diabetes by gender, an important dimension of NCDs in the Caribbean [25]. We chose a time scale from 1990 and projected forward to 2050 in one year time steps.
**Fig 1:** *Summary stock and flow diagram of the core structure of the model for diabetes in the Caribbean.*
The model structure follows a core stock (as boxes) and flow (thick double-lined arrows with valves) structure that represents the movement of people through different glycemic states: normoglycemia (NG), prediabetes (preDM), and diabetes (DM). Added up, these stocks account for the whole of the adult population (20+ years) with no overlap between stocks. There is an inflow of adults for population growth into the normoglycemic stock. People may move from normoglycemia to prediabetes and back to normoglycemia. We assume those with prediabetes may also move into diabetes, and no population-level remission from diabetes. For each stock there is also an outflow for all-cause mortality. The flows are modified by other factors including the ageing of the population, the change in obesity prevalence, and for onset of diabetes; and sub-models for dietary intake (including the consumption of sugar sweetened beverages (SSBs), fruit and vegetable intake), and physical activity (PA).
## Model parametrisation and calibration
The approaches taken here were guided by system dynamics methodology described by Sterman [16]. This model is a reflection of the priorities of stakeholders engaged in the GMB as well as an iterative and collaborative process of testing and revision grounded in the scientific literature. It is, however, a deliberate simplification and seeks to model priorities identified by key stakeholders and for which there was sufficient scientific evidence. We parameterized and calibrated the model using historical trends of diabetes and obesity, and selecting Jamaica as a test case.
A full list of data inputs including time series, calculations, and a detailed accounting of data sources is available in the S1 File. Key data sources and inputs are listed in Table 1.
**Table 1**
| Data input | Source | Summary |
| --- | --- | --- |
| Population inflows, age structures | UN World Population Prospects—2019 Revision [26] | Net population inflow to the normoglycemic stock |
| Baseline estimates of prediabetes and diabetes prevalence and population mean body mass index (BMI) | Jamaica Health and Lifestyle Surveys [19–21], Spanish Town study [22], ICSHIB Cohort [27] | Cross-sectional surveys conducted in Jamaica at various time points |
| Flow (incidence) rates for prediabetes and diabetes onset, and prediabetes remission | Scientific literature [28–30] | Estimates were taken from systematic reviews of longitudinal cohort studies studying the progression of hyperglycemia in various populations |
| Physical activity estimates | Interpolation using modelled trends from Ng and Popkin [31] and Jamaican study data [20, 32] | Physical activity estimates were taken from the literature for studies done in Jamaica and trends interpolated |
| Dietary intake including sugar sweetened beverage consumption | Scientific literature and studies conducted in the Caribbean [33–38] | Interpolated data from studies conducted in Jamaica, Barbados and Latin America |
| Effect sizes for interventions and risk factors | Scientific literature (see S1 File) | Relative risk estimates and effect sizes for interventions taken from systematic reviews and meta-analyses in the literature |
As far as possible, data sources were chosen that were most closely related to the population of interest. We prioritised data inputs and information for testing assumptions from studies conducted in Jamaica, then in the Caribbean region, and finally in the *African diaspora* or pooled estimates from other countries or systematic reviews with meta-analysis.
Where data were not available, we used mean values and pooled estimates from systematic reviews and calibrated inputs based on plausibility and alignment with the historical data available for measured diabetes prevalence and obesity prevalence from Jamaica [25]. We estimated body mass index (BMI) and obesity prevalence from interpolated trends in physical activity and from historical trends in diet. Physical activity estimates were derived from a combination of data points for physical activity obtained from device-based and self-reported cross-sectional surveys in Jamaica [16, 28] and trends and projections estimated from Ng and Popkin [31] to project likely scenarios for decreasing physical activity. Dietary intake estimates were similarly interpolated from data in Jamaica [36] and Barbados [33–35] and combined with global dietary trends [37, 39] and studies done in Latin American countries on ultra-processed food intake [38]. We assume that trends in the future for dietary intake patterns follow a similar trajectory to the past. A detailed description of adjustments and interpolations is available in the S1 File. These were used to estimate caloric intake and expenditure. We use the Harris-Benedict equations [40] to estimate resting metabolic rates and the Forbes constant [41] to estimate weight changes over time from mean caloric imbalance per person in a year in the adult population to arrive at changes in body mass for men and women. The BMI estimates for men and women are used to estimate the prevalence of obesity which has a direct impact on the flow rates for prediabetes and diabetes onset.
We then used the effects of changes over time in obesity prevalence, and population age structure to estimate the incidence of prediabetes in the population and added effects for the change in sugar sweetened beverage intake, fruit and vegetable intake [42], and physical activity for estimating the incidence of diabetes [43] (see Fig 1). Changes in total caloric intake are not modelled to have an independent effect on diabetes incidence. Any reduction in caloric intake will only change diabetes incidence through the change in weight. For dietary intake, SSB intake and fruit and vegetable intake were modelled with an effect on diabetes incidence independent of any effect on caloric balancer (Fig 1, S1 File). We also included an independent effect on total physical activity on diabetes incidence.
## Model implementation
The model was developed using the online Silico App [44], and in R [45] using the deSolve package [46] and methods described by Duggan [47]. Diagramming was done using Vensim PLE Software [48]. The S1 File provides information on how to access the full model and inputs online. Many of the equations for the calculations of prediabetes and diabetes onset and mortality were adapted from Jones et al. [ 8] and for obesity by models by Homer et al. [ 49]. A full accounting of the equations used in the model, sensitivity analyses, and a discussion of how these were developed is available in the S1 File.
## Developing scenarios for testing
The objective of the model is to create a test environment for simulating interventions and changes in determinants necessary to substantially shift diabetes prevalence and obesity prevalence in Jamaica by 2050. To do this we developed three sets of scenarios: testing the necessary changes to achieve global targets for NCDs; scenarios guided by stakeholder determined priorities; and evaluating the impact of single interventions.
## Achieving global targets
The first scenarios tested were the magnitude of change necessary to achieve the targets set by the World Health Organization Global Action Plan (GAP) for the Prevention and Control of NCDs [3] in 2013, which sets as an objective to stop the increase in prevalence of diabetes and obesity among adults by 2025 using 2010 as the baseline. We modified physical activity (total moderate-to-vigorous physical activity (MVPA)) and caloric intake to understand the effect on obesity and diabetes prevalence (%) in adults. We assumed that physical activity continues to decrease over time in line with trends and projections by Ng and Popkin [31] and that caloric intake increases over time in line with global dietary trends [39]. We modelled changes in duration of MVPA and relative percentage decreases in caloric intake from 2010 to 2025. We applied a magnitude change so that the relative trend in decreasing physical activity and increasing caloric intake are maintained. For example, if MVPA is doubled, we simply double the whole of the trend line but assume a continued decreasing trend of MVPA into the future.
## Scenarios relevant to the Caribbean
In consultation with stakeholders both from group-model building sessions in the region and with local experts contributing to this study, we tested a number of scenarios that are of interest to the region and were considered plausible public health strategies for preventing and managing diabetes and obesity. A description of the interventions and combinations of interventions is presented in Table 2 and in the S1 File. Scenarios for obesity reduction do not include any downstream interventions like bariatric surgery because there is no evidence that these have a measurable impact at the population level. As with the scenarios above, we applied a magnitude change in determinants to the inputs so that the relative trends are maintained. Alternative trends and interventions were tested in complementary analyses that are presented in detail in the S1 File.
**Table 2**
| Scenario | Details | Effect sizes and assumptions |
| --- | --- | --- |
| Combined interventions | Combined interventions | Combined interventions |
| Downstream | Targeted interventions of people at high risk of or living with diabetes.Assumes 25% of the pre-diabetes population receives and adheres to a lifestyle intervention to reduce diabetes incidence.One-third of people with diabetes receive and adhere to diabetes self-management education and leads to a reduction in all-cause mortality. | Pooled 19% reduction in diabetes incidence from lifestyle modification interventions [5]Pooled 25% reduction in all-cause mortality in people with diabetes receiving self-management education [50] |
| Modest upstream | A combination of diet and physical activity measures.A 15% reduction in SSBs consumptionA 15% reduction in consumption of other ultra processed foodsA 15% increase in fruit and vegetable consumptionFront-of-package warning labels (FOPL)An additional 15 minutes of MVPA per dayPublic information campaigns on healthy diet and physical activity | A 26% increase in diabetes incidence per unit per day consumption of SSBs [42]A 4% reduction in diabetes incidence per serving per day consumption of fruits and vegetables [42]FOPLs lead to a 6.6% mean reduction in caloric intake, 13% increase in fruit and vegetable intake and a 13% decrease in ultra processed foods [51]Up to 4g/day increase in consumption of fruits and vegetables from an economic model [52]Pooled relative risk increased MVPA in those targeted by 28% [53] |
| Intensive upstream | The same interventions as the modest upstream, but with greater intensity.A 25% reduction in SSBs consumptionA 25% reduction in consumption other ultra processed foodsA 25% increase in fruit and vegetable consumptionFront-of-package warning labels (FOPL)An additional 30 minutes of MVPA per dayPublic information campaigns on physical activity and healthy diet | The effect sizes are the same as those assumed from the moderate intensity interventions. |
| Combined downstream and intensive upstream | A combination of the downstream interventions and the intensive upstream interventions described above | A combination of the downstream and the intensive upstream effects described above |
## Individual policy scenarios
We modelled the effects of the individual scenarios included in the combined scenarios presented in Table 2 on diabetes prevalence and obesity. For these, we constructed dose-response curves which can be found in the S1 File.
## Sensitivity analyses
We conducted sensitivity analyses for diabetes prevalence modifying input variables for assumptions related to incidence, mortality, and effect sizes for interventions. These were done using Latin Hypercube Sampling for multi-variable and univariable analyses. A detailed description and the results of these analyses are available in the S1 File.
## Baseline predictions
Fig 2a–2c presents the output simulated by the model in the historical period of 1990 to 2020 and projecting into the future to 2050 for diabetes prevalence (%), obesity prevalence (%), and mean BMI (kg/m2) (for men and women) in adults, and includes historical survey produced estimates obtained from Jamaican studies(19–22). The predictions assume trends in determinants will continue on their current trajectories so that the population is expected to age (as described by the World Population Prospects) [26], caloric intake, in particular the intake of SSBs and ultra processed foods, will continue to increase and physical activity will continue to decline [31], (full details in S1 File).
**Fig 2:** *Comparison of modelled outcomes (diabetes prevalence (A), obesity prevalence (B), and body mass index (C)) and projections to 2050 to survey measured estimates in adults (20+ years with 95%CI) from Jamaica(19–22).*
These projections allow us to construct a baseline estimate that is realistic and in line with trends in other countries with similar risk factor profiles showing an increase and then stabilising in diabetes incidence [54]. Diabetes prevalence in adults is projected to increase from around $12.5\%$ of adults in 2020 to $15.4\%$ in 2030 and $20.9\%$ by 2050. Obesity prevalence in adults is projected to increase from $29.2\%$ in 2020 to $39.2\%$ by 2050. Women have three times the prevalence of obesity in 2020 than men ($38.4\%$ versus $12.5\%$, respectively), but that difference is expected to reduce by 2050 to $48.8\%$ obesity prevalence in women to $22.2\%$ obesity prevalence in men.
## Scenario testing: Achieving global targets
We modelled possible magnitude changes in dietary intake and in physical activity necessary to achieve the WHO Global Action Plan [3] targets set for 2025 (i.e., stop the increase in prevalence of diabetes and obesity among adults using 2010 as the baseline). We assumed a start date for change from 2010. The results of these scenarios are presented in Fig 3a and 3b.
**Fig 3:** *Scenarios for the magnitude of change in physical activity and caloric intake necessary to achieve global targets for diabetes (A) and obesity (B) prevalence (in adults 20+ years).*
All the scenarios assume trends for ageing and caloric intake will continue increasing, and physical activity will continue decreasing so only a magnitude reduction in caloric intake and physical activity are modelled. The greatest change for both diabetes prevalence and obesity prevalence comes from a combined dramatic reduction in caloric intake ($30\%$ lower overall compared to baseline trends) and a tripling of the estimated total MVPA in 2010. Changes in MVPA are the most powerful determinants decreasing the prevalence of diabetes both via obesity reduction and through reductions in incidence independent of weight change. Changes in caloric intake, in this case, only present a marginal decrease in diabetes prevalence (Fig 3a). If the caloric reduction was assumed to be a result of reducing SSB intake alone, then the reduction in diabetes prevalence would be more pronounced because for this model, SSB intake is the only dietary component associated with diabetes incidence independent of weight change.
For obesity prevalence, any caloric intake reductions and physical activity increases together bring substantial reductions in obesity prevalence (Fig 3b). The only scenario that could achieve the global targets proposed of stopping the rise for both obesity and diabetes prevalence over the course of 15 years is the most intensive scenario including a tripling of MVPA and an overall $30\%$ reduction in caloric intake for the population in 2010 (assuming the relative trend in decreasing physical activity and increasing caloric intake are maintained). Alternative trends for caloric intake and physical activity are discussed in detail in the S1 File. Briefly, when we assume no change in the trends of diabetes and obesity over time, global targets could still only be achieved by tripling the estimated MVPA in 2010 and a $30\%$ reduction in caloric intake levels from 2010. For diabetes prevalence, a tripling of MVPA and a $20\%$ reduction in caloric intake also achieved a magnitude of change to meet the target of no increase in prevalence from 2010 to 2025. In analysis, tripling MVPA above the baseline predicted trend alone was enough to achieve a reduction in diabetes prevalence without changes in caloric intake, but only changes in both determinants could reduce obesity prevalence.
## Scenario testing: Scenarios relevant to the Caribbean
The scenario combinations and individual interventions tested are presented in Table 2, with corresponding future impacts on diabetes and obesity prevalence in adults presented in Fig 4a and 4b and Table 3.
**Fig 4:** *Effects of scenarios for intervention relevant to the Caribbean on diabetes (A) and obesity (B) prevalence in adults (20+ years).* TABLE_PLACEHOLDER:Table 3
## Impact of combined interventions on diabetes prevalence
An intensive campaign that includes a downstream health system component targeting those living with pre-diabetes is most effective at reducing diabetes prevalence. The change in diabetes prevalence from this scenario results in an absolute decrease of 2 percentage points (p.p) in 10 years and 3.9 p.p. over 30 years. But even the modest intensity scenario yields a decrease of 1.2 p.p. in 10 years and 2.3 p.p. in 30 years. The upstream prevention scenarios, including providing diabetes self-management education or intensive lifestyle modification to those at high risk (see Table 2), would also reduce all-cause mortality in people with diabetes beyond what an exclusively downstream intervention can do. Mortality rates in people with diabetes by 2050 would be reduced by $12\%$ in the moderate intensity upstream scenario compared to $8\%$ with the downstream scenario. A combined approach with intensive upstream and a downstream intervention could prevent deaths in people with diabetes by 2050 by $25\%$.
## Impact of combined interventions on obesity prevalence
The scenarios tested have a modest impact on obesity prevalence (Fig 4b). The greatest reduction in obesity prevalence is from the intensive upstream intervention. This scenario results in an absolute decrease in obesity prevalence of 1 p.p. by 2030 and 3.4 p.p. by 2050 compared to the baseline projections. However, the gains from an intensive upstream intervention are only marginally better than one from a moderate upstream intervention which also reduces obesity prevalence by 0.73 p.p. by 2030 and 2.4 p.p. in 2050. Downstream interventions were not included in the modelling for obesity prevalence because the population affected was too small to be reflected in the model.
## Impact of individual interventions on diabetes prevalence
Besides testing scenarios for combined interventions, we also assessed the impact of the individual interventions from Table 2 on diabetes and obesity prevalence (see Table 4). The highest impact from any single intervention to prevent diabetes comes from reductions in sugar sweetened beverage intake. If the intake of SSBs were decreased by at least $15\%$ in 2020, diabetes prevalence would decrease by $2.4\%$ or an absolute reduction of 0.37 p.p. by 2030 and 0.83 p.p. by 2050 compared to the baseline predicted prevalence. The dose-response curves (see S1 File) for reductions in SSB consumption are not linear. The greatest impact is seen in the first five years after a reduction, and that the effect for this impact tapers over time, in part due to other effects like increases in ageing. In addition, each $10\%$ reduction in intake yields a greater, non-linear reduction in diabetes prevalence so that the benefits of a $50\%$ reduction in intake are more than 5 times the benefits of a $10\%$ in intake.
**Table 4**
| Unnamed: 0 | Scenario | Change in diabetes prevalence p.p. (%) | Change in diabetes prevalence p.p. (%).1 | Change in diabetes prevalence p.p. (%).2 | Change in diabetes prevalence p.p. (%).3 |
| --- | --- | --- | --- | --- | --- |
| | Scenario | 2021 | 2025 | 2030 | 2050 |
| | Baseline predicted prevalence | 12.7 | 13.9 | 15.4 | 20.9 |
| Downstream | Lifestyle modification intervention to 25% of pre-diabetes population | -0.05 (-0.4%) | -0.22 (-1.5%) | -0.37 (-2.3%) | -0.69 (-3.3%) |
| Downstream | Diabetes self-management education to 30% of people with diabetes | +0.02 (+0.17%) | +0.1 (+0.7%) | +0.20 (1.3%) | +0.57 (2.7%) |
| Modest intensity | Additional 15 minutes/day of MVPA | -0.05 (-0.4%) | -0.22 (-1.5%) | -0.37 (-2.4%) | -0.69 (-3.3%) |
| Modest intensity | Reduce SSB consumption by 15% | -0.06 (-0.4%) | -0.25 (-1.7%) | -0.42 (-2.7%) | -0.83 (-4.0%) |
| Modest intensity | Reduce ultra processed food consumption by 15% | 0 (0) | -0.002 (-0.01%) | -0.007 (-0.04%) | -0.06 (-0.3%) |
| Modest intensity | Increase fruit and vegetable intake by 15% | -0.002 (-0.02%) | -0.009 (-0.07%) | -0.01 (-0.1%) | -0.03 (-0.1%) |
| High intensity | Additional 30 minutes/day of MVPA | -0.10 (-0.8%) | -0.44 (-3.1%) | -0.75 (-4.8%) | -1.42 (-6.8%) |
| High intensity | Reduce SSB consumption by 25% | -0.29 (-2.2%) | -1.3 (-9.1%) | -2.3 (-14.7%) | -4.8 (-23.1%) |
| High intensity | Reduce ultra processed food consumption by 25% | 0 (0) | -0.003 (-0.02%) | -0.01 (-0.07%) | -0.10 (-0.5%) |
| High intensity | Increase fruit and vegetable intake by 25% | -0.003 (-0.3%) | -0.015 (-0.1%) | -0.02 (-0.1%) | -0.05 (-0.2%) |
| Information* | Public health mass media campaigns and front-of-package labelling | 0(0) | -0.25 (-1.79%) | -0.42 (-2.3%) | -0.82 (-3.9%( |
| | Scenario | Change in obesity prevalence p.p. (%) | Change in obesity prevalence p.p. (%) | Change in obesity prevalence p.p. (%) | Change in obesity prevalence p.p. (%) |
| | Scenario | 2021 | 2025 | 2030 | 2050 |
| | Baseline predicted prevalence | 29.4 | 30.6 | 32.1 | 39.2 |
| Modest intensity | Additional 15 minutes/day of MVPA | -0.008 (-0.03%) | -0.042 (-0.14%) | -0.08 (-0.3%) | -0.27 (-0.7%) |
| Modest intensity | Reduce SSB consumption by 15% | -0.03 (-0.01%) | -0.05 (-0.02%) | -0.11 (-0.4%) | -0.36 (-0.9%) |
| Modest intensity | Reduce UPF consumption by 15% | -0.02 (0.06%) | -0.09 (-0.3%) | -0.18 (-0.6%) | -0.72 (-1.8%) |
| High intensity | Additional 30 minutes/day of MVPA | -0.02 (-0.08%) | -0.08 (-0.3%) | -0.17 (-0.5%) | -0.55 (-1.4%) |
| High intensity | Reduce SSB consumption by 25% | -0.05 (-0.18%) | -0.3 (-0.9%) | -0.6 (-1.7%) | -1.8 (-4.6%) |
| High intensity | Reduce UPF consumption by 25% | -0.03 (-0.09%) | -0.14 (-0.5%) | -0.3 (-1.0%) | -1.2 (-3.1%) |
| Information* | Public health mass media campaigns and front-of-package labelling | 0 (0) | -0.2 (-0.65%) | -0.4 (-1.2%) | -1.0 (-2.6%) |
Of a similar magnitude, adding a mean 15 minutes of MVPA per day above the baseline for the population would lead to a $2.4\%$ decrease in diabetes prevalence by 2030 (or 0.37 p.p.) and a $3.3\%$ decrease by 2050. Other interventions did little to shift diabetes prevalence in the short or long term. More intensive changes yielded greater results. Predictably, adding daily MVPA to 30 min above the baseline reduced diabetes prevalence by -0.75 p.p. in 2030 and -1.42 p.p. by 2050. Reducing SSB consumption by $25\%$ compared to the baseline also produced similar gains (-0.71 p.p by 2030 and -1.42 p.p. by 2050). Other interventions only had a modest impact on reducing diabetes.
## Impact of individual interventions on obesity prevalence
Obesity prevalence was most reduced compared to baseline predictions by reductions in caloric intake. Reducing ultra-processed food consumption by $15\%$ led to a reduction in obesity prevalence of 0.18 p.p. by 2030 and 0.72 p.p. by 2050. If this reduction were increased to $25\%$ compared to baseline, the reduction in obesity would be 0.3 p.p. by 2030 and 1.2 p.p. by 2050. Dose-response curves indicate a non-linear effect on obesity so that every $10\%$ reduction in intake produces greater decreases in prevalence, but unlike with diabetes prevalence, the magnitude of those decreases is greater over time, especially for the highest proportional decrease in intake. In other words, the gains from a $50\%$ decrease in ultra-processed foods after 30 years are more than five times greater than the gains from a $10\%$ decrease over time (see S1 File).
Adding minutes of MVPA above the current baseline was also effective in reducing obesity prevalence. Adding 15 minutes/day of MVPA decreased obesity prevalence by 0.08 p.p. in 2030 and 0.27 p.p. in 2050. The dose-response relationship of MVPA to obesity prevalence was found to be almost linear so that doubling MVPA to an additional 30 minutes/day above the baseline reduced prevalence by 0.17 p.p. in 2030 and 0.55 p.p. in 2050.
In all, individual scenarios all resulted in much smaller gains than those of the combined scenarios.
## Discussion
The alarming rise of type 2 diabetes and obesity in the Caribbean has placed it firmly on the political agenda since at least the Port-of-Spain Declaration in 2007 [13]; however, the prevalence of diabetes and obesity continue to rise in the region [14]. The findings from this model show that for Jamaica, like many parts of the world [55], global targets proposed by the WHO Global Action Plan [3] to stop the rise in obesity and diabetes prevalence by 2025 are unrealistic. The magnitude of change necessary to achieve these targets in the modifiable determinants of obesity (in particular diet and physical activity) are unachievable, especially over the timeframe proposed by the Global Plan. No country in the world has managed to reduce obesity or diabetes prevalence, barring those affected by natural disasters or humanitarian crises [56–58].
## Rethinking global targets
Global targets based on reducing or stabilising prevalence may be unattainable. In a disease where the duration is lifelong, like many NCDs, it is possible to reduce the incidence of a disease yet continue to see a rise in prevalence. Without an outflow that is larger than the inflow, prevalence will continue to rise. For NCDs like diabetes, there is no effective outflow at a population level besides mortality. Studies suggest that with an intensive lifestyle intervention aimed at dietary restriction and weight loss it may be possible to reverse diabetes in individuals [6, 59, 60], but this is not feasible on a large scale in a developing country at the moment. In addition, any interventions aimed at reducing mortality, such as providing diabetes self-management education will prevent deaths and reduce the mortality outflow. This is obviously a good outcome, but will increase diabetes prevalence and thus work against achieving a target based solely on reducing prevalence. Instead, targets should focus on several indicators that track the benefits of improvements over a range of outcomes that include incidence, quality of life, mortality, and rates of complications., Such an approach is supported by Gregg et al. who recommended monitoring targets for the Global Diabetes Compact [61], acknowledging that reliably assessing trends in these is often more challenging and resource intensive than measuring prevalence. The most recent report of the WHO Global Monitoring Framework from 2019 shows progress on implementing NCD guidelines and monitoring for outcomes, but only $53\%$ of countries reported having the six essential technologies for monitoring (including anthropometric measurement, blood glucose, blood pressure, and cholesterol) available routinely in primary care [62].
## Interventions to reduce the burden
Strategies that focus on the prevention of obesity and diabetes show the greatest long-term success in both diabetes prevalence and all-cause mortality for people with diabetes. When we examine the more plausible scenarios, we find that even a more modest change in dietary intake and physical activity has a substantial long term impact on diabetes prevalence compared to the baseline predicted level and also reduces obesity, although to a lesser extent. These changes are not easily achieved, but some interventions have shown promise.
For physical activity, improving infrastructure for active transport and leisure has been shown to increase MVPA in sedentary populations [63–66] as well as developing reliable public transportation systems [67]. Trends in physical activity suggest the greatest losses in MVPA are occurring in occupational physical activity in developing countries [31], and those losses are unlikely to be recovered as jobs become more sedentary. As a result, interventions often focus on increasing leisure-time physical activity where even a small change for sedentary individuals can lead to a reduction in risk for diabetes independent of weight loss [43]. In addition, replacing sedentary time with light physical activity which can include short walks can also lead to weight loss and has been linked to reductions in diabetes incidence [68, 69]. The benefits of increasing physical activity are much wider and include decreasing all-cause mortality, cancer risk reduction, and mental health benefits [70].
There are a number of possible interventions for achieving reductions in caloric intake. Information campaigns including mass media public health education campaigns and front-of-package food labelling have been shown to be effective in calorie reduction [71, 72], Similarly, reducing SSB intake, for which the Caribbean has the highest rate [37] can have a substantial effect on both obesity and diabetes prevalence over time. The Caribbean has made taxing SSBs a priority, but the structure and implementation of those taxes must be considered to achieve a price difference that will effectively reduce intake [73–75]. Taxes on unhealthy foods similar to those implemented in Chile [76] or in combination with public health campaigns as was done in Mexico [73] are used in the model and might achieve a $20\%$ decrease in intake for the population. In our model, SSB intake was one of two dietary components (with fruit and vegetable intake) associated with diabetes incidence independent of weight gain. Thus, if the scenarios presented in Fig 3a focused on achieving the same calorie reduction purely through a reduction in SSB intake, the impact on diabetes prevalence would likely be greater than what is shown here.
Fruit and vegetable intake in the *Caribbean is* very low [77]. Increasing that intake could also improve outcomes and likely with a greater magnitude than what the model shows. There is some evidence that overall energy intake does not decrease when including more fruits and vegetables in the diet, but that weight loss can still occur [78, 79]. The mechanisms by which fresh fruits and vegetable intake affects metabolism may also reduce diabetes incidence [80]. Similarly, reducing ultra processed foods may give benefits beyond weight reduction, and also reduce the incidence of diabetes [81, 82]. Our findings suggest that from a policy perspective, none of these interventions should be attempted in isolation. Rather, a suite of coordinated policies like those implemented in Chile, show the greatest success in shifting dietary patterns across various domains [76]. Downstream interventions in the health system for people at high risk or with diabetes are an important component that can greatly improve the quality of life and reduce disability in those affected [83, 84]. However, it is clear from these scenarios that intervening exclusively downstream is not an effective prevention strategy at the population level.
The commercial determinants of health refer to a broad range of private sector activities that affect the health of populations [85]. Some of the interventions presented here respond to aspects of the commercial determinants of health including price changes on ultra processed foods and front-of-package labelling [86], but other key areas, such as the potential for more comprehensively addressing marketing by producers of unhealthy food were not addressed. This is in part due to a lack of clear evidence on the effects of regulation, particularly for adults. There is good evidence that regulation of marketing to children has the potential to reduce unhealthy diets [87]. Nevertheless, any comprehensive policy action on obesity should include limitations on marketing and make efforts to reduce the reach and influence of the ultra processed food industry [88].
## Targeted interventions
Health systems represent for many countries the first sector to engage in prevention of diabetes. In controlled trials for people at high risk for type 2 diabetes, intensive lifestyle interventions have been shown to be highly effective with lasting benefits [5]. However, when taking into account the small number of people that are engaged in these trials and the challenges of adherence to protocols, our analysis shows these types of interventions give only marginal benefits to the population as a whole. It is important to note that those interventions can provide a great benefit to the people that are engaged. A coordinated strategy for preventing new cases of obesity and diabetes together with a health system strategy to mitigate the effects of those diseases is the most sound approach to achieve many desirable health outcomes beyond just a reduction in prevalence.
For the Caribbean, most populations, including in Jamaica, have a strong gender disparity in physical inactivity and obesity where women have almost three times the rate compared to men [25]. The reasons for the disparities are complex [89] and any intervention targeted to women must take into account this complexity. In addition, a systematic review found that diabetes was commoner among those with a lower education that those with higher education in the Caribbean [25], a social determinant of health, suggesting targeting of interventions to the most vulnerable can be tailored in different ways.
This model shows that intervening in physical activity or obesity reduction for women would have a clear impact on overall population measures of diabetes and obesity and may be an effective targeted approach, especially where resources may be limited. Stakeholders have also focused on targeting interventions to children and youth in the hopes of preventing new cases of obesity and NCDs in adulthood. Any opportunity for prevention can help reduce the burden of NCDs, but it is important to consider that an intervention targeted at youth will take decades to show results [90–92]. Moving away from an obesogenic environment for all of society can ensure those gains made early in life are not lost in adulthood.
## Limitations
Like any model, this system dynamics model is a simplification of reality. It is intended as a tool for understanding the possible magnitude of changes in the outcomes of interest (diabetes and obesity prevalence) by intervening in different points. It is not intended, however, to provide a precise prediction of diabetes prevalence in the near or long term, although we did calibrate the model carefully to reflect the historical data available for Jamaica. We assumed changes from interventions would be apparent within a one year time step, which may be an oversimplification. Interventions can take longer to show results.
One of the most important assumptions underlying the projections in prevalence for obesity and diabetes are the underlying trends in physical activity, caloric intake, and ageing of the population. We rely on published projections of similar plausible models for population ageing [26] and physical activity [31]. Trends for caloric intake were a combination of projections for SSB intake [37], ultra processed food consumption projections [12, 38], and assuming trajectories continue in the next decades. We felt these trends accurately reflected stakeholder input that described reinforcing feedback loops shifting social norms towards more physical inactivity and increasing caloric intake [17, 18]. However, the data on trends in these determinants are lacking for the region and the true trajectories may be different. The literature used to underlie our assumptions do not support a halt to the rise in obesity and diabetes prevalence in the future, nor is there evidence for a decrease in caloric intake or an increase in physical activity at the population level. It is possible that there may be some threshold for which physical activity and caloric intake stabilise, although it is unclear where that level may be. However, we acknowledge that other system dynamics models exploring the obesity epidemic in the United States have noted that in terms of caloric balance, assuming a linear future trend may exaggerate future prevalence estimates [49, 93], and this may be the case in our modelling.
There are a number of places where other simplifications may be underestimating the true impact of changes. For instance, dietary intake patterns can have multiple and synergistic effects on weight reduction and obesity. There is some evidence that shows that consumption of ultra processed foods can lead to excessive caloric intake [81] which means that the effect of reducing these may be underestimated in the model. We did not take into account a detailed dietary pattern including major macronutrients like fat content, salt, or consumption of nuts, all which may have an association with changing weight and diabetes incidence [42]. We also did not take into account the distribution of determinants in the population which in many cases are not normally distributed. For instance, from the self-reported data we do have [19, 20], it is clear that a small portion of highly active individuals may be skewing the mean level of physical activity of a largely sedentary population. In situations such as these, we used a linear relationship for the reduction in diabetes incidence from increases in mean MVPA (see S1 File), but it is possible that intervening with the least active would be the most effective way to shift population-level physical activity.
The model includes the major risk factors for type 2 diabetes (obesity, aspects of diet, and physical inactivity) but there are many other factors that may influence the risk and onset of type 2 diabetes that were not included. For instance, we did not include rates of hypertension, tobacco use (which is relatively low in the English-speaking Caribbean and highly gendered, being commoner in men) [94], or alcohol use which are also known to directly impact diabetes incidence to a lesser extent and for which cost-effective solutions can be implemented [84]. A recently published meta-analysis suggests that some types of antihypertensives can be effective in preventing type 2 diabetes [95]. Finally, it is also possible that the relative impact of obesity on the onset of diabetes is greater for this population than what we assume in the model [96]. If that is the case, any change in obesity is likely to have an even greater effect on reducing diabetes incidence and prevalence.
## Systems thinking for policy and beyond
Our analysis of the global targets proposed in the WHO Global Action Plan for 2025 show them to be overly ambitious if not unachievable. Any future targets to monitor progress on how policies reduce the burden of diabetes and obesity should take into account the complex dynamics including accumulations, feedbacks, and a long time horizon for achieving change in prevalence. Diabetes can also be conceptualised not as a binary condition but as a continuum of risk for complications where it may be appropriate to intervene in different ways at different stages. Any gains shown from interventions presented may be an underestimate as there may be synergistic effects not accounted for in the model. Furthermore, the benefits of reducing obesity go far beyond reductions in diabetes prevalence and can influence quality of life, mental health, cancer risk, cardiovascular disease, and a number of other conditions [97]. Even small relative reductions compared to baseline trends are therefore worth the effort to obtain. This model should help to set realistic goals for policy makers and reinforce the importance of mobilising and coordinating policy across several sectors and using many different approaches at once. Financing and supporting those goals remain a challenge for resource-limited countries, like Jamaica. The CARICOM placed the public health NCD crisis in the region as a key component of a Ten Point Plan for Reparatory Justice for formerly enslaved populations, arguing that there is a direct link between the current high levels NCDs and the historical brutalities of the colonial experience in the Caribbean, including Jamaica [98]. NCD prevention and control are increasingly being seen as critical components of the global health agenda [99] but continue to be underfunded [100].
The scenarios tested here are ambitious, but they echo what many of the policymakers in Jamaica and the region have already known that the whole of the system must be considered and sectors must be engaged to reduce the burden of NCDs. Finally, achieving reductions in NCDs and their determinants is not a goal for a single political cycle, but something for which countries should be prepared to engage in over the long term and for the whole of society.
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|
---
title: Prevalence and determinants of low testosterone levels in men with type 2 diabetes
mellitus; a case-control study in a district hospital in Ghana
authors:
- Dorcas Serwaa
- Folasade Adenike Bello
- Kayode O. Osungbade
- Charles Nkansah
- Felix Osei-Boakye
- Samuel Kwasi Appiah
- Maxwell Hubert Antwi
- Mark Danquah
- Tonnies Abeku Buckman
- Ernest Owusu
journal: PLOS Global Public Health
year: 2021
pmcid: PMC10021198
doi: 10.1371/journal.pgph.0000052
license: CC BY 4.0
---
# Prevalence and determinants of low testosterone levels in men with type 2 diabetes mellitus; a case-control study in a district hospital in Ghana
## Abstract
Diabetes mellitus, an endocrine disorder, has been implicated in many including hypogonadism in men. Given the fact that diabetes mellitus is becoming a fast-growing epidemic and the morbidity associated with it is more disabling than the disease itself. This study sought to assess the prevalence of low testosterone levels and predictors in type 2 diabetes mellitus patients and non-diabetic men in a district hospital in Ghana. This hospital-based case-control study comprised 150 type 2 diabetics and 150 healthy men. A pre-structured questionnaire and patient case notes were used to document relevant demographic and clinical information. Venous blood sample of about 6 ml was taken to measure FBS, HbA1c, FSH, LH, and testosterone levels. All data were analyzed using STATA version 12 (STATA Corporation, Texas, USA). The overall hypogonadism in the study population was $48\%$ ($\frac{144}{300}$). The prevalence of hypogonadism in type 2 diabetic subjects was almost three times more than in healthy men ($70.7\%$ vs $25.3\%$). The odds of having hypogonadism was lower in the men with normal weight and overweight with their underweight counterparts (AOR = 0.33, $95\%$ CI; 0.12–0.96, $$p \leq 0.042$$) and (AOR = 0.29, $95\%$ CI; 0.10–0.84, $$p \leq 0.023$$) respectively. Also, the odds of suffering from hypogonadism was lower in non-smokers compared with smokers (AOR: 0.16, $95\%$ CI; 0.05–0.58, $$p \leq 0.005$$). Participants who were engaged in light (AOR: 0.29, $95\%$ CI; 0.14–0.61, $$p \leq 0.001$$), moderate (AOR: 0.26, $95\%$ CI; 0.13–0.54, $p \leq 0.001$) and heavy (AOR: 0.25, $95\%$ CI; 0.10–0.67, $$p \leq 0.006$$) leisure time activities had lower odds hypogonadal compared to those engaged in sedentary living. Type 2 diabetic men have high incidence of hypogonadism, irrespective of their baseline clinical, lifestyle or demographic characteristics. Smoking and sedentary lifestyle and BMI were associated with hypogonadism in the study population. Routine testosterone assessment and replacement therapy for high risk patients is recommended to prevent the detrimental effect of hypogonadism in diabetic men.
## Introduction
The relationship between sex hormones and Type 2 diabetes mellitus (T2DM) is of great concern in the health sector, given the fact that diabetes mellitus is becoming a fast-growing epidemic and the morbidity associated with it is more disabling than the disease itself. Millions of people around the world are diagnosed with T2DM, many more remain undiagnosed [1]. The world prevalence of diabetes mellitus among adults (aged 20–79 years) in 2010 was $6.4\%$ and expected to increase to $7.7\%$, by 2030 [2]. It is estimated that in 2000, about 7, 146, 000 people in sub-Saharan Africa had diabetes mellitus, with a projected increase to 18, 645, 000 in 2030 [3, 4]. The prevalence of diabetes mellitus in *Ghana is* $6.4\%$ and about $69.9\%$ remain undiagnosed [4]. Diabetes mellitus has been implicated in male sexual dysfunction, libido dissociations, retrograde ejaculation, erectile dysfunction and lack of efficient endocrine control of spermatogenesis [5].
Hypogonadism is characterized by low serum testosterone concentration, followed by numerous clinical features like erectile dysfunction (ED), poor morning erection, low libido, loss of memory, physical decline in strength and health, difficulty in concentration and depression [6–8]. A number of studies have shown high incidence (30–$80\%$) of hypogonadism in men with diabetes mellitus [9–11], a clear evidence of the association between type 2 diabetes and low serum testosterone. Although mediated by a variety of mechanisms, hypogonadism is more common in diabetic than in non-diabetic men in the Western world and in Asia compared to Africa. This could be attributable to the paucity of data on this issue in sub-Saharan African men.
There are very little documented data in Ghana on the prevalence of hypogonadism in both Type 2 diabetic patients and healthy men, because of limited resources and cost-ineffectiveness of screening all men. To the best of our knowledge only one study is available on hypogonadism in diabetes mellitus men in Ghana [5]. This study, therefore, determined the prevalence of hypogonadism among Type 2 diabetic men in a district hospital in Ghana, and non-diabetic controls.
## Study design and study setting
This hospital based case-control study was conducted at Nkenkaasu District Hospital located in the Offinso-North district, in the Ashanti Region, Ghana. The total land area of the Offinso-North district is about 945.9 square kilometres and lies between latitude: 7°20N. 6°50S" and longitude: 1°60W”, 1°45E". The majority of the inhabitants of this district are farmers [12]. The Nkenkaasu District Hospital serves as the main referral facility in the district and its neighboring villages. This Hospital records about 472 cases of diabetes annually, with 427 of them being T2DM, (per the outpatient department’s report).
## Study participants
This study involved 150 type 2 diabetic men who had registered and receiving treatment at the diabetic clinic of Nkenkaasu Government Hospital and 150 control group comprised of apparently healthy blood donors and those visiting their relatives on admission. This study excluded patients on androgens, steroids medications, patients with a history of chronic renal failure, prostate cancer, prostatectomy and castrated men. The exclusion criteria for the healthy group was based on measurement of baseline fasting blood glucose (FBG) ≥ 7.0 mmol/l and HbA1c value ≥$6\%$ [13].
## Sample size determination
The necessary sample size was obtained by employing the Kelsey’s formula:Ncases-Kelsey=r+1rP1-PZα2+Zβ2p1-p22, and P=p1+(rXp2)r+1, where r is the ratio of T2DM to healthy controls, which is 1:1 in this study, Zα2 represents the critical value of the normal dispersion at α/2 (for this study at confidence level of $95\%$, α is 0.05 and the critical value is 1.96), Zβ represents the critical value of the normal distribution at β (this study used a power of $80\%$, β is 0.2 and the critical value is 0.84. p1 represents the percentage of hypogonadism in Ghanaian men with diabetes, which is $35.2\%$, p2 is the percentage of hypogonadism in the control group, which is $6.7\%$ according to Asare-Anane et al. [ 5], and p1-p2 is the smallest difference in proportions that is clinically important.
From the formula above, the minimum number of T2DM required for this study was 33 with corresponding controls of 33. However, this study employed 300 subjects: 150 T2DM patients and 150 healthy controls.
## Collection of information and patients selection
A structured questionnaire and patients’ medical records were used to document relevant demographic and clinical history from the participants. Type 2 diabetes Mellitus was diagnosed through laboratory assessment based on current WHO diagnostic criteria (FBG ≥7.0 mmol/l or 126mg/dl) and HbA1c of <$6.0\%$ [13] and confirmed through a physician’s recommendations.
## Body mass index
Height to the nearest centimetre without shoes and weight to the nearest 0.1 kg in light clothing was estimated. Participants were weighed on a bathroom scale and their heights were measured with a wall-mounted ruler. Body mass index (BMI) was calculated by dividing weight (kg) by height squared (m2). BMI was categorized as: <18.5 (underweight); 18.5 to 24.9 (normal weight); 25 to 29.9 (overweight); and ≥30 (obese) [13].
## Blood pressure (using Korotkoff 1 and 5)
Blood pressure was measured by trained personnel using a mercury sphygmomanometer and a stethoscope. Measurements were taken from the left upper arm after participants sat >5 min in accordance with the recommendations of the American Heart Association. Duplicate measurements were taken with a 5-minutes rest interval between measurements and the mean value was recorded in mmHg. Hypertension was graded as normal when the systolic blood pressure (SBP) was >120 mm Hg and diastolic blood pressure (DBP) >80 mm Hg [14].
## Physical activities
Leisure-time physical activity was measured based on alternatives to the question “How physically active are you during your leisure time?”. Subjects were characterized as having; sedentary leisure time if they perform the following activities reading, watching television, stamp collecting or other sedentary activity; Light leisure-time physical activity if subjects engage in some walking, cycling, or other physical activity under at least four hours per week; Moderate leisure-time physical activity if they perform any of the following activities running, Swimming, tennis, aerobic, heavier gardening, or similar physical activity during at least 2 hours a week; and lastly, Heavy leisure-time physical activity if engaged in heavy training or competitions in running, skiing, swimming, football, etc, which is performed regularly and several times a week [15].
## Sexual health inventory for men (SHIM) questionnaire
The SHIM questionnaire is a basic 5-point questionnaire on erectile dysfunction. Each answer is graded from 0 (no sexual activity or attempts at intercourse) to 5 (very good sexual function). The maximum score patients could obtain will be 25, the minimum was 1. Based on the SHIM questionnaire patients were divided into groups: ≤22; >22 = No ED. This questionnaire was completed by all patients [16].
## Health-related quality of life questionnaire
A basic health-related quality of life questionnaire (EuroQoL group / EQ-5D questionnaire) was completed by all subjects. Although this questionnaire was chosen for its brevity and simplicity. Illiterate subjects were assisted in filling the questionnaire [17].
## Fasting blood glucose (FBG) measurement
Samples for FBG were analyzed using Accu-Chek Advantage Blood Glucose Monitoring System (AC; 3Roche Diagnostics, Indianapolis, IN). Calibration of the instrument was performed at 7:00 am using the test kit glucose control solution. A fingertip capillary whole blood sample was collected from each subject after overnight fasting between 7:00 am and 10:00 am for determination of fasting blood sugar and diabetes mellitus [14].
## Blood sample collection
Whole venous blood of about 6 ml was obtained from each subject via a sterile venepuncture after overnight fasting between 7:00 am and 10: 00 am; 2.0 mL into EDTA for HbA1c and 4.0 mL dispensed into plain tubes at room temperature for 1 hr. after which the supernatant clear fluids were pipetted out to another tube. Plasma was separated after centrifugation (CENTRIFUGE 80–1, Japan). The clear serums were then pipetted into a clean dry test tube and separated into aliquots and frozen at -40 °ċ until analyzed for LH, FSH and testosterone [1]. Hormonal estimation was determined by an enzyme-linked immunosorbent assay (ELISA) technique using automated ELISA washer (BIO-RAD, PW40) and ELISA reader (Mindray, MR-96A) [13, 14].
## Laboratory assay (FSH, LH and testosterone estimation)
All laboratory investigations were done at the Methodist Hospital Laboratory, Wenchi, Bono Region, Ghana. FSH is synthesized and secreted by gonadotrophs of the anterior pituitary gland [18]. FSH was determined using AccuDiag™ FSH ELISA Kit (Omega Diagnostic, Scotland, UK), FSH minimum detection range for AccuDiag™ FSH ELISA *Kit is* 0-200mIU/mL, specificity is $95\%$ and sensitivity is 2.0mIU/ml. The normal reference range of FSH for the laboratory is 1.3–7.4 mIU/ml.
LH is used as an aid in the screening or monitoring of determination of evaluating fertility issues, function of reproductive organs (ovaries or testicles) [18]. LH with Kit (AccuDiag™ LH ELISA Kit: Omega Diagnostic, Scotland, UK). The minimum detection range LH test kit is 0-200mIU/mL, specificity of $95\%$ and 2.0mIU/ml sensitivity. Normal LH value for men according to Methodist hospital laboratory is 1.8–7.4 mIU/ml.
AccuDiag™ Testosterone ELISA Kit (Omega Diagnostic, Scotland, UK) was used for testosterone estimation. The minimum detection limits for testosterone is 0–18 ng/ml, $95\%$ specificity and 0.05 ng/ml sensitivity and normal range of testosterone according to the laboratory is 8.0–12.0 nmol/l.
Collecting and analysing serial serum samples eliminates variability resulting from the episodic secretion of hormones, hence this study evaluated double samples of each participant. Therefore, patients with borderline values were probably transiently suppressed by acute conditions or stress were captured appropriately upon repeat testing.
On the basis of normal ranges and international recommendations, hypogonadism in this study was described as total testosterone levels < 8 nmol/l, with or without signs and symptoms and total testosterone levels > 12 nmol/l was defined as eugonadal.
## Ethical consideration and informed consent
This study was conducted based on the Helsinki Declaration and study protocol, consent forms and participant information material were reviewed and approved by University of Ibadan/University Collage Hospital Ethics Committee (UI/EC/$\frac{18}{0621}$). Also, approval was obtained from the Offinso North District Assembly and the Nkenkaasu Hospital’s Research and Ethics committee.
Written informed consent of individual participants was sought after the aims and objectives of the study had been thoroughly explained to them. Participants either signed or thumb-printed to give their consent, before the commencement of the study and they assured of the confidentiality of their data.
## Statistical analysis
The data were analysed using STATA version 12 (STATA Corporation, Texas, USA). Test for normality was performed using box plot and Kolmogorov-Smirnoff test. Frequencies, percentages, means, and standard deviations were calculated to enable comparison of characteristics between T2DM subjects and the healthy group. Besides the descriptive analysis, the independent t-test was used for the comparison between the categorical and continuous variables among the groups, results were expressed as mean ± SD. Correlation analysis was performed to estimate the relationships between testosterone levels and demographic and clinical variables. Binary logistic regression analysis was done and all independent variables at $p \leq 0.10$ were taken to multivariable logistic regression analysis by backward elimination to identify sociodemographic and lifestyle predictors of hypogonadism. The statistical significance of variables at final model was declared at $p \leq 0.05$ and $95\%$ confidence level for the adjusted odds ratio.
## Results
Most of the study participants were aged 61–70 years 137(45.0), had normal weight 170(56.7), were engaged in farming 135(45.0), non-alcohol consumers 286(95.3), non-smokers 281(93.7), had good Health-related Quality of Life (HRQoL), 256(85.3), engaged in moderate leisure time activity 103(34.3) and had erectile dysfunction 257(85.7). There was statistically significant difference between the type 2 diabetic men and the healthy controls when categorized by age ($$p \leq 0.003$$), body mass index ($p \leq 0.001$), smoking ($p \leq 0.001$), HRQoL ($$p \leq 0.014$$) leisure time activity ($p \leq 0.001$) and erectile function status ($p \leq 0.001$) (Table 1).
**Table 1**
| Characteristics | Total | Type 2 Diabetics | Healthy controls | P value |
| --- | --- | --- | --- | --- |
| | N (%) | N (%) | N(%) | |
| Age (years) | | | | |
| 41–50 | 88(29.3) | 35(23.3) | 53(35.3) | 0.003* |
| 51–60 | 77(25.7) | 33(22.0) | 44(29.3) | 0.003* |
| 61–70 | 137(45.0) | 82(54.7) | 53(35.3) | 0.003* |
| BMI (kg/m 2 ) | | | | |
| Underweight | 25(8.3) | 20(13.3) | 5(3.3) | <0.001* |
| Normal weight | 170(56.7) | 89(59.3) | 81(54.0) | <0.001* |
| Overweight | 91(30.3) | 32(21.3) | 59(39.3) | <0.001* |
| Obese | 14(4.7) | 9(5.0) | 5(3.3) | <0.001* |
| Occupation | | | | |
| Farming | 135(45.0) | 60(40.0) | 75(50.0) | 0.107 |
| Trading | 47(15.7) | 37(24.7) | 21(140.) | 0.107 |
| Civil Servants | 58(19.3) | 23(15.3) | 24(16.0) | 0.107 |
| Unemployed | 60(20.0) | 30(20.0) | 30(20.0) | 0.107 |
| Alcohol consumption | | | | |
| Yes | 14(4.7) | 9(6.0) | 5(3.3) | 0.206 |
| No | 286(95.3) | 141(94.0) | 145(96.7) | 0.206 |
| Smoking | | | | |
| Yes | 19(6.3) | 19(12.7) | 0(0.0) | <0.001* |
| No | 281(93.7) | 131(87.3) | 150(100.0) | <0.001* |
| HRQoL | | | | |
| Poor | 44(14.7) | 30(20.0) | 14(9.3) | 0.014* |
| Good | 256(85.3) | 120(80.0) | 136(90.7) | 0.014* |
| Leisure Time Activity | | | | |
| Sedentary | 75(25.0) | 56(37.3) | 19(12.7) | <0.001* |
| Light | 92(30.7) | 36(24.0) | 56(37.3) | <0.001* |
| Moderate | 103(34.3) | 44(29.3) | 59(37.3) | <0.001* |
| Heavy | 30(10.0) | 14(9.3) | 16(10.7) | <0.001* |
| Erectile function status | | | | |
| No ED | 43(14.3) | 4(2.7) | 39(26.0) | <0.001* |
| ED | 257(85.7) | 146(97.3) | 111(74.0) | <0.001* |
There was no statistically significant difference between the ages the type 2 diabetic men and the healthy controls (58.25±9.71 vs 56.34±9.40, $$p \leq 0.084$$). The body mass index (BMI) of the healthy controls was significantly higher than the type 2 diabetic men (24.01±3.38 and 23.05±4.04, $$p \leq 0.026$$). The type 2 diabetic men had significantly higher systolic blood pressure (SBP) (145.76±24.77 vs 134.94±25.36, $p \leq 0.001$), diastolic blood pressure (DBP) (86.74±12.91 vs 82.15±9.01, $p \leq 0.001$), fasting blood sugar (FBS) (10.33±5.57 vs 5.84±0.61, $p \leq 0.001$) and glycated hemoglobin (HbA1c) levels (8.06±2.58 vs 4.78±0.59, $p \leq 0.001$) compared with the healthy controls. The biochemical analysis revealed, mean serum follicle stimulating hormone (FSH) (8.85±5.05 vs 7.19±4.68, $$p \leq 0.003$$), luteinizing hormone (LH) (7.08±3.90 vs 6.18±3.58, $$p \leq 0.017$$) and testosterone (13.01±7.85 vs 7.66±5.45, $p \leq 0.001$) levels were significantly higher in the healthy controls relative to the type 2 diabetic men (Table 2).
**Table 2**
| Parameters | Type 2 Diabetics (n = 150) | Healthy controls (n = 150) | 95% CI | P value |
| --- | --- | --- | --- | --- |
| Age (yrs) | 58.25±9.71 | 56.34±9.40 | -0.258–4.084 | 0.084 |
| BMI (Kg/m2) | 23.05±4.04 | 24.01±3.38 | -1.810-(-0.118) | 0.026* |
| SBP (mmHg) | 145.76±24.77 | 134.94±25.36 | 5.124–16.516 | <0.001* |
| DBP (mmHg) | 86.74±12.91 | 82.15±9.01 | 2.047–7.126 | <0.001* |
| FBS (mmol/L) | 10.33±5.57 | 5.84±0.61 | 3.588–5.394 | <0.001* |
| HbA1C (%) | 8.06±2.58 | 4.78±0.59 | 2.854–3.707 | <0.001* |
| FSH (mIu/ml) | 7.19±4.68 | 8.85±5.05 | -2.767-(-0.555) | 0.003* |
| LH (mIu/ml) | 6.18±3.58 | 7.08±3.90 | -1.642-(-0.162) | 0.017* |
| Testosterone (nmol/l) | 7.66±5.45 | 13.01±7.85 | -6.887-(-3.818) | <0.001* |
## Correlation of testosterone (T) with fasting blood sugar among the participants
Fig 1 shows the correlation between testosterone levels and fasting blood sugar (FBG) levels among the study participants. A statistically significant positive correlation was observed between free testosterone levels and FBS ($r = 0.233$, $p \leq 0.001$).
**Fig 1:** *Correlation between testosterone levels and fasting blood glucose.r = Correlation coefficient. p<0.05 was considered statistically significant.*
## Correlation of testosterone (T) with glycated hemoglobin (HbA1c) among the participants
Fig 2 shows the correlation between testosterone levels and glycated haemoglobin (HbA1c) levels among the study participants. A statistically significant inverse correlation existed between the testosterone levels and HbA1c levels of the study participants ($r = 0.225$, $p \leq 0.001$).
**Fig 2:** *Correlation between testosterone levels and glycated hemoglobin (HbA1c) levels among the study participants.HbA1c = Glycated haemoglobin, r = Correlation coefficient. p<0.05 was considered statistically significant.*
## Prevalence of hypogonadism in the study population
Fig 3 shows the percentage distribution of testosterone levels of the diabetic and non-diabetic subjects. The overall hypogonadism in the study population was $48\%$ ($\frac{144}{300}$). The prevalence of hypogonadism in the type 2 diabetic subjects (T< 8 nmol/l) was almost three times more than healthy men ($70.7\%$ vs $25.3\%$). Also, 9 ($6.0\%$) and 39 ($26\%$) had testosterone levels between 8–12 nmol/L for the type 2 diabetic men and non-diabetic men respectively. In addition, 35 ($23.3\%$) and 73 ($48.7\%$) were eugonadal for type 2 diabetic and non-diabetic men respectively. Chi square analysis revealed a statistically significant positive association between Type 2 Diabetes Mellitus and hypogonadism as indicated by the p-value from the chi-square analysis ($p \leq 0.001$).
**Fig 3:** *Percentage distribution of categorized testosterone levels.*
The distribution of categorized testosterone levels for the diabetic men was $70.7\%$, $6.0\%$ and $23.3\%$ for <8 nmol/L, between (8–12 nmol/L and >12 nmol/L respectively. Distribution of categorized testosterone levels for the non-diabetic group was $24.5\%$, $26.0\%$ and $48.7\%$ for < 8 nmol/L, between (8–12 nmol/L and >12 nmol/L.
## Clinical and hormonal parameters of type 2 diabetic and the healthy hypogonadal men
The mean age between the type 2 diabetic and the apparently healthy hypogonadal men were not significantly different (57.48±9.62 vs 56.92±9.94, $$p \leq 0.761$$). Similarly, no significant differences were observed between BMI (22.94±3.98 vs 24.18±3.92, $$p \leq 0.099$$), SBP (144.42±23.39 vs 134.50±30.81, $$p \leq 0.076$$) and DBP (85.57±12.76 vs 83.32±7.30, $$p \leq 0.192$$) of type 2 diabetic and the apparently healthy hypogonadal men. With respect to the LH levels, no significant difference was observed between type 2 diabetic and the apparently healthy hypogonadal men (5.80±3.90 vs 5.80±4.65, $$p \leq 0.996$$). The healthy hypogonadal men were significantly different from the type 2 diabetic hypogonadal men with reference to FBG (10.75±6.21 vs 5.82±0.81, $p \leq 0.001$), HbA1c (8.06±2.68 vs 4. 87±0.61, p = <0.001) and FSH (7.61±5.16 vs 10.62±5.33, $$p \leq 0.003$$) level (Table 3).
**Table 3**
| Parameters | Type 2 Diabetics | Healthy controls | 95% CI | P value |
| --- | --- | --- | --- | --- |
| Age (yrs) | 57.48±9.62 | 56.92±9.94 | -3.067–4.188 | 0.761 |
| BMI (Kg/m2) | 22.94±3.98 | 24.18±3.92 | -2.732–0.242 | 0.099 |
| SBP (mmHg) | 144.42±23.39 | 134.50±30.81 | -1.088–20.937 | 0.076 |
| DBP (mmHg) | 85.57±12.76 | 83.32±7.30 | -10146-5.646 | 0.192 |
| FBG (mmol/L) | 10.75±6.21 | 5.82±0.81 | 3.707–6.153 | <0.001* |
| HbA1C (%) | 8.06±2.68 | 4. 87±0.61 | 2.636–3.738 | <0.001* |
| FSH (mIu/ml) | 7.61±5.16 | 10.62±5.33 | -4.950-(-1.060) | 0.003* |
| LH (mIu/ml) | 6.31±3.95 | 7.09±2.82 | -1.968–0.401 | 0.192 |
## Binary and multivariable logistic regression about determinants of hypogonadism among the study participants
In both the bivariate and multivariate logistic regression analyses, BMI, smoking and leisure time activity (sedentary lifestyle) were associated with hypogonadism in the study population. The odds of having hypogonadism was lower in the men with normal weight and overweight with their underweight counterparts (AOR = 0.33, $95\%$ CI; 0.12–0.96, $$p \leq 0.042$$) and (AOR = 0.29, $95\%$ CI; 0.10–0.84, $$p \leq 0.023$$) respectively. Also, the odds of suffering from hypogonadism was lower in non-smokers compared with smokers (AOR: 0.16, $95\%$ CI; 0.05–0.58, $$p \leq 0.005$$). Participants who were engaged in light (AOR: 0.29, $95\%$ CI; 0.14–0.61, $$p \leq 0.001$$), moderate (AOR: 0.26, $95\%$ CI; 0.13–0.54, $p \leq 0.001$) and heavy (AOR: 0.25, $95\%$ CI; 0.10–0.67, $$p \leq 0.006$$) leisure time activities had lower odds hypogonadal compared to those engaged in sedentary living (Table 4).
**Table 4**
| Characteristics | HYPOGONADISM | HYPOGONADISM.1 | Unadjusted OR[95% CI] | P value | Adjusted OR[95% CI] | P value.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | Yes | No | | | | |
| | N = 144 | N = 156 | | | | |
| Age (years) | | | | | | |
| 41–50 | 38(43.2) | 50(56.8) | 1 | | 1 | |
| 51–60 | 42(54.5) | 35(45.5) | 1.58[0.85–2.92] | 0.146 | 1.80[0.90–3.90] | 0.094 |
| 61–70 | 64(47.4) | 71(52.6) | 1.89[0.69–2.04] | 0.536 | 1.20[0.57,2.40] | 0.666 |
| BMI (kg/m 2 ) | | | | | | |
| Underweight | 17(68.0) | 8(32.0) | 1 | | 1 | |
| Normal weight | 84(49.4) | 86(50.6) | 0.46[0.19–1.12] | 0.088 | 0.33[0.12–0.96] | 0.042* |
| Overweight | 36(39.6) | 55(60.4) | 0.31[0.12–1.79] | 0.014* | 0.29[0.10–0.84] | 0.023* |
| Obese | 7(50.0) | 7(50.0) | 0.47[0.12–1.80] | 0.271 | 0.63[0.12,3.21] | 0.575 |
| Occupation | | | | | | |
| Unemployed | 24(40.0) | 36(60.0) | 1 | | 1 | |
| Farming | 70(51.9) | 65(48.1) | 1.62[0.87–2.99] | 0.128 | 2.11[0.90–4.93] | 0.086 |
| Trading | 33(56.9) | 25(43.1) | 1.98[0.95–4.12] | 0.068 | 1.93[0.75–4.98] | 0.176 |
| Civil Servants | 17(36.2) | 30(63.8) | 0.85[0.39–1.87] | 0.686 | 0.82[0.30–2.21] | 0.688 |
| Alcohol consumption | | | | | | |
| Yes | 8(57.1) | 6(42.9) | 1 | | 1 | |
| No | 136(47.6) | 150(52.4) | 0.68[0.43–2.01] | 0.485 | 0.62[0.18–2.16] | 0.458 |
| Smoking | | | | | | |
| Yes | 15(78.9) | 129(45.9) | 1 | | 1 | |
| No | 4(21.1) | 152(54.1) | 0.23[0.07,0.70] | 0.010* | 0.16[0.05–0.58] | 0.005* |
| HRQoL | | | | | | |
| Poor | 20(45.5) | 24(54.5) | 1 | | 1 | |
| Good | 124(48.4) | 132(51.6) | 1.10[0.59–2.14] | 0.715 | 1.30[0.63–2.68] | 0.342 |
| Leisure Time Activity | | | | | | |
| Sedentary | 49(65.3) | 26(34.7) | 1 | | 1 | |
| Light | 40(43.5) | 52(56.5) | 0.41[0.22–0.78] | 0.005* | 0.29[0.14–0.61] | 0.001* |
| Moderate | 43(41.7) | 60(58.3) | 0.38[0.21–0.70] | 0.002* | 0.26[0.13–0.54] | <0.001* |
| Heavy | 12(40.0) | 18(60.0) | 0.35[0.15–0.85] | 0.019* | 0.25[0.10–0.67] | 0.006* |
| Erectile function status | | | | | | |
| No ED | 15(34.9) | 28(65.1) | 1 | | | |
| ED | 129(50.2) | 128(48.9) | 1.88[0.96–3.69] | 0.066 | 1.30[0.60–2.78] | 0.517 |
## Discussion
The testosterone hormone has a major impact on men’s overall health and well-being. This hospital-based case-control study sort to ascertain the prevalence and determinants of low testosterone levels in Type 2 diabetes mellitus Ghanaian men compared to non-diabetic controls. The findings revealed that, the mean age of diabetic men in this sample was not substantially different from that of non-diabetic men. The main occupation of the inhabitants of this district is farming, therefore it was not surprising that most of the study participants were farmers. Compared to the type 2 diabetic men, the healthy men recorded the highest number of smokers and alcohol consumers. It is likely that health education provided to diabetic patients during their routine clinics encouraged some to quit smoking and drinking excessively as part of lifestyle modification. More so, the use of questionnaires to assess smoking and alcohol consumption status may have a social desirability issue diminishing response rate. The majority of the diabetic men had a good quality of life compared with healthy controls. It is plausible that the diabetic patients are educated on the implication of worrying and overthinking on blood glucose control, hence the observed difference. A sedentary lifestyle was observed more in the healthy group compared with the diabetics. This is probably because of sedentary lifestyle modification in most diabetic men due to their condition.
Erectile dysfunction rate was quite high in our study subjects irrespective of their diabetic status and it seems normal at this age group. According to a recent analysis on the prevalence of sexual dysfunction the prevalence of ED was $1\%$–$10\%$ in men younger than 40 years, $2\%$–$9\%$ among men between 40 and 49 years, and it increased to $20\%$–$40\%$ among men between 60–69 years, reaching the highest rate in men older than 70 years ($50\%$–$100\%$) [19]. In the Massachusetts Male Aging Study, diabetic men showed a threefold probability of having ED than men without diabetes; moreover, the age-adjusted risk of ED was doubled in diabetic men compared with those without diabetes [20]. Similar erectile dysfunction rates were also found in France, where $39\%$ of men aged 18 to 70 reported erectile dysfunction [21]. Another report by Thai Erectile Dysfunction Epidemiological Study Group (TEDES) among men aged 40 to 70 revealed an erectile dysfunction prevalence of $37.5\%$ [22]. The findings of this study defining the age group for erectile dysfunction does not rule out ED at early or late stage, therefore categorizing our age group around 40–70 years could not change already known facts the most important factor is the stage of the diabetes. Among the diabetic patients, as age increases and/ or the condition progresses, the risk of developing peripheral neuropathy, hypertension, and impotency would also elevate, which might be the reason for an increased odds of ED.
The findings of the study depicted that the T2DM cohort had a lower BMI than the control cohort. Even though the average BMI of the diabetic men was significantly lower than the healthy men, both were within the normal range. This contradicts the results of [17] that reported that males with diabetes have a higher average BMI than their non-diabetic counterparts. In the pathogenesis of T2DM, lower body mass index (BMI) is consistently associated with reduced type 2 diabetes risk, among people with varied family history, genetic risk factors and weight, while in established T2DM patients weight loss has been shown to meliorate glycaemic control, with severe calorie restraint even reversing the progression of T2DM [23, 24]. The mechanisms for this BMI paradox are not fully understood, but proposed explanations include T2DM individuals lose weight and become frail as a result of underlying illnesses that cause wasting. A study by Peyrot et al. [ 25], into possible psychological barriers to diabetes care, also found that many participants with T2DM felt very anxious and ashamed about their weight and thus reducing their weight reduces their experience of weight stigma.
Another possible explanation is the genetic predisposition in T2DM patients. According to Habbu et al. [ 26], more South Asians developed T2DM at BMI below 30 kg/m2 ($38\%$) than White Europeans ($26\%$) and African-Caribbeans ($29\%$). This suggest a possible low BMI among T2DM patients in our study subjects. Lastly, life style interventions that target diets and weight-loss have shown demonstrable benefit for reducing the risk of type II diabetes in high-risk and pre-diabetic individuals but have not been well-studied in people at lower risk of diabetes. These findings suggest that all individuals can substantially reduce their type 2 diabetes risk through weight loss, and support the broad deployment of weight loss interventions to individuals at all levels of diabetes risk as a public health measure [24]. In our present study since our diabetic cohorts visit the clinic regularly and visit the dietician, there might be measures going on to help reduce the risk of the infection, which might not be seen in the control cohort who might be moving around freely without any restrictions on diet. There was a proportional significant difference between diabetic and non-diabetic subjects for normal weight and overweight. According to Hu et al. [ 27], overweight is the single most significant defining factor of type 2 diabetes; therefore, it was not shocking to see that more diabetic men were overweight and obese than healthy men.
The mean systolic and diastolic blood pressures were significantly higher in diabetic men. Previous studies have asserted that the most adverse outcome of type 2 diabetes mellitus is hypertension because of the complications like diabetic nephropathy, increased exchangeable sodium, insulin resistance and peripheral vascular resistance associated with the disease [28, 29]. Other previous studies have also shown a significant mean difference in body mass index, systolic and diastolic blood pressures and fasting blood glucose between diabetic men and control groups [30, 31]. Also, a significant elevation of FBS and HbA1c levels was identified in the diabetic group compared to the non-diabetics. The elevated FBS and poor glycaemic control have been found to be directly proportional to the severity of the diabetes mellitus. The increasing FBS level and poor glycaemic control in the diabetic group were also in agreement with many other research findings [1, 18, 32].
The biochemical findings of this study showed a highly significant reduction in FSH, LH and testosterone levels were observed in the diabetic group compared to the healthy group. This agrees with the finding of Dhindsa et al. [ 33], which stated a significantly lower FSH and LH concentrations in the diabetic group compared with the controls. This finding also agrees in part with a study conducted by Hussein & Al-qaisi [1], except that their study reported an increased LH level in diabetic group. The diminished gonadotropin secretion in our diabetic subjects might have resulted in insufficient testicular stimulation, hence a reduction in testosterone secretion. Low testosterone levels in the diabetic men may interfere with potency, spermatogenesis and consequently fertility. Our findings showed a significant inverse relationship between low testosterone and FBS and HbA1c. Glycaemia is known to affect Leydig cell function directly causing primary hypogonadism and therefore the association between FBS, HbA1c and reduced total testosterone concentration might be an adverse effect of glycaemia on the testicular microvasculature. The low testosterone levels observed could also be a result of glucose not reaching the cells due to insulin insensitivity, to provide enough energy for the various metabolic processes involved in maintaining testosterone levels [9].
The study depicted that, most of our study participants had low testosterone level. Most of the study participants were at the age group of 40 to 70 years. Studies have shown that mean testicular volume and gonadal function diminishes at this ages. The mean testicular volume tends to increase between 11 and 30 years of age, remains constant between 30 and 60 years of age, and decreases gradually every year after age 60 [34]. Few data on hypogonadism in aging men are available because of the deficiency of evidence regarding the exact criteria for distinguishing testosterone deficiency in older men who do not have pathological hypogonadism [35–37]. The Massachusetts Male Aging Study, using both total testosterone and calculated free testosterone, gave crude prevalence estimates for hypogonadism in men from age 40 to 69 years, ranging at baseline from 6.0 to $12.3\%$ [38]. This is in accordance with the results of this study. Mahmoud et al. [ 39], found the mean testicular volume in men over 75 years to be $31\%$ less than in men between 18 and 40 years of age. This difference is associated with significantly higher mean serum levels of gonadotropins and lower serum free testosterone. Interestingly, when the incidence of hypogonadism was determined by decades, nearly all of the categories of illness were more prevalent in men aged 50–70 years [40], which is consistent with our study.
This study showed that hypogonadism is a common defect in type 2 diabetic men, affecting more than half of the study group, irrespective of their baseline clinical, lifestyle or demographic characteristics. Drugs commonly implicated to induce mild to moderate reduction in serum testosterone levels include B blocker antihypertensive and anticholesterols (statins) which are mostly prescribed for the management of hypertension in T2DM and hypertension co-morbidity [41]. The high prevalence of hypogonadism in the study population raises important issues about its possible consequences the sexual, reproductive and general health (libido, erectile dysfunction, body musculature, abdominal adiposity, bone density, mood, and cognition) of our study population. The Endocrine Society has recommended routine examination and replacement therapy for diabetic patients [42]. However, most facilities in Ghana have not adopted it because of limited resources and cost-ineffectiveness of screening all men for hypogonadism.
A study conducted in Ghana reported a hypogonadism prevalence of $35.2\%$ in men with type 2 diabetes [5]. Unlike this previous study which was conducted in urban setting and a teaching hospital, our study was carried out in a peri-urban setting and district hospital. Other previous studies have shown a prevalence of 30–$80\%$ in men with type 2 diabetes [43–45]. The disparities in prevalence could be attributed to the difference in population examined, the definition used for the diagnosis of hypogonadism and the sample size.
Clinical and hormonal parameters of type 2 diabetic and healthy hypogonadal men were again determined. The mean age, BMI, SBP, DBP and LH between the type 2 diabetic and the apparently healthy hypogonadal men were not significantly different. This finding did not agree with a study that have linked decreasing testosterone levels with aging even in healthy men [11]. The multivariate analysis from our study indicated that BMI, smoking and leisure time activity (sedentary lifestyle) were associated with hypogonadism in the study population. Most cross-sectional studies have shown a positive association between smoking and total or free testosterone levels [46, 47]. Also, some studies have shown a significant association between BMI and hypogonadism [5, 48, 49], while another had reported no relationship between testosterone and BMI [50]. The lack of physical exercise activity contributes to lowering down the testosterone hormone level and indeed beside the effect of obesity [9].
There are few limitations in the study. Firstly, this was a case-control design, which made it impossible to determine whether diabetes preceded or followed the decline in hormone levels. The study did not measure Estradiol (E2) to ascertain any trend because it has been shown that low testosterone levels in diabetes could also be as a result of their increased conversion to E2. The study was also limited by the advanced age of the participants (41–70 years), hence the high prevalence of hypogonadism may have been masked by the age bracket of the study participants. To help address this limitation, we recommend future studies to consider participants below 40 years of age. In the face of these limitations, this study gives significant data for the occurrence and predictors of hypogonadism among Ghanaian men with T2D in the district.
## Conclusion
The study recorded a worrying prevalence of hypogonadism even among the healthy control group but with Type 2 diabetic men having a high incidence of hypogonadism. This study also demonstrates that FSH and LH concentrations are certainly lower in a relatively large number of the males with type II diabetes compared with the healthy men. Body Mass Index, smoking and leisure time activity (sedentary lifestyle) were associated with hypogonadism in the study population. Given a large number of individuals with diabetes worldwide, the high prevalence of hypogonadism in type 2 diabetes raises important issues about its possible consequences on the sexual, reproductive and general health of our study population. A further study is recommended to carry out to holistically assess the prevalence of gonadal deficiencies in subjects with prediabetes patients aged between 20–40 years at the district hospital.
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|
---
title: Prevalence of dyslipidemia and associated risk factors among newly diagnosed
Type-2 Diabetes Mellitus (T2DM) patients in Kushtia, Bangladesh
authors:
- Md. Saad Ahmmed
- Suvasish Das Shuvo
- Dipak Kumar Paul
- M. R. Karim
- Md. Kamruzzaman
- Niaz Mahmud
- Md. Jannatul Ferdaus
- Md. Toufiq Elahi
journal: PLOS Global Public Health
year: 2021
pmcid: PMC10021199
doi: 10.1371/journal.pgph.0000003
license: CC BY 4.0
---
# Prevalence of dyslipidemia and associated risk factors among newly diagnosed Type-2 Diabetes Mellitus (T2DM) patients in Kushtia, Bangladesh
## Abstract
Dyslipidemia is considered a significant modifiable risk factor for type-2 diabetes mellitus (T2DM) and has become one of the emerging health problems throughout the world. In Bangladesh, data on dyslipidemia among newly diagnosed T2DM patients are comparatively inadequate. This study aimed to evaluate the prevalence of dyslipidemia and its associated risk factors in newly diagnosed T2DM patients. This cross-sectional study was conducted by a well-structured questionnaire from 132 newly diagnosed type-2 diabetic patients attending the Mujibur Rahman Memorial Diabetic Hospital in Kushtia, Bangladesh. Data regarding socio-demographic, anthropometric, fasting blood glucose, total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were collected from all the respondents. The association between dyslipidemia and its associated factors was analyzed using the multivariate logit regression model. The findings suggest that the prevalence rate of dyslipidemia was $75.7\%$ in female and $72.6\%$ in male T2DM patients. The odds of having dyslipidemia were 1.74 ($95\%$ Cl: 1.58–1.87) times significantly higher in female ($p \leq 0.001$). The other factors associated with dyslipidemia encompassed age between 30–39 years (OR: 2.32, $95\%$ CI: 1.97–2.69), obesity (OR: 2.63, $95\%$ CI: 2.27–2.90), waist circumferences of male ≥90 and female ≥80 (OR: 1.65, $95\%$ CI: 1.59–1.89), hypertensive patients (OR: 1.51, $95\%$ CI: 1.45–1.74), physically inactive (OR: 3.25, $95\%$ CI: 1.84–4.68), and current smoker or tobacco user (OR: 1.93, $95\%$ CI: 1.85–2.13). This study concluded that the high prevalence of dyslipidemia was found among newly diagnosed type-2 diabetes patients and associated with gender, age, BMI, waist circumference, poor physical activity, and smoking, or tobacco use. This result will support increase awareness of dyslipidemia and its associated risk factors among type-2 diabetes patients.
## 1. Introduction
Diabetes mellitus (DM) is a chronic disease condition associated with hyperglycemia resulting from an imbalance in insulin secretion and insulin action or cooperation of [1, 2]. Estimation studies have confirmed that the incidence of DM in people worldwide in 2019 was 463 million and is anticipated to reach 642 million by 2040, becoming one of the biggest global public health problems [3]. Of all cases of diabetes, more than $90\%$ are detected as type-2 diabetes mellitus (T2DM) around the world [3]. Several metabolic syndromes (MetS) including dyslipidemia, hyperglycemia, and hypertension become a channel to exacerbating cardiovascular disease (CVD) risk factors in T2DM patients [4, 5]. Metabolic syndrome (MetS) comprises several situations: elevated glucose and blood pressure level and abnormal cholesterol or triglyceride levels [6]. In particular, the MetS is recognized by a condition whereby the body’s cells cannot take up glucose from the blood [7].
In recent years, there has been much attention given to understanding the MetS, which is found to be responsible for predicting T2DM [7, 8]. The most common significant component of MetS that occurs in T2DM is dyslipidemia, characterized by hypertriglyceridemia, reduced high-density lipoproteins (HDL) cholesterol levels, and an increased concentration of small dense low-density lipoproteins (LDL) particles [7, 8]. Dyslipidemia is a common phenomenon in type-2 diabetic patients characterized by an abnormal lipid profile because insulin resistance or deficiency affects key enzymes and pathways in lipid metabolism [9, 10]. Globally, the prevalence of dyslipidemia is gradually increasing (≥$80\%$ to $90\%$) in developing countries like Ethiopia [11], Kenya [12], Sri Lanka [13], India [14], and Bangladesh [15, 16]. Cardiovascular risk is significantly increased among patients with diabetes by the presence of dyslipidemia [7]. Studies show that approximately 70–$80\%$ of people having diabetes will die of cardiovascular disease [17, 18]. Over the years, to elucidate the involvement and impact, researchers worldwide have been investigating the association between T2DM and dyslipidemia.
Globally, several community-based studies found that blood pressure, fasting blood glucose, BMI, age, poor physical activity, dietary habits, smoking/tobacco use, and other lifestyle changes have contributed as the major risk factors for increasing the prevalence of dyslipidemia among T2DM patients [11, 16, 19–21]. For example, in some Asian countries, several studies observed a rising trend in the prevalence of dyslipidemia and other metabolic syndromes in T2DM patients [13, 22–25]. However, very few studies are conducted in Bangladesh to assess the prevalence of dyslipidemia in T2DM patients [15, 16]. Insofar as the author’s knowledge, factors associated with dyslipidemia among T2DM patients in Bangladesh are still relatively scarce. In the wake of the rising incidence of diabetes in Bangladesh, there is an urgency to initiate investigation towards evaluating the prevalence and associated risk factors of dyslipidemia among newly diagnosed T2DM patients. So this study will help health professionals and health policymakers to provide better management, appropriate program intervention, and treatment approaches to dyslipidemia. Therefore, to understand the magnitude of dyslipidemia in newly diagnosed T2DM patients and fill the gap of knowledge, this study aimed to evaluate the prevalence of dyslipidemia and to find out the associated risk factors among newly diagnosed T2DM patients.
## 2.1 Study design and settings
A total of 132 subjects with a new diagnosis of T2DM were included in this cross-sectional study. The study was conducted at the Mujibur Rahman Memorial Diabetic Hospital, Hospital Road, at Kushtia District, Bangladesh, from March to November 2017. A purposive sampling technique was deployed for various reasons. Firstly, we need to collect newly diagnosed T2DM patients. Secondly, this study needs to exclude previously detected diabetes patients and pregnant women with diabetes. Finally, purposive sampling was used to recruit participants who can provide in-depth and detailed information about the phenomenon under investigation.
## 2.2 Data collection instrument and techniques
A self-administrated and semi-structured questionnaire was used to gather the data through a face-to-face interview. The questionnaire was structured in three sections included socio-demographic characteristics, blood specimen collection, and anthropometric assessment. Two trained interviewers and a medical technologist were employed to collect the data. The survey questionnaire was formatted in the English language then converted to the Bengali language for easy understanding. After collecting information, the questionnaire was then translated back into the English version. Before the pilot study, the questionnaire was pre-tested two times at the same study place. The pre-tests outcomes were satisfactory and expected that’s why no modification was needed in the final questionnaire. However, the pre-tested data were not used in the final data analysis. Prior to the final data collection, the aim of the study was explained in detail to each study participant. A written agreement was obtained from each study participant, and the study participants were given the full right to withdraw from the study at any time. Data were collected from the participants at the selected diabetic hospital, where each questionnaire took an average of 20 minutes to be administrated.
## 2.3 Exclusion and inclusion criteria
We exclude those T2DM patients who were previously detected as diabetic positive. Pregnant women were excluded though they were first seen as diabetic positive. Those T2DM patients below 30 years and above 70 years were excluded. Persons who were firstly detected as diabetic positive but had been taking drugs to treat hypertension were also excluded. Patients achieving the ADA criteria for diagnosis of type 2 DM: HbA1c > $6.5\%$, or FBS > 126 mg/dl, or PPBS > 200 mg/dl15 and aged ≥30 years, who attended/admitted in the medicine OPD/IPD with symptoms and signs of diabetes mellitus for the first time, were included as study subject [26, 27]. Dyslipidemia was diagnosed if patients had one or more lipid profile parameters outside the target values recommended by the American Diabetes Association (ADA) [28, 29].
## 2.4 Specimen collection, blood sampling, and biochemical analysis
Fasting overnight venous blood samples (nearly 6ml) were drawn from 132 freshly diagnosed T2DMS patients by a well-trained medical technologist into vacutainer tubes from each diabetic individual. Some standard guidelines were followed to determine the Diagnostic Criteria [30]. The blood was allowed to the left for a while without anticoagulants to allow blood to clot. Then serum sample was obtained by centrifugation at room temperature by Rotina 46 Hettich centrifuge, Japan at 4000 rpm/10 minutes. For each selected subject, overnight fasting blood samples were analyzed for fasting blood glucose (FBG) and lipid profiles, namely total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Dyslipidemia was assessed according to the United States National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP-III) model guideline [31]. The Serum glucose, serum cholesterol, serum triglycerides, serum High-Density Lipoprotein (HDL), and Low-Density Lipoprotein (LDL) were determined using the Bio-systems kit (HUMAN GmbH, Wiesbaden, Germany).
## 2.5 Anthropometrics assessment and other variables
Ensuing the standard recommendations of the World Health Organization, trained personnel were recruited to collect the anthropometric measurements. The participants were measured in light clothing and without shoes [29]. Body weight was measured to the nearest 0.1 kg and height to the nearest 0.1 cm. Body mass index (BMI) was then calculated by standard BMI formula [30]. Waist circumference was measured at the midpoint between the lower rib and iliac crest with a flexible steel tape measure to the nearest centimeter while the subjects were in the standing position at the end of gentle expiration [32, 33]. Blood pressure was measured using a sphygmomanometer by the auscultator method [34]. The classification guidelines were used during the study [34]. Gender, age, physical activity, and smoking or tobacco use were recorded with a questionnaire.
## 2.6 Statistical analysis
Descriptive statistics and frequency tables of the baseline characteristics were calculated to describe the study variables. The data were analyzed using the relevant tests of significance, such as the unpaired t-test and Chi-square test. The analytical frameworks used for data analysis were embedded in STATA. Multivariate logit regression analyses were performed using odds ratios (ORs) and $95\%$ CIs to evaluate the independent factors (gender, age, BMI, waist circumference, blood pressure type, physical activity, and smoking/tobacco use) associated with the risk of dyslipidemia. P-value ≤0.05 was considered as the level of significance.
## 2.7 Ethical statement
This study’s protocol was approved by the Research Ethical Committee (REC) of the Department of Applied Nutrition and Food Technology, Islamic University, Kushtia, 7003, Bangladesh. Ethical clearance was also obtained from the ethical committee of the Hospital.
## 3.1 Prevalence of dyslipidemia among newly diagnosed T2DM patients
Newly diagnosed type-2 diabetic patients’ characteristics and dyslipidemia prevalence are stated in Table 1. Among the total respondents, the majority was female ($53\%$). The prevalence of dyslipidemia in males and females was $72.6\%$ and $75.7\%$. In terms of age categories, this table also presents that the prevalence of dyslipidemia was higher among 40–49 years aged ($76.6\%$) and 60–70 years aged ($74.1\%$) respondents. Nearly, $34.9\%$ and $14.4\%$ were overweight and obese, whereas the prevalence of dyslipidemia was also higher among overweight ($72.4\%$) and obese ($74.9\%$) patients, respectively. The prevalence of dyslipidemia in hypertensive patients was $73.5\%$. Moreover, $74.8\%$ and $73.5\%$ currently smoker/tobacco users and past smoker/tobacco user patients were dyslipidemia.
**Table 1**
| Characteristics | Total n (%) | Dyslipidemia n (%) |
| --- | --- | --- |
| Gender | | |
| Male | 62 (47.0) | 45 (72.6) |
| Female | 70 (53.0) | 53 (75.7) |
| Age (Years) | | |
| 30–39 | 35 (26.5) | 22 (62.4) |
| 40–49 | 43 (32.6) | 33 (76.6) |
| 50–59 | 35 (26.5) | 25 (71.4) |
| 60–70 | 19 (14.4) | 14 (74.1) |
| Body Mass Index (BMI) | | |
| Underweight | 4 (3.1) | 3 (75.0) |
| Normal | 63 (47.7) | 44 (69.9) |
| Overweight | 46 (34.8) | 33 (72.4) |
| Obese | 19 (14.4) | 14 (74.9) |
| Waist circumference (cm) | | |
| Male < 90, Female < 80 | 67 (50.7) | 43 (72.3) |
| Male ≥90, Female ≥80 | 65 (49.3) | 49 (74.9) |
| Blood pressure types | | |
| Normotensive | 54 (40.9) | 37 (67.8) |
| Hypertensive | 78 (59.1) | 57 (73.5) |
| Physical exercise | | |
| No | 83 (63.1) | 66 (79.2) |
| Yes | 49 (36.9) | 28 (57.2) |
| Smoking/Tobacco use | | |
| No | 43 (32.5) | 27 (63.7) |
| Yes | 59 (44.6) | 44 (74.8) |
| Ex-smoker/ Tobacco user | 30 (22.9) | 22 (73.5) |
Table 2 illustrates the significance of gender in different clinical health statuses. Respondents’ gender was found significant ($p \leq 0.05$ and $p \leq 0.01$) to the BMI and blood pressure. The majority of female respondents were found overweight and obese than their male counterparts, and they were $38.6\%$ and $20\%$ for female respondents and $30.6\%$ and $8.1\%$ for male respondents, respectively. However, the gender was not found significant to the respondents’ blood LDL, TG, HDL, and TC levels. Most of the respondents ($59.1\%$) had high blood pressure, where an equal number of people had desirable and risk levels of LDL in the blood. However, most of the study participants had a risk level of blood TG, HDL, and TC; $57.6\%$, $74.2\%$, and $52.3\%$, respectively.
**Table 2**
| Variables | Male n (%) | Female n (%) | Total n (%) | Chi2—value¥ | P—value† |
| --- | --- | --- | --- | --- | --- |
| Age category | | | | | |
| 30–39 years | 14 (22.6) | 21 (30) | 35 (26.5) | 2.27 | 0.518 |
| 40–49 years | 24 (38.7) | 19 (27.1) | 43 (32.6) | 2.27 | 0.518 |
| 50–59 years | 15 (24.2) | 20 (28.6) | 35 (26.5) | 2.27 | 0.518 |
| 60–70 years | 9 (14.5) | 10 (14.3) | 19 (14.4) | 2.27 | 0.518 |
| BMI category | | | | | |
| Underweight | 1 (1.6) | 3 (4.3) | 4 (3.1) | 8.12 | 0.044 |
| Normal weight | 37 (59.7) | 26 (37.1) | 63 (47.7) | 8.12 | 0.044 |
| Overweight | 19 (30.6) | 27 (38.6) | 46 (34.8) | 8.12 | 0.044 |
| Obese | 5 (8.1) | 14 (20.0) | 19 (14.4) | 8.12 | 0.044 |
| Waist circumference (cm) | | | | | |
| Male < 90, Female < 80 | 38 (61.3) | 29 (41.4) | 67 (50.7) | 3.65 | 0.735 |
| Male ≥90, Female ≥80 | 24 (38.7) | 41 (58.6) | 65 (49.3) | 3.65 | 0.735 |
| Blood pressure types | | | | | |
| Normotensive | 33 (53.2) | 21 (30.0) | 54 (40.9) | 7.33 | 0.007 |
| Hypertensive | 29 (46.8) | 49 (70.0) | 78 (59.1) | 7.33 | 0.007 |
| Smoking/Tobacco use | | | | | |
| No | 12 (19.4) | 31 (44.3) | 43 (32.5) | 9.45 | 0.03 |
| Yes | 35 (56.4) | 24 (34.3) | 59 (44.6) | 9.45 | 0.03 |
| Ex-smoker/ Tobacco user | 15 (24.2) | 15 (21.4) | 30 (22.9) | 9.45 | 0.03 |
| Physical exercise | | | | | |
| No | 32 (51.7) | 51 (72.6) | 83 (63.1) | 15.62 | 0.001 |
| Yes | 30 (48.3) | 19 (27.4) | 49 (36.9) | 15.62 | 0.001 |
| Level of LDL | | | | | |
| Desirable (<130mg/dL) | 35 (56.5) | 31 (44.3) | 66 (50.0) | 1.94 | 0.111 |
| Risk (>130mg/ dL) | 27 (43.5) | 39 (55.7) | 66 (50.0) | 1.94 | 0.111 |
| Level of TG | | | | | |
| Desirable (<150 mg/dL) | 29 (46.8) | 27 (38.6) | 56 (42.4) | 0.91 | 0.341 |
| Risk (>150 mg/dL) | 33 (53.2) | 43 (61.4) | 76 (57.6) | 0.91 | 0.341 |
| Level of HDL | | | | | |
| Desirable (>40 mg/dL) | 17 (27.4) | 17 (24.3) | 34 (25.8) | 0.16 | 0.681 |
| Risk (<40 mg/dL) | 45 (72.6) | 53 (75.7) | 98 (74.2) | 0.16 | 0.681 |
| Level of TC | | | | | |
| Normal (<200 mg/dL) | 27 (43.5) | 36 (51.4) | 63 (47.7) | 0.81 | 0.366 |
| Abnormal (>200 mg/dL) | 35 (56.5) | 34 (48.6) | 69 (52.3) | 0.81 | 0.366 |
The significance of the blood pressure types on the respondents’ different clinical health statuses is demonstrated in Table 3. The blood pressure type was found significant (p ≤ 0.05 and p≤ 0.01) to all clinical health status of the studied population except the LDL level in the blood. Overall, $40.9\%$ and $59.1\%$ of respondents had normotensive and hypertensive blood pressure, while $70\%$ of the hypertensive and $53.2\%$ of the normotensive respondents were female and male, respectively. Majority of the hypertensive respondents were also in the 50–59 years age group. In the BMI category, $46.2\%$ and $12.8\%$ of the hypertensive respondents were overweight and obese. The TG level in blood had significance ($p \leq 0.01$) to blood pressure type and $71.8\%$ of the hypertensive respondents had a risk level (>150 mg/dL) of blood TG. Around $80\%$ of the hypertensive respondents and $64.8\%$ of the normotensive respondents had a risk level (<40 mg/dL) of blood HDL. Moreover, blood pressure was identified as a more significant ($p \leq 0.01$) factor to the level of TC in blood, where $61.5\%$ of the hypertensive people had abnormal levels (>200 mg/dL) of blood TC.
**Table 3**
| Variables | Normotensive n (%) | Hypertensive n (%) | Total n (%) | Chi2-value¥ | P- value† |
| --- | --- | --- | --- | --- | --- |
| Gender | | | | | |
| Male | 33 (53.2) | 21 (30.0) | 54 (40.9) | 7.33 | 0.007 |
| Female | 29 (46.8) | 49 (70.0) | 78 (59.1) | 7.33 | 0.007 |
| Age category | | | | | |
| 30–39 years | 20 (37) | 15 (19.2) | 35 (26.5) | 17.57 | 0.001 |
| 40–49 years | 23 (42.6) | 20 (25.6) | 43 (32.6) | 17.57 | 0.001 |
| 50–59 years | 5 (9.3) | 30 (38.5) | 35 (26.5) | 17.57 | 0.001 |
| 60–70 years | 6 (11.1) | 13 (16.7) | 19 (14.4) | 17.57 | 0.001 |
| BMI category | | | | | |
| Underweight | 2 (3.7) | 2 (2.5) | 4 (3.1) | 8.12 | 0.044 |
| Normal weight | 33 (61.1) | 30 (38.5) | 63 (47.7) | 8.12 | 0.044 |
| Overweight | 10 (18.5) | 36 (46.2) | 46 (34.8) | 8.12 | 0.044 |
| Obese | 9 (16.7) | 10 (12.8) | 19 (14.4) | 8.12 | 0.044 |
| Waist circumference (cm) | | | | | |
| Male <90, Female <80 | 35 (64.8) | 32 (41.0) | 67 (50.7) | 7.64 | 0.03 |
| Male ≥90, Female ≥80 | 19 (35.2) | 46 (59.0) | 65 (49.3) | 7.64 | 0.03 |
| Smoking/Tobacco use | | | | | |
| No | 23 (42.6) | 20 (25.7) | 43 (32.5) | 11.92 | 0.002 |
| Yes | 15 (27.8) | 44 (56.4) | 59 (44.6) | 11.92 | 0.002 |
| Ex-smoker/ Tobacco user | 16 (29.6) | 14 (17.9) | 30 (22.9) | 11.92 | 0.002 |
| Physical exercise | | | | | |
| No | 17 (31.5) | 66 (84.6) | 83 (40.9) | 15.62 | 0.001 |
| Yes | 37 (68.5) | 12 (15.4) | 49 (59.1) | 15.62 | 0.001 |
| Level of LDL | | | | | |
| Desirable (<130mg/dL) | 32 (59.3) | 34 (43.6) | 66 (50.0) | 3.13 | 0.055 |
| Risk (>130mg/ dL) | 22 (40.7) | 44 (56.4) | 66 (50.0) | 3.13 | 0.055 |
| Level of TG | | | | | |
| Desirable (<150 mg/dL) | 34 (63.0) | 22 (28.2) | 56 (42.4) | 15.78 | 0.001 |
| Risk (>150 mg/dL) | 20 (37.0) | 56 (71.8) | 76 (57.6) | 15.78 | 0.001 |
| Level of HDL | | | | | |
| Desirable (>40 mg/dL) | 19 (35.2) | 15 (19.2) | 34 (25.8) | 4.24 | 0.039 |
| Risk (<40 mg/dL) | 35 (64.8) | 63 (80.8) | 98 (74.2) | 4.24 | 0.039 |
| Level of TC | | | | | |
| Normal (<200 mg/dL) | 33 (61.1) | 30 (38.5) | 63 (47.7) | 6.56 | 0.01 |
| Abnormal (>200 mg/dL) | 21 (38.9) | 48 (61.5) | 69 (52.3) | 6.56 | 0.01 |
Table 4 illustrates the significance level of the gender and the blood pressure type on the different variable and their mean value. This table shows that gender was not a significant factor in the mean value of the different variables. Simultaneously, blood pressure had a high significance ($p \leq 0.01$) level of effect on the respondents’ clinical status. The respondents’ average age in the hypertensive group was around 50 years, and they were overweight (BMI > 24.99). In terms of total cholesterol (TC) and triglycerides (TG) level in the blood, these were found high in hypertensive respondents, and the values were 212.6mg/dL and 229.3mg/dL, respectively. Moreover, systolic and diastolic blood pressures were also found at a high level in hypertensive people, and the values were 138.9mmHg, and 96.1mmHg, respectively.
**Table 4**
| Variables | Gender | Gender.1 | t-value£ | p-value£ | BP types | BP types.1 | t-value£.1 | p-value£.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Male | Female | t-value£ | p-value£ | Normotensive | Hypertensive | t-value£ | p-value£ |
| Age | 46.5±10.8 | 46.33±10.5 | -0.11 | 0.91 | 42.5±10.1 | 49.1±10.1 | -3.71 | 0.001 |
| BMI | 24.4±3.5 | 25.82±4.4 | 1.96 | 0.05 | 23.7±4.4 | 25.1±5.8 | -3.57 | 0.001 |
| FBG Level | 9.4±3.9 | 9.36±4.1 | -0.06 | 0.94 | 9.6±4.3 | 9.2±3.8 | 0.57 | 0.566 |
| A. 75g glu. | 17.5±4.2 | 17.47±4.0 | -0.04 | 0.96 | 17.2±4.2 | 17.7±4.0 | -0.67 | 0.498 |
| TC (mg/dL) | 204.9±40.9 | 218.70±75.8 | -0.33 | 0.74 | 190.4±38.3 | 212.6±45.3 | -2.94 | 0.004 |
| TG (mg/dL) | 208.1±59.1 | 218.70±75.8 | 0.48 | 0.62 | 196.9±52.1 | 229.3±71.0 | -2.86 | 0.005 |
| LDL (mg/dL) | 110.2±31.5 | 117.17±33.9 | 1.21 | 0.22 | 109.7±35.5 | 116.8±30.8 | -1.22 | 0.224 |
| HDL (mg/dL) | 34.7±6.5 | 36.11±6.8 | 1.23 | 1.43 | 36.5±8.1 | 34.6±5.4 | 1.49 | 0.139 |
| S. pressure | 131.4±12.7 | 131.79±13.7 | 0.18 | 0.85 | 121.0±11.4 | 138.9±8.5 | -10.30 | 0.001 |
| D. pressure | 87.5±9.1 | 90.07±10.6 | 1.47 | 0.14 | 78.4±5.7 | 96.1±4.2 | -20.35 | 0.001 |
## 3.2 Factors associated with dyslipidemia among newly diagnosed T2DM patients
Multivariate logit regression analyses were used to determine the factors associated with dyslipidemia in T2DM patients are presented in Table 5. The odds of having dyslipidemia was 1.74 times ($95\%$ CI: 1.58–1.87) higher in female compared with male counterparts ($p \leq 0.001$). The patients aged between 40–49 years had 2.32 times ($95\%$ CI: 1.97–2.69) higher odds in comparison with those between 30–39 years ($p \leq 0.001$). Besides, patients aged 50–59 years and 60–70 years had 1.87 times and 1.71 times increased the risk of having dyslipidemia than patients aged between 30–39 years. The results also shows that patients with a higher BMI were more likely to dyslipidemia disease, where obesity is associated with 2.63 times ($95\%$ CI: 2.27–2.90) increased odds than underweight ($p \leq 0.001$). Male with a waist circumference ≥90 cm and female with a waist circumference ≥80 cm had 1.65 times higher odds of having dyslipidemia ($p \leq 0.001$). Remarkably, the hypertensive (OR: 2.51, $95\%$ CI: 1.45–3.74) and physically inactive (OR: 3.25, $95\%$ CI: 1.84–4.68) patients were associated with increased odds of having dyslipidemia relative to normotensive and physically inactive patients (p ≤0.001). Moreover, the odds of having dyslipidemia were 1.93 times ($95\%$ CI: 1.85–2.13) and 1.72 times ($95\%$ CI: 1.65–1.90) higher in current and ex-smoker/tobacco users compared with non-smoker/tobacco users ($p \leq 0.04$).
**Table 5**
| Variables | Total (%) | Dyslipidemia (%) | Odds ratio (95% CI) | P—value |
| --- | --- | --- | --- | --- |
| Gender | | | | |
| Male | 47.0 | 72.6 | 1 | |
| Female | 53.0 | 75.7 | 1.74 (1.58–1.87) | <0.001 |
| Age (years) | | | | |
| 30–39 | 26.5 | 62.4 | 1 | |
| 40–49 | 32.6 | 76.6 | 2.32 (1.97–2.69) | 0.003 |
| 50–59 | 26.5 | 71.4 | 1.87 (1.75–2.10) | < 0.001 |
| 60–70 | 14.4 | 74.1 | 1.71 (1.59–1.84) | < 0.001 |
| BMI categories | | | | |
| Underweight | 3.1 | 61.6 | 1 | |
| Normal weight | 47.7 | 69.8 | 1.26 (1.20–1.42) | 0.06 |
| Overweight | 34.8 | 72.4 | 2.08 (1.73–2.23) | < 0.001 |
| Obese | 14.4 | 74.9 | 2.63 (2.27–2.90) | < 0.001 |
| Waist circumference (cm) | | | | |
| Male < 90, Female < 80 | 50.7 | 72.3 | 1 | |
| Male ≥90, Female ≥80 | 49.3 | 74.9 | 1.65 (1.59–1.89) | < 0.001 |
| Blood pressure types | | | | |
| Normotensive | 40.9 | 67.8 | 1 | |
| Hypertensive | 59.1 | 73.5 | 2.51 (1.45–3.74) | < 0.001 |
| Physical exercise | | | | |
| Yes | 36.9 | 57.2 | 1 | |
| No | 63.1 | 79.2 | 3.25 (1.84–4.68) | 0.001 |
| Smoking/Tobacco use | | | | |
| No | 32.5 | 63.7 | 1 | |
| Yes | 44.6 | 74.8 | 1.93 (1.85–2.13) | 0.04 |
| Ex-smoker/ Tobacco user | 22.9 | 73.5 | 1.72 (1.65–1.90) | 0.01 |
## 4. Discussion
The prevalence of dyslipidemia is gradually increasing in developing countries because of economic development, modifiable lifestyle, and physical inactivity [15, 19, 20, 35]. This present study was piloted to assess the prevalence of dyslipidemia and associated risk factors among newly diagnosed type-2 diabetes patients visiting Kushtia diabetic hospital, Bangladesh. In this recent study, the total prevalence rate of dyslipidemia was high in both male ($72.6\%$) and female respondents ($75.7\%$). This finding is also similar to other studies in different countries that agreed with the high prevalence [13, 15, 16, 22]. This study also estimated that the prevalence rate of lipid profiles was also in alarming conditions that were LDL ($50\%$), TG ($57.60\%$), HDL ($74.20\%$), and TC ($52.30\%$), respectively. A recent study in the southern region of Bangladesh showed that the prevalence of LDL, TG, HDL, and TC was at high level [15]. On the contrary, some previous studies in different countries were found that the dyslipidemia prevalence rate was high in studied populations while they had the risk level of serum LDL, TG, HDL, and TC [19, 36, 37]. Additionally, recent studies conducted in Ethiopia, Thailand, and Nepal explained that serum lipid profile was the significant factor for increasing the blood pressure level of diabetes [11, 23, 24]. Expectedly, the lipid profile namely, TG, HDL, and TC level patients, among them the level of TG and TC were found as the more significant (p≤0.01) factors [38].
In contrast with the prevalence of dyslipidemia, this study also evaluated some potential factors that may increase the risk of dyslipidemia among newly diagnosed type-2 diabetic patients. This study found that females had higher odds of being dyslipidemia in type-2 diabetes patients. Similar studies reported that female diabetic patients were significantly associated with dyslipidemia, which could be ascribed to their working hours and work spheres [1, 11, 13]. This study also showed that increased age was positively associated with dyslipidemia. This result is in line with other studies [11, 19, 20, 24, 35, 38]. Though no evidence has yet been identified that age directly impacts serum lipid profiles but inherited genetic characteristics, insulin resistance and degenerative processes might be associated with age [20]. Another study found that increasing age was associated with dyslipidemia in type-2 diabetic patients because of their workload and poor physical activity [14, 39]. The study revealed that obesity was significantly associated with dyslipidemia in T2DM patients ($p \leq 0.001$). Similar findings also found in other studies, dyslipidemia is more prevalent among T2DM obese patients compared with normal-weight people [15, 24, 40, 41]. Different studies also reported that obesity helps to release a high amount of free fatty acids by lipolysis which leads to developing hypertriglyceridemia. Along with, the liver also increased the production of very-low-density lipoprotein and triglyceride that are the potential contributor to developing CVD and atherosclerosis diseases in T2DM patients [40, 41]. Moreover, researchers also have examined that obesity increase lipid profile and significantly impact on HbA1c level, as a result of insulin inactivity [42, 43].
Additionally, this study confirmed that hypertension was significantly associated with the prevalence of dyslipidemia in diabetic patients which is similar to those reported in other studies [11, 35, 44]. Another important finding of this study showed that physical inactivity was significantly associated with dyslipidemia among T2DM patients. This finding was consistent with other studies from Ethiopia [11], China [21], Thailand [24], and Saudi Arabia [19]. Several recent studies explained that poor physical activity and dietary habits potentially increase the blood glucose level which may lead to dyslipidemia in T2DM patients [19, 45]. From the pieces of evidence, it has been concluded that regular exercise helps to control glycemic and lipid profile in diabetes patients. The results also showed that current smoking was significantly related to an increase in the risk of dyslipidemia prevalence in diabetic patients. This is following similar studies that found an association between dyslipidemia and smoking [19, 24, 25, 46]. Importantly, other studies also investigated that smoking might increase the LDL-cholesterol, TG but decreases the HDL-cholesterol level that promotes dyslipidemia [46–48].
Finally, the findings of this study along with some other studies reported that the prevalence of dyslipidemia is increasing at an alarming rate in our country. Therefore the implementation of comprehensive national public policy is urgently needed. The risk factors of increasing dyslipidemia can be reduced by the implementation of healthy public policy, adequate knowledge regarding controlling factors of diabetes, and healthy lifestyle interventions. The primary health care facilities should execute regular follow-up, monitor, proper advice, and intervention programs to reduce the prevalence of dyslipidemia among type 2 diabetes patients.
However, this study has also some potential limitations. Firstly, this was a single assessment study of blood samples in newly diagnosed T2DM patients to determine the prevalence of dyslipidemia. Secondly, dietary diversity was not assessed to identify the effect of serum lipid. Thirdly, this study might be lead to self-reported bias due to the questionnaire survey. Finally, this cross-sectional design confines to address the causative relationships amid associated risk factors and dyslipidemia prevalence.
## 5. Conclusion
In summary, this study may be concluded that the prevalence of dyslipidemia among newly diagnosed type-2 diabetes was considerably high in both male and female respondents. Associated factors including gender, age, BMI, waist circumferences, BP type, poor physical activity, and smoking or tobacco use increased the risk ratio of dyslipidemia. These findings might be supportive for planning effective policies and raising awareness regarding the better management of lipid profiles to reduce the prevalence of dyslipidemia in diabetic patients. Therefore, this study highlights the emergency need for screening lipid profile, treatment, and implementation of diabetic education knowledge about dyslipidemia among T2DM patients.
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|
---
title: 'Patterns of multimorbidity in India: A nationally representative cross-sectional
study of individuals aged 15 to 49 years'
authors:
- Jonas Prenissl
- Jan-Walter De Neve
- Nikkil Sudharsanan
- Jennifer Manne-Goehler
- Viswanathan Mohan
- Ashish Awasthi
- Dorairaj Prabhakaran
- Ambuj Roy
- Nikhil Tandon
- Justine I. Davies
- Rifat Atun
- Till Bärnighausen
- Lindsay M. Jaacks
- Sebastian Vollmer
- Pascal Geldsetzer
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021201
doi: 10.1371/journal.pgph.0000587
license: CC BY 4.0
---
# Patterns of multimorbidity in India: A nationally representative cross-sectional study of individuals aged 15 to 49 years
## Abstract
There is a dearth of evidence on the epidemiology of multimorbidity in low- and middle-income countries. This study aimed to determine the prevalence of multimorbidity in India and its variation among states and population groups. We analyzed data from a nationally representative household survey conducted in 2015–2016 among individuals aged 15 to 49 years. Multimorbidity was defined as having two or more conditions out of five common chronic morbidities in India: anemia, asthma, diabetes, hypertension, and obesity. We disaggregated multimorbidity prevalence by condition, state, rural versus urban areas, district-level wealth, and individual-level sociodemographic characteristics. 712,822 individuals were included in the analysis. The prevalence of multimorbidity was 7·$2\%$ ($95\%$ CI, 7·$1\%$ - 7·$4\%$), and was higher in urban (9·$7\%$ [$95\%$ CI, 9·$4\%$ - 10·$1\%$]) than in rural (5·$8\%$ [$95\%$ CI, 5·$7\%$ - 6·$0\%$]) areas. The three most prevalent morbidity combinations were hypertension with obesity (2·$9\%$ [$95\%$ CI, 2·$8\%$ - 3·$1\%$]), hypertension with anemia (2·$2\%$ [$95\%$ CI, 2·$1\%$– 2·$3\%$]), and obesity with anemia (1·$2\%$ [$95\%$ CI, 1·$1\%$– 1·$2\%$]). The age-standardized multimorbidity prevalence varied from 3·$4\%$ ($95\%$ CI: 3·$0\%$ - 3·$8\%$) in Chhattisgarh to 16·$9\%$ ($95\%$ CI: 13·$2\%$ - 21·$5\%$) in Puducherry. Being a woman, being married, not currently smoking, greater household wealth, and living in urban areas were all associated with a higher risk of multimorbidity. Multimorbidity is common among young and middle-aged adults in India. This study can inform screening guidelines for chronic conditions and the targeting of relevant policies and interventions to those most in need.
## Introduction
Low-income and middle-income countries (LMICs) are facing a rapidly increasing disease burden from chronic diseases [1], largely due to population aging and changes in lifestyle [2, 3]. This epidemiological transition has been accompanied by rising levels of multimorbidity, which the World Health Organization (WHO) defined as the coexistence of two or more chronic conditions [4]. Multimorbidity is associated with high levels of healthcare service utilization and out-of-pocket expenditures [5, 6], high mortality [7–9], low quality of life [10], reduced functional status [7, 11], and high costs to the health system [12, 13]. Because healthcare financing and provision in LMICs is primarily focused on single diseases (e.g., HIV, malaria, or iron-deficiency anemia) through vertical health programs [14, 15], effectively dealing with this rise in multimorbidity will require fundamental and large-scale reforms that move towards a more horizontal and patient-centered healthcare delivery and financing system. Evidence on the epidemiology of multimorbidity in LMICs will be essential for guiding these efforts [16–18].
Multimorbidity in *India is* of particular global health importance as India’s population size accounts for more than one sixth of the world’s population [19], and because India has experienced an especially rapid epidemiological transition from acute infectious diseases to one predominated by chronic non-communicable conditions [20]. However, different states in India are in very different stages of this epidemiological transition [20]. As such, it is imperative for studies on multimorbidity in India to be sufficiently large and representative at state and district level to compare and contrast findings across and within states. While there have been several large studies on single chronic conditions [21–23], studies on multimorbidity in India have thus far been limited to samples in specific locales within certain states or small healthcare facility-based studies [9, 24, 25].
We conducted this analysis to address this important lack of evidence on occurence of multimorbidity and its associations by using nationally representative data on five of the most common chronic morbidities in India [20], namely anemia, asthma, diabetes, hypertension, and obesity. We also included HIV in our multimorbidity definition in a random subsample of participants that underwent an HIV test during the survey. These conditions constitute all chronic conditions that were measured in the most recent nationally representative health survey in India. Specifically, to inform the urgency with which health systems in different parts of this large and heterogeneous country need to transition from vertically organized, single-disease-focused healthcare delivery to a more horizontal approach with a focus on co-occurring chronic conditions, this study aimed to determine i) the prevalence of multimorbidity and specific chronic morbidity combinations at the national level in India, and ii) how the prevalence of multimorbidity varies among states and population subgroups within India.
## Data sources
Because individual-level data from the fifth National Family Health Survey (NFHS) has not yet been made available, we analyzed data from the NFHS-4. The NFHS-4 is a household survey that was carried out between 2015 and 2016, and covered all states and union territories. The NFHS-4 used a two-stage cluster random sampling design (with district and rural versus urban location as strata), whereby primary sampling units (PSUs)–villages in rural areas and census enumeration blocks in urban areas–were selected with probability proportional to population size in the first stage. In the second stage, households within each PSU were selected through systematic random sampling, whereby the first household was selected randomly, and then every xth household was sampled. Additional details on the sampling procedure are given in S1 Text. Owing to the survey’s focus on maternal and child health, the NFHS-4 sampled women aged 15–49 years in all selected households but sampled men aged 15 to 54 years in only a subsample of $15\%$ of selected households. All men aged 15 to 54 years were sampled in these $15\%$ of households, regardless of their relationship to any women sampled in the household. We only included individuals aged 15–49 years in our analysis to ensure comparability of gender estimates. An interviewer administered a questionnaire to all eligible individuals in the selected households. The response rate (for both the questionnaire and physical measurements detailed below) was 96·$7\%$ for women and 91·$9\%$ for men.
Before every interview, a respondent’s informed consent for participation in the survey was obtained. Special statements were included at both the beginning of the Household Questionnaire and the Individual Questionnaires. The statements explicitly explained the purpose of the survey. These statements also assured that all respondents were aware that participation in the survey is completely voluntary and that it is their right to refuse to answer any questions or stop the interview at any point [26].
## Ascertaining and defining morbidities
The NFHS-4 assessed five chronic morbidities: anemia, asthma, diabetes, hypertension, and obesity. In addition, a random subsample of 200,951 participants of all NFHS-4 participants were offered an HIV-test. Asthma was assessed through self-report, through a yes or no answer to the question “Do you currently have asthma?”. Other conditions used clinical or anthropometric measures in their derivation.
We defined anemia as a hemoglobin capillary blood concentration <11 g/dl, corresponding to moderate or severe anemia according to the 2011 WHO guidelines [27]. Prior to applying this cutoff, haemoglobin values were adjusted for smoking status (ascertained through self-report) and altitude (measured separately for each PSU with GPS devices) using formulas from the US Centers for Disease Control [28]. The NFHS-4 team measured hemoglobin using the HemoCueHb 201+ (HemoCue AB, Ängelholm, Sweden) and a capillary blood sample.
Diabetes was defined as having a raised blood glucose or having responded with ‘yes’ to at least one of “Do you currently have diabetes?” or “Have you sought treatment for this issue [diabetes]?”. The NFHS-team measured blood glucose using a handheld blood glucometer (FreeStyle Optium H [Abbott Laboratories, Abbott Park, USA]), whereby participants were not instructed to fast prior to the measurement. The capillary blood glucose measurement was converted to a plasma-equivalent value by multiplying with 1·11 [29]. We defined raised blood glucose as a plasma-equivalent glucose concentration ≥200 mg/dL (11·1 mmol/L) if not fasted, and ≥126 mg/dL (7·0 mmol/L) if fasted [30]. Participants were specifically asked about their fasting status. Fasting was defined as reporting no intake of food or drink, except plain water, for at least eight hours prior to the glucose sample being taken.
We defined hypertension as having a raised blood pressure (BP) or having responded with ‘yes’ to at least one of “Were you told on two or more different occasions by a doctor or other health professional that you had hypertension or high blood pressure?” or “To lower your blood pressure, are you now taking a prescribed medicine?”. We defined raised BP as having a mean systolic BP ≥140mmHg or a mean diastolic BP ≥90mmHg. The NFHS-4 team measured BP three times in the upper left arm with an electronic upper arm monitor (Omron HEM-8712 [Omron Corporation, Kyoto, Japan]), with at least five minutes between each measurement (and five minutes of quiet sitting prior to the first measurement). As is generally the standard in household surveys [31–33], we used only the last two measurements to compute mean systolic and diastolic BP.
Based on cutoffs specific to South Asia [34], we defined obesity as a Body Mass Index (BMI) ≥27·5 kg/m2. The NFHS-team measured weight using the SECA 874 U digital floor scale (seca GmbH, Hamburg, Germany) and height using the SECA 213 stadiometer (seca GmbH, Hamburg, Germany).
HIV was defined via an HIV blood test. A finger-prick blood specimen was taken among all participants (200,951 individuals) in a random subsample of households. All samples were first tested using an ELISA (enzyme-linked immunosorbent assay) test (Microlisa HIV, J. Mitra & Co. Pvt., New Delhi, India). Samples that tested positive, as well as a random sample of two percent of negative tests, were retested using a different ELISA test (SD Bioline HIV-$\frac{1}{2}$, Abbott Laboratories, Abbott Park, IL, USA). A positive result on both ELISA tests was recorded as HIV-positive. In the case of discordant results between the two ELISA tests, both ELISA tests were repeated in parallel. If the results still remained discordant, a Western Blot Test (Bio-Rad) was conducted at the National AIDS Research Institute (NARI) in Pune. The result of the Western Blot Test was then considered definitive.
## Sociodemographic variables
We examined how the prevalence of multimorbidity varied by the following sociodemographic variables asked in the survey questions: age, sex, education, household wealth quintile, marital status (currently married or not), current smoking, current consumption of smokeless tobacco, rural vs urban location, and state. Household wealth quintile was calculated separately for rural and urban locations using data on household ownership of 25 durable goods and seven key housing characteristics. Using the methodology developed by Filmer and Pritchett [35], we extracted the first component in a principal component analysis of these variables, and then divided this continuous asset index into quintiles. This is the standard approach used by all Demographic and Health Surveys [36]. More detail on the computation of the household wealth quintiles is provided in S2 Text. Additionally, as a measure of a district’s economic development, we also computed a district wealth quintile by calculating, separately for rural and urban areas, the median asset index in each district and then dividing districts into quintiles based on this value.
## Statistical analysis
Our analysis proceeded in four steps. First, we calculated national-level prevalence estimates for multimorbidity by age and rural vs urban areas, whereby all prevalence estimates in this manuscript used sampling weights that accounted for the survey design (including the higher probability of sampling women than men) and, unless prevalence was disaggregated by age, were age-standardized using the Global Burden of Disease Project’s age structure for India for 2015 [37]. We defined multimorbidity as having two or more of the five chronic conditions (anemia, asthma, diabetes, hypertension, and obesity) examined in this study. Second, we estimated the prevalence for each possible two- and three-morbidity combination among these five chronic morbidities. Third, we studied how the prevalence of multimorbidity varied among states by mapping prevalence by state. Fourth, to ascertain how the prevalence of multimorbidity varied by individual- and district-level characteristics, we regressed, separately for rural and urban areas, multimorbidity (as a binary variable) onto participants’ sociodemographic characteristics, district wealth quintile, and a random intercept for each district. We used Poisson regression models with a robust error structure, because it is a valid regression model for binary outcome data and yields a risk ratio (RR) [38], which is generally more easily interpreted than an Odds Ratio [39]. Standard errors were adjusted for clustering at the level of the PSU [40]. Fifth, to study patterns of multimorbidity among Indian adults living with HIV, we computed the prevalence of anemia, asthma, diabetes, hypertension, and obesity among those participants who had a positive HIV test. All analyses were complete case analyses and implemented in R (version 3.3.2; R Foundation).
## Ethics
This analysis received a determination of “not human subjects research” by the institutional review board of the Harvard T.H. Chan School of Public Health on 9 May 2018 because the authors had access to pseudonymized data only.
## Sample characteristics
749,119 individuals aged 15–49 years participated in the survey. 36,297 (4·$8\%$) had a missing value for at least one morbidity, leaving 712,822 individuals (617,374 women and 95,448 men) for inclusion in the analysis (S1 Fig). The sample characteristics of those excluded from the analysis are shown in S1 Table. 26·$5\%$ (188,954 /712,822) of the analysis sample had no formal education, 68·$8\%$ (490,$\frac{644}{712}$,822) were married, 29·$6\%$ (210,$\frac{798}{712}$,822) were living in an urban area, and 5·$4\%$ (38,$\frac{460}{712}$,822) reported currently smoking (Table 1). 1·$1\%$ (8,$\frac{067}{712}$,822) of participants reported fasting at the time of the blood glucose measurement. Among those who underwent an HIV test, 0·$23\%$ ($\frac{452}{200}$,951) were HIV-positive. The sample characteristics among HIV-positive participants are shown in S2 Table.
**Table 1**
| Characteristic | Total | Women | Men |
| --- | --- | --- | --- |
| n | 712822 | 617374 | 95448 |
| Age Group, n (%) | | | |
| 15–24 years | 24,1854 (33·9) | 209,193 (33·9) | 32,661 (34·2) |
| 25–34 years | 21,1087 (29·6) | 182,843 (29·6) | 28,244 (29·6) |
| 35–44 years | 18,0555 (25·3) | 156,456 (25·3) | 24,099 (25·2) |
| 45–49 years | 79,326 (11·1) | 68,882 (11·2) | 10,444 (10·9) |
| Missing, n (%) | 0 (0·0) | 0 (0·0) | 0 (0·0) |
| Education, n (%) | | | |
| No formal education | 188,954 (26·5) | 177,264 (28·7) | 11,690 (12·2) |
| Some primary school | 42,242 (5·9) | 36,409 (5·9) | 5,833 (6·1) |
| Completed primary school | 47,709 (6·7) | 41,750 (6·8) | 5,959 (6·2) |
| Completed middle school | 286,622 (40·2) | 240,930 (39·0) | 45,692 (47·9) |
| Completed secondary school | 64,001 (9·0) | 52,762 (8·5) | 11,239 (11·8) |
| > Secondary school | 83,294 (11·7) | 68,259 (11·1) | 15,035 (15·8) |
| Missing, n (%) | 0 (0·0) | 0 (0·0) | 0 (0·0) |
| Household wealth quintile, n (%) | | | |
| Q1 (Poorest) | 131,502 (18·4) | 114873 (18·6) | 16,629 (17·4) |
| Q2 | 141,413 (19·8) | 122833 (19·9) | 18,580 (19·5) |
| Q3 | 146,764 (20·6) | 127152 (20·6) | 19,612 (20·5) |
| Q4 | 144,701 (20·3) | 124674 (20·2) | 20,027 (21·0) |
| Q5 (Richest) | 148,442 (20·8) | 127842 (20·7) | 20,600 (21·6) |
| Missing, n (%) | 0 (0·0) | 0 (0·0) | 0 (0·0) |
| Currently married, n (%) | 490,644 (68·8) | 432960 (70·1) | 57,684 (60·4) |
| Missing, n (%) | 0 (0·0) | 0 (0·0) | 0 (0·0) |
| Urban area, n (%) | 210,798 (29·6) | 180802 (29·3) | 29,996 (31·4) |
| Missing, n (%) | 0 (0·0) | 0 (0·0) | 0 (0·0) |
| Smokes tobacco, n (%) | 38,460 (5·4) | 12930 (2·1) | 25,530 (26·7) |
| Missing, n (%) | 0 (0·0) | 0 (0·0) | 0 (0·0) |
| Uses smokeless tobacco, n (%) | 86,730 (12·2) | 55689 (9·0) | 31,041 (32·5) |
| Missing, n (%) | 0 (0·0) | 0 (0·0) | 0 (0·0) |
| Morbidity, n(%) | | | |
| Anemia | 169,558 (23·8) | 165187 (26·8) | 4,371 (4·6) |
| Asthma | 11,379 (1·6) | 10308 (1·7) | 1,071 (1·1) |
| Diabetes | 18,979 (2·7) | 15876 (2·6) | 3,103 (3·3) |
| Hypertension | 118,889 (16·7) | 101461 (16·4) | 17,428 (18·3) |
| Obesity | 63,766 (8·9) | 57212 (9·3) | 6,554 (6·9) |
## Prevalence of multimorbidity at the national level
35·$8\%$ ($95\%$ CI, 35·$4\%$ - 36·$1\%$) of participants had at least one of the five morbidities examined. At the national level, the prevalence of multimorbidity was 7·$2\%$ ($95\%$ CI, 7·$1\%$ - 7·$4\%$). Multimorbidity prevalence was strongly associated with increasing age (Fig 1 and S3 Table). Among those with at least one morbidity, 20·$2\%$ ($95\%$ CI, 19·$9\%$ - 20·$6\%$) had two or more morbidities, 2·$9\%$ ($95\%$ CI, 2·$8\%$– 3·$1\%$) three or more morbidities, 0·$3\%$ ($95\%$ CI, 0·$2\%$– 0·$3\%$) four or five morbidities, and 0·$01\%$ ($95\%$ CI, 0·$003\%$– 0·$012\%$) five morbidities. The prevalence of multimorbidity was higher in urban than in rural areas (9·$7\%$ [$95\%$ CI, 9·$4\%$– 10·$1\%$] vs 5·$8\%$ [$95\%$ CI, 5·$7\%$– 6·$0\%$], respectively; $p \leq 0$·001).
**Fig 1:** *Prevalence of morbidity and multimorbidity by age and urbanicity in India1.1 The prevalence of each number of morbidities (with 95% confidence intervals) is shown in S3 Table.*
## National prevalence of different morbidity combinations
Fig 2 shows the national prevalence of all possible combinations of two and three morbidities among the five morbidities studied. The two most common multimorbidity combinations both included hypertension: hypertension with obesity (2·$9\%$ [$95\%$ CI, 2·$8\%$– 3·$1\%$]), and hypertension with anemia (2·$2\%$ [$95\%$ CI, 2·$1\%$– 2·$3\%$]). The most common three-morbidity combination was diabetes with hypertension and obesity (0·$4\%$ [$95\%$ CI, 0·$3\%$ - 0·$4\%$]).
**Fig 2:** *National prevalence of all two- and three-morbidity combinations1.1 95% confidence intervals can be found in S4 Table.*
## Variation of multimorbidity prevalence among states
Among states and union territories, the age-standardized prevalence of multimorbidity ranged from 3·$4\%$ ($95\%$ CI, 3·$0\%$ - 3·$8\%$) in Chhattisgarh to 16·$9\%$ ($95\%$ CI, 13·$2\%$ - 21·$5\%$) in Puducherry (Fig 3 and S5 Table). Among states, multimorbidity prevalence was highest in urban areas of the South Indian states of Andhra Pradesh, Tamil Nadu, and Telangana, and lowest in rural areas of Madhya Pradesh and Rajasthan. The prevalence of multimorbidity was higher in urban areas than in rural areas in all Indian states except Meghalaya and Punjab. The states with the smallest absolute difference in multimorbidity prevalence between rural and urban areas were Punjab (10·$0\%$ [$95\%$ CI 8·$9\%$– 11·$2\%$] in rural areas vs 9·$7\%$ [$95\%$ CI 8·$5\%$– 11·$0\%$] in urban areas), Kerala (5·$8\%$ [$95\%$ CI 5·$0\%$– 6·$9\%$] in rural areas vs 6·$3\%$ [$95\%$ CI 5·$3\%$– 7·$5\%$] in urban areas), and Goa (7·$1\%$ [$95\%$ CI 5·$0\%$– 9·$9\%$] in rural areas vs 7·$5\%$ [$95\%$ CI 5·$2\%$– 10·$8\%$] in urban areas).
**Fig 3:** *Age-standardized prevalence of multimorbidity by state and rural vs urban location1,2,3.1 Point estimates and 95% CIs for each state and union territory can be found in S5 Table. 2 AN indicates Andaman and Nicobar Islands; AP, Andhra Pradesh; AR, Arunachal Pradesh; AS, Assam; BR, Bihar; CG, Chhattisgarh; CH, Chandigarh; DN, Dadra and Nagar Haveli; DL, Delhi; DD, Daman and Diu; GA, Goa; GJ, Gujarat; HR, Haryana; HP, Himachal Pradesh; JH, Jharkhand; JK, Jammu and Kashmir; KA, Karnataka; KL, Kerala; LD, Lakshadweep; MP, Madhya Pradesh; MH, Maharashtra; MN, Manipur; ML, Meghalaya; MZ, Mizoram; NL, Nagaland; OD, Odisha (Orissa); PB, Punjab; PY, Puducherry; RJ, Rajasthan; SK, Sikkim; TN, Tamil Nadu; TS, Telangana State; TR, Tripura; UP, Uttar Pradesh; UK, Uttarakhand (Uttaranchal); WB, West Bengal. 3 The map used for this figure was sourced from Survey of India, the national survey and mapping organization of India, Department of Science & Technology, Government of India.*
## Variation of multimorbidity prevalence by individual- and district-level characteristics
Table 2 shows results from covariate-adjusted regression models. We find that i) women had a substantially higher risk of suffering from multimorbidity than men (RR of 1·95 [$95\%$ CI, 1·86–2·04] in rural and 1·76 [$95\%$ CI, 1·67–1·86] in urban areas); ii) increasing household wealth quintile was associated with multimorbidity in both rural and urban areas; and iii) smoking tobacco was negatively associated with multimorbidity. The median household wealth in a district was not associated with individuals’ risk of having a multimorbidity. We identified no consistent associations with educational attainment. Results from covariate-unadjusted regressions were similar (S6 Table).
**Table 2**
| Unnamed: 0 | Rural | Rural.1 | Urban | Urban.1 |
| --- | --- | --- | --- | --- |
| | RR (95% CI) | P | RR (95% CI) | P |
| Female | 1·95 (1·86–2·04) | <0·001 | 1·76 (1·67–1·86) | <0·001 |
| Age group | | | | |
| 15–24 years | 1·00 (Ref.) | | 1·00 (Ref.) | |
| 25–34 years | 2·21 (2·12–2·30) | <0·001 | 2·65 (2·51–2·80) | <0·001 |
| 35–44 years | 3·95 (3·79–4·11) | <0·001 | 5·05 (4·79–5·33) | <0·001 |
| 45–49 years | 5·42 (5·19–5·66) | <0·001 | 6·84 (6·47–7·23) | <0·001 |
| Household wealth quintile | | | | |
| Q1 (Poorest) | 1·00 (Ref.) | | 1·00 (Ref.) | |
| Q2 | 1·10 (1·06–1·15) | <0·001 | 1·31 (1·25–1·37) | <0·001 |
| Q3 | 1·27 (1·22–1·32) | <0·001 | 1·52 (1·45–1·59) | <0·001 |
| Q4 | 1·55 (1·48–1·61) | <0·001 | 1·64 (1·56–1·72) | <0·001 |
| Q5 (Richest) | 2·05 (1·96–2·14) | <0·001 | 1·72 (1·64–1·81) | <0·001 |
| Education | | | | |
| No formal education | 1·00 (Ref.) | | 1·00 (Ref.) | |
| Some primary school | 1·13 (1·08–1·18) | <0·001 | 1·08 (1·02–1·15) | 0·011 |
| Completed primary school | 1·13 (1·08–1·17) | <0·001 | 1·10 (1·04–1·17) | <0·001 |
| Completed middle school | 1·09 (1·06–1·12) | <0·001 | 1·09 (1·05–1·13) | <0·001 |
| Completed secondary school | 1·08 (1·03–1·14) | 0·001 | 1·01 (0·96–1·07) | 0·607 |
| > Secondary school | 1·04 (0·99–1·09) | 0·128 | 0·92 (0·87–0·96) | <0·001 |
| Currently married | 1·37 (1·32–1·42) | <0·001 | 1·30 (1·25–1·35) | <0·001 |
| Currently smoking | 0·89 (0·84–0·94) | <0·001 | 0·91 (0·84–0·97) | 0·008 |
| Currently using smokeless tobacco | 1·05 (1·01–1·09) | 0·006 | 1·03 (0·98–1·08) | 0·220 |
| District wealth quintile | | | | |
| Q1 (Poorest) | 1·00 (Ref.) | | 1·00 (Ref.) | |
| Q2 | 0·95 (0·88–1·03) | 0·220 | 1·01 (0·93–1·10) | 0·829 |
| Q3 | 0·93 (0·85–1·01) | 0·079 | 1·02 (0·94–1·12) | 0·611 |
| Q4 | 0·96 (0·88–1·05) | 0·399 | 1·06 (0·97–1·16) | 0·197 |
| Q5 (Richest) | 1·10 (1·00–1·22) | 0·058 | 1·03 (0·93–1·13) | 0·606 |
## Subgroup analysis among HIV-positive participants
As was the case among the entire study population, the two most common comorbidities among the 469 participants with a positive HIV-test were hypertension (25·$1\%$, $95\%$ CI, 16·$3\%$ - 36·$7\%$) and anemia (13·$4\%$, $95\%$ CI, 9·$5\%$ - 18·$7\%$) (S7 Table). With a prevalence of 6·$8\%$ ($95\%$ CI, 1·$7\%$– 23·$2\%$), asthma was far more common among those with HIV than among the entire study population. The most prevalent three-morbidity combinations among HIV-positive participants were HIV with hypertension and obesity (7·$6\%$, $95\%$ CI, 2·$2\%$ - 22·$9\%$) followed by HIV with asthma and obesity (4·$8\%$, $95\%$ CI, 0·$7\%$ - $26.5\%$).
## Discussion
This nationally representative study found a high prevalence of multimorbidity among young and middle-aged adults. This finding highlights the need to avoid single-disease-focused vertical programs in South Asia, which have been a mainstay of healthcare financing and delivery in LMICs. However, it is important to note that the shift away from single-disease-centered to person-centered care is more urgent for some states in India than others as we identified a vast degree of variation in the prevalence of multimorbidity between states, with urban areas in South India having a particularly high prevalence. Lastly, we found that being a woman, married, a non-smoker, living in a household with higher wealth, and living in urban areas were all associated with a higher risk of multimorbidity. This information is not only critical for targeting of appropriate interventions or prevention strategies to reach those most in need; they also imply that the prevalence of multimorbidity will rise in the future as India’s population ages and continues to undergo rapid economic development and urbanization [19, 41].
Preventing and effectively managing multimorbidity will require a shift towards a person- rather than disease- or episode-focused health system. Achieving continuity of person-centered care, across primary care and between primary and secondary care, is a major challenge for all health systems, but particularly so in India, which has multiple nationally managed vertical disease programs while much of primary and hospital care is managed by states [42]. To our knowledge, the National Programme for Prevention & Control of Cancer, Diabetes, Cardiovascular Diseases & Stroke (NPCDCS), which does not include anemia, asthma, and HIV, is the only nation-wide program in India that is focused on multiple related conditions. India also has a disproportionately large private care sector, in a setting where public spending on health care is one of the lowest worldwide [43, 44]. The private healthcare sector in *India is* highly fragmented and consists of a multitude of small independent providers with little to no coordination across providers [44]. The public healthcare system is also fragmented due to the presence of multiple disease-centered vertical programs, which operate in parallel to primary and secondary healthcare [42]. In addition, the majority of patient records are still paper-based and shared sporadically between healthcare facilities [45]. In addition to increased funding for primary care, the use of interdisciplinary professional care teams supported by integrated electronic care records and directed by clinical guidelines for managing common comorbidities could go a long way in transforming India’s health system to more effectively prevent and manage multimorbidity.
India has recently embarked on a major effort to develop integrated and comprehensive primary care services nationwide. Specifically, one of the two main components of India’s recently launched national health reform, Ayushman Bharat (Healthy India), is the establishment of 150,000 so-called health and wellness centers by 2022. In addition to maternal and child health services, these centers will place an emphasis on the prevention and treatment of chronic morbidities, particularly non-communicable diseases, such as diabetes and hypertension [46]. Our analysis can aid national health reforms in several ways. First, our study highlights in which states the need for care for multimorbidity is greatest, which can inform the geographic placement of health and wellness centers. Second, we show which morbidity combinations are most common, including among individuals with HIV, which can inform screening guidelines for healthcare workers at health and wellness centers and other healthcare facilities. Third, we determine which population groups are most likely to suffer from multimorbidity, which can inform not only screening guidelines but also the design of relevant interventions, such as health campaigns and community health worker interventions.
While studies in high-income settings tend to report a higher prevalence of multimorbidity among socioeconomically disadvantaged groups [47, 48], we found a positive association between household wealth and multimorbidity. However, our results do not suggest that multimorbidity is only a health problem among the wealthier strata of Indian society. For instance, the prevalence of multimorbidity in the lowest household wealth quintile was still considerable at 4·$8\%$ ($95\%$ CI, 4·$6\%$ - 5·$1\%$). In addition, there was no clear association between these outcomes and educational attainment, which is another important indicator of socioeconomic status. Regardless, it may well be that the associations of multimorbidity with household wealth and education in India might become more similar to those seen in high-income settings as India continues to develop economically [49]. Another potentially surprising association in our regression analysis was the negative correlation between current smoking and multimorbidity. This association may be, among other potential reasons, a result of a negative association between smoking and obesity (which in turn is a risk factor for diabetes and hypertension) or of those diagnosed with a chronic morbidity being more likely to quit or underreport smoking.
This is by far the largest representative study of multimorbidity in South Asia to date. In fact, a recent systematic review on the prevalence of multimorbidity in South Asia identified 13 studies, of which only one–conducted among 320 adults in a rural area of one district–was carried out after 2010, and only three did not exclusively rely on self-report [24, 50]. The largest of these 13 studies had a sample size of 44,514 adults, was conducted in one neighborhood of Bangalore, and relied entirely on self-report to define morbidities [51]. Nonetheless, despite the limited literature on this subject, the overall body of evidence in LMICs suggests that the prevalence of multimorbidity is substantial in these settings [52].
This study has several limitations. First and foremost, while we were able to include many of the most important morbidities that are thought to affect India’s population [1, 53], the NFHS-4 did not assess an exhaustive list of these conditions. As such, the prevalence of multimorbidity in this analysis should not be interpreted as referring to the presence of two or more chronic conditions in general, but instead only has having two or more conditions of the five conditions that were assessed as part of the NFHS-4. Second, our dataset is only representative for individuals aged 15–49 years. However, given the country’s relatively young population structure, these age groups constituted an estimated 75·$1\%$ of India’s total population above the age of 15 years in 2015 [54]. Third, asthma, currently smoking, and consumption of smokeless tobacco were all defined through self-report only. We were unable to identify biomarker-defined smoking and smokeless tobacco consumption prevalence estimates for India. However, for asthma, our prevalence estimate of 1·$5\%$ ($95\%$ CI, 1·$4\%$ - 1·$6\%$) was similar to the one obtained in a population-based study of 169,575 participants aged ≥15 years in 12 districts of India, which estimated a prevalence of 2·$1\%$ (no CI provided) using a detailed questionnaire on asthma symptoms [55]. Fourth, for those previously undiagnosed with diabetes, the definition of diabetes was based on a one-time capillary blood glucose measurement, which is insufficient for a clinical diagnosis of diabetes, especially since most participants were not fasted at the time of the measurement [56]. Fifth, men constituted merely 13·$4\%$ of participants in the NFHS-4. However, we used sampling weights to adjust for this higher probability of sampling men, and the absolute number of men included [95,448] was sufficiently high to obtain precise prevalence estimates for men. Lastly, HIV tests were conducted in a relatively small subgroup of 200,951 participants, leading to less precise multimorbidity prevalence estimates among participants living with HIV, which was the main reason for performing the HIV analysis separately.
India is facing a high prevalence of multimorbidity that may increase rapidly over the coming decades. Urgent reforms are needed to shift the health system’s focus away from episodic care for acute conditions towards longitudinal, integrated, and person-centered care. In addition, many of these chronic conditions share common risk factors–for example, air pollution is an important risk factor for hypertension and asthma [57–59], and poor diet quality is a risk factor for both anemia and obesity [60, 61]. Thus, population-based approaches targeting these underlying risk factors could have important effects. By providing the first detailed, nationally representative account of multimorbidity in India–including which states and population groups face the highest prevalence, and which morbidity combinations are most common–this study furnishes important evidence to provide both an impetus for future reforms, as well as inform the targeting of policies and interventions as part of India’s ongoing healthcare reform efforts.
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|
---
title: 'The impact of long-term conditions on disability-free life expectancy: A systematic
review'
authors:
- Ilianna Lourida
- Holly Q. Bennett
- Fiona Beyer
- Andrew Kingston
- Carol Jagger
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021208
doi: 10.1371/journal.pgph.0000745
license: CC BY 4.0
---
# The impact of long-term conditions on disability-free life expectancy: A systematic review
## Abstract
Although leading causes of death are regularly reported, there is disagreement on which long-term conditions (LTCs) reduce disability-free life expectancy (DFLE) the most. We aimed to estimate increases in DFLE associated with elimination of a range of LTCs. This is a comprehensive systematic review and meta-analysis of studies assessing the effects of LTCs on health expectancy (HE). MEDLINE, Embase, HMIC, Science Citation Index, and Social Science Citation Index were systematically searched for studies published in English from July 2007 to July 2020 with updated searches from inception to April 8, 2021. LTCs considered included: arthritis, diabetes, cardiovascular disease including stroke and peripheral vascular disease, respiratory disease, visual and hearing impairment, dementia, cognitive impairment, depression, cancer, and comorbidity. Studies were included if they estimated HE outcomes (disability-free, active or healthy life expectancy) at age 50 or older for individuals with and without the LTC. Study selection and quality assessment were undertaken by teams of independent reviewers. Meta-analysis was feasible if three or more studies assessed the impact of the same LTC on the same HE at the same age using comparable methods, with narrative syntheses for the remaining studies. Studies reporting Years of Life Lost (YLL), Years of Life with Disability (YLD) and Disability Adjusted Life Years (DALYs = YLL+YLD) were included but reported separately as incomparable with other HE outcomes (PROSPERO registration: CRD42020196049). Searches returned 6072 unique records, yielding 404 eligible for full text retrieval from which 30 DFLE-related and 7 DALY-related were eligible for inclusion. Thirteen studies reported a single condition, and 17 studies reported on more than one condition (two to nine LTCs). Only seven studies examined the impact of comorbidities. Random effects meta-analyses were feasible for a subgroup of studies examining diabetes (four studies) or respiratory diseases (three studies) on DFLE. From pooled results, individuals at age 65 without diabetes gain on average 2.28 years disability-free compared to those with diabetes ($95\%$ CI: 0.57–3.99, $p \leq 0.01$, I2 = $96.7\%$), whilst individuals without respiratory diseases gain on average 1.47 years compared to those with respiratory diseases ($95\%$ CI: 0.77–2.17, $p \leq 0.01$, I2 = $79.8\%$). Eliminating diabetes, stroke, hypertension or arthritis would result in compression of disability. Of the seven longitudinal studies assessing the impact of multiple LTCs, three found that stroke had the greatest effect on DFLE for both genders. This study is the first to systematically quantify the impact of LTCs on both HE and LE at a global level, to assess potential compression of disability. Diabetes, stroke, hypertension and arthritis had a greater effect on DFLE than LE and so elimination would result in compression of disability. Guidelines for reporting HE outcomes would assist data synthesis in the future, which would in turn aid public health policy.
## Introduction
Chronic or long-term conditions (LTCs) affect the health and quality of life of millions of people globally and exert heavy pressure on health care services. A long-term condition is a condition that cannot, at present, be cured but is controlled by medication and/or other treatment/therapies. It is estimated that nearly $40\%$ of the UK and US populations have at least one LTC [1], while one in three older adults live with multiple LTCs, and these figures are projected to rise dramatically by 2035 [2]. LTCs including heart disease, diabetes, cancer, and respiratory diseases have been among the leading causes of death globally in recent decades although mortality rates have declined due to improved medical care and availability [3]. However, the increased longevity coupled with the rising prevalence of LTCs has led to concerns about the impact on disability burden especially among older people. Therefore, the focus is gradually shifting from mortality and life expectancy (LE) as the measure of success to the need to improve quality of life and using disability-free life expectancy (DFLE; the number of years from a particular age spent free of disability) or healthy life expectancy (HLE; the number of years from a particular age spent in good health) (See S1 Methods for full definitions).
Previous research has shown that individual LTCs such as diabetes [4,5], arthritis [6], depression [7,8], and sensory impairments [9] have a significant effect on DFLE. Other studies have attempted to break down the disability burden of multiple LTCs and have identified heart disease [10–12], stroke [8,13] or dementia [14] as the most important causes of DFLE and/or LE loss. In addition, a few studies have examined the issue of comorbidity trying to assess whether disability could be attributed to one or more LTCs [8,10,13,15]. The potential changes in DFLE and LE resulting from the elimination of various LTCs has also been estimated in some populations [12,16–18] to determine whether better control of these conditions would postpone the onset of disability. The gains in disability-free and total life expectancy can vary depending on the age at which estimates are reported, and especially the condition that is hypothetically eliminated.
The Global Burden of Disease studies provide country-specific and global assessments of Years of Life Lost (YLL), Years of Life with Disability (YLD) and Disability Adjusted Life Years (DALYs = YLL+YLD). However, DALYs are typically reported for all ages, and there is currently no consensus on which LTCs are the main contributors to reductions in DFLE at older ages, or which LTCs if eliminated will result in greater gains in DFLE than LE driving compression of disability. This is important to inform health policy and priority setting in terms of prevention and treatment of LTCs, design of appropriate care packages, and for identifying research and funding priorities for specific conditions. Despite the growing number of studies assessing the impact of common LTCs on health expectancy outcomes, no systematic review has synthesised the available evidence.
Our aim was to address this gap by conducting a systematic review of the literature to assess the effect of a range of LTCs, singly and in combination, on disability-free and total life expectancy, and specifically, which LTCs have a greater effect on DFLE than LE. A fuller description of these key concepts is provided in S1 Methods.
## Data sources and searches
The systematic review was conducted following PRISMA guidelines (S1 Checklist), and the general principles published by the NHS Centre for Reviews and Dissemination (CRD) [19,20]. A protocol was developed following discussion with methods and topic experts and is registered with PROSPERO (PROSPERO 2020: CRD42020196049).
The search strategy was developed by an experienced information specialist (FB) in collaboration with the review team based on the concepts [long-term conditions] AND [disability-free life expectancy]. The search was developed for MEDLINE (OVID, see S2 Methods) using thesaurus headings, and title, abstract and keyword field terms adapted as appropriate for the other electronic databases (MEDLINE and In-Process & Other Non-indexed citations, Embase [via OVID], HMIC Health Management Information Consortium, Science Citation Index, Social Science Citation Index [via Web of Science]). Searches were initially limited to studies published in English from July 2007 to July 2020, 2007 being selected as the date of the publication of the first paper reporting the effect of LTCs on disability-free life expectancy [41]. Update searches were made as comprehensive as possible and conducted from inception to 8 April 2021. Studies were excluded by record type in MEDLINE and Embase if they were editorials, opinion pieces or letters. Reference lists of all included articles were checked for additional relevant studies.
## Eligibility criteria
The aim of the review was to assess the effect of LTCs, singly and in combination, on disability-free (DFLE) and total life expectancy (LE), and specifically, whether LTCs had a greater effect on DFLE or LE. If the former, then elimination of the LTC could result in a compression of disability. To ensure that no studies on DFLE were missed, we included wider health expectancy terms of health-adjusted LE (HALE) and Healthy Life Expectancy (HLE). LTCs included non-communicable chronic, long-term or life-limiting conditions or diseases. Relevant LTCs were identified through previous research [21,22]: arthritis, diabetes, cardiovascular disease including stroke and peripheral vascular disease, respiratory disease, visual and hearing impairment, dementia, cognitive impairment, depression, cancer, and comorbidity/multimorbidity. For this review, obesity was considered a risk factor for disease rather than a chronic condition or disease. Therefore, studies reporting obesity as a single exposure were not eligible. Studies were included if they measured the effect of any of the above LTCs on health expectancy outcomes, primarily disability-free, active or healthy life expectancy reported at age 50 and older compared to individuals without the LTC. Studies reporting estimates at birth only were excluded. Outcomes reported as gains in DFLE or HALE with and without a specific LTC (or multimorbidity) and estimates from projections of DFLE were also eligible. Total life expectancy was eligible as an outcome only when reported alongside the main outcomes (DFLE, ALE, HALE).
## Study selection
Search results were downloaded to Endnote version X9 (Clarivate, Philadelphia, PA) and de-duplicated before being imported to the Rayyan reference management software [23] for screening. Starting with a random sample of 100 citations, titles and abstracts were screened for relevance independently by pairs of reviewers (IL and HB, AK or CJ). Following clarification of concepts and criteria, the team completed the screening process with randomly assigned citations for each pair of reviewers. Disagreements were resolved by discussion adjudicated by a third reviewer if necessary. The full text of relevant articles was retrieved and screened in the same way using the predefined eligibility criteria.
The electronic searches yielded 6,072 unique citations. Screening of titles and abstracts against the eligibility criteria resulted in the retrieval of the full text of 404 articles of which 29 DFLE-related and seven DALY-related were eligible for inclusion. One additional article was identified through reference list checking of the included studies. In total, 37 articles met the inclusion criteria (Fig 1).
**Fig 1:** *Study selection process for the systematic review.*
## Data extraction
Two reviewers piloted and refined a data extraction form using two randomly selected studies (IL and CJ). Data for the remaining studies were extracted independently by pairs of reviewers in a structured form using Excel. Publication details (first author, year, country), study and population characteristics (study design, years of analysis and number of follow-ups where relevant, sample size, age, % men/women), LTCs, outcomes and their measurement, brief methods of analysis, age at which outcomes were reported (prioritising age 65 where possible), and quantitative results for LE and health expectancies with and without the LTC were recorded. Discrepancies were resolved by discussion and involvement of a third reviewer (CJ) where necessary. Authors of four papers were contacted for clarification or additional information.
Searches retrieved papers from the GBD group reporting Years of Life Lost (YLL), Years of Life with Disability (YLD) and Disability Adjusted Life Years (DALYs = YLL+YLD). These studies were screened separately, and grouped into global, regional, and national estimates. We focused on global estimates reporting estimates at more than one time point and excluded any studies that did not provide estimates of both YLL and YLD for individual conditions. However, estimates of YLD were reported for all ages, or age-adjusted, and thus were not directly comparable with DFLE/HALE estimates at older ages. We therefore use these studies to compare the relative ranking of our selected LTCs, and how these have changed over time.
## Quality assessment
The quality of included studies was assessed by one reviewer and checked by a second. In an amendment to the protocol, a modified checklist was used combining items from the Joanna Briggs Institute (JBI) critical appraisal tools for prevalence and longitudinal studies [24,25] with criteria previously used by Freedman [26] for evaluating surveys of trends in self-reported disability. Full details are given in S3 Methods.
## Data synthesis and analysis
Studies were categorised by study design, LTC, and health expectancy outcome, and summaries of these characteristics were provided in tabular and narrative form. Meta-analyses to estimate summary measures of the impact of the LTCs on health expectancy at a specific age were considered to be feasible for three or more studies assessing the impact of the same LTC on the same health expectancy outcome (e.g., DFLE with disability measured by performance in ADLs) at the same age (e.g., at 65) using comparable methods. The mean difference and $95\%$ confidence intervals in DFLE/HALE (years) between those without and those with the LTC were calculated providing sufficient information was reported in the included studies. Data were pooled using random-effects models and presented in forest plots separately for men and women. Statistical heterogeneity was explored through the I2 and the Q tests according to specific categories (low = $25\%$, moderate = $50\%$, and high = $75\%$). Funnel plots were used to evaluate potential publication bias. Statistical analyses were conducted using Stata (StataCorp 2019 Stata Statistical Software: Release 16). Studies that could not be included in meta-analyses due to important differences in key characteristics (e.g., study design, age at which estimates were reported, outcome, data availability) were synthesised narratively.
## Study characteristics
The characteristics of the included DFLE-related studies are presented in Table 1. One of the identified studies was an independent report [27] and the rest were peer-reviewed publications spanning from 1991 to 2020. Thirteen studies were cross-sectional and 17 longitudinal, with data from the USA ($$n = 10$$), Canada ($$n = 4$$), UK ($$n = 3$$), Australia ($$n = 2$$), Brazil ($$n = 2$$), China ($$n = 3$$); and Mexico, Japan, Taiwan, Singapore, Denmark, and the Netherlands (one study each). Three studies reported the impact of LTCs on DFLE at more than one time point [5,28,29]. Most studies reported outcomes for populations aged 60 years or older, and over half of studies ($63\%$) reported estimates at more than one age. Gender distribution varied, with all but three studies [10,28,30] presenting results separately for men and women, and a fourth study stratifying results by sex and race/ethnicity [13]. The number of assessments in longitudinal studies ranged from two to eight with a maximum length of follow-up of 15 years. Most studies reported using nationally representative data for non-institutionalised populations, with self-reported diagnosis of the LTCs coded using standardised criteria- mostly the International Classification of Diseases. Details of the quality assessment items and ratings are shown in S1 Results and S1 and S2 Tables.
**Table 1**
| Study (year) | Survey | LTCs measured | Survey years (n of measurements) | Population (age, sample size) | Outcomes (disability measurement) | Method used to calculate DFLE (or HALE) |
| --- | --- | --- | --- | --- | --- | --- |
| Cross-sectional studies | Cross-sectional studies | Cross-sectional studies | Cross-sectional studies | Cross-sectional studies | Cross-sectional studies | Cross-sectional studies |
| Bronnum-Hansen (2006) [12] | Danish Health Interview Survey 2000 | Chronic obstructive lung disease, Diabetes, Cerebrovascular disease, Ischaemic heart diseases, Neoplasms | 1995–1999 (1) | ≥65 years;3,009 ppts | LE, Expected lifetime without long-standing illness | Sullivan,Cause elimination |
| Campolina (2013) [16] | Saude, Bem-Estare Envelhecimento (SABE) [Health, Wellbeing and Ageing] study (Brazil) | Cancer,Cerebrovascular disease, Diabetes,Heart disease, (systemic arterial) Hypertension,Lung disease | 2000 (1) | ≥60 years;2,143 ppts | Gain in LE, DFLE (ADLs) | Sullivan,Cause elimination |
| Campolina (2014) [17] | Saude, Bem-Estare Envelhecimento (SABE) [Health, Wellbeing and Ageing] study (Brazil) | Cancer,Cerebrovascular disease,Diabetes,Heart disease, (systemic arterial) Hypertension,Lung disease | 2010 (1) | ≥60 years;907 ppts | Gain in LE, DFLE (ADLs) | Sullivan,Cause elimination |
| Chen (2014) [39] | The 2006 ChinaDisability Survey | Cerebrovascular disease, Hearing loss,Osteoarthritis | 2006 (1) | ≥60 years;354,859 ppts | Life expectancy with disability (LED) (abnormalities in anatomical structure or loss of a certain organ or function (either psychological or physiological), and lost ability to perform an activity in normal way) | Sullivan |
| Hu (2019) [29] | Global Burden of Disease Study 2016 for China | Cancer, Cardiovascular diseases, Chronic respiratory diseases,Diabetes | 1990, 2016 (1) | ≥50 years;unclear | Gain in DFLE (Global burden of disease YLD) | Sullivan, Cause elimination |
| Huo (2016) [34] | Australian Survey of Disability, Ageing and Carers, and AustralianDiabetes, Obesity and Lifestyle study | Diabetes | 2011–2012 (1) | ≥50 years;unclear | Gain in LE, DFLE (ADLs, mobility, communication) | Chiang, Sullivan, Cause elimination |
| Manton (1991) [35] | National Long Term Care Surveys (USA) | Dementia | 1983–1984 (1) | ≥65 years;(unlear) | Gain in ALE (ADLs) | Cause elimination |
| Murtaugh (2011) [14] | National Mortality Followback Survey (USA) | Arthritis, COPD,Dementia, Diabetes,Heart attack, Stroke | 1993 (1) | Unclear | LE, DLE (physical strength & endurance, ADLs, IADLs, mobility) | Projected lifetime risk |
| Nusselder (1996) [18] | Dutch National Survey ofGeneral Practice | Cancer, Chronic non-specific lung disease,Diabetes, Heart disease | 1987–1988 (1) | ≥15 years;10,147 ppts | Gain in LE, DFLE (OECD indicator) | Sullivan,Cause elimination |
| Mathers (1999) [11] | Survey of disability, ageing and carers (Australia) | Cerebrovascular disease, COPD, Diabetes, Hearing loss, Hypertensive diseaseIschaemic heart disease, Osteoarthritis, Rheumatoid arthritis | 1993 (1) | ≥5 years;unclear | Gain in LE, HALE (range of disabilities, impairments and handicap) | Cause elimination |
| Public Health Agency of Canada report (2012) [27] | Canadian Community Health Survey | Diabetes, Hypertension, + comorbidity | 2000–2005 (1) | ≥20 years;200,809 (diabetes), 173,567 (hypertension) ppts | Gains in LE, HALE (physiological or psychological functioning measured by HUI3) | Sullivan, cause elimination |
| | Canadian Community Health Survey | Cancer | 2002–2005 (1) | >0 years;156,020 ppts | LE, HALE (physiological or psychological functioning measured by HUI3) | Sullivan,Cause elimination |
| Sikdar (2010) [36] | Canadian Community Health Survey (restricted to Newfoundland and Labrador residents) | Diabetes | 2001–2005 (1) | ≥15 years;3,567 ppts | Gains in LE, HALE (physiological or psychological functioning measured by HUI3) | Sullivan,Cause elimination |
| Steensma (2016) [37] | Canadian Community Health Survey | Depression | 2009–2010 (1) | ≥20 years;103,815 ppts | Period LE, HALE (physiological or psychological functioning measured by HUI3) | Sullivan |
| Longitudinal studies | Longitudinal studies | Longitudinal studies | Longitudinal studies | Longitudinal studies | Longitudinal studies | Longitudinal studies |
| Andrade (2010) [4] | Mexican Health and Aging Study | Diabetes | 2001–2003 (2) | ≥50 years;11,929 for ADLs, 11,944 for IADLs,11,935 for Nagi | LE, DFLE (ADLs, IADLs & Nagi physical performance limitations) | Multi-state life tables |
| Bardenheier (2016a) [5] | Health and Retirement Study | Diabetes | Cohort I: 1992–2002 (6)Cohort II: 2002–2012 (6) | 50–70 years;Cohort I: 9,754, Cohort II: 3,027 ppts | LE, DFLE (ADLs, IADLs & mobility) | Multi-state life tables |
| Bardenheier (2016b) [31] | Health and Retirement Study | Diabetes | 1998–2012 (8) | ≥50 years;20,008 ppts | LE, DFLE (ADLs, IADLs & mobility) | Multi-state life tables |
| Belanger (2002) [38] | National Population Health Survey (Canada) | Arthritis,Cancer,Diabetes | 1994–1996 (2) | ≥45 years;8,009 ppts | LE, DFLE (activity limitations, dependency) | Multi-state life tables |
| Chiu (2019) [32] | Nihon University JapaneseLongitudinal Study of Aging | Stroke | 1999–2009 (5) | ≥65 years;4,833 ppts | LE, DFLE (ADLs, IADLs) | Multi-state life tables |
| Diehr (1998) [30] | Cardiovascular Health Study (USA) | Cardiovascular disease | Unclear | ≥65 years;5,201 ppts | HLE (self-reported health status) | Transition probabilities |
| Dodge (2003) [33] | MoVIES survey (USA) | Alzheimer’s disease | 1989–1995 (3?) | ≥70 years;1,201 ppts | LE, DLE (IADLs) | Multi-state life tables |
| Fang (2009) [28] | Beijing Multidimensional Longitudinal study on Aging (China) | Stroke | Cohort I: 1992–1997 (2)Cohort II: 2000–2004 (2) | ≥55 years;Cohort I: 3,227, Cohort II: 2,837 ppts | LE, ALE (WHO disability scale) | Multi-state life tables |
| Hayward (1998) [40] | Longitudinal Study of Aging (USA) | Heart disease, cerebrovascular disease, cancer, + comorbidity | 1984–1990 (3) | ≥70 years;7,527 ppts | LE, ALE (ADLs, IADLs) | Multi-state life tables, cause elimination |
| Jagger (2003) [10] | GP health assessments (UK) | Diabetes | 1990–1999 (5) | ≥75 years;2,474 ppts | LE, ALE (ADLs) | Multi-state life tables |
| Jagger (2007) [41] | MRC Cognitive Function and Ageing Study (UK) | Arthritis, Coronary heart disease, Chronic airway obstruction, Diabetes, Peripheral vascular disease, Stroke, Visual impairment, Hearing impairment, Cognitive impairment, + comorbidity (arthritis and comorbidity, CHD and comorbidity) | 1992–2002 (4) | ≥65 years; 12,881 ppts | LE, DFLE (ADLs, IADLs) | Multi-state life tables |
| Laditka (2016) [13] | Panel Study of Income Dynamics (USA) | Arthritis, Depression, Diabetes, Heart disease, Hypertension, Lung disease, Memory, Stroke, + comorbidity (diabetes and combinations of the other LTCs) | 1999–2011 (7) | ≥55 years;2,118 ppts | LE, DFLE (ADLs) | Multi-state life tables |
| Liang (2020) [15] | Taiwan LongitudinalStudy on Aging | Diabetes, Hypertension, + comorbidity | 1996–2011 (4) | ≥50 years;5,131 ppts | LE, DFLE (ADLs) | Multi-state life tables |
| Pérès (2008) [7] | MRC Cognitive Function and Ageing Study (UK) | Depression, Emotional problems, + Multimorbidity | 1998–2008 (4) | ≥65 years;11,022 ppts | LE, DFLE (ADLs, IADLs) | Multi-state life tables |
| Reynolds (2008a) [6] | Asset and Health Dynamics Among the Oldest Old-AHEAD (USA) | Arthritis | 1993–1998 (3) | ≥70 years;7,381 ppts | LE, ALE (ADLs) | Multi-state life tables |
| Reynolds (2008b) [8] | Asset and Health Dynamics Among the Oldest Old-AHEAD (USA) | Depression, Cancer, Diabetes, Heart disease, Stroke, + comorbidity (depression and each of the other LTCs) | 1993–1998 (3) | ≥70 years;7,381 ppts | LE, ALE (ADLs) | Multi-state life tables |
| Tareque (2019) [9] | Panel on Health and Ageing in Singaporean Elderly | Visual impairment, Hearing impairment, + comorbidity | 2009–2015 (3) | ≥60 years;3,452 ppts | LE, DFLE (physical function limitations & ADLs, IADLs) | Multi-state life tables |
## Overview of LTCs and outcomes
The LTCs specified in the inclusion criteria and identified in the included studies were mainly self-reported. Thirteen studies reported a single condition [4–6,10,28,30–37], while the remaining 17 [7–18,27,29,38–40] reported the impact of more than one condition ranging from two to nine LTCs. Despite the high number of studies reporting more than one LTC, only seven [8,9,13,15,27,40,41] examined the impact of specified multiple LTCs on DFLE or HALE.
Twenty-five studies reported disability-related outcomes (DFLE, ALE, DLE), four HALE [11,27,30,36], and for one [12], the outcome was expected lifetime without long-standing illness. Disability was mostly measured as limitations in ADLs or in combination with IADLs and/or other functions (e.g., physical strength and endurance, mobility, communication). Health-related quality of life was typically measured using the Health Utilities Index Mark instrument towards the estimation of HALE or self-rated health status (excellent, very good, good, fair, poor).
## Effect of LTCs on health expectancy
We report the two LTCs for which meta-analysis was deemed feasible (diabetes, respiratory diseases). We report in brief the remaining conditions (cardiovascular disease, hypertension, cerebrovascular disease, cancer, arthritis, sensory loss, dementia and cognitive impairment, depression) with more detail provided in S2 Results.
## Diabetes
Nineteen studies [4,5,8,10–18,27,29,31,34,36,38,41] (Table 2) examined the impact of diabetes on health expectancy. Four out of the 19 studies (representing five population samples) provided estimates of SEs or $95\%$ CI of DFLE at age 65 and could be included in a meta-analysis. The meta-analysis showed individuals at age 65 without diabetes gain on average 2 years disability-free compared to those with diabetes (pooled overall mean difference in DFLE = 2.28 years, $95\%$ CI: 0.57–3.99, $p \leq 0.01$, I2 = $96.7\%$). Women gained a slightly higher number of years (pooled mean difference in DFLE = 2.51 years, $95\%$ CI: -0.28–5.31, $p \leq 0.01$, I2 = $96.6\%$) than men (pooled mean difference in DFLE = 2.06 years, $95\%$ CI: -0.24–4.36, $p \leq 0.01$, I2 = $96.4\%$; Fig 2). The funnel plot indicated potential publication bias (S1 Fig). Results of the Egger test for small-study effects suggested this is unlikely to be problematic ($$p \leq 0.10$$). Meta-analysis of LE estimates for the above four studies was not possible due to insufficient data.
**Fig 2:** *Meta-analysis of mean difference in disability-free life expectancy (years) of individuals without diabetes compared to those with diabetes (based on data from 4 out of 19 studies reporting the impact of diabetes on disability-free life expectancy; Hu et al 2019 reported estimates for two population samples, A = in 1990, B = in 2016).* TABLE_PLACEHOLDER:Table 2 Six additional studies [10,11,14,15,18,36] evaluated the effect of diabetes at age 65. Of these, three [11,18,36] focussed on elimination of diabetes, reporting small gains in LE (range of gain in LE: 0.10 to 1.8 years), and in two studies [18,36], the gain in DFLE years was greater than that in LE signifying a compression of disability after diabetes elimination. Results of the nine studies reporting DFLE at other ages suggest a similar pattern with stronger impact reported for younger ages and some variation by gender (details provided in S2 Results). Most of the studies therefore concluded that elimination of diabetes would result in a compression of disability.
## Respiratory diseases
Nine studies examined the impact of respiratory diseases (including COPD, bronchitis, emphysema, asthma) on health expectancy (Table 3), of which three -representing four population samples- were included in a meta-analysis. Pooled results indicated that at age 65 individuals without respiratory diseases gain on average 1.5 years disability-free compared to those with respiratory diseases (pooled overall mean difference in DFLE = 1.47 years, $95\%$ CI: 0.77–2.17, $p \leq 0.01$, I2 = $79.8\%$). Men gained a similar number of years (pooled mean difference in DFLE = 1.40 years, $95\%$ CI: 0.50–2.30, $p \leq 0.01$, I2 = $77.7\%$) to women (pooled mean difference in DFLE = 1.54 years, $95\%$ CI: 0.35–2.73, $p \leq 0.01$, I2 = $82.1\%$; Fig 3). The funnel plot indicated potential publication bias (S2 Fig) but results of Egger’s test for small-study effects suggested bias is not problematic ($$p \leq 0.56$$). Meta-analysis of LE estimates for the above three studies was not possible due to insufficient data.
**Fig 3:** *Meta-analysis of mean difference in disability-free life expectancy (years) of individuals without respiratory diseases compared to those with respiratory diseases (based on data from 3 out of 9 studies reporting the impact of respiratory diseases on disability-free life expectancy; Hu et al 2019 reported estimates for two population samples, A = in 1990, B = in 2016).* TABLE_PLACEHOLDER:Table 3 The remaining six studies [11,13,14,16–18] examining the impact of respiratory diseases reported estimates at 55, 60, and 65 years. Comparison of the difference in LE and DFLE between those without and with the LTC was available for five studies, and indicated that respiratory diseases had a greater impact on DFLE than LE in women in three of the studies [13,16,17] analysing mostly post-2000 cohort data (LE gain, range: 1.85–6.5 years; DFLE gain, range: 7.6–13.5 years). No substantial difference was observed for women in the other two studies [11,14], whereas results for men were mixed.
## Other health conditions
Twelve studies (seven cross-sectional and five longitudinal) assessed the impact of cardiovascular diseases on health expectancy at different ages [8,10–14,16–18,29,30,40] (Table 4). These studies show mixed evidence as to whether eliminating cardiovascular disease would result in compression or expansion of disability, with variation by age and gender. Four cross-sectional [11,16,17,27] and two longitudinal [13,15] studies assessed the effect of hypertension on health expectancy at various ages (Table 5). All but one [15] suggested that elimination of hypertension would lead to substantial gains in LE and DFLE resulting in a compression of disability. Six studies [11,12,16,17,39,40] evaluated the impact of cerebrovascular diseases on health expectancy (Table 6) with a further six on stroke specifically [8,10,13,14,28,32] (Table 7). While there was no clear evidence for compression of disability from eliminating cerebrovascular disease (Table 6), there was for stroke (Table 7). Ten studies evaluated the impact of cancer (Table 8) [8,11,12,16–18,27,29,38,40]. Apart from one [17], all reported that cancer had a major impact on LE, and a lesser impact on DFLE reduction; elimination would therefore likely result in an expansion of disability. Seven studies [6,10,13,14,38] examined the effect of arthritis [11,39] on health expectancy (Table 9). Arthritis was associated with a small loss of LE, generally greater for women. All studies reported a greater effect on DFLE than LE, resulting in a compression of disability if eliminated. Four studies [9–11,39] reported the impact of hearing impairment of which two [9,10] also included the impact of visual impairment on LE and DFLE (Table 10). Hearing loss/impairment had a small impact on LE and a greater impact on DFLE reduction [10,11]. A similar pattern was observed for visual impairment although the impact was slightly greater than that of hearing impairment [10,11]. Five studies examined the impact of dementia [14,33,35] or cognitive impairment [10,13] (Table 11). These conditions generally resulted in shorter LE, generally greater for women than men, but with greater effects on DFLE. Four studies [7,8,13,37] examined the impact of depression on LE and DFLE (Table 12). Depression and emotional problems had a greater impact on reduction of DFLE (or HALE in one study [37]) than LE although differences in DFLE between men and women were not consistent across studies (see S2 Results for full details).
## Ranking of LTCs based on studies assessing multiple conditions
Sixteen of the included studies assessed multiple LTCs for ages between 55 and 70 in different clusters. As cross-sectional studies that derived LTCs from cause of death data will underestimate the impact of non-fatal conditions such as arthritis, we report findings by type of study.
Of the nine cross-sectional studies assessing the impact of multiple LTCs, six found the greatest impact on DFLE was for elimination of heart disease and other circulatory diseases [11,12,16–18,29]. Diabetes and, to a lesser extent, cancer featured among the top conditions that would generate gains in DFLE or in the proportion of years lived free of disability if eliminated [12,14,16–18,27]. Of the seven longitudinal studies assessing the impact of multiple LTCs, stroke followed by diabetes were the conditions with the greatest impact on LE loss in the majority of studies assessing those LTCs [6,10,15,38] (stroke, range: 2.7 to 15.1 years; diabetes, range: 4.0 to 13.5 years). Three found the greatest effect on DFLE was also attributed to stroke for both genders.
We identified six studies reporting global YLDs estimates [42–47] and one study reporting HALE at birth [48] for multiple relevant LTCs. Nine LTCs included in the systematic review also featured among the most common causes of global YLDs (ranking was not available for all LTCs included in the review). *Studies* generally reported YLDs in thousands, for all ages, and both sexes combined except one study that reported estimates for men and women separately [42]. Although the Global Burden of Disease (GBD) studies have used slightly different methods to calculate YLDs across the years, S3 Fig shows the ranking of these nine LTCs from 1990 to 2019 for comparison (lower rankings indicate greater disability attributed to the condition) and S4 Fig the number of YLDs in thousands for each relevant LTC. Depressive disorders were reported in five studies [42,44–47] and remained among the top five causes of global YLDs from 1990 to 2017. Diabetes has been consistently among the top ten causes of YLDs globally showing an increasing trend in the rankings (higher YLDs) in the same studies, followed by COPD which has been ranked between 5th and 14th cause of YLDs between 1990 and 2017 [42,44–47]. Hearing loss was among the top causes in two studies [46,47] ranging from 5th to 13th most common cause of YLDs. Increasing global trends in YLDs were also observed for osteoarthritis in the last two decades ranging from 17th place in 1990 to 11th in 2010 and 12th in 2016 [42,44–47]. Stroke [42,44] and other cerebrovascular diseases [45] were reported in three studies and have also been associated with increased YLDs in recent years. Dementia [45–47] and ischaemic heart disease [44,45,47] were among the leading causes of YLDs in three studies shifting from rankings near the 30th places to the mid-20s in the period between 1990 and 2016, again indicating increased YLDs for those conditions. YLDs for cardiovascular diseases doubled from 17.7 million (CI: 12.9 to 22.5 million) to 34.4 million (CI: 24.9 to 43.6 million) over the period 1990–2019 [43].
## Discussion
This systematic review provides the first comprehensive evidence synthesis of the effect of a range of LTCs on disability-free or healthy, and total life expectancy. For two LTCs meta-analyses could be performed, resulting in estimated gains of 2 years disability-free at age 65 for those without compared to those with diabetes (pooled mean difference in DFLE from four studies = 2.28 years, $95\%$ CI: 0.57–3.99, $p \leq 0.01$, I2 = $96.7\%$), and gains of 1.5 years disability-free at age 65 for those without compared to those with respiratory disease (pooled mean difference in DFLE = 1.47 years, $95\%$ CI: 0.77–2.17, $p \leq 0.01$, I2 = $79.8\%$). Narrative synthesis of remaining studies suggested that many LTCs have a greater effect on DFLE/HALE than LE, suggesting that elimination of certain conditions including stroke, diabetes, hypertension, and arthritis may result in compression of disability. Evidence for the remaining conditions (e.g., respiratory, cancer, dementia) is mixed.
Diabetes is known for multiple vascular and neuropathic complications and increased risk of disability including difficulties with the ability to carry out ADLs and loss of mobility [49]. Findings from longitudinal data included in this review, and taking into account incidence and recovery from disability, also show that individuals with diabetes have an earlier onset of disability compared to those without the condition, and a lower probability of recovering from functional limitations [4,31]. Although there is some evidence from US cohorts for gains in DFLE in people with diabetes in the past 20 years [5], included studies highlight that diabetes still has a substantial impact on disability-free years. Estimates from the Global Burden of Disease Study [44] also place diabetes among the top ten leading causes of increased years with disability in recent decades. With population ageing, the prevalence of diabetes will also increase and so will the need for disability-related health resources.
Although not all of the included studies assessed all the LTCs considered in the systematic review, data from sixteen studies assessing multiple LTCs allowed a crude ranking of these conditions in terms of their impact on DFLE and LE. Stroke had the strongest impact on DFLE (range in years: 4.8–24.2) and it was greater than that on LE, especially when individuals were initially classified as ADL/IADL disabled. Similarly, hypertension and cardiovascular disease were among the LTCs with the greatest effect on DFLE and LE with most studies signifying a compression of disability after disease elimination. Arthritis was the only non-fatal condition within the top five LTCs affecting DFLE more than LE. Arthritis was associated with a small loss of LE which was generally greater in women, and studies indicated that elimination of the condition would result in one of the greatest gains in DFLE/HALE. Although not directly comparable due to different estimation methods, the identified DALY-related studies also show increasing trends in global YLDs for stroke, cardiovascular diseases, and osteoarthritis which further supports the high disability burden linked to these LTCs and the potential gains from their prevention and improved management.
Evidence suggests that multimorbidity predicts future functional decline, with greater decline in people with higher number of LTCs and greater disease severity [50]. The few studies that assessed the effect of comorbidities in this review show a rather complex picture where multiple LTCs appear to generally reduce DFLE and occasionally LE compared to those without the conditions, but the effect is not necessarily additive and seems to vary by the combination of LTCs studied. In recent years, there has been a movement toward research of clusters of chronic conditions and implementation of services based on comorbidity/multimorbidity. However, there is limited evidence from longitudinal data on the disease combinations that are more or less disabling, especially in terms of DFLE, despite projections indicating that complex multimorbidity (four or more diseases) will double in the next 15 years and gains in LE will be spent mostly with complex multimorbidity [2]. Understanding how multimorbidity combinations relate to disabled and disability-free life expectancy is therefore an important step towards planning for appropriate future health and social care provision and designing interventions.
This review has several strengths and limitations. We performed comprehensive search strategies to identify published research including major electronic databases and reference list checking. Along with synthesising evidence narratively across the LTCs, we conducted meta-analyses for a subgroup of studies. However, this was feasible only for two LTCs (diabetes, respiratory diseases) and only for DFLE as the outcome. We also note it is likely the presented Egger tests and funnel plots were underpowered to detect small-study effects bias, while the high heterogeneity observed could not be further explored by subgroup analysis or meta-regressions given the limited number of included studies. Underreporting of HE estimates within the primary studies, particularly standard errors or $95\%$ confidence intervals for LE and DFLE estimates was a major limitation that prevented further quantitative analyses for most of the LTCs. Other methodological differences such as the small number of studies for some LTCs, different outcomes (DFLE versus HALE) reported at different ages (e.g., 55, 60, 65) also meant that many studies did not meet the predefined criteria for meta-analyses. We assessed the quality of the studies, but we had to supplement a standard tool since no assessment tool is available for HE studies. LTCs were mostly self-reported, which means that people with undiagnosed conditions would be classified as not having the LTC, thereby potentially underestimating the difference in DFLE between people with and without the LTC. Guidelines for the identification and management of conditions, such as diabetes and hypertension, have also changed over the decades covered in the included studies which may have also biased DFLE and LE differences between those with and without the LTC. Disability measurement was also largely based on self-report and agreement with objective measurements may vary by LTC status.
Most of the LTCs with the greatest effect on DFLE reviewed here are strongly associated with unhealthy lifestyles. Early interventions to reduce known risk factors such as smoking, physical inactivity and obesity in younger adults could prevent, delay or significantly reduce disability and allow individuals to live independently with minimal or mild disability in older age. Healthier lifestyles including healthy dietary patterns have been associated with reduced risk of many chronic conditions including diabetes, cardiovascular disease and dementia, and there are also findings supporting reduced risk of developing self-reported disability [51]. An intensive lifestyle intervention targeting weight loss and improved fitness was related to a $50\%$ slower decline in physical disability among overweight adults with diabetes and an increase in number of disability-free years compared to a group receiving diabetes support and education [52,53]. Several studies have also shown that pain and disability improve with short-term exercise programmes in patients with osteoarthritis [54]. Many older adults perceive health as functional capability rather than physical fitness, with the ability to master daily life as a vital component [55]. Therefore, improving the number of disability-free years over time is as important as focusing on the prevention and management of LTCs, to allow older adults to be able to do the things to which they attribute value. A recent review [56] indicated that changing both personal and contextual factors can help older adults engage in ADL and IADL. Interventions that included exercise, cognitive behavioural therapy, problem-solving and environmental modifications as the main components were likely to be more effective at reducing disability [56].
Our novel evidence synthesis of the impact of LTCs on DFLE at older ages has identified a number of LTCs that, if eliminated, have the potential to make substantial gains in DFLE. Further studies are needed to provide stronger evidence for many of the LTCs considered, as well as combinations of LTCs to assess specific multiple conditions. However, all further studies should ensure reporting of the uncertainty around estimates of the gain in both DFLE/HALE and LE, to enable further meta-analyses.
## Transfer Alert
This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.
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|
---
title: 'Understanding the role of traditional healers in the HIV care cascade: Findings
from a qualitative study among stakeholders in Mwanza, Tanzania'
authors:
- Dunstan J. Matungwa
- Richie Hong
- Jeremiah Kidola
- Daniel Pungu
- Matthew Ponticiello
- Robert Peck
- Radhika Sundararajan
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021224
doi: 10.1371/journal.pgph.0000674
license: CC BY 4.0
---
# Understanding the role of traditional healers in the HIV care cascade: Findings from a qualitative study among stakeholders in Mwanza, Tanzania
## Abstract
Tanzania is HIV-endemic, with $5\%$ prevalence. However, less than half of Tanzanians are aware of their HIV status, and only $75\%$ of adult Tanzanians living with HIV are on antiretroviral therapy (ART). Informal healthcare providers, such as traditional healers, frequently serve as the first line of healthcare in Tanzania. How traditional healers interact with people living with HIV (PLWH) remains unknown. This study sought to understand gaps in HIV care and explore how traditional healers interface with PLWH along the HIV care cascade. We conducted a qualitative study in Mwanza, Tanzania, between November 2019 and May 2020. We invited 15 traditional healers, 15 clients of traditional healers, 15 biomedical healthcare facility staff, and 15 PLWH to participate in a single qualitative interview. Two community focus groups were held with eight male and eight female participants. Participants were 18 years of age or older. Individual experiences with traditional healers and biomedical healthcare facilities, as well as perceptions of traditional healers with respect to HIV care, were explored through interviews. Using a content-analysis approach, codes were grouped into a framework that characterized how traditional healers engage with PLWH throughout the HIV care cascade. PLWH engaged with traditional healers throughout the HIV care cascade, from pre- to post-HIV diagnosis. Traditional healers were described in some cases as facilitating HIV testing, while others were described as delaying testing by providing traditional treatments for HIV symptoms. Traditional medications were frequently used concurrently with ARTs by PLWH. There was concern that healers contributed to ART nonadherence as some PLWH used traditional therapies in search of a “cure” for HIV. Our findings suggest that traditional healers interact with PLWH throughout the HIV care continuum and that collaboration between traditional healers and biomedical healthcare professionals and facilities is needed to improve HIV treatment outcomes.
## Introduction
Human immunodeficiency virus (HIV) is endemic in Tanzania. As of 2018, the country had an HIV prevalence of $5\%$ and approximately 1.4 million people living with HIV (PLWH) [1,2]. The World Health Organization (WHO) and the Tanzanian Ministry of Health advocate for the “test-and-treat” HIV control strategy, which is predicated on access to HIV testing and antiretroviral therapy (ART) treatment to identify and reduce viral loads in PLWH [3]. Despite Tanzania’s high HIV prevalence and the international push to increase HIV testing [4], in 2018 still less than $50\%$ of Tanzanians were aware of their HIV status [2], and only $75\%$ of adult PLWH in Tanzania were on ART [5]. As such, Tanzania—like much of sub-Saharan Africa—is lagging behind the HIV treatment targets set out by the Joint United Nations Program on HIV/AIDS (UNAIDS) to end the HIV epidemic: $95\%$ of PLWH know their HIV status; $95\%$ of people who know their status are on treatment; and $95\%$ of people on treatment have undetectable viral load by 2030 [6]. To achieve these goals, increasing entry in and retention at each step of the HIV care cascade is necessary.
In low resource settings, PLWH face numerous challenges in entering and remaining in HIV care. Previous studies have reported that some of these challenges include shortage of healthcare workers to attend patients, as well as structural barriers to accessing treatment such as long distance to HIV clinical facilities, high transportation costs, and hours of waiting for services [7–9]. In addition, PLWH may avoid clinics due to perceived stigma and discrimination by healthcare workers, as well as a lack of trust in biomedical care [10,11]. These factors have driven disengagement from care and suboptimal adherence to ART regimens [12]. Implementation scientists have advocated for novel, community-based programs as one approach to engaging with PLWH in these settings [13]. However, in order to effectively engage all PLWH, the HIV care cascade must be embedded in socially, culturally, and structurally-informed programs for initiating and maintaining care.
Traditional healers have been proposed as a community-based strategy for enhancing the HIV care cascade [14,15]. Collaboration with traditional healers could be particularly impactful in sub-Saharan Africa where the WHO estimates that $80\%$ of the population seeks their services on a regular basis [16]. Tanzania’s health sector, like in other sub-Saharan African countries, is pluralistic, with people receiving treatment from traditional healers in conjunction with, or in place of, biomedical institutions [17–19].
Evidence suggests that PLWH use the traditional health sector at all stages of the HIV care continuum [20]. However, there is no consensus on how the use of the traditional healthcare impacts engagement with HIV services. According to some studies in Tanzania [21,22], and elsewhere in sub-Saharan Africa [23–30], traditional healers serve as a bottleneck in the HIV care cascade, delaying HIV diagnosis and treatment and decreasing adherence to ART. Other studies found that PLWH who used traditional herbal medicines did so to alleviate medication side effects and did not demonstrate poor ART adherence [31]. Studies have also illustrated that traditional healers provide tailored, individualized care for PLWH [32], which improves the quality of life among their clients living with HIV [18]. The improved quality of life has been attributed to traditional healers’ provision of psychosocial support and a familiar cultural context for healthcare delivery [33]. We, and others, have shown that when trained by healthcare professionals, traditional healers are both willing and able to facilitate HIV testing among their clients [34–36].
Therefore, while there is no clear consensus on how traditional healers impact engagement with the HIV care cascade, a nuanced understanding of these complex relationships is needed to develop interventions to improve uptake of HIV services. The goal of this study was to shed light on this area of controversy through an in-depth examination of how traditional healers engage with PLWH at all stages in the HIV care cascade in an HIV endemic region. We consider the HIV care cascade for PLWH to begin from the pre-diagnosis phase and continue through linkage to HIV clinical care, ART use, and retention in HIV care.
## Research design and setting
To collect data from key stakeholders in the HIV care cascade, we conducted a qualitative study that included in-depth interviews and focus group discussions [37]. The study was conducted in Mwanza, a port city on the shore of Lake Victoria, and the second largest city in Tanzania. People in Mwanza and the neighboring regions (of Simiyu, Shinyanga, and Tabora), where the Sukuma and Nyamwezi ethnic groups are dominant, have a long, rich, and popular history of practicing and using traditional and alternative medicine [38,39]. Like in many other parts of the country, people in Mwanza city depend on and use biomedical healthcare services, alternative medicine, and traditional medicine [17–19]. The city also has public and private biomedical clinics providing free HIV testing, among other services. Traditional healers—herbalists, birth attendants, spiritualists, and bone setters [40]—are found throughout the city and across villages. Traditional healers’ operations in Tanzania are overseen and regulated by the Traditional and Alternative Medicines Act (No. 23 of 2002) [41]. One of the key provisions of this law is that every traditional healer must be formally registered with the Traditional and Alternative Health Practice Council under the Ministry of Health [41].
## Sampling and recruitment
Using purposive sampling, we identified and recruited participants from five key stakeholder groups in the HIV care cascade: [1] HIV clinic staff, [2] PLWH, [3] traditional healers, [4] adult people receiving care from traditional healers, and [5] adult community members (women and men). Eligible participants were aged ≥18 years, members of one of the five aforementioned stakeholder groups who provided a written informed consent, and agreed to be audio-recorded. With the assistance of clinicians in charge, HIV clinic staff—medical doctors, nurses, and pharmacists ($$n = 15$$)—were identified and recruited from five HIV clinics in Mwanza city. PLWH ($$n = 15$$), like HIV clinic staff, were also identified and recruited at the same five HIV clinics. Traditional healers ($$n = 15$$) were identified from a list of registered and currently practicing healers from the Mwanza Regional Traditional and Alternative Medicine Coordinator. Traditional healers were contacted by phone and invited to take part in the study. Adults receiving treatment or care from traditional healers ($$n = 15$$) were identified and recruited at the traditional healers’ delivery facilities following completion of their treatment or care. We recruited in this manner to avoid the impression that participating in the study was a condition for receiving treatment or care from the traditional healer.
The sample size was set at 15 interviews per group for HIV clinic staff, PLWH, traditional healers, and adults receiving care from traditional healers. Previous studies show that, within homogenous groups, a sample size of nine interviewees is adequate to achieve code saturation [42]. A total of 60 semi-structured, one-hour interviews were conducted. Adult community members (eight men and eight women) were recruited from a single community on the outskirts of Mwanza city—with the help of the community leaders—to participate in two focus groups. Lasting approximately one hour, focus group discussions were intended to elucidate an understanding of common community perspectives on traditional healing practices and HIV. Focus group discussion guides followed a similar structure as the individual interview guides. Each group consisted of eight participants of the same gender. We conducted two focus groups because prior research has shown that two to three focus groups are sufficient for generating and identifying meaningful themes [43].
## Data collection
Between December 2019 and June 2020, stakeholders were invited to participate in either a single semi-structured interview or a focus group discussion. All invitees agreed to participate. Four Tanzanian research assistants (two males and two females) and a study coordinator (co-author DP)—all fluent in Kiswahili (the Tanzanian national language) and English—conducted interviews and focus groups. Prior to data collection, the research assistants and the study coordinator were oriented to the research question and study objectives, as well as given a refresher training on how to collect qualitative data using semi-structured interview and focus group discussion guides.
Interviews and focus groups were audio recorded and conducted in Kiswahili in a private location obtained at or around the site where participants were recruited. Semi-structured guides were used to ensure that topics were consistent across interviews and focus groups (S1 Text). The main interview topic was to understand how traditional healers engage with PLWH prior to and after HIV diagnosis. Before fieldwork, semi-structured guides were pilot tested with one member of each stakeholder group. Data collected during the pilot test were excluded from the study dataset.
## Data management and analysis
Interviews and focus group discussion transcripts were analyzed as part of a single dataset. Professional Tanzanian translators fluent in both Swahili and English transcribed the audio recordings verbatim in Kiswahili, then into English for analysis. Author DJM reviewed all Kiswahili to English transcripts for quality and translational integrity. Three authors (RH, MP and RS) independently reviewed the transcripts and created a coding scheme relevant to traditional healers’ engagement with PLWH prior to and following HIV diagnosis. A phenomenological framework was used for data analysis as we sought to understand participant perspectives and experiences within the contexts of their own worldviews [44,45]. Codes were produced using an open-coding approach and refined through constant comparison [46]. The three authors discussed and agreed on the final coding scheme. Final codes were grouped into themes and analyzed using content analysis approach [47]. Finally, representative quotations were selected to illustrate the study findings.
The study team used diverse strategies to ensure the trustworthiness of this qualitative study across the quality criteria in qualitative research: credibility, transferability, dependability, confirmability, and reflexivity [48,49]. Credibility was ensured by using three strategies: prolonged engagement, triangulation, and persistent observation. With regard to prolonged engagement, the study coordinator and the research assistants asked the participants the main and follow-up questions and encouraged them to support their responses with concrete examples of situations they were describing. With regard to triangulation, data for this study were collected from diverse groups of stakeholders (data triangulation), using interviews and focus group discussions (method triangulation), and were coded, analyzed and interpreted by authors RH, MP, and RS (investigator triangulation). And with regard to persistent observation, authors RH, MP, and RS read and re-read the data transcripts to learn about how traditional healers engage with PLWH before and after HIV diagnosis.
To enable the transferability of this study, we have described the study site and setting in Mwanza city, Tanzania, where use of traditional medicine is in common despite the presence of modern health facilities. We have also described the study participants’ demographic features, sampling procedure, recruitment strategies, inclusion and exclusion criteria, and the sample size. Dependability and confirmability of findings were also ensured by having three authors (RH, MP, and RS) code the data. Thus, themes emerging from the data were identified by separate authors and endorsed by the three authors through consensus. Lastly, an iterative analytical strategy allowing for the reflexive evaluation of data was followed, where authors acknowledged and challenged the role of one another’s positionality in data coding, analysis, and interpretation [48,49].
## Research ethics approvals
This study received ethics approvals from the Medical Research Coordinating Committee (MRCC) in Tanzania (Protocol no. NIMR/HQ/R.8a/Vol. IX/2136) and Weill Cornell Medicine (Protocol no. 19–04020274). This work was funded by the Weill Cornell Kellen Faculty Fellowship. Funders had no role in the research design or decision to publish this work. Clinicians in charge of HIV clinics and traditional healers provided consent for recruiting patients at their clinics. All participants provided a written consent to participate in the interviews or focus groups. A copy of the signed consent form was given to the participant for their records. All participants received de-identified study numbers to maximize confidentiality. Since the nature of the focus groups impedes researchers from guaranteeing complete confidentiality (as some participants may disclose the content of the interview or focus group with non-participants), participants were urged to respect fellow participants’ privacy and to not share the content of the discussions with non-participants [44]. Each participant received 10,000 Tanzanian Shillings (~4 USD) as a compensation for their time to participate.
## Characteristics of study participants
Overall, 60 semi-structured interviews were conducted among HIV clinic staff ($$n = 15$$), PLWH ($$n = 15$$), traditional healers ($$n = 15$$), and clients of traditional healers ($$n = 15$$). Two focus groups were conducted with community members: adult men ($$n = 8$$) and women ($$n = 8$$). Summary characteristics of participants are shown in Table 1. The majority of participants were male ($$n = 42$$, $55.7\%$). Traditional healers were older than their patients, HIV clinic staff, and PLWH. Most traditional healers and their clients had primary or secondary school education while most of HIV clinic staff had at least a post-secondary school education certificate (required for positions such as nursing jobs).
**Table 1**
| Characteristics | Traditional Healers(n = 15) | Clients of Traditional Healers (n = 15) | HIV clinic staff(n = 15) | PLWH (n = 15) | Male focus group (n = 8) | Female focus group (n = 8) |
| --- | --- | --- | --- | --- | --- | --- |
| Female | 3 (20%) | 5 (33.3%) | 10 (66.7%) | 8 (53.3%) | 0 (100%) | 8 (100%) |
| Age in years, median (IQR) | 50 (42–64) | 36 (29–45) | 47 (30–53) | 47 (40–55) | 28 (26–41) | 35.5 (27.5–44.5) |
| Highest level of education | Primary, 9 (60%)Secondary, 6 (40%) | None, 1 (6.7%)Primary, 7 (46.7%)Secondary, 4 (26.7%)Diploma, 1 (6.7%)University, 2 (13.3%) | Certificate, 8 (53.3%)Diploma, 5 (33.3%)University, 2 (13.3) % | None, 1 (6.7%)Primary, 5 (33.3%)Secondary, 8 (53.3%)Diploma, 1 (6.7%) | Primary, 2 (25%)Secondary, 3 (37.5%)Certificate, 1 (12.5%)Diploma, 2 (25%) | Primary, 5 (62.5)Secondary, 2 (25)Certificate, 1 (12.5) |
| Occupation | Traditional healer, 15 (100%) | Business owner, 4 (26.7%)Skilled laborer, 7 (46.7%)Unskilled laborer, 1 (6.7%)Unemployed, 1 (6.7%) | Nurse, 13 (86.7%)Pharmacist, 1 (6.7%)Medical doctor, 1 (6.7%) | Business owner, 10 (66.6%)Skilled laborer, 1 (6.7%)Unskilled laborer, 3 (20%)Unemployed, 1 (6.7%) | Business owner, 4 (50%)Skilled laborer 2 (25%)Unskilled laborer 2 (25%) | Business owner, 4 (50%)Skilled laborer, 2 (25%)Unskilled laborer, 2 (25%) |
## Overview of the results
We categorize traditional healers’ engagement with PLWH in two periods—prior to and following HIV diagnosis—and describe the variety of forms it takes in each period. Our findings show that, before HIV diagnosis, traditional healers’ engagement with PLWH takes three forms: [1] traditional healers have knowledge of HIV and sometimes refer PLWH for voluntary HIV testing and counseling at HIV clinic facilities, [2] consulting traditional healers is a regular step in the care cascade for PLWH, and [3] patients’ decision to attend traditional healers delays HIV testing. After HIV diagnosis, traditional healers’ engagement with PLWH takes four forms: [1] PLWH seek out traditional medication for HIV cure, [2] PLWH deny HIV diagnosis and return to traditional healers for continued treatments, [3] PLWH stop visiting traditional healers and exclusively use biomedical healthcare services, and [4] PLWH use traditional treatment and ART concurrently. This figure summarizes traditional healers’ engagement with PLWH before and after HIV diagnosis (Fig 1).
**Fig 1:** *Schematic representation of traditional healers’ (TH) engagement with people living with HIV (PLWH) before and after HIV diagnosis.*
## Traditional healers engage with PLWH prior to HIV diagnosis
Based on participants’ narratives, traditional healers’ engagement with PLWH prior to HIV diagnosis takes three forms. First, traditional healers refer their clients to healthcare facilities for HIV testing. Participants described that most traditional healers are knowledgeable about HIV symptoms and that when they observe such symptoms in their clients, they refer them for HIV testing. In this way, rather than being the barrier, traditional healers facilitate HIV diagnosis, the first and crucial step in the HIV care cascade.
Second, participants reported that people, irrespective of their HIV status, regularly seek treatment and care from traditional healers. Healthcare facility staff were familiar with this process, and they regarded it as a normal part of healthcare seeking, as most people in Tanzania utilize multiple therapeutic modalities to manage their health.
Third, participants reported that, in some cases, treatment and care seeking from traditional healers creates delays to HIV testing and diagnosis. On the one hand, this happens because patients and their families believe that certain symptoms reflect traditional illnesses, and therefore require traditional treatments. Some participants reflected on how beliefs in the power of traditional and faith healing practices delays HIV testing and diagnosis.
Our data also indicate that delays to HIV testing and diagnosis occur because HIV is outside the purview of traditional healing, and in some cases, healers do not recognize its signs and symptoms. For example, one PLWH explained that his HIV symptoms were initially misinterpreted by the traditional healer as yellow fever: Prolonged traditional treatment bolsters delays in HIV testing and diagnosis, which in some cases can lead to suffering and death. Another participant described the experience of a friend as follows:
## Traditional healers engage with PLWH following HIV diagnosis
Based on participants’ narratives, we found that traditional healers’ engagement with PLWH after HIV diagnosis takes four forms. First, PLWH may visit traditional healers in pursuit of a cure to HIV because of the belief in the preternatural etiology of their HIV infection or increased efficacy of traditional medicine. A HIV clinic nurse described the experience of a patient who sought a cure from a traditional healer: In another case, a PLWH disengaged from HIV care after visiting a healer who promised a cure for HIV, and he believed that he no longer needed to continue ART: In some cases, following a new diagnosis PLWH may deny their status and seek care from traditional healers instead of health facilities. Respondents reported that PLWH may deny their HIV diagnosis because they believe that they are in fact suffering from a spiritual illness.
Some PLWH may also mistrust biomedicine in general, so they do not accept the results of the HIV tests and recommendations provided by HIV clinic staff. Instead, they may hope that symptoms could be attributed to “traditional” etiologies, and thereby cured via traditional therapies.
Third, in some cases after HIV diagnosis, some PLWH choose to use biomedical care exclusively without interacting with traditional healers again.
Finally, following HIV diagnosis, some PLWH may receive concurrent treatment and care from both healthcare facilities and traditional healers. Based on the participants’ narratives, two underlying factors drive this phenomenon. The first factor is that some PLWH on ART use traditional medicine as way to gain relief from HIV symptoms or ART side effects.
The second factor is that other PLWH on ART receive care from traditional healers for psychosocial support regarding their HIV diagnosis and care. This psychosocial support helps PLWH, among other things, with ART adherence and remaining in HIV care.
## Discussion
Our findings clarify how traditional healers impact the HIV care cascade, showing that PLWH interact with traditional healers throughout the entire cascade. Prior to HIV diagnosis, traditional healers [1] assist in the identification of HIV symptoms and encourage their clients to get tested, [2] are a normal step in the HIV care cascade, and neither facilitate nor delay HIV testing, and [3] provide traditional therapies for the treatment of their patients’ symptoms. We found that patients and their families make the decision to pursue traditional therapies for illness symptoms, which can delay HIV testing and care, while in other cases healers failed to recognize signs and symptoms of HIV infection. After HIV diagnosis, traditional healers [1] could siphon patients away from biomedical treatment by promising a cure for HIV or providing treatment to PLWH denying their diagnosis, [2] may not receive PLWH, as the latter prefer exclusive use of biomedical facilities for HIV care, [3] may offer traditional treatment concurrently with ART; and [4] provide psychosocial support for PLWH to remain in HIV care. Traditional healers have the potential to impact engagement with HIV care in many different ways including facilitating engagement with HIV services.
Our findings point to a chasm between healthcare facilities’ staff perceptions and the reported lived experiences of HIV patients who receive care from traditional healers. Healthcare facility staff frequently reported that traditional healers act as a barrier to HIV testing and HIV medication adherence, with anecdotes of patients arriving at healthcare facilities in late-stage HIV with poor outcomes. Others shared stories they had heard from their colleagues, although they had no personal experience working with such patients. This tendency shifts blame to traditional healers for delaying care and contributing to morbidity and mortality [21]. Conversely, our data suggests that traditional healers do not instruct patients to avoid HIV testing or treatment. Instead, individual patients—often in consultation with some of their family members—voluntarily choose traditional medicine to treat their ailments.
Staff at healthcare facilities also expressed concerns that traditional healers could persuade PLWH to disengage with ART by purporting to cure HIV. We also noted accounts of PLWH using traditional medicine for treatment and care while denying their HIV diagnosis, even though no traditional healer participating in this study endorsed having a cure for HIV. The driver of ART disengagement in these cases appears to lie with the individual PLWH and their families who choose where and how to receive treatment and care. The experiences of the healthcare facility staff contrast with traditional healers, who all reported that HIV required biomedical diagnosis and treatment and consequently, referred clients to healthcare facilities to receive testing. Our findings are consistent with previous studies, which found that $75\%$ of Tanzanian traditional healers recognized HIV symptoms and appropriately referred patients to biomedical care [50]. However, we note accounts from PLWH demonstrating that HIV is outside of the purview of traditional healing, and HIV symptoms have gone unrecognized. In Tanzania, traditional healers do not receive formal training or education on recognizing HIV symptoms, and they are not part of community HIV control programs.
Many PLWH reported exclusive healthcare facility utilization following HIV diagnosis, opting not to return to traditional healers. In contrast, traditional healers reported that they often provide supportive care for PLWH and that concurrent use of traditional medicine along with ART is common. Taken in conjunction with prior studies that have shown that patients do not disclose their use of traditional medications to biomedical facilities, lack of disclosure may be attributed to the stigma that healthcare facility staff have against traditional healers, or the fact that healthcare facility staff do not ask their patients if they seek out treatment and care from traditional healers [51]. Other studies have demonstrated similar negative perspectives of traditional medicine among biomedical healthcare facility staff who voice concerns with unstandardized treatments and possible drug interactions when used with biomedical medications [21]. The perspective that biomedicine and traditional medicine are incompatible may actually fuel the siphoning of patients away from biomedical healthcare facilities, as PLWH may feel compelled to choose one modality of treatment over another. In many cases, traditional healers may be preferred as they are more accessible in communities compared with biomedical healthcare facilities, where HIV clinical treatment facilities carry stigma [50]. We and others have also shown that patients choose to receive care from traditional healers instead of medical clinics due to trusting relationships with healers and the perception that healers provide more psychosocial support [34,36,52–54]. Modern medicine has also been criticized for failing to resolve all health problems, and thus, traditional medicine promises to fill the void in the biomedicine health care system [51].
Given that traditional healers interface with PLWH throughout the HIV care cascade, some have proposed formally integrating traditional healers into the HIV control programs [55–58]. Our data from all stakeholder groups, including healthcare facility staff, largely agree that improving HIV treatment and care for PLWH would require a functional collaboration between traditional healers and biomedical healthcare professionals and facilities. Healthcare facility staff and PLWH all recognize the prominent role traditional healers play in their communities. Because they are integrated and accessible in communities, traditional healers are able to reach a wider range of individuals than biomedical facilities. As such, traditional healers may be “trusted messengers” in their communities who could improve entry and retention in the HIV care cascade [59]. The current lack of collaboration between traditional healers and the biomedical system create spaces for PLWH to fall through the cracks and not get tested or treated for HIV [35]. Consequently, HIV service delivery in Tanzania could benefit from a formal collaboration between traditional healers and clinic-based medical providers.
This study has some limitations. First, we did not speak with any PLWH receiving care at traditional healers’ delivery facilities, and therefore may not have captured the perspectives of PLWH who are not in HIV care. Second, the sample of medical doctors among healthcare facility staff that we worked with is small. We therefore might have missed ideas from this important group in the HIV care cascade. Third, only healers registered with the Traditional and Alternative Health Practice Council were included in this study. The views of those healers who have not registered may vary from those registered. We also acknowledge that social desirability bias may have impacted participant responses. It is possible that healers and PLWH may have perceived interviewers as connected with biomedical care/providers, and therefore been inclined to endorse a strictly biomedical perspective on HIV. Finally, qualitative data are highly contextual, and therefore, further research is needed to evaluate if our findings are transferable to other contexts where traditional healers are commonly utilized.
## Conclusion
Traditional healers engage with PLWH throughout the HIV care cascade. For medically pluralistic regions such as Tanzania where people, regardless of their HIV status, use both traditional medicine and biomedicine, we should consider formally integrating traditional healers into HIV treatment and care as partners throughout the cascade. However, there is need for further studies to establish how best this integration can be practically implemented. In the meantime, there is a critical need for improving communication between healthcare facility staff and traditional healers to better serve PLWH. The differences in attitudes and beliefs shared between the healthcare facility staff, traditional healers, and PLWH may be driving forces behind why a formal, functional collaboration between traditional healers and healthcare facility staff has been elusive. As such, PLWH can fall through the cracks of a fragmented healthcare system and either fail to get diagnosed or treated. If traditional healers and healthcare facility staff can better understand each other, communicate, and work in solidarity, we will significantly improve PLWH treatment and outcomes.
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|
---
title: 'Prevalence and inequality in persistent undiagnosed, untreated, and uncontrolled
hypertension: Evidence from a cohort of older Mexicans'
authors:
- C. M. Dieteren
- O. O’Donnell
- I. Bonfrer
journal: PLOS Global Public Health
year: 2021
pmcid: PMC10021230
doi: 10.1371/journal.pgph.0000114
license: CC BY 4.0
---
# Prevalence and inequality in persistent undiagnosed, untreated, and uncontrolled hypertension: Evidence from a cohort of older Mexicans
## Abstract
Hypertension is the leading risk factor for cardiovascular diseases (CVDs) and substantial gaps in diagnosis, treatment and control signal failure to avert premature deaths. Our aim was to estimate the prevalence and assess the socioeconomic distribution of hypertension that remained undiagnosed, untreated, and uncontrolled for at least five years among older Mexicans and to estimate rates of transition from those states to diagnosis, treatment and control. We used data from a cohort of Mexicans aged 50+ in two waves of the WHO Study on Global AGEing and adult health (SAGE) collected in 2009 and 2014. Blood pressure was measured, hypertension diagnosis and treatment self-reported. We estimated prevalence and transition rates over five years and calculated concentration indices to identify socioeconomic inequalities using a wealth index. Using probit models, we identify characteristics of those facing the greatest barriers in receiving hypertension care. More than 60 percent of individuals with full item response ($$n = 945$$) were classified as hypertensive. Over one third of those undiagnosed continued to be in that state five years later. More than two fifths of those initially untreated remained so, and over three fifths of those initially uncontrolled failed to achieve continued blood pressure control. While being classified as hypertensive was more concentrated among the rich, missing diagnosis, treatment and control were more prevalent among the poor. Men, singles, rural dwellers, uninsured, and those with overweight were more likely to have persistent undiagnosed, untreated, and uncontrolled hypertension. There is room for improvement in both hypertension diagnosis and treatment in Mexico. Clinical and public health attention is required, even for those who initially had their hypertension controlled. To ensure more equitable hypertension care and effectively prevent premature deaths, increased diagnosis and long-term treatment efforts should especially be directed towards men, singles, uninsured, and those with overweight.
## Introduction
Hypertension is the leading global risk factor for cardiovascular diseases (CVDs) [1]. Worldwide, there are substantial gaps in diagnosis, treatment, and control of hypertension [2–7], signaling failures to prevent CVDs and avert millions of premature deaths [8]. In middle-income countries, where hypertension prevalence is rising [9, 10], populations are ageing, and health systems are straining to cope with the double burden of disease, gaps in diagnosis and management of hypertension [5, 11–13] can take a heavy toll on population health.
In high-income countries, hypertension tends to be more prevalent among lower socioeconomic groups [14]. In low- and middle-income countries (LMIC), evidence on the socioeconomic gradient in hypertension is mixed, which may reflect changes in the gradient as countries move through the epidemiological transition [2, 15–17]. There is evidence, however, that the socially disadvantaged in LMIC have worse access to hypertension care [5, 18] and so potentially suffer great ill-health as a consequence of uncontrolled hypertension. More effective and equitable targeting of hypertension screening and treatment requires improved understanding of the sociodemographic groups that face the greatest barriers in accessing these services.
In Mexico, which has the highest prevalence of overweight in the world [19] and non-communicable diseases (NCDs) account for $80\%$ of all deaths [20], estimated hypertension prevalence in the adult population aged 18 years and older was $25.5\%$ in 2016, and increased substantially with age with a prevalence near $50\%$ at the age of 60 [21, 22]. Among adults with hypertension, $40\%$ were estimated to be undiagnosed, $21\%$ were untreated, and $55\%$ had not achieved blood pressure control in 2016 [21].
Estimates of diagnosis, treatment, and control of hypertension are valuable for monitoring and targeting of CVD prevention. However, the cross-sectional nature of most of this evidence is limiting in two respects. First, it does not provide information about persistent undiagnosed, untreated, and uncontrolled hypertension. Given that the risks of severe health consequences rise steeply with the duration of exposure to uncontrolled hypertension [23], it is important to establish prevalence of the condition that remains undiagnosed, untreated, and uncontrolled for an extended period of time and how these prevalence rates vary with sociodemographic characteristics. Second, a cross-sectional approach does not allow estimation of rates of transition from undiagnosed to diagnosed, untreated to treated, and uncontrolled to controlled hypertension, nor can it reveal reverse transitions from treated to untreated and controlled to uncontrolled, both of which indicate failures in hypertension management and treatment adherence.
These limitations can be addressed by following a cohort over time to identify the proportion and type of people who remain undiagnosed, untreated, and uncontrolled for an extended period, as well as rates of transition to more favorable states. This study aimed to estimate the prevalence and socioeconomic distribution of hypertension that remained undiagnosed, untreated, and uncontrolled for at least five years among Mexicans aged 50 years and older and to estimate rates of transition from those states to diagnosis, treatment, and control. To help target improvements in hypertension screening and management on vulnerable groups, we aimed to identify sociodemographic characteristics associated with remaining in an unfavorable hypertension state.
## Sample
We used longitudinal data from the Mexican sample of the World Health Organization (WHO) Study on Global AGEing and adult health (SAGE) [6]. Our study focused on adults aged 50 years and older. Mexico is the only one of six countries participating in SAGE to have made longitudinal data publicly available (as of 2021).
The sample for Wave 1 (November 2009–January 2010) was based on the 2003 WHO World Health Survey (WHS) for Mexico (hereafter, Wave 0). A total of 96 strata were defined over 32 states and three levels of urbanicity (rural, urban, and metropolitan) [6]. A nationally representative sample was obtained in Wave 0 by conducting cluster random sampling with Basic Geo-Statistical Areas forming the primary sample units (PSUs). In total, 40,000 households were randomly sampled [24]. To obtain the Wave 1 sample, probability sampling was used to select 211 PSUs from the 797 sampled in Wave 0 [25]. In each selected rural and urban PSU, all Wave 0 individuals who had been aged 50 years or older in 2003 were included in the Wave 1 target sample (ibid). In each of the selected metropolitan PSUs, a random sample of $90\%$ of individuals aged 50 years and older in 2003 were included. In addition, a systematic sample of 1000 individuals from Wave 0 who had been aged 18–49 across all selected PSUs were included as the primary sample.
Wave 1 had a relatively low response rate of $53\%$. Response was lower for middle-aged adults aged 50–59 years ($42\%$) than for younger adults aged 18–49 years ($58\%$). The low response rate has been attributed to the short time available for field work, which left little time to revisit sampled households that did answer during the initial visit in this wave [6], but no further information on the average characteristics of those missing has been made available. An interval of six-seven years between Wave 0 and Wave 1 also contributed to a high rate of attrition and a low response in the latter wave [6].
SAGE Wave 2 was conducted in July-October 2014. The target sample included all individuals who participated in Wave 1, plus those aged 50 years and older in 2014 who were not in the Wave 0 (or Wave 1) sample but who lived in a household that included someone from that sample [6]. The Wave 2 response rate was $83\%$ for households and $81\%$ for individuals.
The SAGE sample was designed, after weighting, to be nationally representative for the population aged 50 years and older at the time of each wave. We restricted the analysis sample to this age range and to respondents observed in both Wave 1 and Wave 2. To maximize the size of this cohort, we selected respondents aged 50 years and older in Wave 2 who also participated in Wave 1. Some of these respondents were therefore slightly younger than 50 in Wave 1. Then, we excluded respondents that had missing data on any of the hypertension measurements. The final step was to exclude respondents that had missing data on any of the other relevant covariates in Wave 2 (see Flow Chart Fig 1).
**Fig 1:** *Participant flow chart.Notes: BP measured indicates that BP was measured and hypertension diagnosis and treatment were reported, allowing hypertension status to be established.*
## Hypertension
Blood pressure (BP) was measured using a Boso Medistar Wrist Blood Pressure Monitor Model S during a home visit [26]. Three measurements were taken, with a minimum of one minute between each. Each participant was asked: “Have you ever been diagnosed with high blood pressure (hypertension)?” A positive response was followed with: “Have you been taking any medications or other treatment for it during a) the last 2 weeks, b) the last 12 months?” We classified a participant as having hypertension (HTN) if a) the last two measurements gave a mean systolic BP ≥140 mm Hg or mean diastolic BP ≥ 90 mm Hg, or b) they reported ever having been diagnosed with high BP [13, 27]. Those classified as having hypertension were then categorized as: a) diagnosed, if they reported ever having been diagnosed (HTN Diagnosed); b) treated, if they reported taking medication or another treatment (HTN Treated); and, c) controlled, if they had measured systolic BP < 140 mm Hg and measured diastolic BP < 90 mm Hg (HTN Controlled). The other respondents, either with or without a classification of hypertension, were classified as being undiagnosed, untreated, or uncontrolled defined analogously (see Table 1).
**Table 1**
| All hypertension (HTN) | Systolic BP ≥140 mm Hg OR diastolic BP ≥ 90 mm Hg |
| --- | --- |
| All hypertension (HTN) | OR self-reported to have ever been diagnosed with high BP |
| HTN Diagnosed | HTN AND self-reported to have ever been diagnosed with high BP |
| HTN Undiagnosed | HTN AND self-reported never having been diagnosed with high BP |
| HTN Treated | HTN AND self-reported taking medication or other treatment for high BP in previous 2 weeks |
| HTN Untreated | HTN AND self-reported not taking medication or other treatment for hypertension in previous 2 weeks |
| HTN Controlled | HTN AND systolic BP < 140 mm Hg AND diastolic BP <90 mm Hg |
| HTN Uncontrolled | HTN AND systolic BP ≥140 mm Hg OR diastolic BP ≥ 90 mm Hg |
While we recognized that clinically diagnosed hypertension is a chronic condition, we did not classify a participant as necessarily having hypertension in Wave 2 if they were classified with the condition in Wave 1. The reason was that we did not observe clinical diagnoses made on the basis of BP measurements on multiple occasions. Measured BP ≥$\frac{140}{90}$ mm Hg on a single occasion could be a false positive. By classifying respondents in each wave using only their measured BP and self-reported diagnoses from that wave, we avoided contaminating Wave 2 classifications with Wave 1 measurement errors.
## Wealth index
To examine socioeconomic inequality in hypertension and its diagnosis, treatment, and control, we used a wealth index to proxy socioeconomic status. The index was the first principal component from analysis of each participant’s reported possession of household durable assets and financial resources, as well as the building materials, sanitation, and water supply of their house [28]. S1 Table shows the list of variables included in this analysis.
## Covariates
We examined associations between persistent undiagnosed, untreated, and uncontrolled hypertension and both sociodemographic and lifestyle characteristics that may plausibly have been related to the risk of hypertension or with access to health services that deliver hypertension care. Specifically, we examined associations with sex and age that are risk factors for hypertension and with cohabiting status, rural/urban location, wealth (index), and health insurance that may each be related with access to care [29, 30]. In addition, we examined associations with smoking, alcohol consumption, and Body Mass Index (BMI) that may each be related to hypertension risks [29, 31, 32]. BMI was calculated from height and weight measured by a healthcare professional at the time of the interview. We categorized respondents as: normal weight (BMI <25.0), overweight (25.0–29.9) and obese (>29.9) [33]. Very few respondents had a BMI lower than 20.0 ($$n = 19$$), we included them in the “normal weight” category.
## Statistical analyses
We estimated percentages of the cohort aged 50 years and older in 2014 classified as hypertensive in each wave and in both waves. We also estimated percentages of the cohort with undiagnosed, untreated, and uncontrolled hypertension, unconditionally on being classified as hypertensive. We used transition matrices and visual representations to summarize movements between hypertension states from Wave 1 to Wave 2. We also examined how the probability of having uncontrolled hypertension in Wave 2 differed between those who were diagnosed and undiagnosed in Wave 1. We did the same for those treated and untreated in Wave 1.
We measured socioeconomic inequality prevalence of each hypertension state using a concentration index equal to the (scaled) covariance between an indicator of that state and wealth index rank [34]. A positive (negative) value indicated that richer (poorer) individuals were more likely to be in that state.
We estimated probit models of persistent (from Wave 1 to Wave 2) undiagnosed, untreated, and uncontrolled hypertension and used them to obtain averaged marginal effects that indicated by how much the probability of remaining in each of these states for at least 5 years varied with covariates. We also conducted a probit regression to estimate how the probability of transitioning from undiagnosed hypertension in Wave 1 to diagnosed in Wave 2 varied with covariates. The sample used for this analysis consisted of those undiagnosed in Wave 1. We conducted analogous analyses to examine variation in the transition probabilities between untreated and treated and between uncontrolled and controlled.
We did not apply sampling weights since these were not available at cohort level. We assessed representativeness of the cohort by comparing its sociodemographic composition with that of the full Wave 2 cross-sectional sample with sampling weights representative of the population aged 50 years and older in 2014. We took account of stratification and cluster sampling in all statistical interference. STATA 16.0 was used for all analyses.
## Sample description
Of the 2,998 Wave 2 respondents aged 50 years and older, 1,740 ($58\%$) participated in Wave 1 (Fig 1). In this cohort, valid BP measures were obtained in both waves for 1,254 ($72\%$), and 945 ($54\%$) had full item response on all measures and variables used in the analyses. We present results obtained from the latter, analysis sample. Estimates of prevalence and transition rates obtained from the larger sample with BP measures in both waves (BP sample) were highly consistent and are given in the S2–S4 Tables.
Table 2 shows characteristics of the analysis sample of Wave 2 respondents aged 50 years and older who participated also in Wave 1 and had full item response. For comparison, the table also shows characteristics of all Wave 2 respondents aged 50 years and older that were weighted to be representative of the Mexican population in that age range [26]. On average, the analysis sample was about eight years older than the full cross-section sample, since new respondents added in Wave 2 were younger than those who had participated in Wave 1. Sample differences in BP and hypertension reflect the difference in average age. Analysis sample respondents were more likely to be rural, have health insurance and abstain from alcohol.
**Table 2**
| Unnamed: 0 | Analysis sample—observed in Waves 1 & 2(N = 945) | Comparison sample—observed in Wave 2(N = 2,998) |
| --- | --- | --- |
| | Mean (SD) | Mean (SD) |
| Age | 70.7 (8.0) | 62.5 (9.3) |
| Systolic blood pressure | 141.8 (23.2) | 138.8 (22.0) |
| Diastolic blood pressure | 76.6 (11.0) | 78.9 (11.0) |
| | No. (%) | No. (%) |
| Classified as hypertensive | | |
| Yes | 609 (64.4) | 1,675 (55.9) |
| No | 336 (35.6) | 1,323 (44.1) |
| Sex | | |
| Female | 523 (55.3) | 1,613 (53.8) |
| Male | 422 (44.7) | 1,385 (46.2) |
| Cohabiting | | |
| Yes | 625 (66.1) | 2,105 (70.2) |
| No | 320 (33.9) | 893 (29.8) |
| Location | | |
| Urban | 645 (68.3) | 2,356 (78.6) |
| Rural | 300 (31.8) | 642 (21.4) |
| Health insurance | | |
| Yes | 845 (89.4) | 2,508 (83.7) |
| No | 100 (10.6) | 490 (16.3) |
| Smoker | | |
| Yes | 104 (11.0) | 375 (12.5) |
| No | 841 (89.0) | 2,623 (87.5) |
| Drinks alcohol | | |
| Yes | 438 (46.3) | 1,844 (61.5) |
| No | 507 (53.7) | 1,154 (38.5) |
| BMI | | |
| Normal | 270 (28.6) | 670 (23.2) |
| Overweight | 397 (42.0) | 1,236 (41.2) |
| Obese | 278 (29.4) | 1,065 (35.5) |
## Hypertension prevalence, diagnosis, treatment and control
Table 3 shows estimates of the prevalence of all hypertension and percentages of the cohort with undiagnosed, untreated, and uncontrolled hypertension in each wave. It also shows estimates of the percent of respondents with these outcomes in both waves. We estimated that $62.4\%$ ($95\%$ CI, 58.9 to 65.9) of the cohort was classified as having hypertension in Wave 1. Around five years later when the same respondents were observed in Wave 2, $64.4\%$ ($95\%$ CI, 61.0 to 67.7) were classified as having hypertension. The difference between the prevalence rates was not significant ($$P \leq 0.364$$). More than half of the cohort ($51.1\%$; $95\%$ CI, 47.5 to 54.7) was classified as having hypertension in both waves. This percentage is lower than the prevalence in either wave because some respondents ($$n = 107$$) transitioned from being classified as hypertensive in Wave 1 to normotensive in Wave 2 (see Table 4). These transitions arise for two reasons. First, measured BP on a single occasion, in a non-clinical setting, can be above the hypertension thresholds in Wave 1 and below the thresholds in Wave 2. If such respondents report in Wave 2 that they have never been diagnosed with high BP/hypertension, then they will not be classified as having hypertension in Wave 2. These cases may have been false positives in Wave 1. Second, a participant could report having ever been diagnosed with hypertension in Wave 1 but in Wave 2 report never having had such a diagnosis. Such reporting implies a measurement error, either in Wave 1 or Wave 2.
We estimated that in Wave 1, $30.3\%$ ($95\%$ CI, 27.2 to 33.5) of the cohort had undiagnosed hypertension, $36.0\%$ ($95\%$ CI, 32.8 to 39.3) had untreated hypertension, and $55.7\%$ ($95\%$ CI, 52.1 to 59.2) had uncontrolled hypertension. In Wave 2, the prevalence rates of undiagnosed, untreated, and uncontrolled hypertension were estimated to be $22.2\%$ ($95\%$ CI, 19.7 to 24.9), $27.1\%$ ($95\%$ CI, 24.4 to 30.0), and $48.7\%$ ($95\%$ CI, 45.4 to 52.0) respectively. Between the two waves, there was a significant reduction in the prevalence of hypertension that was undiagnosed ($$P \leq 0.000$$), untreated ($$P \leq 0.000$$), and uncontrolled ($$P \leq 0.000$$). Over one-tenth ($11.3\%$; $95\%$ CI, 9.5 to 13.4) were classified as having undiagnosed hypertension in both waves. We estimated that $15.3\%$ ($95\%$ CI, 13.2 to 17.8) had untreated hypertension over the five years spanning the two waves, and more than one third ($34.7\%$; $95\%$ CI, 31.7 to 37.9) persistently had uncontrolled hypertension.
## Transitions between hypertension states
Fig 2 and Table 4 show transitions between hypertension states. Panel A shows transitions between three states defined by hypertension and diagnosis. Of the 355 respondents (229+48+78) who did not have hypertension in Wave 1, $13.5\%$ ($95\%$ CI, 10.4 to 17.4) were classified with hypertension and had been diagnosed by Wave 2. A larger percentage ($22\%$; $95\%$ CI, 18.3 to 26.1) of those initially not classified with hypertension were classified as having the condition in Wave 2 but had not been diagnosed. This means that more than three fifths ($62\%$ = 22/(13.5+22)) of those who became hypertensive were undiagnosed. Of the 286 respondents (85+94+107) who were classified as having hypertension in Wave 1 but reported never having been diagnosed, $37.4\%$ ($95\%$ CI, 32.7 to 42.4) remained undiagnosed five years later, while $32.9\%$ ($95\%$ CI, 27.8 to 38.3) acquired a diagnosis and $29.7\%$ ($95\%$ CI, 24.3 to 35.7) were reclassified, on the basis of measured BP and reported diagnosis, as not being hypertensive in Wave 2. A small fraction ($8.3\%$; $95\%$ CI, 5.6 to 11.9) of the 304 respondents (22+257+25) who were classified as having hypertension and reported ever having been diagnosed in Wave 1 had BP above the hypertension thresholds in Wave 2 but at that time they reported, inconsistently, that they had never been diagnosed.
**Fig 2:** *Transitions between hypertension states.Notes. Each figure in the top panel gives a visual representation of the data presented in the respective transition matrix table below it. The sample size is 945 for each panel.*
Panel B shows that $11.6\%$ ($95\%$ CI, 8.6 to 15.4) of those not classified with hypertension in Wave 1 were classified as having hypertension and in receipt of treatment in Wave 2, while $23.9\%$ ($95\%$ CI, 20.2 to 28.2) were reclassified as having untreated hypertension. There was considerable persistence in treatment: $82.4\%$ ($95\%$ CI, 77.3 to 86.6) of those who were being treated for hypertension in Wave 1 continued to be in treatment five years later. Over one tenth ($10.4\%$; $95\%$ CI, 7.3 to 14.6) of those initially under treatment were no longer treated in Wave 2 but were still classified as having hypertension. A small but sizeable percentage ($7.2\%$; $95\%$ CI, 4.9 to 10.6) of those who were being treated in Wave 1 were classified as not having hypertension in Wave 2, which implies that they reported in that wave, inconsistently with their previous reported treatment, never having been diagnosed with hypertension. More than two fifths ($42.7\%$; $95\%$ CI, 37.9 to 47.5) of those initially classified as having untreated hypertension were still untreated. Almost a third ($31.2\%$; $95\%$ CI, 26.6 to 36.2) of those with untreated hypertension in Wave 1 were under treatment in Wave 2.
Panel C reveals that $28.7\%$ ($95\%$ CI, 24.7 to 33.2) of those who were free of a hypertension classification in Wave 1 had uncontrolled hypertension five years later. Among the relatively small number (64 = 6+28+30) identified as having controlled hypertension in Wave 1, almost half ($46.9\%$; $95\%$ CI, 33.7 to 60.5) moved to uncontrolled hypertension. Among the much larger number (526 = 101+97+328) who had uncontrolled hypertension in Wave 1, $62.4\%$ ($95\%$ CI, 57.8 to 66.7) were still in this state in Wave 2, while only $18.4\%$ ($95\%$ CI, 15.3 to 22.0) achieving BP control. Further analyses revealed that those with diagnosed hypertension in Wave 1 were more than twice as likely as those initially undiagnosed to have their BP controlled in Wave 2 (S5 Table). There was a similar difference between those initially treated and untreated in their relative likelihoods of achieving BP control by the end of the study period.
## Socioeconomic inequality
Table 5 shows concentration indices that measure wealth-related inequality in each hypertension indicator. The positive concentration indices imply that a hypertension classification was more prevalent among wealthier respondents in each wave and that wealthier respondents were more likely to be classified as hypertensive in both waves. However, all of the $95\%$ confidence intervals include zero so there was no evidence of statistically significant inequality in hypertension prevalence. The next three rows of the table show concentration indices for undiagnosed, untreated, and uncontrolled hypertension in each wave and in both waves. All the point estimates of these concentration indices are negative, indicating that poorer respondents were more likely to have undiagnosed, untreated, and uncontrolled hypertension. However, all of the $95\%$ confidence intervals include zero, and thus the inequality apparent in the sample was not statistically significant.
**Table 5**
| Unnamed: 0 | N = 945 | N = 945.1 | N = 945.2 |
| --- | --- | --- | --- |
| | Wave 1 | Wave 2 | Both waves |
| | Concentration index [95% CI] | Concentration index [95% CI] | Concentration index [95% CI] |
| All hypertension (HTN) | 0.007 [-0.06, 0.08] | 0.026 [-0.04, 0.09] | 0.028 [-0.04, 0.10] |
| HTN Undiagnosed | -0.039 [-0.11, 0.02] | -0.026 [-0.09, 0.03] | -0.007 [-0.05, 0.04] |
| HTN Untreated | -0.068 [-0.14, 0.00] | -0.039 [-0.11, 0.03] | -0.050 [-0.10, 0.00] |
| HTN Uncontrolled | -0.029 [-0.10, 0.04] | -0.029 [-0.10, 0.04] | -0.047 [-0.11, 0.02] |
## Multivariable analysis
Table 6 contains results of respondents’ characteristics regressed on having persistent undiagnosed, untreated and uncontrolled hypertension in both waves. Men had a 10 percentage point (pp) higher probability of remaining undiagnosed. They were also significantly more likely than women to remain untreated (by 9 pp, with p-value <0.05) and uncontrolled (by 3 pp), although the $95\%$ CI for the latter estimate includes zero. Singles were significantly more likely to remain undiagnosed (4 pp, with p-value <0.05) compared to cohabiting respondents. Rural dwellers in the sample were more likely to have persistent undiagnosed, untreated, and uncontrolled hypertension, although only their estimated 9 pp higher probability of remaining uncontrolled has a $95\%$ CI that does not include zero. Those without health insurance were 4 pp more likely to remain undiagnosed (significant with p-value < 0.05). Those with overweight were significantly more likely to remain untreated or uncontrolled in both waves, respectively with 5 and 13 pp. Compared with abstainers, consumers of alcohol were less likely to remain undiagnosed (3 pp) and untreated (4 pp) with a p-value < 0.05. Analyses of variation in the probabilities of transitioning from undiagnosed to diagnosed, untreated to treated, uncontrolled to controlled revealed that men were significantly less likely to make each of these transitions (S6 Table). Smokers were significantly more likely to move from undiagnosed to diagnosed.
**Table 6**
| Unnamed: 0 | All respondents (N = 945) | All respondents (N = 945).1 | All respondents (N = 945).2 |
| --- | --- | --- | --- |
| | HTN Undiagnosed in both waves | HTN Untreated in both waves | HTN Uncontrolled in both waves |
| | ME [95% CI] (P-value) | ME [95% CI] (P-value) | ME [95% CI] (P-value) |
| Sex | | | |
| Female | Ref | Ref | Ref |
| Male | 0.10 [0.06, 0.15] (0.000) | 0.09 [0.03, 0.14)] (0.002) | 0.03 [-0.04, 0.11] (0.366) |
| Age (years) | -0.00 [-0.00, 0.00] (0.900) | 0.00 [-0.00, 0.00] (0.068) | 0.01 [0.01, 0.01] (0.001) |
| Cohabiting | | | |
| Yes | Ref | Ref | Ref |
| No | 0.04 [0.01, 0.07] (0.007) | 0.05 [-0.00, 0.07] (0.061) | 0.06 [-0.01, 0.12] (0.107) |
| Living area | | | |
| Urban | Ref | Ref | Ref |
| Rural | 0.02 [-0.00, 0.05] (0.083) | 0.03 [-0.1, 0.07] (0.178) | 0.09 [0.02, 0.16] (0.013) |
| Health insurance | | | |
| Health insurance | Ref | Ref | Ref |
| No health insurance | 0.04 [0.01, 0.08] (0.046) | 0.05 [-0.00, 0.10] (0.103) | 0.07 [-0.03, 0.16] (0.171) |
| Wealth status | | | |
| Tercile 1 | Ref | Ref | Ref |
| Tercile 2 | -0.01 [-0.04, 0.03] (0.710) | -0.02 [-0.06, 0.03] (0.431) | -0.03 [-0.11, 0.04] (0.411) |
| Tercile 3 | 0.02 [-0.02, 0.05] (0.334) | -0.02 [-0.01, 0.03] (0.480) | -0.01 [-0.09, 0.07] (0.766) |
| Body weight | | | |
| Normal weight | Ref | Ref | Ref |
| Overweight | 0.02 [-0.01, 0.05] (0.284) | 0.05 [0.01, 0.10] (0.023) | 0.13 [0.05, 0.20] (0.001) |
| Obese | -0.00 [-0.04, 0.04] (0.872) | 0.04 [-0.01, 0.08] (0.134) | 0.16 [0.08, 0.25] (0.000) |
| Smoker | | | |
| No | Ref | Ref | Ref |
| Yes | 0.00 [-0.04, 0.04] (0.931) | 0.02 [-0.04, 0.08] (0.480) | 0.07 [-0.03, 0.17] (0.182) |
| Alcohol consumption | | | |
| No | Ref | Ref | Ref |
| Yes | -0.03 [-0.06, -0.01] (0.017) | -0.04 [-0.08, -0.00] (0.048) | -0.04 [-0.11–0.03] (0.250) |
## Discussion
This study is among the first to provide a longitudinal perspective on diagnosis, treatment, and control of hypertension [35, 36] in a middle-income country. We used data from a cohort of Mexicans aged 50 years and older in two waves of the WHO Study on Global AGEing and adult health (SAGE) collected in 2009 and 2014. We found a substantial prevalence of hypertension ($64\%$). Prevalence of undiagnosed, untreated, and uncontrolled hypertension significantly decreased over the five year period to reach $22\%$, $27\%$, and $49\%$, respectively. More than one third of those classified as having undiagnosed hypertension were still in this state five years later, more than two fifths of those initially untreated remained untreated, and over three fifths of those initially with uncontrolled hypertension failed to achieve BP control by the end of the period. The likelihood of experiencing continued uncontrolled hypertension was much higher than the chances of achieving BP control, which signals substantial losses in population health since CVD risks rise steeply with the duration of exposure to uncontrolled hypertension [37]. These estimates confirm substantial persistence of unfavorable hypertension states, ongoing failures of the health system to find patients who had fallen through the cracks of hypertension care, and lack of patient adherence to treatment. We cannot claim that these findings would necessarily extend beyond Mexico. They may, however, motivate estimation of the prevalence of persistent undiagnosed, untreated, and uncontrolled hypertension in other countries.
We are aware of two other studies that took a longitudinal approach. One study conducted in Ghana, did not assess transitions between the hypertension states but did report similar factors associated with hypertension diagnosis i.e. residing in urban areas and having health insurance [36]. The other (preprint) study is a multi-country including Mexico with similarities to our longitudinal design [35]. The transition rates from undiagnosed to diagnosed, and untreated to treated are like in our study close to $30\%$, while we find a tree times higher rate for treatment continuity. In line with our work, they find men and rural dwellers to be less likely to advance forward through the continuum of hypertension care. The difference in treatment continuity might be driven by differences in the average characteristics of both cohorts. The cohort used by Mauer et al. [ 35] is derived from the Mexican Family Life Survey which also includes those aged 40 to 49 years old and had it first wave a few years earlier [2005]. The second wave was apparently collected over a prolonged period of time (2009 till 2012). Those timing differences might have resulted in a lower observed treatment continuity given that the cohort was followed over a longer and less strictly defined time period.
Our approach allowed for reclassifications from hypertensive to normotensive between waves and we found that such transitions are far from uncommon. Approximately, these are as common as moving to a diagnosed, treated, or controlled state. They do not derive from a false premise that someone with clinically diagnosed hypertension can be cured. In this study, a participant could have been reclassified as not having hypertension because their BP fell from being above the hypertension thresholds when measured (on a single occasion) at Wave 1 to below these thresholds at Wave 2 and they reported never having been diagnosed with hypertension at Wave 2. Reclassification could also occur if the participant never had BP above the thresholds but inconsistently reported having been diagnosed with hypertension at Wave 1 but never having been diagnosed at Wave 2. Each reason for reclassification derives from a measurement error—a false positive in the first case, inconsistent reporting of diagnosis in the second—that would bias cross-sectional estimates of hypertension diagnosis, treatment, and control. While these errors suggest that cross-sectional studies have likely overestimated rates of undiagnosed, untreated, and uncontrolled hypertension, this is not sufficient reason for less policy concern about these indicators of gaps in hypertension screening and management.
We compared how the probability of achieving BP control differed between those who had been diagnosed five years earlier and those who had not. The initially diagnosed were more than twice as likely to have controlled BP after five years. This supports the case for effective implementation of opportunistic or population-based screening for hypertension. The rate of persistent untreated hypertension was high and the initially treated were more than twice as likely as the untreated to have achieved BP control after five years. This points to the need for improvements in hypertension management, as well as screening. The potential health gains from such improvements are clear [37] given evidence that antihypertensives are highly cost-effective [38], as are lifestyle changes if they can be achieved. There was a high degree of persistence in treatment: more than four fifths of those who were under treatment at the beginning of the period continued with treatment five years later. Taken together, these results suggest that diagnosing people and getting them on treatment is the primary challenge, while maintaining continuity of care is arguably of a secondary order. That said, multiple studies have shown that half of patients prescribed antihypertensives stopped taking them within a year [38–40]. Lack of treatment adherence is a recognized global concern [41]. The high rate of persistent uncontrolled hypertension we find provides further support for making frequent follow-up of patients who have not achieved BP control a key component of a healthcare team’s concerted effort to improve adherence [42].
In the study cohort, hypertension was slightly more prevalent among the wealthier. This adds to already conflicting evidence from Latin America regarding socioeconomic inequality since it is reported that individuals with a lower SES had a higher risk for an elevated blood pressure, while another study summarizes recent evidence from LMIC settings with the majority of the studies confirming the positive relationship between socioeconomic status and chronic conditions (including hypertension) [43, 44]. Furthermore, evidence from a low-income setting in Mexico revealed that using two different aspects of SES showed an inverse association with elevated blood pressure [45]. In our sample, we found that less wealthy individuals were slightly more likely to have persistent undiagnosed hypertension and more likely to have persistent untreated and uncontrolled hypertension, however, these differences were not significant. Previous evidence showed that the performance of health systems in LMICs regarding the management of hypertension was poor: not even halve of those with hypertension were diagnosed, only one third were taking medication and $10\%$ had their blood pressure under control [46]. Moreover, individuals with a lower household wealth were more likely to be lost to care before reaching the phase of blood pressure control [46]. The fact that, at least in the sample, the wealthier were more likely to have hypertension but less likely to have undiagnosed (as well as untreated and uncontrolled) hypertension suggests that the former positive wealth gradient in hypertension prevalence is partly due to the wealthier being more likely to get diagnosed. We found that, compared with abstainers, alcohol consumers were less likely to remain undiagnosed. The rate of alcohol consumers in our sample was lower compared to the comparison sample, therefore we have difficulties with interpreting this finding. Furthermore, we found that in the sample, men, those living alone, rural dwellers, uninsured, and those with overweight were more likely to have persistent undiagnosed, untreated, and uncontrolled hypertension. These sociodemographic groups appeared to have been most exposed to deficiencies in hypertension screening and management, and possibly most laxed in adherence to treatment. Other studies, though cross-sectional, observe similar characteristics (e.g. having health insurance, educated, married, living area) for individuals who were less likely to have (undiagnosed, untreated, uncontrolled) hypertension [17, 18, 47]. Previous evidence suggested that enrollees in Mexico’s flagship Seguro Popular universal coverage program had better access to health care, including diagnosis and treatment of hypertension [48, 49]. In line with this, we found that sample respondents that did not have health insurance were more likely to experience persistent undiagnosed, untreated, and uncontrolled hypertension, although this was only statistically significant for persistent undiagnosed hypertension. Finally, we found that women are more likely to become diagnosed, treated and controlled, and thus receive diagnoses or treatment or reach controlled hypertension.
## Limitations
We restricted the sample to respondents who responded to both Wave 1 and Wave 2. The low response rate in Wave 1, as well as attrition between waves, potentially made the study cohort unrepresentative of the Mexican population aged 50 years and older at the time of Wave 2 [2014]. Comparison with the Wave 2 cross-section sample weighted to be representative of the population aged 50 years and older showed that the cohort was older, and, consequently, had higher rates of hypertension, rural dwellers, and health insurance coverage, and it was less likely to be cohabiting and to drink alcohol. Our results should be interpreted with these differences in mind. They do not necessarily hold for the population of Mexico aged 50 years and older in 2014, although they are likely to be more representative for an older population. Selective attrition could also potentially leave the cohort unrepresentative with respect to unobserved characteristics that are related to hypertension and its management.
Respondents who had an elevated BP reading in Wave 1 were informed of this and advised to seek medical advice. Consequently, we would expect rates of persistent undiagnosed, untreated, and uncontrolled hypertension to have been lower in the study cohort than they were in the population. For this reason, the high rates we found are of even greater concern.
The main limitation of this and most hypertension awareness, treatment, and control studies is that BP was measured on a single occasion in each wave. While it was measured multiple times on one occasion, it would have been better if there was a longer time between these two periods. Hypertension is usually diagnosed from BP measurements made on at least two occasions. This might have increased the number of false positives among those identified as having hypertension. The true rate of undiagnosed hypertension in each wave—not persistent undiagnosed hypertension between waves—is likely to be lower than estimated. However, the longitudinal perspective taken in this study provided insight into this measurement error problem that is missing from cross-sectional studies. We estimated that $30\%$ ($$n = 85$$) of those identified as having undiagnosed hypertension in Wave 1 were identified as not having hypertension in Wave 2. The respective rates for untreated and uncontrolled hypertension were $26\%$ ($$n = 89$$) and $19\%$ ($$n = 101$$). These estimates suggest that false positive may well cause substantial upward bias in cross-section estimates of these rates [50]. The focus of this study was not on a cross-sectional snapshot but on persistent gaps in hypertension diagnosis and management over a 5-year period. Classification errors, while still present, are less problematic from this longitudinal perspective.
Our study covered the period 2009–2014. Since then, the Mexican Institute of Social Security (IMSS) has tried to tilt its model of care towards prevention [51] and has introduced several integrated programs of care [52, 53]. It could be that these policies have improved hypertension screening and management and corrected some of the care deficiencies suggested by our estimates.
## Policy implications
Our estimates of substantial rates of persistent undiagnosed, untreated, and uncontrolled hypertension suggest that clinical and public health interventions are required to improve hypertension screening and care. A regular BP check during healthcare visits for other conditions may lead to more and earlier diagnoses. Our results show that this could be particularly relevant for those who are male, single, rural dwellers, uninsured or overweight. The substantial rate of transition from controlled to uncontrolled hypertension suggests that policies to improve treatment adherence care continuity would be particularly valuable. Association of persistent undiagnosed hypertension with lack of health insurance suggests that improving effective coverage for primary care, or even just making people aware of their insurance entitlement, may help close gaps in hypertension care.
## Conclusions
Our study showed that a large proportion of the Mexican older population with hypertension remained undiagnosed, untreated, and uncontrolled for at least five years and that these hypertensive stages have a dynamic character. We show that there is room for improvement in hypertension diagnosis, long-term treatment adherence and hypertension control. To ensure more equitable hypertension management and effectively prevent premature deaths, increased diagnosis and long-term treatment efforts should be directed towards men, those living alone, rural dwellers, uninsured and those with overweight.
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---
title: Provider perspectives on emotional health care for adults with type 2 diabetes
mellitus in the Dominican Republic
authors:
- Deshira D. Wallace
- Nastacia M. Pereira
- Humberto Gonzalez Rodriguez
- Clare Barrington
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021239
doi: 10.1371/journal.pgph.0000537
license: CC BY 4.0
---
# Provider perspectives on emotional health care for adults with type 2 diabetes mellitus in the Dominican Republic
## Abstract
The emotional burden of type 2 diabetes mellitus (T2D) can complicate self-management. Exploring the feasibility of mental and physical health co-management in limited-resourced settings is needed. Thus, we assessed providers’ awareness of the emotional burden their patients experience and their roles in supporting their patients with T2D. We conducted a formative qualitative study using in-depth interviews with 14 providers, including physicians, nurses, and community health workers recruited at two rural health clinics in the Dominican Republic. We coded transcripts using inductive and deductive codes and developed themes through iterative comparative analysis. All providers recognized that patients experience an emotional burden managing life with T2D. Some providers viewed the provision of emotional support as integral to their role and believed that they could do so. Others viewed it as the responsibility of the family or expressed the need for additional guidance on how to provide emotional support. Providers also identified several barriers to integrating emotional support into routine clinical care including personality characteristics, lack of training, and insufficient staffing. While providers recognize the need for emotional support, they identified individual, clinical, and systems-level barriers. Strategies to address these barriers include training specific providers on emotional support provision, balancing workload, and building or strengthening referral systems.
## Introduction
As the prevalence of type 2 diabetes mellitus (T2D) increases globally, there is a need to develop low-cost interventions to improve and sustain diabetes self-management and overall wellbeing [1]. Managing T2D requires modifying and adhering to lifelong behaviors including medication use, healthy eating, and physical activity. However, difficulty coping with and adapting to a diabetes diagnosis and barriers to accessing care, among other factors, can complicate adherence to self-management and lead to diabetes-related distress, which encompasses stress, anxiety, sadness, and depressive symptoms [2, 3]. Both diabetes and non-diabetes related distress have been associated with poor adherence to T2D self-management behaviors, which leads to poorly controlled blood glucose levels and complications, such as kidney failure or nerve damage [4–6]. Therefore, it is important to address stress and overall emotional health as part of T2D management.
Compelling evidence supports the benefits of low-cost, community-based interventions, that provide social support for healthy eating, physical activity, and other health behaviors to improve T2D and associated clinical outcomes [7–9]. Such interventions generally involve family, peers, and community health workers as sources of emotional, material, instrumental, and informational support [10–12]. For example, Latinos with T2D living in Chicago, Illinois were enrolled in a program that focused on addressing T2D-related distress called Compañeros en Salud, which involved regular phone calls, and group classes and activities as a means of providing instrumental and emotional support [7]. Participants in the program had a significant decline in HbA1c levels over their two years of participation [7]. Although there is evidence of the importance of providing emotional support to improve diabetes-related outcomes, healthcare providers including nurses and physicians, largely focus on providing informational support in the form of health education about diabetes self-management and less on emotional support and diabetes-related stress [13–15].
We sought to improve understanding of the perspectives of physicians, nurses, and community health workers (hereafter known as providers to maintain confidentiality) on the emotional burden of diabetes management, and their capacity to support patients in identifying managing and reducing associated stress, anxiety, or depressive symptoms. We explored three primary questions: 1) How do providers describe and understand the emotional burden associated with living with T2D? 2) How do providers describe their role in supporting the emotional health needs of patients? and 3) What barriers exist to integrating emotional support into routine clinical care?
## Study setting
We conducted this formative qualitative study in two rural clinics in the central valley region of the Dominican Republic between May and July 2018. An estimated $8.1\%$ to $9.3\%$ of Dominicans live with T2D, and prevalence of T2D is estimated to rise to $10.3\%$ by 2045 [16]. Qualitative studies conducted in the Dominican Republic found that diabetes-related stress begins at diagnosis and persists throughout the self-management process [13]. Furthermore, people with T2D reported that they often experienced a significant emotional burden as a result of their T2D; however, they lacked sufficient emotional support to cope with this emotional burden [13, 17, 18].
Access to primary and specialized diabetes care is limited in the rural areas of the Dominican Republic. To address this gap, in 2010 a US-based non-profit group, Chronic Care International (CCI) and a Dominican-based non-governmental organization, Institute for Latin American Concern (ILAC) partnered to develop a community-based diabetes and hypertension program in two rural clinics with the intent of improving health care quality and outcomes [19]. Across the two clinics there are three Dominican physicians, two nurses, nine community health workers (locally referred to as cooperadores), and one program manager. Patients receive free consultations with a physician, medications, and education on T2D self-management from the cooperadores. In addition, nurses and cooperadores collect patient vitals (e.g., blood pressure, weight, and fasting blood glucose) and visit patient homes to support self-management using problem solving and goal-setting techniques. At the time of this study, the clinics served over 1,000 patients living with type 2 diabetes mellitus.
## Study team
Our study team consisted of graduate students in public health, a research coordinator, and the principal investigator, who are all fluent in Spanish. The first, second, and third authors are of Latin American descent. The senior author has worked with the community for over 20 years and the study team has engaged in collaborative research with the clinic teams for 5 years. This familiarity with the context and time spent in the community facilitated our access to participants and our ability to reflect on participant narratives throughout the data collection and analysis processes. The primary interviewer resided in one of the clinic communities during data collection, which provided the opportunity for observing the overall context of people’s lives and aided in interpretation of our findings.
## Participants & data collection
We purposefully sampled providers from both participating clinics as they were information-rich informants based on their engagement with individuals with T2D. All providers ($$n = 14$$) from both clinics agreed to participate including three doctors, two nurses, and nine cooperadores. Participants’ mean age was 52 and they had worked at the clinic for a range of 1–8 years. Informed by our past research in this setting [13, 17, 18], we wrote the interview guide in Spanish and organized it according to the study’s three main topics: 1) how providers described the emotional burden of diabetes; 2) how they described their role in supporting the emotional health needs of patients; and 3) barriers to integrating emotional support into routine clinical care.
After obtaining verbal informed consent, we conducted audio-recorded, semi-structured in-depth interviews in-person, in Spanish, across various locations including the clinics, providers’ homes, and a community preschool. We completed fourteen initial in-depth interviews and six follow-up interviews for a total of 20 interviews. Follow-up interviews were conducted to further explore salient themes that arose during initial interviews. The fourteen initial interviews ranged from 25–93 minutes (average 53 minutes). The six follow-up interviews ranged in length from 8–28 minutes (average 18 minutes). Interviews were transcribed verbatim by a university-educated Dominican psychologist. Participants received an incentive worth USD $5. This study, including informed consent materials, was approved by the Institutional Review Board at the University of North Carolina at Chapel Hill.
## Data analysis
Data analysis involved a multi-step iterative process informed by Thomas’ [20] inductive approach. Following each interview, we completed field notes describing the interviewer-participant dynamic, the setting in which the interview took place, and salient themes that arose during the interview. Throughout data collection, field notes were used to guide revisions to the interview guide and to probe on recurring themes in subsequent interviews.
Preliminary analysis involved reading Spanish-language transcripts and checking transcription accuracy against the audio files and triangulating against field notes and observations [21]. These activities informed the development of a codebook, which was used to examine the three primary study questions and additional inductive themes such as “causes of stress” and “familial conflict.” Using Dedoose 8.1 (Los Angeles, CA), an online qualitative analysis software program, we coded each transcript, observed code co-occurrences, and created matrices to help organize illustrative quotes and explore connections across themes. After the initial round of coding and matrix creation, we revised the codebook and recoded each transcript to capture previously unidentified themes. We then examined patterns across participants to assess their perspectives of the emotional burden of living with T2D. All data were analyzed in Spanish; however, all quotes presented below were translated by our bilingual study team.
## Ethics statement
This study was conducted according to the guidelines of the Declaration of Helsinki. We did not include participant information alongside quotes to protect confidentiality. As per approved procedure, verbal consent was obtained from participants prior to data collection. The providers in the study were made aware of the study goals and the privacy of all participants has been protected.
## Results
We first present providers’ perspectives on the emotional burden of living with T2D followed by their perceptions of their roles and responsibilities in alleviating this burden, and finally the perceived challenges to integrating emotional support into the existing clinic structure. To protect participant confidentiality, we do not present identifying information about participants (e.g., gender, occupation, age) and refer to all participants as “providers”. Overall, providers recognized the emotional burden associated with patients’ T2D self-management. However, they offered varying levels of emotional support to patients and provided contrasting viewpoints on their role and capacity in providing emotional support. Some providers viewed the provision of emotional support as integral to their role and believed themselves to be fully equipped to do so. Others, however, viewed providing emotional support as the responsibility of the family or expressed the need for additional guidance on delivering stress management techniques as a means of providing emotional support. Finally, providers identified several factors as barriers to integrating emotional support into routine clinical care.
## Perceptions of the emotional burden of diabetes
Providers were keenly attuned to the emotional burden of T2D among their patients. The sources of stress providers listed were in close alignment with how patients described their stressors in previous research [13, 17]. Primarily, providers identified sources of stress related to diagnosis, long-term management, and interpersonal issues within the family. Related to diabetes diagnosis, providers described that the emotional burden their patients felt came from the uncertainty about whether they would be able to live a “normal life” and fear of falling ill or premature death.
As described by providers, the notion of T2D disturbing patients’ “normal” lives was a common source of stress. To assuage the stress of the unknown, providers primarily worked to educate their patients and dispel inadequate or misinformation.
In addition to fear of morbidity and mortality, providers explained that their patients experienced stress related to adherence to dietary recommendations: Patients’ stress related to engaging in recommended dietary behaviors was viewed as both the consequence of limited access to healthy foods as well as a conscious decision by patients to continue eating their preferred, yet non-recommended, foods. While both aspects of patients’ dietary challenges were acknowledged, the latter reflects that providers believed patients were at least partially responsible for their T2D and subsequent struggles to manage their condition because of their food preferences. Providers described how the patients’ emotional burden changed depending on their ability to effectively manage their T2D. For example, patient emotional burden reduced once patients established and were able to maintain a routine for their self-management. However, there were examples of patients’ emotional burden heightening as they tried to change their diets to recommended foods, yet they encountered structural barriers such as poor access due to lack of availability or high cost.
The clinic environment also contributed to the emotional burden experienced by patients. Specifically, providers perceived that their patients experienced stress when they came in for appointments and were told by providers that they did not achieve desired improvements in their clinical outcomes despite their best efforts to make appropriate lifestyle changes. Providers acknowledged that patients felt frustrated making substantial lifestyle changes that were not always reflected in their glucose levels. Providers shared that the uncertainty around being able to successfully manage their glucose levels lead some patients to believe that having diabetes “is worse than having HIV.” Finally, family-level factors, specifically disruptions in interpersonal relationships and lack of familial support with T2D self-management, were identified as salient sources of stress across every interview. While providers believed that many patients received support from their families, they also believed that patients struggled because their families were not concerned enough or did not do enough to support patients’ diabetes management. One provider offered up common grievances they heard from patients regarding the support received from family: In contrast, reflecting the high levels of migration from these communities, providers also noted that some patients experienced stress due to being alone and isolated from family.
Notably, the quotes above highlight an important gendered pattern of women feeling less supported by their male partners in their diabetes management. Gender also played a role in inequitable caretaking responsibilities and social isolation, with participants noting that their women patients were often burdened by having to care for their families while not receiving the same levels of family support as compared to the men in the program. Participants shared that some patients may forego self-management regimens or struggle to engage with treatment regimens because they must take care of loved ones with little, or sometimes no support from other family members.
## Perceived roles
Providers expressed diverging viewpoints on their role and capacity to address the emotional burden related to T2D experienced by their patients. Most commonly, providers described their primary role as providers of instrumental and informational support for their patients, which is congruent with previous findings from patient interviews [13]. Instrumental support arose in descriptions of their job responsibilities with examples including dispensing patients’ medications, raising funds to support clinic functions, or accompanying patients to medical visits outside of the clinic. Informational support consisted of educating patients on the three fundamental aspects of diabetes self-management (i.e., medication adherence, healthy eating, and physical activity). Providers regarded education as a critical component of patient support, with one provider asserting that education was the “most important” aspect of patients’ care needs and would “solve all the problems [they face].” In addition to instrumental and informational support, providers also recognized the importance of emotional support as an essential component of the clinic services and their roles, with one provider saying, “We are the patients’ mantel of tears […] As I always say, the doctor is more of a counselor than a doctor.” ( Provider 5) They further asserted, “if [we have] 100 patients, all 100 will need some form of counseling” in the form of encouragement and emotional support. Importantly, they believed that “A patient cannot improve if you do not look at what’s going on with them emotionally,” emphasizing the importance of addressing patients’ contexts and acknowledging the multiple factors affecting their ability to manage their condition. In fact, three providers spoke of visiting patients at their homes to offer emotional support. One provider in particular spoke about the home visitation program, which was developed for patients who were struggling with controlling their T2D, and noted that it was in these home visits that they could support patients when “they experienced a state of depression or felt emotionally burdened” as well as support patients and their families diabetes management strategies (Provider 4).They explained that this individualized follow-up was logistically challenging; however to account for this, they engaged family and neighbors as other sources of support for the patient.
Some providers appreciated the importance of emotional support and felt comfortable providing it; however, many expressed uncertainty or discomfort and believed that emotional support should primarily come from family, “trusting in God”, or mental health professionals. One provider specified why the family’s role, while variable, was critical in providing emotional support: Although family were deemed as essential sources of emotional support, providers acknowledged their roles in providing support to their patients as well. Additionally, participants noted other clinic staff who they perceived possessed the appropriate disposition to provide emotional support. Specifically, they spoke of the qualities these team members. As one provider mentioned “a person who is humble…who you can talk to and listens to you, makes time for you and does not make you feel rushed,” as important characteristics that best equipped providers to support patients’ emotional needs.
## Barriers to supporting patients
While none of the participants expressed total unwillingness to provide emotional support, several described important challenges that hindered their ability and preparedness to do so. Across both clinics, the broad consensus was that inadequate training, lack of trained personnel, and limited contact with patients beyond the clinical encounter challenged their ability to adequately support the emotional needs of their patients.
Providers noted that they were trained to provide educational talks around T2D self-management and specific clinical services for their patients, and that they were not trained in providing emotional and mental health support. For example, educational talks primarily covered topics central to T2D self-management, such as glucose control, healthy eating, and exercise. Clinic services were often directly related to measurable outcomes in diabetes care, specifically measuring high blood pressure, lipids, and blood glucose levels, or distributing medications and tracking educational materials discussed during appointments. For some, activities outside of the scope of clinic tasks caused feelings of discomfort among providers, as expressed by one: This quote highlights how this provider was more comfortable providing informational rather than emotional support. In addition to inadequate training on the provision of emotional support, providers also cited limited time during clinical encounters coupled with limited exposure to patients outside the clinic as another barrier. Lastly, providers expressed concern over the limited staff available to provide adequate levels of care should they take on a more active emotional support role. This was a particularly relevant concern as providers noted an increasing patient population requiring support for their T2D management. However, several providers mentioned times when they provided emotional support to patients and identified certain providers who excelled in doing so, or the necessary characteristics for those who can most successfully provide emotional support, specifically patience, understanding or empathy, and a willingness to be patient-facing.
## Discussion
This study provides insights into providers’ perspectives on the emotional wellbeing of patients with T2D in the rural Dominican Republic as well as their role in addressing emotional health needs. We found that providers’ perceptions of the emotional burden of T2D was consistent with patient’s narratives of stress described in past research, indicating that they appreciated that stress was a salient issue among patients [17]. Within the context of the T2D clinics in the DR, patients have previously reported experiencing increased distress related to managing their T2D and a desire for more emotional support from providers [13]. This agreement between provider and patients in terms of their appreciation of the emotional burden of T2D could be an enabling factor for future interventions that aim to support emotional wellbeing.
The American Diabetes Association recommends assessment and referral, as needed, for mental health concerns, such as diabetes-related distress, anxiety, and depression [22]. However, this recommendation requires existing referral systems or mental health service integration in primary care settings, which is not widely available or feasible, especially in low and middle income countries [23]. In clinic environments that are not tied to referral systems, delivery of comprehensive diabetes self-management support systems, such as mental health resources, may be uncoordinated meaning that health care is provided by separate systems with little communication and follow-up between providers.
A recent review by Werfalli et al. [ 24] found that in low- and middle-income countries task shifting may be a potential solution to limited referral systems. In task shifting, a task normally performed by physicians is transferred to a health professional with different education and training or to a person specifically trained to perform a specific task, without having formal health education [24]. Task shifting can involve community health workers or patient peers working with patients to manage chronic conditions and overall wellbeing. For example, a Guatemalan program identified and trained community health workers to provide education and support to Mayan adults with T2D [25]. In addition to diabetes education, each community health worker had a caseload of 15–20 patients and held diabetes clubs to create a social network of support and also met with patients individually to provide emotional support to deal with the stigma and sadness they experienced with living with T2D [25]. At follow-up, HbA1c values significantly decreased, and qualitative findings from focus groups with participants found improved coping mechanisms, empowerment, and experiences with social support [25]. While programs like this one in Guatemala have significantly reduced distress and improved wellbeing [26], some providers in this study were apprehensive in being tasked to provide this form of support due to a lack of training. Programs that have effectively addressed emotional and mental health needs of patients explicitly trained community health workers on the provision of mental, emotional, and social health [27]. Rothschild et al. [ 27] assessed a community health worker-based intervention among Mexican Americans with T2D who taught general self-management skills, problem solving, seeking social support from family and friends, and managing stress.
Although task shifting could improve integration of emotional support into clinical care, there is a need to develop and strengthen capacity. Providers in this study identified their lack of training in mental health as the main barrier to providing such support to patients, which has been documented in studies with providers in Mexico [28] and the DR [29]. Of note, at the time of this study, the Chronic Care International multi-disciplinary team was continuing a capacity building process with these Dominican providers around mental health, including initial training on depressive symptom screening and cognitive behavioral counseling to address multiple dimensions of distress. Recent studies on provider perspectives on delivering mental health services in primary care settings in resource-limited areas such as Mexico [28] and the Dominican Republic [29] have also identified similar results. As resources are often limited, leveraging the strengths of existing staff by formally training those who are providing emotional support in informal ways, while reducing their others clinical tasks, may be an alternative. Comprehensive trainings can include topics related to stress-management, emotional well-being, and provide roleplaying exercises to improve providers’ self-efficacy to provide this type of support.
Providers also acknowledged their limited exposure to patients outside of their clinic appointments as a major barrier to addressing patient emotional wellbeing. Therefore, providers often considered patients’ family members as the ideal group to support patients. An important consideration is how access to familial support is gendered and may not be available for every patient. Providers noted that women with T2D often felt less supported in the home than men. This observation is consistent with T2D studies that have found that women report higher levels of depression, diabetes distress, emotional burden, and diabetes regimen-related stress than men [13, 30, 31]. Furthermore, the onus of who provides social support in the home often falls on women regardless of T2D status compared to men highlighting an imbalance of who is more likely to address emotional health needs for people with T2D [32, 33]. Establishing an emotional support network from peers and family members is essential for care, which may require training families on how to provide emotional support for those with T2D in the household. Overall, developing and strengthening social support networks can help remediate personal distress and improving the well-being of patients. Reflecting this, in the summer of 2018, the diabetes program that the providers we interviewed worked with, in collaboration with their newly formed Patient Associations, expanded the social support system with the formation of peer support groups, known as Groups of 5. The goal was to train peer leaders on diabetes management and social support provision. Future research and evaluation are needed to assess the impact of these efforts as well as the pathways of influence.
Some providers in our study reflected a willingness to provide more emotional support but also identified two key barriers: time and capacity to add additional activities to their workflow as a barrier [34, 35]. Therefore, there is a need to identify ways to integrate emotional support into the existing system of care. However, not every provider is equipped to do so. Key provider characteristics identified were patience, understanding, and a willingness to be patient-facing, all of which were identified as central to the role of cooperadores. A systematic review on facilitators to successful management of T2D in Latin America and the Caribbean found that positive attitude of health professionals, consistent trainings of health providers, using patient centered recommendations improved disease management [31]. Whereas more paternalistic approaches to provider-patient communication was a barrier to successful T2D management [31]. This is in line with our study focused on patients’ social support experiences, in which some patients mentioned that paternalistic tones from providers, particularly physicians, contributed to their feelings of distress [13]. Relatedly, a recent study focused on mental health services in the Dominican Republic from the perspective of providers found that non-mental health trained providers can carry stigmatizing attitudes towards patients presenting with mental health needs, which may influence their desire to manage their patient’s other conditions [29]. Thus, while the integration of emotional support in diabetes care is important, this integration requires staff who can provide that care in a patient-centered and empathetic way. In addition to improving ways to integrate emotional support with the clinic, developing and strengthening mental health referral systems by linking the clinics with local partners is another way of increasing the capacity of these community-based programs and improve the health and well-being of adults living with T2D.
Limitations of the study are that our results may not be transferable to other clinical contexts outside of the DR or primary clinics not focused on diabetes care. Although we interviewed physicians, nurses, and community health workers, additional interviews with physicians and nurses could have provided more clinician-specific perspectives, particularly clinics that function without collaborations with community health workers. However, the context of providing mental and emotional health care for adults with T2D can inform interventions and programs in similar resource-contrained settings.
## Conclusions
Providers were aware of the emotional burden their patients face managing T2D in rural areas of the Dominican Republic but lacked time and capacity to provide emotional support. Family was identified as the primary source of emotional support, followed by providers who had the identified skills and capacity to work with patients on emotional and mental health needs. Training on stress-management and emotional wellbeing specific to diabetes was identified as a needed next step to leverage existing providers towards being sources of emotional support for their patients. More research is needed on the feasibility and acceptability of implementing an integrated health model in rural community clinics.
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|
---
title: 'Alcohol consumption and related disorders in Iran: Results from the National
Surveillance of Non-Communicable Diseases’ Survey (STEPs) 2016'
authors:
- Negar Rezaei
- Naser Ahmadi
- Mehran Shams Beyranvand
- Milad Hasan
- Kimiya Gohari
- Moein Yoosefi
- Shirin Djalalinia
- Sahar Saeedi Moghaddam
- Mitra Modirian
- Forough Pazhuheian
- Alireza Mahdavihezaveh
- Ghobad Moradi
- Farnaz Delavari
- Bagher Larijani
- Farshad Farzadfar
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021244
doi: 10.1371/journal.pgph.0000107
license: CC BY 4.0
---
# Alcohol consumption and related disorders in Iran: Results from the National Surveillance of Non-Communicable Diseases’ Survey (STEPs) 2016
## Abstract
### Background
Alcohol consumption is a public health concern which is illegal in Iran. Moreover, due to cultural and religious beliefs, the available population-based research findings on alcohol consumption are inadequate. We aimed to provide an estimate on alcohol consumption using a large-scale population-based survey in Iran.
### Materials and methods
The National Surveillance of Non-Communicable Risk Factors in Iran was a population-based survey conducted in 2016. The epidemiologic distribution of alcohol consumption and its related disorders were assessed using weighted survey methods and multiple logistic regression models. Age standardized rates were calculated using Iran’s national population census in 2016.
### Results
At the national level, the prevalence rates of lifetime and current alcohol consumption were $8.00\%$ ($95\%$ CI: 7.67–8.32) and $4.04\%$ ($95\%$ CI: 3.81–4.27), respectively. The highest prevalence was reported among 25 to 34 year-olds. Individuals of higher socioeconomic status consumed significantly greater levels of alcohol. At provincial level, the highest and lowest percentages of the current alcohol drinking rates in Iran’s provinces were, $23.92\%$ ($95\%$ CI: 17.56–30.28) and $0.4\%$ ($95\%$ CI: 0–1.18) in males, $1.58\%$ ($95\%$ CI: 0.22–2.94) and $0\%$ in females, respectively. In urban regions, the highest alcohol consumption rate was more than 22 times greater than the lowest alcohol consumption rate. Current alcohol drinkers were 2 times more prone to injury as compared to nondrinkers (ORadj: 2.0, $95\%$CI: 1.7, 2.3).
### Conclusion
In Iran, the prevalence of alcohol consumption is low, although there is a considerable variation of alcohol consumption at provincial level as well as in different gender groups. Therefore, preventive WHO—recommended measures should be adopted more seriously by vulnerable groups.
## Introduction
Globally, alcohol use disorder caused 2.1 deaths per 100,000 persons in 2019 [1]. The death rate was 5.44 per 100,000 in the WHO European Region and 0.61 per 100,000 in the WHO Eastern Mediterranean Region (EMR) [1]. The total alcohol per capita consumption in the population aged over 15 years demonstrates an increasing pattern from 5.5 liters of pure alcohol (ethanol) in 2005 to 6.4 liters in 2016 [2, 3]. This pattern was steady in the EMR, and showed a decline in the European Region [2, 3]. The mortality rate due to alcohol disorder in Iran was estimated at 0.28 per 100,000 in 2019 [1]. The total alcohol per capita consumption was reported to be 0.1 liters [4].
Alcohol is associated with many disorders, including dependency, alcohol-related deaths, mental and physical disorders, cardiovascular disorders, liver disease, violent and anti-social behaviors in adolescents and young people, and traffic injuries [2, 5–7]. A total of 389,100 cases of cancers were related to alcohol worldwide, representing $3.6\%$ of all cancers ($5.2\%$ in men and $1.7\%$ in women) [8]. Alcohol consumption is a risk factor among young people, causing disability or even death [9, 10].
Civil laws regarding alcohol trade and use in Islamic countries are considerably different, as alcohol consumption is forbidden in Islam. In Iran, the prevalence of alcohol consumption was reported at 352.4 per 100,000 persons in 2019 [1]. Hence, alcoholic beverages are either homemade or distributed through the black market [11, 12]. This form of alcohol consumption leads to various disorders such as methanol toxicity and at times even death [6, 12, 13]. Furthermore, the prevalence of alcohol consumption is underreported due to stigmas and prohibition rules [6, 11]. Therefore, reports on the prevalence of alcohol consumption are limited [14, 15] and there is no comprehensive population-based study to evaluate the provincial prevalence of alcohol consumption and its association with relevant disorders. Thus, we aimed to estimate the overall prevalence of alcohol consumption by age, gender, and province and its association with stroke, cardiac disorders, fatty liver disease, traffic injuries, and a healthy diet using a population-based survey.
## Overview
The National Surveillance of Non-Communicable Risk Factors in Iran was conducted on 31,050 people over the age of 18 years. It was a cross-sectional population-based household survey of non-communicable diseases (NCD) risk factors by sex and age carried out at the provincial level in 2016. The survey was conducted with the objective of continuously collecting information in three phases: data collection of NCD related risk factors using a questionnaire, gathering the required information through physical examinations and performing laboratory measurements. Determination of non-communicable risk factors including alcohol consumption was one of this survey’s first goals [16]. After collecting data on age, sex, and different demographics from all family members, we included family members who were 18 years or older. We also excluded people with serious physical and mental illnesses who could not be interviewed. We filled out a questionnaire, measured anthropometric indices, and collected blood and urine samples of all the participants for laboratory controls (laboratory controls were performed for individuals 25 years and older).
## Sampling
A cluster random sampling frame was considered for proportional to size sampling. For our sampling frame, we used the countrywide postal code database, which incorporates the addresses of all residential homes inside the country. Through a scientific choice of samples proportional to size, we chose samples proportional to the dimensions of rural and urban regions inside every province. A total of 3105 clusters and 31,050 Iranian adults aged 18 ≤ were selected. We used the province of Ilam–with the lowest population in Iran–to calculate the minimum sample at $95\%$ confidence interval. 384 samples were considered as the baseline of our calculations. Sample sizes of other provinces were estimated based on the population ratios of each province to Ilam province. To account for the effect of sampling design and to control non–response error, $10\%$ was added to the estimated sample size of each province. To reduce costs and increase productivity, for provinces with 800 or more samples calculated through weighting, it was decided that half the calculated sample size be considered along with double the weight of estimates. We considered these sampling weights in the analysis of results using survey analysis in STATA. This Survey was carried out following WHO’s recommendation to control NCDs. Additional detailed information on sample size calculation, questionnaire validation and study implementation can be found elsewhere [16]. Eventually, 31,050 participants were included in the study, and 30,541 accept to complete the questionnaires. Overall, 29,068 participants completed all items in the alcohol section’s questionnaire. Therefore, the response rate, which is calculated by dividing the number of participants who answered all the questions of the alcohol section by the total number of individuals included in the study, was approximately $93.6\%$.
## Definition of variables
The tool used to evaluate alcohol consumption in this study was based on WHO’s guidelines [17]. The questionnaire was first completed by interviewers who had been trained in research centers and later assigned to the fields. It included questions about lifetime and current alcohol consumption (frequency of alcohol consumed in the past year), binge drinking (defined as frequency of 6 standard drinks or more in one episode), and frequent heavy episodic drinking (defined as frequency of monthly or more episodes of binge drinking during the past year). The question on current alcohol consumption was “Did you drink alcohol in the past 12 months? Yes/ No”. The question on lifetime consumption was “Did you ever drink alcohol in your life?”. Driving under the influence (DUI) of alcohol was asked by the question “Have you ever driven a car while you were drunk? Yes/No”. A ‘standard drink’ is a measure of alcohol consumption representing a fixed amount of pure alcohol, used for future recommendations on alcohol consumption and its associated health risks. This definition varies in different countries [11]. In Iran, the amount is 10 grams, similar to Australia, and is lower than the United States and Japan [11]. As alcohol consumption is banned due to religion and law in Iran, we defined a standard drink for the participants by providing them an image of the amount of a standard drink for their better understanding. This was emphasized during the interviewers’ training sessions.
Cardiovascular disorders were defined as a patient’s response to the question “Have you been diagnosed with a cardiac disorder or stroke in the past 12 months?” Fatty liver disease was defined by an ALT level higher than 40 unites per liter. Injuries were defined as a participant’s response to the question “Did you sustain any injury in the past 12 months?” A healthy diet was defined as two servings of fish and 5 servings of vegetables or fruits per week. The wealth index (SES), which was derived from the household assets survey was analyzed using principal component analysis (PCA) and categorized into five quintiles. Physical activity was assessed by the IPAC questionnaire [18] and defined as a MET less than 6 hours.
## Ethical considerations
The Ethical Committee of the National Institute for Medical Research Development (NIMAD) approved this study under the registration code ID: IR.NIMAD.REC.1394.032. Participation in the study was voluntary. The objectives of the study were explained to all eligible individuals and written informed consent was thereafter obtained.
## Statistical analysis
First, we used weighted survey statistical methods for the descriptive reports, and prevalence. Second, age-standardization of prevalence rates was performed using the 2016 National Population Census [19] data obtained from Iran’s Statistical Center. These age-standardized measures were provided in maps. Third, the models were built distinctly to evaluate the association between alcohol consumption and the defined variables using univariate and multiple logistic regression analysis. The outcomes were cardiovascular disorders [16], suspected fatty liver disease [20], injuries [16], and healthy diet [17, 21]; the exposure was current alcohol consumption.
Univariate analysis was performed to calculate crude Odds Ratio (ORc), and $95\%$ Confidence Interval (CI) for each pair of exposure and outcome. Wealth index (SES) [16], sex, age, smoking and physical activity [7, 22–24] were confounding variables. They were tested separately for their associations with outcomes and exposure, and all met the criteria for being confounders, so they were adjusted in the final multiple regression model to control for confounding bias. The multiple logistic regression model was used to calculate the adjusted odds ratio (ORadj), and $95\%$ CIs applying the Enter method.
All the analyses were performed with STATA software version 14.0 (Stata Corp, College Station, TX, USA). The R statistical software version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for the plots.
## Results
The participants’ mean age was 44.4 years (range: 18 to 100 years). The majority of the participants were female ($52.08\%$), non-smokers ($85.37\%$), physically inactive ($56.36\%$), and urban residents ($71.09\%$). Table 1 shows the characteristics of the study participants.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Alcohol Drinking Status | Alcohol Drinking Status.1 | Alcohol Drinking Status.2 | Alcohol Drinking Status.3 |
| --- | --- | --- | --- | --- | --- |
| | | Lifetime alcohol drinking (n = 29875) | Lifetime alcohol drinking (n = 29875) | Current alcohol drinking (n = 29869) | Current alcohol drinking (n = 29869) |
| Variables | Categories | Prevalence (95%CI) | P-value* | Prevalence (95%CI) | P-value* |
| Sex | Male | 15.3 (14.6,15.9) | <0.001 | 7.6 (7.2,8.1) | <0.001 |
| Sex | Female | 1.3 (1.2,1.5) | <0.001 | 0.8 (0.6,0.9) | <0.001 |
| Age | 18–24 years | 8.8 (7.7,9.9) | <0.001 | 6.6 (5.7,7.6) | <0.001 |
| Age | 25–34 years | 11.5 (10.7,12.2) | <0.001 | 6.9 (6.3,7.6) | <0.001 |
| Age | 35–44 years | 9.3 (8.5,10.0) | <0.001 | 4.5 (4.0,5.0) | <0.001 |
| Age | 45–54 years | 6.6 (6.0,7.2) | <0.001 | 2.5 (2.0,2.9) | <0.001 |
| Age | 55–64 years | 4.9 (4.2,5.5) | <0.001 | 1.7 (1.3,2.1) | <0.001 |
| Age | 65–70 years | 4.8 (3.7,6.0) | <0.001 | 1.1 (0.5,1.6) | <0.001 |
| Age | >70 | 4.1 (3.3,4.9) | <0.001 | 0.6 (0.2,1.0) | <0.001 |
| Wealth index | Poor | 5.4 (4.8,6.0) | <0.001 | 2.6 (2.2,3.1) | <0.001 |
| Wealth index | Second quintile | 7.4 (6.7,8.1) | <0.001 | 3.4 (2.9,3.9) | <0.001 |
| Wealth index | Third quintile | 8.1 (7.3,8.9) | <0.001 | 3.9 (3.3,4.4) | <0.001 |
| Wealth index | Forth quintile | 9.4 (8.6,10.2) | <0.001 | 4.8 (4.2,5.4) | <0.001 |
| Wealth index | Rich | 10.1 (9.3,11.0) | <0.001 | 5.6 (5.0,6.2) | <0.001 |
| Smoking | Yes | 26.6 (25.3,28.0) | <0.001 | 12.3 (11.3,13.3) | <0.001 |
| Smoking | No | 4.8 (4.5,5.1) | <0.001 | 2.6 (2.4,2.8) | <0.001 |
| Low physical activity | Yes | 5.7 (5.3,6.1) | <0.001 | 2.8 (2.5,3.0) | <0.001 |
| Low physical activity | No | 9.2 (8.6,9.7) | <0.001 | 7.6 (6.6,8.6) | <0.001 |
| Residential status | Rural | 7.1 (6.5,7.6) | <0.001 | 3.4 (3.0,3.8) | <0.001 |
| Residential status | Urban | 8.4 (8.0,8.8) | <0.001 | 4.3 (4,4.6) | <0.001 |
At the national level, $8.00\%$ ($95\%$ CI: 7.67–8.32) and $4.04\%$ ($95\%$ CI: 3.8–4.3) of the participants declared lifetime and current alcohol consumption, respectively. The highest prevalence of lifetime and current alcohol consumption was reported among 25 to 34 year-olds. The prevalence of both types of alcohol consumption reduced significantly with an increase in age (p value for trend <0.001) (Fig 1). In our study, socioeconomic status (wealth index) and prevalence of current alcohol consumption were directly proportional. Persons of high SES consumed nearly twice as much alcohol compared to persons of low SES (Table 1). The response rate to frequency of alcohol use in the past year was $89.73\%$ ($\frac{1040}{1159}$). Among respondent participants, $73.36\%$ ($\frac{763}{1040}$) drank less than 1 day per month, $14.90\%$ ($\frac{155}{1040}$) drank 1 to 2 days per month and only $0.20\%$ ($\frac{21}{1040}$) drank every day (Fig 2). Regarding lifetime and current alcohol consumption, the difference in prevalence rates between males and females was significant ($15.27\%$ and $7.61\%$ in males, vs $1.35\%$ and $0.78\%$ in females, respectively). Among the (predominantly male) respondents who lived in urban regions with a mean (SE) age of 35.78 (2.81) years, approximately $0.1\%$ binge drank every day. All in all, $1.5\%$ of participants binge drank less than 12 times a year and less than $1\%$ practiced frequent heavy episodic drinking. However the prevalence of binge drinking among current alcohol consumers in daily, weekly, monthly, and less than 12 months a year, are $1.60\%$, 6.49, 15.14, and $35.03\%$, respectively (Table 2).
**Fig 1:** *Distribution of alcohol drinking among age group by sex and residential status.* **Fig 2:** *Drinking frequency in last 12 months (at national level by age groups).* TABLE_PLACEHOLDER:Table 2 At the provincial level, the highest and lowest percentages of age-standardized current alcohol consumption were, respectively, $13.22\%$ ($95\%$ CI: 7.66–18.77) and $0.58\%$ ($95\%$ CI: 0–1.51) in urban regions and $7.88\%$ ($95\%$ CI: 3.98–11.77) and $0\%$ in rural regions. In other words, in urban regions, the highest current alcohol consumption rate was 22 times greater than the lowest current alcohol consumption rate (Fig 3). At the provincial level, the highest and lowest rates of age-standardized current alcohol consumption were, respectively, $23.92\%$ ($95\%$ CI: 17.56–30.28) and $0.4\%$ ($95\%$ CI: 0–1.18) in males, $1.58\%$ ($95\%$ CI: 0.22–2.94) and $0\%$ in females, and $12.49\%$ ($95\%$ CI: 9.23–15.75) and $0.46\%$ ($95\%$ CI: 0–1.09), in both genders (Fig 4).
**Fig 3:** *Provincial distribution of alcohol drinking (%) within the past year.* **Fig 4:** *Current alcohol drinking prevalence map of Iran, 2016, https://www.openstreetmap.org/copyright.*
In Tehran (the capital of Iran), the prevalence rates of current alcohol consumption for all ages of both genders, males, and females were, $4.27\%$ (95 CI: 3.69–4.85), $7.39\%$ (95 CI: 6.31–8.46), and $1.32\%$ (95 CI: 0.87–1.78), respectively.
Results of the crude model (ORC) showed that compared to nondrinkers, current alcohol consumption respondents had lower risks of cardiac disorders and stroke by $70\%$ and $80\%$, respectively (ORc cardiac disorder: 0.3, $95\%$ CI: 0.2, 0.8; ORc stroke: 0.2, $95\%$ CI: 0.7, 0.9). However, these reports were not significant when adjusting the model (ORadj) for sex, age, smoking, wealth index and physical activity confounders. Current alcohol consumption was associated with cardiac diseases but was not significant (ORadj cardiac disorder: 1.2, $95\%$CI: 0.8, 1.8, ORadj stroke: 0.6, $95\%$ CI: 0.1, 2.4). Current alcohol drinkers were more likely to have poor dietary habits and fatty liver disease as compared to nondrinkers (ORc bad dietary intake: 1.9, $95\%$ CI: 1.6, 2.4, ORc fatty liver: 1.8, $95\%$ CI: 1.4, 2.2), but this association was not significant (ORadj) when controlling the aforementioned confounding variables (ORadj bad dietary intake: 1, $95\%$ CI: 0.8, 1.2, ORadj fatty liver: 1, $95\%$ CI: 0.8, 1.3). Alcohol drinkers were more prone to traffic injuries. Current alcohol drinkers sustained traffic injuries 6 times more than nondrinkers (ORc injury: 6.4, $95\%$CI: 5.7, 7.3). The odds ratio reduced by half when adjusting for confounders and was statistically significant (ORadj injury: 2.0, $95\%$CI: 1.7, 2.3) (Table 3, Fig 5).
**Fig 5:** *Association of alcohol related disorders with alcohol consumption in logistic regression models: Result from the National Surveillance of Non-Communicable Disease Survey (STEPs).* TABLE_PLACEHOLDER:Table 3
## Discussion
The prevalence of lifetime and current alcohol consumption was $8.00\%$ ($95\%$ CI: 7.67–8.32) and $4.04\%$ ($95\%$ CI: 3.8–4.3), respectively. The highest prevalence was reported among 25 to 34-year-old individuals. At the provincial level, the differences between the highest and lowest percentages of current alcohol consumption in males and females, and between urban and rural regions were nearly 23, 12, 12, and 7 percent, respectively. Moreover, current alcohol consumption respondents were susceptible to injury twice as much as nondrinkers.
Most studies conducted in Iran have discretely examined urban or lifetime prevalence, whereas this study has focused on urban, rural, male, female, age-standardized and all-age prevalence rates of current alcohol consumption [14, 15, 25, 26]. For instance, in the capitals of five provinces the prevalence of lifetime alcohol use was reported at a rate of $28\%$ [15, 25]. Another population based study reported the frequency of alcohol use among the general population aged over 15 at about $2.31\%$. Males had almost 8 times higher prevalence of lifetime alcohol consumption compared to females ($4.13\%$ versus $0.56\%$) [14]. The huge difference observed in current alcohol consumption between provinces–especially among females and rural regions- may be explained by the diversion in cultural and religious beliefs among different genders and age groups, and is consistent with other studies conducted in Iran [25, 26]. Alcohol consumption among men and women varies noticeably in different countries, from nearly 10 to 20 percent to approximately 70 to 90 percent [27–29]. These rates are even lower in Islamic countries compared to other countries across the globe [30]. In Iran, women are more likely to be socially active and less exposed to people with high-risk behaviors such as narcotic drug abuse or alcohol misuse [6, 27, 31]. One report suggests an inverse relationship between socioeconomic status and alcohol consumption [31]. Nonetheless, other reports show a direct association [32], which is consistent with this study’s results. Age is directly associated with alcohol consumption. Reports show that 18 to 24-year-olds have the highest alcohol consumption rate, and the rate decreases with increasing age [33]. This study showed that alcohol consumption increased among 25 to 34-year-olds and then decreased with age, which is consistent with other reports from Iran [10, 26].
For many years, research studies indicated that light alcohol consumption had benefits for coronary diseases and stroke [34, 35]. The ‘J-shaped’ dose response of alcohol consumption and all-cause mortality, stroke, and heart disease have been misused by alcohol industries [35–39]. At the time of this study, alcohol-attributable cancer mortality rates were estimated at $5.8\%$ [40]. However, several studies have reported that, irrespective of the dose consumed, alcohol has no protective effect on all-cause mortality and cancer incidence [37, 38, 41]. In this study, the protective effect of alcohol was seen in the univariate model with cardiac disease and stroke. Nevertheless, after adjusting for confounders, the association was no longer significant, which is consistent with the new systematic reviews available on this topic [38]. A meta-analysis did not find any relationship between alcohol consumption and reduced heart disease mortality, which implies that alcohol does not enhance health [37, 39]. However, this study is a cross-sectional study prone to unmeasured confounders that could not be adjusted for their associations and future studies are warranted on this topic. Reports indicate alcohol consumption association with fatty liver disease [40, 42]. However, in this study, we could not find any association. Worldwide, $32\%$ of alcohol-attributable deaths result from accidental injuries, whereas $13.7\%$ result from intentional injuries [3]. This situation is even worse and alarming in low-income countries with an increasing rate of alcohol consumption and underreporting bias in policymaking reports [3, 43, 44]. Based on our findings, the risk of injury increased more than two times in alcohol drinkers when compared to nondrinkers, which is consistent with earlier studies [43, 44].
With regards to the limitations of this study, one is that its methodological design cannot assesse causality. Although we could capture many sociodemographic confounders, there are still residual confounding which needs more detail on drugs and medication, addiction to alcohol, alcohol consumption withdraw, exact amount of alcohol consumption. We could not capture these variables due to social and legal and these information are beyond the aims of the STEPs survey [15]. However, it does illustrate a picture of the current situation of alcohol consumption prevalence in Iran. We recommend conducting cohort studies to assess the causality with assessing potential confounders during follow ups. Nevertheless, the strength of this study is its well-designed, population-weighted, cross-sectional, provincial level survey from 30 provinces of Iran. Hence, it could represent close-to-reality estimates of the existing conditions and associations between alcohol consumption and related disorders in Iran. To our knowledge, this is the first study to assess these associations to this extent, adjusting for confounders such as sex, age, wealth index and physical activity. Another limitation is the stigma of alcohol consumption in Iran due to religious and legal bans. In this study, we are not sure whether the negative responses to alcohol consumption were accurate, due to the cultural, social, legal and religious stigmas attached to it in Iran. So we estimate an under-reporting bias in the results. This selection bias could be due to the under-representation of heavy and problem drinkers, and those with severely poor health in health survey. We recommend conducting future studies using specific questionnaires and applying methodological and statistical methods [45, 46] in order to detect under-estimation and non-response bias on alcohol consumption in Iran.
## Conclusion
Although, the prevalence of alcohol consumption in *Iran is* lower than in developed countries, there is a considerable variation of alcohol consumption at provincial level as well as different gender groups. Also there is an association with injuries. In this regard, preventive social measures should be adapted more seriously [25, 47]. Given WHO’s global strategy [48] to reduce the harm of alcohol consumption (of the ten components of national action regarding this issue), policy makers and stakeholders in Iran could focus on awareness, health services’ response, community action, reducing alcohol intoxication, reducing the public health impact of illicit, informally produced alcohol, and monitoring & surveillance. Social, individual and public empowerment is recommended through educational programs and fighting stigmas attached to alcohol consumption. This will be accomplished through public health advocacy. Protective strategies must be considered to reduce the harm of alcohol consumption among high risk groups.
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|
---
title: Community-based management of chronic obstructive pulmonary disease in Nepal—Designing
and implementing a training program for Female Community Health Volunteers
authors:
- Tara Ballav Adhikari
- Bishal Gyawali
- Anupa Rijal
- Abhishek Sapkota
- Marieann Högman
- Arjun Karki
- Torben Sigsgaard
- Dinesh Neupane
- Per Kallestrup
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021247
doi: 10.1371/journal.pgph.0000253
license: CC BY 4.0
---
# Community-based management of chronic obstructive pulmonary disease in Nepal—Designing and implementing a training program for Female Community Health Volunteers
## Abstract
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality in Nepal. Female community health volunteers (FCHVs) have proven effective in the delivery of reproductive, maternal, and child health services in Nepal and recently in the prevention and management of hypertension and type 2 diabetes. Evidence on their roles in COPD management is not yet available. The aim of this study was to develop, implement, and evaluate a training program for FCHVs regarding COPD prevention and management. The training program was part of a cluster-randomized trial of a 12-month intervention to improve COPD outcomes in a semi-urban area of Western Nepal. A six-day workshop consisting of thirty hours of training was developed for FCHVs. Training materials incorporated introduction to COPD, risk factors and symptoms, COPD status assessment guide for FCHVs, guidance on breathing techniques, and exercises for people living with COPD. Pre- and post-test questionnaires were administered to assess the change in knowledge of FCHVs, post training skills assessment followed by semi-structured interviews assessed FCHVs’ satisfaction with the training program. The findings of the pre- and post- test assessments showed a significant improvement in FCHVs’ COPD-related knowledge from a median (interquartile range) score of 12 (3–16) before to 21 (21–22) ($p \leq 0.001$) after the training program. The qualitative assessment revealed the feasibility of FCHVs’ training on COPD and their acceptability to deliver the intervention package within the community. It also indicated that implementing future training with an extended period and a few days break in-between could enhance the effectiveness. Training of FCHVs in COPD management is feasible and leads to improvement in knowledge. The motivation shown by FCHVs to deliver the intervention could inform and guide community programs and policies for COPD prevention and management in Nepal and similar settings.
## Introduction
Globally, chronic obstructive pulmonary disease (COPD) has an increasing prevalence and constitutes a substantial socioeconomic burden [1]. In 2017, above $3\%$ of disability-adjusted life years and 3.2 million deaths were ascribed to COPD worldwide [2]. A recently published meta-analysis estimated a $12\%$ global prevalence of COPD [3]. More than $90\%$ of COPD-related deaths occur in low-and middle-income countries (LMICs) [4]. COPD is the most prevalent non-communicable diseases (NCDs) in Nepal, with a $12\%$ prevalence in adults [5]. In 2016, nearly one million Nepalese people were suffering from COPD, twice as many as in 1990 [6]. The higher burden of COPD in Nepal can be attributed to a high rate of tobacco smoking [7], substantial use of biomass fuel for cooking [8], poor outdoor air quality [9], exposure to second-hand smoking [8], and a demographic development with an aging population [10].
One of the major challenges in COPD prevention and management in Nepal’s already weakened health system, is the shortage of COPD specialists. The number of practicing pulmonologists in *Nepal is* relatively low, and most are based in the capital city Kathmandu or other big cities. Lack of proper diagnostics, treatment, and medications at peripheral health centers in combination with a low level of health literacy and awareness of the disease in the population constitute important challenges facing the increasing burden of COPD in Nepal [11–13]. COPD diagnosis is mostly limited to symptomatic assessment without objective confirmation by spirometry [12], potentially leading to underdiagnosis and misdiagnosis.
In a scenario with health workforce shortage, the shifting of tasks such as screening, education, referral, and follow-up to non-physicians such as community health workers (CHWs) can be an effective way to ensure the delivery of health care services [14]. The World Health Organization recommends optimizing community-based programs involving CHWs to prevent and manage NCDs, including COPD [15]. It is widely recognized that CHWs play an important role in a wide range of health behavior improvement and health outcome initiatives [16, 17]. Recently, CHW-led interventions have shown positive results in preventing and managing NCDs, including type 2 diabetes and hypertension [18]. Training programs for CHWs in monitoring and care of COPD patients in a rural part of India [19], and educational interventions for CHWs to increase awareness on COPD at a community level in Uganda [20] are some of the promising examples of CHWs’ engagement in community-based management of COPD in LMICs.
A network of CHWs, Female Community Health Volunteers (FCHVs), has been operating within the Nepalese health system for the last three decades [21]. FCHVs are local women selected by mothers’ groups in the communities receiving a short 18 days training. They serve as a bridge between the community and the health system, increasing health service utilization, promoting healthy behavior, and raising health awareness in the community [22]. Recent studies have shown promising results of the work of FCHVs in type 2 diabetes and hypertension management in Nepal [23, 24]. However, FCHVs’ involvement in COPD prevention and management has not yet been explored.
A research project entitled ’Community-based management of COPD in Nepal—a cluster-randomized controlled trial (COBIN-P)’ has been implemented in a collaboration between Aarhus University in Denmark and Nepal Development Society in a semi-urban area of Pokhara Metropolitan city of western Nepal [25]. This ongoing study aims to assess the effectiveness of an FCHV-led intervention to prevent COPD and improve disease management among adults with COPD. This study reports on the development and evaluation of the feasibility and preliminary effectiveness of a training program to strengthen and extend the knowledge of FCHVs in an intervention to prevent and manage COPD at community level.
## Study setting and data collection
This study is part of the COBIN-P project. This two-arm cluster randomized controlled trial is ongoing in the semi-urban area of Pokhara Metropolitan city (former Lekhnath Municipality) of Western Nepal [25]. As a part of the study intervention, FCHVs were trained to raise awareness of prevention and management of COPD at the community level through household visits. FCHVs’ knowledge of COPD was assessed before and after the training program. Eight informants were recruited by convenience sampling to reach data saturation and eight in-depth interviews provided data on FCHVs’ feedback on the training program. The training program and data collection took place from December 2019 to February 2020. Similarly, prior to developing and designing the training program, eight stakeholders from the community (three FCHVs, two health assistants, one medical officer) and two health education experts were purposively selected.
## Developing a training program and educational materials for FCHVs
The study team developed the training program, including training curriculum and tools, based on an extensive literature review of similar CHWs-led interventions on the management of COPD and other NCDs. The team reviewed the health education materials previously used in tobacco and tuberculosis control, hypertension, and diabetes management programs in Nepal [26, 27]. The curriculum was developed by triangulating with domain experts, National Health Education and Information Center (NHEICC) professional staff, district health administrators, physicians, and FCHVs. As some of the community people and FCHVs were illiterate, locally appropriate tools were also developed, including pictorial flip charts and brochures for screening and management of COPD (S1 Appendix).
Based on a literature review, the training program was designed to provide FCHVs with knowledge regarding COPD risk factors, signs, and symptoms, management of COPD via screening, basic breathing techniques, stamina, endurance-building exercises, and knowledge about healthy lifestyle and medication adherence. Likewise, FCHVs were also trained to use educational materials during their home visits, and to identify and refer patients to the nearest health facility, and properly maintain recording and reporting forms. Every session was followed by skills-building sessions. Training was guided by a facilitator’s manual, ’Community Based Prevention and Management of COPD in Nepal: Female Community Health Volunteer’s Training Facilitator Guide’. The facilitators’ guide was developed to assist facilitators in being familiar with the module contents, session objectives, and delivery methods. The training materials, including the facilitator guide, were developed following procedures adopted for other COBIN studies on diabetes and hypertension [26, 27], and Engage-Tuberculosis Training of Community Health Workers and Community Volunteers: Facilitator’s Guide [28]. Experts were consulted for suggestions to be incorporated into the training program. Pilot testing of the training matrieals and program was performed, and adaptations were made. The FCHVs received training for 30 hours during a period of six days (S1 Table).
## Training of FCHVs
There were 123 FCHVs working in the study area. Fifty-seven FCHVs from the randomly assigned seven intervention clusters were invited for a one day orientation and assessment session. During the first day of training, FCHVs were informed about the COBIN-P project and introduced to COPD. FCHVs were then assessed for the minimum requirements of reading, writing, motivation, and availability to attend the next five days of training and the one-year intervention period. A total of 23 FCHVs fulfilled the requirements and were enrolled for the next five days of training. One FCHV dropped out of the training after two days due to personal reasons. The training program was delivered by the principal investigator (TBA), a medical officer from the Ministry of Health and Population who was also a certified trainer of World Health Organization package of essential NCDs, a consultant pulmonologist, and two health staffs from the local organization Nepal Development Society with previous experience in NCDs-related training. The training sessions were conducted in the local language (Nepali) using PowerPoint presentations, videos, demonstrations, and group exercises. The six-day FCHVs training package consisted of seven units and 16 lessons summarized in S1 Table.
## Evaluation
The training program was evaluated using both quantitative and qualitative methods. FCHVs completed a short questionnaire in the local language measuring their degree of COPD knowledge before and after training. The knowledge assessment questionnaire (S2 Appendix and S3 Appendix) was adapted from previous studies [20, 29]. Participants completed the questionnaire in strict confidentiality. Similarly, the skills of FCHVs were assessed on teaching breathing exercises, stamina and endurance building exercise, skills on using a flip chart, and COPD status assessment guide during and at the last day of the training using a skill assessment evaluation form. All FCHVs performed the required tasks during the assessments. Also, the field supervisor regularly monitored and supervised the FCHVs throughout the phase of trial implementation using an evaluation and supervision form (S4 Appendix). We conducted in-depth interviews with three FCHVs, two health assistants and one medical doctor, at the primary health care centers in the study area while designing the training package and educational material for the intervention. Through these pre-training interviews, we explored the health-seeking behavior for COPD, provision of health service delivery for COPD, and the importance and components of the community-based program to prevent and manage COPD. One month after the training, eight FCHVs were interviewed to assess their experiences with the training, obstacles, and challenges in delivering the intervention in the community using topic guide for the interview (S5 Appendix and S6 Appendix). Two trained researchers (TBA and AS) with a degree in public health and previous experiences in facilitating in-depth interviews conducted the interviews.
## Data analysis
Demographic characteristics of FCHVs were summarized using means and percentages. Each knowledge statement was recorded as a dummy variable, scoring 1 for correct and 0 for incorrect responses. The proportion of participants with correct responses for every statement on the questionnaires was determined and compared before and after using the McNemar test. The overall score was calculated by adding the dummy score 1 and 0 before and after the training. The overall change in knowledge score before and after the training was assessed using the Wilcoxon signed-rank test. All statistical tests were two-sided at alpha = 0.05. Data were entered in Epi Data version 4.0 (The EpiData Association, Odense, Denmark) and analyzed using STATA version 15.1 (StataCorp. Texas, USA). Data are presented as mean ± standard deviation (SD). FCHVs providing verbal consent and permission to audio-record the interviews were included in the qualitative study. The interviews were audio-recorded in the local language, transcribed verbatim and later translated into English. A thematic analysis was conducted for transcripts from interviews [30]. It included the steps of iterative reading of interview transcripts, creating initial codes, arranging the codes, and generating a thematic map of the analysis. Two researchers (TBA and AS) analyzed the qualitative data independently, and consensus regarding the nature and coding of emerging themes was reached through discussion with other team members.
## Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the review board of the Nepal Health Research Council (Approval number: 30–2019), and written informed consent was obtained from all participants.
## Quantitative findings
Twenty-two FCHVs participated in this study with a mean age of 45 ± 8 years. The majority belonged to an advantaged ethnic community with average schooling years of 9 ± 2 years. The mean years of working as an FCHV was 17 ± 10 years.
Six FCHVs had never heard of COPD before and more than $40\%$ were not aware that it was a lung disease. Only three FCHVs were aware of the breathing test for COPD diagnosis and the importance of vaccination for pneumonia and influenza. Likewise, none of the FCHVs had knowledge of and skills in breathing exercises and techniques for people living with COPD. Prior to training, the median (interquartile range) correct COPD-related knowledge score of FCHVs was 12 (3–16); after training this increased to 21 (21–22) ($p \leq 0.001$) (Table 1).
**Table 1**
| COPD knowledge statements | Correct in pre-test | Correct in post-test | p-value |
| --- | --- | --- | --- |
| COPD knowledge statements | Number (%) | Number (%) | p-value |
| Heard about COPD | 16 (73) | 22 (100) | 0.03† |
| COPD is a disease of the Lung. | 13 (59) | 22 (100) | 0.04† |
| COPD is most prevalent NCD in Nepal | 14 (64) | 20 (91) | <0.001† |
| COPD is a chronic condition | 13 (59) | 22 (100) | <0.001† |
| COPD is a preventable disease | 16 (73) | 21 (96) | <0.001† |
| COPD is unusual in people below the age of 40 years | 17 (77) | 22 (100) | <0.001† |
| In COPD, there is usually gradual worsening over time | 12 (55) | 22 (100) | 0.002† |
| Breathing tests confirm COPD | 3 (14) | 18 (82) | <0.001† |
| In COPD, oxygen levels in the blood are always low | 4 (18) | 20 (91) | <0.001† |
| Knowledge on symptoms of COPD | Knowledge on symptoms of COPD | Knowledge on symptoms of COPD | Knowledge on symptoms of COPD |
| Cough | 10 (46) | 22 (100) | <0.001† |
| Phlegm production | 13 (59) | 22 (100) | 0.001† |
| Shortness of Breath | 13 (59) | 22 (100) | 0.004† |
| Wheezing | 7 (32) | 22 (100) | <0.001† |
| Don’t know any symptoms | 7 (32) | 0 (0) | 0.02† |
| Knowledge of risk factors of COPD | Knowledge of risk factors of COPD | Knowledge of risk factors of COPD | Knowledge of risk factors of COPD |
| Tobacco smoking | 11 (50) | 22 (100) | 0.001† |
| Biomass fuel smoke | 12 (55) | 22 (100) | 0.002† |
| Outdoor air pollution | 8 (36) | 21 (96) | <0.001† |
| Alcoholism | 17 (77) | 22 (100) | 0.06† |
| Don’t know any risk factors | 6 (27) | 0 (0) | 0.03† |
| Knowledge of biomass fuels, smoking and COPD | Knowledge of biomass fuels, smoking and COPD | Knowledge of biomass fuels, smoking and COPD | Knowledge of biomass fuels, smoking and COPD |
| Stopping smoking will keep COPD from getting worse | 13 (59) | 22 (100) | 0.004† |
| Avoiding biomass fuel smoke prevents disease from getting worse | 5 (23) | 22 (100) | <0.001† |
| Cigarette smoking or second-hand smoke causes most cases of COPD | 9 (41) | 22 (100) | <0.001† |
| Knowledge of treatment and medication of COPD | Knowledge of treatment and medication of COPD | Knowledge of treatment and medication of COPD | Knowledge of treatment and medication of COPD |
| People with COPD should get vaccinated against influenza and pneumonia | 3 (14) | 22 (100) | <0.001† |
| COPD medicines (inhalers) prevent the disease from getting worse | 15 (68) | 22 (100) | 0.02† |
| Knows about breathing exercise and techniques for COPD | 0 (22) | 21 (96) | <0.001† |
| Walking and physical activity helps to improve fitness and lung health | 7 (32) | 20 (91) | <0.001† |
| Overall Median Score (Inetrquartile range) | 12 (3–16) | 21 (21–22) | <0.001†† |
## Stakeholder interviews before designing the training materials
Consultative meetings and interviews with eight concerned stakeholders from the community (three FCHVs, two health assistants, one medical officer) and health education experts (two public health officers from NHEICC) were conducted before designing the training package. All interviews highlighted the importance of the FCHV training on COPD management, particularly the improvement of the knowledge of COPD and its risk factors. FCHVs reported that training would enhance early consultations of symptomatic cases in health centers.
## Challenges in COPD prevention and management
Two kinds of barriers in COPD prevention and management in the community were identified, including the lack of knowledge and awareness regarding the disease and insufficient services related to COPD at the health centers.
Similarly, the degrading outdoor air quality was also considered a challenge in COPD prevention and management. One of the FCHVs said: "We can request them to make their kitchen smokeless based on their capacity. How can we protect the dust of roads which are under construction?" ( FCHV 1)
## Increased knowledge of and skills in COPD after the training
FCHVs reported that the training enhanced their knowledge regarding COPD. One FCHV stated: "I was not aware of other lung health problems other than pneumonia, (lung) cancer and asthma. Now, I can share information with community (people) about COPD and ways to prevent it." ( FCHV 4) The FCHVs acknowledged that training enhanced their skills to assess COPD status with simple questions and that made them able to help people living with COPD in their community.
## Satisfaction with the training package
The trained FCHVs were pleased with the training package and the way it was delivered methodology. The diversity in trainers from senior pulmonologists to local health workers was also well received by the FCHVs.
However, some mentioned time limitations in training considering the novelty of COPD to participants. " *Being a* new disease and different kind of information, (I) had a difficult time to catch (grasp) everything. Few more days of training with breaks for a few days could be (better)." ( FCHV 9)
## Possible challenges to the implementation of the program
FCHVs considered that the community people were receptive to their work. People living with COPD were very responsive during home visits. However, they underlined a few challenges, mainly among people without the disease who expressed less interest in hearing about treatment.
One of the FCHVs highlighted financial and health system challenges for the implementation: "Ok, I referred some of them to health posts, and if they do not find proper service and treatment there, then they do not want us to hear in next (home) visit. Many poor people cannot go to Kathmandu (tertiary hospitals). Then I have to be quiet despite (poor) health when I go (visit) them". ( FCHV 5) Likewise, FCHVs demanded extra incentives for additional roles. " We are providing new services to the community, but incentives are almost unchanged. Therefore, with work related to a new disease, we wish for extra monetary support for us." ( FHCV 8)
## Suggestions for future improvement of training
FCHVs regarded the training as very important to curb the rising burden of COPD in the community, with few suggestions on an integrated program for NCDs. They highlighted the need for refresher training on a timely basis.
## Discussion
In this study, we developed, implemented, and evaluated a first-of its kind, COPD prevention and management training program for FCHVs in Nepal. We found that following participation in this structured training program, FCHVs’ knowledge of COPD significantly ($p \leq 0.001$) increased from a median knowledge score of 12 to 21 (total score = 24). Before the training, a relatively low number of FCHVs knew that smoking causes COPD and that avoiding biomass fuel smoke prevents the disease from worsening. Similarly, only three out of 22 FCHVs knew the importance of influenza and pneumonia vaccinations for patients with COPD. Overall, we observed a knowledge insufficiency in the fundamental domains of symptoms, risk factors, and treatment of COPD among FCHVs, which significantly improved after our training program.
One of the strengths of this FCHVs training was that the training was based on a comprehensive literature review, stakeholder meetings, utilization of existing nationally endorsed health promotion messages, and tailored to the community of the study area. The training was delivered through presentations, videos, role plays, demonstrations, and group exercises, as recommended by previous studies [19, 31]. All these elements increased the effectiveness of the training. We believe this training package with necessary community-specific modifications can be replicated in similar settings in other parts of Nepal. It must be considered that some FCHVs found that continuous training for six days was challenging to participants to comprehend all the new knowledge at once, although we incorporated fun games, local singing, and dancing in between sessions to engage participants and to give a sense of break so that the training was not overwhelming. The qualitative assessment of FCHVs showed motivation and interest among FCHVs in delivering the intervention in the community, and FCHVs perceived that the community was responsive. FCHVs became confident in using the status assessment guide and making referrals. In the context of Nepal, where the objective diagnosis of COPD in primary healthcare centers is very low, and COPD is largely undiagnosed in the community [12, 32], FCHVs can be trained in using short screening questionnaires to screen potential COPD cases in the community. Several short and simple COPD screening tools are being used elsewhere [33], which could be validated in Nepal for community-based screening of COPD by FCHVs.
A noteworthy finding from the qualitative assessment indicated that community-based COPD prevention and management would be challenging to implement and less effective unless local health facilities are strengthened in terms of diagnosis, management, and treatment. Therefore, along with community-based interventions, efforts should be made to strengthen the local health facility’s capacity to effectively diagnose and treat COPD at an affordable cost. So those potential COPD patients referred by CHWs from the community who otherwise would have been missed are identified early and treated appropriately. Also the strengthening of the health system’s readiness and response to CHWs referrals with proper diagnosis, treatment, and medication is needed to increase the trust and effectiveness of CHWs in the community [34].
Patient activation and empowerment for self-management are essential in managing COPD and improving quality of life [35]. In our study, FCHVs were confident in delivering home-based counseling on COPD and demonstrating breathing techniques, stamina and endurance building exercises, support on symptoms assessment, and bridging the gap to health services. A feasibility study conducted in the UK showed lay health workers being acceptable and supportive in uptake and completion of pulmonary rehabilitation in COPD [36]. This sheds light on the need for future research on FCHVs involvement in pulmonary rehabilitation for COPD in resource-limited settings like Nepal.
The widely discussed remuneration and overburdening of CHWs with multiple roles is a matter of concern and also revealed in our qualitative findings [34, 37]. The remuneration of CHWs is considered an essential factor for motivation [37]. In our research, we provided transportation costs as remuneration for their work following the government guidelines of FCHVs incentives. FCHVs also suggested that integrated training, i.e., training on diabetes, hypertension, and COPD could be important as number of households with multiple NCDs are increasing in the community. The integrated package of NCDs for CHWs routine work would eventually reduce the burden and empower FCHVs to help community people comprehensively, but further research and policy discourses on this aspect are needed.
This study presented the development and evaluation of a training program for implementing an FCHV-led model of COPD prevention and management in a community in Nepal. This study had some limitations. The relatively small sample size of trained FCHVs and conveniently selected participants could limit the generalizability of our findings. FCHVs received our training program well; however, this research was implemented by an organization with previous experience in implementing FCHV-led interventions in type 2 diabetes and hypertension management at the same study site [23, 38]. Therefore, FCHVs may have overstated the effect because they do not want to be impolite to the research team. Nevertheless, when triangulating with a knowledge questionnaire, our findings were confirmed. Also, we cannot exclude that FCHVs might face some challenges in counseling some male participants in the context of patriarchal societal norms. However, noting the three-decade well-accepted presence and experience of FCHVs working in the community might partially address this issue. Other COBIN interventions were also implemented mobilizing FCHVs, and they did not face this challenge. We expect there would not be cultural issues that FCHVs may face in their interaction with male COPD patients [23, 38]. Likewise, counseling is considered an important component of this training program, and FCHVs are well-trained in culturally appropriate counseling. Finally, the COPD knowledge questionnaire in this study was adapted from previous studies conducted in Uganda and Turkey [20, 29]; therefore, contextualization and validation of the COPD knowledge assessment questionnaire in Nepal seems necessary for future studies.
The current COPD-related national program and policies in Nepal do not address COPD in the community and do not provide adequate directions to minimize the potential public health impact [39]. Thus, despite these limitations, this study as the first of its kind in Nepal to develop a training program for FCHVs to prevent and manage COPD, opens a discourse for further research in mobilizing local health resources to strengthen community health system to combat COPD.
## Conclusions
The findings of this study suggest that a short training program for FCHVs in COPD management is feasible in a semi-urban area of Nepal and improves knowledge significantly. We found that FCHVs were motivated and wished to expand their knowledge of COPD. This approach could be a promising model to mobilize health care resources and skills to address the increasing burden of COPD and its risk factors in Nepal and similar settings.
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|
---
title: Mobile health intervention for promotion of eye health literacy
authors:
- Indra Prasad Sharma
- Monica Chaudhry
- Dhanapati Sharma
- Raju Kaiti
journal: PLOS Global Public Health
year: 2021
pmcid: PMC10021255
doi: 10.1371/journal.pgph.0000025
license: CC BY 4.0
---
# Mobile health intervention for promotion of eye health literacy
## Abstract
### Purpose
Improving eye health awareness in the underserved population is a universal eye health priority. The ubiquity of cell phones and internet usage provides new and innovative opportunities for health promotion. This study evaluated the effect of mobile health intervention (text message link) to promote eye health literacy (EHL) of priority ocular morbidities.
### Methods
This study was an intervention evaluation and employed a two-armed pre-test post-test approach. Baseline assessment on EHL was performed on 424 university students. Participants were categorised into intervention and control groups, using the 1:1 allocation ratio. The intervention and control group received a text message alone and text message with a link, respectively. EHL was assessed via a self-administered questionnaire. The primary outcome measures were changes in EHL scores between baseline and one month post-intervention. Descriptive analysis was performed to assess the cost-effectiveness of the intervention.
### Results
With low attrition and a response rate of $95.6\%$, 409 responses were eligible for analysis. The mean age of the participants ($49.4\%$ males and $50.6\%$ of females) was 19.9±1.68 years. Baseline EHL scores were low, and there was no correlation with a demographic profile (all $p \leq 0.05$, CI $95\%$). The demographic characteristics were similar between the two groups (for all, $P \leq 0.05$, CI $95\%$) at baseline. The EHL scores improved in both groups between the pre-and post-test assessment; however, improvements were statistically significant only in the control group. The one-month post-intervention EHL scores were also higher in the intervention group compared to the control (p≤0.001, CI $95\%$ for all). The total cost incurred for the intervention used was 11.5 USD.
### Conclusion
Text message link demonstrated effectiveness for improving the EHL scores; the low baseline EHL scores substantially improved with intervention. The text message link intervention is a cost-effective method and could be considered in advocating for eye health in developing countries, particularly during global emergencies.
## Introduction
Global eye health is an emerging public health challenge of the 21st century. With the global population growing and aging, more people are developing and living with visual impairment (VI) [1]. VI poses a tremendous socioeconomic burden and affects the quality of life, ultimately plunging individuals into the vicious circle of poverty [2, 3]. The eye health progress is not keeping pace with needs, and we continue to face enormous challenges in elimination avoidable VI [4]. The World Report on Vision (WRV 2019) estimated that at least 2.2 billion people have VI, of which almost half could be prevented or has yet to be addressed. Uncorrected refractive error ($43\%$) followed by unoperated cataract ($33\%$), glaucoma ($2\%$), and diabetic retinopathy ($1\%$) are the leading causes of VI and are considered the priority ocular morbidities [5]. Literature suggests that with raised eye health awareness and provision of primary eye care services, $\frac{4}{5}$th of VI are avoidable [5].
To improve universal eye health, the WRV recommends raising eye health awareness, engaging communities and empowering people about eye care needs. Literature acknowledges that the awareness and knowledge of common ocular morbidities are poor among the general population, causing a major barrier to uptake of eye care services [6–9]. The Low middle-income countries (LMICs) face ophthalmic human resources and financial constraints; providing eye health services and information to the population is a confronting task. Screening camps and awareness programs must meet to provide advocacy [10]. Elevating eye health literacy (EHL) within the key audiences plays a paramount role in eliminating avoidable blindness. During this COVID-19 pandemic, the need to identify and utilize cost-effective public health intervention is felt more than ever. In the LMICs, traditional ways of health education are time and resource-consuming and are least workable during pandemics. It calls for exploring the effectiveness of mobile health interventions that could aid health policymakers in planning interventions.
eHealth Technologies have emerged as an inexpensive, fast, and dynamic method of disseminating health information [11]. Besides with vast penetration and widespread reach, text messages with reliable links make it suitable for public health practices. However, with merely a few studies conducted to evaluate the effectiveness of text messages in health advocacy, the tool remains underutilized by public health professionals and policymakers [12–16]. Furthermore, eye health promotion has not received adequate priority [17]. With the ubiquity of mobile phone usage in a diverse population, mobile health interventions appear to be a promising medium for improving health education for all ages [18–20].
Considering the proliferation of mobile phones, and easy internet access amongst a diverse population, the study aimed to assess the effect of mobile health intervention (text message link) to promote EHL of priority eye diseases among university students. The hypotheses were; [1] the intervention group will have a higher EHL score after mobile health intervention (text message link) than of baseline, and [2] the intervention group will have a higher post intervention EHL score compared to the control group.
## Design
This two-armed, parallel group (1:1) pre-test post-test questionnaire-based study evaluated the effect of mobile health intervention (text message link). Prior to commencement, study design and protocol got approved by the Institutional Ethical Committee (IRC) of the Amity University Haryana (AUH), Haryana, India (Reference: AUH/EC/D/$\frac{2016}{31}$). The study adhered to the tenets of the Declaration of Helsinki for human participants and followed the principles of Good Clinical Practice (GCP).
## Participants
Eligible participants were; [1] university students and [2] aged above 18 years. The study enrolled participants owning a smartphone with an active phone number and anticipating participation throughout the study. To reduce study bias, enrollment excluded students pursuing courses in optometry and visual sciences. Participants were invited through the university website and recruited between January and March 2018. It was implemented in three stages: baseline assessment, allocation and intervention, and post-intervention evaluation within six months (January to July 2018). On completing the study, participants received a free comprehensive eye examination at the Amity Optometry Clinic.
## Procedure
Data collectors attended a one-day training session covering necessary skills in data collection techniques, confidentiality, and privacy assurance. Participants fulfilling eligibility criteria were enrolled, informed that they were recruited for an interventional study and asked to sign an informed consent. Each participant was assigned a unique code to mask personal identifiers. Following enrollment, the socio-demographic information and web-enabled personal mobile phone number were recorded. Prior to randomisation and allocation, a EHL baseline assessment (pre-test) was conducted using a self-administered questionnaire. The intervention was assigned and the post-intervention assessment was conducted for both groups on the 30th day after the baseline assessment.
## Development of study questionnaire
A structured questionnaire was developed in English by the investigators [S1 File]. It was designed to assess the EHL (total scores on awareness and knowledge) of cataract, diabetic retinopathy, glaucoma, and refractive error. The questionnaire’s face and content validity were assessed by faculties and reviewed by an expert panel of the university. The reproducibility and validity of the questionnaire were verified through a pilot study comprising $10\%$ ($$n = 40$$) of the study population. The experience and feedback received from the pilot study were used to resolve the discrepancies and refine the questionnaire.
## Awareness
The first question, which evaluated the individual’s awareness, comprised whether the respondent had ever heard the name of the disease. Close-ended responses (yes or no) were recorded. Scores for yes and no were recorded as 1 and 0, respectively.
## Knowledge
Responses to open-ended questions on symptoms and treatment options for each ocular condition were assessed. Providing at least one simple and correct symptom and treatment option of the disease was considered having knowledge; correct as 1 and incorrect as 0. Overall knowledge score was the total of scores on knowledge of symptoms and treatment options. The correct and incorrect responses participants provided are documented in S1 Table.
## Intervention
The intervention was a mobile health intervention (text message link) delivered as a text message. Two short text messages (SMS) were tailored; [1] a text message thanking participants for taking part in the baseline study (control text message) and, [2] a text message thanking participants for taking part in the baseline study along with a hyperlink (http://www.who.int/blindness/causes/priority/en/) (intervention text message) [Fig 1]. The hyperlink opened the WHO website on priority eye diseases containing a brief description of the background, causes, common symptoms, and treatment options for the priority eye diseases; cataract, glaucoma, diabetic retinopathy, and refractive error. The WHO website was chosen to provide reliable and consistent information about eye diseases. Participants allocated to control and intervention groups received the control text message and intervention text message, respectively, on the fifth day of baseline assessment.
**Fig 1:** *Tailored text message for intervention and control group.*
## Outcome measures
All outcomes were self-reported and collected through a survey. The primary outcome measures included: [1] awareness, [2] knowledge, [3] changes in EHL (awareness and knowledge) scores for cataract, glaucoma, diabetic retinopathy, and refractive error from baseline to one-month post-test. Having heard about the disease was considered as having awareness and demonstrating some understanding about the symptoms and treatment options was considered having knowledge. The responses received from the participants are shown in S1 Table. The secondary outcome denoted the assessment of the cost-effectiveness of the intervention during the study period (presented separately).
## Sampling and sample size
An a priori power calculation was conducted to determine the sample size required. Assuming an effect size of 0.04 (small) between the two groups, and an alpha error of 0.05 (two-tailed), it required 328 participants to give power (1 - β) of over $95\%$. Considering a follow-up rate of $80\%$, and attrition at $15\%$, 442 questionnaires were distributed during the pre-intervention test. The sampling frame used student enrollment numbers from the university registry and participants selected by random selection, generated using Microsoft Excel.
## Randomization and blinding
After the baseline assessment (pre-test), the randomization was performed by the statistician. A list of participant numbers (unique to this study) was prepared, and computer-generated randomization allocated the participants to intervention and control groups in a 1:1 allocation sequence. The allocation was concealed from the participants, study staff, and investigators until the intervention was assigned. The data were collected by trained optometrists and analyzed by a statistician; both blinded to the intervention throughout the study. The participants and investigators delivering intervention could not be blinded due to the nature of the study.
## Data management and statistical analysis
Data coding, quality control, and data entry were done using established procedures. The questionnaires were pre-coded to minimize data coding errors. Before data entry, forms were checked for errors and necessary corrections made. Data were double entered by two different investigators using Epi-data version 3.1. Tools and checks of Epi-data software were used to control data entry errors and data cleaning performed.
Demographic characteristics of participants at baseline were compared using the Fisher exact test, Pearson chi-square test, and ANOVA. Descriptive tests were used to analyze baseline EHL assessment. Testing of hypothesis, for between-group changes in EHL scores at one-month, was performed using McNemar test and Wilcoxon matched-pairs test. All data analysis was two-sided at a $5\%$ significance level and performed using the Statistical Package for Social Sciences (SPSS 21).
## Recruitment
A total of 450 students were assessed for eligibility; 428 recruited, 424 randomised and 409 response analysed. Using the 1:1 allocation approach, 424 were randomized into intervention and control groups equally ($$n = 212$$). After the post-intervention assessment, only 409 responses (203 control group and 206 intervention group) were eligible for the statistical analysis (response rate, $95.6\%$) The participant flow from enrollment through analysis is depicted in Fig 2.
**Fig 2:** *The participant flow from enrollment through analysis.*
## Baseline characteristics
The mean age of the participants ($49.4\%$ males and $50.6\%$ females) was 19.9±1.68 (range 18–26) years. At the baseline, the demographic characteristics were similar between the two groups (for all, $P \leq 0.05$, CI $95\%$). Notably, there were more undergraduate students and of Hindu ethnicity. There was no significant correlation between the EHL scores and demographic profile (all $p \leq 0.05$, CI $95\%$). The baseline demographic characteristics of the study participants are summarised in Table 1. The baseline EHL scores of the study participants are summarized in Fig 3.
**Fig 3:** *The baseline EHL scores of the study participants.* TABLE_PLACEHOLDER:Table 1
## Primary outcome
A statistical comparison found that both groups demonstrated better EHL (awareness and knowledge) scores at post-test compared to baseline assessment. Table 2 shows the comparison of EHL score in the two groups, pre and post text message link intervention. The intervention group had a higher EHL score (p≤0.001, CI $95\%$ for all) after mobile health intervention (text message link) than of baseline. This finding supported hypothesis 1.
**Table 2**
| Disease | Outcome measure | Control Group n(%) | Control Group n(%).1 | Control Group n(%).2 | Intervention Group n(%) | Intervention Group n(%).1 | Intervention Group n(%).2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Disease | Outcome measure | Pre-test | Post-test | p-value | Pre-test | Post-test | p-value |
| Cataract | Awareness | 142 (67.0) | 142 (69.5) | 0.89 | 139 (65.6) | 153 (74.27) | <0.000 |
| Cataract | RKS | 86 (40.6) | 88 (42.0) | 0.50 | 71 (33.5) | 82 (39.8) | 0.001 |
| Cataract | RKT | 40 (18.9) | 48 (23.6) | 0.01 | 34 (16.0) | 48 (23.3) | <0.000 |
| Glaucoma | Awareness | 48 (22.6) | 50 (24.6) | 0.50 | 64 (30.2) | 95 (46.1) | <0.000 |
| Glaucoma | RKS | 19 (9.0) | 19 (9.4) | 0.97 | 18 (8.5) | 42 (20.3) | <0.000 |
| Glaucoma | RKT | 11 (5.2) | 15 (7.4) | 0.125 | 6 (2.8) | 24 (11.6) | <0.000 |
| Diabetic retinopathy | Awareness | 49 (23.1) | 55 (27.1) | .210 | 53 (25) | 85 (41.2) | <0.000 |
| Diabetic retinopathy | RKS | 12 (5.6) | 17 (8.4) | 0.063 | 10 (4.7) | 31 (15.4) | <0.000 |
| Diabetic retinopathy | RKT | 8 (3.8) | 12 (5.9) | 0.125 | 6 (2.8) | 28 (13.6) | <0.000 |
| Refractive error | Awareness | 77 (36.3) | 90 (44.3) | <0.000 | 75 (35.4) | 115 (55.8) | <0.000 |
| Refractive error | RKS | 49 (23.1) | 56 (27.6) | 0.016 | 43 (20.3) | 67 (33.0) | <0.000 |
| Refractive error | RKT | 54 (25.5) | 65 (32.0) | 0.080 | 63 (29.7) | 98 (47.5) | 0.001 |
The changes in EHL scores between the pre-and post-test assessment between the two groups is shown in Table 3. At one-month post-test, the intervention group had a higher EHL score after intervention than that of the control group (p≤0.001, CI $95\%$ for all). Thus, hypothesis 2 was supported.
**Table 3**
| Ocular Condition | Control Group n(%) | Control Group n(%).1 | Control Group n(%).2 | Control Group n(%).3 | Intervention Group n(%) | Intervention Group n(%).1 | Intervention Group n(%).2 | Intervention Group n(%).3 | (Time x Group) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Ocular Condition | Baseline EHL score | Post-test EHL score | Mean difference (95% CI) | p-value | Baseline EHL score | Post-test EHL score | Mean difference (95% CI) | p-value | p-value |
| Cataract | 1.34±1.05 | 1.35±1.05 | 0.010 (0.00 to 0.02) | 0.16 | 1.18±1.03 | 1.23±1.08 | 0.04 (0.02 to 0.07) | 0.003 | 0.001 |
| Glaucoma | 0.38±0.78 | 0.39±0.76 | 0.010 (0.00 to 0.02) | 0.16 | 0.46±0.71 | 0.58±0.71 | 0.12 (0.07 to 0.16) | 0.000 | 0.001 |
| Diabetic Retinopathy | 0.36±0.76 | 0.37±0.77 | 0.010 (0.00 to 0.02) | 0.16 | 0.32±0.61 | 0.46±0.64 | 0.14 (0.09 to 0.18) | 0.000 | 0.001 |
| Refractive Error | 0.63±0.95 | 0.65±0.95 | 0.15 (0.00 to0.32) | 0.08 | 0.63±0.95 | 0.7±0.92 | 0.10 (0.06 to 0.14) | 0.000 | 0.003 |
## Secondary outcome
In the intervention group, $87.8\%$ (181 of 206) participants responded they opened the hyperlink in the text message, while $83.4\%$ (172 of 206) participants found it useful. Each text message costs us an average of INR 0.50; the total cost was INR 818 (USD 11.5).
## Discussion
Finding cost-effective methods for eye health promotion is an eye health priority, particularly in the LMICs. There is firm evidence that mobile phone messages can successfully promote healthcare, improve medication adherence, and change health behaviour [21–23]. This study has added fresh evidence supporting mobile health interventions (mobile text link) could effectively promote eye-health; the first of its kind to the best of investigators knowledge. Evidence of any effective eye health promotion methods that could benefit public health planning and advocacy is a crucial part of VISION 2020: Right to Sight [24]. With ubiquitous access and an increasingly popular communication platform even in the LMICs, text message tool is a well-established intervention for public health [25]. Recognizing that the number of characters in text messaging limits adequate dissemination of health information, this study aimed to capture if text message links could help mhealth interventions reach their full potential as a health advocacy strategy.
The baseline assessment suggests that the awareness of priority eye conditions was relatively poor amongst the study population; cataracts $68.7\%$, glaucoma $27.4\%$, diabetic retinopathy $24.9\%$, and refractive error $37.2\%$. Nevertheless, it is worthy to note that these findings align with the previous studies conducted in Asia [26, 27]. The knowledge of these common eye conditions is also poor, and correlates well with literature. A Low EHL score could mean inadequate or ineffective eye health advocacy programs in the community.
With the advancements in technologies over the past decade, a revolution has been occurring in health promotion [28]. In this study, the most notable finding was that, in the intervention group, the ESL scores after the intervention showed significant improvements for all four ocular conditions ($p \leq 0.05$, CI $95\%$, for all). As hypothesized, EHL scores were significantly higher for intervention than that of the control group at one month. Though no studies were available to compare our findings on eye health, the results were consonant with the observations on other health care interventions [14, 15]. Text message link was also employed effectively as an online survey tool for longitudinal data collection [29]. The study hypotheses were supported by the results, establishing that text message link intervention could effectively promote EHL.
The improvement in EHL in the group receiving text message link intervention could be attributed to several reasons. The mhealth technologies are portable and online materials can be easily accessed at individual’s convenience with an internet connection. It allows the intervention to claim an individual’s attention at the most convenient time by allowing temporal synchronization of the intervention delivery [30]. In 2019, more than half of the global population (4.13 billion) was connected to the internet via mobile phones [31]. The text message link intervention offers an additional advantage of not requiring to send multiple messages to disseminate a considerable amount of information. As more people use mobile phones throughout the day for various tasks, it is less likely to miss a text message prompt [32].
Although mobile health interventions are presently not utilized in public health for eye care, it was proven cost-effective as reminder programs [30]. Literature suggests a tremendous potential for text message links to positively affect public health intervention, particularly in the LMICs [33]. The cost-effectiveness of the method employed in this study can be established by the cost incurred (less than USD 12) to disseminate advocacy material through a hyperlink to the study population. This study provides shreds of evidence that sending text messages linked to a reliable website could be an effective medium for promoting EHL. While this study used English language-based text messages and a link to a English language website, interventions in vernacular languages could be even more cost-effective.
The COVID 19 pandemic has attacked the health system; ensuring access to health services is the cornerstone of successful health response [34]. During the COVID-19 pandemic more than ever, identifying and implementing effective health advocacy strategies is critical to enabling a better global response. A text message link is a tool to disseminate and reinforce information for eye health promotion effectively. Given its effectiveness, the text message link intervention could be considered a tool for equalizing access to information to address health disparities in minority populations.
## Limitation
While evidence in this study is largely in favour of text message link interventions, this study was not without limitations. The study cohort comprised the same university students, so the possibility of diffusion could not be eliminated. *The* generalization of the findings is also limited to university students. In the future, multicenter randomised controlled trials (RCTs) could address issues on how the program can be made more cost-effective. Registration of the study as a randomized controlled trial (RCT) was not possible within the stipulated time.
## Conclusion
The evaluation of mobile health intervention in this study provided evidence that text message links could improve EHL. The substantially low baseline EHL scores amongst educated individuals are of concern and call for the adoption of effective strategies for eye health promotion. Significantly higher EHL scores in the intervention group establish that text message links help promote eye health. Text message link intervention is also cost effective, and it could be used for promoting eye health, particularly by the LMICs during the current coronavirus pandemic.
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|
---
title: 'Prehypertension and its predictors among older adolescents: A cross-sectional
study from eastern Nepal'
authors:
- Jeevan Thapa
- Shyam Sundar Budhathoki
- Surya Raj Niraula
- Sagar Pandey
- Nishant Thakur
- Paras K. Pokharel
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021258
doi: 10.1371/journal.pgph.0001117
license: CC BY 4.0
---
# Prehypertension and its predictors among older adolescents: A cross-sectional study from eastern Nepal
## Abstract
Prehypertension is a state of transition between normal blood pressure and hypertension. Adolescent prehypertension is a strong predictor of hypertension in adults and is now considered for cardiovascular intervention or risk reduction. This study was conducted among adolescents to assess the burden of pre-hypertension and its predictors. A cross-sectional study was conducted among grade 11 and 12 students in three districts in eastern Nepal namely Jhapa, Morang and Sunsari. Sampling was done using a multistage stratified proportionate random method. A semi-structured questionnaire adapted from the WHO STEPwise approach to the non-communicable disease risk factor surveillance (STEPS) instrument was used as a study tool after modification and pre-testing in addition to the anthropometric and blood pressure measurements by the investigators. The prevalence of prehypertension was assessed along with the identification of its predictors through multivariable binary logistic regression modelling. A total of 806 participants aged 15 to 19 years, with $57.1\%$ female, participated in the study. Prehypertension was found in $20.8\%$ ($24.6\%$ in males and $18.0\%$ in females) of the participants, while $7.1\%$ of them were hypertensive ($9.2\%$ males and $5.4\%$ females). Obesity and central obesity were seen among $6.3\%$ and $17.7\%$ of the respondents respectively. Age, sex, ethnicity and obesity were found to be significantly associated with prehypertension. A significant proportion of prehypertension was seen among the adolescent population along with a notable presence of risk factors such as smoking, alcohol consumption, obesity, and eating out. This warrants careful consideration and identification of relevant strategies to reduce the burden of prehypertension via school-based interventions to reduce the modifiable risk factors.
## Introduction
The seventh report of the Joint National Committee on the prevention, detection, evaluation and treatment of high blood pressure (JNC 7) defines normal blood pressure as a systolic blood pressure less than 120 and diastolic blood pressure less than 80mm Hg. Prehypertension, on the other hand, is a state of high normal blood pressure and is defined as a systolic blood pressure of 120–139 mmHg and/or a diastolic blood pressure of 80–89 mmHg in adults aged ≥18 years of age as per JNC 7 [1]. The fourth report on the diagnosis, evaluation and treatment of high blood pressure in children and adolescents by the National High Blood Pressure Education Program Working Group (NHBPEP) defines prehypertension for children and adolescents (˂18 years) as an average systolic blood pressure or diastolic blood pressure levels that are ≥ 90th percentile but <95th percentile for age, gender and height. In addition, BP ≥$\frac{120}{80}$ mm Hg, even if this figure is <90th percentile, is considered prehypertension in adolescents according to NHBPEP [2].
The World Health Organization (WHO) defines adolescents as those people between 10–19 years of age [3]. In addition to being a transition period to adulthood, adolescence is one of the most dynamic stages of human development where they start to make individual choices and develop behaviours that often persist in adulthood [4]. Nearly two-thirds of premature non-communicable disease (NCD) deaths and one-third of disease burden among adults are associated with conditions or behaviour that began in adolescence including tobacco and alcohol use, physical inactivity and unhealthy diet [5]. Furthermore, there is strong evidence of hypertension in adults has its origin in childhood [6–8]. Adolescent prehypertension is found to be a strong predictor of hypertension in adults. Studies have shown that systolic blood pressure above the age and gender-specific blood pressure values predicted an increased risk of hypertension and metabolic syndrome later in life [9]. Prehypertension, despite being a transition phase between normal blood pressure (BP) and hypertension, has been reported as an independent risk factor for cardiovascular events and stroke [10,11]. The rate of progression of prehypertension to hypertension was found to be $7\%$ per year in a study that examined the longitudinal blood pressure outcomes for adolescents [12].
Prehypertension is reported in a high proportion of adults with a prevalence ranging from $21.9\%$ in China [13] to $52\%$ in Iran [14] in early 2000. These differences in the prevalence of prehypertension could be due to differences in the proportion of risk factors among the population. In China, the mean BMI among male and female participants was 22.55 and 22.99 kg/m2 respectively [13]; while in Iran the mean adjusted BMI among male and female participants was 25.2 and 27.4 kg/m2 respectively [14]. Among younger population, its prevalence ranges from 9.2–$16.4\%$ in South Africa [15,16] to $12.3\%$ - $24.5\%$ in India [17–21]. Early detection of prehypertension has been accepted as a potential opportunity for cardiovascular disease risk reduction. It necessitates lifestyle interventions to prevent or delay the progression to hypertension [1]. In Nepal, the pooled prevalence of pre-hypertension among adults is reported at $35.4\%$ [22]. A single cross-sectional study using the data from two trial cohorts conducted in Sarlahi district of Nepal in the 1990s with participants belonging to the 9–23 year age group, reported the prevalence of prehypertension in the range of 11.6–$13.3\%$ [23].
There were recommendations for alignment of hypertension evaluation and treatment across the transition from adolescence to adulthood. American College of Cardiology/American Heart Association (ACC/AHA) and the American Academy of Pediatrics updated and aligned the hypertension definitions in the “2017 High Blood Pressure Clinical Practice Guidelines” [24]. The updated guidelines have eliminated the term “pre-hypertension” and favoured the term “Elevated Blood Pressure” for the blood pressure group between normal and hypertension, and have removed the stage 3 hypertension category. The percentile-based categories for hypertension have been retained at younger ages less than 13 years, while for more than 13 years; the definition has been simplified and is the same as that of adulthood. It defines normal BP as SBP <120 mmHg and DBP <80 mmHg; while elevated blood pressure is defined as SBP 120–129 mmHg and DBP <80 mmHg. Stage 1 *Hypertension is* defined with SBP 130–139 mmHg or DBP 80-89mmHg; and Stage 2 as SBP ≥ 140mmHg or DBP ≥ 90 mmHg. However, in the Nepali setting, the term pre-hypertension is still being used, as can be seen in many recent studies based on the previous definitions [22,25,26]. We have also adopted the previous definition (based on the JNC 7th and 4th Report) for hypertension categories and assessed the prevalence and risk factors of pre-hypertension. However, there are chances that the burden of pre-hypertension might have been underestimated by the previous definition used in this study.
With increasing evidence that risk factors for prehypertension and hypertension start to develop early in life and literature reporting the increasing prevalence of prehypertension globally, research must be focused on prehypertension among the adolescent population in Nepal. Though there are various studies reporting hypertension and pre-hypertension among adults [22,25,26], there is a dearth of literature reporting on prehypertension among adolescents in Nepal. More seminal research evidence is particularly important as there are initiatives [27,28] aiming to address cardiovascular health among school children in Nepal. More research on adolescents and school children could help build a strong evidence base for justifying interventions and formulation of policies. Hence this study, therefore, aims to assess the burden of prehypertension among adolescents and identify the predictors of prehypertension in Nepal.
## Ethics statement
The ethical approval was obtained from the institutional review committee of BP Koirala Institute of Health Sciences (code no. IRC/$\frac{0876}{016}$). Permission for the study was also sought from the selected schools, along with written consent from participants aged 18 years and above. For younger participants, in addition to written assent from participants, written consent was also obtained from their parents/legal guardians before proceeding with the survey. For parents/legal guardians who were unable to read, information regarding the objectives of the study, expected time duration to complete the survey, an overview of the format of the questionnaire, voluntary participation along with a declaration of confidentiality and anonymity were explained by the investigator beforehand. All of the ethical standards were followed while conducting the study.
## Study design
It was a cross-sectional study done among students attending grades 11 and 12 in eastern terai districts of Nepal, viz Jhapa, Morang and Sunsari. The study was conducted from March 2017 to February 2018.
## Participants
The study participants included adolescent students studying in grades 11 and 12 in public or private institutions who consented to participation. There were a total of 290 eligible public and private institutions running secondary school programs in the Jhapa, Morang and Sunsari districts, with 71,722 students registered for grade 11 and 12 board examinations as per the information provided by Higher Secondary Education Board (HSEB) Office, Biratnagar, Nepal.
## Study size and sampling technique
The sample size was calculated based on a cohort study conducted in Sarlahi, Nepal, where the prevalence of prehypertension was found to be $11.6\%$ among youth including older adolescents [23]. At a $95\%$ confidence interval with a $20\%$ margin of error and inflation of sample size by $10\%$ to address non-response, a final sample size of 806 was reached.
Multistage, stratified proportionate random sampling was done to obtain the representative sample. In the first stage of sampling, eligible public and private institutions in three districts with students studying in grades 11 and 12 were identified. All of those public and private schools were listed separately for each district and assigned unique codes. Subsequently, a serial number was assigned in ascending order of their codes for both public and private schools in each district. This was followed by the second stage of sampling where two computer-generated random numbers representing the serial number of schools were generated for each school type i.e. public and private in each of the three districts. This summed up to a total of 12 schools, four from each district.
The data obtained from the HSEB, office, Biratnagar regarding the total number of students studying in selected schools was confirmed by contacting the selected schools which were then used to obtain an estimate of the number of participants required from the school. The selected school authority was explained about the study, and permission was obtained to conduct the study. In the final stage of sampling, a list of total students in the selected school was prepared and arranged in ascending order of their roll numbers. Students were listed alphabetically on a first-name basis in case of unavailability of roll numbers. Then a unique serial number was assigned to all students. Finally, computer-generated random numbers, which represented the unique serial number, were used to make a decision on which students to include in the study. A flowchart summarizing the process of sampling is depicted in Fig 1 below.
**Fig 1:** *Flowchart depicting the process of multistage stratified proportionate random sampling.*
With the help of the school administration, the selected students were contacted, and explained about the study and a suitable day was picked for data collection, without hampering their study. The institution was visited on the days of data collection and appropriate tools were used for data collection. Those absent on the day of data collection were contacted and scheduled on subsequent days for data collection based on their convenience.
## Study tools
Data collection was done using a pre-tested semi-structured questionnaire along with the measurement of anthropometric parameters and blood pressure of the participants. The semi-structured questionnaire was adapted from the WHO STEPS Instrument for Non-communicable disease risk factor surveillance [29]. Nepali translated version of the questionnaire was used for data collection since students were comfortable answering questions framed in their local language. Native speakers with a good comprehension of the English language initially translated the questionnaire into Nepali which was subsequently translated into English by another person who was not previously acquainted with the English version of the questionnaire. Finally, the original version of the questionnaire in English and the back-translated version into English were matched to ensure the validity of the Nepali translation.
Apart from the socio-demographic characteristics of the participants, the questionnaire collected information regarding maternal education, the use of tobacco and tobacco products, alcohol consumption, dietary habits and salt intake, physical activities and personal as well as family history of high blood pressure and diabetes through face-to-face interviews. Lastly, measurements of height, weight, waist circumference and hip circumference were included under anthropometric measurements. Height was measured with participants in bare feet using the principle of a stadiometer. Weight was measured with a regularly calibrated digital scale on a flat surface and participants were on bare feet without holding onto anything. Waist and hip circumference were measured using the WHO STEPS protocol [30]. A non-stretchable tape was used and the measurements were recorded to the nearest centimetres in all cases. Blood pressure was measured using a calibrated aneroid sphygmomanometer after 15 minutes of rest. Measurement was taken on the right arm in a sitting position following the standard procedure. A total of 3 readings were taken, with a gap of 3 minutes in between each reading, and the mean of the 2nd and 3rd readings was recorded.
The interview as well as anthropometric and blood pressure measurement was done by the investigator himself, using a similar technique in all participants. However, the anthropometric measurement of female participants was done in presence of a female teacher.
## Operational definitions for the variables
Ethnicity: It was categorized into 6 groups as per Health Management Information System (HMIS) classification ethnicity [31].
Type of institution: It was categorized as public if the institution was owned by the government or community, or private if the institution was owned by the private sector as a profit-based institution.
Mother’s literacy: The participant’s mother was considered literate if she was able to read all or part of a sentence.
Smoking and alcohol use: Participant was categorized as ever smoker and ever drank alcohol if they had ever smoked or drank alcohol respectively.
Dietary habit: Adequate serving was considered if their diet considered 5 servings of fruits and vegetables per day. We used a dietary showcard used by WHO STEPS to display the serving sizes of fruits and vegetables to the participants, so they could report their intake.
Type of oil used for cooking: It was categorized as mustard oil, sunflower oil, butter/ghee and others, as reported by the participants.
Adding salt to food: It was defined as the frequency of adding table salt before eating or during eating.
Physical activity: Metabolic equivalent of task (MET score) was calculated for every participant based on the information about the different activities and their duration. Based on MET score, they were labelled as Low level (<600), Moderate level (600 - <3000) and High level (3000 and above).
Eating out: The participants who responded having a meal (breakfast, lunch or dinner) from restaurants/shops on average per week were categorized as eating out, while others were categorized as not to be eating out. Adolescents tend to eat fast food such as chowmein (fried noodles), momo (dumplings), burgers, pizza, etc. along with soft drinks [32] as they eat out, and the culture of eating out has been increasing among adolescents in our country.
Family history of hypertension or diabetes: It was defined by the presence of hypertension or diabetes in parents, grandparents or siblings in their family.
Raised blood glucose: The participants were said to have raised blood glucose if they ever had their blood glucose measured, and were told by health care workers that they had raised blood glucose.
Prehypertension and hypertension: It was defined based on the Fourth Report on the Diagnosis, Evaluation and T/t of High blood pressure in children and adolescents by NHBPEP for those aged less than 18 years [2]. For those who were aged 18 years and older, JNC 7 criteria were used [1] (Table 1).
**Table 1**
| Blood Pressure Classification | For <18 years | For 18 years and older | For 18 years and older.1 |
| --- | --- | --- | --- |
| Blood Pressure Classification | SBP or DBP | SBP(mm Hg) | DBP(mm Hg) |
| Normal | < 90th percentile | <120 | And <80 |
| Prehypertension | ≥ 90th and < 95th percentile | 120–139 | Or 80–89 |
| Hypertension | ≥ 95th percentile | ≥ 140–159 | Or ≥ 90 |
Body Mass Index (BMI): BMI was calculated, and interpreted according to BMI for age growth charts for boys and girls aged 2 to 20 years as per the CDC paediatrics growth charts [33]. BMI-for-age at or above the 95th percentile was categorized as obese and that between 85th and 95th percentile as overweight.
Central obesity: A waist-height ratio of ≥ 0.5 was used to define central obesity for both males and females.
## Statistical analysis
Data were entered in Microsoft excel, cleaned and coded. It was then exported into Statistical Package for Social Sciences (SPSS) version 16 for analysis. Categorical data were presented in counts and percentages, while continuous data were presented in mean, standard deviation, median and quartiles as appropriate.
Prevalence of prehypertension and hypertension was calculated, and the association of prehypertension with predictors was assessed excluding the hypertensive population. A Chi-square test was applied to assess the association of categorical predictor variables such as age group, sex, ethnicity, etc. with prehypertension. An independent sample t-test was used to compare continuous variables such as age (in years), BMI, sleep duration, etc among the normal and pre-hypertensive groups (if they differed statistically significantly). If these continuous variables were not normally distributed, the Mann-Whitney U test was used to compare the differences between these variables among the normal and hypertensive groups. Karl Pearson’s correlation coefficient was obtained to assess the correlation between age, BMI, waist-height ratio, waist-hip ratio, systolic blood pressure, diastolic blood pressure and mean arterial pressure.
Univariable binary logistic regression analysis of predictors was done to calculate the relation of predictors with prehypertension. Crude odd’s ratio and its $95\%$ confidence interval were calculated along with its p-value. All the variables with corresponding p-value <0.25 were tested for collinearity to consider for the first multivariable model. All the non-collinear variables (with Variation Inflation Factor <2) were then taken in the first multivariable binary logistic regression model. All the ten variables (viz. age, sex, ethnicity, mother’s literacy, smoking, eating outside the home, sleep duration, family h/o HTN, obesity and central obesity) were found to be non-collinear and were considered in the first model. Deviance of the first model (i.e. -2 log-likelihood) was calculated. The non-significant predictors from the model were removed stepwise, by comparing the changes in the -2 log-likelihood of the model. If the removal of a variable brought significant change in the model, then the variable was retained, or else dropped from the model. The family history of hypertension was purposefully retained in the model due to its clinical significance despite no statistical significance in the model based on our data. The final model (model II) thus obtained using multivariable binary regression analysis consisted of six predictors (age, sex, ethnicity, eating outside the home, family history of hypertension and obesity) of prehypertension.
## Results
The study included 806 participants, who had been studying in public and private institutions in grades 11 and 12 in the three terai districts of the eastern region. All the students selected for the study consented to study and participated. Data was collected from all these students (in first or subsequent visits) with no non-response.
The age of the participants ranged from 15 to 19 years, with a mean (± SD) age of 17.3 (± 0.9) years. There were $57.1\%$ female participants, while $51.0\%$ were Janjati by ethnicity. More than half ($52.1\%$) of students were from public institutions. Among the participants, $22\%$ were ever smokers, while $37.3\%$ were ever drinkers of alcohol. Only $3.2\%$ of them had adequate servings of fruits and vegetables. Obesity based on BMI was seen among $6.3\%$ of them, while $17.7\%$ were found to be centrally obese (Table 2).
**Table 2**
| Characteristics | Characteristics.1 | Frequency | Percentage |
| --- | --- | --- | --- |
| Age | <18 years | 489 | 60.7 |
| Age | ≥18 years | 317 | 39.3 |
| Age | Mean ± SD (years) | 17.3 ± 0.9 | 17.3 ± 0.9 |
| Sex | Female | 460 | 57.1 |
| Sex | Male | 346 | 42.9 |
| Ethnicity | Janjati | 411 | 51.0 |
| Ethnicity | Others | 395 | 49.0 |
| Type of institution | Public | 420 | 52.1 |
| Type of institution | Private | 386 | 47.9 |
| Mothers education | Literate | 539 | 66.9 |
| Mothers education | Illiterate | 267 | 33.1 |
| Ever smoker | No | 629 | 78.0 |
| Ever smoker | Yes | 177 | 22.0 |
| Ever drank alcohol | No | 505 | 62.7 |
| Ever drank alcohol | Yes | 301 | 37.3 |
| Adequate servings | No | 780 | 96.8 |
| Adequate servings | Yes | 26 | 3.2 |
| Type of oil used in cooking | Mustard oil | 355 | 44.0 |
| Type of oil used in cooking | Sunflower oil | 420 | 52.1 |
| Type of oil used in cooking | Butter/Ghee | 8 | 1.0 |
| Type of oil used in cooking | Others | 23 | 2.9 |
| Adding salt to food | Always/Often | 76 | 9.4 |
| Adding salt to food | Sometimes/Rarely/ Never | 730 | 90.6 |
| Physical activity (MET score) | Low level (<600) | 330 | 40.9 |
| Physical activity (MET score) | Moderate level (600 - <3000) | 384 | 47.6 |
| Physical activity (MET score) | High level (3000 and above) | 92 | 11.4 |
| Eating outside home | No | 198 | 24.6 |
| Eating outside home | Yes | 608 | 75.4 |
| Sleep duration | ≥ 7 hours | 602 | 74.7 |
| Sleep duration | < 7 hours | 204 | 25.3 |
| Sleep duration | Mean ± SD (hours) | 7.37 ± 1.29 | 7.37 ± 1.29 |
| Family history of HTN | No | 555 | 68.9 |
| Family history of HTN | Yes | 251 | 31.1 |
| Family history of DM | No | 715 | 88.7 |
| Family history of DM | Yes | 91 | 11.3 |
| Raised blood glucose (n = 86)# | No | 33 | 86.8 |
| Raised blood glucose (n = 86)# | Yes | 5 | 13.2 |
| Obesity | No | 755 | 93.7 |
| Obesity | Yes | 51 | 6.3 |
| Central obesity | No | 663 | 82.3 |
| Central obesity | Yes | 143 | 17.7 |
| Total | Total | 806 | 100.0 |
Prehypertension was found to be present among $20.8\%$ ($\frac{168}{806}$) of them with $24.6\%$ in males and $18.0\%$ in females, while hypertension was observed among $7.1\%$ ($\frac{57}{806}$) of them with $9.2\%$ in males and $5.4\%$ in females (Fig 2).
**Fig 2:** *Prevalence of prehypertension and hypertension stratified by sex (n = 806).*
In the bivariate analysis of factors associated with prehypertension, age, sex, ethnicity, smoking, eating outside the home, hypertension in the family, obesity and central obesity were found to be statistically significant ($p \leq 0.05$). There were significant differences in means of age, BMI, waist-hip ratio and waist-height ratio among the pre-hypertensive and normal populations. ( Table 3).
**Table 3**
| Characteristics | Characteristics.1 | Prehypertension | Prehypertension.1 | Total | p-value |
| --- | --- | --- | --- | --- | --- |
| Characteristics | Characteristics | Yes (n = 168) | No (n = 581) | Total | p-value |
| Age | <18 years | 74(16.2%) | 384(83.8%) | 458(100%) | <0.001 |
| Age | ≥18 years | 94(32.3%) | 197(67.7%) | 291(100%) | <0.001 |
| Age | Mean ± SD | 17.6 ± 0.9 | 17.23 ± 0.9 | 17.32± 1.0 | <0.001 |
| Sex | Female | 83(19.1%) | 352(80.9%) | 435(100%) | 0.010 |
| Sex | Male | 85(27.1%) | 229(72.9%) | 314(100%) | 0.010 |
| Ethnicity | Janjati | 107(28.1%) | 274(71.9%) | 381(100%) | <0.001 |
| Ethnicity | Others | 61(16.6%) | 307(83.4%) | 368(100%) | <0.001 |
| Type of institution | Public | 83(21.1%) | 311(78.9%) | 394(100%) | 0.346 |
| Type of institution | Private | 85(23.9%) | 270(76.1%) | 355(100%) | 0.346 |
| Mother’s education | Literate | 105(20.9%) | 398(79.1%) | 503(100%) | 0.145 |
| Mother’s education | Illiterate | 63(25.6%) | 183(74.4%) | 246(100%) | 0.145 |
| Ever smoker | No | 118(20.2%) | 465(79.8%) | 583(100%) | 0.007 |
| Ever smoker | Yes | 50(30.1%) | 116(69.9%) | 166(100%) | 0.007 |
| Ever drank alcohol | No | 102(21.6%) | 370(78.4%) | 472(100%) | 0.483 |
| Ever drank alcohol | Yes | 66(23.8%) | 211(76.2%) | 277(100%) | 0.483 |
| Adequate servings | No | 164(22.7%) | 560(77.3%) | 724(100%) | 0.433 |
| Adequate servings | Yes | 4(16.0%) | 21(84.0%) | 25(100%) | 0.433 |
| Total servings (per day) | Median (Q1, Q3) | 1.57 (0.86, 2.55) | 1.43 (0.96, 2.43) | 1.43 (0.86, 2.43) | 0.739# |
| Adding salt to food | Always/ Often | 18 (25.7%) | 52 (74.3%) | 70 (100%) | 0.489 |
| Adding salt to food | Sometimes/Rarely/ Never | 150 (22.1%) | 529 (77.9%) | 679 (100%) | 0.489 |
| Type of oil used in cooking | Mustard oil | 73 (22.1%) | 258 (77.9%) | 331 (100%) | 0.884* |
| Type of oil used in cooking | Sunflower oil | 88 (22.7%) | 299 (77.3%) | 387 (100%) | 0.884* |
| Type of oil used in cooking | Butter/Ghee | 1 (12.5%) | 7 (87.5%) | 8 (100%) | 0.884* |
| Type of oil used in cooking | Others | 6 (26.1%) | 17 (73.9%) | 23 (100%) | 0.884* |
| Physical activity (MET score) | Low level(<600 MET) | 66 (21.4%) | 243 (78.6%) | 309 (100%) | 0.732 |
| Physical activity (MET score) | Moderate level(600 - <3000) | 80 (22.7%) | 273 (77.3%) | 353 (100%) | 0.732 |
| Physical activity (MET score) | High level(3000 and above) | 22 (25.3%) | 65 (74.7%) | 87 (100%) | 0.732 |
| Eating outside home | No | 30(16.4%) | 153(83.6%) | 183(100%) | 0.024 |
| Eating outside home | Yes | 138(24.4%) | 428(75.6%) | 566(100%) | 0.024 |
| Sleep duration (hrs) | ≥ 7 hours | 117(20.8%) | 445(79.2%) | 562(100%) | 0.067 |
| Sleep duration (hrs) | <7 hours | 51(27.3%) | 136(72.7%) | 187(100%) | 0.067 |
| Sleep duration (hrs) | Mean ± SD | 7.21 ± 1.14 | 7.42 ± 1.31 | 7.37 ± 1.28 | 0.067 |
| HTN in family | No | 104(20.1%) | 413(79.9%) | 517(100%) | 0.023 |
| HTN in family | Yes | 64(27.6%) | 168(72.4%) | 232(100%) | 0.023 |
| Diabetes in family | No | 146(21.9%) | 519(78.1%) | 665(100%) | 0.381 |
| Diabetes in family | Yes | 22(26.2%) | 62(73.8%) | 84(100%) | 0.381 |
| Raised blood glucose (n = 86) | No | 6 (20%) | 24 (80%) | 30 (100%) | 0.395* |
| Raised blood glucose (n = 86) | Yes | 1 (50%) | 1 (50%) | 2 (100%) | 0.395* |
| Waist hip ratio | Mean ± SD | 0.81 ± 0.07 | 0.79 ± 0.06 | 0.8 ± 0.06 | 0.015## |
| Waist height ratio | Mean ± SD | 0.45 ± 0.05 | 0.43 ± 0.04 | 0.43 ± 0.05 | <0.001## |
| BMI (kg/m2) | Mean ± SD | 21.41 ± 3.25 | 19.55 ± 2.95 | 19.97 ± 3.12 | <0.001## |
| Obesity | No | 149(21.2%) | 555(78.8%) | 704(100%) | 0.001 |
| Obesity | Yes | 19(42.2%) | 26(57.8%) | 45(100%) | 0.001 |
| Central obesity | No | 145(21.2%) | 540 (78.8%) | 685(100%) | 0.007 |
| Central obesity | Yes | 23(35.9%) | 41(64.1%) | 64(100%) | 0.007 |
| Total | Total | 168(22.4%) | 581(77.6%) | 749(100%) | |
Age showed a weak positive correlation with systolic blood pressure and mean arterial pressure, which was statistically significant. BMI and waist height ratio also showed weak positive correlation with systolic blood pressure, diastolic blood pressure and mean arterial pressure, and the correlation was statistically significant ($p \leq 0.01$) (Table 4).
**Table 4**
| Characters | BMI | Waist hip ratio | Waist height ratio | SBP | DBP | MAP |
| --- | --- | --- | --- | --- | --- | --- |
| Age | .125** | 0.069 | 0.054 | .115** | 0.035 | .076* |
| BMI | 1 | .204** | .599** | .224** | .188** | .227** |
| Waist hip ratio | .204** | 1 | .623** | .123** | 0.028 | .075* |
| Waist height ratio | .599** | .623** | 1 | .158** | .090* | .132** |
| SBP | .224** | .123** | .158** | 1 | .580** | .843** |
| DBP | .188** | 0.028 | .090* | .580** | 1 | .927** |
| MAP | .227** | .075* | .132** | .843** | .927** | 1 |
In the final multivariable model (model II), participants aged ≥ 18 years were found to be 2.27 ($95\%$ CI 1.59, 3.26) times likely to have hypertension than those aged less than 18 years ($p \leq 0.001$). Males were found to be 1.51 times more likely to have prehypertension than females. Participants belonging to the Janjati ethnicity were 1.76 times more likely to be pre-hypertensive than others. Pre-hypertensive was also significantly higher among those who ever ate outside the home, had family history of hypertension, or were obese. ( Table 5) The adjusted odd’s ratio obtained for predictors in the final model has been shown in Fig 3.
**Fig 3:** *Adjusted odds ratio of predictors of PHTN based on final model (Model II).* TABLE_PLACEHOLDER:Table 5
## Discussion
With the rising incidence of NCD and ongoing change in epidemiological determinants that favour NCD, the need to assess the burden of NCD and its predictors is ever increasing, especially among the adolescent age group. This study accordingly reported the burden of prehypertension and its predictors among students of grades 11 and 12 in Jhapa, Morang and Sunsari districts of Nepal. One-fifth of the adolescents ($20.8\%$) in the study were found to be pre-hypertensive, while $7.1\%$ of them were found to be hypertensive. About $6.3\%$ of them were found to be overweight and obese whereas $17.7\%$ had central obesity. A quarter of the study participants were found to be ever smokers and a third of them ever drank alcohol. We found a significant association between age, sex, ethnicity and obesity with prehypertension.
The prevalence of pre-hypertension in this study was found to be similar to the study done among 250 medical students of KIST medical college in Nepal, where the prevalence of pre-hypertension was also $20.8\%$. However, for the study, the average of two readings was recorded and classified, and the study participants’ age ranged from 17 years to 25 years [25]. The prevalence of prehypertension in this study also correlated with the prevalence reported in a school-based study in Kerala and Shimla, India where the respective prevalence was $24.5\%$ and $22.3\%$ [20,21]. On the other hand, the reported prevalence of prehypertension and hypertension during the follow-up study of Nepal Nutrition Intervention Project Sarlahi (NNIPS) cohorts aged 9–23 years was comparatively lower, with prehypertension ranging from $11.6\%$ to $13.3\%$ and hypertension ranging from $4.7\%$ to $6.4\%$ [23]. An explanation for this discrepancy might be due to the increasing trend of prehypertension and hypertension with an increase in risk factors prevalence in the current scenario compared to that from 2006 to 2008 when the study was conducted.
The study has reported an increase in the proportion of prehypertension in a higher age group, with age being positively and significantly correlated with systolic blood pressure and mean arterial pressure. Age is statistically significant in the final model in our study. This was, however, an expected finding since an age-related increase in blood pressure has been a proven concept in human ageing [34,35]. A higher proportion of prehypertension among male adolescents obtained from the final model in this study corroborates with a study in Kerala with a similar risk reported among the adolescents [21]. The higher blood pressure in males than females is attributed to differences in cardiovascular effects of testicular and ovarian hormones as well as the role of sex chromosomes [36].
The proportion of prehypertension was found to be highest among Janjati ethnic group in this study. Although studies reporting a similar prevalence of prehypertension among Janjati in different study settings are lacking, several studies have reported similar findings among ethnic groups having a closer resemblance to Janjati ethnic groups of eastern Terai. For example, the Dhulikhel heart study conducted between 2013 to 2015 among 752 participants reported a higher prevalence of prehypertension among Newars ($54.8\%$) compared to Brahmin/Chhetri ($39.7\%$) and others ($48.5\%$) [26]. Similarly, a community-based cross-sectional study of hypertension in Duwakot, Bhaktapur by Vaidya A. reported that Tibeto-Burmans (which include Tamang, Rai, Limbu, Sherpa, etc.) had a higher prevalence of hypertension ($25.3\%$) compared to Indo-Aryans (which includes Brahmin, Chhetri, people of terai and Tharu) where the number was only $14\%$. An independent ethnic variation in the blood pressure distribution among the Nepalese population possibly acting independently of various lifestyle determinants of hypertension was hypothesized as a reason for the findings of the study [37]. In addition, various socio-cultural practices in different ethnicities, like the obligatory provision of alcohol during festivals, might have a role to play in the observed findings.
This study reported a significant positive correlation between BMI and systolic blood pressure and diastolic blood pressure. The proportion of prehypertensive overweight/obese adolescents in this study was double as compared to normal or underweight adolescents. In addition, overweight/obese individuals had an adjusted odds of 2.32 (p-value- 0.011) than those with normal or underweight. The effect of an increase in blood pressure with an increase in BMI has been extensively reported in the medical literature [38–40]. Furthermore, a significant positive correlation of BMI with systolic blood pressure and diastolic blood pressure among children and adolescents is also reported in studies conducted in various states of India [17,21,41–43]. In addition to the association of BMI with high blood pressure, it was also reported to be strongly associated with the development of atherosclerotic lesions in the aorta and coronary arteries [44]. This signifies the importance of early detection and management of prehypertension and its risk factors to prevent or delay the development of atherosclerotic changes. Lastly, waist-hip ratio and waist-height ratio also showed a significant positive correlation between systolic blood pressure and diastolic blood pressure. The positive association between waist-height ratio and the presence of hypertension was also demonstrated in a cross-sectional study among adolescents aged 10–17 years in Brazil [45]. In addition, in a meta-analysis of 24 cross-sectional studies and ten prospective studies with more than 500,000 participants, the waist-height ratio was found to be more favourable than BMI to detect cardiometabolic risks, elevated blood pressure being one of them [46]. This helps further emphasize the seriousness of the positive association of waist-height ratio with systolic blood pressure and diastolic blood pressure in this study and the need for immediate and long term intervention programs to address the issue.
## Limitations
Blood pressure measurement tends to decrease with repeated measures over a single visit, as well as repeated measures in different visits. So, it is suggested to obtain multiple measurements over time before diagnosing hypertension [47]. However, in this study, all three measurements were taken in a single visit. This might have overestimated the burden of pre-hypertension.
Due to the cross-sectional design of the study, factors associated with prehypertension in the study need to be carefully interpreted. Nepal is a geographically and demographically diverse country, the finds of this study which is primarily conducted among adolescents in the eastern districts of the country might affect the generalizability of the findings.
## Conclusion
Prehypertension was notably present alongside hypertension. Modifiable risk factors such as smoking, eating outside the home, BMI and central obesity found in this study urges careful attention to these factors in health promotion activities focusing on the adolescents in Nepal. Alongside considering packages for managing prehypertension and prehypertension targeting adolescents, future research and health policies need to consider adolescents as an important population for hypertension control programs.
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|
---
title: The links of fine airborne particulate matter exposure to occurrence of cardiovascular
and metabolic diseases in Michigan, USA
authors:
- El Hussain Shamsa
- Zhenfeng Song
- Hyunbae Kim
- Falah Shamsa
- Linda D. Hazlett
- Kezhong Zhang
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021276
doi: 10.1371/journal.pgph.0000707
license: CC BY 4.0
---
# The links of fine airborne particulate matter exposure to occurrence of cardiovascular and metabolic diseases in Michigan, USA
## Abstract
Air pollutants, particularly airborne particulate matter with aerodynamic diameter < 2.5μm (PM2.5), have been linked to the increase in mortality and morbidity associated with cardiovascular and metabolic diseases. In this study, we investigated the dose-risk relationships between PM2.5 concentrations and occurrences of cardiovascular and metabolic diseases as well as the confounding socioeconomic factors in Michigan, USA, where PM2.5 levels are generally considered acceptable. Multivariate linear regression analyses were performed to investigate the relationship between health outcome and annual ground-level PM2.5 concentrations of 82 counties in Michigan. The analyses revelated significant linear dose-response associations between PM2.5 concentrations and cardiovascular disease (CVD) hospitalization. A 10 μg/m3 increase in PM2.5 exposure was found to be associated with a $3.0\%$ increase in total CVD, $0.45\%$ increase in Stroke, and a $0.3\%$ increase in Hypertension hospitalization rates in Medicare beneficiaries. While the hospitalization rates of Total Stroke, Hemorrhagic Stroke, and Hypertension in urbanized counties were significantly higher than those of rural counties, the death rates of coronary heart disease and ischemic stroke in urbanized counties were significantly lower than those of rural counties. These results were correlated with the facts that PM2.5 levels in urbanized counties were significantly higher than that in rural counties and that the percentage of the population with health insurance and the median household income in rural counties were significantly lower. While obesity prevalence showed evidence of a weak positive correlation (ρ = 0.20, p-value = 0.078) with PM2.5 levels, there was no significant dose-response association between county diabetes prevalence rates and PM2.5 exposure in Michigan. In summary, this study revealed strong dose-response associations between PM2.5 concentrations and CVD incidence in Michigan, USA. The socioeconomic factors, such as access to healthcare resources and median household income, represent important confounding factors that could override the impact of PM2.5 exposure on CVD mortality.
## Introduction
Air pollution is a sustained world-wide public health concern for the general population. Accumulating evidence has linked exposure to high-levels of airborne particulate matter in fine and ultrafine ranges (aerodynamic diameter < 2.5 μm, PM2.5) to increased mortality and morbidity associated with cardiovascular and metabolic diseases [1–5]. There is a linear dose-risk relationship between PM2.5 concentrations and occurrence of cardiovascular or metabolic disease, even in the countries within guidelines for EPA PM2.5 exposure limits [6]. Long-term PM2.5 exposure increases the risk of cardiovascular, metabolic, and neurodegenerative diseases to an even greater degree and effects may be even more pronounced in susceptible sub-populations such as older adults, those with lower socioeconomic status, and individuals with pre-existing conditions. Most recently, epidemiological studies indicated that short-term exposure to polluted air, even at levels generally considered “acceptable,” may impair mental ability in elderly people in US [7]. Long-term exposure to PM2.5 is associated with an increased risk of anosmia, the inability to smell [8]. In developing countries, such as China, India, and Latin America, where daily and annual PM2.5 levels range from 100 to 200 μg/m3, detrimental effects of PM2.5 exposure on public health have been grossly underestimated. Traffic-related PM2.5 is a complex mixture of particles and gases from gasoline and diesel engines, together with dust from wear of road surfaces, tires, and brakes [9, 10]. Airborne PM2.5 exhibits an incremental capacity to penetrate to the most distal airway units and potentially the systemic circulation [11, 12]. Studies have demonstrated that these PM2.5 particles cause cytotoxic effects, especially upon forming complexes of PM2.5 particles rather than as a single or few particles [11]. Recent studies by our group and others in the field have addressed that traffic-related PM2.5 may promote cardiovascular and metabolic diseases, possibly by: exaggerating systemic inflammation [1, 4, 5], causing oxidative and endoplasmic reticulum (ER) stress damages [13–15], and disrupting energy homeostasis [4, 16]. While the link between PM2.5 pollution and occurrence of cardiovascular and metabolic diseases has been established, dose-response associations between PM2.5 concentrations and the subtypes of CVD or metabolic diseases, especially in the areas where PM2.5 levels are generally considered “acceptable”, have not been precisely defined.
In this study, we investigated the dose-risk relationships between PM2.5 concentrations and occurrence of cardiovascular and metabolic diseases in Michigan, USA, where the levels of PM2.5 are generally considered “acceptable” compared to the developing countries. Michigan is a representative state of the Midwestern region of the USA with a complex environmental and socioeconomic background, representing an ideal state for studying complex relationships between PM2.5 exposure and health outcome. The state of Michigan possesses a typical urban/rural county status and racial diversity as well as counties of specific health interest, such as Wayne County. Particularly, the great *Detroit area* possesses a number of industrial facilities and transportation routes. Residents in this area form a racially diverse community, including approximately $40\%$ Hispanic, $27\%$ African American, $30\%$ White, Arabic, Native American, and others within a highly urbanized, industrial environment dominated by auto factories, steel mills, metal finishing industries, and waste processing. Our studies reveal that the levels of PM2.5 exposure were significantly associated with increased hospitalization rates of cardiovascular diseases (CVD), Stroke, and Hypertension in Michigan. Interestingly, while the hospitalization rate of CVD in urbanized counties was higher than that in rural counties, the death rates of CVD in rural counties were significantly higher than that in urbanized counties, implicating the impact of socioeconomic factors, such as access to healthcare resources and median household income, in conjunction with air pollution-associated mortality and morbidity.
## Data collection
Annual data was collected for county Diabetes prevalence rate from 2010 to 2016 from the CDC National Health Interview Survey (NIHS) (https://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html#). Prevalence was deemed as a more appropriate measure for rates of diabetes, as diabetes is a chronic disease treated on an outpatient basis, whereas the other diseases studied tend to be more acute events, thus hospitalization rates would not be an accurate representation of diabetes rates within Michigan. Diabetes death rate data was obtained using Michigan Department of Health and Human Services (MDHHS) yearly county mortality datasets (https://www.mdch.state.mi.us/osr/deaths/DiabetesUS.asp). All other health outcome data (hospitalizations and deaths) was retrieved from the CDC’s Division for Heart Disease and Stroke Prevention (DHDSP) (https://www.cdc.gov/dhdsp/index.htm). Based on the US Census Bureau database, 2016 Michigan population estimate is 9,950,571. The number of 2016 Michigan county Original Medicare *Beneficiaries is* 1,270,636. Biannual data was collected for Total Cardiovascular Disease (CVD), Coronary Heart Disease (CHD), Heart Disease (HD), Total Stroke, Ischemic Stroke, and Hemorrhagic Stroke hospitalizations and deaths as well as Hypertension hospitalizations. Death rate data was not available for both hypertension and obesity, likely because they are generally considered secondary causes of death rather than the primary cause of death. All hospitalization data was collected for Medicare Beneficiaries ages 65+ for all races and genders and was reported as Hospitalizations per 1,000 Medicare Beneficiaries; this data will be referred to as “hospitalizations” throughout the study. Death data was collected for all ages, races and genders, and was reported as Deaths per 100,000 population (all ages, races, and genders); this data will be referred to as “deaths” throughout the study. Hospitalization rate and death rate are calculated as follows: HospitalizationRate(%)=Hospitalizations1,000MedicareBeneficiariesx$100\%$ [1] DeathRate(%)=Deaths100,000peopleincountyx$100\%$ [2] International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM, for hospitalizations before 2015) and ICD-10-CM (for hospitalizations after 2015) codes for each health outcome are listed below in S1 Table. All data for the confounding variables mentioned below were also obtained from the CDC’s DHDSP. Data for 82 counties in the state of Michigan was collected. This included all Michigan counties with the exception of Keweenaw County, Michigan, which was excluded due to lack of sufficient data.
Annual concentrations of ground-level fine particulate matter (PM2.5, measured in μg/m3) using high-resolution 0.01°x0.01° gridded data retrieved from multiple satellite, surface-level monitor, and simulated data sources (S1 Fig) [17]. These gridded datasets were processed into county-wise measurements across the state of Michigan by averaging all grid values whose coordinates are contained within a respective county polygon. Coordinates for county polygons were acquired from the Homeland Infrastructure Foundation-Level Data (HIFLD) public domain (https://hifld-geoplatform.hub.arcgis.com/).
## Statistical analyses
Linear regression analysis was used to evaluate for strength of association between each health outcome and PM2.5 concentration. Health outcome and PM2.5 data were averaged over the study period, from 2010 to 2016. Two multivariate models were calculated: one with disease hospitalizations as the dependent variables and the other with disease deaths as the outcome variables. One multiple linear regression model was calculated using diabetes prevalence rate as the dependent variable. All three models were constructed using PM2.5 concentration as the explanatory variable of interest.
Additionally, each model was adjusted for the following 15 county-level confounding factors: obesity prevalence (ages 20+), leisure time physical inactivity (ages 20+), % black, % Hispanic, % above age 65, number of hospitals, % without health insurance, % with less than high school education (ages 25+), % poverty, median home income, county urban/rural status, % blood pressure medication nonadherence (Medicare Part D beneficiaries), % cholesterol-lowering medication nonadherence (Medicare Part D beneficiaries), % high cholesterol (among adults ages 18+ screened in 5 years), and % current smoker status (ages 18+) (Table 2). The inclusion of these confounding variables controls for lifestyle, race, age, access to healthcare, socioeconomic factors, and cardiovascular health predictors—each of which has been established to be associated with cardiovascular and metabolic disease incidence and mortality.
Given the ecological nature of the study, we calculated Moran indices (Moran I) to test for spatial autocorrelation in each measured outcome with respect to county distance. County weights used in calculating each Moran I were obtained by using an inverse distance matrix, where counties were represented as a single [longitude, latitude] point within each county polygon and gridded Euclidean distances were computed between each point [18]. The Moran I significance test was then performed to test the null hypothesis of spatial independence, and all outcome variables except diabetes prevalence showed statistically significant differences from expected values ($p \leq 0.05$), indicating significant spatial autocorrelation in these outcomes. To accommodate for this spatial autocorrelation, spatial correlation was accounted for in the parameter estimates of the two multivariate regression models, as detailed below.
Eq [3] shows the multivariate linear regression model for disease hospitalization rates: Y1=α1+β1,PM2.5X1,PM2.5+β1,1X1,1+⋯+β1,12X1,12+ε1⋯Y7=α7+β7,PM2.5X7,PM2.5+β7,1X7,1+⋯+β7,12X7,12+ε7 [3] Where Y1, …, Y7 represent outcome variables: total CVD, CHD, HD, total stroke, ischemic stroke, hemorrhagic stroke, and hypertension hospitalization rates. Eq [4] shows the multivariate linear regression model for disease death rates: Y1=α1+β1,PM2.5X1,PM2.5+β1,1X1,1+⋯+β1,12X1,12+ε1⋯Y6=α6+β6,PM2.5X6,PM2.5+β6,1X6,1+⋯+β6,12X6,12+ε6 [4] Where Y1, …, Y6 represent outcome variables: total CVD, CHD, HD, total stroke, ischemic stroke, and hemorrhagic stroke death rates. Eq [5] shows the multiple linear regression model for diabetes prevalence rate: Y=α+βPM2.5XPM2.5+β1X1+⋯+β12X12+ε [5] Where Y represents the outcome variable diabetes prevalence rate. In each of the equations above, αi represents the intercept, Xi, PM2.5 represents the explanatory variable average county PM2.5 concentration and βi,PM2.5 is its respective regression coefficient, Xi,1, …, Xi,12 represents each of the 12 confounding variables and βi,1, …, βi,12 are their respective regression coefficients. The errors εi in Eqs [3] and [4] are taken to be spatially correlated with respect to county proximity (determined by using a single point within each county polygon, as described above). This correlation was computed using an exponential correlation structure, in which the correlation degrades with an exponential rate of decay with respect to distance. Regression coefficient estimates were then obtained by maximum likelihood (ML) estimation [19]. The error vector ε in Eq [5] was taken to consist of independently normally distributed errors, as there was no significant spatial autocorrelation in diabetes prevalence data, as described above. The normality assumption is validated with the adjustment of the random errors for spatial correlation, and the multivariate linear regression models detailed above are deemed appropriate for our data analysis. A summary of the results of the regression analyses is shown in Table 1.
**Table 1**
| Unnamed: 0 | Multivariate Multiple Regressiona | Multivariate Multiple Regressiona.1 | Univariate Simple Regressionb | Univariate Simple Regressionb.1 |
| --- | --- | --- | --- | --- |
| Health Outcome | βPM2.5 [95% CI] | P-value | β [95% CI] | P-value |
| Total CVD | | | | |
| Hospitalizations | 3.006 [0.760, 5.252] | 0.011 | 3.364 (1.959, 4.769) | < 0.001 |
| Deaths | 4.537 [0.803, 8.271] | 0.020 | 2.472 (-0.802, 5.746) | 0.137 |
| Coronary Heart Disease | | | | |
| Hospitalizations | 1.039 [0.267, 1.812] | 0.010 | 0.502 (-0.009, 1.013) | 0.054 |
| Deaths | 1.693 [-1.596, 4.982] | 0.317 | -0.496 (-3.085, 2.094) | 0.704 |
| Heart Disease | | | | |
| Hospitalizations | 2.604 [0.662, 4.547] | 0.011 | 2.760 (1.561, 3.959) | < 0.001 |
| Deaths | 3.382 [-0.399, 7.163] | 0.084 | 1.997 (-1.086, 5.080) | 0.201 |
| Total Stroke | | | | |
| Hospitalizations | 0.448 [0.197, 0.698] | < 0.001 | 0.526 (0.380, 0.671) | < 0.001 |
| Deaths | 0.078 [-0.697, 0.854] | 0.844 | -0.234 (-0.668, 0.199) | 0.285 |
| Ischemic Stroke | | | | |
| Hospitalizations | 0.371 [0.143, 0.598] | 0.002 | 0.368 (0.237, 0.499) | < 0.001 |
| Deaths | 0.331 [-0.733, 0.072] | 0.112 | -0.373 (-0.601, -0.146) | 0.002 |
| Hemorrhagic Stroke | | | | |
| Hospitalizations | 0.039 [0.014, 0.065] | 0.004 | 0.071 (0.057, 0.084) | < 0.001 |
| Deaths | 0.084 [-0.068, 0.235] | 0.283 | 0.005 (-0.075, 0.084) | 0.908 |
| Hypertension Hospitalizations | 0.290 (0.042, 0.538) | 0.025 | 0.525 (0.370, 0.679) | < 0.001 |
| Diabetes Prevalence | -0.095 (-0.214, 0.023) | 0.119 | 0.012 (-0.085, 0.110) | 0.801 |
Table 2 shows descriptive statistics for each of the variables included in the study for all 82 counties (Total), as well as for urban and rural counties. Non-parametric Wilcoxon Rank Sum tests were used in comparing means of urban vs. rural counties. P-values < 0.05 are considered statistically significant in this study. Lastly, Spearman’s correlation coefficients were calculated between all variables and are shown as a correlation matrix in S2 Table. All statistical analyses in this study were performed using R (version 3.6.2).
**Table 2**
| Outcome | Total (n = 82 counties) | Total (n = 82 counties).1 | Total (n = 82 counties).2 | Urban (n = 26 counties) | Rural (n = 56 counties) | Unnamed: 6 | Unnamed: 7 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | Mean (SD) | Min | Max | Mean (SD) | Mean (SD) | Fold | P-value |
| Total CVD | | | | | | | |
| Hospitalization Rate | 70.08 (14.50) | 38.83 | 103.93 | 75.10 (13.62) | 67.75 (14.42) | 1.108 | 0.088 |
| Death Rate | 246.7 (30.23) | 162.6 | 318.2 | 243.4 (31.78) | 248.3 (29.65) | 0.980 | 0.417 |
| Coronary Heart Disease | | | | | | | |
| Hospitalization Rate | 17.55 (4.76) | 10.23 | 32.57 | 17.21 (5.24) | 17.70 (4.56) | 0.972 | 0.449 |
| Death Rate | 127.3 (23.61) | 75.7 | 176.43 | 118.41 (23.67) | 131.5 (22.61) | 0.900 | 0.008 |
| Heart Disease | | | | | | | |
| Hospitalization Rate | 52.39 (12.27) | 27.87 | 85.17 | 56.35 (11.68) | 50.55 (12.19) | 1.115 | 0.088 |
| Death Rate | 193.6 (28.37) | 119.9 | 258.4 | 189.8 (28.49) | 195.3 (28.40) | 0.972 | 0.302 |
| Total Stroke | | | | | | | |
| Hospitalization Rate | 11.60 (1.70) | 7.28 | 16.10 | 12.54 (1.38) | 11.16 (1.66) | 1.124 | 0.002 |
| Death Rate | 38.42 (3.98) | 30.97 | 52.57 | 37.73 (4.83) | 38.75 (3.51) | 0.974 | 0.062 |
| Ischemic Stroke | | | | | | | |
| Hospitalization Rate | 9.40 (1.41) | 5.90 | 12.37 | 9.95 (1.11) | 9.15 (1.47) | 1.087 | 0.051 |
| Death Rate | 20.47 (2.21) | 16.47 | 29.63 | 19.48 (1.83) | 20.93 (2.23) | 0.931 | 0.005 |
| Hemorrhagic Stroke | | | | | | | |
| Hospitalization Rate | 1.39 (0.19) | 0.90 | 2.00 | 1.55 (0.13) | 1.32 (0.17) | 1.174 | < 0.001 |
| Death Rate | 9.20 (0.72) | 7.57 | 11.53 | 9.20 (0.84) | 9.20 (0.67) | 1.000 | 0.799 |
| Hypertension Hosp. Rate | 2.89 (1.76) | 0.83 | 10.17 | 4.25 (2.30) | 2.25 (0.95) | 1.889 | < 0.001 |
| Diabetes Prevalence | 9.39 (0.89) | 7.47 | 11.14 | 9.38 (1.03) | 9.40 (0.82) | 0.998 | 0.972 |
| PM2.5 (μg/m3) | 8.14 (2.03) | 4.94 | 12.08 | 10.15 (0.90) | 7.21 (1.70) | 1.408 | < 0.001 |
| Obesity (%) | 33.38 (4.33) | 23.40 | 43.10 | 33.32 (4.15) | 33.41 (4.45) | 0.997 | 0.996 |
| Leisure Physical Inactivity (%) | 23.33 (3.65) | 13.90 | 30.30 | 22.86 (3.12) | 23.55 (3.89) | 0.971 | 0.431 |
| Black (%) | 3.82 (5.96) | 0.10 | 38.70 | 8.40 (8.47) | 1.69 (2.25) | 4.970 | < 0.001 |
| Hispanic (%) | 3.54 (2.58) | 1.00 | 14.80 | 5.11 (2.45) | 2.81 (2.32) | 1.819 | < 0.001 |
| White (%) | 87.72 (8.91) | 49.50 | 96.00 | 81.13 (10.54) | 90.79 (6.03) | 0.894 | < 0.001 |
| Age above 65 (%) | 20.54 (5.13) | 11.70 | 35.50 | 16.37 (1.87) | 22.48 (5.01) | 0.728 | < 0.001 |
| # Hospitals | 1.56 (1.98) | 0 | 14 | 2.81 (3.06) | 0.98 (0.67) | 2.867 | < 0.001 |
| No Health Insurance (%) | 6.92 (1.27) | 4.10 | 11.50 | 5.90 (0.96) | 7.39 (1.12) | 0.798 | < 0.001 |
| Less than High School Education (%) | 9.64 (2.83) | 4.60 | 17.00 | 8.79 (2.51) | 10.03 (2.90) | 0.876 | 0.051 |
| Poverty (%) | 13.92 (3.68) | 5.00 | 23.40 | 12.93 (4.11) | 14.38 (3.41) | 0.899 | 0.19 |
| Median Household Income | 51,561 (9,501) | 36000 | 84000 | 59,346 (10,107) | 47,946 (6,675) | 1.238 | < 0.001 |
| Median Home Value | 125,585 (37,805) | 68000 | 256000 | 147,192 (40,165) | 115,554 (32,380) | 1.274 | < 0.001 |
| High cholesterol (%) | 38.45 (3.29) | 30.00 | 45.30 | 35.98 (2.38) | 39.59 (3.02) | 0.909 | < 0.001 |
| Cholesterol Med Nonadherence (%) | 14.55 (1.23) | 12.20 | 20.20 | 15.41 (1.58) | 14.14 (0.77) | 1.090 | < 0.001 |
| Blood Pressure Med Nonadherence (%) | 19.08 (1.26) | 16.50 | 25.30 | 19.91 (1.75) | 18.69 (0.69) | 1.065 | < 0.001 |
| Current Smokers (%) | 21.06 (2.45) | 13.90 | 26.20 | 19.80 (2.71) | 21.65 (2.10) | 0.915 | 0.004 |
## A dose-response association between PM2.5 exposure and hospitalization of cardiovascular disease, stroke, and hypertension, but not diabetes, exists in the state of Michigan
Health outcome data for each of the 82 Michigan counties studied was plotted against average county PM2.5 concentration, and Spearman’s correlation coefficient (ρ) was calculated for each plot (Figs 1–3). As shown in Fig 1, Total Cardiovascular Disease (CVD, Spearman’s ρ = 0.46), Coronary Heart Disease (CHD, ρ = 0.25), Heart Disease (HD, ρ = 0.47), Total Stroke (ρ = 0.59), Ischemic Stroke (ρ = 0.46), Hemorrhagic Stroke (ρ = 0.76), and Hypertension (HTN, ρ = 0.68) hospitalizations have strong positive correlations with PM2.5 concentrations over the 82 counties studied. Unlike the hospitalizations, deaths of cardiovascular disease did not appear to be significantly correlated with PM2.5 levels in Michigan (Fig 3). Of note, however, total CVD deaths have a weak positive correlation with average PM2.5 concentration (ρ = 0.18). Interestingly, Ischemic stroke was found to have a strongly negative correlation (ρ = -0.32) while Total Stroke had a weak negative correlation (ρ = -0.19) with average PM2.5 concentration. Studies have linked PM2.5 exposure to increased incidences of metabolic syndrome, particularly diabetes mellitus [20–23]. However, diabetes prevalence rates showed no correlation (ρ = 0.04) with county PM2.5 concentrations in Michigan, while obesity prevalence rates did show a weak positive correlation (ρ = 0.20, p-value = 0.078) with county PM2.5 concentrations, though it was not statistically significant (Fig 2).
**Fig 1:** *Scatterplots showing average PM2.5 (x axis) vs. Hospitalizations (y axis) for: A. Total Cardiovascular Disease (CVD), B. Coronary Heart Disease (CHD), C. Heart Disease (HD), D. Hypertension, E. Total Stroke, F. Ischemic Stroke, G. Hemorrhagic Stroke. Hospitalizations have units: Disease hospitalizations per 1000 Medicare beneficiaries (ages 65+) within the county. Individual points represent average values for each of the 82 counties included in the study. Best fit line for each plot was obtained by simple linear regression. Table shows Spearman rank correlation coefficient (ρ) calculated for each plot as well as the p-value for each correlation coefficient. P-values < 0.05 are statistically significant.* **Fig 2:** *Scatterplots showing average PM2.5 (x axis) vs. Prevalence Rate (y axis) for: A. Diabetes and B. Obesity. Prevalence Rates are shown as a percentage value. Individual points represent average values for each of the 82 counties included in the study. Best fit line for each plot was obtained by simple linear regression. Table shows Spearman rank correlation coefficient (ρ) calculated for each plot as well as the p-value for each correlation coefficient. P-values < 0.05 are statistically significant.* **Fig 3:** *Scatterplots showing average PM2.5 vs. death rate A. Total Cardiovascular Disease (CVD), B. Coronary Heart Disease (CHD), C. Heart Disease (HD), D. Total Stroke, E. Ischemic Stroke, and F. Hemorrhagic Stroke. Deaths have units: Disease deaths per 100,000 people within the county, regardless of ages or Medicare status (Y axis). Individual points represent values for each of the 82 counties included in the study. Best fit line for each plot obtained by simple linear regression. Table shows Spearman rank correlation coefficient (ρ) calculated for each plot as well as the p-value for each correlation coefficient. P-values < 0.05 are statistically significant.*
We performed multivariate multiple linear regression analyses to assess for associations between ground-level PM2.5 concentrations and county health outcome data for the years 2010 to 2016. These models were adjusted for major health determinants that are well-known to be linked to cardiovascular, cerebrovascular, and metabolic diseases, such as obesity and sedentary lifestyle, race, socioeconomic status, access to healthcare, hyperlipidemia, and medication nonadherence. Adjusting for these factors allows for correction of the concurrent impact of confounding factors, as well as comparison with simple linear regression results to analyze the degree of distortion caused by these factors. As shown in Table 1, all cardiovascular disease and cerebrovascular disease hospitalizations were found to be significantly positively associated with PM2.5 when accounting for the confounding factors. For each 1 μg/m3 increase in PM2.5 concentration, it was found that total CVD hospitalizations increased by 3.006 hospitalizations per 1,000 Medicare Beneficiaries ($3.01\%$ increase in hospitalization rate per 10 μg/m3, p-value = 0.011), while cardiovascular disease subsets HD hospitalizations and CHD hospitalizations increase by 1.039 ($1.04\%$ increase in hospitalization rate per 10 μg/m3, p-value = 0.010) and 2.604 ($2.64\%$ increase in hospitalization rate per 10 μg/m3, p-value = 0.011), respectively. Furthermore, a 1 unit increase in PM2.5 concentration was also found to be associated with an increase in the hospitalizations of total stroke and its subsets ischemic stroke and hemorrhagic stroke by 0.448 ($0.45\%$ increase per 10 μg/m3, p-value < 0.001), 0.371 ($0.37\%$ increase per 10 μg/m3, p-value = 0.002), and 0.039 ($0.04\%$ increase per 10 μg/m3, p-value = 0.004) hospitalizations per 1,000 Medicare Beneficiaries, respectively. Hypertension hospitalizations were also found to increase by 0.290 for each 1 μg/m3 increase in PM2.5 concentration ($0.29\%$ increase per 10 μg/m3, p-value = 0.025). Diabetes prevalence rate had no significant association with PM2.5 concentration.
Of the diseases studied, only Total CVD death rates were found to be significantly associated with PM2.5 concentration, while mortality rates of CVD subsets CHD and HD and all stroke subsets showed no significant association with PM2.5 when adjusting for confounding factors (Table 1, Fig 3). A 1 μg/m3 increase in county PM2.5 concentration was associated with an increase in Total CVD deaths by 4.537 per 100,000 people ($0.05\%$ increase in death rate per 10 μg/m3, p-value = 0.020) (Table 1). Heart disease mortality rates showed some association with average county PM2.5 (β = 3.382, p-value = 0.084), but this association was not statistically significant. Though Ischemic Stroke deaths were found to have a significant negative association by simple linear regression (p-value = 0.002), this association disappeared once the model was adjusted for confounding factors (p-value = 0.659). This indicates that the strong negative correlation between Ischemic Stroke deaths and PM2.5 concentration (Fig 3) noted above is likely due to interactions between PM2.5 concentration and confounding factors resulting in a potentially misleading apparent association between PM2.5 and Ischemic stroke mortality. This was also seen with diabetes deaths, where simple linear regression showed a significant negative association (p-value = 6.64 x 10−6) which disappeared in the multiple linear regression model which adjusted for confounding factors (p-value = 0.807) (Table 1).
## Urbanized counties showed significantly higher PM2.5 concentrations and hospitalization rates of stroke and hypertension than rural counties
We compared the hospitalization and death rates of cardiovascular and metabolic disease between urbanized and rural counties in Michigan. As shown in Fig 4 and Table 2, urban counties were found to have significantly higher PM2.5 levels than rural counties (p-value < 0.001). Disease hospitalization rates for Total CVD (p-value = 0.088), HD (p-value = 0.088), Total Stroke (p-value = 0.002), Ischemic Stroke (p-value = 0.051), Hemorrhagic Stroke (p-value < 0.001), and Hypertension (p-value = 0.008) in urban counties were higher than those in rural counties, while CHD hospitalization rates and diabetes prevalence rates were similar in urban vs. rural counties. Opposite to hospitalization rates, CHD, Total Stroke, and Ischemic Stroke death rates were significantly lower in the urban counties, compared to those in the rural counties (p-values = 0.008, 0.062, and 0.005, respectively) (Fig 4, Table 2).
**Fig 4:** *Bar graphs comparing average urban vs. rural county values for A. PM2.5 concentration, B. Disease Hospitalization Rate, C. Prevalence rate for obesity and diabetes, and D. Disease Death Rate. * represents a statistically significant (p-value < 0.05) difference between urban and rural values. Error bars depict standard error.*
We compared values of various socioeconomic factors in urban versus rural counties to further unveil possible explanations for the above-mentioned trends in hospitalization and death rates in urban compared to rural counties. Percent Black and Hispanic populations (p-values < 0.001) were significantly larger in urban compared to rural counties. Interestingly, though important population-level cardiovascular disease predictors, such as elderly age (above 65), high cholesterol, and current smoker percentages, were significantly higher in rural counties, cholesterol lowering medication and blood pressure medication nonadherence were significantly higher in urban counties (all p-values < 0.001). The number of hospitals on average (p-value < 0.001), percentage of the population with health insurance (p-value < 0.001), and median household income (p-value < 0.001) were all significantly higher in urban counties compared to rural counties (Table 2), indicating that residents of urban counties in Michigan had better access to healthcare as well as greater resources to seek medical aid with.
## Discussion
In this retrospective study, we confirmed the link between PM2.5 concentrations and CVD and cerebrovascular diseases and made significant expansions on past studies to show strong dose-response links between PM2.5 concentrations and CVD subtypes, Ischemic Stoke, Hemorrhagic Stroke, and Hypertension in Michigan, USA, where the levels of PM2.5 are generally considered “acceptable.” While the EPA’s annual standard for PM2.5 levels is 12 μg/m3 [24], the counties we analyzed had, on average over the study period, a mean PM2.5 level of 8.14 μg/m3 and a maximum of 12.08 μg/m3 (Table 1). We demonstrated a strong and significant dose-response association, in which the level of PM2.5 exposure is positively associated with the occurrence of both CVD and cerebrovascular diseases, despite the fact that Michigan counties were on average within the EPA standard for PM2.5. Thus, though levels below this range may be less harmful, they are still significantly associated with disease. This is a significant finding, as it not only highlights the dramatic health consequences even small levels of PM2.5 may carry. The strong dose-response links between PM2.5 concentrations and Ischemic Stroke, Hemorrhagic Stroke, and Hypertension, among the CVD subtypes, are significant, as these associations have not been precisely defined in the past.
The state of Michigan has a complex urban/rural county status and diverse communities with variable healthcare resources and household income. Michigan represents an ideal place to study the complex impact of environmental and socioeconomic factors in public health. We found that percent Black and Hispanic populations are significantly higher in urban compared to rural counties in Michigan. Furthermore, we found that cholesterol and blood pressure controlling medication nonadherence rates were significantly higher in urban counties. Black and Hispanic races and medication nonadherence have been well documented to be associated with worsened cardiovascular health outcomes. This is further evidenced by our findings of their highly positive correlations with disease hospitalization rates in our study (S2 Table). Though we note that these urban risk factors are directly opposed by the significantly higher percentages of elderly aged residents, percentages of current smokers, and prevalence of high cholesterol in the rural counties, it is arguable that the urban risk factors were seen to have much stronger correlations with hospitalization rates (S2 Table). Thus, in addition to the strong positive associations we have shown between average county PM2.5 levels and cardiovascular and cerebrovascular disease hospitalization rates, the factors of race and medication nonadherence may provide a partial explanation to our findings that the hospitalization rates of cardiovascular and cerebrovascular diseases were significantly higher in urban compared to rural counties.
Importantly, our studies revealed that the associations between disease death rates and PM2.5 exposure may not be consistent with the associations seen between hospitalization rates and PM2.5 among these diverse populations. This was especially seen when observing the strong negative correlation between Ischemic Stroke deaths and PM2.5 concentration. Though Ischemic Stroke death rate showed a significant negative correlation with PM2.5 concentration, regression analysis adjusted for confounders showed no association between the two variables. Thus, possible explanation for the negative correlation seen is that counties with higher PM2.5 tend to be more developed. We used urban status as a measure of the degree of development and found that PM2.5 levels were significantly higher while Ischemic Stroke death rates were significantly lower in urbanized counties compared to rural counties. Furthermore, the number of hospitals on average, the percentage of the population with health insurance, and median household income were significantly higher in urban counties; thus, urban counties have more healthcare resources and greater access to healthcare than rural counties. As timely medical attention is critical to Ischemic Stroke survival, this is a likely explanation for the significantly lower death rates of Ischemic Stroke in urban counties, thus resulting in the observed negative correlation between Ischemic Stroke death rates and PM2.5 concentrations. Simple linear regression did show a strong negative association between Ischemic Stroke deaths and average PM2.5 concentration, however this association completely disappeared in the multivariate multiple linear regression model which corrected for confounding factors–this is further evidence that the correlation between Ischemic Stroke and PM2.5 levels is due to distortion by confounding factors, such as access to healthcare. Similar reasoning can also be applied to the other diseases studied. While the hospitalization rates for total CVD, HD, total stroke, hemorrhagic stroke, and hypertension were significantly higher in urban counties, the death rates of CHD and total stroke were all significantly lower in urban counties than in rural counties. This, again can be attributed to the decreased access to healthcare resources in rural counties. This observation suggests that the access to healthcare resources and household income could be important factors that mitigate the impact of environmental factors, such as PM2.5 exposure, in CVD-associated mortality.
It has been reported that PM2.5 exposure is associated with the prevalence of metabolic syndrome, particularly diabetes mellitus [20–23]. However, our studies showed that PM2.5 may not be associated with the prevalence of diabetes in a state where the link between PM2.5 and CVD and obesity was confirmed. The complex factors involving this discrepancy remains to be elucidated in future studies.
The conclusion that PM2.5 is a causative risk factor for CVD is consistent with the previous studies with the populations in Asia [25, 26], Europe [27, 28], and USA [29, 30]. The strengths of our study include a large sample size, investigation of CVD subtypes, and adjustment for and analysis of the effects of multiple confounding factors, including obesity, race, health care resources, household income, and variables directly linked to cardiovascular health, such as elderly age, smoking, high cholesterol prevalence, and blood pressure and cholesterol controlling medication nonadherence. A multivariate multiple linear regression model was used to assess for associations between health outcomes and PM2.5 concentrations while adjusting for these confounders. Some limitations are present in our study. It is a retrospective, CDC data-based study drawn from survey of public domain but not from the community. A potential bias in case-control studies is discerning the temporal relationship between risk factors and clinical outcomes, because of the complex relationships between PM2.5 exposure and disease occurrence. Additionally, the assessment of some outcomes and covariates relies on self-reporting that may involve unknown misclassifications. Individual variations in long-term exposures to PM2.5 are not trackable. Due to the nature of self-reporting and the dependency on the existing state records, we were limited in addressing the issues of unknown misclassification and individual variations. Nevertheless, to circumvent the weaknesses related to our study, we conducted the analysis with multiple CVD-related risk factors and demographics as well as socioeconomic confounding factors. Indeed, the positive correlation between risk factors and CVD indicated by our analyses were consistent with the established conclusions [31]. This validated the accuracy of our analyses, despite the use of retrospective data, in predicting the relationship between PM2.5 concentrations and cardiovascular and metabolic diseases.
## Conclusion
The hospitalization rates of Total CVD, Stroke and Hypertension are significantly associated with the levels of PM2.5 exposure in Michigan, USA, where the air quality is generally considered as acceptable. While the hospitalization rates of CVD, Stroke, and Hypertension in rural counties were significantly lower than those in urbanized counties, the death rates of CVD and Stroke subtypes were significantly higher in rural counties, partially attributed to the decreased access to healthcare resources and lower median household income. These findings indicate that the socioeconomic factors, such as access to healthcare resources and median household income, are significantly associated with the incidence of air pollution-associated mortality and morbidity.
The findings of the current study indicate that PM2.5 exposure in the USA is an important and major risk factor for both CVD and cerebrovascular disease incidence and mortality. Development of specific guidelines for the prevention and treatment of CVD and cerebrovascular disease needs to consider these findings. Both control of PM2.5 levels and improvement in healthcare resources should be warranted. Such guidelines should outline appropriate and safe treatment to prevent CVD and cerebrovascular disease in patient populations that are at increased risk due to levels of PM2.5 exposure, with the ultimate goal of preventing progressive disease development within the population.
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|
---
title: 'Built environment as a risk factor for adult overweight and obesity: Evidence
from a longitudinal geospatial analysis in Indonesia'
authors:
- Alka Dev
- Jennifer Brite
- Frank W. Heiland
- Deborah Balk
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021279
doi: 10.1371/journal.pgph.0000481
license: CC BY 4.0
---
# Built environment as a risk factor for adult overweight and obesity: Evidence from a longitudinal geospatial analysis in Indonesia
## Abstract
Indonesia has nearly doubled its urban population in the past three decades. In this period, the prevalence of overweight and obesity in Indonesia has also nearly doubled. We examined 1993–2014 panel data from the Indonesian Family Life Survey (IFLS) to determine the extent to which the increase in one’s built environment contributed to a corresponding increase in adult overweight and obesity during this period. We estimated longitudinal regression models for body mass index (BMI) and being overweight or obese using novel matched geospatial measures of built-up land area. Living in a more built-up area was associated with greater BMI and risk of being overweight or obese. The contribution of the built environment was estimated to be small but statistically significant even after accounting for individuals’ initial BMI. We discuss the findings considering the evidence on nutritional and technological transitions affecting food consumption patterns and physical activity levels in urban and rural areas.
## Introduction
Indonesia has seen substantial improvements in development and health indicators in the past three decades. Since 1993, the standard of living, measured as per capita real gross domestic product (GDP), has doubled from USD $4,800 to USD $11,100 [1]. The infant mortality rate has fallen from 62 deaths per 1000 live births in 1990 to 20 in 2019, while maternal mortality has fallen from 272 deaths per 100,000 live births in 2000 to 177 in 2017 [2, 3]. Life expectancy has risen from 62 years in 1990 to 72 years in 2019 and the share of the urban population has grown from $30.5\%$ to $56.0\%$ [4, 5]. Survey data show that the proportions of overweight and obese individuals have also doubled since the mid-1990s, among both men and women [6]. Rising incomes and technological advances are contributing to this development. Studies using cross-sectional Demographic Health Survey (DHS) data in low and middle-income countries (LMICs) found a positive association between BMI and per capita GDP with the middle classes in the richer countries having the highest odds of being overweight or obese [7, 8]. Rates of overweight and obesity were also higher among urban women compared to rural women and among those who were wealthier [9]. However, given the reliance on cross-sectional data in LMICs, it is not always feasible to examine the relationship between urbanization and overweight or obesity outcomes over time in large rapidly developing countries such as Indonesia.
The main feature of development in Indonesia during the past three decades has been rapid urbanization. Jakarta has become one of the world’s largest cities and while it operates as the core urban center of the country, it is only one of several large cities and towns in Indonesia with populations over one million people [10]. With urbanization playing a key role in Indonesian life, it is important to find more sophisticated methods to measure urban conditions than the typical urban/rural dichotomy often found in surveys. For example, our study in India found a strong, positive city-size gradient with respect to proportions overweight [9]. One particular data product, the Global Human Settlement Layer (GHSL) described in detail below, is available as a time series covering the period 1975 to 2014, making it ideal to consistently describe the degree of built-up density as a proxy for urbanization [11]. Since most surveys capture urbanization as a single stratum only—urban vs. rural—new datasets that allow us to determine a fuller urban continuum are important for capturing recent changes and understanding the urban demographic future, which is of great interest to both researchers and policymakers in global health and development [12].
Using longitudinal data from the Indonesian Family Life Survey (IFLS), which spans the period 1993 to 2014, we investigated whether urbanization, as measured by increases in the built environment, could explain the rise in overweight and obesity in Indonesia during the past three decades. The IFLS data provide a unique window into this key period of modernization in Indonesia. High-quality panel data like the IFLS are rare, especially in the developing world. We used four waves of the IFLS, allowing us to follow the weight trajectories of individuals for 21 years in large representative samples. The longitudinal nature of these data permits statistical inferences that are more robust to individual heterogeneity than evidence from (repeated) cross-sections such as the DHS or from treating IFLS panels as cross-sections.
To advance the analysis of the role of urbanization, we supplemented the IFLS urban-rural designations with satellite-based measures of the proportion of land area that is built up over the period of the survey, which we spatially matched to the respondent’s survey cluster location. The Global Human Settlement Layer database defines built-up as manmade objects including buildings, associated structures, and civil works [13]. We refer to these alternative measures as indicators of urbanization, which describes conditions that are particular to an urban setting or that are found to a much greater extent in urban areas [14]. These longitudinal data allow us to examine the determinants of obesity in the context of a developing country undergoing rapid development. This is of scientific interest because relatively little is known about the relationship between changes in the built environment and individual BMI outcomes during times of economic and urban transition, accounting for lagged exposures. We also analyzed how our measure of land use compares, in terms of its explanatory contribution, to using a binary urban-rural measure.
The remainder of the paper is organized as follows: In Section II, we briefly discuss the related theoretical and empirical literature. Section III describes the IFLS data, the choice of sample, and the measures used in the analysis. Section IV describes the analytical approach and presents evidence from basic descriptive and multivariate analyses. In section V, we discuss the findings and conclusions.
## Indonesia Family Life Survey (IFLS)—panels 1–5
The IFLS spans 21 years of Indonesian economic development between 1993 and 2014. It is a national longitudinal survey in Indonesia, consisting of five panels: 1993, 1997, 2000, 2007–08, and 2014. The first panel was representative of approximately $83\%$ of the Indonesian population, covering 13 of 27 provinces. The initial sampling frame was stratified on provinces and enumerated based on a prior national survey and census of which 321 enumeration areas (EAs) or clusters were randomly selected. The final sample included 7,224 partially or fully completed households; $48\%$ of which were urban. Re-contact rates across panels were greater than $90\%$. To maintain an equal duration of seven years between panels, we utilized data from the 1993, 2000, 2007, and 2014 panels.
We included all respondents ages 18 or over who were assessed for health measurements in all four waves and who were never underweight, resulting in a sample size of 3,770 men and women. We excluded underweights as weight gain in this subgroup would largely confer a health benefit ($$n = 1$$,345). The restriction of complete panel data across the 21 years (4 waves) resulted in disproportionally dropping older individuals and men (as of 1993). Further analysis showed that those dropped tended to be from more urban and built-up areas but had comparable BMI values (by sex) to those who were included in all four panels.
Two trained nurses assessed all individuals for health measurements during the survey, unless participants were too ill or pregnant, as determined by them at the time of the interview. Height was measured in centimeters and weight was measured in kilograms. Biologically implausible (<100 or >198.8) or missing heights were replaced with heights from other waves where possible while people with biologically implausible weights (<20 or >167.6) were removed from the sample altogether. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2). Pregnant women were excluded from the sample. Respondents were also excluded from the analysis if they were missing height or weight data or had biologically implausible BMIs (<10 or >50).
The geographic location of each survey cluster (i.e. a collection of households in the sampling frame) was made available for restricted use.
## Global Human Settlement Layer (GHSL) dataset
We used data from the Joint Research Council (JRC) of the European Commission’s GHSL Built-Up Grid Project which integrates existing information on global human settlement with new information extracted from available remote sensed (RS) imagery, largely Landsat, on above-ground buildings [15]. A newly released data product, built-up area is defined as any given area (at 38-meter resolution) where more than $50\%$ of the area contains above-ground buildings. The GHSL definition of buildings includes both permanent and temporary structures. An aggregated data product, at roughly 300 meters resolution, sums the dichotomous 38-meter pixels to generate a measure of the percentage of land area that is built up. Higher values of built-up area serve as a measure of higher levels of urbanization. These data are increasingly being used as proxies for urbanization [16, 17]. The GHSL classification schema does not assume any embedded urban/rural dichotomy.
The dataset supporting the conclusions of this article is available in the Open Science Framework repository [18].
## Analysis
Our analytical strategy (described in greater detail in Section IV below) consisted of a series of regressions predicting an individual’s BMI and risk of being overweight or obese based on individual/household-level and geographic determinants of weight. We exploited the variation in weight outcomes and weight determinants that exists between individuals as well as within individuals (over time) in our four-panel dataset. Fig 1 illustrates the data layers of interest, described below: urban-rural strata as determined by the survey team, built-up area from GHSL, and the cluster-level proportion of overweight or obese (because individual-level BMI cannot be rendered on the map) with a 2km buffer around each cluster.
**Fig 1:** *Illustration of data integration: Survey clusters and built-up area (stylized view).*
## Dependent variables
Our main outcomes of interest were individual BMI and the risk of being overweight or obese. We used continuous BMI and also constructed a binary variable of whether a person was overweight or obese versus normal weight for each panel. The following cut-offs were used for analysis with BMI as a categorical outcome: 18.5–22.9 (normal), 23–29.9 (overweight), and > 30 (obese). Overweight is generally defined as a BMI of 25kg/m2 or higher while obesity is defined as having a BMI of 30kg/m2 or higher [19]. However, a BMI cutoff of 23 kg/m2 has been recommended for Asian populations who might be at higher risk of type 2 diabetes and cardiovascular disease at lower BMIs than the existing WHO cut-off point of 25kg/m2 for overweight [20]. To accommodate for BMI threshold differences among Asians, we used the overweight cut-off recommended for Asian populations in our analysis [21].
## Predictor variables
Dichotomous urban-rural. IFLS included a binary measure based on the National Socioeconomic Survey (SUSENAS), a nationally representative survey that distinguished between urban and rural areas based on five criteria: 1) Population density, 2) Proportion of agriculture households, 3) Access to urban facilities (schools, market/shops, hospital, cinema, hotels/motels, % of household using telephone, % of household using electricity), 4) Availability of public supporting facilities (main street lighting, commercial banks, public phone), and 5) Proportion of land used for other than housing [22]. The sampling frame used in the 1993 IFLS utilized a scoring system based on the first three items in the criteria listed above while the last two items were introduced in the 2000 SUSENAS. The IFLS redefined urban and rural with each panel.
Built-up area. GHSL data are available for 4 periods centering around our target years: 1975, 1990, 2000, and 2014, making it the first-ever spatial layer indicating change over time in built-up areas. We examined several different radii when conducting data analysis (2, 5, and 10km) and found the results to be not qualitatively different. We chose 2 km because we felt conditions most proximal to a respondents’ home would have the greatest impact on obesity risk. Buffers were used to measure the built-up character of neighborhoods (proxying for urban) rather than simply the level of built-up at the cluster location; further, they were not intended to measure walkability, which would depend on many factors including the presence of dedicated pedestrian networks such as sidewalks or walking paths, which cannot be specified in these data [23]. Mean built-up raster values for the 2km IFLS clusters were calculated for 1990, 2000, and 2014 in ArcGIS 10.8 and then converted to percentage built-up [24]. Annual percent growth rates between 1990, 2000, and 2014 were used to impute built-up percentages in 1993 and 2007. Change in the percent built-up was also calculated for the periods 1993–2000, 2000–2007, and 2007–2014 for each cluster. Built-up percentage values were matched to individuals based on their IFLS cluster-ID. To get a sense of these inputs, Fig 2 shows the changes in built-up area in Indonesia, with provincial boundaries, between 1990 and 2014. Fig 3 zooms in to show two anonymized locations–one of a small urban area or town that we call less built-up and another relatively more built-up area, at the same two points in time. Overlaid on this map are the IFLS cluster designations for urban-rural strata to show how dichotomous urban-rural designations may mask underlying urban characteristics–that is, a rural area that is proximate to highly built-up area (left-side panels) as well as an urban cluster in areas with little to no built-up land (right-side panels).
**Fig 2:** *Indonesia provinces and Global Human Settlement Layer (GHS) data, Built-up Land Area (%), Java close-up, 1990–201.Source data: Pesaresi et al., 2016 downloadable from: https://ghsl.jrc.ec.europa.eu/ghs_bu2019.php.* **Fig 3:** *Built-up area close-ups with example, anonymized cluster locations, IFLS.NB: Maps are shown without identifying information to protect the confidentiality of survey respondents. Percent of cluster population that is overweight or obese is shown as this is the dependent variable, though the unit of analysis is individual not cluster as shown. Percentages are not given for any cluster with fewer than 10 respondents in any of the four panels.*
Because we made use of novel data typically not used in health and behavioral studies, we pause to describe the built-up measure we adopted. As shown in Figs 2 and 3, even small cities achieve high levels ($80\%$ or higher) of built-up area at their cores. Our analysis does not consider other spatial characteristics such as total built-up area in the vicinity of the indexed cluster, which could approximate city-size, or connectivity of the indexed location to nearby ones: that is, we do not distinguish between a $40\%$ built-up area cluster that is on the outskirts of Jakarta from $40\%$ built-up area cluster on the periphery of a much smaller city or town [25].
Fig 4 boxplots further depict the heterogeneity of urban clusters by panel-specific classification. For each panel, the distribution of percentage built-up (y-axis) is shown by urban-rural strata of the cluster (x-axis) with the interquartile range (25–$75\%$) shown in the box, and the median value shown by the line within the box. Rural locations have a concentrated and low-level built-up distribution. The median built-up percentage is less than $5\%$ in all panels, though the interquartile increases from about 10 to 15 percentage points from 1997 to 2014. Urban locations, in contrast, have a much greater median percent built-up, approximately $40\%$ in all panels, and much greater dispersion or heterogeneity in the degree built-up of places classified as urban. Neither the median nor the range change much over the 21-year observation period although there is a small increase in median urban built up and a widening of box for rural clusters suggesting, as expected, that all areas are getting more built-up over time.
**Fig 4:** *Boxplots of percent built-up by urban-rural clusters, 1993–2014 (IFLS).*
Other location-based measures. We also constructed binary indicators for residence on the island of Java, Sumatra, and “Other” to account for the potential confounding from Java–the largest, most urban, and most densely populated island.
## Covariates
We included commonly-used socio-economic, cultural, and health behavior variables as covariates [26–28]. Age and age-squared at the time of each survey were introduced as continuous variables in every model. Age was self-reported, and we restricted our sample to 18 years and older at time of first panel. Education level at the time of the survey was introduced as a categorical variable and included the following six groups: none, elementary, junior high, senior high, college or higher, and other. The ‘other’ category generally included vocational and religious schools. Across waves, the educational questions became more detailed regarding schools outside the standard educational system (such as Muslim schools and schools for the disabled), resulting in an increase in the other category, particularly in the most recent survey.
Marital status at the time of the survey was introduced as a categorical variable and included the following three groups: never married, married, and widowed or other. Religion at the time of the survey was introduced as a categorical variable and included the following three groups: Muslim, Christian, and Hindu/Buddhist/other. Smoking was recorded as whether a person was a smoker at the time of the survey or not. Dummy variables were created for all missing values for any covariate, and missing data were re-coded to zero. Additional covariates of interest, such as income, occupation, and food expenditures were not consistently available or viable to use for all four panels, and regrettably, we cannot account for these possible mechanisms. We did not consider migration for work to be a significant issue in the sample as the cohort was interviewed and BMI measured over several panels at the same location.
## Modeling approach
Utilizing the longitudinal nature of the IFLS data, we estimated a series of multivariate regressions using two types of model specifications: “total input” models and “value-added” models. The idea behind the total input approach is to account for all determinants of weight including any relevant current and past exposures, as well as weight-related preferences and endowments. In our total input models, we predicted individuals’ weight outcomes in a given survey year based on individual-level, household-level, and geographic variables. Since the IFLS data—like most data sets—are limited in terms of input variables, especially past exposures, the total input models faced the risk of being mis-specified due to omitted factors. Therefore, in the value-added models, we specified current BMI status as a function of BMI in the previous wave (7 years earlier), along with the same set of individual/household-level and geographic factors. The idea was that lagged BMI would account for any unobserved (earlier) determinants of weight, resulting in more conservative estimates of the impact of built-up on BMI and overweight/obesity [29].
Specifically, we pooled the panel data from four IFLS waves to obtain three 7-year periods for each respondent: 1993–2000, 2000–2007, and 2007–2014. In each period, individuals’ BMI at the end of the period was modeled with contemporaneous measures of age, age squared (divided by 100), education, religion, marital status, and smoking. We examined the role of physical built-up with two strategies: In one set of specifications, we used contemporaneous built-up (“% built-up area of current residence”). In another, we used 7-year lagged built-up (“% built-up area of residence in previous panel”) and change in built-up between waves (“change in % built-up area since previous panel”). The latter specification is a generalization of the former since it is mathematically equivalent to a specification with current and lagged built-up.
The same model specifications were then estimated using a binary overweight/obese variable as the outcome to estimate the (linear) probability of being overweight/obese at the end of each period. The value-added models included individuals’ (7-year) lagged BMI values. In addition to models combining men and women, we also estimated models stratified by sex (shown in S1–S8 Tables). Each model was evaluated for best fit using the overall R2 statistic.
## Ethics statement
This study was reviewed and exempted by the Institutional Review Board at Baruch College of the City University of New York (IRB File #2015–0426).
## Sample means and proportions
Tables 1 and 2 provide means and proportions of key variables in our longitudinal analytic sample by survey panel, sex, and rural-urban residence. Overall, there were significantly more women ($$n = 6$$,918 or $61.2\%$) than men ($$n = 4$$,392 or $38.8\%$) in our panel. As shown in Table 1, on average, individuals in our panel were 36 years old in the first wave [1993]. Individuals were of age 18 and above at baseline, with the vast majority being concentrated between 18 and 59 years, as shown in Table 2. This reflects our age restriction as well as the fact that the survey sought to sample one couple over 50 years in each household, if possible. Those residing in areas classified as urban in the IFLS tended to be slightly older compared to those in rural areas. Men were about 2 years older than women in both rural and urban areas across all waves.
As shown in Table 1, mean BMI values rose steadily over the 21-year observation period from 22.4 in 1993 (IFLS1) to 24.8 in 2014 (IFLS5). Women in every panel had mean BMIs that exceeded those of men, regardless of urban or rural residence; this sex-differential became more pronounced after the first wave. Consistent with the trends in BMI, the proportion of panel members classified as (Asian) overweight or obese rose dramatically from $33.5\%$ in 1993 to $62.8\%$ in 2014 (see Table 2). The biggest jump in the proportion overweight or obese occurred between 1993 and 2000. More urban and rural women were classified as overweight or obese than men; up to nearly 1.5 times more in urban areas and up to twice as many in rural areas.
As Indonesians aged over the observed life span, they were also increasingly likely to live in more urban environments. From the sample sizes in Table 1, we can infer that the percentage of individuals residing in areas classified by IFLS as urban rose from $38.2\%$ in 1993 to $53.3\%$ in 2014. Consistent with rapid urbanization, average built-up percent in all locations increased steadily from $19.8\%$ in the 1993 panel to $25.8\%$ in the 2014 panel. As expected, the mean built-up percentage was much higher in urban than rural areas, by a factor of four or more in each panel. Table 1 suggests that physical environment becomes more built up (at least until 2007) even in locations that remained classified as rural in the IFLS.
Table 2 also reports on three demographic characteristics: education, marital status, and religion. As expected, several respondents attained more education during the observation period. The majority of men and women had an elementary school education with junior or senior high as the next largest category. A greater proportion of men in urban areas had a senior high or college education. Most men and women were also married in both urban and rural areas but the proportions who were widowed or divorced rose throughout the observation period. As expected of a majority Islamic country, about $88\%$ of the sample was Muslim.
In terms of missing observations, 26 people in 1993 were missing religion and 8 of them were missing education level. These were recoded to an ‘other’ category for regression analysis. Approximately $4\%$ and $3\%$ of rural women were breastfeeding at the time of the survey in 2000 and 2007, respectively. Among urban women, $4\%$ and $2\%$ were breastfeeding in 2000 and 2007, respectively. Urban and rural women had similar parities in 2000 and 2007: 2.8 live births per woman in 2000 and 2.9 live births per woman in 2007. Only $3.8\%$ of urban women but $56\%$ of urban men smoked in 2007 and the proportions were higher for rural men and women. Most people did not move (>$90\%$ in all three periods). Among movers, urban-to-urban movers were the larger share. Since we only selected individuals who were interviewed in each panel, attrition bias was a concern. Fuwa found that attrition from a household panel had little quantitative importance for per capita household consumption in other similar surveys [30]. IFLS survey administrators suggest that reasons for individual movers are associated with several sociodemographic factors, some of which are not observed at baseline, and vary with the distance moved [31]. Therefore, we do not consider attrition (at household or individual levels) to be a significant factor in predicting BMI. Age, education, marital status, smoking, and religion were used as controls for regressions with 2000, 2007, and 2014 BMI outcomes.
Fig 5 shows the proportion of the population that is overweight or obese by quintiles of built-up area for each IFLS panel. The overwhelming trend is upward. Yet, there is a wide range in these proportions: in the most current wave, $58\%$ of persons living in the least built-up area (less than $20\%$ of the area is built up) are overweight or obese in comparison to $75\%$ of those living in the most built-up area (more than $80\%$ built up). For the other three panels, we observe similar differences by built-up area quintiles. Across survey waves, we see increasing fractions of overweight or obese, however, this may be due, in large part, to aging of individuals across the survey years, which is unaccounted here but adjusted for in the multivariate analysis below.
**Fig 5:** *Proportion overweight and obese by built-up categories, IFLS 1993–2014.*
## Pooled sample
We first discuss the results from pooled samples, combining the 1,464 men and 2,306 women respondents. Tables 3 and 4 show estimates of BMI regressions and Tables 5 and 6 show results for overweight/obese from linear probability regression models. The tables in the “b” series show the valued-added models discussed above as they add a person’s lagged BMI to the specifications in Tables “a”. Each table shows estimates from eight different regressions studying the relationship between physical built-up and weight, all adjusted for age, age squared, and period dummies (2007–2014, 2000–2007, and 1993–2000, which served as reference). Models 1 and 2 focus on the standard binary measure of urban vs. rural location provided in the IFLS, with Model 1 using current urban residence and Model 2 using lagged urban residence. Models 3 to 8 focus on our continuous measure of urbanicity, degree of built-up, using specifications with current built-up (Models 3, 5 and 7) or lagged built-up and period change in built-up (Models 3, 6 and 8), with different sets of controls.
Turning to the BMI regression results in Tables 3 and 4, we found that location of residence was an important predictor of BMI. Individuals in locations classified as urban had greater BMI values on average than rural residents. Based on Model 1 in Table 3, using current location data, the predicted urban-rural gap in mean BMI was 1.3 points. This is a significant difference both in statistical and practical terms. Using the lagged location (location 7 years prior) instead of the current one (contemporaneous to BMI measurement) returned a similar estimate of the urban-rural gap (see Model 2).
Looking at the estimates from specifications with our continuous measure of built-up percentage (“percent built-up area”), as shown in Models 3–8 in Table 3, we observed that the degree of built-up is strongly positively associated with BMI. For example, BMI values were about 0.22 BMI points higher on average in areas that were 10 percentage points more built up (based on Model 3).
The relationship between physical environment and BMI was robust to controls for island location (Sumatra or all others combined, versus Java as the reference), as shown in Model 5. However, it was reduced by $20\%$ with the inclusion of socio-economic background variables (educational attainment and marital status) and a dummy for being a smoker (Model 7). Adding these controls also improved the overall fit of the model as indicated by the higher R2 (0.18 vs 0.15). The decline is consistent with the idea that socio-economic status and smoking are part of the mechanisms through which urbanization impacts weight.
Models 4, 6, and 8 in Table 3 report estimates for specifications using lagged built-up as a predictor along with the change in built-up during the preceding 7-year period. Lagged built-up percentage performed similarly to contemporaneous built-up in that it predicted BMI well and this association was robust to controls for island residence but diminished when adjusting for the potential confounders discussed above. The coefficient on change in built-up during a 7-year period between waves was estimated to be positive but not statistically significant. Conceptually, we would expect past built-up level (7-year lag) and subsequent change to both matter and, statistically, we found that they are in fact jointly significant. ( As discussed above, this setup is mathematically equivalent to a specification with current and lagged built-up, which is a generalization of the model with only current built-up. The estimate on built-up change equals the coefficient on current built-up obtained from regressing BMI on current and lagged built-up.).
How does our built-up measure compare to the rural-urban dichotomy indicator? The results suggest that both measures perform similarly in terms of their contribution to explaining BMI: R2 is $14.9\%$ in the model with current urban-rural location dummy (Model 1) and $15.3\%$ in the model with 7-year lagged urban dummy (Model 2) and in the models with current (or lagged) built-up measures (Models 3 & 4). Evaluated at the average change in the proportion urban (+15.1 percentage points, based on Table 1) observed over the entire 21-year survey period, Model 1, suggests that the trend in urbanization as captured by the urban-rural dummy added 0.19 BMI points. Using our physical built-up measure, Model 3 predicts that the 6-percentage point average increase in built-up that occurred between 1993 and 2014 (see Table 1) translates into a BMI increase of 0.13 points (both calculations are based on models that adjust for age and sex).
A concern with the estimates in Table 3 is that they may over- or understate the contribution of period-specific determinants of weight such as built-up because of confounding with unmeasured past inputs and endowments in the weight determination process. The value-added regressions attempt to address this concern by conditioning on lagged BMI of the person, i.e., BMI in the previous panel. By doing this, we are focusing explicitly on the variation in individuals’ weight during each 7-year period.
Table 4 shows the results of the value-added models that are based on the same eight specifications as in Table 3, but with lagged BMI added to each. As expected, these “value-added” specifications had a much better fit overall (higher R2 values) as individuals’ past BMI was highly predictive of their current BMI. The estimates associated with the built-up measures were smaller–and substantially so in some cases–than in Table 3. This pattern is consistent with the idea that the earlier estimates tended to overstate the built-up contribution to weight.
While the built environment was found to be a robust predictor of BMI across models, the implied contribution of built-up to overall, population-level BMI is rather small. For example, Model 5 in Table 4 suggests that a 10-percentage point increase in built-up between panel waves is associated with a 0.042 point rise in BMI. Since BMI values rose by about 0.8 points and built-up increased by about 2 percentage points on average in each 7-year period (see Table 1), this estimate suggests that a greater built environment contributed about $1.1\%$ to the rise in mean BMI for the IFLS cohorts. ( Based on Model 7 the contribution is $0.8\%$.) Given the structure of the value-added specification, this may represent a lower bound (i.e. conservative) estimate of the impact of an increase in built-up on weight.
The results from models of binary overweight/obese showed similar patterns as the analysis of mean BMI above. Looking at Tables 5 and 6, the standard measure of urban vs. rural location predicted the probability of being (Asian) overweight or obese well, with individuals in urban locations being more likely to be overweight or obese. Contemporaneous and lagged measures of the degree of built-up also predicted this risk well. Looking across Tables 5 and 6, change in built-up is statistically significant in Models 6 and 8 of the value-added specifications.
## Sex-stratified tables
Looking at the stratified analysis by sex in S1–S8 Tables, we find that overall patterns are very similar among Indonesian men and women. Location and built-up land use tend to play similar roles for men and women for BMI and overweight/obesity. Generally, we found that the models were a better fit for men than women. Also, some statistically significant estimates in the pooled analysis were no longer significant in the stratified analysis, likely the result of the smaller sample sizes.
## Discussion
Indonesia has experienced rapid economic growth and urbanization in the past three decades. In this time, the prevalence of overweight and obesity has also doubled. We examined 21 years of the IFLS panel data (1993–2014) to investigate the role of changing built environment on observed increases in mean BMI and proportion of individuals overweight and obese. We estimated longitudinal regression models using newly available matched geospatial measures of the percentage of land area that is built up. Three other studies have examined obesity trends in Indonesia using data from the same survey and found BMIs are rising among the entire population, particularly among urban women [6, 32, 33]. Our study built on those findings by expanding the focus explicitly to the role of urbanization using satellite data to classify built-up, and analyzing change over time for individual BMI observations.
We found that living in more built-up areas was associated with greater BMI and risk of becoming overweight or obese. The effect sizes associated with the built environment were estimated to be small but statistically significant even in our most conservative models that accounted for individuals’ initial BMI. The results suggest that urbanization, as captured by a $6\%$-point average increase in built-up land area, accounted for a relatively small portion (around $1\%$) of the rise in overweight and obesity in Indonesia. The contribution is similar to using the urban-rural dichotomous measure available in the IFLS survey.
To put these changes in context, $50\%$ built-up thresholds are used in global work as an indicator of urbanization, but suburban locations are characterized by much lower levels of built-up area (for example, as low as $15\%$ in the US) while city centers of large urban areas are much higher (typically close to $80\%$ or more on average in the United States) [15, 34]. A recent study of Indonesia’s Sulawesi Province using built-up data from GHSL finds urbanization unfolding outside areas officially designated with implications not only for the provision of municipal services but also the potential to misclassify relevant aspects of the health and well-being [35]. A recent study in China that evaluated urbanization trajectories found a higher risk of being overweight and obese among men in areas with more urbanized features with no differences observed among women by urbanization trajectories [36]. As urbanization proceeds in Indonesia, it is expected that built-up percentages will increase much more than the $6\%$-point change we observed in the past which poses an increased risk of overweight and obesity over time. China and India provide different models of potential built-up related BMI outcomes in Indonesia. As we noted in India, city size also matters, and small towns or suburbs may eventually see additional risk as they undergo urban and nutrition transitions related to their proximity to larger urban areas [9, 37]. Our understanding of the drivers of overweight and obesity over time in the context of unplanned and rapid urbanization in low and middle-income countries is still evolving [38]. While neighborhood walkability, crime, and access to green spaces and healthy foods are known correlates of obesity among adults and children in western nations [39, 40], country-appropriate measures of similar determinants of health in poorer nations are still lacking. In a global study of urban walkability, Indonesia’s suburbs, like many in South and Southeast Asia, are noted for their sprawling expanses, lack of sidewalks, and development of gate communities associated with an increasing middle-class, factors all of which contribute to a lack of walkability [41]. Further, a recent analysis using GHSL built-up finds that increases in density lead to lower levels of social capital, in particular trust in one’s neighbors and participation in the community [42]. Future work on health outcomes should formally examine the role of these factors for Indonesia, including the use of fine-resolution census and other spatial data that were not available for the current study.
Finally, in Indonesia, the fast pace of urbanization has not been matched with sufficient infrastructure and services [43]. According to the World Bank, although the country’s economy grew by an average of $5.8\%$ in the mid to late 2000s, in the wake of the Asian financial crisis only $3\%$ of GDP was invested in infrastructure per year on average, compared to $10\%$ in China [44]. For example, due to inadequate transportation, commutes in Jakarta can average up to 2.5 hours regardless of public or private transport [45]. Traditionally the country has been divided into kota (municipalities) and kabupaten (non-urban districts). However, since 1990, urban populations have grown faster in kabupaten than in kota, and within the kabupaten non-statutory towns are common. This is a challenge as the governance structure in a kabupaten is often not equipped to manage urban development. Insufficient infrastructure can have immediate impacts on health through lack of access to safe drinking water and sewage removal, but also chronic diseases and obesity through mechanisms such as neighborhoods with poor walkability and inadequate green spaces [46]. Rapidly developing countries may miss out on opportunities for healthy urban planning if urbanization outpaces infrastructure spending.
## Strengths and limitations
The present study had several strengths worth noting. First, we employed a novel, direct measure of built-up area, which was able to distinguish urban residential areas with more granularity than administrative or other definition-based boundaries, and which could capture aspects of the built environment that affect BMI-related behaviors such as walking. These alternative measures build on a more traditional urban-rural comparison. Second, we used individual-level panel data, which allowed us to examine weight trajectories over long periods and to control for individuals’ initial (lagged) BMIs. The results confirmed the importance of individual heterogeneity in the weight determination process and suggest that studies that fail to account for this may overestimate the contribution of background factors such as urbanization. Finally, BMI mismeasurement was not a concern here since weight and height were measured by health professionals rather than self-reported [47–49].
Given the limited set of consistently measured variables available in the IFLS, we could not carefully examine specific causal pathways between urbanization and obesity. Several plausible explanations have been put forth. Economic development and technological change can result in weight gain by lowering calorie prices and rising incomes, making it affordable to increase calorie consumption, and by increasing the opportunity cost of meal preparation at home–resulting in the consumption of calorically denser food [50]. Some argue that a nutrition transition is well underway in developing countries such that processed foods rich in calories from fat and sugar have become more widely available, particularly in urban areas [7]. By 2030, the combined effect of wider availability of fast-food products, higher caloric intake from refined and processed foods, and sedentary work and life conditions associated with urban living, could contribute to a $75\%$ increase in the prevalence of overweight and obesity among adults ages 20 years and older worldwide [51].
Food and built environments in cities elsewhere have been shown to interact to produce ‘obesogenic’ environments that can spatially pattern higher prevalence of overweight and obesity [52]. Greater access to local supermarkets versus neighborhood convenience stores was associated with lower BMI in American urban adolescents, especially among those with higher SES [53]. Another possibly important mechanism is decreasing calorie expenditure through less physical activity. In the developed world, urban sprawl and suburbanization after World War II led to increased reliance on vehicular transport instead of walking or biking and limited physical activity at work and home, with corresponding increases in BMI [54, 55].
Less is known about the ways the built environment affects physical activity in the developing world. Given that our measures of urbanization remained important predictors even after accounting for socio-economic factors, the results are consistent with the presence (and potential longer-term impact) of urban-rural differences in physical activity. This interpretation is in line with an overview of DHS surveys that found a link between urbanization and more sedentary lifestyles in developing countries [56]. In major cities across Ghana, Zimbabwe, Bulgaria, and Nigeria, urban residents have become more vulnerable to unhealthy weight gain, in part due to macro-level economic trends that promote the consumption of energy-dense processed foods and sedentary working conditions [57–59].
## Conclusions
Simple urban-rural dichotomies that are generated from sampling frames tell us little about why some health outcomes are worse in urban settings. Satellite-derived spatially-oriented proxy measures should be able to tell us more about the character, size, and density of those urban settings, as well as potential commuting needs within those settings (for example living in core vs. peri-urban areas). However, as we found, satellite measures of built-up do not completely capture the nature of human interaction with their built environment. Additional research is needed to determine the optimal way to use GHS or similar spatial data sets that characterize the built environment and measure how people navigate it (such as the use of walking and common transportation routes) to more deeply examine the role of urbanization on BMI and other health effects. Additional measures of urbanization that can be generated through satellite measures and importantly, be linked to economic development (such as by use of night-time lights or road data, or data estimating the vertical dimensions of urbanization) would be important to understand changes in population health in the developing world. Furthermore, an exploration of access to safe public spaces for physical activity given cultural restrictions based on sex, ethnicity, or religious identity is warranted in countries such as Indonesia where public and private spaces may be inequitably accessible. Obesity is recognized as a major public health problem in wealthier countries. This study has unambiguously shown that rising BMI values and rising proportions of overweight or obese are major concerns in poorer countries like Indonesia, becoming only more important as urbanization unfolds. This will ultimately result in higher rates of chronic disease.
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|
---
title: 'National health policies and strategies for addressing chronic kidney disease:
Data from the International Society of Nephrology Global Kidney Health Atlas'
authors:
- Brendon L. Neuen
- Aminu K. Bello
- Adeera Levin
- Meaghan Lunney
- Mohamed A. Osman
- Feng Ye
- Gloria E. Ashuntantang
- Ezequiel Bellorin-Font
- Mohammed Benghanem Gharbi
- Sara Davison
- Mohammad Ghnaimat
- Paul Harden
- Vivekanand Jha
- Kamyar Kalantar-Zadeh
- Peter G. Kerr
- Scott Klarenbach
- Csaba P. Kovesdy
- Valerie Luyckx
- Shahrzad Ossareh
- Jeffrey Perl
- Harun Ur Rashid
- Eric Rondeau
- Emily J. See
- Syed Saad
- Laura Sola
- Irma Tchokhonelidze
- Vladimir Tesar
- Kriang Tungsanga
- Rumeyza Turan Kazancioglu
- Angela Yee-Moon Wang
- Chih-Wei Yang
- Alexander Zemchenkov
- Ming-hui Zhao
- Kitty J. Jager
- Fergus J. Caskey
- Vlado Perkovic
- Kailash K. Jindal
- Ikechi G. Okpechi
- Marcello Tonelli
- John Feehally
- David C. Harris
- David W. Johnson
journal: PLOS Global Public Health
year: 2023
pmcid: PMC10021302
doi: 10.1371/journal.pgph.0001467
license: CC BY 4.0
---
# National health policies and strategies for addressing chronic kidney disease: Data from the International Society of Nephrology Global Kidney Health Atlas
## Abstract
National strategies for addressing chronic kidney disease (CKD) are crucial to improving kidney health. We sought to describe country-level variations in non-communicable disease (NCD) strategies and CKD-specific policies across different regions and income levels worldwide. The International Society of Nephrology Global Kidney Health Atlas (GKHA) was a multinational cross-sectional survey conducted between July and October 2018. Responses from key opinion leaders in each country regarding national NCD strategies, the presence and scope of CKD-specific policies, and government recognition of CKD as a health priority were described overall and according to region and income level. 160 countries participated in the GKHA survey, comprising $97.8\%$ of the world’s population. Seventy-four ($47\%$) countries had an established national NCD strategy, and 53 ($34\%$) countries reported the existence of CKD-specific policies, with substantial variation across regions and income levels. Where CKD-specific policies existed, non-dialysis CKD care was variably addressed. 79 ($51\%$) countries identified government recognition of CKD as a health priority. Low- and low-middle income countries were less likely to have strategies and policies for addressing CKD and have governments which recognise it as a health priority. The existence of CKD-specific policies, and a national NCD strategy more broadly, varied substantially across different regions around the world but was overall suboptimal, with major discrepancies between the burden of CKD in many countries and governmental recognition of CKD as a health priority. Greater recognition of CKD within national health policy is critical to improving kidney healthcare globally.
## Introduction
Chronic kidney disease (CKD) is a major global health issue. Estimates indicate that almost $10\%$ of the world’s population are affected by some form of CKD accounting for 1.2 million deaths annually [1]. It is projected that by 2040, CKD will be the 5th leading cause of years of life lost globally–one of the largest projected increases of any major noncommunicable disease (NCD) [2]. In many regions around the world, including Central Latin America, Sub-Saharan Africa and Central Asia, growth in the burden of CKD due to diabetes and hypertension is outpacing that of population growth and aging, with low- and middle-income countries disproportionately affected [3].
The large and growing global burden of CKD has far reaching implications for individuals and health systems. Most people with CKD die prematurely due to cardiovascular disease before reaching kidney failure, with poorer outcomes observed especially for those with diabetes [4]. CKD is therefore a ‘risk multiplier’ for other priority NCDs, particularly cardiovascular disease. Because of the increasing global prevalence of CKD, the number of people projected to require kidney replacement therapy in the form of dialysis or kidney transplantation is also projected to increase. About 2.6 million people were estimated to have received dialysis or undergone kidney transplantation for kidney failure in 2010, and this number is projected to more than double by 2030 [5]. Notwithstanding impacts on patients, families and their caregivers, the growing burden of CKD has major economic implications, given the high cost of providing kidney care, especially dialysis and transplant services [6].
These realities highlight the importance of recognizing and prioritizing CKD care through a broader NCD strategy and through CKD-specific policies. Previously however, CKD has not been given the priority it deserves as a cause, consequence and risk multiplier of many other priority NCDs. The United Nations Sustainable Development Goals (SDGs) have set a target for reducing premature mortality from priority NCDs by a third by 2030 [7]. Whilst CKD is not formally addressed, there is increasing recognition that targeting shared risk factors of priority NCDs such as diabetes and cardiovascular disease, as well as addressing other SDGs relevant to health have the potential to accelerate progress towards reducing the global burden of CKD [8].
The International Society of Nephrology (ISN) Global Kidney Health Atlas (GKHA), now in its second iteration, sought to provide important information on CKD risk factors, the burden and consequences of CKD, and gaps in specific kidney care areas in different countries around the world [9]. The ISN GKHA also collected granular data on national strategies and policies for CKD care, with a view to benchmarking capacity to deliver kidney healthcare and serving as an advocacy tool for health system improvement.
We herein report on national NCD and CKD-specific strategies and policies for addressing CKD around the world to better understand variations across regions and country income levels.
## Methods
The second iteration of the GKHA was a cross-sectional survey conducted by the International Society of Nephrology (ISN). Detailed descriptions of the sampling approach, survey development, data handling, statistical analysis, and main results have been previously published [10, 11]. Briefly, two approaches were used to gather the data for the study: desk research and an online survey of key stakeholders from each participating country.
## Desk research
We performed a comprehensive review of the literature with an information specialist to synthesize national health policies and strategies for addressing CKD. Data were collected from government reports, national registries, and published as well as grey literature. This included data from the World Health Organization Global Health Observatory and the WHO Non-Communicable Disease Strategy. As part of this review, we identified the existence of national NCD strategies and whether CKD was included as part of an NCD strategic plan or addressed directly in a stand-alone policy. The existence and type of CKD-specific policies were also evaluated. We assessed specific kidney conditions (non-dialysis CKD, dialysis, and transplantation) covered by national policies, where available. Finally, government recognition of CKD as a priority in policy documents and the presence of national advocacy groups were also assessed. Data from desk research were supplemented by country-specific survey data.
## Country survey
We conducted a survey as an online questionnaire. The survey was administered electronically to representatives of all 182 countries with ISN affiliate societies between July and October 2018. We identified three key opinion leaders from each country: [1] a leader or president of a nephrology society, [2] a leader of a consumer representative organization, and [3] a policymaker, thus ensuring a diverse representation of perspectives from survey responders. Invitations were sent to key opinion leaders in each country to participate in the survey (available in English, French and Spanish), which included a link to the survey’s online portal (www.redcapcloud.com).
## Patient and public involvement
Patient care organisations (kidney foundations, patients’ associations) were involved in the development of the survey. The following organisations were involved in the survey, and their representatives were also selected to respond to the survey: European Kidney Patients’ Federation, International Federation of Kidney Foundations, Kidney Foundation of Canada, Kidney Health Australia and the US National Kidney Foundation.
## Statistical analysis
The data are presented as numbers (percentages) for categorical variables, means and standard deviations for normally distributed data, and medians with interquartile ranges, or medians with minimum and maximum values for non-normally distributed data. Survey data were analyzed and stratified based on the 4 World Bank income groups (low, low-middle, middle, and high-income) and the 10 ISN regions (Africa, Eastern and Central Europe, Latin America and the Carribbean, Middle East, NIS and Russia, North America, North and East Asia, Oceania and South East Asia, South Asia, and Western Europe). The results of the online survey were reported in accordance with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) guidelines.28 *The data* were analyzed using Stata 14 software (Stata Corporation).
## Results
Out of 182 invited countries, 160 ($88\%$) participated in the GKHA survey, comprising $97.8\%$ of the world’s population. One hundred and fifty-four countries responded to questions about national NCD and CKD strategies. Non-respondent countries were evenly distributed across regions and income groups and mostly represented smaller countries. Full details on response rates and population coverage of the survey have been previously published.
## National NCD and CKD-specific strategies
Seventy-four ($47\%$) countries had an established national NCD strategy, with substantial variability across regions. Most countries in North America, North and East Asia, Oceania and South East Asia had a coordinated strategy for addressing NCDs (67, 71 and $80\%$, respectively), whilst only 27, 29 and $34\%$ of countries in the Middle East, South Asia, and Africa, respectively, addressed NCDs through a national strategy (Table 1). $32\%$ and $37\%$ of low- and low-middle income countries, respectively, had a national NCD strategy (Table 1). The likelihood of having a national strategy increased across increasing World Bank income classification groups (S1 Fig). A national strategy was under development in 21 ($14\%$) countries, whilst 42 ($27\%$) had neither a strategy in place nor in development. Across all regions, only a minority of countries had a specific strategy for improving CKD care, mostly incorporated within existing NCD strategies (S2 Fig).
**Table 1**
| Location | National non-communicable chronic disease strategy | National non-communicable chronic disease strategy.1 | National non-communicable chronic disease strategy.2 | National non-communicable chronic disease strategy.3 | National non-communicable chronic disease strategy.4 |
| --- | --- | --- | --- | --- | --- |
| Location | Yes | Under development | No | Unknown | n |
| Overall | 73(47) | 21(14) | 42(27) | 18(12) | 154 |
| ISN regions: | | | | | |
| • Africa | 14(34) | 5(12) | 16(39) | 6(15) | 41 |
| • Eastern & Central Europe | 9(47) | 4(21) | 5(26) | 1(5) | 19 |
| • Latin America & the Caribbean | 10(56) | 3(17) | 4(22) | 1(6) | 18 |
| • Middle East | 3(27) | 1(9) | 6(55) | 1(9) | 11 |
| • NIS & Russia | 2(29) | 2(29) | 2(29) | 1(14) | 7 |
| • North America | 6(67) | 1(11) | 1(11) | 1(11) | 9 |
| • North & East Asia | 5(71) | 0(0) | 1(14) | 1(14) | 7 |
| • Oceania & South East Asia | 12(80) | 1(7) | 1(7) | 1(7) | 15 |
| • South Asia | 2(29) | 3(43) | 2(29) | 0(0) | 7 |
| • Western Europe | 10(50) | 1(5) | 4(20) | 5(25) | 20 |
| World Bank Groups: | | | | | |
| • Low income | 7(32) | 3(14) | 9(41) | 3(14) | 22 |
| • Lower-middle income | 13(37) | 9(26) | 9(26) | 4(11) | 35 |
| • Upper-middle income | 18(44) | 6(15) | 13(32) | 4(10) | 41 |
| • High income | 35(63) | 3(5) | 11(20) | 7(13) | 56 |
## Existence and type of CKD-specific policies
The existence of CKD-specific policies, both at national and regional levels, varied substantially according to country income (Table 2). Overall, 53 ($34\%$) countries reported the existence of CKD-specific policies, with no low-income countries and $29\%$ of low-middle and upper-middle income countries having CKD-specific policies (Table 2). More than half of countries in North and East Asia and Eastern and Central Europe had CKD-specific policies (57 and $58\%$, respectively) compared to Russia and NIS and Africa (0 and $15\%$, respectively). For those with CKD-specific policies, almost all were national policies with a small proportion of countries in upper-middle and high-income groups having both national and regional policies (S3 Fig).
**Table 2**
| Location | CKD-specific policies | CKD-specific policies.1 | CKD-specific policies.2 | CKD-specific policies.3 |
| --- | --- | --- | --- | --- |
| Location | Yes | No | Unknown | n |
| Overall | 53(34) | 94(61) | 7(5) | 154 |
| ISN regions: | | | | |
| • Africa | 6(15) | 34(83) | 1(2) | 41 |
| • Eastern & Central Europe | 11(58) | 8(42) | 0(0) | 19 |
| • Latin America & the Caribbean | 7(39) | 10(56) | 1(6) | 18 |
| • Middle East | 4(36) | 7(64) | 0(0) | 11 |
| • NIS & Russia | 0(0) | 6(86) | 1(14) | 7 |
| • North America | 3(33) | 4(44) | 2(22) | 9 |
| • North & East Asia | 4(57) | 3(43) | 0(0) | 7 |
| • Oceania & South East Asia | 8(53) | 7(47) | 0(0) | 15 |
| • South Asia | 1(14) | 6(86) | 0(0) | 7 |
| • Western Europe | 9(45) | 9(45) | 2(10) | 20 |
| World Bank Groups: | | | | |
| • Low income | 0(0) | 22(100) | 0(0) | 22 |
| • Lower-middle income | 10(29) | 24(69) | 1(3) | 35 |
| • Upper-middle income | 12(29) | 26(63) | 3(7) | 41 |
| • High income | 31(55) | 22(39) | 3(5) | 56 |
## Specific kidney conditions covered by national policies
Dialysis and transplantation were more frequently covered in CKD-specific national policies compared to general NCD policies (Fig 1). However, dialysis and transplantation were not universally covered in CKD-specific national policies in many regions (Fig 1). Non-dialysis CKD care was not universally covered in CKD-specific or general NCD national policies outside of North and East Asia. No countries in South Asia addressed non-dialysis CKD care in CKD-specific or general NCD national policies, whilst no countries in Africa addressed non-dialysis CKD care through CKD-specific policies (Fig 1).
**Fig 1:** *Kidney conditions covered by CKD-specific and general NCD strategies.*
## Government support and advocacy group for AKI, CKD, and kidney failure treatment and prevention
Governments more frequently recognized CKD and kidney failure as health priorities compared to acute kidney injury (Fig 2). Overall, 79 ($51\%$) countries reported that governments recognized CKD as a health priority. This varied substantially across regions with $27\%$ of low-income and $43\%$ of low-middle income countries recognizing CKD as a health priority, compared to 63 and $57\%$ of upper middle- and high-income countries, respectively (Table 3). Like governmental priorities for specific kidney diseases, advocacy groups for acute kidney injury were less common than for CKD and kidney failure (Fig 2). However, the proportions of countries in which advocacy organizations existed for CKD and/or kidney failure were generally low, particularly in low- and low-middle income countries (Table 3).
**Fig 2:** *Government recognition as a priority (A) and availability of an advocacy group (B) for AKI, CKD, and kidney failure treatment and prevention.* TABLE_PLACEHOLDER:Table 3
## Discussion
In this comprehensive multinational survey of kidney care in 160 countries, we made three main observations with regards to national health policies and strategies for addressing CKD. Firstly, less than half of countries had an established NCD strategy, and overall, only a third of countries had a CKD specific policy, with substantial variation across income levels and regions on both accounts. Secondly, where CKD-specific policies existed, non-dialysis CKD care was variably addressed despite this population encompassing the vast majority of people living with CKD. Thirdly, CKD was identified as a health priority by governments in only half of countries overall, and even less frequently in low and low-middle income countries, where the largest increases in disease burden are occurring. Taken together, these findings indicate that CKD remains substantially underprioritized globally relative to its current and projected burden on individuals and health systems.
Best available data indicate that CKD has a major effect on global health, both as a direct cause of morbidity and mortality and as a risk multiplier for cardiovascular disease [12]. A large proportion of the burden of CKD is concentrated in low- and middle-income countries, particularly Oceania, sub-Saharan Africa and Latin America [3]. It is these same countries that are least equipped to provide equitable access to treatment for kidney failure due to the enormous cost of dialysis and kidney transplantation [6, 13]. In 2010, only half of people worldwide who required dialysis were able to access this life-sustaining treatment, predominantly due to cost [5]. COVID-19 has further exposed and amplified healthcare inequalities, [14] with people living with CKD, especially those with kidney failure, at much greater risk of serious morbidity and mortality [15]. Our findings indicate a major discrepancy between the burden of CKD in many countries and national health policy settings as evidenced by the absence of CKD specific policies and government recognition of CKD as a national priority.
Given the potential for national policies and statements to influence healthcare priorities, our findings argue for urgent attention from governments and policy makers. Indeed, developing and implementing national CKD-specific policies, or integrating CKD into existing or planned NCD strategies, may assist governments and key stakeholders to evaluate and identify gaps in care and advocate for health system improvement. Where they exist, CKD registries provide critical insights, for example temporal trends in quality of care, changes in epidemiology over time and health service utilisation. This information can serve several important purposes including informing the development of CKD policies and guidelines, benchmarking implementation strategies, and serving as a tool for advocacy to urge policy makers to tackle primary prevention of NCDs and reform towards Universal Health Coverage. Multiple registries across different health systems indicate that the use of therapies with proven benefits in CKD remains unacceptably low; in India; less than half of patients with mild-moderate CKD received renin-angiotensin system blockade and statins, with similar findings observed in high-income countries [16–18]. Therefore, when coupled with robust health information systems, CKD-specific policies may help to accelerate implementation science in CKD by providing a local standard by which to evaluate the quality of kidney care–a key priority identified by the ISN to improve global kidney health [19]. Additionally, revaluating the role of screening and early detection of CKD within national NCD and CKD policies will be important as new therapies become increasingly available [20]. Of note, sodium glucose cotransporter 2 inhibitors, are now included in the World Health Organization Essential Medicines List. If access to these agents is expanded in an affordable manner, there is the potential to substantially improve outcomes and influence the epidemiology of CKD (and cardiovascular disease) in many in low- and middle-income countries [21–25]. In this respect, integration of CKD into existing or planned national NCD policies and strategies may be less daunting for policy makers and strengthen commitment towards addressing common risk factors such as diabetes and hypertension, and coexisting conditions such as cardiovascular disease and heart failure.
Whilst international clinical practice guidelines, position papers and scientific statements provide overarching recommendations about best practice, country-specific policies and strategies play an important role in interpreting these recommendations in the context of local disease prevalence patterns and resource availability. For example, strategies and polices for addressing CKD of unknown aetiology are most relevant to Latin America, whereas specific strategies for diabetic kidney disease may be more relevant in parts of Oceania and South East Asia [26, 27]. The availability of kidney replacement therapy also varies widely across regions, and clearly has important implications for CKD-specific national policies [28, 29].
However, the development and implementation of national NCD and CKD-specific strategies and policies in isolation is unlikely to substantially improve kidney health. Supporting countries to develop CKD-specific policies must be coupled with much more widespread testing for albuminuria in high-risk individuals, such as those with type 2 diabetes (which remains poor even in high income countries), [30] greater access to proven therapies, [31] and the establishment and refinement of health information systems to evaluate quality of care [32]. Efforts to address shared risk factors for other NCDs, such as hypertension and diabetes, are likely to result in the greatest gains, and should be a major focus in all national NCDs policies [6].
This study has several important strengths. The ISN GKHA is one of the largest ever health-related country capacity reviews undertaken and all regions and World Bank income levels were well represented in the study. The combined use of desk research and survey responses from key on-the-ground stakeholders and national and regional leaders ensured the data were accurate. The survey was based on a well validated framework for assessing healthcare capacity based on the World Health Organisation health system building blocks. However, our findings should be interpreted in light of some limitations. While the survey was completed by national leaders, key consumer representative organizations and policymakers, responses may not have captured variations within large countries where policies may have varied across jurisdictions. Responses may also have been affected by respondents’ knowledge and experience and been in part subjective. More granular details on the nature of CKD-specific policies, including variations in local NCD or CKD-specific policies (e.g. local pathways of care) within countries were not collected given the size and scope of the GKHA. The GKHA also did not collect information on the implementation of NCD or CKD specific policies, both at a national or local level, which highlights the value of studies that may be able to summarize variations in adherence to guidelines and outcomes. Such implementation studies will be critical to ensure the translation of evidence and guidelines to improve population health for CKD and related NCDs. Nevertheless, this study provides the most comprehensive global overview to date of national health policies and strategies for addressing CKD, which may serve as an advocacy tool to promote the development of national strategies and policies. Future iterations of the GKHA can also be used to gauge progress over time.
In summary, the existence of CKD-specific policies, and a national NCD strategy more broadly, varied substantially across different regions around the world but was suboptimal overall. Greater recognition of CKD within national health policies and NCD strategies is critical to improving kidney healthcare globally.
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|
---
title: 'Emerging trends and research foci of berberine on tumor from 2002 to 2021:
A bibliometric article of the literature from WoSCC'
authors:
- Runzhu Yuan
- Yao Tan
- Ping-Hui Sun
- Bo Qin
- Zhen Liang
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10021304
doi: 10.3389/fphar.2023.1122890
license: CC BY 4.0
---
# Emerging trends and research foci of berberine on tumor from 2002 to 2021: A bibliometric article of the literature from WoSCC
## Abstract
Background: Cancer, also known as a malignant tumor, is caused by the activation of oncogenes, which leads to the uncontrolled proliferation of cells that results in swelling. According to the World Health Organization (WHO), cancer is one of the main causes of death worldwide. The main variables limiting the efficacy of anti-tumor treatments are side effects and drug resistance. The search for natural, safe, low toxicity, and efficient chemical compounds in tumor research is essential. Berberine is a pentacyclic isoquinoline quaternary ammonium alkaloid isolated from Berberis and Coptis that has long been used in clinical settings. Studies in recent years have reported the use of berberine in cancer treatment. In this study, we performed a bibliometric analysis of berberine- and tumor-related research.
Materials and methods: Relevant articles from January 1, 2002, to December 31, 2021, were identified from the Web of Science Core Collection (WOSCC) of Clarivate Analytics. Microsoft Excel, CiteSpace, VOSviewer, and an online platform were used for the literary metrology analysis.
Results: A total of 1368 publications had unique characteristics. Publications from China were the most common (783 articles), and Y. B. Feng (from China) was the most productive author, with the highest total citations. China Medical University (Taiwan) and Sun Yat-sen University (China) were the two organizations with the largest numbers of publications (36 each). Frontiers in Pharmacology was the most commonly occurring journal (29 articles). The present body of research is focused on the mechanism, molecular docking, and oxidative stress of berberine in tumors.
Conclusion: Research on berberine and tumors was thoroughly reviewed using knowledge map and bibliometric methods. The results of this study reveal the dynamic evolution of berberine and tumor research and provide a basis for strategic planning in cancer research.
## 1 Introduction
In every nation worldwide, cancer ranks as a primary cause of mortality and a significant roadblock to increasing life expectancy (Sung et al., 2021). The mortality and incidence rates of malignant tumors have increased globally (Mullard, 2020). The World Health Organization (WHO) estimates for 2019 suggest that cancer is the third or fourth leading cause of death before the age of 70 years in 23 countries (among 183 nations in total) and the first or second leading cause in 112 countries (Sung et al., 2021). The development of cancer treatments has faced significant obstacles. While both traditional and modern approaches (surgery, radiation, chemotherapy, targeted therapy, and immunotherapy) have good efficacy, these treatment modalities also have negative consequences on patient quality of life, including the development of simultaneous resistance to multiple drugs and dermatologic toxicities (Szakacs et al., 2006; Nastiuk and Krolewski, 2016; Lacouture and Sibaud, 2018). One of the main clinical therapies for cancer is chemotherapy; however, cancer is prone to relapse and drug resistance, and most chemotherapy treatments fail; therefore, efforts to develop anti-cancer drugs are needed. Additionally, toxic side effects caused by chemotherapy also affect the quality of life of patients with cancer. Therefore, identifying anti-tumor medicines with minimal toxicity and high efficacy is crucial for the treatment of tumors.
In recent decades, scientists have conducted numerous clinical and laboratory studies on traditional Chinese medicine. Natural compounds have many medicinal properties, including multiple action targets, low toxicity, low side effects, few adverse reactions, and high safety and effectiveness. These compounds have been gradually applied to cancer treatment due to their safety, availability, accessibility, and low cost. Natural compounds have a variety of anti-cancer effects, including suppression of tumor cell growth, induction of tumor cell death, prevention of tumor spread and angiogenesis, regulation of tumor autophagy, reversal of tumor drug resistance, regulation of body immunity, and influence on tumor metabolic reprogramming (Yang et al., 2021). In addition, natural therapy can prevent many issues, increase tumor cell sensitivity to conventional treatment, reduce side effects, enhance patient quality of life, and extend patient lives to cure cancer (Sun et al., 2022a; Sun et al., 2022b). Therefore, natural medicines are receiving increasing attention. Berberine (BBR) is an isoquinoline alkaloid obtained from the Chinese herb *Coptis chinensis* and other Berberis species (Song et al., 2020). It is the main component of Coptidis Rhizome (CR), known as Huanglian in Chinese. Because of its pharmacological characteristics, BBR has been used as a drug to treat diseases. As a secondary metabolite of plants, it has several pharmacological characteristics (Song et al., 2020), including treatment efficacy for cardiovascular and metabolic disease (Feng et al., 2019), polycystic ovary syndrome (Zhang et al., 2021b), and non-alcoholic fatty liver disease (Koperska et al., 2022), in addition to anti-inflammatory (Kuo et al., 2004), antioxidant (Zhuang et al., 2018), and antibacterial (Li et al., 2019) properties. In recent years, BBR has also been used in the research of cancers. For example, BBR binds RXRα to suppress β-catenin signaling, leading to the inhibition of colorectal cancer proliferation (Ruan et al., 2017); BBR also regulates the HMGB1–TLR4 axis to repress the metastasis of breast cancer in vitro and in vivo (Zheng et al., 2021). Moreover, BBR exerts therapeutic actions on gastric cancer by multi-step actions such as inhibiting cell proliferation, migration, and angiogenesis (Liu et al., 2022a); BBR also combines with cisplatin to induce necroptosis and apoptosis in ovarian cancer (Liu et al., 2019). In recent years, more studies on BBR have been conducted in vitro and in vivo, which have largely proved the reproducibility and transformation potential of BBR’s anti-tumor effect in in vitro cell and in vivo animal models.
Using measurement techniques from mathematics and statistics, bibliometrics analysis assesses and forecasts the current state of science and technology by utilizing the features of the literature system as its research subject (Chen et al., 2014; Yu et al., 2020). By examining and evaluating the quantity and quality of scientific literature associated with a specific topic, bibliometric evaluation can objectively assess the state and level of development of that field (Musbahi et al., 2022). Bibliometrics assists researchers in swiftly identifying the information context in the target field, including the annual publishing trends of the literature, catalog of publishing institutes or journals, and popular research (Yin et al., 2022). The knowledge structure can be understood more methodically and intuitively using this approach, and frontiers or hotspots in a particular research area can be identified (Musbahi et al., 2022). Therefore, the present bibliometrics study aimed to investigate the role of BBR in tumors and to offer a broad perspective and roadmap for future research on BBR in pan-carcinoma treatment.
## 2.1 Data source and searching strategies
Published studies from January 1, 2002, to December 31, 2021, related to BBR and tumors were collected from the Science Citation Index Expanded (SCI-E) of the Web of Science (WoS) Core Collection (WOSCC) of Clarivate Analytics. To guarantee the reliability and accuracy of the findings, we performed pertinent pretests and improved the retrieval method. The retrieval method is illustrated in Figure 1. We used the WoS engine to search for terms related to BBR and cancer that were obtained from the Medical Subject Headings (MeSH) in PubMed. The search formula was TS = (Berberine or Umbellatine) and TS = (Tumor or Neoplasm or Tumors or Neoplasia or Neoplasias or Cancer or Cancers or “Malignant Neoplasm” or Malignancy or Malignancies or “Malignant Neoplasms” or “Neoplasm, Malignant” or “Neoplasms, Malignant” or “Benign Neoplasms” or “Benign Neoplasm” or “Neoplasms Benign” or “Neoplasm Benign”). Only publications written in English were included. The reasons for exclusion from the study were 1) no connection between BBR and tumors of any kind; 2) meeting abstract, correction, editorial material, proceeding paper, retracted publication, book chapters, early access, retraction, etc.; and 3) publications in a language other than English. A total of 1,368 papers were obtained.
**FIGURE 1:** *Flowchart of the screening process.*
## 2.2 Data collection
All retrieved documents were used for the bibliometric analysis. We extracted information including titles, annual publications, countries and institutes, authors, references, keywords, and scientific cooperation analysis. Data were independently extracted from a set of articles by TY and YRZ. SP-H mediated the outcomes if a disagreement arose.
## 2.3 Data analysis and visualization
We used CiteSpace (version 6.3. R3), VOSviewer (version 1.6.18), Bibliometrix (version 3.1.4), R language (version 3.6.3), and Microsoft Excel 365 for data analysis and presentation.
CiteSpace, developed by Prof. Chao-mei Chen, is a bibliometric mapping analytical tool used worldwide, most predominantly in China (Qin et al., 2022). It is a free program for analyzing, identifying, and visualizing trends and patterns in scientific literature and was chosen as the analysis target (Pan et al., 2017). In our investigation, the specifics of the CiteSpace settings were as follows: time slices of 1 year each were taken from 2002 to 2021. The links (strength: cosine; scope: inside slices) and term sources (title, abstract, author keywords, and keywords plus) were set to the default values. Items with a g-index citation or occurrence were selected for this study. Links that were not necessary were removed using Pathfinder.
VOSviewer, developed by van Eck and Waltman, creates visual network maps of scientific information, including bibliometric network analysis (van Eck and Waltman, 2010). We used VOSviewer to create network, overlay, and density visualization maps. We chose the “full counting” method for our analysis. For keywords and co-cited journals, the thresholds for the minimum numbers were set at 100 and 5, respectively. Keywords or co-cited journals are displayed as nodes in the form of a network through a visualization diagram.
Visualization of the annual publication numbers; country, institute, author, and journal rankings; cited reference bursts; and most globally and locally cited Local Cited Documents, was performed using Microsoft Excel 365. Additionally, we performed a descriptive study of writer output over time and keyword evolution in Bibliometrix (Cheng et al., 2022). Bibliometrix (https://www.bibliometrix.org) is an open-source instrument for quantitative scient-metric research. Machine-learning software was used to evaluate the distribution of each component examined in the bibliometric investigation. The variables were annual scientific production, average citations per year, most impactful journals by H-index or total citations (TC), top journals’ production over time, most relevant authors, most globally and locally cited documents, most pertinent affiliations, country-level scientific output, international collaboration network, country of origin of the corresponding author, top producing nations over time, historical direct citation network, most widely cited publications, most pertinent keywords, and keyword cluster analysis. Impact factor (IF) and partition information for the journals referred to the “Journal Citation Report (JCR) 2022.” These analytical methods offer unbiased and varied viewpoints on the function of berberine in tumor formation. The variables are presented as numbers and percentages in the descriptive study. p-values were not reported because no comparisons were made.
## 3.1 Global publication outputs
We assessed the historical development process, current research conditions, and forecasted future trends in development by statistically analyzing the general usage of BBR pharmacological functions in the number of publications over time.
We counted the number of papers related to “berberine and tumors” from 1972 to 2021 and collected 1,587 articles for bibliographic records from the WOSCC. Ultimately, 1,368 articles were eligible for the next stage of analysis based on the exclusion criteria of publication time, document type, and language. Figure 2A shows the number of articles published each year and the cumulative number of articles published from 2002 to 2021. The overall trend has increased over the past 20 years. The greatest number of papers was published in 2021, with 180 research articles. The exponential curve equation for the rise in literature production was $y = 11.819$e0.2604x, which conforms to Price’s law. The simulation curve had a relatively strong coefficient of determination (R 2 = 0.9543), which fitted the annual literature growth trend well. Based on this curve, we predicted that the number of annual articles will steadily increase, indicating increased interest in research on BBR and tumors.
**FIGURE 2:** *Global trends of publications about berberine and tumor. (A) The number of articles published each year and the accumulative number of articles from 2002 to 2021. (B) The annual number of papers published by nations from 2002 to 2021.*
## 3.2 Distributions of countries/regions
Statistics on the research countries reporting on publications related to “berberine and tumors” showed the stages of BBR pharmacology development in each country and made comparisons easier. According to WoS, many countries have participated in research in the last 20 years. Table 1 lists the top 10 productive countries. China had the most publications (783 articles), accounting for $57.24\%$ of the total, followed by the United States and India (142 articles each, accounting for $10.38\%$ each, respectively), South Korea ($$n = 97$$), Italy ($$n = 61$$), Iran ($$n = 60$$), Japan ($$n = 42$$), Egypt ($$n = 30$$), Germany ($$n = 26$$), and Poland ($$n = 24$$).
**TABLE 1**
| Rank | Countries | Article count | Percentage (n/1368) | H-index | Total citations | Average citation per article |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | China | 783 | 57.24% | 70 | 23603 | 30.14 |
| 2 | United States | 142 | 10.38% | 48 | 6886 | 48.49 |
| 3 | India | 142 | 10.38% | 35 | 4137 | 29.13 |
| 4 | Republic of Korea | 97 | 7.09% | 34 | 3198 | 32.97 |
| 5 | Italy | 61 | 4.46% | 25 | 2257 | 37.0 |
| 6 | Iran | 60 | 4.39% | 22 | 1800 | 30.0 |
| 7 | Japan | 42 | 3.07% | 22 | 1403 | 33.4 |
| 8 | Egypt | 30 | 2.19% | 14 | 615 | 20.5 |
| 9 | Germany | 26 | 1.90% | 16 | 1117 | 42.96 |
| 10 | Poland | 24 | 1.75% | 14 | 539 | 22.46 |
Publications from China ranked first in total number of citations, but the average number of citation per paper was very low, only $30.14\%$, compared to the top 10 countries. Although the number of articles published by Germany was limited compared to China, the average citation per article ranked second among the top 10 countries. This finding suggested that the publications were of very high quality and had certain reference values. The H-index is a mixed quantitative indicator that considers both the number of posts and the required number of citations and can be used to identify influential researchers (Hirsch, 2005). China’s researchers had the highest H-indexes, demonstrating the significant impact of their publications. After 2008, most global publications came from China. Figure 2B shows the total number of papers published worldwide from 2002 to 2021.
Using an online bibliometric analysis platform, we evaluated the significance of nations in the visualization of cooperative networks. Katz and Martin, two scientists, defined scientific cooperation as the study of academics collaborating to create new scientific knowledge, known as scientific collaboration (Katz and Martin, 1997). Figure 3A shows partnerships between countries, among which articles from China showed the most aggressive cooperation with other countries; most often between China and the United States. We conducted a visual analysis using VOSviewer (Figure 3B). The circles in the map represent countries and the lines indicate the connections between them. The larger the area of the circle, the larger the contribution of these countries to this field. We found that China made significant contributions to research on BBR and cancer. A density map can be used to determine the number of publications in each nation. The lighter the color, the greater the number of publications. The density map was used to determine the number of publications in each nation (Figure 3C). As shown in Figure 3C, China had the highest number of publications. Figure 3D shows that American publications were generally concentrated before 2016, whereas Chinese, Indian, Italian, and Iranian articles were concentrated after 2016.
**FIGURE 3:** *Overview of national publications. (A) The scientific collaborations among worldwide countries. (B) Visual analysis of cooperation between countries based on VOSviewer. (C) A density visualization map of number of publications in each nation. (D) Median time chart of the number of articles issued by each country.*
## 3.3 Distribution of institutes
The top 10 productive institutes are listed in Table 2. China Medical University (Taiwan) and Sun Yat-Sen University (China) had the most publications, with 36 articles each. China Medical University (Taiwan) showed the highest total number of citations [1,417] and H-index [21], with an average number of citations of 39.26, demonstrating a high level of acclaim for its articles. We used VOSviewer to depict the cooperation between institutes in Figure 4A. Figure 4B shows each institute’s cooperation timeline from 2002 to 2021; the node color on this map corresponds to each institute’s cooperation respective average appearing year (AAY). Jilin University and Mashhad University of Medical Sciences had relatively fresh entries compared to the nations shown in purple, such as China Medical University (Taiwan), according to the color gradient in the lower right corner.
## 3.4 Authors
The top 10 authors in terms of the number of articles in the past 20 years are listed in Table 3. Y. B. Feng of the University of Hong Kong, was the most prolific author (21 articles) and also received the highest number of total citations [1,281] and had an H-index of 17, demonstrating his significant contributions to the field. O Paolo from Naxospharma published 17 articles, while L Paolo and K Gopinatha Suresh (Indian Institution of Chemical Biology) both had H-index values of 12.
**TABLE 3**
| Rank | Author | Article count | H-index | Country | Total number of citations | Average number of citations per article | Institution |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Feng, Y. B | 21 | 17 | China | 1281 | 61.0 | University of Hong Kong |
| 2 | Paolo. L | 17 | 12 | Italy | 879 | 51.71 | Naxospharma |
| 3 | Gopinatha Suresh. K | 16 | 12 | Indian | 510 | 31.88 | Indian Institute of Chemical Biology |
| 4 | Tan, H.-Y | 13 | 9 | China | 408 | 31.38 | Hong Kong Baptist University |
| 5 | Wang, Y. T | 11 | 11 | China | 1094 | 99.45 | University of Macau |
| 6 | Zheng, X | 11 | 9 | China | 275 | 25.0 | Jilin University |
| 7 | Bhupendra M. M | 10 | 8 | Republic of Korea | 129 | 12.9 | Konkuk University |
| 8 | Shao, D | 10 | 9 | China | 256 | 25.6 | Jilin University |
| 9 | Chen, L | 10 | 8 | China | 297 | 29.7 | Jilin University |
| 10 | Doo Hwan. K | 10 | 8 | Republic of Korea | 129 | 12.9 | Konkuk University |
## 3.5 Journals and co-cited journals
The top 10 journals according to the number of publications are shown in Table 4 and Figure 5. The top 10 journals published 252 papers, or $18.42\%$ of all the included publications. Frontiers in Pharmacology (IF = 5.9879) was the most popular journal with 29 articles, followed by Molecules (IF = 4.9269) with 28 articles. PLOS One (IF = 3.7521) was the most frequently cited journal (1,388 citations). The distribution of journals across fields, evolution of citation trajectories, and drift of scientific research centers can be displayed using the dual-map overlay of journals (Chen and Leydesdorff, 2014; Zhang et al., 2021a). In our dual-map overlay analysis (Figure 5E), the left side shows the distribution of the journals where the citing documents were located, representing the main disciplines of science mapping, while the right side shows the distribution of the journals corresponding to the cited literature, representing which disciplines were mainly cited by science mapping. The results showed that research related to BBR and tumors was mainly associated with the areas of molecular/biology/immunology and was often cited by molecular/biology/genetics researchers.
## 3.6 Co-cited references
We quantified the knowledge and development of research on a particular topic by evaluating the significant nodes and clusters in the co-cited reference network (Yin et al., 2022). We used CiteSpace to create a timeline-visualized diagram network map of the co-cited articles, which was divided into 19 clusters (Table 5). The co-cited reference network is shown in Figure 6A, in which the node size is the proportion of the research frequency, color is the proportion of the time slice, and the connections are represented by lines. We further conducted cluster analysis on these co-cited articles based on the keywords in those articles (Figure 6B). By removing phrases from the titles of the cited publications, we identified the main research hotspots. Figure 6C shows a timeline of references in the field of BBR and tumors, as determined by CiteSpace. We observed a shift in the focus of research over time. The development of clusters 2 (interleukin 1 beta), 9 (structure-based molecular modeling), and 10 (herb) occurred the earliest, indicating that early research focused more on BBR structure and pharmacology. Clusters 1 (Daxx), 6 (prostate cancer), and 7 (tumor necrosis factor-alpha) occurred between 2007 and 2011, indicating a focus on the anti-cancer molecular mechanism of BBR. Clusters 15 (carbohydrate), 3 (photodynamic therapy), 4 (non-small cell lung cancer), and 11 (gut microbiota) are current research hotspots. Figure 6D shows the top 10 references with the strongest citation bursts, highlighting their significance in the fields of BBR and tumor-related research. In addition, we list the total citations of articles and local citations of articles in Tables 6 and Table 7, respectively.
## 3.7 Analysis of keywords
Keywords are generally regarded as a significant index to reflect research frontiers and hotspots in a certain topic (Zhong And Song, 2008; Wu et al., 2021). We produced a map showing the co-occurrence of keywords using VOSviewer. The cooperation network (Figure 7A) clearly shows keywords changing from 2002 to 2021. At present, the keywords are main focused on “berberine”, “apoptosis” and “cancer”, and so on. Figure 7B shows the evolution of keywords in a typical publishing year. Initially,research on BBR mainly focused on “alkaloid”, and “tumor necrosis factor-alpha”; then, the research keywords shifted towards “prostate cancer”, “rapamycin” and “p53”; finally, the most recent keywords have become more diverse, with topics including “mechanism”, “cancer” and “gut microbiota”, and so on. Figure 7C shows the frequency of keywords in different time periods, the larger the size of the node, the higher the search frequency. In conclusion, “apoptosis”, “non-small cell lung cancer”, and “gut microbiota” were the most frequently searched terms.
**FIGURE 7:** *Analysis of keywords (A) the changing trend of research keywords from 2002 to 2021. (B) The evolution of keyword frequency. (C) The network map of keywords. Minimum number of occurrences of keywords ≥5.*
## 4 Discussion
Chemotherapy and radiation therapy are standard treatments for patients with cancer. However, the resistance of malignant tumors to these treatments and the occurrence of major organ damage severely restrict the clinical outcomes of this disease [35]. Moreover, current cancer treatments are inefficient, and the surgical prognosis is poor because tumor cells can invade and spread after treatment with a variety of chemical medications (Colagiuri et al., 2013). Natural herbal nutraceuticals have recently gained increased attention among popular clinical medications owing to their safety, adaptability to overcome therapeutic resistance, potential to reduce the adverse side effects of cancer treatments, low cost, and wide availability (Choudhari et al., 2019). Natural remedies can not only prevent drug resistance [38] but also have anti-tumor effects through various signaling pathways (Kumar and Adki, 2018; Al-Bari et al., 2021; Hashem et al., 2022). The prognosis of patients with malignant tumors can be improved by combining Chinese herbal therapies with other therapies (Mignani et al., 2018; Rejhová et al., 2018). BBR is a multi-target Chinese medicine monomer compound that not only regulates the growth of cancer cells but also acts as a combination chemotherapy drug for the treatment of tumors.
In this study, we conducted a bibliometric analysis to evaluate the hotspots and development trends of research on disciplines connected to BBR and tumors. We analyzed a total of 1,368 studies published from January 1, 2001, to December 31, 2021, and found that research on BBR and tumors has become increasingly frequent, suggesting that BBR may be a natural medication for treating tumors. China had the most relevant publications in the last 20 years, but the average number of citations per article was only $30.14\%$, significantly lower than that of other nations in terms of publications, suggesting that articles from China still have room for improvement. Among the top 10 countries with the greatest number of articles, China engages the most in international collaborations, primarily with the United States. China Medical University (Taiwan) published the most studies related to BBR and tumors worldwide. However, the University of Hong Kong ranked first in terms of average citations per article. Y. B. Feng, an author based in China, was the most productive and had the highest total citations. The journal that published the most articles was Frontiers in Pharmacology (29 articles), followed by Molecules (28 articles). We also analyzed the reference with the strongest citation burst; the latest literature of the co-cited reference was “Berberine, an epiphany against cancer,” which was published in Molecules in 2014. This article summarizes the molecular targets of BBR, several mechanisms by which BBR inhibits cancer, and the potential discovery of BBR derivatives for anti-cancer drugs (Ortiz et al., 2014). Our analysis of cooperation between countries, institutes, and institutes, showed that academic cooperation can promote the development of clinical and academic research.
## 4.1 Mechanism of berberine on cancers
As shown in Figure 6B, BBR has been used in the treatment of lung cancer (#13), prostate cancer (#6), and non-small-cell lung cancer, and has been a research hotspot in recent years (Figure 6C). The mechanisms of action of BBR in cancer have also been studied and mainly consist of inhibiting migration and invasion, inducing apoptosis, arresting the cell cycle, inhibiting proliferation, and promoting autophagy in tumors.
## 4.1.1 Berberine can inhibit the migration and invasion of tumors
In clinical practice, most patients experience the invasion and migration of cancer cells from a single focus to other distant tissues. In 2015, research on migration and invasion became a hot topic (Figure 7B). A wide variety of migratory and invasion mechanisms are involved in cancer (Figure 6C). In triple-negative breast cancer cells, BBR reduces interleukin-8 (IL-8) expression by blocking the EGFR/MEK/ERK signaling pathway to prevent cell invasion and metastasis (Kim et al., 2018). BBR also affected cell migration and invasion in the human hepatoma HepG2 cell line by inhibiting the transforming growth factor (TGF-beta/Smad) signaling pathway (Du et al., 2021). Understanding cellular and molecular changes in these various migration/invasion programs will enable us to develop novel therapeutic approaches and provide insights into the spread of cancer cells.
## 4.1.2 Berberine can promote tumor cell apoptosis
Apoptosis was the main keyword in BBR-related research (Figure 7A) and the median time of the research boom occurred around 2016 (Figure 7C). Apoptosis is an evolutionarily conserved cell death system responsible for eliminating cells during embryonic development and maintaining organismal homeostasis (Singh et al., 2019). The pathways of apoptosis are divided into the exogenous pathway, endogenous pathway, caspase cascade reaction, and caspase effects. Many biochemical processes, such as a loss of mitochondrial membrane potential, release of cytochrome C into the cytoplasm, expression of Bcl-2 family and caspase family proteins, and cleavage of poly ADP ribose polymerase, induced apoptosis in the skin squamous cell carcinoma A431 cell line after treatment with BBR (Wartenberg et al., 2003). Moreover, BBR induced apoptosis involved in the caspase-related mitochondrial pathway compared to normal liver HL-7702 cells, which was mediated by adenosine monophosphate-activated protein kinase (AMPK) and reduced the survival of human hepatoma HepG2, SMMC-7721, and Bel-7402 cells (Mitani et al., 2001). To reach the goal of treatment, researchers are currently modeling small molecules with apoptosis-promoting protein activity, which renders cells susceptible to mitochondrial apoptosis and triggers apoptosis.
## 4.1.3 Berberine can block the cell cycle of tumor cells
Research on the effect of BBR on cell cycle arrest started around 2012 but was mainly concentrated in 2017 (Figure 7B). To some degree, changes in the cell cycle can either slow or accelerate cancer development. Previous studies demonstrated that BBR inhibits the growth of certain tumor cells by regulating their cell cycle. A study on the mechanism of BBR in breast cancer showed that the alkaloid BBR prevented breast cancer cells from entering the S phase and increased cancer cell sensitivity to treatment (Kim et al., 2010). In a human chondrosarcoma cell HBT-94 model, BBR activated the PI3K/Akt and p38 signaling pathways, increased the protein levels of p53 and p21, and triggered G2/M phase arrest, thus demonstrating anti-cancer effects (Eo et al., 2014). Gene stability is maintained by cell cycle arrest and cancer formation is significantly influenced by the incidence of cell cycle-regulated gene alterations. If DNA is damaged during a healthy cell cycle, the cell cycle stalls at a relevant checkpoint. Cell cycle arrest gives cells more time to repair damage, which lowers the likelihood of mutations and prevents tumor development. Therefore, targeting cell cycle checkpoints may significantly enhance cancer treatment.
## 4.1.4 Berberine can inhibit tumor proliferation
Numerous studies have reported that BBR regulates cell signal transduction by inhibiting tumor cell proliferation. As shown in Figure 7B, the keyword “tumor proliferation” mainly appeared in 2014. BBR inhibited the growth of human colon cancer cell lines Caco-2 and Lovo by reducing citrate synthase activity (Mantena et al., 2006). Moreover, BBR inhibited melanoma by increasing miRNA-582-5p and miRNA-188-5p levels and decreasing the expression of cell cycle-related proteins in melanoma A375 cells (Lin et al., 2005). Research on the mechanism associated with tumor proliferation mainly focuses on material metabolism; therefore, a deeper understanding of the molecular connections between cellular metabolism and growth regulation may ultimately result in more effective cancer treatments.
## 4.1.5 Berberine can induce autophagy of tumor cells
The cooperation network shown in Figure 7A shows that the keyword “autophagy” is closely connected with BBR. Autophagy, a type of programmed cell death, is crucial for preserving intracellular homeostasis. BBR induces autophagy by inhibiting the ERK$\frac{1}{2}$ signaling pathway in glioblastoma and further reduces temozolomide resistance (Qu et al., 2020). By inducing cytostatic autophagy and regulating the MAPK/mTOR/p70S6K and Akt signaling pathways, BBR inhibits the development of human gastric cancer cells both in vitro and in vivo (Zhang et al., 2020a). Despite substantial research on the regulation of autophagy by BBR in recent years, the precise mechanism remains unclear.
## 4.2 Applications of berberine
Figure 6 and Figure 7 list several conditions that can be regulated by BBR, including non-small-cell lung cancer, breast cancer, colorectal cancer, gut microbiota, and photodynamic therapy. Among these, the application of BBR in cancer has been the focus of research in recent years. The results are summarized in Table 8.
**TABLE 8**
| Application | Cell line | Effect | Reference |
| --- | --- | --- | --- |
| Non-small-cell lung cancer | A549, PC9, H1650, and H1299 | Activation of the p38α MAPK signaling pathway | Wu et al. (2021) |
| Non-small-cell lung cancer | A549, PC9, H1650, and H1299 | Induction of the protein expression of p53 and FOXO3a | Wu et al. (2021) |
| Non-small-cell lung cancer | A549, PC9, H1650, and H1299 | Inhibit proliferation and induce apoptosis | Wu et al. (2021) |
| Non-small-cell lung cancer | A549, H157, H358, H460, H1299, H1975, and Lewis cells | Enhance tumor-infiltrating T-cell immunity and attenuate the activation of MDSCs and Tregs | Yang et al. (2021) |
| Non-small-cell lung cancer | A549, H157, H358, H460, H1299, H1975, and Lewis cells | Trigger PD-L1 degradation through the ubiquitin (Ub)/proteasome-dependent pathway | Yang et al. (2021) |
| Non-small-cell lung cancer | A549, NCI-H1299, and BEAS-2B | Suppression of epithelial–mesenchymal transition | Yin et al. (2022) |
| Non-small-cell lung cancer | A549, NCI-H1299, and BEAS-2B | Trigger cell cycle arrest | Yin et al. (2022) |
| Non-small-cell lung cancer | A549, NCI-H1299, and BEAS-2B | Suppression of HIF-1α expression | Yin et al. (2022) |
| Non-small-cell lung cancer | HCC827/AR, HCC827/AR0.5, HCC827/AR2, HCC827/ER, PC-9/AR, and PC-9/GR/AR | Act as a naturally existing MET inhibitor | Yu et al. (2020) |
| Non-small-cell lung cancer | HCC827/AR, HCC827/AR0.5, HCC827/AR2, HCC827/ER, PC-9/AR, and PC-9/GR/AR | Enhance induction of apoptosis through Bim elevation and Mcl-1 reduction | Yu et al. (2020) |
| Breast cancer | HEK293, SMCC-7721, and ZR-75-30 | Decrease the expression of ephrin-B2 and its PDZ-binding proteins | Zhang et al. (2021a) |
| Breast cancer | HEK293, SMCC-7721, and ZR-75-30 | Downregulate the phosphorylation of VEGFR2 and downstream signaling members (AKT and Erk1/2) | Zhang et al. (2021a) |
| Breast cancer | HEK293, SMCC-7721, and ZR-75-30 | Downregulate the expression of MMP-2 and MMP-9 | Zhang et al. (2021a) |
| Breast cancer | MCF-7 and MCF-7/MDR | Enhance sensitivity in drug-resistance breast cancer cells | Zhang et al. (2020a), Zhang et al. (2021b) |
| Breast cancer | MCF-7 and MCF-7/MDR | Induce apoptosis | Zhang et al. (2020a), Zhang et al. (2021b) |
| Breast cancer | MDA-MB-231 and BT549 | Induce DSB | Zhang et al. (2020b) |
| Breast cancer | MDA-MB-231 and BT549 | Increase the release of cytochrome c | Zhang et al. (2020b) |
| Breast cancer | MDA-MB-231 and BT549 | Trigger the caspase-9-dependent apoptosis | Zhang et al. (2020b) |
| Colorectal cancer | HT-29, SW480, and NIH3T3-Light2 cell lines | Suppression of the paracrine sonic hedgehog (SHH) signaling | Zhang et al. (2020c) |
| Colorectal cancer | HT-29, SW480, and NIH3T3-Light2 cell lines | Inhibition of the secretion and expression of SHH protein | Zhang et al. (2020c) |
| Colorectal cancer | HCT116 and SW480 | Inhibit proliferation and induce G0/G1 phase arrest | Zhao et al. (2017) |
| Colorectal cancer | HCT116 and SW480 | Knockdown of IGF2BP3 could suppress the PI3K/AKT pathway to inhibit cell proliferation and cycle transition | Zhao et al. (2017) |
| Colorectal cancer | HCT-8, HCT-116, and HT-29 | Suppress lipogenesis via promotion of PLZF-mediated SCAP ubiquitination | Zhao et al. (2022) |
| Colorectal cancer | HCT116, SW48, RKO, Caco-2, SW480, and HT-29 | Suppression of DNA replication | Zheng et al. (2014) |
| Gut microbiota | Brain neuron cells | Accelerate the production of L-DOPA by intestinal bacteria | Zheng et al. (2021) |
| Gut microbiota | P. mirabilis, S. boydii, and B. fragilis (intestinal bacterial strains) | Reduce the biosynthesis of TMAO by interacting with the enzyme/coenzyme containing (CutC) and (FMO) | Zhong and Song (2008) |
| Gut microbiota | β-cells | Inhibit the biotransformation of DCA by Ruminococcus bromii | Zhuang et al. (2018) |
| Photodynamic therapy | ACHN, 786-O, and HK-2 | Increase reactive oxygen species | [71] |
| Photodynamic therapy | ACHN, 786-O, and HK-2 | Increase autophagy levels and apoptosis by caspase 3 activity | [71] |
| Photodynamic therapy | A375, M8, SK-Mel-19, and the cisplatin-resistant cell lines A375/DDP, M8/DDP, and SK-Mel-19/DDP | Activate the P38 MAPK signaling pathway | [72] |
## 4.2.1 Non-small-cell lung cancer
Figure 6C shows a research hotspot focused on non-small cell lung cancer. Several mechanisms by which BBR inhibits non-small cell lung cancer have been reported. For example, Zheng et al. [ 2014] reported that BBR induces apoptosis of NSCLC cells to prevent growth and is involved in activating the p38α MAPK signaling pathway and subsequent increased protein expression of p53 and FOXO3a. BBR plays also an anti-tumor role from the perspective of immunity, as it can specifically bind to glutamic acid 76 of constitutive photomorphogenic-9 signalosome 5 (CSN5) and inhibit the PD-1/PD-L1 axis through its deubiquitylation activity, leading to the PD-L1 ubiquitination and destruction (Liu et al., 2020). In addition, BBR and its derivatives are considered potential drugs for the treatment of NSCLC. Liu et al. [ 2021] demonstrated that the derivative of BBR, demethyleneberberine (DMB), exerts an anti-tumor effect leading to cell arrest and cellular senescence in NSCLC. In addition, BBR can be combined with other drugs such as osimertinib (Chen et al., 2022). These findings demonstrate that BBR exerts anti-tumor effects through various mechanisms.
## 4.2.2 Breast cancer
According to the keyword analysis (Figure 7A), “breast cancer” is also an aspect of BBR research. Ma et al. [ 2017] reported that BBR prevents the proliferation and migration of breast cancer ZR-75-30 cells by regulating ephrin-B2. BBR also increases chemosensitivity, reverses hypoxia-induced chemoresistance, and further induces apoptosis in breast cancer (Pan et al., 2017b; Pan et al., 2017c). Moreover, Zhao et al. [ 2017] reported that BBR inhibits triple-negative breast cancer. BBR induces caspase-9/cytochrome c-mediated apoptosis both in vitro and in vivo to inhibit the proliferation of TNBC cells. Thus, BBR has been used to treat breast cancer.
## 4.2.3 Colorectal cancer
BBR inhibits colorectal tumor development A previous study showed that BBR reduces paracrine sonic hedgehog (SHH) signaling, which in turn reduces colon cancer growth in vitro and in vivo (Shen et al., 2021). This revealed a novel molecular mechanism for the anti-cancer effects of BBR. BBR inhibits proliferation through cell cycle arrest-related pathways. BBR has been reported to induce G0/G1 phase arrest in colorectal cancer cells by downregulating the targeted gene IGF2BP3 (Zhang et al., 2020c). Moreover, BBR showed potential anti-migration and anti-invasion properties in cell lines including HCT-8, HCT-116, and HT-29. BBR reduces lipogenesis and the spread of colon cancer cells by promoting PLZF-mediated SCAP ubiquitination (Liu et al., 2022b). Meanwhile, several studies have reported on BBR combined with other drugs, for example, the combination with Andrographis to treat colorectal cancer (Zhao et al., 2022). BBR is well known as a potential drug for the treatment of colon cancer.
## 4.2.4 Gut microbiota
Figure 6C shows that cluster 11 (gut microbiota) is a current research hotspot. The gut microbiota includes a large number and a wide range of species that are interdependent and interact with the host. The occurrence, development, and prognosis of many human diseases are closely related to intestinal flora. In 2021, Wang et al. ( 2021b) introduced oral BBR to accelerate the production of L-dopa by intestinal bacteria. The L-DOPA produced by intestinal bacteria enters the brain through circulation and is converted to dopamine, significantly increasing the brain dopamine levels of mice and improving PD expression. Ma et al. [ 2022] demonstrated that oral BBR reduced the biosynthesis of trimethylamine-N-oxide (TMAO), an atherogenic metabolite derived from the gut microbiota in the intestine, by interacting with the enzyme/coenzyme containing choline trimethylamine lyase (CutC) and lutein monooxygenase (FMO) in the intestinal microbiota, thus playing a role in the treatment of atherosclerosis. In addition, the human intestinal microbiome is a promising target for the treatment of type 2 diabetes. BBR reduces blood sugar levels by inhibiting the biotransformation of DCA by *Ruminococcus bromii* (Zhang et al., 2020b). Intestinal microorganisms affect the occurrence and development of metabolic diseases by regulating the metabolism of sugars, lipids, and amino acids and are inextricably linked with diseases of the neuropsychiatric, cardiovascular, urinary, and other systems. Therefore, it is of great significance to study the correlation between intestinal flora and diseases for the prevention and treatment of diseases and the maintenance of human health.
## 4.2.5 Photodynamic therapy
BBR is well known for its anti-inflammatory, antioxidant, anti-diabetes, anti-obesity, and anti-cancer properties; however, little is known about its photosensitive properties, and it might serve as a new kind of photodynamic therapeutic agent. Our analysis of co-cited references (Figure 6C) showed that photodynamic therapy is a hotspot of current research in BBR. For example, a study that assessed the effects of BBR on PDT in renal cancer cell lines reported increased reactive oxygen species (ROS) levels after treatment with BBR associated with PDT, which was accompanied by increased autophagy levels and apoptosis due to caspase 3 activity (Lopes et al., 2020). Wang et al. ( 2021a) reported that a combination of cisplatin and BBR-PDT played a role in cisplatin-resistant melanoma cells. The experimental findings showed that mitochondrial apoptotic pathways that depend on caspases were the mode of cell death and that BBR photodynamic therapy modulated apoptosis by activating the P38 MAPK signaling pathway. The number of articles on BBR photosensitivity therapy is currently small, and most have focused on the treatment and application of cancer, which may suggest that photosensitivity therapy could provide a new method for cancer treatment.
BBR is a natural compound with great biological activity that is effective against various diseases. The literature review showed that increasing numbers of BBR derivatives have been created and used in disease research. BBR can be used as a combination drug in the study of drug-resistant cell lines and has shown significant effects. The ability of BBR to disrupt intracellular pathways and its intrinsic features has been the subject of numerous investigations in recent years. More importantly, BBR has been investigated as a curative medication in both animal models and human cell lines. However, there remain many issues to resolve; for example, BBR alone has not been tested in humans and it has weak water solubility, reduced oral absorption, and low bioavailability. Therefore, future studies should focus on the clinical use of BBR.
## 5 Conclusion
This study analyzed publications, research topics, research hotspots, and development trends of research in the field of BBR and tumors through systematic bibliometric analysis. Chinese researchers have produced the most publications; however, articles published in the United States ranked first in terms of average citations per article. The most prolific universities are China Medical University (Taiwan) and Sun Yat-sen University. The terms “mechanism,” “molecular docking,” and “oxidative stress” are now popular study topics related to BBR in tumors.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.
## Author contributions
All authors contributed significantly to the work that was published, whether it was in ideation, study design, data collection, analysis, etc. All authors also participated in writing, revising, or critically evaluating the article; gave their final approval for the published version; decided on the journal to which the article would be submitted; and agreed to be responsible for all aspects of the work.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Disability-adjusted life years associated with chronic comorbidities among
people living with and without HIV: Estimating health burden in British Columbia,
Canada'
authors:
- Ni Gusti Ayu Nanditha
- Jielin Zhu
- Lu Wang
- Jacek Kopec
- Robert S. Hogg
- Julio S. G. Montaner
- Viviane D. Lima
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021313
doi: 10.1371/journal.pgph.0001138
license: CC BY 4.0
---
# Disability-adjusted life years associated with chronic comorbidities among people living with and without HIV: Estimating health burden in British Columbia, Canada
## Abstract
Life span of people living with HIV (PLWH) has increased dramatically with the advent of modern antiretroviral therapy. As a result, comorbidities have emerged as a significant concern in this population. To describe the burden of chronic comorbidities among PLWH and HIV-negative individuals in British Columbia (BC), Canada, we estimated disability-adjusted life years (DALYs) related to these comorbidities. Based on a population-based cohort in BC, antiretroviral-treated adult PLWH and 1:4 age-sex-matched HIV-negative controls were followed for ≥1 year during 2001–2012. DALYs combined years of life lost to premature mortality (YLLs) and due to disability (YLDs), and were estimated following the Global Burden of Diseases’ approaches. DALYs associated with non-AIDS-defining cancers, diabetes, osteoarthritis, hypertension, dementia, cardiovascular (CVD), kidney, liver and chronic obstructive pulmonary diseases were each measured for 2008–2012. Among PLWH, DALYs attributed to non-AIDS-related cancers were also estimated for 2013–2020. We observed that at baseline, our matched cohort consisted of $82\%$ males with a median age of 40 years (25th-75th percentiles: 34–47). During 2008–2012, 7042 PLWH and 30,640 HIV-negative individuals were alive, where PLWH experienced a twofold higher DALYs associated with chronic comorbidities (770.2 years/1000 people [$95\%$ credible intervals: 710.2, 831.6] vs. 359.0 [336.0, 382.2]). Non-AIDS-defining cancers and CVD contributed the highest DALYs in both populations, driven by YLLs rather than YLDs. Among PLWH, we estimated increasing DALYs attributable to non-AIDS-defining cancers with 91.7 years/1000 people (77.4, 106.0) in 2013 vs. 97.6 (81.0, 115.2) in 2020. In this study, we showed that PLWH experience a disproportionate burden of chronic comorbidities compared to HIV-negative individuals. The observed disparities may relate to differential health behaviors, residual HIV-related inflammation, and ART-related toxicities. As aging shapes future healthcare needs, our findings highlight the need to enhance prevention and management of comorbidities as part of HIV care.
## Introduction
As advances in antiretroviral therapy (ART) have dramatically increased longevity among people living with HIV (PLWH), the increased burden of non-AIDS chronic comorbidities has emerged as a major concern [1–4]. Compared to the general population, PLWH had reportedly a higher risk of developing chronic comorbidities such as non-AIDS-related cancers, kidney, lung, and cardiovascular diseases [5, 6], which often co-occurred [7, 8]. In British Columbia (BC), Canada, where ART has been available free of charge since 1992 and more than $60\%$ of known PLWH in 2020 were 50 years or older [9], chronic comorbidities were diagnosed up to 10 years earlier among PLWH than HIV-negative controls [10]. Chronic comorbidities have also increasingly become the predominant cause of death among PLWH [11, 12], with chronic comorbidities making up to $78\%$ of all deaths among BC’s PLWH in 2017, an increase from $35\%$ in 2004 [13].
Disability-adjusted life years (DALYs) are a comprehensive metric of disease burden, accounting for both morbidity and mortality [14]. DALYs quantify the gap between the perfect health and observed health status based on years of life lost to premature deaths (YLLs) and years of healthy life lost due to disability (YLDs). DALYs were constructed as part of the first annual Global Burden of Diseases (GBD) study in the early 1990s [15]. Under the collaboration between the Institute of Health Metrics and Evaluation and the World Health Organization, the GBD has since undergone annual updates and several methodological changes, with the latest iteration investigating the health effects of 369 diseases and injuries in 204 countries in 2019 [16, 17]. Countries have also adopted DALYs to estimate their nation’s overall and cause-specific disease burden [18–24], often projecting the future burden of diseases to aid policy making and clinical service planning [25–27]. Note that despite many settings adopting DALYs as a metric, methodological choices frequently differed to address disparities in data availability and quality across countries and diseases [28].
In this study, we estimated and compared the burden of nine chronic comorbidities among PLWH and a population of age-sex-matched HIV-negative controls in BC by estimating YLLs, YLDs and DALYs associated with these comorbidities during 2008–2012. Among PLWH, the study also estimated the disease burden associated with non-AIDS-defining cancers from 2013 to 2020. To achieve these aims, we used a population-based cohort with linkages to key provincial health databases. We supplemented the GBD methodology with robust methodological approaches that catered to the BC setting to address potential biases arising from comparing PLWH and HIV-negative controls, and the use of administrative data for health research.
## Ethics statement
Ethics approval for the COAST study was granted from the University of British Columbia/Providence Health Care Research Ethics Board (H09-02905; H16-02036) and Simon Fraser University Office of Research Ethics (#2013 s0566). The study complies with the BC Freedom of Information and Protection of Privacy Act (FIPPA) and did not require informed consent as it is conducted retrospectively for research and statistical purposes only using anonymized data.
## Data sources
We obtained longitudinal de-identified individual-level data from the Comparative Outcomes And Service Utilization Trends (COAST) study, which comprises all diagnosed adult PLWH and a $10\%$ random sample of the general population in BC followed from 1996 to 2013 [29]. COAST was established through confidential linkages between two data sources: i) the BC Centre for Excellence in HIV/AIDS Drug Treatment Program (DTP), which provides demographic, treatment and laboratory information of all ART-treated PLWH in BC [30]; and ii) Population Data BC, which houses various provincial administrative health data of all BC residents [31–36]. COAST and associated data linkages have been detailed elsewhere [29]. While the abovementioned linkages between the DTP and provincial administrative datasets, as part of COAST, ended on March 31, 2013, the DTP dataset has been updated beyond this period. We thus obtained demographic data (i.e., age and sex at birth) of PLWH initiating and receiving ART through the DTP in BC during 2013–2020 from the DTP dataset.
## Study design
In this population-based matched-cohort study, eligible participants were aged ≥19 years and followed for ≥1 year in COAST between January 1, 2001 and December 31, 2012. Each ART-treated PLWH was matched to four HIV-negative individuals by birth year and sex at birth (Note B in S1 Text). For PLWH, baseline was the latest among known positive HIV serostatus, 19th birthday, five years since earliest administrative record (either provincial Medical Service Plan registration or the first identified healthcare encounter), or January 1, 2001. For HIV-negative controls, baseline date was assigned to match their paired PLWH. Participants were observed until death, loss-to-follow-up, or December 31, 2012, whichever was earliest.
For the period after 2012, we considered PLWH aged ≥19 years who received treatment through the DTP between January 1, 2013 and December 31, 2020. For these PLWH, baseline was the latest between the first treatment date and January 1, 2013. They were then followed for ≥1 year until the earliest among death, last contact date and December 31, 2020.
## Outcome variables
Our outcomes were YLLs, YLDs and DALYs associated with nine chronic comorbidities highly prevalent among PLWH and BC’s general population [10, 37]: cardiovascular diseases (CVD), kidney disease, liver disease, chronic obstructive pulmonary disease (COPD), non-AIDS-defining cancers (excluding Kaposi sarcoma, non-Hodgkin’s lymphoma and cervical cancers; hereinafter referred to as cancers), diabetes, osteoarthritis, hypertension, and Alzheimer’s and/or non-HIV-related dementia (Alzheimer’s/dementia). Following age-restrictions in the case-finding algorithms, individuals considered for hypertension, COPD and Alzheimer’s/dementia analyses were older than 20, 35 and 40 years, respectively. Whenever possible, we estimated YLLs, YLDs and DALYs following the methodology used in the GBD 2019 [16, 17].
## YLDs
YLDs quantified time lived in states of suboptimal health as the sum of the number of prevalent cases at a time period multiplied by the corresponding disability weight [16, 17]. The BC Cancer Agency Registry identified cancer cases [35]. The remaining comorbidity diagnoses were ascertained by case-finding algorithms using the International Classification of Disease Ninth and Tenth Revisions (ICD $\frac{9}{10}$) codes and Canada-wide Drug Identification Numbers (Table A in S1 Text), which have been utilized in previous publications [10, 38]. These algorithms were applied to provincial administrative datasets including hospitalizations and day-surgeries [32], physician visits, laboratory and diagnostic procedures [33], and prescription drug dispensation [36]. For each year, we measured period prevalence; a comorbidity was prevalent if an individual was alive for ≥1 day in a given year and an existing diagnosis was identified within a 5-year observation window (i.e., at any point that year and/or within four years prior) [10]. Each comorbidity was associated with severity-specific disability weights between 0 and 1, representing a numerical assessment of non-fatal health loss, where 0 equaled full health and 1 equaled death [16, 17]. We used disability weights from the GBD 2019 (Table B in S1 Text), derived from largescale surveys of the general population, as opposed to health experts, across different cultural contexts [39, 40]. Given administrative data’s inability to ascertain disease severity, to approximate comorbidity-specific severity distributions in our study populations, we conducted a literature review to obtain distributions from BC or comparable settings (Table B in S1 Text).
## YLLs
YLLs measured time lost to premature death as the number of deaths during a time period multiplied by a standard life expectancy at the age of death, which represents the maximum life span of healthy individuals receiving appropriate health services [16, 17]. We used the theoretical minimum risk life table from the GBD 2019 (Table C in S1 Text), constructed based on the lowest observed age-specific mortality rates for both sexes from locations with populations over five million in 2016 [41]. The underlying cause of death, defined as the disease or injury that initiated the series of events leading directly to death, was collected through BC Vital Statistic Agency’s mortality database. For consistency between YLLs and YLDs, we captured comorbidity-specific deaths using the same ICD 10 codes used to ascertain prevalent cases for YLD calculations (Table A in S1 Text).
## DALYs
DALYs associated with each comorbidity were calculated as the sum of corresponding YLLs and YLDs, where one DALY depicted one lost year of healthy life due to a specific cause [16, 17].
## Analytical approaches
Categorical variables were compared using the Fisher’s exact test and continuous variables were compared using the Kruskal-Wallis test [42]. Statistical analyses were performed using R software version 3.2.2 (R Core Team, Vienna, Austria) or SAS software version 9.4 (SAS, Cary, North Carolina, United States), while data simulations were coded in Python and conducted using the NUMPY library [43]. This study consisted of a two-pronged analysis. Of note, our data were exhaustive (i.e., no missing values), with all eligible individuals included in each analysis.
## Comparison of burden of chronic comorbidities among PLWH and HIV-negative controls, 2008–2012
We compared YLLs, YLDs and DALYs, overall and per 1000 people, associated with the abovementioned chronic comorbidities across HIV status for a combined 2008–2012 period. The combined estimates were chosen to ensure sufficient observations, particularly of mortality cases. Further, to ensure combined estimates that are most contemporary, we restricted our analyses to the last five years of COAST data (i.e., 2008–2012). To measure uncertainties around the point estimate of each outcome, the Monte Carlo simulation approach was used to estimate the $95\%$ credible interval (CrI) [44]. Unlike a confidence interval which derived from hypothesized repeats of an experiment, a CrI illustrates a range containing a percentage (e.g., $95\%$) of probable estimates based on observed data [45]. Hence, the lower and upper limits of a $95\%$ CrI are the 2.5th and 97.5th percentiles, within which there is a $95\%$ probability that the true estimate may lie given the evidence provided by our observed data. For YLLs, we conducted non-parametric bootstrapping, comprising 10,000 bootstrap samples with sample lengths equaled comorbidity-specific death counts [46]. For YLDs, data simulation was also repeated 10,000 times. For each simulation, disease severity was randomly assigned based on literature-derived probabilistic distributions. Subsequently, a number within the range of corresponding severity-specific disability weights was randomly assigned [28]. YLDs were then adjusted for the presence of other comorbidities at the individual level, assuming multiplicative combined disability weights (Note A in S1 Text) [16, 26]. The 10,000 simulated values from YLLs and YLDs were then randomly paired and summed to estimate the $95\%$ CrI of DALYs [28]. Additionally, given an ample number of prevalent cases, we sub-analyzed YLD estimates for 2008–2012 stratified by ethnicity (White, non-White, unknown), history of injection drug use (people who have ever injected drugs [PWID], non-PWID, unknown) and sex at birth (male, female).
During 2008–2012, 7042 PLWH and 30,640 HIV-negative controls were alive, leading to 5356.5 and 10,945.7 years in estimated DALYs associated with the selected chronic comorbidities, respectively. Overall DALYs were two times higher among PLWH compared to HIV-negative controls (770.2 years/1000 people [$95\%$ CrI: 710.2, 831.6] vs. 359.0 [336.0, 382.2], respectively), with PLWH experiencing higher DALYs associated with most comorbidities, except hypertension, diabetes, and osteoarthritis (Fig 1). Except for hypertension and osteoarthritis (in both populations) and Alzheimer’s/dementia (among PLWH only), the burden of most comorbidities was driven by YLLs (Fig 2) rather than YLDs (Fig 3). Consequently, the rankings of DALYs were largely identical to those of YLLs, with cancers and CVD as predominant contributors constituting over $70\%$ of the total estimated YLLs and DALYs in both populations. Among PLWH, COPD and liver diseases contributed the third and fourth highest YLLs and DALYs, as did diabetes and COPD among HIV-negative controls. Note, however, that DALYs related to COPD were 12 times higher among PLWH.
**Fig 1:** *Cumulative DALYs (2008–2012) per 1000 people, with 95% credible intervals, associated with nine selected chronic comorbidities, ranked from highest to lowest, by HIV status in British Columbia, Canada.Note: PLWH: people living with HIV; DALYs: disability-adjusted life years; 95% CrI: 95% credible intervals; ALZD: Alzheimer’s and/or non-HIV-related dementia (denominator included only individuals aged 40 years and older); Cancers: non-AIDS-defining cancer; COPD: chronic obstructive pulmonary disease (denominator included only individuals aged 35 years and older); CVD: cardiovascular diseases; KID: kidney diseases; LVR: liver diseases. Horizontal scales differ between PLWH and HIV-negative controls for illustration purposes.* **Fig 2:** *Cumulative YLLs (2008–2012) per 1000 people, with 95% credible intervals, associated with nine selected chronic comorbidities, ranked from highest to lowest, by HIV status in British Columbia, Canada.Note: PLWH: people living with HIV; YLLs: years life lost due to premature mortality; 95% CrI: 95% credible intervals; ALZD: Alzheimer’s and/or non-HIV-related dementia (denominator included only individuals aged 40 years and older); Cancers: non-AIDS-defining cancer; COPD: chronic obstructive pulmonary disease (denominator included only individuals aged 35 years and older); CVD: cardiovascular diseases; KID: kidney diseases; LVR: liver diseases. Horizontal scales differ for each graph and between PLWH and HIV-negative controls for illustration purposes.* **Fig 3:** *Cumulative YLDs (2008–2012) per 1000 people, with 95% credible intervals, associated with nine selected chronic comorbidities, ranked from highest to lowest, by HIV status in British Columbia, Canada.Note: PLWH: people living with HIV; YLDs: years of healthy life lost due to disability; 95% CrI: 95% credible intervals; ALZD: Alzheimer’s and/or non-HIV-related dementia (denominator included only individuals aged 40 years and older); Cancers: non-AIDS-defining cancer; COPD: chronic obstructive pulmonary disease (denominator included only individuals aged 35 years and older); CVD: cardiovascular diseases; KID: kidney diseases; LVR: liver diseases. Horizontal scales differ for each graph and between PLWH and HIV-negative controls for illustration purposes.*
## Estimation of burden of cancers among PLWH, 2013–2020
With COAST data limited until 2012, we used additional data from the DTP to estimate YLLs, YLDs and DALYs associated with cancers (non-AIDS-defining only) among PLWH for 2013–2020. The DTP dataset provides demographic information on PLWH receiving ART in BC, but, unlike COAST, is not linked to healthcare utilization datasets (e.g., hospitalization, physician visit and prescription datasets). Given the absence of information on HIV-negative individuals in the DTP dataset, our analysis was restricted to PLWH. Cancers were chosen due to its adequate number of observed mortality cases among PLWH in the COAST study, where, due to the required ≥1 year follow-up, no deaths were observed in 2001 (i.e., the first calendar year of the study follow-up period). Accordingly, we employed generalized linear mixed-effects regression to model the number of deaths on age, sex, and observation year for 2002–2012, assuming a negative binomial distribution and log link (Note C in S1 Text) [47, 48]. The resulted model was then applied to the DTP dataset to estimate the annual number of cancers-specific deaths in each age-sex subgroup for 2013–2020. To estimate annual total YLLs along with the $95\%$ CrI, we conducted parametric bootstrapping with normal approximation using standard errors associated with the projected number of deaths in each subgroup [49]. Annual age-sex-standardized YLLs per 1000 people were estimated by an identical bootstrapping process using the estimates of age-sex-specific deaths in Canada’s 2011 census population, the reference population [50]. We adapted the original GBD projections which estimated YLDs and DALYs based on the last observed YLLs-to-YLDs ratio [51, 52]. Given the fluctuation in our observed YLLs-to-YLDs ratios over the years, however, we estimated annual total and age-sex-standardized YLDs and DALYs, alongside their respective $95\%$ CrI, based on the mean of all observed YLLs-to-YLDs ratios instead.
Of the 9601 eligible PLWH enrolled in the DTP during 2013–2020, $83\%$ were male with median age at baseline of 47 years, (25th-75th percentile: 38–54) and median follow-up of 8 years (5–8) (Table E in S1 Text). Population pyramids in Fig B in S1 Text illustrated changes in the age and sex distribution over the years highlighting that PLWH in BC were aging. Through 2020, we estimated an increasing trend in cancers-associated YLLs and YLDs (Fig C in S1 Text) as well as DALYs (Fig 5), overall and per 1000 PLWH. The trends in age-sex-standardized YLLs, YLDs and DALYs, however, were projected to decrease over time. In 2020, we estimated 97.6 years ($95\%$ CrI: 81.0, 115.2) in DALYs associated with cancers per 1000 people; the estimated DALYs were largely driven by YLLs with 89.3 years per 1000 people (74.1, 105.4).
**Fig 5:** *Fitted (2002–2012) and predicted (2013–2020) DALYs, with 95% credible intervals, associated with non-AIDS-defining cancers among people living with HIV in British Columbia, Canada.Note: DALYs: disability-adjusted life years. Direct age-sex-standardization was done using Canada’s 2011 census as reference population. Vertical scales differ for each graph for illustration purposes.*
## Results
During 2001–2012, 8,031 PLWH and 32,124 HIV-negative controls met the eligibility criteria. The two populations were identical in age at baseline (median: 40 years, [25th-75th percentile: 34–47]) and sex at birth ($82\%$ male), but differed slightly in the follow-up time (median: 9 years [5–12] for PLWH vs. 11 years [6–12] for HIV-negative controls) (Table D in S1 Text). Among PLWH, $38\%$ were PWID and $38\%$ identified as White. Fig A in S1 Text illustrates the participant selection process.
## Sub-analyses of YLDs
Among PLWH, during 2008–2012, YLDs associated with the selected chronic comorbidities did not vary significantly by ethnicity but varied greatly by history of injection drug use and sex at birth (Fig 4). YLDs associated with Alzheimer’s/dementia, for instance, were substantially elevated among PWID, while COPD and liver diseases were substantially elevated among PWID and female PLWH. YLDs associated with cancers, CVD and diabetes were higher among male PLWH.
**Fig 4:** *Cumulative YLDs (2008–2012) per 1000 people, with 95% credible intervals, associated with nine selected chronic comorbidities among people living with HIV in British Columbia, Canada, stratified by ethnicity (A), history of injection drug use (B) and sex at birth (C). Note: PLWH: people living with HIV; YLDs: years of healthy life lost due to disability; PWID: PLWH who have ever injected drugs; ALZD: Alzheimer’s and/or non-HIV-related dementia; CANCERS: non-AIDS-defining cancers; COPD: chronic obstructive pulmonary disease; CVD: cardiovascular diseases; DM: diabetes mellitus; HTN: hypertension; KID: kidney diseases; LVR: liver diseases; OA: osteoarthritis. Vertical scales differ for each graph for illustration purposes.*
## Discussion
To our knowledge, our study is the first to use DALYs to compare the burden of diseases among PLWH and age-sex-matched HIV-negative individuals. Using a large provincial administrative database which spanned over a decade, our results showed that ART-treated PLWH experienced more than double the burden of selected chronic comorbidities than their HIV-negative counterparts. In both populations, cancers and CVD contributed the most DALYs, predominantly driven by premature mortality. Diabetes and hypertension played major roles in the health burden of HIV-negative individuals, as did COPD and liver diseases in PLWH, particularly PLWH who were female or had a history of injection drug use. Additionally, among PLWH, we estimated that DALYs attributable to non-AIDS-defining cancers would continue to increase in the short term, although the age-sex standardized DALYs would decrease. Together, our findings underscore the role of different chronic comorbidities in the health burden of PLWH and the disparities of this burden compared to HIV-negative individuals, which should be considered in the prioritization of resources and interventions that address the healthcare needs of this aging population.
Consistent with observations in BC’s PLWH and HIV-negative controls, cancers and CVD contributed the highest number of DALYs in Canada and other high-resource countries [23, 53, 54]. In both study populations, YLLs contributed more than $90\%$ of cancers-associated DALYs, congruent with findings from Japan, the Netherlands and Australia [23, 24, 54]. Similarly, the dominance of YLLs in DALYs attributable to CVD, COPD, liver, and kidney diseases have also been examined elsewhere [21–23, 55–57]. This evidence stresses the importance of strategies to prolong life and reduce preventable deaths, such as secondary and tertiary prevention focusing on early detection and minimizing adverse outcomes [58], in alleviating the burden related to these chronic comorbidities. Conversely, the burden of hypertension, which is a known risk factor for other prominent chronic comorbidities, and osteoarthritis, to which no deaths were attributed in our populations and globally [59], was driven by YLDs. In mitigating the impact of these diseases on population health, the role of primary prevention, aimed at improving awareness of risk factors and altering health risk behaviors, should thus be highlighted.
The lack of prior studies examining the disparities in DALYs across HIV status precludes any direct comparisons of our findings. However, the observed disparities, including the two-fold higher burden of chronic comorbidities among PLWH, were aligned with previous findings regarding prevalence [4, 60, 61] and mortality [62–66]. While cancers and CVD contributed significantly to the health burden of both study populations, COPD and liver diseases were also prominent among PLWH, as were diabetes and hypertension among HIV-negative controls. This discrepancy emphasizes the presence of differential health behaviors in both populations and the role of factors related to HIV infection and treatment, which should be considered when addressing the disproportionate disease burden among PLWH. First-generation nucleoside reverse transcriptase inhibitors, for example, have been associated with an increased risk of liver fibrosis [67]. Alcohol use, which is associated with a higher level of cirrhosis biomarkers in HIV-hepatitis C co-infected individuals, and smoking, a major risk factor of COPD, have also been found more prevalent among PLWH [68–71]. Additionally, among PLWH, our findings bolster the growing body of literature [72–75], and underline the merit of considering sex differences and history of injection drug use in formulating appropriate chronic disease interventions in this population.
Among BC’s PLWH, we estimated an increasing trend in DALYs associated with cancers through 2020, where annual DALY estimates per 1000 people in our population were consistently double those in Canada’s general population [76]. Concurrently, we also estimated a decreasing trend in corresponding age-sex-standardized DALYs. These trends agree with the 2012–2019 trends in the global all-cause and Canada’s cancers-associated DALYs [17, 76], and demonstrate the impact of population growth and aging in the progression of the disease burden, reinforcing the need to better understand how aging shapes a population’s healthcare needs. Particularly, the increasing DALYs represent the magnitude of the burden imposed on the healthcare system [17], which should be met with adequate infrastructure, human resources, and services. Our findings thus reaffirm present policies and interventions surrounding prevention, early screening, and management of cancers in BC, while simultaneously promoting the evolvement of these efforts, anticipating changes in population structure and needs. Future studies should project DALYs associated with other comorbidities to obtain a comprehensive outlook on the future overall health burden of PLWH, which will further elucidate optimal allocations and prioritizations of healthcare resources.
This study has some limitations. First, administrative health data, while important for clinical and public health policy-making, were not collected for research purposes. To mitigate administrative data’s susceptibility to coding errors, whenever possible, we utilized published BC Ministry of Health’s case-finding algorithms to identify prevalent comorbidity diagnoses from hospitalizations and day-surgeries, physician visits, laboratory and diagnostic procedures, and prescription drug dispensation datasets, considering BC-specific claims-related practices. Moreover, with over 10 years of follow-up and a judicious 5-year lookback window [77], we are confident in our ability to capture most prevalent cases in both populations, upholding the validity of our YLD estimations. Second, the scarcity of information on disease severity distribution is a widely recognized problem [18, 78, 79]. With administrative data lacking information on the severity of comorbidity diagnoses, we employed literature-derived disease severity distribution, using data from BC or Canada whenever available and applying the same distribution to both PLWH and HIV-negative controls. Further, the lack of empirical data on alternative severity distributions of each comorbidity or how these distributions may vary within and between the two study populations prevented us from conducting meaningful sensitivity analyses. Instead, to account for the uncertainty surrounding the severity distribution, the probabilistic resampling of disease severity status and random assignment of the corresponding disability weight were conducted for each of the 10,000 simulations. Notwithstanding, given that DALYs associated with most diseases were predominantly driven by YLLs, the observed disparities in DALYs between PLWH and HIV-negative individuals should persist despite this limitation. Third, administrative data rendered us unable to consider socioeconomic and lifestyle differences (e.g., alcohol use and smoking) in our analyses, although our sub-analyses unveiled substantial differences in YLDs of major comorbidities among PLWH by sex and history of injection drug use. Fourth, low mortality cases prevented further age- or sex-specific measurements of YLLs and DALYs, although, for YLDs, we were able to highlight key differential burdens across sex groups. Fifth, the estimated burden of non-AIDS-defining cancers among PLWH for 2013–2020 should be interpreted in light of methodological constraints and assumptions. For instance, the lack of socioeconomic and lifestyle data prevented us from conducting more sophisticated modelling to estimate cancers-specific YLLs [80]. Additionally, adapting earlier GBD projections [51, 52], cancers-specific YLDs were estimated based on the mean of all observed YLLs-to-YLDs ratios. Lastly, COAST data are currently limited to December 2012. Note, however, that during 2012–2019, the relative DALY contributions of non-communicable diseases in Canada’s general population were largely unchanged [53]. Furthermore, the methodology established in this study can be readily applied to other settings to compare the contemporary burden of chronic comorbidities between PLWH and HIV-negative controls.
In summary, our study highlights that, compared to age-sex-matched HIV-negative controls from the same geographical setting, PLWH experienced excess DALYs attributable to chronic comorbidities, with the largest contribution from premature mortality associated with non-AIDS-defining cancers and CVD. Our study also underscores the role of aging in shaping the upward trajectories of disease burden and, consequently, determining the future healthcare needs among PLWH. Overall, these findings advocate for intervention strategies that integrate primary, secondary, and tertiary prevention of chronic comorbidities into a comprehensive HIV care model, with the goals to reduce preventable deaths and morbidity and, ultimately, facilitate healthy and successful aging in this population. Factors that likely influence the observed disparities both within PLWH and between PLWH and HIV-negative individuals, such as health behaviors, sex differences, residual HIV-related inflammation, and ART-related toxicities, should also be considered in these strategies. Further research on the current and projected overall disease burden should be prioritized to ensure timely implementation of evidence-based policies and investments to ensure adequate healthcare personnel and infrastructure.
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|
---
title: Understanding barriers and facilitators to clinic attendance and medication
adherence among adults with hypertensive urgency in Tanzania
authors:
- Godfrey A. Kisigo
- Onike C. Mcharo
- John L. Robert
- Robert N. Peck
- Radhika Sundararajan
- Elialilia S. Okello
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021323
doi: 10.1371/journal.pgph.0000919
license: CC BY 4.0
---
# Understanding barriers and facilitators to clinic attendance and medication adherence among adults with hypertensive urgency in Tanzania
## Abstract
Hypertensive urgency is a major risk factor for cardiovascular events and premature deaths. Lack of medication adherence is associated with poor health outcomes among patients with hypertensive urgency in resource-limited settings. To inform the development of tailored interventions to improve health outcomes in this population, this study aimed at understanding facilitators and barriers to clinic attendance and medication adherence among Tanzanian adults with hypertensive urgency. We conducted in-depth interviews with 38 purposively selected participants from three groups: 1) patients with hypertension attending hypertension clinic, 2) patients with hypertension not attending hypertension clinic, and 3) clinic health workers. Interviews were conducted using a semi-structured guide which included open-ended questions with prompts to encourage detailed responses. In their narrative, patients and healthcare workers discussed 21 types of barriers/facilitators to clinic attendance and medication adherence: 12 common to both behaviors (traditional medicine, knowledge and awareness, stigma, social support, insurance, reminder cues, symptoms, self-efficacy, peer support, specialized care, social services, religious beliefs); 6 distinct to clinic attendance (transport, clinic location, appointment, patient-provider interaction, service fragmentation, quality of care); and 3 distinct to medication adherence (drug stock, side effects, medicine beliefs). The majority of identified barriers/facilitators overlap between clinic attendance and medication adherence. The identified barriers may be surmountable using tailored supportive intervention approaches, such as peer counselors, to help patients overcome social challenges of clinic attendance and medication adherence.
## Introduction
Hypertension affects nearly 1 billion adults worldwide and is the single most significant risk factor for premature mortality globally [1, 2]. In Tanzania, the prevalence of hypertension is high in both urban and rural areas [3], but only $20\%$ of Tanzanian adults with hypertension are aware of their diagnosis, and among these, less than $1\%$ are controlled [4]. As a result, adults with untreated or uncontrolled hypertension develop complications, including hypertensive urgency [5]—defined as a blood pressure >$\frac{180}{120}$ mmHg without new or progressive target organ damage. This complication is a major risk factor for cardiovascular events and mortality [6, 7].
In Tanzania, we have documented that $20\%$ of patients with hypertensive urgency were hospitalized and $26\%$ died within 6 months of being seen in the outpatient clinic [8]. Participants at the highest risk of poor outcomes were those who self-reported non-adherence to antihypertensive medications at the time of enrollment. These findings highlighted the urgent need for tailored intervention to improve clinic attendance and medication adherence so as to improve long-term outcomes in patients with hypertensive urgency [9].
However, evidence to inform interventions to improve hypertension treatments among adults presenting with hypertensive urgency is lacking. Several studies have elicited barriers and enablers to hypertension care in Sub-Saharan Africa (SSA), but none focused on patients with hypertensive urgency [10–14]. We need to better understand the perspective of patients with hypertensive urgency in Africa and their healthcare providers in order to design targeted interventions to help patients with hypertensive urgency. Therefore, this study aimed at understanding factors that drive and inhibit clinic attendance and medication adherence among Tanzanian adults with hypertensive urgency as reported by patients and healthcare providers.
## Overview
This study was part of formative research to inform intervention design for adult patients with hypertensive urgency attending outpatient clinic in Mwanza, Tanzania. The study purposively selected 38 individuals relevant to outpatient care of hypertensive urgency to complete in-depth interviews. The development of semi-structured interview guides and qualitative data analysis were guided by the Andersen’s Behavioral Model of Health Services Use (ABMHSU) [15], which is introduced succinctly below.
## Theoretical framework
The ABMHSU provides a theoretical framework for understanding how patient and environmental factors impacts health behaviors and outcomes. These factors can be grouped into seven domains: patient characteristics (predisposing characteristics, enabling factors, perceived need), health care system environment (system, clinic, provider) and external environment (Fig 1). Multiple studies have used the ABMHSU to evaluate the use of health services among patients with chronic illness for purposes of identifying resolvable barriers to service utilizations in order to develop interventions to improve outcomes [16–18].
**Fig 1:** *The Andersen’s behavioral model of health services use (adapted from Andersen) [15].*
In this study, we define predisposing factors as those factors that influence decision-making about hypertension, including attitudes, knowledge, and perceived control. Enabling factors included having appropriate community and individual-level resources necessary for accessing care. The perceived needs domain was defined as how individuals view their own health and functional state, which can be influenced positively or negatively by their perceived severity of health. The health care environment entails primary elements of healthcare services that create the health care environment for the patient. Finally, the external environment includes physical, political, and economic factors unrelated to the health care environment.
## Study site and setting
This qualitative study was conducted as part of a larger study to develop an outpatient intervention to improve care among adults with hypertensive urgency. We conducted 38 qualitative interviews with three stakeholder groups relevant to the outpatient care for hypertensive urgency: 1) hypertensive urgency patients who have successfully attended the outpatient clinic; 2) hypertensive urgency patients who have not attended the clinic, and 3) healthcare workers. These interviews explored facilitators and barriers to outpatient clinic attendance and anti-hypertensive medication adherence. Data collection occurred between March and May 2020. The study received ethical approval from the Weill Cornell Medicine [Ref: 19–11021145] and the Tanzanian National Institute for Medical Research [Ref: NIMR/HQ/R.8a/Vol. IX/3349].
The study was conducted in the outpatient medical clinics of three hospital facilities in Mwanza City, Tanzania. The three study sites all together serve approximately 3000 adults living with hypertension annually. All clinics were following the national guidelines for treating hypertension [19]. Anti-hypertensive medications are widely available and inexpensive by high-income countries’ standards but can still be cost-prohibitive for most Tanzanians who live on <$100/month. Patients pay for services through a combination of insurance, government subsidy, and out-of-pocket contributions.
## Participants
Outpatients with hypertensive urgency were eligible to participate in the study if they were aged 18 years and above, fluent in Swahili, and able to provide informed consent. Patients with hypertension were categorized as not attending the outpatient clinic if they missed clinic appointments for >3 months. Healthcare workers were invited to participate in the study if they worked in the outpatient hypertension clinic at one of the three hospitals where participants were recruited.
## Procedures
Hypertensive urgency patients and healthcare workers were recruited at the clinic by the study team. Hypertensive urgency patients were selected focusing on their experience with hypertension urgency, either being a frequent attainder or non-attainders of the clinic. Non-attainders were identified from the previous cohort study of hypertensive patients conducted by RNP in the same hospital facilities of the current study. To establish non-attendance for >3 months, we assessed data on self-reported medication adherence and reviewed the medical record of clinic attendance. All participants in the previous cohort had contact information and provided their preferences on how they could be reached for future follow-up. Using the contact information extracted from patient files, the health worker contacted potential participants to provide brief information about the study.
All potential participants interested in joining the study were referred to the study team to receive additional information, confirm eligibility, and complete written informed consent procedures. Upon enrollment, participants were invited to schedule a time and choose their preferred location for the interview. Face-to-face interviews were conducted in Swahili, by trained Tanzanian researchers who had prior experience with qualitative research and had no affiliation with the clinic providing the hypertension care. Interviews took approximately one hour and were audio-recorded with the participant’s consent. Participants were given an incentive of 5000 Tanzanian Shillings (approximately $2.50) per interview. Following the completion of the interview, the audio files were transcribed and translated verbatim into English by research assistants fluent in both English and Swahili.
## Data collection instrument
The development of the interview guide was informed by the ABMHSU (see Fig 1) described above. The guide included open-ended questions and probes related to facilitators and barriers to hypertension care. Each section of the guide began with an opening question, followed by potential probes to be used to explore the topic in greater depth. Examples of questions include “What are the things that have enabled you to come to your hypertension clinic visits and take your hypertensive medications?”, “ What are the barriers you have faced attending your clinic appointments?” and “How do you think patients feel about services provided at the outpatient hypertension clinic?” The guide was contextualized according to the patient’s status (i.e., attending or not attending clinic) and healthcare worker occupation (e.g., doctor, nurse). The semi-structured guides were piloted among three hypertensive patients and two healthcare workers prior to the commencement of data collection; data from these pilot interviews were not included in the analysis.
## Analysis
Data were analyzed using principles of applied thematic analysis [20] and consensual qualitative research [21]. Two team members [GAK, EO] independently reviewed transcripts to generate preliminary codes, which informed the development of a structured codebook focused on identifying the facilitators and barriers to clinic attendance and medication adherence. The 38 transcripts were uploaded to NVivo version 12 [22] and coded independently by two team members [OM, GAK] using the final codebook. After coding, code-level queries were run, analytic memos were written to synthesize the content and make comparisons across participant groups. These memos followed an established template of ABMHSU domains to extract and synthesize the core meaning from text related to each theme and to identify representative quotes. In writing memos, barriers and facilitators were analyzed as “barriers/facilitators” because participants’ discussions about clinic attendance and medication adherence behaviors often included their challenges and successes. At each step, coding team members [OM, GAK, and EO] returned to the original data to ensure that participants’ narratives and perspectives were retained. Disagreements were discussed until consensus was reached.
## Characteristic of study participants
A total of 38 participants participated in in-depth interviews; their ages ranged from 28 years to 77 years. An overview of the demographic information of the study participants can be found in Table 1.
**Table 1**
| Participants’ characteristics | Patient with Hypertension attending clinic (n = 13) | Patient with Hypertension not attending clinic (n = 11) | Healthcare workers (n = 14) |
| --- | --- | --- | --- |
| Age, mean (range) | 56 (43–67) | 54 (45–77) | 42 (28–58) |
| Gender | | | |
| Female n (%) | 8 (61%) | 3 (27%) | 11 (78%) |
| Level of Education | | | |
| Primary School n (%) | 7 (54%) | 7 (64%) | |
| Secondary school and above n (%) | 6 (46%) | 4 (36%) | 14 (100%) |
| Occupation n (%) | | | |
| Employed by others | 3 (23%) | 3 (27%) | 14 (100%) |
| Self Employed | 10 (77%) | 5 (45%) | - |
| Retired | - | 1 (9%) | - |
| Home maker | - | 2 (18%) | - |
| Health Insurance cover n (%) | | | |
| Insured | 4 (31%) | 1 (9%) | |
| Not insured | 9 (69%) | 10 (91%) | |
| Cadre n (%) | | | |
| Clinical assistants | | | 2 (14%) |
| Nurses | | | 8 (57%) |
| Doctors | | | 4 (29%) |
In their narrative, patients and healthcare workers discussed 21 types of barriers/facilitators to clinic attendance and medication adherence: 12 common to both behaviours, 6 distinct to clinic attendance, and 3 distinct to medication adherence (Table 2).
**Table 2**
| Attendance alone | Attendance and adherence | Adherence alone |
| --- | --- | --- |
| Transport to clinic | Traditional medicine | Drug stock |
| Clinic location | Knowledge and awareness of HTN | Medication side effects |
| Clinic appointment | Stigma | Medicine beliefs |
| Patient-provider interaction | Social support | |
| Service fragmentation | Health insurance | |
| Quality of care | Reminder cues | |
| | HTN symptoms | |
| | Self-efficacy | |
| | Peer support | |
| | Specialized care | |
| | Social services | |
| | Religious beliefs | |
Barriers/facilitators are presented based on the ABMHSU patient (predisposing characteristics, enabling factors, and perceived need) and environment (health system, clinic, and provider) domains (Fig 2). Tables 3 and 4 report barriers/facilitators with illustrative core ideas. S1 Table highlights detailed description of barriers and facilitators as identified by the study participants.
**Fig 2:** *Barriers and facilitators for clinic attendance and medication adherence among hypertensive urgency patients in Tanzania.* TABLE_PLACEHOLDER:Table 3 TABLE_PLACEHOLDER:Table 4
## Predisposing characteristics
Patients and healthcare workers reported five barriers/facilitators under the predisposing characteristics domain. These barriers/facilitators were traditional medicine, religious beliefs, medicine beliefs, knowledge and awareness of hypertension, and stigma. Among these, traditional medicines were frequently reported as an alternative to allopathic medicine by patients, and they were identified as a critical barrier to clinic attendance and medication adherence by healthcare workers. In this context, we refer to traditional medicine as remedies made from plants prescribed by traditional healers. The preferential use of traditional medicine was primarily driven by the belief that hypertension is caused by witchcraft. Also, traditional medicines are low-priced and could be used for a short period compared to expensive, lifelong allopathic medicine. In this manner, traditional medicines were perceived as curative for hypertension instead of allopathic medicines that are lifelong. Furthermore, some patients reported that traditional medicines were more accessible and perceived to be more efficacious than allopathic medicine.
Most healthcare providers pointed out that solid religious belief in healing hypertension by prayer was a barrier to clinic attendance and medication adherence. Because of this belief, patients with hypertension were likely to stop attending clinics and quit using medicines prescribed by their healthcare providers.
Some patients described medicine beliefs as a barrier to medication adherence. It was believed that long-term use of hospital medicine is harmful and could result in adverse health outcomes. For example, it was generally believed that diabetes in people with hypertension was directly linked to the longstanding use of antihypertensive medicine. This belief was linked to some patients’ decisions to use traditional medicine.
Healthcare workers discussed proper knowledge and hypertension awareness as important barriers/facilitators to clinic attendance and medication adherence. Patients with poor knowledge of the cause, symptoms, and treatment of hypertension were observed to have poor clinic attendance and medication adherence. Nevertheless, healthcare workers noted that patients who are aware of their hypertension status have excellent records on clinic attendance and report good adherence to anti-hypertensive medicine.
Although patients were aware of the importance of adhering to hypertensive medication, they pointed out the stigma attached to taking lifelong medicine. According to patients, the public perception of taking medication daily was associated with the treatment of HIV, making patients reluctant to attend a monthly clinic and adhere to the medication regimen.
## Enabling factors
According to interviewed patients and healthcare workers, five facilitators/barriers were designated to the enabling factors domain. These barriers/facilitators were social support, transport to clinic, health insurance, medication side effects, and reminder cues. The paragraphs below provide a brief description of each barrier/facilitator.
Most patients reported that social support facilitated clinic attendance and medication adherence. This support was mainly from their close family members such as a spouse, children, and other immediate relatives. The assistance from these family members was likely to be available when a patient can disclose their hypertension status to the family. Social supporters reminded the patient when to attend the clinic and take medication, escorted the patient to the clinic whenever needed, and provided financial and moral support.
Some patients, especially those living far from the outpatient clinic, reported the cost of traveling to and from the clinic as a significant barrier to clinic attendance. Patients without financial support could not navigate this challenge.
Both patients and healthcare workers noted that health insurance subscription was a critical facilitator to clinic attendance and medication adherence. The cost of treatment (i.e., consultation and investigation fees, medicine prices) was entirely covered by the health insurance. Thus, patients with insurance cover do not bear the burden of treatment costs, unlike patients without insurance subscriptions, whom most could not afford.
While hypertension can be safely treated in most individuals, side effects of drugs are relatively frequent and could have notable effects on quality of life. A few patients shared their experiences regarding the side effects of antihypertensive medicines that led to poor medication adherence.
Reminder cues were reported by patients and healthcare workers as a facilitator to clinic attendance and medication adherence. Patients used several methods to remember taking antihypertensive medicines, including setting alarms on mobile phones and putting pillboxes at the bedside. Similarly, healthcare workers called patients a day before to remind them about the upcoming clinic appointment.
## Perceived need
The analysis of the perceived need domain revealed two barriers/facilitators, including hypertension symptoms and perceived self-efficacy (i.e., belief about one’s ability to handle hypertension). According to healthcare providers, most patients diagnosed with hypertension deny the diagnosis mainly due to a lack of symptoms. As a result, most patients will not attend the clinic or take antihypertensive medicine because they believe they are not sick. On the contrary, patients who accepted the diagnosis and believed that they could handle hypertension reported attending the clinic regularly and had good adherence.
## System factors
Patients in our study identified three services that impacted their health care environment. These services were specialized care, drug stock, and social welfare services. The majority of the patients emphasized that services provided by specialized doctors enhanced clinic attendance and medication adherence. However, not all clinics had enough specialized doctors to attend to overflowing patients per clinic. Furthermore, patients observed that antihypertensive drugs were often out of stock at public clinics, where they are sold at a subsidized price. Therefore, the drug stock affected medication adherence, especially for those who could not afford to purchase drugs at private pharmacies. Lastly, the availability of social welfare services at the clinic facilitated clinic attendance and medication adherence. Among the services provided under the social welfare services were cost exemption for those who could afford treatment costs and a pre-identified group of patients or diseases.
## Clinic factors
Patients’ and healthcare workers’ narratives highlighted five clinic barriers/facilitators; clinic appointment, service fragmentation, clinic location, quality of care, and peer support. All of these barriers/facilitators, except peer support, were linked to clinic attendance alone. Most patients were given monthly clinic appointments for clinical evaluation and drug refills. Due to a shortage of healthcare workers, patients reported spending a significant time in the clinic, which affected their routine. Furthermore, service fragmentation at the clinic made it hard for patients to complete clinic appointments in time, especially unaccompanied elderly patients. Patients felt that organizing all services at one point would expedite service provision. A few patients reported that quality of care enhanced their clinic attendance. This observation was evidently among patients with health insurance at some clinics where patients with insurance were given differentiated care. Healthcare providers felt that incorporating peer support programs at the standard of care would increase clinic attendance and medication adherence. Thus, patients who had hypertensive urgency and managed to get blood pressure under control would be used to provide education to their peers.
## Provider factors
Some patients encountered incompetent health care providers who were incapable of meeting their needs during the monthly clinic visit. This interaction with incompetent providers resulted in the negative experience of using health care services available in the clinic. For example, patients mentioned that some health care providers are using offensive language and ignore their questions about hypertension. For some patients, this experience discouraged them from continuing to attend clinic appointments.
## Discussion
Hypertensive urgency is common in clinics in Africa, and over $\frac{1}{3}$ of adults with hypertensive urgency will either be hospitalized or die within one year after presenting with hypertensive urgency. Hospitalization and death in patients with hypertensive urgency are strongly linked with poor clinic attendance and poor medication adherence. This study used qualitative methods guided by ABMHSU to explore barriers/ facilitators to clinic attendance and medication adherence among adults with hypertension urgency. This study represents a necessary step in contextualizing barriers/facilitators to clinic attendance and medication adherence among adults with hypertension urgency in Mwanza to develop a context-appropriate intervention for this group.
Our data reflects 21 barriers/facilitators to clinic attendance and medication adherence among patients with hypertensive urgency. The majority (12 out of 21) of the barriers/facilitators affected both behaviors. This overlap of barriers/facilitators across behaviors in the care continuum has been observed in several studies that assessed barriers and facilitators to chronic disease care engagement [16, 17]. However, it is worth noting that the magnitude of the effect of the barrier/facilitator often differs between behaviors. For example, traditional medicine hampered medication adherence more than clinic attendance in this study. This finding may be useful in the design of intervention so that targeting a set of facilitators/barriers might improve clinic attendance and medication adherence. Nevertheless, there should be a recognition that some facilitators/barriers are related to only one behavior, such as medication side effects that affect adherence alone.
Findings from the current study show that beliefs about traditional medicines can act as barriers to clinic attendance and medication adherence. In this study, participants’ beliefs about traditional medicine’s availability, accessibility, and efficacy influenced help-seeking behavior for hypertension. Similar findings have been reported by previous studies of patients with hypertension in Tanzania, Columbia, South Africa and Nepal [23–26]. Such finding shows the importance of asking about the history of the use of alternative, traditional, or supplemental therapy from patients during the clinical encounter as a missed opportunity to address not only the underlying beliefs in the efficacy of traditional medicine but also to further counsel the patients about the nature and course of HTN.
Fear about the effect of poisoning from long-term use of hospital medicine for hypertension was reported to affect medication adherence. A significant correlation between beliefs about medication was found among hypertensive patients in Ghana and Nigeria [27], with patients who were worried about the adverse effects of antihypertensive drugs less likely to be adherent to their medications. Qualitative studies in Pakistan [28], Malaysia [29], and Nigeria [30] found that lack of faith in hospital medicine and perceived side effects hindered adherence to antihypertensive medication. Improving patients’ knowledge and attitudes to hospital medications may empower patients to be more concerned about their health and hence more involved in their treatment.
The health providers and patients in this study reported the ability to afford HTN treatment as a significant barrier to medication adherence. For patients living below the poverty line, the cost of purchasing antihypertensive poses a substantial burden to themselves and their dependents. According to our study participants’ narratives, this burden triggers stress and affects their general wellbeing. Consultation fee and treatment cost exemption, having health insurance were identified as important facilitators. These findings are consistent with other studies [26, 30, 31]. Participants in our study also pointed out the distance to a health facility as an important barrier to clinic attendance and adherence to antihypertensive medication. Longer distance to health facility contributed to non-adherence of hypertensive patients in Ethiopia [32].
In our study, participants identified lack of symptoms as one of the reasons people with hypertensive urgency may not attend the clinic or take hypertensive medications. Studies in Nigeria, India and Congo have shown that the absence of symptoms significantly contributed to poor compliance to hypertensive therapy [33–36].
Our data shows that lack of both emotional and material support was a major barrier to clinic attendance and medication adherence among people with hypertension urgency. Other studies have reported that an absence of family support has a strong negative effect on adherence among hypertensive patients in Ethiopia [37] and Nigeria [38]. Likewise, studies in Congo reported that patients who received support from family members, and particularly reminders about taking their medications, were more likely to be adherent to their antihypertensive medications [35, 39].
Health systems factors, including lack of specialists, cost of medication, length of the clinic visit, and the fragmented nature of services offered, were among the critical barriers to clinic attendance. Weak health systems have been identified as a significant obstacle in effectively responding to the rising burden of chronic conditions such as hypertension in developing countries [40]. Calls have been made to recognize and analyze the complex interactions between health systems and their effects on hypertension management in developing countries [41]. Factors such as confidence in the physician’s knowledge or ability have been found to be significantly related to medication adherence in developed countries. Studies have found dissatisfaction with the health services and treatment providers influenced adherence significantly among patients with hypertension [42, 43]. Hypertensive individuals who were satisfied with the care received are more likely to adhere to the proposed medication treatment. Inconvenient clinic operating hours, long waiting times have also been found to be inhibitors of adherence [44, 45].
In our study, traditional medicine was among the salient barriers to clinic attendance and medication adherence. Prospective intervention designs could consider engaging with traditional healers or educating traditional healers to improve adherence for people with hypertension in communities where concurrent traditional medicine use is common. Previous studies [46–50] demonstrate that partnerships with traditional healers can improve health education for chronic diseases.
## Strengths and limitations
This was a qualitative study aimed at examining context-specific barriers and facilitators to hypertension clinic attendance and adherence to antihypertension. The use of open-ended questions helped to gather rich information on participants’ views and experiences. Our participants included both patients attending and those that had defaulted on regular clinic attendance and medication as well as health service providers. Such triangulation of data sources helped provide an in-depth understanding of context-specific barriers and facilitators from different perspectives. However, some study limitations should be considered, including the exploratory nature of this qualitative study, and confinement to a small sample of patients and health workers in three health facilities limit the generalizability of our findings to other population.
## Conclusion
The current study identified 21 barriers/facilitators to clinic attendance and medication adherence among patients with hypertensive urgency. The barriers to clinic attendance and medication adherence may be surmountable using tailored supportive intervention approaches, such as peer counsellors, to help patients overcome social challenges of clinic attendance and medication adherence.
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|
---
title: Ethnic disparities in mortality and group-specific risk factors in the UK Biobank
authors:
- Kara Keun Lee
- Emily T. Norris
- Lavanya Rishishwar
- Andrew B. Conley
- Leonardo Mariño-Ramírez
- John F. McDonald
- I. King Jordan
journal: PLOS Global Public Health
year: 2023
pmcid: PMC10021328
doi: 10.1371/journal.pgph.0001560
license: CC0 1.0
---
# Ethnic disparities in mortality and group-specific risk factors in the UK Biobank
## Abstract
Despite a substantial overall decrease in mortality, disparities among ethnic minorities in developed countries persist. This study investigated mortality disparities and their associated risk factors for the three largest ethnic groups in the United Kingdom: Asian, Black, and White. Study participants were sampled from the UK Biobank (UKB), a prospective cohort enrolled between 2006 and 2010. Genetics, biological samples, and health information and outcomes data of UKB participants were downloaded and data-fields were prioritized based on participants with death registry records. Kaplan-Meier method was used to evaluate survival differences among ethnic groups; survival random forest feature selection followed by Cox proportional-hazard modeling was used to identify and estimate the effects of shared and ethnic group-specific mortality risk factors. The White ethnic group showed significantly worse survival probability than the Asian and Black groups. In all three ethnic groups, endoscopy and colonoscopy procedures showed significant protective effects on overall mortality. Asian and Black women show lower relative risk of mortality than men, whereas no significant effect of sex was seen for the White group. The strongest ethnic group-specific mortality associations were ischemic heart disease for Asians, COVID-19 for Blacks, and cancers of respiratory/intrathoracic organs for Whites. Mental health-related diagnoses, including substance abuse, anxiety, and depression, were a major risk factor for overall mortality in the Asian group. The effect of mental health on Asian mortality, particularly for digestive cancers, was exacerbated by an observed hesitance to answer mental health questions, possibly related to cultural stigma. C-reactive protein (CRP) serum levels were associated with both overall and cause-specific mortality due to COVID-19 and digestive cancers in the Black group, where elevated CRP has previously been linked to psychosocial stress due to discrimination. Our results point to mortality risk factors that are group-specific and modifiable, supporting targeted interventions towards greater health equity.
## Introduction
Despite the progress made in improving mortality rate, life expectancy, and disease survival outcomes in the last century, health disparities between various population groups persist and remain a major global health issue. Mortality rates are a key indicator of a population’s overall health status and have been long tracked and documented in countries including the United Kingdom (UK) and the United States (US) since 1901 and 1890, respectively [1, 2]. While the mortality gap between race and ethnicity groups have narrowed, the decreasing trend has leveled off in recent years: mortality disparity continues to exist and is variously complex across different populations, geographies, and mortality causes [2, 3]. In particular, the ongoing pandemic of Coronavirus disease 2019 (COVID-19) exemplifies the profound adverse effects of disparity, demonstrated by the disproportionate burden and number of deaths among high-risk and medically underserved racial and ethnic minority groups [4, 5].
Both environmental and genetic factors, with increasing evidence for interaction between environment and genetics through epigenetic mechanisms, have been cited as contributors of health disparities [6–10]. Specifically, the role of differential socioeconomic status (SES), access to healthcare, and allostatic load in mortality disparities had been previously cited as significant risk factors for disparity in mortality [6, 11, 12]. However, studies that explore the potential contributions of many other mortality risk factors together, including health behaviors, medical histories, and dietary factors, are scarce. Moreover, much of health disparity research currently focuses on describing the areas and sizes of disparity, by testing a stratified population in a single model with race and ethnicity as a predictor, and therefore lack information on underlying risk factors specific to each group. In order to effectively reduce disparity, it is crucial to first understand the key contributors to overall and prevalent-cause mortalities specific to each ethnicity, which can be taken to suggest targeted interventions with the greatest likelihood of impact for each group.
The aim of this study was to investigate the disparity in mortality patterns and identify important phenotypic risk factors for the three largest ethnic groups in the UK by using the United Kingdom Biobank (UKB) prospective cohort study, a National Health Service initiative for building health registry of 500,000 people aged between 40 and 69 from 2006 to 2010 [13]. By leveraging UKB’s comprehensive data spanning physical measures, lifestyle, blood and urine biomarkers, imaging, genetic, and linked medical and death registry records, coupled with group-specific feature selection methods and survival models, we aimed to identify top mortality risk factors that are measurable and potentially modifiable [14]. We hope that these findings can inform precise strategies for each ethnicity with the goal of improving mortality for all.
## Ethics statement
Ethics approval for the UKB was obtained from the North West Multi-centre Research Ethics Committee (MREC) for the United Kingdom, the Patient Information Advisory Group (PIAG) for England and Wales, and the Community Health Index Advisory Group (CHIAG) for Scotland (see https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics).
## UK biobank data & preparation
Data-fields for each individual in the study cohort were downloaded on $\frac{3}{18}$/2021 from UKB. With ~$6\%$ of the study cohort having experienced death, we prioritized our study on data-fields applicable to the individuals who had death registry records: we applied a series of automated and manual filters to the data-fields, starting with keeping fields that had values for individuals with death records (2,512 non-unique data-fields). The second filter was to keep data-fields with ≥$80\%$ record completeness ($$n = 326$$), followed by manual filtering to merge related records and transform field responses as needed. Diagnosis fields (field 41202) were grouped based on ICD-10 blocks, and operation fields (field 41272) were grouped according to the Chapters as defined in the UKB Data Showcase. For each of these fields, we transformed the binary occurrence of a diagnosis or operation for an individual into a count of the ICD-10 block or operation chapter to not be too granular when defining features for our models. The final set of 240 data-fields was used as features for our model selection. Further, all data-fields were categorized in accordance with UKB’s “Primary Category of Origin” (S1 Fig).
## Genetic ancestry inference
GA inference was performed to estimate six ancestry proportions (African, European, East Asian, Central Asian, South Asian, and West Asian) for 477,205 UKB participants using their whole genome genotypes (WGG) characterized using the UK Biobank Axiom Array or the UK BiLEVE Axiom Array [15]. Participant WGG were merged and harmonized with whole genome sequence (WGS) data from global reference populations, the 1000 Genomes Project (1KGP) and the Human Genome Diversity Project (HGDP), as indicated in S1 Table [16–18]. WGG and WGS variant data were merged to include variants present in all three datasets with variant strand flips and identifier inconsistencies corrected and were filtered for sample missingness <$5\%$ and a minor allele frequency >$1\%$. The merged genome variant data set was pruned for linkage disequilibrium using PLINK v2 with ‘—indep pairwise 100 10 0.05’ [19]. Principal component analysis (PCA) on genome variant dataset followed using the FastPCA program implemented in PLINK v2 (S2 Fig) [20]. Finally, genome-wide GA inference that analyzes the PCA data from global reference populations and non-reference individuals with non-negative least squares (NNLS) was implemented using the Rye algorithm as previously described [21].
## Feature selection
Top feature selection of mortality risk factors for each of the three ethnic groups was based on combined ranking of results from Cox proportional-hazard (Cox-PH) modeling and random survival forest model using survival and randomForestSRC package, respectively, in R version 3.6.1 [22, 23]. Univariable Cox-PH model evaluated the importance of each variable for the ethnic groups and their overall or cause-specific mortality predictions based on concordance or Harrell’s C-index. Follow-up times were calculated as the time between UKB study enrollment and either death (OS = 1) or last data download in years. Most common level of categories or median numerical values in each group were set as reference level with age at diagnosis included as fixed covariate in the Cox-PH models. In addition, random forest models were also constructed using rfsrc function (ntree = 1000, nsplit = 10, nodesize = 15) with imputation allowed for missing numerical values based on random forest or impute.rfsrc. Thereafter, variables were ranked on importance based on minimal depth using the var.select function. Feature selection was using random forest minimal depth method and optimal number of features in the model was validated using cross validation (CV), where average C-index for random survival forest model for each top feature set (size increasing by 5) across 5 repeats of 5-fold CV were calculated. Optimal number of features for multivariable model was based on the minimal numbers of features yielding C-index within $0.5\%$ of the max C-index across the 5 repeats (S4 Fig). The rankings from Cox-PH and random forest were averaged to provide the final list of top mortality risk factors for multivariable modeling.
## Survival modeling analysis
Multivariable Cox-PH survival models were constructed for each ethnic group using all of Asian and Black participant data (Table 1), while 10,000 subsampled participants from the White ethnic group (random sampling without replacement; 20,000 for lung and bronchus cancer model to ensure >100 events) were used for modeling. Optimal seed for random subsampling were selected that preserves the mortality proportions in the three enrollment age categories (≤ 50, 51–65, and ≥ 66) and sex categories of the entire White cohort (Table 1). In addition to overall mortality models, cause-specific mortality models were subsequently constructed for selected causes of death based on relative frequency and standardized residuals of chi-squared test: COVID-19 (Black), ischemic heart disease (Asian), lung and bronchus cancers (White), and digestive cancers (all). General model construction started with the optimal number of selected risk factors determined in the feature selection step and were reduced using backward stepwise selection method based on Akaike information criterion. Age at enrollment categories, sex, and six GA proportions were included in the model as fixed covariates. Proportional hazards assumption was also checked using the cox.zph function in the survival R-package and the covariates were dropped or stratified when violating the assumption. Finally, significant risk factors in final model were rescaled and transformed as necessary, especially for blood biochemistries with expected ranges or threshold for normal (S2 Table).
**Table 1**
| Characteristic | Full Cohort | Asian group | Black group | White group |
| --- | --- | --- | --- | --- |
| | (N = 490,610) | (n = 9,877) | (n = 8,038) | (n = 472,695) |
| Sex (% in ethnic group): | | | | |
| Female | 266,650 (54.35) | 4,582 (46.39) | 4,639 (57.71) | 257,429 (54.46) |
| Male | 223,960 (45.65) | 5,295 (53.61) | 3,399 (42.29) | 215,266 (45.54) |
| Age of enrollment (%): | | | | |
| ≤ 50 | 127,103 (25.91) | 4,121 (41.72) | 4,019 (50.00) | 118,963 (25.17) |
| 51–65 | 290,698 (59.25) | 4,762 (48.21) | 3,337 (41.52) | 282,599 (59.78) |
| ≥ 66 | 72,809 (14.84) | 994 (10.06) | 682 (8.48) | 71,133 (15.05) |
| Overall survival (OS = 1 or dead %) | 32790 (6.68) | 458 (4.64) | 335 (4.17) | 31,997 (6.77) |
| Cause-specific (% in dead): | | | | |
| Digestive neoplasm† | 5107 (15.17) | 42 (9.17) | 45 (13.43) | 5020 (15.69) |
| Ischemic heart disease† | 3513 (10.71) | 102 (22.27) | 27 (8.06) | 3384 (10.58) |
| COVID-19† | 623 (1.90) | 17 (3.71) | 26 (7.76) | 580 (1.81) |
| Respiratory/intrathoracic organ (bronchus & lung) neoplasms† | 2943 (8.98) | 19 (4.15) | 17 (5.07) | 2907 (9.09) |
## Ethnicity interaction analysis
In order to assess potential differential effects of mortality risk factors across the three ethnic groups, Cox-PH models of pooled samples Black, Asian, and randomly subsampled White participants in UKB for all-cause and digestive cancers mortality were constructed. These models were subjected to same model selection and checking steps as previously described, starting with a full model containing all GA-and-mortality specific selected features. For significant predictors in the final model (α = 0.05), interaction terms with ethnicity (Asian*RiskFactor and Black*RiskFactor, with White as reference) were added to evaluate for significant interaction between ethnicity and mortality risk factors [24]. Forest plot of interaction results were plotted using the plot_model function in the sjPlot R-package.
## Genetic ancestry and mortality patterns
Three main ethnic groups were assigned based on self-identified ethnic background: White (British/Irish/Any other white background/White), Asian (Indian/ Pakistani/Bangladeshi/any other Asian background/Asian or Asian British), and Black (African/Caribbean/any other Black background). Other ethnic groups were not included in this study due to low sample size, comprising <$1\%$ of the total and dead datasets (Fig 1A and 1B). Of the 33,393 death records, Whites made up $95.82\%$ followed by Asians with $1.37\%$ and Blacks with $1\%$.
**Fig 1:** *Ethnicity and genetic ancestry in the UKB.Participant ethnic group percentages for (A) the entire UKB cohort and (B) participants with mortality data. (C) Average genetic ancestry proportions for Asian, Black, and White ethnic groups. (D) Individual participant ancestry group proportions stratified by ethnicity. Continental ancestry group proportions are shown as: African (blue), East Asian (green), European (yellow), Central Asian (plum), South Asian (red), and West Asian (brown).*
To provide ethnic group-specific models with more objective and granular ancestry information, GA inference was performed. Six GA proportions were estimated for 477,205 participants or $97.28\%$ of the three-ethnic group dataset. Whites were predominantly of European ancestry ($98.65\%$), followed by West Asian ($0.69\%$) and Central Asian ($0.33\%$) (Fig 1C). Blacks were also dominantly of a single ancestry, African ($88.85\%$), followed by European ($7.45\%$) and West Asian ($1.82\%$). Asians were comparatively more admixed, however, with South Asian ($54.78\%$), Central Asian ($33.52\%$), East Asian ($4.69\%$), European ($3.39\%$), and West Asian ($3.21\%$) (Fig 1C and 1D).
There were observable differences in age of enrollment across the ethnic groups (i.e., Whites were enrolled at median age of 58 years, compared to Asians at 53 Blacks at 50.5), while the follow-up times were consistent with median of 12 years for all three groups (Fig 2A). Kaplan Meier (KM) survival probability curves for each ethnic group and pairwise log-rank test of difference in curves showed significant difference for Asian vs. Whites and Blacks vs. Whites (Fig 2B). Differences also existed between top causes of death and their associations across ethnic groups. Primary reasons of death from Death *Registry data* coded in International Classification of Diseases, Tenth Revision, Clinical Modification (ICD10) were analyzed at the block level in each ethnic group and Chi-square test of independence was performed for association between causes of mortality and ethnicity. For Asians, the top causes were ischemic heart diseases ($22.27\%$), followed by primary malignant neoplasms or cancers of digestive organs ($9.17\%$), while deaths from digestive cancers were most frequent for Blacks ($13.43\%$) and Whites ($15.69\%$) (S3 Fig). Pearson’s Chi-squared test showed significant association between top causes of mortality and ethnicity with particularly strong positive associations between ischemic heart disease and Asians (std.residual = 8.44), COVID-19 and Blacks (std.residual = 7.97), and respiratory/intrathoracic organ cancers and Whites (std.residual = 4.26) (Fig 2C). Based on the differences observed for mortality and causes of death, all downstream analyses were performed separately for each ethnic group.
**Fig 2:** *Ethnicity and mortality in the UKB.(A) Study enrollment age and follow-up time distributions for Asian (red), Black (blue), and White (yellow) ethnic groups. (B) Kaplan-Meier curves showing survival probabilities over time for Asian, Black and White ethnic groups. P-values for ethnic group pairwise log-rank test of survival curves are shown. (C) Associations between ethnicity and specific mortality causes as measured by Pearson’s standardized residuals from Chi-square test of independence.*
## Overall mortality
Feature selected mortality risk factors (S3 Table) were analyzed in multivariable Cox-PH models and their effect sizes or hazard ratio (HR) were estimated. For overall mortality, previous in-patient diagnoses, such as neoplasms, and operations and/or procedures, such as on heart, arteries, and veins or on respiratory track, had greatest impact on overall mortality in all three groups (Fig 3A). However, some diagnoses were uniquely important or more significant to specific ethnic groups. For example, having mental and behavioral diagnoses increased relative risk of mortality by $60\%$ in Asians (HR = 1.598, CI$95\%$ = [1.236,2.066], $$p \leq 0.00035$$), while having infectious and parasitic diseases increased the risk by nearly 2.5 times in Blacks (HR = 2.472 [1.829,3.342], $p \leq 0.0001$) (S4A Table). In contrast, operation on digestive organs including upper endoscopy and colonoscopy was the only type of in-patient procedures associated with reduced mortality risk in all three groups (HRAsian = 0.743 [0.571,0.967], $$p \leq 0.027$$; HRBlack = 0.479 [0.345,0.665], $p \leq 0.0001$; HRWhite = 0.694 [0.559,0.863], $p \leq 0.0001$) (Fig 3A). Being female was associated with reduced mortality risk by more than $36\%$ compared to males in Asian and Black group but not in Whites. Several blood and urine biomarkers showed significant effect on overall mortality of Asians including cystatin-C (HR = 1.115 [1.051,1.183], $$p \leq 0.00031$$) and aspartate aminotransferases (HR = 1.069 [1.036,1.102], $p \leq 0.0001$). For Blacks, increase in CRP levels (mg/L) was highly associated with increase in mortality risk (HR = 1.028 [1.013,1.043], $$p \leq 0.00034$$), while apolipoprotein (ApoA) levels (10 mg/dL) reduced mortality risk by $14\%$ (HR = 0.862 [0.759,0.978], $$p \leq 0.021$$). Moreover, having paid employment status or being self-employed decreased mortality risk by $36\%$ in Blacks (HR = 0.642 [0.476,0.866], $$p \leq 0.0038$$). Significant environmental and sociodemographic risk factors for overall mortality in Whites were past smoking status (smoked on most or all days; HR = 1.408 [1.081,1.833], $$p \leq 0.011$$) and receipt of disability support or allowance (none; HR = 0.545 [0.405,0.733], $p \leq 0.0001$). In all three ethnic groups, GA informed the models but did display significant effects on overall or cause-specific mortalities.
**Fig 3:** *Ethnicity and mortality risk factors.Risk factor-mortality associations, as measured by Cox proportional hazard ratios (with 95% CIs), are shown for Asian (red), Black (blue), and White (yellow) ethnic groups. Significance of association measured in p-values are indicated in stars. Associations are shown for (A) overall (all-cause) mortality and (B) digestive cancer mortality. Mortality risk factor categories include: age, blood biochemistry, previous in-patient disease diagnoses (diagnoses), lifestyle and environmental measures, medications (meds.), previous in-patient operations and/or procedures (operations), sex, sociodemographic factors (soc-dem), general health, mental health, and physical measures (phys.).*
## Cause-specific mortality: Digestive cancers
Previous diagnoses of infectious and parasitic diseases were important risk factor in all three groups, increasing the relative risk of death by at least 2 folds (HRAsian = 5.141 [2.266,11.660], $p \leq 0.0001$; HRBlack = 2.253 [1.101,4.608], $$p \leq 0.026$$; HRWhite = 2.069 [1.328,3.223], $$p \leq 0.0013$$) (Fig 3B). In addition, operation on digestive organs was the largest risk factor in all three groups, since endoscopy and colonoscopy are the main methods used in digestive cancer diagnosis (S4B Table). Unique or group-specific patterns were observed including increase in glucose levels (mmol/L) and standing height (cm) both significantly associated with increase in relative mortality risk by $18\%$ and $3\%$, respectively, per unit change in Whites (HRglucose = 1.175 [1.029,1.343], $$p \leq 0.017$$; HRs.height = 1.034 [1.001,1.068], $$p \leq 0.044$$). Increase in CRP levels was again highly associated with increase in mortality risk for Blacks (HR = 1.053 [1.022,1.086], $$p \leq 0.00084$$). For Asians, consumption of butter as main spread type doubled the risk of digestive cancer mortality than those consuming other spread types or margarine (HR = 2.187 [1.040,4.597], $$p \leq 0.039$$). Hesitancy to answering questions (i.e., choosing “prefer not to answer” or “don’t know” instead of “yes” or “no”) related to mental state or illness, such as “have seen a psychiatrist for nerves/anxiety/depression” or “have sensitive or hurt feelings”, was associated with higher risk of digestive neoplasm mortality in Asians (HRsensitive = 2.677 [1.001,7.16], $$p \leq 0.049$$; HRpsychiatrist = 3.408 [1.109,10.474], $$p \leq 0.032$$). Moreover, Asians who self-reporting their overall health as “excellent” showed greater risk of mortality than those indicated as having “good” health (HR = 4.759 [1.365,16.590], $$p \leq 0.014$$).
## Cause-specific mortality: COVID-19, ischemic heart disease, and lung & bronchus cancers
For COVID-19 deaths in Blacks, males had increased relative risk of mortality by $72\%$ compared to females (HRfemale = 0.285 [0.108,0.750], $$p \leq 0.011$$), and history of hospitalization due to influenza and pneumonia (HR = 13.905 [1.779,108.709], $$p \leq 0.012$$) and receiving ventilation support (HR = 4.841 [1.949,12.026], $$p \leq 0.039$$) were most significant predictors of mortality for diagnosis and operations, respectively (S5A Fig; S4C Table). Similar to overall and digestive cancer mortalities, increase in CRP was associated with increased mortality risk to COVID-19 in Blacks (HR = 1.043 [1.004,1.082], $$p \leq 0.030$$). Increase in waist circumference (cm) was another highly significant risk factor unique to COVID-19 risk of death (HR = 1.059 [1.028,1.091], $$p \leq 0.00014$$). For ischemic heart disease deaths in Asians, increase in cystatin-C, being male, and past smoking (smoked most or all days) all significantly increased the mortality risk (HRcystatinC = 1.194 [1.035,1.378], $$p \leq 0.015$$; HRfemale = 0.071 [0.015,0.339], $$p \leq 0.00090$$; HRsmoking = 2.847 [1.461,5.550], $$p \leq 0.039$$) (S5B Fig). For deaths due to lung and bronchus cancers in Whites, having previously diagnosed chronic lower respiratory disease (HR = 1.859 [1.190,2.904], $$p \leq 0.0065$$), procedures on respiratory tract (HR = 9.069 [5.894,13.954], $p \leq 0.0001$), and increase in alkaline phosphatase (HR = 1.073 [1.025,1.122], $$p \leq 0.0023$$) increased mortality risk, while increase in ApoA (HR = 0.896 [0.834,0.963], $$p \leq 0.0030$$) and fresh fruit and breakfast cereal intake (HRfruit = 0.924 [0.877,0.973], $$p \leq 0.0027$$; HRcereal = 0.956 [0.922,0.991], $$p \leq 0.015$$) lowered the mortality risk for Whites (S5C Fig).
## Mortality risk factor interactions with ethnicity
Two pooled-sample multivariable survival models were tested for interactions between Asian and Black ethnic groups and 31 all-cause mortality risk factors (Fig 4) and 15 digestive cancer mortality risk factors (Fig 5). For all-cause mortality, there was one significant interaction between Asian ethnicity and operations and/or procedures on genitourinary systems (HR = 1.477 [1.084,2.014], $$p \leq 0.014$$) and three significant interactions between Black ethnicity and blood biomarkers: creatinine (HR = 1.011 [1.003,1.019], $$p \leq 0.0099$$), glycated hemoglobin (HbA1c) (HR = 0.985 [0.972,0.999], $$p \leq 0.0042$$), and Cystatin-C (HR = 0.886 [0.813,0.967], $$p \leq 0.0066$$) (S4D Table). Thus, genitourinary systems-related operations in Asians and higher creatine level in Blacks had significantly greater adverse effect on overall survival compared to their effects in the White group, while higher levels of cystatin-C and HbA1c had less adverse effect in Blacks compared to their effects in Whites. For digestive cancer mortality, one significant interaction between Black ethnicity and CRP emerged (HR = 1.101 [1.029,1.178], $$p \leq 0.0051$$), showing a greater adverse effect of increased CRP levels in Blacks compared to Whites (S4E Table).
**Fig 4:** *Risk factor interactions with ethnicity on overall mortality.Forest plots showing HR and 95% CI bars of ethnicity-by-risk-factor interactions tested in the two pooled sample survival models combining Whites, Blacks, and Asians for overall (all-cause) mortality. Significant interactions with Blacks (blue) and Asians (red) are highlighted with stars showing significant level (* for <0.05; ** for <0.01).* **Fig 5:** *Risk factor interactions with ethnicity on digestive cancer mortality.Forest plots showing HR and 95% CI bars of ethnicity-by-risk-factor interactions tested in the two pooled sample survival models combining Whites, Blacks, and Asians for digestive cancer mortality. Significant interactions with Blacks (blue) are highlighted with stars showing significant level (* for <0.05; ** for <0.01).*
## Discussion
In this study, we characterized mortality patterns across three largest ethnic groups in the UK and identified significant mortality risk factors for each, using group-specific feature selection and survival modeling of the UKB data. Our study demonstrated that mortality disparity exists and assessed the impact of shared and group-specific mortality risk factors for overall and other leading cause-specific mortalities per ethnicity.
Differential survival was seen between ethnic groups with Whites showing worse survival probability compared to Black and Asians in UKB. The same trend was observed in the recent analysis of death registration from England and Wales by the Office for National Statistics in the UK, which reported that Whites had higher all-cause mortality rates than other ethnic groups between 2012 to 2019 [25]. The top causes of death associated with each ethnicity also varied, delineating diseases that increase mortality and are in need of intervention for each group.
Feature selection of top risk factors and survival analysis results elucidated both general and targeted strategies for reducing mortality and disparity across ethnicities. Serious preexisting medical conditions, based on ICD10 and preventive or diagnostic OPCS Classification of Interventions and Procedures, had the greatest impact on mortalities in all three ethnic groups: neoplasms increased relative risk of overall mortality by over 2 folds, while exams of digestive organs showed a protective effect, reducing the risk of overall mortality by at least $25\%$. Thus, focusing on cancer prevention and surveillance methods, such as receiving endoscopic exam of gastrointestinal tract, colon, and lower bowel, may be healthful in reducing the overall mortality in the UK irrespective of ethnicity [26].
Conversely, other preexisting medical conditions and diagnoses were ethnic-specific including the mental and behavioral diseases and Asian mortality. The relative risk of mortality in Asians, who are mostly South Asians of Indian, Pakistani, and Bangladeshi origin in the UKB, increased by $60\%$ when previously diagnosed with mental illnesses related to psychoactive substance abuse, organic mental disorders, anxiety, and depression (S5 Table). Asian participants also evaded directly answering mental health-related questions, and this observed hesitancy was significantly associated with a greater risk of mortality in Asians dying of digestive cancers. High prevalence of mental disorders and reluctance to discuss mental illness have also been reported in both India and for Asian Indian communities in the US, where the perception of mental health issues has been marred by social stigma and cultural shame, which has contributed to avoidance in psychological diagnosis and care [27–30]. Moreover, several studies have indicated that South Asian immigrants experience high rates of mental health disorders, which has been linked to reduction in general life expectancy and worse disease outcomes including cancer [31–34]. These finding underscore the importance of sociocultural factors and mental health and its significant impact on Asian mortality. Reducing stigmatization and increasing awareness of mental illnesses and access to related care represent targeted opportunities for Asians.
Several blood and urine biomarkers showed specific associations with ethnicity and mortality. CRP, a biomarker for chronic inflammation, is an important mortality risk factor for Blacks, as evidenced by the significant association between increased CRP levels and greater risk of both overall and cause-specific deaths due to COVID-19 and digestive cancers. Elevated CRP levels have been previously associated with diseases including diabetes and cardiovascular disease and were found in African Americans and Black ethnic groups at higher concentrations [35–37]. Chronic inflammation, as measured by CRP and other blood biomarkers, has been linked to physiological responses to psychosocial stressors, including exposure to discrimination [38]. Here, CRP showed a significant interaction with Black ethnicity in the pooled survival model for digestive cancer mortality, suggesting a greater adverse effect of elevated CRP in Blacks compared to Whites. This finding further reinforces the importance of CRP as mortality risk factor in the Black ethnic group. Similarly, increased levels of cystatin-C, a marker of renal function, in Asians and their overall and ischemic heart disease deaths, and glucose in Whites dying from digestive cancers, were significantly increased the risk of mortality. Glucose was found to promote invasion and metastasis of colon cancer cells, which fit with our finding as a significant risk factor for White mortality from digestive cancers [39]. HbA1c, an indicator of average blood glucose level in the last 90 days, was also associated with greater risk of overall mortality in both Whites and Asians. Meanwhile, HbA1c negatively interacted with Black ethnicity, suggesting that Blacks experience a reduced adverse effect of elevated HbA1c on their overall survival compared to Whites. Alternatively, the ‘diminished returns’ hypothesis suggests that given the presence of numerous other risk factors in the Black group, the presence or absence of individual-level exposures, such as HbA1c, are less significant for Black than White individuals [24, 40–42]. Consistent with diminished returns, absent elevated levels of specific exposures including HbA1c, Whites are expected to live longer, whereas Black mortality changes substantially less across different levels of the same risk factors. Additionally, high concentrations of cystatin-C have been linked to greater risk of heart failure and death in persons with coronary heart disease (CHD) [43]. Our results reinforce and further suggest that cystatin-C is an important overall mortality risk predictor for Asians and Asians dying of CHD. Finally, the protective effect with increased level of ApoA for Black overall mortality and White mortality due to lung and bronchus cancers also aligns with previous reporting of inverse correlation between ApoA levels and risk of developing lung, colorectal, breast, and ovarian cancers [44]. Biomarkers of this kind are routinely used to assess health and disease status and have been linked to various factors, such as genetics, dietary and behavioral, and environmental pollutants [45, 46]. Identification of measurable biomarkers with ethnic group-specific effects can inform precise strategies and potential biological targets for reducing associated mortality.
Several modifiable risk factors for mortality related to physical measures, dietary habits, and health behaviors were also identified including lessening the consumption of butter for Asians (digestive cancer), increasing fresh fruit and breakfast cereal intake and quitting smoking for Whites (lung and bronchus cancer), and lowering waist circumference for Blacks (COVID-19). Waist circumference, but not body mass index or weight, was feature selected as a significant risk factor for COVID-19 deaths in Blacks, suggesting that adiposity around the waist may be more effective predictor of COVID-19 deaths than other related body measurements [47]. These key environmental factors are modifiable and can help reduce mortality risks.
Lastly, two sociodemographic risk factors, disability living allowance for Whites and having paid or self-employment status for Blacks, impacted the overall mortality. However, other socioeconomic measure of deprivation, such as Townsend index scores, was not found to be a significant risk to ethnic-specific mortality. This is likely attributable to within-group similarities in SES of participants of same ethnicity, which may also explain the lack of significant associations with genetic ancestry proportions and ethnic-specific mortalities. Moreover, this demonstrates that the effects of individual-level risk factors highlighted in this study, intermediate and proximal, outweigh that of SES, a distal risk factor, when we independently investigated the main contributors of mortality in each ethnicity [48]. A number of proximal risk factors also showed significant interactions with ethnic groups, adding to growing evidence of the unequal effects of the same risk factors on mortality across different race and ethnicity groups [24, 40–42]. Therefore, population-specific study design that inform targeted risk factors and strategies for reducing mortalities will be critical to overcoming disparities across race and ethnicity groups.
There are several limitations to this study. First, The UKB data is sampling of people living across the UK with median age of enrollment over 50. There may be bias of results due to left truncation, since the analysis was based on participants who had survived to the late enrollment age. In addition, race and ethnicity classification varies between countries for it is a socially defined membership and self-identified based on shared heritage, culture, and social experiences. While the UK ethnic groups studied here approximately correspond to US racial groups, evidenced by similarity to genetic ancestry proportions of White and African Americans, the social experiences and potential health implications could be different [49]. Thus, the extent to which the study findings may be transferable to other countries like the US is uncertain and may require additional validation using data based on the population of interest.
## Conclusions
Our findings demonstrate the ethic differences in mortality and associated risk factors that may contribute to the observed disparities. Several measurable and modifiable blood biomarkers and environmental and behavioral factors were identified including unexpected associations between ethnic mortality disparities, mental health, and systemic stress, some of which showed differential effects on mortality across ethnic groups. These results underscore the importance of population-specific studies that can help decompose health disparities and inform targeted interventions towards greater health equity.
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|
---
title: 'Hypertension screening, prevalence, treatment, and control at a large private
hospital in Kampala, Uganda: A retrospective analysis'
authors:
- Usnish Majumdar
- Rose Nanyonga Clarke
- Andrew E. Moran
- Patrick Doupe
- Darinka D. Gadikota-Klumpers
- Agaba Gidio
- Dennis Ssentamu
- David J. Heller
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021338
doi: 10.1371/journal.pgph.0000386
license: CC BY 4.0
---
# Hypertension screening, prevalence, treatment, and control at a large private hospital in Kampala, Uganda: A retrospective analysis
## Abstract
Adult hypertension prevalence in *Uganda is* $27\%$, but only $8\%$ are aware of their diagnosis, accordingly treatment and control levels are limited. The private sector provides at least half of care nationwide, but little is known about its effectiveness in hypertension control. We analyzed clinical data from 39 235 outpatient visits among 17 777 adult patients from July 2017 to August 2018 at Uganda’s largest private hospital. We calculated blood pressure screening rate at every visit, and hypertension prevalence, medication treatment, and control rates among the 5 090 patients with two or more blood pressure checks who received any medications from the hospital’s pharmacy. We defined hypertension in this group as 1) an average of two blood pressure measurements at separate consecutive visits, higher than 140 mm Hg systolic or 90 mm Hg diastolic, 2) receipt of any antihypertensive medication, or 3) the use of a hypertension electronic medical record code. We deemed hypertension control as normotensive at the most recent check. 12 821 ($72.1\%$) of patients received at least 1 blood pressure check. Among the 5 090 patients above, 2 121 ($41.6\%$) had hypertension ($33.4\%$ age-standardized to a world population standard): 1 915 ($37.6\%$) with elevated blood pressure, and 170 ($3.3\%$) were normotensive but receiving medication. 838 ($39.4\%$) of patients with hypertension received medication at least once. Overall, $18.3\%$ of patients achieved control ($27\%$ of treated patients, and $15\%$ of untreated patients). Hypertension is common and incompletely controlled in this Ugandan private-sector population, suggesting several avenues for novel interventions.
## Introduction
Hypertension is among the leading risk factors for human mortality worldwide and is both more common and less well-controlled in many low-income countries, especially in sub-Saharan Africa, than in more affluent ones. Uganda, with one of the most rapidly-growing populations in the world, is no exception. Its estimated adult hypertension prevalence is now $28.9\%$ in urban areas and $25.8\%$ in rural areas, up from 14–$18\%$ as of 2005 [1]. Its rising prevalence is multifactorial, but sedentary lifestyles, an increasingly “Western” diet of simple carbohydrates and fats, and increasing use of alcohol and tobacco are key contributors [2, 3]. Hypertension and other non-communicable diseases cause $33\%$ of all mortality in Uganda, but only $8\%$ of persons with hypertension are aware of their diagnosis, and $3.6\%$ achieve blood pressure control [4–6].
Fortunately, novel models for hypertension control in Uganda have begun to emerge. Some, for instance, have leveraged novel HIV/AIDS universal test-and-treat programs to screen patients for elevated blood pressure [7–9]. Others have leveraged nurses to screen for and treat hypertension and other NCDs as a result of physician shortages, a strategy called task-sharing [10, 11]. Years of experience have suggested how to prevent and remedy supply and medication shortages as well as gaps in patient data-tracking [12]. The majority of this work, however, has occurred in public-sector clinics, even though the private sector provides the majority of outpatient care for many Ugandans [13]. Recent work suggests that private-sector hypertension patients struggle to educate themselves on blood pressure control, while the physicians that treat them lack time or resources to aid their disease self-management, suggesting that nurse-led behavior change interventions for hypertension may be impactful in private as well as public contexts [10, 14]. However, little is known about the epidemiology of hypertension in the Ugandan private sector—or its baseline level of treatment or control. To address this gap, we analyzed clinical data from 39 235 outpatient visits among 17 777 patients at the largest private hospital in the nation.
## Ethics statement
The protocol and data analysis plan for this study were approved by the Program for the Protection of Human Subjects (PPHS) at the Icahn School of Medicine at Mount Sinai, the Research Ethics Committee (REC) at the Clarke International University, and the Uganda National Council for Science and Technology. Because all data were de-identified prior to analysis, the requirement for informed consent of individuals in the data set was waived by each of these oversight boards.
## Design
We performed a retrospective observational study of electronic medical record (EMR) data and pharmacy invoicing data at a large private hospital in central Kampala, Uganda’s capital. This private hospital system consists of a cluster of multi-specialty and primary care clinics located on site of a main hospital campus in Kampala which serves as the ‘hub’ of the health system, which additionally serves rural Ugandans through a network of smaller, rural primary care clinics. In this study, we evaluate the primary care-seeking population at the main hospital campus, the largest site of this health system. The data set did not capture the racial or ethnic background of participants, though the hospital serves a population that is chiefly African but also comprises minority South Asian and non-Hispanic white populations among others. Sex-specific results are as presented below.
## Study population and data set
The study population included all patients aged 18 or older seen at all outpatient primary care visits from July 2017 to August 2018. We included only visits to the hospital’s general internal medicine clinic and family medicine clinic, because each such provider (unlike, for instance, an urgent care or surgical clinic) is expected to actively screen for, diagnose, and treat hypertension over the course of longitudinal (not one-off) care. We excluded all inpatient data, emergency department visits, as well as all other outpatient visits, including those at specialty clinics that would require referral from a general clinic. At every visit to the included clinics, hospital protocol requires blood pressure to be checked and recorded by a nurse in a sitting position prior to being seen by a doctor. The hospital uses the automated Edan M3A vital signs monitor for all blood pressure readings. By training protocol, blood pressure is checked once per visit, in either arm, with the patient in the seated position and the arm supported and at heart level. The hospital recorded all clinical data using the Navision EMR platform, a product of Microsoft Dynamics 365 Business Central. We used blood pressure data as recorded by that system, without direct observation of its measurement.
In addition to blood pressure values, we acquired visit characteristics (doctor type, insurance status), patient characteristics (age, sex) and all items procured at the hospital pharmacy (drugs and consumables). When documented, we also included diagnosis codes associated with each clinical encounter. These were missing in only $15.8\%$ of visits overall, and $13.2\%$ of persons with hypertension. All patients and doctor names were de-identified and represented by codes. The data set comprised 39 235 unique visits and 17 777 unique patients (Fig 1). We selected and cleaned the data for analysis via freely available Python packages [15].
**Fig 1:** *Inclusion and exclusion flowchart.All study members and their inclusion or exclusion from calculation of screening rates, as well as those included or excluded from calculation of prevalence, treatment, and control rates.*
## Outcomes
Our four main outcomes were rates of hypertension screening, prevalence, treatment with medication, and control. We defined a patient as screened for hypertension at a given visit if both systolic and diastolic blood pressure were documented in the Navision EMR. We defined the prevalence of hypertension via those who patients who met at least one of three criteria: 1) a documented blood pressure value of greater than or equal to 140 mm Hg systolic or 90 mm Hg diastolic on an average of any two consecutive checks (as per US Eighth Joint National Committee, or JNC-8, guidelines); 2) a documented diagnosis of hypertension (regardless of blood pressure value); or 3) documented procurement of one or more anti-hypertensive medications [see S1 Table] at the hospital pharmacy [16]. We did not include any upper or lower bounds on the duration of time between two visits to include in this analysis. We defined a patient as receiving treatment for hypertension who received any anti-hypertensive medication at the hospital pharmacy. Of note, this definition of treatment does not include non-pharmacologic measures that are not captured in this data–namely diet and lifestyle modifications. We defined a patient as achieving hypertension control whose most recent blood pressure value was under both 140 mm Hg systolic and 90 mm Hg diastolic, also per JNC-8 guidelines.
We calculated the rate of blood pressure screening for all visits—and therefore all patients—in the data set—a total of 39 235 visits among 17 777 patients. However, we calculated the prevalence of hypertension; the proportion of hypertension under treatment; and the proportion of hypertension under control only among patients who had 1) at least two separate blood pressure measurements and 2) at least one drug of any kind dispensed from the hospital pharmacy during the study period. The first prevalence criterion allowed us to focus only on patients whose hypertension status could be properly ascertained per JNC-8 guidelines. The second prevalence criterion allowed us to focus only on patients whose preferred pharmacy was at the hospital (as opposed to an outside facility) to accurately gauge who did and did not receive medication for hypertension. While the hospital pharmacy represents one of the largest pharmacies in the area, patients can also fill prescriptions at one of numerous local pharmacies. To our knowledge, many patients can and do pick up medications at both the hospital pharmacy and their local pharmacies, as a matter of cost and convenience. Of note, only $5\%$ [268] of patients with two BP measurements during the study period had not picked up a medication from the hospital pharmacy. These two criteria–two BP measurements and hospital pharmacy use—narrowed our sample size to 17 858 visits among 5 090 patients.
Because diagnosis strings appeared in the EMR via a free-text field (i.e. not a standardized set of codes), the data set referred to a hypertension diagnosis in multiple ways, such as “hypertension—essential”, “hypertension”, and “htn”. We defined documented hypertension diagnosis for a given patient as a coded string containing the phrase “hypertension” or “htn”, excluding strings that represent prehypertension (“prehypertension”, “prehtn”, etc.), intracranial hypertension, portal hypertension, and pulmonary hypertension appearing at any time during the study period. “ High blood pressure” was not documented as hypertension, as this term was not found frequently in the data set and carries diagnostic ambiguity. Medications dispensed for hypertension at the hospital pharmacy were not free-text but selected from a list, so we used no such criteria—rather, we identified 48 medications for hypertension treatment [see S1 Table] and defined a patient as under hypertension treatment (and therefore experiencing hypertension) who received such a medication at the hospital pharmacy at any time during the study period. *To* generate this list of medications, we classified all dispensed agents in the dataset according to their Anatomical Therapeutic Chemical (ATC) category and subcategory per World Health Organization criteria. This approach yielded 13 distinct categories and combinations of medications: calcium channel blockers (CCBs); beta blockers; thiazide diuretics; angiotensin-converting enzyme inhibitors (ACE-Is); angiotensin receptor blockers (ARBs); potassium-sparing diuretics; loop diuretics; peripheral alpha-adrenoreceptor antagonists; central alpha-adrenoceptor agonists; combination alpha-beta blockers, arteriolar smooth muscle relaxants; combination ARB-thiazide diuretics; and ARB-CCB combination agents. We presumed that the indication for any use of any such agent in the data set was the treatment of hypertension. Lastly, we age-standardized any hypertension rate to the WHO 2000–2025 World standard population.
## Statistical analysis
In order to compare both the study sample (17 777 patients) and the subset of this sample chosen for evaluation of prevalence, treatment, and control [5 090], we performed a Cramer’s V-test (derived from the χ2 statistic but more suitable for large sample sizes) on the distribution of ages among selected ranges (18–30, 30–45, 45–65, and 65+), sex, and insurance status.
We examined potential associated factors via multiple logistic regression of each of the four main outcome variables: screening (at visit level), prevalence (at patient level); treatment (at patient level); and blood pressure control (at patient level). Age, sex, and the total number of outpatient generalist visits during the study period were included as independent variables. We also included payment type as an independent variable: because all hospital patients lacking insurance must pay in cash (self-pay), we treated payment type as a binary variable comprising either cash or insurance. Patients who used both methods of payment during the study period were assigned the payment type they used most frequently. For the regression examining blood pressure control as an outcome variable, we additionally included blood pressure treatment as an independent variable.
*We* generated odds ratios and $95\%$ confidence intervals for each analysis, holding a p value < 0.05 to be statistically significant. All regressions and statistical tests were performed in Python by utilizing the statsmodels package, an econometric and statistical modeling toolkit, as well as other freely available packages such as pandas, a general-purpose data management library [17, 18].
## Baseline characteristics
The screening sample consisted of 39 235 visits and 17 777 patients ranging from 18 to 97 years old (mean: 36.8), whereas the subset of these patients chosen to analyze prevalence, treatment, and control consisted of 17 858 visits and 5 090 patients ranging from 18 to 88 years old (mean: 37.7). More than half the original screening sample are described by diagnosis field as “general checkup” of which the leading diagnoses were diabetes, heart failure, angina, and hypertension. Among these 5 090 patients, the mean number of visits during the study period was 4.25 (minimum 2, median 3, 25th percentile 2, 75th percentile 5, maximum 32). The median interval between consecutive visits was 29 days (mean 59 days, 25th percentile 6, 75th percentile 91 days, max 399 days–the duration of the study period). The distribution of sex, age bins, and insurance status are not significantly different between the original study population and sample (Cramer’s V test, $$p \leq 0.99$$ [age], 0.87 [sex], 0.62 [insurance status]), as can be seen in Table 1 and Fig 2. See S1 Fig for a Venn diagram comparing the different hypertension criteria included in the evaluation of prevalence.
**Fig 2:** *Cascade of care.Bars in graph depict extent of correct, and imperfect, treatment and control across steps of care cascade.* TABLE_PLACEHOLDER:Table 1
## Screening rates
Among 39 235 eligible visits, 25 352 ($65\%$) recorded both a systolic and diastolic blood pressure. Among 17 777 eligible patients, 12,821 ($72.1\%$) of patients received at least 1 complete blood pressure measurement during the study period (Table 2). In a multiple logistic regression predicting screening with age, gender, number of visits, and insurance status, the male gender was associated with decreased odds of screening (OR 0.86, $95\%$ CI 0.80–0.92, $p \leq 0.001$). Number of visits (OR 1.78, $95\%$ CI 1.71–1.86, $p \leq 0.001$) and insurance status (OR 2.73, $95\%$ CI 2.54–2.92, $p \leq 0.001$), were associated with increased odds of screening, as shown in Fig 3.
**Fig 3:** *Multiple logistic regressions.Each multivariate logistic regression model predicts the likelihood of an individual being screened for hypertension, having hypertension, treated for hypertension, or controlled.* TABLE_PLACEHOLDER:Table 2
## Prevalence
Among 5 090 patients in the prevalence and treatment sample (who had at least 2 blood pressure measurements and at least 1 hospital pharmacy record during the study period), 2 121 ($42\%$) had hypertension per EMR code, receipt of antihypertensive therapy, or by blood pressure measurements (Table 2). The age-standardized prevalence of hypertension in this sample was $33.4\%$. Among 2 121 patients, 1 915 met hypertension criteria by blood pressure measurements, 838 by receipt of antihypertensive therapy, and 668 by EMR code. See S1 Fig for a Venn diagram comparing the different hypertension criteria included in the evaluation of prevalence. Of the 2 085 patients who had hypertension by blood pressure or receipt of antihypertensive medications, 632 ($30\%$) were diagnosed as hypertensive by EMR code. 36 patients had a ‘hypertension’ EMR code alone.
Overall, older patients had higher rates of hypertension prevalence than younger patients ($79\%$ in 66+, as compared to $23\%$ among ages 18–30), as did male patients ($46\%$) when compared to female patients ($38\%$). In a multiple logistic regression describing hypertension prevalence and its correlates, older age (OR 1.06, $95\%$ CI 1.05–1.06, $p \leq 0.001$), male gender (OR 1.15, $95\%$ CI 1.04–1.28, $$p \leq 0.007$$), number of visits (OR 1.13, $95\%$ CI 1.11–1.14, $p \leq 0.001$), and insured status (OR 1.21, $95\%$ CI 1.05–1.39, $$p \leq 0.007$$) were associated with increased likelihood of hypertension, as shown in Fig 3.
## Treatment (Medication)
Of 2 121 patients with hypertension per study criteria, 838 ($39.5\%$) received antihypertensive medication from the hospital pharmacy during the study period (Table 2). The 646 hypertensive patients identified by diagnosis code had a higher rate of medication treatment ($83.4\%$) than those meeting criteria for hypertensive but without a diagnosis code ($37.9\%$). Overall, 838 patients received 4 776 itemized prescriptions throughout the study period. Calcium-channel blockers (1 916 prescriptions, $40\%$) and angiotensin-receptor blocker / thiazide diuretic combination pills (1 465 prescriptions, $31\%$) comprised the majority of medications prescribed (Table 3). In a multiple logistic regression describing medication treatment of hypertension, older age (OR 1.05, $95\%$ CI 1.04–1.06, $p \leq 0.0001$), number of visits (OR 1.05, $95\%$ CI 1.04–1.06, $p \leq 0.0001$), and insured status (OR 1.60, $95\%$ CI 1.25–2.06, $p \leq 0.0001$) were associated with increased likelihood of hypertension treatment (Fig 3).
**Table 3**
| Antihypertensive Category | Number of prescriptions | Number of prescriptions.1 | Most common drug in category |
| --- | --- | --- | --- |
| CCB | 1 916 | (40%) | amlodipine 10mg |
| ARB + Thiazide Diuretic | 1 465 | (31%) | losartan 50mg + hydrochlorothiazide 12.5mg |
| Beta Blockers | 552 | (12%) | bisprolol 5mg |
| ARB | 339 | (7%) | losartan 50mg |
| Loop Diuretic | 95 | (2%) | furosemide 40mg |
| Thiazide Diuretic | 94 | (2%) | bendrofluazide 5mg |
| Alpha + Beta Blockers | 79 | (1.6%) | carvedilol 6.25mg |
| ARB + CCB | 78 | (1.6%) | losartan 50mg + amlodipine 5mg |
| ACE Inhibitor | 57 | (1.2%) | enalapril 5mg |
| K- sparing Diuretic | 47 | (1.0%) | spironolactone 25mg |
| Arteriolar smooth muscle relaxants | 41 | (0.9%) | hydralazine 25mg |
| Alpha- and Beta Blockers | 9 | (0.2%) | labetolol 100mg |
| Central Alpha-adrenoceptor agonist | 2 | (0.04%) | clonidine 10mcg |
| Peripheral | 2 | (0.04%) | alfuzosine 10mg |
## Control
Among 2 121 patients with hypertension, 422 ($19.5\%$) were controlled (normotensive at their last visit). Rates of control were similar across age and gender, but higher in the medication-treated group ($27\%$) than in the untreated group ($15\%$) (Table 2). Of 1 915 patients meeting criteria by clinical measurements alone, those who had a baseline SBP of >160mm Hg systolic had a lower rate of control ($8.4\%$) than those with a baseline SBP 140-160mm Hg ($12.4\%$). In a multiple logistic regression describing control of hypertension, antihypertensive treatment (OR 2.02, $95\%$ CI 1.60–2.55, $p \leq 0.001$) was associated with increased likelihood of treatment control, whereas number of visits (OR 0.97, $95\%$ CI 0.96–0.98, $$p \leq 0.01$$) and insured status (OR 0.68, $95\%$ CI 0.51–0.91, $$p \leq 0.01$$) were associated with decreased likelihood of treatment control.
## Discussion
Our analysis of one year of blood pressure data in this large private health system sample—in an under-studied and low-resource setting—reveals a high prevalence of hypertension ($42\%$, age-standardized to $33\%$) relative to those reported in high-income countries (25.3–$31.6\%$), low-middle income countries (age-standardized 31.2–$31.7\%$), and prior estimates in urban Uganda ($28.9\%$) [5, 19]. Rates of treatment with medication ($39.4\%$) and control ($20\%$ overall; $27\%$ if medicines and $15\%$ without) were also significantly less than in high-resource settings such as the US or OECD countries [19, 20]. Compared to low- and middle-income countries overall, treatment rate in this cohort was significantly greater ($39.4\%$) than reported averages ($29.0\%$) [19]. However, control rates were lower ($19.5\%$) than comparable populations in sub-Saharan Africa ($22.6\%$)—yet greater than documented in previous work in Uganda ($9.4\%$) [21, 22]. These outcomes were particularly favorable relative to rural Uganda, where over $90\%$ of hypertension is undiagnosed and under $5\%$ of those diagnosed achieve control [5, 7]. In any case, insofar as the “cascade” of hypertension care causes cumulative attrition at each stage, there exist significant opportunities for this private hospital setting to improve its hypertension management strategy at a population level [9]. If those 4 956 patients unscreened for hypertension (no blood pressure measurements at visit) have the same $42\%$ prevalence rate as the study sample (screened twice, picked up medications at hospital pharmacy), some 2 081 persons may have undiagnosed hypertension undetected by our approach.
This private Ugandan hospital, in which approximately $60\%$ of patients have access to health insurance and others pay in cash, likely serves a significantly higher socioeconomic status population than the Ugandan public sector, yet concurrently a lower-income population than most US hospitals (given the income disparity between even the highest-income Ugandan households and the lowest-income US ones). Its control outcomes for hypertension control between the Ugandan and US population averages may therefore reflect how starkly resource access disparities drive health outcomes: an intermediate level of patient and health system affluence between those two poles yields intermediate clinical results [23, 24]. A prior study of the relationship between individual income level and hypertension control found that, in urban clinics among lower-income Sub-Saharan countries, lower individual wealth was associated with both lower likelihood of control and higher grade of hypertension [22]. In the US, those with insurance have nearly twice the rate of hypertension control (43–$54\%$) than those without ($24\%$) [25].
Relative to other hypertension control studies in Uganda and the East African region, our facility-level findings demonstrate higher levels of blood pressure treatment and control than in the general population, yet also offer insight into how routine primary care operates–outside of a pilot study or other research setting. For example, the Sustainable East African Research in Community Health (SEARCH) study achieved $75\%$ medication treatment and $46\%$ blood pressure control among 3,380 persons linked to hypertension care in Uganda and Kenya–significantly greater than observed in this population–but in a program not yet integrated within routine care [8]. Similarly, the Linkage And Retention to hypertension care in rural Kenya (LARK) achieved a mean 13 mm systolic blood pressure decrease in persons with hypertension treated with smartphone behavior counseling and medication, with $26\%$ control rate, in an intervention now being scaled and integrated with microfinance support, but not yet a part of routine care in western Kenya [9, 26, 27]. Given that the private sector provides more than half of health care in Uganda, and given than hypertension care cascade analyses in both private and public sector clinics in East Africa are uncommon, our work provides novel insights into both the baseline state of usual care in a higher-resource Ugandan clinic, and thereby opportunities to strengthen it through improvements in medication treatment and adherence–work now underway at this site [13, 14, 28–31].
The significant care disparities between insured (those with better access to healthcare) and uninsured patients within the hospital cohort further supports this hypothesis. Among persons meeting study criteria for hypertension, $34\%$ percent of the uninsured received medication treatment, compared to $40\%$ percent of the insured. After correcting for medication treatment status, insurance status was no longer correlated with hypertension control, suggesting that differential receipt of medications between these two groups explains the observed control disparity. Because of limitations in our study approach (which documented as treated only those who picked up a medicine at the hospital pharmacy, regardless of what was prescribed), we cannot establish to what extent insured persons were more likely to be offered medicine by their physician, as opposed to more likely (and more financially able) to access it. It is suggestive, however, that in the population with enough BP measurements and hospital pharmacy records, $81\%$ were insured versus $60\%$ in the general screening population. Further research should seek to better understand the barriers to medication treatment for patients with hypertension at this and similar hospitals in low-income countries, be it inability to afford medication, therapeutic inertia, reluctance to take medication, or other causes.
Regardless of treatment disparities, however, $27\%$ of those treated for hypertension with medication achieved blood pressure control. This result, which also compares negatively with high-resource settings (in the US, $59.5\%$), reinforces prior data from high-income settings that access to hypertensive medication (widely known to be an essential therapy but nonetheless widely unavailable in Uganda)- is nonetheless insufficient to achieve blood pressure control on its own [32, 33]. The gap in control rates between treated patients in this cohort and treated patients in high-income settings suggests further disparities: in access to the support required to adhere to antihypertensive medicines, as well as in access to counseling treatment around lifestyle and diet that complements antihypertensive pharmacotherapy treatment.
This finding suggests a separate opportunity for further research: into the extent, barriers, and potential solutions that address medication adherence for hypertension and other non-communicable diseases in Uganda and similar settings. Although medication nonadherence for chronic disease is under extensive study in the United States, data are more sparse in Uganda [7, 9, 34, 35]. Previous work on hypertension control barriers in Uganda details frequent and unpredictable medication stockouts, and limited evidence suggests that inconsistent access to medications may drive loss to follow-up [12, 36]. Nonetheless, it remains unclear whether inconsistent medication access impairs medication adherence relative to an uninterrupted medication supply, as most prior work on medication adherence in Uganda has occurred only in the setting of inconsistent medication access.
Hypertension medication adherence research in hospital cohorts such as this—where a patient’s receipt or non-receipt of medicine at the hospital pharmacy can be confirmed, and where a sizable cohort of patients already access medicine consistently—could address this question. Previous work in this hospital system has demonstrated that a hypertension “adherence club” resulted in significant blood pressure control relative to baseline and relative to the results above, but this study lacked a control group and the effect of medication adherence counseling (as opposed to behavior counseling of other types) remains unclear [10]. Qualitative studies in this setting further reinforced patient and provider interest in hypertension education and adherence counseling [14, 28]. Work in this setting of relative medication abundance could inform hypertension behavior counseling in the Ugandan public sector, where adherence research remains hampered by frequent stockouts but hypertension remains common and public education limited [14, 24, 28, 37, 38].
Our work has several notable limitations, many of which stem from our use of routine care data in this study. Although we used one year of retrospective cohort data, we analyzed the key outcomes of screening, prevalence, and medication treatment cross-sectionally, i.e., we considered a subject to meet these three criteria for the entire study period if they achieved it at any point during the year. We therefore do not make causal inferences regarding how and why these variables correlate. For example, insurance status may correlate with hypertension status due to underlying greater prevalence of hypertension in the insured; or conversely a greater proclivity for those with hypertension to seek insurance. Moreover, these data rely on inference and may not fully reflect underlying behaviors or outcomes: for instance, hypertension may not be less common in the uninsured but merely less often recorded by the physician—due to less frequent blood pressure checks, but perhaps also less diagnostic documentation or medication treatment. Similarly, the uninsured may not in fact be less often prescribed medication for hypertension than the insured, but merely more likely to purchase hypertension medication at a non-hospital pharmacy (and therefore be labeled as ‘untreated’ in our analysis). We worked to mitigate these limitations, however, by adjusting for measured confounders (i.e. treatment as a confounder of the relationship between insurance and control) and also limiting our analyses to those known to use the hospital pharmacy.
Another limitation stems from not placing upper or lower bounds on the interval between consecutive blood pressure measurements considered for hypertension prevalence–visits too closely spaced may limit the accuracy of our approach. Fortunately, visits only one or two days apart were uncommon (median interval 29 days, 25th percentile 6 days), and likely had a minimal effect on study findings. The short study duration (just over 1 year) is also another potential limitation in this study–given the large proportion of patients who were screened for hypertension but only visited the clinic once during the study period, extending the study period may well have captured cases of hypertension missed during the current period. Limiting our analysis to those known use to the hospital pharmacy may limit the external validity of this study, as it is not well known what causes a patient to seek medications at the hospital pharmacy versus elsewhere. Additionally, should a patient who otherwise picked up medications at the hospital pharmacy choose to fill a prescription at a local pharmacy, this would have not been captured in our data and led to an underestimate of treatment.
Treatment of hypertension had a narrow definition in this study–namely, pharmacologic treatment by one of the 48 antihypertensive agents. Notably, this does not include the many non-pharmacologic approaches to hypertension management, including myriad diet and lifestyle interventions. This may additionally explain the relatively weak association between treatment and control (OR close to 2), Separately, any of these antihypertensive agents carry secondary indications for non-hypertensive disorders–for example, spironolactone functions not only as an antihypertensive but also as a mainstay of treatment in some cirrhosis and heart failure patients. Our conservative approach in this setting–including all medications with any indication in hypertension–may result in an overestimate of treatment for hypertension. In describing prevalence, treatment, and control, we utilized age and number of visits as continuous variables in our logistic regression models–one potential limitation of this approach is that the effects of these variables may not be linear.
We measured hypertension control, unlike the other variables above, based only on the most recent blood pressure check conducted. This approach, common to hypertension research, allowed us to some extent to use a retrospective cohort approach, as patients typically underwent hypertension treatment, or obtained multiple visits for hypertension, before that blood pressure check at the end of the study period. However, it remains unclear whether the number of blood pressure visits correlates negatively with control due to [1] prior lack of control prompting more visits or [2] the frequent visits negatively influencing the control outcome. The natural physiologic variability in blood pressure is a further limitation: the most recent single documented blood pressure is not always reflective of underlying hypertensive control, just as the two or more documented blood pressures meeting our criteria for hypertension over the study year may overlook those who met such criteria during years prior but not documented or treated. Furthermore, we were unable to confirm whether the patient was correctly positioned or the cuff properly sized and applied to optimally measure blood pressure. Nonetheless, even after setting the hypertension threshold at a conservatively high level of 140 mm Hg systolic or 90 mm diastolic to improve specificity in our prevalence and control data, we found both a high hypertension prevalence and a large fraction of those whose pressure remained elevated despite treatment with medication.
## Conclusions
This study provides novel data on hypertension screening, prevalence, and pharmacotherapy treatment in a leading private-sector Ugandan hospital with a large on-site pharmacy. Little data has been reported on hypertension in the private sector in sub-Saharan Africa, and here we report high hypertension prevalence with medication treatment and control rates worse than in high-income countries but superior to rural Ugandan districts. Our findings suggest significant barriers to hypertension control in Uganda even in a setting where pharmacotherapy is nominally universally available. Further avenues for research include identifying and addressing barriers to medication adherence (if not access) and behavior change. Successful care models could benefit the Ugandan public sector and other low-resource settings that face similar challenges.
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|
---
title: 'Changes in sugar-sweetened beverage consumption in the first two years (2018
– 2020) of San Francisco’s tax: A prospective longitudinal study'
authors:
- Lynn D. Silver
- Alisa A. Padon
- Libo Li
- Bethany J. Simard
- Thomas K. Greenfield
journal: PLOS Global Public Health
year: 2023
pmcid: PMC10021346
doi: 10.1371/journal.pgph.0001219
license: CC BY 4.0
---
# Changes in sugar-sweetened beverage consumption in the first two years (2018 – 2020) of San Francisco’s tax: A prospective longitudinal study
## Abstract
### Background
Sugar sweetened beverage (SSB) taxes are a promising strategy to decrease SSB consumption, and their inequitable health impacts, while raising revenue to meet social objectives. In 2016, San Francisco passed a one cent per ounce tax on SSBs. This study compared SSB consumption in San Francisco to that in San José, before and after tax implementation in 2018.
### Methods & findings
A longitudinal panel of adults ($$n = 1$$,443) was surveyed from zip codes in San Francisco and San José, CA with higher densities of Black and Latino residents, racial/ethnic groups with higher SSB consumption in California. SSB consumption was measured at baseline ($\frac{11}{17}$-$\frac{1}{18}$), one- ($\frac{11}{18}$-$\frac{1}{19}$), and two-years ($\frac{11}{19}$-$\frac{1}{20}$) after the SSB tax was implemented in January 2018. Average daily SSB consumption (in ounces) was ascertained using the BevQ-15 instrument and modeled as both continuous and binary (high consumption: ≥6 oz (178 ml) versus low consumption: <6 oz) daily beverage intake measures. *Weighted* generalized linear models (GLMs) estimated difference-in-differences of SSB consumption between cities by including variables for year, city, and their interaction, adjusting for demographics and sampling source. In San Francisco, average SSB consumption in the sample declined by $34.1\%$ (-3.68 oz, $$p \leq 0.004$$) from baseline to 2 years post-tax, versus San José which declined $16.5\%$ by 2 years post-tax (-1.29 oz, $$p \leq 0.157$$), a non-significant difference-in-differences (-$17.6\%$, adjusted AMR = 0.79, $$p \leq 0.224$$). The probability of high SSB intake in San Francisco declined significantly more than in San José from baseline to 2-years post-tax (AOR[interaction] = 0.49, $$p \leq 0.031$$). The difference-in-differences of odds of high consumption, examining the interaction between cities, time and poverty, was far greater (AOR[city*year 2*federal poverty level] = 0.12, $$p \leq 0.010$$) among those living below $200\%$ of the federal poverty level 2-years post-tax.
### Conclusions
Average SSB intake declined significantly in San Francisco post-tax, but the difference in differences between cities over time did not vary significantly. Likelihood of high SSB intake declined significantly more in San Francisco by year 2 and more so among low-income respondents.
## Introduction
It is well documented that sugar sweetened beverages (SSB) contribute to obesity, diabetes, heart disease, cancer, and other illness [1] and to health inequities in these conditions [2,3]. Consumption of SSBs also exhibits a dose-response relationship with all-cause mortality [4]. The unequal burden of diet-driven metabolic disease has also been a contributor to inequitable impact of the COVID-19 pandemic, highlighting the continuing urgency of identifying effective prevention policies [5,6].
Taxation of SSBs is a public health and fiscal policy that has expanded globally over the past decade, now implemented in over 45 countries [7,8]. The policy seeks to respond to the negative health effects of consumption by creating financial incentives to reduce consumption, promote reformulation, and guide consumers to healthier beverage intake. Simultaneously, these fiscal policies have been used to raise revenue for needed social objectives such as food security, improved nutrition, early childhood education and other purposes. In one fiscal year in 7 U.S. cities with taxes, revenue from SSB taxes totaled $134 million, of which $67\%$ was invested in human and community capital and $28\%$ in healthy food access and other health-related investments [9,10].
In the United States, SSB tax policies have been passed by the Navajo Nation [2013], Philadelphia, PA [2017], Boulder, CO [2017], Seattle, WA [2017], and 4 cities in the California “Bay Area”: Berkeley [2013], Oakland [2016], San Francisco [2016] [11] and Albany [2017], CA. An additional tax was passed by Cook County, IL but was repealed after being in effect for 4 months. These taxes varied in design by the size of the tax (ranging from 1 to 2 cents per ounce), inclusion, or exclusion of non-calorically sweetened beverages, and use of revenue. The San Francisco tax applied solely to sugar sweetened beverages with >25 calories per 12 ounces and is a volume-based tax of one cent per ounce applied to distributors, as were all the Bay Area taxes. After Philadelphia, San Francisco, with 875,000 residents, is the largest city currently taxing SSBs in the US. In 2022, SSB taxes continue under debate from Rhode Island to Kazakhstan, highlighting the relevance of ongoing policy evaluation [12,13].
A growing body of evaluation research has emerged, both in the U.S. and globally. The majority of these studies demonstrate partial or complete pass-through of the taxes to retail beverage prices, reductions in their sale or consumption to varying degrees, and reformulation to reduce sugar content where that affected taxation rates [7,14]. In the U.S., using commercial sales data, Silver et al. found a significant $9.6\%$ decline in volume sales of SSBs in the first year after the Berkeley tax [15]. Similarly, using commercial sales data, Léger found a decline of taxed beverages of $14\%$ in Oakland relative to Sacramento over the first year, but $46\%$ of this decrease was offset by border sales [16]. Powell found a $27\%$ decline in SSB volume sales in the first 4 months of the repealed Cook County tax [17]. Roberto et al. found a $26.6\%$ decline in volume sales in Philadelphia in the first year post-implementation of a 1.5 cent per ounce tax when corrected for cross-border shopping [18]. Powell found a significant decline of $22\%$ in volume of taxed beverages sold in the first year of the Seattle 1.75 cent per ounce tax [19]. Studies of individual consumption and of receipts have been less positive. A non-significant $19\%$ decline in taxed ounces consumed was noted in Berkeley’s first year [15], and using an individual respondents’ receipt-based approach, significant reductions at 12 months were not seen in Philadelphia [20]. Regarding the contemporaneous Oakland and San Francisco taxes, Falbe et al. found complete to near complete pass-through of the tax to prices the first year [21]. Cawley and Frisvold studied receipts of households with children in four US cities with taxes, comparing 6 months before and six-month post-tax. They found significant reductions in Philadelphia relative to synthetic controls but no significant reduction in the combined Oakland, San Francisco, and Seattle sample [22].
Fewer studies have longer-term analyses of tax outcomes. In the U.S. Lee et al. found a decline of 0.55 in frequency of SSB consumption per day in low-income communities three years after the Berkeley tax, significantly greater than the comparison areas [23]. Two years after tax implementation, purchases of taxed beverages declined by $42\%$ in Philadelphia compared with Baltimore, and declines were larger amongst shoppers in low-income neighborhoods [24]. Globally, a study of Mexico’s beverage tax reported a $7.6\%$ decline in taxed beverage purchases and a $2.1\%$ increase in nontaxed beverage purchases over 2 years but lacked a control group [25]. A study of a longitudinal cohort in Mexico after three years found increases in SSB non-consumers and reductions in heavy consumers [26]. In Saudi Arabia, after a much larger $50\%$ tax on carbonated drinks started in 2017 and $5\%$ VAT in 2018, volume sales declined by $40.7\%$ after two years relative to pre-tax trends, and $31\%$ compared to untaxed comparison sites [27]. A smaller 10–$20\%$ tiered tax in Catalunya implemented in 2017 was accompanied by a $12.1\%$ decline in regular cola purchases over the first two years [28], and a $39\%$ percent decline in prevalence of at least once weekly SSB consumption in 12 to 40 year olds from low-income neighborhoods after one year, compared to a control city [29].
Using a natural experiment, difference-in-differences approach with a longitudinal panel, this study assessed to what extent the modest one cent per fluid ounce (29.6 ml) of SSB tax implemented in January 2018 in San Francisco was associated with reductions in self-reported SSB consumption at one- and two-years post implementation in San Francisco and a control city, and whether effects differed based on race/ethnicity and/or socioeconomic status.
## Methods
The study used a longitudinal survey of San Francisco and San José residents to compare changes in SSB consumption in the first two years after San Francisco implemented the SSB tax on January 1st, 2018. An online consent process was employed, and potential respondents were informed that the study was about family nutrition and eating habits to minimize response bias related to SSB-relevant questions. The study was granted a waiver of documentation of consent and approved by the Institutional Review Boards of the Public Health Institute, protocol # I16-024 and ICF Macro, Inc. ICF collected all three waves of data by web survey: baseline (B) just prior to and in the first 24 days of the tax from $\frac{12}{2017}$-$\frac{1}{2018}$, first follow-up (F1) about one-year (10–15 months) post-tax implementation from $\frac{11}{2018}$-$\frac{3}{2019}$, and second follow-up (F2) about two years (22–25 months) post-tax implementation from $\frac{11}{2019}$-$\frac{1}{2020.}$ We calculated Euclidean Mahalanobis distances, a method of measuring differences between points in a vector and a mean [30], between standardized city-level measures for San Francisco and potential control cities. U.S. Census data on population size, race/ethnicity, median age, median income, education, and proportion in poverty were compared using Stata’s nnmatch command, and corrected for large-sample bias due to the multiple continuous covariates [31]. San José was the city with the shortest Mahalanobis distances from San Francisco [32,33]. San José is also geographically proximate to San Francisco; the borders are separated by approximately 64 km [34]. Both cities are in California, a state with low overall SSB consumption, and have had similar exposure to other factors reducing consumption such as educational campaigns.
To understand the impact of the tax on those most likely to be at higher risk of health effects, we used a circumscribed sampling frame consisting of the most racially dense neighborhoods within each city, allowing recruitment of more Black and Latino participants, who, while a small part of the population (San Francisco and San Jose were $5.0\%$ and $2.8\%$ non-Hispanic Black, and $15.2\%$ and $31.6\%$ Hispanic, respectively) [35], are often higher consumers of SSBs in the U.S. To achieve the racial/ethnic targets, 9 zip codes in San Francisco and 13 zip codes in San José were selected based on having at least $10\%$ or $4\%$ Black population or $30\%$ or $35\%$ Latino population, respectively. Proportion cut-offs varied according to each city’s specific racial/ethnic make-up. The 9 San Francisco zip codes covered an estimated $65\%$ of the city’s non-Hispanic Black population and $63\%$ of the Latino population [35]. The 13 San José zip codes covered an estimated $67\%$ of the city’s Black population and $55\%$ of the Latino population [35]. In comparison to the overall San Francisco population, the population of the zip codes from which we drew our sample were less likely to be white, more likely to be Latino or Black, and more likely to have low educational attainment [35]. The population of zip codes from which we drew our San José sample were less likely to be Asian compared to the general population of the city [35].
To obtain sufficient sample size to detect modest changes in consumption within budgetary and policy implementation time-sensitivity constraints, and given California’s rapidly declining random digit dial response rates [36], we used a complex sampling design. For the baseline survey, we recruited respondents from 3 sources: mailed address-based sampling (ABS), a web-based non-probability panel (NP), and a panel that was originally recruited via random digit dial (RDD) for an earlier study to assess a 2016 San Francisco SSB warning label law. Response rates from this earlier study were low ($46.7\%$ for landline; $12.5\%$ for cell). This led to a shift to mailed address-based sampling pushing respondents to web surveys for this tax evaluation study, while retaining earlier RDD panel respondents. This was complemented with recruitment from a non-probability web panel, whose members reported higher levels of SSB consumption.
Based on Cohen’s power calculation methods, assuming a $5\%$ significance level and $25\%$ attrition of sample over time, an initial sample with 1100 individuals from each city would give an over $98\%$ power of detecting a small interaction effect between intervention and wave on continuous outcome variables (Cohen’s $f = .10$ or Cohen’s $d = .20$) [37]. Respondents were eligible if they were [1] aged 18 or older, and [2] residents of San Francisco or San José from within the sampling frame. Questionnaires were completed in English, Spanish and Chinese. In total, 7,424 completed questionnaires were collected, 3,736 in San Francisco and 3,688 in San José, with 2,614 at baseline (B), 2,410 at follow-up time 1 (F1), and 2,400 at follow-up time 2 (F2) (see Fig A in S1 Text). The response rates for repeat respondents, referred to as RR3, represents the percentage of completions among all eligible records in the sample, which was $42\%$ for each of the follow-up surveys. Given the recruitment methods used, assessment of reasons for non-participation was not possible. Respondents were included in the analysis if they participated in B and F1 only ($$n = 198$$) or B, F1 and F2 ($$n = 1$$,257). Respondents were then excluded if they ever reported beverage intake greater than 400 ounces (11.8 liters; $$n = 11$$) or had missing covariates at 2 time points of data, one of which was baseline ($$n = 1$$). The resulting final analytic sample was 1,443 repeat respondents (SF: $$n = 722$$, SJ: $$n = 721$$; see Table 1), and based on Hedeker et al, [38] should yield $95\%$ power to detect a small linear interaction between city and time. Respondents who only participated at baseline or were otherwise excluded were more likely to be slightly older, Black or Latino, of lower education (San Francisco only) or economic status (San José only), from the non-probability sample, and have a higher baseline SSB consumption. Due to recruiting challenges, 463 participants in our final sample ($21\%$ of the San Francisco sample, $42\%$ of the San José sample) completed the baseline survey within the first 24 days after tax implementation, potentially capturing a baseline beverage intake for SF participants already impacted by the SSB tax, which could underestimate the effect. However, sensitivity analyses excluding them showed no significant differences (Table C in S1 Text), and it therefore appears unlikely that the tax had an effect on beverage intake in the first 3 weeks. With these and sample size considerations, the main analysis was conducted including these participants.
**Table 1**
| Unnamed: 0 | Total | San Francisco | San José | p–Valuea |
| --- | --- | --- | --- | --- |
| | (n = 1,443) | (n = 721) | (n = 722) | |
| Age, years, weighted median (IQR) | 40 (29–55) | 40 (29–55) | 40 (29–55) | 0.087 |
| | n (weighted %) | n (weighted %) | n (weighted %) | |
| Sex | | | | |
| Male | 654 (51.3) | 325 (52.2) | 329 (50.6) | 0.764 |
| Female | 789 (48.7) | 397 (47.8) | 392 (49.4) | |
| Race/Ethnicity | | | | |
| White | 803 (33.2) | 410 (30.2) | 393 (35.2) | <0.001 |
| Asian | 317 (30.3) | 163 (34.3) | 154 (27.6) | |
| Latino | 202 (26.5) | 75 (23.0) | 127 (28.8) | |
| Black | 72 (5.2) | 55 (10.7) | 17 (1.5) | |
| Other | 49 (4.9) | 19 (1.9) | 30 (6.8) | |
| Education | | | | |
| High School or Less | 108 (37.5) | 53 (37.8) | 55 (37.4) | 0.567 |
| Some College | 314 (24.8) | 138 (22.3) | 176 (26.4) | |
| 4–year College | 551 (24.3) | 290 (26.5) | 261 (22.8) | |
| Graduate or Professional School | 470 (13.4) | 241 (13.4) | 229 (13.4) | |
| Federal Poverty Level | | | | |
| Less than 200% | 243 (31.4) | 140 (37.7) | 103 (27.1) | 0.043 |
| 200% or greater | 1,200 (68.6) | 582 (62.3) | 618 (72.9) | |
| Sampling Source | | | | |
| Random digit dialing | 462 (33.7) | 231 (35.5) | 231 (31.1) | 0.459 |
| Address–based | 569 (38.9) | 307 (39.2) | 262 (38.3) | |
| Non–probability | 412 (27.4) | 183 (25.3) | 229 (30.6) | |
To correct for respondent attrition and disproportionate sampling probabilities introduced by the sampling design, baseline sample weights were constructed for the final sample and survival probabilities over F1 and F2 were estimated by logistic models. The baseline sample weights were then multiplied by the corresponding survival weights before post-stratification via raking [39] to adjust the baseline sample weights and provide a closer match between the sample and the population across the post-strata defined by city, sex, race, age, and education groups.
## SSB consumption
The main outcome was daily sugar-sweetened beverage consumption, measured in ounces (29.6 ml). Usual beverage intake was assessed using the Beverage Intake Questionnaire (BevQ-15) a validated instrument which has correlated well with dietary intake studies [40–42]. The BevQ-15, modified for web administration, estimates mean daily intake of 15 beverage types, including water, $100\%$ juice, tea/coffee, milks, alcohol, diet beverages and SSBs. Consumption of other beverages was ascertained by asking, “Did you drink any other kinds of drinks–for example smoothies, kombucha or horchata?”; up to 5 beverages could be specified. When appropriate, these were reclassified as one of the 15 beverage types (e.g., Champagne classified as alcohol). For each type, participants indicated how many times in the past month they drank the beverage on a 13-category scale. Those who drank the beverage more than once a week were asked how much they drank each occasion using 5 size response categories: “less than 6 ounces (<178ml)”, “8 ounces (237 ml), “12 ounces (355 ml)”, “16 ounces (474 ml)” and “More than 20 ounces (>592 ml).” Responses from the two questions were combined to calculate consumption per day in ounces, using 4 oz (118 ml) as the proxy value for “less than 6 ounces” and 20 oz as the proxy value for “More than 20 ounces,” as recommended by the BevQ developer. Average SSB consumption was determined by summing the estimated daily intake of regular soda, sweet tea, sweetened energy drinks, sweetened juice drinks/ades, and qualifying “other” beverages. In addition to a continuous measure of beverage consumption, a dichotomous measure of high and low daily SSB intake was constructed. High SSB consumption was defined as drinking at least 6 oz (178 ml) of SSBs per day, based on the 75th percentile (5.71 oz/169 ml) of baseline consumption rounded to the nearest ounce, and lower consumption as less than 6 oz. To account for possible non-linearities in the relationship between tax exposure and SSB intake, high SSB consumption was also examined using 4 oz, 8 oz, and 12 oz thresholds (Table D in S1 Text).
## Demographics
Demographic variables included continuous age, sex (male, female), and education (less than or some college versus four-year college or more). Race/ethnicity was dichotomized (white or *Asian versus* Black, Latino/a, Native Hawaiian or Pacific Islander, American Indian/Alaska Native, or some other race) according to trends in SSB consumption and demographic composition of the study area. In California. white and Asian populations drink fewer SSB’s on average than other racial/ethnic groups [43], and, together, composed over half of the population in San Francisco (white: $41.2\%$, Asian: $33.5\%$) and San Jose (white: $27.0\%$, Asian: $33.5\%$) during the study period [35]. Federal poverty level, calculated from reported household income range and number of dependents, was categorized into 2 groups (less than $200\%$ versus greater than or equal to $200\%$). The $200\%$ federal poverty level cut-off was used, because slightly below one third ($31.4\%$) of the weighted sample had household incomes below $200\%$ of FPL, which allowed for comparisons of low versus medium/high socioeconomic status. Due to the stability of federal poverty level between waves, missing values at baseline were imputed with values at F1 for 4 participants, and missing values at F1were imputed with values at F2 for 1 participant.
## Outside-city exposure
The number of days spent in San Francisco in the past 30 days was ascertained of all respondents to capture exposure to San Francisco’s SSB tax regardless of respondent’s place of residence. San Francisco residents were asked, “On how many days in the last 30 days were you outside the City of San Francisco?” and San José residents were asked, “On how many days did you go to the City of San Francisco in the past 30 days?” Number of days spent in San Francisco was calculated as the number of days San José residents traveled to San Francisco in the past 30 days, and the remainder of 30 less the days on which San Francisco residents traveled outside of San Francisco in the past 30 days. The number of days spent in San Francisco was then dichotomized as 16 days or greater (more than half) versus 15 days or fewer (less than or equal to half) spent in San Francisco. Cross-border shopping was measured by asking, “In the past year, have you changed the store or stores where you usually buy your non-alcoholic beverages like soda, juice, water, etc. ( not beer, wine, or hard liquor)? If so, did you change to buy more in a store or stores [in/outside of] your current city?”
## Analysis
Weighted frequencies assessed the distribution of sex, race/ethnicity, baseline education, and baseline federal poverty level, overall, by city, by sampling source, and by baseline survey date (prior to January 1st, 2018, versus January 1st-24th). The significance of differences between cities was assessed using Pearson’s chi-2 test of independence. Weighted medians and interquartile ranges of sample age at baseline were determined overall and by city, and the difference between cities was tested using Mood’s median test. Differences across sampling sources and baseline survey date were assessed using the same methods.
## Difference-in-differences GLM analysis 1
Weighted SSB consumption means and $95\%$ confidence intervals (CI’s) were calculated at each time point by city. To detect effects of the SSB tax, we ran a sample weighted generalized linear model (GLM) with a Gamma distribution and a log link and accounted for intra-person correlation using clustered errors to compare the differences in SSB consumption between San Francisco and San José from baseline to years 1 and 2 post-tax implementation. A Gamma GLM was used in light of the right-skewedness, nonnegative nature, and presence of SSB non-consumers in beverage intake measures. The structure of the GLM is below: ln(E[SSBit])=β0+β1t1+β2t2+β3xi+β4(t1*xi)+β5(t2*xi)+βzZit+εit SSBit represents beverage intake for individual i at time t. Two dummy-coded time variables representing 1-year (t1) and 2-years (t2) post tax, an indicator for respondent’s city (xi), and interactions between the two dummy-coded time variables and city were included in the model. We controlled for age, sex, race/ethnicity, education, federal poverty level, and sampling source (represented as Zit vector above). Estimated marginal means of SSB consumption for both cities at each time point were calculated based on adjusted model results using Stata’s margins command [44].
## Difference-in-differences GLM analysis 2
Weighted proportions and $95\%$ CIs of high SSB consumption were calculated at each time point by city. To compare differences in probability of high SSB consumption (6 oz/178 ml or more daily) pre- and post-tax implementation between cities, we modeled the dichotomized high/low SSB consumption outcome using a binomial GLM with a logit link. As before, the model accounted for intra-person correlation using clustered errors and controlled for the same covariates as in the Gamma GLMs. Average predicted probability estimates were calculated based on adjusted model results using Stata’s margins command [44]. This model was also used to assess likelihood of change in cross-border shopping pre- and post-tax implementation.
## Difference in-differences across socioeconomic status and race/ethnicity GLM analysis 3
To examine whether difference-in-differences of SSB consumption between cities varied by race/ethnicity or socioeconomic status, the Gamma and binomial GLMs were rerun three times with additional 3-way interactions between the year, city, and one of the following variables: race/ethnicity, educational attainment, and federal poverty level. We examined whether the difference-in-differences of SSB consumption varied by sampling source using the same method.
## Sensitivity analyses
Sensitivity analyses were run using both two-way interaction models, exchanging city of residence with the dichotomized days spent in San Francisco in the past 30 days as the measure of exposure to SSB taxes (<16 days versus ≥16 days in past 30 days) to model the cumulative exposure to the SSB tax from pre- to 2-years post tax. One hundred and thirty-one respondents were excluded from the sensitivity analysis (SF: $$n = 119$$, SJ: $$n = 12$$), because they changed tax exposure categories between year 1 and year 2 post-tax implementation, i.e., they spent 16 days or more in San Francisco at the F1 and fewer than 16 days at F2, or vice-versa. All analyses were performed using Stata 16.1 [45].
These sensitivity analyses exchanged city of residence with the dichotomized days (<16 versus ≥ 16 days) spent in San Francisco in the past 30 days as the measure of exposure to SSB taxes in the main Gamma and binomial GLM’s. In the analyses, 505 of the 602 San Francisco residents spent 16 days or more days in San Francisco compared to 5 of the 710 San José residents. The sensitivity analyses produced findings consistent with both SSB consumption (ounces) and high SSB consumption models (Tables G and H in S1 Text).
## Results
Characteristics of the sample can be seen in Table 1. San Francisco participants were more likely to live under the federal poverty level, more likely to identify as Black, and less likely to identify as another race when compared to San José participants. Across sampling sources, the only statistically significant difference in characteristics was that the ABS sample had slightly higher median age (42 years) than the RDD (38 years) and NP (39 years) samples (Table A in S1 Text). Participants who took the baseline survey prior to January 1, 2018 (tax implementation date) were significantly younger than those who took the survey between January 1st and 24th (median age in SF at baseline: pre-January 1st = 38 versus post-December 31st = 46; in SJ: pre-January 1st = 37 versus post-December 31st = 44). No other significant demographic differences were found between these two groups (Table B in S1 Text).
## Difference-in-differences GLM analysis 1 for SSB consumption
At baseline (B), the unadjusted weighted sample average of daily SSB consumption was 10.87 oz (322 ml) ($95\%$ CI: 7.95, 13.78) in San Francisco and 7.26 oz (215 ml) ($95\%$ CI: 5.59, 8.93) in San José; one-year post-tax, average consumption had declined to 8.56 oz (253 ml) ($95\%$ CI: 6.01, 11.11) in San Francisco and 6.78 oz (201 ml) ($95\%$ CI: 4.71, 8.84) in San José; and two-years post-tax, average consumption was 6.42 oz (190 ml) ($95\%$ CI: 4.19, 8.65) in San Francisco and 6.32 oz (187 ml) ($95\%$ CI: 4.65, 7.98) in San José, a $40.9\%$ decline in San Francisco (-4.45 oz, $$p \leq 0.003$$) and $13\%$ (-0.94, $$p \leq 0.397$$) in San José, a non-significant difference-in-differences of $27.9\%$ between cities between baseline and F2. Table 2 presents unadjusted and adjusted arithmetic mean ratios (AMRs; exponentiated coefficients) and $95\%$ Cis from the Gamma GLM assessing the difference-in-differences in SSB consumption. In the adjusted model, San Francisco residents consumed significantly more SSBs daily, on average, than residents of San José at each time point. While there was a marked downward trend in SSB consumption post-tax that was greater in San Francisco, the difference-in-differences between the cities was non-significant (year 1: adjusted AMR = 0.79, $$p \leq 0.308$$; year 2: adjusted AMR = 0.79, $$p \leq 0.224$$). Predicted mean SSB consumption decreased 2.36 oz more in San Francisco than San José between baseline and 1 year post-tax and 2.40 oz more between pre-tax and 2 years post-taxed. Predicted mean consumption with $95\%$ CIs pre- and post-tax are displayed in Fig 1. In San Francisco, SSB consumption declined by $28.0\%$ from baseline to 1-year post-tax (-3.02 oz, $$p \leq 0.001$$) and by $34.1\%$ at 2 years post-tax (-3.68 oz, $$p \leq 0.004$$). In San José, predicted mean SSB consumption declined by $8.5\%$ 1-year post-tax (-0.66 oz, $$p \leq 0.668$$) and by $16.5\%$ (-1.29 oz, $$p \leq 0.157$$) at 2 years post-tax, a non-significant difference of $17.6\%$ in change over time by year 2 between cities. When excluding the 463 participants who took the baseline survey within the first month of tax implementation, the adjusted Gamma GLM’s yielded results consistent with those of the full sample with respect to the difference-in-difference analysis (Table C in S1 Text).
**Fig 1:** *Predicted mean consumption of sugar-sweetened beverages (ounces) with $95\%$ confidence intervals in San Francisco and San José before, one, and two years after San Francisco’s sugar sweetened beverages tax implementation ($$n = 1$$,443).Note: Results based on Gamma GLM with 2-way interactions between time (pre-tax, 1-year post-tax, and 2 years post-tax) and city (San Francisco, San José), controlling for age, sex, race/ethnicity, education, federal poverty level and sampling source.* TABLE_PLACEHOLDER:Table 2
## Difference-in-differences GLM analysis 2 for high SSB consumption
At baseline, based on the unadjusted model, $37.3\%$ ($95\%$ CI: 29.6, 44.9) of San Francisco respondents and $28.7\%$ ($95\%$ CI: 22.3, 35.2) of San José respondents were high SSB consumers (≥ 6 ounces/178 ml daily); one-year post-tax, $33.0\%$ ($95\%$ CI: 25.6, 40.4) of San Francisco and $29.0\%$ ($95\%$ CI: 22.3, 35.7) of San José respondents were high consumers; and two-years post-tax $22.7\%$ ($95\%$ CI:16.0, 29.5) of San Francisco and $28.2\%$ ($95\%$ CI: 21.3, 35.1) of San José respondents were high consumers. The overall unadjusted two year $14.5\%$ decline for San Francisco residents and $1\%$ decline in San José were significantly different. In the adjusted binomial GLM, between the baseline and 1-year post-tax, there were no significant changes in odds of high SSB consumption in either San Francisco or San José (AOR[main effect] = 1.52, $$p \leq 0.080$$; AOR[interaction] = 0.79, $$p \leq 0.440$$). After 2 years, the odds of high intake decreased significantly for San Francisco residents and significantly more for San Francisco than for San José residents (AOR[interaction] = 0.49, $$p \leq 0.031$$; Table 3). Average adjusted predicted probabilities of high intake with $95\%$ CIs pre- and post-tax are displayed in Fig 2. In San Francisco, the probability of consuming greater than 6 ounces (178 ml) per day decreased by $4.3\%$ pre-tax to 1-year post-tax and decreased by $13.6\%$ at 2 years post-tax. In San José, the probability of consuming 6 or more ounces (178 ml) per day increased less than $1\%$ by 1-year post-tax and decreased by less than $1\%$ at 2 years post-tax, a difference in change over time of $13.2\%$ between cities. In sensitivity analyses examining high consumption using alternative thresholds for “high”, and in those excluding respondents from the first month of implementation, the adjusted binomial GLM’s for Year 2 yielded AOR’s for the interactions that were similar in magnitude and direction to those for the analysis using the 75th percentile threshold; however, significance was not maintained in several models (Tables D and E in S1 Text).
**Fig 2:** *Average adjusted predicted probability of high SSB consumptiona with $95\%$ confidence intervals in San Francisco and San Jose before, one, and two years after San Francisco’s sugar sweetened beverages tax implementation ($$n = 1$$,443).Note: Results based on binomial GLM with 2-way interactions between time (pre-tax, 1-year post-tax, and 2 years post-tax) and city (San Francisco, San José), controlling for age, sex, race/ethnicity, education, federal poverty level and sampling source. aHigh SSB consumption refers to daily SSB consumption of ≥ 6 ounces per day.* TABLE_PLACEHOLDER:Table 3
## Difference-in-differences across socioeconomic status and race/ethnicity GLM analyses 3
In the main binomial GLM analysis, we found that the odds of high intake decreased significantly for San Francisco residents only. The results of the 3-way-interaction model between federal poverty level (less than $200\%$ versus greater than or equal to $200\%$), city, and time suggest that this difference-in-differences between cities pre- to 2 years post-tax is far greater among those living under $200\%$ of the federal poverty level than those living at $200\%$ or above (AOR[interaction] = 0.12, $$p \leq 0.010$$; Table F in S1 Text). The predicted probabilities of this model are presented in Table 4. Among those living under $200\%$ of the federal poverty level, the average predicted probability of high SSB consumption decreased $23.6\%$ in San Francisco and increased $17.7\%$ in San José, yielding a difference-in-differences of $41.2\%$. Among those living at $200\%$ or greater of the federal poverty level, there was less than 1 percentage point difference-in-differences.
**Table 4**
| Unnamed: 0 | Pre-tax | 1 Year post-tax | 2 Years post-tax |
| --- | --- | --- | --- |
| Income as % Federal Poverty Level | Average probability estimate (95% CI) | Average probability estimate (95% CI) | Average probability estimate (95% CI) |
| Less than 200% | | | |
| San Francisco | 45.29 (31.81,58.78) | 39.71 (25.08,54.34) | 21.73 (10.29,33.18) |
| San José | 28.19 (15.19,41.20) | 29.77 (18.57,40.97) | 45.86 (28.47,63.24) |
| 200% or greater | | | |
| San Francisco | 32.35 (25.44,39.26) | 28.64 (21.00,36.28) | 24.32 (15.94,32.70) |
| San Jose | 28.55 (21.60,35.49) | 28.69 (21.08,36.31) | 21.17 (14.61,27.73) |
The three-way interaction binomial models examining probability of high SSB consumption for education and race/ethnicity had non-significant interactions (not shown).
In addition, for the continuous outcome of SSB consumption measured in ounces, the Gamma GLMs examining change over time with 3-way interactions between demographic (federal poverty level, race/ethnicity, and education), year, and city were non-significant, suggesting that the difference-in-differences of overall SSB consumption between cities did not vary significantly by socioeconomic status or race/ethnicity (not shown). We found no evidence that the difference-in-differences in the Gamma and binomial models varied by sample source (not shown).
## Cross border shopping
San Francisco residents were significantly more likely to report changing to buy more at locations outside their city compared to their San José counterparts at every time point ($4.4\%$ vs $1.3\%$ at baseline; $6.1\%$ vs. $0.9\%$ at F1; and $3.9\%$ vs $2.0\%$ at F2). However, the difference-in-differences of the adjusted binomial GLM over time was not significant from pre-tax to year 1 (AOR[interaction] = 2.11, $$p \leq 0.387$$) or year 2 post-tax (AOR[interaction] = 0.55, $$p \leq 0.511$$), suggesting the SSB tax did not significantly affect rate of change in cross-border shopping practices (Table I in S1 Text).
## Discussion
The difference-in-differences findings at two years in the studied areas are supportive of the effectiveness of a one cent per ounce tax on SSBs in reducing high SSB consumption. Average SSB intake fell significantly in San Francisco at one and two years post tax, but difference in differences between cities was not significant. Our findings provide stronger evidence of effectiveness in reduction of SSB consumption, though limited to high consumption, than the findings of Cawley and Frisvold who did not find change in the combined Oakland, San Francisco and *Seattle data* in the first six months post tax [22]. Findings of greater reductions in lower-income consumers are consistent with greater sensitivity to price changes among this population and with tax evaluation findings from Mexico, Thailand and Catalunya [29,46,47]. In contrast, in Chile, with high sugary beverages (≥6.25g/100ml) taxed $8\%$ more than less sugary beverages, both Nakamura and Caro found decreases in SSB purchase volumes (of -$21.6\%$ and—$3.4\%$, respectively) that were greater in higher socioeconomic groups [48,49].
Our findings suggest that the likelihood of being a “high” SSB consumer, even at the level of six ounces (178 ml) per day, declined significantly more in these San Francisco neighborhoods than in those of San José between the baseline and the end of the second year of the tax. Of note, in these relatively low SSB consuming cities, the top quartile of SSB consumption was above 6 ounces, similar to Sánchez-Romero (≥one serving per day) [26] but lower than the highest mortality quartile of Anderson’s United Kingdom study assessing SSB consumption and mortality risk (>2 servings a day) [4]. Here, the difference-in-differences was more pronounced amongst those living in poverty, below $200\%$ of the federal poverty level, compared with those living at least $200\%$ above the federal poverty level, consistent with findings from Mexico [46]. Differences by city over time did not significantly vary based on race/ethnicity or education, although those factors were significantly associated with likelihood of being a high SSB consumer. This study did not examine the role of mediating factors such as attitudes towards SSBs and perception of harm, which may also be factors influencing behavior change. There was no significant difference-in-differences in change to cross-border shopping reported, a factor which can influence both tax impact on SSB intake and tax revenue for taxes limited to a city. Cross-border shopping may have been constrained by both San Francisco’s water-bound geography and the fact that three nearby cities also tax SSBs. While this study was limited to the outcome of self-reported SSB consumption, further research using different conceptual approaches and assessment of longer-term health outcomes are needed to fully assess these policies.
## Strengths
Strengths of the study include the longer time frame of two years post-tax, the use of individual consumption data, the large sample size, the assessment of potential moderating factors, and oversampling of Blacks and Latinos who are typically higher consuming racial and ethnic groups that constitute a small but important part of the intervention and control city populations. Use of individual consumption data may also help capture the impact of the tax in restaurant and other settings not reflected in studies based on electronic sales data.
## Limitations
Nevertheless, certain limitations should be noted. First, because the sample was primarily drawn from neighborhoods with greater density of Black and Latino residents, it is therefore not representative of the two cities as a whole. Second, the control city, San José, is close to the intervention site (64 km) and other nearby Bay Area cities with newly adopted tax policies, sharing certain media markets, distribution systems and public health practices. This could potentially lead to underestimation of differences in tax effects if there was regional pricing of beverages by chain retailers, for example. San José may also have shared the “risk signaling” effect of the Bay Area SSB tax measures [50]. Cawley and Frisvold found greater difference-in-differences using a broader synthetic national control than controls in areas adjacent to taxing cities [22]. Similarly, if social norms around beverage consumption in the region underwent broader shifts because of the taxes, it could reduce the difference-in-differences. Other policies were also put into place during an overlapping time period, including elimination of SSB sales at a major employer and on city property in San Francisco, and educational campaigns continued in both cities. *In* general, there has been a secular trend in California towards reduced consumption of SSBs between 2011 and 2018 in all age groups, and a $10\%$ decline in calories nationally from non-alcoholic beverages between 2014 and 2020 [51,52]. This may be a result of taxation policies, threat of taxation, education, or other SSB policy interventions and consequent shifts in product portfolios. Thus, results may be conservative. Baseline data collection extended 23 days into the initial implementation period of the tax to collect sufficient respondents, which could also lead to underestimation of effect, although sensitivity analyses does not suggest that to be the case.
Recruitment challenges for this time sensitive work necessitated a complex sampling design. For these reasons we applied weighting to account for the various sample sources and focused solely on change over time in those respondents who were captured on follow-up at one or more time points. While baseline participants from the non-probability sample were more likely to drop out, sampling source-based attrition rates did not differ by city. We also found no evidence of difference-in-differences findings varying by sampling source. We accounted for the differential attrition by predicting the probability of continuing participation at each wave by city, demographic characteristics and their SSB consumption and then inversely weighting the estimated probabilities before the post-stratification. Despite this, we recognize there may be still concern that the final sample is less representative of our population of interest because of this type of attrition. Consumption is based on self-reported estimates of the preceding month’s consumption, and though other U.S. data have shown month-to-month SSB consumption to be relatively stable [53], monthly recall may be less reliable than 24-hour dietary intake. The lack of statistical significance in the overall consumption outcome despite reasonable sample size also highlights the difficulties in using self-reported consumption and survey research to assess these policies. With high cost and declining response rates [36,54], using telephone or mail survey methods to detect significant changes over time and between locations can be challenging for a behavior with high variance. Use of sales-based electronic data may offer greater power and a more reliable picture of SSB-related changes in a geographic area but is limited in its ability to permit sub-group or individual-level analyses and may provide less complete capture of sites of purchase or consumption. It is also notable that these are two cities with far lower proportions of racial and ethnic groups with high SSB consumption than Philadelphia, which may account for variation in policy impact across cities. This study did not examine the justice of the economic burden or benefits of the tax, but the significant intake reduction in low-income high consumers suggests potential benefit for those at highest risk for health harms.
Finally, this study reflected a relatively modest size tax, limited to sugar sweetened beverages above 25 kcal per 12 ounces, and not tiered for sugar content, and therefore cannot be generalized to the wide variety of taxation models in use globally. Notably, the World Health Organization has recommended SSB taxes of $20\%$ or greater to prevent noncommunicable disease [55].
## Policy implications
This study contributes to the growing body of literature assessing impact of sugar sweetened beverage tax policies. It suggests that even a modest size tax can be effective in reducing high consumption, especially for those of lower income, and that reduction grows over two years, but that higher taxation and/or additional taxation strategies may be necessary to significantly reduce population wide SSB consumption patterns.
## Conclusion
In summary, two years after implementation, the modest San Francisco SSB tax appears to be associated with significantly greater reductions in the likelihood of being a high SSB consumer in the studied neighborhoods, especially amongst those living in poverty. A -$34.1\%$ change in overall consumption of SSBs in ounces in San *Francisco versus* a -$16.5\%$ change in San José by year two was a non-significant difference-in-differences. Longer periods of study may be valuable for assessing tax effects.
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|
---
title: 'Mortality rate and cause among people with intellectual disabilities in South
Korea: A nationwide representative cross-sectional study from 2015 to 2019'
authors:
- Ye-Soon Kim
- Joo-Hee Kim
- Sooyoung Kwon
- Seunghee Ho
journal: PLOS Global Public Health
year: 2022
pmcid: PMC10021350
doi: 10.1371/journal.pgph.0000744
license: CC BY 4.0
---
# Mortality rate and cause among people with intellectual disabilities in South Korea: A nationwide representative cross-sectional study from 2015 to 2019
## Abstract
People with intellectual disabilities (ID) age faster and have a higher prevalence of degenerative diseases. The aim of this study was to identify the patterns/ causes of death among people with ID. We conducted a nationwide, retrospective, cross-sectional study of people with ID in South Korea. The database was compiled by merging data of people registered with ID, based on the 2015–2019 Standards from the Ministry of Health and Welfare, with the cause of death data published by the Korea National Statistical Office. The International Classification of Diseases-10 (ICD-10) was used to categorize causes of death. The mortality and standardized mortality rates were calculated and major causes of death were analyzed. As of 2019, the mortality rate of people with ID in South Korea was 784.6. The rate showed an irregular pattern, increasing or decreasing year by year (increasing from 725 in 2015 to 834 in 2018). The mortality rate of people with ID was approximately 1.4 times higher than the general population; the standardized mortality rate was 3.2 times higher. The main causes of death ($48\%$) in people with ID were, in order of prevalence, circulatory diseases (cerebrovascular disease), neoplasms (malignant neoplasms of the digestive system), and diseases of the respiratory system (pneumonia). The leading causes of death (>$60\%$) in the general population were, in order of prevalence, neoplasms, diseases of the circulatory system, and respiratory system diseases. An accurate understanding of the causes of death of people with ID is important to promote the development and application of health promotion programs and management thereof.
## Introduction
As of 2020, the number of people with intellectual disabilities (ID) in South Korea was 217,108 as registered by the South Korean government [1]. As with the general population, the average age at death of people with ID has increased in recent years. In South Korea, the average age at death of people with ID was 46.4 years in 2008. The average age at death increased by 0.8 years each year, and the average age reached 55.9 years in 2018 [2]. However, at the national level, worldwide, the mortality rate, death pattern, and cause of death in people with ID have not been actively studied [3]. There are only few studies on deaths of people with ID in South Korea. In addition, to the best of our knowledge, there have been no systematic reviews or meta-analyses of the causes of death in people with ID. A small number of related studies, identified through a search engine, were conducted on the death rate of deaf individuals or of all persons with disabilities and specific causes of death [4–8]. Previous studies on the mortality rate and cause of death of people with ID have assessed diverse target groups, study designs, and data analyses. The design of previous studies was of cross-sectional and cohort, and the study subjects were mainly the elderly age group [9–11]. Therefore, it is difficult to draw conclusions about the mortality rates and causes of death of people with ID, which are defined differently. People with ID are reported to age faster than the general population and they have more chronic and degenerative diseases [9–12]. It has also been reported that people with ID have a higher prevalence of mental health problems [13–15].
The Korean government has implemented a system to register and manage people with disabilities [16]. The South Korean government is probably one of the few countries with a system to register and manage all people diagnosed with ID. The system of “Bokjiro,” a welfare portal of the Ministry of Health and Welfare, includes persons with ID who receive services and support under the Welfare Act for Persons with Disabilities. The “Bokjiro” system contains personal information about people with ID. However, the death certificate of the National Statistical Office of Korea does not have a variable that can identify people with disabilities. A database of the causes of death of those with ID was established by linking information registered in the “Bokjiro” system, a population of people with ID in Korea, and death data from the National Statistical Office. The Korean government enacted the “Act on the Protection and Support of the Rights and Support of Persons with Developmental Disabilities Act”(Developmental Disabilities Act) in November 2015 and the “Act on the Guarantee of the Rights to Health and Medical Access for Persons with Disabilities Act” (Health Rights Act for Persons with Disabilities) in December 2017 [17, 18]. It is expected that the environment and life expectancy of people with ID will change due to the enactment of the Health Rights Act for Persons with Disabilities. Considering these positive social developments and changes in the living environments, it is estimated that there will be changes in the mortality rate and cause of death of people with ID in the future. In addition, it is desirable to know whether there is a difference in mortality rates between people with ID and the general population.
This study primarily aimed to determine the mortality rate of people with ID in comparison with that of the general population in South Korea. People with ID are expected to have higher mortality rates than the general population. The secondary objective was to provide internationally comparable standardized mortality data of people with ID in South Korea.
## Data source
We obtained data on the people with a registered disabilities by the Ministry of Health and Welfare, from the end of the 2015–2019 period. In addition, data on the cause of death by the National Statistical Office were merged to establish a cause of death database for people with disabilities. A person with a registered disability is a person with a disability who, in accordance with Article 32 of the Welfare Act for the Disabled, or their legal representative or guardian prescribed by Presidential Decree, has registered their disability status and other matters prescribed by the Ordinance of the Ministry of Health and Welfare with the Special Self-Governing City Mayor, Special Self-Governing Province Governor, Si/Gun/Gu head [16]. The registration data for persons with disabilities include major variables such as gender, age, major type of disability, and comprehensive disability grade. The date, age, and cause of death are coded in the cause of death database for the disabled. The causes of death of the disabled were listed using the International Classification of Diseases-10 (ICD-10) codes. The cause of death information was recorded on the death certificate, and the World Health Organization (WHO) 103 classification criteria were used. This study used secondary data of deceased individuals. Therefore, it was practically impossible to obtain the consent of the research subjects during the course of the study, so the written consent of the research subjects was exempted. We study was approved by National Rehabilitation Institute Clinical Research Review Committee (NRC-2012-04-026).
## Statistical analysis
We presented descriptive statistics for all-cause mortality and mortality in people with ID. Mortality rate was calculated from the crude and age-adjusted mortality rates of people with ID in comparison with those of the general population in South Korea. The crude mortality rate is the ratio of the number of deaths during the year to the average population in that year. This value is expressed per 100,000 people. The age-adjusted mortality rate is the ratio of the number of deaths in a given age group to the population in that age group, expressed per 100,000 people. The age-adjusted mortality rate is expressed as standardized mortality rate (SMR). Data were analyzed using SAS 9.4 statistical software (SAS Institute Inc, Cary, NC).
## Demographic characteristics of people with intellectual disabilities and the general population
In South Korea, the mortality rate in people with ID was 794.9 in 2015 and 784.6 in 2019. The mortality rate for the general population was 541.5 in 2015 and 574.8 in 2019. The survey mortality rate was approximately 1.4 times higher for people with ID than for the general population. The mortality rate in males was high in both groups. Among the deaths of people with ID, those aged 0–59 years accounted for more than $50\%$ of deaths of people with ID ($61.98\%$ in 2015 and $56.32\%$ in 2019). However, in deaths of the general population, those aged 0–59 years accounted for less than $20\%$ of deaths in the general population. Among the deaths of people with ID, deaths of those aged ≥60 years increased from approximately $38.02\%$ in 2015 to $43.69\%$ in 2019. Among the general population, deaths of those aged ≥60 years decreased from approximately $90.58\%$ in 2015 to $83.48\%$ in 2019 (Table 1). People with ID, especially the elderly, had an increased mortality rate. An aging population is believed to be one of the reasons for this finding.
**Table 1**
| Unnamed: 0 | Deaths among people with intellectual disability (ID) | Deaths among people with intellectual disability (ID).1 | Deaths among people with intellectual disability (ID).2 | Deaths among people with intellectual disability (ID).3 | Deaths among general population (GP) | Deaths among general population (GP).1 | Deaths among general population (GP).2 | Deaths among general population (GP).3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | 2015 | 2015 | 2019 | 2019 | 2015 | 2015 | 2019 | 2019 |
| | n (%) | CMR | n (%) | CMR | n (%) | CMR | n (%) | CMR |
| Total | 1486 | 794.9 | 1646 | 784.6 | 275895 | 541.5 | 295110 | 574.8 |
| Sex | Sex | Sex | Sex | Sex | Sex | Sex | Sex | Sex |
| Males | 961 (64.67) | 852.4 | 1,076 (65.37) | 850.9 | 150,449 (54.53) | 591.0 | 160,322 (54.33) | 626.0 |
| Females | 525 (35.33) | 707.5 | 570 (34.63) | 683.9 | 125,446 (45.47) | 492.1 | 134,788 (45.67) | 523.9 |
| Age group | Age group | Age group | Age group | Age group | Age group | Age group | Age group | Age group |
| 0–19 years | 67 (4.51) | 150.6 | 49 (2.98) | 109.6 | 2,589 (0.94) | 24.8 | 2,027 (0.69) | 21.9 |
| 20–39 years | 284(19.11) | 371.4 | 249 (15.13) | 282.5 | 8,052 (2.92) | 56.2 | 7,466 (2.53) | 53.7 |
| 40–59 years | 570 (38.36) | 1084.8 | 629 (38.21) | 1049.2 | 42,914 (15.55) | 253.4 | 39,211 (13.29) | 232.6 |
| 60–79 years | 496 (33.38) | 3868.0 | 606 (36.82) | 3195.0 | 109,661 (39.75) | 1378.4 | 107,733 (36.51) | 1128.0 |
| 80+ years | 69 (4.64) | 11201.3 | 113 (6.87) | 10700.8 | 112,638 (50.83) | 8584.7 | 138,602 (46.97) | 7833.7 |
| Duration of disability | Duration of disability | Duration of disability | Duration of disability | Duration of disability | Duration of disability | Duration of disability | Duration of disability | Duration of disability |
| < 10 years | 414 (27.86) | 532.0 | 266 (16.16) | 401.3 | | | | |
| 10–19 years | 746 (50.20) | 914.7 | 801 (48.66) | 823.6 | | | | |
| 20+ years | 325 (21.87) | 1291.7 | 579 (35.18) | 1174.9 | | | | |
Table 2 shows the mortality rates for each group from 2015 to 2019. The mortality rate of people with ID showed an irregular pattern, increasing or decreasing year by year, increasing from 725 in 2015 to 834 in 2018. By contrast, the survey mortality rate of the general population increased from 541 in 2015 to 582 in 2018, but it decreased slightly in 2019. The mortality rate in people with ID was 1.4 times higher than that of the general population, while the SMR was 3.2 times higher than that of the general population.
**Table 2**
| Years | Deaths among people with intellectual disability (ID) | Deaths among people with intellectual disability (ID).1 | Deaths among people with intellectual disability (ID).2 | Deaths among general population (GP) | Deaths among general population (GP).1 | Deaths among general population (GP).2 |
| --- | --- | --- | --- | --- | --- | --- |
| Years | N | CMR | SMR | N | CMR | SMR |
| 2015 | 1486 | 794.9 | 1105.5 | 275895 | 541.5 | 347.6 |
| 2016 | 1578 | 820.1 | 1138.4 | 280827 | 549.4 | 337.2 |
| 2017 | 1436 | 725.2 | 988.2 | 285534 | 557.3 | 324.3 |
| 2018 | 1701 | 834.8 | 1080.2 | 298820 | 582.5 | 322.6 |
| 2019 | 1646 | 784.6 | 987.7 | 285110 | 574.8 | 305.4 |
## Causes of death in people with intellectual disabilities and the general population by year
Tables 3 and 4 present the number of deaths by cause, mortality rate, and SMR for people with ID and the general population. The main causes of death in people with ID were, in order of prevalence, circulatory diseases, neoplasms, and respiratory diseases. The main causes of death in the general population were, in order of prevalence, neoplasms, circulatory diseases, and respiratory diseases. These top three causes of death accounted for >$60\%$ of the total deaths in the general population. Among the major causes of death for people with ID, circulatory disease ranked first in the top five causes for the past five years, but the top second and third causes showed a change.
Most of the causes of death in people with ID had a higher mortality rate than that in the general population. However, in the case of neoplasms, the general population had a higher mortality rate than people with ID. The SMRs for all causes of death were higher in people with ID than in the general population. In addition, while the general population showed a certain pattern in which most causes of death increased or decreased, most of the causes of death in people with ID showed an irregular pattern. Among the causes of death in people with ID, only digestive diseases (K00-K93) showed a decreasing pattern of mortality.
Among the causes of death, congenital anomalies, deformities, and chromosomal abnormalities (Q00-Q99) showed the largest difference. The difference in mortality rates was more than 20 times between people with ID and the general population, followed by diseases of blood and hematopoietic organs and immune mechanisms. The next rankings were specific disorders (D50-D89); neurological diseases (G00-G99); skin and subcutaneous tissue diseases (L00-L99); and pregnancy, childbirth, and postpartum (O00-O99).
## Three leading causes of death within each elected major cause of death, based on ICD-10 categories among people with intellectual disabilities
Based on the ICD-10 codes, three major causes of death were additionally analyzed within each major cause of death in people with ID (Tables 5–7). Cerebrovascular disease (I60-I69) was the most common cause of death among people with ID who died due to diseases of the circulatory system. Other forms of heart disease (I30-I52) and ischemic heart disease (I20-I25) followed in prevalence. Conversely, acute rheumatic fever (I00-I02), chronic rheumatic heart disease (I05-I09), diseases of the veins, lymphatic vessels, and lymph nodes not elsewhere classified (I80-I89), and other unspecified disorders of the circulatory system (I95-I99) were infrequent. The mortality rates for ischemic heart disease (I20-I25), other forms of heart disease (I30-I52), and cerebrovascular disease (I60-I69) in 2015 were 27.3, 31.6, and 76.0, respectively. These diseases showed an irregular pattern of increasing or decreasing each year. Disease showed an irregular pattern when comparing 2015 and 2019, and the mortality rates in 2019 were 22.4, 39.6, and 62.9, respectively. There were decreased rates of cerebrovascular disease (I60-I69) and ischemic heart disease (I20-I25), but other forms of heart disease (I30-I52) showed an increasing pattern.
In addition, neoplasms of the digestive organs (C15-C26) and respiratory and intrathoracic organs (C30-39) were common among people with ID who died due to neoplasia. This was followed by malignancies of the breast (C50), female reproductive system (C51-C58), eyes, brain and other parts of the central nervous system (C69-C72), lymph, malignant neoplasms, stated or presumed to be primary, of lymphoid, hematopoietic and related tissues (C81-96), etc. In addition, there were no deaths due to independent (primary) multi-site malignant neoplasms (C97). Neoplasms of the digestive tract (C15-C26) and respiratory and intrathoracic organs (C30-C39) were reported from 2015 to 2017. Conversely, breast neoplasms (C50) showed an increasing trend from 2015 to 2017 which then decreased from 2017 to 2019 (Table 6).
Influenza and pneumonia (J09-J18) were the most common diseases among people with ID who died due to respiratory system diseases. Next, our analysis showed that chronic lower respiratory disease (J40-J47) and lung disease caused by external factors (J60-J70) were the most frequent. Acute upper respiratory tract infection (J00-J06), other acute lower respiratory tract infections (J20-J22), and other diseases of the upper respiratory tract (J30-J39) were rarely found among the causes of death of people with ID. Influenza and pneumonia (J09-J18) accounted for more than $70\%$ of the deaths of people with ID, showing an irregular pattern of increasing or decreasing year by year. Conversely, the mortality rate for chronic lower respiratory diseases (J40-J47) decreased every year from 12.3 in 2015 to 6.7 in 2019. The mortality rate of pulmonary diseases (J60-J70) caused by external factors increased every year until 2018, but declined in 2019 (Table 7).
## Discussion
This study considered the death patterns and specific causes of death of people with ID using the national database of causes of death for people with disabilities in South Korea. We investigated death rates and causes of death of people with ID who were registered with the national database from 2015 to 2019.
Our results revealed that the mortality rate of people with ID was approximately 1.4 times higher (SMR: 3.2 times higher) than that of the general population, and that the average age at death was low. Similar results were reported in the United States [19], Canada [20], United Kingdom [21], Australia [22], and Sweden [15, 23]. Males with ID had higher mortality and mortality rates than females [15, 20, 22, 23]. In the general population, the mortality rate in males was higher than that in females. Approximately $61.98\%$ of the deaths of people with ID occurred in those aged <60 years, and the mortality rate was high among young people. This is consistent with the findings of Schoufour et al. [ 20] and Reppermund et al. [ 22].
Our study further examined the causes of death among people with ID in South Korea. The leading cause of death in people with ID was diseases of the circulatory system. Furthermore, circulatory system diseases among people with ID were more common than those among the general population. A similar observation was reported by a previous study, which revealed diseases of the circulatory system as the leading cause of death in people with ID [15]. In the current study, malignant neoplasm was the second most common cause of death in people with ID, followed by respiratory diseases. Studies by Patja et al. [ 24] (Finnish), Glover et al. [ 21] (United Kingdom), and Hirvikoski et al. [ 25] (Swedish) reported similar results on the common causes of death in people with ID.
Our study aimed to elucidate the major causes of death in people with ID. In addition, we compared the data of people with ID to those of the general population. The top three causes of death of people with ID in South Korea were circulatory system diseases, neoplasm, and diseases of the respiratory tract. A United Kingdom national population-based study found that adults with ID had the highest number of diseases in the order of circulatory system, respiratory system, neoplasm, nervous system, and digestive system [21]. In Sweden, the population with ID aged>55 years had the highest number of diseases in the order of circulatory system, respiratory system, neoplasia, nervous system, and mental and behavioral disorders [15]. According to data from the Australian cohort database for ID, the circulatory system, neoplasms, nervous system and sense organ disorders, and respiratory system rank highest [26]. In the Netherlands, population with ID aged>50 years had the highest number of diseases in the order of respiratory system, neoplasia, and circulatory system [27]. In addition, the South Korean population with ID had a higher mortality rate from all diseases than the general population. The rate was particularly high in patients with cerebrovascular diseases. Studies have shown that the population with ID is more vulnerable to cerebrovascular disease risk factors, morbidity, and mortality [28, 29].
This study used the data of people with ID who were registered in South Korea. Only few countries have a registry of persons with disabilities at the national level [15]. This study fills the gap in knowledge about the causes of death among people with ID. The main causes of death among people with ID could be more accurately identified by linking Korea’s data on registered persons with disabilities with data on the causes of death from the National Statistical Office. The death pattern of this population could be analyzed through the constructed ID death database, which could be helpful in health care planning for people with ID in the future. Considering the increasing evidence in this field, a systematic review or analysis of the pattern of causes of death in the life cycle of people with ID is necessary.
## Limitations
Although a database for the cause of death for people with ID in Korea was established, the database for the cause of death for the non-disabled could not be established. As such, data from the general population were used instead of the non-disabled; nence, it appears to be a database that includes people with ID. However, registered persons with disabilities, including those with ID, comprise approximately $5\%$ of the Korean population. Finally, it can be said that there is no significant difference between the non-disabled death database and the general population death database. Therefore, the incidence and hazard risk of ID cannot be calculated owing to the characteristics of the data. Korea has a system in place for registering persons with ID. However, it is impossible to identify persons with ID who are not registered. In addition, children with ID may have been omitted from the registry. In the case of children, it is presumed that the lack of registration may be due to parents’ reluctance to register their children.
## Conclusions
Our study showed that the mortality rate for people with ID in South Korea was 1.4 times higher than that of the general population. In addition, our findings revealed that the average age at death of people with ID was low, meaning that these individuals had a shorter lifespan. The main causes of death in people with ID were circulatory disease (mainly cerebrovascular disease), neoplasm (digestive system neoplasm), and respiratory disease (mainly pneumonia). For the health management of people with ID, it is necessary to understand the cause of death statistics and to develop and apply customized health maintenance and management programs based on the data.
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