Role of Proinflammatory Cytokines and Genetic Factors Related to Metabolic Alterations in People Living with HIV/AIDS

Abstract

Metabolic alterations are a common problem in people living with HIV (PLHIV), as a result of a stage of chronic inflammation that affects the homeostasis of the organism. Prolonged exposure to antiretroviral therapy has been associated with developing lipodystrophies that modify lipoprotein metabolism and inflammatory markers such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), which are mediators of the immune response. The study aimed to associate TNF-α and IL-6 levels with their polymorphisms and metabolic alterations in PLIHV. We hypothesized that TNF-α and IL-6 levels and their polymorphisms are associated with metabolic alterations. In total, 185 PLHIV and 51 HIV-negative people were included. Biochemical parameters were determined by colorimetric assay, cytokine levels by immunoassay, and allelic discrimination by quantitative polymerase chain reaction. A correlation was found between TNF-α levels and the variables cholesterol (r = −0.171, P = 0.020) and high-density lipoprotein (HDL) (r = −0.245, P = 0.001). There are associations between HDL levels (P = 0.011) and GG genotype of rs1800629. The results suggest a metabolic alteration related to the constant immune response, especially the production of proinflammatory cytokines such as TNF-α and IL-6. It was observed that genetic factors may influence metabolism alteration, mainly in lipids.

Introduction

Metabolic alterations are a frequently occurring problem in people living with HIV (PLHIV); this is because of the stage of chronic inflammation (Godfrey et al., 2019). Lipoperoxidation occurs because of the activation of the immune system, causing an imbalance in the antioxidant system, stimulating endothelial lipase and phospholipase A2, and generating metabolic alterations. In previous studies in PLHIV, the prevalence of alterations in the lipid profile is high, from 67.5% to 70% (Aurpibul et al., 2020; Schilke et al., 2020).

In addition, with the introduction of antiretroviral therapy (ART), benefits have been reported for PLHIV in terms of decreased mortality, but comorbidities arise because of chronic inflammation. Side effects of ART have also been described, especially on lipid levels, particularly with the scheme that includes protease inhibitors (PIs), increasing the risk of developing dyslipidemia, type 2 diabetes (DM2), or cardiovascular disease (Babu et al., 2019; Girma et al., 2023).

Besides dyslipidemias, it has also been described that between 10% and 83% of PLHIV on ART develop lipodystrophy, which, in turn, triggers diseases such as DM2, insulin resistance, obesity, or dyslipidemia, which are associated with inflammatory markers such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) secreted by macrophages infiltrating adipose tissue and lipocytes (French et al., 2015; Mohammadi et al., 2017; Namdev et al., 2023).

TNF-α is secreted mainly by macrophages, T lymphocytes, and endothelial cells. It acts in an autocrine, paracrine, and/or systemic manner; it is an important proinflammatory cytokine because it influences HIV-1 progression, favoring both viral replication and cell apoptosis. Its expression is regulated by the TNF-α gene, located on the short arm of chromosome 6, in the cytogenetic band 6p21.1-21.3, next to the major histocompatibility complex (Ahir et al., 2015; Fragoso et al., 2014).

On the contrary, IL-6 has anti- and proinflammatory properties and plays an important role in the organization of the immune response. It is secreted by different cell types, commonly by T lymphocytes, monocytes, and macrophages. The IL-6 gene is located on chromosome 7, at band 7p21 (El-Maadawy et al., 2019). In addition, the presence of SNPs rs1800629 (-308 G<A) and rs1800797 (−597>G) in the TNF-α and IL-6 genes, respectively, has been described as a risk factor for the development of type 2 diabetes mellitus (T2DM) for Mexican and Chinese population (Boeta-López et al., 2017; Lara et al., 2019).

We hypothesized that TNF-α and IL-6 polymorphisms are associated with their plasma levels and metabolic alterations in PLHIV on ART. The study aimed to associate TNF-α and IL-6 levels with polymorphisms and metabolic alterations in PLHIV on ART.

Materials and Methods

Patients

This was a cross-sectional, analytical, and observational study, where 185 PLHIV (144 men and 41 women) and 51 HIV-negative people (31 men and 20 women) were included, assigned from Outpatient Center for AIDS and Sexually Transmitted Infections Prevention and Care (CAPASITS) in Torreon, Coahuila, and from Hospital Integrated Care Services (SAIH) in Gomez Palacio, Durango, both in Mexico. Inclusion criteria were people over 18 years old, with HIV infection diagnosis, and under ART. A control group without HIV infection was also included, comprising 51 participants with a negative serological test for HIV as inclusion criteria, derived from the blood bank of the General Hospital of the cities Gomez Palacio, Durango and Torreon, Coahuila, in Mexico. People with any type of cancer or lymphoma, under chemotherapy treatment, and with tuberculosis or hepatitis infection were excluded from both study groups. After signing the informed consent, the participants were surveyed to obtain sociodemographic and clinical data, including age, place of residence, time of infection, and ART data, as applicable. Only for participants with a positive HIV diagnosis, a medical history consultation was carried out to obtain data of CD4⁺ cell count, viral RNA copy number, and ART regimens for at least 1 year. The project was approved by the Bioethics Committee of the Faculty of Medicine of the Autonomous University of Coahuila in Torreon, Mexico, with the official number C.B/02–11-19.

Anthropometric measurements

For all participants, the following measurements and cutoff points were considered: height (m), weight (kg), and waist circumference (cm) <88 in women and <102 in men. Body mass index (BMI) (kg/m²) was classified according to the World Health Organization criteria: (1) underweight (<18.5 kg/m²), (2) normal weight (18.5–24.9 kg/m²), (3) overweight (25–29.9 kg/m²), and (4) obesity (≥30 kg/m²).

Biochemical measurements

Blood samples were collected after a period of fasting, and they were subsequently centrifuged at 3,000 RPM for 10 min to obtain blood serum. There were determined glucose (mg/dL), total cholesterol (mg/dL), high-density lipoprotein (HDL) (mg/dL), low-density lipoprotein (LDL) (mg/dL), very-low-density lipoprotein (VLDL) (mg/dL), and triglycerides (mg/dL). All measurements were analyzed by colorimetry on a Vitros^® 250 automated dry chemical analyzer according to the manufacturer’s specifications. Plasma levels of TNF-α and IL-6 were performed by immunoassays based on Luminex xMAP technology, using the Milliplex Map^® kit. The methodology was performed according to the manufacturer’s instructions.

DNA extraction and quantification procedure from whole blood

Blood for all samples was collected in Ethylenediaminetetraacetic acid) tubes, from which the leukocyte layer was obtained and the DNA extraction was carried out by means of the Dodecyltrimethylammonium Bromide-Cetyltrimethylammonium Bromide method (Gustincich et al., 1991).

The quantification and purity of the extracted DNA were performed by spectrophotometry with the NanoDrop 1000^® equipment, considering the DNA–protein ratio greater than 1.8 for optimal DNA quality. In addition, standardized aliquots were made for quantitative polymerase chain reaction at 20 ng/dL.

Genotyping for TNF-α (rs1800629) e IL-6 (rs1800797) SNPs

Samples were amplified with predesigned TaqMan^® technology probes, following the supplier’s instructions through real-time PCR equipment 3700 from Applied Biosystem. Genotyping was performed with TaqMan probes, with the sequences for polymorphisms:rs1800629 (TNF-α)5′GAGGCAATAGGTTTTGAGGGGCATG[A/G]GGACGGGGTTCAGCCTCCAGGGGTCC-3′rs1800797 (IL-6)5′TGAAGTAACTGCACGAAATTTGAGG[A/G]TGGCCAGGCAGTTCTACAACAGCCG-3′

Statistical analysis

The Kolmogorov–Smirnov test was used on continuous variables to determine the sample distribution. Sociodemographic and clinical variables with a normal distribution were described with the use of mean, standard deviation (SD), and standard error (SE). Student’s t-test was used for the comparison of means between two groups, and analysis of variance was used for parametric variables with more than three groups. To determine the relation between variables, Pearson’s correlation was used, and finally, through multiple linear regression analysis, significant associations were determined after univariate analysis. P value <0.05 was considered statistically significant. Data were analyzed using the statistical package SPSS version 26 (IBM Corp, 2019; IBM SPSS Statistics for Windows).

Results

Sociodemographic characteristics

We included 185 HIV seropositive cases (PLHIV) and a control group of 51 HIV seronegative individuals. The mean age for PLHIV is 39.22 ± 10.86 and the seronegative group 35.98 ± 11.77 with no statistically significant difference [95% confidence interval [CI], P > 0.05). Regarding ART, it is observed that the most prescribed scheme is the one including 2 nucleoside reverse transcriptase inhibitors (NRTIs) + 1 non-nucleoside reverse transcriptase inhibitor (NNRTIs), followed by 2 NRTIs + 2 protease inhibitors (PIs). Viral load and CD4⁺ values are mostly within normal values (Table 1).

Table 1.

Sociodemographic Characteristics

	PLHIV n = 185	Control group n = 51
Gender	n (%)	n (%)
Male	144 (78)	31 (61)
Female	41 (22)	20 (39)
Age	39.22 ± 10.86	35.98 ± 11.77
ART	n (%)
1 NRTIs + 1 NNRTIs + 2 PIs	2 (1.1)	NA
2 NRTIs + 1 INIs	4 (2.2)	NA
2 NRTIs + 1 NNRTIs	114 (61.6)	NA
2 NRTIs + 2 PIs	61 (33)	NA
3 NRTIs + 2 PIs	1 (0.5)	NA
Years of infection	9.72 ± 6.47	NA
Years in ART	7.17 ± 6.16	NA
Viral load	39 [20–416107]	NA
CD4⁺	503 [42–1280]	NA

Results expressed in frequencies and percentages, mean ± standard deviation, and median (min–max).

ART, antiretroviral therapy; INIs, integrase inhibitors; NNRTIs, non-nucleoside reverse transcriptase inhibitors; NRTIs, nucleoside reverse transcriptase inhibitors; PIs, protease inhibitors; PLHIV, people living with HIV.

Biochemical and anthropometric variables and lipid profile in PLHIV

Anthropometric variables such as weight, height, BMI, and waist circumference were measured, where it was observed that for both groups, the BMI and waist circumference were above normal values but statistically significant differences. The biochemical variables in PLHIV are within normal parameters, with the exception of triglycerides. The group that was included as a control presented values outside the range for most of the variables. When comparing means, statistically significant differences were observed for almost all biochemical variables, except HDL (Table 2). The results of the lipid profile in PLHIV alone show elevated triglyceride values in 55.7% (Table 3).

Table 2.

Biochemical and Anthropometric Variables

	PLHIV n = 185	Control group n = 51	P
Weight (kg)	71.97 ± 14.48	86.66 ± 15.72	0.546
Height (m)	1.67 ± 0.08	1.69 ± 0.07	0.170
BMI	25.59 ± 4.98	30.23 ± 5.08	0.610
Waist circumference (cm)	90.48 ± 13.47	99.48 ± 12.82	0.326
Glucose (mg/dL)	91.43 ± 30.64	128.4 ± 68.96	<0.0001
Cholesterol (mg/dL)	175.07 ± 38.36	216.75 ± 53.65	0.010
Triglycerides (mg/dL)	218.9 ± 202.51	195.33 ± 84.57	0.005
HDL (mg/dL)	46.3 ± 13.43	28.82 ± 12.72	0.227
VLDL (mg/dL)	37.85 ± 22.52	39.06 ± 16.91	0.018
LDL (mg/dL)	90.91 ± 31.14	148.85 ± 50.11	<0.0001
TNF-α (LOG10)	0.86 ± 0.24	1.03 ± 0.09	<0.0001
IL-6 (LOG10)	1.27 ± 0.41	1.71 ± 0.57	<0.0001

Results expressed as mean ± standard deviation.

The P value was calculated using the Student t-test.

Significant values are given in bold.

BMI, body mass index; IL-6, interleukin-6; HDL, high-density cholesterol; LDL, low-density cholesterol; TNF-α, tumor necrosis factor alpha; VLDL, very-low-density cholesterol.

Table 3.

Lipid Profile in PLHIV

Lipids	High		Normal		P
Lipids	n (%)		n (%)		P
Cholesterol	47 (25.4)	224.02 ± 21.58	138 (74.6)	158.39 ± 26.79	<0.0001
Triglycerides	103 (55.7)	310.65 ± 232.99	82 (44.3)	103.65 ± 25.66	<0.0001
VLDL	67 (36.2)	60.36 ± 22.23	118 (63.8)	25.07 ± 8.00	<0.0001
LDL	19 (10.3)	146.6 ± 19.26	166 (89.7)	84.53 ± 25.36	<0.0001

	Low		Normal
HDL	64 (34.6)	32.56 ± 5.09	121 (65.4)	53.57 ± 10.45	<0.0001

Results expressed as mean ± standard deviation.

The P value was calculated using the Student t-test.

Significant values are given in bold.

Correlations of TNF-α and IL-6 levels with biochemical and anthropometric variables

Upon analysis, the correlation of TNF-α levels with cholesterol (r = −0.171, 95% CI, P = 0.020) and HDL (r = −0.245, 95% CI, P = 0.001) variables (Tables 4 and 5) was only found in the group of PLHIV. No correlation was found in the control group.

Table 4.

Correlations Between TNF-α Levels and Biochemical and Anthropometric Variables in People Living with HIV

Correlation	R	P
TNF-α–Glucose	−0.012	0.872
TNF-α–Cholesterol	−0.171	0.020
TNF-α–Triglycerides	0.070	0.347
TNF-α–HDL	−0.245	0.001
TNF-α–VLDL	0.009	0.904
TNF-α–LDL	−0.111	0.133
TNF-α–Weight	−0.030	0.687
TNF-α–BMI	−0.054	0.465
TNF-α–Waist circumference	−0.004	0.959

Pearson correlation.

Significant values are given in bold.

Table 5.

Correlations Between IL-6 Levels and Biochemical and Anthropometric Variables in People Living with HIV

Correlation	R	P
IL-6–Glucose	−0.047	0.527
IL-6–Cholesterol	0.131	0.076
IL-6–Triglycerides	0.014	0.847
IL-6–HDL	0.07	0.341
IL-6–VLDL	0.031	0.672
IL-6–LDL	0.108	0.143
IL-6–Weight	0.006	0.931
IL-6–BMI	−0.029	0.695
IL-6–Waist circumference	−0.107	0.176

Pearson correlation.

Association of TNF-α and IL-6 polymorphisms with metabolic alterations

Contingency table analysis was performed to determine if there is any association between TNF-α (rs1800629) and IL-6 (rs1800797) polymorphisms with metabolic alterations. We only found an association between the TNF-α polymorphism and HDL levels [odds ratio [OR] 3.26, 95% CI, P = 0.011), using the rs1800629 Single Nucleotide Polymorphism (SNP) genotypes (Table 6). For the IL-6 rs1800797 polymorphism, a P value >0.05 was obtained, indicating no association between the variables (Table 7).

Table 6.

Association of the rs1800629 Polymorphism of the TNF-α Gene with Biochemical Variables and BMI

SNP TNF-α rs1800629	Normal n (%)	High n (%)	OR	CI	P ^a
Glucose
GG	152 (88.9)	13 (92.9)	0.615	0.076–4.971	0.646
AG	19 (11.1)	1 (7.1)	0.615	0.076–4.971	0.646
Cholesterol
GG	123 (89.1)	42 (89.4)	0.976	0.335–2.849	0.965
AG	15 (10.9)	5 (10.6)	0.976	0.335–2.849	0.965
Triglycerides
GG	75 (91.5)	90 (87.4)	1.548	0.588–4.077	0.374
AG	7 (8.5)	13 (12.6)	1.548	0.588–4.077	0.374
HDL	Normal	Low
GG	113 (93.4)	52 (81.3)	3.26	1.257–8.454	0.011
AG	8 (6.6)	12 (18.8)	3.26	1.257–8.454	0.011
VLDL	Normal	High
GG	108 (91.5)	57 (85.1)	1.895	0.745–4.819	0.174
AG	10 (8.5)	10 (14.9)	1.895	0.745–4.819	0.174
LDL
GG	147 (88.6)	18 (94.7)	0.43	0.054–3.405	0.411
AG	19 (11.4)	1 (5.3)	0.43	0.054–3.405	0.411
TNF-α
GG	162 (89)	3 (100)	1.019	0.998–1.040	0.543
AG	20 (11)	0 (0)	1.019	0.998–1.040	0.543
BMI	Normal weight	Overweight/Obesity
GG	86 (87.8)	78 (90.7)	0.735	0.286–1.892	0.522
AG	12 (12.2)	8 (9.3)	0.735	0.286–1.892	0.522

Significant values are given in bold.

Chi-squared test.

CI, confidence interval; OR, odds ratio.

Table 7.

Association of the rs1800797 Polymorphism of the IL-6 Gene with Biochemical Variables and BMI

SNP IL-6 rs1800797	Normal n (%)	High n (%)	OR	CI	P ^a
Glucose
GG	147 (86)	10 (71.4)	2.45	0.711–8.443	0.145
AG	24 (14)	4 (28.6)	2.45	0.711–8.443	0.145
Cholesterol
GG	118 (85.5)	39 (83)	1.21	0.494–2.966	0.676
AG	20 (14.5)	8 (17)	1.21	0.494–2.966	0.676
Triglycerides
GG	68 (82.9)	89 (86.4)	0.764	0.342–1.709	0.512
AG	14 (17.1)	14 (13.6)	0.764	0.342–1.709	0.512
HDL	Normal	Low
GG	105 (86.8)	52 (81.3)	1.514	0.668–3.435	0.318
AG	16 (13.2)	12 (18.8)	1.514	0.668–3.435	0.318
VLDL	Normal	High
GG	100 (84.7)	57 (85.1)	0.975	0.421–2.255	0.952
AG	18 (15.3)	10 (14.9)	0.975	0.421–2.255	0.952
LDL
GG	141 (84.9)	16 (84.2)	1.058	0.287–3.897	0.933
AG	25 (15.1)	3 (15.8)	1.058	0.287–3.897	0.933
IL-6
GG	139 (84.2)	18 (90)	0.594	0.130–2.715	0.497
AG	26 (15.8)	2 (10)	0.594	0.130–2.715	0.497
BMI	Normal weight	Overweight/Obesity
GG	84 (85.7)	72 (83.7)	1.167	0.522–2.609	0.707
AG	14 (14.3)	14 (16.3)	1.167	0.522–2.609	0.707

Chi-squared test.

Lipid profile according to ART

When analyzing the lipid profile according to the treatment regimen, high values were observed in the regimens that include PIs. (Table 8). An analysis was performed to determine whether there is an association between the ART scheme containing at least one PI with any metabolic alteration. It was observed that there is an association between the combination of antiretroviral drugs containing PIs with triglyceride levels (OR 2.191, 95% CI, P = 0.014) and VLDL (OR 2.861, 95% CI, P = 0.001) (Supplementary Table S1).

Table 8.

Lipid Profile According to ART

ART	n	Cholesterol	Triglycerides	HDL	VLDL	LDL
1 NRTIs 1 NNRTIs 2 PIs	2	205.5 ± 16.26	863.5 ± 1017.52	52.5 ± 41.71	39.4 ± 14.99	113.6 ± 10.46
2 NRTIs 1 INIs	4	180.5 ± 20.56	121.5 ± 87.06	57.25 ± 16.68	24.3 ± 17.41	98.95 ± 16.61
2 NRTIs 1 NNRTIs	114	175.86 ± 36.80	191.83 ± 146.32	46.86 ± 13.01	35.70 ± 22.40	93.3 ± 29.73
2 NRTIs 2 PIs	61	175.09 ± 41.49	256.24 ± 224.56	45.39 ± 12.72	42.62 ± 23.33	87.07 ± 34.07
3 NRTIs 2 PIs	1	88	150	23	30	35

Discussion

ART has decreased morbidity and mortality; however, metabolic problems have arisen as a consequence of immune activation, inflammation, and lipoprotein modification, as certain inflammatory parameters, such as proinflammatory cytokines (IL-6 and TNF-α), sometimes remain elevated for a prolonged period of time (Menezes et al., 2018; Rockstroh et al., 2010; Wada et al., 2015). In our study, it was observed that most PLHIV are in normal weight, but present alterations in triglyceride levels, being higher in people with obesity/overweight, suggest an association with disease progression, as well as an impairment in lipid metabolism, causing hypertriglyceridemia (see Table 2).

Regarding the correlation of TNF-α and IL-6 levels with the biochemical profile, we found a relationship for TNF-α with cholesterol and HDL values (see Table 4). This can be attributed to lipid variations in chronic inflammation processes, related to the phase of the disease. The levels and composition of HDL are affected by the action of some cytokines and molecules released during inflammatory processes (Grao et al., 2022; Marín et al., 2017). The failure to find correlations between IL-6 and biochemical variants may be because of the fact that most were within normal parameters, as well as under ART, which can decrease IL-6 levels to near-normal values (Table 5). In a study by Sachdeva et al., a decrease in IL-6 and TNF-α was observed after starting ART, which is consistent with our results; IL-6 and TNF-α values were found to be decreased in PLWHA, all under treatment (Sachdeva et al., 2010).

A study by Ghareeb found a correlation between TNF-α-308 (A/G) polymorphism and risk factors for MetS and the A allele and low HDL values as a predictor of developing MetS (Ghareeb et al., 2021). The outcome analysis showed a possible association between the AG genotype of rs1800629 with HDL values, suggesting that TNF-α genotypes may be modifying HDL metabolism (see Table 6). A meta-analysis by Sookoian SC in 2005 suggested that individuals carrying the -308 A/G polymorphism of TNF-α gene had increased obesity-related comorbidities. Bayley et al. indicate that the promoter variant at position -308 leads to an increased transcription rate of the TNF-α gene, followed by elevated TNF-α concentrations and decreased insulin resistance (Bayley et al., 2001; Sookoian et al., 2005).

On the contrary, the AG genotype of rs1800797 (IL-6 gene) does not appear to be affecting metabolism, as no association was found (Table 7). Boeta et al. reported that the rs1800797 variant did not show associations with metabolic traits but was associated with higher IL-6 levels in a study of Mexican Americans in south Texas and that the rs1800797 variant did not show associations with metabolic traits (Boeta-López et al., 2017). Up to now, there are few studies on the polymorphism of the -597 A/G gene, which suggests that it should be studied in greater depth.

Regarding the associations of ART regimens, we observed that those containing PIs may be influencing lipid metabolism, as they are associated with triglyceride and VLDL levels (Table 8 and Supplementary Table S1). Although the quality of life of PLHIV has improved, this population faces other comorbidities such as alterations in lipid metabolism, sometimes as a consequence of exposure to ART. Alterations in lipid metabolism vary depending on the type of treatment regimen, especially those that include PIs, as they inhibit sterol regulatory element transporter proteins and Apo B degradation (Ezeugwunne et al., 2019). The most common effects of PIs are increased central obesity and lipoatrophy and altered triglyceride, cholesterol, LDL, and HDL levels. It has been shown that apart from the type of ART, the time of exposure to antiretrovirals generates an alteration in lipid metabolism (Ahmed et al., 2018; Flint et al., 2009; Waters and Hsue, 2019).

One of the strengths of this study was that serum levels of IL-6 and TNF-α as well as their polymorphisms were measured. No association was found between the rs1800797 polymorphism of the IL-6 gene with alterations in the lipid profile, as in previous studies, but higher serum levels of IL-6 have been reported. A limitation of our research could be the sample size, so no further associations were found. In addition, it would be of great interest to follow this population over time to detect changes in the lipid profile, changes in body weight, as well as CD4⁺ count and viral load.

Conclusions

Our results suggest a relationship between metabolic alterations and the constant immune response, especially the production of proinflammatory cytokines such as TNF-α and IL-6, so it is suggested to continue investigating whether these cytokines can be used as a biomarker to know the response to ART and the metabolic status of people under a certain scheme. It was also observed that other components may be influencing the alteration of metabolism, such as genetic factors and treatment regimens.

Footnotes

Acknowledgments

The authors thank to Dr. Gerardo Zapata Ortiz, Abril Venegas Chairez, director and nurse of the CAPASITS, and Dr. Enrique Castañeda, Guerrero, director of the Care and from SAIH, as well as the staff of each care center for supporting the authors in this research.

Authors’ Contributions

C.A.M.-F. performed writing—original draft, conceptualization, and investigation. J.G.P.-R. performed investigation and writing—reviewing and editing. A.I.U.-R., M.E.G.-P., and A.A.M.-P. performed writing—reviewing and formal analysis. S.K.S.-F. contributed to resources. F.C.L.-M. performed project administration and supervision.

Author Disclosure Statement

All authors declare no conflict of interest.

Funding Information

This study was funded by Scientific and Technological Research (FONCYT) COAH-2019-C13-C054.

Supplementary Material

Supplementary Table S1

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