Comparative Analysis of Serum Adiponectin and its Genetic Polymorphisms in Metabolic Syndrome and Healthy Group-an Observational Study

Uzma Zafar1*, Zaima Ali1, Ambreen Tauseef2 and Saba Khaliq3

1Department of Physiology, Lahore Medical and Dental College, University of Health Sciences, Lahore, Pakistan

2Department of Physiology, CMH Lahore Medical College and Institute of Dentistry, Lahore, Pakistan

3Department of Physiology, University of Health Sciences, Lahore, Pakistan

ABSTRACT

The present study was executed to determine the association of adiponectin gene polymorphisms rs1501299 +276 G>T and rs266729 -11377 C>G with metabolic syndrome and to compare serum adiponectin levels in its polymorphic genotypes. It was an observational study conducted at University of Health Sciences, Lahore. Study approval was granted by institutional Review/Ethical Board. Informed consent was taken from all the participants. The venous blood sample was taken after an overnight fast of 8-10 h. Blood was secured for DNA analysis and biochemical tests. Adiponectin rs266729(-11377 C>G) and rs1501299(+276G>T) genotypes were determined by ARMS PCR. Serum adiponectin levels were measured by ELISA. The current study included 200 metabolic syndrome cases and 200 healthy subjects. The minor allele ‘G’ of -11377 C>G [p=0.013; OR (CI)=1.47(1.08-1.999)] and the major ‘GG’ genotype of +276 G>T [(p=0.027; OR(CI)=1.56(1.05-2.32)] were associated with the increased risk of metabolic syndrome. The major ‘CC’ genotype of -11377 C>G was associated with the decreased risk [(p=0.004; OR (CI)=0.55(0.36-0.83)] of metabolic syndrome. Serum adiponectin was higher significantly in the wild genotype (CC) of -11377 C>G in cases and controls. Serum adiponectin correlated inversely with blood pressure, waist circumference and fasting serum glucose; and it exhibited positive correlation with serum high density lipoproteins in the controls while no significant relation was found in the cases. The major “GG’’ variant of adiponectin +276 G>T was associated with the increased risk whereas the major “CC” variant -11377 C>G was associated with the decreased risk of metabolic syndrome. It is concluded that the serum adiponectin levels were significantly less in the minor “GG” variant of -11377 C>G in metabolic syndrome as well as in the healthy group.

Article Information

Received 24 January 2023

Revised 20 June 2023

Accepted 09 May 2023

Available online 30 June 2023

(early access)

Published 14 August 2024

Authors’ Contribution

UZ designed the study. UZ, ZA, AT and SK did field work, performed data analysis, wrote and gave final approval of the manuscript.

Key words

Adiponectin gene polymorphism, Metabolic syndrome, Genotype variants

DOI: https://dx.doi.org/10.17582/journal.pjz/20230124100125

* Corresponding author: uzma.zargham@gmail.com, uzma.zargham@lmdc.edu.pk

0030-9923/2024/0005-2391 $ 9.00/0

Copyright 2024 by the authors. Licensee Zoological Society of Pakistan.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

INTRODUCTION

The world-wide metabolic syndrome is regarded a major public health issue (Manaf et al., 2021). It is a multicausal disorder and develops in a slow gradual way atherogenic dyslipidemia, central obesity and Metabolic syndrome is a collection of derangements such as hypertension. Almost all these features are intimately associated with insulin resistance (Fahed et al., 2022). The complications of this disorder such as diabetes mellitus and cardiovascular catastrophes exert huge burden on patients and health care set up (Howlader et al., 2021). Diagnostic criteria for metabolic syndrome has been defined by various organizations, “International Diabetes Federation” presented a set criteria in which large waist circumference representing intra-abdominal fat accumulation was considered a mandatory component of metabolic syndrome (Han and Lean, 2016). Current literature has revealed that adipose mass is an active endocrine organ rather than an inert fat depot. It integrates various endocrine, inflammatory and metabolic signals that regulate homeostasis. A variety of adipocytokines have been documented to be released from adipose tissue into the circulation. These include resistin, adiponectin, interleukin-6, tumor necrosis factor alpha and many more (Cinkajzlová et al., 2022).

Adiponectin enhances insulin sensitization at tissue level by modulating cellular and metabolic functions. Although adipose organ is the major site of origin of adiponectin, it is also synthesized in skeletal muscles, liver and bone marrow cells. Adiponectin produces its physiological effects by acting on skeletal muscles predominantly through adiponectin receptor 1 and liver cells predominantly through adiponectin receptor 2. Once these receptors are activated serial signal transduction is through activation of adenosine monophosphate dependent protein kinase pathway and peroxisome proliferator activated receptor gamma pathway resulting in an increased fatty acid oxidation and decreased hepatic glucose synthesis (Yibby et al., 2020). Low serum adiponectin levels are found to be associated with insulin resistance, metabolic syndrome, type 2 diabetes mellitus and perivisceral/central adiposity (Nguyen, 2020).

Genome-based linkage studies on adiponectin gene have revealed direct association of adiponectin genetic polymorphisms with insulin resistance and type 2 diabetes mellitus. The rs1501299 G>T genetic variant of adiponectin at 276th position of intron 2 was intimately linked with type 2 diabetes mellitus and pathogenesis of insulin resistance. The wild GG genotype of 276 G>T instead of TT was found to be a risk for type 2 diabetes mellitus and related with the low adiponectin levels (Alimi et al., 2021a). Adiponectin promoter polymorphism rs266729 -11377 C>G was also reported to be significantly associated with the risk of nonalcoholic fatty liver disease and altered blood lipid profile in obese subjects (Gasparotto et al., 2017; Zheng et al., 2022). However, a previous study from Pakistan did not show significant association between adiponectin promoter polymorphism and type 2 diabetes mellitus (Nadeem et al., 2017). Another study from Pakistan reported lack of association between adiponectin rs1501299 G>T polymorphism and polycystic ovaries (Ahmed et al., 2019). The conflicting diverse results might be due to variable sample size or genetic, ethnic or clinical heterogeneity of different populations. There is still no definite or recognized genomic DNA marker for clinical application. The present study was executed to determine the association of adiponectin gene polymorphisms rs1501299 +276G>T and rs266729 -11377 C>G with metabolic syndrome and to compare serum adiponectin levels in different polymorphic genotypes.

MATERIALS AND METHODS

Study population

It was an observational cross-sectional study conducted at the Physiology Department of University of Health Sciences. Total 400 participants were recruited; 200 were cases of metabolic syndrome and 200 controls. Study population was estimated by WHO calculator. Significance level was taken as 0.05 and power of study was 80%. Sample size equation utilized was “Hypothesis test for two population proportions”; minor allele proportion of rs266729 -11377C>G was considered (Hashemi et al., 2013). Subjects were selected from Sheikh Zayed Hospital, Lahore from June to November 2016.

Diagnostic criteria of metabolic syndrome

Metabolic syndrome identification was based upon “International Diabetes Federation” definition. All recruited male patients were centrally obese with waist circumference of more or equal to 90 cm plus two out of the four other features stated as; (1) triglycerides in serum ≥ 150 mg/dl or on its treatment, (2) high density lipoprotein cholesterol less than 40 mg/dl or on its treatment, (3) Blood pressure greater than 130/85 or on antihypertensives, and (4) Fasting serum glucose > 100mg/dl or on its treatment (Fahed et al., 2022).

Age-matched controls were taken from healthy population. Those were selected as controls; having waist circumference ≤ 90 cm and no evidence of hypertension or diabetes mellitus. There was also no history of intake of anti-lipid, anti-diabetic and anti-hypertensive medications. Blood pressure and plasma glucose (fasting and 2 h postprandial) of the controls were checked on two separate days. Subjects selected were having fasting plasma glucose less than 100mg/dl, 2 h after meals less than 140 mg/dl and BP less than 130/85. All those having evidence of chronic infective or inflammatory metabolic states were excluded (Sinnott et al., 2015).

Anthropometric and biochemical measurements

After subject selection informed consent was undertaken followed by collection of demographic and clinical measurements. Waist circumference was taken mid-way between the last rib (its lower border) and the top rim of the antero-superior iliac crest. Blood pressure was measured from the left arm in sitting position after the rest of 10-12 min. Two measurements were taken after 5 min interval and an average of two was noted. The venous blood sample was taken from the subjects after an overnight fast of 8-10 h (Sinnott et al., 2015). Blood was secured at -200 oC for DNA extraction and analysis; serum was stored at -800 oC for biochemical tests. Serum adiponectin levels were measured by ELISA using ELISA kits (Elabscience, Germany) (E-EL-H0004).

Analysis of adiponectin gene polymorphisms

DNA extraction done with the Favor-Prep Blood Genomic DNA Extraction Kit (Taiwan, China) as per given guidelines. Adiponectin rs266729(-11377 C>G) genotype was determined by ARMS PCR with following primers:

Outer F5’-GGACTGTGGAGATGATATCTGGGGGGCA-3’,

R5’-TGGCCTAGAAGCAGCCTGGAGAACTGGA-’,

Inner F5’ CTTGCAAGAACCGGCTCAGATCCTCCC-3’,

R5’ GAGCTGTTCTACTGCTATTAGCTCTGC-3’

PCR reaction made was of total 15µL containing 0.3 µL of each primer, 2 µL (50ng) genomic DNA, 7.5µL 2X master mix and 4.3µL double distilled water. The PCR cycling conditions were initial denaturation for 5 min at 95oC followed by 30 cycles of denaturation for 30 sec at 95oC, annealing for 30s at 62oC, extension for 27 sec at 72oC with a final extension at 72oC for 5 min to allow for complete extension of all PCR products. The amplicons were run for 45 min on 3% agarose gel stained with ethidium bromide. The PCR product was checked under UV gel documentation system (Bio Rad, USA). The product sizes for rs266729 were 299-bp for control band, 155-bp for C allele, and 201-bp for G-allele (Hashemi et al., 2013).

Adiponectin rs1501299 (+276G>T) genotype was determined by ARMS PCR with following primers;

Outer F5’-GAGCTGTTCTACTGCTATTAGCTCTGC-3’

R5’-GAATATGAATGTACTGGGAATAGGGATG-3’

Inner F5’-CCTCCTACACTGATATAAACTATATGAGGG-3’

R5’TGTGTCTAGGCCTTAGTTAATAATGAACGA-3’

PCR reaction made was of total 15µL containing 0.3 µL of each primer, 2µL (50ng) genomic DNA, 7.5µL 2X PCR master mix and 4.3µL double distilled water. The PCR cycling conditions were initial period of denaturation for 5 min at 95oC initially, followed by 30 denaturation cycles for 30 sec at 95oC, annealing for 27 sec at 61oC and extension for 25 sec at 72oC followed by a final extension at 72oC for 5 min to allow for complete amplification of all PCR products. The amplicons were run for 45 min on 3% agarose gel stained with ethidium bromide. The PCR product was checked under ultra-violet gel documentation system (Bio Rad, USA). The product sizes of rs1501299 were; for G allele 244-bp, T allele 292-bp and control band 476-bp (Hashemi et al., 2013).

Statistical analysis

The data analysis was conducted using SPSS version 22.0 (Statistical Package for Social Sciences). Mann-Whitney U was applied to compare non-normally distributed quantitative variables between the cases and the controls. Allelic and genotypic proportions were checked by Hardy Weinberg equation with online (OEGE) tool of gene epidemiology. Distribution was consistent with Hardy Weinberg law p=0.05 (Bińkowski, 2022). For the comparison of the frequency and determination of the genotypic association with the study groups, three genetic models (Co-dominant, wild/dominant, and mutant/recessive) were designed. With chi-square test (χ2) gene and allele frequencies/proportions of two groups were compared followed by the calculation of the odds ratio. Logistic regression analysis was utilized to find the association between metabolic syndrome and gene frequencies after control of all genotype models. Serum adiponectin was compared between the groups by Mann-Whitney U statistics. Correlation of serum adiponectin with metabolic syndrome related traits was determined with spearman test. A p of less than 0.05 was taken as of statistical significance.

RESULTS

Clinical characteristics of the study population

Study population included 200 cases of metabolic syndrome and 200 healthy controls. The mean (± standard deviation) age of Met S group was 48.03 (±8.03) years and of healthy was 47.04 (±8.19) years. No significant difference was found among the mean ages. Duration course of metabolic syndrome in 60% subjects was less than one a year and in the rest 40% it was more than a year. In the metabolic syndrome study group; 80% were diabetics, 10% were with impaired fasting sugar, 79% hypertensives or on its treatment, 92% were with impaired lipid profile. Anthropometric and biochemical parameters such as waist circumference, blood pressure, serum triglyceride, insulin and glucose levels were higher significantly in cases as compared to the controls. These characteristics were already described in a previous study (Zafar et al., 2019).

Genotype frequency of rs266729 -11377 C>G and rs1501299 +276 G>T

Allelic and genotype frequencies of adiponectin polymorphisms are presented in the Tables I-III. The overall frequency of the major allele C of rs266729 -11377 C>G was 0.71 and the minor allele G was 0.29 in the present study. The minor allele ‘G’ was significantly associated with the increased risk of metabolic syndrome [p=0.013; OR (CI)= 1.47(1.08-1.999)] and the major ‘CC’ genotype was associated with the decreased risk [(p=0.004; OR (CI)=0.55(0.36-0.83)] of metabolic syndrome.

The overall frequency of the major allele G of rs1501299 G>T was 0.75 and the minor allele T was 0.25 in the present study. The major ‘GG’ genotype of +276 G>T was associated with the increased risk [(p=0.027; OR(CI)= 1.56(1.05-2.32)] of metabolic syndrome. These associations remained significant after controlling the confounders such as age and serum lipid status.

 

Table I. Comparison of genotypes and allelic frequency of adiponectin rs266729 (-11377 C>G) polymorphism among study groups.

Genotypes	Metabolic syndrome (%)	Healthy group (%)	p value	Odds ratio and confidence interval
Co-dominant model
CC	83(41.5%)	111(55.5%)	0.02*	Odds ratio not computed
CG	100(50%)	76(38%)
GG	17(8.5%)	13(6.5%)
Total	200	200
Allelic model
G	134(33.5%)	102(25.5%)	0.013*	1.47(1.08 to 1.99)
C	266(66.5%)	298(74.5%)
Total	400	400
Recessive model
GG	18(9%)	14(7%)	0.461	1.31(0.63 to 2.72)
CG+CC	182(91%)	186(93%)
Total	200	200
Dominant model
CC	83(41.5%)	112(56%)	0.004*	0.55(0.36 to 0.83)
CG+GG	117(58.5%)	88(44%)
Total	200	200

 

A Chi square test was utilized to calculate the p value, Confidence interval and Odds ratio. A “p” of < 0.05 is statistically significant*.

 

Table II. Comparison of genotypes and allelic frequency of adiponectin rs1501299 (276G>T) polymorphism among study groups.

Genotypes	Metabolic syndrome (%)	Healthy group (%)	p value	Odds ratio and confidence interval
Co-dominant model
GG	120((60%)	96(48%)	0.055
GT	72(36%)	93(46.5%)
TT	8(4%)	11(5.5%)
Total	200	200
Allelic model
G	312(78%)	285(71.25%)	0.000*	2.67(1.99 to 3.59)
T	88(22%)	115(28.75%)
	400	400
Dominant model
GG	119(59.5%)	97(48.5%)	0.027*	1.56(1.05 to 2.32)
GT+TT	81(40.5%)	103(51.5%)
Total	200	200
Recessive model
TT	8(4%)	11(5.5%)	0.481	0.72(0.28 to 1.82)
GT+TT	192(96%)	189(94.5%)
Total	200	200

 

A Chi square test was utilized to calculate the p value, Confidence interval and Odds ratio. A “p” of < 0.05 is statistically significant*.

 

Table III. Association of adiponectin genotypes with metabolic syndrome after controlling for co-variates.

Co-variates	B	Sig	Exp(β)	CI
Rs266729dominant	0.599	0.005*	0.55	0.36 to 0.83
Rs266729recessive	0.070	0.860	0.49	0.49 to 2.32
Rs1501299dominant	-0.466	0.028*	1.56	1.05 to 2.33
Rs151299recessive	0.049	0.921	0.39	0.39 to 2.78
Age	0.301	0.240	0.82	0.82 to 2.23
Fasting lipids	-0.304	0.966	0.67	0.67 to 1.52

 

Binary logistic regression analysis was applied to calculate p-value, confidence interval and Exp(β) coefficient. A “p” of < 0.05 is statistically significant*. Study groups are taken as dependent variable.

 

Comparison of serum adiponectin among the study groups

Serum adiponectin level (Table IV) was significantly higher in controls as compared to the cases (p=0.000). On comparison of serum adiponectin levels in different adiponectin genotypes its level was significantly less in metabolic syndrome (p=0.028) and healthy group (p=0.008) in the minor ‘GG’ genotype of -11377 C>G.

 

Table IV. Comparative analysis of serum adiponectin between the study groups and within each group in different genotype models.

Serum adiponectin levels (in μg/ml) Among study groups	Metabolic syndrome	Healthy group	p value*
	2.88 (1.04-2.44)	4.01 (2.67-5.8)	0.000
rs266729 (-11377 C > G) minor genotype model within the study groups
GG	1.8 (0.44-2.3)	2.03 (0.93-3.18)
CG+CC	3.07(1.19-4.04)	4.46 (2.88-5.97)
p-value** (within groups)	0.028*	0.008*
rs1501299 (276G > T) major genotype model within the study groups
GG	2.67(0.92-4.23)	3.92(2.5-5.9)
GT+TT	3.04(1.61-4.93)	4.85(2.88-5.83)
p-value**(within groups)	0.704	0.274

 

*Mann Whitney U test was applied to compare serum adiponectin levels between metabolic syndrome and healthy group. **Mann Whitney U test was applied to compare serum adiponectin levels in risk genotypes of adiponectin within each study group. Serum adiponectin- expressed as median (interquartile range) due its non-normal distribution. A “p” of < 0.05 is statistically significant.

 

Correlation of serum adiponectin with metabolic syndrome traits

There was significant correlation of serum adiponectin with blood pressure, waist circumference, serum high density lipoproteins and triglycerides in the healthy group (Table V) however no significant correlation of serum adiponectin was found with these traits in the metabolic syndrome group. Linear regression analysis was applied (Table VI) taking serum adiponectin as a dependent variable and controlling all the covariates such as age, waist circumference, blood pressure, serum triglycerides, high density lipoproteins and glucose. Correlation of waist circumference with serum adiponectin (Fig. 1) remained significant (p=0.001).

 

Table V. Correlation of serum adiponectin with metabolic syndrome traits.

Variables	Metabolic syndrome rho(p-value)	Healthy group rho(p-value)
Systolic blood pressure	0.031(0.736)	-0.322(0.027*)
Diastolic blood pressure	0.058(0.531)	-0.305(0.037*)
Waist circumference	-0.099(0.289)	-0.298(0.042*)
Fasting serum glucose	0.013(0.188)	-0.291(0.047*)
Fasting serum high density lipoproteins	0.036(0.702)	0.308(0.035*)
Fasting serum triglycerides	-0.065(0.487)	0.218(0.141)

 

Spearman rho test was applied to determine the correlation of serum adiponectin with the traits of metabolic syndrome. A p of less than 0.05* is of statistical significance.

 

Table VI. Linear regression analysis to determine the significant correlation of adiponectin after controlling the co-variates.

Predictors of adiponectin	Standardized coefficients beta	t	significance
Systolic blood pressure	-0.014	-0.124	0.902
Diastolic blood pressure	-0.003	-0.028	0.978
Waist circumference	-0.293	-3.34	0.001*
Fasting serum glucose	-0.041	-0.458	0.648
Fasting serum high density lipoproteins	0.001	0.014	0.980
Fasting serum triglycerides	0.210	0.210	0.834

 

Linear regression analysis is applied to determine significant predictor of serum adiponectin. Serum adiponectin is dependent variable. Other co-variates are independent. A p of less than 0.05* is of statistical significance.

 

DISCUSSION

In the present study, two adiponectin gene polymorphisms rs1501299 (+276 G>T) and rs266729 (-11377 C>G) were evaluated for their possible association with the metabolic syndrome. The wild type or major ‘G’ allele and GG genotype of adiponectin +276 G>T genetic

 

variant was found to be associated significantly with the risk of metabolic syndrome. Our study reported major allelic frequency of 0.75 for G allele and minor allelic frequency of 0.25 for the T allele. However, a study on non-diabetic Korean men revealed minor T allelic frequency of 0.38 and significant association of mutant or TT genotype with low adiponectin levels (Song et al., 2018). Another study on post-menopausal North-West Indian women reported minor allelic frequency of 0.35 and significant association of minor TT genotype with the increased cardiovascular risk and oxidative stress (Amrita et al., 2021). In our study the proportion of wild GG genotype was considerably higher in metabolic syndrome than the control group. The minor allelic frequency as determined in our study (0.25) was comparable to that of another study on Iranian population (0.28), however no significant association of rs1501299 polymorphism was found with type 2 diabetes mellitus in Iranians (Alimi et al., 2021b). In a study on Chinese coal mine workers minor allelic frequency was 0.26 and major GG genotype was reported to be the risk genotype for hypertension (Hu et al., 2022). In Saudis major GG genotype was associated with increased risk of type 2 diabetes mellitus. This result is in concordance with our study, however minor allele frequency in Saudi population was higher as compared to ours, i.e., 0.4 versus 0.25 (Al-Nbaheen, 2022). In another meta-analysis, T allele of +276G>T was found to reduce the risk of coronary artery disease in Caucasians and Asians (Yang et al., 2012).

In the current study, the wild or major CC adiponectin genotype of rs266729 (-11377 C>G) was found to be associated significantly with the decreased risk whereas minor allele G was associated with the increased risk of metabolic syndrome. This finding is in agreement with that of a previous systematic review that reported considerable correlation of adiponectin gene rs266729 C>G with non-alcoholic fatty liver disease (Zheng et al., 2022). The minor allelic G frequency in this study was 0.29 which is comparable to the minor allele frequency of 0.30 reported in another study from Pakistan on Rajput population. However, no association of minor rs266729 C>G genotype was found with type 2 diabetes mellitus in that study (Nadeem et al., 2017). In a study on Iranian population significant association of rs266729 C>G minor genotype was found with type 2 diabetes mellitus and minor allelic G frequency (0.3) in this study was also comparable to the frequency reported in the present study (Alimi et al., 2021b). The rs266729 minor genotype was also reported to be associated with the increased risk of type2 diabetes mellitus and metabolic syndrome in Veitnamese Kinh population (Truong et al., 2022). Another meta-analysis revealed that rs266729 C>G variant was associated with the increased risk of type2 diabetes mellitus in Asian population (Chu et al., 2013).

Serum adiponectin level in the present study was significantly less in centrally obese, insulin resistant, metabolic syndrome group as compared to the healthy group. Several postulated mechanisms may explain decrease adiponectin production of the adipose tissue in metabolic syndrome; such as hypoxia, oxidative insult, increased tumor necrosis factor alpha levels in obese adipocytes. Chronic gradual inflammation results in low adiponectin levels in obese metabolically unhealthy subjects (Marques et al., 2021; Singhal et al., 2022; Sparrenberger et al., 2019; Yosaee et al., 2019). Similar findings were reported by the studies from Pakistan conducted upon diabetic and pre-diabetic subjects (Ahan et al., 2014; Najam et al., 2014). The results of this study were also in concordance with those of a previous study on Brazilian adolescents; that reported downward trend in adiponectin with the increasing traits of metabolic syndrome (Sparrenberger et al., 2019).

In the present study, serum adiponectin levels were significantly less in metabolic syndrome as compared to the healthy controls however no significant correlation of serum adiponectin was found with anthropometric or biochemical traits of insulin resistance in metabolic syndrome. These results were not in agreement with those of the previous studies that reported positive association of serum adiponectin with components of metabolic syndrome (Marjani et al., 2022; Yang et al., 2022; Zaha et al., 2020). In our population mean adiponectin levels were very low as compared to the other populations. Dysregulated adiponectin production by obese and inflamed adipose tissue might result in alteration of adiponectin relation with anthropometric and biochemical parameters in the diseased group in our study (Nigro et al., 2014). In the healthy group significant inverse correlation of serum adiponectin was found with blood pressure, waist circumference and serum glucose and positive correlation was there with serum high density lipoproteins. These results were supported by the previous studies that reported association of serum adiponectin with anthropometric measurements in healthy non-diabetic population (Bonneau et al., 2014; Foula et al., 2020; Sparrenberger et al., 2019; Zaidi et al., 2022).

Serum adiponectin was also low significantly in the recessive GG genotype of rs266729 in both healthy and metabolic syndrome group however no significant difference was observed in different genotypes of rs1501299. The impact of rs266729 polymorphism on serum adiponectin suggests that it is a heritable trait and its expression can be modified by genetic variations. This result is also consistent with a previous study conducted on type2 diabetic Jordanians (Alfaqih et al., 2022). Another study conducted on Iranian population reported lack of association of rs266729 -11377 C>G genotype with type 2 diabetes mellitus and serum adiponectin levels in its different genotypes (Karimi et al., 2018). The diversity of results suggests that the heterogenous and multifactor heritability of metabolic syndrome is a challenge for understanding the factors associated with this condition and related traits (Rana et al., 2022).

CONCLUSION

The major “GG’’ genotypic variant of adiponectin +276 G>T was significantly associated with the increased risk whereas the major “CC” variant -11377 C>G was associated with the decreased risk of metabolic syndrome. Serum adiponectin levels were significantly less in the minor “GG” variant of -11377 C>G in metabolic syndrome as well as in the healthy group. Adiponectin gene variants by regulating adiponectin expression can be the possible immune modifiers. Further studies are required to ascertain the role of genetic variants and adiponectin in prediction of metabolic syndrome and its traits.

Acknowledgement

We are thankful to the administration University of Health Sciences, Lahore.

Funding

This study was approved by the institutional review/ethical board (No: UH/education/126-16/1267/5-5-2016).

IRB approval

The study was approved by the Advanced Studies & Research Board of University of Health Sciences, Lahore (No: UHS/Educational/126-16/1267).

Ethical statement

Informed written consent was taken from the participants of the study.

Statement of conflict of interest

The authors have declared no conflict of interests.

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