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Abstract

Background:

Adiponectin, an adipokine produced by adipocytes, is frequently downregulated in obesity-related conditions. This study aimed to investigate the relationship between two adiponectin gene variants (+45T>G and +276G>T), adipokine levels, and their association with metabolic syndrome (MetS) in North Indian adult women.

Methods:

A total of 541 women were genotyped for single nucleotide polymorphisms, including 269 MetS women (National Cholesterol Education Program-Adult Treatment Panel III criteria) and 272 women without MetS (wMetS). Circulating adipokine, lipid profile, glucose, insulin, and homeostatic model assessment-insulin resistance (HOMA-IR) were measured.

Results:

Significant differences (P < 0.01) were observed in adipokine levels, lipid profile, glucose, insulin, HOMA-IR, and waist-to-hip ratio between MetS and wMetS women. Combined mutant genotype (TG+GG) at +45T>G was significantly less frequent (P = 0.017) in MetS women, while the mutant G allele frequency was higher (P = 0.008) compared with the wild type T allele. For the +276G>T variant, the mutant T allele was significantly less frequent (P = 0.027) in MetS than wMetS women. The GG genotype at +45T>G and the TT genotype at +276G>T were strongly associated with reduced adiponectin levels, increased leptin levels, and HOMA-IR (all P < 0.001) in MetS women.

Conclusions:

Our findings suggest that adiponectin gene variants, along with reduced adiponectin levels and increased HOMA-IR, may significantly contribute to the pathogenesis of MetS.

Introduction

Metabolic syndrome (MetS) is a prevalent metabolic disorder characterized by a cluster of risk factors, including increased waist circumference (WC), elevated triglyceride (TG) levels, low HDL-cholesterol (HDL-C), hyperglycemia, and hypertension.¹ Central adiposity, a key component of MetS, is more common in women than in men,² largely due to the strong correlation between WC and increased adiposity.

Adipose tissue, a crucial endocrine organ, plays a significant role in regulating whole-body metabolism and other essential functions related to inflammation and immune responses. There is considerable interest in understanding how adipose tissue-derived adipokines might mediate the relationship between body fat distribution and insulin sensitivity. Adiponectin, one of the most studied adipokines, is an insulin-sensitizing hormone, also known as APM1 or adipoQ. This adipose tissue-specific protein consists of 247 amino acids and plays a crucial role in energy homeostasis and insulin sensitivity.³ It has anti-inflammatory properties that affect the NF-κB pathway and enhances insulin action on peripheral tissues. Another significant adipokine, leptin, acts as an “adiposity signal” that modulates appetite and maintains energy balance,⁴ potentially contributing to the development of MetS. In humans, obesity is often associated with increased leptin levels and decreased adiponectin levels. Adiponectin is located on chromosome 3q27, a region identified as a susceptibility locus for MetS, type 2 diabetes, and coronary artery disease in a genome-wide scan study.⁵ The most commonly studied single nucleotide polymorphism (SNP) variants of adiponectin are located at positions +45T>G and +276G>T, present on exon 2 and intron 2, respectively.

Numerous studies have explored the association between these variants and circulating adiponectin levels, visceral obesity, insulin resistance (IR) syndrome, and metabolic risk factors in humans.^6–8 However, the association between these adiponectin SNPs and MetS has not been extensively studied in age-matched adult women from North India.

This study aims to investigate the association of metabolic risk and adipokine gene variants in adult women from this diverse region in India. Specifically, the study focuses on (1) the frequency distribution of adiponectin gene variants (+45T>G and +276G>T); (2) the impact of these gene variants on phenotypic, clinical, and biochemical profiles; (3) the association of adiponectin gene variants with circulating adipokine levels, homeostatic model assessment-insulin resistance (HOMA-IR), and metabolic risk factors; and (4) the relationship among adiponectin SNPs, HOMA-IR, circulating adipokine levels, and the presence of MetS and associated risk factors. Thus, we examined the genotypic variability of adiponectin and its association with MetS in adult women.

Materials and Methods

Study population

This case–control study was conducted at King George’s Medical University (KGMU), Lucknow, India. A total of 541 adult women aged 20–40 years were enrolled from the KGMU outpatient department and the general population of Uttar Pradesh, a Northern region of India. The study participants were divided into two groups: 269 women with MetS (mean age 31.91 ± 6.05) (according to National Cholesterol Education Program-Adult Treatment Panel [NCEP-ATP] III criteria, 2001),⁹ and 272 age-matched healthy women without MetS (wMetS) (mean age 30.96 ± 7.01), who were nonalcoholic, nondiabetic, and free of cardiac, respiratory, inflammatory, endocrine, or metabolic diseases. Women who were pregnant, lactating, had gynecological or obstetrical issues, or were on medications including hormone replacement therapy were excluded from the study (Supplementary Fig. S1). A structured questionnaire collected information on medical, personal, family, dietary, and menstrual history. Institutional ethics review committee of KGMU, Lucknow, approved the study (Reference Code: XXI ECM/P7, No. 1854-R. Cell-06-07). The research work was completed in accordance with the Declaration of Helsinki as revised in 2013. Written informed consent for the participation in the study was obtained prior to enrollment from all the participant women.

Study material and laboratory measurements

Each participant was assessed for body mass index (BMI), height, weight, blood pressure (BP), WC, hip circumference (HC), and waist-to-hip ratio (WHR). BMI was calculated as body weight (kg) divided by height (m²), and WHR was used to measure central obesity by dividing WC (measured at the narrowest point above the hip) by HC (measured at the greatest gluteal protrusion). Women were classified as having MetS if they met three or more of the NCEP-ATP III criteria⁹: central obesity (WC >88 cm), hypertension (systolic BP >130 mm Hg or diastolic BP >85 mm Hg), hypertriglyceridemia (TG >150 mg/dL), HDL-C (<50 mg/dL), or fasting plasma glucose >110 mg/dL.

Venous blood samples were collected in the morning after an overnight fast on the 10th day of menstruation. From 5 mL of blood, plasma and serum samples were either analyzed immediately or stored at −80°C. Commercial enzymatic test kits were used to determine blood plasma glucose and serum lipid profile by GOD-POD and enzymatic methods, respectively (Randox Laboratories Ltd., UK). Adiponectin (R&D Systems Inc., USA; sensitivity 0.25 ng/mL, intra-assay coefficient of variation 3.4%) and leptin (Diagnostics Biochem Canada Inc., Canada; sensitivity 0.50 ng/mL, intra-assay coefficient of variation 4.3%) levels were measured using sandwich enzyme-linked immunosorbent assay methods, and fasting plasma insulin was assessed by immunoradiometric assay (Immunotech Radiova, Prague). IR was calculated using the HOMA¹⁰ formula: [fasting plasma glucose (mmol/L) × fasting insulin (µU/mL)]/22.5.

Screening of adiponectin gene (+45T>G and +276G>T)

Genomic DNA was extracted from 3 mL of venous blood collected in an EDTA vial using a commercial genomic DNA purification kit (Qiagen, USA). Genotyping of adiponectin was performed using polymerase chain reaction (PCR) on a Thermo Cycler Instrument (Bio-Rad Inc., USA), followed by restriction fragment length polymorphism analysis (Supplementary Fig. S2). For adiponectin +45T>G, the forward primer was 5′-TCCTTTGTAGGTCCCAACT-3′ and the reverse primer was 5′-GCAGCAAAGCCAAAGTCTTG-3′. The PCR conditions were 95°C for 5 min, 35 cycles of 45 s at 94°C, 45 s at 57.5°C, and 45 s at 72°C, followed by a 7 min extension at 72°C. The 503 bp PCR fragment was digested with BspH1/Pag1 at 37°C. For adiponectin +276G>T, the forward primer was 5′-AGAAAGCAGCTCCTAGAAGT-3′ and the reverse primer was 5′-GGCACCATCTACACTCATCC-3′. The PCR conditions were 95°C for 4 min, 35 cycles of 45 s at 94°C, 45 s at 57.5°C, and 45 s at 72°C, followed by a 7 min extension at 72°C. The 518 bp fragment was typed using BgL1 at 37°C. Each PCR reaction was performed in a total volume of 25 μL containing 3–3.5 mm/L MgCl₂, 0.5 mm of each dNTP (Bangalore Genei, India), 0.2 μm of each primer, 2 U of Taq DNA polymerase (Bangalore Genei, India), and 10 ng of genomic DNA. The genotyping products were separated by electrophoresis on 2% (w/v) agarose gels and visualized by ethidium bromide staining.

Statistical analysis

Genotype and allele distributions were compared between MetS and wMetS women using the Chi-square test (χ²). Hardy–Weinberg equilibrium (HWE) was assessed using the χ² test. Cochran–Armitage trend test was also used to assess the genotype effects of adiponectin gene variants. Continuous data were compared using the two-sample Student’s t-test, while categorical data were analyzed using the χ² test with Yates’s correction. Pearson correlation (r) was used to examine associations between adiponectin gene variants with anthropometric and biochemical variables. Multiple logistic regression analysis was used to estimate the association of adiponectin genes with MetS risk and its components, with a P value < 0.05 considered statistically significant. GraphPad Prism (version 10.0) software was used for analysis.

Results

In this study, a total of 541 adult women (272 wMetS and 269 MetS) were screened to investigate the frequency of adiponectin gene variants at positions +45T>G and +276G>T. An independent samples t-test was conducted to compare anthropometric, clinical, and biochemical markers between MetS and wMetS women (Table 1, Fig. 1). Although, the women in both groups were age-matched, there was no significant difference in mean age (P > 0.05). Significant differences (P < 0.001) were found between the two groups in adiponectin and leptin levels, anthropometric variables, lipid profiles, glucose, insulin, and HOMA-IR.

FIG. 1.

Anthropometric variables, risk factors, and circulating adipokine levels in North Indian women with and without metabolic syndrome. P value <0.001** (significant). BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; FPI, fasting plasma insulin; HDL-C, high density lipoprotein-cholesterol; HOMA-IR, homeostasis model assessment-insulin resistance; LDL-C, low density lipoprotein-cholesterol; MetS, metabolic syndrome; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; VLDL, very low-density lipoprotein; WC, waist circumference; WHR, waist-to-hip ratio.

Table 1.

Comparison of Demographic, Anthropometric, and Biochemical Variables Between MetS and wMetS Women

Variables	wMetS (n = 272)	MetS (n = 269)	P value
Age (years)	30.96 ± 7.01	31.91 ± 6.05	NS
Anthropometric variables
WC (cm)	74.11 ± 10.44	88.71 ± 13.99	^*
WHR	0.82 ± 0.05	0.87 ± 0.05	^*
BMI (kg/m²)	22.98 ± 4.36	28.10 ± 5.09	^*
Blood pressure
SBP (mm Hg)	118.36 ± 8.82	132.20 ± 11.97	^*
DBP (mm Hg)	80.25 ± 6.50	88.17 ± 7.80	^*
Lipid profile
TC (mg/dL)	147.33 ± 28.49	171.80 ± 33.9	^*
HDL (mg/dL)	43.75 ± 6.46	41.73 ± 4.85	^*
TG (mg/dL)	106.86 ± 23.15	143.20 ± 38.31	^*
LDL (mg/dL)	101.60 ± 31.46	82.20 ± 27.26	^*
VLDL (mg/dL)	21.36 ± 4.62	28.64 ± 7.66	^*
Glucose homeostatis
FPG (mg/dL)	91.08 ± 10.40	105.01 ± 17.68	^*
FPI (μU/mL)	8.48 ± 5.79	12.24 ± 8.42	^*
HOMA-IR	1.93 ± 1.39	3.31 ± 2.58	^*
Adipokine levels
Adiponectin (ng/mL)	29.56 ± 13.46	20.21 ± 10.85	^*
Leptin (ng/mL)	9.56 ± 6.88	15.77 ± 10.47	^*

Values are expressed as mean ± SD.

P values shown as <0.0001 (significant) and >0.05 (nonsignificant, NS) were calculated between wMetS and MetS women.

BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment-insulin resistance; LDL, low-density lipoprotein; MetS, metabolic syndrome; NS, nonsignificant; SBP, systolic blood pressure; SD, standard deviation; TC, total cholesterol; TG, triglyceride; VLDL, very low-density lipoprotein; WC, waist circumference; WHR, waist-to-hip ratio; wMetS, without metabolic syndrome.

Table 2 presents the frequencies of the adiponectin gene variants at positions +45T>G and +276G>T in the MetS and wMetS women groups. The genotype distributions for both SNPs, +45T>G (χ² = 0.088; P = 0.767) and +276G>T (χ² = 0.157; P = 0.692), were in HWE. The frequency of the combined mutant TG+GG genotype at +45T>G (χ² = 5.707, P < 0.05) and the mutant G allele (χ² = 7.030, P = 0.008) differed significantly between the MetS and wMetS women groups, with notable significance for the TG and GG genotypes. For adiponectin +276G>T, only the mutant T allele (χ² = 4.840, P = 0.028) showed a significant difference between the two groups, while genotype frequencies did not differ significantly (P > 0.05). Additionally, the genotypic effects of adiponectin gene variations were also statistically different between MetS and wMetS women (+45T>G; P = 0.0064 and +276G>T; P = 0.0263) (Supplementary Table S1).

Table 2.

Frequencies of Adiponectin Gene Variants (+45T>G and +276G>T) in MetS and wMetS Women

SNPs	Genotype/alleles	wMetS n = 272 (%)	MetS n = 269 (%)	OR (95% CI)	P value
+45T>G	TT^δ	178 (65.4)	148 (55.0)	Reference
	TG	85 (31.3)	103 (38.3)	1.46 (1.02–2.09)	0.050
	GG	9 (3.3)	18 (6.7)	2.41 (1.05–5.51)	0.054
	TG+GG	94 (34.6)	121 (45.0)	1.55 (1.10–2.91)	0.017^*
	T^δδ	441 (81.1)	399 (74.2)	Ref
	G	103 (18.9)	139 (25.8)	1.49 (1.12–1.99)	0.008^**
+276G>T	GG^δ	152 (55.9)	128 (47.6)	Reference
	GT	101 (37.1)	111 (41.3)	1.31 (0.91–1.87)	0.171
	TT	19 (7.0)	30 (11.2)	1.88 (1.01–3.49)	0.064
	GT+TT	120 (44.1)	141 (52.4)	1.40 (0.99–1.96)	0.065
	G^δδ	405 (74.5)	367 (68.2)	Reference
	T	139 (25.6)	171 (31.8)	1.36 (1.04–1.77)	0.028^*

Wildtype genotype^δ and wildtype allele^δδ (taken as reference) compared with respective genotype and allele.

P values shown as <0.05 and ^**<0.01 (significant).

CI, confidence interval; OR, odds ratio; SNPs, single nucleotide polymorphisms.

Table 3 analyzes the association between adiponectin variants and anthropometric and bio-clinical variables in the MetS women group. The results indicate that WHR and adiponectin levels were significantly associated with +45T>G frequency (P < 0.05 and P < 0.01, respectively). However, no associations were found with other parameters in the wMetS group (data not shown). In contrast, the adiponectin +276G>T variant was strongly associated with phenotypic variables such as WC, WHR, BMI, BP, and circulating leptin, except for adiponectin levels, lipid profiles, glucose, insulin, and HOMA-IR. In the wMetS group, only WC and HDL-C were significantly associated with the +276G>T variant (data not shown).

Table 3.

Distribution of Adiponectin Gene Variants (+45T>G and +276G>T) Among Women with MetS (n = 269)

+45T>G variants	TT (n = 148)	TG (n = 103)	GG (n = 18)	P value
WC (cm)	87.53 ± 13.47	89.54 ± 14.50	93.72 ± 14.51	NS
WHR	0.87 ± 0.05	0.88 ± 0.05	0.91 ± 0.04	0.004
BMI (kg/m²)	27.51 ± 5.04	28.73 ± 5.23	29.39 ± 4.33	NS
SBP (mm Hg)	131.41 ± 12.47	132.80 ± 11.92	135.67 ± 6.41	NS
DBP (mm Hg)	87.35 ± 7.55	88.76 ± 7.98	91.56 ± 8.67	NS
TC (mg/dL)	171.10 ± 34.99	173.18 ± 33.48	170.29 ± 29.18	NS
TG (mg/dL)	143.02 ± 37.84	145.13 ± 40.48	133.68 ± 28.42	NS
HDL-C (mg/dL)	41.82 ± 4.91	41.44 ± 4.72	42.61 ± 5.26	NS
TC-HDL-C	4.14 ± 0.96	4.24 ± 0.98	4.06 ± 0.92	NS
FPG (mg/dL)	104.23 ± 17.25	106.48 ± 18.05	103.07 ± 19.55	NS
Insulin (µU/mL)	12.06 ± 8.01	12.41 ± 8.96	12.89 ± 9.09	NS
HOMA-IR	3.25 ± 2.48	3.37 ± 2.63	3.61 ± 3.22	NS
Adiponectin (ng/mL)	21.39 ± 10.65	19.73 ± 11.76	13.20 ± 5.91	0.0096
Leptin (ng/mL)	15.62 ± 10.56	15.73 ± 9.32	17.28 ± 15.56	NS

+276G>T variants	GG (n = 128)	GT (n = 111)	TT (n = 30)	Value
WC (cm)	87.30 ± 13.38	88.82 ± 14.51	94.33 ± 13.58	0.046
WHR	0.87 ± 0.05	0.87 ± 0.05	0.91 ± 0.05	0.0003
BMI (kg/m²)	27.38 ± 4.65	28.23 ± 5.28	30.70 ± 5.49	0.005
SBP (mm Hg)	130.27 ± 13.54	134.34 ± 10.52	132.77 ± 8.23	0.030
DBP (mm Hg)	86.69 ± 7.95	89.78 ± 7.33	88.53 ± 8.30	0.009
TC (mg/dL)	169.14 ± 33.65	173.11 ± 36.70	178.65 ± 22.47	NS
TG (mg/dL)	140.96 ± 34.85	145.99 ± 44.42	142.51 ± 26.63	NS
HDL-C (mg/dL)	42.05 ± 4.71	41.31 ± 5.03	41.92 ± 4.82	NS
TC-HDL-C	4.07 ± 0.92	4.25 ± 1.06	4.32 ± 0.75	NS
FPG (mg/dL)	107.04 ± 18.34	103.39 ± 17.50	102.37 ± 14.85	NS
Insulin (µU/mL)	11.88 ± 7.80	12.54 ± 9.26	12.74 ± 7.97	NS
HOMA-IR	3.28 ± 2.49	3.36 ± 2.77	3.35 ± 2.34	NS
Adiponectin (ng/mL)	20.99 ± 10.64	20.22 ± 10.82	16.84 ± 11.57	NS
Leptin (ng/mL)	14.75 ± 9.35	15.60 ± 9.64	20.75 ± 15.77	0.018

All values are expressed in mean ± SD. One-way analysis of variance.

P values are shown as <0.05 (significant) and >0.05 (nonsignificant, NS).

HDL-C, high-density lipoprotein-cholesterol.

In Table 4 (a and b), a multiple regression analysis was performed to identify the strongest associations between adiponectin +45G and +276T mutant alleles and various variables across all women combined. The independent variables included BMI, WC, WHR, HDL-C, TG, low-density lipoprotein-cholesterol, total cholesterol (TC)/HDL-C, fasting glucose, insulin, IR, and leptin levels. The multivariate regression analysis revealed that WHR and adiponectin were the strongest and most significant predictors associated with the +45 mutant G allele, while glucose and systolic BP were strongly associated with the +276 mutant T allele.

Table 4.

Associations Between Predictor Variables in Adult Women of North India (n = 541)

Predictors	Coeff.	SE	t ratio	95% CI		P value
Predictors	Coeff.	SE	t ratio	Lower	Upper	P value
(a) presence or absence of the mutant allele (G) of adiponectin +45T>G gene
Constant	0.502	0.205	2.451	0.101	0.904	0.015
WC	−0.014	0.062	0.232	−0.136	0.108	NS
WHR	0.242	0.117	2.065	0.012	0.471	0.039
BMI	−0.076	0.121	0.627	−0.313	0.161	NS
Glucose	0.007	0.059	0.113	−0.109	0.123	NS
SBP	0.105	0.068	1.528	−0.029	0.239	NS
DBP	0.003	0.065	0.051	−0.124	0.130	NS
TC	0.004	0.119	0.037	−0.228	0.237	NS
TG	−0.107	0.291	0.369	−0.678	0.463	NS
HDL-C	0.041	0.084	0.490	−0.124	0.207	NS
LDL-C	−0.013	0.091	0.143	−0.191	0.165	NS
TC-HDL-C	0.022	0.052	0.428	−0.079	0.123	NS
Insulin	−0.041	0.099	0.412	−0.234	0.153	NS
HOMA-IR	0.045	0.062	0.723	−0.076	0.165	NS
Adiponectin	0.301	0.112	2.711	0.083	0.518	0.007
Leptin	−0.076	0.054	1.392	−0.183	0.031	NS
(b) Presence or absence of the mutant allele (T) of adiponectin +276G>T gene
Constant	0.455	0.212	2.141	0.038	0.871	0.033
WC	0.036	0.064	0.566	−0.090	0.163	NS
WHR	0.007	0.121	0.055	−0.231	0.245	NS
BMI	0.037	0.125	0.293	−0.209	0.282	NS
Glucose	−0.133	0.061	2.170	−0.254	−0.013	0.030
SBP	0.178	0.071	2.505	0.039	0.317	0.013
DBP	−0.100	0.067	1.491	−0.232	0.032	NS
TC	−0.054	0.123	0.440	−0.295	0.187	NS
TG	−0.434	0.302	1.437	−1.026	0.158	NS
HDL-C	−0.041	0.088	0.470	−0.213	0.130	NS
LDL-C	0.048	0.094	0.510	−0.136	0.232	NS
TC-HDL-C	0.056	0.054	1.037	−0.049	0.161	NS
Insulin	−0.029	0.102	0.286	−0.230	0.171	NS
HOMA-IR	0.115	0.064	1.803	−0.010	0.240	NS
Adiponectin	−0.038	0.115	0.334	−0.264	0.187	NS
Leptin	−0.078	0.056	1.380	−0.189	0.033	NS

(a) Values transformed in 0 (TT, n = 326) and 1 (TG+GG, n = 215).

(b) Values transformed in 0 (GG, n = 280) and 1 (GT+TT, n = 261).

P values are shown as <0.05 (significant) and >0.05 (nonsignificant, NS).

Coeff., coefficient; LDL-C, low-density lipoprotein-cholesterol; SE, standard error.

Table 5 illustrates the relationship between adiponectin and metabolic risk factors (Fig. 2, Fig. 3). Overall, adiponectin was inversely related to adiposity, fasting glucose, HOMA-IR, and leptin, and positively associated with HDL-C. In women with MetS, adiponectin showed an inverse correlation with WC, WHR, BMI, and leptin, and a positive correlation with HDL-C, while no significant associations were observed with other metabolic risk factors.

FIG. 2.

Pearson correlation of serum adiponectin (ng/mL) level with metabolic risk factors in MetS women of North India (n = 269). P value <0.001 (significant) and P > 0.05 (nonsignificant). r, correlation coefficient.

FIG. 3.

Pearson correlation (r) of serum adiponectin level (ng/mL) with leptin level and other risk factors in MetS women (n = 269). P value <0.001 (significant) and P > 0.05 (nonsignificant).

Table 5.

Pearson Correlation (r) of Circulating Adiponectin Levels with Anthropometric and Biochemical Variables in North Indian Adult Women (n = 541)

Variable	Total women (N = 541)		wMetS (N = 272)		MetS (N = 269)
Variable	r	P value	r	P value	r	P value
WC, cm	−0.5201	<0.0001	−0.4429	<0.0001	−0.4256	<0.0001
WHR	−0.4804	<0.0001	−0.3842	<0.0001	−0.4075	<0.0001
BMI, kg/m²	−0.593	<0.0001	−0.5168	<0.0001	−0.531	<0.0001
SBP, mm Hg	−0.226	<0.0001	−0.0459	NS	−0.03099	NS
DBP, mm Hg	−0.1957	<0.0001	−0.03619	NS	−0.0225	NS
TC, mg/dL	−0.2267	<0.0001	−0.1716	0.0045	−0.05068	NS
TG, mg/dL	−0.243	<0.0001	−0.1346	0.0264	−0.04657	NS
HDL-C, mg/dL	0.2012	<0.0001	0.1959	0.0012	0.07495	NS
LDL-C, mg/dL	−0.2306	<0.0001	−0.2029	0.0008	−0.0624	NS
TC-HDL-C ratio	−0.2888	<0.0001	−0.27	<0.0001	−0.08268	NS
FPG, mg/dL	−0.1887	<0.0001	−0.05096	NS	−0.03642	NS
FPI, µU/mL	−0.1334	0.0019	−0.04603	NS	−0.05276	NS
HOMA-IR	−0.1637	0.0001	−0.04283	NS	−0.07436	NS
Leptin, ng/mL	−0.4101	<0.0001	−0.3221	<0.0001	−0.3711	<0.0001

P values are shown as <0.05 (significant) and >0.05 (nonsignificant, NS).

FPI, fasting plasma insulin.

Discussion

The association between adiponectin gene variations and components of MetS, obesity, and HOMA-IR has been extensively studied across various ethnic populations.^11–13 However, in South India, research on adiponectin and leptin levels is limited. To date, no such studies have specifically investigated the association between adiponectin polymorphisms at loci +45T>G and +276G>T with adiponectin and leptin levels, as well as IR, in adult MetS women.

The present study demonstrated that the adiponectin SNPs at +45T>G and +276G>T are significantly associated with higher HOMA-IR and reduced adiponectin level in MetS women. These particular SNPs were selected based on past literature and their higher allele frequency in SNP databases. The study also revealed significant differences in serum lipid profiles, including TC, TG, HDL-C, plasma glucose, insulin, HOMA-IR, and serum adiponectin and leptin levels between MetS and wMetS women. Moreover, consistent with previous studies, our findings showed that lower serum adiponectin levels are associated with lower HDL-C or dyslipidemia, obesity, high BP, and MetS.^14–19 Collectively, these human studies underscore the importance of adiponectin as a key biomarker for MetS.

A study conducted on White and Pima Indian populations by Weyer et al.²⁰ found that lower adiponectin levels are more strongly related to the degree of HOMA-IR and hyperinsulinemia than to overall adiposity or glucose intolerance. Evidence suggests that adiponectin plays a significant role in contributing to HOMA-IR and MetS. Due to its insulin-sensitizing properties, adiponectin may influence glucose metabolism by stimulating pancreatic insulin secretion in vivo.²¹ These findings indicate that clinically significant hypo-adiponectinemia is a key feature of MetS, potentially contributing to the increased risk of HOMA-IR. Further studies, particularly in men, are needed to validate these associations.

Adiponectin is often referred to as the “fat-burning molecule” because it helps redirect fatty acids to muscle tissue for oxidation. Like leptin, adiponectin appears to prevent fat deposition in insulin-sensitive tissues by increasing fat oxidation, activating insulin signaling, and upregulating molecules involved in fatty acid transport, oxidation, and energy dissipation, thereby enhancing insulin sensitivity.²² Leptin, a circulating hormone, is responsible for regulating body weight and energy homeostasis. Fernandez-Real et al.²³ found that obese individual had greater serum leptin levels than individual with normal body weight. Similarly, our study revealed that serum leptin levels are higher in MetS women compared with wMetS women. It is well established that leptin levels are several times higher in women, suggesting a potentially greater effect on the sympathetic nervous system than in men.²⁴ In addition to its role in adiposity, leptin also influences insulin sensitivity and insulin secretion, exerting direct effects on several metabolic actions of insulin and stimulating protein synthesis in insulin-sensitive target cells.

In a genome-wide scan aimed at identifying genetic loci associated with susceptibility to MetS traits, Kissebah et al.²⁵ pinpointed a region on chromosome 3q27 that overlaps with the adiponectin gene’s location on the human genome. Our findings revealed that the presence of the mutant TG+GG genotype at the +T45G site and the mutant GT+TT genotype at the +G276T site of the adiponectin gene increased the likelihood of having high-risk genotypes by 1.55-fold and 1.40-fold, respectively. Similarly, the mutant G allele at +T45G and the mutant T allele at +G276T were associated with a 1.49-fold and 1.36-fold increased risk, respectively, when comparing MetS and wMetS women. Interestingly, a significant difference in the frequency of the TG+GG genotypes was observed at +45T>G, while the GT+TT genotypes at +276G>T did not show a significant difference between MetS and wMetS women. These results suggest that the rate of disease progression varies among individuals, likely due to differences in their genetic susceptibility to MetS.²⁶

We also demonstrated that polymorphisms at positions +T45G and +G276T of the adiponectin gene may serve as genetic risk factors influencing circulating adiponectin levels in MetS women. These adiponectin polymorphisms likely contribute to variations in serum adiponectin levels. Our findings are consistent with previous studies that demonstrated a significant association between the +45T>G and +276G>T polymorphisms and obesity, insulin sensitivity, and adiponectin levels^11,27,28 in MetS and wMetS women. Previous studies demonstrated that individuals with the G allele at +45T>G typically had lower levels of adiponectin, which may lead to reduced activation of AMP-activated protein kinase (AMPK), an enzyme necessary for glucose metabolism.²⁹ A higher risk of IR and elevated HOMA-IR readings are eventually caused by this decreased AMPK activity, which also interferes with insulin signaling pathways, hinders glucose absorption in skeletal muscles, and lessens hepatic gluconeogenesis suppression.²⁹ Similarly, individuals with the T allele at +276G>T had lower levels of adiponectin, which may have decreased AMPK activation.³⁰ Adiponectin expression has been demonstrated to be influenced by this polymorphism. A strong correlation between the +276 variant and circulating adiponectin, HOMA-IR, and MetS was found in a study by De Luis et al.³⁰ Overall, these investigations show that both SNPs are associated with MetS, circulating adiponectin and HOMA-IR.

Gene–environment interactions play a crucial role in modulating adiponectin levels. Adiponectin gene responds differently to lifestyle factors such as diet, physical activity, and overall adiposity.^19,31 For instance, individuals carrying specific polymorphisms may experience a greater decrease in adiponectin levels when exposed to high-fat diets or sedentary behavior. On the contrary, the negative consequences of these inherited predispositions can be mitigated by maintaining a healthy diet and engaging in frequent exercise. Interestingly, those with greater body fat percentages seem to be more affected by the +276G>T polymorphism, indicating a gene–environment interaction in which adiposity increases genetic susceptibility to IR.³² Additionally, ethnic differences influence the prevalence and impact of adiponectin polymorphisms. A study on the Punjabi population in North India has demonstrated a significant association between the +276G>T SNP and obesity-related traits,¹⁹ indicating that genetic susceptibility to metabolic syndrome may vary across populations. However, inconsistencies remain, as some studies have failed to replicate these associations, highlighting the complexity of genetic and environmental interactions in metabolic disorders.^33,34 Thus, a comprehensive understanding of these gene–environment interactions is essential for developing targeted prevention and treatment strategies for metabolic disorders.

Our findings also reveal significant associations between anthropometric variables (WC, WHR, BMI, BP), lipid profile (TC, TG, TC-HDL ratio), and biochemical parameters such as fasting insulin concentration and serum leptin levels with the mutant genotypes of the +45T>G and +276G>T adiponectin polymorphisms in MetS women. Interestingly, no other studies have examined the relationship between these polymorphisms and serum leptin levels in North Indian adult women with MetS. Our study supports the hypothesis that these adiponectin SNPs (+45T>G and +276G>T) are directly associated with lower adiponectin levels, higher leptin levels, elevated HOMA-IR, and other metabolic risk factors in MetS women, particularly within the North Indian population. Furthermore, adiponectin +45T>G polymorphism has been associated with undesirable lipid profiles, such as elevated TG and lower HDL-C, which exacerbate metabolic disorders.²⁹ Consistent with previous studies,^35,36 our findings indicate that circulating adiponectin levels are positively correlated with HDL-C and insulin sensitivity³⁷ while being negatively correlated with serum leptin levels, HOMA-IR, WHR, adiposity, and MetS.^20,38,39

The limitations of this study include a relatively small sample size and exclusion of male participants of North India, which may affect the generalizability of the findings to other populations or ethnic groups. To the best of our knowledge, this study presents strong evidence of a link between adiponectin gene variants and HOMA-IR and leptin levels in adult MetS women from North India. These results suggest that these adiponectin variants significantly influence adiponectin and leptin levels, insulin sensitivity, and related risk factors in MetS women of North Indian descent.

Conclusions

Our findings suggest that reduced circulating adiponectin may serve as an independent risk factor and potential biomarker for MetS. Future research could reveal that reduced adiponectin levels specifically signal certain MetS risk factors rather than the entire spectrum currently used for diagnosis. Furthermore, our study indicates that adiponectin gene variants at +45T>G and +276G>T might play a protective role in the development of MetS, providing important insights into the genetic underpinnings of the syndrome. However, the influence of other genetic and environmental factors should not be disregarded.

Footnotes

Acknowledgments

The authors would like to thank the adult participants who participated in this study. They also thank physicians and residents of the Department of Medicine and technical staff of Physiology, KGMU, Lucknow, India, for their generous support in the study.

Authors’ Contributions

Conceptualization: A.G. and V.G. Data curation: A.G., A.K.S., P.G. and V.G. Formal analysis: A.G., A.K.S., P.G., V.G., and A.O. Funding acquisition: V.G. and A.G. Investigation: A.G., A.K.S., and V.G. Methodology: A.G., A.K.S., V.G., and A.O. Project administration: A.G. and V.G. Resources: A.G., A.K.S., P.G., V.G., and A.O. Software: A.G., A.K.S., and P.G. Supervision: V.G. Validation: A.G. and V.G. Visualization: A.G. and V.G. Writing—original draft: A.G. Writing—review and editing: A.G., A.K.S., P.G., V.G., and A.O.

Data Sharing Statement

Upon submission, authors agree to make any materials, data, and associated protocols available upon request.

Author Disclosure Statement

The authors declare no conflict of interest.

Funding Information

This work was supported by a grant of Indian Council of Medical Research, New Delhi (Grant No. 3/1/2/2/06-RHN). However, the funders had no role in study design, data collection and analysis, decision to publish or preparation of the article.

Supplementary Material

Abbreviations Used

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Supplementary Material

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Association of Adiponectin Gene Variants (+45T>G and +276G>T) with Adipokine Levels and Metabolic Syndrome in North Indian Adult Women

Abstract

Background:

Methods:

Results:

Conclusions:

Introduction

Materials and Methods

Study population

Study material and laboratory measurements

Screening of adiponectin gene (+45T>G and +276G>T)

Statistical analysis

Results

Discussion

Conclusions

Footnotes

Acknowledgments

Authors’ Contributions

Data Sharing Statement

Author Disclosure Statement

Funding Information

Supplementary Material

Abbreviations Used

References

Supplementary Material