Variation of Adipokines in Obese versus Non Obese Patients with Polycystic Ovary Syndrome and its Association to Visceral Adiposity Index: A Cross-sectional Study

Adiponectin, High density lipoprotein cholesterol, Leptin, Resistin, Triglyceride, Visceral fat

PCOS is an endocrine condition among women of childbearing age. The prevalence of this syndrome has shown considerable variation, ranging from 3.7-22.5% of the Indian population [1], with one of the contributing factors being its heterogeneous presentation [2]. Before the standardisation of the definition of PCOS by the National Institute of Health (NIH), diagnosis was based more on menstrual abnormalities and clinical manifestations of hyperandrogenism rather than on endocrinological criteria [3]. It is proposed that PCOS is a result of the maladaptation of the timing of pubertal changes, which are unable to transition to an oestrogenic state [4]. The Rotterdam criteria for the diagnosis of PCOS are all-inclusive, providing a better opportunity to study related metabolic disorders. Typical symptoms include androgenemia, cystic ovaries, anovulation/oligo-ovulation, and insulin resistance. Metabolic issues such as compensatory hyperinsulinaemia, dyslipidaemia, and hypertension are also linked to the syndrome [5].

The prevalence of obesity among people with polycystic ovaries is well-documented, and research has demonstrated that obesity increases insulin resistance and hyperandrogenism [6]. The prevalence of android-type obesity is high, and there is a greater correlation between upper body obesity and anovulation [7]. The pathology associated with obesity is more closely linked to visceral adipose tissue than to overall obesity [8]. This is because obesity disrupts both the endocrine and metabolic functions of adipose tissue [9]. Visceral adipose tissue is more secretory in nature, and the metabolic-endocrinological changes lead to an increased release of fatty acids, steroid hormones, and proinflammatory molecules, also termed proinflammatory adipokines. Leptin is the key adipokine controlling adipose tissues. It has been observed that leptin has a linear relationship with body fat and a gradual decrease in leptin receptors, resulting in the accumulation of triglycerides in peripheral tissue [10]. This makes it an ideal biomarker for adiposity. Additionally, it has been suggested that leptin modulates the production of gonadotropin-releasing hormones and thus influences reproductive function [11].

In contrast, adiponectin is the most abundant and widely distributed adipokine secreted by adipocytes. Adiponectin inhibits the liver’s gluconeogenesis process, enhances insulin sensitivity, and significantly reduces triglyceride buildup. Adiponectin decreases the production of androgens in follicular theca cells, which are otherwise stimulated by insulin. A meta-analysis reported decreased levels of adiponectin and insulin resistance in non obese PCOS patients when compared to a non obese control group [12]. The cysteine-rich adipokine resistin is a known antagonist of glucose homeostasis, with a positive association with insulin resistance, obesity, and Type 2 diabetes [13].

The role of body fat prediction using BMI has its own reliability; studies have shown that regardless of BMI, women with PCOS have increased visceral fat deposition, which may be related to disturbed adipokine secretion [14-17]. Both non obese and obese phenotypes of PCOS patients exhibit insulin resistance, indicating the involvement of other factors. A previous study found that despite the severity of metabolic and hormonal disturbances being more pronounced in obese PCOS patients, non obese PCOS patients also show considerable metabolic abnormalities when compared to their age-matched controls [18]. Hence, the purpose of the study was to address the gap in understanding the variation in adipokine secretion between non obese and obese PCOS phenotypes, which may influence the characteristics of PCOS patients.

The present study was conducted to assess different adipokines in non obese and obese phenotypes of PCOS while measuring VAI in both groups. Additionally, it aimed to observe the relationship between circulating adipokines and visceral adiposity in non obese and obese PCOS patients.

Materials and Methods

This cross-sectional study was conducted between November 2019 and August 2021 at the Mayo Institute of Medical Sciences in Barabanki, Uttar Pradesh, India. The study was approved by the Institutional Ethics Committee (approval letter no. MIMS/Ex/2019/199, dated 19/11/2019).

Inclusion criteria: A total of 144 consecutive PCOS patients aged between 18 and 38 years were studied. Patients were diagnosed based on the Rotterdam criteria [5], which include the presence of two out of three of the following criteria: clinical or biological hyperandrogenism, chronic anovulation, and/or polycystic ovaries, after the exclusion of thyroid disease, late-onset congenital adrenal hyperplasia, hyperprolactinaemia, and androgen-secreting tumours. To ensure accuracy, patients underwent clinical examination, sonography, and biochemical and hormonal assays. The Ferriman-Gallwey-Lorenzo score [19] was used to assess hirsutism, which defines clinical hyperandrogenism. Biochemical hyperandrogenism was evaluated by estimating serum testosterone, using a cut-off level of >34 ng/dL.

Exclusion criteria: Subjects under 18 years of age and over 38 years of age, as well as those with late-onset congenital adrenal hyperplasia, thyroid disease, hyperprolactinaemia, or androgen-secreting tumours, were excluded from the study. Additionally, subjects using medication (including oral contraceptives), a hormonal intrauterine device, or those who are pregnant or lactating were also excluded from the study.

Sample size calculation: Sample size was determined using the formula

where, n=sample size, t-value=confidence level at 95% (standard value 1.96) p-value=estimated prevalence (10%) [20]. m=Margin of error (Standard value of 0.05). Calculated sample size was 139.

Study Procedure

Anthropometric measurements, including height, weight, WC and HC, were carried out using standard procedures. BMI was estimated using the standard formula. PCOS patients were divided into two groups:

The obese PCOS group (BMI >25) and

The non obese PCOS group (BMI ≤25) [21].

A body fat scale was used to estimate BFP based on the electrical impedance method. A prevalidated, sex-specific VAI was defined as follows:

This was used as an indirect expression of visceral fat function [22].

In all women with PCOS metabolic profile was obtained by estimating fasting blood glucose, lipid profile and fasting insulin [Table/Fig-1] [23,24].

[Table/Fig-1]:

Cut-off values of parameters [23,24].

Criteria	Method of estimation	Cut-off limits
Fasting plasma glucose	GOD-POD (glucose oxidase-peroxidase)	≥6.1 mmol/L (110 mg/dL) [23]
Dyslipidaemia	Enzymatic direct estimation	TG ≥1.7 mmol/L (150 mg/dL) [23]
Dyslipidaemia	Enzymatic direct estimation	HDL-C <40 mg/dL (male), <50 mg/dL (female) [23]
Fasting insulin	ELISA	FI >7.35 μU/mL [24]

Five mL venous blood were drawn and used for the study. The separated serum was stored at -20°C for the estimation of leptin, adiponectin, and resistin using ELISA. For the estimation of leptin, the Demeditec ELISA kit from Germany, Catalogue ID DEE007, based on a sandwich assay, was used. Resistin was estimated using the Human Resistin (RETN) ELISA kit from Sincere Biotech, China, Catalogue ID E13651978. The Adiponectin ELISA kit from Demeditec, Germany, Catalogue ID DEE009, was used for the estimation of serum adiponectin.

Statistical Analysis

Statistical analysis were conducted using Jamovi 2.4.6 software. Results were expressed as Mean±Standard Error of the Mean (SE). The non obese and obese groups of PCOS patients were compared using an Independent t-test, with Welch’s correction applied wherever necessary for better transparency of the data. Relationships between adipokines and components of obesity were analysed using Pearson’s r correlation coefficient and weighted linear regression. A p-value <0.05 was considered statistically significant.

Results

The obese and non obese groups were age-matched. Obesity-related components such as BMI (p-value <0.001), BFP (p-value <0.001), WC (p-value <0.001), and HC (p-value=0.002) were found to be significantly higher in the obese group compared to the non obese group. An interesting observation was that, although the mean VAI was apparently comparable between the groups, it was significantly higher in the non obese PCOS group (p-value=0.034). The independent t-test revealed that adiponectin (p-value <0.001) was significantly lower in the obese PCOS group, whereas leptin (p-value <0.001) and resistin (p-value <0.001) were significantly higher in the obese group [Table/Fig-2].

[Table/Fig-2]:

Descriptive statistics of PCOS population, non obese and obese PCOS groups. t-test for non-obese and obese PCOS groups.

Parameters	PCOS (n=144)	Non obese PCOS (n=71)	Obese PCOS (n=73)	t statistics	p-value
Age (years)	27.47±0.33	27.13±0.41	27.79±0.52	Student t -0.992	0.323
BMI (kg/m2)	26.37±0.26	23.84±0.10	28.84±0.31	Welch’s t -15.157	<0.001
Body Fat Percentage (BFP) (kg)	31.68±0.46	27.13±0.31	36.11±0.46	Welch’s t -16.024	<0.001
Waist (cm)	92.76±0.47	91.02±0.55	94.46±0.72	Student t -3.753	<0.001
Hip (cm)	99.73±0.21	99.07±0.34	100.37±0.22	Welch’s t -3.153	0.002
Visceral Adiposity Index (VAI)	3.33±0.04	3.42±0.06	3.24±0.05	Welch’s t 2.139	0.034
Adiponectin (μg/mL)	10.28±0.27	13.05±0.16	7.39±0.24	Welch’s t 18.320	<0.001
Resistin (ng/mL)	5.41±0.09	4.57±0.06	6.23±0.09	Welch’s t -13.926	<0.001
Leptin (ng/mL)	10.95±0.17	9.71±0.22	12.14±0.18	Student t -8.246	<0.001
Triglyceride (mg/dL)	144.81±1.59	130.90±1.16	158.34±1.89	Welch’s t -12.33	<0.001
HDL-C (mg/dL)	38.49±0.18	39.66±0.24	37.36±0.21	Student’s t 7.02	<0.001

Values expressed as (Mean±SE), p<0.05 considered as significant, #Levene’s test is significant (p<0.05), suggesting a violation of the assumption of equal variances. Welch’s Correction applied

Correlation analysis showed that VAI had no significant correlations with BMI, BFP, or any of the three adipokines in the overall studied PCOS population. In the overall PCOS patients, VAI was found to be correlated only with hip circumference (r-value=0.187, p-value=0.025). Among non obese PCOS patients, all obesity indicators correlated significantly, except for the Waist-to-Hip Ratio (WHR) (r-value=0.056, p-value=0.641) [Table/Fig-3]. Furthermore, no significant correlation was observed between VAI and the three studied adipokines among the obese group patients. However, a significant positive correlation was found between resistin (r-value=0.302, p<0.05) and leptin (r-value=0.308, p<0.05) with VAI in the non obese PCOS group [Table/Fig-4].

[Table/Fig-3]:

Correlation between Visceral Adiposity Index (VAI) and individual obesity indicators in studied PCOS population, non obese PCOS patients and obese PCOS patients.

Obesity indicators	PCOS population (n=144)		Non obese PCOS (n=71)		Obese PCOS (n=73)
	r-value	p-value	r-value	p-value	r-value	p-value
BMI	-0.074	0.376	0.371	0.001*	0.028	0.815
Body Fat Percentage (BFP)	-0.048	0.565	0.391	<0.001*	-0.019	0.874
Waist circumference	0.024	0.773	0.262	0.027*	-0.085	0.477
Hip circumference	0.187	0.025*	0.370	0.002*	0.004	0.974
Waist to Hip Ratio	-0.061	0.465	0.056	0.641	-0.103	0.387

p<0.05 considered as significant

[Table/Fig-4]:

Correlation between Visceral Adiposity Index (VAI) and different Adipokines among obese and non obese PCOS patients.

	Obese PCOS		Non obese PCOS
	r-value	p-value	r-value	p-value
Adiponectin	0.079	0.508	-0.110	0.363
Resistin	-0.129	0.277	0.302	0.011*
Leptin	0.040	0.735	0.308	0.009*

p<0.05 considered as significant

None of the obesity indicators correlated with VAI among the obese PCOS group. Utilising the data with weighted regression analysis, it was observed that leptin appears to be a significant predictor of VAI (p-value=0.024, p<0.05) when the regression model (r-value=0.269, F=2.70, p-value=0.033, p<0.05) is weighted with BMI [Table/Fig-5].

[Table/Fig-5]:

Weighted linear regression model for analysing the adipokines and obesity as predictor for VAI.

Weighted linear regression model
Model fit measures
			Overall model test
Model	R	R2	F	df1	df2	p
VAI	0.269	0.072	2.70	4	139	0.033
Model coefficients VAI
Predictor	Estimate		SE	t		p-value
Intercept*	2.4908		0.5897	4.224		<0.001
Adiponectin	0.0169		0.0254	0.666		0.506
Resistin	0.0397		0.0630	0.630		0.530
Leptin	0.0547		0.0240	2.278		0.024#
Obese (1-0)	-0.2959		0.1647	-1.797		0.075

*Represents reference level; Note. Weighted by ‘BMI’; #represent significant (p<0.05)

Discussion

Adipokines have been found to alter metabolic and endocrine signaling in women with PCOS. The role of adipokines is primarily studied by quantifying their levels in different PCOS phenotypes. White adipose tissue is considered the primary source of adiponectin, with some contribution from brown adipose tissue. It has been observed that the level of adiponectin in obese PCOS females is lower compared to normal females, while in non obese PCOS, adiponectin levels are higher compared to normal controls [25]. The present study corroborates this observation, showing that the adiponectin level in the obese group (7.39±0.24 μg/mL) was significantly (t-value=18.32, p<0.001) lower than the adiponectin level in the non obese PCOS group (13.05±0.16). Adiponectin increases the insulin sensitivity of ovarian follicles and promotes follicular growth by enhancing sensitivity to gonadotropins through its receptors, AdipoR1 and AdipoR2, positively affecting ovulation [26]. A comparison of the findings in the present study with contrasting studies is shown in [Table/Fig-6] [27-31].

[Table/Fig-6]:

Studies related to Adipokines, Visceral Adiposity Index (VAI) in PCOS patients [27-31].

Previous studies	Sample size/population	Observation
Shirazi FKH et al., (2021), Iran [27]	83 subjects	HMW adipponectin was found to be lower in obese PCOS patients.
Kale-Gurbuz T et al., (2013), Turkey [28]	38 subjects	Among PCOS patients adiponectin negatively correlated with BMI, and leptin positively correlated with BMI.
Raeisi T (2021), Iran [29]	Meta-analysis, 38 articles (2424 cases, 1906 controls)	Resistin levels were significantly higher in PCOS women compared with non PCOS controls as well as in obese PCOS women vs. obese controls, and in non obese PCOS women compared with non obese controls, but not in obese PCOS vs. non obese PCOS patients.
Yilmaz M et al., (2008), Iran [30]	76 subjects	Plasma resistin levels were similar among obese and non obese women with PCOS.
Joshi B et al., (2018), India [31]	106 subjects	Mean VAI among PCOS (3.02) was significantly higher than normal controls (2.81). Classic and Mild Phenotypes had high VAI.
Maitra C et al., (2024), India	144 subjects	VAI was higher in non obese PCOS patients. Significant positive association between leptin and VAI in PCOS patients was observed.

HMW: High molecular weight; PCOS: Polycystic ovarian syndrome; BMI: Body mass index

Metabolic syndrome has a prominent relationship with obesity, total adipose tissue content, and endocrine factors. Adiponectin levels inversely correlate with visceral obesity; however, a similar correlative association has not been observed between adiponectin and the VAI in PCOS females with PCOS. The actual interaction of adiponectin in the development of metabolic syndrome is still a subject of ongoing research [32]. It has been reported that serum leptin levels are higher in women suffering from PCOS. In the present study, observations revealed that the leptin level in the obese PCOS group (12.14±0.18 ng/mL) was significantly higher (t-value=-8.246, p<0.001) than that in the non obese PCOS group (9.71±0.22 ng/mL). A significant positive association between leptin and VAI in PCOS patients was observed. Despite the higher BMI and total fat percentage in the obese PCOS group, present study observations suggest a higher VAI in the non obese PCOS group. The VAI is a relatively new indicator of visceral adipose tissue distribution and is considered an indirect predictor of cardiometabolic risk. Studies involving different populations of patients with PCOS, type 2 diabetes, non alcoholic liver disease, and endocrine disorders have led to the assertion that VAI is a valuable marker for adipose tissue dysfunction [33].

However, in the present study, no significant correlation between VAI and other obesity markers: BMI (r-value=-0.074, p-value=0.376), BFP (r-value=-0.048, p-value=0.565), waist circumference (r-value=0.024, p-value=0.773), and WHR (r-value=-0.061, p-value=0.465) were observed. Interestingly, hip circumference showed a mild but significant correlation (r-value=0.187, p-value=0.025, p<0.05), which was contradictory to most of the available studies that observed a significant correlation between waist circumference and VAI [34]. Hereby, authors would like to inform readers that all other studied obesity indicators correlated significantly with one another. The present study finds that VAI correlates significantly with BMI, BFP, waist circumference, and hip circumference in non obese PCOS patients (BMI <25). Present study data did not reflect such correlations in the obese PCOS subgroup [Table/Fig-3].

It is postulated that early and consistent exposure to androgens impacts body fat distribution, leading to greater accumulation of fat in the central region in individuals with PCOS. Studies have shown that Asian Indians have more body fat and less muscle mass than the Western population with the same weight and BMI, according to data from the National Health Survey (2019-21). It is noteworthy that the study population in present study belonged to a state where the prevalence of obesity ranges from 35-50%. In the present study, it was observed that only one out of 71 non obese PCOS patients had a waist circumference of less than 80 cm. This supports the notion that, despite being categorised as non obese, abdominal or visceral adiposity exists in these PCOS patients. Out of the 71 non obese patients, one had a BFP greater than 32%.

In an Indian study involving PCOS patients, it was found that with each unit increase in Visceral VAI, the risk of metabolic syndrome increases by 5.23 times. A higher cut-off of 2.73 has the potential to predict metabolic risk among PCOS patients. The study also observed that the mean VAI is significantly higher in PCOS patients (3.02) compared to normal female controls (2.81) [31]. The present cross-sectional study showed the relationship between adiposity indices and adipokines; however, the authors believe that a longitudinal study design is required to elevate the research to a level where we can observe whether altered secretion of adipokines is a cause or effect of metabolic derangement in PCOS.

Limitation(s)

In the present study, WHR and percent body fat were used as proxy markers of obesity. For a more accurate analysis, a Dual Energy X-ray Absorptiometry (DEXA) scan would be more suitable.

Conclusion(s)

In conclusion, irrespective of total BFP and BMI, VAI was higher in non obese PCOS patients. Furthermore, the VAI in both non obese and obese patients was significantly above the established cut-off range for metabolic risk factors. An imbalance in adipokines may lead to an accumulation of visceral fat without significantly affecting BMI. Therefore, it is recommended that all PCOS patients, regardless of their non obese or obese phenotype, undergo metabolic profiling as a low-cost tool for early intervention, ultimately improving patient outcomes and quality of life.

Values expressed as (Mean±SE), p<0.05 considered as significant, #Levene’s test is significant (p<0.05), suggesting a violation of the assumption of equal variances. Welch’s Correction applied
p<0.05 considered as significant
p<0.05 considered as significant
*Represents reference level; Note. Weighted by ‘BMI’; #represent significant (p<0.05)
HMW: High molecular weight; PCOS: Polycystic ovarian syndrome; BMI: Body mass index

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