Fat Distribution and its Correlation with Insulin Resistance, Androgen Markers, and Proinflammatory Cytokines in Polycystic Ovary Syndrome

Abstract

The high cardiometabolic risk associated with polycystic ovary syndrome (PCOS) may be linked to central fat accumulation. This study compared fat distribution between women with PCOS and controls matched by body mass index. It also sought to determine if insulin resistance (IR), androgens, or inflammatory markers correlate with body composition parameters in PCOS patients. In total, thirty-five women with PCOS and 37 controls, aged 18–40 years, were included. Hormonal/metabolic profiles, inflammatory biomarkers [tumor necrosis factor-alpha (TNF-α and interleukin-6 (IL-6)], anthropometry (waist circumference, waist-to-hip ratio, lipid accumulation product [LAP], visceral adiposity index [VAI]), and body composition assessed through dual-energy X-ray absorptiometry were assessed. The PCOS group exhibited significantly higher androgen levels and markers of IR. However, levels of TNF-α and IL-6 were comparable between the groups. Despite having similar total body fat mass (FM), the PCOS group had excessive central fat, including increased truncal FM and visceral adipose tissue (VAT). In PCOS, androgens were not associated with body fat or its distribution. IL-6 was positively correlated with total and truncal FM, while insulinemia and the homeostatic model assessment for IR were positively associated with VAT, as well as with total and truncal FM. Although anthropometric measurements and indices were positively associated with DXA-derived central FM parameters, our data suggest that LAP is the most effective tool for assessing central fat deposition and metabolic dysfunction in the PCOS patients studied herein. Furthermore, in this population, IR, rather than androgens or proinflammatory cytokines, is more closely associated with abdominal obesity.

Keywords

body composition - trunk fat mass - visceral fat mass - hyperandrogenism - tumor necrosis factor-alpha - interleukin-6

Introduction

Polycystic ovary syndrome (PCOS) is the most common hormonal disorder among women of reproductive age, affecting approximately 5–10% of this population globally [1]. In addition to chronic anovulation, hyperandrogenism, and polycystic ovaries, PCOS is often associated with insulin resistance (IR) [2] and an increased lifelong risk of cardiovascular risk factors (CVRFs) [3]. Metabolic damage in women with PCOS is particularly pronounced in those with excess abdominal visceral adipose tissue (VAT) [4]. This abdominal obesity, classically related to female hyperandrogenism [5], worsens IR by increasing free fatty acid production, which leads to defects in postreceptor insulin action [6], and causes an imbalance in proinflammatory and proatherogenic cytokine production [7]. Indeed, reduced adiponectin levels and higher circulating levels of many adipokines, cytokines, and inflammatory markers – including leptin, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), plasminogen activator inhibitor-1, soluble intercellular adhesion molecule-1, and high-sensitivity C-reactive protein – have been reported in PCOS [8] [9] [10], although the role of central fat deposition in these changes has not been fully established.

Recently, some authors have shown that, in addition to VAT, total body fat and subcutaneous truncal/abdominal fat also correlate with IR, metabolic parameters, and hormonal profiles [11] [12], suggesting that assessing body fat distribution could help estimate metabolic risk in both sexes, including women with PCOS. Computed tomography (CT) and magnetic resonance imaging (MRI) are currently considered the gold standard for distinguishing between intraabdominal and subcutaneous fat. However, their high costs and the ionizing radiation associated with CT limit their widespread use [13]. Dual X-ray absorptiometry (DXA) scanning is an accessible technique that is less costly, highly accurate, and operator-independent. It involves significantly lower radiation exposure and is a well-validated indirect method for estimating the amount of visceral fat [14]. DXA studies have shown increased trunk fat in patients with PCOS compared to controls [4] [15]; however, whether this central fat depot can be used to screen and monitor cardiometabolic risk in PCOS remains to be determined [16].

Therefore, we aimed to compare fat distribution between patients with PCOS and body mass index (BMI)-matched controls, focusing particularly on whether IR, androgen levels, and inflammatory markers correlate with body composition parameters in PCOS, using anthropometric adiposity indices and measurements, and DXA.

Subjects and Methods

Patients and study design

Women aged between 18 and 40 years with a BMI<35 kg/m² were recruited from the outpatient clinics of Pedro Ernesto University Hospital in Rio de Janeiro, Brazil, and through social media. A BMI≥35 kg/m², pregnancy, chronic diseases, regular practice of aerobic or anaerobic exercise, history of smoking, alcohol consumption, or use of anabolic steroids, hormonal contraceptives, or metformin in the last three months were the exclusion criteria. Further screening based on medical history, physical examination, and laboratory tests was conducted on the remaining women. Those who were not eumenorrheic or had androgen levels outside the normal range were excluded from the control group. Additionally, those who did not meet the PCOS diagnostic criteria outlined in the 2018 International Evidence-Based Guidelines, which are based on the Rotterdam Consensus [17], were excluded from the PCOS group. Hirsutism was defined using the semiquantitative modified Ferriman–Gallwey score (mFG score)≥6, and biochemical hyperandrogenism was identified by elevated serum levels of total testosterone (T), delta-4 androstenedione (A4), and dihydroepiandrostenedione sulfate (DHEAS), and/or an increased free androgen index (FAI). Chronic anovulation was identified by oligomenorrhea, defined as fewer than eight cycles per year, or amenorrhea, defined as the absence of menstrual bleeding for at least 90 days, not due to pregnancy. Using an ultrasound device with a transducer frequency of≤8 megahertz, polycystic ovarian morphology was identified by 12 or more follicles measuring 2–9 mm in diameter and/or an ovarian volume of≥10 ml in at least one ovary. All patients tested negative for hyperprolactinemia, Cushing’s syndrome, androgen-secreting tumors, and nonclassical 21-hydroxylase deficiency.

Anthropometry and body composition assessments

Weights and heights were recorded using a digital scale and a stadiometer, respectively; waist circumference (WC) was measured at the midpoint between the lower rib margin and the iliac crest, and hip circumference at the level of the greater trochanters. BMI and waist-hip ratio (WHR) were calculated. Body composition was assessed using Lunar iDXA equipment (GE Healthcare, Madison, WI, USA). A single qualified technician conducted DXA scans to measure body fat percentage, total FM in kilograms (kg), truncal FM (kg), truncal fat percentage, lean mass [LM (kg)], android and gynoid FM, android and gynoid fat percentages and estimated VAT (g). The DXA CoreScan mode in the enCORE software, version 17 (GE Healthcare), was used to analyze VAT. The following indices were calculated based on fat distribution: FM and LM in kg/height in m² (FM and LM indices), FM in kg/LM in kg (F/L ratio), truncal FM in kg/legs FM in kg (fat distribution index, FDI), and the ratio of the percentage of arms and truncal fat to the percentage of legs fat (upper/lower fat ratio) [18] [19] [20].

Laboratory evaluations

Blood samples were collected after an overnight fast, during the early follicular phase of a spontaneous or induced menstrual cycle, or randomly from patients with amenorrhea. Serum glucose, glycated hemoglobin (HbA1c), triglyceride (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-c) levels were measured using commercial automated methods. The calculation of low-density lipoprotein cholesterol (LDL-c) and the homeostatic model assessment for IR (HOMA-IR) was performed using established formulas. The levels of sex hormone-binding globulin (SHBG), T, A4, DHEAS, and insulin were measured using chemiluminescence immunoassay (Immulite 2000, Siemens). TNF-α and IL-6 levels were measured using an enzyme-linked immunosorbent assay with an ELX800 Microplate reader (BioTek). FAI, lipid accumulation product (LAP), and visceral adiposity index (VAI) were calculated using the following formulas: FAI=[T (nmol/l)/SHBG (nmol/l)×100 [21]; LAP=[WC (cm)−58]×TG (mmol/l) [22]; and VAI={WC (cm)/36.58+[1.89×BMI (kg/m²)]}×[TG (mmol/l)/0.81]×[1.52/HDL-C (mmol/l)] [23].

Ethical approval

The local ethics committee of Pedro Ernesto University Hospital approved this cross-sectional study (approval no. 96886918.3.0000.5259). All participants provided written informed consent after a detailed explanation of the nature, objectives, and possible risks of the study.

Statistical analysis

Data normality was assessed using the Shapiro–Wilk test. Results were presented as mean±standard deviation, median (interquartile range), or frequency (n, %) as appropriate. The unpaired Student’s t-test and Chi-square test were used to compare continuous and categorical variables between groups, respectively. Pearson correlation coefficients were calculated to explore the associations among various measures – metabolic, inflammatory, and hormonal profiles, anthropometric indices and measurements of central fat accumulation, and DXA parameters. All analyses were conducted using NCSS^M statistical software (Kaysville, UT, USA), and statistical significance was set at p<0.05. Sample size calculation was performed using G*Power 3.1.9.4 and considered the comparison between the two groups {sample size estimation of 54 patients with an actual power of 0.9502120 [t-tests; means: difference between two dependent means – matched pairs; two-tailed; effect size of 0.50; α error probability of 0.05; and statistical power (1 – β) of 0.95]} and the correlation {sample size estimation of 71 patients with an actual power of 0.9521292 [t-tests; correlation: point biserial model; two-tailed; effect size of 0.40; α error probability of 0.05; and statistical power (1 – β) of 0.95]} employed to investigate our hypothesis further.

Results

In total, 72 women were included (35 PCOS, 37 Controls). [Table 1] presents the clinical and laboratory characteristics of the groups. Notably, BMI was comparable between the two groups; however, class I obesity was present in 28.6% of the PCOS patients compared to 13.5% of the controls, respectively. The PCOS group was younger (p=0.04) and had significantly higher rates of acanthosis nigricans and familial hyperandrogenism than the control group. The average mFG score for patients with PCOS was 10.43±6.60. WC and WHR were significantly higher in the PCOS group.

Table 1 Clinical and laboratory characteristics of the study groups.

Variables	Control (n=37)	PCOS (n=35)	p-Value
Demographic/clinical characteristics
Age (years)	30.6±6.7	28.0±5.3*	0.04
Race (%)
White	18 (48.6)	11 (31.4)	0.32
Black	2 (5.4)	3 (8.6)	–
Other	17 (45.6)	21 (60)	–
Family history of PCOS	5 (13.5)	13 (37.1)*	0.02
Modified Ferriman–Gallwey score	–	10.43±6.60	–
Weight (kg)	65.1±10.9	67.8±9.9	0.14
BMI (kg/m²)	25.2±4.2	26.6±3.6	0.07
Normal	20 (54.1)	12 (34.3)	0.17
Overweight	12 (32.4)	13 (37.1)	–
Class 1 obesity	5 (13.5)	10 (28.6)	–
WC (cm)	75 [69.5–81.2]	80 [74–88]*	0.02
Waist/hip ratio	0.76±0.05	0.80±0.06*	<0.001
VAI	2.05 [1.46–3.45]	2.95 [2.01–3.56]*	0.05
LAP	12.85 [8.36–21.27]	19.30 [13.21–30.90]	0.98
Acanthosis nigricans	3 (8.1)	12 (34.3)*	<0.001
Systolic blood pressure (mmHg)	115.8±11.6	114.2±9.7	0.37
Diastolic blood pressure (mmHg)	74.5±9.9	72.3±9.3	0.17
Fasting glucose (mg/dl)	87.5±8.2	87.7±8.5	0.90
Insulin (mU/l)	10.4 [7.4–12.8]	13.2 [9.5–16.1]*	<0.001
HOMA-IR	2.24±0.82	3.07±1.30*	<0.001
HbA1c (mmol/mol)	30±4	33±5*	0.03
Total cholesterol (mg/dl)	172 [149.5–200.5]	175 [159–229]	0.06
HDL-c (mg/dl)	52 [[45]–61]	47 [[42]–59]	0.11
LDL-c (mg/dl)	101.4 [85.1–123]	116 [97–144.2]*	0.02
Triglycerides (mg/dl)	72±25.2	84.8±29.2*	0.04
TNF-α (pg/ml)	0.74 [0.60–0.87]	0.74 [0.63–0.90]	0.11
IL-6 (pg/ml)	1.28 [0.83–1.75]	1.20 [0.96–2.03]	0.26
T (ng/ml)	0.303±0.132	0.450±0.141*	<0.001
SHBG (nmol/l)	92.2 [74.9–132]	60 [42.6–82]*	<0.001
FAI	1.13 [0.63–1.52]	2.64 [1.51–4.15]*	<0.001
A4 (nmol/l)	2.05±0.64	2.86±1.08*	<0.001
DHEAS (μg/dl)	138 [114.8–182.5]	206 [132–266]*	0.02

PCOS: Polycystic ovary syndrome; BMI: Body mass index; WC: Waist circumference; VAI: Visceral adiposity index; LAP: Lipid accumulation product; HOMA-IR: Homeostasis model assessment of insulin resistance; HbA1c: Glycated hemoglobin; LDL-c: Low-density lipoprotein cholesterol; HDL-c: High-density lipoprotein cholesterol; TNF-α: Tumoral necrosis factor-alpha; IL-6: Interleukin-6; T: Total testosterone; SHBG: Sex hormone-binding globulin; FAI: Free androgen index; A4: Delta-4 androstenedione; DHEAS: Dehydroepiandrosterone sulfate; LH: Luteinizing hormone; FSH: Follicle stimulating hormone; PRL: Prolactin; 17-OHP: 17-Hydroxyprogesterone; TSH: Thyroid stimulating hormone. ^*p-Value, unpaired Student’s t-test or Chi-square test; results expressed as mean±standard deviation, medians [1st–3rd quartiles] or n (%).

The PCOS group exhibited significantly higher levels of T, A4, FAI, and DHEAS, and lower levels of SHBG compared to the control group. Fasting glucose levels were similar; however, insulin, HOMA-IR, and HbA1c levels were significantly elevated in patients with PCOS. Moreover, differences were observed in TG and LDL-c levels, while TC, HDL-c, LAP, TNF-α, and IL-6 levels remained consistent between the two groups. A trend toward increased VAI in patients with PCOS was also observed.

[Table 2] shows the DXA body composition results for the groups. Patients with PCOS exhibited similar total body fat but had an excess of truncal FM and a higher percentage of truncal fat compared to the control group. The LM, LM and FM indices, and gynoid fat levels were similar across the groups. The F/L ratio was significantly higher in patients with PCOS compared to controls. The distribution of upper and lower body fat was significantly higher in the PCOS group, as indicated by the FDI and the upper/lower fat ratio. Women with PCOS were also characterized by significantly increased android FM, percentage of android fat, and VAT.

Table 2 Body composition of the study groups assessed using Dual-energy X-ray absorptiometry.

Variables	Control (n=37)	PCOS (n=35)	p-Value
Body fat mass (kg)	23.49±8.35	26.02±6.65	0.07
Body fat (%)	36.62±7.80	39.41±5.80*	0.05
Trunk fat mass (kg)	9.92 [7.23–13.08]	13.11 [7.96–16.75]*	0.02
Trunk fat (%)	35.64±9.81	40.48±8.40*	0.01
Lean mass (kg)	39.22±5.04	39.15±4.53	0.49
FM index (kg/m²)	9.10±3.34	10.19±2.68	0.06
LM index (kg/m2)	15.10 [13.88–16.70]	15.52 [14.26–16.67]	0.19
F/L ratio	0.601±0.201	0.664±0.155*	<0.001
FDI	1.122 [0.934–1.364]	1.225 [1.045–1.630]*	<0.001
Upper/lower fat ratio	1.920±0.190	2.030±0.269*	<0.001
Android fat mass (kg)	1.41 [0.93–1.95]	1.90 [1.26–2.56]*	0.02
Android fat (%)	35.2±11.9	41.2±9.7*	<0.001
Gynoid fat mass (kg)	4.55±1.51	4.84±1.17	0.15
Gynoid fat (%)	41.9±7.7	43.9±5.5	0.09
Visceral adipose tissue (g)	249 [144–518]	487 [262–813]*	<0.001

PCOS: Polycystic ovary syndrome; FM index: Fat mass index; LM index: Lean mass index; F/L ratio: Fat/lean mass ratio; FDI: Fat Distribution Index. * p-Value, unpaired Student’s t-test or Chi-square test; results expressed as mean±standard deviation, medians [1st–3rd quartiles] or n (%).

[Table 3] presents the associations between metabolic, hormonal, and inflammatory profiles and anthropometric adiposity measurements/indices and DXA body composition parameters in the PCOS group. Insulin and HOMA-IR showed positive correlations with WC, LAP, VAI, total body FM, trunk FM, and VAT. Although no significant associations were found between androgens and anthropometry or DXA body composition measurements, SHBG was significantly inversely associated with BMI, WC, LAP, and total FM, and it also showed a trend toward an inverse correlation with VAI. A significant positive association was observed between IL-6 and body FM, trunk FM, F/L ratio, android FM, and VAI. This association also approached statistical significance with gynoid FM and VAT. Similar correlations were observed in the control group; however, specific differences were identified and are detailed in the Supporting Information (Table 1S).

Table 3 Correlations between metabolic, hormonal, and inflammatory profiles and anthropometric adiposity indices/measurements and dual X-ray absorptiometry body composition parameters in PCOS.

	Fasting glucose	Insulin	HOMA-IR	HbA1c	IL-6	TNF-α	T	SHBG	FAI	A4	DHEAS
BMI	0.08 (p = 0.63)	0.37 (p=0.03)	0.36 (p=0.05)	0.24 (p=0.16)	0.17 (p=0.33)	–0.23 (p=0.19)	–0.14 (p=0.42)	–0.47 (p<0.001)	0.23 (p=0.18)	–0.15 (p=0.39)	–0.04 (p=0.79)
LAP	0.37 (p=0.03)	0.59 (p<0.001)	0.63 (p<0.001)	0.48 (p<0.001)	0.32 (p=0.06)	–0.08 (p=0.64)	–0.13 (p=0.46)	–0.39 (p=0.02)	0.03 (p=0.87)	–0.03 (p=0.86)	–0.20 (p=0.25)
VAI	0.16 (p=0.36)	0.40 (p=0.01)	0.40 (p=0.02)	0.39 (p=0.02)	0.37 (p=0.02)	0.02 (p=0.91)	0.26 (p=0.13)	–0.34 (p=0.05)	0.18 (p=0.32)	0.18 (p=0.29)	0.11 (p=0.55)
WC	0.29 (p=0.10)	0.39 (p=0.02)	0.39 (p=0.02)	0.36 (p=0.03)	0.20 (p=0.26)	–0.15 (p=0.39)	–0.25 (p=0.16)	–0.48 (p<0.001)	0.18 (p=0.31)	–0.10 (p=0.56)	–0.00 (p=0.95)
Waist/hip ratio	0.46 (p<0.001)	0.09 (p=0.60)	0.14 (p=0.46)	0.30 (p=0.07)	0.11 (p=0.60)	–0.21 (p=0.20)	–0.22 (p=0.22)	–0.18 (p=0.30)	–0.06 (p=0.72)	0.13 (p=0.44)	–0.11 (p=0.55)
Body fat mass (kg)	0.36 (p=0.05)	0.39 (p=0.02)	0.49 (p<0.001)	0.31 (p=0.07)	0.37 (p=0.03)	–0.27 (p=0.12)	–0.15 (p=0.40)	–0.37 (p=0.03)	0.00 (p=0.96)	–0.08 (p=0.64)	–0.15 (p=0.40)
Trunk fat mass (kg)	0.36 (p=0.03)	0.37 (p=0.03)	0.47 (p<0.001)	0.36 (p=0.04)	0.40 (p=0.02)	–0.22 (p=0.20)	–0.14 (p=0.41)	–0.32 (p=0.06)	–0.05 (p=0.79)	–0.04 (p=0.81)	–0.10 (p=0.57)
F/L ratio	0.37 (p=0.03)	0.28 (p=0.10)	0.41 (p=0.02)	0.33 (p=0.05)	0.38 (p=0.02)	–0.34 (p=0.05)	–0.04 (p=0.84)	–0.25 (p=0.14)	–0.12 (p=0.50)	0.04 (p=0.80)	–0.22 (p=0.19)
Upper/lower fat ratio	r=0.23; (p=0.19)	r=0.19; (p=0.27)	r=0.23; (p=0.21)	r=0.23; (p=0.19)	0.37 (p=0.03)	–0.12 (p=0.41)	–0.08 (p=0.62)	–0.27 (p=0.12)	–0.02 (p=0.88)	0.01 (p=0.94)	0.02 (p=0.91)
Android fat mass (kg)	0.38 (p=0.02)	0.34 (p=0.05)	0.44 (p=0.02)	0.36 (p=0.03)	0.36 (p=0.03)	–0.20 (p=0.24)	–0.12 (p=0.49)	–0.31 (p=0.07)	–0.05 (p=0.79)	0.02 (p=0.88)	–0.06 (p=0.72)
Gynoid fat mass (kg)	0.27 (p=0.12)	0.37 (p=0.03)	0.47 (p<0.001)	0.28 (p=0.11)	0.34 (p=0.05)	–0.25 (p=0.15)	–0.21 (p=0.22)	–0.35 (p=0.04)	0.00 (p=0.96)	–0.18 (p=0.30)	–0.17 (p=0.32)
Visceral adipose tissue (g)	0.31 (p=0.07)	0.41 (p=0.01)	0.46 (p<0.001)	0.31 (p=0.07)	0.33 (p=0.05)	–0.16 (p=0.35)	–0.10 (p=0.60)	–0.24 (p=0.16)	0.00 (p=0.97)	–0.03 (p=0.84)	–0.22 (p=0.19)

PCOS: Polycystic ovary syndrome; HOMA-IR: Homeostasis model assessment of insulin resistance; HbA1c: Glycated hemoglobin; IL–6: Interleukin-6; TNF-α: Tumor Necrosis Factor-alpha; FAI: Free Androgen Index; A4: Delta-4 androstenedione; T: Total testosterone; SHBG: Sex hormone–binding globulin; DHEAS: Dehydroepiandrosterone sulfate; BMI: Body mass index; LAP: Lipid accumulation product; VAI: Visceral adiposity index; WC: Waist circumference; F/L ratio: fat/lean mass ratio.

In the PCOS group, LAP, VAI, WC, and WHR showed positive correlations with trunk FM and VAT. Specifically, LAP correlated with r=0.80 and 0.84 (p<0.001); VAI with r=0.41 and 0.45 (p≤0.02); WC with r=0.84 and 0.75 (p<0.001), and WHR with r=0.43 and 0.45 (p<0.001) (data not shown).

Discussion

In this study, we aimed to evaluate fat distribution in women with PCOS and to clarify its complex relationship with IR parameters, androgen levels, and inflammation. The results indicated that women with PCOS exhibit greater truncal and visceral fat deposition, as well as an unfavorable metabolic profile compared to BMI-matched controls with similar total body fat. LAP was the most effective anthropometric parameter for the clinical assessment of women with PCOS. Furthermore, while biochemical hyperandrogenism did not significantly correlate with total body fat or its distribution, IR was more closely associated with central fat deposition than inflammatory cytokines.

PCOS is characterized by central fat deposition, regardless of whether the BMI indicates normal weight, overweight, or obesity [24]. This excessive abdominal adiposity – which may be associated with IR, elevated androgen levels, and chronic inflammation – significantly increases cardiometabolic risk [25]. In line with our findings, based on DXA measurements, most previous studies have reported similar LM, an increased F/L ratio, and excessive truncal fat accumulation in women with PCOS compared to BMI-matched controls. However, in contrast to our results, these studies also described elevated total FM in these women [18] [20]. Moreover, an increase in fat deposition in the android region in normal-weight women with PCOS has been documented [26], aligning with alterations in anthropometric measures indicative of excess abdominal adiposity, such as higher WC and WHR. These findings support our results and highlight the usefulness of WC and WHR measurements for assessing central fat deposition in clinical practice. Glintborg et al. [18] reported a strong association of truncal FM, as measured by DXA, with WC in patients with PCOS. Additionally, they found that truncal FM and WC were equally effective predictors of IR, serving as useful clinical tools for indirectly detecting this metabolic dysfunction. In our study, WC, but not WHR, correlated with IR parameters and exhibited a negative correlation with SHBG, which may indicate IR [27]. The suitability of using simple anthropometric parameters, such as WC, to predict and monitor visceral obesity and cardiometabolic risk in women with PCOS was later confirmed [28].

Contextually, LAP and VAI – indicators that combine anthropometric measurements and blood lipid values – have emerged as important, noninvasive, and cost-effective markers of IR [29] and VAT dysfunction [30]. We observed that the PCOS group tended to have a higher VAI. Moreover, VAI was associated with IL-6, which is particularly noteworthy as it suggests that VAI may be a more precise indicator of chronic low-grade inflammation in adipose tissue than LAP. Corroborating our data, Durmus et al. [31] found that VAI was higher in overweight and obese women with PCOS than in overweight and/or obese controls, as well as in normal-weight women with PCOS. Additionally, VAI was associated with specific metabolic and inflammatory parameters. These findings have also been reported in lean patients [32] and may be linked to the severity of anovulation in PCOS [33]. We observed that both VAI and LAP were positively correlated with truncal FM, VAT, and IR. Furthermore, a significant inverse relationship between SHBG and LAP was documented, and LAP demonstrated a stronger correlation with the densitometric measurements of central fat compared to VAI. The superiority of LAP in estimating central fat depot amounts using the DXA method has been previously reported [18]. In the present study, both WC and LAP were correlated with DXA-derived central fat measures and IR markers in women with PCOS. However, the association between IR and LAP was the strongest among all the anthropometric parameters analyzed in our PCOS sample, suggesting that LAP should be the primary tool for the clinical assessment of patients with this condition.

PCOS is a heterogeneous disorder, and the mechanisms underlying IR in this syndrome are particularly complex because of potential intrinsic IR in muscle and hepatic tissues, as well as in various fat depots regardless of obesity – although significantly exacerbated by obesity [34] [35]. Reportedly, metabolic parameters and hormone profiles in PCOS are correlated with total body fat, visceral fat, and subcutaneous truncal/abdominal fat [11] [12]. The abnormal morphology and function of adipose cells in PCOS, characterized by changes in adipocyte size and altered expression and secretion of leptin/adiponectin, are also potential mechanisms likely linked to elevated androgen levels [36] [37]. By quantifying FM using DXA, we observed a direct correlation between total FM, truncal FM, and estimated visceral fat with surrogate markers of IR in women with PCOS. Puder et al. [38] reported an inverse correlation between total FM and central/truncal FM, as assessed by DXA, with insulin sensitivity. This finding is consistent with studies using established gold standard methods. In a small cohort of overweight women with PCOS, Barber et al. [39] reported a correlation between MRI-measured intraabdominal fat and HOMA-IR. Similarly, Penaforte et al. [40] observed that insulin resistant women with PCOS exhibited significantly greater amounts of truncal and visceral abdominal fat than non-insulin resistant women with PCOS – assessed using CT imaging. Furthermore, a systematic review and meta-analysis of 30 eligible studies concluded that central FM is elevated in women with PCOS and is directly associated with increased fasting insulin levels [41].

Some studies have also shown that central fat accumulation is positively associated with circulating androgen levels, as well as with insulin levels. Employing DXA and MRI, Dumesic et al. found that androgen levels were significantly higher in patients with PCOS than in controls, and they observed a correlation between intraabdominal FM and both fasting insulin and androgen levels [26]. Conversely, free T has been associated with all FM measures assessed by DXA and identified as an independent predictor of IR [18]. Herein, despite the notably higher levels of T, A4, FAI, and DHEAS levels, no significant association was observed between these androgens and the total amount of body fat or its distribution as measured by DXA, nor with any of the anthropometric indices and measurements. Therefore, androgens have been shown to be unreliable indicators of body adiposity in PCOS, consistent with findings from a study that reported excess androgens to be independent of body fat quantity and distribution, as assessed by DXA in women with PCOS [38]. Similarly, Tosi et al. [42] studied the body composition of women with PCOS using DXA and observed that total and central FM were independent predictors of insulin sensitivity but not of T levels. The varying results suggest that the impact of androgens on FM in PCOS needs more investigation.

We assessed the status of chronic low-grade inflammation and its association with body fat distribution, focusing on the measurement of TNF-α and IL-6 levels, cytokines primarily secreted by inflammatory cells in visceral fat depots [43]. Established data suggest that TNF-α and IL-6 play roles in the development of obesity-related IR [44] [45] and may contribute to hyperandrogenism [8] by directly affecting ovarian and adrenal function [46]. Consequently, numerous researchers have investigated the associations between these adipocytokines and PCOS. Similarly to our findings, a meta-analysis by Morreale et al. [47] found no significant differences in the levels of TNF-α and IL-6 between patients with PCOS and controls. Shroff et al. [48] evaluated obese women with PCOS and IR using DEXA and found no statistically significant differences in TNF-α and IL-6 levels between the PCOS group and a control group matched for total FM. Conversely, the same study found that total FM was associated with IL-6 and TNF-α levels, but central FM was not. However, the study did not differentiate between visceral and subcutaneous fat within the central FM. Our research identified a direct relationship between IL-6 and various measures of adiposity, including total FM, truncal FM, F/L ratio, and android FM in PCOS, but only a trend toward an association with VAT. This further supports the notion that IL-6 could be considered a potential marker of both total body fat and central fat excess in PCOS, a finding that aligns with previous research [49] [50]. However, its role as a possible indicator of abdominal visceral fat remains uncertain.

The potential limitations of this study are acknowledged. The women with PCOS were slightly younger on average than those in the control group. However, the age difference between the groups was marginally significant and not substantial enough to influence body composition or the development of metabolic and inflammatory disturbances. The cross-sectional design of this study limits our ability to infer causality directly. While our assessment of body composition is validated and accurate, it is not as precise as methods such as MRI or CT imaging. This highlights the need for additional controlled and prospective studies using DXA, with age-matched controls and larger sample sizes.

Despite these limitations, our findings indicate that our predominantly overweight PCOS patients, compared with BMI-matched controls, exhibited elevated levels of circulating androgens, similar TNF-α and IL-6 concentrations, a greater degree of IR, and more central fat accumulation, as evidenced by higher WC, WHR, truncal FM, and VAT, even though their total FM was similar to that of the controls. Among the anthropometric parameters in the PCOS cohort of the present study, WC and LAP were useful for assessing IR and both truncal and visceral fat; however, LAP showed a stronger association with IR markers. This suggests that LAP is a more comprehensive tool and should be prioritized for routine clinical evaluation of central fat accumulation and metabolic dysfunction in these women. Finally, our results indicate that IR, rather than hyperandrogenemia or proinflammatory cytokines, is more closely associated with central/visceral fat deposition in PCOS.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgement

The authors are grateful to the Laboratory for Clinical and Experimental Research on Vascular Biology (BioVasc) at the Biomedical Center, State University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil, for conducting the assays of the inflammatory biomarkers assessed in this study. We also wish to thank the Interdisciplinary Nutritional Assessment Laboratory (LIAN) at the Institute of Nutrition, State University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil, for performing the DXA exams for this research.

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Publication History

Received: 28 May 2024

Accepted after revision: 04 August 2024

Article published online:
03 September 2024