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Body composition analysis in women with polycystic ovary syndrome: a cross-sectional study from the Tehran Lipid and Glucose Study (TLGS)

BMC Endocrine Disorders volume 24, Article number: 251 (2024) Cite this article

Abstract

Background

Obesity is associated with the development of polycystic ovarian syndrome (PCOS), a complex endocrine disorder. However, the correlation between body composition and PCOS in women has not been thoroughly investigated. This study aimed to examine body composition using bioelectrical impedance analysis (BIA) in women with and without PCOS in a population-based study within the Tehran Lipid and Glucose Study (TLGS).

Methods

We conducted a cross-sectional study among non-menopausal women aged 18–45 who underwent BIA in phase VII of the TLGS. A total of 150 participants with PCOS and 240 without PCOS were included based on the Rotterdam criteria. Baseline demographic, anthropometric, laboratory, and body composition parameters were compared between the two groups.

Results

The mean age was 33.7 ± 7.45 years in the PCOS group and 35.49 ± 7.05 years in the control group. The mean BMI was 27.0 ± 4.0 kg/m2 in the PCOS group and 27.1 ± 4.4 kg/m2 in the control group. No significant differences were found in body composition parameters between the two groups as assessed by BIA. Additionally, there were no correlations between body composition and hormone parameters in PCOS patients.

Conclusion

In this sample of non-referral patients with PCOS, the use of BIA did not provide added value beyond conventional anthropometric measures for assessing body composition. Further longitudinal research is needed to determine whether body composition analysis can enhance PCOS evaluation.

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Background

Polycystic ovary syndrome (PCOS) is the most common cause of anovulatory infertility and endocrine dysfunction in women of reproductive age, affecting approximately 8–13% of this population [1, 2]. The clinical phenotype of PCOS is characterized by infertility due to anovulation, irregular menstrual cycles, and symptoms of androgen excess [3]. Additionally, PCOS has been linked to co-occurring chronic metabolic conditions, particularly increased insulin resistance [4]. The etiology of PCOS is multifactorial, with both genetic and environmental factors playing a role in its pathogenesis [4]. Obesity and excess abdominal adipose tissue exacerbate the metabolic and endocrine abnormalities involved in the syndrome’s pathogenesis [5]. Obesity induces insulin resistance and compensatory hyperinsulinemia, promoting adipogenesis and inhibiting lipolysis, which sensitizes thecal cells to LH activation and amplifies functional ovarian hyperandrogenism via increased androgen production [6]. The prevalence of PCOS is higher in women with obesity compared to their normal-weight counterparts [7]. Moreover, PCOS patients have a higher risk of obesity and visceral adiposity than healthy controls [8, 9].

The percentage of body fat is closely associated with the risk of developing chronic diseases such as hypertension, dyslipidemia, diabetes, and coronary heart disease [10, 11]. Although surrogate measures of body fatness such as BMI, waist circumference, waist-hip ratio, and skinfold thickness are commonly used in epidemiological studies, they have limitations in accurately classifying individuals according to their body composition [12, 13]. Multiple techniques have been used to measure the percent body fat, but they are often inconvenient and impractical to undertake in the field, making their application in large epidemiological research limited [14]. Bioelectrical impedance analysis (BIA) is a relatively simple, quick, inexpensive, and non-invasive procedure that provides reliable body composition measurements [15]. Therefore, studying body composition using BIA could provide a deeper understanding of the disease’s physiology, as it appears to play a role in the reproductive and metabolic dysfunction of PCOS.

However, the few case-control studies that have been conducted on the distribution of fat and body composition in women with PCOS have yielded inconsistent findings [16,17,18,19,20,21,22]. Therefore, this study aimed to compare the body composition parameters assessed by BIA and the hormonal parameters of PCOS patients with those of a healthy control group and to determine the effect of PCOS on body composition parameters.

Materials and methods

The study participants were recruited from phase VII of the Tehran Lipid and Glucose Study (TLGS), an ongoing prospective cohort study [23]. The TLGS comprises two major components: a cross-sectional study of non-communicable diseases and associated risk factors (phase I, 1999–2001) and a prospective follow-up study at 3-year intervals (phase II, 2002–2005, phase III, 2006–2008, phase IV, 2009–2011, phase V, 2012–2015, phase VI, 2016–2018, and phase VII, 2019–2022). For this cross-sectional analysis, 1630 women between the ages of 18 and 45 years were initially enrolled in phase VII of the TLGS, and 1223 of them underwent BIA to assess body composition. Subsequently, women with only elevated androgen levels, oligo/anovulation, polycystic morphology, or an uncertain status according to the women’s questionnaire were excluded from the study, leaving 433 premenopausal women for analysis. A standardized previously published questionnaire was used to collect information on menstrual dates and regularity, hirsutism, acne, and reproductive history for the eligible women [24]. Individuals with a BMI less than 18 or greater than 40, diabetes, hypothyroidism, hyperprolactinemia, Cushing’s syndrome, renal failure defined as a GFR less than 60 ml/min, and hysterectomy were also excluded. The remaining participants were separated into two groups: a control group of 240 individuals and a PCOS group of 150 individuals. The study procedure is summarized in Fig. 1.

Fig. 1
figure 1

Flow diagram of participant enrolment

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Assessment of body composition by BIA

Anthropometric measurements were performed in accordance with WHO guidelines, including weight, height, and waist circumference (WC) [25]. A digital electronic weighing scale (Seca 707, Hanover, MD, USA) with a range of 0.1–150 kg was used to measure weight, recorded to the nearest 100 g, while participants were shoeless and minimally clothed. Height and WC were measured using an unstretched tape measure; height was recorded in the standing position with a sensitivity of one millimeter, and WC was measured at the level of the umbilicus without applying any pressure, with the value rounded to the nearest 0.1 cm. All measurements were performed by trained staff according to the study protocol.

Body composition was determined using a portable BIA device (InBody 370, Biospace, Seoul, South Korea). Prior to impedance analysis, participants were instructed to fast overnight or for a minimum of 4–5 h, abstain from exercise for at least 12 h before the test, abstain from alcohol for at least 24 h, maintain balanced hydration, and lie in the supine position for at least 5 min prior to examination. Resistance to alternating current (500 A at 50/60 kHz) was determined while the patient stood on the analyzer’s platform and interpreted using the manufacturer’s software’s “standard” option. The following body composition variables were measured: BMI (kg/m2), waist-hip ratio (WHR), body weight (BW) (kg), fat mass (FM) (kg), fat-free mass (FFM), skeletal muscle mass (SMM) (kg), soft lean mass (SLM), total body water (TBW) (%), percent of body fat (PBF) (%), appendicular skeletal muscle mass (ASM), trunk FM, and basal metabolic rate (BMR) (kcal). In this study, the intraclass correlation coefficient (ICC) was calculated to analyze agreement in the measured values of the BIA device at a group level [26]. A sample of 15 women and 16 men were selected based on the relevant criteria, and a single operator performed body composition analyses on both groups twice, three days apart. Men had a mean age of 24 ± 6.4 years, while women had a mean age of 35 ± 10.8 years. The ICC and 95% confidence interval (CI) calculated for PBF and FFM were 0.996 (0.991–0.998) and 0.998 (0.997–0.999), respectively, indicating high reliability of the measurements. Furthermore, the mean difference between the two measurements of PBF and FFM was (0.04 ± 1.11) and (0.10 ± 1.04), respectively, which indicates good agreement given the proximity to zero.

Metabolic and hormonal parameters

All participants underwent a physical examination and blood sampling for metabolic and hormonal parameters, as previously reported [23]. Blood samples were collected from the patients during the first five days of their menstrual cycle, after 12 h of overnight fasting, for hormonal testing. Enzyme Immuno Assay technique (EIA kit, Diagnostic Biochem Canada Inc.) was used to measure total testosterone, androstenedione, and dehydroepiandrosterone sulfate (DHEAS) levels in the blood. The sensitivities of the assays were 0.076, 0.174, and 0.031 nmol/l, respectively, while the coefficients of variation within the assays were 7.6%, 6.7%, and 5.8%, respectively. Sex hormone-binding protein (SHBG) levels were determined using the Immuno-Enzyme Metric Assay (IEMA kit, Diagnostic Biochem Canada Inc., Ontario, Canada). The sensitivity and coefficient of variation of the assay were 0.1 nmol/l and 7.9%, respectively. The free androgen index (FAI) was calculated using the formula: [tT (nmol/l) 100/SHBG (nmol/l)]. Fasting blood glucose was measured using the glucose oxidase assay, and lipid factors (triglycerides, HDL, and cholesterol) were measured using enzymatic methods.

To assess physical activity, the Modifiable Activity Questionnaire (MAQ) was used, which evaluates physical activity in leisure, occupation, and in-house activities. The Persian-translated version of MAQ has been validated and shown to be reliable for evaluating physical activity in TLGS participants [27]. Instead of physical activity duration, we used the average metabolic equivalent tasks (METs) score to define physical activity. Participants were categorized as low physically active if they could not achieve 600 METs per week in minutes.

Definitions

PCOS was defined by the Rotterdam criteria as the presence of two or more of the following symptoms or signs: (1) oligo-anovulation; (2) clinical or biochemical hyperandrogenism; and/or (3) polycystic ovarian morphology on ultrasound, in the absence of another disorder causing similar symptoms [28]. Hirsutism was evaluated using the modified Ferriman-Gallwey (mFG) scoring method by a trained general practitioner [29].

Statistical analysis

The normality of continuous variables was assessed using the one-sample Kolmogorov–Smirnoff test. Variables that followed a normal distribution were expressed as mean (standard deviation), while those with a skewed distribution were expressed as median with interquartile range (IQR). Participant characteristics were compared between the PCOS and control groups using two independent sample t-tests or the Mann-Whitney U test for nonparametric equivalence. Categorical variables were expressed as percentages and compared using Pearson’s test. Pearson or Spearman correlation coefficient tests were used to examine the correlation between hormones and body composition variables. Generalized linear regression models with identity or logit link function were used to analyze the relationship between PCOS status and different continuous or categorical outcome variables, with adjustment for confounding variables. For non-normally distributed variables, the median regression model was used. Statistical analysis was performed using Stata software (version 12; STATA Inc., College Station, TX, USA), and significance was considered at p < 0.05.

Ethical approval

The protocol of the present study, conducted in accordance with the principles of the Declaration of Helsinki, was approved by the ethics committee of the Research Institute of Endocrine Sciences of Shahid Beheshti University of Medical Sciences. All participants provided written informed consent.

Results

This study included 390 women, with 150 in the PCOS group and 240 in the control group. The mean age of the PCOS patients was significantly lower than the control group (33.7 ± 7.4 vs. 35.4 ± 7.0 years, p = 0.01). The PCOS and control groups had similar mean BMIs of 27.0 ± 4.0 kg/m2 and 27.1 ± 4.4 kg/m2, respectively (p = 0.350). Obesity rates were also comparable between the groups, with 25.3% of PCOS patients and 23.3% of controls being obese. No significant between-group differences existed in other anthropometric measures (Table 1). Compared to the control group, women with PCOS exhibited higher levels of total testosterone, androstenedione, DHEAS, and FSH. In contrast, SHBG concentrations were markedly lower in the PCOS group. The mFG score, a measure of hirsutism, was significantly higher in women with PCOS (p < 0.001). Body composition analysis revealed no significant differences in fat mass, lean mass, or other components between the two groups (Table 2). The most common PCOS phenotype observed was characterized by hyperandrogenism and oligoanovulation symptoms (Fig. 2).

Table 1 Comparison of demographic, anthropometric, and hormonal indices of PCOS and non-PCOS
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Table 2 Participants’ anthropometric and body composition characteristics
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Fig. 2
figure 2

Distribution of PCOS phenotypes among patients in the study. One hundred forty-four patients presented symptoms of hyperandrogenism, 106 patients experienced oligo-anovulation, and 83 patients had polycystic ovaries. PCOM, polycystic ovary morphology; HA, hyperandrogenism; OAV, oligo-anovulation

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No correlations existed between body composition parameters and hormonal levels in women with PCOS (Table 3). PCOS as an independent factor did not significantly impact body composition measures in unadjusted or age-adjusted models (Models I and II, Table 4). However, after adjusting for age, BMI, smoking status, physical activity, and number of deliveries (Model III), PCOS patients exhibited a statistically significant 0.07% decrease in FMR compared to controls.

Table 3 Correlation between body composition parameters and hormone parameters in the PCOS group
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Table 4 Association of PCOS with body composition parameters (multivariate analysis)
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Discussion

This study is the largest to examine the role of BIA in determining body composition parameters among non-referral patients with milder symptoms of PCOS. No significant differences in body composition or anthropometric measures were found between women with PCOS and the control groups. Additionally, PCOS as an independent factor did not significantly impact body composition measures in either unadjusted or age-adjusted models. However, in a fully adjusted model, we observed a statistically significant, though not clinically considerable, correlation between PCOS and a 0.07% reduction in FMR.

PCOS is a highly prevalent endocrine disorder in women of reproductive age that can lead to metabolic dysfunction and infertility [1]. Obesity and excess abdominal adiposity, which are common in PCOS, may contribute to the syndrome’s pathogenesis by inducing insulin resistance [7]. Traditional anthropometric measures cannot adequately characterize fat distribution, volume, or percentage and distinguish fat from muscle mass. Consequently, research has increasingly focused on body composition indicators that better quantify fat and muscle, with BIA regarded as an efficient method for population studies due to its accessibility, simplicity, and low cost [7]. However, limited studies have examined BIA-derived body composition measures in women with PCOS. Differing study designs, patient selection criteria, PCOS severity, sample sizes, and age ranges have yielded conflicting results, with some studies finding increased adiposity [22] and others reporting no significant body composition abnormalities in PCOS versus controls [16,17,18].

Our investigation found no significant differences in body fat parameters (PBF, FM, and VFL) between women with and without PCOS, consistent with most other studies [16,17,18,19,20,21]. In 2021, Bizon et al. [16] also reported no between-group differences in FM, PBF, or VFA using BIA in 55 women across various PCOS phenotypes [27]. Similarly, Attle et al. [21]. observed comparable PBF and VFA in 50 PCOS patients versus controls. We also found no significant differences in anthropometric measures like BMI, WC, WHR, and HC, aligning with previous work [20, 21]. However, contrasting our results, Ezhe et al. [22] reported significantly higher PBF, FM, and FMR along with increased BMI and WHR in 60 PCOS women compared to controls. While greater FM and altered distribution along with reduced skeletal muscle mass can substantially impact metabolic disease progression [22], numerous factors may modulate FM including age, ethnicity, smoking, physical activity, cultural behaviors, psychological factors, diet, and genetics [21].

In the present study, women with PCOS showed no differences in FFM or SMM compared to controls, consistent with all previous investigations [16,17,18,19,20,21,22]. Both FFM and SMM significantly impact the development of cardiometabolic disorders and changes in insulin sensitivity [30]. Chronic exposure to estrogen or gonadotropins in PCOS patients may counteract the anabolic effects of androgens on SMM and FFM [31]. However, other factors like diet, lifestyle, smoking status, race/ethnicity, and chronic inflammation can also modulate these outcomes. Further researches are warranted to clarify the complex interplay between hormonal milieu, lifestyle factors, and body composition in PCOS.

It has been hypothesized that women with PCOS have an altered BMR, contributing to obesity and difficulty losing weight [18]. BMR accounts for 50–70% of total daily energy expenditure and reflects resting energy usage without food consumption [18]. FFM is the strongest predictor of BMR in both normal weight and obese individuals [32] and is incorporated into Cunningham’s formula along with lean body mass to estimate BMR using BIA. In the present study, no difference existed in FFM between PCOS patients and controls, likely explaining the lack of difference observed in BMR, consistent with two other studies [16, 18].

In this study, no significant correlations were found between androgen levels and anthropometric or body composition measures. However, Bizon et al. [16] reported positive correlations between free testosterone and all body composition parameters and negative correlations between SHBG and body composition. SHBG levels are proportional to visceral adiposity; decreased SHBG in abdominal obesity supports links between free androgens, visceral fat accumulation, and insulin resistance [33, 34]. Hyperinsulinemia in these women may also amplify androgen effects by inhibiting SHBG synthesis [35, 36]. Altuntas et al. [20] observed correlations between total/free testosterone and FFM [11]. The conflicting correlations in other studies may stem from higher free androgen and lower SHBG levels, as well as recruiting women with more severe PCOS from referral clinics, whereas our population had milder hyperandrogenism and disease severity.

A concerning trend of alarmingly low physical activity, especially among women, has been consistently documented in the Middle East and North Africa (MENA) region [37]. This poses serious health risks, as sedentary lifestyles have been linked to rising rates of insulin resistance and related metabolic diseases [38]. Mirroring previous research, our study found over half of the participants in both groups exhibited low physical activity levels. These findings underscore significant public health issues in the MENA region that demand attention. The impact of physical inactivity on insulin resistance applies not just to women with PCOS, but overall population health. Urgent public health action is needed to develop and implement interventions aimed at increasing physical activity and reducing sedentary time.

Our study has some limitations worth noting. First, we were unable to validate the BIA measurements using the DEXA gold standard method, although previous studies have found population-level agreement between the two techniques [39]. Second, BIA was performed randomly during the menstrual cycle phases. Third, distinct PCOS phenotypes could not be analyzed due to insufficient statistical power. Fourth, the cross-sectional design and lack of case-control matching introduce potential biases, although we adjusted for confounders. Finally, we could not definitively exclude hypercortisolemia without complete laboratory data, but we did exclude clinical Cushing’s syndrome. Major strengths include the population-based design capturing non-referral patients with milder symptoms and precise anthropometric and body composition measures by trained staff.

Increased body fat mass is a common feature of PCOS that exacerbates metabolic dysfunction and cardiovascular risk [40, 41]. Accurately assessing body composition is therefore critical for the clinical management of PCOS patients. BIA is a widely used, noninvasive technique to estimate body composition [15], but its reliability and validity in PCOS remain unclear. In this study, BIA detected no significant differences in body composition parameters between PCOS patients and controls. These findings suggest BIA may not adequately differentiate body composition in PCOS, carrying important clinical implications. The results highlight the limitations of BIA for body composition assessment in PCOS and the need to develop more precise, reliable methods tailored to this population. As body fat disturbances underpin PCOS metabolic abnormalities, an inability to accurately quantify adiposity using common clinical techniques like BIA represents a major barrier to effective treatment and risk stratification. Further research should explore alternative or modified approaches to improve body composition analysis in PCOS patients.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

PCOS:

Polycystic ovary syndrome

BMI:

Body mass index

BIA:

Bioelectrical impedance analysis

TLGS:

Tehran Lipid and Glucose Study

WHR:

Waist-hip ratio

BW:

Body weight

FM:

Fat mass

FFM:

Fat-free mass

SMM:

Skeletal muscle mass

SLM:

Soft lean mass

TBW:

Total body water

PBF:

Percent of body fat

ASM:

Appendicular skeletal muscle mass

BMR:

Basal metabolic rate

CI:

Confidence interval

DHEAS:

Dehydroepiandrosterone sulfate

SHBG:

Sex hormone-binding protein

METs:

Metabolic equivalent tasks

mFG:

Modified Ferriman-Gallwey

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Acknowledgements

The authors express their appreciation to participants of District 13, Tehran, for their enthusiastic support in this study.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Anahita Zakeri, Majid Valizadeh & Farhad Hosseinpanah

  2. Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Maryam Rahmati & Fahimeh Ramezani Tehrani

  3. Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Amir Abbas Momenan

  4. Endocrinology Division, Department of Internal Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mahtab Niroomand

  5. Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Fereidoun Azizi

  6. Center for Obesity Research, Innovation and Education (CORIE), Hartford HealthCare, Hartford, USA

    Amir Ebadinejad

Authors
  1. Anahita Zakeri
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  2. Amir Ebadinejad
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  3. Maryam Rahmati
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  4. Amir Abbas Momenan
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  5. Mahtab Niroomand
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  6. Majid Valizadeh
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  7. Fereidoun Azizi
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  8. Fahimeh Ramezani Tehrani
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  9. Farhad Hosseinpanah
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Contributions

A.Z: Data collection, project development, Writing - Original draft preparation; A.E: Data management, Writing - Original draft preparation; M.R: Data analysis; AA.M: Data collection, Writing - Review & Editing; M.N: Validation, Writing - Review & Editing; M.V: Validation, Writing - Review & Editing; F.A: Resources, Supervision, Writing - Review & Editing; F.RT: Project development, Methodology, Supervision, Writing - Review & Editing; F.H: Conceptualization, Methodology, Supervision, Writing - Review & Editing.

Corresponding authors

Correspondence to Fahimeh Ramezani Tehrani or Farhad Hosseinpanah.

Ethics declarations

Ethical approval and consent to participate

The protocol of the present study, conducted in accordance with principles of the Declaration of Helsinki, was approved by the ethics committee of the Research Institute of Endocrine Sciences of Shahid Beheshti University of Medical Sciences. All participants provided written informed consent.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Zakeri, A., Ebadinejad, A., Rahmati, M. et al. Body composition analysis in women with polycystic ovary syndrome: a cross-sectional study from the Tehran Lipid and Glucose Study (TLGS). BMC Endocr Disord 24, 251 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-024-01783-2

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-024-01783-2

Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

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