Hirsutism and Anthropometric Profiles Among Subjects with Polycystic Ovarian Morphology? A Cross-Sectional Analysis

Abstract

Background: Polycystic ovarian syndrome (PCOS) is increasingly being diagnosed and treated with sometimes variable lifestyle advice and pharmacological interventions. Obesity is considered as the sole culprit and variable definitions in clinics compound the understanding of pathogenic heterogeneity of this syndrome. We evaluated the differences between various simple to calculate anthropometric indices along with some anthropometric-biochemical equations in subjects with or without PCOS. Objective: To compare traditional measures like waist to hip and height ratio (WHpR and WHtR), BMI, newer markers depicting central obesity like Abdominal Volume index(AVI), Body roundness index (BRI), A Body Shape index (ABSI), Conicity index (C-index) along with biochemical-anthropometric equations like lipid Accumulation Products (LAP), Visceral Adiposity Index (VAI) and Chinese Visceral Adiposity Index (CVAI) for diagnosing PCOS as per the Rotterdam criteria Design: Cross-sectional analysis Place & Study Duration: Naval hospital, Islamabad from Jan2018 to July2019 Subjects and Methods: From our finally evaluated 333 female subjects we initially compared the differences for the presence of hirsutism as per modified Ferrimen Gallwey scores and biochemical hyperandrogenism by measuring free androgen index (Total testosterone/SHBG x 1000. We evaluated waist circumference, BMI, WHpR, WHtR, AVI, BRI, ABSI, C-index along with biochemical-anthropometric equations like LAP, VAI and CVAI for differences in subjects diagnosed to have PCOS by Rotterdam criteria or ultrasonography alone. Results: Differences in hirsutism as defined by modified FG score between subjects defined to have PCOS or otherwise as per Rotterdam defined criteria were as [(PCOS=169, Mean=17.33 + 9.05) (No PCOS=164, Mean=8.21 + 5.74), p< 0.001] and ultrasound [(PCOS=87, Mean=16.95 + 9.57) (No PCOS=246, Mean=11.38 + 8.51), p< 0.001]. Similarly, the differences in FAI between subjects defined to have PCOS or otherwise as per Rotterdam criteria and ultrasound were as [(PCOS=169, Mean=6.41 + 4.88) (No PCOS=164, Mean=2.77 + 1.79), p< 0.001] and [(PCOS=87, Mean=5.75 + 5.01) (No PCOS=246, Mean=4.22 + 3.68), p= 0.011]. Anthropometric measures and anthropometric-mathematical equations were raised in non-PCOS subjects than DOI: 10.14302/issn.2574-450X.jom-19-3000 Freely Available Online www.openaccesspub.org | JOM CC-license DOI : 10.14302/issn.2574-450X.jom-19-3000 Vol-1 Issue 2 Pg. no.45 Introduction Polycystic ovarian morphology or Polycystic ovarian syndrome (PCOS), termed in common parlance as the “The thief of womanhood” has emerged in recent times as a spectrum of disorders starting from menorrhagia, hirsutism to infertility. [1] The disorder not just carries with it the stigmata of reproductive disorders but also linked with various metabolic risks including insulin resistance and dyslipidemia.[2] Though not much appreciated in developing countries, the problem is growing at an alarming pace within sub-continental community with prevalence touching up to 9% of the population in young females. [3] Literature review provides variable results in terms of PCOS association with clinical and metabolic risk factors. Firstly, researchers have discovered different phenotypes of PCOS as regards to their clinic-pathological correlates, which vary as per the geographical zone one belongs to. [4]The usual diagnosis revolves around establishing not just the clinical presentation including disturbances in a menstrual cycle like oligo or anovulation, but reliance has been placed on various biochemical, endocrine parameters and radiological findings for labeling subjects with PCOS. [5] Secondly, there is data to support that available evidence is suggestive of inconsistent data regarding biochemical and endocrine parameters, probably due to inherent imprecision related to analytical techniques or rapidly changing hormonal cycle within the females. [6] Finally, multiple criteria starting from NIH from 1992, to Rotterdam and AEPCOS are available to define PCOS, but yet consensus among authorities in far from converging to a common solution. [7,4,8] Furthermore, local data apart from its scarcity, still has been able to identify differences in phenotype especially for the subcontinental population. [9] While the clinical presentations due to symptoms vary across the cultures, authorities in past have been suggesting a simpler way to diagnose or suspect PCOS, where the role of anthropometric measurements can play a major role especially in resourcescarce countries. [10] Earlier studies in this Pazderska et al have indirectly shown waist circumference to predict cardiometabolic disease in subjects with PCOS thus highlighting the usefulness of anthropometry in this condition. [11] Another Chinese study by Huang et al identified anthropometric indices and lipid peroxidation products to be useful in identifying the underlying risk in PCOS. [12] Techatraisak et al have identified visceral adiposity index and other related measures like BMI to have the potential of predicting PCOS. [13] In recent times we had several new anthropometry based indices along with conventional parameters and anthropometric measures and Corresponding author: Sikandar Hayat Khan, Department of pathology PNS HAFEEZ, Email: [email protected]