|Year : 2019 | Volume
| Issue : 1 | Page : 20-26
Distribution of anthropometric, clinical, and metabolic profiles of women with polycystic ovary syndrome across the four regions of India
Jasneet Kaur1, Madhuri Patil2, Sujata Kar3, Padma Rekha Jirge4, Nalini Mahajan1
1 Department of Reproductive Medicine, Mother and Child Hospital, New Delhi, India
2 Dr. Patil's Fertility and Endoscopy Clinic, Bengaluru, Karnataka, India
3 Department of Obstetrics and Gynaecology, Kar Clinic and Hospital Pvt. Ltd, Bhubaneswar, Odisha, India
4 Sushrut Assisted Conception Clinic, Kolhapur, Maharashtra, India
|Date of Web Publication||25-Jun-2019|
Dr. Jasneet Kaur
Mother and Child Hospital, D.59 Defence Colony, New Delhi - 110 024
Source of Support: None, Conflict of Interest: None
Context: Polycystic ovary syndrome (PCOS), one of the most common endocrine disorders encountered in women of reproductive age, is associated with an increased prevalence of metabolic syndrome (MetS). Development of PCOS and its phenotypic expressions is influenced by genetic, ethnic and environmental factors. Due to the vast cultural diversity in our country, differences in the prevalence of MetS may exist across different regions.
Aims and Objectives: To study the distribution of anthropometric, clinical, and metabolic profiles of women with PCOS across the four regions of India.
Materials and Methods: A multicentric prospective study using data collected from four tertiary Assisted Reproductive Centres across four different regions of India was carried out between January 2017 and December 2017. A total of 651 women were diagnosed with PCOS, with 178 belonging to North, 209 to East, 115 to West and 149 to South India. A comparison of the metabolic and anthropometric profiles of women with PCOS was made across the four different ethnic regions of India.
Statistical Analysis: Quantitative variables were compared using the Mann–Whitney test and qualitative variables using the Chi-square test.P < 0.05 was considered statistically significant.
Results: North Indian women had the highest mean body mass index (BMI) – 27.53 ± 4.55 kg/m2 and a higher waist circumference (89.93 ± 14.53 cm) compared to women from South and West India (P = 0.0001). The prevalence of MetS (41.98%) and impaired glucose tolerance (IGT) (37.04%) was also highest in North India followed by East India. PCOS women from East India were lean but had the highest waist-to-hip ratio (WHR) 0.9 ± 0.05 (P = 0.0001) and dyslipidemia. Using multivariate logistical regression analysis, age >25 years, BMI >25 kg/m2, and WHR >0.8 had a strong association with MetS.
Conclusion: Prevalence of MetS is high among Indian PCOS women, with women from the North and East India having the worst metabolic profiles. IGT is the main driver for MetS.
Keywords: Ethnicity, impaired glucose tolerance, Indian, metabolic syndrome, polycystic ovary syndrome
|How to cite this article:|
Kaur J, Patil M, Kar S, Jirge PR, Mahajan N. Distribution of anthropometric, clinical, and metabolic profiles of women with polycystic ovary syndrome across the four regions of India. Onco Fertil J 2019;2:20-6
|How to cite this URL:|
Kaur J, Patil M, Kar S, Jirge PR, Mahajan N. Distribution of anthropometric, clinical, and metabolic profiles of women with polycystic ovary syndrome across the four regions of India. Onco Fertil J [serial online] 2019 [cited 2020 Jan 20];2:20-6. Available from: http://www.tofjonline.org/text.asp?2019/2/1/20/261256
| Introduction|| |
Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders encountered in women of reproductive age., In addition to its clinical manifestations of oligomenorrhea, androgen excess, and polycystic appearing ovaries on ultrasound, PCOS is also associated with an increased prevalence of metabolic syndrome (MetS)., MetS comprises a cluster of factors that increase the risk for cardiovascular disease, diabetes, stroke, and atherosclerosis., Development of PCOS and its phenotypic expressions is influenced by genetic, ethnic, and environmental factors. The prevalence of MetS in the Indian PCOS populations is reported to be 37.5%, with central obesity as one of its significant predictors, being >six-fold higher than the US White population. A significantly increased prevalence of elevated fasting glucose, impaired glucose tolerance (IGT), MetS, and type 2 diabetes has been described in the South Asian general populations when compared with Whites. While India has one of the highest prevalences of MetS, background rates vary according to the degree of urbanization, region, socioeconomic and dietary factors. Due to the rich and vast cultural diversity of our country, differences in the prevalence of MetS may exist across different regions. The aim of this study was to identify differences in the clinical and metabolic manifestations of PCOS women in the four regions of India – North, South, East, and West, by comparing their metabolic and anthropometric profiles. Identification of differences in the prevalence of MetS and its components in a particular region could aid physicians in counseling patients for screening and early intervention.
Aims and objectives of the study
- To compare the metabolic, clinical and anthropometric profiles of women with PCOS across the four different ethnic regions of India and to identify differences in the driving factor of MetS
- To compare the metabolic profiles among the various phenotypes of PCOS.
| Materials and Methods|| |
Study design, size, and duration
A multicentric prospective study using data collected from four tertiary assisted reproductive centers across four different regions of India was carried out between January 2017 and December 2017. PCOS was diagnosed according to the 2003 Rotterdam criteria satisfying at least two of the following three features: (i) oligo or anovulation (OA), (ii) clinical and/or biochemical hyperandrogenism, and (iii) ultrasound appearance of polycystic ovaries with the exclusion of other causes. A total of 651 women were diagnosed with PCOS, with 178 belonging to North, 209 to East, 115 to West, and 149 to South India. Oligomenorrhea was defined as an intermenstrual interval of >35 days and <8 menstrual bleeds in a year. Secondary amenorrhea was defined as absent menstrual bleeding over a period of 90 days. Polycystic appearing ovaries were defined by the presence of 25 follicles of 2–9 mm on cycle 2/3 diagnosed on transvaginal ultrasound in one/both ovaries using 8 MHz probe/or ovarian volume >10 mm3.
A complete clinical history including family history and an analysis of the ethnic background was obtained from all participants. Assessment of hirsutism was done according to the modified Ferriman–Gallwey (mFG) score. Clinical hyperandrogenism was diagnosed with an mFG score >8. Anthropometric measurements, including weight, height, waist circumference (WC) (measured in the horizontal plane midway between the lowest ribs and the iliac crest), body mass index (BMI), systolic blood pressure (SBP), and diastolic BP (DBP), were noted. Hormonal profile (cycle day 2 luteinizing hormone, follicle-stimulating hormone, and estradiol levels) and biochemical tests were performed. Blood collection was done after a fasting of 8–10 h for analysis of lipid profile and oral glucose tolerance test. Lipid profile was estimated by using enzymatic colorimetric technique, and the criteria adopted were in consonance with the NCEP-ATP III guidelines.
MetS was diagnosed when at least three of the following criteria were present: (1) WC of ≥80 cm, (2) BP of ≥130/85 mmHg, (3) fasting blood sugar of ≥100 mg/dl, (4) triglycerides (TGs) of ≥150 mg/dl, and (5) high-density lipoprotein (HDL) of ≤50 mg/dl. Women with PCOS were assigned one of the four phenotypes of PCOS according to the Androgen Excess and PCOS classification 2012 Phenotype A – clinical and/or biochemical hyperandrogenism (HA) + OA + polycystic ovarian morphology (PCOM); Phenotype B – HA+OA; Phenotype C – HA+PCOM; and Phenotype D – OA+PCOM.
A comparison of the metabolic, clinical, and anthropometric profiles of women with PCOS was made across the four different ethnic regions of India and among the four PCOS phenotypes.
Categorical variables were presented in number and percentage (%), and continuous variables were presented as mean ± standard deviation and median. Normality of the data was tested by Kolmogorov–Smirnov test. If the normality was rejected, then nonparametric test was used. Quantitative variables were compared using unpaired t-test/Mann–Whitney test (when the data sets were not normally distributed) between the two groups. Qualitative variables were correlated using Chi-square test/Fisher's exact test. Univariate and multivariate logistic regression analyses were used to assess the association of central line-associated bloodstream infection with various parameters. P < 0.05 was considered statistically significant. Data analysis was done using the Statistical Package for the Social Sciences (SPSS) software version 21.0 (SPSS Inc., Chicago, IL, USA).
| Results|| |
A total of 651 women were diagnosed with PCOS, with 178 belonging to North, 209 to East, 115 to West, and 149 to South India.
Anthropometrically, PCOS women from North India had the highest mean BMI (27.53 ± 4.55 kg/m2) (P = 0.0001) and a higher WC (89.93 ± 14.53 cm) compared to women from South and West India (P = 0.0001). However, PCOS women from the eastern region of India had the highest waist-to-hip ratio (WHR) 0.9 ± 0.05 (P = 0.0001). BP measurements of the ethnic groups revealed that both SBP and DBP were highest among North Indians with a mean of 117.32 ± 11.57 and 76.99 ± 9.09 mm Hg, respectively (P = 0.0001). Analysis of lipid profile among the groups showed that the total cholesterol and low-density lipoprotein (LDL) were significantly higher in North Indian PCOS women 175.78 ± 38.89 and 106.62 ± 27.59 mg/dl compared to those from South and West India (P < 0.0001). On the other hand, women from East India had significantly higher very LDL (VLDL) (27.57 ± 13.95) and lower HDL (39.97 ± 7.99) mg/dl values than those from the other regions (P < 0.0001) [Table 1].
|Table 1: Demographic variables of the four different ethnic regions of India|
Click here to view
Looking at inheritance patterns, a family history of diabetes mellitus (DM) was present in 49% of patients with PCOS. The strongest association was seen in patients from North India 70%, followed by 52% from South and East India individually and 7.83% from West India (P = 0.0001). Thirty-five percent of the PCOS patients had a positive family history of hypertension (HT), with the strongest association being found in East (53%), followed by North (35%), South (17%), and West India (3.5%) (P = 0.001). Overall, the diagnosis of MetS was made in 37% of patients with PCOS. The prevalence was highest in North India (41.98%). IGT was also most prevalent in North India (37.04%) being significantly higher than the southern and western regions (P = 0.007) [Table 2].
|Table 2: Distribution of the variables amongst the four different regions of India|
Click here to view
A study of PCOS phenotypes revealed that overall, “Phenotype A” was the most prevalent and “B” was the least prevalent phenotype, with the frequency of different phenotypes being, A – 58%, B – 3%, C – 18%, and D – 21%. A region-wise analysis showed a similar phenotype distribution pattern [Table 3]. Phenotype A women had a significantly higher WC (88.68 ± 12.55 cm) than women with other phenotypes (P = 0.0002), and also phenotype A women had the highest WHR (72%) with a mean of 0.89 ± 0.09 (P = 0.0001). Comparison of metabolic patterns revealed that “phenotype A” women had the lowest HDL (P = 0.0001) and the highest VLDL (P = 0.019) and LDL (P = 0.0003) values [Table 4].
|Table 3: Distribution of the PCOS phenotypes across the four regions of India|
Click here to view
|Table 4: Demographic variables amongst the four different phenotypes of PCOS|
Click here to view
The prevalence of MetS was highest in phenotype A – 43%, followed by B – 39%, C – 31%, and least in phenotype D – 28% (P = 0.035). Interestingly, there was no difference in the prevalence of IGT in the four phenotypes (P = 0.5) [Table 5]. MetS appeared to be more common in hyperandrogenic PCOS women (A/B/C) – 85% than the nonhyperandrogenic type D (15%) (P = 0.028) [Figure 1].
|Table 5: Distribution of the variables amongst the four phenotypes of PCOS|
Click here to view
|Figure 1: Distribution of metabolic syndrome amongst the hyper and non-hyperandrogenic phenotypes of PCOS|
Click here to view
Individually, using univariate logistical regression analysis, age >25 years ([OR] =1.589; 95% confidence interval [CI] =1.043–2.423; P = 0.031), BMI >25 kg/m2 (OR = 3.42; 95% CI = 2.242–5.233; P = 0.0001), WC >80 cm (OR = 4.233; 95% CI = 2.557–7.006; P = 0.0001), family history of DM (OR = 1.551; 95% CI = 1.070–2.246; P = 0.020), and WHR >0.8 (OR = 2.635; 95% CI = 1.742–3.984; P = 0.0001) had a strong association with MetS. Using multivariate logistical regression analysis, age >25 years, BMI >25 kg/m2, and WHR >0.8 had a strong association with MetS [Table 6].
|Table 6: Multivariate logistical regression analysis showing correlation of variables with metabolic syndrome|
Click here to view
| Discussion|| |
PCOS is a complex, heterogeneous, endocrine disorder that affects women from adolescence to menopause. Women with PCOS are at a higher risk of developing MetS because of the associated hyperinsulinemia, obesity, and hyperlipidemia. These factors along with a genetic predisposition are known to play a major role in the pathogenesis of MetS. Dokras et al. reported that PCOS women had an eleven-fold higher risk of developing MetS compared to non-PCOS women. Because environmental, genetic, and ethnic factors influence the development of PCOS and its phenotypic expression, the authors have sought to determine if the prevalence of MetS and the clustering of its components vary among women of different ethnicity.
Tillin et al. stated that the prevalence of MetS was three-fold higher in South Asians compared to European women (30.8% vs. 9.1%, P < 0.0001). Assessment of individual components revealed an increased rate of IGT (44.2% vs. 21.3%, P < 0.0001), higher BP (32.4% vs. 20.7%, P < 0.0002), dyslipidemia (37.8% vs. 20.7%, P < 0.0003), and central obesity (65.8% vs. 30.1%, P < 0.0001) in the South Asian group. A study by Pandit et al. reaffirmed that South Asians had the highest prevalence of MetS, with variations depending on the degree of urbanization and regional and socioeconomic factors. Chan et al. addressing six ethnic groups, across four continents, found that the prevalence of MetS was six-fold higher in the Indian cohort compared to the US White population, with the driver being the elevated fasting glucose component. The Indian patients in this group were primarily from the eastern region of India. In our study, the prevalence of MetS in the Indian PCOS women was 37%, which was similar to the 37.5% reported by Kar (2013). This is significantly higher than studies done on Caucasian PCOS women, with the prevalence being 8.2% in an Italian study, 33.8% in German women, and 28.3% in US Whites.
The increased propensity of South Asians to develop MetS appears to be related to their unique phenotype which is characterized by an increased WC and WHR, excessive body fat mass, increased insulin resistance, and an atherogenic dyslipidemia, marked by increased TG and low levels of HDL-cholesterol.,, An attempt is also being made to identify distinctive genetic markers that potentially make South Asians more susceptible to cardiometabolic risks.
Sociocultural factors and dietary habits along with the regional genetic variations might influence metabolic profiles across regions. It has been suggested that comparison of different populations in a country can point to genetic predispositions, whereas differences within a racially similar group living in different countries may reflect environmental influences. As dietary habits and sociocultural and economic factors vary so vastly within India, we studied the metabolic and anthropometric profiles of women in the northern, southern, eastern, and western regions of India in order to identify differences in the prevalence and driving factors of MetS. This in turn would promote counseling and early screening of the specific regional MetS drivers.
Our study revealed that PCOS women from North and East India had the poorest metabolic profiles, hence the highest prevalence of MetS (41.98% and 40.3%, respectively).
North Indian women had the highest mean BMI – 27.53 ± 4.55 kg/m2. (P = 0.0001) among all groups and a higher WC (89.93 ± 14.53 cm) compared to women from South and West India (P = 0.0001). Analysis of lipid profile showed that the total cholesterol and LDL were significantly higher in North Indian PCOS women (175.78 ± 38.89 and 106.62 ± 27.59 mg/dl, respectively) compared to those from South and West India (P < 0.0001). The prevalence of MetS (41.98%) and IGT (37.04%) was also highest in North India. These findings could be attributed to changes in lifestyle and dietary habits related to rapid urbanization, as well as to a genetic contribution.
Interestingly, PCOS women from the eastern region of India were lean but had the highest WHR – 0.9 ± 0.05 (P = 0.0001). They also exhibited a significantly altered lipid profile with a higher VLDL value (27.57 ± 13.95 mg/dl) 17 and lower HDL (39.97 ± 7.99 mg/dl) value than those from the other regions (P < 0.0001). IGT appeared to be the driver for MetS in North India (36.04%) followed closely by East India (26.8%). Hyperlipidemia appeared to be the risk factor in East and North India. High BMI was an additional risk factor among North Indians. PCOS women from South and West India had the lowest prevalence of MetS; whether it is related to their dietary habits or genetic composition needs to be determined.
Body fat patterns and altered lipid profiles are perhaps making Indians, particularly in the northern and eastern regions, more prone to develop MetS. Joshi observed that the socioeconomic status, lifestyle, and dietary habits such as high consumptions of saturated fat and cholesterol and low intake of fiber and antioxidants could lead to varied clustering of the parameters of MetS. Maladaptation, “stress response” causing hypothalamic pituitary activation, and increased smoking and alcohol drinking may be additional contributory factors causing variation in the different ethnic populations, regions, and countries.
A study of PCOS phenotypes revealed that “Phenotype A” was the most prevalent phenotype and “B” was the least prevalent phenotype in our study, with the frequency of different phenotypes being, A – 58%, B – 3%, C – 18%, and D – 21%. Kar also observed phenotype A to be the most prevalent and phenotype C to be the least prevalent. Chauhan et al., however, observed phenotype C to be the most prevalent phenotype and B to be the least prevalent phenotype. The difference in the prevalence of PCOS phenotypes reported by different Indian authors could be explained by the differences in the recruitment of patients (infertility/gynecology clinic).
Evaluation of metabolic components among PCOS phenotypes indicated that women with phenotype A had the lowest HDL (P = 0.0001) and the highest VLDL (P = 0.019) and LDL (P = 0.0003) values, with the highest prevalence of MetS of 43%. This is in corroboration with studies done by various authors which have stated that phenotype A has an increased cardiometabolic risk., In our study, MetS appeared to be more common in the hyperandrogenic (HA) phenotypes (A/B/C) (85%) than the nonhyperandrogenic phenotype D (15%) (P = 0.028). A study done on Nordic women stated that the prevalence of MS was twofold higher in the HA-PCOS population compared with the nonhyperandrogenic phenotypes, suggesting that hyperandrogenism also is implicated, at least in the long term, in the adverse metabolic profile seen in PCOS. This association was reiterated in another Indian study which reported a four-to-five-fold higher (37%–50%) prevalence of MetS in HA phenotypes compared to nonhyperandrogenic phenotype (10%). This underpins the importance of accurate identification of PCOS phenotypes, particularly androgenic status, when evaluating metabolic risks because it has long-term health implications.
Using multivariate logistical regression analysis, we found that age >25 years, BMI >25 kg/m2, and a WHR >0.8 had a strong association with MetS in Indian patients, emphasizing that obesity in PCOS exacerbates symptoms and has long-term negative health consequences. A family history of diabetes was also a strong risk factor for MetS.
| Conclusion|| |
The present study confirms that the prevalence of MetS is high among Indian PCOS women. Regional differences prevail with women from North and East India having the worst metabolic profiles. IGT appears to be the driving component across regions, with the highest propensity in North India followed by East India. Obesity is high in North India, which adds to the MetS risk. In East India though women are lean, they have hyperlipidemia. Our data reaffirm that ethnic and regional differences even within a single community play an important role in the clinical and metabolic manifestations of PCOS. This emphasizes the importance of early screening for MetS in PCOS women to initiate cardiovascular risk reduction strategies. A family history of DM and HT should be sought during evaluation as they are risk factors for MetS.
Limitation of our study
A larger sample size encompassing more states in all the four regions would give a more accurate analysis.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Carmina E, Lobo RA. Polycystic ovary syndrome (PCOS): Arguably the most common endocrinopathy is associated with significant morbidity in women. J Clin Endocrinol Metab 1999;84:1897-9.
Ehrmann DA. Polycystic ovary syndrome. N
Engl J Med 2005;352:1223-36.
Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertil Steril 2004;81:19-25.
Dokras A, Bochner M, Hollinrake E, Markham S, Vanvoorhis B, Jagasia DH. Screening women with polycystic ovary syndrome for metabolic syndrome. Obstet Gynecol 2005;106:131-7.
Moran LJ, Misso ML, Wild RA, Norman RJ. Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: A systematic review and meta-analysis. Hum Reprod Update 2010;16:347-63.
Wild RA, Carmina E, Diamanti-Kandarakis E, Dokras A, Escobar-Morreale HF, Futterweit W, et al.
Assessment of cardiovascular risk and prevention of cardiovascular disease in women with the polycystic ovary syndrome: A consensus statement by the Androgen excess and polycystic ovary syndrome (AE-PCOS) society. J Clin Endocrinol Metab 2010;95:2038-49.
Dokras A. Cardiovascular disease risk in women with PCOS. Steroids 2013;78:773-6.
Ehrmann DA, Kasza K, Azziz R, Legro RS, Ghazzi MN; PCOS/Troglitazone Study Group. Effects of race and family history of type 2 diabetes on metabolic status of women with polycystic ovary syndrome. J Clin Endocrinol Metab 2005;90:66-71.
Wijeyaratne CN, Seneviratne Rde A, Dahanayake S, Kumarapeli V, Palipane E, Kuruppu N, et al.
Phenotype and metabolic profile of South Asian women with polycystic ovary syndrome (PCOS): Results of a large database from a specialist endocrine clinic. Hum Reprod 2011;26:202-13.
Pandit K, Goswami S, Ghosh S, Mukhopadhyay P, Chowdhury S. Metabolic syndrome in South Asians. Indian J Endocrinol Metab 2012;16:44-55.
Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al.
Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific statement. Circulation 2005;112:2735-52.
Johnson TR, Kaplan LK, Ouyang P, Rizza RA. Evidence – Based Methodology Workshop on Polycystic Ovary Syndrome. Bethesda, Maryland: National Institutes of Health; 2012.
Tillin T, Forouhi N, Johnston DG, McKeigue PM, Chaturvedi N, Godsland IF. Metabolic syndrome and coronary heart disease in South Asians, African-Caribbeans and White Europeans: A UK population-based cross-sectional study. Diabetologia 2005;48:649-56.
Chan JL, Kar S, Vanky E, Morin-Papunen L, Piltonen T, Puurunen J, et al.
Racial and ethnic differences in the prevalence of metabolic syndrome and its components of metabolic syndrome in women with polycystic ovary syndrome: A regional cross-sectional study. Am J Obstet Gynecol 2017;217:189.e1.
Kar S. Anthropometric, clinical, and metabolic comparisons of the four Rotterdam PCOS phenotypes: A prospective study of PCOS women. J Hum Reprod Sci 2013;6:194-200.
] [Full text]
Carmina E, Napoli N, Longo RA, Rini GB, Lobo RA. Metabolic syndrome in polycystic ovary syndrome (PCOS): Lower prevalence in Southern Italy than in the USA and the influence of criteria for the diagnosis of PCOS. Eur J Endocrinol 2006;154:141-5.
Hahn S, Tan S, Sack S, Kimmig R, Quadbeck B, Mann K, et al.
Prevalence of the metabolic syndrome in German women with polycystic ovary syndrome. Exp Clin Endocrinol Diabetes 2007;115:130-5.
Misra A, Misra R, Wijesuriya M, Banerjee D. The metabolic syndrome in South Asians: Continuing escalation and possible solutions. Indian J Med Res 2007;125:345-54.
] [Full text]
McKeigue PM, Pierpoint T, Ferrie JE, Marmot MG. Relationship of glucose intolerance and hyperinsulinaemia to body fat pattern in South Asians and Europeans. Diabetologia 1992;35:785-91.
McKeigue PM, Marmot MG, Syndercombe Court YD, Cottier DE, Rahman S, Riemersma RA. Diabetes, hyperinsulinaemia, and coronary risk factors in Bangladeshis in East London. Br Heart J 1988;60:390-6.
Prasad DS, Kabir Z, Dash AK, Das BC. Prevalence and risk factors for metabolic syndrome in Asian Indians: A community study from urban Eastern India. J Cardiovasc Dis Res 2012;3:204-11. [Full text]
Joshi SR. Metabolic syndrome – Emerging clusters of the Indian phenotype. J Assoc Physicians India 2003;51:445-6.
Joshi B, Mukherjee S, Patil A, Purandare A, Chauhan S, Vaidya R, et al.
Across-sectional study of polycystic ovarian syndrome among adolescent and young girls in Mumbai, India. Indian J Endocrinol Metab 2014;18:317-24.
Shroff R, Syrop CH, Davis W, Van Voorhis BJ, Dokras A. Risk of metabolic complications in the new PCOS phenotypes based on the Rotterdam criteria. Fertil Steril 2007;88:1389-95.
Welt CK, Gudmundsson JA, Arason G, Adams J, Palsdottir H, Gudlaugsdottir G, et al.
Characterizing discrete subsets of polycystic ovary syndrome as defined by the Rotterdam criteria: The impact of weight on phenotype and metabolic features. J Clin Endocrinol Metab 2006;91:4842-8.
Pinola P, Puukka K, Piltonen TT, Puurunen J, Vanky E, Sundström-Poromaa I, et al.
Normo- and hyperandrogenic women with polycystic ovary syndrome exhibit an adverse metabolic profile through life. Fertil Steril 2017;107:788-95.e2.
Sharma S, Majumdar A. Prevalence of metabolic syndrome in relation to body mass index and polycystic ovarian syndrome in Indian women. J Hum Reprod Sci 2015;8:202-8.
] [Full text]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]