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Although current evidence suggests increased risk of obesity, insulin resistance, and metabolic alterations in patients with polycystic ovary syndrome (PCOS), especially of a hyperandrogenic phenotype, the impact of each one of these variables on muscle mass remains uncertain. In this case-control study, we evaluated clinical and hormonal characteristics related to lean body mass according to the different PCOS phenotypes. We performed clinical, metabolic, and hormonal assessments and evaluated body compartments by dual-energy X-ray absorptiometry in 133 women of reproductive age. Creatinine served as an indirect marker of lean mass. Median age was 28 (range, 17–37) years. Women with phenotypes A and B (n = 59) had higher body mass index (BMI) and metabolic syndrome prevalence than those with phenotype C (n = 23) and controls (n = 51) (p<0.005). Women with phenotypes A and B also had higher Ferriman-Gallwey score (p<0.001), insulin levels (p = 0.006), HOMA-IR (p = 0.008), testosterone (p = 0.008), free androgen index (FAI) (p<0.001), fat mass index (FMI) (p = 0.015), android-to-gynoid fat ratio (p = 0.036), and bone mineral density (BMD) at lumbar spine (p = 0.027) and total femur (p = 0.013) than controls. Median appendicular lean mass index (ALMI) was higher in phenotypes A and B than in controls (7.01 [IQR, 6.33–8.02] vs. 6.69 [IQR, 5.94–7.09], p = 0.024), but it did not differ significantly from that in phenotype C (6.60 [IQR, 6.16–7.22], p = 0.222). Even after adjusting for BMI, ALMI correlated positively with creatinine in women with phenotypes A and B (rho = 0.319, p = 0.023) but not in those with phenotype C (p = 0.238) or controls (p = 0.097). In multivariate linear regression analyses, ALMI was positively associated with insulin, FAI, FMI, and total femur BMD. The present results suggest that fasting insulin, FAI, fat mass, and total femur BMD were positively associated with increased lean mass in women with PCOS phenotypes A and B. [ABSTRACT FROM AUTHOR]

Background: Globally by 2030, 38% of the world's population would be overweight, and another 20% would be obese. This has led to rising concerns regarding how swiftly and substantially the world is moving towards this epidemic of "globesity". India too is facing an increased burden of overweight and obese population. The changing dietary patterns are significantly associated with the increasing prevalence of overweight/obesity and related complications, especially among women. Hence, the present study aims to observe the spatial patterns of overweight or obesity among women in reproductive age group in India and factors associated with it. Methods: The study analyzed data from a cross-sectional nationwide household survey, i.e. National Family Health Survey (NFHS-4), 2015–16. The primary outcome variable of this study was overweight/obesity among reproductive-age women, which was measured through the body mass index (BMI) of the women. Bivariate and multivariate logistic regression analysis was used to analyze the data. Additionally, for spatial analysis in terms of overweight/obesity among women in India, univariate and bivariate Moran's I index measurements were used along with the usage of spatial regression models. Results: The value of spatial-autocorrelation for overweight or obese was 0.64, which depicts the moderately high prevalence of the overweight/obesity coverage over districts of India. The overall prevalence overweight/obesity among women in India is around 25% and higher proportion of women from urban areas (37.8%), and non-poor (33.4%) economic group reported to be overweight or obese. From spatial lag model, the lag coefficient was found to be 0.28, implying that a change in the prevalence of overweight/obesity among women in a certain district may statistically lag the prevalence of overweight/obesity by 28% in the neighbouring districts. There were significantly high clustering of overweight/obese women and non-poor wealth quintiles in 132 districts, mainly from states of Punjab, Haryana, Gujarat, Maharashtra, Kerala, Tamil Nadu, Karnataka and Andhra Pradesh. Additionally, there was high-high clustering of overweight/obese women and those who ever had caesarean in 82 districts, mostly from Kerala, Tamil Nadu, Andhra Pradesh and Karnataka. Conclusion: The spatial patterns on the prevalence of overweight and obesity in India show that the women belonging to the southern states' districts are more overweight or obese in comparison to other states. The determinants like older age, higher education, urban residence, higher economic status are the key factors contributing to the prevalence of overweight or obesity among women in the reproductive age group. The study concludes and recommends an urgent need of interventions catering to urban women belonging to higher socio-economic status, to reduce the risks of health consequences due to overweight and obesity. [ABSTRACT FROM AUTHOR]

Background: Malnutrition imposes enormous costs resulting from lost investments in human capital and increased healthcare expenditures. There is a dearth of research focusing on the prediction of women's body mass index (BMI) and malnutrition outcomes (underweight, overweight, and obesity) in developing countries. This paper attempts to fill out this knowledge gap by predicting the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health, and demographic features. Methods: Data from the 2017–18 Bangladesh Demographic and Health Survey and a series of supervised machine learning (SML) techniques are used. Additionally, this study circumvents the imbalanced distribution problem in obesity classification by utilizing an oversampling approach. Results: Study findings demonstrate that the support vector machine and k-nearest neighbor are the two best-performing methods in BMI prediction based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The combined predictor algorithms consistently yield top specificity, Cohen's kappa, F1-score, and AUC in classifying the malnutrition status, and their performance is robust to alternative standards. The feature importance ranking based on several nonparametric and combined predictors indicates that socioeconomic status, women's age, and breastfeeding status are the most important features in predicting women's nutritional outcomes. Furthermore, the conditional inference trees corroborate that those three features, along with the partner's educational attainment and employment status, significantly predict malnutrition risks. Conclusion: To the best of our knowledge, this is the first study that predicts BMI and one of the pioneer studies to classify all three malnutrition outcomes for women of childbearing age in Bangladesh, let alone in any lower-middle income country, using SML techniques. Moreover, in the context of Bangladesh, this paper is the first to identify and rank features that are critical in predicting nutritional outcomes using several feature selection algorithms. The estimators from this study predict the outcomes of interest most accurately and efficiently compared to other existing studies in the relevant literature. Therefore, study findings can aid policymakers in designing policy and programmatic approaches to address the double burden of malnutrition among Bangladeshi women, thereby reducing the country's economic burden. [ABSTRACT FROM AUTHOR]