OBJECTIVE:The objective of this study was to test the hypothesis that AMH and antral follicle count (AFC) are good predictors of ovarian response to controlled ovarian stimulation and to compare them.MATERIALS AND METHODS:This observational cross-sectional study included 56 subjects aged between 25 and 42 years who were enrolled between 1st January and 31st December 2010 for their first intracytoplasmic sperm injection (ICSI) program. Baseline hormone profiles including serum levels of Estradiol (E2), Follicle-stimulating hormone (FSH), Luteinizing hormone (LH), and Anti-mullerian Hormone (AMH) were determined on day 3 of the previous cycle. The antral follicle count measurements were performed on days 3-5 of the same menstrual cycle. Antral follicles within the bilateral ovaries between 2-6 mm were recorded. The subjects were treated with long protocol for ovarian stimulation. Ovulation was induced with 10,000 IU of human chorionic gonadotropin (hCG) when at least 3 follicles attained the size of more than 17 mm. Transvaginal oocyte retrieval was performed under ultrasound guidance 36 hours after hCG administration. An oocyte count less than 4 and absence of follicular growth with controlled ovarian hyper stimulation was considered as poor ovarian response. Oocyte count of 4 or more was considered as normal ovarian response.RESULTS:Statistical analysis was performed using SPSS software trail version 16.0. Subjects were divided into 2 groups, depending on the ovarian response. The mean oocyte counts were 12.27 ± 6.06 and 2.22 ± 1.24 in normal and poor responders, respectively, (P = 001). Multiple regression analysis revealed AMH and antral follicle count as predictors of ovarian response (β coefficient ± SE for AMH was 1.618 ± 0.602 (P = 0.01) and for AFC, it was, 0.528 ± 0.175 (P = 0.004). AFC was found to be a better predictor of ovarian response compared to AMH in controlled ovarian hyper stimulation.CONCLUSION:The observations made in this study revealed that both AMH and AFC are good predictors of ovarian response; AFC being a better predictor compared to AMH.

Over a period of time, anthropometric parameters have evolved into reliable indicators for predicting the incidence of diabetes mellitus. A number of studies have shown correlations between anthropometry and lipid profiles in healthy volunteers. This study examined correlations between anthropometry and lipid profile in type 2 diabetics. The limited observations made in this study reveal that anthropometric parameters are not ideal for predicting lipid profile abnormalities in type 2 diabetics.

Background:Polycystic ovary syndrome (PCOS) is the most common endocrinopathy in reproductive age women and is associated with both reproductive and metabolic abnormalities. Recent studies have demonstrated an early onset of abnormal cardiovascular risk profile in women with PCOS. Abnormal lipid profile patterns are common in women with PCOS, and these abnormalities are not uniform in all populations. Anthropometry is a simple and commonly used research tool for assessing metabolic risk in women with PCOS. Therefore, this study examined the correlations between anthropometric parameters and lipid profile in women with PCOS.Objectives:The objectives of the study were (1) To study the anthropometric profile of women with PCOS, (2) To examine the lipid profile pattern of these women with PCOS and (3) To see whether there exists any correlation between these anthropometric parameters and lipid profile.Materials and Methods:This observational cross-sectional study examined anthropometry and lipid profile in 86 married women with PCOS in the age group of 18–35 years and correlated them by using Pearson’s correlation coefficient.Results:More than 80% of the women with PCOS demonstrated abnormal anthropometric parameters, and in more than 70% women, lipid abnormalities such as low levels of high-density lipoprotein (HDL) cholesterol and high levels of triglycerides and low-density lipoprotein cholesterol were observed. Significant positive correlations were seen between body mass index (BMI) and triglycerides (P ≤ 0.001) and waist circumference (WC) and triglycerides (P ≤ 0.029). Negative correlations were observed between BMI and HDL cholesterol (P ≤ 0.013).Conclusion:This study revealed that BMI and WC are the most important anthropometric parameters correlated to dyslipidemia in the south Indian women with PCOS.

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a b s t r a c tBackground: Rheumatic heart disease (RHD) is still a public health issue in many countries in the world, and particularly in Southeast Asia. India, for example, contributes 25%e50% of the global burden of RHD. Clinic-based and epidemiological studies on RHD in India have used different methodologies and clinical criteria to estimate RHD burden in India. The present study employs strict clinical criteria, including echocardiography, to estimate RHD prevalence and associated clinical complications in a large unique rural population in southern India covered through a governmental health insurance scheme. Materials and methods: Total 44,164 eligible patients were screened from 238 primary care health centers in rural southern India between October 2007 and March 2012 using strict clinical criteria and objective ascertainment. A total of 403 patients aged 15 years or above were finally analyzed based on both the inclusion and exclusion criteria. Detailed information on both demographic and clinical characteristics was obtained through personal interviews and clinical examinations. Descriptive analyses were performed, including age standardization. Results: The age-standardized RHD prevalence rate was 9.7/1000 populationsdmore common in younger age groups (<44 years) and relatively high among females. Pulmonary hypertension was the most common clinical complication followed by CHF, tricuspid regurgitation, as well as infective endocarditis. More than two-thirds had no past history of RHD or penicillin prophylaxis. Conclusions: RHD rates are still high in rural India among populations covered through governmental health insurance scheme. Both primary and secondary preventive measures, including widespread coverage of penicillin prophylaxis, must be considered mainstay tools to both prevent and reduce RHD burden in endemic populations, including rural India.