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Bhavadharini, B., Mahalakshmi, M.M., Anjana, R.M., Maheswari, K., Uma, R., Deepa, M., et al. (2016) Prevalence of Gestational Diabetes Mellitus in Urban and Rural Tamil Nadu Using IADPSG and WHO 1999 Criteria (WINGS 6). Clinical Diabetes and Endocrinology, 2, Article No. 8.
https://doi.org/10.1186/s40842-016-0028-6

has been cited by the following article:

• TITLE: Association between Anthropometry and Gestational Diabetes Mellitus

  AUTHORS: Thusitha Nandana Bodhinarayana, Madhava Karunarathne

  KEYWORDS: Anthropometry, Gestational Diabetes, Predictions

  JOURNAL NAME: Open Journal of Obstetrics and Gynecology, Vol.13 No.3, March 29, 2023

  ABSTRACT: Introduction: The predictive value of various anthropometric indicators for identifying benefits or risksfor maternal health outcomes of pregnancy is discussed around the globe. Anthropometric measurements can be a cost effective, efficient method of screening for gestational diabetes mellitus (GDM) especially, in developing countries with low-cost health care settings. Objectives: To determine a relationship between anthropometric measurements and GDM and to assess the importance/suitability of an anthropometric measurement in predicting GDM. Methods: A correlational study with the evaluation of diagnostic test accuracy was conducted among 48 pregnant women at period of amenorrhoea (POA) of 26 - 34 weeks of gestation. The obstetrics ward and the antenatal clinic of Peradeniya teaching hospital was the study setting. Systematic random sampling technique was used for participant selection. Singleton pregnancies with Body Mass Index (BMI) less than 30.0 kg/m2 were included. Women with pre-existing diabetes and medical disorders were excluded. Mid arm circumference (MAC), tricipital skin fold thickness (TSFT), bicipital skin fold thickness (BSFT) were measured according to the National Health and Nutrition Examination Survey (NHANES) anthropometry manual. An interviewer-administered questionnaire was applied to collect the data. Data was evaluated in accordance with the objectives by using SPSS version 25. Results: Mean age of the participants was 29.67 years (SD = 4.76 years). Mean height and weight of the study participants were 154.93 cm and 67.45 kg respectively. Mean BMI value was recorded as 28.13 kgm2. Mean mid arm circumference was 9.43 cm. According to the multivariate analysis done by using logistic regression, calculated TSFT and BSFT values were independently associated with GDM in the population. Successful prediction can be achieved by using the BMI and the body weight (AUC 0.5). 24.8 kg/m2 is taken as the best cut off value to predict GDM (Sn = 79.2; Sp = 29.2). Best cut off value for body weight appears as 60 kg (Sn = 79.2; Sp = 32.3) and the best cut off value for height is 150 cm (Sn = 80.0; Sp = 25.0). When the predictive variables are compared with each other, highest predictive ability was recorded by the body mass index (AUC = 0.632). Predictability of TFT and BSFT appeared significant. 27.0 cm can be considered as the most accurate cut off value of MAC (Sn = 80; Sp = 30). Best cut off values for BSFT and TSFT were 22 mm (Sn = 80; Sp = 60) and 10.5 mm (Sn = 83.3; Sp