Detection of metabolic disorders for African women in a rural South African setting : a case of Ga-Dikgale Limpopo Province, South Africa - Phaho, Mmalehu Francin (My University, 2024)
The waist circumference cut-off point for diagnosing metabolic syndrome in Sub-Saharan Africa is based on standards established for European populations. The purpose of this study was to determine the prevalence of metabolic syndrome and other related disorders and to determine the waist circumference cut-off point that effectively discriminates between African women with and without metabolic syndrome. Initially, the study participants with metabolic syndrome were identified using the National Cholesterol Education Program - Third Adult Treatment Panel criteria, which was subsequently adapted to the International Diabetes Federation definition. According to the National Cholesterol Education Program - Third Adult Treatment Panel definition, metabolic syndrome is present if at least three of the following criteria are met: Triglycerides ≥1.7 mmol/L, High-density lipoprotein cholesterol <1.29 mmol/L, Glucose ≥5.6 mmol/L, Systolic Blood Pressure ≥ 130 mmHg, or Diastolic Blood Pressure ≥ 85 mmHg. The prevalence of metabolic syndrome and obesity (body mass index ≥30 kg) were 19% and 30%, respectively. The optimal waist circumference for diagnosing metabolic syndrome was obtained using receiver operating characteristic curve analysis and was found to be 88 cm. Machine learning methods, including logistic regression, linear discriminant analysis and random forest were employed to further validate the cut-off point. The 88 cm cut-off point demonstrated superior performance compared to the European 80 cm cut-off pint, based on prediction accuracy, specificity and positive predictive value. The study highlights how important ii it is to have population-specific cut-off points for correctly diagnosing metabolic syndrome in order to reduce the risk of misdiagnosis and related complications. The findings advocate for using the 88 cm cut-off point, which differs with the recommended cut-off point of 80 cm. This is a as a quick and cost-effective measure for identifying obesity, potentially improving public health interventions for African populations.
Thesis (M. Sc. (Statics)) -- University of Limpopo, 2024
Keywords: Obesity; Waist circumference; Metabolic syndrome; Machine learning; Metabolic syndrome; Obesity; Machine learning; Metabolism -- Disorders; Waists (Clothing)