Changes in Obesity Phenotype Distribution in Mixed-ancestry South Africans in Cape Town Between 2008/09 and 2014/16

Saarah Fatoma Davids; Tandi Edith Matsha; Nasheeta Peer; Rajiv Timothy Erasmus; Andre Pascal Kengne

doi:10.3389/fendo.2019.00753

Changes in Obesity Phenotype Distribution in Mixed-ancestry South Africans in Cape Town Between 2008/09 and 2014/16

Front Endocrinol (Lausanne). 2019 Nov 6:10:753. doi: 10.3389/fendo.2019.00753. eCollection 2019.

Authors

Saarah Fatoma Davids^{1

2}, Tandi Edith Matsha², Nasheeta Peer^{1

3}, Rajiv Timothy Erasmus⁴, Andre Pascal Kengne^{1

3}

Affiliations

¹ Department of Medicine, Faculty of Health Science, University of Cape Town, Cape Town, South Africa.
² SAMRC/CPUT/Cardiometabolic Health Research Unit, Department of Biomedical Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Bellville, South Africa.
³ Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa.
⁴ Department of Chemical Pathology, Faculty of Medicine and Health Sciences, and National Health Laboratory Service (NHLS), Stellenbosch University, Cape Town, South Africa.

Abstract

Background: The concept of obesity phenotypes encompasses a different approach to evaluating the relationship between obesity and cardiometabolic diseases. Considering the minimal research on obesity phenotypes in Africa, we investigated these changes from 2008/09 to 2014/16 in the mixed ancestry population in Cape Town, South Africa. Methods: In all, 928 (2008/09) and 1969 (2014/16) ≥20 year old participants were included in two community-based cross-sectional studies. For obesity phenotype classification, a combination of body mass index (BMI) categories and prevalent cardiometabolic disease risk factors were used, with the presence of ≥2 cardiometabolic abnormalities defining abnormal metabolic status. Interaction tests were used to investigate changes in their distribution across the years of study. Results: Distribution of BMI categories differed significantly between the 2 years; normal weight, overweight and obese: 27.4, 27.4, and 45.3% in 2008/09 vs. 34.2, 23.6, and 42.2% in 2014/16 (p = 0.001). There was no differential effect in the distribution of obesity phenotypes pattern across the two time-points (interaction p = 0.126). Across BMI categories, levels of cardiometabolic risk factors linearly deteriorated in both metabolically healthy and abnormal participants (all p ≤ 0.018 for linear trends). Findings were not sensitive to the number of metabolic abnormalities included in the definition of obesity phenotypes. Conclusions: Our study showed negligible differences in obesity phenotypes over time, but a high burden of metabolic abnormalities among normal weight participants, and a significant proportion of metabolically health obese individuals. Further investigation is needed to improve risk stratification and cost-effective identification of individuals at high risk for cardiometabolic diseases.

Keywords: Africa; body mass index; cardiometabolic risk factors; obesity phenotype; trends.