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Scientific Letter
Body composition estimates from bioelectrical impedance and its association with cardiovascular risk
Submitted: 07 May 2024 | Published: 09 October 2024
About the author(s)
Jesne Kistan, Department of Public Health Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South AfricaJeffrey Wing, Department of Internal Medicine, Wits Health Consortium, Johannesburg, South Africa
Khanyisile Tshabalala, Department of Public Health Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa; and, Steve Biko Academic Hospital, Pretoria, South Africa
Wesley van Hougenhouck-Tulleken, Department of Nephrology, Faculty of Medicine, Sefako Makgatho Health Sciences University, Pretoria, South Africa
Debashis Basu, Department of Public Health Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa; and, Steve Biko Academic Hospital, Pretoria, South Africa
Abstract
Background: Screening for traditional risk factors of cardiovascular disease is well known in primary healthcare (PHC) settings. However, other risk factors through newer tools (such as bioelectrical impedance analysis [BIA]) could also be predictors of increased cardiovascular risk (CVR). Body composition estimates (body fat percentage, body water percentage, body lean mass) by BIA and its association to CVR have been studied with variable results.
Aim: This study assesses the body composition estimates and their association with CVR in the South African PHC setting.
Methods: A retrospective record analysis was conducted on a cohort of de-identified patients utilising the ABBY® Health Check Machine at a PHC facility in South Africa between May 2020 and August 2022. The ABBY Machine estimates body fat percentage (BF%) and body water percentage (BW%) estimates from BIA. Cardiovascular risk based on the Framingham-risk-score was stratified into high, medium and low CVR. An analysis of variance was used to determine mean differences of BF% and BW% among these groups.
Results: A total of 4008 records (n = 4008) were used in the final analysis. The majority of patients were female (70.1%) with a mean age of 33.6 years. Higher mean BF% (35.75% vs. 31.10% vs. 27.73%; p < 0.0001) and lower mean BW% (49.46% vs. 53.15% vs. 56.18%; p = 0000) were found to be significantly associated with high CVR.
Lessons Learnt: This study demonstrated the use of newer technologies that could assist in the identification of CVR in low resource PHC settings.
Keywords
Sustainable Development Goal
Metrics
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