Metabolic Syndrome and Its Component Factors Among Corporate Company Employees in Kampala Uganda

TL;DR

This study finds that metabolic syndrome is common among corporate employees in Kampala, Uganda, with obesity and blood pressure being key factors.

Contribution

The study identifies key determinants of metabolic syndrome in a corporate employee population in Uganda using cross-sectional survey data.

Findings

The prevalence of metabolic syndrome was 22.8% using NCEP criteria and 28.4% using IDF criteria.

Age, BMI, and perceived cardiometabolic risk were significant determinants of metabolic syndrome.

Central obesity and low HDL-C explained most of the variance in metabolic dysregulation.

Abstract

Metabolic syndrome (MetS), a cluster of metabolic dysregulations indicative of increased cardiometabolic risk is on the rise in Sub-Saharan Africa. The study aimed to determine the prevalence of MetS and its components, among corporate employees in Kampala, Uganda.

A cross-sectional survey was undertaken among 408 adults who were employees from seven corporate companies in Kampala, using the WHO STEPwise NCD screening approach. Metabolic syndrome was measured using the National Cholesterol Education Program Adult Treatment Panel (ATPIII) and the International Diabetes Federation (IDF) criteria with the waist circumference (WC) cut-off points adapted for Sub-Saharan African populations.

The mean population age (standard deviation [SD]) of the respondents was 34years (± 8.87) years and 52% of them were females. The prevalence of MetS was 22.8% (NCEP ATPIII) and 28.4% (IDF). Of the respondents who did not have MetS, 75% had at least one metabolic dysregulation. Of those respondents with MetS, only 31% perceived that they were at risk of this cluster of metabolic dysregulations. In this analysis, we observed that the systolic blood pressure (BP) and the body mass index (BMI) correlated strongly (r = 0.81 and r = 0.71) with the diastolic BP and waist circumference (WC), respectively. Age on the other hand correlated fairly with the WC and BMI (both r values = 0.46). Principal component analysis showed that the greatest loadings in principle factors one, two and three were from central obesity, with low HDL-C explaining 60.8% variance in the population. Age, BMI, family history of having cardiometabolic disorders, and perceived cardiometabolic disease risk (CMR) were associated with an increased risk of MetS by 5, 8.86, 1.55, and 2.73 (all P values were < 0.05) respectively in this group of respondents. These risks remained for age, BMI and perceived CMR after removing the confounding effects of education status, marital status and family history of cardiometabolic disease. Being single on the other hand, was associated with a reduced risk of MetS (0.23, p < 0.009).

While the primary contributors to the high prevalence of MetS among corporate employees in Kampala, Uganda were the high BP, high WC and high fasting blood sugar (FBS); age, BMI and perceived CMR were the key determinants of MetS. Future MetS interventions should aim to control and monitor obesity indicators in this population. Additionally, the findings inform targeted screening parameters for cardiometabolic risk assessment and suggest the need for further research into a weighted algorithm for MetS in this population.

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