Obesity phenotypes in urban and rural Cameroonians: a cross-sectional study

Journal Article

2015

Authors
Journal Title

Diabetology & Metabolic Syndrome

Journal ISSN
Volume Title
Publisher

BioMed Central Ltd

Publisher

University of Cape Town

Series
Abstract
BACKGROUND: Despite the increasing prevalence of diabetes and other health consequences of obesity, little is known on the metabolic profile across categories of body mass index (BMI) among African populations. We therefore assessed the prevalence and distribution of body size phenotypes among urban and rural Cameroonians. METHODS: Adults (n=1628; 41% rural dwellers) aged 24-74 years in 1994 provided data on BMI and metabolic health, defined on the basis of elevated levels of blood pressure (BP); triglycerides, fasting plasma glucose (FPG), and insulin resistance as assessed with homeostasis model assessment (HOMA). Cross-classification of BMI categories and metabolic status (healthy/unhealthy) created six groups. Metabolic measures include elevated blood pressure; elevated triglycerides ([greater than or equal to]150 mg/dL or 1.69mmo/L), elevated fasting plasma glucose ([greater than or equal to]100 mg/dl or 5.6 mmol/L or documented use of antidiabetic medications), and elevated homeostasis model assessment of insulin resistance value (HOMA-IR>90th percentile). RESULTS: A total of 25.2% of participants were overweight yet metabolically healthy (<1 abnormality) and 10.1% were obese yet metabolically healthy, whereas 1.4% were normal weight but metabolically abnormal ([greater than or equal to]2 abnormalities). Proportion of rural dwellers with abnormal metabolic phenotype across normal-weight, overweight, obese categories were 2.9%, 0.8% and 0.3%, respectively; and 0 .3%, 2.2% and 2.6% among urban dwellers. Metabolically abnormal participants increased linearly across BMI categories (p<0.001). BMI categories and metabolic status interacted to affect age, gender, BMI, FPG, triglycerides, and BP status distributions (all p<0.04). Metabolic status and residence (rural vs. urban) interacted to influence the distribution across BMI categories of diastolic BP, BMI, waist circumference, fasting and 2-hour glucose, triglycerides, HOMA-IR, and prevalent diabetes (all p<0.005), with differential occurrence of BMI categories and metabolic status among urban and rural participants. CONCLUSIONS: Metabolic healthy obesity and obesity with a favorable cardiometabolic profile are not uncommon among Cameroonians, including among rural dwellers; but the latter group tended to have a better profile.
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