To Fast Or Not To Fast: Lipid Measurement And Cardiovascular Disease Risk Estimation In Rural Sub-Saharan Africa

JOURNAL OF GLOBAL HEALTH(2020)

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摘要
Background Cardiovascular disease (CVD) morbidity and mortality are increasing in sub-Saharan Africa (sSA), highlighting the need for tools to enable CVD risk stratification in the region. Although non-HDL-cholesterol (nHDL-C) has been promoted as a method to measure lipids without a requirement for fasting in the USA, its diagnostic validity has not been assessed in sSA. We sought to estimate: 1) the association between LDL-cholesterol (LDL-C) and nHDL-C, 2) the impact of fasting on their measurement, and 3) their correlation with carotid atherosclerosis, within a rural Ugandan population with high HIV prevalence.Methods We collected traditional CVD risk factors, blood for serum lipid levels, self-reported fasting status, and performed carotid ultrasonography in 301 participants in rural Uganda. We fit regression models, stratified by fasting status, to estimate associations between carotid intima media thickness (cIMT), LDL-C, and nHDL-C.Results Median age was 50 years (interquartile range = 46-54), 49% were female, 51% were HIV-positive, and at the time of blood collection, 70% had fasted overnight. Mean LDL-C, nHDL-C, and triglycerides in the non-fasting and fasting groups were 85 vs 88 mg/dL (P=0.39), 114 vs 114 mg/dL (P=0.98), and 130 vs 114 mg/dL (P=0.05) mg/dL, respectively. In unadjusted models, mean cIMT (mm) was associated with both increased LDL-C (beta=0.0078 per 10mg/dL., P< 0.01.) and nHDL-C (beta = 0.0075, P< 0.01), and these. relationships were similar irrespective of fasting status. After adjustment for traditional CVD risk factors, we observed similar associations, albeit with muted effect sizes within the fasting group.Conclusions We found a high correlation between LDL-C and nHDL-C, and both were correlated with cIMT, irrespective of fasting or HIV serostatus in rural Uganda. Our findings support use of either fasting or non-fasting serum lipids for CVD risk estimation in rural sSA.
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