A Review of Diabetes Prediction Equations in African Descent Populations.

Abstract

Background: Predicting undiagnosed diabetes is a critical step toward addressing the diabetes epidemic in populations of African descent worldwide.Objective: To review characteristics of equations developed, tested, or modified to predict diabetes in African descent populations.Methods: Using PubMed, Scopus, and Embase databases, a scoping review yielded 585 research articles. After removal of duplicates (n = 205), 380 articles were reviewed. After title and abstract review 328 articles did not meet inclusion criteria and were excluded. Fifty-two articles were retained. However, full text review revealed that 44 of the 52 articles did not report findings by AROC or C-statistic in African descent populations. Therefore, eight articles remained.Results: The 8 articles reported on a total of 15 prediction equation studies. The prediction equations were of two types. Prevalence prediction equations (n = 9) detected undiagnosed diabetes and were based on non-invasive variables only. Non-invasive variables included demographics, blood pressure and measures of body size. Incidence prediction equations (n = 6) predicted risk of developing diabetes and used either non-invasive variables or both non-invasive and invasive. Invasive variables required blood tests and included fasting glucose, high density lipoprotein-cholesterol (HDL), triglycerides (TG), and A1C. Prevalence prediction studies were conducted in the United States, Africa and Europe. Incidence prediction studies were conducted only in the United States. In all these studies, the performance of diabetes prediction equations was assessed by area under the receiver operator characteristics curve (AROC) or the C-statistic. Therefore, we evaluated the efficacy of these equations based on standard criteria, specifically discrimination by either AROC or C-statistic were defined as: Poor (0.50 – 0.69); Acceptable (0.70 – 0.79); Excellent (0.80 – 0.89); or Outstanding (0.90 – 1.00). Prediction equations based only on non-invasive variables reported to have poor to acceptable detection of diabetes with AROC or C-statistic 0.64 – 0.79. In contrast, prediction equations which were based on both non-invasive and invasive variables had excellent diabetes detection with AROC or C-statistic 0.80 – 0.82.Conclusion: Equations which use a combination of non-invasive and invasive variables appear to be superior in the prediction of diabetes in African descent populations than equations that rely on non-invasive variables alone.