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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.

SUBMITTER: Mugeni R

PROVIDER: S-EPMC6779831 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Review of Diabetes Prediction Equations in African Descent Populations.

Mugeni Regine R Aduwo Jessica Y JY Briker Sara M SM Hormenu Thomas T Sumner Anne E AE Horlyck-Romanovsky Margrethe F MF

Frontiers in endocrinology 20191001

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 abstrac ...[more]

PMID: 31632346

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Project description:BackgroundThe ancestry of African-descended Americans is known to be drawn from three distinct populations: African, European, and Native American. While many studies consider this continental admixture, few account for the genetically distinct sources of ancestry within Africa--the continent with the highest genetic variation. Here, we dissect the within-Africa genetic ancestry of various populations of the Americas self-identified as having primarily African ancestry using uniparentally inherited mitochondrial DNA.Methods and principal findingsWe first confirmed that our results obtained using uniparentally-derived group admixture estimates are correlated with the average autosomal-derived individual admixture estimates (hence are relevant to genomic ancestry) by assessing continental admixture using both types of markers (mtDNA and Y-chromosome vs. ancestry informative markers). We then focused on the within-Africa maternal ancestry, mining our comprehensive database of published mtDNA variation (?5800 individuals from 143 African populations) that helped us thoroughly dissect the African mtDNA pool. Using this well-defined African mtDNA variation, we quantified the relative contributions of maternal genetic ancestry from multiple W/WC/SW/SE (West to South East) African populations to the different pools of today's African-descended Americans of North and South America and the Caribbean.ConclusionsOur analysis revealed that both continental admixture and within-Africa admixture may be critical to achieving an adequate understanding of the ancestry of African-descended Americans. While continental ancestry reflects gender-specific admixture processes influenced by different socio-historical practices in the Americas, the within-Africa maternal ancestry reflects the diverse colonial histories of the slave trade. We have confirmed that there is a genetic thread connecting Africa and the Americas, where each colonial system supplied their colonies in the Americas with slaves from African colonies they controlled or that were available for them at the time. This historical connection is reflected in different relative contributions from populations of W/WC/SW/SE Africa to geographically distinct Africa-derived populations of the Americas, adding to the complexity of genomic ancestry in groups ostensibly united by the same demographic label.

Dataset Information

A Review of Diabetes Prediction Equations in African Descent Populations.

Publications

A Review of Diabetes Prediction Equations in African Descent Populations.

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