Regine Mugeni1, Thomas Hormenu2, Arsène Hobabagabo1, Elyssa M Shoup2, Christopher W DuBose2, Anne E Sumner1, Margrethe F Horlyck-Romanovsky3. 1. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States; National Institute of Minority Health and Health Disparities (NIMHD), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States. 2. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States. 3. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States; City University of New York, Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY, United States. Electronic address: MargretheHR@brooklyn.cuny.edu.
Abstract
AIMS: Seventy percent of Africans living with diabetes are undiagnosed. Identifying who should be referred for testing is critical. Therefore we evaluated the ability of the Atherosclerosis Risk in Communities (ARIC) diabetes prediction equation with A1C added (ARIC + A1C) to identify diabetes in 451 African-born blacks living in America (66% male; age 38 ± 10y (mean ± SD); BMI 27.5 ± 4.4 kg/m2). METHODS: All participants denied a history of diabetes. OGTTs were performed. Diabetes diagnosis required 2-h glucose ≥200 mg/dL. The five non-invasive (Age, parent history of diabetes, waist circumference, height, systolic blood pressure) and four invasive variables (Fasting glucose (FPG), A1C, triglycerides (TG), HDL) were obtained. Four models were tested: Model-1: Full ARIC + A1C equation; Model-2: All five non-invasive variables with one invasive variable excluded at a time; Model-3: All five non-invasive variables with one invasive variable included at a time; Model-4: Each invasive variable singly. Area under the receiver operator characteristic curve (AROC) predicted diabetes. Youden Index identified optimal cut-points. RESULTS: Diabetes occurred in 7% (30/451). Model-1, the full ARIC + A1C equation, AROC = 0.83. Model-2: With FPG excluded, AROC = 0.77 (P = 0.038), but when A1C, HDL or TG were excluded AROC remained unchanged. Model-3 with all non-invasive variables and FPG alone, AROC=0.87; but with A1C, TG or HDL included AROC declined to ≤0.76. Model-4: FPG as a single predictor, AROC = 0.87. A1C, TG, or HDL as single predictors all had AROC ≤ 0.74. Optimal cut-point for FPG was 100 mg/dL. CONCLUSIONS: To detect diabetes, FPG performed as well as the nine-variable updated ARIC + A1C equation. Published by Elsevier Ltd.
AIMS: Seventy percent of Africans living with diabetes are undiagnosed. Identifying who should be referred for testing is critical. Therefore we evaluated the ability of the Atherosclerosis Risk in Communities (ARIC) diabetes prediction equation with A1C added (ARIC + A1C) to identify diabetes in 451 African-born blacks living in America (66% male; age 38 ± 10y (mean ± SD); BMI 27.5 ± 4.4 kg/m2). METHODS: All participants denied a history of diabetes. OGTTs were performed. Diabetes diagnosis required 2-h glucose ≥200 mg/dL. The five non-invasive (Age, parent history of diabetes, waist circumference, height, systolic blood pressure) and four invasive variables (Fasting glucose (FPG), A1C, triglycerides (TG), HDL) were obtained. Four models were tested: Model-1: Full ARIC + A1C equation; Model-2: All five non-invasive variables with one invasive variable excluded at a time; Model-3: All five non-invasive variables with one invasive variable included at a time; Model-4: Each invasive variable singly. Area under the receiver operator characteristic curve (AROC) predicted diabetes. Youden Index identified optimal cut-points. RESULTS:Diabetes occurred in 7% (30/451). Model-1, the full ARIC + A1C equation, AROC = 0.83. Model-2: With FPG excluded, AROC = 0.77 (P = 0.038), but when A1C, HDL or TG were excluded AROC remained unchanged. Model-3 with all non-invasive variables and FPG alone, AROC=0.87; but with A1C, TG or HDL included AROC declined to ≤0.76. Model-4: FPG as a single predictor, AROC = 0.87. A1C, TG, or HDL as single predictors all had AROC ≤ 0.74. Optimal cut-point for FPG was 100 mg/dL. CONCLUSIONS: To detect diabetes, FPG performed as well as the nine-variable updated ARIC + A1C equation. Published by Elsevier Ltd.
Authors: Fisaha Haile Tesfay; Christina Zorbas; Laura Alston; Kathryn Backholer; Steven J Bowe; Catherine M Bennett Journal: Front Public Health Date: 2022-08-03
Authors: Annemarie Wentzel; Arielle C Patterson; M Grace Duhuze Karera; Zoe C Waldman; Blayne R Schenk; Christopher W DuBose; Anne E Sumner; Margrethe F Horlyck-Romanovsky Journal: Front Public Health Date: 2022-09-23