Improved Detection of Abnormal Glucose Tolerance in Africans: The Value of Combining Hemoglobin A1c With Glycated Albumin.

OBJECTIVE: In African-born Blacks living in America, we determined by BMI category 1) prevalence of abnormal glucose tolerance (Abnl-GT) and 2) diagnostic value and reproducibility of hemoglobin A1c (HbA1c), fructosamine, and glycated albumin (GA). RESEARCH DESIGN AND METHODS: Participants (n = 416; male, 66%; BMI 27.7 ± 4.5 kg/m2 [mean ± SD]) had an oral glucose tolerance test with HbA1c, GA, and fructosamine assayed. These glycemic markers were repeated 11 ± 7 days later. Abnl-GT diagnosis required 0 h ≥5.6 mmol/L (≥100 mg/dL) and/or 2 h ≥7.8 mmol/L (≥140 mg/dL). Thresholds for HbA1c, GA, and fructosamine were the values at the 75th percentile for the population (39 mmol/mol [5.7%], 14.2%, and 234 µmol/L, respectively). RESULTS: Abnl-GT prevalence in the nonobese was 34% versus 42% in the obese (P = 0.124). Reproducibility was excellent for HbA1c and GA (both κ ≥ 0.8), but moderate for fructosamine (κ = 0.6). Focusing on HbA1c and GA in the nonobese, we found as single tests the sensitivities of HbA1c and GA were 36% versus 37% (P = 0.529). Combining HbA1c and GA, sensitivity increased to 58% because GA identified 37% of Africans with Abnl-GT not detected by HbA1c (P value for both tests vs. HbA1c alone was <0.001). For the obese, sensitivities for HbA1c, GA, and the combined tests were 60%, 27%, and 67%, respectively. Combined test sensitivity did not differ from HbA1c alone (P = 0.25) because GA detected only 10% of obese Africans with Abnl-GT not detected by HbA1c. CONCLUSIONS: Adding GA to HbA1c improves detection of Abnl-GT in nonobese Africans.

Stress Measured by Allostatic Load Varies by Reason for Immigration, Age at Immigration, and Number of Children: The Africans in America Study.

Stress leads to physiologic dysfunction and cardiometabolic disease. Allostatic load score (ALS) measures stress-induced cardiovascular, metabolic, and inflammatory biomarkers. We estimated the odds of high ALS by reason for and age at immigration, duration of American residence, number of children, and socioeconomic status in 193 African immigrants (male: 65%, age 41 ± 10 y (mean ± Standard Deviation (SD)), range 22-65 y). ALS was calculated with High-ALS defined as ALS ≥ 3.0 and Low-ALS defined as ALS < 3.0. Oral glucose tolerance tests (OGTT) were performed, the cardiovascular disease (CVD) risk estimated, and TNF-α, an inflammatory cytokine, measured. Logistic regression was used to estimate odds of High-ALS. In the High- and Low-ALS groups, ALS were 4.0 ± 1.2 vs. 1.3 ± 0.7, diabetes prevalence: 14% vs. 4%, CVD risk: 23% vs. 8%, TNF-α levels: 15 ± 9 vs. 11 ± 6 pg/mL, respectively (all p ≤ 0.01). Immigrants were more likely to be in the High-ALS group if their reason for immigration was work or asylum/refugee (OR 2.18, p = 0.013), their age at immigration was ≥30 y (OR 3.28, p < 0.001), their duration of residence in United States was ≥10 y (OR 3.16, p = 0.001), or their number of children was ≥3 (OR 2.67, p = 0.019). Education, income, health insurance, marital status, and gender did not affect High-ALS odds. Factors adversely influencing allostatic load and cardiometabolic health in African immigrants were age at and reason for immigration, duration of residence in America, and number of children.

Identifying Africans with undiagnosed diabetes: Fasting plasma glucose is similar to the hemoglobin A1C updated Atherosclerosis Risk in Communities diabetes prediction equation.

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.