Mugeni Regine, Hormenu Thomas, Hobabagabo Arsène, Shoup Elyssa M, DuBose Christopher W, Sumner Anne E, Horlyck-Romanovsky Margrethe F
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.
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States.
Prim Care Diabetes. 2020 Oct;14(5):501-507. doi: 10.1016/j.pcd.2020.02.007. Epub 2020 Mar 13.
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/m).
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.
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.
To detect diabetes, FPG performed as well as the nine-variable updated ARIC + A1C equation.
70% 的非洲糖尿病患者未被诊断出来。确定哪些人应该被转诊进行检测至关重要。因此，我们评估了在社区动脉粥样硬化风险（ARIC）糖尿病预测方程中加入糖化血红蛋白（A1C）（ARIC + A1C）来识别451名出生于非洲、生活在美国的黑人（66% 为男性；年龄38±10岁（均值±标准差）；体重指数27.5±4.4kg/m²）是否患有糖尿病的能力。
所有参与者均否认有糖尿病病史。进行了口服葡萄糖耐量试验（OGTT）。糖尿病诊断要求2小时血糖≥200mg/dL。获取了五个非侵入性变量（年龄、糖尿病家族史、腰围、身高、收缩压）和四个侵入性变量（空腹血糖（FPG）、A1C、甘油三酯（TG）、高密度脂蛋白（HDL））。测试了四个模型：模型1：完整的ARIC + A1C方程；模型2：一次排除一个侵入性变量的所有五个非侵入性变量；模型3：一次纳入一个侵入性变量的所有五个非侵入性变量；模型4：每个侵入性变量单独使用。受试者工作特征曲线下面积（AROC）预测糖尿病。约登指数确定最佳切点。
7%（30/451）的人患有糖尿病。模型1，即完整的ARIC + A1C方程，AROC = 0.83。模型2：排除FPG后，AROC = 0.77（P = 0.038），但排除A1C、HDL或TG时，AROC保持不变。模型3包含所有非侵入性变量和单独的FPG时，AROC = 0.87；但纳入A1C、TG或HDL时，AROC降至≤0.76。模型4：FPG作为单一预测指标，AROC = 0.87。A1C、TG或HDL作为单一预测指标时，AROC均≤0.74。FPG的最佳切点为100mg/dL。
为检测糖尿病，FPG的表现与包含九个变量的更新后的ARIC + A1C方程相当。
