Rauh Simone P, Heymans Martijn W, Koopman Anitra D M, Nijpels Giel, Stehouwer Coen D, Thorand Barbara, Rathmann Wolfgang, Meisinger Christa, Peters Annette, de Las Heras Gala Tonia, Glümer Charlotte, Pedersen Oluf, Cederberg Henna, Kuusisto Johanna, Laakso Markku, Pearson Ewan R, Franks Paul W, Rutters Femke, Dekker Jacqueline M
Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands.
Department of General Practice and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands.
PLoS One. 2017 Feb 10;12(2):e0171816. doi: 10.1371/journal.pone.0171816. eCollection 2017.
AIMS/HYPOTHESIS: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors.
Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM.
At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model's intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort.
CONCLUSIONS/INTERPRETATION: In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent.
目的/假设：开发一种预测模型，该模型能够预测非糖尿病普通人群六年后的糖化血红蛋白（HbA1c）水平，模型纳入先前已使用的、易于获取的预测指标。
将来自三个基于人群的队列研究（Hoorn研究、Inter99研究、KORA S4/F4研究）的5762名初始非糖尿病受试者的数据进行合并，以预测随访六年时的HbA1c水平。通过向后选择法，年龄、体重指数（BMI）、腰围、使用抗高血压药物情况、当前吸烟状况以及糖尿病家族史被纳入特定性别的线性回归模型。为使系数的过度拟合最小化，我们采用自抽样技术进行内部验证。使用决定系数（R2）、分类表（比较最高/最低50%的HbA1c水平）和校准图来评估可解释方差、辨别力和校准情况。该模型在基于人群的队列研究METSIM的2765名非糖尿病受试者中进行了外部验证。
在基线时，平均HbA1c水平为5.6%（38 mmol/mol）。经过平均六年的随访，平均HbA1c水平为5.7%（39 mmol/mol）。校准图显示，Inter99队列中预测的HbA1c水平被低估，而在Hoorn和KORA队列中被高估，这表明应针对每个队列调整模型的截距以改善预测。女性的敏感性和特异性（95%可信区间）分别为55.7%（53.9，57.5）和56.9%（55.1，58.7），男性分别为54.6%（52.7，56.5）和54.3%（52.4，56.2）。外部验证显示在METSIM队列中有相似的表现。
结论/解读：在非糖尿病人群中，我们的DIRECT - DETECT预测模型，尽管纳入了易于获取的预测指标，但其可解释方差相对较低，辨别性能中等，但有助于区分未来最高和最低的HbA1c水平。HbA1c的绝对数值因队列而异。
