University Medical Center Utrecht, Utrecht, Netherlands; University of Cape Town and South African Medical Research Council, Cape Town, South Africa; The George Institute for Global Health, Sydney, NSW, Australia.
University Medical Center Utrecht, Utrecht, Netherlands.
Lancet Diabetes Endocrinol. 2014 Jan;2(1):19-29. doi: 10.1016/S2213-8587(13)70103-7. Epub 2013 Oct 8.
The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.
We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27,779 individuals from eight European countries, of whom 12,403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm).
We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups.
Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.
The European Union.
目前尚未对现有预测 2 型糖尿病模型在不同人群中的表现进行比较。我们验证了现有的非实验室模型，并评估了在欧洲人群中预测性能的可变性。
我们选择了基于欧洲人群的非侵入性预测模型，并使用来自欧洲八国的 EPIC-InterAct 病例队列样本的数据（27779 名个体，其中 12403 名患有糖尿病）对其进行验证。我们评估了前 10 年随访的模型区分度和校准情况。首先，根据各国糖尿病发病率调整了模型。我们对每个国家和按性别、年龄（<60 岁与≥60 岁）、BMI（<25 kg/m2 与≥25 kg/m2）和腰围（男性<102 cm 与≥102 cm；女性<88 cm 与≥88 cm）划分的亚组进行了主要分析。
我们验证了 12 种预测模型。区分度可接受至良好：C 统计量总体范围为 0.76（95%CI 0.72-0.80）至 0.81（0.77-0.84），男性为 0.73（0.70-0.76）至 0.79（0.74-0.83），女性为 0.78（0.74-0.82）至 0.81（0.80-0.82）。除了一个模型外，所有模型的区分度均存在显著异质性（p<0.0001）。大多数模型的校准情况良好，且在各国间一致（p>0.05），除了 3 个模型外。然而，有 2 个模型高估了风险，DPoRT 模型高估了 34%（95%CI 29-39%），剑桥模型高估了 40%（28-52%）。在年龄小于 60 岁或腰围较小的个体中，区分度始终优于年龄至少 60 岁或腰围较大的个体。对于 BMI，模式不一致。所有模型均高估了 BMI<25 kg/m2 的个体的风险。对于年龄和腰围亚组，校准模式不一致。
现有的糖尿病预测模型可用于识别普通人群中 2 型糖尿病高危个体。然而，每个模型的性能因国家、年龄、性别和肥胖程度而异。
欧盟。
