Type 2 diabetes mellitus (T2DM) accounts for ~90% of all cases of diabetes which are estimated with an annual world death rate of 1.6 million in 2016. Early detection of T2D high-risk patients can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources for laboratory testing, there is a need for accurate yet accessible prediction models based on non-laboratory parameters. This paper introduces one non-laboratory model which is highly accessible to the general population and one highly precise yet simple laboratory model. Both models are provided as an accessible scoreboard form and also as a logistic regression model. We based the models on data from 44,879 non-diabetic, UK Biobank participants, aged 40-65, predicting the risk of T2D onset within the next 7.3 years (SD 2.3). The non-laboratory prediction model for T2DM onset probability incorporated the following covariates: sex, age, weight, height, waist, hips-circumferences, waist-to-hip Ratio (WHR) and Body-Mass Index (BMI). This logistic regression model achieved an Area Under the Receiver Operating Curve (auROC) of 0.82 (0.79-0.85 95% CI) and an odds ratio (OR) between the upper and lower prevalence deciles of x77 (28-98). We further analysed the contribution of laboratory-based parameters and devised a blood-test model based on just five blood tests. In this model, we included age, sex, Glycated Hemoglobin (HbA1c%), reticulocyte count, Gamma Glutamyl-Transferase, Triglycerides, and HDL cholesterol to predict T2D onset. This logistic-regression model achieved an auROC of 0.89 (0.86-0.91) and a deciles' OR of x87 (27-152). Using the scoreboard results, the Anthropometrics model classified three risk groups, a group with 1%(1-2%); a group with 9% (7-11%) probability, and a group with a 15% (7-23%) risk of developing T2D. The Five blood tests scoreboard model, further classified into four risk groups: 0.9% (0.7%-1%); 8%(6-11%); 18%(14-22%) and a high risk group of 38%(23-54%) of developing T2D. We analysed several more comprehensive models which included genotyping data and other environmental factors and found that it did not provide cost efficient benefits over the five blood tests model. The Five blood tests and anthropometric models, both in their logistic regression form and scoreboard form, outperform the commonly used non-laboratory models, the Finnish Diabetes Risk Score (FINDRISC) and the German Diabetes Risk Score (GDRS). When trained using our data, the FINDRISC achieved an auROC of 0.75 (0.71-0.78), and the GDRS auROC resulted in 0.58 (0.54-0.62), respectively.

中文翻译：

---

使用基于逻辑回归的记分板预测 2 型糖尿病的发作

2 型糖尿病 (T2DM) 占所有糖尿病病例的约 90%，据估计，2016 年世界每年死亡率为 160 万。早期发现 T2D 高危患者可以通过改变降低发病率在生活方式、饮食或药物方面。由于社会人口状况较低的人群更容易患 T2D，并且实验室测试资源可能有限，因此需要基于非实验室参数的准确且可访问的预测模型。本文介绍了一种对一般人群非常容易访问的非实验室模型和一种高度精确但简单的实验室模型。两种模型都作为可访问的记分板形式提供，也作为逻辑回归模型提供。我们的模型基于来自 44,879 名非糖尿病英国生物银行参与者的数据，40-65 岁，预测未来 7.3 年内发生 T2D 的风险 (SD 2.3)。T2DM 发病概率的非实验室预测模型包含以下协变量：性别、年龄、体重、身高、腰围、臀围、腰臀比 (WHR) 和体重指数 (BMI)。该逻辑回归模型实现了 0.82 (0.79-0.85 95% CI) 的接受者操作曲线下面积 (auROC) 和 x77 (28-98) 的上下流行率十分位数之间的优势比 (OR)。我们进一步分析了实验室参数的贡献，并设计了一个仅基于五次血液测试的血液测试模型。在这个模型中，我们包括年龄、性别、糖化血红蛋白 (HbA1c%)、网织红细胞计数、γ-谷氨酰转移酶、甘油三酯和高密度脂蛋白胆固醇来预测 T2D 发作。该逻辑回归模型的 auROC 为 0.89 (0. 86-0.91) 和 x87 (27-152) 的十分位数 OR。使用记分牌结果，人体测量学模型将三个风险组分类，一组为 1%(1-2%)；一组有 9% (7-11%) 的概率，一组有 15% (7-23%) 的风险发展为 T2D。五项血液测试记分牌模型，进一步分为四个风险组：0.9% (0.7%-1%)；8%(6-11%)；18%(14-22%) 和 38%(23-54%) 的高危人群发展为 T2D。我们分析了几个更全面的模型，其中包括基因分型数据和其他环境因素，发现与五种血液测试模型相比，它没有提供具有成本效益的优势。五项血液测试和人体测量模型，无论是逻辑回归形式还是记分板形式，都优于常用的非实验室模型，芬兰糖尿病风险评分 (FINDRISC) 和德国糖尿病风险评分 (GDRS)。当使用我们的数据进行训练时，FINDRISC 的 auROC 分别为 0.75 (0.71-0.78)，GDRS auROC 的结果分别为 0.58 (0.54-0.62)。