Data-driven assessment, contextualisation and implementation of 134 variables in the risk for type 2 diabetes: an analysis of Lifelines, a prospective cohort study in the Netherlands.
Data-driven assessment, contextualisation and implementation of 134 variables in the risk for type 2 diabetes: an analysis of Lifelines, a prospective cohort study in the Netherlands.
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2型糖尿病风险中134个变量的数据驱动评估、情境化和实施：对荷兰一项前瞻性队列研究生命线的分析
DOI：
10.1007/s00125-021-05419-1
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发表时间：
2021-06
期刊：
影响因子：
8.2
通讯作者：
Patel CJ
中科院分区：
文献类型：
--
作者：
van der Meer TP;Wolffenbuttel BHR;Patel CJ
我们的目的是评估和情境化134个潜在的风险变量的发展为2型糖尿病，并确定其适用性的风险预测。来自荷兰生命线队列的总共96，534名基线糖尿病患者（372，007人-年）被纳入。我们采用风险变量广泛关联研究（RV-WAS）设计，独立筛选和重复2型糖尿病5年发病率的风险变量。对于确定的变量，我们将HR置于情境中，计算相关性，并使用自举和交叉验证的套索回归模型评估其在不同临床背景下的稳健性和独特贡献。我们评估了风险的变化，或“HR概率”，当顺序分配变量给模型时。我们确定了63个风险变量，与生活质量指标和非心血管药物（即，质子泵抑制剂、抗哮喘药）。对于连续变量，HbA 1c增加1 SD，即，3.39 mmol/mol（0.31%）的风险相当于葡萄糖增加0.53 mmol/l、腰围增加19.8 cm、BMI增加8.34 kg/m2、HDL-胆固醇增加0.67 mmol/l和尿酸增加0.14 mmol/l。其他变量需要增加> 3SD，这在生理学上是不现实的，或者在人群中很少发生。虽然适度相关，包括四个变量满足预测模型。与非侵入性变量相比，侵入性变量（血糖和HbA 1c除外）的贡献很小。血糖、HbA 1c和糖尿病家族史解释了疾病风险的一个独特部分。将风险变量添加到饱和模型中可能会影响模型中已有变量的HR。许多变量与2型糖尿病的发展之间的关联很弱或不一致，只有少数变量可以可靠地解释疾病风险。新发现的风险变量将产生很少超过既定的因素，现有的预测模型可以简化。一个系统的，数据驱动的方法来识别风险变量预测2型糖尿病是必要的精准医学的实践。在线版本包含同行评审但未经编辑的补充材料，可通过10.1007/s 00125 -021-05419-1获得。
We aimed to assess and contextualise 134 potential risk variables for the development of type 2 diabetes and to determine their applicability in risk prediction. A total of 96,534 people without baseline diabetes (372,007 person-years) from the Dutch Lifelines cohort were included. We used a risk variable-wide association study (RV-WAS) design to independently screen and replicate risk variables for 5-year incidence of type 2 diabetes. For identified variables, we contextualised HRs, calculated correlations and assessed their robustness and unique contribution in different clinical contexts using bootstrapped and cross-validated lasso regression models. We evaluated the change in risk, or ‘HR trajectory’, when sequentially assigning variables to a model. We identified 63 risk variables, with novel associations for quality-of-life indicators and non-cardiovascular medications (i.e., proton-pump inhibitors, anti-asthmatics). For continuous variables, the increase of 1 SD of HbA1c, i.e., 3.39 mmol/mol (0.31%), was equivalent in risk to an increase of 0.53 mmol/l of glucose, 19.8 cm of waist circumference, 8.34 kg/m2 of BMI, 0.67 mmol/l of HDL-cholesterol, and 0.14 mmol/l of uric acid. Other variables required an increase of >3 SD, which is not physiologically realistic or a rare occurrence in the population. Though moderately correlated, the inclusion of four variables satiated prediction models. Invasive variables, except for glucose and HbA1c, contributed little compared with non-invasive variables. Glucose, HbA1c and family history of diabetes explained a unique part of disease risk. Adding risk variables to a satiated model can impact the HRs of variables already in the model. Many variables show weak or inconsistent associations with the development of type 2 diabetes, and only a handful can reliably explain disease risk. Newly discovered risk variables will yield little over established factors, and existing prediction models can be simplified. A systematic, data-driven approach to identify risk variables for the prediction of type 2 diabetes is necessary for the practice of precision medicine. The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-021-05419-1.
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影响因子：
37.8
作者：
Tzoulaki I;Patel CJ;Okamura T;Chan Q;Brown IJ;Miura K;Ueshima H;Zhao L;Van Horn L;Daviglus ML;Stamler J;Butte AJ;Ioannidis JP;Elliott P
通讯作者：
Elliott P
影响因子：
168.9
作者：
Tabak, Adam G.;Jokela, Markus;Akbaraly, Tasnime N.;Brunner, Eric J.;Kivimaki, Mika;Witte, Daniel R.
通讯作者：
Witte, Daniel R.
影响因子：
16.2
作者：
Vangipurapu, Jagadish;Fernandes Silva, Lilian;Laakso, Markku
通讯作者：
Laakso, Markku
影响因子：
3.7
作者：
Bellou V;Belbasis L;Tzoulaki I;Evangelou E
通讯作者：
Evangelou E
DOI：
10.1186/s13690-016-0144-x
发表时间：
2016
期刊：
Archives of public health = Archives belges de sante publique
影响因子：
--
作者：
Zijlema WL;Smidt N;Klijs B;Morley DW;Gulliver J;de Hoogh K;Scholtens S;Rosmalen JG;Stolk RP
通讯作者：
Stolk RP