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杨会芳, 袁璐, 吴结凤, 等. 基于国家基本公共卫生服务体检的中老年人2型糖尿病风险预测模型构建[J]. koko体育app 学报(医学版), 2024, 55(3): 662-670. DOI:
引用本文: 杨会芳, 袁璐, 吴结凤, 等. 基于国家基本公共卫生服务体检的中老年人2型糖尿病风险预测模型构建[J]. koko体育app 学报(医学版), 2024, 55(3): 662-670. DOI:
YANG Huifang, YUAN Lu, WU Jiefeng, et al. Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(3): 662-670. DOI:
Citation: YANG Huifang, YUAN Luꦫ, WU Jiefeng, et al. Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(3): 662-670. DOI🍌:

基于国家基本公共卫生服务体检的中老年人2型糖尿病风险预测模型构建

Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service

  • 摘要:
    目的 利用基本公共卫生服务体检数据,通过Meta分析构建普适于中老年人群的2型糖尿病(diabetes mellitus type 2, T2DM) logistic函数风险预测模型。
    方法 模型构建:计算机检索中英文数据库关于T2DM危险因素的队列研究,Meta合并危险因素效应值(odds ratio, OR),转换为logistic函数的偏回归系数β;常数项α通过合并各队列研究的发病率进行估计。模型验证:使用受试者操作特征( receiver operator characteristic, ROC)曲线,以成都市某社区卫生服务中心2017年–2022年7602名初次体检未患T2DM者的基公卫体检随访数据验证模型预测效果。
    结果 危险因素Meta分析具有统计学意义的基公卫体检条目有10个,来自40项队列研究,分别为年龄、中心性肥胖、吸烟、缺乏运动、空腹血糖受损、低高密度脂蛋白胆固醇(high-density lipoprotein cholesterol, HDL-C)、高血压、体质量指数(body mass index, BMI)、甘油三酯-葡萄糖(triglyceride glucose, TYG)指数和糖尿病家族史,OR(95%置信区间)为1.04(1.03,1.05)、1.55(1.29,1.88)、1.36(1.11,1.66)、1.26(1.07,1.49)、3.93(2.94,5.24)、1.14(1.06,1.23)、1.47(1.34,1.61)、1.11(1.05,1.18)、2.15(1.75,2.62)、1.66(1.55,1.78);37项研究报告了发病率,合并发病率(95%置信区间)为0.08(0.07,0.09),常数α为-2.442。将构建的T2DM风险预测模型在7602例基公卫体检数据中进行外部验证,曲线下面积(95%置信区间)为0.794(0.771,0.816)。
    结论 利用基公卫健康体检数据构建的T2DM风险预测模型具有较好的预测性能,可作为中老年人群T2DM风险预测的实用工具。
     
    Abstract:
    Objective  To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2 (T2DM) in the middle-aged and elderly populations based on the results of a Meta-analysis, and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service.
    Methods  Cohort studies evaluating T2DM risks were identified in Chinese and English databases. The logistic model utilized Meta-combined effect values such as the odds ratio (OR) to derive β, the partial regression coefficient, of the logistic model. The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model. Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service. The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7602 individuals who did not have T2DM at their baseline medical checkups done at the community health center. This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people.
    Results A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM, including age, central obesity, smoking, physical inactivity, impaired fasting glucose, a reduced level of high-density lipoprotein cholesterol (HDL-C), hypertension, body mass index (BMI), triglyceride glucose (TYG) index, and a family history of diabetes, with the OR values and 95% confidence interval (CI) being 1.04 (1.03, 1.05), 1.55 (1.29, 1.88), 1.36 (1.11, 1.66), 1.26 (1.07, 1.49), 3.93 (2.94, 5.24), 1.14 (1.06, 1.23), 1.47 (1.34, 1.61), 1.11 (1.05, 1.18), 2.15 (1.75, 2.62), and 1.66 (1.55, 1.78), respectively, and the combined β values being 0.039, 0.438, 0.307, 0.231, 1.369, 0.131, 0.385, 0.104, 0.765, and 0.507, respectively. A total of 37 studies reported the incidence rate, with the combined incidence being 0.08 (0.07, 0.09) and the parameter α being -2.442 for the logistic model. The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7602 individuals who had medical checkups and were followed up for at least once. External validation results showed that the predictive model had an area under curve (AUC) of 0.794 (0.771, 0.816), accuracy of 74.5%, sensitivity of 71.0%, and specificity of 74.7% in the 7602 individuals.
    Conclusion The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.
     

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