Establishment of a metabolic syndrome factors-based coronary heart disease risk prediction model for rural residents in Xinjiang Uygur Autonomous Region
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摘要:
目的 基于代谢综合征（MS）因子构建新疆农村居民冠状动脉粥样硬化性心脏病（CHD）发病风险预测模型，为该人群CHD的防治工作提供参考依据。 方法 采用多阶段分层整群随机抽样方法分别于2010年4月、2012年12月和2016年11月在新疆伊犁新源县、喀什伽师县和第三师51团抽取16853名农村居民进行基线调查，于2013 — 2017年对新源县和喀什伽师县、2019 — 2021年对第三师51团进行3次随访调查，以随访满5年的13647名农村居民作为研究对象，随机抽取其中2/3作为训练样本（9155人），剩余1/3作为验证样本（4492人）；对训练样本中3206例MS人群进行因子分析，研究MS潜在聚集模式并提取与CHD相关的潜在因子，采用多因素Cox比例风险回归分析方法构建CHD发病风险预测模型，并绘制受试者工作特征曲线（ROC）评价模型的预测效能。 结果 新疆农村居民的CHD累计发病率为4.94%，训练样本和验证样本的CHD累计发病率均为4.94%；因子分析结果显示，训练样本MS患者中共提取出肥胖因子、血压因子、血脂血糖因子、肾代谢因子、蛋白因子、肝酶因子、心肌酶因子和胆红素因子8个潜在因子构建CHD预测模型，累计方差贡献率为77.905%；多因素Cox比例风险回归分析结果显示，女性、年龄较高、肥胖因子、胆红素因子、血压因子和血脂血糖因子均为训练样本和验证样本CHD发病的危险因素；ROC曲线分析结果显示，训练样本ROC曲线下面积（AUC）为0.762（95%CI = 0.742～0.782），验证样本AUC为0.774（95%CI = 0.742～0.805）。 结论 基于MS因子构建的新疆农村居民CHD预测模型适用于当地居民CHD发病的风险预测，可用于该人群CHD 的指导预防工作。 -
关键词:
- 冠状动脉粥样硬化性心脏病（CHD） /
- 预测模型 /
- 代谢综合征（MS）因子 /
- 农村居民
Abstract:Objective To construct a metabolic syndrome (MS) factors-based model for predicting the risk of coronary heart disease (CHD) among rural residents of Xinjiang Uygur Autonomous Region (Xinjiang) for CHD prevention and treatment in the population. Methods Complete baseline information were collected through face-to-face questionnaire interview, physical examination and laboratory tests from 16 853 of 18 524 adult rural residents (aged ≥ 18years) recruited with multistage stratified cluster random sampling at two counties of Xinjiang and a division of Xinjiang Production and Construction Corps during April 2010, December 2012 and November 2016. Subsequent follow-up surveys were carried out among the eligible participants from April 2013 to July 2021 and finally 3 647 participants with a follow-up period of five years and more (averagely 5.28 ± 1.67 years) and without CHD at the baseline survey were included in the analysis, of which, two-thirds (n = 9 155) were randomly assigned into a training set and one-third (n = 4 492) into a verification set. Factor analysis was performed base on the data of 3 206 MS sufferers identified in the participants of training set to explore potential clustering pattern of MS components and probable CHD-related factors. Multivariate Cox proportional risk regression analysis was used to construct prediction model for CHD risk and receiver operating characteristic curve (ROC) analysis was adopted to evaluate the efficiency of the prediction model constructed. Results The cumulative incidence of CHD was 4.94% for all the participants and for those of training and verification set. Training set data-based factor analysis revealed potential CHD-related factors for prediction model construction: obesity, blood pressure, blood lipid/glucose, renal metabolism, blood protein, liver enzyme, myocardial enzyme, and bilirubin and the 8 factors could explain 77.905% of the cumulative variance. Cox proportional hazard regression analysis showed that being female, at older age, obesity, low bilirubin, high blood pressure, and high blood lipid and glucose were risk factors of CHD for the participants of training and verification set. The area under the ROC curve (AUC) was 70.62 (95% confidence interval [95%CI]: 0.742 – 0.782) for the prediction derived from training set data and 0.774 (95%CI: 0.742 – 0.805) for the prediction derived from validation set data. Conclusion A MS factor-based prediction model for predicting CHD risk among rural residents in Xinjiang was constructed and the model could be used in CHD risk assessment and management in local population. -
图 1 新疆农村居民训练样本和验证样本ROC曲线
Figure 1. Receiver operating characteristic curve for established coronary heart disease risk prediction model applied to the data from 9 155 adult residents of the training set and 4 492 adult residents of the varification set in a 5-year follow up study in rural Xinjiang Uygur Autonomous Region
表 1 训练样本不同组别新疆农村居民基线特征比较
Table 1. Mean age, physical indexes, blood pressure, and metabolic indicators by metabolic syndrome status for 9 155 adult residents of the training set at baseline surveys of 2012 and 2016 in rural Xinjiang Uygur Autonomous Region
特征 MS组 非MS组 t 值 P 值 年龄（岁） 46.91 ± 13.03 40.51 ± 13.70 – 21.702 < 0.001 BMI 27.40 ± 4.76 23.91 ± 3.98 – 37.352 < 0.001 腰围（cm） 96.51 ± 11.77 84.92 ± 11.97 – 44.474 < 0.001 臀围（cm） 103.33 ± 9.00 96.55 ± 8.25 – 36.350 < 0.001 SBP（mm Hg） 139.50 ± 21.34 123.93 ± 18.60 – 36.247 < 0.001 DBP（mm Hg） 82.89 ± 13.34 75.67 ± 12.56 – 25.668 < 0.001 TC（mmol/L） 4.76 ± 1.27 4.31 ± 1.10 – 17.828 < 0.001 TG（mmol/L） 2.26 ± 1.55 1.17 ± 0.83 – 43.532 < 0.001 LDL-C（mmol/L） 2.66 ± 0.86 2.39 ± 0.78 – 15.408 < 0.001 HDL-C（mmol/L） 1.26 ± 0.57 1.29 ± 0.50 11.610 < 0.001 FPG（mmol/L） 5.45 ± 2.44 4.52 ± 1.00 – 25.774 < 0.001 Scr（mmol/L） 66.26 ± 17.37 63.12 ± 15.81 – 8.760 < 0.001 UA（μmol/L） 252.49 ± 81.02 232.87 ± 71.21 – 11.979 < 0.001 TP（g/L） 74.01 ± 10.70 73.09 ± 8.27 – 4.562 < 0.001 ALB（g/L） 30.09 ± 8.24 28.89 ± 6.32 – 7.755 < 0.001 AST（IU/L） 24.79 ± 14.22 24.07 ± 13.42 – 2.399 0.016 ALT（IU/L） 25.86 ± 19.29 21.06 ± 16.50 – 12.506 < 0.001 LDH（IU/L） 176.91 ± 63.25 176.08 ± 61.15 – 0.613 0.540 α-HBDH（IU/L） 148.76 ± 67.04 143.72 ± 57.13 – 3.786 < 0.001 TBIL（μmol/L） 10.44 ± 6.37 10.71 ± 5.83 2.032 0.042 IBIL（μmol/L） 6.80 ± 5.38 7.16 ± 4.80 3.280 0.001 
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表 2 验证样本不同组别新疆农村居民基线特征比较
Table 2. Mean age, physical indexes, blood pressure, and metabolic indicators by metabolic syndrome status for 4 492 adult residents of the varification set at baseline surveys of 2012 and 2016 in rural Xinjiang Uygur Autonomous Region
特征 MS组 非MS组 t 值 P 值 年龄（岁） 47.13 ± 13.16 40.44 ± 13.41 – 15.979 < 0.001 BMI 27.19 ± 4.63 24.07 ± 4.05 – 23.271 < 0.001 腰围（cm） 95.81 ± 11.88 85.30 ± 11.82 – 28.214 < 0.001 臀围（cm） 102.93 ± 90 96.96 ± 8.30 – 22.226 < 0.001 SBP（mm Hg） 139.70 ± 21.07 124.70 ± 19.55 – 23.750 < 0.001 DBP（mm Hg） 83.13 ± 12.92 75.64 ± 12.81 – 18.524 < 0.001 TC（mmol/L） 4.69 ± 1.23 4.31 ± 1.08 – 10.818 < 0.001 TG（mmol/L） 2.23 ± 1.71 1.17 ± 0.87 – 27.624 < 0.001 LDL-C（mmol/L） 2.63 ± 0.83 2.41 ± 0.78 – 9.016 < 0.001 HDL-C（mmol/L） 1.23 ± 0.50 1.40 ± 0.47 11.627 < 0.001 FPG（mmol/L） 5.38 ± 2.36 4.52 ± 0.91 – 17.440 < 0.001 Scr（mmol/L） 66.21 ± 17.24 63.70 ± 16.80 – 4.698 < 0.001 UA（μmol/L） 253.67 ± 80.65 233.70 ± 77.46 – 8.082 < 0.001 TP（g/L） 73.97 ± 10.29 73.09 ± 8.08 – 3.133 0.002 ALB（g/L） 30.25 ± 7.99 28.78 ± 6.27 – 6.748 < 0.001 AST（IU/L） 25.20 ± 18.11 24.42 ± 19.68 – 1.292 < 0.001 ALT（IU/L） 26.25 ± 22.74 21.60 ± 24.76 – 6.131 0.196 LDH（IU/L） 177.48 ± 63.62 177.31 ± 62.32 – 0.090 0.928 α-HBDH（IU/L） 146.85 ± 57.07 145.58 ± 61.68 – 0.672 0.502 TBIL（μmol/L） 10.30 ± 5.84 10.89 ± 6.04 3.165 0.002 IBIL（μmol/L） 6.76 ± 4.91 7.25 ± 5.03 3.072 0.002
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表 3 采用主成分分析法对MS患者18个生化指标旋转后因子负荷矩阵
Table 3. Load matrix after index rotation for 3 physical indexes, systolic/diastolic blood pressure and 15 metabolic indicators against 8 independent metabolic syndrome factors derived from principal component analysis based on the data of 3 206 metabolic syndrome cases identified in participants of the training set recruited in rural Xinjiang Uygur Autonomous Region
变量 肥胖因子 胆红素因子 蛋白因子 心肌酶因子 血压因子 肝酶因子 肾代谢因子 血脂血糖因子 BMI 0.847 腰围（cm） 0.894 臀围（cm） 0.916 TBIL（μmol/L） 0.971 IBIL（μmol/L） 0.978 TP（g/L） 0.936 ALB（g/L） 0.944 LDH（IU/L） 0.931 α-HBDH（IU/L） 0.927 SBP（mm Hg） 0.870 DBP（mm Hg） 0.891 AST（IU/L） 0.882 ALT（IU/L） 0.812 Scr（mmol/L） 0.836 UA（μmol/L） 0.778 TC（mmol/L） 0.591 TG（mmol/L） 0.700 FPG（mmol/L） 0.684 特征值 3.361 2.149 1.845 1.667 1.533 1.357 1.093 1.018 方差贡献率（%） 13.715 10.750 10.555 9.803 8.759 8.370 8.289 7.663 累积方差贡献率（%） 13.715 24.465 35.020 44.824 53.583 61.953 70.242 77.905
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