中国麻风皮肤病杂志 ›› 2023, Vol. 39 ›› Issue (4): 225-230.doi: 10.12144/zgmfskin202304225

• 论著 • 上一篇    下一篇

基于遗传和流行病学危险因素建立麻风发病的风险预测模型

槐鹏程*,王真真*,孔瑶瑶,初同胜,刘殿昌,李聪聪,姚梦园,李洪达,靳传洋,袁召君,刘盟盟,李文超,刘红,刘健,张福仁
  

  1. 山东第一医科大学附属皮肤病医院（山东省皮肤病医院），山东省皮肤病性病防治研究所，济南，250022
    *共同第一作者
  • 出版日期:2023-04-15 发布日期:2023-03-27

Optimizing a predictive model of leprosy in the Chinese population based on genetic and epidemiological risk factors

HUAI Pengcheng*, WANG Zhenzhen*, KONG Yaoyao, CHU Tongsheng, LIU Dianchang, LI Congcong, YAO Mengyuan, LI Hongda, JIN Chuanyang, YUAN Zhaojun, LIU Mengmeng, LI Wenchao, LIU Hong, LIU Jian, ZHANG Furen   

  1. Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250022, China
    * Co-first authors
  • Online:2023-04-15 Published:2023-03-27

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摘要： 目的：本研究的目的是利用遗传因素和流行病学因素来构建麻风发病的风险预测模型。方法：以山东省10个地市的21个麻风累计发病例数超过500例的县为研究地区，共纳入816例麻风受累者，3847例密切接触者。通过《麻风患者登记表》和病历收集患者的流行病学及临床信息。采用Logistic回归筛选纳入模型的变量，通过遗传因素和流行病学因素的组合来构建麻风发病的风险预测模型。通过曲线下面积（AUC）来评价每个模型的预测能力。结果：纳入3个流行病学影响因素和25个遗传风险位点信息，麻风模型的预测能力最佳，发现阶段AUC达0.821（95% CI: 0.801~0.842），验证阶段AUC达0.812（95% CI:0.789~0.835）。此外，个体自身因素（AUC=0.750, 95% CI:0.726~0.773）与先证者（传染源）因素（AUC=0.745, 95% CI:0.718~0.772）在麻风发病风险预测模型的构建中发挥几乎相同的作用。在模型灵敏度和特异度最优时，截断值为0.202，高风险人群患麻风的概率是低风险人群的8.5倍。结论：基于遗传学和流行病学因素构建的麻风发病预测模型展现出很好的预测能力，对麻风高危个体的识别和精准化学预防的开展具有十分重要的意义。

关键词: 麻风, 预测模型, 流行病学因素, 遗传因素

Abstract:

Objective: To optimize a predictive model of leprosy by including epidemiological risk factors and genetic variants in the Chinese population. Methods: 816 patients with leprosy and their 3847 contacts were recruited from 10 cities in Shandong Province, China. Epidemiological information was collected from the Leprosy Patients Registration Form and medical records. The Logistic regression technique was used to select the optimal risk features and develop predictive models based on different combinations of genetic and epidemiological risk factors. The discriminatory capability of each model was evaluated using the area under the curve (AUC). Results: By including 3 epidemiological factors and 25 variants, the discriminatory capability of the optimal predictive model of leprosy was 0.821 (95% CI: 0.801-0.842) in the discovery stage and 0.812 (95% CI:0.789-0.835) in the validation stage. In addition, the discriminatory capacity for leprosy associated with self-related factors (AUC=0.750, 95% CI:0.726-0.773) was similar to that for factors associated with index cases (AUC=0.745, 95% CI:0.718-0.772). The cut-off value was 0.202 based on the optimal sensitivity and specificity, and individuals in the high-risk group had a 8.5 times greater likelihood of developing leprosy than those in the low-risk group. Conclusion: This optimizing model based on genetic and epidemiological risk factors shows perfect discriminatory capability for leprosy, which is of great significance for the identification of high-risk populations and the implementation of precise chemical prophylaxis.

Key words: leprosy, predictive model, epidemiological factors, genetic variant