Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, 210042, China.
Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College; Suzhou Institute of Systems Medicine, Suzhou, China.
EBioMedicine. 2021 Jun;68:103408. doi: 10.1016/j.ebiom.2021.103408. Epub 2021 May 26.
There is a high incidence of leprosy among house-contacts compared with the general population. We aimed to establish a predictive model using these genetic factors along with epidemiological factors to predict leprosy risk of leprosy household contacts (HHCs).
Weighted genetic risk score (wGRS) encompassing genome wide association studies (GWAS) variants and five non-genetic factors were examined in a case-control design associated with leprosy risk including 589 cases and 647 controls from leprosy HHCs. We constructed a risk prediction nomogram and evaluated its performance by concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling with 1000 resamples and a prospective design including 1100 HHCs of leprosy patients.
The C-index for the risk model was 0·792 (95% confidence interval [CI] 0·768-0·817), and was confirmed to be 0·780 through bootstrapping validation. The calibration curve for the probability of leprosy showed good agreement between the prediction of the nomogram and actual observation. HHCs were then divided into the low-risk group (nomogram score ≤ 81) and the high-risk group (nomogram score > 81). In prospective analysis, 12 of 1100 participants had leprosy during 63 months' follow-up. We generated the nomogram for leprosy in the validation cohort (C-index 0·773 [95%CI 0·658-0·888], sensitivity75·0%, specificity 66·8%). Interpretation The nomogram achieved an effective prediction of leprosy in HHCs. Using the model, the risk of an individual contact developing leprosy can be determined, which can lead to a rational preventive choice for tracing higher-risk leprosy contacts.
The ministry of health of China, ministry of science and technology of China, Chinese academy of medical sciences, Jiangsu provincial department of science and technology, Nanjing municipal science and technology bureau.
与一般人群相比，麻风病患者的家庭接触者发病率较高。本研究旨在建立一个预测模型，该模型使用这些遗传因素和流行病学因素来预测麻风病家庭接触者（HHC）的麻风病风险。
在一项与麻风病风险相关的病例对照研究中，我们检查了包含全基因组关联研究（GWAS）变异和 5 个非遗传因素的加权遗传风险评分（wGRS），该研究共纳入了 589 例麻风病病例和 647 例对照，这些对照均来自麻风病 HHC。我们构建了风险预测列线图，并通过一致性指数（C 指数）和校准曲线来评估其性能。通过 1000 次重采样的bootstrap 验证和包括 1100 例麻风病患者的前瞻性设计对结果进行了验证。
风险模型的 C 指数为 0.792（95%置信区间[CI]0.768-0.817），通过 bootstrap 验证确认其值为 0.780。麻风病概率的校准曲线显示列线图预测与实际观察结果之间具有良好的一致性。然后将 HHC 分为低风险组（列线图评分≤81）和高风险组（列线图评分>81）。在前瞻性分析中，1100 名参与者中有 12 名在 63 个月的随访期间发生了麻风病。我们在验证队列中生成了麻风病列线图（C 指数 0.773[95%CI 0.658-0.888]，敏感性 75.0%，特异性 66.8%）。
该列线图可有效预测 HHC 中的麻风病。使用该模型可以确定个体接触者患麻风病的风险，从而为追踪更高风险的麻风病接触者做出合理的预防选择。
中国卫生部、中国科技部、中国医学科学院、江苏省科技厅、南京市科技局。
