Dataset Information

Predictive nomogram for leprosy using genetic and epidemiological risk factors in Southwestern China: Case-control and prospective analyses.

ABSTRACT:

Background

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).

Methods

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.

Finding

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.

Funding

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.

SUBMITTER: Long SY

PROVIDER: S-EPMC8176313 | biostudies-literature |

REPOSITORIES: biostudies-literature

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