Prediction of leprosy in the Chinese population based on a weighted genetic risk score

Models for predicting the risk of illness in leprosy contacts in Brazil: Leprosy prediction models in Brazilian contacts

OBJECTIVE

This study aims to develop and validate predictive models that assess the risk of leprosy development among contacts, contributing to an enhanced understanding of disease occurrence in this population.

METHODS

A cohort of 600 contacts of people with leprosy treated at the National Reference Center for Leprosy and Health Dermatology at the Federal University of Uberlândia (CREDESH/HC-UFU) was followed up between 2002 and 2022. The database was divided into two parts: two-third to construct the disease risk score and one-third to validate this score. Multivariate logistic regression models were used to construct the disease score.

RESULTS

Of the four models constructed, model 3, which included the variables anti-phenolic glycolipid I immunoglobulin M positive, absence of Bacillus Calmette-Guérin vaccine scar and age ≥60 years, was considered the best for identifying a higher risk of illness, with a specificity of 89.2%, a positive predictive value of 60% and an accuracy of 78%.

CONCLUSIONS

Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols.

Contribution of infectious diseases to the selection of ADH1B and ALDH2 gene variants in Asian populations

BACKGROUND

The gene variants ADH1B*2 (Arg48His, rs1229984) and ALDH2*2 (Glu504Lys, rs671) are common in East Asian populations but rare in other populations. We propose that selective pressures from pathogen exposure and dietary changes during the neolithic transition favored these variants. Thus, their current association with differences in alcohol sensitivity likely results from phenotypic plasticity rather than direct natural selection.

METHODS

Samples sourced from the Allele Frequency Database (ALFRED) were utilized to compute the average frequency of ADH1B*2 and ALDH2*2 across 88 and 61 countries, respectively. Following computation of the average national allele frequencies, we tested the significance of their correlations with ecological variables. Subsequently, we subjected them to Principal Component Analysis (PCA) and Elastic Net regularization. For comprehensive evaluation, we collected individual-level phenotypic associations, compiling a Phenome-Wide Association Study (PheWAS) spanning multiple ethnicities.

RESULTS

Following multiple testing correction, ADH1B*2 displayed significant correlations with Neolithic transition timing (r = 0.405, p.adj = 2.013e-03, n = 57) and historical trypanosome burden (r = -0.418, p.adj = 0.013, n = 57). The first two components of PCA explained 47.7% of the total variability across countries, with the top three contributors being the historical indices of population density and trypanosome and leprosy burdens. Historical burdens of the Mycobacteria tuberculosis and leprosy were the sole predictive variables with positive coefficients that survived Elastic Net regularization.

CONCLUSIONS

Our analyses suggest that Mycobacteria may have played a role in the joint selection of ADH1B*2 and ALDH2*2, expanding the "toxic aldehyde hypothesis" to include Mycobacterium leprae. Additionally, our hypothesis, linked to dietary shifts from rice domestication, emphasizes nutritional deficiencies as a key element in the selective pressure exerted by Mycobacteria. This offers a plausible explanation for the high frequency of ADH1B*2 and ALDH2*2 in Asian populations.

Leprosy: The age-old companion of humans - Re-evaluation and comparative analysis of Avar-period cases with Hansen's disease from the Danube-Tisza Interfluve, Hungary

In recent years, our knowledge of leprosy in the past has substantially been enriched. Nonetheless, much still remains to be discovered, especially in regions and periods from where no written sources are available. To fill in some research gaps, we provide the comparative analysis of eight Avar-period leprosy cases from the Danube-Tisza Interfluve (Hungary). In every case, to reconstruct the biological consequences of leprosy, the detected bony changes were linked with palaeopathological and modern medical information. To reconstruct the social consequences of being affected by leprosy, conceptualisation of the examined individuals' treatment in death was conducted. In every case, the disease resulted in deformation and disfigurement of the involved anatomical areas (rhinomaxillary region, feet, and/or hands) with difficulties in conducting certain physical activities. These would have been disadvantageous for the examined individuals and limited or changed their possibilities to participate in social situations. The most severe cases would have required continuous support from others to survive. Our findings indicate that, despite their very visible disease and associated debility, the examined communities did not segregate leprosy sufferers but provided and cared for them, and maintained a strong enough social network that made their survival possible even after becoming incapable of self-sufficiency.

Prediction of the occurrence of leprosy reactions based on Bayesian networks

Introduction

Leprosy reactions (LR) are severe episodes of intense activation of the host inflammatory response of uncertain etiology, today the leading cause of permanent nerve damage in leprosy patients. Several genetic and non-genetic risk factors for LR have been described; however, there are limited attempts to combine this information to estimate the risk of a leprosy patient developing LR. Here we present an artificial intelligence (AI)-based system that can assess LR risk using clinical, demographic, and genetic data.

Methods

The study includes four datasets from different regions of Brazil, totalizing 1,450 leprosy patients followed prospectively for at least 2 years to assess the occurrence of LR. Data mining using WEKA software was performed following a two-step protocol to select the variables included in the AI system, based on Bayesian Networks, and developed using the NETICA software.

Results

Analysis of the complete database resulted in a system able to estimate LR risk with 82.7% accuracy, 79.3% sensitivity, and 86.2% specificity. When using only databases for which host genetic information associated with LR was included, the performance increased to 87.7% accuracy, 85.7% sensitivity, and 89.4% specificity.

Conclusion

We produced an easy-to-use, online, free-access system that identifies leprosy patients at risk of developing LR. Risk assessment of LR for individual patients may detect candidates for close monitoring, with a potentially positive impact on the prevention of permanent disabilities, the quality of life of the patients, and upon leprosy control programs.

Modelling the functional genomics of Parkinson's disease in Caenorhabditis elegans: LRRK2 and beyond

For decades, Parkinson's disease (PD) cases have been genetically categorised into familial, when caused by mutations in single genes with a clear inheritance pattern in affected families, or idiopathic, in the absence of an evident monogenic determinant. Recently, genome-wide association studies (GWAS) have revealed how common genetic variability can explain up to 36% of PD heritability and that PD manifestation is often determined by multiple variants at different genetic loci. Thus, one of the current challenges in PD research stands in modelling the complex genetic architecture of this condition and translating this into functional studies. Caenorhabditis elegans provide a profound advantage as a reductionist, economical model for PD research, with a short lifecycle, straightforward genome engineering and high conservation of PD relevant neural, cellular and molecular pathways. Functional models of PD genes utilising C. elegans show many phenotypes recapitulating pathologies observed in PD. When contrasted with mammalian in vivo and in vitro models, these are frequently validated, suggesting relevance of C. elegans in the development of novel PD functional models. This review will discuss how the nematode C. elegans PD models have contributed to the uncovering of molecular and cellular mechanisms of disease, with a focus on the genes most commonly found as causative in familial PD and risk factors in idiopathic PD. Specifically, we will examine the current knowledge on a central player in both familial and idiopathic PD, Leucine-rich repeat kinase 2 (LRRK2) and how it connects to multiple PD associated GWAS candidates and Mendelian disease-causing genes.

Evaluation of antibody detection against the NDO-BSA, LID-1 and NDO-LID antigens as confirmatory tests to support the diagnosis of leprosy in Yunnan province, southwest China

Although multidrug therapy (MDT) has been widely used for the treatment of leprosy for nearly 40 y, the disease remains a public health concern in some areas. The early detection of leprosy cases is vital to interrupt Mycobacterium leprae transmission, but currently diagnosis is typically achieved during the recognition of clinical symptoms by professional staff performing physical examinations in conjunction with microbiological assessment of slit skin smears (SSSs) and histopathology. In the last 10 y, serum antibody detection tests have emerged to aid leprosy diagnosis. Here we evaluated the ability of antigens NDO-BSA and LID-1 (ML0405 and ML2331) and the conjugate of these, NDO-LID, to detect antibodies in the sera of 113 leprosy patients and 166 control individuals in Yunnan province in southwest China. We found that each antigen was readily detected by sera from multibacillary (MB) patients, with sensitivities of 97.3%, 97.3% and 98.6% for NDO-BSA, LID-1 and NDO-LID, respectively. Even among paucibacillary (PB) patients the antigens detected antibodies in 74.4%, 56.4% and 69.2% of serum samples, respectively. Receiver operating characteristics (ROC) curve analysis indicated that, irrespective of the leprosy case classification as MB or PB, the detection efficiency obtained with NDO-LID was better than that obtained with the other two antigens (with LID-1 being a slightly better than NDO-BSA). Our results indicate the utility of NDO-LID in assisting in the diagnosis of PB and MB leprosy patients and that these antibody detection assays represent powerful diagnostic tools. We suggest that could be implemented into the procedures of local health centres in leprosy-endemic regions to assist in earlier diagnosis.

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

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.

The role of an active surveillance strategy of targeting household and neighborhood contacts related to leprosy cases released from treatment in a low-endemic area of China

Objective

Early diagnosis remains the primary goal for leprosy management programs. This study aims to determine whether active surveillance of patients with leprosy and their contact individuals increased identification of latent leprosy cases in the low-endemic areas.

Methods

This cross-sectional survey was carried out between October 2014 and August 2016 in 21 counties throughout Shandong Province. The survey was conducted among patients with leprosy released from treatment (RFT) and their contacts from both household and neighbors.

Results

A total of 2,210 RFT patients and 9,742 contacts comprising 7877 household contacts (HHCs), including 5,844 genetic related family members (GRFMs) and 2033 non-genetic related family members and 1,865 contacts living in neighboring houses (neighbor contacts, NCs), were recruited. Among identified individuals, one relapsed and 13 were newly diagnosed, giving a detection rate of 0.12%, corresponding to 120 times the passive case detection rate. Detection rates were similar for HHCs and NCs (0.114% vs. 0.214%, P = 0.287). Analysis of the family history of leprosy patients revealed clustering of newly diagnosed cases and association with residential coordinates of previously-diagnosed multibacillary leprosy cases.

Conclusion

Active case-finding programs are feasible and contributes to early case detection by tracking HHCs and NCs in low-endemic areas.

Leprosy has been eliminated as a public health problem in 1994 in Shandong Province. However, the district continues to report a relatively high number of cases of leprosy infection involving deformity. Several studies have shown that individuals in contact with people infected with leprosy are at high risk of developing the disease. Subclinical infections among such individuals are important in the chain of M. leprae transmission. Some hyperendemic areas show growing interest in active case finding (ACF). Recent data from the World Health Organization (WHO) show that high rates of relapsed patients, grade 2 disability (G2D) since 2011, and the extensive family history of leprosy among people in Shandong Province P.R. China, indicate a need to reconsider the current approach to leprosy prevention. Active case finding was conducted in 21 counties of Shandong Province among patients with leprosy released from treatment (RFT) and their contacts. We achieved a detection rate of 0.12%, which was much higher than the rate for passive case finding. Our ACF program confirmed the need to implement this strategy among families and neighbors of RFT patients in historically high-endemic areas of leprosy. The program could reduce the risk of G2D by facilitating early detection and treatment, thereby reducing the disease burden.

Assessing the performance of genome-wide association studies for predicting disease risk

To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.