| Leprosy,a disease caused by infection with Mycobacterium leprae,remains an important public health problem with heavy disease burden in some areas of China.The introduction of multidrug therapy(MDT)to.leprosy programmes in the mid-1980s resulted in a signicant reduction in the prevalence of the disease,from 5.4 million cases at that time to a few hundred thousand currently.Global leprosy strategies were built around this target until the elimination of leprosy as a public health problem was achieved in 2000 at global level and subsequently at national level by most countries in 2005.The 5-year global leprosy strategies since then have focused on the reduction of disease burden measured in terms of new cases with visible deformities or grade-2 disabilities(G2D).Leprosy as a kind of chronic infectious diseases,the pathogen of leprosy is mycobacteria leprea,since the 19th century,in vitro culture and vaccine development has not been able to success;The way for spread of leprosy is unknown until now.The main source for the spread of leprosy is the bacteria carriers of leprosy,the incubation period can be up to 10 years,the typical symptoms are difficult to diagnosis,patients with delayed diagnosis results in new cases with high grade-2 disabilities rate,which is the important reason for the stigma and discrimination.Although researchers have examined the feasibility of preventive treatments for leprosy,including chemo-and immunoprophylaxis,further information is needed related to the cost and effectiveness of these interventions.However,the chemo-and immunoprophylaxis has some drawbacks,by including following items:firstly,there is no unified international standard chemical prevention treatment;secondly,the chemical prevention target population is too large,which need the high cost;thirdly,there is no sensitive monitoring indicators for the curative effect of chemical prevention appraisal;fourthly,chemical prevention drugs can cause adverse drug reactions,such as dapsone(DDS)can cause serious drug hypersensitivity syndrome.Genetic epidemiology,twin studies and familial aggregation analysis have confirmed that leprosy has a strong genetic predisposition,with estimated heritability of up to 57%.Our research group has done series of leprosy genetics research,based on the foundation of national nature fund(30771943)and in shandong province science and technology research plan(2006GG2302029),we have built the genetic resource library including the 350 family and 350 sporadic cases and nearly 15000 cases of normal controls to match the genetic resource.The genome wide association studies(GWASs)have identified 32 susceptibility loci for leprosy,including:HLA-DR,RIPK2,LRRK2,NOD2,CCDC122,C13orf31,TNFSF15,et al.and published the papers in the following journals:New England Journal of Medicine,Nature Genetics,The American Journal of Human Genetics,Human Molecular Genetics and etal.With the development of genetic tools,leprosy risk prediction models should be developed,as they have been used for complex diseases such as Inflammatory bowel disease and age-related macular degeneration.In combination with genetic and antigen detection technologies,risk-prediction models could help to diagnose patients with latent leprosy.Chemoprophylaxis for these patients could halt disease progression and transmission,as well as facilitate early detection of nerve involvement,thereby reducing the disability and disease burdens associated with leprosy.This study includes two parts:Stage I:Risk prediction model of leprosy based on a weighted genetic risk score.Stage ?:Verification of the prediction model in leprosy affected patients and their close contact individuals.V?Stage I Risk prediction model of leprosy based on a weighted genetic risk score.Objective:Utilizing the 30 genetic variants to construct a risk prediction model through a weighted genetic risk score(GRS)in a Chinese set,in order to lay the foundation for precision chemoprophylaxis.Methods:1.SNP selections,genotyping and quality controlA total of 30 independent variants with minor allele frequencies>0.01 at a genome-wide significance level were selected from our previous GWASs and one candidate gene study(S1 Table).The genotyping data from 1,572 patients and 2,484 control subjects was derived from our published GWASs database.The remaining subjects in the discovery set(1,692 people affected by leprosy and 1,330 controls)were genotyped according to the manufactures'protocol using the Quant Studio 12K Flex platform(Life Technologies,ABI,USA).Variants went through the following quality control filters:call rate>97%per variant and Hardy-Weinberg Equilibrium P>1.0×10-3 in controls.Five variants with?3%missing data were eliminated.Subjects with missing data on one or more genetic variants of interest were also excluded from the analysis.Ultimately,a total of 25 variants and 2,144 leprosy patients and 2,671 control subjects were included in the analyses.2.GRS calculation and ROC curvesFour risk models were constructed based on the leprosy risk variants.Model 1:Based on the GRS method using 25 SNPs;Model 2:Based on the GRS method using 7 SNPs with P values<5E-8 in Table 1;Model 3:Based on Bayesian network method using 25 SNPs;Model 4:Based on the Bayesian network method,7 SNPs with P values<5E-8 in Table 1 were constructed.The models based on GRS were constructed as the sum of the risk alleles weighted by the ? coefficient of each allele from a multivariate logistic regression of genetic covariates(weighted GRS).Model 1 included all genetic risk variants with a P value<0.05,while only the top variants whose P values reached genome-wide significance(P<5.0 × 10-8)in the discovery set were used to create GRS in model 2.This was because model 2 aimed to investigate the effectiveness of the simplified model.The Hosmer-Lemeshow test was used to evaluate for goodness of fit for the logistic regression models.Receiver-operating characteristics(ROC)curves were applied to assess the discriminatory ability of the risk models.The area under the curve(AUC)and the 95%confidence intervals(CI)were calculated for each model.DeLong's test from the pROC R package was used to test for statistically significant differences in AUCs obtained from different models.3.GRS cut-off values and risk predictionTo further assess the performance of the model,the probability(risk)cut-offs,sensitivity,specificity,and the number of subject needed for screening to prevent one case of leprosy were calculated in the discovery set.A positive likelihood ratio(PLR)above 5 was defined as having moderate evidence for leprosy,whereas a negative likelihood ratio(NLR)below 0.2 was considered to provide moderate evidence to exclude leprosy.GRS cut-off values were selected based on the optimal PLR,NLR and the maximum sensitivity and specificity.To evaluate the risk between individuals in our study,subjects were divided into three risk groups according to optimal PLR and NLR at corresponding GRS cut-off values.Those with a predicted risk higher than that given by a cut-off value were defined as high-risk individuals.Results:1.study subjectsAfter excluding subjects with any missing data,2,144 patients and 2,671 controls were analyzed.2.The optimal GRS xThe median GRS value of model 1 in the leprosy patients was 23.94 ± 3.57 and 20.67±3.59 in the controls,which was significantly different in favor of the patients(P =1.01 × 10-152,odds ratio(OR)= 1.29,95%CI:1.27-1.32).In all models,the P value of Hosmer-Lemeshow test was>0.05(from model 1 to 4,P values were 0.060,0.093,0.627,0.712,respectively),indicating that the four risk models have a certain fitting effect.ROC curves and AUC were applied to assess the discriminatory ability of the risk models.The risk prediction model based on wGRS of 25 variants is the best,and the area under the curve(AUC)is 0.743.3.GRS cut-off value and risk prediction for higher-risk individualsTwo cut-off values(18.17 and 28.06),corresponding to a NLR of 0.2 and PLR of 5.0,were selected as the threshold for low-(4.94%of people affected by leprosy,24.19%of controls)and high-risk groups(12.45%of people affected by leprosy,2.47%of controls).Subjects with a GRS between 18.17 and 28.06 were treated as belonging to the intermediate group.When comparing the high-and low-risk discovery groups to one another,the odds of developing leprosy was significantly higher in the subjects in the high-risk group than those individuals in the low risk group(OR = 24.65,95%CI:17.57-34.60).In order to detect leprosy in 64.9%of the people affected by leprosy,39.31%higher-risk contact subjects should receive preventive treatment.Conclusion:we have constructed a risk prediction model with good discrimination capacity using genetic variants associated with leprosy.This model may not only be used with reasonable confidence in identifying higher-risk contact subjects,but may also assist physicians in the control of leprosy by making decision to trace higher-risk contact individuals.Stage 2:Verification of the prediction model in leprosy affected patients and theirclose contact individuals.Objective:1.Early detection and early diagnosis of latent infection among people affected by leprosy and close contact individuals.2.Apply risk prediction models to screen high-risk individuals in leprosy affected leprosy and close contact individuals.3.Assess the infection status of leprae among the contact individuals and follow up high-risk contact individuals based on the risk prediction results.Subjects and Methods1.The study took place in 20 cities,which account for 80%of people affected by leproy in the history of Shandong Province,China.The cities were selected based on the past number of leprosy patients recorded in the database abstracted from the National Leprosy Recording and Reporting System.Our study population consisted of people who were previously diagnosed with leprosy and registered in LEPMIS,HHCs,and NCs,defined as neighbors who lived within 200 meters of any index case.We empirically established this distance,based on the findings of Barreto et al.All patients,HHCs,and NCs provided their informed consent to participate in the study.2.A team of four professional leprosy control personnel and two medical students visited consenting families of previously diagnosed patients and their HHCs.3.HHCs responded to a verbally administered questionnaire that included questions on age,profession,household income,schooling,residential history,and personal or family history of diabetes,hypertension,tuberculosis,allergies,and leprosy.HHCs or their parents flled out a one-page self-image form(SIF)that collected information about the symptoms and clinical signs of leprosy,as well as details of contact with leprosy patients.Skin,nerves,and other organs involved in leprosy(e.g.,eyes)were examined,and any deformities were documented.?Dermal scraping and sensitivity testing with monofilament nylon fibers(esthesiometer)were performed as needed.Subjects were counseled regarding the clinical progression of leprosy and the importance of taking their medicines regularly.Persons with lesions suggestive of leprosy were referred to the Shandong Provincial Institute of Dermatology and Venereology Department for further examination.4.Clinical classification of each case was confirmed through histopathology of skin biopsies and using the criteria proposed by Ridley and Jopling.After confirmation of the diagnosis,new cases were started on multidrug therapy,as recommended by the WHO.PB patients are diagnosed according to their medical history,by eliciting a definite sensory deficit in the skin lesions(other than nodules and infiltration),and have definite contact with PALs.In the absence of two clinical cardinal signs and when there is a strong suspicion of leprosy,a slit skin smear was taken from both ear lobes and one of the lesions to test for acid-test bacilli.5.Data were stored in Microsoft Excel XP and analyzed with STATISTICA software(release 6.1,StatSoft,USA).6.Screening chip was used to genotype people affected by leprosy and close contact individuals,to determine the risk variants they carried,and finally using the optimal risk prediction model to screen high-risk groups.7.Leprosy specific antigen(PGL-1)test and PCR test among the contact individuals and assess the leprae infection status.Results:1.We examined 2210 index PALs and 9742 contacts,including 7877 household contacts(HHCs)and 1865 contacts living in neighboring houses(neighbor contacts,NCs),in the low-endemic area of Shandong,China.We confirmed 14 new cases of leprosy,including two cases of active multibacillary leprosy(MB)and one relapsed patient.Detection rate was 0.12%of examined people,corresponding to 120 times the passive case detection rate.Sex distribution was similar for the HHC and NC groups(P = 0.1068),but the NC group was older(P<0.001).Analysis of family history of leprosy patients revealed clustering of newly diagnosed cases and association with residential coordinates of previously diagnosed MB cases.2.The PGL-1 positive rate and nasal carrying rate of PCR among the people affected by leprosy and their close contact individuals were 4.67%and 0.3%,respectively;however,the positive rate of PGL antibody was not significantly different between close contacts and neighbors in the family.3.The risk model was validated in the people affected by leprosy and close contact population.The distribution of GRS showed that the peak of the case group was significantly higher to the right,indicating that the model had a good discrimination ability AUC=0.707.4.According to the GRS risk assessment,the highest risk group with a GRS greater than 28.06 is 10.68 times the risk of the lowest risk group(GRS<= 18.17).5.Monitoring and follow-up 29 cases of close contacts can detect 1 case of leprosy,which greatly reduced the scope of the monitoring population and laid a theoretical foundation for"precise" chemoprophylaxis.Conclusion:1.Screening for leprosy close contacts is a necessary way of early detection and early diagnosis,which plays a crucial role in reducing disability of leprosy.2.The leprosy risk prediction model has been well validated in the population of people affected by leprosy and close contact individuals,and this study will contribute a lot to the future implementation of"precise" chemoprophylaxis. |
PDF Full Text Request