| BackgroundSystemic lupus erythematosus (SLE) is the prototype of complex autoimmune diseases characterized by abnormal activation and apoptosis of T/B cells, which lead to failure of immune tolerance, autoimmune disorder, immune complex deposition, and the occurrence of numerous autoantibodies to nuclear and cytoplasmic antigens associated with a diverse array of clinical manifestations. It may affect different organ and systems, such as skin, joints, central, and peripheral nervous system, kidneys, and liver. Recently, SLE has become another important public health issue in China besides breast cancer. Although scientists have carried out a lot of scientific research on SLE, the pathogenesis and etiology of SLE still have not been clearly elucidated. General view is that SLE is a polygenic disease, which caused by an interaction of genetic, environmental, behavioral, and psychosocial factors. Genetic factors are the fundamental factors of the occurrence of SLE that provides genetic susceptibility for SLE while environmental factors are external factors of SLE that promote disease onset, combination of these two factors lead to the occurrence of SLE. Recent candidate gene and genome-wide association studies (GWAS) led to the discovery and validation of multiple susceptibility loci for SLE. But the heritability of SLE was only40%-60%, even in the same genetic background, the cases distribution of SLE still exists obvious national and regional differences. SLE is not common in Africa, but SLE prevalence of African-American grow up in Europe or the United States significantly higher than whites, reach to207/100000. Overall, the prevalence of SLE is about17/100000-48/100000in the world,15/100000-50/100000in USA and20/100000-40/100000in Europe, which is lower than the United States. The prevalence of SLE in Asian also presents significant regional differences, from30/100000to100/100000that is close to Americans but higher than the Europeans. These findings suggest us in addition to genetic factors, there must be some environmental factors cause different cases distribution of SLE between the countries and the regions.As an important part of the geographical environment, climate factors have been confirmed to play an important role in autoimmune diseases (such as rheumatoid arthritis (RA) and psoriasis). Climate factors like temperature, atmospheric pressure and ultraviolet ray (UV) could influence the progress of diseases by influencing inflammatory mediators. In all environmental factors that induce SLE, UV has always been considered as one of the main factors, therefore speculated that summer and autumn which have the strongest UV are high-incidence seasons of SLE. However, some researchers have pointed out there are more new-onset and recurrence SLE patients in winter and spring than that in autumn and summer, there is an "anti-seasonal" phenomenon of SLE disease activity. The number of activity SLE patients was correlated with average temperature and other climatic factors in this region. Further study on the spatial and temporal distribution pattern of SLE cases is helpful to discover SLE related climate factor, in order to focus on the vulnerable groups formulate scientific "Seasonal" treatment programs. Moreover, because SLE is a polygenic disease with different clinical manifestations which caused by combination of various environmental factors with a certain genetic background, we should also consider the role of genetic factors which is the fundamental endopathic in the study of climate factors on the spatial and temporal distribution of SLE. Multiple domestic and international studies have found multiple polymorphism loci strongly correlated to climate variables, such as temperature and latitude by calculating bayes factor or the Spearman correlation coefficient. Some of these polymorphism loci have been confirmed to participate in the immune response. Climate variables can affect role of these polymorphism loci in the process of disease by changing the allele frequency distribution. For example, latitude may modify the distribution of risk allele C to polymorphism loci rs1042522of TP53on cancer risk. SNP rs4613763of PTGER4genes which is strongly correlated with relative humidity in summer was related to Crohn’s disease. Especially, SNP rs6074022located in the upstream promoter regions of PCDH18strongly correlated with summer solar radiation and SNP rs2187668located in the regions of the first intron of HLA-DQA1strongly correlated with relative humidity was confirmed related to SLE genetic susceptibility.The above study shows that genetic mutations and climatic factors may play important roles in the development of SLE. There may be a space-time clustering of SLE cases distribution. Climatic factors and their related gene mutation may be one of the reasons for these temporal and spatial features. In this study, we will collect the cases distribution data of SLE patients from Department of Rheumatology and Immunology of the First Affiliated Hospital of Anhui Medical University and Anhui Provincial Hospital and climate data of Anhui province in different age span in order to explore the spatial and temporal distribution of SLE and the effect of climatic factors; on the pathogenesis of SLE. At the same time, we will chose a case-control study using Sequenom MassArray(?) SNP detection technology to detect SNPs(rs2313132, rs1042522, rs10484554and rs5743810) of the climate-related genes(PCDH18, TP53, HLA-C and TLR6) in SLE patients and normal controls, SLE patients with different clinical manifestations and SLE patients in five different geomorphic unit (Huaibei Plain, Jianghuai Undulating Plain, Mountains in West Anhui, Yanjiang Hilly Plain, Mountains in South Anhui), in order to investigate the relationship between genetic susceptibility of SLE and the polymorphic loci of strongly climate-related genes. The completion of this project will be helpful in finding new climate variables that affect the onset of SLE, revealing the impact of genetic mutations in the process, thereby further reveal the pathogenesis of SLE, and providing a scientific basis for developing new treatment program of SLE.ObjectiveExplore the temporal and spatial distribution characteristics of SLE by analyzing space and time distribution of SLE cases from the two hospitals. Using generalized additive model and multiple stepwise regression analysis to find strongly climate-related risk factors associated with SLE. Comparing the allele distributions and genotype frequencies about SNPs(rs2313132, rs1042522, rs10484554and rs5743810) of the strongly climate-related genes(PCDH18, TP53, HLA-C and TLR6) in SLE patients and normal controls, SLE patients with different clinical manifestations and SLE patients in five different geomorphic unit (Huaibei Plain, Jianghuai Undulating Plain, Mountains in West Anhui, Yanjiang Hilly Plain, Mountains in South Anhui), in order to explore correlation between climate factor, gene single nucleotide polymorphisms and genetic susceptibility to SLE and clinical symptoms combining with landforms. Formulating scientific "Seasonal" treatment programs for vulnerable groups, guide the SLE patients to cope with various bad weathers.MethodsWe conducted the study into three phases. In the first stage, we analyzed the spatial and temporal distribution characteristic of3365SLE cases date from Department of Rheumatology and Immunology of the First Affiliated Hospital of Anhui Medical University and Anhui Provincial Hospital from2000to2012, all these patients have clearly time of onset, residential address and terrain (ascertain the first onset time of SLE cases and each patient calculated only once in order to avoid repeat count)). Using software Epi Info2002establish geographic information database of SLE patients from the two hospitals as the unit of prefecture, and digital map at the prefecture-level (1:250000) was processed. According to the physiognomy and the terrain, analyzing cases distribution of SLE from the two hospitals among the five geomorphic units (Huaibei Plain, Jianghuai Undulating Plain, Mountains in West Anhui, Yanjiang Hilly Plain, Mountains in South Anhui), using one-way analysis of variance (ANOVA) of software SPSS11.0to analyze if there is statistically significant difference of the number of new onset SLE cases among the five geomorphic units. Arrange cases distribution of the number of new onset SLE cases in the two hospitals from2000to2012yearly and monthly, using ANOVA of software SPSS11.0to find if there is statistically significant difference of the number of new onset SLE cases among the four seasons in order to reveal if there is a obvious "seasonal distribution pattern" of SLE case distribution.In the second stage, we analyzed the climate variables that affect progress of SLE. Annual value data set and month value data set come from China Meteorological Data Sharing Service Network (http://cdc.cma.gov.cn/), using SPSS11.0software to draw scatter plot of yearly climate variables and yearly number of new-onset SLE patients, R software for time series plot of monthly climate variables and monthly number of new-onset SLE patients, generalized additive model for exponential smoothing diagram of monthly climate variables and monthly number of new-onset SLE patients and multicollinearity diagnostic method of software SPSS11.0to diagnose if there existence any collinearity of various climatic factors, and if there it is, using principal component analysis (PCA) to extract the main climate information instead of the original principal climate component, multivariate stepwise regression analysis was used to establish multiple regression equations of climatic and logarithm of new-onset SLE patients.In the third stage, analyzing SNPs of strongly climate-related genes to susceptibility and clinical manifestation of SLE. We recruited1470SLE patient samples from Department of Rheumatology and Immunology of the First Affiliated Hospital of Anhui Medical University and Anhui Provincial Hospital. All SLE patients were according to the1997revised American College of Rheumatology (ACR) classification criteria and diagnosed by the two vice-director level and above specialists.2283normal controls were recruited from healthy volunteers, all of them must meet the following conditions: first, themselves without any diagnostic criteria for SLE; second, themselves and their immediate family members without history of any autoimmune disease; third, health in the last month and no use of hormones and immunosuppressant drugs; fourth, no history of any major diseases. After obtaining the informed consent of the experimental objects, we collected data by self-designed questionnaire and5ml EDTA anti-coagulated venous blood samples from all studied subjects. Sequenom MassArray(?) SNP detection technology was used to study SNPs (rs2313132, rs1042522, rs10484554and rs5743810) of the climate-related genes (PCDH18, TP53, HLA-C and TLR6). Epi Data3.0and Epi Info2002softwares were used for establish the database, double entry and error detection. SPSS11.0software was used to analyze data. The chi-square test for count data analyses; t-test for measurement data analyses; Logistic regression analysis was used to calculate odds ratios (ORs) and P values after adjustment for gender, age etc.; PCA analysis was used for multiple collinearity diagnosis of climate factors; Spearman analysis was used for correlation study; Stata10.0software was used to calculate the Hardy-Weinberg equilibrium (HWE). The statistical significance defined as P<0.05and calculated based on two-sided tests.ResultsIn the first stage,3365cases of SLE patients were subordinated in order to analyze time-space distribution of the SLE cases from the two hospitals. But when performing statistical analysis of spatial distribution of SLE cases from the two hospitals, considering both hospitals are located in Hefei, SLE patients in Hefei incorporated will affect statistical analysis of the the spatial clusters of SLE cases, so we delete911SLE patients from Hefei city and the three counties around.(1)3365cases of SLE had been investigated from2000to2012in the two hospitals. There is an upward trend of annual new SLE cases from2000to2012in the two hospitals, there is no significant seasonal distribution pattern of cases distribution of SLE when using ANOVA analysis, but there were more new cases of SLE in June and July each year.(2) With the exception of Hefei city and the three counties around, there are2454cases of SLE from the two hospitals located in68cities in Anhui Province. ANOVA analysis found that number of cumulative SLE cases among different geomorphic units present a significant statistical differences (F=6.428, p<0.000), distribution of SLE cases from the two hospitals has obvious spatial distribution characteristics. SLE patients mainly spreading over the central plains and the western hills mountain of Anhui Province.In the second stage,3365cases of SLE patients were subordinated in order to analyze the correlation between the climate variables and SLE. According to the meteorological monitoring data from China Meteorological Data Sharing Service Network (http://cdc.cma.gov.cn/), we chose average wind speed, maximum wind speed, extremely wind speed, sunshine duration, the percentage of sunshine, average pressure, extreme high pressure, extreme minimum pressure, average temperature, average temperature anomaly, the average maximum temperature, mean minimum temperature, maximum temperature extremes, extreme minimum temperature, average relative humidity, minimum relative humidity, precipitation and precipitation anomaly percentage to conduct a correlation analysis between climate variables and SLE.(1) Scatter plot (R2=0.903) and spearman correlation analysis (rs=-0.839, p<0.01) showed that there may be a linear relationship between annual average wind speed and annual new SLE cases from2000to2012in the two hospitals.(2) Multiple linear statistical analysis indicates there existence collinearity among yearly climate variables. To yearly climate variables, four principal components were extracted with a cumulative contribution rate of79.798%. Component1represented yearly average air pressure, yearly average temperature, yearly mean maximum temperature, yearly mean minimum temperature, yearly average temperature departure, yearly average relative humidity, yearly minimum relative humidity, yearly maximum wind speed and yearly maximum wind speed, mainly reflecting the pressure, temperature and humidity information; Component2represented yearly precipitation, yearly sunshine hours and percentage of yearly sunshine hours, mainly reflecting the sunlight and precipitation information; Component3represented yearly extreme maximum air pressure, yearly mean maximum temperature and yearly mean minimum temperature e, mainly reflecting the extreme pressure and temperature information; Component4represented yearly extreme minimum pressure and yearly average wind speed, mainly reflecting the velocity information.(3) Multiple stepwise regression analysis showed that the first principal component of the annual climate variables (mainly reflects the pressure, temperature and humidity information)(p<0.05) and the fourth principal component of the annual climate variables (mainly reflects the wind speed)(p<0.01) were negatively related to logarithmic of annual new SLE cases from2000to2012in the two hospitals. Regression equation of the principal component of annual climate variables and logarithmic of annual new SLE cases from2000to2012in the two hospitals was Log (number of yearly SLE patients)=2.371-0.165Component4-0.096Component1.(4) Time series plot showed that there is a temporal hysteresis of monthly climate variables and monthly new SLE cases from2000to2012in the two hospitals. Wind speed, pressure and relative humidity may be negatively correlated with monthly new SLE cases from2000to2012in the two hospitals but air temperature and precipitation may be positively correlated with monthly new SLE cases from2000to2012in the two hospitals.(5) Exponential smoothing diagram showed that there is a nonlinear correlation between monthly climate variables and monthly new SLE cases from2000to2012in the two hospitals. When the temperature exceeds20℃, risk of SLE began to increased rapidly, Peak at about27℃then decreased. When wind speed is greater than2.2m/s, risk of SLE began to rapidly reduce. When the precipitation over150millimeters, risk of SLE began to increase rapidly, Peak at about200millimeters then decreased. When air pressure exceeds101000mpa, risk of SLE began to increase rapidly.(6) Multiple linear statistical analysis indicates there existence collinearity among monthly climate variables. To monthly climate variables, three principal components were extracted with a cumulative contribution rate of78.46%. Component1represented monthly average air pressure, monthly extreme maximum air pressure, monthly extreme minimum pressure, monthly average temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly extreme minimum temperature, monthly extreme maximum temperature and monthly precipitation, mainly reflecting the pressure, temperature and precipitation information; Component2represented monthly sunshine hours, percentage of monthly sunshine hours, monthly average relative humidity and month minimum relative humidity, primarily reflecting the sunlight and humidity information; Component3represented monthly average wind speed, monthly maximum wind speed and monthly maximum wind speed, primarily reflecting the velocity information.(7) Multiple stepwise regression analysis showed that the first principal component of monthly climate variables (mainly reflecting the pressure, temperature and precipitation information) were positive related to logarithmic of monthly new SLE cases from2000to2012in the two hospitals (p<0.01). But the third principal component of monthly climate variables (primarily reflecting the velocity information) were negatively related to logarithmic of monthly new SLE cases from12000to2012in the two hospitals (p<0.01). Regression equation of the principal component of monthly climate variables and logarithmic of monthly new SLE cases from2000to2012in the two hospitals was Log (number of monthly SLE patients)=21.548-4.377Componen3+3.647Component1. In the third stage,1470cases of SLE patients and2,283cases of healthy controls were subordinated. Using Sequenom MassArray(?) SNP detection technology tests SNPs rs2313132, rs1042522, rs1048455and rs5743810of the genes PCDH18, TP53, HLA-C and TLR6which strongly correlated with the climate factors.(1)HWE test:There were no deviations from HWE observed in both SLE patients and normal controls in each polymorphism (rs2313132:x2=0.021,p=0.8851;rs1042522: x2=0.198, p=0.6567; rs10484554:x2=0.366,p=0.5454; rs5743810:x2=0.013, p=0.9081)(2) Association analysis of SNPs of the gene which strongly correlated with the climate factors and the susceptibility of SLEa) SNP rs2313132of PCDH18, genotype frequencies for GG, AG and AA were1case (0.07%),106cases (7.21%) and1363cases (92.72%) in the1470SLE patient group and2cases (0.09%),138cases (6.04%) and2143cases (93.87%) in the2283normal control group. Allele and genotype frequencies of rs2313132showed no significant difference between the SLE patient group and the normal control group (p>0.05);b) SNP rs10484554of HLA-C, genotype frequencies for TT, CT and CC were22cases (1.50%),290cases (19.73%) and1158cases (78.77%) in the1470SLE patient group and31cases (1.36%),445cases (19.49%) and1807cases (79.15%) in the2283normal control group. Allele and genotype frequencies of rs10484554showed no significant difference between the SLE patient group and the normal control group (p>0.05);c) SNP rs5743810of TLR6, genotype frequencies for CT and CC were8cases (0.54%) and1462cases (99.46%) in the1470SLE patient group and11cases (0.48%) and2272cases (99.52%) in the2283normal control group. Allele and genotype frequencies of rs5743810showed no significant difference between the SLE patient group and the normal control group (p>0.05);d) SNP rs1042522of TP53, genotype frequencies for CC, GC and GG were269cases (18.30%),694(47.21%) and507cases (34.49%) in the1470SLE patient group and465cases (20.37%),1119(49.01%) and699cases (30.62%) in the2283normal control group. Allele and genotype frequencies of rs1042522showed significant statistical significance between the SLE patient group and the normal control group (C vs. G:p=0.01, OR=0.89,95%CI:0.81-0.97; CC vs. GG:p=0.02, OR=0.80,95%CI:0.66-0.96; GCvs.GG:p=0.04, OR=0.86,95%CI:0.74-0.99).(3) Association analysis of SNPs of the gene which strongly correlated with the climate factors and clinical symptoms of SLE patientsa) SNP rs2313132of PCDH18was correlated with leukopenia (G vs. A:P=0.03, OR=1.65,95%CI:1.04-2.63; AG vs. AA:p=0.02, OR=1.78,95%CI:1.10to2.86), but unrelated to other clinical symptoms;b) SNP rs1042522of TP53, was correlated with discoid erythema (C vs G:p=0.04, OR=1.25,95%CI:1.00-1.55; CC vs. GG:p=0.04, OR=1.54,95%CI:1.01-2.36) but unrelated to other clinical symptoms;c) SNP rs10484554of HLA-C, was correlated with leukopenia (TT vs. CC:p=0.03, OR=2.77,95%CI:1.11-6.89), alopecia (TT vs. CC:p=0.04, OR=2.46,95%CI:1.05-5.74) and fever (T vs. C:p<0.00, OR=1.51,95%CI:1.17-1.95; TT vs. CC:p<0.00, OR=3.97,95%CI:1.70-9.27) but unrelated to other clinical symptoms;d) SNP rs5743810of TLR6, was correlated with pericarditis (T vs. C:p=0.04, OR=8.09,95%CI:1.62-40.53; CT vs. CC:p=0.01, OR=8.26,95%CI:1.6241.60), oral ulcer (T vs. C:p=0.01, OR=7.33,95%CI:1.44-25.41; TT vs. CC: p=0.01, OR=7.39,95%CI:1.44-25.80) and the light-sensitive (T vs. C:p=0.03, OR=6.04,95%CI:1.44-25.41; TT vs. CC:p=0.01, OR=6.10,95%CI:1.44-25.80) but unrelated to other clinical symptoms. (4) SNPs of the gene which strongly correlated with the climate factors in SLE patients from different landformsAllele and genotype frequencies for SNP rs2313132of PCDH18(x2=17.58, p=0.025), rs10484554of HLA-C (x2=17.49,p=0.025) and rs5743810of TLR6(x2=3116.75, p<0.000) had significant statistical significance among SLE patients from different geomorphic units, but Allele and genotype frequencies for rs1042522of TP53had no statistically significant among SLE patients from different geomorphic units(x2=4.83, p=0.776).Conclusions(1) We didn’t found any obvious "seasonal distribution pattern" of SLE cases distribution from the two hospitals in2000to2012, but there were more new cases of SLE in June and July each year. Distribution of SLE cases from the two hospitals has obvious spatial clusters. SLE patients mainly spreading over the central plains and the western hills mountain of Anhui Province. The main climate factors of SLE are pressure, temperature, precipitation and wind speed.(2) SNP rs1042522of TP53gene which has has a strong correlation with minimum winter temperature, latitude and summer downward solar radiation may be associated with susceptibility of SLE in Chinese population.(3) SNPs rs2313132of PCDH18which has has a strong correlation with summer solar radiation was correlated with leucopenia, rs1042522of TP53which has has a strong correlation with minimum winter temperature, latitude and summer downward solar radiation was correlated with discoid erythema, rs10484554of HLA-C which has has a strong correlation with summer precipitation rate was correlated with leucopenia, alopecia and fever, rs5743810of TLR6which has has a strong correlation with winter solar radiation was correlated with pericarditis, oral ulcer and the light-sensitive. (4) SNPs rs2313132of PCDH18, rs10484554of HLA-C and rs5743810of TLR6had significant statistical significance among SLE patients from different geomorphic units These results suggest that the gene-environment interactions impact together to increases the risk of SLE. |
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