Zhongshang Yuan

Spatio-temporal analysis of the relationship between climate and hand, foot, and mouth disease in Shandong province, China, 2008–2012

BackgroundHand, foot, and mouth disease (HFMD) is the most common communicable disease in China. Shandong Province is one of the most seriously affected areas. The distribution of HFMD had spatial heterogeneity and seasonal characteristic in this setting. The aim of this study was to explore the associations between climate and HFMD by a Bayesian approach from spatio-temporal interactions perspective.MethodsThe HFMD data of Shandong Province during 2008–2012 were derived from the China National Disease Surveillance Reporting and Management System. And six climatic indicators were obtained from the Meteorological Bureau of Shandong Province. The global spatial autocorrelation statistic (Moran’s I) was used to detect the spatial autocorrelation of HFMD cases in each year. The optimal one among four Bayesian models was further adopted to estimate the relative risk of the occurrence of HFMD via Markov chain Monte Carlo.ResultsThe annual average incidence rate of HFMD was 104.40 per 100,000 in Shandong Province. Positive spatial autocorrelation appeared at county level (Moran’s I ≥0.30, P < 0.001). The best fitting Spatio-temporal interactive model showed that annual average temperature, annual average pressure, annual average relative humidity, annual average wind speed and annual sunshine hours were significantly positive related to the occurrence of HFMD. The estimated relative risk of 36, 87, 91, 79, 65 out of 140 counties for 2008–2012 respectively were significantly more than 1.ConclusionsThere were obvious spatio-temporal heterogeneity of HFMD in Shandong Province, and the climatic indicators were associated with the epidemic of HFMD. Bayesian approach should be recommended to capture the spatial-temporal pattern of HFMD.

Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models

OBJECTIVES The aim of this study was to quantify the relationship between meteorological factors and the occurrence of hand, foot, and mouth disease (HFMD) among children in Shandong Province, China, at a county level, using spatial panel data models. METHODS Descriptive analysis was applied to describe the epidemic characteristics of HFMD from January 2008 to December 2012, and then a global autocorrelation statistic (Morans I) was used to detect the spatial autocorrelation of HFMD in each year. Finally, spatial panel data models were performed to explore the association between the incidence of HFMD and meteorological factors. RESULTS Morans I at the county level were high, from 0.30 to 0.45 (p < 0.001), indicating the existence of a high spatial autocorrelation on HFMD. Spatial panel data models are more appropriate to describe the data. Results showed that the incidences of HFMD in Shandong Province, China were significantly associated with average temperature, relative humidity, vapor pressure, and wind speed. CONCLUSIONS Spatial panel data models are useful when longitudinal data with multiple units are available and spatial autocorrelation exists. The association found between HFMD and meteorological factors makes a contribution towards advancing knowledge with respect to the causality of HFMD and has policy implications for HFMD prevention and control.

Detection for gene-gene co-association via kernel canonical correlation analysis

BackgroundCurrently, most methods for detecting gene-gene interaction (GGI) in genomewide association studies (GWASs) are limited in their use of single nucleotide polymorphism (SNP) as the unit of association. One way to address this drawback is to consider higher level units such as genes or regions in the analysis. Earlier we proposed a statistic based on canonical correlations (CCU) as a gene-based method for detecting gene-gene co-association. However, it can only capture linear relationship and not nonlinear correlation between genes. We therefore proposed a counterpart (KCCU) based on kernel canonical correlation analysis (KCCA).ResultsThrough simulation the KCCU statistic was shown to be a valid test and more powerful than CCU statistic with respect to sample size and interaction odds ratio. Analysis of data from regions involving three genes on rheumatoid arthritis (RA) from Genetic Analysis Workshop 16 (GAW16) indicated that only KCCU statistic was able to identify interactions reported earlier.ConclusionsKCCU statistic is a valid and powerful gene-based method for detecting gene-gene co-association.

Gene- or region-based association study via kernel principal component analysis

BackgroundIn genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits). Kernel principal component analysis combined with logistic regression test (KPCA-LRT) has been successfully used in classifying gene expression data. Nevertheless, the purpose of association study is to detect the correlation between genetic variations and disease rather than to classify the sample, and the genomic data is categorical rather than numerical. Recently, although the kernel-based logistic regression model in association study has been proposed by projecting the nonlinear original SNPs data into a linear feature space, it is still impacted by multicolinearity between the projections, which may lead to loss of power. We, therefore, proposed a KPCA-LRT model to avoid the multicolinearity.ResultsSimulation results showed that KPCA-LRT was always more powerful than principal component analysis combined with logistic regression test (PCA-LRT) at different sample sizes, different significant levels and different relative risks, especially at the genewide level (1E-5) and lower relative risks (RR = 1.2, 1.3). Application to the four gene regions of rheumatoid arthritis (RA) data from Genetic Analysis Workshop16 (GAW16) indicated that KPCA-LRT had better performance than single-locus test and PCA-LRT.ConclusionsKPCA-LRT is a valid and powerful gene- or region-based method for the analysis of GWAS data set, especially under lower relative risks and lower significant levels.

The Impact of Ambient Temperature on Childhood HFMD Incidence in Inland and Coastal Area: A Two-City Study in Shandong Province, China.

Hand, foot and mouth disease (HFMD) has been a substantial burden throughout the Asia-Pacific countries over the past decades. For the purposes of disease prevention and climate change health impact assessment, it is important to understand the temperature–disease association for HFMD in different geographical locations. This study aims to assess the impact of temperature on HFMD incidence in an inland city and a coastal city and investigate the heterogeneity of temperature–disease associations. Daily morbidity data and meteorological variables of the study areas were collected for the period from 2007 to 2012. A total of 108,377 HFMD cases were included in this study. A distributed lag non-linear model (DLNM) with Poisson distribution was used to examine the nonlinear lagged effects of daily mean temperature on HFMD incidence. After controlling potential confounders, temperature showed significant association with HFMD incidence and the two cities demonstrated different impact modes (I2 = 96.1%; p < 0.01). The results highlight the effect of temperature on HFMD incidence and the impact pattern may be modified by geographical localities. Our findings can be a practical reference for the early warning and intervention strategies of HFMD.

A Latent Variable Partial Least Squares Path Modeling Approach to Regional Association and Polygenic Effect with Applications to a Human Obesity Study

Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a range of effect and sample sizes. When applied to 12 candidate regions in data (N = 2,417) from the European Prospective Investigation of Cancer (EPIC)-Norfolk study, a region in FTO was found to have stronger association (rs7204609∼rs9939881 at the first intron P = 4.29×10−7) than single SNP analysis (all with P>10−4) and a latent quantitative phenotype was obtained using a subset sample of EPIC-Norfolk (N = 12,559). We believe our method is appropriate for assessment of regional association and polygenic effect on a single or multiple traits.

Association between obesity indices and type 2 diabetes mellitus among middle-aged and elderly people in Jinan, China: a cross-sectional study

Background The relationship between obesity and type 2 diabetes mellitus (T2DM) varies with geographical area and race. Objectives To investigate the prevalence of T2DM and the proportion of subjects with undiagnosed T2DM. In addition, to compare the associations between different obesity indices and T2DM for middle-aged and elderly people from six communities in Jinan, China. Setting A cross-sectional study was designed and the study subjects were chosen from blocks which were randomly selected in the 6 communities of Jinan, China in 2011–2012. Participants A total of 3277 residents aged ≥50 years were eligible for this study, but 1563 people were excluded because they did not provide anthropometric data such as height, weight, waist circumference (WC), hip circumference, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, triglyceride (TG), total cholesterol (TC) or information about their current medication use. Hence, 1714 participants were included in the final data analysis. Results The prevalence of T2DM among people aged ≥50 years was 16.6% (19.3% for men and 15.3% for women) and the proportion of patients with undiagnosed T2DM was 32.7%. Compared with the lowest levels of body mass index (BMI), WC, waist-to-hip ratio or waist-to-stature ratio (WSR), the ORs and 95% CIs of the highest levels for men, after adjusting for age, smoking, alcohol drinking, regular exercise, hypertension, TG and TC, were 1.607 (0.804 to 3.210), 2.189 (1.118 to 4.285), 1.873 (0.968 to 3.623) and 2.572 (1.301 to 5.083), respectively, and for women, 2.764 (1.622 to 4.712), 2.407 (1.455 to 3.985), 2.500 (1.484 to 4.211) and 2.452 (1.447 to 4.155), respectively. Conclusions Among adults aged ≥50 years in Jinan, China, the best indicator of the relationship between obesity and T2DM is WSR for men and BMI for women, respectively.

Association between white blood cell count and non-alcoholic fatty liver disease in urban Han Chinese: a prospective cohort study.

Objectives The white blood cell (WBC) count is a simple and convenient marker of inflammation for use in medical practice; however, its association with non-alcoholic fatty liver disease (NAFLD) has not been determined. We examined the relationship between WBC and NAFLD to provide a convenient and useful marker for the prediction of NAFLD. Setting A longitudinal cohort participating in a large health check-up programme for the Chinese population was selected and followed up from 2005 to 2011. Participants A total of 21 307 male and female participants without NAFLD who underwent health check-ups at least twice between 2005 and 2011 were included in this study. 15 201 participants (7286 men and 7915 women) were eligible for inclusion. Results The baseline distribution of age, WBC, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum total protein (TP), albumin (ALB) and globin (GLO) and the prevalence of males, hypertension, hyperglycaemia, smoking and regular exercise were significantly different between the incident NAFLD and non-NAFLD groups (p<0.05). Cox proportional hazards regression analysis was performed to estimate the HRs and 95% CIs of WBC, which predicted the occurrence of NAFLD. Compared with the lowest WBC quartile (Q1), the HRs and 95% CIs of the other WBC quartiles (Q2, Q3 and Q4) for incident NAFLD were 1.090 (0.978 to 1.215), 1.174 (1.055 to 1.305) and 1.152 (1.035 to 1.281), respectively, after adjusting for age, gender, smoking, regular exercise, BMI, hypertension, hyperglycaemia, TC, TG, HDL-C, LDL-C, ALB and GLO. Conclusions Our study clearly showed that WBC count was a significant factor associated with incident NAFLD in Han Chinese.

Lipotoxicity, a Potential Risk Factor for the Increasing Prevalence of Subclinical Hypothyroidism?

CONTEXT Subclinical hypothyroidism (SCH) is an important public health problem worldwide for its increasing prevalence and potential deleterious effects, whereas its etiology has not been fully elucidated. Lipotoxicity exerts extensive and serious impact on human health, but so far, the potential effect of lipotoxicity on thyroid is unclear. OBJECTIVE The objective of the study was to assess the association between serum triglyceride levels and the risk for SCH. DESIGN, PARTICIPANTS, AND METHODS We conducted a population-based case-control study. A total of 24 100 subjects with similar and stable iodine nutrition status were recruited from China. Cases of 5033 SCH patients were identified and equal controls were matched by age, gender, and region. Conditional logistic regression was used to analyze the association between serum triglyceride levels and risk for SCH. RESULTS Hypertriglyceridemia was associated with an approximately 35% increased risk for SCH in both men (odds ratio 1.325; 95% confidence interval 1.002-1.753) and women (odds ratio 1.397; 95% confidence interval 1.217-1.604), even after adjustment for potential confounders. Notably, the risk for SCH increased progressively following the elevation of serum triglyceride levels. Compared with individuals with serum triglyceride levels less than 0.97 mmol/L, the risk for SCH increased approximately 1.9-fold in men and 1.4-fold in women, respectively, when triglyceride levels were greater than 1.99 mmol/L. CONCLUSION Our findings suggested that hypertriglyceridemia was positively associated with the risk for SCH.

From interaction to co-association --a Fisher r-to-z transformation-based simple statistic for real world genome-wide association study.

Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher r-to-z transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality.