Type 2 Diabetes (T2D) and other chronic diseases are caused by a complex combination of many genetic and environmental factors. Few methods are available to comprehensively associate specific ...physical environmental factors with disease. We conducted a pilot Environmental-Wide Association Study (EWAS), in which epidemiological data are comprehensively and systematically interpreted in a manner analogous to a Genome Wide Association Study (GWAS).
We performed multiple cross-sectional analyses associating 266 unique environmental factors with clinical status for T2D defined by fasting blood sugar (FBG) concentration > or =126 mg/dL. We utilized available Centers for Disease Control (CDC) National Health and Nutrition Examination Survey (NHANES) cohorts from years 1999 to 2006. Within cohort sample numbers ranged from 503 to 3,318. Logistic regression models were adjusted for age, sex, body mass index (BMI), ethnicity, and an estimate of socioeconomic status (SES). As in GWAS, multiple comparisons were controlled and significant findings were validated with other cohorts. We discovered significant associations for the pesticide-derivative heptachlor epoxide (adjusted OR in three combined cohorts of 1.7 for a 1 SD change in exposure amount; p<0.001), and the vitamin gamma-tocopherol (adjusted OR 1.5; p<0.001). Higher concentrations of polychlorinated biphenyls (PCBs) such as PCB170 (adjusted OR 2.2; p<0.001) were also found. Protective factors associated with T2D included beta-carotenes (adjusted OR 0.6; p<0.001).
Despite difficulty in ascertaining causality, the potential for novel factors of large effect associated with T2D justify the use of EWAS to create hypotheses regarding the broad contribution of the environment to disease. Even in this study based on prior collected epidemiological measures, environmental factors can be found with effect sizes comparable to the best loci yet found by GWAS.
Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs ...associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).
Despite substantial interest in the species diversity of the human microbiome and its role in disease, the scale of its genetic diversity, which is fundamental to deciphering human-microbe ...interactions, has not been quantified. Here, we conducted a cross-study meta-analysis of metagenomes from two human body niches, the mouth and gut, covering 3,655 samples from 13 studies. We found staggering genetic heterogeneity in the dataset, identifying a total of 45,666,334 non-redundant genes (23,961,508 oral and 22,254,436 gut) at the 95% identity level. Fifty percent of all genes were “singletons,” or unique to a single metagenomic sample. Singletons were enriched for different functions (compared with non-singletons) and arose from sub-population-specific microbial strains. Overall, these results provide potential bases for the unexplained heterogeneity observed in microbiome-derived human phenotypes. One the basis of these data, we built a resource, which can be accessed at https://microbial-genes.bio.
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•Cross-study meta-analysis of metagenomes covering 3,655 samples from two body sites•Meta-analysis uncovers staggering microbial gene diversity•50% of all genes in a metagenomic sample are individual-specific or “singletons”•Individual’s microbiomes can be fingerprinted via rare microbial strains
Tierney et al. presents a meta-analysis of metagenomes covering 3,655 samples from two body sites. They identify 45,666,334 non-redundant genes in the human oral and gut microbiome, and half of every person’s microbial gene content is completely unique. These rare genes, denotes singletons, predominantly arise from extremely rare microbial strains.
Human blood glucose levels have likely evolved toward their current point of stability over hundreds of thousands of years. The robust population stability of this trait is called canalization. It ...has been represented by a hyperbolic function of two variables: insulin sensitivity and insulin response. Environmental changes due to global migration may have pushed some human subpopulations to different points of stability. We hypothesized that there may be ethnic differences in the optimal states in the relationship between insulin sensitivity and insulin response.
We identified studies that measured the insulin sensitivity index (SI) and acute insulin response to glucose (AIRg) in three major ethnic groups: Africans, Caucasians, and East Asians. We identified 74 study cohorts comprising 3,813 individuals (19 African cohorts, 31 Caucasian, and 24 East Asian). We calculated the hyperbolic relationship using the mean values of SI and AIRg in the healthy cohorts with normal glucose tolerance.
We found that Caucasian subpopulations were located around the middle point of the hyperbola, while African and East Asian subpopulations are located around unstable extreme points, where a small change in one variable is associated with a large nonlinear change in the other variable.
Our findings suggest that the genetic background of Africans and East Asians makes them more and differentially susceptible to diabetes than Caucasians. This ethnic stratification could be implicated in the different natural courses of diabetes onset.
Abstract Objectives Model specification—what adjusting variables are analytically modeled—may influence results of observational associations. We present a standardized approach to quantify the ...variability of results obtained with choices of adjustments called the “vibration of effects” (VoE). Study Design and Setting We estimated the VoE for 417 clinical, environmental, and physiological variables in association with all-cause mortality using National Health and Nutrition Examination Survey data. We selected 13 variables as adjustment covariates and computed 8,192 Cox models for each of 417 variables' associations with all-cause mortality. Results We present the VoE by assessing the variance of the effect size and in the −log10( P -value) obtained by different combinations of adjustments. We present whether there are multimodality patterns in effect sizes and P -values and the trajectory of results with increasing adjustments. For 31% of the 417 variables, we observed a Janus effect, with the effect being in opposite direction in the 99th versus the 1st percentile of analyses. For example, the vitamin E variant α-tocopherol had a VoE that indicated higher and lower risk for mortality. Conclusion Estimating VoE offers empirical estimates of associations are under different model specifications. When VoE is large, claims for observational associations should be very cautious.
We analysed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 ...individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status (SES), air pollution and climate) in each phenotype. Mean heritability (h
= 0.311) and shared environmental variance (c
= 0.088) were higher than variance attributed to specific environmental factors such as zip-code-level SES (var
= 0.002), daily air quality (var
= 0.0004), and average temperature (var
= 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h
= 0.433, c
= 0.241) and average monthly cost (h
= 0.290, c
= 0.302). All results are available using our Claims Analysis of Twin Correlation and Heritability (CaTCH) web application.
Abstract
Motivation
Investigating the aggregate burden of environmental factors on human traits and diseases requires consideration of the entire ‘exposome’. However, current studies primarily focus ...on a single exposure or a handful of exposures at a time, without considering how multiple exposures may be simultaneously associated with each other or with the phenotype. Polyexposure risk scores (PXS) have been shown to predict and stratify risk for disease beyond or complementary to genetic and clinical risk. PXStools provides an analytical package to standardize exposome-wide studies as well as derive and validate polyexposure risk scores.
Implementation
PXStools is a package for the statistical R.
General features
The package allows users to (i) conduct exposure-wide association studies; (ii) derive and validate polyexposure risk scores with and without accounting for exposure interactions, using new approaches in regression modelling (hierarchical lasso);(iii) compare goodness of fit between models with and without multiple exposures; and (iv) visualize results. A data frame with a unique identifier, phenotype and exposures is needed as the only input. Various customizations are allowed including data preprocessing (removing missing or unwanted responses), covariates adjustment, multiple hypothesis correction and model specification (linear, logistic, survival).
Availability
The PXStools source code is freely available on Github at https://github.com/yixuanh/PXStools.
To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction ...beyond traditional clinical risk factors.
We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA
levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively.
In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively.
For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.
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