Environment-Wide Association Study of Blood Pressure in the National Health and Nutrition Examination Survey (1999-2012)

Differential effects of environmental exposures on clinically relevant endophenotypes between sexes

Sex and gender differences play a crucial role in health and disease outcomes. This study used data from the National Health and Nutrition Examination Survey to explore how environmental exposures affect health-related traits differently in males and females. We utilized a sex-stratified phenomic environment-wide association study (PheEWAS), which allowed the identification of associations across a wide range of phenotypes and environmental exposures. We examined associations between 272 environmental exposures, including smoking-related exposures such as cotinine levels and smoking habits, and 58 clinically relevant blood phenotypes, such as serum albumin and homocysteine levels. Our analysis identified 119 sex-specific associations. For example, smoking-related exposures had a stronger impact on increasing homocysteine, hemoglobin, and hematocrit levels in females while reducing serum albumin and bilirubin levels and increasing c-reactive protein levels more significantly in males. These findings suggest mechanisms by which smoking exposure may pose higher cardiovascular risks and greater induced hypoxia for women, and greater inflammatory and immune responses in men. The results highlight the importance of considering sex differences in biomedical research. Understanding these differences can help develop more personalized and effective health interventions and improve clinical outcomes for both men and women.

Associations between maternal exposure to multiple metals and metalloids and blood pressure in preschool children: A mixture-based approach

BACKGROUND

Exposure to metals during pregnancy can potentially influence blood pressure (BP) in children, but few studies have examined the mixed effects of prenatal metal exposure on childhood BP. We aimed to assess the individual and combined effects of prenatal metal and metalloid exposure on BP in preschool children.

METHODS

A total of 217 mother-child pairs were selected from the Zhuang Birth Cohort in Guangxi, China. The maternal plasma concentrations of 20 metals [e.g. lead (Pb), rubidium (Rb), cesium (Cs), and zinc (Zn)] in early pregnancy were measured by inductively coupled plasmamass spectrometry. Childhood BP was measured in August 2021. The effects of prenatal metal exposure on childhood BP were explored by generalized linear models, restricted cubic spline and Bayesian kernel machine regression (BKMR) models.

RESULTS

In total children, each unit increase in the log10-transformed maternal Rb concentration was associated with a 10.82-mmHg decrease (95% CI: -19.40, -2.24) in childhood diastolic BP (DBP), and each unit increase in the log10-transformed maternal Cs and Zn concentrations was associated with a 9.67-mmHg (95% CI: -16.72, -2.61) and 4.37-mmHg (95% CI: -8.68, -0.062) decrease in childhood pulse pressure (PP), respectively. The log10-transformed Rb and Cs concentrations were linearly related to DBP (P nonlinear=0.603) and PP (P nonlinear=0.962), respectively. Furthermore, an inverse association was observed between the log10-transformed Cs concentration and PP (β =-12.18; 95% CI: -22.82, -1.54) in girls, and between the log10-transformed Rb concentration and DBP (β =-12.54; 95% CI: -23.87, -1.21) in boys, while there was an increasing association between the log10-transformed Pb concentration and DBP there was an increasing in boys (β =6.06; 95% CI: 0.36, 11.77). Additionally, a U-shaped relationship was observed between the log10-transformed Pb concentration and SBP (P nonlinear=0.015) and DBP (P nonlinear=0.041) in boys. Although there was no statistically signiffcant difference, there was an inverse trend in the combined effect of maternal metal mixture exposure on childhood BP among both the total children and girls in BKMR.

CONCLUSIONS

Prenatal exposure to both individual and mixtures of metals and metalloids influences BP in preschool children, potentially leading to nonlinear and sex-specific effects.

Association of serum five heavy metals level with all-cause and cause-specific mortality: a large population-based cohort study

The study aimed to explore the association between five heavy metals exposure (Cadmium, Lead, Mercury, Manganese, and Selenium) and mortality [all-cause, cardiovascular disease (CVD), and cancer-related]. We integrated the data into the National Health and Nutrition Examination Survey from 2011 to 2018 years. A total of 16,092 participants were recruited. The link between heavy metals exposure and mortality was analyzed by constructing a restricted cubic spline (RCS) curve, Cox proportional hazard regression model, and subgroup analysis. The RCS curve was used to show a positive linear relationship between Cadmium, Lead, and all-cause mortality. In contrast, there was a negative linear correlation between Mercury and all-cause mortality. Additionally, Manganese and Selenium also had a J-shaped and L-shaped link with all-cause mortality. The positive linear, positive linear, negative liner, J-shaped, and L-shaped relationships were observed for Cadmium, Lead, Mercury, Manganese, and Selenium and CVD mortality, respectively. Cadmium, Lead, Mercury, and Selenium were observed to exhibit positive linear, U-shaped, negative linear, and L-shaped relationships with cancer-related mortality, respectively. There was an increase and then a decrease in the link between Manganese and cancer-related morality. This study revealed the correlation between the content of different elements and different types of mortality in the U.S. general population.

A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization

Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.

Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK

BACKGROUND

Risk for COVID-19 positivity and hospitalization due to diverse environmental and sociodemographic factors may change as the pandemic progresses.

METHODS

We investigated the association of 360 exposures sampled before COVID-19 outcomes for participants in the UK Biobank, including 9268 and 38,837 non-overlapping participants, sampled at July 17, 2020 and February 2, 2021, respectively. The 360 exposures included clinical biomarkers (e.g., BMI), health indicators (e.g., doctor-diagnosed diabetes), and environmental/behavioral variables (e.g., air pollution) measured 10-14 years before the COVID-19 time periods.

RESULTS

Here we show, for example, "participant having son and/or daughter in household" was associated with an increase in incidence from 20% to 32% (risk difference of 12%) between timepoints. Furthermore, we find age to be increasingly associated with COVID-19 positivity over time from Risk Ratio [RR] (per 10-year age increase) of 0.81 to 0.6 (hospitalization RR from 1.18 to 2.63, respectively).

CONCLUSIONS

Our data-driven approach demonstrates that time of pandemic plays a role in identifying risk factors associated with positivity and hospitalization.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.

An exposome-wide association study on body mass index in adolescents using the National Health and Nutrition Examination Survey (NHANES) 2003-2004 and 2013-2014 data

Excess weight is a public health challenge affecting millions worldwide, including younger age groups. The human exposome concept presents a novel opportunity to comprehensively characterize all non-genetic disease determinants at susceptible time windows. This study aimed to describe the association between multiple lifestyle and clinical exposures and body mass index (BMI) in adolescents using the exposome framework. We conducted an exposome-wide association (ExWAS) study using U.S. National Health and Nutrition Examination Survey (NHANES) 2003–2004 wave for discovery of associations between study population characteristics and zBMI, and used the 2013–2014 wave to replicate analysis. We included non-diabetic and non-pregnant adolescents aged 12–18 years. We performed univariable and multivariable linear regression analysis adjusted for age, sex, race/ethnicity, household smoking, and income to poverty ratio, and corrected for false-discovery rate (FDR). A total of 1899 and 1224 participants were eligible from 2003–2004 and 2013–2014 survey waves. Weighted proportions of overweight were 18.4% and 18.5% whereas those for obese were 18.1% and 20.6% in 2003–2004 and 2013–2014, respectively. Retained exposure agents included 75 laboratory (clinical and biomarkers of environmental chemical exposures) and 64 lifestyle (63 dietary and 1 physical activity) variables. After FDR correction, univariable regression identified 27 and 12 predictors in discovery and replication datasets, respectively, while multivariable regression identified 22 and 9 predictors in discovery and replication datasets, respectively. Six were significant in both datasets: alanine aminotransferase, gamma glutamyl transferase, segmented neutrophils number, triglycerides; uric acid and white blood cell count. In this ExWAS study using NHANES data, we described associations between zBMI, nutritional, clinical and environmental factors in adolescents. Future studies are warranted to investigate the role of the identified predictors as early-stage biomarkers of increased BMI and associated pathologies among adolescents and to replicate findings to other populations.

Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits

The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits  to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising > 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits. 'Physical activity' and 'General Health' were the exposure categories containing the highest number of 'tentative signals' in analyses assessing the average association of lifestyle variables, while 'Tobacco use' was the top category for the 10-year change association analyses. Eleven modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to the domains: (i) Smoking, (ii) Beverage (filtered coffee), (iii) physical activity, (iv) alcohol intake, and (v) specific variables related to Nordic lifestyle (hunting/fishing during leisure time and boiled coffee consumption). We used an agnostic, data-driven approach to assess a wide range of established and novel risk factors for cardiometabolic disease. Our findings highlight key variables, along with their respective effect estimates, that might be prioritised for subsequent prediction models and lifestyle interventions.

Drug prescriptions and dementia incidence: a medication-wide association study of 17000 dementia cases among half a million participants

BACKGROUND

Previous studies have suggested that some medications may influence dementia risk. We conducted a hypothesis-generating medication-wide association study to investigate systematically the association between all prescription medications and incident dementia.

METHODS

We used a population-based cohort within the Secure Anonymised Information Linkage (SAIL) databank, comprising routinely-collected primary care, hospital admissions and mortality data from Wales, UK. We included all participants born after 1910 and registered with a SAIL general practice at ≤60 years old. Follow-up was from each participant's 60th birthday to the earliest of dementia diagnosis, deregistration from a SAIL general practice, death or the end of 2018. We considered participants exposed to a medication if they received ≥1 prescription for any of 744 medications before or during follow-up. We adjusted for sex, smoking and socioeconomic status. The outcome was any all-cause dementia code in primary care, hospital or mortality data during follow-up. We used Cox regression to calculate hazard ratios and Bonferroni-corrected p values.

RESULTS

Of 551 344 participants, 16 998 (3%) developed dementia (median follow-up was 17 years for people who developed dementia, 10 years for those without dementia). Of 744 medications, 221 (30%) were associated with dementia. Of these, 217 (98%) were associated with increased dementia incidence, many clustering around certain indications. Four medications (all vaccines) were associated with a lower dementia incidence.

CONCLUSIONS

Almost a third of medications were associated with dementia. The clustering of many drugs around certain indications may provide insights into early manifestations of dementia. We encourage further investigation of hypotheses generated by these results.

Identification of Novel Phenotypes Correlated with CKD: A Phenotype-Wide Association Study

Background: A comprehensive understanding of phenotypes related to CKD will facilitate the identification and management of CKD. We aimed to panoramically test and validate associations between multiple phenotypes and CKD using a phenotype-wide association study (PheWAS). Methods: 15,815 subjects from cross-sectional cohorts of the National Health and Nutrition Examination Survey (1999-2006) were randomly 50:50 split into training and testing sets. CKD was defined as eGFR < 60 mL/min/1.73m². We performed logistic regression analyses between each of 985 phenotypes with CKD in the training set (false discovery rate < 1%) and validated in the testing set (false discovery rate < 1% ). Random forest (RF) model, Nagelkerke's Pseudo-R², and the area under the receiver operating characteristic (AUROC) were used to validate the identified phenotypes. Results: We identified 18 phenotypes significantly related to CKD, among which retinol, red cell distribution width (RDW), and C-peptide were less researched. The top 5 identified phenotypes were blood urea nitrogen (BUN), homocysteine (HCY), retinol, parathyroid hormone (PTH), and osmolality in RF importance ranking. Besides, BUN, HCY, PTH, retinol, and uric acid were the most important phenotypes based on Pseudo-R². AUROC of the RF model was 0.951 (full model) and 0.914 (top 5 phenotypes). Conclusion: Our study demonstrated associations between multiple phenotypes with CKD from a holistic view, including 3 novel phenotypes: retinol, RDW, and C-peptide. Our findings provided valid evidence for the identification of novel biomarkers for CKD.

Environmental exposure to metals and the risk of high blood pressure: a cross-sectional study from NHANES 2015-2016

Exposure to metal pollution can be caused from inhalation, ingestion, or absorption from air, water, or food. Chronic exposure to trace amounts of metals can lead to high blood pressure, or hypertension, and other chronic diseases. The rationale of our study was to determine if there was a correlation between nineteen forms of urinary metal concentrations and high blood pressure, defined as ≥ 130 mm Hg systolic or ≥ 80 mm Hg diastolic, in the adult US population, to understand the possible impacts of metal exposure on humans. Five types of urinary arsenic species and fourteen types of urinary metals were studied to examine their correlation with high blood pressure. We used the dataset from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) for the study. A specialized complex survey design analysis package was used in analyzing the NHANES data. We used pairwise t tests and the logit regression models to study the correlation between urinary arsenic (five types) and urinary metal (fourteen types) concentrations and high blood pressure. The total study population analyzed included 4037 adults aged 20 years and older, of whom 57.9% of males and 51.7% of females had high blood pressure. Urinary arsenous acid (OR: 2.053, 95% CI: 1.045, 4.035), tin (OR: 1.983, 95% CI: 1.169, 3.364), and cesium (OR: 2.176, 95% CI: 1.013, 4.675) were associated with increased odds of high blood pressure. The other four types of urinary arsenic and twelve types of urinary metals were not associated with high blood pressure. Our results determined that exposure to environmental metals such as arsenous acid, tin, and cesium can be associated with high blood pressure. Further investigation is suggested to support our findings.

The exposome in practice: an exploratory panel study of biomarkers of air pollutant exposure in Chinese people aged 60-69 years (China BAPE Study)

The exposome overhauls conventional environmental health impact research paradigms and provides a novel methodological framework that comprehensively addresses the complex, highly dynamic interplays of exogenous exposures, endogenous exposures, and modifiable factors in humans. Holistic assessments of the adverse health effects and systematic elucidation of the mechanisms underlying environmental exposures are major scientific challenges with widespread societal implications. However, to date, few studies have comprehensively and simultaneously measured airborne pollutant exposures and explored the associated biomarkers in susceptible healthy elderly subjects, potentially resulting in the suboptimal assessment and management of health risks. To demonstrate the exposome paradigm, we describe the rationale and design of a comprehensive biomarker and biomonitoring panel study to systematically explore the association between individual airborne exposure and adverse health outcomes. We used a combination of personal monitoring for airborne pollutants, extensive human biomonitoring, advanced omics analysis, confounding information, and statistical methods. We established an exploratory panel study of Biomarkers of Air Pollutant Exposure in Chinese people aged 60-69 years (China BAPE), which included 76 healthy residents from a representative community in Jinan City, Shandong Province. During the period between September 2018 and January 2019, we conducted prospective longitudinal monitoring with a 3-day assessment every month. This project: (1) leveraged advanced tools for personal airborne exposure monitoring (external exposures); (2) comprehensively characterized biological samples for exogenous and endogenous compounds (e.g., targeted and untargeted monitoring) and multi-omics scale measurements to explore potential biomarkers and putative toxicity pathways; and (3) systematically evaluated the relationships between personal exposure to air pollutants, and novel biomarkers of exposures and effects using exposome-wide association study approaches. These findings will contribute to our understanding of the mechanisms underlying the adverse health impacts of air pollution exposures and identify potential adverse clinical outcomes that can facilitate the development of effective prevention and targeted intervention techniques.

Exposure to heavy metals and trace minerals in first trimester and maternal blood pressure change over gestation

BACKGROUND

While several heavy metals and trace minerals have been linked with hypertensive disorders during pregnancy (HDP) in women, no studies have estimated the relationship of exposure to these chemicals, both independently and as a mixture, with systolic blood pressure (SBP) or diastolic blood pressure (DBP) over gestation.

OBJECTIVES

We examined individual and joint effects of 1^st trimester chemicals with SBP and DBP over gestation, and whether those chemicals were associated with HDP.

METHODS

We used data from 1832 non-obese pregnant women with low-risk antenatal profiles from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Fetal Growth Studies - Singleton cohort (2009-2013). In plasma collected from women at 8-13 weeks' gestation (baseline enrollment), we measured heavy metals, barium (Ba), cesium (Cs), antimony (Sb), as well as trace minerals, cobalt (Co), copper (Cu), molybdenum (Mo), selenium (Se), and zinc (Zn). We obtained BP at baseline and throughout gestation until delivery and diagnosis of HDP from medical records. We used Bayesian Kernel Machine Regression (BKMR) as well as traditional linear and logistic regressions to examine the cross-sectional associations of chemicals with baseline BP and HDP. We used linear mixed effect regression to examine longitudinal associations between chemicals and rate of weekly change in BP in each trimester. We adjusted for sociodemographic and lifestyle factors and pre-pregnancy body mass index in all models.

RESULTS

BKMR revealed that comparing the entire chemical mixture at the 90^th percentile vs. the 50^th percentile was associated with a 1.61 mmHg (95% CI: 0.41, 2.81) higher SBP and a 1.09 mmHg (0.10, 2.09) higher DBP. No interactions were observed between chemicals. Accounting for chemical co-exposure in BKMR, each interquartile range (IQR) increment in Cu was associated with a 0.67 mmHg (0.02, 1.32) higher SBP and a 0.60 mmHg (0.08, 1.12) higher DBP at baseline; each IQR increment in Se was associated with a 0.67 mmHg (0.05, 1.29) higher SBP but not DBP. In longitudinal analyses, women with higher (i.e., above median concentration) baseline Cu had a 0.09 mmHg (0.01, 0.17) and 0.06 mmHg (0.001, 0.12) larger weekly decrease in SBP and DBP in 2^nd trimester, respectively. Women with higher baseline Ba had a 0.12 mmHg (0.04, 0.20) larger weekly increase in SBP in 2^nd trimester, while women with higher Cs had a 0.05 mmHg (0.01, 0.10) larger weekly increase in DBP in 3^rd trimester. None of the chemicals examined were significantly associated with HDP.

CONCLUSIONS

In this multi-ethnic cohort of women with low antenatal risk, plasma metals and trace minerals in early pregnancy, both individually and as a mixture, were statistically significantly associated with BP during gestation in small magnitude and in different directions, but not with HDP. The implications of these findings for women's postpartum BP and future cardiovascular health remains to be investigated.

Metal biomarker mixtures and blood pressure in the United States: cross-sectional findings from the 1999-2006 National Health and Nutrition Examination Survey (NHANES)

BACKGROUND

The objective of this study was to identify conditional relationships between multiple metal biomarkers that predict systolic and diastolic blood pressure in the non-institutionalized United States adult population below the age of 60.

METHODS

We used inorganic exposure biomarker data and blood pressure data from three cycles (1999-2004) of the National Health and Nutrition Examination Survey (NHANES) to construct regression trees for blood pressure among adults ages 20-60 (adjusted for age, sex, body mass index, race, and smoking status) to identify predictors of systolic (SBP) and diastolic blood pressure (DBP). We also considered relationships among non-Hispanic black, Mexican-American, and white adults separately.

RESULTS

The following metal exposure biomarkers were conditionally predictive of SBP and/or DBP in the full sample: antimony (Sb), barium (Ba), cadmium (Cd), cesium (Cs), lead (Pb), tungsten (W) and molybdenum (Mo). The highest average SBP (> 120 mmHg) was observed among those with low Sb (≤ 0.21 μg/dL) high Cd (> 0.22 μg/g creatinine) and high Pb (> 2.55 μg/dL) biomarkers. Those with the highest average DBP had high urinary W levels (> 0.10 μg/g creatinine) in combination with either urinary Sb > 0.17 μg/g creatinine or those with urinary Sb ≤ 0.17 μg/g creatinine, but with high blood Pb levels (> 1.35 μg/dL). Predictors differed by ethnicity, with Cd as the main predictor of SBP among non-Hispanic black adults, and Pb not selected by the algorithm as a predictor of SBP among non-Hispanic white adults.

CONCLUSIONS

Combinations of metal biomarkers have different apparent relationships with blood pressure. Additional research in toxicological experimental models and in epidemiological studies is warranted to evaluate the suggested possible toxicological interactions between Sb, Cd, and Pb; and between W, Sb, and Pb; for cardiovascular (e.g., blood pressure) health. We also think future epidemiological research on inorganic exposure sets in relation to health outcomes like blood pressure might benefit from stratification by race and ethnicity.

What about the environment? Leveraging multi-omic datasets to characterize the environment's role in human health

The environment plays an important role in mediating human health. In this session we consider research addressing ways to overcome the challenges associated with studying the multifaceted and ever-changing environment. Environmental health research has a need for technological and methodological advances which will further our knowledge of how exposures precipitate complex phenotypes and exacerbate disease.

Association of 152 Biomarker Reference Intervals with All-Cause Mortality in Participants of a General United States Survey from 1999 to 2010

BACKGROUND:

Physicians sometimes consider whether or not to perform diagnostic testing in healthy people, but it is unknown whether nonextreme values of diagnostic tests typically encountered in such populations have any predictive ability, in particular for risk of death. The goal of this study was to quantify the associations among population reference intervals of 152 common biomarkers with all-cause mortality in a representative, nondiseased sample of adults in the United States.

METHODS:

The study used an observational cohort derived from the National Health and Nutrition Examination Survey (NHANES), a representative sample of the United States population consisting of 6 survey waves from 1999 to 2010 with linked mortality data (unweighted N=30 651) and a median followup of 6.1 years. We deployed an X-wide association study (XWAS) approach to systematically perform association testing of 152 diagnostic tests with all-cause mortality.

RESULTS:

After controlling for multiple hypotheses, we found that the values within reference intervals (10–90th percentiles) of 20 common biomarkers used as diagnostic tests or clinical measures were associated with all-cause mortality, including serum albumin, red cell distribution width, serum alkaline phosphatase, and others after adjusting for age (linear and quadratic terms), sex, race, income, chronic illness, and prior-year healthcare utilization. All biomarkers combined, however, explained only an additional 0.8% of the variance of mortality risk. We found modest year-to-year changes, or changes in association from survey wave to survey wave from 1999 to 2010 in the association sizes of biomarkers.

CONCLUSIONS:

Reference and nonoutlying variation in common biomarkers are consistently associated with mortality risk in the US population, but their additive contribution in explaining mortality risk is minor.

Environment-wide association study on childhood obesity in the U.S

BACKGROUND

Childhood obesity is a national public health issue with increasing prevalence. It has been linked to diet, lack of physical activity, and genetic susceptibility, with more recent evidence that it could also result from environmental factors. Studies linking it to environmental factors are limited, unsystematic, incomprehensive, and inconclusive.

OBJECTIVE

To conduct an environment-wide association study (EWAS) to comprehensively investigate all the environmental factors available in a nationally representative sample of children to determine factors associated with childhood obesity.

METHODS

We utilized the 1999-2016 National Health and Nutrition Examination Survey (NHANES) datasets and included all children/adolescents (6-17 years). Obesity was measured using body mass index and waist to height ratio. A multinomial and binary logistic regression were used adjusting for age, sex, race/ethnicity, creatinine, calorie intake, physical activity, screen time, limitation to physical activities, and socioeconomic status. We then controlled for multiple hypothesis testing and validated our findings on a different cohort of children.

RESULTS

We found that metals such as beryllium (OR: 3.305 CI: 1.460-7.479) and platinum (OR: 1.346 CI: 1.107-1.636); vitamins such as gamma-tocopherol (OR: 8.297 CI: 5.683-12.114) and delta-tocopherol (OR: 1.841 CI:1.476-2.297); heterocyclic aromatic amines such as 2-Amino-9H-pyrido (2,3-b) indole (OR: 1.323 CI: 1.083-1.617) and 2-Amino-3-methyl-9H-pyriodo(2,3-b)indole (OR: 2.799 CI: 1.442-5.433); polycyclic aromatic amines such as 9- fluorene (OR: 1.509 CI: 1.230-1.851) and 4-phenanthrene (OR: 2.828 CI: 1.632-4.899); and caffeine metabolites such as 1,3,7-trimethyluric acid (OR: 1.22 CI: 1.029-1.414) and 1,3,7-trimethylxanthine (OR: 1.258 CI: 1.075-1.473) were positively and significantly associated with childhood obesity.

CONCLUSION

Following the unique concept of EWAS, certain environmental factors were associated with childhood obesity. Further studies are required to confirm these associations while investigating their mechanisms of action.

A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease

Environmental factors, and in particular diet, are known to play a key role in the development of Coronary Heart Disease. Many of these factors were unveiled by detailed nutritional epidemiology studies, focusing on the role of a single nutrient or food at a time. Here, we apply an Environment-Wide Association Study approach to Nurses' Health Study data to explore comprehensively and agnostically the association of 257 nutrients and 117 foods with coronary heart disease risk (acute myocardial infarction and fatal coronary heart disease). After accounting for multiple testing, we identify 16 food items and 37 nutrients that show statistically significant association - while adjusting for potential confounding and control variables such as physical activity, smoking, calorie intake, and medication use - among which 38 associations were validated in Nurses' Health Study II. Our implementation of Environment-Wide Association Study successfully reproduces prior knowledge of diet-coronary heart disease associations in the epidemiological literature, and helps us detect new associations that were only marginally studied, opening potential avenues for further extensive experimental validation. We also show that Environment-Wide Association Study allows us to identify a bipartite food-nutrient network, highlighting which foods drive the associations of specific nutrients with coronary heart disease risk.

Laboratory-wide association study of survival with prostate cancer

BACKGROUND

Estimates of overall patient health are essential to inform treatment decisions for patients diagnosed with cancer. The authors applied XWAS methods, herein referred to as "laboratory-wide association study (LWAS)", to evaluate associations between routinely collected laboratory tests and survival in veterans with prostate cancer.

METHODS

The authors identified 133,878 patients who were diagnosed with prostate cancer between 2000 and 2013 in the Veterans Health Administration using any laboratory tests collected within 6 months of diagnosis (3,345,083 results). Using the LWAS framework, the false-discovery rate was used to test the association between multiple laboratory tests and survival, and these results were validated using training, testing, and validation cohorts.

RESULTS

A total of 31 laboratory tests associated with survival met stringent LWAS criteria. LWAS confirmed markers of prostate cancer biology (prostate-specific antigen: hazard ratio [HR], 1.07 [95% confidence interval (95% CI), 1.06-1.08]; and alkaline phosphatase: HR, 1.22 [95% CI, 1.20-1.24]) as well laboratory tests of general health (eg, serum albumin: HR, 0.78 [95% CI, 0.76-0.80]; and creatinine: HR, 1.05 [95% CI, 1.03-1.07]) and inflammation (leukocyte count: HR, 1.23 [95% CI, 1.98-1.26]; and erythrocyte sedimentation rate: HR, 1.33 [95% CI, 1.09-1.61]). In addition, the authors derived and validated separate models for patients with localized and advanced disease, identifying 28 laboratory markers and 15 laboratory markers, respectively, in each cohort.

CONCLUSIONS

The authors identified routinely collected laboratory data associated with survival for patients with prostate cancer using LWAS methodologies, including markers of prostate cancer biology, overall health, and inflammation. Broadening consideration of determinants of survival beyond those related to cancer itself could help to inform the design of clinical trials and aid in shared decision making.

LAY SUMMARY

This article examined routine laboratory tests associated with survival among veterans with prostate cancer. Using laboratory-wide association studies, the authors identified 31 laboratory tests associated with survival that can be used to inform the design of clinical trials and aid patients in shared decision making.

Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005-2008

Several circulating biomarkers are reported to be associated with diabetic retinopathy (DR). However, their relative contributions to DR compared to known risk factors, such as hyperglycaemia, hypertension, and hyperlipidaemia, remain unclear. In this data driven study, we used novel models to evaluate the associations of over 400 laboratory parameters with DR compared to the established risk factors. Methods: we performed an environment-wide association study (EWAS) of laboratory parameters available in National Health and Nutrition Examination Survey (NHANES) 2007–2008 in individuals with diabetes with DR as the outcome (test set). We employed independent variable (feature) selection approaches, including parallelised univariate regression modelling, Principal Component Analysis (PCA), penalised regression, and RandomForest™. These models were replicated in NHANES 2005–2006 (replication set). Our test and replication sets consisted of 1025 and 637 individuals with available DR status and laboratory data respectively. Glycohemoglobin (HbA1c) was the strongest risk factor for DR. Our PCA-based approach produced a model that incorporated 18 principal components (PCs) that had an Area under the Curve (AUC) 0.796 (95% CI 0.761–0.832), while penalised regression identified a 9-feature model with 78.51% accuracy and AUC 0.74 (95% CI 0.72–0.77). RandomForest™ identified a 31-feature model with 78.4% accuracy and AUC 0.71 (95% CI 0.65–0.77). On grouping the selected variables in our RandomForest™, hyperglycaemia alone achieved AUC 0.72 (95% CI 0.68–0.76). The AUC increased to 0.84 (95% CI 0.78–0.9) when the model also included hypertension, hypercholesterolemia, haematocrit, renal, and liver function tests.

Environment-Wide Association Study (En WAS) of Prenatal and Perinatal Factors Associated With Autistic Traits: A Population-Based Study

A combination of genetic and environmental factors contributes to the origins of autism spectrum disorder (ASD). While a number of studies have described specific environmental factors associating with emerging ASD, studies that compare and contrast multiple environmental factors in the same study are lacking. Thus, the goal of this study was to perform a prospective, data-driven environmental-wide association study of pre- and perinatal factors associated with the later development of autistic symptoms in childhood. The participants included 3891 6-year-old children from a birth cohort with pre- and perinatal data. Autistic symptoms were measured using the Social Responsiveness Scale in all children. Prior to any analyses, the sample was randomly split into a discovery set (2920) and a test set (921). Multiple linear regression analyses were performed for each of 920 variables, correcting for six of the most common covariates in epidemiological studies. We found 111 different pre- and perinatal factors associated with autistic traits during childhood. In secondary analyses where we controlled for parental psychopathology, 23 variables in the domains of family and interpersonal relationships were associated with the development of autistic symptoms during childhood. In conclusion, a data-driven approach was used to identify a number of pre- and perinatal risk factors associating with higher childhood autistic symptoms. These factors include measures of parental psychopathology and family and interpersonal relationships. These measures could potentially be used for the early identification of those at increased risk to develop ASD. LAY SUMMARY: A combination of genetic and environmental factors contributes to the development of autism spectrum disorder (ASD). Each environmental factor may affect the risk of ASD. In a study on 6-year-old children, a number of pre- and perinatal risk factors were identified that are associated with autistic symptoms in childhood. These factors include measures of parental psychopathology and family and interpersonal relationships. These variables could potentially serve as markers to identify those at increased risk to develop ASD or autistic symptoms. Autism Res 2020, 13: 1582-1600. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

Environment-Wide Association Study of CKD

BACKGROUND AND OBJECTIVES

Exposure to environmental chemicals has been recognized as one of the possible contributors to CKD. We aimed to identify environmental chemicals that are associated with CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We analyzed the data obtained from a total of 46,748 adults who participated in the National Health and Nutrition Examination Survey (1999-2016). Associations of chemicals measured in urine or blood (n=262) with albuminuria (urine albumin-to-creatinine ratio ≥30 mg/g), reduced eGFR (<60 ml/min per 1.73 m²), and a composite of albuminuria or reduced eGFR were tested and validated using the environment-wide association study approach.

RESULTS

Among 262 environmental chemicals, seven (3%) chemicals showed significant associations with increased risk of albuminuria, reduced eGFR, or the composite outcome. These chemicals included metals and other chemicals that have not previously been associated with CKD. Serum and urine cotinines, blood 2,5-dimethylfuran (a volatile organic compound), and blood cadmium were associated with albuminuria. Blood lead and cadmium were associated with reduced eGFR. Blood cadmium and lead and three volatile compounds (blood 2,5-dimethylfuran, blood furan, and urinary phenylglyoxylic acid) were associated with the composite outcome. A total of 23 chemicals, including serum perfluorooctanoic acid, seven urinary metals, three urinary arsenics, urinary nitrate and thiocyanate, three urinary polycyclic aromatic hydrocarbons, and seven volatile organic compounds, were associated with lower risks of one or more manifestations of CKD.

CONCLUSIONS

A number of chemicals were identified as potential risk factors for CKD among the general population.

Design and methodology challenges of environment-wide association studies: A systematic review

Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.

The Secure Anonymised Information Linkage databank Dementia e-cohort (SAIL-DeC)

Introduction

The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care.

Objectives

We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers.

Methods

SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors.

Results

From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years.

Conclusion

We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

While genome-wide association studies are an established method of identifying genetic variants associated with disease, environment-wide association studies (EWAS) highlight the contribution of nongenetic components to complex phenotypes. However, the lack of high-throughput quality control (QC) pipelines for EWAS data lends itself to analysis plans where the data are cleaned after a first-pass analysis, which can lead to bias, or are cleaned manually, which is arduous and susceptible to user error. We offer a novel software, CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures (CLARITE), as a tool to efficiently clean environmental data, perform regression analysis, and visualize results on a single platform through user-guided automation. It exists as both an R package and a Python package. Though CLARITE focuses on EWAS, it is intended to also improve the QC process for phenotypes and clinical lab measures for a variety of downstream analyses, including phenome-wide association studies and gene-environment interaction studies. With the goal of demonstrating the utility of CLARITE, we performed a novel EWAS in the National Health and Nutrition Examination Survey (NHANES) (N overall Discovery=9063, N overall Replication=9874) for body mass index (BMI) and over 300 environment variables post-QC, adjusting for sex, age, race, socioeconomic status, and survey year. The analysis used survey weights along with cluster and strata information in order to account for the complex survey design. Sixteen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were serum levels of g-tocopherol (vitamin E) (Discovery Bonferroni p: 8.67x10^-12, Replication Bonferroni p: 2.70x10^-9) and iron (Discovery Bonferroni p: 1.09x10^-8, Replication Bonferroni p: 1.73x10^-10). Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes. As such, exposures predictive of BMI may be useful for covariate and/or interaction assessment of metabolic-related traits. CLARITE allows improved data quality for EWAS, gene-environment interactions, and phenome-wide association studies by establishing a high-throughput quality control infrastructure. Thus, CLARITE is recommended for studying the environmental factors underlying complex disease.

Exposure to Environmental Pollutants and Their Association with Biomarkers of Aging: A Multipollutant Approach

Mitochondrial DNA (mtDNA) content and telomere length are putative aging biomarkers and are sensitive to environmental stressors, including pollutants. Our objective was to identify, from a set of environmental exposures, which exposure is associated with leukocyte mtDNA content and telomere length in adults. This study includes 175 adults from 50 to 65 years old from the cross-sectional Flemish Environment and Health study, of whom leukocyte telomere length and mtDNA content were determined using qPCR. The levels of exposure of seven metals, 11 organohalogens, and four perfluorinated compounds (PFHxS, PFNA, PFOA, PFOS) were measured. We performed sparse partial least-squares regression analyses followed by ordinary least-squares regression to assess the multipollutant associations. While accounting for possible confounders and coexposures, we identified that urinary cadmium (6.52%, 95% confidence interval, 1.06, 12.28), serum hexachlorobenzene (2.89%, 018, 5.68), and perfluorooctanesulfonic acid (11.38%, 5.97, 17.08) exposure were positively associated ( p < 0.05) with mtDNA content, while urinary copper (-9.88%, -14.82, -4.66) and serum perfluorohexanesulfonic acid (-4.75%, -8.79, -0.54) exposure were inversely associated with mtDNA content. Urinary antimony (2.69%, 0.45, 4.99) and mercury (1.91%, 0.42, 3.43) exposure were positively associated with leukocyte telomere length, while urinary copper (-3.52%, -6.60, -0.34) and serum perfluorooctanesulfonic acid (-3.64%, -6.60, -0.60) showed an inverse association. Our findings support the hypothesis that environmental pollutants interact with molecular hallmarks of aging.

Exposome-wide association study of semen quality: Systematic discovery of endocrine disrupting chemical biomarkers in fertility require large sample sizes

OBJECTIVES

Exposome-wide association studies (EWAS) are a systematic and unbiased way to investigate multiple environmental factors associated with phenotype. We applied EWAS to study semen quality and queried the sample size requirements to detect modest associations in a reproductive cohort.

STUDY DESIGN AND SETTING

We conducted 1) a multivariate EWAS of 128 endocrine disrupting chemicals (EDCs) from 15 chemical classes measured in urine/serum relative to 7 semen quality endpoints in a prospective cohort study comprising 473 men and 2) estimated the sample size requirements for EWAS etiologic investigations.

RESULTS

None of the EDCs were associated with semen quality endpoints after adjusting for multiple tests. However, several EDCs (e.g., polychlorinated biphenyl congeners 99, 105, 114, and 167) were associated with raw p < 0.05. In a post hoc statistical power analysis with the observed effect sizes, we determined that EWAS research in male fertility will require a mean sample size of 2696 men (1795-3625) to attain a power of 0.8. The average size of four published studies is 201 men.

CONCLUSION

Existing cohort studies with hundreds of participants are underpowered (<0.8) for EWAS-related investigations. Merging cohorts to ensure a sufficient sample size can facilitate the use of EWAS methods for assessing EDC mixtures that impact semen quality.

Toward a panoramic perspective of the association between environmental factors and cardiovascular disease: An environment-wide association study from National Health and Nutrition Examination Survey 1999-2014

OBJECTIVES

An environment-wide association study (EWAS) may be useful to comprehensively test and validate associations between environmental factors and cardiovascular disease (CVD) in an unbiased manner.

APPROACH AND RESULTS

Data from National Health and Nutrition Examination Survey (1999-2014) were randomly 50:50 spilt into training set and testing set. CVD was ascertained by a self-reported diagnosis of myocardial infarction, coronary heart disease or stroke. We performed multiple linear regression analyses associating 203 environmental factors and 132 clinical phenotypes with CVD in training set (false discovery rate < 5%) and significant factors were validated in the testing set (P < 0.05). Random forest (RF) model was used for multicollinearity elimination and variable importance ranking. Discriminative power of factors for CVD was calculated by area under the receiver operating characteristic (AUROC). Overall, 43,568 participants with 4084 (9.4%) CVD were included. After adjusting for age, sex, race, body mass index, blood pressure and socio-economic level, we identified 5 environmental variables and 19 clinical phenotypes associated with CVD in training and testing dataset. Top five factors in RF importance ranking were: waist, glucose, uric acid, and red cell distribution width and glycated hemoglobin. AUROC of the RF model was 0.816 (top 5 factors) and 0.819 (full model). Sensitivity analyses reveal no specific moderators of the associations.

CONCLUSION

Our systematic evaluation provides new knowledge on the complex array of environmental correlates of CVD. These identified correlates may serve as a complementary approach to CVD risk assessment. Our findings need to be probed in further observational and interventional studies.

The Exposome Research Paradigm: an Opportunity to Understand the Environmental Basis for Human Health and Disease

PURPOSE OF REVIEW

This paper presents an overview of the exposome research paradigm with particular application to understanding human reproduction and development and its implications for health across a lifespan.

RECENT FINDINGS

The exposome research paradigm has generated considerable discussion about its feasibility and utility for delineating the impact of environmental exposures on human health. Early initiatives are underway, including smaller proof-of-principle studies and larger concerted efforts. Despite the notable challenges underlying the exposome paradigm, analytic techniques are being developed to handle its untargeted approach and correlated and multi-level or hierarchical data structures such initiatives generate, while considering multiple comparisons. The relatively short intervals for critical and sensitive windows of human reproduction and development seem well suited for exposome research and may revolutionize our understanding of later onset diseases. Early initiatives suggest that the exposome paradigm is feasible, but its utility remains to be established with applications to population human health research.

Yale school of public health symposium on lifetime exposures and human health: the exposome; summary and future reflections

The exposome is defined as "the totality of environmental exposures encountered from birth to death" and was developed to address the need for comprehensive environmental exposure assessment to better understand disease etiology. Due to the complexity of the exposome, significant efforts have been made to develop technologies for longitudinal, internal and external exposure monitoring, and bioinformatics to integrate and analyze datasets generated. Our objectives were to bring together leaders in the field of exposomics, at a recent Symposium on "Lifetime Exposures and Human Health: The Exposome," held at Yale School of Public Health. Our aim was to highlight the most recent technological advancements for measurement of the exposome, bioinformatics development, current limitations, and future needs in environmental health. In the discussions, an emphasis was placed on moving away from a one-chemical one-health outcome model toward a new paradigm of monitoring the totality of exposures that individuals may experience over their lifetime. This is critical to better understand the underlying biological impact on human health, particularly during windows of susceptibility. Recent advancements in metabolomics and bioinformatics are driving the field forward in biomonitoring and understanding the biological impact, and the technological and logistical challenges involved in the analyses were highlighted. In conclusion, further developments and support are needed for large-scale biomonitoring and management of big data, standardization for exposure and data analyses, bioinformatics tools for co-exposure or mixture analyses, and methods for data sharing.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.