AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.

Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women's Health Initiative (WHI), a prospective cohort study including randomized clinical trials and observational study to improve postmenopausal women's health with one of the main focuses on cardiovascular disease. Our AESurv model effectively learned participant representations in low-dimensional latent space and achieved better model performance (concordance index-C index of 0.864 ± 0.009 and time-to-event mean area under the receiver operating characteristic curve-AUROC of 0.905 ± 0.009) than other survival analysis models (Cox proportional hazard, Cox proportional hazard deep neural network survival analysis, random survival forest, and gradient boosting survival analysis models) in the SHS. We further validated the AESurv model in WHI and also achieved the best model performance. The AESurv model can be used for accurate CHD prediction and assist health care professionals and patients to perform early intervention strategies. We suggest using AESurv model for future time-to-event CHD prediction based on DNA methylation features.

Association of Urinary Metals With Cardiovascular Disease Incidence and All-Cause Mortality in the Multi-Ethnic Study of Atherosclerosis (MESA).

BACKGROUND: Exposure to metals has been associated with cardiovascular disease (CVD) end points and mortality, yet prospective evidence is limited beyond arsenic, cadmium, and lead. In this study, we assessed the prospective association of urinary metals with incident CVD and all-cause mortality in a racially diverse population of US adults from MESA (the Multi-Ethnic Study of Atherosclerosis). METHODS: We included 6599 participants (mean [SD] age, 62.1 [10.2] years; 53% female) with urinary metals available at baseline (2000 to 2001) and followed through December 2019. We used Cox proportional hazards models to estimate the adjusted hazard ratio and 95% CI of CVD and all-cause mortality by baseline urinary levels of cadmium, tungsten, and uranium (nonessential metals), and cobalt, copper, and zinc (essential metals). The joint association of the 6 metals as a mixture and the corresponding 10-year survival probability was calculated using Cox Elastic-Net. RESULTS: During follow-up, 1162 participants developed CVD, and 1844 participants died. In models adjusted by behavioral and clinical indicators, the hazard ratios (95% CI) for incident CVD and all-cause mortality comparing the highest with the lowest quartile were, respectively: 1.25 (1.03, 1.53) and 1.68 (1.43, 1.96) for cadmium; 1.20 (1.01, 1.42) and 1.16 (1.01, 1.33) for tungsten; 1.32 (1.08, 1.62) and 1.32 (1.12, 1.56) for uranium; 1.24 (1.03, 1.48) and 1.37 (1.19, 1.58) for cobalt; 1.42 (1.18, 1.70) and 1.50 (1.29, 1.74) for copper; and 1.21 (1.01, 1.45) and 1.38 (1.20, 1.59) for zinc. A positive linear dose-response was identified for cadmium and copper with both end points. The adjusted hazard ratios (95% CI) for an interquartile range (IQR) increase in the mixture of these 6 urinary metals and the corresponding 10-year survival probability difference (95% CI) were 1.29 (1.11, 1.56) and -1.1% (-2.0, -0.05) for incident CVD and 1.66 (1.47, 1.91) and -2.0% (-2.6, -1.5) for all-cause mortality. CONCLUSIONS: This epidemiological study in US adults indicates that urinary metal levels are associated with increased CVD risk and mortality. These findings can inform the development of novel preventive strategies to improve cardiovascular health.

Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multimarker in the Strong Heart Study.

The statistical analysis of omics data poses a great computational challenge given their ultra-high-dimensional nature and frequent between-features correlation. In this work, we extended the iterative sure independence screening (ISIS) algorithm by pairing ISIS with elastic-net (Enet) and 2 versions of adaptive elastic-net (adaptive elastic-net (AEnet) and multistep adaptive elastic-net (MSAEnet)) to efficiently improve feature selection and effect estimation in omics research. We subsequently used genome-wide human blood DNA methylation data from American Indian participants in the Strong Heart Study (n = 2235 participants; measured in 1989-1991) to compare the performance (predictive accuracy, coefficient estimation, and computational efficiency) of ISIS-paired regularization methods with that of a bayesian shrinkage and traditional linear regression to identify an epigenomic multimarker of body mass index (BMI). ISIS-AEnet outperformed the other methods in prediction. In biological pathway enrichment analysis of genes annotated to BMI-related differentially methylated positions, ISIS-AEnet captured most of the enriched pathways in common for at least 2 of all the evaluated methods. ISIS-AEnet can favor biological discovery because it identifies the most robust biological pathways while achieving an optimal balance between bias and efficient feature selection. In the extended SIS R package, we also implemented ISIS paired with Cox and logistic regression for time-to-event and binary endpoints, respectively, and a bootstrap approach for the estimation of regression coefficients.

Blood DNA methylation and liver cancer in American Indians: evidence from the Strong Heart Study.

PURPOSE: Liver cancer incidence among American Indians/Alaska Natives has risen over the past 20 years. Peripheral blood DNA methylation may be associated with liver cancer and could be used as a biomarker for cancer risk. We evaluated the association of blood DNA methylation with risk of liver cancer. METHODS: We conducted a prospective cohort study in 2324 American Indians, between age 45 and 75 years, from Arizona, Oklahoma, North Dakota and South Dakota who participated in the Strong Heart Study between 1989 and 1991. Liver cancer deaths (n = 21) were ascertained using death certificates obtained through 2017. The mean follow-up duration (SD) for non-cases was 25.1 (5.6) years and for cases, 11.0 (8.8) years. DNA methylation was assessed from blood samples collected at baseline using MethylationEPIC BeadChip 850 K arrays. We used Cox regression models adjusted for age, sex, center, body mass index, low-density lipoprotein cholesterol, smoking, alcohol consumption, and immune cell proportions to examine the associations. RESULTS: We identified 9 CpG sites associated with liver cancer. cg16057201 annotated to MRFAP1) was hypermethylated among cases vs. non-cases (hazard ratio (HR) for one standard deviation increase in methylation was 1.25 (95% CI 1.14, 1.37). The other eight CpGs were hypomethylated and the corresponding HRs (95% CI) ranged from 0.58 (0.44, 0.75) for cg04967787 (annotated to PPRC1) to 0.77 (0.67, 0.88) for cg08550308. We also assessed 7 differentially methylated CpG sites associated with liver cancer in previous studies. The adjusted HR for cg15079934 (annotated to LPS1) was 1.93 (95% CI 1.10, 3.39). CONCLUSIONS: Blood DNA methylation may be associated with liver cancer mortality and may be altered during the development of liver cancer.

Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

BACKGROUND: Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD. METHODS: Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis. RESULTS: A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic. CONCLUSIONS: Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

Toxic Metals and Subclinical Atherosclerosis in Carotid, Femoral, and Coronary Vascular Territories: The Aragon Workers Health Study.

OBJECTIVE: Studies evaluating the association of metals with subclinical atherosclerosis are mostly limited to carotid arteries. We assessed individual and joint associations of nonessential metals exposure with subclinical atherosclerosis in 3 vascular territories. Approach and Results: One thousand eight hundred seventy-three Aragon Workers Health Study participants had urinary determinations of inorganic arsenic species, barium, cadmium, chromium, antimony, titanium, uranium, vanadium, and tungsten. Plaque presence in carotid and femoral arteries was determined by ultrasound. Coronary Agatston calcium score ≥1 was determined by computed tomography scan. Median arsenic, barium, cadmium, chromium, antimony, titanium, uranium, vanadium, and tungsten levels were 1.83, 1.98, 0.27, 1.18, 0.05, 9.8, 0.03, 0.66, and 0.23 µg/g creatinine, respectively. The adjusted odds ratio (95% CI) for subclinical atherosclerosis presence in at least one territory was 1.25 (1.03-1.51) for arsenic, 1.67 (1.22-2.29) for cadmium, and 1.26 (1.04-1.52) for titanium. These associations were driven by arsenic and cadmium in carotid, cadmium and titanium in femoral, and titanium in coronary territories and mostly remained after additional adjustment for the other relevant metals. Titanium, cadmium, and antimony also showed positive associations with alternative definitions of increased coronary calcium. Bayesian Kernel Machine Regression analysis simultaneously evaluating metal associations suggested an interaction between arsenic and the joint cadmium-titanium exposure. CONCLUSIONS: Our results support arsenic and cadmium and identify titanium and potentially antimony as atherosclerosis risk factors. Exposure reduction and mitigation interventions of these metals may decrease cardiovascular risk in individuals without clinical disease.

Healthy lifestyle, metabolomics and incident type 2 diabetes in a population-based cohort from Spain.

BACKGROUND: The contribution of metabolomic factors to the association of healthy lifestyle with type 2 diabetes risk is unknown. We assessed the association of a composite measure of lifestyle with plasma metabolite profiles and incident type 2 diabetes, and whether relevant metabolites can explain the prospective association between healthy lifestyle and incident type 2 diabetes. METHODS: A Healthy Lifestyle Score (HLS) (5-point scale including diet, physical activity, smoking status, alcohol consumption and BMI) was estimated in 1016 Hortega Study participants, who had targeted plasma metabolomic determinations at baseline examination in 2001-2003, and were followed-up to 2015 to ascertain incident type 2 diabetes. RESULTS: The HLS was cross-sectionally associated with 32 (out of 49) plasma metabolites (2.5% false discovery rate). In the subset of 830 participants without prevalent type 2 diabetes, the rate ratio (RR) and rate difference (RD) of incident type 2 diabetes (n cases = 51) per one-point increase in HLS was, respectively, 0.69 (95% CI, 0.51, 0.93), and - 8.23 (95% CI, - 16.34, - 0.13)/10,000 person-years. In single-metabolite models, most of the HLS-related metabolites were prospectively associated with incident type 2 diabetes. In probit Bayesian Kernel Machine Regression, these prospective associations were mostly driven by medium HDL particle concentration and phenylpropionate, followed by small LDL particle concentration, which jointly accounted for ~ 50% of the HLS-related decrease in incident type 2 diabetes. CONCLUSIONS: The HLS showed a strong inverse association with incident type 2 diabetes, which was largely explained by plasma metabolites measured years before the clinical diagnosis.

Effects of e-liquid flavor, nicotine content, and puff duration on metal emissions from electronic cigarettes.

Vaping is the action of inhaling and exhaling aerosols from electronic cigarettes. The aerosols contain various amounts of toxic chemicals, including metals. The purpose of this study was to evaluate factors that can influence metal levels, including flavor and nicotine content in the e-liquid, and puff duration. Aerosols were collected from both closed-system (cartridge-based) and open-system e-cigarettes using e-liquids with different flavors (fruit, tobacco, and menthol), nicotine content (0, 6, 24, and 59 mg/mL), and different puff durations (1, 2, and 4 s). The concentrations of 14 metals in the collected aerosols were measured using inductively coupled plasma mass spectroscopy. Aerosol concentrations of As, Fe, and Mn varied significantly among fruit, tobacco, and menthol flavors in both closed-system and open-system devices. Concentrations of Al, Fe, Sn, and U were significantly higher in tobacco or menthol flavored aerosols compared to fruit flavors in closed-system devices. Aerosol W levels were significantly higher in tobacco flavored aerosols compared to fruit flavors in open-system devices. Concentrations of As, Fe, and Mn were higher in tobacco flavored aerosols compared to menthol flavors in both types of devices. The median Pb concentration decreased significantly from 15.8 to 0.88 µg/kg when nicotine content increased from 0 to 59 mg/mL, and median Ni concentration was 9.60 times higher in aerosols with nicotine of 59 mg/mL compared to 24 mg/mL (11.9 vs. 1.24 µg/kg) for closed-system devices. No significant differences were observed in aerosol metal concentrations for different puff durations. Aerosol metal concentrations varied widely between different flavors and nicotine content but not by puff duration. Flavor and nicotine content of the e-liquid could be potential factors in metal emissions. Some elements showed higher concentrations under certain conditions, highlighting the urgent need of developing strict product regulations, especially on e-liquid composition and nicotine content to inform e-cigarette users about metal exposure through vaping.

Arsenic exposure and human blood DNA methylation and hydroxymethylation profiles in two diverse populations from Bangladesh and Spain.

BACKGROUND: Associations of arsenic (As) with the sum of 5-mC and 5-hmC levels have been reported; however, As exposure-related differences of the separated 5-mC and 5-hmC markers have rarely been studied. METHODS: In this study, we evaluated the association of arsenic exposure biomarkers and 5-mC and 5-hmC in 30 healthy men (43-55 years) from the Aragon Workers Health Study (AWHS) (Spain) and 31 healthy men (31-50 years) from the Folic Acid and Creatinine Trial (FACT) (Bangladesh). We conducted 5-mC and 5-hmC profiling using Infinium MethylationEPIC arrays, on paired standard and modified (ox-BS in AWHS and TAB in FACT) bisulfite converted blood DNA samples. RESULTS: The median for the sum of urine inorganic and methylated As species (ΣAs) (µg/L) was 12.5 for AWHS and 89.6 for FACT. The median of blood As (µg/L) was 8.8 for AWHS and 10.2 for FACT. At a statistical significance p-value cut-off of 0.01, the differentially methylated (DMP) and hydroxymethylated (DHP) positions were mostly located in different genomic sites. Several DMPs and DHPs were consistently found in AWHS and FACT both for urine ΣAs and blood models, being of special interest those attributed to the DIP2C gene. Three DMPs (annotated to CLEC12A) for AWHS and one DHP (annotated to NPLOC4) for FACT remained statistically significant after false discovery rate (FDR) correction. Pathways related to chronic diseases including cardiovascular, cancer and neurological were enriched. CONCLUSIONS: While we identified common 5-hmC and 5-mC signatures in two populations exposed to varying levels of inorganic As, differences in As-related epigenetic sites across the study populations may additionally reflect low and high As-specific associations. This work contributes a deeper understanding of potential epigenetic dysregulations of As. However, further research is needed to confirm biological consequences associated with DIP2C epigenetic regulation and to investigate the role of 5-hmC and 5-mC separately in As-induced health disorders at different exposure levels.

Critical Care Requirements Under Uncontrolled Transmission of SARS-CoV-2.

Objectives. To estimate the critical care bed capacity that would be required to admit all critical COVID-19 cases in a setting of unchecked SARS-CoV-2 transmission, both with and without elderly-specific protection measures.Methods. Using electronic health records of all 2432 COVID-19 patients hospitalized in a large hospital in Madrid, Spain, between February 28 and April 23, 2020, we estimated the number of critical care beds needed to admit all critical care patients. To mimic a hypothetical intervention that halves SARS-CoV-2 infections among the elderly, we randomly excluded 50% of patients aged 65 years and older.Results. Critical care requirements peaked at 49 beds per 100 000 on April 1-2 weeks after the start of a national lockdown. After randomly excluding 50% of elderly patients, the estimated peak was 39 beds per 100 000.Conclusions. Under unchecked SARS-CoV-2 transmission, peak critical care requirements in Madrid were at least fivefold higher than prepandemic capacity. Under a hypothetical intervention that halves infections among the elderly, critical care peak requirements would have exceeded the prepandemic capacity of most high-income countries.Public Health Implications. Pandemic control strategies that rely exclusively on protecting the elderly are likely to overwhelm health care systems.

Cost-effectiveness of a Contact Intervention and a Psychotherapeutic Program for Post-discharge Suicide Prevention.

OBJECTIVE: To determine the cost-effectiveness of 2 strategies for post-discharge suicide prevention, an Enhanced Contact intervention based on repeated in-person and telephone contacts, and an individual 2-month long problem-solving Psychotherapy program, in comparison to facilitated access to outpatient care following a suicide attempt. METHODS: We conducted a cost-effectiveness analysis based on a decision tree between January and December 2019. Comparative effectiveness estimates were obtained from an observational study conducted between 2013 and 2017 in Madrid, Spain. Electronic health care records documented resource use (including extra-hospital emergency care, mortality, inpatient admission, and disability leave). Direct cost data were derived from Madrid's official list of public health care prices. Indirect cost data were derived from Spain's National Institute of Statistics. RESULTS: Both augmentation strategies were more cost-effective than a single priority outpatient appointment considering reasonable thresholds of willingness to pay. Under the base-case scenario, Enhanced Contact and Psychotherapy incurred, respectively, €2,340 and 6,260 per averted attempt, compared to a single priority appointment. Deterministic and probabilistic sensitivity analyses showed both augmentation strategies to remain cost-effective under several scenarios. Enhanced Contact was slightly cost minimizing in comparison to Psychotherapy (base-case scenario: €-196 per averted attempt). CONCLUSIONS: Two post-discharge suicide prevention strategies based on Enhanced Contact and Psychotherapy were cost-effective in comparison to a single priority appointment. Increasing contacts between suicide attempters and mental health-care providers was slightly cost minimizing compared to psychotherapy.

DNA methylation and adiposity phenotypes: an epigenome-wide association study among adults in the Strong Heart Study.

BACKGROUND: Elevated adiposity is often posited by medical and public health researchers to be a risk factor associated with cardiovascular disease, diabetes, and other diseases. These health challenges are now thought to be reflected in epigenetic modifications to DNA molecules, such as DNA methylation, which can alter gene expression. METHODS: Here we report the results of three Epigenome Wide Association Studies (EWAS) in which we assessed the differential methylation of DNA (obtained from peripheral blood) associated with three adiposity phenotypes (BMI, waist circumference, and impedance-measured percent body fat) among American Indian adult participants in the Strong Heart Study. RESULTS: We found differential methylation at 8264 CpG sites associated with at least one of our three response variables. Of the three adiposity proxies we measured, waist circumference had the highest number of associated differentially methylated CpGs, while percent body fat was associated with the lowest. Because both waist circumference and percent body fat relate to physiology, we focused interpretations on these variables. We found a low degree of overlap between these two variables in our gene ontology enrichment and Differentially Methylated Region analyses, supporting that waist circumference and percent body fat measurements represent biologically distinct concepts. CONCLUSIONS: We interpret these general findings to indicate that highly significant regions of the genome (DMR) and synthesis pathways (GO) in waist circumference analyses are more likely to be associated with the presence of visceral/abdominal fat than more general measures of adiposity. Our findings confirmed numerous CpG sites previously found to be differentially methylated in association with adiposity phenotypes, while we also found new differentially methylated CpG sites and regions not previously identified.

MLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions.

Accurately measuring epigenetic marks such as 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level, requires combining data from DNA processing methods including traditional (BS), oxidative (oxBS) or Tet-Assisted (TAB) bisulfite conversion. We introduce the R package MLML2R, which provides maximum likelihood estimates (MLE) of 5-mC and 5-hmC proportions. While all other available R packages provide 5-mC and 5-hmC MLEs only for the oxBS+BS combination, MLML2R also provides MLE for TAB combinations. For combinations of any two of the methods, we derived the pool-adjacent-violators algorithm (PAVA) exact constrained MLE in analytical form. For the three methods combination, we implemented both the iterative method by Qu et al. [Qu, J., M. Zhou, Q. Song, E. E. Hong and A. D. Smith (2013): "Mlml: consistent simultaneous estimates of dna methylation and hydroxymethylation," Bioinformatics, 29, 2645-2646.], and also a novel non iterative approximation using Lagrange multipliers. The newly proposed non iterative solutions greatly decrease computational time, common bottlenecks when processing high-throughput data. The MLML2R package is flexible as it takes as input both, preprocessed intensities from Infinium Methylation arrays and counts from Next Generation Sequencing technologies. The MLML2R package is freely available at https://CRAN.R-project.org/package=MLML2R.

Metal concentrations in electronic cigarette aerosol: Effect of open-system and closed-system devices and power settings.

BACKGROUND: Electronic cigarettes (E-cigarettes) generate aerosol containing metal contaminants. Our goals were to quantify aerosol metal concentrations and to compare the effects of power setting and device type (closed-system vs. open-system) on metal release. METHODS: Aerosol samples were collected from two closed-system devices (a cigalike and pod) and two open-system devices (mods). Each open-system device was operated at three different power settings to examine the effect of device power on metal release. Concentrations of 14 metals in e-cigarette aerosol collected via droplet deposition were measured using inductively coupled plasma mass spectroscopy. Aerosol metal concentrations were reported as mass fractions (µg/kg) in the e-liquid. RESULTS: For open-system device 1 (OD1), median arsenic (As), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), antimony (Sb), tin (Sn), and zinc (Zn) concentrations increased 14, 54, 17, 30, 41, 96, 14, 81, 631, and 7-fold when the device power was increased from low (20 W) to intermediate (40 W) setting. When the power was further increased from intermediate (40 W) to high (80 W) setting, concentrations of As, Cr, Cu, Mn, Ni, and Sb did not change significantly. For open-system device 2 (OD2), Cr and Mn concentrations increased significantly when device power was increased from low (40 W) to intermediate (120 W) setting, and then decreased significantly when power was further increased from intermediate (120 W) to high (200 W) setting. Among the four devices, aerosol metal concentrations were higher for the open-system than the closed-system devices, except for aluminum (Al) and uranium (U). For Cr, median (interquartile range) concentrations (µg/kg) from the open-system devices were 2.51 (1.55, 4.23) and 15.6 (7.88, 54.5) vs. 0.39 (0.05, 0.72) and 0.41 (0.34, 0.57) for the closed-system devices. For Ni, concentrations (µg/kg) from the open-system devices were 793 (508, 1169) and 2148 (851, 3397) vs. 1.32 (0.39, 3.35) and 11.9 (10.7, 22.7) from the closed-system devices. Inhalation of 0% and 100% of samples from OD1, 7.4% and 88.9% from OD2 by typical e-cigarette users would exceed chronic minimum risk levels (MRL) of Mn and Ni, respectively. No MRL exceedance was predicted for the closed-system devices. A large fraction of users of OD1 (100%) and OD2 (77.8%) would be exposed to Ni levels higher than those from reference tobacco cigarette 3R4F. CONCLUSIONS: Our findings suggest that power setting and device type affect metal release from devices to aerosol which would subsequently be inhaled by users. Metal concentrations from open-system devices first increased with device power, and then leveled off for most metals. Open-system devices generate aerosol with higher metal concentrations than closed-system devices. These findings inform tobacco regulatory science, policy makers and health professionals on potential metal health risks associated with e-cigarette use, design and manufacturing.

Methods for the Analysis of Multiple Epigenomic Mediators in Environmental Epidemiology.

PURPOSE OF REVIEW: Epigenetic changes can be highly influenced by environmental factors and have in turn been proposed to influence chronic disease. Being able to quantify to which extent epigenomic processes are mediators of the association between environmental exposures and diseases is of interest for epidemiologic research. In this review, we summarize the proposed mediation analysis methods with applications to epigenomic data. RECENT FINDINGS: The ultra-high dimensionality and high correlations that characterize omics data have hindered the precise quantification of mediated effects. Several methods have been proposed to deal with mediation in high-dimensional settings, including methods that incorporate dimensionality reduction techniques to the mediation algorithm. Although important methodological advances have been conducted in the previous years, key challenges such as the development of sensitivity analyses, dealing with mediator-mediator interactions, including environmental mixtures as exposures, or the integration of different omic data should be the focus of future methodological developments for epigenomic mediation analysis.

Causal mediation for uncausally related mediators in the context of survival analysis.

Objective: The study of the potential intermediate effect of several variables on the association between an exposure and a time-to-event outcome is a question of interest in epidemiologic research. However, to our knowledge, no tools have been developed for the evaluation of multiple correlated mediators in a survival setting. Methods: In this work, we extended the multimediate algorithm, which conducts mediation analysis in the context of multiple uncausally correlated mediators, to a time-to-event setting using the semiparametric additive hazards model. We theoretically demonstrated that, under certain assumptions, indirect, direct and total effects can be calculated using the counterfactual framework with collapsible survival models. We also adapted the algorithm to accommodate exposure-mediator interactions. Results and conclusions: Using simulations, we demonstrated that our algorithm performs better than the product of coefficients method, even for uncorrelated mediators. The additive hazards model quantifies the effects as rate differences, which constitute a measure of impact, with applications that can be highly informative for public health. Our algorithm can be found in the R package multimediate, which is available in Github.

Urinary Volatile Organic Compound Metabolites are Associated with High Blood Pressure Among Non-smoking Participants in the National Health and Nutrition Examination Survey (2011-2018).

Background: Volatile organic compounds (VOCs) are ubiquitous environmental pollutants. Exposure to VOCs is associated with cardiovascular disease (CVD) risk factors, including elevated blood pressure (BP) in susceptible populations. However, research in the general population, particularly among non-smoking adults, is limited. We hypothesized that higher VOC exposure is associated with higher BP and hypertension, among non-smokers. Methods: We included four cycles of data (2011-2018) of non-smoking adults (n=4,430) from the National Health and Nutrition Examination Survey (NHANES). Urinary VOC metabolites were measured by ultra-performance liquid chromatography-mass spectrometry, adjusted for urine dilution, and log-transformed. We estimated mean differences in BP using linear models and prevalence ratio of stage 2 hypertension using modified Poisson models with robust standard errors. Models were adjusted for age, sex, race and ethnicity, education, body mass index, estimated glomerular filtration rate and NHANES cycle. Results: Participants were 54% female, with a median age of 48 years, 32.3% had hypertension, and 7.9% had diabetes. The mean differences (95% CI) in systolic BP were 1.61 (0.07, 3.15) and 2.46 (1.01, 3.92) mmHg when comparing the highest to lowest quartile of urinary acrolein (CEMA) and 1,3-butadiene (DHBMA) metabolites. The prevalence ratios (PR) for hypertension were 1.06 (1.02, 1.09) and 1.05 (1.01, 1.09) when comparing the highest to lowest quartiles of urinary acrolein (CEMA) and 1,3-butadiene (DHBMA), respectively. Conclusions: Exposure to VOCs may be relevant yet understudied environmental contributors to CVD risk in the non-smoking, US population.

Heart Failure Risk Prediction in a Population With a High Burden of Diabetes: Evidence From the Strong Heart Study.

BACKGROUND: Despite the high burden of diabetes and cardiovascular risk factors in American Indian communities in the United States, prospective studies of heart failure (HF) in this population group are scarce, and the generalizability of previous HF risk scales may be limited. We developed a parsimonious HF risk prediction equation that accounts for relevant risk factors affecting American Indian communities, focusing on diabetes and kidney damage. METHODS AND RESULTS: A total of 3059 participants from the SHS (Strong Heart Study) (56±8 years of age, 58% women) were included. Five hundred seven developed HF. Progressively adjusted Cox proportional hazards models were used to identify risk factors for HF and HF subtypes. Predictors of risk at 5 and 10 years included older age (hazard ratio [HR], 1.79 [95% CI, 1.43-2.25]; HR, 1.68 [95% CI, 1.44-1.95]), smoking (HR, 2.26 [95% CI, 1.23-4.13]; HR, 2.08 [95% CI, 1.41-3.06]), macroalbuminuria (HR, 8.38 [95% CI, 4.44-15.83]; HR, 5.20 [95% CI, 3.42-7.9]), microalbuminuria (HR, 2.72 [95% CI, 1.51-4.90]; HR, 1.92 [95% CI, 1.33, 2.78]), and previous myocardial infarction (HR, 6.58 [95% CI, 2.54-17.03]; HR, 3.87 [95% CI, 2.29-6.54]), respectively. These predictors, together with diabetes diagnosis and glycated hemoglobin were significant at 10 and 28 years. High discrimination performance was achieved (C index, 0.81 [95% CI, 0.76-0.84]; C index, 0.78 [95% CI, 0.75-0.81]; and C index, 0.77 [95% CI, 0.74-0.78] at 5, 10, and up to 28 years of follow up, respectively). Some associations varied across HF subtypes, although diabetes, albuminuria, and previous myocardial infarction were associated with all subtypes. CONCLUSIONS: This prospective study of HF risk factors in American Indian communities identifies that smoking, body mass index, and indicators of diabetes control and kidney damage (glycated hemoglobin and albuminuria) are major determinants of HF. Our findings can improve HF risk assessment in populations with a high burden of diabetes.

An epigenome-wide study of selenium status and DNA methylation in the Strong Heart Study.

BACKGROUND: Selenium (Se) is an essential nutrient linked to adverse health endpoints at low and high levels. The mechanisms behind these relationships remain unclear and there is a need to further understand the epigenetic impacts of Se and their relationship to disease. We investigated the association between urinary Se levels and DNA methylation (DNAm) in the Strong Heart Study (SHS), a prospective study of cardiovascular disease (CVD) among American Indians adults. METHODS: Selenium concentrations were measured in urine (collected in 1989-1991) using inductively coupled plasma mass spectrometry among 1,357 participants free of CVD and diabetes. DNAm in whole blood was measured cross-sectionally using the Illumina MethylationEPIC BeadChip (850 K) Array. We used epigenome-wide robust linear regressions and elastic net to identify differentially methylated cytosine-guanine dinucleotide (CpG) sites associated with urinary Se levels. RESULTS: The mean (standard deviation) urinary Se concentration was 51.8 (25.1) µg/g creatinine. Across 788,368 CpG sites, five differentially methylated positions (DMP) (hypermethylated: cg00163554, cg18212762, cg11270656, and hypomethylated: cg25194720, cg00886293) were significantly associated with Se in linear regressions after accounting for multiple comparisons (false discovery rate p-value: 0.10). The top hypermethylated DMP (cg00163554) was annotated to the Disco Interacting Protein 2 Homolog C (DIP2C) gene, which relates to transcription factor binding. Elastic net models selected 425 hypo- and hyper-methylated DMPs associated with urinary Se, including three sites (cg00163554 [DIP2C], cg18212762 [MAP4K2], cg11270656 [GPIHBP1]) identified in linear regressions. CONCLUSIONS: Urinary Se was associated with minimal changes in DNAm in adults from American Indian communities across the Southwest and the Great Plains in the United States, suggesting that other mechanisms may be driving health impacts. Future analyses should explore other mechanistic biomarkers in human populations, determine these relationships prospectively, and investigate the potential role of differentially methylated sites with disease endpoints.

Method validation for (ultra)-trace element concentrations in urine for small sample volumes in large epidemiological studies: application to the population-based epidemiological multi-ethnic study of atherosclerosis (MESA).

Analysis of essential and non-essential trace elements in urine has emerged as a valuable tool for assessing occupational and environmental exposures, diagnosing nutritional status and guiding public health and health care intervention. Our study focused on the analysis of trace elements in urine samples from the Multi-Ethnic Study of Atherosclerosis (MESA), a precious resource for health research with limited sample volumes. Here we provide a comprehensive and sensitive method for the analysis of 18 elements using only 100 µL of urine. Method sensitivity, accuracy, and precision were assessed. The analysis by inductively coupled plasma mass spectrometry (ICP-MS) included the measurement of antimony (Sb), arsenic (As), barium (Ba), cadmium (Cd), cesium (Cs), cobalt (Co), copper (Cu), gadolinium (Gd), lead (Pb), manganese (Mn), molybdenum (Mo), nickel (Ni), selenium (Se), strontium (Sr), thallium (Tl), tungsten (W), uranium (U), and zinc (Zn). Further, we reported urinary trace element concentrations by covariates including gender, ethnicity/race, smoking and location. The results showed good accuracy and sensitivity of the ICP-MS method with the limit of detections rangings between 0.001 µg L-1 for U to 6.2 µg L-1 for Zn. Intra-day precision for MESA urine analysis varied between 1.4% for Mo and 26% for Mn (average 6.4% for all elements). The average inter-day precision for most elements was <8.5% except for Gd (20%), U (16%) and Mn (19%) due to very low urinary concentrations. Urinary mean concentrations of non-essential elements followed the order of Sr > As > Cs > Ni > Ba > Pb > Cd > Gd > Tl > W > U. The order of urinary mean concentrations for essential trace elements was Zn > Se > Mo > Cu > Co > Mn. Non-adjusted mean concentration of non-essential trace elements in urine from MESA participants follow the order Sr > As > Cs > Ni > Ba > Pb > Cd > Gd > Tl > W > U. The unadjusted urinary mean concentrations of essential trace elements decrease from Zn > Se > Mo > Cu > Co > Mn.