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.

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

The statistical analysis of omics data poses a great computational challenge given its 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 two versions of adaptive Enet (AEnet and MSAEnet) to efficiently improve feature selection and effect estimation in omics research. We subsequently used genome-wide human blood DNA methylation data from American Indians of the Strong Heart Study (N=2,235 participants), measured in 1989-1991, to compare the performance (predictive accuracy, coefficient estimation and computational efficiency) of SIS-paired regularization methods to Bayesian shrinkage and traditional linear regression to identify epigenomic multi-marker of body mass index. 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 two 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 bootstrap confidence intervals for the estimated 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.

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.

Arsenic Exposure and Epigenetic Aging: The Association with Cardiovascular Disease and All-Cause Mortality in the Strong Heart Study.

BACKGROUND: Inorganic arsenic (As) may increase the risk of cardiovascular disease (CVD) and all-cause mortality through accelerated aging, which can be estimated using epigenetic-based measures. OBJECTIVES: We evaluated three DNA methylation-based aging measures (PhenoAge, GrimAge, DunedinPACE) (epigenetic aging measures) as potential mediators of the previously reported association of As exposure with CVD incidence, CVD mortality, and all-cause mortality in the Strong Heart Study (SHS), an epidemiological cohort of American Indian adults. METHODS: Blood DNA methylation and urinary As levels were measured in 2,323 SHS participants (41.5% men, mean age of 55 years old). PhenoAge and GrimAge values were calculated using a residual-based method. We tested the association of urinary As with epigenetic aging measures using linear regression, the association of epigenetic aging measures with the three health outcomes using additive hazards models, and the mediation of As-related CVD incidence, CVD mortality, and all-cause mortality by epigenetic aging measures using the product of coefficients method. RESULTS: SHS participants with higher vs. lower urinary As levels had similar PhenoAge age, older GrimAge age, and faster DunedinPACE. An interquartile range increase in urinary As was associated with higher of PhenoAge age acceleration [mean difference (95% confidence interval)=0.48 (0.17, 0.80) years], GrimAge age acceleration [0.80 (0.60, 1.00) years], and DunedinPACE [0.011 (0.005, 0.018)], after adjusting for age, sex, center location, genetic components, smoking status, and body mass index. Of the 347 incident CVD events per 100,000 person-years associated with a doubling in As exposure, 21.3% (9.1, 57.1) and 22.6% (9.5, 56.9), were attributable to differences in GrimAge and DunedinPACE, respectively. DISCUSSION: Arsenic exposure was associated with older GrimAge and faster DunedinPACE measures of biological age. Furthermore, accelerated biological aging measured from DNA methylation accounted for a relevant fraction of As-associated risk for CVD, CVD mortality, and all-cause mortality in the SHS, supporting the role of As in accelerated aging. Research of the biological underpinnings can contribute to a better understanding of the role of aging in arsenic-related disease. https://doi.org/10.1289/EHP11981.

Traffic Density Exposure, Oxidative Stress Biomarkers and Plasma Metabolomics in a Population-Based Sample: The Hortega Study.

Exposure to traffic-related air pollution (TRAP) generates oxidative stress, with downstream effects at the metabolic level. Human studies of traffic density and metabolomic markers, however, are rare. The main objective of this study was to evaluate the cross-sectional association between traffic density in the street of residence with oxidative stress and metabolomic profiles measured in a population-based sample from Spain. We also explored in silico the potential biological implications of the findings. Secondarily, we assessed the contribution of oxidative stress to the association between exposure to traffic density and variation in plasma metabolite levels. Traffic density was defined as the average daily traffic volume over an entire year within a buffer of 50 m around the participants' residence. Plasma metabolomic profiles and urine oxidative stress biomarkers were measured in samples from 1181 Hortega Study participants by nuclear magnetic resonance spectroscopy and high-performance liquid chromatography, respectively. Traffic density was associated with 7 (out of 49) plasma metabolites, including amino acids, fatty acids, products of bacterial and energy metabolism and fluid balance metabolites. Regarding urine oxidative stress biomarkers, traffic associations were positive for GSSG/GSH% and negative for MDA. A total of 12 KEGG pathways were linked to traffic-related metabolites. In a protein network from genes included in over-represented pathways and 63 redox-related candidate genes, we observed relevant proteins from the glutathione cycle. GSSG/GSH% and MDA accounted for 14.6% and 12.2% of changes in isobutyrate and the CH2CH2CO fatty acid moiety, respectively, which is attributable to traffic exposure. At the population level, exposure to traffic density was associated with specific urine oxidative stress and plasma metabolites. Although our results support a role of oxidative stress as a biological intermediary of traffic-related metabolic alterations, with potential implications for the co-bacterial and lipid metabolism, additional mechanistic and prospective studies are needed to confirm our findings.

Urinary Metal Levels and Coronary Artery Calcification: Longitudinal Evidence in the Multi-Ethnic Study of Atherosclerosis (MESA).

Objective: Growing evidence indicates that exposure to metals are risk factors for cardiovascular disease (CVD). We hypothesized that higher urinary levels of metals with prior evidence of an association with CVD, including non-essential (cadmium , tungsten, and uranium) and essential (cobalt, copper, and zinc) metals are associated with baseline and rate of change of coronary artery calcium (CAC) progression, a subclinical marker of atherosclerotic CVD. Methods: We analyzed data from 6,418 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) with spot urinary metal levels at baseline (2000-2002) and 1-4 repeated measures of spatially weighted coronary calcium score (SWCS) over a ten-year period. SWCS is a unitless measure of CAC highly correlated to the Agatston score but with numerical values assigned to individuals with Agatston score=0. We used linear mixed effect models to assess the association of baseline urinary metal levels with baseline SWCS, annual change in SWCS, and SWCS over ten years of follow-up. Urinary metals (adjusted to µg/g creatinine) and SWCS were log transformed. Models were progressively adjusted for baseline sociodemographic factors, estimated glomerular filtration rate, lifestyle factors, and clinical factors. Results: At baseline, the median and interquartile range (25th, 75th) of SWCS was 6.3 (0.7, 58.2). For urinary cadmium, the fully adjusted geometric mean ratio (GMR) (95%Cl) of SWCS comparing the highest to the lowest quartile was 1.51 (1.32, 1.74) at baseline and 1.75 (1.47, 2.07) at ten years of follow-up. For urinary tungsten, uranium, and cobalt the corresponding GMRs at ten years of follow-up were 1.45 (1.23, 1.71), 1.39 (1.17, 1.64), and 1.47 (1.25, 1.74), respectively. For copper and zinc, the association was attenuated with adjustment for clinical risk factors; GMRs at ten years of follow-up before and after adjustment for clinical risk factors were 1.55 (1.30, 1.84) and 1.33 (1.12, 1.58), respectively, for copper and 1.85 (1.56, 2.19) and 1.57 (1.33, 1.85) for zinc. Conclusion: Higher levels of cadmium, tungsten, uranium, cobalt, copper, and zinc, as measured in urine, were associated with subclinical CVD at baseline and at follow-up. These findings support the hypothesis that metals are pro-atherogenic factors.

Smoking, blood DNA methylation sites and lung cancer risk.

Altered DNA methylation (DNAm) might be a biological intermediary in the pathway from smoking to lung cancer. In this study, we investigated the contribution of differential blood DNAm to explain the association between smoking and lung cancer incidence. Blood DNAm was measured in 2321 Strong Heart Study (SHS) participants. Incident lung cancer was assessed as time to event diagnoses. We conducted mediation analysis, including validation with DNAm and paired gene expression data from the Framingham Heart Study (FHS). In the SHS, current versus never smoking and pack-years single-mediator models showed, respectively, 29 and 21 differentially methylated positions (DMPs) for lung cancer with statistically significant mediated effects (14 of 20 available, and five of 14 available, positions, replicated, respectively, in FHS). In FHS, replicated DMPs showed gene expression downregulation largely in trans, and were related to biological pathways in cancer. The multimediator model identified that DMPs annotated to the genes AHRR and IER3 jointly explained a substantial proportion of lung cancer. Thus, the association of smoking with lung cancer was partly explained by differences in baseline blood DNAm at few relevant sites. Experimental studies are needed to confirm the biological role of identified eQTMs and to evaluate potential implications for early detection and control of lung cancer.

Metal mixtures and DNA methylation measures of biological aging in American Indian populations.

INTRODUCTION: Native American communities suffer disproportionately from elevated metal exposures and increased risk for cardiovascular diseases and diabetes. DNA methylation is a sensitive biomarker of aging-related processes and novel epigenetic-based "clocks" can be used to estimate accelerated biological aging that may underlie increased risk. Metals alter DNA methylation, yet little is known about their individual and combined impact on epigenetic age acceleration. Our objective was to investigate the associations of metals on several DNA methylation-based aging measures in the Strong Heart Study (SHS) cohort. METHODS: Blood DNA methylation data from 2,301 SHS participants was used to calculate age acceleration of epigenetic clocks (PhenoAge, GrimAge, DunedinPACE, Hannum, Horvath). Urinary metals [arsenic (As), cadmium (Cd), tungsten (W), zinc (Zn), selenium (Se), molybdenum (Mo)] were creatinine-adjusted and categorized into quartiles. We examined associations of individual metals through linear regression models and used Bayesian Kernel Machine Regression (BKMR) for the impact of the total metal mixture on epigenetic age acceleration. RESULTS: The mixture of nonessential metals (W, As, Cd) was associated with greater GrimAge acceleration and DunedinPACE, while the essential metal mixture (Se, Zn, Mo) was associated with lower epigenetic age acceleration. Cd was associated with increased epigenetic age acceleration across all clocks and BKMR analysis suggested nonlinear associations between Se and DunedinPACE, GrimAge, and PhenoAge acceleration. No interactions between individual metals were observed. The associations between Cd, Zn, and epigenetic age acceleration were greater in never smokers in comparison to current/former smokers. CONCLUSION: Nonessential metals were positively associated with greater epigenetic age acceleration, with strongest associations observed between Cd and DunedinPACE and GrimAge acceleration. In contrast, essential metals were associated with lower epigenetic aging. Examining the influence of metal mixtures on epigenetic age acceleration can provide insight into metals and aging-related diseases.

Maternal DNA methylation signatures of arsenic exposure is associated with adult offspring insulin resistance in the Strong Heart Study.

Exposure to low to moderate arsenic (As) levels has been associated with type 2 diabetes (T2D) and other chronic diseases in American Indian communities. Prenatal exposure to As may also increase the risk for T2D in adulthood, and maternal As has been associated with adult offspring metabolic health measurements. We hypothesized that T2D-related outcomes in adult offspring born to women exposed to low to moderate As can be evaluated utilizing a maternally-derived molecular biosignature of As exposure. Herein, we evaluated the association of maternal DNA methylation with incident T2D and insulin resistance (Homeostatic model assessment of insulin resistance [HOMA2-IR]) in adult offspring. For DNA methylation, we used 20 differentially methylated cytosine-guanine dinucleotides (CpG) previously associated with the sum of inorganic and methylated As species (ΣAs) in urine in the Strong Heart Study (SHS). Of these 20 CpGs, we found six CpGs nominally associated (p < 0.05) with HOMA2-IR in a fully adjusted model that included clinically relevant covariates and offspring adiposity measurements; a similar model that adjusted instead for maternal adiposity measurements found three CpGs nominally associated with HOMA2-IR, two of which overlapped the offspring adiposity model. After adjusting for multiple comparisons, cg03036214 remained associated with HOMA2-IR (q < 0.10) in the offspring adiposity model. The odds ratio of incident T2D increased with an increase in maternal DNA methylation at one HOMA2-IR associated CpG in the model adjusting for offspring adiposity, cg12116137, whereas adjusting for maternal adiposity had a minimal effect on the association. Our data suggests offspring adiposity, rather than maternal adiposity, potentially influences the effects of maternal DNAm signatures on offspring metabolic health parameters. Here, we have presented evidence supporting a role for epigenetic biosignatures of maternal As exposure as a potential biomarker for evaluating risk of T2D-related outcomes in offspring later in life.

DNA methylation analysis is used to identify novel genetic loci associated with circulating fibrinogen levels in blood.

BACKGROUND: Fibrinogen plays an essential role in blood coagulation and inflammation. Circulating fibrinogen levels may be determined based on interindividual differences in DNA methylation at cytosine-phosphate-guanine (CpG) sites and vice versa. OBJECTIVES: To perform an EWAS to examine an association between blood DNA methylation levels and circulating fibrinogen levels to better understand its biological and pathophysiological actions. METHODS: We performed an epigenome-wide association study of circulating fibrinogen levels in 18 037 White, Black, American Indian, and Hispanic participants, representing 14 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium. Circulating leukocyte DNA methylation was measured using the Illumina 450K array in 12 904 participants and using the EPIC array in 5133 participants. In each study, an epigenome-wide association study of fibrinogen was performed using linear mixed models adjusted for potential confounders. Study-specific results were combined using array-specific meta-analysis, followed by cross-replication of epigenome-wide significant associations. We compared models with and without CRP adjustment to examine the role of inflammation. RESULTS: We identified 208 and 87 significant CpG sites associated with fibrinogen levels from the 450K (p < 1.03 × 10-7) and EPIC arrays (p < 5.78 × 10-8), respectively. There were 78 associations from the 450K array that replicated in the EPIC array and 26 vice versa. After accounting for overlapping sites, there were 83 replicated CpG sites located in 61 loci, of which only 4 have been previously reported for fibrinogen. The examples of genes located near these CpG sites were SOCS3 and AIM2, which are involved in inflammatory pathways. The associations of all 83 replicated CpG sites were attenuated after CRP adjustment, although many remained significant. CONCLUSION: We identified 83 CpG sites associated with circulating fibrinogen levels. These associations are partially driven by inflammatory pathways shared by both fibrinogen and CRP.

Association of single and joint metals with albuminuria and estimated glomerular filtration longitudinal change in middle-aged adults from Spain: The Aragon workers health study.

The nephrotoxicity of low-chronic metal exposures is unclear, especially considering several metals simultaneously. We assessed the individual and joint association of metals with longitudinal change in renal endpoints in Aragon Workers Health Study participants with available measures of essential (cobalt [Co], copper [Cu], molybdenum [Mo] and zinc [Zn]) and non-essential (As, barium [Ba], Cd, chromium [Cr], antimony [Sb], titanium [Ti], uranium [U], vanadium [V] and tungsten [W]) urine metals and albumin-to-creatinine ratio (ACR) (N = 707) and estimated glomerular filtration rate (eGFR) (N = 1493) change. Median levels were 0.24, 7.0, 18.6, 295, 3.1, 1.9, 0.28, 1.16, 9.7, 0.66, 0.22 µg/g for Co, Cu, Mo, Zn, As, Ba, Cd, Cr, Sb, Ti, V and W, respectively, and 52.5 and 27.2 ng/g for Sb and U, respectively. In single metal analysis, higher As, Cr and W concentrations were associated with increasing ACR annual change. Higher Zn, As and Cr concentrations were associated with decreasing eGFR annual change. The shape of the longitudinal dose-responses, however, was compatible with a nephrotoxic role for all metals, both in ACR and eGFR models. In joint metal analysis, both higher mixtures of Cu-Zn-As-Ba-Ti-U-V-W and Co-Cd-Cr-Sb-V-W showed associations with increasing ACR and decreasing eGFR annual change. As and Cr were main drivers of the ACR change joint metal association. For the eGFR change joint metal association, while Zn and Cr were main drivers, other metals also contributed substantially. We identified potential interactions for As, Zn and W by other metals with ACR change, but not with eGFR change. Our findings support that Zn, As, Cr and W and suggestively other metals, are nephrotoxic at relatively low exposure levels. Metal exposure reduction and mitigation interventions may improve prevention and decrease the burden of renal disease in the population.

Epigenetics of type 2 diabetes and diabetes-related outcomes in the Strong Heart Study.

BACKGROUND: The prevalence of type 2 diabetes has dramatically increased in the past years. Increasing evidence supports that blood DNA methylation, the best studied epigenetic mark, is related to diabetes risk. Few prospective studies, however, are available. We studied the association of blood DNA methylation with diabetes in the Strong Heart Study. We used limma, Iterative Sure Independence Screening and Cox regression to study the association of blood DNA methylation with fasting glucose, HOMA-IR and incident type 2 diabetes among 1312 American Indians from the Strong Heart Study. DNA methylation was measured using Illumina's MethylationEPIC beadchip. We also assessed the biological relevance of our findings using bioinformatics analyses. RESULTS: Among the 358 differentially methylated positions (DMPs) that were cross-sectionally associated either with fasting glucose or HOMA-IR, 49 were prospectively associated with incident type 2 diabetes, although no DMPs remained significant after multiple comparisons correction. Multiple of the top DMPs were annotated to genes with relevant functions for diabetes including SREBF1, associated with obesity, type 2 diabetes and insulin sensitivity; ABCG1, involved in cholesterol and phospholipids transport; and HDAC1, of the HDAC family. (HDAC inhibitors have been proposed as an emerging treatment for diabetes and its complications.) CONCLUSIONS: Our results suggest that differences in peripheral blood DNA methylation are related to cross-sectional markers of glucose metabolism and insulin activity. While some of these DMPs were modestly associated with prospective incident type 2 diabetes, they did not survive multiple testing. Common DMPs with diabetes epigenome-wide association studies from other populations suggest a partially common epigenomic signature of glucose and insulin activity.

Epigenetic Biomarkers of Lead Exposure and Cardiovascular Disease: Prospective Evidence in the Strong Heart Study.

Background Lead is a cardiotoxic metal with a variety of adverse health effects. In the absence of data on bone lead exposure, epigenetic biomarkers can serve as indicators of cumulative lead exposure and body burden. Herein, we leveraged novel epigenetic biomarkers of lead exposure to investigate their association with cardiovascular disease (CVD) incidence and mortality. Methods and Results Blood DNA methylation was measured using the Illumina MethylationEPIC BeadChip among 2231 participants of the Strong Heart Study (SHS) at baseline (1989-1991). Epigenetic biomarkers of lead levels in blood, patella, and tibia were estimated using previously identified cytosine-guanine dinucleotide (CpG) sites. CVD incidence and mortality data were available through 2017. Median concentrations of lead epigenetic biomarkers were 13.8 µg/g, 21.3 µg/g, and 2.9 µg/dL in tibia, patella, and blood, respectively. In adjusted models, the hazard ratio (HR) (95% CI) of CVD mortality per doubling increase in lead epigenetic biomarkers were 1.42 (1.07-1.87) for tibia lead, 1.22 (0.93-1.60) for patella lead, and 1.57 (1.16-2.11) for blood lead. The corresponding HRs for incident CVD were 0.99 (0.83-1.19), 1.07 (0.89-1.29), and 1.06 (0.87-1.30). The association between the tibia lead epigenetic biomarker and CVD mortality was modified by sex (interaction P value: 0.014), with men at increased risk (HR, 1.42 [95% CI, 1.17-1.72]) compared with women (HR, 1.04 [95% CI, 0.89-1.22]). Conclusions Tibia and blood epigenetic biomarkers were associated with increased risk of CVD mortality, potentially reflecting the cardiovascular impact of cumulative and recent lead exposures. These findings support that epigenetic biomarkers of lead exposure may capture some of the disease risk associated with lead exposure.

Urinary Zinc and Incident Type 2 Diabetes: Prospective Evidence From the Strong Heart Study.

OBJECTIVE: Hyperglycemia can increase urinary zinc excretion. We evaluated the association of higher urinary zinc level with new diagnosis of incident type 2 diabetes mellitus (T2DM) in adult populations with a high burden of T2DM from AZ, OK, and ND and SD. We also assessed the cross-sectional association of urinary zinc levels with prevalent prediabetes. RESEARCH DESIGN AND METHODS: We included 1,339 adults free of T2DM at baseline (1989-1991) followed through 1998-1999 in the Strong Heart Study (SHS) and 1,905 family members of SHS participants followed as part of the Strong Heart Family Study (SHFS) through 2006-2009. RESULTS: T2DM incidence was 14.7% (mean follow-up 6.6 years) in the SHS and 13.5% (mean follow-up 5.6 years) in the SHFS. After adjustment for sex, site, education, smoking status, BMI, and estimated glomerular filtration rate, the hazard ratio of T2DM in comparing 75th vs. 25th percentiles of urinary zinc distribution was 1.21 (95% CI 1.08, 1.36) in the SHS and 1.12 (0.96, 1.31) in the SHFS. These associations were attenuated but significant in the SHS after adjustment for HOMA of insulin resistance (HOMA-IR) score. With exclusion of participants with prediabetes at baseline, urinary zinc remained significantly associated with T2DM in the SHS. In cross-sectional analyses, prediabetes was associated with higher urinary zinc levels. CONCLUSIONS: Urinary zinc levels were associated with T2DM incidence and prediabetes prevalence even after adjustment for HOMA-IR in populations with a high burden of T2DM. These results highlight the importance of zinc metabolism in diabetes development.

Thromboprophylaxis with standard-dose vs. flexible-dose heparin for hospitalized COVID-19 patients: a target trial emulation.

OBJECTIVES: To compare mortality of hospitalized COVID-19 patients under two low-molecular weight heparin (LMWH) thromboprophylaxis strategies: standard dose and variable dose (standard dose increased to intermediate dose in the presence of laboratory abnormalities indicating an increased thrombosis risk). STUDY DESIGN AND SETTING: Target trial emulation using observational data from 2,613 adults admitted with a COVID-19 diagnosis in Madrid, Spain between March 16 and April 15, 2020. RESULTS: A total of 1,284 patients were eligible. Among 503 patients without increased baseline thrombotic risk, 28-day mortality risk (95% confidence interval [CI]) was 9.0% (6.6, 11.7) under the standard dose strategy and 5.6% (3.3, 8.3) under the variable dose strategy; risk difference 3.4% (95% CI: -0.24, 6.9); mortality hazard ratio 1.61 (95% CI: 0.97, 2.89). Among 781 patients with increased baseline thrombotic risk, the 28-day mortality risk was 25.8% (22.7, 29.0) under the standard dose strategy and 18.1% (9.3, 28.9) under the intermediate dose strategy; risk difference 7.7% (95% CI: -3.5, 17.2); mortality hazard ratio 1.45 (95% CI: 0.81, 3.17). Major bleeding and LMWH-induced coagulopathy were rare under all strategies. CONCLUSION: Escalating anticoagulation intensity after signs of thrombosis risk may increase the survival of hospitalized COVID-19 patients. However, effect estimates were imprecise and additional studies are warranted.

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.

An epigenome-wide study of DNA methylation profiles and lung function among American Indians in the Strong Heart Study.

BACKGROUND: Epigenetic modifications, including DNA methylation (DNAm), are often related to environmental exposures, and are increasingly recognized as key processes in the pathogenesis of chronic lung disease. American Indian communities have a high burden of lung disease compared to the national average. The objective of this study was to investigate the association of DNAm and lung function in the Strong Heart Study (SHS). We conducted a cross-sectional study of American Indian adults, 45-74 years of age who participated in the SHS. DNAm was measured using the Illumina Infinium Human MethylationEPIC platform at baseline (1989-1991). Lung function was measured via spirometry, including forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), at visit 2 (1993-1995). Airflow limitation was defined as FEV1 < 70% predicted and FEV1/FVC < 0.7, restriction was defined as FEV1/FVC > 0.7 and FVC < 80% predicted, and normal spirometry was defined as FEV1/FVC > 0.7, FEV1 > 70% predicted, FVC > 80% predicted. We used elastic-net models to select relevant CpGs for lung function and spirometry-defined lung disease. We also conducted bioinformatic analyses to evaluate the biological plausibility of the findings. RESULTS: Among 1677 participants, 21.2% had spirometry-defined airflow limitation and 13.6% had spirometry-defined restrictive pattern lung function. Elastic-net models selected 1118 Differentially Methylated Positions (DMPs) as predictors of airflow limitation and 1385 for restrictive pattern lung function. A total of 12 DMPs overlapped between airflow limitation and restrictive pattern. EGFR, MAPK1 and PRPF8 genes were the most connected nodes in the protein-protein interaction network. Many of the DMPs targeted genes with biological roles related to lung function such as protein kinases. CONCLUSION: We found multiple differentially methylated CpG sites associated with chronic lung disease. These signals could contribute to better understand molecular mechanisms involved in lung disease, as assessed systemically, as well as to identify patterns that could be useful for diagnostic purposes. Further experimental and longitudinal studies are needed to assess whether DNA methylation has a causal role in lung disease.

Integrative analysis of clinical and epigenetic biomarkers of mortality.

DNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome-wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow-up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry-stratified meta-analysis of all-cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10-7 , of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm-based prediction models for all-cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C-index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, PMR  = 4.1 × 10-4 ) and negatively associated with longevity (Beta = -1.9, PMR  = 0.02). Pathway analysis revealed that genes associated with mortality-related CpGs are enriched for immune- and cancer-related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk.