The role of blood DNA methylation in environment-related chronic disease: a biostatistical toolkit

La epigenética se refiere al estudio de las marcas químicas que alteran la expresión génica sin cambiar la secuencia genética. Los factores ambientales y conductuales son conocidos modificadores de la epigenética, resultando así en cambios heredables que pueden dar lugar a alteraciones en procesos biológicos esenciales y, por consiguiente, al desarrollo de enfermedades. La metilación del ADN es la marca epigenética más estudiada. Existe amplia evidencia científica de la asociación entre factores ambientales tales como tabaco y metales, y desregulaciones en la metilación del ADN. Asimismo, existe amplia evidencia de la asociación entre desregulaciones en metilación del ADN y enfermedades crónicas, en especial para el cáncer. Sin embargo, aún está por descifrar si estas asociaciones son causales o suceden debido a que la metilación del ADN es un biomarcador de otros procesos biológicos alterados, siendo estos procesos los que influyen en las enfermedades de forma causal. Para evaluar el papel de la metilación del ADN en la asociación entre los factores ambientales y las enfermedades crónicas, se requieren métodos estadísticos apropiados para el análisis de datos de muy altas dimensiones y altamente correlacionados. En primer lugar, debemos ser capaces de seleccionar qué posiciones genómicas de metilación están asociadas con nuestra variable respuesta de interés. Los métodos existentes para selección de variables y estimación de efectos pierden capacidad predictiva y presentan sesgos en contextos de dimensiones muy altas. Además, no cuantifican la incertidumbre estadística. Una vez seleccionado el conjunto relevante de posiciones de metilación asociadas con nuestra variable respuesta, el análisis de mediación es una herramienta útil para cuantificar el potencial efecto intermedio de estas posiciones de metilación en la asociación entre factores ambientales y enfermedades crónicas. El contexto más probable es que varias marcas de metilación (y no una única marca) sean intermediarias entre estos dos procesos, estando además posiblemente correlacionadas. Por otro lado, es habitual que las variables respuesta analizadas en contextos epidemiológicos sean de supervivencia, con el fin de incorporar al modelo el tiempo hasta el evento de salud. Sin embargo, hasta la fecha, no se han desarrollado algoritmos de mediación que incorporen múltiples mediadores correlacionados en el contexto de análisis de supervivencia. Así pues, esta tesis consta de dos objetivos principales, el primero relacionado con la selección de variables en muy altas dimensiones, y el segundo relacionado con el análisis de mediación múltiple para datos de supervivencia. Resumen del objetivo 1. El primer objetivo de esta tesis consiste en extender la herramienta estadística Iterative Sure Independence Screening (ISIS), que realiza selección de variables en contextos de muy altas dimensiones, para mejorar su capacidad predictiva, su estimación de efectos y para incorporar la incertidumbre estadística. Para ello, hemos combinado el algoritmo ISIS con los métodos de regularización llamados elastic-net y adaptive elastic-net (Aenet), y además hemos incluido un algoritmo para el cálculo de intervalos de confianza basados en booststrap. Esta extensión ha sido incluida en el paquete SIS de R, que está disponible en el repositorio público CRAN. En la línea de este objetivo, esta tesis incluye dos aplicaciones prácticas del algoritmo ISIS. Para ello, hemos usado datos del Strong Heart Study (SHS), la cohorte prospectiva de indios americanos con más participantes y de mayor duración. La primera aplicación es metodológica y evalúa las mejoras introducidas por nuestra extensión del paquete. El algoritmo ISIS pareado con Aenet presenta una mejora en capacidad predictiva con respecto a la versión original de ISIS para variables respuesta continuas y binarias, no así para variables respuesta de supervivencia. Además, al parear ISIS con Aenet, se obtiene una estimación de efectos más consistente debido al cumplimiento de la propiedad de oracle. Nuestro análisis bioinformático revela que también da lugar a una selección más robusta de variables desde el punto de vista biológico. La segunda aplicación es un estudio epidemiológico que evalúa el potencial rol intermedio de los cambios en metilación del ADN en la ampliamente documentada asociación entre arsénico y enfermedad cardiovascular. Empleamos el algoritmo ISIS pareado con Aenet para seleccionar las posiciones de metilación asociadas con la enfermedad cardiovascular, y posteriormente realizamos un análisis de mediación simple en esas posiciones. Encontramos efectos mediados estadísticamente significativos en 21 y 15 posiciones diferencialmente metiladas (DMPs) para incidencia cardiovascular y mortalidad cardiovascular, respectivamente. Además, de las 21 DMPs con efectos mediados significativos para enfermedad cardiovascular, seis fueron replicadas en tres cohortes americanas independientes (Framingham Heart Study, Women’s Health Initiative y Multi-Ethnic Study of Atherosclerosis) con la misma dirección de asociación. Los genes asociados a las posiciones de metilación significativas en nuestro análisis de mediación también fueron replicados en un estudio animal con ratones. Las funciones biológicas de estos genes, ampliamente relacionadas con la enfermedad cardiovascular, proporcionan evidencia de la robustez de los resultados. Resumen del objetivo 2. El segundo objetivo de la tesis se centra en la extensión del algoritmo multimediate, que realiza análisis de mediación múltiple para mediadores correlacionados, a datos de supervivencia. El doctor Allan Jerolon desarrolló este algoritmo para variables respuesta continuas y binarias. Utilizando los modelos aditivos de Lin-Ying, hemos extendido los resultados teóricos para la identificación de efectos mediados, así como el propio algoritmo, al contexto de supervivencia. Asimismo, hemos adaptado el algoritmo multimediate para la incorporación de potenciales interacciones entre la exposición y el mediador. Este algoritmo está disponible en el siguiente repositorio de Github: https://github.com/AllanJe/multimediate. En este segundo objetivo, también se incluyen dos aplicaciones a datos de este algoritmo. La primera es un estudio de simulación en el que se muestra la superioridad del algoritmo multimediate con respecto a la mediación simple, incluso en el caso de mediadores no correlacionados. La segunda aplicación es un estudio epidemiológico en el que estudiamos el potencial papel intermedio de la metilación del ADN en la asociación entre el tabaco y los cánceres relacionados con el tabaco usando datos del SHS. Utilizamos el algoritmo ISIS pareado con elastic-net para seleccionar posiciones de metilación asociadas con cáncer, y posteriormente aplicamos el algoritmo multimediate para evaluar varias posiciones de metilación como potenciales mediadores conjuntos en la asociaci´on entre el tabaco y el cáncer. El algoritmo multimediate detectó un efecto mediado conjunto del 81.3 % atribuible a tres posiciones de metilacion para el cáncer de pulmón, y del 64.4 % atribuible a cuatro posiciones de metilación para una variable respuesta combinada de todos los cánceres asociados con el tabaco de los que disponíamos datos (pulmón, esófago-estómago, colorrectal, hígado, páncreas y riñón). Asimismo, los resultados del análisis de mediación fueron ampliamente replicados en una población independiente (Framingham Heart Study), en la que también llevamos a cabo validación funcional con datos de expresión génica. En general, encontramos una asociación inversa entre metilación del ADN y expresión génica en las posiciones de metilación identificadas en nuestro análisis de mediación. Además de estos dos objetivos principales, esta tesis presenta un breve apartado relacionado con la expresión génica, el proceso directamente influenciado por la metilación del ADN. Incluso obteniendo efectos mediados significativos de la metilación del ADN en la asociación entre exposiciones ambientales y enfermedades crónicas, desconocemos si este efecto es causal o no, debido, entre otras razones, a que podrían existir confusores no medidos. Así pues, estudiar los procesos que son influenciados por la metilación del ADN podría contribuir a evaluar su papel en las enfermedades crónicas. La expresión génica medida en forma de secuenciación de células individuales (scRNAseq) se sitúa a la vanguardia de la investigación de los datos ómicos, debido a su capacidad para capturar y evaluar la heterogeneidad celular. Sin embargo, estos datos presentan retos estadisticos para su análisis debido a las grandes proporciones de ceros que se obtienen en las mediciones de la expresión génica para cada gen y célula. Además de evaluar diferencias en medias de expresión entre grupos, las diferencias en variabilidad de expresión han demostrado ser biológicamente relevantes. Varios métodos han sido desarrollados para la identificación de variabilidad diferencial en datos ómicos, aunque no para datos de scRNAseq. En esta tesis hemos evaluado, usando datos simulados, cómo influye la presencia de ceros en los métodos estadísticos utilizados para la identificación de genes diferencialmente variables en datos de scRNAseq. Hemos concluido que la presencia de altas proporciones de ceros da lugar a varianzas y p-valores inflados, así como a subidas en las tasas de descubrimientos falsos. El algoritmo distinct, que utiliza tests de permutaciones para identificar diferencias en distribuciones entre grupos, es el que mejores resultados presenta en cuanto a equilibrio entre tasa de verdaderos descubrimientos y de falsos descubrimientos. En resumen, esta tesis ha contribuido al área científica de los datos ómicos, tanto mediante el desarrollo métodos estadísticos innovadores para el análisis de datos de metilación del ADN, como realizando contribuciones a la evidencia epidemiológica relacionada con metilación del ADN en asociación con exposiciones ambientales y enfermedades crónicas.Epigenetic changes refer to modifications that alter gene expression without changing the genomic sequence. Environmental and behavioral factors are well-known epigenetic modifiers, leading to heritable changes that might disrupt essential biological processes and, in turn, influence the development of disease. DNA methylation is the most widely studied epigenetic mark. Scientific evidence supports the association between environmental factors, such as smoking and metals, and DNA methylation dysregulations. In addition, the evidence supports the association between DNA methylation dysregulations and chronic disease, especially for cancer. However, it is unknown whether these associations are causal or happen due to DNA methylation being a biomarker of other disrupted biological processes. In order to evaluate the potential role of genome-wide DNA methylation on the association between environmental factors and chronic disease, appropriate statistical methods for the analysis of ultra-high dimensional and highly correlated data are needed. To begin with, we need to select which methylation sites in the genome are associated with our outcome of interest. Existing methods for variable selection and effect estimation lose predictive ability and are subject to bias in ultra-high dimensional settings. Additionally, they are not able to quantify statistical uncertainty. Once we get to select the set of epigenomic features associated with our outcome, mediation analysis is a valuable tool to quantify the potential intermediate effect of these methylation sites on the association between environmental factors and chronic disease. The most biologically plausible scenario is that several correlated DNA methylation marks (as opposed to a single one) are mediators between an exposure and an outcome. On the other hand, it is common to consider time-to-event outcomes in epidemiological settings, in order to incorporate the time in which the outcome happened into the statistical model. However, to date, no mediation analysis algorithms able to deal with multiple correlated mediators with survival outcomes have been developed. Thus, this thesis has two main objectives, the first one related to variable selection in ultra-high dimensional settings, and the second one focused on multiple mediation analysis with survival outcomes. Abstract of objective 1. The first objective of this thesis arises from the need to extend the Iterative Sure Independence Screening (ISIS) statistical tool, which conducts variable selection for ultra-high dimensional data, in order to improve its predictive accuracy, effect estimation and to incorporate statistical uncertainty. The objective was to pair the ISIS algorithm with two shrinkage methods: elastic-net and adaptive elastic-net (Aenet), and to include an algorithm for calculation of bootstrap-based confidence intervals. This extension of ISIS has been added to the SIS R package, which is available in the CRAN repository. As part of this first objective, this dissertation shows two applications of the ISIS algorithm. For this purpose, we used data from the Strong Heart Study (SHS), the largest and longest prospective cohort of American Indians. The first application aimed to evaluate the improvements introduced by our extension of ISIS (Aenet, elastic-net, MSAenet) as compared to other shrinkage methods implemented in the original version. The ISIS algorithm paired with Aenet provides increased predictive ability as compared to the original ISIS version, especially for continuous and binary outcomes. Additionally, by pairing ISIS with Aenet, a more consistent effect estimation is obtained because Aenet fulfills the oracle property. Our bioinformatics analysis reveals that it also leads to a more robust variable selection in terms of subsequent biological pathway enrichment. The second application is an epidemiologic study in which we evaluate the potential intermediate role of single DNA methylation sites on the well-documented association between arsenic and cardiovascular disease (CVD). We used the ISIS algorithm paired with Aenet to select methylation sites associated with CVD, and we subsequently conducted a simple mediation analysis (one marker at a time) in the selected sites. We found statistically significant mediated effects for 21 and 15 differentially methylated positions (DMPs) for CVD incidence and mortality, respectively. In addition, six of the 21 DMPs showing statistically significant mediated effects for CVD incidence were replicated in three independent American cohorts (the Framingham Heart Study, Women's Health Initiative y Multi-Ethnic Study of Atherosclerosis) with the same direction in the association. The genes annotated to methylation sites with statistically significant mediated effects were also replicated in a mouse model. The biological plausibility of those genes in CVD provides additional robustness of the results. Abstract of objective 2. The second objective of this thesis focuses on the extension of the multimediate algorithm, which conducts mediation analysis in the context of multiple correlated mediators, to survival outcomes. Jerolon and colleagues developed this algorithm for continuous and binary outcomes. Using the Lin-Ying additive models, we extended the multimediate algorithm as well as the theoretical results for identification of mediated effects to time-to-event data. In addition, we adapted the multimediate algorithm to incorporate potential exposure-mediator interactions. The extension of the algorithm to survival outcomes is available in the following Github repository: https://github.com/AllanJe/multimediate. The extension including exposure-mediator interactions will soon be posted in the same repository. As part of this second objective, we also included two data applications of this algorithm. The first application is a simulation study in which we prove the better performance of the multimediate algorithm as compared to simple mediation analysis, even in settings of uncorrelated mediators. The second data application is an epidemiologic study in which we investigate the potential intermediate role of multiple, potentially correlated, DNA methylation marks on the association between smoking and smoking-related cancers using data from the SHS. We first used the ISIS algorithm paired with elastic-net to select DNA methylation sites associated with cancer. Subsequently, we applied the multimediate algorithm to evaluate several methylation sites as potential mediators on the association between smoking and cancer. The algorithm identified a joint mediated effect of 81.3 % attributable to three DMPs for lung cancer, and of 64.4 % attributable to four DMPs for a combined endpoint including all smoking-related cancers available (lung, esophagus-stomach, colorectal, liver, pancreatic and kidney). The results of the mediation analysis were largely replicated in an independent population (the Framingham Heart Study), in which we also conducted functional validation using gene expression data. In general, we found inverse association between DNA methylation and gene expression for the methylation sites identified in our mediation analysis. In addition to these two main objectives, this thesis presents a short section focused on gene expression, the biological process directly influenced by DNA methylation, which points to future research lines. Even if mediated effects of DNA methylation on the association between environmental factors and chronic disease are identified, this does not necessarily imply causality, as unmeasured confounders and other sources of bias might exist. Thus, investigating the biological processes influenced by DNA methylation might help as functional support of its role in chronic disease. In particular, gene expression measured in single cells (scRNAseq) is at the forefront of omics data research, as it enables the characterization of cell heterogeneity. However, these data present statistical challenges due to high proportions of zeros obtained in gene expression measurements for each individual gene and cell. In addition to evaluating differences in means of gene expression across groups, differences in variability have shown to be biologically relevant. Several methods have been developed for the evaluation of differential variability in omics data. However, these methods are not specific for scRNAseq data. In this thesis, we have used simulations to evaluate the impact of high proportions of zero counts in statistical methods for the identification of differentially variable genes in scRNAseq data. We found that high proportions of zeros lead to inflated variances and p-values, as well as higher false discovery rates. The distinct algorithm, which uses permutation tests to identify differences in distributions across groups, shows the best performance in terms of compromise between false discovery and true positive rates. In summary, this thesis has contributed to the field of omics data research, both by providing novel statistical methods for DNA methylation data analysis, which can also be used for other omics, and by contributing to the body of epidemiological evidence that supports a role of environmental epigenetics in chronic disease

Papel de la epigenética en la epidemiología ambiental de las enfermedades crónicas

Incluye: PDF de la presentación y video del seminario.Estudio sobre el papel de la epigenética en la epidemiología ambiental de las enfermedades crónicas. Se divide en dos proyectos: "Selección de variables en el contexto de datos ómicos" (aplicación: Arsénico, metilación del ADN y enfermedad cardiovascular) y "Modelo multimediador para mediadores correlacionados en el contexto de análisis de supervivencia" (aplicación: Tabaco, metilación del ADN y cáncer de pulmón).N

Comparison of methods for analyzing environmental mixtures effects on survival outcomes and application to a population-based cohort study

The estimation of the effect of environmental exposures and overall mixtures on a survival time outcome is common in environmental epidemiological studies. While advanced statistical methods are increasingly being used for mixture analyses, their applicability and performance for survival outcomes has yet to be explored. We identified readily available methods for analyzing an environmental mixture's effect on a survival outcome and assessed their performance via simulations replicating various real-life scenarios. Using prespecified criteria, we selected Bayesian Additive Regression Trees (BART), Cox Elastic Net, Cox Proportional Hazards (PH) with and without penalized splines, Gaussian Process Regression (GPR) and Multivariate Adaptive Regression Splines (MARS) to compare the bias and efficiency produced when estimating individual exposure, overall mixture, and interaction effects on a survival outcome. We illustrate the selected methods in a real-world data application. We estimated the effects of arsenic, cadmium, molybdenum, selenium, tungsten, and zinc on incidence of cardiovascular disease in American Indians using data from the Strong Heart Study (SHS). In the simulation study, there was a consistent bias-variance trade off. The more flexible models (BART, GPR and MARS) were found to be most advantageous in the presence of nonproportional hazards, where the Cox models often did not capture the true effects due to their higher bias and lower variance. In the SHS, estimates of the effect of selenium and the overall mixture indicated negative effects, but the magnitudes of the estimated effects varied across methods. In practice, we recommend evaluating if findings are consistent across methods

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.S

Cadmium, Smoking, and Human Blood DNA Methylation Profiles in Adults from the Strong Heart Study.

The epigenetic effects of individual environmental toxicants in tobacco remain largely unexplored. Cadmium (Cd) has been associated with smoking-related health effects, and its concentration in tobacco smoke is higher in comparison with other metals. We studied the association of Cd and smoking exposures with human blood DNA methylation (DNAm) profiles. We also evaluated the implication of findings to relevant methylation pathways and the potential contribution of Cd exposure from smoking to explain the association between smoking and site-specific DNAm. We conducted an epigenome-wide association study of urine Cd and self-reported smoking (current and former vs. never, and cumulative smoking dose) with blood DNAm in 790,026 CpGs (methylation sites) measured with the Illumina Infinium Human MethylationEPIC (Illumina Inc.) platform in 2,325 adults 45-74 years of age who participated in the Strong Heart Study in 1989-1991. In a mediation analysis, we estimated the amount of change in DNAm associated with smoking that can be independently attributed to increases in urine Cd concentrations from smoking. We also conducted enrichment analyses and in silico protein-protein interaction networks to explore the biological relevance of the findings. At a false discovery rate (FDR)-corrected level of 0.05, we found 6 differentially methylated positions (DMPs) for Cd; 288 and 17, respectively, for current and former smoking status; and 77 for cigarette pack-years. Enrichment analyses of these DMPs displayed enrichment of 58 and 6 Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene sets, respectively, including biological pathways for cancer and cardiovascular disease. In in silico protein-to-protein networks, we observed key proteins in DNAm pathways directly and indirectly connected to Cd- and smoking-DMPs. Among DMPs that were significant for both Cd and current smoking (annotated to PRSS23, AHRR, F2RL3, RARA, and 2q37.1), we found statistically significant contributions of Cd to smoking-related DNAm. Beyond replicating well-known smoking epigenetic signatures, we found novel DMPs related to smoking. Moreover, increases in smoking-related Cd exposure were associated with differential DNAm. Our integrative analysis supports a biological link for Cd and smoking-associated health effects, including the possibility that Cd is partly responsible for smoking toxicity through epigenetic changes. https://doi.org/10.1289/EHP6345.This work was supported by grants by the National Heart, Lung, and Blood Institute (NHLBI) (under contract numbers 75N92019D00027, 75N92019D00028, 75N92019D00029, & 75N92019D00030) and previous grants (R01HL090863, R01HL109315, R01HL109301, R01HL109284, R01HL109282, and R01HL109319 and cooperative agreements U01HL41642, U01HL41652, U01HL41654, U01HL65520, and U01HL65521), by the National Institute of Health Sciences (R01ES021367, R01ES025216, P42ES010349, P30ES009089), by the Spanish Funds for Research In Health Sciences, Carlos III Health Institute, co-funded by European Regional Development Fund (CP12/03080 and PI15/00071), by Chilean CONICYT/FONDECYT-POSTDOCTORADO Nº3180486 (A.L.R.-C) and a fellowship from “La Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DR19/11740016.”S

Associations of maternal arsenic exposure with adult fasting glucose and insulin resistance in the Strong Heart Study and Strong Heart Family Study

Experimental and prospective epidemiologic evidence suggest that arsenic exposure has diabetogenic effects. However, little is known about how family exposure to arsenic may affect risk for type 2 diabetes (T2D)-related outcomes in adulthood. We evaluated the association of both maternal and offspring arsenic exposure with fasting glucose and incident T2D in 466 participants of the Strong Heart Family Study. Total arsenic (ΣAs) exposure was calculated as the sum of inorganic arsenic (iAs) and methylated (MMA, DMA) arsenic species in maternal and offspring baseline urine. Median maternal ΣAs at baseline (1989-91) was 7.6 µg/g creatinine, while median offspring ΣAs at baseline (2001-03) was 4.5 µg/g creatinine. Median offspring glucose in 2006-2009 was 94 mg/dL, and 79 participants developed T2D. The fully adjusted mean difference (95% CI) for offspring glucose was 4.40 (-3.46, 12.26) mg/dL per IQR increase in maternal ΣAs vs. 2.72 (-4.91 to 10.34) mg/dL per IQR increase in offspring ΣAs. The fully adjusted odds ratio (95%CI) of incident T2D was 1.35 (1.07, 1.69) for an IQR increase in maternal ΣAs and 1.15 (0.92, 1.43) for offspring ΣAs. The association of maternal ΣAs with T2D outcomes were attenuated with adjustment for offspring adiposity markers. Familial exposure to arsenic, as measured in mothers 15-20 years before offspring follow-up, is associated with increased odds of offspring T2D. More research is needed to confirm findings and better understand the importance of family exposure to arsenic in adult-onset diabetes.This study was supported by the National Institute of EnvironmentalHealth Sciences, Unites States (P42ES010349, P30ES009089,R01ES028758, R01ES025216).N.T., P.F.-L., and A.N.-A. contributed to the preparation of researchdata and writing of the manuscript. N.T, M.J.S, A.D.-R., M.T.-P., M.G.-P., and A.N.-A. contributed to the statistical analysis. B.V.H., J.M., K.N.,J.G.U., and S.C. contributed as the primary investigators of the SHS andSHFS, and to the preparation of the research data. K.A.F. and W.G.contributed to the arsenic measurements in the SHS and SHFS partici-pants. A.N.-A. is the guarantor of this work and, as such, had full accessto all the data in the study and takes responsibility for the integrity ofthe data and the accuracy of the data analysis.S

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.This work was supported by the Strategic Action for Research in Health sciences [PI10/0082, PI13/01848, PI14/00874, PI16/01402, PI11/00726, PI16/609, PI16/1512, PI18/287, PI19/319 and PI20/00896], the GUTMOM Project (JPI-A Healthy Diet for a Healthy Life INTIMIC-085, State Secretary of R + D + I PCIN-2017-117), the Cátedra de Epidemiología y Control del Riesgo Cardiovascular at UAM (#820024), the State Agency for Research (PID2019-108973RB-C21 and C22), the Valencia Government (GRUPOS 03/101; PROMETEO/2009/029 and ACOMP/2013/039), the Castilla-Leon Government (GRS/279/A/08) and European Network of Excellence Ingenious Hypercare (EPSS- 037093) from the European Commission; CIBER Fisiopatología Obesidad y Nutrición (CIBEROBN) (CIBER-02-08-2009, CB06/03 and CB12/03/30016). MSP holds a Ramón y Cajal contract (RYC-2018-025069-I) from the Ministry of Science, Innovation and Universities. MDV holds a “Predoctoral Training in Health Research” contract (FI20/00162) from the Carlos III Health Institute. MGP and ADR received the support of a fellowship from “la Caixa” Foundation (ID 100010434, fellowship codes LCF/BQ/IN18/11660001, and LCF/BQ/DR19/11740016, respectively). PO received the support of a Sara Borrell contract from the Carlos III Health Institute (reference CD16/00255). The Strategic Action for Research in Health Sciences, CIBEROBN are initiatives from Carlos III Health Institute Madrid and co-funded by the European Social Fund “The ESF - investing in your future”. The State Agency for Research and Carlos III Health Institute belong to the Spanish Ministry of Science and Innovation. The funding bodies had no role in the study design, data collection and analysis, interpretation of results, manuscript preparation or in the decision to submit this manuscript for publication.S

Gene-environment interaction analysis of redox-related metals and genetic variants with plasma metabolic patterns in a general population from Spain: The Hortega Study

Background: Limited studies have evaluated the joint influence of redox-related metals and genetic variation on metabolic pathways. We analyzed the association of 11 metals with metabolic patterns, and the interacting role of candidate genetic variants, in 1145 participants from the Hortega Study, a population-based sample from Spain. Methods: Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu), selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS, respectively. We summarized 54 plasma metabolites, measured with targeted NMR, by estimating metabolic principal components (mPC). Redox-related SNPs (N = 291) were measured by oligo-ligation assay. Results: In our study, the association with metabolic principal component (mPC) 1 (reflecting non-essential and essential amino acids, including branched chain, and bacterial co-metabolism versus fatty acids and VLDL subclasses) was positive for Se and Zn, but inverse for Cu, arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2 (reflecting essential amino acids, including aromatic, and bacterial co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3 (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse for Co. The association for mPC4 (reflecting HDL subclasses) was positive for Sb, but inverse for plasma Zn. These associations were mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of interactions with genetic variants associated to enriched endocrine, cardiovascular and neurological pathways. Conclusions: Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several metabolic patterns involved in chronic disease. Carriers of redox-related variants may have differential susceptibility to metabolic alterations associated to excessive exposure to metals.This work was supported by the Strategic Action for Research in Health sciences [CP12/03080, PI15/00071, PI10/0082, PI13/01848, PI14/00874, PI16/01402, PI21/00506 and PI11/00726], CIBER Fisio patología Obesidad y Nutrición (CIBEROBN) (CIBER-02-08-2009, CB06/03 and CB12/03/30,016), the State Agency for Research (PID2019-108973RB- C21 and C22), the Valencia Government (GRUPOS 03/101; PROMETEO/2009/029 and ACOMP/2013/039, IDI FEDER/2021/072 and GRISOLIAP/2021/119), the Castilla-Leon Government (GRS/279/A/08) and European Network of Excellence Ingenious Hypercare (EPSS-037093) from the European Commission. The Strategic Action for Research in Health sciences, CIBERDEM and CIBEROBN are initiatives from Carlos III Health Institute Madrid and cofunded with European Funds for Regional Development (FEDER). The State Agency for Research and Carlos III Health Institute belong to the Spanish Ministry of Science and Innovation. ADR received the support of a fellowship from “la Caixa” Foundation (ID 100010434) (fellowship code “LCF/BQ/DR19/11740016”). MGP received the support of a fellowship from “la Caixa” Foundation (ID 100010434, fellowship code LCFLCF/BQ/DI18/11660001). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.S

Meta-analyses identify DNA methylation associated with kidney function and damage

Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation was associated with the respective trait. The majority of these findings showed directionally consistent associations with the respective clinical outcomes chronic kidney disease and moderately increased albuminuria. Associations of DNA methylation with kidney function, such as CpGs at JAZF1, PELI1 and CHD2 were validated in kidney tissue. Methylation at PHRF1, LDB2, CSRNP1 and IRF5 indicated causal effects on kidney function. Enrichment analyses revealed pathways related to hemostasis and blood cell migration for estimated glomerular filtration rate, and immune cell activation and response for urinary albumin-to-creatinineratio-associated CpGs

Urinary metals and metal mixtures and oxidative stress biomarkers in an adult population from Spain: The Hortega Study

INTRODUCTION: Few studies have investigated the role of exposure to metals and metal mixtures on oxidative stress in the general population. OBJECTIVES: We evaluated the cross-sectional association of urinary metal and metal mixtures with urinary oxidative stress biomarkers, including oxidized to reduced glutathione ratio (GSSG/GSH), malondialdehyde (MDA), and 8‑oxo‑7,8‑dihydroguanine (8-oxo-dG), in a representative sample of a general population from Spain (Hortega Study). METHODS: Urine antimony (Sb), barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), molybdenum (Mo), vanadium (V) and zinc (Zn) were measured by ICPMS in 1440 Hortega Study participants. RESULTS: The geometric mean ratios (GMRs) of GSSG/GSH comparing the 80th to the 20th percentiles of metal distributions were 1.15 (95% confidence intervals [95% CI]: 1.03-1.27) for Mo, 1.17 (1.05-1.31) for Ba, 1.23 (1.04-1.46) for Cr and 1.18 (1.00-1.40) for V. For MDA, the corresponding GMRs (95% CI) were 1.13 (1.03-1.24) for Zn and 1.12 (1.02-1.23) for Cd. In 8-oxo-dG models, the corresponding GMR (95% CI) were 1.12 (1.01-1.23) for Zn and 1.09 (0.99-1.20) for Cd. Cr for GSSG/GSH and Zn for MDA and 8-oxo-dG drove most of the observed associations. Principal component (PC) 1 (largely reflecting non-essential metals) was positively associated with GSSG/GSH. The association of PC2 (largely reflecting essential metals) was positive for GSSG/GSH but inverse for MDA. CONCLUSIONS: Urine Ba, Cd, Cr, Mo, V and Zn were positively associated with oxidative stress measures at metal exposure levels relevant for the general population. The potential health consequences of environmental, including nutritional, exposure to these metals warrants further investigation