DNA methylation in blood as a mediator of the association of mid-childhood body mass index with cardio-metabolic risk score in early adolescence

Speeding up interval estimation for R 2 -based mediation effect of high-dimensional mediators via cross-fitting

Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for theR 2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of theR 2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Next Generation, Modifiable Cardiometabolic Biomarkers: Mitochondrial Adaptation and Metabolic Resilience: A Scientific Statement From the American Heart Association

Cardiometabolic risk is increasing in prevalence across the life span with disproportionate ramifications for youth at socioeconomic disadvantage. Established risk factors and associated disease progression are harder to reverse as they become entrenched over time; if current trends are unchecked, the consequences for individual and societal wellness will become untenable. Interrelated root causes of ectopic adiposity and insulin resistance are understood but identified late in the trajectory of systemic metabolic dysregulation when traditional cardiometabolic risk factors cross current diagnostic thresholds of disease. Thus, children at cardiometabolic risk are often exposed to suboptimal metabolism over years before they present with clinical symptoms, at which point life-long reliance on pharmacotherapy may only mitigate but not reverse the risk. Leading-edge indicators are needed to detect the earliest departure from healthy metabolism, so that targeted, primordial, and primary prevention of cardiometabolic risk is possible. Better understanding of biomarkers that reflect the earliest transitions to dysmetabolism, beginning in utero, ideally biomarkers that are also mechanistic/causal and modifiable, is critically needed. This scientific statement explores emerging biomarkers of cardiometabolic risk across rapidly evolving and interrelated "omic" fields of research (the epigenome, microbiome, metabolome, lipidome, and inflammasome). Connections in each domain to mitochondrial function are identified that may mediate the favorable responses of each of the omic biomarkers featured to a heart-healthy lifestyle, notably to nutritional interventions. Fuller implementation of evidence-based nutrition must address environmental and socioeconomic disparities that can either facilitate or impede response to therapy.

Epigenetics of Early Cardiometabolic Disease: Mechanisms and Precision Medicine

Epigenetics has transformed our understanding of the molecular basis of complex diseases, including cardiovascular and metabolic disorders. This review offers a comprehensive overview of the current state of knowledge on epigenetic processes implicated in cardiovascular and metabolic diseases, highlighting the potential of DNA methylation as a precision medicine biomarker and examining the impact of social determinants of health, gut bacterial epigenomics, noncoding RNA, and epitranscriptomics on disease development and progression. We discuss challenges and barriers to advancing cardiometabolic epigenetics research, along with the opportunities for novel preventive strategies, targeted therapies, and personalized medicine approaches that may arise from a better understanding of epigenetic processes. Emerging technologies, such as single-cell sequencing and epigenetic editing, hold the potential to further enhance our ability to dissect the complex interplay between genetic, environmental, and lifestyle factors. To translate research findings into clinical practice, interdisciplinary collaborations, technical and ethical considerations, and accessibility of resources and knowledge are crucial. Ultimately, the field of epigenetics has the potential to revolutionize the way we approach cardiovascular and metabolic diseases, paving the way for precision medicine and personalized health care, and improving the lives of millions of individuals worldwide affected by these conditions.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

DNA methylation and aeroallergen sensitization: The chicken or the egg?

Background

DNA methylation (DNAm) is considered a plausible pathway through which genetic and environmental factors may influence the development of allergies. However, causality has yet to be determined as it is unknown whether DNAm is rather a cause or consequence of allergic sensitization. Here, we investigated the direction of the observed associations between well-known environmental and genetic determinants of allergy, DNAm, and aeroallergen sensitization using a combination of high-dimensional and causal mediation analyses.

Methods

Using prospectively collected data from the German LISA birth cohort from two time windows (6–10 years: N = 234; 10–15 years: N = 167), we tested whether DNAm is a cause or a consequence of aeroallergen sensitization (specific immunoglobulin E > 0.35kU/l) by conducting mediation analyses for both effect directions using maternal smoking during pregnancy, family history of allergies, and a polygenic risk score (PRS) for any allergic disease as exposure variables. We evaluated individual CpG sites (EPIC BeadChip) and allergy-related methylation risk scores (MRS) as potential mediators in the mediation analyses. We applied three high-dimensional mediation approaches (HIMA, DACT, gHMA) and validated results using causal mediation analyses. A replication of results was attempted in the Swedish BAMSE cohort.

Results

Using high-dimensional methods, we identified five CpGs as mediators of prenatal exposures to sensitization with significant (adjusted p < 0.05) indirect effects in the causal mediation analysis (maternal smoking: two CpGs, family history: one, PRS: two). None of these CpGs could be replicated in BAMSE. The effect of family history on allergy-related MRS was significantly mediated by aeroallergen sensitization (proportions mediated: 33.7–49.6%), suggesting changes in DNAm occurred post-sensitization.

Conclusion

The results indicate that DNAm may be a cause or consequence of aeroallergen sensitization depending on genomic location. Allergy-related MRS, identified as a potential cause of sensitization, can be considered as a cross-sectional biomarker of disease. Differential DNAm in individual CpGs, identified as mediators of the development of sensitization, could be used as clinical predictors of disease development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01332-5.

Recent progress in epigenetics of obesity

Nowadays, obesity is one of the largest public health problems worldwide. In the last few decades, there has been a marked increase in the obesity epidemic and its related comorbidities. Worldwide, more than 2.2 billion people (33%) are affected by overweight or obesity (712 million, 10%) and its associated metabolic complications. Although a high heritability of obesity has been estimated, the genetic variants conducted from genetic association studies only partially explain the variation of body mass index. This has led to a growing interest in understanding the potential role of epigenetics as a key regulator of gene-environment interactions on the development of obesity and its associated complications. Rapid advances in epigenetic research methods and reduced costs of epigenome-wide association studies have led to a great expansion of population-based studies. The field of epigenetics and metabolic diseases such as obesity has advanced rapidly in a short period of time. The main epigenetic mechanisms include DNA methylation, histone modifications, microRNA (miRNA)-mediated regulation and so on. DNA methylation is the most investigated epigenetic mechanism. Preliminary evidence from animal and human studies supports the effect of epigenetics on obesity. Studies of epigenome-wide association studies and genome-wide histone modifications from different biological specimens such as blood samples (newborn, children, adolescent, youth, woman, man, twin, race, and meta-analysis), adipose tissues, skeletal muscle cells, placenta, and saliva have reported the differential expression status of multiple genes before and after obesity interventions and have identified multiple candidate genes and biological markers. These findings may improve the understanding of the complex etiology of obesity and its related comorbidities, and help to predict an individual's risk of obesity at a young age and open possibilities for introducing targeted prevention and treatment strategies.

Estimation of total mediation effect for high-dimensional omics mediators

BACKGROUND

Environmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to various health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings, and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (R[Formula: see text]) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework.

RESULTS

Based on extensive simulations, we compare our measure and estimation procedure with several frequently used mediation measures, including product, proportion, and ratio measures. Our R[Formula: see text]-based second-moment measure has small bias and variance under the correctly specified model. To mitigate potential bias induced by non-mediators, we examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate control, to exclude the non-mediators. We establish the consistency of the proposed estimation procedures and introduce a resampling-based confidence interval. By applying the proposed estimation procedure, we found that 38% of the age-related variations in systolic blood pressure can be explained by gene expression profiles in the Framingham Heart Study of 1711 individuals. An R package "RsqMed" is available on CRAN.

CONCLUSION

R-squared (R[Formula: see text]) is an effective and efficient measure for total mediation effect especially under high-dimensional setting.

DNA methylation as a mediator of associations between the environment and chronic diseases: A scoping review on application of mediation analysis

DNA methylation (DNAm) is one of the most studied epigenetic modifications. DNAm has emerged as a key biological mechanism and biomarkers to test associations between environmental exposure and outcomes in epidemiological studies. Although previous studies have focused on associations between DNAm and either exposure/outcomes, it is useful to test for mediation of the association between exposure and outcome by DNAm. The purpose of this scoping review is to introduce the methodological essence of statistical mediation analysis and to examine emerging epidemiological research applying mediation analyses. We conducted this scoping review for published peer-reviewed journals on this topic using online databases (PubMed, Scopus, Cochrane, and CINAHL) ending in December 2020. We extracted a total of 219 articles by initial screening. After reviewing titles, abstracts, and full texts, a total of 69 articles were eligible for this review. The breakdown of studies assigned to each category was 13 for smoking (18.8%), 8 for dietary intake and famine (11.6%), 6 for other lifestyle factors (8.7%), 8 for clinical endpoints (11.6%), 22 for environmental chemical exposures (31.9%), 2 for socioeconomic status (SES) (2.9%), and 10 for genetic factors and race (14.5%). In this review, we provide an exposure-wide summary for the mediation analysis using DNAm levels. However, we found heterogenous methods and interpretations in mediation analysis with typical issues such as different cell compositions and tissue-specificity. Further accumulation of evidence with diverse exposures, populations and with rigorous methodology will be expected to provide further insight in the role of DNAm in disease susceptibility.

Longitudinal data reveal strong genetic and weak non-genetic components of ethnicity-dependent blood DNA methylation levels

Epigenetic architecture is influenced by genetic and environmental factors, but little is known about their relative contributions or longitudinal dynamics. Here, we studied DNA methylation (DNAm) at over 750,000 CpG sites in mononuclear blood cells collected at birth and age 7 from 196 children of primarily self-reported Black and Hispanic ethnicities to study race-associated DNAm patterns. We developed a novel Bayesian method for high-dimensional longitudinal data and showed that race-associated DNAm patterns at birth and age 7 are nearly identical. Additionally, we estimated that up to 51% of all self-reported race-associated CpGs had race-dependent DNAm levels that were mediated through local genotype and, quite surprisingly, found that genetic factors explained an overwhelming majority of the variation in DNAm levels at other, previously identified, environmentally-associated CpGs. These results indicate that race-associated blood DNAm patterns in particular, and blood DNAm levels in general, are primarily driven by genetic factors, and are not as sensitive to environmental exposures as previously suggested, at least during the first 7 years of life.

Gene-based mediation analysis in epigenetic studies

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

DNA methylome analysis identifies BMI-related epigenetic changes associated with non-small cell lung cancer susceptibility

Background

Body mass index (BMI) has been reported to be inversely associated with incident risk of non‐small cell lung cancer (NSCLC). However, the underlying mechanism is still unclear. This study aimed to investigate the role of DNA methylation in the relationship between BMI and NSCLC.

Methods

We carried out a genome‐wide DNA methylation study of BMI in peripheral blood among 2266 Chinese participants by using Illumina Methylation arrays. For the BMI‐related DNA methylation changes, their associations with NSCLC risk were further analyzed and their mediation effects on BMI‐NSCLC association were also evaluated.

Results

The methylation levels of four CpGs (cg12593793, cg17061862, cg11024682, and cg06500161, annotated to LMNA, ZNF143, SREBF1, and ABCG1, respectively) were found to be significantly associated with BMI. Methylation levels of cg12593793, cg11024682, and cg06500161 were observed to be inversely associated with NSCLC risk [OR (95%CI) =0.22 (0.16, 0.31), 0.39 (0.30, 0.50), and 0.66 (0.53, 0.82), respectively]. Additionally, cg11024682 in SREBF1 and cg06500161 in ABCG1 mediated 45.3% and 19.5% of the association between BMI and decreased NSCLC risk, respectively.

Conclusions

In this study, we identified four DNA methylation sites associated with BMI in the Chinese populations at the genome‐wide significant level. We also found that the BMI‐related methylations of SREBF1 and ABCG1 could mediate about a quintile‐to‐half of the effect of BMI on reduced NSCLC risk, which adds a potential mechanism underlying this association.

The mediation effect of serum metabolites on the relationship between long-term smoking exposure and esophageal squamous cell carcinoma

BACKGROUND

Long-term smoking exposure will increase the risk of esophageal squamous cell carcinoma (ESCC), whereas the mechanism is still unclear. We conducted a cross-sectional study to explore whether serum metabolites mediate the occurrence of ESCC caused by cigarette smoking.

METHODS

Serum metabolic profiles and lifestyle information of 464 participants were analyzed. Multiple logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of smoking exposure to ESCC risk. High-dimensional mediation analysis and univariate mediation analysis were performed to screen potential intermediate metabolites of smoking exposure for ESCC.

RESULTS

Ever smoking was associated with a 3.11-fold increase of ESCC risk (OR = 3.11, 95% CI 1.63-6.05), and for each cigarette-years increase in smoking index, ESCC risk increased by 56% (OR = 1.56, 95% CI 1.18-2.13). A total of 5 metabolites were screened as mediators by high-dimensional mediation analysis. In addition, glutamine, histidine, and cholic acid were further proved existing mediation effects according to univariate mediation analysis. And the proportions of mediation of histidine and glutamine were 40.47 and 30.00%, respectively. The mediation effect of cholic acid was 8.98% according to the analysis of smoking index.

CONCLUSIONS

Our findings suggest that cigarette smoking contributed to incident ESCC, which may be mediated by glutamine, histidine and cholic acid.

IMAGE: high-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

Identifying genetic variants that are associated with methylation variation-an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping-is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches.

Body Mass Index Drives Changes in DNA Methylation: A Longitudinal Study

RATIONALE

Previous EWASs (Epigenome-Wide Association Studies) suggest that obesity may be the cause, not a consequence, of changes in DNA methylation (DNAm). However, longitudinal observations are lacking.

OBJECTIVE

To identify 5'-cytosine-phosphate-guanine-3' in DNA (CpG) sites associated with body mass index (BMI) and examine the temporal relationship between dynamic changes in DNAm and BMI in a longitudinal cohort.

METHODS AND RESULTS

Race-specific EWASs were performed in 995 whites and 490 blacks from the Bogalusa Heart Study. Suggestive CpG sites were further replicated in 252 whites and 228 blacks from the Georgia Stress and Heart Study. Cross-lagged panel analysis was used to examine the temporal relationship between DNAm and BMI in 439 whites and 201 blacks who were examined twice 6.2 years apart. In discovery and replication samples, 349 CpG sites (266 novel) in whites and 36 (21 novel) in blacks were identified to be robustly associated with BMI, with 8 (1 novel) CpG sites overlapping between the 2 races. Cross-lagged panel analyses showed significant unidirectional paths (P_FDR <0.05) from baseline BMI to follow-up DNAm at 18 CpG sites in whites and 7 in blacks; no CpG sites showed significant paths from DNAm at baseline to BMI at follow-up. Baseline BMI was associated with a DNAm score (calculated from DNAm levels at the associated CpG sites) at follow-up (P<0.001 both in blacks and in whites).

CONCLUSIONS

The findings provide strong evidence that obesity is the cause, not a consequence, of changes in DNAm over time.