AHRR Methylation is a Significant Predictor of Mortality Risk in Framingham Heart Study

Aromatic Hydrocarbon Receptor Repressor (AHRR) is a biomarker of ambient air pollution exposure and Coronary Artery Disease (CAD)

Two hundred and twenty subjects were recruited while undergoing cardiac catheterization. AHRR cg05575921 methylation was shown to be significantly decreased in ever smokers compared to never smokers (Mean± SD = 64.2 ± 17.2 vs 80.1 ± 11.1 respectively; P < 0.0001). In addition, higher urinary levels of 2-OHNAP and 2-OHFLU were significantly associated with more AHRR cg05575921 hypomethylation, even after correcting for smoking (β[95%CI]= -4.161[-7.553, -0.769]; P = 0.016 and -5.190[-9.761, -0.618]; P = 0.026, respectively) but not 1-OHPYR (β[95%CI]= -3.545 [-10.935, 3.845]; P = 0.345). Additionally, hypomethylation of AHRR ROI was significantly associated with obstructive coronary artery disease (CAD) after adjusting for smoking, age, sex, diabetes and dyslipidemia (OR [95%CI] = 1.024[1.000 - 1.048]; P = 0.046). Results of this study necessitate further validation to potentially consider clinical incorporation of AHRR methylation status as an early predictive biomarker for the potential association between ambient air pollution and CAD.

Refining epigenetic prediction of chronological and biological age

BACKGROUND

Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.

METHODS

First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study).

RESULTS

Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.

CONCLUSIONS

The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

The association between DNA methylation of 6p21.33 and AHRR in blood and coronary heart disease in Chinese population

BACKGROUND

Early detection could significantly improve the prognosis of coronary heart disease (CHD). In-invitro diagnostic technique may provide a solution when sufficient biomarkers could be identified. Pertinent associations between blood-based aberrant DNA methylation and smoking, the pathogenesis of atherosclerosis, and CHD have been robustly demonstrated and replicated, but that studies in Chinese populations are rare. The blood-based methylation of aryl-hydrocarbon receptor repressor (AHRR) cg05575921 and 6p21.33 cg06126421 has been associated with cardiovascular mortality in Caucasians. Here, we aim to investigate whether the AHRR and 6p21.33 methylation in the blood is associated with CHD in the Chinese population.

METHODS

In this case-control study, 180 CHD patients recruited at their first registration in our study center, and 184 controls randomly selected from the people who participated in the annual health examination were enrolled. Methylation intensities of 19 CpG sites, including AHRR cg05575921, 6p21.33 cg06126421, and their flanking CpG sites, were quantified by mass spectrometry. The association between methylation intensities and CHD was estimated by logistic regression analyses adjusted for covariant.

RESULTS

Compared to the controls, lower methylation of 6p21.33_CpG_4.5/cg06126421 was independently associated with increased odds of being a CHD patient (OR per - 10% methylation = 1.42 after adjustment for age, gender, and batch effect; p = 0.032 by multiple testing corrections). No association between blood-based AHRR methylation and CHD was found.

CONCLUSIONS

6p21.33 methylation exhibits a significant association with CHD. The combination of 6p21.33 methylation and conventional risk factors might be an intermediate step towards the early detection of CHD.

Digital methylation assessments of alcohol and cigarette consumption account for common variance in accelerated epigenetic ageing

Smoking and Heavy Alcohol Consumption (HAC) are established risk factors for myriad complex disorders of ageing. Yet many prior studies of Epigenetic Ageing (EA) have shown only modest effects of smoking and drinking on accelerated ageing. One potential reason for this conundrum might be the reliance of some prior EA studies on self-reported substance use, which may be unreliable in many samples. To test whether novel, non-self-reported indices would show a stronger association of smoking and HAC to EA, we used methylation sensitive digital PCR (MSdPCR) and data from 437 African American subjects from Wave 7 of the Family and Community Health Study Offspring Cohort to examine the effects of subjective and objective measures of smoking and HAC on 7 indices of EA. Because of limited overall correlations between the various EA indices, we examined patterns of association separately for each index. Consistent with expectations, MSdPCR assessments of smoking and HAC, but not self-reported alcohol consumption, were strongly correlated with accelerated EA. MSdPCR assessments of smoking and HAC accounted for 57% of GrimAge acceleration and the shared variance in GrimAge and DunedinPOAM accelerated EA. We conclude that MSdPCR assessments of smoking and HAC are valuable tools for understanding EA, represent directly targetable conditions for the prevention of premature ageing, and substantially improve upon self-reported assessment of smoking and HAC.

The relationship of smoking to cg05575921 methylation in blood and saliva DNA samples from several studies

Numerous studies have shown that cg05575921 methylation decreases in response to smoking. However, secondary to methodological issues, the magnitude and dose dependency of that response is as of yet unclear. This lack of certainty is a barrier to the use of DNA methylation clinically to assess and monitor smoking status. To better define this relationship, we conducted a joint analysis of methylation sensitive PCR digital (MSdPCR) assessments of cg05575921 methylation in whole blood and/or saliva DNA to smoking using samples from 421 smokers and 423 biochemically confirmed non-smokers from 4 previously published studies. We found that cg05575921 methylation manifested a curvilinear dose dependent decrease in response to increasing cigarette consumption. In whole blood DNA, the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) of cg05575921 methylation for predicting daily smoking status was 0.98. In saliva DNA, the gross AUC was 0.91 with correction for cellular heterogeneity improving the AUC to 0.94. Methylation status was significantly associated with the Fagerstrom Test for Nicotine Dependence score, but with significant sampling heterogeneity. We conclude that MSdPCR assessments of cg05575921 methylation are a potentially powerful, clinically implementable tool for the assessment and management of smoking.

Blood DNA Methylation and Incident Coronary Heart Disease: Evidence From the Strong Heart Study

Importance

American Indian communities experience a high burden of coronary heart disease (CHD). Strategies are needed to identify individuals at risk and implement preventive interventions.

Objective

To investigate the association of blood DNA methylation (DNAm) with incident CHD using a large number of methylation sites (cytosine-phosphate-guanine [CpG]) in a single model.

Design, Setting, and Participants

This prospective study, including a discovery cohort (the Strong Heart Study [SHS]) and 4 additional cohorts (the Women's Health Initiative [WHI], the Framingham Heart Study [FHS], the Atherosclerosis Risk in Communities Study ([ARIC]-Black, and ARIC-White), evaluated 12 American Indian communities in 4 US states; African American women, Hispanic women, and White women throughout the US; White men and White women from Massachusetts; and Black men and women and White men and women from 4 US communities. A total of 2321 men and women (mean [SD] follow-up, 19.1 [9.2] years) were included in the SHS, 1874 women (mean [SD] follow-up, 15.8 [5.9] years) in the WHI, 2128 men and women (mean [SD] follow-up, 7.7 [1.8] years) in the FHS, 2114 men and women (mean [SD] follow-up, 20.9 [7.2] years) in the ARIC-Black, and 931 men and women (mean [SD] follow-up, 20.9 [7.2] years) in the ARIC-White. Data were collected from May 1989 to December 2018 and analyzed from February 2019 to May 2021.

Exposure

Blood DNA methylation.

Main Outcome and Measure

Using a high-dimensional time-to-event elastic-net model for the association of 407 224 CpG sites with incident CHD in the SHS (749 events), this study selected the differentially methylated CpG positions (DMPs) selected in the SHS and evaluated them in the WHI (531 events), FHS (143 events), ARIC-Black (350 events), and ARIC-White (121 events) cohorts.

Results

The median (IQR) age of participants in SHS was 55 (49-62) years, and 1359 participants (58.6%) were women. Elastic-net models selected 505 DMPs associated with incident CHD in the SHS beyond established risk factors, center, blood cell counts, and genetic principal components. Among those DMPs, 33 were commonly selected in 3 or 4 of the other cohorts and the pooled hazard ratios from the standard Cox models were significant at P < .05 for 10 of the DMPs. For example, the hazard ratio (95% CI) for CHD comparing the 90th and 10th percentiles of differentially methylated CpGs was 0.86 (0.78-0.95) for cg16604233 (tagged to COL11A2) and 1.23 (1.08-1.39) for cg09926486 (tagged to FRMD5). Some of the DMPs were consistent in the direction of the association; others showed associations in opposite directions across cohorts. Untargeted independent elastic-net models of CHD showed distinct DMPs, genes, and network of genes in the 5 cohorts.

Conclusions and Relevance

In this multi-cohort study, blood-based DNAm findings supported an association between a complex blood epigenomic signature and CHD that was largely different across populations.

Digital Predictors of Morbidity, Hospitalization, and Mortality Among Older Adults: A Systematic Review and Meta-Analysis

The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has rapidly enabled new opportunities for predictive health monitoring. Leveraging digital health tools to track parameters relevant to human health is particularly important for the older segments of the population as old age is associated with multimorbidity and higher care needs. In order to assess the potential of these digital health technologies to improve health outcomes, it is paramount to investigate which digitally measurable parameters can effectively improve health outcomes among the elderly population. Currently, there is a lack of systematic evidence on this topic due to the inherent heterogeneity of the digital health domain and the lack of clinical validation of both novel prototypes and marketed devices. For this reason, the aim of the current study is to synthesize and systematically analyse which digitally measurable data may be effectively collected through digital health devices to improve health outcomes for older people. Using a modified PICO process and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we provide the results of a systematic review and subsequent meta-analysis of digitally measurable predictors of morbidity, hospitalization, and mortality among older adults aged 65 or older. These findings can inform both technology developers and clinicians involved in the design, development and clinical implementation of digital health technologies for elderly citizens.

Bacteria-related changes in host DNA methylation and the risk for CRC

Colorectal cancer (CRC) is the second most common cause of cancer deaths in men and women combined. Colon-tumor growth is multistage and the result of the accumulation of spontaneous mutations and epigenetic events that silence tumor-suppressor genes and activate oncogenes. Environmental factors are primary contributors to these somatic gene alterations, which account for the increase in incidence of CRC in western countries. In recent decades, gut microbiota and their metabolites have been recognized as essential contributing factors to CRC, and now serve as biomarkers for the diagnosis and prognosis of CRC. In the present review, we highlight holistic approaches to understanding how gut microbiota contributes to CRC. We particularly focus herein on bacteria-related changes in host DNA methylation and the risk for CRC.