The Association of Epigenetic Age Acceleration and Multimorbidity at Age 90 in the Women's Health Initiative

BACKGROUND

Epigenetic age acceleration (EAA), a measure of accelerated biological aging, has been associated with an increased risk of several age-related chronic conditions. This is the first study to prospectively examine the relationship between EAA and both multimorbidity count and a weighted multimorbidity score among long-lived postmenopausal women.

METHODS

We included 1 951 women from the Women's Health Initiative who could have survived to age 90. EAA was estimated using the Horvath pan-tissue, Hannum, PhenoAge, and GrimAge "clocks." Twelve chronic conditions were included in the multimorbidity count. The multimorbidity score was weighted for each morbidity's relationship with mortality in the study population. Using mixed-effects Poisson and linear regression models that included baseline covariates associated with both EAA and multimorbidity, we estimated relative risks (RRs) and 95% confidence intervals (CIs) for the relationships between each EAA measure at the study baseline with both multimorbidity count and weighted multimorbidity score at age 90, respectively.

RESULTS

For every one standard deviation increase in AgeAccelPheno, the rate of multimorbidity accumulation increased 6% (RR = 1.06; 95% CI = 1.01-1.12; p = .025) and the multimorbidity score by 7% (RR = 1.07; 95% CI = 1.01-1.13; p = .014) for women who survived to age 90. The results for a one standard deviation increase in AgeAccelHorvath, AgeAccelHannum, and AgeAccelGrim with multimorbidity accumulation and score were weaker compared to AgeAccelPheno, and the latter 2 did not reach statistical significance.

CONCLUSION

AgeAccelPheno and AgeAccelHannum may predict multimorbidity count and score at age 90 in older women and, thus, may be useful as a biomarker predictor of multimorbidity burden in the last decades of life.

Probing the diabetes and colorectal cancer relationship using gene - environment interaction analyses

BACKGROUND

Diabetes is an established risk factor for colorectal cancer. However, the mechanisms underlying this relationship still require investigation and it is not known if the association is modified by genetic variants. To address these questions, we undertook a genome-wide gene-environment interaction analysis.

METHODS

We used data from 3 genetic consortia (CCFR, CORECT, GECCO; 31,318 colorectal cancer cases/41,499 controls) and undertook genome-wide gene-environment interaction analyses with colorectal cancer risk, including interaction tests of genetics(G)xdiabetes (1-degree of freedom; d.f.) and joint testing of Gxdiabetes, G-colorectal cancer association (2-d.f. joint test) and G-diabetes correlation (3-d.f. joint test).

RESULTS

Based on the joint tests, we found that the association of diabetes with colorectal cancer risk is modified by loci on chromosomes 8q24.11 (rs3802177, SLC30A8 - ORAA: 1.62, 95% CI: 1.34-1.96; ORAG: 1.41, 95% CI: 1.30-1.54; ORGG: 1.22, 95% CI: 1.13-1.31; p-value3-d.f.: 5.46 × 10-11) and 13q14.13 (rs9526201, LRCH1 - ORGG: 2.11, 95% CI: 1.56-2.83; ORGA: 1.52, 95% CI: 1.38-1.68; ORAA: 1.13, 95% CI: 1.06-1.21; p-value2-d.f.: 7.84 × 10-09).

DISCUSSION

These results suggest that variation in genes related to insulin signaling (SLC30A8) and immune function (LRCH1) may modify the association of diabetes with colorectal cancer risk and provide novel insights into the biology underlying the diabetes and colorectal cancer relationship.

A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer.

SIGNIFICANCE

This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

Polygenic risk scores point toward potential genetic mechanisms of type 2 myocardial infarction in people with HIV

BACKGROUND

People with human immunodeficiency virus (HIV) infection (PWH) are at higher risk of myocardial infarction (MI) than those without HIV. About half of MIs in PWH are type 2 (T2MI), resulting from mismatch between myocardial oxygen supply and demand, in contrast to type 1 MI (T1MI), which is due to primary plaque rupture or coronary thrombosis. Despite worse survival and rising incidence in the general population, evidence-based treatment recommendations for T2MI are lacking. We used polygenic risk scores (PRS) to explore genetic mechanisms of T2MI compared to T1MI in PWH.

METHODS

We derived 115 PRS for MI-related traits in 9541 PWH enrolled in the Centers for AIDS Research Network of Integrated Clinical Systems cohort with adjudicated T1MI and T2MI. We applied multivariate logistic regression analyses to determine the association with T1MI and T2MI. Based on initial findings, we performed gene set enrichment analysis of the top variants composing PRS associated with T2MI.

RESULTS

We found that T1MI was strongly associated with PRS for cardiovascular disease, lipid profiles, and metabolic traits. In contrast, PRS for alcohol dependence and cholecystitis, significantly enriched in energy metabolism pathways, were predictive of T2MI risk. The association remained after the adjustment for actual alcohol consumption.

CONCLUSIONS

We demonstrate distinct genetic traits associated with T1MI and T2MI among PWH further highlighting their etiological differences and supporting the role of energy regulation in T2MI pathogenesis.

Analysis of Epigenetic Age Acceleration and Healthy Longevity Among Older US Women

IMPORTANCE

Accelerated biological aging is associated with decreased physical capability and cognitive functioning, which are associated with increased risk of morbidity and mortality.

OBJECTIVE

We investigated associations between epigenetic age acceleration (EAA), a biomarker associated with aging, and healthy longevity among older women.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was a secondary analysis of participants in the Women's Health Initiative (WHI) who were eligible to survive to age 90 years by September 30, 2020. Participants were located in multiple centers. This study was restricted to women with genome-wide DNA methylation data, generated from baseline blood samples within 3 WHI ancillary studies. Median (IQR) follow-up times from baseline were 21.6 (19.6-22.9) years and 21.4 (19.8-22.7) years for women who survived to age 90 years with and without intact mobility, respectively, and 13.2 (8.8-16.7) for women who did not survive to age 90 years. Data were analyzed from December 2020 to July 2021.

EXPOSURES

EAA was estimated using 4 established "clocks": Horvath pantissue, Hannum, Pheno, and Grim.

MAIN OUTCOMES AND MEASURES

Using multinomial logistic regression, odds ratios (ORs) and 95% CIs were estimated for 3 healthy longevity outcomes for each clock: survival to age 90 years with intact mobility, survival to age 90 years without intact mobility, and no survival to age 90 years.

RESULTS

Among 1813 women, there were 464 women (mean [SD] age at baseline, 71.6 [3.5] years) who survived to age 90 years with intact mobility and cognitive functioning, 420 women (mean [SD] age at baseline, 71.3 [3.2] years) who survived to age 90 years without intact mobility and cognitive functioning, and 929 women (mean [SD] age at baseline, 70.2 [3.4] years) who did not survive to age 90 years. Women who survived to age 90 years with intact mobility and cognitive function were healthier at baseline compared with women who survived without those outcomes or who did not survive to age 90 years (eg, 143 women [30.8%] vs 101 women [24.0%] and 202 women [21.7%] with 0 chronic conditions). The odds of surviving to age 90 years with intact mobility were lower for every 1 SD increase in EAA compared with those who did not survive to age 90 years as measured by AgeAccelHorvath (OR, 0.82; 95% CI, 0.69-0.96; P = .01), AgeAccelHannum (OR, 0.67; 95% CI, 0.56-0.80; P < .001), AgeAccelPheno (OR, 0.60; 95% CI, 0.51-0.72; P < .001), and AgeAccelGrim (OR, 0.68; 95% CI, 0.55-0.84; P < .001). ORs were similar for women who survived to age 90 years with intact mobility and cognitive function (eg, AgeAccelHorvath: OR per 1 SD increase in EAA, 0.83; 95% CI, 0.71-0.98; P = .03) compared with women who did not survive to age 90 years.

CONCLUSIONS AND RELEVANCE

These findings suggest that EAA may be a valid biomarker associated with healthy longevity among older women and may be used for risk stratification and risk estimation of future functional and cognitive aging. Outcomes suggest that future studies may focus on the potential for public health interventions to counteract EAA and its association with poor health outcomes to lower disease burden while increasing longevity.

Associations between DNA methylation and BMI vary by metabolic health status: a potential link to disparate cardiovascular outcomes

Background

Body mass index (BMI), a well-known risk factor for poor cardiovascular outcomes, is associated with differential DNA methylation (DNAm). Similarly, metabolic health has also been associated with changes in DNAm. It is unclear how overall metabolic health outside of BMI may modify the relationship between BMI and methylation profiles, and what consequences this may have on downstream cardiovascular disease. The purpose of this study was to identify cytosine-phosphate-guanine (CpG) sites at which the association between BMI and DNAm could be modified by overall metabolic health.

Results

The discovery study population was derived from three Women’s Health Initiative (WHI) ancillary studies (n = 3977) and two Atherosclerosis Risk in Communities (ARIC) ancillary studies (n = 3520). Findings were validated in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort (n = 1200). Generalized linear models regressed methylation β values on the interaction between BMI and metabolic health Z score (BMI × MHZ) adjusted for BMI, MHZ, cell composition, chip number and location, study characteristics, top three ancestry principal components, smoking, age, ethnicity (WHI), and sex (ARIC). Among the 429,566 sites examined, differential associations between BMI × MHZ and DNAm were identified at 22 CpG sites (FDR q < 0.05), with one site replicated in MESA (cg18989722, in the TRAPPC9 gene). Three of the 22 sites were associated with incident coronary heart disease (CHD) in WHI. For each 0.01 unit increase in DNAm β value, the risk of incident CHD increased by 9% in one site and decreased by 6–10% in two sites over 25 years.

Conclusions

Differential associations between DNAm and BMI by MHZ were identified at 22 sites, one of which was validated (cg18989722) and three of which were predictive of incident CHD. These sites are located in several genes related to NF-kappa-B signaling, suggesting a potential role for inflammation between DNA methylation and BMI-associated metabolic health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01194-3.

Epigenome-wide association study of diet quality in the Women's Health Initiative and TwinsUK cohort

BACKGROUND

Diet quality is a risk factor for chronic disease and mortality. Differential DNA methylation across the epigenome has been associated with chronic disease risk. Whether diet quality is associated with differential methylation is unknown. This study assessed whether diet quality was associated with differential DNA methylation measured across 445 548 loci in the Women's Health Initiative (WHI) and the TwinsUK cohort.

DESIGN

The discovery cohort consisted of 4355 women from the WHI. The replication cohort consisted of 571 mono- and dizygotic twins from the TwinsUK cohort. DNA methylation was measured in whole blood using the Illumina Infinium HumanMethylation450 Beadchip. Diet quality was assessed using the Alternative Healthy Eating Index 2010 (AHEI-2010). A meta-analysis, stratified by study cohort, was performed using generalized linear models that regressed methylation on AHEI-2010, adjusting for cell composition, chip number and location, study characteristics, principal components of genetic relatedness, age, smoking status, race/ethnicity and body mass index (BMI). Statistical significance was defined as a false discovery rate < 0.05. Significant sites were tested for replication in the TwinsUK cohort, with significant replication defined by P < 0.05 and a consistent direction.

RESULTS

Diet quality was significantly associated with differential DNA methylation at 428 cytosine-phosphate-guanine (CpG) sites in the discovery cohort. A total of 24 CpG sites were consistent with replication in the TwinsUK cohort, more than would be expected by chance (P = 2.7x10-4), with one site replicated in both the blood and adipose tissue (cg16379999 located in the body of SEL1L).

CONCLUSIONS

Diet quality was associated with methylation at 24 CpG sites, several of which have been associated with adiposity, inflammation and dysglycaemia. These findings may provide insight into pathways through which diet influences chronic disease.

Epigenome-wide Association Study of Diet Quality in the Women's Health Initiative (OR31-06-19)

Objectives

This study examined the influence of diet on the methylome by analyzing 428,019 cytosine-guanine nucleotide pair (CpG) sites and assessing whether diet quality was associated with differential methylation patterns.

Methods

The study population included 4529 women from the Women's Health Initiative (WHI) observation and clinical trial from three ancillary studies: EMPC, BAA23, and AS311. DNA methylation was measured from whole blood samples using the Illumina Infinium HumanMethylation450 Beadchip. Diet quality was assessed using the Alternative Healthy Eating Index 2010 (AHEI-2010). An epigenome-wide association study (EWAS) meta-analysis, stratified by study cohort, was done using generalized linear models by regressing methylation β values (β = Methylated probes/[Methylated + Unmethylated probes]) for each CpG site on the primary exposure, AHEI, adjusting for cell composition, chip number and location, study characteristics, principle components of genetic relatedness, age, ethnicity and BMI. Significance was set at Holm-Bonferroni P < 0.05.

Results

Demographic characteristics are described by quartile of AHEI with Quartile 4 equivalent to the healthiest diet (highest score) and Quartile 1 equivalent to the poorest diet (lowest score) in Table 1. We found diet quality was significantly associated with 340 CpG sites after false discovery correction (Figure 1). While statistically significant, effect sizes were small (∼0.0003). These findings suggest that, on average, as AHEI increases by one SD (10.1 units), methylation changes by only ±0.003 in associated CpG sites. When examining the top 20 CpG sites (Table 2), several sites were located in genes critical to metabolism, including cg26137868 in the FOXA2 gene and cg20006924 in the RORA gene, both related to the regulation of glucose and fat metabolism and 3 CpG sites in the SLC18A2, SLC2A14, and SLC16A3 genes related to nutrient transport.

Conclusions

This is the first reported EWAS examining the relationship between diet quality and methylation in humans. While diet quality was statistically associated with many CpG sites, the effect sizes were small. Further investigation is required to understand the relationship between diet quality and the methylome.

Funding Sources

NA.

Supporting Tables, Images and/or Graphs

Tumor expression of adiponectin receptor 2 and lethal prostate cancer

To investigate the role of adiponectin receptor 2 (AdipoR2) in aggressive prostate cancer we used immunohistochemistry to characterize AdipoR2 protein expression in tumor tissue for 866 men with prostate cancer from the Physicians' Health Study and the Health Professionals Follow-up Study. AdipoR2 tumor expression was not associated with measures of obesity, pathological tumor stage or prostate-specific antigen (PSA) at diagnosis. However, AdipoR2 expression was positively associated with proliferation as measured by Ki-67 expression quartiles (P-trend < 0.0001), with expression of fatty acid synthase (P-trend = 0.001), and with two measures of angiogenesis (P-trend < 0.1). An inverse association was observed with apoptosis as assessed by the TUNEL assay (P-trend = 0.006). Using Cox proportional hazards regression and controlling for age at diagnosis, Gleason score, year of diagnosis category, cohort and baseline BMI, we identified a statistically significant trend for the association between quartile of AdipoR2 expression and lethal prostate cancer (P-trend = 0.02). The hazard ratio for lethal prostate cancer for the two highest quartiles, as compared to the two lowest quartiles, of AdipoR2 expression was 1.9 (95% confidence interval [CI]: 1.2-3.0). Results were similar when additionally controlling for categories of PSA at diagnosis and Ki-67 expression quartiles. These results strengthen the evidence for the role of AdipoR2 in prostate cancer progression.