DNA methyltransferase 1 (DNMT1) promotes cyst growth and epigenetic age acceleration in autosomal dominant polycystic kidney disease.

Alteration of DNA methylation leads to diverse diseases, and the dynamic changes of DNA methylation (DNAm) on sets of CpG dinucleotides in mammalian genomes are termed "DNAm age" and "epigenetic clocks" that can predict chronological age. However, whether and how dysregulation of DNA methylation promotes cyst progression and epigenetic age acceleration in autosomal dominant polycystic kidney disease (ADPKD) remains elusive. Here, we show that DNA methyltransferase 1 (DNMT1) is upregulated in cystic kidney epithelial cells and tissues and that knockout of Dnmt1 and targeting DNMT1 with hydralazine, a safe demethylating agent, delays cyst growth in Pkd1 mutant kidneys and extends life span of Pkd1 conditional knockout mice. With methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq), DNMT1 chromatin immunoprecipitation (ChIP)-sequencing and RNA-sequencing analysis, we identified two novel DNMT1 targets, PTPRM and PTPN22 (members of the protein tyrosine phosphatase family). PTPRM and PTPN22 function as mediators of DNMT1 and the phosphorylation and activation of PKD-associated signaling pathways, including ERK, mTOR and STAT3. With whole-genome bisulfide sequencing in kidneys of patients with ADPKD versus normal individuals, we found that the methylation of epigenetic clock-associated genes was dysregulated, supporting that epigenetic age is accelerated in the kidneys of patients with ADPKD. Furthermore, five epigenetic clock-associated genes, including Hsd17b14, Itpkb, Mbnl1, Rassf5 and Plk2, were identified. Thus, the diverse biological roles of these five genes suggest that their methylation status may not only predict epigenetic age acceleration but also contribute to disease progression in ADPKD.

Evaluation of different computational methods for DNA methylation-based biological age.

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

Epigenetic age acceleration in follicular fluid and markers of ovarian response among women undergoing IVF.

STUDY QUESTION: Are markers of epigenetic age acceleration in follicular fluid associated with outcomes of ovarian stimulation? SUMMARY ANSWER: Increased epigenetic age acceleration of follicular fluid using the Horvath clock, but not other epigenetic clocks (GrimAge and Granulosa Cell), was associated with lower peak estradiol levels and decreased number of total and mature oocytes. WHAT IS KNOWN ALREADY: In granulosa cells, there are inconsistent findings between epigenetic age acceleration and ovarian response outcomes. STUDY DESIGN, SIZE, DURATION: Our study included 61 women undergoing IVF at an academic fertility clinic in the New England area who were part of the Environment and Reproductive Health Study (2006-2016). PARTICIPANTS/MATERIALS, SETTING, METHODS: Participants provided a follicular fluid sample during oocyte retrieval. DNA methylation of follicular fluid was assessed using a genome-wide methylation screening tool. Three established epigenetic clocks (Horvath, GrimAge, and Granulosa Cell) were used to predict DNA-methylation-based epigenetic age. To calculate the age acceleration, we regressed epigenetic age on chronological age and extracted the residuals. The association between epigenetic age acceleration and ovarian response outcomes (peak estradiol levels, follicle stimulation hormone, number of total, and mature oocytes) was assessed using linear and Poisson regression adjusted for chronological age, three surrogate variables (to account for cellular heterogeneity), race, smoking status, initial infertility diagnosis, and stimulation protocol. MAIN RESULTS AND ROLE OF CHANCE: Compared to the median chronological age of our participants (34 years), the Horvath clock predicted, on an average, a younger epigenetic age (median: 24.2 years) while the GrimAge (median: 38.6 years) and Granulosa Cell (median: 39.0 years) clocks predicted, on an average, an older epigenetic age. Age acceleration based on the Horvath clock was associated with lower peak estradiol levels (-819.4 unit decrease in peak estradiol levels per standard deviation increase; 95% CI: -1265.7, -373.1) and fewer total (% change in total oocytes retrieved per standard deviation increase: -21.8%; 95% CI: -37.1%, -2.8%) and mature oocytes retrieved (% change in mature oocytes retrieved per standard deviation increase: -23.8%; 95% CI: -39.9%, -3.4%). The age acceleration based on the two other epigenetic clocks was not associated with markers of ovarian response. LIMITATIONS, REASONS FOR CAUTION: Our sample size was small and we did not specifically isolate granulosa cells from follicular fluid samples so our samples could have included mixed cell types. WIDER IMPLICATIONS OF THE FINDINGS: Our results highlight that certain epigenetic clocks may be predictive of ovarian stimulation outcomes when applied to follicular fluid; however, the inconsistent findings for specific clocks across studies indicate a need for further research to better understand the clinical utility of epigenetic clocks to improve IVF treatment. STUDY FUNDING/COMPETING INTEREST(S): The study was supported by grants ES009718, ES022955, ES000002, and ES026648 from the National Institute of Environmental Health Sciences (NIEHS) and a pilot grant from the NIEHS-funded HERCULES Center at Emory University (P30 ES019776). RBH was supported by the Emory University NIH Training Grant (T32-ES012870). TRIAL REGISTRATION NUMBER: N/A.

Threshold and combined effects of heavy metals on the risk of phenotypic age acceleration among U.S. adults.

Accumulation of heavy metals in the body has been shown to affect the phenotypic age (PhenoAge). However, the combined and threshold effects of blood heavy metals on the risk of PhenoAge acceleration (PhenoAgeAccel) are not well understood. A cross-sectional study was conducted using blood heavy metal data (N = 7763, age ≥18 years) from the 2015-2018 National Health and Nutrition Examination Survey. PhenoAgeAccel was calculated from actual age and nine biomarkers. Multiple regression equations were used to describe the relationship between heavy metals and PhenoAgeAccel. Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling was used to explore the relationship between the combined effects of heavy metals and PhenoAgeAccel. Threshold effect and multiple regression analyses were performed to explore the linear and nonlinear relationships between heavy metals and PhenoAgeAccel. Threshold effect analysis showed that blood mercury (Hg) concentration was linearly associated with PhenoAgeAccel. In contrast, lead (Pb), cadmium (Cd), manganese (Mn), and combined exposure were nonlinearly associated with PhenoAgeAccel. In addition, the combination of Pb, Cd, Hg, and Mn significantly affected PhenoAgeAccel. The risk of PhenoAgeAccel was increased by 207% (P < 0.0001). Meanwhile, a threshold relationship was found between blood Pb, Cd, Mn, and the occurrence of PhenoAgeAccel. Overall, our results indicate that combined exposure to heavy metals may increase the risk of PhenoAgeAccel. This study underscores the need to reduce heavy metal pollution in the environment and provides a reference threshold for future studies.

Epigenetic age acceleration and the risk of frailty, and persistent activities of daily living (ADL) disability.

BACKGROUND: Epigenetic ageing is among the most promising ageing biomarkers and may be a useful marker of physical function decline, beyond chronological age. This study investigated whether epigenetic age acceleration (AA) is associated with the change in frailty scores over 7 years and the 7-year risk of incident frailty and persistent Activities of Daily Living (ADL) disability among 560 Australians (50.7% females) aged ≥70 years. METHODS: Seven AA indices, including GrimAge, GrimAge2, FitAge and DunedinPACE, were estimated from baseline peripheral-blood DNA-methylation. Frailty was assessed using both the 67-item deficit-accumulation frailty index (FI) and Fried phenotype (Fried). Persistent ADL disability was defined as loss of ability to perform one or more basic ADLs for at least 6 months. Linear mixed models and Cox proportional-hazard regression models were used as appropriate. RESULTS: Accelerated GrimAge, GrimAge2, FitAge and DunedinPACE at baseline were associated with increasing FI scores per year (adjusted-Beta ranged from 0.0015 to 0.0021, P < 0.05), and accelerated GrimAge and GrimAge2 were associated with an increased risk of incident FI-defined frailty (adjusted-HRs 1.43 and 1.39, respectively, P < 0.05). The association between DunedinPACE and the change in FI scores was stronger in females (adjusted-Beta 0.0029, P 0.001 than in males (adjusted-Beta 0.0002, P 0.81). DunedinPACE, but not the other AA measures, was also associated with worsening Fried scores (adjusted-Beta 0.0175, P 0.04). No associations were observed with persistent ADL disability. CONCLUSION: Epigenetic AA in later life is associated with increasing frailty scores per year and the risk of incident FI-defined frailty.

Subclinical atherosclerosis and accelerated epigenetic age mediated by inflammation: a multi-omics study.

AIMS: Epigenetic age is emerging as a personalized and accurate predictor of biological age. The aim of this article is to assess the association of subclinical atherosclerosis with accelerated epigenetic age and to investigate the underlying mechanisms mediating this association. METHODS AND RESULTS: Whole blood methylomics, transcriptomics, and plasma proteomics were obtained for 391 participants of the Progression of Early Subclinical Atherosclerosis study. Epigenetic age was calculated from methylomics data for each participant. Its divergence from chronological age is termed epigenetic age acceleration. Subclinical atherosclerosis burden was estimated by multi-territory 2D/3D vascular ultrasound and by coronary artery calcification. In healthy individuals, the presence, extension, and progression of subclinical atherosclerosis were associated with a significant acceleration of the Grim epigenetic age, a predictor of health and lifespan, regardless of traditional cardiovascular risk factors. Individuals with an accelerated Grim epigenetic age were characterized by an increased systemic inflammation and associated with a score of low-grade, chronic inflammation. Mediation analysis using transcriptomics and proteomics data revealed key pro-inflammatory pathways (IL6, Inflammasome, and IL10) and genes (IL1B, OSM, TLR5, and CD14) mediating the association between subclinical atherosclerosis and epigenetic age acceleration. CONCLUSION: The presence, extension, and progression of subclinical atherosclerosis in middle-aged asymptomatic individuals are associated with an acceleration in the Grim epigenetic age. Mediation analysis using transcriptomics and proteomics data suggests a key role of systemic inflammation in this association, reinforcing the relevance of interventions on inflammation to prevent cardiovascular disease.

Causal benefits of 25 dietary intakes on epigenetic ageing: a Mendelian randomisation study.

DNA methylation GrimAge acceleration (DMGA) and intrinsic epigenetic age acceleration (IEAA) are important physiological markers for assessing the ageing process. Evidence from cross-sectional studies suggests that some dietary intake is associated with DMGA and IEAA. However, the causal relationship between them has yet to be elucidated. This Mendelian randomisation study uses genetic variants associated with different dietary intakes as instrumental variables to explore the causal benefits of multiple dietary intakes on DMGA and IEAA. Cheese intake, dark chocolate intake, average weekly red wine intake, dried fruit intake, fresh fruit intake, porridge intake, cereal intake, and liver intake had a negative causal association with DMGA, and poultry intake and doughnut intake had a positive causal association with DMGA (p < 0.05). Muesli and bran cereal intake had a negative causal association with IEAA, and pineapple intake had a positive causal association with IEAA (p < 0.05). Dietary intake positively causally associated with IEAA or DMGA may have accelerated biological ageing; conversely, dietary intake negatively causally associated with IEAA or DMGA may have contributed to delaying biological ageing. Based on genetic evidence, this study demonstrated some significant causal benefits of dietary intake on DMGA and IEAA, suggesting the possibility of intervening in DNA methylation acceleration and epigenetic age acceleration by adjusting these food intakes, thereby promoting health and delaying ageing. However, the findings of this study are exploratory and preliminary and need to be supported and validated by evidence from further clinical studies and mechanistic studies.

Sleep Duration Polygenic Risk and Phenotype: Associations with Biomarkers of Accelerated Aging in the Baltimore Longitudinal Study of Aging.

We sought to explore whether genetic risk for, and self-reported, short sleep are associated with biological aging and whether age and sex moderate these associations. Participants were a subset of individuals from the Baltimore Longitudinal Study of Aging who had complete data on self-reported sleep (n = 567) or genotype (n = 367). Outcomes included: Intrinsic Horvath age, Hannum age, PhenoAge, GrimAge, and DNAm-based estimates of plasminogen activator inhibitor-1 (PAI-1) and granulocyte count. Results demonstrated that polygenic risk for short sleep was positively associated with granulocyte count; compared to those reporting <6 hr sleep, those reporting >7 hr demonstrated faster PhenoAge and GrimAge acceleration and higher estimated PAI-1. Polygenic risk for short sleep and self-reported sleep duration interacted with age and sex in their associations with some of the outcomes. Findings highlight that polygenic risk for short sleep and self-reported long sleep is associated with variation in the epigenetic landscape and subsequently aging.

Causal effects of denture wearing on epigenetic age acceleration and the mediating pathways: a mendelian randomization study.

BACKGROUND: The epigenetic-age acceleration (EAA) represents the difference between chronological age and epigenetic age, reflecting accelerated biological aging. Observational studies suggested that oral disorders may impact DNA methylation patterns and aging, but their causal relationship remains largely unexplored. This study aimed to investigate potential causal associations between dental traits and EAA, as well as to identify possible mediators. METHODS: Using summary statistics of genome-wide association studies of predominantly European ancestry, we conducted univariable and multivariable Mendelian randomization (MR) to estimate the overall and independent effects of ten dental traits (dentures, bleeding gums, painful gums, loose teeth, toothache, ulcers, periodontitis, number of teeth, and two measures of caries) on four EAA subtypes (GrimAge acceleration [GrimAA], PhenoAge acceleration [PhenoAA], HannumAge acceleration [HannumAA] and intrinsic EAA [IEAA]), and used two-step Mendelian randomization to evaluate twelve potential mediators of the associations. Comprehensive sensitivity analyses were used to verity the robustness, heterogeneity, and pleiotropy. RESULTS: Univariable inverse variance weighted MR analyses revealed a causal effect of dentures on greater GrimAA (ß: 2.47, 95% CI: 0.93-4.01, p = 0.002), PhenoAA (ß: 3.00, 95% CI: 1.15-4.85, p = 0.001), and HannumAA (ß: 1.96, 95% CI: 0.58-3.33, p = 0.005). In multivariable MR, the associations remained significant after adjusting for periodontitis, caries, number of teeth and bleeding gums. Three out of 12 aging risk factors were identified as mediators of the association between dentures and EAA, including body mass index, body fat percentage, and waist circumference. No evidence for reverse causality and pleiotropy were detected (p > 0.05). CONCLUSIONS: Our findings supported the causal effects of genetic liability for denture wearing on epigenetic aging, with partial mediation by obesity. More attention should be paid to the obesity-monitoring and management for slowing EAA among denture wearers.

Associations of Early-Life Adversity With Later-Life Epigenetic Aging Profiles in the Multi-Ethnic Study of Atherosclerosis.

Epigenetic biomarkers of accelerated aging have been widely used to predict disease risk and may enhance our understanding of biological mechanisms between early-life adversity and disparities in aging. With respect to childhood adversity, most studies have used parental education or childhood disadvantage and/or have not examined the role played by socioemotional or physical abuse and trauma in epigenetic profiles at older ages. This study leveraged data from the Multi-Ethnic Study of Atherosclerosis (MESA) on experiences of threat and deprivation in participants' early lives (i.e., before the age of 18 years) to examine whether exposure to specific dimensions of early-life adversity is associated with epigenetic profiles at older ages that are indicative of accelerated biological aging. The sample included 842 MESA respondents with DNA methylation data collected between 2010 and 2012 who answered questions on early-life adversities in a 2018-2019 telephone follow-up. We found that experiences of deprivation, but not threat, were associated with later-life GrimAge epigenetic aging signatures that were developed to predict mortality risk. Results indicated that smoking behavior partially mediates this association, which suggests that lifestyle behaviors may act as downstream mechanisms between parental deprivation in early life and accelerated epigenetic aging in later life.

Cord blood epigenome-wide meta-analysis in six European-based child cohorts identifies signatures linked to rapid weight growth.

BACKGROUND: Rapid postnatal growth may result from exposure in utero or early life to adverse conditions and has been associated with diseases later in life and, in particular, with childhood obesity. DNA methylation, interfacing early-life exposures and subsequent diseases, is a possible mechanism underlying early-life programming. METHODS: Here, a meta-analysis of Illumina HumanMethylation 450K/EPIC-array associations of cord blood DNA methylation at single CpG sites and CpG genomic regions with rapid weight growth at 1 year of age (defined with reference to WHO growth charts) was conducted in six European-based child cohorts (ALSPAC, ENVIRONAGE, Generation XXI, INMA, Piccolipiù, and RHEA, N = 2003). The association of gestational age acceleration (calculated using the Bohlin epigenetic clock) with rapid weight growth was also explored via meta-analysis. Follow-up analyses of identified DNA methylation signals included prediction of rapid weight growth, mediation of the effect of conventional risk factors on rapid weight growth, integration with transcriptomics and metabolomics, association with overweight in childhood (between 4 and 8 years), and comparison with previous findings. RESULTS: Forty-seven CpGs were associated with rapid weight growth at suggestive p-value <1e-05 and, among them, three CpGs (cg14459032, cg25953130 annotated to ARID5B, and cg00049440 annotated to KLF9) passed the genome-wide significance level (p-value <1.25e-07). Sixteen differentially methylated regions (DMRs) were identified as associated with rapid weight growth at false discovery rate (FDR)-adjusted/Siddak p-values < 0.01. Gestational age acceleration was associated with decreasing risk of rapid weight growth (p-value = 9.75e-04). Identified DNA methylation signals slightly increased the prediction of rapid weight growth in addition to conventional risk factors. Among the identified signals, three CpGs partially mediated the effect of gestational age on rapid weight growth. Both CpGs (N=3) and DMRs (N=3) were associated with differential expression of transcripts (N=10 and 7, respectively), including long non-coding RNAs. An AURKC DMR was associated with childhood overweight. We observed enrichment of CpGs previously reported associated with birthweight. CONCLUSIONS: Our findings provide evidence of the association between cord blood DNA methylation and rapid weight growth and suggest links with prenatal exposures and association with childhood obesity providing opportunities for early prevention.

Parenting Practices May Buffer the Impact of Adversity on Epigenetic Age Acceleration Among Young Children With Developmental Delays.

This study examined whether children exposed to adversity would exhibit lower epigenetic age acceleration in the context of improved parenting. Children with developmental delays and externalizing behavior problems (N = 62; Mage = 36.26 months; 70.97% boys, 29.03% girls; 71% Latinx, 22.6% Black) were drawn from a larger randomized controlled trial (RCT), which randomized them to receive Internet-delivered parent-child interaction therapy (iPCIT; n = 30) or community referrals as usual (RAU; n = 32). Epigenetic age acceleration was estimated with the pediatric buccal epigenetic clock, using saliva. Adversity was assessed using parent, family, and neighborhood-level cumulative-risk indicators. Adversity interacted with Time 2 (T2) observations of positive and negative-parenting practices to predict epigenetic age acceleration 1.5 years later, regardless of treatment assignment. Children exposed to more adversity displayed lower epigenetic age acceleration when parents evidenced increased positive (b = -0.15, p = .001) and decreased negative (b = -0.12, p = .01) parenting practices.

The effects of epigenetic age and its acceleration on surface area, cortical thickness, and volume in young adults.

DNA methylation age has been used in recent studies as an epigenetic marker of accelerated cellular aging, whose contribution to the brain structural changes was lately acknowledged. We aimed to characterize the association of epigenetic age (i.e. estimated DNA methylation age) and its acceleration with surface area, cortical thickness, and volume in healthy young adults. Using the multi-tissue method (Horvath S. DNA methylation age of human tissues and cell types. 2013. Genome Biol 14), epigenetic age was computed with saliva sample. Epigenetic age acceleration was derived from residuals after adjusting epigenetic age for chronological age. Multiple regression models were computed for 148 brain regions for surface area, cortical thickness, and volume using epigenetic age or accelerated epigenetic age as a predictor and controlling for sex. Epigenetic age was associated with surface area reduction of the left insula. It was also associated with cortical thinning and volume reduction in multiple regions, with prominent changes of cortical thickness in the left temporal regions and of volume in the bilateral orbital gyri. Finally, accelerated epigenetic age was negatively associated with right cuneus gyrus volume. Our findings suggest that understanding the mechanisms of epigenetic age acceleration in young individuals may yield valuable insights into the relationship between epigenetic aging and the cortical change and on the early development of neurocognitive pathology among young adults.

Longitudinal Changes in Epigenetic Age Acceleration in Aviremic Human Immunodeficiency Virus-Infected Recipients of Long-term Antiretroviral Treatment.

BACKGROUND: Human immunodeficiency virus (HIV) infection induces epigenetic age acceleration (EAA), but it remains unclear whether epigenetic aging continues to accelerate during successful antiretroviral therapy (ART) and prolonged virological suppression. METHODS: We longitudinally analyzed 63 long-term aviremic HIV-infected adults. Using blood DNA methylation patterns, we calculated EAA measures based on 3 epigenetic clocks (Horvath's clock, PhenoAge, and GrimAge). We recorded the emergence of serious AIDS-related and non-AIDS-related events throughout the study to assess its association with EAA. RESULTS: All participants were on stable ART and were virologically suppressed. After 4 years of follow-up, PhenoAge-EAA and GrimAge-EAA showed no differences, whereas Horvath-EAA slightly decreased (median difference, -0.53 years; P = .015). Longitudinal changes in EAA measures were independent of changes in CD4 cell counts, the ART regimen, or other HIV-related factors. Nineteen percent of participants experienced a serious clinical event during the study. Horvath-EAA was significantly higher at baseline in participants with clinical events (P = .027). After adjusting for confounders, we found a trend toward an association of higher levels of all EAA measures at baseline with serious clinical events. CONCLUSIONS: Epigenetic aging did not accelerate in long-term aviremic HIV-infected adults after 4 years of successful ART. EAA measures deserve further study as potential tools for predicting clinical events.

A new monocyte epigenetic clock reveals nonlinear effects of alcohol consumption on biological aging in three independent cohorts (N = 2242).

BACKGROUND: Assessing the effect of alcohol consumption on biological age is essential for understanding alcohol use-related comorbidities and mortality. Previously developed epigenetic clocks are mainly based on DNA methylation in heterogeneous cell types, which provide limited knowledge on the impacts of alcohol consumption at the individual cellular level. Evidence shows that monocytes play an important role in both alcohol-induced pathophysiology and the aging process. In this study, we developed a novel monocyte-based DNA methylation clock (MonoDNAmAge) to assess the impact of alcohol consumption on monocyte age. METHODS: A machine learning method was applied to select a set of chronological age-associated DNA methylation CpG sites from 1202 monocyte methylomes. Pearson correlation was tested between MonoDNAmAge and chronological age in three independent cohorts (Ntotal  = 2242). Using the MonoDNAmAge clock and four established clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge), we then evaluated the effect of alcohol consumption on epigenetic aging in the three cohorts [i.e., Yale Stress Center Community Study (YSCCS), Veteran Aging Cohort Study (VACS), Women's Interagency HIV Study (WIHS)] using linear and quadratic models. RESULTS: The MonoDNAmAge, comprised of 186 CpG sites, was moderately to strongly correlated with chronological age in the three cohorts (r = 0.90, p = 3.12E-181 in YSCCS; r = 0.54, p = 1.75E-96 in VACS; r = 0.66, p = 1.50E-60 in WIHS). More importantly, we found a nonlinear association between MonoDNAmAge and alcohol consumption (pmodel  = 4.55E-08, px2  = 7.80E-08 in YSCCS; pmodel  = 1.85E-02, px2  = 3.46E-02 in VACS). Heavy alcohol consumption increased EAAMonoDNAmAge up to 1.60 years while light alcohol consumption decreased EAAMonoDNAmAge up to 2.66 years. These results were corroborated by the four established epigenetic clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge). CONCLUSIONS: The results suggest a nonlinear relationship between alcohol consumption and its effects on epigenetic age. Considering adverse effects of alcohol consumption on health, nonlinear effects of alcohol use should be interpreted with caution. The findings, for the first time, highlight the complex effects of alcohol consumption on biological aging.

Hormetic association between perceived stress and human epigenetic aging based on resilience capacity.

Chronic stress is associated with deleterious health outcomes and mortality risk. A potential mechanism by which stress affects healthspan and lifespan is acceleration of cellular aging. Biologic age prediction models, termed epigenetic clocks, have been developed to estimate biologic age differences among people with the same chronologic age. This study evaluates the simultaneous impact of perceived chronic stress and resilience on Grim Age acceleration. The perceived stress score (PSS) and Connor-Davidson Resilience Scale (CD-RISC) were used to measure chronic stress and resilience, respectively. DNA was extracted from whole blood and analyzed using the MethylationEPIC BeadChip. GrimAge estimates were calculated using the methylation age calculator. Forty-seven business executives were categorized by levels of high or low stress and resilience scores. Compared to participants with low stress and high resilience, those with low stress and low resilience demonstrated the strongest association with Grim Age acceleration (p = 0.044), after controlling for age and estimated cellular proportions. Interestingly, among participants with low resilience, those with high perceived stress had a weaker association with Grim Age acceleration than participants with low perceived stress. However, among participants with high resilience, low perceived stress had a weaker association with Grim Age acceleration than high perceived stress. Our findings suggest that the impact of perceived stress on epigenetic age acceleration may differ based on resilience capacity, with a potential paradoxical beneficial effect among those with low resilience.

Association between depression and epigenetic age acceleration: A co-twin control study.

INTRODUCTION: Prior studies have shown inconsistent findings of an association between depression and epigenetic aging. DNA methylation (DNAm) age acceleration can measure biological aging. We adopted a robust co-twin control study design to examine whether depression is associated with DNAm age acceleration after accounting for the potential confounding influences of genetics and family environment. METHODS: We analyzed data on a sub-cohort of the Vietnam Era Twin Registry. A total of 291 twins participated at baseline and 177 at follow-up visit after a mean of 11.7 years, with 111 participants having DNA samples for both time points. Depression was measured using the Beck Depression Inventory II (BDI-II). Six measures of DNAm age acceleration were computed at each time point, including Horvath's DNAm age acceleration (HorvathAA), intrinsic epigenetic age acceleration (IEAA), Hannum's DNAm age acceleration (HannumAA), extrinsic epigenetic age acceleration (EEAA), GrimAge acceleration (GrimAA), and PhenoAge acceleration (PhenoAA). Mixed-effects modeling was used to assess the within-pair association between depression and DNAm age acceleration. RESULTS: At baseline, a 10-unit higher BDI-II total score was associated with HannumAA (0.73 years, 95% confidence interval [CI] 0.13-1.33, p = .019) and EEAA (0.94 years, 95% CI 0.22-1.66, p = .012). At follow-up, 10-unit higher BDI-II score was associated with PhenoAA (1.32 years, 95% CI 0.18-2.47, p = .027). CONCLUSION: We identified that depression is associated with higher levels of DNAm age acceleration. Further investigation is warranted to better understand the underlying mechanisms for the potential causal relationship between depression and accelerated aging.

Exploring the relationship between DNA methylation age measures and psychoneurological symptoms in women with early-stage breast cancer.

PURPOSE: The epigenetic clock has been acknowledged as an indicator for molecular aging, but few studies have examined possible associations of DNA methylation (DNAm) age or age acceleration (AA) with symptom burden in individuals who are treated for cancer. This study explored the association of DNAm age or AA with psychoneurological (PN) symptoms, including cognitive impairment, fatigue, sleep disturbances, pain, and depressive symptoms, in breast cancer survivors over a 2-year period. METHODS: We measured PN symptoms using reliable instruments and DNAm levels by Infinium HumanMethylation450K BeadChip (N = 72). DNAm age was calculated by the Horvath, Grim, and Hannum-based intrinsic and extrinsic age estimations. AA was defined by the residual regressing estimated epigenetic age on chronological age. Mixed regression models were fitted for AA and changes in AA to study the association over time. Separate linear regression models and a mixed-effects model were fitted for AA at each time point. RESULTS: Horvath-AA, Grim-AA, and extrinsic epigenetic AA were significantly changed over time, while intrinsic epigenetic AA did not exhibit any temporal changes. Increased AA was associated with greater anxiety and fatigue, as well as worse cognitive memory, adjusting for race, BMI, income, chemotherapy, radiation therapy, and chronological age. Increased DNAm age was associated with greater anxiety over 2 years. CONCLUSION: Our findings suggest DNAm age and AA may be associated with PN symptoms over the course of cancer treatment and survivorship. Some PN symptoms may be amenable to preventive interventions targeted to epigenetic clocks that influence aging-associated processes.

Differences in DNA Methylation-Based Age Prediction Within Twin Pairs Discordant for Cancer.

DNA methylation-based age acceleration (DNAmAA) is associated with cancer, with both cancer tissue and blood showing increased DNAmAA. We aimed to investigate whether DNAmAA is associated with cancer risk within twin pairs discordant for cancer, and whether DNAmAA has the potential to serve as a biomarker for such. The study included 47 monozygotic and 48 same-sex-dizygotic cancer-discordant twin pairs from the Finnish Twin Cohort study with blood samples available between 17 and 31 years after the cancer diagnosis. We studied all cancers (95 pairs), then separately breast cancer (24 pairs) and all sites other than breast cancer (71 pairs). DNAmAA was calculated for seven models: Horvath, Horvath intrinsic epigenetic age acceleration, Hannum, Hannum intrinsic epigenetic age acceleration, Hannum extrinsic epigenetic age acceleration, PhenoAge and GrimAge. Within-pair differences in DNAmAA were analyzed by paired t tests and linear regression. Twin pairs sampled before cancer diagnosis did not differ significantly in DNAmAA. However, the within-pair differences in DNAmAA before cancer diagnosis increased significantly the closer the cancer diagnosis was, and this acceleration extended for years after the diagnosis. Pairs sampled after the diagnosis differed for DNAmAA with the Horvath models capturing cancer diagnosis-associated DNAmAA across all three cancer groupings. The results suggest that DNAmAA in blood is associated with cancer diagnosis. This may be due to epigenetic alterations in relation to cancer, its treatment or associated lifestyle changes. Based on the current study, the biomarker potential of DNAmAA in blood appears to be limited.

Association of Phenotypic Aging Marker with comorbidities, frailty and inflammatory markers in people living with HIV.

BACKGROUND: Aging characteristics in people living with HIV (PLWH) are heterogeneous, and the identification of risk factors associated with aging-related comorbidities such as neurocognitive impairment (NCI) and frailty is important. We evaluated predictors of novel aging markers, phenotypic age (PhenoAge) and phenotypic age acceleration (PAA) and their association with comorbidities, frailty, and NCI. METHODS: In a cohort of PLWH and age- and sex-matched HIV-negative controls, we calculated PhenoAge using chronological age and 9 biomarkers from complete blood counts, inflammatory, metabolic-, liver- and kidney-related parameters. PAA was calculated as the difference between chronological age and PhenoAge. Multivariate logistic regression models were used to identify the factors associated with higher (>median) PAA. Area under the receiver operating characteristics curve (AUROC) was used to assess model discrimination for frailty. RESULTS: Among 333 PLWH and 102 HIV-negative controls (38% female), the median phenotypic age (49.4 vs. 48.5 years, p = 0.54) and PAA (- 6.7 vs. -7.5, p = 0.24) was slightly higher and PAA slightly less in PLWH although this did not reach statistical significance. In multivariate analysis, male sex (adjusted odds ratio = 1.68 [95%CI = 1.03-2.73]), current smoking (2.74 [1.30-5.79]), diabetes mellitus (2.97 [1.48-5.99]), hypertension (1.67 [1.02-2.72]), frailty (3.82 [1.33-10.93]), and higher IL-6 levels (1.09 [1.04-1.15]), but not HIV status and NCI, were independently associated with higher PAA. PhenoAge marker discriminated frailty better than chronological age alone (AUROC: 0.75 [0.66-0.85] vs. 0.65 [0.55-0.77], p = 0.04). In the analysis restricted to PLWH, PhenoAge alone predicted frailty better than chronological age alone (AUROC: 0.7412 vs. 0.6499, P = 0.09) and VACS index (AUROC: 0.7412 vs. 0.6811, P = 0.34) despite not statistically significant. CONCLUSIONS: While PLWH did not appear to have accelerated aging in our cohort, the phenotypic aging marker was significantly associated with systemic inflammation, frailty, and cardiovascular disease risk factors. This simple aging marker could be useful to identify high-risk PLWH within a similar chronological age group.