DNA methylation age of blood predicts all-cause mortality in later life

Multifunctional scaffolds for bone repair following age-related biological decline: Promising prospects for smart biomaterial-driven technologies

The repair of large bone defects due to trauma, disease, and infection can be exceptionally challenging in the elderly. Despite best clinical practice, bone regeneration within contemporary, surgically implanted synthetic scaffolds is often problematic, inconsistent, and insufficient where additional osteobiological support is required to restore bone. Emergent smart multifunctional biomaterials may drive important and dynamic cellular crosstalk that directly targets, signals, stimulates, and promotes an innate bone repair response following age-related biological decline and when in the presence of disease or infection. However, their role remains largely undetermined. By highlighting their mechanism/s and mode/s of action, this review spotlights smart technologies that favorably align in their conceivable ability to directly target and enhance bone repair and thus are highly promising for future discovery for use in the elderly. The four degrees of interactive scaffold smartness are presented, with a focus on bioactive, bioresponsive, and the yet-to-be-developed autonomous scaffold activity. Further, cell- and biomolecular-assisted approaches were excluded, allowing for contemporary examination of the capabilities, demands, vision, and future requisites of next-generation biomaterial-induced technologies only. Data strongly supports that smart scaffolds hold significant promise in the promotion of bone repair in patients with a reduced osteobiological response. Importantly, many techniques have yet to be tested in preclinical models of aging. Thus, greater clarity on their proficiency to counteract the many unresolved challenges within the scope of aging bone is highly warranted and is arguably the next frontier in the field. This review demonstrates that the use of multifunctional smart synthetic scaffolds with an engineered strategy to circumvent the biological insufficiencies associated with aging bone is a viable route for achieving next-generation therapeutic success in the elderly population.

Interplay of education and DNA methylation age on cognitive impairment: insights from the Health and Retirement Study

Few studies have assessed the association of educational attainment on dementia and cognitive impairment through DNA methylation age acceleration, while accommodating exposure-mediator interaction effects. We evaluated the mediation role of six epigenetic clocks with dementia, cognitive impairment non-dementia, and normal cognition, while accommodating exposure-mediator interaction effects. To understand the joint association of low education (≤12 years) and DNA methylation age acceleration (yes/no) in relation to cognitive impairment, we used weighted logistic regression, adjusting for chronological age, sex, race/ethnicity, and cell type composition. We performed four-way mediation and interaction decomposition analysis. Analyses were conducted on 2016 venous blood study participants from the Health and Retirement Study (N = 3724). Both GrimAge acceleration (OR = 1.6 95%CI 1.3-2.1) and low educational attainment (OR = 2.4 95%CI 1.9-3.0) were associated with higher odds of cognitive impairment in a mutually adjusted logistic model. We found additive interaction associations between low education and GrimAge acceleration on dementia. We observed that 6-8% of the association of education on dementia was mediated through GrimAge acceleration. While mediation effects were small, the portion of the association of education on dementia due to additive interaction with GrimAge acceleration was between 23.6 and 29.2%. These results support the interplay of social disadvantage and biological aging processes on impaired cognition.

Prenatal and early-life air pollutant exposure and epigenetic aging acceleration

BACKGROUND

This study investigated the association of prenatal and early childhood exposure to air pollution with epigenetic age acceleration (EAA) at six years of age using the Environment and Development of Children Cohort (EDC Cohort) MATERIALS & METHODS: Air pollution, including particulate matter [< 2.5 µm (PM2.5) and < 10 µm (PM10) in an aerodynamic diameter], nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were estimated based on the residential address for two periods: 1) during the whole pregnancy, and 2) for one year before the follow-up in children at six years of age. The methylation levels in whole blood at six years of age were measured, and the methylation clocks, including Horvath's clock, Horvath's skin and blood clock, PedBE, and Wu's clock, were estimated. Multivariate linear regression models were constructed to analyze the association between EAA and air pollutants.

RESULTS

A total of 76 children in EDC cohort were enrolled in this study. During the whole pregnancy, interquartile range (IQR) increases in exposure to PM2.5 (4.56 μg/m3) and CO (0.156 ppm) were associated with 0.406 years and 0.799 years of EAA (Horvath's clock), respectively. An IQR increase in PM2.5 (4.76 μg/m3) for one year before the child was six years of age was associated with 0.509 years of EAA (Horvath's clock) and 0.289 years of EAA (Wu's clock). PM10 (4.30 μg/m3) and O3 (0.003 ppm) exposure in the period were also associated with EAA in Horvath's clock (0.280 years) and EAA in Horvath's skin and blood clock (0.163 years), respectively.

CONCLUSION

We found that prenatal and childhood exposure to ambient air pollutants is associated with EAA among children. The results suggest that air pollution could induce excess biological aging even in prenatal and early life.

Newborn DNA methylation age differentiates long-term weight trajectories: the Boston Birth Cohort

BACKGROUND

Gestational age (GEAA) estimated by newborn DNA methylation (GAmAge) is associated with maternal prenatal exposures and immediate birth outcomes. However, the association of GAmAge with long-term overweight or obesity (OWO) trajectories is yet to be determined.

METHODS

GAmAge was calculated for 831 children from a US predominantly urban, low-income, multi-ethnic birth cohort based on cord blood DNA methylation profile using Illumina EPIC array. Repeated anthropometric measurements aligned with pediatric primary care schedule allowed us to calculate body-mass-index percentiles (BMIPCT) at specific age and to define long-term weight trajectories from birth to 18 years.

RESULTS

GAmAge was associated with BMIPCT trajectories, defined by 4 groups: stable (consistent OWO: "early OWO"; constant normal weight: "NW") or non-stable (OWO by year 1 of follow-up: "late OWO"; OWO by year 6 of follow-up: "NW to very late OWO"). GAmAge differentiated between the group with consistently normal BMIPCT pattern and the non-stable groups with late and very late OWO development. Such differentiation was observed in the age periods of birth to 1year, 3years, 6years, 10years, and 14years (p < 0.05 for all). The findings persisted after adjusting for GEAA, maternal smoking, delivery method, and child's sex in multivariate models. Birth weight was a mediator for the GAmAge effect on OWO status for specific groups at multiple age periods.

CONCLUSIONS

GAmAge is associated with BMIPCT trajectories from birth to age 18 years, independent of GEAA and birth weight. If further confirmed, GAmAge may serve as an early biomarker for predicting BMI trajectory to inform early risk assessment and prevention of OWO.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT03228875).

A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans

Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.

Common DNA sequence variation influences epigenetic aging in African populations

Aging is associated with genome-wide changes in DNA methylation in humans, facilitating the development of epigenetic age prediction models. However, most of these models have been trained primarily on European-ancestry individuals, and none account for the impact of methylation quantitative trait loci (meQTL). To address these gaps, we analyzed the relationships between age, genotype, and CpG methylation in 3 understudied populations: central African Baka (n = 35), southern African ‡Khomani San (n = 52), and southern African Himba (n = 51). We find that published prediction methods yield higher mean errors in these cohorts compared to European-ancestry individuals, and find that unaccounted-for DNA sequence variation may be a significant factor underlying this loss of accuracy. We leverage information about the associations between DNA genotype and CpG methylation to develop an age predictor that is minimally influenced by meQTL, and show that this model remains accurate across a broad range of genetic backgrounds. Intriguingly, we also find that the older individuals and those exhibiting relatively lower epigenetic age acceleration in our cohorts tend to carry more epigenetic age-reducing genetic variants, suggesting a novel mechanism by which heritable factors can influence longevity.

Association of biological age acceleration with cardiac morphology, function, and incident heart failure: insights from UK Biobank participants

AIMS

Advanced age is associated with an increased risk of adverse cardiovascular events. The relationship between biological age acceleration (BAA), cardiac size, cardiac function, and heart failure (HF) is not well-defined.

METHODS AND RESULTS

Utilizing the UK Biobank cohort, we assessed biological age using the Klemera-Doubal and PhenoAge methods. BAA was quantified by residual analysis compared with chronological age. Cardiovascular magnetic resonance (CMR) imaging provided detailed insights into cardiac structure and function. We employed multivariate regression to examine links between BAA and CMR-derived cardiac phenotypes. Cox proportional hazard regression models analysis was applied to explore the causative relationship between BAA and HF. Additionally, Mendelian randomization was used to investigate the genetic underpinnings of these associations. A significant correlation was found between increased BAA and deleterious changes in cardiac structure, such as diminished left ventricular mass, lower overall ventricular volume, and reduced stroke volumes across ventricles and atria. Throughout a median follow-up of 13.8 years, participants with greater biological aging showed a heightened risk of HF [26% per standard deviation (SD) increase in KDM-BA acceleration, 95% confidence intervals (CI): 23-28%; 33% per SD increase in PhenoAge acceleration, 95% CI: 32-35%]. Mendelian randomization analysis suggests a likely causal link between BAA, vital cardiac metrics, and HF risk.

CONCLUSION

In this cohort, accelerated biological aging may serve as a risk indicator for altered cardiac dimensions, functionality, and the onset of heart failure among middle-aged and elderly adults. It holds promise as a focal point for evaluating risk and developing targeted interventions.

DNA methylation-estimated phenotypes, telomere length and risk of ischemic stroke: epigenetic age acceleration of screening and a Mendelian randomization study

BACKGROUND

Aging is a complex biological process that may be accelerated in certain pathological conditions. DNA methylation age (DNAmAge) has emerged as a biomarker for biological age, which can differ from chronological age. This research peels back the layers of the relationship between fast-forward aging and ischemic stroke, poking and prodding the potential two-way causal influences between stroke and biological aging indicators.

METHODS

We analyzed a cohort of ischemic stroke patients, comparing DNAmAge with chronological age to measure age acceleration. We assessed variations in age acceleration among stroke subtypes and between sexes. Using Mendelian randomization, we examined the causal links between stroke, aging biomarkers like telomere length, and age acceleration's effect on stroke risk.

RESULTS

Our investigation reveals a pronounced association between ischemic stroke and age acceleration, most notably in patients with cardioembolic strokes, who exhibited a striking median difference of 9 years between DNAmAge and chronological age. Furthermore, age acceleration differed significantly across stroke subtypes and was higher in women than in men. In terms of causality, MR analysis indicated a modest negative effect of stroke on telomere length, but no causal effect of age phenotypes on stroke or its subtypes. However, some indication of a potential causal effect of ischemic stroke on PhenoAge acceleration was observed.

CONCLUSION

The study provides insight into the relationship between DNAmAge and ischemic stroke, particularly cardioembolic stroke, and suggests possible gender differences. These insights carry profound clinical significance and set stage for future investigations into the entwined pathways of stroke and accelerated aging.

An atlas of genetic effects on the monocyte methylome across European and African populations

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Cell-type-specific effects of age and sex on human cortical neurons

Altered transcriptional and epigenetic regulation of brain cell types may contribute to cognitive changes with advanced age. Using single-nucleus multi-omic DNA methylation and transcriptome sequencing (snmCT-seq) in frontal cortex from young adult and aged donors, we found widespread age- and sex-related variation in specific neuron types. The proportion of inhibitory SST- and VIP-expressing neurons was reduced in aged donors. Excitatory neurons had more profound age-related changes in their gene expression and DNA methylation than inhibitory cells. Hundreds of genes involved in synaptic activity, including EGR1, were less expressed in aged adults. Genes located in subtelomeric regions increased their expression with age and correlated with reduced telomere length. We further mapped cell-type-specific sex differences in gene expression and X-inactivation escape genes. Multi-omic single-nucleus epigenomes and transcriptomes provide new insight into the effects of age and sex on human neurons.

Epigenetics, Microbiome and Personalized Medicine: Focus on Kidney Disease

Personalized medicine, which involves modifying treatment strategies/drug dosages based on massive laboratory/imaging data, faces large statistical and study design problems. The authors believe that the use of continuous multidimensional data, such as those regarding gut microbiota, or binary multidimensional systems properly transformed into a continuous variable, such as the epigenetic clock, offer an advantageous scenario for the design of trials of personalized medicine. We will discuss examples focusing on kidney diseases, specifically on IgA nephropathy. While gut dysbiosis can provide a treatment strategy to restore the standard gut microbiota using probiotics, transforming epigenetic omics data into epigenetic clocks offers a promising tool for personalized acute and chronic kidney disease care. Epigenetic clocks involve a complex transformation of DNA methylome data into estimated biological age. These clocks can identify people at high risk of developing kidney problems even before symptoms appear. Some of the effects of both the epigenetic clock and microbiota on kidney diseases seem to be mediated by endothelial dysfunction. These "big data" (epigenetic clocks and microbiota) can help tailor treatment plans by pinpointing patients likely to experience rapid declines or those who might not need overly aggressive therapies.

Social and environmental predictors of gut microbiome age in wild baboons

Understanding why some individuals age faster than others is essential to evolutionary biology and geroscience, but measuring variation in biological age is difficult. One solution may lie in measuring gut microbiome composition because microbiota change with many age-related factors (e.g., immunity and behavior). Here we create a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting "microbiome clock" predicts host chronological age. Deviations from the clock's predictions are linked to demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and high social status (both sexes). However, an individual's "microbiome age" does not predict the attainment of developmental milestones or lifespan. Hence, the microbiome clock accurately reflects age and some social and environmental conditions, but not the pace of development or mortality risk.

Epigenome-wide association study of prostate cancer in African American men identified differentially methylated genes

INTRODUCTION

Men with African ancestry have the highest incidence and mortality rates of prostate cancer (PCa) worldwide.

METHODS

This study aimed to identify differentially methylated genes between tumor vs. adjacent normal and aggressive vs. indolent PCa in 121 African American patients. Epigenome-wide DNA methylation patterns in tumor DNA were assessed using the human Illumina Methylation EPIC V1 array.

RESULTS

Around 5,139 differentially methylated CpG-sites (q < 0.01, lΔβl > 0.2) were identified when comparing normal vs. tumor, with an overall trend of hypermethylation in prostate tumors.  Multiple representative differentially methylated regions (DMRs), including immune-related genes, such as CD40, Galectin3, OX40L, and STING, were detected in prostate tumors when compared to adjacent normal tissues. Based on an epigenetic clock model, we observed that tumors' total number of stem cell divisions and the stem cell division rate were significantly higher than adjacent normal tissues. Regarding PCa aggressiveness, 2,061 differentially methylated CpG-sites (q < 0.05, lΔβl > .05) were identified when the grade group (GG)1 was compared with GG4/5. Among these 2,061 CpG sites, 155 probes were consistently significant in more than one comparison. Among these genes, several immune system genes, such as COL18A1, S100A2, ITGA4, HLA-C, and ADCYAP1, have previously been linked to tumor progression in PCa.

CONCLUSION

Several differentially methylated genes involved in immune-oncologic pathways associated with disease risk or aggressiveness were identified. In addition, 261 African American-specific differentially methylated genes related to the risk of PCa were identified. These results can shedlight on potential mechanisms contributing to PCa disparities in the African American Population.

Association Between Phenotypic Age and the Risk of Mortality in Patients With Heart Failure: A Retrospective Cohort Study

BACKGROUND

Chronological age (CA) is an imperfect proxy for the true biological aging state of the body. As novel measures of biological aging, Phenotypic age (PhenoAge) and Phenotypic age acceleration (PhenoAgeAccel), have been shown to identify morbidity and mortality risks in the general population.

HYPOTHESIS

PhenoAge and PhenoAgeAccel might be associated with mortality in heart failure (HF) patients.

METHODS

This cohort study extracted adult data from the National Health and Nutrition Examination Survey (NHANES) databases. Weighted univariable and multivariable Cox models were performed to analyze the effect of PhenoAge and PhenoAgeAccel on all-cause mortality in HF patients, and hazard ratio (HR) with 95% confidence intervals (CI) was calculated.

RESULTS

In total, 845 HF patients were identified, with 626 all-cause mortality patients. The findings suggested that (1) each 1- and 10-year increase in PhenoAge were associated with a 3% (HR = 1.03, 95% CI: 1.03-1.04) and 41% (HR = 1.41, 95% CI: 1.29-1.54) increased risk of all-cause mortality, respectively; (2) when the PhenoAgeAccel < 0 as reference, the ≥ 0 group was associated with higher risk of all-cause mortality (HR = 1.91, 95% CI = 1.49-2.45). Subgroup analyses showed that (1) older PhenoAge was associated with an increased risk of all-cause mortality in all subgroups; (2) when the PhenoAgeAccel < 0 as a reference, PhenoAgeAccel ≥ 0 was associated with a higher risk of all-cause mortality in all subgroups.

CONCLUSION

Older PhenoAge was associated with an increased risk of all-cause mortality in HF patients. PhenoAge and PhenoAgeAccel can be used as convenient tools to facilitate the identification of at-risk individuals with HF and the evaluation of intervention efficacy.

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.

Epigenetic clock in the aorta and age-related endothelial dysfunction in mice

While epigenetic age (EA) of mouse blood can be determined using DNA methylation analysis at three CpG sites in the Prima1, Hsf4 and Kcns1 genes it is not known whether this approach is useful for predicting vascular biological age. In this study we validated the 3-CpG estimator for age prediction in mouse blood, developed a new predictive model for EA in mouse aorta, and assessed whether epigenetic age acceleration (EAA) measured with blood and aorta samples correlates with age-dependent endothelial dysfunction. Endothelial function was characterized in vivo by MRI in 8-96-week-old C57BL/6 mice. Arterial stiffness was measured by USG-doppler. EA-related changes within 41 CpG sites in Prima1, Kcns1 and Hsf4 loci, were analyzed in the aorta and blood using bisulfite amplicon high-throughput sequencing. Progressive age-dependent endothelial dysfunction and changes in arterial stiffness were observed in 36-96-week-old C57BL/6 mice. Methylation levels of the investigated loci correlated with chronological age in blood and the aorta. The new model for EA estimation in aorta included three cytosines located in the Kcns1 and Hsf4, explained R2 = 87.8% of the variation in age, and predicted age with an mean absolute error of 9.6 weeks in the independent test set. EAA in the aorta was associated with endothelial dysfunction in the abdominal aorta and femoral artery what was consistent with the EAA direction estimated in blood samples. The rate of vascular biological ageing in mice, reflected by the age-dependent systemic endothelial dysfunction, could be estimated using DNA methylation measurements at three loci in aorta and blood samples.

Models for depression recognition and efficacy assessment based on clinical and sequencing data

Major depression is a complex psychiatric disorder that includes genetic, neurological, and cognitive factors. Early detection and intervention can prevent progression, and help select the best treatment. Traditional clinical diagnosis tends to be subjective and misdiagnosed. Based on this, this study leverages clinical scale assessments and sequencing data to construct disease prediction models. Firstly, data undergoes preprocessing involving normalization and other requisite procedures. Feature engineering is then applied to curate subsets of features, culminating in the construction of a model through the implementation of machine learning and deep learning algorithms. In this study, 18 features with significant differences between patients and healthy controls were selected. The depression recognition model was constructed by deep learning with an accuracy of 87.26 % and an AUC of 91.56 %, which can effectively distinguish patients with depression from healthy controls. In addition, 33 features selected by recursive feature elimination method were used to construct a prognostic effect model of patients after 2 weeks of treatment, with an accuracy of 75.94 % and an AUC of 83.33 %. The results show that the deep learning algorithm based on clinical and sequencing data has good accuracy and provides an objective and accurate method for the diagnosis and pharmacodynamic prediction of depression. Furthermore, the selected differential features can serve as candidate biomarkers to provide valuable clues for diagnosis and efficacy prediction.

DNA Methylation-derived biological age and long-term mortality risk in subjects with type 2 diabetes

BACKGROUND

Individuals with type 2 diabetes (T2D) face an increased mortality risk, not fully captured by canonical risk factors. Biological age estimation through DNA methylation (DNAm), i.e. the epigenetic clocks, is emerging as a possible tool to improve risk stratification for multiple outcomes. However, whether these tools predict mortality independently of canonical risk factors in subjects with T2D is unknown.

METHODS

Among a cohort of 568 T2D patients followed for 16.8 years, we selected a subgroup of 50 subjects, 27 survived and 23 deceased at present, passing the quality check and balanced for all risk factors after propensity score matching. We analyzed DNAm from peripheral blood leukocytes using the Infinium Human MethylationEPIC BeadChip (Illumina) to evaluate biological aging through previously validated epigenetic clocks and assess the DNAm-estimated levels of selected inflammatory proteins and blood cell counts. We tested the associations of these estimates with mortality using two-stage residual-outcome regression analysis, creating a reference model on data from the group of survived patients.

RESULTS

Deceased subjects had higher median epigenetic age expressed with DNAmPhenoAge algorithm (57.49 [54.72; 60.58] years. vs. 53.40 [49.73; 56.75] years; p = 0.012), and accelerated DunedinPoAm pace of aging (1.05 [1.02; 1.11] vs. 1.02 [0.98; 1.06]; p = 0.012). DNAm PhenoAge (HR 1.16, 95% CI 1.05-1.28; p = 0.004) and DunedinPoAm (HR 3.65, 95% CI 1.43-9.35; p = 0.007) showed an association with mortality independently of canonical risk factors. The epigenetic predictors of 3 chronic inflammation-related proteins, i.e. CXCL10, CXCL11 and enRAGE, C-reactive protein methylation risk score and DNAm-based estimates of exhausted CD8 + T cell counts were higher in deceased subjects when compared to survived.

CONCLUSIONS

These findings suggest that biological aging, as estimated through existing epigenetic tools, is associated with mortality risk in individuals with T2D, independently of common risk factors and that increased DNAm-surrogates of inflammatory protein levels characterize deceased T2D patients. Replication in larger cohorts is needed to assess the potential of this approach to refine mortality risk in T2D.

Psychoneuroimmunology in multiple myeloma and autologous hematopoietic stem cell transplant: Opportunities for research among patients and caregivers

Multiple myeloma (MM) is an incurable cancer and is the leading indication for autologous hematopoietic stem cell transplantation (HSCT). To be eligible for HSCT, a patient must have a caregiver, as caregivers play a central role in HSCT preparation and recovery. MM patients remain on treatment indefinitely, and thus patients and their caregivers face long-term challenges including the intensity of HSCT and perpetual therapy after transplant. Importantly, both patients and their caregivers show heightened depressive and anxiety symptoms, with dyadic correspondence evidenced and caregivers' distress often exceeding that of patients. An extensive psychoneuroimmunology (PNI) literature links distress with health via immune and neuroendocrine dysregulation as well as biological aging. However, data on PNI in the context of multiple myeloma - in patients or caregivers - are remarkably limited. Distress in MM patients has been associated with poorer outcomes including higher inflammation, greater one year post-HSCT hospital readmissions, and worse overall survival. Further, anxiety and depression are linked to biological aging and may contribute to the poor long-term health of both patients and caregivers. Because MM generally affects older adults, individual differences in biological aging may represent an important modifier of MM biology and HSCT treatment outcomes. There are a number of clinical scenarios in which biologically younger people could be prescribed more intensive therapies, with potential for greater benefit, by using a personalized cancer therapy approach based on the quantification of physiologic reserve. Further, despite considerable psychological demands, the effects of distress on health among MM caregivers is largely unexamined. Within this context, the current critical review highlights gaps in knowledge at the intersection of HSCT, inflammation, and biological aging in the context of MM. Research in this area hold promise for opportunities for novel and impactful psychoneuroimmunology (PNI) research to enhance health outcomes, quality of life, and longevity among both MM patients and their caregivers.

Socioeconomic Status, Lifestyle, and DNA Methylation Age Among Racially and Ethnically Diverse Adults: NIMHD Social Epigenomics Program

Importance

Variation in DNA methylation at specific loci estimates biological age, which is associated with morbidity, mortality, and social experiences. Aging estimates known as epigenetic clocks, including the Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), were trained on data predominately from individuals of European ancestry; however, limited research has explored DunedinPACE in underrepresented populations experiencing health disparities.

Objective

To investigate associations of neighborhood and individual sociobehavioral factors with biological aging in a racially and ethnically diverse population.

Design, Setting, and Participants

This cohort study, part of the Multiethnic Cohort study conducted from May 1993 to September 1996 to examine racial and ethnic disparities in chronic diseases, integrated biospecimen and self-reported data collected between April 2004 and November 2005 from healthy Hawaii residents aged 45 to 76 years. These participants self-identified as of Japanese American, Native Hawaiian, or White racial and ethnic background. Data were analyzed from January 2022 to May 2024.

Main Outcomes and Measures

DNA methylation data were generated from monocytes enriched from cryopreserved lymphocytes and used to derive DunedinPACE scores from November 2017 to June 2021. Neighborhood social economic status (NSES) was estimated from 1990 US Census Bureau data to include factors such as educational level, occupation, and income. Individual-level factors analyzed included educational level, body mass index (BMI), physical activity (PA), and diet quality measured by the Healthy Eating Index (HEI). Linear regression analysis of DunedinPACE scores was used to examine their associations with NSES and sociobehavioral variables.

Results

A total of 376 participants were included (113 [30.1%] Japanese American, 144 [38.3%] Native Hawaiian, and 119 [31.6%] White; 189 [50.3%] were female). Mean (SE) age was 57.81 (0.38) years. Overall, mean (SE) DunedinPACE scores were significantly higher among females than among males (1.28 [0.01] vs 1.25 [0.01]; P = .005); correlated negatively with NSES (R = -0.09; P = .08), HEI (R = -0.11; P = .03), and educational attainment (R = -0.15; P = .003) and positively with BMI (R = 0.31; P < .001); and varied by race and ethnicity. Native Hawaiian participants exhibited a higher mean (SE) DunedinPACE score (1.31 [0.01]) compared with Japanese American (1.25 [0.01]; P < .001) or White (1.22 [0.01]; P < .001) participants. Controlling for age, sex, HEI, BMI, and NSES, linear regression analyses revealed a negative association between educational level and DunedinPACE score among Japanese American (β, -0.005 [95% CI, -0.013 to 0.002]; P = .03) and Native Hawaiian (β, -0.003 [95% CI, -0.011 to 0.005]; P = .08) participants, yet this association was positive among White participants (β, 0.007; 95% CI, -0.001 to 0.015; P = .09). Moderate to vigorous PA was associated with lower DunedinPACE scores only among Native Hawaiian participants (β, -0.006; 95% CI, -0.011 to -0.001; P = .005), independent of NSES.

Conclusions and Relevance

In this study of a racially and ethnically diverse sample of 376 adults, low NSES was associated with a higher rate of biological aging measured by DunedinPACE score, yet individual-level factors such as educational level and physical activity affected this association, which varied by race and ethnicity. These findings support sociobehavioral interventions in addressing health inequities.

Sociodemographic and Lifestyle Factors and Epigenetic Aging in US Young Adults: NIMHD Social Epigenomics Program

Importance

Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most research is based on samples of older adults who already have measurable chronic disease.

Objective

To investigate whether and how sociodemographic and lifestyle characteristics are associated with biological aging in a younger adult sample across a wide array of epigenetic clock measures.

Design, Setting, and Participants

This cohort study was conducted using data from the National Longitudinal Study of Adolescent to Adult Health, a US representative cohort of adolescents in grades 7 to 12 in 1994 followed up for 25 years to 2018 over 5 interview waves. Participants who provided blood samples at wave V (2016-2018) were analyzed, with samples tested for DNA methylation (DNAm) in 2021 to 2024. Data were analyzed from February 2023 to May 2024.

Exposure

Sociodemographic (sex, race and ethnicity, immigrant status, socioeconomic status, and geographic location) and lifestyle (obesity status by body mass index [BMI] in categories of reference range or underweight [<25], overweight [25 to <30], obesity [30 to <40], and severe obesity [≥40]; exercise level; tobacco use; and alcohol use) characteristics were assessed.

Main Outcome and Measure

Biological aging assessed from banked blood DNAm using 16 epigenetic clocks.

Results

Data were analyzed from 4237 participants (mean [SD] age, 38.4 [2.0] years; percentage [SE], 51.3% [0.01] female and 48.7% [0.01] male; percentage [SE], 2.7% [<0.01] Asian or Pacific Islander, 16.7% [0.02] Black, 8.7% [0.01] Hispanic, and 71.0% [0.03] White). Sociodemographic and lifestyle factors were more often associated with biological aging in clocks trained to estimate morbidity and mortality (eg, PhenoAge, GrimAge, and DunedinPACE) than clocks trained to estimate chronological age (eg, Horvath). For example, the β for an annual income less than $25 000 vs $100 000 or more was 1.99 years (95% CI, 0.45 to 3.52 years) for PhenoAgeAA, 1.70 years (95% CI, 0.68 to 2.72 years) for GrimAgeAA, 0.33 SD (95% CI, 0.17 to 0.48 SD) for DunedinPACE, and -0.17 years (95% CI, -1.08 to 0.74 years) for Horvath1AA. Lower education, lower income, higher obesity levels, no exercise, and tobacco use were associated with faster biological aging across several clocks; associations with GrimAge were particularly robust (no college vs college or higher: β = 2.63 years; 95% CI, 1.67-3.58 years; lower vs higher annual income: <$25 000 vs ≥$100 000: β = 1.70 years; 95% CI, 0.68-2.72 years; severe obesity vs no obesity: β = 1.57 years; 95% CI, 0.51-2.63 years; no weekly exercise vs ≥5 bouts/week: β = 1.33 years; 95% CI, 0.67-1.99 years; current vs no smoking: β = 7.16 years; 95% CI, 6.25-8.07 years).

Conclusions and Relevance

This study found that important social and lifestyle factors were associated with biological aging in a nationally representative cohort of younger adults. These findings suggest that molecular processes underlying disease risk may be identified in adults entering midlife before disease is manifest and inform interventions aimed at reducing social inequalities in heathy aging and longevity.

Bidirectional relationship between epigenetic age and brain health events

Chronological age offers an imperfect estimate of the molecular changes that occur with aging. Epigenetic age, which is derived from DNA methylation data, provides a more nuanced representation of aging-related biological processes. This study examines the bidirectional relationship between epigenetic age and the occurrence of brain health events (stroke, dementia, and late-life depression). Using data from the Health and Retirement Study, we analyzed blood samples from over 4,000 participants to determine how epigenetic age relates to past and future brain health events. Study participants with a prior brain health event prior to blood collection were 4% epigenetically older (beta 0.04, SE 0.01), suggesting that these conditions are associated with faster aging than that captured by chronological age. Furthermore, a one standard deviation increase in epigenetic age was associated with 70% higher odds of experiencing a brain health event in the next four years after blood collection (OR 1.70, 95%CI 1.16-2.50), indicating that epigenetic age is not just a consequence but also a predictor of poor brain health. Both results were replicated through Mendelian Randomization analyses, supporting their causal nature. Our findings support the utilization of epigenetic age as a useful biomarker to evaluate the role of interventions aimed at preventing and promoting recovery after a brain health event.

Maximizing Insights from Longitudinal Epigenetic Age Data: Simulations, Applications, and Practical Guidance

Background

Epigenetic Age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional - using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (i) their choice of model; (ii) the primary outcome (EA vs. EAA); and (iii) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.

Results

Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered an accelerated EA rate in males and an advanced EA that decelerates over time in children with higher birthweight.

Conclusion

Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

The influence of epigenetic biological age on key complications and outcomes in aneurysmal subarachnoid haemorrhage

BACKGROUND

We aimed to investigate the association between DNA-methylation biological age (B-age) calculated as age acceleration (ageAcc) and key aneurysmal subarachnoid haemorrhage (aSAH) complications such as vasospasm, delayed cerebral ischaemia (DCI), poor outcome, and mortality.

METHODS

We conducted a prospective study involving 277 patients with aSAH. B-age was determined in whole blood samples using five epigenetic clocks: Hannum's, Horvath's, Levine's and both versions of Zhang's clocks. Age acceleration was calculated as the residual obtained from regressing out the effect of C-age on the mismatch between C-age and B-age. We then tested the association between ageAcc and vasospasm, DCI and 12-month poor outcome (mRS 3-5) and mortality using linear regression models adjusted for confounders.

RESULTS

Average C-age was 55.0 years, with 66.8% being female. Vasospasm occurred in 143 cases (51.6%), DCI in 70 (25.3%) and poor outcomes in 99 (35.7%), with a mortality rate of 20.6%. Lower ageAcc was linked to vasospasm in Horvath's and Levine's clocks, whereas increased ageAcc was associated with 12-month mortality in Hannum's clock. No significant differences in ageAcc were found for DCI or poor outcome at 12 months with other clocks.

CONCLUSIONS

Our study indicates that B-age is independently associated with vasospasm and 12-month mortality in patients with aSAH. These findings underscore the potential role of epigenetics in understanding the pathophysiology of aSAH-related complications and outcomes.

Associations of childhood, adolescence, and midlife cognitive function with DNA methylation age acceleration in midlife

Prior studies showed increased age acceleration (AgeAccel) is associated with worse cognitive function among old adults. We examine the associations of childhood, adolescence and midlife cognition with AgeAccel based on DNA methylation (DNAm) in midlife. Data are from 359 participants who had cognition measured in childhood and adolescence in the Child Health and Development study, and had cognition, blood based DNAm measured during midlife in the Disparities study. Childhood cognition was measured by Raven's Progressive Matrices and Peabody Picture Vocabulary Test (PPVT). Adolescent cognition was measured only by PPVT. Midlife cognition included Wechsler Test of Adult Reading (WTAR), Verbal Fluency (VF), Digit Symbol (DS). AgeAccel measures including Horvath, Hannum, PhenoAge, GrimAge and DunedinPACE were calculated from DNAm. Linear regressions adjusted for potential confounders were utilized to examine the association between each cognitive measure in relation to each AgeAccel. There are no significant associations between childhood cognition and midlife AgeAccel. A 1-unit increase in adolescent PPVT, which measures crystalized intelligence, is associated with 0.048-year decrease of aging measured by GrimAge and this association is attenuated after adjustment for adult socioeconomic status. Midlife crystalized intelligence measure WTAR is negatively associated with PhenoAge and DunedinPACE, and midlife fluid intelligence measure (DS) is negatively associated with GrimAge, PhenoAge and DunedinPACE. AgeAccel is not associated with VF in midlife. In conclusion, our study showed the potential role of cognitive functions at younger ages in the process of biological aging. We also showed a potential relationship of both crystalized and fluid intelligence with aging acceleration.

The prognostic value of serum α-klotho in age-related diseases among the US population: A prospective population-based cohort study

Objective

α-Klotho is a potential biological marker of aging with satisfactory clinical applicability. However, its prognostic significance in age-related diseases has largely been undermined. Therefore, we aimed to report the prognostic value of serum α-klotho levels in age-related diseases.

Methods

Participants with available serum α-klotho data from the National Health and Nutrition Examination Survey (2007-2016) were included. Their survival status was collected at 7.62 ± 2.99 years after serum α-klotho data was collected, and the endpoint was all-cause and cardiovascular mortality. A Cox regression model was established to examine the association between serum α-klotho levels and all-cause and cardiovascular mortality.

Results

The present study included 13,746 U.S. adults with a survey-weighted mean age of 56.19 ± 10.42 years old. Of these, 52.2 % were female and 72.9 % were non-Hispanic whites. The optimal cutoff value of serum α-klotho for predicting all-cause mortality risk in the general population was 603.5 pg/ml. Individuals with low serum α-klotho (<603.5 pg/ml) had a significantly higher risk of all-cause (adjusted HR: 1.34(1.18-1.52), P < 0.001) and cardiovascular mortality (adjusted HR: 1.63(1.27-2.10), P < 0.001). Subgroup analysis showed that low serum α-klotho level was an independent risk factor for all-cause and cardiovascular mortality in people with hypertension, congestive heart failure, diabetes mellitus, and emphysema, while it was an independent risk factor for all-cause mortality in patients with renal insufficiency.

Conclusion

A low serum α-klotho concentration (<603.5 pg/ml) could serve as a marker of all-cause and cardiovascular mortality in the general population and in people with age-related diseases, including hypertension, congestive heart failure, diabetes mellitus, and emphysema.

Nature of epigenetic aging from a single-cell perspective

Age-related changes in DNA methylation (DNAm) form the basis of the most robust predictors of age-epigenetic clocks-but a clear mechanistic understanding of exactly which aspects of aging are quantified by these clocks is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the dynamics of tissue and single-cell DNAm in mice. We compare these changes during early development with those observed during adult aging in mice, and corroborate our analyses with a single-cell RNA sequencing analysis within the same multiomics dataset. We show that epigenetic aging involves co-regulated changes as well as a major stochastic component, and this is consistent with transcriptional patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns. Together, our analyses increase our understanding of the basis of epigenetic clocks and highlight potential opportunities for targeting aging and evaluating longevity interventions.

Short Sleep and Insomnia Are Associated With Accelerated Epigenetic Age

OBJECTIVE

Short sleep and insomnia are each associated with a greater risk of age-related disease, which suggests that insufficient sleep may accelerate biological aging. We examine whether short sleep and insomnia alone or together relates to epigenetic age among older adults.

METHODS

A total of 3795 men (46.3%) and women aged 56 to 100 years from the Health and Retirement Study were included. Insomnia was defined as reporting at least one insomnia symptom (difficulty falling asleep, waking up at night, or waking up too early in the morning) and feeling unrested when waking up most of the time. Those reporting <6 hours of bedtime were categorized as short sleepers. Three second- or third-generation epigenetic age acceleration clocks were derived from the 2016 Health and Retirement Study Venous Blood Study. The linear regression analysis was adjusted for age, sex, race/ethnicity, education, and obesity status.

RESULTS

Insomnia and short sleep were associated with acceleration of GrimAge of 0.49 (95% confidence interval [CI] = 0.03-0.94 years; p = .04) and 1.29 (95% CI = 0.52-2.07 years; p = .002) years, respectively, as well as a faster pace of aging (DunedinPACE; 0.018 [95% CI = 0.004-0.033; p = .02] and 0.022 [95% CI = -0.004 to 0.048; p = .11]). Compared with healthy sleepers, individuals with the combination of short sleep and insomnia had an accelerated GrimAge (0.97 years; 95% CI = 0.07-1.87 years, p = .04) and a greater DunedinPACE (0.032; 95% CI = 0.003-0.060, p = .04).

CONCLUSIONS

Our findings indicate that short sleep, insomnia, and the combination of the two are linked to epigenetic age acceleration, suggesting that these individuals have an older biological age that may contribute to risk of comorbidity and mortality.

Quantifying the stochastic component of epigenetic aging

DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock's predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66-75% of the accuracy underpinning Horvath's clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang's clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath's age acceleration in males and PhenoAge's age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.

Targeting the epigenetically older individuals for geroprotective trials: the use of DNA methylation clocks

Chronological age is the most important risk factor for the incidence of age-related diseases. The pace of ageing determines the magnitude of that risk and can be expressed as biological age. Targeting fundamental pathways of human aging with geroprotectors has the potential to lower the biological age and therewith prolong the healthspan, the period of life one spends in good health. Target populations for geroprotective interventions should be chosen based on the ageing mechanisms being addressed and the expected effect of the geroprotector on the primary outcome. Biomarkers of ageing, such as DNA methylation age, can be used to select populations for geroprotective interventions and as a surrogate outcome. Here, the use of DNA methylation clocks for selecting target populations for geroprotective intervention is explored.

The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual's survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual's aging status.

Relationships of depression and antidepressant use with epigenetic age acceleration and all-cause mortality among postmenopausal women

We investigated relations of depressive symptoms, antidepressant use, and epigenetic age acceleration with all-cause mortality risk among postmenopausal women. Data were analyzed from ≤1,900 participants in the Women's Health Initiative study testing four-way decomposition models. After a median 20.4y follow-up, 1,161 deaths occurred. Approximately 11% had elevated depressive symptoms (EDS+), 7% were taking antidepressant medication at baseline (ANTIDEP+), while 16.5% fell into either category (EDS_ANTIDEP+). Baseline ANTIDEP+, longitudinal transition into ANTIDEP+ and accelerated epigenetic aging directly predicted increased mortality risk. GrimAge DNA methylation age acceleration (AgeAccelGrim) partially mediated total effects of baseline ANTIDEP+ and EDS_ANTIDEP+ on all-cause mortality risk in socio-demographic factors-adjusted models (Pure Indirect Effect >0, P < 0.05; Total Effect >0, P < 0.05). Thus, higher AgeAccelGrim partially explained the relationship between antidepressant use and increased all-cause mortality risk, though only prior to controlling for lifestyle and health-related factors. Antidepressant use and epigenetic age acceleration independently predicted increased all-cause mortality risk. Further studies are needed in varying populations.

Predicting exacerbation of renal function by DNA methylation clock and DNA damage of urinary shedding cells: a pilot study

Recent reports have shown the feasibility of measuring biological age from DNA methylation levels in blood cells from specific regions identified by machine learning, collectively known as the epigenetic clock or DNA methylation clock. While extensive research has explored the association of the DNA methylation clock with cardiovascular diseases, cancer, and Alzheimer's disease, its relationship with kidney diseases remains largely unexplored. In particular, it is unclear whether the DNA methylation clock could serve as a predictor of worsening kidney function. In this pilot study involving 20 subjects, we investigated the association between the DNA methylation clock and subsequent deterioration of renal function. Additionally, we noninvasively evaluated DNA damage in urinary shedding cells using a previously reported method to examine the correlation with the DNA methylation clock and worsening kidney function. Our findings revealed that patients with an accelerated DNA methylation clock exhibited increased DNA damage in urinary shedding cells, along with a higher rate of eGFR decline. Moreover, in cases of advanced CKD (G4-5), the DNA damage in urinary shedding cells was significantly increased, highlighting the interplay between elevated DNA damage and eGFR decline. This study suggests the potential role of the DNA methylation clock and urinary DNA damage as predictive markers for the progression of chronic kidney disease.

Combined effect of unrelated donor age and HLA peptide-binding motif match status on HCT outcomes

ABSTRACT

An HLA-mismatched unrelated donor who is class I peptide-binding motif (PBM)-matched is preferred over a PBM-mismatched donor. We hypothesized that using a younger donor (aged ≤35 years vs >35 years) could compensate for the inferior overall survival (OS) associated with PBM mismatches. We compared 6 groups: HLA-matched/younger donor (n = 10 531), HLA-matched/older donor (n = 3572), PBM-matched/younger donor (n = 357), PBM-matched/older donor (n = 257), PBM-mismatched/younger donor (n = 616), and PBM-mismatched/older donor (n = 339) in patients undergoing transplantation with conventional graft-versus-host disease prophylaxis. In multivariate analysis, HLA-matched/younger donors were associated with superior OS relative to any other group. Pairwise comparisons showed that donor age significantly impacted OS in both HLA-matched and HLA-mismatched groups. Moreover, younger donors appeared to negate the detrimental effect of PBM mismatching: the PBM-matched/younger donor group had similar OS as the HLA-matched/older donor group and the PBM-mismatched/younger donor group had similar OS as the PBM-matched/older donor group. Our study suggests that older unrelated donor age and PBM mismatching confer similarly adverse effects on OS and the impacts are additive, a finding which may widen the "acceptable" donor pool. The best OS is observed with HLA-matched/younger donors and the worst with PBM-mismatched/older donors. These findings should be validated with other data sets and with posttransplantation cyclophosphamide-based prophylaxis.

The effect of epigenetic aging on neurodegenerative diseases: a Mendelian randomization study

Background

Aging has always been considered as a risk factor for neurodegenerative diseases, but there are individual differences and its mechanism is not yet clear. Epigenetics may unveil the relationship between aging and neurodegenerative diseases.

Methods

Our study employed a bidirectional two-sample Mendelian randomization (MR) design to assess the potential causal association between epigenetic aging and neurodegenerative diseases. We utilized publicly available summary datasets from several genome-wide association studies (GWAS). Our investigation focused on multiple measures of epigenetic age as potential exposures and outcomes, while the occurrence of neurodegenerative diseases served as potential exposures and outcomes. Sensitivity analyses confirmed the accuracy of the results.

Results

The results show a significant decrease in risk of Parkinson's disease with GrimAge (OR = 0.8862, 95% CI 0.7914-0.9924, p = 0.03638). Additionally, we identified that HannumAge was linked to an increased risk of Multiple Sclerosis (OR = 1.0707, 95% CI 1.0056-1.1401, p = 0.03295). Furthermore, we also found that estimated plasminogen activator inhibitor-1(PAI-1) levels demonstrated an increased risk for Alzheimer's disease (OR = 1.0001, 95% CI 1.0000-1.0002, p = 0.04425). Beyond that, we did not observe any causal associations between epigenetic age and neurodegenerative diseases risk.

Conclusion

The findings firstly provide evidence for causal association of epigenetic aging and neurodegenerative diseases. Exploring neurodegenerative diseases from an epigenetic perspective may contribute to diagnosis, prognosis, and treatment of neurodegenerative diseases.

Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes

To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n = 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6 months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation.

Blood Test-Based Age Acceleration Is Inversely Associated with High-Volume Sports Activity

PURPOSE

We develop blood test-based aging clocks and examine how these clocks reflect high-volume sports activity.

METHODS

We use blood tests and body metrics data of 421 Hungarian athletes and 283 age-matched controls (mean age, 24.1 and 23.9 yr, respectively), the latter selected from a group of healthy Caucasians of the National Health and Nutrition Examination Survey (NHANES) to represent the general population ( n = 11,412). We train two age prediction models (i.e., aging clocks) using the NHANES dataset: the first model relies on blood test parameters only, whereas the second one additionally incorporates body measurements and sex.

RESULTS

We find lower age acceleration among athletes compared with the age-matched controls with a median value of -1.7 and 1.4 yr, P < 0.0001. BMI is positively associated with age acceleration among the age-matched controls ( r = 0.17, P < 0.01) and the unrestricted NHANES population ( r = 0.11, P < 0.001). We find no association between BMI and age acceleration within the athlete dataset. Instead, age acceleration is positively associated with body fat percentage ( r = 0.21, P < 0.05) and negatively associated with skeletal muscle mass (Pearson r = -0.18, P < 0.05) among athletes. The most important blood test features in age predictions were serum ferritin, mean cell volume, blood urea nitrogen, and albumin levels.

CONCLUSIONS

We develop and apply blood test-based aging clocks to adult athletes and healthy controls. The data suggest that high-volume sports activity is associated with slowed biological aging. Here, we propose an alternative, promising application of routine blood tests.

Accelerated epigenetic clock aging in maternal peripheral blood and preterm birth

BACKGROUND

Epigenetic clocks use CpG DNA methylation to estimate biological age. Acceleration is associated with cancer, heart disease, and shorter life span. Few studies evaluate DNA methylation age and pregnancy outcomes. AgeAccelGrim is a novel epigenetic clock that combines 7 DNA methylation components.

OBJECTIVE

This study aimed to determine whether maternal biological aging (via AgeAccelGrim) is associated with early preterm birth.

STUDY DESIGN

A prospective cohort of patients with singleton pregnancies and at high risk of spontaneous preterm birth delivering at a tertiary university hospital were included in this study. Genome-wide CpG methylation was measured using the Illumina EPIC BeadChip (Illumina, Inc, San Diego, CA) from maternal blood samples obtained at <28 weeks of gestation. AgeAccelGrim and its 7 DNA methylation components were estimated by the Horvath DNA methylation age online tool. Positive values are associated with accelerated biological aging, whereas negative values are associated with slower biological aging relative to each subject's age. The primary outcome was preterm birth at <34 weeks of gestation (any indication). The secondary outcomes were preterm birth at <37 and <28 weeks of gestation. AgeAccelGrim was analyzed as a continuous variable and in quartiles. Exploratory analyses evaluated each of the 7 DNA methylation components included in the composite AgeAccelGrim. Data were analyzed by chi-square test, t test, rank-sum test, logistic regression (controlling a priori for maternal age, cell counts, low socioeconomic status, and gestational age at the time of sample collection), and Kaplan-Meier survival analyses. The log-rank test was used to test the equality of the survival functions.

RESULTS

Overall, 163 patients met the inclusion criteria. Of the patients, 48%, 39%, and 21% delivered at <37, <34, and <28 weeks of gestation, respectively. The median AgeAccelGrim was -0.35 years (interquartile range, -2.24 to 1.31) for those delivering at term. Those delivering preterm had higher AgeAccelGrim values that were inversely proportional to delivery gestational age (preterm birth at <37 weeks of gestation: +0.40 years [interquartile range: -1.21 to +2.28]; preterm birth at <34 weeks of gestation: +0.51 years [interquartile range: -1.05 to +2.67]; preterm birth at <28 weeks of gestation: +1.05 years [interquartile range: -0.72 to +2.72]). Estimated DNA methylation of the 7 epigenetic clock component values was increased among those with preterm birth at <34 weeks of gestation, although the differences were only significant for DNA methylation of plasminogen activation inhibitor 1. In regression models, AgeAcccelGrim was associated with an elevated risk of preterm birth with increasing magnitude for increasing severity of preterm birth. For each 1-year increase in the AgeAccelGrim value (ie, each 1-year increase in biological age compared with chronologic age), the adjusted odds of preterm birth were 11% (adjusted odds ratio, 1.11; 95% confidence interval, 1.00-1.24), 13% (adjusted odds ratio, 1.13; 95% confidence interval, 1.01-1.26), and 18% (adjusted odds ratio, 1.18; 95% confidence interval, 1.04-1.35) higher for preterm birth at <37, <34, and <28 weeks of gestation, respectively. Similarly, individuals with accelerated biological aging (≥75th percentile AgeAccelGrim) had more than double the odds of preterm birth at <34 weeks of gestation (adjusted odds ratio, 2.36; 95% confidence interval, 1.10-5.08) and more than triple the odds of preterm birth at <28 weeks of gestation (adjusted odds ratio, 3.89; 95% confidence interval, 1.61-9.38). The adjusted odds ratio for preterm birth at <37 weeks of gestation was 1.73 but spanned the null (adjusted odds ratio, 1.73; 95% confidence interval, 0.81-3.69). In Kaplan-Meier survival analyses, those in the highest AgeAccelGrim quartile delivered the earliest (log-rank P value of <.001).

CONCLUSION

Accelerated biological aging was associated with preterm birth among high-risk patients. Future research confirming these findings and elucidating factors that slow biological aging may improve birth outcomes.

Accelerated epigenetic aging in alcohol dependence

Alcohol dependence poses a global health threat associated with aging and reduced life expectancy. Recently, aging research through deoxyribonucleic acid (DNA) methylation has gained attention. New epigenetic clocks have been developed; however, no study has investigated GrimAge components, GrimAge2 components and DunedinPACE in patients with alcohol dependence. In this study, we aimed to perform epigenetic clock analysis to evaluate epigenetic age acceleration and DNA methylation-based age-predictive components in patients with alcohol dependence and controls. We utilized publicly available DNA methylation data (GSE98876) for our analysis. Additionally, we compared the values of the same items before and after the patients underwent a treatment program. The dataset comprised 23 controls and 24 patients. We observed that DunedinPACE accelerated more in patients with alcohol dependence. AgeAccelGrim and AgeAccelGrim2 decelerated more after the treatment program than before, and beta-2-microglobulin and Cystatin C decreased after the treatment program than before. These findings are crucial as they affect the cranial nerve area, potentially contributing to cognitive dysfunction and psychiatric symptoms in patients with alcohol dependence.

Role of mitochondrial potassium channels in ageing

Ageing is described as an inevitable decline in body functions over time and an increase in susceptibility to age-related diseases. Therefore, the increase of life expectancy is also viewed as a condition in which many elderly will develop age-related diseases and disabilities, such as cardiovascular, metabolic, neurological and oncological ones. Currently, several recognized cellular hallmarks of senescence are taken in consideration to evaluate the level of biological ageing and are the topic to plan preventive/curative anti-ageing interventions, including genomic instability, epigenetic alterations, and mitochondrial dysfunction. In this scenario, alterations in the function/expression of mitochondrial ion channels have been found in ageing and associated to an impairment of calcium cycling and a reduced mitochondrial membrane potential. Although several ion channels have been described at mitochondrial level, undoubtedly the mitochondrial potassium (mitoK) channels are the most investigated. Therefore, this review summarized the evidence that sheds to light a correlation between age-related diseases and alteration of mitoK channels, focusing the attention of the main age-related diseases, i.e. cardiovascular, neurological and oncological ones.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.

Blood-Based Epigenetic Age Acceleration and Incident Colorectal Cancer Risk: Findings from a Population-Based Case-Control Study

This study investigates the association between epigenetic age acceleration (EAA) derived from DNA methylation and the risk of incident colorectal cancer (CRC). We utilized data from a random population sample of 9,360 individuals (men and women, aged 45-69) from the HAPIEE Study who had been followed up for 16 years. A nested case-control design yielded 35 incident CRC cases and 354 matched controls. Six baseline epigenetic age (EA) measures (Horvath, Hannum, PhenoAge, Skin and Blood (SB), BLUP, and Elastic Net (EN)) were calculated along with their respective EAAs. After adjustment, the odds ratios (ORs) for CRC risk per decile increase in EAA ranged from 1.20 (95% CI: 1.04-1.39) to 1.44 (95% CI: 1.21-1.76) for the Horvath, Hannum, PhenoAge, and BLUP measures. Conversely, the SB and EN EAA measures showed borderline inverse associations with ORs of 0.86-0.87 (95% CI: 0.76-0.99). Tertile analysis reinforced a positive association between CRC risk and four EAA measures (Horvath, Hannum, PhenoAge, and BLUP) and a modest inverse relationship with EN EAA. Our findings from a prospective population-based-case-control study indicate a direct association between incident CRC and four markers of accelerated baseline epigenetic age. In contrast, two markers showed a negative association or no association. These results warrant further exploration in larger cohorts and may have implications for CRC risk assessment and prevention.

Accelerated epigenetic age is associated with whole-brain functional connectivity and impaired cognitive performance in older adults

While chronological age is a strong predictor for health-related risk factors, it is an incomplete metric that fails to fully characterize the unique aging process of individuals with different genetic makeup, neurodevelopment, and environmental experiences. Recent advances in epigenomic array technologies have made it possible to generate DNA methylation-based biomarkers of biological aging, which may be useful in predicting a myriad of cognitive abilities and functional brain network organization across older individuals. It is currently unclear which cognitive domains are negatively correlated with epigenetic age above and beyond chronological age, and it is unknown if functional brain organization is an important mechanism for explaining these associations. In this study, individuals with accelerated epigenetic age (i.e. AgeAccelGrim) performed worse on tasks that spanned a wide variety of cognitive faculties including both fluid and crystallized intelligence (N = 103, average age = 68.98 years, 73 females, 30 males). Additionally, fMRI connectome-based predictive models suggested a mediating mechanism of functional connectivity on epigenetic age acceleration-cognition associations primarily in medial temporal lobe and limbic structures. This research highlights the important role of epigenetic aging processes on the development and maintenance of healthy cognitive capacities and function of the aging brain.

A Comparison of 5 Measures of Accelerated Biological Aging and Their Association With Incident Cardiovascular Disease: The CARDIA Study

BACKGROUND

Accelerated biological aging is an increasingly popular way to track the acceleration of biology over time that may not be captured by calendar time. Biological aging has been linked to external and internal chronic stressors and has the potential to be used clinically to understand a person's personalized functioning and predict future disease. We compared the association of different measures of biological aging and incident cardiovascular disease (CVD) overall and by race.

METHODS AND RESULTS

We used multiple informants models to compare the strength of clinical marker-derived age acceleration, 5 measures of epigenetic age acceleration (intrinsic and extrinsic epigenetic age acceleration, GrimAge acceleration, and PhenoAge acceleration), and 1 established clinical predictor of future CVD, Framingham 10-year risk score, with incident CVD over an 11-year period (2007-2018). Participants were 913 self-identified Black or White (41% and 59%, respectively) female or male (51% and 49%, respectively) individuals enrolled in the US-based CARDIA (Coronary Artery Risk Development in Young Adults) cohort study. The analytic baseline for this study was the 20-year follow-up examination (2005-2006; median age 45 years). We also included race-specific analysis. We found that all measures were modestly correlated with one another. However, clinical marker-derived age acceleration and Framingham 10-year risk score were more strongly associated with incident CVD than all the epigenetic measures. Clinical marker-derived age acceleration and Framingham 10-year risk score were not significantly different than one another in their association with incident CVD.

CONCLUSIONS

The type of accelerated aging measure should be taken into consideration when comparing their association with clinical outcomes. A multisystem clinical composite shows associations with incident CVD equally to a well-known clinical predictor.

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

Meta-analysis of epigenetic aging in schizophrenia reveals multifaceted relationships with age, sex, illness duration, and polygenic risk

BACKGROUND

The study of biological age acceleration may help identify at-risk individuals and reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a whole blood DNAm sample of 1090 SCZ cases and 1206 controls across four European cohorts, we performed a meta-analysis of differential aging using three DNAm clocks (i.e., Hannum, Horvath, and Levine). To dissect how DNAm aging contributes to SCZ, we integrated information on duration of illness and SCZ polygenic risk, as well as stratified our analyses by chronological age and biological sex.

RESULTS

We found that blood-based DNAm aging is significantly altered in SCZ independent from duration of the illness since onset. We observed sex-specific and nonlinear age effects that differed between clocks and point to possible distinct age windows of altered aging in SCZ. Most notably, intrinsic cellular age (Horvath clock) is decelerated in SCZ cases in young adulthood, while phenotypic age (Levine clock) is accelerated in later adulthood compared to controls. Accelerated phenotypic aging was most pronounced in women with SCZ carrying a high polygenic burden with an age acceleration of + 3.82 years (CI 2.02-5.61, P = 1.1E-03). Phenotypic aging and SCZ polygenic risk contributed additively to the illness and together explained up to 14.38% of the variance in disease status.

CONCLUSIONS

Our study contributes to the growing body of evidence of altered DNAm aging in SCZ and points to intrinsic age deceleration in younger adulthood and phenotypic age acceleration in later adulthood in SCZ. Since increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. Our study did not find that DNAm aging could be explained by the duration of illness of patients, but we did observe age- and sex-specific effects that warrant further investigation. Finally, our results show that combining genetic and epigenetic predictors can improve predictions of disease outcomes and may help with disease management in schizophrenia.

DNA methylation and chromatin accessibility predict age in the domestic dog

Across mammals, the epigenome is highly predictive of chronological age. These "epigenetic clocks," most of which have been built using DNA methylation (DNAm) profiles, have gained traction as biomarkers of aging and organismal health. While the ability of DNAm to predict chronological age has been repeatedly demonstrated, the ability of other epigenetic features to predict age remains unclear. Here, we use two types of epigenetic information-DNAm, and chromatin accessibility as measured by ATAC-seq-to develop age predictors in peripheral blood mononuclear cells sampled from a population of domesticated dogs. We measured DNAm and ATAC-seq profiles for 71 dogs, building separate predictive clocks from each, as well as the combined dataset. We also use fluorescence-assisted cell sorting to quantify major lymphoid populations for each sample. We found that chromatin accessibility can accurately predict chronological age (R2 ATAC = 26%), though less accurately than the DNAm clock (R2 DNAm = 33%), and the clock built from the combined datasets was comparable to both (R2 combined = 29%). We also observed various populations of CD62L+ T cells significantly correlated with dog age. Finally, we found that all three clocks selected features that were in or near at least two protein-coding genes: BAIAP2 and SCARF2, both previously implicated in processes related to cognitive or neurological impairment. Taken together, these results highlight the potential of chromatin accessibility as a complementary epigenetic resource for modeling and investigating biologic age.

Cumulative Stress Across the Life Course and Biological Aging in Adulthood

OBJECTIVE

Psychosocial stressors have been linked with accelerated biological aging in adults; however, few studies have examined stressors across the life course in relation to biological aging.

METHODS

In 359 individuals (57% White, 34% Black) from the Child Health and Development Studies Disparities study, economic (income, education, financial strain), social (parent-child relations, caretaker responsibilities) and traumatic (death of a sibling or child, violence exposure) stressors were assessed at multiple time points (birth and ages 9, 15, and 50 years). Experiences of major discrimination were assessed at age 50. Life period stress scores were then assessed as childhood (birth-age 15 years) and adulthood (age 50 years). At age 50 years, participants provided blood samples, and DNA methylation was assessed with the EPIC BeadChip. Epigenetic age was estimated using six epigenetic clocks (Horvath, Hannum, Skin and Blood age, PhenoAge, GrimAge, Dunedin Pace of Aging). Age acceleration was determined using residuals from regressing chronologic age on each of the epigenetic age metrics. Telomere length was assessed using the quantitative polymerase chain reaction-based methods.

RESULTS

In linear regression models adjusted for race and gender, total life stress, and childhood and adult stress independently predicted accelerated aging based on GrimAge and faster pace of aging based on the DunedinPace. Associations were attenuated after adjusting for smoking status. In sex-stratified analyses, greater childhood stress was associated with accelerated epigenetic aging among women but not men. No associations were noted with telomere length.

CONCLUSIONS

We found that cumulative stressors across the life course were associated with accelerated epigenetic age, with differences by sex (e.g., accelerated among women). Further research of this association in large and diverse samples is needed.

DNA methylation marker identification and poly-methylation risk score in prediction of healthspan termination

Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13-1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6, CTSK). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.