DNAmFitAge: biological age indicator incorporating physical fitness

Exercise epigenetics is fueled by cell bioenergetics: Supporting role on brain plasticity and cognition

Exercise has the unique aptitude to benefit overall health of body and brain. Evidence indicates that the effects of exercise can be saved in the epigenome for considerable time to elevate the threshold for various diseases. The action of exercise on epigenetic regulation seems central to building an "epigenetic memory" to influence long-term brain function and behavior. As an intrinsic bioenergetic process, exercise engages the function of the mitochondria and redox pathways to impinge upon molecular mechanisms that regulate synaptic plasticity and learning and memory. We discuss how the action of exercise uses mechanisms of bioenergetics to support a "epigenetic memory" with long-term implications for neural and behavioral plasticity. This information is crucial for directing the power of exercise to reduce the burden of neurological and psychiatric disorders.

methscore: a comprehensive R function for DNA methylation-based health predictors

MOTIVATION

DNA methylation-based predictors of various biological metrics have been widely published and are becoming valuable tools in epidemiologic studies of epigenetics and personalized medicine. However, generating these predictors from original source software and web servers is complex and time consuming. Furthermore, different predictors were often derived based on data from different types of arrays, where array differences and batch effects can make predictors difficult to compare across studies.

RESULTS

We integrate these published methods into a single R function to produce 158 previously published predictors for chronological age, biological age, exposures, lifestyle traits and serum protein levels using both classical and principal component-based methods. To mitigate batch and array differences, we also provide a modified RCP method (ref-RCP) that normalize input DNA methylation data to reference data prior to estimation. Evaluations in real datasets show that this approach improves estimate precision and comparability across studies.

AVAILABILITY AND IMPLEMENTATION

The function was included in software package ENmix, and is freely available from Bioconductor website (https://www.bioconductor.org/packages/release/bioc/html/ENmix.html).

[Genetics, epigenetics, and environmental factors in life expectancy-What role does nature-versus-nurture play in aging?]

In Germany and worldwide, the average age of the population is continuously rising. With this general increase in chronological age, the focus on biological age, meaning the actual health and fitness status, is becoming more and more important. The key question is to what extent the age-related decline in fitness is genetically predetermined or malleable by environmental factors and lifestyle.Many epigenetic studies in aging research have provided interesting insights in this nature-versus-nurture debate. In most model organisms, aging is associated with specific epigenetic changes, which can be countered by certain interventions like moderate caloric restriction or increased physical activity. Since these interventions also have positive effects on lifespan and health, epigenetics appears to be the interface between environmental factors and the aging process. This notion is supported by the fact that an epigenetic drift occurs through the life course of identical twins, which is related to the different manifestations of aging symptoms. Furthermore, biological age can be determined with high precision based on DNA methylation patterns, further emphasizing the importance of epigenetics in aging.This article provides an overview of the importance of genetic and epigenetic parameters for life expectancy. A major focus will be on the possibilities of maintaining a young epigenome through lifestyle and environmental factors, thereby slowing down biological aging.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2,923 plasma proteins assessed using the Olink Explore 3072 assay®. The Spearman correlation between PAC proteomic age and chronological age was 0.77. A total of 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of least absolute shrinkage and selection operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

Epigenetic age oscillates during the day

Since their introduction, epigenetic clocks have been extensively used in aging, human disease, and rejuvenation studies. In this article, we report an intriguing pattern: epigenetic age predictions display a 24-h periodicity. We tested a circadian blood sample collection using 17 epigenetic clocks addressing different aspects of aging. Thirteen clocks exhibited significant oscillations with the youngest and oldest age estimates around midnight and noon, respectively. In addition, daily oscillations were consistent with the changes of epigenetic age across different times of day observed in an independant populational dataset. While these oscillations can in part be attributed to variations in white blood cell type composition, cell count correction methods might not fully resolve the issue. Furthermore, some epigenetic clocks exhibited 24-h periodicity even in the purified fraction of neutrophils pointing at plausible contributions of intracellular epigenomic oscillations. Evidence for circadian variation in epigenetic clocks emphasizes the importance of the time-of-day for obtaining accurate estimates of epigenetic age.

Analysis of epigenetic clocks links yoga, sleep, education, reduced meat intake, coffee, and a SOCS2 gene variant to slower epigenetic aging

DNA methylation (DNAm) clocks hold promise for measuring biological age, useful for guiding clinical interventions and forensic identification. This study compared the commonly used DNAm clocks, using DNA methylation and SNP data generated from nearly 1000 human blood or buccal swab samples. We evaluated different preprocessing methods for age estimation, investigated the association of epigenetic age acceleration (EAA) with various lifestyle and sociodemographic factors, and undertook a series of novel genome-wide association analyses for different EAA measures to find associated genetic variants. Our results highlighted the Skin&Blood clock with ssNoob normalization as the most accurate predictor of chronological age. We provided novel evidence for an association between the practice of yoga and a reduction in the pace of aging (DunedinPACE). Increased sleep and physical activity were associated with lower mortality risk score (MRS) in our dataset. University degree, vegetable consumption, and coffee intake were associated with reduced levels of epigenetic aging, whereas smoking, higher BMI, meat consumption, and manual occupation correlated well with faster epigenetic aging, with FitAge, GrimAge, and DunedinPACE clocks showing the most robust associations. In addition, we found a novel association signal for SOCS2 rs73218878 (p = 2.87 × 10-8) and accelerated GrimAge. Our study emphasizes the importance of an optimized DNAm analysis workflow for accurate estimation of epigenetic age, which may influence downstream analyses. The results support the influence of genetic background on EAA. The associated SOCS2 is a member of the suppressor of cytokine signaling family known for its role in human longevity. The reported association between various risk factors and EAA has practical implications for the development of health programs to improve quality of life and reduce premature mortality associated with age-related diseases.

Alterations of the gut microbiome are associated with epigenetic age acceleration and physical fitness

Epigenetic clocks can measure aging and predict the incidence of diseases and mortality. Higher levels of physical fitness are associated with a slower aging process and a healthier lifespan. Microbiome alterations occur in various diseases and during the aging process, yet their relation to epigenetic clocks is not explored. To fill this gap, we collected metagenomic (from stool), epigenetic (from blood), and exercise-related data from physically active individuals and, by applying epigenetic clocks, we examined the relationship between gut flora, blood-based epigenetic age acceleration, and physical fitness. We revealed that an increased entropy in the gut microbiome of physically active middle-aged/old individuals is associated with accelerated epigenetic aging, decreased fitness, or impaired health status. We also observed that a slower epigenetic aging and higher fitness level can be linked to altered abundance of some bacterial species often linked to anti-inflammatory effects. Overall our data suggest that alterations in the microbiome can be associated with epigenetic age acceleration and physical fitness.

pyaging: a Python-based compendium of GPU-optimized aging clocks

MOTIVATION

Aging is intricately linked to diseases and mortality. It is reflected in molecular changes across various tissues which can be leveraged for the development of biomarkers of aging using machine learning models, known as aging clocks. Despite advancements in the field, a significant challenge remains: the lack of robust, Python-based software tools for integrating and comparing these diverse models. This gap highlights the need for comprehensive solutions that can handle the complexity and variety of data in aging research.

RESULTS

To address this gap, I introduce pyaging, a comprehensive open-source Python package designed to facilitate aging research. pyaging harmonizes dozens of aging clocks, covering a range of molecular data types such as DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package is not limited to traditional model types; it features a diverse array, from linear and principal component models to neural networks and automatic relevance determination models. Thanks to a PyTorch-based backend that enables GPU acceleration, pyaging is capable of rapid inference, even when dealing with large datasets and complex models. In addition, the package's support for multi-species analysis extends its utility across various organisms, including humans, various mammals, and Caenorhabditis elegans.

AVAILABILITY AND IMPLEMENTATION

pyaging is accessible on GitHub, at https://github.com/rsinghlab/pyaging, and the distribution is available on PyPi, at https://pypi.org/project/pyaging/. The software is also archived on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10335011.

The Semmelweis Study: a longitudinal occupational cohort study within the framework of the Semmelweis Caring University Model Program for supporting healthy aging

The Semmelweis Study is a prospective occupational cohort study that seeks to enroll all employees of Semmelweis University (Budapest, Hungary) aged 25 years and older, with a population of 8866 people, 70.5% of whom are women. The study builds on the successful experiences of the Whitehall II study and aims to investigate the complex relationships between lifestyle, environmental, and occupational risk factors, and the development and progression of chronic age-associated diseases. An important goal of the Semmelweis Study is to identify groups of people who are aging unsuccessfully and therefore have an increased risk of developing age-associated diseases. To achieve this, the study takes a multidisciplinary approach, collecting economic, social, psychological, cognitive, health, and biological data. The Semmelweis Study comprises a baseline data collection with open healthcare data linkage, followed by repeated data collection waves every 5 years. Data are collected through computer-assisted self-completed questionnaires, followed by a physical health examination, physiological measurements, and the assessment of biomarkers. This article provides a comprehensive overview of the Semmelweis Study, including its origin, context, objectives, design, relevance, and expected contributions.

Associations between cardiorespiratory fitness and lifestyle-related factors with DNA methylation-based ageing clocks in older men: WASEDA'S Health Study

DNA methylation-based age estimators (DNAm ageing clocks) are currently one of the most promising biomarkers for predicting biological age. However, the relationships between cardiorespiratory fitness (CRF), measured directly by expiratory gas analysis, and DNAm ageing clocks are largely unknown. We investigated the relationships between CRF and the age-adjusted value from the residuals of the regression of DNAm ageing clock to chronological age (DNAmAgeAcceleration: DNAmAgeAccel) and attempted to determine the relative contribution of CRF to DNAmAgeAccel in the presence of other lifestyle factors. DNA samples from 144 Japanese men aged 65-72 years were used to appraise first- (i.e., DNAmHorvath and DNAmHannum) and second- (i.e., DNAmPhenoAge, DNAmGrimAge, and DNAmFitAge) generation DNAm ageing clocks. Various surveys and measurements were conducted, including physical fitness, body composition, blood biochemical parameters, nutrient intake, smoking, alcohol consumption, disease status, sleep status, and chronotype. Both oxygen uptake at ventilatory threshold (VO2 /kg at VT) and peak oxygen uptake (VO2 /kg at Peak) showed a significant negative correlation with GrimAgeAccel, even after adjustments for chronological age and smoking and drinking status. Notably, VO2 /kg at VT and VO2 /kg at Peak above the reference value were also associated with delayed GrimAgeAccel. Multiple regression analysis showed that calf circumference, serum triglyceride, carbohydrate intake, and smoking status, rather than CRF, contributed more to GrimAgeAccel and FitAgeAccel. In conclusion, although the contribution of CRF to GrimAgeAccel and FitAgeAccel is relatively low compared to lifestyle-related factors such as smoking, the results suggest that the maintenance of CRF is associated with delayed biological ageing in older men.

Epigenetic and "redoxogenetic" adaptation to physical exercise

Exercise-induced adaptation is achieved by altering the epigenetic landscape of the entire genome leading to the expression of genes involved in various processes including regulatory, metabolic, adaptive, immune, and myogenic functions. Clinical and experimental data suggest that the methylation pattern/levels of promoter/enhancer is not linearly correlated with gene expression and proteome levels during physical activity implying a level of complexity and interplay with other regulatory modulators. It has been shown that a higher level of physical fitness is associated with a slower DNA methylation-based aging clock. There is strong evidence supporting exercise-induced ROS being a key regulatory mediator through overlapping events, both as signaling entities and through oxidative modifications to various protein mediators and DNA molecules. ROS generated by physical activity shapes epigenome both directly and indirectly, a complexity we are beginning to unravel within the epigenetic arrangement. Oxidative modification of guanine to 8-oxoguanine is a non-genotoxic alteration, does not distort DNA helix and serves as an epigenetic-like mark. The reader and eraser of oxidized guanine is the 8-oxoguanine DNA glycosylase 1, contributing to changes in gene expression. In fact, it can modulate methylation patterns of promoters/enhancers consequently leading to multiple phenotypic changes. Here, we provide evidence and discuss the potential roles of exercise-induced ROS in altering cytosine methylation patterns during muscle adaptation processes.

CellBiAge: Improved single-cell age classification using data binarization

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods.

Host fecal DNA specific methylation signatures mark gut dysbiosis and inflammation in children affected by autism spectrum disorder

The gut-brain axis involves several bidirectional pathway communications including microbiome, bacterial metabolites, neurotransmitters as well as immune system and is perturbed both in brain and in gastrointestinal disorders. Consistently, microbiota-gut-brain axis has been found altered in autism spectrum disorder (ASD). We reasoned that such alterations occurring in ASD may impact both on methylation signatures of human host fecal DNA (HFD) and possibly on the types of human cells shed in the stools from intestinal tract giving origin to HFD. To test this hypothesis, we have performed whole genome methylation analysis of HFD from an age-restricted cohort of young children with ASD (N = 8) and healthy controls (N = 7). In the same cohort we have previously investigated the fecal microbiota composition and here we refined such analysis and searched for eventual associations with data derived from HFD methylome analysis. Our results showed that specific epigenetic signatures in human fecal DNA, especially at genes related to inflammation, associated with the disease. By applying methylation-based deconvolution algorithm, we found that the HFD derived mainly from immune cells and the relative abundance of those differed between patients and controls. Consistently, most of differentially methylated regions fitted with genes involved in inflammatory response. Interestingly, using Horvath epigenetic clock, we found that ASD affected children showed both epigenetic and microbiota age accelerated. We believe that the present unprecedented approach may be useful for the identification of the ASD associated HFD epigenetic signatures and may be potentially extended to other brain disorders and intestinal inflammatory diseases.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications

BACKGROUND

DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals.

OBJECTIVE

This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application.

METHODS

The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics.

CONCLUSION

The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.

DNA methylation clock DNAmFitAge shows regular exercise is associated with slower aging and systemic adaptation

DNAmPhenoAge, DNAmGrimAge, and the newly developed DNAmFitAge are DNA methylation (DNAm)-based biomarkers that reflect the individual aging process. Here, we examine the relationship between physical fitness and DNAm-based biomarkers in adults aged 33-88 with a wide range of physical fitness (including athletes with long-term training history). Higher levels of VO2max (ρ = 0.2, p = 6.4E - 4, r = 0.19, p = 1.2E - 3), Jumpmax (p = 0.11, p = 5.5E - 2, r = 0.13, p = 2.8E - 2), Gripmax (ρ = 0.17, p = 3.5E - 3, r = 0.16, p = 5.6E - 3), and HDL levels (ρ = 0.18, p = 1.95E - 3, r = 0.19, p = 1.1E - 3) are associated with better verbal short-term memory. In addition, verbal short-term memory is associated with decelerated aging assessed with the new DNAm biomarker FitAgeAcceleration (ρ: - 0.18, p = 0.0017). DNAmFitAge can distinguish high-fitness individuals from low/medium-fitness individuals better than existing DNAm biomarkers and estimates a younger biological age in the high-fit males and females (1.5 and 2.0 years younger, respectively). Our research shows that regular physical exercise contributes to observable physiological and methylation differences which are beneficial to the aging process. DNAmFitAge has now emerged as a new biological marker of quality of life.

Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks

BACKGROUND

DNA methylation analysis has proven to be a powerful tool for age assessment. However, the implementation of epigenetic age prediction in diagnostics or routine forensic casework requires appropriate laboratory methods. In this study, we aimed to compare the performance of large-scale DNA methylation analysis protocols that show promise in terms of accuracy, throughput, multiplexing capacity, and high sensitivity.

RESULTS

The protocols were designed to target a predefined panel of 161 genomic CG/CA sites from four known estimators of epigenetic age-related parameters, optimized and validated using artificially methylated controls or blood samples. We successfully targeted 96% of these loci using two enrichment protocols: Ion AmpliSeq™, an amplicon-based method integrated with Ion Torrent S5, and SureSelectXT Methyl-Seq, a hybridization-based method followed by MiSeq FGx sequencing. Both protocols demonstrated high accuracy and robustness. Although hybridization assays have greater multiplexing capabilities, the best overall performance was observed for the amplicon-based protocol with the lowest variability in DNA methylation at 25 ng of starting DNA, mean observed marker coverage of ~ 6.7 k reads, and accuracy of methylation quantification with a mean absolute difference between observed and expected methylation beta value of 0.054. The Ion AmpliSeq method correlated strongly with genome-scale EPIC microarray data (R = 0.91) and showed superiority in terms of methylation measurement accuracy. Method-to-method bias was accounted for by the use of linear transformation, which provided a highly accurate prediction of calendar age with a mean absolute error of less than 5 years for the VISAGE and Hannum age clocks used. The pace of aging (PoAm) and the mortality risk score (MRS) estimators included in our panel represent next-generation clocks, were found to have low to moderate correlations with the VISAGE and Hannum models (R < 0.75), and thus may capture different aspects of epigenetic aging.

CONCLUSIONS

We propose a laboratory tool that allows the quantification of DNA methylation in cytosines underlying four different clocks, thus providing broad information on epigenetic aging while maintaining a reasonable number of CpG markers, opening the way to a wide range of applications in forensics, medicine, and healthcare.