Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression

Background Epigenetic clocks use DNA methylation (DNAm) levels of specific sets of CpG dinucleotides to accurately predict individual chronological age. A popular application of these clocks is to explore whether the deviation of predicted age from chronological age is associated with disease phenotypes, where this deviation is interpreted as a potential biomarker of biological age. This wide application, however, contrasts with the limited insight in the processes that may drive the running of epigenetic clocks. Results We perform a functional genomics analysis on four epigenetic clocks, including Hannum’s blood predictor and Horvath’s multi-tissue predictor, using blood DNA methylome and transcriptome data from 3132 individuals. The four clocks result in similar predictions of individual chronological age, and their constituting CpGs are correlated in DNAm level and are enriched for similar histone modifications and chromatin states. Interestingly, DNAm levels of CpGs from the clocks are commonly associated with gene expression in trans. The gene sets involved are highly overlapping and enriched for T cell processes. Further analysis of the transcriptome and methylome of sorted blood cell types identifies differences in DNAm between naive and activated T and NK cells as a probable contributor to the clocks. Indeed, within the same donor, the four epigenetic clocks predict naive cells to be up to 40 years younger than activated cells. Conclusions The ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells, an established feature of immuno-senescence. This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes.

Rat leukocyte population dynamics predicts a window for intervention in aging.

Age-associated changes in human hematopoiesis have been mostly recapitulated in mouse models; but not much has been explored in rats, a physiologically closer model to humans. To establish whether rat hematopoiesis closely mirrors humans’, we examined the peripheral blood of rats throughout their lifespan. Significant age-associated changes showed distinctive population shifts predictive of age. A divergence between predicted versus chronological age changes was indicative of fragility; thus, these data may be a valuable tool to identify underlying diseases or as a surrogate predictor for intervention efficacy. Notably, several blood parameters and DNA methylation alterations defined specific leverage points during aging, supporting non-linear aging effects and highlighting a roadmap for interventions at these junctures. Overall, we present a simple set of rat blood metrics that can provide a window into their health and inform the implementation of interventions in a model system physiologically relevant for humans.

Correlation of cutaneous blood microcirculation parameters with biological age in diabetic patients

Известно, что с возрастом и при ряде хронических заболеваний происходит снижение реактивности микроциркуляторного звена кровообращения. Целью данного исследования стала оценка взаимосвязи параметров кожной микроциркуляции с биологическим и хронологическим возрастом у пациентов с сахарным диабетом. В исследование были включены 11 человек с сахарным диабетом [медиана возраста 57 (51; 64) лет]; биологический возраст определяли с помощью калькулятора Aging.AI. Показатели кожной микроциркуляции оценивали с помощью метода лазерной допплеровской флоуметрии в ходе окклюзионно-теплового теста. Для анализа взаимосвязи количественных параметров рассчитывали коэффициенты ранговой корреляции Спирмена. Были выявлены значимые множественные отрицательные корреляции биологического возраста с показателями реактивности микрососудов как при тепловом, так и при окклюзионном воздействии (сила корреляций от -0,618 до -0,97, p<0,05). У лиц с сахарным диабетом снижение реактивности микрососудов в большей мере ассоциировано с биологическим возрастом, чем с хронологическим. It has been established that the age together with the number of chronic diseases cause the decrease of the reactivity of the microcirculatory bed. This study aims to evaluate the relationship between cutaneous microcirculation parameters and biological and chronological age of patients with diabetes mellitus. 11 diabetic patients (median age 57 (51; 64) years) were examined in course of this study; biological age was figured by Aging.AI calculator. Cutaneous microcirculation parameters were measured by laser Doppler flowmetry with an occlusion-heating test. Spearman’s rank correlation coefficients were calculated to analyze the relationships between quantitative parameters. Significant multiple negative correlations of biological age and microvascular reactivity indices on exposure to both heat and occlusion (correlation strength from -0,618, to -0,97, p<0,05) were found. Diabetic patients have decreased microvascular reactivity that is more associated with biological age than with chronological age.

Associations Between Blood Pressure and Accelerated DNA Methylation Aging

Background Individuals of the same chronological age may exhibit diverse susceptibilities to death. However, few studies have investigated the associations between blood pressure and the accelerated aging. Methods and Results A cross‐sectional study was conducted in 288 adults aged ≥50 years. We assessed the DNA methylation‐based measures of biological age using CpG sites on the Illumina HumanMethylationEPIC BeadChip. Epigenetic age acceleration metrics were derived by regressing residuals (ΔAge) and ratios (aging rate) of DNA methylation age on chronological age. Dose‐response relationships between blood pressure and epigenetic age acceleration were quantified using multiple linear regression and restricted cubic regression models. We found that each 10–mm Hg increase in systolic blood pressure was associated with 0.608 (95% CI, 0.231–0.984) years increase in ΔAge and 0.007 (95% CI, 0.002–0.012) increase in aging rate; meanwhile, for pulse pressure, the increase was 1.12 (95% CI, 0.625–1.61) years for ΔAge and 0.013 (95% CI, 0.007–0.020) for aging rate. Subgroup analysis showed that the significant associations of systolic blood pressure and pulse pressure with epigenetic age acceleration appeared to be limited to women, although interactions between blood pressure and sex were not significant ( P values for interaction >0.05). The combination of women and hypertension was associated with a much higher increase in ΔAge (β [95% CI], 4.05 [1.07–7.02]) and aging rate (β [95% CI], 0.047 [0.008–0.087]), compared with male participants without hypertension. Conclusions Our findings suggested that high systolic blood pressure and pulse pressure were associated with the epigenetic age acceleration, providing important clues for relationships between blood pressure and epigenetic aging.

PathoClock and PhysioClock in mice recapitulate human multimorbidity and heterogeneous aging

Background: Multimorbidity is a public health concern and an essential component of aging and healthspan but understudied because investigative tools are lacking that can be translatable to capture similarities and differences of the aging process across species and variability between individuals and individual organs. Methods: To help address this need, body organ disease number (BODN) borrowed from human studies was applied to C57BL/6 (B6) and CB6F1 mouse strains at 8, 16, 24, and 32 months of age, as a measure of systems morbidity based on pathology lesions to develop a mouse PathoClock resembling clinically-based Body Clock in humans, using Bayesian inference. A mouse PhysioClock was also developed based on measures of physiological domains including cardiovascular, neuromuscular, and cognitive function in the same two mouse strains so that alignment with BODN was predictable. Results: Between- and within-age variabilities in PathoClock and PhysioClock, as well as between-strain variabilities. Both PathoClock and PhysioClock correlated with chronological age more strongly in CB6F1 than C57BL/6. Prediction models were then developed, designated as PathoAge and PhysioAge, using regression models of pathology and physiology measures on chronological age. PathoAge better predicted chronological age than PhysioAge as the predicted chronological and observed chronological age for PhysioAge were complex rather than linear. Conclusion: PathoClock and PhathoAge can be used to capture biological changes that predict BODN, a metric developed in humans, and compare multimorbidity across species. These mouse clocks are potential translational tools that could be used in aging intervention studies. Keywords: Multimorbidity, aging, pathology, physiology, pathoClock, physioClock, pathoAge, physioAge