Biomarkers Of Aging Research Articles

BayesAge 2.0: A Maximum Likelihood Algorithm to Predict Transcriptomic Age.

Aging is a complex biological process influenced by various factors, including genetic and environmental influences. In this study, we present BayesAge 2.0, an improved version of our maximum likelihood algorithm designed for predicting transcriptomic age (tAge) from RNA-seq data. Building on the original BayesAge framework, which was developed for epigenetic age prediction, BayesAge 2.0 integrates a Poisson distribution to model count-based gene expression data and employs LOWESS smoothing to capture non-linear gene-age relationships. BayesAge 2.0 provides significant improvements over traditional linear models, such as Elastic Net regression. Specifically, it addresses issues of age bias in predictions, with minimal age-associated bias observed in residuals. Its computational efficiency further distinguishes it from traditional models, as reference construction and cross-validation are completed more quickly compared to Elastic Net regression, which requires extensive hyperparameter tuning. Overall, BayesAge 2.0 represents a notable advance in transcriptomic age prediction, offering a robust, accurate, and efficient tool for aging research and biomarker development.

Challenges and recommendations for the translation of biomarkers of aging.

Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.

Effects of Intrahypothalamic Administration of microRNA Inhibitors and Mimetics on Blood Plasma Biomarkers in the Rat Aging.

The effect of let-7a-5p, miR-9a-3p, miR-132-3p, miR-218a-5p microRNA inhibitors and mimetics, when administered into the dorsomedial nucleus of the hypothalamus (DMN), on the markers of age-related changes in blood plasma in 3-month-old and 24-month-old male rats was studied. In the 24-month-old control rats, the content of C-reactive protein (CRP) increased, and the level of myoglobin decreased compared to the 3-month-old animals. After te administration of miRNA inhibitors, the level of CRP significantly increased, and the content of myoglobin decreased, and after the administration of miRNA mimetics, opposite changes were observed. We found no significant differences in the content of somatotropic hormone and testosterone between the control and experimental groups, as well as between the 3-month-old and 24-month-old animals.

Fundamental equations linking methylation dynamics to maximum lifespan in mammals

We describe a framework that addresses concern that the rate of change in any aging biomarker displays a trivial inverse relation with maximum lifespan. We apply this framework to methylation data from the Mammalian Methylation Consortium. We study the relationship of lifespan with the average rate of change in methylation (AROCM) from two datasets: one with 90 dog breeds and the other with 125 mammalian species. After examining 54 chromatin states, we conclude three key findings: First, a reciprocal relationship exists between the AROCM in bivalent promoter regions and maximum mammalian lifespan: AROCM ∝\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\propto$$\\end{document} 1/MaxLifespan. Second, the correlation between average methylation and age bears no relation to maximum lifespan, Cor(Methyl,Age) ⊥ MaxLifespan. Third, the rate of methylation change in young animals is related to that in old animals: Young animals’ AROCM ∝\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\propto$$\\end{document} Old AROCM. These findings critically hinge on the chromatin context, as different results emerge in other chromatin contexts.

Ferritin as a biomarker of aging: geroprotective peptides of standardized human placental hydrolysate. A review

Ferritin, an iron transport protein, is an acute phase protein of inflammation and oxidative stress (OS), a biomarker of cytolysis and ferroptosis. Inflammation, OS and iron overload are characteristic processes of the pathophysiology of aging. Human placental hydrolysates (HPHs) are promising hepatoprotective agents for anti-aging therapy. The goal of the team of authors was to systematize data on ferritin as a marker of aging and to identify peptides that counteract the aging pathophysiology, including through the regulation of iron and ferritin metabolism, in the HPH Laennec (manufactured by Japan Bioproducts). The results of basic and clinical studies confirm the above relationships and indicate that blood ferritin levels characterize the chronological and biological aging of the human body.

Comprehensive analysis of molecular, physiological, and functional biomarkers of aging with neurological diseases using Mendelian randomization.

An increasing burden of neurological diseases (NDs) has been a public health challenge in an aging society. Age, especially biological age, is the most important risk factor for NDs. Identification of biomarkers of aging to capture NDs might lead to a better understanding of the underlying mechanisms of pathological brain aging and the implementation of effective intervention. We conducted a comprehensive two-sample Mendelian Randomization (MR) study to investigate the association between various biomarkers of aging and three leading causes of NDs: Alzheimer's disease (AD), vascular dementia (VaD), and ischemic stroke. Publicly available GWAS summary statistics on people from European ancestry were obtained for six molecular biomarkers, two physiological biomarkers, and eight functional biomarkers, and three NDs. Genetic variants serving as instrumental variables (IVs) were identified for each biomarker. The MR analysis included inverse variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO. We found that short telomere length and decrease in appendicular lean mass were associated with an increased risk for AD (OR IVW = 1.12 per 1SD decrease, 95% confidence interval 1.02-1.22, and OR IVW = 1.11, 1.06-1.16, respectively), whereas high frailty index showed a protective effect for AD. Accelerated BioAge appeared to be associated with increased risk for ischemic stroke (OR IVW = 1.3 per year in BioAge acceleration, 95% CI 1.19-1.41). Our findings implied a causal association of short telomere length and a decrease in appendicular lean mass with an increased risk for AD, while BioAge appeared to be a good biomarker for ischemic stroke. Further studies are needed to validate these associations and explore underlying mechanisms.

Disparities in Telomere length by Sexual Orientation in Adults from the Genetic Epidemiology Research on Aging Cohort.

The weathering hypothesis proposes that marginalized people experience faster biologic aging due to cumulative stress which translates to chronic disease disparities. We assessed telomere length (TL) differences, an aging biomarker, by sexual orientation (bisexual, gay/lesbian, straight) among 102,258 individuals enrolled in the Resource for Genetic Epidemiology Research on Aging Cohort during 2008 through 2011 (mean age of 60.6 years, 58% female, and 7.6% bisexual/gay/lesbian). We used linear models to estimate differences in telomere length, stratified by sex/gender and adjusted for age (at salivary sample) and socio-demographic variables and Kitagawa-Blinder-Oaxaca decomposition to quantify contributions of participant factors on TL differences. Among females, there was no significant difference in age-adjusted telomere length by sexual orientation after adjustment for socio-demographics (ref: straight; bisexual 0.007, 95%CI: -0.03 to 0.04; lesbian: 0.005, 95%CI: -0.02 to 0.03). Among males, only gay (-0.04, 95%CI: -0.06 to -0.02) but not bisexual (-0.02, 95%CI: -0.06 to 0.02) men had significantly shorter age-adjusted telomere length compared to straight men after adjusting for socio-demographic variables. Decomposition analysis identified ever smoking and marital status as significant drivers of the gay-straight disparity. Studies confirming our findings are needed and the implications of shorter telomeres on gay men's health requires further investigation.

Prenatal and early life exposure to fine particulate matter and telomere length in early childhood

BackgroundTelomere length is a biomarker of molecular aging that may be impacted by air pollution exposure starting in utero. We aimed to examine the association between prenatal and early life exposure to fine particulate matter (PM2.5) and leukocyte telomere length (LTL) in children and explore sex differences. MethodsAnalyses included 384 mother–child pairs enrolled in the Programming Research in Obesity, Growth, and Environmental Stressors (PROGRESS) birth cohort in Mexico City. Exposure to PM2.5 was estimated at the residential level using a satellite based spatio-temporally resolved prediction model. Average relative LTL was measured in DNA isolated from blood collected at age 4–6 years using quantitative real-time polymerase chain reaction. Linear regression models were used to examine the association between average PM2.5 across pregnancy, individual trimesters, first postnatal year, and LTL. Models were adjusted for maternal age and education at enrollment, prenatal environmental tobacco smoke exposure, child sex, age, and body mass index z-score at LTL measurement. Effect modification by sex was investigated with interaction terms and stratification. ResultsIn trimester specific models, we found an association between 2nd trimester PM2.5 and elongated LTL (β: 4.34, 95%CI [0.42, 8.42], per 5 μg/m3 increase). There was suggestive effect modification by sex on average 2nd trimester PM2.5 with stronger associations seen in females compared to males (β: 7.12, [95%CI: 0.98, 13.6] and β: 1.43 [95%CI: −3.46, 6.57]) per 5 μg/m3 increase respectively. ConclusionSecond trimester PM2.5 levels were associated with changes in LTL in early childhood. Understanding temporal and sex differences in PM2.5 exposure may provide insights into telomere dynamics over early life.

Exploring the potential of epigenetic clocks in aging research

The process of aging is a notable risk factor for numerous age-related illnesses. Hence, a reliable technique for evaluating biological age or the pace of aging is crucial for understanding the aging process and its influence on the progression of disease. Epigenetic alterations are recognized as a prominent biomarker of aging, and epigenetic clocks formulated on this basis have been shown to provide precise estimations of chronological age. Extensive research has validated the effectiveness of epigenetic clocks in determining aging rates, identifying risk factors for aging, evaluating the impact of anti-aging interventions, and predicting the emergence of age-related diseases. This review provides a detailed overview of the theoretical principles underlying the development of epigenetic clocks and their utility in aging research. Furthermore, it explores the existing obstacles and possibilities linked to epigenetic clocks and proposes potential avenues for future studies in this field.

Artificial intelligence for the study of human ageing: a systematic literature review

As society experiences accelerated ageing, understanding the complex biological processes of human ageing, which are affected by a large number of variables and factors, becomes increasingly crucial. Artificial intelligence (AI) presents a promising avenue for ageing research, offering the ability to detect patterns, make accurate predictions, and extract valuable insights from large volumes of complex, heterogeneous data. As ageing research increasingly leverages AI techniques, we present a timely systematic literature review to explore the current state-of-the-art in this field following a rigorous and transparent review methodology. As a result, a total of 77 articles have been identified, summarised, and categorised based on their characteristics. AI techniques, such as machine learning and deep learning, have been extensively used to analyse diverse datasets, comprising imaging, genetic, behavioural, and contextual data. Findings showcase the potential of AI in predicting age-related outcomes, developing ageing biomarkers, and determining factors associated with healthy ageing. However, challenges related to data quality, interpretability of AI models, and privacy and ethical considerations have also been identified. Despite the advancements, novel approaches suggest that there is still room for improvement to provide personalised AI-driven healthcare services and promote active ageing initiatives with the ultimate goal of enhancing the quality of life and well-being of older adults.Graphical abstractOverview of the literature review.

A hand-in-hand phenomenon in older adults: Increased risk of frailty in geriatric outpatients associated with handgrip strength asymmetry and weakness

ObjectivesHandgrip strength (HGS) is accepted as a predictor of overall health status and a biomarker of aging, besides negative health outcomes and mortality. While differences in HGS between the dominant and non-dominant hands are expected, substantial discrepancies may signal impaired muscle function. This study aims to investigate whether handgrip asymmetry can serve as a reliable indicator of frailty in a diverse population of older adults. DesignCross-sectional study Setting and participantsA total of 512 patients aged 65 years and older who were admitted to the geriatric medicine outpatient clinic of a university hospital were enrolled in the study. MethodsThe Clinical Frailty Scale (CFS) was used to assess the overall level of frailty of the study population. The highest recorded HGS values from the non-dominant and dominant hand were used to calculate the HGS ratio. Those with a HGS ratio of dominant and non-dominant hand <0.90 or >1.10 were defined as HGS asymmetry. ResultsOf the whole study group, 61.1% was female and the mean age was 73.2 ± 6.1 years. The ratio of the participants living with frailty was 57.6% (n=219). The number of patients with HGS asymmetry was 264 (51.6%), and 48.4% (n=248) of the study population had symmetric HGS. The normal and symmetric HGS was found in 40.2% of the non-frail group, whereas it was 23.7% in patients living with frailty. Furthermore, the ratio of low and asymmetric HGS was 16.3% in the non-frail group, and 35.0% in the patients living with frailty (p<0.001). The presence of asymmetric and low HGS increased the risk of frailty three times independently of other confounding factors (OR:3.08; 95% CI:1.48-6.43; p=0.003). Conclusions and ImplicationsIdentifying HGS asymmetry along with low HGS as potential indicators of frailty may provide clinicians with a clear and quantifiable criterion for assessing older patients.

Animal Models Relevant for Geroscience: Current Trends and Future Perspectives in Biomarkers, and Measures of Biological Aging.

For centuries, aging was considered inevitable and immutable. Geroscience provides the conceptual framework to shift this focus toward a new view that regards aging as an active biological process, and the biological age of an individual as a modifiable entity. Significant steps forward have been made toward the identification of biomarkers for and measures of biological age, yet knowledge gaps in geroscience are still numerous. Animal models of aging are the focus of this perspective, which discusses how experimental design can be optimized to inform and refine the development of translationally relevant measures and biomarkers of biological age. We provide recommendations to the field, including: the design of longitudinal studies in which subjects are deeply phenotyped via repeated multilevel behavioral/social/molecular assays; the need to consider sociobehavioral variables relevant for the species studied; and finally, the importance of assessing age of onset, severity of pathologies, and age-at-death. We highlight approaches to integrate biomarkers and measures of functional impairment using machine learning approaches designed to estimate biological age as well as to predict future health declines and mortality. We expect that advances in animal models of aging will be crucial for the future of translational geroscience but also for the next chapter of medicine.

Comparison of facial skin ageing in healthy Asian and Caucasian females quantified by in vivo line-field confocal optical coherence tomography 3D imaging.

Quantitative biomarkers of facial skin aging were investigated in 109 healthy Asian female volunteers, aged 20 to 70 years. In vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging, enhanced by Artificial Intelligence (AI)-based quantification algorithms, was utilized to compute various metrics, including stratum corneum thickness (SC), viable epidermal (VE) thickness, and Dermal-Epidermal Junction (DEJ) undulation along with cellular metrics for the temple, cheekbone, and mandible. Comparison with data from a cohort of healthy Caucasian volunteers revealed similarities in the variations of stratum corneum and viable epidermis layers, as well as cellular shape and size with age in both ethnic groups. However, specific findings emerged, such as larger, more heterogeneous nuclei in both layers, demonstrated by an increase in nuclei volume and their standard deviation, and increased network atypia, all showing significant age-related variations. Caucasian females exhibited a flatter and more homogeneous epidermis, evidenced by a decreased standard deviation of the number of layers, and a less dense cellular network with fewer cells per layer, indicated by a decrease in cell surface density. Ethnicity-wise comparisons highlighted distinct biological features specific to each population. Asian individuals showed significantly higher DEJ undulation, higher compactness, and lower cell network atypia compared to their Caucasian counterparts across age groups. Differences in stratum corneum and viable epidermal thickness on the cheekbone were also significant. LC-OCT 3D imaging provides valuable insights into the aging process in different populations and underscores inherent biological differences between Caucasian and Asian female volunteers.

A retinal biomarker of biological age based on composite clinical phenotypic information.

A retinal biomarker of biological age based on composite clinical phenotypic information.

ScEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood

Ageing is the accumulation of changes and decline of function of organisms over time. The concept and biomarkers of biological age have been established, notably DNA methylation-based clocks. The emergence of single-cell DNA methylation profiling methods opens the possibility of studying the biological age of individual cells. Here, we generate a large single-cell DNA methylation and transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages. The number of genes expressed increases with age, but gene-specific changes are small. We next develop scEpiAge, a single-cell DNA methylation age predictor, which can accurately predict age in (very sparse) publicly available datasets, and also in single cells. DNA methylation age distribution is wider than technically expected, indicating epigenetic age heterogeneity and functional differences. Our work provides a foundation for single-cell and sparse data epigenetic age predictors, validates their functionality and highlights epigenetic heterogeneity during ageing.

Adipose tissue–derived adipsin marks human aging in non-type 2 diabetes population

IntroductionAdipsin or complement factor D is an adipokine that augments insulin secretion, is altered in various degrees of obesity, and is involved in alternative complement pathway. However, whether adipsin has...

Brain age prediction via cross-stratified ensemble learning

As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.

ImAge quantitates aging and rejuvenation.

For efficient, cost-effective and personalized healthcare, biomarkers that capture aspects of functional, biological aging, thus predicting disease risk and lifespan more accurately and reliably than chronological age, are essential. We developed an imaging-based chromatin and epigenetic age (ImAge) that captures intrinsic age-related trajectories of the spatial organization of chromatin and epigenetic marks in single nuclei, in mice. We show that such trajectories readily emerge as principal changes in each individual dataset without regression on chronological age, and that ImAge can be computed using several epigenetic marks and DNA labeling. We find that interventions known to affect biological aging induce corresponding effects on ImAge, including increased ImAge upon chemotherapy treatment and decreased ImAge upon caloric restriction and partial reprogramming by transient OSKM expression in liver and skeletal muscle. Further, ImAge readouts from chronologically identical mice inversely correlated with their locomotor activity, suggesting that ImAge may capture elements of biological and functional age. In sum, we developed ImAge, an imaging-based biomarker of aging with single-cell resolution rooted in the analysis of spatial organization of epigenetic marks.

Lumican is Both a Novel Risk Factor and Potential Plasma Biomarker for Vascular Aging, Capable of Promoting VSMCs Senescence through Interacting with Integrin α2β1.

Vascular aging, a common pathogenesis of senile chronic diseases, significantly increases morbidity and mortality in older adults; its intricate cellular and molecular mechanisms necessitate further investigation. Lumican (LUM) and integrin α2β1(ITGα2β1) are profibrotic extracellular matrix proteins and vital cell regulatory receptors, respectively. However, their roles in vascular aging remain unclear. This study sought to elucidate the connection between LUM and vascular aging as well as the biological mechanism of LUM/ITGα2β1 in this process. Using an enzyme-linked immunosorbent assay, we discovered that plasma LUM was elevated in vascular aging individuals and was positively correlated with brachial-ankle pulse wave velocity. Additionally, immunohistochemical and western blot analyses confirmed LUM upregulation in arteries of older adults and aged mice, as well as in senescent vascular smooth cells (VSMCs). Wild-type and LUM semiknockout (Lum-/+) mice, along with primary VSMCs extracted from these mice, were exposed to angiotensin II (Ang II) to induce stress-induced senescence model. LUM semiknockout mitigated Ang Ⅱ-induced arteriosclerosis, hypertension, vascular aging and remodeling in mice. Both in vitro and in vivo studies revealed that LUM deficiency suppressed p53, p21, collagen 1 and collagen 3 upregulation and synthetic phenotype formation in VSMCs stimulated by Ang Ⅱ. Treating VSMCs with a ITGα2β1 antagonist reversed the aforementioned changes triggered by LUM proteins. Briefly, LUM functions as a potential marker and risk factor for vascular aging and promotes pathological changes by affecting ITGα2β1 in VSMCs. This study introduces a novel molecular target for the early diagnosis and treatment of vascular aging and age-related vascular diseases.

Association Between Body Composition Patterns, Cardiovascular Disease, and Risk of Neurodegenerative Disease in the UK Biobank.

Accumulating evidence connects diverse components of body composition (e.g., fat, muscle, and bone) to neurodegenerative disease risk, yet their interplay remains underexplored. This study examines the associations between patterns of body composition and the risk of neurodegenerative diseases, exploring the mediating role of cardiovascular diseases (CVDs). This retrospective analysis used data from the UK Biobank, a prospective community-based cohort study. We included participants free of neurodegenerative diseases and with requisite body composition measurements at recruitment, who were followed from 5 years after recruitment until April 1, 2023, to identify incident neurodegenerative diseases. We assessed the associations between different components and major patterns of body composition (identified by principal component analysis) with the risk of neurodegenerative diseases, using multivariable Cox models. Analyses were stratified by disease susceptibility, indexed by polygenetic risk scores for Alzheimer and Parkinson diseases, APOE genotype, and family history of neurodegenerative diseases. Furthermore, we performed mediation analysis to estimate the contribution of CVDs to these associations. In addition, in a subcohort of 40,790 participants, we examined the relationship between body composition patterns and brain aging biomarkers (i.e., brain atrophy and cerebral small vessel disease). Among 412,691 participants (mean age 56.0 years, 55.1% female), 8,224 new cases of neurodegenerative diseases were identified over an average follow-up of 9.1 years. Patterns identified as "fat-to-lean mass," "muscle strength," "bone density," and "leg-dominant fat distribution" were associated with a lower rate of neurodegenerative diseases (hazard ratio [HR] = 0.74-0.94) while "central obesity" and "arm-dominant fat distribution" patterns were associated with a higher rate (HR = 1.13-1.18). Stratification analysis yielded comparable risk estimates across different susceptibility groups. Notably, 10.7%-35.3% of the observed associations were mediated by CVDs, particularly cerebrovascular diseases. The subcohort analysis of brain aging biomarkers corroborated the findings for "central obesity," "muscle strength," and "arm-dominant fat distribution" patterns. Our analyses demonstrated robust associations of body composition patterns featured by "central obesity," "muscle strength," and "arm-dominant fat distribution" with both neurodegenerative diseases and brain aging, which were partially mediated by CVDs. These findings underscore the potential of improving body composition and early CVD management in mitigating risk of neurodegenerative diseases.