Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

DNA Methylation Clocks in Aging: Categories, Causes, and Consequences.

Age-associated changes to the mammalian DNA methylome are well documented and thought to promote diseases of aging, such as cancer. Recent studies have identified collections of individual methylation sites whose aggregate methylation status measures chronological age, referred to as the DNA methylation clock. DNA methylation may also have value as a biomarker of healthy versus unhealthy aging and disease risk; in other words, a biological clock. Here we consider the relationship between the chronological and biological clocks, their underlying mechanisms, potential consequences, and their utility as biomarkers and as targets for intervention to promote healthy aging and longevity.

Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells.

An individual's chronological age does not always correspond to the health of different tissues in their body, especially in cases of disease. Therefore, estimating and contrasting the physiological age of tissues with an individual's chronological age may be a useful tool to diagnose disease and its progression. In this study, we present novel metrics to quantify the loss of phylogenetic diversity in hematopoietic stem cells (HSCs), which are precursors to most blood cell types and are associated with many blood-related diseases. These metrics showed an excellent correspondence with an age-related increase in blood cancer incidence, enabling a model to estimate the phylogeny-derived age (phyloAge) of HSCs present in an individual. The HSC phyloAge was generally older than the chronological age of patients suffering from myeloproliferative neoplasms (MPNs). We present a model that relates excess HSC aging with increased MPN risk. It predicted an over 200 times greater risk based on the HSC phylogenies of the youngest MPN patients analyzed. Our new metrics are designed to be robust to sampling biases and do not rely on prior knowledge of driver mutations or physiological assessments. Consequently, they complement conventional biomarker-based methods to estimate physiological age and disease risk.

Clonal hematopoiesis in patients with HIV and cancer.

BACKGROUND: Cancer-related deaths for people living with HIV (PWH) are increasing due to longer life expectancies and disparately poor cancer-related outcomes. We hypothesize that advanced biological aging contributes to cancer-related morbidity and mortality for PWH and cancer. We sought to determine the impact of clonal hematopoiesis (CH) on cancer disparities in PWH. METHODS: We conducted a retrospective study to compare the prevalence and clinical outcomes of CH in PWH and people without HIV (PWoH) and cancer. Included in the study were PWH and similar PWoH based on tumor site, age, tumor sequence, and cancer treatment status. Biological aging was also measured using epigenetic methylation clocks. RESULTS: In 136 patients with cancer, PWH had twice the prevalence of CH compared to similar PWoH (23% vs 11%, p=0.07). After adjusting for patient characteristics, PWH were four-times more likely to have CH than PWoH (OR 4.1, 95% CI 1.3-13.9, p=0.02). The effect of CH on survival was most pronounced in PWH, who had a 5-year survival rate of 38% if they had CH (vs 59% if no CH), compared to PWoH who had a 5-year survival rate of 75% if they had CH (vs 83% if no CH). CONCLUSION: This study provides the first evidence that PWH may have a higher prevalence of CH than PWoH with the same cancers. CH may be an independent biological aging risk factor contributing to inferior survival for PWH and cancer.

Relation between frailty and hypertension is partially mediated by physical activity among males and females in the Canadian Longitudinal Study on Aging.

Frailty reflects the heterogeneity in aging and may lead to the development of hypertension and heart disease, but the frailty-cardiovascular relationship and whether physical activity modifies this relationship in males and females are unclear. We tested whether higher frailty was positively associated with hypertension and heart disease in males and females and whether habitual movement mediated this relationship. The relationship between baseline frailty with follow-up hypertension and heart disease was investigated using the Canadian Longitudinal Study on Aging at 3-year follow-up data (males: n = 13,095; females: n = 13,601). Frailty at baseline was determined via a 73-item deficit-based index, activity at follow-up was determined via the Physical Activity Scale for the Elderly, and cardiovascular function was self-reported. Higher baseline frailty level was associated with a greater likelihood of hypertension and heart disease at follow-up, with covariate-adjusted odds ratios of 1.08-1.09 (all, P < 0.001) for a 0.01 increase in frailty index score. Among males and females, sitting time and strenuous physical activity were independently associated with hypertension, with these activity behaviors being partial mediators (except male-sitting time) for the frailty-hypertension relationship (explained 5-10% of relationship). The strength of this relationship was stronger among females. Only light-moderate activity partially mediated the relationship (∼6%) between frailty and heart disease in females, but no activity measure was a mediator for males. Higher frailty levels were associated with a greater incidence of hypertension and heart disease, and strategies that target increases in physical activity and reducing sitting may partially uncouple this relationship with hypertension, particularly among females.NEW & NOTEWORTHY Longitudinally, our study demonstrates that higher baseline frailty levels are associated with an increased risk of hypertension and heart disease in a large sample of Canadian males and females. Movement partially mediated this relationship, particularly among females.

Value of combining biological age with assessment of individual frailty to optimize management of cancer treated with targeted therapies: model of chronic myeloid leukemia treated with tyrosine kinase inhibitors (BIO-TIMER trial).

BACKGROUND: In the era of targeted therapies, the influence of aging on cancer management varies from one patient to another. Assessing individual frailty using geriatric tools has its limitations, and is not appropriate for all patients especially the youngest one. Thus, assessing the complementary value of a potential biomarker of individual aging is a promising field of investigation. The chronic myeloid leukemia model allows us to address this question with obvious advantages: longest experience in the use of tyrosine kinase inhibitors, standardization of therapeutic management and response with minimal residual disease and no effect on age-related diseases. Therefore, the aim of the BIO-TIMER study is to assess the biological age of chronic myeloid leukemia or non-malignant cells in patients treated with tyrosine kinase inhibitors and to determine its relevance, in association or not with individual frailty to optimize the personalised management of each patient. METHODS: The BIO-TIMER study is a multi-center, prospective, longitudinal study aiming to evaluate the value of combining biological age determination by DNA methylation profile with individual frailty assessment to personalize the management of chronic myeloid leukemia patients treated with tyrosine kinase inhibitors. Blood samples will be collected at diagnosis, 3 months and 12 months after treatment initiation. Individual frailty and quality of life will be assess at diagnosis, 6 months after treatment initiation, and then annually for 3 years. Tolerance to tyrosine kinase inhibitors will also be assessed during the 3-year follow-up. The study plans to recruit 321 patients and recruitment started in November 2023. DISCUSSION: The assessment of individual frailty should make it possible to personalize the treatment and care of patients. The BIO-TIMER study will provide new data on the role of aging in the management of chronic myeloid leukemia patients treated with tyrosine kinase inhibitors, which could influence clinical decision-making. TRIAL REGISTRATION: ClinicalTrials.gov , ID NCT06130787; registered on November 14, 2023.

Regular moderate physical exercise decreases Glycan Age index of biological age and reduces inflammatory potential of Immunoglobulin G.

Physical inactivity and obesity are growing concerns, negatively impacting the general population. Moderate physical activity is known to have a beneficial anti-inflammatory effect. N-glycosylation of immunoglobulin G (IgG) reflects changes in the inflammatory potential of IgG. In this study, GlycanAge index of biological age (GlycanAge), one of the first commercially used biomarkers of aging, was employed to assess effects of exercise intensity in three different groups of athletes: professional competing athletes, regularly moderate active individuals and newly involved recreational individuals, compared to the group of inactive individuals. GlycanAge was significantly lower in the active group compared to the inactive group (ß = -7.437, p.adj = 7.85E-03), and nominally significant and increased in professional athletes compared to the active group (ß = 7.546, p = 3.20E-02). Competing female athletes had significantly higher GlycanAge comparing to active females exercising moderately (ß = 20.206, p.adj = 2.71E-02), while the latter had significantly lower GlycanAge when compared with the inactive counterparts (ß = -9.762, p.adj = 4.68E-02). Regular, life-long moderate exercise has an anti-inflammatory effect in both female and male population, demonstrated by lower GlycanAge index, and it has great potential to mitigate growing issues related to obesity and a sedentary lifestyle, which are relentlessly increasing world-wide.

Estimated median density identifies donor age and sex differences in red blood cell biological age.

BACKGROUND: Donors possess heterogeneous red cell concentrates (RCCs) in terms of the biological age of their red blood cells (RBCs) as a direct result of various donor-dependent factors influencing rates of erythropoiesis. This study aimed to estimate the median biological age of RBCs in RCCs based on donor age and sex to investigate inherent differences in blood products' biological ages over hypothermic storage using estimated median densities (EMDs). STUDY DESIGN: Sixty RCCs were collected from four donor groups; male and female teenagers (17-19 years old) and seniors (75+ years old). A Percoll density-based separation approach was used to quantify the EMDs indicative of biological age. EMD and mean corpuscular hemoglobin (MCHC) were compared by correlation analyses. RESULTS: Differences in the median biological age of RCC units were observed with male donors having significantly higher EMDs compared to females (p < .001). Teen male donors possessed the highest EMDs with significantly elevated levels of biologically aged RBCs compared to both female donor groups, regardless of storage duration (p < .05). Throughout most of the 42-day storage period, senior donors, particularly senior females, demonstrated the strongest correlation between EMD and MCHC (R2 > 0.5). CONCLUSIONS: This study provides further evidence that there are inherent differences between the biological age profiles of RBCs between blood donors of different sex and age. Our findings further highlight that biological age may contribute to RBC quality during storage and that donor characteristics need to be considered when evaluating transfusion safety and efficacy.

Red cell concentrates from teen male donors contain poor-quality biologically older cells.

BACKGROUND AND OBJECTIVES: Donor factors influence the quality characteristics of red cell concentrates (RCCs) and the lesions that develop in these heterogeneous blood products during hypothermic storage. Teen male donors' RCCs contain elevated levels of biologically old red blood cells (RBCs). The aim of this study was to interrogate the quality of units of different donor ages and sexes to unravel the complex interplay between donor characteristics, long-term cold storage and, for the first time, RBC biological age. MATERIALS AND METHODS: RCCs from teen males, teen females, senior males and senior females were density-separated into less-dense/young (Y-RBCs) and dense/old RBCs (O-RBCs) throughout hypothermic storage for testing. The unseparated and density-separated cells were tested for haematological parameters, stress (oxidative and osmotic) haemolysis and oxygen affinity (p50). RESULTS: The O-RBCs obtained from teen donor samples, particularly males, had smaller mean corpuscular volumes and higher mean corpuscular haemoglobin concentrations. While biological age did not significantly affect oxygen affinity, biologically aged O-RBCs from stored RCCs exhibited increased oxidative haemolysis and decreased osmotic fragility, with teenage male RCCs exhibiting the highest propensity to haemolyse. CONCLUSION: Previously, donor age and sex were shown to have an impact on the biological age distribution of RBCs within RCCs. Herein, we demonstrated that RBC biological age, particularly O-RBCs, which are found more prevalently in male teens, to be a driving factor of several aspects of poor blood product quality. This study emphasizes that donor factors should continue to be considered for their potential impacts on transfusion outcomes.

Joint association of biological aging and lifestyle with risks of cancer incidence and mortality: A cohort study in the UK Biobank.

BACKGROUND: Aging is a risk factor for cancer incidence and mortality. Biological aging can reflect the aging degree of the body better than chronological age and can be aggravated by unhealthy lifestyle factors. We aimed to assess the joint effect of biological aging and lifestyle with risks of cancer incidence and mortality. METHODS: This study included a total of 281,889 participants aged 37 to 73 from the UK Biobank database. Biological age was derived from chronological age and 9 clinical blood indicators, and lifestyle score was constructed by body mass index, smoking status, alcohol consumption, physical activity, and diet. Multivariate Cox hazard proportional regression model was used to analyze the independent and joint association of biological aging and lifestyle with risks of cancer incidence and mortality, respectively. RESULTS: Over a median follow-up period of 12.3 years, we found that older biological age was associated with increased risks of overall cancer, digestive system cancers, lung, breast and renal cancers incidence and mortality (HRs: 1.12-2.25). In the joint analysis of biological aging and lifestyle with risks of cancer incidence and mortality, compared with unhealthy lifestyle and younger biological age, individuals with healthy lifestyle and older biological age had decreased risks of incidence (8% âˆ¼ 60%) and mortality (20% âˆ¼ 63%) for overall, esophageal, colorectal, pancreatic and lung cancers. CONCLUSIONS: Biological aging may be an important risk factor for cancer morbidity and mortality. A healthier lifestyle is more likely to mitigate the adverse effects of biological aging on overall cancer and some site-specific cancers.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data.

BACKGROUND: There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆. METHODS: We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. RESULTS: The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. CONCLUSIONS: Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

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.

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

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

Retirement Makes You Old? Causal Effect of Retirement on Biological Age.

Retirement is a critical life event for older people. Health scholars have scrutinized the health effects of retirement, but its consequences on age-related diseases and mortality are unclear. We extend this body of research by integrating measurements of biological age, representing the physiological decline preceding disease onset. Using data from the UK Biobank and a fuzzy regression discontinuity design, we estimated the effects of retirement on two biomarker-based biological age measures. Results showed that retirement significantly increases biological age for those induced to retire by the State Pension eligibility by 0.871-2.503 years, depending on sex and specific biological age measurement. Given the emerging scientific discussion about direct interventions to biological age to achieve additional improvements in population health, the positive effect of retirement on biological age has important implications for an increase in the State Pension eligibility age and its potential consequences on population health, public health care policy, and older people's labor force participation. Overall, this study provides novel empirical evidence contributing to the question of what social factors make people old.

Association of Biological Age with Tumor Microenvironment in Patients with Esophageal Adenocarcinoma.

INTRODUCTION: Esophageal cancer is the seventh most common cancer worldwide and typically tends to manifest at an older age. Marked heterogeneity in time-dependent functional decline in older adults results in varying grades of clinically manifest patient fitness or frailty. The biological age-related adaptations that accompany functional decline have been shown to modulate the non-malignant cells comprising the tumor microenvironment (TME). In the current work, we studied the association between biological age and TME characteristics in patients with esophageal adenocarcinoma. METHODS: We comparatively assessed intratumoral histologic stroma quantity, tumor immune cell infiltrate, and blood leukocyte and thrombocyte count in 72 patients stratified over 3 strata of biological age (younger <70 years, fit older ≥70 years, and frail older adults ≥70 years), as defined by a geriatric assessment. RESULTS: Frailty in older adults was predictive of decreased intratumoral stroma quantity (B = -14.66% stroma, p = 0.022) relative to tumors in chronological-age-matched fit older adults. Moreover, in comparison to younger adults, frail older adults (p = 0.032), but not fit older adults (p = 0.302), demonstrated a lower blood thrombocyte count at the time of diagnosis. Lastly, we found an increased proportion of tumors with a histologic desert TME histotype, comprising low stroma quantity and low immune cell infiltration, in frail older adults. CONCLUSION: Our results illustrate the stromal-reprogramming effects of biological age and provide a biological underpinning for the clinical relevance of assessing frailty in patients with esophageal adenocarcinoma, further justifying the need for standardized geriatric assessment in geriatric cancer patients.

Living in poverty and accelerated biological aging: evidence from population-representative sample of U.S. adults.

BACKGROUND: Biological aging reflects a decline in the functions and integrity of the human body that is closely related to chronological aging. A variety of biomarkers have been found to predict biological age. Biological age higher than chronological age (biological age acceleration) indicates an accelerated state of biological aging and a higher risk of premature morbidity and mortality. This study investigated how socioeconomic disadvantages influence biological aging. METHODS: The data from the National Health and Nutrition Examination Survey (NHANES) IV, including 10 nationally representative cross-sectional surveys between 1999-2018, were utilized. The analytic sample consisted of N = 48,348 individuals (20-84 years). We used a total of 11 biomarkers for estimating the biological age. Our main outcome was biological age acceleration, indexed by PhenoAge acceleration (PAA) and Klemera-Doubal biological age acceleration (KDM-A). Poverty was measured as a ratio of family income to the poverty thresholds defined by the U.S. Census Bureau, adjusted annually for inflation and family size (5 categories). The PAA and KDM-A were regressed on poverty levels, age, their interaction, education, sex, race, and a data collection wave. Sample weights were used to make the estimates representative of the U.S. adult population. RESULTS: The results showed that higher poverty was associated with accelerated biological aging (PAA: unstandardized coefficient B = 1.38 p <.001, KDM: B = 0.96, p = .026 when comparing the highest and the lowest poverty level categories), above and beyond other covariates. The association between PAA and KDM-A and age was U-shaped. Importantly, there was an interaction between poverty levels and age (p <.001), as the effect of poverty was most pronounced in middle-aged categories while it was modest in younger and elderly groups. CONCLUSION: In a nationally representative US adult population, we found that higher poverty was positively associated with the acceleration of biological age, particularly among middle-aged persons.

Chronological age-based vs. Bio-banded friendly football tournament: Effects on physical performance and spatial exploration behaviour in 7v7 match play.

Biological age-based competitions have been suggested to mitigate maturation bias among youth football players. This study analysed the physical demands and spatial exploration behaviour when playing football matches against biological or chronological age-based opposition. One-hundred and sixteen U-13 and U-14 male regional-level football players were recruited from five Portuguese football academies. Each player completed eight games, four per condition (chronological and biological-based), on the same day. Games were played in 7 vs. 7 formats, with 20-min duration, and conformed to standard officiating and rule procedures. The following variables were considered from GPS-data in each game: distance covered, high-intensity accelerations and decelerations, body impacts, and average and peak speeds. Furthermore, average, and peak heart rates (HRpeak) and spatial exploration index were collected. Players covered significantly less distance (ES = 1.11), performed fewer decelerations (ES = 0.82), achieved lower average speeds (ES = 1.15), had fewer body impacts (ES = 0.94), and recorded lower HRpeak (ES = 0.56) in the biological age-based competition compared to the chronological-age-based. No significant differences were found between the two formats in spatial exploration. Both game formats offer distinct physical demands at the external and internal levels. Finally, practitioners can manipulate the type of opposition to get distinct physical demands to mitigate the effect of maturation bias during development.

Shorter donor leukocyte telomere length is associated with poor peripheral blood stem cell mobilization induced by granulocyte colony-stimulating factor.

BACKGROUND/PURPOSE: Insufficient numbers of peripheral blood stem cells (PBSC) after granulocyte colony-stimulating factor (G-CSF) mobilization occurs in a significant proportion of PBSC collections, often from older age donors. Telomere length (TL) is often used as an indicator of an individual's biological age. This study aimed to investigate the relationship between donors' leukocyte TL and the outcome of G-CSF-induced PBSC mobilization in healthy unrelated donors. METHODS: Donors' leukocyte TLs and the outcome of G-CSF-induced PBSC mobilization, as assessed by pre-harvest CD34+ cell counts, were analyzed in 39 healthy PBSC donors. TL in a non-mobilized general population (n = 90) was included as a control group. G-CSF mobilization effect was categorized into three groups according to pre-harvest CD34+ cell count: poor (≤25/µL, PMD), intermediate (between 25 and 180/µL), and good (≥180/µl, GMD). RESULTS: Leukocyte TL of PBSC donors correlated well with pre-harvest CD34+ cell counts (r = 0.645, p < 0.001). Leukocyte TLs of PMDs (n = 8) were significantly shorter than those of GMDs (n = 9) and non-mobilization controls (p < 0.05). Moreover, all PMD TLs were below the 50th percentile, and 62.5% of PMDs had TLs below the 10th percentile of age-matched control participants. In contrast, no GMD TLs were below the 10th percentile; in fact, 33.3% (3/9) of them were above the 90th percentile. CONCLUSION: Our results indicate that shorter donor leukocyte TL is associated with poor G-CSF-induced PBSC mobilization. TL, which represents a donor's biological age, could be a potential predictor for mobilization outcome.

The Gut and Skin Microbiome and Its Association with Aging Clocks.

Aging clocks are predictive models of biological age derived from age-related changes, such as epigenetic changes, blood biomarkers, and, more recently, the microbiome. Gut and skin microbiota regulate more than barrier and immune function. Recent studies have shown that human microbiomes may predict aging. In this narrative review, we aim to discuss how the gut and skin microbiomes influence aging clocks as well as clarify the distinction between chronological and biological age. A literature search was performed on PubMed/MEDLINE databases with the following keywords: "skin microbiome" OR "gut microbiome" AND "aging clock" OR "epigenetic". Gut and skin microbiomes may be utilized to create aging clocks based on taxonomy, biodiversity, and functionality. The top contributing microbiota or metabolic pathways in these aging clocks may influence aging clock predictions and biological age. Furthermore, gut and skin microbiota may directly and indirectly influence aging clocks through the regulation of clock genes and the production of metabolites that serve as substrates or enzymatic regulators. Microbiome-based aging clock models may have therapeutic potential. However, more research is needed to advance our understanding of the role of microbiota in aging clocks.

[Novel integrative multi-omics strategies of human's biological age computation.]

Multi-omics methods for analysing postgenomic data have become firmly established in the tools of molecular gerontology only in recent years, since previously there were no comprehensive integrative approaches adequate to the task of calculating biological age. This paper provides an overview of existing papers on multi-omics integrative approaches in calculating the biological age of a human. An analysis of the most common options for integrating methylomic, transcriptomic, proteomic, microbiomic and metabolomic datasets was carried out. We defined (1) concatenation (machine learning), in which models are developed using a concatenated data matrix, formed by combining multiple omics data sets; (2) fusion model approaches that create multiple intermediate submodels for different omics data to then build a final integrated model from the various intermediate submodels; and (3) transformation methods (via artificial intelligence) that first transform each of the single omics data sets into core plots or matrices, and then combine them all into one graph before building an integral complex model. It is unlikely that multi-omics approaches will find application in anti-aging personalized medicine, but they will undoubtedly deepen and expand the understanding of the fundamental processes standing behind the phenomenon of the biological aging clocks.