BACKGROUND: Multiple epidemiological studies have observed the connection between aging and brain volumes. The concept of accelerated biological aging (BA) is more powerful for observing the degree of aging of an individual than chronologic age (CA). The objective of this study is to explore the relationship between BA and brain volumes.
METHODS: BA was measured from clinical traits using two blood-chemistry algorithms, the Klemera-Doubal method (KDM) and the PhenoAge. The two age acceleration biomarkers were calculated by the residuals from regressing CA, termed \"KDM-acceleration\" and \"PhenoAge-acceleration\". Brain volumes were from brain magnetic resonance imaging (MRI) data. After adjustment for confounding factors, general linear regression models were used to examine associations between KDM-acceleration and PhenoAge-acceleration and brain volumes, respectively. Additionally, we stratified participants by sex, age, and the four quartiles of the Townsend Deprivation Index (TDI) for extra subgroup analysis.
RESULTS: 14,725 participants with available information were enrolled. After full adjustment, we observed negative associations between KDM-acceleration and brain volumes, such as gray matter (β = -0.029), white matter (β = -0.021), gray and white matter (β = -0.026), and hippocampus (β = -0.011 for left and β = -0.014 for right). There were also negative associations between PhenoAge-acceleration and brain volumes, such as white matter (β = -0.008), gray and white matter (β = -0.010), thalamus (β = -0.012 for left and β = -0.012 for right). In the subgroup analysis stratified by sex, age, and the four quartiles of TDI, the association between KDM-acceleration and PhenoAge-acceleration and brain volumes still existed. In subgroup analyses, the variation in associations suggests that socioeconomic and biological factors may differentially influence brain aging.
CONCLUSIONS: Our research indicated that more advanced BA was associated with less brain tissue.

BACKGROUND: Individuals with bipolar disorders (BD) have an estimated loss of life expectancy around 10-15 years. Several laboratory-measured biomarkers of accelerated aging exist (e.g., telomere length), however with a questionable transferability to bedside. There is a need for easily and inexpensively measurable markers of aging, usable in routine practice, such as BioAge.
METHODS: We calculated BioAge that estimates biological age based on routine blood tests and a physical exam, in a sample of 2220 outpatients with BD. We investigated associations between BioAge Acceleration (BioAgeAccel), which is an indicator of accelerated aging, and sociodemographic variables, clinical variables, and current psychotropic medication use.
RESULTS: Mean chronological age was 40.2 (±12.9). Mean BioAge was 39.1 (±12.4). Mean BioAgeAccel was 0.08 (±1.8). A minority of individuals (15%) had a BioAgeAccel above 2 years. Multivariable analyses suggested strong associations between a higher BioAgeAccel and younger age, male sex, overweight and sleep disturbances. Regarding current psychotropic medication use, discrepancies between univariate and multivariate analyses were observed.
CONCLUSIONS: A minority of individuals with BD had an accelerated aging as measured by BioAge. We identified associations with potentially modifiable factors, such as higher body mass index and sleep disturbances, that are however nonspecific to BD. These results require replications in independent samples of individuals with BD, and comparisons with a control group matched for age and gender. Longitudinal studies are also required to test whether any change in metabolic health, or sleep might decrease BioAgeAccel.

In recent years, there has been a paradigm shift with regards to ageing, challenging its traditional perception as an inevitable and natural process. Researchers have collectively identified hallmarks of ageing, nine of which were initially proposed in 2013 and expanded in 2023 to include disabled macroautophagy, chronic inflammation, and dysbiosis, enhancing our understanding of the ageing process at microscopic, cellular, and system-wide levels. Strategies to manipulate these hallmarks present opportunities for slowing, preventing, or reversing age-related diseases, thereby promoting longevity. The interdependence of these hallmarks underscores the necessity of a comprehensive, systems-based approach to address the complex processes contributing to ageing. As a primary risk factor for various diseases, ageing diminishes healthspan, leading to extended periods of compromised health and multiple age-related conditions towards the end of life. The significant gap between healthspan and lifespan holds substantial economic and societal implications. The inaugural Longevity Med Summit (4-5 May 2023, Cascais, Portugal) provided an international forum to discuss the academic and industry landscape of healthy longevity research, preventive medicine and clinical practice to enhance healthspan.

Biological age (BA) captures detrimental age-related changes. The best-known and most-used BA indicators include DNA methylation-based epigenetic clocks and telomere length (TL). The most common biological sample material for epidemiological aging studies, whole blood, is composed of different cell types. We aimed to compare differences in BAs between blood cell types and assessed the BA indicators\' cell type-specific associations with chronological age (CA). An analysis of DNA methylation-based BA indicators, including TL, methylation level at cg16867657 in ELOVL2, as well as the Hannum, Horvath, DNAmPhenoAge, and DunedinPACE epigenetic clocks, was performed on 428 biological samples of 12 blood cell types. BA values were different in the majority of the pairwise comparisons between cell types, as well as in comparison to whole blood (p < 0.05). DNAmPhenoAge showed the largest cell type differences, up to 44.5 years and DNA methylation-based TL showed the lowest differences. T cells generally had the \"youngest\" BA values, with differences across subsets, whereas monocytes had the \"oldest\" values. All BA indicators, except DunedinPACE, strongly correlated with CA within a cell type. Some differences such as DNAmPhenoAge-difference between naïve CD4 + T cells and monocytes were constant regardless of the blood donor\'s CA (range 20-80 years), while for DunedinPACE they were not. In conclusion, DNA methylation-based indicators of BA exhibit cell type-specific characteristics. Our results have implications for understanding the molecular mechanisms underlying epigenetic clocks and underscore the importance of considering cell composition when utilizing them as indicators for the success of aging interventions.

OBJECTIVE: To validate AnthropoAge, a new metric of biological age (BA), for prediction of all-cause mortality and age-related outcomes and characterize population-specific aging patterns using multinational longitudinal cohorts.
METHODS: We analyzed harmonized multinational data from the Gateway to Global Aging, including studies from the US, England, Mexico, Costa Rica, and China. We used body mass index and waist-to-height ratio to estimate AnthropoAge and AnthropoAgeAccel in participants aged 50-90 years old as proxies of BA and age acceleration, respectively. We compared the predictive capacity for all-cause mortality of AnthropoAge and chronological age (CA) using Cox models, described aging trends in all countries and explored the utility of longitudinal assessments of AnthropoAgeAccel to predict new-onset functional decline and age-related diseases using generalized estimating equations (GEE).
RESULTS: Using data from 55,628 participants, we found AnthropoAge (c-statistic 0.772) outperformed CA (0.76) for prediction of mortality independently of comorbidities, sex, race/ethnicity, education, and lifestyle; this result was replicated in most countries individually except for Mexico. Individuals with accelerated aging had a ~39% higher risk of death, and AnthropoAge also identified trends of faster biological aging per year. In longitudinal analyses, higher AnthropoAgeAccel values were independently predictive of self-reported health deterioration and new-onset deficits in basic/instrumental activities of daily living (ADL/IADL), diabetes, hypertension, cancer, chronic lung disease, myocardial infarction, and stroke.
CONCLUSIONS: AnthropoAge is a robust and reproducible BA metric associated with age-related outcomes. Its implementation could facilitate modeling trends of biological aging acceleration in different populations, although recalibration may enhance its utility in underrepresented populations such as individuals from Latin America.

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.

UNASSIGNED: The study aimed to examine the association between the systemic immune-inflammation index (SII), a contemporary metric of systemic inflammatory response, and biological aging, which are closely interconnected processes.
UNASSIGNED: This cross-sectional study utilized 10 cycles of data from the NHANES database spanning from 1990 to 2018. The study examined the relationship between the SII index, calculated as P * N/L, where P represents preoperative peripheral platelet count, N represents neutrophil count, and L represents lymphocyte count, and biological aging. Biological aging was assessed through various methods, such as phenotypic age, phenotypic age acceleration (PhenoAgeAccel), biological age, and biological age acceleration (BioAgeAccel). Correlations were analyzed using weighted linear regression and subgroup analysis.
UNASSIGNED: Among the 7,491 participants analyzed, the average age was 45.26 ± 0.34 years, with 52.16% being female. The average phenotypic and biological ages were 40.06 ± 0.36 and 45.89 ± 0.32 years, respectively. Following adjustment for potential confounders, elevated SII scores were linked to increased phenotypic age, biological age, Phenotypic age acceleration, and Biological age acceleration. Positive correlations were observed between health behavior and health factor scores and biological aging, with stronger associations seen for health factors. In health factor-specific analyses, the β coefficient was notably higher for high BMI. The robust positive associations between SII scores and both phenotypic age and biological age in the stratified analyses were consistently observed across all strata.
UNASSIGNED: The evidence from the NHANES data indicate that SII may serve as a valuable marker for assessing different facets of aging and health outcomes, such as mortality and the aging process. Additional research is warranted to comprehensively elucidate the implications of SII in the aging process and its utility as a clinical instrument for evaluating and addressing age-related ailments.

Various approaches exist to quantify the aging process and estimate biological age on an individual level. Frailty indices based on an age-related accumulation of physical deficits have been developed for human use and translated into mouse models. However, declines observed in aging are not limited to physical functioning but also involve social capabilities. The concept of \"social frailty\" has been recently introduced into human literature, but no index of social frailty exists for laboratory mice yet. To fill this gap, we developed a mouse Social Frailty Index (mSFI) consisting of seven distinct assays designed to quantify social functioning which is relatively simple to execute and is minimally invasive. Application of the mSFI in group-housed male C57BL/6 mice demonstrated a progressively elevated levels of social frailty through the lifespan. Conversely, group-housed females C57BL/6 mice manifested social frailty only at a very old age. Female mice also showed significantly lower mSFI score from 10 months of age onward when compared to males. We also applied the mSFI in male C57BL/6 mice under chronic subordination stress and in chronic isolation, both of which induced larger increases in social frailty compared to age-matched group-housed males. Lastly, we show that the mSFI is enhanced in mouse models that show accelerated biological aging such as progeroid Ercc1-/Δ and Xpg-/- mice of both sexes compared to age matched littermate wild types. In summary, the mSFI represents a novel index to quantify trajectories of biological aging in mice and may help elucidate links between impaired social behavior and the aging process.

Chronological age reveals the number of years an individual has lived since birth. By contrast, biological age varies between individuals of the same chronological age at a rate reflective of physiological decline. Differing rates of physiological decline are related to longevity and result from genetics, environment, behavior, and disease. The creation of methylation biological age predictors is a long-standing challenge in aging research due to the lack of individual pre-mortem longevity data. The consistent differences in longevity between domestic dog breeds enable the construction of biological age estimators which can, in turn, be contrasted with methylation measurements to elucidate mechanisms of biological aging. We draw on three flagship methylation studies using distinct measurement platforms and tissues to assess the feasibility of creating biological age methylation clocks in the dog. We expand epigenetic clock building strategies to accommodate phylogenetic relationships between individuals, thus controlling for the use of breed standard metrics. We observe that biological age methylation clocks are affected by population stratification and require heavy parameterization to achieve effective predictions. Finally, we observe that methylation-related markers reflecting biological age signals are rare and do not colocalize between datasets.

UNASSIGNED: Chronological age (CAge), biological age (BAge), and accelerated age (AAge) are all important for aging-related diseases. CAge is a known risk factor for benign prostatic hyperplasia (BPH); However, the evidence of association of BAge and AAge with BPH is limited. This study aimed to evaluate the association of CAge, Bage, and AAge with BPH in a large prospective cohort.
UNASSIGNED: A total of 135,933 males without BPH at enrolment were extracted from the UK biobank. We calculated three BAge measures (Klemera-Doubal method, KDM; PhenoAge; homeostatic dysregulation, HD) based on 16 biomarkers. Additionally, we calculated KDM-BAge and PhenoAge-BAge measures based on the Levine method. The KDM-AAge and PhenoAge-AAge were assessed by the difference between CAge and BAge and were standardized (mean = 0 and standard deviation [SD] = 1). Cox proportional hazard models were applied to assess the associations of CAge, Bage, and AAge with incident BPH risk.
UNASSIGNED: During a median follow-up of 13.150 years, 11,811 (8.690%) incident BPH were identified. Advanced CAge and BAge measures were associated with an increased risk of BPH, showing threshold effects at a later age (all P for nonlinearity <0.001). Nonlinear relationships between AAge measures and risk of BPH were also found for KDM-AAge (P = 0.041) and PhenoAge-AAge (P = 0.020). Compared to the balance comparison group (-1 SD < AAge < 1 SD), the accelerated aging group (AAge > 2 SD) had a significantly elevated BPH risk with hazard ratio (HR) of 1.115 (95% CI, 1.000-1.223) for KDM-AAge and 1.180 (95% CI, 1.068-1.303) for PhenoAge-AAge, respectively. For PhenoAge-AAge, subgroup analysis of the accelerated aging group showed an increased HR of 1.904 (95% CI, 1.374-2.639) in males with CAge <50 years and 1.233 (95% CI, 1.088-1.397) in those having testosterone levels <12 nmol/L. Moreover, AAge-associated risk of BPH was independent of and additive to genetic risk.
UNASSIGNED: Biological aging is an independent and modifiable risk factor for BPH. We suggest performing active health interventions to slow biological aging, which will help mitigate the progression of prostate aging and further reduce the burden of BPH.