Biological age (BA), reflecting aging-related health decline beyond chronological age, varies among individuals. While previous research explored associations of maternal pregnancy-related body size with offspring health outcomes, its implications for BA in young adults remain unclear. Utilizing longitudinal data of 1,148 mother-offspring pairs from the Jerusalem Perinatal Study, we analyzed associations of maternal pre-pregnancy BMI and gestational weight gain (GWG) with offspring Klemera-Doubal method (KDM)-based BA at age 32, and potential familial life-course underlying mechanisms. Maternal pregnancy-related body size, adjusted for sociodemographic/lifestyle factors was associated with offspring BA (βmaternal pre-pregnancy BMI=0.183,95%CI:0.098,0.267;βGWG=0.093,95%CI:0.021,0.165). Association of GWG with BA was largely direct (90%,95%CI,44%,100%), while association with maternal pre-pregnancy BMI was partially mediated through adolescent BMI (36%,95%CI=18%,75%), with both associations eliminated after adjustment for offspring adult BMI. Associations persisted after adjusting for offspring polygenic risk score for BMI (βmaternal pre-pregnancy BMI=0.128;95%CI=0.023,0.234; βGWG=0.102;95%CI=0.006,0.198), and somewhat altered after adjustment for maternal cardiometabolic conditions (βmaternal pre-pregnancy BMI=0.144,95%CI=0.059, 0.230). Impact on GWG associations was negligible. Thus, perinatal obesogenic environment contributes to offspring BA beyond sociodemographic factors and maternal cardiometabolic history, yet intergenerational transmission of obesity seems to underlie these associations. Nonetheless, the period between adolescence and young adulthood could be targeted for weight-reducing interventions, ultimately promoting healthy aging.

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.

Accelerated biological aging may be associated with increased risk of esophageal adenocarcinoma (EAC). However, its relationship with genetic variation, and its effect on improving risk population stratification, remains unknown. We performed an exposome association study to determine potential associated factors associated with EAC. To quantify biological age and its difference from chronological age, we calculated the BioAge10 and Biological Age Acceleration (BioAgeAccel) based on chronological age and nine biomarkers. Multivariable Cox regression models for 362,310 participants from the UK Biobank with a median follow-up of 13.70 years were performed. We established a weighted polygenic risk score (wPRS) associated with EAC, to assess joint and interaction effects with BioAgeAccel. Four indicators were used to evaluate their interaction effects, and we fitted curves to evaluate the risk stratification ability of BioAgeAccel. Compared with biologically younger participants, those older had higher risk of EAC, with adjusted HR of 1.79 (95%CI: 1.52-2.10). Compared with low wPRS and biologically younger group, the high wPRS and biologically older group had a 4.30-fold increase in HR (95% CI: 2.78-6.66), at meanwhile, 1.15-fold relative excess risk was detected (95% CI: 0.30-2.75), and 22% of the overall EAC risk was attributable to the interactive effects (95% CI: 12%-31%). The 10-year absolute incidence risk indicates that biologically older individuals should begin screening procedures 4.18 years in advance, while youngers can postpone screening by 4.96 years, compared with general population. BioAgeAccel interacted positively with genetic variation and increased risk of EAC, it could serve as a novel indicator for predicting incidence.

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.