GeroScience最新文献

Aging is the progressive decline in function at the cellular, tissue, and organismal levels that ultimately leads to mortality. The longevity of an organism is influenced by various internal and external factors, including nutrition, exercise, metabolic dysfunction, genomic instability, and epigenetic imbalance. Histone modifications, such as acetylation, methylation, phosphorylation, and ubiquitination, play a critical role in aging. These modifications illustrate histone changes crucial for regulating chromatin architecture and gene expression. The epigenetic clock signifies the impact of aging on our cells and is essential for accurately determining biological age. It demonstrates significant associations with histone modifications, underscoring their mechanistic importance in age-related changes. Histone alterations are closely associated with age-related diseases such as cancer, neurological disorders, and cardiovascular conditions. While the mechanistic function of histone modifications in biological aging is well-established, clinical application remains constrained. Emerging clinical studies targeting histone-modifying enzymes are beginning to investigate their potential as therapeutic options for age-related disorders. This review seeks to provide a comprehensive examination of histone modifications, including acetylation, methylation, and phosphorylation, as well as the impact of histone alterations on biological age, age-related diseases, and the importance of the epigenetic clock, with the aim of enhancing understanding of the epigenetic mechanisms associated with aging and facilitating the development of therapies to promote healthy aging. In this review, we also focus on the current status of such clinical trials and discuss future directions for translating these epigenetic insights into clinical applications.

Although mechanistic studies support a beneficial effect of several dietary flavonoids on telomere length (TL), to our knowledge no studies have examined associations between habitual flavonoid intakes and TL in population-based studies. We examined the associations between habitual intake of major flavonoid subclasses (flavonols, flavones, flavanones, flavan-3-ols and their polymers, and anthocyanins) and TL in a cross-sectional analysis of 4,944 disease-free females from the Nurses' Health Study (NHS). Flavonoid intakes were collected using food frequency questionnaire data, and TL was measured in peripheral blood leukocytes using quantitative real-time polymerase chain reaction. Multivariable-adjusted least squares mean leukocyte TL (z scores and corresponding standard error [SE]) for total and all flavonoid subclasses were calculated using generalized linear models. Although no individual flavonoid subclass was significantly associated with TL in the overall population, when we restricted analyses to younger and middle-aged participants (aged < 55 y), a higher anthocyanin intake associated with longer TL (least squares means ± SE: 0.06 ± 0.06 for the highest versus 0.24 ± 0.07 for the lowest quintile; P-trend = 0.042), corresponding to 6.6 (95% CI, 1.7-14.5) years of aging. In food-based analyses, participants aged < 55 y who consumed more berries had longer TL (Mean TL: 0.10 ± 0.04 for never/rarely; 0.21 ± 0.04 for ≤ 1 serving/week; 0.44 ± 0.26 for ≥ 2 servings/week; P = 0.033 for trend). Similarly, a higher anthocyanin intake was associated with longer TL among pre-menopausal females (0.46 ± 0.11 for the highest versus 0.02 ± 0.08 for the lowest quintile; P-trend = 0.001). Overall, habitual flavonoid intake was not associated with TL attrition in this cross-sectional analysis. However, in females aged < 55 y a higher anthocyanin intake was associated with longer TL, which raises the possibility that anthocyanin-rich foods may promote healthy aging and warrants further investigation with respect to age.

Falls are a leading cause of injury in older adults, making risk prediction a clinical priority. While many machine learning (ML) models exist, they typically provide a static assessment, predicting risk for a single, fixed timeframe. This approach overlooks how the clinical drivers of risk evolve over time, failing to distinguish between acute factors that signal an imminent fall and chronic conditions that confer long-term vulnerability. This study uses an explainable ML framework across multiple time horizons to unlock deeper clinical insights into the temporal nature of fall risk. We conducted a retrospective, matched case-control study using electronic health record (EHR) data from 99,078 patients who fell and 99,078 matched controls. Seven ML models were trained to predict fall risk across seven distinct prediction windows (3, 6, 12, 24, 36, 48, and 60 months). The best-performing model for each horizon, consistently XGBoost, was interpreted using SHAP (SHapley Additive exPlanations) to identify how the importance of clinical and demographic predictors changed over time. A clear performance trade-off emerged across time horizons. Short-term models (3-12 months) delivered balanced discrimination (best model XGBoost, AUC ≈ 0.75), while long-term models became progressively better at identifying eventual fallers (recall ≈ 80% at 60 months) at the cost of lower specificity (≈ 46%). SHAP analysis revealed distinct temporal patterns: short-term risk was driven by acute conditions like syncope, respiratory symptoms, and urinary tract infections, while long-term risk was predicted by chronic, cumulative factors such as spondylopathies, nutritional deficiencies, and benign neoplasms. Three primary risk trajectories (increasing, steady, and decreasing) were identified, each corresponding to distinct underlying clinical profiles. Fall risk is a dynamic process, not a static state. By analyzing risk across multiple timeframes, we can distinguish between acute triggers requiring immediate intervention and chronic vulnerabilities demanding long-term management. This multi-horizon framework provides a data-driven foundation for a new paradigm in fall prevention: moving beyond generic "high-risk" labels to personalized, temporally aware strategies that align the type and timing of interventions with the specific nature of a patient's evolving risk.

Untreated hypertension is a risk factor for late-onset Alzheimer's disease (AD); however, this association is not well understood. The aim of this study was to reveal protein signatures that bridge the pathophysiological changes in hypertension to AD. Using untargeted proteomics, we analyzed brain samples from aged (30- and 40-week-old) spontaneously hypertensive rats (SHRs, n = 12), and age-matched normotensive Wistar Kyoto (WKY, n = 8) controls, and human AD patients (Braak stages 4-6) (n = 30), cerebral amyloid angiopathy (CAA) (n = 5), non-demented controls (Braak stages 0-3) (n = 37). Differential expression and pathway analyses in SHRs highlighted the 'extracellular exosome' pathway. This pathway also showed significant associations to differentially expressed proteins in AD and CAA patients. Comparison between species identified 24 proteins in SHRs and AD, whose trajectory pattern over the progression of hypertension, aligned with those observed during AD Braak stage progression, compared to respective controls. The proteins were similarly associated with extracellular exosomes. Immunostaining and spatial proteomics support vesicle accumulation and dysregulated exosome protein signatures in the cerebrovasculature of both SHR and AD brains. Additionally, the extracellular exosome pathway-association was not identified in a traditional model of familial AD (5xFAD). Our findings demonstrate cross-species translatability between AD and the SHR and provide novel mechanistic insights into a shared dysregulation of cerebral artery-associated exosomes.

Our ability to sense the position and movement of our limbs is essential for all activities of daily living. This ability arises from the signal sent by muscle spindles to the brain. While there is clear evidence for age-related changes in the quantity of muscle spindles and in their sensitivity, behavioral assessment of age-related changes in position sense have produced mixed findings even though it is taken as textbook knowledge that proprioception declines with age. Yet, study results are difficult to compare since there is no golden standard for assessment of proprioception. Therefore, we measured upper limb proprioception across several standard proprioceptive tasks together with key factors that could influence behavioral results such as touch, motor function, and cognition in 37 young (19-32 years old) and 35 older (53-71 years old) adults. We tested age-related differences in behavioral outcomes and their associations across tasks. Our results showed that age-related effects were variable, ranging from tasks where older participants performed better to tasks where they exhibit large age-related declines. Age-related declines in position sense are outcome-dependent, sometimes requiring large samples to detect small effects. The results were confirmed by meta-analysis based on data from hundreds of participants tested in our laboratory on the exact same tasks. Associations between outcome variables across or within proprioceptive tasks were overall negligible to weak. In conclusion, age-related changes in proprioception are limited in size, task- and outcome-dependent, and current tasks used to assess proprioception do not provide consistent evidence of age-related impairment in upper limb proprioception.

Frailty, a hallmark of systemic vulnerability in aging populations, is increasingly recognized in the clinical management of chronic respiratory diseases (CRDs). Molecular mechanism underpinning the relationships remain insufficiently elucidated. This study hence aimed to investigate whether proteomic biomarkers-circulating plasma proteins reflecting systemic inflammation, metabolism, and tissue remodeling-are associated with CRDs and may serve as potential mediators of the observed links. We analyzed data from a population-based cohort of 22,802 adults with proteomic measurements. Frailty was assessed by three phenotypes, including frailty index, physical frailty, and psychological frailty. Related-proteomic signatures were estimated by both linear and elastic regression models. Cox regression models were applied to explore the associations of frailty phenotypes and their proteomic signatures with incident CRDs, including asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF), as well as lung function outcomes, with full adjustments for potential confounders. Furthermore, mediation analyses were conducted to explore underlying mechanisms, complemented by pathway enrichment analyses to reveal relevant biological functions. Over a median follow-up of 13.2 years, 617 participants developed asthma, 701 developed COPD, and 228 developed IPF. Higher frailty index was associated with elevated risks of asthma (HR 1.95, 95% CI 1.64 to 2.31) and COPD (2.02, 1.71 to 2.38). Corresponding proteomic signature also related to increased risks of asthma (HR 1.22, 1.11 to 1.34) and COPD (1.65, 1.53 to 1.78). Mediation analysis suggested that the frailty index partially mediated the association with COPD, accounting for 26.0% (95% CI: 18.7 to 37.5%) of the total effect, respectively, particularly for GDF15, WFDC2, and PLAUR. Pathway enrichment analysis showed that these mediating proteins were predominantly involved in immune activation, inflammatory signaling, and metabolic stress responses. Frailty phenotypes contribute to elevated CRDs risks, partly through proteomic dysregulation in inflammatory and metabolic pathways.

Understanding oocyte and ovarian aging has become critically important, as trends in family planning evolve, with many women choosing to have children later in life. The ovary, a crucial organ in female reproduction, is particularly susceptible to age-related changes and is one of the organs that exhibit functional deterioration most distinctly with age. The aging of female reproductive systems also affects longevity and various health outcomes. A better understanding of both oocyte and ovarian aging will lay the cornerstone to elucidate the phenomenon of longevity in women. Here, clinical data from 400 women of various ages undergoing intracytoplasmic sperm injection (ICSI) have been analyzed, including Anti-Müllerian Hormone (AMH) and Follicle-Stimulating Hormone (FSH) levels, the number of recovered oocytes, blastocyst rates, pregnancy rates, and live birth rates. Our analyses revealed significant differences in the aforementioned rates between patients of young and advanced age. For the biomarker analysis, we further utilised a novel predictive performance of age-associated gene expression signatures for oocyte aging, demonstrating its potential to provide molecular-level insights into oocyte quality over time. By analyzing RNA sequencing data generated from human oocytes of different ages, a genome-wide landscape of age-associated gene expression has been described. Additionally, metabolome profiling has been performed on young and reproductively aged mice, serving as a model for human ovaries. Changes in metabolites of the murine ovaries during aging have been recorded. In conjunction with traditional biomarkers, multiomics data represent a transformative approach in reproductive health, and they may offer personalised risk assessments and interventions to mitigate age-related fertility decline in women. Our metabolome profiling provides a valuable resource for elucidating the metabolomic basis of ovarian aging. Our findings offer novel insights into systemic shifts associated with oocyte and ovarian aging. This integrated approach may unlock new avenues for fertility preservation, ovarian rejuvenation, and assisted reproduction.

Polluting cooking fuels are a major source of household air pollution and may reduce life satisfaction (LS). Yet, this relationship remains unexplored in India where polluting fuel use is still widespread. We utilized cross-sectional data from the Longitudinal Aging Study in India, a nationally representative study of individuals aged 45 years and above. Cooking fuel type was classified as households using clean fuels (CF) or non-CF. Based on kitchen type, non-CF was categorized into non-CF in separate kitchen, and non-CF without separate kitchen. We used multivariable regression to examine the association between polluting cooking fuels and LS and explored gender differences in this association. The analytical sample comprised 62,822 respondents (mean age 59.56 ± 10.52 years). The use of non-CF in separate and non-separate kitchen was 27.08% (n = 17,010) and 18.89% (n = 11,868), respectively. We observed that LS scores were highest among households using CF, followed by those using non-CF in separate kitchen, and lowest among households using non-CF without separate room for cooking. Additionally, in the fully adjusted model, a dose-response association with LS was observed corresponding to different levels of polluting cooking fuels (non-CF in separate kitchen: β = -0.51 [95% CI: -0.71, -0.31]; non-CF without separate kitchen: β = -1.42 [95% CI: -1.65, -1.19]). Multiple sensitivity analyses support the robustness of our findings. Gender was a significant effect modifier, with the interaction model revealing a stronger negative association between non-CF use and LS among women than among men. Our findings highlight the need for policies that promote an equitable clean fuel transition to enhance life satisfaction and well-being.