Experimental gerontology最新文献

Type 2 diabetes mellitus (T2DM) causes microvascular injury across multiple organs, but whether its cumulative microvascular burden contributes to sensory decline remains unclear. In this population-based cohort of 493,272 UK Biobank participants, including 43,332 with T2DM, we examined the association between diabetes-related microvascular complications (nephropathy, retinopathy, neuropathy) and incident hearing loss. Complications were identified from hospital records (ICD-10 E11.2/E11.3/E11.4) and categorized by count. Hearing loss (ICD-10 H90-H91) was ascertained from linked hospital records, death registries, and self-reports. Compared with participants without diabetes, adjusted hazard ratios (HRs; 95% CIs) for hearing loss were 1.28 (1.22-1.34) for T2DM without complications, 1.83 (1.65-2.02) for one complication, and 2.13 (1.75-2.59) for two or more. Within T2DM, risk increased stepwise with complication count (one vs none: 1.45 [1.31-1.60]; ≥2 vs none: 1.75 [1.44-2.13]). Associations were stronger in participants <60 years, those with glycated hemoglobin ≥6.5%, and insulin users. Mediation analyses showed partial indirect effects of estimated glomerular filtration rate and C-reactive protein, consistent with contributions of renal dysfunction and systemic inflammation. Findings were robust across demographic and clinical subgroups. These results indicate that the cumulative burden of microvascular complications is independently associated with higher risk of hearing loss, supporting integration of auditory evaluation into comprehensive diabetes care, particularly for individuals with multiple complications or poor glycemic control.

Objective: This study aimed to systematically investigate the association between osteoporosis (OP) and age-related macular degeneration (AMD), including its subtypes, by integrating observational evidence with causal inference approaches.

Methods: We first conducted a systematic literature search and meta-analysis of relevant observational studies up to August 2025 to evaluate the observational association between OP and AMD. Subsequently, using summary data from genome-wide association studies, we performed a bidirectional two-sample Mendelian randomization (MR) analysis to examine the causal relationship between OP and four AMD subtypes. The primary causal estimates were derived using the inverse variance weighted method, and multiple sensitivity analyses were conducted to verify the robustness of the results.

Results: The meta-analysis, which included three studies, indicated that osteoporosis increases the risk of AMD in women (OR: 1.46; 95% CI: 1.12-1.91, P = 0.0049). Under Bonferroni correction, the MR analysis showed that genetically predicted osteoporosis significantly increased the risk of dry AMD (IVW OR: 1.26; 95% CI: 1.10-1.44, P = 0.00087. Bayesian weighted MR OR: 1.25; 95% CI: 1.05-1.49, P = 0.01162.), while no significant causal effects were observed for wet, early, or late AMD. Reverse MR analysis did not indicate any causal effect of AMD on OP. No significant horizontal pleiotropy or heterogeneity was detected in any of the analyses.

Conclusion: Osteoporosis may increase the risk of AMD in women. The MR analysis provides some genetic evidence supporting a potential causal link between osteoporosis and dry AMD. In clinical practice, patients with osteoporosis-particularly elderly women-should be considered at potential high risk for dry AMD, and early fundus screening may facilitate the early detection and intervention of dry AMD.

Identification of ground contact timings (GCT) is critical for monitoring mobility in older adults. Laboratory methods are precise but limited to controlled environments, restricting their applicability in real-world settings. Treadmills allow extended measurements but fail to reflect the variability of overground walking. We evaluated the performance of deep learning models trained on treadmill data from young adults and their generalizability to treadmill and outdoor walking in older adults. We also explored transfer learning to enhance predictions by fine-tuning models with older adults' treadmill and outdoor walking data. Foot-mounted inertial measurement unit (IMU) walking data was collected from 20 young adults on treadmills and 26 older adults on treadmills and outdoor level, incline, and decline terrains. Ground truth GCTs were derived using pressure insoles (young adults) and manually-annotated motion capture (older adults). A fully connected neural network, a convolutional neural network (CNN), and a bidirectional long short-term memory network were trained on IMU data. Transfer learning was applied incrementally by fine-tuning the best-performing model with older adults' data. Model performance was evaluated on unseen outdoor data from 6 participants using F1-score and mean absolute error (MAE). The CNN achieved the highest F1-scores (0.9864 - treadmill, 0.9637 - outdoor level, 0.9538 - incline, and 0.9029 - decline walking) and the lowest MAE. Fine-tuning improved treadmill F1-scores up to n=10, while outdoor level scores plateaued at n=5. Decline walking showed poorer performance, highlighting the need for advanced modeling strategies. These findings underscore the potential of transfer learning for real-world mobility monitoring.

Although neuromuscular decline is well documented with aging, emerging evidence indicates that it may begin as early as midlife, around age 50. As this stage represents a critical window for early intervention, the present study investigated age- and sex-related differences in muscle activation using high-density surface electromyography (HD-sEMG) of the biceps brachii (BB). Physically active individuals were categorized into three age groups: young (20-30 years), middle-aged (45-55 years), and older adults (65-75 years). HD-sEMG signals were recorded during isometric contractions at 20%, 40%, and 60% of maximal voluntary contraction (MVC). Muscle activation amplitude, spatial distribution, and signal complexity were analyzed. Although strength remained similar across groups, RMS amplitude was primarily influenced by contraction level, with age-related differences emerging in an intensity-dependent manner. In men, older participants exhibited lower RMS amplitudes compared to younger men at higher contraction levels (60% MVC, p<0.05). In women, middle-aged participants consistently exhibited lower RMS amplitudes across contraction levels, accompanied by altered spatial organization of muscle activation, reflected by higher RMS CoV and lower modified entropy at moderate-to-high contraction intensities (p<0.05). Signal complexity, assessed using sample entropy, did not show robust age-related differences, although descriptive trends toward lower values were observed in older adults at low contraction level. Taken together, these findings suggest that midlife, in women, may be characterized by subtle, task-dependent neuromuscular reorganization rather than a generalized decline. Early identification of such changes using HD-sEMG metrics may support timely interventions aimed at preserving neuromuscular function across the lifespan.

Background: Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by cognitive dysfunction. The discovery and identification of non-cognitive symptoms in the preclinical stage hold promise for early diagnosis and intervention. Previous studies have shown that diagnosed AD patients commonly exhibit alterations in sarcopenia-related indicators, which might represent early symptoms of progression from mild cognitive impairment (MCI) to AD.

Methods: This study used 3-month-old APP/PS1 transgenic (AD) mice and C57BL/6J wild-type (WT) mice. Hindlimbs were immobilized with plaster casts for 2 weeks. After immobilization, body, brain, and muscle weights were measured. Behavioral tests were conducted. Immunofluorescence staining was used to assess muscle morphology and analyze oligodendrocyte precursor cells (OPCs) lineage-related indicators.

Results: Hindlimb immobilization induced sarcopenia in both AD and WT mice, manifested as decreased body, brain, gastrocnemius (Gas), and soleus (Sol) muscle weights. Immobilized mice showed decreased motor ability and impaired exploration behavior. Long-term spatial learning and memory were also affected. Muscle histological analysis revealed that AD mice exhibited baseline muscle fiber type switching. After immobilization, AD mice showed increased proportions of MyHC IIa fast-twitch fibers in the Sol and MyHC IIb fast-twitch fibers in the tibialis anterior (TA). At the central nervous system level, immobilization inhibited the OPCs proliferation and significantly increased activation of microglia and astrocytes of immobilized mice.

Conclusion: Hindlimb immobilization-induced sarcopenia correlated with slow-to-fast fiber transformation, reduced OPCs proliferation, and enhanced neuroinflammation. This study highlights the importance of sarcopenia in the progression of AD-related white matter pathology.

Objective: Disulfidptosis is a newly recognized form of regulated cell death characterized by actin cytoskeleton collapse under disulfide stress. Recent studies suggest it may significantly contribute to osteoarthritis (OA), though its exact role in OA development remains unclear. This study aims to analyze the expression of disulfidptosis-related genes in OA, identifying potential biomarkers and candidate therapeutic leads.

Methods: Transcriptomic data from six datasets related to OA and single-cell RNA sequencing data were sourced from the GEO database. To identify differentially expressed genes (DEGs) associated with disulfidptosis, we employed robust rank aggregation. We then utilized functional enrichment analysis, constructed a protein-protein interaction network, and applied machine learning models to prioritize key genes of interest. A single-cell analysis was conducted to evaluate gene expression within specific subsets of chondrocytes. Additionally, we predicted drug-target binding affinity using the GraphDTA model and performed molecular docking studies to assess drug-to-target binding affinity. Finally, immunohistochemistry, RT-qPCR, and Western blotting were performed for experimental validation.

Results: We identified several DEGs related to disulfidptosis, including SLC2A3, PDLIM1, and SLC3A2, which were found to be downregulated in OA tissues, particularly in pre-fibrocartilage chondrocyte subtypes. Our functional analysis indicated that these genes were enriched in pathways associated with cytoskeleton organization and the oxidative stress response. Furthermore, machine learning models demonstrated that these genes have significant diagnostic potential across various datasets. Using GraphDTA for drug prediction and molecular docking, we discovered that talaroflavone exhibits a strong binding affinity to SLC2A3, with highest predicted affinity (13.98) and a binding energy of -8.8 kcal/mol. Experimental validation confirmed the downregulation of SLC2A3 in OA-affected cartilage. Functional assays indicated that modulating SLC2A3 could alleviate cartilage matrix degradation and oxidative stress, suggesting a potential functional association between SLC2A3 and OA progression.

Conclusion: This study providing functional evidence for a potential association between disulfidptosis-related genes and OA, while highlighting SLC2A3 as a potential functional regulator for further investigation. Additionally, was identified talaroflavone as a computationally predicted lead compound that warrants future experimental validation. These findings not only enhance our understanding of the molecular pathways underlying OA but also lay the groundwork for potential development of targeted therapeutic strategies.

Detecting heterogeneity in treatment response enriches the interpretation of gerontologic trials. In aging research, estimating the intervention's effect on clinically meaningful outcomes poses analytical challenges when outcomes are truncated by death. For example, in the Whole Systems Demonstrator trial, a large cluster-randomized study evaluating telecare among older adults, the overall effect of the intervention on quality of life was found to be null. However, this marginal intervention estimate obscures potential heterogeneity of individuals responding to the intervention, particularly among those who survive to the end of follow-up. To explore this heterogeneity, we adopt a causal framework grounded in principal stratification, targeting the Survivor Average Causal Effect (SACE)-the treatment effect among "always-survivors," or those who would survive regardless of treatment assignment. We extend this framework using Bayesian Additive Regression Trees (BART), a nonparametric machine learning method, to flexibly model both latent principal strata and stratum-specific potential outcomes. This enables the estimation of the Conditional SACE (CSACE), allowing us to uncover variation in treatment effects across subgroups defined by baseline characteristics. Our analysis reveals that despite the null average effect, some subgroups experience distinct quality of life benefits (or lack thereof) from telecare, highlighting opportunities for more personalized intervention strategies. This study demonstrates how embedding machine learning methods, such as BART, within a principled causal inference framework can offer deeper insights into trial data with complex features including truncation by death and clustering-key considerations in analyzing pragmatic gerontology trials.

Background and aim: Advances in human microbiome research have highlighted its influence on host health. This study aimed to characterize the oral microbiome (OM) and gut microbiome (GM) and to examine their relationships with systemic fatty acid and cytokine profiles across different age groups in healthy adults.

Methods: Participants aged 18-76 years without diagnosed diseases were grouped into young (18-29 years), middle-aged (30-49 years), and older adults (≥50 years). Blood, dental plaque, and fecal samples were collected. OM and GM composition were evaluated using 16 rRNA sequencing. Circulating free fatty acids (FFAs) were quantified by gas chromatography-mass spectrometry, and serum cytokines were assessed using flow cytometry.

Results: In the OM, Fusobacterium and Haemophilus were notably abundant in young adults, while Haemophilus and Neisseria predominated in middle-aged adults. In older adults, Neisseria and Capnocytophaga were the most prevalent oral genera. In the GM, Bacteroides was the most prevalent genus across all age groups, followed by Faecalibacterium, Blautia, and Prevotella_9. Additionally, circulating levels of decanoic, hexadecanoic, and octadecanoic acids, as well as the cytokine IP-10, were higher in young adults compared with the other age groups.

Conclusion: To our knowledge, this study is the first to characterize and correlate the diversity of both the OM and GM with systemic FFA and cytokine profiles in a cohort of healthy adults, highlighting the critical role of age in shaping microbiome composition and associated metabolites. Integrating microbial profiling with serum FFA and cytokine measurements enhances our understanding of how the microbiome may influence health and disease risk across the adult lifespan.