Frontiers in aging最新文献

Background: Unwanted loneliness among older adults has become a growing public health concern, particularly in rural contexts characterized by population aging, depopulation, and limited access to services. While many interventions focus on functional and social dimensions, the subjective and emotional roots of loneliness remain insufficiently explored. This study aims to identify profiles at greater risk of loneliness and to analyze personal and community activities that may help mitigate it in highly depopulated rural municipalities.

Methods: A cross-sectional quantitative study was conducted in five rural areas of western Salamanca, in municipalities with fewer than 500 inhabitants. A total of 153 individuals aged 60 and over participated. Structured interviews were carried out, including a sociodemographic questionnaire, variables related to available services, personal and community activities, and the UCLA Loneliness Scale Version 3. Data was analyzed using descriptive statistics and chi-square tests.

Results: 41.2% of participants reported moderate or severe levels of loneliness. Living alone, having a low educational level, and not engaging in social or leisure activities were associated with higher levels of loneliness. Significant associations were found between loneliness and variables such as living alone (p = 0.012), not talking on the phone (p = 0.024), not reading (p = 0.010), and not engaging in community activities like going out, spending time with family, or exercising (p < 0.01). The availability of services in the municipality showed no statistically significant relationship.

Conclusion: In rural contexts, loneliness among older adults appears to be more strongly influenced by the quality and frequency of social relationships than by the mere availability of services. Interventions should address not only structural or functional needs but also the emotional and relational dimensions, through personalized and community-based strategies that promote overall wellbeing. It is crucial to distinguish between social isolation and subjective loneliness, and to guide policies toward the strengthening of human connection.

The rising demands of an aging population underscore the need for digital health technologies (DHTs) in elderly care. Traditional services, which rely heavily on face-to-face, human-dependent interactions by medical and nursing staff, may struggle to meet these needs amid growing healthcare workforce shortages. This review examines the emerging role of DHTs-including wearable sensors, artificial intelligence (AI), robotics, and virtual reality (VR)-in supporting healthy aging. These technologies offer promising solutions to challenges such as chronic disease management, mobility impairments, cognitive decline, and social isolation. However, despite their potential, their adoption and integration into care systems remain limited due to a range of barriers. While we identify these barriers in detail, we also aim to open a broader discussion on why the urgency to act is particularly pronounced in Europe. The continent faces a sharp demographic shift, combined with uneven digital readiness across countries, under-resourced long-term care systems, and regional disparities in access to innovation. To fully harness the potential of DHTs in promoting healthy aging, this review outlines six strategic priorities: improving user-centered design; expanding real-world validation; promoting digital inclusion; addressing ethical and regulatory concerns; defining appropriate reimbursement and funding pathways; and innovating care delivery models. Advancing these areas will be critical to ensuring that DHTs contribute meaningfully to equitable and sustainable aging.

Background: Sarcopenia, the progressive decline in skeletal muscle mass and function, is a major public health concern linked to falls, hospitalization and loss of independence among older adults. Initially defined by reduced muscle mass, later also by reduced muscle strength and function, it is now recognized that standard diagnostic tools do not fully capture complexity of sarcopenia. Tensiomyography (TMG) is a non-invasive method that assesses skeletal muscle contractile parameters, which undergo change with aging and sarcopenia. The aims of this exploratory study are to determine whether TMG could be a method for contractile parameters assessment in sarcopenia classification and to evaluate the relationship between TMG-derived parameters and sarcopenia classification tests.

Methods: We included 654 older adults (70.6% women) and included demographics, sarcopenia classification (EWGSOP2, SDOC), muscle strength (handgrip strength, five sit-to-stand), TMG of three leg muscles, muscle mass (bioimpedance), and physical performance (gait speed, timed up-and-go). MANOVA was used to analyze contractile properties and due to low agreement between classifications, we used both classifications. A partial correlation for each sex was conducted to determine the associations between sarcopenia classification tests and TMG-derived parameters of delay time (Td), contraction time (Tc), radial displacement (Dm) and contraction velocity (Vc), controlling for age.

Results: One-way MANOVA confirmed difference between sarcopenic and non-sarcopenic participants according to EWGSOP2 and SDOC classifications in TMG-derived contractile parameters in all three muscles, Td was consistently longer and Dm consistently lower in sarcopenic individuals. Post-hoc univariate tests further demonstrated specific differences due to sarcopenia presence. Age-adjusted partial correlations were weak to moderate, ranging between -0.430 and 0.369.

Conclusion: Sarcopenic individuals exhibited longer Td and Tc, and smaller Dm, though not consistently across all muscles. Td was longer in all three muscles, reflecting electromechnical delays linked to aging. Dm was consistently lower, suggesting increased muscle stiffness. Correlations between TMG parameters and sarcopenia classification tests indicated that shorter Td and Tc, higher Dm, and greater Vc were associated with higher muscle volume, muscle strength and performance. The findings indicate that TMG parameters may be associated with neuromuscular degeneration and sarcopenia, supporting further exploration of muscle- and sex-specific differences.

Chronic psychological stress refers to repeated or prolonged exposure to adverse social or emotional threats that exceed an individual's adaptive capacity. It is recognized as a risk factor for aging-associated diseases. A growing body of research has shown that there is a link between chronic psychological stress and accelerated aging. Here, we highlight recent findings on the interconnected relationship between chronic psychological stress and major aging hallmarks, including mitochondrial dysfunction, telomere attrition, cellular senescence, epigenetic alterations, inflammation, and genomic instability. We discuss the mechanisms by which chronic psychological stress may drive this effect and explore intervention strategies that could mitigate its adverse effects and promote healthy aging. Moreover, we address current research gaps and propose future research directions to improve our understanding of the intricate relationship between psychological stress and aging.

Background: Nicotinamide adenine dinucleotide (NAD⁺) is a cofactor for NAD⁺-dependent enzymes that regulate DNA repair, cellular metabolism, and immune function. Supplementation with NAD⁺ and its precursors is commonly used to prevent age-related disease and extend healthspan. Commercial clinical and wellness settings increasingly provide intravenous (IV) NAD⁺ and nicotinamide riboside (NR) despite limited evaluation of safety and effectiveness. These considerations are important, as NAD⁺ and its precursors differ in reported side effects, cellular uptake, and metabolism. This study sought to compare commercially administered NAD⁺ IV and NR IV in humans by evaluating infusion time, tolerability, safety markers, and metabolic outcomes.

Methods: A retrospective review of electronic medical records was conducted in clients from a commercial setting. Participants received four consecutive days of 500 mg NAD⁺ IV or NR IV, with 30 days follow-up. Primary outcomes included reported symptoms, total infusion time, blood pressure, resting heart rate, and biomarker assessments (ALT, AST, hsCRP, BUN/creatinine, and TSH). Exploratory analyses included metabolic biomarkers (HbA1c, fasting glucose, HDL-C, LDL-C, and triglycerides).

Results: Tolerability differed between groups. Participants that received NAD⁺ IV reported moderate to severe gastrointestinal symptoms, increased heart rate, and chest pressure during infusions. Participants receiving NR IV experienced minor tongue, jaw, and arm tingling and mild cramping during infusion. All symptoms resolved upon infusion completion. Moderate to severe symptoms with NAD⁺ IV resulted in longer infusion times compared to NR IV, averaging 97 min versus 37 min, respectively. No significant changes were observed in ALT, AST, hsCRP, BUN/creatinine, or TSH. Alkaline phosphatase (ALP) decreased significantly in the NAD⁺ IV group only, with values remaining within normal reference ranges. The NR IV group demonstrated a significant reduction in HbA1c, whereas the NAD⁺ IV group showed a significant reduction in HDL-C. Neither group exhibited changes in fasting glucose or LDL-C over the 30-day period.

Conclusion: This study directly compared commercially administered NAD⁺ IV and NR IV, providing preliminary real-world evidence of infusion tolerability and short-term safety. Exploratory metabolic outcomes were variable and warrant further investigation. Additional studies are needed to evaluate dosage and effectiveness beyond 30 days.

Naïve CD8⁺ T cells are key players of adaptive immunity, but their heterogeneity and age-related changes are not fully understood. This study aimed to compare naïve CD8⁺ T cell subsets defined by different combinations of markers, namely, N_CCR7 (CD45RA⁺CCR7⁺), N_CD28 (CD45RA⁺CD28⁺), N_CD27 (CD45RA⁺CD27⁺), and phenotypically most "true-naïve"-like, N_TN (CD45RA⁺CCR7⁺CD28⁺CD27⁺CD57^-). Peripheral blood was harvested from donors of various ages and the phenotype of the four subsets of naïve CD8⁺ T cells was analyzed. N_CD27 and N_TN cells showed similar phenotypes with low expression of differentiation markers, pro-inflammatory cytokines, and effector molecules. Furthermore, they exhibited optimal mitochondrial fitness, low senescence markers, reduced apoptosis, and high proliferation potential. Hierarchical clustering identified cluster one including N_CD27 and N_TN, with lower expression of differentiation markers and pro-inflammatory molecules, and cluster 2, including N_CCR7 and N_CD28 cells, in which these parameters were more expressed. Age-related changes were observed in all subsets, although they were less pronounced for the N_CD27 and N_TN subsets. Taken together, this study demonstrates significant heterogeneity among naïve CD8⁺ T cell subsets, with N_TN cells representing the most bona fide naïve phenotype and N_CD27 showing a partially similar phenotype. These findings significantly enhance our understanding of naïve CD8⁺ T cell biology and function.

Background: The periorbital skin area is particularly susceptible to aging compared to other facial regions due to its unique anatomical features and frequent muscle movements. This leads to early development of wrinkles and discoloration, which affect one's appearance. Because of these characteristics, the eye-region skin serves as a representative indicator reflecting both skin aging and overall health status.

Objectives: This study aims to develop and validate a straightforward, non-invasive method to evaluate changes in the eye-region skin as reliable markers of aging and overall physiological condition.

Methods: We analyzed facial images from 2,515 Korean women aged 20-69 and evaluated various periorbital features, including wrinkles, morphological characteristics, and pigmented spots, using skin measurement devices and computational image analysis techniques. To assess skin aging and health status, we developed age prediction models based on different combinations of these periorbital features for each individual. Subsequently, Principal Component Analysis (PCA) was performed to summarize disease history variables for each participant, and the correlation between the first principal component (PC1) and periorbital skin age was evaluated using Pearson correlation analysis.

Results: Periorbital skin features showed significant associations with chronological age. We developed nine distinct age prediction models by combining different subsets of these features, each producing a unique aging score. Among them, seven models demonstrated strong correlations with actual age (r > 0.7), confirming their predictive reliability. These individual model outputs were collectively considered as unified aging markers representing periorbital skin age. To evaluate clinical relevance, we analyzed the association between periorbital skin age derived from the model incorporating all skin features and disease history. Periorbital skin age showed significant associations with five out of seven diseases individually, as well as with the PC1 summarizing all disease histories collectively.

Conclusion: This study establishes 'periorbital skin age' as a non-invasive biomarker that effectively reflects both the progression of skin aging and underlying medical conditions. Our findings highlight the potential utility of eye-region skin assessment in clinical and health monitoring settings, offering a practical tool for evaluating physiological aging and disease risk.

This case report describes the management of a 61-year-old female patient with metastatic adenoid cystic carcinoma and chronic refractory cancer pain. Initially treated with spinal cord stimulation (SCS), she later developed tolerance and inadequate analgesia. An intrathecal drug delivery system (IDDS) was subsequently implanted, and combination therapy with intrathecal morphine and sufentanil was initiated. This regimen achieved significant pain relief, reduced Numerical Rating Scale (NRS) scores, and improved sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI). The case highlights the potential efficacy of combining neuromodulation and intrathecal analgesia in managing complex cancer pain. However, due to limited data on sufentanil use in IDDS and the absence of standardized conversion protocols, further studies are warranted.

Aging is driven by interconnected genetic, epigenetic, molecular, and physiological processes spanning from unicellular to organismal levels. The surge in high-throughput data, from clinical and imaging to multi-omics, has outpaced traditional analysis methods; driving the integration of artificial intelligence (AI) into aging research. This comprehensive review examines the application of machine learning, deep learning, and computer vision across four canonical aging models (yeast, Caenorhabditis elegans, Drosophila melanogaster, and mice), highlighting AI's role in lifespan prediction, biomarker and gene discovery, aging-clock construction, and assay automation via automated animal counting and imaging. However, only 3% of the reviewed studies incorporated in vivo biological validation with common issues including small and imbalanced datasets, dataset bias, prediction noise, lack of cross-species analyses, absence of cytotoxicity testing, and overreliance on synthetic data. These drawbacks pose AI as just an aiding tool rather than a standalone solution, and without improvements in these sectors, AI-derived findings should be considered hypothesis generating rather than definitive conclusions. To address these issues, we propose the development of a standardized scoring system, AI Quality Assessment Metric (AI-QAM), for aging research that will evaluate studies on six criteria: (1) dataset size, (2) feature dimensionality, (3) biological validation type, (4) species diversity, (5) model generalizability, and (6) interpretability. Moreover, to mitigate the problem of lacking a unifying of a framework integrating AI approaches with biological mechanisms of aging, we present a conceptual framework, mapping AI applications across biological levels and aging hallmarks. AI will fulfill its potential in aging research only when it is firmly grounded in biological principles, systematically benchmarked, and rigorously validated through experimental studies.

Introduction: Cross-sectional and interventional studies have demonstrated that sleep has a significant impact on waking brain function, including alertness and cognitive performance. Few studies have assessed whether spontaneous night-to-night variation in sleep is associated with variation in brain function within an individual. How this compares to inter-individual variation in sleep and cognition and their associations also remains largely unknown. These questions are of particular interest in the context of aging because both sleep and cognitive abilities are altered.

Methods: Furthermore, older people have been reported to be less sensitive to sleep loss. Here, we investigated the relationship between sleep and cognition by quantifying associations between intra-individual variation in sleep and cognition, along with associations between inter-individual variation in sleep and cognition, in 35 cognitively intact older adults (70.8 ± 4.9 years; mean ± SD; 14 female individuals) living in the community. Subjective and actigraphic sleep measures and daily digital assessments of cognition (9 cognitive tests; 19 variables) were obtained over a 2-week period. The cognitive test battery probed a wide range of cognitive functions, including reaction time, working memory, attention, and problem-solving. Principal component analysis (PCA) identified four principal sleep components, namely, sleep duration, sleep efficiency, subjective sleep quality, and nap effect. Mixed model analyses were conducted with mean and deviation-from-the-mean cognitive variables to quantify how inter- and intra-individual variations in sleep were associated with inter- and intra-individual variations in cognition.

Results: Longer sleep duration was associated with faster reaction times in both the inter- and intra-individual analyses and with reduced errors in the inter-individual analyses. Higher sleep efficiency was associated with faster reaction times in both the intra- and inter-individual analyses. In contrast, aspects of cognition relating to learning, visual memory, verbal reasoning, and verbal fluency were not associated with sleep.

Discussion: These data show that, in older people, some aspects of waking function are sensitive to night-to-night variation in sleep duration and efficiency, implying that interventions targeting these aspects of sleep may be beneficial for waking function in aging.