Gerontology最新文献

Background: Orthopedic diseases, including osteoarthritis, osteoporosis, and age-related musculoskeletal disorders, significantly impact quality of life and are becoming increasingly prevalent with aging populations. A growing body of evidence highlights the role of cellular senescence in the pathogenesis of these conditions. Senescent cells (SCs), characterized by irreversible cell cycle arrest, accumulate in tissues over time, contributing to inflammation, tissue degeneration, and impaired regeneration. The emerging field of senolytics, which aims to selectively eliminate these SCs, has garnered attention as a novel therapeutic strategy in orthopedics.

Summary: Senolytic drugs, including small molecules, peptides, and natural compounds, have shown promise in preclinical models and early clinical trials for the treatment of various age-related diseases. In orthopedics, senolytics have been investigated for their potential to ameliorate cartilage degradation, bone fragility, and other degenerative changes associated with aging. Recent studies have demonstrated that targeting SCs in musculoskeletal tissues can improve tissue function, reduce inflammation, and promote regeneration. Although the majority of research is still in the preclinical phase, the positive outcomes from animal studies and early clinical trials suggest that senolytic drugs may offer new therapeutic avenues for orthopedic diseases.

Key messages: Senolytic therapies hold significant potential for treating orthopedic diseases by targeting the underlying cellular senescence that contributes to tissue degeneration and inflammation. Preclinical and clinical studies indicate that senolytic drugs may enhance tissue repair, alleviate symptoms, and slow disease progression in musculoskeletal disorders. Further research is needed to optimize drug efficacy, ensure safety, and identify patient populations that may benefit the most from these treatments. The development of senolytic drugs could revolutionize the management of aging-related orthopedic diseases, providing a more targeted and effective approach than current treatments.

Introduction: DNA damage in chondrocytes has been found to be associated with osteoarthritis (OA) and could be a primary pathological mechanism of the disease. Here, we performed transcriptomic analysis of human chondrocytes using existing RNA-seq datasets to characterize DNA damage repair pathway alterations associated with OA status.

Methods: We collected 9 public RNA-seq datasets of cartilage samples in the Gene Expression Omnibus from 57 OA patients and 35 non-OA controls. We identified differentially expressed genes (DEGs), examined enriched pathways, and predicted regulatory networks of the DNA damage response (DDR) in OA by comparing RNA-seq data from OA and non-OA chondrocytes. Furthermore, we evaluated the potential associations between DDR-related gene signatures and OA status.

Results: We identified 490 upregulated and 350 downregulated DEGs in OA. The upregulated DEGs are significantly enriched in DDR pathways, including the Fanconi anemia, mismatch repair, and base excision repair pathways. A total of 10 significant DDR downstream pathways were enriched and upregulated in OA, including DNA replication, DNA repair, and cell cycle pathways in relation to the DDR. Finally, we identified 9 core genes for DNA damage repair in OA (DDR-OA genes) as potential targets for OA biomarkers. Three of these genes are known to be associated with both DDR processes and OA pathology.

Conclusion: Elevated expression of DDR-related genes and enhanced activity of DDR signaling pathways were observed in conjunction with OA onset and progression. Our computational analysis prioritizes identified DDR-OA genes as high-confidence candidates for further experimental investigation.

Introduction: Frailty is a syndrome characterized by increased vulnerability to stressful events due to diminished metabolic, neuromuscular, and functional reserves. It has been associated with poor outcomes in patients with cardiovascular diseases. This study compares the eyeball test and the Fried test (considered the "gold standard") for frailty assessment in elderly patients in an electrophysiology clinic.

Methods: This prospective study included stable patients aged 75 years and older who were assessed in an electrophysiology clinic. Frailty assessment was conducted using both the Eyeball and Fried tests. Patients were first evaluated by an electrophysiologist who used the Eyeball test to grade frailty on a Fried-type scale (1-5, with frailty defined as a score ≥3). Subsequently, each patient underwent objective measurements of frailty using the Fried test.

Results: A total of 201 patients with a median age of 82 ± 7 years were included in the study, with 62% being males. According to the Fried test, 81 (40%) patients were classified as frail. The majority of frail patients were over 80 years old. The eyeball test demonstrated an 88.9% sensitivity and a 78.3% specificity in diagnosing frailty. Accordingly, positive and negative predictive values were 73.5% and 91.2%, respectively.

Conclusion: The eyeball test is highly accurate for ruling out frailty in elderly outpatients in an electrophysiology clinic. However, when frailty is suspected based on the eyeball test, an additional objective test, such as the Fried test, should be used to confirm the diagnosis.

Background: With prevalence rising from 5 to 13% (60-70 years) to as high as 50% (>80 years), sarcopenia is associated with frailty, falls, and up to a 41% 3-year mortality in high-risk cohorts. Early recognition is hampered by variability in definitions and limited access to imaging in many regions.

Summary: Comprehensive Geriatric Assessment (CGA) offers an integrated framework - spanning physical, functional, cognitive, psychological, and social domains - to improve the screening, diagnosis, and longitudinal monitoring of sarcopenia in older adults.

Key messages: (1) CGA-guided care enables tailored interventions that combine protein-rich nutrition, progressive-resistance exercise and - in selected cases - emerging pharmacological agents. (2) Economic analyses indicate CGA can be cost-neutral or cost-saving when targeted to high-risk groups, but workforce requirements challenge its scalability outside well-resourced centers. (3) Evidence remains heterogeneous and drawn largely from urban, high-income settings; caution is required when generalizing outcomes to rural or low-resource environments. (4) Future research should standardize a muscle-specific CGA core set, test implementation in diverse health systems, and evaluate digital tools that reduce staff time without widening the digital divide.

Introduction: The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of older populations. However, the diagnosis of osteoporosis and people's understanding of it are seriously insufficient. We aim to develop a deep learning model to automatically measure bone mineral density (BMD) and improve the diagnostic rate of osteoporosis.

Methods: The images of 801 subjects with 2,080 vertebral bodies who underwent chest or abdominal paired computer tomography (CT) and quantitative computer tomography (QCT) scanning was retrieved from June 2020 to January 2022. The BMD of T11-L4 vertebral bodies was measured by QCT. Developing a multistage deep learning-based model to simulate the segmentation of the vertebral body and predict BMD. The subjects were randomly divided into training dataset, validation dataset and test dataset. Analyze the fitting effect between the BMD measured by the model and the standard BMD by QCT. Accuracy, precision, recall and f1-score were used to analyze the diagnostic performance according to categorization criterion measured by QCT.

Results: 410 males (51.2%) and 391 females (48.8%) were included in this study. Among them, there were 154 (19.2%) males and 118 (14.7%) females aged 23-44; 182 (22.7%) males and 205 (25.6%) females aged 45-64; 74 (9.2%) males and 68 (8.5%) females aged 65-84. The number of vertebral bodies in the training dataset, the validation dataset, and the test dataset was 1433, 243, 404, respectively. In each dataset, the BMD of males and females decreases with age. There was a significant correlation between the BMD measured by the model and QCT, with the coefficient of determination (R2) 0.95-0.97. The diagnostic accuracy based on the model in the three datasets was 0.88, 0.91, and 0.91, respectively.

Conclusion: The proposed multistage deep learning-based model can achieve automatic measurement of vertebral BMD and performed well in the prediction of osteoporosis.

Introduction: Studying what older adults say can provide important insights into cognitive, affective, and social aspects of aging. Available language analysis tools generally require audio-recorded speech to be transcribed into verbatim text, a task that has historically been performed by humans. However, recent advances in AI-based language processing open up the possibility of replacing this time- and resource-intensive task with fully automatic speech to text.

Methods: This study evaluates the accuracy of two common automatic speech-to-text tools - OpenAI's Whisper and otter.ai - relative to human-corrected transcripts. Based on two speech tasks completed by 238 older adults, we used the Linguistic Inquiry and Word Count (LIWC) to compare language features of text generated by each transcription method. The study further assessed the degree to which manual tagging of filler words (e.g., "like," "well") common in spoken language impacts the validity of the analysis.

Results: The AI-based LIWC features evidenced very high convergence with the LIWC features derived from the human-corrected transcripts (average r = 0.98). Further, the manual tagging of filler words did not impact the validity for all LIWC features except the categories filler words and netspeak.

Conclusion: These findings support that Whisper and otter.ai are valuable tools for language analysis in aging research and provide further evidence that automatic speech to text with state-of-the art AI tools is ready for psychological language research.

Introduction: Cardiovascular disease (CVD) is more likely to occur in old people with mildly reduced kidney function. We aimed to identify target features in this cohort to reduce cardiovascular death using deep learning models.

Methods: A total of 12,650 older people (age ≥60) with mildly reduced kidney function from Tianjin Community Health Promotion Prospective Study were recruited from 2014 to 2020. Cardiovascular death was verified by the death certificates from the provincial vital statistics offices. Mildly reduced kidney function was defined when estimated glomerular filtration rate (eGFR) between 45 mL/min/1.73 m2 ≤ and 90 mL/min/1.73 m2. Data were analyzed using Cox regression, random survival forest (RSF), DeepHit (DH), and Dynamic DH (DDH). Concordance Index (C-index) and Brier Score (B-S) were used to compare the models' performances.

Results: During the follow-up of 7 years, 838 people died of CVD (6.62%). Age, gender, hypertension, diabetes, and eGFR were closely related to cardiovascular death. Both accuracy and precision of models, predictive performance gets better as the number of follow-up visits increases. In predicting cardiovascular death, the C-index and B-S value of COX were only 0.711 and 0.001 at the first follow-up, and values were 0.767 and 0.073 at last time, respectively. This trend is similar in the other three models, with the DDH model standing, which showed the individual survival prediction with more accuracy at different time points (for the 6-year survival prediction, the C-index = 0.797 and B-S = 0.022 for the average of all time points) than the Cox, RSF, and DH.

Conclusion: A novel deep learning algorithm used in our study has shown its superior performance in the prediction of individual dynamics in longitudinal studies, which improves predictive power with increasing data input over time.

Introduction: The relationship between cognitive impairment and a phenotype comprising low muscle strength coupled with excess adiposity, representative of sarcopenic obesity, is not well defined. The present study aimed to elucidate the relationship between low hand grip strength (HGS), representative of "probable sarcopenia," coupled with obesity, thus representing "probable sarcopenic obesity" and cognitive impairment.

Methods: Logistic regression models were implemented between probable sarcopenia and cognitive impairment in older men residing in Northern Ireland within the Prospective Epidemiological Study of Myocardial Infarction (PRIME)-COG cohort, a nested study in the PRIME cohort. In addition, associations across BMI strata were evaluated, including probable sarcopenic obesity (low HGS and BMI ≥30 kg/m2). Models were adjusted for demographics, cardiometabolic disease and risk factors, APOE-ε4, and lifestyle behaviours.

Results: Among 792 men (79.1, SD 3.2 years), low HGS was associated with a significantly increased odds ratio (OR) of cognitive impairment (OR 2.14; 95% confidence intervals [CIs] 1.51-3.03, p < 0.001). The risk was broadly consistent across BMI strata, including men with probable sarcopenic obesity (OR 2.36 [95% CI: 0.85-6.35], p = 0.05). The consistent risk across BMI strata was supported by a non-significant interaction between BMI and probable sarcopenia (likelihood ratio test, p = 0.772).

Conclusions: Probable sarcopenia, indicated by low HGS, was associated with an increased risk of cognitive impairment in older men, with risk consistent across BMI strata, including men living with probable sarcopenic obesity. Our findings have clinical relevance, suggesting that phenotypes comprising low muscle strength, in the presence of excess adiposity, must not be overlooked and appropriate interventions explored to attenuate physical perturbations which could carry significance towards ameliorating cognitive function in ageing.

Introduction: The global aging population poses significant challenges to healthcare, with frailty, balance impairment, and fall risks being prominent issues. However, the conventional clinical assessments often fail to detect early signs of these conditions. This study aimed to explore the potential of metabolomics in early identification of biomarkers related to frailty, poor balance, and fall risks in older adults.

Methods: We analyzed plasma samples from 110 participants aged 25-98 years using untargeted metabolomic analysis. Clinical assessments, including Instrumental Activities of Daily Living (IADL), Morse Fall Risk Scale, Timed Up and Go (TUG), and Fried Frailty Criteria, were performed. We examined the correlation between metabolomic results, aging-related blood tests, and clinical assessments. Statistical analysis and pathway analysis were used to identify key metabolic alterations.

Results: The metabolomics analysis identified 914 metabolites matching in the human metabolome database, with 293 metabolites significantly correlated with age. Metabolomic profiles showed distinct alterations in older adults, with significant metabolic changes observed in the old-old group, particularly in pathways related to lipid metabolism, sphingolipid signaling, and fatty acid metabolism. A new age classification based on metabolic profiles revealed significant differences in frailty risks across groups, with metabolic signatures linked to poor balance and fall risks.

Conclusion: Metabolomics offers a promising approach to identify early biomarkers of frailty, balance impairment, and fall risks in older adults. The integration of metabolic profiles with clinical assessments could lead to more precise and personalized healthcare interventions, improving fall prevention strategies and frailty management. Future studies with larger cohorts are needed to validate these findings and explore the clinical utility of Metabolomics in aging-related healthcare.