Clinical Interventions in Aging最新文献

Background: This multi-center study aims to develop and validate the Prealbumin-adjusted Prognostic Nutritional Index (PAPNI), hypothesizing that it would enhance prognostic prediction for hip fracture patients compared to traditional indices.

Methods: Data from 771 patients (Cohort 1) and 673 patients (Cohort 2) were retrospectively analyzed. The PAPNI was formulated by substituting albumin with prealbumin in the Prognostic Nutritional Index (PNI) formula, with its weight optimized using receiver operating characteristic (ROC) curves. All individuals were followed up for 1 year. The relationships between PAPNI and outcomes were explored.

Results: In both cohorts, PAPNI demonstrated superior predictive accuracy for 1-year mortality and free walking ability compared to PNI, prealbumin, and lymphocyte count alone. Patients with low PAPNI exhibited significantly higher mortality rates and lower free walking rates. Multivariate analyses confirmed PAPNI as an independent predictor for outcomes of hip fracture.

Conclusion: PAPNI, incorporating prealbumin, offers a more accurate and convenient method for predicting postoperative survival and functional recovery of hip fractures, providing a basis for early nutritional intervention.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders globally, where early diagnosis plays a pivotal role in delaying disease progression and improving patient outcomes. In recent years, the rapid, multidisciplinary advances in molecular imaging and emerging technologies have significantly advanced our understanding of AD pathogenesis, early diagnosis, and intervention strategies. Imaging tools such as positron emission tomography (PET) and magnetic resonance imaging (MRI), alongside emerging technologies like retinal imaging, nanosensors, and quantum dots (QDs), are continuously enhancing AD diagnostic pathways. Studies on the gut microbiome and extracellular vesicles (EVs) offer novel insights into AD pathogenesis. Furthermore, AI-driven multimodal data fusion techniques hold great promise for improving diagnostic accuracy. Future research will increasingly focus on multi-target synergistic intervention strategies, standardization of multimodal imaging, and the integration of AI with molecular diagnostics and treatment to enable early detection and personalized precision therapy for AD.

Purpose: This study aimed to evaluate the association between physical and cognitive function and long-term outcomes in elderly cardiovascular disease (CVD) patients, using the Short Physical Performance Battery (SPPB) and the Mini-Mental State Examination (MMSE).

Patients and methods: In this prospective cohort study, 524 patients aged ≥65 years hospitalized in the Department of Cardiology at Beijing Hospital from September 2018 to April 2019 were evaluated. Baseline demographic, clinical, laboratory, and functional data were collected, and physical and cognitive function were assessed using SPPB and MMSE scores. Patients were followed for all-cause mortality over a 5-year period. Kaplan-Meier survival analysis and Cox proportional hazards models were used to examine the mortality risk associated with impairments.

Results: Physical impairment was identified in 28.2% and cognitive impairment in 12.4% of patients. The combination of both impairments was associated with a 5.47-fold increased mortality risk (HR: 5.47; 95% CI: 2.78-10.78; p<0.001). Each 1-point increase in SPPB and MMSE scores correlated with a 16.3% and 8.7% reduction in mortality risk, respectively. Cognitive function, particularly attention and calculation ability, has emerged as a significant predictor of survival.

Conclusion: Combined physical and cognitive impairments are prevalent in elderly CVD patients and strongly predict poor long-term prognosis. Routine assessment of cognitive function alongside physical performance can improve clinical decision-making, intervention strategies, and patient management, offering the potential to enhance outcomes in this high-risk population.

Background: There is an urgent need for a convenient and incidental method to assess the bone health status of the population, especially in primary-level hospitals lacking specialized bone density testing equipment. This study aims to investigate the association between multiple vertebral Hounsfield Unit (HU) value clusters and bone mass subtypes using an unsupervised learning approach, providing a practical tool for incidental osteoporosis screening in clinical settings.

Materials and methods: This retrospective study included subjects who underwent chest CT and quantitative CT (QCT) from January 2023 to December 2024. Vertebral HU values (T7-T12) were measured on chest CT images. Intergroup comparisons (normal, osteopenia, and osteoporosis) in clinical findings and CT values were performed using Pearson χ² test and one-way analysis of variance. An unsupervised k-means clustering was applied to vertebral CT values across the cohort.

Results: The study comprised 455 participants (260 males, 195 females) with a median age of 60 years (interquartile range, 51-67 years), who were classified into three groups: normal bone mass, 253 cases; osteopenia, 152 cases; osteoporosis, 50 cases. Among 455 participants, age inversely correlated with bone mass. Vertebrae HU values (T7-T12) exhibited significant stepwise declines from normal to osteopenia to osteoporosis (OP) groups. The clustering analysis revealed five distinct subtypes: cluster 1 strongly correlated with OP (45 of 72 cases), cluster 4 with osteopenia (107 of 146 cases), and clusters 2, 3, and 5 with normal bone mass (31 of 31 cases; 90 of 107 cases; 97 of 99 cases).

Conclusion: Unsupervised clustering of T7-T12 vertebral HU values effectively stratifies bone mass subtypes, offering an efficient, CT-based screening method for skeletal health assessment, especially valuable in resource-limited primary-level hospitals lacking dedicated bone densitometry.

Background: Extracellular vesicles (EVs) play a pivotal role in driving aging, serving as vehicles for the transmission of aging-related signals. Decay-accelerating factors (DAFs), also known as CD55, a crucial complement regulator, exhibits age-associated alterations, yet its expression on circulating EVs remains poorly characterized. This study aims to investigate differences in plasma EVs and CD55 positive EVs concentrations in younger and older adults, and explore their correlations with age and blood glucose levels.

Methods: In this study, 40 older adults (≥65 years) undergoing routine physical examinations at Beijing Jishuitan Hospital and 40 gender-matched younger adults (<65 years) were enrolled prospectively. Blood glucose levels were measured using the standard glucose oxidase method. Circulating EVs concentrations were quantified by flow cytometry, including circulating total EVs, endothelial EVs (EEVs), platelet EVs (PEVs), red blood cell EVs (REVs), lymphocyte EVs (LEVs), monocyte EVs (MEVs), neutrophil EVs (NEVs), white blood cell EVs (WEVs), and CD55-positive EVs.

Results: Compared to younger adults, older adults exhibited significantly lower concentrations of LEVs, MEVs, NEVs, and WEVs (p < 0.05). Concentrations of CD55 positive EVs, including CD55+EEVs, CD55+PEVs, CD55+LEVs, CD55+MEVs, CD55+NEVs, and CD55+WEVs, were markedly reduced in older adults (p < 0.05). Notably, CD55+PEVs showed the strongest negative correlation with age (r = - 0.6228, p < 0.001). Furthermore, significant inverse correlations were found between blood glucose levels and concentrations of CD55+EEVs (p = 0.001), CD55+PEVs (p = 0.006), CD55+LEVs (p < 0.001), CD55+MEVs (p = 0.001), and CD55+WEVs (p = 0.003).

Conclusion: Our findings indicate significant differences in plasma EVs subtypes between older and younger adults and demonstrate robust negative correlations between CD55+ EVs subtypes and both aging and elevated glucose levels. Thus, these results suggest CD55+ EVs as a critical contributor to metabolic disorders in aging, offering potential risk assessment and monitoring strategies in older populations.

Background: Frailty and osteoporotic vertebral compression fractures (OVCFs) exhibit bidirectional causality, yet the impact of percutaneous kyphoplasty (PKP) on frailty progression remains unclear. This study developed machine learning (ML) models to predict post-PKP frailty and identify key predictors.

Methods: A retrospective cohort of 4599 PKP patients was categorized into frailty/non-frailty groups based on two-year follow-up. Variables included preoperative baseline data, imaging parameters (fracture number/segments, Genant classification, T2 hyperintensity), clinical characteristics (osteoporosis severity, Visual Analogue Scale scores, residual low back pain [LBP]), and surgical details. After data splitting (4:1 ratio), features were selected to train and optimize ML models, with performance evaluated via area under the curve (AUC). The ML model with the best performance was selected as our final model while using it for external validation. SHAP analysis determined predictor contributions.

Results: Key features (residual LBP, Genant classification, etc) informed model development. Hyperparameter optimization enhanced performance, with Extreme Gradient Boost achieving superior prediction (AUC 0.950, 95% CI 0.934-0.965). The model still maintains a good performance in the external test set, with an AUC of 0.845 (95% CI 0.805-0.884). SHAP identified residual LBP, Genant classification, and postoperative recumbency duration as top predictors.

Conclusion: ML models effectively predict post-PKP frailty, highlighting modifiable risk factors. Standardized anti-osteoporosis therapy, residual LBP prevention, and reduced postoperative recumbency may mitigate frailty risk.

Background: Maintaining long-term engagement in physical activity programs among older adults remains challenging. This study examines actual and potential sustainability and adherence to a home-based online exercise program at six- and twelve-month follow-ups, while also analyzing perceived barriers, facilitators, and benefits.

Methods: This follow-up study included 82 independent, home-dwelling older adults (mean age 73.7 ± 3.4; 77% female) who had previously completed a 12-week randomized controlled trial. 6- and 12-months sustainability and adherence were assessed using a custom-designed online questionnaire. Actual sustainability measured ongoing participation, while potential sustainability reflected willingness to engage in regular exercise. Participants were classified as regular, irregular, or non-exercisers, with follow-up closed-ended, semi-open, and open-ended questions exploring influencing factors. Adherence was categorized as high, moderate, or none, based on participation frequency and consistency. Data analysis combined quantitative (descriptive statistics) and qualitative (thematic analysis) approaches.

Results: At six months, 79.3% of participants remained engaged, decreasing to 70.7% at twelve months, while potential sustainability remained high at 89% and 78.1%, respectively. High adherence was observed in 51.3% at six months and 47.8% at twelve months. Barriers included a lack of motivation and time, while social support and external incentives facilitated adherence. Most reported benefits among exercisers at the six- and twelve-month follow-ups were improved well-being (43/42), flexibility (41/37), and energy (20/20).

Conclusion: This study confirms that long-term sustainability and adherence to home-based online exercise programs are achievable. The findings underscore the importance of understanding and flexible engagement models and highlight the potential for integrating similar strategies into future exercise programs for older adults.

Background: Laparoscopic cholecystectomy (LC) is commonly recommended following endoscopic retrograde cholangiopancreatography (ERCP) to reduce the risk of recurrent biliary events (RBE). However, in geriatric patients, this strategy remains controversial due to increased surgical risk, comorbid conditions, and decreased physiologic reserve. This study aimed to evaluate the outcomes of delayed LC in geriatric patients after ERCP for bile duct stone removal, emphasizing the role of individualized treatment planning.

Methods: We retrospectively reviewed medical records of patients aged ≥75 years who underwent ERCP at Her Royal Highness Princess Maha Chakri Sirindhorn Medical Center, Srinakharinwirot University, from January 2018 to December 2023. Patients were grouped based on whether they underwent delayed LC (>6 weeks post-ERCP). Clinical characteristics, RBE incidence, and mortality were analyzed. Multivariate logistic regression was used to identify predictors of RBE.

Results: Of 152 geriatric patients (mean age 82.3 years; 48% male), 53.3% had an ASA score ≥3, and the mean Charlson Comorbidity Index was 4.8. Delayed LC was performed in 34.2% of patients. RBE occurred in 8.6% overall, with a median onset of 390 days. The incidence of RBE was significantly lower in patients who underwent delayed LC compared with those managed without surgery (3.8% vs 11.0%, p = 0.035). RBE-related mortality was 0.7%, and surgery-related mortality was 1.9%. Multivariate analysis identified age ≥82 years as the only independent predictor of RBE (OR 5.7, 95% CI 2.15-25.05, p = 0.014).

Conclusion: Subsequent LC should be considered in geriatric patients to reduce the risk of RBE after ERCP. However, given the low rates of RBE and mortality observed, a wait-and-see strategy may be a reasonable alternative in selected high-risk patients. Treatment decisions should be individualized, considering each patient's comorbidities, surgical risk, and overall health status.

The incidence of chronic kidney disease (CKD) has been consistently rising in recent years. This trend is particularly concerning in the aging population, where the prevalence of CKD and cardiovascular disease is disproportionately high. Among CKD patients, cardiovascular disease stands as the primary prognostic risk factor and leading cause of mortality. Early cardiac function assessment is critical, especially in older adults, for diagnosis and treatment guidance due to their atypical presentations and high risk. Due to its convenience, lack of radiation, and excellent repeatability, ultrasound has become a widely used method for evaluating cardiac function. Furthermore, with the rapid advancements in medical technology, numerous innovative ultrasound techniques have been introduced for assessing cardiac function.

Purpose: The development of home-based clinical interventions and healthcare supported by digital tools has rapidly advanced in recent years, promising improvements in preventive and personalized treatment, especially for aging chronic patients. However, many systems are launched without feedback from healthcare experts, essential for understanding their strengths, limitations, and areas for improvement. This study had two objectives: first, to gather expert opinions on the qualities and limitations of current home-centred healthcare trends for aging patients; second, as a case study, to obtain feedback on a novel system, Time-Integrated Healthcare (TI-Health), integrating these trends. TI-Health uses real-time data to combine bio-psycho-social information through big data and machine learning, enabling health status prediction, diagnosis, prevention, and treatment.

Participants and methods: A Delphi group of 60 experts from 9 countries, all with experience using digital health systems, was surveyed. A questionnaire was administered to the group focused on the limitations and qualities of current home-based digital healthcare trends. In addition, the Delphi group provided feedback on the TI-Health system.

Results: A majority of experts believed that personalized healthcare delivered in the patient's home will shape the future of healthcare services. The Delphi group identified key qualities of these trends: improved service integration, enhanced patient comfort, cost reduction, and a deeper understanding of the patient's context. They also highlighted challenges, such as barriers to inter-agency collaboration and data sharing, resistance to change among healthcare professionals, and the need for strong leadership to drive the adoption of new systems. Based on the insights from the Delphi group, this work presents a refined conceptualization of the TI-Health system.

Conclusion: Designing home-based clinical systems such as TI-Health should prioritize effective inter-agency coordination, ensuring data protection and fostering collaborative leadership throughout the implementation process. These factors are crucial to the successful adoption and utilization of new home-based digital health solutions.