Lăcrămioara Luminița Apescaritei Apostol

Frailty is a dynamic state of reduced reserve whose expression reflects clinical, functional, cognitive, psychological, social, and environmental factors. This structured narrative review examines its conceptual models, determinants, consequences, multidimensional assessment, holistic interventions, and coordination across hospital, community, and home care. The literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Embase up to September 2026, supplemented by World Health Organization (WHO) documents, Google Scholar, and citation tracking. Physical phenotype, deficit-accumulation, multidimensional, judgement-based, and electronic approaches address different clinical questions and should not be treated as interchangeable. Brief case finding is most useful when it leads to assessment of modifiable contributors and a care plan aligned with the older person’s goals. Evidence most consistently supports progressive multicomponent exercise; nutritional support, medication review, falls prevention, and cognitive or social interventions should target identified needs. Hospital geriatric care, structured discharge, community follow-up, and home-based services require coordination, shared information, and reassessment, although evidence for particular models remains heterogeneous. Under-recognition, fragmented services, limited multidisciplinary capacity, caregiver burden, and unequal access constrain implementation. Frailty care is best understood as a longitudinal, person-centred process. Future research should test clinically meaningful outcomes, reproducible pathways, and equity in digital and non-digital delivery.

Cognitive disorders, from mild cognitive impairment to more serious dementia, are becoming more common and are directly related to the increased longevity and quality of life throughout the world. This demographic dynamic places a tremendous pressure on health care systems and fosters vigorous research in the international medical community, with the aim of identifying disease-modifying treatments able to stop or slow the progression of these neurodegenerative diseases. In total, 28 molecules are presently under development in this therapeutic portfolio at various phases of clinical studies. The main purpose of this research is to conduct a detailed analysis of therapeutic agents in Phase III clinical trials, a key phase for evaluating efficacy and safety. Another section describes monoclonal antibodies, a class of drugs that has already demonstrated impressive results as DMTs by specifically targeting pathogenic processes. Besides DMTs, there are several innovative approaches under research for treating acute exacerbations and symptom relief in neurodegenerative diseases. This review aims to critically synthesize the therapeutic landscape in dementia, pointing to promising directions for future clinical research. Of the 28 candidates evaluated, 7.1% met the primary efficacy endpoints in Phase III, 50.0% showed minimal or no efficacy, and 42.9% await definitive results. While monoclonal antibodies demonstrate a modest slowing of clinical progression in early-stage disease, many candidate compounds have failed, underscoring the ongoing difficulties of late-stage pharmaceutical development.

Frailty syndrome and Alzheimer’s disease are prevalent conditions in the elderly that are associated with aging, decreased quality of life, and a significant healthcare burden. Evidence for a relationship between physical frailty and neurodegenerative decline is accumulating. This study analyzed two independent datasets, a frailty dataset based on gait and mobility parameters and an AD dataset with clinical, functional and lifestyle variables, in order to evaluate and compare their classification performance using machine learning. Features were optimized using dimensionality reduction techniques to keep predictors of clinical significance and hyperparameter optimized Random Forest models were built to develop the best model. Evaluation was performed with Accuracy, F1-score, Matthews Correlation Coefficient and Area Under the Curve. The results showed that the models constructed on the whole AD dataset achieved maximum predictive power with an accuracy of 0.946, which was slightly increased to an accuracy of 0.948 after the selection of significant features. Diagnostic models based on frailty were able to demonstrate an ACC predictive capacity of 0.6418, and in terms of feature selection, improvements appeared in all indicators. Regarding the features derived from Alzheimer’s disease associated with geriatric frailty, they managed to surpass the ACC frailty features of 0.741 alone, suggesting some intercalation mechanisms between neurodegeneration and physical vulnerability. These findings show that machine learning algorithms accompanied by feature selection improve clinical discrimination and prediction of frailty and neurodegenerative disorders, which offers a promising aspect for geriatric assessment. The frailty models analyzed demonstrated an ACC predictive capacity of 0.6418, even though feature selection improved all indicators. Alzheimer’s disease-derived features associated with frailty outperformed features in the frailty dataset with an ACC of 0.741, suggesting the mechanism of overlap between neurodegeneration and physical vulnerability. These results support the theory of a motor-cognitive aging continuum, indicating that algorithmic machine learning techniques coupled with feature selection mainly provide computational validation for the biological intersection of neurodegeneration and physical frailty, rather than forming an independent predictive clinical model. Using these algorithms the study highlights shared pathophysiological mechanisms, providing a significant insight into systemic geriatric deterioration.