28 papers
Geriatric medicine focuses on the unique health needs of older adults, addressing complex conditions that often arise as we age. This field goes beyond treating single diseases to manage the whole person, considering how multiple chronic issues, medication interactions, and physical changes affect quality of life. By prioritizing functional ability and dignity, researchers in this space aim to help people live healthier, more independent lives for as long as possible.
Gist.Science makes the latest findings in this vital area accessible by processing every new preprint from medRxiv in this category. We transform these raw scientific reports into both plain-language overviews for general readers and detailed technical summaries for specialists, ensuring no one is left behind by complex jargon. Below are the latest papers in geriatric medicine, updated regularly as new research becomes available on medRxiv.
In a contemporary and diverse cohort, most established Alzheimer's disease risk algorithms showed limited discriminatory power for clinical and biomarker outcomes, but including a functional assessment measure significantly improved their ability to distinguish between healthy cognition, mild cognitive impairment, and Alzheimer's disease.
This comparative study of Spain, Italy, Chile, and Colombia reveals that while all four nations have established policy frameworks to address multimorbidity and frailty in rapidly aging populations, the primary challenge lies in overcoming implementation barriers such as fragmented services and workforce constraints to achieve effective health-social care integration.
This study of 5,042 NHANES adults reveals that the protective effect of physical activity against type 2 diabetes is significantly modified by both obesity and age, being strongest in normal-weight and middle-aged individuals while diminishing or disappearing in those with obesity or older age, thereby supporting the need for stratified prevention strategies.
This study demonstrates that while PET-anchored and distribution-based thresholds for plasma pTau-217 yield similar classifications at the extreme ends of the biomarker distribution, they diverge significantly in the intermediate range, suggesting that the choice of thresholding method rather than the analytical platform is the primary driver of positivity status and should be tailored to the specific clinical or research application.
This study utilizes a validated synthetic cohort of older Ghanaian adults to demonstrate that STOPP/START v3 criteria identify significantly higher rates of potentially inappropriate prescribing than the AGS Beers 2023 criteria, thereby establishing synthetic data modeling as a scalable, privacy-preserving solution for auditing medication safety in data-scarce sub-Saharan health systems.
This cross-sectional study of older adults in a Kazakhstani social-care institution reveals a double burden of malnutrition characterized by high rates of overweight, obesity, and cardiometabolic risk coexisting with widespread micronutrient deficiencies, particularly vitamin D, challenging the typical perception of undernutrition in residential care settings.
This study utilizes a generator-matrix causal-inference framework to demonstrate that the majority of human mortality acceleration is driven by latent dynamics largely invisible to current measurable biomarkers, which show no causal effect on lifespan and remain resistant to cellular reprogramming.
This cross-sectional study of the FREEDOM cohort reveals that cognitive impairment is highly prevalent and strongly associated with physical frailty and functional dependence in community-dwelling older adults, with modifiable risk factors such as low BMI, hypertension, alcohol consumption, smoking, and polypharmacy identified as key predictors of poor cognitive performance.
This study leverages CHARLS data to develop a dynamic nomogram integrating routine blood markers and mental health indicators, revealing that depression is a dominant predictor of disability in elderly cardiometabolic multimorbidity patients and that the risk relationship plateaus after three comorbidities.
This study demonstrates that a bagging classifier machine learning model, trained on a population-based dataset from Hospital Universitario Parc Tauli, can effectively and proactively identify patients with advanced chronic conditions (MACA) with high sensitivity (0.91) and AUC (0.90) using ten key clinical and functional predictors.