Objective: To better define the role of nonsteroidal antiinflammatory drugs (NSAID) in cognitive decline of the elderly.
Methods: Population based inception cohort of the rural elderly. NSAID user status was characterized as high dose, low/medium dose, or nonuser at 2 successive in-person interviews 3 years apart (F3 and F6). Respondents were from the Iowa 65+ Rural Health Study, one of the 4 cohorts of the National Institute on Aging's Established Populations for Epidemiologic Studies of the Elderly. Memory decline was assessed by a change in immediate word recall between F3 and F6. Multivariable logistic regression models were created to determine important predictors of the F3 to F6 recall memory decline in groups with poor, average, and good word recall at the F3 interview. Specific NSAID were compared to assess which drugs, if any, were associated with memory decline.
Results: The 2 factors most strongly associated with a significant immediate word recall decline between F3 and F6 among individuals in the average F3 word recall group were limitation in functional status [odds ratio (OR) = 2.31, 95% Confidence Interval (CI) (1.51, 3.52)] and high dose NSAID use [OR = 2.04, 95% CI (1.07, 3.89)]. No single NSAID agent was significantly more strongly associated with word recall decline than the others. However, in exploratory analyses use of high dose NSAID of the proprionic acid family neared significance for recall decline [OR = 3.17, 95% CI (0.92, 10.9).
Conclusion: In elderly respondents with average baseline recall memory, high dose NSAID were a significant risk factor for longitudinal memory decline in this community based cohort. Although a large scale clinical trial is needed to definitely address this issue, we provide further evidence that NSAID play a role in cognitive dysfunction in the elderly.
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Ther Adv Musculoskelet Dis
January 2025
Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 158 Shangtang Road, Hangzhou, Zhejiang 310014, China.
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Elife
January 2025
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University & IDG/McGovern Institute for Brain Research, Beijing, China.
Speech comprehension involves the dynamic interplay of multiple cognitive processes, from basic sound perception, to linguistic encoding, and finally to complex semantic-conceptual interpretations. How the brain handles the diverse streams of information processing remains poorly understood. Applying Hidden Markov Modeling to fMRI data obtained during spoken narrative comprehension, we reveal that the whole brain networks predominantly oscillate within a tripartite latent state space.
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January 2025
Institute of Mathematical Sciences Centre for Health Analytics and Modelling (CHaM), Strathmore University, Nairobi, Kenya.
Background: Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data.
View Article and Find Full Text PDFJ Epidemiol Community Health
January 2025
Epidemiology, University of Michigan, Ann Arbor, Michigan, USA.
Background: While social support is associated with better cognitive health among cancer-free individuals, this relationship is understudied among cancer survivors. We investigated whether overall social support before and after a cancer diagnosis is related to post-diagnosis memory ageing, overall and by sex/gender.
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Prehosp Emerg Care
January 2025
Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.
Objectives: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data.
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