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BackgroundParticipant eligibility for the A4 Study was determined by amyloid PET imaging. Given the disadvantages of amyloid PET imaging in accessibility and cost, blood-based biomarkers may serve as a sufficient biomarker and more cost-effective screening tool for patient enrollment into preclinical AD trials.ObjectiveTo determine if a blood-based screening test can adequately identify amyloid burden in participants screened into a preclinical AD trial.MethodsIn this cross-sectional study, 224 participants from the A4 Study received an amyloid PET scan (18Florbetapir) within 90 days of blood sample collection. Blood samples from all study participants were processed within 2 h after phlebotomy. Plasma amyloid measures were quantified by Shimazdu and C2 N Diagnostics using mass spectrometry-based platforms. A corresponding subset of blood samples (n = 100) was processed within 24 h after phlebotomy and analyzed by C2 N.ResultsPlasma Aβ42/Aβ40 demonstrated the highest association for Aβ accumulation in the brain with an AUC 0.76 (95%CI = 0.69, 0.82) at C2 N and 0.80 (95%CI = 0.75, 0.86) at Shimadzu. Blood samples processed to plasma within 2 h after phlebotomy provided a better prediction of amyloid PET status than blood samples processed within 24 h (AUC 0.80 versus 0.64; p

Aim: Muscle mass and strength correlate with cognitive function; however, it remains unclear whether dynapenia (i.e., muscle weakness with preserved muscle mass) is relevant. This study aimed to explore whether dynapenia is associated with global cognitive function in community‐dwelling older Japanese adults. Methods: This cross‐sectional study used data from the Integrated Research Initiative for Living Well with Dementia Cohort Study, which pooled data from five community‐based geriatric cohorts. Dynapenia was defined as muscle weakness without muscle mass loss according to the Asian Working Group for Sarcopenia criteria. Cognitive function was assessed using the Mini‐Mental State Examination (MMSE). An ordered logistic regression analysis was conducted with dynapenia as the exposure and with cognitive decline stages, defined as an MMSE score of 27–30 for normal cognition, 24–26 for possible cognitive decline, and <24 for cognitive decline, as the outcome, stratified by sex and adjusted for age, muscle mass, education, alcohol consumption, smoking habits, living alone, and non‐communicable diseases. Results: We analyzed data for 3338 participants (2162 female) with preserved muscle mass. Of these, 449 (13.5%) had dynapenia, and 79 (2.4%) exhibited cognitive decline. Multivariate odds ratios (95% confidence interval) for cognitive decline among those with dynapenia, compared with those without dynapenia, were 1.51 (1.02–2.24) for males and 2.08 (1.51–2.86) for females. Conclusions: Muscle weakness is associated with cognitive decline, even in individuals with preserved muscle mass. Further studies are needed to better understand the association between muscle weakness and cognitive decline over time in order to develop dementia prevention strategies for those with dynapenia. Geriatr Gerontol Int 2024; 24: 123–129. [ABSTRACT FROM AUTHOR]

We studied frailty and subjective cognitive decline (SCD) trajectories in older Japanese adults and evaluated the influence of various factors on these trajectories. We analyzed data from 1157 non-demented adults aged 70 and above from 2013 to 2019. Frailty was assessed using the self-administered Kihon Checklist (KCL), a Japanese frailty index. SCD was evaluated using the questionnaire of the Subjective Memory Complaints scale. Through group-based joint trajectory models, we discerned three frailty trajectories: non-progressive (n = 775), moderate progressive (n = 312), and rapid progressive (n = 70); and three SCD trajectories: non-progressive (n = 302), moderate progressive (n = 625), and rapid progressive (n = 230). Individuals in the rapid progressive SCD trajectory had a 32.2% probability of also being in the rapid progressive frailty trajectory. In contrast, those in the non-progressive SCD trajectory had zero probability of being in the rapid progressive frailty trajectory. Both the rapid progressive frailty and SCD groups combined had a higher incidence of depressive symptoms and slow gait speed. Our results have found that frailty and SCD share a similar trajectory in Japanese older adults. Additionally, rapid progressive frailty and SCD were associated with the highest risk of depressive symptoms and slow gait speed. Thus, interventions targeting both frailty and cognitive decline should prioritize mental health enhancement and gait speed improvement. [ABSTRACT FROM AUTHOR]

Introduction: We examined the relationship between previous fluctuations in Mini-Mental State Examination (MMSE) scores, future changes in MMSE scores, and attrition from follow-up surveys, which helps in a more comprehensive interpretation of repeatedly collected MMSE scores. Methods: This 4-year longitudinal study included 2,073 community-dwelling older adults aged ≥65 years in Japan. The MMSE was administered at baseline (T0), 2 years (T1), and 4 years (T2) follow-up. We performed multinomial logistic regression analysis with the dependent variable, indicating the change in MMSE score from T1 to T2 (categorized as increase, no change [reference category], and decrease) and attrition at T2. The independent variables included the change in MMSE scores from T0 to T1 and MMSE scores at T0 and T1. Results: The mean MMSE score was 29 across the three time points. A one-point decrease in MMSE score from T0 to T1 was associated with 79% (95% confidence interval: 1.62, 1.97) higher odds of an increase in MMSE score from T1 to T2 and 28% (1.17, 1.40) higher odds of attrition at T2. A one-point decrement in the MMSE score at T0 and T1 was also associated with an increase in the MMSE score from T1 to T2 and attrition at T2. Conclusion: Focusing on cognitive fluctuation for 2 years, rather than cognitive function at a point in time, would have no remarkable advantage when focusing on future cognitive function and attrition. Our results emphasize the need for further studies to identify factors that distinguish between those who continue to attend follow-up surveys and show improvements in cognitive test scores and those who drop out. [ABSTRACT FROM AUTHOR]

Background and Objectives Readmission-related health care reforms have shifted their focus from all-cause readmissions (ACR) to potentially avoidable readmissions (PAR). However, little is known about the utility of analytic tools from administrative data in predicting PAR. This study determined whether 30-day ACR or 30-day PAR is more predictable using tools that assess frailty, comorbidities, and activities of daily living (ADL) from administrative data. Research Design and Methods This retrospective cohort study was conducted at a large general acute care hospital in Tokyo, Japan. We analyzed patients aged ≥70 years who had been admitted to and discharged from the subject hospital between July 2016 and February 2021. Using administrative data, we assessed each patient's Hospital Frailty Risk Score, Charlson Comorbidity Index, and Barthel Index on admission. To determine the influence of each tool on readmission predictions, we constructed logistic regression models with different combinations of independent variables for predicting unplanned ACR and PAR within 30 days of discharge. Results Among 16 313 study patients, 4.1% experienced 30-day ACR and 1.8% experienced 30-day PAR. The full model (including sex, age, annual household income, frailty, comorbidities, and ADL as independent variables) for 30-day PAR showed better discrimination (C-statistic: 0.79, 95% confidence interval: 0.77–0.82) than the full model for 30-day ACR (0.73, 0.71–0.75). The other prediction models for 30-day PAR also had consistently better discrimination than their corresponding models for 30-day ACR. Discussion and Implications PAR is more predictable than ACR when using tools that assess frailty, comorbidities, and ADL from administrative data. Our PAR prediction model may contribute to the accurate identification of at-risk patients in clinical settings who would benefit from transitional care interventions. [ABSTRACT FROM AUTHOR]

Background: With increasingly aging societies, a comprehensive strategy for dementia research is important. The Organized Registration for the Assessment of dementia by the Nationwide General consortium toward Effective treatment (ORANGE) Registry is the first longitudinal multicenter prospective trial-ready cohort in Japan.Objective: To establish a large cohort for use in clinical trials and research in Japan.Methods: This registry, based on communities, hospitals, and nursing homes, covers three dementia stages (preclinical, mild cognitive impairment [MCI], and advanced dementia), and includes more than 30 hospitals. We analyzed enrollment and 1-year follow-up data for disease progression.Results: There were 1450 registered patients (649 men, 801 women; mean age, 77.92±6.70 years; mean Mini-Mental State Examination [MMSE] score, 25.19±2.76). The conversion rates from MCI to dementia and MCI to normal were 14.3% and 1.1%, respectively. High Clinical Dementia Rating score (odds ratio [OR] = 11.085, 95% confidence interval [CI]:1.619-75.913, p = 0.014), low MMSE score (OR = 0.835, 95% CI: 0.761-0.917, p < 0.001), high Geriatric Depression Scale score (OR = 1.093, 95% CI: 1.005-1.189, p = 0.038), and low body mass index (OR = 0.895, 95% CI: 0.829-0.967, p = 0.005) at enrollment were significant factors for conversion.Conclusion: The ORANGE MCI Registry is an established registry that facilitates creation of trial-ready cohorts to accelerate promotion of clinical trials with low reversion rates as it originates from a hospital. One-year follow-up analysis suggested assessing various factors for conversion risk. Further analyses will be possible in future with registry expansion. We will continue to refine this registry, including how it can be used more efficiently. [ABSTRACT FROM AUTHOR]

The article discusses the importance of early identification of prefrailty and frailty in older adults within the community, emphasizing collaboration among healthcare professionals for proactive frailty interventions. It introduces the Prefrailty scale for Appearance Observation by Nurses (PAON) as a simple yet effective tool for identifying prefrailty through clinical observations by nurses, highlighting its potential to improve early detection and intervention strategies for frailty.