JAR life最新文献

Background: Daytime sleepiness is common in older adults and may result from poor nighttime sleep due to sleep disordered breathing, fragmented sleep, or other sleep disorders. Daytime sleepiness may be associated with cognition in older adults.

Objectives: We investigated the association between self-reported daytime sleepiness and cognitive function in the Look AHEAD clinical trial.

Design: Observational follow-up of a randomized clinical trial of an intensive lifestyle intervention.

Setting: Clinic.

Participants: Participants (n=1,778) aged 45-76 years at baseline with type 2 diabetes and overweight or obesity.

Interventions: Participants were randomized to an intensive lifestyle intervention for weight loss or a control condition of diabetes support and education.

Measurements: Participants provided self-reported levels of daytime sleepiness at baseline and years 12-13. Cognitive function was assessed with a neurocognitive battery at years 12-13 and 18-20.

Results: Participants who reported having frequent daytime sleepiness (often or always) performed significantly worse than others on the cognitive composite (-0.35; p-value=0.014) after controlling for covariates. When stratified by intervention arm, participants assigned to the intensive lifestyle intervention who reported often/always having daytime sleepiness performed worse on Digit Symbol Coding (-0.63; p-value=0.05) and Trail Making Part-B (-0.56; p-value=0.02) after controlling for covariates. Statistical interactions revealed associations between daytime sleepiness and the following covariates: race and ethnicity, APOE ε4 carrier status, baseline history of cardiovascular disease, and depression.

Conclusions: Daytime sleepiness over ~13 years predicted poorer cognitive performance in older individuals who, by virtue of having diabetes and overweight/obesity, are at high risk for sleep disorders and cognitive impairment.

Background: The criteria for use of Alzheimer's disease (AD) drug Leqembi recommended by the Department of Veterans Affairs (VA) include patients aged 65 years or older with mild cognitive impairment (MCI) or mild AD. Comorbidities that include hypertension, hyperlipidemia, and diabetes are common among these patients.

Objectives: Our objective is to investigate the comparative effectiveness of the administration of one, two, or three medications belonging to the categories of angiotensin receptor blockers (ARBs), angiotensin-converting enzyme inhibitors (ACEIs), Beta Blockers, Statins, and Metformin, for their potential to delay the clinical onset of AD and provide a window of opportunity for therapeutic intervention.

Design: Retrospective matched case-control study.

Setting: Data from the Department of Veterans Affairs national corporate data warehouse.

Participants: We conducted an analysis of 122,351 participants (13,611 with AD and 108,740 without AD), aged 65-89, who began at least one of the prescribed medication classes under investigation between October 1998 and April 2018.

Measurements: We utilized Cox proportional hazard regressions, both with and without propensity score weighting, to estimate hazard ratios (HR) associated with the use of different medication combinations for the pre-symptomatic survival time of AD onset. Additionally, we employed a supervised machine learning algorithm (random forest) to assess the relative importance of various therapies in predicting the occurrence of AD.

Result: Adding Metformin to the combination of ACEI+Beta Blocker (HR = 0.56, 95% CI (0.41, 0.77)) reduced the risk of AD onset compared to ACEI monotherapy alone (HR = 0.91, (0.85, 0.98)), Beta Blocker monotherapy (HR = 0.86, 95% CI (0.80, 0.92)), or combined ACEI+Beta Blocker (HR=0.85, 95%CI (0.77, 0.94)), when statin prescribers were used as a reference. Prescriptions of ARB alone or the combination of ARB with Beta Blocker showed an association with a lower risk of AD onset.

Conclusion: Selected medications for the treatment of multiple chronic conditions among elderly individuals with hypertension, hyperlipidemia, and diabetes as monotherapy or combination therapies lengthen the pre-symptomatic period before the onset of AD.

Depressive symptoms the most prevalent clinical condition in the field of mood disorders in older populations. Depressive symptoms are associated to poorer morbidity and mortality, and is considered a component of frailty and intrinsic capacity. Dementia could overlap with DS in clinical and brain abnormalities. Moreover, there are sex-differences in the field of Neuro- and Gero-science. To date, no review has addressed the neuro-anatomical basis of DS in older adults using magnetic resonance imaging (MRI), neither has investigated the discrimination of dementia nor sex-differences. This narrative review investigated studies about older adults; depressive symptoms evaluation via MRI, and published in English or Spanish over the past 7 years. Moreover, it evaluated dementia discrimination and sex-related differences. The most accurate evidence showed cerebral small vessel disease as a predictor of depressive symptoms worsening. Most studies were cross-sectional, with a coarse dementia screening and sex-unrepresentative samples. Cingulate cortex and hippocampus showed a negative association to depressive symptoms, and Precuneus cortex a positive association; although these inferences require further investigation. Additional research is needed to identify the brain imaging signature of depressive symptoms in older population (if any), and if this would be associated with sex and individuals'level of frailty and intrinsic capacity.

Objectives: In this pilot study, we have evaluated the specific metabolic and immune-related benefits of the AFO-202 strain and N-163 strain of black yeast Aureobasidium pullulans-produced beta 1,3-1,6 glucan in healthy human subjects.

Methods: Sixteen healthy Japanese male volunteers (aged 40 to 60 years) took part in this clinical trial. They were divided into four groups (n = 4 each): Group I consumed AFO-202 beta-glucan (2 sachets of 1 g each per day), IA for 35 days and IB for 21 days; Group II consumed a combination of AFO-202 beta-glucan (2 sachets of 1 g each) and N-163 beta-glucan (1 sachet of 15 g gel each per day), IIA for 35 days and IIB for 21 days.

Results: Decrease in HbA1C and glycated albumin (GA), significant increase of eosinophils and monocytes and marginal decrease in D-dimer levels, decrease in neutrophil-to-lymphocyte ratio (NLR), with an increase in the lymphocyte-to-CRP ratio (LCR) and leukocyte-to-CRP ratio (LeCR) was observed in Group I between pre- and post-treatment. Decrease in total and LDL cholesterol, a decrease of CD11b, serum ferritin, galectin-3 and fibrinogen were profound in Group II between pre- and post-treatment. However, there was no statistically significant difference between day 21 and day 35 among the groups.

Conclusion: This outcome warrants larger clinical trials to explore the potentials of these safe food supplements in the prevention and prophylaxis of diseases due to dysregulated metabolism, such as fatty liver disease, and infections such as COVID-19 in which balanced immunomodulation are of utmost importance, besides their administration as an adjunct to existing therapeutic approaches of both communicable and non-communicable diseases.

Cost estimates for care for those with dementia and other cognitive impairments are rising globally, estimated to reach US $1 trillion by 2025. Lack of specialized personnel, infrastructure, diagnostic capabilities, and healthcare access impedes the timely identification of patients progressing to dementia, particularly in underserved populations. International healthcare infrastructure may be unable to handle existing cases in addition to a sudden increase due to undiagnosed cognitive impairment and dementia. Healthcare bioinformatics offers a potential route for quicker access to healthcare services; however, a better preparedness plan must be implemented now if expected demands are to be met. The most critical consideration for implementing artificial intelligence/machine learning (AI/ML) -driven clinical decision intelligence applications (CDIA) is ensuring patients and practitioners take action on the information provided.

Appropriate intervention and care in detecting cognitive impairment early are essential to effectively prevent the progression of cognitive deterioration. Diagnostic voice analysis is a noninvasive and inexpensive screening method that could be useful for detecting cognitive deterioration at earlier stages such as mild cognitive impairment. We aimed to distinguish between patients with dementia or mild cognitive impairment and healthy controls by using purely acoustic features (i.e., nonlinguistic features) extracted from two simple phrases. Voice was analyzed on 195 recordings from 150 patients (age, 45-95 years). We applied a machine learning algorithm (LightGBM; Microsoft, Redmond, WA, USA) to test whether the healthy control, mild cognitive impairment, and dementia groups could be accurately classified, based on acoustic features. Our algorithm performed well: area under the curve was 0.81 and accuracy, 66.7% for the 3-class classification. Thus, our vocal biomarker is useful for automated assistance in diagnosing early cognitive deterioration.

Background: Exercise, education, and social engagement are critical interventions for older adults for a healthy life expectancy and to improve their physical function.

Objective: To conduct a combined exercise and education (CEE) program for improved social engagement and physical function of older adults.

Design: Based on a short-term program we conducted in our previous study, in this study, the program was conducted for half the number of participants of the earlier study but for a longer duration.

Setting: A community of older adults in Ami, Japan, was the setting of the study.

Participants: 23 healthy older adults >65 years living in the community were the participants in the study.

Interventions: Five 80-minute sessions conducted once in two weeks comprised 60-min exercise instruction and 20-min educational lectures per session on health. We examined the improvement in physical and social engagement before and after participation. Physical function and health-related questionnaire data were collected before and after the program.

Results: Data analysis from 15 participants showed improved physical performance but no effect on social engagement.

Conclusions: A higher program frequency, rather than program duration, may be vital to improving exercise performance and social engagement and maximizing the effects of high group cohesion in small groups. Further studies are needed to develop more effective interventions to extend healthy life expectancy.