As the numbers of older adults continue to increase globally, the need for facilitating healthy aging has become critical. While a physically healthy lifestyle, including exercise and diet, is important, recent research has highlighted a major impact of psychosocial determinants of health, such as resilience, wisdom, positive social connections, and mental well-being, on whole health. This article focuses on keeping the mind and brain healthy with psychosocially active aging.
View Article and Find Full Text PDFBeneficial associations between higher fruit and vegetable intakes and risk of depression appear to exist but few studies have focused on adults aged 45 + years and the potential that associations are due to residual confounding has not been tested. This longitudinal study of twins (n = 3483, age 45-90 years) from Australia, Denmark, Sweden and USA, assessed the associations between baseline fruit/vegetable intake and depressive symptoms over 5-11 years using linear mixed effects models. Intakes from food frequency questionnaires were trichotomized.
View Article and Find Full Text PDFBackground: We aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors.
Methods: Data were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.
Alzheimer's disease (AD) is a devastating neurodegenerative condition that affects memory and cognition, characterized by neuronal loss and currently lacking a cure. Mutations in (Presenilin 1) are among the most common causes of early-onset familial AD (fAD). While changes in neuronal excitability are believed to be early indicators of AD progression, the link between mutations and neuronal excitability remains to be fully elucidated.
View Article and Find Full Text PDFBackground: We conducted a secondary analysis of a cohort study to examine the World Falls Guidelines algorithm's ability to stratify older people into sizable fall risk groups or whether minor modifications were necessary to achieve this.
Methods: Six hundred and ninety-three community-living people aged 70-90 years (52.4% women) were stratified into low, intermediate and high fall risk groups using the original algorithm and a modified algorithm applying broader Timed Up and Go test screening with a >10-s cut point (originally >15 s).
Background: Urban neighbourhood environments may impact older adults' cognitive health. However, longitudinal studies examining key environmental correlates of cognitive health are lacking. We estimated cross-sectional and longitudinal associations of neighbourhood built and natural environments and ambient air pollution with multiple cognitive health outcomes in Australian urban dwellers aged 60+ years.
View Article and Find Full Text PDFDown syndrome regression disorder (DSRD) is a rare condition involving subacute cognitive decline, loss of previously acquired developmental skills, and prominent neuropsychiatric symptoms, particularly catatonia, in people with Down syndrome. It is thought to involve both autoimmune and neuropsychiatric mechanisms. Research, however, is largely restricted to case studies and retrospective case series and is particularly limited in terms of prospective longitudinal follow-up.
View Article and Find Full Text PDFBackground: Few studies evaluated the contribution of long-term elevated blood pressure (BP) towards dementia and deaths. We examined the association between cumulative BP (cBP) load and dementia, cognitive decline, all-cause and cardiovascular deaths in older Australians. We also explored whether seated versus standing BP were associated with these outcomes.
View Article and Find Full Text PDFStructural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model.
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