The search for interventions to slow down and even reverse aging is a burgeoning field. The literature cites hundreds of supposedly beneficial pharmacological and genetic interventions in model organisms: mice, rats, flies and worms, where research into physiology is routinely accompanied by lifespan data. However, when experimental animals from one article live as long as controls from another article, comparing the results of interventions across studies can yield misleading outcomes. Theoretically, all lifespan data are ripe for re-analysis: we could contrast the molecular targets and pathways across studies and help focus the further search for interventions. Alas, the results of most longevity studies are difficult to compare. This is in part because there are no clear, universally accepted standards for conducting such experiments or even for reporting such data. The situation is worsened by the fact that the authors often do not describe experimental conditions completely. As a result, works on longevity make up a set of precedents, each of which might be interesting in its own right, yet incoherent and incomparable at least for the reason that in a general context, it may indicate, for example, not prolonging the life of an average organism, but compensating for any genetic abnormalities of a particular sample or inappropriate living conditions. Here we point out specific issues and propose solutions for quality control by checking both inter- and intra-study consistency of lifespan data.
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http://dx.doi.org/10.18632/aging.205604 | DOI Listing |
Background: There is an urgent need for new therapeutic and diagnostic targets for Alzheimer's disease (AD). Dementia afflicts roughly 55 million individuals worldwide, and the prevalence is increasing with longer lifespans and the absence of preventive therapies. Given the demonstrated heterogeneity of Alzheimer's disease in biological and genetic components, it is critical to identify new therapeutic approaches.
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December 2024
Edith Cowan University, Perth, Western Australia, Australia.
Background: Accumulation of amyloid beta 42 (Aβ42) senile plaques is the most critical event leading to Alzheimer's disease (AD). Currently approved drugs for AD have not been able to effectively modify the disease. This has caused increasing research interests in health beneficial nutritious plant foods as viable alternative therapy to prevent or manage AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA, USA.
Background: Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations are underrepresented in Alzheimer's disease and related dementias (ADRD) research, despite being the fastest growing racial group in the United States. The Collaborative Approach for AANHPI Research and Education (CARE) registry aims to create a sustainable research recruitment source to address this need.
Method: Participants can enroll online, by phone, or in-person by completing an enrollment survey in English, Chinese (Simplified/Traditional), Hindi, Korean, Samoan, or Vietnamese.
Alzheimers Dement
December 2024
Murdoch University, Perth, Western Australia, Australia.
Background: Research on cognitive reserve (CR) in individuals aged 80 years old and above has resulted in inconsistent findings, mostly showing a relationship with baseline cognitive abilities but not follow up assessments. The effects of amyloid burden on the relationship between CR, cognitive decline and dementia in oldest old warrants further study in the presence of APOE e4. We hypothesised that CR in oldest old (≥80 yrs old) adults will result in different trajectories, depending on being amyloid PET positive or negative.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
National University of Singapore, Singapore, Singapore, Singapore.
Background: Past studies examining sleep-cognition relationships mostly employed univariate approaches, which are subject to problems such as multicollinearity and multiple comparisons. Further, results from small sample univariate analyses are difficult to compare, precluding the identification of the aspects of sleep health associated with a particular cognitive domain(s). The current study used a multivariate approach to identify key sleep metrics and cognitive domains that contribute to the maximum sleep-cognition covariance in healthy older adults.
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