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Record W4406024325 · doi:10.1002/alz.090906

The Multisite SuperAging Research Initiative: Enrollment and Scientific Progress

2024· article· en· W4406024325 on OpenAlexaffabout
Emily Rogalskı, Adam Martersteck, Angela Roberts, Matthew J. Huentelman, Ozioma C. Okonkwo, Phyllis Timpo, Hannah Peirce, Chandler Zolliecoffer, Rhiana Schafer, Rebecca Devine, Janessa Engelmeyer, Changiz Geula, Elizabeth Addison, Ignazio S. Piras, Antoine R Trammell, Gabrielle A Lincoln, Felicia C. Goldstein, Karen Van Ooteghem, Bill McIlroy, Robert Bartha, Elizabeth Finger, Ivan Culum, Andrew Kean Seng Lim, Richard H. Swartz, Nathan Gill, Amanda Cook Maher

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsHealth Sciences CentreUniversity of WaterlooSunnybrook Health Science CentreWestern University
Fundersnot available
KeywordsPsychosocialGerontologyCognitive declineBiobankExposomeCognitionPsychologyMedicineDementiaDiseasePsychiatryEnvironmental healthBioinformaticsPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The multisite SuperAging Research Initiative (SRI) was established in 2021 to identify resilience and resistance factors promoting cognitive healthspan through a harmonized multidisciplinary protocol with prospective data collection. The designation of SuperAger is reserved for individuals age 80+ with episodic memory performance that is at least average for those 2-3 decades younger. Research studies of this relatively uncommon phenotype allow for investigations of fundamental importance to the neurobiology of brain aging, resilience, resistance, and avoidance of cognitive decline related to "average aging" and more severe impairments associated with Alzheimer's and related dementias (ADRD). The SRI is focused on increasing participant diversity and deep phenotyping through the enrollment of 500+ participants into a multi-component longitudinal protocol. This presentation will summarize the engagement, recruitment, and baseline characteristics as well as the initial scientific findings from the cohort. METHOD: Enrollment and harmonized data collection occurs across North American sites in the U.S. and Canada. The SRI includes three Cores (Administrative/Biostatistics, Clinical/Imaging, and Biospecimen/Neuropathology) and two Research Projects. The Core infrastructure provides behavioral, health, genetic, environmental, socioeconomic, psychosocial, neuropsychologic, neuroimaging, and neuropathologic measurements. Project 1 uses state-of-the-art wearable technology to obtain quantitative measurements of daily life (e.g., sleep, physical activity, and social engagement) to determine whether SuperAgers have relatively preserved physiologic and behavioral 'complexity'. Project 2 uses transcriptomic, genetic, and protein profiling to examine central and peripheral immune and inflammatory system parameters. RESULT: Community-engaged research practices have been implemented, yielding enrollment of >170 participants (ages 80-108) into the multicomponent harmonized protocol. Approximately half of the participants have been co-enrolled into Project 1 and the majority have provided biospecimens for Project 2. Additional engagement and recruitment strategies are being deployed and evaluated for efficacy to promote increased enrollment of diverse participants. CONCLUSION: The multidisciplinary study of 80+ year-olds with exceptional memory including imaging, blood draw, and unsupervised remote data collection using wearable technologies is feasible. The prospective study of SuperAgers is uncommon but holds promise for identifying mechanisms of resilience and resistance. Outcomes have potential relevance for isolating modifiable factors for promoting healthspan and avoiding ADRD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.354
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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