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Record W4312087289 · doi:10.1002/alz.066407

The SuperAging Research Initiative: A multisite consortium focused on identifying factors promoting extraordinary cognitive aging

2022· article· en· W4312087289 on OpenAlexaffabout
Emily Rogalskı, Matthew J. Huentelman, Angela Roberts, Amanda Cook Maher, Bill McIlroy, Karen Van Ooteghem, Elizabeth Finger, Andrew Lim, Ozioma C. Okonkwo, Felicia C. Goldstein, Ihab Hajjar, Todd B. Parrish, Denise Scholtens, Stephanie Gutierrez, Sandra Weıntraub, Changiz Geula, M.‐Marsel Mesulam

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHealth Sciences CentreToronto Rehabilitation InstituteUniversity of WaterlooSunnybrook Health Science CentreWestern University
Fundersnot available
KeywordsPsychosocialNeuropathologyGerontologyCognitionPsychologySocioeconomic statusCohortMedicineNeurosciencePsychiatryDiseasePathologyEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Abstract Background The designation of SuperAger is reserved for individuals age 80+ who have episodic memory capacity that would be considered at least average for those 2‐3 decades younger. The presence of such outliers raises questions of fundamental importance to the neurobiology of brain aging. Have these superior memory performers resisted age‐related changes, or have they simply started from a much higher baseline? Do they have identifiable peculiarities of genetic background? Is there something special about their brain structure or perhaps their resistance to age‐related processes such as neurofibrillary degeneration and amyloid deposition? These are the questions that were initially addressed by the Northwestern SuperAging Project, which identified unique results encompassing cognitive, psychosocial, molecular, and neuropathologic markers that characterize SuperAgers. Obstacles to further progress have been the relative rarity of this phenotype and, consequently, the barriers to racial diversity in the cohort. Methods To address these challenges, we established the SuperAging Research Initiative, a multicenter study focused on increased minority representation, to identify behavioral, health, biologic, genetic, environmental, socioeconomic, psychosocial, neuroanatomic, and neuropathologic factors associated with SuperAging. Results Here we provide the organizational structure and progress to date of the SuperAging Research Initiative, which includes three Cores (Administrative/Biostatistics, Clinical/Imaging, and Biospecimen/Neuropathology) and two Research Projects. Enrollment (n = 500) is planned across four US Sites located in Illinois, Wisconsin, Michigan, and Georgia, and a Canadian Site in Southwest Ontario, with a focus on enrollment of Black SuperAgers and Cognitively Average Elderly Controls with similar demographics. Project 1 uses state‐of‐the‐art wearable technology to obtain quantitative everyday measurements of life sleep, physical activity, autonomic responsivity, and social engagement to determine whether SuperAgers have relatively preserved physiologic and behavioral ‘complexity’ compared to Controls. Project 2 focuses on transcriptomic, genetic, and protein profiling to examine central and peripheral immune and inflammatory system parameters of SuperAgers. Conclusions By identifying factors contributing to superior memory performance in old age, outcomes may help isolate modifiable factors that promote healthspan and perhaps also prevent age‐related brain diseases such as Alzheimer’s disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0020.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.132
GPT teacher head0.402
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2022
Admission routes2
Has abstractyes

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