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Record W3024592681 · doi:10.1093/gerona/glaa116

APOE ε4 and the Influence of Sex, Age, Vascular Risk Factors, and Ethnicity on Cognitive Decline

2020· article· en· W3024592681 on OpenAlexaff
Steve R. Makkar, Darren M. Lipnicki, Nicole A. Kochan, E. Costa, Maria Fernanda Lima‐Costa, Breno S. Diniz, Carol Brayne, Blossom C. M. Stephan, Fiona E. Matthews, Juan J. Llibre Rodríguez, Jorge J. Llibre‐Guerra, Adolfo J. Valhuerdi-Cepero, Richard B. Lipton, Mindy J. Katz, Cuiling Wang, Karen Ritchie, Sophie Carles, Isabelle Carrière, Nikolaos Scarmeas, Mary Yannakoulia, Mary H. Kosmidis, Linda Lam, Wai Chi Chan, Ada W. T. Fung, Antonio Guaita, Roberta Vaccaro, Annalisa Davin, Ki Woong Kim, Ji Won Han, Seung Wan Suh, Steffi G. Riedel‐Heller, Susanne Roehr, Alexander Pabst, Mary Ganguli, Tiffany F. Hughes, Beth E. Snitz, Kaarin J. Anstey, Nicolas Cherbuin, Simon Easteal, Mary N. Haan, Allison E. Aiello, Kristina Dang, Tze Pin Ng, Qi Gao, Ma Shwe Zin Nyunt, Henry Brodaty, Julian N. Trollor, Yvonne Leung, Jessica Lo, Perminder S. Sachdev

Bibliographic record

VenueThe Journals of Gerontology Series A · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCarolina Population Center, University of North Carolina at Chapel HillEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeBiomedical Research CouncilNational Medical Research CouncilNational Health and Medical Research CouncilUniversity of New South WalesMinistério da SaúdeFundação de Amparo à Pesquisa do Estado de Minas GeraisMinistry of Health and WelfareWellcome TrustEuropean Social FundAgency for Science, Technology and ResearchFondo Nacional de Desarrollo Científico y TecnológicoMedical Research CouncilNational Institute on AgingAlzheimer's AssociationNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsCognitive declineDemographyApolipoprotein ECognitionEthnic groupGerontologyPsychologyMedicineModerationInternal medicineDementiaPsychiatryDisease

Abstract

fetched live from OpenAlex

We aimed to examine the relationship between Apolipoprotein E ε4 (APOE*4) carriage on cognitive decline, and whether these associations were moderated by sex, baseline age, ethnicity, and vascular risk factors. Participants were 19,225 individuals aged 54-103 years from 15 longitudinal cohort studies with a mean follow-up duration ranging between 1.2 and 10.7 years. Two-step individual participant data meta-analysis was used to pool results of study-wise analyses predicting memory and general cognitive decline from carriage of one or two APOE*4 alleles, and moderation of these associations by age, sex, vascular risk factors, and ethnicity. Separate pooled estimates were calculated in both men and women who were younger (ie, 62 years) and older (ie, 80 years) at baseline. Results showed that APOE*4 carriage was related to faster general cognitive decline in women, and faster memory decline in men. A stronger dose-dependent effect was observed in older men, with faster general cognitive and memory decline in those carrying two versus one APOE*4 allele. Vascular risk factors were related to an increased effect of APOE*4 on memory decline in younger women, but a weaker effect of APOE*4 on general cognitive decline in older men. The relationship between APOE*4 carriage and memory decline was larger in older-aged Asians than Whites. In sum, APOE*4 is related to cognitive decline in men and women, although these effects are enhanced by age and carriage of two APOE*4 alleles in men, a higher numbers of vascular risk factors during the early stages of late adulthood in women, and Asian ethnicity.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.348
Teacher spread0.300 · 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 designObservational
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

Citations42
Published2020
Admission routes1
Has abstractyes

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