MétaCan
Menu
Back to cohort
Record W2148327825 · doi:10.1093/gerona/glp036

Insulin-Like Growth Factors and Leukocyte Telomere Length: The Cardiovascular Health Study

2009· article· en· W2148327825 on OpenAlexaff
Robert C. Kaplan, Annette L. Fitzpatrick, Michaël Pollak, Jonathan P. Gardner, N. S. Jenny, Aileen P. McGinn, Lewis H. Kuller, Howard D. Strickler, Masayuki Kimura, Bruce M. Psaty, Ariel Aviv

Bibliographic record

VenueThe Journals of Gerontology Series A · 2009
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsTelomereConfoundingInternal medicineInsulin-like growth factorGrowth factorEndocrinologyBody mass indexMedicineInsulinDiabetes mellitusBiologyReceptorGeneticsDNA

Abstract

fetched live from OpenAlex

The insulin-like growth factor (IGF) axis may affect immune cell replicative potential and telomere dynamics. Among 551 adults 65 years and older, leukocyte telomere length (LTL), insulin-like growth factor-1 (IGF-1), and insulin-like growth factor-binding proteins 1 and 3 (IGFBP-1, IGFBP-3) were measured. Multivariate linear regression was used to model the association of LTL with IGF-1 and IGFBPs, while controlling for confounding and increasing precision by adjusting for covariates. We observed a significant association between higher IGF-1 and longer LTL after adjustment for age, sex, race, smoking status, body mass index, hypertension, diabetes, and serum lipids. The results suggested an increase of .08 kb in LTL for each standard deviation increase of IGF-1 (p = .04). IGFBP-1 and IGFBP-3 were not significantly associated with LTL. High IGF-1 may be an independent predictor of longer LTL, consistent with prior evidence suggesting a role for IGF-1 in mechanisms relating to telomere maintenance.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.316
Teacher spread0.257 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series ASame topicTelomeres, Telomerase, and SenescenceFrench-language works237,207