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Abstract P002: Evidence For Smoking Dependent Genetic Effects on C-reactive Protein Levels in a Multi-ethnic Cohort Setting: The Care Consortium.

2012· article· en· W961798284 on OpenAlexaff
Jaclyn Ellis, Jeremy Walston, Josée Dupuis, Emma K. Larkin, Maja Barbalić, Brendan J. Keating, Jon Peter Durda, Ervin R. Fox, Yan Meng, Taylor Young, Renate B. Schnabel, Ramachandran S. Vasan, James S. Pankow, Guillaume Lettre, Ethan M. Lange, Christie M. Ballantyne, Myron D. Gross, James Wilson, Nora L. Nock, George Papanicolaou, Russell P. Tracy, Alex P. Reiner, Emelia J. Benjamin, Leslie A. Lange

Bibliographic record

VenueCirculation · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineC-reactive proteinCohortFramingham Risk ScoreInternal medicineBiomarkerEthnic groupCohort studyDiseaseDemographyGerontologyInflammationGenetics

Abstract

fetched live from OpenAlex

INTRODUCTION: C-reactive protein (CRP) is a heritable biomarker of systemic inflammation and a predictor of cardiovascular disease (CVD). Cigarette smoking is a major risk factor in the development of CVD and has been shown to affect circulating levels of CRP. Therefore, we sought to determine how this important environmental exposure may influence genetic associations with CRP in a multi-ethnic setting. METHODS: Using the ITMAT Broad-CARe (IBC) SNP array, a custom 50,000 SNP gene-centric array having dense coverage of over 2,000 candidate genes for CVD pathways, we performed a meta-analysis of up to 26,065 participants of European descent and 7,584 participants of African descent for association with log-CRP level within smoking status stratum. The 2 smoking strata were: never smokers and ever smokers (comprising of current and former smokers). We conducted IBC-wide association scans for CRP within cohort-, race- and smoking-stratum and meta-analyzed by race. Samples were from the Candidate gene Association Resource (CARe) cohorts (Atherosclerosis Risk in Communities Study, Framingham Heart Study, Cardiovascular Health Study, Cleveland Family Study , Coronary Artery Risk Development in Young Adults Study, Jackson Heart Study, and Multi-Ethnic Study of Atherosclerosis Study). Results were considered to be panel wide statistically significant if p<2.2×10−6. RESULTS: The overall sample size for ever smokers (never smokers) was 11,698 (10,344) in European Americans and 3,448 (4,330) in African Americans. The per-allele beta coefficients for genes previously established to be associated with CRP and present on the IBC chip ( CRP, APOE, GCKR, IL6R, LEPR, HNF1A, NLRP3 ) were very similar in magnitude between smoking strata in European Americans. However, in the African Americans, the estimated per-allele CRP and IL6R betas were 2-times larger for the ever smokers as compared to the never smokers. In the European American analysis, one gene not previously reported for association with CRP reached IBC-wide significance for a CRP-lowering effect in the never smokers ( GSTT1 , p=4.8E-07 for SNP rs405597 ), but not in the ever smokers (p=0.078). CONCLUSION: This large scale candidate gene based meta-analysis identified one novel locus for CRP ( GSTT1 ) associated with serum CRP levels in those reporting having never regularly smoked. Polymorphisms in GSTT1 , which plays a role in detoxification, have previously been reported to interact with smoking for other phenotypes including birth weight and colorectal cancer. We also observed evidence that smoking modifies the effects for previously established loci CRP and IL6R in African Americans. These results may identify important context genetic specific effects that influence chronic inflammation.

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)0.026
metaresearch head score (Gemma)0.068
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 score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.068
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.012
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0040.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.101
GPT teacher head0.375
Teacher spread0.274 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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