MétaCan
Menu
Back to cohort

Abstract 010: Favorable Associations of a Healthy Diet with Fasting Glucose or Insulin are Not Modified by Fasting Glucose- or Insulin-Associated Genotypes in 51,289 Non-Diabetic Individuals from 15 Cohorts

2012· article· en· W62201741 on OpenAlexaff
Jennifer A. Nettleton, Marie‐France Hivert, Nicola M. McKeown, Dariush Mozaffarian, Toshiko Tanaka, Mary K. Wojczynski, Adela Hruby, Luc Djoussé, Julius S. Ngwa, George Dedoussis, James B. Meigs, Paul W. Franks

Bibliographic record

VenueCirculation · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineMediterranean dietQuartileRed meatInsulinCohortFood groupFasting glucoseDiabetes mellitusType 2 diabetesInternal medicineInsulin resistanceEndocrinologyEnvironmental health

Abstract

fetched live from OpenAlex

Background: Genome-wide association studies (GWAS) have identified several genetic loci that influence fasting glucose (FG) and insulin (FI). Whether the favorable relation between eating a healthy diet and FG or FI is the same regardless of genetic risk is unknown. Objective: We studied 15 well-characterized U.S., Northern European and Mediterranean cohorts that had dietary and genetic data (maximum N = 51,289) to test whether genotype and healthy diet interact to influence FG or FI concentrations. Design: Within each cohort, we constructed a healthy diet score comprising foods with previous evidence of associations with metabolic risk: whole grains, fish, fruits, vegetables, nuts/seeds (favorable food groups) and red meat, sweets, sugared beverages, fried potatoes (unfavorable food groups). Intakes of each food group were categorized in quartiles and assigned ascending values (0, 1, 2, 3) for favorable foods and descending values (3, 2, 1, 0) for unfavorable foods. These values were summed to generate an overall diet score (range: 0 to 27 points), with higher scores representing healthier diets. We used multivariable linear regression including an additive genetic model within cohorts followed by inverse weighted meta-analysis of all cohorts to quantify 1) associations between healthy diet and FG and FI and 2) interactions of healthy diet with 16 established FG- or two FI-associated loci on FG and FI concentrations. Results: Healthier diets (per additional diet score unit) were associated with lower FG (β: -0.004; 95% CI: -0.005, -0.003 mmol/L, p: <0.001) and lower ln(FI) (β: -0.008; 95% CI: -0.009, -0.007 pmol/L, p: <0.001) with adjustment for demographic, lifestyle and physiological factors including body mass index. The relations between healthy diet and FG and FI were the same regardless of genotype for any individual SNP (interaction p: 0.22 - 0.99) or the sum of risk alleles across the 16 FG-related SNPs (unweighted genetic risk score, p: 0.71). We estimated that modest differences in diet score could offset the small genetic risk associated with per risk allele increases in common variants associated with FG. For example, the mean effect size across all 16 FG-raising alleles was ∼0.03 mmol/L greater FG per FG-raising allele, which compares in magnitude to the effect size of an approximate 1.5-SD increase in diet score, i.e., towards a healthier diet: ∼7 score units x diet score β -0.004 = -0.028 mmol/L lower FG). Conclusions: A healthy diet score allowing summarization of dietary intake as an environmental exposure across diverse cohorts is favorably associated with FG and FI concentrations regardless of genotype at FG or FI-associated loci. Modest dietary differences are far larger than an individual’s apparent genetic risk at these loci.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCirculationSame topicNutrition, Genetics, and DiseaseFrench-language works237,207