Longitudinal Associations of Phospholipid and Cholesteryl Ester Fatty Acids With Disorders Underlying Diabetes
Bibliographic record
Abstract
CONTEXT: Specific serum fatty acid (FA) profiles predict the development of incident type 2 diabetes; however, limited longitudinal data exist exploring their role in the progression of insulin sensitivity (IS) and β-cell function. OBJECTIVE: To examine the longitudinal associations of the FA composition of serum phospholipid (PL) and cholesteryl ester (CE) fractions with IS and β-cell function over 6 years. DESIGN: The Prospective Metabolism and Islet Cell Evaluation (PROMISE) cohort is a longitudinal observational study, with clinic visits occurring every 3 years. Three visits have been completed, totaling 6 years of follow-up. SETTING: Individuals (n = 477) at risk for diabetes recruited from the general population in London and Toronto, Canada. MAIN OUTCOME MEASURES: Values from an oral glucose tolerance test were used to compute 1/HOMA-IR and the Matsuda index for IS, the insulinogenic index over HOMA-IR, and the insulin secretion-sensitivity index-2 for β-cell function. Thin-layer chromatograph and gas chromatograph quantified FA. Generalized estimating equations were used for the analysis. RESULTS: IS and β-cell function declined by 8.3-19.4% over 6 years. In fully adjusted generalized estimating equation models, PL cis-vaccenate (18:1n-7) was positively associated with all outcomes, whereas γ-linolenate (GLA; 18:3n-6) and stearate (18:0) were negatively associated with IS. Tests for time interactions revealed that PL eicosadienoate (20:2n-6) and palmitate (16:0) and CE dihomo-γ-linolenate (20:3n-6), GLA, and palmitate had stronger associations with the outcomes after longer follow-up. CONCLUSIONS: In a Canadian population at risk for diabetes, we found that higher PL stearate and GLA and lower cis-vaccenic acid predicted consistently lower IS and β-cell function over 6 years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".