Using an erythrocyte fatty acid fingerprint to predict risk of all-cause mortality: the Framingham Offspring Cohort
Bibliographic record
Abstract
BACKGROUND: RBC long-chain omega-3 (n-3) fatty acid (FA) percentages (of total fatty acids) are associated with lower risk for total mortality, but it is unknown if a suite of FAs could improve risk prediction. OBJECTIVES: The objective of this study was to compare a combination of RBC FA levels with standard risk factors for cardiovascular disease (CVD) in predicting risk of all-cause mortality. METHODS: Framingham Offspring Cohort participants without prevalent CVD having RBC FA measurements and relevant baseline clinical covariates (n = 2240) were evaluated during 11 y of follow-up. A forward, stepwise approach was used to systematically evaluate the association of 8 standard risk factors (age, sex, total cholesterol, HDL cholesterol, hypertension treatment, systolic blood pressure, smoking status, and prevalent diabetes) and 28 FA metrics with all-cause mortality. A 10-fold cross-validation process was used to build and validate models adjusted for age and sex. RESULTS: Four of 28 FA metrics [14:0, 16:1n-7, 22:0, and omega-3 index (O3I; 20:5n-3 + 22:6n-3)] appeared in ≥5 of the discovery models as significant predictors of all-cause mortality. In age- and sex-adjusted models, a model with 4 FA metrics was at least as good at predicting all-cause mortality as a model including the remaining 6 standard risk factors (C-statistic: 0.778; 95% CI: 0.759, 0.797; compared with C-statistic: 0.777; 95% CI: 0.753, 0.802). A model with 4 FA metrics plus smoking and diabetes (FA + Sm + D) had a higher C-statistic (0.790; 95% CI: 0.770, 0.811) compared with the FA (P < 0.01) or Sm + D models alone (C-statistic: 0.766; 95% CI: 0.739, 0.794; P < 0.001). A variety of other highly correlated FAs could be substituted for 14:0, 16:1n-7, 22:0, or O3I with similar predicted outcomes. CONCLUSIONS: In this community-based population in their mid-60s, RBC FA patterns were as predictive of risk for death during the next 11 y as standard risk factors. Replication is needed in other cohorts to validate this FA fingerprint as a predictor of all-cause mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".