Individual serum saturated fatty acids and markers of chronic subclinical inflammation: the Insulin Resistance Atherosclerosis Study
Bibliographic record
Abstract
Recent evidence has documented distinct effects of individual saturated FAs (SFAs) on cardiometabolic outcomes, with potential protective effects from odd- and very long-chain SFAs (VLSFAs). Cross-sectional and prospective associations of individual serum SFAs (12:0, 14:0, 15:0, 16:0, 18:0, 20:0, 22:0, and total SFA) with proinflammatory biomarkers and adiponectin were investigated in 555 adults from the IRAS. Principal component analysis (PCA) of proinflammatory markers yielded three clusters: principal component (PC) 1: fibrinogen, white cell count, C-reactive protein; PC 2: plasminogen activator inhibitor-1 (PAI-1), TNF-α, IL-18; PC 3: IL-6 and IL-8. Cross-sectional analyses on proinflammatory PCs and adiponectin, and prospective analyses on 5 year PAI-1 and fibrinogen concentrations were conducted with multiple regression. Total SFA and 16:0 were positively associated with PC 1 and PC 2, and negatively associated with adiponectin. The 14:0 was positively associated with PC 1 and negatively associated with adiponectin. In contrast, 15:0, 20:0, and 22:0 were negatively associated with PC 2, and 20:0 and 22:0 were positively associated with adiponectin. The 18:0 was negatively associated with PC 3. Prospectively, 15:0, 18:0, 20:0, and 22:0 were negatively associated with 5 year PAI-1 concentrations. The results demonstrate that individual SFAs have distinct roles in subclinical inflammation, highlighting the unique metabolic impacts of individual SFAs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".