Identification of Phenotypic Lipidomic Signatures in Response to Long Chain n‐3 Polyunsaturated Fatty Acid Supplementation in Humans
Bibliographic record
Abstract
Background Supplementation with long chain n‐3 polyunsaturated fatty acids is used to reduce total circulating triacylglycerol (TAG) concentrations. However, in about 30% of people, supplementation with long chain n‐3 polyunsaturated fatty acids does not result in decreased plasma TAG. Lipidomic analysis may provide insight into this inter‐individual variability. Methods Lipidomic analyses using targeted, mass spectrometry were performed on plasma samples obtained from a clinical study in which participants were supplemented with 3 g/day of long chain n‐3 in the form of fish oil capsules over a 6‐week period. TAG species and cholesteryl esters (CE) were quantified for 130 participants pre‐ and post‐supplementation. Participants were segregated into 3 potential responder phenotypes: (1) positive responder (R pos ; TAG decrease), (2) non‐responder (R non ; lacking TAG change), and (3) negative responder (R neg ; TAG increase) representing 67%, 18%, and 15% of the study participants, respectively. Separation of the 3 phenotypes was attributed to differential responses in TAG with 50 to 54 carbons with 1 to 4 desaturations. Elevated TAG with higher carbon number and desaturation were common to all phenotypes following supplementation. Using the TAG responder phenotype for grouping, decreases in total CE and specific CE occurred in the R pos phenotype versus the R neg phenotype with intermediate responses in the R non phenotype. CE 20:5, containing eicosapentaenoic acid (20:5n‐3), was elevated in all phenotypes. A classifier combining lipidomic and genomic features was built to discriminate triacylglycerol response phenotypes and reached a high predictive performance with a balanced accuracy of 75%. Conclusions These data identify lipidomic signatures, TAG and CE, associated with long chain n‐3 response p henotypes and identify a novel phenotype based upon CE changes. Registration URL: https://www.ClinicalTrials.gov ; Unique Identifier: NCT01343342.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".