Development of Fatty Acid Reference Ranges and Relationship with Lipid Biomarkers in Middle-Aged Healthy Singaporean Men and Women
Bibliographic record
Abstract
Dietary fatty acids (FA) are essential for overall human health, yet individual FA reference ranges have yet to be established. Developing individual FA reference ranges can provide context to reported concentrations and whether an individual displays deficient, or excess amounts of FA. Reference ranges of sixty-seven individual FA (μmol/L) were profiled and analyzed using gas chromatography with a flame ionization detector from serum samples collected from 476 middle-aged Singaporean males (BMI:23.3 ± 2.9) and females (BMI:21.8 ± 3.6). Measures of triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and total cholesterol (TC) (mmol/L) were also collected. The mean FA concentration seen in this cohort (11,458 ± 2478 was similar to that of overweight North American cohorts assessed in past studies. Ten biologically relevant FA were compared between sexes, with females exhibiting significantly higher concentrations in four FA (p < 0.05). A multiple regression model revealed the ten FA contributed significantly to nearly all lipid biomarkers (p < 0.05). A majority of participants who had FA concentrations in the ≥95th percentile also exhibited TG, HDL, LDL, and TC levels in the “high” risk classification of developing cardiovascular disease. Future studies profiling individual FA reference ranges in many unique, global cohorts are necessary to develop cut-off values of individual FA concentrations highly related to disease-risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".