Serum fatty acid profiling within distinct lipid fractions provides a more robust indicator of insulin resistance in humans than total triglyceride and fatty acid profiles
Bibliographic record
Abstract
The need to better predict the development of insulin resistance (IR) and type 2 diabetes (T2D) is necessary as the prevalence of T2D is projected to increase in Canada and cost Canadians ∼$18 billion by 2030. The International Diabetes Federation currently advises the use of total blood triglycerides (TGs) as a predictor for identifying individuals at risk of developing IR and T2D. However, it has been demonstrated that total TG levels do not reliably reflect IR across multiple ethnicities. This suggests that a more accurate marker of IR is required. Interestingly, recent evidence suggests that distinct fatty acids (FAs) in blood TGs can reflect an individual’s IR status more accurately than total TGs. It is therefore reasonable to postulate that an analysis of the FAs within TGs and other blood lipids could lead to the discovery of a more accurate marker of IR. Over the course of 3 studies, the following thesis aimed to demonstrate that distinct FAs within serum lipids are associated with markers of IR more robustly than total TGs or total FAs. First, a large cross-sectional cohort that comprised young adults was used to demonstrate the associations between individual serum FAs and markers of IR (e.g., fasting glucose). Second, a population of Caucasian men that varied in IR-risk was examined to demonstrate that specific serum FAs within TGs associate with markers of IR more strongly than total TG levels. Finally, a cohort of individuals was investigated to demonstrate the improved ability of phospholipid and TG FA profiles (compared with total FAs) to distinguish individuals varying in metabolic health. This thesis demonstrated that circulating FAs associate with markers of IR and that these associations vary between individuals of different sex and ethnicity. Furthermore, the findings of this thesis indicate that compared with total serum TGs, specific FAs within lipid fractions may provide a more accurate means to identify individuals at increased risk of developing IR and T2D. Collectively, this thesis encourages future investigations of FAs within serum lipid fractions to uncover an improved method for identifying individuals at risk of IR and T2D.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".