Ethnic- and sex-specific associations between plasma fatty acids and markers of insulin resistance in healthy young adults
Bibliographic record
Abstract
BACKGROUND: Although evidence indicates that fatty acids (FA) can affect insulin resistance (IR), not all FA contribute equally to the process. Indeed, monounsaturated FA (MUFA) and polyunsaturated FA (PUFA) are reported to reduce IR, whereas saturated FA (SFA) and trans FA appear to increase IR. However, it is not yet clear how individual FA are associated with markers of IR, and whether these relationships are influenced by ethnicity and/or sex. Therefore, the goal of this study was to examine the ethnic- and sex-specific relationships between plasma FA and markers of IR in a cohort of healthy young Caucasian, East Asian, and South Asian adults. METHODS: Gas chromatography was used to quantify fasting plasma FA from young Canadian adults (22.6 ± 0.1 yrs) of Caucasian (n = 461), East Asian (n = 362), or South Asian (n = 104) descent. Linear regression models were used to investigate associations between plasma FA and markers of IR (i.e. fasting insulin, glucose, and HOMA-IR) according to ethnicity and sex. RESULTS: Numerous significant associations (P < 0.05, adjusted for multiple testing) were identified between individual FA and markers of IR, with the majority identified in Caucasians. For SFA, positive associations were found between 14:0 and fasting insulin and HOMA-IR in Caucasian and East Asian populations, and 18:0 and fasting glucose in Caucasians only. Several positive associations were also found for specific MUFA (18:1t11 and 18:1t6-8 with HOMA-IR, and 18:1c9 with fasting glucose) and PUFA (18:2n6 with fasting glucose and 18:2c9t11 with HOMA-IR) in Caucasian adults only. Most of the aforementioned associations were stronger in males compared to females. Interestingly, no significant associations were found between FA and markers of IR in South Asian adults. CONCLUSIONS: We report numerous associations between plasma FA and markers of IR in Caucasian and East Asian populations, but not in South Asian individuals. Furthermore, these associations appeared to be more robust in men. This demonstrates the importance of investigating associations between FA and markers of IR in an ethnic- and sex-specific manner in order to better understand the contribution of plasma FA to the development of IR and type-2 diabetes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".