Unfavorable Associations Between Serum Trimethylamine N-Oxide and L-Carnitine Levels With Components of Metabolic Syndrome in the Newfoundland Population
Bibliographic record
Abstract
Background: We aimed to study the relationships between serum Trimethylamine N-oxide (TMAO) and L-carnitine levels with metabolic syndrome profiles, including obesity, blood pressure, serum lipids, serum glucose and insulin resistance (IR)-related index in humans. Methods: Cross-sectional study was performed in 1081 subjects from the CODING study in Newfoundland. Serum TMAO and L-carnitine levels were quantified by LC-MS/MS. Metabolic markers were measured in all subjects using fasting blood samples. Partial correlation and linear regression analysis was employed after systematicly controlling the major confounding factors, such as age, gender, calorie intake and physical activity level. Results: Serum L-carnitine level was positively correlated with serum triglyceride (TG), serum insulin, IR in males with normal fasting glucose (p<0.05 for all) and positively correlated with only serum TG (p<0.05) in those with hyperglycemia. In females, significant positive correlations were identified between serum L-carnitine level with obesity, serum total cholesterol, glucose, insulin, and IR in those with normal fasting glucose level (p<0.05 for all), while none was found in those with hyperglycemia. Serum TMAO level was only identified to be positively correlated with serum insulin level and IR in hyperglycemic males (p<0.05 for all). Conclusions: Serum L-carnitine level was significantly associated with an unfavorable MS profile mainly in subjects with normal serum glucose level, while serum TMAO level was associated a unfavorable MS profile in subjects with hyperglycemia. The gender difference warrants further investigations.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".