Plasma fatty acids are associated with circulating adiponectin in overweight adolescent girls
Bibliographic record
Abstract
Objective: Recent evidence suggests insulin resistance develops as a result of dietary fat quality and plasma fatty acids are a good marker to reflect recent dietary exposure. Adiponectin could be involved in the pathway in which dietary fatty acids influence insulin sensitivity. The purpose of this study was to describe how dietary fatty acids impact adiponectin concentrations. Methods: We examined the plasma fatty acids of 180 adolescent daughters born to mothers with and without gestational diabetes. Overweight was defined as ≥ 85th percentile based on CDC 2000 BMI‐for‐age growth charts. Fasting adiponectin concentrations were measured using a commercial assay. Results: There were no differences in fatty acid concentrations between normal weight and overweight adolescents. However, the relation between fatty acids and adiponectin varied by body weight. Among the overweight teens, n‐6 fatty acids were positively related with circulating adiponectin (r = 0.36, P = 0.01). In addition, adiponectin was significantly and negatively associated with monounsaturated fatty acids (r = −0.29, P = 0.04) and n‐3 fatty acids (r = −0.35, P = 0.01). Conclusions: Obesity may impact the relationship between markers of fat intake and adiponectin. Unlike previous findings in healthy subjects, n‐3 is inversely associated with circulating adiponectin levels in overweight adolescent girls. Obesity and insulin resistance have been proposed to result in alterations of fatty acid desaturase activities leading to differences in fatty acid composition thus potentially explaining these findings in this subgroup of adolescents. CIHR Funding.
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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".