<scp>ApoB‐lipoprotein</scp> remnant dyslipidemia and <scp>high‐fat</scp> meal intolerance is associated with markers of cardiometabolic risk in youth with obesity
Bibliographic record
Abstract
Summary Introduction Cardiovascular disease (CVD) originates in childhood and risk is exacerbated in obesity. Mechanisms of the etiologic link between early adiposity and CVD‐risk remain unclear. Postprandial or non‐fasting dyslipidemia is characterized by elevated plasma triglycerides (TG) and intestinal‐apolipoprotein(apo)B48‐remnants following a high‐fat meal and is a known CVD‐risk factor in adults. The aim of this study was to determine (a) whether the fasting concentration of apoB48‐remnants can predict impaired non‐fasting apoB48‐lipoprotein metabolism (fat intolerance) and (b) the relationship of these biomarkers with cardiometabolic risk factors in youth with or without obesity. Methods We assessed fasting and non‐fasting lipids in youth without obesity (n = 22, 10 males, 12 females) and youth with obesity (n = 13, 5 males, 8 females) with a mean BMI Z‐score of 0.19 ± 0.70 and 2.25 ± 0.31 (P = .04), respectively. Results Fasting and non‐fasting apoB48‐remnants were elevated in youth with obesity compared to youth without obesity (apoB48: 18.04 ± 1.96 vs 8.09 ± 0.59, P < .0001, and apoB48AUC: 173.0 ± 20.86 vs 61.99 ± 3.44, P < .001). Furthermore, fasting plasma apoB48‐remnants were positively correlated with the non‐fasting response in apoB48AUC (r = 0.84, P < .0001) as well as other cardiometabolic risk factors including HOMA‐IR (r = 0.61, P < .001) and leptin (r = 0.56, P < .0001). Conclusion Fasting apoB48‐remnants are elevated in youth with obesity and predict apoB48 postprandial dyslipidemia. ApoB48‐remnants are associated with the extent of fat intolerance and appear to be potential biomarker of CVD‐risk in youth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".