Diagnostic Value of Postprandial Triglyceride Testing in Healthy Subjects:A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND/AIM: Triglycerides (TGs) are measured in studies evaluating changes in non-fasting lipid profiles after a fat tolerance test (FTT); however, the optimal timing for TG measurements after the oral fat load is unclear. The aim of this study was to evaluate how non-fasting TG levels vary after an oral FTT in healthy subjects. METHODS: This meta-analysis included 113 studies with >5 participants of Caucasian race that were indexed in PubMed from its inception through March 2010, using the search term "postprandial lipemia". We only included studies that provided mean values and standard deviation (SD) (or standard error of the mean) for TG measurements at baseline (=fasting) and for at least one other time-point. Exclusion criteria included uncommon sampling time-points after the FTT, baseline TGs≥2.0 mmol/L (≥177mg/dl), and a body mass index ≥30kg/m(2). RESULTS: All studies combined, weighted mean±SD TG values in mmol/L were 1.25±0.32 fasting, 1.82±0.40 at 2 h, 2.31±0.62 at 4 h, 1.87±0.63 at 6 h, and 1.69±0.80 at 8 h. After stratifying studies based on fat quantity in the test meal (<40, ≥40-<50, ≥50-<60, ≥60-<70, ≥70-<80, ≥80-<90, ≥90-<100, ≥100-<110, ≥110-120, ≥120 g), the highest standardized mean difference in TG levels from fasting levels was found in those having an oral fat load of ≥70 g and <80 g, and at 4 h (difference=1.74 mmol/L; p<0.001). CONCLUSION: The 4 h time-point after an oral fat load during a FTT was the most representative measurement of TGs. The highest standardized mean difference of TGs was found after a meal containing 70-79g of fat. The relevance of these two key parameters determined in healthy subjects should be considered for further developments of an oral FFT for clinical purposes.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".