Tissue‐Specific Responses of Lipoprotein Lipase to Dietary Macronutrient Composition as a Predictor of Weight Gain Over 4 Years
Bibliographic record
Abstract
This study evaluated if the effect of dietary macronutrient composition on adipose tissue lipoprotein lipase (ATLPL) and skeletal muscle lipoprotein lipase (SMLPL) predicted the long-term (over 4 years) changes in body weight and composition in free-living adults. Using a crossover design, 39 healthy subjects (n = 24 normal weight, n = 7 overweight, n = 8 obese) each followed a 2-week isocaloric high-carbohydrate (HC; 55% CHO:25% fat) and high-fat (HF; 30% CHO:50% fat) diet. On day 15 of each diet, biopsies were performed in the fasted state and 6 h after a meal. Body weight and composition were measured annually over 4 years. The outcomes for body weight, fat mass and % body fat were assessed using a linear two-stage mixed model. The mean (±SEM) increase in body weight and fat mass over 4 years was 0.29 ± 0.15 kg/year (P = 0.063) and 0.31 ± 0.15 kg/year (P = 0.051), respectively. The most consistent predictors of future body weight and fat changes were the ΔATLPL and ΔSMLPL responses (0-6 h) to a HC diet/meal. For the HC diet/meal, the subjects who had an increase in ATLPL activity/cell gained more % body fat over 4 years (P = 0.006) whereas subjects who had a decrease in SMLPL activity/g also had an increase in fat mass (P = 0.021). No significant relationships were observed between fasting ATLPL and SMLPL or enzyme responses to meals and any of the outcomes following the HF diet. In free-living adults the variability in tissue-specific lipoprotein lipase (LPL) responsiveness to a HC diet/meal predicts longitudinal changes in body composition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".