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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".