High Fat and Sugar Consumption During <i>Ad Libitum</i> Intake Predicts Weight Gain
Bibliographic record
Abstract
OBJECTIVE: To determine how macronutrients accompanying foods with high energy density (EnDen) affect energy intake and weight gain. METHODS: ) ate ad libitum for 3 days. Food intake was expressed as the mean daily intake (in kilocalories) and the percentage of weight-maintaining energy needs (%WMEN). EnDen was expressed as the ratio of intake (in kilocalories) to food weight (in grams). Food choices were expressed as absolute and percent intake (kilocalories), categorized as high in fat (HF; ≥ 45% kcal) or low in fat (LF; < 20% kcal), and further categorized as high in complex carbohydrates (≥ 30% kcal), high in simple sugars (HSS; ≥ 30% kcal), or high in protein (HP; ≥ 13% kcal). Follow-up weights were available from 99 subjects (65 males, range: 6 months to 11 years). RESULTS: EnDen was associated with BMI (r = 0.28, P < 0.0001), percent body fat (r = 0.18, P = 0.007), and percent intake from HF/HP (r = 0.34, P < 0.0001), HF/HSS (r = 0.31, P < 0.0001), LF/HP (r = -0.37, P < 0.0001) and LF/HSS (r = -0.68, P < 0.0001). The %WMEN was associated with EnDen (r = 0.16, P = 0.01), HF/HSS (r = 0.33, P < 0.0001), and LF/HP intake (r = -0.25, P = 0.0002). In a multivariate model, only HF/HSS intake remained a significant predictor of %WMEN (β = 1.4% per 1% change, P < 0.0001). The percent intake from HF/HSS (r = 0.23, P = 0.02), not EnDen (P = 0.54), was associated with weight gain, even after adjusting for follow-up time (in years) and covariates. CONCLUSIONS: Relatively greater consumption of HF/HSS foods independently predicted overeating and weight gain. Nutrient compositions of high-EnDen foods may be important for weight management.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".