High intake of dairy during energy restriction does not affect energy balance or the intestinal microflora compared with low dairy intake in overweight individuals in a randomized controlled trial
Bibliographic record
Abstract
During weight loss, dairy calcium is proposed to accelerate weight and fat-mass loss through increased fecal fat excretion. The primary objective was to investigate if a high-dairy energy-restricted diet is superior to low dairy in terms of changes in body weight, body composition, and fecal fat excretion over 24 weeks. Secondary objectives included fecal energy and calcium excretion, resting energy expenditure, blood pressure, lipid metabolism, and gut microbiota. In a randomized, parallel-arm intervention study, 11 men and 69 women (body mass index, 30.6 ± 0.3 kg/m2; age, 44 ± 1 years) were allocated to a 500-kcal (2100 kJ) –deficit diet that was either high (HD: 1500 mg calcium/day) or low (LD: 600 mg calcium/day) in dairy products for 24 weeks. Habitual calcium intake was ∼1000 mg/day. Body weight loss (HD: –6.6 ± 1.3 kg, LD: –7.9 ± 1.5 kg, P = 0.73), fat-mass loss (HD: –7.8% ± 1.3%, LD: –8.5% ± 1.1%, P = 0.76), changes in fecal fat excretion (HD: –0.57 ± 0.76 g, LD: 0.46 ± 0.70 g, P = 0.12), and microbiota composition were similar for the groups over 24 weeks. However, total fat-mass loss was positively associated with relative abundance of Papillibacter (P = 0.017) independent of diet group. Consumption of a high-dairy diet did not increase fecal fat or accelerate weight and fat-mass loss beyond energy restriction over 24 weeks in overweight and obese adults with a habitual calcium intake of ∼1000 mg/day. However, this study indicates that Papillibacter is involved in body compositional changes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".