Higher Dietary Protein Intake is Associated with Lower Body Fat in the Newfoundland Population
Bibliographic record
Abstract
BACKGROUND: Increased protein proportions in the diet combined with energy restriction has been shown to enhance weight loss during dietary intervention. It is not known if the beneficial effect of dietary protein exists in the general population under normal living conditions without a negative energy balance. METHODS: A total of 1834 participants (n = 443 men, n = 1391 women) were recruited from the CODING study. Participants' dietary macronutrient compositions were determined through a Willett FFQ. Body composition variables including percent body fat (%BF), percent trunk fat (%TF), percent total lean mass (%LM), and percent trunk lean mass (%TLM) were determined using DXA. Major confounding factors including age, physical activity levels, total caloric intake, carbohydrate intake, menopausal status, smoking status and medication use were controlled for in all analyses. RESULTS: Significant inverse relationships were observed between dietary protein intake (g/kg body weight/day) and weight, waist circumference, waist-to-hip ratio, BMI, %BF, and %TF (P < 0.001). Significant positive relationships were observed with %LM and %TLM (P < 0.001). Additionally, significant differences in weight (12.7 kg in men, 11.4 kg in women), BMI (4.1 BMI units in men, 4.2 units in women), and %BF (7.6% in men, 6.0% in women) were observed between low and high dietary protein consuming groups (P < 0.001). Dietary protein explained 11% of the total variation in %BF in the NL population. CONCLUSION: This study provides strong evidence that higher protein intake, even in the absence of energy restriction, is associated with a more favorable body composition in the general population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".