Effects of higher- versus lower-protein diets on health outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Numerous randomised controlled trials (RCTs) published in first tier medical journals have evaluated the health effects of diets high in protein. We conducted a rigorous systematic review of RCTs comparing higher- and lower-protein diets. METHODS: We searched several electronic databases up to July 2011 for studies focusing on patient-important outcomes (for example, cardiovascular disease) and secondary outcomes such as risk factors for chronic disease (for example, adiposity). RESULTS: We identified 111 articles reporting on 74 trials. Pooled effect sizes using standardised mean differences (SMDs) were small to moderate and favoured higher-protein diets for weight loss (SMD -0.36, 95% confidence interval (CI) -0.56 to -0.17), body mass index (-0.37, CI -0.56 to 0.19), waist circumference (-0.43, CI -0.69 to -0.16), blood pressure (systolic: -0.21, CI -0.32 to -0.09 and diastolic: -0.18, CI -0.29 to -0.06), high-density lipoproteins (HDL 0.25, CI 0.07 to 0.44), fasting insulin (-0.20, CI -0.39 to -0.01) and triglycerides (-0.51, CI -0.78 to -0.24). Sensitivity analysis of studies with lower risk of bias abolished the effect on HDL and fasting insulin, and reduced the effect on triglycerides. We observed nonsignificant effects on total cholesterol, low-density lipoproteins, C-reactive protein, HbA1c, fasting blood glucose, and surrogates for bone and kidney health. Adverse gastrointestinal events were more common with high-protein diets. Multivariable meta-regression analysis showed no significant dose response with higher protein intake. CONCLUSIONS: Higher-protein diets probably improve adiposity, blood pressure and triglyceride levels, but these effects are small and need to be weighed against the potential for harms.
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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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.031 | 0.054 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".