Effect of dietary composition of weight loss diets on high‐sensitivity c‐reactive protein: The Randomized POUNDS LOST trial
Bibliographic record
Abstract
OBJECTIVE: Overweight and obesity are associated with increased high-sensitivity C-reactive protein (hsCRP) levels. The purpose of this study was to determine if weight loss diets differing in fat, protein, or carbohydrate composition differentially reduce hsCRP. DESIGN AND METHODS: POUNDS (preventing overweight using novel dietary strategies) LOST was a 2-year trial of overweight and obese adults randomly allocated to one of four weight loss diets with targeted percentages of energy derived from fat, protein, and carbohydrates (20, 15, 65%; 20, 25, 55%; 40, 15, 45%; 40, 25, 35%, respectively). hsCRP was measured at baseline, 6, and 24 months among 710 participants, and adiposity as measured by dual X-ray absorptiometry (N = 340) or abdominal computed tomography (N = 126) was correlated with hsCRP change. RESULTS: At 6 months, hsCRP was reduced in all trial participants by -24.7% (Interquartile range (IQR) +7%, -50%), weight by -6.7% (IQR -3%, -11%), and waist circumference by -6.0% (IQR -3%, -10%) (all P < 0.002), with no significant differences according to dietary composition. The percent change in hsCRP at 6 and 24 months correlated modestly with change in weight, waist circumference, fasting insulin, fasting glucose, HOMA, and most lipid levels. Reductions in hsCRP persisted despite ∼ 50% regain of weight by 24 months. The percent change in hsCRP at 24 months significantly correlated with changes in total body fat (r = 0.42), total abdominal adiposity (r = 0.52), subcutaneous abdominal adiposity (r = 0.52), visceral adiposity (r = 0.47), and hepatic tissue density (r = -0.34) (all P < 0.0006). CONCLUSION: Weight loss decreased hsCRP by similar magnitude, irrespective of dietary composition. Clinicians concerned about inflammation and cardiovascular risk should recommend weight loss diets most likely to succeed for their patients.
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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) | 0.000 | 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".