Effects of Low-Fat, Mediterranean, or Low-Carbohydrate Weight Loss Diets on Serum Urate and Cardiometabolic Risk Factors: A Secondary Analysis of the Dietary Intervention Randomized Controlled Trial (DIRECT)
Bibliographic record
Abstract
OBJECTIVE Weight loss diets may reduce serum urate (SU) by lowering insulin resistance while providing cardiometabolic benefits, something urate-lowering drugs have not shown in trials. We aimed to examine the effects of weight loss diets on SU and cardiometabolic risk factors. RESEARCH DESIGN AND METHODS This secondary study of the Dietary Intervention Randomized Controlled Trial (DIRECT) used stored samples from 235 participants with moderate obesity randomly assigned to low-fat, restricted-calorie (n = 85); Mediterranean, restricted-calorie (n = 76); or low-carbohydrate, non–restricted-calorie (n = 74) diets. We examined SU changes at 6 and 24 months overall and among those with hyperuricemia (SU ≥416 μmol/L), a relevant subgroup at risk for gout. RESULTS Among all participants, average SU decreases were 48 μmol/L at 6 months and 18 μmol/L at 24 months, with no differences between diets (P > 0.05). Body weight, HDL cholesterol (HDL-C), total cholesterol:HDL-C ratio, triglycerides, and insulin concentrations also improved in all three groups (P < 0.05 at 6 months). Adjusting for covariates, changes in weight and fasting plasma insulin concentrations remained associated with SU changes (P < 0.05). SU reductions among those with hyperuricemia were 113, 119, and 143 μmol/L at 6 months for low-fat, Mediterranean, and low-carbohydrate diets (all P for within-group comparison < 0.001; P > 0.05 for between-group comparisons) and 65, 77, and 83 μmol/L, respectively, at 24 months (all P for within-group comparison < 0.01; P > 0.05 for between-group comparisons). CONCLUSIONS Nonpurine-focused weight loss diets may simultaneously improve SU and cardiovascular risk factors likely mediated by reducing adiposity and insulin resistance. These dietary options could provide personalized pathways to suit patient comorbidity and preferences for adherence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".