Effects of replacing meat and fish with pulse intake on circulating fibroblast growth factor-23 levels during a 12-week dietary weight-loss intervention using the Four-Food-Group Point Method: a pilot randomized controlled study
Bibliographic record
Abstract
Fibroblast growth factor-23 (FGF23) is a phosphaturic hormone secreted by the bone in response to dietary phosphate intake. High circulating FGF23 levels is an early sign of cardiovascular disease in individuals with obesity. This randomized controlled trial investigated the effects of replacing meat and fish with pulse intake on circulating FGF23 levels during a dietary weight-loss intervention. Sixteen middle-aged and older individuals with overweight and obesity (63.7 ± 5.1 years of age) were randomly assigned to control and plant groups. All participants attended a dietary weight-loss class once per week for 12 weeks. Participants in the plant group replaced meat and fish with pulse intake. Circulating FGF23 levels were measured before and after the intervention. Both groups showed reductions in body weight (control: 73.3 kg to 66.9 kg, plant: 78.8 kg to 73.1 kg, P < 0.001 for time effect). Plant-based protein intake was significantly higher in the plant group than in the control group (control: 7.3% of energy to 7.7% vs. plant: 7.0% of energy to 9.2%; P = 0.002 for group × time effect). However, the circulating FGF23 levels did not change in either group. Our results suggest that dietary weight-loss intervention promoting plant-based protein intake does not decrease circulating FGF23 levels. As this trial is one of the few to examine the effects of dietary weight-loss interventions on circulating FGF23 levels, additional intervention studies are needed. The protocol was registered in the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN000048081; registered on 6/18/2022).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".