Intermittent Energy Restriction Combined with a High-Protein/Low-Protein Diet: Effects on Body Weight, Satiety, and Inflammation: A Pilot Study
Bibliographic record
Abstract
Intermittent energy restricted (IER) diets have become popular as a body weight management approach. In this pilot study, we investigated if an IER diet would reduce systemic inflammation and if maintaining an elevated protein level while on an IER diet would enhance satiety. Six healthy women, aged 33–55 years with a BMI of 27–33 kg/m2, were randomized to first adhere to either a low- or high-protein IER diet using whole foods for three weeks. They then returned to their regular diets for a week, after which they adhered to the second diet for three weeks. Each test diet consisted of three low-energy intake days followed by four isocaloric energy intake days. The diets differed only in protein content. High-sensitivity C-reactive protein (hs-CRP), glucose, satiety, body weight, and waist circumference were measured at the beginning and end of each dietary intervention. Most participants showed reductions in hs-CRP levels from baseline on both IER diets but reported greater satiety when adhering to the higher protein IER diet. Overall, the IER diets reduced body weight and appeared to decrease inflammation in these overweight women, and the higher protein version enhanced satiety, which may lead to greater long-term dietary adherence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".