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Record W4377115546 · doi:10.3390/obesities3020015

Intermittent Energy Restriction Combined with a High-Protein/Low-Protein Diet: Effects on Body Weight, Satiety, and Inflammation: A Pilot Study

2023· article· en· W4377115546 on OpenAlexaff
Nada Alzhrani, J Bryant

Bibliographic record

VenueObesities · 2023
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOverweightWaistMedicineC-reactive proteinIntermittent fastingWeight lossInternal medicineBody weightObesityHigh-protein dietInflammationEndocrinologyAnimal scienceBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.237
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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