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Record W4399221968 · doi:10.3390/nu16111721

Effects of Early and Late Time-Restricted Feeding on Parameters of Metabolic Health: An Explorative Literature Assessment

2024· article· en· W4399221968 on OpenAlexaff
Froso Petridi, Jan M.W. Geurts, Jean Nyakayiru, Anne Schaafsma, Dedmer Schaafsma, Ruth C. R. Meex, Cécile M. Singh‐Povel

Bibliographic record

VenueNutrients · 2024
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsImpact
FundersFrieslandCampina
KeywordsMealCircadian rhythmInsulin resistanceMedicineBlood pressurePhysiologyRandomized controlled trialInsulinEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Chrono-nutrition (meal timing) aligns food consumption with one's circadian rhythm. The first meal (e.g., breakfast) likely promotes synchronization of peripheral circadian clocks, thereby supporting metabolic health. Time-restricted feeding (TRF) has been shown to reduce body weight (BW) and/or improve cardiovascular biomarkers. In this explorative literature assessment, 13 TRF randomized controlled trials (RCTs) were selected from PubMed and Scopus to evaluate the effects of early (eTRF: first meal before 10:30 a.m.) and late TRF (lTRF: first meal after 11:30 a.m.) on parameters of metabolic health. Although distinct variations in study design were evident between reports, TRF consistently decreased energy intake (EI) and BW, and improved insulin resistance as well as systolic blood pressure. eTRF seemed to have a greater beneficial effect than lTRF on insulin resistance (HOMA-IR). Importantly, most studies did not appear to consider chronotype in their evaluation, which may have underestimated TRF effects. TRF intervention may be a promising approach for risk reduction of human metabolic diseases. To conclusively determine benefits of TRF and identify clear differences between eTRF and lTRF, future studies should be longer-term (≥8 weeks) with well-defined (differences in) feeding windows, include participants chronotypically matching the intervention, and compare outcomes to those of control groups without any dietary limitations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.334
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2024
Admission routes1
Has abstractyes

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