MétaCan
Menu
Back to cohort
Record W4392641850 · doi:10.1016/j.isci.2024.109468

Creatine and low-dose lithium supplementation separately alter energy expenditure, body mass, and adipose metabolism for the promotion of thermogenesis

2024· article· en· W4392641850 on OpenAlexafffund
Michael S. Finch, G.L. Gardner, Jessica L. Braun, Mia S. Geromella, John R. Murphy, Kaitlyn Colonna, Ruban Dhaliwal, Alejandra Retta, Amro H. Mohammad, Jeffrey A. Stuart, Paul J. LeBlanc, Val A. Fajardo, Brian D. Roy, R.E.K. MacPherson

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchBrock University
KeywordsThermogenesisBrown adipose tissueEndocrinologyInternal medicineAdipose tissueCreatineLithium (medication)White adipose tissueThermogeninLipolysisBiologyMedicine

Abstract

fetched live from OpenAlex

Nutraceutical approaches to promote adipose tissue thermogenesis may help to prevent obesity onset. Creatine is a critical regulator of adipose metabolic function and low-dose lithium supplementation has been shown to promote adipose thermogenesis. In the present study, we sought to directly compare the two supplements for their effects on adipose metabolism and thermogenesis. We show that both supplements increase daily energy expenditure (EE) and reduce body mass in male Sprague-Dawley rats. Lithium increased brown adipose tissue (BAT) mitochondrial and lipolytic proteins that are associated with thermogenesis, while creatine increased BAT UCP1 and mitochondrial respiration. The BAT thermogenic findings were not observed in females. White adipose tissue and skeletal muscle markers of thermogenesis were unaltered with the supplements. Together, the data show that low-dose lithium and creatine have diverging effects on markers of BAT thermogenesis and that each increase daily EE and lower body mass in a sex-dependent manner.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.276

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 head0.022
GPT teacher head0.313
Teacher spread0.290 · 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 designBench or experimental
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

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicAdipose Tissue and MetabolismFrench-language works237,207