Lithium and creatine supplementation, alone and in combination, for the promotion of adipose thermogenesis
Bibliographic record
Abstract
Neutraceutical approaches to enhancing energy expenditure (EE) and adipose browning may provide a method of preventing obesity development. GSK3 has been identified as a negative regulator of UCP1. We have previously observed lithium, an inhibitor of GSK3, to increase UCP1 content and cellular respiration in 3T3-L1 adipocytes (0.5mM) and mice treated with 10mg/kg/day have increased EE, UCP1 and white adipose (WAT) multilocular phenotype. Creatine metabolism is also emerging as a critical regulator of adipose metabolism, and we have previously observed increased mitochondrial markers with creatine monohydrate supplementation. This study aimed to investigate the impact of supplemental lithium, creatine and their combination on EE and adipose tissue form and function. Male and female Sprague-Dawley rats were divided into four experimental groups for 6 weeks: (1) control, (2) lithium-supplemented (200 mg/L), (3) creatine-supplemented (5 g/L), (4) dual-supplemented (n=8 per group). All interventions increased EE and reduced body mass in males. In males, brown adipose (BAT) UCP1 was unaltered with lithium, however several mitochondrial and lipolytic markers were increased as well as inhibitory GSK3 serine9 phosphorylation. Creatine increased BAT UCP1 but had no effect on WAT thermogenic markers and lowered WAT mitochondrial and lipolytic markers. In females, none of the observed effects on EE, body weight and thermogenic proteins observed in males carried through to females. In female WAT, the treatments resulted in lower protein content of select mitochondrial and lipolytic proteins. Lithium supplementation was found to increase markers of BAT thermogenesis which likely contributes to the increased EE and reduced body mass in males. Creatine supplementation had a similar effect on EE and increased UCP1 protein content in the BAT of males. There was however no additive effect of the treatments, and all the results were independent of any evidence for a WAT browning/beinging effect suggesting that BAT is the main target of the treatments. None of the above observed effects of the treatments were translated to the females and could be, at least in part, due to sex differences in the adipose response to the treatments as GSK3 was differentially affected by lithium across sexes and depots.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".