Fecal microbiota composition is related to brown adipose tissue 18F-fluorodeoxyglucose uptake in young adults
Bibliographic record
Abstract
Abstract Objective Human brown adipose tissue (BAT) has gained considerable attention as a potential therapeutic target for obesity and its related cardiometabolic diseases; however, whether the gut microbiota might be an efficient stimulus to activate BAT metabolism remains to be ascertained. We aimed to investigate the association of fecal microbiota composition with BAT volume and activity and mean radiodensity in young adults. Methods 82 young adults (58 women, 21.8 ± 2.2 years old) participated in this cross-sectional study. DNA was extracted from fecal samples and 16S rRNA sequencing was performed to analyse the fecal microbiota composition. BAT was determined via a static 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography-computed tomography scan (PET/CT) after a 2 h personalized cooling protocol. 18 F-FDG uptake was also quantified in white adipose tissue (WAT) and skeletal muscles. Results The relative abundance of Akkermansia , Lachnospiraceae sp. and Ruminococcus genera was negatively correlated with BAT volume, BAT SUVmean and BAT SUVpeak (all rho ≤ − 0.232, P ≤ 0.027), whereas the relative abundance of Bifidobacterium genus was positively correlated with BAT SUVmean and BAT SUVpeak (all rho ≥ 0.262, P ≤ 0.012). On the other hand, the relative abundance of Sutterellaceae and Bifidobacteriaceae families was positively correlated with 18 F-FDG uptake by WAT and skeletal muscles (all rho ≥ 0.213, P ≤ 0.042). All the analyses were adjusted for the PET/CT scan date as a proxy of seasonality. Conclusion Our results suggest that fecal microbiota composition is involved in the regulation of BAT and glucose uptake by other tissues in young adults. Further studies are needed to confirm these findings. Clinical trial information ClinicalTrials.gov no. NCT02365129 (registered 18 February 2015).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".