Multi-omics Integrative Investigation of Fatty Acid Metabolism in Obese and Lean Subcutaneous Tissue
Bibliographic record
Abstract
Abstract White adipose tissue (WAT) plays a central role in whole-body energy homeostasis through storage and release of fatty acids. A deeper understanding of the complex and highly integrated pathways regulating WAT fatty acid metabolism, and how they are altered with obesity, is necessary for diagnostic and therapeutic innovations in nutritional disorders. In this multi-omics study, we investigated the influence of obesity on fatty acid metabolism in human subcutaneous adipose tissue (SAT) using an approach that integrated transcriptomic, peptidomic, and fatty acid analyses. Notably, all analyses were conducted in the same adipose tissue sample from each participant, thus minimizing the chance of spurious results. In a sample of SAT from the periumbilical abdominal region of obese ( n = 11, mean body mass index [BMI] = 35.0 ± 1.2 kg/m 2 ) and lean subjects ( n = 9, mean BMI = 22.1 ± 0.5 kg/m 2 ), we found that obese SAT tended to have higher relative amounts of specific monounsaturated fatty acids and n-6 polyunsaturated fatty acids, and lower amounts of saturated fatty acids ( p < 0.05). These changes were associated with differential regulation of lipogenic and lipolytic pathways in obese SAT. Fatty acid analysis showed changes in estimated fatty acid desaturase and elongase activities between lean and obese SAT ( p < 0.05). Biomarkers of lipogenesis (e.g., fatty acid synthase protein) were differentially regulated between lean and obese SAT. These changes were noted in conjunction with increases in extracellular matrix remodeling proteins. Transcriptomic data revealed that the key regulators of lipolysis were reduced in obese SAT. This integrative multi-omics analysis collectively shows that obese SAT has a distinct fatty acid signature compared to lean SAT and the pathways underlying fatty acid metabolism are broadly regulated at the level of gene expression and protein abundance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".