Plasma Fatty Acid Profiles Modulate <scp>PPARγ</scp> Expression in Adipose Tissue: A Lipidomic Insight Into Obesity‐Related Metabolic Dysregulation
Bibliographic record
Abstract
AIM: This study aimed to investigate the relationship between plasma fatty acids (FAs), FA-derived factors and PPARγ expression in visceral and subcutaneous adipose tissues (VAT and SAT) of obese and nonobese adults. METHODS: ). Anthropometric and biochemical measurements were taken, and plasma fatty acids (FAs) were analysed using gas chromatography flame ionisation detection (GC/FID). PPARγ mRNA levels were measured through real-time RT-qPCR. RESULTS: Obese individuals had higher PPARγ gene expression in both VAT and SAT compared to nonobese participants (p < 0.001). Eighteen FFAs and three new FA-derived factors were identified in both groups, accounting for 69% of the variance in nonobese individuals and 71% in obese individuals. After adjusting for confounding factors, saturated FA (SFA) was associated with PPARγ expression in the SAT of the nonobese group (β = -0.12, p = 0.019). Additionally, total FAs (β = -0.02, p = 0.017), SFA (β = -0.06, p = 0.048), monounsaturated FA (MUFA) (β = -0.08, p = 0.020), polyunsaturated FA (PUFA) (β = -0.03, p = 0.039) and omega-6 FA (β = -0.03, p = 0.040) were associated with VAT PPARγ expression among obese individuals. Conversely, an inverse correlation was observed between factor I of FAs and SAT PPARγ expression in nonobese individuals (β = -0.15; p = 0.027). CONCLUSION: These findings suggest that alterations in plasma FA profiles are associated with PPARγ gene expression, particularly in obese individuals. This fact highlights the potential role of dietary FAs in metabolic regulation and health issues related to obesity.
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 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) | 0.000 | 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".