The interplay between white adipose tissue, adipokines, and structural gray matter changes
Bibliographic record
Abstract
The growing global obesity issue emphasizes the importance of understanding its health implications. Previous research has identified consistent alterations in gray matter (GM) volume in connection with obesity. Given the various implications of distinct fat compartments and the potential role of adipose tissue-derived adipokines in brain health, a more detailed investigation of adiposity is required. This study investigates a sample of 65 males with varying body mass indices to explore the relationship between various fat compartments, adipokine levels, and volumetric GM variations, aiming to provide a deeper understanding of the interplay between adiposity, brain structure, and metabolic signals. Whole-body magnetic resonance imaging (MRI) was used to assess total, visceral, and subcutaneous adipose tissue, while MR spectroscopy was performed to capture liver fat content. For the assessment of adipokine levels leptin and adiponectin concentrations were measured, and structural brain images underwent cortical and subcortical segmentation for GM volume and thickness. A predictive modeling approach with leave-one-out cross-validation was used to predict body composition metrics and adipokine levels based on structural GM data. Our investigation revealed diminished GM volume and thickness correlated with elevated leptin levels in areas crucial for appetite regulation, decision-making, and cognitive control, including the anterior insula, orbitofrontal cortex, and anterior cingulate cortex. These findings suggest a potential adverse impact of heightened leptin concentrations on brain health and eating habits. Contrary to expectations, our investigation found no significant relationship between GM volume and any of the measured fat compartments. This result prompts the need for further research to elucidate the relationship between obesity, adipokines, and brain structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".