Greater ectopic fat deposition and liver fibroinflammation and lower skeletal muscle mass in people with type 2 diabetes
Bibliographic record
Abstract
Abstract Objective Type 2 diabetes (T2D) is associated with significant end‐organ damage and ectopic fat accumulation. Multiparametric magnetic resonance imaging (MRI) can provide a rapid, noninvasive assessment of multiorgan and body composition. The primary objective of this study was to investigate differences in visceral adiposity, ectopic fat accumulation, body composition, and relevant biomarkers between people with and without T2D. Methods Participant demographics, routine biochemistry, and multiparametric MRI scans of the liver, pancreas, visceral and subcutaneous adipose tissue, and skeletal muscle were analyzed from 266 participants (131 with T2D and 135 without T2D) who were matched for age, gender, and BMI. Wilcoxon and χ2 tests were performed to calculate differences between groups. Results Participants with T2D had significantly elevated liver fat (7.4% vs. 5.3%, p = 0.011) and fibroinflammation (as assessed by corrected T1 [cT1]; 730 milliseconds vs. 709 milliseconds, p = 0.019), despite there being no differences in liver biochemistry, serum aspartate aminotransferase (p = 0.35), or alanine transaminase concentration (p = 0.11). Significantly lower measures of skeletal muscle index (45.2 cm2/m2 vs. 50.6 cm2/m2, p = 0.003) and high‐density lipoprotein cholesterol (1.1 mmol/L vs. 1.3 mmol/L, p < 0.0001) were observed in participants with T2D. Conclusions Multiparametric MRI revealed significantly elevated liver fat and fibroinflammation in participants with T2D, despite normal liver biochemistry. This study corroborates findings of significantly lower measures of skeletal muscle and high‐density lipoprotein cholesterol in participants with T2D versus those without T2D.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".