Effects of Insulin Glargine and Liraglutide Therapy on Liver Fat as Measured by Magnetic Resonance in Patients With Type 2 Diabetes: A Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: This study determined the effects of insulin versus liraglutide therapy on liver fat in patients with type 2 diabetes inadequately controlled with oral agents therapy, including metformin. RESEARCH DESIGN AND METHODS: Thirty-five patients with type 2 diabetes inadequately controlled on metformin monotherapy or in combination with other oral antidiabetic medications were randomized to receive insulin glargine or liraglutide therapy for 12 weeks. The liver proton density fat fraction (PDFF) was measured by MRS. The mean liver PDFF, the total liver volume, and the total liver fat index were measured by MRI. The Student t test, the Fisher exact test, and repeated-measures ANOVA were used for statistical analysis. RESULTS: Insulin treatment was associated with a significant improvement in glycated hemoglobin (7.9% to 7.2% [62.5 to 55.2 mmol/mol], P = 0.005), a trend toward a decrease in MRS-PDFF (12.6% to 9.9%, P = 0.06), and a significant decrease in liver mean MRI-PDFF (13.8% to 10.6%, P = 0.005), liver volume (2,010.6 to 1,858.7 mL, P = 0.01), and the total liver fat index (304.4 vs. 209.3 % ⋅ mL, P = 0.01). Liraglutide treatment was also associated with a significant improvement in glycated hemoglobin (7.6% to 6.7% [59.8 to 50.2 mmol/mol], P < 0.001) but did not change MRS-PDFF (P = 0.80), liver mean MRI-PDFF (P = 0.15), liver volume (P = 0.30), or the total liver fat index (P = 0.39). CONCLUSIONS: The administration of insulin glargine therapy reduced the liver fat burden in patients with type 2 diabetes. However, the improvements in the liver fat fraction and glycemia control were not significantly different from those in the liraglutide group.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".