Differing Effect of the Sodium-Glucose Cotransporter 2 Inhibitor Ipragliflozin on the Decrease of Fat Mass vs. Lean Mass in Patients With or Without Metformin Therapy
Bibliographic record
Abstract
BACKGROUND: We previously reported changes of body composition determined by dual-energy X-ray absorptiometry after treatment with ipragliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor. In that study, mean body weight was decreased by 3.5 kg (4.3% of the baseline value) after ipragliflozin treatment at 50 mg/day, with fat mass and lean mass showing similar reductions of 1.7 and 1.8 kg, respectively. A long-term decrease of lean mass in patients treated with SGLT2 inhibitors may be associated with loss of skeletal muscle, which could potentially have an impact on quality of life. METHODS: analysis, we investigated whether changes of body composition were influenced by other medications for diabetes in 20 patients (11 men and nine women) who received ipragliflozin for 24 weeks. RESULTS: When we divided the patients into two subgroups with or without metformin treatment, fat mass showed a significant decrease in the ipragliflozin + metformin subgroup and a significantly greater decrease compared to the ipragliflozin subgroup (2.0 kg; 95% confidence interval (CI): 0.1 - 3.9; P = 0.038). Lean mass was significantly decreased in the ipragliflozin subgroup, but the decrease showed no significant difference from that in the ipragliflozin + metformin subgroup (1.9 kg; 95% CI: -4.1 - 0.3; P = 0.087). No significant differences of body composition changes were observed with other antidiabetic agents. CONCLUSIONS: More desirable weight reduction due to preferential fat loss and less muscle loss may be achieved by combining an SGLT2 inhibitor with metformin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".