Effect of the Dipeptidyl Peptidase-4 Inhibitor Sitagliptin on Muscle Mass and the Muscle/Fat Ratio in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Background: We previously reported the effect of sitagliptin or glimepiride treatment for 24 weeks on body composition in Japanese overweight and obese patients with type 2 diabetes. Although the degree of HbA1c reduction was similar between the two groups, significant reduction of intrahepatic lipid (IHL), determined by proton magnetic resonance spectroscopy ( 1 H-MRCP), and fat mass (FM), determined by dual-energy X-ray absorptiometry (DXA), was observed in the sitagliptin group but not in the glimepiride group. As both IHL and FM are known as associating factors of insulin resistance, these reductions may lead to improvement of insulin sensitivity, which in turn may contribute to sitagliptin-induced amelioration of glycemic control. On the other hand, muscle and muscle/fat ratio were also reported to be positively correlated with insulin sensitivity, but we did not evaluate these factors. Methods: DXA separates the whole body into three major components, bone mass (BM), FM and fat and bone-free mass (FBFM), and measures the weight of each component. FBFM is normally used as a good marker of muscle mass; therefore, in this post-hoc analysis, we investigated whether sitagliptin treatment for 24 weeks influenced the FBFM and FBFM/FM ratio. Results: After 24 weeks, the FBFM and FBFM/FM ratio significantly increased in the sitagliptin group (47.6 ± 10.3 to 48.8 ± 11.0 kg, P < 0.05 and 2.0 ± 0.8 to 2.1 ± 0.8, P < 0.05), but not in the glimepiride group (49.7 ± 10.6 to 49.3 ± 9.9, P = 0.655 and 2.1 ± 0.9 to 2.0 ± 0.7, P = 0.855). The mean change of FBFM and FBFM/FM ratio from baseline to 24 weeks in the sitagliptin and glimepiride groups was 1.24 ± 2.01 (sitagliptin group) vs. -0.34 ± 2.63 kg (glimepiride group) (P = 0.074) and 0.13 ± 0.17 (sitagliptin group) vs. -0.11 ± 0.30 (glimepiride group) (P < 0.05), respectively. Conclusions: Sitagliptin 24-week treatment demonstrated not only reduction of body fat and liver fat but also an increase of muscle and muscle/fat ratio. These changes may partly explain the mechanism underlining sitagliptin-induced improvement of glycemic control. J Clin Med Res. 2020;12(2):122-126 doi: https://doi.org/10.14740/jocmr4078
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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 | 0.001 |
| 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 | 0.001 |
| 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".