Sodium-Glucose Co-Transporter 2 Inhibitors Increase Serum Level of Total Procollagen Type 1 Amino-Terminal Propeptide and Bone Strength in Japanese Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Diabetes mellitus is known to be associated with an increased risk of bone fracture. We investigated the effects of sodium-glucose co-transporter 2 (SGLT2) inhibitors on bone strength and density in type 2 diabetic patients. Methods: Nineteen Japanese patients with type 2 diabetes mellitus were administered 2.5 mg/day of luseogliflozin or 5 mg/day of dapagliflozin for 6 months. Serum levels of tartrate-resistant acid phosphatase 5b (TRACP-5b), an osteoclastic marker, and total procollagen type 1 amino-terminal propeptide (P1NP), a bone formation marker, were measured and compared with those before the treatment. Bone strength was measured by quantitative ultrasound (QUS), and bone density was evaluated by dual-energy X-ray absorptiometry (DEXA). Results: SGLT2 inhibitors significantly increased the calcaneal bone strength as measured by QUS compared to that in young adult mean. However, there was no effect in the lumbar spine density as measured by DEXA after administration. The drug treatment had no effect on serum TRACP-5b, but significantly increased serum P1NP. Conclusions: The results imply that the SGLT2 inhibitors improve bone strength while the inhibitors had no effect on bone density. These results suggest that the increase in bone strength was due to improved bone quality through an increase in serum P1NP level. J Endocrinol Metab. 2020;10(3-4):89-93 doi: https://doi.org/10.14740/jem683
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".