Canagliflozin-induced adaptive metabolism in bone
Bibliographic record
Abstract
Sodium-glucose transporter-2 inhibitor (SGLT2i) drugs are widely used for lowering blood glucose levels independent of insulin. Beyond this, these drugs induce various metabolic changes, including weight loss and impaired bone integrity. A significant gap exists in understanding SGLT2i-induced skeletal changes, as SGLT2 is not expressed in osteoblasts or osteocytes, which use glucose to remodel the bone matrix. We studied the impact of 1, 3, or 6 months of canagliflozin (CANA), an SGLT2i treatment, on the skeleton of 6-month-old genetically heterogeneous UM-HET3 mice. Significant metabolic adaptations to CANA were evident as early as 1.5 months after treatment, specifically in male mice. CANA-treated male mice exhibited notable reductions in body weight and decreased proinflammatory and bone remodeling markers associated with reduced cortical bone remodeling indices. Bone tissue metabolome indicated enrichment in metabolites related to amino acid transport and tryptophan catabolism in CANA-treated male mice. In contrast, CANA-treated female mice showed increases in nucleic acid metabolism. An integrOmics approach of source-matched bone tissue metabolome and bone marrow RNA sequencing indicated a positive correlation between the two omics data sets in male mice. Three clusters of transcripts and metabolites involved in energy metabolism, oxidative stress response, and cellular proliferation and differentiation were reduced in CANA-treated male mice. In conclusion, CANA affects bone metabolism mainly via the "glucose restriction state" it induces and impacts bone cell proliferation and differentiation. These findings underline the effects of SGLT2i on bone health and highlight the need to consider sex-specific responses when developing clinical treatments that alter substrate availability.
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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) | 0.000 | 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 | 0.001 |
| 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".