Effects of SGLT2 ablation or inhibition on corticosterone secretion in high-fat-fed mice: exploring a nexus with cytokine levels
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Despite recent therapeutic advances, achieving optimal glycaemic control remains a challenge in managing type 2 diabetes. Sodium-glucose cotransporter 2 (SGLT2) inhibitors have emerged as effective treatments by promoting urinary glucose excretion. However, the full scope of their mechanisms extends beyond glycaemic control. At present, their immunometabolic effects remain elusive. METHODS: To investigate the effects of SGLT2 inhibition or deletion, we compared the metabolic and immune phenotype between high-fat-diet-fed control mice, mice treated chronically with dapagliflozin, and total-body Slc5a2-knockout mice. RESULTS: SGLT2-null mice exhibited better glucose tolerance and insulin sensitivity (blood glucose during IPGTT AUC 0-90 min 1175 ± 57.4 mmol/l × min, mean ± SEM) compared with control (AUC 0-90 min 1857 ± 117.9 mmol/l × min, p=0.05) or dapagliflozin-treated mice (AUC 0-90 min 1506 ± 68.72 mmol/l × min, p=0.09), independent of glycosuria and body weight. Moreover, SGLT2-null mice demonstrated physiological regulation of corticosterone secretion, with lower morning levels than control mice (p<0.01). Systemic cytokine profiling also unveiled significant alterations in inflammatory mediators, particularly IL-6. Furthermore, unbiased proteomic analysis demonstrated downregulation of acute-phase proteins and upregulation of glutathione-related proteins, suggesting a role in the modulation of antioxidant responses. Conversely, IL-6 treatment increased SGLT2 expression in human kidney HK2 cells, suggesting a role for cytokines in the effects of hyperglycaemia. CONCLUSIONS/INTERPRETATION: Collectively, our data elucidate a potential interplay between SGLT2 activity, immune modulation and metabolic homeostasis, as well as a potential feedback loop between SGLT2 expression and cytokine concentration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".