Sodium‐glucose cotransporter 2 inhibition does not reduce hepatic steatosis in overweight, insulin‐resistant patients without type 2 diabetes
Bibliographic record
Abstract
BACKGROUND AND AIM: Non-alcoholic fatty liver disease (NAFLD) is rapidly becoming the leading indication for liver transplant and is associated with increased cardiovascular and liver mortality, yet there are no licensed therapies. Sodium-glucose cotransporter 2 (SGLT2) inhibitors are widely used for their glucose-lowering effects in patients with type 2 diabetes (T2D). Preclinical models have suggested a beneficial impact on NAFLD, but clinical data are limited, and there are currently no data on patients without T2D. We aimed to investigate the impact of SGLT2 inhibition on NAFLD in overweight, nondiabetic patients and establish the effect these agents may have on the processes that regulate hepatic steatosis in vivo. METHODS: = 10) using gold-standard noninvasive assessments of NAFLD phenotype, including magnetic resonance spectroscopy, two-step hyperinsulinemic euglycemic clamps, and stable isotope tracers to assess lipid and glucose metabolism. Investigations were performed before and after a 12-week treatment with the SGLT2 inhibitor, dapagliflozin. RESULTS: Despite a body weight reduction of 4.4 kg, hepatic steatosis was unchanged following treatment. Hepatic glucose production increased, and there was impairment of glucose disposal during the low-dose insulin infusion. Although circulating, nonesterified, fatty acid levels did not change, the ability of insulin to suppress lipolysis was reduced. CONCLUSIONS: SGLT2 inhibition for 12 weeks does not improve hepatic steatosis in patients without T2D. Additional studies in patients with established T2D or impairments of fasting or postprandial glucose homeostasis are needed to determine whether SGLT2 inhibition represents a viable therapeutic strategy for NAFLD. (http://clinicaltrials.gov Number NCT02696941).
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".