Treatment of diabetes mellitus in patients with chronic hepatitis B: Are SGLT2 inhibitors hitting the sweet spot?
Bibliographic record
Abstract
Although currently available nucleo(s)tide analogs may completely suppress serum HBV DNA levels in the vast majority of patients with chronic hepatitis B (CHB), antiviral therapy cannot completely abolish the risk of HCC. The persistent risk of HCC in these patients is at least partially attributable to HBV integration in the host genome, persistent intrahepatic replication, and other potentially carcinogenic HBV-related processes that are not inhibited by NUC therapy. In addition, emerging data show that metabolic syndrome and associated fatty liver disease in concomitance are independent predictors of HCC risk among patients with CHB.1 This is especially relevant since the prevalence of these conditions is increasing rapidly in the aging CHB population.2 Importantly, the excess risk of adverse liver-related outcomes seems closely related to the glycemic burden and is further exacerbated by the presence of other metabolic comorbidities.3,4 Currently available data therefore support careful assessment of metabolic health in patients with CHB, as well as close monitoring and treatment of diabetes mellitus and other elements of metabolic dysfunction. An important question that still remains is which anti-diabetic to choose for patients co-affected with CHB and type 2 diabetes mellitus (T2DM). In the current issue of Hepatology, Lee et al5 report on the results of an important study, in which they addressed the association between use of sodium glucose co-transporter 2 inhibitors (SGLT2i), a new class of antidiabetics, and incident HCC among patients with CHB with T2DM. For their study, the authors identified all patients with T2DM and CHB from the Hong Kong Hospital Authority database, which covers care provided to the vast majority of Hong Kong residents. Many patients enrolled in the current study were on nucleo(s)tide analog therapy at baseline or initiated antiviral therapy during follow-up. As expected in a CHB population with T2DM, a significant proportion already had cirrhosis (~24%). After propensity score matching, the investigators compared the risk of HCC among 1000 patients treated with SGLT2 versus 1000 subjects with T2DM and CHB who received (combinations of) other antidiabetics. In their main analysis, use of SGLT2i was associated with a significantly reduced cumulative incidence of HCC (2.6 vs 4.6%) over a median follow-up time of 17 months. The findings presented by Lee and colleagues corroborate previous reports that have suggested beneficial effects of SGLT2i use on HCC risk and outcomes in chronic liver disease, although the exact pathophysiological mechanisms behind these effects remain to be defined.6 Preclinical studies have linked the observed antitumor effects associated with SGLT2i use to a myriad of potential mechanisms of action. Examples include inhibition of cell proliferation through actions on AKT/mTOR and β-catenin pathways, inhibition of inflammation and oxidative stress through alterations of fatty acid metabolism, oxidative phosphorylation metabolism and reactive oxygen species, and antiproliferative effects associated with inhibition of VEGF and cell-cycle inhibition. Importantly, a recent meta-analysis of clinical trials also showed a reduced overall risk of cancer among patients treated with SGLT2i, further supporting the findings reported in the study by Lee and colleagues.7 Although the presented reduction in HCC risk with SGLT2i therapy in the current study was substantial, important questions remain to be answered. First, previous studies suggest that strict glycemic control may reduce the risk of HCC among CHB with T2DM.3 Unfortunately, follow-up data on glucose and/or HbA1c levels were not available in the current study, and it is, therefore, not possible to rule out the possibility that the lower risk of HCC observed in the SGLT2i group was attributable to more successful control of glycemic burden and not related to the SGLT2i drug class per se. Furthermore, although the authors used extensive and complex statistical modeling techniques to overcome some of the limitations associated with imbalances across treatment groups in retrospective data sets, additional adjustments for time-varying factors such as glycemic burden could further improve robust estimation of the effect of SGLT2i on HCC risk. Another question is whether the reduced risk of HCC observed with SGLT2i is related to benefits associated with regression of NASH. Recent studies indicate that SGLT2i may improve indicators of steatosis and fibrosis among patients with NASH, an effect that is not limited to patients with T2DM.8,9 Unfortunately, information on hepatic steatosis was not available for most patients in the current study, although the authors attempted to circumvent this by using the hepatic steatosis index (HSI, based on presence of DM, BMI, and AST/ALT) to identify patients most likely affected by hepatic steatosis. Importantly, the accuracy of this index is uncertain, especially among subjects with CHB, and only 30.4% of the study cohort had sufficient information available to calculate the score. Nevertheless, the effect estimates varied across subgroups with and without steatosis based on the HSI; the HR for HCC was 0.58 for subjects with steatosis and 0.92 for subjects without steatosis. These HRs suggest a much stronger effect in patients with steatosis than in those without, although the limited number of subjects per group precludes careful subgroup analysis across steatosis strata. The current study can therefore not rule out that the benefits observed with SGLT2i may be more pronounced, or even limited to, subjects with concomitant fatty liver disease. This requires further elucidation in future studies to prioritize the use of SGLT2i to those patients who are most likely to benefit from these compounds. In light of this, it is also important to note that another class of antidiabetics, the glucagon-like peptide-1 receptor agonists, also improves the indicators of NASH regardless of T2DM status.10 A careful comparison of liver-related outcomes with SGLT2i versus glucagon-like peptide-1 receptor agonists is therefore also of major clinical interest. Taken together, the current study by Lee and colleagues supports previous findings from (pre)clinical studies that show a reduced risk of malignancy associated with SGLT2i use and adds new insight into the importance of antidiabetic drug class on the risk of HCC in patients coaffected by T2DM and CHB. Based on the findings reported in this study, use of SGLT2i should be encouraged in eligible patients with T2DM and CHB. Further studies are required to demonstrate whether these findings are related to class-specific antitumor effects, improved glycemic control, and/or whether the risk reduction is related to beneficial effects associated with regression of NASH.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".