SGLT2i reduces risk of developing HCC in patients with co-existing type 2 diabetes and hepatitis B infection: A territory-wide cohort study in Hong Kong
Bibliographic record
Abstract
BACKGROUND AND AIMS: Type 2 diabetes (T2D) and chronic hepatitis B infection (CHB) are risk factors of HCC. Sodium glucose co-transporter 2 inhibitors (SGLT2i) inhibit HCC oncogenesis in preclinical studies. However, clinical studies are lacking. This study aimed to evaluate the impact of SGLT2i use on incident HCC using a territory-wide cohort of exclusively patients with co-existing T2D and CHB. APPROACH AND RESULTS: Patients with co-existing T2D and CHB between 2015 and 2020 were identified from the representative electronic database of the Hong Kong Hospital Authority. Patients with and without SGLT2i use were 1:1 matched by propensity score for their demographics, biochemistry results, liver-related characteristics, and background medications. Cox proportional hazards regression model was used to assess the association between SGLT2i use and incident HCC. A total of 2,000 patients with co-existing T2D and CHB (1,000 in each SGLT2i and non-SGLT2i group; 79.7% on anti-HBV therapy at baseline) were included after propensity-score matching. Over a follow-up of 3,704 person-years, the incidence rates of HCC were 1.39 and 2.52 cases per 100 person-year in SGLT2i and non-SGLT2i groups, respectively. SGLT2i use was associated with a significantly lower risk of incident HCC (HR 0.54, 95%CI: 0.33-0.88, p =0.013). The association remained similar regardless of sex, age, glycemic control, diabetes duration, presence of cirrhosis and hepatic steatosis, timing of anti-HBV therapy, and background antidiabetic agents including dipeptidyl peptidase-4 inhibitors, insulin, or glitazones (all p interaction>0.05). CONCLUSIONS: Among patients with co-existing T2D and CHB, SGLT2i use was associated with a lower risk of incident HCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".