Lower Risks of New-Onset Hepatocellular Carcinoma in Patients With Type 2 Diabetes Mellitus Treated With SGLT2 Inhibitors Versus DPP4 Inhibitors

Authors:
Oscar Hou In Chou Division of Clinical Pharmacology and Therapeutics, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
Diabetes Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China

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Jing Ning Wuwei Hospital of Traditional Chinese Medicine, Wuwei, Gansu, China

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Raymond Ngai Chiu Chan Diabetes Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China

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Cheuk To Chung Diabetes Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China

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Helen Huang Diabetes Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China

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Kenrick Ng Department of Medical Oncology, University College London Hospital, London, UK
Department of Medical Oncology, St Bartholomew’s Hospital, London, UK

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Edward Christopher Dee Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY

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Sharen Lee Diabetes Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China

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Apichat Kaewdech Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand

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Ariel K Man Chow Department of Health Sciences, School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China

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Nancy Kwan Man Department of Surgery, The University of Hong Kong, Hong Kong Special Administrative Region (SAR), and Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hong Kong, China

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Tong Liu Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China

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Fengshi Jing Faculty of Data Science, City University of Macau, Macao SAR, China
UNC Project-China, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC

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Bernard Man Yung Cheung Division of Clinical Pharmacology and Therapeutics, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China

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Gary Tse Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China

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Jiandong Zhou Department of Family Medicine and Primary Care (host department), Department of Pharmacology and Pharmacy, and School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China

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Background: Type 2 diabetes mellitus (T2DM) may be a risk factor for development of hepatocellular carcinoma (HCC). The association between risk of developing HCC and treatment with sodium-glucose cotransporter-2 inhibitors (SGLT2i) versus dipeptidyl peptidase-4 inhibitors (DPP4i) is currently unknown. This study aimed to compare the risk of new-onset HCC in patients treated with SGLT2i versus DPP4i. Methods: This was a retrospective cohort study of patients with T2DM in Hong Kong receiving either SGLT2i or DPP4i between January 1, 2015, and December 31, 2020. Patients with concurrent DPP4i and SGLT2i use were excluded. Propensity score matching (1:1 ratio) was performed by using the nearest neighbor search. Multivariable Cox regression was applied to identify significant predictors. Results: A total of 62,699 patients were included (SGLT2i, n=22,154; DPP4i, n=40,545). After matching (n=44,308), 166 patients (0.37%) developed HCC: 36 in the SGLT2i group and 130 in the DPP4i group over 240,269 person-years. Overall, SGLT2i use was associated with lower risks of HCC (hazard ratio [HR], 0.42; 95% CI, 0.28–0.79) compared with DPP4i after adjustments. The association between SGLT2i and HCC development remained significant in patients with cirrhosis or advanced fibrosis (HR, 0.12; 95% CI, 0.04–0.41), hepatitis B virus (HBV) infection (HR, 0.32; 95% CI, 0.17–0.59), or hepatitis C virus (HCV) infection (HR, 0.41; 95% CI, 0.22–0.80). The results were consistent in different risk models, propensity score approaches, and sensitivity analyses. Conclusions: SGLT2i use was associated with a lower risk of HCC compared with DPP4i use after adjustments, and in the context of cirrhosis, advanced fibrosis, HBV infection, and HCV infection.

Background

Over the past several decades, the incidence of hepatocellular carcinoma (HCC) has continued to increase.1 In 2020, HCC was the sixth most common cancer worldwide, accounting for most cases of liver cancers, and was ranked the third leading cause of cancer death.2 The geographic distribution of disease burden varies significantly, with the highest incidence rates observed in the Western Pacific.3 Hong Kong has a high rate of HCC due to the prevalence of hepatitis B virus (HBV), which accounted for 80% of HCC episodes from 1992 to 2016.4 The prognosis for advanced-stage HCC remains poor because symptoms rarely appear in the early stages of disease, and patients at high risk may not have access to timely surveillance.5

Recent work has demonstrated that patients with type 2 diabetes mellitus (T2DM) may be at increased risk of developing HCC. A systematic review and meta-analysis revealed that patients with diabetes had a 2.31-fold increased risk of HCC and a 2.43-fold increased risk of HCC-related death compared with nondiabetic patients.6 Meanwhile, antidiabetic medications such as metformin have demonstrated protective effects against the disease.7,8 This led to the growing interest in exploring the long-term effects of novel antidiabetic medications such as sodium-glucose cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP4i) in HCC.

Previously, it was found that SGLT2i were associated with lower risks of cancer compared with DPP4i.9 Presently, several antidiabetic medications have shown promising antitumor effects against HCC.10,11 For instance, Giorda et al12 suggested that both SGLT2i and DPP4i have a protective role against HCC among patients with T2DM. Lee et al13 showed that SGLT2i was associated with a lower risk of HCC among patients with T2DM with concomitant HBV infection.13 Several studies with a relatively short follow-up suggested that SGLT2i may reduce the risk of nonalcoholic fatty liver disease (NAFLD), which may be linked to HCC.1416 Additionally, a case report observed spontaneous regression of HCC with reduction of angiogenesis-related cytokines post-SGLT2i treatment.17

DPP4i is an incretin-based antidiabetic drug that inhibits glucagon-like peptide-1 (GLP-1) degradation.18 DPP4i was demonstrated to be associated with a lower risk of HCC among patients with hepatitis C virus (HCV) in a retrospective cohort study.19 Indeed, a meta-analysis demonstrated that DPP4i does not increase the risk of developing overall cancer compared with placebo or other drugs. However, direct comparison of SGLT2i versus DPP4i in new-onset HCC remains limited. Therefore, this study aims to compare the association of SGLT2i versus DPP4i on the risk of new-onset HCC in patients with T2DM from Hong Kong.

Methods

Study Design and Population

This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (HKU/HA HKWC IRB) (UW-20-250) and New Territories East Cluster-Chinese University of Hong Kong (NTEC-UCHK) Clinical Research Ethnics Committee (2018.309, 2018.643), and complies with the Declaration of Helsinki.

This was a retrospective, territory-wide cohort study of patients with T2DM treated with SGLT2i or DPP4i between January 1, 2015, and December 31, 2020, in Hong Kong. Patients were observed until December 31, 2020, or until death. The patients were identified from the Clinical Data Analysis and Reporting System (CDARS), a territory-wide database that centralizes patient information from individual local hospitals to establish comprehensive medical data, including clinical characteristics, disease diagnosis, laboratory results, and drug treatment details. The system has been used by local teams in Hong Kong to conduct epidemiologic studies.2022 The minimum drug duration for either SGLT2i or DPP4i was 1 week. Exclusion criteria were as follows: (1) concurrent DPP4i and SGLT2i use, (2) incomplete demographics, (3) missing mortality data, (4) age <18 years, (5) previous HCC diagnosis, (6) development of new-onset HCC <1 year after drug exposure, or (7) death within 30 days after drug exposure (Figure 1). A GLP-1 agonist (GLP-1a) cohort, which comprised patients receiving GLP-1a between January 1, 2015, and December 31, 2020, was included for sensitivity analysis to demonstrate the relative effects among the second-line oral antidiabetic agents.

Figure 1.
Figure 1.

Procedures of data processing.

Abbreviations: ALT, alanine aminotransferase; APRI, AST-to-platelet ratio index; AST, aspartate aminotransferase; B/A ratio, bilirubin-to-albumin ratio; DPP4i, dipeptidyl peptidase-4 inhibitor; FIB-4 index, fibrosis-4 index; HbA1c, hemoglobin A1c; HCC, hepatocellular carcinoma; MDRD, modification of diet in renal disease; NLR, neutrophil-to-lymphocyte ratio; SGLT2i, sodium-glucose cotransporter-2 inhibitor; T2DM, type 2 diabetes mellitus.

Citation: Journal of the National Comprehensive Cancer Network 22, 2D; 10.6004/jnccn.2023.7118

Patient demographics included sex and age at initial drug treatment (baseline). Clinical and biochemical data were extracted for this study. Previous comorbidities were extracted using ICD-9 codes (see Table S1 in the supplementary material, available online with this article). Patients receiving financial aid were defined as those receiving Comprehensive Social Security Assistance (CSSA), a higher disability allowance, a normal disability allowance, a waiver, and other financial aid in Hong Kong. Charlson’s comorbidity index was also calculated. The duration and frequency of SGLT2i and DPP4i use were calculated. Data were extracted on non-SGLT2i/DPP4i medications as well as baseline laboratory examinations, including CBC, renal and liver biochemical tests, HBV and HCV testing, and lipid and glucose profiles. Diabetes duration was calculated based on the earliest date among the first date of (1) diagnosis using ICD-9 codes, (2) hemoglobin A1c (HbA1c) level ≥6.5%, (3) fasting glucose level ≥7.0 mmol/L or random glucose level of 11.1 mmol/L, and (4) use of antidiabetic medications. HBV infection was defined by either ICD-9 codes for HBV infection or positive hepatitis surface antigen (HBsAg) laboratory results. HCV infection was defined by either ICD-9 codes for HCV infection or positive anti-HCV laboratory results. Cirrhosis and advanced fibrosis were defined by either ICD-9 codes (571.0–571.6, 571.8, 571.9), fibrosis-4 (FIB-4) index >3.67, or the aspartate aminotransferase (AST)-to-platelet ratio index (APRI) >1.5, as suggested in previous publications.23,24 The estimated glomerular filtration rate (eGFR) was calculated using the abbreviated modification of diet in renal disease (MDRD) formula.25

Adverse Outcomes and Statistical Analysis

Primary outcomes of this study were new-onset HCC (ICD-9 code 155) and time to HCC. Mortality data were obtained from the Hong Kong Death Registry, a population-based official government registry with the registered death records of all Hong Kong citizens linked to CDARS. Mortality was recorded using ICD-10 codes. The endpoint date of interest for eligible patients was the event presentation date. The endpoint for those without primary outcome presentation was the mortality date or the endpoint of the study (December 31, 2020).

Descriptive statistics were used to summarize baseline clinical and biochemical characteristics of patients treated with SGLT2i and DPP4i. For baseline clinical characteristics, continuous variables were presented as means and 95% confidence intervals/standard deviations and categorical variables were presented as total number and percentage. Continuous variables were compared using the 2-tailed Mann-Whitney U test, whereas the 2-tailed chi-square test with Yates correction was used to test 2 × 2 contingency data. Propensity score matching with 1:1 ratio for SGLT2i use versus DPP4i use based on demographics, prior comorbidities, non-SGLT2i/DPP4i medications, abbreviated MDRD, calculated biomarkers, HbA1c level, fasting glucose level, and duration from T2DM diagnosis to initial drug exposure was performed using the nearest neighbor search strategy. Propensity score matching was conducted using Stata, version 16.0 (StataCorp LLC).

Baseline characteristics of patients treated with SGLT2i versus DPP4i before and after matching were compared using standardized mean difference (SMD), with SMD <0.20 regarded as good balance between the groups. Cox proportional hazards regression model was used to identify significant risk predictors of adverse study outcomes after adjusting for significant demographics, past comorbidities, non-SGLT2i/DPP4i medications, abbreviated MDRD, fasting glucose level, HbA1c level, and duration from earliest date of T2DM diagnosis to initial drug exposure date. The log-log plot was used to verify the proportionality assumption for the Cox proportional hazards regression model. Cumulative incidence curves were constructed for the primary and secondary outcomes. Subgroup analysis was conducted to confirm the association among patients with clinically important predictors. The difference in the associations among the subgroups was assessed by P for interaction.

Cause-specific and subdistribution hazard models were conducted to consider possible competing risks. Multiple propensity score approaches were used, including propensity score stratification,26 propensity score with inverse probability of treatment weighting (IPTW),27 and propensity score with stable inverse probability weighting.28 A sensitivity analysis was conducted to test the effects of different minimum drug durations on the outcomes. Patients with stage IV–V chronic kidney disease (eGFR <30 mL/min/1.73 m2) and those undergoing peritoneal dialysis or hemodialysis, in whom SGLT2i use may be contraindicated, were excluded from this analysis, as were patients receiving financial aid. However, patients who developed HCC within 1 year after medication initiation were included.

The as-treat approach was adopted, in which patients were censored at treatment discontinuation or switching of the comparison medications. The 3-arm sensitivity analysis involving GLP-1a using stabilized IPTW were conducted to test the association with the primary outcome and choice among the novel second-line antidiabetic medications. The negative control outcome was suggested to detect the residual bias and confounding factors due to unobserved confounders. Venous thromboembolism was used as the negative control in the falsification analysis, such that the observed significant association in the falsification analysis should be attributed to bias. Hazard ratios (HR), 95% confidence intervals, and P values are reported. Statistical significance was defined as P<.05. All statistical analyses were performed with RStudio version 1.1.456 (RStudio, Inc.) and Python 3.6 (Python Software Foundation).

Results

Baseline Characteristics

This was a retrospective, territory-wide cohort study of 76,147 patients with T2DM treated with SGLT2i or DPP4i between January 1, 2015, and December 31, 2020, in Hong Kong. Patients enrolled during that period were observed until December 31, 2020, or until death. Patients were excluded if they were treated with SGLT2i and DPP4i concurrently (n=12,858), had incomplete demographics (n=17), had missing mortality data (n=13), were age <18 years (n=135), died within 30 days after drug exposure (n=295), had a prior HCC diagnosis (n=84), or developed new-onset HCC <1 year after drug exposure (n=46) (Figure 1).

After exclusion, this study included a total of 62,699 patients with T2DM (mean age, 62.8 years [SD, 12.2]; 55.18% males). In all, 22,154 (35.33%) patients received SGLT2i, and 40,545 (64.67%) received DPP4i. The SGLT2i and DPP4i cohorts were comparable after matching, and there was no violation of the proportional hazard assumption (Supplementary Figure S1). In the matched cohort, 166 (0.42%) patients developed HCC. Patient characteristics are shown in Supplementary Tables S2 and S3.

The time to develop HCC was 3.78 years (IQR, 2.60–4.27 years) among those who received SGLT2i and 2.92 years (IQR, 1.63–3.50 years) among those who received DPP4i. After a follow-up of 240,268.5 person-years, the incidence of HCC was lower among those who received SGLT2i (incidence per 1,000 person-years, 0.29 [95% CI, 0.21–0.40] vs 1.10 [95% CI, 0.92–1.32] for DPP4i). The time to cancer-related mortality was 4.14 years (IQR, 3.40–4.68 years) among those who received SGLT2i and 3.76 years (IQR, 2.82–4.33 years) among those who received DPP4i. Those who received SGLT2i had a lower incidence of cancer-related mortality (incidence per 1,000 person-years, 1.06 [95% CI, 0.88–1.25] vs 5.91 [95% CI, 5.47–6.36] for DPP4i). The time to all-cause mortality was 4.21 years (IQR, 3.39–4.78 years) among those who received SGLT2i and 3.60 years (IQR, 2.88–4.33 years) among those who received DPP4i. Those who received SGLT2i had a lower incidence of all-cause mortality (incidence per 1,000 person-years, 4.98 [95% CI, 4.59–5.39] vs 23.59 [95% CI, 22.71–24.48] for DPP4i) (Table 1).

Table 1.

Incidence and Multivariate Cox Regression Models in the Cohort Before and After 1:1 Propensity Score Matching

Table 1.

Significant Predictors of the Study Outcomes

In the multivariate Cox proportional hazards regression model, SGLT2i use was associated with a lower risk of HCC (HR, 0.42; 95% CI, 0.28–0.79) after adjustments for significant demographics, past comorbidities, non-SGLT2i/DPP4i medications, abbreviated MDRD, fasting glucose level, HbA1c level, and duration from the earliest T2DM diagnosis date to the initial drug exposure date (Table 1, Supplementary Table S4). SGLT2i use was also associated with lower risks of cancer-related mortality (HR, 0.31; 95% CI, 0.19–0.41) and all-cause mortality (HR, 0.30; 95% CI, 0.26–0.41) after adjustments. The cumulative incidence curves demonstrated that SGLT2i use was associated with a lower cumulative hazard for HCC, cancer-related mortality, and all-cause mortality after matching (Figure 2).

Figure 2.
Figure 2.

Cumulative incidence curves for new-onset HCC, cancer-relortality, and all-cause mortality stratified by exposure to SGLT2i and DPP4i before (A, B, and C, respectively) and after (D, E, and F, respectively) propensity score matching (1:1).

Abbreviations: DPP4i, dipeptidyl peptidase-4 inhibitor; HCC, hepatocellular carcinoma; SGLT2i, sodium-glucose cotransporter-2 inhibitor.

Citation: Journal of the National Comprehensive Cancer Network 22, 2D; 10.6004/jnccn.2023.7118

Subgroup Analysis

In the subgroup analysis, SGLT2i use was associated a lower risk of HCC regardless of gender (P=.341 for interaction) and age (P=.651 for interaction) (Figure 3). SGLT2i use was also associated with a lower risk of HCC among patients regardless of history of HBV infection (P=.39 for interaction). Characteristics of patients with and without a history of HBV infection are presented in Supplementary Table S5. Among HBV-negative patients, SGLT2i use was associated with lower risks of HCC after adjustments for significant demographics, past comorbidities, and non-SGLT2i/DPP4i medications (HR, 0.31; 95% CI, 0.19–0.46), with the same association observed among HBV-positive patients (HR, 0.34; 95% CI, 0.21–0.66). SGLT2i use was also associated with a lower risk of cancer-related mortality (HR, 0.22; 95% CI, 0.11–0.35) and all-cause mortality (HR, 0.35; 95% CI, 0.22–0.55) among HBV-positive patients (Supplementary Table S6). SGLT2i use remained associated with a lower risk of HCC compared with DPP4i use regardless of the duration of HBV infection. Furthermore, SGLT2i use was associated with a lower risk of HCC regardless of hepatitis B e antigen (HBeAg) seroconversion (P=.411 for interaction). Finally, SGLT2i use was associated with a lower risk of HCC regardless of the history of HCV infection (P=.12 for interaction), the presence of diagnosed NAFLD (P=.93 for interaction), or cancer (P=.762 for interaction) (Figure 3). However, because the duration of HCV infection was longer, SGLT2i use was associated with an increased risk of HCC compared with DPP4i use (Supplementary Figure S2).

Figure 3.
Figure 3.

Subgroup analyses for the association between SGLT2i versus DPP4i and new-onset hepatocellular carcinoma in the matched cohort.

Abbreviations: Anti-HBe, hepatitis B e antibody; DPP4i, dipeptidyl peptidase-4 inhibitor; HBV, hepatitis B virus; HCV, hepatitis C virus; HR, hazard ratio; NAFLD, nonalcoholic fatty liver disease; SGLT2i, sodium-glucose cotransporter-2 inhibitor.

Citation: Journal of the National Comprehensive Cancer Network 22, 2D; 10.6004/jnccn.2023.7118

Supplementary Figure S3 shows the marginal interaction effects and the adverse outcomes of the calculated biomarkers and exposure to SGLT2i and DPP4i for new-onset HCC and cancer-related mortality in the matched cohort (1:1). SGLT2i use was associated with a lower risk of HCC compared with DPP4i use across most abbreviated MDRD, apart from patients with low abbreviated MDRD.

Regarding patients with cirrhosis and advanced fibrosis, SGLT2i use was associated with a lower risk of HCC compared with DPP4i use across all APRIs and FIB-4 indexes, and the differences increase as these indexes increase. This indicated that the effects of SGLT2i on HCC might be greater among patients with more advanced cirrhosis and fibrosis (Supplementary Figure S3).

Relationship of SGLT2i and DPP4i to NAFLD

In this study, 945 (2.45%) patients were diagnosed with NAFLD that required medical attention during the follow-up period. SGLT2i use was associated with lower risks of NAFLD (HR, 0.51; 95% CI, 0.33–0.69) after adjustment (Supplementary Table S7). The results remained significant across the cause-specific hazard models, subdistribution hazard models, and different propensity score approaches. The cumulative incidence curves demonstrated that SGLT2i use was associated with a lower cumulative hazard for newly diagnosed NAFLD (Supplementary Figure S4).

Sensitivity Analyses

Sensitivity analyses were performed to confirm the predictive ability of the models. Results of the cause-specific hazard models, subdistribution hazard models, and different propensity score approaches demonstrated that different models did not change the point estimates for either the primary or the secondary outcomes (P<.05 for all) (Supplementary Table S8). The minimum drug duration threshold in the matched cohort was also considered. After excluding patients with <4 weeks, 12 weeks, or 52 weeks of exposure, the association remained significant (Supplementary Table S9).

Furthermore, the association between SGLT2i use and HCC remained significant upon excluding patients with stage 4/5 chronic kidney disease, those undergoing peritoneal dialysis, and those undergoing hemodialysis. The results remained consistent when including patients who developed HCC within 1 year after starting medications and excluding patients receiving financial aid (Supplementary Table S10). The as-treat approach was also adopted in the sensitivity analysis, in which patients were censored at treatment discontinuation or switching of the comparison medications. SGLT2i use was associated with lower risk of HCC compared with DPP4i use when using the as-treat approach (Supplementary Table S11). A 3-arm analysis incorporating the GLP-1a cohort was conducted using stabilized IPTW, including patients only on SGLT2i, DPP4i, or GLP-1a (Supplementary Table S12). However, the association of GLP-1a with HCC compared with that of SGLT2i or DPP4i was not statistically significant.

Falsification Analysis

Venous thromboembolism was used as the negative control outcome in the falsification analysis for the comparison between SGLT2i and DPP4i (Supplementary Table S10). Results demonstrated that there was no statistically significant risk of venous thromboembolism among those treated with SGLT2i versus DPP4i after adjustment (HR, 1.14; 95% CI, 0.89–1.32; P=.7665) (Supplementary Table S13).

Discussion

In this territory-wide retrospective cohort study, we used real-world data from routine clinical practice to compare the association between SGLT2i versus DPP4i use and HCC. Our findings demonstrated that SGLT2i use was associated with a 58% lower risk of HCC compared with DPP4i. The results remained significant among patients with cirrhosis, advanced fibrosis, HBV infection, or HCV infection.

Comparison With Previous Studies

Patients with T2DM have a 2.5-fold increased risk of developing HCC.29,30 The literature generally supports the notion that SGLT2i and DPP4i are beneficial in hepatic diseases but lack direct comparisons. Results from our study suggest that patients treated with SGLT2i have a lower risk of new-onset HCC compared with those treated with DPP4i. Giorda et al12 suggested that SGLT2i, DPP4i, and GLP-1a might have a protective role against HCC among patients with T2DM. Previously, antidiabetic agents such as metformin, thiazolidinediones, and GLP-1 analogs were shown to improve the manifestations of HCC in patients with T2DM.31,32 Our findings should not be interpreted as suggesting that DPP4i use is associated with an increased risk of HCC. DPP4i use itself might still help prevent the development of HCC. The incidence of HCC in patients with T2DM reported in the Republic of Korea was >1 per 1,000 person-years, which is similar to the incidence of HCC among DPP4i users reported in our study (1.10 per 1,000 person-years).33 Our results suggested that the decrease in the risk of HCC might be a drug-induced positive phenomenon instead. Indeed, DPP4 is an important molecule involved in the development of HCC, such that inhibition of DPP4 may help prevent HCC among patients with HCV infection. However, further randomized controlled trials are needed to confirm the causation relationship between SGLT2i use and the reduction of HCC risk.

Chronic HBV infection is still prevalent in Asia despite universal vaccination for individuals aged >20 years. Our findings showed that among HBV-positive patients, SGLT2i use lowered the risk of HCC compared with DPP4i use (HR, 0.32; 95% CI, 0.17–0.59) (Figure 3). Our findings aligned with those of a previous study, which suggested that among patients with T2DM with concomitant HBV infection, SGLT2i use was associated with a lower risk of HCC compared with nonuse.13 This study also found that the association remained significant among patients with or without HBeAg seroconversion. Recent studies showed that the risk of HCC persisted even after HBeAg seroconversion.34 Notably, lower platelet and albumin and elevated AST levels were significant predictors of HCC risk upon HBeAg seroconversion.

Furthermore, SGLT2i use was also associated with a lower risk of HCC compared with DPP4i use among patients infected with HCV. Although studies have shown that DPP4 was associated with a reduced risk of HCC in patients with T2DM and chronic HCV infection, our findings suggested that SGLT2i use might have exerted a greater effect than inhibiting DPP4.19,35 It was suggested that among patients with HCV infection, advanced chronic hepatitis or cirrhosis would be the significant predictor of HCC risk.34

In our results, SGLT2i use reduced HCC risk regardless of the presence of cirrhosis or advanced fibrosis. The association remained significant across all severities of liver fibrosis and cirrhosis, as reflected by the APRI and FIB-4 index (Supplementary Table S4A).36 Similar to findings in another previous study, SGLT2i use reduced the risk of death and improved the survival of patients with T2DM and cirrhosis when compared with DPP4i use. A systematic review demonstrated no hepatic benefit associated with DPP4i use in patients with hepatic steatosis, but showed significant risk reductions in patients receiving SGLT2i.37 Furthermore, the risk reduction increase further among patients with more advanced fibrosis, as inferred from the above 2 biomarkers.

As the prevalence of NAFLD continues to increase, managing diabetes becomes more pressing, considering the risk of developing HCC. It was found that use of SGLT2i or DPP4i reduced the risk of NAFLD.38,39 Therefore, that patient subgroup with NAFLD reflects a degree of importance in HCC prevention. Nevertheless, a prospective study is necessary to validate these assumptions. Our identification of patients diagnosed with NAFLD was made using only ICD-9 codes; however, we were unable to identify patients with undiagnosed NAFLD using other assessment tools such as FibroScan due to the limitations of the registry data.

Potential Underlying Mechanisms

Several mechanisms have been proposed to explain the relationship between SGLT2i and HCC. It was hypothesized that SGLT2i inhibited de novo lipogenesis by inhibiting the expression of the FAS gene involved in fatty acid biosynthesis, which decreases fatty acid production and reduces steatosis.40 In addition, SGLT2i has demonstrated anti-inflammatory and antisteatosis properties, preventing progression to HCC.41 Furthermore, SGLT2 receptors are highly expressed in liver tumors because of their increased demand for glucose for ATP synthesis and overall growth.42 Meanwhile, DPP4i was also suggested to reduce the risk of HCC via several mechanisms. It was previously suggested that GLP-1 might reduce the risk of HCC through activation of lymphocyte chemotaxis and downregulation of the pentose phosphate pathway.43,44 However, some conflicting results suggest that DPP4i may play a role in the progression of nonalcoholic steatohepatitis–related HCC by suppressing p62 and Keap1.44 Future research is needed to confirm the effects of SGLT2i and DPP4i in preventing new-onset HCC.

Clinical Implications

Given the importance and mortality related to HCC in patients with T2DM, despite the relatively low case numbers, there is a need to investigate how SGLT2i and DPP4i may modify the risk for this disease.45 The present study used data from routine clinical practice, which may influence the choice of second-line antidiabetic therapy in patients with T2DM in terms of the risk for HCC. The findings from our study show that SGLT2i may help prevent HCC compared with DPP4i. Among all patients with T2DM, the number needed to treat (NNT) for SGLT2i to reduce the risk of HCC was 238.10 (Supplementary Table S14). Meanwhile, the NNTs for HBV-positive patients and those with cirrhosis or advanced fibrosis were 42.37 and 36.50, respectively, which were lower than the NNT for SGLT2i to reduce the risk of major adverse cardiovascular events compared with controls.46 Generally, SGLT2i was found to reduce the risk of malignancies.9 Patient groups treated with SGLT2i had a lower risk of hematologic and urinary tract malignancies in a nationwide study conducted by Rokszin et al.47 By exploring the association of treatment with SGLT2i and DPP4i with HCC, we add to the growing body of evidence supporting the use of antidiabetic agents in preventing cancer. Further investigations are needed to confirm the causation relationship between SGLT2i/DPP4i use and the risk of new-onset HCC.

Limitations

This study had several limitations. First, given the observational nature of this study, there is inherent undercoding, coding errors, and missing data, leading to information bias. Importantly, information on predictive variables, such as smoking, body mass index, and alcohol use, were not available from CDARS.48 To address this, extensive laboratory results and comorbidities related to cardiovascular disease and HCC were included to infer possible risk variables indirectly. We also conducted a falsification analysis between SGLT2i and DPP4i to minimize the risk of residual confounding, and the results did not falsify our findings. The retrospective design also necessitates the presentation of associations but not causal links between SGLT2i/DPP4i use and the risk of new-onset HCC.

Second, medication adherence can be assessed only indirectly through prescription refills but not through direct measurement of drug exposure. Third, we must acknowledge the existing ambiguity regarding the definitions of NAFLD. Despite a large proportion of patients with T2DM and coexisting NAFLD, the percentage of patients with T2DM and diagnosed NAFLD is significantly lower, and therefore we were only able to confirm the association among patients diagnosed with NAFLD. Our definition of NAFLD was established in accordance with the ICD-9 code; however, nuances in our diagnostic criteria relative to other studies could influence the generalizability of our data results within that subgroup of patients.49 Furthermore, given the relatively small number of primary outcomes (0.37%), the findings should be carefully considered. Further multinational follow-up studies with larger sample sizes and longer follow-up are needed. Finally, the duration of the drug exposure was not controlled. This may suggest that study results are susceptible to immortal time and time-lag biases. The impact of these biases could be reduced by including a time-dependent analysis.

Conclusions

SGLT2i use was associated with lower risks for new-onset HCC compared with DPP4i use after propensity score matching and adjustments. This association remained significant among patients with cirrhosis or advanced fibrosis, HBV infection, or HCV infection. The results support the need for further evaluation in the prospective setting.

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Submitted April 15, 2023; final revision received November 25, 2023; accepted for publication November 28, 2023.

O.H.I. Chow and J. Ning contributed equally and are co–first authors.

Author contributions: Data acquisition: Chou, Chung, Lee. Data review: Chou, Ning, Tse, Zhou. Data analysis: Chou, Ning, Zhou. Data interpretation: Chou, Ning, Chan, Chung, Huang. Supervision: Cheung, Tse, Zhou. Manuscript writing: Chou, Huang, Ng, Dee, Lee, Kaewdech, Chow, Man, Liu, Jing. Manuscript revision: Chou, Ning, Dee, Lee, Kaewdech, Chow, Man, Liu, Cheung, Tse, Zhou.

Disclosures: The authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Data availability statement: An anonymized version without identifiable or personal information is available from the corresponding authors upon reasonable request for research purposes.

Guarantor statement: All authors approved the final version of the manuscript. G. Tse is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Supplementary material: Supplementary material associated with this article is available online at https://doi.org/10.6004/jnccn.2023.7118. The supplementary material has been supplied by the author(s) and appears in its originally submitted form. It has not been edited or vetted by JNCCN. All contents and opinions are solely those of the author. Any comments or questions related to the supplementary materials should be directed to the corresponding author.

Correspondence: Gary Tse, MD, PhD, Kent and Medway Medical School, Pears Building, Canterbury, CT2 7FS UK. Email: gary.tse@kmms.ac.uk; and
Jiandong Zhou, PhD, Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 3/F., Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China. Email: jdzhou@hku.hk

Supplementary Materials

  • Collapse
  • Expand
  • Figure 1.

    Procedures of data processing.

    Abbreviations: ALT, alanine aminotransferase; APRI, AST-to-platelet ratio index; AST, aspartate aminotransferase; B/A ratio, bilirubin-to-albumin ratio; DPP4i, dipeptidyl peptidase-4 inhibitor; FIB-4 index, fibrosis-4 index; HbA1c, hemoglobin A1c; HCC, hepatocellular carcinoma; MDRD, modification of diet in renal disease; NLR, neutrophil-to-lymphocyte ratio; SGLT2i, sodium-glucose cotransporter-2 inhibitor; T2DM, type 2 diabetes mellitus.

  • Figure 2.

    Cumulative incidence curves for new-onset HCC, cancer-relortality, and all-cause mortality stratified by exposure to SGLT2i and DPP4i before (A, B, and C, respectively) and after (D, E, and F, respectively) propensity score matching (1:1).

    Abbreviations: DPP4i, dipeptidyl peptidase-4 inhibitor; HCC, hepatocellular carcinoma; SGLT2i, sodium-glucose cotransporter-2 inhibitor.

  • Figure 3.

    Subgroup analyses for the association between SGLT2i versus DPP4i and new-onset hepatocellular carcinoma in the matched cohort.

    Abbreviations: Anti-HBe, hepatitis B e antibody; DPP4i, dipeptidyl peptidase-4 inhibitor; HBV, hepatitis B virus; HCV, hepatitis C virus; HR, hazard ratio; NAFLD, nonalcoholic fatty liver disease; SGLT2i, sodium-glucose cotransporter-2 inhibitor.

  • 1.

    Petrick JL, Kelly SP, Altekruse SF, et al. Future of hepatocellular carcinoma incidence in the United States forecast through 2030. J Clin Oncol 2016;34:17871794.

  • 2.

    Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209249.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Sayiner M, Golabi P, Younossi ZM. Disease burden of hepatocellular carcinoma: a global perspective. Dig Dis Sci 2019;64:910917.

  • 4.

    Chui AMN, Yau TCC, Cheung TT. An overview in management of hepatocellular carcinoma in Hong Kong using the Hong Kong Liver Cancer (HKLC) staging system. Glob Health Med 2020;2:312318.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Lai CL, Lam KC, Wong KP, et al. Clinical features of hepatocellular carcinoma: review of 211 patients in Hong Kong. Cancer 1981;47:27462755.

  • 6.

    Wang P, Kang D, Cao W, et al. Diabetes mellitus and risk of hepatocellular carcinoma: a systematic review and meta-analysis. Diabetes Metab Res Rev 2012;28:109122.

  • 7.

    Wainwright P, Scorletti E, Byrne CD. Type 2 diabetes and hepatocellular carcinoma: risk factors and pathogenesis. Curr Diab Rep 2017;17:20.

  • 8.

    Cunha V, Cotrim HP, Rocha R, et al. Metformin in the prevention of hepatocellular carcinoma in diabetic patients: a systematic review. Ann Hepatol 2020;19:232237.

  • 9.

    Chung CT, Lakhani I, Chou OHI, et al. Sodium-glucose cotransporter 2 inhibitors versus dipeptidyl peptidase 4 inhibitors on new-onset overall cancer in type 2 diabetes mellitus: a population-based study. Cancer Med 2023;12:1229912315.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Schulte L, Scheiner B, Voigtländer T, et al. Treatment with metformin is associated with a prolonged survival in patients with hepatocellular carcinoma. Liver Int 2019;39:714726.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Plaz Torres MC, Jaffe A, Perry R, et al. Diabetes medications and risk of HCC. Hepatology 2022;76:18801897.

  • 12.

    Giorda CB, Picariello R, Tartaglino B, et al. Hepatocellular carcinoma in a large cohort of type 2 diabetes patients. Diabetes Res Clin Pract 2023;200:110684.

  • 13.

    Lee CH, Mak LY, Tang EHM, et al. 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. Hepatology 2023;78:15691580.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Kontana A, Tziomalos K. Role of sodium-glucose co-transporter-2 inhibitors in the management of nonalcoholic fatty liver disease. World J Gastroenterol 2019;25:36643668.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Eriksson JW, Lundkvist P, Jansson PA, et al. Effects of dapagliflozin and n-3 carboxylic acids on non-alcoholic fatty liver disease in people with type 2 diabetes: a double-blind randomised placebo-controlled study. Diabetologia 2018;61:19231934.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Shao SC, Kuo LT, Chien RN, et al. SGLT2 inhibitors in patients with type 2 diabetes with non-alcoholic fatty liver diseases: an umbrella review of systematic reviews. BMJ Open Diabetes Res Care 2020;8:e001956.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Kawaguchi T, Nakano D, Okamura S, et al. Spontaneous regression of hepatocellular carcinoma with reduction in angiogenesis-related cytokines after treatment with sodium-glucose cotransporter 2 inhibitor in a cirrhotic patient with diabetes mellitus. Hepatol Res 2019;49:479486.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Zhao M, Chen J, Yuan Y, et al. Dipeptidyl peptidase-4 inhibitors and cancer risk in patients with type 2 diabetes: a meta-analysis of randomized clinical trials. Sci Rep 2017;7:8273.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Hsu WH, Sue SP, Liang HL, et al. Dipeptidyl peptidase 4 inhibitors decrease the risk of hepatocellular carcinoma in patients with chronic hepatitis C infection and type 2 diabetes mellitus: a nationwide study in Taiwan. Front Public Health 2021;9:711723.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Chou OHI, Zhou J, Lee TTL, et al. Comparisons of the risk of myopericarditis between COVID-19 patients and individuals receiving COVID-19 vaccines: a population-based study. Clin Res Cardiol 2022;111:10981103.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Zhou J, Lee S, Wang X, et al. Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong. NPJ Digit Med 2021;4:66.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Zhou J, Wang X, Lee S, et al. Proton pump inhibitor or famotidine use and severe COVID-19 disease: a propensity score-matched territory-wide study. Gut 2021;70:20122013.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Zhang J, Liu F, Song T, et al. Liver fibrosis scores and clinical outcomes in patients with COVID-19. Front Med (Lausanne) 2022;9:829423.

  • 24.

    Loaeza-del-Castillo A, Paz-Pineda F, Oviedo-Cárdenas E, et al. AST to platelet ratio index (APRI) for the noninvasive evaluation of liver fibrosis. Ann Hepatol 2008;7:350357.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Soliman AR, Fathy A, Khashab S, et al. Comparison of abbreviated modification of diet in renal disease formula (aMDRD) and the Cockroft-Gault adjusted for body surface (aCG) equations in stable renal transplant patients and living kidney donors. Ren Fail 2013;35:9497.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011;46:399424.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med 2015;34:36613679.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Avagyan V, Vansteelandt S. Stable inverse probability weighting estimation for longitudinal studies. Scand J Stat 2021;48:10461067.

  • 29.

    Mantovani A, Targher G. Type 2 diabetes mellitus and risk of hepatocellular carcinoma: spotlight on nonalcoholic fatty liver disease. Ann Transl Med 2017;5:270.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Huang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol 2021;18:223238.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Tacelli M, Celsa C, Magro B, et al. Antidiabetic drugs in NAFLD: the accomplishment of two goals at once? Pharmaceuticals (Basel) 2018;11:121.

  • 32.

    Kramer JR, Natarajan Y, Dai J, et al. Effect of diabetes medications and glycemic control on risk of hepatocellular cancer in patients with nonalcoholic fatty liver disease. Hepatology 2022;75:14201428.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Yoo JJ, Cho EJ, Han K, et al. Glucose variability and risk of hepatocellular carcinoma in patients with diabetes: a nationwide population-based study. Cancer Epidemiol Biomarkers Prev 2021;30:974981.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Murata K, Sugimoto K, Shiraki K, et al. Relative predictive factors for hepatocellular carcinoma after HBeAg seroconversion in HBV infection. World J Gastroenterol 2005;11:68486852.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Hollande C, Boussier J, Mottez E, et al. Safety of sitagliptin in treatment of hepatocellular carcinoma in chronic liver disease patients. Liver Cancer Int 2021;2:7381.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Saffo S, Kaplan DE, Mahmud N, et al. Impact of SGLT2 inhibitors in comparison with DPP4 inhibitors on ascites and death in veterans with cirrhosis on metformin. Diabetes Obes Metab 2021;23:24022408.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Dougherty JA, Guirguis E, Thornby KA. A systematic review of newer antidiabetic agents in the treatment of nonalcoholic fatty liver disease. Ann Pharmacother 2021;55:6579.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Han E, Lee YH, Lee BW, et al. Ipragliflozin additively ameliorates non-alcoholic fatty liver disease in patients with type 2 diabetes controlled with metformin and pioglitazone: a 24-week randomized controlled trial. J Clin Med 2020;9:259.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Pradhan R, Yin H, Yu O, et al. Glucagon-like peptide 1 receptor agonists and sodium-glucose cotransporter 2 inhibitors and risk of nonalcoholic fatty liver disease among patients with type 2 diabetes. Diabetes Care 2022;45:819829.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Jensen-Urstad AP, Semenkovich CF. Fatty acid synthase and liver triglyceride metabolism: housekeeper or messenger? Biochim Biophys Acta 2012;1821:747753.

  • 41.

    Jojima T, Wakamatsu S, Kase M, et al. The SGLT2 inhibitor canagliflozin prevents carcinogenesis in a mouse model of diabetes and non-alcoholic steatohepatitis-related hepatocarcinogenesis: association with SGLT2 expression in hepatocellular carcinoma. Int J Mol Sci 2019;20:5237.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Ganapathy V, Thangaraju M, Prasad PD. Nutrient transporters in cancer: relevance to Warburg hypothesis and beyond. Pharmacol Ther 2009;121:2940.

  • 43.

    Nishina S, Yamauchi A, Kawaguchi T, et al. Dipeptidyl peptidase 4 inhibitors reduce hepatocellular carcinoma by activating lymphocyte chemotaxis in mice. Cell Mol Gastroenterol Hepatol 2018;7:115134.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Kawaguchi T, Nakano D, Koga H, et al. Effects of a DPP4 inhibitor on progression of NASH-related HCC and the p62/ Keap1/Nrf2-pentose phosphate pathway in a mouse model. Liver Cancer 2019;8:359372.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    De Souza A, Irfan K, Masud F, et al. Diabetes type 2 and pancreatic cancer: a history unfolding. JOP 2016;17:144148.

  • 46.

    McKee A, Al-Khazaali A, Albert SG. Glucagon-like peptide-1 receptor agonists versus sodium-glucose cotransporter inhibitors for treatment of T2DM. J Endocr Soc 2020;4:bvaa037.

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