Abstract

Introduction

Fatty liver disease (FLD), which is characterized by the accumulation of fat in the liver, is a prevalent liver disorder with significant global impact^1,2. This condition is a hepatic manifestation of metabolic syndrome and is closely linked to insulin resistance and type 2 diabetes mellitus (T2DM)^2–4. Individuals with diabetes are at a higher risk of developing FLD, and diabetes increases the risk of progression to more severe liver diseases, such as liver cirrhosis or hepatocellular carcinoma (HCC)^5,6. Various studies have indicated that patients with concurrent FLD and T2DM are significantly more likely to develop HCC^6–8. With the incidence of FLD and T2DM increasing worldwide, managing the risk of progression to HCC in these patient populations is becoming a critical concern.

Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are a class of drugs primarily used to treat T2DM⁹. Beyond their role in lowering blood glucose levels, emerging research suggests SGLT2i may offer additional benefits in liver diseases, including non-alcoholic fatty liver disease (NAFLD) and HCC^10–12. Pre-clinical studies have shown that SGLT2i can decrease hepatic steatosis, enhance insulin sensitivity, and reduce liver inflammation and fibrosis^13,14. Several clinical studies have shown that the use of SGLT2i in patients with NAFLD improves liver function and serum markers of liver injury^11,15. Additionally, the use of SGLT2i in T2DM patients has been observed to reduce the risk of HCC development¹⁶. However, the relationship between SGLT2i and other cancer types has yielded mixed outcomes; while some studies report a reduced risk of cancer, such as lung and gastrointestinal cancers, others have raised concerns over increased risks of bladder cancer¹⁷. Given the relatively recent introduction of SGLT2i in the market, there is a critical need for further research involving long-term follow-up and the use of clinical big data to more thoroughly investigate the cancer incidence associated with SGLT2i use.

In healthcare research, the use of big data has become increasingly vital, particularly for identifying trends, patterns, and correlations within vast datasets¹⁸. Our study used data from the Health Insurance Review and Assessment Service (HIRA) of Korea, which is an extensive database encompassing a wide array of patient information¹⁹. The use of such big datasets offers a unique opportunity to conduct comprehensive and detailed analyses of large populations. This approach enabled us to observe real-world outcomes, overcome the limitations of small sample sizes, and enhance the generalizability of our findings.

Our study aimed to evaluate the impact of SGLT2i on cancer development, with a specific focus on HCC, in patients with co-existing FLD and T2DM, using a nationwide Korean cohort from the HIRA. We conducted our analysis on two distinct subpopulations of patients coexisting with FLD and T2DM. The first cohort included patients with T2DM and NAFLD, after excluding those with other chronic liver diseases, such as chronic viral hepatitis (CVH), alcoholic liver disease, and autoimmune liver disease from patients with FLD and T2DM. The second cohort comprised high-risk individuals with HCC who were diagnosed with CVH among patients with FLD and T2DM. In addition to assessing the impact of SGLT2i on HCC incidence, we also examined various demographic and clinical factors to identify independent risk factors for HCC in these patient groups, leveraging an extensive dataset to provide insights into effective HCC risk management strategies.

Results

Baseline characteristics and incidence rate of cancers in the NAFLD-T2DM cohort

We identified 201,542 patients with co-existing NAFLD and T2DM. Of these patients, 55,770 (27.7%) were in the SGLT2i group and 145,772 (72.3%) were in the non-SGLT2i group. The median [interquartile range (IQR)] of the follow-up time was 3.56 (2.17–5.10) years for all, 3.01 (1.94–4.52) years for SLGT2i group, and 3.77 (2.30–5.34) years for Non-SGLT2i group. This selection was conducted after excluding patients diagnosed with chronic liver diseases including CVH, alcoholic liver disease, and autoimmune liver disease. After 1:1 PS matching, a balanced cohort of 107,972 patients was established for analysis and evenly divided into 53,986 patients (50.0%) in the SGLT2i group and 53,986 patients (50.0%) in the non-SGLT2i group (Fig. 1). In PS-matched cohort, the median (IQR) of the follow-up time was 3.04 (1.94–4.55) years for all, 3.05 (1.95–4.56) years for SLGT2i group, and 3.03 (1.93–4.54) years for Non-SGLT2i group. There was no significant difference in follow-up period between the two groups (P=0.445).

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Figure 1

Schematic representation of cohort derivation for this study. T2DM type 2 diabetes mellitus, NAFLD non-alcoholic fatty liver disease, FLD fatty liver disease, CVH chronic viral hepatitis, SGLT2i sodium-glucose cotransporter-2 inhibitor, PSM propensity score matching.

Supplementary Table ¹ and Figure ^S1A (Love plot) confirm the successful adjustment of covariate differences between groups following PS matching. In this cohort, PS matching effectively standardized the mean differences, with all variables achieving an aSMD of less than 0.1, demonstrating excellent balance across covariates. These results underscore the robustness of the matching process and comparability of the groups for subsequent analyses. Table ​Table11 illustrates comprehensive patient characteristics before and after PS matching.

Table 1

Baseline characteristics of pre- and post-PS matched NAFLD-T2DM cohort.

Characteristics	Pre-PS matching cohort			Post-PS matched cohort
	SGLT2i users (N=55,770)	Non-SGLT2i users (N=145,772 )	aSMD	SGLT2i users (N=53,986 )	Non-SGLT2i users (N=53,986)	aSMD
Age, years (mean± SD)	49.72 (12.91)	56.29 (12.02)	0.527	50.37 (12.55)	50.61 (12.48)	0.02
Sex, male, n (%)	31,703 (56.8)	84,868 (58.2)	0.028	30,697 (56.9)	30,731 (56.9)	0.001
Index year
2015	4160 (7.5)	22,956 (15.7)	0.354	4153 (7.7)	3977 (7.4)	0.013
2016	6697 (12.0)	25,095 (17.2)		6649 (12.3)	6625 (12.3)
2017	8890 (15.9)	24,021 (16.5)		8699 (16.1)	8747 (16.2)
2018	9716 (17.4)	24,204 (16.6)		9463 (17.5)	9438 (17.5)
2019	13,071 (23.4)	24,555 (16.8)		12,420 (23.0)	12,467 (23.1)
2020	13,236 (23.7)	24,941 (17.1)		12,602 (23.3)	12,732 (23.6)
CCI index
0	3631 (6.5)	13,236 (9.1)	0.108	3613 (6.7)	3602 (6.7)	0.005
1	9376 (16.8)	26,568 (18.2)		9151 (17.0)	9187 (17.0)
2	16,349 (29.3)	40,424 (27.7)		15,755 (29.2)	15,856 (29.4)
≤3	26,414 (47.4)	65,544 (45.0)		25,467 (47.2)	25,341 (46.9)
Comorbidities
Hypertension	25,196 (45.2)	66,474 (45.6)	0.008	24,703 (45.8)	22,681 (42.0)	0.076
Dyslipidemia	35,496 (63.6)	83,122 (57.0)	0.136	34,180 (63.3)	34,161 (63.3)	0.001
Cerebrovascular disease	1580 (2.8)	3175 (2.2)	0.042	1557 (2.9)	938 (1.7)	0.076
Coronary artery disease	1281 (2.3)	4561 (3.1)	0.051	1272 (2.4)	1351 (2.5)	0.01
Peripheral vascular disease	1642 (2.9)	3140 (2.2)	0.05	1633 (3.0)	859 (1.6)	0.096
Heart failure	4073 (7.3)	13,074 (9.0)	0.061	4021 (7.4)	4390 (8.1)	0.026
Hypothyroidism	2202 (3.9)	5030 (3.5)	0.026	2121 (3.9)	1977 (3.7)	0.014
Obesity or overweight	262 (0.5)	214 (0.1)	0.058	230 (0.4)	121 (0.2)	0.035
Chronic kidney disease	352 (0.6)	879 (0.6)	0.004	331 (0.6)	320 (0.6)	0.003
Liver cirrhosis	131 (0.2)	405 (0.3)	0.008	129 (0.2)	140 (0.3)	0.004
Chronic respiratory disease	351 (0.6)	878 (0.6)	0.003	330 (0.6)	320 (0.6)	0.002
Co-medications
Aspirin	6526 (11.7)	18,618 (12.8)	0.033	6492 (12.0)	5193 (9.6)	0.078
Clopidogrel	2698 (4.8)	6312 (4.3)	0.024	2685 (5.0)	1818 (3.4)	0.08
Antiplatelet, others	1880 (3.4)	5933 (4.1)	0.037	1872 (3.5)	1854 (3.4)	0.002
Statin	24,068 (43.2)	57,996 (39.8)	0.068	23,579 (43.7)	22,249 (41.2)	0.05
Ezetimibe	5353 (9.6)	9952 (6.8)	0.101	5217 (9.7)	4573 (8.5)	0.042
Fibrates	3729 (6.7)	8063 (5.5)	0.048	3600 (6.7)	3875 (7.2)	0.02
ACEi	632 (1.1)	1327 (0.9)	0.022	622 (1.2)	355 (0.7)	0.052
ARB	18,006 (32.3)	46,487 (31.9)	0.008	17,701 (32.8)	16,357 (30.3)	0.054
CCB	11,793 (21.1)	33,246 (22.8)	0.04	11,591 (21.5)	11,362 (21.0)	0.01
Beta blocker	5628 (10.1)	13,738 (9.4)	0.022	5500 (10.2)	4576 (8.5)	0.059
Level of T2DM treatment
Level I	47,658 (85.5)	127,376 (87.4)	0.066	46,278 (85.7)	46,332 (85.8)	0.005
Level II	3203 (5.7)	8108 (5.6)		3096 (5.7)	3121 (5.8)
Level III	4909 (8.8)	10,288 (7.1)		4612 (8.5)	4533 (8.4)

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SGLT2i sodium-glucose cotransporter-2 inhibitor, NAFLD non-alcoholic fatty liver disease, T2DM type 2 diabetes mellitus, CVH chronic viral hepatitis, PSM propensity score, aSMD absolute standardized mean difference, CCI Charlson Comorbidity Index, ACEi angiotensin-converting enzyme inhibitor, ARB angiotensin II receptor blocker, CCB calcium channel blocker.

Table ​Table22 displays the number of cancer cases, person-years, and IR per 10,000 person-years [95% CI] for each type of cancer according to SGLT2i exposure status in the NAFLD-T2DM cohort both before and after PS matching. Figure 3A shows a forest plot of the HRs for each cancer type. In the pre-matching analysis, non-SGLT2i users exhibited significant HRs for the occurrence of “total cancer”, HCC, CCC, stomach cancer, colorectal cancer, pancreatic cancer, lung cancer, prostate cancer, and “other cancers”. However, after PS matching, the statistically significant differences in cancer risk between the two groups disappeared.

Table 2

Incident rate per 10,000 person year of the malignancies according to SGLT2i usage in the pre- and post-PS matched NAFLD-T2DM cohort.

	Pre-PS matched cohort (N=201,542)						Post-PS matched cohort (N=107,972)
	Cancer cases		Person-year		Incidence rate per 10,000 person year (95% CI)		Cancer cases		Person-year		Incidence rate per 10,000 person year (95% CI)
	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users
Total cancer	1046	4535	186,705.42	566,243.17	56.0 (52.7–59.5)	80.1 (77.8–82.5)	1036	1034	182,187.4	181,929.2	56.9 (53.5–60.4)	56.8 (53.5–60.4)
Hepatocellular carcinoma	67	326	188,137.25	573,129.17	3.6 (2.8–4.5)	5.7 (5.1–6.3)	67	59	183,610.3	183,367.3	3.6 (2.9–4.6)	3.2 (2.4–4.2)
Cholangiocarcinoma	4	50	188,235.58	573,559.92	0.2 (0.1–0.6)	0.9 (0.7–1.2)	4	6	183,708.7	183,435.2	0.2 (0.1–0.6)	0.3.(0.2–0.7)
Stomach cancer	97	567	188,094.25	572,564.67	5.2 (4.2–6.3)	9.9 (9.1–10.8)	97	124	183,567.3	183,245.5	5.3 (4.3–6.4)	6.8 (5.7–8.1)
Colorectal cancer	101	489	188,081.25	572,736.75	5.4 (4.4–6.5)	8.5 (7.8–9.3)	99	109	183,556.6	183,270.1	5.4 (4.4–6.6)	5.9 (4.9–7.2)
Esophagus cancer	6	44	188,228.5	573,546.83	0.3 (0.1–0.7)	0.8 (0.6–1.0)	6	6	183,701.6	183,432.8	0.3 (0.1–0.7)	0.3 (0.1–0.7)
Pancreas cancer	37	196	188,205	573,411.92	2.0 (1.4–2.7)	3.4 (3.0–3.9)	37	41	183,678.1	183,393.4	2.0 (1.5–2.8)	2.2 (1.6–3.0)
Lung cancer	89	413	188,127.83	573,053.08	4.7 (3.8–5.38)	7.2 (6.5–7.9)	89	66	183,600.9	183,353.5	4.8 (3.9–6.0)	3.6 (2.8–4.6)
Bladder cancer	43	149	188,177.67	573,304.5	2.3 (1.7–3.1)	2.6 (2.2–3.1)	43	42	183,650.8	183,371.4	2.3 (1.7–3.2)	5.7 (2.3–1.7)
Prostate cancer	52	329	103,664.42	332,421.75	5.0 (3.8–6.6)	9.9 (8.9–11.0)	52	44	101,202.6	103,408.4	5.1 (3.9–6.7)	4.3 (3.2–5.7)
Breast cancer	118	337	84,300.58	240,001.5	14.0 (11.7–16.8)	14.0 (12.6–15.6)	118	118	82,235.5	79,776	14.4 (120–17.2)	14.8 (12.3–17.7)
Cervical cancer	10	45	84,477.75	240,560.92	1.2 (0.6–2.2)	1.9 (0.4–2.5)	9	9	82,416.08	79,955.67	1.1 (0.6–2.1)	1.1 (0.6–2.2)
Other cancers	422	1590	187,603.42	571,036.08	22.5 (20.4–24.7)	27.8 (26.5–29.2	415	410	183,079.8	182,834.8	22.7 (20.6–25.0)	22.4 (20.4–24.7)

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SGLT2i sodium-glucose cotransporter-2 inhibitor, NAFLD non-alcoholic fatty liver disease, T2DM type 2 diabetes mellitus, CVH chronic viral hepatitis, PSM propensity score matching, HR hazard ratio, CI confidence interval.

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Figure 3

Forrest plots of the hazard ratio of each cancer according to SGLT2i usage in the NAFLD-T2DM cohort and FLD-T2DM-CVH cohort. (A) NAFLD-T2DM cohort. (B) FLD-T2DM-CVH cohort. SGLT2i sodium-glucose cotransporter-2 inhibitor, PSM propensity score matching, NAFLD non-alcoholic fatty liver disease, T2DM type 2 diabetes mellitus, FLD fatty liver disease, CVH chronic viral hepatitis.

Survival analysis of HCC and other cancers in the PS-matched NAFLD-T2DM cohort according to SGLT2i usage

Figure 2A shows the Kaplan–Meier curves comparing the probability of HCC incidence between SGLT2i users and non-SGLT2i users within the PS-matched NAFLD-T2DM cohort. No significant differences were observed not only in HCC development but also in other types of cancer, indicating that SGLT2i usage does not statistically influence cancer incidence in this cohort (Fig. ^S1).

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Figure 2

Comparison of Kaplan–Meier curves of HCC occurrence according to SGLT2i exposure in PS-matched NAFLD-T2DM cohort and PS-matched FLD-T2DM-CVH cohort. HCC hepatocellular carcinoma, SGLT2i sodium-glucose cotransporter-2 inhibitor, PS propensity score, NAFLD non-alcoholic fatty liver disease, T2DM type 2 diabetes mellitus, FLD fatty liver disease, CVH chronic viral hepatitis.

Subsequently, Cox proportional hazards analysis was conducted to identify the independent variables affecting HCC occurrence in this cohort (Table ​(Table3).3). In the univariate Cox regression analysis, older age; male sex; comorbidities such as hypertension, hypothyroidism, and liver cirrhosis; and the use of aspirin, beta-blockers, calcium channel blockers, and fibrates were identified as significant risk factors for HCC occurrence. In the multivariate Cox regression analysis, which included variables with a P value<0.1 and SGLT2i used from the univariate analysis, older age (HR=1.08, 95% CI=1.06–1.10, P<0.001), male sex (HR=2.79, 95% CI=1.87–4.14, P<0.001), hypothyroidism (HR=2.43, 95% CI=1.21–4.87, P=0.013), liver cirrhosis (HR=17.88, 95% CI=8.19–39.03, P<0.001), statin use (HR=0.59, 95% CI=0.36–0.96, P=0.035), and fibrate use (HR=0.14, 95% CI=0.02–0.99, P=0.049) were identified as independent risk factors for HCC occurrence. The Concordance index of this model was 0.805, with a standard error (SE) of 0.029.

Table 3

Univariate and multivariate Cox regression analysis to identify risk factors associated with HCC occurrence in the PS-matched NAFLD-T2DM cohort.

Variables	Univariate			Multivariate
	HR	95% CI	P value	HR	95% CI	P value
Age, year	1.07	1.05–1.09	<0.001	1.08	1.06–1.10	<0.001
Sex, female	1.97	1.34–2.90	<0.001	2.79	1.87–4.14	<0.001
SGLT2i use, no	0.88	0.62–1.45	0.472	0.87	0.61–1.24	0.435
CCI index, 0 or 1 vs.≥2	1.20	0.77–1.85	0.424
Comorbidities
Hypertension	1.70	1.20–2.42	0.003	1.30	0.75–2.26	0.349
Dyslipidemia	1.37	0.51–1.04	0.079	0.71	0.45–1.12	0.138
Cerebrovascular disease	0.80	0.20–3.24	0.756
Peripheral vascular disease	1.00	0.32–3.16	0.994
Heart failure	0.94	0.48–1.86	0.868
Hypothyroidism	2.00	1.02–3.94	0.045	2.43	1.21–4.87	0.013
Chronic kidney disease	1.58	0.22–11.28	0.650
Chronic respiratory disease	2.06	0.51–8.31	0.312
End stage renal disease	1.58	0.22–11.30	0.649
Liver cirrhosis	29.71	13.86–63.70	<0.001	17.88	8.19–39.03	<0.001
Co-medications
Aspirin	1.60	1.01–2.54	0.044	1.01	0.61–1.67	0.972
Clopidogrel	1.72	0.84–3.52	0.138
Statin	0.71	0.50–1.04	0.080	0.59	0.36–0.96	0.035
Ezetimibe	0.35	0.11–1.11	0.074	0.44	0.14–1.42	0.170
Fibrates	0.13	0.02–0.93	0.043	0.14	0.02–0.99	0.049
Angiotensin-converting enzyme inhibitor	0.80	0.11–5.71	0.822
Angiotensin II receptor blocker	1.39	0.97–2.00	0.071	0.95	0.57–1.57	0.838
Calcium channel blocker	1.59	1.08–2.35	0.018	1.08	0.67–1.74	0.743
Beta-blocker	1.70	1.02–2.77	0.041	1.13	0.67–1.92	0.643
Metformin	1.22	0.80–1.86	0.350
Level of antidiabetic treatment, level 1 vs.≥2	1.31	0.81–2.11	0.276

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PS propensity score, NAFLD non-alcoholic fatty liver disease, T2DM type 2 diabetes mellitus, HR hazard ratio, CI confidence interval, SGLT2i sodium-glucose cotransporter-2 inhibitor, CCI Charlson Comorbidity Index.

Concordance=0.806 (standard error=0.029).

Baseline characteristics and incidence rate of cancers in the FLD-T2DM-CVH cohort

In this subset, 4936 patients with CVH along with co-existing NAFLD and T2DM were identified. Among them, 1440 (29.2%) were categorized into the SGLT2i group and 3,496 (70.8%) into the non-SGLT2i group. The median (IQR) of the follow-up period was 3.50 (2.18–4.95) years for all, 3.06 (2.04–4.46) years for SLGT2i group, and 3.67 (2.25–5.17) years for Non-SGLT2i group. Following 1:1 PS matching, an eligible cohort for analysis was formed, consisting of patients with an equal distribution of 1,399 patients (50.0%) in both the SGLT2i and non-SGLT2i groups (Fig. 1). In the PS-matched FLD-T2DM-CVH cohort, the median [IQR] of the follow-up time was 3.13 (2.07–4.48) years for all, 3.07 (2.03–4.48) years for SLGT2i group, and 3.19 (2.10–4.48) years for non-SGLT2i group. There was no significant difference in follow-up period between the two groups (P=0.529).

Supplementary Table ² and Fig. ^S1B (Love plot) confirm the successful adjustment of covariate differences between groups following PS matching. Significant discrepancies between groups were noted before PS matching; however, PS matching effectively standardized the mean differences in the PS-matched cohort, with all variables achieving an aSMD of less than 0.1, demonstrating excellent balance across covariates. These results underscore the robustness of the matching process and comparability of the groups for subsequent analyses. Table ​Table44 illustrates comprehensive patient characteristics before and after PS matching.

Table 4

Baseline characteristics of pre- and post-PS matched FLD-T2DM-CVH cohort.

characteristics	Pre-PS matched cohort (N=4936)			Post-PS matched cohort (N=2798)
	SGLT2i users (N=1440)	Non-SGLT2i users (N=3.496 )	aSMD	SGLT2i users (N=1399 )	Non-SGLT2i users (N=1399)	aSMD
Age, years (mean± SD)	51.76 (11.19)	56.45 (10.82)	0.427	52.27 (10.85)	52.31 (10.99)	0.004
Sex, male, n (%)	848 (58.9)	2197 (62.8)	0.081	831 (59.4)	828 (59.2)	0.004
Index year
2015	99 (6.9)	535 (15.3)	0.341	98 (7.0)	101 (7.2)	0.042
2016	172 (11.9)	577 (16.5)		172 (12.3)	164 (11.7)
2017	251 (17.4)	577 (16.5)		243 (17.4)	258 (18.4)
2018	259 (18.0)	582 (16.6)		251 (17.9)	259 (18.5)
2019	363 (25.2)	609 (17.4)		345 (24.7)	328 (23.4)
2020	296 (20.6)	616 (17.6)		290 (20.7)	289 (20.7)
CCI
0	0 (0)	0 (0)	0.083	0 (0)	0 (0)	0.02
1	49 (3.4)	161 (4.6)		48 (3.4)	50 (3.6)
2	397 (27.6)	1039 (29.7)		392 (28.0)	380 (27.2)
≤3	994 (69.0)	2296 (65.7)		959 (68.5)	969 (69.3)
Comorbidities
Hypertension	779 (54.1)	1838 (52.6)	0.031	758 (54.2)	759 (54.3)	0.001
Dyslipidemia	1143 (79.4)	2454 (70.2)	0.213	1104 (78.9)	1106 (79.1)	0.004
Heart failure	64 (4.4)	108 (3.1)	0.071	60 (4.3)	58 (4.1)	0.007
Cerebrovascular disease	51 (3.5)	150 (4.3)	0.039	50 (3.6)	62 (4.4)	0.044
Coronary artery disease	64 (4.4)	98 (2.8)	0.088	62 (4.4)	47 (3.4)	0.055
Peripheral vascular disease	138 (9.6)	407 (11.6)	0.067	132 (9.4)	170 (12.2)	0.088
Hypothyroidism	116 (8.1)	231 (6.6)	0.056	112 (8.0)	101 (7.2)	0.03
Obesity or overweight	5 (0.3)	10 (0.3)	0.011	4 (0.3)	5 (0.4)	0.013
Chronic kidney disease	17 (1.2)	35 (1.0)	0.017	17 (1.2)	19 (1.4)	0.013
Alcoholic liver disease	65 (4.5)	205 (5.9)	0.061	63 (4.5)	56 (4.0)	0.025
Primary biliary cholangitis	2 (0.1)	2 (0.1)	0.026	1 (0.1)	1 (0.1)	<0.001
Chronic viral hepatitis B	1018 (70.7)	2386 (68.2)	0.053	992 (70.9)	991 (70.8)	0.002
Chronic viral hepatitis C	196 (13.6)	638 (18.2)	0.127	192 (13.7)	187 (13.4)	0.01
Liver cirrhosis	24 (1.7)	64 (1.8)	0.013	66 (4.7)	63 (4.5)	0.01
Co-medications
Aspirin	169 (11.7)	450 (12.9)	0.035	167 (11.9)	154 (11.0)	0.029
Clopidogrel	76 (5.3)	165 (4.7)	0.026	75 (5.4)	71 (5.1)	0.013
Antiplatelet, others	72 (5.0)	148 (4.2)	0.037	69 (4.9)	58 (4.1)	0.038
Statin	681 (47.3)	1442 (41.2)	0.122	665 (47.5)	655 (46.8)	0.014
Ezetimibe	162 (11.2)	259 (7.4)	0.132	156 (11.2)	156 (11.2)	<0.001
Fibrates	97 (6.7)	187 (5.3)	0.058	93 (6.6)	84 (6.0)	0.026
ACEi	28 (1.9)	35 (1.0)	0.078	25 (1.8)	23 (1.6)	0.011
ARB	579 (40.2)	1291 (36.9)	0.067	565 (40.4)	574 (41.0)	0.013
CCB	364 (25.3)	918 (26.3)	0.022	355 (25.4)	377 (26.9)	0.036
Beta blocker	182 (12.6)	405 (11.6)	0.032	176 (12.6)	170 (12.2)	0.013
Level of T2DM treatment
Level I	1212 (84.2)	3080 (88.1)	0.121	1182 (84.5)	1193 (85.3)	0.04
Level II	74 (5.1)	157 (4.5)		70 (5.0)	75 (5.4)
Level III	154 (10.7)	259 (7.4)		147 (10.5)	131 (9.4)

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PS propensity score, FLD fatty liver disease, T2DM type 2 diabetes mellitus, CVH chronic viral hepatitis, SGLT2i sodium-glucose cotransporter-2 inhibitor, aSMD absolute standardized mean difference, CCI Charlson Comorbidity Index, ACEi angiotensin-converting enzyme inhibitor, ARB angiotensin II receptor blocker, CCB calcium channel blocker.

Table ​Table55 shows the number of cancer cases, person-years, and IR per 10,000 person-years for each cancer type according to SGLT2i exposure in the FLD-T2DM-CVH cohort before and after PS matching. Notably, the crude IR per 10,000 person-years of HCC was significantly higher in the FLD-T2DM-CVH cohort (IR per 10,000 person-years: 57.3, 95% CI 18.4–71.6) compared to the NAFLD-T2DM cohort (IR per 10,000 person-years: 5.2, 95% CI 3.6–5.7). Interestingly, in both pre-and post-PS matching, the IR per 10,000 person-years of HCC was markedly higher in the non-SGLT2i group (Pre-PS matching: 18.4 vs 71.6, and post-PS matching: 18.8 vs 41.7, for SGLT2i users and non-SGLT2i users, respectively).

Table 5

Incident rate per 10,000 person year of the malignancies according to SGLT2i use in the pre- and post-PS matched FLD-T2DM-CVH cohort.

	Pre-PS matched cohort (N=4936)						Post-PS matched cohort (N=2798)
	Cancer cases		Person-year		Incidence rate per 10,000 person year (95% CI)		Cancer cases		Person-year		Incidence rate per 10,000 person year (95% CI)
	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users	SGLT2i users	Non-SGLT2i users
Total cancers	38	194	4850.3	13,238.8	78.3 (57.0–107.7)	146.5 (127.3–168.7)	38	37	4727.3	4772.8	80.4 (58.5–110.5)	77.5 (56.2–107.0)
Hepatocellular carcinoma	9	96	4900.2	13,412.1	18.4 (9.6–35.3)	71.6 (58.6–87.4)	9	21	4777.3	4804.1	18.8 (9.8–36.2)	41.7 (28.5–67.0)
Cholangiocarcinoma	1	3	4908.4	13,557.8	2.0 (0.3–14.5)	2.2 (0.7–6.9)	1	1	4785.5	4839.1	2.1 (0.3–14.8)	2.1 (0.3–14.7)
Stomach cancer	4	11	4905	13,543.1	8.2 (3.1–21.7)	8.1 (4.5–14.7)	4	0	4782.1	4839.5	8.44 (3.1–22.3)	0
Colorectal cancer	1	11	4910.2	13,539	2.0 (0.3–14.5)	8.1 (4.5–14.7)	1	2	4787.3	4838.6	2.1 (0.3–14.8)	4.1 (1.0–16.5)
Esophagus cancer	0	1	4911.7	13,561.2	0	0.7 (0.1–5.2)	0	1	4788.8	4837.1	0	2.1 (0.3–14.7)
Pancreas cancer	2	4	4911.3	13,561.4	4.1 (1.0–16.3)	2.9 (1.1–7.9)	2	0	4788.3	4839.5	4.2 (1.0–16.7)	0
Lung cancer	0	13	4911.7	13,541.7	0	9.6 (5.6–16.5)	0	2	4788.8	4832.8	4.1 (1.0–16.5)	0
Bladder cancer	1	3	4909.6	13,562.1	2.0 (0.3–14.5)	2.2 (0.7–6.9)	1	0	4786.7	4839.5	2.1 (0.3–14.8)	0
Prostate cancer	1	5	2834.2	8547.2	3.6 (0.5–25.0)	5.8 (2.14–14.1)	1	2	2786.5	2873.2	3.6 (0.5–25.5)	7.0 (1.7–27.8)
Breast cancer	5	7	2069.3	4987.8	24.2 (10.1–58.1)	14.0 (6.7–29.4)	5	2	1994.0	1955.3	25.1 (10.4–30.2)	10.2 (2.6–40.9)
Cervical cancer	0	2	2076.8	5004.3	0	4.0 (1.0–16.0)	0	0	2001.6	1962.3	0	0
Other cancers	14	38	4883.9	13,501.8	28.7 (17.0–48.4)	28.1 (20.5–38.7)	14	6	4761	4829.8	29.4 (17.4–49.7)	12.4 (5.6–27.7)

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SGLT2i sodium-glucose cotransporter-2 inhibitor, FLD fatty liver disease, T2DM type 2 diabetes mellitus, CVH chronic viral hepatitis, PS propensity score, HR hazard ratio, CI confidence interval.

While the IR per 10,000 person-years for HCC increased by more than tenfold in the FLD-T2DM-CVH cohort, the number of cases of other cancer types decreased as the cohort size diminished. As shown in Table ​Table5,5, for several cancer types, the number of cases was less than 10. Due to concerns such as lack of statistical power, risk of overestimation, adherence to the Events Per Variable rule, and model fit issues, HRs could not be calculated for these types of cancer. We could analyze HR of HCC, “total cancer”, and “other cancer” in both pre- and post-PM matched cohorts. The risk of HCC occurrence in non-SGLT2i users was significantly higher in both cohorts; before matching [crude HR=3.58 (1.80–7.09)] and in the PS-matched cohort [adjusted HR=2.32 (1.06–5.06)]. The risk of “total cancer” showed significant HR in the pre-matched cohort; however, this significance disappeared in the post-PS-matched cohort (Fig. 3B).

Survival analysis of HCC and other cancers in the PS matched FLD-T2DM-CVH cohort according to SGLT2i usage

Figure 2b displays the Kaplan–Meier curves comparing HCC occurrence between SGLT2i and non-SGLT2i users within the PS-matched FLD-T2DM-CVH cohort. SGLT2i users had a significantly lower risk of developing HCC (P=0.03). There were no significant differences in the occurrence of “total cancers” and “other cancers” between the two groups (Fig. ^S3).

Subsequently, Cox proportional hazards analysis was conducted in the PS-matched FLD-T2DM-CVH cohort (Table ​(Table6).6). In the univariate Cox regression analysis, older age and comorbidities such as dyslipidemia, heart failure, and liver cirrhosis, as well as the use of SGLT2i, statins, and antiviral treatment, were significantly associated with the occurrence of HCC. To adjust for covariates, we performed a multivariate Cox regression analysis by entering variables with a P value<0.1 from the univariate analysis. Sex was also included in the multivariate analysis, although it was not a significant factor in the univariate analysis. This is because it is considered a basic variable for adjustment. In multivariate analysis, SGLT2i usage [HR=2.22 (1.01–4.87), P=0.047] was identified as an independent risk factor of HCC occurrence along with older age [HR=1.07 (1.03–1.10), P<0.001], male sex [HR=2.23 (1.00–5.26), P=0.049], and liver cirrhosis [HR=7.33 (3.31–16.21), P<0.001]. The C-index of this model was 0.882 with an SE of 0.056.

Table 6

Univariate and multivariate Cox regression analysis to identify risk factors associated with HCC occurrence in the PS matched FLD-T2DM-CVH cohort (N=2798).

Variables	Univariate			Multivariate
	HR	95% CI	P value	HR	95% CI	P value
Age, year	1.09	1.02–1.09	0.004	1.07	1.03–1.10	<0.001
Sex, male	1.95	0.87–4.39	0.105	2.30	1.00–5.26	0.049
SGLT2i use, no	2.32	1.06–5.06	0.035	2.22	1.01–4.87	0. 047
CCI,≤2 vs 3	0.84	0.40–1.81	0.671
Comorbidities
Hypertension	0.96	0.47–1.96	0.907
Dyslipidemia	0.41	0.20–0.85	0.017	0.61	0.27–1.36	0.226
Heart failure	3.07	0.93–10.13	0.065
Cerebrovascular disease	0.78	0.11–5.70	0.803
Peripheral vascular disease	0.96	0.29–3.16	0.943
Alcoholic liver disease	2.24	0.68–7.39	0.185
Liver cirrhosis	12.47	5.93–26.23	<0.001	7.33	3.31–16.21	<0.001
Chronic viral hepatitis B	1.77	0.72–4.32	0.213
Chronic viral hepatitis C	1.17	0.45–3.06	0.746
Co-medications
Aspirin	0.83	0.25–2.750	0.766
Statin	0.29	0.12–0.71	0.006	0.37	0.14–1.05	0.006
Ezetimibe	0.35	0.05–2.53	0.295
Angiotensin II receptor blocker	0.86	0.41–1.80	0.681
Calcium channel blocker	1.07	0.47–2.40	0.870
Beta-blocker	1.12	0.39–3.19	0.840
Metformin	1.62	0.72–3.65	0.241
Antiviral treatment	2.80	1.25–6.29	0.013	2.02	0.86–4.76	0.108
Level of antidiabetic treatment, level 1 vs≥2	0.66	0.20–2.18	0.499

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HCC hepatocellular carcinoma, PSM propensity score matching, FLD fatty liver disease, T2DM type 2 diabetes mellitus, CVH chronic viral hepatitis, NAFLD non-alcoholic fatty liver disease, SGLT2i sodium-glucose cotransporter-2 inhibitor.

Concordance index=0.882 (standard error=0.056).

Discussion

This study undertook a comprehensive analysis using large-scale healthcare data to investigate the influence of SGLT2i on cancer development, with emphasis on HCC, in a cohort with co-existing FLD and T2DM. By leveraging high-quality data from the HIRA Service of Korea. This study enriches the field with valuable insights into practical implications and outcomes in the clinical setting. Our findings in the NAFLD-T2DM cohort indicated no significant differences in the incidence of HCC and other types of cancers based on SGLT2i use. However, in the HCC high-risk group of patients, the FLD-T2DM-CVH cohort, the use of SGLT2i was significantly associated with a lower incidence of HCC, even after PS matching and multivariate Cox analysis, highlighting its potential protective effect in this particular subgroup.

A previous systematic review and meta-analysis investigating the association between SGLT2i and cancer risk in T2DM patients found no significant increase in the overall cancer risk, consistent with our findings in the NAFLD-T2DM cohort¹⁷. This prior research, encompassing 46 randomized controlled trials, indicated an increased risk of bladder cancer with SGLT2 inhibitor use but suggested a potential protective effect against gastrointestinal cancers. However, the authors state that further long-term studies are recommended owing to the short-term nature of the trials included in the study. In our study on patients with FLD and T2DM, the use of SGLT2i was not associated with an increased risk of bladder cancer, and the potential protective effect against gastrointestinal cancer was not statistically significant. Chou et al.²⁰ reported a protective effect of SGLT2i against HCC compared to dipeptidyl peptidase-4 inhibitors in T2DM patients using data from Hong Kong's National Health Care System. In our study, using data from the Korean HIRA Service, we initially observed a trend towards lower crude IRs of HCC and other cancer types among SGLT2i users within the NAFLD-T2DM cohort. In addition, a significant increase in the HRs of various types of cancers, including HCC, was observed in non-SGLT2i users before matching. However, this trend did not reach statistical significance after PS matching, which was adjusted for discrepancies in person-years attributable to the relatively recent introduction of SGLT2i compared to other oral hypoglycemic agents (OHA). This may be due to differences in the observed person-years between SGLT2i users and non-users. Specifically, SGLT2i users demonstrated relatively shorter person-years than non-users, resulting in an apparent increase in the IR of various cancers in the SGLT2i user group before matching.

While SGLT2i did not demonstrate a statistically significant association with HCC incidence in patients with NAFLD and T2DM, multivariate Cox analysis identified several factors associated with increased HCC risk in this population. These included older age, male sex, presence of hypothyroidism, and liver cirrhosis. Furthermore, the use of statins and fibrates has been associated with a lower incidence of HCC. This observation aligns with the existing research, underscoring the potential protective effects of statins and fibrates against HCC. Previous research has demonstrated that statins may confer a protective benefit in the chemoprevention and treatment of several cancers, including HCC^21–24. Recently, Zou et al.²⁵ suggested an association between statin and reduced risk of HCC development in NAFLD patients by using the Optum de-identified Clinformatics database. Additionally, a large-scale case–control study in Taiwan revealed a significant inverse association between fibrate use and the incidence of liver cancer²⁶. The study demonstrated that fibrate use was associated with significantly lower odds of liver cancer in a dose-dependent manner, indicating a protective effect of fibrates against liver cancer. While our study contributes to the understanding of SGLT2i's role in various types of cancer risk, particularly in a specific cohort of patients with NAFLD and T2DM, it also highlights the importance of considering the protective effects of other medications, such as statins and fibrates, in managing HCC risk in this cohort.

In our FLD-T2DM-CVH cohort, we noted a notably higher crude incidence rate of HCC compared to the NAFLD-T2DM cohort. This difference is attributed not only to viral infection but also to variations in HCC screening strategies for both cohorts. CVH is a well-known risk factor for HCC, and it is recommended by various expert groups that patients with CVH should undergo biannual HCC screening^27–29. On the other hand, in patients without CVH or liver cirrhosis, regular HCC screening is not recommended. Considering the significant differences in HCC risk and HCC screening strategies based on CVH status, we conducted separate analyses for patients with CVH and those NAFLD patients without CVH to minimize potential biases. Interestingly, within the CVH cohort with higher HCC risk, we noted a pronounced protective effect of SGLT2i against development of HCC. This finding is in line with the concepts of risk difference effect and relative risk reduction, suggesting that therapeutic interventions might offer greater absolute benefits in populations at a higher baseline risk^30,31. The underlying theory suggests that individuals at an elevated risk of a condition may gain more from interventions due to their higher initial risk, potentially preventing a greater number of adverse outcomes³¹. Despite the limitations of our study design and dataset which prevent a detailed statistical analysis to fully quantify this effect, the observed trend highlights the importance of considering baseline risk when evaluating treatment outcomes. This insight is particularly pertinent for clinicians seeking to optimize therapeutic strategies for patients with diverse risk profiles, emphasizing the need for tailored approaches based on individual patient risk factors. Further research is needed to explore this differential effect more comprehensively, possibly by incorporating more detailed data on baseline risk and utilizing statistical methods to assess the interaction effects between treatment efficacy and specific risk factors for HCC in patients. This finding aligns with a territory-wide cohort study conducted in Hong Kong, which reported that SGLT2i use was associated with a lower risk of HCC development in patients with co-existing T2DM and chronic hepatitis B infection³². These results suggest the potential protective effects of SGLT2 inhibitors against HCC development in high-risk patients, reinforcing the importance of targeted therapeutic strategies for managing HCC risk in patients with diabetes and chronic viral hepatitis.

The strength of our study lies in its large sample size and utilization of a national database, enabling a robust statistical approach and enhancing the generalizability of our findings. Nevertheless, we acknowledge the presence of inherent limitations, notably the study's retrospective and observational nature, which could introduce biases and the potential for residual confounding factors that might not be fully eliminated through statistical adjustments. In addition to, critical individual patient variables, such as height, weight, and blood glucose levels, which can significantly influence the outcomes, were not directly measured in our study. To mitigate these constraints, we incorporated several variables capable of indirectly representing the baseline health status of patients, including diagnoses related to obesity and the intensity of glycemic control treatments. Notably, in the Korean healthcare system, the prescription of OHAs and insulin is determined by initial HbA1c levels, offering a surrogate marker for assessing patients' baseline glycemic control. This methodology, while not directly measuring each variable, provides a practical and indirect assessment of patients' health conditions that could address, at least partially, some of the limitations mentioned. Furthermore, the potential underdiagnosis of early-stage HCC among non-cirrhotic patients without CVH presents an additional limitation. Our reliance on ICD-10 codes for identifying FLD, T2DM, and any cancers might not capture all instances of early-stage HCC, especially given the lack of established recommendations for HCC screening in non-cirrhotic patients. To address this concern, we employed a wash-out period strategy, however, we recognize that this measure cannot fully overcome the challenges associated with underdiagnosis of early-stage HCC. It indicates the need for future studies to develop more precise diagnostic criteria and screening protocols for this patient population.

In conclusion, within the NAFLD-T2DM cohort, SGLT2i did not demonstrate a statistically significant effect in reducing the risk of developing HCC. In contrast, our analysis within the FLD-T2DM-CVH cohort indicates a significant association between SGLT2i use and a decreased risk of HCC, highlighting their potential as a preventive strategy in patients with a higher risk profile of HCC. Nevertheless, it is important to recognize that our study is based on retrospective cohort data, underscoring the need for future research through prospective cohort studies to further validate these findings.

Methods

Author contributions

Concept and design: Jae Youn Cheong, Hyo Jung Cho. Acquisition, analysis, or interpretation of data: Hyo Jung Cho, Eunyoung Lee. Drafting of the manuscript: Hyo Jung Cho. Critical review of the manuscript for important intellectual content: Soon Sun Kim. Statistical analysis: Hyo Jung Cho, Eunyoung Lee. Supervision: Soon Sun Kim. Obtained funding: Jae Youn Cheong, Hyo Jung Cho.

Funding

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR22C1734, HR21C1003, HI23C0901), and National IT Industry Promotion Agency (NIPA) grant funded by the Korea government (MSIT) (No. S0252-21-1001).

Data availability

The data that support the findings of this study are available from the Health Insurance Review and Assessment Service (HIRA) of South Korea but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Health Insurance Review and Assessment Service (HIRA) of South Korea.

Competing interests

The authors declare no competing interests.

Footnotes

References