Effect of renin-angiotensin system inhibitor in incident cancer among chronic hepatitis B patients: An emulated target trial using a nationwide cohort

Abstract

Background

Although the renin-angiotensin system (RAS) has been reported to be associated with cancer development, the anticancer effects of RAS inhibitors (RASi) remain controversial.

Objectives

This study aimed to investigate the effect of RASi use on cancer incidence in chronic hepatitis B (CHB) patients.

Design

We designed a series of pragmatic trials for each week and followed the patients until the cancer diagnosis, death, or end of follow-up.

Methods

We analyzed CHB patients aged 40–84 years from the nationwide database between 2009 and 2017. We used 3:1 propensity score matching.

Results

Among 15,477 RASi non-users and 5263 RASi users, 2002 developed cancer. The adjusted hazard ratio (HR) for all cancer in RASi users was 0.89 [95% confidence interval (CI): 0.81–0.99]. The adjusted HR (95% CI) of hepatocellular carcinoma (HCC) and extrahepatic cancer were 0.79 (0.65–0.96) and 0.93 (0.82–1.04), respectively. When RASi was further divided, the adjusted HR (95% CI) for cancer of the angiotensin-converting enzyme inhibitor user and the angiotensin II receptor blocker user were 0.66 (0.50–0.87) and 0.93 (0.84–1.03), respectively.

Conclusion

RASi use was associated with a decreased incidence of all cancers, particularly HCC, in CHB patients, suggesting a chemopreventive effect of RASi in this population.

Keywords

chronic hepatitis B renin-angiotensin system inhibitor cancer hepatocellular carcinoma

Introduction

Chronic hepatitis B (CHB) infection is a leading cause of hepatocellular carcinoma (HCC).¹ HCC has the fifth-highest cancer incidence and the second-highest mortality rate of all cancers globally.² Even with high genetic barrier antiviral therapy for hepatitis B virus (HBV), the 5-year cumulative incidence of HCC is between 3.06% and 5.09% in CHB patients.³ Efforts are being made to further reduce the risk of HCC in CHB, and studies suggest that aspirin,⁴ metformin,⁵ and statins⁶ have chemoprevention effects on HCC. CHB is also associated with an increased risk of multiple extrahepatic cancers.⁷

The renin-angiotensin system (RAS) is a hormone system that has two pathways: (a) a classical pathway consisting of ACE/angiotensin II (AngII)/type 1 angiotensin receptor (AT₁R), and (b) an alternative pathway consisting of ACE2/Ang (1–7)/Mas receptor.⁸ With a balance between the two pathways, it controls blood pressure, tissue perfusion, and fluid balance at the endocrine level, and also controls proliferation, apoptosis, angiogenesis, and inflammation at the paracrine level.⁹ Preclinical studies have shown that the classical pathway plays a role in carcinogenesis and tumor progression.¹⁰ Yet, clinical studies with RAS inhibitors (RASi) have shown contradictory results. In 1998, a single-center retrospective study reported that angiotensin-converting enzyme inhibitor (ACEi) and angiotensin II receptor blocker (ARB) lowered the incidence of cancer by 28% in hypertensive patients.¹¹ On the other hand, a meta-analysis of 9 randomized controlled trials (RCTs) indicated that ARB increased cancer incidence by 25%.¹² Yet, the FDA announced that ARB administration does not increase the risk of cancer development.¹³ Furthermore, a recent meta-analysis concluded that the use of ACEi and ARB does not increase cancer incidence, further supporting the safety of these medications in the context of cancer risk.¹⁴

Recent in vitro and in vivo studies have highlighted the prospect that RASi has anti-fibrotic and anti-tumor effects, and might lead to a reduction in the incidence of cancer, including HCC.^15,16 As studies are now being performed on potential medications that might reduce the incidence of cancer in CHB patients, it would be worth investigating whether RASi use can reduce cancer incidence in CHB patients. However, clinical data are still lacking, and the results have been inconsistent.^17–21 Thus, we performed a target trial emulation (TTE) using a nationwide population-based cohort to identify the effect of RASi on cancer incidence among CHB patients.

Methods

Data sources and study population

We performed a trial emulation study using data from the Korean National Health Insurance Service (K-NHIS) database. The K-NHIS covers approximately 97% of Koreans, while the 3% of remaining Koreans who cannot afford national insurance are covered by the Medical Aid Program (MAP).²² Therefore, the K-NHIS database represents the entire population of South Korea. The K-NHIS claims database contains information on demographics, medical treatment, procedures, prescription drugs, diagnostic codes, and hospital use. Furthermore, the K-NHIS claims database includes national health screening results. A standardized national health screening program is provided by the K-NHIS every two years for all insured persons.²³ This program includes a self-administered questionnaire on medical history and lifestyle habits, anthropometric measurements, and laboratory tests (Supplementary material 1).²³ The participation rate among the target population is approximately 76%.²³ Use of the K-NHIS database is permitted if the study protocols are approved by the government's official review committee (protocol number: NHIS-2021-1-575). The need for informed consent was waived, as this study was conducted using anonymized claims data.

We included all 40–84 years old CHB patients who were defined as having any HBV (International Classification of Diseases, 10^th Revision [ICD-10]; B18.0, B18.1, B18.10, B18.18, or Z22.5) code in any claim or death certificate during the study period. To reduce selection bias, we restricted CHB patients to those who had hypertension (ICD-10; I10–13, I15), diabetes (ICD-10; E11–14), or heart failure (ICD-10; I110, I130, I1132, I255, I420, I425–429, I43, I50, I971), which is the most common indication for RASi.

Among CHB patients with hypertension, diabetes, or heart failure, we selected patients who underwent at least one health screening exam and did not receive a RASi prescription prior to the exam between January 1, 2009, and December 31, 2017. We excluded participants with a history of cancer, HIV or HCV infection, liver cirrhosis, end-stage renal disease [ICD-10: N17 or special medical aid codes (V001, V003, or V005)], kidney transplantation (R3280), or dialysis (O7020, O9991, O7075, O2016, O2019, O7076, O7077, O2013) prior to the health screening examination. We also excluded participants with jaundice (ICD-10; R17), ascites (ICD-10; R18), and hepatic failure (ICD-10; K720), which potentially impacted the RASi prescription. Participants who died on the health-screening day were also excluded. Among the eligible participants, those who had missing data on confounding factors were excluded. The Institutional Review Board of the Samsung Medical Center approved the study and waived the requirement for informed consent because the K-NHIS data were de-identified.

Measurements

K-NHIS claims for inpatient and outpatient visits, procedures, and prescriptions were coded using the ICD-10.²⁴ As the K-NHIS routinely audits the claims, such data are considered reliable and used in numerous peer-reviewed publications.^23,25

The study exposure was the use of the RASi which was defined as the presence of RASi prescriptions identified from the Korean Drug and Anatomical Therapeutic Chemical Codes (Supplemental material 2).

The primary endpoint was the development of cancer of any type. Cancer was defined as the presence of a cancer-specific insurance claim code (V193). The secondary endpoints were the development of HCC and extrahepatic cancer. HCC was defined as the presence of a cancer-specific insurance claim code (V193 code) with the C22.0 code which is ICD-10 code for HCC.

For the covariates, we included age, sex, body mass index, smoking status, drinking status, physical activity, total cholesterol, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol; liver enzymes including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and gamma-glutamyl transferase (GGT); comorbidity including history of myocardial infarction, stroke, revascularization, coronary heart disease, hypertension, and diabetes; and concomitant medications including calcium channel blockers, aspirin, statins, beta-blockers, warfarin, and antiviral medications. Concomitant medications were defined as those prescribed 90 days prior to the index date.

Assigned groups and follow-up

We emulated a pragmatic sequence of trials or pseudo-trials by aligning the eligibility window, treatment assignment, and start of follow-up between the biological and non-biological study arms.²⁶ To emulate a trial of effectiveness of RASi on clinical outcomes in CHB patients, we identified CHB patients who met the eligibility criteria on the day of health screening visits and followed them until incident cancer, death, or December 2019, whichever occurred first. We classified participants into the RASi user group if they received RASi prescriptions during the week, which was the enrollment period, and into the RASi non-user group if they did not.

Next, using an approach previously described,²⁷ we emulated a second trial with a new baseline a week after the first trial, and continued this every subsequent week until the week of the 180^th day after the health screening visit for the participants with hypertension, diabetes, or heart failure codes at the initial health screening visit. Participants who met the exclusion criteria, developed cancer, or were assigned to the RASi group in a previous trial were excluded, and all others were reclassified into two groups according to whether they had a RASi prescription in the next seven days or not.

We repeated the entire process of health screening between days 0 and 180. Each participant could contribute as an eligible individual to as many trials as possible. Since the participant could develop an event and then have a RASi prescription even within a week, we emulated a series of trials with a 1-week enrollment period. Thus, participants who developed an event within a week were excluded and followed-up until the end date of the trial. The emulation of sequential trials is a valid and efficient procedure if participants meet the eligibility criteria at several time points.²⁸

We then performed propensity score (PS) matching to minimize the potential impact of confounders on the outcomes. In the analysis, the covariate variables were updated at the start of each trial. Multivariable logistic regression estimated the PS for RASi users using the following variables at each trial entry date: all the covariates in Table 1 at year-month of the entry of each trial. We implemented a 3:1 PS nearest-neighbor matching with a caliper of 0.15 on the PS scale. Differences in baseline covariates between the two groups were evaluated before and after the PS matching using an absolute standardized difference with a value of > 0.15 indicating a significant difference. In the analysis, we pooled data from all trials into a single model and included the day of the trial's baseline. A standardized mean difference (SMD) between the RASi user and non-user groups was estimated to compare the distribution of the variables used for matching.

Table 1.

Baseline characteristics.

	RASi nonuserN = 15,477	RASi userN = 5263	SMD
Age, years	60.4 (9.6)	60.5 (9.6)	0.01
Sex			0.004
Male	8561 (55.3)	2921 (55.5)
Female	6916 (44.7)	2342 (44.5)
BMI
Underweight (<18.5 kg/m²)	200 (1.3)	67 (1.3)	−0.002
Normal (18.5–23 kg/m²)	3524 (22.8)	1153 (21.9)	−0.021
Overweight (23–25 kg/m²)	3826 (24.7)	1273 (24.2)	−0.012
Obesity (>25 kg/m²)	7924 (51.2)	2770 (52.6)	0.029
Unknown	3 (0.0)	0 (0.0)	0
Drinking
None	9261 (59.8)	3170 (60.2)	0.008
Moderate	5522 (35.7)	1848 (35.1)	−0.077
Heavy	643 (4.2)	231 (4.4)	0.012
Unknown	51 (0.3)	14 (0.3)	−0.012
Smoking
None	9084 (58.7)	3066 (58.3)	−0.009
Past	2928 (18.9)	1035 (19.7)	0.019
Current	3448 (22.3)	1158 (22.0)	−0.007
Unknown	17 (0.1)	4 (0.1)	−0.011
Physical activity (per week)
None	12,390 (80.1)	4182 (79.5)	−0.015
Regular	3029 (19.6)	1065 (20.2)	0.017
Unknown	58 (0.4)	16 (0.3)	−0.012
Total cholesterol, mg/dl	190.40 (52.39)	189.57 (41.27)	−0.012
HDL cholesterol, mg/dl	53.01 (19.00)	52.91 (18.37)	−0.004
LDL cholesterol, mg/dl	110.36 (54.59)	109.34 (37.02)	−0.015
AST, U/L	34.39 (47.27)	35.81 (48.63)	0.021
ALT, U/L	34.82 (61.78)	36.88 (66.54)	0.023
GGT, U/L	55.96 (97.30)	55.53 (86.58)	−0.003
Systolic blood pressure, mmHg	133.27 (17.53)	132.93 (17.16)	−0.014
Diastolic blood pressure, mmHg	81.52 (11.50)	81.42 (11.47)	−0.006
eGFR, mL/min/1.73 m²	84.6 (27.6)	84.4 (29.2)	−0.007
Myocardial infarction (%)	571 (3.7)	266 (5.1)	0.069
Stroke (%)	2592 (16.7)	861 (16.4)	−0.01
Revascularization (%)	790 (5.1)	404 (7.7)	0.105
Concomitant medications	92 (0.6)	30 (0.6)
Calcium channel blocker (%)	652 (4.2)	342 (6.5)	0.037
Aspirin (%)	688 (4.4)	358 (6.8)	0.102
Statin (%)	906 (5.9)	367 (7.0)	0.102
Beta-blocker (%)	40 (0.3)	18 (0.3)	0.046
Warfarin (%)	421 (2.7)	142 (2.7)	0.015
Antiviral medications (%)	571 (3.7)	266 (5.1)	−0.001

Values were presented as n (%) or mean (SD). RASi: renin-angiotensin system inhibitor; SMD: standard mean difference; BMI: body mass index; HDL: high-density lipoprotein; LDL: low-density lipoprotein; AST: aspartate aminotransferase; ALT: alanine aminotransferase; GGT: gamma-glutamyl transferase; eGFR: estimated glomerular filtration rate.

Statistical analysis

The primary analysis was intention-to-treat analysis. The cumulative incidence of each outcome was estimated using the Kaplan-Meier method, and log-rank tests were applied to evaluate differences between the groups. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated for the incidence of clinical outcomes using a Cox regression model. Although we performed PS matching, covariates with SMD > 0.1, indicating potential imbalance between matched groups, were further adjusted in the survival analysis. We also performed an analysis by type of RASi compared to the control group.

We examined the proportional hazard assumption using plots of the log (-log) survival function and Schoenfeld residuals. All p values were 2-sided, and a p value of less than 0.05 was considered significant. Analyses were performed using SAS^® Visual Analytics (SAS Institute Inc., USA) and R 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Of the 1,879,022 potential person-trials, we excluded participants who had a history of any cancer (N = 247,927), HIV (N = 321) or HCV (N = 57,040) infection, liver cirrhosis (N = 266,705), end-stage renal disease, chronic kidney disease, kidney transplantation, or dialysis (N = 2550). We also excluded participants with a history of jaundice, ascites, or hepatic failure (N = 28,859). Furthermore, participants who died on the health-screening day (N = 1760) were also excluded. Thus, 1,389,525 person-trials met the eligibility criteria, and 288,687 person-trials were excluded because of missing data on confounding factors. The final sample size was 1,100,838. Among them, we selected three person-trials for the control group using the RASi non-users. Thus, we obtained matched 15,477 RASi non-users and 5263 matched RASi users (Figure 1). The mean follow-up period was 8 years. The mean (standard deviation) age of the study participants was 60.4 years and 55% of the participants were men (Table 1). All SMDs of the difference between RASi users and non-users were less than 0.1, except for revascularization, use of aspirin and statin (Figure 2). Compared to RASi non-users, RASi users were more likely to receive revascularization and use aspirin and statin (Table 1).

Figure 1.

Flow chart of study participants.

Figure 2.

Covariate balance check.

During the follow-up, 2,002 participants developed cancer, and the incidence rates of cancer in RASi non-users and RASi users were 1.9 and 1.8 per 100 person-years, respectively (Table 2). The fully adjusted HR for incident cancer comparing RASi users to RASi non-users was 0.89 (95% CI: 0.81, 0.99). The fully adjusted HR for incident HCC and extrahepatic cancer comparing RASi users to RASi nonusers were 0.79 (95% CI: 0.65, 0.96) and 0.93 (95% CI: 0.82, 1.04), respectively (Table 2).

Table 2.

Risk for incidence of cancer by use of renin-angiotensin system inhibitor (RASi).

	Crude HR(95% CI)	Adjusted HR(95% CI)
All cancer
Any types
Non-user	Reference	Reference
User	0.90 (0.81, 0.99)	0.89 (0.81, 0.99)
ACEi
Non-user	Reference	Reference
User	0.71 (0.54, 0.93)	0.66 (0.50, 0.87)
ARB
Non-user	Reference	Reference
User	0.92 (0.83, 1.03)	0.93 (0.84, 1.03)
Type of cancer
Hepatocellular carcinoma
Any types
Non-user	Reference	Reference
User	0.78 (0.64, 0.95)	0.79 (0.65, 0.96)
ACEi
Non-user	Reference	Reference
User	0.56 (0.32, 0.97)	0.57 (0.32, 0.99)
ARB
Non-user	Reference	Reference
User	0.82 (0.67, 1.00)	0.82 (0.67, 1.01)
Extrahepatic cancer
Any types
Non-user	Reference	Reference
User	0.94 (0.83, 1.05)	0.93 (0.82, 1.04)
ACEi
Non-user	Reference	Reference
User	0.77 (0.57, 1.05)	0.70 (0.51, 0.96)
ARB
Non-user	Reference	Reference
User	0.96 (0.85, 1.09)	0.96 (0.85, 1.09)

HR: hazard ratio; CI: confidence interval; ACEi: angiotensin-converting enzyme inhibitor; ARB: angiotensin II receptor blocker.

Adjusted for covariates with SMD greater than 0.1.

On subgroup analysis by type of RASi, the association between RASi and a lower risk of cancer development (adjusted HR = 0.66; 95% CI: 0.50, 0.87), HCC (adjusted HR = 0.57; 95% CI: 0.32, 0.99), and extrahepatic cancer (adjusted HR = 0.70; 95% CI: 0.51, 0.96) showed a stronger effect in ACEi than in ARB (Table 2).

Discussion

This study is the first to emulate a hypothetical randomized trial investigating the effect of RASi on cancer incidence among patients with CHB. This study benefitted from employing the capabilities of the nationwide database, K-NHIS, while compensating for the potential shortcomings of retrospective studies. We found that RASi use was associated with a lower risk of developing all cancers, especially HCC, in patients with CHB. When RASi was further divided into ACEi and ARB, ACEi significantly lowered the risk of HCC, extrahepatic cancer, and all cancers, whereas ARB only showed a trend. These results suggest a potential role for RASi as a chemopreventive agent for the prevention of cancer development, especially HCC, in CHB patients.

Preclinical and clinical studies have showed that RASi may confer protective effects against cancer. Clinical studies have revealed that RASi significantly lower the risk of various cancers, including lung, female-specific,¹¹ esophageal,²⁹ and prostate cancer³⁰ in hypertensive patients. The biological mechanisms underlying these protective effects involve the modulation of classical RASi pathway. RASi can inhibit tumor growth, metastasis, and angiogenesis by blocking the AT₁R, which facilitates cell proliferation, inflammation, and angiogenesis through pathways such as MAPK/ERK and PI3K/Akt. Conversely, the alternative RASi pathway involving ACE2/Ang-(1–7)/Mas receptor exerts anti-tumor effects by inhibiting cell proliferation and angiogenesis.¹⁰

In addition, RASi might have an additional inhibitory effect on HCC. It is assumed that the RASi use inhibits the local hepatic RAS, hence decreasing portal pressure.³¹ Furthermore, it has been observed that RASi reduce hepatic fibrosis by reducing the fibrotic activity of hepatic stellate cells.^15,32–34 Clinical data has shown that RASi use reduced HCC recurrence and increased survival.^35–37 However, a nationwide cohort study with RASi users among CHB patients receiving antiviral medications showed that the incidence of HCC was not reduced in RASi users compared to RASi nonusers, with an adjusted HR of 0.97 (95% CI:0.80–1.16).¹⁷ These results may be different from ours because they comprised only patients who started to take RASi within six months from the index date and had a relatively short 4-year follow-up period.

In the present study, the risk of cancer was lower in ACEi users than in ARB users. This finding is consistent with the previous study by Zhang et al. in which only ACEi, not ARBs, reduced liver-related events (a composite endpoint comprising complications from cirrhosis and liver cancer).¹⁸ These findings may be explained by the fact that ACEi affects both the RAS and kallikrein-kinin system. ACE, the target of ACEi, degrades bradykinin as part of the kallikrein-kinin system. Therefore, the administration of ACEi increases bradykinin concentration. Bradykinin was reported to have shown anti-fibrotic properties in vitro,³⁸ and to reduce hepatic fibrosis in vivo.³⁹ Therefore, ACEi may have a stronger effect in preventing HCC development than ARB.

Our study is strengthened by the application of advanced analytic methods that account for measured differences between treatment arms, which otherwise confound analyses and preclude causal inference, and also reduce the issue of immortal-time bias. TTE draws attention to the timing of eligibility and treatment assignment, two standard design features of randomized experiments that can lead to bias when mismatched in observational analyses.²⁸ A previous cohort study that used sequential TTE⁴⁰ applied TTE to evaluate the effects of emulated “randomization” to intervention on subsequent cancer risk. By simultaneously determining eligibility and assigning treatment at the start of a study, we similarly assigned treatment at the “start” of induction, before subsequently emulating the randomization process using coarsened exact matching.

This study had some limitations. First, even if the TTE approach was utilized in this study as a retrospective cohort analysis, the results do not substitute for RCTs. Ultimately, large-scale RCTs are required to confirm the findings of this study. Second, the K-NHIS has limited information on CHB stage, such as the status of HBeAg, anti-HBeAb, and HBV DNA titers. However, this disadvantage was offset by including antiviral treatment prescription information as an adjusting variable because it would be administered when the reimbursement conditions were fit. Third, RASi use was substituted for prescriptions. Since the K-NHIS does not have information on drug compliance, it cannot be confirmed whether the prescribed medication was actually administered. Fourth, the follow-up period from 2009 to 2017 may not capture the full latency period required for the development of certain solid cancers, which often exceeds 10 years. Therefore, our study may not fully reflect the long-term cancer risks associated with antihypertensive medications. Finally, despite employing robust methods such as sequential emulation trial techniques and propensity score matching to adjust for known confounders, there remains the potential influence of unmeasured confounders. These could affect the observed associations between RAS inhibitor use and cancer incidence, particularly HCC. Unmeasured variables, such as genetic predispositions, environmental exposures, or variations in healthcare access, could impact the outcomes. Therefore, caution should be exercised when interpreting the findings. The strength of this study was that it attempted to overcome the limitations of a retrospective study by employing TTE. In addition, various clinical and laboratory measurements were performed.

Conclusion

In conclusion, this study showed that RASi users with CHB are at a lower risk of developing cancer, particularly HCC. Further prospective studies are required to verify the results of the present study.

Supplemental Material

sj-pdf-1-jra-10.1177_14703203241294037 - Supplemental material for Effect of renin-angiotensin system inhibitor in incident cancer among chronic hepatitis B patients: An emulated target trial using a nationwide cohort

Supplemental material, sj-pdf-1-jra-10.1177_14703203241294037 for Effect of renin-angiotensin system inhibitor in incident cancer among chronic hepatitis B patients: An emulated target trial using a nationwide cohort by Yewan Park, Danbee Kang, Dong Hyun Sinn, Hyunsoo Kim, Yun Soo Hong, Juhee Cho and Geum-Youn Gwak in Journal of the Renin-Angiotensin-Aldosterone System

Supplemental Material

sj-docx-2-jra-10.1177_14703203241294037 - Supplemental material for Effect of renin-angiotensin system inhibitor in incident cancer among chronic hepatitis B patients: An emulated target trial using a nationwide cohort

Supplemental material, sj-docx-2-jra-10.1177_14703203241294037 for Effect of renin-angiotensin system inhibitor in incident cancer among chronic hepatitis B patients: An emulated target trial using a nationwide cohort by Yewan Park, Danbee Kang, Dong Hyun Sinn, Hyunsoo Kim, Yun Soo Hong, Juhee Cho and Geum-Youn Gwak in Journal of the Renin-Angiotensin-Aldosterone System

Footnotes

Abbreviations

Acknowledgments

None

Author contributions

Y.P., D.K., J.C., and G.Y.G.: study design, D.K. and H.K.: data collection and statistical analysis, Y.P. and D.K.: drafting of the manuscript, D.H.S., Y.S.H., J.C., and G.Y.G.: critical review of the manuscript. D.H.S., J.C., and G.Y.G.: study supervision. All the authors have full access to all of the data and take responsibility for the integrity of the data and the accuracy of the data analysis.

Availability of data and materials

The data and materials used in this study are not publicly available due to restrictions imposed by the Korean National Health Insurance Service (KNHIS). Access to the dataset is limited to authorized personnel in accordance with regulatory guidelines and privacy policies.

Consent for publication

Not applicable

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics approval and consent to participate

The Institutional Review Board of the Samsung Medical Center approved the study and waived the requirement for informed consent because the K-NHIS data were de-identified. (IRB number: SMC 2020-06-095)

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Geum-Youn Gwak

Supplemental material

Supplemental material for this article is available online.

Use of AI software

AI software was used only for the purpose of grammar checking in the preparation of the manuscript. No other content generation or analysis was performed using AI tools. All research, data analysis, and interpretations were conducted independently by the authors.

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