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Abstract
Objective We aimed to determine the association of statins with progression to a high risk for advanced fibrosis in primary care patients with metabolic dysfunction-associated steatotic liver disease (MASLD).
Design This retrospective cohort study of electronic health record data included patients with MASLD and an initial low or indeterminate risk for advanced fibrosis, determined by Fibrosis-4 Index (FIB-4) score (<2.67). Patients were followed from the index FIB-4 until the primary outcome of a high-risk FIB-4 (≥2.67) or the end of the study period. Prescription for a statin during follow-up was the primary exposure. We developed Cox regression models for the time to a high-risk FIB-4 score with statin therapy as the primary covariate and adjusting for baseline fibrosis risk, demographic and comorbidity variables.
Results The cohort of 1238 patients with MASLD was followed for a mean of 3.3 years, with 47% of patients receiving a prescription for a statin, and 18% of patients progressing to a high-risk FIB-4. In the adjusted Cox model with statin prescription as the primary exposure, statins were associated with a lower risk (HR 0.60; 95% CI 0.45 to 0.80) of progressing to a FIB-4≥2.67. In the adjusted Cox models with statin prescription intensity as the exposure, moderate (HR 0.60; 95% CI 0.42 to 0.84) and high intensity (HR 0.61; 95% CI 0.42 to 0.88) statins were associated with a lower risk of progressing to a high-risk FIB-4.
Conclusion Statin prescriptions, and specifically moderate and high intensity statin prescriptions, demonstrate a protective association with fibrosis risk progression in primary care patients with MASLD.
- FIBROSIS
- CHRONIC LIVER DISEASE
- Non-alcoholic Fatty Liver Disease
Data availability statement
No data are available. Data come from the electronic health record (EHR), EPIC Clarity Database (EPIC, WI) and the Enterprise Data Warehouse at MUSC. This data include protected health information and are not currently available in a data repository.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Statin medications play an integral role in reducing cardiovascular event risk in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), but their association with the risk of liver fibrosis is not known.
WHAT THIS STUDY ADDS
This retrospective cohort study demonstrates the protective association of statins, specifically moderate and high intensity statins, with the time to a high risk for advanced fibrosis in patients with MASLD.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
A prospective cohort study of patients with MASLD is needed to better understand the relationship of statins and progression to advanced fibrosis.
Introduction
Statin, or 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor, medications play a foundational role in the primary care mission to reduce atherosclerotic cardiovascular events and cardiovascular death from coronary artery, cerebrovascular and peripheral arterial disease.1 Statins are prescribed for primary prevention of future myocardial infarction and stroke, and as secondary prevention for patients already experiencing cardiovascular events.2 3
Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly non-alcoholic fatty liver disease (NAFLD)) is a highly prevalent chronic liver disease affecting an estimated 30% of the US population and maybe a cardiovascular risk factor independent of other, often coinciding, cardiometabolic conditions (eg, diabetes and dyslipidaemia).4–6 Current MASLD management guidelines in primary care stress the need to address cardiovascular disease (CVD) risk in affected patients, but statins are not recommended for all patients with MASLD and existing cardiovascular risk assessment tools do not include MASLD as a risk modifier.1 3 7
While cardiometabolic risk management is a feature of recent MASLD guidance statements, the principal focus in primary care is to identify patients with MASLD at high risk for advanced fibrosis, as advanced fibrosis is the single best predictor of future severe liver disease outcomes including cirrhosis, complications of cirrhosis, hepatocellular carcinoma and need for liver transplantation.7–10 Using validated non-invasive tools like the Fibrosis-4 Index (FIB-4) and vibration-controlled elastography, primary care clinicians can accurately assess advanced fibrosis risk and refer patients at high risk to hepatology specialists for ongoing MASLD evaluation and care. Patients with MASLD at low risk for advanced fibrosis are to remain in primary care, but the management goals and targets for therapy in these patients are incompletely described and not universally agreed on.11–13 Current recommendations include prescribing weight loss strategies, reducing the intake of sugar-sweetened beverages and addressing cardiometabolic risk factors.7 In addition to providing needed cardiovascular risk reduction to patients with MASLD, statins have also shown potential benefits for chronic liver disease, demonstrating an association with a lower hazard of incident cirrhosis, hepatocellular carcinoma and liver-related mortality in studies of retrospective data.14–16 Whether statins can play a role in treating patients with MASLD at low risk for advanced fibrosis and preventing fibrosis progression is not known.
Previous studies demonstrate a potentially protective relationship of statin medications with fibrosis risk in patients with MASLD.17–20 Many of these retrospective studies, however, do not evaluate changes in fibrosis risk with longitudinal data, consider statin usage as the primary exposure or analyse the relationship between statin intensity and fibrosis outcomes. In this study, we aimed to determine the relationship between statin prescribing, and the intensity of statin prescribing, with progression to a high risk for advanced fibrosis in primary care patients with low-risk MASLD. We hypothesised that statin prescriptions would be associated with a lower hazard of advanced fibrosis risk escalation.
Methods
Study design
This retrospective cohort study examined the association of statin prescribing with time to a high risk for advanced fibrosis in patients with MASLD and low- or indeterminate- advanced fibrosis risk as baseline. We used Cox regression models and treated statin prescriptions, and statin prescription intensity, as primary exposures. FIB-4 scores were used for assessing advanced fibrosis risk.21
Setting
We studied electronic health record (EHR) data from an internal medicine primary care clinic at an academic medical centre in the southeastern United States from July 2012 through December 2021.
Study sample
Patients with a diagnosis of MASLD (formerly NAFLD) by International Classification of Diseases (ICD)-9/10 code (571.8, K76.0 or K75.8) or radiographic imaging results with hepatic steatosis were identified. Those with a competing chronic liver disease diagnosis, cirrhosis, complication of cirrhosis, hepatocellular carcinoma or history of liver transplant at baseline (by ICD-9/10 code, online supplemental table 1) were excluded.22 Remaining patients with an MASLD diagnosis or hepatic steatosis finding on imaging were evaluated for available laboratory inputs to calculate a FIB-4 score within 1 year before or after the first MASLD diagnosis or image. FIB-4 score calculation required a platelet count, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) values, and patient age at the aminotransferase results (FIB-4=[Age×AST]/[Platelets×√ALT]).21 To avoid using lab results indicative of acute liver inflammation, we limited qualifying FIB-4 inputs to ALT and AST values <350 IU/L. Platelet counts needed to come from the day of the ALT and AST results, or within the preceding 6 months. Once calculated, FIB-4 scores were categorised by advanced fibrosis risk; low: FIB-4<1.3; indeterminate: 1.3≤FIB-4< 2.67 and high: FIB-4≥2.67.23–25 The first FIB-4 score calculated at the time of MASLD ascertainment served as the index FIB-4 and subsequent scores were calculated for the remainder of the study period, with FIB-4s separated by at least 6 months to ensure that inputs were never used for more than one score.
Supplemental material
Once FIB-4 scores were calculated, we also excluded: (1) patients with a high-risk FIB-4 (≥2.67) at baseline; (2) patients without two FIB-4s during the study period and (3) patients without 12 months of prescription data preceding the index FIB-4 were also excluded.
Outcomes
Time to a high-risk advanced fibrosis risk assessment was the primary outcome of interest. High risk for advanced fibrosis was determined by a FIB-4≥2.67 during follow-up. Patients were followed from the date of the index FIB-4 (within 1 year of MASLD ascertainment) until the occurrence of a high-risk FIB-4 or the end of the study period.
Exposure
Prescription of a statin medication was the primary exposure of interest. Patient EHR data were reviewed to identify transmitted and printed prescriptions for any statin medication during follow-up and the year preceding the index FIB-4. Statin prescriptions were categorised by intensity (low/moderate/high) based on definitions from the American College of Cardiology and the American Heart Association (online supplemental table 2).2 Statin prescriptions during the follow-up period were treated as follows: (1) dichotomous fixed covariates (yes/no) and (2) multilevel categorical fixed covariates by the intensity of the first and last prescription. Statin prescription data in the 1 year preceding the index FIB-4 were used to determine the statin exposure at baseline for the time-varying analyses.
Covariates
Demographic and clinical data were collected from the EHR. Demographic variables included age (continuous), sex (female/male), race (non-white, white), marital status (married/unmarried) and smoking status (yes/no). The race variable was dichotomised due to the relatively low proportion of clinic patients who are identified as non-black and non-white (<3%). Marital status was included as a recognised variable associated with disparate health outcomes.26 Comorbidity variables included Body Mass Index (BMI, kg/m2) and diagnosis codes (ICD-9/10) within the EHR during follow-up for hypertension, diabetes mellitus, CVD (composite of coronary artery disease, peripheral arterial disease and cerebrovascular disease), hypothyroidism and chronic kidney disease (CKD). Diagnostic codes were identified using validated Elixhauser coding algorithms.27 28 Comorbidities were chosen based on their relationship with MASLD, statin prescribing and advanced fibrosis outcomes.2 4 29 Cohort patients with lipid panel results, including total cholesterol, high-density lipoprotein and triglycerides, at the beginning of follow-up or in the preceding 3 years had their 10-year atherosclerotic cardiovascular disease (ASCVD) risk calculated and categorised as 10-year ASCVD risk ≥7.5% (an indication for prescribing statins in primary care) or <7.5%.30 31
Data sources
All data elements came from the EHR, EPIC Clarity Database (EPIC, WI) and the Enterprise Data Warehouse at Medical University of South Carolina.
Statistical analysis
Univariate analyses were performed to describe the characteristics of the cohort overall and by statin prescription status (yes/no) during follow-up. Continuous variables were compared using two sample t tests and categorical variables were compared using χ2 tests. Statin prescribing, statin intensity, demographic and clinical variables were compared by high risk for advanced fibrosis outcome status. Two sample t tests were used to compare age and BMI while χ2 tests were used to compare the categorical variables.
We developed unadjusted and adjusted Cox regression models for the time to high risk for advanced fibrosis using different structures of the statin prescription exposure variable. First, we developed Cox models with statin prescription during follow-up as a dichotomous fixed covariate (yes/no). We performed a stepwise adjustment by FIB-4 fibrosis risk (low (FIB-4<1.3)/indeterminate (1.3≤FIB-4<2.67)) at baseline, then fully adjusting by sex, race, marital status, smoking status, BMI, hypertension, diabetes, CVD, hypothyroidism and CKD. Next, we developed unadjusted and adjusted Cox regression models with statin intensity (none/low/moderate/high) as a fixed covariate, with one set of models using the first statin prescription identified during follow-up and another set using the last statin prescription detected during the follow-up period. We also performed sensitivity analyses by developing unadjusted and adjusted Cox regression models for the outcome of time to a high-risk FIB-4 with 10-year ASCVD risk as a covariate (categorised as 10-year ASCVD risk ≥7.5% or <7.5%) and models for the subset of the cohort with FIB-4 scores <1.3 at baseline.30 We also developed unadjusted and adjusted Cox regression models for the subset of the cohort with only FIB-4 scores <1.3 at baseline. All models were tested for multicollinearity using variance inflation factor. SAS V. 9.4 (Cary, North Carolina, USA) was used for all statistical analyses.
Results
In total, 1238 patients with MASLD and an index FIB-4 score <2.67 were included in the cohort (online supplemental figure 1). Included patients had a mean age of 53.7 years, were 64% female and 40% were non-white (table 1). The cohort had a mean BMI of 33 kg/m2 and 35%, 32% and 18% had diabetes, CVD and CKD, respectively. At baseline, 64% (795) of patients had a FIB-4 score at low risk (FIB-4<1.3) and 36% (443) of patients had a FIB-4 at indeterminate risk (1.3≤<FIB-4<2.67) for advanced fibrosis. Of the cohort, 858 (69%) of patients had qualifying inputs for a 10-year ASCVD risk calculation and 502 patients (59% of those with inputs (858)) had a 10-year ASCVD risk ≥7.5%.
Cohort characteristics overall and by receipt of a statin prescription (n=1238)
Patients were followed for a mean 3.3 (±2.5) years. Of the cohort, 47% (586) received a prescription for a statin during follow-up and 18% (220) progressed to a high risk for advanced fibrosis (FIB-4≥2.67, figure 1). There was no difference in the proportion of patients receiving a prescription for a statin during follow-up by progression to high fibrosis risk (48% vs 47%, p=0.898) and no difference in the intensity of the statin prescription by high-risk FIB-4 outcome (p=0.968, table 2). Patients progressing to a high-risk FIB-4 were older, had a lower BMI and had higher proportions of hypertension, diabetes, CVD and CKD compared with patients without a high-risk FIB-4 score. Of the 502 cohort patients with inputs for 10-year ASCVD risk calculation and a 10-year risk ≥7.5%, 69% (345) received a prescription for statin therapy during follow-up. By the end of follow-up, 82 patients (6.6%) received a diagnosis for a severe liver disease outcome comprising cirrhosis, a complication of cirrhosis or hepatocellular carcinoma. A significantly lower proportion of patients with a statin prescription had a severe liver disease outcome (4.8%) compared with patients without a statin script (8.3%, p=0.01).
Kaplan-Meier survival curves for the outcome of progression to a high risk for advanced fibrosis (FIB-4≥2.67) by statin prescription. FIB-4, Fibrosis-4 Index.
Patient characteristics by progression to a FIB-4 score at high risk (FIB-4≥2.67) for advanced fibrosis
In the Cox regression models with statin prescription as a dichotomous, time-fixed exposure variable, a statin prescription had no association with progression to a high-risk FIB-4 (HR 0.89; 95% CI 0.68 to 1.16) in the unadjusted model (table 3). After adjusting for advanced fibrosis risk at baseline, a prescription for a statin was associated with a lower hazard of a high fibrosis risk (HR 0.73; 95% CI 0.56 to 0.95). In the Cox regression model adjusting for baseline fibrosis risk, demographic and comorbidity variables, a statin prescription during follow-up was associated with a significantly lower risk of developing a FIB-4≥2.67 (HR 0.60; 95% CI 0.45 to 0.80) compared with patients not receiving a statin prescription. In the Cox regression models with statin prescription as a four-level categorical, time-fixed variable by the intensity of the first statin prescribed, no statin prescription intensities were associated with time to a high-risk FIB-4 in the unadjusted model. After adjusting for baseline fibrosis risk, no significant association was seen between prescribed statin intensity and time to high risk for advanced fibrosis. In the fully adjusted model, moderate (HR 0.60; 95% CI 0.42 to 0.84) and high intensity (HR 0.61; 95% CI 0.42 to 0.88) statin prescriptions were associated with a lower hazard of progressing to a high-risk FIB-4 compared with patients not receiving a statin prescription. There was no significant association seen with the prescription of low intensity statins. Similar findings resulted in the series of Cox regression models using the intensity of the last statin prescribed during follow-up (table 3). The fully adjusted models are available in online supplemental table 3.
Estimated HRs and 95% CIs for the unadjusted and adjusted Cox regression models for the time to a high advanced fibrosis risk assessment (FIB-4≥2.67) with any statin prescription (A), the intensity of the first statin prescription (B) and the intensity of the final statin prescription (C) as the primary exposures
The sensitivity analyses with Cox regression models adjusting for ASCVD risk yielded similar results (online supplemental table 4) as did the models performed for the subset of the cohort with an index FIB-4<1.3 (online supplemental table 5).
Discussion
The study results demonstrate that prescriptions for statin therapy are associated with a reduced risk of progression to high risk for advanced fibrosis (HR 0.60; 95% CI 0.45 to 0.80) in patients with low-risk MASLD. Further, moderate (HR 0.60; 95% CI 0.42 to 0.84) and high intensity (HR 0.61; 95% CI 0.42 to 0.88) statin prescriptions are associated with a lower hazard of progressing to MASLD with a high risk for advanced fibrosis compared with patients with no statin prescriptions.
Generalised use of statin therapies in the primary care management of MASLD is going to be an important consideration in future iterations of MASLD care guidelines. Recommendations targeted towards primary care clinicians have begun to stress the need for cardiovascular risk assessment and reduction in patients with MASLD, but current care trends suggest that clinicians have not yet prioritised statin prescribing in this population and that many patients with MASLD lack routine lipid test results necessary for calculating the 10-year risk of ASCVD.3 7 12 32 In our study, only 47% of patients with high burdens of cardiometabolic risk factors received a prescription for a statin at any point during the mean 3.3 years of follow-up. More problematically, 31% of patients with inputs for 10-year ASCVD risk calculation and a 10-year ASCVD risk ≥7.5% did not receive a statin prescription. CVD is the leading cause of death in patients with MASLD and MASLD is associated with increased cardiovascular risk in affected patients.5 33 Future statin prescribing risk assessment tools and guidelines for primary prevention of ASCVD should consider inclusion of MASLD as a risk factor for future cardiovascular events.1 3
Beyond reducing cardiovascular risk, primary care efforts in MASLD management will need to focus on ways to prevent progression to advanced fibrosis. Improving MASLD detection and performing advanced fibrosis risk assessment will identify more patients in primary care with MASLD at initially low risk for fibrosis.11 In addition to determining the optimal timing for serial, non-invasive advanced fibrosis assessment in these low-risk patients with MASLD, care plans with evidence-based interventions for reducing future advanced fibrosis risk are needed.7 Weight loss of 7%–10% of body weight mediated by lifestyle changes has been shown to halt and even reverse fibrosis in patients with MASLD.34 Evidence for the impact of pharmacological and surgical weight loss on hepatic fibrosis change continues to emerge, but these weight loss interventions have demonstrated reductions in metabolic dysfunction-associated steatohepatitis activity.35 36 Exercise, independent of weight loss, will also be a hallmark of MASLD therapy as evidence shows that exercise training can reduce hepatic fat content.37 Prospective studies scrutinising the relationship between statin therapy, MASLD and advanced fibrosis progression are needed, as prescribing statins could be a crucial adjunct to weight loss and exercise in primary care MASLD management.7 9 25
We recognise limitations in this study. First, we recognise that FIB-4 advanced fibrosis risk is not a perfect surrogate for advanced fibrosis (F3/F4) from histology. However, FIB-4 has a relatively high AUROC (area under the receiver operating characteristic curve) for predicting advanced fibrosis, is broadly available in primary care and will be the primary signal that primary care clinicians follow in future management of patients with MASLD.23–25 Thus, though FIB-4 is not an ideal outcome, it is a reasonable one. Future work augmenting FIB-4 scores with liver stiffness measurements would improve this analysis. Also, our use of ICD-9/10 codes for chronic liver disease and comorbidity identification is a limitation as these codes in administrative data can suffer from low sensitivity. Notably, there is no ICD code for MASLD during this study, so we chose to rely on NAFLD codes and radiographic reports of hepatic steatosis. Additionally, our statin exposure data are not perfect. We are confident in our ability to detect all statin prescriptions, characterised by intensity, in the EHR, but we cannot guarantee patients’ adherence to therapy, and we cannot estimate cumulative statin exposure prior to follow-up. We anticipate this concern would bias our data towards the null hypothesis. There is also a threat from immortal time bias in this study, as healthier patients are likely to have longer follow-up times and greater opportunity to receive statin therapies. Future work to address this concern would include performing a landmark analysis or a Cox regression analysis with time-dependent covariates. Further, in our adjusted Cox models, we do not have the ability to determine whether statin usage was for primary or secondary prevention of cardiovascular events. Adjustment by cardiovascular diagnoses (coronary artery, cerebrovascular and peripheral arterial disease) helps mitigate this concern, and the interaction term of statin prescription and a CVD diagnosis was not significant in the model. We also have incomplete data for the 10-year ASCVD risk assessment in this cohort, which is an important consideration for a study evaluating statin prescribing. Though this missing data is a limitation, our sensitivity analyses accounting for ASCVD risk in patients with complete data yield similar results. Additionally, the follow-up time (mean 3.3 years) may have been too brief to fully address our question. Longer follow-up may be necessary to see clinically meaningful changes in advanced fibrosis. Lastly, this study comes from a single centre, threatening generalisability. While this is true, we think the diversity of the sample (45% non-white) and the high burden of cardiometabolic risk factors reflect other primary care practices throughout the USA.
Conclusion
Statins play a role in reducing cardiovascular risk for primary care patients with MASLD, but their association with progression to advanced fibrosis is less clear. Increasing the intensity of cardiovascular risk assessment for patients with MASLD may increase statin prescribing in this population and additional work is needed to determine the impact of statins on fibrosis progression in primary care patients with MASLD.
Data availability statement
No data are available. Data come from the electronic health record (EHR), EPIC Clarity Database (EPIC, WI) and the Enterprise Data Warehouse at MUSC. This data include protected health information and are not currently available in a data repository.
Ethics statements
Patient consent for publication
Ethics approval
This study (Pro00056541) involves human participants and was approved by the Institutional Review Board at the Medical University of South Carolina (MUSC).
Acknowledgments
All contributors to this project and manuscript are recognised as authors.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Presented at This study and data were presented at the European Association for the Study of the Liver (EASL) International Liver Congress (ILC) in Milan, Italy on 9 June 2024.
Contributors AS, JZ and MG were responsible for the conceptualisation of the study. JZ, JM and CB were responsible for data procurement, cleaning and storage. AS, JZ and MG were responsible for the methodology and analysis plan. All authors (AS, JZ, CAP, WPM, DGK, JM, CB, PDM and MG) participated in the interpretation of data. AS authored the original draft, and all authors edited the final version. All authors approve of the final submitted version. AS is the guarantor.
Funding National Institute of Diabetes and Digestive and Kidney Diseases (NIH/NIDDK K23DK118200 PI: AS; R03DK129558 PI: AS). This project was also supported by the South Carolina Clinical & Translational Research Institute with an academic home at the Medical University of South Carolina CTSA National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) under UL1 TR001450.
Competing interests All authors report no conflicts of interest with this work. AS has previously consulted for Pfizer and Novo Nordisk.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.