Advertisement

Development and validation of a model to predict incident chronic liver disease in the general population: The CLivD score

Open AccessPublished:March 25, 2022DOI:https://doi.org/10.1016/j.jhep.2022.02.021

      Highlights

      • Liver disease tends to develop silently without symptoms and thus the diagnosis is often delayed.
      • To improve early risk prediction, we developed and validated the CLivD score for use in the general population.
      • The CLivD score is based on age, sex, alcohol use, waist-hip ratio, diabetes, smoking, with or without GGT values.
      • The CLivD score provides accurate predictions of 15-year risk for future severe liver disease.
      • The CLivD score could be used as part of health counseling, and for planning further liver investigations and follow-up.

      Background & Aims

      Current screening strategies for chronic liver disease focus on detection of subclinical advanced liver fibrosis but cannot identify those at high future risk of severe liver disease. Our aim was to develop and validate a risk prediction model for incident chronic liver disease in the general population based on widely available factors.

      Methods

      Multivariable Cox regression analyses were used to develop prediction models for liver-related outcomes with and without laboratory measures (Modellab and Modelnon-lab) in 25,760 individuals aged 40–70 years. Their data were sourced from the Finnish population-based health examination surveys FINRISK 1992-2012 and Health 2000 (derivation cohort). The models were externally validated in the Whitehall II (n = 5,058) and Copenhagen City Heart Study (CCHS) (n = 3,049) cohorts.

      Results

      The absolute rate of incident liver outcomes per 100,000 person-years ranged from 53 to 144. The final prediction model included age, sex, alcohol use (drinks/week), waist–hip ratio, diabetes, and smoking, and Modellab also included gamma-glutamyltransferase values. Internally validated Wolbers’ C-statistics were 0.77 for Modellab and 0.75 for Modelnon-lab, while apparent 15-year AUCs were 0.84 (95% CI 0.75-0.93) and 0.82 (95% CI 0.74-0.91). The models identified a small proportion (<2%) of the population with >10% absolute 15-year risk for liver events. Of all liver events, only 10% occurred in participants in the lowest risk category. In the validation cohorts, 15-year AUCs were 0.78 (Modellab) and 0.65 (Modelnon-lab) in the CCHS cohort, and 0.78 (Modelnon-lab) in the Whitehall II cohort.

      Conclusions

      Based on widely available risk factors, the Chronic Liver Disease (CLivD) score can be used to predict risk of future advanced liver disease in the general population.

      Lay summary

      Liver disease often progresses silently without symptoms and thus the diagnosis is often delayed until severe complications occur and prognosis becomes poor. In order to identify individuals in the general population who have a high risk of developing severe liver disease in the future, we developed and validated a Chronic Liver Disease (CLivD) risk prediction score, based on age, sex, alcohol use, waist-hip ratio, diabetes, and smoking, with or without measurement of the liver enzyme gamma-glutamyltransferase. The CLivD score can be used as part of health counseling, and for planning further liver investigations and follow-up.

      Graphical abstract

      Keywords

      Linked Article

      Introduction

      Liver disease is increasingly contributing to the global healthcare burden,
      • Asrani S.K.
      • Devarbhavi H.
      • Eaton J.
      • Kamath P.S.
      Burden of liver diseases in the world.
      and cirrhosis is the eleventh most common cause of death. Liver disease tends to develop without signs or symptoms and thus is often detected in the late stages based on complications such as ascites, jaundice, and variceal bleeding, with markedly poor prognosis.
      • Williams R.
      • Aspinall R.
      • Bellis M.
      • Camps-Walsh G.
      • Cramp M.
      • Dhawan A.
      • et al.
      Addressing liver disease in the UK: a blueprint for attaining excellence in health care and reducing premature mortality from lifestyle issues of excess consumption of alcohol, obesity, and viral hepatitis.
      In the UK, for instance, 50% of patients receive their diagnosis of cirrhosis following an emergency admission to the hospital because of complications of end-stage disease,
      • Ratib S.
      • Fleming K.M.
      • Crooks C.J.
      • Aithal G.P.
      • West J.
      1 and 5 year survival estimates for people with cirrhosis of the liver in England, 1998-2009: a large population study.
      even though most of these patients have had prior contacts with primary healthcare.
      • Verrill C.
      • Smith S.
      • Sheron N.
      Are the opportunities to prevent alcohol related liver deaths in the UK in primary or secondary care? A retrospective clinical review and prospective interview study.
      Identifying at-risk individuals before progression to advanced liver disease is an imperative. Early diagnosis could allow for risk communication with the affected patient, implementation of targeted lifestyle interventions, and consistent liver evaluation and follow-up. Existing screening strategies are based on currently acknowledged population-level risk factors, such as diabetes, obesity, and alcohol use. However, relying on these factors makes the number needed to screen unrealistically high,
      • Hudson M.
      • Sheron N.
      • Rowe I.A.
      • Hirschfield G.M.
      Should we screen for cirrhosis?.
      along with carrying significant uncertainty regarding the risk of liver disease progression.
      Liver investigations triggered by abnormal aminotransferases alone or the detection of steatosis on imaging will miss a significant number of patients with liver disease.
      • Harris R.
      • Harman D.J.
      • Card T.R.
      • Aithal G.P.
      • Guha I.N.
      Prevalence of clinically significant liver disease within the general population, as defined by non-invasive markers of liver fibrosis: a systematic review.
      Thanks to low positive-predictive values, reliance on these findings can lead to resource-consuming specialized investigations and overdiagnosis in many individuals who will never develop clinical liver disease.
      • Donnan P.T.
      • McLernon D.
      • Dillon J.F.
      • Ryder S.
      • Roderick P.
      • Sullivan F.
      • et al.
      Development of a decision support tool for primary care management of patients with abnormal liver function tests without clinically apparent liver disease: a record-linkage population cohort study and decision analysis (ALFIE).
      ,
      • Rowe I.A.
      Too much medicine: overdiagnosis and overtreatment of non-alcoholic fatty liver disease.
      Current risk stratification focuses on assessing the amount of liver fibrosis, but simple non-invasive liver fibrosis tests, such as non-alcoholic fatty liver disease (NAFLD) fibrosis score, fibrosis-4 (FIB-4), or aspartate aminotransferase to platelet ratio index (APRI), are of limited value in individuals in the general population compared to in highly selected NAFLD cohorts from specialist centers.
      • Caballeria L.
      • Pera G.
      • Arteaga I.
      • Rodriguez L.
      • Aluma A.
      • Morillas R.M.
      • et al.
      High prevalence of liver fibrosis among European adults with unknown liver disease: a population-based study.
      ,
      • Armstrong M.J.
      • Schmidt-Martin D.
      • Rowe I.A.
      • Newsome P.N.
      Caution in using non-invasive scoring systems in NAFLD beyond highly selected study populations.
      Significant alcohol use also impairs the performance of these tests,
      • Thiele M.
      • Madsen B.S.
      • Hansen J.F.
      • Detlefsen S.
      • Antonsen S.
      • Krag A.
      Accuracy of the enhanced liver fibrosis test vs. Fibrotest, elastography, and indirect markers in detection of advanced fibrosis in patients with alcoholic liver disease.
      and they were not originally designed to predict clinical liver outcomes.
      • Hagström H.
      • Nasr P.
      • Ekstedt M.
      • Stål P.
      • Hultcrantz R.
      • Kechagias S.
      Accuracy of noninvasive scoring systems in assessing risk of death and liver-related endpoints in patients with nonalcoholic fatty liver disease.
      • Hagström H.
      • Talbäck M.
      • Andreasson A.
      • Walldius G.
      • Hammar N.
      Ability of noninvasive scoring systems to identify individuals in the population at risk for severe liver disease.
      • Åberg F.
      Liver fibrosis scores in the general population: better risk indices are needed.
      Direct fibrosis biomarkers and special imaging methods, such as elastography, are additionally limited by cost, accessibility, and suboptimal performance in identifying early fibrosis stages and in the presence of alcoholic steatohepatitis.
      • Nguyen-Khac E.
      • Thiele M.
      • Voican C.
      • Nahon P.
      • Moreno C.
      • Boursier J.
      • et al.
      Non-invasive diagnosis of liver fibrosis in patients with alcohol-related liver disease by transient elastography: an individual patient data meta-analysis.
      ,
      • Pavlov C.S.
      • Casazza G.
      • Nikolova D.
      • Tsochatzis E.
      • Gluud C.
      Systematic review with meta-analysis: diagnostic accuracy of transient elastography for staging of fibrosis in people with alcoholic liver disease.
      All fibrosis tests reflect only the current state of the liver and not the factors driving disease progression.
      Given that metabolic factors are also important in alcoholic-related liver disease and that alcohol use seems to affect metabolic liver disease,
      • Åberg F.
      • Färkkilä M.
      Drinking and obesity: alcoholic liver disease/nonalcoholic fatty liver disease interactions.
      population risk assessment should consider the combined contribution of alcohol and metabolic factors.
      • Åberg F.
      • Färkkilä M.
      Drinking and obesity: alcoholic liver disease/nonalcoholic fatty liver disease interactions.
      • Eslam M.
      • Sanyal A.J.
      • George J.
      International Consensus Panel
      MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease.
      • Åberg F.
      • Puukka P.
      • Salomaa V.
      • Männistö S.
      • Lundqvist A.
      • Valsta L.
      • et al.
      Combined effects of alcohol and metabolic disorders in patients with chronic liver disease.
      Risk prediction models that account for the combined contribution of several risk factors, analogous to the Framingham risk score or the European SCORE used in cardiovascular medicine,
      • Damen J.A.
      • Hooft L.
      • Schuit E.
      • Debray T.P.A.
      • Collins G.S.
      • Tzoulaki I.
      • et al.
      Prediction models for cardiovascular disease risk in the general population: systematic review.
      would offer the opportunity to risk-stratify individuals before advanced liver disease arises.
      Our aim was to develop and validate a simple prediction model – the Chronic Liver Disease (CLivD) score – to quantify the risk of incident clinical liver disease in individuals in the general population, based on widely available and easily reproducible risk factors.

      Material and methods

      This study was conducted and reported in accordance with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prediction or Diagnosis) guidelines.
      • Collins G.S.
      • Reitsma J.B.
      • Altman D.G.
      • Moons K.G.
      Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.
      Written informed consent from participants and research ethical approvals were obtained in all study cohorts.

      Derivation cohort (Finland)

      The population-based sample used for development of the risk model was extracted from the national FINRISK Studies from 1992, 1997, 2002, 2007, and 2012, and the Health 2000 survey from 2000-2001.
      • Aromaa A.
      • Koskinen S.
      Health and functional capacity in Finland. Baseline results of the Health 2000 health examination survey.
      ,
      • Borodulin K.
      • Tolonen H.
      • Jousilahti P.
      • Jula A.
      • Juolevi A.
      • Koskinen S.
      • et al.
      Cohort profile: the national FINRISK study.
      FINRISK studies are cross-sectional health-examination surveys that have been conducted in a systematic and standardized fashion by the Finnish Institute for Health and Welfare (previously National Public Health Institute) in Finland every 5 years since 1972. The surveys provide data on adults (25–74 years) from 4–6 regions in Finland. The sample were randomly drawn from the Finnish National Population Register and were stratified by region, sex, and 10-year age groups. The number of invitees has varied during 1992–2012 from 7,927 to 13,500, and participation rates have ranged from 65% to 76%.
      • Borodulin K.
      • Tolonen H.
      • Jousilahti P.
      • Jula A.
      • Juolevi A.
      • Koskinen S.
      • et al.
      Cohort profile: the national FINRISK study.
      The Health 2000 Survey was also coordinated by the Finnish Institute for Health and Welfare (previously National Public Health Institute), and originally comprised 8,028 adults aged ≥30 years, with a participation rate in the full examinations of 80%.
      • Aromaa A.
      • Koskinen S.
      Health and functional capacity in Finland. Baseline results of the Health 2000 health examination survey.
      The cohort is considered representative of the entire Finnish population through a regional 2-stage stratified cluster sampling procedure. The methods, measurements, and protocols used in the FINRISK and Health 2000 studies are described in the supplementary information (p. 2–3).
      The present study included individuals who were aged 40-70 years at baseline. We excluded those with a baseline diagnosis of liver disease in any registry (ICD-10: K70-K77, C22.0; ICD8/9: 570-573, 155.0); with a diagnosis of chronic viral hepatitis (ICD-10: B18); and current alcohol abstainers (i.e. they had used alcohol earlier and then stopped) (Table S1).
      Follow-up data were obtained from several nationwide electronic health registers through linkage using the unique personal identity code assigned to all Finnish residents, as explained in detail in the supplementary information (p. 2–3). Follow-up ended in December 2016.

      Validation cohort (Whitehall II)

      The Whitehall II study is an ongoing cohort study of UK civil servants. A total of 10,308 adults (6,895 men and 3,413 women, aged 35–55) were originally recruited during 1985–1988 from London-based offices. Follow-up clinical examinations have taken place every 4-5 years since that time, with each wave taking 2 years to complete. Participants were linked electronically to national hospitalization, cancer, and mortality registers up to December 2019.
      • Kivimäki M.
      • Batty G.D.
      • Singh-Manoux A.
      • Britton A.
      • Brunner E.J.
      • Shipley M.J.
      Validity of cardiovascular disease event ascertainment using linkage to UK hospital records.
      In studies of chronic diseases, the sensitivity and specificity of the Hospital Episode Statistics (HES) database are high.
      • Kivimäki M.
      • Batty G.D.
      • Singh-Manoux A.
      • Britton A.
      • Brunner E.J.
      • Shipley M.J.
      Validity of cardiovascular disease event ascertainment using linkage to UK hospital records.
      ,
      • Sommerlad A.
      • Perera G.
      • Singh-Manoux A.
      • Lewis G.
      • Stewart R.
      • Livingston G.
      Accuracy of general hospital dementia diagnoses in England: sensitivity, specificity, and predictors of diagnostic accuracy 2008-2016.
      Because the hospitalization register achieved a high level of national coverage from January 1997 onward, we set the start of follow-up time at the Whitehall II study’s fifth follow-up examination (phase 5), which was undertaken in 1997–1999 and included 7,870 participants. We then applied the same exclusion criteria as in the derivation cohort (Table S1), except that we were could not fully exclude known baseline liver disease before 1997 based on registry coding.

      Validation cohort (Copenhagen City Heart Study, CCHS)

      The CCHS originally comprised a random sample of 19,698 individuals drawn from the Copenhagen Population Register in January 1976, from an urban population of ∼90,000 inhabitants aged ≥20 years, as previously described.
      The Copenhagen city heart study.
      Although additional samples have been included in follow-up examinations (1981–1983, 1991–1994, and 2001–2003), a total of 3,092 individuals have been examined in all 4 examinations. Because of the availability of necessary variables, we set the start of follow-up time at the fourth examination conducted in 2001–2003, comprising 6,238 individuals (49.5% of those invited). Participants were linked to well-validated Danish nationwide hospitalization, cancer, and mortality registers, which have previously been used successfully for liver outcomes.
      • Gellert-Kristensen H.
      • Richardson T.G.
      • Davey Smith G.
      • Nordestgaard B.G.
      • Tybjaerg-Hansen A.
      • Stender S.
      Combined effect of PNPLA3, TM6SF2, and HSD17B13 variants on risk of cirrhosis and hepatocellular carcinoma in the general population.
      We applied the same exclusion criteria as above (Table S1).

      Definition of liver outcomes

      Study endpoints were fatal and non-fatal advanced liver disease (requiring hospital admission or causing liver cancer or liver-related death). The ICD codes used for defining the outcomes are listed in Table S2.

      Candidate predictors and missing data

      The primary candidate variables of interest were objective, readily available, and reproducible factors identified a priori based on previously published data, clinical rationale, and their ease of use in primary care settings (Tables S3 and S4). Alcohol use and smoking data were based on questionnaires (supplementary information, p. 3–4). Waist and hip circumferences were measured using standard techniques.
      • Aromaa A.
      • Koskinen S.
      Health and functional capacity in Finland. Baseline results of the Health 2000 health examination survey.
      ,
      • Borodulin K.
      • Tolonen H.
      • Jousilahti P.
      • Jula A.
      • Juolevi A.
      • Koskinen S.
      • et al.
      Cohort profile: the national FINRISK study.
      Diabetes was defined as a fasting serum glucose ≥7.0 mmol/L (126 mg/dl), taking diabetic medication, or having a prior known diabetes diagnosis.
      Baseline data with ≤5% missingness were imputed by multiple imputation using the predictive mean matching method (supplementary information, p. 6). In the derivation cohort, data on exercise, binge drinking, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and homeostasis model assessment of insulin resistance (HOMA-IR) were missing in ≥15% because these variables had not been assessed in all sub-cohorts. Missingness rates for all other variables were 0%–5% (Table S3).

      Statistical analyses

      We developed two types of risk prediction models: one based on non-laboratory measures only (Modelnon-lab) and one including laboratory measures (Modellab). Candidate variables were tested for association with liver outcomes by univariable and multivariable Cox regression analyses with incident liver disease within 15 years as the outcome. Predictors with univariate p <0.2 were considered in multivariable analysis. Of correlated variables (Spearman correlation coefficient >0.6), we chose the variable judged to be clinically more important. The final model was selected by a combination of backward and forward stepwise elimination techniques (supplementary information, pp. 6–14); however, age was retained in the model as a measure of exposure time regardless of statistical significance. Non-linear associations were investigated using restricted cubic splines. Two-way interactions among the variables in the final model and with sex were investigated and included in the final model if they improved model performance based on the Akaike information criterion, C-statistic and likelihood ratio test. Variables with >5% missingness rates were subsequently tested in the complete-case dataset for whether they improved model performance (supplementary information, p. 11–12).
      From the final models (Modelnon-lab and Modellab), we calculated prognostic risk scores for each person as a linear predictor, i.e. a weighted sum of the variables in the model, where the weights were the Cox regression coefficients. A high-risk score indicates higher risk of liver events. Based on this risk score, participants were classified into 4 risk groups defined by the predicted 15-year absolute cumulative probability of liver events using the cut-offs 0.5%, 5%, and 10%. We considered 15-year risk because of the time it usually takes for clinical liver endpoints to develop from early-stage liver disease,
      • Hagström H.
      • Nasr P.
      • Ekstedt M.
      • Hammar U.
      • Stål P.
      • Hultcrantz R.
      • et al.
      Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD.
      and risk stratification on a shorter timescale would have risked suboptimal discrimination. The cumulative probability calculation was based on cause-specific Cox regression considering death without liver disease as a competing-risk event.
      • Ozenne B.
      • Sorensen A.
      • Scheike T.
      • Torp-Pedersen C.
      • Gerds T.
      riskRegression: predicting the risk of an event using Cox regression models.
      We assessed cause-specific Cox regression model performance in terms of discrimination (Wolbers’ C-statistic and time-dependent AUC) and calibration. Internal validation was based on bootstrap re-sampling. The Aalen-Johansen competing-risk method was used to estimate the cumulative incidence of liver outcomes within risk groups. Using the prognostic scores, we assessed the model’s C-statistic in subgroups of the derivation cohort by sex, alcohol risk use (average alcohol intake >30 g/day for men and >20 g/day for women), and baseline NAFLD (fatty liver index ≥30
      • Bedogni G.
      • Bellentani S.
      • Miglioli L.
      • Masutti F.
      • Passalacqua M.
      • Castiglione A.
      • et al.
      The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population.
      and non-risk drinking). To address the impact of possible pre-existing undiagnosed liver disease, we also performed landmark analysis set at 1 or 3 years after baseline. We also performed analyses by all liver events (including milder forms of liver disease; ICD-10: K70-K77, C22.0; ICD8/9: 570-573, 155.0), liver death, all-cause death, and incident cardiovascular disease (defined as previously described).
      • Åberg F.
      • Puukka P.
      • Salomaa V.
      • Männistö S.
      • Lundqvist A.
      • Valsta L.
      • et al.
      Risks of light and moderate alcohol use in fatty liver disease: follow-up of population cohorts.
      External validation of Modelnon-lab was performed in the Whitehall II and CCHS cohorts, but we externally validated Modellab only in the CCHS cohort because gamma-glutamyltransferase (GGT) was unavailable in Whitehall II. Data were analyzed with R software version 3.6.1.

      Results

      The Finnish derivation cohort comprised 25,760 individuals, the Whitehall II cohort had 5,058 individuals, and the CCHS cohort had 3,049 individuals (Table 1). Compared with the Finnish cohort, the Whitehall II cohort had a higher proportion of men (70% vs. 48%), fewer lifetime alcohol abstainers (3% vs. 9%), fewer smokers (12% vs. 23%), and less diabetes (4% vs. 8%). The CCHS cohort had a slightly higher mean age (56.8 vs. 54.0 years), more alcohol use (13 vs. 8 drinks/week), more smokers (36% vs. 23%), and less diabetes (4% vs. 8%) than the Finnish cohort (Table 1).
      Table 1Baseline demographics in the Finnish derivation cohort and the UK validation cohort.
      Derivation cohortValidation cohort
      CountryFinlandUKDenmark
      N25,7605,0583,049
      Age54.1 (8.5)55.6 (6.0)56.8 (8.6)
      Sex
       Men12,354 (48.0)3,520 (69.6)1,447 (47.5)
       Women13,406 (52.0)1,538 (30.4)1,602 (52.5)
      Alcohol use (drinks per week)
      1 drink = 10 g ethanol.
      8.1 (14.6)11.3 (11.7)13.3 (12.2)
      Lifetime alcohol abstainer2,299 (8.9)160 (3.2)
      Current smoker5,833 (22.8)592 (11.7)1,110 (36.4)
      Waist–hip ratio0.91 (0.09)0.89 (0.09)0.87 (0.09)
      Diabetes2,151 (8.4)224 (4.4)111 (3.6)
      Gamma-glutamyltransferase (U/L)37.1 (57.1)41.5 (36.9)
      Additional variables tested in the derivation cohort only
      Binge drinking
      Defined as 5 or more alcoholic drinks per occasion.
       Less often8,717 (73.2)
       Monthly1,670 (14.0)
       Weekly or more often1,516 (12.7)
      Smoking
       Never13,509 (53.4)
       Previous smoker6,194 (24.4)
       <10 cigarettes/day1,276 (5.0)
       10–19 cigarettes/day2,086 (8.2)
       20+ cigarettes/day2,291 (9.0)
      Exercise (>20 minutes)
       At least 2 times a week13,509 (57.9)
       2–4 times a month5,660 (26.0)
       Less often3,499 (16.1)
      Waist circumference (cm)92.2 (13.5)
      Body mass index (kg/m2)27.4 (4.6)
      Alanine aminotransferase (U/L)27.60 (18.11)
      Aspartate aminotransferase (U/L)28.85 (14.09)
      Low-density lipoprotein cholesterol (mmol/L)3.54 (0.96)
      High-density lipoprotein cholesterol (mmol/L)1.43 (0.39)
      Non–high-density lipoprotein cholesterol (mmol/L)4.28 (1.09)
      Triglycerides (mmol/L)1.54 (1.00)
      Results are given as n (%) or mean (± SD).
      1 drink = 10 g ethanol.
      ∗∗ Defined as 5 or more alcoholic drinks per occasion.
      Median follow-up time was 12.9 years (IQR 7.8–17.8; range 0.0–23.0; 318,616.0 person-years) in the derivation cohort, 21.6 years (IQR 21.2–21.8; range 0.1–22.4; 102,710.3 person-years) in the Whitehall II cohort, and 16.0 years (IQR 15.5–16.6, range 0.3–17.2, 45,027.4 person-years) in the CCHS cohort. The number of incident liver events (hospitalization, cancer, or death) was 273 in the derivation cohort, 54 in Whitehall II, and 64 in CCHS, and the absolute rates of incident liver outcomes per 100,000 person-years were 85.7, 52.6, and 144.4, respectively. All-cause mortality rates per 100,000 person-years were 937.4 in the derivation cohort, 810.0 in Whitehall II, and 1,494.6 in CCHS. Median years from baseline to first liver event were 9.3 (IQR 4.5–12.8) in the derivation cohort, 14.4 (IQR 9.4–17.5) in Whitehall II, and 15.5 (IQR 9.1–16.4) in CCHS.

      Model development, performance measure, and internal validation

      The phases of the model-building procedures are detailed in the supplementary information (pp. 8–14). The final multivariable model based on non-laboratory values only (Modelnon-lab) included age, sex, waist-hip ratio, average alcohol consumption, diabetes, and smoking status. The model with laboratory values (Modellab) included all of these variables and GGT (Fig. 1). There was a significant interaction between sex and smoking (effect of smoking stronger for men) and between sex and GGT (effect of GGT stronger for women) (Fig. S4).
      Figure thumbnail gr1
      Fig. 1HRs of model variables for liver-related outcomes and relationship between alcohol use and liver-related outcomes.
      Forest plot showing the HRs and 95% CIs for liver-related outcomes and the variables in (A) Modellab and (B) Modelnon-lab, and plots showing the non-linear relationship between alcohol use and liver-related outcomes. GGT, gamma-glutamyltransferase; HRs, hazard ratios; WHR, waist-hip ratio.
      Table 2 shows apparent and internal validation performance statistics of both models. Modellab had an optimism-corrected Wolbers’ C-statistic of 0.77 for discrimination of incident liver disease and an apparent 15-year AUC of 0.84 (Table 2). Calibration plots are shown in Fig. S13). For Modelnon-lab, the optimism-corrected C-statistic was 0.75, and apparent 15-year AUC, 0.82. Over the 15-year follow-up, the apparent C-statistic was ∼0.8 in Modellab, and Modelnon-lab (Fig. S6). In all sensitivity analyses, the apparent C-statistic remained >0.75 for Modellab and >0.71 for Modelnon-lab (Table 3). Model discrimination improved with restriction of outcomes to liver-related deaths only (Table 3).
      Table 2Model diagnostics.
      ModellabModelnon-lab
      Derivation cohort
      Wolbers’ C-statistic, apparent0.8160.790
      Wolbers’ C-statistic, optimism corrected
      Determined by bootstrapping 200 samples of the derivation data.
      0.7710.747
      Time-dependent AUC at 15 years, apparent0.841 (0.753-0.929)0.823 (0.736-0.909)
      Whitehall II (validation cohort)
      Wolbers’ C-statistic0.739
      Time-dependent AUC at 15 years0.789 (0.695-0.882)
      Copenhagen City Heart Study (validation cohort)
      Wolbers’ C-statistic0.7770.652
      Time-dependent AUC at 15 years0.777 (0.683-0.871)0.653 (0.549-0.756)
      95% CIs given in parentheses.
      Determined by bootstrapping 200 samples of the derivation data.
      Table 3Sensitivity analyses in the derivation cohort showing apparent Wolbers’ C-statistic as a measure of model performance.
      SubgroupsModellabModelnon-lab
      NLiver eventsC-statisticC-statistic
      Landmark at 1 year of follow-up25,6502560.8190.792
      Landmark at 3 years of follow-up21,8272240.8120.782
      Men12,3541940.8110.797
      Women13,406790.7810.712
      Complete-case analysis24,2292620.8150.789
      Alcohol risk drinkers
      Average alcohol intake >30 g/day for men and >20 g/day for women.
      1,704860.7670.711
      Non-risk drinkers22,7831490.7590.719
      Non-alcoholic fatty liver disease
      Fatty liver index >30 and a non-risk drinker.
      13,1531180.7620.714
      ModellabModelnon-lab
      Alternative outcomesNEventsC-statisticC-statistic
      All liver events
      ICD-10 codes K70-77 and C22.0, and ICD-8/9 codes 570-573, 155.0.
      24,2294070.7180.686
      Liver death24,2291530.8360.812
      All-cause death24,2292,9930.6950.682
      Cardiovascular events24,2292,7530.6390.629
      Average alcohol intake >30 g/day for men and >20 g/day for women.
      ∗∗ Fatty liver index >30 and a non-risk drinker.
      ∗∗∗ ICD-10 codes K70-77 and C22.0, and ICD-8/9 codes 570-573, 155.0.
      In a subpopulation of 1,253 individuals from the FINRISK cohorts with available platelet data, apparent Wolbers’ C were 0.72 for FIB-4 and 0.70 for APRI, compared to 0.88 for Modellab and 0.86 for Modelnon-lab.

      Risk stratification

      Fig. 2 shows the cumulative incidences of liver events by risk group, considering death without liver disease as a competing-risk event. The models could identify a small proportion (<2%) of the population with >10% absolute risk of developing liver events within 15 years. In the minimal-risk groups, 15-year risks were <0.4%, and only 10% of all liver events in the population occurred in the minimal-risk group.
      Figure thumbnail gr2
      Fig. 2Cumulative incidence of liver-related outcomes by risk group.
      Cumulative incidence of liver-related outcomes by risk group estimated by (A) Modellab and (B) Modelnon-lab in the derivation cohort using the Aalen-Johansen cumulative incidence function. Risk groups were defined by the predicted 15-year probability of liver outcomes as <0.05% (minimal), 0.05%–4.9% (low), 5%–9.9% (intermediate), and ≥10% (high).

      Nomogram

      Fig. 3, Fig. 4 show the nomograms for estimation of an individual’s 15-year (Modellab and Modelnon-lab) absolute risks of advanced clinical liver disease based on cause-specific Cox regression.
      Figure thumbnail gr3
      Fig. 3Nomogram to calculate an individual’s 15-year absolute risks of developing clinical liver-related outcomes based on the risk prediction model with laboratory data (Modellab).
      GGT, gamma-glutamyltransferase.
      Figure thumbnail gr4
      Fig. 4Nomograms to calculate an individual’s 15-year absolute risk of developing clinical liver-related outcomes based on the risk prediction model without laboratory data (Modelnon-lab).

      External validation

      Applying a cause-specific Cox model with the risk score based on Modellab as a single covariate to the CCHS cohort gave a Wolbers’ C-statistic of 0.78 and a 15-year AUC of 0.78 (95% CI 0.68-0.87) (Table 2). For Modelnon-lab, the corresponding C-statistics and 15-year AUCs were 0.65 and 0.65 (95% CI 0.55–0.76) in the CCHS cohort, and 0.74 and 0.79 (95% CI 0.70–0.88) in the Whitehall II cohort (Table 2). Comparisons of hazard ratios and absolute incidence estimates between risk groups in the various cohorts are shown in Fig. S11 and S12 and in Table S9. Assessment of relatedness between the derivation and Whitehall II samples are shown in the supplementary information, p. 17–18 and Fig. S14.
      In the Whitehall II cohort, the Modelnon-lab score increased during follow-up among both those who developed liver events and those who did not, but the score was consistently higher in the liver-event group (Fig. S15).

      Discussion

      We have developed and validated a chronic liver disease risk prediction model – the CLivD score – based on affordable and widely available variables to quantify an individual’s absolute risk of developing advanced chronic liver disease. Use of this novel model enables the identification of high-risk individuals in the general population before development of advanced liver fibrosis, considering the combined contribution of several risk factors and avoiding dichotomization between alcohol risk drinkers or non-risk drinkers.
      • Åberg F.
      • Färkkilä M.
      Drinking and obesity: alcoholic liver disease/nonalcoholic fatty liver disease interactions.
      ,
      • Åberg F.
      • Helenius-Hietala J.
      • Puukka P.
      • Färkkilä M.
      • Jula A.
      Interaction between alcohol consumption and metabolic syndrome in predicting severe liver disease in the general population.
      ,
      • Boyle M.
      • Masson S.
      • Anstee Q.M.
      The bidirectional impacts of alcohol consumption and the metabolic syndrome: cofactors for progressive fatty liver disease.
      Risk estimation with the non-laboratory version of the model can be completed by anyone online or using color-coded scoring sheets (Fig. 3, Fig. 4; example shown in Fig. S16) without the need for a single blood test, for example, as part of liver-oriented public health campaigns. This accessibility could increase the applicability and dissemination of risk estimations in the general population.
      In the derivation cohort, optimism-corrected Wolbers’ C-statistics as a measure of model discrimination in the competing-risk setting were 0.77 for the model with laboratory data and 0.75 for the model without laboratory data. In the validation cohort, the C-statistic for the model with GGT was 0.78. These estimates are reasonable considering that the models were developed to predict rare outcomes in unselected populations. For comparison, for the largest multinational risk charts with laboratory data to predict 10-year cardiovascular disease risk, the C-statistic varies in a range of 0.69–0.83 by country.
      WHO CVD Risk Chart Working Group
      World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions.
      The non-laboratory factors included in the CLivD score (age, sex, alcohol use, abdominal obesity, diabetes, and smoking) are acknowledged population risk factors for cirrhosis and have an established or suspected causal relationship with liver fibrosis and/or liver cancer.
      • Caballeria L.
      • Pera G.
      • Arteaga I.
      • Rodriguez L.
      • Aluma A.
      • Morillas R.M.
      • et al.
      High prevalence of liver fibrosis among European adults with unknown liver disease: a population-based study.
      ,
      • Åberg F.
      • Helenius-Hietala J.
      • Puukka P.
      • Färkkilä M.
      • Jula A.
      Interaction between alcohol consumption and metabolic syndrome in predicting severe liver disease in the general population.
      ,
      • Poynard T.
      • Lebray P.
      • Ingiliz P.
      • Varaut A.
      • Varsat B.
      • Ngo Y.
      • et al.
      Prevalence of liver fibrosis and risk factors in a general population using non-invasive biomarkers (FibroTest).
      • Koehler E.M.
      • Plompen E.P.
      • Schouten J.N.
      • Hansen B.E.
      • Darwish Murad S.
      • Taimr P.
      • et al.
      Presence of diabetes mellitus and steatosis is associated with liver stiffness in a general population: the Rotterdam study.
      • Roerecke M.
      • Vafaei A.
      • Hasan O.S.M.
      • Chrystoja B.R.
      • Cruz M.
      • Lee R.
      • et al.
      Alcohol consumption and risk of liver cirrhosis: a systematic review and meta-analysis.
      • Andreasson A.
      • Carlsson A.C.
      • Onnerhag K.
      • Hagström H.
      Waist/hip ratio better predicts development of severe liver disease within 20 years than body mass index: a population-based cohort study.
      • Zein C.O.
      • Unalp A.
      • Colvin R.
      • Liu Y.C.
      • McCullough A.J.
      Nonalcoholic Steatohepatitis Clinical Research Network
      Smoking and severity of hepatic fibrosis in nonalcoholic fatty liver disease.
      • Rutledge S.M.
      • Asgharpour A.
      Smoking and liver disease.
      • Liu B.
      • Balkwill A.
      • Roddam A.
      • Brown A.
      • Beral V.
      Million Women Study Collaborators
      Separate and joint effects of alcohol and smoking on the risks of cirrhosis and gallbladder disease in middle-aged women.
      In this context, age is a measure of exposure time and not a risk factor per se. Flexible non-linear analyses revealed that even light alcohol use was associated with liver outcomes, which is in agreement with previous longitudinal studies.
      • Roerecke M.
      • Vafaei A.
      • Hasan O.S.M.
      • Chrystoja B.R.
      • Cruz M.
      • Lee R.
      • et al.
      Alcohol consumption and risk of liver cirrhosis: a systematic review and meta-analysis.
      GGT is a readily available biomarker and more sensitive and accurate than ALT or AST for predicting future liver disease.
      • Hagström H.
      • Talbäck M.
      • Andreasson A.
      • Walldius G.
      • Hammar N.
      Ability of noninvasive scoring systems to identify individuals in the population at risk for severe liver disease.
      ,
      • McLernon D.J.
      • Donnan P.T.
      • Ryder S.
      • Roderick
      • Sullivan F.M.
      • Rosenberg W.
      • et al.
      Health outcomes following liver function testing in primary care: a retrospective cohort study.
      However, the correlation between serum GGT and severity of liver disease in cross-sectional studies is only modest.
      • Hagström H.
      • Talbäck M.
      • Andreasson A.
      • Walldius G.
      • Hammar N.
      Ability of noninvasive scoring systems to identify individuals in the population at risk for severe liver disease.
      ,
      • Westerbacka J.
      • Corner A.
      • Tiikkainen M.
      • Tamminen M.
      • Vehkavaara S.
      • Häkkinen A.-M.
      • et al.
      Women and men have similar amounts of liver and intra-abdominal fat, despite more subcutaneous fat in women: implications for sex differences in markers of cardiovascular risk.
      ,
      • Petta S.
      • Macaluso F.S.
      • Barcellona M.R.
      • Camma C.
      • Cabibi D.
      • Di Marco V.
      • et al.
      Serum gamma-glutamyl transferase levels, insulin resistance and liver fibrosis in patients with chronic liver diseases.
      By reflecting whole-body oxidative stress, serum GGT may instead mirror mechanisms that lead to disease and thus serve as a marker of disease risk rather than of existing liver disease.
      • Koenig G.
      • Seneff S.
      Gamma-glutamyltransferase: a predictive biomarker of cellular antioxidant inadequacy and disease risk.
      ,
      • Lee D.H.
      • Blomhoff R.
      • Jacobs D.R.
      Is serum gamma glutamyltransferase a marker of oxidative stress?.
      Oxidative stress mainly related to mitochondrial dysfunction is indeed considered pivotal in the pathophysiology of chronic liver disease and cirrhosis.
      • Cichoz-Lach H.
      • Michalak A.
      Oxidative stress as a crucial factor in liver diseases.
      ,
      • Masarone M.
      • Rosato V.
      • Dallio M.
      • Gravina A.G.
      • Aglitti A.
      • Loguercio C.
      • et al.
      Role of oxidative stress in pathophysiology of nonalcoholic fatty liver disease.
      Although GGT has traditionally been used as a marker of alcohol intake, the correlation between GGT and amount of alcohol intake is poor (r <0.3).
      • Sillanaukee P.
      • Massot N.
      • Jousilahti P.
      • Vartiainen E.
      • Sundvall J.
      • Olsson U.
      • et al.
      Dose response of laboratory markers to alcohol consumption in a general population.
      The prediction models are based on hazard rates derived from several combined, large, and well-characterized Finnish population cohorts with longitudinal follow-up for clinically relevant liver outcomes (hospital admission, cancer, death) ascertained from reliable national registries. We were able to assess multiple acknowledged risk factors identified a priori and account for their complex non-linear relationships and joint contribution. Because they are built on reproducible and widely available risk factors, the models likely can be applied with relative ease in clinical practice by nurses or general practitioners and are amenable to further external validation. Although waist–hip ratio was measured in this study according to standardized protocols, accuracy of self-measurements of this ratio is good.
      • Roberts C.A.
      • Wilder L.B.
      • Jackson R.T.
      • Moy T.F.
      • Becker D.M.
      Accuracy of self-measurement of waist and hip circumference in men and women.
      ,
      • Barrios P.
      • Martin-Biggers J.
      • Quick V.
      • Byrd-Bredbenner C.
      Reliability and criterion validity of self-measured waist, hip, and neck circumferences.
      Strengths of our study include external validation in UK and Danish populations with similar inclusion and exclusion criteria, and similar definitions of exposures and outcomes as in the derivation cohort. The ability to include liver-related hospitalizations is a major strength of our study, because this inclusion reduces the risk of omitting cases with clinically significant liver disease. Differences in incidence rates among cohorts are likely the result of a different case mix and different length of follow-up.
      Study limitations include an uncertainty in risk estimates when dealing with relatively rare outcomes and long-term predictions, as is the case with clinical liver disease. The low number of outcomes in the validation cohorts limited our ability to obtain accurate incidence estimates by risk subgroup, and for this reason, we did not perform model re-calibration. We assessed risk factors only once, at baseline, but this is often also the reality in the clinic when making predictions of future risks. More validation studies are needed in larger samples and in ethnically diverse populations. More study is also needed before the models can be applied in those with chronic viral hepatitis or abstainers with a history of alcohol consumption, since both of these were exclusion criteria in this study. We acknowledge that reliance on registry linkage omits undiagnosed liver disease and less severe cases that may have been largely managed in primary care, but we specifically sought to examine complicated liver disease, not subclinical liver fibrosis. Future studies should analyze whether inclusion of additional variables could improve model performance.

      Comparison to previous studies

      Current population-based screening strategies focus on detecting prevalent subclinical advanced liver fibrosis using various non-invasive fibrosis tests, such as FIB-4 and APRI.
      • Gines P.
      • Graupera I.
      • Lammert F.
      • Angeli P.
      • Caballeria L.
      • Krag A.
      • et al.
      Screening for liver fibrosis in the general population: a call for action.
      However, non-invasive fibrosis tests were not designed for screening the general population or predicting clinical liver-related outcomes.
      • Caballeria L.
      • Pera G.
      • Arteaga I.
      • Rodriguez L.
      • Aluma A.
      • Morillas R.M.
      • et al.
      High prevalence of liver fibrosis among European adults with unknown liver disease: a population-based study.
      ,
      • Younossi Z.M.
      • Henry L.
      Are noninvasive scoring systems for persons with chronic liver disease ready for prime time?.
      In a smaller subpopulation analysis, C-statistics for FIB-4 and APRI were 0.72 and 0.70, respectively. These are comparable to those reported in a large Swedish population-based study (0.70 and 0.67), although it is unclear whether competing risks were adequately accounted for in that study.
      • Hagström H.
      • Talbäck M.
      • Andreasson A.
      • Walldius G.
      • Hammar N.
      Ability of noninvasive scoring systems to identify individuals in the population at risk for severe liver disease.
      Importantly, in that latter study, 65%–69% of liver outcomes within 10 years occurred in the low-risk categories, so that FIB-4 or APRI screening would have missed them.
      • Hagström H.
      • Talbäck M.
      • Andreasson A.
      • Walldius G.
      • Hammar N.
      Ability of noninvasive scoring systems to identify individuals in the population at risk for severe liver disease.
      In comparison, in our study, only 8%–10% of liver outcomes within 15 years occurred in the lowest laboratory-based CLivD-score category. Further comparison to non-invasive fibrosis tests is needed in larger cohorts.
      A strong advantage of our risk factor-based model, compared to a specific diagnosis of advanced liver fibrosis, is the ability to identify high-risk persons before they progress to advanced fibrosis. The CLivD score could complement fibrosis tests by serving as the initial basis for further fibrosis testing and follow-up. Such targeted fibrosis screening could substantially reduce the numbers needed to screen and the false-positive rates among screened individuals, but this requires specific investigation.
      Identification of individuals in the general population who are at risk of future liver disease can support informing them about liver-related risks and how to reduce these risks. Knowledge about being at high risk can support healthy lifestyle changes, such as a reduction of harmful drinking.
      • Sheron N.
      • Moore M.
      • O'Brien W.
      • Harris S.
      • Roderick P.
      Feasibility of detection and intervention for alcohol-related liver disease in the community: the Alcohol and Liver Disease Detection study (ALDDeS).
      Furthermore, the prediction model could help target lifestyle intervention resources and therapeutic decisions based on risk, and possibly also help assess response to such interventions.
      Alcohol use, smoking, abdominal obesity, and serum GGT are all modifiable.
      • Mazzotti A.
      • Caletti M.T.
      • Brodosi L.
      • Di Comizio S.
      • Forchielli M.L.
      • Petta S.
      • et al.
      An internet-based approach for lifestyle changes in patients with NAFLD: two-year effects on weight loss and surrogate markers.
      • Hellstrand M.
      • Simonsson B.
      • Engström S.
      • Nilsson K.W.
      • Molarius A.
      A health dialogue intervention reduces cardiovascular risk factor levels: a population based randomised controlled trial in Swedish primary care setting with 1-year follow-up.
      • Eriksson M.K.
      • Franks P.W.
      • Eliasson M.
      A 3-year randomized trial of lifestyle intervention for cardiovascular risk reduction in the primary care setting: the Swedish Björknäs study.
      • Tuomilehto J.
      • Lindström J.
      • Eriksson J.G.
      • Valle T.T.
      • Hämäläinen H.
      • Ilanne-Parikka P.
      • et al.
      Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.
      • Vilar-Gomez E.
      • Martinez-Perez Y.
      • Calzadilla-Bertot L.
      • Torres-Gonzalez A.
      • Gra-Oramas B.
      • Gonzalez-Fabian L.
      • et al.
      Weight loss through lifestyle modification significantly reduces features of nonalcoholic steatohepatitis.
      • St George A.
      • Bauman A.
      • Johnston A.
      • Farrell G.
      • Chey T.
      • George J.
      Effect of a lifestyle intervention in patients with abnormal liver enzymes and metabolic risk factors.
      • Mehta G.
      • Macdonald S.
      • Cronberg A.
      • Rosselli M.
      • Khera-Butler T.
      • Sumpter C.
      • et al.
      Short-term abstinence from alcohol and changes in cardiovascular risk factors, liver function tests and cancer-related growth factors: a prospective observational study.
      The change in serum GGT level correlates with the improvement in hepatic steatosis following lifestyle interventions.
      • Lazo M.
      • Solga S.F.
      • Horska A.
      • Bonekamp S.
      • Diehl A.M.
      • Brancati F.L.
      • et al.
      Effect of a 12-month intensive lifestyle intervention on hepatic steatosis in adults with type 2 diabetes.
      Interventions targeted at reducing an individual’s CLivD score or components of the score would likely lead to reductions in the risk of clinical liver disease, but this possibility also needs further study. In addition, the CLivD score should not replace current diagnostic tests when there is suspicion of prevalent liver disease.
      In conclusion, the CLivD score is a simple prediction model based on easily accessible risk factors for predicting future risk of advanced chronic liver disease. Using the CLivD score, risk estimation can be performed by anyone through the internet or by using the related scoring sheets. The model identifies individuals at high risk and provides data to support lifestyle changes. At the primary health-care level, the CLivD score can be used to identify individuals who should be referred for further liver assessment. More validation studies and a health-economics evaluation of this approach are needed.

      Abbreviations

      ALT, alanine aminotransferase; APRI, aspartate aminotransferase to platelet ratio index; AST, aspartate aminotransferase; CLivD score, Chronic Liver Disease score; FIB-4, fibrosis-4; GGT, gamma-glutamyltransferase; HES, Hospital Episode Statistics; HOMA-IR, homeostasis model assessment of insulin resistance; ICD, International Classification of Diseases; NAFLD, non-alcoholic fatty liver disease.

      Financial support

      Dr. Åberg was supported by the Mary and Georg Ehrnrooth Foundation, Medicinska Understödsföreningen Liv och Hälsa, Finska Läkaresällskapet, Academy of Finland (#338544), and Sigrid Jusélius Foundation. Dr. Luukkonen was supported by the Novo Nordisk, Sigrid Jusélius, and Instrumentarium Science Foundations. Dr. Salomaa was supported by the Finnish Foundation for Cardiovascular Research. Dr. Nordestgaard was supported by the Danish Heart Foundation and the Research Foundation for the Capital Region of Denmark. The researchers are all independent of the funders. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

      Authors’ contributions

      Study concept: FÅ, PKL, MF, ABu, AJ. Study design and enrollment of participants and data collection: FÅ, PKL, ABu, ABr, SEB, BGN, PP, VS., SM, AL, MP, AJ, MF. Data analysis: FÅ, PKL, ABu, KMP, MB, SEB, PP. Data interpretation: FÅ, PKL, ABu, ABr, KMP, MB, SEB, BGN, PP, VS., SM, AL, MP, AJ, MF. First draft: FÅ. Critical revision for important intellectual content: FÅ, PKL, ABu, ABr, KMP, MB, SEB, BGN, PP, VS., SM, AL, MP, AJ, MF.

      Data availability statement

      FINRISK and Health 2000 data are available from the THL biobank based on a research application, as explained on the website of the THL biobank (https://thl.fi/en/web/thl-biobank/for-researchers). Whitehall II data are available to bona fide researchers for research purposes. Please refer to the Whitehall II data sharing policy at http://www.ucl.ac.uk/whitehallII/data-sharing. Copenhagen City Heart Study data are also available through research application.

      Conflicts of interest

      The authors declare that they have no conflict of interest regarding the content of this manuscript.
      Please refer to the accompanying ICMJE disclosure forms for further details.

      Acknowledgments

      FINRISK and Health 2000 data used for the research were obtained from THL Biobank. We thank all study participants for their generous participation at THL Biobank and the FINRISK 1992-2012 studies and Health 2000 Survey. We thank all participants in the Whitehall II Study and the Whitehall II researchers and support staff who make the study possible. Whitehall II data are available to qualified researchers for research purposes. Please refer to the Whitehall II data sharing policy at http://www.ucl.ac.uk/whitehallII/data-sharing. The UK Medical Research Council (MR/K013351/1; G0902037), British Heart Foundation (RG/13/2/30098), and US National Institutes of Health (R01HL36310, R01AG013196) have supported collection of data in the Whitehall II Study. We also thank participants and staff at the Copenhagen City Heart Study. We thank Professor Thomas Gerds for his valuable statistical advice.

      Supplementary data

      The following are the supplementary data to this article:

      References

        • Asrani S.K.
        • Devarbhavi H.
        • Eaton J.
        • Kamath P.S.
        Burden of liver diseases in the world.
        J Hepatol. 2019; 70: 151-171
        • Williams R.
        • Aspinall R.
        • Bellis M.
        • Camps-Walsh G.
        • Cramp M.
        • Dhawan A.
        • et al.
        Addressing liver disease in the UK: a blueprint for attaining excellence in health care and reducing premature mortality from lifestyle issues of excess consumption of alcohol, obesity, and viral hepatitis.
        Lancet. 2014; 384: 1953-1997
        • Ratib S.
        • Fleming K.M.
        • Crooks C.J.
        • Aithal G.P.
        • West J.
        1 and 5 year survival estimates for people with cirrhosis of the liver in England, 1998-2009: a large population study.
        J Hepatol. 2014; 60: 282-289
        • Verrill C.
        • Smith S.
        • Sheron N.
        Are the opportunities to prevent alcohol related liver deaths in the UK in primary or secondary care? A retrospective clinical review and prospective interview study.
        Subst Abuse Treat Prev Policy. 2006; 1: 16-597X
        • Hudson M.
        • Sheron N.
        • Rowe I.A.
        • Hirschfield G.M.
        Should we screen for cirrhosis?.
        BMJ. 2017; 358: j3233
        • Harris R.
        • Harman D.J.
        • Card T.R.
        • Aithal G.P.
        • Guha I.N.
        Prevalence of clinically significant liver disease within the general population, as defined by non-invasive markers of liver fibrosis: a systematic review.
        Lancet Gastroenterol Hepatol. 2017; 2: 288-297
        • Donnan P.T.
        • McLernon D.
        • Dillon J.F.
        • Ryder S.
        • Roderick P.
        • Sullivan F.
        • et al.
        Development of a decision support tool for primary care management of patients with abnormal liver function tests without clinically apparent liver disease: a record-linkage population cohort study and decision analysis (ALFIE).
        Health Technol Assess. 2009; 13 (iii-iv, ix-xi)
        • Rowe I.A.
        Too much medicine: overdiagnosis and overtreatment of non-alcoholic fatty liver disease.
        Lancet Gastroenterol Hepatol. 2018; 3: 66-72
        • Caballeria L.
        • Pera G.
        • Arteaga I.
        • Rodriguez L.
        • Aluma A.
        • Morillas R.M.
        • et al.
        High prevalence of liver fibrosis among European adults with unknown liver disease: a population-based study.
        Clin Gastroenterol Hepatol. 2018; 16: 1138-1145.e5
        • Armstrong M.J.
        • Schmidt-Martin D.
        • Rowe I.A.
        • Newsome P.N.
        Caution in using non-invasive scoring systems in NAFLD beyond highly selected study populations.
        Am J Gastroenterol. 2017; 112: 653-654
        • Thiele M.
        • Madsen B.S.
        • Hansen J.F.
        • Detlefsen S.
        • Antonsen S.
        • Krag A.
        Accuracy of the enhanced liver fibrosis test vs. Fibrotest, elastography, and indirect markers in detection of advanced fibrosis in patients with alcoholic liver disease.
        Gastroenterology. 2018; 154: 1369-1379
        • Hagström H.
        • Nasr P.
        • Ekstedt M.
        • Stål P.
        • Hultcrantz R.
        • Kechagias S.
        Accuracy of noninvasive scoring systems in assessing risk of death and liver-related endpoints in patients with nonalcoholic fatty liver disease.
        Clin Gastroenterol Hepatol. 2019; 17: 1148-1156.e4
        • Hagström H.
        • Talbäck M.
        • Andreasson A.
        • Walldius G.
        • Hammar N.
        Ability of noninvasive scoring systems to identify individuals in the population at risk for severe liver disease.
        Gastroenterology. 2020; 158: 200-214
        • Åberg F.
        Liver fibrosis scores in the general population: better risk indices are needed.
        Hepatology. 2018; 67: 1186
        • Nguyen-Khac E.
        • Thiele M.
        • Voican C.
        • Nahon P.
        • Moreno C.
        • Boursier J.
        • et al.
        Non-invasive diagnosis of liver fibrosis in patients with alcohol-related liver disease by transient elastography: an individual patient data meta-analysis.
        Lancet Gastroenterol Hepatol. 2018; 3: 614-625
        • Pavlov C.S.
        • Casazza G.
        • Nikolova D.
        • Tsochatzis E.
        • Gluud C.
        Systematic review with meta-analysis: diagnostic accuracy of transient elastography for staging of fibrosis in people with alcoholic liver disease.
        Aliment Pharmacol Ther. 2016; 43: 575-585
        • Åberg F.
        • Färkkilä M.
        Drinking and obesity: alcoholic liver disease/nonalcoholic fatty liver disease interactions.
        Semin Liver Dis. 2020; 40: 154-162
        • Eslam M.
        • Sanyal A.J.
        • George J.
        • International Consensus Panel
        MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease.
        Gastroenterology. 2020; 158: 1999-2014.e1
        • Åberg F.
        • Puukka P.
        • Salomaa V.
        • Männistö S.
        • Lundqvist A.
        • Valsta L.
        • et al.
        Combined effects of alcohol and metabolic disorders in patients with chronic liver disease.
        Clin Gastroenterol Hepatol. 2020; 18: 995-997.e2
        • Damen J.A.
        • Hooft L.
        • Schuit E.
        • Debray T.P.A.
        • Collins G.S.
        • Tzoulaki I.
        • et al.
        Prediction models for cardiovascular disease risk in the general population: systematic review.
        BMJ. 2016; 353: i2416
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.G.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.
        Ann Intern Med. 2015; 162: 55-63
        • Aromaa A.
        • Koskinen S.
        Health and functional capacity in Finland. Baseline results of the Health 2000 health examination survey.
        Publications of National Public Health Institute, Series B 12/2004, Helsinki, Finland2004
        • Borodulin K.
        • Tolonen H.
        • Jousilahti P.
        • Jula A.
        • Juolevi A.
        • Koskinen S.
        • et al.
        Cohort profile: the national FINRISK study.
        Int J Epidemiol. 2018; 47 (696-696i)
        • Kivimäki M.
        • Batty G.D.
        • Singh-Manoux A.
        • Britton A.
        • Brunner E.J.
        • Shipley M.J.
        Validity of cardiovascular disease event ascertainment using linkage to UK hospital records.
        Epidemiology. 2017; 28: 735-739
        • Sommerlad A.
        • Perera G.
        • Singh-Manoux A.
        • Lewis G.
        • Stewart R.
        • Livingston G.
        Accuracy of general hospital dementia diagnoses in England: sensitivity, specificity, and predictors of diagnostic accuracy 2008-2016.
        Alzheimers Dement. 2018; 14: 933-943
      1. The Copenhagen city heart study.
        Eur Heart J Suppl. 2001; 3: H1-H83
        • Gellert-Kristensen H.
        • Richardson T.G.
        • Davey Smith G.
        • Nordestgaard B.G.
        • Tybjaerg-Hansen A.
        • Stender S.
        Combined effect of PNPLA3, TM6SF2, and HSD17B13 variants on risk of cirrhosis and hepatocellular carcinoma in the general population.
        Hepatology. 2020; 72: 845-856
        • Hagström H.
        • Nasr P.
        • Ekstedt M.
        • Hammar U.
        • Stål P.
        • Hultcrantz R.
        • et al.
        Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD.
        J Hepatol. 2017; 67: 1265-1273
        • Ozenne B.
        • Sorensen A.
        • Scheike T.
        • Torp-Pedersen C.
        • Gerds T.
        riskRegression: predicting the risk of an event using Cox regression models.
        R J. 2017; 9: 440-460
        • Bedogni G.
        • Bellentani S.
        • Miglioli L.
        • Masutti F.
        • Passalacqua M.
        • Castiglione A.
        • et al.
        The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population.
        BMC Gastroenterol. 2006; 6: 33-230X
        • Åberg F.
        • Puukka P.
        • Salomaa V.
        • Männistö S.
        • Lundqvist A.
        • Valsta L.
        • et al.
        Risks of light and moderate alcohol use in fatty liver disease: follow-up of population cohorts.
        Hepatology. 2020; 71: 835-848
        • Åberg F.
        • Helenius-Hietala J.
        • Puukka P.
        • Färkkilä M.
        • Jula A.
        Interaction between alcohol consumption and metabolic syndrome in predicting severe liver disease in the general population.
        Hepatology. 2018; 67: 2141-2149
        • Boyle M.
        • Masson S.
        • Anstee Q.M.
        The bidirectional impacts of alcohol consumption and the metabolic syndrome: cofactors for progressive fatty liver disease.
        J Hepatol. 2018; 68: 251-267
        • WHO CVD Risk Chart Working Group
        World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions.
        Lancet Glob Health. 2019; 7: e1332-e1345
        • Poynard T.
        • Lebray P.
        • Ingiliz P.
        • Varaut A.
        • Varsat B.
        • Ngo Y.
        • et al.
        Prevalence of liver fibrosis and risk factors in a general population using non-invasive biomarkers (FibroTest).
        BMC Gastroenterol. 2010; 10: 40-230X
        • Koehler E.M.
        • Plompen E.P.
        • Schouten J.N.
        • Hansen B.E.
        • Darwish Murad S.
        • Taimr P.
        • et al.
        Presence of diabetes mellitus and steatosis is associated with liver stiffness in a general population: the Rotterdam study.
        Hepatology. 2016; 63: 138-147
        • Roerecke M.
        • Vafaei A.
        • Hasan O.S.M.
        • Chrystoja B.R.
        • Cruz M.
        • Lee R.
        • et al.
        Alcohol consumption and risk of liver cirrhosis: a systematic review and meta-analysis.
        Am J Gastroenterol. 2019; 114: 1574-1586
        • Andreasson A.
        • Carlsson A.C.
        • Onnerhag K.
        • Hagström H.
        Waist/hip ratio better predicts development of severe liver disease within 20 years than body mass index: a population-based cohort study.
        Clin Gastroenterol Hepatol. 2017; 15: 1294-1301.e2
        • Zein C.O.
        • Unalp A.
        • Colvin R.
        • Liu Y.C.
        • McCullough A.J.
        • Nonalcoholic Steatohepatitis Clinical Research Network
        Smoking and severity of hepatic fibrosis in nonalcoholic fatty liver disease.
        J Hepatol. 2011; 54: 753-759
        • Rutledge S.M.
        • Asgharpour A.
        Smoking and liver disease.
        Gastroenterol Hepatol (N Y). 2020; 16: 617-625
        • Liu B.
        • Balkwill A.
        • Roddam A.
        • Brown A.
        • Beral V.
        • Million Women Study Collaborators
        Separate and joint effects of alcohol and smoking on the risks of cirrhosis and gallbladder disease in middle-aged women.
        Am J Epidemiol. 2009; 169: 153-160
        • McLernon D.J.
        • Donnan P.T.
        • Ryder S.
        • Roderick
        • Sullivan F.M.
        • Rosenberg W.
        • et al.
        Health outcomes following liver function testing in primary care: a retrospective cohort study.
        Fam Pract. 2009; 26: 251-259
        • Westerbacka J.
        • Corner A.
        • Tiikkainen M.
        • Tamminen M.
        • Vehkavaara S.
        • Häkkinen A.-M.
        • et al.
        Women and men have similar amounts of liver and intra-abdominal fat, despite more subcutaneous fat in women: implications for sex differences in markers of cardiovascular risk.
        Diabetologia. 2004; 47: 1360-1369
        • Petta S.
        • Macaluso F.S.
        • Barcellona M.R.
        • Camma C.
        • Cabibi D.
        • Di Marco V.
        • et al.
        Serum gamma-glutamyl transferase levels, insulin resistance and liver fibrosis in patients with chronic liver diseases.
        PLoS One. 2012; 7: e51165
        • Koenig G.
        • Seneff S.
        Gamma-glutamyltransferase: a predictive biomarker of cellular antioxidant inadequacy and disease risk.
        Dis Markers. 2015; 2015: 818570
        • Lee D.H.
        • Blomhoff R.
        • Jacobs D.R.
        Is serum gamma glutamyltransferase a marker of oxidative stress?.
        Free Radic Res. 2004; 38: 535-539
        • Cichoz-Lach H.
        • Michalak A.
        Oxidative stress as a crucial factor in liver diseases.
        World J Gastroenterol. 2014; 20: 8082-8091
        • Masarone M.
        • Rosato V.
        • Dallio M.
        • Gravina A.G.
        • Aglitti A.
        • Loguercio C.
        • et al.
        Role of oxidative stress in pathophysiology of nonalcoholic fatty liver disease.
        Oxid Med Cell Longev. 2018; 2018: 9547613
        • Sillanaukee P.
        • Massot N.
        • Jousilahti P.
        • Vartiainen E.
        • Sundvall J.
        • Olsson U.
        • et al.
        Dose response of laboratory markers to alcohol consumption in a general population.
        Am J Epidemiol. 2000; 152: 747-751
        • Roberts C.A.
        • Wilder L.B.
        • Jackson R.T.
        • Moy T.F.
        • Becker D.M.
        Accuracy of self-measurement of waist and hip circumference in men and women.
        J Am Diet Assoc. 1997; 97: 534-536
        • Barrios P.
        • Martin-Biggers J.
        • Quick V.
        • Byrd-Bredbenner C.
        Reliability and criterion validity of self-measured waist, hip, and neck circumferences.
        BMC Med Res Methodol. 2016; 16: 49-4016
        • Gines P.
        • Graupera I.
        • Lammert F.
        • Angeli P.
        • Caballeria L.
        • Krag A.
        • et al.
        Screening for liver fibrosis in the general population: a call for action.
        Lancet Gastroenterol Hepatol. 2016; 1: 256-260
        • Younossi Z.M.
        • Henry L.
        Are noninvasive scoring systems for persons with chronic liver disease ready for prime time?.
        Gastroenterology. 2020; 158: 40-42
        • Sheron N.
        • Moore M.
        • O'Brien W.
        • Harris S.
        • Roderick P.
        Feasibility of detection and intervention for alcohol-related liver disease in the community: the Alcohol and Liver Disease Detection study (ALDDeS).
        Br J Gen Pract. 2013; 63: e698-e705
        • Mazzotti A.
        • Caletti M.T.
        • Brodosi L.
        • Di Comizio S.
        • Forchielli M.L.
        • Petta S.
        • et al.
        An internet-based approach for lifestyle changes in patients with NAFLD: two-year effects on weight loss and surrogate markers.
        J Hepatol. 2018; 69: 1155-1163
        • Hellstrand M.
        • Simonsson B.
        • Engström S.
        • Nilsson K.W.
        • Molarius A.
        A health dialogue intervention reduces cardiovascular risk factor levels: a population based randomised controlled trial in Swedish primary care setting with 1-year follow-up.
        BMC Public Health. 2017; 17 (669-017)
        • Eriksson M.K.
        • Franks P.W.
        • Eliasson M.
        A 3-year randomized trial of lifestyle intervention for cardiovascular risk reduction in the primary care setting: the Swedish Björknäs study.
        PLoS One. 2009; 4e5195
        • Tuomilehto J.
        • Lindström J.
        • Eriksson J.G.
        • Valle T.T.
        • Hämäläinen H.
        • Ilanne-Parikka P.
        • et al.
        Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.
        N Engl J Med. 2001; 344: 1343-1350
        • Vilar-Gomez E.
        • Martinez-Perez Y.
        • Calzadilla-Bertot L.
        • Torres-Gonzalez A.
        • Gra-Oramas B.
        • Gonzalez-Fabian L.
        • et al.
        Weight loss through lifestyle modification significantly reduces features of nonalcoholic steatohepatitis.
        Gastroenterology. 2015; 149 (quiz e14): 367-378.e5
        • St George A.
        • Bauman A.
        • Johnston A.
        • Farrell G.
        • Chey T.
        • George J.
        Effect of a lifestyle intervention in patients with abnormal liver enzymes and metabolic risk factors.
        J Gastroenterol Hepatol. 2009; 24: 399-407
        • Mehta G.
        • Macdonald S.
        • Cronberg A.
        • Rosselli M.
        • Khera-Butler T.
        • Sumpter C.
        • et al.
        Short-term abstinence from alcohol and changes in cardiovascular risk factors, liver function tests and cancer-related growth factors: a prospective observational study.
        BMJ Open. 2018; 8: e020673-e022017
        • Lazo M.
        • Solga S.F.
        • Horska A.
        • Bonekamp S.
        • Diehl A.M.
        • Brancati F.L.
        • et al.
        Effect of a 12-month intensive lifestyle intervention on hepatic steatosis in adults with type 2 diabetes.
        Diabetes Care. 2010; 33: 2156-2163