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ADAMTSL2 protein and a soluble biomarker signature identify at-risk non-alcoholic steatohepatitis and fibrosis in adults with NAFLD

Open AccessPublished:September 30, 2021DOI:https://doi.org/10.1016/j.jhep.2021.09.026

      Highlights

      • Aptamer-based profiling platform used to find proteins that non-invasively identify significant fibrosis in 316 adults with NAFLD.
      • An 8-protein panel can distinguish NAFL/NASH fibrosis stage 0-1 from fibrosis stage 2-4 with an AUROC of 0.87-0.89.
      • The ADAMTSL2 protein alone can distinguish NAFL/NASH fibrosis stage 0-1 from fibrosis stage 2-4 with an AUROC of 0.83-0.86.
      • Both the 8-protein panel and ADAMTSL2 showed superior performance to the NAFLD fibrosis score and fibrosis-4 score.

      Background & Aims

      Identifying fibrosis in non-alcoholic fatty liver disease (NAFLD) is essential to predict liver-related outcomes and guide treatment decisions. A protein-based signature of fibrosis could serve as a valuable, non-invasive diagnostic tool. This study sought to identify circulating proteins associated with fibrosis in NAFLD.

      Methods

      We used aptamer-based proteomics to measure 4,783 proteins in 2 cohorts (Cohort A and B). Targeted, quantitative assays coupling aptamer-based protein pull down and mass spectrometry (SPMS) validated the profiling results in a bariatric and NAFLD cohort (Cohort C and D, respectively). Generalized linear modeling-logistic regression assessed the ability of candidate proteins to classify fibrosis.

      Results

      From the multiplex profiling, 16 proteins differed significantly by fibrosis in cohorts A (n = 62) and B (n = 98). Quantitative and robust SPMS assays were developed for 8 proteins and validated in Cohorts C (n = 71) and D (n = 84). The A disintegrin and metalloproteinase with thrombospondin motifs like 2 (ADAMTSL2) protein accurately distinguished non-alcoholic fatty liver (NAFL)/non-alcoholic steatohepatitis (NASH) with fibrosis stage 0-1 (F0-1) from at-risk NASH with fibrosis stage 2-4, with AUROCs of 0.83 and 0.86 in Cohorts C and D, respectively, and from NASH with significant fibrosis (F2-3), with AUROCs of 0.80 and 0.83 in Cohorts C and D, respectively. An 8-protein panel distinguished NAFL/NASH F0-1 from at-risk NASH (AUROCs 0.90 and 0.87 in Cohort C and D, respectively) and NASH F2-3 (AUROCs 0.89 and 0.83 in Cohorts C and D, respectively). The 8-protein panel and ADAMTSL2 protein had superior performance to the NAFLD fibrosis score and fibrosis-4 score.

      Conclusion

      The ADAMTSL2 protein and an 8-protein soluble biomarker panel are highly associated with at-risk NASH and significant fibrosis; they exhibited superior diagnostic performance compared to standard of care fibrosis scores.

      Lay Summary

      Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of liver disease worldwide. Diagnosing NAFLD and identifying fibrosis (scarring of the liver) currently requires a liver biopsy. Our study identified novel proteins found in the blood which may identify fibrosis without the need for a liver biopsy.

      Graphical abstract

      Keywords

      Linked Article

      • Liquid biomarkers for fibrotic NASH – progress in a complex field
        Journal of HepatologyVol. 76Issue 1
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          Non-alcoholic fatty liver (NAFL) is an important representation of the metabolic syndrome, usually occurring in conjunction with over-nutrition. Approximately 15% of patients with NAFL present with non-alcoholic steatohepatitis (NASH) – defined by a histological NAFLD activity score (NAS) ≥4 – usually with fibrosis; these patients are at high risk of progressing to cirrhosis and hepatocellular carcinoma.1 Patients with NAFL and no to moderate liver fibrosis (histological stages F0-F2) are mainly at risk of cardiovascular events or sequelae of type 2 diabetes,2 while liver-related morbidity and mortality increase exponentially with advanced fibrosis and cirrhosis (stages F3-F4).
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      See Editorial, pages 5–7

      Introduction

      Non-alcoholic fatty liver disease (NAFLD) is an accelerating cause of liver disease worldwide, impacting 25% of adults.
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      While the large number and range of protein concentrations can make assessment challenging, advances in large-scale proteomic profiling allow for the rapid detection and relative quantification of thousands of proteins from a single sample. One such multiplexed affinity assay (SomaScan®) uses modified single-stranded DNA aptamers to specifically bind target proteins in a high-throughput assay format with capture and microarray readouts.
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      This method has been successfully utilized to discover biomarkers including those associated with cardiovascular and neurologic disorders.
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      The present study applied the aptamer-based profiling platform to plasma and serum from adults with histologically defined NAFLD for the identification of proteins associated with fibrosis and the development of a disease panel for the non-invasive identification of adults with at-risk NASH.

      Patients and methods

      Design, setting and participants

      We conducted a nested case-control study drawing from 3 unique, prospectively enrolled cohorts of adults with NAFLD; Cohort A, (Discovery Cohort) from the Massachusetts General Hospital (MGH) Study of Brain Natriuretic Peptide in Bariatric Surgery, Cohort B (Discovery Cohort) and Cohort C (Validation Cohort), both from the MGH NAFLD Cohort Registry and Cohort D (Validation Cohort) from the Beth Israel Deaconess Medical Center (BIDMC) NAFLD Clinic Cohort.
      Cohort A (Discovery Cohort) included men and women age ≥18 years who underwent Roux-en-Y gastric bypass including standard of care liver biopsies at the time of bariatric surgery. Plasma was collected at baseline, 1-2 months and 6 months post-operatively.
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      Effect of weight loss after weight loss surgery on plasma N-terminal pro-B-type natriuretic peptide levels.
      Patients with pre-existing cardiac disease, chronic kidney disease and uncontrolled hypertension were excluded.
      The MGH NAFLD Cohort Registry includes more than 2,000 adults with NAFLD, diagnosed by imaging or liver biopsy and individuals who have undergone bariatric surgery with standard of care intra-operative liver biopsies as described previously.
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      Metabolite profiling identifies anandamide as a biomarker of nonalcoholic steatohepatitis.
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      Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes.
      Individuals are followed longitudinally with collection of plasma, serum and, in a subset, liver tissue.
      The NAFLD Clinic Cohort at Beth Israel Deaconess Medical Center includes 205 adults with a histological diagnosis of NAFLD enrolled between December 2009 and 2016 and followed longitudinally.
      For Cohorts B, C and D, patients were selected using the following criteria: i) men and women age ≥18 years; ii) hepatitis C antibody and hepatitis B surface antigen negative and iii) contemporaneous liver biopsy and plasma samples (within 6 months for the MGH cohort in individuals with a clinical biopsy or 6 months before bariatric surgery in those with a biopsy during surgery; within 3 months for the BIDMC Registry). Individuals were excluded in the following cases: i) alcohol use >20 g daily for women or >30 g daily for men; ii) decompensated cirrhosis; iii) chronic use of methotrexate, amiodarone, corticosteroids or tamoxifen; or iv) other causes of chronic liver disease.

      Diagnosis of liver disease

      For Cohort A, pathology reports from liver biopsies underwent independent review by 2 clinicians. Normal liver histology (NLH) was defined by the absence of steatosis, hepatocyte ballooning, lobular inflammation and fibrosis. NAFL was defined by steatosis score ≥1, ballooning score of 0, lobular inflammation score of 0-1 and fibrosis stage 0, or no report of lobular inflammation, ballooning or fibrosis. NASH was defined by steatosis score ≥1, ballooning score ≥1 and lobular inflammation score ≥1. Fibrosis was staged using the modified Brunt criteria.
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      • et al.
      Design and validation of a histological scoring system for nonalcoholic fatty liver disease.
      When specific scores were not available, reports of the presence of ballooning and lobular inflammation were used.
      For Cohorts B, C and D, all biopsies were reviewed by a hepatopathologist in a blinded manner using the NASH Clinical Research Network scoring criteria.
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      Design and validation of a histological scoring system for nonalcoholic fatty liver disease.
      Steatosis, NASH and fibrosis were defined as above. Patients were classified as having NLH, NAFL, NASH F0, NASH F1, NASH F2, NASH F3 and NASH cirrhosis. Subjects with at-risk NASH (NAS ≥4 and F≥2) or NASH with significant fibrosis (F2-3) were grouped for comparisons to those with NAFL/NASH F0-1. Two subjects in validation Cohort C had fibrosis stage ≥2 but NAS3 (with 1 in each component).

      Sample collection

      Fasting plasma samples (Cohorts A-C) or serum samples (Cohort D) were collected for measurement with the multiplex assay. Blood was collected in EDTA-treated tubes for plasma or serum separating tubes for serum, centrifuged at 1.9 g for 15 minutes within 120 minutes of collection, and the supernatant aliquoted and frozen at -80 °C.

      Proteomic profiling assay

      The SomaScan proteomic profiling platform utilizes SOMAmers® (Slow-Off rate Modified Aptamers) that bind to target proteins with high affinity and specificity.
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      Unique motifs and hydrophobic interactions shape the binding of modified DNA ligands to protein targets.
      The expanded assay includes 5,034 SOMAmers, of which 4,783 measure human proteins from 4,137 distinct human genes with femtomole (fM) limits of detection over a wide range of protein levels in plasma or serum (>8 logs of concentration). The platform exhibits median limits of detection and quantification (LOD/LOQ) of 40 fM and 100 fM, respectively and ∼5% coefficients of variation for median intra- and inter-assay variability.
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      Assessment of variability in the SOMAscan assay.
      A hybridization array to capture SOMAmers quantitatively determines the protein present by converting the assay signal (relative fluorescence units) into the relative abundance of an analyte.
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      • Shen D.
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      • Keyes M.J.
      • Shi X.
      • et al.
      Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease.
      ,
      • Sattlecker M.
      • Kiddle S.J.
      • Newhouse S.
      • Proitsi P.
      • Nelson S.
      • Williams S.
      • et al.
      Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology.
      Assays were performed by SomaLogic in collaboration with Novartis according to the protocol described by our group and others.
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      Application of large-scale aptamer-based proteomic profiling to planned myocardial infarctions.
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      Co-regulatory networks of human serum proteins link genetics to disease.
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      Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy.

      Validation using SOMAmer-pulldown mass spectrometry

      SOMAmers are well-suited to multiplexed protein enrichment strategies that use serum or plasma coupled with quantitation by mass spectrometry.
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      ,
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      Proteins are affinity enriched, eluted and digested prior to targeted multiple reaction monitoring (MRM) mass spectrometry. Accurate quantitation is achieved by the use of stable isotope-labeled peptide external calibration curves. We refer to this technique as SOMAmer-pulldown mass spectrometry (SPMS) which allows for both specific and sensitive orthogonal validation while achieving lower limits of protein detection in the nanogram per milliliter range. Performance characteristics of this method were assessed (see supplementary materials and methods) and analytical validation established using a subset of Cohort A (31 individuals). Protein level changes by SPMS were directionally consistent to the relative protein abundance as determined by SomaScan. All SPMS was performed by Novartis.

      SomaScan microarray data analysis

      Open-source microarray analysis software from the R/Bioconductor consortium was used to analyze the microarray data (http://www.bioconductor.org/). arrayQualityMetrics was used for microarray technical quality assessment, and data was background corrected and normalized by SomaLogic using spike in controls and calibrator samples.
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      • Gentleman R.
      • Huber W.
      arrayQualityMetrics--a bioconductor package for quality assessment of microarray data.
      , Differentially measured protein levels were calculated using a moderated t statistic in limma and applying a simple regression model to compare patients grouped by disease stage (as assessed by histology). The p values were adjusted using Benjamini-Hochberg to control for the false discovery rate (FDR). Proteins were considered significantly changed if they had an FDR-adjusted p value <0.05 and a fold change greater than 20%.

      Analysis from SOMAmer-pull down mass spectrometry

      Two analytical approaches, generalized linear modeling-logistic regression (GLM-LR) and random forest classification, were used to assess the ability of a single protein (A disintegrin and metalloproteinase with thrombospondin motifs like 2; ADAMTSL2) or an 8-protein signature to classify fibrotic NASH. Multivariate models were trained using Cohorts C and D, comparing NAFL/NASH F0-1 to either at-risk NASH or NASH F2-3. Random forest classifier for at-risk NASH trained on Cohort D was tested on Cohort C. Machine learning methods were provided through the R/Bioconductor framework using the caret library (package version 6.0-86).
      • Kuhn M.
      Building predictive models in R using the caret package.
      Model performance was evaluated based on the AUROC using pROC (package version_1.17.0.1).
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      • Tiberti N.
      • Lisacek F.
      • Sanchez J.C.
      • Müller M.
      pROC: an open-source package for R and S+ to analyze and compare ROC curves.
      Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the models was determined using standard definitions.
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      • Bland J.M.
      Diagnostic tests 2: predictive values.
      • Altman D.G.
      • Bland J.M.
      Diagnostic tests. 1: sensitivity and specificity.
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      • White B.C.
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      • Bush W.S.
      • Ritchie M.D.
      • Williams S.M.
      • et al.
      A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction.
      Continuous and categorical variables were assessed using the Student’s t test and Fisher’s exact test, respectively.
      The FIB-4 score and NFS were used as comparative fibrosis risk scores in Cohort D, with metadata available to calculate results in 81 patients. The presence of advanced fibrosis was defined as FIB-4 >2.67 (≥36 years) and NFS >0.67 (≥36 years). The absence of advanced fibrosis was defined as FIB-4 <1.3 (36-65 years) or <2.0 (≥65 years) and NFS <-1.455 (36-65 years) or <0.12 (≥65 years).
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      • Petta S.
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      All studies were approved by the MGH and BIDMC institutional review boards and all participants provided written informed consent.

      Results

      Baseline characteristics

      Overall, 315 individuals from 3 bariatric surgery cohorts (Cohort A, n = 62; Cohort B, n = 98; Cohort C, n = 71) and 1 NAFLD cohort (Cohort D, n = 84) were included in the study (Table 1). Mean age ranged from 46 to 50 years, 45-75% of patients were women, and diabetes mellitus was present in 32-58% of patients. Lower mean BMI (p <0.0001), lower prevalence of diabetes (p <0.01) and higher mean alanine and aspartate aminotransferase levels (p <0.0001 and p <0.0005, respectively) were observed when comparing validation Cohort D to Cohort C.
      Table 1Demographics and NAFLD liver histological grading of discovery and validation cohorts.
      VariableSomaScanSOMAmer-pulldown multiplex MRM mass spectrometry
      Discovery Cohort A

      (n = 62)
      Discovery Cohort B

      (n = 98)
      Validation Cohort C

      (n = 71)
      Validation Cohort D

      (n = 84)
      Age, years (SD)46 (11)48 (13)48 (12)50 (13)
      Sex, female – yes (%)47 (75)69 (70)47 (66)38 (45)∗
      Diabetes mellitus – yes (%)29 (46)45 (46)41 (58)27 (32)∗
      BMI (kg/m2)47 (7)45 (6)46 (7)34 (7)∗∗∗
      ALT (U/L)44 (37)27 (15)50 (42)74 (40)∗∗∗
      AST (U/L)32 (24)26 (10)35 (28)50 (24)∗∗
      Alkaline phosphatase (U/L)91 (22)81 (24)88 (33)80 (27)
      Total bilirubin (mg/dl)0.5 (0.2)0.5 (0.3)0.5 (0.2)0.7 (0.8)
      Glucose (mg/dl)113 (38)105 (31)128 (60)113 (40)
      LDL (mg/dl)102 (32)98 (32)101 (36)107 (39)
      HDL (mg/dl)52 (13)44 (12)∗45 (16)46 (12)
      Triglycerides (mg/dl)172 (100)127 (69)∗157 (80)189 (97)
      Liver histology, n (%)
       Normal4 (6)19 (19)10 (14)NA
       NAFL31 (50)30 (31)8 (11)12 (14)
       NASH fibrosis stage 013 (21)9 (9)10 (14)19 (23)
       NASH fibrosis stage 15 (8)23 (23)10 (14)12 (14)
       NASH fibrosis stage 25 (8)10 (10)18 (25)21 (25)
       NASH fibrosis stage 33 (5)5 (5)10 (14)12 (14)
       NASH fibrosis stage 41 (2)2 (2)5 (7)8 (10)
      Data reported as mean (SD) unless otherwise noted. Significant differences between Cohort A/B and Cohort C/D for demographic data reported using one-way ANOVA for continuous variables and Fisher’s Exact test for categorical variables.
      p <0.01, ∗∗p <0.0005, ∗∗∗p <0.0001.
      ALT, alanine aminotransferase; AST, aspartate aminotransferase; MRM, multiple reaction monitoring; NAFL, non-alcoholic fatty liver; NASH, non-alcoholic steatohepatitis.

      Proteomic profiles in discovery cohorts and selection of candidates for validation

      A total of 234 and 304 proteins were identified by SomaScan in Cohorts A and B, respectively, which differed between NLH and any of the NAFLD phenotypes (adjusted p <0.05) (Fig. 1). Twenty-four proteins were significantly different in patients with at-risk NASH (adjusted p <0.05). ADAMTSL2 was included given its biological plausibility, directional consistency and conserved effect size in both cohorts (Cohort A, p = 0.01 and Cohort B, p = 0.08; both adjusted for the 5,034 multiple comparisons). The proteins were refined to 16 candidates that demonstrated stepwise directionally consistent relationships between phenotypes and statistically significant protein values between at-risk NASH compared to NAFL/NASH F0-1 or NLH. These 16 candidates were selected for validation and quantification by SPMS in 2 additional cohorts (C and D). Accurate and robust SPMS assays were established for the following 8 proteins, referred to as the NAFLD fibrosis protein panel (NFPP): ACY1, ADAMTSL2, ADH4, ALDOC, ASL, ENPP7, FBP1 and FTCD. SPMS assays could not be established for 8 proteins (NFASC, CHST9, COLEC11, POR, FAH, SELE, THBS2 and TREM2) owing to challenges in reproducibility, stability and/or sensitivity. Protein levels as determined by SOMAscan in the 2 discovery cohorts and by SPMS in the 2 validation cohorts are presented in Figs. S1 and S2. Statistical results across NAFLD phenotypes in the 2 discovery cohorts are presented in Table S1. Biological function and tissue expression for NFPP proteins is provided in Table S2.
      Figure thumbnail gr1
      Fig. 1Flow diagram of protein selection for NAFLD fibrosis protein panel.
      NAFLD, non-alcoholic fatty liver disease.

      Performance of ADAMTSL2 for distinguishing NASH with significant fibrosis (F2-3) and at-risk NASH

      Of the 8 proteins evaluated, ADAMTSL2 was the strongest individual protein classifier of fibrosis. ADAMTSL2 accurately classified NASH F2-3 or at-risk NASH relative to NAFL/NASH F0-1 with an AUROC of 0.80 (p = 2.3 × 10-3) or 0.83 (p = 1.3 × 10-3) in Cohort C and 0.83 (p = 2.8 × 10-4) or 0.86 (p = 7.1 × 10-7) in Cohort D, respectively (Fig. 2). For patients with at-risk NASH, sensitivities of 70% and 68%, specificities of 79% and 86%, PPVs of 79% and 82%, and NPVs of 69% and 74% were obtained in Cohorts C and D, respectively. The full performance characteristics of ADAMTSL2 (for the classification of significant fibrosis and at-risk NASH) are provided in Table 2.
      Figure thumbnail gr2
      Fig. 2ROC curves of ADAMTSL2 for the identification of NASH F2-3 and at-risk NASH.
      (A) In Cohort C, ADAMTSL2 AUROC = 0.80 (95% CI 0.56-0.81) for NASH F2-3. (B) In Cohort D, ADAMTSL2 AUROC = 0.83 (95% CI 0.65-0.85) for NASH F2-3. (C) In Cohort C, ADAMTSL2 AUROC = 0.83 (95% CI 0.61-0.84) for at-risk NASH. (D) In Cohort D, ADAMTSL2 AUROC = 0.86 (95% CI 0.67-0.86) for at-risk NASH. ROC: receiver operating characteristic; AUROC: area under the receiver operating characteristic curve. 95% confidence intervals based on leave-one-out cross-validation (LOOCV) for GLM-LR.
      Table 2Performance characteristics of ADAMTSL2 and the NFPP by generalized linear modeling logistic regression.
      ComparisonClassifier/ModelCohortAUROC (95% CI)SensitivitySpecificityPPVNPV
      NAFL/NASH F0-1 vs. NASH F2-3ADAMTSL2C0.80 (0.56-0.81)61%79%74%67%
      D0.83 (0.65-0.85)58%91%83%74%
      NFPPC0.89 (0.70-0.91)71%93%91%76%
      D0.83 (0.65-0.85)64%86%78%76%
      NFPP + clinical featuresC0.90 (0.72-0.92)79%89%88%81%
      D0.87 (0.73-0.91)70%93%88%80%
      NAFL/NASH F0-1 vs. at-risk NASHADAMTSL2C0.83 (0.61-0.84)70%79%79%69%
      D0.86 (0.67-0.86)68%86%82%74%
      NFPPC0.90 (0.72-0.92)82%86%87%80%
      D0.87 (0.68-0.87)73%84%81%77%
      NFPP + clinical featuresC0.90 (0.68-0.89)79%82%84%77%
      D0.86 (0.68-0.87)73%84%81%77%
      Model performance was evaluated using the area under the receiver operating characteristic curve, with 95% confidence intervals based on leave-one-out cross-validation (LOOCV) for GLM-LR. AUROC: area under the receiver operating characteristic curve; PPV: positive predictive value; NPV: negative predictive value.

      Performance of the NFPP for distinguishing NASH with significant fibrosis (F2-3) and at-risk NASH

      The NFPP classified NASH F2-3 or at-risk NASH with an AUROC of 0.89 (p = 6.2 × 10-7) or 0.90 (p = 1.2 × 10-6) in Cohort C and 0.83 (p = 2.7 × 10-4) or 0.87 (p = 2.1 × 10-7) in Cohort D, respectively (Fig. 3). For patients with at-risk NASH, sensitivities of 82% and 73%, specificities of 86% and 84%, PPVs of 87% and 81%, and NPVs of 80% and 77% were obtained in Cohorts C and D, respectively. Full performance characteristics are noted in Table 2. Comparable results were observed when applying random forest classification models (Table S3).
      Figure thumbnail gr3
      Fig. 3ROC curves of the NFPP for the identification of NASH F2-3 and at-risk NASH.
      (A) In Cohort C, NFPP AUROC = 0.89 (95% CI 0.70-0.91) and NFPP plus Clinical Features AUROC = 0.90 (95% CI 0.72-0.92) for NASH F2-3. (B) In Cohort D, NFPP AUROC = 0.83 (95% CI 0.65-0.85) and NFPP plus Clinical Features AUROC = 0.87 (95% CI 0.71-0.90) for NASH F2-3. (C) In Cohort C, NFPP AUROC = 0.90 (95% CI 0.72-0.92) and NFPP plus Clinical Features AUROC = 0.90 (95% CI 0.68-0.89) for at-risk NASH. (D) In Cohort D, NFPP AUROC = 0.87 (95% CI 0.68-0.87) and NFPP plus Clinical Features AUROC = 0.86 (95% CI 0.68-0.87) for at-risk NASH. Clinical features include age, BMI, sex, and diabetes status. ROC: receiver operating characteristic; AUROC: area under the receiver operating characteristic curve. 95% confidence intervals based on leave-one-out cross-validation (LOOCV) for GLM-LR.
      Clinical features were evaluated as potential confounders to NASH fibrosis classification using the NFPP. In Cohort C, there were no statistically significant differences by fibrosis group. In Cohort D, the at-risk NASH group had higher mean age (54 ± 12 years vs. 47 ± 13 years, p = 0.013) and diabetes prevalence (54% vs. 12%, p <0.0001) than those with NAFL/NASH F0-1. Sex (66% vs. 45% female, p <0.01) and BMI (46 kg/m2 vs. 34 kg/m2, p <0.001) differed across Cohort C and D, respectively. Including these 4 clinical features in the model resulted in low or minimal impact on performance (Fig. 3 and Table 2).

      Performance of ADAMTSL2 and the NFPP compared to NFS and FIB-4 for distinguishing at-risk NASH

      In Cohort D, the NFS demonstrated an AUROC of 0.70 (p = 0.009) for at-risk NASH classification. Addition of ADAMTSL2 or the NFPP improved performance of the NFS, increasing the AUROC to 0.86 (p = 4.7 × 10-6) or 0.89 (p = 1.8 × 10-9), respectively (Fig. 4A). Sensitivity improved from 55% for NFS alone to 70% and 80% with the addition of ADAMTSL2 or the NFPP, respectively. Specificity improved from 73% for NFS alone to 80% and 85% with the addition of ADAMTSL2 or the NFPP, respectively. Similarly, PPV and NPV improved from 67% and 63%, respectively, for NFS alone to 78% and 73% with the addition of ADAMTSL2 and to 84% and 81% with the addition of the NFPP, respectively (Table S4).
      Figure thumbnail gr4
      Fig. 4ROC curves of the NFS and FIB-4 scores with the addition of ADAMTSL2 and the NFPP for the identification of at-risk NASH in Cohort D.
      (A) NFS AUROC = 0.70 (95% CI 0.53-0.75), NFS plus ADAMTSL2 AUROC = 0.86 (95% CI 0.64-0.84), and NFS plus NFPP AUROC = 0.89 (95% CI 0.73-0.90).
      (B) FIB-4 AUROC = 0.74 (95% CI 0.60-0.81), FIB-4 plus ADAMTSL2 AUROC = 0.86 (95% CI 0.69-0.87), and FIB-4 plus NFPP AUROC = 0.86 (95% CI 0.73-0.90). ROC: receiver operating characteristic; AUROC: area under the receiver operating characteristic curve. 95% confidence intervals based on leave-one-out cross-validation (LOOCV) for GLM-LR.
      In Cohort D, the FIB-4 score had an AUROC of 0.74 (p = 9.8 × 10-5) for identifying at-risk NASH. The addition of ADAMTSL2 or the NFPP improved performance of the FIB-4 score, increasing the AUROC to 0.86 (p = 1.3 × 10-7) or 0.86 (p = 1.8 × 10-9), respectively (Fig. 4B). While sensitivity was 55% for both the NFS and FIB-4, other assay metrics were higher (specificity 88%, PPV 81% and NPV 67%) for FIB-4. The addition of ADAMTSL2 or the NFPP improved performance of the FIB-4 score, with sensitivity of 72% and 75%, specificity of 85% and 90%, PPV of 83% and 88%, and NPV of 76% and 79%, respectively (Table S4).
      Performance characteristics for patients with NASH F2-3 are provided in Table S4.

      Discussion

      Using the SomaScan method, we identified circulating protein biomarkers associated with fibrotic NASH in 2 histologically defined cohorts and validated the findings in 2 additional cohorts using a rigorous, specific, and quantitative complementary technique (SPMS). We identified a single protein (ADAMTSL2) and developed an 8-protein panel (NFPP) that accurately classified at-risk NASH (NAFLD activity score ≥4, F≥2) and NASH with significant fibrosis (F2-3).
      Non-invasive testing to identify fibrosis and risk stratify patients with NAFLD is a growing field. Currently, several algorithmic fibrosis scores are used in clinical practice. The NFS and FIB-4 scores combine clinical variables and laboratory results to predict the presence of fibrosis stage 3-4 with AUROCs of 0.82-0.88 and 0.765.
      • Angulo P.
      • Hui J.M.
      • Marchesini G.
      • Bugianesi E.
      • George J.
      • Farrell G.C.
      • et al.
      The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD.
      ,
      • Sterling R.K.
      • Lissen E.
      • Clumeck N.
      • Sola R.
      • Correa M.C.
      • Montaner J.
      • et al.
      Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection.
      Subsequent evaluation resulted in AUROCs ranging from 0.66-0.71 for the NFS and 0.73-0.76 for FIB-4, depending on the population prevalence of advanced fibrosis.
      • Wong V.W.
      • Adams L.A.
      • de Ledinghen V.
      • Wong G.L.
      • Sookoian S.
      Noninvasive biomarkers in NAFLD and NASH - current progress and future promise.
      ,
      • Karsdal M.A.
      • Daniels S.J.
      • Holm Nielsen S.
      • Bager C.
      • Rasmussen D.G.K.
      • Loomba R.
      • et al.
      Collagen biology and non-invasive biomarkers of liver fibrosis.
      The NIS4 panel, incorporating miR-34a-5p, alpha-2-macroglobulin, YKL-40 and hemoglobin A1C, was recently developed to assess at-risk NASH and demonstrated AUROCs of 0.76-0.83.
      • Harrison S.A.
      • Ratziu V.
      • Boursier J.
      • Francque S.
      • Bedossa P.
      • Majd Z.
      • et al.
      A blood-based biomarker panel (NIS4) for non-invasive diagnosis of non-alcoholic steatohepatitis and liver fibrosis: a prospective derivation and global validation study.
      Pro-C3, a marker of type III collagen formation, distinguishes advanced fibrosis with AUROCs of 0.73-0.78 and when used as part of the ADAPT score with age, diabetes and platelet count, identified advanced fibrosis with an AUROC of 0.86.
      • Boyle M.T.D.
      • Schattenberg J.M.
      • Ratziu V.
      • Bugianessi E.
      • Petta S.
      • Oliveira C.P.
      • et al.
      Performance of the PRO-C3 collagen neo-epitope biomarker in non-alcoholic fatty liver disease.
      ,
      • Daniels S.J.
      • Leeming D.J.
      • Eslam M.
      • Hashem A.M.
      • Nielsen M.J.
      • Krag A.
      • et al.
      ADAPT: an algorithm incorporating PRO-C3 accurately identifies patients with NAFLD and advanced fibrosis.
      In a recent meta-analysis, the ELF score, composed of tissue inhibitor of metalloproteinases 1, hyaluronic acid and PIIINP (N-terminal propeptide of type III procollagen), was highly sensitive for excluding fibrosis but exhibited low PPV and sensitivity for detecting advanced and significant fibrosis except in high prevalence settings (>50%).
      • Vali Y.
      • Lee J.
      • Boursier J.
      • Spijker R.
      • Loffler J.
      • Verheij J.
      • et al.
      Enhanced liver fibrosis test for the non-invasive diagnosis of fibrosis in patients with NAFLD: a systematic review and meta-analysis.
      In the present study, both the NFPP and ADAMTSL2 showed comparable performance in the at-risk population (AUROC 0.83-0.90) and warrant direct comparison to emerging non-invasive biomarkers. The NFPP and ADAMTSL2 maintain their predictive ability in NASH with significant fibrosis (F2-3) (AUROC 0.80-0.89). Evaluation of biomarkers to identify at-risk NASH in the absence of cirrhosis ensures biomarker performance is not driven solely by large differences seen in patients with severe disease.
      The rule-in approach for NAFLD seeks to accurately predict which patients with NASH are at risk of progressive disease. Both the NFPP and ADAMTSL2 exhibit high PPV for classification of at-risk NASH, 87% and 81% for the NFPP and 79% and 82% for ADAMTSL2 in Cohorts C and D, respectively. When compared to existing clinical scores in Cohort D, the NFPP and ADAMTSL2 show superior (NFS PPV 67%) or comparable (FIB-4 PPV 81%) performance. The addition of the NFPP or ADAMTSL2 improved performance of the NFS (PPV 84% and 78%, respectively) and FIB-4 score (PPV 88% and 83%, respectively). Notably, the results suggest addition of a single soluble biomarker to existing clinical scores could extend their performance into F2-4 populations. Thus, for ruling in at-risk NASH, both the NFPP and ADAMTSL2 were superior to the commonly used NFS and FIB-4 score and improved their performance.
      The findings in this report strongly support ADAMTSL2 as a novel circulating classifier of NASH fibrosis. ADAMTSL2 was previously identified as a plasma marker of NASH fibrosis in a preclinical model
      • Fernando H.
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      Liver proteomics in progressive alcoholic steatosis.
      and included as part of a gene expression signature associated with NAFLD progression derived from a meta-analysis of published human studies.
      • Ryaboshapkina M.
      • Hammar M.
      Human hepatic gene expression signature of non-alcoholic fatty liver disease progression, a meta-analysis.
      Existing data suggests a role for ADAMTSL2 in extracellular matrix (ECM) biology. ADAMTSL2 interacts with latent TGF-β-binding protein-1, involved in TGF-β tissue specific sequestration in the ECM, as well as with fibrillin-1 and fibrillin-2, proteins involved in the modulation of microfibril formation.
      • Le Goff C.
      • Morice-Picard F.
      • Dagoneau N.
      • Wang L.W.
      • Perrot C.
      • Crow Y.J.
      • et al.
      ADAMTSL2 mutations in geleophysic dysplasia demonstrate a role for ADAMTS-like proteins in TGF-beta bioavailability regulation.
      ,
      • Hubmacher D.
      • Apte S.S.
      ADAMTS proteins as modulators of microfibril formation and function.
      ADAMTSL2 also interacts with lysyl oxidases which are involved in microfibril formation, ECM-associated signaling and hepatic fibrosis progression.
      • Aviram R.
      • Zaffryar-Eilot S.
      • Hubmacher D.
      • Grunwald H.
      • Maki J.M.
      • Myllyharju J.
      • et al.
      Interactions between lysyl oxidases and ADAMTS proteins suggest a novel crosstalk between two extracellular matrix families.
      Potential interplay between lysyl oxidases and ADAMTSL2 is intriguing given the known role of lysyl oxidases in liver fibrosis and the interest in targeting them for the pharmacological treatment of NASH. Thus, the identification and validation of ADAMTSL2 may provide insight into the pathophysiology of NASH fibrosis.
      The present study has several notable strengths including the use of 3 separate cohorts with distinct populations, those with obesity undergoing bariatric surgery who generally have a lower prevalence of NAFLD fibrosis and those followed at a NAFLD Specialty Clinic where rates of fibrosis are higher than the general population. The SomaScan assay allows for a relatively unbiased approach to detect thousands of proteins at low circulating levels with high precision.
      • Ngo D.
      • Sinha S.
      • Shen D.
      • Kuhn E.W.
      • Keyes M.J.
      • Shi X.
      • et al.
      Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease.
      This was complemented by the use of SPMS to allow orthogonal validation of SOMAmer selectivity, precise quantification of identified proteins and validation in distinct cohorts. Further, results were consistent across serum and plasma samples collected from different centers and of different age.
      Our study is cross-sectional in nature and thus cannot distinguish whether the proteins identified play a role in fibrosis progression or result from it. Future studies must test whether these proteins are associated with an increased risk of progression of fibrotic disease in longitudinal cohorts of NAFLD and other liver conditions not explored here. Sample selection bias is possible for the bariatric surgery cohorts based on the availability of pre-operative serum/plasma, however similar baseline characteristics across all cohorts suggest the measured samples are representative of the larger repository. Eight proteins could not be robustly assessed using the SPMS approach which may have strengthened our classifier score. Heterogeneity of protein levels precluded a focus on distinguishing fibrosis stage 1, reflecting the significant variability of early-stage disease as evidenced by high levels of variations in markers of fibrogenesis in other work.
      • Luo Y.
      • Oseini A.
      • Gagnon R.
      • Charles E.D.
      • Sidik K.
      • Vincent R.
      • et al.
      An evaluation of the collagen fragments related to fibrogenesis and fibrolysis in nonalcoholic steatohepatitis.
      Our study did not examine the performance of all emerging fibrosis biomarkers, including Pro-C3 or the ELF score. Comparisons within the same cohort are warranted to directly compare the performance characteristics of these biomarkers to ADAMTSL2 and the NFPP. Finally, it is important to acknowledge that the findings of any biomarker study are influenced by the chosen platform and our findings were confined to the SomaScan and SPMS platforms.
      The present study utilized a proteomics-based assay to identify 8 circulating biomarkers that accurately identify at-risk patients in need of liver biopsy and aggressive management and may be useful to better understand the pathophysiology of fibrosis development in NAFLD. In particular, ADAMTSL2 warrants further investigation as a non-invasive biomarker of significant and advanced fibrosis. The ability to non-invasively identify patients with the highest risk of disease progression could ultimately contribute to better risk stratification, appropriate disease management and consideration for clinical trial enrollment.

      Abbreviations

      ADAMTSL2, A disintegrin and metalloproteinase with thrombospondin motifs like 2; BIDMC, Beth Israel Deaconess Medical Center; ECM, extracellular matrix; ELF, enhanced liver fibrosis; FDR, false discovery rate; MGH, Massachusetts General Hospital; MRM, multiple reaction monitoring; NAFL, non-alcoholic fatty liver; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; NFPP, NAFLD fibrosis protein panel; NFS, NAFLD fibrosis score; NLH, normal liver histology; NPV, negative predictive value; PPV, positive predictive value; SPMS, SOMAmer-pulldown mass spectrometry.

      Financial support

      This work was supported by the National Institute of Health through R01 DK114144 (KEC), R01HL132320 (REG and TJW), R01HL133870 (REG).

      Authors’ contributions

      KEC, REG, CU, and LLJ designed the study and interpreted the data. RP, ML, RM, SAO, JLG, MMH, DWG, ORM, ERW collected and assembled the data. RP, JL, SMR, JJ, NF, DN, and TJW performed data analysis and/or interpretation. KEC drafted the manuscript. All authors reviewed and approved the final version of the manuscript.

      Data availability statement

      Data are available on request due to privacy/ethical restrictions. SomaScan data are available through a data use agreement with Novartis Institutes for BioMedical Research ([email protected]).

      Conflict of interest

      RP, JL, SMR, JJ, NF, CU & LLJ are employees and stockholders of Novartis. KEC serves on the scientific advisory board for Novo Nordisk and BMS and has received grant funding from Boehringer-Ingelheim, BMS and Novartis. All other authors have no conflicts of interest to declare.
      Please refer to the accompanying ICMJE disclosure forms for further details.

      Supplementary data

      The following are the supplementary data to this article:

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