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Scandinavian Journal of Gastroenterology
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/igas20
Serum levels of endotrophin are associated with
nonalcoholic steatohepatitis
Hannes Hagström, Dawei Bu, Patrik Nasr, Mattias Ekstedt, Hannes Hegmar,
Stergios Kechagias, Ningyan Zhang, Zhiqiang An, Per Stål & Philipp E.
Scherer
To cite this article: Hannes Hagström, Dawei Bu, Patrik Nasr, Mattias Ekstedt, Hannes Hegmar, Stergios Kechagias, Ningyan Zhang, Zhiqiang An, Per Stål & Philipp E. Scherer (2021) Serum levels of endotrophin are associated with nonalcoholic steatohepatitis, Scandinavian Journal of Gastroenterology, 56:4, 437-442, DOI: 10.1080/00365521.2021.1879249
To link to this article: https://doi.org/10.1080/00365521.2021.1879249
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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RESEARCH ARTICLE
Serum levels of endotrophin are associated with nonalcoholic steatohepatitis
Hannes Hagstr€oma,b,c
, Dawei Bud, Patrik Nasre , Mattias Ekstedte , Hannes Hegmara,c, Stergios Kechagiase , Ningyan Zhangf, Zhiqiang Anf, Per Ståla,c and Philipp E. Schererd,g a
Department of Upper GI, Division of Hepatology, Karolinska University Hospital, Stockholm, Sweden;bDepartment of Medicine, Solna, Clinical Epidemiology Unit, Karolinska Institutet, Stockholm, Sweden;cDepartment of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden;dDepartment of Internal Medicine, Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX, USA;
e
Department of Gastroenterology and Hepatology, Department of Health, Medicine and Caring Sciences, Link€oping University, Link€oping, Sweden;fTexas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA;gDepartment of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
ABSTRACT
Background and aims: There are no currently available biomarkers that can accurately indicate the presence of non-alcoholic steatohepatitis (NASH). We investigated the association between endotro-phin, a cleavage product of collagen type 6a3, and disease severity in patients with non-alcoholic fatty liver disease (NAFLD).
Methods: We measured serum endotrophin levels in 211 patients with NAFLD and nine healthy con-trols. Liver biopsy data was available for 141 (67%) of the patients. Associations between endotrophin and the presence of NASH and advanced fibrosis were investigated alone and in combination with standard clinical parameters using logistic regression.
Results: A total of 211 patients were enrolled in this study, consisting of 108 (51%) men and 103 (49%) women with a mean age of 55.6 years. 58 (27%) of the patients had advanced fibrosis. Of those with biopsy data, 87 (62%) had NASH. Serum levels of endotrophin were significantly higher in patients with NAFLD than those in healthy controls (37[±12] vs. 17[±7] ng/mL, p<.001). Serum levels of endotrophin were also significantly higher in patients with NASH than in those without NASH (40[±12] vs. 32[±13] ng/mL, p<.001). A model using age, sex, body mass index and levels of alanine aminotransferase (ALT), glucose and endotrophin effectively predicted the presence of NASH in a der-ivation (AUROC 0.83, 95%CI¼ 0.74–0.92) and validation cohort (AUROC 0.71, 95%CI ¼ 0.54–0.88). There was no significant association between serum levels of endotrophin and advanced fibrosis.
Conclusions: These data suggest that serum endotrophin could be a valuable biomarker for diagnos-ing NASH, but not for detectdiagnos-ing advanced fibrosis in NAFLD.
ARTICLE HISTORY
Received 7 December 2020 Revised 10 January 2021 Accepted 16 January 2021
KEYWORDS
Nonalcoholic fatty liver disease; metabolic-associated fatty liver disease; NASH; fibrosis; diagnostic test
Introduction
Non-alcoholic fatty liver disease (NAFLD) is highly prevalent in the general population, with a close association between obesity and the metabolic syndrome [1–3]. A small minority of patients with NAFLD progress to end-stage liver disease [4,5]. These patients often present in late stages when prog-nosis is dismal [6]. Previous studies have shown that liver fibrosis staging is the best way to predict future liver-related outcomes [5,7,8]. However, fibrosis staging requires liver biopsies. These are invasive and not practical to use as a diagnostic tool in large populations of people with NAFLD. Likewise, there are no approved tests to accurately diagnose non-alcoholic steatohepatitis (NASH), the progressive form of NAFLD. The diagnosis of NASH is a criterion for inclusion in clinical trials. However, there is a high rate of screening fail-ures, often due to the fact that NASH could not be verified on biopsy [9]. Many studies are underway aiming to develop
non-invasive biomarkers to improve diagnosis of liver fibrosis and NASH. Collagen type VI is an important contributor to liver fibrosis [10]. Endotrophin is the C-terminal cleavage
fragment of the collagen VI a3 chain (COL6A3) that is
released from mature collagen VI (COL6) following secretion [11]. Hepatic endotrophin is linked to a poor prognosis in patients with hepatocellular carcinoma [12], and may act as a mediator in the progression of chronic liver diseases [12]. Endotrophin might be a promising non-invasive biomarker for NASH or fibrosis in NAFLD. Here, we evaluated the associ-ation between endotrophin in circulassoci-ation and disease sever-ity in a well-characterized cohort of patients with NAFLD.
Materials and methods Study population
Our study included 211 individuals between 2011 and 2019 at the Karolinska University and the Link€oping University CONTACTHannes Hagstr€om hannes.hagstrom@ki.se Division of Hepatology, Karolinska University Hospital, 141 86 Stockholm, Sweden
Supplemental data for this article can be accessedhere.
ß 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4. 0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
2021, VOL. 56, NO. 4, 437–442
Hospitals, both in Sweden. Patients were recruited following routine evaluation of referrals from primary care. Liver biop-sies were performed as part of the evaluation in 141 of the 211 patients (67%). Transient elastography was performed in 192 patients (91%). Liver biopsy was performed for fibrosis staging in cases where results of elastography were equivo-cal or where other liver diseases could not be ruled out. Liver biopsies were also used for inclusion in another study [13]. Individuals were excluded if they had any other chronic liver disease or consumption of alcohol >30 g/day (for men) or >20 g/day (for women) as defined by consuming >3/2 standard drinks per day (roughly 10 g/unit) as per an AUDIT questionnaire [14]. We also included serum samples from nine healthy controls with a mean age of 49 years who were free of chronic diseases and with a BMI of <25 kg/m2 and normal alanine aminotransferase (ALT) levels. Controls were clinical staff at the Karolinska University Hospital and did not undergo liver biopsy or elastography.
Variables
At the time of evaluation, serum samples were taken and stored in a 80C freezer. The following variables were all assessed at or within 30 days of baseline, defined as the date of serum sampling. Transient elastography was per-formed after at least 3 h of fasting using an M- or XL-probe as appropriate. Liver biopsies were performed under local anaesthesia using a Menghini or true-cut technique after ultrasound guidance. Smoking status included categories of current, past or never smoker. Type 2 diabetes was defined as present if there was a previous diagnosis treated with per
oral medications or insulin, or a fasting glucose of
>6.1 mmol/L. Body mass index (BMI) was defined as body weight (kg) divided by height (m) squared. Routine chemistry analyses performed evaluated levels of platelets, creatinine, ALT, AST and fasting glucose. We calculated the FIB-4 index as age (years)AST (U/L)/[platelets (109/L)ALT1/2(U/L)] [15].
All biopsies were scored by one expert liver pathologist per site (Stockholm/Link€oping), with fibrosis stage defined as per the NASH CRN definition (0–4) [16]. Presence of NASH was defined as per the FLIP algorithm as presence of hepatic steatosis, ballooning and lobular inflammation [17]. Advanced fibrosis was defined as stage 3–4 on liver biopsy if available, or in those without biopsy data but with elastogra-phy as more or equal to 15 kPa. We selected this cut-off to ensure a high specificity of advanced fibrosis.
ELISA based measurements of circulating endotrophin
ELISA based measurements of circulating endotrophin were performed as previously described [18]. Briefly, serum sam-ples (10mL) were prepared from freshly collected blood sam-ples and preserved at80C until analysis. To quantitatively determine the levels of circulating endotrophin in human serum, 96-well MaxiSorp plates (Corning Costar, Corning, NY) were coated with rabbit polyclonal anti-endotrophin antibod-ies prepared in-house at 20 mg/mL. Plasma samples were titrated at a series of dilutions in 1 PBS, then added to an
endotrophin coated plate. A high affinity specific anti-endotrophin antibody (ETPmAb4) was biotinylated using an amine conjugation kit (Fisher Scientific, Waltham, MA), then utilized as secondary detection antibody. Streptavidin conju-gated with HRP (horseradish peroxidase) (Fisher Scientific, Waltham, MA) was used for detection of endotrophin signals, using the dilution suggested by the manufacturer. A purified endotrophin recombinant protein was titrated in a series of concentrations (0–5000 pg/mL) to establish a standard curve for calculation of endotrophin in serum samples.
Statistical analysis
Differences between groups were analysed using a
Mann–Whitney test or a Chi2-test as appropriate. The primary analyses measured differences in endotrophin levels between patients with NASH and those without NASH as determined by biopsy data. The primary analyses also measured differen-ces in endotrophin levels between patients with advanced fibrosis and those without advanced fibrosis using the full dataset. We also compared differences in endotrophin levels between individuals with NAFLD with healthy controls.
For detection of NASH, we randomly split the dataset into two groups consisting of 2/3 of the cohort (derivation cohort) and 1/3 (validation cohort). Model building was performed in the derivation cohort and tested in the validation cohort. We first considered a univariable model, using only endotrophin data. Next, we considered a multivariable model, with predic-tors selected a priori that included age, sex, BMI and levels of fasting glucose and ALT. We then analysed whether adding endotrophin data to this multivariable model could improve its predictive capacity. A similar strategy was used to assess the capacity of endotrophin data to improve prediction of advanced fibrosis, adding endotrophin data to the FIB-4 score. We calculated ROC curves for the predictive capacity of these models to define NASH as per the FLIP algorithm and advanced fibrosis. All analyses were performed using STATA version 16.0 (StataCorp, College Station, TX).
Ethical considerations
The study was approved by the regional ethics committee in Stockholm, Sweden, reference numbers 2011/13-31/1, 2016/ 2137-31 and 2019-02804. All patients enrolled provided oral and written informed consent.
Results
Baseline characteristics for the full NAFLD cohort are pre-sented in Table 1, and stratified on presence of NASH in
Table 2. Additionally, characteristics stratified on if a liver biopsy or not are presented inSupplementary Table 1.
Mean age was 55.5 years, 51% were males and mean BMI was 31.4 kg/m2. Of patients with biopsy data (n¼ 141, 67%), 87 (62%) had NASH and 41 (29%) had advanced fibrosis. Transient elastography was performed in 192 patients (92%). Of these, 13 (7%) had low quality measurements (success rate<60% or IQR >30% of the median liver stiffness). Of the
179 patients with good quality elastography, the mean value measured was 10.9 kPa (SD 9.2). The CAP technology for steatosis assessment was only available in 72/192 (38%) patients with elastography data. Advanced fibrosis was pre-sent in 58/211 (27%) persons in the full cohort. Patient
endo-trophin levels were similar for the two sites
(Karolinska¼ 37.3 ng/mL, Link€oping ¼ 35.5 ng/mL, p ¼ .28). Meanwhile, the full NAFLD cohort had significantly higher endotrophin levels than did healthy controls (36.7 vs. 16.6 ng/mL, p<.001, Figure 1). Patients with NASH had sig-nificantly higher endotrophin levels than did those without NASH (40.1 vs. 32.4 ng/mL, p<.001, Figure 2). Crude and adjusted odds ratios for presence of NASH for candidate pre-dictors using the full cohort are presented inTable 3.
In the derivation cohort, using univariable logistic regres-sion the AUROC for endotrophin to define presence of NASH was modest (AUROC 0.73, 95%CI¼ 0.62–0.84). A multivariable model using age, sex, BMI, levels of fasting glucose and ALT had an ability to diagnose NASH similar to that of endotro-phin levels (AUROC 0.74, 95%CI¼ 0.64–0.84). However, add-ing endotrophin data to the multivariable model significantly
improved the predictive capacity (AUROC 0.83,
95%CI¼ 0.74–0.92, p ¼ .03). The performance of the full model was however lower in the validation cohort (AUROC 0.71, 95%CI 0.54–0.85). The performance of the model was Table 1. Characteristics of the full cohort with NAFLD (n ¼ 211).
Parameter N with data Mean (SD)/N (%) Age (years) 211 55.5 (13.2) Sex (male) 211 108 (51%) Smoking 211 Never 116 (55%) Past 79 (37%) Current 16 (8%)
Type 2 diabetes (yes) 211 81 (38%) Body mass index (kg/m2) 211 31.4 (4.8) Platelets (109) 211 223 (61) ALT (IU/L) 211 74 (55) AST (IU/L) 210 49 (26) FIB-4 score 210 1.65 (1.09) Fasting glucose (mg/dL) 209 134 (47) Endotrophin (ng/mL) 211 36.7 (12.0) Elastographya 179a Stiffness (kPa) 10.9 (9.2) IQR 2.4 (2.6) Success rate (%) 94 (10) CAP (n ¼ 72, dB/m) 321 (46) Biopsy data 141 Fat (1–3) 38 (27%) 1 69 (49%) 2 34 (24%) 3 Lobular inflammation (0–3) 0 29 (21%) 1 62 (44%) 2 41 (29%) 3 9 (6%) Ballooning (0–2) 0 48 (34%) 1 64 (45%) 2 29 (21%) Fibrosis stage 0 20 (14%) 1 46 (33%) 2 34 (24%) 3 26 (18%) 4 15 (11%) NAS score 4.0 (1.7) NASH (yes) 87 (62%)
ALT: alanine aminotransferase; AST: aspartate aminotransferase; CAP: con-trolled attenuation parameter; NASH: non-alcoholic steatohepatitis; T2D: type 2 diabetes.
aOnly including those with success rate>60% and IQR <30%.
Table 2. Characteristics of the cohort stratified on presence of NASH in those with available biopsy data.
Parameter Not NASH (n ¼ 54) NASH (n ¼ 87) p Value Age (years) 54.0 (14.2) 56.0 (12.3) .53 Sex (male) 31 (57%) 48 (55%) .80 Smoking .008 Never 20 (37%) 55 (63%) Past 28 (52%) 24 (28%) Current 6 (11%) 8 (9%)
Type 2 diabetes (yes) 14 (26%) 43 (49%) .006 Body mass index (kg/m2) 30.5 (4.5) 32.1 (5.0) .055
Platelets (109) 219 (53) 218 (64) .55 ALT (IU/L) 71 (46) 87 (69) .11 AST (IU/L) 47 (26) 57 (28) .009 FIB-4 score 1.55 (0.94) 1.81 () .10 Fasting glucose (mg/dL) 123 (37) 151 (55) <.001 Endotrophin (ng/mL) 32.4 (12.9) 40.1 (11.7) <.001 Elastographya Stiffness (kPa) 8.8 (4.9) 13.4 (9.7) <.001 IQR 1.9 (1.3) 3.1 (3.4) .12 Success rate (%) 93 (2) 93 (1) .80 CAP (n ¼ 38, dB/m) 315 (37) 334 (45) .25 Biopsy data Fat (1–3) .001 1 23 (43%) 15 (17%) 2 25 (46%) 44 (51%) 3 6 (11%) 28 (32%) Lobular inflammation (0–3) <.001 0 29 (54%) 0 (0%) 1 18 (33%) 44 (51%) 2 7 (13%) 34 (39%) 3 0 (0%) 9 (10%) Ballooning (0–2) <.001 0 48 (89%) 0 (0%) 1 6 (11%) 58 (67%) 2 0 (0%) 29 (33%) Fibrosis stage .001 0 16 (30%) 4 (5%) 1 18 (33%) 46 (33%) 2 11 (20%) 34 (24%) 3 6 (11%) 26 (18%) 4 3 (6%) 12 (14%) NAS score 2.3 (1.1) 5.1 (1.1) <.001 ALT: alanine aminotransferase; AST: aspartate aminotransferase; CAP: controlled attenuation parameter; NASH: non-alcoholic steatohepatitis; T2D: type 2 diabetes;
a
Only including those with success rate>60% and IQR <30% (n ¼ 121).
Figure 1.Dotplot of levels of endotrophin in patients with NAFLD (n ¼ 211) and healthy controls (n ¼ 9) (p<.001). Bars represent median values and inter-quartile ranges.
slightly higher in patients without type 2 diabetes (AUROC 0.78, 95%CI¼ 0.68–0.89) compared to patients with type 2 diabetes (AUROC 0.73, 95%CI¼ 0.57–0.90).
In the full dataset, endotrophin levels were higher in patients with ballooning grade 1 or 2 than in those with bal-looning grade 0 (39.4 vs. 32.8 ng/mL, p<.001). In contrast, no significant difference was seen between endotrophin levels of patients with lobular inflammation grade 0–1 and those with lobular inflammation grade 2–3 (37.5 vs. 36.5 ng/mL, p ¼ .67). No significant difference was seen between endotrophin levels of patients with a fat score of 1 and those of patients with a fat score of 2 or 3 (38.6 vs. 36.7 ng/mL, p¼.43).
There was no difference in circulating endotrophin levels of patients with (37.4 ng/mL) and without (35.3 ng/mL) advanced fibrosis (p¼.27). This was true also when only using data from patients with available biopsy (38.0 vs. 36.7 ng/mL, p¼.56). Likewise, there was no association between endotrophin levels and liver stiffness by transient elastography (adjusted R2¼0.00, Figure 3). The predictive capacity to detect advanced fibrosis for endotrophin was low in the derivation cohort (AUROC 0.57, 95%CI¼ 0.47–0.67), while the predictive value of FIB-4 was comparable to previ-ous studies (AUROC 0.75, 95%CI¼ 0.66–0.85). Adding endo-trophin data to the FIB-4 score did not improve prediction of advanced fibrosis (AUROC 0.75 vs. 0.75, p¼.68).
Discussion
In this cross-sectional study, we found that circulating endo-trophin was associated with the presence of NASH, which
was primarily driven by higher levels of endotrophin in indi-viduals with hepatocyte ballooning, while lobular inflamma-tion and steatosis contributed less. No associainflamma-tion was found between endotrophin and advanced fibrosis. Endotrophin improved a multivariable model to detect NASH significantly and to acceptable levels (AUROC 0.83). We do not however argue that the developed model should be preferred, as this was mostly a mean to show that endotrophin was able to enhance the diagnostic accuracy of such a model. It would be interesting to see if endotrophin could also enhance the diagnostic accuracy of other models such as the FAST score [19], which could not be investigated here. Additionally, the performance of endotrophin should be further externally validated given the lower AUROCs found in our validation holdout dataset.
Taken together, these results suggest that circulating endotrophin is involved in the ongoing inflammatory process of NASH, but not with chronic non-inflammatory fibrosis. This is in agreement with other evidence indicating endotro-phin is a dynamic product of collagen type VI processing, which is also found in extra-hepatic tissues such as adipose tissue [20]. Importantly, we are assessing circulating endotro-phin in serum. While no correlations were seen with fibrosis, this does not mean that endotrophin is not causally involved in hepatic fibrosis. Endotrophin is a factor predominantly act-ing in the microenvironment where it is produced. As such, during more severe fibrotic stages, the bulk of endotrophin is retained in hepatic tissue and not released into circulation. This is demonstrated by the presence of high levels of endo-trophin in fibrotic lesions [12,18,20,21]. Furthermore, our Figure 2. Dotplot of serum endotrophin levels in patients with and without
NASH (p<.001). Bars represent median values and interquartile ranges.
Table 3.Crude and adjusted odds ratios for presence of NASH for predictors in the multi-variable adjusted model, using the full cohort.
Parameter OR, crude (95%CI) p Values OR, adjusteda(95%CI) p Values Age (years) 1.01 (0.99–1.04) .38 1.02 (0.99–1.05) .20 Sex (male) 0.91 (0.46–1.81) .80 0.87 (0.39–1.92) .73 Fasting glucose (mg/dL) 1.01 (1.00–1.02) .004 1.01 (1.00–1.02) .006 ALT (IU/L) 1.005 (0.998–1.012) .14 1.01 (1.00–1.02) .058 Endotrophin (mg/mL) 1.06 (1.02–1.09) .001 1.06 (1.03–1.10) <.001 BMI (kg/m2) 1.08 1.00–1.16) .06 1.08 (1.00–1.18) .06
ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index; CI: confidence interval; OR: odds ratio.
a
Adjusted for age, sex, ALT, BMI, fasting glucose and endotrophin.
Figure 3. Scatterplot of endotrophin levels and liver stiffness measured in kPa, with a fractional polynomial model fit, showing no clear association between endotrophin levels and liver stiffness.
recent data in a rodent model clearly implicate endotrophin in fibrosis. Accumulation of endotrophin in the liver induced hepatic inflammation and the development of fibrosis with associated insulin resistance. Surprisingly, endotrophin over-expression also led to the emergence of liver cancer within 10 months in the SREBP1a transgenic background [12].
Identification of biomarkers to improve diagnosis of NASH is of high importance for several reasons. First, recruitment in ongoing clinical trials has a high screening failure rate [22,23]; better diagnostics are important to reduce costs. Second, although fibrosis stage is the major predictor of adverse outcomes as shown by our group and others [5,7,8], identification of non-invasive biomarkers of prognosis is urgently needed, as the currently available scoring systems are poor predictors of liver-related outcomes [24–27].
These results can be contrasted to other studies that have tried to identify non-invasive markers or models for the pres-ence of NASH. In a NASH CRN study from 2010, a
multivari-able model using data on basic biochemistry,
anthropometrics, demographics and comorbidities yielded an AUROC of 0.79 for predicting the presence of NASH [28]. Our model required less data to achieve roughly the same pre-dictive capacity. A recent study by Newsome et al. found an AUROC of 0.80 when using elastography data combined with AST data to detect NASH with significant fibrosis [19]. In con-trast, our model did not require elastography data. Hence, it could be easier to use in settings where elastography is not available. It was not possible to evaluate the FAST score plus added endotrophin as we lacked CAP measurements on a large part of the cohort. In general, it should be noted that the general capacity of these models is not yet sufficiently high to be used in clinical practice. However, they could be a means to reduce screening failure rates in clinical trials.
Strengths of this study include a large cohort with biopsy available for the majority of patients enrolled, data from two sites that enhances external generalizability, and a short timeframe between biopsy and serum sampling. We could also compare results from patients to those from healthy controls and had a good balance of male and female study subjects. Limitations include risk for selection bias as a large part of the cohort had NASH. This limitation also applies to
other cohorts from tertiary centres. The presence of
advanced fibrosis was not as high in our cohort as in these other cohorts. There were no biopsy data in controls; how-ever, presence of NAFLD is very unlikely in a healthy, lean population with normal ALT levels.
Collectively, the results of our study show that endotro-phin is a promising biomarker for detecting the presence of NASH, especially when combined with other markers. These results should be validated in other cohorts.
Conclusions
Elevated levels of circulating endotrophin are associated with the presence of NASH, but not with fibrosis, in patients with NAFLD. Our results suggest that endotrophin could be a novel biomarker to better distinguish NASH from simple
steatosis, which could help reduce screen failures in clinical trials and contribute to the diagnosis of NASH.
Ethical approval
The study was approved by the regional ethics committee in Stockholm, Sweden, reference numbers 2011/13-31/1, 2016/2137-31 and 2019-02804. All patients enrolled provided oral and written informed consent.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Hannes Hagstr€om http://orcid.org/0000-0002-8474-1759
Patrik Nasr http://orcid.org/0000-0002-2928-4188
Mattias Ekstedt http://orcid.org/0000-0002-5590-8601
Stergios Kechagias http://orcid.org/0000-0001-7614-739X
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