Increasing metabolic co-morbidities are associated with higher risk of advanced fibrosis in nonalcoholic steatohepatitis.

Hepatic fibrosis and advanced fibrosis in particular is the strongest predictor of liver-related outcomes and mortality among nonalcoholic steatohepatitis (NASH) patients. Understanding prevalence and predictors of NASH with advanced fibrosis is critical for healthcare resource planning. Using a large U.S. clinical laboratory database from 10/1/2017-9/30/2018, adults negative for hepatitis B and hepatitis C and after excluding for alcoholic liver disease and pregnancy were evaluated for prevalence of F3 and F4 fibrosis using a systematic algorithm of five fibrosis-4 (FIB-4) criteria: Criteria 1 (≥F3: >2.67), Criteria 2 (2.67<F3≤4.12 and F4>4.12), Criteria 3 (2.67<F3≤3.15, F4>3.15), Criteria 4 (3.25<F3≤3.5, F4>3.5), Criteria 5 (3.25<F3≤4.12, F4>4.12). Metabolic co-morbidities evaluated included decreased high density lipoprotein (<40 mg/dL men, <50 mg/dL women), high triglycerides (≥150 mg/dL), elevated hemoglobin A1C (≥6.5%). Parallel analyses of patients with specific NAFLD/NASH ICD-9/10 codes from 10/1/2013-9/30/2018 were performed. Multivariate logistic regression models evaluated for predictors of ≥F3 fibrosis. Among patients with NAFLD/NASH ICD-9/10 codes, ≥F3 prevalence ranged from 4.35% - 6.90%, and F4 prevalence ranged from 2.52%- 3.67%. Increasing metabolic co-morbidities was associated with higher risk of ≥F3 fibrosis. Compared to NASH patients without metabolic co-morbidities, NASH with four concurrent metabolic co-morbidities had higher risk of ≥F3 (OR 1.56, 95% CI 1.40-1.73, p<0.001). In summary, prevalence of NASH with advanced fibrosis among U.S. adults was as high as 6.90% and prevalence of NASH with cirrhosis was as high as 3.67%, representing 5.18 million and 2.75 million, respectively, when using an estimate of 75 million U.S. adults with NAFLD. Co-morbid metabolic abnormalities were associated with higher risk of advanced fibrosis among NASH patients.

Introduction

Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease in the United States and globally[1, 2], and is rapidly becoming a leading cause of end-stage liver disease leading to hepatocellular carcinoma (HCC) and need for liver transplantation[3-5]. The presence of fibrosis is the strongest predictor of mortality and liver-related complications among patients with NASH[6-8]. Advanced fibrosis in particular, which is the subset of patients with F3 or greater fibrosis, have significantly greater risks of liver-related complications, risk of HCC, and risk of liver-related and all-cause mortality in NASH patients[6, 8]. Earlier identification of NASH patients with advanced fibrosis has clinical significance, given that these patients need closer monitoring including screening for HCC or varices, more aggressive management of risk factors, and are at greatest need for NASH therapies to halt or reverse steatohepatitis and fibrosis.

The prevalence of NASH with advanced fibrosis is not clear and current estimates are likely underestimates given that existing studies have utilized primarily observation cohort-type or survey-based study designs that underestimate the true prevalence[1, 9, 10]. In addition, modeling studies or systematic reviews aimed at defining NASH and NASH with advanced fibrosis prevalence are based on existing literature, which may be biased given that NASH is likely underdiagnosed given sub-optimal provider and patients awareness, the lack of clear guidelines on who to screen and what tools to screen with, the lack of effective therapies that often drives disease screening efforts, as well as the lack of large datasets with adequate data to assess hepatic fibrosis[11-13]. More accurately understanding prevalence and predictors of advanced fibrosis among NASH patients is important to guide healthcare resource planning such that adequate preventative care and interventions can be effectively targeted to those NASH patients at greatest risk of liver-related complications. To address this gap in knowledge, we aim to utilize a nationally representative U.S. clinical laboratory database to evaluate prevalence of advanced fibrosis using a systematic algorithm of non-invasive fibrosis biomarker (fibrosis-4 (FIB-4)) and to evaluate the impact of metabolic co-morbidities on odds of advanced fibrosis.

Materials and methods

A retrospective cohort study was performed using clinical laboratory data from October 1, 2017 to September 30, 2018 from the Quest Diagnostics Clinical Laboratory Database. Quest Diagnostics has over 145 million patient encounters each year across the United States. Test results are stored in the Quest Diagnostics Informatics Data Warehouse, which is the largest private clinical laboratory data warehouse in the United States and stores approximately 3 billion test results annually. For this Quest Diagnostics Health Trends study, we extracted testing data for individual patients as described below; all data were de-identified prior to analysis. This study was determined to be exempt from institutional review by the Alameda Health System Institutional Review Board.

The study cohort was developed by first identifying unique adults (age ≥ 18 years) with unique identification numbers in the dataset. We then excluded patients with positive hepatitis B virus surface antigen (HBsAg) or positive hepatitis C virus antibody (HCV Ab), as well as patients with unavailable labs to assess FIB-4 (Fig 1). We further excluded patients with alcohol use or alcoholic liver disease related ICD-9/10 codes from 10/1/2013–9/30/2018 and patients with maternal screening at a time that indicated pregnancy when laboratory values were obtained. Among this cohort, prevalence of F3 and F4 fibrosis was assessed using a systematic algorithm consisting of five FIB-4 score criteria based on published literature[14-16]: Criteria 1 (≥F3: >2.67), Criteria 2 (2.674.12), Criteria 3 (2.673.15), Criteria 4 (3.253.5), Criteria 5 (3.254.12). Given our focus on the impact of metabolic co-morbidities on the prevalence of advanced fibrosis, we focused on three laboratory biomarkers(decreased high density lipoprotein (HDL <40 mg/dL in men, <50 mg/dL in women), elevated triglycerides (TG ≥150 mg/dL), and elevated hemoglobin A1C (≥6.5%)) and at least one ICD-9/10 code for hypertension from 10/1/2013–9/30/2018 that are included components of the metabolic syndrome[17, 18]. Prevalence of F3 and F4 fibrosis were calculated in patients with elevated alanine aminotransferase (ALT > 25 U/L women, ALT > 35 U/L men) and one, two, three, or four concurrent metabolic co-morbidities. In addition to the above study cohort, we performed a parallel analysis specifically focusing on patients with NAFLD/NASH ICD-9/10 codes. To generate a larger sample for this parallel analysis, we included patients with NAFLD/NASH ICD-9/10 codes from 10/1/2013–9/30/2018 (Fig 1). Patients with unavailable labs to assess FIB-4, alcohol use or alcoholic liver disease-related ICD-9/10 codes, and patients with evidence of pregnancy were similarly excluded. Histologic data from liver biopsy and radiographic data from imaging-based studies were not available for inclusion in the study. Thus our use of the term NASH to refer to patients with F3 or F4 fibrosis is based on the assumption that those patients with suspected NAFLD and advanced fibrosis must have progressed to the NASH disease state[13].

Fig 1

Flowchart to identify the study cohort.

Prevalence of F3 and F4 fibrosis using each of the above Criteria were calculated and presented as prevalence (%) with 95% confidence intervals (95% CI). Adjusted multivariate logistic regression models were utilized to evaluate predictors of advanced fibrosis (F3/F4 using Criteria 1). The final multivariate model adjusted for sex, age, and number of metabolic co-morbidities. In the final model, age is a continuous variable with a unit constituting 10 years. Statistical analyses were performed using SAS Studio 3.6 on SAS 9.4 (SAS Institute Inc., Cary, NC, USA). A two-tailed p<0.05 indicated statistical significance. This study was granted exempt status from Alameda Health System Institutional Review Board.

Results

From October 1, 2017 to September 30, 2018, 57,874,162 patients with unique identification numbers were identified in the database. After excluding patients with evidence of positive HBsAg or positive HCV Ab, we additionally excluded patients with unavailable labs to calculate FIB-4 scores, those with alcohol use or alcoholic liver disease related ICD-9/10 and patients with evidence of pregnancy (Fig 1). The final overall study cohort consisted of 20,204,004 patients. Among this cohort, 9,246,209 patients had available labs to assess metabolic co-morbidities. We also performed a parallel analysis that specifically focused on patients with NAFLD/NASH ICD-9/10 codes from October 1, 2013 to September 30, 2018, and after excluding patients as previously described, the final NAFLD/NASH ICD-9/10 cohort consisted of 208,749 patients (Fig 1).

Among the overall study cohort, 57.4% were female and mean age was 54.6 years (SD 17.3), 7.4% had elevated ALT, 29.0% had elevated triglycerides, 19.9% had elevated hemoglobin A1C, 24.3% had deceased LDL, and 39.1% had hypertension (Table 1). Overall prevalence of F3/F4 fibrosis using Criteria 1 was 3.12% (95% 3.11–3.13) (Table 2). Increasing number of metabolic co-morbidities was associated with higher prevalence of F3/F4 fibrosis using Criteria 1. For example, prevalence of F3/F4 fibrosis was 5.07% (95% CI 4.98–5.16) in patients with elevated ALT and one metabolic co-morbidity, which increased to 6.68% (6.44–6.92) among patients with elevated ALT and four metabolic co-morbidities, p<0.01. Among the NAFLD/NASH ICD-9/10 cohort, prevalence of F3/F4 fibrosis was 6.90% (95% CI 6.79–7.01). When assessing fibrosis using Criteria 2–5, among the overall study cohort, prevalence of F3 fibrosis ranged from 0.35% - 2.41% and the prevalence of F4 fibrosis ranged from 0.71% - 1.21% (Table 2). As with Criteria 1, similar trends were observed with Criteria 2–5, such that increasing number of metabolic co-morbidities were associated with higher prevalence of F3 and F4 fibrosis. Among the NAFLD/NASH ICD-9/10 cohort, prevalence of F3 fibrosis ranged from 0.68% - 4.38% and prevalence of F4 fibrosis ranged from 2.52% - 3.67% using these Criteria (Table 2).

Table 1

Characteristics of the study cohort.

Variables	Proportion (%)	Frequency (N)
Total		20,204,004
Sex
Female	57.4%	11,579,119
Male	42.6%	8,590,269
Age (mean, SD, Median, Range)	54.6 ± 17.3	55.7 (18–99)
Elevated ALT	7.4%	1,492,650
Elevated Triglycerides	29.0%	4,788,095
Elevated Hemoglobin A1C	19.9%	1,966,854
Decreased HDL	24.3%	3,999,546
Hypertension	39.1%	7,907,090

Note:

Elevated ALT: > 25 U/L in women, > 35 U/L in men

Elevated Triglycerides: > = 150 mg/dL

Elevated Hemoglobin A1C: > = 6.5%

Decreased HDL: <40mg/dL in men, <50 mg/dL in women

Hypertension: ICD 9/10 code for hypertension

Table 2

Prevalence of F3 and F4 fibrosis by different FIB-4 criteria.

		Overall Cohort		NAFLD/NASH ICD-9/10 Cohort
		N = 20,204,004		N = 208,749
	FIB-4 Categories	Prevalence (%)	95% CI	Prevalence (%)	95% CI
Criteria 1	F3/F4 > 2.67	3.12%	3.11–3.13%	6.90%	6.79–7.01%
Criteria 2	F4 > 4.12	0.71%	0.71–0.71%	2.52%	2.45–2.59%
	2.67 < F3 < = 4.12	2.41%	2.40–2.42%	4.38%	4.29–4.47%
Criteria 3	F4 > 3.5	1.21%	1.20–1.21%	3.67%	3.59–3.75%
	2.67 < F3 < = 3.5	1.91%	1.91–1.92%	3.23%	3.16–3.31%
Criteria 4	F4 > 3.5	1.21%	1.20–1.21%	3.67%	3.59–3.75%
	3.25 < F3 < = 3.5	0.35%	0.35–0.35%	0.68%	0.65–0.72%
Criteria 5	F4 > 4.12	0.71%	0.71–0.71%	2.52%	2.45–2.59%
	3.25 < F3 < = 4.12	0.84%	0.84–0.85%	1.83%	1.77–1.89%
		One Metabolic Co-Morbidity		Two Metabolic Co-Morbidities
		N = 231,429		N = 208,943
	FIB-4 Categories	Prevalence (%)	95% CI	Prevalence (%)	95% CI
Criteria 1	F3/F4 > 2.67	5.07%	4.98–5.16%	4.79%	4.70–4.88%
Criteria 2	F4 > 4.12	1.70%	1.64–1.75%	1.46%	1.41–1.51%
	2.67 < F3 < = 4.12	3.37%	3.30–3.44%	3.33%	3.25–3.41%
Criteria 3	F4 > 3.5	2.56%	2.50–2.63%	2.29%	2.23–2.36%
	2.67 < F3 < = 3.5	2.51%	2.44–2.57%	2.50%	2.43–2.57%
Criteria 4	F4 > 3.5	2.56%	2.50–2.63%	2.29%	2.23–2.36%
	3.25 < F3 < = 3.5	0.51%	0.48–0.54%	0.53%	0.49–0.56%
Criteria 5	F4 > 4.12	1.70%	1.64–1.75%	1.46%	1.41–1.51%
	3.25 < F3 < = 4.12	1.38%	1.33–1.43%	1.35%	1.30–1.40%
		Three Metabolic Co-Morbidities		Four Metabolic Co-Morbidities
		N = 117,797		N = 43,035
	FIB-4 Categories	Prevalence (%)	95% CI	Prevalence (%)	95% CI
Criteria 1	F3/F4 > 2.67	5.57%	5.44–5.70%	6.68%	6.44–6.92%
Criteria 2	F4 > 4.12	1.53%	1.46–1.60%	1.62%	1.50–1.74%
	2.67 < F3 < = 4.12	4.04%	3.93–4.15%	5.06%	4.86–5.27%
Criteria 3	F4 > 3.5	2.47%	2.38–2.56%	2.81%	2.66–2.97%
	2.67 < F3 < = 3.5	3.10%	3.00–3.20%	3.87%	3.69–4.05%
Criteria 4	F4 > 3.5	2.47%	2.38–2.56%	2.81%	2.66–2.97%
	3.25 < F3 < = 3.5	0.59%	0.55–0.64%	0.76%	0.68–0.84%
Criteria 5	F4 > 4.12	1.53%	1.46–1.60%	1.62%	1.50–1.74%
	3.25 < F3 < = 4.12	1.53%	1.46–1.60%	1.95%	1.82–2.09%

To further assess the impact of concurrent metabolic co-morbidities on prevalence of advanced fibrosis among individuals with NASH, we specifically focused on the subset of NAFLD/NASH ICD-9/10 cohort and calculated the prevalence of F3/F4 fibrosis in patients with elevated ALT and incremental increasing number of concurrent metabolic co-morbidities (Fig 2). When using Criteria 1, the prevalence of F3/F4 fibrosis among NASH patients increased significantly from 7.82% in patients with elevated ALT and one metabolic co-morbidity to 11.63% to NASH patients with elevated ALT and four concurrent metabolic co-morbidities, p<0.001. Similar trends in prevalence of F3 and F4 fibrosis were observed when using Criteria 2–5, such that individuals with NASH concurrent with four metabolic co-morbidities had higher prevalence of F3 fibrosis (range, 1.42% - 8.65%) and F4 fibrosis (range, 2.98% - 4.90%) than those with one, two, or three concurrent metabolic co-morbidities (Fig 2).

Fig 2

Prevalence of F3 and F4 fibrosis among NAFLD/NASH ICD-9/10 patients with increasing number of metabolic co-morbidities.

On adjusted multivariable logistic regression analysis evaluating the odds of F3/F4 fibrosis (Criteria 1) among NAFLD/NASH ICD-9/10 cohort patients, males with NASH had significantly greater odds of F3/F4 fibrosis compared to females with NASH (OR 1.22, 95% CI 1.16–1.27, p<0.001) (Table 3). Increasing age was also associated with higher odds of F3/F4 fibrosis (OR for each 10 years, 2.12, 95% CI 2.08–2.17). Compared to NASH patients without any of our assessed metabolic co-morbidities, increasing number of metabolic comorbidities was associated with increasing odds of F3/F4 fibrosis (Table 3).

Table 3

Odds of F3 or greater fibrosis using criteria 1 among NAFLD/NASH ICD-9/10 diagnosis code patients.

	Adjusted Odds Ratio	95% CI	P-Value
Sex
Female	1.00	Reference	-
Male	1.22	1.16–1.27	< 0.001
Age (each 10 years)	2.12	2.08–2.17	< 0.001
Metabolic Co-Morbidities
None	1.00	Reference	-
One	1.18	1.08–1.29	< 0.001
Two	1.28	1.18–1.40	< 0.001
Three	1.34	1.23–1.47	< 0.001
Four	1.56	1.40–1.73	< 0.001

Discussion

Among a large U.S. national clinical laboratory database, when focusing specifically on patients with NAFLD/NASH ICD-9/10 diagnosis codes, prevalence of advanced fibrosis ranged from 4.35% - 6.90%, and the prevalence of F4 fibrosis/cirrhosis ranged from 2.52%– 3.67%. Using an estimate of 75 million adults with nonalcoholic fatty liver disease in the U.S., the prevalence of NASH with advanced fibrosis is as high as 5.18 million adults, and the prevalence of NASH cirrhosis is as high as 2.75 million adults.

Given the likely under-diagnosis and thus under-estimate of NASH when relying only on ICD-9/10 coding, we evaluated a larger cohort of patients with available labs to assess ALT and concurrent metabolic co-morbidities after excluding those with HBV or HCV and excluding those with pregnancy or potential alcoholic liver disease, without relying on ICD-9/10 coding. Among this cohort, we observed significantly higher prevalence and risk of advanced fibrosis associated with increasing number of co-morbidities. For example, prevalence of advanced fibrosis in patients with elevated ALT and four concurrent metabolic co-morbidities is as high as 6.68%, and the prevalence of cirrhosis in patients with elevated ALT and four concurrent metabolic co-morbidities is as high as 2.81%. Given the methods employed, it would be a reasonable estimate that these patients with elevated ALT and four concurrent metabolic co-morbidities have NASH, thus making the estimates of advanced fibrosis and cirrhosis prevalence among this group quite concerning.

To our knowledge, this study is one of the first to utilize a large national clinical laboratory database to refine prevalence estimates of patients with NASH and advanced fibrosis. Previous studies evaluating prevalence of NASH and advanced fibrosis have utilized cross sectional survey-based study designs that have may have significantly underestimated true prevalence. For example, three recent studies utilizing data from the National Health and Nutrition Examination Survey (NHANES) evaluated prevalence and predictors of advanced fibrosis among patients with suspected NAFLD based on algorithms that combined patient reported history and co-morbidities and serological biomarkers[9, 10, 19]. Using the U.S. fatty liver index to identify adults with NAFLD and NAFLD fibrosis score to assess for presence of fibrosis, Le, et al. reported a 30.0% prevalence of NAFLD among U.S. adults from 1999–2012 NHANES, among which 10.3% had advanced fibrosis[19]. Similarly, Kabbany, which focused on the latter 2009–2012 NHANES dataset, observed a prevalence of 0.84–1.75% for NASH advanced fibrosis and prevalence of 0.178% for NASH cirrhosis[10]. Most recently, Wong, et al focused on the 2011–2014 NHANES reported 21.9% prevalence of NAFLD among U.S. adults, among which 2.3–9.7% had advanced fibrosis, corresponding to 5.0 million adults[9]. While these previous studies provide some estimates of NASH with advanced fibrosis, they are likely underestimates given the limitations of NHANES, including inherent biases from survey-based methods such as recall bias or reporter bias. Furthermore, misclassification bias due to reliance of surrogates of disease states to identify NASH may have affected prevalence estimates. Our current study utilizes national clinical laboratory data that captures a large proportion of the U.S. adult population and utilized objective serology based measures for identifying NASH with advanced fibrosis, which improved the generalizability and accuracy of our results.

Given that development of hepatic fibrosis and advanced fibrosis in particular is the strongest predictor of liver-related outcomes and mortality in patients with NASH[6, 8, 20], understanding which NASH populations are at greatest risk of progression to advanced fibrosis is of critical clinical significance. Focusing on four components of the metabolic syndrome, our study demonstrated increasing number of concurrent metabolic co-morbidities to be associated with increasing risks for advanced fibrosis in NASH patients. The detrimental impact of metabolic disease is well known, and Stepanova, et al. using the NHANES data observed that concurrent metabolic syndrome was associated with increased liver-related and all-cause mortality in patients with chronic liver disease[21]. Similarly, Younossi, et al. observed that metabolic syndrome among NAFLD patients in particular, was also associated with increased risk of overall, liver-related, and cardiovascular mortality[22]. Insulin resistance and diabetes mellitus have demonstrated strong associations with increased risks of advanced fibrosis in NASH patients[23-27]. For example, Petta, et al. evaluated 863 patients with biopsy proven NAFLD and observed that among patients < 55 years, the risk of advanced fibrosis was primarily driven by insulin resistance and visceral adiposity, whereas risk of advanced fibrosis in NAFLD patients 55 years and older, low HDL and insulin resistance were the main factors[24]. In our current study, we focused on three laboratory biomarker-biomarker based metabolic abnormalities–HDL, TG, and hemoglobin A1C, given that these are specific components of the metabolic syndrome. Data for waist circumference and presence of hypertension (the other two variables in the metabolic syndrome definition) were not available for assessment. Among our NASH cohort, we observed significantly higher risk of advanced fibrosis associated with presence of metabolic co-morbidities, with the highest risk group (those with four concurrent metabolic abnormalities) having a 56% higher risk of advanced fibrosis compared to patients without any metabolic co-morbidities. These are clinically significant observations and highlight the importance of focusing on optimizing the management of metabolic co-morbidities among NASH patients to prevent further disease progression.

As this is one of the first studies to utilize national clinical laboratory data to provide estimates of NASH with advanced fibrosis prevalence among U.S. adults, our observations provide important epidemiology data to guide healthcare resource planning. The dataset used captures a large sample of U.S. adults undergoing routine laboratory testing, which improves the generalizability of our study. While our overall study cohort identified probable NASH patients primarily through exclusion of chronic HBV, chronic HCV and exclusion of patients who were pregnant or had potential alcoholic liver disease (based on ICD-9/10 coding), we acknowledge the limitation of not having specific data on alcohol consumption. To address this limitation, we performed parallel analyses using similar exclusion criteria that specifically focused on patients with NAFLD/NASH ICD-9/10 diagnosis codes and performed similar assessments of F3 and F4 prevalence. While we utilized objective laboratory criteria to identify metabolic abnormalities, it is possible that some patients were on lipid-lowering therapies or diabetes medications that may have contributed to normal laboratory values, which would have contributed to an underestimation of the prevalence of metabolic abnormalities in this subset. However, this limitation likely biased our estimates towards a more conservative estimate of prevalence which is preferred over the potential type 1 error of overestimation. Despite these limitations, our current data provide important generalizable data on prevalence and predictors of NASH advanced fibrosis among U.S. adults.

In conclusion, among a large nationally representative clinical laboratory database of U.S. adults, the prevalence of NASH with advanced fibrosis was as high as 6.68% and the prevalence of NASH with cirrhosis was as high as 2.81%, which represents 5.18 million and 2.75 million adults, respectively when using an estimate of 75 million U.S. adults with NAFLD.[1] Co-morbid metabolic abnormalities were associated with significantly higher risk of advanced fibrosis among NASH patients. These data highlight the significant clinical and economic impact that NASH will play on healthcare systems.

18 Jun 2019

PONE-D-19-14625

Increasing Metabolic Co-Morbidities are Associated with Higher Risk of Advanced Fibrosis in Nonalcoholic Steatohepatitis

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Reviewer #1: This study was performed by a group of experts, aiming to utilize a nationally representative U.S. clinical laboratory database to evaluate prevalence of advanced fibrosis using a systematic algorithm of non-invasive fibrosis biomarker (FIB-4) and to evaluate the impact of metabolic co-morbidities on odds of advanced fibrosis. The findings are important. It would be even nice to improve the quality of figures by using colour figures of higher resolution.

Reviewer #2: The Authors conducted the retrospective study aiming to evaluate prevalence of advanced fibrosis and to evaluate the impact of metabolic co-morbidities on odds of advanced fibrosis based on a large-scale database. They found that the prevalence of advanced fibrosis ranged from 6.18% - 9.07%, and the prevalence of F4 fibrosis/cirrhosis ranged from 3.91% – 5.35%. They concluded that co-morbid metabolic abnormalities were associated with higher risk of advanced fibrosis among NASH patients. Although the study touched the important issue indicating the parallel association between disease severity and metabolic abnormalities in NAFLD patients, some points need clarification.

1. The definition of NAFLD/NASH in the current study was based on the ICD 9/10 diagnosis coding. The validation of the diagnosis should at least be performed to its best. The exclusion of alcoholism, pregnancy-related fatty change could be the confounding factors.

2. The Authors used FIB-4 as the non-invasive serum panel for fibrosis assessment. The performance could be validated by APRI.

3. Hypertension is one of the main components of metabolic abnormalities. How come the results if the diagnosis coding of hypertension were added to enhance the accuracy of metabolic syndrome in the current study.

4. The conclusion depicted "prevalence of NASH ..., representing 6.80 million and 4.01 million, respectively." , which could not be sufficiently supported by the results. A mild form is suggested.

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Reviewer #1: No

Reviewer #2: No

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2 Jul 2019

To:

Dr. Joerg Heber

Editor-in-Chief, PLOS ONE

Dr. Ming-Lung Yu

Academic Editor, PLOS ONE

RE: Manuscript (PONE-D-19-14625)

" Increasing Metabolic Co-Morbidities are Associated with Higher Risk of Advanced Fibrosis in Nonalcoholic Steatohepatitis"

Dear Dr. Yu and Dr. Heber

My co-authors and I are grateful of the reviewers’ comments and your continued consideration of our manuscript for publication in PLOS ONE. Please find below our point-by-point responses to the reviewers’ comments and suggestions. We have also made revisions in the manuscript text to incorporate the changes resulting from these reviewer comments. We have included two versions of our revised manuscript – track changes and clean version - as per your instructions.

Comments to the Author(s):

Reviewer #1: This study was performed by a group of experts, aiming to utilize a nationally representative U.S. clinical laboratory database to evaluate prevalence of advanced fibrosis using a systematic algorithm of non-invasive fibrosis biomarker (FIB-4) and to evaluate the impact of metabolic co-morbidities on odds of advanced fibrosis. The findings are important. It would be even nice to improve the quality of figures by using colour figures of higher resolution.

Response:

Thank you for this suggestion – we have revised our figures to be of color and improved the resolution of our submitted figure files.

Reviewer #2: The Authors conducted the retrospective study aiming to evaluate prevalence of advanced fibrosis and to evaluate the impact of metabolic co-morbidities on odds of advanced fibrosis based on a large-scale database. They found that the prevalence of advanced fibrosis ranged from 6.18% - 9.07%, and the prevalence of F4 fibrosis/cirrhosis ranged from 3.91% – 5.35%. They concluded that co-morbid metabolic abnormalities were associated with higher risk of advanced fibrosis among NASH patients. Although the study touched the important issue indicating the parallel association between disease severity and metabolic abnormalities in NAFLD patients, some points need clarification.

1. The definition of NAFLD/NASH in the current study was based on the ICD 9/10 diagnosis coding. The validation of the diagnosis should at least be performed to its best. The exclusion of alcoholism, pregnancy-related fatty change could be the confounding factors.

Response:

Thank you for this very important question. This is one of the stated limitations of this database in that our diagnosis and identification of NAFLD/NASH relies on the data that we have available in the current dataset. We tried to utilize a systematic algorithm, and agree with this reviewer that even among the cohort of patients with ICD-9/10 codes, we were not able to perfectly exclude all other potential concurrent disease states. For example, we did not have data on alcohol consumption and thus were not able to accurately capture this. However, based on this reviewer’s very insightful feedback, we did go back and incorporate two additional levels of exclusion criteria to further validate our study findings. We implemented exclusion criteria of any patients with ICD-9/10 codes for alcohol use/abuse or alcoholic liver disease. We further reviewed the Quest Diagnostics database and were able to exclude any individual with evidence if pregnancy. We have completely revised our entire study, which is reflected in an updated figure 1 flow chart as well as updated all of the results and relevant methods section. Thank you again for this feedback as we feel that the revisions arising out of this suggestion has truly strengthened our manuscript.

Reviewer #2:

2. The Authors used FIB-4 as the non-invasive serum panel for fibrosis assessment. The performance could be validated by APRI.

Response:

Thank you for this suggestion. While we did in fact perform similar analyses using APRI, we chose not to include this in our analyses. Firstly, when evaluating the performance characteristics of different non-invasive methods for assessing hepatic fibrosis, FIB-4 has significantly better performance characteristics (higher AUROC) than APRI, and thus our analyses focused on FIB-4 given the better sensitivity and specificity (Xiao, et al. Hepatology 2017;66:1486-1501). Furthermore, recent studies demonstrated that the performance characteristics of these non-invasive markers are affected by age, and a recent study by McPherson et al. (Am J Gastroenterol 2017;112:740-51) developed a revised cut-off of FIB-4>2.67, which we were able to incorporate into our analyses. However, no such analyses have been evaluated with APRI, and thus the performance characteristics may be worse than originally thought. Secondly, our analysis was specifically focused on evaluating patients with F3 or greater fibrosis, which can be done with the FIB-4 based algorithm that we illustrated. However, currently validated cutpoints for APRI are only validated for assessing at the F2 and F4 levels, and have not specifically been validated for the F3 fibrosis level. For these reasons and limitations, we felt strongly about not including the APRI data in our manuscript. However, the data that we did analyze demonstrated similar trends, such that increasing number of metabolic co-morbidities also correlated with increasing risk of hepatic fibrosis when using APRI-based cutpoints.

Reviewer #2:

3. Hypertension is one of the main components of metabolic abnormalities. How come the results if the diagnosis coding of hypertension were added to enhance the accuracy of metabolic syndrome in the current study.

Response:

Thank you for this comment. This is a great suggestion. We have completely revised our entire analysis and updated our manuscript to include hypertension as a fourth metabolic co-morbidity analyzed. Given that blood pressure measurements and medication data were not available for analysis, we utilized ICD=9/10 coding to identify patient with hypertension. Thank you for this important suggestion as you can see by adding hypertension, it actually strengthened our analysis of the correlation between metabolic co-morbidities and the presence of advanced fibrosis in the multivariate model.

Reviewer #2:

4. The conclusion depicted "prevalence of NASH ..., representing 6.80 million and 4.01 million, respectively." , which could not be sufficiently supported by the results. A mild form is suggested.

Response:

Thank you for raising this important point. These estimates were based on a estimated national NAFLD prevalence of 75 million based on a recent systematic review (Rinella ME. JAMA. 2015;313(22):2263-73). We neglected to mention this 75 million estimate as our basis. We have revised our conclusion section incorporate this.

We thank the editors and reviewers for the helpful comments and suggestions that have improved our manuscript significantly and have made our manuscript more valuable to the readers of PLOS ONE. We hope that you will find the response to the above comments and the corresponding manuscript revisions acceptable. Thank you again for the opportunity to revise our manuscript for your continued consideration.

Sincerely,

Robert J. Wong, M.D., M.S., F.A.C.G.

Assistant Clinical Professor of Medicine

Division of Gastroenterology and Hepatology

Alameda Health System – Highland Hospital

Highland Care Pavilion – 5th Floor, Endoscopy Unit

1411 East 31st Street

Oakland, CA 94602

Phone: 510-437-6531

Email: rowong@alamedahealthsystem.org

Submitted filename: Response to Reviewers_PLOS One_final.docx

Click here for additional data file.

22 Jul 2019

Increasing Metabolic Co-Morbidities are Associated with Higher Risk of Advanced Fibrosis in Nonalcoholic Steatohepatitis

PONE-D-19-14625R1

Dear Dr. Wong,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Ming-Lung Yu, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I have no additional comments.

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Jee-Fu Huang

24 Jul 2019

PONE-D-19-14625R1

Increasing Metabolic Co-Morbidities are Associated with Higher Risk of Advanced Fibrosis in Nonalcoholic Steatohepatitis

Dear Dr. Wong:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Ming-Lung Yu

Academic Editor

PLOS ONE