Association between cytokines and liver histology in children with nonalcoholic fatty liver disease

Emily R Perito; Veeral Ajmera; Nathan M Bass; Philip Rosenthal; Joel E Lavine; Jeffrey B Schwimmer; Katherine P Yates; Anna Mae Diehl; Jean P Molleston; Karen F Murray; Ann Scheimann; Ryan Gill; David Glidden; Bradley Aouizerat; Nonalcoholic Steatohepatitis Clinical Research Network

doi:10.1002/hep4.1068

. 2017 Jul 17;1(7):609–622. doi: 10.1002/hep4.1068

Association between cytokines and liver histology in children with nonalcoholic fatty liver disease

Emily R Perito ^1,^✉, Veeral Ajmera ¹, Nathan M Bass ¹, Philip Rosenthal ¹, Joel E Lavine ², Jeffrey B Schwimmer ³, Katherine P Yates ⁴, Anna Mae Diehl ⁵, Jean P Molleston ⁶, Karen F Murray ⁷, Ann Scheimann ⁸, Ryan Gill ¹, David Glidden ¹, Bradley Aouizerat ^1,⁸; Nonalcoholic Steatohepatitis Clinical Research Network

PMCID: PMC5679472 NIHMSID: NIHMS887215 PMID: 29130075

Abstract

Reliable noninvasive markers to characterize inflammation, hepatocellular ballooning, and fibrosis in nonalcoholic fatty liver disease (NAFLD) are lacking. We investigated the relationship between plasma cytokine levels and features of NAFLD histology to gain insight into cellular pathways driving nonalcoholic steatohepatitis (NASH) and to identify potential noninvasive discriminators of NAFLD severity and pattern. Cytokines were measured from plasma obtained at enrollment in pediatric participants in NASH Clinical Research Network studies with liver biopsy‐proven NAFLD. Cytokines were chosen a priori as possible discriminators of NASH and its components. Minimization of Akaike information criterion was used to determine cytokines retained in multivariable models. Of 235 subjects, 31% had “Definite NASH” on liver histology, 43% had “Borderline NASH,” and 25% had NAFLD but not NASH. Total plasminogen activator inhibitor 1 (PAI1) and activated PAI1 levels were higher in pediatric participants with Definite NASH and with lobular inflammation. Interleukin‐8 (IL‐8) was higher in those with stage 3‐4 fibrosis and lobular inflammation. Soluble IL‐2 receptor alpha was higher in children with stage 3‐4 fibrosis and portal inflammation. In multivariable analysis, PAI1 variables were discriminators of Borderline/Definite NASH, Definite NASH, lobular inflammation, and ballooning. IL‐8 increased with steatosis and fibrosis severity; soluble IL‐2 receptor alpha increased with fibrosis severity and portal inflammation. IL‐7 decreased with portal inflammation and fibrosis severity. Conclusion: Plasma cytokines associated with histology varied considerably among NASH features, suggesting promising avenues for investigation. More targeted analysis is needed to identify the role of these markers in NAFLD and to evaluate their potential as noninvasive discriminators of disease severity. (Hepatology Communications 2017;1:609–622)

Abbreviations

AIC: Akaike information criterion
ALT: alanine aminotransferase
aPAI1: activated plasminogen activator inhibitor 1
AST: aspartate aminotransferase
BMI: body mass index
CI: confidence interval
hpf: high‐powered field
IGF‐II: insulin‐like growth factor 2
IL: interleukin
MMP‐9: matrix metallopeptidase 9
NAFLD: nonalcoholic fatty liver disease
NASH: nonalcoholic steatohepatitis
NASH CRN: NASH Clinical Research Network
OR: odds ratio
OSA: obstructive sleep apnea
PAI1: plasminogen activator inhibitor 1
sIL‐1RI: soluble IL‐1 receptor 1
TGF: transforming growth factor
TNF: tumor necrosis factor
TONIC: treatment of nonalcoholic fatty liver disease in children
tPAI1: total plasminogen activator inhibitor 1
VEGF: vascular endothelial growth factor

Introduction

Nonalcoholic fatty liver disease (NAFLD) is the most common pediatric liver disease in the United States. A subset of children with NAFLD have nonalcoholic steatohepatitis (NASH), which can progress to cirrhosis1 and increases the risk for morbidities, including diabetes, cardiovascular disease, and hepatocellular carcinoma.2, 3 Routine imaging and blood tests are helpful to identify children and adolescents with NAFLD, but liver biopsy is currently required for diagnosis of NASH and for staging NASH severity.

Immune system pathways that drive hepatic steatosis, inflammation, apoptosis, and fibrosis in NASH are insufficiently understood. In addition, diagnosing NASH severity and histologic pattern requires a liver biopsy, an invasive procedure that relies on a limited liver sample. We lack reliable noninvasive markers for inflammation severity, hepatocellular ballooning, fibrosis, and the age‐specific pattern of NASH histology. To address these two gaps, we have recently reported on assocations between NASH histologic severity and cytokine levels in adult NASH Clinical Research Network (NASH CRN) participants.4 NASH histologic patterns stereotypically differ between children and adults, although overlap is observed. This parallel investigation of cytokine associations with histology in pediatric NASH CRN participants provides novel insight into differences and similarities. By examining a panel of cytokines in a large pediatric cohort, we provide direction for more targeted investigations of promising biomarkers.

Children can have the “adult‐type” zone 3 NASH, with pericentral/sinusoidal fibrosis and primarily lobular inflammation, with or without ballooned hepatocytes. However, they can also have a “pediatric” NASH, called zone 1 NASH, characterized by periportal (i.e., zone 1) or panacinar steatosis, portal fibrosis, and primarily portal inflammation. It is not yet clear whether zone 1 NASH is a precursor to or distinct entity from zone 3 NASH, nor how their natural histories differ, although advanced fibrosis and cirrhosis can be an end stage for either.5

Previous studies of biomarkers for NASH in children examined a limited number of biomarkers in small cohorts.6, 7, 8, 9, 10, 11, 12, 13, 14 Most studies did not evaluate NASH histology, instead relying on surrogate markers or imaging as a substitute for diagnosis.8, 11, 12, 13, 14 Plasminogen activator inhibitor 1 (PAI1) has been associated with NAFLD in three studies in children9, 10, 15 as well as adult studies; but other markers, including tumor necrosis factor alpha (TNF‐α), interleukin (IL)‐6, IL‐10, and resistin have been inconsistent predictors of NAFLD in smaller cohorts of adults and children (n = 40‐65).7, 9, 10, 16, 17, 18

This study provides a unique examination of a set of plasma biomarkers in a large cohort of children with biopsy‐proven NAFLD. Cytokines and other analytes were chosen a priori as possible NASH predictors based on their known role in steatosis, inflammation, fibrosis, angiogenesis, or glucose metabolism. Our aims were to gain insight into the cellular pathways driving NASH and to refine the search for potential noninvasive biomarkers of NASH severity and subtype. We also evaluated associations in children with borderline zone 3 and borderline zone 1 NASH to investigate whether processes driving NASH in these two groups appear to be similar or distinct.

Patients and Methods

STUDY DESIGN

This was a cross‐sectional study using enrollment data from pediatric participants in the NASH CRN, a multicenter network sponsored by the National Institute of Diabetes, Digestive and Kidney Diseases since 2002.19 Study participants were drawn from two groups within the NASH CRN: (1) Treatment of NAFLD in Children (TONIC) trial (ClinicalTrials.gov Identifier:NCT00063635) and (2) NAFLD Database study, an observational cohort. Both studies have Institutional Review Board approval at all clinical centers participating in the NASH CRN. Written permission was obtained prior to participation from a parent/guardian; written assent was obtained from children ≥8 years old. NASH CRN Pediatric and Resource centers are listed in the Acknowledgment section.

STUDY POPULATION

The NAFLD Database was an observational study of participants 2 years and older with NAFLD. Eligibility for the NAFLD Database study included having either a histologic diagnosis of NAFLD or cryptogenic cirrhosis with suspected NAFLD based on imaging studies. The TONIC trial was a phase IIb, double‐masked, randomized, placebo‐controlled trial of metformin or vitamin E versus placebo in children 8‐17 years of age with NAFLD. Participants in TONIC were required to have a baseline alanine aminotransferase (ALT) value of 60 U/L or greater and biopsy‐proven NAFLD.20 Exclusion criteria for both studies included no or restricted alcohol intake, other chronic liver diseases, history of parenteral nutrition, bariatric or hepatobiliary surgery, human immunodeficiency virus infection, inborn errors of metabolism, or short‐bowel syndrome. Additional exclusion criteria for TONIC included age less than 8 years, diagnosis of diabetes, cirrhosis, use of drugs associated with NAFLD, antidiabetic or anti‐NAFLD drugs, metabolic acidosis, and renal dysfunction. Enrollment began in September 2004 for the NAFLD Database and in August 2005 for TONIC.21 Data collection for this analysis was completed by April 2010. Both studies have been described.20, 22 Participants from both studies age 17 years and younger (total TONIC, n = 173; NAFLD Database, n = 116) were eligible for inclusion in the current study if they had a fasting plasma sample available within 6 months of liver biopsy reviewed by the central Pathology Committee (TONIC, n = 173; NAFLD Database, n = 79) and had at least two plasma aliquots available for analysis (TONIC, n = 162; NAFLD Database, n = 73).

CLINICAL AND LABORATORY ASSESSMENT

Demographic data and self‐reported doctor‐diagnosed comorbidities were obtained via structured interview and questionnaires. Height, weight, and waist/hip measurements were taken in duplicate while standing, wearing light clothing, and averaged for analyses. Height and weight were measured without shoes to the nearest 0.1 cm and 0.1 kg, respectively. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. The BMI z‐score was determined according to age, sex, height, and weight based on data from the Centers for Disease Control and Prevention 2000 reference population.23

Fasting whole blood samples were obtained via venipuncture after an overnight fast of 8 hours or more and processed for plasma and serum within 2 hours. Laboratory assays were performed at individual clinical centers and included platelet count (10⁹ cells/L), bilirubin (mg/dL), ALT (U/L), aspartate aminotransferase (AST; U/L), triglycerides and cholesterol fractions (mg/dL), glucose (mg/dL), and insulin (mU/mL). Homeostasis model assessment of insulin resistance was calculated from fasting insulin and glucose values.24

HISTOLOGIC EVALUATION

Biopsy specimens were evaluated centrally by the NASH CRN Pathology Committee according to the validated histologic scoring system by Kleiner et al.25 for steatosis (grade 0 [<5% macrovesicular fat], grade 1 [5%‐33%], grade 2 [34%‐66%], and grade 3 [>66%]), portal inflammation (0‐2 [none, mild, more than mild]), lobular inflammation (0‐3 [none, <2 foci per high‐powered field [hpf], 2‐4 foci, >4 foci per 20× field]), ballooning degeneration (0‐2 [none, few, many]), and fibrosis (stage 0, stage 1a [mild perisinusoidal], stage 1b [moderate perisinusoidal], stage 1c [portal/periportal fibrosis only], stage 2 [zone 3 and periportal], stage 3 [bridging fibrosis], and stage 4 [cirrhosis]).

A diagnostic categorization was determined for each case: NAFLD, Not NASH, Borderline Zone 3, Borderline Zone 1, or Definite NASH.22, 26 Borderline Zone 1 NASH presents mainly in children and is characterized by periportal fibrosis with steatosis involving zone 1 and minimal or no zone 3 injury.26 Definite NASH unequivocally fulfilled previously defined criteria for steatohepatitis,25 whereas the category NAFLD, Not NASH encompasses cases of NAFLD in which diagnostic NASH features are absent.

PLASMA BIOMARKER MULTIPLEX ASSAY

Thirty‐two plasma biomarkers were chosen a priori as possible predictors of NASH or its components (steatosis, inflammation, fibrosis), energy homeostasis, angiogenesis, or metabolism of lipids, glucose, and insulin. Biomarkers were measured from an enrollment sample for all children, which was prior to beginning medications for those in the TONIC trial. Blood samples were obtained by venipuncture and plasma collected by centrifugation and stored at –80°C. Plasma samples were measured in duplicate using the Luminex Multiplex platform (Millipore, St. Louis, MO) and processed according to standard protocol. Detection by Luminex is comparable to that with enzyme‐linked immunosorbent assay, but the required input plasma volume is drastically reduced. Quality control procedures were employed to ensure high‐quality data for downstream analyses.

The coefficient of variation for plasma biomarkers was required to be less than 20%, a criterion met for all biomarkers included in this analysis (Supporting Table S1). Biomarkers available in less than 90% of the sample were excluded (glucagon‐like peptide 1, soluble vascular endothelial growth factor receptor 1, IL‐1α, IL‐12/40 kDa subunit, IL‐13, transforming growth factor beta 3 [TGF‐β3]).

STATISTICAL ANALYSIS

This analysis considered clinical and plasma biomarker predictors of histologic outcomes. Histologic outcomes were collapsed into binary categories as follows: NASH diagnosis (Not/Borderline NASH versus Definite NASH, Not versus Definite NASH), steatosis severity (steatosis ≤33% versus steatosis >33%), inflammatory pattern and severity (lobular, <2 foci per hpf versus ≥2 per hpf; portal, none/mild versus more than mild), hepatocellular ballooning (none versus few/many), and fibrosis severity. For fibrosis severity, two categorizations were considered: (1) no fibrosis (stage 0) versus any fibrosis (stage 1‐4) and (2) no/minimal fibrosis (stage 0‐2) versus significant fibrosis (stage 3‐4).

Descriptive statistics and frequency distributions were generated on the sample demographic and clinical characteristics and plasma biomarker measurements. The Student t test accounting for unequal variances (Tables 1 and 2; Supporting Tables S2‐S4) and logistic regression (Tables 3 and 4) were used for bivariate analysis of associations between continuous clinical characteristics or biomarker discriminators and histologic outcomes, and Pearson's chi‐square test was used for categorical clinical characteristics (Table 1). All odds ratios (ORs) for cytokines in univariable and multivariable analysis are reported per 0.5 SD change in the cytokine based on SDs for the total group as reported in Table 2. Significance using the Benjamini‐Hochberg correction with a false discovery rate of 10% also provided for univariate logistic regression of cytokine changes with outcomes.27

Table 1.

CLINICAL AND DEMOGRAPHIC CHARACTERISTICS OF THE COHORT BY NASH DIAGNOSIS

	Total (n = 235)	Not NASH (n = 62)	Borderline Zone 1 NASH (n = 53)	Borderline Zone 3 NASH (n = 47)	Definite NASH (n = 73)
	%	%	%	%	%
Female	22.4	26.7	13.2	26.1	23.3
Race
Hispanic	59.5	61.2	64.2	58.7	54.7
White, non‐Hispanic	33.6	31.2	32.1	32.6	36.9
Black, non‐Hispanic	1.7	1.7	0	4.4	1.3
Other, non‐Hispanic	5.2	5.0	3.8	4.4	6.8
	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD
Age at biopsy (years)	13.1 ± 2.6	13.6 ± 2.9	11.8 ± 2.0	13.6 ± 2.8	13.5 ± 2.3
BMI z‐score	2.3 ± 0.4	2.3 ± 0.7	2.3 ± 0.3	2.3 ± 0.4	2.3 ± 0.3
ALT (U/L)	107.5 ± 74.6	76.4 ± 42.9	104.6 ± 53.4	97.5 ± 61.9	143.5 ± 99.7
AST (U/L)	62.7 ± 40.2	45.8 ± 25.3	61.3 ± 31.7	54.1 ± 26.6	83.6 ± 52.9
Total bilirubin (mg/dL)	0.7 ± 0.3	0.7 ± 0.4	0.7 ± 0.3	0.7 ± 0.3	0.6 ± 0.3
Triglycerides (mg/dL)	147.7 ± 94.6	126.9 ± 59.0	137.3 ± 109.7	151.0 ± 108.5	170.4 ± 94.5
HDL cholesterol (mg/dL)	37.9 ± 9	37.3 ± 7.7	41.6 ± 10.8	38.0 ± 10.4	35.6 ± 6.8
HOMA‐IR	8.3 ± 10.7	7.5 ± 9.4	6.9 ± 6.1	7.2 ± 5.6	10.5 ± 15.1

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Abbreviations: HDL, high‐density lipoprotein; HOMA‐IR, homeostasis model assessment of insulin resistance.

Table 2.

PLASMA BIOMARKER LEVELS AND NASH DIAGNOSIS

		Total	Not/Borderline NASH (n = 162)	Definite NASH (n = 73)
	n	Mean ± SD	Mean ± SD	Mean ± SD	P
Adiponectin (μg/mL)	233	13.1 ± 7.48	13.20 ± 7.26	13.00 ± 7.98	0.92
aPAI1 (ng/mL)	233	54.97 ± 43.96	50.46 ± 45.09	64.87 ± 39.93	0.02
FGF2 (pg/mL)	232	98.6 ± 141.3	93.8 ± 146	109.1 ± 130.9	0.42
Fibrinogen (mg/mL)	234	4.39 ± 2.11	4.44 ± 1.69	4.31 ± 2.84	0.72
Haptoglobin (mg/mL)	235	2.47 ± 1.27	2.48 ± 1.27	2.44 ± 1.28	0.81
IFN‐γ (pg/mL)	218	8.7 ± 21.3	8.3 ± 21.3	9.6 ± 21.5	0.69
IGF‐II (mg/mL)	234	1.83 ± 0.99	1.79 ± 0.99	1.89 ± 1.02	0.48
IL‐1b (pg/mL)	215	0.5 ± 0.8	0.5 ± 0.8	0.6 ± 0.6	0.78
IL‐2 (pg/mL)	226	3.7 ± 8.8	3.6 ± 9.8	4.0 ± 5.8	0.65
IL‐4 (pg/mL)	226	24.8 ± 31.9	24.5 ± 32.5	25.4 ± 30.7	0.84
IL‐5 (pg/mL)	230	1.0 ± 1.8	0.9 ± 1.9	1.1 ± 1.6	0.52
IL‐6 (pg/mL)	235	15.2 ± 91.1	16.7 ± 108.6	12.0 ± 24.0	0.6
IL‐7 (pg/mL)	226	5.0 ± 6.4	5.2 ± 7.4	4.7 ± 3.5	0.47
IL‐8 (pg/mL)	235	3.1 ± 2.0	2.9 ± 2.2	3.4 ± 1.5	0.07
IL‐10 (pg/mL)	234	24.9 ± 99.4	26.1 ± 118.2	22.2 ± 27.5	0.69
MCP‐1 (pg/mL)	235	246.5 ± 100.6	246.9 ± 105.3	245.7 ± 90	0.93
MMP‐9 (ng/mL)	235	63.9 ± 33.3	64.1 ± 35.7	63.4 ± 27.6	0.88
Resistin (ng/mL)	234	14.89 ± 6.32	14.91 ± 6.49	14.87 ± 5.97	0.97
sFasL (pg/mL)	227	107.6 ± 71.4	110.2 ± 78.8	102.1 ± 51.1	0.36
sIL‐1RI (pg/mL)	235	33.8 ± 23.0	32.2 ± 23.8	37.2 ± 32.4	0.11
sIL‐2Rα (ng/mL)	235	0.79 ± 0.36	0.79 ± 0.34	0.79 ± 0.41	0.91
sIL‐6R (ng/mL)	235	21.24 ± 5.39	21.33 ± 5.37	21.04 ± 5.49	0.71
TGF‐β1 (ng/mL)	235	6.86 ± 7.61	7.11 ± 8.63	6.28 ± 4.59	0.34
TGF‐β2 (pg/mL)	223	359.6 ± 343.1	377.9 ± 401.1	321.2 ± 161.2	0.13
TNF‐α (pg/mL)	235	8.4 ± 8.8	7.6 ± 5.0	10.1 ± 13.9	0.13
tPAI1 (ng/mL)	235	45.8 ± 24.8	43.2 ± 24.3	51.4 ± 25.1	0.02
VEGF (pg/mL)	235	551.3 ± 1083.4	473.9 ± 981.7	723 ± 1271.3	0.14

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Abbreviations: FGF2, fibroblast growth factor 2; IFN‐γ, interferon gamma; MCP‐1, monocyte chemoattractant protein‐1; sFasL, soluble Fas ligand.

Table 3.

UNADJUSTED OR (95% CI) FOR CHANGE IN CYTOKINE LEVEL BY 0.5 SD BY HISTOLOGIC OUTCOME

	Definite NASH (vs. Not/Borderline)		Any Fibrosis (stage 1‐4)		Significant Fibrosis (stage 3‐4)		Steatosis (>33%)
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Adiponectin (μg/mL)	0.99	0.86‐1.14	0.91	0.80‐1.04	1.01	0.84‐1.21	0.95	0.82‐1.09
aPAI1 (ng/mL)	1.18a	1.01‐1.38	1.09	0.93‐1.28	1.15	0.99‐1.35	1.13	0.95‐1.35
FGF‐2 (pg/mL)	1.05	0.92‐1.20	0.92	0.80‐1.06	1.05	0.91‐1.22	0.97	0.85‐1.10
Fibrinogen (mg/mL)	0.97	0.84‐1.12	1.03	0.89‐1.19	1.09	0.94‐1.27	0.92	0.80‐1.05
Haptoglobin (mg/mL)	0.98	0.82‐1.18	0.95	0.83‐1.10	0.98	0.82‐1.18	0.92	0.80‐1.06
IFN‐γ (pg/mL)	1.03	0.90‐1.18	0.99	0.86‐1.13	0.86	0.57‐1.29	0.94	0.82‐1.07
IGF‐II (mg/mL)	1.05	0.92‐1.21	0.98	0.85‐1.12	0.90	0.73‐1.09	1.18a	1.00‐1.38
IL‐1b (pg/mL)	1.02	0.88‐1.18	0.96	0.83‐1.11	0.87	0.65‐1.17	0.90	0.77‐1.04
IL‐2 (pg/mL)	1.03	0.90‐1.17	0.90	0.76‐1.05	0.77	0.44‐1.35	0.93	0.81‐1.07
IL‐4 (pg/mL)	1.01	0.88‐1.16	1.02	0.88‐1.18	0.89	0.70‐1.15	0.98	0.85‐1.13
IL‐5 (pg/mL)	1.04	0.91‐1.19	0.98	0.86‐1.12	0.92	0.69‐1.22	1.03	0.87‐1.20
IL‐6 (pg/mL)	0.97	0.81‐1.16	0.89	0.69‐1.15	0.98	0.78‐1.23	1.05	0.84‐1.31
IL‐7 (pg/mL)	0.96	0.82‐1.12	0.89	0.77‐1.03	0.77	0.55‐1.09	0.98	0.85‐1.12
IL‐8 (pg/mL)	1.11	0.97‐1.27	1.13	0.97‐1.33	1.20a	1.03‐1.39	1.12	0.95‐1.32
IL‐10 (pg/mL)	0.98	0.83‐1.15	0.93	0.79‐1.09	1.01	0.86‐1.19	1.06	0.83‐1.35
MCP‐1 (pg/mL)	0.99	0.87‐1.14	1.01	0.88‐1.16	1.13	0.97‐1.32	1.03	0.89‐1.19
MMP‐9 (ng/mL)	0.99	0.86‐1.14	1.13	0.96‐1.32	0.98	0.82‐1.18	0.88	0.77‐1.01
Resistin (ng/mL)	1.00	0.87‐1.15	1.08	0.93‐1.25	1.16	0.99‐1.36	1.08	0.93‐1.26
sFasL (pg/mL)	0.94	0.81‐1.09	1.02	0.88‐1.17	0.98	0.81‐1.18	1.14	0.96‐1.36
sIL‐1RI (pg/mL)	1.11	0.97‐1.27	1.00	0.87‐1.14	1.05	0.89‐1.24	1.11	0.93‐1.32
sIL‐2Rα (ng/mL)	0.99	0.86‐1.14	1.13	0.97‐1.31	1.39b	1.18‐1.64	0.98	0.85‐1.12
sIL‐6R (ng/mL)	0.97	0.85‐1.12	0.99	0.86‐1.13	1.20	0.99‐1.44	1.00	0.86‐1.15
TGF‐β1 (ng/mL)	0.94	0.81‐1.10	1.05	0.91‐1.21	0.89	0.70‐1.12	1.00	0.87‐1.16
TGF‐β2 (pg/mL)	0.90	0.75‐1.08	1.04	0.89‐1.22	0.96	0.77‐1.18	0.99	0.85‐1.14
TNF‐α (pg/mL)	1.18	0.97‐1.44	1.10	0.89‐1.326	0.87	0.62‐1.23	1.06	0.88‐1.29
tPAI1 (ng/mL)	1.17a	1.02‐1.34	1.07	0.92‐1.23	1.09	0.92‐1.29	1.08	0.93‐1.25
VEGF (pg/mL)	1.11	0.97‐1.27	1.04	0.89‐1.20	1.02	0.86‐1.21	1.01	0.87‐1.16

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P ≤ 0.05 before correction for multiple comparisons.

P ≤ 0.05 before correction and significance retained with Benjamini‐Hochberg correction.

Abbreviations: FGF2, fibroblast growth factor 2; IFN‐γ, interferon gamma; MCP‐1, monocyte chemoattractant protein‐1; sFasL, soluble Fas ligand.

Table 4.

UNADJUSTED OR (95% CI) FOR CHANGE IN CYTOKINE LEVEL BY 0.5 SD BY HISTOLOGIC OUTCOME

	Lobular Inflammation (≥2 foci per hpf)		Portal Inflammation (mild/more than mild)		Hepatocellular Ballooning
	OR	95% CI	OR	95% CI	OR	95% CI
Adiponectin (μg/mL)	1.03	0.91‐1.17	1.04	0.82‐1.30	1.02	0.90‐1.17
aPAI1 (ng/mL)	1.29b	1.09‐1.52	1.17	0.86‐1.59	1.14	0.98‐1.33
FGF‐2 (pg/mL)	1.26a	1.01‐1.59	1.15	0.76‐1.72	1.04	0.91‐1.20
Fibrinogen (mg/mL)	0.93	0.81‐1.07	1.02	0.81‐1.30	0.96	0.84‐1.10
Haptoglobin (mg/mL)	1.00	0.88‐1.14	0.80a	0.65‐0.98	0.99	0.87‐1.13
IFN‐γ (pg/mL)	1.06	0.92‐1.22	1.11	0.73‐1.68	0.99	0.87‐1.13
IGF‐II (mg/mL)	0.99	0.87‐1.13	0.98	0.79‐1.22	1.01	0.89‐1.15
IL‐1b (pg/mL)	1.06	0.92‐1.23	1.10	0.79‐1.53	1.01	0.88‐1.16
IL‐2 (pg/mL)	1.00	0.88‐1.14	0.99	0.80‐1.22	1.10	0.92‐1.31
IL‐4 (pg/mL)	1.06	0.93‐1.21	0.97	0.79‐1.19	0.97	0.85‐1.11
IL‐5 (pg/mL)	0.98	0.86‐1.12	1.21	0.72‐2.05	0.98	0.85‐1.11
IL‐6 (pg/mL)	1.25	0.66‐2.37	1.46	0.30‐7.06	1.21	0.67‐2.17
IL‐7 (pg/mL)	0.96	0.84‐1.10	0.95	0.80‐1.14	1.01	0.89‐1.15
IL‐8 (pg/mL)	1.14a	1.00‐1.31	1.06	0.83‐1.36	1.06	0.93‐1.20
IL‐10 (pg/mL)	0.93	0.76‐1.14	2.47	0.35‐17.54	0.94	0.79‐1.12
MCP‐1 (pg/mL)	1.12	0.98‐1.29	0.96	0.79‐1.18	1.02	0.90‐1.16
MMP‐9 (ng/mL)	1.11	0.97‐1.27	1.48a	1.02‐2.14	1.09	0.95‐1.24
Resistin (ng/mL)	1.14	0.99‐1.30	0.96	0.78‐1.18	0.94	0.83‐1.07
sFasL (pg/mL)	0.95	0.83‐1.08	1.18	0.88‐1.58	0.91	0.79‐1.04
sIL‐1RI (pg/mL)	0.95	0.83‐1.08	1.06	0.82‐1.38	1.13	0.98‐1.30
sIL‐2Rα (ng/mL)	1.03	0.90‐1.17	1.48a	1.07‐2.06	0.99	0.87‐1.12
sIL‐6R (ng/mL)	1.17a	1.02‐1.33	1.09	0.88‐1.37	0.98	0.86‐1.11
TGF‐β1 (ng/mL)	0.86	0.74‐1.00	0.99	0.80‐1.23	0.90	0.79‐1.04
TGF‐β2 (pg/mL)	0.84	0.70‐1.00	0.98	0.80‐1.21	0.90	0.78‐1.05
TNF‐α (pg/mL)	0.99	0.87‐1.12	1.21	0.75‐1.94	1.12	0.93‐1.35
tPAI1 (ng/mL)	1.22b	1.06‐1.40	1.12	0.87‐1.44	1.18a	1.03‐1.35
VEGF (pg/mL)	1.11	0.96‐1.28	1.43	0.78‐2.60	1.14	0.97‐1.33

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P ≤ 0.05 before correction for multiple comparisons.

P ≤ 0.05 before correction and significance retained with Benjamini‐Hochberg correction.

Abbreviations: FGF2, fibroblast growth factor 2; IFN‐γ, interferon gamma; MCP‐1, monocyte chemoattractant protein‐1; sFasL, soluble Fas ligand.

Given the number of predictors considered and the exploratory aim of this analysis, predictors were chosen for the final multivariable models to minimize the Akaike information criterion (AIC) (Table 5). AIC minimization allows choice of the model that best fits the data with penalization for the number of predictors included; it is an empiric method for optimizing model fit without overfitting. The AIC statistic is calculated using a model's log‐likelihood, the number of parameters included in the model, and the sample size. AIC for candidate models are compared, and the model with the lowest AIC is retained. AIC comparisons are automated, as described below, such that all variable combinations can be tested. The order of variable inclusion/exclusion does not impact results, thus avoiding incorrect selection that can be caused by stepwise selection methods in data sets with many predictors.

Table 5.

PREDICTORS ASSOCIATED WITH NASH AND HISTOLOGIC COMPONENTS IN MULTIVARIABLE ANALYSISa

NAFLD Group Comparison	Predictor (per unit changeb)	Odds Ratio	95% CI	P
Definite NASH (vs. Borderline/Not NASH)	Age (1 year)	1.12	0.99‐1.26	0.07
	AST (20 U/L)	1.56	1.29‐1.88	<0.001
	HDL cholesterol (5 mg/dL)	0.78	0.64‐0.95	0.01
	TNF‐α (4.4 pg/mL)	1.19	0.97‐1.46	0.10
	χ² = 42.46, P < 0.0005, n = 230
Definite NASH (vs. Not NASH)
	AST (20 U/L)	2.18	1.49‐3.19	<0.0001
	Triglycerides (20 mg/dL)	1.15	1.02‐1.28	0.02
	VEGF (514.5 pg/mL)	1.30	0.98‐1.71	0.07
	χ² = 45.58, P < 0.0005, n = 134
Steatosis >33%	Male	1.81	0.88‐3.78	0.11
	Age (1 year)	0.87	0.76‐0.99	0.03
	BMI z‐score (1)	1.96	0.83‐4.64	0.13
	ALT (20 U/L)	1.09	0.99‐1.22	0.09
	IGF‐II (1 pg/mL)	1.28	1.06‐1.55	0.01
	MMP‐9 (16.5 ng/mL)	0.84	0.72‐0.98	0.02
	Resistin (3.15 ng/mL)	1.13	0.95‐1.33	0.17
	sFasL (35.5 pg/mL)	1.09	0.92‐1.28	0.33
	χ² = 25.95, P = 0.001, n = 229
Stage 1‐4 fibrosis	AST (20 U/L)	1.30	1.05‐1.61	0.02
	IL‐5 (0.9 pg/mL)	1.28	0.92‐1.78	0.14
	IL‐7 (3.2 pg/mL)	0.62	0.46‐0.84	0.002
	IL‐8 (1 pg/mL)	1.23	0.95‐1.59	0.11
	MMP‐9 (16.5 ng/mL)	1.20	1.01‐1.41	0.03
	χ² = 26.14, P = 0.0001, n = 233
Stage 3‐4 fibrosis	AST (20 IU/mL)	1.40	1.13‐1.74	0.002
	IL‐4 (16 pg/mL)	0.63	0.36‐1.09	0.10
	IL‐7 (3.2 pg/mL)	0.32	0.15‐0.66	0.002
	IL‐8 (1 pg/mL)	1.77	1.31‐2.38	<0.0005
	IL‐10 (50 pg/mL)	2.20	1.21‐3.98	0.01
	sIL‐2rα (0.18 ng/mL)	1.48	1.20‐1.84	<0.0005
	χ² = 60.45, P < 0.0005, n = 234
Mild/more than mild portal inflammation	Haptoglobin (0.65 mg/mL)	0.80	0.65‐1.00	0.05
	sIL‐2rα (0.18 ng/mL)	1.45	1.04‐2.02	0.03
	MMP‐9 (16.5 ng/mL)	1.31	0.93‐1.84	0.12
	χ² = 14.94, P = 0.002, n = 235
Lobular inflammation (≥2 foci per hpf)	Male	0.47	0.22‐0.98	0.04
	AST (per 20 U/L)	1.35	0.89‐2.05	0.16
	ALT (per 20 U/L)	1.18	0.95‐1.48	0.14
	HDL cholesterol (per 5 mg/dL)	0.86	0.72‐1.04	0.13
	Resistin (per 3.15 ng/mL)	1.20	1.02‐1.42	0.03
	sIL‐2rα (per 0.18 ng/mL)	0.85	0.72‐1.00	0.06
	sIL‐6R (per 2.7 ng/mL)	1.26	1.07‐1.48	0.005
	TGF‐β2 (per 171.5 pg/mL)	0.62	0.47‐0.82	0.001
	tPAI1 (per 12.4 ng/mL)	1.37	1.13‐1.65	0.001
	χ² = 71.67, P <0.0005, n = 227
Hepatocellular ballooning	AST (per 20 U/L)	1.26	1.06‐1.50	0.008
	Triglycerides (per 20 mg/dL)	1.09	1.02‐1.16	0.02
	TGF‐β1 (per 3.8 pg/mL)	0.78	0.65‐0.94	0.008
	tPAI1 (per 12.4 ng/mL)	1.23	1.04‐1.45	0.02
	χ² = 30.10, P <0.0005, n = 233

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Models include imputation of missing cytokine and clinical values by progressive mean matching and chained iterative equations. See Supporting Table S5 for multivariate models without imputed missing values.

Per unit change represents 0.5 SD change for cytokine biomarkers and clinically significant change for clinical predictors.

Abbreviations: HDL, high‐density lipoprotein; sFasL, soluble Fas ligand

First, all cytokines were competed in a logistic regression model. The model with the lowest AIC, i.e., the best data fit with penalization for the number of predictors included, was retained for each outcome. That limited groups of cytokines competed in a second logistic regression model with clinical predictors chosen a priori for their association with NASH diagnosis and severity (age, sex, BMI z‐score, AST, ALT, triglycerides, high‐density lipoprotein, and homeostasis model assessment of insulin resistance). Cytokine and clinical predictors retained in the AIC‐minimized model were re‐entered into a final logistic regression to obtain the results reported in Table 5. Thus, inclusion in this final model was based on AIC values, not on P values. Sensitivity analysis was done excluding those children with Borderline Zone 1 NASH.

For multivariable models, missing values for cytokine and clinical data were imputed using a multiple imputations technique with progressive mean matching and iterative chained equations. To allow for automated AIC minimization, the mean of 20 imputed values was retained and used for predictor selection in the same process described above. All statistical analyses were performed using STATA (StataCorp LP, College Station, TX). Multivariable model results without multiple imputations are reported in Supporting Table S5.

Results

CHARACTERISTICS OF THE STUDY POPULATION

Included in this study were 235 subjects, 73 from the NAFLD Database and 162 from the TONIC trial. Children from the NAFLD Database and TONIC were grouped together because there were no differences in their participation or care prior to collection of baseline data. Of the 235 children and adolescents included, 4 had no steatosis, 58 were classified as NAFLD, Not NASH (25%), and 73 (31%) were categorized as Definite NASH. The remaining 100 had Borderline NASH, with 47 of those (20% of total) having a Borderline Zone 3 NASH pattern and 53 (23% of total) a Borderline Zone 1 pattern. Clinical characteristics associated with NASH are noted in Table 1 and have been described in additional detail in previous NASH CRN analyses.20, 22

Children classified as Borderline Zone 1 NASH were younger than those with Borderline/Definite Zone 3 NASH (11.8 ± 2.0 years versus 13.5 ± 2.5, P < 0.0005), but there were no differences by sex, race, ethnicity, BMI z‐score, transaminases, glucose, or insulin.

PLASMA BIOMARKERS ASSOCIATED WITH NASH DIAGNOSIS

In bivariate analysis, participants with Definite NASH had significantly higher levels of activated PAI1 (aPAI1) and total PAI1 (tPAI1) than those with Not/Borderline NASH (Tables 2 and 3). In multivariable regression models, Definite NASH was associated with higher TNF‐α levels as well as clinical predictors (Table 5).22

Comparison was also made between children with Definite NASH (n = 73) and those with Not NASH (n = 62), excluding those with Borderline NASH. Those with Definite NASH had significantly higher tPAI1 (51.4 ± 25.1 ng/mL versus 38.8 ± 21.9 ng/mL; P = 0.002), aPAI1 (64.8 ± 35.9 versus 45.8 ± 35.9 ng/mL; P = 0.004), and IL‐8 (3.37 ± 1.54 pg/mL versus 2.46 ± 1.89 pg/mL; P = 0.003) than those with Not NASH.

In sensitivity analysis excluding participants with Borderline Zone 1 NASH, children with Definite Zone 3 NASH (n = 108) had higher aPAI1 (64.9 ± 39.9 versus 44.8 ± 31.2; P = 0.0004), tPAI1 (51.4 ± 21.0 versus 40.7 ± 25.1; P = 0.003), IL‐8 (3.38 ± 1.54 versus 2.70 ± 1.98; P = 0.01), and vascular endothelial growth factor (VEGF; 723.0 ± 1271.3 versus 404.2 ± 609.9; P = 0.05) levels than those with Not/Borderline Zone 3 NASH (n = 73). In multivariable analysis excluding those with Borderline Zone 1 NASH, significant discriminators of Definite NASH versus Not/Borderline Zone 3 NASH were aPAI1 (OR, 1.25 per 0.5 SD increase; 95% confidence interval [CI], 1.01‐1.56; P = 0.04), AST (OR, 1.65 per 20 IU/L increase; 95% CI, 1.30‐2.11; P <0.005), and triglycerides (OR, 1.06 per 20 mg/dL increase; 95% CI, 0.98‐1.14; P = 0.14).

Soluble IL‐1 receptor 1 (sIL‐1RI) was higher in children with Borderline/Definite Zone 3 NASH (37.06 ± 25.39 pg/mL) than those with Borderline Zone 1 NASH (30.29 ± 16.37 pg/mL; P = 0.04). There were no other significant differences in analyte levels between participants with Borderline/Definite Zone 3 and Zone 1 NASH (data not shown).

PLASMA BIOMARKERS ASSOCIATED WITH STEATOSIS GRADE

Forty percent of participants had >66% steatosis (grade 3), 32% had 34%‐66% (grade 2), 26% had 5%‐33% (grade 1), and 2% had <5% steatosis (grade 0). Participants with moderate to severe steatosis (grade 2‐3) had higher levels of insulin‐like growth factor 2 (IGF‐II; P = 0.03, Table 3; Supporting Table S3). In multivariable modeling, steatosis >33% was associated with higher IGF‐II, resistin, and soluble Fas ligand levels and lower matrix metallopeptidase 9 (MMP‐9) levels (Table 4). When subjects with Borderline Zone 1 NASH were excluded for sensitivity analysis, IGF‐II remained significantly associated with moderate‐severe steatosis (OR, 1.85; 95% CI, 1.16‐2.96; P = 0.01); IL‐1β was also retained in this model (OR, 0.69; 95% CI, 0.41‐1.18; P = 0.18) with ALT, BMI z‐score, and age.

PLASMA BIOMARKERS ASSOCIATED WITH FIBROSIS STAGE

Thirty‐two percent of participants had no fibrosis on liver biopsy, 38% had stage 1 (perisinusoidal or periportal only), 15% had stage 2, 14% stage 3, and 1% stage 4 fibrosis. None of the biomarker distributions were significantly different between those with no fibrosis and those with stage 1‐4 fibrosis (Table 3). However, IL‐8 and sIL‐2Rα were significantly higher in those who had stage 3‐4 fibrosis compared to stage 0‐2, and the latter retained statistical significance after correction for multiple comparisons (Table 3).

In multivariable analysis, IL‐7 levels were lower in participants that had any fibrosis (versus none) and stage 3‐4 fibrosis (versus 0‐1) (Table 4). Stage 3‐4 fibrosis was also associated with lower IL‐4 and higher IL‐8, IL‐10, and sIL‐2Rα (Table 4).

Compared to children with fibrosis in a zone 1 pattern (periportal only), children with any fibrosis in an adult NASH pattern had higher levels of IL‐5 (1.12 ± 1.65 versus 0.60 ± 0.45, P = 0.004), IL‐10 (23.41 ± 35.79 versus 14.80 ± 15.00, P = 0.04), and IL‐1b (0.58 ± 0.62 versus 0.41 ± 0.40, P = 0.05) with no significant differences in other cytokine levels between the two groups (data not shown).

When participants with Zone 1 NASH fibrosis (periportal only) were excluded, those with any fibrosis in an adult NASH pattern (stage 1a, 1b, or 2‐4; n = 98) had higher IL‐8 levels (3.41 ± 2.25 pg/mL) than those with no fibrosis (2.77 ± 1.96 pg/mL; P = 0.05, n = 75). There were no statistically significant differences in other biomarker levels between those with adult NASH pattern fibrosis and those with no fibrosis. Those with significant fibrosis (stage 3‐4; n = 138) had higher IL‐8 (3.87 ± 3.05 pg/mL versus 2.95 ± 1.83 pg/mL in stage 0‐2 fibrosis; n = 35), sIL‐2rα (1.04 ± 0.07 ng/mL versus 0.72 ± 0.03 ng/mL), and sIL‐6R (22.83 ± 4.74 ng/mL versus 20.93 ± 5.22 ng/mL) levels as well as lower IL‐7 levels (3.64 ± 3.91 pg/mL versus 5.52 ± 7.40 pg/mL; P = 0.05).

In multivariable modeling excluding those with Zone 1 NASH fibrosis (periportal only), any fibrosis (stage 1‐4) in an adult NASH pattern was associated with increasing IL‐5 (OR, 1.80 per 0.9 pg/mL increase; 95% CI, 1.16‐2.79; P = 0.01) and IL‐8 levels (OR, 1.41 per 1 pg/mL increase; 95% CI, 1.06‐1.87; P = 0.02) as well as decreasing IL‐7 (OR, 0.46 per 3.2 pg/mL increase; 95% CI, 0.30‐0.72; P = 0.001) and increasing AST (OR, 1.39 per 20 IU/L increase; 95% CI, 1.09‐1.78; P = 0.008). In the same group, fibrosis stage 3‐4 (versus 0‐2) remained associated with decreasing IL‐7 and increasing AST as well as increasing sIL‐2Rα (OR, 1.56 per 0.18 ng/mL increase; 95% CI, 1.22‐1.98; P < 0.0005) and IL‐10 (OR, 2.77 per 50 /mL increase; 95% CI, 1.27‐6.04; P = 0.01).

PLASMA BIOMARKERS ASSOCIATED WITH INFLAMMATION AND HEPATOCELLULAR BALLOONING

Lobular inflammation was seen in <2 foci per 20× field in 53% of participants, 2‐4 foci in 42%, and more than 4 foci in 5% (n = 235). Compared to participants with <2 foci of lobular inflammation, those with ≥2 foci had higher levels of aPAI1 and fibroblast growth factor 2 that remained significant after correction for multiple comparisons. They had higher tPAI1, IL‐8, and sIL‐6R and lower levels of TGF‐β1 and TGF‐β2 before correction (Table 4). In multivariable modeling, lobular inflammation was significantly associated with higher resistin, sIL‐6R, and tPAI1 levels and lower TGF‐β2 and sIL‐2Rα levels (Table 4).

Chronic portal inflammation was absent in 9% of children, mild in 81%, and more than mild in 10%. Compared to those with no portal inflammation, those with mild or more than mild portal inflammation had higher levels of MMP‐9 and sIL‐2Rα and lower levels of haptoglobin (Table 5; Supporting Table S4). In multivariable models, portal inflammation was associated with higher levels of MMP‐9 and sIL‐2Rα and lower levels of haptoglobin (Table 4).

Hepatocyte ballooning was seen in 49% of participants. Those with ballooning had higher levels of tPAI1 with no significant differences in other biomarkers (Table 5; Supporting Table S3). The association of ballooning with higher tPAI1, as well as lower TGF‐β1, was significant in multivariable analysis (Table 4).

In the sample excluding those with Borderline Zone 1 NASH, participants with ballooning had higher levels of tPAI1 (49.3 ± 24.7 versus 40.0 ± 20.4; P = 0.006), aPAI1 (59.4 ± 37.9 versus 45.2 ± 32.8; P = 0.007), sIL‐1RI (38.1 ± 27.5 versus 30.9 ± 20.0; P = 0.04), IL‐8 (3.22 ± 1.63 versus 2.68 ± 2.03; P = 0.05), and VEGF (667.8 ± 1148.1 versus 373.7 ± 593.1; P = 0.03). Only 3% of pediatric participants had Mallory bodies on baseline liver biopsy.

Discussion

This is the first report of an array of plasma biomarkers related to metabolism, inflammation, and fibrosis measured simultaneously with liver biopsy in children and adolescents with NAFLD. The NASH CRN cohort offers a unique opportunity to characterize differences in plasma biomarker levels by histologic pattern and severity. Given the significant number of cytokines evaluated, our aim was to identify promising targets for future research.

In multivariable models, clinical characteristics, such as sex, age, and AST, were associated most consistently with overall NASH diagnosis and particular histologic components. In addition, several analytes emerged repeatedly in bivariate and multivariable analyses as significantly associated with NASH histologic components. These patterns provide targets for future research into the inflammatory, fibrotic, and other pathways that drive NAFLD development and progression.

One of the most consistent findings was the association between NASH features and tPAI1 and aPAI1. In bivariate analysis, increased tPAI1 and aPAI1 were associated with Definite NASH and lobular inflammation. In multivariable analysis, tPAI1 was associated with lobular inflammation and hepatocellular ballooning.

In the parallel analysis of cytokines in the adult NASH CRN cohort, aPAI1 was the only cytokine that discriminated between Definite NASH and Borderline/Not NASH.4 This association between PAI1 levels and NAFLD has been reported in other cohorts of adults with NASH.28, 29 PAI1 has primarily been studied for its role in atherosclerosis and thrombotic disease.30 It is not clear if PAI1 is itself mediating inflammation and damage in the NASH liver or if NASH inflammation induces PAI1 release, which then contributes to systemic cardiovascular disease.29 The mechanisms are deserving of further investigation as NAFLD is emerging as a significant risk factor for cardiovascular events in adults, with severity of liver disease correlating with cardiovascular event risk.31 PAI1 has also been reported as a predictor of NASH and NAFLD activity score in obese children9 and as a predictor of steatosis severity in another cohort.15 The investigators hypothesized that PAI1 might be involved in NASH hepatic inflammation; indeed tPAI1 retained significance as a predictor in multivariable modeling of lobular inflammation and aPAI1 of portal inflammation in our cohort.

Interestingly, PAI1 levels are also increased in patients with obstructive sleep apnea (OSA).32 In children with NAFLD, OSA has recently been associated with fibrosis severity, and their hypoxemia severity correlated with inflammation severity and increasing NAS.33

IL‐8 was also a significant discriminator of NASH severity across multiple histologic components. It was associated with fibrosis in overall multivariable analysis, with NASH diagnosis in multivariable analysis excluding children with pediatric (Borderline Zone 1) NASH, and with lobular inflammation in bivariate analysis. IL‐8 was also associated with fibrosis in the adult NASH CRN cohort.4 IL‐8 is a proinflammatory cytokine released from macrophages, endothelium, and other cells and is primarily responsible for recruiting neutrophils to sites of injury. Elevated IL‐8 levels have been associated with both NASH34 and alcoholic liver disease.35 Lipid accumulation in hepatocytes may induce production of IL‐8.36 IL‐8 levels were higher in children with hepatic steatosis measured by magnetic resonance imaging in one study.8

IL‐7 decreased with increasing fibrosis stage. IL‐7 is generally considered a proinflammatory cytokine secreted by adipose tissue as well as other cell types. In high‐fat‐fed mice, IL‐7 deficiency has been associated with glucose intolerance.37 An association of lower IL‐7 levels with higher fibrosis stage has been reported in an adult NASH cohort38 but does not seem to have been investigated in pediatric NASH.

Similarly, increased sIL‐2Rα was associated with fibrosis stage and with portal and lobular inflammation. It also emerged as a significant predictor of fibrosis in the adult NASH CRN cohort.4 sIL‐2Rα is a marker of T‐cell activation in the plasma. It is produced by T cells and then shed from their surface, although its role as a mediator of inflammation is not well characterized. Elevated sIL‐2Rα levels have been reported in patients with chronic hepatitis C39 and hepatocellular carcinoma.40 In children, sIL‐2Rα was recently identified as a marker of poor prognosis in pediatric acute liver failure, with levels significantly higher in those children who died or required liver transplantation.41

MMP‐9 levels increased with portal inflammation and any fibrosis but decreased with steatosis severity. Elevated plasma MMP‐9 has been reported in adults with NASH compared to healthy controls42 and with viral hepatitis43 but has not been studied in pediatric subjects with NAFLD to our knowledge. MMP‐9 levels decreased in both obese children and adults with OSA who were successfully treated, again suggesting that hypoxia may play a role in NASH.32 The pattern of MMP‐9 increase with inflammation and fibrosis but decrease with steatosis severity is intriguing. In light of theories that isolating free fatty acids in triglyceride droplets, thereby increasing steatosis, may be protective against NASH, the pattern we found suggests that MMP‐9 may be involved in NASH‐mediated damage.

Additional analytes had associations with only one histologic component, suggesting that they may play a more targeted role in NASH. For example, IGF‐II and IL‐1β emerged only in association with steatosis severity. IGF‐II is secreted primarily by hepatocytes and is known to be involved in fetal pancreatic β‐cell development, but its role in children and adults is not well characterized. Possibly related to its homology to insulin and weak affinity for the insulin receptor, it may play a role in glucose and fat metabolism.44 Elevations have been reported in obese individuals.45 Overexpression of IGF‐II in mice leads to an increase in pancreatic β‐cell mass, insulin hypersecretion, insulin resistance, and hepatic steatosis,46, 47 but an association with hepatic steatosis in humans has not been reported. Lower levels of IGF‐II in children with NAFLD fibrosis was recently observed but was not associated with steatosis in a smaller cohort of obese children48 but was not observed in our study. Higher levels of IGF‐II with increased steatosis and lower levels of IGF‐II with advanced fibrosis and ballooning were also seen in the adult NASH CRN cohort.4

A decrease in IL‐1β was seen with increasing steatosis in our cohort and in adult NASH in other cohorts.34, 49 IL‐1β is a proinflammatory cytokine that appears to impact liver insulin sensitivity, with lower levels associated with insulin resistance; higher levels are profibrotic. IL‐1β deficiency in mice protected against inflammation in mice with steatosis.50

One limitation of this study is that the cohort did not include control subjects. Ideally, a control group would include overweight pediatric patients with liver biopsy available but without NAFLD or other liver disease. Unfortunately, liver biopsies on this group of children are rare for clinical purposes, and it is difficult to justify the risk for research purposes. A control group of nonoverweight children with liver biopsies would not be directly applicable to NASH associations because increased cytokine levels could reflect the low‐grade inflammation associated with obesity and insulin resistance instead of just NASH.

Our cohort does explore the utility of plasma biomarkers in differentiating severity of NAFLD and its components among those with biopsy‐proven fatty liver disease. As a cross‐sectional study, it identifies correlations between analyte levels and NASH histology. We relied on serum levels of analytes, so our analyses do not identify the tissue driving cytokine levels, i.e., adipose, muscle, liver, or other, or actual hepatic levels. Additional longitudinal research of cytokines in serum and liver or other tissues is warranted to investigate the role of these analytes in differentiating between those with and without NAFLD and in NASH progression.

Another limitation of this study is that we examined multiple plasma biomarkers and outcomes in these analyses, increasing the risk of false‐positive associations between plasma biomarkers and NASH. However, our analyte choices were hypothesis driven and selected a priori based on their known role in processes related to NASH. We have reported actual levels in Supporting Tables S2‐S4 so that the reader may put the associations in context. Statistical analysis was designed to minimize spurious associations by using AIC minimization techniques to select predictors for our multivariable models and competing the biomarkers against established clinical discriminators of NASH severity. Using AIC for model selection allowed us to account for the large number of cytokines evaluated while minimizing information lost in the whittling of the model. It does not rely on statistical significance from univariable analysis to choose predictors for consideration in the multivariable models or on nested models, allowing more flexibility in considering the contribution of each cytokine relative to the others. However, AIC minimization is a relatively liberal technique as concerns predictor retention in final models; our study intended to provide direction to future work and not to definitively identify biomarkers ready for clinical application.

Future investigations are needed to validate and understand associations between the plasma biomarkers and NASH histology identified in this analysis. Of particular interest will be to study whether changes in these biomarker levels correspond to changes in NASH histology over time and to further study their etiologic roles in NASH progression or improvement and to assess whether any might prove useful as noninvasive biomarkers. Correlation of baseline biomarker levels with NASH progression may potentially allow for prognostic testing at initial diagnosis. A central puzzle in pediatric NASH remains the accurate prediction of which patients will progress to cirrhosis and end‐stage liver disease in adulthood; this study highlights cytokines deserving of further exploration as biomarkers.

Supporting information

Additional Supporting Information may be found at onlinelibrary.wiley.com/doi/10.1002/hep4.1068/suppinfo.

Supporting Information Table 1.

Click here for additional data file.^{(69.5KB, doc)}

Supporting Information Table 2.

Click here for additional data file.^{(78.5KB, doc)}

Supporting Information Table 3.

Click here for additional data file.^{(76KB, doc)}

Supporting Information Table 4.

Click here for additional data file.^{(77.5KB, doc)}

Supporting Information Table 5.

Click here for additional data file.^{(100.5KB, doc)}

Acknowledgment

We appreciate and acknowledge all members of the NASH CRN, Pediatric, and Resource Centers: Baylor College of Medicine, Houston, TX: Stephanie H. Abrams, M.D., M.S. (2007‐2013); Sarah Barlow, M.D.; Ryan Himes, M.D.; Rajesh Krisnamurthy, M.D.; Leanel Maldonado, R.N. (2007‐2012); Rory Mahabir. Baylor College of Medicine Institutional Review Board (IRB) for Baylor College of Medicine and Affiliated Hospitals, Cincinnati Children's Hospital Medical Center, Cincinnati, OH: Kimberlee Bernstein, B.S., C.C.R.P.; Kristin Bramlage, M.D.; Kim Cecil, Ph.D.; Stephanie DeVore, M.S.P.H (2009‐2011); Rohit Kohli, M.D.; Kathleen Lake, M.S.W. (2009‐2012); Daniel Podberesky, M.D. (2009‐2014); Alex Towbin, M.D.; Stavra Xanthakos, M.D. Cinncinati Children's Hospital Medical Center IRB. Columbia University, New York, NY: Gerald Behr, M.D.; Joel E. Lavine, M.D., Ph.D.; Jay H. Lefkowitch, M.D.; Ali Mencin, M.D.; Elena Reynoso, M.D. Columbia University Medical Center IRB. Emory University, Atlanta, GA: Adina Alazraki, M.D.; Rebecca Cleeton, M.P.H., C.C.R.P.; Saul Karpen, M.D., Ph.D.; Jessica Cruz Munos (2013‐2015); Nicholas Raviele (2012‐2014); Miriam Vos, M.D., M.S.P.H, F.A.H.A. Emory University IRB. Indiana University School of Medicine, Indianapolis, IN: Molly Bozic, M.D.; Oscar W. Cummings, M.D.; Ann Klipsch, R.N.; Jean P. Molleston, M.D.; Sarah Munson, R.N.; Kumar Sandrasegaran, M.D.; Girish Subbarao, M.D. Indiana University/ Purdue University Indianapolis and Clarian IRB. Johns Hopkins Hospital, Baltimore, MD: Kimberly Kafka, R.N.; Ann Scheimann, M.D. Western IRB (Johns Hopkins). Northwestern University, Feinberg School of Medicine/Ann & Robert H. Lurie Children's Hospital of Chicago: Katie Amsden, M.P.H.; Mark H. Fishbein, M.D.; Elizabeth Kirwan, R.N.; Saeed Mohammad, M.D.; Cynthia Rigsby, M.D.; Lisa Sharda, R.D.; Peter F. Whitington, M.D. Children's Memorial Hospital IRB. Saint Louis University, St Louis, MO: Sarah Barlow, M.D. (2002‐2007); Jose Derdoy, M.D. (2007‐2011); Ajay Jain M.D.; Debra King, R.N.; Pat Osmack; Joan Siegner, R.N. (2004‐2015); Susan Stewart, R.N. (2004‐2015); Susan Torretta; Kristina Wriston, R.N. Saint Louis Unversity IRB. University at Buffalo, Buffalo, NY: Susan S. Baker, M.D., Ph.D.; Lixin Zhu, Ph.D. University of California San Diego, San Diego, CA: Jonathon Africa, M.D.; Jorge Angeles, M.D.; Sandra Arroyo, M.D.; Hannah Awai, M.D.; Cynthia Behling, M.D., Ph.D.; Craig Bross; Janis Durelle; Michael Middleton, M.D., Ph.D.; Kimberly Newton, M.D.; Melissa Paiz; Jennifer Sanford; Jeffrey B. Schwimmer, M.D.; Claude Sirlin, M.D.; Patricia Ugalde‐Nicalo, M.D.; Mariana Dominguez Villarreal. University of California, San Diego Human Research Protections Program. University of California San Francisco, San Francisco, CA: Bradley Aouizerat, Ph.D.; Jesse Courtier, M.D.; Linda D. Ferrell, M.D.; Shannon Fleck, M.P.H.; Ryan Gill, M.D., Ph.D.; Camille Langlois, M.S.; Emily Rothbaum Perito, M.D.; Philip Rosenthal, M.D.; Patrika Tsai, M.D. University of California San Francisco Committee on Human Research. University of Washington Medical Center and Seattle Children's Hospital, Seattle, WA: Kara Cooper; Simon Horslen, M.B., Ch.B.; Evelyn Hsu, M.D.; Karen Murray, M.D.; Randolph Otto, M.D.; Matthew Yeh, M.D., Ph.D.; Melissa Young. University of Washington Human Subjects Review Committee Washington University, St. Louis, MO: Elizabeth M. Brunt, M.D.; Kathryn Fowler, M.D. Resource centers include: National Cancer Institute, Bethesda, MD: David E. Kleiner, M.D., Ph.D. National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD: Sherry Brown, M.S.; Edward C. Doo, M.D.; Jay H. Hoofnagle, M.D.; Patricia R. Robuck, Ph.D., M.P.H. (2002‐2011); Averell Sherker, M.D.; Rebecca Torrance, R.N., M.S. Johns Hopkins University, Bloomberg School of Public Health (Data Coordinating Center), Baltimore, MD: Patricia Belt, B.S.; Jeanne M. Clark, M.D., M.P.H.; Michele Donithan, M.H.S.; Erin Hallinan, M.H.S.; Milana Isaacson, B.S.; Kevin P. May, M.S.; Laura Miriel, B.S.; Alice Sternberg, Sc.M.; James Tonascia, Ph.D.; Mark Van Natta, M.H.S.; Ivana Vaughn, M.P.H.; Laura Wilson, Sc.M.; Katherine Yates, Sc.M. Johns Hopkins University Committee on Human Research.

Potential conflict of interest: Nothing to report.

The Nonalcoholic Steatohepatitis Clinical Research Network is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (grants U01DK061718, U01DK061728, U01DK061731, U01DK061732, U01DK061734, U01DK061737, U01DK061738, U01DK061730, U01DK061713). Additional support is received from the National Center for Advancing Translational Sciences (grants UL1TR000077, UL1TR000150, UL1TR000424, UL1TR000006, UL1TR000448, UL1TR000040, UL1TR000100, UL1TR000004, UL1TR000423, UL1TR000454).This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute. E.P. receives support from an NIH‐NIDDK K23 (DK099253‐01A1). V.A. is supported by T32 5T32DK060414‐13 (Dr. Maher, Principal Investigator, University of California, San Francisco) from the National Institute of Diabetes and Digestive and Kidney Diseases.

REFERENCES

1. Loomba R, Sirlin CB, Schwimmer JB, Lavine JE. Advances in pediatric nonalcoholic fatty liver disease. Hepatology 2009;50:1282‐1293. [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Ballestri S, Zona S, Targher G, Romagnoli D, Baldelli E, Nascimbeni F, et al. Nonalcoholic fatty liver disease is associated with an almost twofold increased risk of incident type 2 diabetes and metabolic syndrome. Evidence from a systematic review and meta‐analysis. J Gastroenterol Hepatol 2016;31:936‐944. [DOI] [PubMed] [Google Scholar]
3. Law YM, Yim R, Agatisa P, Boyle GJ, Miller SA, Lawrence K, et al. Lipid profiles in pediatric thoracic transplant recipients are determined by their immunosuppressive regimens. J Heart Lung Transplant 2006;25:276‐282. [DOI] [PubMed] [Google Scholar]
4. Ajmera V, Perito ER, Bass NM, Terrault NA, Yates KP, Gill R, et al.; NASH Clinical Research Network . Novel plasma biomarkers associated with liver disease severity in adults with nonalcoholic fatty liver disease. Hepatology 2017;65:65‐77. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Crespo M, Lappe S, Feldstein AE, Alkhouri N. Similarities and differences between pediatric and adult nonalcoholic fatty liver disease. Metabolism 2016;65:1161‐1171. [DOI] [PubMed] [Google Scholar]
6. Cianfarani S, Inzaghi E, Alisi A, Germani D, Puglianiello A, Nobili V. Insulin‐like growth factor‐I and ‐II levels are associated with the progression of nonalcoholic fatty liver disease in obese children. J Pediatr 2014;165:92‐98. [DOI] [PubMed] [Google Scholar]
7. Nobili V, Cutrera R, Liccardo D, Pavone M, Devito R, Giorgio V, et al. Obstructive sleep apnea syndrome affects liver histology and inflammatory cell activation in pediatric nonalcoholic fatty liver disease, regardless of obesity/insulin resistance. Am J Respir Crit Care Med 2014;189:66‐76. [DOI] [PubMed] [Google Scholar]
8. Kim JS, Le KA, Mahurkar S, Davis JN, Goran MI. Influence of elevated liver fat on circulating adipocytokines and insulin resistance in obese Hispanic adolescents. Pediatr Obes 2012;7:158‐164. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Alisi A, Manco M, Devito R, Piemonte F, Nobili V. Endotoxin and plasminogen activator inhibitor‐1 serum levels associated with nonalcoholic steatohepatitis in children. J Pediatr Gastroenterol Nutr 2010;50:645‐649. [DOI] [PubMed] [Google Scholar]
10. Fitzpatrick E, Dew TK, Quaglia A, Sherwood RA, Mitry RR, Dhawan A. Analysis of adipokine concentrations in paediatric non‐alcoholic fatty liver disease. Pediatr Obes 2012;7:471‐479. [DOI] [PubMed] [Google Scholar]
11. Louthan MV, Barve S, McClain CJ, Joshi‐Barve S. Decreased serum adiponectin: an early event in pediatric nonalcoholic fatty liver disease. J Pediatr 2005;147:835‐838. [DOI] [PubMed] [Google Scholar]
12. Cali AM, De Oliveira AM, Kim H, Chen S, Reyes‐Mugica M, Escalera S, et al. Glucose dysregulation and hepatic steatosis in obese adolescents: is there a link? Hepatology 2009;49:1896‐1903. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Lebensztejn DM, Wojtkowska M, Skiba E, Werpachowska I, Tobolczyk J, Kaczmarski M. Serum concentration of adiponectin, leptin and resistin in obese children with non‐alcoholic fatty liver disease. Adv Med Sci 2009;54:177‐182. [DOI] [PubMed] [Google Scholar]
14. Zou CC, Liang L, Hong F, Fu JF, Zhao ZY. Serum adiponectin, resistin levels and non‐alcoholic fatty liver disease in obese children. Endocr J 2005;52:519‐524. [DOI] [PubMed] [Google Scholar]
15. Holzberg JR, Jin R, Le NA, Ziegler TR, Brunt EM, McClain CJ, et al. Plasminogen activator inhibitor‐1 predicts quantity of hepatic steatosis independent of insulin resistance and body weight. J Pediatr Gastroenterol Nutr 2016;62;819‐823. [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Abiru S, Migita K, Maeda Y, Daikoku M, Ito M, Ohata K, et al. Serum cytokine and soluble cytokine receptor levels in patients with non‐alcoholic steatohepatitis. Liver Int 2006;26:39‐45. [DOI] [PubMed] [Google Scholar]
17. Pagano C, Soardo G, Pilon C, Milocco C, Basan L, Milan G, et al. Increased serum resistin in nonalcoholic fatty liver disease is related to liver disease severity and not to insulin resistance. J Clin Endocrinol Metab 2006;91:1081‐1086. [DOI] [PubMed] [Google Scholar]
18. Bahcecioglu IH, Yalniz M, Ataseven H, Ilhan N, Ozercan IH, Seckin D, et al. Levels of serum hyaluronic acid, TNF‐alpha and IL‐8 in patients with nonalcoholic steatohepatitis. Hepatogastroenterology 2005;52:1549‐1553. [PubMed] [Google Scholar]
19. Lavine JE, Schwimmer JB; Nonalcoholic Steatohepatitis‐Clinical Research Network . Pediatric initiatives within the Nonalcoholic Steatohepatitis‐Clinical Research Network (NASH CNR). J Pediatr Gastroenterol Nutr 2003;37:220‐221. [DOI] [PubMed] [Google Scholar]
20. Lavine JE, Schwimmer JB, Molleston JP, Scheimann AO, Murray KF, Abrams SH, et al.; Nonalcoholic Steatohepatitis Clinical Research Network Research Group . Treatment of nonalcoholic fatty liver disease in children: TONIC trial design. Contemp Clin Trials 2010;31:62‐70. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Lavine JE, Schwimmer JB, Van Natta ML, Molleston JP, Murray KF, Rosenthal P, et al.; Nonalcoholic Steatohepatitis Clinical Research Network . Effect of vitamin E or metformin for treatment of nonalcoholic fatty liver disease in children and adolescents: the TONIC randomized controlled trial. JAMA 2011;305:1659‐1668. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Patton HM, Lavine JE, Van Natta ML, Schwimmer JB, Kleiner D, Molleston J; Nonalcoholic Steatohepatitis Clinical Research Network . Clinical correlates of histopathology in pediatric nonalcoholic steatohepatitis. Gastroenterology 2008;135:1961‐1971.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Ogden CL, Kuczmarski RJ, Flegal KM, Mei Z, Guo S, Wei R, et al. Centers for Disease Control and Prevention 2000 growth charts for the United States: improvements to the 1977 National Center for Health Statistics version. Pediatrics 2002;109:45‐60. [DOI] [PubMed] [Google Scholar]
24. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta‐cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412‐419. [DOI] [PubMed] [Google Scholar]
25. Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, et al.; Nonalcoholic Steatohepatitis Clinical Research Network . Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005;41:1313‐1321. [DOI] [PubMed] [Google Scholar]
26. Schwimmer JB, Behling C, Newbury R, Deutsch R, Nievergelt C, Schork NJ, et al. Histopathology of pediatric nonalcoholic fatty liver disease. Hepatology 2005;42:641‐649. [DOI] [PubMed] [Google Scholar]
27. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B (Methodological) 1995;57:289‐300. [Google Scholar]
28. Verrijken A, Francque S, Mertens I, Prawitt J, Caron S, Hubens G, et al. Prothrombotic factors in histologically proven nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology 2014;59:121‐129. [DOI] [PubMed] [Google Scholar]
29. Targher G, Chonchol M, Miele L, Zoppini G, Pichiri I, Muggeo M. Nonalcoholic fatty liver disease as a contributor to hypercoagulation and thrombophilia in the metabolic syndrome. Semin Thromb Hemost 2009;35:277‐287. [DOI] [PubMed] [Google Scholar]
30. Barrera F, George J. Prothrombotic factors and nonalcoholic fatty liver disease: an additional link to cardiovascular risk? Hepatology 2014;59:16‐18. [DOI] [PubMed] [Google Scholar]
31. Targher G, Byrne CD, Lonardo A, Zoppini G, Barbui C. Non‐alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta‐analysis. J Hepatol 2016;65:589‐600. [DOI] [PubMed] [Google Scholar]
32. Kheirandish‐Gozal L, Gileles‐Hillel A, Alonso‐Alvarez ML, Peris E, Bhattacharjee R, Teran‐Santos J, et al. Effects of adenotonsillectomy on plasma inflammatory biomarkers in obese children with obstructive sleep apnea: a community‐based study. Int J Obes (Lond) 2015;39:1094‐1100. [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Sundaram SS, Sokol RJ, Capocelli KE, Pan Z, Sullivan JS, Robbins K, et al. Obstructive sleep apnea and hypoxemia are associated with advanced liver histology in pediatric nonalcoholic fatty liver disease. J Pediatr 2014;164:699‐706.e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
34. du Plessis J, van Pelt J, Korf H, Mathieu C, van der Schueren B, Lannoo M, et al. Association of adipose tissue inflammation with histologic severity of nonalcoholic fatty liver disease. Gastroenterology 2015;149:635‐648.e14. [DOI] [PubMed] [Google Scholar]
35. Huang YS, Chan CY, Wu JC, Pai CH, Chao Y, Lee SD. Serum levels of interleukin‐8 in alcoholic liver disease: relationship with disease stage, biochemical parameters and survival. J Hepatol 1996;24:377‐384. [DOI] [PubMed] [Google Scholar]
36. Joshi‐Barve S, Barve SS, Amancherla K, Gobejishvili L, Hill D, Cave M, et al. Palmitic acid induces production of proinflammatory cytokine interleukin‐8 from hepatocytes. Hepatology 2007;46:823‐830. [DOI] [PubMed] [Google Scholar]
37. Lucas S, Taront S, Magnan C, Fauconnier L, Delacre M, Macia L, et al. Interleukin‐7 regulates adipose tissue mass and insulin sensitivity in high‐fat diet‐fed mice through lymphocyte‐dependent and independent mechanisms. PLoS One 2012;7:e40351. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Estep M, Abawi M, Jarrar M, Wang L, Stepanova M, Elariny H, et al. Association of obestatin, ghrelin, and inflammatory cytokines in obese patients with non‐alcoholic fatty liver disease. Obes Surg 2011;21:1750‐1757. [DOI] [PubMed] [Google Scholar]
39. Naveau S, Balian A, Degos F, Daurat V, Chevret S, Gayno S, et al. Prognostic value of the soluble interleukin‐2 receptor in chronic hepatitis C treated with interferon‐alfa. Multicenter GER‐CYT 04 Group. J Hepatol 1999;31:612‐617. [DOI] [PubMed] [Google Scholar]
40. Izzo F, Cremona F, Delrio P, Leonardi E, Castello G, Pignata S, et al. Soluble interleukin‐2 receptor levels in hepatocellular cancer: a more sensitive marker than alfa fetoprotein. Ann Surg Oncol 1999;6:178‐185. [DOI] [PubMed] [Google Scholar]
41. Bucuvalas J, Filipovich L, Yazigi N, Narkewicz MR, Ng V, Belle SH, et al. Immunophenotype predicts outcome in pediatric acute liver failure. J Pediatr Gastroenterol Nutr 2013;56:311‐315. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. D'Amico F, Consolo M, Amoroso A, Skarmoutsou E, Mauceri B, Stivala F, et al. Liver immunolocalization and plasma levels of MMP‐9 in non‐alcoholic steatohepatitis (NASH) and hepatitis C infection. Acta Histochem 2010;112:474‐481. [DOI] [PubMed] [Google Scholar]
43. Ljumovic D, Diamantis I, Alegakis AK, Kouroumalis EA. Differential expression of matrix metalloproteinases in viral and non‐viral chronic liver diseases. Clin Chim Acta 2004;349(1‐2):203‐211. [DOI] [PubMed] [Google Scholar]
44. Cianfarani S. Insulin‐like growth factor‐II: new roles for an old actor. Front Endocrinol (Lausanne) 2012;3:118. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Frystyk J, Skjaerbaek C, Vestbo E, Fisker S, Orskov H. Circulating levels of free insulin‐like growth factors in obese subjects: the impact of type 2 diabetes. Diabetes Metab Res Rev 1999;15:314‐322. [DOI] [PubMed] [Google Scholar]
46. Simon Y, Kessler SM, Bohle RM, Haybaeck J, Kiemer AK. The insulin‐like growth factor 2 (IGF2) mRNA‐binding protein p62/IGF2BP2‐2 as a promoter of NAFLD and HCC? Gut 2014;63:861‐863. [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Devedjian JC, George M, Casellas A, Pujol A, Visa J, Pelegrin M, et al. Transgenic mice overexpressing insulin‐like growth factor‐II in beta cells develop type 2 diabetes. J Clin Invest 2000;105:731‐740. [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Cianfarani S, Inzaghi E, Alisi A, Germani D, Puglianiello A, Nobili V. Insulin‐like growth factor‐I and ‐II levels are associated with the progression of nonalcoholic fatty liver disease in obese children. J Pediatr 2014;165:92‐98. [DOI] [PubMed] [Google Scholar]
49. Mehta R, Birerdinc A, Wang L, Younoszai Z, Moazzez A, Elariny H, et al. Expression of energy metabolism related genes in the gastric tissue of obese individuals with non‐alcoholic fatty liver disease. BMC Gastroenterol 2014;14:72. [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Petrasek J, Bala S, Csak T, Lippai D, Kodys K, Menashy V, et al. IL‐1 receptor antagonist ameliorates inflammasome‐dependent alcoholic steatohepatitis in mice. J Clin Invest 2012;122:3476‐3489. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional Supporting Information may be found at onlinelibrary.wiley.com/doi/10.1002/hep4.1068/suppinfo.

Supporting Information Table 1.

Click here for additional data file.^{(69.5KB, doc)}

Supporting Information Table 2.

Click here for additional data file.^{(78.5KB, doc)}

Supporting Information Table 3.

Click here for additional data file.^{(76KB, doc)}

Supporting Information Table 4.

Click here for additional data file.^{(77.5KB, doc)}

Supporting Information Table 5.

Click here for additional data file.^{(100.5KB, doc)}

[hep41068-bib-0001] 1. Loomba R, Sirlin CB, Schwimmer JB, Lavine JE. Advances in pediatric nonalcoholic fatty liver disease. Hepatology 2009;50:1282‐1293. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0002] 2. Ballestri S, Zona S, Targher G, Romagnoli D, Baldelli E, Nascimbeni F, et al. Nonalcoholic fatty liver disease is associated with an almost twofold increased risk of incident type 2 diabetes and metabolic syndrome. Evidence from a systematic review and meta‐analysis. J Gastroenterol Hepatol 2016;31:936‐944. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0003] 3. Law YM, Yim R, Agatisa P, Boyle GJ, Miller SA, Lawrence K, et al. Lipid profiles in pediatric thoracic transplant recipients are determined by their immunosuppressive regimens. J Heart Lung Transplant 2006;25:276‐282. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0004] 4. Ajmera V, Perito ER, Bass NM, Terrault NA, Yates KP, Gill R, et al.; NASH Clinical Research Network . Novel plasma biomarkers associated with liver disease severity in adults with nonalcoholic fatty liver disease. Hepatology 2017;65:65‐77. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0005] 5. Crespo M, Lappe S, Feldstein AE, Alkhouri N. Similarities and differences between pediatric and adult nonalcoholic fatty liver disease. Metabolism 2016;65:1161‐1171. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0006] 6. Cianfarani S, Inzaghi E, Alisi A, Germani D, Puglianiello A, Nobili V. Insulin‐like growth factor‐I and ‐II levels are associated with the progression of nonalcoholic fatty liver disease in obese children. J Pediatr 2014;165:92‐98. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0007] 7. Nobili V, Cutrera R, Liccardo D, Pavone M, Devito R, Giorgio V, et al. Obstructive sleep apnea syndrome affects liver histology and inflammatory cell activation in pediatric nonalcoholic fatty liver disease, regardless of obesity/insulin resistance. Am J Respir Crit Care Med 2014;189:66‐76. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0008] 8. Kim JS, Le KA, Mahurkar S, Davis JN, Goran MI. Influence of elevated liver fat on circulating adipocytokines and insulin resistance in obese Hispanic adolescents. Pediatr Obes 2012;7:158‐164. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0009] 9. Alisi A, Manco M, Devito R, Piemonte F, Nobili V. Endotoxin and plasminogen activator inhibitor‐1 serum levels associated with nonalcoholic steatohepatitis in children. J Pediatr Gastroenterol Nutr 2010;50:645‐649. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0010] 10. Fitzpatrick E, Dew TK, Quaglia A, Sherwood RA, Mitry RR, Dhawan A. Analysis of adipokine concentrations in paediatric non‐alcoholic fatty liver disease. Pediatr Obes 2012;7:471‐479. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0011] 11. Louthan MV, Barve S, McClain CJ, Joshi‐Barve S. Decreased serum adiponectin: an early event in pediatric nonalcoholic fatty liver disease. J Pediatr 2005;147:835‐838. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0012] 12. Cali AM, De Oliveira AM, Kim H, Chen S, Reyes‐Mugica M, Escalera S, et al. Glucose dysregulation and hepatic steatosis in obese adolescents: is there a link? Hepatology 2009;49:1896‐1903. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0013] 13. Lebensztejn DM, Wojtkowska M, Skiba E, Werpachowska I, Tobolczyk J, Kaczmarski M. Serum concentration of adiponectin, leptin and resistin in obese children with non‐alcoholic fatty liver disease. Adv Med Sci 2009;54:177‐182. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0014] 14. Zou CC, Liang L, Hong F, Fu JF, Zhao ZY. Serum adiponectin, resistin levels and non‐alcoholic fatty liver disease in obese children. Endocr J 2005;52:519‐524. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0015] 15. Holzberg JR, Jin R, Le NA, Ziegler TR, Brunt EM, McClain CJ, et al. Plasminogen activator inhibitor‐1 predicts quantity of hepatic steatosis independent of insulin resistance and body weight. J Pediatr Gastroenterol Nutr 2016;62;819‐823. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0016] 16. Abiru S, Migita K, Maeda Y, Daikoku M, Ito M, Ohata K, et al. Serum cytokine and soluble cytokine receptor levels in patients with non‐alcoholic steatohepatitis. Liver Int 2006;26:39‐45. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0017] 17. Pagano C, Soardo G, Pilon C, Milocco C, Basan L, Milan G, et al. Increased serum resistin in nonalcoholic fatty liver disease is related to liver disease severity and not to insulin resistance. J Clin Endocrinol Metab 2006;91:1081‐1086. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0018] 18. Bahcecioglu IH, Yalniz M, Ataseven H, Ilhan N, Ozercan IH, Seckin D, et al. Levels of serum hyaluronic acid, TNF‐alpha and IL‐8 in patients with nonalcoholic steatohepatitis. Hepatogastroenterology 2005;52:1549‐1553. [PubMed] [Google Scholar]

[hep41068-bib-0019] 19. Lavine JE, Schwimmer JB; Nonalcoholic Steatohepatitis‐Clinical Research Network . Pediatric initiatives within the Nonalcoholic Steatohepatitis‐Clinical Research Network (NASH CNR). J Pediatr Gastroenterol Nutr 2003;37:220‐221. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0020] 20. Lavine JE, Schwimmer JB, Molleston JP, Scheimann AO, Murray KF, Abrams SH, et al.; Nonalcoholic Steatohepatitis Clinical Research Network Research Group . Treatment of nonalcoholic fatty liver disease in children: TONIC trial design. Contemp Clin Trials 2010;31:62‐70. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0021] 21. Lavine JE, Schwimmer JB, Van Natta ML, Molleston JP, Murray KF, Rosenthal P, et al.; Nonalcoholic Steatohepatitis Clinical Research Network . Effect of vitamin E or metformin for treatment of nonalcoholic fatty liver disease in children and adolescents: the TONIC randomized controlled trial. JAMA 2011;305:1659‐1668. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0022] 22. Patton HM, Lavine JE, Van Natta ML, Schwimmer JB, Kleiner D, Molleston J; Nonalcoholic Steatohepatitis Clinical Research Network . Clinical correlates of histopathology in pediatric nonalcoholic steatohepatitis. Gastroenterology 2008;135:1961‐1971.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0023] 23. Ogden CL, Kuczmarski RJ, Flegal KM, Mei Z, Guo S, Wei R, et al. Centers for Disease Control and Prevention 2000 growth charts for the United States: improvements to the 1977 National Center for Health Statistics version. Pediatrics 2002;109:45‐60. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0024] 24. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta‐cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412‐419. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0025] 25. Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, et al.; Nonalcoholic Steatohepatitis Clinical Research Network . Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005;41:1313‐1321. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0026] 26. Schwimmer JB, Behling C, Newbury R, Deutsch R, Nievergelt C, Schork NJ, et al. Histopathology of pediatric nonalcoholic fatty liver disease. Hepatology 2005;42:641‐649. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0027] 27. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B (Methodological) 1995;57:289‐300. [Google Scholar]

[hep41068-bib-0028] 28. Verrijken A, Francque S, Mertens I, Prawitt J, Caron S, Hubens G, et al. Prothrombotic factors in histologically proven nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology 2014;59:121‐129. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0029] 29. Targher G, Chonchol M, Miele L, Zoppini G, Pichiri I, Muggeo M. Nonalcoholic fatty liver disease as a contributor to hypercoagulation and thrombophilia in the metabolic syndrome. Semin Thromb Hemost 2009;35:277‐287. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0030] 30. Barrera F, George J. Prothrombotic factors and nonalcoholic fatty liver disease: an additional link to cardiovascular risk? Hepatology 2014;59:16‐18. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0031] 31. Targher G, Byrne CD, Lonardo A, Zoppini G, Barbui C. Non‐alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta‐analysis. J Hepatol 2016;65:589‐600. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0032] 32. Kheirandish‐Gozal L, Gileles‐Hillel A, Alonso‐Alvarez ML, Peris E, Bhattacharjee R, Teran‐Santos J, et al. Effects of adenotonsillectomy on plasma inflammatory biomarkers in obese children with obstructive sleep apnea: a community‐based study. Int J Obes (Lond) 2015;39:1094‐1100. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0033] 33. Sundaram SS, Sokol RJ, Capocelli KE, Pan Z, Sullivan JS, Robbins K, et al. Obstructive sleep apnea and hypoxemia are associated with advanced liver histology in pediatric nonalcoholic fatty liver disease. J Pediatr 2014;164:699‐706.e1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0034] 34. du Plessis J, van Pelt J, Korf H, Mathieu C, van der Schueren B, Lannoo M, et al. Association of adipose tissue inflammation with histologic severity of nonalcoholic fatty liver disease. Gastroenterology 2015;149:635‐648.e14. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0035] 35. Huang YS, Chan CY, Wu JC, Pai CH, Chao Y, Lee SD. Serum levels of interleukin‐8 in alcoholic liver disease: relationship with disease stage, biochemical parameters and survival. J Hepatol 1996;24:377‐384. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0036] 36. Joshi‐Barve S, Barve SS, Amancherla K, Gobejishvili L, Hill D, Cave M, et al. Palmitic acid induces production of proinflammatory cytokine interleukin‐8 from hepatocytes. Hepatology 2007;46:823‐830. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0037] 37. Lucas S, Taront S, Magnan C, Fauconnier L, Delacre M, Macia L, et al. Interleukin‐7 regulates adipose tissue mass and insulin sensitivity in high‐fat diet‐fed mice through lymphocyte‐dependent and independent mechanisms. PLoS One 2012;7:e40351. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0038] 38. Estep M, Abawi M, Jarrar M, Wang L, Stepanova M, Elariny H, et al. Association of obestatin, ghrelin, and inflammatory cytokines in obese patients with non‐alcoholic fatty liver disease. Obes Surg 2011;21:1750‐1757. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0039] 39. Naveau S, Balian A, Degos F, Daurat V, Chevret S, Gayno S, et al. Prognostic value of the soluble interleukin‐2 receptor in chronic hepatitis C treated with interferon‐alfa. Multicenter GER‐CYT 04 Group. J Hepatol 1999;31:612‐617. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0040] 40. Izzo F, Cremona F, Delrio P, Leonardi E, Castello G, Pignata S, et al. Soluble interleukin‐2 receptor levels in hepatocellular cancer: a more sensitive marker than alfa fetoprotein. Ann Surg Oncol 1999;6:178‐185. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0041] 41. Bucuvalas J, Filipovich L, Yazigi N, Narkewicz MR, Ng V, Belle SH, et al. Immunophenotype predicts outcome in pediatric acute liver failure. J Pediatr Gastroenterol Nutr 2013;56:311‐315. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0042] 42. D'Amico F, Consolo M, Amoroso A, Skarmoutsou E, Mauceri B, Stivala F, et al. Liver immunolocalization and plasma levels of MMP‐9 in non‐alcoholic steatohepatitis (NASH) and hepatitis C infection. Acta Histochem 2010;112:474‐481. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0043] 43. Ljumovic D, Diamantis I, Alegakis AK, Kouroumalis EA. Differential expression of matrix metalloproteinases in viral and non‐viral chronic liver diseases. Clin Chim Acta 2004;349(1‐2):203‐211. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0044] 44. Cianfarani S. Insulin‐like growth factor‐II: new roles for an old actor. Front Endocrinol (Lausanne) 2012;3:118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0045] 45. Frystyk J, Skjaerbaek C, Vestbo E, Fisker S, Orskov H. Circulating levels of free insulin‐like growth factors in obese subjects: the impact of type 2 diabetes. Diabetes Metab Res Rev 1999;15:314‐322. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0046] 46. Simon Y, Kessler SM, Bohle RM, Haybaeck J, Kiemer AK. The insulin‐like growth factor 2 (IGF2) mRNA‐binding protein p62/IGF2BP2‐2 as a promoter of NAFLD and HCC? Gut 2014;63:861‐863. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0047] 47. Devedjian JC, George M, Casellas A, Pujol A, Visa J, Pelegrin M, et al. Transgenic mice overexpressing insulin‐like growth factor‐II in beta cells develop type 2 diabetes. J Clin Invest 2000;105:731‐740. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0048] 48. Cianfarani S, Inzaghi E, Alisi A, Germani D, Puglianiello A, Nobili V. Insulin‐like growth factor‐I and ‐II levels are associated with the progression of nonalcoholic fatty liver disease in obese children. J Pediatr 2014;165:92‐98. [DOI] [PubMed] [Google Scholar]

[hep41068-bib-0049] 49. Mehta R, Birerdinc A, Wang L, Younoszai Z, Moazzez A, Elariny H, et al. Expression of energy metabolism related genes in the gastric tissue of obese individuals with non‐alcoholic fatty liver disease. BMC Gastroenterol 2014;14:72. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hep41068-bib-0050] 50. Petrasek J, Bala S, Csak T, Lippai D, Kodys K, Menashy V, et al. IL‐1 receptor antagonist ameliorates inflammasome‐dependent alcoholic steatohepatitis in mice. J Clin Invest 2012;122:3476‐3489. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Association between cytokines and liver histology in children with nonalcoholic fatty liver disease

Emily R Perito

Veeral Ajmera

Nathan M Bass

Philip Rosenthal

Joel E Lavine

Jeffrey B Schwimmer

Katherine P Yates

Anna Mae Diehl

Jean P Molleston

Karen F Murray

Ann Scheimann

Ryan Gill

David Glidden

Bradley Aouizerat

Abstract

Abbreviations

Introduction

Patients and Methods

STUDY DESIGN

STUDY POPULATION

CLINICAL AND LABORATORY ASSESSMENT

HISTOLOGIC EVALUATION

PLASMA BIOMARKER MULTIPLEX ASSAY

STATISTICAL ANALYSIS

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Results

CHARACTERISTICS OF THE STUDY POPULATION

PLASMA BIOMARKERS ASSOCIATED WITH NASH DIAGNOSIS

PLASMA BIOMARKERS ASSOCIATED WITH STEATOSIS GRADE

PLASMA BIOMARKERS ASSOCIATED WITH FIBROSIS STAGE

PLASMA BIOMARKERS ASSOCIATED WITH INFLAMMATION AND HEPATOCELLULAR BALLOONING

Discussion

Supporting information

Acknowledgment

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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