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Multicenter Study
. 2020 Nov;159(5):1731-1751.e10.
doi: 10.1053/j.gastro.2020.07.034. Epub 2020 Jul 23.

Progression of Fatty Liver Disease in Children Receiving Standard of Care Lifestyle Advice

Collaborators, Affiliations
Multicenter Study

Progression of Fatty Liver Disease in Children Receiving Standard of Care Lifestyle Advice

Stavra A Xanthakos et al. Gastroenterology. 2020 Nov.

Abstract

Background & aims: Nonalcoholic fatty liver disease (NAFLD) is the most common pediatric chronic liver disease. Little is known about outcomes in recognized youth.

Methods: We compared paired liver biopsies from 122 of 139 children with NAFLD (74% male; 64% white; 71% Hispanic; mean age, 13 ± 3 years; age range, 8-17 years) who received placebo and standard of care lifestyle advice in 2 double-blind, randomized clinical trials within the nonalcoholic steatohepatitis (NASH) clinical research network from 2005 through 2015. We analyzed histologic changes with respect to baseline and longitudinal change in clinical variables using regression analysis.

Results: At enrollment, 31% of the children had definite NASH, 34% had borderline zone 1 NASH, 13% had borderline zone 3 NASH, and 21% had fatty liver but not NASH. Over a mean period of 1.6 ± 0.4 years, borderline or definite NASH resolved in 29% of the children, whereas 18% of the children with fatty liver or borderline NASH developed definite NASH. Fibrosis improved in 34% of the children but worsened in 23%. Any progression to definite NASH and/or in fibrosis was associated with adolescent age, and higher waist circumference, levels of alanine or aspartate aminotransferase, total and low-density lipoprotein cholesterol at baseline (<0.05), and over follow-up time, with increasing level of alanine aminotransferase, hemoglobin A1C (P<.05), gamma-glutamyl transferase and development of type 2 diabetes (P<.01). Increasing level of gamma-glutamyl transferase was also associated with reduced odds of any improvement (P = .003).

Conclusions: One-third of children with NAFLD enrolled in placebo groups of clinical trials had histologic features of progression within 2 years, in association with increasing obesity and serum levels of aminotransferases and loss of glucose homeostasis.

Keywords: ALT; Cirrhosis; Histology; Natural History.

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Conflict of interest statement

Conflict of Interest

These authors disclose the following: Cynthia Behling is a consultant to ICON and Covance. Elizabeth Brunt is a consultant to Perspectum Diagnostics and Histoindex. Ajay Jain is a consultant to Alexion. Joel Lavine is a consultant for Surrozen, Intercept, and Novo Nordisk. He received grant support from Genfit. Tamir Miloh is a consultant to Alexion. Jean Molleston has received research support from Gilead, Abbvie, Albireo, and Shire/Mirum. Karen Murray is a consultant to Gilead and Albireo. Philip Rosenthal has received research support from Gilead, Abbvie, BMS, Roche/Genentech, Retrophin, Merck, Albireo, Mirum, and Arrowhead and has served as a consultant for Albireo, Audentes, Retrophin, Gilead, Abbvie, Dicerna, Arrowhead and Mirum. Arun Sanyal is president of Sanyal Biotechnology and has stock options in Genfit, Akarna, Tiziana, Indalo, Durect Inversago, and Galmed. He has served as a consultant to Astra Zeneca, Nitto Denko, Conatus, Nimbus, Salix, Tobira, Takeda, Jannsen, Gilead, Terns, Birdrock, Merck, Valeant, Boehringer-Ingelheim, Bristol Myers Squibb, Lilly, Hemoshear, Zafgen, Novartis, Novo Nordisk, Pfizer, Exhalenz and Genfit. He has been an unpaid consultant to Intercept, Echosens, Immuron, Galectin, Fractyl, Syntlogic, Affimune, Chemomab, Zydus, Nordic Bioscience, Albireo, Prosciento, Surrozen. His institution has received grant support from Gilead, Salix, Tobira, Bristol Myers, Shire, Intercept, Merck, Astra Zeneca, Malinckrodt, Cumberland and Novartis. He receives royalties from Elsevier and UptoDate. Ann Scheimann has received research support from Millendo Therapeutics, Insys Therapeutics, Albireo, and serves on a Data Safety Monitoring Board for Levo Therapeutics. Jeffrey Schwimmer has received grants from Galmed, Genfit, and Intercept. Miriam Vos has received grants from Gemphire, Resonance Health, and Shire, and is a consultant for Boehringer Ingelheim, Bristol Myers Squibb, Immuron, Intercept, Mallinckrodt, Novo Nordisk, Target Pharmasolutions, and AMRA. Stavra Xanthakos has received grants from Axcella Health and Target PharmaSolutions. She receives royalties from UptoDate. The remaining authors disclose no conflicts.

Figures

Figure 1.
Figure 1.
Percentage of children with (A) NAFLD Activity Score, (B) NASH diagnosis, and (C) fibrosis stage, at first and last biopsy. First biopsy denoted in blue color and last biopsy denoted in orange color in each panel.
Figure 2.
Figure 2.
Mean change from baseline in characteristics associated with progression vs same/improved NASH. Panels include (on y-axis) the following: (A) ALT (U/L), (B) AST (U/L), (C) GGT (U/L), (D) LDL cholesterol (mg/dL), (E) hemoglobin A1C (A1C) (%), and (F) BMI z-score (BMIz). The x-axis indicates the weeks of observation. Same/improved NASH is denoted by black solid lines, whereas progression in NASH is indicated by blue-dashed lines. Generalized estimating equations (GEEs) were used to account for repeated visits per child for multiple linear regression models of the change in laboratory test or BMI z-score in relation to the histological outcome indicator adjusted for the baseline value of the lab test or BMI z-score, the visit code and the interaction of visit by histological outcome, respectively; Probability (2-sided) then determined by testing the histological effect and the interaction term.
Figure 3.
Figure 3.
Mean change from baseline in characteristics associated with progression vs same/improved fibrosis. Panels include (on y-axis): (A) ALT (U/L), (B) AST (U/L), (C) GGT (U/L), (D) LDL cholesterol (mg/dL), (E) hemoglobin A1C (A1C) (%), and (F) BMI z-score (BMIz). The x-axis indicates the weeks of observation. Same/improved fibrosis is denoted by black solid lines, while progression in fibrosis is indicated by blue-dashed lines. Generalized estimating equations (GEEs) were used to account for repeated visits for multiple regression models of the change in laboratory test or BMI z-score in relation to the histological outcome indicator adjusted for the baseline value of the laboratory test or BMI z-score, the visit code, and the interaction of visit by histological outcome, respectively. Probability (2-sided) then determined by testing the histological effect and the interaction term.

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