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Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is the commonest liver condition in the western world and is directly linked to obesity and the metabolic syndrome. Elevated body mass index is regarded as a major risk factor of NAFL (steatosis) and NAFLD fibrosis. Using data from the Avon Longi...

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Autores principales: Abeysekera, Kushala W. M., Orr, James G., Gordon, Fiona H., Howe, Laura D., Hamilton-Shield, Julian, Heron, Jon, Hickman, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245230/
https://www.ncbi.nlm.nih.gov/pubmed/35773644
http://dx.doi.org/10.1186/s12876-022-02401-y
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author Abeysekera, Kushala W. M.
Orr, James G.
Gordon, Fiona H.
Howe, Laura D.
Hamilton-Shield, Julian
Heron, Jon
Hickman, Matthew
author_facet Abeysekera, Kushala W. M.
Orr, James G.
Gordon, Fiona H.
Howe, Laura D.
Hamilton-Shield, Julian
Heron, Jon
Hickman, Matthew
author_sort Abeysekera, Kushala W. M.
collection PubMed
description BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is the commonest liver condition in the western world and is directly linked to obesity and the metabolic syndrome. Elevated body mass index is regarded as a major risk factor of NAFL (steatosis) and NAFLD fibrosis. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we sought to investigate whether other variables from adolescence could improve prediction of future NAFL and NAFLD fibrosis risk at 24 years, above BMI and sex. METHODS: Aged 24 years, 4018 ALSPAC participants had transient elastography (TE) and controlled attenuation parameter (CAP) measurement using Echosens 502 Touch. 513 participants with harmful alcohol consumption were excluded. Logistic regression models examined which variables measured at 17 years were predictive of NAFL and NAFLD fibrosis in young adults. Predictors included sex, BMI, central adiposity, lipid profile, blood pressure, liver function tests, homeostatic model assessment for insulin resistance (HOMA-IR), and ultrasound defined NAFL at 17 years (when examining fibrosis outcomes). A model including all these variables was termed “routine clinical measures”. Models were compared using area under the receiver operator curve (AUROC) and Bayesian Information Criterion (BIC), analysis, which penalises model complexity. Models were tested in all participants and those with overweight or obese standardised BMIs (BMI SDS) centiles at the 17-year time point. A decision curve analysis (DCA) was performed to evaluate the clinical utility of models in overweight and obese adolescents predicting NAFLD fibrosis at a threshold probability of 0.1. RESULTS: The “routine clinical measures” model had the highest AUROC for predicting NAFL in all adolescent participants (AUROC 0.79 [SD 0.00]) and those with an overweight/obese BMI SDS centile (AUROC 0.77 [SD 0.01]). According to BIC analysis, insulin resistance was the best predictor of NAFL in all adolescents, whilst central adiposity was the best predictor in those with an overweight/obese BMI SDS centile. The “routine clinical measures” model also had the highest AUROC for predicting NAFLD fibrosis in all adolescent participants (AUROC 0.78 [SD 0.02]) and participants with an overweight/obese BMI SDS centile (AUROC 0.84 [SD 0.03]). However, following BIC analysis, BMI was the best predictor of NAFLD fibrosis in all adolescents including those with an overweight/obese BMI SDS centile. A decision curve analysis examining overweight/obese adolescent participants showed the model that had the greatest net benefit for increased NAFLD fibrosis detection, above a treat all overweight and obese adolescents’ assumption, was the “routine clinical measures” model. However, the net benefit was marginal (0.0054 [0.0034–0.0075]). CONCLUSION: In adolescents, routine clinical measures were not superior to central adiposity and BMI at predicting NAFL and NAFLD fibrosis respectively in young adulthood. Additional routine clinical measurements do provide incremental benefit in detecting true positive fibrosis cases, but the benefit is small. Thus, to reduce morbidity and mortality associated with NASH cirrhosis in adults, the ultimate end point of NAFLD, the focus must be on obesity management at a population level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-022-02401-y.
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spelling pubmed-92452302022-07-01 Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis Abeysekera, Kushala W. M. Orr, James G. Gordon, Fiona H. Howe, Laura D. Hamilton-Shield, Julian Heron, Jon Hickman, Matthew BMC Gastroenterol Research BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is the commonest liver condition in the western world and is directly linked to obesity and the metabolic syndrome. Elevated body mass index is regarded as a major risk factor of NAFL (steatosis) and NAFLD fibrosis. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we sought to investigate whether other variables from adolescence could improve prediction of future NAFL and NAFLD fibrosis risk at 24 years, above BMI and sex. METHODS: Aged 24 years, 4018 ALSPAC participants had transient elastography (TE) and controlled attenuation parameter (CAP) measurement using Echosens 502 Touch. 513 participants with harmful alcohol consumption were excluded. Logistic regression models examined which variables measured at 17 years were predictive of NAFL and NAFLD fibrosis in young adults. Predictors included sex, BMI, central adiposity, lipid profile, blood pressure, liver function tests, homeostatic model assessment for insulin resistance (HOMA-IR), and ultrasound defined NAFL at 17 years (when examining fibrosis outcomes). A model including all these variables was termed “routine clinical measures”. Models were compared using area under the receiver operator curve (AUROC) and Bayesian Information Criterion (BIC), analysis, which penalises model complexity. Models were tested in all participants and those with overweight or obese standardised BMIs (BMI SDS) centiles at the 17-year time point. A decision curve analysis (DCA) was performed to evaluate the clinical utility of models in overweight and obese adolescents predicting NAFLD fibrosis at a threshold probability of 0.1. RESULTS: The “routine clinical measures” model had the highest AUROC for predicting NAFL in all adolescent participants (AUROC 0.79 [SD 0.00]) and those with an overweight/obese BMI SDS centile (AUROC 0.77 [SD 0.01]). According to BIC analysis, insulin resistance was the best predictor of NAFL in all adolescents, whilst central adiposity was the best predictor in those with an overweight/obese BMI SDS centile. The “routine clinical measures” model also had the highest AUROC for predicting NAFLD fibrosis in all adolescent participants (AUROC 0.78 [SD 0.02]) and participants with an overweight/obese BMI SDS centile (AUROC 0.84 [SD 0.03]). However, following BIC analysis, BMI was the best predictor of NAFLD fibrosis in all adolescents including those with an overweight/obese BMI SDS centile. A decision curve analysis examining overweight/obese adolescent participants showed the model that had the greatest net benefit for increased NAFLD fibrosis detection, above a treat all overweight and obese adolescents’ assumption, was the “routine clinical measures” model. However, the net benefit was marginal (0.0054 [0.0034–0.0075]). CONCLUSION: In adolescents, routine clinical measures were not superior to central adiposity and BMI at predicting NAFL and NAFLD fibrosis respectively in young adulthood. Additional routine clinical measurements do provide incremental benefit in detecting true positive fibrosis cases, but the benefit is small. Thus, to reduce morbidity and mortality associated with NASH cirrhosis in adults, the ultimate end point of NAFLD, the focus must be on obesity management at a population level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-022-02401-y. BioMed Central 2022-06-30 /pmc/articles/PMC9245230/ /pubmed/35773644 http://dx.doi.org/10.1186/s12876-022-02401-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Abeysekera, Kushala W. M.
Orr, James G.
Gordon, Fiona H.
Howe, Laura D.
Hamilton-Shield, Julian
Heron, Jon
Hickman, Matthew
Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis
title Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis
title_full Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis
title_fullStr Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis
title_full_unstemmed Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis
title_short Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis
title_sort evaluating future risk of nafld in adolescents: a prediction and decision curve analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245230/
https://www.ncbi.nlm.nih.gov/pubmed/35773644
http://dx.doi.org/10.1186/s12876-022-02401-y
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