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. 2024 Feb 26;8(3):e0375.
doi: 10.1097/HC9.0000000000000375. eCollection 2024 Mar 1.

Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics

Helaina E Huneault  1 , Alasdair E Gent  2 , Catherine C Cohen  3 , Zhulin He  4 , Zachery R Jarrell  5 , Rishikesan Kamaleswaran  2 , Miriam B Vos  1   6   7
Affiliations

Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics

Helaina E Huneault et al. Hepatol Commun. .

Abstract

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, is the most common liver disease in children. Liver biopsy remains the gold standard for diagnosis, although more efficient screening methods are needed. We previously developed a novel NAFLD screening panel in youth using machine learning applied to high-resolution metabolomics and clinical phenotype data. Our objective was to validate this panel in a separate cohort, which consisted of a combined cross-sectional sample of 161 children with stored frozen samples (75% male, 12.8±2.6 years of age, body mass index 31.0±7.0 kg/m2, 81% with MASLD, 58% Hispanic race/ethnicity).

Methods: Clinical data were collected from all children, and high-resolution metabolomics was performed using their fasting serum samples. MASLD was assessed by MRI-proton density fat fraction or liver biopsy and cardiometabolic factors. Our previously developed panel included waist circumference, triglycerides, whole-body insulin sensitivity index, 3 amino acids, 2 phospholipids, dihydrothymine, and 2 unknowns. To improve feasibility, a simplified version without the unknowns was utilized in the present study. Since the panel was modified, the data were split into training (67%) and test (33%) sets to assess the validity of the panel.

Results: Our present highest-performing modified model, with 4 clinical variables and 8 metabolomics features, achieved an AUROC of 0.92, 95% sensitivity, and 80% specificity for detecting MASLD in the test set.

Conclusions: Therefore, this panel has promising potential for use as a screening tool for MASLD in youth.

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Conflict of interest statement

Miriam B. Vos serves as a consultant to Boehringer Ingelheim, Novo Nordisk, Eli Lilly, Intercept, Takeda, and Alberio. She has stock or stock options in Thiogenesis and Tern Pharmaceuticals. Her institution has received research grants (or in-kind research services) from Target Real World Evidence, Quest, Labcorp, and Sonic Incytes Medical Corp. The remaining authors have no conflicts to report.

Figures

FIGURE 1
FIGURE 1
SHAP “beeswarm” plot and bar graph for random forest Model #1. “Beeswarm” plot illustrating the distribution of Shapley values for each variable, with the color signifying the value of the respective data point (A) and a bar graph ranking the variables by importance to the model, based on the mean of the absolute value of all Shapley values for that variable (B). Abbreviations: LysoPC, lysophosphatidylcholine; LysoPE, lysophosphatidylethanolamine; SHAP, Shapley Additive exPlanations; TG, triglycerides; WC, waist circumference.
FIGURE 2
FIGURE 2
SHAP “beeswarm” plot and bar graph for random forest Model #2. “Beeswarm” plot illustrating the distribution of Shapley values for each variable, with the color signifying the value of the respective data point (A) and a bar graph ranking the variables by importance to the model, based on the mean of the absolute value of all Shapley values for that variable (B). Abbreviations: HOMA-IR, homeostatic model assessment for insulin resistance; LysoPC, lysophosphatidylcholine; LysoPE, lysophosphatidylethanolamine; SHAP, Shapley Additive exPlanations; TG, triglycerides; WC, waist circumference.
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