A Framework for Selecting Zebrafish Models of Metabolic Diseases: Modeling Strategies, Phenotypic Validation, Mechanistic Investigation, and Efficacy Evaluation.

Metabolic diseases have diverse etiologies, prolonged courses, and multiorgan involvement, making selection of animal models matched to the research objective and disease stage essential. Zebrafish develop rapidly, are genetically tractable, and support in vivo imaging and high-throughput screening, enabling studies of obesity and dyslipidemia, diabetes and its complications, fatty liver disease, atherosclerosis, and inherited metabolic diseases. However, zebrafish models differ in developmental stage, induction conditions, phenotypic evidence, and fidelity to human disease, and a single abnormal phenotype rarely supports a complete disease designation or mechanistic conclusion. This review compares dietary, chemical, genetic, and combined models in terms of modeling characteristics, representative phenotypes, applications, and limitations. It summarizes morphological, biochemical, and functional assays, in vivo imaging, omics, and automated quantification for phenotypic validation; discusses metabolic imbalance, mitochondrial dysfunction and oxidative stress, inflammation and immunometabolism, and interorgan crosstalk; and evaluates drug-screening applications and translational limitations. We propose a model-selection framework based on research objective and disease stage, developmental stage, modeling strategy, and core-phenotype validation. Minimum validation criteria define appropriate disease terminology and inference. A larval rapid-screening-to-adult-systemic-validation strategy, multilevel outcome assessment, and cross-model validation may improve the reliability and translational value of zebrafish research on metabolic diseases.