Metabolic liver diseases presenting with neonatal cholestasis: at the crossroad between old and new paradigms.

Metabolic liver diseases (MLD) are an important group of disorders presenting with neonatal cholestasis (NC). The spectrum of liver involvement is wide and the presumptive diagnosis is traditionally based on clinical and laboratory findings. Recently, next-generation sequencing (NGS) panels have emerged as an appealing tool to diagnose neonatal/infantile cholestatic disorders. The aim of this study was to identify clinical phenotypes of liver injury and contribute to find a diagnostic methodology that integrates new molecular diagnostic tools. We retrospectively analyzed the clinical and biochemical features of 16 patients with MLD and NC. Patients were categorized into three groups: A-NC with liver failure (N = 8): tyrosinemia type I (n = 2), classic galactosemia (n = 5), mitochondrial DNA depletion syndrome (n = 1); B-NC evolving with chronic liver disease (N = 5): argininemia (n = 2); mitochondrial cytopathy (n = 1); congenital disorders of glycosylation type Ia (n = 1); Zellweger syndrome (n = 1); and C-transient NC (N = 3): Niemann-Pick type C (n = 2), citrullinemia type II (n = 1).Conclusion: MLD presenting with NC can be categorized into three main clinical phenotypes of liver injury. We highlight transient NC as a clue for MLD that must be pursued. New molecular diagnostic tools can play a key role, but application criteria must be established to make them cost-effective. What is Known: • Metabolic liver diseases are an important group of disorders presenting with neonatal cholestasis. • The diagnostic approach is challenging and traditionally based on clinical and laboratory findings. Next-generation sequencing is a recent and rapidly developing tool in pediatric hepatology. What is New: • We provide a liver-targeted characterization of metabolic liver diseases presenting with neonatal cholestasis, categorizing them into three clinical phenotypes that may narrow the diagnostic possibilities. • A clinical decision-making algorithm is proposed, in which the NGS technology is integrated.