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Background: In erythropoietic protoporphyria (EPP), which presents with severe painful phototoxicity, progressive deposition of protoporphyrins in hepatocytes and bile canaliculi may result in liver disease. Clinically EPP related liver disease ranges from mildly elevated liver enzymes to cirrhosis and acute cholestatic hepatic failure. The prevalence of liver disease in EPP, and factors predicting the risk of developing liver disease, have not been defined in a large series of unselected EPP patients. Aim: To determine the prevalence of liver disease in EPP-patients. Methods: A single-center prospective unselected cohort study of 114 adult EPP patients, who underwent routine laboratory testing, abdominal ultrasonography and transient elastography to assess the presence of steatosis (controlled attenuation parameter,dB/m) and liver stiffness (kPa). Results: 114 adult EPP patients were included. Elevated liver enzymes were found in 6.2% of the patients. Liver steatosis was detected in 29.0%, and significant fibrosis as assessed with liver stiffness measurements was present in 9.6% of patients. BMI positively predicted CAP-values ( p = 0.026); and protoporphyrin IX levels ( p = 0.043) positively predicted liver stiffness. Conclusions: This study demonstrates a prevalence of hepatic steatosis and fibrosis in adult EPP-patients comparable to that found in the general population. Protoporphyrin IX levels correlate with increased liver stiffness in EPP.

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.