Clinical metabolomics and metabolic diagnostics
Diagnostics, Genetic metabolic disease, Metabolomics
Research aim
Our research, focusing on metabolic disturbances, aims to improve diagnostics, and to contribute to the development of novel therapies. The impact extends from technology development for individual patient care to a broader understanding of human disease.
About us
Two main lines of research are actively pursued in our group. Our Clinical Metabolomics line is technology-driven and focuses on untargeted metabolomics workflows, development of algorithms for data handling and discovery and validation of biomarkers to monitor and screen for inherited and acquired metabolic disorders. The second line of research centers around obtaining fundamental insight in pathophysiology of genetic disorders.
Together, our goal is to unravel the intricate biochemical mechanisms underlying (genetic) diseases, improving diagnostics, treatment, and preventive strategies. Our approach is multidisciplinary, combining molecular biology, biochemistry, mass spectrometry, metabolomics, and data science.
We aim to build a bridge between the vast potential of metabolomics and clinical application. Our work has led to embedding metabolomic signatures into diagnostic processes, helping to flag metabolic alterations earlier and more comprehensively than traditional screens and has revealed pathophysiological insight in rare disorders. We partner with clinicians, companies, and experts in related fields.
Method development
Current method development focuses on advancing the scope of clinical metabolomics and bringing clinical meaning to this complex data.
- Biomarker identification and implementation: having integrated metabolomics in routine diagnostic processes we are uniquely positioned to discover novel biomarkers and clinically validate them simultaneously within the diagnostic context.
- Longitudinal metabolomics for monitoring therapeutic interventions. By tracking metabolic profiles over time, we assess responses to e.g. drug treatments, thereby enabling personalized therapeutic monitoring.
- Using bioinformatics and advanced statistics we build algorithms that integrate metabolomics with clinical data, genomic data, large-scale laboratory parameters and databases. These multimodal pipelines help bridge the gap between biochemical, molecular and clinical phenotypes, improving the diagnostic yield in metabolic disorders and preparing for automation of advanced diagnostic processes.
- We employ mechanistic modeling and flux analysis to reveal how metabolites move through metabolic networks in real time, adding a layer of mechanistic insight on top of static concentrations.
Diseases
Disorders we focus on include:
- PLPHP deficiency, a vitamin B6-responsive genetic epilepsy, is only partly understood. Using different models, including zebrafish and yeast, we investigate its molecular mechanism.
- Defects in the malate aspartate shuttle impact energy metabolism. We investigate the broader metabolic consequences and potential treatments by metabolomics and fluxomics in cellular models and patient samples.
- For hereditary anemias, including pyruvate kinase deficiency and sickle cell disease, we develop new methodologies to improve diagnostics and prognostics and to monitor effects of treatment.
- In chronic fatigue, exercise intolerance and post exertional malaise (including ME/CFS and long Covid) we study disturbances of metabolism. We pay specific attention to glutamine and glucose as carbon sources for energy production in immune cells.
We advance diagnostics by untargeted metabolomics to identify metabolic signatures, enabling early detection, personalized treatments, and a deeper understanding of disease mechanisms. Data analytics, including algorithms, aids in diagnostic pattern recognition and biomarker discovery.