Judith Jans

I am head of Metabolic Diagnostics at the Wilhelmina Childrens Hospital and associate professor Clinical Metabolomics.

After obtaining a PhD in genetics at Erasmus university and completing four years of postdoctoral studies at University of California, Berkeley, I returned to the Netherlands and joined the University Medical Centre Utrecht.  I trained and registered as a laboratory specialist in Clinical Genetics, specializing in diagnostics for patients with inborn metabolic disorders. Furthermore, I coordinate and carry responsibility for the scientific training of the SUMMA and am a member of the educational council of SUMMA.

Clinical Metabolomics

Research in my group focuses on developing, improving and valorizing Clinical Metabolomics. Clinical Metabolomics refers to the comprehensive measurement and analysis of small molecules (metabolites) in biological specimens—such as blood, urine or cerebrospinal fluid— of individual patients using advanced mass spectrometry and bioinformatics. With metabolomics we capture the current biochemical phenotype of a patient, reflecting both genetic influences and environmental factors, and thereby offers real‑time insight into physiological or pathological states. This approach enables detection of disease‑specific metabolic signatures and biomarkers, supporting early and precise diagnosis of conditions, including inborn metabolic disorders. 

Method development

I aim to build a bridge between the vast potential of metabolomics and tangible 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.

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.

Valorisation

By integrating metabolomics with clinical and genomic data, our framework greatly enhances both accuracy and depth: we identify rare diagnoses, interpret variants of uncertain significance, follow therapeutic response over time, and personalize treatment strategies for individual patient. A key challenge is making this technology accessible, meaningful, and sustainable for individual patients with ultra‑rare diseases. We address this by expanding our scope, automating workflows, building advanced algorithms, linking to big clinical datasets, and scaling implementation for broader applicability.

We also utilize clinical metabolomics in combination with fluxomics to elucidate disease pathophysiology—such as in the case of malate–aspartate shuttle defects—where our research revealed critical mechanistic insights, candidate biomarkers, and potential treatment strategies. In addition, our metabolomic investigations have contributed to the discovery of many novel rare inherited metabolic diseases

Through close collaboration with clinicians, we continuously adapt our methodologies and data accessibility to align with patient care needs. With the current exciting emerging therapies —such as mRNA therapies, gene therapy, stem cell transplantation, and gene correction— we focus our metabolomic profiling to provide functional readouts of efficacy.

The protocols we develop have also proven applicable beyond classical metabolic disorders—extending into areas like hemolytic anemias, long‑COVID, and other complex conditions—demonstrating the broader translational leverage of metabolomics in clinical research and diagnostics.

In summary, my work is centered on developing the technological and algorithmic framework for untargeted metabolomics, dynamic metabolic modeling, and biochemical-clinical‑genomic integration. We create tools that support early diagnosis, functional variant interpretation, therapeutic monitoring, biomarker validation and translational research. We aim to embed these methods into clinical practice, improving care for patients with metabolic and complex diseases through precise metabolic insights.