Experimental Clinical Chemistry

The group mainly works with metabolomics, machine learning (ML), and artificial intelligence (AI) to study multiple sclerosis and chronic pain. We study disease mechanisms and the role of environmental factors in disease development and severity. We also utilize immunobased techniques for protein analyses, perform register-based studies, and participate in clinical trials. We especially focus on developing reliable ways to measure AI's confidence in its predictions for individual patients, aiming to make these tools trustworthy for future use in healthcare.

Description of our research

We are mainly working along three research lines. One is the development of new methods for the diagnosis and prognosis of neurological diseases. The second is to investigate the role of environmental contaminants in the development and severity of diseases with an autoimmune component. Thirdly, the team also develops methods and assays for measuring a broad range of small molecules (metabolomics/lipidomics), environmental contaminants (e.g., PFAS and PCBs), and therapeutic drugs using mass spectrometry.

Enabling precision medicine in multiple sclerosis

Multiple sclerosis (MS) is a chronic neurological disease primarily affecting young people in Sweden, leading to severe disability and often premature death. It is an autoimmune disease where the immune system attacks the body's tissues. In Sweden, MS is the most common cause of mobility impairment in young people, and each year, approximately one thousand people become ill with MS.

A significant challenge is to identify patients with a faster disease progression or at risk of developing secondary progressive MS (SPMS). Proactive recognition of patients with progressive disease could limit exposure to ineffective medications and their side effects and be a valuable tool for clinical practitioners.

This project addresses this challenge by developing a highly accurate predictive model trained on electronic health records data and novel biomarkers. To be helpful within a clinical setting, we apply conformal prediction to deliver valid measures of uncertainty in predictions at the level of the individual patient. Proactive recognition of patients with progressive disease could limit exposure to ineffective medications and their side effects. The project, funded by the Swedish Research Council (VR), NEURO Sweden, and the Åke Wiberg Foundation, further supports its potential impact on MS treatment and management.

Autoimmune diseases and the role of environmental exposures

The prevalence of autoimmune diseases, including conditions like multiple sclerosis and rheumatoid arthritis, is rising, with women being particularly affected. This rise cannot be fully explained by genetics or known risk factors like smoking and obesity, suggesting that environmental contaminants, such as perfluorinated substances (PFAS) in products like kitchenware, may play a role. We use large-scale cohort studies, like EIMS and EpiHealth, to investigate the interaction between genetics and the exposome—the totality of environmental exposures—to better understand the risk of developing autoimmune diseases. The project, funded by grants from FORMAS, NEURO Sweden, and others, aims to identify environmental pollutants that may contribute to these diseases and inform stricter regulations to prevent exposure.

Metabolomics, lipidomics, and the exposome: the stethoscope for the twenty-first century

Metabolomics and lipidomics, which involve identifying and quantifying small molecules in the human body, offer new diagnostic biomarkers for diseases and can personalize treatment responses. The exposome concept captures environmental exposures that may be more influential than genetic factors in chronic diseases, representing an individual’s lifetime exposures. High-resolution mass spectrometry (HRMS) is critical to assessing the exposome despite the challenges posed by low concentrations of environmental contaminants. Using ultra-high-performance liquid chromatography (UHPLC) coupled with HRMS, we develop methods to measure these small molecules, revealing links between exposures and diseases and for a better understanding of disease mechanisms. The project is funded by grants from FORMAS and with support from Region Uppsala.

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