Klementy Shchetynsky

Klementy Shchetynsky

Research Specialist
Visiting address: L8:05, CMM Karolinska Universitetssjukhuset Solna, 17176 Stockholm
Postal address: K8 Klinisk neurovetenskap, K8 Neuro Kockum, 171 77 Stockholm
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About me

  • Klementy Shchetynsky’s work uses statistical genetics, computational modeling, and integrative multi-omics analyses to dissect the complex genetic factors driving autoimmune and neurological diseases. He works on development and application of analytical methods to translate genetic association signals into mechanistic insights and clinically actionable biomarkers. Through active collaboration in international consortia, he contributes to advancing personalized medicine approaches for diseases such as multiple sclerosis and rheumatoid arthritis, focusing on improving disease prognosis, treatment response prediction, and therapeutic target identification.

Research

  • Klementy is a computational geneticist specializing in the genetic architecture of neurological and immune-mediated diseases. His research focuses on leveraging large-scale human genetic and multi-omics datasets to uncover genetic risk factors and molecular mechanisms underlying multiple sclerosis, as well as other neuroinflammatory and autoimmune diseases. By integrating  statistical genetics, machine learning, clinical,  and lifestyle data he aims to improve disease prediction, stratification, and the development of personalized therapeutic strategies. This work would not be possible without access to rich clinical and genetic data from Swedish national registries and international consortia, enabling novel insights into disease progression and treatment response.

Articles

All other publications

Grants

  • Improving prediction of multiple sclerosis, algorithmic approaches and biomarkers
    Region Stockholm ALF Medicin
    1 January 2026 - 31 December 2028
    Multiple Sclerosis (MS), a complex autoimmune disorder affecting mainly young adults, with a higher prevalence in women. Its diverse symptoms and inherent progression complexity hinder early detection and personalized prognostic evaluations. This proposal aspires to enhance health outcomes, elucidate MS’s intricate mechanisms, and reduce neurological disabilities through innovative, transformative methodologies. The objective is to develop advanced predictive models by harnessing extensive multimodal data, facilitating early detection, and personalized interventions. The project is organized in 4 different but interconnected tasks and will run for 3 years. We will integrate clinical, lifestyle, and 'omics' data. By targeting the prodromal phase of MS, we aspire to devise impactful intervention strategies and project disease progression, with Artificial Intelligence (AI) serving as the cornerstone for analyzing data. We will test the MS prediction among first degree relatives. We will also identify biomarkers for MS risk. A special focus will be on role of sex hormones. Employing advanced analytical techniques, network theories, autoencoders and deep learning, we will probe the multifaceted nature of MS using one of the world’s leading MS-specific databases. This will contribute to earlier diagnosis of MS and opening up for reduction of the disease by personalized changes in lifestyle and possibly preventative treatments in the future.
  • Swedish Research Council
    1 January 2025 - 31 December 2028
    Multiple Sclerosis (MS), a complex autoimmune disorder affecting mainly young adults, with a higher prevalence in women. Its diverse symptoms and inherent progression complexity hinder early detection and personalized prognostic evaluations. This proposal aspires to enhance health outcomes, elucidate MS’s intricate mechanisms, and reduce neurological disabilities through innovative, transformative methodologies.The objective is to develop advanced predictive models by harnessing extensive multimodal data, facilitating early detection, and personalized interventions. The project is organized in 6 different but interconnected tasks and will run for 5 years. We will integrate clinical, lifestyle, and ´omics´ data. By targeting the prodromal phase of MS, we aspire to devise impactful intervention strategies and project disease progression, with Artificial Intelligence (AI) serving as the cornerstone for analyzing data. We will test the MS prediction among first degree relatives. We will also identify biomarkers for MS risk and severity and genetic factors associated with MS severity. A special focus will be on role of sex hormones.Employing advanced analytical techniques, network theories, autoencoders and deep learning, we will probe the multifaceted nature of MS using one of the world’s leading MS-specific databases. This will contribute to more comprehensive and individualized MS treatment approaches.
  • Swedish Research Council
    1 January 2021 - 31 December 2024

Employments

  • Research Specialist, Department of Clinical Neuroscience, Karolinska Institutet, 2021-

Degrees and Education

  • Degree Of Doctor Of Philosophy, Department of Medicine, Solna, Karolinska Institutet, 2015
  • Degree Of Master Of Medical Science Two Years With A Major In, Karolinska Institutet, 2009

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