Project

Early Metabolic Programming Affects the Hypothalamus, Leading to Eating Disorders – EMPATHY

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Project value: 4,964,807.01 PLN
Funding value for PORT: 1,303,949.75 PLN
Project implementation period: 01/05/2025 – 31/05/2027
Project leader: Ali Jawaid, PhD

Consortium:

  • Vilnius University – Lithuania (Coordinator)
  • Łukasiewicz Research Network – PORT Polish Center for Technology Development (Partner)
  • INSERM – University of Bordeaux – France (Partner)
  • University and ETH Zürich – Switzerland (Partner)
  • University of South Bohemia (USB) – Czech Republic (Partner)

Eating disorders are complex neuropsychiatric conditions that lead to severe physical, psychological, and social consequences. Despite extensive research, their etiology remains insufficiently understood, and effective intervention strategies have yet to be developed. Increasing evidence highlights the critical role of maternal factors during the prenatal and perinatal periods—such as poor diet or obesity—in disrupting brain development in offspring and increasing the risk of eating disorders. Particular attention is directed toward the impact of a maternal high-fat diet on the development of the hypothalamus, a brain region essential for regulating appetite and metabolism.

The aim of the project is to determine how perinatal maternal metabolic programming modulates interactions between microglia and neurons in the hypothalamus, leading to increased susceptibility of offspring to eating disorders. To this end, the study will employ experimental models including hypothalamic neurons and microglia derived from stem cells, organotypic brain cultures, as well as animal models—mouse and Drosophila.

In parallel, translational validation of the findings will be conducted by analyzing lipids and non-coding RNAs present in human milk as potential mediators of signaling between maternal metabolism and offspring brain development. As a result, the project will deepen our understanding of the molecular and cellular mechanisms underlying eating disorders and enable the identification of potential metabolic targets and optimal intervention windows to reduce their risk.

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