BMIgap: a new tool to quantify brain signatures of weight
Author Information
Author(s): Koutsouleris Nikolaos, Khuntia Adyasha Tejaswi, Popovic David, Sarisik Elif, Buciuman Madalina O., Pedersen Mads L., Westlye Lars T., Andreassen Ole, Meyer-Lindenberg Andreas, Kambeitz Joseph, Salokangas Raimo, Hietala Jarmo, Bertolino Alessandro, Borgwardt Stefan, Brambilla Paolo, Upthegrove Rachel, Wood Stephen, Lencer Rebekka, Meisenzahl Eva, Falkai Peter, Schwarz Emanuel, Wiegand Ariane
Primary Institution: Ludwig-Maximilians-University
Hypothesis
Can individualized BMI prediction models derived from brain imaging help identify individuals at risk for weight gain?
Conclusion
The study proposes BMIgap as a potential brain-derived measure to identify at-risk individuals and improve metabolic risk control.
Supporting Evidence
- Individuals with schizophrenia showed a higher BMIgap compared to healthy controls.
- BMIgap was positively correlated with future weight gain, especially in younger individuals.
- The BMI predictor had a mean absolute error of 2.75 kg/m2 in healthy individuals.
- Lower grey matter volume in specific brain regions was predictive of higher BMI.
- Shared brain patterns of BMI and schizophrenia were linked to illness duration and hospitalization frequency.
Takeaway
Researchers created a new tool called BMIgap to help understand how brain structure relates to weight gain, especially in people with mental health issues.
Methodology
The study used support-vector machines and whole-brain voxel-wise grey matter volume to generate and validate a BMI predictor.
Limitations
The study lacked individuals with very high or low BMI and metabolic markers to further investigate the relationship between brain structure and clinical features.
Participant Demographics
Included healthy individuals aged 15-75 years and patients with schizophrenia, clinical high-risk states for psychosis, and recent-onset depression.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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