A polygenic score for schizophrenia predicts glycemic control
Schizophrenia is substantially comorbid with type 2 diabetes (T2D), but the molecular basis of this effect is incompletely understood. Here, we show that a cortical schizophrenia expression score predicts glycemic control from pancreatic islet cell expression. We used machine learning to identify a...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
18 December 2017
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| In: |
Translational Psychiatry
Year: 2017, Volume: 7, Issue: 12, Pages: 1-9 |
| ISSN: | 2158-3188 |
| DOI: | 10.1038/s41398-017-0044-z |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1038/s41398-017-0044-z Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41398-017-0044-z 
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| Author Notes: | Han Cao, Junfang Chen, Andreas Meyer-Lindenberg, Emanuel Schwarz |
| Summary: | Schizophrenia is substantially comorbid with type 2 diabetes (T2D), but the molecular basis of this effect is incompletely understood. Here, we show that a cortical schizophrenia expression score predicts glycemic control from pancreatic islet cell expression. We used machine learning to identify a cortical expression signature in 212 schizophrenia patients and controls, which explained ~25% of the illness-associated variance. The algorithm was predicted in expression data from 51 subjects (9 with T2D), explained up to 26.3% of the variance in the glycemic control indicator HbA1c and could significantly differentiate T2D patients from controls. The cross-tissue prediction was driven by processes previously linked to diabetes. Genes contributing to this prediction were involved in the electron transport chain as well as kidney development and support oxidative stress as a molecular process underlying the comorbidity between both conditions. Together, the present results suggest a molecular commonality between schizophrenia and glycemic markers of type 2 diabetes. |
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| Item Description: | Gesehen am 16.04.2018 |
| Physical Description: | Online Resource |
| ISSN: | 2158-3188 |
| DOI: | 10.1038/s41398-017-0044-z |