SchizophreniaGenetic & Genomic MedicinePeer-reviewed journal article
Genomic insights into the comorbidity between type 2 diabetes and schizophrenia.
Ana Luiza ArrudaiD, Golam Mohammed KhandakeriD, Andrew P. MorrisiD, George Davey SmithiD, Laura M. HuckinsiD, Eleftheria ZegginiiD
Abstract
Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify high-confidence effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.
- organism: human
- assay: bulk RNA-seq
- assay: multiome
- research focus: GWAS / association mapping
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 6
- FWCI
- 1.59
- Field percentile
- top 19%
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