Co-shared genetics and possible risk gene pathway partially explain the comorbidity of schizophrenia, major depressive disorder, type 2 diabetes, and metabolic syndrome.
Teodor T PostolacheLaura Del Bosque-PlataSerge JabbourMichael VergareRongling WuClaudia GragnoliPublished in: American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics (2019)
Schizophrenia (SCZ) and major depressive disorder (MDD) in treatment-naive patients are associated with increased risk for type 2 diabetes (T2D) and metabolic syndrome (MetS). SCZ, MDD, T2D, and MetS are often comorbid and their comorbidity increases cardiovascular risk: Some risk genes are likely co-shared by them. For instance, transcription factor 7-like 2 (TCF7L2) and proteasome 26S subunit, non-ATPase 9 (PSMD9) are two genes independently reported as contributing to T2D and SCZ, and PSMD9 to MDD as well. However, there are scarce data on the shared genetic risk among SCZ, MDD, T2D, and/or MetS. Here, we briefly describe T2D, MetS, SCZ, and MDD and their genetic architecture. Next, we report separately about the comorbidity of SCZ and MDD with T2D and MetS, and their respective genetic overlap. We propose a novel hypothesis that genes of the prolactin (PRL)-pathway may be implicated in the comorbidity of these disorders. The inherited predisposition of patients with SCZ and MDD to psychoneuroendocrine dysfunction may confer increased risk of T2D and MetS. We illustrate a strategy to identify risk variants in each disorder and in their comorbid psychoneuroendocrine and mental-metabolic dysfunctions, advocating for studies of genetically homogeneous and phenotype-rich families. The results will guide future studies of the shared predisposition and molecular genetics of new homogeneous endophenotypes of SCZ, MDD, and metabolic impairment.
Keyphrases
- major depressive disorder
- bipolar disorder
- genome wide
- type diabetes
- metabolic syndrome
- copy number
- transcription factor
- genome wide identification
- end stage renal disease
- cardiovascular disease
- chronic kidney disease
- gene expression
- glycemic control
- mental health
- machine learning
- big data
- uric acid
- cardiovascular risk factors
- weight loss
- data analysis
- replacement therapy