ABSTRACT
Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases 'type 2 diabetes, coronary artery disease, hypertension' and/or for the risk factors 'blood pressure, obesity, plasma lipid levels, insulin and glucose related traits'. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and 'depression' or 'depressive disorder' or 'depressive symptoms' or 'bipolar disorder' or 'lithium treatment response in bipolar disorder', or 'serotonin reuptake inhibitors treatment response in major depression'. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.
Subject(s)
Bipolar Disorder/genetics , Coronary Artery Disease/genetics , Depressive Disorder, Major/genetics , Diabetes Mellitus, Type 2/genetics , Hypertension/genetics , Obesity/genetics , Antimanic Agents/therapeutic use , Bipolar Disorder/drug therapy , Blood Pressure/genetics , Depression/genetics , Depressive Disorder, Major/drug therapy , Genome-Wide Association Study , Glucose/metabolism , Humans , Insulin , Lipid Metabolism/genetics , Lithium Compounds/therapeutic use , Selective Serotonin Reuptake Inhibitors/therapeutic useABSTRACT
Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview.
Subject(s)
Bipolar Disorder/psychology , Prodromal Symptoms , Psychotic Disorders/psychology , Schizophrenia, Paranoid/psychology , Adolescent , Bayes Theorem , Bipolar Disorder/metabolism , Bipolar Disorder/physiopathology , Child , Cohort Studies , Disease Progression , Electroencephalography , Fatty Acids/metabolism , Female , Humans , Male , Membrane Lipids/metabolism , Odds Ratio , Oxidative Stress , Probability , Psychotic Disorders/metabolism , Psychotic Disorders/physiopathology , ROC Curve , Risk , Risk Assessment , Schizophrenia, Paranoid/metabolism , Schizophrenia, Paranoid/physiopathology , Young AdultABSTRACT
Clathrin-mediated endocytosis (CME) is the best-characterized mechanism governing cellular membrane and protein trafficking. In this hypothesis review, we integrate recent evidence implicating CME and related cellular trafficking mechanisms in the pathophysiology of psychotic disorders such as schizophrenia and bipolar disorder. The evidence includes proteomic and genomic findings implicating proteins and genes of the clathrin interactome. Additionally, several important candidate genes for schizophrenia, such as dysbindin, are involved in processes closely linked to CME and membrane trafficking. We discuss that key aspects of psychosis neuropathology such as synaptic dysfunction, white matter changes and aberrant neurodevelopment are all influenced by clathrin-dependent processes, and that other cellular trafficking mechanisms previously linked to psychoses interact with the clathrin interactome in important ways. Furthermore, many antipsychotic drugs have been shown to affect clathrin-interacting proteins. We propose that the targeted pharmacological manipulation of the clathrin interactome may offer fruitful opportunities for novel treatments of schizophrenia.
