OBJECTIVES: The association of bipolar disorder with early and excessive cardiovascular disease was identified over a century ago. Nonetheless, the vascular-bipolar link remains underrecognized, particularly with regard to how this link can contribute to our understanding of pathogenesis and treatment. METHODS: An international group of experts completed a selective review of the literature, distilling core themes, identifying limitations and gaps in the literature, and highlighting future directions to bridge these gaps. RESULTS: The association between bipolar disorder and vascular disease is large in magnitude, consistent across studies, and independent of confounding variables where assessed. The vascular-bipolar link is multifactorial and is difficult to study given the latency between the onset of bipolar disorder, often in adolescence or early adulthood, and subsequent vascular disease, which usually occurs decades later. As a result, studies have often focused on risk factors for vascular disease or intermediate phenotypes, such as structural and functional vascular imaging measures. There is interest in identifying the most relevant mediators of this relationship, including lifestyle (eg, smoking, diet, exercise), medications, and systemic biological mediators (eg, inflammation). Nonetheless, there is a paucity of treatment studies that deliberately engage these mediators, and thus far no treatment studies have focused on engaging vascular imaging targets. CONCLUSIONS: Further research focused on the vascular-bipolar link holds promise for gleaning insights regarding the underlying causes of bipolar disorder, identifying novel treatment approaches, and mitigating disparities in cardiovascular outcomes for people with bipolar disorder.

• Keywords
• atherosclerosis, bipolar disorder, cardiovascular disease, prevention, stroke, vascular,
• MeSH
• Bipolar Disorder * complications   MeSH
• Adult MeSH
• Cardiovascular Diseases * epidemiology   MeSH
• Smoking MeSH
• Humans MeSH
• Adolescent MeSH
• Advisory Committees MeSH
• Risk Factors MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

BACKGROUND: Brain imaging is of limited diagnostic use in psychiatry owing to clinical heterogeneity and low sensitivity/specificity of between-group neuroimaging differences. Machine learning (ML) may better translate neuroimaging to the level of individual participants. Studying unaffected offspring of parents with bipolar disorders (BD) decreases clinical heterogeneity and thus increases sensitivity for detection of biomarkers. The present study used ML to identify individuals at genetic high risk (HR) for BD based on brain structure. METHODS: We studied unaffected and affected relatives of BD probands recruited from 2 sites (Halifax, Canada, and Prague, Czech Republic). Each participant was individually matched by age and sex to controls without personal or family history of psychiatric disorders. We applied support vector machines (SVM) and Gaussian process classifiers (GPC) to structural MRI. RESULTS: We included 45 unaffected and 36 affected relatives of BD probands matched by age and sex on an individual basis to healthy controls. The SVM of white matter distinguished unaffected HR from control participants (accuracy = 68.9%, p = 0.001), with similar accuracy for the GPC (65.6%, p = 0.002) or when analyzing data from each site separately. Differentiation of the more clinically heterogeneous affected familiar group from healthy controls was less accurate (accuracy = 59.7%, p = 0.05). Machine learning applied to grey matter did not distinguish either the unaffected HR or affected familial groups from controls. The regions that most contributed to between-group discrimination included white matter of the inferior/middle frontal gyrus, inferior/middle temporal gyrus and precuneus. LIMITATIONS: Although we recruited 126 participants, ML benefits from even larger samples. CONCLUSION: Machine learning applied to white but not grey matter distinguished unaffected participants at high and low genetic risk for BD based on regions previously implicated in the pathophysiology of BD.

• MeSH
• White Matter anatomy & histology   pathology   MeSH
• Bipolar Disorder diagnosis   genetics   MeSH
• Adult MeSH
• Genetic Predisposition to Disease * MeSH
• Cohort Studies MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Adolescent MeSH
• Young Adult MeSH
• Neuroimaging * MeSH
• Prefrontal Cortex anatomy & histology   pathology   MeSH
• Risk Factors MeSH
• Gray Matter anatomy & histology   pathology   MeSH
• Temporal Lobe anatomy & histology   pathology   MeSH
• Machine Learning * MeSH
• Support Vector Machine MeSH
• Parietal Lobe anatomy & histology   pathology   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH
• Geographicals
• Czech Republic MeSH
• Canada MeSH

Type 2 diabetes mellitus (T2DM) damages the brain, especially the hippocampus, and frequently co-occurs with bipolar disorders (BD). Reduced hippocampal volumes are found only in some studies of BD subjects and may thus be secondary to the presence of certain clinical variables. Studying BD patients with abnormal glucose metabolism could help identify preventable risk factors for hippocampal atrophy in BD. We compared brain structure using optimized voxel-based morphometry of 1.5T MRI scans in 33 BD subjects with impaired glucose metabolism (19 with insulin resistance/glucose intolerance (IR/GI), 14 with T2DM), 15 euglycemic BD participants and 11 euglycemic, nonpsychiatric controls. The group of BD patients with IR, GI or T2DM had significantly smaller hippocampal volumes than the euglycemic BD participants (corrected p=0.02) or euglycemic, nonpsychiatric controls (corrected p=0.004). Already the BD subjects with IR/GI had smaller hippocampal volumes than euglycemic BD participants (t(32)=-3.15, p=0.004). Age was significantly more negatively associated with hippocampal volumes in BD subjects with IR/GI/T2DM than in the euglycemic BD participants (F(2, 44)=9.96, p=0.0003). The gray matter reductions in dysglycemic subjects extended to the cerebral cortex, including the insula. In conclusion, this is the first study demonstrating that T2DM or even prediabetes may be risk factors for smaller hippocampal and cortical volumes in BD. Abnormal glucose metabolism may accelerate the age-related decline in hippocampal volumes in BD. These findings raise the possibility that improving diabetes care among BD subjects and intervening already at the level of prediabetes could slow brain aging in BD.

• MeSH
• Bipolar Disorder complications   metabolism   pathology   MeSH
• Diabetes Mellitus, Type 2 complications   pathology   MeSH
• Adult MeSH
• Insulin Resistance * MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain pathology   MeSH
• Image Processing, Computer-Assisted MeSH
• Glucose Intolerance MeSH
• Cross-Sectional Studies MeSH
• Aging pathology   MeSH
• Organ Size MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH