Type 2 Diabetes Mellitus: A Potentially Modifiable Risk Factor for Neurochemical Brain Changes in Bipolar Disorders

Hájek, Tomáš; Calkin, Cynthia; Blagdon, Ryan; Slaney, Claire; Alda, Martin

doi:10.1016/j.biopsych.2013.11.007

Cited by 44 publications

(35 citation statements)

References 75 publications

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“…In other words, are the putative neurodegenerative changes attributable to BD per se, or are they a consequence of commonly comorbid illnesses or lifestyle sequelae? A recent study reported that BD subjects with type 2 diabetes and/or insulin resistance had lower NAA and creatine levels in the PFC than euglycemic BD subjects and healthy controls (Hajek et al, 2013). Potentially consistent with these data, Bond et al (2013) found a correlation between elevated body mass index (BMI) and GM/WM reductions in the multiple brain regions including the mPFC, in recovered first-episode mania patients but not healthy controls.…”

Section: Future Directionsmentioning

confidence: 69%

Neuropathological and neuromorphometric abnormalities in bipolar disorder: View from the medial prefrontal cortical network

Savitz

Price

Drevets

2014

Neuroscience & Biobehavioral Reviews

125

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Section: Future Directionsmentioning

confidence: 69%

Neuropathological and neuromorphometric abnormalities in bipolar disorder: View from the medial prefrontal cortical network

Savitz

Price

Drevets

2014

Neuroscience & Biobehavioral Reviews

125

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“…There is replicated evidence suggesting that repeated episodes of illness, 8,9 comorbid conditions, 13,14 or exposure to medications [10][11][12] introduce heterogeneity, which may mask/overcome the primary changes indicative of the risk for BD. 16,18,19 The findings emphasize the need to control for clinical heterogeneity, for example by recruiting unaffected participants at high genetic risk for the illness.…”

Section: J Psychiatry Neurosci 2015;40(5)mentioning

confidence: 99%

“…For example, brain changes in patients with bipolar disorders (BD) may represent biological markers of BD, but also the consequences of illness episodes, 8,9 exposure to medications [10][11][12] or comorbid conditions. 13,14 The changes secondary to illness burden, medication exposure or comorbid conditions have limited diagnostic potential as they occur only later in the course of BD. The biomarkers that could be used diagnostically (i.e., the brain changes that reflect the susceptibility for BD) are typically small.…”

Section: Introductionmentioning

confidence: 99%

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Hájek

Cooke

Kopeček

et al. 2015

jpn

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IntroductionThe diagnostic system in psychiatry continues to be based on behavioural symptoms rather than biomarkers. This complicates clinical work and research as it introduces marked hetero geneity. Neuroimaging has the unique ability to noninvasively investigate brain structure and function. Yet, the diag nostic promise of neuroimaging in psychiatry has not been fully realized. Brain imaging studies have shown replicated evidence for neuroanatomical changes in groups of participants with psychiatric disorders relative to controls. However, these statistical group differences have low specificity and sensitivity and thus are of limited diagnostic use on the level of individual participants. 1-3The problem of low sensitivity and specificity may be overcome by novel methods of neuroimaging analyses, such as machine learning (ML).1,2 Traditional methods of MRI data analysis focus on relatively large, localized and spatially segregated patterns of between-group differences. 4 In contrast, the multivariate ML techniques target patterns of relatively minor alterations distributed throughout the whole brain, 1 which may better characterize the abnormalities found in individuals with psychiatric disorders.5 These techniques bring neuroimaging analyses to the level of individual participants and potentially allow for their diagnostic use.The use of neuroimaging for diagnostic purposes in psychiatry is further complicated by clinical heterogeneity.6,7 Not all neuroimaging findings in psychiatric patients are of diagnostic use. For example, brain changes in patients with bipolar disorders (BD) may represent biological markers of BD, but also the consequences of illness episodes, 8,9 exposure to medications 10-12 or comorbid conditions. 13,14 The changes 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 mo...

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“…According to Hajek DM-T2 can itself cause an effect on the brain causing mental illness [27]. Hajek stated DM-T2 frequently co-occurs with bipolar affective disorder.…”

Section: Review Findingsmentioning

confidence: 99%

A Service Improvement Opportunity for Pharmacist Independent Prescribers? A Literature Review Examining the Relationship Between Poor Diabetes Control and the Co-Existence of Mental Health Issues

Bibi

Morrissey

Ball

2019

Int J Curr Pharm Sci

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Diabetes and mental illness are clinically managed by diverse pathways. However, an association between the two has been observed and evidence is growing that when poorly addressed, therapy adherence is low and outcomes are poor. To date, no intervention has been shown to provide sustained improvements in adherence, outcome, quality of life or provider cost savings. This literature review was undertaken as a foundation to a research project to examine whether there is an opportunity for current independent prescriber pharmacists, with or without further training, could as part of a primary care team, contribute their expertise to achieving better and sustainable outcomes for these conditions, where current treatment models are heavily medication-dependent. It reinforces the idea that these conditions are interlinked but by poorly understood mechanisms and suggests that a new approach is required in order to improve outcomes for this complex patient group.

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Type 2 Diabetes Mellitus: A Potentially Modifiable Risk Factor for Neurochemical Brain Changes in Bipolar Disorders

Cited by 44 publications

References 75 publications

Neuropathological and neuromorphometric abnormalities in bipolar disorder: View from the medial prefrontal cortical network

Neuropathological and neuromorphometric abnormalities in bipolar disorder: View from the medial prefrontal cortical network

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

A Service Improvement Opportunity for Pharmacist Independent Prescribers? A Literature Review Examining the Relationship Between Poor Diabetes Control and the Co-Existence of Mental Health Issues

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