The association of kynurenine pathway metabolites with symptom severity and clinical features of bipolar disorder: An overview

Background. The balance between neurotoxic and neuroprotective effects of kynurenine pathway (KP) components has been recently proposed as a key element in the pathophysiology of bipolar disorder (BD) and related mood episodes. This comprehensive overview explored the link of KP with symptom severity and other clinical features of BD. Methods. We searched Medline, Embase, and PsycInfo electronic databases for studies assessing the association of peripheral and/or central concentrations of KP metabolites with putative clinical features, including symptom severity and other clinical domains in BD. Results. We included the findings of 13 observational studies investigating the possible variations of KP metabolites according to symptom severity, psychotic features, suicidal behaviors, and sleep disturbances in BD. Studies testing the relationship between KP metabolites and depression severity generated mixed and inconsistent findings. No statistically significant correlations with manic symptoms were found. Moreover, heterogeneous variations of the KP across different clinical domains were shown. Few available studies found (a) higher levels of cerebrospinal fluid kynurenic acid and lower of plasma quinolinic acid in BD with psychotic features, (b) lower central and peripheral picolinic acid levels in BD with suicide attempts, and (c) no significant correlations between KP metabolites and BD-related sleep disturbances. Conclusions. An imbalance of KP metabolism toward the neurotoxic branches is likely to occur in people with BD, though evidence on variations according to specific clinical features of BD is less clear. Additional research is needed to clarify the role of KP in the etiopathogenesis of BD and related clinical features

The Functional DRD3 Ser9Gly Polymorphism (rs6280) Is Pleiotropic, Affecting Reward as Well as Movement

Abnormalities of motivation and behavior in the context of reward are a fundamental component of addiction and mood disorders. Here we test the effect of a functional missense mutation in the dopamine 3 receptor (DRD3) gene (ser9gly, rs6280) on reward-associated dopamine (DA) release in the striatum. Twenty-six healthy controls (HCs) and 10 unmedicated subjects with major depressive disorder (MDD) completed two positron emission tomography (PET) scans with [11C]raclopride using the bolus plus constant infusion method. On one occasion subjects completed a sensorimotor task (control condition) and on another occasion subjects completed a gambling task (reward condition). A linear regression analysis controlling for age, sex, diagnosis, and self-reported anhedonia indicated that during receipt of unpredictable monetary reward the glycine allele was associated with a greater reduction in D2/3 receptor binding (i.e., increased reward-related DA release) in the middle (anterior) caudate (p<0.01) and the ventral striatum (p<0.05). The possible functional effect of the ser9gly polymorphism on DA release is consistent with previous work demonstrating that the glycine allele yields D3 autoreceptors that have a higher affinity for DA and display more robust intracellular signaling. Preclinical evidence indicates that chronic stress and aversive stimulation induce activation of the DA system, raising the possibility that the glycine allele, by virtue of its facilitatory effect on striatal DA release, increases susceptibility to hyperdopaminergic responses that have previously been associated with stress, addiction, and psychosis

Association of concussion history with psychiatric symptoms, limbic system structure, and kynurenine pathway metabolites in healthy, collegiate-aged athletes

Psychiatric outcomes are commonly observed in individuals with repeated concussions, though their underlying mechanism is unknown. One potential mechanism linking concussion with psychiatric symptoms is inflammation-induced activation of the kynurenine pathway, which is thought to play a role in the pathogenesis of mood disorders. Here, we investigated the association of prior concussion with multiple psychiatric-related outcomes in otherwise healthy male and female collegiate-aged athletes (N = 212) with varying histories of concussion recruited from the community. Specially, we tested the hypotheses that concussion history is associated with worse psychiatric symptoms, limbic system structural abnormalities (hippocampal volume, white matter microstructure assessed using neurite orientation dispersion and density imaging; NODDI), and elevations in kynurenine pathway (KP) metabolites (e.g., Quinolinic acid; QuinA). Given known sex-effects on concussion risk and recovery, psychiatric outcomes, and the kynurenine pathway, the moderating effect of sex was considered for all analyses. More concussions were associated with greater depression, anxiety, and anhedonia symptoms in female athletes (ps ≤ 0.005) and greater depression symptoms in male athletes (p = 0.011). More concussions were associated with smaller bilateral hippocampal tail (ps &lt; 0.010) and left hippocampal body (p &lt; 0.001) volumes across male and female athletes. Prior concussion was also associated with elevations in the orientation dispersion index (ODI) and lower intracellular volume fraction in several white matter tracts including the in uncinate fasciculus, cingulum-gyrus, and forceps major and minor, with evidence of female-specific associations in select regions. Regarding serum KP metabolites, more concussions were associated with elevated QuinA in females and lower tryptophan in males (ps ≤ 0.010). Finally, serum levels of QuinA were associated with elevated ODI (male and female athletes) and worse anxiety symptoms (females only), while higher ODI in female athletes and smaller hippocampal volumes in male athletes were associated with more severe anxiety and depression symptoms (ps ≤ 0.05). These data suggest that cumulative concussion is associated with psychiatric symptoms and limbic system structure in healthy athletes, with increased susceptibility to these effects in female athletes. Moreover, the associations of outcomes with serum KP metabolites highlight the KP as one potential molecular pathway underlying these observations.</p

Trends in Suicidology: Personality as an Endophenotype for Molecular Genetic Investigations

In studying the genetics of suicide, should personality be used as an endophenotype (an intermediate trait lying somewhere on the developmental pathway from genes to phenotype)

Translational studies in the complex role of neurotransmitter systems in anxiety and anxiety disorders

Discovery of innovative anxiolytics is severely hampering. Existing anxiolytics are developed decades ago and are still the therapeutics of choice. Moreover, lack of new drug targets forecasts a severe jeopardy in the future treatment of the huge population of CNS-diseased patients. We simply lack the knowledge on what is wrong in brains of anxious people (normal and diseased). Translational research, based on interacting clinical and preclinical research, is extremely urgent. In this endeavor, genetic and genomic approaches are part of the spectrum of contributing factors. We focus on three druggable targets: serotonin transporter, 5-HT1A, and GABAA receptors. It is still uncertain whether and how these targets are involved in normal and diseased anxiety processes. For serotonergic anxiolytics, the slow onset of action points to indirect effects leading to plasticity changes in brain systems leading to reduced anxiety. For GABAA benzodiazepine drugs, acute anxiolytic effects are found indicating primary mechanisms directly influencing anxiety processes. Close translational collaboration between fundamental academic and discovery research will lead to badly needed breakthroughs in the search for new anxiolytics.</p

Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles&nbsp; and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated&nbsp;by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression

Mega-analysis of association between obesity and cortical morphology in bipolar disorders:ENIGMA study in 2832 participants

Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD