Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness—ENIGMA study in 2436 individuals

McWhinney, Sean R.; Abé, Christoph; Alda, Martin; Benedetti, Francesco; Bøen, Erlend; Bonnín, Caterina del Mar; Borgers, Tiana; Canales-Rodríguez, Erick Jorge; Cannon, Dara M.; Dannlowski, Udo; Díaz-Zuluaga, Ana M.; Dietze, Lorielle M F; Elvsåshagen, Torbjørn; Eyler, Lisa T.; Fullerton, Janice M.; Goikolea, José Manuel; Goltermann, Janik; Grotegerd, Dominik; Haarman, Bartholomeus C M; Hahn, Tim; Howells, Fleur M.; Ingvar, Martin; Kircher, Tilo; Krug, Axel; Kuplicki, Rayus; Landén, Mikael; Lemke, Hannah; Liberg, Benny; López‐Jaramillo, Carlos; Malt, Ulrik Fredrik; Martyn, Fiona; Mazza, Elena; McDonald, Colm; McPhilemy, Genevieve; Meier, Sandra; Meinert, Susanne; Meller, Tina; Melloni, Elisa; Mitchell, Philip B.; Nabulsi, Leila; Nenadić, Igor; Ophoff, Roel A.; Overs, Bronwyn; Pfarr, Julia‐Katharina; Pineda-Zapata, Julián A; Pomarol‐Clotet, Edith; Raduà, Joaquim; Repple, Jonathan; Richter, Maike; Ringwald, Kai G.; Roberts, Gloria; Ross, Alex; Salvador, Raymond; Savitz, Jonathan; Schmitt, Simon; Schofield, Peter R.; Sim, Kang; Stein, Dan J.; Stein, Frederike; Temmingh, Henk; Thiel, Katharina; Thomopoulos, Sophia I.; Haren, Neeltje E.M. van; Gestel, Holly Van; Vargas, Cristian; Vieta, Eduard; Vreeker, Annabel; Waltemate, Lena; Yatham, Lakshmi N.; Ching, Christopher R. K.; Andreassen, Ole A.; Thompson, Paul M.; Hájek, Tomáš

doi:10.1111/bdi.13172

Cited by 9 publications

(15 citation statements)

References 60 publications

(137 reference statements)

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“…The ENIGMA-BD Working Group aims to improve replication and generalizability of neuroimaging studies of BD by combining existing, independently collected neuroimaging samples of BD from around the world (Ching et al, 2022;Hibar et al, 2018Hibar et al, , 2016McWhinney et al, 2021aMcWhinney et al, , 2022aNunes et al, 2020). Seventeen independently collected ENIGMA-BD samples from 13 countries on six continents contributed individual-level structural MRI data, medication information, specifically medications used at the time of scanning for the following medication categories (lithium, first-, second-generation antipsychotics, anticonvulsants, antidepressants), and body mass index (BMI) values from a total of 1231 individuals with BD and 1601 healthy controls.…”

Section: Participating Sitesmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Participating Sitesmentioning

confidence: 99%

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

et al. 2023

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“…We found that there were no strictly separate clusters in brain imaging data and that the boundaries between BD and controls were not clear, i.e. many controls fell into the cluster together with BD individuals, while some BD individuals clustered with controls (McWhinney, Abé, et al, 2022 ). The cluster assignment of individuals in part depended on continuous variables including age and BMI and effectively resulted in categorizing of these continuous variables, which is not optimal.…”

Section: Introductionmentioning

confidence: 84%

“…It is increasingly clear that brain alterations in severe mental illnesses (SMI) are multifactorial. Aside from the diagnosis, they also reflect the effects of additional clinical factors, including medications (Hajek et al, 2012 ; McWhinney, Abé, et al, 2022 ; Van Gestel et al, 2019 ), and comorbid psychiatric or physical conditions, such as obesity (McWhinney, Abé, et al, 2021 ; McWhinney, Brosch, et al, 2022 ; McWhinney, Kolenic, et al, 2021 ) and diabetes (Hajek et al, 2014 , 2016 ). Understanding the brain changes in SMI and translating these findings into clinical settings requires sensitive and replicable methods that link patterns of brain alterations to system‐level variables.…”

Section: Introductionmentioning

confidence: 99%

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

McWhinney,

Hlinka,

Bakstein

et al. 2024

Human Brain Mapping

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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.

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“…It is well established that obesity is a risk factor for mood disorders and vice versa [50][51][52]. In addition, obesity is consistently associated with cognitive impairment [53] and neurostructural alterations [54][55][56][57]. There is some evidence suggesting that weight loss may improve brain structure [58] which could also positively impact cognitive functions [59] and/or psychiatric symptoms [60].…”

Section: Discussionmentioning

confidence: 99%

Are the antidepressant effects of insulin-sensitizing medications related to improvements in metabolic markers?

2022

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Insulin-sensitizing medications were originally used in psychiatric practice to treat weight gain and other metabolic side effects that accompany the use of mood stabilizers, antipsychotics, and some antidepressants. However, in recent studies these medications have been shown to cause improvement in depressive symptoms, creating a potential new indication outside of metabolic regulation. However, it is still unclear whether the antidepressant properties of these medications are associated with improvements in metabolic markers. We performed a systematic search of the literature following PRISMA guidelines of studies investigating antidepressant effects of insulin-sensitizing medications. We specifically focused on whether any improvements in depressive symptoms were connected to the improvement of metabolic dysfunction. Majority of the studies included in this review reported significant improvement in depressive symptoms following treatment with insulin-sensitizing medications. Nine out of the fifteen included studies assessed for a correlation between improvement in symptoms and changes in metabolic markers and only two of the nine studies found such association, with effect sizes ranging from R2 = 0.26–0.38. The metabolic variables, which correlated with improvements in depressive symptoms included oral glucose tolerance test, fasting plasma glucose and glycosylated hemoglobin following treatment with pioglitazone or metformin. The use of insulin-sensitizing medications has a clear positive impact on depressive symptoms. However, it seems that the symptom improvement may be unrelated to improvement in metabolic markers or weight. It is unclear which additional mechanisms play a role in the observed clinical improvement. Some alternative options include inflammatory, neuroinflammatory changes, improvements in cognitive functioning or brain structure. Future studies of insulin-sensitizing medications should measure metabolic markers and study the links between changes in metabolic markers and changes in depression. Additionally, it is important to use novel outcomes in these studies, such as changes in cognitive functioning and to investigate not only acute, but also prophylactic treatment effects.

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Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness—ENIGMA study in 2436 individuals

Cited by 9 publications

References 60 publications

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

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

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

Are the antidepressant effects of insulin-sensitizing medications related to improvements in metabolic markers?

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