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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.

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.

ObjectivesThe distinction between bipolar I disorder (BD-I) and bipolar II disorder (BD-II) has been a topic of long-lasting debate. This study examined differences between BD-I and BD-II in a large, global sample of OABD, focusing on general functioning, cognition and somatic burden as these domains are often affected in OABD.MethodsCross-sectional analyses were conducted with data from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) database. The sample included 963 participants aged ≥50 years (714 BD-I, 249 BD-II). Sociodemographic and clinical factors were compared between BD subtypes including adjustment for study cohort. Multivariable analyses were conducted with generalized linear mixed models (GLMMs) and estimated associations between BD subtype and (1) general functioning (GAF), (2) cognitive performance (g-score) and (3) somatic burden, with study cohort as random intercept.ResultsAfter adjustment for study cohort, BD-II patients more often had a late onset ≥50 years (p = 0.008) and more current severe depression (p = 0.041). BD-I patients were more likely to have a history of psychiatric hospitalization (p

OBJECTIVES: There is limited information on the characteristics of older adults with bipolar disorder (OABD) treated with lithium, along with safety concerns about its use by older adults. The aim of the present study is to describe the demographic and clinical characteristics of OABD receiving lithium therapy, using data from the Global Ageing & Geriatric Experiments in Bipolar Disorder (GAGE-BD). EXPERIMENTAL PROCEDURES: Cross-sectional analysis of the GAGE-BD dataset to determine differences and similarities between lithium users and non-users. We analysed data from 986 participants aged 50 years or older (mean age 63.5 years; 57.5% females) from 12 study sites. Two subgroups (Lithium; Non-lithium) were defined according to the current prescription of lithium. We compared several outcomes between these groups, controlling for age, gender, and study site. RESULTS: OABD treated with lithium had lower scores on depression rating scales and were less likely to be categorised as with moderate or severe depression. There was a lower proportion of lithium users than non-users among those with evidence of rapid cycling and non-bipolar psychiatric diagnoses. Assessment of global cognitive state and functionality indicated better performance among lithium users. The current use of antipsychotics was less frequent among lithium users, who also reported fewer cardiovascular comorbidities than non-users. CONCLUSION: We found several potentially relevant differences in the clinical profile of OABD treated with lithium compared with those treated with other mood stabilisers. However, the interpretation of the present results must take into account the methodological limitations inherent to the cross-sectional approach and data harmonisation.

OBJECTIVES: Persistent functional impairment is common in bipolar disorder (BD) and is influenced by a number of demographic, clinical, and cognitive features. The goal of this project was to estimate and compare the influence of key factors on community function in multiple cohorts of well-characterized samples of individuals with BD. METHODS: Thirteen cohorts from 7 countries included n = 5882 individuals with BD across multiple sites. The statistical approach consisted of a systematic uniform application of analyses across sites. Each site performed a logistic regression analysis with empirically derived higher versus lower function as the dependent variable and selected clinical and demographic variables as predictors. RESULTS: We found high rates of functional impairment, ranging from 41 to 75%. Lower community functioning was associated with depressive symptoms in 10 of 12 of the cohorts that included this variable in the analysis. Lower levels of education, a greater number of prior mood episodes, the presence of a comorbid substance use disorder, and a greater total number of psychotropic medications were also associated with low functioning. CONCLUSIONS: The bipolar clinical research community is poised to work together to characterize the multi-dimensional contributors to impairment and address the barriers that impede patients complete recovery. We must also identify the core features which enable many to thrive and live successfully with BD. A large-scale, worldwide, prospective longitudinal study focused squarely on BD and its heterogeneous presentations will serve as a platform for discovery and promote major advances toward optimizing outcomes for every individual with this illness.

OBJECTIVE: Some individuals with bipolar disorder (BD) experience manic and depressive symptoms concurrently, but data are limited on symptom mixity in older age bipolar disorder (OABD). Using the Global Aging & Geriatric Experiments in Bipolar Disorder Database, we characterized mixity in OABD and associations with everyday function. METHODS: The sample (n = 805), from 12 international studies, included cases with both mania and depression severity ratings at a single timepoint. Four mixity groups were created: asymptomatic (A), mixed (Mix), depressed only (Dep), and manic only (Man). Generalized linear mixed models used mixity group as the predictor variable; cohort was included as a random intercept. Everyday function was assessed with the Global Assessment of Functioning score. RESULTS: Group proportions were Mix (69.6%; n = 560), followed by Dep (18.4%; n = 148), then A (7.8%; n = 63), then Man (4.2%; n= 34); levels of depression and mania were similar in Mix compared to Dep and Man, respectively. Everyday function was lowest in Mix, highest in A, and intermediate in Man and Dep. Within Mix, severity of depression was the main driver of worse functioning. Groups differed in years of education, with A higher than all others, but did not differ by age, gender, employment status, BD subtype, or age of onset. CONCLUSIONS: Mixed features predominate in a cross-sectional, global OABD sample and are associated with worse everyday function. Among those with mixed symptoms, functional status relates strongly to current depression severity. Future studies should include cognitive and other biological variables as well as longitudinal designs to allow for evaluation of causal effects.

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.

ObjectivesAntipsychotic drugs (APS) are widely used to treat patients with bipolar disorder (BD), but there is limited information in older-age bipolar disorder (OABD). This analysis of the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) investigated characteristics of OABD patients prescribed APS vs. those not prescribed APS.Experimental designThe observational analysis used baseline, cross-sectional data from 16 international studies for adults aged ≥ 50 years with BD comprising 1,007 individuals with mean age 63.2 years (SD = 9.0), 57.4% women, and mean age of onset 31.6 years (SD = 15.0). The dependent variable was current APS treatment status. The independent variables included demographic and clinical variables, and a random effect for study, that were included in generalized mixed models.Principal observations46.6% of individuals (n = 469) were using APS. The multivariate model results suggest that those treated with APS were younger (p = 0.01), less likely to be employed (p < 0.001), had more psychiatric hospitalizations (p = 0.009) and were less likely to be on lithium (p < 0.001). Of individuals on APS, only 6.6% of those (n = 27) were on first-generation antipsychotics (FGAs) and experienced a greater burden of psychiatric hospitalizations (p = 0.012).ConclusionsAPS are widely prescribed in OABD, observed in nearly half of this sample with great variation across sites. Individuals with OABD on APS have more severe illness, more frequent hospitalizations and are more often unemployed vs. those not on APS. Future studies need to examine longitudinal outcomes in OABD prescribed APS to characterize underlying causal relationships.

ObjectiveLiterature on older-age bipolar disorder (OABD) is limited. This first-ever analysis of the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) investigated associations among age, BD symptoms, comorbidity, and functioning.MethodsThis analysis used harmonized, baseline, cross-sectional data from 19 international studies (N = 1377). Standardized measures included the Young Mania Rating Scale (YMRS), Hamilton Depression Rating Scale (HAM-D), Montgomery-Asberg Depression Rating Scale (MADRS), and Global Assessment of Functioning (GAF).ResultsMean sample age was 60.8 years (standard deviation [SD] 12.2 years), 55% female, 72% BD I. Mood symptom severity was low: mean total YMRS score of 4.3 (SD 5.4) and moderate-to-severe depression in only 22%. Controlled for sample effects, both manic and depressive symptom severity appeared lower among older individuals (p's

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

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.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

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  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 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.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Recent diffusion imaging studies using free-water (FW) elimination have shown increased FW in gray matter (GM) and white matter (WM) in first-episode psychosis (FEP) and lower corrected fractional anisotropy (FAt) in WM in chronic schizophrenia. However, little is known about the longitudinal stability and clinical significance of these findings. To determine tissue-specific FW and FAt abnormalities in FEP, as part of a multicenter Spanish study, 132 FEP and 108 healthy controls (HC) were clinically characterized and underwent structural and diffusion-weighted MRI scanning. FEP subjects were classified as schizophrenia spectrum disorder (SSD) or non-SSD. Of these subjects, 45 FEP and 41 HC were longitudinally assessed and rescanned after 2 years. FA and FW tissue-specific measurements were cross-sectional and longitudinally compared between groups using voxel-wise analyses in the skeletonized WM and vertex-wise analyses in the GM surface. SSD and non-SSD subjects showed (a) higher baseline FW in temporal regions and in whole GM average (P.adj(SSD vs HC) = .003, P.adj(Non-SSD vs HC) = .040) and (b) lower baseline FAt in several WM tracts. SSD, but not non-SSD, showed (a) higher FW in several WM tracts and in whole WM (P.adj(SSD vs HC)= .049) and (b) a significant FW decrease over time in temporal cortical regions and in whole GM average (P.adj = .011). Increased extracellular FW in the brain is a reliable finding in FEP, and in SSD appears to decrease over the early course of the illness. FAt abnormalities are stable during the first years of psychosis.

Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr

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Introduction: The IMPACT BCN trial—a parallel‐group randomized clinical trial where 1221 pregnant women at high risk for small‐for‐gestational age (SGA) newborns were randomly allocated at 19‐ to 23‐week gestation into three groups: Mediterranean diet, Mindfulness‐based Stress reduction or non‐intervention—has demonstrated a positive effect of Mediterranean diet and Stress reduction in the prevention of SGA. However, the mechanism of action of these interventions remains still unclear. The aim of this study is to investigate the effect of Mediterranean diet and Stress reduction on placental volume and perfusion. Material and Methods: Participants in the Mediterranean diet group received monthly individual and group educational sessions, and free provision of extra‐virgin olive oil and walnuts. Women in the Stress reduction group underwent an 8‐week Stress reduction program adapted for pregnancy, consisting of weekly 2.5‐h and one full‐day sessions. Non‐intervention group was based on usual care. Placental volume and perfusion were assessed in a subgroup of randomly selected women (n = 165) using magnetic resonance (MR) at 36‐week gestation. Small placental volume was defined as MR estimated volume <10th centile. Perfusion was assessed by intravoxel incoherent motion. Results: While mean MR placental volume was similar among the study groups, both interventions were associated with a lower prevalence of small placental volume (3.9% Mediterranean diet and 5% stress reduction vs. 17% non‐intervention; p = 0.03 and p = 0.04, respectively). Logistic regression showed that small placental volume was significantly associated with higher risk of SGA in both study groups (OR 7.48 [1.99–28.09] in Mediterranean diet and 20.44 [5.13–81.4] in Stress reduction). Mediation analysis showed that the effect of Mediterranean diet on SGA can be decomposed by a direct effect and an indirect effect (56.6%) mediated by a small placental volume. Similarly, the effect of Stress reduction on SGA is partially mediated (45.3%) by a small placental volume. Results on placental intravoxel incoherent motion perfusion fraction and diffusion coefficient were similar among the study groups. Conclusions: Structured interventions during pregnancy based on Mediterranean diet or Stress reduction are associated with a lower proportion of small placentas, which is consistent with the previously observed beneficial effects of these interventions on fetal growth. [ABSTRACT FROM AUTHOR]

Objective: Bipolar Disorder (BD) is a severe mental illness associated with high rates of general medical comorbidity, reduced life expectancy, and premature mortality. Although BD has been associated with high medical hospitalization, the factors that contribute to this risk remain largely unexplored. We used baseline medical and psychiatric records to develop a supervised machine learning model to predict general medical admissions after discharge from psychiatric hospitalization. Methods: In this retrospective three-year cohort study of 71 patients diagnosed with BD (mean age=52.19 years, females=56.33%), lasso regression models combining medical and psychiatric records, as well as those using them separately, were fitted and their predictive power was estimated using a leaveone- out cross-validation procedure. Results: The proportion of medical admissions in patients with BD was higher compared with age- and sex-matched hospitalizations in the same region (25.4% vs. 8.48%). The lasso model fairly accurately predicted the outcome (area under the curve [AUC]=69.5%, 95%C.I.=55--84.1; sensitivity=61.1%, specificity=75.5%, balanced accuracy=68.3%). Notably, pre-existing cardiovascular, neurological, or osteomuscular diseases collectively accounted for more than 90% of the influence on the model. The accuracy of the model based on medical records was slightly inferior (AUC=68.7%, 95%C.I. = 54.6-82.9), while that of the model based on psychiatric records only was below chance (AUC=61.8%, 95% C.I.=46.2--77.4). Conclusion: Our findings support the need to monitor medical comorbidities during clinical decision-making to tailor and implement effective preventive measures in people with BD. Further research with larger sample sizes and prospective cohorts is warranted to replicate these findings and validate the predictive model. [ABSTRACT FROM AUTHOR]