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.

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.

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.

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.

The consequences for the COVID-19 pandemic in the newborns of affected mothers remains unknown. Previous clinical experiences with other infections during pregnancy lead to considered pregnant women and their offspring especially vulnerable for SARS-COV-2. That is, the underlying physiopathological changes caused by the infection (e.g. storm of cytokines, micro-coagulation in placenta or vertical transmission) could clearly compromise fetal neurodevelopment.

To analyze the impact of maternal SARS-COV-2 infection during pregnancy in early neurodevelopment of infants gestated during the COVID-19 pandemic period compared to those gestated immediately prior (2017-2021).

212 pregnant women (14% infected) were followed throughout their pregnancy and postpartum, including newborn development. SARS-COV-2 infection was serologically confirmed during pregnancy. The Brazelton Neonatal Assessment Scale (NBAS) was administered at 6 weeks old by a trained neonatologist to evaluate neurological, social and behavioral aspects of newborn’s functioning. Differences in NBAS scores between cases and controls were tested by ANOVAs. All the analysis were adjusted for maternal age, sociodemographic status, anxious-depressive symptomatology, infant’s sex and gestational age at birth and NBAS, and for the period of gestation (previous or during COVID-19 pandemic).

NBAS social interactive dimension was significantly decreased in those infants exposed to prenatal SARS-COV-2 (F=4.248, p=.043), particularly when the infection occurred before the week 20 of gestation. Gestation during COVID-19 pandemic did not alter NBAS subscales.

SARS-COV-2 infection during pregnancy seems to be associated with lower NBAS scores on social dimension in 6 weeks old exposed newborns.

No significant relationships.

Maternal stress during pregnancy influences fetal neurodevelopment, especially by the dysregulation of the HPA axis. However, less is known about whether maltreatment or stressful life experiences previous to pregnancy influence on developmental outcomes in the offspring.

To analyze newborns’ neurobehavioral profiles in a cohort of healthy pregnant women, according to 1) childhood and recent maternal adverse experiences and 2) mother-infant attachment.

150 women were followed during the three trimesters of pregnancy. CTQ and AAT tests were employed to evaluate childhood and recent experiences of maltreatment, while infant and recent adverse experiences were evaluated using ETI-SR and SRSS, respectively. Newborns neurobehavioral profiles were defined at 8 weeks using the Neonatal Behavioral Assessment Scale (NBAS) and their temperament was assessed with IBQ. PBQ and PAI scales were employed to assess mother-infant attachment. A linear regression model was performed, adjusting for possible confounders.

Maternal childhood sexual abuse seems to be associated with greater difficulties in the newborns control of reactivity to external stimuli (β=0,517; p-value=0.001), while recent maternal stressful experiences are related to difficulties for states regulation (β=0,29; p-value=0,038). Regarding attachment, maltreated mothers tend to show ambivalent and avoidant styles. Interestingly, postnatal mother-infant attachment seems to modulate autonomous, motor and social-interactive abilities in the offspring (β=-0,227; p-value=0,033 // β=-0,329; p-value=0,006).

Newborns from mothers exposed to maltreatment and negative life events previous to pregnancy show difficulties to organize and regulate the reactions to psychosocial stimuli. Future studies must disentangle whether maternal attachment style is a modulator of this association.

No significant relationships.

To examine the cross-sectional and longitudinal (2-year follow-up) associations between dietary diversity (DD) and depressive symptoms.

An energy-adjusted dietary diversity score (DDS) was assessed using a validated FFQ and was categorised into quartiles (Q). The variety in each food group was classified into four categories of diversity (C). Depressive symptoms were assessed with Beck Depression Inventory-II (Beck II) questionnaire and depression cases defined as physician-diagnosed or Beck II >= 18. Linear and logistic regression models were used.

Spanish older adults with metabolic syndrome (MetS).

A total of 6625 adults aged 55–75 years from the PREDIMED-Plus study with overweight or obesity and MetS.

Total DDS was inversely and statistically significantly associated with depression in the cross-sectional analysis conducted; OR Q4 v. Q1 = 0·76 (95 % CI (0·64, 0·90)). This was driven by high diversity compared to low diversity (C3 v. C1) of vegetables (OR = 0·75, 95 % CI (0·57, 0·93)), cereals (OR = 0·72 (95 % CI (0·56, 0·94)) and proteins (OR = 0·27, 95 % CI (0·11, 0·62)). In the longitudinal analysis, there was no significant association between the baseline DDS and changes in depressive symptoms after 2 years of follow-up, except for DD in vegetables C4 v. C1 = (β = 0·70, 95 % CI (0·05, 1·35)).

According to our results, DD is inversely associated with depressive symptoms, but eating more diverse does not seem to reduce the risk of future depression. Additional longitudinal studies (with longer follow-up) are needed to confirm these findings.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning.

Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error.

Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose–response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified).

Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.

In young patients, the cause of ischemic stroke (IS) remains often cryptogenic despite presence of traditional vascular risk factors (VRFs). Since arterial hypertension (AH) is considered the most important one, we aimed to evaluate the impact of AH and blood pressure (BP) levels after discharge on risk of recurrent IS (RIS) in young patients.

The study set consisted of acute IS patients < 50 years of age enrolled in the prospective Heart and Ischemic STrOke Relationship studY registered on ClinicalTrials.gov (NCT01541163). Cause of IS was assessed according to the ASCOD classification.

Out of 319 enrolled patients <50 years of age (179 males, mean age 41.1 ± 7.8 years), AH was present in 120 (37.6%) of them. No difference was found in the rates of etiological subtypes of IS between patients with and without AH. Patients with AH were older, had more VRF, used more frequently antiplatelets prior IS, and had more RIS (10 vs. 1%, p = 0.002) during a follow-up (FUP) with median of 25 months. Multivariate logistic regression stepwise model showed the prior use of antiplatelets as only predictor of RIS (p = 0.011, OR: 6.125; 95% CI: 1.510–24.837). Patients with elevated BP levels on BP Holter 1 month after discharge did not have increased rate of RIS during FUP (3.8 vs. 1.7%, p = 1.000).

AH occurred in 37.6% of young IS patients. Patients with AH had more frequently RIS. Prior use of antiplatelets was found only predictor of RIS in young IS patients with AH.