Obesity, dyslipidemia and brain age in first-episode psychosis

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Prediction of chronological age from resting-state EEG power in the first three years of life

The infant brain undergoes rapid and significant developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging data can provide insights into typical and atypical brain development. We utilized longitudinal resting-state EEG data from 457 typically developing infants, comprising 938 recordings, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 of 0.82 and a mean absolute error of 92.4 days. Aperiodic offset and periodic theta, alpha, and beta power were identified as key predictors of age via Shapley values. Application of the model to EEG data from infants later diagnosed with autism spectrum disorder or Down syndrome revealed significant underestimations of chronological age. This study establishes the feasibility of using EEG to assess brain maturation in early childhood and supports its potential as a clinical tool for early identification of alterations in brain development.

Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis

BACKGROUND AND HYPOTHESIS

Brain development/aging is not uniform across individuals,spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or agingmap onto specific symptom facets.

STUDY DESIGN

Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms.

STUDY RESULTS

ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity.

CONCLUSIONS

Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.

Dissecting unique and common variance across body and brain health indicators using age prediction

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

Alterations of the gut microbiota in patients with schizophrenia

Introduction

Schizophrenia is a complex psychiatric disorder, of which molecular pathogenesis remains largely unknown. Accumulating evidence suggest that gut microbiota may affect brain function via the complex gut-brain axis, which may be a potential contributor to schizophrenia. However, the alteration of gut microbiota showed high heterogeneity across different studies. Therefore, this study aims to identify the consistently altered gut microbial taxa associated with schizophrenia.

Methods

We conducted a systematic search and synthesis of the up-to-date human gut microbiome studies on schizophrenia, and performed vote counting analyses to identify consistently changed microbiota. Further, we investigated the effects of potential confounders on the alteration of gut microbiota.

Results

We obtained 30 available clinical studies, and found that there was no strong evidence to support significant differences in α-diversity and β-diversity between schizophrenic patients and healthy controls. Among 428 differential gut microbial taxa collected from original studies, we found that 8 gut microbial taxa were consistently up-regulated in schizophrenic patients, including Proteobacteria, Gammaproteobacteria, Lactobacillaceae, Enterobacteriaceae, Lactobacillus, Succinivibrio, Prevotella and Acidaminococcus. While 5 taxa were consistently down-regulated in schizophrenia, including Fusicatenibacter, Faecalibacterium, Roseburia, Coprococcus and Anaerostipes.

Discussion

These findings suggested that gut microbial changes in patients with schizophrenia were characterized by the depletion of anti-inflammatory butyrate-producing genera, and the enrichment of certain opportunistic bacteria genera and probiotics. This study contributes to further understanding the role of gut microbiota in schizophrenia, and developing microbiota-based diagnosis and therapy for schizophrenia.

A Blood-Based Lipid Profile Associated With Hippocampal Volume and Brain Resting-State Activation Within Obese Adults from the UK Biobank

Objectives: Obesity and dyslipidemia may be associated with hippocampal alterations and may increase the risk of neurodegeneration. This study studied hippocampal anatomical and functional association with a lipid profile based on high-density lipoprotein, low-density lipoprotein, and triglyceride related to dyslipidemia in obese and nonobese adults. A whole-brain analysis was also conducted to examine the effect of dyslipidemia on resting-state function across the brain. Participants and Methods: In total, 553 UK Biobank participants comprised three groups based on body mass index (BMI) rankings: obese adults with high BMI (OHigh, n = 184, 32.7 kg/m2 ≤ BMI ≤53.4 kg/m2), obese adults with a lower BMI (OLow, n = 182, 30.3 kg/m2 ≤ BMI ≤32.6 kg/m2), and nonobese controls (n = 187). Structural MRI and functional MRI data were accessed. The fractional amplitude of low-frequency fluctuations (fALFFs) maps was calculated to reflect resting-state brain activity. A lipid health factor was created using principal component analysis. Linear models tested for associations between the lipid health score and hippocampal MRI readouts. Results: With a higher lipid health factor corresponding to a lower dyslipidemia risk, we found a positive correlation between hippocampal volume with the lipid health factor exclusively in group OLow (p = 0.01). We also found a positive association between the lipid health factor and hippocampal fALFF in group OHigh (p = 0.02). Additional fALFF voxel-wise analysis to group OHigh also implicated that the premotor cortex, amygdala, thalamus, subcallosal cortex, temporal fusiform cortex, and middle temporal gyrus brain regions are related with lipid. Conclusion: The study finds novel associations among circulating lipid, hippocampal structure, and hippocampal function exclusively in the obese adults.

Isolating the impact of antipsychotic medication on metabolic health: Secondary analysis of a randomized controlled trial of antipsychotic medication versus placebo in antipsychotic medication naïve first-episode psychosis (the STAGES study)

BACKGROUND

Cardiovascular and metabolic diseases are the leading contributors to the early mortality associated with psychotic disorders. To date, it has not been possible to disentangle the effect of medication and non-medication factors on the physical health of people with a first episode of psychosis (FEP). This study aimed to isolate the effects of antipsychotic medication on anthropometric measurements, fasting glucose and lipids.

METHODS

This study utilized data from a triple-blind randomized placebo-controlled trial comparing two groups of antipsychotic-naïve young people with a FEP who were randomized to receive a second-generation antipsychotic medication (FEP-medication group) or placebo (FEP-placebo group) for 6 months. Twenty-seven control participants were also recruited.

RESULTS

Eighty-one participants commenced the trial; 69.1% completed at least 3 months of the intervention and 33.3% completed the full 6 months. The FEP-placebo group gained a mean of 2.4 kg (±4.9) compared to 1.1 kg (±4.9) in the control participants (t = 0.76, p = .45). After controlling for multiple analyses, there was no difference in blood pressure, waist circumference or heart rate between the FEP-placebo group and controls. After 6 months, the FEP medication group had gained 4.1 kg (±4.5), higher than those receiving placebo but not statistically significant (t = 0.8, p = .44). There were no differences in fasting glucose or lipids between the FEP groups after 3 months.

CONCLUSIONS

While limited by small numbers and high attrition, these findings indicate that some of the metabolic complications observed in psychotic disorders could be attributable to factors other than medication. This emphasizes the need to deliver physical health interventions early in the course of FEP.

The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity

Background

Obesity negatively impacts multiple bodily systems, including the central nervous system. Retrospective studies that estimated chronological age from neuroimaging have found accelerated brain aging in obesity, but it is unclear how this estimation would be affected by weight loss following a lifestyle intervention.

Methods

In a sub-study of 102 participants of the Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS) trial, we tested the effect of weight loss following 18 months of lifestyle intervention on predicted brain age based on magnetic resonance imaging (MRI)-assessed resting-state functional connectivity (RSFC). We further examined how dynamics in multiple health factors, including anthropometric measurements, blood biomarkers, and fat deposition, can account for changes in brain age.

Results

To establish our method, we first demonstrated that our model could successfully predict chronological age from RSFC in three cohorts (n=291;358;102). We then found that among the DIRECT-PLUS participants, 1% of body weight loss resulted in an 8.9 months' attenuation of brain age. Attenuation of brain age was significantly associated with improved liver biomarkers, decreased liver fat, and visceral and deep subcutaneous adipose tissues after 18 months of intervention. Finally, we showed that lower consumption of processed food, sweets and beverages were associated with attenuated brain age.

Conclusions

Successful weight loss following lifestyle intervention might have a beneficial effect on the trajectory of brain aging.

Funding

The German Research Foundation (DFG), German Research Foundation - project number 209933838 - SFB 1052; B11, Israel Ministry of Health grant 87472511 (to I Shai); Israel Ministry of Science and Technology grant 3-13604 (to I Shai); and the California Walnuts Commission 09933838 SFB 105 (to I Shai).

Metabolic profile in first episode drug naïve patients with psychosis and its relation to cognitive functions and social cognition: a case control study

1st episode drug naïve patients with psychosis might be at higher risk for cardiometabolic disturbances which could affect the different cognitive, and executive functions and domains of social cognition. This study aimed to study the metabolic parameters in 1st episode drug naïve patients with psychosis, to evaluate the relation of these cardiometabolic domains to the cognitive, executive functions, and social cognition. Socio-demographic characteristics of 150 first episode drug naïve patients with psychosis and 120 matched healthy control groups were collected. The current study also assessed the cardiometabolic profile and cognitive functions in both groups. Social cognition was examined by Edinburgh Social Cognition Test. The study revealed a statistically significant difference in parameters of metabolic profile among the studied groups (p < 0.001*), the scores of cognitive and executive tests were statistically significantly different (p < 0.001*). In addition, the patient's group has lowered scores of domains of social cognition (p < 0.001*). Also, the mean affective theory of mind was negatively correlated with the conflict cost of the Flanker test (r = -.185* p value = .023). The total cholesterol level (r = - 0.241**, p value = .003) and level of triglycerides (r = - 0.241**, p value = 0.003) were negatively correlated with the interpersonal domain of social cognition, the total cholesterol level is positively correlated to the total score of social cognition (r = 0.202*, p value = 0.013). Patients with 1st episode drug naïve psychosis showed disturbed cardiometabolic parameters which have deleterious effects on cognitive functions and social cognition.

Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies

Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psychotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was obtained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature.

Halting the Metabolic Complications of Antipsychotic Medication in Patients with a First Episode of Psychosis: How Far Can We Go with the Mediterranean Diet? A Pilot Study

Patients with first-episode psychosis (FEP) often adopt unhealthy dietary patterns, with a risk of weight gain and metabolic and cardiovascular disease. In 21 FEP patients receiving nutritional intervention based on the Mediterranean diet (MedDiet), we explored differences in anthropometric and biometric parameters, according to their antipsychotic (AP) medication: AP1, associated with a lower risk, or AP2, associated with a higher risk of weight gain and metabolic complications. The blood biochemical profile was recorded before and after dietary intervention, and dietary habits and body composition were monitored for six months. Following intervention, all of the patients recorded significant increases in the consumption of fruit and vegetables and decreases in red meat and poultry consumption, with closer adherence to the MedDiet and a reduction in the daily intake of calories, carbohydrates, and sodium. Vegetable consumption and energy, protein, and carbohydrate intake were lower in AP1 patients than in AP2 patients. There was no significant weight gain overall. A reduction was demonstrated in total and LDL cholesterol, sodium, urea, and iron (lower in AP1 patients). It was evident that AP medication affected blood levels of lipids, urea, and iron of FEP patients, but MedDiet nutritional intervention led to a significant improvement in their eating habits, with a restriction in weight gain and a decrease in blood sodium and urea.

Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry

It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.

The age of violence: Mapping brain age in psychosis and psychopathy

Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.

Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.

Associations between BMI and brain structures involved in food intake regulation in first-episode schizophrenia spectrum disorders and healthy controls

Structural brain differences have been described in first-episode schizophrenia spectrum disorders (FES), and often overlap with those evident in the metabolic syndrome (MetS). We examined the associations between body mass index (BMI) and brain structures involved in food intake regulation in minimally treated FES patients (n = 117) compared to healthy controls (n = 117). The effects of FES diagnosis, BMI and their interactions on our selected prefrontal cortical thickness and subcortical gray matter volume regions of interest (ROIs) were investigated with hierarchical multivariate regressions, followed by post-hoc regressions for the individual ROIs. In a secondary analysis, we examined the relationships of other MetS risk factors and psychopathology with the brain ROIs. Both illness and BMI significantly predicted the grouped prefrontal cortical thickness ROIs, whereas only BMI predicted the grouped subcortical volume ROIs. For the individual ROIs, schizophrenia diagnosis predicted thinner left and right frontal pole and right lateral OFC thickness, and increased BMI predicted thinner left and right caudal ACC thickness. There were no significant main or interaction effects for diagnosis and BMI on any of the individual subcortical volume ROIs. Secondary analyses suggest associations between several brain ROIs and individual MetS risk factors, but not with psychopathology. Our findings indicate differential, independent effects for FES diagnosis and BMI on brain structures. Limited evidence suggests that the BMI effects are more prominent in FES. Exploratory analyses suggest associations between other MetS risk factors and some brain ROIs.

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

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.

Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood.

Impact of Weight Loss on Brain Age: Improved Brain Health Following Bariatric Surgery

Individuals living with obesity tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM) measurements. We used three distinct datasets to perform this study: 1) CamCAN dataset to train the brain age prediction model, 2) Human Connectome Project (HCP) dataset to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n=87, age: 44.0±9.2 years, BMI: 43.9±4.2 kg/m2) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. As expected, our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (T-value = 7.08, p-value < 0.0001). We also found significant improvement in brain health, indicated by a decrease of 2.9 and 5.6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p-value < 0.0005 for both). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions (mainly in somatomotor, visual, and ventral attention networks) at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (T-value_BMI=4.29, T-value_SBP=4.67, T-value_DBP=4.12, T-value_HOMA-IR=3.16, all p-values < 0.05). In conclusion, these results suggest that obesity-related brain health abnormalities (as measured by delta age) might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations.

Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins

Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.

Mind the gap: Performance metric evaluation in brain-age prediction

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R² ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R² values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy-also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

•

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions.

•

We investigated adipose tissue distribution from body magnetic resonance imaging (MRI) in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI).

•

The results indicated older-appearing brains in people with higher measures of adipose tissue, and accelerated ageing over the course of the study period in people with higher measures of adipose tissue.

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18–94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19–86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) – the difference between actual age and the prediction of the brain’s biological age – at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.

Schizophrenia Patients With Prevotella-Enterotype Have a Higher Risk of Obesity

Recent studies have indicated the critical influence of gut microbiota on the occurrence of obesity. There is a significant risk of obesity in people with schizophrenia. This work proposed that the disorder of gut microbiota in patients with schizophrenia was based on microbial enterotypes. Ninety-seven patients with schizophrenia and 69 matched health controls were eligible. The fresh feces of all the subjects were collected and used to complete 16S rRNA sequence. Statistical analysis was performed to identify the intestinal type of gut microbiota and analyze their potential effects on metabolic function. The patients with enterotype-P had a higher BMI than that of the others. Several differences in the gut microbes of enterotype-P were found between the patients and the controls. Proteobacteria and Firmicutes had significantly higher abundance in the patients' group with enterotype-P. The Bacteroidetes had higher abundance in health controls with enterotype-P. Different metabolic pathways of the microbiota with the enterotype-P were identified in the subjects categorized in different BMI intervals. The schizophrenia patients had a significantly higher BMI than that of health controls. The patients with enterotype-P had a higher BMI. Therefore, the enterotype-P might have a critical influence on a variety of metabolic pathways to disturb the metabolism of glucose and lipid in human body.

Brain age in mood and psychotic disorders: a systematic review and meta-analysis

OBJECTIVE

To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders.

METHODS

A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder.

RESULTS

A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals.

CONCLUSION

Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.

Association between lipid metabolism and cognitive function in patients with schizophrenia

BACKGROUND

The association between blood lipids and cognitive function in schizophrenia is still controversial. Thus, the present study aimed to verify the association between various lipid parameters and cognitive impairment in schizophrenic patients and potential lipid pathways.

METHODS

A total of 447 adult inpatients with schizophrenia were divided into cognitive normal and cognitive impairment groups based on the Mini-Mental State Examination with a cut-off of 26. The blood lipid parameters were defined as abnormal levels based on the guideline. The liquid chromatography-mass spectrometry method was used to preliminarily explore the potential lipid metabolism pathway associated with cognitive impairment.

RESULTS

There were 368 (82.3%) patients who had cognitive impairment. Herein, apolipoprotein B was positively associated with cognitive function in overall patients and age (≥45 and <45 years) and sex subgroups. After excluding patients with hypertension and diabetes, ApoB was still significantly associated with cognitive function in all the patients. The associations between other lipid parameters, including non-high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglyceride, and cognitive impairment were heterogeneous in age and sex subgroups. In contrast, total cholesterol and apolipoprotein A1 were not significantly associated with cognitive impairment. Metabolomics analysis showed that metabolic pathway mainly involved sphingolipid metabolism. Meanwhile, sphinganine and 3-dehydrosphinganine were positively correlated with lipid parameters and decreased in patients with cognitive impairment as compared to those with normal cognition.

CONCLUSIONS

The present study suggests a positive association between lipids and cognitive function in schizophrenic patients and needs to be further verified by a prospective study.

The effect of Alzheimer's disease risk factors on brain aging in normal Chineses: Cognitive aging and cognitive reserve

Aging has been recognized as a major driving force of the Alzheimer's disease's (AD) progression, however, the relationship between brain aging and AD is still unclear. There is also a lack of studies investigating the influence of AD risk factors on brain aging in cognitively normal people. Here, the "Brain Age Gap Estimation" (BrainAGE) framework was applied to investigate the effects of AD risk factors on individual brain aging. Across a total of 165 cognitively normal elderly subjects, although no significant difference was observed in the BrainAGE scores among the three groups, AD risk dose (i.e., the number of AD risk factors) is tend to associated with an increased BrainAGE scores (high-risk > middle risk > low risk). Female exhibited more advanced brain aging (P = 0.004), and higher education years were associated with preserved brain aging (P < 0.001). APOE-ɛ4 (P = 0.846) and family history (FH) of dementia (P = 0.209) did not increase BrainAGE scores. When comparing 52 aMCI patients with 38 cognitively normal controls from ADNI dataset, aMCI patients showed significantly increased BrainAGE scores. BrainAGE scores were negatively correlated with CSF Aβ42 levels in the aMCI group (r = -0.275, P = 0.048). With an accuracy of 68.9%, BrainAGE outperformed APOE-ɛ4 and hippocampus gray matter volume (GMV) in predicting aMCI. In conclusion, AD is independently associated with structural changes in the brain that reflect advanced aging. Potentially, BrainAGE combined with APOE-ɛ4 and hippocampus GMV could be used as a pre-screening tool in early-stage AD.

Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report

OBJECTIVES

Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response.

METHODS

Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data.

RESULTS

Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD.

CONCLUSION

We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.

Machine learning for brain age prediction: Introduction to methods and clinical applications

The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.

Factors associated with brain ageing - a systematic review

Background

Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing.

Methods

This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible.

Results

A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable.

Conclusion

This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets.

Trial registration

A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02331-4.

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study

BACKGROUND

Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learning and structural magnetic resonance imaging (MRI) to study the impact of psychotic illness and obesity on brain ageing/neuroprogression shortly after illness onset.

METHODS

We acquired 2 prospective MRI scans on average 1.61 years apart in 183 FEP and 155 control individuals. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants and calculated BrainAGE by subtracting chronological from the estimated brain age.

RESULTS

Individuals with FEP had a higher initial BrainAGE than controls (3.39 ± 6.36 vs 1.72 ± 5.56 years; β = 1.68, t(336) = 2.59, P = .01), but similar annual rates of brain ageing over time (1.28 ± 2.40 vs 1.07±1.74 estimated years/actual year; t(333) = 0.93, P = .18). Across both cohorts, greater baseline body mass index (BMI) predicted faster brain ageing (β = 0.08, t(333) = 2.59, P = .01). For each additional BMI point, the brain aged by an additional month per year. Worsening of functioning over time (Global Assessment of Functioning; β = -0.04, t(164) = -2.48, P = .01) and increases especially in negative symptoms on the Positive and Negative Syndrome Scale (β = 0.11, t(175) = 3.11, P = .002) were associated with faster brain ageing in FEP.

CONCLUSIONS

Brain alterations in psychosis are manifest already during the first episode and over time get worse in those with worsening clinical outcomes or higher baseline BMI. As baseline BMI predicted faster brain ageing, obesity may represent a modifiable risk factor in FEP that is linked with psychiatric outcomes via effects on brain structure.

Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia

Schizophrenia patients show signs of accelerated aging in cognitive and physiological domains. Both schizophrenia and accelerated aging, as measured by MRI brain images and epigenetic clocks, are correlated with increased mortality. However, the association between these aging measures have not yet been studied in schizophrenia patients. In schizophrenia patients and healthy subjects, accelerated aging was assessed in brain tissue using a longitudinal MRI (N = 715 scans; mean scan interval 3.4 year) and in blood using two epigenetic age clocks (N = 172). Differences ('gaps') between estimated ages and chronological ages were calculated, as well as the acceleration rate of brain aging. The correlations between these aging measures as well as with polygenic risk scores for schizophrenia (PRS; N = 394) were investigated. Brain aging and epigenetic aging were not significantly correlated. Polygenic risk for schizophrenia was significantly correlated with brain age gap, brain age acceleration rate, and negatively correlated with DNAmAge gap, but not with PhenoAge gap. However, after controlling for disease status and multiple comparisons correction, these effects were no longer significant. Our results imply that the (accelerated) aging observed in the brain and blood reflect distinct biological processes. Our findings will require replication in a larger cohort.

Accelerated brain aging predicts impulsivity and symptom severity in depression

Multiple structural and functional neuroimaging measures vary over the course of the lifespan and can be used to predict chronological age. Accelerated brain aging, as quantified by deviations in the MRI-based predicted age with respect to chronological age, is associated with risk for neurodegenerative conditions, bipolar disorder, and mortality. Whether age-related changes in resting-state functional connectivity are accelerated in major depressive disorder (MDD) is unknown, and, if so, it is unclear if these changes contribute to specific cognitive weaknesses that often occur in MDD. Here, we delineated age-related functional connectivity changes in a large sample of normal control subjects and tested whether brain aging is accelerated in MDD. Furthermore, we tested whether accelerated brain aging predicts individual differences in cognitive function. We trained a support vector regression model predicting age using resting-state functional connectivity in 710 healthy adults aged 18-89. We applied this model trained on normal aging subjects to a sample of actively depressed MDD participants (n = 109). The difference between predicted brain age and chronological age was 2.11 years greater (p = 0.015) in MDD patients compared to control participants. An older MDD brain age was significantly associated with increased impulsivity and, in males, increased depressive severity. Unexpectedly, accelerated brain aging was also associated with increased placebo response in a sham-controlled trial of high-frequency repetitive transcranial magnetic stimulation targeting the dorsomedial prefrontal cortex. Our results indicate that MDD is associated with accelerated brain aging, and that accelerated aging is selectively associated with greater impulsivity and depression severity.

Prevalence of obesity and clinical and metabolic correlates in first-episode schizophrenia relative to healthy controls

OBJECTIVE

People with schizophrenia exhibit a high obesity rate. However, little is known about the prevalence of obesity and its relationship with clinical symptoms and metabolic indicators in first-episode drug-naïve (FEDN) schizophrenia.

METHODS

Demographic and lipid parameters were gathered from 297 FEDN schizophrenia and 325 healthy controls. The patients' symptomatology was evaluated by the Positive and Negative Syndrome Scale (PANSS).

RESULTS

The obesity rate of FEDN patients was 10.77%, similar to that of controls (10.5%). The prevalence of overweight plus obesity of patients was 44.8%, significantly higher than that of controls (36.6%). Compared with non-obese patients, obese patients had higher levels of cholesterol (4.81 ± 0.93 vs 4.22 ± 1.00 mmol/L), triglyceride (0.27 ± 0.21 vs 0.14 ± 0.24 mg/dL), low-density lipoprotein (0.48 ± 0.12 vs 0.40 ± 0.12 mg/dL), greater ratio of triglyceride/high-density lipoprotein (2.01 ± 1.23 vs 1.44 ± 1.26), and higher PANSS positive symptom subscale score (29.81 ± 6.29 vs 27.05 ± 6.15), general psychopathology subscale score (70.75 ± 11.74 vs 66.87 ± 11.37), and total score (149.81 ± 21.08 vs 140.64 ± 21.58), but lower high-density lipoprotein level (1.09 ± 0.21 vs 1.27 ± 0.34 mg/dL) (all p < 0.05). Correlation analysis revealed that body mass index (BMI) was positively correlated with triglyceride, cholesterol, high-density lipoprotein, low-density lipoprotein, triglyceride/high-density lipoprotein ratio, PANSS positive symptoms, general psychopathology, and total scores (all p < 0.05, r = 0.124 ~ 0.335). Multiple regression analysis confirmed that PANSS positive symptoms, total score, and cholesterol level were significantly associated with BMI (all p < 0.05, β: 0.126-0.162).

CONCLUSION

There was no significant difference in the prevalence of obesity between FEDN patients and the control group. Moreover, BMI was positively associated with positive symptom severity in FEDN patients.

Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability

Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians. Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site. Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model. Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.

Cognitive ability and metabolic physical health in first-episode psychosis

Cognitive impairments are a core feature of first-episode psychosis (FEP), arising before illness onset and antipsychotic exposure. Individuals with chronic psychosis experience poorer physical health while taking antipsychotic medication, but health disparities may be evident at FEP onset, prior to antipsychotic exposure. Given the links between cognition and physical health in healthy populations, the aim was to explore whether cognition and physical health are associated in FEP, which could inform early physical health interventions for cognition in FEP. Participants were aged 15 to 25 and included 86 individuals experiencing FEP with limited antipsychotic exposure and duration of untreated psychosis of ≤six months, and 43 age- and sex-matched controls. Individuals with FEP performed significantly poorer than controls in most cognitive domains (Cohen's d = 0.38 to 1.59). Groups were similar in metabolic health measures, excluding a significantly faster heart rate in FEP (d = 0.68). Through hierarchical regression analyses, we found that in the overall sample, BMI was negatively related to current IQ after controlling for education and group (FEP/control). Relationships between BMI and cognition were consistent across the FEP and healthy control groups. In FEP, current IQ and working memory were negatively correlated with lipid profiles. Findings suggest that in FEP, impaired cognition is exhibited earlier than physical health problems, and that compared to controls, similar relationships with cognition are demonstrated. Causal pathways and trajectories of relationships between health and cognition in FEP require investigation, especially as antipsychotic medications are introduced. The findings have implications for cognitive and health interventions.

Sex-specific differences in the association between body mass index and brain aging in young adults: Findings from the human connectome project

BACKGROUND

This report evaluated sex-specific differences in the association between brain aging and body mass index (BMI) in young adults using the publicly available data from the Human Connectome Project (HCP).

METHODS

Participants of HCP with available structural imaging and BMI data were included [n = 1112; mean age = 28.80 (SD = 3.70); mean BMI = 26.53 (SD = 5.20); males n = 507, females n = 605]. Predicted brain age was generated using raw T1-weighted MRI scan and a Gaussian Processes regression model. The difference (Δ aging) between brain age predicted by structural imaging and chronological age was computed. A linear regression model was used with Δ aging as the dependent variable, and sex, BMI, and BMI-by-sex interaction as independent variables of interest, and race, ethnicity, income, and education as covariates.

RESULTS

There was a significant BMI-by-sex interaction for Δ aging (p = 0.041). Higher BMI was associated with greater brain aging in both sexes. However, this association was substantially stronger in males (β = 0.215; SE = 0.050; p < 0.0001) than in females (β = 0.122; SE = 0.035; p = 0.0005).

CONCLUSION

We found evidence suggesting that higher BMI is associated with greater brain aging in adults. Furthermore, the association between higher BMI and greater brain aging was stronger in males than in females. Future studies are needed to explore the mechanistic pathways that link higher BMI to greater brain aging and whether weight-loss interventions, such as exercise, can reverse higher BMI-associated greater brain aging.

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R ² = 0.88; support vector regression, MAE = 4.42 years, R ² = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R ² = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders

Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict "healthy" brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.

Physical health interventions for patients who have experienced a first episode of psychosis: a narrative review

Objectives:

Service users with severe psychiatric illnesses, such as schizophrenia, major depressive disorder and bipolar disorder, are more likely to suffer from ill health. There is evidence that lifestyle interventions, for example, exercise, dietary advice and smoking cessation programmes for service users with severe mental illness can be of health benefit. This review was carried out to identify the literature pertaining to physical health interventions for service users who have experienced a first-episode psychosis (FEP), to examine the nature of the interventions which were carried out and to assess these interventions in terms of feasibility and efficacy.

Methods:

A narrative review was conducted in August 2019 by searching ‘Pubmed’ and ‘Embase’ electronic databases. Studies investigating the effect a physical health intervention had on service users who had experienced a FEP were included in the review.

Results:

Fifteen studies met inclusion criteria: 12 quantitative studies and 3 qualitative. Exercise, dietary advice, smoking cessation and motivational coaching were some of the physical health interventions utilised in the identified studies. Positive effects were seen in terms of physical health markers wherever they were investigated, particularly when the intervention was delivered early. The impact on psychiatric symptoms and longer-term impacts on health were less frequently assessed.

Conclusions:

Physical health interventions have a positive impact on service users who have experienced a FEP. More research is warranted in this area in Ireland. These studies should include controls, have longer follow-up periods and should assess the impact on psychiatric health.

Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression

Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain-age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ² = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ² = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ² = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ² = 5.89, p = 0.0152) and working memory performance (Wald χ² = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ² = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.

Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors

The brain-age paradigm is proving increasingly useful for exploring aging-related disease and can predict important future health outcomes. Most brain-age research uses structural neuroimaging to index brain volume. However, aging affects multiple aspects of brain structure and function, which can be examined using multimodality neuroimaging. Using UK Biobank, brain-age was modeled in n = 2205 healthy people with T1-weighted MRI, T2-FLAIR, T2∗, diffusion-MRI, task fMRI, and resting-state fMRI. In a held-out healthy validation set (n = 520), chronological age was accurately predicted (r = 0.78, mean absolute error = 3.55 years) using LASSO regression, higher than using any modality separately. Thirty-four neuroimaging phenotypes were deemed informative by the regression (after bootstrapping); predominantly gray-matter volume and white-matter microstructure measures. When applied to new individuals from UK Biobank (n = 14,701), significant associations with multimodality brain-predicted age difference (brain-PAD) were found for stroke history, diabetes diagnosis, smoking, alcohol intake and some, but not all, cognitive measures (corrected p < 0.05). Multimodality neuroimaging can improve brain-age prediction, and derived brain-PAD values are sensitive to biomedical and lifestyle factors that negatively impact brain and cognitive health.

•

Brain-age was predicted from 6 different neuroimaging modalities.

•

Combined multi-modality brain-age was more accurate than any single modality.

•

Thirty-four neuroimaging measures were informative for brain-age prediction.

•

Informative measures generally reflect brain volume and white-matter microstructure.

•

Brain-age was associated with stroke, diabetes, smoking, alcohol and cognition.

Metabolic Parameters in Patients with Prader-Willi Syndrome and DiGeorge Syndrome with Respect to Psychopathological Manifestation

PURPOSE

The purpose of our study was to compare the metabolic parameters in two genetic syndromes with a proven high risk of developing psychiatric comorbidities. These comorbidities, especially mood and psychotic disorders, may be associated with a risk of obesity, type 2 diabetes and other components of metabolic syndrome regardless of antipsychotic treatment.

PATIENTS AND METHODS

Two groups of children diagnosed with Prader - Willi syndrome (PWS) (n = 20) and DiGeorge syndrome (DGS) (n = 18), aged 7-18 years, were enrolled. Behavioral symptoms and co-occurring psychopathological symptoms were assessed using the Child Behavior Checklist (CBCL). The levels of following biochemical parameters were measured: glucose, insulin, high-sensitivity C-reactive protein, total cholesterol, low- and high-density lipoproteins (LDL and HDL), triglycerides and non-HDL cholesterol. Additionally, the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was calculated.

RESULTS

There were significantly higher levels of insulin and non-HDL in patients with PWS compared to those with DGS. The scores of four CBCL subscales (social problems, thought problems, delinquent behavior and aggressive behavior) were significantly higher in PWS patients. Higher scores of the CBCL-thought problems were associated with significantly higher levels of insulin as well as HOMA-IR.

CONCLUSION

Patients with PWS seem to be more prone to develop subclinical metabolic dysregulation, in terms of elevated non-HDL levels and insulin levels, compared to DGS patients. Altered insulin sensitivity, present in both groups, even though it is not a specific risk factor, might be related to thought problems associated with psychosis.

Higher Body-Mass Index and Lower Gray Matter Volumes in First Episode of Psychosis

BACKGROUND

Neurostructural alterations are often reported in first episode of psychosis (FEP), but there is heterogeneity in the direction and location of findings between individual studies. The reasons for this heterogeneity remain unknown. Obesity is disproportionately frequent already early in the course of psychosis and is associated with smaller brain volumes. Thus, we hypothesized that obesity may contribute to brain changes in FEP.

METHOD

We analyzed MRI scans from 120 participants with FEP and 114 healthy participants. In primary analyses, we performed voxel-based morphometry (VBM) with small volume corrections to regions associated with FEP or obesity in previous meta-analyses. In secondary analyses, we performed whole-brain VBM analyses.

RESULTS

In primary analyses, we found that when controlling for BMI, FEP had lower GM volume than healthy participants in a) left fronto-temporal region (pTFCE = 0.008) and b) left postcentral gyrus (pTFCE = 0.043). When controlling for FEP, BMI was associated with lower GM volume in left cerebellum (pTFCE < 0.001). In secondary analyses, we found that when controlling for BMI, FEP had lower GM volume than healthy participants in the a) cerebellum (pTFCE = 0.004), b) left frontal (pTFCE = 0.024), and c) right temporal cortex (pTFCE = 0.031). When controlling for FEP, BMI was associated with lower GM volume in cerebellum (pTFCE = 0.004). Levels of C-reactive protein, HDL and LDL-cholesterol correlated with obesity related neurostructural alterations.

CONCLUSIONS

This study suggests that higher BMI, which is frequent in FEP, may contribute to cerebellar alterations in schizophrenia. As previous studies showed that obesity-related brain alterations may be reversible, our findings raise the possibility that improving the screening for and treatment of obesity and associated metabolic changes could preserve brain structure in FEP.

Brain age in bipolar disorders: Effects of lithium treatment

OBJECTIVE

Bipolar disorders increase the risk of dementia and show biological and brain alterations, which resemble accelerated aging. Lithium may counter some of these processes and lower the risk of dementia. However, until now no study has specifically investigated the effects of Li on brain age.

METHODS

We acquired structural magnetic resonance imaging scans from 84 participants with bipolar disorders (41 with and 43 without Li treatment) and 45 controls. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants, and calculated BrainAGE by subtracting chronological from the estimated brain age.

RESULTS

BrainAGE was significantly greater in non-Li relative to Li or control participants, F(2, 125) = 10.22, p < 0.001, with no differences between the Li treated and control groups. The estimated brain age was significantly higher than the chronological age in the non-Li (4.28 ± 6.33 years, matched t(42) = 4.43, p < 0.001), but not the Li-treated group (0.48 ± 7.60 years, not significant). Even Li-treated participants with partial prophylactic treatment response showed lower BrainAGE than the non-Li group, F(1, 64) = 4.80, p = 0.03.

CONCLUSIONS

Bipolar disorders were associated with greater, whereas Li treatment with lower discrepancy between brain and chronological age. These findings support the neuroprotective effects of Li, which were sufficiently pronounced to affect a complex, multivariate measure of brain structure. The association between Li treatment and BrainAGE was independent of long-term thymoprophylactic response and thus may generalize beyond bipolar disorders, to neurodegenerative disorders.

Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging

Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the BrainAGE (brain age estimation gap) framework allows machine-learning based detection of a gap between age estimated from high-resolution MRI scans an chronological age, and thus an indicator of systems-level accelerated aging. We analysed 3T high-resolution structural MRI scans in 38 major depression patients (without co-morbid axis I or II disorders) and 40 healthy controls using the BrainAGE method to test the hypothesis of accelerated aging in (non-psychotic) major depression. We found no significant difference (or trend) for elevated BrainAGE in this pilot sample. Unlike previous findings in schizophrenia (and partially bipolar disorder), unipolar depression per se does not seem to be associated with accelerated aging patterns across the brain. However, given the limitations of the sample, further study is needed to test for effects in subgroups with comorbidities, as well as longitudinal designs.

Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?

With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The "Brain Age Gap Estimation (BrainAGE)" method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the BrainAGE method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future, BrainAGE and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.

Accelerating research on biological aging and mental health: Current challenges and future directions

Aging is associated with complex biological changes that can be accelerated, slowed, or even temporarily reversed by biological and non-biological factors. This article focuses on the link between biological aging, psychological stressors, and mental illness. Rather than comprehensively reviewing this rapidly expanding field, we highlight challenges in this area of research and propose potential strategies to accelerate progress in this field. This effort requires the interaction of scientists across disciplines - including biology, psychiatry, psychology, and epidemiology; and across levels of analysis that emphasize different outcome measures - functional capacity, physiological, cellular, and molecular. Dialogues across disciplines and levels of analysis naturally lead to new opportunities for discovery but also to stimulating challenges. Some important challenges consist of 1) establishing the best objective and predictive biological age indicators or combinations of indicators, 2) identifying the basis for inter-individual differences in the rate of biological aging, and 3) examining to what extent interventions can delay, halt or temporarily reverse aging trajectories. Discovering how psychological states influence biological aging, and vice versa, has the potential to create novel and exciting opportunities for healthcare and possibly yield insights into the fundamental mechanisms that drive human aging.