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

Effects of obesity on aging brain and cognitive decline: A cohort study from the UK Biobank

Objective

To investigate the impact of obesity on brain structure and cognition using large neuroimaging and genetic data.

Methods

Associations between body mass index (BMI), gray matter volume (GMV), whiter matter hyper-intensities (WMH), and fluid intelligence score (FIS) were estimated in 30283 participants from the UK Biobank. Longitudinal data analysis was conducted. Genome-wide association studies were applied to explore the genetic loci associations among BMI, GMV, WMH, and FIS. Mendelian Randomization analyses were applied to further estimate the effects of obesity on changes in the brain and cognition.

Results

The observational analysis revealed that BMI was negatively associated with GMV (r = -0.15, p < 1 × 10-24) and positively associated with WMH (r = 0.08, p < 1 × 10-16). The change in BMI was negatively associated with the change in GMV (r = -0.04, p < 5 × 10-5). Genetic overlap was observed among BMI, GMV, and FIS at SBK1 (rs2726032), SGF29 (rs17707300), TUFM (rs3088215), AKAP6 (rs1051695), IL27 (rs4788084), and SPI1 (rs3740689 and rs935914). The MR analysis provided evidence that higher BMI was associated with lower GMV (β=-1119.12, p = 5.77 ×10-6), higher WMH (β=42.76, p = 6.37 ×10-4), and lower FIS (β=-0.081, p = 1.92 ×10-23).

Conclusions

The phenotypic and genetic association between obesity and aging brain and cognitive decline suggested that weight control could be a promising strategy for slowing the aging brain.

Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis

Introduction

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, brain age research in psychiatry has primarily examined mood and psychotic disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group.

Methods

3D brain s tructural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1,134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effect models examined group differences in brain-PAD and moderation by age.

Results

No significant group difference in brain-PAD manifested ( β diagnosis (SE)=0.37 years (0.43), p =0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH ( β diagnosis-by-age =-0.08 (0.03), pFDR =0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post-hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; β diagnosis =1.20 (0.60), p <0.05, mixed-effects d (95% confidence interval)=0.14 (0.00-0.28)), but not older participants (36-75 years; β diagnosis =0.07 (0.65), p =0.91).

Conclusions

Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post-hoc analyses showed a subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

Brain Age Gap as a Predictor of Early Treatment Response and Functional Outcomes in First-Episode Schizophrenia: A Longitudinal Study: L'écart d'âge cérébral comme prédicteur de la réponse en début de traitement et des résultats fonctionnels dans un premier épisode de schizophrénie : une étude longitudinale

OBJECTIVES

Accelerated brain aging, i.e., the age-related structural changes in the brain appearing earlier than expected from one's chronological age, is a feature that is now well established in schizophrenia. Often interpreted as a feature of a progressive pathophysiological process that typifies schizophrenia, its prognostic relevance is still unclear. We investigate its role in response to antipsychotic treatment in first-episode schizophrenia.

METHODS

We recruited 49 drug-naive patients with schizophrenia who were then treated with risperidone at a standard dose range of 2-6 mg/day. We followed them up for 3 months to categorize their response status. We acquired T1-weighted anatomical images and used the XGboost method to evaluate individual brain age. The brain age gap (BAG) is the difference between the predicted brain age and chronological age.

RESULTS

Patients with FES had more pronounced BAG compared to healthy subjects, and this difference was primarily driven by those who did not respond adequately after 12 weeks of treatment. BAG did not worsen significantly over the 12-week period, indicating a lack of prominent brain-ageing effect induced by the early antipsychotic exposure per se. However, highly symptomatic patients had a more prominent increase in BAG, while patients with higher BAG when initiating treatment later showed lower gains in global functioning upon treatment, highlighting the prognostic value of BAG measures in FES.

CONCLUSIONS

Accelerated brain aging is a feature of first-episode schizophrenia that is more likely to be seen among those who will not respond adequately to first-line antipsychotic use. Given that early poor response indicates later treatment resistance, measuring BAG using structural MRI in the first 12 weeks of treatment initiation may provide useful prognostic information when considering second-line treatments in schizophrenia.

Excess cardiometabolic risk in children and adolescents initiating antipsychotic treatment compared to young adults: results from a nationwide cohort study

Antipsychotic treatment is associated with cardiometabolic risks that may be especially detrimental to children and adolescents. In this Danish population-based cohort study, we included individuals with psychiatric diagnoses who initiated antipsychotics in 2000-2021 at age 6-31 years. We assessed the risk of cardiometabolic adverse events up to 10 years following incident exposure to antipsychotics, compared to age- and sex-matched unexposed individuals with psychiatric diagnoses. Cox regression models were used to calculate hazard ratios (HRs) after adjustment using high-dimensional propensity scores, including age, sex, calendar time, hospital diagnoses, and prescription drug use. HRs were compared between incident exposure in youths (6-17 years) and young adults (18-31 years), and between incident exposure in children (6-11 years) and adolescents (12-17 years). The total cohort consisted of 335,093 individuals, including 36,092 subjects exposed to antipsychotics (children and adolescents: 8,547, mean follow-up: 6.8±3.2 years; young adults: 27,545, mean follow-up: 6.5±3.4 years) and 299,001 age-, sex- and calendar-matched unexposed subjects. The incidence rate of cardiometabolic events was higher for young adults initiating antipsychotics than for children and adolescents (23.2 vs. 14.1 events/1,000 person-years). However, the adjusted excess risk of cardiometabolic events was significantly higher in exposed compared to unexposed children and adolescents (HR=1.87, 95% CI: 1.71-2.05) than in exposed compared to unexposed young adults (HR=1.46, 95% CI: 1.40-1.51) (p<0.001). The excess risk of cardiometabolic events was even higher when antipsychotic treatment was initiated before age 12 years (HR=2.44; 95% CI: 1.99-2.98) than at age 12-17 years (HR=1.69, 95% CI: 1.52-1.87) (p=0.012). Concerning specific cardiometabolic outcomes, there was an effect of age at antipsychotic initiation on the risks of metabolic syndrome (p=0.011) and obesity (p<0.001), that were higher among children and adolescents than young adults. Thus, initiation of antipsychotic treatment before age 18 years is associated with an excess risk of cardiometabolic events compared to age- and sex-matched youths with psychiatric disorders but unexposed to antipsychotics. The excess cardiometabolic risk is significantly higher than that of individuals who start antipsychotic treatment in early adulthood, and significantly higher for treatment onset in childhood compared to adolescence. On the basis of these findings, recommendations are provided about the use of antipsychotics in children and adolescents.

Fasting insulinemia as biomarker of illness relapse in patients with severe mental illness?

Severe Mental Illness (SMI) is often associated with metabolic alteration and/or metabolic syndrome, which may determine an increased mortality due to a further increased cardiovascular risk. The relationship with metabolic syndrome is often bidirectional, resulting in a pathoplastic effect of these dysmetabolisms. Among the several hormones involved, insulin appears to play a key role, albeit not entirely clear. The aim of our real-world cross-sectional observational study is to investigate a set of metabolic biomarkers of illness relapse/recurrence/onset in a cohort of 310 adult SMI inpatients consecutively admitted to the Psychiatry Clinic of the Azienda Ospedaliero Universitaria of Marche, in Ancona (Italy), between February 2021 and February 2024. According to the stepwise multivariate regression model, a higher number of acute episodes per year was positively predicted by the age of illness onset, the lifetime number of suicidal attempts and fasting insulinemia and negatively by the participant's age. A second stepwise multivariate regression model using only the metabolic characteristics as independent variables, found that a higher number of acute episodes per year was predicted positively by the fasting insulinemia and red blood cells and negatively by the abdominal circumference. Overall, our findings could provide practical implications for the treatment and management of SMI patients, emphasizing the importance of monitoring and managing metabolic factors, particularly insulinemia, metabolic syndrome and insulin resistance. Finally, insulinemia could potentially act as metabolic biomarker of illness relapse, though more larger and longitudinal studies should be carried out to confirm these results.

Brain metabolic profiling of schizophrenia: a path towards a better understanding of the neuropathogenesis of psychosis

Schizophrenia (SCZ) is a complex psychotic syndrome whose pathogenesis involves countless protagonists, none of which, to date, can fully explain how this disorder develops. In this narrative review, an overview of the biochemical impairment is offered according to several perspectives. Indeed, the metabolic framework behind SCZ dopaminergic hypotheses, glutamate - gamma-amynobutyric acid dysregulation, norepinephrine and serotonin, calcium channel dysfunction is addressed together with the energetic impairment, involving glucose and lipids in SCZ etiopathogenesis, in order to highlight the multilevel pathways affected in this neuropsychiatric disorder. Furthermore, neuroinflammation is analyzed, by virtue of its important role, widely investigated in recent years, in neurodegeneration. Tracing the neurotransmitter activity at the brain level by assessing the metabolic network behind the abovementioned molecules puts into light as unavoidable the need for future studies to adopt an integrate approach to address SCZ pathological and clinical picture. The combination of all these factors, essential in acquiring an overview on the complexity of SCZ pathophysiology represents a crucial step in the development of a more targeted management of SCZ patients.

Sex differences in the association of overweight with cognitive performance in individuals with first-episode psychosis

Cognitive deficits and overweight are prominent challenges in the treatment of psychosis, which have a direct impact on patients' quality of life. We aim to determine whether there is an association of overweight with cognitive performance and whether there are sex differences in this association. We included 170 individuals with first-episode psychosis (FEP) (mean age 23.08 years, 32.9% females) attending an early intervention service who underwent clinical, biometric, and cognitive assessments by the MATRICS Consensus Cognitive Battery. A set of two-way analyses of covariance (ANCOVAs) were conducted for each cognitive test. Sex, overweight, and their interaction were included as factors. Nearly 34% of the participants were overweight without differences between males and females. The excess of weight did not exert any main effect on cognition; however, overweight females performed significantly worse than non-overweight females in processing speed, verbal learning and memory, reasoning and problem-solving, and global cognitive function, whereas in males, there were no differences. Our findings highlight that sex matters in the study of metabolic and cognitive factors in FEP to develop targeted interventions based on sex perspectives.

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation

BACKGROUND

Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders.

HYPOTHESIS

Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD).

STUDY DESIGN

From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored.

STUDY RESULTS

The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01).

CONCLUSIONS

ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.

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 aging map 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.

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.

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

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

Sex-difference in the association between social drinking, structural brain aging and cognitive function in older individuals free of cognitive impairment

Background

We investigated a potential sex difference in the relationship between alcohol consumption, brain age gap and cognitive function in older adults without cognitive impairment from the population-based Mayo Clinic Study of Aging.

Methods

Self-reported alcohol consumption was collected using the food-frequency questionnaire. A battery of cognitive testing assessed performance in four different domains: attention, memory, language, and visuospatial. Brain magnetic resonance imaging (MRI) was conducted using 3-T scanners (Signa; GE Healthcare). Brain age was estimated using the Brain-Age Regression Analysis and Computational Utility Software (BARACUS). We calculated the brain age gap as the difference between predicted brain age and chronological age.

Results

The sample consisted of 269 participants [55% men (n=148) and 45% women (n=121) with a mean age of 79.2 ± 4.6 and 79.5 ± 4.7 years respectively]. Women had significantly better performance compared to men in memory, (1.12 ± 0.87 vs 0.57 ± 0.89, P<0.0001) language (0.66 ± 0.8 vs 0.33 ± 0.72, P=0.0006) and attention (0.79 ± 0.87 vs 0.39 ± 0.83, P=0.0002) z-scores. Men scored higher in visuospatial skills (0.71 ± 0.91 vs 0.44 ± 0.90, P=0.016). Compared to participants who reported zero alcohol drinking (n=121), those who reported alcohol consumption over the year prior to study enrollment (n=148) scored significantly higher in all four cognitive domains [memory: F3,268 = 5.257, P=0.002, Language: F3,258 = 12.047, P<0.001, Attention: F3,260 = 22.036, P<0.001, and Visuospatial: F3,261 = 9.326, P<0.001] after correcting for age and years of education. In addition, we found a significant positive correlation between alcohol consumption and the brain age gap (P=0.03). Post hoc regression analysis for each sex with language z-score revealed a significant negative correlation between brain age gap and language z-scores in women only (P=0.008).

Conclusion

Among older adults who report alcohol drinking, there is a positive association between higher average daily alcohol consumption and accelerated brain aging despite the fact that drinkers had better cognitive performance compared to zero drinkers. In women only, accelerated brain aging is associated with worse performance in language cognitive domain. Older adult women seem to be vulnerable to the negative effects of alcohol on brain structure and on certain cognitive functions.

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.

The Gaze of Schizophrenia Patients Captured by Bottom-up Saliency

Schizophrenia (SCHZ) notably impacts various human perceptual modalities, including vision. Prior research has identified marked abnormalities in perceptual organization in SCHZ, predominantly attributed to deficits in bottom-up processing. Our study introduces a novel paradigm to differentiate the roles of top-down and bottom-up processes in visual perception in SCHZ. We analysed eye-tracking fixation ground truth maps from 28 SCHZ patients and 25 healthy controls (HC), comparing these with two mathematical models of visual saliency: one bottom-up, based on the physical attributes of images, and the other top-down, incorporating machine learning. While the bottom-up (GBVS) model revealed no significant overall differences between groups (beta = 0.01, p = 0.281, with a marginal increase in SCHZ patients), it did show enhanced performance by SCHZ patients with highly salient images. Conversely, the top-down (EML-Net) model indicated no general group difference (beta = -0.03, p = 0.206, lower in SCHZ patients) but highlighted significantly reduced performance in SCHZ patients for images depicting social interactions (beta = -0.06, p < 0.001). Over time, the disparity between the groups diminished for both models. The previously reported bottom-up bias in SCHZ patients was apparent only during the initial stages of visual exploration and corresponded with progressively shorter fixation durations in this group. Our research proposes an innovative approach to understanding early visual information processing in SCHZ patients, shedding light on the interplay between bottom-up perception and top-down cognition.

Relative brain age is associated with socioeconomic status and anxiety/depression problems in youth

Brain age, a measure of biological aging in the brain, has been linked to psychiatric illness, principally in adult populations. Components of socioeconomic status (SES) associate with differences in brain structure and psychiatric risk across the lifespan. This study aimed to investigate the influence of SES on brain aging in childhood and adolescence, a period of rapid neurodevelopment and peak onset for many psychiatric disorders. We reanalyzed data from the Healthy Brain Network to examine the influence of SES components (occupational prestige, public assistance enrollment, parent education, and household income-to-needs ratio [INR]) on relative brain age (RBA). Analyses included 470 youth (5-17 years; 61.3% men), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Household income was 3.95 ± 2.33 (mean ± SD) times the federal poverty threshold. RBA quantified differences between chronological age and brain age using covariation patterns of morphological features and total volumes. We also examined associations between RBA and psychiatric symptoms (Child Behavior Checklist [CBCL]). Models covaried for sex, scan location, and parent psychiatric diagnoses. In a linear regression, lower RBA is associated with lower parent occupational prestige (p = .01), lower public assistance enrollment (p = .03), and more parent psychiatric diagnoses (p = .01), but not parent education or INR. Lower parent occupational prestige (p = .02) and lower RBA (p = .04) are associated with higher CBCL anxious/depressed scores. Our findings underscore the importance of including SES components in developmental brain research. Delayed brain aging may represent a potential biological pathway from SES to psychiatric risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Central obesity and its association with retinal age gap: insights from the UK Biobank study

BACKGROUND

Conflicting evidence exists on the association between ageing and obesity. Retinal age derived from fundus images has been validated as a novel biomarker of ageing. In this study, we aim to investigate the association between different anthropometric phenotypes based on body mass index (BMI) and waist circumference (WC) and the retinal age gap (retinal age minus chronological age).

METHODS

A total of 35,550 participants with BMI, WC and qualified retinal imaging data available were included to investigate the association between anthropometric groups and retinal ageing. Participants were stratified into 7 different body composition groups based on BMI and WC (Normal-weight/Normal WC, Overweight/Normal WC, Mild obesity/Normal WC, Normal-weight/High WC, Overweight/High WC, Mild obesity/High WC, and Severe obesity/High WC). Linear regression and logistic regression models were fitted to investigate the association between the seven anthropometric groups and retinal age gap as continuous and categorical outcomes, respectively.

RESULTS

A total of 35,550 participants (55.6% females) with a mean age 56.8 ± 8.04 years were included in the study. Individuals in the Overweight/High WC, Mild obesity/High WC and Severe obesity/High WC groups were associated with an increase in the retinal age gap, compared with those in the Normal Weight/Normal WC group (β = 0.264, 95% CI: 0.105-0.424, P =0.001; β = 0.226, 95% CI: 0.082-0.371, P = 0.002; β = 0.273, 95% CI: 0.081-0.465, P = 0.005; respectively) in fully adjusted models. Similar findings were noted in the association between the anthropometric groups and retinal ageing process as a categorical outcome.

CONCLUSION

A significant positive association exists between central obesity and accelerated ageing indexed by retinal age gaps, highlighting the significance of maintaining a healthy body shape.

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

BACKGROUND

Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.

METHODS

We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.

RESULTS

BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.

CONCLUSIONS

We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Mania-related effects on structural brain changes in bipolar disorder - a narrative review of the evidence

Cross-sectional neuroimaging studies show that bipolar disorder is associated with structural brain abnormalities, predominantly observed in prefrontal and temporal cortex, cingulate gyrus, and subcortical regions. However, longitudinal studies are needed to elucidate whether these abnormalities presage disease onset or are consequences of disease processes, and to identify potential contributing factors. Here, we narratively review and summarize longitudinal structural magnetic resonance imaging studies that relate imaging outcomes to manic episodes. First, we conclude that longitudinal brain imaging studies suggest an association of bipolar disorder with aberrant brain changes, including both deviant decreases and increases in morphometric measures. Second, we conclude that manic episodes have been related to accelerated cortical volume and thickness decreases, with the most consistent findings occurring in prefrontal brain areas. Importantly, evidence also suggests that in contrast to healthy controls, who in general show age-related cortical decline, brain metrics remain stable or increase during euthymic periods in bipolar disorder patients, potentially reflecting structural recovering mechanisms. The findings stress the importance of preventing manic episodes. We further propose a model of prefrontal cortical trajectories in relation to the occurrence of manic episodes. Finally, we discuss potential mechanisms at play, remaining limitations, and future directions.

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

Is musical engagement enough to keep the brain young?

Music-making and engagement in music-related activities have shown procognitive benefits for healthy and pathological populations, suggesting reductions in brain aging. A previous brain aging study, using Brain Age Gap Estimation (BrainAGE), showed that professional and amateur-musicians had younger appearing brains than non-musicians. Our study sought to replicate those findings and analyze if musical training or active musical engagement was necessary to produce an age-decelerating effect in a cohort of healthy individuals. We scanned 125 healthy controls and investigated if musician status, and if musical behaviors, namely active engagement (AE) and musical training (MT) [as measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)], had effects on brain aging. Our findings suggest that musician status is not related to BrainAGE score, although involvement in current physical activity is. Although neither MT nor AE subscales of the Gold-MSI are predictive for BrainAGE scores, dispositional resilience, namely the ability to deal with challenge, is related to both musical behaviors and sensitivity to musical pleasure. While the study failed to replicate the findings in a previous brain aging study, musical training and active musical engagement are related to the resilience factor of challenge. This finding may reveal how such musical behaviors can potentially strengthen the brain's resilience to age, which may tap into a type of neurocognitive reserve.

Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis

BACKGROUND

Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown.

METHODS

We assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set.

RESULTS

The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%).

CONCLUSIONS

We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.

Effect of Glucagon-like Peptide-1 Receptor Agonists on Cardio-Metabolic Risk Factors among Obese/Overweight Individuals Treated with Antipsychotic Drug Classes: An Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials

Introduction: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) constitute a drug class primarily developed for the treatment of subjects with type 2 diabetes, although they have also provided significant benefit for subjects with obesity without underlying diabetes. Individuals with psychotic disorders who are receiving antipsychotic treatment are a patient population at risk of developing obesity, which is linked to other metabolic disturbances. Methods: We searched PubMed and the Cochrane Library from inception to 1 December 2022, for randomized controlled trials (RCTs) enrolling obese or overweight adult subjects with an underlying psychotic disorder treated with antipsychotic drugs, randomized either to GLP-1RAs or a control. We set as the primary efficacy outcome the change in body weight and as secondary efficacy outcomes the change in body mass index (BMI) and in waist circumference, along with indices of glycemia, lipid profile, and blood pressure. Results: We pooled data from 4 trials (2 with liraglutide and 2 with exenatide) in a total of 199 enrolled subjects. GLP-1RA treatment, compared to control, resulted in a significant decrease in body weight by 3.8 kg [mean difference (MD) = −3.80, 95% CI; −6.35 to −1.24, I2 = 64%]. In addition, GLP-1RA treatment led to a significant decrease in BMI, compared to control, of 1.04 kg/m2 (MD = −1.04, 95% CI; −1.92 to −0.17, I2 = 35%). However, no significant effect on waist circumference was shown (MD = −3.2, 95% CI; −6.47 to 0.08, I2 = 88%). A significant improvement in glycemia and lipid profiles was also demonstrated with GLP-1RAs. No subgroup difference between liraglutide and exenatide was shown, and the use of GLP-1RAs did not increase the risk for treatment discontinuation compared to the control group. Conclusion: Treatment with GLP-1RAs can significantly improve weight loss and other cardiometabolic risk factors in obese people taking antipsychotic medications.

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.

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

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

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.

Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

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.

GLP-1 agonists: superior for mind and body in antipsychotic-treated patients?

Antipsychotics (APDs) represent a core treatment for severe mental disorders (SMEs). Providing symptomatic relief, APDs do not exert therapeutic effects on another clinically significant domain of serious mental disorders, cognitive impairment. Moreover, adverse metabolic effects (diabetes, weight gain, dyslipidemia, and increased cardiovascular risk) are common during treatment with APDs. Among pharmacological candidates reversing APD-induced metabolic adverse effects, glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1 RAs), approved for both diabetes and recently for obesity treatment, stand out due to their favorable effects on peripheral metabolic parameters. Interestingly, GLP-1 RAs are also proposed to have pro-cognitive effects. Particularly in terms of dual therapeutic mechanisms potentially improving both central nervous system (CNS) deficits and metabolic burden, GLP-1 RAs open a new perspective and assume a clinically advantageous position.

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.

Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia

Background

Accelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms.

Methods

Global and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T1-weighted structural neuroimaging data from 84 patients (aged 16-35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16-37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters.

Results

HYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE.

Discussion

Accelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.

Factors Influencing Change in Brain-Predicted Age Difference in a Cohort of Healthy Older Individuals

Background

There is considerable variability in the rate at which we age biologically, and the brain is particularly susceptible to the effects of aging.

Objective

We examined the test-retest reliability of brain age at one- and three-year intervals and identified characteristics that predict the longitudinal change in brain-predicted age difference (brain-PAD, defined by deviations of brain age from chronological age).

Methods

T1-weighted magnetic resonance images were acquired at three timepoints from 497 community-dwelling adults (73.8±3.5 years at baseline, 48% were female). Brain age was estimated from whole brain volume, using a publicly available algorithm trained on an independent dataset. Linear mixed models were used, adjusting for sex, age, and age2.

Results

Excellent retest reliability of brain age was observed over one and three years. We identified a significant sex difference in brain-PAD, where a faster rate of brain aging (worsening in brain age relative to chronological age) was observed in men, and this finding replicated in secondary analyses. The effect size, however, was relatively weak, equivalent to 0.16 years difference per year. A higher score in physical health related quality of life and verbal fluency were associated with a faster rate of brain aging, while depression was linked to a slower rate of brain aging, but these findings were not robust.

Conclusion

Our study provides consistent evidence that older men have slightly faster brain atrophy than women. Given the sparsity of longitudinal research on brain age in older populations, future prospective studies are needed to confirm our findings.

Neuroimaging-derived brain age is associated with life satisfaction in cognitively unimpaired elderly: A community-based study

With the widespread increase in elderly populations, the quality of life and mental health in old age are issues of great interest. The human brain changes with age, and the brain aging process is biologically complex and varies widely among individuals. In this cross-sectional study, to clarify the effects of mental health, as well as common metabolic factors (e.g., diabetes) on healthy brain aging in late life, we analyzed structural brain MRI findings to examine the relationship between predicted brain age and life satisfaction, depressive symptoms, resilience, and lifestyle-related factors in elderly community-living individuals with unimpaired cognitive function. We extracted data from a community-based cohort study in Arakawa Ward, Tokyo. T1-weighted images of 773 elderly participants aged ≥65 years were analyzed, and the predicted brain age of each subject was calculated by machine learning from anatomically standardized gray-matter images. Specifically, we examined the relationships between the brain-predicted age difference (Brain-PAD: real age subtracted from predicted age) and life satisfaction, depressive symptoms, resilience, alcohol consumption, smoking, diabetes, hypertension, and dyslipidemia. Brain-PAD showed significant negative correlations with life satisfaction (Spearman's rs= -0.102, p = 0.005) and resilience (rs= -0.105, p = 0.004). In a multiple regression analysis, life satisfaction (p = 0.038), alcohol use (p = 0.040), and diabetes (p = 0.002) were independently correlated with Brain-PAD. Thus, in the cognitively unimpaired elderly, higher life satisfaction was associated with a 'younger' brain, whereas diabetes and alcohol use had negative impacts on life satisfaction. Subjective life satisfaction, as well as the prevention of diabetes and alcohol use, may protect the brain from accelerated aging.

CoMET: a randomised controlled trial of co-commencement of metformin versus placebo as an adjunctive treatment to attenuate weight gain in patients with schizophrenia newly commenced on clozapine

BACKGROUND

There is limited evidence on interventions to minimise weight gain at clozapine commencement. We compared the effect of adjunctive metformin versus placebo at clozapine initiation.

METHODS

People with schizophrenia commencing on clozapine were randomised to either metformin or placebo for 24 weeks. The primary outcome was difference in the change of body weight. Secondary outcomes included comparative rates of weight gain of more than 5%, overall weight gain/loss, and differences in metabolic and psychosis outcomes.

RESULTS

The study was closed prematurely in March 2020 due to COVID-19 restrictions. Ten participants were randomised to each of the metformin and placebo groups. Eight metformin group and five placebo group participants completed the trial and were included in the analysis. The study was insufficiently powered to detect difference between the metformin and placebo groups for the primary outcome of change in weight (0.09 kg vs 2.88 kg, p = 0.231). In terms of secondary outcomes, people in the metformin group were significantly less likely to gain >5% of their body weight (12.5% vs 80%, p = 0.015) and were more likely to lose weight (37.5% vs 0% p = 0.024) compared to placebo. There was no difference between the groups in terms of adverse drug reactions (ADRs).

CONCLUSION

While limited by the forced premature closure of the trial due to COVID19, the findings from this randomised controlled trial are promising. Clozapine and metformin co-commencement may be a promising treatment to prevent clozapine-associated weight gain, especially given the low rates of ADRs associated with metformin. This supports the consideration of use of metformin to prevent weight gain in people initiated on clozapine; however, further studies are needed to confirm this finding.

TRIAL REGISTRATION

ACTRN12617001547336.

Microbiome First Medicine in Health and Safety

Microbiome First Medicine is a suggested 21st century healthcare paradigm that prioritizes the entire human, the human superorganism, beginning with the microbiome. To date, much of medicine has protected and treated patients as if they were a single species. This has resulted in unintended damage to the microbiome and an epidemic of chronic disorders [e.g., noncommunicable diseases and conditions (NCDs)]. Along with NCDs came loss of colonization resistance, increased susceptibility to infectious diseases, and increasing multimorbidity and polypharmacy over the life course. To move toward sustainable healthcare, the human microbiome needs to be front and center. This paper presents microbiome-human physiology from the view of systems biology regulation. It also details the ongoing NCD epidemic including the role of existing drugs and other factors that damage the human microbiome. Examples are provided for two entryway NCDs, asthma and obesity, regarding their extensive network of comorbid NCDs. Finally, the challenges of ensuring safety for the microbiome are detailed. Under Microbiome-First Medicine and considering the importance of keystone bacteria and critical windows of development, changes in even a few microbiota-prioritized medical decisions could make a significant difference in health across the life course.