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 +3.64 years (95% CI: 3.01, 4.26; I2 = 55.28%) compared to controls, after adjusting for age and sex (Cohen's d = 0.50). 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.

Inflammaging and Brain: Curcumin and Its Beneficial Potential as Regulator of Microglia Activation

Inflammaging is a term used to describe the tight relationship between low-grade chronic inflammation and aging that occurs during physiological aging in the absence of evident infection. This condition has been linked to a broad spectrum of age-related disorders in various organs including the brain. Inflammaging represents a highly significant risk factor for the development and progression of age-related conditions, including neurodegenerative diseases which are characterized by the progressive dysfunction and degeneration of neurons in the brain and peripheral nervous system. Curcumin is a widely studied polyphenol isolated from Curcuma longa with a variety of pharmacologic properties. It is well-known for its healing properties and has been extensively used in Asian medicine to treat a variety of illness conditions. The number of studies that suggest beneficial effects of curcumin on brain pathologies and age-related diseases is increasing. Curcumin is able to inhibit the formation of reactive-oxygen species and other pro-inflammatory mediators that are believed to play a pivotal role in many age-related diseases. Curcumin has been recently proposed as a potential useful remedy against neurodegenerative disorders and brain ageing. In light of this, our current review aims to discuss the potential positive effects of Curcumin on the possibility to control inflammaging emphasizing the possible modulation of inflammaging processes in neurodegenerative diseases.

Inflammaging in Major Depressive Disorder: Associations between markers of inflammation and brain ageing in depression across two large community-based cohorts

Background: Major Depressive Disorder (MDD) is associated with accelerated ageing trajectories including functional markers of ageing, cellular ageing and markers of poor brain health. The biological mechanisms underlying these associations remain poorly understood. Chronic inflammation is also associated with advanced ageing; however, the degree to which long-term inflammation plays a role in ageing in MDD remains unclear, partly due to difficulties differentiating long-term inflammation from acute cross-sectional measures. Methods: Here, we use a longer-term measure of inflammation: a DNA methylation-based marker of C-reactive protein (DNAm CRP), in a large cohort of individuals deeply phenotyped for MDD (Generation Scotland, GS, N=804). We investigate associations between DNAm CRP and serum CRP using linear modelling with two brain ageing neuroimaging-derived phenotypes: (i) a machine learning based measure of brain-predicted age difference (brain-PAD) and (ii) white matter hyperintensities (WMH). We then examine inflammation by depression interaction effects for these brain ageing phenotypes. We sought to replicate findings in an independent sample of older community-dwelling adults (Lothian Birth Cohort 1936, LBC1936; N=615). Results: DNAm CRP was significantly associated with increased brain-PAD (β=0.111, p=0.015), which was replicated in the independent sample with a similar significant effect size (β=0.114, p=0.012). There were no associations between the inflammation markers and WMH phenotypes in the GS-imaging sample, however in the LBC1936 sample, DNAm CRP was significantly associated with both Wahlund infratentorial (β=0.15, PFDR= 0.006) and Fazekas deep white matter hyperintensity scores (β= 0.116, PFDR=0.033). There were no interaction effects between inflammation and MDD in either cohort. Conclusions: This study found robust associations between a longer-term marker of inflammation and brain ageing as measured by brain-PAD, consistent across two large independent samples. However, we found no evidence for interaction effects between inflammation and MDD on any brain ageing phenotype in these community-based cohorts. These findings provide evidence that chronic inflammation is associated with increased brain ageing, which is not specific to MDD. Future work should investigate these relationships in clinical samples including with other inflammatory biomarkers and should furthermore aim to determine causal directionality.

Local Brain-Age: A U-Net Model

We propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18–90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps.” To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication

Background Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. Aims To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. Method The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. Results The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014). Conclusions The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Vinexin contributes to autophagic decline in brain ageing across species

Autophagic decline is considered a hallmark of ageing. The activity of this intracytoplasmic degradation pathway decreases with age in many tissues and autophagy induction ameliorates ageing in many organisms, including mice. Autophagy is a critical protective pathway in neurons and ageing is the primary risk factor for common neurodegenerative diseases. Here, we describe that autophagosome biogenesis declines with age in mouse brains and that this correlates with increased expression of the SORBS3 gene (encoding vinexin) in older mouse and human brain tissue. We characterise vinexin as a negative regulator of autophagy. SORBS3 knockdown increases F-actin structures, which compete with YAP/TAZ for binding to their negative regulators, angiomotins, in the cytosol. This promotes YAP/TAZ translocation into the nucleus, thereby increasing YAP/TAZ transcriptional activity and autophagy. Our data therefore suggest brain autophagy decreases with age in mammals and that this is likely, in part, mediated by increasing levels of vinexin.