Immunological/inflammatory factors are implicated in the development of psychosis. Complement is a key driver of inflammation; however, it remains unknown which factor is better at predicting the onset of psychosis. This study aimed to compare the alteration and predictive performance of inflammation and complement in individuals at clinical high risk (CHR). We enrolled 49 individuals at CHR and 26 healthy controls (HCs). Twenty-five patients at CHR had converted to psychosis (converter) by the 3-year follow-up. Inflammatory cytokines, including interleukin (IL)-1β, 6, 8, 10, tumor necrosis factor-alpha (TNF-alpha), macrophage colony-stimulating factor levels, and complement proteins (C1q, C2, C3, C3b, C4, C4b, C5, C5a, factor B, D, I, H) were measured by enzyme-linked immunosorbent assay at baseline. Except for TNF- alpha, none of the inflammatory cytokines reached a significant level in either the comparison of CHR individuals and HC or between CHR-converters and non-converters. The C5, C3, D, I, and H levels were significantly lower (C5, p = 0.006; C3, p = 0.009; D, p = 0.026; I, p = 0.016; H, p = 0.019) in the CHR group than in the HC group. Compared to non-converters, converters had significantly lower levels of C5 (p = 0.012) and C5a (p = 0.007). None of the inflammatory factors, but many complement factors, showed significant correlations with changes in general function and symptoms. None of the inflammatory markers, except for C5a and C5, were significant in the discrimination of conversion outcomes in CHR individuals. Our results suggest that altered complement levels in the CHR population are more associated with conversion to psychosis than inflammatory factors. Therefore, an activated complement system may precede the first-episode of psychosis and contribute to neurological pathogenesis at the CHR stage.

OBJECTIVE: In a previous epidemiological study, we reported on the ascertainment and outcomes of clinical high risk (CHR) individuals at the Shanghai Mental Health Center (SMHC, 2011 cohort). The current study compares demographic and clinical characteristics, including conversion rates, of this sample with a subsequently recruited, independent CHR sample and with published data from western samples. METHOD: A new sample of 100 CHR subjects (2013 cohort) was selected based on screening and semi-structured interviews. Both studies used the Structured Interview for Prodromal Syndromes (SIPS) for CHR assessment and conducted a naturalistic two-year follow-up. The two cohorts were compared on conversion rates, demographic and clinical characteristics, psychosis risk symptoms, and risk factors for psychotic conversion. RESULTS: Ninety one (91%) of the 2013 cohort subjects completed the clinical two-year follow-up and 25 (27.5% of the 91) converted to a psychotic disorder over the follow-up period. A comparison of conversions to full psychosis between the 2013 and the 2011 cohorts showed no significant difference in time to conversion (Pairwise comparison: χ2=0.3, p=0.562). Both cohort studies showed that CHR subjects with more severe clinical symptoms at baseline and decline in functioning were more likely to convert to psychosis. CONCLUSIONS: Conversion rates in this new, independent Chinese sample are similar to those reported in non-Chinese samples and to the 2011 cohort. Future research is needed to examine whether the implementation of early intervention for CHR/prodromal symptoms reduces the risk of psychosis and decreases the conversion rate.

Background

Cognitive training may contribute to the ability to maintain cognitive function in healthy elderly adults. Whether genotype modifies training effects remains unknown.

Objective

Assess influence of APOE on cognitive function over time in community-dwelling elderly adults participating in multi-domain cognitive training.

Methods

Healthy individuals ≥70 years of age were screened from one urban community in Shanghai. 145 healthy Chinese older adults met inclusion criteria and were assigned to intervention (n = 88) or control (n = 57) groups. Multi-domain cognitive training involved 24 sessions of different content taking place over 12 weeks. Neuropsychological testing was administered at baseline, immediately after training, six months and twelve months post-intervention; composite measures of cognitive function were identified via factor analysis.

Results

Three factors explained the majority of variance in function (verbal memory, processing speed, executive function). The intervention attenuated 12-month declines in processing speed, regardless of APOE genotype (p = 0.047). Executive function declined in APOEɛ4 carriers over 12 months, regardless of intervention (p = 0.056). There was a significant interaction after 12 months where intervention ɛ4 carriers had better processing speed than ɛ4 controls (p = 0.003). Intervention ɛ2 carriers had better executive function immediately after training (p = 0.02) and had better verbal memory 6-months post-intervention (p = 0.04). These effects remained significant after false-discovery rate correction.

Conclusion

Multi-domain cognitive training reduces declines in processing speed over time. APOEɛ4 is associated with reductions in executive function over time, and training may attenuate ɛ4-associated declines in processing speed. APOEɛ2 carriers may also benefit from training, particularly on measures of executive function and verbal memory.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.