T10. HERITABILITY OF AMYGDALA ACTIVITY AND ITS GENOME WIDE ASSOCIATION WITH THE SCHIZOPHRENIA RISK LOCUS OF MIR137

It is well known that heritability plays a prominent role in risk for schizophrenia, and that this brain disorder is crucially characterized by emotional symptoms. Less known is how heritability shapes brain activity during emotion processing and whether this brain phenotype is also associated with genetic variation increasing risk for schizophrenia. Here, we implemented a multi-step, data-driven approach in order to assess the relevance of the link between heritability, genetic variation, and schizophrenia for brain activity during emotion processing

Grey matter volume alterations in patients with schizophrenia and unaffected siblings show region-specific effects of genetic risk and disease-related factors

Patients with schizophrenia (scz) display a pattern of grey matter abnormalities in the prefrontal cortex (PFC), the thalamus and the cerebellum, as shown by voxel-based morphometry studies. Furthermore, post-mortem studies indicate in patients neuronal loss in specific sub- regions of the thalamus such as the mediodorsal thalamic nucleus (MD). However, it is unclear to what extent these alterations in schizophrenia are associated with the genetic risk or with state-specific factors. The present study investigated the association between genetic risk for schizophrenia and grey matter volume abnormalities

Heritability of amygdala reactivity to angry faces and its replicable association with the schizophrenia risk locus ofmiR-137

Background: Among healthy participants, the interindividual variability of brain response to facial emotions is associated with genetic variation, including common risk variants for schizophrenia, a heritable brain disorder characterized by anomalies in emotion processing. We aimed to identify genetic variants associated with heritable brain activity during processing of facial emotions among healthy participants and to explore the impact of these identified variants among patients with schizophrenia. Methods: We conducted a data-driven stepwise study including samples of healthy twins, unrelated healthy participants and patients with schizophrenia. Participants approached or avoided pictures of faces with negative emotional valence during functional magnetic resonance imaging (fMRI). Results: We investigated 3 samples of healthy participants - including 28 healthy twin pairs, 289 unrelated healthy participants (genome-wide association study [GWAS] discovery sample) and 90 unrelated healthy participants (replication sample) - and 1 sample of 48 patients with schizophrenia. Among healthy twins, we identified the amygdala as the brain region with the highest heritability during processing of angry faces (heritability estimate 0.54, p < 0.001). Subsequent GWAS in both discovery and replication samples of healthy non-twins indicated that amygdala activity was associated with a polymorphism in the miR-137 locus (rs1198575), a micro-RNA strongly involved in risk for schizophrenia. A significant effect in the same direction was found among patients with schizophrenia (p = 0.03). Limitations: The limited sample size available for GWAS analyses may require further replication of results. Conclusion: Our data-driven approach shows preliminary evidence that amygdala activity, as evaluated with our task, is heritable. Our genetic associations preliminarily suggest a role for miR-137 in brain activity during explicit processing of facial emotions among healthy participants and patients with schizophrenia, pointing to the amygdala as a brain region whose activity is related to miR-137

[Development of a conceptual model for interpretation of monitoring indicators of childhood obesity prevention from the Italian National Prevention Plan] Sviluppo di un modello concettuale di riferimento per l\u2019interpretazione degli indicatori di prevenzione dell\u2019obesit\ue0 infantile e nell'adolescenza nel Piano nazionale della prevenzione

INTRODUZIONE: il Piano nazionale della prevenzione (PNP) 2014-2018 ha indicato alle Regioni obiettivi e strategie per la prevenzione dell\u2019obesit\ue0 e si \ue8 dotato di un sistema di valutazione d\u2019impatto. OBIETTIVI: sviluppare un modello per l\u2019interpretazione delle variazioni negli indicatori relativi all\u2019obesit\ue0 infantile e nell\u2019adolescenza. METODI: da una revisione sistematica, DPSEEA (\uabforze trainanti\ubb, \uabpressioni\ubb, \uabstato\ubb, \uabesposizione\ubb, \uabeffetto\ubb, \uabazioni\ubb) \ue8 risultata la cornice concettuale pi\uf9 completa e applicabile. Un gruppo di esperti ha identificato determinanti e nessi causali e possano agire gli interventi proposti dal PNP come su questi. Il modello finale \ue8 stato ottenuto attraverso un processo iterativo che ha coinvolto un comitato consultivo di decisori e rappresentanti di societ\ue0 scientifiche. RISULTATI: \uabforze trainanti\ubb identificate sono legate a profitto dell\u2019industria alimentare, all\u2019alimentazione scolastica, al contesto domestico. Tra le \uabPressioni\ubb rientrano abitudini familiari, offerta delle mense scolastiche e fattori socioculturali, contesto di vita, organizzazione dell\u2019attivit\ue0 fisica (AF) scolastica ed extra-scolastica. Nello \uabstato\ubb sono inclusi: frequente consumo di cibo confezionato, alta quantit\ue0 di cibo ipercalorico disponibile, mancanza del pasto in famiglia, consumo di bevande gasate e zuccherate, interruzione precoce dell\u2019allattamento al seno, scarsa fruibilit\ue0 di spazi esterni, frequente uso del trasporto privato e scarsa offerta di AF nelle scuole. \uabEsposizione\ubb sono le opportunit\ue0 di AF e apporto calorico, che agiscono su \uabeffetto\ubb (prevalenza di obesit\ue0). CONCLUSIONI: il modello interpretativo colloca le \uabazioni\ubb e i meccanismi che dovrebbero modificare AF e apporto calorico, in una sequenza causale, rendendo esplicito il costrutto degli indicatori di monitoraggio e d\u2019impatto.BACKGROUND: the Italian National Prevention Plan (PNP) posed the standard to be achieved by Regions for the prevention of obesity in childhood and adolescence. The PNP also set up a monitoring system to assess the impact of implemented policies. OBJECTIVES: to develop a conceptual model to facilitate interpretation of variation in outcome indicators. METHODS: after a systematic review, the DPSEEA (\uabDriving forces\ubb, \uabPressures\ubb, \uabState\ubb, \uabExposure\ubb, \uabEffect\ubb, \uabActions!) was identified as the more appropriate framework to assess the results of preventive policies. Factors for each component of the framework were identified and indicators that allow measuring the changing of each of these factors were defined. RESULTS: the included \uabdriving forces\ubb were related to the profit-led food industry, to the nutrition environment at school, and to household-level factors. Among the \uabpressures\ubb, parenting behaviours, food provided by school canteens, sociocultural factors, social context, physical activity (PA), opportunities at school or after-school were included. In the State, the high consumption of processed food, the large quantities of high-calorie food easy available, the consumption of carbonated and sugar-sweetened beverages, the reduced social function of mealtimes in families, the early cessation of breastfeeding, the reduction of outdoors activity, active transportation, and PA at school for children were identified. The \uabexposure\ubb factors were the reduced opportunities of doing PA and the over-consumption of calories that influence the \uabeffect\ubb, described as the prevalence of children and adolescents affected by obesity. CONCLUSIONS: through the DPSEEA, a conceptual model was set up; it allows to place in the causal chain the \uabactions\ubb and the mechanisms through which these actions should impact on the \uabexposure\ubb (PA and over-consumption of calories), making the rationale of process and impact indicators explicit

Functional genetic variation of the cannabinoid receptor 1 and cannabis use interact on prefrontal connectivity and related working memory behavior

Cannabinoid signaling is involved in different brain functions and it is mediated by the cannabinoid receptor 1 (CNR1), which is encoded by the CNR1 gene. Previous evidence suggests an association between cognition and cannabis use. The logical interaction between genetically determined cannabinoid signaling and cannabis use has not been determined. Therefore, we investigated whether CNR1 variation predicts CNR1 prefrontal mRNA expression in postmortem prefrontal human tissue. Then, we studied whether functional variation in CNR1 and cannabis exposure interact in modulating prefrontal function and related behavior during working memory processing. Thus, 208 healthy subjects (113 males) were genotyped for the relevant functional SNP and were evaluated for cannabis use by the Cannabis Experience Questionnaire. All individuals performed the 2-back working memory task during functional magnetic resonance imaging. CNR1 rs1406977 was associated with prefrontal mRNA and individuals carrying a G allele had reduced CNR1 prefrontal mRNA levels compared with AA subjects. Moreover, functional connectivity MRI demonstrated that G carriers who were also cannabis users had greater functional connectivity in the left ventrolateral prefrontal cortex and reduced working memory behavioral accuracy during the 2-back task compared with the other groups. Overall, our results indicate that the deleterious effects of cannabis use are more evident on a specific genetic background related to its receptor expression