Final Report Summary - METSY (Neuroimaging platform for characterisation of metabolic co-morbidities in psychotic disorders)
Executive Summary:
Psychotic disorders are associated with metabolic abnormalities including alterations in glucose and lipid metabolism. A major challenge in the treatment of psychosis is to identify patients with vulnerable metabolic profiles who may be at risk of developing cardiometabolic co-morbidities. It is established that both central and peripheral metabolic organs use lipids to control energy balance and regulate peripheral insulin sensitivity. The endocannabinoid system, implicated in the regulation of glucose and lipid metabolism, has been shown to be dysregulated in psychosis. It is an open research question how these endocannabinoid abnormalities relate to metabolic changes in psychosis. The METSY project was established with the aim to identify and evaluate multi-modal peripheral and neuroimaging markers that may be able to predict the onset and prognosis of psychiatric and metabolic symptoms in patients at risk of developing psychosis and first episode psychosis patients. The project brought together clinicians, researchers and industry partners in the domains of psychiatry, neuroimaging, metabolic research, systems biology and bioinformatics.
Given the intrinsic complexity and widespread role of lipid metabolism, a systems biology approach which combines molecular, structural and functional neuroimaging methods with detailed metabolic characterisation and multi-variate network analysis is essential in order to identify how lipid dysregulation may contribute to psychotic disorders. METSY applied comprehensive metabolomics as well as neuroimaging (MRI and PET) in first-episode (FEP) or clinical high-risk (CHR) for psychosis in studies across the four clinical sites. The project led to several key findings including detailed prospective characterisation of inflammation/metabolism in FEP, predictive signatures of psychosis in CHR patients, identification of FEP patients at highest risk of rapid weight gain by metabolomics, and identification of dysregulation of the endocannabinoid system in the brain of FEP/CHR patients as well as in the periphery. A decision support system, integrating clinical, neuropsychological and neuroimaging data, was also developed in order to aid clinical decision making in psychosis.
Knowledge of common and specific mechanisms may aid the etiopathogenic understanding of psychotic and metabolic disorders, facilitate early disease detection, aid treatment selection and elucidate new targets for pharmacological treatments. Other expected impacts of the project are (1) new validated multi-modal markers for early disease detection and monitoring, (2) new tools for the identification of subjects who may benefit from specific treatment (3) discovery of new avenues for disease prevention and therapy, and (4) new tools and processes for applying brain imaging in personalised medicine. Over the lifetime of the project, the topic of metabolic co-morbidities in psychotic has gained increasing prominence in the field of psychosis research. For example, most recently, METSY participants organized a special session on this topic at the 6th Biennial Schizophrenia International Research Society Conference in Florence, Italy (April 2018), SIRS 2018.
Project Context and Objectives:
The METSY overall objective was to identify, prioritize and evaluate multi-modal blood and neuroimaging markers with diagnostic potential for prediction and monitoring of psychotic disorders and associated metabolic co-morbidities.
The main objective of METSY was met by combinations of clinical research, state-of-the-art technologies and systems biology, divided into six specific objectives:
O1. To apply neuroimaging strategies to characterise structural and metabolic changes in the brain during the first stages of psychosis.
O2. To apply imaging strategies to characterise the endocannabinoid pathways in the brain and relate them to lipid molecular networks.
O3. To characterise genetic and lipid molecular networks as measured in biofluids in early psychosis and identify how these networks associate with patient outcomes.
O4. To develop and demonstrate methodology for combined PET and MRI imaging.
O5. To develop bioinformatics tools to integrate brain image information with clinical and molecular profile data.
O6. To identify, prioritize and evaluate multi-modal circulating and neuroimaging markers with diagnostic potential for prediction and monitoring of psychotic disorders and associated metabolic co-morbidities.
Figure 1. METSY S/T structure.
Research comprised of three activity areas, articulated into six S/T work packages (WPs), as shown in Figure 1:
(1) human cohort studies (WPs 1-3),
(2) methodology developments (WPs 4 and 5), and
(3) translational research (WP 6).
METSY also included two WPs dedicated to dissemination and management. The S/T WPs followed the progression of the work plan and reflected the six specific objectives.
WP 1 pursued detailed neuroimaging and neuropsychology characterisation in longitudinal studies involving patients at-risk or with first episode of psychosis. FEP and CHR subjects and matched healthy controls were followed up simultaneously with analogous methodology in order to extract neuroimaging information useful for characterization of the development of psychosis and associated metabolic outcomes.
WP 2 pursued detailed neuroimaging and metabolic studies of endocannabinoid pathways including synthesis and degradation systems. More accurate methods for direct quantification of CB1 receptors have been recently developed. In collaboration with NIH a CB1 tracer ([18F]FMPEP-d2) was validated and was be used in the project proposal in Turku (P1) and London (P6) with inter-center methodology harmonization process.
WP 3 pursued detailed metabolic characterization, metabolomics as well as studied immune/oxidative stress markers in the cohorts included in WP 1. Data was analysed in collaboration with WP 5.
WP 4 developed methods for combined PET and MR image acquisition and analysis. Consecutive baseline scans, i.e. CB1R and the presynaptic dopamine synthesis tracer [18F]DOPA were performed and the binding outcome interactions (correlation and hub analyses) was a starting point for methodology testing. Early protocols were utilized first in healthy volunteers and then later in early pilot patients. Optimised protocols for the PET/MR hybrid camera (Philips IngenuityTF) were developed and evaluated. Dedicated software packages, specifically adapted to match the requirements of WP1 and WP2 for combined PET and MRI data visualization and analysis were developed on the Imalytics Research Workstation (Philips Research, Aachen, DE).
WP 5 pursued statistical developments to integrate image data with other phenotypic data, including from ‘omics’ analyses, aiming to extract the signals of potential diagnostic value. Semantic modelling was used to annotate these data with biological and literature-based annotations. Disease State Index was evaluated as a decision-support system in psychosis.
WP 6 validated the multi-modal circulating and neuroimage markers which are sensitive to metabolic disturbances in the brain of at-risk or psychotic patients using an independent prospective sample series from 250 first-episode patients and their healthy controls, which were not included as part of biomarker discovery in WPs 1-3.
Project Results:
Scientific background
Unhealthy lifestyles and pharmacological side effects have been suggested to be a major cause of excess mortality rates in patients with psychotic disorders. Schizophrenia patients exhibiting negative symptoms such as anhedonia and social withdrawal are more prone to becoming overweight and developing metabolic syndrome, which may in turn increase the risk of cardiovascular morbidity (Arango et al., 2011). Additionally, the use of antipsychotic medication, especially second generation antipsychotics, has been consistently associated with weight gain, insulin resistance and the development of metabolic syndrome (Correll et al., 2011; Howes et al., 2004; Jin et al., 2004; Newcomer, 2005), which seems to be more marked in younger people (De Hert et al., 2011). After only six months of treatment with specific second-generation antipsychotics, the percentage of previously drug naïve first episode psychosis patients at risk of developing the metabolic syndrome rises from 17% to 40% (Fraguas et al., 2008). This evidence suggests that these psychotropic drugs target brain regions involved in regulating energy balance and metabolism.
However, pharmacological side effects and unhealthy lifestyles only explain a fraction of the metabolic co-morbidities shown in psychosis. Abnormal glucose homeostasis, hyperinsulinemia and accumulation of visceral fat are already evident in drug-naïve first episode psychosis patients, independently of obesity (Kirkpatrick et al., 2012; Pillinger et al., 2017). In the WHO World Health Survey, as compared with the absence of symptoms, having one psychotic symptom was associated with higher odds (OR 1.71; 95% CI, 1.61-1.81) of diabetes mellitus in the general population, with increasing likelihood as the number of psychotic symptoms increased (Nuevo et al., 2011). Furthermore, unaffected first-degree relatives of people with schizophrenia also have higher rates of diabetes mellitus (19-30%) compared to the general population (1.2-6.3%) (Mukherjee et al., 1989). Some recent genetic studies have detected genes that increase the risk of both schizophrenia and type 2 diabetes (T2D) (Hansen et al., 2011); however, there have been negative findings as well (Kajio et al., 2014; Padmanabhan et al., 2016). Taken together, these observations suggest that metabolic disturbances associated with obesity may contribute to the etiopathogenesis of psychosis.
The role of cannabis use in increasing the relative risk for the development of psychosis is well established (Marconi et al., 2016). The endocannabinoid system is comprised of lipid-derived endogenous cannabinoid ligands, enzymes involved in the synthesis and degradation of these ligands and the cannabinoid 1 and 2 receptors which have affinity to these endogenous cannabinoid ligands. The cannabinoid 1 receptor has been postulated to be dysregulated in both psychotic and metabolic diseases (Gatta-Cherifi and Cota, 2015; Lu and Mackie, 2016). The CB1R is a G-protein coupled receptor widely distributed centrally throughout the cortex, striatum, hippocampus and cerebellum. However, CB1Rs are also distributed in the periphery throughout the gastrointestinal tract, liver, adipose tissue and adrenal glands (Pagotto et al., 2006). The CB1R has been implicated in the etiology of metabolic diseases based on evidence that CB1R agonists dysregulate both glucose and lipid metabolism (Scheen and Paquot, 2009). In line with these findings, selective CB1R antagonists have been demonstrated to be effective for weight-loss leading to favorable changes in both lipid and glucose levels (Colombo et al., 1998). However, further research is warranted to investigate how endocannabinoid dysregulation in psychosis relates to metabolic abnormalities in psychosis.
Metabolomics studies
Metabolomics is a comprehensive study of small molecules (i.e. metabolites) in cells, tissues and biofluids, including their biochemical transformation and responses to environmental and genetic perturbations. Metabolomics provides new tools to study the etiopathology of psychotic disorders as well as metabolic dysregulation arising following the use of antipsychotics (He et al., 2012; Kaddurah-Daouk et al., 2007; McEvoy et al., 2013; Oresic et al., 2012; Oresic et al., 2011b; Paredes et al., 2014). However, metabolomics has also played an important role in unravelling putative biomarkers and underlying pathways in several other diseases of the central nervous system (Quinones and Kaddurah-Daouk, 2009), including major depressive disorder (Ali-Sisto et al., 2016; Kaddurah-Daouk et al., 2012), Autism spectrum disorder (West et al., 2014), Alzheimer’s (Han et al., 2011; Kaddurah-Daouk et al., 2011; Oresic et al., 2011a; Trushina et al., 2013) and Parkinsons (Ahmed et al., 2009; Bogdanov et al., 2008; Hatano et al., 2016) diseases. Since the metabolome is sensitive to both genetic and environmental factors, such as drug exposure, metabolomics was chosen as a key ‘omics’ platform for molecular phenotyping in the METSY project.
Studying the metabolome in a population-based study, Oresic and colleagues found that schizophrenia was associated with elevated serum levels of specific triglycerides, hyperinsulinemia, and the upregulation of the serum amino acid proline (Oresic et al., 2011b). Using a network approach, the metabolic profiles were combined with other clinical and lifestyle data to create a diagnostic model which discriminated schizophrenia from other psychotic illnesses. As part of the METSY project, metabolomics has also been applied to study the metabolite profiles predicting weight gain and the development of other metabolic abnormalities in patients with first-episode psychosis (Suvitaival et al., 2016), where weight gain was associated with increased levels of triglycerides with low carbon number and double bond count at baseline (Figure 2). These lipids are known to be associated with increased liver fat (Luukkonen et al., 2016; Oresic et al., 2013). These preliminary results suggest that the first-episode psychosis patients who are at the highest risk of rapid weight gain, tend to have increased levels of lipids linked to liver fat prior to becoming obese. However, it is unclear whether there is a common biological mechanism underlying metabolic changes shown in first-episode psychosis. Validation metabolomics studies have been conducted by ORU & UTU in the final stage of the METSY project in FEP (KCL, UTU, SERMAS) and CHR (KCL, EU-GEI cohort) cohorts, aiming to validate lipid signatures associated with weight gain as well as to discover or confirm metabolic signatures associated with FEP and CHR. A total of 866 samples were analysed by lipidomics, incl. 206 in the EU-GEI cohort (CHR individuals). If confirmed, the lipid signatures associated with weight-gain in FEP patients may be clinically useful, as it may help identify the most vulnerable individuals who may benefit from metabolic therapy (anti-obesity/diabetes) in addition to antipsychotic therapy.
Figure 2. Lipidomic profiles and weight gain in FEP patients (Suvitaival et al., 2016). (a) Relative weight gain (blue crosses) from baseline as a function of time in the FEP case group. The median increase in body mass was 3 kg and 11 kg from baseline to the two-month and one-year follow-up points, respectively. Nonlinear Gaussian process regression model was fit on the weight gain data to visually highlight the trend. (b) Association between the level of triacylglycerols (TGs) at baseline and the two-month follow-up weight gain (Spearman correlation; color of the points) with respect to the number of carbon atoms (x-axis) and the number of double bonds between carbon atoms (y-axis). The baseline levels of saturated and mono-unsaturated compounds (y=0 and y=1, respectively) are associated with short-term weight gain (red color). The coefficient of determination (R2) of the linear model for the association as a function of triacylglycerol carbon number and double bond count are shown in the x-axis and y-axis labels, respectively (both p<0.05).
Neuroimaging studies
Background
An extensive body of literature over the last 40 years has documented subtle but widespread structural and functional changes in the brains of patients with non-affective and affective psychotic disorders. These changes are usually most prominent in fronto-temporal regions but it is now evident that these changes are more widespread, extending to posterior brain regions (Brugger and Howes, 2017). The progression of structural brain changes, particularly grey matter volume loss, has been found in the early onset schizophrenia, including both adult and adolescent-onset cases (Arango et al., 2012; Cahn et al., 2009). These volumetric changes are also shown in antipsychotic- naïve patients and become greater over time (Haijma et al., 2013), and have been correlated with poor clinical outcomes (Arango et al., 2012). Interestingly, volumetric reductions in frontal and temporal grey matter have also been linked to weight gain in healthy subjects (Minichino et al., 2017). These findings suggest that volumetric changes in the structure of the brain are related to the severity of clinical and metabolic changes in psychosis.
In vivo molecular imaging studies have consistently shown that un-medicated patients with schizophrenia exhibit an increase in striatal dopamine synthesis and release (Hietala et al., 1995; Howes and Murray, 2014; Laruelle et al., 1996). However, it is clear that dopamine dysregulation in psychosis is part of a larger problem in the connectome involving also other neurotransmitter pathways, in particular the glutamate and GABA systems. The endocannabinoid receptor CB1R, located on pre-synaptic nerve terminals of glutamatergic and GABAergic nerve terminals, plays a fundamental neuro-modulatory role in the brain due to its ability to inhibit the release of both excitatory and inhibitory neurotransmitters. CB1R begin modulating the fine tuning of excitatory/inhibitory neurotransmitter release during periods of pre- and postnatal brain development (Harkany et al., 2007) , thought to be central in the etiology of schizophrenia-spectrum disorders. Previous attempts to quantify the CB1R in vivo in schizophrenia have been largely unsuccessful due to high levels of tracer lipophilicity (Yasuno et al., 2008), the use of irreversible tracers and the failure to use arterial blood sampling to quantify the tracer kinetics (Ceccarini et al., 2013; Wong et al., 2010). However, it is now possible to elucidate the role of CB1R in patients with psychosis due to the development of specific positron emission tomography (PET) radiotracers, such as[11C]OMAR, [11C]MEPPEP and [18F]FMPEP-d2. These tracers bind reversibly with high specificity to CB1R in healthy volunteers and have appropriate kinetic properties for compartmental modeling of receptor availability as well as good test-retest reliability (Normandin et al., 2015; Terry et al., 2010; Terry et al., 2009; Tsujikawa et al., 2014). A recent study using arterial blood sampling and appropriate quantification techniques found that medication naïve schizophrenia patients abstaining from cannabis use showed a down-regulation of the CB1R in the hypothalamus, hippocampus, amygdala, caudate and insula (Ranganathan et al., 2016).
PET and MRI are established neuroimaging tools, but generally used independently. Recently, a hybrid PET/MR system, which allows for acquisition of such complementary information consecutively in the same study session without repositioning of the subject has been established. This system provides truly simultaneous, complementary information on different aspects of brain function (e.g. CBR1 availability, white matter integrity) by the different modalities without the temporal limitations of conducting separate PET and MRI scans. MRI-based data on brain morphology and white matter tract integrity have been used to quantify structural connectivity patterns of the brain of the cannabinoid systems as measured with PET and network connectivity, such as the default mode network (DMN) in the brain. The DMN is activated when the brain is at wakeful rest and not focusing on the outer world but rather engaged with internal tasks (e.g. daydreaming, spontaneous thoughts, memories). DMN is usually regarded as a predominantly context-independent phenomenon. Despite the fact that resting state functional magnetic resonance imaging (R-fMRI) has become a powerful tool to explore the dysconnectivity of brain networks in psychotic disorders, very little is known about the role of specific neurotransmitters involved in emergence and maintaining DMN activity.
Patient recruitment
Final recruitment within METSY reached the following final numbers for neuroimaging (as well as metabolomics studies): 268 subjects with FEP (goal 250), 29 subjects with clinical high risk for psychosis (CHR) (goal 100) and 231 healthy controls (HC) were recruited. Of them, 207 FEP subjects have an MRI, all of the CHR subjects have an MRI and 210 HC participants have an MRI at baseline. Regarding laboratory data, baseline data are available for 213 FEP subjects, 28 CHR subjects and 193 HC participants. The final effort carried out by all centres allowed an important and significant increase in the numbers of FEP and HC subjects. The final impulse in the recruitment of controls, matched by general socio-demographic characteristics with FEP helped increase the final size sample. However, the final number of CHR subjects is far from the target for different reasons. Different health care systems in different European countries have different ways to assess the CHR. For example, in Finland and in Spain, patients no longer enter the psychiatric health care system at this stage, making it very difficult to recruit subjects at risk. In this regard, from the beginning of the study, only THL, UTU and KCL committed themselves to recruit CHR subjects.
During this period, effort has been directed towards follow-up of the participants in the study. The 1-year follow-up is almost finished, with still a number of patients scheduled for evaluations. Database design has been completed and final minor issues with clinical harmonization have been solved. Baseline clinical data has been sent from clinical sites to Biomax and integrated in the common database. Clinical centres have sent baseline imaging data to SERMAS for storage and analysis and processed data has afterwards been sent to Biomax for inclusion in the database.
Data harmonisation and processing
Amongst all participating centres, the following aspects were harmonized:
• Clinical diagnostic interview: After looking for differences among the clinical and the research versions of the Structured Clinical Interview for DSM-IV (SCID-I), only slight variance was found between them. However, as version used did not affect the aim of the instrument application, that is to establish a clinical diagnosis in Axis I, all participants agreed on continuing using the SCID-I version they had (UTU, THL and KCL use the SCID-I research version and SERMAS the SCID-I clinical version). Only one site (SERMAS) is enrolling minors, and therefore Kiddie-SADS-Present and Lifetime Version (K-SADS-PL) is used for kids´ assessment. Diagnosis will be included in the database.
• Severity of psychotic symptoms: To assess severity of psychotic symptoms, THL is using the Brief Psychiatric Rating Scale (BPRS-Extended) complemented by SANS avolition, anhedonia and alogia, whilst UTU, KCL and SERMAS use the Positive and Negative Syndrome Scale (PANSS). After examining this discordance, all centres agreed on continue using scales as they were doing, as total scores of BPRS and PANSS could be interchangeable without affecting internal validity of both scales (Ventura et al. 1993). PANSS and BPRS will be converted into standard scores for further analysis. All items of PANSS and BPRS will be included in the database (including the modifications used in THL) and afterwards conversion into standard scores will also be included. PANSS interrater reliability was later obtained between evaluators at different sites (see below in interrater reliability).
• Prodromal psychotic symptoms: To assess the presence of prodromal psychotic symptoms, all sites agreed on using the Structured Interview for Prodromal Symptoms (SIPS 5.0). SIPS interrater reliability was later obtained between evaluators at different sites (as described in the 18th month report).
• Global Functioning: All centres agreed on exclusively using the Global Assessment of Functioning scale (GAF) to assess global functioning. They also agreed on deleting the SOFAS scale that some centres were administering. GAF interrater reliability was later obtained between evaluators at different sites (as described in the 18th month report).
• Diet: After looking through the diet questionnaires in each sites, all centres agreed that each one will have their own diet scales due to the diversity of dietary habits in each country. With the DIET scale used by SERMAS, dietary habits can be classified in 3 categories: healthy, need changes, and unhealthy.
• BMI and waist perimeter, cannabis use, smoking, exercise, socioeconomic status (SES), years of education and race were also harmonized. THL exercise assessment will be used by each centre.
• Cannabis: Agreements on cannabis included: Joints per week is not a reliable measure, so instead current/Lifetime use/abuse/dependence and >50 times/days current/lifetime will be used.
Interrater reliability for GAF, PANSS, and SIPS was established (as described in the 18th month report).
Regarding harmonization of neuropsychological data, measures used to assess IQ, attention, processing speed, and working memory have been harmonized. Both direct scores (raw) and standardized scores (T-scores) have been added in the main database. The standardized scores are calculated by each site with the normative data of their own country. Merging data between sites will be made with standardized scores. Variables will be created when at least two sites use the same/compatible test. Each site will provide information regarding which neuropsychological tests have and have not available normative data in their language. Research domains for higher executive functions will be adjusted depending on the different analyses. The use of the following measures has been prioritized: Vocabulary WAIS-III (IQ), TMT-A and CPT (Attention), TMT-B (Executive function) Digit Symbol Coding WAIS-III (Processing Speed), Categories FAS (Verbal Processing Speed), Letter number sequencing-WAIS-III (Working Memory), WMS-III-WAIS-III (Working Memory), HVLT-R (verbal learning) and BVMT (visual learning).
Finally, during this period, clinical sites have sent MRI images and these MRI images have been processed with the same pipeline. The T1-weighted images of all METSY participants have been analysed in FreeSurfer (v5.3) to provide detailed anatomical information customized for each participant. The FreeSurfer analysis stream includes intensity bias field removal, skull stripping, and assigning a neuro-anatomical label (e.g. hippocampus, amygdala, etc.) to each voxel. In addition to the volume-based analysis, FreeSurfer constructs models of the pial and white surface. These surfaces have been used to quantify cortical thickness, surface area, and volume at regional and vertex-wise scales for each METSY participant.
In order to detect and correct artefacts introduced during collection of the Diffusion Tensor Imaging scan, a quality control protocol for METSY subjects have been implemented. First, artefacts related to intensity are detected by computing the normalized correlation between intensity in successive slices across the diffusion volume. Any diffusion volumes containing one or more artefacts are excluded. Next, eddy-current and head motion correction is performed using Fmri Software Library (FSL) tools. Finally, machine-related (i.e. B0 field inhomogeneity) spatial distortions are corrected by warping each participant's T2-b0 image to the anatomical T2-weighted image of the same individual. Anatomically constrained probabilistic diffusion tractography have been carried out using the Tracts Constrained by UnderLying Anatomy (TRACULA) tool within FreeSurfer using default settings (Yendicki et al. 2011). Mean values of fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity of major white matter tracts have been calculated for each METSY participant.
Previously, a reliability study was conducted to assess whether images acquired at the different acquisition centers (Madrid, London, Turku, and Helsinki) could be combined in a mega-analysis. Five healthy volunteers travelled to each site and images were acquired. Images were uploaded to the DICOMSERVER in Madrid and processed using FreeSurfer (for T1-weighted images) and TRACULA (for Diffusion Tensor Images). The results of the reliability study for T1 were already. For DTI, the harmonization process included five volunteers were scanned in all sites (Figure 3):
• Fundación Cien (Madrid, Spain)
• AMI – Centre (Espoo, Finland)
• PET – Centre (Turku, Finland)
• King’s College Institute of Psychiatry (London, United Kingdom)
Figure 3. Harmonisation of MRI/DTI data across the four study centres.
CB1R PET analyses
Recruitment targets for patients with FEP and Controls were well achieved (Table 1). Recruitment of CHR cases numbers was abandoned based on early decisions to focus on baseline FEPs and controls.
Table 1. WP2: Subjects (n) scanned with CB1R PET with complete data
PET data UTU KCL Total/goal
HC 22 20 42/35
FEP 15 20 35/35
CHR 0 0 0/35
Total 37 40 77
UTU and KCL PET and MRI data pre-processing has been harmonized. Modelling results using 2TCM and MTGA Logan plot are in line with previous [18F]FMPEP-d2 and [11C]MEPPEP studies. Preliminary findings from the collected samples (Table 1) are: (a) a lower CB1R availability of FEPs in both sites (Figure 4) and (b) lower CB1R availability of females in the UTU control sample. Logan plot Parametric images (DVtot) show regional associations between CB1R availability and cognitive capacity in HCs as well as CB1R availability and BPRS psychotic symptoms in male FEPs.’
All available matching serum samples from UTU and KCL have been analysed using the endocannabinoid platform developed by UTU in collaboration with ORU. Preliminary results are: (a) lower circulating levels of OEA and AA in FEPs in the UTU sample. There were no significant differences between patients and controls in the KCL sample; and (b) association of 1+2-AG and OEA to hippocampal CB1R availability in HCs but not FEPs as measured by PET and [18F]FMPEP-d2. Endocannabinoids have also, by the end of the project, been analysed in FEP samples from THL as well as in CHR samples from the EU-GEI cohort.
Together, the preliminary data generated within METSY dies suggest that the endocannabinoid system is dysregulated in gender-specific manner in the brain of FEP patients and that there may be an association between the endocannabinoid systems in the brain and in the periphery. The final studies performed in METSY (EU-GEI) as well as future studies will need to determine if the endocannabinoid system may be a mechanistic underlying link between the metabolic co-morbidities incl. elevated liver fat and early psychosis.
Figure 4. Cannabinoid 1 receptor availability was significantly lower in first episode psychosis patients relative to healthy volunteers as determined by [11C]MePPEP PET quantification (A) and [18F]FMPEP-d2 PET quantification (B).
Integrative bioinformatics platform
The METSY bioinformatics platform is comprised of three inter-related components (Frank et al., 2018) (Figure 5):
1. Network analysis to integrate heterogeneous data (multi-omics, in vivo molecular neuroimaging, structural neuroimaging, functional neuroimaging and psychosocial);
2. Semantic modelling to annotate heterogeneous data with biological and literature-based annotations;
3. Development of a decision support system to facilitate decision-making in the clinic based on multi-modal diagnostic information.
Extracting predictive biomarkers from multiple types of information requires the integration and correlation of existing knowledge and data from diverse sources and formats. Network construction and analysis is a promising approach facilitating data integration that is increasingly used in disease related research (Barabasi, 2007; Hofree et al., 2013). In this approach, networks are constructed from associations between variables and are integrated with prior knowledge that is also represented in a network form. Currently, most prior knowledge is not readily accessible for analysis since it exists in different repositories for structured (comprising about 1400 public databases on molecular biology related information (Galperin and Fernandez-Suarez, 2012)) and unstructured data such as high-content imaging, physiological, biochemical and clinical data. Bioinformatics methods, developed to bridge multiple sources and scales of knowledge into semantic networks, have recently been extended to imaging data and computational models (Maier et al., 2011). Another challenge that can be approached by networks is the representation of gained knowledge, e.g. how do changes of a specific receptor detected by PET imaging influence our prior knowledge about the overall phenomenon. Current neuroimaging methods are focused on correlation of voxel pattern to outcomes, largely neglecting existing mechanistic and structural information during the analysis.
Figure 5. Outline of the METSY bioinformatics platform, bridging the systems medicine research approaches with the applications in the clinic. The platform integrates three components: network analysis, semantic modelling and decision support system. (A) Network analysis to integrate heterogeneous data (multi-omics, in vivo molecular neuroimaging, structural neuroimaging, functional neuroimaging and psychosocial) based on partical correlations. (B) Semantic modelling to annotate heterogeneous data with biological and literature-based annotations, representing knowledge as network which integrates associations otherwise separated in individual data sources. Integration is based on mapping of equivalentmeaning and objects across all information types relevant in a life science project. (C) Development of a decision support system to facilitate decision-making in the clinic based on multi-modal diagnostic information.
In order to provide systematic and structured information suitable for algorithmic analysis, METSY structured the current knowledge (i.e. scientific literature, implicit expert knowledge, databases) into concepts, which can be mapped to the experimental and clinical data (Figure 6). Using this approach, concepts relevant to a specific research area can be retrieved from the literature or defined by expert consensus and implemented as software concepts. In psychosis research, relevant concepts are for example “brain area”, “symptom” or “metabolite” and associations such as “causes” or “is consumed by”. Within the METSY project, we will apply the BioXM Knowledge Management Environment (Losko and Heumann, 2009; Maier et al., 2011), which will allow us to adapt existing concepts throughout the course of the project using a graphical editor. Semantic mapping approaches can also be used to identify defined concepts from structured resources such as ontologies, neuroanatomical or functional atlases, databases or literature-mining. For example “brain area” might be populated from the Human anatomy atlas (Rosse and Mejino, 2003) and the FreeSurfer neuroanatomy atlas (Desikan et al., 2006); while “metabolites” might be derived from the Human Metabolome Database (Wishart et al., 2013) and different symptoms associated to psychosis might be retrieved by automatic literature-mining. In this process, information from different sources can be mapped to the same concepts based on their meaning (semantics) and thereby integrated. This process can be automated for data extraction from various sources based on descriptions of the contained data and its format (metadata); however, some data extraction requires manual selection in cases where the source relates to specific areas of expertise (i.e. identifying the similarity of different neuroanatomical atlases).
Figure 6. Example of integrative analysis of connectome and gene expression data by using the semantic approach. Coloured dots indicate gene expression values for FKBP5 (taken from Human Allen Brain Atlas). Red colours indicate high expression values whereas blue colours indicate low values. In addition, we selected prefrontal cortex circuitry and display structural and functional connection strengths measured by DTI and fMRI, respectively. Structural connectivity is depicted by line thickness. Red line colouring indicates strong functional connectivity while blue indicates anti-correlated activity between the connected brain areas. Connection strengths are taken from the NKI_AVRG dataset - the average connectivity of all connectomes of the NKI Rockland study from the Human Connectome Project. Datasets available through the USC Multimodal Connectivity Database. All brain coordinates were transformed to a unified coordinate frame specified by the MNI-152 standard brain.
Within METSY, this approach allowed us to integrate structural brain connectivity data from the USC Multimodal Connectivity Database (UMCD) (Brown et al., 2012) with functional brain area information from the Brede database (Nielsen, 2014) and brain gene expression data from the Allen Brain atlas (Hawrylycz et al., 2012). To this end, an experienced neuroanatomist manually mapped the areas of the Craddock200 atlas used by UMCD to the Brede WOROI ontology (Brede) and Human Allen Brain Atlas (Allen Brain) using MNI coordinates as common denominator. For example, left hippocampus (Craddock200) was mapped to 107 Left hippocampus (Brede WOROI) and 4249001 hippocampal formation, left (Human Allen Brain Atlas). Individual level data from the three sources was subsequently uploaded into the METSY knowledge portal which may be searched and visualized based on any of the mapped atlases. As an example, a DTI tract might state “in schizophrenic patient A, left hippocampus is connected with mammillary body by strength 91 while a functional association might be “left hippocampus and mammillary body are correlated with connectivity 0.008 during resting state in healthy volunteers” and finally post-mortem expression data may indicate certain genes expressed in left hippocampus and mammillary body. Such mappings enable us to directly compute potential functional and molecular consequences of differences shown between schizophrenia patients and healthy volunteers, which are relevant to clinical decision making.
Decision support system
Using integrated data from the METSY knowledge base, a novel clinical decision support and data visualization framework was adapted and applied to tackle heterogeneous patient information. The main focus of the framework was to provide a comprehensive overview of the patient’s disease state (Mattila et al., 2011), which denotes a patient’s degree of similarity to a previously diagnosed disease population. This was archived by implementing the disease state index (DSI) method and disease state fingerprint (DSF) visualizations (Mattila et al., 2012) for the data contained within the METSY knowledge base. The DSF visualization clearly discloses how different components of the patient data contribute to the DSI, facilitating rapid interpretation of the information. The same methods were previously applied to examine Alzheimers disease and dementias in EU projects PredictAD, PredictND and VPH-DARE@IT.
DSI is a supervised machine learning algorithm, which quantifies the disease state of the patient. The method computes the statistical distributions for each measurement and uses them to quantify the disease state of the patient. The method produces a single variable for the patient, ranging between zero and one. An index value close to zero denotes that the patient has values similar to healthy subjects. By contrast, if the index is close to one, the measurements are more similar to diagnosed patients. The DSI can quantify a score, even if not all measures are available. The DSI classifier is accompanied by a disease state fingerprint (DSF) (Mattila et al., 2012) visualization. The DSF has a tree structure, which represents the structure of the DSI classifier, highlighting which measures have the strongest prognostic value.
Within METSY, the DSI was used to combine volumetric data from MRI, psychiatric measures, clinical measures and selected metabolomics data (Figure 5). The DSI was trained and tested with volumetric MRI, psychiatric and clinical measures selected based on earlier knowledge from the psychotic disorders. The metabolomics measures were selected based on the machine learning methods with dependency detection.
References
Ahmed, S.S. Santosh, W., Kumar, S., and Christlet, H.T. (2009). Metabolic profiling of Parkinson's disease: evidence of biomarker from gene expression analysis and rapid neural network detection. J Biomed Sci 16, 63.
Ali-Sisto, T., Tolmunen, T., Toffol, E., Viinamaki, H., Mantyselka, P., Valkonen-Korhonen, M., Honkalampi, K., Ruusunen, A., Velagapudi, V., and Lehto, S.M. (2016). Purine metabolism is dysregulated in patients with major depressive disorder. Psychoneuroendocrinology 70, 25-32.
Arango, C., Bobes, J., Kirkpatrick, B., Garcia-Garcia, M., and Rejas, J. (2011). Psychopathology, coronary heart disease and metabolic syndrome in schizophrenia spectrum patients with deficit versus non-deficit schizophrenia: findings from the CLAMORS study. Eur Neuropsychopharmacol 21, 867-875.
Arango, C., Rapado-Castro, M., Reig, S., Castro-Fornieles, J., Gonzalez-Pinto, A., Otero, S., Baeza, I., Moreno, C., Graell, M., Janssen, J., et al. (2012). Progressive brain changes in children and adolescents with first-episode psychosis. Arch Gen Psychiatry 69, 16-26.
Barabasi, A.-L. (2007). Network Medicine -- From Obesity to the "Diseasome". N Engl J Med 357, 404-407.
Bogdanov, M., Matson, W.R. Wang, L., Matson, T., Saunders-Pullman, R., Bressman, S.S. and Flint Beal, M. (2008). Metabolomic profiling to develop blood biomarkers for Parkinson's disease. Brain 131, 389-396.
Brown, J.A. Rudie, J.D. Bandrowski, A., Van Horn, J.D. and Bookheimer, S.Y. (2012). The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Front Neuroinform 6, 28.
Brugger, S.P. and Howes, O.D. (2017). Heterogeneity and Homogeneity of Regional Brain Structure in Schizophrenia: A Meta-analysis. JAMA Psychiatry 74, 1104-1111.
Cahn, W., Rais, M., Stigter, F.P. van Haren, N.E. Caspers, E., Hulshoff Pol, H.E. Xu, Z., Schnack, H.G. and Kahn, R.S. (2009). Psychosis and brain volume changes during the first five years of schizophrenia. Eur Neuropsychopharmacol 19, 147-151.
Ceccarini, J., De Hert, M., Van Winkel, R., Peuskens, J., Bormans, G., Kranaster, L., Enning, F., Koethe, D., Leweke, F.M. and Van Laere, K. (2013). Increased ventral striatal CB1 receptor binding is related to negative symptoms in drug-free patients with schizophrenia. Neuroimage 79, 304-312.
Colombo, G., Agabio, R., Diaz, G., Lobina, C., Reali, R., and Gessa, G.L. (1998). Appetite suppression and weight loss after the cannabinoid antagonist SR 141716. Life Sci 63, PL113-117.
Correll, C.U. Kane, J.M. and Manu, P. (2011). Obesity and coronary risk in patients treated with second-generation antipsychotics. Eur Arch Psychiatry Clin Neurosci 261, 417-423.
De Hert, M., Dobbelaere, M., Sheridan, E.M. Cohen, D., and Correll, C.U. (2011). Metabolic and endocrine adverse effects of second-generation antipsychotics in children and adolescents: A systematic review of randomized, placebo controlled trials and guidelines for clinical practice. Eur Psychiatry 26, 144-158.
Desikan, R.S. Segonne, F., Fischl, B., Quinn, B.T. Dickerson, B.C. Blacker, D., Buckner, R.L. Dale, A.M. Maguire, R.P. Hyman, B.T. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-980.
Fornito, A., Zalesky, A., Pantelis, C., and Bullmore, E.T. (2012). Schizophrenia, neuroimaging and connectomics. Neuroimage 62, 2296-2314.
Fraguas, D., Merchan-Naranjo, J., Laita, P., Parellada, M., Moreno, D., Ruiz-Sancho, A., Cifuentes, A., Giraldez, M., and Arango, C. (2008). Metabolic and hormonal side effects in children and adolescents treated with second-generation antipsychotics. J Clin Psychiatry 69, 1166-1175.
Frank, E., Maier, D., Pajula, J., Suvitaival, T., Borgan, F., Butz-Ostendorf, M., Fischer, A., Hietala, J., Howes, O., Hyotylainen, T., et al. (2018). Platform for systems medicine research and diagnostic applications in psychotic disorders-The METSY project. Eur Psychiatry 50, 40-46.
Galperin, M.Y. and Fernandez-Suarez, X.M. (2012). The 2012 Nucleic Acids Research Database Issue and the online Molecular Biology Database Collection. Nucleic Acids Res 40, D1-8.
Gatta-Cherifi, B., and Cota, D. (2015). Endocannabinoids and Metabolic Disorders. Handb Exp Pharmacol 231, 367-391.
Haijma, S.V. Van Haren, N., Cahn, W., Koolschijn, P.C. Hulshoff Pol, H.E. and Kahn, R.S. (2013). Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr Bull 39, 1129-1138.
Han, X., Rozen, S., Boyle, S.H. Hellegers, C., Cheng, H., Burke, J.R. Welsh-Bohmer, K.A. Doraiswamy, P.M. and Kaddurah-Daouk, R. (2011). Metabolomics in early Alzheimer's disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One 6, e21643.
Hansen, T., Ingason, A., Djurovic, S., Melle, I., Fenger, M., Gustafsson, O., Jakobsen, K.D. Rasmussen, H.B. Tosato, S., Rietschel, M., et al. (2011). At-risk variant in TCF7L2 for type II diabetes increases risk of schizophrenia. Biol Psychiatry 70, 59-63.
Harkany, T., Guzman, M., Galve-Roperh, I., Berghuis, P., Devi, L.A. and Mackie, K. (2007). The emerging functions of endocannabinoid signaling during CNS development. Trends Pharmacol Sci 28, 83-92.
Hatano, T., Saiki, S., Okuzumi, A., Mohney, R.P. and Hattori, N. (2016). Identification of novel biomarkers for Parkinson's disease by metabolomic technologies. J Neurol Neurosurg Psychiatry 87, 295-301.
Hawrylycz, M.J. Lein, E.S. Guillozet-Bongaarts, A.L. Shen, E.H. Ng, L., Miller, J.A. van de Lagemaat, L.N. Smith, K.A. Ebbert, A., Riley, Z.L. et al. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391-399.
He, Y., Yu, Z., Giegling, I., Xie, L., Hartmann, A.M. Prehn, C., Adamski, J., Kahn, R., Li, Y., Illig, T., et al. (2012). Schizophrenia shows a unique metabolomics signature in plasma. Transl Psychiatry 2, e149.
Hietala, J., Syvalahti, E., Vuorio, K., Rakkolainen, V., Bergman, J., Haaparanta, M., Solin, O., Kuoppamaki, M., Kirvela, O., Ruotsalainen, U., et al. (1995). Presynaptic dopamine function in striatum of neuroleptic-naive schizophrenic patients. Lancet 346, 1130-1131.
Hofree, M., Shen, J.P. Carter, H., Gross, A., and Ideker, T. (2013). Network-based stratification of tumor mutations. Nat Methods 10, 1108-1115.
Howes, O.D. Bhatnagar, A., Gaughran, F.P. Amiel, S.A. Murray, R.M. and Pilowsky, L.S. (2004). A prospective study of impairment in glucose control caused by clozapine without changes in insulin resistance. Am J Psychiatry 161, 361-363.
Howes, O.D. and Murray, R.M. (2014). Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet 383, 1677-1687.
Jin, H., Meyer, J.M. and Jeste, D.V. (2004). Atypical antipsychotics and glucose dysregulation: a systematic review. Schizophr Res 71, 195-212.
Kaddurah-Daouk, R., McEvoy, J., Baillie, R.A. Lee, D., Yao, J.K. Doraiswamy, P.M. and Krishnan, K.R.R. (2007). Metabolomic mapping of atypical antipsychotic effects in schizophrenia. Mol Psychiatry 12, 934-945.
Kaddurah-Daouk, R., Rozen, S., Matson, W., Han, X., Hulette, C.M. Burke, J.R. Doraiswamy, P.M. and Welsh-Bohmer, K.A. (2011). Metabolomic changes in autopsy-confirmed Alzheimer's disease. Alzheimers Dement 7, 309-317.
Kaddurah-Daouk, R., Yuan, P., Boyle, S.H. Matson, W., Wang, Z., Zeng, Z.B. Zhu, H., Dougherty, G.G. Yao, J.K. Chen, G., et al. (2012). Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile. Sci Rep 2, 667.
Kajio, Y., Kondo, K., Saito, T., Iwayama, Y., Aleksic, B., Yamada, K., Toyota, T., Hattori, E., Ujike, H., Inada, T., et al. (2014). Genetic association study between the detected risk variants based upon type II diabetes GWAS and psychotic disorders in the Japanese population. J Hum Genet 59, 54-56.
Kirkpatrick, B., Miller, B.J. Garcia-Rizo, C., Fernandez-Egea, E., and Bernardo, M. (2012). Is abnormal glucose tolerance in antipsychotic-naive patients with nonaffective psychosis confounded by poor health habits? Schizophr Bull 38, 280-284.
Laruelle, M., Abi-Dargham, A., van Dyck, C.H. Gil, R., D'Souza, C.D. Erdos, J., McCance, E., Rosenblatt, W., Fingado, C., Zoghbi, S.S. et al. (1996). Single photon emission computerized tomography imaging of amphetamine-induced dopamine release in drug-free schizophrenic subjects. Proc Natl Acad Sci U S A 93, 9235-9240.
Leweke, F.M. Giuffrida, A., Koethe, D., Schreiber, D., Nolden, B.M. Kranaster, L., Neatby, M.A. Schneider, M., Gerth, C.W. Hellmich, M., et al. (2007). Anandamide levels in cerebrospinal fluid of first-episode schizophrenic patients: impact of cannabis use. Schizophr Res 94, 29-36.
Losko, S., and Heumann, K. (2009). Semantic data integration and knowledge management to represent biological network associations. Methods Mol Biol 563, 241-258.
Lu, H.C. and Mackie, K. (2016). An Introduction to the Endogenous Cannabinoid System. Biol Psychiatry 79, 516-525.
Luukkonen, P.K. Zhou, Y., Sadevirta, S., Leivonen, M., Arola, J., Oresic, M., Hyotylainen, T., and Yki-Jarvinen, H. (2016). Hepatic ceramides dissociate steatosis and insulin resistance in patients with non-alcoholic fatty liver disease. J Hepatol 64, 1167-1175.
Maier, D., Kalus, W., Wolff, M., Kalko, S.G. Roca, J., Marin de Mas, I., Turan, N., Cascante, M., Falciani, F., Hernandez, M., et al. (2011). Knowledge management for systems biology a general and visually driven framework applied to translational medicine. BMC Syst Biol 5, 38.
Marconi, A., Di Forti, M., Lewis, C.M. Murray, R.M. and Vassos, E. (2016). Meta-analysis of the Association Between the Level of Cannabis Use and Risk of Psychosis. Schizophr Bull 42, 1262-1269.
Mattila, J., Koikkalainen, J., Virkki, A., Simonsen, A., van Gils, M., Waldemar, G., Soininen, H., Lotjonen, J., and Alzheimer's Disease Neuroimaging, I. (2011). A disease state fingerprint for evaluation of Alzheimer's disease. J Alzheimers Dis 27, 163-176.
Mattila, J., Koikkalainen, J., Virkki, A., van Gils, M., Lotjonen, J., and Alzheimer's Disease Neuroimaging, I. (2012). Design and application of a generic clinical decision support system for multiscale data. IEEE Trans Biomed Eng 59, 234-240.
McEvoy, J., Baillie, R.A. Zhu, H., Buckley, P., Keshavan, M.S. Nasrallah, H.A. Dougherty, G.G. Yao, J.K. and Kaddurah-Daouk, R. (2013). Lipidomics reveals early metabolic changes in subjects with schizophrenia: effects of atypical antipsychotics. PLoS One 8, e68717.
Minichino, A., Ando, A., Francesconi, M., Salatino, A., Delle Chiaie, R., and Cadenhead, K. (2017). Investigating the link between drug-naive first episode psychoses (FEPs), weight gain abnormalities and brain structural damages: Relevance and implications for therapy. Prog Neuropsychopharmacol Biol Psychiatry 77, 9-22.
Mukherjee, S., Schnur, D.B. and Reddy, R. (1989). Family history of type 2 diabetes in schizophrenic patients. Lancet 1, 495.
Newcomer, J.W. (2005). Second-generation (atypical) antipsychotics and metabolic effects: a comprehensive literature review. CNS Drugs 19 Suppl 1, 1-93.
Nielsen, F.A. (2014). Brede tools and federating online neuroinformatics databases. Neuroinformatics 12, 27-37.
Normandin, M.D. Zheng, M.Q. Lin, K.S. Mason, N.S. Lin, S.F. Ropchan, J., Labaree, D., Henry, S., Williams, W.A. Carson, R.E. et al. (2015). Imaging the cannabinoid CB1 receptor in humans with [11C]OMAR: assessment of kinetic analysis methods, test-retest reproducibility, and gender differences. J Cereb Blood Flow Metab 35, 1313-1322.
Nuevo, R., Chatterji, S., Fraguas, D., Verdes, E., Naidoo, N., Arango, C., and Ayuso-Mateos, J.L. (2011). Increased risk of diabetes mellitus among persons with psychotic symptoms: results from the WHO World Health Survey. J Clin Psychiatry 72, 1592-1599.
Oresic, M., Hyotylainen, T., Herukka, S.K. Sysi-Aho, M., Mattila, I., Seppanan-Laakso, T., Julkunen, V., Gopalacharyulu, P.V. Hallikainen, M., Koikkalainen, J., et al. (2011a). Metabolome in progression to Alzheimer's disease. Transl Psychiatry 1, e57.
Oresic, M., Hyotylainen, T., Kotronen, A., Gopalacharyulu, P., Nygren, H., Arola, J., Castillo, S., Mattila, I., Hakkarainen, A., Borra, R.J. et al. (2013). Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids. Diabetologia 56, 2266-2274.
Oresic, M., Seppanen-Laakso, T., Sun, D., Tang, J., Therman, S., Viehman, R., Mustonen, U., van Erp, T.G. Hyotylainen, T., Thompson, P., et al. (2012). Phospholipids and insulin resistance in psychosis: a lipidomics study of twin pairs discordant for schizophrenia. Genome Med 4, 1.
Oresic, M., Tang, J., Seppanen-Laakso, T., Mattila, I., Saarni, S.E. Saarni, S.I. Lonnqvist, J., Sysi-Aho, M., Hyotylainen, T., Perala, J., et al. (2011b). Metabolome in schizophrenia and other psychotic disorders: a general population-based study. Genome Med 3, 19.
Padmanabhan, J.L. Nanda, P., Tandon, N., Mothi, S.S. Bolo, N., McCarroll, S., Clementz, B.A. Gershon, E.S. Pearlson, G.D. Sweeney, J.A. et al. (2016). Polygenic risk for type 2 diabetes mellitus among individuals with psychosis and their relatives. J Psychiatr Res 77, 52-58.
Pagotto, U., Marsicano, G., Cota, D., Lutz, B., and Pasquali, R. (2006). The emerging role of the endocannabinoid system in endocrine regulation and energy balance. Endocr Rev 27, 73-100.
Paredes, R.M. Quinones, M., Marballi, K., Gao, X., Valdez, C., Ahuja, S.S. Velligan, D., and Walss-Bass, C. (2014). Metabolomic profiling of schizophrenia patients at risk for metabolic syndrome. Int J Neuropsychopharmacol 17, 1139-1148.
Pillinger, T., Beck, K., Gobjila, C., Donocik, J.G. Jauhar, S., and Howes, O.D. (2017). Impaired Glucose Homeostasis in First-Episode Schizophrenia: A Systematic Review and Meta-analysis. JAMA Psychiatry 74, 261-269.
Quinones, M.P. and Kaddurah-Daouk, R. (2009). Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol Dis 35, 165-176.
Ranganathan, M., Cortes-Briones, J., Radhakrishnan, R., Thurnauer, H., Planeta, B., Skosnik, P., Gao, H., Labaree, D., Neumeister, A., Pittman, B., et al. (2016). Reduced Brain Cannabinoid Receptor Availability in Schizophrenia. Biol Psychiatry 79, 997-1005.
Rosse, C., and Mejino, J.L. Jr. (2003). A reference ontology for biomedical informatics: the Foundational Model of Anatomy. J Biomed Inform 36, 478-500.
Scheen, A.J. and Paquot, N. (2009). Use of cannabinoid CB1 receptor antagonists for the treatment of metabolic disorders. Best Pract Res Clin Endocrinol Metab 23, 103-116.
Sporns, O., Tononi, G., and Kotter, R. (2005). The human connectome: A structural description of the human brain. PLoS Comput Biol 1, e42.
Suvitaival, T., Mantere, O., Kieseppa, T., Mattila, I., Poho, P., Hyotylainen, T., Suvisaari, J., and Oresic, M. (2016). Serum metabolite profile associates with the development of metabolic co-morbidities in first-episode psychosis. Transl Psychiatry 6, e951.
Terry, G.E. Hirvonen, J., Liow, J.S. Zoghbi, S.S. Gladding, R., Tauscher, J.T. Schaus, J.M. Phebus, L., Felder, C.C. Morse, C.L. et al. (2010). Imaging and quantitation of cannabinoid CB1 receptors in human and monkey brains using (18)F-labeled inverse agonist radioligands. J Nucl Med 51, 112-120.
Terry, G.E. Liow, J.S. Zoghbi, S.S. Hirvonen, J., Farris, A.G. Lerner, A., Tauscher, J.T. Schaus, J.M. Phebus, L., Felder, C.C. et al. (2009). Quantitation of cannabinoid CB1 receptors in healthy human brain using positron emission tomography and an inverse agonist radioligand. Neuroimage 48, 362-370.
Trushina, E., Dutta, T., Persson, X.M. Mielke, M.M. and Petersen, R.C. (2013). Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics. PLoS One 8, e63644.
Tsujikawa, T., Zoghbi, S.S. Hong, J., Donohue, S.R. Jenko, K.J. Gladding, R.L. Halldin, C., Pike, V.W. Innis, R.B. and Fujita, M. (2014). In vitro and in vivo evaluation of (11)C-SD5024, a novel PET radioligand for human brain imaging of cannabinoid CB1 receptors. Neuroimage 84, 733-741.
West, P.R. Amaral, D.G. Bais, P., Smith, A.M. Egnash, L.A. Ross, M.E. Palmer, J.A. Fontaine, B.R. Conard, K.R. Corbett, B.A. et al. (2014). Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children. PLoS One 9, e112445.
Wishart, D.S. Jewison, T., Guo, A.C. Wilson, M., Knox, C., Liu, Y., Djoumbou, Y., Mandal, R., Aziat, F., Dong, E., et al. (2013). HMDB 3.0--The Human Metabolome Database in 2013. Nucleic Acids Res 41, D801-807.
Wong, D.F. Kuwabara, H., Horti, A.G. Raymont, V., Brasic, J., Guevara, M., Ye, W., Dannals, R.F. Ravert, H.T. Nandi, A., et al. (2010). Quantification of cerebral cannabinoid receptors subtype 1 (CB1) in healthy subjects and schizophrenia by the novel PET radioligand [11C]OMAR. Neuroimage 52, 1505-1513.
Yasuno, F., Brown, A.K. Zoghbi, S.S. Krushinski, J.H. Chernet, E., Tauscher, J., Schaus, J.M. Phebus, L.A. Chesterfield, A.K. Felder, C.C. et al. (2008). The PET radioligand [11C]MePPEP binds reversibly and with high specific signal to cannabinoid CB1 receptors in nonhuman primate brain. Neuropsychopharmacology 33, 259-269.
Potential Impact:
METSY optimized the use of existing MRI and PET technologies for the study of psychotic disorders, with specific focus on their metabolic co-morbidities. Additionally, the project developed the tools (procedures, software) for combined MRI and PET neuroimaging, as well as statistical and bioinformatics tools to integrate this information with other phenotypic data including metabolic characterisation as obtained from metabolomics of biofluids. Specifically, METSY developed and validated multiple innovative multi-modal biomarkers based on neuroimaging and metabolic profiling to the stage where they can be considered for developments towards the implementation in healthcare setting. For clinical biomarker development it is needless to say that the further the biomarker is developed, more value it carries. Discovery studies were carried out in WPs 1-3, while the validation studies were done in WP 6.
Notably, all these activities were highly integrated and include a strong component of computational systems biology (WP 5) and neuroimaging technology development (WP 4). In metabolomics studies, the discovery and validation steps are usually performed by using “global platforms”, covering a broad range of analytes. Once the biomarker analytes are known, a robust and rugged method needs to be developed and validated which may be applicable in clinical setting. As anticipated ion the to the very end of the assay development stage, e.g. as a diagnostic kit ready for use in healthcare setting. However, they were developed far enough that commercial exploitation and pilot studies in healthcare setting can be considered. The commercial exploitation of such biomarkers and related assays will be considered case-by-case according to METSY management procedures, and may involve further in house developments, launching spin-outs, licensing, or industrial partnerships.
For broad acceptance of the biomarker in healthcare setting, biomarker needs to be further confirmed in independent replication studies and its utility needs to be demonstrated in multiple studies, with some leading to publications in notable medical journals. Although these additional studies were not considered within METSY, once the assays and multi-modal panels are developed in WP 6, METSY will consider offering them for applications in other on-going or future studies or projects. The conditions of involvement will be considered on case-by-case basis according to METSY management procedures. In addition to participating SME and industrial partner, also other METSY partners have a strong track record in commercial exploitation of scientific findings, including in launching multiple spinout companies, licensing deals, or industry partnerships across a broad range of business areas.
Encouragement of SME participation and fostering innovation in Europe in line with the Europe2020 agenda.
METSY included one SME in the domain of bioinformatics. In line with Europe 2020 Smart Growth
priorities and strongly facilitating the integration with a large European industry partner,
METSY had strong impact on creating new products/services that generated growth and jobs and helped address social challenges with the help of innovative combination of neuroimaging and metabolic research via the use of state-of-the-art methods of bioinformatics and statistics.
METSY supported the goals of the European Pact for Mental Health.
Affective and non-affective psychoses are relatively prevalent mental illnesses. It has been
estimated that the lifetime prevalence of all psychotic disorders is about 3.5 %. Psychotic bipolar
disorder and psychotic depression are common affective psychoses whereas schizophrenia is the
most common as well as the most severe one among the non-affective psychoses in terms of
functional outcome. Schizophrenia is clinically characterised with a typical onset in adolescence
or early adulthood with disturbances of perception, thinking, behaviour and emotional life. Schizophrenia and other psychotic disorders are also a major public health problem because of their
burden and prevalence. Brain disorders cost Europe almost 800 billion € a year. Among all brain
disorders psychotic disorders come second, only after mood disorders, in terms of cost to Europe. In a recent report it has been estimated that in Europe there are over 5 million people with psychotic
disorders and that the cost to Europe is 93.6 billion € a year. METSY provided better predictive diagnostic tools to detect and monitor psychosis which is directly relevant to two priority areas of the European Pact on Mental Health and Well-being: (II) Mental health in youth and education, and (III) Mental health in workplace settings.
Dissemination and/or exploitation of project results, and management of IP
Dissemination of new knowledge within the scientific community is an intrinsic interest of research,
and aims at strengthening and reinforcing the European research activities by multiplication and
initiation of networking and collaborations beyond the consortium. Dissemination of results and
new knowledge obtained in METSY is of high priority, emphasized by the dedication of a work
package to dissemination issues. Within METSY WP 7 was dedicated on result exploitation:
1. To attain a high level of public awareness of METSY activities and discoveries and of the
relevance to systems medicine
2. To maximize exploitation of METSY discoveries in healthcare and personalised medicine settings
3. To protect METSY intellectual property.
Dissemination of knowledge took place at different levels:
1. Within the METSY consortium (internal meetings and reports);
2. To the scientific community (publication in international peer reviewed journals, presentations
at national and international conferences);
3. To patients as well as healthcare professionals and decision makers via patient organizations;
4. To the broad public.
The project manager was responsible to oversee all dissemination activities, which were also defined in the Consortium Agreement.
Means to disseminate new knowledge within the scientific community were:
1. Publications in high-impact, peer-reviewed international journals
2. Presentations at international conferences
3. METSY Workshops
4. Filing of patent applications
5. Multiplication by recruitment and training of scientists at the PhD and postdoctoral level
6. Open workshops
7. Presentation of METSY, its objective, aims and potentials on a public domain web page.
Means to disseminate to patients as well as healthcare professionals and decision makers were:
1. Organization of special workshops for patients and/or healthcare professionals related to
specific METSY S/T activities and outcomes,
2. Active participation in international conferences, such as organization of METSY sessions at
these meetings. As an already existing example of such activities, M. Orešič (P1) organized a
symposium at 14th International Congress on Schizophrenia Research (April 2013; Orlando/FL/USA) on the topic of molecular biomarkers in schizophrenia.
Means to disseminate to the public were:
1. Press releases in national newspapers, initiated by the information offices at the participating
organizations;
2. Open door events in the participating organizations;
3. Science and Society events;
4. Presentation of METSY, its objective, aims and potentials on a public domain web page.
METSY had specific initiatives to strengthen its dissemination potential:
1. METSY web page. One important means to integrate all dissemination activities was the web page of METSY, where knowledge was made available to the scientific community and
the public. A private domain was established to make internal knowledge easily accessible
for all partners. The private domain includes internal interim reports, pre-views of scientific
publications and internal news. The web page was regularly updated and designed to meet
the needs of the consortium.
2. Preparation of joint papers and position statements. The Steering Committee actively pursued opportunities for METSY to publish joint papers and position statements in the name of
METSY. The METSY joint papers attract more attention to other means of dissemination
such as the METSY web page, and therefore had a multiplication value.
Exploitation of METSY results and management of Intellectual Property
– industry participants
Biomax
The BioXMTM Knowledge Management Environment is developed by Biomax Informatics AG and
is available as a commercial product in life science research and clinical application environments.
Generation of a psychotic disease and metabolic co-morbidities knowledgebase and integration with
newly developed clinical decision support systems (WP 5) broadened the clinical applicability of
the system to psychotic disease (earlier it was focused on pulmonary care).
The experience and research results gained during the project, regarding structuring, mapping and
mining results of brain imaging technologies in the context of diagnostics allowed Biomax to
further extend its established commercial footprint in the field of clinical knowledge management in
research and application. While there is strong competition from large non-European companies
(e.g. Microsoft Almaga Life Sciences) regarding general clinical data management infrastructure
the added value provided by targeted content and knowledge representation, developed in projects
such as METSY, allowed Biomax to provide added value and expand its position in the life science
knowledge management and bioinformatics market with a total volume in 2010 of about $ 3.5
billion and a focus on content in purchase decisions (Frost&Sullivan Market report 2010, RNCOS
Market Outlook 2010, Global Industry Analysts report 2011).
Philips
The Imalytics Research Workstation is developed by Philips Research and is available as a
commercial product. It serves as a platform for new applications to be used in preclinical and
clinical research environments. Project results of WP 4 became new modules on the Imalytics platform, and thereby have the potential for direct commercialisation.
Insights and first prototypes generated in this project were transferred to various business units within Philips Healthcare at a later stage. Developed modules may also be made available on
the clinical workstation IntelliSpace Portal (requiring regulatory approval, which is out of scope for
this project).
Furthermore, the demonstration of the clinical benefits of hybrid PET/MR neuroimaging that were pursued in this project substantiated the need for this innovative hybrid imaging technology
for early prediction and monitoring of psychotic disorders, thus fostering sales to academic hospitals
and specialized neuroimaging centres.
Overview of Intellectual Property (IP) opportunity
The effective management and exploitation of Intellectual Property (IP) was a critical component of
METSY. It is essential that the outcomes of the research are adequately protected in such a way
that they are attractive for commercial exploitation. Consideration must be given to the categories
of IP that are likely products of the Work Packages. For example:
1. Diagnostic biomarkers applicable in healthcare setting. Activities in WPs 1-3 and 6 led to novel biomarkers for the specific clinical outcomes of relevance to healthcare and personalized medicine. The IP may include specific analytical assays for the biomarkers or more
broadly the multi-modal biomarker signatures together with the method to predict the relevant
outcomes using these analyte(s) as well as potentially other information.
2. Technology solutions for neuroimaging. The technology/software developments in WPs 2, 4,
and 5 have a potential to lead to new or improved existing products by the industrial
participants, as well as to novel product ideas, e.g. software tools for diagnostics and patient
monitoring in psychiatric disorders (WPs 4, 5).
Management of IP
As recommended in the FP7 guidelines and according to standard practice, IP was considered in
the following categories:
1. Background (pre-existing IP);
2. Foreground (knowledge generated from the Collaborative Project whether or not it may be
patentable).
Foreground may be owned by the single party that generated the Foreground or jointly owned by
several parties that have contributed to the Foreground. The ownership and rights of use of
Foreground generated through the performance of this collaborative project were defined in the
consortium agreement which will combine standard practice with several innovative features to
enhance the effectiveness through which Foreground can be exploited and/or commercialized. A
summary of the key considerations is provided here:
1. Identification. Academic research scientists will often not recognize valuable foreground that
could form the basis of a patent. This is not surprising since such training is not routinely provided
or available to researchers. The current program addressed this by two actions:
1. it provided training in knowledge protection and transfer, and IP. This action took advantage of the existing technology transfer organizations within METSY. The partner organizations have well developed and professional technology transfer offices. In addition, the program included two SMEs, for which the effective management of IP is an essential part of their activities. METSY offered a workshop taught by experts from the technology transfer organizations and with representatives from SMEs to provide the industry perspective. The workshop focused on how to identify a foreground opportunity that should be protected and on the best strategy for its protection.
2. Technology scouts. This is, relative to academic practice, a highly innovative feature of the
METSY program that has been inspired by current industry practice. Industry employs technology scouts who are trained in the identification of valuable IP and technology partnering opportunities and who attend meetings and conferences or visit biotechnology or academic clusters in order to identify what may be of interest to their company. METSY collected volunteers within the programme (preferably at the Postdoctoral level) who have an interest in the exploitation of foreground and commercialization of research results. These were briefed, mentored and trained by the technology transfer specialists to act as technology scouts within METSY. Their task will be to identify opportunities arising from the research that should be protected and/or exploited. This activity also served to provide a first training to the postdoctoral fellows in industry-relevant actions and may be particularly attractive to those researchers considering a career in industry.
2. Protection. The breadth of claims and positioning of the claims are essential elements in
establishing the value of a patent. Consequently, it is important to consider the full possible breath
of claims that could be made regarding a particular asset of foreground. This was achieved within the current proposal by implementation of a Foreground Evaluation Committee (FEC), which included a blend of expertise that added value to patents by identifying enlarged scope or wider positioning of claims. The FEC was composed of clinical researchers, academic researchers, representatives of the technology transfer offices and a patent attorney. The FEC reviewed each opportunity presented by the technology scouts (or directly by research scientists whenever they should take such initiative) with a view to maximize its value and to recommend an effective patent filing strategy or other form of protection strategy. The FEC also assisted in determining the assignment of ownership of jointly-owned Foreground.
3. Rights of Use. All Foreground generated within METSY is made available to the
consortium for non-commercial research, training and educational purposes. Whenever possible this philosophy is also applied on a Europe-wide scope to any Foreground that has potential for
creation of value in the European research base such that the Foreground is made available
non-exclusively for non-commercial research, training and education to the research community.
Pending confirmation of this and other stipulation by the negotiated consortium agreement, it is
anticipated that any party generating individually Foreground that is the subject of a patented
invention has the right to use and licence such invention at their sole discretion. Rights of use of
patented inventions developed with contributions of more than one partner within METSY is determined by agreement between the concerned partners on an exploitation and licensing plan.
This plan may stipulate that one contributing partner licenses exclusively their share of the joint
invention to another contributing partner for commercialization. Alternatively, each party sharing
the ownership of Foreground may be entitled to issue non-exclusive licenses at their sole discretion
but without the right to sublicense. Please note that patented Background that is required for the
performance of METSY will be provided by each party as a royalty-free non-exclusive license.
Project logo
Other project contacts
Prof. Jarmo Hietala
University of Turku
Kiinamyllynkatu 4-8, building 11B, 20520 TURKU
Phone: +358 2 266 2520
E-mail: jarmo.hietala@utu.fi
Prof. Oliver Howes
King’s College London
PO 67, De Crespigny Park, Denmark Hill, London SE5 8AF
Phone: +44 (0)20 7848 0080 or +44 (0)20 8383 3160
E-mail: oliver.howes@kcl.ac.uk
Prof. Jaana Suvisaari
National Public Health Institute
P.O. Box 30, FI-00271, Mannerheimintie 166, Helsinki, Finland
Phone: +358 29 524 8539
E-mail: jaana.suvisaari@thl.fi
Prof. Mark van Gils
VTT Technical Research Centre of Finland
P.O. Box 1300, 33101 Tampere, Finland
Phone: +358 20 722 3342
E-mail: mark.vangils@vtt.fi
Dr. Dieter Maier
Biomax Informatics AG
Robert-Koch-Str. 2 D-82152 Planegg Germany
Phone: +49 89 895574-0
Email: dieter.maier@biomax.com
Website: http://www.biomax.com
Prof. Tuulia Hyötyläinen
School of Science and Technology
Örebro University
SE-701 82 Örebro, Sweden
Phone: +46 19 303487
Email: tuulia.hyotylainen@oru.se
Psychotic disorders are associated with metabolic abnormalities including alterations in glucose and lipid metabolism. A major challenge in the treatment of psychosis is to identify patients with vulnerable metabolic profiles who may be at risk of developing cardiometabolic co-morbidities. It is established that both central and peripheral metabolic organs use lipids to control energy balance and regulate peripheral insulin sensitivity. The endocannabinoid system, implicated in the regulation of glucose and lipid metabolism, has been shown to be dysregulated in psychosis. It is an open research question how these endocannabinoid abnormalities relate to metabolic changes in psychosis. The METSY project was established with the aim to identify and evaluate multi-modal peripheral and neuroimaging markers that may be able to predict the onset and prognosis of psychiatric and metabolic symptoms in patients at risk of developing psychosis and first episode psychosis patients. The project brought together clinicians, researchers and industry partners in the domains of psychiatry, neuroimaging, metabolic research, systems biology and bioinformatics.
Given the intrinsic complexity and widespread role of lipid metabolism, a systems biology approach which combines molecular, structural and functional neuroimaging methods with detailed metabolic characterisation and multi-variate network analysis is essential in order to identify how lipid dysregulation may contribute to psychotic disorders. METSY applied comprehensive metabolomics as well as neuroimaging (MRI and PET) in first-episode (FEP) or clinical high-risk (CHR) for psychosis in studies across the four clinical sites. The project led to several key findings including detailed prospective characterisation of inflammation/metabolism in FEP, predictive signatures of psychosis in CHR patients, identification of FEP patients at highest risk of rapid weight gain by metabolomics, and identification of dysregulation of the endocannabinoid system in the brain of FEP/CHR patients as well as in the periphery. A decision support system, integrating clinical, neuropsychological and neuroimaging data, was also developed in order to aid clinical decision making in psychosis.
Knowledge of common and specific mechanisms may aid the etiopathogenic understanding of psychotic and metabolic disorders, facilitate early disease detection, aid treatment selection and elucidate new targets for pharmacological treatments. Other expected impacts of the project are (1) new validated multi-modal markers for early disease detection and monitoring, (2) new tools for the identification of subjects who may benefit from specific treatment (3) discovery of new avenues for disease prevention and therapy, and (4) new tools and processes for applying brain imaging in personalised medicine. Over the lifetime of the project, the topic of metabolic co-morbidities in psychotic has gained increasing prominence in the field of psychosis research. For example, most recently, METSY participants organized a special session on this topic at the 6th Biennial Schizophrenia International Research Society Conference in Florence, Italy (April 2018), SIRS 2018.
Project Context and Objectives:
The METSY overall objective was to identify, prioritize and evaluate multi-modal blood and neuroimaging markers with diagnostic potential for prediction and monitoring of psychotic disorders and associated metabolic co-morbidities.
The main objective of METSY was met by combinations of clinical research, state-of-the-art technologies and systems biology, divided into six specific objectives:
O1. To apply neuroimaging strategies to characterise structural and metabolic changes in the brain during the first stages of psychosis.
O2. To apply imaging strategies to characterise the endocannabinoid pathways in the brain and relate them to lipid molecular networks.
O3. To characterise genetic and lipid molecular networks as measured in biofluids in early psychosis and identify how these networks associate with patient outcomes.
O4. To develop and demonstrate methodology for combined PET and MRI imaging.
O5. To develop bioinformatics tools to integrate brain image information with clinical and molecular profile data.
O6. To identify, prioritize and evaluate multi-modal circulating and neuroimaging markers with diagnostic potential for prediction and monitoring of psychotic disorders and associated metabolic co-morbidities.
Figure 1. METSY S/T structure.
Research comprised of three activity areas, articulated into six S/T work packages (WPs), as shown in Figure 1:
(1) human cohort studies (WPs 1-3),
(2) methodology developments (WPs 4 and 5), and
(3) translational research (WP 6).
METSY also included two WPs dedicated to dissemination and management. The S/T WPs followed the progression of the work plan and reflected the six specific objectives.
WP 1 pursued detailed neuroimaging and neuropsychology characterisation in longitudinal studies involving patients at-risk or with first episode of psychosis. FEP and CHR subjects and matched healthy controls were followed up simultaneously with analogous methodology in order to extract neuroimaging information useful for characterization of the development of psychosis and associated metabolic outcomes.
WP 2 pursued detailed neuroimaging and metabolic studies of endocannabinoid pathways including synthesis and degradation systems. More accurate methods for direct quantification of CB1 receptors have been recently developed. In collaboration with NIH a CB1 tracer ([18F]FMPEP-d2) was validated and was be used in the project proposal in Turku (P1) and London (P6) with inter-center methodology harmonization process.
WP 3 pursued detailed metabolic characterization, metabolomics as well as studied immune/oxidative stress markers in the cohorts included in WP 1. Data was analysed in collaboration with WP 5.
WP 4 developed methods for combined PET and MR image acquisition and analysis. Consecutive baseline scans, i.e. CB1R and the presynaptic dopamine synthesis tracer [18F]DOPA were performed and the binding outcome interactions (correlation and hub analyses) was a starting point for methodology testing. Early protocols were utilized first in healthy volunteers and then later in early pilot patients. Optimised protocols for the PET/MR hybrid camera (Philips IngenuityTF) were developed and evaluated. Dedicated software packages, specifically adapted to match the requirements of WP1 and WP2 for combined PET and MRI data visualization and analysis were developed on the Imalytics Research Workstation (Philips Research, Aachen, DE).
WP 5 pursued statistical developments to integrate image data with other phenotypic data, including from ‘omics’ analyses, aiming to extract the signals of potential diagnostic value. Semantic modelling was used to annotate these data with biological and literature-based annotations. Disease State Index was evaluated as a decision-support system in psychosis.
WP 6 validated the multi-modal circulating and neuroimage markers which are sensitive to metabolic disturbances in the brain of at-risk or psychotic patients using an independent prospective sample series from 250 first-episode patients and their healthy controls, which were not included as part of biomarker discovery in WPs 1-3.
Project Results:
Scientific background
Unhealthy lifestyles and pharmacological side effects have been suggested to be a major cause of excess mortality rates in patients with psychotic disorders. Schizophrenia patients exhibiting negative symptoms such as anhedonia and social withdrawal are more prone to becoming overweight and developing metabolic syndrome, which may in turn increase the risk of cardiovascular morbidity (Arango et al., 2011). Additionally, the use of antipsychotic medication, especially second generation antipsychotics, has been consistently associated with weight gain, insulin resistance and the development of metabolic syndrome (Correll et al., 2011; Howes et al., 2004; Jin et al., 2004; Newcomer, 2005), which seems to be more marked in younger people (De Hert et al., 2011). After only six months of treatment with specific second-generation antipsychotics, the percentage of previously drug naïve first episode psychosis patients at risk of developing the metabolic syndrome rises from 17% to 40% (Fraguas et al., 2008). This evidence suggests that these psychotropic drugs target brain regions involved in regulating energy balance and metabolism.
However, pharmacological side effects and unhealthy lifestyles only explain a fraction of the metabolic co-morbidities shown in psychosis. Abnormal glucose homeostasis, hyperinsulinemia and accumulation of visceral fat are already evident in drug-naïve first episode psychosis patients, independently of obesity (Kirkpatrick et al., 2012; Pillinger et al., 2017). In the WHO World Health Survey, as compared with the absence of symptoms, having one psychotic symptom was associated with higher odds (OR 1.71; 95% CI, 1.61-1.81) of diabetes mellitus in the general population, with increasing likelihood as the number of psychotic symptoms increased (Nuevo et al., 2011). Furthermore, unaffected first-degree relatives of people with schizophrenia also have higher rates of diabetes mellitus (19-30%) compared to the general population (1.2-6.3%) (Mukherjee et al., 1989). Some recent genetic studies have detected genes that increase the risk of both schizophrenia and type 2 diabetes (T2D) (Hansen et al., 2011); however, there have been negative findings as well (Kajio et al., 2014; Padmanabhan et al., 2016). Taken together, these observations suggest that metabolic disturbances associated with obesity may contribute to the etiopathogenesis of psychosis.
The role of cannabis use in increasing the relative risk for the development of psychosis is well established (Marconi et al., 2016). The endocannabinoid system is comprised of lipid-derived endogenous cannabinoid ligands, enzymes involved in the synthesis and degradation of these ligands and the cannabinoid 1 and 2 receptors which have affinity to these endogenous cannabinoid ligands. The cannabinoid 1 receptor has been postulated to be dysregulated in both psychotic and metabolic diseases (Gatta-Cherifi and Cota, 2015; Lu and Mackie, 2016). The CB1R is a G-protein coupled receptor widely distributed centrally throughout the cortex, striatum, hippocampus and cerebellum. However, CB1Rs are also distributed in the periphery throughout the gastrointestinal tract, liver, adipose tissue and adrenal glands (Pagotto et al., 2006). The CB1R has been implicated in the etiology of metabolic diseases based on evidence that CB1R agonists dysregulate both glucose and lipid metabolism (Scheen and Paquot, 2009). In line with these findings, selective CB1R antagonists have been demonstrated to be effective for weight-loss leading to favorable changes in both lipid and glucose levels (Colombo et al., 1998). However, further research is warranted to investigate how endocannabinoid dysregulation in psychosis relates to metabolic abnormalities in psychosis.
Metabolomics studies
Metabolomics is a comprehensive study of small molecules (i.e. metabolites) in cells, tissues and biofluids, including their biochemical transformation and responses to environmental and genetic perturbations. Metabolomics provides new tools to study the etiopathology of psychotic disorders as well as metabolic dysregulation arising following the use of antipsychotics (He et al., 2012; Kaddurah-Daouk et al., 2007; McEvoy et al., 2013; Oresic et al., 2012; Oresic et al., 2011b; Paredes et al., 2014). However, metabolomics has also played an important role in unravelling putative biomarkers and underlying pathways in several other diseases of the central nervous system (Quinones and Kaddurah-Daouk, 2009), including major depressive disorder (Ali-Sisto et al., 2016; Kaddurah-Daouk et al., 2012), Autism spectrum disorder (West et al., 2014), Alzheimer’s (Han et al., 2011; Kaddurah-Daouk et al., 2011; Oresic et al., 2011a; Trushina et al., 2013) and Parkinsons (Ahmed et al., 2009; Bogdanov et al., 2008; Hatano et al., 2016) diseases. Since the metabolome is sensitive to both genetic and environmental factors, such as drug exposure, metabolomics was chosen as a key ‘omics’ platform for molecular phenotyping in the METSY project.
Studying the metabolome in a population-based study, Oresic and colleagues found that schizophrenia was associated with elevated serum levels of specific triglycerides, hyperinsulinemia, and the upregulation of the serum amino acid proline (Oresic et al., 2011b). Using a network approach, the metabolic profiles were combined with other clinical and lifestyle data to create a diagnostic model which discriminated schizophrenia from other psychotic illnesses. As part of the METSY project, metabolomics has also been applied to study the metabolite profiles predicting weight gain and the development of other metabolic abnormalities in patients with first-episode psychosis (Suvitaival et al., 2016), where weight gain was associated with increased levels of triglycerides with low carbon number and double bond count at baseline (Figure 2). These lipids are known to be associated with increased liver fat (Luukkonen et al., 2016; Oresic et al., 2013). These preliminary results suggest that the first-episode psychosis patients who are at the highest risk of rapid weight gain, tend to have increased levels of lipids linked to liver fat prior to becoming obese. However, it is unclear whether there is a common biological mechanism underlying metabolic changes shown in first-episode psychosis. Validation metabolomics studies have been conducted by ORU & UTU in the final stage of the METSY project in FEP (KCL, UTU, SERMAS) and CHR (KCL, EU-GEI cohort) cohorts, aiming to validate lipid signatures associated with weight gain as well as to discover or confirm metabolic signatures associated with FEP and CHR. A total of 866 samples were analysed by lipidomics, incl. 206 in the EU-GEI cohort (CHR individuals). If confirmed, the lipid signatures associated with weight-gain in FEP patients may be clinically useful, as it may help identify the most vulnerable individuals who may benefit from metabolic therapy (anti-obesity/diabetes) in addition to antipsychotic therapy.
Figure 2. Lipidomic profiles and weight gain in FEP patients (Suvitaival et al., 2016). (a) Relative weight gain (blue crosses) from baseline as a function of time in the FEP case group. The median increase in body mass was 3 kg and 11 kg from baseline to the two-month and one-year follow-up points, respectively. Nonlinear Gaussian process regression model was fit on the weight gain data to visually highlight the trend. (b) Association between the level of triacylglycerols (TGs) at baseline and the two-month follow-up weight gain (Spearman correlation; color of the points) with respect to the number of carbon atoms (x-axis) and the number of double bonds between carbon atoms (y-axis). The baseline levels of saturated and mono-unsaturated compounds (y=0 and y=1, respectively) are associated with short-term weight gain (red color). The coefficient of determination (R2) of the linear model for the association as a function of triacylglycerol carbon number and double bond count are shown in the x-axis and y-axis labels, respectively (both p<0.05).
Neuroimaging studies
Background
An extensive body of literature over the last 40 years has documented subtle but widespread structural and functional changes in the brains of patients with non-affective and affective psychotic disorders. These changes are usually most prominent in fronto-temporal regions but it is now evident that these changes are more widespread, extending to posterior brain regions (Brugger and Howes, 2017). The progression of structural brain changes, particularly grey matter volume loss, has been found in the early onset schizophrenia, including both adult and adolescent-onset cases (Arango et al., 2012; Cahn et al., 2009). These volumetric changes are also shown in antipsychotic- naïve patients and become greater over time (Haijma et al., 2013), and have been correlated with poor clinical outcomes (Arango et al., 2012). Interestingly, volumetric reductions in frontal and temporal grey matter have also been linked to weight gain in healthy subjects (Minichino et al., 2017). These findings suggest that volumetric changes in the structure of the brain are related to the severity of clinical and metabolic changes in psychosis.
In vivo molecular imaging studies have consistently shown that un-medicated patients with schizophrenia exhibit an increase in striatal dopamine synthesis and release (Hietala et al., 1995; Howes and Murray, 2014; Laruelle et al., 1996). However, it is clear that dopamine dysregulation in psychosis is part of a larger problem in the connectome involving also other neurotransmitter pathways, in particular the glutamate and GABA systems. The endocannabinoid receptor CB1R, located on pre-synaptic nerve terminals of glutamatergic and GABAergic nerve terminals, plays a fundamental neuro-modulatory role in the brain due to its ability to inhibit the release of both excitatory and inhibitory neurotransmitters. CB1R begin modulating the fine tuning of excitatory/inhibitory neurotransmitter release during periods of pre- and postnatal brain development (Harkany et al., 2007) , thought to be central in the etiology of schizophrenia-spectrum disorders. Previous attempts to quantify the CB1R in vivo in schizophrenia have been largely unsuccessful due to high levels of tracer lipophilicity (Yasuno et al., 2008), the use of irreversible tracers and the failure to use arterial blood sampling to quantify the tracer kinetics (Ceccarini et al., 2013; Wong et al., 2010). However, it is now possible to elucidate the role of CB1R in patients with psychosis due to the development of specific positron emission tomography (PET) radiotracers, such as[11C]OMAR, [11C]MEPPEP and [18F]FMPEP-d2. These tracers bind reversibly with high specificity to CB1R in healthy volunteers and have appropriate kinetic properties for compartmental modeling of receptor availability as well as good test-retest reliability (Normandin et al., 2015; Terry et al., 2010; Terry et al., 2009; Tsujikawa et al., 2014). A recent study using arterial blood sampling and appropriate quantification techniques found that medication naïve schizophrenia patients abstaining from cannabis use showed a down-regulation of the CB1R in the hypothalamus, hippocampus, amygdala, caudate and insula (Ranganathan et al., 2016).
PET and MRI are established neuroimaging tools, but generally used independently. Recently, a hybrid PET/MR system, which allows for acquisition of such complementary information consecutively in the same study session without repositioning of the subject has been established. This system provides truly simultaneous, complementary information on different aspects of brain function (e.g. CBR1 availability, white matter integrity) by the different modalities without the temporal limitations of conducting separate PET and MRI scans. MRI-based data on brain morphology and white matter tract integrity have been used to quantify structural connectivity patterns of the brain of the cannabinoid systems as measured with PET and network connectivity, such as the default mode network (DMN) in the brain. The DMN is activated when the brain is at wakeful rest and not focusing on the outer world but rather engaged with internal tasks (e.g. daydreaming, spontaneous thoughts, memories). DMN is usually regarded as a predominantly context-independent phenomenon. Despite the fact that resting state functional magnetic resonance imaging (R-fMRI) has become a powerful tool to explore the dysconnectivity of brain networks in psychotic disorders, very little is known about the role of specific neurotransmitters involved in emergence and maintaining DMN activity.
Patient recruitment
Final recruitment within METSY reached the following final numbers for neuroimaging (as well as metabolomics studies): 268 subjects with FEP (goal 250), 29 subjects with clinical high risk for psychosis (CHR) (goal 100) and 231 healthy controls (HC) were recruited. Of them, 207 FEP subjects have an MRI, all of the CHR subjects have an MRI and 210 HC participants have an MRI at baseline. Regarding laboratory data, baseline data are available for 213 FEP subjects, 28 CHR subjects and 193 HC participants. The final effort carried out by all centres allowed an important and significant increase in the numbers of FEP and HC subjects. The final impulse in the recruitment of controls, matched by general socio-demographic characteristics with FEP helped increase the final size sample. However, the final number of CHR subjects is far from the target for different reasons. Different health care systems in different European countries have different ways to assess the CHR. For example, in Finland and in Spain, patients no longer enter the psychiatric health care system at this stage, making it very difficult to recruit subjects at risk. In this regard, from the beginning of the study, only THL, UTU and KCL committed themselves to recruit CHR subjects.
During this period, effort has been directed towards follow-up of the participants in the study. The 1-year follow-up is almost finished, with still a number of patients scheduled for evaluations. Database design has been completed and final minor issues with clinical harmonization have been solved. Baseline clinical data has been sent from clinical sites to Biomax and integrated in the common database. Clinical centres have sent baseline imaging data to SERMAS for storage and analysis and processed data has afterwards been sent to Biomax for inclusion in the database.
Data harmonisation and processing
Amongst all participating centres, the following aspects were harmonized:
• Clinical diagnostic interview: After looking for differences among the clinical and the research versions of the Structured Clinical Interview for DSM-IV (SCID-I), only slight variance was found between them. However, as version used did not affect the aim of the instrument application, that is to establish a clinical diagnosis in Axis I, all participants agreed on continuing using the SCID-I version they had (UTU, THL and KCL use the SCID-I research version and SERMAS the SCID-I clinical version). Only one site (SERMAS) is enrolling minors, and therefore Kiddie-SADS-Present and Lifetime Version (K-SADS-PL) is used for kids´ assessment. Diagnosis will be included in the database.
• Severity of psychotic symptoms: To assess severity of psychotic symptoms, THL is using the Brief Psychiatric Rating Scale (BPRS-Extended) complemented by SANS avolition, anhedonia and alogia, whilst UTU, KCL and SERMAS use the Positive and Negative Syndrome Scale (PANSS). After examining this discordance, all centres agreed on continue using scales as they were doing, as total scores of BPRS and PANSS could be interchangeable without affecting internal validity of both scales (Ventura et al. 1993). PANSS and BPRS will be converted into standard scores for further analysis. All items of PANSS and BPRS will be included in the database (including the modifications used in THL) and afterwards conversion into standard scores will also be included. PANSS interrater reliability was later obtained between evaluators at different sites (see below in interrater reliability).
• Prodromal psychotic symptoms: To assess the presence of prodromal psychotic symptoms, all sites agreed on using the Structured Interview for Prodromal Symptoms (SIPS 5.0). SIPS interrater reliability was later obtained between evaluators at different sites (as described in the 18th month report).
• Global Functioning: All centres agreed on exclusively using the Global Assessment of Functioning scale (GAF) to assess global functioning. They also agreed on deleting the SOFAS scale that some centres were administering. GAF interrater reliability was later obtained between evaluators at different sites (as described in the 18th month report).
• Diet: After looking through the diet questionnaires in each sites, all centres agreed that each one will have their own diet scales due to the diversity of dietary habits in each country. With the DIET scale used by SERMAS, dietary habits can be classified in 3 categories: healthy, need changes, and unhealthy.
• BMI and waist perimeter, cannabis use, smoking, exercise, socioeconomic status (SES), years of education and race were also harmonized. THL exercise assessment will be used by each centre.
• Cannabis: Agreements on cannabis included: Joints per week is not a reliable measure, so instead current/Lifetime use/abuse/dependence and >50 times/days current/lifetime will be used.
Interrater reliability for GAF, PANSS, and SIPS was established (as described in the 18th month report).
Regarding harmonization of neuropsychological data, measures used to assess IQ, attention, processing speed, and working memory have been harmonized. Both direct scores (raw) and standardized scores (T-scores) have been added in the main database. The standardized scores are calculated by each site with the normative data of their own country. Merging data between sites will be made with standardized scores. Variables will be created when at least two sites use the same/compatible test. Each site will provide information regarding which neuropsychological tests have and have not available normative data in their language. Research domains for higher executive functions will be adjusted depending on the different analyses. The use of the following measures has been prioritized: Vocabulary WAIS-III (IQ), TMT-A and CPT (Attention), TMT-B (Executive function) Digit Symbol Coding WAIS-III (Processing Speed), Categories FAS (Verbal Processing Speed), Letter number sequencing-WAIS-III (Working Memory), WMS-III-WAIS-III (Working Memory), HVLT-R (verbal learning) and BVMT (visual learning).
Finally, during this period, clinical sites have sent MRI images and these MRI images have been processed with the same pipeline. The T1-weighted images of all METSY participants have been analysed in FreeSurfer (v5.3) to provide detailed anatomical information customized for each participant. The FreeSurfer analysis stream includes intensity bias field removal, skull stripping, and assigning a neuro-anatomical label (e.g. hippocampus, amygdala, etc.) to each voxel. In addition to the volume-based analysis, FreeSurfer constructs models of the pial and white surface. These surfaces have been used to quantify cortical thickness, surface area, and volume at regional and vertex-wise scales for each METSY participant.
In order to detect and correct artefacts introduced during collection of the Diffusion Tensor Imaging scan, a quality control protocol for METSY subjects have been implemented. First, artefacts related to intensity are detected by computing the normalized correlation between intensity in successive slices across the diffusion volume. Any diffusion volumes containing one or more artefacts are excluded. Next, eddy-current and head motion correction is performed using Fmri Software Library (FSL) tools. Finally, machine-related (i.e. B0 field inhomogeneity) spatial distortions are corrected by warping each participant's T2-b0 image to the anatomical T2-weighted image of the same individual. Anatomically constrained probabilistic diffusion tractography have been carried out using the Tracts Constrained by UnderLying Anatomy (TRACULA) tool within FreeSurfer using default settings (Yendicki et al. 2011). Mean values of fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity of major white matter tracts have been calculated for each METSY participant.
Previously, a reliability study was conducted to assess whether images acquired at the different acquisition centers (Madrid, London, Turku, and Helsinki) could be combined in a mega-analysis. Five healthy volunteers travelled to each site and images were acquired. Images were uploaded to the DICOMSERVER in Madrid and processed using FreeSurfer (for T1-weighted images) and TRACULA (for Diffusion Tensor Images). The results of the reliability study for T1 were already. For DTI, the harmonization process included five volunteers were scanned in all sites (Figure 3):
• Fundación Cien (Madrid, Spain)
• AMI – Centre (Espoo, Finland)
• PET – Centre (Turku, Finland)
• King’s College Institute of Psychiatry (London, United Kingdom)
Figure 3. Harmonisation of MRI/DTI data across the four study centres.
CB1R PET analyses
Recruitment targets for patients with FEP and Controls were well achieved (Table 1). Recruitment of CHR cases numbers was abandoned based on early decisions to focus on baseline FEPs and controls.
Table 1. WP2: Subjects (n) scanned with CB1R PET with complete data
PET data UTU KCL Total/goal
HC 22 20 42/35
FEP 15 20 35/35
CHR 0 0 0/35
Total 37 40 77
UTU and KCL PET and MRI data pre-processing has been harmonized. Modelling results using 2TCM and MTGA Logan plot are in line with previous [18F]FMPEP-d2 and [11C]MEPPEP studies. Preliminary findings from the collected samples (Table 1) are: (a) a lower CB1R availability of FEPs in both sites (Figure 4) and (b) lower CB1R availability of females in the UTU control sample. Logan plot Parametric images (DVtot) show regional associations between CB1R availability and cognitive capacity in HCs as well as CB1R availability and BPRS psychotic symptoms in male FEPs.’
All available matching serum samples from UTU and KCL have been analysed using the endocannabinoid platform developed by UTU in collaboration with ORU. Preliminary results are: (a) lower circulating levels of OEA and AA in FEPs in the UTU sample. There were no significant differences between patients and controls in the KCL sample; and (b) association of 1+2-AG and OEA to hippocampal CB1R availability in HCs but not FEPs as measured by PET and [18F]FMPEP-d2. Endocannabinoids have also, by the end of the project, been analysed in FEP samples from THL as well as in CHR samples from the EU-GEI cohort.
Together, the preliminary data generated within METSY dies suggest that the endocannabinoid system is dysregulated in gender-specific manner in the brain of FEP patients and that there may be an association between the endocannabinoid systems in the brain and in the periphery. The final studies performed in METSY (EU-GEI) as well as future studies will need to determine if the endocannabinoid system may be a mechanistic underlying link between the metabolic co-morbidities incl. elevated liver fat and early psychosis.
Figure 4. Cannabinoid 1 receptor availability was significantly lower in first episode psychosis patients relative to healthy volunteers as determined by [11C]MePPEP PET quantification (A) and [18F]FMPEP-d2 PET quantification (B).
Integrative bioinformatics platform
The METSY bioinformatics platform is comprised of three inter-related components (Frank et al., 2018) (Figure 5):
1. Network analysis to integrate heterogeneous data (multi-omics, in vivo molecular neuroimaging, structural neuroimaging, functional neuroimaging and psychosocial);
2. Semantic modelling to annotate heterogeneous data with biological and literature-based annotations;
3. Development of a decision support system to facilitate decision-making in the clinic based on multi-modal diagnostic information.
Extracting predictive biomarkers from multiple types of information requires the integration and correlation of existing knowledge and data from diverse sources and formats. Network construction and analysis is a promising approach facilitating data integration that is increasingly used in disease related research (Barabasi, 2007; Hofree et al., 2013). In this approach, networks are constructed from associations between variables and are integrated with prior knowledge that is also represented in a network form. Currently, most prior knowledge is not readily accessible for analysis since it exists in different repositories for structured (comprising about 1400 public databases on molecular biology related information (Galperin and Fernandez-Suarez, 2012)) and unstructured data such as high-content imaging, physiological, biochemical and clinical data. Bioinformatics methods, developed to bridge multiple sources and scales of knowledge into semantic networks, have recently been extended to imaging data and computational models (Maier et al., 2011). Another challenge that can be approached by networks is the representation of gained knowledge, e.g. how do changes of a specific receptor detected by PET imaging influence our prior knowledge about the overall phenomenon. Current neuroimaging methods are focused on correlation of voxel pattern to outcomes, largely neglecting existing mechanistic and structural information during the analysis.
Figure 5. Outline of the METSY bioinformatics platform, bridging the systems medicine research approaches with the applications in the clinic. The platform integrates three components: network analysis, semantic modelling and decision support system. (A) Network analysis to integrate heterogeneous data (multi-omics, in vivo molecular neuroimaging, structural neuroimaging, functional neuroimaging and psychosocial) based on partical correlations. (B) Semantic modelling to annotate heterogeneous data with biological and literature-based annotations, representing knowledge as network which integrates associations otherwise separated in individual data sources. Integration is based on mapping of equivalentmeaning and objects across all information types relevant in a life science project. (C) Development of a decision support system to facilitate decision-making in the clinic based on multi-modal diagnostic information.
In order to provide systematic and structured information suitable for algorithmic analysis, METSY structured the current knowledge (i.e. scientific literature, implicit expert knowledge, databases) into concepts, which can be mapped to the experimental and clinical data (Figure 6). Using this approach, concepts relevant to a specific research area can be retrieved from the literature or defined by expert consensus and implemented as software concepts. In psychosis research, relevant concepts are for example “brain area”, “symptom” or “metabolite” and associations such as “causes” or “is consumed by”. Within the METSY project, we will apply the BioXM Knowledge Management Environment (Losko and Heumann, 2009; Maier et al., 2011), which will allow us to adapt existing concepts throughout the course of the project using a graphical editor. Semantic mapping approaches can also be used to identify defined concepts from structured resources such as ontologies, neuroanatomical or functional atlases, databases or literature-mining. For example “brain area” might be populated from the Human anatomy atlas (Rosse and Mejino, 2003) and the FreeSurfer neuroanatomy atlas (Desikan et al., 2006); while “metabolites” might be derived from the Human Metabolome Database (Wishart et al., 2013) and different symptoms associated to psychosis might be retrieved by automatic literature-mining. In this process, information from different sources can be mapped to the same concepts based on their meaning (semantics) and thereby integrated. This process can be automated for data extraction from various sources based on descriptions of the contained data and its format (metadata); however, some data extraction requires manual selection in cases where the source relates to specific areas of expertise (i.e. identifying the similarity of different neuroanatomical atlases).
Figure 6. Example of integrative analysis of connectome and gene expression data by using the semantic approach. Coloured dots indicate gene expression values for FKBP5 (taken from Human Allen Brain Atlas). Red colours indicate high expression values whereas blue colours indicate low values. In addition, we selected prefrontal cortex circuitry and display structural and functional connection strengths measured by DTI and fMRI, respectively. Structural connectivity is depicted by line thickness. Red line colouring indicates strong functional connectivity while blue indicates anti-correlated activity between the connected brain areas. Connection strengths are taken from the NKI_AVRG dataset - the average connectivity of all connectomes of the NKI Rockland study from the Human Connectome Project. Datasets available through the USC Multimodal Connectivity Database. All brain coordinates were transformed to a unified coordinate frame specified by the MNI-152 standard brain.
Within METSY, this approach allowed us to integrate structural brain connectivity data from the USC Multimodal Connectivity Database (UMCD) (Brown et al., 2012) with functional brain area information from the Brede database (Nielsen, 2014) and brain gene expression data from the Allen Brain atlas (Hawrylycz et al., 2012). To this end, an experienced neuroanatomist manually mapped the areas of the Craddock200 atlas used by UMCD to the Brede WOROI ontology (Brede) and Human Allen Brain Atlas (Allen Brain) using MNI coordinates as common denominator. For example, left hippocampus (Craddock200) was mapped to 107 Left hippocampus (Brede WOROI) and 4249001 hippocampal formation, left (Human Allen Brain Atlas). Individual level data from the three sources was subsequently uploaded into the METSY knowledge portal which may be searched and visualized based on any of the mapped atlases. As an example, a DTI tract might state “in schizophrenic patient A, left hippocampus is connected with mammillary body by strength 91 while a functional association might be “left hippocampus and mammillary body are correlated with connectivity 0.008 during resting state in healthy volunteers” and finally post-mortem expression data may indicate certain genes expressed in left hippocampus and mammillary body. Such mappings enable us to directly compute potential functional and molecular consequences of differences shown between schizophrenia patients and healthy volunteers, which are relevant to clinical decision making.
Decision support system
Using integrated data from the METSY knowledge base, a novel clinical decision support and data visualization framework was adapted and applied to tackle heterogeneous patient information. The main focus of the framework was to provide a comprehensive overview of the patient’s disease state (Mattila et al., 2011), which denotes a patient’s degree of similarity to a previously diagnosed disease population. This was archived by implementing the disease state index (DSI) method and disease state fingerprint (DSF) visualizations (Mattila et al., 2012) for the data contained within the METSY knowledge base. The DSF visualization clearly discloses how different components of the patient data contribute to the DSI, facilitating rapid interpretation of the information. The same methods were previously applied to examine Alzheimers disease and dementias in EU projects PredictAD, PredictND and VPH-DARE@IT.
DSI is a supervised machine learning algorithm, which quantifies the disease state of the patient. The method computes the statistical distributions for each measurement and uses them to quantify the disease state of the patient. The method produces a single variable for the patient, ranging between zero and one. An index value close to zero denotes that the patient has values similar to healthy subjects. By contrast, if the index is close to one, the measurements are more similar to diagnosed patients. The DSI can quantify a score, even if not all measures are available. The DSI classifier is accompanied by a disease state fingerprint (DSF) (Mattila et al., 2012) visualization. The DSF has a tree structure, which represents the structure of the DSI classifier, highlighting which measures have the strongest prognostic value.
Within METSY, the DSI was used to combine volumetric data from MRI, psychiatric measures, clinical measures and selected metabolomics data (Figure 5). The DSI was trained and tested with volumetric MRI, psychiatric and clinical measures selected based on earlier knowledge from the psychotic disorders. The metabolomics measures were selected based on the machine learning methods with dependency detection.
References
Ahmed, S.S. Santosh, W., Kumar, S., and Christlet, H.T. (2009). Metabolic profiling of Parkinson's disease: evidence of biomarker from gene expression analysis and rapid neural network detection. J Biomed Sci 16, 63.
Ali-Sisto, T., Tolmunen, T., Toffol, E., Viinamaki, H., Mantyselka, P., Valkonen-Korhonen, M., Honkalampi, K., Ruusunen, A., Velagapudi, V., and Lehto, S.M. (2016). Purine metabolism is dysregulated in patients with major depressive disorder. Psychoneuroendocrinology 70, 25-32.
Arango, C., Bobes, J., Kirkpatrick, B., Garcia-Garcia, M., and Rejas, J. (2011). Psychopathology, coronary heart disease and metabolic syndrome in schizophrenia spectrum patients with deficit versus non-deficit schizophrenia: findings from the CLAMORS study. Eur Neuropsychopharmacol 21, 867-875.
Arango, C., Rapado-Castro, M., Reig, S., Castro-Fornieles, J., Gonzalez-Pinto, A., Otero, S., Baeza, I., Moreno, C., Graell, M., Janssen, J., et al. (2012). Progressive brain changes in children and adolescents with first-episode psychosis. Arch Gen Psychiatry 69, 16-26.
Barabasi, A.-L. (2007). Network Medicine -- From Obesity to the "Diseasome". N Engl J Med 357, 404-407.
Bogdanov, M., Matson, W.R. Wang, L., Matson, T., Saunders-Pullman, R., Bressman, S.S. and Flint Beal, M. (2008). Metabolomic profiling to develop blood biomarkers for Parkinson's disease. Brain 131, 389-396.
Brown, J.A. Rudie, J.D. Bandrowski, A., Van Horn, J.D. and Bookheimer, S.Y. (2012). The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Front Neuroinform 6, 28.
Brugger, S.P. and Howes, O.D. (2017). Heterogeneity and Homogeneity of Regional Brain Structure in Schizophrenia: A Meta-analysis. JAMA Psychiatry 74, 1104-1111.
Cahn, W., Rais, M., Stigter, F.P. van Haren, N.E. Caspers, E., Hulshoff Pol, H.E. Xu, Z., Schnack, H.G. and Kahn, R.S. (2009). Psychosis and brain volume changes during the first five years of schizophrenia. Eur Neuropsychopharmacol 19, 147-151.
Ceccarini, J., De Hert, M., Van Winkel, R., Peuskens, J., Bormans, G., Kranaster, L., Enning, F., Koethe, D., Leweke, F.M. and Van Laere, K. (2013). Increased ventral striatal CB1 receptor binding is related to negative symptoms in drug-free patients with schizophrenia. Neuroimage 79, 304-312.
Colombo, G., Agabio, R., Diaz, G., Lobina, C., Reali, R., and Gessa, G.L. (1998). Appetite suppression and weight loss after the cannabinoid antagonist SR 141716. Life Sci 63, PL113-117.
Correll, C.U. Kane, J.M. and Manu, P. (2011). Obesity and coronary risk in patients treated with second-generation antipsychotics. Eur Arch Psychiatry Clin Neurosci 261, 417-423.
De Hert, M., Dobbelaere, M., Sheridan, E.M. Cohen, D., and Correll, C.U. (2011). Metabolic and endocrine adverse effects of second-generation antipsychotics in children and adolescents: A systematic review of randomized, placebo controlled trials and guidelines for clinical practice. Eur Psychiatry 26, 144-158.
Desikan, R.S. Segonne, F., Fischl, B., Quinn, B.T. Dickerson, B.C. Blacker, D., Buckner, R.L. Dale, A.M. Maguire, R.P. Hyman, B.T. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-980.
Fornito, A., Zalesky, A., Pantelis, C., and Bullmore, E.T. (2012). Schizophrenia, neuroimaging and connectomics. Neuroimage 62, 2296-2314.
Fraguas, D., Merchan-Naranjo, J., Laita, P., Parellada, M., Moreno, D., Ruiz-Sancho, A., Cifuentes, A., Giraldez, M., and Arango, C. (2008). Metabolic and hormonal side effects in children and adolescents treated with second-generation antipsychotics. J Clin Psychiatry 69, 1166-1175.
Frank, E., Maier, D., Pajula, J., Suvitaival, T., Borgan, F., Butz-Ostendorf, M., Fischer, A., Hietala, J., Howes, O., Hyotylainen, T., et al. (2018). Platform for systems medicine research and diagnostic applications in psychotic disorders-The METSY project. Eur Psychiatry 50, 40-46.
Galperin, M.Y. and Fernandez-Suarez, X.M. (2012). The 2012 Nucleic Acids Research Database Issue and the online Molecular Biology Database Collection. Nucleic Acids Res 40, D1-8.
Gatta-Cherifi, B., and Cota, D. (2015). Endocannabinoids and Metabolic Disorders. Handb Exp Pharmacol 231, 367-391.
Haijma, S.V. Van Haren, N., Cahn, W., Koolschijn, P.C. Hulshoff Pol, H.E. and Kahn, R.S. (2013). Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr Bull 39, 1129-1138.
Han, X., Rozen, S., Boyle, S.H. Hellegers, C., Cheng, H., Burke, J.R. Welsh-Bohmer, K.A. Doraiswamy, P.M. and Kaddurah-Daouk, R. (2011). Metabolomics in early Alzheimer's disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One 6, e21643.
Hansen, T., Ingason, A., Djurovic, S., Melle, I., Fenger, M., Gustafsson, O., Jakobsen, K.D. Rasmussen, H.B. Tosato, S., Rietschel, M., et al. (2011). At-risk variant in TCF7L2 for type II diabetes increases risk of schizophrenia. Biol Psychiatry 70, 59-63.
Harkany, T., Guzman, M., Galve-Roperh, I., Berghuis, P., Devi, L.A. and Mackie, K. (2007). The emerging functions of endocannabinoid signaling during CNS development. Trends Pharmacol Sci 28, 83-92.
Hatano, T., Saiki, S., Okuzumi, A., Mohney, R.P. and Hattori, N. (2016). Identification of novel biomarkers for Parkinson's disease by metabolomic technologies. J Neurol Neurosurg Psychiatry 87, 295-301.
Hawrylycz, M.J. Lein, E.S. Guillozet-Bongaarts, A.L. Shen, E.H. Ng, L., Miller, J.A. van de Lagemaat, L.N. Smith, K.A. Ebbert, A., Riley, Z.L. et al. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391-399.
He, Y., Yu, Z., Giegling, I., Xie, L., Hartmann, A.M. Prehn, C., Adamski, J., Kahn, R., Li, Y., Illig, T., et al. (2012). Schizophrenia shows a unique metabolomics signature in plasma. Transl Psychiatry 2, e149.
Hietala, J., Syvalahti, E., Vuorio, K., Rakkolainen, V., Bergman, J., Haaparanta, M., Solin, O., Kuoppamaki, M., Kirvela, O., Ruotsalainen, U., et al. (1995). Presynaptic dopamine function in striatum of neuroleptic-naive schizophrenic patients. Lancet 346, 1130-1131.
Hofree, M., Shen, J.P. Carter, H., Gross, A., and Ideker, T. (2013). Network-based stratification of tumor mutations. Nat Methods 10, 1108-1115.
Howes, O.D. Bhatnagar, A., Gaughran, F.P. Amiel, S.A. Murray, R.M. and Pilowsky, L.S. (2004). A prospective study of impairment in glucose control caused by clozapine without changes in insulin resistance. Am J Psychiatry 161, 361-363.
Howes, O.D. and Murray, R.M. (2014). Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet 383, 1677-1687.
Jin, H., Meyer, J.M. and Jeste, D.V. (2004). Atypical antipsychotics and glucose dysregulation: a systematic review. Schizophr Res 71, 195-212.
Kaddurah-Daouk, R., McEvoy, J., Baillie, R.A. Lee, D., Yao, J.K. Doraiswamy, P.M. and Krishnan, K.R.R. (2007). Metabolomic mapping of atypical antipsychotic effects in schizophrenia. Mol Psychiatry 12, 934-945.
Kaddurah-Daouk, R., Rozen, S., Matson, W., Han, X., Hulette, C.M. Burke, J.R. Doraiswamy, P.M. and Welsh-Bohmer, K.A. (2011). Metabolomic changes in autopsy-confirmed Alzheimer's disease. Alzheimers Dement 7, 309-317.
Kaddurah-Daouk, R., Yuan, P., Boyle, S.H. Matson, W., Wang, Z., Zeng, Z.B. Zhu, H., Dougherty, G.G. Yao, J.K. Chen, G., et al. (2012). Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile. Sci Rep 2, 667.
Kajio, Y., Kondo, K., Saito, T., Iwayama, Y., Aleksic, B., Yamada, K., Toyota, T., Hattori, E., Ujike, H., Inada, T., et al. (2014). Genetic association study between the detected risk variants based upon type II diabetes GWAS and psychotic disorders in the Japanese population. J Hum Genet 59, 54-56.
Kirkpatrick, B., Miller, B.J. Garcia-Rizo, C., Fernandez-Egea, E., and Bernardo, M. (2012). Is abnormal glucose tolerance in antipsychotic-naive patients with nonaffective psychosis confounded by poor health habits? Schizophr Bull 38, 280-284.
Laruelle, M., Abi-Dargham, A., van Dyck, C.H. Gil, R., D'Souza, C.D. Erdos, J., McCance, E., Rosenblatt, W., Fingado, C., Zoghbi, S.S. et al. (1996). Single photon emission computerized tomography imaging of amphetamine-induced dopamine release in drug-free schizophrenic subjects. Proc Natl Acad Sci U S A 93, 9235-9240.
Leweke, F.M. Giuffrida, A., Koethe, D., Schreiber, D., Nolden, B.M. Kranaster, L., Neatby, M.A. Schneider, M., Gerth, C.W. Hellmich, M., et al. (2007). Anandamide levels in cerebrospinal fluid of first-episode schizophrenic patients: impact of cannabis use. Schizophr Res 94, 29-36.
Losko, S., and Heumann, K. (2009). Semantic data integration and knowledge management to represent biological network associations. Methods Mol Biol 563, 241-258.
Lu, H.C. and Mackie, K. (2016). An Introduction to the Endogenous Cannabinoid System. Biol Psychiatry 79, 516-525.
Luukkonen, P.K. Zhou, Y., Sadevirta, S., Leivonen, M., Arola, J., Oresic, M., Hyotylainen, T., and Yki-Jarvinen, H. (2016). Hepatic ceramides dissociate steatosis and insulin resistance in patients with non-alcoholic fatty liver disease. J Hepatol 64, 1167-1175.
Maier, D., Kalus, W., Wolff, M., Kalko, S.G. Roca, J., Marin de Mas, I., Turan, N., Cascante, M., Falciani, F., Hernandez, M., et al. (2011). Knowledge management for systems biology a general and visually driven framework applied to translational medicine. BMC Syst Biol 5, 38.
Marconi, A., Di Forti, M., Lewis, C.M. Murray, R.M. and Vassos, E. (2016). Meta-analysis of the Association Between the Level of Cannabis Use and Risk of Psychosis. Schizophr Bull 42, 1262-1269.
Mattila, J., Koikkalainen, J., Virkki, A., Simonsen, A., van Gils, M., Waldemar, G., Soininen, H., Lotjonen, J., and Alzheimer's Disease Neuroimaging, I. (2011). A disease state fingerprint for evaluation of Alzheimer's disease. J Alzheimers Dis 27, 163-176.
Mattila, J., Koikkalainen, J., Virkki, A., van Gils, M., Lotjonen, J., and Alzheimer's Disease Neuroimaging, I. (2012). Design and application of a generic clinical decision support system for multiscale data. IEEE Trans Biomed Eng 59, 234-240.
McEvoy, J., Baillie, R.A. Zhu, H., Buckley, P., Keshavan, M.S. Nasrallah, H.A. Dougherty, G.G. Yao, J.K. and Kaddurah-Daouk, R. (2013). Lipidomics reveals early metabolic changes in subjects with schizophrenia: effects of atypical antipsychotics. PLoS One 8, e68717.
Minichino, A., Ando, A., Francesconi, M., Salatino, A., Delle Chiaie, R., and Cadenhead, K. (2017). Investigating the link between drug-naive first episode psychoses (FEPs), weight gain abnormalities and brain structural damages: Relevance and implications for therapy. Prog Neuropsychopharmacol Biol Psychiatry 77, 9-22.
Mukherjee, S., Schnur, D.B. and Reddy, R. (1989). Family history of type 2 diabetes in schizophrenic patients. Lancet 1, 495.
Newcomer, J.W. (2005). Second-generation (atypical) antipsychotics and metabolic effects: a comprehensive literature review. CNS Drugs 19 Suppl 1, 1-93.
Nielsen, F.A. (2014). Brede tools and federating online neuroinformatics databases. Neuroinformatics 12, 27-37.
Normandin, M.D. Zheng, M.Q. Lin, K.S. Mason, N.S. Lin, S.F. Ropchan, J., Labaree, D., Henry, S., Williams, W.A. Carson, R.E. et al. (2015). Imaging the cannabinoid CB1 receptor in humans with [11C]OMAR: assessment of kinetic analysis methods, test-retest reproducibility, and gender differences. J Cereb Blood Flow Metab 35, 1313-1322.
Nuevo, R., Chatterji, S., Fraguas, D., Verdes, E., Naidoo, N., Arango, C., and Ayuso-Mateos, J.L. (2011). Increased risk of diabetes mellitus among persons with psychotic symptoms: results from the WHO World Health Survey. J Clin Psychiatry 72, 1592-1599.
Oresic, M., Hyotylainen, T., Herukka, S.K. Sysi-Aho, M., Mattila, I., Seppanan-Laakso, T., Julkunen, V., Gopalacharyulu, P.V. Hallikainen, M., Koikkalainen, J., et al. (2011a). Metabolome in progression to Alzheimer's disease. Transl Psychiatry 1, e57.
Oresic, M., Hyotylainen, T., Kotronen, A., Gopalacharyulu, P., Nygren, H., Arola, J., Castillo, S., Mattila, I., Hakkarainen, A., Borra, R.J. et al. (2013). Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids. Diabetologia 56, 2266-2274.
Oresic, M., Seppanen-Laakso, T., Sun, D., Tang, J., Therman, S., Viehman, R., Mustonen, U., van Erp, T.G. Hyotylainen, T., Thompson, P., et al. (2012). Phospholipids and insulin resistance in psychosis: a lipidomics study of twin pairs discordant for schizophrenia. Genome Med 4, 1.
Oresic, M., Tang, J., Seppanen-Laakso, T., Mattila, I., Saarni, S.E. Saarni, S.I. Lonnqvist, J., Sysi-Aho, M., Hyotylainen, T., Perala, J., et al. (2011b). Metabolome in schizophrenia and other psychotic disorders: a general population-based study. Genome Med 3, 19.
Padmanabhan, J.L. Nanda, P., Tandon, N., Mothi, S.S. Bolo, N., McCarroll, S., Clementz, B.A. Gershon, E.S. Pearlson, G.D. Sweeney, J.A. et al. (2016). Polygenic risk for type 2 diabetes mellitus among individuals with psychosis and their relatives. J Psychiatr Res 77, 52-58.
Pagotto, U., Marsicano, G., Cota, D., Lutz, B., and Pasquali, R. (2006). The emerging role of the endocannabinoid system in endocrine regulation and energy balance. Endocr Rev 27, 73-100.
Paredes, R.M. Quinones, M., Marballi, K., Gao, X., Valdez, C., Ahuja, S.S. Velligan, D., and Walss-Bass, C. (2014). Metabolomic profiling of schizophrenia patients at risk for metabolic syndrome. Int J Neuropsychopharmacol 17, 1139-1148.
Pillinger, T., Beck, K., Gobjila, C., Donocik, J.G. Jauhar, S., and Howes, O.D. (2017). Impaired Glucose Homeostasis in First-Episode Schizophrenia: A Systematic Review and Meta-analysis. JAMA Psychiatry 74, 261-269.
Quinones, M.P. and Kaddurah-Daouk, R. (2009). Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol Dis 35, 165-176.
Ranganathan, M., Cortes-Briones, J., Radhakrishnan, R., Thurnauer, H., Planeta, B., Skosnik, P., Gao, H., Labaree, D., Neumeister, A., Pittman, B., et al. (2016). Reduced Brain Cannabinoid Receptor Availability in Schizophrenia. Biol Psychiatry 79, 997-1005.
Rosse, C., and Mejino, J.L. Jr. (2003). A reference ontology for biomedical informatics: the Foundational Model of Anatomy. J Biomed Inform 36, 478-500.
Scheen, A.J. and Paquot, N. (2009). Use of cannabinoid CB1 receptor antagonists for the treatment of metabolic disorders. Best Pract Res Clin Endocrinol Metab 23, 103-116.
Sporns, O., Tononi, G., and Kotter, R. (2005). The human connectome: A structural description of the human brain. PLoS Comput Biol 1, e42.
Suvitaival, T., Mantere, O., Kieseppa, T., Mattila, I., Poho, P., Hyotylainen, T., Suvisaari, J., and Oresic, M. (2016). Serum metabolite profile associates with the development of metabolic co-morbidities in first-episode psychosis. Transl Psychiatry 6, e951.
Terry, G.E. Hirvonen, J., Liow, J.S. Zoghbi, S.S. Gladding, R., Tauscher, J.T. Schaus, J.M. Phebus, L., Felder, C.C. Morse, C.L. et al. (2010). Imaging and quantitation of cannabinoid CB1 receptors in human and monkey brains using (18)F-labeled inverse agonist radioligands. J Nucl Med 51, 112-120.
Terry, G.E. Liow, J.S. Zoghbi, S.S. Hirvonen, J., Farris, A.G. Lerner, A., Tauscher, J.T. Schaus, J.M. Phebus, L., Felder, C.C. et al. (2009). Quantitation of cannabinoid CB1 receptors in healthy human brain using positron emission tomography and an inverse agonist radioligand. Neuroimage 48, 362-370.
Trushina, E., Dutta, T., Persson, X.M. Mielke, M.M. and Petersen, R.C. (2013). Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics. PLoS One 8, e63644.
Tsujikawa, T., Zoghbi, S.S. Hong, J., Donohue, S.R. Jenko, K.J. Gladding, R.L. Halldin, C., Pike, V.W. Innis, R.B. and Fujita, M. (2014). In vitro and in vivo evaluation of (11)C-SD5024, a novel PET radioligand for human brain imaging of cannabinoid CB1 receptors. Neuroimage 84, 733-741.
West, P.R. Amaral, D.G. Bais, P., Smith, A.M. Egnash, L.A. Ross, M.E. Palmer, J.A. Fontaine, B.R. Conard, K.R. Corbett, B.A. et al. (2014). Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children. PLoS One 9, e112445.
Wishart, D.S. Jewison, T., Guo, A.C. Wilson, M., Knox, C., Liu, Y., Djoumbou, Y., Mandal, R., Aziat, F., Dong, E., et al. (2013). HMDB 3.0--The Human Metabolome Database in 2013. Nucleic Acids Res 41, D801-807.
Wong, D.F. Kuwabara, H., Horti, A.G. Raymont, V., Brasic, J., Guevara, M., Ye, W., Dannals, R.F. Ravert, H.T. Nandi, A., et al. (2010). Quantification of cerebral cannabinoid receptors subtype 1 (CB1) in healthy subjects and schizophrenia by the novel PET radioligand [11C]OMAR. Neuroimage 52, 1505-1513.
Yasuno, F., Brown, A.K. Zoghbi, S.S. Krushinski, J.H. Chernet, E., Tauscher, J., Schaus, J.M. Phebus, L.A. Chesterfield, A.K. Felder, C.C. et al. (2008). The PET radioligand [11C]MePPEP binds reversibly and with high specific signal to cannabinoid CB1 receptors in nonhuman primate brain. Neuropsychopharmacology 33, 259-269.
Potential Impact:
METSY optimized the use of existing MRI and PET technologies for the study of psychotic disorders, with specific focus on their metabolic co-morbidities. Additionally, the project developed the tools (procedures, software) for combined MRI and PET neuroimaging, as well as statistical and bioinformatics tools to integrate this information with other phenotypic data including metabolic characterisation as obtained from metabolomics of biofluids. Specifically, METSY developed and validated multiple innovative multi-modal biomarkers based on neuroimaging and metabolic profiling to the stage where they can be considered for developments towards the implementation in healthcare setting. For clinical biomarker development it is needless to say that the further the biomarker is developed, more value it carries. Discovery studies were carried out in WPs 1-3, while the validation studies were done in WP 6.
Notably, all these activities were highly integrated and include a strong component of computational systems biology (WP 5) and neuroimaging technology development (WP 4). In metabolomics studies, the discovery and validation steps are usually performed by using “global platforms”, covering a broad range of analytes. Once the biomarker analytes are known, a robust and rugged method needs to be developed and validated which may be applicable in clinical setting. As anticipated ion the to the very end of the assay development stage, e.g. as a diagnostic kit ready for use in healthcare setting. However, they were developed far enough that commercial exploitation and pilot studies in healthcare setting can be considered. The commercial exploitation of such biomarkers and related assays will be considered case-by-case according to METSY management procedures, and may involve further in house developments, launching spin-outs, licensing, or industrial partnerships.
For broad acceptance of the biomarker in healthcare setting, biomarker needs to be further confirmed in independent replication studies and its utility needs to be demonstrated in multiple studies, with some leading to publications in notable medical journals. Although these additional studies were not considered within METSY, once the assays and multi-modal panels are developed in WP 6, METSY will consider offering them for applications in other on-going or future studies or projects. The conditions of involvement will be considered on case-by-case basis according to METSY management procedures. In addition to participating SME and industrial partner, also other METSY partners have a strong track record in commercial exploitation of scientific findings, including in launching multiple spinout companies, licensing deals, or industry partnerships across a broad range of business areas.
Encouragement of SME participation and fostering innovation in Europe in line with the Europe2020 agenda.
METSY included one SME in the domain of bioinformatics. In line with Europe 2020 Smart Growth
priorities and strongly facilitating the integration with a large European industry partner,
METSY had strong impact on creating new products/services that generated growth and jobs and helped address social challenges with the help of innovative combination of neuroimaging and metabolic research via the use of state-of-the-art methods of bioinformatics and statistics.
METSY supported the goals of the European Pact for Mental Health.
Affective and non-affective psychoses are relatively prevalent mental illnesses. It has been
estimated that the lifetime prevalence of all psychotic disorders is about 3.5 %. Psychotic bipolar
disorder and psychotic depression are common affective psychoses whereas schizophrenia is the
most common as well as the most severe one among the non-affective psychoses in terms of
functional outcome. Schizophrenia is clinically characterised with a typical onset in adolescence
or early adulthood with disturbances of perception, thinking, behaviour and emotional life. Schizophrenia and other psychotic disorders are also a major public health problem because of their
burden and prevalence. Brain disorders cost Europe almost 800 billion € a year. Among all brain
disorders psychotic disorders come second, only after mood disorders, in terms of cost to Europe. In a recent report it has been estimated that in Europe there are over 5 million people with psychotic
disorders and that the cost to Europe is 93.6 billion € a year. METSY provided better predictive diagnostic tools to detect and monitor psychosis which is directly relevant to two priority areas of the European Pact on Mental Health and Well-being: (II) Mental health in youth and education, and (III) Mental health in workplace settings.
Dissemination and/or exploitation of project results, and management of IP
Dissemination of new knowledge within the scientific community is an intrinsic interest of research,
and aims at strengthening and reinforcing the European research activities by multiplication and
initiation of networking and collaborations beyond the consortium. Dissemination of results and
new knowledge obtained in METSY is of high priority, emphasized by the dedication of a work
package to dissemination issues. Within METSY WP 7 was dedicated on result exploitation:
1. To attain a high level of public awareness of METSY activities and discoveries and of the
relevance to systems medicine
2. To maximize exploitation of METSY discoveries in healthcare and personalised medicine settings
3. To protect METSY intellectual property.
Dissemination of knowledge took place at different levels:
1. Within the METSY consortium (internal meetings and reports);
2. To the scientific community (publication in international peer reviewed journals, presentations
at national and international conferences);
3. To patients as well as healthcare professionals and decision makers via patient organizations;
4. To the broad public.
The project manager was responsible to oversee all dissemination activities, which were also defined in the Consortium Agreement.
Means to disseminate new knowledge within the scientific community were:
1. Publications in high-impact, peer-reviewed international journals
2. Presentations at international conferences
3. METSY Workshops
4. Filing of patent applications
5. Multiplication by recruitment and training of scientists at the PhD and postdoctoral level
6. Open workshops
7. Presentation of METSY, its objective, aims and potentials on a public domain web page.
Means to disseminate to patients as well as healthcare professionals and decision makers were:
1. Organization of special workshops for patients and/or healthcare professionals related to
specific METSY S/T activities and outcomes,
2. Active participation in international conferences, such as organization of METSY sessions at
these meetings. As an already existing example of such activities, M. Orešič (P1) organized a
symposium at 14th International Congress on Schizophrenia Research (April 2013; Orlando/FL/USA) on the topic of molecular biomarkers in schizophrenia.
Means to disseminate to the public were:
1. Press releases in national newspapers, initiated by the information offices at the participating
organizations;
2. Open door events in the participating organizations;
3. Science and Society events;
4. Presentation of METSY, its objective, aims and potentials on a public domain web page.
METSY had specific initiatives to strengthen its dissemination potential:
1. METSY web page. One important means to integrate all dissemination activities was the web page of METSY, where knowledge was made available to the scientific community and
the public. A private domain was established to make internal knowledge easily accessible
for all partners. The private domain includes internal interim reports, pre-views of scientific
publications and internal news. The web page was regularly updated and designed to meet
the needs of the consortium.
2. Preparation of joint papers and position statements. The Steering Committee actively pursued opportunities for METSY to publish joint papers and position statements in the name of
METSY. The METSY joint papers attract more attention to other means of dissemination
such as the METSY web page, and therefore had a multiplication value.
Exploitation of METSY results and management of Intellectual Property
– industry participants
Biomax
The BioXMTM Knowledge Management Environment is developed by Biomax Informatics AG and
is available as a commercial product in life science research and clinical application environments.
Generation of a psychotic disease and metabolic co-morbidities knowledgebase and integration with
newly developed clinical decision support systems (WP 5) broadened the clinical applicability of
the system to psychotic disease (earlier it was focused on pulmonary care).
The experience and research results gained during the project, regarding structuring, mapping and
mining results of brain imaging technologies in the context of diagnostics allowed Biomax to
further extend its established commercial footprint in the field of clinical knowledge management in
research and application. While there is strong competition from large non-European companies
(e.g. Microsoft Almaga Life Sciences) regarding general clinical data management infrastructure
the added value provided by targeted content and knowledge representation, developed in projects
such as METSY, allowed Biomax to provide added value and expand its position in the life science
knowledge management and bioinformatics market with a total volume in 2010 of about $ 3.5
billion and a focus on content in purchase decisions (Frost&Sullivan Market report 2010, RNCOS
Market Outlook 2010, Global Industry Analysts report 2011).
Philips
The Imalytics Research Workstation is developed by Philips Research and is available as a
commercial product. It serves as a platform for new applications to be used in preclinical and
clinical research environments. Project results of WP 4 became new modules on the Imalytics platform, and thereby have the potential for direct commercialisation.
Insights and first prototypes generated in this project were transferred to various business units within Philips Healthcare at a later stage. Developed modules may also be made available on
the clinical workstation IntelliSpace Portal (requiring regulatory approval, which is out of scope for
this project).
Furthermore, the demonstration of the clinical benefits of hybrid PET/MR neuroimaging that were pursued in this project substantiated the need for this innovative hybrid imaging technology
for early prediction and monitoring of psychotic disorders, thus fostering sales to academic hospitals
and specialized neuroimaging centres.
Overview of Intellectual Property (IP) opportunity
The effective management and exploitation of Intellectual Property (IP) was a critical component of
METSY. It is essential that the outcomes of the research are adequately protected in such a way
that they are attractive for commercial exploitation. Consideration must be given to the categories
of IP that are likely products of the Work Packages. For example:
1. Diagnostic biomarkers applicable in healthcare setting. Activities in WPs 1-3 and 6 led to novel biomarkers for the specific clinical outcomes of relevance to healthcare and personalized medicine. The IP may include specific analytical assays for the biomarkers or more
broadly the multi-modal biomarker signatures together with the method to predict the relevant
outcomes using these analyte(s) as well as potentially other information.
2. Technology solutions for neuroimaging. The technology/software developments in WPs 2, 4,
and 5 have a potential to lead to new or improved existing products by the industrial
participants, as well as to novel product ideas, e.g. software tools for diagnostics and patient
monitoring in psychiatric disorders (WPs 4, 5).
Management of IP
As recommended in the FP7 guidelines and according to standard practice, IP was considered in
the following categories:
1. Background (pre-existing IP);
2. Foreground (knowledge generated from the Collaborative Project whether or not it may be
patentable).
Foreground may be owned by the single party that generated the Foreground or jointly owned by
several parties that have contributed to the Foreground. The ownership and rights of use of
Foreground generated through the performance of this collaborative project were defined in the
consortium agreement which will combine standard practice with several innovative features to
enhance the effectiveness through which Foreground can be exploited and/or commercialized. A
summary of the key considerations is provided here:
1. Identification. Academic research scientists will often not recognize valuable foreground that
could form the basis of a patent. This is not surprising since such training is not routinely provided
or available to researchers. The current program addressed this by two actions:
1. it provided training in knowledge protection and transfer, and IP. This action took advantage of the existing technology transfer organizations within METSY. The partner organizations have well developed and professional technology transfer offices. In addition, the program included two SMEs, for which the effective management of IP is an essential part of their activities. METSY offered a workshop taught by experts from the technology transfer organizations and with representatives from SMEs to provide the industry perspective. The workshop focused on how to identify a foreground opportunity that should be protected and on the best strategy for its protection.
2. Technology scouts. This is, relative to academic practice, a highly innovative feature of the
METSY program that has been inspired by current industry practice. Industry employs technology scouts who are trained in the identification of valuable IP and technology partnering opportunities and who attend meetings and conferences or visit biotechnology or academic clusters in order to identify what may be of interest to their company. METSY collected volunteers within the programme (preferably at the Postdoctoral level) who have an interest in the exploitation of foreground and commercialization of research results. These were briefed, mentored and trained by the technology transfer specialists to act as technology scouts within METSY. Their task will be to identify opportunities arising from the research that should be protected and/or exploited. This activity also served to provide a first training to the postdoctoral fellows in industry-relevant actions and may be particularly attractive to those researchers considering a career in industry.
2. Protection. The breadth of claims and positioning of the claims are essential elements in
establishing the value of a patent. Consequently, it is important to consider the full possible breath
of claims that could be made regarding a particular asset of foreground. This was achieved within the current proposal by implementation of a Foreground Evaluation Committee (FEC), which included a blend of expertise that added value to patents by identifying enlarged scope or wider positioning of claims. The FEC was composed of clinical researchers, academic researchers, representatives of the technology transfer offices and a patent attorney. The FEC reviewed each opportunity presented by the technology scouts (or directly by research scientists whenever they should take such initiative) with a view to maximize its value and to recommend an effective patent filing strategy or other form of protection strategy. The FEC also assisted in determining the assignment of ownership of jointly-owned Foreground.
3. Rights of Use. All Foreground generated within METSY is made available to the
consortium for non-commercial research, training and educational purposes. Whenever possible this philosophy is also applied on a Europe-wide scope to any Foreground that has potential for
creation of value in the European research base such that the Foreground is made available
non-exclusively for non-commercial research, training and education to the research community.
Pending confirmation of this and other stipulation by the negotiated consortium agreement, it is
anticipated that any party generating individually Foreground that is the subject of a patented
invention has the right to use and licence such invention at their sole discretion. Rights of use of
patented inventions developed with contributions of more than one partner within METSY is determined by agreement between the concerned partners on an exploitation and licensing plan.
This plan may stipulate that one contributing partner licenses exclusively their share of the joint
invention to another contributing partner for commercialization. Alternatively, each party sharing
the ownership of Foreground may be entitled to issue non-exclusive licenses at their sole discretion
but without the right to sublicense. Please note that patented Background that is required for the
performance of METSY will be provided by each party as a royalty-free non-exclusive license.
Project logo
Other project contacts
Prof. Jarmo Hietala
University of Turku
Kiinamyllynkatu 4-8, building 11B, 20520 TURKU
Phone: +358 2 266 2520
E-mail: jarmo.hietala@utu.fi
Prof. Oliver Howes
King’s College London
PO 67, De Crespigny Park, Denmark Hill, London SE5 8AF
Phone: +44 (0)20 7848 0080 or +44 (0)20 8383 3160
E-mail: oliver.howes@kcl.ac.uk
Prof. Jaana Suvisaari
National Public Health Institute
P.O. Box 30, FI-00271, Mannerheimintie 166, Helsinki, Finland
Phone: +358 29 524 8539
E-mail: jaana.suvisaari@thl.fi
Prof. Mark van Gils
VTT Technical Research Centre of Finland
P.O. Box 1300, 33101 Tampere, Finland
Phone: +358 20 722 3342
E-mail: mark.vangils@vtt.fi
Dr. Dieter Maier
Biomax Informatics AG
Robert-Koch-Str. 2 D-82152 Planegg Germany
Phone: +49 89 895574-0
Email: dieter.maier@biomax.com
Website: http://www.biomax.com
Prof. Tuulia Hyötyläinen
School of Science and Technology
Örebro University
SE-701 82 Örebro, Sweden
Phone: +46 19 303487
Email: tuulia.hyotylainen@oru.se
