from a better etiological understanding, through valid diagnosis, to more effective health care

Background Autism Spectrum Disorder (ASD) is a severe, lifelong neurodevelopmental disorder with early onset that places a heavy burden on affected individuals and their families. Due to the need for highly specialized health, educational and vocational services, ASD is a cost- intensive disorder, and strain on health care systems increases with increasing age of the affected individual. Methods The ASD-Net will study Germany’s largest cohort of patients with ASD over the lifespan. By combining methodological expertise from all levels of clinical research, the ASD-Net will follow a translational approach necessary to identify neurobiological pathways of different phenotypes and their appropriate identification and treatment. The work of the ASD-Net will be organized into three clusters concentrating on diagnostics, therapy and health economics. In the diagnostic cluster, data from a large, well-characterized sample (N = 2568) will be analyzed to improve the efficiency of diagnostic procedures. Pattern classification methods (machine learning) will be used to identify algorithms for screening purposes. In a second step, the developed algorithm will be tested in an independent sample. In the therapy cluster, we will unravel how an ASD-specific social skills training with concomitant oxytocin administration can modulate behavior through neurobiological pathways. For the first time, we will characterize long-term effects of a social skills training combined with oxytocin treatment on behavioral and neurobiological phenotypes. Also acute effects of oxytocin will be investigated to delineate general and specific effects of additional oxytocin treatment in order to develop biologically plausible models for symptoms and successful therapeutic interventions in ASD. Finally, in the health economics cluster, we will assess service utilization and ASD-related costs in order to identify potential needs and cost savings specifically tailored to Germany. The ASD-Net has been established as part of the German Research Network for Mental Disorders, funded by the BMBF (German Federal Ministry of Education and Research). Discussion The highly integrated structure of the ASD-Net guarantees sustained collaboration of clinicians and researchers to alleviate individual distress, harm, and social disability of patients with ASD and reduce costs to the German health care system. Trial registration Both clinical trials of the ASD- Net are registered in the German Clinical Trials Register: DRKS00008952 (registered on August 4, 2015) and DRKS00010053 (registered on April 8, 2016)

EPIGENETICS, RESILIENCE, COMORBIDITY AND TREATMENT OUTCOME

Personalized or precision medicine is a relatively new promising concept which is gaining momentum in all branches of medicine including psychiatry and neurology. Psychiatry and neurology are medical specialties dealing with diagnosis, prevention and treatment of brain disorders which are the main causes of years lived with disability worldwide as well as shortened life. Despite a huge progress in clinical psychopharmacology and neuropharmacology, the treatment outcome for many psychiatric disorders and neurologic diseases has remained unsatisfactory. With aging, comorbidities are more the rule, than an exception and may significantly influence on the final treatment outcome. Epigenetic modulation, resilience and life style are key determinants of the health and very important issues for understanding therapeutic mechanisms and responses. There is a hope that epigenetic profiling before treatment could be used in near future to increase the likelihood of good treatment response by selecting the appropriate medication. The aim of this paper is to offer an overview of the main aspects of epigenetic modulation, resilience and comorbidities and their role in developing the concept of personalized medicine. While waiting for more precise and reliable treatment guidelines it is possible to increase treatment effectiveness in psychiatry and neurology by enhancing individual resilience of patients and managing comorbidities properly

Genetic vulnerability, environmental exposures and neurodevelopmental disorders : clinical insights and in-vitro consequences

An understanding of how different genetic backgrounds and environmental exposures interact and contribute to diverse medical conditions like neurodevelopmental disorders (NDDs), is key to better health outcomes. A considerable overlap exists in the underlying genetics and physiology, often leading to their co-diagnoses. There is a lack of robust biomarkers for ASD and ADHD. Furthermore, no efficient resource exists to evaluate potential gene-environment interactions during early human neurodevelopment. This thesis addressed these knowledge gaps through five clinical and in-vitro studies. Study I explored the utility of genetic information from exome sequencing in predicting intervention outcomes of a social skills group training (SSGT) clinical trial for ASD. A genetic score was developed for common and rare variants in relevant genetic pathways, followed by generating a predictive machine learning (ML) model for individual responses. Variant carriers demonstrated significantly less improvement after standard care at postintervention. A higher rare variant genetic score for synaptic transmission was linked to less efficacy after SSGT at follow-up, while an opposite effect was observed for regulation of transcription from RNA polymerase II. The ML model emphasised the importance of rare variants in predicting intervention outcomes. Study II deployed urine-based untargeted metabolomics to investigate ASD-related biomarkers in a twin cohort with ultra-high performance liquid chromatography and mass spectrometry (UHPLC-MS). For the first time, any associations with autistic traits were also evaluated. No metabolite was found to be significantly associated with ASD. Based on nominal significance, an elevation in phenylpyruvate and taurine, and a decline in carnitine were detected, amongst others. These were found to be enriched in the arginine and proline metabolism pathway. More nominally significant metabolites were associated with autistic traits, and indole-3-acetate was positively associated with autistic traits within twin pairs. Study III also utilised a twin cohort to detect urinary and faecal metabolites associated with ADHD using nuclear magnetic resonance (NMR) and UHPLC-MS, respectively. Males with ADHD had increased levels of urinary hippurate, a metabolite produced by microbial-host co-metabolism. Hippurate was also negatively associated with intelligence quotient (IQ) levels in males and differentially associated with faecal metabolites from the gut microbiome. ADHD faecal profiles were characterised by higher levels of 1-stearoyl-2-linoleoyl-snglycerol (SLG), flavine adenine dinucleotide (FAD) and 3,7-dimethylurate. Reduced levels of aspartate, xanthine, orotate and other metabolites were also detected. Study IV dissected the impact of six environmental factors (lead, valproic acid, bisphenol A, ethanol, fluoxetine and zinc deficiency) in human induced pluripotent stem cell (iPSC) derived neuronal progenitors after differentiation for 5 days using the fractional factorial experimental design (FFED) coupled with RNA-sequencing. This was followed by a stratified analytical approach. Several gene and pathway level changes, that were both convergent and divergent for the environmental factor exposures, were identified. Pathways related to synaptic function and lipid metabolism were significantly elevated by lead and fluoxetine, respectively. Furthermore, fluoxetine increased the levels of several fatty acids when validated with direct infusion electrospray ionisation mass spectrometry (ESI-MS). Study V evaluated the differential in-vitro effects of four commonly prescribed selectively serotonin reuptake inhibitors (SSRIs: fluoxetine, citalopram, sertraline and paroxetine) in iPSC-derived neuronal progenitors. Total reactive oxygen species (ROS) and adenosine triphosphate (ATP) levels were determined at day 5 and 28 of differentiation. Concurrently, untargeted metabolomics was performed using ESI-MS. Sertraline and paroxetine significantly decreased ROS and ATP levels. Sertraline mediated early metabolite changes at day 5, while both sertraline and paroxetine drove such effects at day 28. Combined effects were driven by LPC 18:0 and LPC 16:0. Overall, metabolites were enriched in phospholipid biosynthesis and amino acid metabolism pathways. In conclusion, this thesis highlighted that genetic information can be used as an indicator for ASD interventions, encouraging further exploration. Urine and faecal metabolites are potential biomarkers for ASD and ADHD, pending validation. A multiplexable resource for studying gene-environment interactions was developed, along with a rich dataset outlining molecular changes in ASD. Lastly, the thesis demonstrated that different SSRIs elicit both shared and unique in-vitro responses, with a need to evaluate probable in-utero effects. The findings can guide future clinical studies to generate greater insights into ASD, ADHD and other conditions with aberrant neurodevelopmental trajectories

Advances in psychotherapy research and precision mental health: Answering the “What works for whom” question for patients with depression

The present doctoral thesis focuses on two articles which are embedded in the field of precision mental health and treatment selection. Study 1 examined if model determined treatment allocation to cognitive behavioral therapy (CBT) or CBT with integrated exposure and emotion-focused elements (CBT-EE) results in better treatment outcomes while using important predictors found for each intervention. Study 2 investigated important predictors in routine care and blended internet- and face-to-face CBT in secondary care, as well as treatment outcomes for treatment allocation using this predictive information. Both studies use a Bayesian approach called Bayesian Model Averaging (BMA) and the Personalized Advantage Index (PAI) for their statistical analyses. After an introduction to the Generic Model of Psychotherapy, the development of process and outcome research and the thematic field of treatment selection and precision medicine, the individual articles will be described and critically reflected in more detail. Possibilities and limits of predicting the optimal treatment for an individual based on algorithms are discussed based on the results of the two studies. Taken together, the two studies provide an important contribution to psychotherapy research as the feasibility of treatment selection using BMA and PAI is shown. Last but not least, implications for future research are discussed and an example of how treatment selection can be transferred into clinical practice is presented

Toward precision medicine in ADHD

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response

The development of psychiatric disorders and adverse behaviors : from context to prediction

Psychiatric disorders by definition cause significant impairment in an individual’s daily functioning. Certain disorders, such as borderline personality disorder (BPD) and eating disorders, have worse prognosis and high mortality rates compared to other psychiatric disorders. Similarly, adverse behaviors such as self-harm, suicide, and crime are often present in individuals with psychiatric disorders. It is of interest to further understand the etiology and associations of BPD and eating disorders to uncover potential avenues and opportunities for intervention. Moreover, prediction modeling has recently come of interest to psychiatric epidemiologists with the rise of large data sets. Prediction modeling may provide valuable information about the nature of risk factors and eventually aid clinical diagnostics and prognostics. Thus, the studies included in this thesis seek to examine the etiology, associations, and prediction approaches of psychiatric disorders and adverse behaviors. Study I examined the individual and familial association between type 1 diabetes (T1D) and eating disorder diagnoses. We used national health care records from Denmark (n = 1,825,920) and Sweden (n = 2,517,277) to calculate the association within individuals, full siblings, half siblings, full cousins, and half cousins. Individuals with T1D had twice the hazard rate ratio of being diagnosed with an eating disorder compared to the general population. There was conflicting evidence for the risk of an eating disorder in full siblings of T1D patients. However, there was no evidence to support a further familial relationship between the two conditions. Study II aimed to illuminate the nature of the correlates for BPD across time, sex, and for their full siblings. We examined 87 variables across psychiatric disorders, somatic illnesses, trauma, and adverse behaviors (such as self-harm). In a sample of 1,969,839 Swedes with 12,175 individuals diagnosed with BPD, we found that BPD was associated with nearly all of the examined variables. The associations were largely consistent across time and between the sexes. Finally, we found that having a sibling diagnosed with BPD was associated with psychiatric disorders, trauma, and adverse behaviors but not somatic illnesses. Study III created a prediction model that could predict who would have high or low psychiatric symptoms at age 15 based on data from parental reports and national health care registers collected at age 9 or 12. Additionally, we compared multiple types of machine learning algorithms to assess predictive performance. The sample included 7,638 twins from the Child and Adolescent Twin Study in Sweden (CATSS). Our model was able to predict the outcome with reasonable performance but is not suitable for use in clinics. Each model performed similarly indicating that researchers with similar data and research questions do not need to forgo standard logistic regression. Study IV aimed to determine if an individual will exhibit suicidal behaviour (self-harm or suicidal thoughts), aggressive behaviour, both, or neither before adulthood with prediction modeling. Through variable importance scores we examined the usefulness of genetic variables within the model. A total of 5,974 participants from CATSS and 2,702 participants from the Netherlands Twin Register (NTR) were included in the study. The model had adequate performance in both the CATSS and NTR datasets for all classes except for the suicidal behaviors class in the NTR, which did not perform better than chance. The included genetic data had higher variable importance scores than questionnaire data completed at age 9 or 12, indicating that genetic biomarkers can be useful when combined with other data types. In conclusion, the development of psychiatric disorders and symptoms are associated with many factors across somatic illnesses, other psychiatric disorders, trauma, and harmful behaviors. The results of this thesis demonstrates the limitations of prediction modeling in psychiatric clinics but highlights their use in research and on the path forward towards personalized medicine