Translational studies on bipolar disorder and anorexia nervosa

Translational medicine aims at closing the gap between basic and clinical sciences in an integrative way. Psychiatry is one of the few medical specialties in which diagnosis is primarily based on clinical observation and all mental disorders are defined by abnormal behaviors and cognitions. The lack of biomarkers supporting diagnostic and therapeutic procedures has been a challenge in psychiatry. A better biological understanding is needed to move the field forward, it will enhance diagnostics and treatment, while reducing the stigma that surrounds mental disorders that are so poorly understood. Over the last years, advances in fundamental sciences like genetics and neuroscience have made it clear that there is shared biology between many psychiatric disorders and that integration of methods might lead to new understandings. The studies presented in this thesis focus on bipolar disorder (BD) and anorexia nervosa (AN), both severe mental disorders with high suicide rates, high heritability, and both lacking in biological understanding. BD, formerly known as manic-depressive disorder, is a mood disorder, characterized by manic or hypomanic episodes, often in combination with depressive episodes. AN is an eating disorder characterized by severe weight loss together with pathological behaviors. This thesis includes five main studies on the biology underlying these disorders, based on large, well characterized cohorts, covering several methods, including genetic, imaging and protein markers, as well as preliminary data on the establishment of in vitro models. Specifically, in study I, we attempted to replicate previously published findings on the association between subphenotypes of bipolar disorder and genetic variations in the AKT1 gene. Using frequentist and Bayesian approaches, as well as publicly available results from genome-wide association studies (GWAS), we were able to reject previously proposed associations. In study II, we explored the effects of genetic variations in genes involved in glutamate regulation on glutamate levels in two brain regions and their associations with other phenotypes. We found that the minor allele of rs3812778/rs3829280 in the 5’-untranslated region of SLC1A2, coding for a glutamate transporter, is associated (1) with increased glutamate levels in the anterior cingulate cortex, (2) with increased expression levels, in several brain regions, of the transmembrane receptor gene CD44, which is implicated in inflammation and brain development, as well as (3) with an increased risk for rapid-cycling in bipolar disorder, potentially linking CD44/SLC1A2 to a more severe phenotype of BD. In study III, we investigated the effects of clinical and genetic parameters on lithium pharmacokinetics in order to better understand lithium biology and improve lithium dose prediction models for bipolar patients, using the ratio between serum lithium and daily lithium intake, as outcome. We were able to confirm the association of several clinical predictors. Although no genome-wide significant locus was found, we report that genetic variation is important and might influence the outcome. Finally, based on the results obtained in the study, we developed a prediction algorithm that can be tested in the clinic. In study IV, we investigated the involvement of neuronal degeneration in AN by studying neurofilament light chain (NfL), a known marker of neurodegeneration, in a case-control setting and found increased levels of NfL in patients with active AN in two different cohorts. In study V, we studied the involvement of inflammation in AN, using a panel of 92 inflammatory markers in a case-control setting and report an aberrant inflammatory profile in patients with active AN, but not in patients that have recovered from AN. These studies exemplify possible approaches that can be taken in translational psychiatry. The integration of clinical, technical and analytical approaches illustrates important learning outcomes for an aspiring clinical scientist in psychiatr

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

How can older adults combat diabetes to achieve a longer and healthier life?

Type 2 diabetes (hereafter, diabetes) and prediabetes are very common in older adults and constitute a great health concern for this population. The objective of this project is to investigate the impact of prediabetes and diabetes on health and survival among older adults, and to identify modifiable factors that may attenuate the risk of diabetes on disability and mortality to prolong survival with independence. Data used in this project were derived from the ongoing population-based Swedish National study on Aging and Care in Kungsholmen (SNAC-K). Study I described the natural history of prediabetes and identified prognostic factors related to different outcomes of prediabetes. We found that among 918 participants with prediabetes at baseline, 204 (22%) reverted back to normoglycemia, 119 (13%) developed diabetes, and 215 (23%) died during the 12-year follow-up. Lower systolic blood pressure, and weight loss, and the absence of heart diseases were associated with the reversion of prediabetes to normoglycemia, whereas obesity was related to its progression to diabetes. Study II examined the association of prediabetes and diabetes with the risk of stroke and subsequent dementia. Among 2,655 dementia-free participants at baseline, a stroke-free cohort and a prevalent stroke cohort were identified based on prevalent stroke. In the stroke-free cohort, 236 participants developed ischemic stroke and 47 developed post-stroke dementia. Diabetes was associated with a higher risk of ischemic stroke and post-stroke dementia. In the prevalent stroke cohort, diabetes was also related to dementia risk. We did not find a significant association between prediabetes and stroke or post-stroke dementia. Study III assessed the association of prediabetes and diabetes with physical function decline and disability progression and explored whether cardiovascular diseases (CVDs) mediate these associations. During a 12-year follow-up, prediabetes accelerated the deterioration in chair stand performance, walking speed, and disability progression, independent of the future development of diabetes. Diabetes led to a faster decline than prediabetes, especially among those with uncontrolled diabetes. CVDs mediated 7.1%, 7.8%, and 20.9% of the associations between prediabetes and chair stand performance, walking speed, and disability progression, respectively. Study IV examined the association of prediabetes and diabetes on a composite outcome of disability or death and further identified modifiable factors that may prolong disability-free survival. Diabetes, but not prediabetes, was associated with a higher risk of disability or death. Compared to diabetes-free participants with a favorable lifestyle profile including the presence of at least one of the healthy behaviours, active leisure activities, or moderate-to-rich social network, those with diabetes and an unfavorable profile had 2.46 times higher risk of the outcomes. However, among participants with diabetes, the risk of the outcome was attenuated (HR 1.19, 95% CI 0.93 to 1.53) in those with a favorable profile, which prolonged disability-free survival by 3 years compared to those with an unfavorable profile. Conclusions. In addition to its associations with stroke and cardiovascular diseases, diabetes could increase the risk of dementia secondary to stroke and accelerate decline in physical function. This decline in physical function might start already during prediabetes. Yet, one out of five older adults with prediabetes could revert back to normoglycemia with lifestyle modifications such as weight management. Diabetes is related to the risk of disability or death among older adults, but a healthy and socially active lifestyle may attenuate this risk and prolong disability-free survival

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables