Open Access

Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD. Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend). Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance. Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too.

Introduction Bipolar disorder (BD) is characterized by recurrent episodes of depression and mania and affects up to 2% of the population worldwide. Patients suffering from bipolar disorder have a reduced life expectancy of up to 10 years. The increased mortality might be due to a higher rate of somatic diseases, especially cardiovascular diseases. There is however also evidence for an increased rate of diabetes mellitus in BD, but the reported prevalence rates vary by large. Material and Methods 85 bipolar disorder patients were recruited in the framework of the BiDi study (Prevalence and clinical features of patients with Bipolar Disorder at High Risk for Type 2 Diabetes (T2D), at prediabetic state and with manifest T2D) in Dresden and Würzburg. T2D and prediabetes were diagnosed measuring HBA1c and an oral glucose tolerance test (oGTT), which at present is the gold standard in diagnosing T2D. The BD sample was compared to an age-, sex- and BMI-matched control population (n = 850) from the Study of Health in Pomerania cohort (SHIP Trend Cohort). Results Patients suffering from BD had a T2D prevalence of 7%, which was not significantly different from the control group (6%). Fasting glucose and impaired glucose tolerance were, contrary to our hypothesis, more often pathological in controls than in BD patients. Nondiabetic and diabetic bipolar patients significantly differed in age, BMI, number of depressive episodes, and disease duration. Discussion When controlled for BMI, in our study there was no significantly increased rate of T2D in BD. We thus suggest that overweight and obesity might be mediating the association between BD and diabetes. Underlying causes could be shared risk genes, medication effects, and lifestyle factors associated with depressive episodes. As the latter two can be modified, attention should be paid to weight changes in BD by monitoring and taking adequate measures to prevent the alarming loss of life years in BD patients.

Background: Depression and obesity are widespread and closely linked. Brain-derived neurotrophic factor (BDNF) and vitamin D are both assumed to be associated with depression and obesity. Little is known about the interplay between vitamin D and BDNF. We explored the putative associations and interactions between serum BDNF and vitamin D levels with depressive symptoms and abdominal obesity in a large population-based cohort. Methods: Data were obtained from the population-based Study of Health in Pomerania (SHIP)-Trend (n = 3,926). The associations of serum BDNF and vitamin D levels with depressive symptoms (measured using the Patient Health Questionnaire) were assessed with binary and multinomial logistic regression models. The associations of serum BDNF and vitamin D levels with obesity (measured by the waist-to-hip ratio [WHR]) were assessed with binary logistic and linear regression models with restricted cubic splines. Results: Logistic regression models revealed inverse associations of vitamin D with depression (OR = 0.966; 95% CI 0.951–0.981) and obesity (OR = 0.976; 95% CI 0.967–0.985). No linear association of serum BDNF with depression or obesity was found. However, linear regression models revealed a U-shaped association of BDNF with WHR (p < 0.001). Conclusion: Vitamin D was inversely associated with depression and obesity. BDNF was associated with abdominal obesity, but not with depression. At the population level, our results support the relevant roles of vitamin D and BDNF in mental and physical health-related outcomes.