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Background
Lower cortisol concentrations in adulthood were repeatedly associated with more severe childhood maltreatment. Additionally, childhood maltreatment was reported to promote health risk behavior, such as smoking or alcohol consumption, and to increase the risk of mental and somatic diseases during adulthood, such as major depressive disorders or obesity. The present study investigated if health risk behavior and disease symptoms in adults mediate the associations between past childhood maltreatment and present basal serum cortisol concentrations.
Methods
Data from two independent adult cohorts of the general population-based Study of Health in Pomerania (SHIP-TREND-0: N = 3,517; SHIP-START-2: N = 1,640) was used. Childhood maltreatment was assessed via the Childhood Trauma Questionnaire (CTQ). Cortisol concentrations were measured in single-point serum samples. Health risk behavior and mental and physical symptoms were used as mediators. Mediation analyses were calculated separately for both cohorts; results were integrated via meta-analyses.
Results
In mediator-separated analyses, associations between childhood maltreatment and basal serum cortisol concentrations were partly mediated by depressive symptoms (BDI-II: βindirect effect = -.011, pFDR = .017, 21.0% mediated) and subjective somatic health complaints (somatic complaints: βindirect effect = -.010, pFDR = .005, 19.4% mediated). In the second step, both mediators were simultaneously integrated into one mediation model. The model replicated the mediation effects of the subjective somatic health complaints (whole model: βindirect effect = -.014, p = .001, 27.6% mediated; BDI-II: βindirect effect = -.006, p = .163, 11.4% mediated, somatic complaints: βindirect effect = -.020, p = .020, 15.5% mediated).
Conclusion
The results support the hypothesis that the long-lasting effects of childhood maltreatment on the stress response system are partly mediated through self-perceived disease symptoms. However, no mediation was found for health risk behavior or physically measured mediators. Mediation models with multiple simultaneous mediators pointed to a relevant overlap between the potential mediators. This overlap should be focused on in future studies.
The hypothalamus–pituitary–adrenal axis is the main physiological stress response system and regulating the release of cortisol. The two corticoid receptors encoded by the genes NR3C1 and NR3C2 are the main players in regulating the physiological response to cortisol. This biological system has been linked to neurocognitive processes and memory, yet the mechanisms remain largely unclear. In two independent general population studies (SHIP, total sample size > 5500), we aim to diseantangle the effects of genetic variation, gene expression and cortisol on verbal memory and memory associated brain volume. Especially for NR3C1 results exhibited a consistent pattern of direct an interactive effects. All three biological layers, genetic variation (rs56149945), gene expression for NR3C1 and cortisol levels, were directly associated with verbal memory. Interactions between these components showed significant effects on verbal memory as well as hippocampal volume. For NR3C2 such a complex association pattern could not be observed. Our analyses revealed that different components of the stress response system are acting together on different aspects of cognition. Complex phenotypes, such as cognition and memory function are regulated by a complex interplay between different genetic and epigenetic features. We promote the glucocorticoid receptor NR3C1 as a main target to focus in the context of verbal memory and provided a mechanistic concept of the interaction between various biological layers spanning NR3C1 function and its effects on memory. Especially the NR3C1 transcript seemed to be a key element in this complex system.
Childhood abuse was inconsistently related to whole-brain cortical thickness in former studies. However, both childhood abuse and cortical thickness have been associated with depressive symptoms. We hypothesised that childhood abuse moderates the association between depressive symptoms and cortical thickness. In 1551 individuals of the general population, associations between whole-brain cortical thickness and the interaction of childhood abuse (emotional, physical, and sexual) and depressive symptoms were analysed using an ANCOVA. Linear regression analyses were used to estimate the same effect on the cortical thickness of 34 separate regions (Desikan-Killiany-atlas). A significant interaction effect of childhood abuse and depressive symptoms was observed for whole-brain cortical thickness (F(2, 1534) = 5.28, p = 0.007). A thinner cortex was associated with depressive symptoms in abused (t value = 2.78, p = 0.025) but not in non-abused participants (t value = − 1.50, p = 0.224). Focussing on non-depressed participants, a thicker whole-brain cortex was found in abused compared to non-abused participants (t value = − 2.79, p = 0.025). Similar interaction effects were observed in 12 out of 34 cortical regions. Our results suggest that childhood abuse is associated with reduced cortical thickness in subjects with depressive symptoms. In abused subjects without depressive symptoms, larger cortical thickness might act compensatory and thus reflect resilience against depressive symptoms.
Introduction
Heart rate variability (HRV), defined as the variability of consecutive heart beats, is an important biomarker for dysregulations of the autonomic nervous system (ANS) and is associated with the development, course, and outcome of a variety of mental and physical health problems. While guidelines recommend using 5 min electrocardiograms (ECG), recent studies showed that 10 s might be sufficient for deriving vagal-mediated HRV. However, the validity and applicability of this approach for risk prediction in epidemiological studies is currently unclear to be used.
Methods
This study evaluates vagal-mediated HRV with ultra-short HRV (usHRV) based on 10 s multichannel ECG recordings of N = 4,245 and N = 2,392 participants of the Study of Health in Pomerania (SHIP) from two waves of the SHIP-TREND cohort, additionally divided into a healthy and health-impaired subgroup. Association of usHRV with HRV derived from long-term ECG recordings (polysomnography: 5 min before falling asleep [N = 1,041]; orthostatic testing: 5 min of rest before probing an orthostatic reaction [N = 1,676]) and their validity with respect to demographic variables and depressive symptoms were investigated.
Results
High correlations (r = .52–.75) were revealed between usHRV and HRV. While controlling for covariates, usHRV was the strongest predictor for HRV. Furthermore, the associations of usHRV and HRV with age, sex, obesity, and depressive symptoms were similar.
Conclusion
This study provides evidence that usHRV derived from 10 s ECG might function as a proxy of vagal-mediated HRV with similar characteristics. This allows the investigation of ANS dysregulation with ECGs that are routinely performed in epidemiological studies to identify protective and risk factors for various mental and physical health problems.
The relationship between Alzheimer's-related brain atrophy patterns and sleep macro-architecture
(2022)
Introduction
Sleep is increasingly recognized as a major risk factor for neurodegenerative disorders such as Alzheimer's disease (AD).
Methods
Using an magnetic resonance imaging (MRI)–based AD score based on clinical data from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) case-control cohort, we investigated the associations between polysomnography-based sleep macro-architecture and AD-related brain atrophy patterns in 712 pre-symptomatic, healthy subjects from the population-based Study of Health in Pomerania.
Results
We identified a robust inverse association between slow-wave sleep and the AD marker (estimate: −0.019; 95% confidence interval: −0.03 to −0.0076; false discovery rate [FDR] = 0.0041), as well as with gray matter (GM) thicknesses in typical individual cortical AD-signature regions. No effects were identified regarding rapid eye movement or non–rapid eye movement (NREM) stage 2 sleep, and NREM stage 1 was positively associated with GM thickness, mainly in the prefrontal cortical regions.
Discussion
There is a cross-sectional relationship between AD-related neurodegenerative patterns and the proportion of sleep spent in slow-wave sleep.
Microbial metabolites measured using NMR may serve as markers for physiological or pathological host–microbe interactions and possibly mediate the beneficial effects of microbiome diversity. Yet, comprehensive analyses of gut microbiome data and the urine NMR metabolome from large general population cohorts are missing. Here, we report the associations between gut microbiota abundances or metrics of alpha diversity, quantified from stool samples using 16S rRNA gene sequencing, with targeted urine NMR metabolites measures from 951 participants of the Study of Health in Pomerania (SHIP). We detected significant genus–metabolite associations for hippurate, succinate, indoxyl sulfate, and formate. Moreover, while replicating the previously reported association between hippurate and measures of alpha diversity, we identified formate and 4-hydroxyphenylacetate as novel markers of gut microbiome alpha diversity. Next, we predicted the urinary concentrations of each metabolite using genus abundances via an elastic net regression methodology. We found profound associations of the microbiome-based hippurate prediction score with markers of liver injury, inflammation, and metabolic health. Moreover, the microbiome-based prediction score for hippurate completely mediated the clinical association pattern of microbial diversity, hinting at a role of benzoate metabolism underlying the positive associations between high alpha diversity and healthy states. In conclusion, large-scale NMR urine metabolomics delivered novel insights into metabolic host–microbiome interactions, identifying pathways of benzoate metabolism as relevant candidates mediating the beneficial health effects of high microbial alpha diversity.
The Study of Health in Pomerania (SHIP), a population-based study from a rural state in northeastern Germany with a relatively poor life expectancy, supplemented its comprehensive examination program in 2008 with whole-body MR imaging at 1.5 T (SHIP-MR). We reviewed more than 100 publications that used the SHIP-MR data and analyzed which sequences already produced fruitful scientific outputs and which manuscripts have been referenced frequently. Upon reviewing the publications about imaging sequences, those that used T1-weighted structured imaging of the brain and a gradient-echo sequence for R2* mapping obtained the highest scientific output; regarding specific body parts examined, most scientific publications focused on MR sequences involving the brain and the (upper) abdomen. We conclude that population-based MR imaging in cohort studies should define more precise goals when allocating imaging time. In addition, quality control measures might include recording the number and impact of published work, preferably on a bi-annual basis and starting 2 years after initiation of the study. Structured teaching courses may enhance the desired output in areas that appear underrepresented.
For the goal of individualized medicine, it is critical to have clinical phenotypes at hand which represent the individual pathophysiology. However, for most of the utilized phenotypes, two individuals with the same phenotype assignment may differ strongly in their underlying biological traits. In this paper, we propose a definition for individualization and a corresponding statistical operationalization, delivering thereby a statistical framework in which the usefulness of a variable in the meaningful differentiation of individuals with the same phenotype can be assessed. Based on this framework, we develop a statistical workflow to derive individualized phenotypes, demonstrating that under specific statistical constraints the prediction error of prediction scores contains information about hidden biological traits not represented in the modeled phenotype of interest, allowing thereby internal differentiation of individuals with the same assigned phenotypic manifestation. We applied our procedure to data of the population-based Study of Health in Pomerania to construct a refined definition of obesity, demonstrating the utility of the definition in prospective survival analyses. Summarizing, we propose a framework for the individualization of phenotypes aiding personalized medicine by shifting the focus in the assessment of prediction models from the model fit to the informational content of the prediction error.
Background: Alexithymia is a personality trait characterized by difficulties in identifying and describing emotions and associated with various psychiatric disorders. Neuroimaging studies found evidence for morphological and functional brain alterations in alexithymic subjects. However, the neurobiological mechanisms underlying alexithymia remain incompletely understood. Methods: We study the association of alexithymia with cortical correlation networks in a large community-dwelling sample of the Study of Health in Pomerania. Our analysis includes data of n = 2,199 individuals (49.4% females, age = 52.1 ± 13.6 years) which were divided into a low and high alexithymic group by a median split of the Toronto Alexithymia Scale. Cortical correlation networks were constructed based on the mean thicknesses of 68 regions, and differences in centralities were investigated. Results: We found a significantly increased centrality of the right paracentral lobule in the high alexithymia network after correction for multiple testing. Several other regions with motoric and sensory functions showed altered centrality on a nominally significant level. Conclusions: Finding increased centrality of the paracentral lobule, a brain area with sensory as well as motoric features and involvement in bowel and bladder voiding, may contribute to explain the association of alexithymia with functional somatic disorders and chronic pain syndromes.
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.
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.
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.
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.