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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.
Our goal was to provide a comprehensive overview of the antibody response to Staphylococcus aureus antigens in the general population as a basis for defining disease-specific profiles and diagnostic signatures. We tested the specific IgG and IgA responses to 79 staphylococcal antigens in 996 individuals from the population-based Study of Health in Pomerania. Using a dilution-based multiplex suspension array, we extended the dynamic range of specific antibody detection to seven orders of magnitude, allowing the precise quantification of high and low abundant antibody specificities in the same sample. The observed IgG and IgA antibody responses were highly heterogeneous with differences between individuals as well as between bacterial antigens that spanned several orders of magnitude. Some antigens elicited significantly more IgG than IgA and vice versa. We confirmed a strong influence of colonization on the antibody response and quantified the influence of sex, smoking, age, body mass index, and serum glucose on anti-staphylococcal IgG and IgA. However, all host parameters tested explain only a small part of the extensive variability in individual response to the different antigens of S. aureus.
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.
Background
Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability.
Method
We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality.
Results
Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable.
Conclusion
We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.
APOE ε4 in Depression-Associated Memory Impairment—Evidence from Genetic and MicroRNA Analyses
(2022)
(1) Background: The aim of this study was to replicate a reported interaction between APOE ε4 status and depression on memory function in two independent, nondemented samples from the general population and to examine the potential role of circulating plasma miRNAs. (2) Methods: The impact of the APOE ε4 allele on verbal memory and the interaction with depression is investigated in two large general-population cohorts from the Study of Health in Pomerania (SHIP, total n = 6286). Additionally, biological insights are gained by examining the potential role of circulating plasma miRNAs as potential epigenetic regulators. Analyses are performed using linear regression models adjusted for relevant biological and environmental covariates. (3) Results: Current depression as well as carrying the APOE ε4 allele were associated with impaired memory performance, with increasing effect for subjects with both risk factors. In a subcohort with available miRNA data subjects with current depressive symptoms and carrying APOE e4 revealed reduced levels of hsa-miR-107, a prominent risk marker for early Alzheimer’s Disease. (4) Conclusions: Our results confirm the effect of depressive symptoms and APOE ε4 status on memory performance. Additionally, miRNA analysis identified hsa-miR-107 as a possible biological link between APOE ε4, depressive symptoms, and cognitive impairment.
Abstract
Objectives
To examine the association between third molars and orofacial pain. We hypothesized that impacted third molars are a cause of orofacial pain.
Methods
Magnetic resonance images of 1808 participants from two population‐based cohorts from Northeastern Germany were analysed to define the status of third molars according to the Pell and Gregory classification. A self‐reported questionnaire and a clinical dental examination were used to detect chronic and acute complaints of orofacial pain, masticatory muscle pain, migraine and other types of headache. Logistic regression models were used to analyse the associations between third molar status and orofacial pain.
Results
Individuals with impacted third molars in the maxilla had a higher chance of chronic orofacial pain than those with erupted third molars (odds ratio 2.19; 95% CI 1.19‐4.02). No such association was detected for third molars in the lower jaw. Third molars were not associated with masticatory muscle pain, migraine or other types of headache.
Conclusions
Impacted maxillary third molars might be a cause of chronic orofacial pain. Thus, physicians should consider the eruption/impaction status of third molars in their decision‐making process when treating patients who complain of orofacial pain.
Abstract
Aim
To examine the associations between bone turnover markers and periodontitis in two cross‐sectional population‐based studies.
Materials and Methods
We used data from two independent adult samples (N = 4993), collected within the Study of Health in Pomerania project, to analyse cross‐sectional associations of N‐procollagen type 1 amino‐terminal propeptide (P1NP), C‐terminal cross‐linking telopeptide, osteocalcin, bone‐specific alkaline phosphatase (BAP), fibroblast growth factor 23, wingless‐type mouse mammary tumour virus integration site family member 5a (WNT5A), and sclerostin values with periodontitis. Confounder‐adjusted gamma and fractional response regression models were applied.
Results
Positive associations were found for P1NP with mean pocket probing depth (PPD; eβ=1.008; 95% confidence interval [CI]: 1.001–1.015), mean clinical attachment loss (mean CAL; eβ=1.027; 95% CI: 1.011–1.044), and proportion of sites with bleeding on probing (%BOP; eβ=1.055; 95% CI: 1.005–1.109). Similar associations were seen for BAP with %BOP (eβ=1.121; 95% CI: 1.042–1.205), proportion of sites with PPD ≥4 mm (%PPD4) (eβ=1.080; 95% CI: 1.005–1.161), and sclerostin with %BOP (eβ=1.308; 95% CI: 1.005–1.704). WNT5A was inversely associated with mean PPD (eβ=0.956; 95% CI: 0.920–0.993) and %PPD4 (eβ=0.794; 95% CI: 0.642–0.982).
Conclusions
This study revealed scattered associations of P1NP, BAP, WNT5A, and sclerostin with periodontitis, but the results are contradictory in the overall context. Associations reported in previous studies could not be confirmed.
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.
Background: Abdominal obesity is a major driver for adverse medical conditions. While an interaction between adipose tissue and thyroid function is thought to exist, to our knowledge, no study has examined the effect of thyroid-stimulating hormone (TSH) on visceral adipose tissue (VAT) in a population-based context. Objective: We determined an association between serum TSH levels and VAT. Methods: A sample of 1,021 female and 956 male adults aged 20-79 years was drawn from registry offices in the cross-sectional, population-based Study of Health in Pomerania Trend (SHIP Trend) in Northeast Germany from 2008 to 2012. Our main exposure was serum TSH levels. Our main outcome was VAT measured using magnetic resonance imaging. The possibly mediating role of leptin on the TSH-VAT association was also assessed. Results: A total of 1,719 participants (87.9%) had serum TSH levels within the reference range. The mean volume of VAT was 5.33 liters for men and 2.83 liters for women. No association between TSH and VAT (β = 0.06, 95% CI: -0.02, 0.14) was observed, and there were no differences detected between sexes. VAT was strongly associated with leptin with a greater effect in women than in men. Leptin was strongly associated with TSH. Conclusions: No association between TSH and VAT was observed. Other biomarkers such as leptin may play a role in the relationship between thyroid function and metabolic risk.
The associations of thyroid function parameters with non-alcoholic fatty liver disease (NAFLD) and hepatic iron overload are not entirely clear. We have cross-sectionally investigated these associations among 2734 participants of two population-based cross-sectional studies of the Study of Health in Pomerania. Serum levels of thyroid-stimulating hormone (TSH), free tri-iodothyronine (fT3), and free thyroxine (fT4) levels were measured. Liver fat content (by proton-density fat fraction) as well as hepatic iron content (by transverse relaxation rate; R2*) were assessed by quantitative MRI. Thyroid function parameters were associated with hepatic fat and iron contents by median and logistic regression models adjusted for confounding. There were no associations between serum TSH levels and liver fat content, NAFLD, or hepatic iron overload. Serum fT4 levels were inversely associated with liver fat content, NAFLD, hepatic iron contents, and hepatic iron overload. Serum fT3 levels as well as the fT3 to fT4 ratio were positively associated with hepatic fat, NAFLD, hepatic iron contents, but not with hepatic iron overload. Associations between fT3 levels and liver fat content were strongest in obese individuals, in which we also observed an inverse association between TSH levels and NAFLD. These findings might be the result of a higher conversion of fT4 to the biologically active form fT3. Our results suggest that a subclinical hyperthyroid state may be associated with NAFLD, particularly in obese individuals. Furthermore, thyroid hormone levels seem to be more strongly associated with increased liver fat content compared to hepatic iron content.