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Abstract
Introduction
Transabdominal ultrasound (US) and magnetic resonance imaging (MRI) are commonly used for the examination of the pancreas in clinical routine. We therefore were interested in the concordance of these two imaging methods for the size measurement of the pancreas and how age, gender, and body mass index (BMI) affect the organ size.
Methods
A total of 342 participants from the Study of Health in Pomerania underwent whole‐body MRI and transabdominal US on the same day, and the diameter of the pancreatic head, body, and tail were measured. The agreement between US and MRI measurements was assessed by Bland and Altman plots. Intraclass correlation coefficients were used to compare observers. A multivariable regression model was applied using the independent variables age, gender, and body mass index.
Results
Compared to MRI, abdominal US returned smaller values for each segment of the pancreas, with a high level of inconsistency between these two methods. The mean difference was 0.39, 0.18, and 0.54 cm for the head, body, and tail, respectively. A high interobserver variability was detected for US. Multivariable analysis showed that pancreatic size in all three segments increased with BMI in both genders whereas pancreatic head and tail size decreased with age, an effect more marked in women.
Conclusions
Agreement of pancreatic size measurements is poor between US and MRI. These limitations should be considered when evaluating morphologic features for pathologic conditions or setting limits of normal size. Adjustments for BMI, gender, and age may also be warranted.
Homoarginine (hArg) is a non-essential cationic amino acid which inhibits hepatic alkaline phosphatases to exert inhibitory effects on bile secretion by targeting intrahepatic biliary epithelium. We analyzed (1) the relationship between hArg and liver biomarkers in two large population-based studies and (2) the impact of hArg supplementation on liver biomarkers. We assessed the relationship between alanine transaminase (ALT), aspartate aminotransferase (AST), γ-glutamyltransferase (GGT), alkaline phosphatases (AP), albumin, total bilirubin, cholinesterase, Quick’s value, liver fat, and Model for End-stage Liver Disease (MELD) and hArg in appropriately adjusted linear regression models. We analyzed the effect of L-hArg supplemention (125 mg L-hArg daily for 4 weeks) on these liver biomarkers. We included 7638 individuals (men: 3705; premenopausal women: 1866, postmenopausal women: 2067). We found positive associations for hArg and ALT (β 0.38 µkatal/L 95% confidence interval (CI): 0.29; 0.48), AST (β 0.29 µkatal/L 95% CI 0.17; 0.41), GGT (β 0.033 µkatal/L 95% CI 0.014; 0.053), Fib-4 score (β 0.08 95% CI 0.03; 0.13), liver fat content (β 0.016% 95% CI 0.006; 0.026), albumin (β 0.030 g/L 95% CI 0.019; 0.040), and cholinesterase (β 0.003 µkatal/L 95% CI 0.002; 0.004) in males. In premenopausal women hArg was positively related with liver fat content (β 0.047% 95%CI 0.013; 0.080) and inversely with albumin (β − 0.057 g/L 95% CI − 0.073; − 0.041). In postmenopausal women hARG was positively associated with AST (β 0.26 µkatal/L 95% CI 0.11; 0.42). hArg supplementation did not affect liver biomarkers. We summarize that hArg may be a marker of liver dysfunction and should be explored further.
Aims
Averaged measurements, but not the progression based on multiple assessments of carotid intima-media thickness, (cIMT) are predictive of cardiovascular disease (CVD) events in individuals. Whether this is true for conventional risk factors is unclear.
Methods and results
An individual participant meta-analysis was used to associate the annualised progression of systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol with future cardiovascular disease risk in 13 prospective cohort studies of the PROG-IMT collaboration (n = 34,072). Follow-up data included information on a combined cardiovascular disease endpoint of myocardial infarction, stroke, or vascular death. In secondary analyses, annualised progression was replaced with average. Log hazard ratios per standard deviation difference were pooled across studies by a random effects meta-analysis. In primary analysis, the annualised progression of total cholesterol was marginally related to a higher cardiovascular disease risk (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.00 to 1.07). The annualised progression of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol was not associated with future cardiovascular disease risk. In secondary analysis, average systolic blood pressure (HR 1.20 95% CI 1.11 to 1.29) and low-density lipoprotein cholesterol (HR 1.09, 95% CI 1.02 to 1.16) were related to a greater, while high-density lipoprotein cholesterol (HR 0.92, 95% CI 0.88 to 0.97) was related to a lower risk of future cardiovascular disease events.
Conclusion
Averaged measurements of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol displayed significant linear relationships with the risk of future cardiovascular disease events. However, there was no clear association between the annualised progression of these conventional risk factors in individuals with the risk of future clinical endpoints.
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.
Representative epidemiologic data on the average volume of the parotid gland in a large population-based MRI survey is non-existent. Within the Study of Health in Pomerania (SHIP), we examined the parotid gland in 1725 non-contrast MRI-scans in T1 weighted sequence of axial layers. Thus, a reliable standard operating procedure (Intraclass Correlation Coefficient > 0.8) could be established. In this study, we found an average, single sided parotid gland volume of 27.82 cm3 (95% confidence interval (CI) 27.15 to 28.50) in male and 21.60 cm3 (95% CI 21.16 to 22.05) in female subjects. We observed positive associations for age, body mass index (BMI), as well as male sex with parotid gland size in a multivariate model. The prevalence of incidental tumors within the parotid gland regardless of dignity was 3.94% in the Northeast German population, slightly higher than assumed. Further epidemiologic investigations regarding primary salivary gland diseases are necessary.
Objective
This study provides a comprehensive overview of the associations of five adipokines (adiponectin, chemerin, galectin‐3, leptin, and resistin) with fat deposits, behavioral risk factors, and metabolic phenotypes.
Methods
Using multivariable linear and logistic regression models, cross‐sectional data from 4,116 participants of the population‐based Study of Health in Pomerania were analyzed.
Results
Participants with obesity showed higher chemerin, galectin‐3, and leptin but showed lower adiponectin concentrations. Independently of other fat compounds, liver fat content, visceral adipose tissue, and subcutaneous adipose tissue (SAT) were inversely associated with adiponectin. Independent positive associations of liver fat content and SAT with chemerin as well as of SAT with galectin‐3 and leptin were observed. Physically inactive participants had higher chemerin and leptin concentrations. Smokers had higher chemerin and galectin‐3 as well as lower leptin. Alcohol consumption was associated with adiponectin (positive) and resistin (inverse). All adipokines were associated with at least one lipid marker. Associations with glucose metabolism were seen for adiponectin, chemerin, galectin‐3, and leptin.
Conclusions
High adiponectin concentrations were related to favorable metabolic conditions, whereas high chemerin, galectin‐3, and leptin were associated with an unfavorable metabolic profile. High leptin seems to be primarily indicative of obesity, whereas high adiponectin and chemerin are associated with a broader range of metabolic phenotypes.
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.