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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.
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.
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.
In 2017, in the Polish-German transborder area of West Pomerania, Mecklenburg-Western Pomerania, and Brandenburg, in collaboration with two centers in Warsaw, a partnership in the field of newborn screening (NBS) for severe primary immunodeficiency diseases (PID), mainly severe combined immunodeficiency (SCID), was initiated. SCID, but also some other severe PID, is a group of disorders characterized by the absence of T and/or B and NK cells. Affected infants are susceptible to life-threatening infections, but early detection gives a chance for effective treatment. The prevalence of SCID in the Polish and German populations is unknown but can be comparable to other countries (1:50,000–100,000). SCID NBS tests are based on real-time polymerase chain reaction (qPCR) and the measurement of a number of T cell receptor excision circles (TREC), kappa-deleting recombination excision circles (KREC), and beta-actin (ACTB) as a quality marker of DNA. This method can also be effective in NBS for other severe PID with T- and/or B-cell lymphopenia, including combined immunodeficiency (CID) or agammaglobulinemia. During the 14 months of collaboration, 44,287 newborns were screened according to the ImmunoIVD protocol. Within 65 positive samples, seven were classified to immediate recall and 58 requested a second sample. Examination of the 58 second samples resulted in recalling one newborn. Confirmatory tests included immunophenotyping of lymphocyte subsets with extension to TCR repertoire, lymphoproliferation tests, radiosensitivity tests, maternal engraftment assays, and molecular tests. Final diagnosis included: one case of T-BlowNK+ SCID, one case of atypical Tlow BlowNK+ CID, one case of autosomal recessive agammaglobulinemia, and one case of Nijmegen breakage syndrome. Among four other positive results, three infants presented with T- and/or B-cell lymphopenia due to either the mother's immunosuppression, prematurity, or unknown reasons, which resolved or almost normalized in the first months of life. One newborn was classified as truly false positive. The overall positive predictive value (PPV) for the diagnosis of severe PID was 50.0%. This is the first population screening study that allowed identification of newborns with T and/or B immunodeficiency in Central and Eastern Europe.
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.
A New Laboratory Workflow Integrating the Free Light Chains Kappa Quotient into Routine CSF Analysis
(2022)
We performed this cohort study to test whether further analysis of intrathecal inflammation can be omitted if the free light chain kappa (FLCκ) quotient is within the reference range in the corresponding quotient diagram. FLCκ concentrations were measured in serum and cerebrospinal fluid (CSF) samples. The intrathecal fraction (IF) of FLCκ was calculated in relation to the hyperbolic reference range. 679 patient samples were used as a discovery cohort (DC). The sensitivity and negative predictive value (NPV) of the FLCκ-IF for the detection of an intrathecal humoral immune response (CSF-specific OCB and/or IF IgG/A/M > 0%) was determined. Based on these data, a diagnostic algorithm was developed and prospectively validated in an independent validation cohort (VC, n = 278). The sensitivity of the FLCκ-IF was 98% in the DC and 97% in the VC with a corresponding NPV of 99%. The use of the FLCκ-IF as a first line analysis would have reduced the Ig and OCB analysis by 62% in the DC and 74% in the VC. The absence of a FLCκ-IF predicts the absence of a humoral intrathecal immune response with a very high NPV of 99%. Thus, integration of our proposed algorithm into routine CSF laboratory analysis could help to reduce analytical efforts.