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Objectives: An inverse relationship between education and cardiovascular risk has been described, however, the combined association of education, income, and neighborhood socioeconomic status with macrovascular disease is less clear. The aim of this study was to evaluate the association of educational level, equivalent household income and area deprivation with macrovascular disease in Germany.
Methods: Cross-sectional data from two representative German population-based studies, SHIP-TREND (n = 3,731) and KORA-F4 (n = 2,870), were analyzed. Multivariable logistic regression models were applied to estimate odds ratios and 95% confidence intervals for the association between socioeconomic determinants and macrovascular disease (defined as self-reported myocardial infarction or stroke).
Results: The study showed a higher odds of prevalent macrovascular disease in men with low and middle educational level compared to men with high education. Area deprivation and equivalent income were not related to myocardial infarction or stroke in any of the models.
Conclusion: Educational level, but not income or area deprivation, is significantly related to the macrovascular disease in men. Effective prevention of macrovascular disease should therefore start with investing in individual education.
Background: There is only limited data on the potential association between thyroid dysfunction and peripheral arterial disease (PAD). Objective: The aim of our study was to investigate the potential association of thyroid function, as defined by serum concentrations of the clinically used primary thyroid function marker thyrotropin [i.e. thyroid-stimulating hormone (TSH)] and 3,5-diiodothyronine (3,5-T<sub>2</sub>), with the ankle-brachial index (ABI) as a marker of PAD. Methods: We used data from 5,818 individuals from three cross-sectional population-based studies conducted in Northeast (SHIP-2 and SHIP-TREND) and Central Germany (CARLA). Measurement of serum TSH concentrations was conducted in one central laboratory for all three studies. In a randomly selected subpopulation of 750 individuals of SHIP-TREND, serum 3,5-T<sub>2</sub> concentrations were measured with a recently developed immunoassay. ABI was measured either by a hand-held Doppler ultrasound using the Huntleigh Dopplex D900 or palpatorily by the OMRON HEM-705CP device. Results: Serum TSH concentrations were not significantly associated with ABI values in any of the three studies. Likewise, groups of individuals with a TSH <0.3 mIU/l or with a TSH ≥3.0 mIU/l had no significantly different ABI values in comparison with individuals with a TSH in the reference range. Analyses regarding TSH within the reference range or serum 3,5-T<sub>2</sub> concentrations did not reveal consistent significant associations with the ABI. No sex-specific associations were detected. Conclusions: The results of our study do not substantiate evidence for an association between thyroid function and PAD, but further studies are needed to investigate the associations of overt forms of thyroid dysfunction with PAD.
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.
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.
Body surface scan anthropometrics are related to cardiorespiratory fitness in the general population
(2022)
The assessment of cardiorespiratory fitness (CRF) is an important tool for prognosis evaluation of cardiovascular events. The gold standard to measure CRF is cardiopulmonary exercise testing (CPET) to determine peak oxygen uptake (VO2peak). However, CPET is not only time consuming but also expensive and is therefore not widely applicable in daily practice. The aim of our study was to analyze, whether and which anthropometric markers derived from a 3D body scanner were related to VO2peak in a general population-based study. We analyzed data (SHIP-START-3) from 3D body scanner and CPET of 1035 subjects (529 women; 51.1%, age range 36–93). A total of 164 anthropometric markers were detected with the 3D body scanner VITUS Smart XXL using the software AnthroScan Professional. Anthropometric measurements were standardized and associated with CRF by sex-stratified linear regression models adjusted for age and height. Anthropometric markers were ranked according to the − log- p values derived from these regression models. In men a greater left and right thigh-knee-ratio, a longer forearm-fingertip length, a greater left thigh circumference and greater left upper arm circumference were most strongly associated with a higher VO2peak. In women a greater left and right thigh circumference, left calf circumference, thigh thickness and right calf circumference were most strongly associated with a higher VO2peak. The detected VO2peak-related anthropometric markers could be helpful in assessing CRF in clinical routine. Commonly used anthropometric markers, e.g. waist and hip circumference, were not among the markers associated with VO2peak.
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.
Life-threatening toxic shock syndrome is often caused by the superantigen toxic shock syndrome toxin-1 (TSST-1) produced by Staphylococcus aureus. A well-known risk factor is the lack of neutralizing antibodies. To identify determinants of the anti-TSST-1 antibody response, we examined 976 participants of the German population-based epidemiological Study of Health in Pomerania (SHIP-TREND-0). We measured anti-TSST-1 antibody levels, analyzed the colonization with TSST-1-encoding S. aureus strains, and performed a genome-wide association analysis of genetic risk factors. TSST-1-specific serum IgG levels varied over a range of 4.2 logs and were elevated by a factor of 12.3 upon nasal colonization with TSST-1-encoding S. aureus. Moreover, the anti-TSST-1 antibody levels were strongly associated with HLA class II gene loci. HLA-DRB1*03:01 and HLA-DQB1*02:01 were positively, and HLA-DRB1*01:01 as well as HLA-DQB1*05:01 negatively associated with the anti-TSST-1 antibody levels. Thus, both toxin exposure and HLA alleles affect the human antibody response to TSST-1.