Background: In the Gutenberg Health Study, a random sample of the population was scanned with vascular ultrasound for early atherosclerosis. A continuous classical risk marker model (waist circumference, HbA1c, LDL/HDL ratio, pack years and pulse pressure) was compared to a model of modern biomarkers (C-reactive protein, troponin I, N-terminal pro B-type natriuretic peptide, copeptin, mid-regional pro-adrenomedullin, and asymmetric dimethylarginine) with regard to the ability of ruling out abnormal intima-media thickness (IMT), respectively, carotid plaques.
Methods: Data of the first consecutive 5,000 participants (aged 35-74 years; 2,540 men, 2,460 women) were analyzed. IMT was measured at both common carotid arteries using an edge detection system. Plaques were defined as protrusion of ≥1.5 mm in common, internal and external carotid artery.
Results: For classical risk factors, in comparison to a model of six modern biomarkers, regarding the variable (a) IMT>0.85 mm negative and positive predictive value (NPV and PPV) were 0.98 and 0.16 for both the classical risk factor model and the biomarker model. The second variable (b) presence of plaque could be ruled out with an NPV of 0.84 and identified with a PPV of 0.61 for classical risk factors, and 0.84 and 0.58 for biomarkers, respectively. Values were calculated using logistic regression analysis.
Conclusion: Classical risk factors allow ruling out pathologic IMT and presence of carotid plaques in a population of primary prevention in a reliable way. Modern biomarkers performed almost equally well but did not provide further information.
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http://dx.doi.org/10.1007/s00392-014-0674-6 | DOI Listing |
Sci Rep
December 2024
Department of Occupational Health and Safety Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Due to the extensive use of explosives, the failure to identify hazards and assess risks in blasting may lead to catastrophic consequences. However, classical risk assessment approaches are limited in their ability to address ambiguity and uncertainty, as well as in assigning weights to the criteria involved in the risk assessment process. This study employs a multi-criteria decision-making system to address these limitations and assess the risks associated with blasting.
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December 2024
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, School of Computer and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.
Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability.
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December 2024
Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830028, China.
Parkinson's disease (PD) is the second most common age-related neurodegenerative disease after Alzheimer's disease. Despite numerous studies, specific age-related factors remain unidentified. This study employed a multi-omics approach to investigate the link between PD and aging.
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December 2024
Department of General Surgical Science, Graduate School of Medicine, Gunma University, 3-39-22 Showa-machi, Maebashi, Gunma, 371-8511, Japan.
The importance of collagen and elastin remains incompletely understood concerning tumor immunity in cancer tissues. This study explored the clinical significance of collagen and elastin deposition on tumor immunity in advanced colorectal cancer patients. The collagen and elastin contents were assessed simultaneously using elastic van Gieson (EVG) histochemical staining.
View Article and Find Full Text PDFThromb Res
December 2024
Department of Medicine, Division of Thrombosis and Hemostasis, Leiden University Medical Center, the Netherlands.
Introduction: In patients with pancreatic cancer, the risk of venous thromboembolism (VTE) is high compared to other cancer types, suggesting that tumor-intrinsic features drive hypercoagulability. Tumor gene expression analysis may help unravel the pathogenesis of VTE in these patients and help to identify high-risk patients.
Aim: To evaluate the association between tumor gene expression patterns and VTE in patients with pancreatic cancer.
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