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Pál Maurovich-Horvat

Researcher at Semmelweis University

Publications -  301
Citations -  9050

Pál Maurovich-Horvat is an academic researcher from Semmelweis University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 33, co-authored 215 publications receiving 6802 citations. Previous affiliations of Pál Maurovich-Horvat include Harvard University & Advanced Technologies Center.

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Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study.

TL;DR: These findings are consistent with the hypothesized role of visceral fat as a unique, pathogenic fat depot and Measurement of VAT may provide a more complete understanding of metabolic risk associated with variation in fat distribution.
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Visceral and Subcutaneous Adipose Tissue Volumes Are Cross-Sectionally Related to Markers of Inflammation and Oxidative Stress The Framingham Heart Study

TL;DR: The present cross-sectional data support an association between both SAT and VAT with inflammation and oxidative stress and suggest that the contribution of visceral fat to inflammation may not be completely accounted for by clinical measures of obesity.
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Comprehensive plaque assessment by coronary CT angiography

TL;DR: The combination of morphologic and functional characteristics of coronary plaques might enable noninvasive detection of vulnerable plaques in the future to reduce mortality and morbidity of coronary heart disease.
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Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography.

TL;DR: It is suggested that volumetric measurements can depict age- and gender-related differences of visceral and subcutaneous abdominal adipose tissue deposition and may substantially improve the predictive value of obesity measures for insulin resistance, type 2 diabetes mellitus and other diseases.
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Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

TL;DR: In this article, the authors developed prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations, including age, sex, symptoms, and risk factors such as diabetes, hypertension, dyslipidaemia, and smoking.