A network-based approach to visualize prevalence and progression of metabolic syndrome components.
<h4>Background</h4>The additional clinical value of clustering cardiovascular risk factors to define the metabolic syndrome (MetS) is still under debate. However, it is unclear which cardiovascular risk factors tend to cluster predominately and how individual risk factor states change ov...
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2012
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oai:doaj.org-article:727991695b664e8ab55a57dc379184d22021-11-18T07:14:56ZA network-based approach to visualize prevalence and progression of metabolic syndrome components.1932-620310.1371/journal.pone.0039461https://doaj.org/article/727991695b664e8ab55a57dc379184d22012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22724019/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>The additional clinical value of clustering cardiovascular risk factors to define the metabolic syndrome (MetS) is still under debate. However, it is unclear which cardiovascular risk factors tend to cluster predominately and how individual risk factor states change over time.<h4>Methods and results</h4>We used data from 3,187 individuals aged 20-79 years from the population-based Study of Health in Pomerania for a network-based approach to visualize clustered MetS risk factor states and their change over a five-year follow-up period. MetS was defined by harmonized Adult Treatment Panel III criteria, and each individual's risk factor burden was classified according to the five MetS components at baseline and follow-up. We used the map generator to depict 32 (2(5)) different states and highlight the most important transitions between the 1,024 (32(2)) possible states in the weighted directed network. At baseline, we found the largest fraction (19.3%) of all individuals free of any MetS risk factors and identified hypertension (15.4%) and central obesity (6.3%), as well as their combination (19.0%), as the most common MetS risk factors. Analyzing risk factor flow over the five-year follow-up, we found that most individuals remained in their risk factor state and that low high-density lipoprotein cholesterol (HDL) (6.3%) was the most prominent additional risk factor beyond hypertension and central obesity. Also among individuals without any MetS risk factor at baseline, low HDL (3.5%), hypertension (2.1%), and central obesity (1.6%) were the first risk factors to manifest during follow-up.<h4>Conclusions</h4>We identified hypertension and central obesity as the predominant MetS risk factor cluster and low HDL concentrations as the most prominent new onset risk factor.Robin HaringMartin RosvallUwe VölkerHenry VölzkeHeyo KroemerMatthias NauckHenri WallaschofskiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 6, p e39461 (2012) |
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Medicine R Science Q |
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Medicine R Science Q Robin Haring Martin Rosvall Uwe Völker Henry Völzke Heyo Kroemer Matthias Nauck Henri Wallaschofski A network-based approach to visualize prevalence and progression of metabolic syndrome components. |
| description |
<h4>Background</h4>The additional clinical value of clustering cardiovascular risk factors to define the metabolic syndrome (MetS) is still under debate. However, it is unclear which cardiovascular risk factors tend to cluster predominately and how individual risk factor states change over time.<h4>Methods and results</h4>We used data from 3,187 individuals aged 20-79 years from the population-based Study of Health in Pomerania for a network-based approach to visualize clustered MetS risk factor states and their change over a five-year follow-up period. MetS was defined by harmonized Adult Treatment Panel III criteria, and each individual's risk factor burden was classified according to the five MetS components at baseline and follow-up. We used the map generator to depict 32 (2(5)) different states and highlight the most important transitions between the 1,024 (32(2)) possible states in the weighted directed network. At baseline, we found the largest fraction (19.3%) of all individuals free of any MetS risk factors and identified hypertension (15.4%) and central obesity (6.3%), as well as their combination (19.0%), as the most common MetS risk factors. Analyzing risk factor flow over the five-year follow-up, we found that most individuals remained in their risk factor state and that low high-density lipoprotein cholesterol (HDL) (6.3%) was the most prominent additional risk factor beyond hypertension and central obesity. Also among individuals without any MetS risk factor at baseline, low HDL (3.5%), hypertension (2.1%), and central obesity (1.6%) were the first risk factors to manifest during follow-up.<h4>Conclusions</h4>We identified hypertension and central obesity as the predominant MetS risk factor cluster and low HDL concentrations as the most prominent new onset risk factor. |
| format |
article |
| author |
Robin Haring Martin Rosvall Uwe Völker Henry Völzke Heyo Kroemer Matthias Nauck Henri Wallaschofski |
| author_facet |
Robin Haring Martin Rosvall Uwe Völker Henry Völzke Heyo Kroemer Matthias Nauck Henri Wallaschofski |
| author_sort |
Robin Haring |
| title |
A network-based approach to visualize prevalence and progression of metabolic syndrome components. |
| title_short |
A network-based approach to visualize prevalence and progression of metabolic syndrome components. |
| title_full |
A network-based approach to visualize prevalence and progression of metabolic syndrome components. |
| title_fullStr |
A network-based approach to visualize prevalence and progression of metabolic syndrome components. |
| title_full_unstemmed |
A network-based approach to visualize prevalence and progression of metabolic syndrome components. |
| title_sort |
network-based approach to visualize prevalence and progression of metabolic syndrome components. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2012 |
| url |
https://doaj.org/article/727991695b664e8ab55a57dc379184d2 |
| work_keys_str_mv |
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