Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study
The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation. Individual participant...
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creator | Wang, Xin Dalmeijer, Geertje W den Ruijter, Hester M Anderson, Todd J Britton, Annie R Dekker, Jacqueline Engström, Gunnar Evans, Greg W de Graaf, Jacqueline Grobbee, Diederick E Hedblad, Bo Holewijn, Suzanne Ikeda, Ai Kauhanen, Jussi Kitagawa, Kazuo Kitamura, Akihiko Kurl, Sudhir Lonn, Eva M Lorenz, Matthias W Mathiesen, Ellisiv B Nijpels, Giel Okazaki, Shuhei Polak, Joseph F Price, Jacqueline F Rembold, Christopher M Rosvall, Maria Rundek, Tatjana Salonen, Jukka T Sitzer, Matthias Stehouwer, Coen D A Tuomainen, Tomi-Pekka Peters, Sanne A E Bots, Michiel L |
description | The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation.
Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group.
Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women.
Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters. |
doi_str_mv | 10.1371/journal.pone.0173393 |
format | Article |
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Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group.
Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women.
Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0173393</identifier><identifier>PMID: 28323823</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age Factors ; Aged ; Analysis ; Arteriosclerosis ; artery ; Atherosclerosis ; Biology and Life Sciences ; Blood ; Blood pressure ; Body weight ; Cardiology ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular Diseases - diagnostic imaging ; Cardiovascular Diseases - epidemiology ; Cardiovascular tests ; Carotid arteries ; Carotid Intima-Media Thickness ; Cholesterol ; Cholesterol - blood ; Clinical medical disciplines: 750 ; clinical-practice ; Cluster Analysis ; Clustering ; Cohort Studies ; Cross-Sectional Studies ; Diabetes ; disease ; Epidemiology ; Ethnic factors ; events ; Female ; general-population ; Health aspects ; Health risk assessment ; Health risks ; Health Sciences ; Humans ; Hypertension ; Hypertension - diagnostic imaging ; Hypertension - epidemiology ; Hälsovetenskap ; Internal medicine ; Klinisk medisinske fag: 750 ; Linear Models ; Male ; malmo diet ; Medical disciplines: 700 ; Medicine ; Medicine and Health Sciences ; Medisinske Fag: 700 ; Men ; Meta-Analysis as Topic ; Metabolism ; Middle Aged ; Minority & ethnic groups ; Neurology ; Nutrition ; Overweight ; Overweight - diagnostic imaging ; Overweight - epidemiology ; Physical Sciences ; Population ; prediction ; Prevention ; Primary care ; progression ; Public health ; Regression analysis ; Regression models ; Risk analysis ; Risk Factors ; rotterdam ; Sex Factors ; Smoking ; Smoking - epidemiology ; Stroke ; Studies ; subclinical atherosclerosis ; VDP</subject><ispartof>PloS one, 2017-03, Vol.12 (3), p.e0173393-e0173393</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>info:eu-repo/semantics/openAccess</rights><rights>2017 Wang et al 2017 Wang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c787t-7ba192308aa4ba9480677487b6bcfbf2db327a12c5c1a3c241198b5e2adce8de3</citedby><cites>FETCH-LOGICAL-c787t-7ba192308aa4ba9480677487b6bcfbf2db327a12c5c1a3c241198b5e2adce8de3</cites><orcidid>0000-0002-9048-6635</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360240/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360240/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,26544,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28323823$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://gup.ub.gu.se/publication/253395$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Xin</creatorcontrib><creatorcontrib>Dalmeijer, Geertje W</creatorcontrib><creatorcontrib>den Ruijter, Hester M</creatorcontrib><creatorcontrib>Anderson, Todd J</creatorcontrib><creatorcontrib>Britton, Annie R</creatorcontrib><creatorcontrib>Dekker, Jacqueline</creatorcontrib><creatorcontrib>Engström, Gunnar</creatorcontrib><creatorcontrib>Evans, Greg W</creatorcontrib><creatorcontrib>de Graaf, Jacqueline</creatorcontrib><creatorcontrib>Grobbee, Diederick E</creatorcontrib><creatorcontrib>Hedblad, Bo</creatorcontrib><creatorcontrib>Holewijn, Suzanne</creatorcontrib><creatorcontrib>Ikeda, Ai</creatorcontrib><creatorcontrib>Kauhanen, Jussi</creatorcontrib><creatorcontrib>Kitagawa, Kazuo</creatorcontrib><creatorcontrib>Kitamura, Akihiko</creatorcontrib><creatorcontrib>Kurl, Sudhir</creatorcontrib><creatorcontrib>Lonn, Eva M</creatorcontrib><creatorcontrib>Lorenz, Matthias W</creatorcontrib><creatorcontrib>Mathiesen, Ellisiv B</creatorcontrib><creatorcontrib>Nijpels, Giel</creatorcontrib><creatorcontrib>Okazaki, Shuhei</creatorcontrib><creatorcontrib>Polak, Joseph F</creatorcontrib><creatorcontrib>Price, Jacqueline F</creatorcontrib><creatorcontrib>Rembold, Christopher M</creatorcontrib><creatorcontrib>Rosvall, Maria</creatorcontrib><creatorcontrib>Rundek, Tatjana</creatorcontrib><creatorcontrib>Salonen, Jukka T</creatorcontrib><creatorcontrib>Sitzer, Matthias</creatorcontrib><creatorcontrib>Stehouwer, Coen D A</creatorcontrib><creatorcontrib>Tuomainen, Tomi-Pekka</creatorcontrib><creatorcontrib>Peters, Sanne A E</creatorcontrib><creatorcontrib>Bots, Michiel L</creatorcontrib><title>Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation.
Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group.
Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women.
Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters.</description><subject>Age Factors</subject><subject>Aged</subject><subject>Analysis</subject><subject>Arteriosclerosis</subject><subject>artery</subject><subject>Atherosclerosis</subject><subject>Biology and Life Sciences</subject><subject>Blood</subject><subject>Blood pressure</subject><subject>Body weight</subject><subject>Cardiology</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - diagnostic imaging</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Cardiovascular tests</subject><subject>Carotid arteries</subject><subject>Carotid Intima-Media Thickness</subject><subject>Cholesterol</subject><subject>Cholesterol - blood</subject><subject>Clinical medical disciplines: 750</subject><subject>clinical-practice</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Cohort Studies</subject><subject>Cross-Sectional Studies</subject><subject>Diabetes</subject><subject>disease</subject><subject>Epidemiology</subject><subject>Ethnic factors</subject><subject>events</subject><subject>Female</subject><subject>general-population</subject><subject>Health aspects</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Health Sciences</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Hypertension - diagnostic imaging</subject><subject>Hypertension - epidemiology</subject><subject>Hälsovetenskap</subject><subject>Internal medicine</subject><subject>Klinisk medisinske fag: 750</subject><subject>Linear Models</subject><subject>Male</subject><subject>malmo diet</subject><subject>Medical disciplines: 700</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Medisinske Fag: 700</subject><subject>Men</subject><subject>Meta-Analysis as Topic</subject><subject>Metabolism</subject><subject>Middle Aged</subject><subject>Minority & ethnic groups</subject><subject>Neurology</subject><subject>Nutrition</subject><subject>Overweight</subject><subject>Overweight - diagnostic imaging</subject><subject>Overweight - epidemiology</subject><subject>Physical Sciences</subject><subject>Population</subject><subject>prediction</subject><subject>Prevention</subject><subject>Primary care</subject><subject>progression</subject><subject>Public health</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Risk analysis</subject><subject>Risk Factors</subject><subject>rotterdam</subject><subject>Sex Factors</subject><subject>Smoking</subject><subject>Smoking - epidemiology</subject><subject>Stroke</subject><subject>Studies</subject><subject>subclinical atherosclerosis</subject><subject>VDP</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>3HK</sourceid><sourceid>DOA</sourceid><recordid>eNqNk11v0zAUhiMEYqPwDxBEQkJwkeKPfDhcIE3VgEpDk1jHrXXsOKm31C62M9i_x6XtaNAuplw4On7e4-P3-CTJS4ymmFb4w5UdnIF-urZGTRGuKK3po-QY15RkJUH08cH_UfLM-yuECsrK8mlyRBgllBF6nPBZP_ignDZdattUgmu0vQEvhx5c6rS_TluQwTqfgmk2-zboJtUm6BVkK9VoSMNSy2ujvP-YLpYqvbw4zebfFqkPQ3P7PHnSQu_Vi906SS4_ny5mX7Oz8y_z2clZJitWhawSgGtCEQPIBdQ5Q2VV5awSpZCtaEkjKKkAE1lIDFSSHOOaiUIRaKRijaKT5PU277q3nu-88Ryzqi4RoXkeifmWaCxc8bWL9btbbkHzvwHrOg4uaNkrLoQkpAZKRU1zVDIgJRJISlLiEtFo3iTJtrn8L7UexChbN6x5DHUD94qTIraliPynXXWDiJ5JZYKDfiQb7xi95J294QWN1efo3_VkbEnQhhvrgGOEaMUxwRWJxLvdEc7-HJQPfKW9VH0PRtlh4wRjNUNFyR6CIsQKjGhE3_yH3m_tjuoguqdNa-Ml5CYpP8lZmefRPxyp6T1U_Bq10jK-4lbH-EjwfiSITFC_QweD93x-8f3h7PmPMfv2gF0q6MPS234I2ho_BvO969Z7p9q7jmHEN0O4d4NvhpDvhjDKXh12-060nzr6B0jjKcI</recordid><startdate>20170321</startdate><enddate>20170321</enddate><creator>Wang, 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of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study</title><author>Wang, Xin ; Dalmeijer, Geertje W ; den Ruijter, Hester M ; Anderson, Todd J ; Britton, Annie R ; Dekker, Jacqueline ; Engström, Gunnar ; Evans, Greg W ; de Graaf, Jacqueline ; Grobbee, Diederick E ; Hedblad, Bo ; Holewijn, Suzanne ; Ikeda, Ai ; Kauhanen, Jussi ; Kitagawa, Kazuo ; Kitamura, Akihiko ; Kurl, Sudhir ; Lonn, Eva M ; Lorenz, Matthias W ; Mathiesen, Ellisiv B ; Nijpels, Giel ; Okazaki, Shuhei ; Polak, Joseph F ; Price, Jacqueline F ; Rembold, Christopher M ; Rosvall, Maria ; Rundek, Tatjana ; Salonen, Jukka T ; Sitzer, Matthias ; Stehouwer, Coen D A ; Tuomainen, Tomi-Pekka ; Peters, Sanne A E ; Bots, Michiel L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c787t-7ba192308aa4ba9480677487b6bcfbf2db327a12c5c1a3c241198b5e2adce8de3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Age Factors</topic><topic>Aged</topic><topic>Analysis</topic><topic>Arteriosclerosis</topic><topic>artery</topic><topic>Atherosclerosis</topic><topic>Biology and Life Sciences</topic><topic>Blood</topic><topic>Blood pressure</topic><topic>Body weight</topic><topic>Cardiology</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Cardiovascular Diseases - diagnostic imaging</topic><topic>Cardiovascular Diseases - epidemiology</topic><topic>Cardiovascular tests</topic><topic>Carotid arteries</topic><topic>Carotid Intima-Media Thickness</topic><topic>Cholesterol</topic><topic>Cholesterol - blood</topic><topic>Clinical medical disciplines: 750</topic><topic>clinical-practice</topic><topic>Cluster Analysis</topic><topic>Clustering</topic><topic>Cohort Studies</topic><topic>Cross-Sectional Studies</topic><topic>Diabetes</topic><topic>disease</topic><topic>Epidemiology</topic><topic>Ethnic factors</topic><topic>events</topic><topic>Female</topic><topic>general-population</topic><topic>Health aspects</topic><topic>Health risk assessment</topic><topic>Health risks</topic><topic>Health Sciences</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Hypertension - diagnostic imaging</topic><topic>Hypertension - epidemiology</topic><topic>Hälsovetenskap</topic><topic>Internal medicine</topic><topic>Klinisk medisinske fag: 750</topic><topic>Linear Models</topic><topic>Male</topic><topic>malmo diet</topic><topic>Medical disciplines: 700</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Medisinske Fag: 700</topic><topic>Men</topic><topic>Meta-Analysis as Topic</topic><topic>Metabolism</topic><topic>Middle Aged</topic><topic>Minority & ethnic groups</topic><topic>Neurology</topic><topic>Nutrition</topic><topic>Overweight</topic><topic>Overweight - diagnostic imaging</topic><topic>Overweight - epidemiology</topic><topic>Physical Sciences</topic><topic>Population</topic><topic>prediction</topic><topic>Prevention</topic><topic>Primary care</topic><topic>progression</topic><topic>Public health</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Risk analysis</topic><topic>Risk Factors</topic><topic>rotterdam</topic><topic>Sex Factors</topic><topic>Smoking</topic><topic>Smoking - epidemiology</topic><topic>Stroke</topic><topic>Studies</topic><topic>subclinical atherosclerosis</topic><topic>VDP</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xin</creatorcontrib><creatorcontrib>Dalmeijer, Geertje W</creatorcontrib><creatorcontrib>den Ruijter, Hester M</creatorcontrib><creatorcontrib>Anderson, Todd J</creatorcontrib><creatorcontrib>Britton, Annie R</creatorcontrib><creatorcontrib>Dekker, Jacqueline</creatorcontrib><creatorcontrib>Engström, Gunnar</creatorcontrib><creatorcontrib>Evans, Greg W</creatorcontrib><creatorcontrib>de Graaf, Jacqueline</creatorcontrib><creatorcontrib>Grobbee, Diederick E</creatorcontrib><creatorcontrib>Hedblad, Bo</creatorcontrib><creatorcontrib>Holewijn, Suzanne</creatorcontrib><creatorcontrib>Ikeda, Ai</creatorcontrib><creatorcontrib>Kauhanen, Jussi</creatorcontrib><creatorcontrib>Kitagawa, Kazuo</creatorcontrib><creatorcontrib>Kitamura, Akihiko</creatorcontrib><creatorcontrib>Kurl, Sudhir</creatorcontrib><creatorcontrib>Lonn, Eva M</creatorcontrib><creatorcontrib>Lorenz, Matthias W</creatorcontrib><creatorcontrib>Mathiesen, Ellisiv B</creatorcontrib><creatorcontrib>Nijpels, Giel</creatorcontrib><creatorcontrib>Okazaki, Shuhei</creatorcontrib><creatorcontrib>Polak, Joseph F</creatorcontrib><creatorcontrib>Price, Jacqueline F</creatorcontrib><creatorcontrib>Rembold, Christopher M</creatorcontrib><creatorcontrib>Rosvall, Maria</creatorcontrib><creatorcontrib>Rundek, Tatjana</creatorcontrib><creatorcontrib>Salonen, Jukka T</creatorcontrib><creatorcontrib>Sitzer, Matthias</creatorcontrib><creatorcontrib>Stehouwer, Coen D A</creatorcontrib><creatorcontrib>Tuomainen, Tomi-Pekka</creatorcontrib><creatorcontrib>Peters, Sanne A E</creatorcontrib><creatorcontrib>Bots, Michiel L</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science 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Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>NORA - Norwegian Open Research Archives</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Göteborgs universitet</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xin</au><au>Dalmeijer, Geertje W</au><au>den Ruijter, Hester M</au><au>Anderson, Todd J</au><au>Britton, Annie R</au><au>Dekker, Jacqueline</au><au>Engström, Gunnar</au><au>Evans, Greg W</au><au>de Graaf, Jacqueline</au><au>Grobbee, Diederick E</au><au>Hedblad, Bo</au><au>Holewijn, Suzanne</au><au>Ikeda, Ai</au><au>Kauhanen, Jussi</au><au>Kitagawa, Kazuo</au><au>Kitamura, Akihiko</au><au>Kurl, Sudhir</au><au>Lonn, Eva M</au><au>Lorenz, Matthias W</au><au>Mathiesen, Ellisiv B</au><au>Nijpels, Giel</au><au>Okazaki, Shuhei</au><au>Polak, Joseph F</au><au>Price, Jacqueline F</au><au>Rembold, Christopher M</au><au>Rosvall, Maria</au><au>Rundek, Tatjana</au><au>Salonen, Jukka T</au><au>Sitzer, Matthias</au><au>Stehouwer, Coen D A</au><au>Tuomainen, Tomi-Pekka</au><au>Peters, Sanne A E</au><au>Bots, Michiel L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-03-21</date><risdate>2017</risdate><volume>12</volume><issue>3</issue><spage>e0173393</spage><epage>e0173393</epage><pages>e0173393-e0173393</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation.
Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group.
Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women.
Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28323823</pmid><doi>10.1371/journal.pone.0173393</doi><tpages>e0173393</tpages><orcidid>https://orcid.org/0000-0002-9048-6635</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2017-03, Vol.12 (3), p.e0173393-e0173393 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_1879602344 |
source | MEDLINE; NORA - Norwegian Open Research Archives; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
subjects | Age Factors Aged Analysis Arteriosclerosis artery Atherosclerosis Biology and Life Sciences Blood Blood pressure Body weight Cardiology Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - diagnostic imaging Cardiovascular Diseases - epidemiology Cardiovascular tests Carotid arteries Carotid Intima-Media Thickness Cholesterol Cholesterol - blood Clinical medical disciplines: 750 clinical-practice Cluster Analysis Clustering Cohort Studies Cross-Sectional Studies Diabetes disease Epidemiology Ethnic factors events Female general-population Health aspects Health risk assessment Health risks Health Sciences Humans Hypertension Hypertension - diagnostic imaging Hypertension - epidemiology Hälsovetenskap Internal medicine Klinisk medisinske fag: 750 Linear Models Male malmo diet Medical disciplines: 700 Medicine Medicine and Health Sciences Medisinske Fag: 700 Men Meta-Analysis as Topic Metabolism Middle Aged Minority & ethnic groups Neurology Nutrition Overweight Overweight - diagnostic imaging Overweight - epidemiology Physical Sciences Population prediction Prevention Primary care progression Public health Regression analysis Regression models Risk analysis Risk Factors rotterdam Sex Factors Smoking Smoking - epidemiology Stroke Studies subclinical atherosclerosis VDP |
title | Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study |
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