Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study
Increases in circulating LDL cholesterol (LDL-C) and high-sensitivity C-reactive protein (hsCRP) concentrations are significant risk factors for cardiovascular disease (CVD). We assessed direct LDL-C and hsCRP concentrations compared to standard risk factors in the Framingham Offspring Study. We use...
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Veröffentlicht in: | Clinical chemistry (Baltimore, Md.) Md.), 2019-09, Vol.65 (9), p.1102-1114 |
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creator | Ikezaki, Hiroaki Fisher, Virginia A Lim, Elise Ai, Masumi Liu, Ching-Ti Adrienne Cupples, L Nakajima, Katsuyuki Asztalos, Bela F Furusyo, Norihiro Schaefer, Ernst J |
description | Increases in circulating LDL cholesterol (LDL-C) and high-sensitivity C-reactive protein (hsCRP) concentrations are significant risk factors for cardiovascular disease (CVD). We assessed direct LDL-C and hsCRP concentrations compared to standard risk factors in the Framingham Offspring Study.
We used stored frozen plasma samples (-80 °C) obtained after an overnight fast from 3147 male and female participants (mean age, 58 years) free of CVD at cycle 6 of the Framingham Offspring Study. Overall, 677 participants (21.5%) had a CVD end point over a median of 16.0 years of follow-up. Total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), direct LDL-C (Denka Seiken and Kyowa Medex methods), and hsCRP (Dade Behring method) concentrations were measured by automated analysis. LDL-C was also calculated by both the Friedewald and Martin methods.
Considering all CVD outcomes on univariate analysis, significant factors included standard risk factors (age, hypertension, HDL-C, hypertension treatment, sex, diabetes, smoking, and TC concentration) and nonstandard risk factors (non-HDL-C, direct LDL-C and calculated LDL-C, TG, and hsCRP concentrations). On multivariate analysis, only the Denka Seiken direct LDL-C and the Dade Behring hsCRP were still significant on Cox regression analysis and improved the net risk reclassification index, but with modest effects. Discordance analysis confirmed the benefit of the Denka Seiken direct LDL-C method for prospective hard CVD endpoints (new-onset myocardial infarction, stroke, and/or CVD death).
Our data indicate that the Denka Seiken direct LDL-C and Dade Behring hsCRP measurements add significant, but modest, information about CVD risk, compared to standard risk factors and/or calculated LDL-C. |
doi_str_mv | 10.1373/clinchem.2019.304600 |
format | Article |
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We used stored frozen plasma samples (-80 °C) obtained after an overnight fast from 3147 male and female participants (mean age, 58 years) free of CVD at cycle 6 of the Framingham Offspring Study. Overall, 677 participants (21.5%) had a CVD end point over a median of 16.0 years of follow-up. Total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), direct LDL-C (Denka Seiken and Kyowa Medex methods), and hsCRP (Dade Behring method) concentrations were measured by automated analysis. LDL-C was also calculated by both the Friedewald and Martin methods.
Considering all CVD outcomes on univariate analysis, significant factors included standard risk factors (age, hypertension, HDL-C, hypertension treatment, sex, diabetes, smoking, and TC concentration) and nonstandard risk factors (non-HDL-C, direct LDL-C and calculated LDL-C, TG, and hsCRP concentrations). On multivariate analysis, only the Denka Seiken direct LDL-C and the Dade Behring hsCRP were still significant on Cox regression analysis and improved the net risk reclassification index, but with modest effects. Discordance analysis confirmed the benefit of the Denka Seiken direct LDL-C method for prospective hard CVD endpoints (new-onset myocardial infarction, stroke, and/or CVD death).
Our data indicate that the Denka Seiken direct LDL-C and Dade Behring hsCRP measurements add significant, but modest, information about CVD risk, compared to standard risk factors and/or calculated LDL-C.</description><identifier>ISSN: 0009-9147</identifier><identifier>EISSN: 1530-8561</identifier><identifier>DOI: 10.1373/clinchem.2019.304600</identifier><identifier>PMID: 31239251</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Biomarkers - blood ; C-reactive protein ; C-Reactive Protein - analysis ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular Diseases - blood ; Cardiovascular Diseases - etiology ; Cerebral infarction ; Cholesterol ; Cholesterol, LDL - blood ; Diabetes mellitus ; Discordance ; Female ; Health risk assessment ; Health risks ; Heart diseases ; High density lipoprotein ; Humans ; Hypertension ; Laboratories ; Lipids ; Low density lipoprotein ; Male ; Mathematical analysis ; Middle Aged ; Multivariate Analysis ; Myocardial infarction ; Offspring ; Proportional Hazards Models ; Prospective Studies ; Proteins ; Reclassification ; Regression analysis ; Risk analysis ; Risk Assessment ; Risk factors ; Smoking ; Statins ; Triglycerides</subject><ispartof>Clinical chemistry (Baltimore, Md.), 2019-09, Vol.65 (9), p.1102-1114</ispartof><rights>2019 American Association for Clinical Chemistry.</rights><rights>Copyright American Association for Clinical Chemistry Sep 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-8d50a24d0da088b26bb12728042383e0384877faaf79f08b6e19f886a0d8e2a03</citedby><cites>FETCH-LOGICAL-c447t-8d50a24d0da088b26bb12728042383e0384877faaf79f08b6e19f886a0d8e2a03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31239251$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ikezaki, Hiroaki</creatorcontrib><creatorcontrib>Fisher, Virginia A</creatorcontrib><creatorcontrib>Lim, Elise</creatorcontrib><creatorcontrib>Ai, Masumi</creatorcontrib><creatorcontrib>Liu, Ching-Ti</creatorcontrib><creatorcontrib>Adrienne Cupples, L</creatorcontrib><creatorcontrib>Nakajima, Katsuyuki</creatorcontrib><creatorcontrib>Asztalos, Bela F</creatorcontrib><creatorcontrib>Furusyo, Norihiro</creatorcontrib><creatorcontrib>Schaefer, Ernst J</creatorcontrib><title>Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study</title><title>Clinical chemistry (Baltimore, Md.)</title><addtitle>Clin Chem</addtitle><description>Increases in circulating LDL cholesterol (LDL-C) and high-sensitivity C-reactive protein (hsCRP) concentrations are significant risk factors for cardiovascular disease (CVD). We assessed direct LDL-C and hsCRP concentrations compared to standard risk factors in the Framingham Offspring Study.
We used stored frozen plasma samples (-80 °C) obtained after an overnight fast from 3147 male and female participants (mean age, 58 years) free of CVD at cycle 6 of the Framingham Offspring Study. Overall, 677 participants (21.5%) had a CVD end point over a median of 16.0 years of follow-up. Total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), direct LDL-C (Denka Seiken and Kyowa Medex methods), and hsCRP (Dade Behring method) concentrations were measured by automated analysis. LDL-C was also calculated by both the Friedewald and Martin methods.
Considering all CVD outcomes on univariate analysis, significant factors included standard risk factors (age, hypertension, HDL-C, hypertension treatment, sex, diabetes, smoking, and TC concentration) and nonstandard risk factors (non-HDL-C, direct LDL-C and calculated LDL-C, TG, and hsCRP concentrations). On multivariate analysis, only the Denka Seiken direct LDL-C and the Dade Behring hsCRP were still significant on Cox regression analysis and improved the net risk reclassification index, but with modest effects. Discordance analysis confirmed the benefit of the Denka Seiken direct LDL-C method for prospective hard CVD endpoints (new-onset myocardial infarction, stroke, and/or CVD death).
Our data indicate that the Denka Seiken direct LDL-C and Dade Behring hsCRP measurements add significant, but modest, information about CVD risk, compared to standard risk factors and/or calculated LDL-C.</description><subject>Biomarkers - blood</subject><subject>C-reactive protein</subject><subject>C-Reactive Protein - analysis</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - blood</subject><subject>Cardiovascular Diseases - etiology</subject><subject>Cerebral infarction</subject><subject>Cholesterol</subject><subject>Cholesterol, LDL - blood</subject><subject>Diabetes mellitus</subject><subject>Discordance</subject><subject>Female</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Heart diseases</subject><subject>High density lipoprotein</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Laboratories</subject><subject>Lipids</subject><subject>Low density lipoprotein</subject><subject>Male</subject><subject>Mathematical analysis</subject><subject>Middle Aged</subject><subject>Multivariate Analysis</subject><subject>Myocardial infarction</subject><subject>Offspring</subject><subject>Proportional Hazards Models</subject><subject>Prospective Studies</subject><subject>Proteins</subject><subject>Reclassification</subject><subject>Regression analysis</subject><subject>Risk analysis</subject><subject>Risk Assessment</subject><subject>Risk factors</subject><subject>Smoking</subject><subject>Statins</subject><subject>Triglycerides</subject><issn>0009-9147</issn><issn>1530-8561</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpdkdtq3DAQhkVpaTbbvkEpgt70xludbMmXwdskhYWEpO2tGVvjrlMfUo0cyDP0paNlk14UhMTA9w8z-hj7IMVGaqu_tEM_tXscN0rIcqOFKYR4xVYy1yJzeSFfs5UQosxKaewJOyW6S6WxrnjLTrRUulS5XLG_2z5gG_lPDLQQr2BolwEier7b7ni1nwekiGEeOEyeV9kNQhv7B-TXYY7YTzydCoLv5wegQzTwbU8IhPymp9_8jAiJRpzigYx75OcBxn76tYeRX3Ud3YdU8Nu4-Md37E0HA-H753fNfpx__V5dZruri2_V2S5rjbExcz4XoIwXHoRzjSqaRiqrnDBKO41CO-Os7QA6W3bCNQXKsnOuAOEdKhB6zT4f-96H-c-S9qvHnlocBphwXqhWypn0gdaqhH76D72blzCl6RJVGm3zIt1rZo5UG2aigF2dthohPNZS1AdZ9Yus-iCrPspKsY_PzZdmRP8v9GJHPwGKBpJi</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Ikezaki, Hiroaki</creator><creator>Fisher, Virginia A</creator><creator>Lim, Elise</creator><creator>Ai, Masumi</creator><creator>Liu, Ching-Ti</creator><creator>Adrienne Cupples, L</creator><creator>Nakajima, Katsuyuki</creator><creator>Asztalos, Bela F</creator><creator>Furusyo, Norihiro</creator><creator>Schaefer, Ernst J</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4U-</scope><scope>7QO</scope><scope>7RV</scope><scope>7TM</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PCBAR</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>RC3</scope><scope>S0X</scope><scope>7X8</scope></search><sort><creationdate>20190901</creationdate><title>Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study</title><author>Ikezaki, Hiroaki ; Fisher, Virginia A ; Lim, Elise ; Ai, Masumi ; Liu, Ching-Ti ; Adrienne Cupples, L ; Nakajima, Katsuyuki ; Asztalos, Bela F ; Furusyo, Norihiro ; Schaefer, Ernst J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-8d50a24d0da088b26bb12728042383e0384877faaf79f08b6e19f886a0d8e2a03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Biomarkers - blood</topic><topic>C-reactive protein</topic><topic>C-Reactive Protein - analysis</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Cardiovascular Diseases - blood</topic><topic>Cardiovascular Diseases - etiology</topic><topic>Cerebral infarction</topic><topic>Cholesterol</topic><topic>Cholesterol, LDL - blood</topic><topic>Diabetes mellitus</topic><topic>Discordance</topic><topic>Female</topic><topic>Health risk assessment</topic><topic>Health risks</topic><topic>Heart diseases</topic><topic>High density lipoprotein</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Laboratories</topic><topic>Lipids</topic><topic>Low density lipoprotein</topic><topic>Male</topic><topic>Mathematical analysis</topic><topic>Middle Aged</topic><topic>Multivariate Analysis</topic><topic>Myocardial infarction</topic><topic>Offspring</topic><topic>Proportional Hazards Models</topic><topic>Prospective Studies</topic><topic>Proteins</topic><topic>Reclassification</topic><topic>Regression analysis</topic><topic>Risk analysis</topic><topic>Risk Assessment</topic><topic>Risk factors</topic><topic>Smoking</topic><topic>Statins</topic><topic>Triglycerides</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ikezaki, Hiroaki</creatorcontrib><creatorcontrib>Fisher, Virginia A</creatorcontrib><creatorcontrib>Lim, Elise</creatorcontrib><creatorcontrib>Ai, Masumi</creatorcontrib><creatorcontrib>Liu, Ching-Ti</creatorcontrib><creatorcontrib>Adrienne Cupples, L</creatorcontrib><creatorcontrib>Nakajima, Katsuyuki</creatorcontrib><creatorcontrib>Asztalos, Bela F</creatorcontrib><creatorcontrib>Furusyo, Norihiro</creatorcontrib><creatorcontrib>Schaefer, Ernst J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>University Readers</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Nucleic Acids Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science 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>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Proquest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection (ProQuest)</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database (ProQuest)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>SIRS Editorial</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical chemistry (Baltimore, Md.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ikezaki, Hiroaki</au><au>Fisher, Virginia A</au><au>Lim, Elise</au><au>Ai, Masumi</au><au>Liu, Ching-Ti</au><au>Adrienne Cupples, L</au><au>Nakajima, Katsuyuki</au><au>Asztalos, Bela F</au><au>Furusyo, Norihiro</au><au>Schaefer, Ernst J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study</atitle><jtitle>Clinical chemistry (Baltimore, Md.)</jtitle><addtitle>Clin Chem</addtitle><date>2019-09-01</date><risdate>2019</risdate><volume>65</volume><issue>9</issue><spage>1102</spage><epage>1114</epage><pages>1102-1114</pages><issn>0009-9147</issn><eissn>1530-8561</eissn><abstract>Increases in circulating LDL cholesterol (LDL-C) and high-sensitivity C-reactive protein (hsCRP) concentrations are significant risk factors for cardiovascular disease (CVD). We assessed direct LDL-C and hsCRP concentrations compared to standard risk factors in the Framingham Offspring Study.
We used stored frozen plasma samples (-80 °C) obtained after an overnight fast from 3147 male and female participants (mean age, 58 years) free of CVD at cycle 6 of the Framingham Offspring Study. Overall, 677 participants (21.5%) had a CVD end point over a median of 16.0 years of follow-up. Total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), direct LDL-C (Denka Seiken and Kyowa Medex methods), and hsCRP (Dade Behring method) concentrations were measured by automated analysis. LDL-C was also calculated by both the Friedewald and Martin methods.
Considering all CVD outcomes on univariate analysis, significant factors included standard risk factors (age, hypertension, HDL-C, hypertension treatment, sex, diabetes, smoking, and TC concentration) and nonstandard risk factors (non-HDL-C, direct LDL-C and calculated LDL-C, TG, and hsCRP concentrations). On multivariate analysis, only the Denka Seiken direct LDL-C and the Dade Behring hsCRP were still significant on Cox regression analysis and improved the net risk reclassification index, but with modest effects. Discordance analysis confirmed the benefit of the Denka Seiken direct LDL-C method for prospective hard CVD endpoints (new-onset myocardial infarction, stroke, and/or CVD death).
Our data indicate that the Denka Seiken direct LDL-C and Dade Behring hsCRP measurements add significant, but modest, information about CVD risk, compared to standard risk factors and/or calculated LDL-C.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>31239251</pmid><doi>10.1373/clinchem.2019.304600</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Biomarkers - blood C-reactive protein C-Reactive Protein - analysis Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - blood Cardiovascular Diseases - etiology Cerebral infarction Cholesterol Cholesterol, LDL - blood Diabetes mellitus Discordance Female Health risk assessment Health risks Heart diseases High density lipoprotein Humans Hypertension Laboratories Lipids Low density lipoprotein Male Mathematical analysis Middle Aged Multivariate Analysis Myocardial infarction Offspring Proportional Hazards Models Prospective Studies Proteins Reclassification Regression analysis Risk analysis Risk Assessment Risk factors Smoking Statins Triglycerides |
title | Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study |
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