Literature DB >> 24881768

New risk markers for cardiovascular prevention.

Guy G De Backer1.   

Abstract

The importance of total cardiovascular (CV) risk estimation before management decisions are taken is well established. Models have been developed that allow physicians to stratify the asymptomatic population in subgroups at low, moderate, high, and very high total CV risk. Most models are based on classical CV risk factors: age, gender, smoking, blood pressure, and lipid levels. The impact of additional risk factors is discussed here, looking separately at the predictive increments of novel biomarkers and of indicators of subclinical atherosclerotic disease. The contribution of biomarkers to the total CV risk estimation is generally modest, and their usage should be limited to subjects at intermediate total CV risk. Detection of subclinical vascular damage may improve total CV risk estimation in asymptomatic subjects who are close to a threshold that could affect management decisions and in whom the chances of re-classification in a different risk category are great. There is, however, an urgent need for trials in which the value of using total CV risk estimation models is tested.

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Year:  2014        PMID: 24881768     DOI: 10.1007/s11883-014-0427-z

Source DB:  PubMed          Journal:  Curr Atheroscler Rep        ISSN: 1523-3804            Impact factor:   5.113


  59 in total

Review 1.  Comparisons of established risk prediction models for cardiovascular disease: systematic review.

Authors:  George C M Siontis; Ioanna Tzoulaki; Konstantinos C Siontis; John P A Ioannidis
Journal:  BMJ       Date:  2012-05-24

2.  2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.

Authors:  Philip Greenland; Joseph S Alpert; George A Beller; Emelia J Benjamin; Matthew J Budoff; Zahi A Fayad; Elyse Foster; Mark A Hlatky; John McB Hodgson; Frederick G Kushner; Michael S Lauer; Leslee J Shaw; Sidney C Smith; Allen J Taylor; William S Weintraub; Nanette K Wenger; Alice K Jacobs
Journal:  Circulation       Date:  2010-11-15       Impact factor: 29.690

3.  An updated coronary risk profile. A statement for health professionals.

Authors:  K M Anderson; P W Wilson; P M Odell; W B Kannel
Journal:  Circulation       Date:  1991-01       Impact factor: 29.690

4.  Interpreting incremental value of markers added to risk prediction models.

Authors:  Michael J Pencina; Ralph B D'Agostino; Karol M Pencina; A Cecile J W Janssens; Philip Greenland
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

5.  Multimarker prediction of coronary heart disease risk: the Women's Health Initiative.

Authors:  Hyeon Chang Kim; Philip Greenland; Jacques E Rossouw; JoAnn E Manson; Barbara B Cochrane; Norman L Lasser; Marian C Limacher; Donald M Lloyd-Jones; Karen L Margolis; Jennifer G Robinson
Journal:  J Am Coll Cardiol       Date:  2010-05-11       Impact factor: 24.094

6.  Common carotid intima-media thickness in cardiovascular risk stratification of older people: the Rotterdam Study.

Authors:  Suzette E Elias-Smale; Maryam Kavousi; Germaine C Verwoert; Michael T Koller; Ewout W Steyerberg; Francesco U S Mattace-Raso; Albert Hofman; Arnold P G Hoeks; Robert S Reneman; Jacqueline C M Witteman
Journal:  Eur J Prev Cardiol       Date:  2011-06-22       Impact factor: 7.804

7.  Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study.

Authors:  Raimund Erbel; Stefan Möhlenkamp; Susanne Moebus; Axel Schmermund; Nils Lehmann; Andreas Stang; Nico Dragano; Dietrich Grönemeyer; Rainer Seibel; Hagen Kälsch; Martina Bröcker-Preuss; Klaus Mann; Johannes Siegrist; Karl-Heinz Jöckel
Journal:  J Am Coll Cardiol       Date:  2010-10-19       Impact factor: 24.094

8.  Fasting versus nonfasting triglycerides and the prediction of cardiovascular risk: do we need to revisit the oral triglyceride tolerance test?

Authors:  Paul M Ridker
Journal:  Clin Chem       Date:  2007-11-12       Impact factor: 8.327

9.  Coronary calcium as a predictor of coronary events in four racial or ethnic groups.

Authors:  Robert Detrano; Alan D Guerci; J Jeffrey Carr; Diane E Bild; Gregory Burke; Aaron R Folsom; Kiang Liu; Steven Shea; Moyses Szklo; David A Bluemke; Daniel H O'Leary; Russell Tracy; Karol Watson; Nathan D Wong; Richard A Kronmal
Journal:  N Engl J Med       Date:  2008-03-27       Impact factor: 91.245

Review 10.  C-reactive protein as a risk factor for coronary heart disease: a systematic review and meta-analyses for the U.S. Preventive Services Task Force.

Authors:  David I Buckley; Rongwei Fu; Michele Freeman; Kevin Rogers; Mark Helfand
Journal:  Ann Intern Med       Date:  2009-10-06       Impact factor: 25.391

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  3 in total

1.  Which Measures of Health Status Assessment are the Most Significant in Organized Cohorts with Low Current Cardiovascular Risk? The Screening Study of Penitentiary Staff in Saratov Region, Russia.

Authors:  Anton R Kiselev; Sergey V Balashov; Olga M Posnenkova; Mikhail D Prokhorov; Vladimir I Gridnev
Journal:  Eurasian J Med       Date:  2016-02

2.  Low serum paraoxonase1 activity levels predict coronary artery disease severity.

Authors:  Ting Sun; Jingchao Hu; Zhaofang Yin; Zuojun Xu; Liang Zhang; Li Fan; Yang Zhuo; Changqian Wang
Journal:  Oncotarget       Date:  2017-03-21

3.  Potential novel biomarkers of cardiovascular dysfunction and disease: cardiotrophin-1, adipokines and galectin-3.

Authors:  Simona Hogas; Stefana C Bilha; Dumitru Branisteanu; Mihai Hogas; Abduzhappar Gaipov; Mehmet Kanbay; Adrian Covic
Journal:  Arch Med Sci       Date:  2016-03-22       Impact factor: 3.318

  3 in total

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