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ORIGINAL CONTRIBUTION

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Audio Interview

Lipid-Related Markersand Cardiovascular Disease PredictionThe Emerging Risk FactorsCollaboration*

ROUTINELY USED RISK PREDIC-tion scores for cardiovascu-lar disease (CVD) contain in-formation on total cholesterol

and high-density lipoprotein choles-terol (HDL-C) and several other con-

ventional risk factors.1,2

There is con-siderable interest in whether CVDprediction can be improved by assess-ment of various additional lipid-related markers either to replace, orsupplement, traditional cholesterolmeasurements in these scores.3

Proposals to replace information ontotal cholesterol and HDL-C withsingleparameters, such as the total choles-terol:HDL-C ratio or nonHDL-C(ie, total cholesterol HDL-C),4,5 havebeen motivated by a desire for greatersimplicity and a belief that these para-meters better reflect the underlyingatherosclerotic process. For example,nonHDL-C reflects the cholesterolcontent of several proatherogeniclipoprotein subfractions (very low-density lipoprotein, intermediate-density lipoprotein, and chylomicronremnants) in addition to low-densitylipoprotein cholesterol.

Similar considerations apply to pro-posals to replace information on totalcholesterol and HDL-C with apolipo-protein B and apolipoprotein A-I.6-9 Be-

cause apolipoprotein B and A-I are theprincipal surface proteinsfound on pro-atherogenic lipoproteins and HDL, re-spectively, they might be more strongly

related to CVD risk than is the choles-terol contained in these lipoproteins.However, perhaps partly due to incon-

clusive epidemiological evidence, thereare conflicting guidelines about the rel-evance of apolipoprotein B and A-I toCVD prediction.1,6-11

There is also debate about the valueof supplementing conventionalriskfac-tors with targeted assessment of lipo-protein(a). In 2010, the European Ath-erosclerosis Society Consensus Panel

recommended lipoprotein(a) measure-ment to augment risk assessment inpeople at intermediate (10%-20%) or

high (

20%) predicted 10-year CVDrisk.12 However, the 2010 AmericanCollege of Cardiology Foundation/AmericanHeart Association TaskForce

For editorial comment see p 2540.

Author Audio Interview available atwww.jama.com.

*Authors/Members Writing Committee and Inves-tigators are listed at the end of this article.Corresponding Author: John Danesh, FRCP, Depart-ment of Public Health and Primary Care, Universityof Cambridge,Worts Causeway,Cambridge, CB18RN,England ([email protected]).

Context The value of assessing various emerging lipid-related markers for predic-tion of first cardiovascular events is debated.

Objective To determine whether adding information on apolipoprotein B and apo-lipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cho-lesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular dis-ease (CVD) risk prediction.

Design, Setting, and Participants Individual records were available for 165 544participants without baseline CVD in 37 prospective cohorts (calendar years of re-cruitment: 1968-2007) with up to 15 126 incident fatal or nonfatal CVD outcomes(10 132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years(interquartile range, 7.6-14 years).

Main Outcome Measures Discrimination of CVD outcomes and reclassificationof participants across predicted 10-year risk categories of low (10%), intermediate(10%-20%), and high (20%) risk.

Results The addition of information on various lipid-related markers to total cho-lesterol, HDL-C, and other conventional risk factors yielded improvement in themodels discrimination: C-index change, 0.0006 (95% CI, 0.0002-0.0009) forthe combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009-0.0023) forlipoprotein(a); and 0.0018 (95% CI, 0.0010-0.0026) for lipoprotein-associatedphospholipase A2 mass. Net reclassification improvements were less than 1% withthe addition of each of these markers to risk scores containing conventional risk fac-tors. We estimated that for 100 000 adults aged 40 years or older, 15 436 would beinitially classified at intermediate risk using conventional risk factors alone. Addi-

tional testing with a combination of apolipoprotein B and A-I would reclassify1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A 2 mass,2.7% of people to a 20% or higher predicted CVD risk category and, therefore, inneed of statin treatment under Adult Treatment Panel III guidelines.

Conclusion In a study of individuals without known CVD, the addition of informa-tion on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-Cled to slight improvement in CVD prediction.

JAMA. 2012;307(23):2499-2506 www.jama.com

2012 American Medical Association. All rights reserved. JAMA, June 20, 2012Vol 307, No. 23 2499Corrected on June 19, 2012

wnloaded From: http://jama.jamanetwork.com/ by Santiago Silva on 06/19/2012

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on Practice Guidelines did not sup-port this recommendation.6-8 Similaruncertainties apply to the incrementalpredictive value of assessing circulat-ing concentrations of lipoproteinassociated phospholipase A2.

8

Complementingprevious reportsfromthis collaboration,13-15 the current analy-sis has 2 objectives. First, to determinewhether replacing information on totalcholesterolandHDL-C with variouslipidparameters improves prediction of first-onset CVD outcomes. Second, to deter-minewhether additionalinformationonapolipoprotein B andA-I, lipoprotein(a),or lipoprotein-associated phospholi-pase A2 to prognostic models contain-ing information on total cholesterol,HDL-C,andother conventional risk fac-tors improves CVD risk prediction.

METHODS

Study Design

Details of this collaboration have beenpublished.16 Eligible prospective stud-ies had information for each partici-pant on total cholesterol, HDL-C, age,sex, smokingstatus, diabetes, andbloodpressure; assayed triglyceride, apolipo-protein B and A-I, lipoprotein(a), orlipoprotein-associatedphospholipase A2mass or activity; had not selected par-ticipants on the basis of having hadpre-

vious CVD (defined in each study at theinitial examination); recorded cause-specific mortality, vascular morbidity(nonfatal myocardial infarction orstroke), or both during follow-upusingwell-defined criteria; and recorded morethan 1 year of follow-up. Because in-formation on directly measured LDL-C,adiposity measures, family history ofCVD, and socioeconomic factors wasavailable only in subsets of the partici-pants, these variables were not in-cludedin the main analysis. eTables 1-4

and eAppendix 1 provide study de-tails, including assay methods, acro-nyms, and references (available at http:// ww w. ja ma .c om ). Da ta fr om th eApolipoprotein Related Mortality RiskStudy (AMORIS) could not be incor-porated into these current analyses be-cause it did not measure baseline lev-els of HDL-C, blood pressure, smoking

status, body mass index, or diabetes(eTable 5).17 In registering fatal out-comes, all contributing studies in thisanalysis used International Classifica-tion of Disease coding to at least 3 dig-its and ascertainment was based on

death certificates, with 29 studies alsoinvolving review of medicalrecords,au-topsy findings, and other supplemen-tary sources. Studies used definitionsof myocardial infarction based onWorld Health Organization or similarcriteria and of stroke based on clinicaland brain imaging features. The studywas approved by the Cambridgeshireethics review committee.

Statistical Analysis

Becauserecentriskscoreshavetendedtocombine coronary heart disease (CHD)

andstrokeoutcomesduetotheexistenceofsharedriskfactorsandtreatments,18theprimary outcome used herein was first-onset CVD, defined as fatal or nonfatalCHD event or anystroke. Wecomparedprognosticmodelsthat replacedinforma-tionontotalcholesterolandHDL-Cwithvarious nontraditional lipid parametersthat have been previously proposed, in-cluding the total cholesterol:HDL-C ra-tio (which is mathematically equivalentto the nonHDL-C:HDL-C ratio); theHDL-C:totalcholesterolratio;nonHDL-

C;apolipoprotein B andA-I;apolipopro-teinB: A-Iratio; apolipoprotein A-I:B ra-tio;totalcholesterol and apolipoproteinA-I;apolipoproteinBandHDL-C,andlog etransformations of ratios.

Wealso evaluatedsupplementing riskscores containing total cholesterol andHDL-C with triglyceride, apolipopro-teinB, apolipoprotein A-I, lipoprotein(a),and lipoprotein-associated phospholi-pase A2 mass or activity. Lipoprotein(a)was modeled nonlinearly by includinglinear and quadratic terms of log-

transformed lipoprotein(a). Because ofdifferences in the mean andstandard de-viation of concentrationsof lipoprotein-associated phospholipase A2 recordedacross studiesusing different assay meth-ods (eTables 3 and4),values were stan-dardized within each study. Cox pro-portional hazards modeling allowed forseparate baseline hazards by study (and,

when appropriate, by trial group) andsexbut estimated common coefficients(logehazard ratios) across studies. We cen-sored deaths from non-CVD causes.Prognostic models werecomparedusingmeasures of risk discrimination and re-

classification.

19-21

We extended our pre-vious methods19 to a 2-stageapproach al-lowing examination of between-studyheterogeneity, calculating the C indexand the D measure, and their changes,within eachstudy separately before pool-ing results. Studies were weighted bynumbers of CVD outcomes (eAppen-dix 2). Between-study heterogeneity inthe risk discrimination measures andtheir changes wasquantifiedby theI2 sta-tistic.22 The proportional hazards as-sumption was satisfied. For partici-pants in studies with at least 10 years of

follow-up, we constructed reclassifica-tion tables using data from studies thathad recorded both fatal and nonfatalCVDoutcomes to examine movementofparticipants between 3 predicted 10-year CVD risk categories (10%, 10%-20%, and 20%) upon addition oflipid-relatedmarkers to conventionalriskfactors8 andsummarized these using thenet reclassification improvement.20

Our clinical modeling involved3 keyassumptions. First, we assumed the useof sequential screening, ie, initial

screening with conventional risk fac-tors alone followed by additional mea-surement of further lipid-related mark-ers in people at 10% to less than 20%predicted 10-year CVD risk. Second, weassumed statin allocation would re-duce CVD risk by 20% in people with-out a history of CVD (including inpeopleat20%predicted 10-year risk).This estimate was derived from rela-tive risk reductions observed with stat-ins in a meta-analysis of randomizedtrials (eAppendix 2).8,23 Third, we as-

sumed a policy of statin allocation perAdult Treatment Panel III guide-lines,24 that is, people at 20% or moreof predicted CVD risk plus others, suchas people with diabetes irrespective oftheir predicted 10-year risk. Analyseswere performed using Stata statisticalsoftware version 11.0 (StataCorp),2-sided P values, and 95% CIs.

LIPID-RELATED MARKERS AND CARDIOVASCULAR DISEASE

2500 JAMA, June 20, 2012Vol 307, No. 23 2012 American Medical Association. All rights reserved.Corrected on June 19, 2012


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RESULTSIndividual records were available for165 544 participants without baselineCVD in 37 prospective cohorts (cal-endar years of recruitment, 1968-2007) with up to 15 126 incident

fatal and nonfatal CVD outcomes( 1 0 1 3 2 C H D a n d 4 9 9 4 s t r o k eevents) recorded during medianfollow-up of 10.4 years (interquartilerange [IQR], 7.6-14 years). TheTABLE describes the baseline charac-teristics of participants and presentsadjusted hazard ratios for CVD withbaseline levels of risk factors (supple-mented by eTables 1-3, availableat http://www.jama.com).

Replacement of Cholesterol

With Other Lipid-Related Markers

Replacing total cholesterol and HDL-Cwith information on various lipid-related markers didnot improve risk dis-crimination or reclassification (FIGURE1

and eTable 6). For example, replace-ment of information on total choles-terol and HDL-C with apolipoproteinB and A-I significantly worsened riskdiscrimination (C-index change:0.0028; P .001) and risk classifica-tion (net reclassification improve-ment: 1.08%; P =.01). No improve-ment in risk discrimination wasobservedin subgroupsdefined by base-line age, sex, elevated triglyceride, his-

tory of diabetes, and other conven-tional risk factors (eg, lipids, bloodpressure, smoking status, metabolicsyndrome), use of lipid- or blood pres-surelowering medications at entry,fasting status, type of assay, predicted

10-year CVD risk, and study design(eFigure 1). In separate analyses ofCHD and stroke as individual out-comes, replacement of information ontotal cholesterol and HDL-C with vari-ous lipid-related markers did not im-prove risk discrimination (eFigure 2).

Addition of Lipid-Related Markers

Prognostic models for CVD that addedlipid-relatedmarkersto modelscontain-

Table. Summary of Available Data and Hazard Ratios for Cardiovascular Disease With Measured Baseline Levels of Risk Factors

Studies With Informationon Apolipoproteins

(26 Studies, 139581 Participants,12 234 CVD Cases)

Studies With Informationon Lipoprotein(a)

(24 Studies, 133 502 Participants,12 639 CVD Cases)

Studies With Informationon Lipoprotein-Associated

Phospholipase A2(11 Studies, 32075 Participants,

6150 CVD Cases)

Mean (SD) orNo. (No. of CVD

Cases)Hazard Ratioa

(95% CI)

Mean (SD) or No.(No. of CVD

Cases)Hazard Ratioa

(95% CI)

Mean (SD) orNo. (No. of CVD

Cases)Hazard Ratioa

(95% CI)

Conventional risk factorsAge at survey, y 56.42 (8.41) 1.87 (1.73-2.02) 56.86 (8.38) 1.81 (1.69-1.93) 63.78 (7.52) 1.62 (1.43-1.83)

SexMen 68 520 (7734) NAb 64 402 (7910) NAb 15 814 (3583) NAb

Women 71 061 (4500) NAb 69 100 (4729) NAb 16 261 (2567) NAb

Current smokingNo 102 261 (7137) 1.0 [Reference] 97 949 (7483) 1.0 [Reference] 21 972 (3677) 1.0 [Reference]

Yes 37 320 (5097) 1.79 (1.66-1.94) 35 553 (5156) 1.87 (1.73-2.02) 10 103 (2473) 1.63 (1.38-1.91)

History of diabetesNo 131 610 (10 722) 1.0 [Reference] 126 328 (11 103) 1.0 [Reference] 29 904 (5534) 1.0 [Reference]

Yes 7971 (1512) 2.04 (1.76-2.35) 7174 (1536) 2.05 (1.77-2.38) 2171 (616) 1.76 (1.57-1.98)

Systolic blood pressure, mm Hg 135.19 (18.38) 1.31 (1.26-1.37) 134.17 (18.15) 1.34 (1.29-1.38) 138.88 (21.04) 1.29 (1.24-1.35)

Traditional lipids, mg/dLTotal cholesterol 226 (42.5) 1.22 (1.17-1.27) 229 (42.1) 1.19 (1.15-1.24) 225 (41.7) 1.13 (1.05-1.22)

HDL-C 51.4 (14.7) 0.83 (0.78-0.87) 50.6 (14.7) 0.82 (0.77-0.88) 52.5 (12.7) 0.85 (0.77-0.94)

Triglyceridec 115 (80-168)d 1.19 (1.15-1.23) 115 (80-168)d 1.18 (1.14-1.22) 97 (80-142)d 1.11 (1.05-1.16)

Lipid-related markersNonHDL-C, mg/dL 175 (43.6) 1.27 (1.22-1.33) 178 (43.2) 1.25 (1.19-1.31) 173 (42.9) 1.18 (1.10-1.27)

Total-C:HDL-C ratioc 4.4 (3.5-5.4)d 1.32 (1.24-1.39) 4.4 (3.5-5.5)d 1.31 (1.23-1.39) 4.4 (3.6-5.4)d 1.20 (1.09-1.32)

Apolipoprotein B, mg/dL 110 (29) 1.24 (1.19-1.29)

Apolipoprotein A-I, mg/dL 146 (32) 0.87 (0.84-0.90)

Apolipoprotein B:A-I ratioc 0.7 (0.6-0.9)d 1.30 (1.24-1.36)

Lipoprotein(a), mg/dLc 10.9 (4.4-28.0)d 1.13 (1.09-1.18)

Lipoprotein-associated phospholipase A2Activity NAe 1.12 (1.04-1.20)

Mass NAe 1.15 (1.09-1.21)

Abbreviations: CVD, cardiovascular disease; HDL-C, high-density lipoprotein cholesterol; NA, not applicable.SI conversionfactors: To convert totalcholesterol and HDL-Cfrom mg/dLto mmol/L, multiply by 0.0259; triglycerides from mg/dLto mmol/L, multiply by 0.0113; lipoprotein(a)from

mg/dL to mmol/L, multiply by 0.357; and apolipoprotein B and A-I from mg/dL to g/L, multiply by 0.01.a Hazard ratio (95% confidence interval) per 1 SD higher age, systolic blood pressure, measured biomarker level or compared to relevant reference category. Hazard ratios were

adjusted for age, smoking status, systolic blood pressure, and history of diabetes, where appropriate.b Models were stratified by sex.cVariables were loge transformed.d Median and interquartile range.e Concentrations of lipoptoteinassociated phospholipase A2 were standardized to a mean (SD) of 0 (1) within each study due to different assays yielding different absolute levels.




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ingtotalcholesterolandHDL-Candotherconventional risk factors changed theCindexbytheamountsshowninFIGURE2and eTable 7, available at http://www.jama.com. However, none of theselipid-related markers significantly im-proved CVD risk classification. Again,broadly similar results to those ob-served overallfor thelipid-relatedmark-ers were found in clinically relevantsubgroups, including participants who

reported using lipid-lowering medica-tions at entry. Although there was ten-tative evidence of effect-modification insome groups (eFigures 3-6), cautiousinterpretation is required given the mul-tiplicity of comparisons made. First,apolipoprotein A-I and B, as well aslipoprotein(a), could improve CVDpre-diction more in individuals with highertotal cholesterol or in people initiallyclassified at 10% to less than 20% pre-dicted 10-year risk(P.001 andP=.02,respectively; eFigures 3 and 4). Sec-

ond, the addition of apolipoprotein Band A-I could preferentially improveCVD risk discrimination in men(P=.01), participants using blood pres-surelowering medications at entry(P =.005), and individuals with lowerHDL-C (P=.022; eFigure 3). Third, theaddition of apolipoprotein B andA-I sig-nificantly improved risk discrimina-

tion for CHD (C-index increase of0.0010; P.001) but not for stroke (C-index increase of 0.0002; P =.30). Bycontrast, addition of lipoprotein(a) orlipoprotein-associatedphospholipase A2mass provided improvements for CHDthat were similar to those for stroke(eFigure 7).

Similar results to those describedabove were observed in analyses thatused the D measure (eFigures8 and 9),

or that were restricted to studies withat least 10 years of follow-up (eFigure10). Levels of lipid-related markers con-tributed relatively little to heteroge-neity in the study-specific C index,which was mostly due to differing ageranges across cohorts (eFigures11-15).We could not reliably evaluate the ef-fect of joint assessment of apolipopro-tein B and A-I, lipoprotein(a), andlipoprotein-associatedphospholipase A2because only about 10% of the partici-pants in this analysis had concomitant

information on all these parameters.Clinical Modeling

We modeled a population of 100 000adults aged 40 yearsor older with simi-lar age structure as the European stan-dard population and an age- and sex-specific incidence of CVD as in thecurrent study; 15 436 people would be

initially classified at 10% to less than20% 10-year predicted CVD risk usingconventional risk factors alone, ofwhom 13 622 would remain after ex-cluding those recommended for statintreatment by Adult Treatment Panel IIIguidelines (such as people with diabe-tes irrespective of their predicted 10-year risk24 (FIGURE3 and eAppendix 2).For these 13 622 people, assessment oflipoprotein(a) would reclassify 555

people (4.1%) to 20% or greater pre-dicted risk, 86 of whom would be ex-pected to have a CVD event within 10years; assessment of lipoprotein-associated phospholipase A2 masswould reclassify 365 people (2.7%), 72of whom would be expected to have aCVD event within 10 years; and assess-ment of the combination of apolipo-protein B or A-I would reclassify 154people (1.1%), 16 of whom would beexpected to have a CVD event within10 years (eFigure 16). Assuming statin

allocation per the Adult TreatmentPanel III guidelines,24 such targeted as-sessment could help prevent about 17(ie, 0.2086) extra CVD outcomesover 10 years for those additionallytested for lipoprotein(a), 14 (0.2072)extra CVD outcomes over 10 years forthose tested for lipoprotein-associatedphospholipaseA2 mass,or 3 (0.2016)

Figure 1. Changes in Cardiovascular Disease Risk Discrimination and Reclassification When Replacing Cholesterol Markers With Lipid-RelatedMarkers

Worsens Risk

Prediction

Improves Risk

Prediction

C-Index Change (95% CI)

(26 Studies

139581 Participants12234 CVD Cases)

Net Reclassification

Improvement, % (95% CI)

(17 Studies 77886 Participants5435 CVD Cases)

Replacement With Lipid-Related Markers

0.0098 (0.0114 to 0.0082)a 2.74 (3.77 to 1.71)aTotal cholesterol: HDL-C ratio0.0024 (0.0032 to 0.0016)a 0.48 (1.16 to 0.21)Non-HDL-C

Apolipoprotein

0.0028 (0.0039 to 0.0017)a 1.08 (1.94 to 0.22)bB and A-I0.0056 (0.0070 to 0.0043)a 2.03 (2.98 to 1.09)aB:A-I ratio0.0025 (0.0035 to 0.0014)a 0.58 (1.36 to 0.20)Total cholesterol and Apolipoprotein A-I0.0020 (0.0029 to 0.0011)a 1.33 (2.07 to 0.59)aApolipoprotein B and HDL-C

[Reference] [Reference]Conventional Risk Factors

0.015 0.010 0.005 0 0.005


The model analyzed patients with conventional risk factors of age, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoproteincholesterol (HDL-C), each of which were included as individual linear terms. The models were stratified by sex. Overall, the C-index for a model containing conven-tional cardiovascular disease (CVD) risk factors was 0.7244 (95% CI, 0.7200-0.7289). The net reclassification improvement analysis was calculated only for partici-pants in studies that had at least 10 years of follow-up.a P.001 for comparison against the model containing conventional risk factors.b

P.05 for comparison against the model containing conventional risk factors.




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extra CVD outcomes over 10 years forthose tested for a combination of apo-lipoprotein B or A-I. In other words,

such targeted assessment of individu-als at intermediate CVDrisk could helpprevent 1 extra CVD outcome over 10years for every 801 assessed for lipo-protein(a) (ie, 13 622/17), 973 as-sessed for lipoprotein-associated phos-pholipase A2 mass (13 622/14), and4541 assessed for the combination ofapolipoprotein B and A-I (13 622/3).Under these circumstances, statinswould be newly allocated to about 33of 801 people (4.1%) assessed for lipo-protein(a), 26 of 973 people (2.7%) as-

sessed for lipoprotein-associated phos-pholipase A2 mass,or 50 of 4541 (1.1%)assessed for the combination of apoli-poprotein B and A-I. Alternatively, as-suming use of the more selective statinallocation policies in Canada9 or theUnited Kingdom, then the numbersneeded to screen listed above shouldeach be multiplied by 0.6.

Figure 2. Changes in Cardiovascular Disease Risk Discrimination and Classification After Adding Lipid-Related Markers

Worsens

Risk

Prediction

Improves

Risk

Prediction

C-Index Change

(95% CI)Studies Part ic ipants CVD Casesa

Net Reclassification

Improvement, %

(95% CI)

Addition of Lipid-Related

Markers

Triglyceride

Apolipoproteins

0.0025 0 0.0025 0.0050


133919 11700 [Reference]24 [Reference]Conventional risk factors

Lipoprotein(a)

133502 12639 [Reference]24 [Reference]Conventional risk factors

Lipoprotein-associated

phospholipase A228567 5299 [Reference]8 [Reference]Conventional risk factors

0.49 (0.76 to 0.21)cPlus triglycerideb 0.0000 (0.0001 to 0.0002)

0.17 (0.52 to 0.18)B 0.0001 (0.0002 to 0.0003)

0.20 (0.49 to 0.08)A-I 0.0005 (0.0002 to 0.0007)d

0.10 (0.49 to 0.29)B and A-I 0.0006 (0.0002 to 0.0009)d

0.05 (0.59 to 0.70)Plus lipoprotein(a)e 0.0016 (0.0009 to 0.0023)d

Plus l ipoprotein-associated

phospholipase A2 activity0.21 (0.45 to 0.86)0.0001 (0.0003 to 0.0006)

28494 5859 [Reference]8 [Reference]Conventional risk factors0.0018 (0.0010 to 0.0026)d 0.81 (0.15 to 1.77)Plus l ipoprotein-associated

phospholipase A2 mass

139581 12234 [Reference]26 [Reference]Conventional risk factorsPlus apolipoprotein

No. of

The model containing conventional risk factors include age, systolic blood pressure, smoking status, history of diabetes, total and high-density lipoprotein cholesterol(HDL-C), each included as individual linear terms. Models were stratified by sex.a Net reclassification improvement wascalculatedonly forparticipants in studies withat least 10 years of follow-up. Change in C-indexaddinglipoprotein(a) greater than30mg/dL was 0.0001 (95% CI, 0.0001 to 0.0003).b Triglyceride values were log-transformed.c P.05 for comparison against model containing conventional risk factors.d P.001 for comparison against model containing conventional risk factors.eLipoprotein(a) was modeled nonlinearly by including linear and quadratic terms of log-transformed lipoprotein(a).

Figure 3. Modeling of Reclassification per 100 000 People Initially Screened WithConventional Risk Factors and Then Additional Targeted Assessment of Lipid-Related Markers

15436 Have 10% to

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COMMENTIn contrast with some existing guide-lines,1,6,7,9 the current analysis hasshown that replacement of informa-tion on total cholesterol and HDL-Cwith various lipid parameters does not

improve CVD prediction. For ex-ample, none of the following mea-sures were superior to total choles-terol and HDL-C when they replacedtraditional cholesterol measurements inrisk prediction scores: the total choles-terol:HDL-Cratio; nonHDL-C; the lin-ear combination of apolipoprotein BandA-I; or the apolipoprotein B:A-I ra-tio. Furthermore, replacement of totalcholesterol and HDL-C with apolipo-protein B and A-I actually signifi-cantly worsened risk discrimination.These findings applied to clinically rel-

evant subpopulations, including peoplewith diabetes and people with el-evated triglyceride levels.

With regards to the value of addinginformation on various emerging lipid-related markers to risk scores alreadycontaining total cholesterol, HDL-C,and other conventional risk factors, weobserved slight potential for improve-ment in CVD prediction. This conclu-sion was suggested by the followinganalyses. First, we showed that each ofthe lipid-relatedmarkers studied herein

slightly increased CVD prediction whenusing measures (eg, the C index and Dmeasure) that are independent of clini-cal risk categories. Second, we foundthat none of these markers signifi-cantly improved reclassification of par-ticipants across the clinical risk cutofflevels that are currently used to in-form treatment decisions. Third, wemodeled a scenario assuming targetedlipid-related marker assessment inpeople judged as being at intermedi-ate risk (10%- 20% 10-year pre-

dicted CVD risk) after initial screen-ing by conventional risk factors alone.If such targeted measurement were tobe coupled with allocation of statins perUS Adult Treatment Panel III guide-lines,24 then our data suggest that itcould help prevent 1 extra CVD out-come over 10 years for approximatelyevery 4500 peopleadditionally screened

with a combination of apolipoproteinB andA-I, or about800 peoplescreenedwith lipoprotein(a), or about 1000people screened with lipoprotein-associated phospholipase A2 mass.

The generalizability of our findings

has been enhanced by inclusion of datafrom 165 000 participants in 15 coun-tries and by the general lack of hetero-geneity in the results. To enhance va-lidity, we have restricted analysis toprospective studies with extended fol-low-up. For example, although somelarge retrospective case-control stud-ies have reported stronger associa-tions of apolipoprotein B and A-I withCHD than those observed herein, it re-mains uncertain to what extent this dif-ference might be explained by factorssuch as changes in lipid levels ob-

served in the hours after the onset ofinfarction in case-control studies ofacute myocardial infarction.25,26 In con-trast with literature-basedreviews,27 ouraccess to individual participant data hasenabled time-to-event analysis, analy-sisof clinically relevant subgroups, andconsistent comparison across studies.To estimate incremental improve-ment in CVD prediction, we have stud-ied only people with complete infor-mation on conventional risk factors.Our findings are consistent with a sepa-

rate andcomplementary analyses of theevidence from randomized trials of pa-tients treated with statins.2,28

This study has potential limita-tions. Our analysis does not, of course,address etiological and therapeuticquestions being explored in random-ized trials. Reclassification analyses areintrinsically sensitive to choice of fol-low-up interval and clinical risk cat-egories. Somewhat greater clinical im-pact than suggested by our analysiswould be estimated if we had used less

conservativemodeling assumptions(eg,use of more effective statin regimens23

and longer time horizons) or alterna-tive disease outcomes (such as an ex-clusive focus on CHD rather than onCHD plus stroke). Conversely, ourclinical models could have overesti-mated potential benefits of assessinglipid-related markers because not all

people eligible for statins will receivethem or be willing, adherent, or able totake them.31 Although we did not findthat our results varied importantly byassay methods used, further study ofthis issue is needed, perhaps particu-

larly for lipid-related markers for whichmeasurements have only recently beenstandardised.12,32 Furthermore, largestudies are needed to assess whetherconcurrent assesment of lipopro-tein(a) concentration and apolipopro-tein(a) isoform size confers greater im-provement in CVD prediction thanlipoprotein(a) alone (such assessmentwas not possible in the current studybecause it lacked concomitant data onsuch isoforms). This study had a lim-ited ability to study lipid-related mark-ers in combination with one anotherand to investigate populations not ofEuropean descent.

In summary, in a study of individu-als without known cardiovascular dis-ease, replacing informationon total cho-lesteroland HDL-C withapolipoproteinBandapolipoproteinA-IworsenedCVDprediction.Furthermore, addition ofthecombinationapolipoprotein B and A-I,lipoprotein(a),or lipoprotein-associatedphospholipase A2 toriskscorescontain-ingtotalcholesterolandHDL-Cprovidedslightimprovement in CVD prediction.

Theclinicalbenefitsofusinganyofthesebiomarkers remains to be established.

Authors/Writing Group: Emanuele Di Angelantonio,MD, Pei Gao, PhD, Lisa Pennells, PhD, StephenKaptoge, PhD, Department of Public Health and Pri-mary Care, University of Cambridge, Cambridge, En-gland; Muriel Caslake, PhD, Institute of Cardiovas-cular and Medical Sciences, University of Glasgow,Glasgow,Scotland;Alexander Thompson, PhD, AdamS. Butterworth, PhD, Nadeem Sarwar, PhD, DavidWormser, PhD, and Danish Saleheen, MD, Depart-ment of Public Health and Primary Care, Universityof Cambridge; Christie M. Ballantyne, MD, Depart-ment of Medicine, BaylorCollege of Medicine, Hous-ton, Texas;BruceM. Psaty,MD, Departments of Medi-cine, Epidemiology,and Health Services,University ofWashington, Seattle; Johan Sundstrom, MD, Upp-sala ClinicalResearch Center,Uppsala University, Upp-

sala, Sweden; Paul M Ridker, MD, Division of Pre-ventive Medicine, Brigham and Womens Hospital,Boston, Massachusetts; Dorothea Nagel, PhD, Klini-kum der Universitat Munchen, LMU, Munchen, Ger-many; RichardF. Gillum, MD, Center forDiseaseCon-trol andPrevention, Washington, DC; IanFord,PhD,Robertson Centre for Biostatistics, University ofGlasgow; Pierre Ducimetiere, PhD, INSERM, France;Stefan Kiechl, MD, Department of Neurology,Medical UniversityInnsbruck,Innsbruck, Austria; Wolf-g a n g Ko e n ig , M D, De p a rtme n t o f In te rn a lMedicineII-Cardiology,University of Ulm Medical Cen-




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ter, Ulm,Germany;RobinP. F. Dullaart,MD, Univer-sity Hospital Groningen, University Medical CenterGroningen, Groningen, the Netherlands; GerdAssmann, MD, Assmann-Stiftung fur Pravention,Ger-many; Ralph B. DAgostino, Sr, PhD, Department ofMathematics and Statistics, Boston University, Bos-ton; Gilles R. Dagenais, MD, Department de medi-cine, Institut universitairede cardiologie et pneumolo-gie de Quebec, Quebec, Canada; Jackie A. Cooper,

MSc, Centre for Cardiovascular Genetics, UniversityCollege London, England; Daan Kromhout, PhD, Di-vision of Human Nutrition, Wageningen University,Wageningen, the Netherlands; Altan Onat, MD, In-stitute for Experimental Medical Research,Istanbul Uni-versity, Istanbul, Turkey; Robert W. Tipping, MS, MerckResearch Laboratories, Philadelphia, Pennsylvania;Agustn Gomez-de-la-Camara, MD, Unidad de inves-tigation, Hospital 12 de Octubre, Madrid, Spain;Annika Rosengren,MD, Departmentof Medicine,Sahl-grenska Academy, University of Gothenburg,Goteborg, Sweden; Susan E. Sutherland, PhD, Medi-cal Universityof South Carolina; JohnGallacher, PhD,Departmentof Epidemiology, Cardiff University, Car-diff,Wales; F.GerryR. Fowkes, FRCPE, Wolfson Unit,Public Health Sciences, University of Edinburgh, Ed-inburgh; Edoardo Casiglia, MD, Department of Clini-cal and Experimental Medicine, University of Pa-dova, Padova,Italy; Albert Hofman, MD, Departmentof Epidemiology, Erasmus Medical Center, Rotter-dam, the Netherlands; Veikko Salomaa, MD, De-partment of Epidemiology, National Institute forHealth and Welfare, Helsinki, Finland; ElizabethBarrett-Connor, MD, Department of Family and Pre-ventive Medicine, Division of Epidemiology, Univer-sityof California, SanDiego; Robert Clarke, MD, Clini-cal Trials Service Unit, University of Oxford, Oxford;Eric Brunner, PhD, Department of Epidemiology andPublic Health, University College London; J. WouterJukema, MD, Departmentof Cardiology, Leiden Uni-versity Medical Center, Leiden, the Netherlands; LeonA. Simons, MD, Lipid Research Department, Univer-sityof NewSouthWales,Darlinghurst,Australia; Man-jinder Sandhu, PhD, Department of Public Health andPrimary Care; Nicholas J. Wareham, FRCP, MRC Epi-demiology Unit, University of Cambridge; Kay-TeeKhaw,FMedSci, Department of Public Health andPri-mary Care, University of Cambridge; JussiKauhanen,MD, University of Eastern Finland, Kuopio, Finland;

Jukka T. Salonen, MD, Metabolic Analytical ServicesInc and University of Helsinki, Helsinki; William J.Howard, MD, MedStar Health Research Institute,Washington Hospital Center, Washington, DC; BrgeG. Nordestgaard, MD, Department of Clinical Bio-chemistry, University of Copenhagen, Copenhagen,Denmark; Angela M. Wood, PhD, and Simon G.Thompson, FMedSci, Department of PublicHealth andPrimary Care, University of Cambridge; S. MatthijsBoekholdt, MD, Academic Medical Center, Amster-dam,the Netherlands; Naveed Sattar,FRCP, andChrisPackard, PhD, Institute of Cardiovascular and Medi-cal Sciences, University of Glasgow; VilmundurGudnason, MD, Icelandic Heart Association and Uni-versityof Iceland, Reykjavik,Iceland;and John Danesh,FRCP,Departmentof Public Health andPrimaryCare,University of Cambridge.Author Contributions: DrDi Angelantoniohadfullac-cess to all the data in the study and takes responsi-

bility for the integrity of the data and the accuracy ofthedata analysis. Dr Di Angelantonio, Dr Gao, Dr Pen-nells, andDr Kaptogecontributed equallyto thestudy.Study concept and design: Di Angelantonio, Gao,Pennells, Kaptoge, A. Thompson, Sarwar, Gillum,Kiechl, Hofman, Sandhu, Khaw, Kauhanen, Sattar,Packard, Danesh.Acquisition of data: Di Angelantonio, Kaptoge,A. Thompson, Sarwar, Wormser, Ballantyne, Psaty,Ridker, Nagel, Gillum, Ford, Ducimetiere, Kiechl,Koenig, Assmann, DAgostino, Dagenais, Cooper,Kromhout, Tipping, Gomez-de-la-Camara, Rosengren,

Sutherland, Gallacher, Fowkes, Casiglia, Salomaa,Barrett-Connor, Clarke, Brunner, Jukema, Simons,Sandhu, Wareham, Khaw, Kauhanen, Salonen,Howard, Nordestgaard, Wood, Boekholdt, Sattar,Packard, Gudnason, Danesh.Analysis and interpretation of data:Di Angelantonio,Gao, Pennells, Kaptoge, Caslake, A. Thompson,Butterworth, Sarwar, Wormser, Saleheen,Sundstrom,Ridker, Dullaart, Onat,Sutherland, Salomaa, Simons,

Nordestgaard, Wood,S. G. Thompson,Sattar, Packard,Danesh.Drafting of themanuscript: Di Angelantonio, Pennells,Kaptoge, A. Thompson, Butterworth, Sarwar,Kromhout, Casiglia, Salomaa, Wood,Packard, Danesh.

Critical revision of the manuscript for important in-tellectual content: Di Angelantonio, Gao, Pennells,Kaptoge, Caslake, Thompson,Butterworth, Saleheen,Ballantyne, Psaty, Ri dker, Sundstrom, Nagel, Gillum,Ford, Ducimetiere, Kiechl, Koenig, Dullaart,Assmann,DAgostino, Dagenais, Cooper, Kromhout, Onat,Tipping, Gomez-de-la-Camara, Rosengren,Sutherland,Gallacher,Fowkes, Casiglia, Salomaa,Barrett-Connor,Clarke, Kauhanen, Salonen, Howard, Nordestgaard,S. G. Thompson, Boekholdt, Sattar, Gudnason,Danesh.Statisticalanalysis:Gao,Pennells, Kaptoge,Wormser,Saleheen, Gillum, DAgostino, Sutherland, Brunner,Jukema, Simons, Sandhu, Wareham, Khaw, Wood,S. G. Thompson.

Obtained funding: Caslake, A. Thompson, Psaty,Koenig, Dullaart, Kromhout, Rosengren, Fowkes,Casiglia, Hofman, Salomaa, Barrett-Connor,Brunner,Jukema, Wareham, Khaw, Kauhanen, Salonen,Nordestgaard, S. G. Thompson, Packard, Gudnason,Danesh.Administrative, technical, or material support:Di Angelantonio,Caslake, Ridker,Ford, Kiechl,Koenig,Dullaart, Assmann, Dagenais, Kromhout, Onat,Tipping, Sutherland, Salomaa, Barrett-Connor,Jukema,Simons, Khaw, Packard, Danesh.Studysupervision:Di Angelantonio, Pennells, Kaptoge,Caslake, Wormser, Gomez-de-la-Camara, Gallacher,Fowkes,Casiglia, Hofman,Salomaa, Brunner, Jukema,Simons, Kauhanen, Wood, S. G. Thompson, Sattar,Danesh.Conflict of InterestDisclosures: All authorshave com-pleted and submitted the ICMJE Form for Disclosureof Potential Conflicts of Interest. Dr Di Angelantonio

reported that he has received research funding fromtheBritish HeartFoundationand EuropeanUnion; com-pensationfor consultancy and lecturersfrom Lead-upMedicalNetwork,Merck Sharp andDohme, andJohnWiley & Sons; support for travel or accommodationsfrom Pfizer; and royalties from Elsevier (France). DrGao reported that she has received research fundingfromthe BritishHeart Foundationand theMedical Re-search Council. Dr Pennells reported that she has re-ceived research funding from the Medical ResearchCouncil. Dr Kaptoge reportedthat hehas receivedre-search funding from British Heart Foundation, Medi-cal Research Council, UK National Institute of HealthResearch, and Cambridge Biomedical Research Cen-tre. Dr Caslake reported that she has received re-search funding from Roche and Merck Sharp andDohme and support for travel and accommodationsfrom Randox Laboratories and Denka Seiken. Dr A.Thompsonreportedthat he hasreceivedresearchfund-

ingfrom theBritishHeart Foundation andMedicalRe-searchCouncil; hasbeenan employee ofRocheProd-ucts Ltd since September 2011; and has receivedcompensation from GlaxoSmithKline for giving lec-tures. Dr Sarwar reported that he has been an em-ployee of Pfizer Inc since April 2011. Dr Wormser re-ported thathe has receivedresearchfundingfrom theBritish HeartFoundationand Medical ResearchCoun-cil. Dr Saleheen reported that he has received re-search funding from the National Institute of Healthand Wellcome Trust. Dr Ballantyne reported that shehas received research funding from Abbott, Amarin,

AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline,Gentech, Kowa, Merck, Novartis, Roche, Sanofi-Synthelabo, Takeda, National Institutes of Health,American Diabetes Association, and American HeartAssociationand compensationfor consultancyand forlectures from Abbott, Adnexus, Amarin, Amylin, As-traZeneca, Bristol-Myers Squibb, Esperion, Gene-tech, GlaxoSmithKline, Idera Pharma, Kowa, Merck,Novartis,Omthera, Pfizer, Resverlogix, Roche, Sanofi-

Synthelabo, and Takeda. Dr Psaty reported that heserves on a data safety and monitoring board for aclinical trial of a device funded by the manufacturerZoll LieCor andon theSteeringCommittee forthe YaleOpen-Data Project funded by Medtronic. Dr Ridkerreported that he is listed as a coinventor on patentsheld by the Brigham and Womens Hospital that re-late to the use of inflammatory biomarkers in cardio-vascular disease and diabetes that have been li-censedto AstraZenecaand Seimens; receivedresearchfunding from AstraZeneca and Novartis; and com-pensation for consultancy from Merck, Vascular Bio-genics, ISISInc, and Genzyme.Dr Kiechl reportedthathe has received research funding from the Fonds zurForderung der wissenschaftlichenForschung. Dr Koenigreported that he has received research funding fromthe Else Kroner Fresenius Foundation, Vifor Com-pany, European Union, Federal Ministry of Educa-tion andResearchand Boehringer Ingelheim; andcom-

pensation for consultancy and lecturers from Roche,Cerenis, Anthera, BioInvet, Novartis, AstraZeneca,Merck Sharp and Dohme, Roche, and Boehringer In-gelheim. Dr Assmann reported that he has receivedcompensationfor consultancy andlecturersfrom MerckSharp and Dohme, and Residual Risk Reduction Ini-tiative and has served on board for the Residual RiskReductionInitiative. Dr Dagenaisreportedthat he hasreceived research funding from the National Heart,Lung, and Blood Institute, National Institute of Dia-betes and Digestive and Kidney Diseases, CanadianInstitutes Health Research, and Heart and Stroke ofCanada and compensation for expert testimony andlecturers from Abbott and Boehringer Ingelheim. DrKromhout reportedthat he hasreceivedresearchfund-ing from the NetherlandsPreventionFoundation. MrTipping reported that he is an employee of Merck.DrRosengren reported that she has received researchfunding fromthe SwedishResearchCouncil andSwed-

ish Heart andLung Foundation.Dr Barrett-Connor re-ported that she has received research funding fromthe National Institutes of Health. Dr Clarke reportedthat he has received research funding from the Brit-ish Heart Foundation. Dr Brunner reportedthat he hasreceived research funding from the Medical Re-search Council and British HeartFoundation.Dr Jukemareported that he has received research funding fromthe European Union. Dr Sandhu reported that he hasreceivedresearch fundingfrom theBritishHeart Foun-dation. Dr Wareham reportedthat hehas received re-search funding from the Medical Research Council.Dr Khawreported thatshe hasreceived researchfund-ing from the UK Medical Research Council and Can-cer Research. Dr Howard reported that he has re-ceived compensation forconsultancy andlecturersfromMerck,Abbott,and GlaxoSmithKline. Dr S. G. Thomp-sonreportedthat he hasreceivedresearchfundingfromthe British Heart Foundation. Dr Sattar reported that

he has received research funding from Pfizer and hasserved on the advisory board for Niaspan for MerckSharp and Dohme. Dr Packard reported that he hasreceived compensation for consultancy and lecturersfrom AstraZeneca, Roche, and Merck Sharp andDohme. Dr Danesh reported that he has received re-search funding from the British Heart Foundation,BUPA Foundation, Cambridge Biomedical ResearchCentre, Denka, diaDexus, European Union, Euro-pean Research Council, Evelyn Trust, Fogarty Inter-national Centre, GlaxoSmithKline, Medical ResearchCouncil, Merck Sharp and Dohme, National Heart,




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Lung, and Blood Institute, National Institute of Neu-rological Disorders and Stroke, National Institute forHealth Research, Novartis, Pfizer, Roche, WellcomeTrust, and UK Biobank and has served on advisoryboards for Merck, Pfizer, andNovartis. All other mem-bers of the writing committee declare that they haveno conflicts of interest.Investigators of Participating Studies: AFTCAPS: R.W. Tipping; ARIC: C. M. Ballantyne, R. Hoogeveen,

S. S. Virani, C. Ndumele, and V. Nambi; BRUN: J.Willeit, P. Willeit, A. Mayr, P. Santer, and S. Kiechl;CaPS: J. Gallacher, J. W. G. Yarnell, and Y. Ben-Shlomo; CASTEL: E. Casiglia andV. Tikhonoff;CHARL:S. E. Sutherland, P.J. Nietert, J. E. Keil, and D. L. Bach-man; CHS: B.M. Psaty,M. Cushman,R. P.Tracy, andN. Jenny, see http://www.chs-nhlbi.orgfor acknowl-edgments; COPEN: B. G. Nordestgaard, A. Tybjrg-Hansen, R. Frikke-Schmidt, M. Benn, and P. R.Kamstrup; DRECE: A. Gomez de la Ca mara, J. A.Gutierrez-Fuentes, J. A. Go mez Gerique, and M. A.Rubio Herrera; DUBBO: L. A. Simons, Y. Friedlander,and J. McCallum; EAS: F.G.R. Fowkes, J. F. Price, A.J. Lee, and J. Bolton; EPICNOR: M. Sandhu, N. J.Wareham, and K-T Khaw; FINRISK92: K. Harald, P.

R. Jousilahti,E. Vartiainen, andV. Salomaa; FRAMOFF:R. B.DAgostino, Sr, P.A. Wolf, R. S.Vasan,and E. J.Benjamin; GRIPS: P. Cremer and D. Nagel; KIHD: J.Kauhanen, J. T. Salonen, K. Nyyssonen, and T-PTuomainen;MOGERAUG1: W. Koenig, C.Meisinger,A. Doring, andW. Mraz;MOSWEGOT: A. Rosengren,L. Wilhelmsen,and G. Lappas; NHANES III:R.F.GillumandJ. Kwagyan; NPHS II: J.A. Cooper, and K.A. Bauer;PREVEND: R. P. F. Dullaart, S. J. L. Bakker, R. T.

Gansevoort, P. vander Harst, andH. L. Hillege; PRIME:P. Ducimetiere, P. Amouyel,D. Arveiler,A. Evans, andJ. Ferrires; PROCAM: H. Schulte and G. Assmann;PROSPER: J . W. Ju k e ma , D. J . S to tt, R. G. J .Westendorp, andB. M. Buckley; QUEBEC: B. Cantin,B. Lamarche, J-P Despres, and G. R. Dagenais;RANCHO: E. Barrett-Connor, D. L. Wingard, and L.B. Daniels; REYK: V. Gudnason, T. Aspelund, G.Sigurdsson, B. Thorsson, and G. Eiriksdottir; ROTT: J.C. M. Witteman, I. Kardys, A. Dehghan, M. A. Ikram,and A. Hofman; SHS: W. J. Howard, B. V. Howard,Y.Zhang,L. Best, and J.Umans; TARFS: A.Onat andG. Can; ULSAM: J. Sundstrom, L. Lind, V. Giedraitis,and E. Ingelsson; WHITE1: M. Marmot, R. Clarke, R.Collins, and A. Fletcher; WHITE2: E. Brunner, M.

Shipley,and M. Kivimaki;WHS: P.M Ridker, J. Buring,N. Rifai, and N. Cook; WOSCOPS: I. Ford, and M.Robertson; and ZUTE: E.J. M.Feskens,J. M.Geleijnse,and D, Kromhout.Data Management Team: M. Walker, S. Watson.CoordinatingCentre: M. Alexander,A. S. Butterworth,E. Di Angelantonio, O. H. Franco, P. Gao, R. Gobin,J. M. Gregson, P. Haycock, S. Kaptoge, L. Pennells,D. Saleheen, J. Sanderson, N. Sarwar, A. Thompson,

S. G. Thompson,M. Walker, S. Watson, A. M. Wood,D. Wormser, X. Zhao, and J. Danesh (principal inves-tigator).Funding/Support: The study was funded by grantsfrom the British Heart Foundation; UK Medical Re-search Council; and UK National Institute of HealthResearch, Cambridge Biomedical Research Centre.Role of theSponsor: None of these organizations wereinvolved in the design and conduct of the study; col-lection, management, analysis, and interpretation ofthe data; and preparation, review, or approval of themanuscript.Online-Only Material: The 7 eTables, 16 eFigures, 2eAppendixes, and Author Audio Interview are avail-able at http://www.jama.com.

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