Supplementary Online Content The Emerging Risk Factors Collaboration. Lipoprotein(a) Concentration and the Risk of Coronary Heart Disease, Stroke, and Nonvascular Mortality. JAMA. 2009;302(4):412-423. eAppendix 1. Description of Methods Used in the Emerging Risk Factors Collaboration eAppendix 2. Rationale for Using Non-HDL Cholesterol Rather Than Calculated LDL Cholesterol in the Current Analyses eAppendix 3. List of Acronyms for Studies Included in the Current Report eTable 1. Baseline Characteristics, Blood Handling, Storage and Assay Characteristics of Studies Contributing Data to the Current Analysis eTable 2. Characterisation of Baseline and Incident Cardiovascular Disease Outcomes in Studies Contributing Data to the Current Analysis eTable 3. Results From Quadratic Models for the Association Between Usual Lp(a) Levels and the Risk of CHD eTable 4. Risk Ratios for CHD for 3.5-fold (ie, 1-SD) Higher Usual Levels of Lp(a), Further Adjusted for Usual Values of Various Potential Confounding Factors eTable 5. Parallel Analyses of the Association of Lp(a) With Disease Risk: (a) for Comparison of Individuals in Extreme Thirds of Baseline Level Distributions, and (b) per 3.5-fold (ie, 1-SD) Higher Baseline Level eFigure 1. Study Specific Adjusted Risk Ratios for CHD, Corresponding to the Adjusted Risk Ratio in Table 2 eFigure 2. Mean Lp(a) Levels by Cohort and (a) Assay Method Principle, or (b) Whether the Assay Method Used Was Sensitive to Apolipoprotein(a) Isoform Variation eFigure 3. Within-Person Variability in Lipoprotein(a) Levels (Regression Dilution Ratios† Stratified by Study and Repeat) eFigure 4. Age- and Sex-Adjusted Risk Ratios for Various Vascular and Non-Vascular Endpoints per 3.5 Fold Higher Usual Lp(a) Levels eFigure 5. Risk Ratios for Coronary Heart Disease by Fifths of Usual Lp(a) Levels, After Excluding the First 5 Years of Follow-up eFigure 6. Risk Ratios for Coronary Heart Disease per 3.5-fold Higher Usual Lp(a) Levels, by Strata of Various Study Level Characteristics eFigure 7. Direct Comparison of Adjusted Risk Ratios for CHD Between Lp(a) and Non-HDL-C, for a 1-SD Higher† Baseline or Usual Levels eFigure 8. Association of Lp(a) With Fatal Vascular and Non-Vascular Outcomes in Analyses That Did Not Censor for Nonfatal Events† – RRs are per 3.5-fold Higher Usual Lp(a) Levels Adjusted for Cardiovascular Risk Factors This supplementary material has been provided by the authors to give readers additional information about their work. © 2007 American Medical Association. All rights reserved. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eAppendix 1. Description of Methods Used in the Emerging Risk Factors Collaboration Study selection criteria Eligible prospective studies (reported as observational cohort studies, nested case-control or case-cohort subsets) had: (1) data on relevant exposure variables available from baseline measurements; (2) at least 1 year of follow-up; (3) participants not selected on the basis of having previous cardiovascular disease; and (4) information on causespecific mortality and/or major cardiovascular morbidity collected during follow-up.1 Studies were prioritised for inclusion if they were known to have recorded >20,000 person years at risk and were conducted in Western populations. Most studies were identified in previously published meta-analyses,2-11 with additional studies identified through updated computerassisted literature searches of databases, scanning of reference lists, hand-searching of relevant journals and correspondence with authors of relevant reports. As only a handful of relevant studies could not provide data,12-15 >90% of relevant incident CHD cases in known Western studies are estimated to have been included. Data collection Anonymised data were sought from collaborators on many characteristics recorded at the baseline and subsequent surveys during follow-up.1 Information on categorical variables, such as alcohol consumption status, physical activity and smoking status, was systematically re-coded to maximise comparability among studies. For each individual, data were sought on the following outcomes and on their dates of occurrence: non-fatal CHD; non-fatal stroke; cause-specific mortality (or at least fatal CHD and fatal stroke) and other cardiovascular outcomes. Precise details of the diagnostic criteria used for the definition of incident cases were sought from each study (as were data on the completeness of follow-up). Principal analyses were based on events classified according to the International Classification of Diseases (ICD) or, where this was not available, on study-specific classification systems. Attribution of death referred to the primary cause provided (or, in its absence, the underlying cause provided). The influence on the findings of differential cause of death coding (across cohorts, calendar year and age at death) was small. Data obtained from each participating study were checked for internal consistency and any queries then referred back, in confidence, to the study collaborator(s), before harmonisation to a standard format. The content of the data was unchanged by this process, and computer-generated detailed summary tabulations based on the converted data were reviewed and confirmed by collaborators. Data have been stored securely and anonymously at the coordinating centre. Statistical methods 95% confidence intervals (CIs) and two-sided p-values were used. Studies contributing 10 or fewer outcomes to any particular analysis were excluded. Regression analyses−The main analyses were based on Cox proportional hazards (PH) models16 estimated for each study separately, with logistic regression used for “nested” casecontrol studies (see below). The PH models were stratified by sex and, if applicable, randomised group. So for each study s=1…S, with strata k=1…Ks (for most studies Ks=2 just for the two sexes) and individuals i=1…ns with exposure of interest Esi and other covariates Xsi, the hazard at time t after baseline were modelled as: log(hski (t | Esi , X si )) = log h0sk (t ) + β s Esi + γ s X si . (1) The evolution of risk over time was thus modelled independently for each stratum in each study, as represented by the non-parametric baseline hazards h0sk(t). The βs were the parameters of interest, being the log hazard ratios per unit increase in the exposure in study s, adjusted for the confounding effects of the covariates Xsi. These estimated log hazard ratios were combined over studies using random-effects meta-analysis. Parallel analyses involved fixed-effect models,17-19 writing the variance of the estimated βs as vs, the randomeffects meta-analysis model is: βˆs = β s + ε s ; where ε s ~ N(0,υs ) β s = β + ηs ; where η s ~ N(0,τ 2 ). Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 (2) Here β is the average log hazard ratio, whose estimate combines within-study information on the relationship between exposure and risk, while allowing for heterogeneity between studies as represented by the variance τ2, although potential sources of heterogeneity were specifically investigated (see below). A standard moment estimator of τ2 was used.20 Nested casecontrol studies were analysed with similar methods to those described above, but involved logistic regression.21 For individually-matched studies, conditional logistic regression was used, whereas unconditional logistic regression was used in frequency-matched studies, including matching factors as covariates. Such analyses either provided estimates of hazard ratios (if matched controls were selected to be disease-free at the time the case had an event), or odds ratios (if the selected controls were disease-free at the end of the study). Provided the disease is relatively rare (say fewer than 10% of the study’s participants), odds ratios approximate hazard ratios and it is reasonable to combine these estimates. For nested case-cohort studies, weighted analyses allowed for the fact that, by design, cases who were not in the randomly selected sub-cohort were also included in the analyses.22 A modified PH regression model then provided estimates of log hazard ratios with robust standard errors.23 To investigate shape of relationships, exposure variables were divided into quantile groups based on overall distribution across studies. The hazard or odds ratios in each quantile group, compared to the lowest group, were estimated using Cox PH regression or logistic regression in each study separately. These risk ratios were pooled across studies using multivariate random-effects meta-analysis,24,25 and floating absolute risks were estimated,26,27 which were then plotted against the mean exposure level in each quantile group. Estimation of floating absolute risks does not alter their values, but ascribes an appropriate variance to the log of the risk ratio for each group, including even the reference group with a risk ratio of 1 (rather than having one group arbitrarily chosen to have a relative risk of 1 with no associated variation). This allows the values to be compared informatively (ie, with known variance) between any pair of exposure categories, rather than only between each exposure category and the arbitrarily chosen reference group. Subsidiary analyses involved use of fractional polynomials28 to investigate curvature. To investigate confounding, adjustment was made progressively for increasing numbers of potential confounding factors (including correction for measurement error and within-person variation in these covariates: see below). Use of simple linear terms for age at baseline was generally sufficient, but empirical comparisons were made of alternatives as sensitivity analyses (eg, adjustment or stratification by age categories at baseline, and inclusion of polynomial terms and interactions with other covariates, especially sex). Similar considerations applied to adjustment for other covariates. The change in the Wald χ2 statistic provides an indication of the reduction in the evidence of association and/or increase in uncertainty following adjustment.18,29,30 Measurement error and within-person variation−Measurement error and within-person variability in an exposure variable can cause any association of disease with the current usual level of the exposure to be underestimated.31-33 The degree of underestimation, or regression dilution bias,31,32 was quantified by regressing serial measurements of the exposure on baseline exposure and confounder values34 to provide a regression dilution ratio (RDR). Within-person variability and measurement error in confounders can also affect the observed exposuredisease association, and this bias may either exaggerate or obscure associations. A regression calibration technique adapted to the context of multiple studies was used to correct for multivariate measurement error.35 For each variable measured with error, a regression calibration model incorporating between-study and between-individual heterogeneity was constructed amongst studies s=1…S with individuals i=1…,ns providing repeat measurements r=1…rsi as: Esir = α + (β + us )Esi + λ X si + wsi + ε sir ; where us ~ N(0, σ u2 ), wsi ~ N(0, σ w2 ) and ε sir ~ N(0, σ e2 ), Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 (3) and where Esir and Esi denote repeat and baseline measurements of the error-prone variable respectively, Xsi are other baseline covariates, σ u2 denotes between-study heterogeneity on the estimated RDR value β, and σ w2 and σ e2 denote individual-specific and residual variation respectively. Empirical Bayes conditional expectations (ie, expected usual levels) for all individuals were estimated from model (3) and used in the main analyses. The regression calibration model (3) was extended to allow for measurement error time trends, resulting in timedependent expected usual levels which were entered into a time-dependent Cox PH model. These methods assume disease risk depends on the current usual exposure (and confounder) levels.36 Joint effects−Potential effect modifiers measured at the individual level, such as age or other risk markers, were assessed using within-study information.37,38 A 2-stage procedure was adopted. Study-specific estimates of interaction terms δs for the potential effect modifier Xsi were estimated from model (4) and subsequently combined using random-effects metaanalysis, as in (2): log(hski (t | E si , X si )) = log h0 sk (t ) + β s Esi + γ s X si + δ s E si X si . (4) The overall interaction was then based only on within-study information. Model (4) was extended to include adjustments for other confounders, and indeed their interactions with the exposure of interest; this enabled investigation of whether a particular interaction was confounded by other main effects or interactions. Potential effect modifiers measured at the study level, such as population type or the laboratory methods, were assessed entirely on between-study comparisons using random effects meta-regression.39 Using the estimates of βs from (1), model (2) has been extended to include a study level covariate Xs by writing: βˆs = β s + ε s ; where ε s ~ N(0,υs ) β s = β + δ B X s + ηs ; where η s ~ N(0,τ 2 ) (5) where δB is the between-study interaction term, with statistical significance assessed allowing for the residual between-study heterogeneity τ 2. Proportionality of hazards−This was evaluated in each study separately by including an interaction between the exposure and time, or by the commonly used diagnostic based on Schoenfeld residuals. 40 which gives a χ12 statistic for each study. These independent χ12 statistics were summed across the S studies, yielding a χ s2 statistic testing the hypothesis that PH holds in each study. However, because this approach is not a powerful test against the plausible alternative hypothesis that hazard ratios tend to decline with time in all studies, the interaction terms between the exposure and time were pooled over studies using random-effects meta-analysis. This provided an “average” interaction term and corresponding test statistic. Heterogeneity and reporting biases−In addition to the standard χ 2 test for heterogeneity,41 the impact of heterogeneity was expressed in terms of I2,42 the percentage of variance in the estimated log hazard ratios from each study that is attributable to between-study variation as opposed to sampling variation. The potential effect of study size on risk estimates was investigated by: (i) χ 2 tests comparing studies with ≥500 cases vs those with <500 cases; (ii) meta-regression; and (iii) visual tests (forest and/or funnel plots).43 Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 Assessment of cross-sectional correlates−Unadjusted Pearson correlation coefficients were pooled across cohorts by random-effects meta-analysis of Fisher’s Z transformation of cohortand sex-specific correlation coefficients.44 Associations of exposure variables with various characteristics were then assessed using a linear mixed model that included a study-level random gradient for the relationship between the correlate and exposure of interest, but a fixed constant for each study. The main effect of cohort was modelled as a separate fixed effect. Continuous variables were divided into tenths based on the overall distribution in males and females combined, allowing assessment of shape of associations without imposing a priori any particular shape. Natural logarithms were used to achieve approximately symmetrical distributions for positively skewed variables. Categorical variables were modelled similarly to the risk-factor tenths, except dummy variables were also used in the random effects equation since there was no natural monotonic ordering of the categories. From each fitted mixed model, overall adjusted means and 95% CI by sex within the tenths of continuous markers, or category for categorical variables, were obtained. These adjusted mean values were used to assess the shape of the association by plotting the mean (95% CI) of the exposure variable against the mean marker value within each tenth. An inverse-variance weighted polynomial was superimposed across the adjusted means to assess whether the overall association was consistent with a linear or a quadratic shape. Censoring of outcomes−For participants who had multiple events (eg, two CHD events at separate time points, or a CHD event followed by another type of event such as a stroke or death from cancer), analyses in the ERFC focused on first events.1 Thus, in an analysis of CHD events, participants were followed until their first CHD event, or censored at the time of other non-fatal cardiovascular events, such as stroke, or death from other causes. Individuals were not censored at the time of cardiovascular investigations or interventions, such as angiography or coronary bypass operations, or at the diagnosis of angina. The rationale for this was that major cardiovascular events, such as first non-fatal MI or stroke, may disrupt the association between baseline risk factors and subsequent disease risk. The incidence of angina and coronary interventions was, however, not recorded reliably enough in sufficient studies to consider censoring for them. The potential biases that arise through these decisions on censoring were addressed through sensitivity analyses and by implementing alternative censoring criteria. In general, such changes had only minimal effects. eAppendix 1 References 1. The Emerging Risk Factors Collaboration. The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. Eur J Epidemiol 2007; 22:839-869. 2. Hokanson JE, Austin MA. Plasma triglyceride level is a risk factor for cardiovascular disease independent of high-density lipoprotein cholesterol level: a meta-analysis of populationbased prospective studies. J Cardiovasc Risk 1996; 3(2):213-219. 3. Sarwar N, Danesh J, Eiriksdottir G et al. Triglycerides and the Risk of Coronary Heart Disease. 10 158 Incident Cases Among 262 525 Participants in 29 Western Prospective Studies. Circulation 2006; 115(4):450-458. 4. Thompson A, Danesh J. Associations between apolipoprotein B, apolipoprotein AI, the apolipoprotein B/AI ratio and coronary heart disease: a literature-based meta-analysis of prospective studies. J Intern Med 2006; 259(5):481-492. 5. Craig WY, Neveux LM, Palomaki GE, Cleveland MM, Haddow JE. Lipoprotein(a) as a risk factor for ischemic heart disease: metaanalysis of prospective studies. Clin Chem 1998; 44(11):2301-2306. 6. Danesh J, Collins R, Peto R. Lipoprotein(a) and coronary heart disease. Meta-analysis of prospective studies. Circulation 2000; 102(10):1082-1085. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 7. Danesh J, Whincup P, Walker M et al. Low grade inflammation and coronary heart disease: prospective study and updated meta-analyses. BMJ 2000; 321(7255):199-204. 8. Danesh J, Wheeler JG, Hirschfield GM et al. C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. N Engl J Med 2004; 350(14):1387-1397. 9. Danesh J, Collins R, Appleby P, Peto R. Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA 1998; 279(18):1477-1482. 10. Wheeler JG, Mussolino ME, Gillum RF, Danesh J. Associations between differential leucocyte count and incident coronary heart disease: 1764 incident cases from seven prospective studies of 30,374 individuals. Eur Heart J 2004; 25(15):1287-1292. 11. Gordon DJ, Probstfield JL, Garrison RJ et al. High-density lipoprotein cholesterol and cardiovascular disease. Four prospective American studies. Circulation 1989; 79(1):8-15. 12. Nguyen TT, Ellefson RD, Hodge DO, Bailey KR, Kottke TE, bu-Lebdeh HS. Predictive value of electrophoretically detected lipoprotein(a) for coronary heart disease and cerebrovascular disease in a community-based cohort of 9936 men and women. Circulation 1997; 96(5):1390-1397. 13. Farquhar JW, Fortmann SP, Maccoby N et al. The Stanford Five-City Project: design and methods. Am J Epidemiol 1985; 122(2):323-334. 14. Schaefer EJ, Lamon-Fava S, Jenner JL et al. Lipoprotein(a) levels and risk of coronary heart disease in men. The lipid Research Clinics Coronary Primary Prevention Trial. JAMA 1994; 271(13):999-1003. 15. Frick MH, Elo O, Haapa K et al. Helsinki Heart Study: primary-prevention trial with gemfibrozil in middle-aged men with dyslipidemia. Safety of treatment, changes in risk factors, and incidence of coronary heart disease. N Engl J Med 1987; 317(20):1237-1245. 16. Cox DR. Regression Models and Life-Tables. JRSSB 1972; 34(2):187-220. 17. Prospective Studies Collaboration. Collaborative overview ('meta-analysis') of prospective observational studies of the associations of usual blood pressure and usual cholesterol levels with common causes of death: protocol for the second cycle of the Prospective Studies Collaboration. J Cardiovasc Risk 1999; 6(5):315-320. 18. Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360(9349):1903-1913. 19. Prospective Studies Collaboration. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths. Lancet 2007; 370(9602):1829-1839. 20. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7(3):177-188. 21. Breslow NE, Day NE. Statistical methods in cancer research. Volume I - The analysis of case-control studies. IARC Sci Publ 1980;(32):5-338. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 22. Prentice R. A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika 1986; 73(1):1-11. 23. Barlow WE, Ichikawa L, Rosner D, Izumi S. Analysis of case-cohort designs. J Clin Epidemiol 1999; 52(12):1165-1172. 24. White IR. Multivariate random-effects meta-analysis. Stata Journal 2009; 9(1):40-56 25. Berkey CS, Hoaglin DC, Antczak-Bouckoms A, Mosteller F, Colditz GA. Meta-analysis of multiple outcomes by regression with random effects. Stat Med 1998; 17(22):2537-2550. 26. Easton DF, Peto J, Babiker AG. Floating absolute risk: an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group. Stat Med 1991; 10(7):1025-1035. 27. Plummer M. Improved estimates of floating absolute risk. Stat Med 2004; 23(1):93-104. 28. Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol 1999; 28(5):964-974. 29. The Fibrinogen Studies Collaboration. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. JAMA 2005; 294(14):1799-1809. 30. The Fibrinogen Studies Collaboration. Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies. Statistics in Medicine 2009;28:1067-92 31. Clarke R, Shipley M, Lewington S et al. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am J Epidemiol 1999; 150(4):341-353. 32. MacMahon S, Peto R, Cutler J et al. Blood pressure, stroke, and coronary heart disease. Part 1, Prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet 1990; 335(8692):765-774. 33. The Fibrinogen Studies Collaboration. Regression dilution methods for meta-analysis: assessing long-term variability in plasma fibrinogen among 27 247 adults in 15 prospective studies. Int J Epidemiol 2006; 35(6):1570-1578. 34. Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med 1989; 8(9):1051-1069. 35. Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am J Epidemiol 1990; 132(4):734-745. 36. Frost C, White IR. The effect of measurement error in risk factors that change over time in cohort studies: do simple methods overcorrect for 'regression dilution'? Int J Epidemiol 2005; 34(6):1359-1368. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eAppendix 2. Rationale for Using Non-HDL Cholesterol Rather Than Calculated LDL Cholesterol in the Current Analyses Because direct measurement of low density lipoprotein cholesterol (LDL-C) has been relatively uncommon in long-term prospective studies, most studies have tended to use the Friedewald equation1 to estimate LDL-C values from the measured concentrations of total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), and triglycerides (divided by a constant): Calculated LDL-C = TC – HDL-C – (triglycerides/2.2) (1) where triglycerides/2.2 approximates the concentration of cholesterol carried in very low density lipoprotein (VLDL-C) when the units of measurement are mmol/l (if mg/dl are used, the constant is 5.0). Non-HDL-C (calculated as the difference between TC and HDL-C) can be substituted into equation 1: Calculated LDL-C = non-HDL-C – (triglycerides/2.2) (2) As a consequence, any regression model (eg, Cox proportional hazards model for survival data, or logistic regression model for case-control data) that concomitantly includes calculated LDL-C, HDL-C and triglycerides, is simply a mathematical rearrangement of a model that includes non-HDL-C, HDL-C and triglycerides, as shown below. Consider a survival model for the log hazard ratio (HR) including non-HDL-C, HDL-C and triglycerides (TG), where the mutually adjusted coefficients for each term are given by β1, β2 and β3, respectively: logHR = β1 non-HDL-C + β2 HDL-C + β3 TG (3) Adding and subtracting (β1/2.2) TG and simplifying yields: logHR = β1 non-HDL-C + β2 HDL-C + β3 TG + (β1/2.2 – β1/2.2) TG = β1 (non-HDL-C – TG/2.2) + β2 HDL-C + (β3 + β1/2.2) TG Substituting calculated LDL-C for (non-HDL-C – TG/2.2) as in equation 2 gives: logHR = β1 calculated LDL-C + β2 HDL-C + (β3 + β1/2.2) TG (4) Comparing equations 3 and 4 demonstrates that: • the calculated LDL-C parameter (β1 in equation 4) equals the non-HDL-C parameter (β1 in equation 3) in any model that also includes HDL-C and triglycerides. Indeed, any of the coefficients in equation 4 can be calculated from those in equation 3, should the need arise. • when calculated LDL-C, HDL-C and triglycerides are included in the same model (equation 4), the coefficient for triglycerides is biased by β1/2.2 (or β1/5.0 if mg/dl are used) compared to equation 3. This means that even if triglycerides concentration was not associated with the outcome of interest in equation 3 (ie. β3 = 0), then it would appear to have an association of (β1/2.2) when adjusted for calculated LDL-C and HDL-C in equation 4. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eAppendix 3. List of Acronyms for Studies Included in the Current Report AFTCAPS (Air Force/Texas Coronary Atherosclerosis Prevention Study) ARIC (Atherosclerosis Risk in Communities Study) ATTICA (Attica Study) BRHS (British Regional Heart Study) BRUN (Bruneck Study) BUPA (British Union Provident Association) CHARL (Charleston Heart Study) CHS (Cardiovascular Health Study) COPEN (Copenhagen City Heart Study) DUBBO (Dubbo Study of the Elderly) EAS (Edinburgh Artery Study) FIA (First Myocardial Infarction in Northern Sweden) FINRISK 92 (Finrisk Cohort 1992) FLETCHER (Fletcher Challenge Blood Study) FRAMOFF (Framingham Offspring Cohort) GOH (The Glucose Intolerance, Obesity and Hypertension Study) GOTO33 (Göteborg Study 1933) GRIPS (Göttingen Risk Incidence and Prevalence Study) HPFS (Health Professionals Follow-up Study) KIHD (Kuopio Ischaemic Heart Disease Study) MRFIT (Multiple Risk Factor Intervention Trial 1) NHANES III (National Health and Nutrition Examination Survey III) NHS (Nurses’ Health Study) NPHS II (Northwick Park Heart Study II) PRIME (Prospective Epidemiological Study of Myocardial Infarction) PROCAM (Prospective Cardiovascular Münster Study) QUEBEC (Quebec Cardiovascular Study) REYK (Reykjavik Study) SHS (Strong Heart Study) TARFS (Turkish Adult Risk Factor Study) ULSAM (Uppsala Longitudinal Study of Adult Men) USPHS (U.S. Physicians Health Study) Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 WHITE II (Whitehall II Study) WHS (Women’s Health Study) WOSCOPS (West of Scotland Coronary Prevention Study) ZUTE (Zutphen Elderly Study) Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eAppendix 3. List of Acronyms for Studies Included in the Current Report AFTCAPS (Air Force/Texas Coronary Atherosclerosis Prevention Study) ARIC (Atherosclerosis Risk in Communities Study) ATTICA (Attica Study) BRHS (British Regional Heart Study) BRUN (Bruneck Study) BUPA (British Union Provident Association) CHARL (Charleston Heart Study) CHS (Cardiovascular Health Study) COPEN (Copenhagen City Heart Study) DUBBO (Dubbo Study of the Elderly) EAS (Edinburgh Artery Study) FIA (First Myocardial Infarction in Northern Sweden) FINRISK 92 (Finrisk Cohort 1992) FLETCHER (Fletcher Challenge Blood Study) FRAMOFF (Framingham Offspring Cohort) GOH (The Glucose Intolerance, Obesity and Hypertension Study) GOTO33 (Göteborg Study 1933) GRIPS (Göttingen Risk Incidence and Prevalence Study) HPFS (Health Professionals Follow-up Study) KIHD (Kuopio Ischaemic Heart Disease Study) MRFIT (Multiple Risk Factor Intervention Trial 1) NHANES III (National Health and Nutrition Examination Survey III) NHS (Nurses’ Health Study) NPHS II (Northwick Park Heart Study II) PRIME (Prospective Epidemiological Study of Myocardial Infarction) PROCAM (Prospective Cardiovascular Münster Study) QUEBEC (Quebec Cardiovascular Study) REYK (Reykjavik Study) SHS (Strong Heart Study) TARFS (Turkish Adult Risk Factor Study) ULSAM (Uppsala Longitudinal Study of Adult Men) USPHS (U.S. Physicians Health Study) Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 WHITE II (Whitehall II Study) WHS (Women’s Health Study) WOSCOPS (West of Scotland Coronary Prevention Study) ZUTE (Zutphen Elderly Study) Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eTable 1. Baseline Characteristics, Blood Handling, Storage and Assay Characteristics of Studies Contributing Data to the Current Analysis Study Country Isoform sensitivity Population source / Baseline sampling year Fasting status†/ duration Blood sample Storage duration Storage temperature Assay method (source) Assay Standard Antibody used Fasted / > 8 hrs Fasted / > 8 hrs Fated / > 8 hrs Fasted / > 8 hrs NS serum NS NS NS NS NS NS ELISA (In-house) ITA (NS) In-house Anti-apo(a) PAb No NS NS NS ELISA (Immuno) ELISA (Terumo) ELISA (Genetech) ITA (DAKO) ELISA (Biopool) ELISA (Biopool) IRMA (Pharmacia) ITA (DiaSorin SPQIII) ITA (K-Assay) ELISA (Immuno) IRMA (Pharmacia) ELISA (Strategic Diagnostics) ELISA (Biopool) ELISA (In-house) EID (Behringwerke) ELISA (Biopool) ELISA (Terumo) INA (Behring) IRMA (Pharmacia) In-house No Manufacturer C: Anti-apo(a) PAb D: Anti-apo(a) Ab D: anti-Lp(a) MAb Yes In-house D: Anti apo(a) MAb Yes Manufacturer Rabbit anti-Lp(a) Pab Yes Manufacturer C: Anti-Lp(a) PAb D: Anti-Lp(a) MAb C: Anti-Lp(a) PAb D: Anti-Lp(a) MAb Two site anti-apo(a) MAb NS No Cohort studies AFTCAPS USA ARIC 2001 USA ATTICA Greece Popln. screening / 1990-93 complete Household 1987-89 listings/Random Popln. register / Random 2000-1 BRUN 1999 Italy Popln. register / Random 1990 CHARL USA CHS1 2003 USA Household listing / Random Medicare lists / Random COPEN 2008 Denmark 2 DUBBO 2002 Australia 27 EAS 2001 Scotland 1960-61 1989-93 Popln. register / Random 1991-94 Electoral roll / Complete GP list / Random 1988-89 1987-88 FINRISK92 2005 FRAMOFF 1996 GOH Finland Israel Offspring & spouse to 1991-95 FHS / Complete Popln. register / Random 1969-73 GRIPS 1997 Germany Occupational / Complete 1982 KIHD Finland Popln. register / Random 1984-89 NHANES3 USA Census list / Cluster USA Popln. register / Random 1992 1988-1994 NPHSII 2001 UK GP list / Complete 1989-94 PRIME 2002 France / Ireland‡ PROCAM Germany 1996 QUEBEC Canada 1998 SHS 2002 USA General Popln. / Quota 1991-94 TARFS Turkey ULSAM Sweden WHITE2 WHS 2006 UK USA WOSCOPS 2000 UK Occupational / Complete 1975-2001 Popln. register / Random 1985-86 Tribal rolls / Complete 1989-92 Household listings / Random Popln. screening / complete Civil servant / Complete Health professionals / Complete Heart screening clinic / Complete 1990 1970-74 1985-88 1993-2004 1989-91 Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 0 Plasma 1 week -1 yr Frozen, -70 C Serum < 1 week Fresh Plasma <1 week Fresh NS 1-5 yrs Frozen, -70 0C Fasted / > 8 hrs Non-fasted Plasma 1 week-1 yr Serum 1 week-2 yrs Frozen, -80 0C Fasted / > 8 hrs Fasted/ > 8 hrs Fasted / 4-8 hrs Fasted / > 8 hrs Fasted / > 8 hrs Fasted / 4-8 hrs Fasted / > 8 hrs Fasted / > 6 hrs Non-fasted Serum < 1 week Fresh Fasted / > 8 hrs Fasted / > 8 hrs Fasted / > 8 hrs Fasted / > 8 hrs Fasted / > 8 hrs Fasted / . 8 hrs Fasted / NS 3/4 Fasted / >8 hrs Fasted / > 8 hrs Serum Serum 5-10 yr 1 week- 1 yr 0 Frozen, -50 C 0 Frozen, -70 C 0 Plasma 1–5 yrs Frozen, -80 C Plasma <1 week Fresh Serum 5-10 yrs Frozen, -90 0C Serum 2-6 yrs Frozen, -20 0C Serum 1 week–1 yr Frozen, -20 0C 0 Serum 1 week-1 yr Frozen, -80 C Plasma <1 week Fresh Serum <1 week Fresh 0 Plasma 5-10 yrs Frozen, -70 C Plasma NS NS Plasma NS Frozen, -75 0C Serum > 10 yrs Frozen, -150 0C Serum Plasma NS >10 yrs Frozen, -80 0C Frozen, -150 0C Plasma 1-5 yrs Frozen, -70 0C ITA (NS) ITA (Denka Seiken) ELISA (Innogenetics) Manufacturer Manufacturer Manufacturer Manufacturer No No Yes Yes Manufacturer Goat anti-Lp(a) antisera C: Anti-apo(a) PAb D: Anti-apo(a) MAb Two site anti-apo(a) MAb C: Anti-Lp(a) MAb D: Anti-Lp(a) PAb C: Anti-Lp(a) MAb D: Anti-Lp(a) PAb C: Anti-Apo(a) Mab D: Anti-ApoB MAb Rabbit anti-Lp(a) antisera C: Anti-Lp(a) MAb D: Anti-Lp(a) PAb C: MAb; D: Pab NS NS No Manufacturer Two site anti-apo(a) MAb NS Anti-Lp(a) PAb No Manufacturer Manufacturer Manufacturer Manufacturer NS Immuno CDC NS Manufacturer NS C: Anti-apo(a) Mab D: Anti-apoB PAb No No Yes No No No No Yes Yes No No ZUTE The Popln. register / Random 1990 Netherlands Non-fasted Serum NS Frozen, -20 0C NS Fasted / NS Serum >10 yrs Frozen, -40 0C ELISA Nested case-control studies (individually matched) BUPA 1994 FIA 1998 UK Sweden Medical center list / 1975-82 Complete Popln. register / Random 1985-99 FLETCHER 2007 HPFS New Zealand Occupational , electoral 1992-94 roll / complete, random USA Occupational / Complete 1994 MRFIT 2001 USA NHS 2005 USA Popln. screening / 1973-76 Complete Occupational / Complete 1990 Fasted / 4 hrs Non-fasted Plasma Plasma 6-19 yrs UK GOTO33 1993 Sweden REYK 2008 Iceland USPHS 1993 USA GP lists / Random 1978-80 Popln. register / 1983-84 Complete Popln. register / 1967-91 Complete Occupational / Complete 1982 Frozen, -80 C 0 >10 yrs Frozen, -70 C 2/3 Fasted / NS Plasma 5 -10 yrs Frozen , -130 0C Fasted / > 8 hrs Fasted / Variable Plasma >10 yrs Frozen, -50 0C Plasma 5-10 yrs Frozen , -130 0C Serum >10 yrs Frozen, -20 0C Nested case-control studies (frequency matched) BRHS 0 Non-fasted Fasted / > 8 hrs Fasted / > 8 hrs Non-fasted 0 Serum 5-10 yrs Frozen, -70 C Serum >10 yrs Frozen, -20 0C Plasma >10 yrs Frozen, -80 0C †Fasting status at blood sampling; ‡Northern Ireland; Popln. indicates Population; NS indicates not stated Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 NS (Biopool) NS ELISA (Hypehn Biomed) ELISA (Hyphen Biomed) ITA (Denka Seiken) ELISA (Strategic Diagnostics) ITA (Denka Seiken) ELISA (Hyphen Biomed) ELISA (Biopool) ELISA (Hyphen Biomed) ELISA (Biopool) In-house Manufacturer Manufacturer NS NS C: Anti-Lp(a) Pab D: Anti-Lp(a) MAb Mono-specific antiapo(a) PAb C: Anti-apo(a) MAb D: Anti-apoB PAb Anti-Lp(a) PAb No No No No Yes Manufacturer C: Anti-Lp(a) Mab D: Anti-Lp(a) PAb Anti-Lp(a) PAb Manufacturer NS No Manufacturer C: Anti-Lp(a) PAb D: Anti-Lp(a) MAb C: Anti-apo(a) MAb D: Anti-apoB PAb C: Anti-Lp(a) Mab D: Anti-Lp(a) Pab No Manufacturer Manufacturer Manufacturer No No No eFigure 6. Risk ratios for coronary heart disease per 3.5 fold higher usual Lp(a) levels, by strata of various study level characteristics. Variable/ subgroup No. of studies No. of individuals RR (95% CI) Heterogeneity p-value No. of cases Ethnicity† White Black Other 29 3 2 95753 4546 4207 7540 261 426 1.14 (1.09, 1.19) 1.05 (0.90, 1.23) 1.36 (0.88, 2.09) 0.52 18 10 2 54301 49981 2363 5138 2819 405 1.16 (1.10, 1.23) 1.08 (1.02, 1.15) 1.14 (1.02, 1.28) 0.39 17 13 45650 60995 4631 3731 1.17 (1.10, 1.24) 1.09 (1.04, 1.15) 0.18 11 19 24517 82128 2332 6030 1.17 (1.07, 1.28) 1.11 (1.07, 1.15) 0.26 4 6 16 13409 32128 48530 533 2009 5182 1.18 (1.07, 1.30) 1.07 (1.02, 1.13) 1.14 (1.07, 1.22) 0.48 4 15 8 13409 71359 13486 533 3710 3103 1.18 (1.07, 1.30) 1.16 (1.08, 1.24) 1.09 (1.02, 1.17) 0.50 20 4 4 58455 38284 8776 6483 861 955 1.13 (1.07, 1.20) 1.18 (1.06, 1.32) 1.11 (1.03, 1.19) 0.75 22 6 2 79749 25766 1130 6561 1738 63 1.14 (1.09, 1.20) 1.11 (1.01, 1.22) 1.10 (0.82, 1.46) 0.83 Geographical region Western Europe North America Other Blood sample Serum Plasma Fasting status Non-fasted Fasted Storage duration† < 1 wk 1wk-1yr > 1yr Storage temperature† Fresh -70 or less -20 to -70 Assay method† ELISA ITA/INA OTHER Isoform sensitivity‡ insensitive sensitive unknown .75 1 1.5 2.5 RR per 3.5 fold higher Lp(a) levels CI indicates confidence interval. Sizes of data markers are proportional to the inverse of the variance of the risk ratios. Risk ratios are adjusted for age, and usual levels of systolic blood pressure, smoking status, history of diabetes, body mass index and total cholesterol, and stratified, where appropriate, by sex and study group. †Although a total of 30 studies have contributed to the analyses, for different characteristics different number of studies had relevant data; for Storage duration 4 studies, for Storage temperature 3 studies, and for Assay method 2 studies did not have relevant data. For Ethnicity, the no. of studies add to 34 because 4 studies contributed to 2 categories. ‡Isoform sensitivity refers to whether the result of an assay is affected by apolipoprotein(a) isoform variation. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eTable 3. Results From Quadratic Models for the Association Between Usual Lp(a) Levels and the Risk of CHD Study No. of observations No. of cases Association with CHD risk: RR (95% CI) per 3.5 fold higher level Main effect Quadratic term Cohort studies 902 21 1.15(0.72,1.84) 14033 850 1.15(1.05,1.25) 798 53 1.10(0.74,1.63) 165 19 0.73(0.37,1.45) 3860 592 1.01(0.91,1.13) 7487 283 1.16(1.03,1.32) 2008 273 1.26(1.07,1.47) 637 54 1.41(0.97,2.07) 2201 92 1.03(0.80,1.33) 2850 109 1.35(1.03,1.76) 5784 299 1.31(1.13,1.51) 1996 386 1.01(0.90,1.15) 4496 107 1.26(0.99,1.61) 2375 157 1.12(0.92,1.37) 7441 115 1.44(1.12,1.86) 3198 94 1.67(1.07,2.60) 2012 53 1.15(0.81,1.64) 3837 416 1.11(0.97,1.27) 1400 3 0.82(0.21,3.12) 1866 485 1.14(1.03,1.26) 7903 170 1.27(1.03,1.57) 27791 227 0.92(0.78,1.09) 4617 299 1.02(0.89,1.18) 305 42 1.11(0.75,1.66) Nested case-control studies (individually matched) BUPA 1994 1184 208 1.55(1.24,1.93) FIA 1998 1454 510 1.34(1.10,1.64) FLETCHER 2007 372 134 1.31(0.94,1.82) HPFS 691 220 1.15(0.94,1.40) MRFIT 2001 736 246 0.90(0.73,1.10) NHS 2005 687 234 1.27(1.00,1.61) Nested case-control studies (frequency matched) BRHS 1561 461 1.06(0.91,1.23) GOTO33 1993 128 16 1.28(0.66,2.46) REYK 2008 5771 1850 1.30(1.21,1.39) USPHS 1993 652 243 1.19(0.97,1.46) Overall† 123,198 ‡ 9321 1.16 (1.11,1.21) AFTCAPS ARIC 2001 BRUN 1999 CHARL CHS1 2003 COPEN 2008 DUBBO 2002 EAS 2001 FINRISK92 2005 FRAMOFF 1996 GRIPS 1997 KIHD NHANES3 NPHSII 2001 PRIME 2002 PROCAM 1996 QUEBEC 1998 SHS 2002 TARFS ULSAM WHITE2 WHS 2006 WOSCOPS 2000 ZUTE 1.04(0.73,1.47) 1.05(0.96,1.14) 1.05(0.76,1.43) 0.66(0.29,1.52) 0.98(0.87,1.10) 1.12(1.01,1.23) 0.91(0.79,1.05) 0.80(0.51,1.25) 1.05(0.82,1.33) 0.98(0.76,1.27) 1.14(1.04,1.23) 1.18(1.06,1.32) 1.02(0.79,1.32) 1.04(0.81,1.35) 0.90(0.74,1.10) 0.84(0.60,1.18) 0.95(0.66,1.38) 1.01(0.94,1.07) 1.49(0.31,7.16) 1.09(1.00,1.19) 1.03(0.89,1.20) 1.33(1.15,1.53) 1.00(0.87,1.16) 1.06(0.72,1.55) 1.03(0.82,1.28) 1.27(0.95,1.69) 1.02(0.76,1.38) 0.93(0.76,1.14) 0.97(0.80,1.17) 1.00(0.83,1.20) 1.06(0.97,1.16) 2.01(0.95,4.22) 1.00(0.93,1.09) 1.06(0.84,1.33) 1.05 (1.02,1.08)* Risk ratios are adjusted for baseline age and, where appropriate, stratified by sex and study group. † Overall effect calculated by combining study specific estimates for main effect and quadratic terms for the loge Lp(a) using multivariate random-effects meta-analysis. ‡Overall number is less than study total because 2 studies (ATTICA and GOH) did not contribute CHD endpoints.*P-value for comparison of the linear versus the quadratic model = 0.003. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eTable 4. Risk Ratios for CHD for 3.5-fold (ie, 1-SD) Higher Usual Levels of Lp(a), Further Adjusted for Usual Values of Various Potential Confounding Factors Subset No. of studies No. of subjects No. of cases 26 96675 5728 1.13 1.15 1.14 1.17 1.19) 1.21) 1.20) 1.24) 23 25 25 30 Plus Non-HDL-C & HDL-C Plus Apo-B & Apo-AI 15 75560 3540 1.21 (1.12 , 1.31) 1.18 (1.09 , 1.27) 22 18 Fibrinogen† Basic ‡ Plus Non-HDL-C & HDL-C Further adjustment for fibrinogen 19 87708 4227 1.16 (1.09 , 1.24) 1.13 (1.06 , 1.21) 20 12 CRP† Basic ‡ Plus Non-HDL-C & HDL Further adjustment for CRP 19 55146 3375 1.09 (1.04 , 1.15) 1.09 (1.04 , 1.13) 11 13 Lipid markers† Adjustment Basic ‡ Plus total cholesterol Basic ‡ Plus Non-HDL-C & HDL-C Basic ‡ Plus Non-HDL-C, HDL-C & log-triglycerides Basic ‡ Plus total cholesterol corrected for Lp(a) cholesterol§ Apolipoproteins† Basic Basic ‡ ‡ RR (95% CI) Wald χ12 ‡ (1.08 (1.09 (1.08 (1.11 , , , , †Analysis was restricted to participants with complete information on sex, study group and respective confounding variables for each subset. Basic adjustment includes age and usual values of systolic blood pressure, smoking, history of diabetes and body mass index. Risk ratios are stratified by sex and study group where appropriate. Studies with fewer than 10 events were excluded from these analyses. § Correction for the cholesterol content of Lp(a) particles was made by subtracting estimated Lp(a) cholesterol values from total cholesterol; Lp(a) cholesterol was estimated from Lp(a) total mass using the following equation: Lp(a) cholesterol (mg/dl) = 0.15*Lp(a) (mg/dl)+1.24 (Clinical Chemistry 1998; 44(8):1629-40) Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eTable 5. Parallel Analyses of the Association of Lp(a) With Disease Risk: (a) for Comparison of Individuals in Extreme Thirds of Baseline Level Distributions, and (b) per 3.5-fold (ie, 1-SD) Higher Baseline Level Coronary heart disease 106645 individuals 8362 cases 30 cohorts I2 Risk ratio 2 Wald χ1 (95% CI) (95% CI) Ischaemic stroke 69539 individuals 1684 cases 13 cohorts I2 Risk ratio 2 Wald χ1 (95% CI) (95% CI) a) Top vs. bottom thirds of Lp(a) distribution With adjustment for... Age & sex 1.33 (1.23 , 1.45) Plus systolic blood pressure 1.34 (1.23 , 1.45) Plus smoking status 1.33 (1.23 , 1.45) Plus history of diabetes 1.35 (1.24 , 1.47) Plus body mass index 1.35 (1.25 , 1.47) Plus total cholesterol 1.27 (1.17 , 1.38) 46 47 45 49 50 33 42 42 44 45 42 40 (11 , 63) (11 , 63) (13 , 63) (15 , 64) (10 , 63) (6 , 62) 1.19 1.17 1.17 1.19 1.19 1.18 (1.01 (1.01 (1.01 (1.02 (1.03 (1.02 , , , , , , 1.41) 1.36) 1.36) 1.38) 1.38) 1.37) 5 4 4 5 6 5 17 (0 , 56) 0 (0 , 57) 0 (0 , 57) 0 (0 , 57) 0 (0 , 57) 0 (0 , 57) b) Per 3.5 fold higher baseline With adjustment for... Age & sex Plus systolic blood pressure Plus smoking status Plus history of diabetes Plus body mass index Plus total cholesterol 33 34 32 35 38 25 62 62 63 64 63 59 (43 (44 (45 (46 (46 (39 1.08 1.07 1.08 1.08 1.09 1.08 (1.00 (1.00 (1.00 (1.01 (1.02 (1.01 , , , , , , 1.17) 1.15) 1.15) 1.16) 1.16) 1.16) 4 4 4 6 6 5 52 38 37 33 31 35 Lp(a) level 1.13 1.14 1.13 1.14 1.15 1.11 (1.09 (1.09 (1.09 (1.09 (1.10 (1.07 , , , , , , 1.18) 1.19) 1.18) 1.19) 1.20) 1.16) , , , , , , 74) 75) 75) 75) 75) 73) Analyses were restricted to participants with complete information on sex and all confounding variables. Risk ratios are stratified by sex and study group where appropriate. Studies with less than 10 events were excluded from analysis. Note: I2 is a measure of consistency across studies: the percentage of variance in estimated log RRs that is attributable to between study variations as opposed to sampling variation. Values of I2 close to 0 indicate lack of evidence of heterogeneity Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 (9 (0 (0 (0 (0 (0 , , , , , , 74) 68) 67) 65) 64) 66) eFigure 1. Study specific adjusted risk ratios for CHD, corresponding the adjusted risk ratio in Table 2. Cohort No. of individuals GOTO33 BUPA QUEBEC AFTCAPS ZUTE EAS BRUN NHANES3 FINRISK PROCAM PRIME FLETCHER NPHSII WHITE2 WHS USPHS NHS MRFIT COPEN DUBBO GRIPS WOSCOPS KIHD ULSAM FIA SHS BRHS CHS1 ARIC REYKCON 126 110 617 826 304 622 798 2457 2190 3185 7431 368 2367 7720 22667 515 609 736 7288 1995 5783 4617 1981 1420 1083 3728 1553 3837 13989 5723 No. of cases RR (95% CI) 16 19 20 21 42 53 53 60 92 94 114 133 157 168 206 210 218 246 269 272 299 299 383 386 394 407 458 588 843 1842 1.64 1.76 0.81 1.19 1.05 1.27 1.03 1.07 1.05 1.22 1.28 1.13 1.09 1.32 1.05 1.15 1.32 0.89 1.14 1.15 1.61 1.02 1.08 1.12 1.21 1.19 1.01 1.03 1.07 1.14 (0.82, (1.00, (0.51, (0.72, (0.74, (0.95, (0.72, (0.80, (0.85, (0.95, (1.05, (0.92, (0.92, (1.10, (0.92, (0.94, (1.09, (0.71, (1.01, (1.01, (1.40, (0.92, (0.96, (1.01, (1.03, (1.04, (0.88, (0.93, (1.00, (1.09, 3.27) 3.13) 1.29) 1.96) 1.50) 1.70) 1.47) 1.43) 1.31) 1.56) 1.56) 1.39) 1.30) 1.59) 1.20) 1.41) 1.61) 1.12) 1.28) 1.31) 1.86) 1.13) 1.22) 1.24) 1.43) 1.37) 1.17) 1.14) 1.15) 1.20) Overall (random-effects) 1.13 (1.09, 1.18) Overall (fixed-effect) 1.13 (1.10, 1.15) .5 1 2 4 RR per 3.5 fold higher usual Lp(a) levels CI indicates confidence interval. Sizes of data markers are proportional to the inverse of the variance of the risk ratios. The overall adjusted RR in studies with greater than 500 CHD cases (1.09, 1.03-1.16) was not significantly different from that of studies with less than 500 cases (1.15, 1.09-1.21) (heterogeneity p-value=0.36). Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eFigure 2. Mean Lp(a) levels by cohort and (a) assay method principle, or (b) whether the assay method used was sensitive to apolipoprotein(a) isoform variation. Other WHS NHS ATTICA HPFS FRAMOFF COPEN GOH WHITE2 PROCAM ULSAM KIHD 32 ITA / INA NS 4 8 16 ELISA FINRISK92 AFTCAPS ZUTE GRIPS PRIME FLETCHER ARIC WOSCOPS BUPA QUEBEC FIA NHANES3 TARFS BRUN CHS1 NPHSII GOTO33 DUBBO CHARL SHS REYK BRHS USPHS EAS MRFIT 2 Geometric mean Lp(a), mg/dl (95% CI) - log scale (A) Assay method 32 sensitive NS 4 8 16 Not sensitive COPEN GOH NHANES3 WHITE2 AFTCAPS ATTICA ZUTE CHARL CHS1 FRAMOFF WOSCOPS BUPA QUEBEC FIA MRFIT SHS DUBBO GRIPS WHS NHS PRIME FINRISK92 HPFS FLETCHER ARIC NPHSII TARFS GOTO33 KIHD BRUN REYK BRHS ULSAM USPHS EAS PROCAM 2 Geometric mean Lp(a), mg/dl (95% CI) - log scale (B) Isoform sensitivity CI indicates confidence interval; NS indicates not specified; ELISA: Enzyme Linked Immunosorbent Assay; ITA: Immunoturbidimetric Assay; INA: Immunonephelometric Assay;. Error bars indicate 95% CI of the mean Lp(a) level in each study. Meta-regression showed that there was no statistically significant difference in mean loge Lp(a) between groups of studies defined by (a) assay method (p=0.10), or (b) isoform sensitivity (p= 0.98) Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eFigure 3. Within-person variability in lipoprotein(a) levels (regression dilution ratios† stratified by study and repeat). Regression dilution ratio and 95% CI 1 FLETCHER AFTCAPS TARFS COPEN REYK ULSAM TARFS 0.8 PROCAM 0.6 PROCAM 0.4 0.2 No. of repeats = 6597 Mean RDR† = 0.87 (95% CI, 0.81-0.93) 0 0 3 6 9 12 15 18 21 24 Time since baseline (years) RDR indicates regression dilution ratio; CI confidence interval. † The values provided are estimates of RDR at mean log-Lp(a) concentration, as the models allowed variation to vary by level. RDRs are adjusted for age and sex. Data shown for repeat measures involving more than 25 individuals. The solid and broken lines indicate the overall RDR and its 95%CI, respectively. Sizes of data markers are proportional to the inverse of the variance of the study specific RDRs. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eFigure 4. Age- and sex- adjusted risk ratios for various vascular and non-vascular endpoints per 3.5 fold higher usual Lp(a) levels. Endpoint No of studies No. of individuals No. of Cases RR (95% CI) 33 121633 9314 1.17 (1.13, 1.22) Coronary death† 27 107873 2523 1.20 (1.13, 1.27) Non-fatal MI† 29 113892 6632 1.16 (1.11, 1.21) Ischaemic stroke 14 78466 1890 1.10 (1.02, 1.19) Unclassified stroke 12 51305 720 1.03 (0.93, 1.15) Haemorrhagic stroke 9 62040 305 1.12 (0.97, 1.29) Non-vascular deaths 29 117237 8094 0.98 (0.94, 1.01) 23 105716 3945 1.00 (0.96, 1.04) Smoking-related cancer deaths 17 67963 1481 1.00 (0.95, 1.06) Other cancer deaths 22 105239 2452 1.01 (0.94, 1.08) 24 114220 4101 0.95 (0.89, 1.00) Non-fatal MI and coronary death All tumour deaths Other non-vascular deaths .8 1 1.2 1.4 1.6 Risk ratio per 3.5 fold higher Lp(a) level CI indicates confidence interval. Sizes of data markers are proportional to the inverse of the variance of the risk ratios. Risk ratios are adjusted for baseline age and stratified, where appropriate, by sex and study group. Studies involving fewer than 10 cases of any outcome were excluded from the analysis of that outcome. † These subtotals do not add to the total number of CHD outcome in the first row because some nested case-control studies did not subdivide outcomes into coronary death or non-fatal MI Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eFigure 5. Risk ratios for coronary heart disease by fifths of usual Lp(a) levels, after excluding the first 5 years of follow-up. b) Further adjustment† a) Adjustment for age and sex only Non-fatal MI and coronary death: 5237 Cases Risk ratio and 95% CI (log scale) Non-fatal MI and coronary death: 5717 Cases 2 2 1.8 1.8 1.6 1.6 1.4 1.4 1.2 1.2 1 1 .9 .9 3 6 12 24 48 96 Usual Lp(a) (mg/dl) Geometric mean (log scale) 192 3 6 12 24 48 96 192 Usual Lp(a) (mg/dl) Geometric mean (log scale) CI indicates confidence interval. CIs were calculated using floating absolute risk technique. †Further adjustment for systolic blood pressure, smoking status, history of diabetes, body mass index and total cholesterol. Sizes of data markers are proportional to the inverse of the variance of the risk ratios. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eFigure 6. Risk ratios for coronary heart disease per 3.5 fold higher usual Lp(a) levels, by strata of various study level characteristics. Variable/ subgroup No. of studies No. of individuals RR (95% CI) Heterogeneity p-value No. of cases Ethnicity† White Black Other 29 3 2 95753 4546 4207 7540 261 426 1.14 (1.09, 1.19) 1.05 (0.90, 1.23) 1.36 (0.88, 2.09) 0.52 18 10 2 54301 49981 2363 5138 2819 405 1.16 (1.10, 1.23) 1.08 (1.02, 1.15) 1.14 (1.02, 1.28) 0.39 17 13 45650 60995 4631 3731 1.17 (1.10, 1.24) 1.09 (1.04, 1.15) 0.18 11 19 24517 82128 2332 6030 1.17 (1.07, 1.28) 1.11 (1.07, 1.15) 0.26 4 6 16 13409 32128 48530 533 2009 5182 1.18 (1.07, 1.30) 1.07 (1.02, 1.13) 1.14 (1.07, 1.22) 0.48 4 15 8 13409 71359 13486 533 3710 3103 1.18 (1.07, 1.30) 1.16 (1.08, 1.24) 1.09 (1.02, 1.17) 0.50 20 4 4 58455 38284 8776 6483 861 955 1.13 (1.07, 1.20) 1.18 (1.06, 1.32) 1.11 (1.03, 1.19) 0.75 22 6 2 79749 25766 1130 6561 1738 63 1.14 (1.09, 1.20) 1.11 (1.01, 1.22) 1.10 (0.82, 1.46) 0.83 Geographical region Western Europe North America Other Blood sample Serum Plasma Fasting status Non-fasted Fasted Storage duration† < 1 wk 1wk-1yr > 1yr Storage temperature† Fresh -70 or less -20 to -70 Assay method† ELISA ITA/INA OTHER Isoform sensitivity‡ insensitive sensitive unknown .75 1 1.5 2.5 RR per 3.5 fold higher Lp(a) levels CI indicates confidence interval. Sizes of data markers are proportional to the inverse of the variance of the risk ratios. Risk ratios are adjusted for age, and usual levels of systolic blood pressure, smoking status, history of diabetes, body mass index and total cholesterol, and stratified, where appropriate, by sex and study group. †Although a total of 30 studies have contributed to the analyses, for different characteristics different number of studies had relevant data; for Storage duration 4 studies, for Storage temperature 3 studies, and for Assay method 2 studies did not have relevant data. For Ethnicity, the no. of studies add to 34 because 4 studies contributed to 2 categories. ‡Isoform sensitivity refers to whether the result of an assay is affected by apolipoprotein(a) isoform variation. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eFigure 7. Direct comparison of adjusted risk ratios for CHD between Lp(a) and non-HDL-C, for a 1SD higher† baseline or usual levels RR (95% CI) Baseline Lp(a) 1.11 (1.05, 1.17) Non-HDL-c 1.48 (1.34, 1.64) Usual Lp(a) 1.14 (1.09, 1.20) Non-HDL-c 1.66 (1.40, 1.96) .75 1 1.5 2.5 RR per 1 SD higher level CI indicates confidence interval. Sizes of data markers are proportional to the inverse of the variance of the risk ratios.* † RRs presented are for 1-SD increase in loge Lp(a) or non-HDL-C levels. Analyses were based on data from 26 cohorts involving 97,049 and 5766 cases. Risk ratios were mutually adjusted for each other, and baseline age, and usual levels of systolic blood pressure, smoking status, history of diabetes, body mass index and HDL-C Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014 eFigure 8. Association of Lp(a) with fatal vascular and non-vascular outcomes in analyses that did not censor for nonfatal events† – RRs are per 3.5 fold higher usual Lp(a) levels adjusted for cardiovascular risk factors. Endpoint No. of studies No. of individuals No. of cases RR (95% CI) Coronary death 26 101604 3575 1.15 (1.08, 1.22) Fatal MI 25 95915 1744 1.20 (1.10, 1.30) All vascular deaths 28 103452 5553 1.10 (1.06, 1.14) Non-vascular deaths 26 103170 8390 1.01 (0.99, 1.04) All tumour deaths 21 92326 3945 1.00 (0.97, 1.04) Smoking-related cancer deaths 16 63555 1576 0.99 (0.94, 1.05) Other cancer deaths 20 92209 2357 1.03 (0.97, 1.09) 22 100378 4409 1.02 (0.98, 1.07) Other non-vascular deaths .8 1 1.2 1.4 1.6 Risk ratio per 3.5 fold higher Lp(a) level CI indicates confidence interval. Sizes of data markers are proportional to the inverse of the variance of the risk ratios †Compared to the corresponding main analyses (Figure 2), analyses that did not censor for non-fatal events involved additional 1917 vascular and 1122 non-vascular fatal outcomes. Risk ratios are adjusted for baseline age, smoking status, systolic blood pressure, history of diabetes, body mass index and total cholesterol, and stratified, where appropriate, by sex and study group. Studies involving fewer than 10 cases of any outcome were excluded from the analysis of the outcome. Downloaded From: http://jama.jamanetwork.com/ on 06/09/2014
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