- Email: [email protected]
Genetics of atherosclerosis: The power of plaque burden and progression
Atherosclerosis 223 (2012) 98e101
Contents lists available at SciVerse ScienceDirect
Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Invited commentary
Genetics of atherosclerosis: The power of plaque burden and progression Invited Commentary on Dong C, Beecham A, Wang L, Blanton SH, Rundek T, Sacco RL. Follow-Up Association Study of Linkage Regions Reveals Multiple Candidate Genes for Carotid Plaque in Dominicans Atherosclerosis 223 (1) (2012) 177e183 J. David Spence* Stroke Prevention & Atherosclerosis Research Centre, Robarts Research Institute, Western University, 1400 Western Road, London, ON, Canada N6G 2V2
a r t i c l e i n f o Article history: Received 24 March 2012 Accepted 29 March 2012 Available online 12 May 2012
In this issue of Atherosclerosis, Dong et al. [1] report their findings in a followup association study of linkage regions they previously identified in a genome-wide association study [2]. They genotyped 3712 single nucleotide polymorphisms (SNPs) under the four linkage regions identified in their earlier study of 100 extended Dominican families, using family-based association tests to investigate associations with carotid plaque. Promising SNPs were evaluated in an independent population-based Hispanic subcohort of 941 participants in the Northern Manhattan Study (NOMAS). They found evidence for association with six loci/genes, with at least three of these replicated in the NOMAS cohort. However, the associations within or near these genes explained only a small proportion of the observed linkage. The authors concluded that “Further studies with in-depth re-sequencing are needed to uncover both rare and common functional variants that contribute to the susceptibility to atherosclerosis”. An important aspect of their study is that it is one of the first to study associations with the burden of carotid plaque, measured as total plaque area. It is perhaps not sufficiently recognized that various phenotypes of atherosclerosis that are the subject of genetic research are very different, and that these differences are crucial to the associations that will be uncovered [3,4]. Case/control studies comparing people who have had myocardial infarction with controls that have not had such an event require very large samples, and will reveal
DOI of original article: 10.1016/j.atherosclerosis.2012.03.025. * Tel.: þ1 519 663 3113; fax: þ1 519 63 3018. E-mail address: [email protected]. URL: http://www.robarts.ca/sparc 0021-9150/$ e see front matter Ó 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2012.03.040
associations with many factors involved in the occurrence of such events: a myocardial infarction is the culmination of many processes, each with underlying genetic contributions, as reviewed by Hegele in 1997 [5]. Initiation of fatty streaks, development and progression of plaque (and the risk factors contributing to plaque formation such as lipid metabolism, hypertension, hyperhomocysteinemia and others), plaque inflammation and rupture, thrombosis, fibrinolysis and other factors culminating in myocardial infarction will all have genetic components. Even tobacco smoking will have genetic components [6] relating to such factors as nicotine metabolism, depression, susceptibility to addiction and other personality traits. Events such as myocardial infarction are therefore extremely messy phenotypes. Among controls who have not yet had an event, it can be expected that many will have extensive atherosclerosis that has not yet manifested as an event. One approach to simplifying the problem is to use phenotypes based on assessing the burden of preclinical phenotypes of atherosclerosis; for example by measuring coronary calcium, carotid intima-media thickness (IMT), carotid plaque or stenosis of coronary, carotid or femoral arteries. However even this approach is more complicated than it may seem. Coronary calcium can also be expected to have genetic associations that are different from those of other phenotypes of atherosclerosis, since not all plaques are calcified, and there are biological processes that are particular to calcification [7,8]. Biological differences among ultrasound phenotypes of “atherosclerosis” can be seen by comparing the proportion of variance explained (R2) by coronary risk factors in multiple regression, with various phenotypes of “atherosclerosis” as the
J.D. Spence / Atherosclerosis 223 (2012) 98e101
99
Table 1 Stepwise linear multiple regression of baseline total plaque area. Coefficientsa
(Constant) Age Sex (F ¼ 1, M ¼ 0) Smoking status Diabetes Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Smoking (pack-years) Systolic pressure Diastolic pressure On lipid medication On blood pressure medication
Unstandardized coefficients
Standardized coefficients
B
Std. error
Beta
0.596 0.017 0.175 0.122 0.093 0.040 0.010 0.049 0.015 0.002
0.070 0.001 0.016 0.014 0.022 0.019 0.010 0.025 0.018 0.000
0.549 0.186 0.176 0.065 0.095 0.022 0.045 0.033 0.088
8.514 30.133 11.003 8.846 4.167 2.091 1.014 1.936 0.789 4.372
0.000 0.000 0.000 0.000 0.000 0.037 0.311 0.053 0.430 0.000
0.003 0.003 0.106
0.000 0.001 0.016
0.139 0.069 0.111
6.974 3.628 6.634
0.000 0.000 0.000
0.078
0.015
0.083
5.127
0.000
R2 ¼ 0.573 (The R2 for raw plaque area was 0.397). a Dependent variable: total plaque area normalized transformation.
t
by
Sig.
a
cube
root
dependent variable. Traditional coronary risk factors explain approximately half of carotid total plaque area (TPA), as shown in Table 1, but only 15e17% of IMT and only 13% of carotid stenosis measured by ultrasound [4,9]. The association of Lp(a) with stenosis and occlusion but not plaque area [10] is an example of this principle; the reverse for plasma homocysteine [4] is another. Carotid IMT measured in the distal common carotid where there is no plaque is biologically (and therefore genetically) different from IMT measured in the bulb and including plaque thickness [11,12]; the former will tend to be associated with genetic factors contributing to hypertension, whereas the latter (if cases with and without plaque included in the IMT measurement were not lumped together e a serious problem) would more closely relate to genetic factors affecting plaque. Pollex and Hegele reviewed in 2006 [13]
Table 2 Linear multiple regression of plaque progression from baseline to a year later. Coefficientsa Baseline variables
(Constant) Age Sex (F ¼ 1, M ¼ 0) Smoking status Diabetes Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Smoking (pack-years) Systolic pressure Diastolic pressure On lipid medication On blood pressure medication
Unstandardized coefficients
Standardized coefficients
t
Sig.
B
Std. error
Beta
0.028 0.002 0.032 0.012 0.034 0.168 0.036 0.090 0.170 0.001
0.256 0.002 0.056 0.053 0.083 0.093 0.046 0.106 0.087 0.002
0.029 0.021 0.010 0.014 0.232 0.040 0.053 0.242 0.024
0.108 0.729 0.564 0.225 0.408 1.800 0.784 0.854 1.951 0.538
0.914 0.466 0.573 0.822 0.683 0.072 0.433 0.393 0.051 0.591
0.003 0.004 0.010 0.034
0.002 0.003 0.055 0.053
0.099 0.069 0.007 0.022
2.231 1.655 0.186 0.644
0.026 0.098 0.853 0.519
R2 ¼ 0.016. a Dependent variable: plaque progression from baseline to one year later.
Fig. 1. Unexplained atherosclerosis and protection from atherosclerosis. Measured carotid plaque area is shown on the X-axis, with predicted plaque area from the regression model shown in Table 1. The two individuals shown by the arrows are outliers; their residual scores (shown to the right of the arrows) are the quantitative traits, and are similar to their standard deviation above and below the regression line.
the differences in genetic associations with IMT versus plaque burden. A phenotype that is more closely related biologically to atherosclerosis than IMT [14], and which is also a stronger predictor of cardiovascular events than IMT [11,15], is plaque burden, measured as carotid total plaque area (or in future, plaque volume [16] or vessel wall volume [17]). A powerful phenotype for assessing genetic effects on atherosclerosis is a quantitative trait computed from the residual score (amounting to the distance off the regression line) of plaque burden adjusted for coronary risk factors in multiple regression, described in this journal in 1999 [18]. I call this unexplained atherosclerosis. This is illustrated in Fig. 1 and Table 3. With Hegele and colleagues, we have used this approach to show that the D9N variant of lipoprotein lipase [4], mannose binding lectin [19] and polymorphisms
Table 3 Examples of unexplained atherosclerosis and protection. These are the values used in the regression model to compute the standardized residual scores for the two individuals whose distance off the regression line are shown in Fig. 1. Baseline
Age Sex Diabetic Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Systolic pressure Diastolic pressure Smoking (pack-years) Plaque area Residual score
Unexplained
Protected
46 Male No 5.02 1.54 1.03 3.29 170 105 30 704 3.68
82 Male No 4.04 2.0 0.72 2.40 167 85 195 173 2.31
100
J.D. Spence / Atherosclerosis 223 (2012) 98e101
progression, as shown in Table 2. This makes it possible to see the effect of new risk factors/therapeutic targets. Examples of this include the effect of the blood pressure rise during mental stress [23], and plasma aldosterone [24]. Fig. 2 and Table 4 show examples of extreme outliers with unexplained progression and regression of atherosclerosis. 1. Conclusion Measuring the burden of atherosclerosis, and of atherosclerosis progression, affords the opportunity of assessing genetic associations with atherosclerosis in a much cleaner way than do studies of association of events such as myocardial infarction. The quantitative traits described here have great potential for uncovering new therapeutic targets against atherosclerosis and its complications. Statement of originality This commentary has not been published previously, although some of the concepts have previously been published. The figures are original, but Fig. 1 is similar to figures that have been published before (previous versions were based on different variables in the regression model, and did not have arrows identifying individual outliers). Table 1 is similar to tables that have been published previously, but again based on different variables in the regression model, and on a different sample of patients. Fig. 2. Unexplained progression and regression of plaque. The X-axis shows the progression of total plaque area from baseline to a year later; the Y axis shows predicted progression. The arrows identify two outliers, with their residual scores shown above the arrows. Their residual scores are the quantitative traits, and are similar to their standard deviation above and below the regression line.
of PCK1 [20] and PPARg [21] are significantly associated with carotid plaque area; in some of these studies the associations with plaque area were different from those with IMT. With Lanktree and colleagues we have calculated [22] that by using the extremes of the distribution (the 5% of outliers at each extreme), the sample size required for genome-wide association studies can be reduced by three quarters. An even more powerful phenotype, particularly for identifying new therapeutic targets in atherosclerosis, is unexplained progression/regression of atherosclerosis. It is more powerful because baseline plaque area is the result of the cumulative effects of the risk factors shown in Table 1, over a lifetime. As shown in Table 1, age accounts for more than half of the explained variance (the Beta is 0.549). However, age accounts for only 3% of plaque Table 4 Examples of unexplained progression and regression. These are the values used in the regression model to compute the standardized residual scores for the two individuals whose distance off the regression line is shown in Fig. 2. Standardized residual scores are similar to standard deviations. Progression
Age Sex Diabetic Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Systolic pressure Diastolic pressure Smoking (pack-years) Plaque progression (mm2) Residual score
Unexplained progression
Unexplained regression
75 Male No 3.70 0.86 1.45 1.86 150 76 20 29.20 5.59
83 Male No 5.15 2.77 0.82 3.10 151 81 0 27.90 5.35
References [1] Dong C, Beecham A, Wang L, Blanton SH, Rundek T, Sacco RL. Follow-up association study of linkage regions reveals multiple candidate genes for carotid plaque in dominicans. Atherosclerosis 2012;223(1):177e83. [2] Dong C, Beecham A, Slifer S, Wang L, Blanton SH, Wright CB, et al. Genomewide linkage and peakwide association analyses of carotid plaque in Caribbean hispanics. Stroke 2010;41:2750e6. [3] Spence JD, Hegele RA. Noninvasive phenotypes of atherosclerosis. Arterioscler Thromb Vasc Biol 2004;24:e188e9. [4] Spence JD, Hegele RA. Noninvasive phenotypes of atherosclerosis: similar windows but different views. Stroke 2004;35:649e53. [5] Hegele RA. The genetic basis of atherosclerosis. Int J Clin Lab Res 1997;27: 2e13. [6] Li MD, Cheng R, Ma JZ, Swan GE. A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction 2003;98:23e31. [7] Shao JS, Cai J, Towler DA. Molecular mechanisms of vascular calcification: lessons learned from the aorta. Arterioscler Thromb Vasc Biol 2006;26: 1423e30. [8] Towler DA, Demer LL. Thematic series on the pathobiology of vascular calcification: an introduction. Circ Res 2011;108:1378e80. [9] Al Shali K, House AA, Hanley AJ, Khan HM, Harris SB, Mamakeesick M, et al. Differences between carotid wall morphological phenotypes measured by ultrasound in one, two and three dimensions. Atherosclerosis 2005;178: 319e25. [10] Klein JH, Hegele RA, Hackam DG, Koschinsky ML, Huff MW, Spence JD. Lipoprotein(a) is associated differentially with carotid stenosis, occlusion, and total plaque area. Arterioscler Thromb Vasc Biol 2008;28:1851e6. [11] Inaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intimamedia thickness, more accurately predicts coronary artery disease events: a meta-analysis. Atherosclerosis 2011;220:128e33. [12] Spence JD. Carotid plaque measurement is superior to IMT. Invited editorial comment on: carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysisYoichi Inaba, M.D., Jennifer A. Chen M.D., Steven R. Bergmann M.D., Ph.D. Atherosclerosis 2011;220:34e5. [13] Pollex RL, Hegele R. Genetic determinants of carotid ultrasound traits. Curr Atheroscler Rep 2006;8:206e15. [14] Finn AV, Kolodgie FD, Virmani R. Correlation between carotid intimal/medial thickness and atherosclerosis. A point of view from pathology. Arterioscler Thromb Vasc Biol 2010;30:177e81. [15] Spence JD, Rundek T. Toward clinical applications of carotid ultrasound: intima-media thickness, plaque area, and three-dimensional phenotypes. In: Nicolaides AN, Beach KW, Kiriacou E, Pattichis CS, editors. Ultrasound and carotid bifurcation atherosclerosis. London: Springer-Verlag; 2012. p. 431e48. [16] Ainsworth CD, Blake CC, Tamayo A, Beletsky V, Fenster A, Spence JD. 3D ultrasound measurement of change in carotid plaque volume: a tool for rapid evaluation of new therapies. Stroke 2005;36:1904e9.
J.D. Spence / Atherosclerosis 223 (2012) 98e101 [17] Egger M, Spence JD, Fenster A, Parraga G. Validation of 3D ultrasound vessel wall volume: an imaging phenotype of carotid atherosclerosis. Ultrasound Med Biol 2007;33:905e14. [18] Spence JD, Barnett PA, Bulman DE, Hegele RA. An approach to ascertain probands with a non traditional risk factor for carotid atherosclerosis. Atherosclerosis 1999;144:429e34. [19] Hegele RA, Ban MR, Anderson CM, Spence JD. Infection-susceptibility alleles of mannose-binding lectin are associated with increased carotid plaque area. J Investig Med 2003;48:198e202. [20] Hegele RA, Al-Shali KZ, House AA, Hanley AJ, Harris SB, Mamakeesick M, et al. Disparate associations of a functional promoter polymorphism in PCK1 with carotid wall ultrasound traits. Stroke 2005;36:2566e70.
101
[21] Al Shali KZ, House AA, Hanley AJ, Khan HM, Harris SB, Zinman B, et al. Genetic variation in PPARG encoding peroxisome proliferator-activated receptor gamma associated with carotid atherosclerosis. Stroke 2004;35: 2036e40. [22] Lanktree MB, Hegele RA, Schork NJ, Spence JD. Extremes of unexplained variation as a phenotype: an efficient approach for genome-wide association studies of cardiovascular disease. Circ Cardiovasc Genet 2010;3:215e21. [23] Barnett PA, Spence JD, Manuck SB, Jennings JR. Psychological stress and the progression of carotid artery disease. J Hypertens 1997;15:49e55. [24] de Rita O, Hackam DG, Spence JD. Vascular effects of aldosterone in human subjects: plasma aldosterone and carotid total plaque area. Can J Cardiol, in press.
Contents lists available at SciVerse ScienceDirect
Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Invited commentary
Genetics of atherosclerosis: The power of plaque burden and progression Invited Commentary on Dong C, Beecham A, Wang L, Blanton SH, Rundek T, Sacco RL. Follow-Up Association Study of Linkage Regions Reveals Multiple Candidate Genes for Carotid Plaque in Dominicans Atherosclerosis 223 (1) (2012) 177e183 J. David Spence* Stroke Prevention & Atherosclerosis Research Centre, Robarts Research Institute, Western University, 1400 Western Road, London, ON, Canada N6G 2V2
a r t i c l e i n f o Article history: Received 24 March 2012 Accepted 29 March 2012 Available online 12 May 2012
In this issue of Atherosclerosis, Dong et al. [1] report their findings in a followup association study of linkage regions they previously identified in a genome-wide association study [2]. They genotyped 3712 single nucleotide polymorphisms (SNPs) under the four linkage regions identified in their earlier study of 100 extended Dominican families, using family-based association tests to investigate associations with carotid plaque. Promising SNPs were evaluated in an independent population-based Hispanic subcohort of 941 participants in the Northern Manhattan Study (NOMAS). They found evidence for association with six loci/genes, with at least three of these replicated in the NOMAS cohort. However, the associations within or near these genes explained only a small proportion of the observed linkage. The authors concluded that “Further studies with in-depth re-sequencing are needed to uncover both rare and common functional variants that contribute to the susceptibility to atherosclerosis”. An important aspect of their study is that it is one of the first to study associations with the burden of carotid plaque, measured as total plaque area. It is perhaps not sufficiently recognized that various phenotypes of atherosclerosis that are the subject of genetic research are very different, and that these differences are crucial to the associations that will be uncovered [3,4]. Case/control studies comparing people who have had myocardial infarction with controls that have not had such an event require very large samples, and will reveal
DOI of original article: 10.1016/j.atherosclerosis.2012.03.025. * Tel.: þ1 519 663 3113; fax: þ1 519 63 3018. E-mail address: [email protected]. URL: http://www.robarts.ca/sparc 0021-9150/$ e see front matter Ó 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2012.03.040
associations with many factors involved in the occurrence of such events: a myocardial infarction is the culmination of many processes, each with underlying genetic contributions, as reviewed by Hegele in 1997 [5]. Initiation of fatty streaks, development and progression of plaque (and the risk factors contributing to plaque formation such as lipid metabolism, hypertension, hyperhomocysteinemia and others), plaque inflammation and rupture, thrombosis, fibrinolysis and other factors culminating in myocardial infarction will all have genetic components. Even tobacco smoking will have genetic components [6] relating to such factors as nicotine metabolism, depression, susceptibility to addiction and other personality traits. Events such as myocardial infarction are therefore extremely messy phenotypes. Among controls who have not yet had an event, it can be expected that many will have extensive atherosclerosis that has not yet manifested as an event. One approach to simplifying the problem is to use phenotypes based on assessing the burden of preclinical phenotypes of atherosclerosis; for example by measuring coronary calcium, carotid intima-media thickness (IMT), carotid plaque or stenosis of coronary, carotid or femoral arteries. However even this approach is more complicated than it may seem. Coronary calcium can also be expected to have genetic associations that are different from those of other phenotypes of atherosclerosis, since not all plaques are calcified, and there are biological processes that are particular to calcification [7,8]. Biological differences among ultrasound phenotypes of “atherosclerosis” can be seen by comparing the proportion of variance explained (R2) by coronary risk factors in multiple regression, with various phenotypes of “atherosclerosis” as the
J.D. Spence / Atherosclerosis 223 (2012) 98e101
99
Table 1 Stepwise linear multiple regression of baseline total plaque area. Coefficientsa
(Constant) Age Sex (F ¼ 1, M ¼ 0) Smoking status Diabetes Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Smoking (pack-years) Systolic pressure Diastolic pressure On lipid medication On blood pressure medication
Unstandardized coefficients
Standardized coefficients
B
Std. error
Beta
0.596 0.017 0.175 0.122 0.093 0.040 0.010 0.049 0.015 0.002
0.070 0.001 0.016 0.014 0.022 0.019 0.010 0.025 0.018 0.000
0.549 0.186 0.176 0.065 0.095 0.022 0.045 0.033 0.088
8.514 30.133 11.003 8.846 4.167 2.091 1.014 1.936 0.789 4.372
0.000 0.000 0.000 0.000 0.000 0.037 0.311 0.053 0.430 0.000
0.003 0.003 0.106
0.000 0.001 0.016
0.139 0.069 0.111
6.974 3.628 6.634
0.000 0.000 0.000
0.078
0.015
0.083
5.127
0.000
R2 ¼ 0.573 (The R2 for raw plaque area was 0.397). a Dependent variable: total plaque area normalized transformation.
t
by
Sig.
a
cube
root
dependent variable. Traditional coronary risk factors explain approximately half of carotid total plaque area (TPA), as shown in Table 1, but only 15e17% of IMT and only 13% of carotid stenosis measured by ultrasound [4,9]. The association of Lp(a) with stenosis and occlusion but not plaque area [10] is an example of this principle; the reverse for plasma homocysteine [4] is another. Carotid IMT measured in the distal common carotid where there is no plaque is biologically (and therefore genetically) different from IMT measured in the bulb and including plaque thickness [11,12]; the former will tend to be associated with genetic factors contributing to hypertension, whereas the latter (if cases with and without plaque included in the IMT measurement were not lumped together e a serious problem) would more closely relate to genetic factors affecting plaque. Pollex and Hegele reviewed in 2006 [13]
Table 2 Linear multiple regression of plaque progression from baseline to a year later. Coefficientsa Baseline variables
(Constant) Age Sex (F ¼ 1, M ¼ 0) Smoking status Diabetes Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Smoking (pack-years) Systolic pressure Diastolic pressure On lipid medication On blood pressure medication
Unstandardized coefficients
Standardized coefficients
t
Sig.
B
Std. error
Beta
0.028 0.002 0.032 0.012 0.034 0.168 0.036 0.090 0.170 0.001
0.256 0.002 0.056 0.053 0.083 0.093 0.046 0.106 0.087 0.002
0.029 0.021 0.010 0.014 0.232 0.040 0.053 0.242 0.024
0.108 0.729 0.564 0.225 0.408 1.800 0.784 0.854 1.951 0.538
0.914 0.466 0.573 0.822 0.683 0.072 0.433 0.393 0.051 0.591
0.003 0.004 0.010 0.034
0.002 0.003 0.055 0.053
0.099 0.069 0.007 0.022
2.231 1.655 0.186 0.644
0.026 0.098 0.853 0.519
R2 ¼ 0.016. a Dependent variable: plaque progression from baseline to one year later.
Fig. 1. Unexplained atherosclerosis and protection from atherosclerosis. Measured carotid plaque area is shown on the X-axis, with predicted plaque area from the regression model shown in Table 1. The two individuals shown by the arrows are outliers; their residual scores (shown to the right of the arrows) are the quantitative traits, and are similar to their standard deviation above and below the regression line.
the differences in genetic associations with IMT versus plaque burden. A phenotype that is more closely related biologically to atherosclerosis than IMT [14], and which is also a stronger predictor of cardiovascular events than IMT [11,15], is plaque burden, measured as carotid total plaque area (or in future, plaque volume [16] or vessel wall volume [17]). A powerful phenotype for assessing genetic effects on atherosclerosis is a quantitative trait computed from the residual score (amounting to the distance off the regression line) of plaque burden adjusted for coronary risk factors in multiple regression, described in this journal in 1999 [18]. I call this unexplained atherosclerosis. This is illustrated in Fig. 1 and Table 3. With Hegele and colleagues, we have used this approach to show that the D9N variant of lipoprotein lipase [4], mannose binding lectin [19] and polymorphisms
Table 3 Examples of unexplained atherosclerosis and protection. These are the values used in the regression model to compute the standardized residual scores for the two individuals whose distance off the regression line are shown in Fig. 1. Baseline
Age Sex Diabetic Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Systolic pressure Diastolic pressure Smoking (pack-years) Plaque area Residual score
Unexplained
Protected
46 Male No 5.02 1.54 1.03 3.29 170 105 30 704 3.68
82 Male No 4.04 2.0 0.72 2.40 167 85 195 173 2.31
100
J.D. Spence / Atherosclerosis 223 (2012) 98e101
progression, as shown in Table 2. This makes it possible to see the effect of new risk factors/therapeutic targets. Examples of this include the effect of the blood pressure rise during mental stress [23], and plasma aldosterone [24]. Fig. 2 and Table 4 show examples of extreme outliers with unexplained progression and regression of atherosclerosis. 1. Conclusion Measuring the burden of atherosclerosis, and of atherosclerosis progression, affords the opportunity of assessing genetic associations with atherosclerosis in a much cleaner way than do studies of association of events such as myocardial infarction. The quantitative traits described here have great potential for uncovering new therapeutic targets against atherosclerosis and its complications. Statement of originality This commentary has not been published previously, although some of the concepts have previously been published. The figures are original, but Fig. 1 is similar to figures that have been published before (previous versions were based on different variables in the regression model, and did not have arrows identifying individual outliers). Table 1 is similar to tables that have been published previously, but again based on different variables in the regression model, and on a different sample of patients. Fig. 2. Unexplained progression and regression of plaque. The X-axis shows the progression of total plaque area from baseline to a year later; the Y axis shows predicted progression. The arrows identify two outliers, with their residual scores shown above the arrows. Their residual scores are the quantitative traits, and are similar to their standard deviation above and below the regression line.
of PCK1 [20] and PPARg [21] are significantly associated with carotid plaque area; in some of these studies the associations with plaque area were different from those with IMT. With Lanktree and colleagues we have calculated [22] that by using the extremes of the distribution (the 5% of outliers at each extreme), the sample size required for genome-wide association studies can be reduced by three quarters. An even more powerful phenotype, particularly for identifying new therapeutic targets in atherosclerosis, is unexplained progression/regression of atherosclerosis. It is more powerful because baseline plaque area is the result of the cumulative effects of the risk factors shown in Table 1, over a lifetime. As shown in Table 1, age accounts for more than half of the explained variance (the Beta is 0.549). However, age accounts for only 3% of plaque Table 4 Examples of unexplained progression and regression. These are the values used in the regression model to compute the standardized residual scores for the two individuals whose distance off the regression line is shown in Fig. 2. Standardized residual scores are similar to standard deviations. Progression
Age Sex Diabetic Total cholesterol Triglycerides HDL cholesterol LDL cholesterol Systolic pressure Diastolic pressure Smoking (pack-years) Plaque progression (mm2) Residual score
Unexplained progression
Unexplained regression
75 Male No 3.70 0.86 1.45 1.86 150 76 20 29.20 5.59
83 Male No 5.15 2.77 0.82 3.10 151 81 0 27.90 5.35
References [1] Dong C, Beecham A, Wang L, Blanton SH, Rundek T, Sacco RL. Follow-up association study of linkage regions reveals multiple candidate genes for carotid plaque in dominicans. Atherosclerosis 2012;223(1):177e83. [2] Dong C, Beecham A, Slifer S, Wang L, Blanton SH, Wright CB, et al. Genomewide linkage and peakwide association analyses of carotid plaque in Caribbean hispanics. Stroke 2010;41:2750e6. [3] Spence JD, Hegele RA. Noninvasive phenotypes of atherosclerosis. Arterioscler Thromb Vasc Biol 2004;24:e188e9. [4] Spence JD, Hegele RA. Noninvasive phenotypes of atherosclerosis: similar windows but different views. Stroke 2004;35:649e53. [5] Hegele RA. The genetic basis of atherosclerosis. Int J Clin Lab Res 1997;27: 2e13. [6] Li MD, Cheng R, Ma JZ, Swan GE. A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction 2003;98:23e31. [7] Shao JS, Cai J, Towler DA. Molecular mechanisms of vascular calcification: lessons learned from the aorta. Arterioscler Thromb Vasc Biol 2006;26: 1423e30. [8] Towler DA, Demer LL. Thematic series on the pathobiology of vascular calcification: an introduction. Circ Res 2011;108:1378e80. [9] Al Shali K, House AA, Hanley AJ, Khan HM, Harris SB, Mamakeesick M, et al. Differences between carotid wall morphological phenotypes measured by ultrasound in one, two and three dimensions. Atherosclerosis 2005;178: 319e25. [10] Klein JH, Hegele RA, Hackam DG, Koschinsky ML, Huff MW, Spence JD. Lipoprotein(a) is associated differentially with carotid stenosis, occlusion, and total plaque area. Arterioscler Thromb Vasc Biol 2008;28:1851e6. [11] Inaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intimamedia thickness, more accurately predicts coronary artery disease events: a meta-analysis. Atherosclerosis 2011;220:128e33. [12] Spence JD. Carotid plaque measurement is superior to IMT. Invited editorial comment on: carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysisYoichi Inaba, M.D., Jennifer A. Chen M.D., Steven R. Bergmann M.D., Ph.D. Atherosclerosis 2011;220:34e5. [13] Pollex RL, Hegele R. Genetic determinants of carotid ultrasound traits. Curr Atheroscler Rep 2006;8:206e15. [14] Finn AV, Kolodgie FD, Virmani R. Correlation between carotid intimal/medial thickness and atherosclerosis. A point of view from pathology. Arterioscler Thromb Vasc Biol 2010;30:177e81. [15] Spence JD, Rundek T. Toward clinical applications of carotid ultrasound: intima-media thickness, plaque area, and three-dimensional phenotypes. In: Nicolaides AN, Beach KW, Kiriacou E, Pattichis CS, editors. Ultrasound and carotid bifurcation atherosclerosis. London: Springer-Verlag; 2012. p. 431e48. [16] Ainsworth CD, Blake CC, Tamayo A, Beletsky V, Fenster A, Spence JD. 3D ultrasound measurement of change in carotid plaque volume: a tool for rapid evaluation of new therapies. Stroke 2005;36:1904e9.
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