- Email: [email protected]
Age-associated cardiovascular risk and metabolomics of mitochondrial dysfunction
Atherosclerosis 232 (2014) 257e258
Contents lists available at ScienceDirect
Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Invited commentary
Age-associated cardiovascular risk and metabolomics of mitochondrial dysfunction Gian Paolo Fadini a, b, * a b
Department of Medicine, University of Padova, Italy Venetian Institute of Molecular Medicine, Padova, Italy
a r t i c l e i n f o Article history: Received 30 October 2013 Accepted 31 October 2013 Available online 19 November 2013 Keywords: Stratification Systems biology Statistics
Although the prevalence and incidence of cardiovascular disease (CVD) increases with age, the prediction of future CV events in the elderly is challenging. Most CV risk scores are applicable to subjects younger than 65e75 years and their transferability to older individuals is questionable. In addition, classical CV risk factors tend to loose their impact in very old persons, owing to the “survivor effect”, or they even turn from harmful to protective (e.g. “obesity paradox”) [1e3]. Furthermore, the pathophysiology of CVD in the elderly may be different from that of younger subjects: vascular stiffness and calcification, haemodynamic and autonomic dysregulation, small artery fragility, anaemia, and impaired renal function are highly prevalent and contribute to CV events in this population [4]. The molecular and cellular mechanisms driving vascular senescence are still incompletely understood, but studies in experimental animal models have indicated a central role of mitochondria in determining lifespan and cardiovascular ageing, through regulation of glutathione content, antioxidant defences, and electron transport chain [5]. In addition to processes generating oxidative stress, a generalized mitochondrial dysfunction can lead to dysregulation of mitochondrial turnover, driving severe impairments in cellular bioenergetics. Studying the complex molecular machinery of mitochondrial function in intact organisms is
DOI of original article: http://dx.doi.org/10.1016/j.atherosclerosis.2013.10.029. * Dept. of Medicine, Via Giustiniani 2, 35128 Padova, Italy. Tel.: þ39 049 8214318; fax: þ39 049 8212184. E-mail addresses: [email protected], [email protected]. 0021-9150/$ e see front matter Ó 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atherosclerosis.2013.10.038
challenging, especially in humans. “Omics” approaches, including genomics, proteomics and metabolomics, are gaining increasing interests for their ability to address pathological processes using unbiased data mining techniques. Integrating results coming from different -omics can yield a comprehensive “systems biology” overview of a given disease, providing a benchmark for future developments [6,7]. In recent years, this methodology is being successfully applied to cardiovascular risk prediction [8,9]. In this issue of Atherosclerosis, Rizza et al. [10] from the Department of Systems Medicine at the University of Rome Tor Vergata report the results of a small longitudinal study on elderly subjects, investigating whether a mass spectrometry-based metabolomic profiling targeted to mitochondrial metabolites is able to improve prediction of future cardiovascular events, over and beyond traditional risk scores. Out of 49 metabolites related to mitochondrial function, a factorial analysis identified a group composed by medium- and long-chain acylcarnitines that was independently associated with the incidence of major adverse cardiovascular events (MACE). Carnitine-acylcarnitines shuttle long chain fatty acids to the mitochondrial matrix for b-oxidation. Excess generation of mitochondrial oxidative stress is associated with dysfunction of the shuttle, a condition that can result in increased plasma acylcarnitines [11,12]. Therefore, circulating acylcarnitines concentrations can reflect ongoing mitochondrial dysfunction. Importantly, not only were acylcarnitine concentrations associated with incident MACE, but they were also able to improve risk stratification, as shown by statistical metrics specifically designed to address this issue, namely the integrated discrimination improvement (IDI) and the net reclassification improvement (NRI) [13]. Although metabolomic profiling was unable to significantly change C-statistics, the improvement in patients’ risk classification shown by IDI and NRI is consistent with a good performance of the factor e medium- and long-chain acylcarnitines e as a new cardiovascular risk biomarker. Indeed, it should be noted that improvements in C-statistics are rarely obtained using surrogate biomarkers and, based on C-statistics, even established risk factors (e.g. HDL cholesterol) might be dismissed as non-significant. The findings reported by Rizza et al. come from a small cohort of 67 elderly subjects and therefore need to be replicated in larger populations. To account for this limitation, the authors used the
258
G.P. Fadini / Atherosclerosis 232 (2014) 257e258
random survival forest (RSF) methodology, which is suitable for analysing time-to-event data in small-sized samples. In this model, randomization is introduced in two forms: (i) a high number of randomly drawn bootstrap samples of the data are used to grow trees; (ii) at each node of the tree, randomly selected subsets of variables are chosen as candidate covariates. The bootstrapping procedure renders results less dependent on the specific characteristics of the cohort under investigation and on small numbers of cases driving statistical significance. The selection of variables in the model is also randomly rotated allowing a more reliable final model, derived from averaged trees. This statistical approach has clear advances over conventional survival methods, used in similar studies [14], that rely on restrictive assumptions such as proportional hazards, non-collinearity, parametric testing, and stepwise procedures for variable selection [15]. In conclusion, the study by Rizzo et al. has the advantages over previous similar investigations [14,16,17] that statistical analyses has been tailored to answer the specific question of whether metabolomic profiling offers additional information beyond traditional risk assessment in a small population of elderly subjects at high CV risk, in whom however, predictive variables cannot be selected a priori. Based on such enthusiastic results, the application of this methodology to larger cohorts is warranted. These “systems medicine” studies have the potential to explore the relationships between cardiovascular risk and biological processes otherwise not amenable to clinical investigation, such as mitochondrial metabolism.
Conflict of interest None declared.
Acknowledgements None.
References [1] Anderson KM, Castelli WP, Levy D. Cholesterol and mortality. 30 years of follow-up from the Framingham study. J Am Med Assoc 1987;257:2176e80. [2] Kannel WB, D’Agostino RB, Silbershatz H. Blood pressure and cardiovascular morbidity and mortality rates in the elderly. Am Heart J 1997;134:758e63. [3] Kovacic JC, Lee P, Baber U, et al. Inverse relationship between body mass index and coronary artery calcification in patients with clinically significant coronary lesions. Atherosclerosis 2012;221:176e82. [4] Freitas WM, Carvalho LS, Moura FA, Sposito AC. Atherosclerotic disease in octogenarians: a challenge for science and clinical practice. Atherosclerosis 2012;225:281e9. [5] Dai DF, Rabinovitch PS, Ungvari Z. Mitochondria and cardiovascular aging. Circ Res 2012;110:1109e24. [6] Stock J. The emerging role of lipidomics. Atherosclerosis 2012;221:38e40. [7] Didangelos A, Stegemann C, Mayr M. The -omics era: proteomics and lipidomics in vascular research. Atherosclerosis 2012;221:12e7. [8] Shah SH, Kraus WE, Newgard CB. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 2012;126:1110e20. [9] Wang Z, Tang WH, Cho L, Brennan DM, Hazen SL. Targeted metabolomic evaluation of arginine methylation and cardiovascular risks: potential mechanisms beyond nitric oxide synthase inhibition. Arterioscler Thromb Vasc Biol 2009;29:1383e91. [10] Rizza S, Copetti M, Rossi C, et al. Metabolomics signature improves the prediction of cardiovascular events in elderly subjects. Atherosclerosis 2014;232(2):260e4. [11] Setoyama D, Fujimura Y, Miura D. Metabolomics reveals that carnitine palmitoyltransferase-1 is a novel target for oxidative inactivation in human cells. Genes Cells 2013. [12] McGill MR, Li F, Sharpe MR, et al. Circulating acylcarnitines as biomarkers of mitochondrial dysfunction after acetaminophen overdose in mice and humans. Arch Toxicol 2013. [13] Jensen JM, Voss M, Hansen VB, et al. Risk stratification of patients suspected of coronary artery disease: comparison of five different models. Atherosclerosis 2012;220:557e62. [14] Shah SH, Sun JL, Stevens RD, et al. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am Heart J 2012;163:844e50. e841. [15] Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat 2008;2:841e60. [16] Shah AA, Craig DM, Sebek JK, et al. Metabolic profiles predict adverse events after coronary artery bypass grafting. J Thorac Cardiovasc Surg 2012;143: 873e8. [17] Shah SH, Bain JR, Muehlbauer MJ, et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ Cardiovasc Genet 2010;3:207e14.
Contents lists available at ScienceDirect
Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Invited commentary
Age-associated cardiovascular risk and metabolomics of mitochondrial dysfunction Gian Paolo Fadini a, b, * a b
Department of Medicine, University of Padova, Italy Venetian Institute of Molecular Medicine, Padova, Italy
a r t i c l e i n f o Article history: Received 30 October 2013 Accepted 31 October 2013 Available online 19 November 2013 Keywords: Stratification Systems biology Statistics
Although the prevalence and incidence of cardiovascular disease (CVD) increases with age, the prediction of future CV events in the elderly is challenging. Most CV risk scores are applicable to subjects younger than 65e75 years and their transferability to older individuals is questionable. In addition, classical CV risk factors tend to loose their impact in very old persons, owing to the “survivor effect”, or they even turn from harmful to protective (e.g. “obesity paradox”) [1e3]. Furthermore, the pathophysiology of CVD in the elderly may be different from that of younger subjects: vascular stiffness and calcification, haemodynamic and autonomic dysregulation, small artery fragility, anaemia, and impaired renal function are highly prevalent and contribute to CV events in this population [4]. The molecular and cellular mechanisms driving vascular senescence are still incompletely understood, but studies in experimental animal models have indicated a central role of mitochondria in determining lifespan and cardiovascular ageing, through regulation of glutathione content, antioxidant defences, and electron transport chain [5]. In addition to processes generating oxidative stress, a generalized mitochondrial dysfunction can lead to dysregulation of mitochondrial turnover, driving severe impairments in cellular bioenergetics. Studying the complex molecular machinery of mitochondrial function in intact organisms is
DOI of original article: http://dx.doi.org/10.1016/j.atherosclerosis.2013.10.029. * Dept. of Medicine, Via Giustiniani 2, 35128 Padova, Italy. Tel.: þ39 049 8214318; fax: þ39 049 8212184. E-mail addresses: [email protected], [email protected]. 0021-9150/$ e see front matter Ó 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atherosclerosis.2013.10.038
challenging, especially in humans. “Omics” approaches, including genomics, proteomics and metabolomics, are gaining increasing interests for their ability to address pathological processes using unbiased data mining techniques. Integrating results coming from different -omics can yield a comprehensive “systems biology” overview of a given disease, providing a benchmark for future developments [6,7]. In recent years, this methodology is being successfully applied to cardiovascular risk prediction [8,9]. In this issue of Atherosclerosis, Rizza et al. [10] from the Department of Systems Medicine at the University of Rome Tor Vergata report the results of a small longitudinal study on elderly subjects, investigating whether a mass spectrometry-based metabolomic profiling targeted to mitochondrial metabolites is able to improve prediction of future cardiovascular events, over and beyond traditional risk scores. Out of 49 metabolites related to mitochondrial function, a factorial analysis identified a group composed by medium- and long-chain acylcarnitines that was independently associated with the incidence of major adverse cardiovascular events (MACE). Carnitine-acylcarnitines shuttle long chain fatty acids to the mitochondrial matrix for b-oxidation. Excess generation of mitochondrial oxidative stress is associated with dysfunction of the shuttle, a condition that can result in increased plasma acylcarnitines [11,12]. Therefore, circulating acylcarnitines concentrations can reflect ongoing mitochondrial dysfunction. Importantly, not only were acylcarnitine concentrations associated with incident MACE, but they were also able to improve risk stratification, as shown by statistical metrics specifically designed to address this issue, namely the integrated discrimination improvement (IDI) and the net reclassification improvement (NRI) [13]. Although metabolomic profiling was unable to significantly change C-statistics, the improvement in patients’ risk classification shown by IDI and NRI is consistent with a good performance of the factor e medium- and long-chain acylcarnitines e as a new cardiovascular risk biomarker. Indeed, it should be noted that improvements in C-statistics are rarely obtained using surrogate biomarkers and, based on C-statistics, even established risk factors (e.g. HDL cholesterol) might be dismissed as non-significant. The findings reported by Rizza et al. come from a small cohort of 67 elderly subjects and therefore need to be replicated in larger populations. To account for this limitation, the authors used the
258
G.P. Fadini / Atherosclerosis 232 (2014) 257e258
random survival forest (RSF) methodology, which is suitable for analysing time-to-event data in small-sized samples. In this model, randomization is introduced in two forms: (i) a high number of randomly drawn bootstrap samples of the data are used to grow trees; (ii) at each node of the tree, randomly selected subsets of variables are chosen as candidate covariates. The bootstrapping procedure renders results less dependent on the specific characteristics of the cohort under investigation and on small numbers of cases driving statistical significance. The selection of variables in the model is also randomly rotated allowing a more reliable final model, derived from averaged trees. This statistical approach has clear advances over conventional survival methods, used in similar studies [14], that rely on restrictive assumptions such as proportional hazards, non-collinearity, parametric testing, and stepwise procedures for variable selection [15]. In conclusion, the study by Rizzo et al. has the advantages over previous similar investigations [14,16,17] that statistical analyses has been tailored to answer the specific question of whether metabolomic profiling offers additional information beyond traditional risk assessment in a small population of elderly subjects at high CV risk, in whom however, predictive variables cannot be selected a priori. Based on such enthusiastic results, the application of this methodology to larger cohorts is warranted. These “systems medicine” studies have the potential to explore the relationships between cardiovascular risk and biological processes otherwise not amenable to clinical investigation, such as mitochondrial metabolism.
Conflict of interest None declared.
Acknowledgements None.
References [1] Anderson KM, Castelli WP, Levy D. Cholesterol and mortality. 30 years of follow-up from the Framingham study. J Am Med Assoc 1987;257:2176e80. [2] Kannel WB, D’Agostino RB, Silbershatz H. Blood pressure and cardiovascular morbidity and mortality rates in the elderly. Am Heart J 1997;134:758e63. [3] Kovacic JC, Lee P, Baber U, et al. Inverse relationship between body mass index and coronary artery calcification in patients with clinically significant coronary lesions. Atherosclerosis 2012;221:176e82. [4] Freitas WM, Carvalho LS, Moura FA, Sposito AC. Atherosclerotic disease in octogenarians: a challenge for science and clinical practice. Atherosclerosis 2012;225:281e9. [5] Dai DF, Rabinovitch PS, Ungvari Z. Mitochondria and cardiovascular aging. Circ Res 2012;110:1109e24. [6] Stock J. The emerging role of lipidomics. Atherosclerosis 2012;221:38e40. [7] Didangelos A, Stegemann C, Mayr M. The -omics era: proteomics and lipidomics in vascular research. Atherosclerosis 2012;221:12e7. [8] Shah SH, Kraus WE, Newgard CB. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 2012;126:1110e20. [9] Wang Z, Tang WH, Cho L, Brennan DM, Hazen SL. Targeted metabolomic evaluation of arginine methylation and cardiovascular risks: potential mechanisms beyond nitric oxide synthase inhibition. Arterioscler Thromb Vasc Biol 2009;29:1383e91. [10] Rizza S, Copetti M, Rossi C, et al. Metabolomics signature improves the prediction of cardiovascular events in elderly subjects. Atherosclerosis 2014;232(2):260e4. [11] Setoyama D, Fujimura Y, Miura D. Metabolomics reveals that carnitine palmitoyltransferase-1 is a novel target for oxidative inactivation in human cells. Genes Cells 2013. [12] McGill MR, Li F, Sharpe MR, et al. Circulating acylcarnitines as biomarkers of mitochondrial dysfunction after acetaminophen overdose in mice and humans. Arch Toxicol 2013. [13] Jensen JM, Voss M, Hansen VB, et al. Risk stratification of patients suspected of coronary artery disease: comparison of five different models. Atherosclerosis 2012;220:557e62. [14] Shah SH, Sun JL, Stevens RD, et al. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am Heart J 2012;163:844e50. e841. [15] Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat 2008;2:841e60. [16] Shah AA, Craig DM, Sebek JK, et al. Metabolic profiles predict adverse events after coronary artery bypass grafting. J Thorac Cardiovasc Surg 2012;143: 873e8. [17] Shah SH, Bain JR, Muehlbauer MJ, et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ Cardiovasc Genet 2010;3:207e14.