Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts

Peter Würtz; Aki S. Havulinna; Pasi Soininen; Tuulia Tynkkynen; David Prieto-Merino; Therese Tillin; Anahita Ghorbani; Anna Artati; Qin Wang; Mika Tiainen; Antti J. Kangas; Johannes Kettunen; Jari Kaikkonen; Vera Mikkilä; Antti Jula; Mika Kähönen; Terho Lehtimäki; Debbie A. Lawlor; Tom R. Gaunt; Alun D. Hughes; Naveed Sattar; Thomas Illig; Jerzy Adamski; Thomas J. Wang; Markus Perola; Samuli Ripatti; Ramachandran S. Vasan; Olli T. Raitakari; Robert E. Gerszten; Juan Pablo Casas; Nish Chaturvedi; Mika Ala-Korpela; Veikko Salomaa

doi:10.1161/CIRCULATIONAHA.114.013116

Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts

Peter Würtz, Aki S. Havulinna, Pasi Soininen, Tuulia Tynkkynen, David Prieto-Merino, Therese Tillin, Anahita Ghorbani, Anna Artati, Qin Wang, Mika Tiainen, Antti J. Kangas, Johannes Kettunen, Jari Kaikkonen, Vera Mikkilä, Antti Jula, Mika Kähönen, Terho Lehtimäki, Debbie A. Lawlor, Tom R. Gaunt, Alun D. HughesNaveed Sattar, Thomas Illig, Jerzy Adamski, Thomas J. Wang, Markus Perola, Samuli Ripatti, Ramachandran S. Vasan, Olli T. Raitakari, Robert E. Gerszten, Juan Pablo Casas, Nish Chaturvedi, Mika Ala-Korpela, Veikko Salomaa

Research output: Contribution to journal › Article › peer-review

468 Scopus citations

Abstract

Background - High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors. Methods and Results - We applied quantitative nuclear magnetic resonance metabolomics to identify the biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the National Finnish FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the Southall and Brent Revisited (SABRE) study (n=2622; 573 events) and British Women's Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes mellitus, and medication. When further adjusting for routine lipids, 4 metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation, 1.18; 95% confidence interval, 1.12-1.24; P=4×10^-10) and monounsaturated fatty acid levels (1.17; 1.11-1.24; P=1×10^-8) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89; 0.84-0.94; P=6×10^-5) and docosahexaenoic acid levels (0.90; 0.86-0.95; P=5×10^-5) were associated with lower risk. A risk score incorporating these 4 biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the 2 validation cohorts (relative integrated discrimination improvement, 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5% to 10% risk range (net reclassification, 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289). Conclusions - Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.

Original language	English (US)
Pages (from-to)	774-785
Number of pages	12
Journal	Circulation
Volume	131
Issue number	9
DOIs	https://doi.org/10.1161/CIRCULATIONAHA.114.013116
State	Published - 2015
Externally published	Yes

Keywords

Amino acids
Biological markers
Fatty acids
Metabolomics
Risk factors

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine
Physiology (medical)

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10.1161/CIRCULATIONAHA.114.013116

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Würtz, P., Havulinna, A. S., Soininen, P., Tynkkynen, T., Prieto-Merino, D., Tillin, T., Ghorbani, A., Artati, A., Wang, Q., Tiainen, M., Kangas, A. J., Kettunen, J., Kaikkonen, J., Mikkilä, V., Jula, A., Kähönen, M., Lehtimäki, T., Lawlor, D. A., Gaunt, T. R., ... Salomaa, V. (2015). Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts. Circulation, 131(9), 774-785. https://doi.org/10.1161/CIRCULATIONAHA.114.013116

Würtz, P, Havulinna, AS, Soininen, P, Tynkkynen, T, Prieto-Merino, D, Tillin, T, Ghorbani, A, Artati, A, Wang, Q, Tiainen, M, Kangas, AJ, Kettunen, J, Kaikkonen, J, Mikkilä, V, Jula, A, Kähönen, M, Lehtimäki, T, Lawlor, DA, Gaunt, TR, Hughes, AD, Sattar, N, Illig, T, Adamski, J, Wang, TJ, Perola, M, Ripatti, S, Vasan, RS, Raitakari, OT, Gerszten, RE, Casas, JP, Chaturvedi, N, Ala-Korpela, M & Salomaa, V 2015, 'Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts', Circulation, vol. 131, no. 9, pp. 774-785. https://doi.org/10.1161/CIRCULATIONAHA.114.013116

@article{e17b373fac19436cb9a537e6efda7bb4,

title = "Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts",

abstract = "Background - High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors. Methods and Results - We applied quantitative nuclear magnetic resonance metabolomics to identify the biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the National Finnish FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the Southall and Brent Revisited (SABRE) study (n=2622; 573 events) and British Women's Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes mellitus, and medication. When further adjusting for routine lipids, 4 metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation, 1.18; 95% confidence interval, 1.12-1.24; P=4×10-10) and monounsaturated fatty acid levels (1.17; 1.11-1.24; P=1×10-8) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89; 0.84-0.94; P=6×10-5) and docosahexaenoic acid levels (0.90; 0.86-0.95; P=5×10-5) were associated with lower risk. A risk score incorporating these 4 biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the 2 validation cohorts (relative integrated discrimination improvement, 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5% to 10% risk range (net reclassification, 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289). Conclusions - Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.",

keywords = "Amino acids, Biological markers, Fatty acids, Metabolomics, Risk factors",

author = "Peter W{\"u}rtz and Havulinna, {Aki S.} and Pasi Soininen and Tuulia Tynkkynen and David Prieto-Merino and Therese Tillin and Anahita Ghorbani and Anna Artati and Qin Wang and Mika Tiainen and Kangas, {Antti J.} and Johannes Kettunen and Jari Kaikkonen and Vera Mikkil{\"a} and Antti Jula and Mika K{\"a}h{\"o}nen and Terho Lehtim{\"a}ki and Lawlor, {Debbie A.} and Gaunt, {Tom R.} and Hughes, {Alun D.} and Naveed Sattar and Thomas Illig and Jerzy Adamski and Wang, {Thomas J.} and Markus Perola and Samuli Ripatti and Vasan, {Ramachandran S.} and Raitakari, {Olli T.} and Gerszten, {Robert E.} and Casas, {Juan Pablo} and Nish Chaturvedi and Mika Ala-Korpela and Veikko Salomaa",

note = "Publisher Copyright: {\textcopyright} 2015 American Heart Association, Inc.",

year = "2015",

doi = "10.1161/CIRCULATIONAHA.114.013116",

language = "English (US)",

volume = "131",

pages = "774--785",

journal = "Circulation",

issn = "0009-7322",

publisher = "Lippincott Williams and Wilkins",

number = "9",

}

TY - JOUR

T1 - Metabolite profiling and cardiovascular event risk

T2 - A prospective study of 3 population-based cohorts

AU - Würtz, Peter

AU - Havulinna, Aki S.

AU - Soininen, Pasi

AU - Tynkkynen, Tuulia

AU - Prieto-Merino, David

AU - Tillin, Therese

AU - Ghorbani, Anahita

AU - Artati, Anna

AU - Wang, Qin

AU - Tiainen, Mika

AU - Kangas, Antti J.

AU - Kettunen, Johannes

AU - Kaikkonen, Jari

AU - Mikkilä, Vera

AU - Jula, Antti

AU - Kähönen, Mika

AU - Lehtimäki, Terho

AU - Lawlor, Debbie A.

AU - Gaunt, Tom R.

AU - Hughes, Alun D.

AU - Sattar, Naveed

AU - Illig, Thomas

AU - Adamski, Jerzy

AU - Wang, Thomas J.

AU - Perola, Markus

AU - Ripatti, Samuli

AU - Vasan, Ramachandran S.

AU - Raitakari, Olli T.

AU - Gerszten, Robert E.

AU - Casas, Juan Pablo

AU - Chaturvedi, Nish

AU - Ala-Korpela, Mika

AU - Salomaa, Veikko

PY - 2015

Y1 - 2015

N2 - Background - High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors. Methods and Results - We applied quantitative nuclear magnetic resonance metabolomics to identify the biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the National Finnish FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the Southall and Brent Revisited (SABRE) study (n=2622; 573 events) and British Women's Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes mellitus, and medication. When further adjusting for routine lipids, 4 metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation, 1.18; 95% confidence interval, 1.12-1.24; P=4×10-10) and monounsaturated fatty acid levels (1.17; 1.11-1.24; P=1×10-8) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89; 0.84-0.94; P=6×10-5) and docosahexaenoic acid levels (0.90; 0.86-0.95; P=5×10-5) were associated with lower risk. A risk score incorporating these 4 biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the 2 validation cohorts (relative integrated discrimination improvement, 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5% to 10% risk range (net reclassification, 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289). Conclusions - Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.

AB - Background - High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors. Methods and Results - We applied quantitative nuclear magnetic resonance metabolomics to identify the biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the National Finnish FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the Southall and Brent Revisited (SABRE) study (n=2622; 573 events) and British Women's Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes mellitus, and medication. When further adjusting for routine lipids, 4 metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation, 1.18; 95% confidence interval, 1.12-1.24; P=4×10-10) and monounsaturated fatty acid levels (1.17; 1.11-1.24; P=1×10-8) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89; 0.84-0.94; P=6×10-5) and docosahexaenoic acid levels (0.90; 0.86-0.95; P=5×10-5) were associated with lower risk. A risk score incorporating these 4 biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the 2 validation cohorts (relative integrated discrimination improvement, 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5% to 10% risk range (net reclassification, 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289). Conclusions - Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.

KW - Amino acids

KW - Biological markers

KW - Fatty acids

KW - Metabolomics

KW - Risk factors

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SN - 0009-7322

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EP - 785

JO - Circulation

JF - Circulation

IS - 9

ER -

Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts

Abstract

Keywords

ASJC Scopus subject areas

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