RESEARCH ARTICLE
Metabolomics and Incidence of Atrial Fibrillation in African Americans: The Atherosclerosis Risk in Communities (ARIC) Study Alvaro Alonso1*, Bing Yu2, Waqas T. Qureshi3, Morgan E. Grams4,5, Elizabeth Selvin5,6, Elsayed Z. Soliman7,8, Laura R. Loehr9, Lin Y. Chen10, Sunil K. Agarwal11, Danny Alexander12, Eric Boerwinkle2,13
OPEN ACCESS Citation: Alonso A, Yu B, Qureshi WT, Grams ME, Selvin E, Soliman EZ, et al. (2015) Metabolomics and Incidence of Atrial Fibrillation in African Americans: The Atherosclerosis Risk in Communities (ARIC) Study. PLoS ONE 10(11): e0142610. doi:10.1371/ journal.pone.0142610 Editor: Tanja Zeller, Medical University Hamburg, University Heart Center, GERMANY
1 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America, 2 Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America, 3 Division of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America, 4 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America, 5 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America, 6 Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland, United States of America, 7 Epidemiological Cardiology Research Center (EPICARE), Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 8 Department of Medicine-Cardiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 9 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America, 10 Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America, 11 Mount Sinai Heart Hospital, New York, New York, United States of America, 12 Metabolon, Inc., Durham, North Carolina, United States of America, 13 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America * [email protected]
Received: July 10, 2015 Accepted: October 23, 2015 Published: November 6, 2015 Copyright: © 2015 Alonso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Study participants did not consent to have their data publicly available and freely accessible and, therefore, we cannot make share publicly the data. However, the data underlying our work can be obtained through two mechanisms. First, interested investigators can contact the ARIC Coordinating Center at the University of North Carolina – Chapel Hill. Details about the procedures for data request can be found in the following website: http://www2.cscc.unc.edu/aric/distributionagreements. Second, most ARIC data can be also obtained from BioLINCC, a repository maintained by
Abstract Background Atrial fibrillation (AF) is a common arrhythmia. Application of metabolomic approaches, which may identify novel pathways and biomarkers of disease risk, to a longitudinal epidemiologic study of AF has been limited.
Methods We determined the prospective association of 118 serum metabolites identified through untargeted metabolomics profiling with the incidence of newly-diagnosed AF in 1919 African-American men and women from the Atherosclerosis Risk in Communities study without AF at baseline (1987–1989). Incident AF cases through 2011 were ascertained from study electrocardiograms, hospital discharge codes, and death certificates.
PLOS ONE | DOI:10.1371/journal.pone.0142610 November 6, 2015
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Metabolomics in Atrial Fibrillation
the National Heart, Lung, and Blood Institute. The BioLINCC website (https://biolincc.nhlbi.nih.gov/) includes detailed information about the available data and the process to obtain such data. Any interested researcher could obtain a de-identified, minimal dataset needed to replicate or reprove your study findings pending ethical approval following any of the two mentioned mechanisms. Funding: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HSN268201100012C). The metabolomics research was sponsored by the National Human Genome Research Institute (3U01HG004402-02S1). This work was additionally supported by National Heart, Lung, and Blood Institute grant RC1HL099452 to Dr. Alonso and by National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK089174 and K24DK106414 to Dr. Selvin. The metabolomics measurements were sponsored by National Human Genome Research Institute (3U01HG004402-02S1). Reagents for the ALT, AST, and GGT assays were donated by Roche Diagnostics. Author DA is employed by Metabolon, Inc., a commercial entity. Metabolon, Inc. provided support in the form of salaries for this author [DA], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contribution’ section. Similarly, the remaining funders did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Danny Alexander is an employee of Metabolon, Inc. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.
Results During a median follow-up of 22 years, we identified 183 incident AF cases. In Cox proportional hazards models adjusted for age, sex, smoking, body mass index, systolic blood pressure, use of antihypertensive medication, diabetes, prevalent heart failure, prevalent coronary heart disease, and kidney function, two conjugated bile acids (glycolithocholate sulfate and glycocholenate sulfate) were significantly associated with AF risk after correcting for multiple comparisons (p90%) and surveillance of local hospitals, and hospitalization discharge codes are recorded. AF was considered present if ICD-9-CM codes 427.31 or 427.32 were present in a hospitalization in any position not accompanied by a procedure code for open cardiac surgery. This approach for case ascertainment has demonstrated adequate validity in the ARIC cohort and other studies.[10, 12] Finally, AF was considered present if the death certificate included ICD-9 code 427.3 or ICD-10 code I48.
Assessment of other covariates At baseline, information on age, sex, race, and smoking status was self-reported. Alcohol consumption was ascertained by an interviewer-administered questionnaire. Height and weight were measured with the participant lightly dressed. Body mass index was calculated as weight in kilograms divided by height in meters squared. Sitting blood pressure was measured three times using a random-zero sphygmomanometer after five minutes of rest, and the second and third measurements were averaged. Diabetes was defined as a fasting blood glucose 126 mg/ dL, non–fasting blood glucose 200 mg/dL, a self-reported physician diagnosis of diabetes, or current use of antidiabetic medication. Estimated glomerular filtration rate (eGFRCKD-EPI) was
PLOS ONE | DOI:10.1371/journal.pone.0142610 November 6, 2015
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Metabolomics in Atrial Fibrillation
calculated from serum creatinine using the CKD-EPI equation.[13] Serum albumin was measured with a Coulter DACOS (Coulter Diagnostics) using Coulter’s bromcresol green colorimetric assay. Liver enzymes (aspartate aminotransferase, alanine aminotransferase, gammaglutamyl transpeptidase) were measured in serum samples collected at visit 2 (1990–1992) using Roche reagents on the Roche Modular P800 Chemistry analyzer (Roche Diagnostics Corporation). Prevalent heart failure was defined using the Gothenburg criteria,[14] while prevalent coronary heart disease was considered present if the participant self-reported a history of myocardial infarction, coronary bypass surgery, or coronary angioplasty, or had evidence of a previous myocardial infarction by ECG at the baseline visit.
Statistical analysis The association of each metabolite with newly-diagnosed AF was evaluated using Cox proportional hazards regression. Time to follow-up was defined as the time between the baseline examination and the incidence of AF, death, loss to follow-up, or December 31, 2011, whichever occurred first. Metabolites with
Metabolomics and Incidence of Atrial Fibrillation in African Americans: The Atherosclerosis Risk in Communities (ARIC) Study Alvaro Alonso1*, Bing Yu2, Waqas T. Qureshi3, Morgan E. Grams4,5, Elizabeth Selvin5,6, Elsayed Z. Soliman7,8, Laura R. Loehr9, Lin Y. Chen10, Sunil K. Agarwal11, Danny Alexander12, Eric Boerwinkle2,13
OPEN ACCESS Citation: Alonso A, Yu B, Qureshi WT, Grams ME, Selvin E, Soliman EZ, et al. (2015) Metabolomics and Incidence of Atrial Fibrillation in African Americans: The Atherosclerosis Risk in Communities (ARIC) Study. PLoS ONE 10(11): e0142610. doi:10.1371/ journal.pone.0142610 Editor: Tanja Zeller, Medical University Hamburg, University Heart Center, GERMANY
1 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America, 2 Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America, 3 Division of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America, 4 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America, 5 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America, 6 Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland, United States of America, 7 Epidemiological Cardiology Research Center (EPICARE), Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 8 Department of Medicine-Cardiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 9 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America, 10 Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America, 11 Mount Sinai Heart Hospital, New York, New York, United States of America, 12 Metabolon, Inc., Durham, North Carolina, United States of America, 13 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America * [email protected]
Received: July 10, 2015 Accepted: October 23, 2015 Published: November 6, 2015 Copyright: © 2015 Alonso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Study participants did not consent to have their data publicly available and freely accessible and, therefore, we cannot make share publicly the data. However, the data underlying our work can be obtained through two mechanisms. First, interested investigators can contact the ARIC Coordinating Center at the University of North Carolina – Chapel Hill. Details about the procedures for data request can be found in the following website: http://www2.cscc.unc.edu/aric/distributionagreements. Second, most ARIC data can be also obtained from BioLINCC, a repository maintained by
Abstract Background Atrial fibrillation (AF) is a common arrhythmia. Application of metabolomic approaches, which may identify novel pathways and biomarkers of disease risk, to a longitudinal epidemiologic study of AF has been limited.
Methods We determined the prospective association of 118 serum metabolites identified through untargeted metabolomics profiling with the incidence of newly-diagnosed AF in 1919 African-American men and women from the Atherosclerosis Risk in Communities study without AF at baseline (1987–1989). Incident AF cases through 2011 were ascertained from study electrocardiograms, hospital discharge codes, and death certificates.
PLOS ONE | DOI:10.1371/journal.pone.0142610 November 6, 2015
1 / 10
Metabolomics in Atrial Fibrillation
the National Heart, Lung, and Blood Institute. The BioLINCC website (https://biolincc.nhlbi.nih.gov/) includes detailed information about the available data and the process to obtain such data. Any interested researcher could obtain a de-identified, minimal dataset needed to replicate or reprove your study findings pending ethical approval following any of the two mentioned mechanisms. Funding: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HSN268201100012C). The metabolomics research was sponsored by the National Human Genome Research Institute (3U01HG004402-02S1). This work was additionally supported by National Heart, Lung, and Blood Institute grant RC1HL099452 to Dr. Alonso and by National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK089174 and K24DK106414 to Dr. Selvin. The metabolomics measurements were sponsored by National Human Genome Research Institute (3U01HG004402-02S1). Reagents for the ALT, AST, and GGT assays were donated by Roche Diagnostics. Author DA is employed by Metabolon, Inc., a commercial entity. Metabolon, Inc. provided support in the form of salaries for this author [DA], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contribution’ section. Similarly, the remaining funders did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Danny Alexander is an employee of Metabolon, Inc. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.
Results During a median follow-up of 22 years, we identified 183 incident AF cases. In Cox proportional hazards models adjusted for age, sex, smoking, body mass index, systolic blood pressure, use of antihypertensive medication, diabetes, prevalent heart failure, prevalent coronary heart disease, and kidney function, two conjugated bile acids (glycolithocholate sulfate and glycocholenate sulfate) were significantly associated with AF risk after correcting for multiple comparisons (p90%) and surveillance of local hospitals, and hospitalization discharge codes are recorded. AF was considered present if ICD-9-CM codes 427.31 or 427.32 were present in a hospitalization in any position not accompanied by a procedure code for open cardiac surgery. This approach for case ascertainment has demonstrated adequate validity in the ARIC cohort and other studies.[10, 12] Finally, AF was considered present if the death certificate included ICD-9 code 427.3 or ICD-10 code I48.
Assessment of other covariates At baseline, information on age, sex, race, and smoking status was self-reported. Alcohol consumption was ascertained by an interviewer-administered questionnaire. Height and weight were measured with the participant lightly dressed. Body mass index was calculated as weight in kilograms divided by height in meters squared. Sitting blood pressure was measured three times using a random-zero sphygmomanometer after five minutes of rest, and the second and third measurements were averaged. Diabetes was defined as a fasting blood glucose 126 mg/ dL, non–fasting blood glucose 200 mg/dL, a self-reported physician diagnosis of diabetes, or current use of antidiabetic medication. Estimated glomerular filtration rate (eGFRCKD-EPI) was
PLOS ONE | DOI:10.1371/journal.pone.0142610 November 6, 2015
3 / 10
Metabolomics in Atrial Fibrillation
calculated from serum creatinine using the CKD-EPI equation.[13] Serum albumin was measured with a Coulter DACOS (Coulter Diagnostics) using Coulter’s bromcresol green colorimetric assay. Liver enzymes (aspartate aminotransferase, alanine aminotransferase, gammaglutamyl transpeptidase) were measured in serum samples collected at visit 2 (1990–1992) using Roche reagents on the Roche Modular P800 Chemistry analyzer (Roche Diagnostics Corporation). Prevalent heart failure was defined using the Gothenburg criteria,[14] while prevalent coronary heart disease was considered present if the participant self-reported a history of myocardial infarction, coronary bypass surgery, or coronary angioplasty, or had evidence of a previous myocardial infarction by ECG at the baseline visit.
Statistical analysis The association of each metabolite with newly-diagnosed AF was evaluated using Cox proportional hazards regression. Time to follow-up was defined as the time between the baseline examination and the incidence of AF, death, loss to follow-up, or December 31, 2011, whichever occurred first. Metabolites with
