Metabolomic Profiling in Relation to New-Onset Atrial Fibrillation (from the Framingham Heart Study)

Previous studies have shown several metabolic biomarkers to be associated with prevalent and incident atrial fibrillation (AF), but the results have not been replicated. We investigated metabolite profiles of 2,458 European ancestry participants from the Framingham Heart Study without AF at the index examination and followed them for 10 years for new-onset AF. Amino acids, organic acids, lipids, and other plasma metabolites were profiled by liquid chromatography–tandem mass spectrometry using fasting plasma samples. We conducted Cox proportional hazard analyses for association between metabolites and new-onset AF. We performed hypothesis-generating analysis to identify novel metabolites and hypothesis-testing analysis to confirm the previously reported associations between metabolites and AF. Mean age was 55.1 ± 9.9 years, and 53% were women. Incident AF developed in 156 participants (6.3%) in 10 years of follow-up. A total of 217 metabolites were examined, consisting of 54 positively charged metabolites, 59 negatively charged metabolites, and 104 lipids. None of the 217 metabolites met our a priori specified Bonferroni corrected level of significance in the multivariate analyses. We were unable to replicate previous results demonstrating associations between metabolites that we had measured and AF. In conclusion, in our metabolomics approach, none of the metabolites we tested were significantly associated with the risk of future AF.

Previous metabolomics investigations have focused on identifying metabolic pathways responsible for the initiation and maintenance of the arrhythmia in patients with known atrial fibrillation (AF) or postoperative AF. Recently, the Atherosclerotic Risk in Communities Study identified bile acids glycolithocholate sulfate and glycocholenate sulfate as markers of increased risk of new-onset AF. In the present study, we aimed to identify novel metabolic markers and to confirm the association between previously reported metabolites in relation to new-onset AF.


We studied participants from the Framingham Heart Study Offspring cohort, which was initiated in 1971. Participants (n = 5,124) underwent medical and laboratory evaluation every 4 to 8 years. Our study involved the fifth examination, consisting of 3,799 participants evaluated from 1991 to 1995. Metabolites were measured on 2,526 participants, in whom 49 were excluded because of prevalent AF and 19 because of missing covariates. Institutional Review Boards at Boston University Medical Center and Massachusetts General Hospital approved the study protocols. All participants provided written informed consent.

Fasting EDTA plasma metabolites were analyzed using targeted liquid chromatography–tandem mass spectrometry using 3 methods focusing on amino acids and amines, organic acids, and lipids. Data were acquired using either an AB SCIEX 4000 QTRAP triple quadrupole mass spectrometer (positively charged polar compounds and lipids) or an AB SCIEX 5500 QTRAP triple quadrupole mass spectrometer (negatively charged polar compounds). Briefly, polar, positively charged metabolites were separated using hydrophobic interaction liquid chromatography and analyzed using multiple reaction monitoring in the positive ion mode. Polar, negatively charged compounds, including central and polar phosphorylated metabolites, were separated using a Luna NH2 column (150 × 2 mm, Luna NH2; Phenomenex, Torrance, California) and analyzed using multiple reaction monitoring in the negative ion mode. Lipids were separated on a Prosphere C4 HPLC column and underwent full scan mass spectrometer analysis in the positive ion mode. MultiQuant software version 1.2 (AB SCIEX, Concord, Ontario, Canada) was used for automated peak integration and manual review of data quality before statistical analysis. For all 3 profiling platforms, a pooled plasma sample also was run after every 20 samples, and the peak areas in samples were normalized to metabolite peak areas in the nearest pooled plasma. We have previously published that coefficient of variabilities (CVs) for ∼80% of the analytes are <20%.

Physicians measured systolic and diastolic blood pressures twice in seated participants. Medications and tobacco use were ascertained by self-report. Tobacco use was defined as routine smoking of ≥1 cigarettes/day within the year before the Framingham Heart Study clinic visit. Diabetes was defined by fasting serum glucose of ≥126 mg/dl or use of insulin or oral hypoglycemic agents. Serum lipid and glucose concentrations were collected after an overnight fast. Myocardial infarction and heart failure were determined by a panel of 3 physicians who examined hospital and outpatient records of the participants, using previously reported criteria.

The presence of AF was determined from participant records from the Framingham Heart Study clinic as well as both other ambulatory clinic and inpatient hospital records and Holter monitoring. Participants were diagnosed with AF if either AF or atrial flutter was noted on electrocardiogram. Cardiologists at the Framingham Heart Study confirmed the incident AF electrocardiographic diagnoses.

We present baseline characteristics as mean ± standard deviation for continuous covariates and counts (%) for dichotomous covariates. Each metabolite was rank normalized before the analysis using Blom’s method. For the 209 metabolites, we used the corrected p values of ≤0.00024 (0.05/209) for hypothesis generating. For the 8 metabolites previously reported in the literature to be associated with AF (β hydroxybuterate, glycine, phosphocreatine, glucose, creatine, alanine, glutamine, betaine ), we used the Bonferroni corrected significance level of p ≤0.00625 (0.05/8) for hypothesis testing. We conducted Cox proportional hazard analyses for association between baseline metabolite (rank normalized values) and incident AF, adjusting for age and gender. We additionally adjusted for height, weight, systolic and diastolic blood pressures, current tobacco use, antihypertensive medication use, diabetes, myocardial infarction, heart failure, and statin use. We analyzed 10-year risk of AF by censoring on death, last contact, or 10 years from examination 5 date, whichever came first. Hazard ratios (HR) are expressed per SD of the metabolites. Analyses were conducted with SAS version 9.4 software (SAS Institute, Cary, North Carolina).

Only gold members can continue reading. Log In or Register to continue

Stay updated, free articles. Join our Telegram channel

Nov 20, 2016 | Posted by in CARDIOLOGY | Comments Off on Metabolomic Profiling in Relation to New-Onset Atrial Fibrillation (from the Framingham Heart Study)

Full access? Get Clinical Tree

Get Clinical Tree app for offline access