Mark Trinder1, Kaavya Paruchuri2, Sara Haidermota3, Rachel Bernardo4, Seyedeh Maryam Zekavat5, Thomas Gilliland2, James Januzzi6, Pradeep Natarajan7. 1. Centre for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard, Cambridge, Massachusetts, USA. Electronic address: https://twitter.com/marketrinder. 2. Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard, Cambridge, Massachusetts, USA; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA. 3. Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard, Cambridge, Massachusetts, USA; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. 4. Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. 5. Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard, Cambridge, Massachusetts, USA; Computational Biology and Bioinformatics Program, Yale University, New Haven, Connecticut, USA. 6. Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA. 7. Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard, Cambridge, Massachusetts, USA; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA. Electronic address: pnatarajan@mgh.harvard.edu.
Abstract
BACKGROUND: When indicated, guidelines recommend measurement of lipoprotein(a) for cardiovascular risk assessment. However, temporal variability in lipoprotein(a) is not well understood, and it is unclear if repeat testing may help refine risk prediction of coronary artery disease (CAD). OBJECTIVES: The authors examined the stability of repeat lipoprotein(a) measurements and the association between instability in lipoprotein(a) molar concentration with incident CAD. METHODS: The authors assessed the correlation between baseline and first follow-up measurements of lipoprotein(a) in the UK Biobank (n = 16,017 unrelated individuals). The association between change in lipoprotein(a) molar concentration and incident CAD was assessed among 15,432 participants using Cox proportional hazards models. RESULTS: Baseline and follow-up lipoprotein(a) molar concentration were significantly correlated over a median of 4.42 years (IQR: 3.69-4.93 years; Spearman rho = 0.96; P < 0.0001). The correlation between baseline and follow-up lipoprotein(a) molar concentration were stable across time between measurements of <3 (rho = 0.96), 3-4 (rho = 0.97), 4-5 (rho = 0.96), and >5 years (rho = 0.96). Although there were negligible-to-modest associations between statin use and changes in lipoprotein(a) molar concentration, statin usage was associated with a significant increase in lipoprotein(a) among individuals with baseline levels ≥70 nmol/L. Follow-up lipoprotein(a) molar concentration was significantly associated with risk of incident CAD (HR per 120 nmol/L: 1.32 [95% CI: 1.16-1.50]; P = 0.0002). However, the delta between follow-up and baseline lipoprotein(a) molar concentration was not significantly associated with incident CAD independent of follow-up lipoprotein(a) (P = 0.98). CONCLUSIONS: These findings suggest that, in the absence of therapies substantially altering lipoprotein(a), a single accurate measurement of lipoprotein(a) molar concentration is an efficient method to inform CAD risk.
BACKGROUND: When indicated, guidelines recommend measurement of lipoprotein(a) for cardiovascular risk assessment. However, temporal variability in lipoprotein(a) is not well understood, and it is unclear if repeat testing may help refine risk prediction of coronary artery disease (CAD). OBJECTIVES: The authors examined the stability of repeat lipoprotein(a) measurements and the association between instability in lipoprotein(a) molar concentration with incident CAD. METHODS: The authors assessed the correlation between baseline and first follow-up measurements of lipoprotein(a) in the UK Biobank (n = 16,017 unrelated individuals). The association between change in lipoprotein(a) molar concentration and incident CAD was assessed among 15,432 participants using Cox proportional hazards models. RESULTS: Baseline and follow-up lipoprotein(a) molar concentration were significantly correlated over a median of 4.42 years (IQR: 3.69-4.93 years; Spearman rho = 0.96; P < 0.0001). The correlation between baseline and follow-up lipoprotein(a) molar concentration were stable across time between measurements of <3 (rho = 0.96), 3-4 (rho = 0.97), 4-5 (rho = 0.96), and >5 years (rho = 0.96). Although there were negligible-to-modest associations between statin use and changes in lipoprotein(a) molar concentration, statin usage was associated with a significant increase in lipoprotein(a) among individuals with baseline levels ≥70 nmol/L. Follow-up lipoprotein(a) molar concentration was significantly associated with risk of incident CAD (HR per 120 nmol/L: 1.32 [95% CI: 1.16-1.50]; P = 0.0002). However, the delta between follow-up and baseline lipoprotein(a) molar concentration was not significantly associated with incident CAD independent of follow-up lipoprotein(a) (P = 0.98). CONCLUSIONS: These findings suggest that, in the absence of therapies substantially altering lipoprotein(a), a single accurate measurement of lipoprotein(a) molar concentration is an efficient method to inform CAD risk.
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