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Published on: October 12, 2017
Repeat Measures of Lipoprotein(a) Molar Concentration and Cardiovascular Risk
Mark Trinder1, Kaavya Paruchuri2, Sara Haidermota3
1Centre 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.
Insights
Repeat lipoprotein(a) measurements show high stability over time. A single measurement of lipoprotein(a) is sufficient for assessing coronary artery disease (CAD) risk, as changes do not independently predict future events.
Area of Science:
- Cardiovascular Medicine
- Clinical Chemistry
- Genetics and Genomics
Background:
- Lipoprotein(a) [Lp(a)] measurement is recommended for cardiovascular risk assessment.
- Temporal variability of Lp(a) and its impact on coronary artery disease (CAD) risk prediction are not well understood.
Purpose of the Study:
- To examine the stability of repeat Lp(a) measurements.
- To assess the association between Lp(a) instability and incident CAD.
Main Methods:
- Assessed correlation between baseline and follow-up Lp(a) molar concentration in 16,017 UK Biobank participants.
- Used Cox proportional hazards models to evaluate the association between Lp(a) changes and incident CAD in 15,432 participants.
Main Results:
- Baseline and follow-up Lp(a) molar concentrations were highly correlated (Spearman rho = 0.96) over a median of 4.42 years, with stable correlations across different time intervals.
- Follow-up Lp(a) molar concentration was significantly associated with incident CAD risk (HR per 120 nmol/L: 1.32).
- Changes in Lp(a) molar concentration were not significantly associated with incident CAD independent of follow-up Lp(a) levels.
Conclusions:
- A single, accurate Lp(a) molar concentration measurement is an efficient tool for informing CAD risk.
- Repeat Lp(a) testing is not necessary for refining CAD risk prediction in the absence of therapies that significantly alter Lp(a) levels.
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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