Abstract
PURPOSE
The LipidCardio Study was established for in-depth analyses of cardiovascular risk factors, providing well-defined cardiovascular and metabolic phenotypes. In particular, the role of lipoproteins in the pathobiological process and treatment of cardiovascular disease (CVD) will be a main focus.
PARTICIPANTS
1005 individuals aged 21 years and older undergoing cardiac catheterisation during 17 months at a tertiary academic cardiology centre were enrolled (troponin-positive acute coronary syndrome was exclusion criterion). The baseline data not only contain detailed phenotyping, broad biochemical parameters, genetic data, but also standardised personal and family history, a screening test for cognitive impairment, pulse wave analysis and measurements of hand grip strength, among others. Blood samples were stored in a biobank for future analyses.
FINDINGS TO DATE
The mean age of the participants at enrolment was 70.9±11.1 years (70% male). Coronary angiography provided evidence of obstructive coronary artery disease (CAD) in 69.9% of participants. Those with evidence of CAD were significantly more likely to be male, inactive, diabetic and with a family history of CVD than participants without CAD.About 20% of patients had lipoprotein(a) (Lp(a)) concentrations above 106.9 nmol/L (fifth quintile). These patients had significantly increased odds of obstructive CAD compared with participants in quintiles 1-4 (crude OR 1.70, 95% CI 1.17 to 2.48, p=0.005). There was reasonable evidence that with increasing severity of CAD the odds of having elevated Lp(a) increased. We were able to replicate the established strong association between specified single nucleotide polymorphisms (SNPs) in the LPA gene (rs10455872, rs3798220 and rs186696265) and the APOE gene (rs7412), and the concentration of Lp(a), validating our phenotype database and biobank.
FUTURE PLANS
Mortality information will be obtained in 2 year intervals. Follow-up phone interviews will be conducted at 3 and 6 years after enrolment. We seek to cooperate with other researchers, for example, by sharing data and biobank samples.
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