Polymorphism of the cystatin C gene in patients with acute coronary syndromes: Results from the PLATelet inhibition and patient Outcomes study.

Akerblom A, Eriksson N, Wallentin L, Siegbahn A, Barratt BJ, Becker RC, Budaj A, Himmelmann A, Husted S, Storey RF, Johansson A, James SK, PLATO Investigators

Am. Heart J. 168 (1) 96-102.e2 [2014-07-00; online 2014-04-04]

Elevated cystatin C concentration is an independent risk factor for cardiovascular (CV) events in patients with acute coronary syndromes. Genetic polymorphisms in CST3 influence cystatin C levels, but their relationship to outcomes is unclear. We measured cystatin C concentrations in plasma, obtained within 24 hours of admission, in 16,279 acute coronary syndrome patients from the PLATO trial. In 9,978 patients, we performed a genome-wide association study with up to 2.5 million single nucleotide polymorphisms. Single nucleotide polymorphisms affecting cystatin C levels were evaluated in relation to the first occurrence of myocardial infarction (MI) or CV death within 1 year using Cox regression analysis. Several single nucleotide polymorphisms were associated with cystatin C levels, most significantly rs6048952 (P = 7.82 × 10(-16)) adjacent to CST3. Median cystatin C concentrations per genotype were 0.85 mg/L (A/A), 0.80 mg/L (A/G), and 0.73 mg/L (G/G). Modeled as additive, the allelic effect, multivariable adjusted, was -0.045 mg/L per G allele for rs6048952. The multivariable adjusted c-statistic regarding the combined end point (CV death or MI) was 0.6735. Adding cystatin C or genotype-adjusted cystatin C levels resulted in c-statistics of 0.6761 and 0.6758, respectively. The multivariable adjusted hazard ratios per G allele at rs6048952 in the entire population were 0.94 (95% CI 0.83-1.06) for CV death or MI and 0.88 (95% CI 0.71-1.08) for CV death. Genetic polymorphisms affect cystatin C concentrations independently of kidney function. However, the polymorphisms were not observed to be associated with outcome, nor did they improve risk prediction or discriminative models.

NGI Uppsala (SNP&SEQ Technology Platform) [Service]

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PubMed 24952865

DOI 10.1016/j.ahj.2014.03.010

Crossref 10.1016/j.ahj.2014.03.010

S0002-8703(14)00153-7