Mean platelet volume and myocardial infarction

The genetics of normal platelet reactivity

Genetic and environmental factors contribute to a substantial variation in platelet function seen among normal persons. Candidate gene association studies represent a valiant effort to define the genetic component in an era where genetic tools were limited, but the single nucleotide polymorphisms identified in those studies need to be validated by more objective, comprehensive approaches, such as genome-wide association studies (GWASs) of quantitative functional traits in much larger cohorts of more carefully selected normal subjects. During the past year, platelet count and mean platelet volume, which indirectly affect platelet function, were the subjects of GWAS. The majority of the GWAS signals were located to noncoding regions, a consistent outcome of all GWAS to date, suggesting a major role for mechanisms that alter phenotype at the level of transcription or posttranscriptional modifications. Of 15 quantitative trait loci associated with mean platelet volume and platelet count, one located at 12q24 is also a risk locus for coronary artery disease. In most cases, the effect sizes of individual quantitative trait loci are admittedly small, but the results of these studies have led to new insight into regulators of hematopoiesis and megakaryopoiesis that would otherwise be unapparent and difficult to define.

A genome-wide association study identifies three loci associated with mean platelet volume

Mean platelet volume (MPV) is increased in myocardial and cerebral infarction and is an independent and strong predictor for postevent morbidity and mortality. We conducted a genome-wide association study (GWAS), the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K study, and found MPV to be strongly associated with three common single-nucleotide polymorphisms (SNPs): rs7961894 located within intron 3 of WDR66 on chromosome 12q24.31, rs12485738 upstream of the ARHGEF3 on chromosome 3p13-p21, and rs2138852 located upstream of TAOK1 on chromosome 17q11.2. We replicated all three SNPs in another GWAS from the UK and in two population-based samples from Germany. In a combined analysis including 10,048 subjects, the SNPs had p values of 7.24 x 10(-48) for rs7961894, 3.81 x 10(-27) for rs12485738, and 7.19 x 10(-28) for rs2138852. These three quantitative trait loci together accounted for 4%-5% of the variance in MPV. In-depth sequence analysis of WDR66 in 382 samples from the extremes revealed 20 new variants and a haplotype with three coding SNPs and one SNP at the transcription start site associated with MPV (p = 6.8 x 10(-5)). In addition, expression analysis indicated a direct correlation of WDR66 transcripts and MPV. These findings may not only enhance our understanding of platelet activation and function, but may also provide a focus for several novel research avenues.

Role of reticulated platelets and platelet size heterogeneity on platelet activity after dual antiplatelet therapy with aspirin and clopidogrel in patients with stable coronary artery disease

OBJECTIVES

The aim of this study was to evaluate the relationship between reticulated platelets (RPs), platelet size, and platelet function in patients with stable coronary artery disease (CAD) taking aspirin and clopidogrel.

BACKGROUND

Reticulated platelets are young platelets that are larger and possibly more active than non-RPs.

METHODS

Flow cytometry was used to measure RPs after staining with thiazole orange and to define the upper 20% and lower 20% of platelets by size. Platelet aggregation was measured with light transmission aggregometry (LTA); platelet activation was assessed by measuring activated platelet surface expression of P-selectin and glycoprotein (GP) IIb/IIIa.

RESULTS

Ninety patients were recruited and stratified into tertiles of %RPs. Patients in the upper tertile displayed greater platelet aggregation to 5-mumol/l adenosine diphosphate (ADP) (50.7 +/- 16.4% vs. 34.2 +/- 17.3%, p < 0.001), 1.5-mmol/l arachidonic acid (AA) (27.3 +/- 16.9% vs. 11.7 +/- 9.3%, p < 0.001), and 1-mug/ml collagen (18 +/- 11.6% vs. 12.1 +/- 8.7%, p < 0.05) and greater expression of GP IIb/IIIa (4.7 +/- 1.8% vs. 3.1 +/- 2.2%, p < 0.001). Frequency of low response to aspirin (AA LTA >20%) was higher in the upper tertile (53% vs. 17%, p < 0.001) compared with the lower tertile; low response to clopidogrel (ADP LTA >50%) was also elevated in the upper tertile (50% vs. 13%, p = 0.003). The larger platelet gate had a higher % of RPs compared with the smaller gate (15.4 +/- 16.7% vs. 1.7 +/- 2.3%, p < 0.001) and greater GP IIb/IIIa (5.7 +/- 3.1 vs. 2.1 +/- 1.2, p < 0.001) and P-selectin expression (7.8 +/- 4.9 vs. 4.6 +/- 2.7, p < 0.001).

CONCLUSIONS

The proportion of circulating RPs strongly correlates with response to antiplatelet therapy in patients with stable CAD. Large platelets exhibit increased reactivity despite dual antiplatelet therapy, compared with smaller platelets.

The mouse as a model for human biology: a resource guide for complex trait analysis

The mouse has been a powerful force in elucidating the genetic basis of human physiology and pathophysiology. From its beginnings as the model organism for cancer research and transplantation biology to the present, when dissection of the genetic basis of complex disease is at the forefront of genomics research, an enormous and remarkable mouse resource infrastructure has accumulated. This review summarizes those resources and provides practical guidelines for their use, particularly in the analysis of quantitative traits.

Quantitative trait loci for baseline erythroid traits

A substantial genetic contribution underlies variation in baseline peripheral blood counts. We performed quantitative trait locus/loci (QTL) analyses to identify chromosome (Chr) regions harboring genes influencing the baseline erythroid parameters in F2 intercrosses between NZW/LacJ, SM/J, and C57BLKS/J inbred mice. We identified multiple significant QTL for red blood cell (RBC) count, hemoglobin (Hgb) and hematocrit (Hct) levels, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean cell hemoglobin concentration (CHCM). We identified four RBC count QTL: Rbcq1 (Chr 1, peak LOD score at 62 cM,), Rbcq2 (Chr 4, 60 cM), Rbcq3 (Chr 11, 34 cM), and Rbcq4 (Chr 10, 60 cM). Three MCV QTL were identified: Mcvq1 (Chr 7, 30 cM), Mvcq2 (Chr 11, 6 cM), and Mcvq3 (Chr 10, 60 cM). Single significant loci for Hgb (Hgbq1, Chr 16, 32 cM), Hct (Hctq1, Chr 3, 42 cM), and MCH (Mchq1, Chr 10, 60 cM) were identified. The data support the existence of a common RBC/MCH/MCV locus on Chr 10. Two QTL for CHCM (Chcmq1, Chr 2, 48 cM; Chcmq2, Chr 9, 44 cM) and an interaction between Chcmq2 with a locus on Chr 19 were identified. These analyses emphasize the genetic complexity underlying the regulation of erythroid peripheral blood traits in normal populations and suggest that genes not previously recognized as significantly impacting normal erythropoiesis exist.

Quantitative trait loci for baseline white blood cell count, platelet count, and mean platelet volume

A substantial genetic contribution to baseline peripheral blood counts has been established. We performed quantitative trait locus/loci (QTL) analyses to identify chromosome (Chr) regions harboring genes influencing the baseline white blood cell (WBC) count, platelet (Plt) count, and mean platelet volume (MPV) in F(2) intercrosses between NZW/LacJ, SM/J, and C57BLKS/J inbred mice. We identified six significant WBC QTL: Wbcq1 (peak LOD score at 38 cM, Chr 1), Wbcq2 (42 cM, Chr 3), Wbcq3 (0 cM, Chr 15), Wbcq4 (58 cM, Chr 1), Wbcq5 (82 cM, Chr 1), and Wbcq6 (8 cM, Chr 14). Three significant Plt QTL were identified: Pltq1 (24 cM, Chr 2), Pltq2 (36 cM, Chr 7), and Pltq3 (10 cM, Chr 12). Two significant MPV QTL were identified, Mpvq1 (62 cM, Chr 15) and Mpvq2 (44 cM, Chr 8). In total, the WBC QTL accounted for up to 31% of the total variance in baseline WBC count, while the Plt and MPV QTL accounted for up to 30% and 49% of the total variance, respectively. These analyses underscore the genetic complexity underlying these traits in normal populations and provide the basis for future studies to identify novel genes involved in the regulation of mammalian hematopoiesis.