This Article Abstract Figures Only Full Text (PDF) Online Appendix Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (5) Citing Articles via Google Scholar Google Scholar Articles by Jacquemin, B. Articles by Peters, A. Search for Related Content PubMed Articles by Jacquemin, B. Articles by Peters, A.

CLINICAL RESEARCH: GENETICS AND GENOMICS

Common Genetic Polymorphisms and Haplotypes of Fibrinogen Alpha, Beta, and Gamma Chains Affect Fibrinogen Levels and the Response to Proinflammatory Stimulation in Myocardial Infarction Survivors

The AIRGENE Study

Bénédicte Jacquemin, MD, PhD^_*, Charalambos Antoniades, MD, PhD^,,_*, Fredrik Nyberg, MD, PhD^,||, Estel Plana, MSc^_*, Martina Müller, MSc, PhD^#,_**, Sonja Greven, MSc, PhD^#,,, Veikko Salomaa, MD, PhD, Jordi Sunyer, MD, PhD^_*,||||, Tom Bellander, MD, PhD, Alexandros-Georgios Chalamandaris, MSc^¶¶, Ricardo Pistelli, MD^##, Wolfgang Koenig, MD, PhD^¶ and Annette Peters, MSc, PhD^#

^_* Centre for Environmental Research, Municipal Institute of Medical Research, Barcelona, Spain
1st Cardiology Department, Athens University Medical School, Athens, Greece
Department of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
^|| AstraZeneca R&D Mölndal, Mölndal, Sweden
^¶ Department of Internal Medicine II Cardiology, University of Ulm Medical Centre, Ulm, Germany
^# Institute of Epidemiology, Research Units "Air Pollution Health Effects" and "Chronic Disease Epidemiology" German Research Center for Environmental Health (GmbH), Neuherberg, Germany
^_** LMU-IBE, Chair of Epidemiology, University of Munich, Neuherberg, Germany
LMU Institute of Statistics, University of Munich, Munich, Germany
Department of Biostatistics, John Hopkins University, Baltimore, Maryland
National Public Health Institute (KTL), Helsinki, Finland
^|||| Universitat Pompeu Fabra, Barcelona, Spain
^¶¶ Department of Hygiene and Epidemiology, Athens University Medical School, Athens, Greece
^## Catholic University, Rome, Italy

Manuscript received February 29, 2008; revised manuscript received May 28, 2008, accepted June 2, 2008.

^_* Reprint request and correspondence: Dr. Charalambos Antoniades, Athens University Medical School, 1st Cardiology Department, Ilias 8, Gerakas Attikis, 153 44, Athens, Greece (Email: antoniades{at}vodafone.net.gr).

	Abstract

Top Abstract Methods Results Discussion Conclusions Appendix References

 
Objectives: This study was designed to investigate whether single nucleotide polymorphisms (SNPs) and haplotypes of the fibrinogen gene-cluster (fibrinogen chains alpha [FGA], beta [FGB], and gamma [FGG]) could explain the inter- and intraindividual variability of fibrinogen levels in patients with atherosclerosis. We also searched for genetic determinants affecting the responses of fibrinogen genes to proinflammatory stimulation.

Background: The mechanisms regulating fibrinogen levels are not fully understood, and they are likely to be regulated by complex gene–environment interactions.

Methods: In the AIRGENE study, 895 survivors of myocardial infarction from 5 European cities were followed prospectively for 6 to 8 months, and plasma fibrinogen, interleukin (IL)-6, and C-reactive protein levels were determined monthly. We analyzed 21 SNPs and the corresponding haplotypes in the 3 fibrinogen genes.

Results: Eight SNPs in FGA and FGB were significantly associated with fibrinogen levels. Similarly, 2 different haplotypes in FGA and 3 in FGB were also associated with mean fibrinogen levels. The IL-6 levels had a significant impact on the associations between SNPs/haplotypes in FGA/FGB and fibrinogen levels. We also identified SNPs and haplotypes in FGA and FGB with strong impact on the intraindividual variability of fibrinogen during the follow-up period.

Conclusions: We identified common SNPs and haplotypes on FGA/FGB genes, explaining the interindividual and intraindividual variability of fibrinogen levels, in patients with a history of myocardial infarction. We have also identified for the first time, SNPs/haplotypes on FGA/FGB whose effects on fibrinogen expression are modified by the underlying IL-6 levels. These findings may have an impact on risk stratification and the design of genetically guided therapeutic approaches in patients with advanced atherosclerosis.

Key Words: fibrinogen • genetics • inflammation • myocardial infarction

Abbreviations and Acronyms   CRP = C-reactive protein   FGA = fibrinogen chain alpha   FGB = fibrinogen chain beta   FGG = fibrinogen chain gamma   IL = interleukin   LD = linkage disequilibrium   MI = myocardial infarction   SNP = single nucleotide polymorphism

Plasma fibrinogen appears to have high variability in the general population, because in addition to its seasonal variations (6), its expression is affected by the presence of mild inflammatory stimuli induced by smoking (7), aging or severe atherosclerosis (1). However, increasing evidence suggests that 50% of the total variability in fibrinogen levels is determined by genetic factors (8). Several single nucleotide polymorphisms (SNPs) have been identified in the genes encoding the 3 fibrinogen chains alpha, beta, and gamma (FGA, FGB, and FGG genes, respectively). Because the synthesis of the beta chain is rate-limiting for fibrinogen's synthesis in vitro (9), recent research has focused mainly on the genetic variability of FGB. Some SNPs in FGB have been associated with variation in plasma fibrinogen levels (10,11). Although sequences responsive to interleukin (IL)-6 (which is the main mediator of acute phase-induced fibrinogen synthesis) are present in the promoter regions of the genes encoding all 3 fibrinogen chains, recent evidence suggests that SNPs on the beta chain have a major regulatory function in fibrinogen synthesis in response to IL-6 (12). This genetically determined sensitivity of fibrinogen expression to IL-6 may also explain previous reports that specific SNPs on fibrinogen genes affect plasma fibrinogen levels only in subgroups with higher IL-6 levels such as smokers (13), or in subjects with an enhanced acute phase response as determined by high C-reactive protein (CRP) levels (14).

In the present study, we hypothesized that SNPs and haplotypes of the fibrinogen genes FGA, FGB, and FGG may affect mean plasma fibrinogen levels in patients with a history of myocardial infarction (MI). We also examined whether these SNPs and the haplotypes defined by them modify the complex relationship between fibrinogen, IL-6, and CRP in this population.

	Methods

Top Abstract Methods Results Discussion Conclusions Appendix References

 
Study population and protocol.   From a total cohort of 1,003 patients with a history of MI participating in the AIRGENE study (15), 895 had suitable blood samples for the present study. The patients were recruited from 5 European cities: Augsburg (Germany), Barcelona (Spain), Helsinki (Finland), Rome (Italy), and Stockholm (Sweden). Details of the design of the study are given elsewhere (15). Subjects were recruited through hospital- (Helsinki, Rome) or population-based registries of patients with MI (Augsburg, Barcelona, Stockholm). The inclusion criteria were survival after MI 3 months to 6 years before entry into the study and age between 35 and 80 years. Because of the rapid changes in the medical management of MI survivors during the last few years, the limit of 6 years after MI was used to increase study group homogeneity. Exclusion criteria were MI or interventional coronary procedure (percutaneous transluminal coronary angioplasty, coronary artery bypass graft) less than 3 months before the beginning of the study; residence outside the defined study area; an extended period of absence from the study area planned during the study period; a major illness that would prevent compliance with the study protocol; and chronic inflammatory diseases and/or anti-inflammatory medication (that might modify the biomarkers considered in the study). Further, samples from visits where the patient indicated having had an infection, surgery, or a major dental intervention in the 3 days before the visit were excluded and subsequently subjects with <2 remaining valid blood samples were completely excluded from the study. Other cardiovascular morbidities, such as heart failure, were not exclusion criteria.

The fieldwork consisted of a baseline visit, where an extensive questionnaire to characterize the patient was administered; blood pressure was measured, the body mass index determined, a 12-lead resting electrocardiogram was recorded; and a venous blood sample was drawn to measure blood count, total cholesterol, high density lipoprotein cholesterol and glycosylated hemoglobin HbA1c, and inflammatory markers (fibrinogen, IL-6, and CRP) and to extract deoxyribonucleic acid. Patients were followed up prospectively for an average of 6 monthly visits. At each monthly visit, the inflammatory markers were measured and a short questionnaire was completed to characterize the 24 h previous to the visit in terms of time spent in traffic, amount of exercise, tea and alcohol consumption, smoking, and exposure to environmental tobacco smoke, to detect an acute infection and to record changes in medication.

Biochemical measurements.   Blood samples were stored at 4°C until centrifugation. The resulting plasma samples were stored at –80°C until shipped to the central laboratory in Ulm, Germany, where high-sensitivity CRP and fibrinogen were analyzed by immunonephelometry on a BNII analyzer (Dade Behring Marburg GmbH, Marburg, Germany, for both CRP and fibrinogen). For IL-6, a high-sensitivity colorimetric sandwich enzyme-linked immunoadsorbent assay (R&D Systems GmbH, Abingdon, United Kingdom) was used. For quality control, 102 fibrinogen duplicates were obtained; the mean of the coefficients of variation in the duplicates was 6.1% (from 2.5% in Augsburg to 11.2% in Rome). The interassay coefficient of variation was 3.6% at 2.35 g/l.

Genotyping.   Out of a total of 134 SNPs genotyped for the AIRGENE study, 27 SNPs located in the genes FGA, FGB, and FGG were investigated for the present study. We selected all up-to-then known haplotype tagging SNPs, possibly functional SNPs, as well as DNA variants showing an association in other studies with cardiovascular disease or related phenotypes. For SNP selection, the National Centre for Biotechnology SNP database dbSNP Build 124, based on NCBI Human Genome Build 35.1 (16), the Innate Immunity Program for Genomic Applications database (17), and the SeattleSNPs Program for Genomic Applications (18) were used.

Genotyping was carried out by using the MassARRAY system (Sequenom, San Diego, California) (19). All reactions (PCR amplification, base extension) were carried out in a Tetrad PCR thermal cycler (MJ Research, St. Bruno, Quebec).

Eleven SNPs were genotyped in FGA, 8 in FGB, and 8 in FGG. For 2 SNPs (1 in FGB and 1 in FGG), no assay could be established or the assay did not provide valid results. Two SNPs (1 in FGA and 1 in FGG) were monomorphic in the study population and 2 (both in FGA) had a minor allele frequency below 1%. Finally, 21 fibrinogen gene SNPs were taken into the statistical analyses (rs2070022, rs6050, rs2070018, rs2070016, rs2070014, rs2070011, rs10012555, and rs2070006 in FGA; rs1800791, rs1800790, rs1800788, rs2227399, rs6056, rs4220, and rs2227421 in FGB; and rs2066865, rs1049636, rs2066861, rs2066860, rs1800792, and rs2066854 in FGG). The average success rate for these 21 SNPs in the 3 genes was 93%.

For quality control, sex determination was performed for all samples by amplification of a partial sequence of the amelogenin gene (AMELX) and thus validated genotyping assays. No samples had to be excluded for this analysis.

Negative controls were included on all genotyping plates. For 30% of samples randomly selected and genotyped in duplicate, the replication rate was 99.82%. Each SNP was tested for departures from Hardy-Weinberg equilibrium by means of a chi-square test or Fisher exact test depending on allele frequency. None of the SNPs in the 3 selected genes deviated significantly from Hardy-Weinberg equilibrium.

Estimation of haplotypes.   The genewise regions appropriate for haplotype estimation and haplotype association analysis were identified through linkage disequilibrium (LD) analysis. Haplotype blocks were defined as regions with Lewontin's D' >0.8 between consecutive SNPs. Only individuals with complete SNP information were included in the haplotype estimation. To reduce the number of subjects with missing genotypes, highly correlated SNPs were used to impute missing values. In each group of highly correlated SNPs (r² >0.95), missing values in the SNP with the highest call rate were imputed from SNPs with lower call rates, which subsequently were excluded from further analysis. In FGA, rs2070022 was complemented using information from rs2070014. In FGB, rs6056 was filled up using rs4220, and missing values in rs2227399 were imputed based on rs1800790. For FGG, a group of 3 highly correlated SNPs was identified (rs2066861, rs2066865, rs2066854) with rs2066861 having the highest call rate. The updated versions of the SNPs rs6056, rs2227399, and rs2066861 entered haplotype estimation, but the 5 SNPs that were used for imputing missing values had to be excluded.

Haplotype estimation was performed for each gene using the expectation-maximization algorithm presented by Schaid et al. (20) with SNPs entered in gene-specific reading direction. To avoid bias resulting from genetic variation between populations, haplotypes were estimated for each center separately. Analysis of individually estimated haplotypes identified only 1 individual with a most probable haplotype pair in gene FGG having a probability of 0.74. All other estimated most probable haplotype pairs in all 3 genes exceeded a probability of 0.94, with an overall ambiguity rate of 8.9%. Additional information obtained through alternative haplotype pairs, therefore, was assumed to be small and only the individually most probable pair of haplotypes was modeled in association analysis.

Statistical analysis.   Statistical analysis was performed in 3 steps. In the first step, we identified the nongenetic variables associated with fibrinogen levels; then we identified the SNPs associated with the fibrinogen levels; and finally we created a final model including the SNPs and the variables that fulfilled the confounder definition. This procedure was first done using all the centers and then city by city. Covariates were selected from a list of potential confounders as those found to change the estimated SNP effect by more than 10%. Time invariant (from the baseline questionnaire) as well as time variant (from the short questionnaire) variables with potential effects on fibrinogen levels were entered into the model one by one based on the criterion of showing the minimum Akaike information criterion value. Final adjustment was made for city, gender, age, body mass index, HbA1c, and smoothed trend.

Power calculations suggested that the study sample was able to detect a 16% difference in fibrinogen levels between genotypes, at a genotype frequency of 15% and alpha = 0.05 with a power of 80%.

As the outcome variable, we used the logarithm of the measured fibrinogen values to normalize its distribution. This implies that effects estimated can be expressed as percentage change of the geometric mean in the plasma fibrinogen levels.

We used mixed effects models with random patient intercepts to account for the serial correlation among repeated measurements within the framework of additive mixed models to allow for nonparametric exposure-response functions (21).

Besides the 21 fibrinogen SNPs, we also tested 6 IL-6 and 8 CRP SNPs. For each SNP, we both calculated the effect only adjusting for city and applied the confounder model to obtain an adjusted effect on the plasma fibrinogen level. For association analyses between the different SNPs and the fibrinogen levels, the original significance level of alpha = 0.05 was corrected for multiple testing following the Sidak method. The number of tests was calculated as the effective number of independent loci among the SNPs analyzed according to Li and Ji (22). Finally, we constructed a mutually adjusted model with SNPs associated to fibrinogen levels in the univariate analysis. In the multiple adjusted model, we included one by one based on the criterion of lowest Akaike information criterion, the SNPs showing a significant association in the previous models; furthermore, we excluded any SNP(s) having a high LD (r² >0.8) with the last SNP introduced in the model to avoid collinearity. The common homozygote genotype of each SNP was used as reference. The same confounder model was used to estimate the association between haplotypes and fibrinogen levels. As SNP analyses indicate a generally present additive effect, an additive effect is assumed in haplotype association analysis. Therefore, haplotypes were coded as count variables with the most frequent haplotype of each gene as reference.

For haplotype association analysis, only haplotypes with estimated frequency of at least 0.05 in any center were coded as single variables. All other haplotypes were pooled in a group of rare haplotypes unless this group turned out to be significant. It turned out to be the case for a rare haplotype in FGB gene, and we extracted the most frequent haplotype from this group for separate analysis.

We also investigated interactions between the significant SNPs of the fibrinogen genes and IL-6 or CRP levels in affecting fibrinogen levels. We tested interactions using continuous IL-6 and CRP, and we stratified them by quartiles to obtain the estimations. The same procedure was followed for interactions with haplotypes. Finally, we analyzed the intraindividual variability in fibrinogen levels related to each SNP by using the intraindividual standard deviations of our repeated measurements of the logarithmically transformed fibrinogen in each individual as the outcome variable, adjusting for individual mean logarithmically transformed fibrinogen and the final set of confounder variables described earlier.

	Results

Top Abstract Methods Results Discussion Conclusions Appendix References

 
Demographic characteristics and fibrinogen levels.   The demographic characteristics of the participants are presented in Table 1. To minimize the variability of fibrinogen levels, we performed serial measurements for each patient, across a period of 6 to 8 months (the average number of monthly visits per subject for the participating centers was approximately 6 except in Barcelona where it was around 7). The median fibrinogen level in the overall study population was 3.5 g/l; however, there was a significant difference in fibrinogen levels between patients from different geographical areas (lowest in Rome [median: 3.0 g/l] and highest in Barcelona [median: 4.1 g/l]). City of residence was the strongest predictor of fibrinogen levels in our population, despite the fact that 99.7% of the study participants were Caucasians.

View larger version (26K): [in this window] [in a new window] [Download PPT slide]   Figure 1 LD Maps for FGA, FGB, and FGG Linkage disequilibrium (LD) maps for the 3 fibrinogen chain genes: fibrinogen chain alpha (FGA), beta (FGB), and gamma (FGG). D' (red) and r2 (blue) measures of LD are shown.

Associations between SNPs and fibrinogen levels.   Eight SNPs (out of 21 included in the study) were significantly associated with plasma fibrinogen levels in the analyses that included 1 SNP at a time (Table 2): 1) for FGA: rs2070011, rs2070006, rs2070022, rs2070014; and 2) for FGB: rs1800790, rs2227399, rs6056, rs4220.

Four of these SNPs held the calculated significance level corrected for multiple testing (alpha = 0.0026): 1) for FGA: rs2070011; and 2) for FGB: rs1800790, rs2227399, rs6056. Two SNPs (FGA rs2070011 and FGB rs1800790s) remained in the mutually adjusted model. FGB rs1800790s explained 1.9% of the variability in the fibrinogen concentration and FGA rs2070011 explained 1.2%, in the crude bivariate model. When both SNPs were included in the same model, 3.4% of the variability in the fibrinogen concentration was explained by the genetic variants, and 31.37% of the variability was explained by the nongenetic covariates.

None of the tested SNPs in the FGG, IL-6, or CRP genes showed a significant association with fibrinogen levels (data not shown).

Associations between haplotypes and fibrinogen levels.   We found that 1 haplotype of the FGA gene, TTTAACG (defined by the minor allele in rs6050 as described in Table 3), showed significant association with fibrinogen levels. In addition, interestingly, 1 haplotype of FGA (CTCGATG), defined by SNP rs2070016, was also associated with fibrinogen levels, although this SNP was not significantly associated in the SNP analyses.

We also observed that 3 haplotypes in the FGB gene were associated with plasma fibrinogen levels. Two were defined only by SNPs associated with fibrinogen levels (Table 2): GCGTA was the only haplotype with the minor allele in rs6056, and GCGCA was defined by the allelic combination in both SNPs (rs2227399 and rs6056). The third haplotype (GTTCA) associated with fibrinogen levels was mainly defined by 1 SNP not associated with fibrinogen levels in single SNP analyses (rs1800788). The distinction between this haplotype and the rare variant ATTCA (frequency 0.1%) can be made in rs1800791.

None of the haplotypes in the FGG gene showed a significant association with fibrinogen levels as in the SNPs analyses.

Effect of SNPs and haplotypes on the response to low-grade inflammation.   Individual means of plasma fibrinogen levels in the overall population were significantly correlated with both IL-6 (r = 0.40, 95% confidence interval [CI]: 0.38 to 0.42) and CRP mean levels (r = 0.53, 95% CI: 0.51 to 0.55). To test whether the examined SNPs/haplotypes modify the response of fibrinogen genes to proinflammatory stimuli, we first examined whether plasma levels of IL-6 and CRP had any impact on the relationship between SNPs and fibrinogen levels (Table 4). Indeed, there was a significant interaction between IL-6 levels and the association of rs2070011 in FGA with fibrinogen levels and a borderline interaction between CRP levels and the association of the same SNP with fibrinogen levels. Interestingly, the haplotype effect patterns of all 3 fibrinogen genes showed significant heterogeneity across quartiles of IL-6 levels, indicating effect modification at the haplotype level. The IL-6 levels appeared particularly to significantly modify the association between fibrinogen levels and 1 haplotype of FGA, 2 haplotypes of FGB (1 significantly and 1 borderline), and 2 haplotypes of FGG (both borderline) (Table 5), none of which showed significant overall association with fibrinogen levels (Table 3).

	Discussion

Top Abstract Methods Results Discussion Conclusions Appendix References

 
In the present study, we found that 8 SNPs in the FGA and FGB genes had significant associations with plasma fibrinogen levels when evaluated one by one. Furthermore, we show that specific haplotypes of FGA, FGB, and FGG appear to modify the expression of fibrinogen in response to proinflammatory stimuli such as IL-6, because their effects on fibrinogen levels became stronger in the presence of high IL-6 levels. We also provide evidence that SNPs and haplotypes on both FGA and FGB genes may be implicated in the normally observed intraindividual variability of fibrinogen levels.

It is now widely accepted that systemic low-grade inflammation is a key feature in atherogenesis (23), and evidence suggests that increased plasma levels of inflammatory biomarkers such as fibrinogen are associated with increased cardiovascular risk (1,23). It is therefore of importance to determine the genetic variables that affect the expression of fibrinogen cluster genes and their response to the underlying proinflammatory stimuli.

Genetic determinants of fibrinogen levels.   The FGB chain synthesis is considered the rate-limiting factor for the production of fibrinogen (24), and SNPs on the FGB gene have been associated with fibrinogen levels (10,13,25), with G455A (FGB rs18000790) and C148T (FGB rs1800787), most widely studied (8,26). The FGB rs18000790 SNP is probably the most physiologically relevant, because the genetic variants defined by this SNP have distinct nuclear protein-binding properties (26). However, conflicting data (8,27) suggest that complex gene-environment interactions and especially the underlying inflammatory status or disease state could be important modifiers of this relationship (8).

In this study, we found that 4 SNPs in high mutual LD on the FGB gene affect fibrinogen levels, with FGB rs18000790 giving the strongest association. Indeed, the rs1800790 SNP is located in the promoter region of the FGB gene (11) and it seems to be a functional polymorphism, affecting the expression of FGB and hence the synthesis of fibrinogen in experimental models (12,23). On the other hand, we also found similar but slightly weaker associations between fibrinogen levels and the other 3 FGB SNPs in this LD group: rs6056 (C4651T), rs4220 (C7589A), and rs2227399 (G1683T). Although 1 of these could also represent a true relationship, their association with fibrinogen levels may well be indirect through their high LD with the functional FGB rs18000790 SNP in our population.

We also observed that FGA SNPs rs2070011 (C58T, located in the promoter region) and the correlated rs2070006 (C2027T, located in the promoter region) as well as rs2070022 (A6892G, located in the untranslated region) and the correlated rs2070014 (C1374T located in an intronic area) were each individually associated with fibrinogen levels, with rs2070011 giving the strongest association.

More importantly, this is the first study demonstrating that specific haplotypes on fibrinogen cluster genes regulate plasma fibrinogen levels in MI survivors. Indeed, we made the novel observation that 3 haplotypes (1 haplotype in FGA and 2 in FGB) are strong determinants of fibrinogen levels, not entirely as expected from the SNP analyses, suggesting that haplotype analyses could provide an additional future option in evaluating the effects of genetic background on fibrinogen levels.

Other determinants of fibrinogen levels: gene-environment interactions.   It is now widely known that fibrinogen levels vary across different ethnic groups, as a result of genetic differences (28), dietary modifiers and the overall lifestyle (29), with season of the year being an additional important modifier (6). In the present study, we observed that apart from some demographic characteristics known to influence plasma fibrinogen levels (e.g., age, gender, and body mass index) (30), city of residence was the strongest predictor. As 99.7% of study participants were Caucasians, it seems that "city" captures differences beyond ethnicity. In our study, city but not ethnicity was a strong determinant of both fibrinogen levels and the distribution of the examined SNPs/haplotypes. This finding suggests that factors beyond ethnicity (i.e., cultural and dietary habits, weather variations, pollution and many others) may affect fibrinogen levels and genetic differences of European populations are best described by geographical, linguistic, and cultural influences, which are best described by "city of stay" rather than "ethnic group," as previously reported (31).

By performing serial monthly measurements of fibrinogen levels in the same subjects, we were able: 1) to calculate more stable estimates of average individual fibrinogen levels, which is an advantage over most previous studies as it reduces measurement random errors; and 2) to examine the effect of genotype on the intraindividual variability of fibrinogen levels over time. We demonstrate for the first time that the intraindividual variability of plasma fibrinogen is associated with SNPs on the FGB (rs2227421) and possibly on FGA (rs2070016) genes, and more importantly with haplotypes of both FGA and FGB genes. These novel findings imply that the genetic background may be partly responsible for the high intraindividual variability of fibrinogen levels normally observed in patients with atherosclerosis (6,32).

Effects of proinflammatory stimuli on the association between SNPs/haplotypes and fibrinogen.   Interleukin 6 is the main mediator of acute phase-induced fibrinogen synthesis, mainly because it acts on the promoter regions of the genes encoding all 3 fibrinogen chains (12). The SNPs in FGB (12) or FGA (27) may modify the response of these genes to IL-6 in vitro, but at a clinical level, preliminary data suggested that these SNPs affect plasma fibrinogen levels only in subgroups with higher IL-6/CRP levels such as healthy smokers (13,14). However, the possible effect of the haplotypes of FGA, FGB, and FGG on IL-6–induced fibrinogen expression has not previously been investigated.

In this study, we were able to show that SNP rs2070011 in FGA modified the association between IL-6 and fibrinogen levels. Somewhat surprisingly, there was no clear association between the rs18000790 SNP in FGB and the IL-6–induced fibrinogen expression.

In addition to the finding that several SNPs in different areas of the FGA, FGB, and FGG genes affect the response of these genes to IL-6, we also made the novel observation that haplotypes on these genes may have an even stronger effect. Indeed, we observed an effect modification of the IL-6 impact on fibrinogen levels, which is related mainly to 1 haplotype on FGA, 2 haplotypes on FGB, and 2 haplotypes on FGG. These novel findings suggest that the haplotype-based approach may be more accurate than individual SNPs to define complex associations between fibrinogen genes and variations of its plasma levels in response to proinflammatory stimuli.

Genetics of fibrinogen chains: clinical perspectives.   As plasma fibrinogen is considered to be a risk factor for atherosclerosis (23), it is still unclear whether therapeutic strategies targeting fibrinogen levels may have the potential to modify cardiovascular risk. Treatments currently used against atherosclerosis (such as statins or angiotensin-converting enzyme inhibitors) improve clinical outcome in primary and secondary prevention (33), and they also decrease fibrinogen levels (23,34). However, it is unknown whether plasma fibrinogen is a reliable marker to follow the response to these treatments or whether it could ever be used as a therapeutic target itself. As fibrinogen appears to have seasonal variability, only serial measurements can provide useful information regarding the progression of the underlying proinflammatory status. A novel finding of the present study is the suggestion that haplotypes affect the response of fibrinogen genes to low-grade inflammation observed in MI survivors. These findings imply that multiple genetic variants in these genes are of importance and that the described haplotypes could modify the response of fibrinogen levels to anti-inflammatory treatments, such as statins or angiotensin-converting enzyme inhibitors. The present study was not designed to test any pharmacogenomic value of these haplotypes, and prospective clinical trials should be designed to test such a hypothesis.

Study limitations.   A limitation of the present study is the absence of any prospective data examining the effect of these SNPs/haplotypes on long-term clinical outcome. As the study was designed to test the effect of these genotypes on fibrinogen levels and variations throughout a period of 6 months, it was underpowered to detect any impact on clinical events during the follow-up period. In addition, the borderline, nonsignificant effects of these genotypes on clinical status of these patients at baseline suggests that further large-scale prospective clinical studies are required to demonstrate the effect of these SNPs and haplotypes on hard clinical end points.

	Conclusions

Top Abstract Methods Results Discussion Conclusions Appendix References

 
In the present study, we demonstrate that genetic background, as defined by specific SNPs and haplotypes of the FGA, FGB, and FGG genes, represents a major determinant of the expression of fibrinogen in post-MI patients, modifies the response to proinflammatory stimuli, and possibly also modifies the intraindividual variability of fibrinogen levels. These findings may have important implications on risk stratification of patients with advanced atherosclerosis. Although it is still premature to suggest an immediate clinical application of these findings, the results of the present study may guide the design of genetically based approaches for secondary prevention in these patients in the future.

	Appendix

Top Abstract Methods Results Discussion Conclusions Appendix References

 
For a list of the AIRGENE study group partners, please see the online version of this article.

	Footnotes

 
Drs. Jacquemin and Antoniades contributed equally to this work. Parts of this work were supported by grants from the German Ministry of Education and Research (BMBF)/National Genome Research Network (NGFN). Dr. Nyberg, employed by AstraZeneca, is also Adjunct Lecturer in Molecular Epidemiology at Karolinska Institute. AstraZeneca did not contribute any direct financing to this study.

The AIRGENE study was funded as part of the European Union's 5th Framework Programme, key action number 4: "Environment and Health," contract number QLRT-2002-02236.

	References

Top Abstract Methods Results Discussion Conclusions Appendix References

 
1. Danesh J, Lewington S, Thompson SG, et al. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis JAMA 2005;294:1799-1809.[Abstract/Free Full Text]

2. Mosesson MW, Siebenlist KR, Meh DA. The structure and biological features of fibrinogen and fibrin Ann N Y Acad Sci 2001;936:11-30.[Web of Science][Medline]

3. Naito M, Hayashi T, Kuzuya M, Funaki C, Asai K, Kuzuya F. Effects of fibrinogen and fibrin on the migration of vascular smooth muscle cells in vitro Atherosclerosis 1990;83:9-14.[CrossRef][Web of Science][Medline]

4. Yee KO, Schwartz SM. Why atherosclerotic vessels narrow: the fibrin hypothesis Thromb Haemost 1999;82:762-771.[Web of Science][Medline]

5. Koenig W. Fibrin(ogen) in cardiovascular disease: an update Thromb Haemost 2003;89:601-609.[Web of Science][Medline]

6. Hermida C, Calvo C, Ayala DE, et al. Seasonal variation of fibrinogen in dipper and nondipper hypertensive patients Circulation 2003;108:1101-1107.[Abstract/Free Full Text]

7. Scarabin PY, Aillaud MF, Amouyel P, et al. Associations of fibrinogen, factor VII and PAI-1 with baseline findings among 10,500 male participants in a prospective study of myocardial infarction—the PRIME Study. Prospective Epidemiological Study of Myocardial Infarction. Thromb Haemost 1998;80:749-756.[Web of Science][Medline]

8. Voetsch B, Loscalzo J. Genetic determinants of arterial thrombosis Arterioscler Thromb Vasc Biol 2004;24:216-229.[Abstract/Free Full Text]

9. Yu S, Sher B, Kudryk B, Redman CM. Fibrinogen precursors. Order of assembly of fibrinogen chains. J Biol Chem 1984;259:10574-10581.[Abstract/Free Full Text]

10. Kathiresan S, Yang Q, Larson MG, et al. Common genetic variation in five thrombosis genes and relations to plasma hemostatic protein level and cardiovascular disease risk Arterioscler Thromb Vasc Biol 2006;26:1405-1412.[Abstract/Free Full Text]

11. Green FR. Fibrinogen polymorphisms and atherothrombotic disease Ann N Y Acad Sci 2001;936:549-559.[Web of Science][Medline]

12. Verschuur M, de Jong M, Felida L, de Maat MP, Vos HL. A hepatocyte nuclear factor-3 site in the fibrinogen beta promoter is important for interleukin 6-induced expression, and its activity is influenced by the adjacent -148C/T polymorphism J Biol Chem 2005;280:16763-16771.[Abstract/Free Full Text]

13. Behague I, Poirier O, Nicaud V, et al. Beta fibrinogen gene polymorphisms are associated with plasma fibrinogen and coronary artery disease in patients with myocardial infarction. The ECTIM Study. Etude Cas-Temoins sur l'Infarctus du Myocarde. Circulation 1996;93:440-449.[Abstract/Free Full Text]

14. Gardemann A, Schwartz O, Haberbosch W, et al. Positive association of the beta fibrinogen H1/H2 gene variation to basal fibrinogen levels and to the increase in fibrinogen concentration during acute phase reaction but not to coronary artery disease and myocardial infarction Thromb Haemost 1997;77:1120-1126.[Web of Science][Medline]

15. AIRGENE Study GroupPeters A, Scheider A, Greven A, et al. Air pollution and inflammatory response in myocardial infarction survivors: gene-environment-interactions in a high-risk group. Study design of the AIRGENE study. Inhal Toxic 2007;19(Suppl 1):161-175.

16. National Centre for Biotechnology National Centre for Biotechnology SNP database Build 124, based on NCBI Human Genome Build 35.1. January 6, 2005 http://www.ncbi.nlm.nih.gov/SNP/ 2007Accessed January 6, 2005.

17. Innate Immunity Program for Genomic Applications. IIPGA database. http://innateimmunity.net/ 2007Accessed January 6, 2005.

18. SeattleSNPs Program for Genomic Applications http://pga.gs.washington.edu/ 2007Accessed January 6, 2005.

19. Weidinger S, Klopp N, Wagenpfeil S, et al. Association of a STAT 6 haplotype with elevated serum IgE levels in a population based cohort of white adults J Med Genet 2004;41:658-663.[Abstract/Free Full Text]

20. Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. Score tests for association between traits and haplotypes when linkage phase is ambiguous Am J Hum Genet 2002;70:425-434.[CrossRef][Web of Science][Medline]

21. Greven S, Kuchenhoff H, Peters A. Additive mixed models with P-splinesIn: Hinde J, Einbeck J, Newell J, editors. Proceedings of the 21st International Workshop on Statistical Modelling. Amsterdam: Statistical Modelling Society; 2006. pp. 201-207.

22. Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix Heredity 2005;95:221-227.[CrossRef][Web of Science][Medline]

23. Tousoulis D, Antoniades C, Stefanadis C. Assessing inflammatory status in cardiovascular disease Heart 2007;93:1001-1007.[Free Full Text]

24. Roy SN, Mukhopadhyay G, Redman CM. Regulation of fibrinogen assembly. Transfection of Hep G2 cells with B beta cDNA specifically enhances synthesis of the three component chains of fibrinogen. J Biol Chem 1990;265:6389-6393.[Abstract/Free Full Text]

25. Thomas AE, Green FR, Kelleher CH, et al. Variation in the promoter region of the beta fibrinogen gene is associated with plasma fibrinogen levels in smokers and non-smokers Thromb Haemost 1991;65:487-490.[Web of Science][Medline]

26. van't Hooft FM, von Bahr SJ, Silveira A, Iliadou A, Eriksson P, Hamsten A. Two common, functional polymorphisms in the promoter region of the beta-fibrinogen gene contribute to regulation of plasma fibrinogen concentration Arterioscler Thromb Vasc Biol 1999;19:3063-3070.[Abstract/Free Full Text]

27. Friedlander Y, Kark JD, Sinnreich R, Basso F, Humphries SE. Combined segregation and linkage analysis of fibrinogen variability in Israeli families: evidence for two quantitative-trait loci, one of which is linked to a functional variant (-58G > A) in the promoter of the alpha-fibrinogen gene Ann Hum Genet 2003;67:228-241.[CrossRef][Web of Science][Medline]

28. Cook DG, Cappuccio FP, Atkinson RW, et al. Ethnic differences in fibrinogen levels: the role of environmental factors and the beta-fibrinogen gene Am J Epidemiol 2001;153:799-806.[Abstract/Free Full Text]

29. de Maat MP. Effects of diet, drugs, and genes on plasma fibrinogen levels Ann N Y Acad Sci 2001;936:509-521.[Web of Science][Medline]

30. Tybjaerg-Hansen A, Agerholm-Larsen B, Humphries SE, Abildgaard S, Schnohr P, Nordestgaard BG. A common mutation (G-455—> A) in the beta-fibrinogen promoter is an independent predictor of plasma fibrinogen, but not of ischemic heart disease. A study of 9,127 individuals based on the Copenhagen City Heart Study. J Clin Invest 1997;99:3034-3039.[Web of Science][Medline]

31. Zerjal T, Beckman L, Beckman G, et al. Geographical, linguistic, and cultural influences on genetic diversity: Y-chromosomal distribution in Northern European populations Mol Biol Evol 2001;18:1077-1087.[Abstract/Free Full Text]

32. Mavri A, Guzic-Salobir B, Salobir-Pajnic B, Keber I, Stare J, Stegnar M. Seasonal variation of some metabolic and haemostatic risk factors in subjects with and without coronary artery disease Blood Coagul Fibrinolysis 2001;12:359-365.[CrossRef][Web of Science][Medline]

33. Afilalo J, Duque G, Steele R, Jukema JW, de Craen AJ, Eisenberg MJ. Statins for secondary prevention in elderly patients: a hierarchical bayesian meta-analysis J Am Coll Cardiol 2008;51:37-45.[Abstract/Free Full Text]

34. Undas A, Brummel-Ziedins KE, Mann KG. Statins and blood coagulation Arterioscler Thromb Vasc Biol 2005;25:287-294.[Abstract/Free Full Text]

This article has been cited by other articles:

				A. Peters, S. Greven, I. M. Heid, F. Baldari, S. Breitner, T. Bellander, C. Chrysohoou, T. Illig, B. Jacquemin, W. Koenig, et al. Fibrinogen Genes Modify the Fibrinogen Response to Ambient Particulate Matter Am. J. Respir. Crit. Care Med., March 15, 2009; 179(6): 484 - 491. [Abstract] [Full Text] [PDF]

This Article Abstract Figures Only Full Text (PDF) Online Appendix Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (5) Citing Articles via Google Scholar Google Scholar Articles by Jacquemin, B. Articles by Peters, A. Search for Related Content PubMed Articles by Jacquemin, B. Articles by Peters, A.