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Article

A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification

by
Anastasia V. Ponasenko
1,
Maria V. Khutornaya
1,
Anton G. Kutikhin
1,*,
Natalia V. Rutkovskaya
1,
Anna V. Tsepokina
1,
Natalia V. Kondyukova
1,
Arseniy E. Yuzhalin
1,2 and
Leonid S. Barbarash
1
1
Research Institute for Complex Issues of Cardiovascular Diseases, Sosnovy Boulvevard 6, Kemerovo 650002, Russia
2
Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, UK
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2016, 17(9), 1385; https://doi.org/10.3390/ijms17091385
Submission received: 23 June 2016 / Revised: 9 August 2016 / Accepted: 19 August 2016 / Published: 31 August 2016
(This article belongs to the Special Issue Human Single Nucleotide Polymorphisms and Disease Diagnostics)

Abstract

:
Severe bioprosthetic mitral valve calcification is a significant problem in cardiovascular surgery. Unfortunately, clinical markers did not demonstrate efficacy in prediction of severe bioprosthetic mitral valve calcification. Here, we examined whether a genomics-based approach is efficient in predicting the risk of severe bioprosthetic mitral valve calcification. A total of 124 consecutive Russian patients who underwent mitral valve replacement surgery were recruited. We investigated the associations of the inherited variation in innate immunity, lipid metabolism and calcium metabolism genes with severe bioprosthetic mitral valve calcification. Genotyping was conducted utilizing the TaqMan assay. Eight gene polymorphisms were significantly associated with severe bioprosthetic mitral valve calcification and were therefore included into stepwise logistic regression which identified male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as independent predictors of severe bioprosthetic mitral valve calcification. The developed genomics-based model had fair predictive value with area under the receiver operating characteristic (ROC) curve of 0.73. In conclusion, our genomics-based approach is efficient for the prediction of severe bioprosthetic mitral valve calcification.

Graphical Abstract

1. Introduction

Mitral valve calcification, accompanied by inflammation and lipid deposition, is associated with common cardiovascular risk factors and represents an important risk factor of mitral valve disease [1,2]. Currently, there is no efficient approach for the prevention of mitral valve disease progression, with valve replacement being the only treatment option [1]. However, bioprosthetic mitral valves also frequently undergo severe calcification which is able to cause bioprosthetic valve failure and may require repeated valve replacement surgery, a major clinical intervention [1]. Even the widely established Carpentier-Edwards Perimount and Medtronic Mosaic bioprosthetic mitral valves undergo severe calcification in up to 20% of patients <60 years [3,4].
Unfortunately, there is still no clinical model for the prediction of severe bioprosthetic mitral valve calcification. A previous study by our research group did not reveal any significant clinical predictors of this condition [5]. Mitral valve calcification is frequent among family members [6] but genomic markers of native and bioprosthetic mitral valve calcification are still almost unknown [7]. Nevertheless, their identification may assist in revealing the underlying mechanisms of these conditions. This, in turn, may improve treatment of mitral valve disease.
Progress in genotyping technologies resulted in many studies on the association of single nucleotide polymorphisms (SNPs) with human diseases [8]. SNPs can lead to a number of consequences depending on their location in the genome [9]. As known, SNPs within the noncoding regions are able to affect mRNA splicing or even transcription initiation, while SNPs within the coding regions may alter protein folding, stability, and expression, or influence posttranslational modifications [9]. Here, we investigated whether SNPs within innate immunity, lipid metabolism and calcium metabolism genes are significant predictors of severe bioprosthetic mitral valve calcification.

2. Results

We identified eight SNPs being significantly associated with severe bioprosthetic mitral valve calcification (Table 1).
The C allele of the rs1800796 polymorphism within the TLR6 gene, the T allele of the rs1205 polymorphism within the CRP gene, and the G allele of the rs10455872 polymorphism within the LPA gene were associated with decreased risk of severe bioprosthetic mitral valve calcification. In contrast, the A allele of the rs5743810 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, the A/G genotype of the rs1800871 polymorphism and the T/G genotype of the rs1800872 polymorphism within the IL10 gene, and the G/G genotype of the rs13290979 polymorphism within the NOTCH1 gene were associated with increased risk of severe bioprosthetic mitral valve calcification. To perform an additional quality control step, we tested six non-relevant SNPs within the genes encoding coagulation factors and integrin beta 3, a protein responsible for platelet aggregation. Expectedly, we did not find any significant associations with severe bioprosthetic mitral valve calcification.
We then carried out a stepwise logistic regression to reveal independent predictive markers of severe bioprosthetic mitral valve calcification. Out of eight markers revealed by genetic association analysis, only three remained significant (Table 2).
A final model for prediction of severe bioprosthetic mitral valve calcification included male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene. The area under the ROC curve of 0.73 demonstrated the fair predictive value of the model.

3. Discussion

Previous studies vaguely uncovered the genetic susceptibility to mitral annular calcification. Novaro et al. [10] and Tangri et al. [11] did not detect significant associations between polymorphisms within apoE (gene encoding apolipoprotein E), Klotho, β-Klotho, and FGF-23 (genes encoding proteins constituting one of the calcium phosphate homeostasis pathways) genes and mitral annular calcification. Davutoglu and Nacak [12] reported that the I allele of the rs4340 polymorphism within the ACE gene (encoding angiotensin-converting enzyme) correlated with a higher risk of mitral annular calcification. Moreover, a study by Thanassoulis et al. [13] revealed two IL1F9 (gene encoding IL-36γ/IL-1F9 protein) gene polymorphisms, rs17659543 and rs13415097, being significantly associated with higher risk of mitral annular calcification.
However, there are no published data on genetic susceptibility to bioprosthetic mitral valve calcification. In addition, there is no any model for the prediction of bioprosthetic mitral valve calcification. Here we identified the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as the independent predictive markers of severe bioprosthetic mitral valve calcification. Moreover, we developed a predictive model with the fair discriminative power. Nevertheless, area under the receiver operating characteristic (ROC) curve of 0.73 indicates a number of other relevant predictive markers to be discovered.
A previous study by our research group found that the C/T genotype of the rs2229238 polymorphism within the IL6R gene is significantly associated with a higher IL-6 plasma level compared to the C/C and T/T genotypes [14]. It is worth noting that IL-6 is associated with heart valve calcification in general and with mitral annular calcification in particular [15,16]. Therefore, we hypothesize that the C/T genotype of the rs2229238 polymorphism within the IL6R gene may increase IL-6 plasma level and may thus promote bioprosthetic mitral valve calcification.
Our study had a considerable shortcoming: we recruited a relatively small sample due to a limited number of mitral valve replacements. However, we tested six irrelevant SNPs for the occasional associations, expectedly with a negative result. This approach was used to increase statistical confidence when using a small sample size.
Our findings may have clinical applications. A genomics-based model for the prediction of severe bioprosthetic mitral valve calcification can be used in choosing between mechanical and bioprosthetic mitral valves for mitral valve replacement surgery. For carriers of the high risk genotypes, mechanical heart valves which are resistant to calcification may be an appropriate option (reviewed by Bre et al. [17]). Further investigations on larger samples are necessary to confirm our results.

4. Materials and Methods

4.1. Population

Inclusion criteria were: (1) living in Kemerovo Region for ≥2 generations; (2) Russian ethnicity; (3) mitral valve replacement surgery due to mitral valve disease; and (4) written informed consent. Exclusion criteria were: (1) belonging to the immigrant or aboriginal populations; (2) previous cancer diagnosis; (3) concomitant mental disorders and/or autoimmune diseases; and (4) refusal to sign a written informed consent.
We recruited 140 patients admitted to our Research Institute who underwent mitral valve replacement surgery due to mitral valve disease in 2006–2007. After exclusion of 16 patients due to the above-mentioned criteria, the study group finally included 124 patients (Table 3).
Half of them (n = 62) had severe bioprosthetic mitral valve calcification within 8 years post-implantation and therefore represented a case group; remaining subjects (n = 62) without severe bioprosthetic mitral valve calcification were considered as the controls (Table 4). The local ethical committee approved the study protocol. All the participants provided written informed consent after the study was fully explained.
The diagnosis of mitral valve disease and decision on mitral valve replacement surgery were performed in accordance with the respective American guidelines [18]. For the mitral valve replacement, we used KemCor and PeriCor bioprosthetic valves (NeoCor, Russian Federation) crosslinked with ethylene glycol diglycidyl ether for conferring resistance to oxidation and enzymatic degradation [19]. Functional conditions of the bioprosthetic valves were annually assessed by echocardiography. After the explantation of failing bioprosthesis (Figure 1a), bioprosthetic mitral valve calcification was verified by von Kossa staining (Figure 1b) and scanning electron microscopy (Figure 1c).
The study workflow is shown in the Figure 2.

4.2. SNP Selection and Genotyping

For this study, we defined four main criteria for SNP selection: (1) location within innate immunity, lipid metabolism, or calcium metabolism genes; (2) minor allele frequency ≥5% for Russian population tested with HapMap; (3) functional consequences; and (4) few or no studies on the role of the SNP in mitral valve calcification. The National Center for Biotechnology Information dbSNP, SNPinfo, and SNPnexus databases were utilized for the SNP selection [20,21]. In total, we selected 50 SNPs within 24 genes (Table 5).
The procedures of DNA extraction and genotyping were the same as previously described [22,23,24]. Table 5 demonstrates the sequence-specific primers for genotyped SNPs. Laboratory staff was blinded to patient status, and one-tenth of the samples was repeatedly genotyped for quality control.

4.3. Statistical Analysis

The statistical analysis was performed as in [22,23,24] using the SNPStats software [25]. To further define independent predictors of severe bioprosthetic mitral valve calcification, we carried out stepwise logistic regression with the plotting of the ROC curve and area under the curve.

Acknowledgments

There was no financial assistance with the project.

Author Contributions

Anastasia V. Ponasenko, Maria V. Khutornaya, Anton G. Kutikhin and Leonid S. Barbarash conceived and designed the study; Natalia V. Rutkovskaya and Natalia V. Kondyukova collected the patient data; Anna V. Tsepokina and Maria V. Khutornaya collected the blood specimens, isolated DNA and performed genotyping; Anton G. Kutikhin carried out statistical analysis; Anastasia V. Ponasenko, Maria V. Khutornaya, Anton G. Kutikhin and Arseniy E. Yuzhalin wrote the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Bioprosthetic valve calcification: (a) explanted bioprosthetic heart valve; (b) von Kossa staining, scale bar = 50 µm; (c) scanning electron microscopy. Calcified areas are indicated as black circles.
Figure 1. Bioprosthetic valve calcification: (a) explanted bioprosthetic heart valve; (b) von Kossa staining, scale bar = 50 µm; (c) scanning electron microscopy. Calcified areas are indicated as black circles.
Ijms 17 01385 g001
Figure 2. Study workflow.
Figure 2. Study workflow.
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Table 1. Association of the polymorphisms within innate immunity genes, genes of lipid metabolism, and genes of calcium metabolism with severe bioprosthetic mitral valve calcification.
Table 1. Association of the polymorphisms within innate immunity genes, genes of lipid metabolism, and genes of calcium metabolism with severe bioprosthetic mitral valve calcification.
ModelGenotypeWithout Severe Bioprosthetic Mitral Valve CalcificationWith Severe Bioprosthetic Mitral Valve CalcificationOR (95% CI)p-ValueAICHWE
TLR1 rs5743551
CodominantT/T31 (50%)35 (56.5%)1.000.39171.70.06
C/T30 (48.4%)25 (40.3%)0.69 (0.32–1.45)
C/C1 (1.6%)2 (3.2%)2.71 (0.22–33.36)
DominantT/T31 (50%)35 (56.5%)1.000.42170.9
C/T-C/C31 (50%)27 (43.5%)0.74 (0.36–1.54)
RecessiveT/T-C/T61 (98.4%)60 (96.8%)1.000.35170.7
C/C1 (1.6%)2 (3.2%)3.17 (0.26–38.23)
OverdominantT/T-C/C32 (51.6%)37 (59.7%)1.000.27170.4
C/T30 (48.4%)25 (40.3%)0.66 (0.31–1.39)
Log-additive---------0.85 (0.44–1.67)0.64171.4
TLR1 rs5743611
CodominantC/C38 (61.3%)36 (58.1%)1.000.761730.61
C/G21 (33.9%)21 (33.9%)1.00 (0.46–2.19)
G/G3 (4.8%)5 (8.1%)1.74 (0.38–7.96)
DominantC/C38 (61.3%)36 (58.1%)1.000.81171.5
C/G-G/G24 (38.7%)26 (41.9%)1.10 (0.52–2.30)
RecessiveC/C-C/G59 (95.2%)57 (91.9%)1.000.46171
G/G3 (4.8%)5 (8.1%)1.74 (0.39–7.75)
OverdominantC/C-G/G41 (66.1%)41 (66.1%)1.000.89171.6
C/G21 (33.9%)21 (33.9%)0.94 (0.44–2.04)
Log-additive---------1.16 (0.64–2.09)0.62171.3
TLR2 rs5743708
---G/G57 (91.9%)56 (90.3%)1.000.67171.40.99
A/G5 (8.1%)6 (9.7%)1.33 (0.36–4.92)
TLR2 rs3804099
CodominantT/T23 (37.1%)18 (29%)1.000.37171.60.06
C/T33 (53.2%)37 (59.7%)1.80 (0.79–4.13)
C/C6 (9.7%)7 (11.3%)1.35 (0.36–5.06)
DominantT/T23 (37.1%)18 (29%)1.000.18169.8
C/T-C/C39 (62.9%)44 (71%)1.72 (0.77–3.82)
RecessiveT/T-C/T56 (90.3%)55 (88.7%)1.000.93171.6
C/C6 (9.7%)7 (11.3%)0.94 (0.28–3.17)
OverdominantT/T-C/C29 (46.8%)25 (40.3%)1.000.18169.8
C/T33 (53.2%)37 (59.7%)1.68 (0.78–3.60)
Log-additive---------1.34 (0.73–2.46)0.33170.6
TLR4 rs4986790
CodominantA/A53 (85.5%)53 (85.5%)1.000.461720.53
A/G8 (12.9%)9 (14.5%)1.19 (0.41–3.45)
G/G1 (1.6%)0 (0%)0.00 (0.00–0.00)
DominantA/A53 (85.5%)53 (85.5%)1.000.95171.6
A/G-G/G9 (14.5%)9 (14.5%)1.03 (0.37–2.91)
RecessiveA/A-A/G61 (98.4%)62 (100%)1.000.23170.1
G/G1 (1.6%)0 (0%)0.00 (0.00–0.00)
OverdominantA/A-G/G54 (87.1%)53 (85.5%)1.000.73171.5
A/G8 (12.9%)9 (14.5%)1.20 (0.41–3.50)
Log-additive---------0.91 (0.35–2.35)0.85171.5
TLR4 rs4986791
CodominantC/C53 (85.5%)53 (85.5%)1.000.98173.50.17
C/T8 (12.9%)8 (12.9%)1.00 (0.33–2.97)
T/T1 (1.6%)1 (1.6%)1.36 (0.08–23.62)
DominantC/C53 (85.5%)53 (85.5%)1.000.95171.6
C/T-T/T9 (14.5%)9 (14.5%)1.03 (0.37–2.91)
RecessiveC/C-C/T61 (98.4%)61 (98.4%)1.000.83171.5
T/T1 (1.6%)1 (1.6%)1.36 (0.08–23.58)
OverdominantC/C-T/T54 (87.1%)54 (87.1%)1.000.99171.6
C/T8 (12.9%)8 (12.9%)0.99 (0.33–2.95)
Log-additive---------1.05 (0.43–2.56)0.91171.6
TLR6 rs3775073
CodominantT/T12 (19.4%)20 (32.3%)1.000.092168.80.72
T/C32 (51.6%)33 (53.2%)0.71 (0.29–1.75)
C/C18 (29%)9 (14.5%)0.31 (0.10–0.94)
DominantT/T12 (19.4%)20 (32.3%)1.000.18169.8
T/C-C/C50 (80.7%)42 (67.7%)0.56 (0.24–1.32)
RecessiveT/T-T/C44 (71%)53 (85.5%)1.000.04167.4
C/C18 (29%)9 (14.5%)0.39 (0.15–0.98)
OverdominantT/T-C/C30 (48.4%)29 (46.8%)1.000.59171.3
T/C32 (51.6%)33 (53.2%)1.22 (0.58–2.55)
Log-additive---------0.56 (0.32–0.98)0.037167.2
TLR6 rs5743810
CodominantG/G35 (56.5%)24 (38.7%)1.000.09168.80.67
A/G25 (40.3%)30 (48.4%)1.57 (0.73–3.38)
A/A2 (3.2%)8 (12.9%)5.19 (0.97–27.93)
DominantG/G35 (56.5%)24 (38.7%)1.000.11169
A/G-A/A27 (43.5%)38 (61.3%)1.83 (0.87–3.84)
RecessiveG/G-A/G60 (96.8%)54 (87.1%)1.000.062168.1
A/A2 (3.2%)8 (12.9%)4.17 (0.81–21.53)
OverdominantG/G-A/A37 (59.7%)32 (51.6%)1.000.53171.2
A/G25 (40.3%)30 (48.4%)1.26 (0.61–2.64)
Log-additive---------1.87 (1.02–3.44)0.039167.3
TREM1 rs2234246
CodominantC/C15 (24.2%)18 (29%)1.000.54172.30.99
C/T29 (46.8%)33 (53.2%)1.05 (0.43–2.52)
T/T18 (29%)11 (17.7%)0.63 (0.22–1.81)
DominantC/C15 (24.2%)18 (29%)1.000.8171.5
C/T-T/T47 (75.8%)44 (71%)0.90 (0.39–2.07)
RecessiveC/C-C/T44 (71%)51 (82.3%)1.000.27170.3
T/T18 (29%)11 (17.7%)0.61 (0.25–1.47)
OverdominantC/C-T/T33 (53.2%)29 (46.8%)1.000.48171.1
C/T29 (46.8%)33 (53.2%)1.30 (0.63–2.70)
Log-additive---------0.80 (0.47–1.36)0.41170.9
TREM1 rs4711668
CodominantC/C26 (41.9%)21 (33.9%)1.000.461720.85
T/C30 (48.4%)30 (48.4%)1.29 (0.58–2.85)
T/T6 (9.7%)11 (17.7%)2.07 (0.64–6.75)
DominantC/C26 (41.9%)21 (33.9%)1.000.35170.7
T/C-T/T36 (58.1%)41 (66.1%)1.43 (0.67–3.04)
RecessiveC/C-T/C56 (90.3%)51 (82.3%)1.000.29170.4
T/T6 (9.7%)11 (17.7%)1.80 (0.60–5.37)
OverdominantC/C-T/T32 (51.6%)32 (51.6%)1.000.87171.5
T/C30 (48.4%)30 (48.4%)1.07 (0.51–2.21)
Log-additive---------1.40 (0.81–2.41)0.23170.1
TREM1 rs3804277
CodominantC/C16 (25.8%)18 (29%)1.000.52172.30.86
C/T28 (45.2%)33 (53.2%)1.13 (0.47–2.69)
T/T18 (29%)11 (17.7%)0.66 (0.23–1.89)
DominantC/C16 (25.8%)18 (29%)1.000.92171.6
C/T-T/T46 (74.2%)44 (71%)0.96 (0.42–2.18)
RecessiveC/C-C/T44 (71%)51 (82.3%)1.000.27170.3
T/T18 (29%)11 (17.7%)0.61 (0.25–1.47)
OverdominantC/C-T/T34 (54.8%)29 (46.8%)1.000.41170.9
C/T28 (45.2%)33 (53.2%)1.36 (0.66–2.83)
Log-additive---------0.82 (0.49–1.39)0.47171
TREM1 rs2234237
CodominantT/T49 (79%)50 (80.7%)1.000.39171.70.99
A/T13 (21%)11 (17.7%)0.69 (0.27–1.79)
A/A0 (0%)1 (1.6%)0.00 (0.00–0.00)
DominantT/T49 (79%)50 (80.7%)1.000.55171.2
A/T-A/A13 (21%)12 (19.4%)0.76 (0.30–1.92)
RecessiveT/T-A/T62 (100%)61 (98.4%)1.000.26170.3
A/A0 (0%)1 (1.6%)0.00 (0.00–0.00)
OverdominantT/T-A/A49 (79%)51 (82.3%)1.000.41170.9
A/T13 (21%)11 (17.7%)0.67 (0.26–1.74)
Log-additive---------0.86 (0.36–2.05)0.73171.5
TREM1 rs6910730
CodominantA/A49 (79%)48 (77.4%)1.000.49172.20.99
A/G13 (21%)13 (21%)0.84 (0.34–2.10)
G/G0 (0%)1 (1.6%)0.00 (0.00–0.00))
DominantA/A49 (79%)48 (77.4%)1.000.83171.5
A/G-G/G13 (21%)14 (22.6%)0.91 (0.37–2.24)
RecessiveA/A-A/G62 (100%)61 (98.4%)1.000.26170.3
G/G0 (0%)1 (1.6%)0.00 (0.00–0.00)
OverdominantA/A-G/G49 (79%)49 (79%)1.000.67171.4
A/G13 (21%)13 (21%)0.82 (0.33–2.05)
Log-additive---------1.00 (0.43–2.34)1171.6
TREM1 rs1817537
CodominantC/C16 (25.8%)18 (29%)1.000.52172.30.86
C/G28 (45.2%)33 (53.2%)1.13 (0.47–2.69)
G/G18 (29%)11 (17.7%)0.66 (0.23–1.89)
DominantC/C16 (25.8%)18 (29%)1.000.92171.6
C/G-G/G46 (74.2%)44 (71%)0.96 (0.42–2.18)
RecessiveC/C-C/G44 (71%)51 (82.3%)1.000.27170.3
G/G18 (29%)11 (17.7%)0.61 (0.25–1.47)
OverdominantC/C-G/G34 (54.8%)29 (46.8%)1.000.41170.9
C/G28 (45.2%)33 (53.2%)1.36 (0.66–2.83)
Log-additive---------0.82 (0.49–1.39)0.47171
TREM1 rs9471535
CodominantT/T49 (79%)50 (80.7%)1.000.39171.70.99
C/T13 (21%)11 (17.7%)0.69 (0.27–1.79)
C/C0 (0%)1 (1.6%)0.00 (0.00–0.00)
DominantT/T49 (79%)50 (80.7%)1.000.55171.2
C/T-C/C13 (21%)12 (19.4%)0.76 (0.30–1.92)
RecessiveT/T-C/T62 (100%)61 (98.4%)1.000.26170.3
C/C0 (0%)1 (1.6%)0.00 (0.00–0.00)
OverdominantT/T-C/C49 (79%)51 (82.3%)1.000.41170.9
C/T13 (21%)11 (17.7%)0.67 (0.26–1.74)
Log-additive---------0.86 (0.36–2.05)0.73171.5
TREM1 rs7768162
CodominantG/G26 (41.9%)21 (33.9%)1.000.57172.40.25
A/G31 (50%)33 (53.2%)1.35 (0.62–2.96)
A/A5 (8.1%)8 (12.9%)1.88 (0.51–6.85)
DominantG/G26 (41.9%)21 (33.9%)1.000.35170.7
A/G-A/A36 (58.1%)41 (66.1%)1.43 (0.67–3.04)
RecessiveG/G-A/G57 (91.9%)54 (87.1%)1.000.46171
A/A5 (8.1%)8 (12.9%)1.58 (0.47–5.29)
OverdominantG/G-A/A31 (50%)29 (46.8%)1.000.66171.4
A/G31 (50%)33 (53.2%)1.18 (0.57–2.45)
Log-additive---------1.36 (0.77–2.43)0.29170.4
IL1B rs16944
CodominantG/G26 (41.9%)25 (40.3%)1.000.88173.30.42
G/A31 (50%)30 (48.4%)0.92 (0.42–1.99)
A/A5 (8.1%)7 (11.3%)1.27 (0.33–4.79)
DominantG/G26 (41.9%)25 (40.3%)1.000.92171.6
G/A-A/A36 (58.1%)37 (59.7%)0.96 (0.46–2.04)
RecessiveG/G-G/A57 (91.9%)55 (88.7%)1.000.65171.4
A/A5 (8.1%)7 (11.3%)1.33 (0.38–4.66)
OverdominantG/G-A/A31 (50%)32 (51.6%)1.000.72171.4
G/A31 (50%)30 (48.4%)0.88 (0.42–1.82)
Log-additive---------1.04 (0.58–1.86)0.89171.6
IL1B rs1143634
CodominantG/G30 (48.4%)40 (64.5%)1.000.18170.20.48
G/A27 (43.5%)17 (27.4%)0.48 (0.21–1.06)
A/A5 (8.1%)5 (8.1%)0.63 (0.16–2.54)
DominantG/G30 (48.4%)40 (64.5%)1.000.07168.3
G/A-A/A32 (51.6%)22 (35.5%)0.50 (0.24–1.07)
RecessiveG/G-G/A57 (91.9%)57 (91.9%)1.000.79171.5
A/A5 (8.1%)5 (8.1%)0.83 (0.21–3.24)
OverdominantG/G-A/A35 (56.5%)45 (72.6%)1.000.084168.6
G/A27 (43.5%)17 (27.4%)0.51 (0.23–1.10)
Log-additive---------0.64 (0.36–1.15)0.13169.3
IL1F9 rs17659543
CodominantC/C49 (79%)48 (78.7%)1.000.651720.99
C/T12 (19.4%)13 (21.3%)1.03 (0.41–2.55)
T/T1 (1.6%)0 (0%)0.00 (0.00–0.00)
DominantC/C49 (79%)48 (78.7%)1.000.94170.8
C/T-T/T13 (21%)13 (21.3%)0.97 (0.40–2.37)
RecessiveC/C-C/T61 (98.4%)61 (100%)1.000.35170
T/T1 (1.6%)0 (0%)0.00 (0.00–0.00)
OverdominantC/C-T/T50 (80.7%)48 (78.7%)1.000.93170.8
C/T12 (19.4%)13 (21.3%)1.04 (0.42–2.59)
Log-additive---------0.91 (0.39–2.13)0.83170.8
IL6 rs1554606
CodominantT/T17 (27.4%)13 (21%)1.000.59172.50.47
G/T30 (48.4%)37 (59.7%)1.43 (0.58–3.52)
G/G15 (24.2%)12 (19.4%)0.94 (0.32–2.82)
DominantT/T17 (27.4%)13 (21%)1.000.58171.3
G/T-G/G45 (72.6%)49 (79%)1.27 (0.54–3.02)
RecessiveT/T-G/T47 (75.8%)50 (80.7%)1.000.5171.1
G/G15 (24.2%)12 (19.4%)0.73 (0.30–1.80)
OverdominantT/T-G/G32 (51.6%)25 (40.3%)1.000.3170.5
G/T30 (48.4%)37 (59.7%)1.47 (0.70–3.07)
Log-additive---------0.98 (0.57–1.69)0.94171.6
IL6 rs1800796
CodominantG/G53 (85.5%)49 (79%)1.000.69172.80.10
C/G8 (12.9%)11 (17.7%)1.51 (0.54–4.24)
C/C1 (1.6%)2 (3.2%)1.66 (0.14–20.27)
DominantG/G53 (85.5%)49 (79%)1.000.39170.8
C/G-C/C9 (14.5%)13 (21%)1.53 (0.58–4.06)
RecessiveG/G-C/G61 (98.4%)60 (96.8%)1.000.73171.5
C/C1 (1.6%)2 (3.2%)1.54 (0.13–18.69)
OverdominantG/G-C/C54 (87.1%)51 (82.3%)1.000.44171
C/G8 (12.9%)11 (17.7%)1.49 (0.53–4.17)
Log-additive---------1.42 (0.62–3.26)0.4170.9
IL6 rs2069827
CodominantG/G48 (77.4%)51 (82.3%)1.000.68172.80.63
G/T13 (21%)10 (16.1%)0.66 (0.26–1.69)
T/T1 (1.6%)1 (1.6%)0.87 (0.05–15.09)
DominantG/G48 (77.4%)51 (82.3%)1.000.39170.8
G/T-T/T14 (22.6%)11 (17.7%)0.67 (0.27–1.68)
RecessiveG/G-G/T61 (98.4%)61 (98.4%)1.000.97171.6
T/T1 (1.6%)1 (1.6%)0.95 (0.06–16.33)
OverdominantG/G-T/T49 (79%)52 (83.9%)1.000.39170.8
G/T13 (21%)10 (16.1%)0.66 (0.26–1.69)
Log-additive---------0.73 (0.32–1.64)0.44171
IL6R rs2228145
CodominantA/A25 (40.3%)28 (45.2%)1.000.81173.20.99
C/A29 (46.8%)28 (45.2%)1.00 (0.46–2.18)
C/C8 (12.9%)6 (9.7%)0.68 (0.20–2.34)
DominantA/A25 (40.3%)28 (45.2%)1.000.84171.5
C/A-C/C37 (59.7%)34 (54.8%)0.93 (0.44–1.94)
RecessiveA/A-C/A54 (87.1%)56 (90.3%)1.000.52171.2
C/C8 (12.9%)6 (9.7%)0.68 (0.21–2.20)
OverdominantA/A-C/C33 (53.2%)34 (54.8%)1.000.83171.5
C/A29 (46.8%)28 (45.2%)1.08 (0.52–2.27)
Log-additive---------0.88 (0.51–1.53)0.65171.4
IL6R rs2229238
CodominantC/C42 (67.7%)35 (56.5%)1.000.03166.60.30
C/T14 (22.6%)25 (40.3%)2.48 (1.07–5.73)
T/T6 (9.7%)2 (3.2%)0.40 (0.07–2.25)
DominantC/C42 (67.7%)35 (56.5%)1.000.12169.2
C/T-T/T20 (32.3%)27 (43.5%)1.83 (0.84–3.96)
RecessiveC/C-C/T56 (90.3%)60 (96.8%)1.000.13169.3
T/T6 (9.7%)2 (3.2%)0.30 (0.05–1.59)
OverdominantC/C-T/T48 (77.4%)37 (59.7%)1.000.016165.8
C/T14 (22.6%)25 (40.3%)2.70 (1.18–6.16)
Log-additive---------1.21 (0.66–2.21)0.54171.2
IL8 rs2227306
CodominantC/C20 (32.3%)20 (32.3%)1.000.97173.50.72
C/T29 (46.8%)30 (48.4%)1.05 (0.45–2.43)
T/T13 (21%)12 (19.4%)0.92 (0.33–2.61)
DominantC/C20 (32.3%)20 (32.3%)1.000.99171.6
C/T-T/T42 (67.7%)42 (67.7%)1.01 (0.46–2.22)
RecessiveC/C-C/T49 (79%)50 (80.7%)1.000.82171.5
T/T13 (21%)12 (19.4%)0.90 (0.36–2.23)
OverdominantC/C-T/T33 (53.2%)32 (51.6%)1.000.84171.5
C/T29 (46.8%)30 (48.4%)1.08 (0.52–2.26)
Log-additive---------0.97 (0.58–1.62)0.9171.6
IL10 rs1800871
CodominantG/G34 (54.8%)31 (50%)1.000.029166.50.09
A/G24 (38.7%)31 (50%)1.81 (0.83–3.95)
A/A4 (6.5%)0 (0%)0.00 (0.00–0.00)
DominantG/G34 (54.8%)31 (50%)1.000.26170.3
A/G-A/A28 (45.2%)31 (50%)1.55 (0.72–3.32)
RecessiveG/G-A/G58 (93.5%)62 (100%)1.000.029166.8
A/A4 (6.5%)0 (0%)0.00 (0.00–0.00)
OverdominantG/G-A/A38 (61.3%)31 (50%)1.000.07168.3
A/G24 (38.7%)31 (50%)2.02 (0.93–4.38)
Log-additive---------1.15 (0.59–2.24)0.68171.4
IL10 rs1800872
CodominantG/G34 (54.8%)30 (49.2%)1.000.028165.70.09
T/G24 (38.7%)31 (50.8%)1.84 (0.84–4.00)
T/T4 (6.5%)0 (0%)0.00 (0.00–0.00)
DominantG/G34 (54.8%)30 (49.2%)1.000.24169.5
T/G-T/T28 (45.2%)31 (50.8%)1.57 (0.73–3.36)
RecessiveG/G-T/G58 (93.5%)61 (100%)1.000.029166.1
T/T4 (6.5%)0 (0%)0.00 (0.00–0.00)
OverdominantG/G-T/T38 (61.3%)30 (49.2%)1.000.065167.4
T/G24 (38.7%)31 (50.8%)2.05 (0.95–4.44)
Log-additive---------1.17 (0.60–2.27)0.65170.6
IL10 rs1800896
CodominantT/T17 (27.4%)16 (25.8%)1.000.461720.86
T/C30 (48.4%)34 (54.8%)1.41 (0.58–3.41)
C/C15 (24.2%)12 (19.4%)0.79 (0.27–2.34)
DominantT/T17 (27.4%)16 (25.8%)1.000.68171.4
T/C-C/C45 (72.6%)46 (74.2%)1.19 (0.52–2.74)
RecessiveT/T-T/C47 (75.8%)50 (80.7%)1.000.33170.6
C/C15 (24.2%)12 (19.4%)0.64 (0.26–1.58)
OverdominantT/T-C/C32 (51.6%)28 (45.2%)1.000.24170.2
T/C30 (48.4%)34 (54.8%)1.56 (0.74–3.29)
Log-additive---------0.92 (0.54–1.56)0.75171.5
IL12B rs3212227
CodominantT/T38 (61.3%)36 (58.1%)1.000.771730.80
G/T21 (33.9%)22 (35.5%)1.30 (0.59–2.85)
G/G3 (4.8%)4 (6.5%)1.42 (0.28–7.32)
DominantT/T38 (61.3%)36 (58.1%)1.000.47171.1
G/T-G/G24 (38.7%)26 (41.9%)1.32 (0.62–2.79)
RecessiveT/T-G/T59 (95.2%)58 (93.5%)1.000.75171.5
G/G3 (4.8%)4 (6.5%)1.29 (0.26–6.47)
OverdominantT/T-G/G41 (66.1%)40 (64.5%)1.000.55171.2
G/T21 (33.9%)22 (35.5%)1.26 (0.58–2.73)
Log-additive---------1.25 (0.67–2.31)0.48171.1
IL12RB rs375947
CodominantA/A27 (43.5%)26 (41.9%)1.000.77173.10.84
A/G26 (41.9%)29 (46.8%)1.21 (0.56–2.66)
G/G9 (14.5%)7 (11.3%)0.82 (0.25–2.67)
DominantA/A27 (43.5%)26 (41.9%)1.000.77171.5
A/G-G/G35 (56.5%)36 (58.1%)1.11 (0.53–2.33)
RecessiveA/A-A/G53 (85.5%)55 (88.7%)1.000.6171.3
G/G9 (14.5%)7 (11.3%)0.74 (0.25–2.26)
OverdominantA/A-G/G36 (58.1%)33 (53.2%)1.000.52171.2
A/G26 (41.9%)29 (46.8%)1.27 (0.61–2.65)
Log-additive---------0.99 (0.58–1.69)0.96171.6
TNF rs361525
---G/G56 (90.3%)60 (96.8%)1.000.092168.70.99
A/G6 (9.7%)2 (3.2%)0.25 (0.04–1.41)
TNF rs1800629
CodominantG/G48 (77.4%)54 (87.1%)1.000.39171.70.06
A/G11 (17.7%)7 (11.3%)0.60 (0.21–1.73)
A/A3 (4.8%)1 (1.6%)0.31 (0.03–3.19)
DominantG/G48 (77.4%)54 (87.1%)1.000.2170
A/G-A/A14 (22.6%)8 (12.9%)0.53 (0.20–1.42)
RecessiveG/G-A/G59 (95.2%)61 (98.4%)1.000.32170.6
A/A3 (4.8%)1 (1.6%)0.34 (0.03–3.42)
OverdominantG/G-A/A51 (82.3%)55 (88.7%)1.000.38170.8
A/G11 (17.7%)7 (11.3%)0.62 (0.21–1.80)
Log-additive---------0.58 (0.26–1.29)0.17169.7
TNF rs1799964
CodominantT/T41 (66.1%)41 (66.1%)1.000.87173.30.25
C/T17 (27.4%)18 (29%)0.95 (0.42–2.16)
C/C4 (6.5%)3 (4.8%)0.65 (0.13–3.35)
DominantT/T41 (66.1%)41 (66.1%)1.000.78171.5
C/T-C/C21 (33.9%)21 (33.9%)0.90 (0.41–1.94)
RecessiveT/T-C/T58 (93.5%)59 (95.2%)1.000.61171.3
C/C4 (6.5%)3 (4.8%)0.66 (0.13–3.33)
OverdominantT/T-C/C45 (72.6%)44 (71%)1.000.97171.6
C/T17 (27.4%)18 (29%)0.99 (0.44–2.21)
Log-additive---------0.88 (0.47–1.63)0.68171.4
CRP rs3093077
---C/C55 (88.7%)56 (90.3%)1.000.86171.50.99
A/C7 (11.3%)6 (9.7%)1.11 (0.34–3.70)
CRP rs1130864
CodominantG/G33 (53.2%)22 (35.5%)1.000.13169.40.99
A/G24 (38.7%)31 (50%)1.98 (0.90–4.34)
A/A5 (8.1%)9 (14.5%)2.72 (0.77–9.59)
DominantG/G33 (53.2%)22 (35.5%)1.000.053167.7
A/G-A/A29 (46.8%)40 (64.5%)2.10 (1.00–4.45)
RecessiveG/G-A/G57 (91.9%)53 (85.5%)1.000.27170.4
A/A5 (8.1%)9 (14.5%)1.93 (0.58–6.36)
OverdominantG/G-A/A38 (61.3%)31 (50%)1.000.2170
A/G24 (38.7%)31 (50%)1.61 (0.77–3.38)
Log-additive---------1.76 (1.00–3.09)0.051167.6
CRP rs1205
CodominantC/C19 (30.6%)28 (45.2%)1.000.09168.80.99
C/T32 (51.6%)27 (43.5%)0.42 (0.18–0.98)
T/T11 (17.7%)7 (11.3%)0.41 (0.13–1.30)
DominantC/C19 (30.6%)28 (45.2%)1.000.028166.8
C/T-T/T43 (69.3%)34 (54.8%)0.42 (0.19–0.93)
RecessiveC/C-C/T51 (82.3%)55 (88.7%)1.000.43170.9
T/T11 (17.7%)7 (11.3%)0.66 (0.23–1.87)
OverdominantC/C-T/T30 (48.4%)35 (56.5%)1.000.12169.1
C/T32 (51.6%)27 (43.5%)0.55 (0.25–1.17)
Log-additive---------0.58 (0.34–1.02)0.052167.8
APOB rs1042031
CodominantC/C43 (71.7%)42 (70%)1.000.84168.20.99
C/T16 (26.7%)16 (26.7%)1.14 (0.48–2.67)
T/T1 (1.7%)2 (3.3%)1.94 (0.15–24.67)
DominantC/C43 (71.7%)42 (70%)1.000.68166.3
C/T-T/T17 (28.3%)18 (30%)1.19 (0.52–2.72)
RecessiveC/C-C/T59 (98.3%)58 (96.7%)1.000.62166.3
T/T1 (1.7%)2 (3.3%)1.89 (0.15–23.70)
OverdominantC/C-T/T44 (73.3%)44 (73.3%)1.000.8166.4
C/T16 (26.7%)16 (26.7%)1.12 (0.48–2.62)
Log-additive---------1.21 (0.58–2.51)0.61166.2
APOB rs6725189
CodominantG/G41 (68.3%)39 (65%)1.000.81168.10.56
G/T17 (28.3%)18 (30%)1.25 (0.55–2.88)
T/T2 (3.3%)3 (5%)1.53 (0.23–10.15)
DominantG/G41 (68.3%)39 (65%)1.000.53166.1
G/T-T/T19 (31.7%)21 (35%)1.29 (0.58–2.84)
RecessiveG/G-G/T58 (96.7%)57 (95%)1.000.71166.4
T/T2 (3.3%)3 (5%)1.43 (0.22–9.29)
OverdominantG/G-T/T43 (71.7%)42 (70%)1.000.63166.3
G/T17 (28.3%)18 (30%)1.22 (0.54–2.78)
Log-additive---------1.25 (0.64–2.42)0.51166.1
APOE rs7412
---C/C50 (80.7%)54 (87.1%)1.000.54171.20.99
C/T12 (19.4%)8 (12.9%)0.73 (0.27–2.00)
APOE rs429358
---T/T51 (82.3%)46 (74.2%)1.000.42170.90.36
C/T11 (17.7%)16 (25.8%)1.45 (0.59–3.57)
LIPC rs1800588
CodominantC/C38 (61.3%)37 (60.7%)1.000.27169.60.44
C/T22 (35.5%)18 (29.5%)0.86 (0.39–1.92)
T/T2 (3.2%)6 (9.8%)3.43 (0.62–19.08)
DominantC/C38 (61.3%)37 (60.7%)1.000.87170.2
C/T-T/T24 (38.7%)24 (39.3%)1.07 (0.50–2.26)
RecessiveC/C-C/T60 (96.8%)55 (90.2%)1.000.11167.7
T/T2 (3.2%)6 (9.8%)3.61 (0.66–19.64)
OverdominantC/C-T/T40 (64.5%)43 (70.5%)1.000.52169.8
C/T22 (35.5%)18 (29.5%)0.77 (0.35–1.69)
Log-additive---------1.26 (0.69–2.30)0.45169.6
LPA rs10455872
---A/A52 (83.9%)59 (96.7%)1.000.019165.30.99
A/G10 (16.1%)2 (3.3%)0.18 (0.04–0.91)
NOTCH1 rs13290979
CodominantA/A26 (41.9%)20 (32.8%)1.000.1168.30.35
A/G28 (45.2%)26 (42.6%)1.28 (0.56–2.93)
G/G8 (12.9%)15 (24.6%)3.15 (1.05–9.46)
DominantA/A26 (41.9%)20 (32.8%)1.000.2169.2
A/G-G/G36 (58.1%)41 (67.2%)1.65 (0.76–3.57)
RecessiveA/A-A/G54 (87.1%)46 (75.4%)1.000.04166.6
G/G8 (12.9%)15 (24.6%)2.75 (1.02–7.43)
OverdominantA/A-G/G34 (54.8%)35 (57.4%)1.000.72170.7
A/G28 (45.2%)26 (42.6%)0.87 (0.42–1.82)
Log-additive---------1.68 (0.99–2.85)0.05167
VDR rs731236
CodominantA/A32 (51.6%)29 (47.5%)1.000.54171.60.67
A/G26 (41.9%)24 (39.3%)1.04 (0.48–2.27)
G/G4 (6.5%)8 (13.1%)2.07 (0.55–7.81)
DominantA/A32 (51.6%)29 (47.5%)1.000.64170.6
A/G-G/G30 (48.4%)32 (52.5%)1.19 (0.57–2.48)
RecessiveA/A-A/G58 (93.5%)53 (86.9%)1.000.27169.6
G/G4 (6.5%)8 (13.1%)2.03 (0.56–7.32)
OverdominantA/A-G/G36 (58.1%)37 (60.7%)1.000.85170.8
A/G26 (41.9%)24 (39.3%)0.93 (0.44–1.96)
Log-additive---------1.27 (0.73–2.22)0.39170.1
VDR rs2228570
CodominantG/G16 (25.8%)19 (31.1%)1.000.62171.90.58
A/G36 (58.1%)29 (47.5%)0.70 (0.30–1.64)
A/A10 (16.1%)13 (21.3%)1.02 (0.34–3.07)
DominantG/G16 (25.8%)19 (31.1%)1.000.53170.4
A/G-A/A46 (74.2%)42 (68.8%)0.77 (0.34–1.74)
RecessiveG/G-A/G52 (83.9%)48 (78.7%)1.000.59170.6
A/A10 (16.1%)13 (21.3%)1.29 (0.50–3.33)
OverdominantG/G-A/A26 (41.9%)32 (52.5%)1.000.33169.9
A/G36 (58.1%)29 (47.5%)0.69 (0.33–1.44)
Log-additive---------0.97 (0.57–1.67)0.91170.8
CASR rs1042636
CodominantA/A47 (75.8%)50 (82%)1.000.25170.10.08
A/G14 (22.6%)8 (13.1%)0.51 (0.19–1.38)
G/G1 (1.6%)3 (4.9%)2.79 (0.26–29.85)
DominantA/A47 (75.8%)50 (82%)1.000.38170.1
A/G-G/G15 (24.2%)11 (18%)0.67 (0.27–1.65)
RecessiveA/A-A/G61 (98.4%)58 (95.1%)1.000.33169.9
G/G1 (1.6%)3 (4.9%)3.04 (0.29–32.42)
OverdominantA/A-G/G48 (77.4%)53 (86.9%)1.000.16168.9
A/G14 (22.6%)8 (13.1%)0.50 (0.19–1.35)
Log-additive---------0.87 (0.42–1.80)0.7170.7
OPG rs3134069
CodominantA/A49 (79%)52 (85.2%)1.000.59171.80.99
A/C12 (19.4%)9 (14.8%)0.80 (0.30–2.12)
C/C1 (1.6%)0 (0%)0.00 (0.00–0.00)
DominantA/A49 (79%)52 (85.2%)1.000.56170.5
A/C-C/C13 (21%)9 (14.8%)0.75 (0.29–1.97)
RecessiveA/A-A/C61 (98.4%)61 (100%)1.000.35170
C/C1 (1.6%)0 (0%)0.00 (0.00–0.00)
OverdominantA/A-C/C50 (80.7%)52 (85.2%)1.000.68170.7
A/C12 (19.4%)9 (14.8%)0.81 (0.31–2.16)
Log-additive---------0.72 (0.29–1.80)0.48170.3
OPG rs2073618
CodominantG/G15 (24.2%)12 (19.7%)1.000.85172.50.07
C/G35 (56.5%)37 (60.7%)1.29 (0.51–3.27)
C/C12 (19.4%)12 (19.7%)1.13 (0.35–3.61)
DominantG/G15 (24.2%)12 (19.7%)1.000.62170.6
C/G-C/C47 (75.8%)49 (80.3%)1.25 (0.51–3.08)
RecessiveG/G-C/G50 (80.7%)49 (80.3%)1.000.88170.8
C/C12 (19.4%)12 (19.7%)0.93 (0.37–2.37)
OverdominantG/G-C/C27 (43.5%)24 (39.3%)1.000.6170.6
C/G35 (56.5%)37 (60.7%)1.22 (0.58–2.57)
Log-additive---------1.07 (0.60–1.91)0.82170.8
OPG rs3102735
CodominantT/T39 (62.9%)46 (75.4%)1.000.391710.53
C/T19 (30.6%)14 (22.9%)0.69 (0.30–1.61)
C/C4 (6.5%)1 (1.6%)0.29 (0.03–2.80)
DominantT/T39 (62.9%)46 (75.4%)1.000.26169.6
C/T-C/C23 (37.1%)15 (24.6%)0.63 (0.28–1.41)
RecessiveT/T-C/T58 (93.5%)60 (98.4%)1.000.28169.7
C/C4 (6.5%)1 (1.6%)0.32 (0.03–3.08)
OverdominantT/T-C/C43 (69.3%)47 (77%)1.000.48170.3
C/T19 (30.6%)14 (22.9%)0.74 (0.32–1.70)
Log-additive---------0.63 (0.32–1.26)0.19169.1
CALCR rs1801197
CodominantA/A32 (51.6%)37 (60.7%)1.000.48171.40.99
A/G27 (43.5%)20 (32.8%)0.64 (0.29–1.39)
G/G3 (4.8%)4 (6.6%)1.19 (0.24–5.98)
DominantA/A32 (51.6%)37 (60.7%)1.000.34169.9
A/G-G/G30 (48.4%)24 (39.3%)0.69 (0.33–1.46)
RecessiveA/A-A/G59 (95.2%)57 (93.4%)1.000.65170.6
G/G3 (4.8%)4 (6.6%)1.43 (0.29–6.99)
OverdominantA/A-G/G35 (56.5%)41 (67.2%)1.000.23169.4
A/G27 (43.5%)20 (32.8%)0.63 (0.29–1.35)
Log-additive---------0.82 (0.45–1.52)0.54170.5
F2 rs1799963
---G/G59 (98.3%)58 (98.3%)1.000.77164.30.99
A/G1 (1.7%)1 (1.7%)0.64 (0.04–11.58)
F5 rs6025
---C/C55 (91.7%)57 (96.6%)1.000.18162.50.99
C/T5 (8.3%)2 (3.4%)0.31 (0.05–1.84)
F5 rs6027
CodominantT/T47 (78.3%)46 (78%)1.000.92166.20.09
C/T11 (18.3%)11 (18.6%)0.86 (0.33–2.29)
C/C2 (3.3%)2 (3.4%)1.33 (0.17–10.31)
DominantT/T47 (78.3%)46 (78%)1.000.87164.3
C/T-C/C13 (21.7%)13 (22%)0.92 (0.37–2.29)
RecessiveT/T-C/T58 (96.7%)57 (96.6%)1.000.77164.3
C/C2 (3.3%)2 (3.4%)1.36 (0.18–10.49)
OverdominantT/T-C/C49 (81.7%)48 (81.4%)1.000.75164.3
C/T11 (18.3%)11 (18.6%)0.85 (0.32–2.25)
Log-additive---------0.99 (0.47–2.07)0.97164.4
F7 rs6046
CodominantG/G52 (86.7%)42 (71.2%)1.000.15162.50.20
A/G7 (11.7%)15 (25.4%)2.55 (0.91–7.16)
A/A1 (1.7%)2 (3.4%)2.94 (0.25–35.06)
DominantG/G52 (86.7%)42 (71.2%)1.000.052160.5
A/G-A/A8 (13.3%)17 (28.8%)2.59 (0.98–6.90)
RecessiveG/G-A/G59 (98.3%)57 (96.6%)1.000.46163.8
A/A1 (1.7%)2 (3.4%)2.48 (0.21–29.33)
OverdominantG/G-A/A53 (88.3%)44 (74.6%)1.000.08161.3
A/G7 (11.7%)15 (25.4%)2.45 (0.88–6.87)
Log-additive---------2.19 (0.94–5.11)0.058160.8
F13A1 rs5985
CodominantC/C39 (65%)37 (62.7%)1.000.33164.10.10
A/C15 (25%)19 (32.2%)1.74 (0.72–4.21)
A/A6 (10%)3 (5.1%)0.66 (0.15–2.91)
DominantC/C39 (65%)37 (62.7%)1.000.4163.7
A/C-A/A21 (35%)22 (37.3%)1.41 (0.63–3.14)
RecessiveC/C-A/C54 (90%)56 (94.9%)1.000.41163.7
A/A6 (10%)3 (5.1%)0.55 (0.13–2.37)
OverdominantC/C-A/A45 (75%)40 (67.8%)1.000.17162.5
A/C15 (25%)19 (32.2%)1.83 (0.77–4.36)
Log-additive---------1.09 (0.60–1.98)0.78164.3
ITGB3 rs5918
CodominantT/T45 (75%)42 (71.2%)1.000.95166.30.08
C/T12 (20%)14 (23.7%)1.11 (0.45–2.77)
C/C3 (5%)3 (5.1%)0.83 (0.15–4.65)
DominantT/T45 (75%)42 (71.2%)1.000.9164.4
C/T-C/C15 (25%)17 (28.8%)1.05 (0.45–2.46)
RecessiveT/T-C/T57 (95%)56 (94.9%)1.000.81164.3
C/C3 (5%)3 (5.1%)0.81 (0.15–4.46)
OverdominantT/T-C/C48 (80%)45 (76.3%)1.000.8164.3
C/T12 (20%)14 (23.7%)1.13 (0.46–2.79)
Log-additive---------1.00 (0.51–1.95)1164.4
Here and below: TLR is for Toll-like receptor, TREM is for triggering receptor expressed on myeloid cells, IL is for interleukin, TNF is for tumor necrosis factor, CRP is for C-reactive protein, APO is for apolipoprotein, LIPC is for hepatic lipase, LPA is for lipoprotein (a), VDR is for vitamin D receptor, CASR is for calcium-sensing receptor, OPG is for osteoprotegerin, CALCR is for calcitonin receptor, ITGB is for integrin beta, OR is for odds ratio, CI is for confidence interval, AIC is for Akaike information criterion, and HWE is for Hardy–Weinberg equilibrium.
Table 2. Brief description of the model predicting the risk of severe bioprosthetic mitral valve calcification after mitral valve replacement surgery, calculated by stepwise logistic regression.
Table 2. Brief description of the model predicting the risk of severe bioprosthetic mitral valve calcification after mitral valve replacement surgery, calculated by stepwise logistic regression.
Clinical Markers
GenderMale gender OR = 2.80 (95% CI = 1.23–6.38)
AgeNo statistically significant association
Coronary artery diseaseNo statistically significant association
Peripheral artery diseaseNo statistically significant association
Arterial hypertensionNo statistically significant association
Diabetes mellitusNo statistically significant association
Genomic Markers
rs3775073 (TLR6)Carriers of T/T genotype: OR = 3.33 (95% CI = 1.14–9.75)
rs2229238 (IL6R)Carriers of C/T genotype: OR = 3.70 (95% CI = 1.48–9.22)
rs10455872 (LPA)Carriers of A/A genotype: OR = 5.67 (95% CI = 1.19–27.09)
rs5743810 (TLR6)No statistically significant association
rs1800871 (IL10)No statistically significant association
rs1800872 (IL10)No statistically significant association
rs1205 (CRP)No statistically significant association
rs13290979 (NOTCH1)No statistically significant association
General Evaluation
Sensitivity59.68% (37 true; 25 false-negatives)
Specificity74.19% (46 true; 16 false-positives)
Percent of cases correctly classified66.94%
Area under the ROC curve0.73 (95% CI = 0.64–0.81)
Standard error0.045
Here and below: ROC is for receiver operating characteristic.
Table 3. Clinical features of the patients who underwent mitral valve replacement surgery.
Table 3. Clinical features of the patients who underwent mitral valve replacement surgery.
FeatureValue, n (%)
Male gender50 (40.32%)
Age ≥ 50 years65 (52.42%)
Mitral stenosis and/or regurgitation with New York Heart Association functional class III-IV symptoms54 (43.55%)
Coronary artery disease14 (11.29%)
Peripheral artery disease6 (4.84%)
Arterial hypertension38 (30.64%)
Diabetes mellitus8 (6.45%)
Severe bioprosthetic mitral valve calcification within 8 years post-implantation62 (50.00%)
Table 4. Basic and echocardiography characteristics of the study population.
Table 4. Basic and echocardiography characteristics of the study population.
FeatureWithout Severe Bioprosthetic Mitral Valve CalcificationWith Severe Bioprosthetic Mitral Valve CalcificationTotalp Value
Basic characteristics
Sample size62 (50.00%)62 (50.00%)124 (100.00%)
Mean age50.60 (48.12–53.08)47.81 (45.68–49.94)49.20 (47.57–50.83)0.09
Standard deviation of mean age9.768.399.17
Male gender19 (30.64%)31 (50.00%)50 (40.32%)0.03
Female gender43 (69.36%)31 (50.00%)74 (59.68%)
Echocardiography characteristics
Left atrial diameter, cm6.70 (6.43–7.01)5.51 (5.22–5.69)6.10 (5.82–6.35)0.02
Left ventricular end-diastolic diameter, cm5.42 (5.23–5.56)5.37 (5.17–5.50)5.39 (5.20–5.53)0.81
Left ventricular end-systolic diameter, cm3.23 (3.05–3.39)3.41 (3.26–3.51)3.32 (3.15–3.45)0.36
Left ventricular end-diastolic volume, cm3139.03 (136.12–143.15)136.56 (134.01–139.76)137.79 (135.06–141.45)0.82
Left ventricular end-systolic volume, cm340.23 (38.23–41.98)45.14 (43.24–47.12)42.68 (40.73–44.55)0.03
Interventricular septal thickness, cm1.04 (0.97–1.12)1.08 (1.02–1.15)1.06 (0.99–1.13)0.89
Left ventricular posterior wall thickness, cm1.03 (0.95–1.08)1.11 (1.00–1.18)1.07 (0.97–1.13)0.72
Left ventricular ejection fraction, %71.00 (67.00–74.00)65.00 (61.00–68.00)68.00 (64.00–71.00)0.03
Right atrial diameter, cm6.00 (5.87–6.16)4.70 (4.62–4.88)5.35 (5.24–5.52)0.03
Right ventricular diameter, cm2.09 (2.01–2.17)2.03 (1.95–2.14)2.06 (1.98–2.15)0.76
Aortic root diameter, cm3.30 (3.12–3.49)3.32 (3.14–3.50)3.31 (3.13–3.49)0.93
Mitral valve area, cm21.72 (1.64–1.79)1.41 (1.35–1.47)1.56 (1.49–1.63)0.02
Table 5. Features of the genotyped polymorphisms.
Table 5. Features of the genotyped polymorphisms.
Single Nucleotide PolymorphismNucleotide SubstitutionChromosomal PositionAmino Acid SubstitutionForward 5′-3′ and Reverse 3′-5′ Polymerase Chain Reaction Primers
TLR1 gene
rs5743551T>C388076545′-upstreamF: agtgggcagggcagtaagggaagct
R: ctcagcactctgaattcctgttttt
rs5743611C>G38800214Arg80ThrF: aacactgatatcaagatactggatt
R: tattatgagaaattatcaaaatcct
TLR2 gene
rs3804099T>C154624656Asn199AsnF: caaaaagtttgaagtcaattcagaa
R: gtaagtcatctgatccttcatatga
rs5743708G>A154626317Arg753GlnF: aagccattccccagcgcttctgcaagctgc
R: gaagataatgaacaccaagacctacctgga
TLR4 gene
rs4986790A>G120475302Asp299GlyF: gattagcatacttagactactacctcgatg
R: attattgacttatttaattgtttgacaaat
rs4986791C>T120475602Thr399IleF: gttgctgttctcaaagtgattttgggacaa
R: agcctaaagtatttagatctgagcttcaat
TLR6 gene
rs3775073T>C38829832Lys421LysF: cactatactctcaacccaagtgcagttttc
R: ttatgtctaccagattccaaagaattccagc
rs5743810A>G38830350Ser249ProF: ttgagggtaaaattcagtaaggttg
R: acctctggtgagttctgataaaaat
TREM-1 gene
rs1817537C>G41244567intronicF: acacagggacagacagatggcaatggaaca
R: aaggccagatgcagagccagtgctatgcag
rs3804277C>T41245172intronicF: ccagcatctctctcacccctcacatggtgg
R: cactcagcatcctcagcatctgccccgatt
rs6910730A>G412466333′-downstreamF: catggagcaacaccaaggtctaggggcaag
R: aatctaggatggattcgtgctgacttccca
rs7768162A>G412555115′-upstreamF: aaagattcctactgctaaataaacaaaaaa
R: taacttggtttcttcaaaggaattgaaata
rs2234246C>T412437403′-UTRF: ggaaggtgagacgctgactttagaaatagc
R: ggtgattacagatttaattcatgttattaa
rs4711668T>C412464733′-downstreamF: gctagtgtggattccactttccagactgga
R: ttggctgaaaggatagttcatattagatga
rs9471535T>C412554905’-upstreamF: aaaatttttaaatttaaataaaaagattcc
R: ctgctaaataaacaaaaaaataacttggtt
rs2234237T>A41250466Thr25SerF: gcccctctttcagttcatacttttcctcag
R: aatttagttgcagctcggagttctataagc
IL1B gene
rs16944A>G1135948675′-upstreamF: taccttgggtgctgttctctgcctc
R: ggagctctctgtcaattgcaggagc
rs1143634G>A113590390Phe105PheF: cataagcctcgttatcccatgtgtc
R: aagaagataggttctgaaatgtgga
IL1F9 gene
rs17659543C>T113716306Not announcedF: tgtacctggacaagaggcataaattggggc
R: gtcttaggaaagcagatatacagccatcct
IL6 gene
rs1554606T>G22768707intronicF: ttagttcatcctgggaaaggtactc
R: cagggccttttccctctctggctgc
rs1800796G>C227662465′-upstreamF: atggccaggcagttctacaacagcc
R: ctcacagggagagccagaacacaga
rs2069827G>T227654565′-upstreamF: gcccaacagaggtcactgttttatc
R: atcttgaagagatctcttcttagca
IL6R gene
rs2228145A>T/C154426970Asp358Val/AlaF: aattttttttttaacctagtgcaag
R: ttcttcttcagtaccactgcccaca
rs2229238T>C1544378963′-UTRF: ccagcagcctggaccctgtggatga
R: aaaacacaaacgggctcagcaaaag
IL8 gene
rs2227306C>T74607055intronicF: aactctaactctttatataggaagt
R: gttcaatgttgtcagttatgactgt
IL10 gene
rs1800871A>G2069466345′-upstreamF: agtgagcaaactgaggcacagagat
R: ttacatcacctgtacaagggtacac
rs1800872T>G2069464075’-upstreamF: ttttactttccagagactggcttcctacag
R: acaggcggggtcacaggatgtgttccaggc
rs1800896T>C2069468975′-upstreamF: tcctcttacctatccctacttcccc
R: tcccaaagaagccttagtagtgttg
IL12B gene
rs3212227T>G1587429503′-UTRF: attgtttcaatgagcatttagcatc
R: aactatacaaatacagcaaagatat
IL12RB gene
rs375947A>G18180451Met365ThrF: aggctgccattcaatgcaatacgtc
R: tgctctgagcccgggctggccaata
TNF gene
rs361525G>A315431015′-upstreamF: ggcccagaagacccccctcggaatc
R: gagcagggaggatggggagtgtgag
rs1800629G>A315430315′-upstreamF: gaggcaataggttttgaggggcatg
R: ggacggggttcagcctccagggtcc
rs1799964T>C315423083′-downstreamF: gcaggggaagcaaaggagaagctgagaaga
R: gaaggaaaagtcagggtctggaggggcggg
CRP gene
rs3093077A>C159679636Not announcedF: ggaatccaggcaagtacgacaaccc
R: tctgagactagtgggcagttgtcct
rs1130864G>A1596830913′-UTRF: cctcaaattctgattcttttggacc
R: tttcccagcatagttaacgagctcc
rs1205C>T1596822333′-UTRF: acttccagtttggcttctgtcctca
R: agtctctctccatgtggcaaacaag
APOB gene
rs1042031C>T21225753Glu4181LysF: caatcagatgcttgactttcatatggaatt
R: ttgagtaactcgtaccaagccatcaaacac
rs6725189G>T21219001Not announcedF: ttcccagcctcagctcaacagagctatggg
R: cagcagtcggccctctctattgttctttcc
APOE gene
rs7412C>T45412079Arg176CysF: ctcctccgcgatgccgatgacctgcagaag
R: gcctggcagtgtaccaggccggggcccgcg
rs429358T>C45411941Cys130ArgF: gcccggctgggcgcggacatggaggacgtg
R: gcggccgcctggtgcagtaccgcggcgagg
LIPC gene
rs1800588C>T587236755′-upstreamF: tctttgcttcttcgtcagctccttttgaca
R: gggggtgaagggttttctgcaccacacttt
LPA gene
rs10455872A>G161010118intronicF: tcagacaccttgttctcagaaccca
R: tgtgtttatacaggttagaggagaa
NOTCH1 gene
rs13290979A>G139425634intronicF: ccagcccagcagtgaagaaactgagcccac
R: accctcctggcctgacctacactcgggctt
VDR gene
rs731236A>G48238757Ile352IleF: tgtgttggacaggcggtcctggatggcctc
R: atcagcgcggcgtcctgcaccccaggacga
rs2228570A>G48272895Met1Thr/Lys/ArgF: ggcagggaagtgctggccgccattgcctcc
R: tccctgtaagaacagcaagcaggccacggt
CASR gene
rs1042636A>G122003769Arg990GlyF: gatgagcctcagaagaacgccatggcccac
R: ggaattctacgcaccagaactccctggagg
OPG gene
rs3134069A>C1199649885′-upstreamF: ggagcttcctacgcgctgaacttctggagt
R: gcctcctcgaggtctttccactagcctcaa
rs2073618G>C119964052Asn3LysF: gggacttaccacgagcgcgcagcacagcaa
R: ttgttcattgtggtccccggaaacctcagg
rs3102735T>C1199650705′-upstreamF: ctttgctctagggttcgctgtctcccccat
R: aattccctggtctagaagttagacttgatg
CALCR gene
rs1801197A>G93055753Leu481ProF: tcgccttggttgttggctggttcattcctc
R: gctcctgatggcagatgtaaattgggatgt
F2 gene
rs1799963G>A467610553′-UTRF: gttcccaataaaagtgactctcagc
R: agcctcaatgctcccagtgctattc
F5 gene
rs6025T>C169519049Gln534ArgF: ttacttcaaggacaaaatacctgtattcct
R: gcctgtccagggatctgctcttacagatta
rs6027T>C169483561Asp2222GlyF: gggtttttgaatgttcaattctagtaaata
R: cacagccaaagagttccaggcgaagtgcaa
F7 gene
rs6046G>A113773159Arg412Gln/Pro/LeuF: acagtggaggcccacatgccacccactacc
R: gggcacgtggtacctgacgggcatcgtcag
F13A1 gene
rs5985C>A6318795Val35LeuF: taccttgcaggttgacgccccggggcacca
R: gccctgaagctccactgtgggcaggtcatc
ITGB3 gene
rs5918T>C45360730Leu59ProF: tttgggctcctgacttacaggccctgcctc
R: gggctcacctcgctgtgacctgaaggagaa

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MDPI and ACS Style

Ponasenko, A.V.; Khutornaya, M.V.; Kutikhin, A.G.; Rutkovskaya, N.V.; Tsepokina, A.V.; Kondyukova, N.V.; Yuzhalin, A.E.; Barbarash, L.S. A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification. Int. J. Mol. Sci. 2016, 17, 1385. https://doi.org/10.3390/ijms17091385

AMA Style

Ponasenko AV, Khutornaya MV, Kutikhin AG, Rutkovskaya NV, Tsepokina AV, Kondyukova NV, Yuzhalin AE, Barbarash LS. A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification. International Journal of Molecular Sciences. 2016; 17(9):1385. https://doi.org/10.3390/ijms17091385

Chicago/Turabian Style

Ponasenko, Anastasia V., Maria V. Khutornaya, Anton G. Kutikhin, Natalia V. Rutkovskaya, Anna V. Tsepokina, Natalia V. Kondyukova, Arseniy E. Yuzhalin, and Leonid S. Barbarash. 2016. "A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification" International Journal of Molecular Sciences 17, no. 9: 1385. https://doi.org/10.3390/ijms17091385

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