VASCULAR SURGERY / BASIC RESEARCH
 
KEYWORDS
TOPICS
ABSTRACT
Introduction:
Observational studies have indicated significant contributions of protein C and protein S to thrombotic diseases, yet the “anticoagulation paradox” in deep venous thrombosis (DVT) remains unresolved. Therefore, we conducted an investigation to discern the causal effects of protein C, protein S and antithrombin-III on DVT risk.

Material and methods:
We employed a two-sample (one to evaluate the gene-exposure relationship and the other to evaluate the gene-outcome relationship) bidirectional Mendelian randomization (MR) framework to assess the causal associations between protein C, protein S, antithrombin-III and DVT.

Results:
Genetic associations with DVT were extracted from a comprehensive genome-wide association study involving 484,598 individuals. In the multivariable MR analysis, the odds ratios for DVT per standard deviation (SD) increase were 1.005 (95% CI: 1.002–1.008; p < 0.001) for protein C, 0.997 (95% CI: 0.992–1.001; p = 0.146) for protein S, and 1.001 (95% CI: 0.998–1.005; p = 0.456) for antithrombin-III. A two-step MR mediation analysis revealed that the association between protein C and DVT was partially mediated by body mass index, with a mediated proportion of 11.4% (95% confidence interval, 2.3% to 79.2%).

Conclusions:
These findings provide insights into the genetic relationship between relative protein C rather than protein S or antithrombin-III levels and DVT, offering potential utility in identifying at-risk patients for DVT development.

 
REFERENCES (31)
1.
Middleton P, Shepherd E, Gomersall JC. Venous thromboembolism prophylaxis for women at risk during pregnancy and the early postnatal period. Cochrane Database Syst Rev 2021; 3: CD001689.
 
2.
Wang X, Ma Y, Hui X, et al. Oral direct thrombin inhibitors or oral factor Xa inhibitors versus conventional anticoagulants for the treatment of deep vein thrombosis. Cochrane Database Syst Rev 2023; 4: CS010956.
 
3.
Li P, Liu Q, Huang Z, et al. miR-10b-5p regulates venous endothelial cells in deep venous thrombosis by targeting MFG-E8. Arch Med Sci 2023. doi: 10.5114/aoms/161674.
 
4.
Luo P, Yuan Q, Wan X, et al. A two-sample Mendelian randomization study of circulating lipids and deep venous thrombosis. Sci Rep 2023; 13: 7432.
 
5.
Trauscht-Van Horn JJ, Capeless EL, Easterling TR, et al. Pregnancy loss and thrombosis with protein C deficiency. Am J Obstet Gynecol 1992; 167: 968-72.
 
6.
Meng Y, Li Y, Ye YJ, et al. Associations between coagulation factor XII, coagulation factor XI, and stability of venous thromboembolism: a case-control study. World J Clin Cases 2022; 10: 2700-9.
 
7.
Spiezia L, Forestan C, Campello E, et al. Persistently high levels of coagulation factor XI as a risk factor for venous thrombosis. J Clin Med 2023; 12: 4890.
 
8.
Müller-Calleja N, Hollerbach A, Royce J, et al. Lipid presentation by the protein C receptor links coagulation with autoimmunity. Science 2021; 371: eabc0956.
 
9.
Scheiner B, Balcar L, Nussbaumer RJ, et al. Factor VIII/protein C ratio independently predicts liver-related events but does not indicate a hypercoagulable state in ACLD. J Hepatol 2022; 76: 1090-9.
 
10.
Abraitis V, Simoliūniene R, Mongirdiene A, et al. Prevalence of activated protein C resistance among women with recurrent miscarriage. Medicina (Kaunas, Lith) 2004; 40: 225-31.
 
11.
Vukovich T, Auberger K, Weil J, et al. Replacement therapy for a homozygous protein C deficiency-state using a concentrate of human protein C and S. Br J Haematol 1988; 70: 435-40.
 
12.
Christiansen SC, Lijfering WM, Næss IA, et al. The relationship between body mass index, activated protein C resistance and risk of venous thrombosis. J Thromb Haemost 2012; 10: 1761-7.
 
13.
Jeremic A, Mikovic Z, Soldatovic I, et al. Follicular and serum levels of vitamin D in women with unexplained infertility and their relationship with in vitro fertilization outcome: an observational pilot study. Arch Med Sci 2021; 17: 1418-22.
 
14.
Gunes N, Çam H. An observational study of serum vitamin d status in critically ill children admitted to the pediatric intensive care unit. Arch Med Sci 2022. doi: 10.5114/aoms/155137.
 
15.
Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 2003; 32: 1-22.
 
16.
Klovaite J, Benn M, Nordestgaard BG. Obesity as a causal risk factor for deep venous thrombosis: a Mendelian randomization study. J Intern Med 2015; 277: 573-84.
 
17.
Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet 2013; 45: 1274-83.
 
18.
Laan SW van der, Harshfield EL, Hemerich D, et al. From lipid locus to drug target through human genomics. Cardiovasc Res 2018; 114: 1258-70.
 
19.
Crone B, Krause AM, Hornsby WE, et al. Translating genetic association of lipid levels for biological and clinical application. Cardiovasc Drug Ther 2021; 35: 617-26.
 
20.
Burgess S, Thompson SG, Collaboration CCG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 2011; 40: 755-64.
 
21.
Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Statist Med 2015; 35: 1880-906.
 
22.
Pietzner M, Wheeler E, Carrasco-Zanini J, et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat Commun 2020; 11: 6397.
 
23.
Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature 2018; 558: 73-9.
 
24.
Hartwig FP, Davies NM, Hemani G, et al. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol 2017; 45: 1717-26..
 
25.
Hartwig FP, Smith GD, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 2017; 46: 1985-98.
 
26.
Bowden J, Smith GD, Haycock PC, et al. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016; 40: 304-14.
 
27.
Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018; 50: 693-8.
 
28.
Burgess S, Bowden J, Fall T, et al. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 2017; 28: 30-42.
 
29.
Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018; 7: e34408.
 
30.
Kujovich JL. Factor V Leiden thrombophilia. Genet Med 2011; 13: 1-16.
 
31.
Catalano PM, Shankar K. Obesity and pregnancy: mechanisms of short term and long term adverse consequences for mother and child. BMJ 2017; 356: j1.
 
eISSN:1896-9151
ISSN:1734-1922
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