The Impact of Genetic and Acquired Risk Factors on Thromboembolic Events: A Retrospective Study

Abstract

This study, involving a cohort of 980 patients with arterial and/or venous events, evaluated the relative importance of genetic and traditional risk factors using a machine learning model approach. The analysis revealed that conventional risk factors—such as age, gender, tobacco use, obesity—and acquired thrombophilia (most prominently antiphospholipid syndrome [APS]) outperformed any genetic risk variants analyzed. Of note, the presence of heterozygous FVL mutations was associated with a reduced arterial risk, whereas carriers of heterozygous factor VII activation protease (FSAP) mutations had a lower risk of venous events.

Thus, by employing diverse statistical approaches, the study showed that acquired risk factors exert the most substantial impact on the development of thrombotic events, except for the well-known role of FVL mutations in venous events.

 

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Keywords

thrombophilia - gene mutations - risk factors - antiphospholipid syndrome

Introduction

Arterial thrombotic events and venous thromboembolism (VTE), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), represent a significant cause of morbidity and mortality worldwide.[1] [2] [3] Various risk factors, both acquired and inherited, are known to heighten the risk of thromboembolic events, typically through mechanisms like blood flow stasis, endothelial injury or increased hypercoagulability.[4] [5] [6] Traditional and acquired risk factors include tobacco use, obesity, arterial hypertension, diabetes mellitus, hypercholesterolemia, oral contraceptives, hormonal replacement therapy, cancer, and antiphospholipid syndrome.[7] Furthermore, an increased risk of thrombosis has been attributed to specific gene mutations. After the discovery of the prothrombotic role of factor V Leiden mutation,[8] other gene polymorphisms have emerged as potential risk factors, such as prothrombin G20210A, plasminogen activator inhibitor-1 4G/4G, and methylenetetrahydrofolate reductase C677T.[9] [10] [11] [12] However, while most patients with thromboembolic events have at least one identifiable risk factor, the predictive strength of these factors varies significantly.[13] For effective management of these patients, it is essential for clinicians to understand the relative impact of the specific risk factors.

Nevertheless, in most studies genetic polymorphisms associated with thrombophilia are evaluated independently from traditional risk factors including a distinction of arterial versus venous risk factors.

The purpose of this study was to examine the occurrence of thrombotic events in a retrospective cohort of individuals who had undergone thrombophilia testing, focusing on traditional and genetic risk factors. Additionally, the study aimed to assess the relative importance of individual genetic and traditional risk factors on arterial and venous thrombotic events.

 

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Materials and Patients

We retrospectively analyzed data from a total of 980 patients ([Table 1]), who underwent thrombophilia assessment including various clinical and laboratory parameters as well as genetic testing of the following polymorphisms or mutations: prothrombin (FII) 20210G/A mutation, factor V Leiden (FVL) 1691G/A mutation, plasminogen activator inhibitor-1 (PAI1) 5G/4G mutation, tissue-type plasminogen activator (t-PA) intron h deletion/insertion, factor VII activation protease (FSAP) G511E Marburg I polymorphism, and methylenetetrahydrofolate reductase (MTHFR) mutation C677T.

Table 1 Clinical and demographic characteristics of the cohort

Characteristics	N (%)
Female sex	637 (65)
Median age, years (IQR)	45 (34–59)
Thrombotic risk factors assessed	617 (63)
Tobacco use	93 (9.4)
Diabetes	53 (5.4)
Hyperlipidemia	220 (22.4)
Hypertension	278 (28.3)
Hormonal estrogen therapy[a]	66 (6.7)
Obesity	153 (15.6)
Antiphospholipid syndrome	64 (6.5)
Active cancer	65 (6.6)
Autoimmune/inflammatory disease (other than APS)[b]	125 (12.8)
Thrombotic events	582 (59.3)
Arterial thrombosis	173 (17.6)
Ischemic stroke	109 (11.1)
Myocardial infarction	37 (3.7)
Other arterial thrombosis[c]	43 (4.3)
Venous thrombosis	459 (46.8)
Deep vein thrombosis	284 (28.9)
Pulmonary embolism	185 (18.8)
Other venous thrombosis[c]	122 (12.4)
Arterial + venous thrombosis	50 (5.1)
Recurrent arterial events	46 (4.6)
Recurrent venous events	145 (14.7)

Abbreviations: APS, antiphospholipid syndrome; IQR, interquartile range.

Notes: ^aCombined oral contraceptives or hormone replacement therapy.

^b Further details in [Supplementary Table S4] (online only).

^c Thrombosis occurring in unclassified locations.

We included all individuals who underwent thrombophilia genetic screening, regardless of their history of thrombotic events. In the case of asymptomatic patients, the indication for testing was based on clinical judgment, with the intention to stratify the thrombotic risk in specific individuals, including subjects with a strong family history of thrombosis, especially if these events occurred at a young age (<50 years) and/or in multiple family members, before starting therapies that increase thrombotic risk, such as combined oral contraceptives or hormone replacement therapy, and patients with underlying clinical conditions that predispose to thrombosis (i.e., autoimmune diseases, cancer).

To further evaluate the relative importance of genetic and traditional risk factors, we employed a machine learning approach, comparing different predictive models, including logistic regression, random forest, and classification tree. The outcome variable was the occurrence of any thrombotic event. Model training and evaluation were conducted using the “Orange data mining” software (version 3.35), applying 10-fold cross-validation to ensure robustness. Predictor variables were selected using the Gini index as a ranking criterion, and the top 10 features were retained for model development ([Supplementary Table S1] (online only)). Predictive performance was primarily assessed using receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC) values ([Fig. 1A, B]). Finally, we applied both binary and multinomial logistic regression models to assess risk factors for any vascular event including different arterial and venous events.

Fig. 1 (A) Receiver operating characteristic (ROC) curves for the prediction of any thrombotic event. (B) Area under the ROC curve (AUC) of the prediction models analyzed. (C) Relative importance of variables in predicting thrombosis, estimated by decrease in AUC of the logistic regression model.

 

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Results

In total, 980 patients were included in the study, with a female to male ratio of 2:1 and a median age of 45 years ([Table 1]). Of this, 156 patients carried FVL mutation (1691G/A mutation; 9 homozygous, 147 heterozygous), 66 had factor II mutation (20210G/A; 1 homozygous, 65 heterozygous), 763 had PAI1 mutation (5G/4G; 298 homozygous, 465 heterozygous), 770 carried t-PA mutations (intron h deletion/insertion; 260 homozygous, 510 heterozygous), 74 were found to have FSAP mutations (G511E Marburg I; 2 homozygous, 72 heterozygous), and finally 517 had MTHFR mutations (C677T; 91 homozygous, 426 heterozygous) ([Table 2]).

Table 2 Prevalence of the genetic mutations

Gene	Homozygous mutated N (%)	Heterozygous mutated N (%)	Any mutation N (%)
Factor V	9 (0.9)	147 (15)	156 (15.9)
Factor II	1 (0.1)	65 (6.6)	66 (6.7)
MTHFR	91 (9.2)	426 (43.4)	517 (52.7)
PAI1	298 (30.4)	465 (47.4)	763 (77.8)
t-PA	260 (26.5)	510 (52)	770 (78.6)
FSAP	2 (0.2)	72 (7.3)	74 (7.6)

Abbreviations: FSAP, factor VII activation protease; MTHFR, methylenetetrahydrofolate reductase; PAI1, plasminogen activator inhibitor-1; t-PA, tissue-type plasminogen activator.

More than half of the patients included (59.3%) experienced a thrombotic event. Among those, 21.1% displayed arterial thrombosis, 70.3% had venous thrombosis, while 8.6% patients presented with both arterial and venous events ([Table 1]). A total of 64 subjects (6.5%) were diagnosed with the autoimmune acquired thrombophilia antiphospholipid syndrome (APS). The remaining individuals underwent screening testing related to familial, obstetric, or other clinical risk evaluation.

The machine learning model revealed several notable findings. Conventional risk factors—such as age, gender, tobacco use, obesity—and acquired thrombophilia (here notably APS) consistently outcompeted genetic risk variants. In particular, age appeared as the most prominent variable, followed by sex, APS, hormonal estrogen therapy, tobacco use, and obesity ([Fig. 1C]).

As an initial step, we conducted an adjusted binary logistic regression analysis, which identified several significant predictors of an increased risk for any thrombosis including age, male sex, hypertension, hormonal estrogen therapy, and obesity. Of note, APS exhibited the highest odds ratio (5.244; 95% CI 2.369–11.605; p < 0.001). None of the genetic mutations assessed were significant predictors in this model ([Supplementary Table S2] (online only)).

Using the multinomial logistic regression model ([Table 3]), we found age, male sex, and APS associated with an increased risk for all three outcomes: arterial thrombosis, venous thrombosis, and both arterial and venous events. Of particular note, APS was associated with the highest risk for arterial manifestations (odds ratio 8.069; p < 0.001). Furthermore, arterial thrombosis was significantly linked to tobacco use, hyperlipidemia, and hypertension ([Fig. 2A]). For venous events, key risk factors included hormonal estrogen therapy, obesity, and homozygous FVL, the latter yielding an odds ratio of 10.467 (p = 0.042) ([Fig. 2B]).

Table 3 Adjusted multinominal logistic regression for thrombotic outcomes

Variable	Arterial thrombosis		Venous thrombosis		Arterial + Venous thrombosis
	Exp (B) (95% confidence interval)	p-value	Exp (B) (95% confidence interval)	p-value	Exp (B) (95% confidence interval)	p-value
Age	1.042 (1.023–1.061)	<0.001	1.059 (1.046–1.073)	<0.001	1.052 (1.027–1.078)	<0.001
Male sex	2.985 (1.765–5.050)	<0.001	3.641 (2.500–5.304)	<0.001	3.423 (1.716–6.831)	<0.001
Tobacco use	4.783 (2.341–9.774)	<0.001	1.424 (0.734–2.764)	0.296	1.502 (0.491–4.592)	0.476
Diabetes	0.716 (0.239–2.142)	0.550	1.088 (0.446–2.652)	0.853	0.614 (0.141–2.667)	0.515
Hyperlipidemia	4.032 (2.328–6.985)	<0.001	0.888 (0.557–1.417)	0.619	3.045 (1.480–6.262)	0.002
Hypertension	2.513 (1.388–4.550)	0.002	1.346 (0.853–2.124)	0.202	2.745 (1.264–5.963)	0.011
Hormonal estrogen therapy	2.236 (0.766–6.530)	0.141	5.240 (2.773–9.900)	<0.001	1.212 (0.148–9.944)	0.858
Obesity	0.978 (0.471–2.032)	0.953	2.554 (1.580–4.128)	<0.001	1.348 (0.537–3.382)	0.525
Antiphospholipid syndrome	8.069 (3.067–21.230)	<0.001	4.383 (1.918–10.013)	<0.001	9.105 (2.924–28.352)	<0.001
Autoimmune/inflammatory disease	1.510 (0.731–3.118)	0.265	1.467 (0.882–2.442)	0.140	2.384 (0.994–5.719)	0.052
Active cancer	1.459 (0.574–3.707)	0.428	0.875 (0.446–1.719)	0.699	1.283 (0.380–4.330)	0.688
Factor V
Heterozygous	0.377 (0.169–0.842)	0.017	0.802 (0.514–1.251)	0.331	0.672 (0.256–1.762)	0.419
Homozygous	–	0.994	10.467 (1.086–100.837)	0.042	–	0.996
Wild type	–	–	–	–	–	–
Factor II
Heterozygous	0.431 (0.136–1.365)	0.152	1.085 (0.566–2.082)	0.806	0.681 (0.171–2.717)	0.587
Homozygous	–	0.998	–	0.997	–	–
Wild type	–	–	–	–	–	–
MHTFR
Heterozygous	0.803 (0.485–1.330)	0.394	0.984 (0.698–1.386)	0.925	0.541 (0.271–1.082)	0.082
Homozygous	0.76 (0.323–1.788)	0.530	0.907 (0.495–1.665)	0.753	0.537 (0.163–1.766)	0.306
Wild type	–	–	–	–	–	–
PAI1
Heterozygous	1.008 (0.533–1.907)	0.980	0.809 (0.532–1.230)	0.321	0.833 (0.372–1.863)	0.656
Homozygous	1.370 (0.703–2.669)	0.355	0.813 (0.515–1.283)	0.374	0.836 (0.35–1.997)	0.686
Wild type	–	–	–	–	–	–
t-PA
Heterozygous	0.958 (0.527–1.741)	0.887	0.860 (0.566–1.307)	0.480	0.697 (0.297–1.640)	0.409
Homozygous	0.528 (0.257–1.083)	0.082	0.883 (0.549–1.422)	0.610	1.022 (0.418–2.498)	0.961
Wild type	–	–	–	–	–	–
FSAP
Heterozygous	1.205 (0.535–2.714)	0.653	0.463 (0.235–0.915)	0.027	1.152 (0.396–3.353)	0.795
Homozygous	–	0.997	–	0.995	–	0.998
Wild type	–	–	–	–	–	–

Abbreviations: FSAP, factor VII activation protease; MTHFR, methylenetetrahydrofolate reductase; PAI1, plasminogen activator inhibitor-1; t-PA, tissue-type plasminogen activator.

Fig. 2 Odds ratios for arterial thrombosis (A), venous thrombosis (B), and both arterial and venous thrombosis occurring in the same patient (C).

Interestingly, heterozygous FVL (1691G/A) mutations appeared to have a protective effect against arterial events (odds ratio 0.377; p = 0.017). Similarly, carriers of heterozygous Marburg I FSAP mutations had a lower risk of venous events (odds ratio of 0.463; p = 0.027).

In patients who experienced both arterial and venous thrombosis, male sex, hyperlipidemia, hypertension, and APS appeared as key risk factors ([Fig. 2C]).

 

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Discussion

This study evaluated hereditary and acquired risk factors among patients undergoing thrombophilia assessment. Notably, it provides compelling evidence that traditional cardiovascular risk factors are the dominant contributors to the occurrence of not only arterial but also venous events, with APS emerging as a shared risk factor for venous and arterial events. In contrast, the role of hereditary (genetic) risk factors, although still relevant, was found to have a comparatively smaller impact. Beyond the well-established significance of the FVL mutation,[8] other genetic factors were not identified as substantial risk contributors.

Of note, this study found that heterozygous carriers of the FVL mutation (1691G/A) were associated with a reduced risk of arterial thrombosis. Similarly, the Copenhagen City Heart Study[14] showed a reduction of arterial events (ischemic stroke, but not myocardial infarction) among both heterozygous and homozygous FVL carriers. The high prevalence of FVL in the general Caucasian population, combined with studies indicating a reduced risk of severe sepsis[15] [16] in individuals with this mutation, has also led to the hypothesis that FVL polymorphism may represent an evolutionary “survival advantage” mutation.[17] However, the association of FVL and arterial events remains controversial. For instance, an increased risk of ischemic stroke has been described for both heterozygous and homozygous FVL.[18] Moreover, a recent meta-analysis[19] found no association between FVL and increased risk of subsequent atherothrombotic events or mortality in high-risk participants with established and treated coronary heart disease. Overall, the current evidence suggests no clear consensus on the role of FVL in arterial events.

The current study also highlighted a novel association between the G534E single nucleotide polymorphism (Marburg I) of FSAP and a reduced risk of venous events. Initially, this mutation has been linked to accelerated atherosclerosis and a low proteolytic activity.[20] Moreover, some authors found an association between Marburg I FSAP mutation and venous thrombosis.[21] [22] However, this result was not confirmed by other studies.[23] [24] FSAP seems to play a dual role in thrombus regulation by activating fibrinolysis while also promoting coagulation through activation of tissue factor (TF) and inhibition of tissue factor pathway inhibitor (TFPI).[25] The Marburg I FSAP variant, associated with reduced proteolytic activity,[26] may lead to lower “procoagulant activity” of FSAP, acting as a protective factor toward venous thrombosis.

Despite these findings, the study underscores that the influence of genetic risk factors, such as FVL and FSAP mutations, remains modest compared with the dominant role of acquired risk factors, which consistently demonstrated significant impact across all statistical models and methodologies.

It is important to note that this study has certain limitations that should be considered when interpreting the results.

First, it is crucial to note that these findings were obtained from a specific at-risk population referred for thrombophilia workup and as such preselected, which may limit the generalizability of the results.

Second, sociodemographic differences between asymptomatic subject and patients with thrombosis could affect the analysis. Indeed, the thrombosis group displayed a higher age and a greater proportion of males ([Supplementary Table S3] (online only)), both known to be risk factors of thrombosis.[27] [28] [29] To mitigate their confounding effect, both variables were included in the adjusted multivariate analysis. Other potential influencing factors, such as education level, income, and access to healthcare, were not available due to the retrospective design of the study. Ideally, prospective cohort studies with broader age ranges and longer follow-up periods would yield more robust and generalizable data.

Finally, the low number of subjects with certain genetic risk alleles, such as homozygous mutations for FVL (1691G/A), FII (20210G/A), and FSAP (G511E Marburg I) as well as the small number of patients with malignancies, restricted the statistical power to draw definitive conclusions for these subgroups.

 

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Conclusions

This retrospective study provides evidence that acquired risk factors predominantly contribute to the development of thrombotic events, except for the well-established role of homozygous FVL mutation on VTE. Interestingly, individuals with heterozygous FVL mutations in our cohort demonstrated a reduced risk of arterial events, while carriers of heterozygous FSAP mutations exhibited a decreased risk of venous events. Thus, while genetic factors cannot be entirely disregarded, their influence on arterial and venous risks appears less significant compared with the predominant influence of partially modifiable cardiovascular risk factors and the presence of APS.

 

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Conflict of Interest

The authors declare that they have no conflict of interest.

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Publikationsverlauf

Eingereicht: 14. Februar 2025

Angenommen: 16. Mai 2025

Artikel online veröffentlicht:
08. Juli 2025