# Sex-specific DNA methylation marks associated with sex-biased risk of recurrence in unprovoked venous thromboembolism

Ohanna C L Bezerra, Marc Rodger, Gaelle Munsch, Michael J Kovacs, Gregoire Le Gal, Pierre E Morange, David-Alexandre Tregouet, Celia M T Greenwood, France Gagnon

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► To cite this version:

Ohanna C L Bezerra, Marc Rodger, Gaelle Munsch, Michael J Kovacs, Gregoire Le Gal, et al. Sex-specific DNA methylation marks associated with sex-biased risk of recurrence in unprovoked venous thromboembolism. Journal of Thrombosis and Haemostasis, 2025, 10.1016/j.jtha.2025.01.004. hal-04956649

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ORIGINAL ARTICLE

# Sex-specific DNA methylation marks associated with sex-biased risk of recurrence in unprovoked venous thromboembolism

Ohanna C. L. Bezerra<fnref n="1" /> ID X | Marc Rodger<fnref n="2" /> | Gaëlle Munsch<fnref n="3" /> | Michael J. Kovacs<fnref n="4" /> |
Grégoire Le Gal<fnref n="5" /> | Pierre-Emmanuel Morange<fnref n="6" /> | David-Alexandre Trégouët<fnref n="3" /> |
Celia M. T. Greenwood<fnref n="7" />,<fnref n="8" /> | France Gagnon<fnref n="1" />,<fnref n="9" />

<figure><img src="image_3.png" /></figure>

Correspondence

France Gagnon, Office of the Vice-Principal of Research and Innovation, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L1C6, Canada.

Email: france.gagnon@utoronto.ca

Abstract

**Background:** Whether to stop oral anticoagulants after a first unprovoked venous thromboembolism (VTE) is challenging, partially due to an intriguingly higher risk of VTE recurrence (rVTE) in men after therapy discontinuation. DNA methylation (DNAm) differences between men and women might underlie this sex-biased rVTE risk difference.

**Objectives:** To investigate sex-specific associations between DNAm at cytosine-phosphate-guanine (CpG) sites and rVTE.

**Methods:** In 417 unprovoked VTE patients, including 101 experiencing recurrences over a 5-year follow-up (REcurrent VEnous thromboembolism Risk Stratification Evaluation [REVERSE] I), we analyzed blood DNAm using the Illumina EPIC array and performed a sex-stratified epigenome-wide association study. We further examined 181 major provoked VTE patients, including 36 recurrences over a 14-year follow-up (the MARseille THrombosis Association [MARTHA]), to investigate whether DNAm is a risk factor for rVTE after anticoagulation therapy.

**Results:** Hypomethylated CpGs at genes TBC1D22B-cg01060850 and ZHX2-cg07808424 in men and DIP2B-ch.12.1038646R and DENND3-cg03401656 in women were associated with rVTE at genome-wide level (P < 7x10⁻<fnref n="8" />). Though not statistically significant, DENND3-cg03401656 had the same direction of effect in MARTHA women. Sensitivity analysis confirmed the robustness of the estimates, including potential confounders, adaptations of the Cox model, non-Europeans, and proximal methylation quantitative trait loci in the association. The associated CpGs were situated at genes for membrane trafficking, corroborating the participation of Rab regulatory proteins in rVTE and transcription factors.

**Conclusion:** We identified DNAm marks as potential risk factors for sex-biased recurrence in unprovoked VTE. Further replication and experimental validation could

<footnote n="1">¹Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada</footnote>

<footnote n="2">²Department of Medicine, McGill University, Montreal, Quebec, Canada</footnote>

<footnote n="3">³Bordeaux Population Health Research Center, Institut national de la santé et de la recherche médicale (INSERM), Unité Mixte de Recherche (UMR) 1219, F-33000, University of Bordeaux, Bordeaux, France</footnote>

<footnote n="4">⁴Department of Medicine, Western University, London, Ontario, Canada</footnote>

<footnote n="5">⁵Department of Medicine, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada</footnote>

<footnote n="6">⁶Cardiovascular and Nutrition Research Center, Aix-Marseille University, Marseille, France</footnote>

<footnote n="7">⁷Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada</footnote>

<footnote n="8">⁸Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada</footnote>

<footnote n="9">⁹Office of the Vice-Principal of Research and Innovation, University of Toronto Mississauga, Mississauga, Ontario, Canada</footnote>
**Funding information** This study was supported by a research grant from the Canadian Institutes of Health Research (grant PJT-162233) and a fellowship from the CANSSI (Canadian Statistical Sciences Institute) Ontario STAGE (Strategic Training for Advanced Genetic Epidemiology) program (grant 2021-02-22-50090040-31). The MARseille THrombosis Association (MARTHA) project was supported by a grant from the Program Hospitalier de Recherche Clinique. The H450M epityping in MARTHA was partially funded by the Canadian Institutes of Health Research (grant MOP 86466) and by the Heart and Stroke Foundation of Canada (grant T6484).

refine our understanding of the regulation of the identified DNAm sites and help optimize personalized decision-making for long-term anticoagulation after a first VTE.

KEYWORDS

DNA methylation, genome-wide association study, recurrence, sex differences, venous thromboembolism

# 1 | INTRODUCTION

Venous thromboembolism (VTE), which encompasses deep vein thrombosis (DVT) and pulmonary embolism, is a multifactorial, potentially fatal condition with substantial morbidity and mortality [1]. Initial treatment of a first VTE is based on 3 to 6 months of anticoagulant therapy [2]. After discontinuation, a higher risk of recurrence (5%-10% per year) is typically observed in individuals who had an unprovoked VTE or weakly provoked VTE compared with those whose VTE was provoked by a major transient risk factor (eg, major surgery or immobilization) [3,4]. In unprovoked VTE, where there is no clear triggering factor and weakly provoked VTE (eg, travel-associated), the optimal duration of treatment is uncertain and often extended (ie, indefinite) in patients who are not at high bleeding risks. The decision of whether to continue the anticoagulants beyond the short-term course entails balancing the benefits of the therapy and the risk of hemorrhagic complications in the presence or absence of provoking risk factors [2].

Men and women are at similar risk of developing a first VTE. In contrast, recurrent VTE (rVTE) has a consistently higher rate in men than in women after anticoagulant cessation: up to 10% per year in men vs 2% to 5% per year in women [5]. The best-known rVTE risk assessment models are based on clinical predictors [6,7]. The “HER-DOO2” decision rule, based on age, obesity, D-dimer, and clinical symptoms, identifies women at low risk of rVTE who can discontinue anticoagulants after completing 5 to 12 months of therapy, but not men (all men are under high risk) [8,9]. Genetic risk factors, including inherited thrombophilia (eg, factor [F]V Leiden [FVL], FII G20210A, protein C and S, and antithrombin deficiency), confer a higher risk for a first VTE [10]. However, while the most common thrombophilia has been included in predictive models (FVL and FII G20210A), it does not appear to predict the risk of recurrence, prompting an investigation into other molecular mechanisms underlying rVTE [11-13].

DNA methylation (DNAm) is an epigenetic mark by which a methyl bonds to cytosines in 5'-C-phosphate-G-3' (CpG) dinucleotides and regulates gene expression [14]. While DNA sequence is invariant across cell types, DNAm is dynamic and varies over time or in response to exposures (eg, medication) between cell types within an individual and between individuals [15]. DNAm variation is strongly influenced by sex and has emerged as a molecular candidate for

mediating the relationship between sex-biased complex diseases and prognostic biomarkers or disease-related exposures [16-19]. Epigenome-wide association studies (EWAS) have revealed many autosomal loci displaying sex-biased DNAm differences in several tissues [20-23]. Although most DNAm differences and associated changes in gene expression are small, they can still be associated with large effects on disease risks [20]. DNAm biomarkers may account for parts of the “missing heritability” [24] and hold the potential for improving personalized risk assessment for first and rVTE [25,26].

Here, we investigated sex-specific epigenetic signatures underlying rVTE risk differences between men and women with unprovoked or weakly provoked VTE after stopping anticoagulant therapy. We conducted a time-to-event EWAS of almost 723 000 autosomal CpGs from the blood of unprovoked VTE participants followed-up over 5 years for rVTE. Additionally, because genetic variants have strong effects on the DNA methylome, we incorporated genotype data on the same individuals into our analyses as another step toward understanding gene regulatory variation and modulation in rVTE. A deep understanding of the molecular downstream mechanisms acting in rVTE may uncover risk predictors to help classify patients at high risk in whom thromboprophylaxis treatment decisions must be made.

# 2 | METHODS

## 2.1 | Overall analytic strategy

Hypothesizing that differential blood DNAm signatures underlie the sex-biased rVTE, we first tested for an association between time to rVTE in unprovoked VTE patients of the REcurrent VEnous thromboembolism Risk Stratification Evaluation (REVERSE) I study and DNAm levels from the Illumina Infinium EPIC array. We aimed for a complementary validation of our identified associations with rVTE by analyzing a study of provoked first VTE, the MARseille THrombosis Association (MARTHA), where methylation was assayed using the Illumina Infinium 450 K array. Second, to better understand the epigenetic molecular mechanisms of rVTE, we mapped the methylation quantitative trait loci (meQTLs) of the rVTE-associated CpGs in REVERSE I. Third, to explore whether DNAm could repress or activate the loci under the association, we examined the
<figure><img src="image_4.png" /><figcaption>FIGURE 1 Study overview. Blood samples of unprovoked venous thromboembolism (VTE) individuals followed-up for recurrence over an average of 5 years were collected while on oral anticoagulants (OTA; REcurrent VEnous thromboembolism Risk Stratification Evaluation [REVERSE] I study). From the Illumina EPIC array, we ended up with 722 418 probes, genome-wide analyzed in 417 European-ancestry individuals. We performed a sex-stratified epigenome-wide association study using the Cox regression model adjusted for the principal components of methylation and cell type proportions plus age at the first VTE. After sensitivity analysis and annotation of the cytosines in 5'-C-phosphate-G-3' (CpGs), we used the MARseille THrombosis Association [MARTHA] study, where 80% of the first VTE were provoked and followed-up for an average of 14 years, as a validation step. To evaluate the genetic influence around the associated methylation loci, we integrated methylation and genotyping data of REVERSE I and examined the methylation quantitative trait loci (cis-meQTL) SNPs (± 250 kb) within the associated CpGs, modeling their effect on VTE recurrence (rVTE). Finally, we compared the transcript levels of the associated genes between individuals with no VTE, VTE, and rVTE using Gene Expression Omnibus datasets. QC, quality control; SNP, single nucleotide polymorphisms.</figcaption></figure>

expression profiles of these genes in individuals with no VTE, first VTE, and rVTE. The overall schema is in Figure 1.

## 2.2 | Study population

REVERSE I is a multicenter prospective cohort study designed to derive a clinical prediction rule for patients at low risk for rVTE after completion of 5 to 7 months of anticoagulant therapy for a first unprovoked VTE [8,27]. All participants were initially treated with >5 days of heparin or low-molecular-weight heparin after their index unprovoked VTE, followed by 5 to 7 months of oral anticoagulants. Enrolment occurred from October 2001 to March 2006, with the last follow-up in 2014. Event-free patients were followed-up for 8 years. During a mean follow-up of 5 years (range, 1 month to 9.77 years), episodes of suspected rVTE were independently adjudicated. Details on recruitment criteria and baseline assessment are in the Supplementary Methods and elsewhere [8,28].

Gender was reported upon enrolment. All individuals retained for analysis were cis-gendered; therefore, we use “sex” throughout. Research ethics board approvals were obtained by all participating centers (Ottawa Hospital Research Ethics Board was the lead institution). The study of genetic and epigenetic data in REVERSE I was approved by the University of Toronto Research Ethics Board. As validation, we used the MARTHA study (Supplementary Methods). Definitions for a first VTE in REVERSE I and MARTHA are in Supplementary Table S1 and MARTHA characteristics are in Supplementary Table S2.

## 2.3 | Measurements

### 2.3.1 | DNAm processing

Genome-wide DNAm was profiled by the Illumina EPIC array in 544 stored blood samples from REVERSE I individuals. The quality control
features of the R package "meffil" [29] were exploited to identify prob-
lematic samples based on I) unusual intensity patterns for control probes
and methylated/unmethylated probes, II) failing correlation with matched
genotyping data, III) outliers of principal component (PC) analysis, and IV)
mismatch between provided gender and estimated sex [29]. Sex was
predicted by "wateRmelon" to check for sample mix-ups [30]. Before
normalization, we removed probes with a detection P value > .01 and <3
beads count in more than 20% of samples. Normalization was based on
the method of "meffil." Control probes in the array were used to guar-
antee that each batch was normalized in the same way. After normali-
zation, we excluded probes overlapping any single nucleotide
polymorphisms (SNP) with a minor allele frequency > 5%, probes located
in X and Y chromosomes, and cross-hybridizing probes [31-33]. We
retained 417 inferred Europeans for the primary analyses at a total of 722
418 autosomal CpGs and included the remaining participants in a multi-
ancestry analysis to explore ancestry influence. The methylation level of
each CpG was represented by the β-value (proportion of total signal in-
tensity from the methylated-specific probe, ranging from 0 unmethylated
to 1 methylated). β-values were logit transformed into methylation levels
(i.e, M-values) because they give more resolution to methylation near 0 or
1 [34]. Methylation data of the MARTHA study are in Supplementary
Methods and elsewhere [35]. Genotyping measurements of REVERSE I
are detailed in Supplementary Methods.

2.3.2 | Cell type proportions

DNAm patterns are cell type-specific. Cell type proportions for pri-
mary blood cell types of REVERSE I were inferred using the robust
partial correlations framework of the package "EpiDISH," using the
IDOLOptimizedCpGs for reference-based deconvolution in whole
blood [36]. To reduce collinearity, we computed PC analysis on the cell
proportion estimates using the *prcomp* R function and used the first 2
PCs for model adjustments.

2.4 | EWAS

2.4.1 | Quality control

To capture unmeasured confounders in the methylation data (eg, hidden
batch effects), we computed PCs using all autosomal estimated methyl-
ation proportions, both using all participants and separately for each sex.
The genomic inflation factor (λ) was initially estimated by comparing the
observed test statistics with those expected under a uniform distribution,
as commonly used in genome-wide association studies. However, given
that genomic inflation in EWAS usually exceeds that observed in
genome-wide association studies, possibly due to many small true asso-
ciations or bias driven by confounding, we also applied a Bayesian method
implemented in the R package "bacon" to estimate and correct for re-
sidual confounding, thereby improving the estimation of the genomic
inflation of methylation data [37,38].

2.4.2 | Sex-specific EWAS

To estimate sex-specific risks for each CpG with rVTE, we performed a
sex-stratified EWAS fitting Cox proportional hazards model using the
R package "survival" [39]. The hazard ratio can be interpreted as the
instantaneous risk ratio for rVTE at time t, considering a 1-unit change
in the M-value for the CpG being analyzed and controlling for the
effects of the covariates. A hazard ratio > 1 indicates that increased
DNAm of a CpG is associated with a reduced probability of survival
(increased risk of rVTE). The validity of the proportional hazards
assumption was confirmed using Schoenfeld residuals. Covariates
included age at baseline VTE, PCs of methylation data (mPCs; mPC1-
3), and PCs of cell type proportions (ctPCs; ctPC1-2). mPCs from the
general cohort or stratified by sex were used as sensitivity analysis.
We applied the Bonferroni correction (P = .05/722 418 = 7x10-8), and
a suggestive threshold (P < 7x10-6) was also considered for female
analysis. As a secondary investigation of the associated loci, we also fit
models including interactions between sex and each CpG's DNAm to
test for differences by sex. To test whether the meQTLs played an
indirect effect in rVTE mediated by the CpGs, we included meQTL
genotypes as predictors in the models and evaluated the evidence for
the causal relationship of genotypes, methylation, and outcome [40].
To visualize survival curves using the package "survminer" [41],
individuals were dichotomized into high and low methylation groups
based on the sex-specific median cutoff for each CpG's methylation
proportions. CpG annotation was acquired from Illumina EPIC
manifest 1.0 B5 (GRCh38). Attempting to improve statistical power to
identify additional candidate loci, we also performed a hypothesis-
driven EWAS, ie, up-weighting CpGs in genes targeted by oral anti-
coagulants [42,43]. This approach, along with the proof-of-concept
study details that led to its hypothesis, are in the Supplementary
Methods and Supplementary Figures S1 and S2.

2.4.3 | Sensitivity analysis

Sensitivity analyses were carried out to assess the robustness of the
regression coefficients and to account for potentially unmeasured
confounders. For the candidate associations, we checked for sensi-
tivity to the choice of the Cox model by reanalyzing the data with (1)
Cox proportional hazard models using methylation residuals after
adjusting for mPCs and ctPCs adjusted by age (since we had few re-
currences and many covariates, we fit residuals to improve the model);
(2) case-base sampling models, which allows smooth baseline risk
estimates [44] and (3) Cox robust models that address robustness to
outliers [45]. Second, we assessed sensitivity to our choice of cova-
riates by adjusting our standard model for (4) body mass index (BMI),
(5) type of first VTE, (6) D-dimer, and (7) including more than 2 ctPCs.
Finally, (8) including genome-wide genotyping PCs as adjustments, we
ran the model in the multiancestry dataset with a total of 452 in-
dividuals (416 Europeans, 17 Africans, 16 Native Americans, 2 East
Asians, and 1 South Asian).
## 2.5 | Molecular characterization

### 2.5.1 | Proximal SNP-CpG associations

We conducted meQTL analyses to evaluate the influence of proximal SNPs within ±250 kb (cis-meQTL) on methylation M-values at our top associated CpGs. We analyzed biallelic SNPs with minor allele frequency > 5% in the European samples of REVERSE I with genotyping and methylation data and tested the association of DNAm levels (dependent variable) with each SNP (coded 0, 1, or 2 for the number of minor alleles) in the respective window using linear models (P < .05). Independent variables were age, sex, mPC1-3, and ctPC1-2 as well as the SNPs. We then estimated the total genetic contribution at each locus by multiple penalized linear regression (least absolute shrinkage and selection operator) using the R package "glmnet," with the penalty parameter chosen to minimize the residual error after 10-fold cross-validation [46]. For visualization, CpG methylation levels were compared across genotypes. The package "biomaRt" was used for SNP annotation [47]. We validated our meQTL results by comparing them with databases of the Genetics of DNA Methylation Consortium (GoDMC) and eQTLGen Consortium and checking reports as expression quantitative trait locus (eQTL) in the Genotype-Tissue Expression (GTEx) portal [48–50]. Details of further analysis on loci characterization are in the Supplementary Methods.

# 3 | RESULTS

## 3.1 | Study population characteristics

Of 455 unprovoked VTE subjects from REVERSE I passing quality control, 417 with European ancestry were included in our primary analyses, of whom 207 were women, and 210 were men (Figure 1, Table 1). Over a 5-year follow-up, men had more incident rVTE events (n = 69, 32% vs n = 32, 15%; P = 5.53x10⁻⁵) and manifested recurrent episodes younger than women (52.9 ± 14.8 vs 57 ± 20.6 years; P = .15). Higher D-dimer after 5 to 7 months of anticoagulation was detected in those who developed rVTE during follow-up. A higher percentage of individuals with DVT as a first VTE type had a recurrent event (72% in men and 60% in women). Time to recurrence after the end of anticoagulant therapy was similar between men (2.4 years) and women (2.5 years). The validation subset of MARTHA (n = 181) involved ~80% provoked VTE individuals, mostly women (77%), on average 15 years younger than men (Supplementary Table S2). Higher recurrence was also observed in men (n = 28% vs n = 17%). MARTHA participants had, on average, a lower BMI (24.1) compared with REVERSE I participants (29.6). Time to recurrence in MARTHA was longer than in REVERSE I, with an average of 5.2 years for men compared with 6.1 for women.

## 3.2 | Sex-specific hypomethylated CpGs associated with rVTE

Our sex-based EWAS predicting rVTE were adjusted for PCs derived from all methylation probes to capture technical factors and residual confounding and PCs from the estimated cell type composition to capture cell type heterogeneity. Sensitivity analyses using sex-specific PCs showed no notable differences in association results compared with global PCs (Supplementary Figure S3). QQ plots ascertained whether models were well adjusted (Figure 2, Supplementary Figure S4). To assess for widespread uncaptured confounding, we estimated λ using both the conventional and "bacon" methods. After "bacon," the distribution of corrected P values showed no evidence of inflation when analyses were adjusted by age + mPC1-3 + ctPC1-2 in men (λ = 1.02; Bayesian λ = 0.99) and women (λ = 1.36; Bayesian λ = 1.00), even though inflation was observed before the correction in women (Figure 2).

In men, DNAm levels at 2 CpG sites were significantly associated with rVTE at our genome-wide significance level (P < 7x10⁻⁸; Figure 2A). The top hit was cg01060850, detected with power > 99% (Supplementary Table S3), located in the TBC1D22B gene body (coefficient = -2.66; P = 3.23x10⁻⁸), followed by cg07808424 (coefficient = -1.55; P = 2.47x10⁻⁸) in the 5'UTR of the ZHX2 gene (Table 2). In women, the only statistically significant CpG site, ch.12.1038646R (coefficient = -0.76; P = 2.79x10⁻⁸), was annotated to the DIP2B gene body (Figure 2B). Since the small number of recurrent events in women will limit statistical power to detect true associations, using a suggestive threshold (P < 7x10⁻⁶), we also examined the top 5 probes in the female EWAS, adjusting for either mPC1-3 or mPC1-4. Therefore, we also investigated a second suggestively significant association in women: cg03401656 (coefficient = -2.15; P = 5.46x10⁻⁷) lies in the gene body of DENND3 (Supplementary Figure S4). When fitting models containing the interaction term with sex, coefficients for the associated probes followed the same direction as in the sex-specific analysis with significant P values for 3 out of the 4 probes (except cg07808424), suggesting moderation by sex of DNAm association and recurrence (Supplementary Table S4). The same top CpGs were associated in the hypothesis-driven EWAS, whereas no prioritized probe or additional loci were revealed (Supplementary Figure S5). The associated probes located genes implicated in vesicular trafficking, signal transduction, and methylation/transcriptional regulation (Supplementary Table S5). In the estimated survival curves, patients exhibiting shorter overall survival (ie, earlier rVTE) tended to have lower methylation levels of cg01060850 and cg07808424 in men but not women (Figure 3A, B). Correspondingly, shorter survival probabilities were observed for hypomethylated ch.12.1038646 and cg03401656 in women but not men (Figure 3C, D). Significant sex-specific hypomethylation on a global level was observed in rVTE for cg07808424, ch.12.1038646R, and cg03401656 (Figure 3).
<table><caption>TABLE 1 The REcurrent VEnous thromboembolism Risk Stratification Evaluation (REVERSE) I study sample characteristics.</caption><thead><tr><th rowspan="3">Characteristics</th><th colspan="3">Total<br/>N = 417</th><th colspan="3">Men<br/>n = 210</th><th colspan="3">Women<br/>n = 207</th></tr><tr><th>VTE</th><th>rVTE</th><th rowspan="2">P value<br/>(95% CI)</th><th>VTE</th><th>rVTE</th><th rowspan="2">P value<br/>(95% CI)</th><th>VTE</th><th>rVTE</th><th rowspan="2">P value<br/>(95% CI)</th></tr><tr><th>n = 316</th><th>n = 101</th><th>n = 141</th><th>n = 69</th><th>n = 175</th><th>n = 32</th></tr></thead><tbody><tr><td>Age at first VTE (y), mean (SD)</td><td>53.5 (17.9)</td><td>54.2 (16.9)</td><td>.72 (-4.90; 3.42)</td><td>56.3 (15.4)</td><td>52.9 (14.8)</td><td>.13 (-1.18; 7.91)</td><td>51.3 (19.4)</td><td>57.0 (20.6)</td><td>.10 (-14.4; 1.50)</td></tr><tr><td>BMI (kg/m²), mean (SD)</td><td>29.0 (7.2)</td><td>30.3 (8.2)</td><td>.30 (-2.01; 0.64)</td><td>29.5 (6.0)</td><td>29.3 (7.3)</td><td>.30 (-0.70; 2.24)</td><td>28.6 (8.0)</td><td>32.6 (9.6)</td><td>.03 (-6.37; -0.22)</td></tr><tr><td>D-dimer (ng/mL), mean (SD)</td><td>298.6 (321.4)</td><td>310.2 (268.6)</td><td>.22 (-49.9; 11.9)</td><td>278.9 (255.2)</td><td>284.3 (261.5)</td><td>.85 (-32; 40.9)</td><td>314.5 (366.4)</td><td>365.3 (279.2)</td><td>.03 (-152; -5.99)</td></tr><tr><td colspan="10">First-type VTE</td></tr><tr><td>DVT only, n (%)</td><td>156 (49)</td><td>69 (68)</td><td>1x10-3 (-0.30; -0.07)</td><td>78 (55)</td><td>50 (72)</td><td>.02 (-0.31; -0.02)</td><td>78 (45)</td><td>19 (60)</td><td>.17 (-0.35; 0.05)</td></tr><tr><td>PE or PE + DVT, n (%)</td><td>160 (51)</td><td>32 (32)</td><td></td><td>63 (45)</td><td>19 (28)</td><td></td><td>97 (55)</td><td>13 (40)</td><td></td></tr><tr><td>FV Leiden heterozygous, n (%)</td><td>47 (14.8)</td><td>18 (17.8)</td><td>.58 (-0.06; 0.11)</td><td>20 (14.1)</td><td>8 (11.5)</td><td>.76 (-0.13; 0.07)</td><td>27 (15.4)</td><td>10 (31.2)</td><td>.06 (-0.03; 0.34)</td></tr><tr><td>FII G20210A, heterozygous, n (%)</td><td>19 (4)</td><td>4 (0.9)</td><td>.59 (-0.07; 0.03)</td><td>9 (4)</td><td>3 (1)</td><td>.77 (-0.09; 0.05)</td><td>10 (4)</td><td>1 (0.4)</td><td>.86 (-0.11; 0.06)</td></tr><tr><td>Family history, n (%)</td><td>77 (24.3)</td><td>28 (27.7)</td><td>.62 (-0.07; 0.13)</td><td>28 (19.8)</td><td>19 (27.5)</td><td>.30 (-0.06; 0.20)</td><td>49 (28)</td><td>9 (28)</td><td>1 (-0.17; 0.16)</td></tr><tr><td>Time to recurrence (y), mean (SD)</td><td>6.0 (2.9)<sup>a</sup></td><td>2.4 (2.3)</td><td>2x10-16 (3.32; 5.26)</td><td>5.6 (3.0)<sup>a</sup></td><td>2.4 (2.4)</td><td>1x10-12 (2.42; 5.04)</td><td>6.7 (2.8)<sup>a</sup></td><td>2.5 (2.4)</td><td>1x10-10 (2.90; 6.24)</td></tr></tbody></table>

Numbers might not be 100% due to missing information. D-dimer was measured by VIDAS at the moment of the first VTE.

BMI, body mass index; DVT, deep vein thrombosis; FII/FV, factor II/V; PE, pulmonary embolism; rVTE, recurrent venous thromboembolism; VIDAS, Versatile Immunoassay Detection Assay System; VTE, unprovoked venous thromboembolism.

<footnote>ª Time to last follow-up in years. The univariate analysis applied the Wilcoxon test and the chi-square test for continuous and categorical variables, respectively.</footnote>
<figure><img src="image_5.png" /><figcaption>FIGURE 2 QQ and Manhattan plots of the sex-stratified time-to-event epigenome-wide association studies (EWAS) of DNA methylation and recurrent VTE. QQ plots show the distribution of the observed log₁₀-fold transformed *P* values against the expected null distribution in (A) men and (B) women. The gray shaded area represents the 95% CI for equality. Cox models were adjusted by age and principal components of methylation and cell type proportions. The conventional genomic inflation factor *λ* and the Bayesian *λ* from "bacon" are given in each QQ plot to quantify the statistical inflation of the *P* values. Manhattan plots display the *P* values from the Cox regression testing the association of each cytosine in 5'-C-phosphate-G-3' with recurrent VTE. The horizontal axis represents chromosomal position, and the vertical axis shows the *P* values on a -log₁₀-fold scale. Dashed red and gray lines mark Bonferroni (*P* < 7x10⁻⁸) and suggestive significance thresholds (*P* < 1x10⁻⁵).</figcaption></figure>

<table><caption>TABLE 2 Cytosine-phosphate-Guanine (CpG) sites whose methylation levels showed sex-specific association at Bonferroni-corrected significance levels in the time-to-event EWAS in the REcurrent VEnous thromboembolism Risk Stratification Evaluation (REVERSE) I.</caption><thead><tr><th rowspan="2">CpG Chr:position</th><th rowspan="2">Gene annotation</th><th rowspan="2">Location at the gene</th><th colspan="3">Men (n = 210)</th><th colspan="3">Women (n = 207)</th></tr><tr><th>Coeff.</th><th>HR (95% CI)</th><th>P value</th><th>Coeff.</th><th>HR (95% CI)</th><th>P value</th></tr></thead><tbody><tr><td>cg01060850<br>chr6:37260002</td><td>TBC1D22B</td><td>Body</td><td>-2.66</td><td>0.06 (0.02; 0.17)</td><td>3.23x10<sup>-8</sup></td><td>0.09</td><td>1.10 (0.33; 3.62)</td><td>.87</td></tr><tr><td>cg07808424<br>chr8:122802461</td><td>ZHX2</td><td>5'UTR</td><td>-1.55</td><td>0.21 (0.12; 0.36)</td><td>2.47x10<sup>-8</sup></td><td>-0.09</td><td>0.40 (0.07; 2.31)</td><td>.30</td></tr><tr><td>ch.12.1038646R<br>chr12:50731142</td><td>DIP2B</td><td>Body</td><td>-0.04</td><td>0.95 (0.76; 1.20)</td><td>.71</td><td>-0.76</td><td>0.46 (0.35; 0.60)</td><td>2.79x10<sup>-8</sup></td></tr><tr><td>cg03401656<br>chr8:141151888</td><td>DENND3</td><td>Body</td><td>0.38</td><td>1.46 (0.54; 3.91)</td><td>.44</td><td>-2.15</td><td>0.11 (0.04; 0.26)</td><td>5.46x10<sup>-7</sup></td></tr></tbody></table>

Gene annotation is according to EPIC manifest v1.0 B5 (University of California, Santa Cruz [UCSC] Genome Browser). HR were calculated using Cox regression with natural log-transformed values.

Chr, chromosome; CpG, name of the cytosine in 5'-C-phosphate-G-3 site; Coeff., regression coefficient (the Cox model coefficient represents the expected change in the log HR for a unit change in the methylation level [ie, M-value]); HR, hazard ratio (HR per 1-unit M-value increase); Position, the genomic location of each site in genome build GRCh38.
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>FIG URE 3 Overall survival curves for first and recurrent venous thromboembolism (rVTE) in the REcurrent VEnous thromboembolism Risk Stratification Evaluation (REVERSE) I. Graphics show independent analyses for cytosine in 5'-C-phosphate-G-3 (A) cg01060850, (B) cg07808424, (C) ch.12.1038646, and (D) cg03401656. Patients were divided into low and high methylation groups using the median cutoff of methylation levels (ie, M-values). The estimated survival curves were calculated based on Cox models for time to rVTE as a function of methylation residuals of principal components and cell type proportions adjusted by age. The x- and y-axes denote survival time in years and the probability of patient survival, respectively. Boxplots of methylation levels showing Wilcoxon test P values for the comparison of DNA methylation between single venous thromboembolism (VTE; men = 141 and women = 175) and recurrent venous thromboembolism (rVTE; men = 69 and women = 32).</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>FIG URE 4 Survival curves and methylation levels of DENND3-cg03401656 in MARTHA. Patients were divided into low and high methylation groups using the median cutoff of methylation levels (ie, *M-values*). The estimated survival curves were calculated based on Cox models for time to recurrent venous thromboembolism (rVTE) as a function of methylation residuals of principal components and cell type proportions adjusted by age. The x- and y-axes denote survival time in years and the probability of patient survival, respectively. Survival curves were not calculated for men in MARTHA due to the small number of events. Boxplots, along with the Wilcoxon test, were plotted to compare DNA methylation between single venous thromboembolism (VTE; men = 30 and women = 115) and rVTE (men = 12 and women = 24).</figcaption></figure>

Among these sex-specific rVTE-associated CpGs, only 1 probe was available for validation in the independent set of MARTHA. DENND3-cg03401656 was suggestively hypomethylated in rVTE compared with VTE in women (Figure 4). The regression coefficient had the same sign as in REVERSE I but showed essentially no evidence of statistical significance (coefficient = -0.56; *P* = .45). Although neighboring correlated CpGs with the male-associated sites were suggestively hypomethylated in men (Supplementary Figure S6), male-derived analysis in MARTHA was limited due to sample size and absence of our top probes.

## 3.3 | Sensitivity analyses

In our sensitivity analyses, associations remained robust after evaluating the impact of including more than 2 ctPCs, BMI, D-dimer, first-type VTE, and FVL in the models (Supplementary Table S6). In the multiancestry dataset, cg07808424 and cg01060850 remained the top 2 hits in men, although cg01060850 did not pass Bonferroni, and ch.12.1038646R continued to be associated in women (Supplementary Figure S7). The inclusion of the ancestral genotype PCs did not change the detected effect sizes of the CpGs. All significant or suggestive associations displayed negative effect estimates, indicating that the CpGs are more likely to be hypomethylated in rVTE. Also, we obtained similar estimates after applying robust adaptations of the Cox model to reduce the potential influence of methylation outliers (Supplementary Table S7).

## 3.4 | Genetic effects on the association between CpGs and rVTE

We carried out a cis-meQTL analysis to investigate the role of proximal genetic variation (within 250 kb) on DNAm in REVERSE I (Supplementary Figures S8 and S9). After least absolute shrinkage and selection operator regression, SNPs were pruned for high linkage disequilibrium (LD) (R² > .7) and defined as cis-meQTLs if the minor allele significantly modulated DNAm. All associated probes had at least 4 SNPs affecting DNAm, many of them previously confirmed as cis-meQTLs for the gene of interest or associated with additional CpGs (100-650 kb) from the candidate site, attesting that single genetic variants are often associated with methylation at numerous nearby CpG sites (Supplementary Table S8). The proportion of variance explained by meQTLs for the methylation sites was 6% (ch.12.1038646R), 20% (cg07808424 and cg03401656), and 54% (cg01060850). In eQTLGen and GoDMC, our identified cis-meQTLs for cg01060850 were reported at genes in the vicinity of *TBC1D22B*. rs55800130 was the only meQTL that also acts as an eQTL for *CMTR1* in fibroblasts, which play an important role in maintaining microenvironment homeostasis [51]. Many meQTLs near ZHX2 were strongly associated with cg07808424 (false discovery rate < .05); 9 of them were validated as ZHX2 meQTLs in public resources. The meQTLs of ch.12.1038646 were also eQTLs for *DIP2B* with relevance in whole blood and cultured fibroblasts (GTEx). At cg03401656-*DENND3*, we corroborated the effect of numerous meQTLs at *DENND3* and adjacent genes, including the synonymous variant rs1045303, also characterized as an eQTL of whole blood and
fibroblasts. To study whether the meQTLs played an indirect effect on rVTE due to DNAm, we modeled the individual and joint effects of the meQTLs of each gene as predictors. Despite some meQTLs demonstrating an association with rVTE, neither *P* values nor regression coefficients of the CpGs were significantly altered when including them, suggesting that the rVTE association depends on the methylation and not the meQTLs (Supplementary Table S9).

## 3.5 | Differentially expressed genes associated with rVTE

To gain insight into the relationship between DNAm differences and gene expression in VTE, we examined the messenger RNA levels for our identified genes between no VTE and VTE groups in Gene Expression Omnibus microarray datasets. The enriched Gene Ontology terms for the top 1000 genes that most vary between the groups of GSE48000 and GSE19151 attested to the relevance of vesicular and transport-related ontologies in VTE (protein binding, cytosol, cytoplasm, extracellular exosome, endoplasmic reticulum, Golgi apparatus, and endosome; Supplementary Figure S10).

TBC1D22B is included among the 1285 upregulated genes in VTE compared with no VTE in GSE48000, and DENND3 among the 187 downregulated genes in the GSE19151 dataset by Fan et al. [52]. TBC1D22B was found to be significantly overexpressed in unprovoked VTE with >2 recurrences compared with no VTE or provoked VTE and displayed a suggestive overexpression (P = .06) compared with unprovoked VTE with 1 recurrence in GSE48000 (Supplementary Figure S11A). The same trend was observed in GSE19151 (Supplementary Figure S11G). The expression of TBC1D22B was considerably increased in rVTE women compared with rVTE men in reference to no VTE in their respective group, suggesting a sex-biased regulation in the context of VTE and a potential association with recurrence. Interestingly, RAB27A, a downregulated gene in recurrent unprovoked VTE at GSE48000, displayed an opposite direction of expression in VTE and rVTE compared with TBC1D22B (Supplementary Figure S11E). The protein interaction network revealed that TBC1D22B directly interacts with CMTR1 and indirectly interacts with RAB27A through TBC1D10A (TBC1 domain family member 10A; Supplementary Figure S12). Furthermore, probes at CMTR1 showed a significant correlation (r = .4; $R^2$ = .6) with TBC1D22B-cg01060850. Because they colocalize in the same topological association domains, it is likely that they share functional relevance and regulatory processes (Supplementary Figure S13).

## 4 | DISCUSSION

Limitations of current clinical decision-making tools to predict high-risk men and women for rVTE after stopping anticoagulation therapy challenge the clinical management of unprovoked VTE. A deeper understanding of the underlying processes of recurrence may help tailor the duration of anticoagulation to high-risk individuals. In a

preliminary candidate gene analysis (see Supplementary Figure S1), we observed sex-specific blood DNAm marks in vitamin K cycle genes (VKORC1 and UBIAD1) relevant to the coagulation cascade and vascular integrity. Here, interrogating almost 730 000 DNAm sites on the genome of unprovoked VTE patients, we identified male- and female-independent DNA hypomethylation marks favoring recurrence in a sex-specific manner, providing a mechanistic explanation for the role of genes previously hypothesized in VTE. These associations remained significant after adjusting for potential confounders and inclusion of meQTLs, suggesting that rVTE might be driven, at least in part, by DNAm levels at vesicle-mediated transport-related genes. Determining whether these associated CpGs can predict rVTE warrants further validation.

Exosomes regulate intercellular communication and signal transduction, and differential methylation at vesicle trafficking regulatory genes might ultimately impact VTE [48,49]. TBC1D22B and DENND3 are differentially expressed in individuals who had a first VTE or rVTE compared with those without [52]. TBC1D22B, a TBC1 domain family (member 22B) gene, codes a member of the guanosine triphosphate (GTP)ase-activating proteins for Rab proteins, which are predominantly expressed in bone marrow [53]. Sex-biased expression has been observed for other members of this family (TBC1D1 and TBC1D4) [54]. DENND3, expressed in blood and immune cells, codes the largest family of Rab-activating guanine nucleotide exchange factors (GEFs) [55]. Rab GTPases are crucial for vesicle-mediated membrane trafficking and cycling between inactive (guanosine diphosphate-bound) and active (GTP-bound) conformations. GEFs mediate their activation, while GTPase-activating proteins terminate their activity [56]. Rab GTPases regulate Weibel-Palade bodies exocytosis, endothelial organelles involved in the release of von Willebrand factor that increases endothelial permeability and vascular tone in angiogenesis and thrombosis. One Rab gene that acts on exome secretion is RAB27A, which has been shown to be highly expressed in VTE patients with 2 or more sustained, unprovoked events compared with those who had 1 or more provoked VTE events [57]. Biesemann et al. [58] revealed that overexpression of TBC1D22B, TBC1D14, TBC1D10A, and TBC1D10B reduced von Willebrand factor secretion, indicating an inhibitory effect on Weibel-Palade bodies' exocytosis. While it is unclear whether TBC1D22B directly modulates RAB27A, we observed an upregulation of TBC1D22B and downregulation of RAB27A in unprovoked VTE with >2 recurrences, pointing toward an inhibitory effect of TBC1D22B on RAB27A, demanding further evaluation. Engagingly, TBC1D3C, also involved in Rab-regulated trafficking, was differentially expressed in DVT patients manifesting early recurrent events postanticoagulation compared with those without recurrence [59]. Furthermore, by activating Rab12, DENND3 mediates autophagy [55]. Studies have shed light on the role of autophagy in hemostasis and thrombosis, but no direct link between GEF-mediated autophagy and VTE has been documented. In our study, even prioritizing coagulation genes, some with sex-biased methylation patterns, no genes of coagulation were associated with rVTE. Rather, we found DNAm marks in genes involved in vesicle trafficking and transcriptional regulation.
Studies have implicated the *DIP2B* gene in cardiovascular diseases and VTE through its modulation of circulating fibrinogen [60]. Elevated fibrinogen increases the risk of residual VTE, but the association with rVTE remains questionable [61,62]. *DIP2B* codes a disco-interacting protein homolog 2 protein family containing binding sites for DNA methyltransferase 1 associated protein 1 and adenosine monophosphate binding sites involved in transcriptional regulation. ZHX2, a zinc-finger and homeoboxes family of transcription factors, acts as a transcriptional repressor and oncogene [63]. Near ZHX2 is another TBC1 family member gene, *TBC1D31*, involved in cilium biogenesis [64]. Several other transcription factors have sex-biased regulatory targeting patterns [20,65]. In mice, ZHX2 is a regulator of sex-biased CYP gene expression, a key drug metabolization enzyme [66]. Besides, ZHX2 positively regulates the hypoxia-inducible factor 1A activity, a trigger compound of VTE [67,68]. Despite detecting the cg07808424-ZHX2 association in men, we do not see it when fitting interactions between sexes. This probe was suggestively associated with rVTE when no sex stratification was done in REVERSE I (data not shown). This might have occurred due to a higher number of recurrences in men, increasing the power to detect associations or potential uncontrolled variables.

There are several strengths to this study. First, we used a cohort of a first unprovoked VTE followed-up for several years, enabling robust time-to-event analysis. While others linked higher levels of global DNAm (before treatment) with rVTE in males [69], we evidenced locus-specific DNAm associated with sex-biased risk of rVTE when participants were under anticoagulants. Second, our sensitivity analyses showed consistent estimates after adjustments for several potential confounders, running adaptations of the Cox model, and considering the cohort that included multiancestry individuals adjusting for heterogeneity on a global scale. The inclusion of different ethnic compositions did not expressively alter the replication of the top probes, although studies with larger proportions of non-European descent individuals are required for generalizability. Third, using the genotype data available in the same individuals, we screened *cis*-meQTL effects in the associated genes. Note that distal genetic influence was not considered in our study, and *cis*-regulatory SNPs might have a pleiotropic effect on multiple genes, not just the sites investigated [58]. To explore the gene regulatory effect on DNAm and rVTE, we evaluated the influence of *cis*-meQTLs on regression coefficients assuming potential causal relationships: (I) the meQTL contributes to rVTE (or to CpG) either directly (SNP → rVTE or SNP → CpG), (II) indirectly through the presence of the CpG (SNP → CpG → rVTE), or (III) the CpG has a direct effect on rVTE (CpG → rVTE). The last condition better represents our findings, suggesting that while some meQTLs were independently associated with DNAm, the association with rVTE was most likely caused by DNAm.

Limitations are also acknowledged. We lacked another methyl-ation dataset of unprovoked VTE. (1) In MARTHA, we were unable to explore independent validation in unprovoked VTE; (2) the MARTHA sample size was insufficient for the sex-stratified analysis in men and had low power for women; (3) MARTHA lacked most of the associated probes of our discovery cohort; and (4) MARTHA had considerable

differences in age and time-to-event compared with REVERSE, which might have impacted the validation of the single probe available in the dataset. Even though cg03401656 exhibited the same direction of association in REVERSE and MARTHA, it is not clear whether methylation regulation plays a major role in rVTE in provoked VTE; any trends could also be attributed to the transient risk factors of a first VTE. In unprovoked VTE, however, since there is no clear risk factor, molecular (dys)regulation acting through methylation and, ultimately, gene expression could prominently influence rVTE risk. Likewise, etiologic disparities between provoked and unprovoked VTE may confound the gene expression level comparisons between VTE and rVTE in Gene Expression Omnibus datasets [70].

A well-established statistical method to remove the effect of cell type heterogeneity was applied in our analyses, but residual confounding might still be possible. The smaller sample size of rVTE in women, which was expected since rates of rVTE are higher in men, inevitably leads to lower statistical power. Our results warrant follow-up, ideally in methylation datasets with larger sample sizes. This is important because little is known about the plausible EWAS effect sizes in complex traits due to the decreasing longitudinal stability of the epigenetic marks and their variance within biological samples [71]. We were unable to elucidate whether the sex-associated CpGs play a causal role in driving rVTE or whether the DNAm changes represent a downstream effect of the anticoagulation therapy. While alterations in DNAm at warfarin regulatory genes, or perhaps direct oral anticoagulants, might influence drug efficacy [72], we could not assess the impact of anticoagulants on methylation in our candidate genes because all participants were under treatment. If anticoagulant activity does alter DNAm, it would affect gene expression. Therefore, analyzing both methylation and gene expression in the same samples is crucial to evaluate if DNAm can serve as a biomarker for high-risk VTE patients prior to their withdrawal from anticoagulant therapy. Notwithstanding, we cannot exclude the possibility that the DNAm differences reflect the influence of other factors that were not controlled, such as environmental factors and other exposures (eg, smoking and medications). It is worth considering that ~20% of the women in REVERSE were on estrogen therapy at the time of the index event, and while the risk of recurrence was not higher in users of estrogen compared with nonusers, we cannot exclude that oral contraceptives could impact methylation [73].

Overall, our findings generated new hypotheses on putative mechanisms by which DNAm regulates rVTE in a sex-biased manner. While our findings warrant further validation, it appears very likely that genes outside the coagulation pathway play a significant role in VTE pathophysiology. Comparing our results with those of a similar European-based VTE cohort and a multiancestry study will be particularly informative. Moreover, the lack of gene expression data in REVERSE I limited our capacity to assess if DNAm levels were followed by a direct impact on the expression of the associated genes. Further transcriptional characterization at those loci would be relevant to better understand the sex-biased risk of rVTE and optimize decision-making on thromboprophylaxis after a first VTE event.
## ACKNOWLEDGMENTS

This study was supported by a research grant from the Canadian Institutes of Health Research (grant PJT-162233) and a fellowship from the CANSSI (Canadian Statistical Sciences Institute) Ontario STAGE (Strategic Training for Advanced Genetic Epidemiology) program. The authors thank Lee Vernich for her diligent support in coordinating the array analyses of the REcurrent VEnous thromboembolism Risk Stratification Evaluation (REVERSE) I study samples, Elham Sabri for sharing her deep knowledge of the REVERSE I data, and Liz Li, Jingjing Cao, and Nicole Roslin of The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada, for assistance in the processing of the methylation and genotyping data from REVERSE I. The MARseille THrombosis Association (MARTHA) project was supported by a grant from the Program Hospitalier de Recherche Clinique. The H450M epityping in MARTHA was partially funded by the Canadian Institutes of Health Research (grant MOP 86466) and by the Heart and Stroke Foundation of Canada (grant T6484). Statistical analyses of the REVERSE and MARTHA datasets were performed using the Niagara supercomputer at the SciNET High Performance Computing Consortium of the University of Toronto and the Centre de Bioinformatique de Bordeaux (CBIB) computing center of the University of Bordeaux, respectively. The authors are thankful to the individuals who participated in the study.

## AUTHOR CONTRIBUTIONS

F.G. designed the study, obtained the funding in collaboration with M.R., and led the overall project. O.C.L.B., C.M.T.G., and F.G. designed the study analytic plan and interpreted the study findings. O.C.L.B. led the implementation of the analyses and drafted and led the manuscript writing. C.M.T.G. and F.G. supported O.C.L.B. in drafting the manuscript. M.R., M.J.K., and G.L.G. designed and led the REcurrent VEnous thromboembolism Risk Stratification Evaluation I cohort study data collection. D.-A.T. and P.E.M. designed and led the MARseille THrombosis Association study data collection. G.M. conducted statistical analysis in the MARTHA study. All authors provided feedback on the manuscript and approved its final version for publication.

## DECLARATION OF COMPETING INTERESTS

There are no competing interests to disclose.

### ORCID

Ohanna C.L. Bezerra https://orcid.org/0000-0002-0798-778X

### X

Ohanna C.L. Bezerra @ohannaclb

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SUPPLEMENTARY MATERIAL

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