Causal relationship between matrix metalloproteinase and pulmonary embolism: a bidirectional two-sample Mendelian randomization study

Abstract

This study evaluated the causal relationship between matrix metalloproteinases (MMPs) and pulmonary embolism using data from the genome-wide association study (GWAS) of pulmonary embolism from the UK Biobank and a GWAS dataset of MMPs based on 5,457 Icelanders aged 65 years and older. MR-Egger, MR-PRESSO, Cochran’s Q, and leave-one-out were used for sensitivity analysis. The Mendelian randomization (MR) analysis, based on the IVW analysis, indicated an elevated risk for pulmonary embolism in association with MMP19 (OR = 1.0009, 95%CI: 1-1.0017, P = 0.041), consistent with the weighted median method results (P = 0.015). In addition, despite the negative result from the IVW method (P = 0.554), the weighted median analysis suggested a reduced risk for pulmonary embolism related to MMP12 (OR = 0.9992, 95%CI: 0.9984-1, P = 0.038). No causal associations were found for the other MMPs (including MMP1, MMP2, MMP3, MMP7, MMP8, MMP9, MMP10, MMP13, MMP14, MMP16, MMP17, and MMP20) on pulmonary embolism (all P > 0.05). The reverse MR analysis revealed no causal associations between pulmonary embolism as exposure and MMPs as outcomes. Sensitivity analyses confirmed the robustness of these findings. In conclusion, this MR analysis revealed the potential causal relationship between MMPs and pulmonary embolism, suggesting that measuring MMPs could help identify people at higher risk of pulmonary embolism, but further research is needed.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-83735-3.

Introduction

Pulmonary embolism is a general term encompassing clinical syndromes in which emboli obstruct the pulmonary artery system, including pulmonary thromboembolism, fat embolism syndrome, amniotic fluid embolism, air embolism, etc¹. Pulmonary embolism is one of the main causes of death from cardiovascular diseases¹. The incidence of pulmonary embolism is about 350,000 cases per year in the United States of America (about 1.17‰)²and about 1 million per year in European countries³. In addition, the incidence of pulmonary embolism in China has increased 10–20 times compared to over 20 years⁴. The mortality of pulmonary embolism is high, causing enormous harm and burden to society. The risk factors for pulmonary embolism are complex and diverse, including genetic factors⁵, surgical and traumatic infections⁶, cardiovascular diseases⁷, tumors⁸, etc. Nevertheless, its exact pathogenesis is still unclear, and exploring the various factors involved is necessary.

Matrix metalloproteinases (MMPs) are Zn²⁺-dependent endopeptidases responsible for the degradation of extracellular matrix during tissue remodeling and play crucial roles in many physiological and pathological processes⁹. MMPs have been implicated in cardiovascular diseases^{10 and have shown elevated plasma levels in patients with pulmonary hypertension secondary to pulmonary embolism,particularly MMP2,MMP7,MMP9,and MMP1211}. Notably, the MMP inhibitor priomastat, when combined with chemotherapy, has been associated with an increased risk of venous thromboembolism in patients with non-small cell lung cancer¹². This finding suggests a potential link between MMP inhibition and thrombotic events, although the exact mechanisms remain unclear.  MMPs are upregulated in various inflammatory conditions and injuries^13,14; pulmonary embolism, a type of vascular injury, leads to the local recruitment of immune cells that play roles in inflammation and the secretion of MMPs¹⁵. However, the association between MMPs and pulmonary embolism has not been systematically examined due to potential biases and confounding factors.Therefore, it remains undetermined whether MMPs play a causal role in the development and progression of pulmonary embolism or if pulmonary embolism influences the MMP levels. Further research is needed to clarify these relationships and to explore the potential therapeutic implications of targeting MMPs in pulmonary embolism management.

Mendelian randomization (MR) builds on Genome-wide association study (GWAS) data by using single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to infer the causal associations between modifiable exposures and clinically relevant outcomes¹⁶. MR leverages the principles of Mendel’s law of inheritance, where alleles are randomly separated during meiosis, reducing bias from confounding factors¹⁷. In addition, since genetic variation precedes disease occurrence, MR can mitigate issues of reverse causality, which often impede epidemiological studies. MR relies on three key assumptions: (1) the relevance assumption (i.e., the genetic variant(s) being used as an instrument for the exposure is associated with the exposure), (2) the independence assumption (i.e., there are no common causes of the genetic variant(s) and the outcome of interest), and (3) the no horizontal pleiotropy assumption (i.e., there is no independent pathway between the genetic variants and the outcome other than through the exposure)¹⁸. MR has been widely applied across medical fields to examine causal relationships. For example, it has been used to investigate the effects of MMPs on bone mineral density¹⁹, cardiovascular diseases²⁰, and breast cancer²¹.

Hence, this study employed a bidirectional two-sample MR design to examine the potential causal association between MMPs and pulmonary embolism.

Methods

Study design

This study investigated the causal relationship between MMPs and pulmonary embolism using publicly available data from the GWAS, with MMPs as exposure factors and pulmonary embolism as the outcome in the forward analysis, and then a reverse MR analysis was performed using pulmonary embolism as the exposure factor and MMPs as the outcomes. The flowchart of this study is shown in Fig. 1.

Fig. 1.

Study design and workflow.

Data Sources

We obtained pooled statistics for all SNPs associated with total MMPs from a previously published large-scale GWAS²². The data contained serum levels of 4135 human proteins from 5457 Icelanders over the age of 65²³. The 14 MMPs concerned in this study were MMP1, MMP2 MMP3, MMP7, MMP8, MMP9, MMP10, MMP12, MMP13, MMP14, MMP16, MMP17, MMP19, and MMP20 (Table S1). For pulmonary embolism, we used GWAS summary data from the UK Biobank, which collects genetic, physical, and health data from approximately 500,000 individuals across the UK. The specific PE dataset included 3940 cases²⁴. All data were sourced from public databases or published studies that have been ethically approved in previous studies; hence, no additional approvals were required for this study.

Instrumental variable selection

IVs included in this study had to meet the following criteria. (1) SNPs related to the genome-wide significance of matrix metalloproteinase were screened, P < 5 × 10⁻⁸. For exposures other than MMP1, the criteria were relaxed to P < 5 × 10⁻⁶ because too few SNPs were screened. (2) SNPs with minor allele frequency (MAF) > 0.01 were selected. (3) According to R² < 0.001 and a window size of 10,000 kb, the linkage disequilibrium (LD) effect between SNPs was eliminated. (4) When the selected IVs did not exist in the summary data of the outcome, SNPs with high LD (R² > 0.8) and the IVs were found as proxy SNPs and replaced. (5) The F-value of each SNP in IVs was calculated to evaluate the intensity of the IVs and to exclude the possible weak instrumental variable bias between IV and exposure factors. The calculation formula was F = R²×(N-2)/(1-R2), where R²is the proportion of variation in exposure that can be explained by SNP in IVs, and the requirement for F-value was > 10²⁵.

MR analysis

The MR analysis investigated the causal associations between MMPs and pulmonary embolism. Four common MR methods were used to perform the analysis. The inverse-variance weighted (IVW, random effects) was used as the main analysis method^25,26. MR-Egger, weighted median, and weighted mode were used as supplementary analysis methods. All statistical analyses were performed using the “Two-Sample MR” R package (version 4.0.5). Scatter charts and sensitivity analysis charts were used for visualization. The false discovery rate (FDR) correction method was applied to correct for multiple testing; FDR P< 0.05 was considered statistically significant²⁷.

Sensitivity analysis

Cochran’s Q test was used to evaluate the heterogeneity among IVs. When P > 0.05, the heterogeneity was considered low, that is, the estimation among IVs was random and had little impact on the IVW results. MR-Egger regression was used to identify potential pleiotropy (P< 0.05 was considered as the presence of directional pleiotropy)²⁸. The MR-pleiotropy residual sum and outlier (MR‐PRESSO) method was used to assess possible outliers (SNPs with P< 0.05), and the causal association was re-estimated after elimination, thus correcting for horizontal pleiotropy²⁹. The leave-one-out analysis is used to test the robustness and consistency of the results³⁰.

Results

SNP selection

First, in the forward analysis, 239 SNPs associated with MMPs were identified. When MMP1, MMP2, MMP3, MMP7, MMP8, MMP9, MMP10, MMP12, MMP13, MMP14, MMP16, MMP17, MMP19, and MMP20 were used as exposures, 7, 18, 19, 20, 28, 20, 14, 23, 15, 9, 14, 19, 22, and 16 relevant IVs were selected, respectively. For these IVs, the calculated mean of the F statistic ranged from 22.96 to 208.00, the minimum values ranged from 20.89 to 44.34, and the maximum value ranged from 26.73 to 1045.16, all F > 10. In the reverse analysis, when using pulmonary embolism as exposures for MR analysis, 8 IVs were selected. The mean values of F-statistics for IVs are 97.92, indicating that they were all robust IVs. Detailed information is provided in Table S2. Within the summary data, there was one unmatched SNP in each of MMP1, MMP9, and MMP19 and two SNPs in MMP17, as well as one unmatched SNP in pulmonary embolism, with no corresponding proxy SNPs being identified for these cases.

Causal effects of matrix metalloproteinase on pulmonary embolism

The causal relationship between each MMP and pulmonary embolism was first analyzed. Notably, MMP19 emerged as a potential risk factor, exhibiting a marginal yet statistically significant association (IVW: OR = 1.0009, 95% confidence interval [CI]: 1.0000–1.0017, P = 0.041). However, this finding failed to attain concordance across alternative methodologies, specifically the weighted median, weighted mode, and MR-Egger approaches, thus casting uncertainty over its robustness. On the other hand, while the IVW analysis did not corroborate a connection between MMP12 and pulmonary embolism, the weighted median analysis disclosed a suggestive protective effect (OR = 0.9992, 95% CI: 0.9984–1.0000, P = 0.038), pointing to divergent outcomes warranting further scrutiny. The incongruent results across these analytical techniques highlight the necessity for supplementary research to ascertain the precise nature of these relationships. Interestingly, MMPs previously implicated in observational studies, i.e., MMP2 (OR = 1.0000, P = 0.998), MMP7 (OR = 0.9999, P = 0.838), and MMP9 (OR = 1.0002, P = 0.707), did not consistently manifest causal associations with pulmonary embolism under rigorous MR analysis (Table 1, Table S3, Fig. 2, and Figure S1). The forest map showed the estimated values around the points (Fig. 3 and Figure S2).

Table 1.

Forward MR analysis of all matrix metalloproteinase as the exposures.

Exposure	Outcome	N.SNPs	Methods	OR (95% CI)	P	Power
MMP1	Pulmonary embolism	6	IVW	0.9998(0.9992–1.0004)	0.511	0.05
MMP10	Pulmonary embolism	14	IVW	1.0003(0.9994–1.0012)	0.477	0.05
MMP12	Pulmonary embolism	22	IVW	0.9998(0.9991–1.0005)	0.554	0.05
MMP13	Pulmonary embolism	15	IVW	0.9997(0.999–1.0004)	0.375	0.05
MMP14	Pulmonary embolism	9	IVW	1.0004(0.9993–1.0015)	0.478	0.05
MMP16	Pulmonary embolism	14	IVW	1.0004(0.9993–1.0015)	0.468	0.05
MMP17	Pulmonary embolism	17	IVW	1(0.9989–1.001)	0.967	0.05
MMP19	Pulmonary embolism	21	IVW	1.0009(1–1.0017)	0.041	0.05
MMP2	Pulmonary embolism	17	IVW	1(0.9988–1.0012)	0.998	0.05
MMP20	Pulmonary embolism	15	IVW	0.9999(0.999–1.0009)	0.838	0.05
MMP3	Pulmonary embolism	19	IVW	0.9996(0.9988–1.0004)	0.34	0.05
MMP7	Pulmonary embolism	20	IVW	0.9999(0.9992–1.0007)	0.838	0.05
MMP8	Pulmonary embolism	27	IVW	1.0001(0.9994–1.0007)	0.873	0.05
MMP9	Pulmonary embolism	19	IVW	1.0002(0.9991–1.0013)	0.707	0.05

Fig. 2.

Scatter plot of SNP effects on matrix metalloproteinase and pulmonary embolism. The 95% CI for the effect size on pulmonary embolism is shown as vertical lines, while the 95% CI for the effect size on matrix metalloproteinase is shown as horizontal lines. The slope of fitted lines represents the estimated MR effect per method. (A) MMP12. (B) MMP19.

Fig. 3.

Forest plot showing results from the Mendelian randomization analysis. (A) MMP12. (B) MMP19.

Next, an extensive sensitivity analysis was conducted to validate the above conclusions. MR- Cochran’s Q heterogeneity test showed heterogeneity for MMP9 as an exposure factor (P = 0.016), while no heterogeneity was observed for the other MMPs (all P > 0.05) (Table 2). Egger regression results showed that the results were not affected by horizontal pleiotropy (all P > 0.05) (Table 2). The MR-PRESSO and leave-one-out analyses revealed no outliers (all P > 0.05) but showed horizontal pleiotropy for the MMP9 analysis (global P = 0.016) (Table 3; Figs. 4 and 5, and Figs. S3 and S4).

Table 2.

The heterogeneity and pleiotropy results of the forward MR analysis.

Exposure	Outcome	Heterogeneity		Pleiotropy
		Q statistic (IVW)	P value	MR-Egger Intercept	P value
MMP1	Pulmonary embolism	5.667	0.34	0	0.538
MMP10	Pulmonary embolism	5.503	0.962	0	0.927
MMP12	Pulmonary embolism	31.844	0.061	0	0.072
MMP13	Pulmonary embolism	20.391	0.118	0	0.175
MMP14	Pulmonary embolism	5.137	0.743	0	0.795
MMP16	Pulmonary embolism	13.593	0.403	0	0.606
MMP17	Pulmonary embolism	11.217	0.796	0	0.709
MMP19	Pulmonary embolism	19.035	0.52	0	0.562
MMP2	Pulmonary embolism	23.541	0.1	0	0.086
MMP20	Pulmonary embolism	8.037	0.887	0	0.864
MMP3	Pulmonary embolism	14.91	0.668	0	0.595
MMP7	Pulmonary embolism	18.045	0.519	0	0.178
MMP8	Pulmonary embolism	29.289	0.298	0	0.315
MMP9	Pulmonary embolism	33.156	0.016	0	0.697

Table 3.

MR-PRESSO analysis for horizontal pleiotropy of the forward MR analysis.

Exposure	Outcome	Raw		Outlier corrected		Global P	Number of outliers	Distortion P
		OR (CI%)	P	OR (CI%)	P
MMP1	Pulmonary embolism	0.9998 (0.9992–1.0004)	0.54	NA	NA	0.5	0	NA
MMP10	Pulmonary embolism	1.0003 (0.9997–1.0009)	0.295	NA	NA	0.935	0	NA
MMP12	Pulmonary embolism	0.9998 (0.9991–1.0006)	0.657	NA	NA	0.065	0	NA
MMP13	Pulmonary embolism	0.9997 (0.999–1.0004)	0.39	NA	NA	0.232	0	NA
MMP14	Pulmonary embolism	1.0004 (0.9995–1.0013)	0.402	NA	NA	0.762	0	NA
MMP16	Pulmonary embolism	1.0004 (0.9993–1.0015)	0.481	NA	NA	0.388	0	NA
MMP17	Pulmonary embolism	1 (0.9991–1.0009)	0.962	NA	NA	0.777	0	NA
MMP19	Pulmonary embolism	1.0008 (1–1.0016)	0.077	NA	NA	0.485	0	NA
MMP2	Pulmonary embolism	1 (0.9988–1.0012)	0.998	NA	NA	0.114	0	NA
MMP20	Pulmonary embolism	1.0001 (0.9993–1.0008)	0.863	NA	NA	0.801	0	NA
MMP3	Pulmonary embolism	0.9996 (0.9989–1.0003)	0.308	NA	NA	0.71	0	NA
MMP7	Pulmonary embolism	0.9999 (0.9992–1.0007)	0.836	NA	NA	0.459	0	NA
MMP8	Pulmonary embolism	1.0001 (0.9994–1.0007)	0.812	NA	NA	0.355	0	NA
MMP9	Pulmonary embolism	1.0002 (0.9991–1.0013)	0.711	NA	NA	0.016	0	NA

Fig. 4.

Funnel plot showing the estimation using the inverse of the standard error of the causal estimate with each individual SNP as a tool. The vertical line represents the estimated causal effect. SNP, single nucleotide polymorphism. (A) MMP12. (B) MMP19.

Fig. 5.

Leave-one-out sensitivity analysis, where each SNP was iteratively removed from the instrumental variables. (A) MMP12. (B) MMP19.

Causal association of pulmonary embolism on matrix metalloproteinases

Pulmonary embolism displayed no causal associations with MMPs (IVW, all P > 0.05), including (Table 4), after FDR adjustment. Cochran’s Q-test revealed heterogeneity for the analysis of pulmonary embolism on MMP8 (P = 0.048), MMP9 (P = 0.004), MMP12 (P = 0.007), and MMP13 (P = 0.018) (Table 5). The MR-Egger intercept test showed no horizontal pleiotropy (Table 5). The MR-PRESSO analysis revealed one outlier for each of MMP9 (P = 0.008), MMP12 (P = 0.013), and MMP13 (P = 0.031) (Table 6). After excluding the outliers and recalculating the IVW analyses, the results for these three MMPs remained similar (IVW, P > 0.05), but heterogeneity was no longer observed (P = 0.112, P = 0.578, and P = 0.109, respectively).

Table 4.

Reverse MR analysis of all matrix metalloproteinase as the outcomes.

Exposure	Outcome	N.SNPs	Methods	OR (95% CI)	P	Power
Pulmonary embolism	MMP10	7	IVW	3.1523 (0.001–10157.8389)	0.7806	0.9997
Pulmonary embolism	MMP12	7	IVW	0.3428 (0–209258.4395)	0.8748	0.3861
Pulmonary embolism	MMP13	7	IVW	9.4486 (0–4097262.0962)	0.7345	1
Pulmonary embolism	MMP14	7	IVW	0.0542 (0–212.9266)	0.4899	0.6751
Pulmonary embolism	MMP16	7	IVW	5.1216 (0.0018–14942.9922)	0.6882	1
Pulmonary embolism	MMP17	7	IVW	0.4348 (1e-04–1453.7222)	0.8406	0.3
Pulmonary embolism	MMP19	7	IVW	1.5556 (7e-04–3278.6755)	0.9099	0.2872
Pulmonary embolism	MMP1	7	IVW	4.8978 (0.0014–17446.7877)	0.7034	1
Pulmonary embolism	MMP20	7	IVW	0.2509 (1e-04–794.1554)	0.7367	0.4786
Pulmonary embolism	MMP2	7	IVW	0.1099 (0–916.1344)	0.6317	0.6217
Pulmonary embolism	MMP3	7	IVW	1.3327 (7e-04–2533.2295)	0.9406	0.1334
Pulmonary embolism	MMP7	7	IVW	1.7976 (6e-04–5017.4447)	0.8848	0.5178
Pulmonary embolism	MMP8	7	IVW	6.0923 (0–1097868.8115)	0.7698	1
Pulmonary embolism	MMP9	7	IVW	7.3955 (0–14417702.6303)	0.7866	1

Table 5.

The heterogeneity and pleiotropy results of the reverse MR analysis.

Exposure	Outcome	Heterogeneity		Pleiotropy
		Q statistic (IVW)	P	MR-Egger Intercept	P
Pulmonary embolism	MMP10	3.805131	0.703028	0.006545	0.765703
Pulmonary embolism	MMP12	17.65158	0.007165	−0.00602	0.878745
Pulmonary embolism	MMP13	15.36226	0.017619	0.014248	0.708856
Pulmonary embolism	MMP14	2.522435	0.865948	0.008097	0.719512
Pulmonary embolism	MMP16	6.488681	0.370727	0.008785	0.708046
Pulmonary embolism	MMP17	7.795581	0.253465	0.012849	0.587685
Pulmonary embolism	MMP19	2.20192	0.900223	−0.01524	0.473993
Pulmonary embolism	MMP1	3.686396	0.719028	0.017432	0.445409
Pulmonary embolism	MMP20	1.117931	0.980744	0.004734	0.828533
Pulmonary embolism	MMP2	7.842038	0.249906	0.003944	0.882676
Pulmonary embolism	MMP3	6.313546	0.388998	−0.02631	0.223535
Pulmonary embolism	MMP7	1.382207	0.966932	0.007914	0.714333
Pulmonary embolism	MMP8	12.70256	0.04801	0.039482	0.234305
Pulmonary embolism	MMP9	19.40475	0.003532	0.01773	0.676628

Table 6.

MR-PRESSO analysis for horizontal pleiotropy of the reverse MR analysis.

Exposure	Outcome	Raw		Outlier corrected		Global P	Number of outliers	Distortion P
		OR (CI%)	P	OR (CI%)	P
Pulmonary embolism	MMP10	3.1523 (0.0051–1960.6262)	0.738428	NA (NA-NA)	NA	0.7331	NA	NA
Pulmonary embolism	MMP12	0.3428 (0–209258.4395)	0.880001	210.1591 (0.1303–338970.5173)	0.215066	0.0131	1	0.2798
Pulmonary embolism	MMP13	9.4486 (0–4097262.0962)	0.746066	886.5531 (0.0062–126430349.5416)	0.313244	0.031	1	0.7467
Pulmonary embolism	MMP14	0.0542 (3e-04–11.5977)	0.327897	NA (NA-NA)	NA	0.8489	NA	NA
Pulmonary embolism	MMP16	5.1216 (0.0018–14942.9922)	0.702117	NA (NA-NA)	NA	0.355	NA	NA
Pulmonary embolism	MMP17	0.4348 (1e-04–1453.7222)	0.847226	NA (NA-NA)	NA	0.2474	NA	NA
Pulmonary embolism	MMP19	1.5556 (0.0151–160.4855)	0.857979	NA (NA-NA)	NA	0.8921	NA	NA
Pulmonary embolism	MMP1	4.8978 (0.0081–2978.265)	0.644351	NA (NA-NA)	NA	0.6913	NA	NA
Pulmonary embolism	MMP20	0.2509 (0.0077–8.1362)	0.465575	NA (NA-NA)	NA	0.9765	NA	NA
Pulmonary embolism	MMP2	0.1099 (0–916.1344)	0.648645	NA (NA-NA)	NA	0.2913	NA	NA
Pulmonary embolism	MMP3	1.3327 (7e-04–2533.2295)	0.942987	NA (NA-NA)	NA	0.3672	NA	NA
Pulmonary embolism	MMP7	1.7976 (0.0399–81.0124)	0.772973	NA (NA-NA)	NA	0.9752	NA	NA
Pulmonary embolism	MMP8	6.0923 (0–1097868.8115)	0.779636	NA (NA-NA)	NA	0.0787	NA	NA
Pulmonary embolism	MMP9	7.3955 (0–14417702.6303)	0.795635	0.0225 (0–2810.0497)	0.554169	0.0083	1	0.2561

Discussion

This MR study investigated whether the MMPs have a causal association with pulmonary embolism, revealing a causal association between MMP19 and pulmonary embolism. In other words, high serum MMP19 levels are causally associated with an increased risk of pulmonary embolism. In addition, while the IVW analysis showed that MMP12 was not associated with the risk of pulmonary embolism, the weighted media analysis showed that higher levels of MMP12 were causally associated with a reduced risk of pulmonary embolism.

The role of MMP19 in pulmonary embolism is an emerging area of interest, albeit less extensively studied compared with some other MMPs. The MR study revealed an OR of 1.0009 for the influence of MMP19 on pulmonary embolism, suggesting a potential albeit mild deleterious effect. This finding aligns with animal studies where MMP19 was highly expressed in thrombi within the inferior vena cava of mice and the vessels themselves, highlighting its possible involvement in deep venous thrombosis²⁰. MMP19 has been shown to enhance the migration and permeability of primary human lung microvascular endothelial cells, regulating vascular integrity³¹. Given that thrombus formation is facilitated by vessel wall injury, stasis, or hypercoagulability, i.e., by factors that disrupt the balance between procoagulant and fibrinolytic systems³², MMP19 could indirectly modulate pulmonary embolism progression by affecting the stability of the vessel wall. Moreover, pulmonary embolism implicates both thrombus formation and resolution. MMPs can either facilitate or inhibit thrombolysis by degrading fibrin networks³³. While specific actions of MMP19 require further investigation, it may regulate fibrin metabolism, impacting clot stability or dissolution rates. Post-pulmonary embolism tissue repair and lung function recovery involve ECM remodeling³⁴. Notably, MMP19 expression significantly increases in the dermis surrounding healing wounds in the adult human skin wound healing organ culture model, implicating its role in ECM reconstruction and tissue regeneration³⁵. Thus, MMP19 could affect the degradation and synthesis of ECM components in the lungs, influencing the reparative process following embolization and, ultimately, lung function restoration. MMP19’s potential effects on vascular endothelial function and platelet activation are noteworthy, as MMP family members are known to regulate these processes³⁶, which may indirectly influence thrombus formation and the risk of embolic events. Therefore, while direct evidence linking MMP19 to the pathogenesis of pulmonary embolism remains limited, the present findings and available literature propose several mechanisms through which MMP19 could contribute to the development and outcomes of pulmonary embolism. Further mechanistic and clinical studies are warranted to elucidate the precise role and therapeutic potential of targeting MMP19 in this context.

Similarly, MMP12 has emerged as a subject of interest in the context of pulmonary embolism, with findings presenting a complex picture of its role. The present MR analysis, leveraging data from the UK Biobank on pulmonary embolism, intriguingly suggests that MMP12 might be a protective factor against pulmonary embolism, aligning with the intricate regulatory roles MMPs play in inflammation and tissue remodeling³⁷. Contrasting the present observations, a Finnish cohort study utilizing data on venous thromboembolism and pulmonary embolism reported higher serum MMP-12 levels associated with an increased risk of PE, evidenced by an OR of 1.06³⁸. Furthermore, another MR study revealed that MMP12 levels were causally associated with venous thromboembolism³⁸. Still, although pulmonary embolism is a type of venous thromboembolism, it has some differences with the other types of thromboembolic diseases. The same study also highlighted a heightened PE risk associated with elevated serum MMP1 levels, whereas MMP3 and MMP7 showed a decreased risk³⁸. It is noteworthy that our MR investigation did not establish causal associations between MMP1, MMP2, or MMP7 and PE. These discrepancies across studies underscore the complexity of MMPs’ involvement in thrombotic disorders and emphasize the need for further research to clarify the specific contexts under which MMP12 exerts its effects, i.e., whether protective or detrimental. Factors such as population-specific genetic backgrounds, environmental influences, and variations in sample characteristics could contribute to the observed differences. Longitudinal and interventional studies, coupled with detailed mechanistic explorations, will be pivotal in deciphering the precise role of MMP12 in the pathophysiology of PE and guiding potential therapeutic interventions.

In recent years, multiple studies have shown that changes in MMP levels are associated with pulmonary embolism. Fibrinolytic therapy with alteplase can increase the concentration of MMPs. In patients with pulmonary embolism treated with alteplase, a transient increase in circulating pro-MMP9 levels was found after acute pulmonary embolism fibrinolysis, indicating that the increase in MMP9 levels may be the basis for the risk of cerebral hemorrhage or other thrombolytic bleeding complications of acute pulmonary embolism³⁹. Several studies using animal models of acute pulmonary embolism observed that increased MMP9 levels were associated with an increased risk of acute pulmonary embolism in rats and dogs, respectively, while MMP2 levels had no effect^40–43. An in vivo study revealed that MMP2 and MMP9 were elevated in a rat model of pulmonary embolism⁴⁴. Pretreatment with atorvastatin contributes to less severe pulmonary hypertension after pulmonary embolism, accompanied by decreased MMP9 levels⁴³. In the present analysis, no causal associations were found between MMP9 and pulmonary embolism, but special attention is needed because the results for MMP9 showed heterogeneity. In a prospective cohort study of 152 patients with pulmonary embolism, no correlation was observed between MMPs and prognosis in patients with pulmonary embolism⁴⁵. A clinical study reported that patients with recurrent glioblastoma treated with irinotecan and doxycycline had progressively elevated levels of MMP9 in the cerebrospinal fluid during treatment, and pulmonary embolism occurred in many patients during treatment⁴⁶. By comparing the plasma protein levels of 119 patients with pulmonary embolism and 70 patients with chronic thromboembolic pulmonary hypertension after pulmonary embolism, differences between MMP9 and MMP2 proteins were found⁴⁷. The present study indicates no association between genetically-predicted MMP9 or MMP2 levels and genetically-predicted pulmonary embolism, but it cannot reveal the mechanisms nor the influence of possible confounders or other factors. Additional studies will be necessary to unravel the relationships between MMP2 or MMP9 and pulmonary embolism.

Overall, these clinical studies have observed that certain MMPs may be associated with the development of pulmonary embolism. However, observational clinical trials have shortcomings, which can only infer possible causality from the observed and recorded data and cannot rule out the interference of confounding factors on the results. In addition, cross-sectional studies cannot be used to determine causality. The present study analyzed the genetic relationship between MMPs and pulmonary embolism, providing conclusive evidence for the involvement of MMPs in pulmonary embolism. Unlike clinically reported observations, the present findings suggest a causal association between high serum levels of MMP19 and an increased risk of pulmonary embolism, which has never been reported before. However, the exact causal relationship between MMP9 and pulmonary embolism that has been a concern in clinical studies has not been confirmed, further reflecting the complexity of the association between MMPs and pulmonary embolism. Therefore, it is necessary to investigate the association between different MMPs and pulmonary embolism through additional mechanistic studies. Nevertheless, the present study suggests that MMPs could be serum markers to evaluate the risk of pulmonary embolism. Future studies should examine the possibility.

Several MMPs are involved in many pathological processes and inflammation^37,48,49. Although pulmonary embolism causes a local lung injury that recruits immune cells⁵⁰, pulmonary embolism as exposure does not appear to influence serum MMP levels. A reason could be that the immune reaction is local, while the present study examined the serum MMP levels. MMPs include Zn²⁺in their three-dimensional structure. Zinc treatment appears to be either inhibitory or stimulatory on MMP activity depending upon the exact physiological conditions of the cell or animal models used, the zinc dose, and treatment duration⁵¹. Zinc levels were not assessed in the present study. Future studies could examine the influence of zinc levels on pulmonary embolism.

This study has several notable strengths. One of the main advantages of this study is the MR design. The study was based on the MR methodology to explore causal associations through genetic data, avoiding confounding bias and reverse causality, which are common limitations in traditional observational studies. By utilizing widely recognized public databases such as the UK Biobank, the study enhances the representativeness and credibility of the data and providing important new evidence in this field. The paper successfully applies various statistical tools such as MR-Egger and MR-PRESSO for sensitivity analysis, effectively validating the robustness and pleiotropy of the data, thus increasing the reliability of the results. The paper discusses the potential application of MMP testing in predicting the risk of pulmonary embolism, suggesting that future blood serum MMP testing could identify high-risk individuals and explore targeted therapies related to MMPs.

Nevertheless, the present study has several limitations. First, the MMP data only covered the Icelandic population, and the age range was > 65. Geographical and ethnic differences (including genetics, environment, diet, environment, and ethnic lifestyle) may affect the characteristics of serum protein levels between individuals and groups, so the knowledge of serum MMP levels in other regions and ethnicities is limited, and different ages and physical conditions were not differentiated in detail in the present study. Therefore, more data on serum MMP levels in large samples, as well as multi-population and multi-age sources, is needed. In addition, the data on pulmonary embolism was from the UK population, and there were only 3940 cases of pulmonary embolism. The findings of this study are limited to European populations, particularly the Icelandic population. Genetic differences in thrombosis-related genes between European and East Asian populations⁵²may affect the generalizability of the results. Further studies involving diverse populations are needed to validate these findings. In addition, the data of patients with pulmonary embolism were not further broken down, such as the type of disease, and these limitations make the findings difficult to generalize to other populations. Furthermore, MR analyses are valid without horizontal pleiotropy, which was observed for MMP9, suggesting that the MMP9 analysis might be invalid. In addition, heterogeneity was observed for the forward analysis of MMP9 and the reverse analyses of MMP8, MMP9, MMP12, and MMP13, indicating that the IVs were not randomly distributed. Still, the IVW analysis is a random-effect model that has some tolerability to heterogeneity⁵³.

In conclusion, this MR study provides evidence of a causal association between MMPs and pulmonary embolism, and more detailed mechanistic studies are needed to elucidate the role of specific MMPs in pulmonary embolism, leading to innovative prevention or treatment strategies targeting MMPs for pulmonary embolism.

Electronic supplementary material

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Author contributions

(I) Conception and design: Xiaowei Gong(II) Administrative support: Yadong Yuan(III) Provision of study materials or patients: Xiaowei Gong(IV) Collection and assembly of data: Xiaowei Gong(V) Data analysis and interpretation: Yadong Yuan(VI) Manuscript writing: All authors.(VII) Final approval of manuscript: All authors.

Funding

This study was funded by grants from the Hebei Province Science and Technology Support Program (246Z7704G). The research was designed, conducted, analyzed, and interpreted by the authors entirely independently of the funding sources.

Data availability

All data generated or analyzed during this study are included in this article and supplementary information files.

Declarations

Ethics approval and consent to participate

This article is a Mendelian randomization study. The data for this study were obtained from publicly available databases and published literature data and did not require ethical approval and written informed consent.

Competing interests

The authors declare no competing interests.