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. 2025 Aug 22;30:787. doi: 10.1186/s40001-025-03071-8

Association of lipoprotein(a) and LPA gene with calcific aortic valve disease

Xinyi Yu 1,✉, Zean Fu 2, Minghuan Yu 3, Yibo Shi 1
PMCID: PMC12372332  PMID: 40847316

Abstract

Objective

To investigate the association between Lp(a) levels and calcific aortic valve disease (CAVD) and the potential molecular mechanism underlying the effect of LPA gene expression on aortic valve calcification (AVC).

Methods

Case–control and cohort studies on the association between Lp(a) and CAVD were searched in the meta-analysis. Meta-analysis was performed using RevMan and Stata. AVC-related gene microarray data were obtained from the GEO database. The Gene Set Variation Analysis (GSVA) algorithm was used to synthetically score each gene set and analyze differences in pathways in the LPA gene high- and low-expression groups. The expression of endothelial markers, interstitial markers and osteogenic markers after Lp(a) intervention in human aortic valve endothelial cells (AVEC) was detected by Western blot.

Results

The risk of CAVD was increased 1.44-fold (95% CI 1.25–1.67, P < 0.05) when Lp(a) concentrations were > 30 mg/dL and 1.95-fold (95% CI 1.93–1.97, P < 0.05) when Lp(a) concentrations were > 50 mg/dL. GSVA results showed that high expression of the LPA gene was associated with TGF-β signaling, oxidative phosphorylation, and reactive oxygen species pathway. Western-blot results showed that after Lp(a) was co-cultured with AVEC for 72 h, the expression of endothelial markers decreased, while the expression of interstitial markers and osteogenic markers increased.

Conclusion

Elevated Lp(a) concentration is a risk factor for CAVD. High expression of the LPA gene (or high concentration of Lp(a)) may cause EndoMT of AVEC by disrupting pathways, such as TGF-β signaling, resulting in CAVD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-025-03071-8.

Keywords: Calcific aortic valve disease, Aortic valve calcification, Lipoprotein(a), LPA gene

Introduction

Calcific aortic valve disease (CAVD), also known as aortic valve calcification (AVC), is a heart valve disease characterized by the thickening and calcification of the aortic valve leaflets. The incidence of CAVD is increasing in the elderly population, and it is the second most common cardiovascular disease after coronary artery disease and hypertension and a common cause of aortic valve stenosis [1]. In addition to the elderly population suffering from degenerative changes owing to valve calcification, people with a congenital bicuspid aortic valve are also more likely to develop CAVD [2]. Currently, CAVD is not a simple age-related degenerative process, but rather the result of a combination of factors, as evidenced by findings from epidemiological, histopathological, and animal studies [3–5].

Lipoprotein(a) (Lp(a)) is a low-density lipoprotein cholesterol-like particle bound to apolipoprotein(a) [6]. Higher plasma Lp(a) levels are correlated with a greater risk of cardiovascular events [7–9]. Lp(a) is also an important mediator in the development and progression of CAVD, as evidenced in an increasing number of studies [6, 10, 11]. Plasma Lp(a) concentrations are primarily determined by the expression levels of the LPA gene, which encodes apolipoprotein(a) [7, 12]. The LPA gene is highly polymorphic [11]. The rs10455872 variant of this gene is associated with an increased risk of aortic valve stenosis [13–15]. The rs3088442 variant is associated with coronary artery disease severity [16].

However, our understanding of the role of Lp(a) in CAVD is incomplete [17]. Owing to the lack of globally standardized Lp(a) assays and the different cutoff values used, the clinical correlation between plasma Lp(a) levels and CAVD remains unclear. Owing to inadequate experimental models, the potential mechanisms of action of LPA in CAVD need to be further investigated. Here, we investigated the association between different Lp(a) levels and CAVD in a meta-analysis to determine the basis for the prevention and monitoring of CAVD. Using bioinformatics, we analyzed the potential molecular mechanism by which the LPA gene affected AVC. Based on the results of meta-analysis and bioinformatics, this study innovatively explored the effects of Lp(a) on human aortic valve endothelial cells (AVEC) and detected the expression of endothelial markers, interstitial markers, and osteogenic markers through real-time quantitative PCR (RT-qPCR) and Western blot analysis.

Methods

Meta-analysis

Search strategy

The meta-analysis was conducted in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) [18]. The protocol was registered in the PROSPERO database (registration number: CRD42023417823). Two authors (Xinyi Yu and Zean Fu) systematically searched electronic databases, including PubMed, Embase, Web of Science, and Cochrane, for publications till April 30, 2023. Search terms included “aortic valve calcification” or “aortic valve stenosis” or “calcific aortic valve stenosis” or “aortic stenosis” or “aortic valve sclerosis”, and “lipoprotein (a)” or “lp (a)” or “lipoprotein”. The reference lists of relevant reviews and original reports were also searched for potential eligible records. The initial screening of eligible studies was based on the titles and abstracts. The comprehensive search strategy for each database has been provided in Supplementary Material 1.

Eligibility criteria and qualitative assessment

Studies were considered eligible if they met the following inclusion criteria: (1) the general population or patients with CAVD (no restrictions on age and gender) constitute the study population; (2) the Lp(a) level was the exposure factor; (3) the study endpoint was CAVD (including AVC and calcific aortic stenosis, among other factors); (4) the study type was case–control or cohort; (5) the language of the publication was English. The studies were excluded if they were (1) case reports, overviews, conference abstracts, etc.; (2) published or reported multiple times; (3) unpublished and inaccessible full-text articles; (4) incomplete relevant data used to obtain evaluable effect values; (5) studies with sample size < 100 individuals.

The quality of the included studies was evaluated using the Newcastle–Ottawa Scale (NOS) [19]. All eligible studies were independently assessed by two authors (Xinyi Yu and Zean Fu). The NOS score ranged from zero to nine points, and studies with an overall score ≥ 4 were classified as high quality.

Data extraction

Data extraction was performed independently by two authors (Xinyi Yu and Zean Fu). In case of disagreements, a third reviewer was consulted (Yibo Shi). Key information extracted from each study included the first author’s name, publication year, country or region where the study was performed, study design, sample size, and follow-up duration. Demographic data, including the number of participants and primary characteristics, were obtained.

Statistical analysis

Meta-analysis was performed using RevMan 5.3 and Stata 15.0. Differences were considered statistically significant at P < 0.05. In the meta-analysis, the odds ratio (OR) was used as the effect size. The effect measures and their corresponding 95% confidence intervals (95% CI) were combined using the effect size. I2 and Q tests were used to assess the heterogeneity of the included literature. I2 > 50% and P < 0.05 indicated heterogeneity between the studies, which was analyzed using a random effects model. I2 ≤ 50% or P > 0.05 indicated lesser heterogeneity, and the solid-state effect model analysis was used. The stability of the results was tested using sensitivity analysis. Subgroup analysis was performed to examine the effect of study characteristics on outcome variables and analyze the reasons for heterogeneity. The literature with study heterogeneity or influential outcomes was excluded using a case-by-case exclusion method, and the results were then recombined. The Egger’s test and Begg’s test were used to determine publication bias. If publication bias existed, the effect of publication bias on the study results was re-estimated using the nonparametric cut-and-patch method.

Bioinformatics study

Data extraction

The gene expression profiles of GSE51472, GSE12644, and GSE83453 were retrieved from the Gene Expression Omnibus (GEO) public database at the National Center for Biotechnology Information (Supplementary Material 2). The experimental platform for GSE51472 is GPL570, which includes data from five normal aortic valve specimens and five AVC specimens. The experimental platform for GSE12644 was GPL570. Expression profile data from 20 patients were included, of which ten were normal aortic valve specimens and ten were AVC specimens. The experimental platform for GSE83453 was GPL10558, and expression profile data from 17 patients were included; eight normal aortic valve specimens and nine AVC specimens were used. The present study employed the RMA algorithm to standardize the data. Subsequently, the SVA algorithm was employed to identify and correct potential unknown batch effects in the samples. The ComBat method was then applied to further eliminate known experimental batch differences, thereby obtaining a more reliable dataset for subsequent analysis.

Co-expression analysis

Correlation analysis (correlation coefficient filter condition of 0.5, P < 0.05) was used to examine the co-expression of LPA genes in the AVC data. After the genes most significantly associated with LPA gene expression were screened, a heat map of genes significantly associated with LPA gene expression was plotted using the"corrplot"package in the R software.

Gene set variation analysis

Patients were divided into high- and low-expression groups according to the median expression of the LPA gene. The hallmark gene sets were retrieved from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb). The Gene Set Variation Analysis (GSVA) algorithm was used to synthetically score each gene set and analyze the differences in pathways in the high- and low-expression groups [20].

Correlation between LPA gene and genes associated with AVC

Through the GeneCards database (https://www.genecards.org/), disease genes related to AVC were obtained. The expression levels of the top 20 genes with the highest Relevance scores were analyzed to examine the inter-group differences in the expression of these disease genes. Pearson’s correlation analysis was used to investigate the correlation between the expression level of the LPA gene and the expression levels of AVC-related genes.

Cell experiment

To investigate the potential role of the TGF-β signaling pathway in incidence of CAVD, human primary AVEC (HUM-iCell-c018, Cellverse) were respectively co-cultured with 0, 2.5, 5, and 10 μg/mL Lp(a) (Ag32907, Proteintech) for 72 h. Following incubation, cells were harvested for Western Blot and RT-qPCR analysis. The detailed information of the reagents and primers is presented in the Supplementary Material 3.

Results

Correlation between Lp(a) levels and CAVD risk

Search results and study characteristics of meta-analysis

A total of 2,131 literature articles were searched. Based on the inclusion and exclusion criteria, 856 duplicate papers, 1,251 papers that did not meet the inclusion criteria, five papers with incomplete data, and seven papers with inaccessible data were excluded after reading the title, abstract, and full text. Twelve papers were eventually included (Fig. 1). Of these, six were case–control studies, including 1,074 cases and 3,192 controls, and six were cohort studies, with 129,943 participants in all. The basic characteristics of the included studies are shown in Supplementary Material 4.

Fig. 1.

Fig. 1

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Flow diagram of the meta-analysis

Association of Lp(a) with CAVD risk

Twelve studies with 134,209 participants were included in this study. Of these, seven studies reported an association between Lp(a) > 30 mg/dL and CAVD, eight studies reported an association between Lp(a) > 50 mg/dL and CAVD, and one study reported an association between Lp(a) > 90 mg/dL and CAVD. Eventually, 16 data sets were included in this study. The results of the heterogeneity test indicated heterogeneity among the data retrieved from the studies (I2 = 86.0%, P < 0.05). Data were combined according to a random effects model. Elevated Lp(a) levels were associated with CAVD (OR = 1.84, 95% CI 1.53–2.22, P < 0.05) (Fig. 2A). Sensitivity analysis revealed that after one study was sequentially excluded from this analysis, the combined effect size of the remaining studies was similar to the total combined effect size of the random effects model, suggesting that the results of the Lp(a) and CAVD correlation study were stable and reliable (Fig. 2B).

Fig. 2.

Fig. 2

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The results of the meta-analysis on the correlation between Lp(a) and CAVD. A Forest plot of the associations between Lp(a) levels and CAVD. B Sensitivity analysis of the associations between Lp(a) and CAVD. C Subgroup analysis of the associations between Lp(a) and CAVD. D Funnel plot: publication bias of the included studies

Subgroup analysis

Subgroup analysis was performed based on the Lp(a) levels. Since only Kamstrup et al. [21] used 90 mg/dL as the cutoff value, this study was excluded when performing subgroup analysis. Heterogeneity tests and sensitivity analyses were performed using the case-by-case exclusion method. After data from Cardoso-Saldaña et al. [22] and Wilkinson et al. [23] were excluded in the > 30 mg/dL group, the I2 was 12%, with P = 0.34. After data from Cao et al. [24] were excluded in the > 50 mg/dL group, the I2 was 0%, with P = 0.79. Therefore, subgroup analysis and data merging were performed according to the fixed effects model. The results are shown in Fig. 2C. Elevated Lp(a) levels were associated with CAVD at Lp(a) > 30 mg/dL (OR = 1.44, 95% CI 1.25–1.67, P < 0.05). Elevated Lp(a) levels were associated with CAVD at Lp(a) > 50 mg/dL (OR = 1.95, 95% CI 1.93–1.97, P < 0.05). The results of the heterogeneity test indicated heterogeneity between the two groups (I2 = 93.8%, P < 0.05). The results of the meta-regression analysis showed that the level of Lp(a) explained the heterogeneity between the groups (Adj R2 = 55.50%, P = 0.116).

Publication bias

The 16 data sets were subjected to Begg’s test (P = 0.163) and Egger's test (P = 0.377); the funnel plot obtained was almost symmetrical (Fig. 2D), suggesting the lack of publication bias. In the subgroup analysis, the P values for the Begg’s test and Egger’s test in the > 30 mg/dL group were 0.086 and 0.129, respectively. The P values for Begg’s test and Egger’s test in the > 50 mg/dL group were 0.548 and 0.905, respectively; again, there was no publication bias.

Potential molecular mechanisms of AVC

In this study, the GSE51472, GSE12644, and GSE83453 datasets were retrieved from the GEO database, and expression profile data from 47 patients were included. This included data from the normal control group (n = 23) and the CAVD group (n = 24). The batch effect of the GEO data was removed using SVA ComBat. The interchip batch effect was reduced after correction (Fig. 3A). In the AVC data, 7,483 genes significantly correlated with LPA gene expression were screened (Supplementary Material 5). The heat map of the TOP 10 genes with correlation coefficients (positive/negative correlation) is shown in Fig. 3B. GSVA results showed that high expression of the LPA gene was associated with the enrichment of signaling pathways such as TGF-β signaling, oxidative phosphorylation, and reactive oxygen species pathway. Low expression of the LPA gene could enrich signaling pathways such as KRAS signaling, inflammation response (Fig. 3C). These pathways are associated with endothelial mesenchymal transformation (EndoMT) [25]. Figure 3D shows that the expression of the ACTA2, COL3A1, COL5A1, MYH11, MYLK, SMAD4, SMAD6, and TGFB2 genes differs between AVC patients and normal individuals. The expression level of the LPA gene is significantly correlated with the expression levels of several AVC-related genes (Fig. 3E).

Fig. 3.

Fig. 3

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The results of the bioinformatics analysis on the correlation between LPA gene and CAVD. A Principal component analysis before and after SVA correction. B Heat maps of the top 10 genes significantly associated with LPA gene expression. C Differences in pathway activities between the high and low LPA gene expression groups. D Differential expression of CAVD-related disease genes between normal aortic valves and calcified aortic valves. * represents P < 0.05, ** represents P < 0.01, ns represents P > 0.05. E The correlation between the expression level of LPA gene and the expression level of aortic valve calcification-related genes

EndoMT and osteoblast-like differentiation of AVEC

The Western Blot results show that after Lp(a) co-cultured with AVEC, the expression levels of endothelial markers (VE-Cadherin and E-Cadherin) decreased, while the expression levels of interstitial markers (N-Cadherin and α-SMA) and osteogenic markers (ALP and RUNX2) increased (Fig. 4A–D).

Fig. 4.

Fig. 4

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Expression of protein and mRNA after co-culture of Lp(a) and AVEC. A The result of Western Blot. The expression of endothelial markers (VE-Cadherin and E-Cadherin) gradually decreased. The expression of interstitial markers (N-Cadherin and α-SMA) and osteogenic markers (ALP and RUNX2) gradually increased. B, C, and D showed the differences between groups of endothelial markers, interstitial markers, and osteogenic markers at the protein scale, respectively. E, F, and G showed the differences between groups of endothelial markers, interstitial markers, and osteogenic markers at the mRNA scale, respectively. * represents P < 0.05, ** represents P < 0.01, *** represents P < 0.001, **** represents P < 0.0001, ns represents P > 0.05

The RT-qPCR results show that when compared with the untreated control group, treatment with 10 μg/mL Lp(a) significantly downregulated endothelial cell markers in AVEC (VE-Cadherin decreased by 0.17-fold, P < 0.0001; E-cadherin decreased by 0.24-fold, P < 0.001). Conversely, mRNA expression of interstitial markers and osteogenic markers was significantly upregulated (N-cadherin increased by 3.59-fold, P < 0.0001; α-SMA increased by 5.32-fold, P < 0.0001); ALP increased by 5.12-fold, P < 0.0001; RUNX2 increased by 6.12-fold, P < 0.0001) (Fig. 4E–G).

Discussion

CAVD was once considered a passive degenerative process, but recent studies have revealed that it is an active disease process driven by native aortic valve cells [26]. AVEC, which covers the surface of the heart valves, is mechanically sensitive and can respond to changes in various circulating factors in the blood [27]. Aortic valve interstitial cells possess a rich variety of cell types within the heart valves and have the ability to differentiate into osteoblasts, playing a key role in the progression of CAVD disease [28]. Currently, CAVD is considered an active disease process involving complex interactions between AVEC, aortic valve interstitial cells, inflammatory cells, and the extracellular matrix [29, 30]. However, the specific role of AVEC in CAVD is still poorly understood.

Prospective, observational, and Mendelian randomization studies have identified the contribution of plasma Lp(a) to CAVD [31–34]. Genetic elevations in Lp(a) were strongly associated with CAVD, regardless of the presence of coronary artery disease [31]. The findings reported by Perrot et al. support additional investigations on the potential usefulness of Lp(a) cascade screening in CAVD [31]. In the present study, the results of the meta-analysis showed that elevated Lp(a) concentration was a risk factor for CAVD, and this result was consistent with the results of previous studies [35]. Subgroup analysis showed that the risk of CAVD was 1.44 times higher at Lp(a) > 30 mg/dL and 1.95 times higher at Lp(a) > 50 mg/dL than the population with normal Lp(a) levels. The results of the heterogeneity test indicated significant heterogeneity between the Lp(a) > 30 mg/dL and Lp(a) > 50 mg/dL groups. The results of the meta-regression analysis showed that the Lp(a) level could explain the heterogeneity between groups (Adj R2=55.50%, P=0.116). Therefore, the study showed that the higher the concentration of Lp(a), the higher the risk of CAVD.

Bourgeois et al. concluded that several mechanisms related to aging, ossification and inflammation may be involved in the development of CAVD in the context of lifelong exposure to high Lp(a) levels [36]. Evidence suggests that the level of circulating Lp(a) is largely driven by Lp(a) synthesis [37]. The concentration of Lp(a) in plasma may be associated with increased expression of the LPA gene in the aortic valve. In the present study, GSVA results suggest that high LPA gene expression may affect AVC by influencing TGF-β signaling, oxidative phosphorylation, or reactive oxygen species pathway. These three signaling pathways are key to EndoMT, suggesting that overexpression of the LPA gene may induce EndoMT in AVEC, leading to CAVD.

As demonstrated in prior research, AVEC can undergo EndoMT to transform into interstitial cells, thereby participating in valve repair and valve calcification [38]. In summary, this study hypothesizes that increased plasma Lp(a) levels disrupt the valvular homeostasis formed by AVECs and interstitial cells, and promote the EndoMT process of AVEC through signaling pathways such as TGF-β signaling, oxidative phosphorylation, or reactive oxygen species pathways, thereby leading to osteoblast-like differentiation and AVC (Fig. 5). The Western Blot and RT-qPCR outcomes appear to support this finding. As the concentration of Lp(a) rises, the expression of endothelial markers decreased, while the expression of interstitial markers and osteogenic markers in AVEC increased.

Fig. 5.

Fig. 5

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Mechanism hypothesis diagram: Lp(a) affects aortic valve endothelial cells, causing endothelial-mesenchymal transition and ultimately leading to aortic valve calcification (AVEC represents aortic valve endothelial cells, endothelial-derived VICs represents valve interstitial cells derived from endothelial cells, osteoblastic VICs represents osteoblast-like valvular interstitial cells)

In this study, we investigated the correlation between Lp(a) levels and CAVD and the potential pathogenic mechanisms using meta-analysis and bioinformatics. However, this study had some limitations. First, the results of the meta-analysis depended on the included studies. Since a few of the included studies classified the severity of CAVD, the results of the meta-analysis were limited. In the future, more prospective and pathway inhibition studies are necessary to explore the correlation between Lp(a) levels and CAVD, as well as the key signaling mechanisms. Second, in this study, the number of available clinical samples was limited. If relevant gene expression could be detected in a larger number of samples, the findings would be of higher clinical value.

Conclusion

Elevated Lp(a) concentration is a risk factor for CAVD, and the higher the Lp(a) concentration, the higher the risk of CAVD. High expression of LPA gene (or high concentration of Lp(a)) may lead to EndoMT of AVEC by interfering with pathways such as TGF-β signaling, leading to CAVD.

Supplementary Information

Supplementary material 1. (23.5KB, doc)
Supplementary material 2. (14KB, docx)
Supplementary material 3. (14KB, docx)
Supplementary material 4. (137.3KB, pdf)
Supplementary material 5. (600.7KB, pdf)
Supplementary material 6. (1.9MB, tif)

Acknowledgements

Figure 5 was created using Servier Medical Art (https://smart.servier.com). Servier Medical Art by Servier is licensed under a Creative Commons Attribution 4.0 Unported License (https://creativecommons.org/licenses/by/4.0/). This study was supported by Postdoctoral Research Initiation Grant for the First Affiliated Hospital of Zhengzhou University (Grant No. 72125).

Abbreviations

CAVD

Calcific aortic valve disease

AVC

Aortic valve calcification

Lp(a)

Lipoprotein(a)

AVEC

Aortic valve endothelial cells

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-analyses

NOS

Newcastle–Ottawa scale

GEO

Gene expression omnibus

GSVA

Gene set variation analysis

OR

Odds ratio

CI

Confidence intervals

RT-qPCR

Real-time quantitative PCR

ALP

Alkaline phosphatase

RUNX2

Runt-related transcription factor 2

EndoMT

Endothelial mesenchymal transformation

Author contributions

Xinyi Yu, Zean Fu, Minghuan Yu, Yibo Shi: Conceptualization, Data curation, Formal analysis, Investigation, Funding acquisition, Project administration, Resources, Writing-original draft, Writing-review & editing.

Funding

Postdoctoral Research Initiation Grant for the First Affiliated Hospital of Zhengzhou University (Grant No. 72125).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Approval number: 2024-KY-1600-002).

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1. (23.5KB, doc)
Supplementary material 2. (14KB, docx)
Supplementary material 3. (14KB, docx)
Supplementary material 4. (137.3KB, pdf)
Supplementary material 5. (600.7KB, pdf)
Supplementary material 6. (1.9MB, tif)

Data Availability Statement

No datasets were generated or analysed during the current study.

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