Association between lipoprotein(a), oxidized phospholipids, and bioprosthetic valve dysfunction following transcatheter aortic valve implantation
Bibliographic record
Abstract
Question: Are elevated lipoprotein(a) [Lp(a)] and oxidized phospholipids (OxPL) levels were associated with the occurrence of bioprosthetic valve dysfunction following transcatheter aortic valve implantation? Findings: Higher Lp(a) is associated with increased risk of bioprosthetic valve dysfunction, including subclinical leaflet thrombosis and early structural valve deterioration. Meaning: Further longitudinal studies are needed to confirm the role of Lp(a) and their associated OxPL in bioprosthetic valve dysfunction and assess whether Lp(a)-lowering therapies could enhance bioprosthetic valve durability. Over the last decade, transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of severe aortic stenosis (AS), offering an alternative to surgery across all surgical risks. Despite the rapidly growing adoption of TAVI, the long-term durability of transcatheter heart valves remains a matter of concern. Structural valve deterioration (SVD) shares similar risk factors and mechanisms with native AS, including oxidized lipid deposition, foam cell formation, and inflammation leading to progressive leaflet calcification, haemodynamic dysfunction and reintervention. Lipoprotein(a) [Lp(a)], has gained attention due to its established role in the initiation and faster progression of native AS.1,2 However, there is very few data on the association between Lp(a) or OxPL levels and bioprosthetic valve dysfunction (BVD) following TAVI.3–5 Thus, this study aimed to investigate the relationship between Lp(a) and the development of BVD following TAVI. This study prospectively enrolled 210 patients with severe AS undergoing TAVI at Québec Heart and Lung Institute from January 2017 to August 2020. Clinical and echocardiographic data were collected at pre-procedure, discharge, and 1-year follow-up, with echocardiographic measurements adjudicated by an echocardiography core laboratory. Bioprosthetic valve structure and haemodynamic function were assessed according to current guidelines and the aetiology and stage of BVD was adjudicated according to VARC-3 standardized definitions.6 Blood samples were collected before TAVI and stored at −80°C. Isoform-independent7 Lp(a), OxPL-apoB, OxPL-apo(a), OxPL on plasminogen and plasminogen were measured using chemiluminescent immunoassays, were performed at UCSD as previously described.8 Continuous variables were assessed for normality and expressed as mean ± SD or median and interquartile range (IQR), according to their distribution. Statistical comparisons were performed using t-tests or Mann–Whitney tests for continuous variables and Chi-squared or Fisher's exact tests for categorical variables, as appropriate. Receiver operating characteristics (ROC) curve and Youden’s index were used to determine the optimal Lp(a) threshold associated with BVD. Univariable logistic regression analyses were used to assess the associations between Lp(a) levels and BVD. Analyses were conducted using SPSS 26.0, with a significance level of P < 0.05. The primary endpoint was the incidence of BVD (including valve leaflet thrombosis and SVD) at 1-year follow-up. The secondary endpoint was all-cause mortality. The mean age of the study population was 79.7 ± 8.2 years and 120 (57.1%) were males, hypertension 186 (88.6%), dyslipidemia 179 (85.2%), diabetes mellitus 75 (35.7%); BMI ≥30 kg/m2 60 (28.6%), renal failure 107 (51.7%), STS score 3.9% (IQR 2.6–5.9), Valve in Valve 36 (17.1%). 25/210 (12%) patients developed BVD at 1 year following TAVI. Of these 25 patients, 9 (36%) had BVD stage 1 (morphological deterioration), 14 (56%) Stage 2 (moderate haemodynamic valve deterioration), and 2 (8%) stage 3 (severe haemodynamic valve deterioration). The aetiology of BVD was SVD in 9/25 (36%), subclinical leaflet thrombosis (SLT) in 6/25 (24%), and undetermined in 10/25 (40%). Lp(a) [38.9 (8.5–123.6) nmol/L] and OxPL-apoB [9.0 (4.7–16.4) nmol/L] levels were significantly higher in patients with BVD. At 1 year post-TAVI, 81/210 (38.6%) patients with Lp(a) ≥ 30 nmol/L had a higher incidence of: (i) Overall BVD (15 [18.5%] vs. 10 [7.8%], P = 0.027; OR [95% CI]: 2.77 [1.08–7.08], P = 0.033), (ii) Stage 2 or 3 BVD (12 [8.6%] vs. 4 [2.3%], P = 0.002; OR [95% CI]: 3.97 [1.00–15.83], P = 0.050), and (iii) Subclinical valve leaflet thrombosis (5 [6.2%] vs. 1 [0.8%], P = 0.022; OR [95% CI]: 8.42 [0.96–76.43], P = 0.054) (Central Illustration). Lp(a) remained independently associated with an increased risk of BVD after adjustment for relevant clinical covariates, including valve-in-valve status. During median follow-up of 2.6 years (IQR 2.2–3.6), 37/210 (17.6%) died. However, neither Lp(a) ≥ 30 nmol/L nor BVD was associated with an increased incidence of all-cause mortality (HR [95% CI]: 1.04 [0.54–2.00], P = 0.89 and HR [95% CI]: 0.99 [0.35–2.82], P = 0.99) respectively. The main findings of this study indicate that elevated Lp(a) and OxPL-apoB levels are associated with increased risk of BVD including SLT and early SVD. Lp(a) is the major lipoprotein carrier of OxPL, which induce calcification, lending insights into the potential mechanistic aetiology of this association. These data are consistent with a recent meta-analysis of studies in pre-existing AS where both Lp(a) and OxPL-apoB were independently associated with progression of AS.9 Lp(a) induces osteogenic differentiation of valvular interstitial cells through its OxPL content that can be inhibited with the E06 monoclonal antibody against OxPL. Elevated Lp(a) may impair valvular interstitial cells through oxidative stress, with OxPL activating nuclear factor-κB, promoting calcification and remodeling.2 The pathophysiology of SVD likely involves mechanical stress, platelet adhesion, lipid-mediated inflammation, and immune responses to the bioprosthetic valve tissues. Although the Lp(a) threshold associated with BVD in the present study was much lower than those typically associated with clinical cardiovascular events, Lp(a) has pro-thrombotic effects that may manifest at low levels of Lp(a). This may explain the increased risk of SLT in patients with elevated Lp(a) levels in this study. The aetiology of BVD was undetermined in 40% of patients due to suboptimal visualization of valve leaflets by transthoracic echocardiography and the fact that transoesophageal echocardiography or contrast-enhanced CT were not systematically performed. However, given that these BVD occurred during the first year following TAVI, it is plausible that most of these undetermined BVD are, in fact, related to valve leaflet thrombosis. A recent study by Shi et al. reported an association between higher plasma levels of Lp(a), C Reactive Protein and SLT.3 Furthermore, several studies have suggested that SLT may, in turn, predispose to early SVD.10,11 Hence, higher Lp(a) levels were associated with increased risk of BVD, including SLT and early SVD. RNA therapeutics and other drugs directly targeting Lp(a) can substantially reduce Lp(a) and OxPL-apoB levels.12 The findings of this study emphasize that patients with elevated Lp(a) undergoing TAVI should receive close echocardiographic follow-up of bioprosthetic valve function. Even though, this study is limited by the small number of primary events and requires confirmation in larger cohorts, these hypothesis-generating findings highlight the need for randomized clinical trials to determine whether Lp(a)-lowering therapies can reduce the risk of BVD and SVD, thereby improving valve durability after TAVI. None declared. Carlos Maximiliano Giuliani (Giuliani (Conceptualization [lead]; Data curation [lead]; Formal analysis [lead]; Investigation [lead]; Methodology [lead]; Writing—original draft [lead]; Writing—review & editing [lead])), Josep Rodés-Cabau [Validation (supporting)], Romain Capoulade [Validation (supporting)], Benoit Arsenault (Validation [supporting]; Visualization [supporting]), Marie Annick Clavel (Validation [supporting]; Visualization [supporting]; Writing—review & editing [supporting]), Jean-Michel Paradis (Validation [supporting]; Visualization [supporting]), Robert DeLarochellière [Validation (supporting)], Anthony Poulin (Validation [supporting]; Visualization [supporting]), Frédéric Beaupré (Validation [supporting]; Visualization [supporting]), Sotirios Tsimikas (Supervision [supporting]; Validation [supporting]), Nancy Côté (Validation [supporting]; Visualization [supporting]), Rami Abu-AlhayjaE28099a [Validation (supporting)], Lionel Tastet [Validation (supporting)], Jeremy Bernard [Validation (supporting)], Julio Farjat Pasos [Validation (supporting)], Jorge Nuche [Validation (supporting)], Marisa Avvedimento [Validation (supporting)], Antonela Mariel Zanuttini [Validation (supporting)], Sébastien Hecht (Conceptualization [supporting]; Validation [supporting]), Jonathan Beaudoin [Validation (supporting)], and Philippe Pibarot (Conceptualization [supporting]; Supervision [lead]; Validation [lead]; Visualization [lead]) No financial support and sponsorship to declare. Dr S.T. is supported by NHLBI grants R01 HL159156 and HL170224 under which the laboratory variables were performed. The data underlying this article will be shared on reasonable request to the corresponding author.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".