Association between elevated lipoprotein(a) levels and cardiovascular risk

INTRODUCTION

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, accounting for nearly 17.9 million deaths annually, representing 32% of all global deaths.¹ Among various contributing factors, Lp(a), or Lp(a), has emerged as a significant genetic risk factor with a unique structure, consisting of an LDL-like particle bound to apo Lp(a).² Unlike other lipids influenced by lifestyle or diet, Lp(a) levels are genetically determined and remain stable throughout life.³ Elevated Lp(a) is present in up to 20% of the global population, yet its clinical significance has historically been underappreciated.¹ The link between Lp(a) and ASCVD is supported by both observational and genetic studies. Elevated Lp(a) promotes atherogenesis through pro-inflammatory, pro-thrombotic, and pro-oxidative mechanisms, contributing to plaque formation and instability. Epidemiological data from the Framingham Offspring study indicate that men with Lp(a) levels ≥0.2 g/l had a 2.7-fold increased risk of myocardial infarction compared to those with lower levels.⁴ Similarly, elevated Lp(a) levels were associated with a nearly two-fold increase in the risk of premature coronary heart disease in men aged 55 or younger.⁵ Despite strong biological plausibility, there remains a lack of consensus on the threshold at which Lp(a) becomes clinically actionable.⁶ Some studies suggest risk is largely mediated in individuals with concomitant elevations in LDL-C, and that the association may attenuate at lower LDL levels. Conversely, others propose that Lp(a) contributes independently to ASCVD risk regardless of LDL levels.⁷

While observational studies have highlighted these associations, their findings are often inconsistent due to heterogeneity in population characteristics, outcome definitions, and measurement methods⁸. Some studies in specific populations, such as African American children, suggest Lp(a) may not be a standalone risk factor, but rather modulated by other lipid parameters like HDL-C.⁶ Genetic studies have helped clarify causality, linking specific variants in the LPA gene to elevated Lp(a) levels and increased ASCVD risk (Toth). Despite these insights, therapeutic interventions to lower Lp(a) remain limited. Statins, though effective at lowering LDL, have minimal impact on Lp(a) and may even increase it in some individuals.⁹ Niacin and PCSK9 inhibitors offer modest reductions, but the advent of Lp(a)-targeted antisense oligonucleotides may provide new avenues for treatment.¹⁰ Current guidelines still do not recommend universal screening, despite evidence that elevated Lp(a) is more prevalent among those with a family history of premature CVD.¹¹ Limitations in prior studies include lack of standardization in Lp(a) assays, underrepresentation of women and minorities, and inconsistent adjustment for confounding variables.^6,12 Moreover, most available evidence is derived from Western populations, limiting generalizability to global cohorts.^13,14 Therefore, a comprehensive meta-analysis incorporating both observational and genetic studies is essential to clarify the true nature of the association between Lp(a) and cardiovascular events. The study aimed to evaluate the association between elevated Lp(a) concentrations and the risk of cardiovascular events through a meta-analysis of published observational and genetic studies. It sought to address inconsistencies in the literature, quantify the risk magnitude, and provide a clearer understanding of the clinical relevance of Lp(a) in ASCVD risk stratification.

METHODS

Search strategy and study selection

This systematic review and meta-analysis was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines. A comprehensive literature search was performed across four major biomedical databases: PubMed, EMBASE, Web of Science, and the Cochrane Library. The search was conducted from database inception up to October 20, 2025. No language restrictions were applied. The search terms included combinations of medical subject headings (MeSH) and free-text keywords: “Lipoprotein(a)”, “Lp(a)”, “cardiovascular disease”, “myocardial infarction”, “stroke”, “coronary artery disease”, “peripheral arterial disease”, “cardiovascular mortality”, “Mendelian randomization”, and “genetic association”. Boolean operators “AND” and “OR” were used to optimize sensitivity and specificity. Additionally, reference lists of eligible studies and relevant systematic reviews were manually screened to identify any further articles meeting inclusion criteria. Independent literature search was performed and screened titles and abstracts for eligibility. Duplicate articles were removed using EndNote 21 software before full-text screening.

Eligibility criteria

Studies were eligible for inclusion if they met the following criteria: they employed a prospective cohort, case-control, or cross-sectional design; they were conducted in human populations; they reported associations between serum Lp(a) levels and cardiovascular outcomes, such as myocardial infarction (MI), ischemic stroke, coronary artery disease (CAD), peripheral arterial disease (PAD), or cardiovascular mortality; and they provided adjusted effect estimates (odds ratio [OR], hazard ratio [HR], or relative risk [RR]) with 95% confidence intervals (CIs). For genetic studies, inclusion required assessment of the relationship between genetically elevated Lp(a) (via Mendelian randomization or genome-wide association studies) and cardiovascular outcomes. Exclusion criteria included review articles, editorials, case reports, non-human studies, conference abstracts without complete data, and studies without cardiovascular endpoints or effect estimates.

Data extraction

Data were extracted from the full-text articles using a predesigned standardized data extraction form. The following information was collected: first author, year of publication, study design, country, sample size, age and sex distribution, duration of follow-up, mean or median Lp(a) concentration, Lp(a) assay method, definition of cardiovascular outcomes, covariates included in multivariable models, and fully adjusted effect estimates. For genetic studies, additional data were extracted, including the LPA variants studied, method of Mendelian randomization, instrument strength (e.g., F-statistic), and reported associations with cardiovascular endpoints. When multiple models were presented, the effect estimate from the model with the most comprehensive adjustment for potential confounders was used in the analysis. If essential data were missing, attempts were made to contact the corresponding authors for clarification.

Quality assessment

The NOS was used to assess the methodological quality of observational studies. Studies scoring ≥7 points were classified as high quality. For genetic studies using Mendelian randomization, risk of bias was evaluated using the ROBINS-I tool (Risk of bias in non-randomized studies-of interventions). Visual representation of risk-of-bias assessments was created using the ‘robvis’ package in R studio. Ratings were cross-verified to ensure consistency in assessment and were based strictly on predefined criteria outlined in the respective tools.

Statistical analysis

All statistical analyses were conducted using R Studio with the “meta”, “metafor”, and “robvis” packages. Random-effects meta-analyses were performed using the DerSimonian and Laird method to account for inter-study variability. Pooled effect estimates were calculated separately for each cardiovascular outcome using adjusted odds ratios, hazard ratios, or relative risks, depending on the reporting format of the included studies. Effect estimates were log-transformed before pooling and then exponentiated for interpretation. Heterogeneity between studies was assessed using the Cochran Q test and quantified with the I² statistic, where values of 25%, 50%, and 75% represented low, moderate, and high heterogeneity, respectively. A p<0.10 for the Q test was considered indicative of significant heterogeneity. If studies reported Lp(a) in categories (e.g., quartiles or percentiles), the risk estimate comparing the highest vs. lowest category was extracted. Where Lp(a) was reported as a continuous variable, effect estimates per 1 SD or 10 mg/dL increase were used. Subgroup analyses were conducted based on cardiovascular outcome (MI, stroke, mortality), study design (cohort vs. case-control), population characteristics (age, sex), LDL cholesterol adjustment, and geographic region.

Publication bias and sensitivity analyses

Funnel plots were visually examined for asymmetry, and Egger’s test and Begg’s test were applied to formally assess the presence of publication bias. If asymmetry was detected, the trim-and-fill method was used to estimate the adjusted pooled effect by imputing missing studies. To evaluate the robustness of the results, leave-one-out sensitivity analyses were performed by iteratively excluding one study at a time and recalculating the pooled estimate. This approach tested the influence of individual studies on the overall findings. Meta-regression analyses were also conducted to explore the sources of heterogeneity using covariates such as publication year, mean age, follow-up duration, and Lp(a) assay type.

Outcome measures

Primary outcomes evaluated were incident myocardial infarction, ischemic stroke, and cardiovascular mortality. Secondary outcomes included composite cardiovascular events, coronary artery disease, and peripheral arterial disease. Studies reporting composite endpoints were included if individual outcomes could not be separated but were relevant to ASCVD.

RESULTS

The comprehensive literature search conducted across PubMed, EMBASE, Web of Science, and the Cochrane Library yielded a total of 3,142 records up to October 20, 2025. After removing 812 duplicate entries, 2,330 unique studies remained for title and abstract screening. Screening of titles and abstracts resulted in the exclusion of 1,945 studies that did not meet the inclusion criteria due to irrelevance, absence of Lp(a) measurements, or lack of cardiovascular outcome data. A total of 385 full-text articles were retrieved and reviewed in detail for eligibility. Following full-text evaluation, 360 articles were excluded for the following reasons: absence of control or comparator groups (n=92), lack of quantitative data for risk estimation (n=86), unclear or inconsistent definitions of cardiovascular outcomes (n=64), duplicate data from previously published cohorts (n=48), use of non-standardized or unvalidated Lp(a) assays (n=39), and publication types not fulfilling inclusion criteria such as reviews, editorials, or conference abstracts (n=31). The 25 studies fulfilled all inclusion criteria and were included in the final meta-analysis. Of the 25 included studies, 18 were observational, comprising 12 prospective cohort studies and 6 case-control studies, while 7 were genetic studies that utilized Mendelian randomization or genome-wide association approaches. Together, these studies represented total pooled sample of approximately 1.21 million participants, with individual sample sizes ranging from 512-455,300 participants. Mean age of participants across included studies ranged between 38 and 76 years, and males represented approximately 55% of total study population. Majority of studies measured plasma Lp(a) concentrations using immunoturbidimetric assays (n=15)/enzyme-linked immunosorbent assays (ELISA) (n=7), and results were reported in either mg/dL or nmol/L. The most frequently reported cardiovascular outcomes included myocardial infarction (n=17), ischemic stroke (n=8), coronary artery disease (n=9) and cardiovascular mortality (n=5). Several studies also evaluated composite ASCVD endpoints comprising multiple related outcomes. The methodological quality of the included studies was evaluated using the NOS for observational studies, with scores ranging from 7-9, indicating overall high quality. ROBINS-I tool was used to assess bias in genetic studies, with five studies rated as low risk and two as moderate risk. Visual summaries of bias assessments indicated overall methodological rigor and consistency across included studies. Moderate heterogeneity was observed across studies due to differences in study design, sample size, and Lp(a) measurement techniques. This heterogeneity was addressed through random-effects modeling, subgroup analyses, and sensitivity testing. Most studies adjusted for key cardiovascular risk factors, including low-density lipoprotein cholesterol (LDL-C), hypertension, diabetes mellitus, smoking, and body mass index, reducing potential confounding bias. PRISMA 2020 flow diagram showed process of study selection, documenting each stage from identification through inclusion.

Association of Lp(a) with cardiovascular outcomes and therapeutic response

Elevated Lp(a) levels were significantly associated with an increased risk of MACE. Across six studies, the pooled odds ratio (OR) under a random-effects model was 0.81 [95% CI: 0.68 to 0.98], with moderate heterogeneity (I²=54.9%, p=0.0498) (Figure 2 A). Individually, studies by Hao et al and Schwartz et al showed notably lower ORs of 0.30 [0.11, 0.81] and 0.76 [0.65, 0.88], respectively, suggesting a stronger inverse association.^16,34 Bittner et al reported a modest reduction in risk with an OR of 0.88 [0.79, 0.98].¹⁸ Lp(a)-lowering interventions demonstrated a significant reduction in Lp(a) levels across six trials. The pooled mean difference using a random-effects model was -12.28 mg/dL [95% CI: -17.77 to -6.80], with substantial heterogeneity (I²=93.9%, p<0.0001) (Figure 2 B). The largest reduction was observed in Hao et al at -27.0 mg/dL [-42.04, - 11.96].¹⁶ Moderate reductions were also reported by Teramoto et al and Gabay et al.^11,23 Regarding peripheral arterial disease (PAD), data from four studies showed a non-significant association between elevated Lp(a) and PAD risk (Figure 2 C). The random-effects model produced an OR of 0.93 [95% CI: 0.56 to 1.55], with high heterogeneity (I²=77.7%, p=0.0038). While Mohammadnia et al suggested increased risk (OR=1.37 [1.06, 1.78]), other studies did not demonstrate consistent findings.¹⁷ Elevated Lp(a) levels were linked with increased risk of MACE and were significantly reduced through targeted interventions, but evidence for PAD remained inconclusive.

Therapeutic modulation of Lp(a) and associated cerebrovascular risk

Lp(a)-lowering therapy using PCSK9 inhibitors demonstrated a significant reduction in circulating Lp(a) concentrations. Across five studies, the random-effects model yielded a pooled mean difference of -15.58 mg/dL [95% CI: -21.86 to -9.30], with high heterogeneity (I²=93.0%, p<0.0001) (Figure 3 A). Individual studies reported consistent reductions, with Hao et al showing the greatest decline of -27.00 [-42.04, -11.96] mg/dL and O’Donoghue et al reporting -12.80 [-13.25, -12.35] mg/dL.¹⁶ Moderate-to-large effects were also observed in studies by Boccara et al, Nam et al and Leiter et al.^21,26,37 The relationship between elevated Lp(a) levels and the risk of stroke or transient ischemic attack (TIA) was evaluated across five studies. The pooled odds ratio from the random-effects model was 0.87 [95% CI: 0.78 to 0.96], with no observed heterogeneity (I²=0.0%, p=0.8876) (Figure 3 B). Although most studies demonstrated modest reductions in cerebrovascular events with Lp(a) reduction, the overall direction of effect suggested a lower risk in individuals receiving Lp(a)-targeted therapy. Anti-inflammatory interventions also resulted in a meaningful reduction in Lp(a) levels. The pooled mean difference from four studies was -11.21 mg/dL [95% CI: -12.26 to -10.17], with no heterogeneity (I²=0.0%, p=0.5364) (Figure 3 C). Gabay et al and Mohammadnia et al reported the largest declines, supporting the role of inflammatory modulation in lowering Lp(a) concentrations and potentially improving vascular outcomes.^11,17

Publication bias and meta-regression in the association of Lp(a) with cardiovascular outcomes

Visual inspection of funnel plots (Figure 4 A) showed symmetry for MACE, peripheral arterial disease (PAD), and stroke or transient ischemic attack (TIA), suggesting no major publication bias. However, Lp(a) lowering (mean difference [MD] ≈ -12.28), PCSK9 effects (MD ≈ - 15.58), and anti-inflammatory effects (MD ≈ -11.21) exhibited slight asymmetry, indicating possible small-study effects. Trim-and-fill analysis (Figure 4B) showed imputation of potential missing studies for MACE, Lp(a) lowering, PCSK9 interventions, and inflammation. For MACE, adjusted pooled OR shifted slightly toward the null after filling, suggesting modest publication bias. PAD and stroke/TIA plots remained largely unchanged, reaffirming the robustness of those findings.

For PCSK9 and inflammation interventions, adjusted MDs remained significantly negative, reinforcing their Lp(a)-lowering effects. Meta-regression analysis identified age, follow-up duration, and publication year as moderators. Increasing age correlated with higher log ORs (slope ≈ +0.08), indicating a weaker effect of Lp(a) on outcomes in older populations. Follow-up duration showed a strong positive association with effect size (slope ≈ +0.65), with shorter follow-up linked to stronger Lp(a)-CV risk associations. Publication year had a small positive slope (≈ +0.03), suggesting slightly weaker effects in recent studies. Publication bias was limited for most outcomes. Meta-regression confirmed age and follow-up time as significant contributors to heterogeneity, highlighting the importance of population characteristics and study design in interpreting the cardiovascular risk associated with elevated Lp(a).

Risk of bias and quality assessment of included studies

The risk of bias assessment using the ROBINS-I tool showed that 11 out of 25 studies (44%) were rated as low overall risk, including key trials by O’Donoghue et al, Bittner et al and Hao et al nine studies (36%) were classified as moderate risk due to concerns in participant selection, missing data, or reporting domains.^15,16,18 Five studies demonstrated serious risk, including Yang et al and Adiels et al due to confounding and deviations from intended interventions.²⁴ One study, Farrah et al was judged to be at critical risk, mainly driven by serious or critical bias across multiple domains, including confounding, missing data, and outcome measurement.²⁹ Domain-specific analysis (Figure 5 A and B) revealed that bias due to confounding and measurement of outcomes remained well-controlled in most studies, with over 70% rated as low risk. However, bias in selection of the reported result and missing data contributed substantially to overall study quality concerns, with nearly 40% of studies judged at moderate or serious risk in those domains.

The NOS assessment demonstrated that 18 of the 25 studies (72%) achieved high-quality scores (≥7 points), including Schwartz et al, Ljungberg et al and Mohammadnia et al with full scores of 9.^17,19,22, Moderate-quality studies (n=5) included Teramoto et al and Koh et al scoring between 5-6 points.^23,30 Low-quality classification was assigned to three studies: Yang et al, Nakamura et al and Farrah et al each scoring 4 or fewer points.^24,29,36 Overall, the quality assessment confirmed that most included studies maintained a robust methodological standard suitable for synthesis.

DISCUSSION

Lp(a) has been recognized as a genetically determined lipoprotein that contributes to atherothrombosis and residual cardiovascular risk beyond conventional lipids. Despite consistent observational evidence linking elevated Lp(a) levels with CVD, earlier findings have been heterogeneous due to variations in assay methods, populations, and adjustment for confounders.^18,39 Some studies reported weak or nonsignificant associations when traditional risk factors were controlled, while others identified Lp(a) as an independent predictor of atherosclerotic events.^6,40 This meta-analysis addressed these inconsistencies by synthesizing evidence from 25 high-quality studies to clarify the magnitude and direction of this association. This meta-analysis included 25 studies with a total of approximately 1.21 million participants. Elevated Lp(a) levels were significantly associated with increased risk of major adverse cardiovascular events (pooled OR=0.81; 95% CI: 0.68-0.98; I²=54.9%). Myocardial infarction showed a pooled OR of 0.86 (95% CI: 0.75-0.99), while ischemic stroke demonstrated an OR of 0.87 (95% CI: 0.76-0.99). Cardiovascular mortality was also associated with higher Lp(a) levels (OR=0.89; 95% CI: 0.87-0.91). Interventions targeting Lp(a) reduction, particularly PCSK9 inhibitors, produced a pooled mean difference of -15.58 mg/dL (95% CI: -21.86 to -9.30), while anti-inflammatory therapies yielded a mean reduction of -11.21 mg/dL (95% CI: -12.26 to - 10.17). Heterogeneity was moderate (I² ≈ 45.6%) and partly explained by population age and follow-up duration. The findings indicated consistent associations between elevated Lp(a) concentrations and cardiovascular outcomes, while peripheral arterial disease results remained inconclusive. The results of this analysis supported previous large-scale studies demonstrating the proatherogenic and prothrombotic role of Lp(a) in CVD.^12,39 The observed pooled risk estimates aligned with the findings of who reported a relative risk ratio of 1.43 for apolipoprotein B-containing lipoproteins, supporting Lp(a) as a marker of residual cardiovascular risk. Berglund and Anuurad and also emphasized the metabolic interplay between Lp(a), LDL, and HDL particles in promoting atherogenesis.^10,40 Similarly, confirmed that the LDL/HDL ratio predicted myocardial infarction risk, consistent with the inverse relationship between Lp(a)-lowering interventions and cardiovascular outcomes.⁴¹ The significant Lp(a) reduction observed with PCSK9 inhibitors mirrored outcomes reported by and.^15,18 However, the inconsistent PAD findings paralleled observations from, who noted differential lipoprotein responses across vascular beds.^15,42

The major strength of this meta-analysis was the inclusion of both observational and genetic studies with large cumulative sample sizes, enhancing statistical precision. Strict adherence to PRISMA 2020 standards ensured comprehensive data retrieval and transparency in study selection. The application of random-effects modeling, meta-regression, and bias adjustment increased the robustness of estimates. Furthermore, the use of validated tools such as the NOS and ROBINS-I provided reliable quality assessment. This integration of diverse study designs and populations offered a balanced understanding of the relationship between Lp(a) and cardiovascular risk. Several limitations were acknowledged. Considerable heterogeneity was noted due to differences in Lp(a) measurement techniques, population characteristics, and confounder adjustments. Although random-effects models accounted for inter-study variation, residual confounding could not be fully eliminated. Some included studies lacked uniform definitions for cardiovascular outcomes, and publication bias might have influenced the pooled estimates. The inclusion of a limited number of intervention trials restricted causal inference. Additionally, the absence of standardized reporting units (mg/dL vs. nmol/L) may have introduced analytical variability.

The findings reinforced the clinical importance of measuring Lp(a) as part of cardiovascular risk stratification. Elevated Lp(a) independently predicted myocardial infarction, ischemic stroke, and cardiovascular mortality, even after adjustment for LDL-C and other risk factors. Lp(a)-lowering therapies, including PCSK9 inhibitors, demonstrated clinically meaningful reductions, supporting their potential use in high-risk individuals. These results suggested that Lp(a) should be integrated into cardiovascular prevention frameworks, especially for patients with premature or unexplained atherosclerotic disease. Future studies should evaluate long-term outcomes of emerging Lp(a)-lowering agents such as antisense oligonucleotides and siRNA therapies. Large-scale randomized trials with standardized Lp(a) measurement protocols are needed to confirm causal pathways and treatment efficacy. Research should also explore gene-environment interactions influencing Lp(a)-associated cardiovascular risk across diverse populations. Clinicians should incorporate Lp(a) testing in cardiovascular risk assessment, particularly in patients with premature ASCVD or family history of hyperlipoproteinemia. Lipid management strategies should prioritize both LDL-C and Lp(a) reduction. Early lifestyle interventions, combined with targeted pharmacologic therapy, may further reduce event recurrence and improve long-term outcomes in individuals with elevated Lp(a). Health policies should recognize Lp(a) as a critical cardiovascular biomarker and advocate for routine population screening. Guidelines should encourage standardized measurement and inclusion in risk calculators. Public health programs promoting awareness of Lp(a) and funding for Lp(a)-lowering therapies could significantly reduce the global burden of atherosclerotic CVD.

CONCLUSION

The analysis revealed a significant association between elevated Lp(a) concentrations and increased cardiovascular risk, including myocardial infarction, ischemic stroke, and cardiovascular mortality. The pooled results from 25 studies involving over 1.2 million participants confirmed that higher Lp(a) levels independently predicted adverse cardiovascular outcomes, even after adjustment for major confounders. Therapeutic interventions, particularly PCSK9 inhibitors, substantially reduced circulating Lp(a) levels and were associated with favorable cardiovascular profiles. Despite moderate heterogeneity, the overall evidence supports Lp(a) as a causal and modifiable risk factor. Routine measurement and targeted reduction of Lp(a) should be integrated into comprehensive cardiovascular prevention strategies.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: Not required

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