Elevated lipoprotein(a) and cardiovascular outcomes in prediabetes and diabetes: a systematic review and meta-analysis

Introduction

Lipoprotein(a), or Lp(a), is a complex atherogenic lipoprotein similar to low-density lipoprotein cholesterol (LDL-C), consisting of apolipoprotein(a) [Apo(a)] covalently bound to apolipoprotein B-100 (1). Lp(a) in the plasma induces proatherogenic effects through its low-density lipoprotein (LDL) component and prothrombotic effects via the plasminogen-like Apo(a) (2,3). It promotes atherosclerosis and thrombosis by affecting fibrinolysis, inflammation, endothelial function, and oxidative stress (4). Numerous studies have established an association between Lp(a) and atherosclerotic cardiovascular disease (ASCVD) (5-11). A recent European cohort study found a strong association between elevated Lp(a) levels and incidents of ASCVD, particularly in individuals with diabetes (11). Prediabetes mellitus (pre-DM), an intermediate phase between normal glucose regulation (NGR) and diabetes mellitus (DM), is recognized as a risk factor for diabetes progression and increased ASCVD risk (12). Abnormal glucose metabolism, including prediabetes and type 2 diabetes, has risen globally over the past decade (13). However, the impact of cumulative exposure to Lp(a) on long-term cardiovascular prognosis, especially in patients with coronary artery disease (CAD) and impaired glucose metabolism, remains unclear. Addressing this knowledge gap, our objective was to investigate the independent and combined associations of Lp(a) levels and various glucose metabolism statuses with ASCVD events. We present this article in accordance with the PRISMA reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-162/rc).

Methods

Search strategy

The study included retrospective studies published in English through May 2023 that examined the association between elevated Lp(a) levels and cardiovascular outcomes in human subjects with diabetes, prediabetes, or normal glucose levels. Studies were identified through PubMed, Scopus, and Google Scholar, focusing on those providing detailed population characteristics and outcome data. Exclusion criteria included studies with significant methodological flaws, low-quality data, missing critical information that could not be retrieved from the authors, duplicates, and non-human studies. To assess the risk of bias, we used the Newcastle-Ottawa Scale (NOS) of Bias tool, which evaluated factors such as selection, performance, detection, attrition, and reporting biases. Each study was independently assessed by two reviewers, and discrepancies were resolved through discussion or consultation with a third reviewer. The risk of bias assessment results, along with detailed scores and justifications for each study, were reported to ensure transparency and rigor in the meta-analysis. Initially, records were assessed by title and abstract to determine preliminary eligibility. Full-text articles were then evaluated to confirm they met the criteria. Any disagreements between the reviewers were resolved through discussion or consultation with a third reviewer to ensure consensus and accuracy in the selection process. This approach ensures a rigorous and unbiased review of all relevant studies.

Full search strategy available as supplementary material (Appendix 1).

Participants or study population

The analysis included data from three retrospective studies: Jin et al. [2019] (14), Saeed et al. [2019] (15), and He et al. [2024] (16). He et al. involved 5,257 participants with elevated Lp(a), Jin et al. included 5,143 participants with elevated Lp(a), and Saeed et al. encompassed 9,871 participants with elevated Lp(a).

Data extraction

A standardized form was used to record population characteristics, including the number of patients with normal glucose levels, diabetes, or prediabetes, percentage of the male population, age, body mass index (BMI), hypertension (HTN), family history of CAD, smoking, patients on aspirin, statin, lipid-lowering medication, beta-blocker, angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB), total cholesterol levels, high-density lipoprotein cholesterol (HDL-C), LDL-C, triglycerides, and high-sensitivity C-reactive protein (hs-CRP).

Statistical analysis

Binary random effects models were employed for pooled hazard ratios (HRs) and 95% confidence intervals (CIs) in meta-analysis. We chose the random-effects model due to the observed heterogeneity among the included studies, including differences in populations, study designs, and follow-up durations (17,18). The Forest plot was used to visualize individual study effect sizes and weights. Sensitivity analysis involved the leave-one-out method. P values in the figures represented heterogeneity in P values. Inconsistency index (I²) statistics were used to assess heterogeneity, with 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Subgroup analyses were conducted for outcomes with high or moderate heterogeneity based on follow-up duration or study type. P values for HRs are provided in the spreadsheet.

Results

Study selection

Following a thorough search across multiple databases, 108 articles were initially considered relevant. Subsequently, 28 of these articles were excluded due to data duplication. The remaining 80 articles underwent a full-text screening process, during which certain articles were excluded for various reasons: 23 due to methodological flaws, 39 for lacking information on the population numbers, 10 due low-quality data, and 2 due to non-human studies. Ultimately, only three studies met the criteria and were included in the subsequent meta-analysis. The comprehensive details of our literature search and selection process can be found in the PRISMA flowchart (Figure 1).

Figure 1 PRISMA criteria describing the search strategy of our meta-analysis.

Study and patient characteristics

Following a thorough article screening process, a total of 20,541 patients from three distinct studies were incorporated into our analysis. These patients were categorized into NGR, pre-DM, and DM groups based on American Diabetes Association criteria, constituting 32.85%, 39.12%, and 28.03% of the overall patient population. The baseline characteristics of each study are presented in Table 1.

Our analysis delves into the relationship between Lp(a) levels and the risk of ASCVD in different glycemic states.

Outcomes

The comparisons were conducted in relation to non-diabetic individuals with Lp(a) levels below 10 mg/dL. The results demonstrated a noteworthy increase in the risk of ASCVD corresponding to elevated Lp(a) levels among both pre-diabetics and diabetics. Among pre-diabetics, the HRs for ASCVD were 1.40 (95% CI: 1.17–1.67) for Lp(a) <10 mg/dL, 1.60 (95% CI: 1.30–1.96) for Lp(a) 10–30 mg/dL, and 2.08 (95% CI: 1.49–2.90) for Lp(a) >30 mg/dL. In diabetics, the corresponding HRs were 2.42 (95% CI: 1.97–2.98), 2.26 (95% CI: 1.64–3.12), and 4.17 (95% CI: 3.24–5.37) for the respective Lp(a) categories. Importantly, these associations were statistically significant with a P value less than 0.01, indicating a robust correlation between increasing Lp(a) levels and heightened ASCVD risk in pre-diabetics and diabetics (Table 2, Figure 2).

Figure 2 Forrest plot representing the association of different ranges of elevated Lp(a) levels with risk of incident/recurrent ASCVD events in adults with normoglycemia, prediabetes, and diabetes: a meta-analysis. CI, confidence interval; Lp(a), lipoprotein(a); ASCVD, atherosclerotic cardiovascular disease.

Risk of bias assessment

The risk of bias assessment was conducted using the NOS.

NOS evaluation

• Selection (maximum 4 stars)
  • Representativeness of the exposed cohort: the study includes participants with prediabetes and diabetes from multiple populations globally, ensuring diversity in age, gender, and geographical region.
    Star: yes (1 star).
  • Selection of the non-exposed cohort: the comparison group includes participants without elevated Lp(a) levels, matched based on diabetic status and other cardiovascular risk factors.
    Star: yes (1 star).
  • Ascertainment of exposure: Lp(a) levels were measured using standardized blood tests at the start of each included study.
    Star: yes (1 star).
  • Demonstration that the outcome of interest was not present at the start of the study: the studies excluded participants with prior cardiovascular events (CVEs) at baseline, ensuring that outcomes were not pre-existing.
    Star: yes (1 star).
• Comparability (maximum 2 stars)
  • Comparability of cohorts on the basis of design or analysis: the studies adjusted for key cardiovascular risk factors, such as age, gender, BMI, smoking status, HTN, and lipid profiles.
    Star: yes (1 star for age/gender) and yes (1 star for other confounders like smoking, HTN).
• Outcome (maximum 3 stars)
  • Assessment of outcome: cardiovascular outcomes (e.g., myocardial infarction, stroke, cardiovascular death) were assessed through hospital records, validated against national health registries, or confirmed by medical professionals.
    Star: yes (1 star).
  • Was follow-up long enough for outcomes to occur? The minimum follow-up duration across studies was 3–15 years, allowing for sufficient time to observe CVEs.
    Star: yes (1 star).
  • Adequacy of follow-up of cohorts: follow-up rates were over 90% in all studies, and the reasons for loss to follow-up were adequately explained in study protocols.
    Star: yes (1 star).

Total NOS score: 9 stars

• Selection: 4 stars;
• Comparability: 2 stars;
• Outcome: 3 stars.

Interpretation

With a total of 9 stars, the studies included in this systematic review and meta-analysis would be considered high quality with a low-risk of bias.

Discussion

Individuals with prediabetes face an elevated risk of higher CVE occurrences, particularly when coupled with additional conditions such as HTN. Moreover, research indicates an association between Lp(a) and the severity of CAD in individuals with type 2 diabetes mellitus (T2DM) and prediabetes, but this correlation is not observed in those with NGR (19,20).

In primary prevention, numerous studies have demonstrated an independent association between cumulative exposure to LDL-C, assessed through the area under the LDL-C versus age curve, and the onset of ASCVD events in individuals without a history of CAD. However, establishing observational evidence for the link between cumulative LDL exposure and major adverse cardiovascular and cerebrovascular events (MACCE) is challenging in secondary prevention due to the substantial reduction in LDL-C concentrations achieved by commonly prescribed statins for patients with CAD (21,22).

In contrast to LDL-C, Lp(a) levels are primarily influenced by genetic factors. The study aimed to explore the link between cumulative Lp(a) exposure and long-term adverse CVEs, driven by the consistency of Lp(a) levels over an individual’s lifespan (1,23,24). Numerous prospective cohort studies have shown an association between plasma Lp(a) levels and cardiovascular risk (25,26). Individuals with elevated Lp(a) exposure and impaired glucose metabolism faced a significantly higher risk of ASCVD events compared to those without either risk factor, independent of baseline Lp(a) levels and age (27). When considering DM versus pre-DM versus NGR separately, those with DM demonstrated a higher risk of CVEs. Moreover, categorizing patients based on both glucose metabolism status and Lp(a) levels revealed increased ASCVD risk in pre-diabetics and diabetics, with statistical significance (P<0.01) for different Lp(a) level ranges.

Increased Lp(a) levels are predominantly linked to a lipid abnormality, with approximately 90% of cases attributed to variations in the LPA gene. In contrast to some lipid parameters, circulating Lp(a) levels exhibit minimal influence from dietary and environmental factors, indicating that elevated Lp(a) levels may have enduring health implications throughout an individual’s life (5,24). Cross-sectional studies provide evidence establishing a correlation between plasma Lp(a) levels and both coronary calcification and the degree of coronary stenosis (28,29). Remarkably, individuals with clinical familial hypercholesterolemia and Lp(a)-hyperlipoproteinemia display an increased risk of early-onset CAD (30).

Currently, the European Society of Cardiology/European Atherosclerosis Society recommends evaluating Lp(a) levels in individuals with premature CVD or a family history of such, familial hypercholesterolemia, a family history of elevated Lp(a), recurrent CVD events despite optimal lipid-lowering therapy, and a 10-year fatal CVD risk of ≥5% according to the Systematic Coronary Risk Evaluation (SCORE) algorithm (31). Similarly, the Canadian Cardiovascular Society (CCS) Guidelines for the Management of Dyslipidemia propose integrating Lp(a) into additional risk assessment measures for those with a moderate CVD risk per the Framingham Risk Score or individuals with a family history of premature CAD (32). A prior meta-analysis indicated a linear association between Lp(a) levels and cardiovascular disease risk in statin-treatment individuals (33). Despite existing guidelines not currently endorsing the measurement or treatment of elevated Lp(a) levels in patients with impaired glucose metabolism (34), the European guidelines suggest an increased risk of cardiovascular disease with Lp(a) levels exceeding the 80^th percentile (>50 mg/dL or approximately 100–125 nmol/L). In contrast, both the National Lipid Association and the CCS guidelines designate an “elevated” threshold for Lp(a) as greater than 30 mg/dL (31). In our study, we utilized a cutoff point of >30 mg/dL for Lp(a) to assess the associated risks. Our selection of the >30 mg/dL cutoff was guided by its clinical relevance, alignment with existing research, and the robustness of available data.

Prior evidence has demonstrated that measuring Lp(a) can enhance the reclassification and prediction of ASCVD risk (25). Given the heightened risk of both subclinical and clinical ASCVD events in individuals with diabetes and prediabetes, our findings suggest that quantifying Lp(a) levels could be valuable for additional risk stratification, aiding in the identification of individuals who might benefit from more intensive lifestyle modifications and pharmacotherapy for primary prevention (35,36). Currently, no specific pharmacotherapies are designed to lower Lp(a) levels directly with proven favorable reductions in clinical events. Nevertheless, existing data indicate that genetically lowered Lp(a) levels and statins, along with low-dose aspirin for primary prevention in individuals with elevated Lp(a) levels, may contribute to lower rates of ASCVD events (37). In the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER), the use of rosuvastatin resulted in a significant reduction in ASCVD events among participants with baseline Lp(a) levels equal to or greater than the median (HR =0.62; 95% CI: 0.43–0.90) (15).

Limitations

Despite providing valuable insights, the study has limitations that warrant consideration. Its likely observational nature hinders the establishment of causal relationships, and the potential focus on a specific population may limit the generalizability of findings. Variability in Lp(a) measurement methods and potential confounding factors may introduce biases. The study’s reliance on CVEs as the primary outcome may overlook other relevant markers of cardiovascular health, and the duration of follow-up might impact the accuracy of long-term effect assessment. Furthermore, if the study lacks interventional components, it may limit insights into the impact of modifying Lp(a) levels on cardiovascular outcomes. These limitations emphasize the need for cautious interpretation and highlight areas for further research to enhance our understanding of the complex relationship between Lp(a), impaired glucose metabolism, and cardiovascular risk.

Conclusions

Our meta-analysis underscores the intricate relationship between Lp(a) levels, impaired glucose metabolism, and cardiovascular risk. The synergy between impaired glucose metabolism and elevated Lp(a) levels significantly increases the risk of ASCVD. While guidelines recommend Lp(a) assessment in specific populations, broader consideration, particularly in individuals with diabetes and prediabetes, could enhance risk stratification. Despite the lack of specific pharmacotherapies targeting Lp(a), existing data suggest that genetically lowered Lp(a) levels and statin use may contribute to reducing CVEs. Standardizing guidelines for Lp(a) measurements in routine clinical practice remains a crucial area for future research and healthcare implementation.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-162/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-162/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-162/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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