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Phenomapping of subgroups in high-Lp(a) patients: a data-driven cluster analysis in RED-CARPET study
Shaozhao Zhang1,2, Xiaoyu Lin3, Rongjian Zhan4
1Cardiology Department, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan 2Nd Road, Guangzhou, 510080, China.
Insights
High levels of lipoprotein (a) [Lp(a)] increase cardiovascular disease risk, but this risk varies by patient phenotype. Identifying these distinct subgroups, particularly males with unhealthy lifestyles, is crucial for targeted ASCVD risk management.
Area of Science:
- Cardiovascular Medicine
- Clinical Research
- Biomarkers
Background:
- Elevated lipoprotein (a) [Lp(a)] is a known risk factor for cardiovascular disease (CVD).
- Clinical characteristics significantly influence the association between high Lp(a) and CVD outcomes.
- Understanding heterogeneity within high Lp(a) populations is essential for refining risk assessment.
Purpose of the Study:
- To explore the heterogeneity of clinical phenotypes in individuals with high Lp(a) levels.
- To investigate the differential relationship between these phenotypes and atherosclerosis cardiovascular disease (ASCVD) risk.
Main Methods:
- Utilized k-means clustering on baseline variables for participants with Lp(a) ≥ 50 mg/dL from the RED-CARPET Study (n=11,629).
- Employed multivariate logistic regression to assess ASCVD risk across identified clusters, using Lp(a) < 50 mg/dL as reference.
- Validated findings using propensity score matching (PSM).
Main Results:
- Four distinct clusters of high Lp(a) individuals were identified: dyslipidemia, aged females, males with unhealthy lifestyles, and anemia/renal insufficiency/hypercoagulability.
- Males with unhealthy lifestyles (cluster 3) exhibited the highest ASCVD risk (OR 2.12, 95% CI 1.62-2.76, p < 0.001) after adjustment.
- No significant ASCVD risk was observed in the anemia/renal insufficiency/hypercoagulability cluster (cluster 4) (OR 0.82, 95% CI 0.58-1.16, p = 0.233).
Conclusions:
- A data-driven approach successfully stratified high Lp(a) individuals into four distinct subgroups.
- These subgroups demonstrate varying levels of ASCVD risk, highlighting the importance of phenotypic characterization.
- Targeted risk management strategies may be beneficial for specific high Lp(a) patient phenotypes.
Background:
The association between high levels of lipoprotein (a) [Lp(a)] and cardiovascular disease (CVD) is influenced by clinical characteristics. We aimed to explore the heterogeneity in high Lp(a) population with different clinical phenotypes and their relationship with atherosclerosis cardiovascular disease (ASCVD) risk.
Methods And Results:
We included 11,629 participants with Lp(a) measurement in RED-CARPET Study (ChiCTR2000039901) from the First Affiliated Hospital of Sun Yat-Sen University. The primary outcome was the occurrence of ASCVD events. The k-means clustering method was performed for baseline variables in participants with high Lp(a) levels (Lp(a) ≥ 50 mg/dL). Multivariate logistic regression model was used to assess the association between high Lp(a) level and ASCVD across clusters, with the low-Lp(a) group (Lp(a) < 50 mg/dL) serving as reference. Propensity score matching (PSM) was used to validate thefindings. High-Lp(a) group was categorized into four clusters: cluster 1 (dyslipidemia); cluster 2 (aged females); cluster 3 (males with an unhealthy lifestyle) and cluster 4 (anemia, renal insufficiency and hypercoagulability). Patients in different clusters exhibited differences in ASCVD risk. Patients with high-Lp(a) had significantly highest risk for ASCVD in cluster 3 (OR 2.12, 95% CI 1.62-2.76, p < 0.001) after adjusting for traditional risk factors. However, no significant association was observed in cluster 4 (OR 0.82, 95% CI 0.58-1.16, p = 0.233). These findings remained consistent after PSM.
Conclusions:
Using a data-driven approach, high-Lp(a) patients can be stratified into four phenotypically distinct subgroups with different ASCVD risk.
