| Title: | Lipoprotein(a) Is Markedly More Atherogenic Than LDL An Apolipoprotein B-Based Genetic Analysis |
| Journal: | Journal of the American College of Cardiology |
| Published: | 1 Jan 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38233012/ |
| DOI: | https://doi.org/10.1016/j.jacc.2023.10.039 |
| Citations: | 31 (31 in last 2 years) as of 8 Aug 2024 |
Publication 9040
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Abstract
BACKGROUND: Lipoprotein(a) (Lp(a)) is recognized as a causal factor for coronary heart disease (CHD) but its atherogenicity relative to that of low-density lipoprotein (LDL) on a per-particle basis is indeterminate.</p>
OBJECTIVES: The authors addressed this issue in a genetic analysis based on the fact that Lp(a) and LDL both contain 1 apolipoprotein B (apoB) per particle.</p>
METHODS: Genome-wide association studies using the UK Biobank population identified 2 clusters of single nucleotide polymorphisms: one comprising 107 variants linked to Lp(a) mass concentration, the other with 143 variants linked to LDL concentration. In these Lp(a) and LDL clusters, the relationship of genetically predicted variation in apoB with CHD risk was assessed.</p>
RESULTS: The Mendelian randomization-derived OR for CHD for a 50 nmol/L higher Lp(a)-apoB was 1.28 (95% CI: 1.24-1.33) compared with 1.04 (95% CI: 1.03-1.05) for the same increment in LDL-apoB. Likewise, use of polygenic scores to rank subjects according to difference in Lp(a)-apoB vs difference in LDL-apoB revealed a greater HR for CHD per 50 nmol/L apoB for the Lp(a) cluster (1.47; 95% CI: 1.36-1.58) compared with the LDL cluster (1.04; 95% CI: 1.02-1.05). From these data, we estimate that the atherogenicity of Lp(a) is approximately 6-fold (point estimate of 6.6; 95% CI: 5.1-8.8) greater than that of LDL on a per-particle basis.</p>
CONCLUSIONS: We conclude that the atherogenicity of Lp(a) (CHD risk quotient per unit increase in particle number) is substantially greater than that of LDL. Therefore, Lp(a) represents a key target for drug-based intervention in a significant proportion of the at-risk population.</p>
7 Authors
- Elias Björnson
- Martin Adiels
- Marja-Riitta Taskinen
- Stephen Burgess
- M John Chapman
- Chris J Packard
- Jan Borén
1 Application
| Application ID | Title |
|---|---|
| 53308 | Predictive Models for Cardio-Metabolic Disease: a Machine Learning Approach |
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