Causal Associations Between Remnant Cholesterol Levels and Atherosclerosis-Related Cardiometabolic Risk Factors: A Bidirectional Mendelian Randomization Analysis

Abstract

Background: Despite the widespread use of lipid-lowering agents, the risk of atherosclerotic cardiovascular disease (ASCVD) remains; this residual risk has been attributed to remnant cholesterol (RC) levels. However, the causal associations between RC levels and various atherosclerosis-related cardiometabolic and vascular risk factors for ASCVD remain unclear. Methods: Using genetic and biochemical data of 108,876 Taiwan Biobank study participants, follow-up data of 31,790 participants, and follow-up imaging data of 18,614 participants, we conducted a genome-wide association study, a Functional Mapping and Annotation analysis, and bidirectional Mendelian randomization analyses to identify the genetic determinants of RC levels and the causal associations between RC levels and various cardiometabolic and vascular risk factors. Results: We found that higher RC levels were associated with higher prevalence or incidence of the analyzed risk factors. The genome-wide association study unveiled 61 lead genetic variants determining RC levels. The Functional Mapping and Annotation analysis revealed 21 gene sets exhibiting strong enrichment signals associated with lipid metabolism. Standard Mendelian randomization models adjusted for nonlipid variables and low-density lipoprotein cholesterol levels unraveled forward causal associations of RC levels with the prevalence of diabetes mellitus, hypertension, microalbuminuria, and metabolic liver disease. Reverse Mendelian randomization analysis revealed the causal association of diabetes mellitus with RC levels. Conclusions: RC levels, mainly influenced by genes associated with lipid metabolism, exhibit causal associations with various cardiometabolic risk factors, including diabetes mellitus, hypertension, microalbuminuria, and metabolic liver disease. This study provides further insights into the role of RC levels in predicting the residual risk of ASCVD.

Keywords: Mendelian randomization; cardiometabolic risk factor; diabetes mellitus; genome-wide association study; metabolic liver disease; remnant cholesterol.

Figure 1

Participant selection for the analysis. For the present study, participants of the Taiwan Biobank study were screened against the inclusion and exclusion criteria. The results of quality control of GWAS data indicated the absence of imputation data and exhibited cryptic relatedness (identity-by-descent value > 0.187). * Exclusion of each cardiometabolic risk factor during the first survey. ** Exclusion of HBV infection, HCV infection, alcohol consumption, or other known causes of chronic liver disease (n = 4012). *** Exclusion of known causes of chronic liver disease (n = 568). Abbreviations: MR: Mendelian randomization; GWAS, genome-wide association study; HL, hyperlipidemia; DM, diabetes mellitus; CKD, chronic kidney disease; CIMT, carotid intimal-medial thickness; NAFLD, nonalcoholic fatty liver disease; MASLD, metabolic dysfunction–associated steatotic liver disease; MAFLD, metabolic dysfunction-associated fatty liver disease; SD: standard deviation.

Figure 2

Diagrams for the approach of bidirectional MR analysis. The bidirectional MR analysis included the forward analysis (direction: from RC levels [exposure] to cardiometabolic and vascular risk factors [outcome]) and the reverse analysis (direction: from the risk factors [exposure] to RC levels [outcome]). The three core instrumental variable assumptions were relevance (I), independence (II), and exclusion restriction (III). Sensitivity analyses were performed to confirm the causal associations through MR analysis with multiple genetic variants.

Figure 3

Associations of RC levels with various cardiometabolic and vascular risk factors. Cardiometabolic and vascular risk factors analyzed in the present study included prevalent conditions such as DM, hypertension, microalbuminuria, NAFLD, MAFLD, MASLD, CKD, carotid plaques, and abnormal CIMT. RC levels were categorized into four equal quartiles, designated Q1 to Q4, with Q4 representing the quartile with the highest numerical values (a–c). To determine significance, p values were obtained from statistical models adjusted for sex, age, body mass index, and current smoking status and calculated using logistic regression. Abbreviations: RC: remnant cholesterol; DM, diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MAFLD, metabolic dysfunction-associated fatty liver disease; CKD, chronic kidney disease; CIMT, carotid intimal–medial thickness.

Figure 4

Associations between remnant cholesterol levels and new-onset cardiometabolic risk factors during the follow-up period. Abbreviations: DM, diabetes mellitus; CKD, chronic kidney disease.

Figure 5

Observational and causal associations between remnant cholesterol (RC) levels and cardiometabolic outcomes. The odds ratios (ORs) showed the effects of RC levels (observational association), in comparison with the weighted genetic risk scores of RC levels (RC-WGRSs) (causal association), on the risk of cardiometabolic outcomes according to a standard increment of 1.0 mmol/L RC levels (all p < 0.0001). Corresponding 95% confidence interval (CI) values indicated the precision of the ORs. To determine significance, p values were obtained from logistic regression models adjusted for age, sex, body mass index, and current smoking status. The abbreviations of outcomes are shown in Figure 2.