The ApoB/LDL-C ratio predicts cardiovascular events in coronary artery disease patients independent of type 2 diabetes status

Abstract A high ApoB/LDL-C ratio reflects small LDL particle size which like type 2 diabetes (T2DM) is associated with insulin resistance. Whether the ApoB/LDL-C ratio and the presence of T2DM are mutually independent predictors of cardiovascular events is unclear and is addressed in the present study. We enrolled a large high-risk cohort of 1200 patients with coronary artery disease. T2DM was diagnosed according to current ADA guidelines. Prospectively, cardiovascular events were recorded over a mean follow-up period of 10.5±4.9 years. At baseline, the ApoB/LDL-C ratio was significantly higher in patients with T2DM (n=422) than in subjects who did not have T2DM (0.81±0.18 vs. 0.74±0.15 p<0.001). During follow-up, 662 patients suffered cardiovascular events. The Apo B/LDL-C ratio predicted cardiovascular events in univariate analysis (HR=1.11 [1.03-1.20] p= 0.006), and the event rate was higher in patients with than in those without T2DM (62.2% vs. 52.3%; p=0.001). In a multivariate adjusted Cox regression model both the ApoB/LDL-C ratio and T2DM significantly predicted cardiovascular events in a mutually independent manner, with standardized adjusted HRs of 1.11 [1.03-1.20]; p=0.008 and 1.39 [1.18- 1.64]; p<0.001, respectively. We conclude that in subjects with coronary artery disease the ApoB/LDL-C and T2DM are mutually independent predictors of cardiovascular events.

Authors

Institutions

Publication Details

Journal
European Heart Journal
Published
2024-10-01
DOI
https://doi.org/10.1093/eurheartj/ehae666.2812
Primary Topic
Lipoproteins and Cardiovascular Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The ApoB/LDL-C ratio predicts cardiovascular events in coronary artery disease patients independent of type 2 diabetes status

ARTHUR MADER, Alexander Vonbank, BARBARA LARCHER, THOMAS PLATTNER et al.
European Heart Journal
Lipoproteins and Cardiovascular Health
article

The ApoB/LDL-C ratio predicts cardiovascular events in coronary artery disease patients independent of type 2 diabetes status

ARTHUR MADER, Alexander Vonbank, BARBARA LARCHER, THOMAS PLATTNER, A Muendlein, Andreas Leiherer, Heinz Drexel, Christoph H. Saely
article en

Abstract

Abstract A high ApoB/LDL-C ratio reflects small LDL particle size which like type 2 diabetes (T2DM) is associated with insulin resistance. Whether the ApoB/LDL-C ratio and the presence of T2DM are mutually independent predictors of cardiovascular events is unclear and is addressed in the present study. We enrolled a large high-risk cohort of 1200 patients with coronary artery disease. T2DM was diagnosed according to current ADA guidelines. Prospectively, cardiovascular events were recorded over a mean follow-up period of 10.5±4.9 years. At baseline, the ApoB/LDL-C ratio was significantly higher in patients with T2DM (n=422) than in subjects who did not have T2DM (0.81±0.18 vs. 0.74±0.15 p<0.001). During follow-up, 662 patients suffered cardiovascular events. The Apo B/LDL-C ratio predicted cardiovascular events in univariate analysis (HR=1.11 [1.03-1.20] p= 0.006), and the event rate was higher in patients with than in those without T2DM (62.2% vs. 52.3%; p=0.001). In a multivariate adjusted Cox regression model both the ApoB/LDL-C ratio and T2DM significantly predicted cardiovascular events in a mutually independent manner, with standardized adjusted HRs of 1.11 [1.03-1.20]; p=0.008 and 1.39 [1.18- 1.64]; p<0.001, respectively. We conclude that in subjects with coronary artery disease the ApoB/LDL-C and T2DM are mutually independent predictors of cardiovascular events.

European Heart JournalVol. 45(Supplement_1)
Landeskrankenhaus Feldkirch (AT), Private University in the Principality of Liechtenstein (LI), Vorarlberg Institute for Vascular Investigation and Treatment (AT)
Openalex Percentile: Top 64%
Lipoproteins and Cardiovascular Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.