Subclinical Atherosclerosis Links Premature and Late-Onset Coronary Artery Disease
Guo, M.; Zhao, M.; Zhao, Y.; Wang, A.; Guo, X.; Tao, L.; Liu, J.
Show abstract
BackgroundDespite established relationship between atherosclerosis and coronary artery disease (CAD), evidence on subclinical atherosclerosis and its differential associations with premature coronary artery disease (PCAD) versus late-onset coronary artery disease (LCAD) remains limited. AimsThis study aims to delve deeper into the associations between subclinical atherosclerosis and the incidence risk of both PCAD and LCAD. MethodsUsing UK Biobank data, we identified PCAD (male <55/female <65 years; n=7,398) and LCAD (male [≥]55/female [≥]65 years; n=39,085) cohorts. Conditional inference tree classification optimized carotid intima-media thickness (cIMT) stratification in both cohorts. ResultsConditional inference tree categorized the PCAD cohort into two subgroups: cIMT [≤]700m and cIMT >700m, with the latter demonstrating a HR of 2.079 for cardiovascular risk. In the LCAD cohort, four cIMT strata were identified: [≤] 620m, 620-763m (HR=1.401), 763-1054m (HR=1.810), and >1054m (HR=2.850). Multivariable-adjusted Cox models demonstrated significant associations between subclinical atherosclerosis and PCAD (HR=2.079, 95%CI:1.477-2.925) and LCAD (HR=1.776, 95%CI:1.455-2.169), highlighting the prognostic value of cIMT stratification in coronary artery disease risk assessment. ConclusionsA UK Biobank prospective cohort study revealed subclinical atherosclerosis significantly associated with PCAD and LCAD risks. Even within conventional cIMT "safe thresholds", incremental increases predicted elevated risks, underscoring limitations of current thresholds. Future research need to develop multimodal frameworks integrating dynamic cIMT trajectories to refine risk stratification and early interventions.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Insulin resistance potentiates the effect of remnant cholesterol on cardiovascular mortality in individuals without diabetes 95%
- Polygenic Hyperlipidemias and Coronary Artery Disease Risk 95%
- LDLR Variant Classification for Improved Cardiovascular Risk Prediction in Familial Hypercholesterolemia 95%
Similar papers in this journal
- Development and validation of a risk prediction algorithm for high-risk populations combining genetic and conventional risk factors of cardiovascular disease 95%
- Lipoprotein(a) and cardiovascular disease: prediction, attributable risk fraction and estimating benefits from novel interventions 94%
- AORTA Gene: Polygenic prediction improves detection of thoracic aortic aneurysm 93%
Similar papers in this journal
- Familial risk of myocardial infarction with non-obstructive and obstructive coronary arteries-A nation-wide cohort study 96%
- Polygenic prediction of coronary heart disease among 130,000 Mexican adults 94%
- Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes 94%
Similar papers in this journal
- Effects of Age and Sex on Systemic Inflammation and Cardiometabolic Function in Individuals with Type 2 Diabetes 95%
- The association between clonal hematopoiesis driver mutations, immune cell function and the vasculometabolic complications of obesity 94%
- Metabolic biomarkers for peripheral artery disease compared with coronary artery disease: Lipoprotein and metabolite profiling of 31,657 individuals from five prospective cohorts 94%
Similar papers in this journal
- Validation of genome-wide polygenic risk scores for coronary artery disease in French Canadians 96%
- A polygenic risk score for coronary heart disease performs well in individuals aged 70 years and older 94%
- A Polygenic Risk Score for Coronary Artery Disease Improves the Prediction of Early-Onset Myocardial Infarction and Mortality in Men 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.