Atherogenic index of plasma is associated with the risk of myocardial infarction: a prospective cohort study
Zhang, Y.; Wu, S.; Tian, X.; Xu, Q.; Xia, X.; Zhang, X.; Li, J.; Chen, S.; Wang, A.; Liu, F.
Show abstract
Background and aimsThe atherogenic index of plasma (AIP) has been confirmed as a contributor of cardiovascular disease. But few evidence on the longitudinal pattern of AIP during follow-up. This study aimed to explore the associations between baseline and long-term AIP with the risk of myocardial infarction (MI). MethodsA total of 98 861 participants without MI at baseline were included from the Kailuan study. The baseline AIP was calculated as log (triglyceride/high-density lipoprotein cholesterol). The long-term AIP was calculated as the updated mean AIP and the number of visits with high AIP. The updated mean AIP was calculated as the mean of AIP from baseline to the first occurrence of MI or to the end of follow-up. The number of visits with high AIP was defined as higher than the cutoff value at the first three visits. Univariable and multivariable Cox proportional hazard models were used to determine the association between AIP and the risk of MI. ResultsDuring a median follow-up of 12.80 years, 1804 participants developed MI. The multivariable models revealed that elevated levels of baseline and updated mean AIP increased the risk of MI, compared with quartile 1 the HR in quartile 4 was 1.63 (95% CI, 1.41-1.88) and 1.59 (95% CI, 1.37-1.83), respectively. Compared to those without high AIP, the risk of individuals with three times was 1.94 (95% CI,1.55-2.45). ConclusionsElevated levels of both baseline and long-term AIP displayed a higher risk of MI.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Age-stratified Prevalence and Relative Prognostic Significance of Traditional Atherosclerotic Risk Factors: A Report from the Nationwide Registry of Percutaneous Coronary Interventions in Japan 97%
- Association of free fatty acids with long-term adverse outcomes in patients with premature myocardial infarction: a prospective cohort study 96%
- Social Networks and Cardiovascular Disease Events in the Jackson Heart Study 95%
Similar papers in this journal
- Catalpol inhibits HHcy-induced EndMT in endothelial cells by modulating ROS/NF-κB signaling 93%
- Obesity, hypertension and tobacco use associated with left ventricular remodelling and hypertrophy in South African Women: Birth to Twenty Plus Cohort 92%
- Postoperative glycemic variability as a predictor for one-year mortality following coronary artery bypass grafting: A retrospective cohort study 92%
Similar papers in this journal
- Metformin Is Associated with Favorable Outcomes in Patients with COVID-19 and Type 2 Diabetes Mellitus 94%
- Abnormal Upregulation of Cardiovascular Disease Biomarker PLA2G7 Induced by Proinflammatory Macrophages in COVID-19 patients 94%
- Klotho plasma levels are an independent predictor of mortality in women with acute coronary syndrome 93%
Similar papers in this journal
- Markers of systemic iron status show sex-specific differences in peripheral artery disease: a cross-sectional analysis of HEIST-DiC and NHANES participants 94%
- Insulin resistance potentiates the effect of remnant cholesterol on cardiovascular mortality in individuals without diabetes 94%
- LDLR Variant Classification for Improved Cardiovascular Risk Prediction in Familial Hypercholesterolemia 94%
Similar papers in this journal
- Predicting long-term prognosis after percutaneous coronary intervention in patients with acute coronary syndromes: a prospective nested case-control analysis for county-level health services 96%
- Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM) 95%
- Nomogram-Based Prognostic Model to predict the High blood pressure in Children and Adolescents —— Finding from 342,736 individual in China 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.