BMC endocrine disorders · 2023
The relation between atherogenic index of plasma and cardiovascular outcomes in prediabetic individuals with unstable angina pectoris.
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Liu Yang, Feng Xunxun, Yang Jiaqi, Zhai Guangyao, Zhang Bin, Guo Qianyun, Zhou Yujie
Аннотация
BACKGROUND: The atherogenic index of plasma (AIP) is a novel biomarker associated with atherosclerosis, and an important risk factor for atherosclerosis, but its relation with cardiovascular prognosis in prediabetic patients with unstable angina pectoris (UAP) is still uncertain.
METHODS: This study included 1096 prediabetic patients with UAP who were subjected to follow-up for a maximum of 30 months, with cardiac death, refractory angina, and non-fatal myocardial infarction (MI) being the primary cardiovascular endpoints.
RESULTS: A significantly increased AIP was observed for the group with primary cardiovascular endpoints. Kaplan-Meier curves corresponding to these endpoints revealed pronounced differences between these two AIP groups (Log-rank P < 0.001). Multivariate Cox proportional hazards analyses highlighted AIP as being independent related to this primary endpoint (HR 1.308, 95% CI: 1.213-1.412, P < 0.001). AIP addition to the baseline risk model improved the prediction of the primary endpoint (AUC: baseline model, 0.622, vs. baseline model + AIP, 0.739, P < 0.001).
CONCLUSIONS: AIP could be used to predict cardiovascular events in prediabetic individuals with UAP.
METHODS: This study included 1096 prediabetic patients with UAP who were subjected to follow-up for a maximum of 30 months, with cardiac death, refractory angina, and non-fatal myocardial infarction (MI) being the primary cardiovascular endpoints.
RESULTS: A significantly increased AIP was observed for the group with primary cardiovascular endpoints. Kaplan-Meier curves corresponding to these endpoints revealed pronounced differences between these two AIP groups (Log-rank P < 0.001). Multivariate Cox proportional hazards analyses highlighted AIP as being independent related to this primary endpoint (HR 1.308, 95% CI: 1.213-1.412, P < 0.001). AIP addition to the baseline risk model improved the prediction of the primary endpoint (AUC: baseline model, 0.622, vs. baseline model + AIP, 0.739, P < 0.001).
CONCLUSIONS: AIP could be used to predict cardiovascular events in prediabetic individuals with UAP.