Li Xu1, Xu Chen2, Jingfen Lu2, Yan Xu3, Honglin Yang2, Xuewen Zhou2, Jun Zhou4, Jianhong Xu5, Hao Shen6. 1. Department of General Surgery, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China. 2. Department of Blood Transfusion and Clinical Laboratory Medicine, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China. 3. Department of Nephrology, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China. 4. Department of Emergency Medicine, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China. 29080127@qq.com. 5. Department of Blood Transfusion and Clinical Laboratory Medicine, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China. 584953989@qq.com. 6. Department of Blood Transfusion and Clinical Laboratory Medicine, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China. shenhaojyk@126.com.
Abstract
BACKGROUND: As a subcomponent of low-density lipoprotein cholesterol (LDL-C), small dense LDL-C (sdLDL-C) has been suggested to be a better predictor of cardiovascular diseases (CVD). The aim of this research was to evaluate the predictive value of the sdLDL-C in cardiovascular events (CVs) in Chinese elderly patients with type 2 diabetes mellitus (DM). METHODS: A total of 386 consecutive type 2 DM patients were included into this study during December 2014 to December 2016. The serum sdLDL-C level of each subject was measured by homogeneous method. During a period of 48-month's follow-up, the occurrence of CVs and associated clinical information were recorded. Receiver operating characteristic (ROC) curves were used to assess the predictive value of serum sdLDL-C to occurrence of major CVs. RESULTS: A total of 92 CVs occurred during the study period. The ROC curve analysis manifested that sdLDL-C in the study population had a matchable discriminatory power (AUC for sdLDL-C was 0.7366, P = 0.003). In addition, Kaplan-Meier event-free survival curves displayed an obvious increase of CVs risk for sdLDL-C ≧ 26 mg/dL (log-rank = 9.10, P = 0.003). This phenomenon had analogous results in patients who received statins at baseline (log rank = 7.336, P = 0.007). Cox regression analysis revealed that the increase in HbA1c, glucose, LDL-C, sdLDL-C, non-high-density lipoprotein cholesterol (non-HDL-C) and apolipoprotein B (ApoB) and the decrease in apolipoprotein AI (ApoAI) were obviously interrelated with heightened CVs risk. Multiple Cox regression demonstrated that the increase of sdLDL-C and hemoglobin A1c (HbA1c) was significantly correlated with CVs. The results of the study indicated that high sdLDL-C level (> 10 mg/dL) was a risk factor for CVs in the multivariate model (HR 1.281, 95% CI 1.225-16.032; P < 0.01). CONCLUSION: sdLDL-C level could be an effective predictor in predicting the future CVs for Chinese elderly patients with type 2 DM and dyslipidemia.
BACKGROUND: As a subcomponent of low-density lipoprotein cholesterol (LDL-C), small dense LDL-C (sdLDL-C) has been suggested to be a better predictor of cardiovascular diseases (CVD). The aim of this research was to evaluate the predictive value of the sdLDL-C in cardiovascular events (CVs) in Chinese elderly patients with type 2 diabetes mellitus (DM). METHODS: A total of 386 consecutive type 2 DMpatients were included into this study during December 2014 to December 2016. The serum sdLDL-C level of each subject was measured by homogeneous method. During a period of 48-month's follow-up, the occurrence of CVs and associated clinical information were recorded. Receiver operating characteristic (ROC) curves were used to assess the predictive value of serum sdLDL-C to occurrence of major CVs. RESULTS: A total of 92 CVs occurred during the study period. The ROC curve analysis manifested that sdLDL-C in the study population had a matchable discriminatory power (AUC for sdLDL-C was 0.7366, P = 0.003). In addition, Kaplan-Meier event-free survival curves displayed an obvious increase of CVs risk for sdLDL-C ≧ 26 mg/dL (log-rank = 9.10, P = 0.003). This phenomenon had analogous results in patients who received statins at baseline (log rank = 7.336, P = 0.007). Cox regression analysis revealed that the increase in HbA1c, glucose, LDL-C, sdLDL-C, non-high-density lipoprotein cholesterol (non-HDL-C) and apolipoprotein B (ApoB) and the decrease in apolipoprotein AI (ApoAI) were obviously interrelated with heightened CVs risk. Multiple Cox regression demonstrated that the increase of sdLDL-C and hemoglobin A1c (HbA1c) was significantly correlated with CVs. The results of the study indicated that high sdLDL-C level (> 10 mg/dL) was a risk factor for CVs in the multivariate model (HR 1.281, 95% CI 1.225-16.032; P < 0.01). CONCLUSION:sdLDL-C level could be an effective predictor in predicting the future CVs for Chinese elderly patients with type 2 DM and dyslipidemia.