High-density lipoprotein cholesterol, apolipoprotein E and atherogenic index of plasma are associated with risk of chronic kidney disease

Objective: This study explored whether lipid disorders or an elevated atherogenic index of plasma (AIP, a risk factor for cardiovascular diseases) could predict major kidney function decline. Methods: We conducted a retrospective 7-year cohort study of 3712 Chinese adults followed up between 2010 and 2017. Major kidney function decline was defined as a ! 30% reduction in the estimated glomerular filtration rate (eGFR) from baseline. Multivariable logistic regression models were used to evaluate the relationship between lipid profiles and major kidney function decline. Smoking habits, waist circumference, and physical activity were not assessed. Results: During the 7-year follow-up, 1.70% (n ¼ 63) of the participants developed major kidney function decline. After adjustment for potential confounders, the odds ratios (ORs) for developing eGFR decline with per standard deviation increase were 1.23 [95% confidence interval (CI): 1.06-1.43] for triglyceride and 2.55 (95% CI: 1.01-6.42) for AIP in all participants. Furthermore, in the stratified analysis, we found sex-related differences; triglyceride and AIP were only independently associated with the risk of eGFR decline in men (OR, 1.27, 95% CI: 1.08-1.48; OR, 3.98, 95% CI: 1.22-12.99, respectively). When the participants were divided into groups according to the baseline lipid status, association was observed only between abnormal AIP and eGFR decline (all p values < 0.05). Conclusion: Our findings suggest that a higher serum triglyceride level or an elevated AIP increases the risk of major kidney function decline in Chinese men with normal kidney function. Thus, assessment of AIP may help identify the risk of eGFR decline.

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Elevated atherogenic index and higher triglyceride increase risk of kidney function decline: a 7-year cohort study in Chinese adults

Huang

Wang

Zhang

et al. 2020

Renal Failure

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Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Yan

Lü

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Objective. To establish a prediction model for the risk evaluation of chronic kidney disease (CKD) to guide the management and prevention of CKD. Methods. A total of 1263 patients with CKD and 1948 patients without CKD admitted to the Tongde Hospital of the Zhejiang Province from January 1, 2008, to December 31, 2018, were retrospectively analyzed. Spearman’s correlation was used to analyze the relationship between CKD and laboratory parameters. XGBoost, random forest, Naive Bayes, support vector machine, and multivariate logistic regression algorithms were employed to establish prediction models for the risk evaluation of CKD. The accuracy, precision, recall, F1 score, and area under the receiver operating curve (AUC) of each model were compared. The new bidirectional encoder representations from transformers with light gradient boosting machine (MD-BERT-LGBM) model was used to process the unstructured data and transform it into researchable unstructured vectors, and the AUC was compared before and after processing. Results. Differences in laboratory parameters between CKD and non-CKD patients were observed. The neutrophil ratio and white blood cell count were significantly associated with the occurrence of CKD. The XGBoost model demonstrated the best prediction effect (accuracy = 0.9088, precision = 0.9175, recall = 0.8244, F1 score = 0.8868, AUC = 0.8244), followed by the random forest model (accuracy = 0.9020, precision = 0.9318, recall = 0.7905, F1 score = 0.581, AUC = 0.9519). Comparatively, the predictions of the Naive Bayes and support vector machine models were inferior to those of the logistic regression model. The AUC of all models was improved to some extent after processing using the new MD-BERT-LGBM model. Conclusion. The new MD-BERT-LGBM model with the inclusion of unstructured data has contributed to the higher accuracy, sensitivity, and specificity of the prediction models. Clinical features such as age, gender, urinary white blood cells, urinary red blood cells, thrombin time, serum creatinine, and total cholesterol were associated with CKD incidence.

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Predictive Value of Plasma Atherogenic Index for Microalbuminuria in Newly Diagnosed Patients with Type 2 Diabetes Mellitus

Qi¹,

Kang²,

Chen³

et al. 2022

DMSO

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Purpose This study aims to explore the predictive value of plasma atherogenic index of plasma (AIP) for microalbuminuria (MAU) in newly diagnosed patients with type 2 diabetes mellitus (T2DM). Methods This study was a retrospective study, which included 335 newly diagnosed T2DM patients. They were divided into microalbuminuria group (group A, n = 105 cases) and no microalbuminuria group (group B, n = 230 cases) according to whether microalbuminuria occurred. General information and laboratory examination indexes of patients were collected, and AIP was calculated. Multivariate logistic regression analysis was used to analyze the independent risk factors of microalbuminuria in T2DM patients, and receiver operating characteristic curve (ROC) was established to evaluate the predictive value of AIP on MAU of newly diagnosed T2DM patients. Results According to general data analysis, AIP level in group A was significantly higher than that in group B (P < 0.05). Multivariate logistic regression analysis showed that AIP was an independent risk factor for microalbuminuria (P < 0.05). The receiver operating characteristic curve showed that the area under the curve (AUC) of AIP was 0.772 (P < 0.05), which had a good predictive value for the occurrence of MAU in newly diagnosed T2DM patients. The waist-hip ratio, triglyceride, high-density lipoprotein cholesterol, fasting blood glucose, glycosylated hemoglobin and AIP were used to make a joint model, and the AUC was 0.841 (P < 0.05), which had a better predictive value for the occurrence of MAU. Conclusions AIP is an independent risk factor and could predict the occurrence of MAU in newly diagnosed T2DM patients. AIP provides clinicians a reliable basis to quickly identify high-risk patients and formulate appropriate treatment strategies.

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High-density lipoprotein cholesterol, apolipoprotein E and atherogenic index of plasma are associated with risk of chronic kidney disease

Cited by 7 publications

References 6 publications

Elevated atherogenic index and higher triglyceride increase risk of kidney function decline: a 7-year cohort study in Chinese adults

Elevated atherogenic index and higher triglyceride increase risk of kidney function decline: a 7-year cohort study in Chinese adults

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Predictive Value of Plasma Atherogenic Index for Microalbuminuria in Newly Diagnosed Patients with Type 2 Diabetes Mellitus

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