LDL-C/apoB and HDL-C/apoA-1 ratios predict incident chronic kidney disease in a large apparently healthy cohort
Introduction
Dyslipidemia may affect the kidney directly by causing deleterious renal lipid disturbances, as well as indirectly through systemic inflammation and oxidative stress, vascular injury, and other signaling molecules with renal action [1], [2]. Since the lipid nephrotoxicity hypothesis was proposed in 1982, increasing evidence has supported the hypothesis that dyslipidemia contributes to the development of renal disease, but the pathophysiology remains uncertain [2]. Feeding a hyperlipidemic diet to an animal model initiates and worsens renal dysfunction. Hypercholesterolemia and hypertriglyceridemia contribute to podocyte injury, which leads to segmental sclerosis associated with secondary damage to the tubulointerstitium [3], [4]. In addition, oxidized LDL induced renal injury in in vitro studies [4], [5], [6]. Mesangial cells express receptors for low density lipoprotein (LDL) and oxidized LDL, which induce proliferation of mesangial cells, increase mesangial matrix deposition, and enhance production of chemokines, cytokines, and growth factors. Chemokines, such as macrophage chemo-attractant protein-1, enhances recruitment of macrophages, which infiltrate the glomerulus and become foam cells [4], [5].
Clinical data also support that dyslipidemia contributes to the development of renal disease [4]. A few large sample sized longitudinal studies have shown that lipid abnormalities are associated with the development of renal disease in individuals who were free of kidney disease [7], [8], [9], [10], [11]. Several other clinical data have shown a relationship between lipid abnormalities and progression of renal disease [4]. However, the evidence is not as strong as it is in experimental studies because results are inconsistent regarding the ability of cholesterol and triglycerides (TGs) to predict the development of renal disease, and small sample sizes limited statistical power [4], [7], [8], [9], [10], [11], [12], [13]. Furthermore, these studies did not measure the full lipid profile [4], [7], [8], [9], [10], [11].
The objective of this study was to evaluate the ability of several lipid variables to predict the development of chronic kidney disease (CKD). Thus, we investigated the longitudinal association between lipid profiles and incident CKD in a large apparently healthy cohort.
Section snippets
Subjects
A large number of people undergo voluntary comprehensive health check-ups each year at Samsung Medical Center, and a considerable proportion of these individuals return for a medical check-up either annually or biennially. All anthropometric information, laboratory tests, radiology imaging results, and coded answers to self-reported questionnaires are stored electronically in medical records. Initial data were obtained from 25,170 individuals >20 years who participated in at least four
Results
Overall, the participants were not obese and had a mean BMI of 24.0 ± 2.8 kg/m2. Mean age of the participants was 50 years, and 67.6% were men. Among the participants, 7.2% had diabetes at baseline. The mean follow-up period was 56.5 ± 14.3 months, during which 356 (3.5%) new cases of CKD occurred. Individuals who developed CKD later were more likely to have diabetes and use hypertensive and lipid medications at baseline (Table 1). After adjusting for age, sex, baseline eGFR, fasting glucose,
Discussion
Baseline TG and HDL-C levels predicted subsequent development of CKD over a mean follow-up of 57 months in this large longitudinal cohort study. Interestingly, a significant longitudinal association was also found between incident CKD and the baseline LDL-C/apoB and HDL-C/apoA-I ratios. In the discrimination capabilities for predicting CKD, no differences were observed between these lipid parameters. On the other hand, the risk for incident CKD was not associated with baseline TC, LDL-C,
Conflict of interest
The authors declared that they do not have anything to disclose regarding conflict of interest with respect to this manuscript.
Financial support
This work was supported by a grant (SMX1161861) from the Samsung Biomedical Research Institute, Republic of Korea.
Author contributions
J.C.B, and J.H.K contributed to the study design.
J.H.J and T.Y.Y acquired the data.
J.C.B, and J.M,H participated in the analysis and interpretation of data.
J.C.B wrote the manuscript which was critically reviewed and edited by S.K and M.K.L.
All authors contributed to the development and revision of the report and approved the final report for submission.
Acknowledgements
We acknowledge the efforts of the health screening group at Samsung Medical Center in Seoul, Korea. This work was supported by a grant (SMX1161861) from the Samsung Biomedical Research Institute, Republic of Korea. This funding source played no role in the study design, collection, analysis, and interpretation of data, the writing of the manuscript, and the decision to submit the manuscript for publication. J.H.K is the guarantor of this work and, as such, had full access to all the data in the
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